A COVID Global Economy: Policy Responses, Inflationary Concerns and Consumer Confidence
By Aaron Tom, Neil Song, Ben Zhao, Luke Melcher, and Tanvi Kigga
Abstract
COVID-19 has drastically changed our lives, and there is no doubt that it has affected the economy as well on a global scale. The global economy in 2020 was characterized by severe decline in early stages of the pandemic and a trend of recovery in later and current stages. The goal of our study is to examine the effect that COVID-19 has had on the global economy, and to to predict trends in economic indicators for each of the countries in our data set through COVID policy. The three economic variables we chose to examine the economy from were consumer confidence index, inflation rate index (CPI), and unemployment rate. We used our data to create a double prediction layer model. We then used 4 different types of models to determine the best model for our data, we also separated data into training and testing data for the model. We then used the model to predict future economic data. Overall there is a positive correlation for consumer confidence, negative correlation for unemployment, and mostly positive correlation for inflation, over the period of July 2021-December 2021. Methods Used We first coded a heatmap based off of the World Bank GDP data for January 2019 of the GDP of most of the countries in the world, in order to gain perspective on the global economy and what countries we could use in our analysis. But to analyze trends in the economy, yearly data wouldn’t work in the timeframe of 2-3 years, so we choose to use the OECD (Organisation for Economic Co-operation and Development) database, which is limited to about 50 countries compared to the 200+ countries in the world. It took quite a while to find the OECD database which contained many economic indicators but also had monthly data. We realized that most main economic indicators like GDP and trade data were done either quarterly or annually. In the end, we found several monthly economic indicators from roughly the past 30 months: unemployment rate, consumer confidence index, and inflation rate. The problem with analyzing all countries through the world bank is that some countries don’t have monthly data, other countries have no data at all, so instead we have normal distribution from 28 countries of the top 100 GDP countries in the world from the OECD. When looking closer at the data, it is actually fairly normally distributed. Our OECD dataset includes countries like US #1, Finland #42 , Latvia #95 (GDP) so we have around a normal distribution of the top 100 GDP countries in the world Our model involves 3 layers: a base predictor layer, a 1st layer, and a 2nd layer which is the prediction layer. The base layer contains: Month, Year, Country, Covid Rate. The 1st layer contains Covid policy data: Public Campaigns, Vaccination Policies, School Closures, Stay at Home Orders. The prediction layer contains economic data: Unemployment Rates, Consumer Confidence, and Inflation Rates
Graphical EDA from Slides
In our EDA (exploratory data analysis), there was a surge in unemployment rates in the beginning of the pandemic over March 2020 to April 2020. Countries that experienced the largest unemployment rates spikes were Costa Rica, Columbia, Greece, Spain, Chile, and the United States. We saw a dip in confidence in the beginning of the pandemic, but rising confidence in recovery months in late 2020 and 2021. Consumer confidence index is a survey that measures how optimistic or pessimistic someone is regarding their financial situation. A high confidence index indicates optimism about the market which means people will spend more money. A low confidence index indicates pessimism about the market which means that people will spend less money, to be conservative, or save money. Inflation grew regardless of the pandemic. We may speculate it is due to the Fed’s role with low interest rates and government stimulus throughout the pandemic. Low interest rates may affect inflation as a larger availability of credit and loans leads to more money in circulation, which leads to rising inflation. To determine the best model for our data we used: naive multiple linear regression, naive random forest, naive XGBoost Random Forest, and naive support vector machine models. After an analysis of the Least Squares Mean Error for each of these methods, we determined that SVM (Support Vector Machine) and XGBoost Random Forest were the best models for our variables. XGBoost is a machine learning algorithm that contains packages which our team used for statistical and prediction analysis. This package is based on boosting, which compiles multiple weak models together to form a stronger comprehensive model. Support vector machine is a machine learning algorithm regression which uses the help of many vectors to improve regression classification results. In the Residual VS fitted graph, we can say that this model is homoscedastic and linear as there are equal amounts of points above and below the line. In the second normal Q-Q plot as most of the points fall on the curve. Therefore, our plot is normal and we can conclude that all assumptions of the linear model are met.
Findings
Analyzing each of the 28 countries would be too much- we will examine one country from 3 different tiers (high GDP, middle GDP, and low GDP) from our specified countries to define and analyze a trend through multiple perspectives. Findings for all 28 countries can be explored through the Rmd file. For high tier- US, middle tier- Finland , low tier- Slovenia. The predictions will be for over the time period July 2021 to December 2021. The projected increase/decrease for unemployment for the US is -11.65% , for Finland -15.5% , and for Slovenia -11.93%. The projected increase/decrease for consumer confidence for the US is 1.42%, for Finland 2.31%, and for Slovenia 0.71%. The projected increase/decrease for inflation rate for the US is 0.22%, for Finland -0.22%, and for Slovenia 0.93%. Overall there is a positive correlation for consumer confidence, negative correlation for unemployment, and mostly positive correlation for inflation. Overall there is a positive correlation for consumer confidence, negative correlation for unemployment, and mostly positive correlation for inflation, over the period of July 2021 - December 2021. Our predictions mostly match expectations of recovery for the global economy in 2021.
Bibliography
“A Gentle Introduction to XGBoost for Applied Machine Learning.” Machine Learning Mastery. Last modified February 17, 2021. Accessed August 4, 2021. https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/. “Global Economic Effects of COVID-19.” Congressional Research Service. Last modified July 9, 2021. Accessed July 25, 2021. https://fas.org/sgp/crs/row/R46270.pdf. Organisation for Economic Co-operation and Development. Accessed August 2, 2021. https://stats.oecd.org/. “Policy Responses to the Coronavirus Pandemic.” Our World in Data. Accessed August 2, 2021. https://ourworldindata.org/policy-responses-covid. Song, Neil, Aaron Tom, Benjamin Zhao, Tanvi Kigga, and Luke Melcher. “Final Project.” GitHub. Accessed August 4, 2021. https://github.com/neilsong/upenn-data-science-academy/tree/master/Final%20Project.
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## [1000] train-rmse:0.105701
## [22:38:12] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "nthreads" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-rmse:82.769875
## Will train until train_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.572792
## [3] train-rmse:58.482998
## [4] train-rmse:49.164600
## [5] train-rmse:41.335327
## [6] train-rmse:34.758064
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## [615] train-rmse:0.002266
## Stopping. Best iteration:
## [609] train-rmse:0.002266## [22:38:12] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "nthreads" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-rmse:96.896080
## Will train until train_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.269157
## [3] train-rmse:78.606117
## [4] train-rmse:70.811279
## [5] train-rmse:63.798573
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## [999] train-rmse:0.249770
## [1000] train-rmse:0.249677## [22:38:13] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "nthreads" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-rmse:7.548731
## Will train until train_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.983161
## [3] train-rmse:6.485629
## [4] train-rmse:6.037310
## [5] train-rmse:5.646307
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## [998] train-rmse:0.399972
## [999] train-rmse:0.399671
## [1000] train-rmse:0.399172## [22:38:14] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "nthreads" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-rmse:0.932100
## Will train until train_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.614025
## [3] train-rmse:0.417054
## [4] train-rmse:0.299367
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## [376] train-rmse:0.009397
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## [381] train-rmse:0.009125
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## [388] train-rmse:0.008666
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## [391] train-rmse:0.008542
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## [612] train-rmse:0.003198
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## [619] train-rmse:0.003132
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## [631] train-rmse:0.002968
## [632] train-rmse:0.002955
## [633] train-rmse:0.002937
## [634] train-rmse:0.002935
## [635] train-rmse:0.002934
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## [642] train-rmse:0.002815
## [643] train-rmse:0.002806
## [644] train-rmse:0.002789
## [645] train-rmse:0.002787
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## [647] train-rmse:0.002759
## [648] train-rmse:0.002752
## [649] train-rmse:0.002747
## [650] train-rmse:0.002741
## [651] train-rmse:0.002730
## [652] train-rmse:0.002722
## [653] train-rmse:0.002713
## [654] train-rmse:0.002706
## [655] train-rmse:0.002693
## [656] train-rmse:0.002692
## [657] train-rmse:0.002691
## [658] train-rmse:0.002691
## [659] train-rmse:0.002691
## [660] train-rmse:0.002691
## [661] train-rmse:0.002691
## [662] train-rmse:0.002691
## [663] train-rmse:0.002691
## Stopping. Best iteration:
## [657] train-rmse:0.002691## [22:38:14] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "nthreads" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-rmse:0.870897
## Will train until train_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.762032
## [3] train-rmse:0.675984
## [4] train-rmse:0.605551
## [5] train-rmse:0.549712
## [6] train-rmse:0.501545
## [7] train-rmse:0.466478
## [8] train-rmse:0.434467
## [9] train-rmse:0.408927
## [10] train-rmse:0.386002
## [11] train-rmse:0.364975
## [12] train-rmse:0.344814
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## [219] train-rmse:0.009068
## [220] train-rmse:0.008955
## [221] train-rmse:0.008785
## [222] train-rmse:0.008518
## [223] train-rmse:0.008435
## [224] train-rmse:0.008340
## [225] train-rmse:0.008267
## [226] train-rmse:0.008186
## [227] train-rmse:0.008036
## [228] train-rmse:0.007878
## [229] train-rmse:0.007745
## [230] train-rmse:0.007632
## [231] train-rmse:0.007474
## [232] train-rmse:0.007327
## [233] train-rmse:0.007224
## [234] train-rmse:0.007089
## [235] train-rmse:0.007036
## [236] train-rmse:0.006952
## [237] train-rmse:0.006859
## [238] train-rmse:0.006774
## [239] train-rmse:0.006652
## [240] train-rmse:0.006603
## [241] train-rmse:0.006466
## [242] train-rmse:0.006330
## [243] train-rmse:0.006255
## [244] train-rmse:0.006128
## [245] train-rmse:0.006035
## [246] train-rmse:0.005943
## [247] train-rmse:0.005899
## [248] train-rmse:0.005823
## [249] train-rmse:0.005716
## [250] train-rmse:0.005662
## [251] train-rmse:0.005593
## [252] train-rmse:0.005518
## [253] train-rmse:0.005408
## [254] train-rmse:0.005353
## [255] train-rmse:0.005345
## [256] train-rmse:0.005228
## [257] train-rmse:0.005152
## [258] train-rmse:0.005131
## [259] train-rmse:0.005095
## [260] train-rmse:0.004993
## [261] train-rmse:0.004929
## [262] train-rmse:0.004800
## [263] train-rmse:0.004784
## [264] train-rmse:0.004757
## [265] train-rmse:0.004727
## [266] train-rmse:0.004651
## [267] train-rmse:0.004560
## [268] train-rmse:0.004501
## [269] train-rmse:0.004391
## [270] train-rmse:0.004291
## [271] train-rmse:0.004223
## [272] train-rmse:0.004124
## [273] train-rmse:0.004106
## [274] train-rmse:0.004015
## [275] train-rmse:0.003923
## [276] train-rmse:0.003838
## [277] train-rmse:0.003753
## [278] train-rmse:0.003747
## [279] train-rmse:0.003662
## [280] train-rmse:0.003603
## [281] train-rmse:0.003590
## [282] train-rmse:0.003545
## [283] train-rmse:0.003488
## [284] train-rmse:0.003456
## [285] train-rmse:0.003426
## [286] train-rmse:0.003375
## [287] train-rmse:0.003314
## [288] train-rmse:0.003291
## [289] train-rmse:0.003251
## [290] train-rmse:0.003231
## [291] train-rmse:0.003221
## [292] train-rmse:0.003180
## [293] train-rmse:0.003125
## [294] train-rmse:0.003101
## [295] train-rmse:0.003089
## [296] train-rmse:0.003049
## [297] train-rmse:0.003010
## [298] train-rmse:0.002932
## [299] train-rmse:0.002872
## [300] train-rmse:0.002853
## [301] train-rmse:0.002827
## [302] train-rmse:0.002827
## [303] train-rmse:0.002827
## [304] train-rmse:0.002827
## [305] train-rmse:0.002827
## [306] train-rmse:0.002827
## [307] train-rmse:0.002827
## Stopping. Best iteration:
## [301] train-rmse:0.002827## [22:38:15] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "nthreads" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-rmse:1.110532
## Will train until train_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.818772
## [3] train-rmse:0.649562
## [4] train-rmse:0.558389
## [5] train-rmse:0.481635
## [6] train-rmse:0.446110
## [7] train-rmse:0.421907
## [8] train-rmse:0.388818
## [9] train-rmse:0.374595
## [10] train-rmse:0.333989
## [11] train-rmse:0.324004
## [12] train-rmse:0.316999
## [13] train-rmse:0.305775
## [14] train-rmse:0.289728
## [15] train-rmse:0.261559
## [16] train-rmse:0.246361
## [17] train-rmse:0.239396
## [18] train-rmse:0.230868
## [19] train-rmse:0.215626
## [20] train-rmse:0.206779
## [21] train-rmse:0.192529
## [22] train-rmse:0.178172
## [23] train-rmse:0.170494
## [24] train-rmse:0.166442
## [25] train-rmse:0.162236
## [26] train-rmse:0.149438
## [27] train-rmse:0.143495
## [28] train-rmse:0.135469
## [29] train-rmse:0.128945
## [30] train-rmse:0.118477
## [31] train-rmse:0.114449
## [32] train-rmse:0.107183
## [33] train-rmse:0.105200
## [34] train-rmse:0.099853
## [35] train-rmse:0.097176
## [36] train-rmse:0.088808
## [37] train-rmse:0.084376
## [38] train-rmse:0.080578
## [39] train-rmse:0.074592
## [40] train-rmse:0.072061
## [41] train-rmse:0.071384
## [42] train-rmse:0.068995
## [43] train-rmse:0.064975
## [44] train-rmse:0.064214
## [45] train-rmse:0.060893
## [46] train-rmse:0.059352
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## [48] train-rmse:0.055670
## [49] train-rmse:0.054460
## [50] train-rmse:0.052593
## [51] train-rmse:0.051195
## [52] train-rmse:0.048313
## [53] train-rmse:0.044676
## [54] train-rmse:0.042820
## [55] train-rmse:0.040135
## [56] train-rmse:0.038881
## [57] train-rmse:0.037422
## [58] train-rmse:0.035942
## [59] train-rmse:0.034021
## [60] train-rmse:0.032170
## [61] train-rmse:0.030737
## [62] train-rmse:0.029481
## [63] train-rmse:0.028539
## [64] train-rmse:0.027523
## [65] train-rmse:0.026461
## [66] train-rmse:0.025446
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## [69] train-rmse:0.022420
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## [72] train-rmse:0.019684
## [73] train-rmse:0.018883
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## [75] train-rmse:0.017397
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## [77] train-rmse:0.015738
## [78] train-rmse:0.015263
## [79] train-rmse:0.014733
## [80] train-rmse:0.014317
## [81] train-rmse:0.013943
## [82] train-rmse:0.013258
## [83] train-rmse:0.012890
## [84] train-rmse:0.012513
## [85] train-rmse:0.011871
## [86] train-rmse:0.011532
## [87] train-rmse:0.011024
## [88] train-rmse:0.010695
## [89] train-rmse:0.010208
## [90] train-rmse:0.009865
## [91] train-rmse:0.009329
## [92] train-rmse:0.009075
## [93] train-rmse:0.008893
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## [95] train-rmse:0.007965
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## [106] train-rmse:0.005400
## [107] train-rmse:0.005347
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## [110] train-rmse:0.004958
## [111] train-rmse:0.004814
## [112] train-rmse:0.004652
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## [115] train-rmse:0.004063
## [116] train-rmse:0.003866
## [117] train-rmse:0.003756
## [118] train-rmse:0.003731
## [119] train-rmse:0.003530
## [120] train-rmse:0.003408
## [121] train-rmse:0.003308
## [122] train-rmse:0.003136
## [123] train-rmse:0.003028
## [124] train-rmse:0.002953
## [125] train-rmse:0.002844
## [126] train-rmse:0.002713
## [127] train-rmse:0.002600
## [128] train-rmse:0.002503
## [129] train-rmse:0.002438
## [130] train-rmse:0.002372
## [131] train-rmse:0.002241
## [132] train-rmse:0.002179
## [133] train-rmse:0.002179
## [134] train-rmse:0.002179
## [135] train-rmse:0.002179
## [136] train-rmse:0.002179
## [137] train-rmse:0.002179
## [138] train-rmse:0.002179
## Stopping. Best iteration:
## [132] train-rmse:0.002179## [22:38:15] WARNING: amalgamation/../src/learner.cc:573:
## Parameters: { "nthreads" } might not be used.
##
## This may not be accurate due to some parameters are only used in language bindings but
## passed down to XGBoost core. Or some parameters are not used but slip through this
## verification. Please open an issue if you find above cases.
##
##
## [1] train-rmse:1.179358
## Will train until train_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.880255
## [3] train-rmse:0.680660
## [4] train-rmse:0.553496
## [5] train-rmse:0.476213
## [6] train-rmse:0.429108
## [7] train-rmse:0.401427
## [8] train-rmse:0.374282
## [9] train-rmse:0.360726
## [10] train-rmse:0.348208
## [11] train-rmse:0.339567
## [12] train-rmse:0.329976
## [13] train-rmse:0.322789
## [14] train-rmse:0.317366
## [15] train-rmse:0.312471
## [16] train-rmse:0.305644
## [17] train-rmse:0.301610
## [18] train-rmse:0.294497
## [19] train-rmse:0.289527
## [20] train-rmse:0.286560
## [21] train-rmse:0.283385
## [22] train-rmse:0.276009
## [23] train-rmse:0.271885
## [24] train-rmse:0.269112
## [25] train-rmse:0.266368
## [26] train-rmse:0.264232
## [27] train-rmse:0.259180
## [28] train-rmse:0.257186
## [29] train-rmse:0.252805
## [30] train-rmse:0.251021
## [31] train-rmse:0.247123
## [32] train-rmse:0.244161
## [33] train-rmse:0.240264
## [34] train-rmse:0.237735
## [35] train-rmse:0.230189
## [36] train-rmse:0.228232
## [37] train-rmse:0.226621
## [38] train-rmse:0.220133
## [39] train-rmse:0.217853
## [40] train-rmse:0.212621
## [41] train-rmse:0.211351
## [42] train-rmse:0.209154
## [43] train-rmse:0.207056
## [44] train-rmse:0.204057
## [45] train-rmse:0.199414
## [46] train-rmse:0.197105
## [47] train-rmse:0.196108
## [48] train-rmse:0.193494
## [49] train-rmse:0.192608
## [50] train-rmse:0.191242
## [51] train-rmse:0.190163
## [52] train-rmse:0.185548
## [53] train-rmse:0.184746
## [54] train-rmse:0.182937
## [55] train-rmse:0.182151
## [56] train-rmse:0.181359
## [57] train-rmse:0.179620
## [58] train-rmse:0.178783
## [59] train-rmse:0.176380
## [60] train-rmse:0.174598
## [61] train-rmse:0.173263
## [62] train-rmse:0.170436
## [63] train-rmse:0.168264
## [64] train-rmse:0.166844
## [65] train-rmse:0.166428
## [66] train-rmse:0.165873
## [67] train-rmse:0.164491
## [68] train-rmse:0.163202
## [69] train-rmse:0.161422
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## [71] train-rmse:0.156696
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## [73] train-rmse:0.154253
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## [76] train-rmse:0.151086
## [77] train-rmse:0.149573
## [78] train-rmse:0.147366
## [79] train-rmse:0.145788
## [80] train-rmse:0.144676
## [81] train-rmse:0.142172
## [82] train-rmse:0.141050
## [83] train-rmse:0.139677
## [84] train-rmse:0.138234
## [85] train-rmse:0.136619
## [86] train-rmse:0.135505
## [87] train-rmse:0.132210
## [88] train-rmse:0.129144
## [89] train-rmse:0.127424
## [90] train-rmse:0.126334
## [91] train-rmse:0.125279
## [92] train-rmse:0.124520
## [93] train-rmse:0.123834
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## [95] train-rmse:0.122244
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## [97] train-rmse:0.119823
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## [100] train-rmse:0.117879
## [101] train-rmse:0.116580
## [102] train-rmse:0.116027
## [103] train-rmse:0.115637
## [104] train-rmse:0.114702
## [105] train-rmse:0.113374
## [106] train-rmse:0.112495
## [107] train-rmse:0.110893
## [108] train-rmse:0.109320
## [109] train-rmse:0.108235
## [110] train-rmse:0.107704
## [111] train-rmse:0.107368
## [112] train-rmse:0.106148
## [113] train-rmse:0.105714
## [114] train-rmse:0.104420
## [115] train-rmse:0.103591
## [116] train-rmse:0.102509
## [117] train-rmse:0.101605
## [118] train-rmse:0.100709
## [119] train-rmse:0.100039
## [120] train-rmse:0.099748
## [121] train-rmse:0.098838
## [122] train-rmse:0.097591
## [123] train-rmse:0.096790
## [124] train-rmse:0.095076
## [125] train-rmse:0.094760
## [126] train-rmse:0.093476
## [127] train-rmse:0.092953
## [128] train-rmse:0.092486
## [129] train-rmse:0.091601
## [130] train-rmse:0.091142
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## [133] train-rmse:0.089588
## [134] train-rmse:0.089336
## [135] train-rmse:0.088786
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## [138] train-rmse:0.087836
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## [159] train-rmse:0.071949
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## [186] train-rmse:0.060934
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## [188] train-rmse:0.059449
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## [191] train-rmse:0.057886
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## [282] train-rmse:0.034060
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## [284] train-rmse:0.033546
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## [288] train-rmse:0.033021
## [289] train-rmse:0.032811
## [290] train-rmse:0.032696
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## [292] train-rmse:0.032435
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## [300] train-rmse:0.031165
## [301] train-rmse:0.030960
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## [311] train-rmse:0.029215
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## [313] train-rmse:0.028780
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## [315] train-rmse:0.028543
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## [317] train-rmse:0.028273
## [318] train-rmse:0.027874
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## [320] train-rmse:0.027592
## [321] train-rmse:0.027422
## [322] train-rmse:0.027189
## [323] train-rmse:0.026941
## [324] train-rmse:0.026775
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## [327] train-rmse:0.026406
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## [330] train-rmse:0.025469
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## [337] train-rmse:0.024553
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## [342] train-rmse:0.024036
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## [347] train-rmse:0.023294
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## [349] train-rmse:0.023046
## [350] train-rmse:0.022930
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## [356] train-rmse:0.022129
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## [361] train-rmse:0.021482
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## [363] train-rmse:0.021265
## [364] train-rmse:0.020982
## [365] train-rmse:0.020973
## [366] train-rmse:0.020892
## [367] train-rmse:0.020862
## [368] train-rmse:0.020716
## [369] train-rmse:0.020601
## [370] train-rmse:0.020468
## [371] train-rmse:0.020343
## [372] train-rmse:0.020231
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## [374] train-rmse:0.020027
## [375] train-rmse:0.020006
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## [378] train-rmse:0.019516
## [379] train-rmse:0.019397
## [380] train-rmse:0.019255
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## [383] train-rmse:0.018896
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## [390] train-rmse:0.018473
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## [395] train-rmse:0.018061
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## [398] train-rmse:0.017830
## [399] train-rmse:0.017691
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## [430] train-rmse:0.015037
## [431] train-rmse:0.014873
## [432] train-rmse:0.014802
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## [435] train-rmse:0.014463
## [436] train-rmse:0.014406
## [437] train-rmse:0.014368
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## [440] train-rmse:0.014086
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## [443] train-rmse:0.014042
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## [446] train-rmse:0.013784
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## [448] train-rmse:0.013660
## [449] train-rmse:0.013606
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## [451] train-rmse:0.013507
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## [453] train-rmse:0.013383
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## [455] train-rmse:0.013256
## [456] train-rmse:0.013199
## [457] train-rmse:0.013151
## [458] train-rmse:0.013120
## [459] train-rmse:0.013054
## [460] train-rmse:0.012969
## [461] train-rmse:0.012961
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## [463] train-rmse:0.012837
## [464] train-rmse:0.012786
## [465] train-rmse:0.012712
## [466] train-rmse:0.012679
## [467] train-rmse:0.012552
## [468] train-rmse:0.012467
## [469] train-rmse:0.012382
## [470] train-rmse:0.012339
## [471] train-rmse:0.012254
## [472] train-rmse:0.012145
## [473] train-rmse:0.011956
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## [475] train-rmse:0.011820
## [476] train-rmse:0.011752
## [477] train-rmse:0.011673
## [478] train-rmse:0.011588
## [479] train-rmse:0.011549
## [480] train-rmse:0.011460
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## [484] train-rmse:0.011220
## [485] train-rmse:0.011194
## [486] train-rmse:0.011114
## [487] train-rmse:0.011052
## [488] train-rmse:0.011001
## [489] train-rmse:0.010983
## [490] train-rmse:0.010920
## [491] train-rmse:0.010889
## [492] train-rmse:0.010843
## [493] train-rmse:0.010775
## [494] train-rmse:0.010753
## [495] train-rmse:0.010693
## [496] train-rmse:0.010583
## [497] train-rmse:0.010559
## [498] train-rmse:0.010522
## [499] train-rmse:0.010412
## [500] train-rmse:0.010358
## [501] train-rmse:0.010333
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## [503] train-rmse:0.010256
## [504] train-rmse:0.010245
## [505] train-rmse:0.010130
## [506] train-rmse:0.010075
## [507] train-rmse:0.010000
## [508] train-rmse:0.009942
## [509] train-rmse:0.009870
## [510] train-rmse:0.009813
## [511] train-rmse:0.009722
## [512] train-rmse:0.009714
## [513] train-rmse:0.009650
## [514] train-rmse:0.009594
## [515] train-rmse:0.009529
## [516] train-rmse:0.009476
## [517] train-rmse:0.009458
## [518] train-rmse:0.009394
## [519] train-rmse:0.009346
## [520] train-rmse:0.009328
## [521] train-rmse:0.009288
## [522] train-rmse:0.009235
## [523] train-rmse:0.009160
## [524] train-rmse:0.009100
## [525] train-rmse:0.009055
## [526] train-rmse:0.008997
## [527] train-rmse:0.008991
## [528] train-rmse:0.008942
## [529] train-rmse:0.008909
## [530] train-rmse:0.008861
## [531] train-rmse:0.008801
## [532] train-rmse:0.008752
## [533] train-rmse:0.008730
## [534] train-rmse:0.008674
## [535] train-rmse:0.008651
## [536] train-rmse:0.008550
## [537] train-rmse:0.008546
## [538] train-rmse:0.008512
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## [540] train-rmse:0.008431
## [541] train-rmse:0.008399
## [542] train-rmse:0.008351
## [543] train-rmse:0.008317
## [544] train-rmse:0.008247
## [545] train-rmse:0.008227
## [546] train-rmse:0.008203
## [547] train-rmse:0.008183
## [548] train-rmse:0.008133
## [549] train-rmse:0.008127
## [550] train-rmse:0.008071
## [551] train-rmse:0.008018
## [552] train-rmse:0.008000
## [553] train-rmse:0.007993
## [554] train-rmse:0.007961
## [555] train-rmse:0.007943
## [556] train-rmse:0.007897
## [557] train-rmse:0.007878
## [558] train-rmse:0.007851
## [559] train-rmse:0.007816
## [560] train-rmse:0.007784
## [561] train-rmse:0.007732
## [562] train-rmse:0.007728
## [563] train-rmse:0.007670
## [564] train-rmse:0.007653
## [565] train-rmse:0.007646
## [566] train-rmse:0.007592
## [567] train-rmse:0.007553
## [568] train-rmse:0.007542
## [569] train-rmse:0.007492
## [570] train-rmse:0.007437
## [571] train-rmse:0.007407
## [572] train-rmse:0.007377
## [573] train-rmse:0.007374
## [574] train-rmse:0.007355
## [575] train-rmse:0.007324
## [576] train-rmse:0.007314
## [577] train-rmse:0.007288
## [578] train-rmse:0.007241
## [579] train-rmse:0.007200
## [580] train-rmse:0.007163
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## [582] train-rmse:0.007117
## [583] train-rmse:0.007070
## [584] train-rmse:0.007014
## [585] train-rmse:0.007008
## [586] train-rmse:0.006976
## [587] train-rmse:0.006938
## [588] train-rmse:0.006904
## [589] train-rmse:0.006864
## [590] train-rmse:0.006838
## [591] train-rmse:0.006815
## [592] train-rmse:0.006786
## [593] train-rmse:0.006735
## [594] train-rmse:0.006698
## [595] train-rmse:0.006645
## [596] train-rmse:0.006576
## [597] train-rmse:0.006535
## [598] train-rmse:0.006514
## [599] train-rmse:0.006504
## [600] train-rmse:0.006474
## [601] train-rmse:0.006462
## [602] train-rmse:0.006436
## [603] train-rmse:0.006404
## [604] train-rmse:0.006368
## [605] train-rmse:0.006342
## [606] train-rmse:0.006311
## [607] train-rmse:0.006264
## [608] train-rmse:0.006246
## [609] train-rmse:0.006221
## [610] train-rmse:0.006200
## [611] train-rmse:0.006165
## [612] train-rmse:0.006133
## [613] train-rmse:0.006120
## [614] train-rmse:0.006090
## [615] train-rmse:0.006082
## [616] train-rmse:0.006049
## [617] train-rmse:0.005990
## [618] train-rmse:0.005951
## [619] train-rmse:0.005938
## [620] train-rmse:0.005886
## [621] train-rmse:0.005869
## [622] train-rmse:0.005849
## [623] train-rmse:0.005835
## [624] train-rmse:0.005810
## [625] train-rmse:0.005773
## [626] train-rmse:0.005740
## [627] train-rmse:0.005699
## [628] train-rmse:0.005684
## [629] train-rmse:0.005647
## [630] train-rmse:0.005601
## [631] train-rmse:0.005573
## [632] train-rmse:0.005555
## [633] train-rmse:0.005528
## [634] train-rmse:0.005516
## [635] train-rmse:0.005487
## [636] train-rmse:0.005462
## [637] train-rmse:0.005442
## [638] train-rmse:0.005423
## [639] train-rmse:0.005403
## [640] train-rmse:0.005379
## [641] train-rmse:0.005366
## [642] train-rmse:0.005355
## [643] train-rmse:0.005349
## [644] train-rmse:0.005323
## [645] train-rmse:0.005298
## [646] train-rmse:0.005294
## [647] train-rmse:0.005280
## [648] train-rmse:0.005255
## [649] train-rmse:0.005231
## [650] train-rmse:0.005224
## [651] train-rmse:0.005189
## [652] train-rmse:0.005135
## [653] train-rmse:0.005100
## [654] train-rmse:0.005083
## [655] train-rmse:0.005052
## [656] train-rmse:0.005024
## [657] train-rmse:0.004991
## [658] train-rmse:0.004973
## [659] train-rmse:0.004951
## [660] train-rmse:0.004942
## [661] train-rmse:0.004926
## [662] train-rmse:0.004898
## [663] train-rmse:0.004868
## [664] train-rmse:0.004830
## [665] train-rmse:0.004809
## [666] train-rmse:0.004796
## [667] train-rmse:0.004769
## [668] train-rmse:0.004757
## [669] train-rmse:0.004734
## [670] train-rmse:0.004731
## [671] train-rmse:0.004722
## [672] train-rmse:0.004686
## [673] train-rmse:0.004666
## [674] train-rmse:0.004638
## [675] train-rmse:0.004599
## [676] train-rmse:0.004574
## [677] train-rmse:0.004557
## [678] train-rmse:0.004527
## [679] train-rmse:0.004487
## [680] train-rmse:0.004476
## [681] train-rmse:0.004445
## [682] train-rmse:0.004434
## [683] train-rmse:0.004426
## [684] train-rmse:0.004413
## [685] train-rmse:0.004399
## [686] train-rmse:0.004374
## [687] train-rmse:0.004354
## [688] train-rmse:0.004344
## [689] train-rmse:0.004326
## [690] train-rmse:0.004323
## [691] train-rmse:0.004302
## [692] train-rmse:0.004294
## [693] train-rmse:0.004281
## [694] train-rmse:0.004264
## [695] train-rmse:0.004244
## [696] train-rmse:0.004201
## [697] train-rmse:0.004182
## [698] train-rmse:0.004169
## [699] train-rmse:0.004162
## [700] train-rmse:0.004140
## [701] train-rmse:0.004125
## [702] train-rmse:0.004092
## [703] train-rmse:0.004076
## [704] train-rmse:0.004053
## [705] train-rmse:0.004038
## [706] train-rmse:0.004007
## [707] train-rmse:0.003993
## [708] train-rmse:0.003965
## [709] train-rmse:0.003946
## [710] train-rmse:0.003925
## [711] train-rmse:0.003905
## [712] train-rmse:0.003878
## [713] train-rmse:0.003866
## [714] train-rmse:0.003843
## [715] train-rmse:0.003827
## [716] train-rmse:0.003814
## [717] train-rmse:0.003794
## [718] train-rmse:0.003775
## [719] train-rmse:0.003760
## [720] train-rmse:0.003746
## [721] train-rmse:0.003733
## [722] train-rmse:0.003723
## [723] train-rmse:0.003721
## [724] train-rmse:0.003704
## [725] train-rmse:0.003682
## [726] train-rmse:0.003682
## [727] train-rmse:0.003682
## [728] train-rmse:0.003682
## [729] train-rmse:0.003682
## [730] train-rmse:0.003682
## [731] train-rmse:0.003682
## Stopping. Best iteration:
## [725] train-rmse:0.003682## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.
## [1] 0.753## [1] 0.0312## [1] 32## [1] 1.19## [1] 0.0312## [1] 32## [1] 2.13## [1] 0.125## [1] 2## [1] 0.137## [1] 0.0625## [1] 32## [1] 0.434## [1] 0.25## [1] 8## [1] 0.52## [1] 0.25## [1] 4## [1] 0.345## [1] 0.0312## [1] 32## [1] train-rmse:88.665695+0.088976 test-rmse:88.666461+0.404980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.835214+0.080050 test-rmse:79.835893+0.413675
## [3] train-rmse:71.885242+0.071998 test-rmse:71.885820+0.421463
## [4] train-rmse:64.728087+0.064733 test-rmse:64.728551+0.428434
## [5] train-rmse:58.284824+0.058174 test-rmse:58.285159+0.434654
## [6] train-rmse:52.484374+0.052257 test-rmse:52.484567+0.440197
## [7] train-rmse:47.262778+0.046908 test-rmse:47.262816+0.445120
## [8] train-rmse:42.562434+0.042074 test-rmse:42.562303+0.449468
## [9] train-rmse:38.331508+0.037710 test-rmse:38.331192+0.453293
## [10] train-rmse:34.523343+0.033759 test-rmse:34.522811+0.456630
## [11] train-rmse:31.095926+0.030181 test-rmse:31.095166+0.459509
## [12] train-rmse:28.011453+0.026945 test-rmse:28.010445+0.461960
## [13] train-rmse:25.235913+0.024012 test-rmse:25.234629+0.463999
## [14] train-rmse:22.738687+0.021363 test-rmse:22.737103+0.465637
## [15] train-rmse:20.492223+0.018971 test-rmse:20.490315+0.466887
## [16] train-rmse:18.471749+0.016823 test-rmse:18.469484+0.467747
## [17] train-rmse:16.654969+0.014903 test-rmse:16.652318+0.468213
## [18] train-rmse:15.021334+0.013222 test-rmse:15.020864+0.456548
## [19] train-rmse:13.552676+0.011643 test-rmse:13.544601+0.458028
## [20] train-rmse:12.232597+0.010248 test-rmse:12.227122+0.446788
## [21] train-rmse:11.046271+0.009054 test-rmse:11.038224+0.443529
## [22] train-rmse:9.980255+0.008025 test-rmse:9.968272+0.443284
## [23] train-rmse:9.023094+0.007243 test-rmse:9.008939+0.438743
## [24] train-rmse:8.164006+0.006919 test-rmse:8.150402+0.432761
## [25] train-rmse:7.393469+0.006909 test-rmse:7.379096+0.426788
## [26] train-rmse:6.703072+0.007057 test-rmse:6.686790+0.424458
## [27] train-rmse:6.085288+0.007552 test-rmse:6.068708+0.418426
## [28] train-rmse:5.532980+0.008304 test-rmse:5.519245+0.408053
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## [30] train-rmse:4.600489+0.010213 test-rmse:4.594970+0.392018
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## [32] train-rmse:3.862690+0.012582 test-rmse:3.860397+0.369796
## [33] train-rmse:3.555588+0.013855 test-rmse:3.554603+0.357976
## [34] train-rmse:3.284309+0.015268 test-rmse:3.287877+0.346969
## [35] train-rmse:3.045633+0.016470 test-rmse:3.050797+0.332103
## [36] train-rmse:2.836047+0.017489 test-rmse:2.845978+0.317524
## [37] train-rmse:2.652766+0.018795 test-rmse:2.669513+0.300805
## [38] train-rmse:2.493238+0.019878 test-rmse:2.514378+0.280145
## [39] train-rmse:2.354711+0.021049 test-rmse:2.379089+0.265032
## [40] train-rmse:2.234943+0.022094 test-rmse:2.265612+0.246200
## [41] train-rmse:2.131615+0.023174 test-rmse:2.167513+0.224969
## [42] train-rmse:2.042864+0.024109 test-rmse:2.084259+0.208171
## [43] train-rmse:1.966769+0.024842 test-rmse:2.014315+0.193288
## [44] train-rmse:1.901578+0.025421 test-rmse:1.955068+0.179009
## [45] train-rmse:1.845874+0.025797 test-rmse:1.905672+0.164749
## [46] train-rmse:1.798192+0.026300 test-rmse:1.864764+0.154294
## [47] train-rmse:1.757437+0.026768 test-rmse:1.828740+0.143916
## [48] train-rmse:1.722471+0.026893 test-rmse:1.792479+0.133593
## [49] train-rmse:1.692650+0.027017 test-rmse:1.770749+0.127906
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## [51] train-rmse:1.644496+0.027142 test-rmse:1.732634+0.118091
## [52] train-rmse:1.625123+0.027103 test-rmse:1.714835+0.115536
## [53] train-rmse:1.608072+0.026895 test-rmse:1.697876+0.115190
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## [62] train-rmse:1.515741+0.025723 test-rmse:1.622405+0.112412
## [63] train-rmse:1.509057+0.025562 test-rmse:1.616240+0.112238
## [64] train-rmse:1.502773+0.025388 test-rmse:1.612215+0.114254
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## [66] train-rmse:1.490971+0.025089 test-rmse:1.603275+0.113735
## [67] train-rmse:1.485414+0.024976 test-rmse:1.603660+0.114531
## [68] train-rmse:1.480038+0.024820 test-rmse:1.595055+0.110439
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## [70] train-rmse:1.469882+0.024491 test-rmse:1.587984+0.111379
## [71] train-rmse:1.465024+0.024388 test-rmse:1.583794+0.110146
## [72] train-rmse:1.460356+0.024308 test-rmse:1.580313+0.109840
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## [76] train-rmse:1.442292+0.024023 test-rmse:1.572665+0.113238
## [77] train-rmse:1.438008+0.023898 test-rmse:1.570687+0.113234
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## [80] train-rmse:1.425634+0.023685 test-rmse:1.558881+0.107172
## [81] train-rmse:1.421640+0.023650 test-rmse:1.555921+0.106504
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## [84] train-rmse:1.409962+0.023528 test-rmse:1.545438+0.104367
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## [98] train-rmse:1.360892+0.022997 test-rmse:1.515031+0.105377
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## [100] train-rmse:1.354468+0.022858 test-rmse:1.511719+0.101865
## [101] train-rmse:1.351313+0.022751 test-rmse:1.507047+0.100594
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## [104] train-rmse:1.342012+0.022467 test-rmse:1.499437+0.102430
## [105] train-rmse:1.338983+0.022408 test-rmse:1.497183+0.103030
## [106] train-rmse:1.336001+0.022355 test-rmse:1.494013+0.101579
## [107] train-rmse:1.333009+0.022288 test-rmse:1.492739+0.099012
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## [110] train-rmse:1.324319+0.022089 test-rmse:1.491584+0.100560
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## [112] train-rmse:1.318684+0.021980 test-rmse:1.484869+0.100631
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## [114] train-rmse:1.313197+0.021849 test-rmse:1.482315+0.097640
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## [118] train-rmse:1.302402+0.021720 test-rmse:1.477799+0.095221
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## [126] train-rmse:1.281920+0.021358 test-rmse:1.461458+0.095645
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## [132] train-rmse:1.267626+0.021081 test-rmse:1.453120+0.092218
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## [135] train-rmse:1.260769+0.021020 test-rmse:1.447388+0.091032
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## [138] train-rmse:1.254088+0.020967 test-rmse:1.442474+0.093734
## [139] train-rmse:1.251892+0.020953 test-rmse:1.440436+0.094316
## [140] train-rmse:1.249701+0.020929 test-rmse:1.439562+0.095391
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## [144] train-rmse:1.241051+0.020868 test-rmse:1.435498+0.091701
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## [149] train-rmse:1.230802+0.020748 test-rmse:1.427142+0.089426
## [150] train-rmse:1.228789+0.020730 test-rmse:1.424342+0.089648
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## [152] train-rmse:1.224864+0.020710 test-rmse:1.420796+0.089954
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## [176] train-rmse:1.181998+0.020503 test-rmse:1.389943+0.087287
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## [182] train-rmse:1.172507+0.020449 test-rmse:1.384558+0.089039
## [183] train-rmse:1.170971+0.020436 test-rmse:1.384326+0.089376
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## [192] train-rmse:1.157595+0.020255 test-rmse:1.372695+0.086352
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## [194] train-rmse:1.154748+0.020208 test-rmse:1.370296+0.086722
## [195] train-rmse:1.153349+0.020192 test-rmse:1.369066+0.086035
## [196] train-rmse:1.151953+0.020177 test-rmse:1.367934+0.086494
## [197] train-rmse:1.150574+0.020159 test-rmse:1.368015+0.087793
## [198] train-rmse:1.149201+0.020139 test-rmse:1.367931+0.085923
## [199] train-rmse:1.147828+0.020111 test-rmse:1.367604+0.086146
## [200] train-rmse:1.146473+0.020092 test-rmse:1.366128+0.085552
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## [202] train-rmse:1.143793+0.020043 test-rmse:1.363554+0.086548
## [203] train-rmse:1.142460+0.020017 test-rmse:1.363462+0.086117
## [204] train-rmse:1.141138+0.019997 test-rmse:1.362548+0.086056
## [205] train-rmse:1.139830+0.019977 test-rmse:1.362604+0.084456
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## [208] train-rmse:1.135957+0.019958 test-rmse:1.358986+0.083483
## [209] train-rmse:1.134667+0.019934 test-rmse:1.358675+0.085461
## [210] train-rmse:1.133388+0.019921 test-rmse:1.357532+0.086148
## [211] train-rmse:1.132132+0.019899 test-rmse:1.355123+0.086770
## [212] train-rmse:1.130883+0.019884 test-rmse:1.353668+0.087425
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## [214] train-rmse:1.128399+0.019856 test-rmse:1.350844+0.088888
## [215] train-rmse:1.127178+0.019846 test-rmse:1.349413+0.089583
## [216] train-rmse:1.125955+0.019837 test-rmse:1.349803+0.087602
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## [220] train-rmse:1.121181+0.019752 test-rmse:1.345538+0.086975
## [221] train-rmse:1.120007+0.019729 test-rmse:1.344384+0.086976
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## [223] train-rmse:1.117687+0.019686 test-rmse:1.343185+0.085805
## [224] train-rmse:1.116541+0.019675 test-rmse:1.342951+0.085455
## [225] train-rmse:1.115388+0.019669 test-rmse:1.342433+0.085376
## [226] train-rmse:1.114257+0.019649 test-rmse:1.340956+0.084467
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## [228] train-rmse:1.112004+0.019625 test-rmse:1.339527+0.084745
## [229] train-rmse:1.110885+0.019607 test-rmse:1.338396+0.085352
## [230] train-rmse:1.109774+0.019585 test-rmse:1.336443+0.086220
## [231] train-rmse:1.108675+0.019570 test-rmse:1.335900+0.086687
## [232] train-rmse:1.107580+0.019550 test-rmse:1.334156+0.086866
## [233] train-rmse:1.106495+0.019532 test-rmse:1.333495+0.087755
## [234] train-rmse:1.105417+0.019519 test-rmse:1.331850+0.087668
## [235] train-rmse:1.104347+0.019498 test-rmse:1.332577+0.085912
## [236] train-rmse:1.103272+0.019467 test-rmse:1.332076+0.086233
## [237] train-rmse:1.102210+0.019445 test-rmse:1.331578+0.086204
## [238] train-rmse:1.101158+0.019420 test-rmse:1.330314+0.086402
## [239] train-rmse:1.100119+0.019401 test-rmse:1.329319+0.086731
## [240] train-rmse:1.099091+0.019381 test-rmse:1.329072+0.087025
## [241] train-rmse:1.098060+0.019358 test-rmse:1.328103+0.087828
## [242] train-rmse:1.097026+0.019343 test-rmse:1.327459+0.087531
## [243] train-rmse:1.096007+0.019321 test-rmse:1.326854+0.087667
## [244] train-rmse:1.094991+0.019301 test-rmse:1.326023+0.087776
## [245] train-rmse:1.093972+0.019274 test-rmse:1.325760+0.087697
## [246] train-rmse:1.092971+0.019262 test-rmse:1.325146+0.087758
## [247] train-rmse:1.091973+0.019244 test-rmse:1.323582+0.088091
## [248] train-rmse:1.090985+0.019232 test-rmse:1.322079+0.089041
## [249] train-rmse:1.090007+0.019216 test-rmse:1.320917+0.088563
## [250] train-rmse:1.089022+0.019189 test-rmse:1.321773+0.087978
## [251] train-rmse:1.088049+0.019171 test-rmse:1.321098+0.087568
## [252] train-rmse:1.087085+0.019159 test-rmse:1.319227+0.086376
## [253] train-rmse:1.086116+0.019134 test-rmse:1.318564+0.085784
## [254] train-rmse:1.085163+0.019117 test-rmse:1.317913+0.086266
## [255] train-rmse:1.084219+0.019107 test-rmse:1.317388+0.086355
## [256] train-rmse:1.083278+0.019090 test-rmse:1.317196+0.085754
## [257] train-rmse:1.082341+0.019079 test-rmse:1.316861+0.086158
## [258] train-rmse:1.081411+0.019066 test-rmse:1.315840+0.086973
## [259] train-rmse:1.080484+0.019050 test-rmse:1.315313+0.086877
## [260] train-rmse:1.079569+0.019030 test-rmse:1.314655+0.086963
## [261] train-rmse:1.078668+0.019011 test-rmse:1.314007+0.087147
## [262] train-rmse:1.077773+0.018992 test-rmse:1.312972+0.087682
## [263] train-rmse:1.076882+0.018973 test-rmse:1.313745+0.088906
## [264] train-rmse:1.075994+0.018958 test-rmse:1.312850+0.089132
## [265] train-rmse:1.075108+0.018937 test-rmse:1.313117+0.087595
## [266] train-rmse:1.074220+0.018912 test-rmse:1.312080+0.086439
## [267] train-rmse:1.073336+0.018894 test-rmse:1.311417+0.086269
## [268] train-rmse:1.072455+0.018868 test-rmse:1.310698+0.086005
## [269] train-rmse:1.071578+0.018843 test-rmse:1.309510+0.086894
## [270] train-rmse:1.070714+0.018820 test-rmse:1.309506+0.086921
## [271] train-rmse:1.069851+0.018796 test-rmse:1.308126+0.087013
## [272] train-rmse:1.068990+0.018772 test-rmse:1.307101+0.087502
## [273] train-rmse:1.068134+0.018759 test-rmse:1.307110+0.088096
## [274] train-rmse:1.067286+0.018746 test-rmse:1.306161+0.089286
## [275] train-rmse:1.066449+0.018742 test-rmse:1.305278+0.088594
## [276] train-rmse:1.065617+0.018733 test-rmse:1.305069+0.088694
## [277] train-rmse:1.064790+0.018720 test-rmse:1.303881+0.089262
## [278] train-rmse:1.063974+0.018706 test-rmse:1.303806+0.089736
## [279] train-rmse:1.063152+0.018689 test-rmse:1.303103+0.089588
## [280] train-rmse:1.062331+0.018670 test-rmse:1.302598+0.089662
## [281] train-rmse:1.061523+0.018648 test-rmse:1.302064+0.090256
## [282] train-rmse:1.060722+0.018632 test-rmse:1.301339+0.089768
## [283] train-rmse:1.059923+0.018600 test-rmse:1.300771+0.089598
## [284] train-rmse:1.059134+0.018581 test-rmse:1.301067+0.089613
## [285] train-rmse:1.058345+0.018568 test-rmse:1.301842+0.088560
## [286] train-rmse:1.057562+0.018549 test-rmse:1.301895+0.088383
## [287] train-rmse:1.056785+0.018530 test-rmse:1.301736+0.088363
## [288] train-rmse:1.056007+0.018511 test-rmse:1.300391+0.088585
## [289] train-rmse:1.055229+0.018491 test-rmse:1.299652+0.088026
## [290] train-rmse:1.054452+0.018469 test-rmse:1.298643+0.088395
## [291] train-rmse:1.053685+0.018453 test-rmse:1.297534+0.087392
## [292] train-rmse:1.052927+0.018434 test-rmse:1.296932+0.088126
## [293] train-rmse:1.052178+0.018410 test-rmse:1.296056+0.088706
## [294] train-rmse:1.051422+0.018386 test-rmse:1.295739+0.088146
## [295] train-rmse:1.050679+0.018359 test-rmse:1.293646+0.089519
## [296] train-rmse:1.049945+0.018343 test-rmse:1.293090+0.089276
## [297] train-rmse:1.049214+0.018322 test-rmse:1.292453+0.089145
## [298] train-rmse:1.048491+0.018305 test-rmse:1.292219+0.088977
## [299] train-rmse:1.047768+0.018281 test-rmse:1.290894+0.089222
## [300] train-rmse:1.047047+0.018259 test-rmse:1.290264+0.089212
## [301] train-rmse:1.046323+0.018239 test-rmse:1.290189+0.090561
## [302] train-rmse:1.045608+0.018215 test-rmse:1.290599+0.090476
## [303] train-rmse:1.044896+0.018195 test-rmse:1.290422+0.090256
## [304] train-rmse:1.044185+0.018180 test-rmse:1.290647+0.090333
## [305] train-rmse:1.043478+0.018165 test-rmse:1.290496+0.090410
## [306] train-rmse:1.042776+0.018148 test-rmse:1.290186+0.090401
## [307] train-rmse:1.042073+0.018130 test-rmse:1.289755+0.090863
## [308] train-rmse:1.041382+0.018112 test-rmse:1.288409+0.090914
## [309] train-rmse:1.040691+0.018094 test-rmse:1.288179+0.091033
## [310] train-rmse:1.039997+0.018065 test-rmse:1.288727+0.089831
## [311] train-rmse:1.039310+0.018042 test-rmse:1.288596+0.090051
## [312] train-rmse:1.038631+0.018016 test-rmse:1.287577+0.090284
## [313] train-rmse:1.037949+0.017997 test-rmse:1.286655+0.090040
## [314] train-rmse:1.037275+0.017974 test-rmse:1.286510+0.089795
## [315] train-rmse:1.036594+0.017949 test-rmse:1.285426+0.089936
## [316] train-rmse:1.035925+0.017933 test-rmse:1.285061+0.089921
## [317] train-rmse:1.035258+0.017923 test-rmse:1.285046+0.089833
## [318] train-rmse:1.034593+0.017906 test-rmse:1.284101+0.089611
## [319] train-rmse:1.033929+0.017898 test-rmse:1.284419+0.089734
## [320] train-rmse:1.033272+0.017884 test-rmse:1.283853+0.090045
## [321] train-rmse:1.032623+0.017876 test-rmse:1.282538+0.090433
## [322] train-rmse:1.031979+0.017866 test-rmse:1.281250+0.088828
## [323] train-rmse:1.031334+0.017853 test-rmse:1.280719+0.088851
## [324] train-rmse:1.030691+0.017843 test-rmse:1.280021+0.088774
## [325] train-rmse:1.030058+0.017825 test-rmse:1.279141+0.088647
## [326] train-rmse:1.029427+0.017813 test-rmse:1.278857+0.089064
## [327] train-rmse:1.028804+0.017799 test-rmse:1.278932+0.088698
## [328] train-rmse:1.028182+0.017782 test-rmse:1.277846+0.089336
## [329] train-rmse:1.027563+0.017766 test-rmse:1.277746+0.089174
## [330] train-rmse:1.026948+0.017750 test-rmse:1.277420+0.089113
## [331] train-rmse:1.026337+0.017739 test-rmse:1.277259+0.088921
## [332] train-rmse:1.025728+0.017726 test-rmse:1.277050+0.089405
## [333] train-rmse:1.025119+0.017716 test-rmse:1.276739+0.089830
## [334] train-rmse:1.024512+0.017706 test-rmse:1.276319+0.090754
## [335] train-rmse:1.023905+0.017694 test-rmse:1.276350+0.091173
## [336] train-rmse:1.023302+0.017685 test-rmse:1.275764+0.090604
## [337] train-rmse:1.022700+0.017674 test-rmse:1.274448+0.089283
## [338] train-rmse:1.022097+0.017666 test-rmse:1.273988+0.089077
## [339] train-rmse:1.021500+0.017655 test-rmse:1.273054+0.089167
## [340] train-rmse:1.020904+0.017645 test-rmse:1.272054+0.088808
## [341] train-rmse:1.020306+0.017637 test-rmse:1.271964+0.089178
## [342] train-rmse:1.019716+0.017631 test-rmse:1.272008+0.088929
## [343] train-rmse:1.019130+0.017622 test-rmse:1.271568+0.089042
## [344] train-rmse:1.018535+0.017603 test-rmse:1.271734+0.087800
## [345] train-rmse:1.017951+0.017589 test-rmse:1.271734+0.087277
## [346] train-rmse:1.017370+0.017582 test-rmse:1.270845+0.086973
## [347] train-rmse:1.016785+0.017573 test-rmse:1.271038+0.086610
## [348] train-rmse:1.016206+0.017566 test-rmse:1.271083+0.087530
## [349] train-rmse:1.015636+0.017556 test-rmse:1.269787+0.087470
## [350] train-rmse:1.015068+0.017545 test-rmse:1.269100+0.087878
## [351] train-rmse:1.014503+0.017540 test-rmse:1.268229+0.087522
## [352] train-rmse:1.013941+0.017535 test-rmse:1.268468+0.086915
## [353] train-rmse:1.013383+0.017529 test-rmse:1.267956+0.086883
## [354] train-rmse:1.012828+0.017520 test-rmse:1.268028+0.086785
## [355] train-rmse:1.012272+0.017509 test-rmse:1.267060+0.087002
## [356] train-rmse:1.011715+0.017496 test-rmse:1.266885+0.087636
## [357] train-rmse:1.011159+0.017495 test-rmse:1.266981+0.087798
## [358] train-rmse:1.010614+0.017491 test-rmse:1.266295+0.088506
## [359] train-rmse:1.010065+0.017493 test-rmse:1.266218+0.088791
## [360] train-rmse:1.009522+0.017487 test-rmse:1.265817+0.088763
## [361] train-rmse:1.008982+0.017479 test-rmse:1.265500+0.088884
## [362] train-rmse:1.008445+0.017472 test-rmse:1.264243+0.089167
## [363] train-rmse:1.007909+0.017462 test-rmse:1.263003+0.088070
## [364] train-rmse:1.007375+0.017455 test-rmse:1.262796+0.087866
## [365] train-rmse:1.006844+0.017447 test-rmse:1.263109+0.088095
## [366] train-rmse:1.006307+0.017444 test-rmse:1.262874+0.087931
## [367] train-rmse:1.005778+0.017438 test-rmse:1.261700+0.087417
## [368] train-rmse:1.005255+0.017436 test-rmse:1.262178+0.087027
## [369] train-rmse:1.004721+0.017432 test-rmse:1.261569+0.087753
## [370] train-rmse:1.004201+0.017435 test-rmse:1.261008+0.087467
## [371] train-rmse:1.003684+0.017434 test-rmse:1.260483+0.087338
## [372] train-rmse:1.003169+0.017432 test-rmse:1.259766+0.087502
## [373] train-rmse:1.002655+0.017432 test-rmse:1.259264+0.087217
## [374] train-rmse:1.002141+0.017431 test-rmse:1.260005+0.086411
## [375] train-rmse:1.001628+0.017427 test-rmse:1.259321+0.086198
## [376] train-rmse:1.001121+0.017426 test-rmse:1.259857+0.085756
## [377] train-rmse:1.000615+0.017426 test-rmse:1.258955+0.085915
## [378] train-rmse:1.000111+0.017426 test-rmse:1.258895+0.086179
## [379] train-rmse:0.999611+0.017420 test-rmse:1.258972+0.086162
## [380] train-rmse:0.999111+0.017419 test-rmse:1.258909+0.086164
## [381] train-rmse:0.998614+0.017417 test-rmse:1.259070+0.085779
## [382] train-rmse:0.998110+0.017404 test-rmse:1.259024+0.085606
## [383] train-rmse:0.997614+0.017405 test-rmse:1.258775+0.085924
## [384] train-rmse:0.997120+0.017404 test-rmse:1.258439+0.085839
## [385] train-rmse:0.996622+0.017409 test-rmse:1.258602+0.086792
## [386] train-rmse:0.996132+0.017405 test-rmse:1.258506+0.087116
## [387] train-rmse:0.995645+0.017402 test-rmse:1.258573+0.086755
## [388] train-rmse:0.995158+0.017402 test-rmse:1.258384+0.086830
## [389] train-rmse:0.994676+0.017400 test-rmse:1.258074+0.087097
## [390] train-rmse:0.994197+0.017400 test-rmse:1.257692+0.087473
## [391] train-rmse:0.993715+0.017398 test-rmse:1.256637+0.088079
## [392] train-rmse:0.993238+0.017399 test-rmse:1.256024+0.086459
## [393] train-rmse:0.992765+0.017404 test-rmse:1.255751+0.086517
## [394] train-rmse:0.992295+0.017407 test-rmse:1.255902+0.086750
## [395] train-rmse:0.991829+0.017408 test-rmse:1.255605+0.086570
## [396] train-rmse:0.991352+0.017406 test-rmse:1.255634+0.087067
## [397] train-rmse:0.990876+0.017399 test-rmse:1.255284+0.086894
## [398] train-rmse:0.990411+0.017400 test-rmse:1.254429+0.087107
## [399] train-rmse:0.989945+0.017403 test-rmse:1.254326+0.087034
## [400] train-rmse:0.989479+0.017405 test-rmse:1.253834+0.086791
## [401] train-rmse:0.989019+0.017407 test-rmse:1.253981+0.086912
## [402] train-rmse:0.988559+0.017413 test-rmse:1.253666+0.087037
## [403] train-rmse:0.988102+0.017414 test-rmse:1.253842+0.086726
## [404] train-rmse:0.987644+0.017415 test-rmse:1.253476+0.086932
## [405] train-rmse:0.987191+0.017415 test-rmse:1.253531+0.086414
## [406] train-rmse:0.986736+0.017419 test-rmse:1.253379+0.086152
## [407] train-rmse:0.986281+0.017417 test-rmse:1.253738+0.086065
## [408] train-rmse:0.985836+0.017418 test-rmse:1.253294+0.086159
## [409] train-rmse:0.985391+0.017419 test-rmse:1.252460+0.086600
## [410] train-rmse:0.984945+0.017420 test-rmse:1.251797+0.086523
## [411] train-rmse:0.984504+0.017422 test-rmse:1.251303+0.086747
## [412] train-rmse:0.984063+0.017421 test-rmse:1.250527+0.086571
## [413] train-rmse:0.983620+0.017422 test-rmse:1.250893+0.087263
## [414] train-rmse:0.983180+0.017421 test-rmse:1.251232+0.086943
## [415] train-rmse:0.982737+0.017428 test-rmse:1.251093+0.087485
## [416] train-rmse:0.982302+0.017429 test-rmse:1.250940+0.087854
## [417] train-rmse:0.981867+0.017429 test-rmse:1.250667+0.087745
## [418] train-rmse:0.981431+0.017427 test-rmse:1.249627+0.087350
## [419] train-rmse:0.980997+0.017428 test-rmse:1.249105+0.087145
## [420] train-rmse:0.980565+0.017433 test-rmse:1.249291+0.086513
## [421] train-rmse:0.980139+0.017432 test-rmse:1.248971+0.086396
## [422] train-rmse:0.979712+0.017426 test-rmse:1.248142+0.086439
## [423] train-rmse:0.979289+0.017427 test-rmse:1.247982+0.086505
## [424] train-rmse:0.978862+0.017432 test-rmse:1.248078+0.086029
## [425] train-rmse:0.978440+0.017432 test-rmse:1.247473+0.086144
## [426] train-rmse:0.978019+0.017434 test-rmse:1.246942+0.085897
## [427] train-rmse:0.977602+0.017441 test-rmse:1.246536+0.085840
## [428] train-rmse:0.977188+0.017446 test-rmse:1.245907+0.085510
## [429] train-rmse:0.976772+0.017455 test-rmse:1.244950+0.084219
## [430] train-rmse:0.976360+0.017456 test-rmse:1.244934+0.084569
## [431] train-rmse:0.975950+0.017453 test-rmse:1.245172+0.084722
## [432] train-rmse:0.975537+0.017453 test-rmse:1.245123+0.084471
## [433] train-rmse:0.975127+0.017455 test-rmse:1.244998+0.084752
## [434] train-rmse:0.974720+0.017455 test-rmse:1.244931+0.084750
## [435] train-rmse:0.974315+0.017454 test-rmse:1.244805+0.085156
## [436] train-rmse:0.973915+0.017454 test-rmse:1.244418+0.085042
## [437] train-rmse:0.973517+0.017453 test-rmse:1.244171+0.085405
## [438] train-rmse:0.973114+0.017455 test-rmse:1.244371+0.085739
## [439] train-rmse:0.972710+0.017448 test-rmse:1.245254+0.085268
## [440] train-rmse:0.972309+0.017449 test-rmse:1.244911+0.085679
## [441] train-rmse:0.971912+0.017450 test-rmse:1.244331+0.085947
## [442] train-rmse:0.971515+0.017448 test-rmse:1.244282+0.085715
## [443] train-rmse:0.971121+0.017450 test-rmse:1.244005+0.085661
## [444] train-rmse:0.970724+0.017451 test-rmse:1.244154+0.086285
## [445] train-rmse:0.970335+0.017452 test-rmse:1.243512+0.086098
## [446] train-rmse:0.969946+0.017449 test-rmse:1.243589+0.085761
## [447] train-rmse:0.969556+0.017453 test-rmse:1.243189+0.085590
## [448] train-rmse:0.969169+0.017455 test-rmse:1.243471+0.085478
## [449] train-rmse:0.968783+0.017457 test-rmse:1.243269+0.085415
## [450] train-rmse:0.968400+0.017459 test-rmse:1.242770+0.085234
## [451] train-rmse:0.968014+0.017458 test-rmse:1.243066+0.085979
## [452] train-rmse:0.967631+0.017462 test-rmse:1.242627+0.086407
## [453] train-rmse:0.967250+0.017466 test-rmse:1.242301+0.085900
## [454] train-rmse:0.966869+0.017471 test-rmse:1.241770+0.085898
## [455] train-rmse:0.966492+0.017472 test-rmse:1.241953+0.085671
## [456] train-rmse:0.966113+0.017468 test-rmse:1.241456+0.085639
## [457] train-rmse:0.965735+0.017471 test-rmse:1.241216+0.085769
## [458] train-rmse:0.965360+0.017476 test-rmse:1.240774+0.085959
## [459] train-rmse:0.964982+0.017486 test-rmse:1.240545+0.086960
## [460] train-rmse:0.964610+0.017493 test-rmse:1.241131+0.087266
## [461] train-rmse:0.964236+0.017501 test-rmse:1.241182+0.086920
## [462] train-rmse:0.963868+0.017508 test-rmse:1.240957+0.086874
## [463] train-rmse:0.963502+0.017513 test-rmse:1.240652+0.087149
## [464] train-rmse:0.963133+0.017521 test-rmse:1.240235+0.086735
## [465] train-rmse:0.962762+0.017521 test-rmse:1.240044+0.087009
## [466] train-rmse:0.962394+0.017524 test-rmse:1.238419+0.085653
## [467] train-rmse:0.962028+0.017524 test-rmse:1.238203+0.085476
## [468] train-rmse:0.961657+0.017527 test-rmse:1.237883+0.085434
## [469] train-rmse:0.961293+0.017533 test-rmse:1.238847+0.084460
## [470] train-rmse:0.960932+0.017538 test-rmse:1.238780+0.084634
## [471] train-rmse:0.960573+0.017542 test-rmse:1.238750+0.084164
## [472] train-rmse:0.960219+0.017547 test-rmse:1.238521+0.084090
## [473] train-rmse:0.959864+0.017552 test-rmse:1.238692+0.084218
## [474] train-rmse:0.959506+0.017558 test-rmse:1.238768+0.084302
## Stopping. Best iteration:
## [468] train-rmse:0.961657+0.017527 test-rmse:1.237883+0.085434
##
## 1
## [1] train-rmse:88.665695+0.088976 test-rmse:88.666461+0.404980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.835214+0.080050 test-rmse:79.835893+0.413675
## [3] train-rmse:71.885242+0.071998 test-rmse:71.885820+0.421463
## [4] train-rmse:64.728087+0.064733 test-rmse:64.728551+0.428434
## [5] train-rmse:58.284824+0.058174 test-rmse:58.285159+0.434654
## [6] train-rmse:52.484374+0.052257 test-rmse:52.484567+0.440197
## [7] train-rmse:47.262778+0.046908 test-rmse:47.262816+0.445120
## [8] train-rmse:42.562434+0.042074 test-rmse:42.562303+0.449468
## [9] train-rmse:38.331508+0.037710 test-rmse:38.331192+0.453293
## [10] train-rmse:34.523343+0.033759 test-rmse:34.522811+0.456630
## [11] train-rmse:31.095926+0.030181 test-rmse:31.095166+0.459509
## [12] train-rmse:28.011453+0.026945 test-rmse:28.010445+0.461960
## [13] train-rmse:25.235913+0.024012 test-rmse:25.234629+0.463999
## [14] train-rmse:22.738687+0.021363 test-rmse:22.737103+0.465637
## [15] train-rmse:20.492223+0.018971 test-rmse:20.490315+0.466887
## [16] train-rmse:18.471749+0.016823 test-rmse:18.469484+0.467747
## [17] train-rmse:16.654969+0.014903 test-rmse:16.652318+0.468213
## [18] train-rmse:15.021334+0.013222 test-rmse:15.020864+0.456548
## [19] train-rmse:13.552638+0.011590 test-rmse:13.550876+0.449119
## [20] train-rmse:12.232204+0.010135 test-rmse:12.222750+0.446257
## [21] train-rmse:11.045206+0.008913 test-rmse:11.035400+0.435400
## [22] train-rmse:9.978030+0.008019 test-rmse:9.966148+0.429625
## [23] train-rmse:9.018742+0.007174 test-rmse:9.000809+0.427222
## [24] train-rmse:8.156825+0.006737 test-rmse:8.145320+0.416046
## [25] train-rmse:7.382623+0.006335 test-rmse:7.368813+0.407825
## [26] train-rmse:6.687697+0.006199 test-rmse:6.680066+0.394512
## [27] train-rmse:6.063817+0.006278 test-rmse:6.054954+0.387910
## [28] train-rmse:5.504728+0.006089 test-rmse:5.498833+0.379448
## [29] train-rmse:5.003695+0.006597 test-rmse:4.997531+0.369393
## [30] train-rmse:4.555438+0.006971 test-rmse:4.552144+0.361533
## [31] train-rmse:4.155410+0.007013 test-rmse:4.152031+0.356011
## [32] train-rmse:3.798181+0.007965 test-rmse:3.798917+0.348658
## [33] train-rmse:3.480425+0.007882 test-rmse:3.490479+0.336623
## [34] train-rmse:3.197386+0.008741 test-rmse:3.211733+0.323150
## [35] train-rmse:2.947292+0.009883 test-rmse:2.970537+0.310529
## [36] train-rmse:2.726225+0.011099 test-rmse:2.754650+0.297279
## [37] train-rmse:2.531626+0.011054 test-rmse:2.563110+0.281378
## [38] train-rmse:2.360084+0.010689 test-rmse:2.402166+0.269516
## [39] train-rmse:2.209549+0.010642 test-rmse:2.259924+0.257042
## [40] train-rmse:2.078889+0.010934 test-rmse:2.139972+0.247216
## [41] train-rmse:1.964759+0.011584 test-rmse:2.034912+0.230664
## [42] train-rmse:1.865176+0.011735 test-rmse:1.946129+0.215627
## [43] train-rmse:1.779480+0.011557 test-rmse:1.867812+0.202941
## [44] train-rmse:1.705572+0.011697 test-rmse:1.800987+0.192181
## [45] train-rmse:1.642020+0.011842 test-rmse:1.748217+0.181679
## [46] train-rmse:1.586074+0.012588 test-rmse:1.702903+0.170285
## [47] train-rmse:1.538074+0.012773 test-rmse:1.661126+0.159043
## [48] train-rmse:1.496895+0.012003 test-rmse:1.629049+0.148082
## [49] train-rmse:1.462072+0.012124 test-rmse:1.601961+0.139067
## [50] train-rmse:1.430791+0.011120 test-rmse:1.578572+0.131733
## [51] train-rmse:1.404476+0.011064 test-rmse:1.556426+0.128339
## [52] train-rmse:1.380232+0.012116 test-rmse:1.533386+0.122259
## [53] train-rmse:1.359284+0.010761 test-rmse:1.518423+0.116701
## [54] train-rmse:1.341063+0.010845 test-rmse:1.504538+0.114705
## [55] train-rmse:1.323624+0.011517 test-rmse:1.492702+0.111689
## [56] train-rmse:1.308540+0.011741 test-rmse:1.480470+0.109325
## [57] train-rmse:1.295463+0.012396 test-rmse:1.471399+0.106024
## [58] train-rmse:1.284149+0.012520 test-rmse:1.461757+0.105146
## [59] train-rmse:1.273449+0.012731 test-rmse:1.453527+0.102499
## [60] train-rmse:1.262597+0.011425 test-rmse:1.445628+0.101168
## [61] train-rmse:1.253081+0.012169 test-rmse:1.439298+0.099704
## [62] train-rmse:1.243640+0.013661 test-rmse:1.433569+0.096373
## [63] train-rmse:1.235062+0.012555 test-rmse:1.427081+0.094231
## [64] train-rmse:1.226692+0.012706 test-rmse:1.423584+0.093783
## [65] train-rmse:1.219217+0.012925 test-rmse:1.418181+0.093899
## [66] train-rmse:1.211317+0.011646 test-rmse:1.411221+0.093313
## [67] train-rmse:1.204206+0.011502 test-rmse:1.406730+0.091086
## [68] train-rmse:1.196591+0.012307 test-rmse:1.405032+0.091753
## [69] train-rmse:1.189641+0.011621 test-rmse:1.402977+0.091157
## [70] train-rmse:1.183895+0.011476 test-rmse:1.398239+0.090076
## [71] train-rmse:1.178000+0.011735 test-rmse:1.392505+0.088055
## [72] train-rmse:1.172300+0.011623 test-rmse:1.388604+0.089307
## [73] train-rmse:1.166186+0.012771 test-rmse:1.380869+0.092128
## [74] train-rmse:1.159405+0.013053 test-rmse:1.376677+0.091777
## [75] train-rmse:1.154030+0.013257 test-rmse:1.374105+0.090524
## [76] train-rmse:1.148085+0.012656 test-rmse:1.372643+0.089152
## [77] train-rmse:1.143253+0.012682 test-rmse:1.368923+0.089953
## [78] train-rmse:1.137850+0.012578 test-rmse:1.364153+0.089503
## [79] train-rmse:1.132466+0.013410 test-rmse:1.355761+0.088702
## [80] train-rmse:1.127134+0.013737 test-rmse:1.350543+0.090423
## [81] train-rmse:1.121132+0.014047 test-rmse:1.346440+0.089797
## [82] train-rmse:1.115874+0.013565 test-rmse:1.345134+0.089827
## [83] train-rmse:1.111531+0.013528 test-rmse:1.341546+0.088989
## [84] train-rmse:1.106950+0.013518 test-rmse:1.338749+0.088996
## [85] train-rmse:1.102447+0.013610 test-rmse:1.334498+0.086219
## [86] train-rmse:1.098208+0.013963 test-rmse:1.332509+0.086029
## [87] train-rmse:1.093218+0.013447 test-rmse:1.326281+0.086091
## [88] train-rmse:1.088461+0.013235 test-rmse:1.323566+0.086681
## [89] train-rmse:1.083923+0.013037 test-rmse:1.320358+0.087568
## [90] train-rmse:1.079225+0.012779 test-rmse:1.319126+0.087058
## [91] train-rmse:1.075174+0.013011 test-rmse:1.316077+0.086893
## [92] train-rmse:1.068309+0.013638 test-rmse:1.311998+0.085770
## [93] train-rmse:1.064089+0.013372 test-rmse:1.306351+0.088362
## [94] train-rmse:1.058663+0.013566 test-rmse:1.304936+0.087426
## [95] train-rmse:1.054416+0.013717 test-rmse:1.302029+0.086665
## [96] train-rmse:1.050777+0.014002 test-rmse:1.298862+0.087543
## [97] train-rmse:1.046937+0.013658 test-rmse:1.296463+0.085787
## [98] train-rmse:1.043175+0.014282 test-rmse:1.293138+0.087564
## [99] train-rmse:1.038724+0.014551 test-rmse:1.290724+0.089079
## [100] train-rmse:1.035445+0.014653 test-rmse:1.289874+0.088592
## [101] train-rmse:1.030954+0.015939 test-rmse:1.285597+0.085810
## [102] train-rmse:1.025732+0.016237 test-rmse:1.283683+0.084370
## [103] train-rmse:1.021421+0.015783 test-rmse:1.280366+0.085559
## [104] train-rmse:1.016901+0.015977 test-rmse:1.278026+0.086194
## [105] train-rmse:1.013377+0.016336 test-rmse:1.277985+0.087824
## [106] train-rmse:1.009098+0.017086 test-rmse:1.274711+0.090343
## [107] train-rmse:1.005852+0.017294 test-rmse:1.272327+0.090554
## [108] train-rmse:1.002170+0.016543 test-rmse:1.270422+0.091051
## [109] train-rmse:0.997930+0.016761 test-rmse:1.266616+0.089724
## [110] train-rmse:0.994731+0.016490 test-rmse:1.265151+0.089321
## [111] train-rmse:0.991844+0.016399 test-rmse:1.262599+0.088635
## [112] train-rmse:0.988465+0.015993 test-rmse:1.260360+0.087739
## [113] train-rmse:0.984698+0.016436 test-rmse:1.258656+0.088870
## [114] train-rmse:0.980682+0.016695 test-rmse:1.256055+0.088245
## [115] train-rmse:0.976049+0.016720 test-rmse:1.252382+0.088092
## [116] train-rmse:0.972855+0.016873 test-rmse:1.250688+0.087196
## [117] train-rmse:0.968636+0.017420 test-rmse:1.246464+0.089151
## [118] train-rmse:0.965596+0.017643 test-rmse:1.246986+0.090805
## [119] train-rmse:0.962855+0.017873 test-rmse:1.245405+0.089520
## [120] train-rmse:0.957798+0.016865 test-rmse:1.239259+0.091196
## [121] train-rmse:0.954822+0.016975 test-rmse:1.237996+0.091231
## [122] train-rmse:0.951660+0.017462 test-rmse:1.235149+0.091469
## [123] train-rmse:0.948004+0.017455 test-rmse:1.234452+0.090571
## [124] train-rmse:0.945176+0.017616 test-rmse:1.233264+0.091067
## [125] train-rmse:0.942667+0.017501 test-rmse:1.231901+0.089812
## [126] train-rmse:0.939824+0.017835 test-rmse:1.227814+0.090533
## [127] train-rmse:0.936831+0.017292 test-rmse:1.225078+0.092216
## [128] train-rmse:0.933398+0.016201 test-rmse:1.225528+0.091571
## [129] train-rmse:0.928609+0.015985 test-rmse:1.224390+0.090350
## [130] train-rmse:0.925890+0.016501 test-rmse:1.223607+0.090782
## [131] train-rmse:0.923561+0.016737 test-rmse:1.222882+0.090409
## [132] train-rmse:0.920145+0.016964 test-rmse:1.219732+0.091566
## [133] train-rmse:0.917154+0.016686 test-rmse:1.217214+0.091095
## [134] train-rmse:0.914625+0.016286 test-rmse:1.215269+0.090883
## [135] train-rmse:0.911127+0.017067 test-rmse:1.213179+0.089520
## [136] train-rmse:0.908763+0.017076 test-rmse:1.212176+0.089577
## [137] train-rmse:0.906586+0.017198 test-rmse:1.210854+0.090728
## [138] train-rmse:0.903495+0.015997 test-rmse:1.208769+0.091480
## [139] train-rmse:0.900669+0.015594 test-rmse:1.206137+0.092723
## [140] train-rmse:0.897330+0.014516 test-rmse:1.205242+0.091257
## [141] train-rmse:0.894416+0.015288 test-rmse:1.203724+0.092401
## [142] train-rmse:0.891834+0.015651 test-rmse:1.202389+0.091953
## [143] train-rmse:0.889623+0.015793 test-rmse:1.200775+0.092348
## [144] train-rmse:0.886724+0.016212 test-rmse:1.198855+0.091215
## [145] train-rmse:0.884251+0.016171 test-rmse:1.197126+0.091924
## [146] train-rmse:0.882344+0.016234 test-rmse:1.196285+0.092918
## [147] train-rmse:0.879111+0.015097 test-rmse:1.193021+0.095088
## [148] train-rmse:0.876031+0.015058 test-rmse:1.191272+0.094697
## [149] train-rmse:0.873668+0.014618 test-rmse:1.190398+0.095818
## [150] train-rmse:0.871212+0.014203 test-rmse:1.189485+0.096252
## [151] train-rmse:0.867559+0.013654 test-rmse:1.188791+0.096663
## [152] train-rmse:0.864801+0.013544 test-rmse:1.188284+0.096609
## [153] train-rmse:0.861641+0.012226 test-rmse:1.186972+0.097082
## [154] train-rmse:0.859209+0.011628 test-rmse:1.185143+0.097850
## [155] train-rmse:0.857145+0.011738 test-rmse:1.183839+0.097425
## [156] train-rmse:0.854271+0.011231 test-rmse:1.181931+0.098428
## [157] train-rmse:0.852551+0.011110 test-rmse:1.180558+0.098466
## [158] train-rmse:0.850126+0.011382 test-rmse:1.178915+0.097666
## [159] train-rmse:0.848278+0.011428 test-rmse:1.178939+0.096785
## [160] train-rmse:0.845362+0.011834 test-rmse:1.177189+0.097398
## [161] train-rmse:0.842380+0.011522 test-rmse:1.176001+0.097588
## [162] train-rmse:0.840235+0.011857 test-rmse:1.173697+0.096635
## [163] train-rmse:0.838412+0.011640 test-rmse:1.172768+0.096711
## [164] train-rmse:0.836452+0.011436 test-rmse:1.169913+0.097942
## [165] train-rmse:0.833089+0.010335 test-rmse:1.168700+0.098125
## [166] train-rmse:0.830570+0.009797 test-rmse:1.166956+0.098598
## [167] train-rmse:0.828465+0.009713 test-rmse:1.166187+0.099031
## [168] train-rmse:0.826589+0.009655 test-rmse:1.165038+0.099215
## [169] train-rmse:0.824773+0.009227 test-rmse:1.164285+0.098536
## [170] train-rmse:0.823146+0.009364 test-rmse:1.163546+0.098608
## [171] train-rmse:0.821102+0.009462 test-rmse:1.163407+0.099123
## [172] train-rmse:0.819032+0.009067 test-rmse:1.161803+0.099931
## [173] train-rmse:0.816706+0.008927 test-rmse:1.161109+0.100486
## [174] train-rmse:0.814994+0.009048 test-rmse:1.159947+0.100880
## [175] train-rmse:0.812435+0.008098 test-rmse:1.156801+0.101874
## [176] train-rmse:0.810930+0.008069 test-rmse:1.156419+0.101530
## [177] train-rmse:0.808745+0.008497 test-rmse:1.156289+0.102117
## [178] train-rmse:0.806773+0.008531 test-rmse:1.154082+0.103530
## [179] train-rmse:0.804723+0.008567 test-rmse:1.153672+0.103686
## [180] train-rmse:0.803262+0.008569 test-rmse:1.152942+0.103767
## [181] train-rmse:0.801467+0.008270 test-rmse:1.151455+0.104594
## [182] train-rmse:0.798049+0.007566 test-rmse:1.150502+0.104926
## [183] train-rmse:0.796576+0.007364 test-rmse:1.149841+0.104347
## [184] train-rmse:0.794670+0.007264 test-rmse:1.147917+0.105163
## [185] train-rmse:0.792049+0.007130 test-rmse:1.148444+0.105313
## [186] train-rmse:0.790752+0.007126 test-rmse:1.147653+0.105704
## [187] train-rmse:0.789057+0.007351 test-rmse:1.147499+0.105241
## [188] train-rmse:0.786644+0.006392 test-rmse:1.147016+0.105620
## [189] train-rmse:0.785112+0.006177 test-rmse:1.147186+0.105280
## [190] train-rmse:0.783875+0.006200 test-rmse:1.145748+0.106472
## [191] train-rmse:0.781584+0.006825 test-rmse:1.144707+0.105917
## [192] train-rmse:0.779530+0.006141 test-rmse:1.143397+0.106772
## [193] train-rmse:0.777248+0.006643 test-rmse:1.140436+0.104800
## [194] train-rmse:0.775070+0.007035 test-rmse:1.140216+0.105606
## [195] train-rmse:0.773646+0.006912 test-rmse:1.138840+0.106760
## [196] train-rmse:0.771690+0.006489 test-rmse:1.138567+0.106049
## [197] train-rmse:0.770277+0.006415 test-rmse:1.137164+0.104814
## [198] train-rmse:0.768361+0.005662 test-rmse:1.136258+0.105589
## [199] train-rmse:0.766306+0.006688 test-rmse:1.134651+0.104802
## [200] train-rmse:0.764891+0.006826 test-rmse:1.133409+0.104663
## [201] train-rmse:0.762883+0.006368 test-rmse:1.131398+0.106082
## [202] train-rmse:0.760602+0.006069 test-rmse:1.129525+0.106778
## [203] train-rmse:0.759359+0.005954 test-rmse:1.128713+0.106751
## [204] train-rmse:0.757843+0.005559 test-rmse:1.128828+0.106982
## [205] train-rmse:0.755603+0.004941 test-rmse:1.128329+0.107637
## [206] train-rmse:0.753723+0.004937 test-rmse:1.128663+0.107083
## [207] train-rmse:0.751715+0.004793 test-rmse:1.127532+0.108079
## [208] train-rmse:0.750198+0.005220 test-rmse:1.127228+0.107467
## [209] train-rmse:0.748396+0.005908 test-rmse:1.127060+0.105946
## [210] train-rmse:0.746979+0.005808 test-rmse:1.126254+0.106023
## [211] train-rmse:0.744951+0.006832 test-rmse:1.123877+0.106487
## [212] train-rmse:0.743426+0.006649 test-rmse:1.122770+0.105563
## [213] train-rmse:0.741484+0.006080 test-rmse:1.121169+0.106839
## [214] train-rmse:0.739868+0.006863 test-rmse:1.119394+0.105998
## [215] train-rmse:0.738232+0.006431 test-rmse:1.118394+0.106233
## [216] train-rmse:0.737170+0.006406 test-rmse:1.117355+0.106272
## [217] train-rmse:0.735192+0.007166 test-rmse:1.115982+0.105292
## [218] train-rmse:0.733427+0.006843 test-rmse:1.115519+0.106274
## [219] train-rmse:0.731774+0.006528 test-rmse:1.115974+0.106527
## [220] train-rmse:0.729838+0.006317 test-rmse:1.114868+0.105556
## [221] train-rmse:0.728249+0.005938 test-rmse:1.113907+0.107488
## [222] train-rmse:0.727001+0.005813 test-rmse:1.113431+0.107685
## [223] train-rmse:0.725003+0.006323 test-rmse:1.112564+0.107640
## [224] train-rmse:0.722905+0.006332 test-rmse:1.111829+0.107996
## [225] train-rmse:0.720961+0.005769 test-rmse:1.110079+0.107498
## [226] train-rmse:0.719777+0.005769 test-rmse:1.109017+0.107874
## [227] train-rmse:0.718447+0.005672 test-rmse:1.108163+0.107793
## [228] train-rmse:0.717032+0.005862 test-rmse:1.107476+0.107899
## [229] train-rmse:0.715532+0.005805 test-rmse:1.106982+0.107661
## [230] train-rmse:0.714501+0.005856 test-rmse:1.107026+0.108025
## [231] train-rmse:0.713446+0.005747 test-rmse:1.106848+0.107941
## [232] train-rmse:0.712046+0.005529 test-rmse:1.106919+0.107915
## [233] train-rmse:0.710568+0.005425 test-rmse:1.105565+0.108874
## [234] train-rmse:0.709311+0.005293 test-rmse:1.105271+0.108584
## [235] train-rmse:0.707871+0.005277 test-rmse:1.104239+0.109928
## [236] train-rmse:0.705951+0.005887 test-rmse:1.101988+0.111294
## [237] train-rmse:0.704564+0.005903 test-rmse:1.100581+0.110894
## [238] train-rmse:0.703472+0.005726 test-rmse:1.099602+0.111709
## [239] train-rmse:0.701903+0.006459 test-rmse:1.099523+0.111201
## [240] train-rmse:0.700431+0.006631 test-rmse:1.098424+0.111362
## [241] train-rmse:0.699018+0.006823 test-rmse:1.098723+0.111380
## [242] train-rmse:0.698035+0.006719 test-rmse:1.098549+0.111693
## [243] train-rmse:0.696107+0.006022 test-rmse:1.097527+0.111155
## [244] train-rmse:0.694697+0.006767 test-rmse:1.096882+0.111106
## [245] train-rmse:0.692502+0.007534 test-rmse:1.095008+0.110985
## [246] train-rmse:0.691329+0.008125 test-rmse:1.094779+0.109679
## [247] train-rmse:0.689926+0.007989 test-rmse:1.094659+0.108661
## [248] train-rmse:0.688913+0.007885 test-rmse:1.093983+0.108312
## [249] train-rmse:0.687737+0.007798 test-rmse:1.093324+0.109124
## [250] train-rmse:0.686716+0.007728 test-rmse:1.093475+0.109753
## [251] train-rmse:0.684973+0.007728 test-rmse:1.093185+0.109696
## [252] train-rmse:0.683684+0.007807 test-rmse:1.093181+0.109205
## [253] train-rmse:0.682260+0.007843 test-rmse:1.092697+0.109186
## [254] train-rmse:0.680784+0.007474 test-rmse:1.090701+0.108357
## [255] train-rmse:0.679638+0.007859 test-rmse:1.090549+0.108530
## [256] train-rmse:0.678852+0.007823 test-rmse:1.090444+0.108289
## [257] train-rmse:0.677137+0.008310 test-rmse:1.090142+0.107370
## [258] train-rmse:0.675831+0.008123 test-rmse:1.089547+0.107939
## [259] train-rmse:0.674898+0.008113 test-rmse:1.088631+0.108226
## [260] train-rmse:0.673341+0.007693 test-rmse:1.086975+0.109028
## [261] train-rmse:0.672089+0.007830 test-rmse:1.086402+0.109101
## [262] train-rmse:0.670537+0.007485 test-rmse:1.085151+0.108415
## [263] train-rmse:0.668132+0.007854 test-rmse:1.081856+0.108423
## [264] train-rmse:0.667143+0.007630 test-rmse:1.081566+0.108376
## [265] train-rmse:0.666056+0.007643 test-rmse:1.081549+0.108897
## [266] train-rmse:0.665154+0.007745 test-rmse:1.081132+0.108741
## [267] train-rmse:0.664212+0.007695 test-rmse:1.080464+0.108380
## [268] train-rmse:0.663084+0.008360 test-rmse:1.079313+0.107586
## [269] train-rmse:0.661882+0.008535 test-rmse:1.079554+0.107233
## [270] train-rmse:0.660720+0.008307 test-rmse:1.078445+0.107779
## [271] train-rmse:0.659864+0.008236 test-rmse:1.078658+0.108306
## [272] train-rmse:0.658466+0.008850 test-rmse:1.078232+0.109035
## [273] train-rmse:0.657711+0.008817 test-rmse:1.077960+0.109190
## [274] train-rmse:0.656545+0.009237 test-rmse:1.077192+0.108841
## [275] train-rmse:0.655323+0.009156 test-rmse:1.076466+0.108377
## [276] train-rmse:0.653955+0.008892 test-rmse:1.075637+0.108875
## [277] train-rmse:0.652343+0.009767 test-rmse:1.074950+0.108662
## [278] train-rmse:0.651199+0.009547 test-rmse:1.074683+0.108562
## [279] train-rmse:0.649729+0.009381 test-rmse:1.074403+0.108195
## [280] train-rmse:0.648340+0.009425 test-rmse:1.073917+0.108767
## [281] train-rmse:0.647492+0.009387 test-rmse:1.073962+0.108828
## [282] train-rmse:0.646016+0.009354 test-rmse:1.072957+0.108368
## [283] train-rmse:0.644822+0.009215 test-rmse:1.072558+0.108444
## [284] train-rmse:0.643740+0.008792 test-rmse:1.071967+0.108052
## [285] train-rmse:0.643065+0.008825 test-rmse:1.072878+0.107666
## [286] train-rmse:0.641805+0.009169 test-rmse:1.072205+0.107169
## [287] train-rmse:0.640594+0.009151 test-rmse:1.071024+0.106988
## [288] train-rmse:0.639696+0.009550 test-rmse:1.070931+0.106642
## [289] train-rmse:0.638282+0.009927 test-rmse:1.070384+0.106496
## [290] train-rmse:0.637252+0.010084 test-rmse:1.069420+0.108098
## [291] train-rmse:0.635827+0.009628 test-rmse:1.068636+0.108328
## [292] train-rmse:0.635089+0.009681 test-rmse:1.067912+0.108893
## [293] train-rmse:0.633703+0.009887 test-rmse:1.065625+0.108487
## [294] train-rmse:0.632870+0.009805 test-rmse:1.064293+0.107643
## [295] train-rmse:0.631096+0.009317 test-rmse:1.062771+0.106721
## [296] train-rmse:0.630263+0.009332 test-rmse:1.062187+0.106912
## [297] train-rmse:0.629340+0.009760 test-rmse:1.061853+0.106428
## [298] train-rmse:0.628060+0.009600 test-rmse:1.061297+0.106279
## [299] train-rmse:0.627083+0.009426 test-rmse:1.060514+0.106124
## [300] train-rmse:0.625314+0.009424 test-rmse:1.060277+0.105395
## [301] train-rmse:0.624370+0.009352 test-rmse:1.060573+0.105247
## [302] train-rmse:0.623593+0.009430 test-rmse:1.060394+0.105330
## [303] train-rmse:0.622572+0.009875 test-rmse:1.060429+0.105161
## [304] train-rmse:0.621438+0.009547 test-rmse:1.061280+0.105228
## [305] train-rmse:0.619849+0.009678 test-rmse:1.059797+0.106435
## [306] train-rmse:0.618618+0.010530 test-rmse:1.059273+0.106390
## [307] train-rmse:0.617694+0.010360 test-rmse:1.058979+0.106325
## [308] train-rmse:0.616861+0.010298 test-rmse:1.058677+0.105918
## [309] train-rmse:0.616010+0.010412 test-rmse:1.057571+0.105191
## [310] train-rmse:0.615231+0.010385 test-rmse:1.057219+0.105315
## [311] train-rmse:0.614590+0.010256 test-rmse:1.057111+0.105689
## [312] train-rmse:0.613507+0.010353 test-rmse:1.057037+0.105455
## [313] train-rmse:0.612741+0.010462 test-rmse:1.056149+0.105011
## [314] train-rmse:0.611853+0.010494 test-rmse:1.056267+0.105102
## [315] train-rmse:0.610880+0.010372 test-rmse:1.056515+0.105180
## [316] train-rmse:0.609928+0.010822 test-rmse:1.055718+0.105122
## [317] train-rmse:0.608707+0.010415 test-rmse:1.055431+0.104923
## [318] train-rmse:0.607529+0.011218 test-rmse:1.054890+0.105455
## [319] train-rmse:0.606723+0.011234 test-rmse:1.054030+0.104995
## [320] train-rmse:0.605669+0.011742 test-rmse:1.054128+0.105176
## [321] train-rmse:0.604620+0.012066 test-rmse:1.053081+0.106231
## [322] train-rmse:0.603598+0.011851 test-rmse:1.052682+0.106281
## [323] train-rmse:0.602387+0.012261 test-rmse:1.052352+0.106099
## [324] train-rmse:0.601139+0.012609 test-rmse:1.050941+0.105426
## [325] train-rmse:0.599761+0.012607 test-rmse:1.051009+0.105505
## [326] train-rmse:0.599194+0.012667 test-rmse:1.050454+0.105258
## [327] train-rmse:0.597184+0.011803 test-rmse:1.050537+0.105692
## [328] train-rmse:0.596341+0.011673 test-rmse:1.051062+0.105655
## [329] train-rmse:0.595436+0.011377 test-rmse:1.051264+0.106029
## [330] train-rmse:0.594833+0.011335 test-rmse:1.050342+0.105444
## [331] train-rmse:0.593887+0.011099 test-rmse:1.049620+0.105597
## [332] train-rmse:0.592963+0.010888 test-rmse:1.049783+0.105934
## [333] train-rmse:0.592023+0.010589 test-rmse:1.049295+0.105954
## [334] train-rmse:0.591227+0.010925 test-rmse:1.048589+0.105599
## [335] train-rmse:0.589978+0.010766 test-rmse:1.048075+0.105281
## [336] train-rmse:0.589153+0.010576 test-rmse:1.047511+0.105798
## [337] train-rmse:0.588285+0.010514 test-rmse:1.047473+0.106466
## [338] train-rmse:0.587417+0.010950 test-rmse:1.047305+0.106662
## [339] train-rmse:0.586166+0.010712 test-rmse:1.046269+0.106364
## [340] train-rmse:0.585179+0.010870 test-rmse:1.045657+0.106422
## [341] train-rmse:0.584280+0.010815 test-rmse:1.045571+0.107680
## [342] train-rmse:0.582980+0.010520 test-rmse:1.045191+0.107307
## [343] train-rmse:0.581693+0.010157 test-rmse:1.044355+0.107229
## [344] train-rmse:0.580767+0.010211 test-rmse:1.043861+0.107203
## [345] train-rmse:0.580194+0.010427 test-rmse:1.043431+0.107122
## [346] train-rmse:0.579458+0.010484 test-rmse:1.043851+0.107380
## [347] train-rmse:0.578890+0.010457 test-rmse:1.043741+0.107308
## [348] train-rmse:0.577876+0.010313 test-rmse:1.042904+0.108715
## [349] train-rmse:0.577174+0.010461 test-rmse:1.042502+0.108580
## [350] train-rmse:0.576544+0.010645 test-rmse:1.042151+0.108501
## [351] train-rmse:0.575645+0.010969 test-rmse:1.041609+0.108380
## [352] train-rmse:0.574706+0.010717 test-rmse:1.041622+0.108276
## [353] train-rmse:0.573934+0.010788 test-rmse:1.041524+0.107662
## [354] train-rmse:0.572812+0.010848 test-rmse:1.041176+0.106673
## [355] train-rmse:0.571270+0.010289 test-rmse:1.040831+0.106629
## [356] train-rmse:0.570087+0.009826 test-rmse:1.041875+0.106636
## [357] train-rmse:0.569159+0.010660 test-rmse:1.040610+0.105535
## [358] train-rmse:0.568246+0.010481 test-rmse:1.040472+0.105584
## [359] train-rmse:0.567707+0.010522 test-rmse:1.040115+0.105607
## [360] train-rmse:0.567029+0.010388 test-rmse:1.039471+0.105347
## [361] train-rmse:0.566480+0.010276 test-rmse:1.039060+0.105429
## [362] train-rmse:0.565685+0.010379 test-rmse:1.038310+0.105490
## [363] train-rmse:0.564532+0.010381 test-rmse:1.038805+0.105494
## [364] train-rmse:0.563870+0.010646 test-rmse:1.038508+0.106092
## [365] train-rmse:0.563072+0.010509 test-rmse:1.037579+0.105865
## [366] train-rmse:0.562644+0.010501 test-rmse:1.037140+0.106037
## [367] train-rmse:0.560917+0.009697 test-rmse:1.035912+0.105399
## [368] train-rmse:0.559737+0.010176 test-rmse:1.035294+0.105549
## [369] train-rmse:0.559116+0.010338 test-rmse:1.035127+0.105631
## [370] train-rmse:0.558431+0.010829 test-rmse:1.035101+0.105347
## [371] train-rmse:0.557620+0.011241 test-rmse:1.035510+0.105176
## [372] train-rmse:0.556791+0.011667 test-rmse:1.034932+0.105921
## [373] train-rmse:0.556119+0.011481 test-rmse:1.035423+0.105747
## [374] train-rmse:0.555432+0.011560 test-rmse:1.035087+0.105557
## [375] train-rmse:0.554370+0.011170 test-rmse:1.035250+0.105747
## [376] train-rmse:0.553751+0.011148 test-rmse:1.034944+0.105736
## [377] train-rmse:0.553304+0.011203 test-rmse:1.034895+0.105150
## [378] train-rmse:0.552801+0.011176 test-rmse:1.034919+0.105112
## [379] train-rmse:0.551735+0.010834 test-rmse:1.034789+0.104699
## [380] train-rmse:0.551180+0.010999 test-rmse:1.034545+0.104956
## [381] train-rmse:0.550344+0.011176 test-rmse:1.035146+0.105229
## [382] train-rmse:0.549817+0.011336 test-rmse:1.034961+0.105195
## [383] train-rmse:0.549144+0.011190 test-rmse:1.034828+0.105570
## [384] train-rmse:0.548346+0.010951 test-rmse:1.034318+0.104862
## [385] train-rmse:0.547641+0.010866 test-rmse:1.033869+0.105322
## [386] train-rmse:0.546947+0.010684 test-rmse:1.033830+0.105208
## [387] train-rmse:0.546164+0.010437 test-rmse:1.033990+0.105107
## [388] train-rmse:0.545674+0.010471 test-rmse:1.033732+0.104642
## [389] train-rmse:0.544898+0.010691 test-rmse:1.032852+0.104367
## [390] train-rmse:0.544030+0.010623 test-rmse:1.032473+0.104269
## [391] train-rmse:0.542957+0.011248 test-rmse:1.030818+0.103966
## [392] train-rmse:0.541724+0.010961 test-rmse:1.030110+0.104092
## [393] train-rmse:0.540240+0.010683 test-rmse:1.029701+0.103667
## [394] train-rmse:0.539651+0.010606 test-rmse:1.029498+0.103835
## [395] train-rmse:0.538844+0.010790 test-rmse:1.029171+0.103623
## [396] train-rmse:0.537885+0.010843 test-rmse:1.029151+0.103605
## [397] train-rmse:0.537044+0.010753 test-rmse:1.029046+0.103699
## [398] train-rmse:0.535947+0.010589 test-rmse:1.029142+0.103822
## [399] train-rmse:0.535165+0.010365 test-rmse:1.029700+0.103865
## [400] train-rmse:0.534717+0.010355 test-rmse:1.029439+0.103754
## [401] train-rmse:0.534302+0.010349 test-rmse:1.029405+0.104053
## [402] train-rmse:0.533470+0.010499 test-rmse:1.029198+0.103186
## [403] train-rmse:0.532969+0.010451 test-rmse:1.029440+0.103100
## Stopping. Best iteration:
## [397] train-rmse:0.537044+0.010753 test-rmse:1.029046+0.103699
##
## 2
## [1] train-rmse:88.665695+0.088976 test-rmse:88.666461+0.404980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.835214+0.080050 test-rmse:79.835893+0.413675
## [3] train-rmse:71.885242+0.071998 test-rmse:71.885820+0.421463
## [4] train-rmse:64.728087+0.064733 test-rmse:64.728551+0.428434
## [5] train-rmse:58.284824+0.058174 test-rmse:58.285159+0.434654
## [6] train-rmse:52.484374+0.052257 test-rmse:52.484567+0.440197
## [7] train-rmse:47.262778+0.046908 test-rmse:47.262816+0.445120
## [8] train-rmse:42.562434+0.042074 test-rmse:42.562303+0.449468
## [9] train-rmse:38.331508+0.037710 test-rmse:38.331192+0.453293
## [10] train-rmse:34.523343+0.033759 test-rmse:34.522811+0.456630
## [11] train-rmse:31.095926+0.030181 test-rmse:31.095166+0.459509
## [12] train-rmse:28.011453+0.026945 test-rmse:28.010445+0.461960
## [13] train-rmse:25.235913+0.024012 test-rmse:25.234629+0.463999
## [14] train-rmse:22.738687+0.021363 test-rmse:22.737103+0.465637
## [15] train-rmse:20.492223+0.018971 test-rmse:20.490315+0.466887
## [16] train-rmse:18.471749+0.016823 test-rmse:18.469484+0.467747
## [17] train-rmse:16.654969+0.014903 test-rmse:16.652318+0.468213
## [18] train-rmse:15.021334+0.013222 test-rmse:15.020864+0.456548
## [19] train-rmse:13.552638+0.011590 test-rmse:13.550876+0.449119
## [20] train-rmse:12.232204+0.010135 test-rmse:12.222750+0.446257
## [21] train-rmse:11.045206+0.008913 test-rmse:11.035400+0.435400
## [22] train-rmse:9.977548+0.008148 test-rmse:9.964506+0.428153
## [23] train-rmse:9.017570+0.007287 test-rmse:9.001551+0.419615
## [24] train-rmse:8.154575+0.006721 test-rmse:8.138352+0.406949
## [25] train-rmse:7.378679+0.005902 test-rmse:7.363665+0.398571
## [26] train-rmse:6.681479+0.005510 test-rmse:6.671474+0.389514
## [27] train-rmse:6.055015+0.005640 test-rmse:6.045592+0.375872
## [28] train-rmse:5.493136+0.005017 test-rmse:5.485153+0.365690
## [29] train-rmse:4.988874+0.004903 test-rmse:4.982656+0.353738
## [30] train-rmse:4.536124+0.004712 test-rmse:4.531850+0.339350
## [31] train-rmse:4.130855+0.005113 test-rmse:4.132149+0.331838
## [32] train-rmse:3.768240+0.006205 test-rmse:3.776049+0.326634
## [33] train-rmse:3.442939+0.006166 test-rmse:3.456337+0.317813
## [34] train-rmse:3.153799+0.006191 test-rmse:3.172286+0.306366
## [35] train-rmse:2.895482+0.006930 test-rmse:2.919982+0.295833
## [36] train-rmse:2.665737+0.007985 test-rmse:2.694852+0.285155
## [37] train-rmse:2.460389+0.008358 test-rmse:2.502118+0.274936
## [38] train-rmse:2.279883+0.007756 test-rmse:2.330656+0.268217
## [39] train-rmse:2.120797+0.008383 test-rmse:2.182400+0.257294
## [40] train-rmse:1.980208+0.008629 test-rmse:2.054770+0.243360
## [41] train-rmse:1.856871+0.010496 test-rmse:1.943643+0.230783
## [42] train-rmse:1.748659+0.011934 test-rmse:1.847181+0.217790
## [43] train-rmse:1.654673+0.013301 test-rmse:1.762182+0.206912
## [44] train-rmse:1.571111+0.012864 test-rmse:1.696539+0.196976
## [45] train-rmse:1.498321+0.011995 test-rmse:1.637953+0.184650
## [46] train-rmse:1.435402+0.012782 test-rmse:1.588724+0.171392
## [47] train-rmse:1.380004+0.014126 test-rmse:1.541337+0.165529
## [48] train-rmse:1.333345+0.014442 test-rmse:1.506098+0.155127
## [49] train-rmse:1.292651+0.015210 test-rmse:1.473547+0.150618
## [50] train-rmse:1.256625+0.014209 test-rmse:1.448805+0.143761
## [51] train-rmse:1.223418+0.015356 test-rmse:1.423117+0.136740
## [52] train-rmse:1.194328+0.015010 test-rmse:1.404151+0.131119
## [53] train-rmse:1.170010+0.015750 test-rmse:1.388500+0.123547
## [54] train-rmse:1.144717+0.016747 test-rmse:1.370911+0.118077
## [55] train-rmse:1.124603+0.017796 test-rmse:1.357998+0.114670
## [56] train-rmse:1.107166+0.018521 test-rmse:1.348497+0.109686
## [57] train-rmse:1.091386+0.017607 test-rmse:1.337939+0.105838
## [58] train-rmse:1.076254+0.018625 test-rmse:1.327581+0.102061
## [59] train-rmse:1.062688+0.020053 test-rmse:1.320418+0.099094
## [60] train-rmse:1.049903+0.020533 test-rmse:1.312104+0.095687
## [61] train-rmse:1.038132+0.021367 test-rmse:1.305712+0.093929
## [62] train-rmse:1.027356+0.019750 test-rmse:1.298222+0.092657
## [63] train-rmse:1.017358+0.020232 test-rmse:1.292510+0.093446
## [64] train-rmse:1.006364+0.021248 test-rmse:1.288475+0.090325
## [65] train-rmse:0.996962+0.020292 test-rmse:1.283337+0.088478
## [66] train-rmse:0.987870+0.020142 test-rmse:1.276131+0.088375
## [67] train-rmse:0.980317+0.019631 test-rmse:1.271724+0.087538
## [68] train-rmse:0.971699+0.020564 test-rmse:1.267674+0.087062
## [69] train-rmse:0.964094+0.019554 test-rmse:1.261455+0.088168
## [70] train-rmse:0.955287+0.017404 test-rmse:1.257709+0.088586
## [71] train-rmse:0.947669+0.018120 test-rmse:1.251829+0.086238
## [72] train-rmse:0.940090+0.018175 test-rmse:1.248575+0.084870
## [73] train-rmse:0.933315+0.018397 test-rmse:1.244102+0.085701
## [74] train-rmse:0.925868+0.018149 test-rmse:1.240008+0.087336
## [75] train-rmse:0.919297+0.018529 test-rmse:1.235820+0.087580
## [76] train-rmse:0.913667+0.018661 test-rmse:1.231392+0.086838
## [77] train-rmse:0.905876+0.018878 test-rmse:1.228498+0.086617
## [78] train-rmse:0.900579+0.018831 test-rmse:1.225208+0.083579
## [79] train-rmse:0.893608+0.018026 test-rmse:1.218340+0.086228
## [80] train-rmse:0.887791+0.017446 test-rmse:1.214930+0.086127
## [81] train-rmse:0.881509+0.016063 test-rmse:1.212181+0.088649
## [82] train-rmse:0.875760+0.016975 test-rmse:1.209930+0.088723
## [83] train-rmse:0.870458+0.016853 test-rmse:1.205538+0.088892
## [84] train-rmse:0.865957+0.016853 test-rmse:1.201200+0.088939
## [85] train-rmse:0.860701+0.017140 test-rmse:1.198210+0.090612
## [86] train-rmse:0.854599+0.017272 test-rmse:1.194822+0.090156
## [87] train-rmse:0.849975+0.017574 test-rmse:1.192399+0.088009
## [88] train-rmse:0.845095+0.016380 test-rmse:1.189661+0.089369
## [89] train-rmse:0.839039+0.015112 test-rmse:1.184564+0.085082
## [90] train-rmse:0.833039+0.016477 test-rmse:1.178333+0.085330
## [91] train-rmse:0.827829+0.016441 test-rmse:1.173741+0.088091
## [92] train-rmse:0.822773+0.016174 test-rmse:1.170924+0.088644
## [93] train-rmse:0.818349+0.015873 test-rmse:1.166658+0.088818
## [94] train-rmse:0.813666+0.016500 test-rmse:1.161777+0.084924
## [95] train-rmse:0.809390+0.016320 test-rmse:1.158777+0.084419
## [96] train-rmse:0.805332+0.015920 test-rmse:1.157354+0.084782
## [97] train-rmse:0.800502+0.015751 test-rmse:1.154518+0.084399
## [98] train-rmse:0.794120+0.016413 test-rmse:1.149541+0.083147
## [99] train-rmse:0.789732+0.016586 test-rmse:1.147596+0.083298
## [100] train-rmse:0.784831+0.017405 test-rmse:1.144604+0.083519
## [101] train-rmse:0.780043+0.017576 test-rmse:1.142778+0.082115
## [102] train-rmse:0.776364+0.016610 test-rmse:1.139357+0.083494
## [103] train-rmse:0.772877+0.017087 test-rmse:1.136000+0.082766
## [104] train-rmse:0.768098+0.016310 test-rmse:1.134052+0.083425
## [105] train-rmse:0.764204+0.015383 test-rmse:1.130289+0.083553
## [106] train-rmse:0.760041+0.015236 test-rmse:1.129091+0.082871
## [107] train-rmse:0.756818+0.015339 test-rmse:1.127834+0.082737
## [108] train-rmse:0.752826+0.015692 test-rmse:1.126147+0.082442
## [109] train-rmse:0.749706+0.016027 test-rmse:1.124198+0.081318
## [110] train-rmse:0.746260+0.015163 test-rmse:1.122070+0.080954
## [111] train-rmse:0.742634+0.015214 test-rmse:1.120327+0.080940
## [112] train-rmse:0.738241+0.015717 test-rmse:1.119226+0.080366
## [113] train-rmse:0.733977+0.015059 test-rmse:1.116972+0.080500
## [114] train-rmse:0.731265+0.014836 test-rmse:1.115008+0.080250
## [115] train-rmse:0.726859+0.015534 test-rmse:1.111552+0.080568
## [116] train-rmse:0.724300+0.015360 test-rmse:1.110596+0.080721
## [117] train-rmse:0.721235+0.015102 test-rmse:1.109932+0.080559
## [118] train-rmse:0.717511+0.015273 test-rmse:1.109236+0.079664
## [119] train-rmse:0.713486+0.015448 test-rmse:1.107268+0.081984
## [120] train-rmse:0.710371+0.015921 test-rmse:1.105551+0.081291
## [121] train-rmse:0.706941+0.016038 test-rmse:1.103778+0.080668
## [122] train-rmse:0.703735+0.015930 test-rmse:1.101802+0.079306
## [123] train-rmse:0.700748+0.015452 test-rmse:1.100242+0.079720
## [124] train-rmse:0.696815+0.015906 test-rmse:1.096581+0.081265
## [125] train-rmse:0.693786+0.016449 test-rmse:1.094419+0.081053
## [126] train-rmse:0.690413+0.015611 test-rmse:1.092992+0.081249
## [127] train-rmse:0.686824+0.015168 test-rmse:1.091627+0.081136
## [128] train-rmse:0.684030+0.014669 test-rmse:1.090267+0.081732
## [129] train-rmse:0.680420+0.015063 test-rmse:1.089511+0.082631
## [130] train-rmse:0.677747+0.014999 test-rmse:1.088494+0.082629
## [131] train-rmse:0.674437+0.015078 test-rmse:1.086982+0.082648
## [132] train-rmse:0.671917+0.014548 test-rmse:1.085842+0.082809
## [133] train-rmse:0.668623+0.014833 test-rmse:1.084439+0.082771
## [134] train-rmse:0.665730+0.014816 test-rmse:1.083503+0.081620
## [135] train-rmse:0.662685+0.013769 test-rmse:1.082075+0.081476
## [136] train-rmse:0.659551+0.013364 test-rmse:1.080631+0.082605
## [137] train-rmse:0.656998+0.013167 test-rmse:1.078798+0.082402
## [138] train-rmse:0.653700+0.013051 test-rmse:1.078440+0.081399
## [139] train-rmse:0.651644+0.012854 test-rmse:1.077547+0.081245
## [140] train-rmse:0.649237+0.013287 test-rmse:1.077026+0.081263
## [141] train-rmse:0.646707+0.013070 test-rmse:1.075970+0.081767
## [142] train-rmse:0.644224+0.012278 test-rmse:1.075250+0.081989
## [143] train-rmse:0.641411+0.012368 test-rmse:1.074779+0.081447
## [144] train-rmse:0.638202+0.012834 test-rmse:1.074449+0.081728
## [145] train-rmse:0.634867+0.013091 test-rmse:1.073043+0.081830
## [146] train-rmse:0.632293+0.013165 test-rmse:1.071426+0.082655
## [147] train-rmse:0.628510+0.013798 test-rmse:1.069025+0.084248
## [148] train-rmse:0.626131+0.013357 test-rmse:1.068621+0.084455
## [149] train-rmse:0.624016+0.013355 test-rmse:1.068124+0.084508
## [150] train-rmse:0.621236+0.012365 test-rmse:1.065823+0.084133
## [151] train-rmse:0.619201+0.012258 test-rmse:1.065353+0.083571
## [152] train-rmse:0.615829+0.012240 test-rmse:1.064484+0.082737
## [153] train-rmse:0.613048+0.011327 test-rmse:1.062299+0.083562
## [154] train-rmse:0.610394+0.010884 test-rmse:1.062209+0.083949
## [155] train-rmse:0.607488+0.011531 test-rmse:1.061650+0.083990
## [156] train-rmse:0.604544+0.012042 test-rmse:1.059483+0.082272
## [157] train-rmse:0.601986+0.011331 test-rmse:1.059658+0.082558
## [158] train-rmse:0.600382+0.011227 test-rmse:1.058286+0.083190
## [159] train-rmse:0.598148+0.011594 test-rmse:1.058124+0.081950
## [160] train-rmse:0.596462+0.011642 test-rmse:1.056004+0.081443
## [161] train-rmse:0.592830+0.011510 test-rmse:1.052775+0.081372
## [162] train-rmse:0.590679+0.011490 test-rmse:1.051857+0.081256
## [163] train-rmse:0.587984+0.011938 test-rmse:1.048842+0.081392
## [164] train-rmse:0.585042+0.011048 test-rmse:1.047269+0.081132
## [165] train-rmse:0.582770+0.010505 test-rmse:1.045466+0.079451
## [166] train-rmse:0.580675+0.011142 test-rmse:1.045260+0.079783
## [167] train-rmse:0.578824+0.011368 test-rmse:1.044504+0.079998
## [168] train-rmse:0.576716+0.011395 test-rmse:1.043440+0.081030
## [169] train-rmse:0.573855+0.011470 test-rmse:1.044926+0.082710
## [170] train-rmse:0.571753+0.011213 test-rmse:1.045726+0.084308
## [171] train-rmse:0.570348+0.011465 test-rmse:1.045519+0.083980
## [172] train-rmse:0.567588+0.012660 test-rmse:1.045263+0.083426
## [173] train-rmse:0.565018+0.013243 test-rmse:1.046998+0.084279
## [174] train-rmse:0.562474+0.012860 test-rmse:1.045943+0.083223
## Stopping. Best iteration:
## [168] train-rmse:0.576716+0.011395 test-rmse:1.043440+0.081030
##
## 3
## [1] train-rmse:88.665695+0.088976 test-rmse:88.666461+0.404980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.835214+0.080050 test-rmse:79.835893+0.413675
## [3] train-rmse:71.885242+0.071998 test-rmse:71.885820+0.421463
## [4] train-rmse:64.728087+0.064733 test-rmse:64.728551+0.428434
## [5] train-rmse:58.284824+0.058174 test-rmse:58.285159+0.434654
## [6] train-rmse:52.484374+0.052257 test-rmse:52.484567+0.440197
## [7] train-rmse:47.262778+0.046908 test-rmse:47.262816+0.445120
## [8] train-rmse:42.562434+0.042074 test-rmse:42.562303+0.449468
## [9] train-rmse:38.331508+0.037710 test-rmse:38.331192+0.453293
## [10] train-rmse:34.523343+0.033759 test-rmse:34.522811+0.456630
## [11] train-rmse:31.095926+0.030181 test-rmse:31.095166+0.459509
## [12] train-rmse:28.011453+0.026945 test-rmse:28.010445+0.461960
## [13] train-rmse:25.235913+0.024012 test-rmse:25.234629+0.463999
## [14] train-rmse:22.738687+0.021363 test-rmse:22.737103+0.465637
## [15] train-rmse:20.492223+0.018971 test-rmse:20.490315+0.466887
## [16] train-rmse:18.471749+0.016823 test-rmse:18.469484+0.467747
## [17] train-rmse:16.654969+0.014903 test-rmse:16.652318+0.468213
## [18] train-rmse:15.021334+0.013222 test-rmse:15.020864+0.456548
## [19] train-rmse:13.552638+0.011590 test-rmse:13.550876+0.449119
## [20] train-rmse:12.232204+0.010135 test-rmse:12.222750+0.446257
## [21] train-rmse:11.045206+0.008913 test-rmse:11.035400+0.435400
## [22] train-rmse:9.977467+0.008102 test-rmse:9.962921+0.427508
## [23] train-rmse:9.017349+0.007255 test-rmse:8.999121+0.417020
## [24] train-rmse:8.153999+0.006940 test-rmse:8.138505+0.407923
## [25] train-rmse:7.377035+0.006059 test-rmse:7.362266+0.393684
## [26] train-rmse:6.678746+0.005816 test-rmse:6.667051+0.379536
## [27] train-rmse:6.051251+0.005637 test-rmse:6.044940+0.368063
## [28] train-rmse:5.487670+0.004816 test-rmse:5.484530+0.351195
## [29] train-rmse:4.980828+0.004387 test-rmse:4.978787+0.338219
## [30] train-rmse:4.525984+0.004128 test-rmse:4.522226+0.329975
## [31] train-rmse:4.116593+0.003738 test-rmse:4.120610+0.323908
## [32] train-rmse:3.750095+0.004081 test-rmse:3.756790+0.316317
## [33] train-rmse:3.420906+0.004089 test-rmse:3.432447+0.300881
## [34] train-rmse:3.125754+0.003966 test-rmse:3.148254+0.287010
## [35] train-rmse:2.862901+0.004892 test-rmse:2.894543+0.275162
## [36] train-rmse:2.627469+0.005990 test-rmse:2.666050+0.267851
## [37] train-rmse:2.416913+0.004719 test-rmse:2.469600+0.256763
## [38] train-rmse:2.230513+0.005435 test-rmse:2.295225+0.245118
## [39] train-rmse:2.063914+0.004794 test-rmse:2.144146+0.231756
## [40] train-rmse:1.915314+0.004914 test-rmse:2.014027+0.224384
## [41] train-rmse:1.783515+0.007448 test-rmse:1.898127+0.213247
## [42] train-rmse:1.666476+0.007154 test-rmse:1.798208+0.204763
## [43] train-rmse:1.566179+0.007122 test-rmse:1.709773+0.195997
## [44] train-rmse:1.474782+0.006850 test-rmse:1.637473+0.185446
## [45] train-rmse:1.396656+0.007268 test-rmse:1.574889+0.176179
## [46] train-rmse:1.325393+0.007537 test-rmse:1.522324+0.166996
## [47] train-rmse:1.263276+0.007981 test-rmse:1.474429+0.162400
## [48] train-rmse:1.209777+0.008545 test-rmse:1.434887+0.153769
## [49] train-rmse:1.163757+0.008516 test-rmse:1.401538+0.145268
## [50] train-rmse:1.122001+0.008643 test-rmse:1.370902+0.140806
## [51] train-rmse:1.083162+0.009492 test-rmse:1.344888+0.136070
## [52] train-rmse:1.051570+0.008279 test-rmse:1.325004+0.129685
## [53] train-rmse:1.023373+0.008078 test-rmse:1.304584+0.126998
## [54] train-rmse:0.996264+0.007328 test-rmse:1.288933+0.122382
## [55] train-rmse:0.971812+0.007820 test-rmse:1.273236+0.118902
## [56] train-rmse:0.950984+0.009638 test-rmse:1.259607+0.115334
## [57] train-rmse:0.933336+0.010063 test-rmse:1.246997+0.113006
## [58] train-rmse:0.915426+0.008436 test-rmse:1.236597+0.111676
## [59] train-rmse:0.899424+0.007435 test-rmse:1.227600+0.112069
## [60] train-rmse:0.885081+0.007935 test-rmse:1.220666+0.108329
## [61] train-rmse:0.869870+0.006846 test-rmse:1.213656+0.107245
## [62] train-rmse:0.857049+0.006701 test-rmse:1.204262+0.105703
## [63] train-rmse:0.845640+0.007496 test-rmse:1.196899+0.104043
## [64] train-rmse:0.835258+0.006953 test-rmse:1.190794+0.102423
## [65] train-rmse:0.823935+0.007981 test-rmse:1.181665+0.097754
## [66] train-rmse:0.814050+0.006851 test-rmse:1.176907+0.095450
## [67] train-rmse:0.804261+0.008057 test-rmse:1.172477+0.095892
## [68] train-rmse:0.794833+0.008028 test-rmse:1.166048+0.092469
## [69] train-rmse:0.785550+0.009316 test-rmse:1.159844+0.091380
## [70] train-rmse:0.777416+0.010099 test-rmse:1.155742+0.090806
## [71] train-rmse:0.769986+0.008397 test-rmse:1.150964+0.091427
## [72] train-rmse:0.762171+0.007275 test-rmse:1.148112+0.090377
## [73] train-rmse:0.755939+0.006821 test-rmse:1.143936+0.089813
## [74] train-rmse:0.748358+0.006740 test-rmse:1.138809+0.089449
## [75] train-rmse:0.740116+0.008462 test-rmse:1.134605+0.090303
## [76] train-rmse:0.733436+0.007304 test-rmse:1.131039+0.090321
## [77] train-rmse:0.726156+0.008537 test-rmse:1.125984+0.091210
## [78] train-rmse:0.719312+0.009214 test-rmse:1.123724+0.089014
## [79] train-rmse:0.712474+0.009731 test-rmse:1.116534+0.085333
## [80] train-rmse:0.707225+0.009345 test-rmse:1.112954+0.085277
## [81] train-rmse:0.700773+0.009794 test-rmse:1.106508+0.082580
## [82] train-rmse:0.695012+0.009054 test-rmse:1.103486+0.083058
## [83] train-rmse:0.689547+0.009084 test-rmse:1.102952+0.081087
## [84] train-rmse:0.684065+0.009426 test-rmse:1.100401+0.081837
## [85] train-rmse:0.677595+0.010166 test-rmse:1.095890+0.082789
## [86] train-rmse:0.672497+0.010962 test-rmse:1.093800+0.082546
## [87] train-rmse:0.667252+0.011136 test-rmse:1.089431+0.081779
## [88] train-rmse:0.661915+0.009627 test-rmse:1.086426+0.082280
## [89] train-rmse:0.656702+0.010694 test-rmse:1.086066+0.082372
## [90] train-rmse:0.651744+0.011011 test-rmse:1.082703+0.082358
## [91] train-rmse:0.646389+0.011843 test-rmse:1.079840+0.081757
## [92] train-rmse:0.641645+0.012541 test-rmse:1.078152+0.082537
## [93] train-rmse:0.636686+0.011754 test-rmse:1.075636+0.081474
## [94] train-rmse:0.631811+0.011963 test-rmse:1.070393+0.080086
## [95] train-rmse:0.627415+0.012657 test-rmse:1.068904+0.078415
## [96] train-rmse:0.622964+0.012583 test-rmse:1.066035+0.078357
## [97] train-rmse:0.618307+0.012482 test-rmse:1.063965+0.078617
## [98] train-rmse:0.612389+0.011156 test-rmse:1.062098+0.078275
## [99] train-rmse:0.607852+0.011753 test-rmse:1.059783+0.080731
## [100] train-rmse:0.604228+0.011820 test-rmse:1.058327+0.082016
## [101] train-rmse:0.600616+0.010809 test-rmse:1.056414+0.082284
## [102] train-rmse:0.595223+0.011516 test-rmse:1.054039+0.081541
## [103] train-rmse:0.591952+0.011644 test-rmse:1.053083+0.081463
## [104] train-rmse:0.587201+0.012139 test-rmse:1.051211+0.080751
## [105] train-rmse:0.583096+0.011748 test-rmse:1.050347+0.078909
## [106] train-rmse:0.578142+0.010978 test-rmse:1.046604+0.078130
## [107] train-rmse:0.574952+0.011171 test-rmse:1.045308+0.077868
## [108] train-rmse:0.571029+0.011893 test-rmse:1.040615+0.076993
## [109] train-rmse:0.565894+0.011485 test-rmse:1.038819+0.076675
## [110] train-rmse:0.562654+0.011426 test-rmse:1.039423+0.077842
## [111] train-rmse:0.558597+0.011558 test-rmse:1.037242+0.078146
## [112] train-rmse:0.554085+0.010771 test-rmse:1.036149+0.078226
## [113] train-rmse:0.550664+0.010113 test-rmse:1.035171+0.077479
## [114] train-rmse:0.546345+0.010882 test-rmse:1.033434+0.077151
## [115] train-rmse:0.542634+0.010860 test-rmse:1.034511+0.078727
## [116] train-rmse:0.538918+0.010592 test-rmse:1.031814+0.078213
## [117] train-rmse:0.535387+0.010797 test-rmse:1.030817+0.078261
## [118] train-rmse:0.532391+0.010707 test-rmse:1.028899+0.078857
## [119] train-rmse:0.529290+0.011108 test-rmse:1.027899+0.079220
## [120] train-rmse:0.525254+0.011479 test-rmse:1.029023+0.080310
## [121] train-rmse:0.522136+0.011355 test-rmse:1.027240+0.080080
## [122] train-rmse:0.519514+0.011953 test-rmse:1.024165+0.078807
## [123] train-rmse:0.515332+0.011968 test-rmse:1.022593+0.078761
## [124] train-rmse:0.512998+0.011825 test-rmse:1.020590+0.079875
## [125] train-rmse:0.509799+0.010883 test-rmse:1.019904+0.079774
## [126] train-rmse:0.506024+0.009539 test-rmse:1.018257+0.079963
## [127] train-rmse:0.502057+0.010304 test-rmse:1.013743+0.077253
## [128] train-rmse:0.498320+0.009405 test-rmse:1.012046+0.076304
## [129] train-rmse:0.496314+0.009747 test-rmse:1.010886+0.076745
## [130] train-rmse:0.492512+0.010171 test-rmse:1.011226+0.076825
## [131] train-rmse:0.489664+0.009443 test-rmse:1.012064+0.077169
## [132] train-rmse:0.486428+0.008225 test-rmse:1.008067+0.076164
## [133] train-rmse:0.482436+0.008112 test-rmse:1.007307+0.076684
## [134] train-rmse:0.478845+0.007552 test-rmse:1.006387+0.076152
## [135] train-rmse:0.476491+0.008108 test-rmse:1.007411+0.077611
## [136] train-rmse:0.473447+0.008202 test-rmse:1.004853+0.076372
## [137] train-rmse:0.470522+0.008488 test-rmse:1.003101+0.076537
## [138] train-rmse:0.467021+0.008199 test-rmse:1.004236+0.076877
## [139] train-rmse:0.463790+0.007967 test-rmse:1.003523+0.076775
## [140] train-rmse:0.461316+0.008252 test-rmse:1.002238+0.076350
## [141] train-rmse:0.457923+0.007610 test-rmse:1.001148+0.075614
## [142] train-rmse:0.455394+0.007414 test-rmse:1.000972+0.075632
## [143] train-rmse:0.452155+0.007455 test-rmse:1.001972+0.076834
## [144] train-rmse:0.449353+0.007460 test-rmse:1.000293+0.076383
## [145] train-rmse:0.446459+0.007878 test-rmse:0.999544+0.075978
## [146] train-rmse:0.444391+0.007658 test-rmse:0.999754+0.075489
## [147] train-rmse:0.441318+0.008198 test-rmse:0.999402+0.075496
## [148] train-rmse:0.438554+0.008407 test-rmse:0.997851+0.075146
## [149] train-rmse:0.436125+0.008319 test-rmse:0.997017+0.074905
## [150] train-rmse:0.434020+0.008595 test-rmse:0.996171+0.075206
## [151] train-rmse:0.431706+0.007512 test-rmse:0.994897+0.075416
## [152] train-rmse:0.429422+0.007245 test-rmse:0.994513+0.076968
## [153] train-rmse:0.427126+0.007693 test-rmse:0.993648+0.076600
## [154] train-rmse:0.424855+0.007412 test-rmse:0.992891+0.076559
## [155] train-rmse:0.422491+0.007499 test-rmse:0.992333+0.076292
## [156] train-rmse:0.420035+0.007709 test-rmse:0.992249+0.075846
## [157] train-rmse:0.418083+0.007332 test-rmse:0.991147+0.074845
## [158] train-rmse:0.416442+0.007308 test-rmse:0.991444+0.075282
## [159] train-rmse:0.413791+0.007326 test-rmse:0.992362+0.075725
## [160] train-rmse:0.412022+0.007436 test-rmse:0.991923+0.076632
## [161] train-rmse:0.409131+0.007506 test-rmse:0.990687+0.076148
## [162] train-rmse:0.406663+0.008118 test-rmse:0.988939+0.075743
## [163] train-rmse:0.403830+0.008170 test-rmse:0.988433+0.075544
## [164] train-rmse:0.401349+0.008931 test-rmse:0.987040+0.074030
## [165] train-rmse:0.399295+0.008297 test-rmse:0.987286+0.073813
## [166] train-rmse:0.396718+0.008521 test-rmse:0.985692+0.072743
## [167] train-rmse:0.394060+0.009075 test-rmse:0.985436+0.071979
## [168] train-rmse:0.392487+0.009254 test-rmse:0.985390+0.072915
## [169] train-rmse:0.390126+0.008911 test-rmse:0.985439+0.072915
## [170] train-rmse:0.387818+0.009249 test-rmse:0.984942+0.072708
## [171] train-rmse:0.386285+0.009326 test-rmse:0.983883+0.072267
## [172] train-rmse:0.384449+0.008804 test-rmse:0.982852+0.073063
## [173] train-rmse:0.382918+0.009097 test-rmse:0.982063+0.073448
## [174] train-rmse:0.381030+0.008784 test-rmse:0.981753+0.074643
## [175] train-rmse:0.378399+0.008929 test-rmse:0.981950+0.075815
## [176] train-rmse:0.376042+0.008357 test-rmse:0.982005+0.076585
## [177] train-rmse:0.373236+0.008043 test-rmse:0.980400+0.075705
## [178] train-rmse:0.371321+0.008131 test-rmse:0.979992+0.075263
## [179] train-rmse:0.369722+0.007484 test-rmse:0.979546+0.075278
## [180] train-rmse:0.367559+0.007413 test-rmse:0.978774+0.075148
## [181] train-rmse:0.365678+0.007395 test-rmse:0.978876+0.075603
## [182] train-rmse:0.363360+0.007727 test-rmse:0.978522+0.074877
## [183] train-rmse:0.361641+0.008168 test-rmse:0.978121+0.074793
## [184] train-rmse:0.359404+0.008480 test-rmse:0.977922+0.074794
## [185] train-rmse:0.357263+0.008299 test-rmse:0.977658+0.074948
## [186] train-rmse:0.355567+0.008306 test-rmse:0.977317+0.075210
## [187] train-rmse:0.353281+0.007772 test-rmse:0.976139+0.075326
## [188] train-rmse:0.350578+0.007935 test-rmse:0.975308+0.074788
## [189] train-rmse:0.349091+0.008844 test-rmse:0.973535+0.073570
## [190] train-rmse:0.347526+0.008521 test-rmse:0.974586+0.074204
## [191] train-rmse:0.345632+0.008366 test-rmse:0.973826+0.073175
## [192] train-rmse:0.343531+0.008698 test-rmse:0.973224+0.072633
## [193] train-rmse:0.341343+0.008646 test-rmse:0.973788+0.071871
## [194] train-rmse:0.339575+0.008169 test-rmse:0.973712+0.072067
## [195] train-rmse:0.337734+0.008410 test-rmse:0.973845+0.072313
## [196] train-rmse:0.336184+0.008465 test-rmse:0.973460+0.071718
## [197] train-rmse:0.334695+0.008654 test-rmse:0.973875+0.071724
## [198] train-rmse:0.332562+0.008477 test-rmse:0.974310+0.071253
## Stopping. Best iteration:
## [192] train-rmse:0.343531+0.008698 test-rmse:0.973224+0.072633
##
## 4
## [1] train-rmse:88.665695+0.088976 test-rmse:88.666461+0.404980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.835214+0.080050 test-rmse:79.835893+0.413675
## [3] train-rmse:71.885242+0.071998 test-rmse:71.885820+0.421463
## [4] train-rmse:64.728087+0.064733 test-rmse:64.728551+0.428434
## [5] train-rmse:58.284824+0.058174 test-rmse:58.285159+0.434654
## [6] train-rmse:52.484374+0.052257 test-rmse:52.484567+0.440197
## [7] train-rmse:47.262778+0.046908 test-rmse:47.262816+0.445120
## [8] train-rmse:42.562434+0.042074 test-rmse:42.562303+0.449468
## [9] train-rmse:38.331508+0.037710 test-rmse:38.331192+0.453293
## [10] train-rmse:34.523343+0.033759 test-rmse:34.522811+0.456630
## [11] train-rmse:31.095926+0.030181 test-rmse:31.095166+0.459509
## [12] train-rmse:28.011453+0.026945 test-rmse:28.010445+0.461960
## [13] train-rmse:25.235913+0.024012 test-rmse:25.234629+0.463999
## [14] train-rmse:22.738687+0.021363 test-rmse:22.737103+0.465637
## [15] train-rmse:20.492223+0.018971 test-rmse:20.490315+0.466887
## [16] train-rmse:18.471749+0.016823 test-rmse:18.469484+0.467747
## [17] train-rmse:16.654969+0.014903 test-rmse:16.652318+0.468213
## [18] train-rmse:15.021334+0.013222 test-rmse:15.020864+0.456548
## [19] train-rmse:13.552638+0.011590 test-rmse:13.550876+0.449119
## [20] train-rmse:12.232204+0.010135 test-rmse:12.222750+0.446257
## [21] train-rmse:11.045206+0.008913 test-rmse:11.035400+0.435400
## [22] train-rmse:9.977437+0.008087 test-rmse:9.961850+0.427085
## [23] train-rmse:9.017246+0.007304 test-rmse:9.003600+0.418966
## [24] train-rmse:8.153400+0.006879 test-rmse:8.135442+0.405985
## [25] train-rmse:7.376223+0.006002 test-rmse:7.358687+0.387746
## [26] train-rmse:6.677960+0.005386 test-rmse:6.667460+0.375209
## [27] train-rmse:6.049415+0.005624 test-rmse:6.040969+0.363101
## [28] train-rmse:5.485107+0.004686 test-rmse:5.478143+0.348993
## [29] train-rmse:4.977172+0.003795 test-rmse:4.970297+0.336528
## [30] train-rmse:4.520442+0.003663 test-rmse:4.520325+0.322914
## [31] train-rmse:4.110031+0.002698 test-rmse:4.112325+0.319790
## [32] train-rmse:3.740829+0.002273 test-rmse:3.750294+0.306242
## [33] train-rmse:3.409035+0.002741 test-rmse:3.428388+0.293507
## [34] train-rmse:3.111739+0.003632 test-rmse:3.131453+0.282949
## [35] train-rmse:2.844751+0.003186 test-rmse:2.874774+0.274099
## [36] train-rmse:2.604950+0.003180 test-rmse:2.646873+0.261132
## [37] train-rmse:2.388845+0.004153 test-rmse:2.443782+0.246163
## [38] train-rmse:2.196900+0.005067 test-rmse:2.268277+0.237109
## [39] train-rmse:2.026011+0.005865 test-rmse:2.113173+0.231621
## [40] train-rmse:1.872878+0.004855 test-rmse:1.974863+0.224322
## [41] train-rmse:1.736563+0.004693 test-rmse:1.858761+0.212726
## [42] train-rmse:1.616836+0.006406 test-rmse:1.751996+0.204367
## [43] train-rmse:1.509329+0.006383 test-rmse:1.662416+0.199235
## [44] train-rmse:1.414040+0.006360 test-rmse:1.589799+0.186074
## [45] train-rmse:1.330165+0.006533 test-rmse:1.525533+0.176845
## [46] train-rmse:1.252364+0.008355 test-rmse:1.468310+0.170656
## [47] train-rmse:1.185098+0.009960 test-rmse:1.417420+0.162849
## [48] train-rmse:1.126240+0.009712 test-rmse:1.377790+0.157704
## [49] train-rmse:1.073937+0.009453 test-rmse:1.340349+0.154146
## [50] train-rmse:1.027644+0.009226 test-rmse:1.309016+0.151434
## [51] train-rmse:0.986823+0.007796 test-rmse:1.282305+0.149147
## [52] train-rmse:0.951952+0.008191 test-rmse:1.261119+0.146077
## [53] train-rmse:0.919483+0.009281 test-rmse:1.242091+0.141481
## [54] train-rmse:0.889168+0.008777 test-rmse:1.225548+0.137938
## [55] train-rmse:0.863369+0.008882 test-rmse:1.213209+0.134968
## [56] train-rmse:0.838097+0.008762 test-rmse:1.197570+0.132732
## [57] train-rmse:0.816679+0.010318 test-rmse:1.185394+0.129618
## [58] train-rmse:0.794550+0.009367 test-rmse:1.173909+0.127526
## [59] train-rmse:0.777364+0.010250 test-rmse:1.163413+0.127713
## [60] train-rmse:0.761223+0.011365 test-rmse:1.150376+0.123481
## [61] train-rmse:0.747424+0.011453 test-rmse:1.142943+0.123276
## [62] train-rmse:0.731691+0.012023 test-rmse:1.135478+0.123300
## [63] train-rmse:0.720120+0.011858 test-rmse:1.127064+0.119879
## [64] train-rmse:0.708589+0.011989 test-rmse:1.120140+0.116379
## [65] train-rmse:0.696824+0.012848 test-rmse:1.116257+0.115129
## [66] train-rmse:0.684685+0.012120 test-rmse:1.111660+0.113651
## [67] train-rmse:0.673950+0.012854 test-rmse:1.105491+0.111563
## [68] train-rmse:0.662472+0.013118 test-rmse:1.101079+0.111413
## [69] train-rmse:0.653616+0.012848 test-rmse:1.096072+0.109885
## [70] train-rmse:0.645084+0.014045 test-rmse:1.091717+0.111480
## [71] train-rmse:0.633867+0.015120 test-rmse:1.085945+0.110298
## [72] train-rmse:0.626069+0.015884 test-rmse:1.083419+0.109978
## [73] train-rmse:0.617154+0.016282 test-rmse:1.077436+0.108133
## [74] train-rmse:0.609814+0.017560 test-rmse:1.074067+0.106709
## [75] train-rmse:0.601902+0.017789 test-rmse:1.072358+0.107540
## [76] train-rmse:0.594758+0.018102 test-rmse:1.066670+0.107965
## [77] train-rmse:0.587299+0.017513 test-rmse:1.061355+0.109578
## [78] train-rmse:0.581064+0.017677 test-rmse:1.056383+0.107682
## [79] train-rmse:0.575310+0.018133 test-rmse:1.056277+0.106754
## [80] train-rmse:0.570111+0.017656 test-rmse:1.053211+0.109506
## [81] train-rmse:0.564120+0.016981 test-rmse:1.050326+0.108137
## [82] train-rmse:0.558749+0.016815 test-rmse:1.046125+0.105106
## [83] train-rmse:0.552964+0.016490 test-rmse:1.043632+0.106490
## [84] train-rmse:0.546760+0.016472 test-rmse:1.043329+0.107188
## [85] train-rmse:0.540968+0.016988 test-rmse:1.039653+0.107683
## [86] train-rmse:0.534046+0.017509 test-rmse:1.038048+0.107231
## [87] train-rmse:0.528802+0.018068 test-rmse:1.036229+0.106579
## [88] train-rmse:0.523306+0.018331 test-rmse:1.033765+0.105947
## [89] train-rmse:0.516808+0.016806 test-rmse:1.030756+0.107135
## [90] train-rmse:0.511963+0.017505 test-rmse:1.027529+0.109097
## [91] train-rmse:0.506360+0.016870 test-rmse:1.024701+0.107499
## [92] train-rmse:0.501931+0.017116 test-rmse:1.023179+0.107937
## [93] train-rmse:0.497306+0.016941 test-rmse:1.022346+0.108073
## [94] train-rmse:0.493167+0.016959 test-rmse:1.020243+0.108102
## [95] train-rmse:0.486409+0.016926 test-rmse:1.016703+0.107259
## [96] train-rmse:0.481599+0.016913 test-rmse:1.014006+0.104602
## [97] train-rmse:0.477677+0.017227 test-rmse:1.010779+0.104333
## [98] train-rmse:0.473876+0.017866 test-rmse:1.009573+0.104287
## [99] train-rmse:0.469750+0.018055 test-rmse:1.008912+0.103987
## [100] train-rmse:0.465823+0.017670 test-rmse:1.007161+0.103335
## [101] train-rmse:0.462384+0.018365 test-rmse:1.006435+0.102403
## [102] train-rmse:0.457508+0.017708 test-rmse:1.005705+0.103019
## [103] train-rmse:0.453358+0.016431 test-rmse:1.003769+0.102519
## [104] train-rmse:0.449238+0.016333 test-rmse:1.002316+0.100978
## [105] train-rmse:0.444373+0.015528 test-rmse:1.000715+0.101084
## [106] train-rmse:0.440495+0.015478 test-rmse:1.000015+0.100705
## [107] train-rmse:0.436105+0.016244 test-rmse:1.000348+0.100788
## [108] train-rmse:0.431669+0.016760 test-rmse:0.998661+0.100881
## [109] train-rmse:0.428218+0.016023 test-rmse:0.996221+0.100432
## [110] train-rmse:0.424695+0.016155 test-rmse:0.995013+0.099317
## [111] train-rmse:0.420991+0.015972 test-rmse:0.992774+0.097688
## [112] train-rmse:0.416993+0.016650 test-rmse:0.991399+0.096892
## [113] train-rmse:0.414298+0.017103 test-rmse:0.989711+0.097103
## [114] train-rmse:0.411648+0.017253 test-rmse:0.988765+0.097023
## [115] train-rmse:0.407965+0.017813 test-rmse:0.988300+0.096996
## [116] train-rmse:0.404502+0.018259 test-rmse:0.987645+0.096921
## [117] train-rmse:0.401105+0.018222 test-rmse:0.987088+0.096622
## [118] train-rmse:0.398765+0.018376 test-rmse:0.986063+0.095735
## [119] train-rmse:0.395283+0.017775 test-rmse:0.985319+0.095800
## [120] train-rmse:0.392035+0.018029 test-rmse:0.983800+0.096171
## [121] train-rmse:0.388952+0.017823 test-rmse:0.983750+0.096340
## [122] train-rmse:0.386022+0.018342 test-rmse:0.983030+0.096062
## [123] train-rmse:0.382702+0.018439 test-rmse:0.982875+0.095498
## [124] train-rmse:0.380254+0.017974 test-rmse:0.982686+0.094924
## [125] train-rmse:0.377953+0.017702 test-rmse:0.980637+0.095449
## [126] train-rmse:0.374597+0.017454 test-rmse:0.981729+0.095595
## [127] train-rmse:0.371152+0.016977 test-rmse:0.980046+0.095072
## [128] train-rmse:0.368087+0.017225 test-rmse:0.979440+0.095823
## [129] train-rmse:0.365487+0.016565 test-rmse:0.978025+0.095454
## [130] train-rmse:0.362518+0.016607 test-rmse:0.977587+0.094794
## [131] train-rmse:0.360117+0.016470 test-rmse:0.976574+0.094012
## [132] train-rmse:0.356509+0.016248 test-rmse:0.975902+0.094827
## [133] train-rmse:0.354197+0.016061 test-rmse:0.975381+0.094806
## [134] train-rmse:0.351023+0.015907 test-rmse:0.975437+0.093919
## [135] train-rmse:0.345886+0.015837 test-rmse:0.975821+0.093408
## [136] train-rmse:0.343594+0.015711 test-rmse:0.974983+0.093059
## [137] train-rmse:0.340980+0.015393 test-rmse:0.974994+0.092284
## [138] train-rmse:0.337542+0.016141 test-rmse:0.973164+0.090926
## [139] train-rmse:0.335675+0.016092 test-rmse:0.973105+0.090934
## [140] train-rmse:0.333200+0.016425 test-rmse:0.972010+0.090836
## [141] train-rmse:0.330637+0.016039 test-rmse:0.970461+0.089947
## [142] train-rmse:0.328133+0.016314 test-rmse:0.968521+0.088979
## [143] train-rmse:0.325583+0.016254 test-rmse:0.968075+0.089425
## [144] train-rmse:0.321535+0.015186 test-rmse:0.968219+0.090175
## [145] train-rmse:0.319029+0.014988 test-rmse:0.968299+0.090121
## [146] train-rmse:0.316356+0.014182 test-rmse:0.968071+0.089429
## [147] train-rmse:0.313971+0.013885 test-rmse:0.966968+0.088544
## [148] train-rmse:0.311317+0.014090 test-rmse:0.964739+0.088993
## [149] train-rmse:0.308259+0.013419 test-rmse:0.964862+0.089023
## [150] train-rmse:0.305981+0.013077 test-rmse:0.964466+0.089831
## [151] train-rmse:0.303349+0.012536 test-rmse:0.963560+0.089872
## [152] train-rmse:0.301467+0.012811 test-rmse:0.963404+0.090212
## [153] train-rmse:0.299195+0.012886 test-rmse:0.963368+0.089934
## [154] train-rmse:0.296718+0.012182 test-rmse:0.963943+0.090808
## [155] train-rmse:0.294200+0.012085 test-rmse:0.963407+0.090404
## [156] train-rmse:0.291524+0.012196 test-rmse:0.962529+0.090383
## [157] train-rmse:0.289128+0.012121 test-rmse:0.960817+0.089130
## [158] train-rmse:0.286726+0.011945 test-rmse:0.960896+0.088705
## [159] train-rmse:0.284105+0.011019 test-rmse:0.960293+0.088614
## [160] train-rmse:0.281037+0.010005 test-rmse:0.959373+0.087862
## [161] train-rmse:0.278726+0.009658 test-rmse:0.958627+0.088111
## [162] train-rmse:0.276381+0.008938 test-rmse:0.958461+0.087971
## [163] train-rmse:0.274119+0.009590 test-rmse:0.957884+0.088181
## [164] train-rmse:0.271787+0.009328 test-rmse:0.957457+0.089349
## [165] train-rmse:0.270293+0.009114 test-rmse:0.957349+0.089564
## [166] train-rmse:0.267799+0.008843 test-rmse:0.957001+0.088770
## [167] train-rmse:0.266045+0.008685 test-rmse:0.956068+0.088567
## [168] train-rmse:0.264530+0.008475 test-rmse:0.956729+0.088959
## [169] train-rmse:0.263167+0.008238 test-rmse:0.955682+0.089317
## [170] train-rmse:0.261688+0.008454 test-rmse:0.955674+0.089541
## [171] train-rmse:0.259601+0.008081 test-rmse:0.956337+0.089985
## [172] train-rmse:0.257372+0.008849 test-rmse:0.955416+0.089130
## [173] train-rmse:0.255728+0.008618 test-rmse:0.955531+0.089180
## [174] train-rmse:0.253585+0.007793 test-rmse:0.955243+0.088396
## [175] train-rmse:0.251461+0.007539 test-rmse:0.955304+0.088130
## [176] train-rmse:0.249505+0.007583 test-rmse:0.953792+0.087589
## [177] train-rmse:0.247394+0.006714 test-rmse:0.953988+0.088444
## [178] train-rmse:0.245739+0.007573 test-rmse:0.953434+0.088444
## [179] train-rmse:0.243832+0.007148 test-rmse:0.953281+0.088231
## [180] train-rmse:0.242033+0.007255 test-rmse:0.952213+0.088581
## [181] train-rmse:0.240162+0.007722 test-rmse:0.952119+0.088868
## [182] train-rmse:0.238539+0.007320 test-rmse:0.951705+0.088273
## [183] train-rmse:0.236968+0.007550 test-rmse:0.951939+0.088063
## [184] train-rmse:0.234810+0.007272 test-rmse:0.952400+0.088384
## [185] train-rmse:0.233014+0.007579 test-rmse:0.952305+0.089109
## [186] train-rmse:0.231589+0.007718 test-rmse:0.951828+0.088384
## [187] train-rmse:0.230176+0.007795 test-rmse:0.951481+0.088638
## [188] train-rmse:0.228433+0.007622 test-rmse:0.951265+0.088898
## [189] train-rmse:0.225942+0.006665 test-rmse:0.950333+0.088691
## [190] train-rmse:0.224316+0.006375 test-rmse:0.949796+0.089324
## [191] train-rmse:0.222351+0.006931 test-rmse:0.949441+0.088988
## [192] train-rmse:0.220936+0.006803 test-rmse:0.949762+0.088753
## [193] train-rmse:0.218845+0.006350 test-rmse:0.949408+0.088644
## [194] train-rmse:0.216895+0.006447 test-rmse:0.948900+0.088127
## [195] train-rmse:0.215042+0.006762 test-rmse:0.949665+0.088458
## [196] train-rmse:0.214263+0.006841 test-rmse:0.949649+0.088424
## [197] train-rmse:0.212793+0.007285 test-rmse:0.949085+0.088742
## [198] train-rmse:0.211462+0.007087 test-rmse:0.948834+0.088194
## [199] train-rmse:0.209319+0.006927 test-rmse:0.949243+0.087873
## [200] train-rmse:0.208035+0.007177 test-rmse:0.949101+0.087541
## [201] train-rmse:0.206647+0.007279 test-rmse:0.949118+0.087722
## [202] train-rmse:0.205637+0.007247 test-rmse:0.949062+0.087378
## [203] train-rmse:0.203487+0.006836 test-rmse:0.948959+0.087690
## [204] train-rmse:0.201993+0.007118 test-rmse:0.948312+0.087602
## [205] train-rmse:0.200249+0.007260 test-rmse:0.947757+0.088089
## [206] train-rmse:0.198349+0.007437 test-rmse:0.947817+0.088058
## [207] train-rmse:0.196747+0.007504 test-rmse:0.947195+0.087512
## [208] train-rmse:0.195820+0.007487 test-rmse:0.946855+0.087390
## [209] train-rmse:0.194501+0.006951 test-rmse:0.946760+0.088110
## [210] train-rmse:0.193561+0.006849 test-rmse:0.946585+0.088112
## [211] train-rmse:0.191931+0.006232 test-rmse:0.946376+0.087644
## [212] train-rmse:0.190316+0.005845 test-rmse:0.946790+0.087320
## [213] train-rmse:0.189350+0.005817 test-rmse:0.946218+0.087040
## [214] train-rmse:0.188092+0.005730 test-rmse:0.946445+0.086906
## [215] train-rmse:0.186655+0.005829 test-rmse:0.945869+0.086031
## [216] train-rmse:0.185331+0.005263 test-rmse:0.945603+0.086166
## [217] train-rmse:0.183945+0.005665 test-rmse:0.945620+0.085966
## [218] train-rmse:0.183180+0.005787 test-rmse:0.945842+0.086081
## [219] train-rmse:0.181937+0.005767 test-rmse:0.945899+0.085898
## [220] train-rmse:0.180802+0.005315 test-rmse:0.945936+0.085879
## [221] train-rmse:0.179208+0.005251 test-rmse:0.945788+0.085686
## [222] train-rmse:0.178251+0.004941 test-rmse:0.945489+0.085834
## [223] train-rmse:0.177008+0.004887 test-rmse:0.945251+0.085658
## [224] train-rmse:0.176065+0.004969 test-rmse:0.945437+0.085549
## [225] train-rmse:0.175036+0.005166 test-rmse:0.945077+0.085292
## [226] train-rmse:0.173748+0.005781 test-rmse:0.944783+0.085455
## [227] train-rmse:0.172708+0.005481 test-rmse:0.945131+0.085712
## [228] train-rmse:0.171558+0.005219 test-rmse:0.945248+0.085779
## [229] train-rmse:0.170632+0.005044 test-rmse:0.945365+0.085804
## [230] train-rmse:0.169464+0.005236 test-rmse:0.945035+0.085804
## [231] train-rmse:0.168632+0.005392 test-rmse:0.944722+0.085677
## [232] train-rmse:0.167246+0.005853 test-rmse:0.944411+0.085432
## [233] train-rmse:0.165983+0.005632 test-rmse:0.944514+0.085188
## [234] train-rmse:0.164573+0.005668 test-rmse:0.944309+0.084798
## [235] train-rmse:0.163368+0.005731 test-rmse:0.944162+0.084616
## [236] train-rmse:0.162436+0.006091 test-rmse:0.944050+0.084552
## [237] train-rmse:0.161075+0.006362 test-rmse:0.943763+0.084417
## [238] train-rmse:0.159528+0.006042 test-rmse:0.943863+0.084538
## [239] train-rmse:0.158534+0.006116 test-rmse:0.943293+0.083933
## [240] train-rmse:0.157277+0.006272 test-rmse:0.943014+0.083863
## [241] train-rmse:0.156157+0.006386 test-rmse:0.942987+0.083755
## [242] train-rmse:0.155196+0.006105 test-rmse:0.942877+0.083557
## [243] train-rmse:0.154435+0.005848 test-rmse:0.942698+0.083665
## [244] train-rmse:0.153484+0.005939 test-rmse:0.942869+0.083981
## [245] train-rmse:0.152519+0.006089 test-rmse:0.942559+0.083690
## [246] train-rmse:0.151010+0.005517 test-rmse:0.942451+0.083508
## [247] train-rmse:0.150203+0.005407 test-rmse:0.942447+0.083774
## [248] train-rmse:0.149427+0.005585 test-rmse:0.942416+0.083672
## [249] train-rmse:0.148005+0.006222 test-rmse:0.942469+0.083676
## [250] train-rmse:0.147300+0.006144 test-rmse:0.942297+0.083643
## [251] train-rmse:0.146145+0.006396 test-rmse:0.942303+0.083546
## [252] train-rmse:0.145333+0.006229 test-rmse:0.942071+0.083750
## [253] train-rmse:0.144072+0.006492 test-rmse:0.941757+0.083835
## [254] train-rmse:0.143411+0.006507 test-rmse:0.941760+0.084008
## [255] train-rmse:0.142656+0.006934 test-rmse:0.941286+0.083783
## [256] train-rmse:0.141897+0.007028 test-rmse:0.941351+0.083607
## [257] train-rmse:0.140946+0.006985 test-rmse:0.941164+0.083859
## [258] train-rmse:0.140246+0.006851 test-rmse:0.940784+0.083696
## [259] train-rmse:0.139240+0.006866 test-rmse:0.940536+0.083801
## [260] train-rmse:0.138416+0.006960 test-rmse:0.940604+0.083812
## [261] train-rmse:0.137724+0.006967 test-rmse:0.940309+0.083879
## [262] train-rmse:0.136840+0.006754 test-rmse:0.940016+0.083659
## [263] train-rmse:0.135754+0.006664 test-rmse:0.940204+0.084264
## [264] train-rmse:0.134586+0.006401 test-rmse:0.940184+0.083966
## [265] train-rmse:0.133842+0.006595 test-rmse:0.939972+0.083797
## [266] train-rmse:0.132827+0.006851 test-rmse:0.940063+0.083880
## [267] train-rmse:0.131848+0.006982 test-rmse:0.939652+0.083881
## [268] train-rmse:0.131122+0.006852 test-rmse:0.939469+0.084052
## [269] train-rmse:0.130682+0.006940 test-rmse:0.939452+0.084127
## [270] train-rmse:0.129879+0.006827 test-rmse:0.939337+0.084004
## [271] train-rmse:0.128814+0.006655 test-rmse:0.939196+0.083793
## [272] train-rmse:0.127816+0.006636 test-rmse:0.939295+0.083513
## [273] train-rmse:0.127281+0.006851 test-rmse:0.939052+0.083403
## [274] train-rmse:0.126739+0.006853 test-rmse:0.939285+0.083676
## [275] train-rmse:0.125724+0.006764 test-rmse:0.939177+0.083429
## [276] train-rmse:0.124714+0.006793 test-rmse:0.939278+0.083462
## [277] train-rmse:0.123990+0.006860 test-rmse:0.939105+0.083435
## [278] train-rmse:0.123137+0.007081 test-rmse:0.939327+0.083274
## [279] train-rmse:0.122394+0.006897 test-rmse:0.939405+0.083342
## Stopping. Best iteration:
## [273] train-rmse:0.127281+0.006851 test-rmse:0.939052+0.083403
##
## 5
## [1] train-rmse:88.665695+0.088976 test-rmse:88.666461+0.404980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.835214+0.080050 test-rmse:79.835893+0.413675
## [3] train-rmse:71.885242+0.071998 test-rmse:71.885820+0.421463
## [4] train-rmse:64.728087+0.064733 test-rmse:64.728551+0.428434
## [5] train-rmse:58.284824+0.058174 test-rmse:58.285159+0.434654
## [6] train-rmse:52.484374+0.052257 test-rmse:52.484567+0.440197
## [7] train-rmse:47.262778+0.046908 test-rmse:47.262816+0.445120
## [8] train-rmse:42.562434+0.042074 test-rmse:42.562303+0.449468
## [9] train-rmse:38.331508+0.037710 test-rmse:38.331192+0.453293
## [10] train-rmse:34.523343+0.033759 test-rmse:34.522811+0.456630
## [11] train-rmse:31.095926+0.030181 test-rmse:31.095166+0.459509
## [12] train-rmse:28.011453+0.026945 test-rmse:28.010445+0.461960
## [13] train-rmse:25.235913+0.024012 test-rmse:25.234629+0.463999
## [14] train-rmse:22.738687+0.021363 test-rmse:22.737103+0.465637
## [15] train-rmse:20.492223+0.018971 test-rmse:20.490315+0.466887
## [16] train-rmse:18.471749+0.016823 test-rmse:18.469484+0.467747
## [17] train-rmse:16.654969+0.014903 test-rmse:16.652318+0.468213
## [18] train-rmse:15.021334+0.013222 test-rmse:15.020864+0.456548
## [19] train-rmse:13.552638+0.011590 test-rmse:13.550876+0.449119
## [20] train-rmse:12.232204+0.010135 test-rmse:12.222750+0.446257
## [21] train-rmse:11.045206+0.008913 test-rmse:11.035400+0.435400
## [22] train-rmse:9.977437+0.008087 test-rmse:9.961850+0.427085
## [23] train-rmse:9.017222+0.007327 test-rmse:9.002905+0.418550
## [24] train-rmse:8.153142+0.007026 test-rmse:8.132628+0.403760
## [25] train-rmse:7.375641+0.006523 test-rmse:7.358086+0.386144
## [26] train-rmse:6.676874+0.006110 test-rmse:6.664854+0.372684
## [27] train-rmse:6.048462+0.006179 test-rmse:6.036334+0.357361
## [28] train-rmse:5.483426+0.005291 test-rmse:5.473213+0.339879
## [29] train-rmse:4.974809+0.004622 test-rmse:4.962442+0.330863
## [30] train-rmse:4.517822+0.004087 test-rmse:4.507892+0.324959
## [31] train-rmse:4.105579+0.003650 test-rmse:4.106107+0.311515
## [32] train-rmse:3.734930+0.002627 test-rmse:3.740299+0.301411
## [33] train-rmse:3.401261+0.003039 test-rmse:3.410442+0.290344
## [34] train-rmse:3.100665+0.004138 test-rmse:3.123887+0.270813
## [35] train-rmse:2.831528+0.004083 test-rmse:2.868586+0.259876
## [36] train-rmse:2.590556+0.004682 test-rmse:2.636841+0.247273
## [37] train-rmse:2.372917+0.004714 test-rmse:2.432736+0.236897
## [38] train-rmse:2.176590+0.005018 test-rmse:2.250900+0.228325
## [39] train-rmse:2.000876+0.003932 test-rmse:2.093256+0.211542
## [40] train-rmse:1.843353+0.003266 test-rmse:1.955518+0.196284
## [41] train-rmse:1.704839+0.004721 test-rmse:1.831358+0.188988
## [42] train-rmse:1.579760+0.006076 test-rmse:1.724393+0.180594
## [43] train-rmse:1.466427+0.006131 test-rmse:1.636521+0.175116
## [44] train-rmse:1.366982+0.006463 test-rmse:1.554056+0.167114
## [45] train-rmse:1.278060+0.006746 test-rmse:1.483212+0.161318
## [46] train-rmse:1.199380+0.007099 test-rmse:1.422570+0.151168
## [47] train-rmse:1.126885+0.008301 test-rmse:1.366786+0.139767
## [48] train-rmse:1.064318+0.009374 test-rmse:1.325082+0.135471
## [49] train-rmse:1.008371+0.009196 test-rmse:1.288832+0.131199
## [50] train-rmse:0.957748+0.009581 test-rmse:1.254673+0.125174
## [51] train-rmse:0.911165+0.008953 test-rmse:1.228957+0.118002
## [52] train-rmse:0.870335+0.009117 test-rmse:1.206568+0.116043
## [53] train-rmse:0.832790+0.008251 test-rmse:1.187527+0.112111
## [54] train-rmse:0.801401+0.009430 test-rmse:1.168955+0.109193
## [55] train-rmse:0.770831+0.008759 test-rmse:1.151561+0.107158
## [56] train-rmse:0.743744+0.008556 test-rmse:1.137683+0.102352
## [57] train-rmse:0.720342+0.007931 test-rmse:1.129534+0.101969
## [58] train-rmse:0.698761+0.007673 test-rmse:1.121406+0.101182
## [59] train-rmse:0.678973+0.007344 test-rmse:1.108604+0.096879
## [60] train-rmse:0.662322+0.006460 test-rmse:1.100298+0.095629
## [61] train-rmse:0.645161+0.008161 test-rmse:1.093907+0.092609
## [62] train-rmse:0.628248+0.008578 test-rmse:1.088278+0.092725
## [63] train-rmse:0.613743+0.008204 test-rmse:1.081271+0.087542
## [64] train-rmse:0.601220+0.009230 test-rmse:1.075273+0.084935
## [65] train-rmse:0.588259+0.009329 test-rmse:1.070195+0.085046
## [66] train-rmse:0.577493+0.009347 test-rmse:1.066949+0.083369
## [67] train-rmse:0.564171+0.008538 test-rmse:1.060836+0.080995
## [68] train-rmse:0.555147+0.008947 test-rmse:1.055406+0.080092
## [69] train-rmse:0.542969+0.009257 test-rmse:1.052370+0.079455
## [70] train-rmse:0.533887+0.008038 test-rmse:1.050760+0.079385
## [71] train-rmse:0.525071+0.010090 test-rmse:1.046829+0.079743
## [72] train-rmse:0.515294+0.008485 test-rmse:1.043426+0.079064
## [73] train-rmse:0.508051+0.009301 test-rmse:1.040300+0.079455
## [74] train-rmse:0.500557+0.009285 test-rmse:1.037318+0.077897
## [75] train-rmse:0.492421+0.008530 test-rmse:1.035971+0.076690
## [76] train-rmse:0.485436+0.009123 test-rmse:1.031434+0.075966
## [77] train-rmse:0.478826+0.008167 test-rmse:1.028707+0.076121
## [78] train-rmse:0.472638+0.008537 test-rmse:1.026620+0.076145
## [79] train-rmse:0.464811+0.007245 test-rmse:1.021973+0.075559
## [80] train-rmse:0.459577+0.007471 test-rmse:1.020232+0.075487
## [81] train-rmse:0.452975+0.008184 test-rmse:1.018717+0.075687
## [82] train-rmse:0.447193+0.006934 test-rmse:1.016286+0.076588
## [83] train-rmse:0.441474+0.007161 test-rmse:1.013827+0.077181
## [84] train-rmse:0.436286+0.007330 test-rmse:1.013268+0.077905
## [85] train-rmse:0.430761+0.006259 test-rmse:1.009028+0.076979
## [86] train-rmse:0.425062+0.006747 test-rmse:1.007343+0.076207
## [87] train-rmse:0.420266+0.007237 test-rmse:1.005201+0.076023
## [88] train-rmse:0.415358+0.007529 test-rmse:1.003976+0.076733
## [89] train-rmse:0.410399+0.006982 test-rmse:1.002576+0.077264
## [90] train-rmse:0.406306+0.006895 test-rmse:0.998659+0.076206
## [91] train-rmse:0.401106+0.007743 test-rmse:0.997701+0.076369
## [92] train-rmse:0.395562+0.006256 test-rmse:0.994453+0.076340
## [93] train-rmse:0.391513+0.007033 test-rmse:0.993220+0.076860
## [94] train-rmse:0.387657+0.006729 test-rmse:0.991834+0.077936
## [95] train-rmse:0.383827+0.006395 test-rmse:0.990486+0.078494
## [96] train-rmse:0.378130+0.006353 test-rmse:0.989015+0.079146
## [97] train-rmse:0.373530+0.006877 test-rmse:0.987105+0.079439
## [98] train-rmse:0.369718+0.006035 test-rmse:0.985841+0.079226
## [99] train-rmse:0.364625+0.005247 test-rmse:0.984581+0.078430
## [100] train-rmse:0.359787+0.004183 test-rmse:0.984597+0.076913
## [101] train-rmse:0.356303+0.004898 test-rmse:0.983773+0.076499
## [102] train-rmse:0.351679+0.004478 test-rmse:0.983153+0.076204
## [103] train-rmse:0.346974+0.004826 test-rmse:0.982334+0.076611
## [104] train-rmse:0.343238+0.004971 test-rmse:0.981702+0.076163
## [105] train-rmse:0.339238+0.005135 test-rmse:0.980144+0.075599
## [106] train-rmse:0.333999+0.006748 test-rmse:0.980077+0.075651
## [107] train-rmse:0.329701+0.007264 test-rmse:0.978608+0.075766
## [108] train-rmse:0.326609+0.008243 test-rmse:0.977807+0.076324
## [109] train-rmse:0.323707+0.007990 test-rmse:0.977153+0.076308
## [110] train-rmse:0.319691+0.009786 test-rmse:0.977026+0.076481
## [111] train-rmse:0.316644+0.009753 test-rmse:0.975971+0.076904
## [112] train-rmse:0.313344+0.009231 test-rmse:0.975724+0.076650
## [113] train-rmse:0.309821+0.009616 test-rmse:0.974438+0.076668
## [114] train-rmse:0.305530+0.009082 test-rmse:0.973607+0.076952
## [115] train-rmse:0.301794+0.008139 test-rmse:0.972441+0.076627
## [116] train-rmse:0.297887+0.008061 test-rmse:0.971773+0.076408
## [117] train-rmse:0.293398+0.008611 test-rmse:0.971509+0.076583
## [118] train-rmse:0.289876+0.009041 test-rmse:0.970998+0.076178
## [119] train-rmse:0.287056+0.008475 test-rmse:0.970547+0.076150
## [120] train-rmse:0.284739+0.008357 test-rmse:0.969587+0.075212
## [121] train-rmse:0.282024+0.008789 test-rmse:0.969355+0.075162
## [122] train-rmse:0.279360+0.008277 test-rmse:0.968385+0.074389
## [123] train-rmse:0.276189+0.008120 test-rmse:0.967295+0.075111
## [124] train-rmse:0.273707+0.008477 test-rmse:0.967657+0.076185
## [125] train-rmse:0.271444+0.008897 test-rmse:0.967219+0.076478
## [126] train-rmse:0.268563+0.009226 test-rmse:0.965995+0.075508
## [127] train-rmse:0.265833+0.009936 test-rmse:0.965593+0.074901
## [128] train-rmse:0.263049+0.009949 test-rmse:0.965505+0.074709
## [129] train-rmse:0.260577+0.009717 test-rmse:0.963729+0.075902
## [130] train-rmse:0.258116+0.009333 test-rmse:0.964041+0.075583
## [131] train-rmse:0.255338+0.009611 test-rmse:0.963771+0.075799
## [132] train-rmse:0.252448+0.010100 test-rmse:0.963393+0.075138
## [133] train-rmse:0.248994+0.010186 test-rmse:0.963363+0.075061
## [134] train-rmse:0.245579+0.010216 test-rmse:0.962294+0.074731
## [135] train-rmse:0.242912+0.010261 test-rmse:0.961521+0.074294
## [136] train-rmse:0.240504+0.009658 test-rmse:0.961115+0.073933
## [137] train-rmse:0.238440+0.009754 test-rmse:0.960669+0.074050
## [138] train-rmse:0.234812+0.009936 test-rmse:0.959933+0.073538
## [139] train-rmse:0.232080+0.009587 test-rmse:0.959022+0.073418
## [140] train-rmse:0.229746+0.009200 test-rmse:0.958856+0.073584
## [141] train-rmse:0.227716+0.009292 test-rmse:0.958313+0.073328
## [142] train-rmse:0.225824+0.009132 test-rmse:0.958744+0.072927
## [143] train-rmse:0.223699+0.008664 test-rmse:0.958706+0.072771
## [144] train-rmse:0.221512+0.009387 test-rmse:0.957796+0.073223
## [145] train-rmse:0.220005+0.009538 test-rmse:0.957786+0.073529
## [146] train-rmse:0.217596+0.008986 test-rmse:0.958068+0.073565
## [147] train-rmse:0.214456+0.008802 test-rmse:0.957315+0.074123
## [148] train-rmse:0.211621+0.009848 test-rmse:0.957272+0.074344
## [149] train-rmse:0.208689+0.010151 test-rmse:0.956951+0.074489
## [150] train-rmse:0.207252+0.010531 test-rmse:0.956360+0.074415
## [151] train-rmse:0.205570+0.010048 test-rmse:0.955974+0.074140
## [152] train-rmse:0.203511+0.010471 test-rmse:0.955504+0.073795
## [153] train-rmse:0.202140+0.010856 test-rmse:0.955477+0.073501
## [154] train-rmse:0.199718+0.010725 test-rmse:0.954505+0.073064
## [155] train-rmse:0.198257+0.010932 test-rmse:0.954344+0.073169
## [156] train-rmse:0.196115+0.011039 test-rmse:0.954056+0.073147
## [157] train-rmse:0.194390+0.010958 test-rmse:0.953809+0.073406
## [158] train-rmse:0.192637+0.010276 test-rmse:0.953497+0.073284
## [159] train-rmse:0.191021+0.010093 test-rmse:0.953183+0.073074
## [160] train-rmse:0.189043+0.010081 test-rmse:0.952322+0.072704
## [161] train-rmse:0.186789+0.009251 test-rmse:0.951754+0.072618
## [162] train-rmse:0.185432+0.009300 test-rmse:0.951329+0.072376
## [163] train-rmse:0.183011+0.009685 test-rmse:0.951154+0.072254
## [164] train-rmse:0.181458+0.009717 test-rmse:0.950834+0.072511
## [165] train-rmse:0.179847+0.009344 test-rmse:0.950742+0.072738
## [166] train-rmse:0.178000+0.009739 test-rmse:0.950465+0.072232
## [167] train-rmse:0.175451+0.009777 test-rmse:0.949991+0.071840
## [168] train-rmse:0.173224+0.009777 test-rmse:0.950223+0.072117
## [169] train-rmse:0.171366+0.010233 test-rmse:0.949923+0.072288
## [170] train-rmse:0.169790+0.010519 test-rmse:0.950105+0.072633
## [171] train-rmse:0.168221+0.010960 test-rmse:0.950140+0.072557
## [172] train-rmse:0.166806+0.011302 test-rmse:0.949748+0.072909
## [173] train-rmse:0.165673+0.011427 test-rmse:0.949246+0.073250
## [174] train-rmse:0.164377+0.011093 test-rmse:0.948802+0.073303
## [175] train-rmse:0.162887+0.010924 test-rmse:0.948817+0.073595
## [176] train-rmse:0.161358+0.011267 test-rmse:0.948389+0.073699
## [177] train-rmse:0.159915+0.011303 test-rmse:0.948131+0.073416
## [178] train-rmse:0.158224+0.011448 test-rmse:0.947902+0.073503
## [179] train-rmse:0.156631+0.011710 test-rmse:0.947934+0.073131
## [180] train-rmse:0.154957+0.012008 test-rmse:0.948149+0.073296
## [181] train-rmse:0.152896+0.012156 test-rmse:0.947816+0.073378
## [182] train-rmse:0.150827+0.011771 test-rmse:0.947753+0.074035
## [183] train-rmse:0.149352+0.011781 test-rmse:0.947964+0.074010
## [184] train-rmse:0.147278+0.011953 test-rmse:0.947678+0.074107
## [185] train-rmse:0.145452+0.012136 test-rmse:0.947653+0.074224
## [186] train-rmse:0.144394+0.011723 test-rmse:0.947247+0.074551
## [187] train-rmse:0.142566+0.012037 test-rmse:0.947393+0.074478
## [188] train-rmse:0.140908+0.011857 test-rmse:0.947348+0.074615
## [189] train-rmse:0.139618+0.012164 test-rmse:0.947233+0.075027
## [190] train-rmse:0.138040+0.012164 test-rmse:0.947023+0.075174
## [191] train-rmse:0.136288+0.012059 test-rmse:0.946657+0.074959
## [192] train-rmse:0.135166+0.012066 test-rmse:0.946714+0.074926
## [193] train-rmse:0.133585+0.011793 test-rmse:0.946857+0.075049
## [194] train-rmse:0.132766+0.011761 test-rmse:0.946726+0.074804
## [195] train-rmse:0.130883+0.011172 test-rmse:0.946594+0.074810
## [196] train-rmse:0.129320+0.010653 test-rmse:0.945904+0.075251
## [197] train-rmse:0.128319+0.010278 test-rmse:0.946030+0.075206
## [198] train-rmse:0.127062+0.010608 test-rmse:0.946058+0.075167
## [199] train-rmse:0.125629+0.010661 test-rmse:0.946393+0.075362
## [200] train-rmse:0.124468+0.010873 test-rmse:0.946146+0.075285
## [201] train-rmse:0.123430+0.010914 test-rmse:0.946018+0.075620
## [202] train-rmse:0.122559+0.010942 test-rmse:0.945736+0.075659
## [203] train-rmse:0.121544+0.010738 test-rmse:0.945916+0.075755
## [204] train-rmse:0.120462+0.010936 test-rmse:0.945704+0.075575
## [205] train-rmse:0.118944+0.010752 test-rmse:0.945133+0.075695
## [206] train-rmse:0.118035+0.010729 test-rmse:0.945017+0.075625
## [207] train-rmse:0.117270+0.010589 test-rmse:0.944934+0.075385
## [208] train-rmse:0.116520+0.010546 test-rmse:0.944776+0.075563
## [209] train-rmse:0.115481+0.010329 test-rmse:0.944365+0.075777
## [210] train-rmse:0.114135+0.010160 test-rmse:0.944232+0.075844
## [211] train-rmse:0.113091+0.010245 test-rmse:0.944033+0.075622
## [212] train-rmse:0.112052+0.010143 test-rmse:0.943839+0.075774
## [213] train-rmse:0.110650+0.009741 test-rmse:0.943498+0.075812
## [214] train-rmse:0.109690+0.009499 test-rmse:0.943411+0.075849
## [215] train-rmse:0.108476+0.009391 test-rmse:0.943098+0.075985
## [216] train-rmse:0.107717+0.009446 test-rmse:0.942770+0.075676
## [217] train-rmse:0.106811+0.009589 test-rmse:0.942729+0.075736
## [218] train-rmse:0.105652+0.009917 test-rmse:0.942618+0.075634
## [219] train-rmse:0.105034+0.009907 test-rmse:0.942447+0.075662
## [220] train-rmse:0.104305+0.009723 test-rmse:0.942151+0.075436
## [221] train-rmse:0.103363+0.009432 test-rmse:0.941881+0.075700
## [222] train-rmse:0.102314+0.009264 test-rmse:0.941877+0.075699
## [223] train-rmse:0.100760+0.009089 test-rmse:0.941848+0.075306
## [224] train-rmse:0.099177+0.009041 test-rmse:0.941618+0.074910
## [225] train-rmse:0.098044+0.009127 test-rmse:0.941255+0.074921
## [226] train-rmse:0.097379+0.009061 test-rmse:0.941465+0.074980
## [227] train-rmse:0.096714+0.008820 test-rmse:0.941509+0.074965
## [228] train-rmse:0.096081+0.008869 test-rmse:0.941436+0.075016
## [229] train-rmse:0.095570+0.008772 test-rmse:0.941423+0.075055
## [230] train-rmse:0.094685+0.008452 test-rmse:0.941176+0.075110
## [231] train-rmse:0.093675+0.008468 test-rmse:0.941106+0.075195
## [232] train-rmse:0.092935+0.008669 test-rmse:0.940896+0.075250
## [233] train-rmse:0.092401+0.008566 test-rmse:0.940892+0.075429
## [234] train-rmse:0.091141+0.008305 test-rmse:0.940548+0.075432
## [235] train-rmse:0.090590+0.008231 test-rmse:0.940317+0.075455
## [236] train-rmse:0.089662+0.007917 test-rmse:0.940230+0.075602
## [237] train-rmse:0.088977+0.007883 test-rmse:0.940351+0.075675
## [238] train-rmse:0.088271+0.007945 test-rmse:0.940273+0.075665
## [239] train-rmse:0.087416+0.007838 test-rmse:0.940234+0.075734
## [240] train-rmse:0.086129+0.007567 test-rmse:0.940368+0.075804
## [241] train-rmse:0.085212+0.007562 test-rmse:0.940063+0.075667
## [242] train-rmse:0.084573+0.007342 test-rmse:0.939951+0.075817
## [243] train-rmse:0.083997+0.007396 test-rmse:0.939834+0.075851
## [244] train-rmse:0.083223+0.007054 test-rmse:0.939741+0.075843
## [245] train-rmse:0.082217+0.006849 test-rmse:0.939360+0.076195
## [246] train-rmse:0.081433+0.006791 test-rmse:0.939394+0.076152
## [247] train-rmse:0.080619+0.006732 test-rmse:0.939386+0.076003
## [248] train-rmse:0.079563+0.006906 test-rmse:0.939317+0.075831
## [249] train-rmse:0.078948+0.006693 test-rmse:0.939369+0.075761
## [250] train-rmse:0.078195+0.006414 test-rmse:0.939356+0.075838
## [251] train-rmse:0.077534+0.006380 test-rmse:0.939323+0.075610
## [252] train-rmse:0.076819+0.006478 test-rmse:0.939241+0.075548
## [253] train-rmse:0.075989+0.006521 test-rmse:0.939265+0.075666
## [254] train-rmse:0.075153+0.006394 test-rmse:0.939512+0.075712
## [255] train-rmse:0.073989+0.006203 test-rmse:0.939437+0.075626
## [256] train-rmse:0.073581+0.006063 test-rmse:0.939297+0.075743
## [257] train-rmse:0.072790+0.006122 test-rmse:0.939204+0.075554
## [258] train-rmse:0.072106+0.006059 test-rmse:0.939195+0.075765
## [259] train-rmse:0.071338+0.006089 test-rmse:0.938911+0.075625
## [260] train-rmse:0.070520+0.005991 test-rmse:0.938527+0.075853
## [261] train-rmse:0.069834+0.006183 test-rmse:0.938442+0.075959
## [262] train-rmse:0.069316+0.006187 test-rmse:0.938345+0.076047
## [263] train-rmse:0.068713+0.006022 test-rmse:0.938240+0.076047
## [264] train-rmse:0.067843+0.005917 test-rmse:0.937989+0.076222
## [265] train-rmse:0.067293+0.006016 test-rmse:0.937969+0.076284
## [266] train-rmse:0.066798+0.005851 test-rmse:0.937957+0.076222
## [267] train-rmse:0.066168+0.005957 test-rmse:0.937997+0.076169
## [268] train-rmse:0.065339+0.005600 test-rmse:0.938127+0.076139
## [269] train-rmse:0.064801+0.005722 test-rmse:0.938061+0.076029
## [270] train-rmse:0.064252+0.005570 test-rmse:0.937950+0.076055
## [271] train-rmse:0.063585+0.005582 test-rmse:0.937930+0.076080
## [272] train-rmse:0.062975+0.005524 test-rmse:0.937959+0.076150
## [273] train-rmse:0.062233+0.005216 test-rmse:0.937783+0.076304
## [274] train-rmse:0.061607+0.005319 test-rmse:0.937712+0.076377
## [275] train-rmse:0.060823+0.004966 test-rmse:0.937709+0.076317
## [276] train-rmse:0.060390+0.005069 test-rmse:0.937552+0.076201
## [277] train-rmse:0.059887+0.004895 test-rmse:0.937447+0.076278
## [278] train-rmse:0.059433+0.004687 test-rmse:0.937473+0.076423
## [279] train-rmse:0.059032+0.004717 test-rmse:0.937249+0.076443
## [280] train-rmse:0.058553+0.004622 test-rmse:0.937128+0.076476
## [281] train-rmse:0.057879+0.004634 test-rmse:0.937148+0.076443
## [282] train-rmse:0.057316+0.004731 test-rmse:0.937156+0.076398
## [283] train-rmse:0.056721+0.004815 test-rmse:0.937067+0.076466
## [284] train-rmse:0.056000+0.004647 test-rmse:0.937050+0.076409
## [285] train-rmse:0.055394+0.004578 test-rmse:0.937056+0.076382
## [286] train-rmse:0.054614+0.004366 test-rmse:0.936928+0.076493
## [287] train-rmse:0.054029+0.004162 test-rmse:0.936826+0.076513
## [288] train-rmse:0.053491+0.004072 test-rmse:0.936877+0.076527
## [289] train-rmse:0.053111+0.004086 test-rmse:0.936721+0.076516
## [290] train-rmse:0.052633+0.003872 test-rmse:0.936661+0.076517
## [291] train-rmse:0.052131+0.003985 test-rmse:0.936610+0.076573
## [292] train-rmse:0.051618+0.003814 test-rmse:0.936477+0.076712
## [293] train-rmse:0.051204+0.003874 test-rmse:0.936397+0.076619
## [294] train-rmse:0.050647+0.003713 test-rmse:0.936410+0.076522
## [295] train-rmse:0.050089+0.003544 test-rmse:0.936393+0.076565
## [296] train-rmse:0.049610+0.003555 test-rmse:0.936402+0.076434
## [297] train-rmse:0.049304+0.003445 test-rmse:0.936348+0.076379
## [298] train-rmse:0.048732+0.003572 test-rmse:0.936310+0.076314
## [299] train-rmse:0.048263+0.003626 test-rmse:0.936267+0.076207
## [300] train-rmse:0.048025+0.003703 test-rmse:0.936254+0.076210
## [301] train-rmse:0.047690+0.003835 test-rmse:0.936222+0.076274
## [302] train-rmse:0.047179+0.003862 test-rmse:0.936166+0.076353
## [303] train-rmse:0.046874+0.003832 test-rmse:0.936107+0.076342
## [304] train-rmse:0.046406+0.003843 test-rmse:0.936051+0.076428
## [305] train-rmse:0.045963+0.003824 test-rmse:0.935969+0.076391
## [306] train-rmse:0.045590+0.003719 test-rmse:0.935939+0.076319
## [307] train-rmse:0.045110+0.003611 test-rmse:0.935806+0.076286
## [308] train-rmse:0.044619+0.003407 test-rmse:0.935799+0.076380
## [309] train-rmse:0.044148+0.003368 test-rmse:0.935921+0.076354
## [310] train-rmse:0.043799+0.003383 test-rmse:0.935863+0.076272
## [311] train-rmse:0.043422+0.003419 test-rmse:0.935855+0.076155
## [312] train-rmse:0.042887+0.003364 test-rmse:0.935855+0.076178
## [313] train-rmse:0.042519+0.003299 test-rmse:0.935850+0.076263
## [314] train-rmse:0.042204+0.003447 test-rmse:0.935854+0.076272
## Stopping. Best iteration:
## [308] train-rmse:0.044619+0.003407 test-rmse:0.935799+0.076380
##
## 6
## [1] train-rmse:88.665695+0.088976 test-rmse:88.666461+0.404980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.835214+0.080050 test-rmse:79.835893+0.413675
## [3] train-rmse:71.885242+0.071998 test-rmse:71.885820+0.421463
## [4] train-rmse:64.728087+0.064733 test-rmse:64.728551+0.428434
## [5] train-rmse:58.284824+0.058174 test-rmse:58.285159+0.434654
## [6] train-rmse:52.484374+0.052257 test-rmse:52.484567+0.440197
## [7] train-rmse:47.262778+0.046908 test-rmse:47.262816+0.445120
## [8] train-rmse:42.562434+0.042074 test-rmse:42.562303+0.449468
## [9] train-rmse:38.331508+0.037710 test-rmse:38.331192+0.453293
## [10] train-rmse:34.523343+0.033759 test-rmse:34.522811+0.456630
## [11] train-rmse:31.095926+0.030181 test-rmse:31.095166+0.459509
## [12] train-rmse:28.011453+0.026945 test-rmse:28.010445+0.461960
## [13] train-rmse:25.235913+0.024012 test-rmse:25.234629+0.463999
## [14] train-rmse:22.738687+0.021363 test-rmse:22.737103+0.465637
## [15] train-rmse:20.492223+0.018971 test-rmse:20.490315+0.466887
## [16] train-rmse:18.471749+0.016823 test-rmse:18.469484+0.467747
## [17] train-rmse:16.654969+0.014903 test-rmse:16.652318+0.468213
## [18] train-rmse:15.021334+0.013222 test-rmse:15.020864+0.456548
## [19] train-rmse:13.552638+0.011590 test-rmse:13.550876+0.449119
## [20] train-rmse:12.232204+0.010135 test-rmse:12.222750+0.446257
## [21] train-rmse:11.045206+0.008913 test-rmse:11.035400+0.435400
## [22] train-rmse:9.977437+0.008087 test-rmse:9.961850+0.427085
## [23] train-rmse:9.017222+0.007327 test-rmse:9.002905+0.418550
## [24] train-rmse:8.153142+0.007026 test-rmse:8.132628+0.403760
## [25] train-rmse:7.375596+0.006519 test-rmse:7.357645+0.385685
## [26] train-rmse:6.676762+0.006198 test-rmse:6.663002+0.372359
## [27] train-rmse:6.048609+0.005331 test-rmse:6.042072+0.356753
## [28] train-rmse:5.483776+0.004307 test-rmse:5.474873+0.345259
## [29] train-rmse:4.974539+0.003700 test-rmse:4.968185+0.332206
## [30] train-rmse:4.516635+0.003296 test-rmse:4.515265+0.320132
## [31] train-rmse:4.103717+0.003215 test-rmse:4.104181+0.307391
## [32] train-rmse:3.732577+0.002477 test-rmse:3.744347+0.292441
## [33] train-rmse:3.398377+0.002382 test-rmse:3.413745+0.285053
## [34] train-rmse:3.097576+0.002912 test-rmse:3.118874+0.273649
## [35] train-rmse:2.826310+0.002548 test-rmse:2.859871+0.259310
## [36] train-rmse:2.583939+0.003976 test-rmse:2.624490+0.248432
## [37] train-rmse:2.364182+0.003782 test-rmse:2.425086+0.233356
## [38] train-rmse:2.164690+0.003606 test-rmse:2.240503+0.221094
## [39] train-rmse:1.987538+0.005321 test-rmse:2.080321+0.214329
## [40] train-rmse:1.828325+0.005303 test-rmse:1.943159+0.210974
## [41] train-rmse:1.686256+0.005427 test-rmse:1.824285+0.205721
## [42] train-rmse:1.556712+0.003061 test-rmse:1.714302+0.198470
## [43] train-rmse:1.441230+0.002871 test-rmse:1.624937+0.189388
## [44] train-rmse:1.334996+0.002384 test-rmse:1.542465+0.177284
## [45] train-rmse:1.242547+0.003217 test-rmse:1.472385+0.168639
## [46] train-rmse:1.158269+0.002591 test-rmse:1.407218+0.162373
## [47] train-rmse:1.085129+0.004125 test-rmse:1.357174+0.156476
## [48] train-rmse:1.016017+0.002760 test-rmse:1.310506+0.147836
## [49] train-rmse:0.955680+0.005483 test-rmse:1.274477+0.140267
## [50] train-rmse:0.900111+0.002976 test-rmse:1.238908+0.136970
## [51] train-rmse:0.850654+0.004819 test-rmse:1.212583+0.132724
## [52] train-rmse:0.806738+0.006845 test-rmse:1.188585+0.131320
## [53] train-rmse:0.767984+0.005790 test-rmse:1.166787+0.129568
## [54] train-rmse:0.732449+0.006261 test-rmse:1.146414+0.129451
## [55] train-rmse:0.699768+0.007300 test-rmse:1.132010+0.128309
## [56] train-rmse:0.671418+0.007511 test-rmse:1.117846+0.124012
## [57] train-rmse:0.645090+0.009353 test-rmse:1.105650+0.120555
## [58] train-rmse:0.621914+0.010649 test-rmse:1.095657+0.119593
## [59] train-rmse:0.602064+0.011571 test-rmse:1.086870+0.119520
## [60] train-rmse:0.581659+0.012937 test-rmse:1.078155+0.115051
## [61] train-rmse:0.562888+0.012051 test-rmse:1.070885+0.114236
## [62] train-rmse:0.544133+0.013156 test-rmse:1.064467+0.112085
## [63] train-rmse:0.530103+0.014889 test-rmse:1.059795+0.110504
## [64] train-rmse:0.515066+0.017636 test-rmse:1.056489+0.108336
## [65] train-rmse:0.502533+0.016319 test-rmse:1.053387+0.108831
## [66] train-rmse:0.490740+0.017282 test-rmse:1.049251+0.108974
## [67] train-rmse:0.476377+0.016601 test-rmse:1.042342+0.105889
## [68] train-rmse:0.465640+0.017482 test-rmse:1.039174+0.106383
## [69] train-rmse:0.456891+0.018060 test-rmse:1.036479+0.105509
## [70] train-rmse:0.446135+0.019648 test-rmse:1.033131+0.104423
## [71] train-rmse:0.438680+0.019050 test-rmse:1.028107+0.103520
## [72] train-rmse:0.428964+0.019527 test-rmse:1.025165+0.101774
## [73] train-rmse:0.419245+0.018723 test-rmse:1.023830+0.102512
## [74] train-rmse:0.410242+0.018323 test-rmse:1.022196+0.102595
## [75] train-rmse:0.402930+0.018990 test-rmse:1.020306+0.102532
## [76] train-rmse:0.395581+0.019579 test-rmse:1.020263+0.100888
## [77] train-rmse:0.389071+0.017827 test-rmse:1.018802+0.100648
## [78] train-rmse:0.382405+0.017261 test-rmse:1.015072+0.101278
## [79] train-rmse:0.375151+0.017582 test-rmse:1.014603+0.101110
## [80] train-rmse:0.369599+0.017946 test-rmse:1.013410+0.101636
## [81] train-rmse:0.364870+0.017425 test-rmse:1.012359+0.102241
## [82] train-rmse:0.358580+0.017322 test-rmse:1.010283+0.102174
## [83] train-rmse:0.352804+0.018725 test-rmse:1.008240+0.101851
## [84] train-rmse:0.348027+0.018517 test-rmse:1.007207+0.103200
## [85] train-rmse:0.342808+0.018123 test-rmse:1.006526+0.103322
## [86] train-rmse:0.335899+0.017128 test-rmse:1.005922+0.103185
## [87] train-rmse:0.330548+0.017067 test-rmse:1.004300+0.103787
## [88] train-rmse:0.325665+0.018992 test-rmse:1.002239+0.103807
## [89] train-rmse:0.320658+0.018801 test-rmse:1.000390+0.102843
## [90] train-rmse:0.315900+0.018231 test-rmse:0.999288+0.102790
## [91] train-rmse:0.310643+0.017501 test-rmse:0.998092+0.103124
## [92] train-rmse:0.306403+0.017586 test-rmse:0.998485+0.102391
## [93] train-rmse:0.301187+0.017327 test-rmse:0.997620+0.102578
## [94] train-rmse:0.295687+0.017155 test-rmse:0.996499+0.102415
## [95] train-rmse:0.292768+0.016766 test-rmse:0.996332+0.103430
## [96] train-rmse:0.287784+0.016091 test-rmse:0.995624+0.103055
## [97] train-rmse:0.283393+0.016388 test-rmse:0.994439+0.103052
## [98] train-rmse:0.279852+0.016071 test-rmse:0.994942+0.105099
## [99] train-rmse:0.276721+0.016368 test-rmse:0.993347+0.105249
## [100] train-rmse:0.272027+0.015557 test-rmse:0.992815+0.105162
## [101] train-rmse:0.268536+0.014798 test-rmse:0.991938+0.104333
## [102] train-rmse:0.264032+0.014646 test-rmse:0.990682+0.102212
## [103] train-rmse:0.260734+0.014881 test-rmse:0.990382+0.102009
## [104] train-rmse:0.257581+0.014322 test-rmse:0.989895+0.101576
## [105] train-rmse:0.253454+0.013594 test-rmse:0.988885+0.101299
## [106] train-rmse:0.249771+0.013663 test-rmse:0.988708+0.101432
## [107] train-rmse:0.246819+0.013592 test-rmse:0.987728+0.101513
## [108] train-rmse:0.243539+0.013495 test-rmse:0.987872+0.101809
## [109] train-rmse:0.238467+0.013658 test-rmse:0.986943+0.101045
## [110] train-rmse:0.235722+0.013043 test-rmse:0.986837+0.101425
## [111] train-rmse:0.233236+0.013893 test-rmse:0.986828+0.100671
## [112] train-rmse:0.229258+0.014611 test-rmse:0.986099+0.100096
## [113] train-rmse:0.225983+0.014846 test-rmse:0.985773+0.100012
## [114] train-rmse:0.223105+0.013933 test-rmse:0.985682+0.099840
## [115] train-rmse:0.219610+0.013555 test-rmse:0.985874+0.099828
## [116] train-rmse:0.215811+0.014228 test-rmse:0.984780+0.099653
## [117] train-rmse:0.212816+0.013814 test-rmse:0.984228+0.099612
## [118] train-rmse:0.209747+0.013056 test-rmse:0.983579+0.100215
## [119] train-rmse:0.207034+0.012354 test-rmse:0.982801+0.100443
## [120] train-rmse:0.204720+0.012142 test-rmse:0.982298+0.100257
## [121] train-rmse:0.201584+0.011689 test-rmse:0.982102+0.100579
## [122] train-rmse:0.198772+0.011616 test-rmse:0.981552+0.100245
## [123] train-rmse:0.196849+0.011576 test-rmse:0.981281+0.099704
## [124] train-rmse:0.193608+0.011384 test-rmse:0.980778+0.099159
## [125] train-rmse:0.191264+0.011462 test-rmse:0.980931+0.099337
## [126] train-rmse:0.188419+0.010724 test-rmse:0.980853+0.099770
## [127] train-rmse:0.186056+0.010696 test-rmse:0.980911+0.099900
## [128] train-rmse:0.183396+0.011690 test-rmse:0.980942+0.099654
## [129] train-rmse:0.180835+0.010965 test-rmse:0.980770+0.100065
## [130] train-rmse:0.178684+0.011288 test-rmse:0.980433+0.099505
## [131] train-rmse:0.176520+0.010909 test-rmse:0.980743+0.099689
## [132] train-rmse:0.173762+0.010686 test-rmse:0.980701+0.099536
## [133] train-rmse:0.171738+0.010748 test-rmse:0.980673+0.099588
## [134] train-rmse:0.170005+0.010399 test-rmse:0.980246+0.099054
## [135] train-rmse:0.167502+0.009976 test-rmse:0.979865+0.098926
## [136] train-rmse:0.165527+0.009853 test-rmse:0.978962+0.098683
## [137] train-rmse:0.163691+0.009846 test-rmse:0.979018+0.098532
## [138] train-rmse:0.161724+0.009205 test-rmse:0.978827+0.098696
## [139] train-rmse:0.159110+0.008934 test-rmse:0.978337+0.098083
## [140] train-rmse:0.156967+0.008776 test-rmse:0.978321+0.098354
## [141] train-rmse:0.155375+0.008960 test-rmse:0.978104+0.098333
## [142] train-rmse:0.153017+0.008595 test-rmse:0.977055+0.097973
## [143] train-rmse:0.150857+0.007657 test-rmse:0.976384+0.098258
## [144] train-rmse:0.148587+0.007272 test-rmse:0.975263+0.097586
## [145] train-rmse:0.146904+0.006789 test-rmse:0.974228+0.097701
## [146] train-rmse:0.145522+0.007037 test-rmse:0.974228+0.098034
## [147] train-rmse:0.143492+0.006991 test-rmse:0.974211+0.098047
## [148] train-rmse:0.141618+0.007132 test-rmse:0.973505+0.097855
## [149] train-rmse:0.140507+0.007159 test-rmse:0.972834+0.097727
## [150] train-rmse:0.138466+0.007342 test-rmse:0.972742+0.097673
## [151] train-rmse:0.136909+0.006935 test-rmse:0.972154+0.097429
## [152] train-rmse:0.135475+0.007030 test-rmse:0.971773+0.097188
## [153] train-rmse:0.133642+0.007141 test-rmse:0.971696+0.097468
## [154] train-rmse:0.131290+0.007135 test-rmse:0.971612+0.097239
## [155] train-rmse:0.129244+0.006961 test-rmse:0.971004+0.097216
## [156] train-rmse:0.127380+0.006981 test-rmse:0.970408+0.096859
## [157] train-rmse:0.125104+0.006296 test-rmse:0.970258+0.096460
## [158] train-rmse:0.123192+0.006611 test-rmse:0.970035+0.096391
## [159] train-rmse:0.121859+0.006734 test-rmse:0.969863+0.096138
## [160] train-rmse:0.120404+0.007340 test-rmse:0.969664+0.095872
## [161] train-rmse:0.118915+0.007486 test-rmse:0.969617+0.095967
## [162] train-rmse:0.117084+0.006987 test-rmse:0.969396+0.096122
## [163] train-rmse:0.116094+0.006992 test-rmse:0.968972+0.095952
## [164] train-rmse:0.114400+0.006627 test-rmse:0.968793+0.095987
## [165] train-rmse:0.112942+0.006576 test-rmse:0.968576+0.096074
## [166] train-rmse:0.111229+0.006296 test-rmse:0.968310+0.096350
## [167] train-rmse:0.110189+0.005930 test-rmse:0.968074+0.096467
## [168] train-rmse:0.109016+0.005987 test-rmse:0.967700+0.096128
## [169] train-rmse:0.107410+0.006165 test-rmse:0.967255+0.095683
## [170] train-rmse:0.106171+0.006004 test-rmse:0.967236+0.095836
## [171] train-rmse:0.104450+0.005762 test-rmse:0.966846+0.096044
## [172] train-rmse:0.103002+0.006135 test-rmse:0.966649+0.096180
## [173] train-rmse:0.101122+0.005662 test-rmse:0.966419+0.096123
## [174] train-rmse:0.099910+0.005503 test-rmse:0.966271+0.096062
## [175] train-rmse:0.098819+0.005175 test-rmse:0.966302+0.095934
## [176] train-rmse:0.097664+0.005245 test-rmse:0.966198+0.095904
## [177] train-rmse:0.096331+0.004993 test-rmse:0.965954+0.095955
## [178] train-rmse:0.094976+0.005106 test-rmse:0.965907+0.095684
## [179] train-rmse:0.093744+0.004965 test-rmse:0.965743+0.095665
## [180] train-rmse:0.092683+0.004825 test-rmse:0.965676+0.095720
## [181] train-rmse:0.092109+0.004843 test-rmse:0.965660+0.095640
## [182] train-rmse:0.090981+0.004361 test-rmse:0.965731+0.095684
## [183] train-rmse:0.089671+0.004246 test-rmse:0.965397+0.095634
## [184] train-rmse:0.088417+0.004017 test-rmse:0.964983+0.095512
## [185] train-rmse:0.086863+0.003430 test-rmse:0.964927+0.095828
## [186] train-rmse:0.085690+0.003274 test-rmse:0.964772+0.095916
## [187] train-rmse:0.084658+0.003202 test-rmse:0.964640+0.095908
## [188] train-rmse:0.083558+0.003195 test-rmse:0.964514+0.095872
## [189] train-rmse:0.082172+0.003099 test-rmse:0.964491+0.095849
## [190] train-rmse:0.081126+0.003258 test-rmse:0.964340+0.095987
## [191] train-rmse:0.080128+0.003360 test-rmse:0.964381+0.096065
## [192] train-rmse:0.079221+0.003030 test-rmse:0.964452+0.096030
## [193] train-rmse:0.078270+0.002697 test-rmse:0.964380+0.095789
## [194] train-rmse:0.077544+0.002589 test-rmse:0.964312+0.095747
## [195] train-rmse:0.076213+0.002426 test-rmse:0.964285+0.095675
## [196] train-rmse:0.074989+0.002016 test-rmse:0.964225+0.095841
## [197] train-rmse:0.074274+0.001919 test-rmse:0.964272+0.095880
## [198] train-rmse:0.073309+0.001903 test-rmse:0.963989+0.095887
## [199] train-rmse:0.072023+0.001871 test-rmse:0.963993+0.096047
## [200] train-rmse:0.071049+0.001668 test-rmse:0.963893+0.096032
## [201] train-rmse:0.069856+0.001828 test-rmse:0.963735+0.095997
## [202] train-rmse:0.069076+0.002217 test-rmse:0.963733+0.096034
## [203] train-rmse:0.068396+0.002282 test-rmse:0.963855+0.096040
## [204] train-rmse:0.067622+0.002531 test-rmse:0.963769+0.096278
## [205] train-rmse:0.066728+0.002639 test-rmse:0.963726+0.096289
## [206] train-rmse:0.066032+0.002663 test-rmse:0.963845+0.096272
## [207] train-rmse:0.065479+0.002506 test-rmse:0.963983+0.096259
## [208] train-rmse:0.064638+0.002358 test-rmse:0.963884+0.096507
## [209] train-rmse:0.064001+0.002247 test-rmse:0.963926+0.096575
## [210] train-rmse:0.063293+0.002568 test-rmse:0.963909+0.096437
## [211] train-rmse:0.062594+0.002620 test-rmse:0.963838+0.096424
## Stopping. Best iteration:
## [205] train-rmse:0.066728+0.002639 test-rmse:0.963726+0.096289
##
## 7
## [1] train-rmse:86.704080+0.086998 test-rmse:86.704831+0.406915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.342288+0.076506 test-rmse:76.342926+0.417100
## [3] train-rmse:67.220384+0.067270 test-rmse:67.220891+0.426011
## [4] train-rmse:59.190229+0.059099 test-rmse:59.190588+0.433783
## [5] train-rmse:52.121395+0.051883 test-rmse:52.121578+0.440539
## [6] train-rmse:45.899076+0.045513 test-rmse:45.899065+0.446386
## [7] train-rmse:40.422199+0.039869 test-rmse:40.421979+0.451412
## [8] train-rmse:35.601835+0.034876 test-rmse:35.601369+0.455694
## [9] train-rmse:31.359680+0.030457 test-rmse:31.358941+0.459291
## [10] train-rmse:27.626835+0.026543 test-rmse:27.625793+0.462249
## [11] train-rmse:24.342670+0.023071 test-rmse:24.341286+0.464606
## [12] train-rmse:21.453838+0.019997 test-rmse:21.452077+0.466382
## [13] train-rmse:18.913416+0.017291 test-rmse:18.911232+0.467585
## [14] train-rmse:16.680124+0.014931 test-rmse:16.677478+0.468208
## [15] train-rmse:14.717049+0.012912 test-rmse:14.717037+0.454197
## [16] train-rmse:12.991951+0.011029 test-rmse:12.983670+0.455294
## [17] train-rmse:11.476346+0.009517 test-rmse:11.471345+0.443079
## [18] train-rmse:10.145233+0.008151 test-rmse:10.138345+0.437914
## [19] train-rmse:8.976784+0.007294 test-rmse:8.962900+0.437113
## [20] train-rmse:7.951891+0.006687 test-rmse:7.938465+0.433674
## [21] train-rmse:7.054369+0.006937 test-rmse:7.042167+0.423506
## [22] train-rmse:6.269129+0.007379 test-rmse:6.256289+0.415896
## [23] train-rmse:5.583360+0.008180 test-rmse:5.570893+0.409264
## [24] train-rmse:4.985856+0.009218 test-rmse:4.977497+0.397643
## [25] train-rmse:4.466506+0.010678 test-rmse:4.458933+0.389700
## [26] train-rmse:4.016339+0.011866 test-rmse:4.013655+0.376027
## [27] train-rmse:3.627186+0.013558 test-rmse:3.622800+0.359596
## [28] train-rmse:3.292384+0.015168 test-rmse:3.294218+0.344284
## [29] train-rmse:3.005499+0.016734 test-rmse:3.010627+0.322856
## [30] train-rmse:2.760947+0.018266 test-rmse:2.772131+0.304294
## [31] train-rmse:2.553549+0.019749 test-rmse:2.574415+0.282468
## [32] train-rmse:2.378588+0.021157 test-rmse:2.407303+0.262411
## [33] train-rmse:2.231700+0.022431 test-rmse:2.268397+0.240867
## [34] train-rmse:2.108995+0.023572 test-rmse:2.145102+0.217420
## [35] train-rmse:2.007121+0.024544 test-rmse:2.049820+0.198811
## [36] train-rmse:1.922507+0.025521 test-rmse:1.972695+0.179145
## [37] train-rmse:1.852647+0.026191 test-rmse:1.910984+0.163123
## [38] train-rmse:1.794808+0.026727 test-rmse:1.857173+0.149043
## [39] train-rmse:1.746817+0.026956 test-rmse:1.816441+0.137269
## [40] train-rmse:1.707142+0.027379 test-rmse:1.781570+0.127688
## [41] train-rmse:1.674071+0.027337 test-rmse:1.756122+0.123290
## [42] train-rmse:1.646455+0.027452 test-rmse:1.724942+0.119143
## [43] train-rmse:1.623093+0.027297 test-rmse:1.707829+0.120631
## [44] train-rmse:1.603251+0.027125 test-rmse:1.692142+0.116654
## [45] train-rmse:1.586231+0.026979 test-rmse:1.677148+0.113688
## [46] train-rmse:1.571346+0.026762 test-rmse:1.666656+0.112107
## [47] train-rmse:1.558221+0.026685 test-rmse:1.656941+0.113871
## [48] train-rmse:1.546512+0.026524 test-rmse:1.647803+0.113556
## [49] train-rmse:1.536030+0.026306 test-rmse:1.640681+0.114801
## [50] train-rmse:1.526445+0.026090 test-rmse:1.633454+0.114529
## [51] train-rmse:1.517701+0.025840 test-rmse:1.621985+0.113353
## [52] train-rmse:1.509631+0.025689 test-rmse:1.617349+0.113383
## [53] train-rmse:1.502053+0.025562 test-rmse:1.610319+0.114488
## [54] train-rmse:1.494911+0.025415 test-rmse:1.602971+0.111574
## [55] train-rmse:1.488129+0.025257 test-rmse:1.600621+0.112637
## [56] train-rmse:1.481572+0.025086 test-rmse:1.596861+0.112030
## [57] train-rmse:1.475211+0.024861 test-rmse:1.593811+0.113216
## [58] train-rmse:1.469131+0.024813 test-rmse:1.590926+0.112989
## [59] train-rmse:1.463309+0.024728 test-rmse:1.585002+0.113719
## [60] train-rmse:1.457653+0.024575 test-rmse:1.582780+0.114276
## [61] train-rmse:1.452173+0.024471 test-rmse:1.577552+0.112129
## [62] train-rmse:1.446804+0.024368 test-rmse:1.569349+0.110418
## [63] train-rmse:1.441539+0.024283 test-rmse:1.564154+0.108196
## [64] train-rmse:1.436415+0.024162 test-rmse:1.561734+0.108474
## [65] train-rmse:1.431380+0.024016 test-rmse:1.559514+0.108280
## [66] train-rmse:1.426447+0.023945 test-rmse:1.554492+0.108998
## [67] train-rmse:1.421622+0.023847 test-rmse:1.552394+0.107991
## [68] train-rmse:1.416784+0.023756 test-rmse:1.552013+0.106979
## [69] train-rmse:1.412078+0.023744 test-rmse:1.548105+0.110357
## [70] train-rmse:1.407396+0.023749 test-rmse:1.544548+0.108771
## [71] train-rmse:1.402794+0.023734 test-rmse:1.544237+0.110174
## [72] train-rmse:1.398245+0.023636 test-rmse:1.538999+0.109321
## [73] train-rmse:1.393912+0.023628 test-rmse:1.531204+0.105876
## [74] train-rmse:1.389673+0.023567 test-rmse:1.527024+0.103315
## [75] train-rmse:1.385444+0.023525 test-rmse:1.525331+0.103423
## [76] train-rmse:1.381227+0.023481 test-rmse:1.520560+0.104564
## [77] train-rmse:1.377064+0.023406 test-rmse:1.519592+0.103859
## [78] train-rmse:1.372969+0.023304 test-rmse:1.518621+0.104653
## [79] train-rmse:1.368855+0.023282 test-rmse:1.519486+0.105644
## [80] train-rmse:1.364890+0.023187 test-rmse:1.516644+0.106943
## [81] train-rmse:1.360960+0.023079 test-rmse:1.515752+0.104127
## [82] train-rmse:1.357059+0.022935 test-rmse:1.516739+0.101886
## [83] train-rmse:1.353272+0.022803 test-rmse:1.516377+0.103903
## [84] train-rmse:1.349480+0.022709 test-rmse:1.510567+0.102675
## [85] train-rmse:1.345742+0.022616 test-rmse:1.507813+0.101531
## [86] train-rmse:1.342033+0.022538 test-rmse:1.501298+0.102092
## [87] train-rmse:1.338384+0.022472 test-rmse:1.497375+0.101975
## [88] train-rmse:1.334717+0.022412 test-rmse:1.491662+0.099283
## [89] train-rmse:1.331138+0.022300 test-rmse:1.491300+0.095995
## [90] train-rmse:1.327663+0.022240 test-rmse:1.490217+0.095654
## [91] train-rmse:1.324201+0.022189 test-rmse:1.488388+0.095270
## [92] train-rmse:1.320718+0.022138 test-rmse:1.486284+0.097165
## [93] train-rmse:1.317301+0.022116 test-rmse:1.485750+0.098134
## [94] train-rmse:1.313948+0.022058 test-rmse:1.484243+0.098120
## [95] train-rmse:1.310628+0.021989 test-rmse:1.482577+0.097243
## [96] train-rmse:1.307346+0.021910 test-rmse:1.480527+0.097825
## [97] train-rmse:1.304086+0.021779 test-rmse:1.474613+0.096583
## [98] train-rmse:1.300894+0.021693 test-rmse:1.471108+0.095032
## [99] train-rmse:1.297782+0.021616 test-rmse:1.467844+0.095079
## [100] train-rmse:1.294695+0.021550 test-rmse:1.467399+0.092767
## [101] train-rmse:1.291591+0.021490 test-rmse:1.466474+0.088793
## [102] train-rmse:1.288544+0.021432 test-rmse:1.466216+0.090894
## [103] train-rmse:1.285473+0.021437 test-rmse:1.463553+0.091874
## [104] train-rmse:1.282448+0.021364 test-rmse:1.459837+0.090280
## [105] train-rmse:1.279489+0.021285 test-rmse:1.458935+0.091216
## [106] train-rmse:1.276542+0.021188 test-rmse:1.457912+0.092207
## [107] train-rmse:1.273636+0.021134 test-rmse:1.456680+0.091883
## [108] train-rmse:1.270768+0.021100 test-rmse:1.454833+0.093890
## [109] train-rmse:1.267939+0.021086 test-rmse:1.454667+0.090962
## [110] train-rmse:1.265145+0.021067 test-rmse:1.453125+0.091262
## [111] train-rmse:1.262352+0.021058 test-rmse:1.453282+0.089433
## [112] train-rmse:1.259625+0.021048 test-rmse:1.447618+0.089020
## [113] train-rmse:1.256940+0.021037 test-rmse:1.444823+0.088422
## [114] train-rmse:1.254275+0.021003 test-rmse:1.443473+0.087460
## [115] train-rmse:1.251630+0.020970 test-rmse:1.442133+0.086687
## [116] train-rmse:1.249011+0.020909 test-rmse:1.440153+0.087890
## [117] train-rmse:1.246431+0.020864 test-rmse:1.438311+0.086330
## [118] train-rmse:1.243871+0.020839 test-rmse:1.437917+0.085661
## [119] train-rmse:1.241318+0.020803 test-rmse:1.435949+0.085732
## [120] train-rmse:1.238779+0.020750 test-rmse:1.432894+0.084554
## [121] train-rmse:1.236242+0.020721 test-rmse:1.429983+0.085918
## [122] train-rmse:1.233736+0.020695 test-rmse:1.425807+0.084982
## [123] train-rmse:1.231258+0.020664 test-rmse:1.425577+0.085855
## [124] train-rmse:1.228795+0.020656 test-rmse:1.424343+0.087511
## [125] train-rmse:1.226350+0.020633 test-rmse:1.422387+0.088127
## [126] train-rmse:1.223974+0.020619 test-rmse:1.423445+0.086133
## [127] train-rmse:1.221629+0.020610 test-rmse:1.421683+0.085329
## [128] train-rmse:1.219278+0.020590 test-rmse:1.420349+0.087110
## [129] train-rmse:1.217002+0.020581 test-rmse:1.418689+0.086321
## [130] train-rmse:1.214726+0.020584 test-rmse:1.418104+0.084024
## [131] train-rmse:1.212492+0.020577 test-rmse:1.416355+0.085135
## [132] train-rmse:1.210227+0.020614 test-rmse:1.412367+0.083628
## [133] train-rmse:1.208011+0.020601 test-rmse:1.407752+0.083296
## [134] train-rmse:1.205836+0.020586 test-rmse:1.405387+0.082702
## [135] train-rmse:1.203664+0.020540 test-rmse:1.404981+0.083773
## [136] train-rmse:1.201532+0.020514 test-rmse:1.404250+0.084421
## [137] train-rmse:1.199412+0.020485 test-rmse:1.402360+0.087495
## [138] train-rmse:1.197311+0.020473 test-rmse:1.400381+0.086455
## [139] train-rmse:1.195228+0.020452 test-rmse:1.398328+0.087219
## [140] train-rmse:1.193159+0.020424 test-rmse:1.397585+0.087581
## [141] train-rmse:1.191070+0.020426 test-rmse:1.395739+0.086793
## [142] train-rmse:1.189026+0.020413 test-rmse:1.395995+0.086482
## [143] train-rmse:1.186998+0.020404 test-rmse:1.394710+0.087910
## [144] train-rmse:1.184969+0.020398 test-rmse:1.394946+0.085728
## [145] train-rmse:1.182958+0.020372 test-rmse:1.392724+0.084502
## [146] train-rmse:1.180974+0.020366 test-rmse:1.391078+0.083571
## [147] train-rmse:1.179046+0.020347 test-rmse:1.391208+0.084112
## [148] train-rmse:1.177127+0.020341 test-rmse:1.388858+0.084694
## [149] train-rmse:1.175185+0.020328 test-rmse:1.386422+0.083666
## [150] train-rmse:1.173312+0.020323 test-rmse:1.384355+0.082985
## [151] train-rmse:1.171438+0.020317 test-rmse:1.382511+0.082565
## [152] train-rmse:1.169597+0.020295 test-rmse:1.379553+0.083631
## [153] train-rmse:1.167773+0.020272 test-rmse:1.377579+0.084769
## [154] train-rmse:1.165969+0.020252 test-rmse:1.376358+0.084233
## [155] train-rmse:1.164168+0.020215 test-rmse:1.376439+0.084952
## [156] train-rmse:1.162378+0.020181 test-rmse:1.374844+0.084330
## [157] train-rmse:1.160610+0.020167 test-rmse:1.374273+0.085139
## [158] train-rmse:1.158854+0.020143 test-rmse:1.373262+0.085945
## [159] train-rmse:1.157127+0.020107 test-rmse:1.373519+0.083483
## [160] train-rmse:1.155423+0.020059 test-rmse:1.372733+0.083418
## [161] train-rmse:1.153730+0.020011 test-rmse:1.371005+0.083198
## [162] train-rmse:1.152054+0.019986 test-rmse:1.369485+0.083699
## [163] train-rmse:1.150386+0.019961 test-rmse:1.368293+0.083077
## [164] train-rmse:1.148699+0.019949 test-rmse:1.365441+0.085040
## [165] train-rmse:1.147061+0.019924 test-rmse:1.364625+0.084742
## [166] train-rmse:1.145429+0.019907 test-rmse:1.362832+0.084800
## [167] train-rmse:1.143812+0.019888 test-rmse:1.360899+0.084790
## [168] train-rmse:1.142211+0.019876 test-rmse:1.358491+0.083701
## [169] train-rmse:1.140616+0.019857 test-rmse:1.359658+0.085150
## [170] train-rmse:1.139023+0.019855 test-rmse:1.358171+0.085026
## [171] train-rmse:1.137447+0.019839 test-rmse:1.355200+0.083848
## [172] train-rmse:1.135878+0.019828 test-rmse:1.353468+0.084678
## [173] train-rmse:1.134344+0.019794 test-rmse:1.353125+0.082877
## [174] train-rmse:1.132825+0.019774 test-rmse:1.352181+0.083657
## [175] train-rmse:1.131315+0.019749 test-rmse:1.351149+0.083196
## [176] train-rmse:1.129804+0.019749 test-rmse:1.350103+0.084134
## [177] train-rmse:1.128305+0.019739 test-rmse:1.348034+0.084932
## [178] train-rmse:1.126814+0.019716 test-rmse:1.347512+0.086000
## [179] train-rmse:1.125330+0.019684 test-rmse:1.349272+0.084476
## [180] train-rmse:1.123844+0.019661 test-rmse:1.347945+0.084544
## [181] train-rmse:1.122360+0.019622 test-rmse:1.346836+0.085217
## [182] train-rmse:1.120939+0.019579 test-rmse:1.345626+0.085146
## [183] train-rmse:1.119528+0.019533 test-rmse:1.343673+0.084560
## [184] train-rmse:1.118139+0.019512 test-rmse:1.342751+0.085136
## [185] train-rmse:1.116754+0.019500 test-rmse:1.342728+0.082737
## [186] train-rmse:1.115372+0.019490 test-rmse:1.341139+0.081605
## [187] train-rmse:1.114011+0.019488 test-rmse:1.340173+0.080048
## [188] train-rmse:1.112660+0.019471 test-rmse:1.338165+0.081547
## [189] train-rmse:1.111310+0.019463 test-rmse:1.336048+0.082537
## [190] train-rmse:1.109976+0.019441 test-rmse:1.335922+0.082898
## [191] train-rmse:1.108656+0.019412 test-rmse:1.335509+0.081818
## [192] train-rmse:1.107349+0.019386 test-rmse:1.333978+0.081016
## [193] train-rmse:1.106049+0.019364 test-rmse:1.332585+0.080449
## [194] train-rmse:1.104755+0.019346 test-rmse:1.331990+0.080949
## [195] train-rmse:1.103471+0.019309 test-rmse:1.331097+0.080834
## [196] train-rmse:1.102182+0.019270 test-rmse:1.329851+0.081230
## [197] train-rmse:1.100902+0.019224 test-rmse:1.329042+0.080844
## [198] train-rmse:1.099628+0.019190 test-rmse:1.327954+0.081756
## [199] train-rmse:1.098373+0.019146 test-rmse:1.326843+0.082302
## [200] train-rmse:1.097113+0.019105 test-rmse:1.325495+0.081727
## [201] train-rmse:1.095872+0.019082 test-rmse:1.326079+0.082471
## [202] train-rmse:1.094649+0.019062 test-rmse:1.325477+0.083336
## [203] train-rmse:1.093434+0.019042 test-rmse:1.324546+0.083648
## [204] train-rmse:1.092227+0.019019 test-rmse:1.321908+0.083522
## [205] train-rmse:1.091030+0.018990 test-rmse:1.320063+0.084305
## [206] train-rmse:1.089841+0.018956 test-rmse:1.319762+0.085450
## [207] train-rmse:1.088637+0.018928 test-rmse:1.319259+0.086011
## [208] train-rmse:1.087463+0.018902 test-rmse:1.317803+0.086402
## [209] train-rmse:1.086310+0.018897 test-rmse:1.317040+0.086404
## [210] train-rmse:1.085162+0.018890 test-rmse:1.317742+0.084666
## [211] train-rmse:1.083999+0.018883 test-rmse:1.317644+0.084823
## [212] train-rmse:1.082858+0.018857 test-rmse:1.316063+0.084739
## [213] train-rmse:1.081728+0.018836 test-rmse:1.314530+0.085939
## [214] train-rmse:1.080610+0.018826 test-rmse:1.314000+0.085736
## [215] train-rmse:1.079497+0.018807 test-rmse:1.312456+0.087195
## [216] train-rmse:1.078400+0.018789 test-rmse:1.311745+0.086687
## [217] train-rmse:1.077302+0.018767 test-rmse:1.310785+0.086716
## [218] train-rmse:1.076221+0.018749 test-rmse:1.310905+0.087450
## [219] train-rmse:1.075132+0.018728 test-rmse:1.309182+0.087610
## [220] train-rmse:1.074068+0.018713 test-rmse:1.308129+0.087111
## [221] train-rmse:1.073014+0.018685 test-rmse:1.307135+0.087569
## [222] train-rmse:1.071965+0.018654 test-rmse:1.307521+0.088124
## [223] train-rmse:1.070937+0.018637 test-rmse:1.306936+0.087363
## [224] train-rmse:1.069908+0.018620 test-rmse:1.306337+0.087176
## [225] train-rmse:1.068885+0.018598 test-rmse:1.305944+0.086739
## [226] train-rmse:1.067862+0.018578 test-rmse:1.304535+0.086987
## [227] train-rmse:1.066855+0.018549 test-rmse:1.302716+0.088044
## [228] train-rmse:1.065848+0.018519 test-rmse:1.302649+0.087904
## [229] train-rmse:1.064836+0.018482 test-rmse:1.302590+0.085552
## [230] train-rmse:1.063843+0.018456 test-rmse:1.300906+0.085384
## [231] train-rmse:1.062867+0.018437 test-rmse:1.300407+0.085297
## [232] train-rmse:1.061888+0.018418 test-rmse:1.300010+0.085449
## [233] train-rmse:1.060914+0.018405 test-rmse:1.299469+0.085556
## [234] train-rmse:1.059943+0.018394 test-rmse:1.298584+0.086255
## [235] train-rmse:1.058991+0.018363 test-rmse:1.298118+0.086383
## [236] train-rmse:1.058051+0.018340 test-rmse:1.297589+0.086410
## [237] train-rmse:1.057116+0.018319 test-rmse:1.296390+0.087180
## [238] train-rmse:1.056169+0.018289 test-rmse:1.295695+0.087815
## [239] train-rmse:1.055244+0.018265 test-rmse:1.295779+0.087279
## [240] train-rmse:1.054323+0.018239 test-rmse:1.293970+0.088872
## [241] train-rmse:1.053414+0.018217 test-rmse:1.293590+0.088718
## [242] train-rmse:1.052497+0.018185 test-rmse:1.293743+0.087690
## [243] train-rmse:1.051589+0.018152 test-rmse:1.293003+0.088525
## [244] train-rmse:1.050683+0.018125 test-rmse:1.292278+0.087810
## [245] train-rmse:1.049789+0.018103 test-rmse:1.292515+0.087801
## [246] train-rmse:1.048897+0.018077 test-rmse:1.291780+0.088181
## [247] train-rmse:1.048004+0.018057 test-rmse:1.290790+0.087730
## [248] train-rmse:1.047105+0.018030 test-rmse:1.290270+0.087616
## [249] train-rmse:1.046221+0.017993 test-rmse:1.289270+0.088154
## [250] train-rmse:1.045344+0.017966 test-rmse:1.287599+0.088387
## [251] train-rmse:1.044473+0.017945 test-rmse:1.286850+0.088490
## [252] train-rmse:1.043613+0.017913 test-rmse:1.287200+0.087366
## [253] train-rmse:1.042769+0.017892 test-rmse:1.285969+0.087207
## [254] train-rmse:1.041934+0.017868 test-rmse:1.286245+0.087157
## [255] train-rmse:1.041099+0.017849 test-rmse:1.285158+0.088180
## [256] train-rmse:1.040270+0.017839 test-rmse:1.284365+0.087507
## [257] train-rmse:1.039440+0.017825 test-rmse:1.285531+0.086447
## [258] train-rmse:1.038622+0.017800 test-rmse:1.284664+0.086974
## [259] train-rmse:1.037808+0.017788 test-rmse:1.283800+0.087044
## [260] train-rmse:1.037012+0.017763 test-rmse:1.283629+0.087063
## [261] train-rmse:1.036212+0.017744 test-rmse:1.283681+0.086667
## [262] train-rmse:1.035415+0.017729 test-rmse:1.283322+0.087051
## [263] train-rmse:1.034619+0.017708 test-rmse:1.283479+0.087045
## [264] train-rmse:1.033825+0.017688 test-rmse:1.280938+0.085916
## [265] train-rmse:1.033041+0.017667 test-rmse:1.278579+0.086546
## [266] train-rmse:1.032262+0.017634 test-rmse:1.278746+0.086496
## [267] train-rmse:1.031485+0.017602 test-rmse:1.278189+0.087102
## [268] train-rmse:1.030712+0.017575 test-rmse:1.277718+0.087913
## [269] train-rmse:1.029942+0.017545 test-rmse:1.277798+0.087788
## [270] train-rmse:1.029183+0.017520 test-rmse:1.276743+0.086983
## [271] train-rmse:1.028421+0.017502 test-rmse:1.276798+0.088029
## [272] train-rmse:1.027663+0.017470 test-rmse:1.275992+0.087713
## [273] train-rmse:1.026922+0.017440 test-rmse:1.275728+0.087900
## [274] train-rmse:1.026174+0.017417 test-rmse:1.275351+0.087108
## [275] train-rmse:1.025435+0.017397 test-rmse:1.274784+0.086796
## [276] train-rmse:1.024704+0.017382 test-rmse:1.274054+0.086726
## [277] train-rmse:1.023974+0.017368 test-rmse:1.274073+0.087193
## [278] train-rmse:1.023245+0.017353 test-rmse:1.274482+0.086001
## [279] train-rmse:1.022518+0.017343 test-rmse:1.273914+0.086043
## [280] train-rmse:1.021798+0.017334 test-rmse:1.273474+0.085783
## [281] train-rmse:1.021075+0.017333 test-rmse:1.273395+0.085919
## [282] train-rmse:1.020364+0.017328 test-rmse:1.273061+0.086413
## [283] train-rmse:1.019656+0.017322 test-rmse:1.272409+0.086794
## [284] train-rmse:1.018952+0.017320 test-rmse:1.271669+0.086676
## [285] train-rmse:1.018250+0.017317 test-rmse:1.270589+0.086703
## [286] train-rmse:1.017552+0.017306 test-rmse:1.268603+0.084853
## [287] train-rmse:1.016860+0.017291 test-rmse:1.268189+0.084279
## [288] train-rmse:1.016166+0.017279 test-rmse:1.267595+0.084484
## [289] train-rmse:1.015478+0.017270 test-rmse:1.266644+0.085627
## [290] train-rmse:1.014796+0.017260 test-rmse:1.266147+0.086327
## [291] train-rmse:1.014113+0.017246 test-rmse:1.265162+0.086347
## [292] train-rmse:1.013443+0.017236 test-rmse:1.264786+0.085493
## [293] train-rmse:1.012768+0.017223 test-rmse:1.264552+0.085989
## [294] train-rmse:1.012095+0.017215 test-rmse:1.264529+0.086026
## [295] train-rmse:1.011429+0.017206 test-rmse:1.264520+0.085597
## [296] train-rmse:1.010754+0.017201 test-rmse:1.264158+0.085794
## [297] train-rmse:1.010089+0.017187 test-rmse:1.263909+0.085756
## [298] train-rmse:1.009435+0.017183 test-rmse:1.264149+0.085718
## [299] train-rmse:1.008785+0.017179 test-rmse:1.264202+0.087147
## [300] train-rmse:1.008143+0.017177 test-rmse:1.263500+0.087277
## [301] train-rmse:1.007486+0.017189 test-rmse:1.262536+0.087991
## [302] train-rmse:1.006849+0.017187 test-rmse:1.263226+0.086967
## [303] train-rmse:1.006217+0.017180 test-rmse:1.262990+0.086601
## [304] train-rmse:1.005581+0.017172 test-rmse:1.262086+0.086689
## [305] train-rmse:1.004950+0.017170 test-rmse:1.262034+0.086464
## [306] train-rmse:1.004316+0.017154 test-rmse:1.260778+0.087091
## [307] train-rmse:1.003689+0.017154 test-rmse:1.260596+0.087555
## [308] train-rmse:1.003060+0.017154 test-rmse:1.260277+0.086839
## [309] train-rmse:1.002441+0.017151 test-rmse:1.260718+0.086903
## [310] train-rmse:1.001822+0.017146 test-rmse:1.260062+0.086734
## [311] train-rmse:1.001201+0.017143 test-rmse:1.259913+0.086151
## [312] train-rmse:1.000583+0.017141 test-rmse:1.259615+0.086088
## [313] train-rmse:0.999975+0.017136 test-rmse:1.260016+0.085594
## [314] train-rmse:0.999368+0.017128 test-rmse:1.259450+0.085029
## [315] train-rmse:0.998763+0.017129 test-rmse:1.257933+0.083366
## [316] train-rmse:0.998170+0.017125 test-rmse:1.257925+0.083719
## [317] train-rmse:0.997571+0.017127 test-rmse:1.257337+0.083783
## [318] train-rmse:0.996970+0.017123 test-rmse:1.257070+0.083984
## [319] train-rmse:0.996386+0.017122 test-rmse:1.256448+0.084483
## [320] train-rmse:0.995792+0.017129 test-rmse:1.255756+0.084181
## [321] train-rmse:0.995208+0.017131 test-rmse:1.255965+0.083396
## [322] train-rmse:0.994629+0.017131 test-rmse:1.255801+0.084662
## [323] train-rmse:0.994051+0.017131 test-rmse:1.255255+0.085245
## [324] train-rmse:0.993476+0.017129 test-rmse:1.254558+0.086150
## [325] train-rmse:0.992904+0.017126 test-rmse:1.254318+0.085605
## [326] train-rmse:0.992324+0.017123 test-rmse:1.254988+0.085253
## [327] train-rmse:0.991742+0.017121 test-rmse:1.254801+0.085258
## [328] train-rmse:0.991159+0.017115 test-rmse:1.254325+0.085511
## [329] train-rmse:0.990602+0.017114 test-rmse:1.253902+0.085459
## [330] train-rmse:0.990043+0.017106 test-rmse:1.253597+0.085601
## [331] train-rmse:0.989490+0.017105 test-rmse:1.252218+0.085641
## [332] train-rmse:0.988943+0.017100 test-rmse:1.251926+0.085848
## [333] train-rmse:0.988391+0.017099 test-rmse:1.251676+0.086034
## [334] train-rmse:0.987839+0.017100 test-rmse:1.252289+0.085127
## [335] train-rmse:0.987301+0.017098 test-rmse:1.251290+0.086010
## [336] train-rmse:0.986765+0.017101 test-rmse:1.251012+0.085428
## [337] train-rmse:0.986225+0.017107 test-rmse:1.251080+0.085792
## [338] train-rmse:0.985691+0.017106 test-rmse:1.249854+0.085521
## [339] train-rmse:0.985161+0.017111 test-rmse:1.249125+0.085454
## [340] train-rmse:0.984629+0.017112 test-rmse:1.249071+0.085413
## [341] train-rmse:0.984100+0.017114 test-rmse:1.248819+0.085087
## [342] train-rmse:0.983563+0.017119 test-rmse:1.248388+0.085359
## [343] train-rmse:0.983039+0.017124 test-rmse:1.248614+0.084951
## [344] train-rmse:0.982515+0.017127 test-rmse:1.246898+0.083512
## [345] train-rmse:0.981996+0.017128 test-rmse:1.245948+0.083252
## [346] train-rmse:0.981477+0.017130 test-rmse:1.245683+0.083552
## [347] train-rmse:0.980954+0.017131 test-rmse:1.245398+0.083284
## [348] train-rmse:0.980429+0.017137 test-rmse:1.245013+0.083352
## [349] train-rmse:0.979912+0.017137 test-rmse:1.244278+0.083768
## [350] train-rmse:0.979391+0.017138 test-rmse:1.244305+0.083739
## [351] train-rmse:0.978880+0.017136 test-rmse:1.243320+0.084057
## [352] train-rmse:0.978367+0.017133 test-rmse:1.243478+0.083618
## [353] train-rmse:0.977850+0.017135 test-rmse:1.243468+0.083432
## [354] train-rmse:0.977340+0.017138 test-rmse:1.243434+0.083886
## [355] train-rmse:0.976841+0.017135 test-rmse:1.243592+0.083520
## [356] train-rmse:0.976344+0.017131 test-rmse:1.243556+0.083003
## [357] train-rmse:0.975846+0.017120 test-rmse:1.243360+0.083189
## Stopping. Best iteration:
## [351] train-rmse:0.978880+0.017136 test-rmse:1.243320+0.084057
##
## 8
## [1] train-rmse:86.704080+0.086998 test-rmse:86.704831+0.406915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.342288+0.076506 test-rmse:76.342926+0.417100
## [3] train-rmse:67.220384+0.067270 test-rmse:67.220891+0.426011
## [4] train-rmse:59.190229+0.059099 test-rmse:59.190588+0.433783
## [5] train-rmse:52.121395+0.051883 test-rmse:52.121578+0.440539
## [6] train-rmse:45.899076+0.045513 test-rmse:45.899065+0.446386
## [7] train-rmse:40.422199+0.039869 test-rmse:40.421979+0.451412
## [8] train-rmse:35.601835+0.034876 test-rmse:35.601369+0.455694
## [9] train-rmse:31.359680+0.030457 test-rmse:31.358941+0.459291
## [10] train-rmse:27.626835+0.026543 test-rmse:27.625793+0.462249
## [11] train-rmse:24.342670+0.023071 test-rmse:24.341286+0.464606
## [12] train-rmse:21.453838+0.019997 test-rmse:21.452077+0.466382
## [13] train-rmse:18.913416+0.017291 test-rmse:18.911232+0.467585
## [14] train-rmse:16.680124+0.014931 test-rmse:16.677478+0.468208
## [15] train-rmse:14.717049+0.012912 test-rmse:14.717037+0.454197
## [16] train-rmse:12.991833+0.011108 test-rmse:12.980617+0.452674
## [17] train-rmse:11.475719+0.009385 test-rmse:11.467150+0.438504
## [18] train-rmse:10.143333+0.007698 test-rmse:10.125890+0.433347
## [19] train-rmse:8.972312+0.006735 test-rmse:8.954921+0.424580
## [20] train-rmse:7.944482+0.006174 test-rmse:7.927380+0.414847
## [21] train-rmse:7.042126+0.005636 test-rmse:7.019776+0.406748
## [22] train-rmse:6.250131+0.005900 test-rmse:6.228907+0.389574
## [23] train-rmse:5.556402+0.006070 test-rmse:5.541040+0.381518
## [24] train-rmse:4.950009+0.006754 test-rmse:4.936426+0.372615
## [25] train-rmse:4.419668+0.007711 test-rmse:4.407692+0.362674
## [26] train-rmse:3.958137+0.008119 test-rmse:3.955575+0.347945
## [27] train-rmse:3.557235+0.008759 test-rmse:3.558009+0.339234
## [28] train-rmse:3.208547+0.009296 test-rmse:3.217133+0.327508
## [29] train-rmse:2.908189+0.010593 test-rmse:2.927775+0.316527
## [30] train-rmse:2.649275+0.010514 test-rmse:2.677204+0.294808
## [31] train-rmse:2.426731+0.011341 test-rmse:2.467638+0.276174
## [32] train-rmse:2.238539+0.012203 test-rmse:2.285279+0.261334
## [33] train-rmse:2.077648+0.013439 test-rmse:2.139274+0.241341
## [34] train-rmse:1.941758+0.014501 test-rmse:2.017363+0.224198
## [35] train-rmse:1.827234+0.015257 test-rmse:1.909494+0.208537
## [36] train-rmse:1.730997+0.014873 test-rmse:1.827242+0.194657
## [37] train-rmse:1.651358+0.015010 test-rmse:1.760242+0.178324
## [38] train-rmse:1.584960+0.014805 test-rmse:1.703788+0.163212
## [39] train-rmse:1.529850+0.015009 test-rmse:1.656900+0.150969
## [40] train-rmse:1.483057+0.015091 test-rmse:1.620779+0.137863
## [41] train-rmse:1.442070+0.017669 test-rmse:1.580902+0.132763
## [42] train-rmse:1.408371+0.018003 test-rmse:1.558355+0.126694
## [43] train-rmse:1.379870+0.018066 test-rmse:1.534090+0.119148
## [44] train-rmse:1.356299+0.018593 test-rmse:1.516273+0.116176
## [45] train-rmse:1.334776+0.018806 test-rmse:1.505502+0.110856
## [46] train-rmse:1.314421+0.016125 test-rmse:1.495477+0.106201
## [47] train-rmse:1.297625+0.017236 test-rmse:1.485312+0.104275
## [48] train-rmse:1.283466+0.017004 test-rmse:1.470524+0.104345
## [49] train-rmse:1.270566+0.017113 test-rmse:1.463942+0.104598
## [50] train-rmse:1.257745+0.018041 test-rmse:1.455908+0.102158
## [51] train-rmse:1.247099+0.018010 test-rmse:1.449907+0.100738
## [52] train-rmse:1.235947+0.017147 test-rmse:1.442733+0.096760
## [53] train-rmse:1.226125+0.017153 test-rmse:1.436204+0.095404
## [54] train-rmse:1.216352+0.017205 test-rmse:1.425924+0.096439
## [55] train-rmse:1.207848+0.017798 test-rmse:1.420473+0.093278
## [56] train-rmse:1.199382+0.016127 test-rmse:1.416798+0.091333
## [57] train-rmse:1.191848+0.016147 test-rmse:1.410022+0.094283
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## [59] train-rmse:1.175934+0.017313 test-rmse:1.398237+0.094785
## [60] train-rmse:1.167248+0.018747 test-rmse:1.393603+0.096214
## [61] train-rmse:1.160500+0.018866 test-rmse:1.386059+0.093470
## [62] train-rmse:1.153882+0.019129 test-rmse:1.382567+0.091987
## [63] train-rmse:1.146630+0.018381 test-rmse:1.378043+0.092114
## [64] train-rmse:1.140563+0.018303 test-rmse:1.374522+0.092312
## [65] train-rmse:1.133561+0.019459 test-rmse:1.369061+0.091641
## [66] train-rmse:1.127507+0.019461 test-rmse:1.366535+0.089086
## [67] train-rmse:1.121461+0.019391 test-rmse:1.362154+0.089054
## [68] train-rmse:1.115067+0.018443 test-rmse:1.355512+0.086979
## [69] train-rmse:1.109546+0.018204 test-rmse:1.352563+0.086436
## [70] train-rmse:1.103501+0.018481 test-rmse:1.346495+0.088135
## [71] train-rmse:1.098022+0.018304 test-rmse:1.342106+0.087054
## [72] train-rmse:1.090660+0.019713 test-rmse:1.337355+0.086733
## [73] train-rmse:1.084746+0.019291 test-rmse:1.333456+0.089143
## [74] train-rmse:1.079472+0.019028 test-rmse:1.329113+0.093105
## [75] train-rmse:1.073005+0.020771 test-rmse:1.324806+0.091746
## [76] train-rmse:1.068025+0.020786 test-rmse:1.320053+0.093253
## [77] train-rmse:1.062771+0.021458 test-rmse:1.317270+0.093240
## [78] train-rmse:1.056449+0.021326 test-rmse:1.314243+0.092541
## [79] train-rmse:1.051751+0.021474 test-rmse:1.310583+0.089716
## [80] train-rmse:1.046398+0.021167 test-rmse:1.307462+0.089638
## [81] train-rmse:1.040793+0.022290 test-rmse:1.300854+0.085999
## [82] train-rmse:1.036466+0.022378 test-rmse:1.295487+0.087583
## [83] train-rmse:1.030589+0.022569 test-rmse:1.293702+0.087182
## [84] train-rmse:1.026083+0.023062 test-rmse:1.292501+0.090529
## [85] train-rmse:1.021978+0.022997 test-rmse:1.288645+0.093978
## [86] train-rmse:1.018150+0.022968 test-rmse:1.287711+0.093773
## [87] train-rmse:1.014206+0.023381 test-rmse:1.284714+0.093880
## [88] train-rmse:1.010434+0.023294 test-rmse:1.281166+0.093304
## [89] train-rmse:1.006329+0.023132 test-rmse:1.280051+0.093208
## [90] train-rmse:1.002155+0.023410 test-rmse:1.277320+0.093663
## [91] train-rmse:0.997598+0.023218 test-rmse:1.274572+0.092528
## [92] train-rmse:0.992393+0.021385 test-rmse:1.271016+0.095315
## [93] train-rmse:0.988796+0.021466 test-rmse:1.267248+0.097230
## [94] train-rmse:0.983960+0.022439 test-rmse:1.262394+0.093390
## [95] train-rmse:0.979563+0.021663 test-rmse:1.261549+0.094216
## [96] train-rmse:0.976193+0.021813 test-rmse:1.257404+0.094491
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## [98] train-rmse:0.969097+0.021159 test-rmse:1.252183+0.095636
## [99] train-rmse:0.964567+0.020944 test-rmse:1.250952+0.096857
## [100] train-rmse:0.960708+0.020951 test-rmse:1.247306+0.098880
## [101] train-rmse:0.956646+0.021234 test-rmse:1.243452+0.098653
## [102] train-rmse:0.953142+0.021944 test-rmse:1.240477+0.098877
## [103] train-rmse:0.949151+0.022815 test-rmse:1.238995+0.097691
## [104] train-rmse:0.944890+0.021670 test-rmse:1.234893+0.097867
## [105] train-rmse:0.941513+0.021208 test-rmse:1.231238+0.097917
## [106] train-rmse:0.937592+0.021165 test-rmse:1.229942+0.098001
## [107] train-rmse:0.933003+0.021575 test-rmse:1.228079+0.097982
## [108] train-rmse:0.929710+0.021819 test-rmse:1.224763+0.097850
## [109] train-rmse:0.926447+0.022029 test-rmse:1.223316+0.097697
## [110] train-rmse:0.923296+0.022146 test-rmse:1.222034+0.098589
## [111] train-rmse:0.918341+0.020087 test-rmse:1.221313+0.100912
## [112] train-rmse:0.914816+0.020448 test-rmse:1.219110+0.101006
## [113] train-rmse:0.912002+0.020488 test-rmse:1.215268+0.101899
## [114] train-rmse:0.907923+0.021544 test-rmse:1.213292+0.102548
## [115] train-rmse:0.905170+0.021749 test-rmse:1.211540+0.103370
## [116] train-rmse:0.901684+0.020294 test-rmse:1.208801+0.106897
## [117] train-rmse:0.898478+0.019781 test-rmse:1.207178+0.105697
## [118] train-rmse:0.895634+0.019194 test-rmse:1.205816+0.105438
## [119] train-rmse:0.892041+0.018336 test-rmse:1.204700+0.105639
## [120] train-rmse:0.888709+0.018509 test-rmse:1.202915+0.105184
## [121] train-rmse:0.885054+0.019040 test-rmse:1.201704+0.104028
## [122] train-rmse:0.880900+0.017547 test-rmse:1.201000+0.102855
## [123] train-rmse:0.878299+0.017783 test-rmse:1.199596+0.103644
## [124] train-rmse:0.875846+0.017869 test-rmse:1.199549+0.103484
## [125] train-rmse:0.872882+0.018397 test-rmse:1.197899+0.103476
## [126] train-rmse:0.870501+0.018395 test-rmse:1.195202+0.102867
## [127] train-rmse:0.867019+0.018553 test-rmse:1.194515+0.104157
## [128] train-rmse:0.863976+0.017473 test-rmse:1.192078+0.104649
## [129] train-rmse:0.861618+0.017740 test-rmse:1.189310+0.104698
## [130] train-rmse:0.858612+0.017467 test-rmse:1.188877+0.105209
## [131] train-rmse:0.855712+0.017916 test-rmse:1.187445+0.107480
## [132] train-rmse:0.853466+0.017702 test-rmse:1.187341+0.106870
## [133] train-rmse:0.850315+0.016306 test-rmse:1.186146+0.107534
## [134] train-rmse:0.848167+0.016139 test-rmse:1.183904+0.108872
## [135] train-rmse:0.845739+0.016288 test-rmse:1.182625+0.108176
## [136] train-rmse:0.843397+0.016587 test-rmse:1.179973+0.107348
## [137] train-rmse:0.840738+0.016538 test-rmse:1.178930+0.107683
## [138] train-rmse:0.838656+0.016444 test-rmse:1.178679+0.108313
## [139] train-rmse:0.836660+0.016581 test-rmse:1.177626+0.109275
## [140] train-rmse:0.834700+0.016703 test-rmse:1.175677+0.109950
## [141] train-rmse:0.831950+0.016757 test-rmse:1.175945+0.107669
## [142] train-rmse:0.829573+0.016435 test-rmse:1.173734+0.109311
## [143] train-rmse:0.827210+0.016379 test-rmse:1.172521+0.108608
## [144] train-rmse:0.824699+0.016575 test-rmse:1.170422+0.108549
## [145] train-rmse:0.822321+0.016801 test-rmse:1.170415+0.108301
## [146] train-rmse:0.819185+0.016502 test-rmse:1.168946+0.108423
## [147] train-rmse:0.816441+0.017287 test-rmse:1.167062+0.109414
## [148] train-rmse:0.814350+0.017515 test-rmse:1.166315+0.109452
## [149] train-rmse:0.812033+0.017822 test-rmse:1.163641+0.108563
## [150] train-rmse:0.809266+0.016621 test-rmse:1.160586+0.109217
## [151] train-rmse:0.807336+0.016588 test-rmse:1.159242+0.109235
## [152] train-rmse:0.805510+0.016715 test-rmse:1.159235+0.109294
## [153] train-rmse:0.802497+0.014900 test-rmse:1.158457+0.110964
## [154] train-rmse:0.800588+0.015034 test-rmse:1.157777+0.111601
## [155] train-rmse:0.798749+0.015051 test-rmse:1.156985+0.111455
## [156] train-rmse:0.796156+0.013536 test-rmse:1.156318+0.110314
## [157] train-rmse:0.793052+0.013196 test-rmse:1.154503+0.113027
## [158] train-rmse:0.791132+0.013295 test-rmse:1.153818+0.113597
## [159] train-rmse:0.789513+0.013461 test-rmse:1.152533+0.114125
## [160] train-rmse:0.786488+0.015523 test-rmse:1.148628+0.111288
## [161] train-rmse:0.784587+0.015305 test-rmse:1.147638+0.112442
## [162] train-rmse:0.782500+0.014564 test-rmse:1.147412+0.111743
## [163] train-rmse:0.780242+0.014994 test-rmse:1.144821+0.110259
## [164] train-rmse:0.777226+0.014217 test-rmse:1.140074+0.110360
## [165] train-rmse:0.774915+0.014566 test-rmse:1.139040+0.110905
## [166] train-rmse:0.773218+0.014530 test-rmse:1.138629+0.110330
## [167] train-rmse:0.771082+0.014472 test-rmse:1.139088+0.111049
## [168] train-rmse:0.768984+0.014595 test-rmse:1.136558+0.110946
## [169] train-rmse:0.767553+0.014695 test-rmse:1.136203+0.109580
## [170] train-rmse:0.765572+0.014732 test-rmse:1.134860+0.109409
## [171] train-rmse:0.763398+0.013649 test-rmse:1.133451+0.110803
## [172] train-rmse:0.761809+0.013277 test-rmse:1.132634+0.110855
## [173] train-rmse:0.760324+0.013204 test-rmse:1.131652+0.111314
## [174] train-rmse:0.758594+0.013180 test-rmse:1.130882+0.111930
## [175] train-rmse:0.756606+0.012523 test-rmse:1.129127+0.113209
## [176] train-rmse:0.754184+0.013034 test-rmse:1.128430+0.112757
## [177] train-rmse:0.752322+0.012651 test-rmse:1.126511+0.113157
## [178] train-rmse:0.750760+0.012376 test-rmse:1.125590+0.113904
## [179] train-rmse:0.749184+0.012731 test-rmse:1.125611+0.113116
## [180] train-rmse:0.747717+0.012695 test-rmse:1.125549+0.112299
## [181] train-rmse:0.745505+0.011437 test-rmse:1.123325+0.113231
## [182] train-rmse:0.743784+0.011412 test-rmse:1.124082+0.112541
## [183] train-rmse:0.740890+0.013223 test-rmse:1.119718+0.108876
## [184] train-rmse:0.739035+0.013389 test-rmse:1.118113+0.108795
## [185] train-rmse:0.736457+0.013179 test-rmse:1.116416+0.108769
## [186] train-rmse:0.734321+0.013106 test-rmse:1.116177+0.109692
## [187] train-rmse:0.731952+0.014832 test-rmse:1.113635+0.109920
## [188] train-rmse:0.730709+0.014954 test-rmse:1.112976+0.108921
## [189] train-rmse:0.729189+0.014769 test-rmse:1.112196+0.108545
## [190] train-rmse:0.726912+0.013984 test-rmse:1.110175+0.110324
## [191] train-rmse:0.724717+0.013502 test-rmse:1.109916+0.110746
## [192] train-rmse:0.722932+0.013256 test-rmse:1.108644+0.111752
## [193] train-rmse:0.720112+0.013244 test-rmse:1.107993+0.110824
## [194] train-rmse:0.718637+0.013412 test-rmse:1.107358+0.110612
## [195] train-rmse:0.717325+0.013420 test-rmse:1.106585+0.111443
## [196] train-rmse:0.715862+0.014047 test-rmse:1.105797+0.112731
## [197] train-rmse:0.714390+0.013799 test-rmse:1.103914+0.112048
## [198] train-rmse:0.713061+0.013760 test-rmse:1.103582+0.111878
## [199] train-rmse:0.710545+0.013592 test-rmse:1.102932+0.112326
## [200] train-rmse:0.708621+0.013820 test-rmse:1.101215+0.111553
## [201] train-rmse:0.706626+0.013859 test-rmse:1.099699+0.111316
## [202] train-rmse:0.705074+0.013805 test-rmse:1.100124+0.111290
## [203] train-rmse:0.703157+0.013619 test-rmse:1.099888+0.112716
## [204] train-rmse:0.701566+0.012866 test-rmse:1.098748+0.113339
## [205] train-rmse:0.700421+0.012780 test-rmse:1.098552+0.112192
## [206] train-rmse:0.698804+0.012861 test-rmse:1.098729+0.111770
## [207] train-rmse:0.697191+0.012194 test-rmse:1.097963+0.110467
## [208] train-rmse:0.695098+0.011129 test-rmse:1.097201+0.111694
## [209] train-rmse:0.693430+0.011261 test-rmse:1.095671+0.111227
## [210] train-rmse:0.692024+0.011675 test-rmse:1.094713+0.111027
## [211] train-rmse:0.689619+0.010560 test-rmse:1.094544+0.109940
## [212] train-rmse:0.687122+0.011396 test-rmse:1.089267+0.108211
## [213] train-rmse:0.685651+0.011156 test-rmse:1.088174+0.108902
## [214] train-rmse:0.684075+0.011246 test-rmse:1.087933+0.109817
## [215] train-rmse:0.683006+0.011289 test-rmse:1.087479+0.109527
## [216] train-rmse:0.681414+0.010565 test-rmse:1.087495+0.108673
## [217] train-rmse:0.679920+0.010265 test-rmse:1.086671+0.110237
## [218] train-rmse:0.677419+0.008825 test-rmse:1.085359+0.110728
## [219] train-rmse:0.676166+0.008699 test-rmse:1.084698+0.110681
## [220] train-rmse:0.674762+0.008598 test-rmse:1.085108+0.108847
## [221] train-rmse:0.672262+0.008827 test-rmse:1.084539+0.108571
## [222] train-rmse:0.671327+0.008774 test-rmse:1.084235+0.108566
## [223] train-rmse:0.669958+0.009108 test-rmse:1.082985+0.108393
## [224] train-rmse:0.668573+0.009083 test-rmse:1.083535+0.108990
## [225] train-rmse:0.667056+0.008742 test-rmse:1.082877+0.108926
## [226] train-rmse:0.665293+0.007613 test-rmse:1.082359+0.109354
## [227] train-rmse:0.664343+0.007524 test-rmse:1.082142+0.108944
## [228] train-rmse:0.663049+0.007391 test-rmse:1.080567+0.108898
## [229] train-rmse:0.661372+0.006997 test-rmse:1.079779+0.108854
## [230] train-rmse:0.660281+0.007087 test-rmse:1.079513+0.108818
## [231] train-rmse:0.659021+0.006952 test-rmse:1.079385+0.108371
## [232] train-rmse:0.658025+0.006871 test-rmse:1.078954+0.107900
## [233] train-rmse:0.656196+0.006286 test-rmse:1.077002+0.106297
## [234] train-rmse:0.655025+0.006501 test-rmse:1.076331+0.106865
## [235] train-rmse:0.653836+0.006679 test-rmse:1.075161+0.107282
## [236] train-rmse:0.652797+0.006384 test-rmse:1.074741+0.107092
## [237] train-rmse:0.651615+0.006080 test-rmse:1.073725+0.107518
## [238] train-rmse:0.650266+0.006117 test-rmse:1.073078+0.109254
## [239] train-rmse:0.648240+0.007275 test-rmse:1.070654+0.106921
## [240] train-rmse:0.646402+0.007488 test-rmse:1.070948+0.106443
## [241] train-rmse:0.644684+0.008106 test-rmse:1.069855+0.106257
## [242] train-rmse:0.643455+0.007838 test-rmse:1.069911+0.106224
## [243] train-rmse:0.642442+0.007859 test-rmse:1.069595+0.105999
## [244] train-rmse:0.641560+0.007944 test-rmse:1.069309+0.106500
## [245] train-rmse:0.640739+0.007835 test-rmse:1.069143+0.106264
## [246] train-rmse:0.639709+0.007790 test-rmse:1.068697+0.106461
## [247] train-rmse:0.638885+0.007778 test-rmse:1.068349+0.106681
## [248] train-rmse:0.637831+0.007804 test-rmse:1.067667+0.107196
## [249] train-rmse:0.636950+0.007521 test-rmse:1.067379+0.107352
## [250] train-rmse:0.635324+0.007549 test-rmse:1.066534+0.107342
## [251] train-rmse:0.634125+0.007432 test-rmse:1.065665+0.107252
## [252] train-rmse:0.633336+0.007536 test-rmse:1.065591+0.106772
## [253] train-rmse:0.632489+0.007645 test-rmse:1.064127+0.106392
## [254] train-rmse:0.631477+0.007690 test-rmse:1.064227+0.106453
## [255] train-rmse:0.630503+0.007558 test-rmse:1.063776+0.106810
## [256] train-rmse:0.629049+0.006686 test-rmse:1.062671+0.107451
## [257] train-rmse:0.628152+0.006577 test-rmse:1.062570+0.108256
## [258] train-rmse:0.626636+0.005955 test-rmse:1.063194+0.108807
## [259] train-rmse:0.624310+0.006129 test-rmse:1.062676+0.107866
## [260] train-rmse:0.623235+0.006006 test-rmse:1.061229+0.108477
## [261] train-rmse:0.621471+0.007116 test-rmse:1.060449+0.108101
## [262] train-rmse:0.619968+0.007651 test-rmse:1.059079+0.106309
## [263] train-rmse:0.618925+0.007406 test-rmse:1.058052+0.107226
## [264] train-rmse:0.617844+0.007420 test-rmse:1.057452+0.107197
## [265] train-rmse:0.616133+0.007487 test-rmse:1.057213+0.106433
## [266] train-rmse:0.615106+0.007499 test-rmse:1.056822+0.106367
## [267] train-rmse:0.613965+0.007641 test-rmse:1.056411+0.106542
## [268] train-rmse:0.612616+0.007549 test-rmse:1.056299+0.106153
## [269] train-rmse:0.611590+0.007471 test-rmse:1.056778+0.105855
## [270] train-rmse:0.610034+0.008312 test-rmse:1.054095+0.103677
## [271] train-rmse:0.608807+0.008227 test-rmse:1.054495+0.103083
## [272] train-rmse:0.608087+0.008273 test-rmse:1.054620+0.102554
## [273] train-rmse:0.606739+0.008174 test-rmse:1.054721+0.102891
## [274] train-rmse:0.605347+0.007818 test-rmse:1.053749+0.104185
## [275] train-rmse:0.604374+0.007504 test-rmse:1.053673+0.104147
## [276] train-rmse:0.603199+0.007593 test-rmse:1.053555+0.104181
## [277] train-rmse:0.601718+0.008173 test-rmse:1.053464+0.103329
## [278] train-rmse:0.600917+0.008141 test-rmse:1.052925+0.103213
## [279] train-rmse:0.599121+0.008674 test-rmse:1.052632+0.103468
## [280] train-rmse:0.597708+0.008709 test-rmse:1.052965+0.103626
## [281] train-rmse:0.596650+0.008630 test-rmse:1.052156+0.103512
## [282] train-rmse:0.594967+0.008915 test-rmse:1.051876+0.102739
## [283] train-rmse:0.593069+0.009853 test-rmse:1.049829+0.102310
## [284] train-rmse:0.591860+0.010071 test-rmse:1.047836+0.101108
## [285] train-rmse:0.590753+0.010142 test-rmse:1.047835+0.101278
## [286] train-rmse:0.589167+0.009826 test-rmse:1.047281+0.102313
## [287] train-rmse:0.588590+0.009802 test-rmse:1.047444+0.102364
## [288] train-rmse:0.587740+0.009972 test-rmse:1.046702+0.101986
## [289] train-rmse:0.586891+0.010048 test-rmse:1.046824+0.101502
## [290] train-rmse:0.585624+0.009764 test-rmse:1.046040+0.102363
## [291] train-rmse:0.584914+0.009831 test-rmse:1.046042+0.101905
## [292] train-rmse:0.582865+0.008232 test-rmse:1.045048+0.102443
## [293] train-rmse:0.581939+0.007941 test-rmse:1.044935+0.102373
## [294] train-rmse:0.581191+0.008133 test-rmse:1.044375+0.101800
## [295] train-rmse:0.580557+0.008208 test-rmse:1.043990+0.101924
## [296] train-rmse:0.579390+0.008503 test-rmse:1.042995+0.101861
## [297] train-rmse:0.578200+0.008277 test-rmse:1.042409+0.102589
## [298] train-rmse:0.576802+0.009197 test-rmse:1.042327+0.102152
## [299] train-rmse:0.575824+0.009226 test-rmse:1.040828+0.102099
## [300] train-rmse:0.575007+0.009423 test-rmse:1.040681+0.101469
## [301] train-rmse:0.573457+0.009189 test-rmse:1.040156+0.101839
## [302] train-rmse:0.572727+0.009059 test-rmse:1.039407+0.101371
## [303] train-rmse:0.571990+0.008966 test-rmse:1.039359+0.101476
## [304] train-rmse:0.571484+0.009007 test-rmse:1.039216+0.101970
## [305] train-rmse:0.570761+0.009226 test-rmse:1.038964+0.101899
## [306] train-rmse:0.569475+0.009295 test-rmse:1.038712+0.102776
## [307] train-rmse:0.568717+0.009259 test-rmse:1.038500+0.102873
## [308] train-rmse:0.566988+0.009061 test-rmse:1.038450+0.103219
## [309] train-rmse:0.566328+0.008973 test-rmse:1.037752+0.102681
## [310] train-rmse:0.565252+0.009128 test-rmse:1.037242+0.102152
## [311] train-rmse:0.564255+0.009226 test-rmse:1.037667+0.102467
## [312] train-rmse:0.563214+0.009532 test-rmse:1.037074+0.102658
## [313] train-rmse:0.561244+0.008473 test-rmse:1.035463+0.101968
## [314] train-rmse:0.560579+0.008391 test-rmse:1.035337+0.101255
## [315] train-rmse:0.559641+0.008581 test-rmse:1.035057+0.102124
## [316] train-rmse:0.558950+0.008837 test-rmse:1.034346+0.101610
## [317] train-rmse:0.557907+0.009075 test-rmse:1.033491+0.102267
## [318] train-rmse:0.556657+0.008288 test-rmse:1.033602+0.101489
## [319] train-rmse:0.555338+0.008554 test-rmse:1.032831+0.100937
## [320] train-rmse:0.554142+0.008307 test-rmse:1.032174+0.101112
## [321] train-rmse:0.553011+0.007940 test-rmse:1.032073+0.100869
## [322] train-rmse:0.552166+0.007944 test-rmse:1.032995+0.101267
## [323] train-rmse:0.550908+0.008313 test-rmse:1.032212+0.101400
## [324] train-rmse:0.548420+0.008596 test-rmse:1.031900+0.101083
## [325] train-rmse:0.547583+0.008866 test-rmse:1.030930+0.101184
## [326] train-rmse:0.545710+0.007753 test-rmse:1.029994+0.101122
## [327] train-rmse:0.544329+0.008314 test-rmse:1.028280+0.100723
## [328] train-rmse:0.543486+0.008393 test-rmse:1.028525+0.101104
## [329] train-rmse:0.542284+0.008192 test-rmse:1.029077+0.101294
## [330] train-rmse:0.540998+0.008440 test-rmse:1.027107+0.100775
## [331] train-rmse:0.540476+0.008484 test-rmse:1.027079+0.100627
## [332] train-rmse:0.539446+0.008670 test-rmse:1.025164+0.100455
## [333] train-rmse:0.538651+0.008632 test-rmse:1.025377+0.100524
## [334] train-rmse:0.538175+0.008607 test-rmse:1.025645+0.100351
## [335] train-rmse:0.537441+0.008239 test-rmse:1.026610+0.099843
## [336] train-rmse:0.536249+0.007544 test-rmse:1.026532+0.099622
## [337] train-rmse:0.534923+0.007846 test-rmse:1.026873+0.099749
## [338] train-rmse:0.534073+0.008061 test-rmse:1.026619+0.100050
## Stopping. Best iteration:
## [332] train-rmse:0.539446+0.008670 test-rmse:1.025164+0.100455
##
## 9
## [1] train-rmse:86.704080+0.086998 test-rmse:86.704831+0.406915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.342288+0.076506 test-rmse:76.342926+0.417100
## [3] train-rmse:67.220384+0.067270 test-rmse:67.220891+0.426011
## [4] train-rmse:59.190229+0.059099 test-rmse:59.190588+0.433783
## [5] train-rmse:52.121395+0.051883 test-rmse:52.121578+0.440539
## [6] train-rmse:45.899076+0.045513 test-rmse:45.899065+0.446386
## [7] train-rmse:40.422199+0.039869 test-rmse:40.421979+0.451412
## [8] train-rmse:35.601835+0.034876 test-rmse:35.601369+0.455694
## [9] train-rmse:31.359680+0.030457 test-rmse:31.358941+0.459291
## [10] train-rmse:27.626835+0.026543 test-rmse:27.625793+0.462249
## [11] train-rmse:24.342670+0.023071 test-rmse:24.341286+0.464606
## [12] train-rmse:21.453838+0.019997 test-rmse:21.452077+0.466382
## [13] train-rmse:18.913416+0.017291 test-rmse:18.911232+0.467585
## [14] train-rmse:16.680124+0.014931 test-rmse:16.677478+0.468208
## [15] train-rmse:14.717049+0.012912 test-rmse:14.717037+0.454197
## [16] train-rmse:12.991833+0.011108 test-rmse:12.980617+0.452674
## [17] train-rmse:11.475719+0.009385 test-rmse:11.467150+0.438504
## [18] train-rmse:10.143096+0.007693 test-rmse:10.131042+0.427921
## [19] train-rmse:8.971152+0.006739 test-rmse:8.953272+0.422391
## [20] train-rmse:7.941756+0.005865 test-rmse:7.925213+0.406111
## [21] train-rmse:7.037091+0.005237 test-rmse:7.024215+0.395413
## [22] train-rmse:6.242095+0.004933 test-rmse:6.232475+0.377173
## [23] train-rmse:5.545368+0.004885 test-rmse:5.540685+0.366689
## [24] train-rmse:4.934418+0.004738 test-rmse:4.929785+0.356840
## [25] train-rmse:4.398644+0.004681 test-rmse:4.402532+0.339622
## [26] train-rmse:3.930207+0.005302 test-rmse:3.938639+0.326907
## [27] train-rmse:3.520380+0.004492 test-rmse:3.539965+0.309889
## [28] train-rmse:3.163704+0.005069 test-rmse:3.188579+0.303191
## [29] train-rmse:2.854341+0.005134 test-rmse:2.885191+0.292800
## [30] train-rmse:2.584439+0.004857 test-rmse:2.624352+0.278255
## [31] train-rmse:2.351231+0.006173 test-rmse:2.401692+0.261377
## [32] train-rmse:2.151224+0.007667 test-rmse:2.214129+0.246748
## [33] train-rmse:1.979010+0.007049 test-rmse:2.054866+0.232996
## [34] train-rmse:1.833406+0.007399 test-rmse:1.925542+0.220043
## [35] train-rmse:1.707353+0.009514 test-rmse:1.819360+0.204657
## [36] train-rmse:1.601453+0.009317 test-rmse:1.725736+0.191957
## [37] train-rmse:1.509911+0.013170 test-rmse:1.650120+0.177409
## [38] train-rmse:1.433103+0.012547 test-rmse:1.589632+0.170777
## [39] train-rmse:1.367858+0.010851 test-rmse:1.535444+0.158861
## [40] train-rmse:1.312752+0.010527 test-rmse:1.494860+0.150065
## [41] train-rmse:1.264542+0.009861 test-rmse:1.464016+0.139268
## [42] train-rmse:1.225347+0.010740 test-rmse:1.432286+0.131465
## [43] train-rmse:1.189490+0.011111 test-rmse:1.408030+0.125432
## [44] train-rmse:1.161012+0.011462 test-rmse:1.388977+0.119893
## [45] train-rmse:1.135430+0.011651 test-rmse:1.377121+0.113096
## [46] train-rmse:1.112565+0.011691 test-rmse:1.365862+0.109924
## [47] train-rmse:1.093293+0.010833 test-rmse:1.352510+0.108685
## [48] train-rmse:1.076020+0.010788 test-rmse:1.342420+0.104002
## [49] train-rmse:1.060163+0.012195 test-rmse:1.331269+0.102579
## [50] train-rmse:1.044690+0.013171 test-rmse:1.319242+0.099994
## [51] train-rmse:1.032244+0.013356 test-rmse:1.311483+0.097621
## [52] train-rmse:1.019802+0.010299 test-rmse:1.302033+0.097176
## [53] train-rmse:1.008191+0.011318 test-rmse:1.293036+0.096687
## [54] train-rmse:0.998519+0.011743 test-rmse:1.287207+0.094869
## [55] train-rmse:0.988116+0.012673 test-rmse:1.281922+0.092568
## [56] train-rmse:0.978391+0.013318 test-rmse:1.276127+0.091420
## [57] train-rmse:0.967640+0.014863 test-rmse:1.269706+0.086700
## [58] train-rmse:0.958774+0.015656 test-rmse:1.263671+0.085165
## [59] train-rmse:0.949566+0.015162 test-rmse:1.259254+0.087307
## [60] train-rmse:0.942191+0.015785 test-rmse:1.252444+0.088168
## [61] train-rmse:0.933919+0.016084 test-rmse:1.247983+0.086091
## [62] train-rmse:0.927061+0.015748 test-rmse:1.242065+0.088068
## [63] train-rmse:0.919211+0.016627 test-rmse:1.238329+0.088186
## [64] train-rmse:0.911484+0.017291 test-rmse:1.231376+0.086956
## [65] train-rmse:0.902673+0.013036 test-rmse:1.226428+0.086835
## [66] train-rmse:0.893249+0.014624 test-rmse:1.222151+0.086008
## [67] train-rmse:0.886203+0.015188 test-rmse:1.216711+0.088169
## [68] train-rmse:0.879075+0.015360 test-rmse:1.215581+0.085840
## [69] train-rmse:0.872236+0.016273 test-rmse:1.208029+0.087210
## [70] train-rmse:0.863754+0.017829 test-rmse:1.203063+0.087230
## [71] train-rmse:0.857883+0.017991 test-rmse:1.199501+0.088125
## [72] train-rmse:0.850680+0.019284 test-rmse:1.197472+0.088904
## [73] train-rmse:0.842898+0.017809 test-rmse:1.190361+0.088421
## [74] train-rmse:0.837519+0.018492 test-rmse:1.187497+0.090668
## [75] train-rmse:0.831463+0.019721 test-rmse:1.184899+0.089588
## [76] train-rmse:0.825660+0.018277 test-rmse:1.181747+0.088802
## [77] train-rmse:0.820376+0.018368 test-rmse:1.179340+0.087902
## [78] train-rmse:0.815145+0.017897 test-rmse:1.172832+0.087845
## [79] train-rmse:0.809104+0.018970 test-rmse:1.169097+0.086976
## [80] train-rmse:0.803937+0.019852 test-rmse:1.166056+0.086220
## [81] train-rmse:0.799415+0.019933 test-rmse:1.165443+0.086134
## [82] train-rmse:0.792853+0.018666 test-rmse:1.160875+0.087851
## [83] train-rmse:0.786054+0.019437 test-rmse:1.157600+0.087853
## [84] train-rmse:0.780079+0.018815 test-rmse:1.153280+0.089705
## [85] train-rmse:0.775205+0.019687 test-rmse:1.149385+0.089990
## [86] train-rmse:0.770328+0.018608 test-rmse:1.145929+0.090262
## [87] train-rmse:0.765286+0.018490 test-rmse:1.141520+0.089837
## [88] train-rmse:0.760588+0.018789 test-rmse:1.139242+0.089024
## [89] train-rmse:0.755584+0.018051 test-rmse:1.137717+0.088393
## [90] train-rmse:0.750993+0.017639 test-rmse:1.135166+0.089356
## [91] train-rmse:0.746272+0.017925 test-rmse:1.132517+0.090073
## [92] train-rmse:0.742645+0.018644 test-rmse:1.131125+0.090185
## [93] train-rmse:0.737007+0.018761 test-rmse:1.127076+0.089280
## [94] train-rmse:0.732356+0.019157 test-rmse:1.125480+0.089202
## [95] train-rmse:0.728396+0.018337 test-rmse:1.123453+0.088768
## [96] train-rmse:0.724007+0.018406 test-rmse:1.121218+0.087772
## [97] train-rmse:0.720293+0.018826 test-rmse:1.120594+0.087042
## [98] train-rmse:0.716211+0.018609 test-rmse:1.118031+0.088949
## [99] train-rmse:0.712767+0.018886 test-rmse:1.116263+0.089398
## [100] train-rmse:0.708601+0.018255 test-rmse:1.111807+0.088920
## [101] train-rmse:0.704216+0.018291 test-rmse:1.110415+0.088782
## [102] train-rmse:0.699559+0.017639 test-rmse:1.107177+0.088212
## [103] train-rmse:0.694240+0.018806 test-rmse:1.103917+0.087677
## [104] train-rmse:0.691153+0.019034 test-rmse:1.103287+0.087090
## [105] train-rmse:0.687632+0.018835 test-rmse:1.100500+0.088444
## [106] train-rmse:0.682102+0.020387 test-rmse:1.097528+0.088948
## [107] train-rmse:0.678154+0.020752 test-rmse:1.095083+0.091265
## [108] train-rmse:0.674018+0.020542 test-rmse:1.094317+0.090498
## [109] train-rmse:0.671693+0.020603 test-rmse:1.094033+0.088979
## [110] train-rmse:0.668016+0.021056 test-rmse:1.091630+0.087863
## [111] train-rmse:0.664726+0.021220 test-rmse:1.090755+0.090679
## [112] train-rmse:0.660485+0.020169 test-rmse:1.089873+0.094050
## [113] train-rmse:0.657405+0.020533 test-rmse:1.087799+0.094576
## [114] train-rmse:0.653853+0.019999 test-rmse:1.086478+0.095506
## [115] train-rmse:0.651058+0.020073 test-rmse:1.085043+0.094925
## [116] train-rmse:0.647083+0.018290 test-rmse:1.082767+0.095269
## [117] train-rmse:0.643738+0.018163 test-rmse:1.081347+0.096402
## [118] train-rmse:0.640519+0.018289 test-rmse:1.080454+0.095459
## [119] train-rmse:0.636962+0.019094 test-rmse:1.079492+0.095320
## [120] train-rmse:0.634611+0.019109 test-rmse:1.078185+0.095807
## [121] train-rmse:0.631673+0.019486 test-rmse:1.079813+0.097553
## [122] train-rmse:0.629634+0.019666 test-rmse:1.079992+0.097281
## [123] train-rmse:0.625256+0.019049 test-rmse:1.077889+0.097695
## [124] train-rmse:0.621630+0.016810 test-rmse:1.077511+0.100071
## [125] train-rmse:0.617197+0.016512 test-rmse:1.076111+0.101728
## [126] train-rmse:0.614210+0.017267 test-rmse:1.073864+0.100287
## [127] train-rmse:0.611750+0.017429 test-rmse:1.072997+0.100617
## [128] train-rmse:0.608234+0.017423 test-rmse:1.071460+0.099905
## [129] train-rmse:0.604212+0.017217 test-rmse:1.068377+0.100510
## [130] train-rmse:0.601718+0.017561 test-rmse:1.067617+0.100540
## [131] train-rmse:0.599745+0.017756 test-rmse:1.066908+0.100981
## [132] train-rmse:0.596439+0.016905 test-rmse:1.065049+0.101787
## [133] train-rmse:0.593663+0.015741 test-rmse:1.063673+0.102919
## [134] train-rmse:0.590906+0.015524 test-rmse:1.060944+0.100891
## [135] train-rmse:0.588785+0.015513 test-rmse:1.059787+0.101060
## [136] train-rmse:0.586436+0.015958 test-rmse:1.058761+0.101652
## [137] train-rmse:0.583195+0.016280 test-rmse:1.058885+0.101089
## [138] train-rmse:0.580981+0.015807 test-rmse:1.057687+0.101455
## [139] train-rmse:0.577723+0.014847 test-rmse:1.057139+0.104184
## [140] train-rmse:0.574049+0.014792 test-rmse:1.057326+0.102973
## [141] train-rmse:0.570971+0.014481 test-rmse:1.054309+0.102953
## [142] train-rmse:0.567641+0.013448 test-rmse:1.053081+0.102132
## [143] train-rmse:0.565610+0.014200 test-rmse:1.049777+0.100452
## [144] train-rmse:0.563906+0.014544 test-rmse:1.049293+0.099898
## [145] train-rmse:0.561923+0.014599 test-rmse:1.049203+0.099996
## [146] train-rmse:0.559682+0.014614 test-rmse:1.048085+0.100419
## [147] train-rmse:0.556616+0.015013 test-rmse:1.046815+0.099735
## [148] train-rmse:0.553113+0.014861 test-rmse:1.044745+0.101782
## [149] train-rmse:0.550525+0.014502 test-rmse:1.043875+0.101629
## [150] train-rmse:0.548296+0.014437 test-rmse:1.039069+0.096277
## [151] train-rmse:0.545903+0.014796 test-rmse:1.038077+0.095039
## [152] train-rmse:0.544026+0.015053 test-rmse:1.038777+0.094520
## [153] train-rmse:0.541745+0.014280 test-rmse:1.039583+0.093504
## [154] train-rmse:0.539156+0.014493 test-rmse:1.038365+0.093291
## [155] train-rmse:0.536077+0.014473 test-rmse:1.035877+0.092481
## [156] train-rmse:0.533493+0.014656 test-rmse:1.034607+0.092870
## [157] train-rmse:0.530683+0.015303 test-rmse:1.034178+0.092686
## [158] train-rmse:0.528134+0.013784 test-rmse:1.033667+0.092904
## [159] train-rmse:0.526349+0.013831 test-rmse:1.033119+0.092737
## [160] train-rmse:0.522321+0.012963 test-rmse:1.032215+0.093890
## [161] train-rmse:0.520378+0.012328 test-rmse:1.031760+0.092564
## [162] train-rmse:0.518496+0.012655 test-rmse:1.031560+0.093022
## [163] train-rmse:0.516841+0.012585 test-rmse:1.031357+0.093345
## [164] train-rmse:0.514116+0.011766 test-rmse:1.029666+0.094147
## [165] train-rmse:0.511672+0.011859 test-rmse:1.028618+0.093340
## [166] train-rmse:0.509969+0.011720 test-rmse:1.028033+0.093400
## [167] train-rmse:0.507353+0.012526 test-rmse:1.025734+0.092253
## [168] train-rmse:0.505086+0.012084 test-rmse:1.025556+0.091839
## [169] train-rmse:0.502995+0.012411 test-rmse:1.025234+0.091100
## [170] train-rmse:0.500473+0.011702 test-rmse:1.025458+0.093285
## [171] train-rmse:0.498420+0.011234 test-rmse:1.023507+0.090606
## [172] train-rmse:0.496127+0.011508 test-rmse:1.024965+0.092385
## [173] train-rmse:0.494431+0.011390 test-rmse:1.024524+0.092589
## [174] train-rmse:0.493141+0.011375 test-rmse:1.023583+0.092351
## [175] train-rmse:0.490919+0.010944 test-rmse:1.023152+0.092249
## [176] train-rmse:0.489650+0.010788 test-rmse:1.023142+0.092544
## [177] train-rmse:0.487730+0.010945 test-rmse:1.023373+0.094370
## [178] train-rmse:0.484924+0.011801 test-rmse:1.023579+0.093439
## [179] train-rmse:0.481377+0.012325 test-rmse:1.023501+0.094444
## [180] train-rmse:0.479025+0.012219 test-rmse:1.023064+0.094654
## [181] train-rmse:0.476936+0.012700 test-rmse:1.021587+0.095457
## [182] train-rmse:0.474365+0.013141 test-rmse:1.019987+0.092599
## [183] train-rmse:0.472425+0.013374 test-rmse:1.019326+0.093915
## [184] train-rmse:0.470109+0.013638 test-rmse:1.019381+0.094026
## [185] train-rmse:0.468789+0.013632 test-rmse:1.018383+0.094255
## [186] train-rmse:0.466276+0.012235 test-rmse:1.017481+0.095080
## [187] train-rmse:0.464232+0.012117 test-rmse:1.016962+0.094585
## [188] train-rmse:0.461107+0.011610 test-rmse:1.018991+0.096366
## [189] train-rmse:0.459261+0.011953 test-rmse:1.018051+0.095160
## [190] train-rmse:0.456555+0.012452 test-rmse:1.017671+0.095505
## [191] train-rmse:0.455357+0.012603 test-rmse:1.016760+0.094755
## [192] train-rmse:0.453367+0.011696 test-rmse:1.015143+0.094561
## [193] train-rmse:0.451423+0.011409 test-rmse:1.014708+0.094870
## [194] train-rmse:0.448766+0.011982 test-rmse:1.013908+0.093925
## [195] train-rmse:0.446901+0.012468 test-rmse:1.013951+0.095871
## [196] train-rmse:0.444973+0.012437 test-rmse:1.012945+0.095751
## [197] train-rmse:0.443235+0.012645 test-rmse:1.011833+0.094737
## [198] train-rmse:0.441703+0.012159 test-rmse:1.010936+0.095263
## [199] train-rmse:0.440097+0.011881 test-rmse:1.009444+0.093014
## [200] train-rmse:0.437686+0.012208 test-rmse:1.008475+0.092891
## [201] train-rmse:0.436340+0.012517 test-rmse:1.008668+0.092572
## [202] train-rmse:0.434320+0.012436 test-rmse:1.008864+0.092151
## [203] train-rmse:0.431816+0.012624 test-rmse:1.005680+0.088613
## [204] train-rmse:0.430343+0.012321 test-rmse:1.005270+0.088501
## [205] train-rmse:0.428601+0.012458 test-rmse:1.004756+0.088131
## [206] train-rmse:0.425877+0.011596 test-rmse:1.003557+0.087451
## [207] train-rmse:0.424627+0.011944 test-rmse:1.003363+0.086774
## [208] train-rmse:0.422641+0.011979 test-rmse:1.003712+0.086310
## [209] train-rmse:0.420897+0.012346 test-rmse:1.003238+0.086246
## [210] train-rmse:0.419360+0.012420 test-rmse:1.002877+0.086432
## [211] train-rmse:0.417834+0.012192 test-rmse:1.003465+0.087983
## [212] train-rmse:0.416384+0.012601 test-rmse:1.003961+0.087585
## [213] train-rmse:0.415545+0.012470 test-rmse:1.003123+0.087959
## [214] train-rmse:0.414074+0.011833 test-rmse:1.003766+0.087819
## [215] train-rmse:0.412659+0.011480 test-rmse:1.003330+0.087804
## [216] train-rmse:0.410573+0.011324 test-rmse:1.001878+0.087807
## [217] train-rmse:0.408743+0.011620 test-rmse:1.001070+0.087992
## [218] train-rmse:0.406878+0.011583 test-rmse:1.000861+0.087450
## [219] train-rmse:0.405001+0.012138 test-rmse:1.001019+0.087529
## [220] train-rmse:0.403826+0.012244 test-rmse:1.000120+0.087419
## [221] train-rmse:0.401993+0.012107 test-rmse:0.999254+0.088618
## [222] train-rmse:0.400077+0.012502 test-rmse:0.999329+0.087992
## [223] train-rmse:0.398991+0.012159 test-rmse:0.998720+0.088151
## [224] train-rmse:0.397054+0.012832 test-rmse:0.998773+0.088613
## [225] train-rmse:0.396145+0.012864 test-rmse:0.998152+0.088204
## [226] train-rmse:0.394690+0.012584 test-rmse:0.998050+0.088177
## [227] train-rmse:0.393521+0.012585 test-rmse:0.997852+0.088014
## [228] train-rmse:0.392544+0.012434 test-rmse:0.997749+0.088545
## [229] train-rmse:0.390934+0.012504 test-rmse:0.997465+0.087719
## [230] train-rmse:0.389550+0.012876 test-rmse:0.996821+0.086877
## [231] train-rmse:0.387099+0.014134 test-rmse:0.996248+0.086783
## [232] train-rmse:0.385896+0.014274 test-rmse:0.995571+0.087013
## [233] train-rmse:0.384892+0.014162 test-rmse:0.994561+0.087187
## [234] train-rmse:0.382849+0.014108 test-rmse:0.993563+0.086385
## [235] train-rmse:0.381361+0.014151 test-rmse:0.993162+0.086081
## [236] train-rmse:0.378996+0.013447 test-rmse:0.993582+0.086192
## [237] train-rmse:0.377725+0.013264 test-rmse:0.992693+0.086495
## [238] train-rmse:0.376084+0.013428 test-rmse:0.989124+0.082835
## [239] train-rmse:0.374999+0.013395 test-rmse:0.988659+0.081785
## [240] train-rmse:0.373607+0.013769 test-rmse:0.988549+0.082042
## [241] train-rmse:0.372129+0.013578 test-rmse:0.988512+0.082211
## [242] train-rmse:0.371046+0.013829 test-rmse:0.988281+0.082671
## [243] train-rmse:0.369564+0.014122 test-rmse:0.987993+0.082370
## [244] train-rmse:0.367068+0.014535 test-rmse:0.988112+0.081972
## [245] train-rmse:0.366040+0.014845 test-rmse:0.988310+0.082217
## [246] train-rmse:0.364780+0.015296 test-rmse:0.987977+0.082505
## [247] train-rmse:0.363084+0.015217 test-rmse:0.988563+0.082366
## [248] train-rmse:0.361598+0.015404 test-rmse:0.987838+0.081612
## [249] train-rmse:0.359951+0.014913 test-rmse:0.987137+0.082670
## [250] train-rmse:0.358882+0.015145 test-rmse:0.986809+0.082083
## [251] train-rmse:0.358096+0.014810 test-rmse:0.986562+0.082680
## [252] train-rmse:0.356340+0.015048 test-rmse:0.986652+0.082805
## [253] train-rmse:0.354282+0.015568 test-rmse:0.985808+0.083576
## [254] train-rmse:0.352994+0.015438 test-rmse:0.985718+0.082668
## [255] train-rmse:0.352060+0.015303 test-rmse:0.986041+0.082549
## [256] train-rmse:0.351113+0.015459 test-rmse:0.985983+0.082868
## [257] train-rmse:0.349807+0.015478 test-rmse:0.986549+0.082956
## [258] train-rmse:0.348717+0.015603 test-rmse:0.987827+0.084759
## [259] train-rmse:0.347684+0.015505 test-rmse:0.989395+0.085875
## [260] train-rmse:0.345802+0.015384 test-rmse:0.985830+0.082896
## Stopping. Best iteration:
## [254] train-rmse:0.352994+0.015438 test-rmse:0.985718+0.082668
##
## 10
## [1] train-rmse:86.704080+0.086998 test-rmse:86.704831+0.406915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.342288+0.076506 test-rmse:76.342926+0.417100
## [3] train-rmse:67.220384+0.067270 test-rmse:67.220891+0.426011
## [4] train-rmse:59.190229+0.059099 test-rmse:59.190588+0.433783
## [5] train-rmse:52.121395+0.051883 test-rmse:52.121578+0.440539
## [6] train-rmse:45.899076+0.045513 test-rmse:45.899065+0.446386
## [7] train-rmse:40.422199+0.039869 test-rmse:40.421979+0.451412
## [8] train-rmse:35.601835+0.034876 test-rmse:35.601369+0.455694
## [9] train-rmse:31.359680+0.030457 test-rmse:31.358941+0.459291
## [10] train-rmse:27.626835+0.026543 test-rmse:27.625793+0.462249
## [11] train-rmse:24.342670+0.023071 test-rmse:24.341286+0.464606
## [12] train-rmse:21.453838+0.019997 test-rmse:21.452077+0.466382
## [13] train-rmse:18.913416+0.017291 test-rmse:18.911232+0.467585
## [14] train-rmse:16.680124+0.014931 test-rmse:16.677478+0.468208
## [15] train-rmse:14.717049+0.012912 test-rmse:14.717037+0.454197
## [16] train-rmse:12.991833+0.011108 test-rmse:12.980617+0.452674
## [17] train-rmse:11.475719+0.009385 test-rmse:11.467150+0.438504
## [18] train-rmse:10.143055+0.007675 test-rmse:10.129140+0.427151
## [19] train-rmse:8.970805+0.006670 test-rmse:8.950908+0.420274
## [20] train-rmse:7.940881+0.005761 test-rmse:7.923774+0.398042
## [21] train-rmse:7.035267+0.004886 test-rmse:7.022973+0.382856
## [22] train-rmse:6.239656+0.004130 test-rmse:6.228714+0.366489
## [23] train-rmse:5.540599+0.004375 test-rmse:5.535328+0.349520
## [24] train-rmse:4.926272+0.003805 test-rmse:4.922588+0.333672
## [25] train-rmse:4.387596+0.003544 test-rmse:4.386799+0.321908
## [26] train-rmse:3.915342+0.004811 test-rmse:3.923805+0.305630
## [27] train-rmse:3.500066+0.004203 test-rmse:3.513140+0.295862
## [28] train-rmse:3.138259+0.004168 test-rmse:3.154216+0.284798
## [29] train-rmse:2.820254+0.004811 test-rmse:2.850427+0.267774
## [30] train-rmse:2.543126+0.003110 test-rmse:2.583970+0.250353
## [31] train-rmse:2.303836+0.004732 test-rmse:2.356156+0.239119
## [32] train-rmse:2.096198+0.007209 test-rmse:2.166274+0.231501
## [33] train-rmse:1.915114+0.006546 test-rmse:2.004724+0.215618
## [34] train-rmse:1.760644+0.005823 test-rmse:1.868427+0.201297
## [35] train-rmse:1.627484+0.007873 test-rmse:1.749707+0.188073
## [36] train-rmse:1.510602+0.007302 test-rmse:1.653987+0.177685
## [37] train-rmse:1.413919+0.008253 test-rmse:1.577743+0.165616
## [38] train-rmse:1.328622+0.009954 test-rmse:1.513877+0.158158
## [39] train-rmse:1.257624+0.009159 test-rmse:1.457135+0.150346
## [40] train-rmse:1.196861+0.009355 test-rmse:1.412351+0.146479
## [41] train-rmse:1.144686+0.011003 test-rmse:1.375775+0.138806
## [42] train-rmse:1.099348+0.013528 test-rmse:1.344080+0.132186
## [43] train-rmse:1.057451+0.016316 test-rmse:1.316189+0.128702
## [44] train-rmse:1.022315+0.014672 test-rmse:1.294110+0.123208
## [45] train-rmse:0.992570+0.013109 test-rmse:1.275631+0.119348
## [46] train-rmse:0.966737+0.015244 test-rmse:1.262441+0.117650
## [47] train-rmse:0.945293+0.016696 test-rmse:1.250188+0.114430
## [48] train-rmse:0.926674+0.016245 test-rmse:1.238905+0.111647
## [49] train-rmse:0.906332+0.015774 test-rmse:1.227595+0.111438
## [50] train-rmse:0.889481+0.017089 test-rmse:1.219218+0.112881
## [51] train-rmse:0.875433+0.018488 test-rmse:1.212891+0.110784
## [52] train-rmse:0.861223+0.018713 test-rmse:1.202975+0.109123
## [53] train-rmse:0.845192+0.018351 test-rmse:1.190458+0.103078
## [54] train-rmse:0.830685+0.015213 test-rmse:1.182269+0.101987
## [55] train-rmse:0.817301+0.014237 test-rmse:1.176063+0.097456
## [56] train-rmse:0.805686+0.014815 test-rmse:1.171601+0.097256
## [57] train-rmse:0.795274+0.017795 test-rmse:1.163265+0.097654
## [58] train-rmse:0.785598+0.018420 test-rmse:1.156876+0.096016
## [59] train-rmse:0.775891+0.018161 test-rmse:1.150525+0.094741
## [60] train-rmse:0.767197+0.016806 test-rmse:1.147721+0.094400
## [61] train-rmse:0.755606+0.015678 test-rmse:1.135810+0.089882
## [62] train-rmse:0.746906+0.016359 test-rmse:1.132956+0.086809
## [63] train-rmse:0.738506+0.017505 test-rmse:1.127141+0.086592
## [64] train-rmse:0.729574+0.018246 test-rmse:1.123242+0.086255
## [65] train-rmse:0.722468+0.020011 test-rmse:1.118068+0.083647
## [66] train-rmse:0.715525+0.020213 test-rmse:1.112940+0.084087
## [67] train-rmse:0.708479+0.020902 test-rmse:1.110054+0.084970
## [68] train-rmse:0.701364+0.020463 test-rmse:1.102819+0.083106
## [69] train-rmse:0.695061+0.019249 test-rmse:1.100267+0.080257
## [70] train-rmse:0.685890+0.018908 test-rmse:1.092542+0.075384
## [71] train-rmse:0.679859+0.018837 test-rmse:1.091357+0.075995
## [72] train-rmse:0.672262+0.018442 test-rmse:1.085841+0.076101
## [73] train-rmse:0.665548+0.018754 test-rmse:1.082520+0.075098
## [74] train-rmse:0.657398+0.016967 test-rmse:1.076988+0.073952
## [75] train-rmse:0.651762+0.017337 test-rmse:1.073481+0.074404
## [76] train-rmse:0.644664+0.017392 test-rmse:1.070522+0.073607
## [77] train-rmse:0.639027+0.018471 test-rmse:1.067089+0.072980
## [78] train-rmse:0.632017+0.017608 test-rmse:1.060201+0.073169
## [79] train-rmse:0.626935+0.017975 test-rmse:1.058939+0.071835
## [80] train-rmse:0.620813+0.017962 test-rmse:1.054754+0.069868
## [81] train-rmse:0.615692+0.019265 test-rmse:1.051806+0.070055
## [82] train-rmse:0.611315+0.019508 test-rmse:1.049421+0.070939
## [83] train-rmse:0.604835+0.018119 test-rmse:1.046421+0.071833
## [84] train-rmse:0.600583+0.018051 test-rmse:1.044093+0.071342
## [85] train-rmse:0.593806+0.016972 test-rmse:1.040526+0.070884
## [86] train-rmse:0.587431+0.016434 test-rmse:1.038235+0.070507
## [87] train-rmse:0.582348+0.015777 test-rmse:1.037421+0.073064
## [88] train-rmse:0.576704+0.015286 test-rmse:1.036407+0.074354
## [89] train-rmse:0.572879+0.015089 test-rmse:1.032616+0.074372
## [90] train-rmse:0.568536+0.015229 test-rmse:1.029301+0.073157
## [91] train-rmse:0.563624+0.015413 test-rmse:1.026316+0.072293
## [92] train-rmse:0.557578+0.016920 test-rmse:1.023178+0.071706
## [93] train-rmse:0.552687+0.017164 test-rmse:1.022557+0.070791
## [94] train-rmse:0.548394+0.017056 test-rmse:1.018953+0.071025
## [95] train-rmse:0.544978+0.016946 test-rmse:1.018571+0.071466
## [96] train-rmse:0.541094+0.016928 test-rmse:1.016435+0.071221
## [97] train-rmse:0.536273+0.016828 test-rmse:1.014458+0.071315
## [98] train-rmse:0.531923+0.017140 test-rmse:1.012040+0.072359
## [99] train-rmse:0.527964+0.018455 test-rmse:1.011088+0.069770
## [100] train-rmse:0.524932+0.018284 test-rmse:1.009985+0.069577
## [101] train-rmse:0.519799+0.017201 test-rmse:1.009130+0.069475
## [102] train-rmse:0.515424+0.016625 test-rmse:1.008363+0.069330
## [103] train-rmse:0.511615+0.015350 test-rmse:1.007395+0.069854
## [104] train-rmse:0.508001+0.015131 test-rmse:1.007097+0.068081
## [105] train-rmse:0.504367+0.015786 test-rmse:1.006597+0.067958
## [106] train-rmse:0.501459+0.016575 test-rmse:1.006180+0.066887
## [107] train-rmse:0.497477+0.016108 test-rmse:1.003957+0.067010
## [108] train-rmse:0.492344+0.014391 test-rmse:1.002141+0.066905
## [109] train-rmse:0.486942+0.014262 test-rmse:1.001140+0.068968
## [110] train-rmse:0.482266+0.014101 test-rmse:0.999099+0.067430
## [111] train-rmse:0.477538+0.012716 test-rmse:0.998589+0.065857
## [112] train-rmse:0.474314+0.013322 test-rmse:0.999203+0.064855
## [113] train-rmse:0.470249+0.013446 test-rmse:0.998476+0.065238
## [114] train-rmse:0.467588+0.013226 test-rmse:0.997313+0.065366
## [115] train-rmse:0.464584+0.013390 test-rmse:0.996732+0.065910
## [116] train-rmse:0.462028+0.013656 test-rmse:0.995573+0.065880
## [117] train-rmse:0.458859+0.014440 test-rmse:0.993471+0.066873
## [118] train-rmse:0.455741+0.013999 test-rmse:0.995611+0.065955
## [119] train-rmse:0.453312+0.013522 test-rmse:0.994880+0.065973
## [120] train-rmse:0.450147+0.013491 test-rmse:0.993140+0.066652
## [121] train-rmse:0.446793+0.013492 test-rmse:0.991093+0.066964
## [122] train-rmse:0.443637+0.013350 test-rmse:0.989543+0.065225
## [123] train-rmse:0.440938+0.013250 test-rmse:0.988985+0.066286
## [124] train-rmse:0.436289+0.013708 test-rmse:0.989593+0.065455
## [125] train-rmse:0.433094+0.013424 test-rmse:0.987760+0.065525
## [126] train-rmse:0.430216+0.014521 test-rmse:0.987592+0.066216
## [127] train-rmse:0.426819+0.013101 test-rmse:0.986923+0.065306
## [128] train-rmse:0.423292+0.012707 test-rmse:0.987521+0.064953
## [129] train-rmse:0.419641+0.013700 test-rmse:0.988126+0.064394
## [130] train-rmse:0.417443+0.013673 test-rmse:0.987547+0.063579
## [131] train-rmse:0.413440+0.012503 test-rmse:0.987408+0.063499
## [132] train-rmse:0.411290+0.012523 test-rmse:0.987138+0.062345
## [133] train-rmse:0.409273+0.012659 test-rmse:0.986470+0.062571
## [134] train-rmse:0.406316+0.013745 test-rmse:0.985087+0.062994
## [135] train-rmse:0.403297+0.013339 test-rmse:0.983628+0.062827
## [136] train-rmse:0.400215+0.013482 test-rmse:0.983478+0.061360
## [137] train-rmse:0.398100+0.013184 test-rmse:0.983476+0.060910
## [138] train-rmse:0.395844+0.013314 test-rmse:0.982135+0.060824
## [139] train-rmse:0.393289+0.012214 test-rmse:0.983126+0.060843
## [140] train-rmse:0.389266+0.011774 test-rmse:0.983129+0.061432
## [141] train-rmse:0.386793+0.012316 test-rmse:0.983735+0.059798
## [142] train-rmse:0.384615+0.012748 test-rmse:0.983194+0.059993
## [143] train-rmse:0.382615+0.012845 test-rmse:0.983991+0.060034
## [144] train-rmse:0.379641+0.013958 test-rmse:0.983534+0.058992
## Stopping. Best iteration:
## [138] train-rmse:0.395844+0.013314 test-rmse:0.982135+0.060824
##
## 11
## [1] train-rmse:86.704080+0.086998 test-rmse:86.704831+0.406915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.342288+0.076506 test-rmse:76.342926+0.417100
## [3] train-rmse:67.220384+0.067270 test-rmse:67.220891+0.426011
## [4] train-rmse:59.190229+0.059099 test-rmse:59.190588+0.433783
## [5] train-rmse:52.121395+0.051883 test-rmse:52.121578+0.440539
## [6] train-rmse:45.899076+0.045513 test-rmse:45.899065+0.446386
## [7] train-rmse:40.422199+0.039869 test-rmse:40.421979+0.451412
## [8] train-rmse:35.601835+0.034876 test-rmse:35.601369+0.455694
## [9] train-rmse:31.359680+0.030457 test-rmse:31.358941+0.459291
## [10] train-rmse:27.626835+0.026543 test-rmse:27.625793+0.462249
## [11] train-rmse:24.342670+0.023071 test-rmse:24.341286+0.464606
## [12] train-rmse:21.453838+0.019997 test-rmse:21.452077+0.466382
## [13] train-rmse:18.913416+0.017291 test-rmse:18.911232+0.467585
## [14] train-rmse:16.680124+0.014931 test-rmse:16.677478+0.468208
## [15] train-rmse:14.717049+0.012912 test-rmse:14.717037+0.454197
## [16] train-rmse:12.991833+0.011108 test-rmse:12.980617+0.452674
## [17] train-rmse:11.475719+0.009385 test-rmse:11.467150+0.438504
## [18] train-rmse:10.143055+0.007675 test-rmse:10.129140+0.427151
## [19] train-rmse:8.970698+0.006836 test-rmse:8.949548+0.418495
## [20] train-rmse:7.940652+0.006047 test-rmse:7.923759+0.397811
## [21] train-rmse:7.034612+0.005403 test-rmse:7.022922+0.382875
## [22] train-rmse:6.237830+0.005355 test-rmse:6.226893+0.366578
## [23] train-rmse:5.538006+0.004430 test-rmse:5.534187+0.345654
## [24] train-rmse:4.922755+0.004016 test-rmse:4.920044+0.335732
## [25] train-rmse:4.382811+0.004370 test-rmse:4.380032+0.321752
## [26] train-rmse:3.907297+0.004005 test-rmse:3.914115+0.304359
## [27] train-rmse:3.490110+0.002727 test-rmse:3.498806+0.298394
## [28] train-rmse:3.122818+0.003839 test-rmse:3.146467+0.280012
## [29] train-rmse:2.800955+0.004778 test-rmse:2.839688+0.262370
## [30] train-rmse:2.520187+0.006565 test-rmse:2.571602+0.249825
## [31] train-rmse:2.273294+0.005187 test-rmse:2.342704+0.238273
## [32] train-rmse:2.057841+0.004450 test-rmse:2.144558+0.225066
## [33] train-rmse:1.869773+0.003843 test-rmse:1.978923+0.214768
## [34] train-rmse:1.709572+0.005938 test-rmse:1.836980+0.202975
## [35] train-rmse:1.567774+0.006286 test-rmse:1.718737+0.196012
## [36] train-rmse:1.447609+0.005907 test-rmse:1.614722+0.187842
## [37] train-rmse:1.342543+0.004133 test-rmse:1.538247+0.177950
## [38] train-rmse:1.250474+0.006412 test-rmse:1.466772+0.168391
## [39] train-rmse:1.171992+0.005452 test-rmse:1.409623+0.161478
## [40] train-rmse:1.102244+0.005861 test-rmse:1.365817+0.152776
## [41] train-rmse:1.044782+0.005891 test-rmse:1.325839+0.145636
## [42] train-rmse:0.990570+0.005212 test-rmse:1.292785+0.138173
## [43] train-rmse:0.945659+0.003795 test-rmse:1.265614+0.133535
## [44] train-rmse:0.908008+0.002999 test-rmse:1.243138+0.129270
## [45] train-rmse:0.876103+0.003192 test-rmse:1.222983+0.126277
## [46] train-rmse:0.848122+0.003886 test-rmse:1.208242+0.121067
## [47] train-rmse:0.820667+0.003947 test-rmse:1.191799+0.117534
## [48] train-rmse:0.796000+0.004021 test-rmse:1.179611+0.116793
## [49] train-rmse:0.775833+0.003918 test-rmse:1.170476+0.114801
## [50] train-rmse:0.755251+0.005424 test-rmse:1.161713+0.114072
## [51] train-rmse:0.736938+0.006503 test-rmse:1.151879+0.113350
## [52] train-rmse:0.722330+0.006359 test-rmse:1.141870+0.109422
## [53] train-rmse:0.709004+0.006758 test-rmse:1.135985+0.106566
## [54] train-rmse:0.695826+0.007725 test-rmse:1.129703+0.105409
## [55] train-rmse:0.683472+0.008072 test-rmse:1.122939+0.105432
## [56] train-rmse:0.670557+0.008993 test-rmse:1.113193+0.099639
## [57] train-rmse:0.656986+0.009235 test-rmse:1.109603+0.099098
## [58] train-rmse:0.645663+0.010709 test-rmse:1.101313+0.098561
## [59] train-rmse:0.635653+0.012894 test-rmse:1.094812+0.098578
## [60] train-rmse:0.624810+0.013866 test-rmse:1.092592+0.097425
## [61] train-rmse:0.614151+0.011939 test-rmse:1.088421+0.099236
## [62] train-rmse:0.605695+0.013252 test-rmse:1.085125+0.098051
## [63] train-rmse:0.597494+0.011828 test-rmse:1.078611+0.094997
## [64] train-rmse:0.589684+0.011791 test-rmse:1.075431+0.096207
## [65] train-rmse:0.582496+0.011318 test-rmse:1.071633+0.096118
## [66] train-rmse:0.573014+0.010305 test-rmse:1.068064+0.097667
## [67] train-rmse:0.564248+0.011964 test-rmse:1.063347+0.097790
## [68] train-rmse:0.557060+0.012473 test-rmse:1.059587+0.096981
## [69] train-rmse:0.549469+0.012809 test-rmse:1.055966+0.096416
## [70] train-rmse:0.542705+0.012059 test-rmse:1.049561+0.094755
## [71] train-rmse:0.536287+0.012233 test-rmse:1.047785+0.093595
## [72] train-rmse:0.529723+0.011253 test-rmse:1.043248+0.093285
## [73] train-rmse:0.523866+0.012614 test-rmse:1.041289+0.095959
## [74] train-rmse:0.516870+0.011812 test-rmse:1.036654+0.094659
## [75] train-rmse:0.510010+0.011468 test-rmse:1.035313+0.094514
## [76] train-rmse:0.502852+0.012892 test-rmse:1.033989+0.093068
## [77] train-rmse:0.497872+0.011818 test-rmse:1.031220+0.093357
## [78] train-rmse:0.493121+0.011819 test-rmse:1.029959+0.092367
## [79] train-rmse:0.487301+0.010948 test-rmse:1.025962+0.090784
## [80] train-rmse:0.482592+0.010999 test-rmse:1.024755+0.089915
## [81] train-rmse:0.476678+0.009451 test-rmse:1.023257+0.091488
## [82] train-rmse:0.472276+0.009469 test-rmse:1.022573+0.092620
## [83] train-rmse:0.466728+0.008771 test-rmse:1.020164+0.092311
## [84] train-rmse:0.459694+0.009301 test-rmse:1.017572+0.092041
## [85] train-rmse:0.455854+0.009020 test-rmse:1.016612+0.093310
## [86] train-rmse:0.450331+0.008764 test-rmse:1.013472+0.091722
## [87] train-rmse:0.445247+0.008231 test-rmse:1.011551+0.089774
## [88] train-rmse:0.440791+0.008135 test-rmse:1.010251+0.089780
## [89] train-rmse:0.436490+0.008457 test-rmse:1.008157+0.090605
## [90] train-rmse:0.431229+0.008756 test-rmse:1.007592+0.091451
## [91] train-rmse:0.426669+0.009203 test-rmse:1.006179+0.091590
## [92] train-rmse:0.423082+0.008967 test-rmse:1.006823+0.091820
## [93] train-rmse:0.419047+0.008963 test-rmse:1.006008+0.091307
## [94] train-rmse:0.415332+0.009451 test-rmse:1.003966+0.092459
## [95] train-rmse:0.410474+0.008402 test-rmse:1.003079+0.092818
## [96] train-rmse:0.403901+0.007559 test-rmse:1.000850+0.092550
## [97] train-rmse:0.400574+0.008156 test-rmse:0.999753+0.092617
## [98] train-rmse:0.397147+0.008900 test-rmse:0.999185+0.093183
## [99] train-rmse:0.393315+0.008629 test-rmse:0.997653+0.093216
## [100] train-rmse:0.389139+0.008984 test-rmse:0.996035+0.092155
## [101] train-rmse:0.384450+0.007995 test-rmse:0.995639+0.092957
## [102] train-rmse:0.380502+0.007773 test-rmse:0.993857+0.092404
## [103] train-rmse:0.375697+0.008593 test-rmse:0.993042+0.091276
## [104] train-rmse:0.372598+0.009337 test-rmse:0.990983+0.091516
## [105] train-rmse:0.369038+0.010107 test-rmse:0.990204+0.090931
## [106] train-rmse:0.365780+0.009752 test-rmse:0.989605+0.091338
## [107] train-rmse:0.362613+0.010170 test-rmse:0.988056+0.092163
## [108] train-rmse:0.360204+0.009855 test-rmse:0.987375+0.091187
## [109] train-rmse:0.356010+0.010238 test-rmse:0.986178+0.091130
## [110] train-rmse:0.351969+0.010952 test-rmse:0.984351+0.091430
## [111] train-rmse:0.348077+0.010513 test-rmse:0.982920+0.092329
## [112] train-rmse:0.343875+0.009796 test-rmse:0.981807+0.092560
## [113] train-rmse:0.340158+0.009856 test-rmse:0.980503+0.091960
## [114] train-rmse:0.336397+0.010441 test-rmse:0.979629+0.091294
## [115] train-rmse:0.333010+0.009960 test-rmse:0.978937+0.091545
## [116] train-rmse:0.328919+0.010820 test-rmse:0.979617+0.092715
## [117] train-rmse:0.325776+0.010671 test-rmse:0.978554+0.092096
## [118] train-rmse:0.322750+0.010954 test-rmse:0.978497+0.092409
## [119] train-rmse:0.319513+0.010339 test-rmse:0.977228+0.092482
## [120] train-rmse:0.316591+0.010307 test-rmse:0.977538+0.092859
## [121] train-rmse:0.314482+0.010062 test-rmse:0.978161+0.092949
## [122] train-rmse:0.310930+0.010028 test-rmse:0.979847+0.092597
## [123] train-rmse:0.308195+0.009416 test-rmse:0.978291+0.093068
## [124] train-rmse:0.305544+0.009831 test-rmse:0.976825+0.092835
## [125] train-rmse:0.302772+0.010576 test-rmse:0.976872+0.092888
## [126] train-rmse:0.300429+0.010672 test-rmse:0.976181+0.093293
## [127] train-rmse:0.295719+0.010528 test-rmse:0.974909+0.092726
## [128] train-rmse:0.293971+0.010104 test-rmse:0.974336+0.091679
## [129] train-rmse:0.291610+0.010711 test-rmse:0.973687+0.091961
## [130] train-rmse:0.289153+0.011250 test-rmse:0.972299+0.090939
## [131] train-rmse:0.286939+0.011909 test-rmse:0.971131+0.090696
## [132] train-rmse:0.285042+0.011429 test-rmse:0.971272+0.090722
## [133] train-rmse:0.283001+0.011591 test-rmse:0.970692+0.090227
## [134] train-rmse:0.280663+0.011604 test-rmse:0.970422+0.090157
## [135] train-rmse:0.276908+0.009248 test-rmse:0.968656+0.090206
## [136] train-rmse:0.274504+0.009477 test-rmse:0.967715+0.089483
## [137] train-rmse:0.271881+0.010005 test-rmse:0.967028+0.089517
## [138] train-rmse:0.269593+0.009096 test-rmse:0.966073+0.089065
## [139] train-rmse:0.267256+0.009716 test-rmse:0.966111+0.088710
## [140] train-rmse:0.264502+0.010253 test-rmse:0.964906+0.088739
## [141] train-rmse:0.262076+0.010666 test-rmse:0.965368+0.088703
## [142] train-rmse:0.259379+0.010903 test-rmse:0.964597+0.088295
## [143] train-rmse:0.257527+0.011362 test-rmse:0.963849+0.087619
## [144] train-rmse:0.254496+0.010297 test-rmse:0.962781+0.088017
## [145] train-rmse:0.251945+0.009660 test-rmse:0.962058+0.088607
## [146] train-rmse:0.250115+0.009326 test-rmse:0.961996+0.088635
## [147] train-rmse:0.247882+0.009269 test-rmse:0.962128+0.088902
## [148] train-rmse:0.245034+0.008289 test-rmse:0.960626+0.088663
## [149] train-rmse:0.242308+0.008810 test-rmse:0.960278+0.087528
## [150] train-rmse:0.240163+0.008911 test-rmse:0.959890+0.087203
## [151] train-rmse:0.238187+0.008626 test-rmse:0.959053+0.087214
## [152] train-rmse:0.235444+0.008374 test-rmse:0.958438+0.087265
## [153] train-rmse:0.232554+0.007333 test-rmse:0.957084+0.088544
## [154] train-rmse:0.229955+0.008208 test-rmse:0.957168+0.088900
## [155] train-rmse:0.228480+0.007940 test-rmse:0.957288+0.088684
## [156] train-rmse:0.226222+0.008884 test-rmse:0.956847+0.088543
## [157] train-rmse:0.224975+0.009272 test-rmse:0.955927+0.089090
## [158] train-rmse:0.223276+0.008944 test-rmse:0.955616+0.090000
## [159] train-rmse:0.221707+0.008845 test-rmse:0.954958+0.089556
## [160] train-rmse:0.219804+0.008868 test-rmse:0.954233+0.089944
## [161] train-rmse:0.217736+0.009911 test-rmse:0.953298+0.089587
## [162] train-rmse:0.215349+0.010175 test-rmse:0.952787+0.089405
## [163] train-rmse:0.212566+0.009243 test-rmse:0.952486+0.089590
## [164] train-rmse:0.211334+0.008938 test-rmse:0.952159+0.089488
## [165] train-rmse:0.209407+0.009043 test-rmse:0.952072+0.089197
## [166] train-rmse:0.206721+0.008141 test-rmse:0.951288+0.088803
## [167] train-rmse:0.205140+0.008430 test-rmse:0.951619+0.089033
## [168] train-rmse:0.202482+0.007883 test-rmse:0.951127+0.088775
## [169] train-rmse:0.201149+0.007770 test-rmse:0.951416+0.088446
## [170] train-rmse:0.199973+0.007928 test-rmse:0.951701+0.088594
## [171] train-rmse:0.197751+0.007497 test-rmse:0.950816+0.088876
## [172] train-rmse:0.196291+0.007848 test-rmse:0.950349+0.088560
## [173] train-rmse:0.194376+0.007538 test-rmse:0.950209+0.087851
## [174] train-rmse:0.191952+0.007828 test-rmse:0.950059+0.087631
## [175] train-rmse:0.190266+0.007962 test-rmse:0.949814+0.087750
## [176] train-rmse:0.188511+0.007740 test-rmse:0.949328+0.087585
## [177] train-rmse:0.186772+0.007805 test-rmse:0.949411+0.087679
## [178] train-rmse:0.185193+0.007448 test-rmse:0.949396+0.087515
## [179] train-rmse:0.183841+0.007071 test-rmse:0.949404+0.087240
## [180] train-rmse:0.181938+0.007378 test-rmse:0.948380+0.087796
## [181] train-rmse:0.180786+0.007781 test-rmse:0.948402+0.088075
## [182] train-rmse:0.178886+0.007677 test-rmse:0.948627+0.088836
## [183] train-rmse:0.177593+0.007491 test-rmse:0.949143+0.089502
## [184] train-rmse:0.176158+0.007011 test-rmse:0.949627+0.089953
## [185] train-rmse:0.175028+0.007098 test-rmse:0.949039+0.089602
## [186] train-rmse:0.173088+0.006481 test-rmse:0.948812+0.089877
## Stopping. Best iteration:
## [180] train-rmse:0.181938+0.007378 test-rmse:0.948380+0.087796
##
## 12
## [1] train-rmse:86.704080+0.086998 test-rmse:86.704831+0.406915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.342288+0.076506 test-rmse:76.342926+0.417100
## [3] train-rmse:67.220384+0.067270 test-rmse:67.220891+0.426011
## [4] train-rmse:59.190229+0.059099 test-rmse:59.190588+0.433783
## [5] train-rmse:52.121395+0.051883 test-rmse:52.121578+0.440539
## [6] train-rmse:45.899076+0.045513 test-rmse:45.899065+0.446386
## [7] train-rmse:40.422199+0.039869 test-rmse:40.421979+0.451412
## [8] train-rmse:35.601835+0.034876 test-rmse:35.601369+0.455694
## [9] train-rmse:31.359680+0.030457 test-rmse:31.358941+0.459291
## [10] train-rmse:27.626835+0.026543 test-rmse:27.625793+0.462249
## [11] train-rmse:24.342670+0.023071 test-rmse:24.341286+0.464606
## [12] train-rmse:21.453838+0.019997 test-rmse:21.452077+0.466382
## [13] train-rmse:18.913416+0.017291 test-rmse:18.911232+0.467585
## [14] train-rmse:16.680124+0.014931 test-rmse:16.677478+0.468208
## [15] train-rmse:14.717049+0.012912 test-rmse:14.717037+0.454197
## [16] train-rmse:12.991833+0.011108 test-rmse:12.980617+0.452674
## [17] train-rmse:11.475719+0.009385 test-rmse:11.467150+0.438504
## [18] train-rmse:10.143055+0.007675 test-rmse:10.129140+0.427151
## [19] train-rmse:8.970594+0.006998 test-rmse:8.951129+0.420565
## [20] train-rmse:7.940393+0.006392 test-rmse:7.924440+0.399271
## [21] train-rmse:7.034083+0.005786 test-rmse:7.021996+0.378463
## [22] train-rmse:6.237705+0.004877 test-rmse:6.224243+0.361078
## [23] train-rmse:5.537108+0.004233 test-rmse:5.531768+0.340030
## [24] train-rmse:4.921336+0.003293 test-rmse:4.911486+0.329909
## [25] train-rmse:4.379399+0.003633 test-rmse:4.380186+0.315199
## [26] train-rmse:3.901963+0.002947 test-rmse:3.907497+0.298529
## [27] train-rmse:3.481934+0.002010 test-rmse:3.495515+0.283696
## [28] train-rmse:3.112788+0.003388 test-rmse:3.130073+0.276865
## [29] train-rmse:2.789583+0.003233 test-rmse:2.822822+0.260453
## [30] train-rmse:2.503696+0.004980 test-rmse:2.553019+0.245390
## [31] train-rmse:2.253766+0.004046 test-rmse:2.319729+0.238977
## [32] train-rmse:2.033991+0.005451 test-rmse:2.118112+0.224690
## [33] train-rmse:1.843204+0.006898 test-rmse:1.946255+0.213855
## [34] train-rmse:1.674573+0.005389 test-rmse:1.806288+0.204142
## [35] train-rmse:1.529390+0.006603 test-rmse:1.684071+0.198368
## [36] train-rmse:1.401023+0.006681 test-rmse:1.581284+0.188235
## [37] train-rmse:1.287080+0.005796 test-rmse:1.495351+0.182270
## [38] train-rmse:1.190826+0.006350 test-rmse:1.424354+0.170469
## [39] train-rmse:1.107082+0.005405 test-rmse:1.369207+0.160281
## [40] train-rmse:1.035265+0.007627 test-rmse:1.317627+0.154360
## [41] train-rmse:0.969493+0.006477 test-rmse:1.273239+0.148488
## [42] train-rmse:0.912657+0.005357 test-rmse:1.239892+0.147021
## [43] train-rmse:0.862928+0.004669 test-rmse:1.211322+0.145540
## [44] train-rmse:0.820173+0.006104 test-rmse:1.186792+0.135439
## [45] train-rmse:0.780902+0.005532 test-rmse:1.166339+0.129327
## [46] train-rmse:0.747629+0.006976 test-rmse:1.153265+0.125459
## [47] train-rmse:0.717309+0.009174 test-rmse:1.138345+0.122156
## [48] train-rmse:0.691392+0.012725 test-rmse:1.124384+0.121553
## [49] train-rmse:0.666819+0.012554 test-rmse:1.111356+0.114680
## [50] train-rmse:0.646586+0.013169 test-rmse:1.103947+0.111727
## [51] train-rmse:0.628650+0.011818 test-rmse:1.094232+0.107254
## [52] train-rmse:0.610056+0.012979 test-rmse:1.087557+0.105382
## [53] train-rmse:0.595081+0.014384 test-rmse:1.082235+0.103300
## [54] train-rmse:0.579720+0.014154 test-rmse:1.075647+0.104424
## [55] train-rmse:0.566743+0.013355 test-rmse:1.069725+0.104013
## [56] train-rmse:0.554959+0.012901 test-rmse:1.066252+0.102118
## [57] train-rmse:0.543265+0.014528 test-rmse:1.058743+0.102460
## [58] train-rmse:0.531229+0.015271 test-rmse:1.054958+0.101044
## [59] train-rmse:0.521945+0.016358 test-rmse:1.051242+0.100384
## [60] train-rmse:0.511653+0.016170 test-rmse:1.047100+0.100830
## [61] train-rmse:0.502842+0.013877 test-rmse:1.041152+0.097941
## [62] train-rmse:0.493996+0.012357 test-rmse:1.037616+0.093982
## [63] train-rmse:0.486152+0.012523 test-rmse:1.033604+0.093045
## [64] train-rmse:0.474495+0.015397 test-rmse:1.028234+0.089977
## [65] train-rmse:0.466683+0.015281 test-rmse:1.025770+0.088533
## [66] train-rmse:0.459672+0.014440 test-rmse:1.024745+0.087544
## [67] train-rmse:0.450737+0.015910 test-rmse:1.020846+0.084856
## [68] train-rmse:0.443054+0.014022 test-rmse:1.016893+0.083783
## [69] train-rmse:0.436098+0.015341 test-rmse:1.016059+0.083629
## [70] train-rmse:0.429066+0.014588 test-rmse:1.012266+0.083862
## [71] train-rmse:0.423191+0.013634 test-rmse:1.011072+0.083701
## [72] train-rmse:0.415965+0.014451 test-rmse:1.009290+0.083948
## [73] train-rmse:0.410243+0.016143 test-rmse:1.007727+0.083831
## [74] train-rmse:0.404703+0.016071 test-rmse:1.006270+0.084088
## [75] train-rmse:0.396776+0.016402 test-rmse:1.003585+0.082654
## [76] train-rmse:0.392398+0.017172 test-rmse:1.001796+0.082475
## [77] train-rmse:0.386225+0.016705 test-rmse:1.001106+0.080131
## [78] train-rmse:0.380024+0.015480 test-rmse:0.999413+0.079723
## [79] train-rmse:0.373376+0.017032 test-rmse:0.998366+0.079696
## [80] train-rmse:0.368662+0.016237 test-rmse:0.996987+0.079637
## [81] train-rmse:0.363862+0.016526 test-rmse:0.996594+0.080906
## [82] train-rmse:0.359124+0.017378 test-rmse:0.995616+0.080054
## [83] train-rmse:0.353209+0.017156 test-rmse:0.995519+0.080627
## [84] train-rmse:0.348701+0.016154 test-rmse:0.995190+0.080765
## [85] train-rmse:0.343866+0.016542 test-rmse:0.993344+0.079997
## [86] train-rmse:0.339182+0.016174 test-rmse:0.993452+0.080102
## [87] train-rmse:0.334709+0.017052 test-rmse:0.991608+0.079175
## [88] train-rmse:0.329453+0.016946 test-rmse:0.990119+0.079898
## [89] train-rmse:0.324356+0.016633 test-rmse:0.990753+0.079879
## [90] train-rmse:0.321064+0.017289 test-rmse:0.990928+0.080042
## [91] train-rmse:0.317640+0.017735 test-rmse:0.989931+0.079477
## [92] train-rmse:0.312953+0.019646 test-rmse:0.990360+0.079555
## [93] train-rmse:0.308637+0.019944 test-rmse:0.989954+0.079186
## [94] train-rmse:0.305132+0.020269 test-rmse:0.988310+0.079675
## [95] train-rmse:0.299457+0.020407 test-rmse:0.988176+0.080113
## [96] train-rmse:0.296111+0.020352 test-rmse:0.986643+0.080253
## [97] train-rmse:0.292542+0.019942 test-rmse:0.984697+0.081032
## [98] train-rmse:0.288410+0.019980 test-rmse:0.983286+0.081232
## [99] train-rmse:0.283618+0.020741 test-rmse:0.982539+0.080923
## [100] train-rmse:0.279727+0.019413 test-rmse:0.982607+0.081111
## [101] train-rmse:0.275532+0.018949 test-rmse:0.981460+0.080765
## [102] train-rmse:0.272854+0.018533 test-rmse:0.981326+0.080972
## [103] train-rmse:0.269511+0.018480 test-rmse:0.981130+0.081178
## [104] train-rmse:0.266493+0.018202 test-rmse:0.980468+0.080155
## [105] train-rmse:0.262324+0.017807 test-rmse:0.979678+0.080400
## [106] train-rmse:0.259527+0.017796 test-rmse:0.980552+0.080944
## [107] train-rmse:0.255637+0.018143 test-rmse:0.979445+0.080919
## [108] train-rmse:0.251620+0.017754 test-rmse:0.979267+0.081236
## [109] train-rmse:0.249297+0.016687 test-rmse:0.978937+0.080930
## [110] train-rmse:0.245640+0.015968 test-rmse:0.978686+0.079374
## [111] train-rmse:0.243069+0.015494 test-rmse:0.978949+0.079743
## [112] train-rmse:0.240518+0.015058 test-rmse:0.979272+0.078281
## [113] train-rmse:0.237936+0.014793 test-rmse:0.979446+0.077994
## [114] train-rmse:0.234642+0.014315 test-rmse:0.979572+0.078034
## [115] train-rmse:0.230584+0.014411 test-rmse:0.978818+0.078090
## [116] train-rmse:0.227795+0.013757 test-rmse:0.977417+0.078408
## [117] train-rmse:0.225011+0.013721 test-rmse:0.975843+0.078000
## [118] train-rmse:0.222484+0.014481 test-rmse:0.974727+0.077253
## [119] train-rmse:0.219080+0.014768 test-rmse:0.974033+0.077420
## [120] train-rmse:0.215991+0.013367 test-rmse:0.972592+0.077347
## [121] train-rmse:0.213631+0.012767 test-rmse:0.972230+0.077579
## [122] train-rmse:0.211712+0.012234 test-rmse:0.972054+0.077551
## [123] train-rmse:0.209605+0.011899 test-rmse:0.971556+0.077731
## [124] train-rmse:0.206377+0.011263 test-rmse:0.970340+0.076718
## [125] train-rmse:0.203984+0.010954 test-rmse:0.969085+0.076909
## [126] train-rmse:0.201251+0.011308 test-rmse:0.968311+0.076302
## [127] train-rmse:0.198626+0.011233 test-rmse:0.969196+0.075636
## [128] train-rmse:0.196550+0.011136 test-rmse:0.969494+0.075220
## [129] train-rmse:0.194111+0.010626 test-rmse:0.969772+0.075288
## [130] train-rmse:0.191597+0.010940 test-rmse:0.968740+0.075029
## [131] train-rmse:0.188987+0.010717 test-rmse:0.968409+0.074922
## [132] train-rmse:0.187230+0.010845 test-rmse:0.968050+0.074307
## [133] train-rmse:0.184733+0.010609 test-rmse:0.967540+0.073524
## [134] train-rmse:0.181759+0.010058 test-rmse:0.967502+0.073002
## [135] train-rmse:0.179861+0.009258 test-rmse:0.966869+0.072374
## [136] train-rmse:0.177692+0.009354 test-rmse:0.967183+0.072421
## [137] train-rmse:0.175782+0.009178 test-rmse:0.967453+0.072400
## [138] train-rmse:0.174494+0.009240 test-rmse:0.966643+0.072573
## [139] train-rmse:0.171864+0.009659 test-rmse:0.966524+0.072418
## [140] train-rmse:0.169469+0.009321 test-rmse:0.965971+0.072168
## [141] train-rmse:0.167554+0.009483 test-rmse:0.965433+0.071793
## [142] train-rmse:0.164971+0.009884 test-rmse:0.965702+0.070653
## [143] train-rmse:0.163362+0.010011 test-rmse:0.965257+0.070817
## [144] train-rmse:0.160874+0.010621 test-rmse:0.964881+0.071479
## [145] train-rmse:0.158723+0.011298 test-rmse:0.964511+0.072003
## [146] train-rmse:0.156627+0.011410 test-rmse:0.964111+0.072254
## [147] train-rmse:0.154645+0.011401 test-rmse:0.963332+0.072491
## [148] train-rmse:0.153028+0.011703 test-rmse:0.963068+0.072166
## [149] train-rmse:0.151855+0.011299 test-rmse:0.963135+0.072486
## [150] train-rmse:0.149921+0.011302 test-rmse:0.962786+0.072562
## [151] train-rmse:0.148265+0.011415 test-rmse:0.962280+0.072566
## [152] train-rmse:0.146487+0.010546 test-rmse:0.962433+0.072687
## [153] train-rmse:0.145251+0.010263 test-rmse:0.961838+0.073071
## [154] train-rmse:0.143683+0.010436 test-rmse:0.961743+0.073719
## [155] train-rmse:0.142435+0.010710 test-rmse:0.961432+0.073543
## [156] train-rmse:0.140123+0.010869 test-rmse:0.961497+0.073146
## [157] train-rmse:0.138368+0.011081 test-rmse:0.961632+0.073427
## [158] train-rmse:0.136783+0.010624 test-rmse:0.961998+0.073318
## [159] train-rmse:0.135260+0.010981 test-rmse:0.961439+0.073285
## [160] train-rmse:0.133459+0.010666 test-rmse:0.961306+0.072865
## [161] train-rmse:0.132177+0.010655 test-rmse:0.961511+0.072993
## [162] train-rmse:0.130696+0.010601 test-rmse:0.961677+0.072844
## [163] train-rmse:0.128970+0.010580 test-rmse:0.961434+0.072952
## [164] train-rmse:0.128078+0.010379 test-rmse:0.961267+0.073240
## [165] train-rmse:0.126609+0.009731 test-rmse:0.960665+0.073518
## [166] train-rmse:0.124510+0.010001 test-rmse:0.960569+0.073606
## [167] train-rmse:0.122613+0.010234 test-rmse:0.960915+0.073475
## [168] train-rmse:0.120984+0.009765 test-rmse:0.961185+0.073556
## [169] train-rmse:0.119224+0.009985 test-rmse:0.961120+0.073509
## [170] train-rmse:0.117225+0.009653 test-rmse:0.961248+0.073834
## [171] train-rmse:0.115772+0.009388 test-rmse:0.961384+0.073919
## [172] train-rmse:0.115151+0.009200 test-rmse:0.961208+0.073969
## Stopping. Best iteration:
## [166] train-rmse:0.124510+0.010001 test-rmse:0.960569+0.073606
##
## 13
## [1] train-rmse:86.704080+0.086998 test-rmse:86.704831+0.406915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.342288+0.076506 test-rmse:76.342926+0.417100
## [3] train-rmse:67.220384+0.067270 test-rmse:67.220891+0.426011
## [4] train-rmse:59.190229+0.059099 test-rmse:59.190588+0.433783
## [5] train-rmse:52.121395+0.051883 test-rmse:52.121578+0.440539
## [6] train-rmse:45.899076+0.045513 test-rmse:45.899065+0.446386
## [7] train-rmse:40.422199+0.039869 test-rmse:40.421979+0.451412
## [8] train-rmse:35.601835+0.034876 test-rmse:35.601369+0.455694
## [9] train-rmse:31.359680+0.030457 test-rmse:31.358941+0.459291
## [10] train-rmse:27.626835+0.026543 test-rmse:27.625793+0.462249
## [11] train-rmse:24.342670+0.023071 test-rmse:24.341286+0.464606
## [12] train-rmse:21.453838+0.019997 test-rmse:21.452077+0.466382
## [13] train-rmse:18.913416+0.017291 test-rmse:18.911232+0.467585
## [14] train-rmse:16.680124+0.014931 test-rmse:16.677478+0.468208
## [15] train-rmse:14.717049+0.012912 test-rmse:14.717037+0.454197
## [16] train-rmse:12.991833+0.011108 test-rmse:12.980617+0.452674
## [17] train-rmse:11.475719+0.009385 test-rmse:11.467150+0.438504
## [18] train-rmse:10.143055+0.007675 test-rmse:10.129140+0.427151
## [19] train-rmse:8.970594+0.006998 test-rmse:8.951129+0.420565
## [20] train-rmse:7.940375+0.006409 test-rmse:7.923416+0.398633
## [21] train-rmse:7.034011+0.005779 test-rmse:7.023178+0.380351
## [22] train-rmse:6.237414+0.004653 test-rmse:6.228478+0.359306
## [23] train-rmse:5.536330+0.004104 test-rmse:5.530238+0.341399
## [24] train-rmse:4.919820+0.003306 test-rmse:4.914515+0.328485
## [25] train-rmse:4.377724+0.003892 test-rmse:4.380428+0.312684
## [26] train-rmse:3.899713+0.003604 test-rmse:3.907662+0.298251
## [27] train-rmse:3.479117+0.002884 test-rmse:3.492796+0.285562
## [28] train-rmse:3.108809+0.003021 test-rmse:3.132075+0.279514
## [29] train-rmse:2.781641+0.002698 test-rmse:2.821122+0.260769
## [30] train-rmse:2.496594+0.003034 test-rmse:2.548273+0.248669
## [31] train-rmse:2.244193+0.003037 test-rmse:2.309772+0.234387
## [32] train-rmse:2.020284+0.003501 test-rmse:2.116104+0.222245
## [33] train-rmse:1.823621+0.002867 test-rmse:1.942473+0.214699
## [34] train-rmse:1.653623+0.005050 test-rmse:1.799246+0.205481
## [35] train-rmse:1.503829+0.004791 test-rmse:1.672562+0.193150
## [36] train-rmse:1.373782+0.004945 test-rmse:1.569713+0.179833
## [37] train-rmse:1.257132+0.004329 test-rmse:1.480403+0.169095
## [38] train-rmse:1.154573+0.003422 test-rmse:1.402954+0.161533
## [39] train-rmse:1.062082+0.003246 test-rmse:1.341325+0.156607
## [40] train-rmse:0.985719+0.003322 test-rmse:1.290190+0.151753
## [41] train-rmse:0.917472+0.000817 test-rmse:1.244278+0.145407
## [42] train-rmse:0.855749+0.003709 test-rmse:1.203907+0.136880
## [43] train-rmse:0.805928+0.004932 test-rmse:1.175399+0.133187
## [44] train-rmse:0.759376+0.005332 test-rmse:1.149161+0.131152
## [45] train-rmse:0.717417+0.007528 test-rmse:1.128992+0.129708
## [46] train-rmse:0.679326+0.005420 test-rmse:1.105479+0.123670
## [47] train-rmse:0.648151+0.007719 test-rmse:1.092549+0.121995
## [48] train-rmse:0.618894+0.008254 test-rmse:1.076936+0.123509
## [49] train-rmse:0.592846+0.009580 test-rmse:1.066839+0.121975
## [50] train-rmse:0.568739+0.012721 test-rmse:1.056441+0.119750
## [51] train-rmse:0.546602+0.012019 test-rmse:1.047933+0.120839
## [52] train-rmse:0.526840+0.014457 test-rmse:1.043075+0.117101
## [53] train-rmse:0.508341+0.016571 test-rmse:1.038945+0.114481
## [54] train-rmse:0.492957+0.018171 test-rmse:1.034513+0.117603
## [55] train-rmse:0.478702+0.018696 test-rmse:1.024936+0.114627
## [56] train-rmse:0.464650+0.018884 test-rmse:1.022861+0.114766
## [57] train-rmse:0.452890+0.018667 test-rmse:1.019400+0.114726
## [58] train-rmse:0.440795+0.020037 test-rmse:1.016093+0.113576
## [59] train-rmse:0.430291+0.018080 test-rmse:1.009624+0.111682
## [60] train-rmse:0.419267+0.017195 test-rmse:1.004283+0.108923
## [61] train-rmse:0.408731+0.018651 test-rmse:1.001990+0.106668
## [62] train-rmse:0.399839+0.019575 test-rmse:0.999856+0.105555
## [63] train-rmse:0.391102+0.019151 test-rmse:0.998066+0.105074
## [64] train-rmse:0.384496+0.018857 test-rmse:0.996715+0.104883
## [65] train-rmse:0.375635+0.017424 test-rmse:0.995586+0.105328
## [66] train-rmse:0.368370+0.017294 test-rmse:0.993661+0.107574
## [67] train-rmse:0.361937+0.017619 test-rmse:0.991450+0.107918
## [68] train-rmse:0.354255+0.017714 test-rmse:0.987652+0.106210
## [69] train-rmse:0.347752+0.016793 test-rmse:0.986312+0.106537
## [70] train-rmse:0.341589+0.015376 test-rmse:0.982111+0.105467
## [71] train-rmse:0.333715+0.014768 test-rmse:0.980812+0.103742
## [72] train-rmse:0.328254+0.015713 test-rmse:0.979760+0.104409
## [73] train-rmse:0.321754+0.015536 test-rmse:0.977830+0.106025
## [74] train-rmse:0.315199+0.014233 test-rmse:0.975856+0.107278
## [75] train-rmse:0.309178+0.013520 test-rmse:0.974887+0.107620
## [76] train-rmse:0.302856+0.012379 test-rmse:0.973609+0.108180
## [77] train-rmse:0.297427+0.012688 test-rmse:0.972522+0.107583
## [78] train-rmse:0.292847+0.011767 test-rmse:0.971877+0.107876
## [79] train-rmse:0.287798+0.012103 test-rmse:0.970427+0.108748
## [80] train-rmse:0.282863+0.010631 test-rmse:0.969246+0.110001
## [81] train-rmse:0.278126+0.010245 test-rmse:0.968275+0.108716
## [82] train-rmse:0.273924+0.010870 test-rmse:0.967543+0.108585
## [83] train-rmse:0.268909+0.010609 test-rmse:0.966862+0.107911
## [84] train-rmse:0.264444+0.011483 test-rmse:0.965723+0.107203
## [85] train-rmse:0.258376+0.010565 test-rmse:0.964212+0.105593
## [86] train-rmse:0.253929+0.010301 test-rmse:0.963548+0.105875
## [87] train-rmse:0.250392+0.010116 test-rmse:0.962601+0.106239
## [88] train-rmse:0.244577+0.008998 test-rmse:0.962930+0.105973
## [89] train-rmse:0.240612+0.009395 test-rmse:0.962452+0.104974
## [90] train-rmse:0.237640+0.009313 test-rmse:0.962053+0.104154
## [91] train-rmse:0.232887+0.009215 test-rmse:0.960360+0.104440
## [92] train-rmse:0.228992+0.009806 test-rmse:0.959507+0.104766
## [93] train-rmse:0.225312+0.010232 test-rmse:0.959742+0.105994
## [94] train-rmse:0.221634+0.009777 test-rmse:0.958814+0.105481
## [95] train-rmse:0.218166+0.008250 test-rmse:0.958089+0.105145
## [96] train-rmse:0.214240+0.007682 test-rmse:0.957973+0.104511
## [97] train-rmse:0.210695+0.007476 test-rmse:0.957378+0.104799
## [98] train-rmse:0.207575+0.007190 test-rmse:0.956218+0.104884
## [99] train-rmse:0.204110+0.007736 test-rmse:0.956448+0.104775
## [100] train-rmse:0.201613+0.007559 test-rmse:0.956108+0.105232
## [101] train-rmse:0.198704+0.008644 test-rmse:0.955913+0.104998
## [102] train-rmse:0.195514+0.008594 test-rmse:0.955442+0.105401
## [103] train-rmse:0.192769+0.008419 test-rmse:0.955982+0.104881
## [104] train-rmse:0.189434+0.008018 test-rmse:0.955283+0.104513
## [105] train-rmse:0.186630+0.007464 test-rmse:0.955034+0.104828
## [106] train-rmse:0.183766+0.007257 test-rmse:0.955017+0.104704
## [107] train-rmse:0.179547+0.008085 test-rmse:0.954023+0.104288
## [108] train-rmse:0.176993+0.008467 test-rmse:0.953725+0.103658
## [109] train-rmse:0.174659+0.008199 test-rmse:0.952917+0.103163
## [110] train-rmse:0.171136+0.008523 test-rmse:0.952290+0.103071
## [111] train-rmse:0.168474+0.007546 test-rmse:0.951245+0.102053
## [112] train-rmse:0.166346+0.007097 test-rmse:0.950729+0.102047
## [113] train-rmse:0.162767+0.007436 test-rmse:0.950314+0.102216
## [114] train-rmse:0.159839+0.007546 test-rmse:0.950428+0.101694
## [115] train-rmse:0.156769+0.007473 test-rmse:0.950002+0.101532
## [116] train-rmse:0.154417+0.007455 test-rmse:0.949937+0.101889
## [117] train-rmse:0.151658+0.006877 test-rmse:0.949956+0.101376
## [118] train-rmse:0.149499+0.006722 test-rmse:0.949069+0.100669
## [119] train-rmse:0.146994+0.007327 test-rmse:0.948952+0.100340
## [120] train-rmse:0.144233+0.007415 test-rmse:0.948663+0.100976
## [121] train-rmse:0.141891+0.007181 test-rmse:0.948268+0.100653
## [122] train-rmse:0.140141+0.007260 test-rmse:0.947950+0.100546
## [123] train-rmse:0.137449+0.007933 test-rmse:0.948111+0.100318
## [124] train-rmse:0.135010+0.008061 test-rmse:0.948984+0.100829
## [125] train-rmse:0.132867+0.008034 test-rmse:0.948479+0.100402
## [126] train-rmse:0.130730+0.007870 test-rmse:0.948456+0.100129
## [127] train-rmse:0.128727+0.008086 test-rmse:0.947892+0.099996
## [128] train-rmse:0.127197+0.008116 test-rmse:0.947793+0.099916
## [129] train-rmse:0.124525+0.007832 test-rmse:0.947500+0.100172
## [130] train-rmse:0.122199+0.007527 test-rmse:0.948117+0.100813
## [131] train-rmse:0.119796+0.006998 test-rmse:0.948010+0.100614
## [132] train-rmse:0.118107+0.007066 test-rmse:0.947630+0.100407
## [133] train-rmse:0.116217+0.007303 test-rmse:0.947464+0.099959
## [134] train-rmse:0.114155+0.007189 test-rmse:0.947394+0.100056
## [135] train-rmse:0.112785+0.007192 test-rmse:0.947060+0.100170
## [136] train-rmse:0.111388+0.007185 test-rmse:0.946980+0.100497
## [137] train-rmse:0.109216+0.007071 test-rmse:0.946682+0.100753
## [138] train-rmse:0.107597+0.007173 test-rmse:0.946436+0.100751
## [139] train-rmse:0.106243+0.007126 test-rmse:0.946122+0.100565
## [140] train-rmse:0.105028+0.007242 test-rmse:0.945984+0.100353
## [141] train-rmse:0.103691+0.007317 test-rmse:0.945986+0.100309
## [142] train-rmse:0.102199+0.007337 test-rmse:0.945883+0.100174
## [143] train-rmse:0.100827+0.007431 test-rmse:0.946083+0.100170
## [144] train-rmse:0.099577+0.007606 test-rmse:0.946078+0.100060
## [145] train-rmse:0.097644+0.007824 test-rmse:0.945969+0.099852
## [146] train-rmse:0.096177+0.007828 test-rmse:0.945504+0.100002
## [147] train-rmse:0.094894+0.007637 test-rmse:0.945516+0.100105
## [148] train-rmse:0.093491+0.007535 test-rmse:0.944926+0.099652
## [149] train-rmse:0.092128+0.007747 test-rmse:0.944844+0.099710
## [150] train-rmse:0.090495+0.007297 test-rmse:0.944462+0.099570
## [151] train-rmse:0.089230+0.007140 test-rmse:0.944649+0.099074
## [152] train-rmse:0.088007+0.007511 test-rmse:0.944587+0.099242
## [153] train-rmse:0.086617+0.007620 test-rmse:0.944292+0.099249
## [154] train-rmse:0.085676+0.007609 test-rmse:0.944465+0.098921
## [155] train-rmse:0.084516+0.007321 test-rmse:0.944321+0.098698
## [156] train-rmse:0.082687+0.007301 test-rmse:0.944337+0.098733
## [157] train-rmse:0.081478+0.007556 test-rmse:0.944024+0.098831
## [158] train-rmse:0.080344+0.007296 test-rmse:0.943372+0.098787
## [159] train-rmse:0.079523+0.007163 test-rmse:0.943324+0.098889
## [160] train-rmse:0.078156+0.007199 test-rmse:0.943416+0.098927
## [161] train-rmse:0.076976+0.006942 test-rmse:0.943478+0.099075
## [162] train-rmse:0.075496+0.006805 test-rmse:0.943231+0.099068
## [163] train-rmse:0.074364+0.006610 test-rmse:0.943124+0.099369
## [164] train-rmse:0.072681+0.006459 test-rmse:0.942947+0.099174
## [165] train-rmse:0.071782+0.006288 test-rmse:0.942939+0.099405
## [166] train-rmse:0.070678+0.006425 test-rmse:0.942889+0.099319
## [167] train-rmse:0.069470+0.006221 test-rmse:0.942808+0.099396
## [168] train-rmse:0.068749+0.006212 test-rmse:0.942755+0.099428
## [169] train-rmse:0.067644+0.006043 test-rmse:0.942619+0.099526
## [170] train-rmse:0.066411+0.005581 test-rmse:0.942379+0.099319
## [171] train-rmse:0.065309+0.005606 test-rmse:0.942357+0.099181
## [172] train-rmse:0.064047+0.005402 test-rmse:0.942122+0.098982
## [173] train-rmse:0.063025+0.005209 test-rmse:0.942207+0.099251
## [174] train-rmse:0.062284+0.005110 test-rmse:0.942020+0.099114
## [175] train-rmse:0.061436+0.005140 test-rmse:0.941943+0.099225
## [176] train-rmse:0.060590+0.005270 test-rmse:0.941991+0.099388
## [177] train-rmse:0.059578+0.005081 test-rmse:0.941820+0.099252
## [178] train-rmse:0.058795+0.005030 test-rmse:0.941632+0.099216
## [179] train-rmse:0.057799+0.004998 test-rmse:0.941491+0.099343
## [180] train-rmse:0.056804+0.004834 test-rmse:0.941410+0.099329
## [181] train-rmse:0.055800+0.004855 test-rmse:0.941392+0.099337
## [182] train-rmse:0.054993+0.004779 test-rmse:0.941454+0.099291
## [183] train-rmse:0.054300+0.004656 test-rmse:0.941353+0.099171
## [184] train-rmse:0.053452+0.004671 test-rmse:0.941442+0.099279
## [185] train-rmse:0.052806+0.004832 test-rmse:0.941465+0.099337
## [186] train-rmse:0.052034+0.004860 test-rmse:0.941459+0.099256
## [187] train-rmse:0.051037+0.004704 test-rmse:0.940921+0.099293
## [188] train-rmse:0.050434+0.004707 test-rmse:0.941056+0.099281
## [189] train-rmse:0.049862+0.004628 test-rmse:0.940845+0.099460
## [190] train-rmse:0.049083+0.004725 test-rmse:0.940646+0.099608
## [191] train-rmse:0.048318+0.004628 test-rmse:0.940628+0.099492
## [192] train-rmse:0.047461+0.004470 test-rmse:0.940472+0.099639
## [193] train-rmse:0.046862+0.004457 test-rmse:0.940378+0.099842
## [194] train-rmse:0.046076+0.004345 test-rmse:0.940423+0.099855
## [195] train-rmse:0.045346+0.004413 test-rmse:0.940438+0.099976
## [196] train-rmse:0.044734+0.004387 test-rmse:0.940486+0.100043
## [197] train-rmse:0.044027+0.004205 test-rmse:0.940570+0.100079
## [198] train-rmse:0.043208+0.004058 test-rmse:0.940525+0.100008
## [199] train-rmse:0.042667+0.004178 test-rmse:0.940518+0.100124
## Stopping. Best iteration:
## [193] train-rmse:0.046862+0.004457 test-rmse:0.940378+0.099842
##
## 14
## [1] train-rmse:84.742499+0.085026 test-rmse:84.743227+0.408848
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.927655+0.073048 test-rmse:72.928244+0.420446
## [3] train-rmse:62.762302+0.062739 test-rmse:62.762725+0.430333
## [4] train-rmse:54.016514+0.053817 test-rmse:54.016744+0.438737
## [5] train-rmse:46.492472+0.046117 test-rmse:46.492479+0.445831
## [6] train-rmse:40.019990+0.039459 test-rmse:40.019755+0.451772
## [7] train-rmse:34.452692+0.033687 test-rmse:34.452158+0.456684
## [8] train-rmse:29.664635+0.028683 test-rmse:29.663766+0.460663
## [9] train-rmse:25.547519+0.024346 test-rmse:25.546270+0.463776
## [10] train-rmse:22.008217+0.020588 test-rmse:22.006533+0.466070
## [11] train-rmse:18.966660+0.017350 test-rmse:18.964487+0.467562
## [12] train-rmse:16.354035+0.014591 test-rmse:16.351313+0.468250
## [13] train-rmse:14.110245+0.012261 test-rmse:14.110562+0.451996
## [14] train-rmse:12.184010+0.010093 test-rmse:12.173134+0.452602
## [15] train-rmse:10.530898+0.008449 test-rmse:10.519598+0.447698
## [16] train-rmse:9.113155+0.007247 test-rmse:9.105301+0.433855
## [17] train-rmse:7.898617+0.006814 test-rmse:7.883248+0.431642
## [18] train-rmse:6.859430+0.006913 test-rmse:6.845067+0.425213
## [19] train-rmse:5.973051+0.007616 test-rmse:5.962075+0.414155
## [20] train-rmse:5.218115+0.008760 test-rmse:5.210028+0.403066
## [21] train-rmse:4.577487+0.010502 test-rmse:4.571826+0.389309
## [22] train-rmse:4.035859+0.012181 test-rmse:4.031004+0.374521
## [23] train-rmse:3.579486+0.013938 test-rmse:3.579301+0.364553
## [24] train-rmse:3.197461+0.015976 test-rmse:3.199516+0.340930
## [25] train-rmse:2.879811+0.017882 test-rmse:2.888447+0.322277
## [26] train-rmse:2.617508+0.019709 test-rmse:2.628649+0.296257
## [27] train-rmse:2.402199+0.021250 test-rmse:2.420778+0.272234
## [28] train-rmse:2.227119+0.022672 test-rmse:2.253942+0.247927
## [29] train-rmse:2.085533+0.024306 test-rmse:2.120294+0.219875
## [30] train-rmse:1.971825+0.025607 test-rmse:2.015106+0.195273
## [31] train-rmse:1.880680+0.026342 test-rmse:1.930531+0.173614
## [32] train-rmse:1.807643+0.026881 test-rmse:1.864625+0.158112
## [33] train-rmse:1.749528+0.027355 test-rmse:1.818068+0.144137
## [34] train-rmse:1.702832+0.027433 test-rmse:1.777041+0.131970
## [35] train-rmse:1.665310+0.027447 test-rmse:1.743669+0.123417
## [36] train-rmse:1.634672+0.027219 test-rmse:1.720439+0.116937
## [37] train-rmse:1.609485+0.027159 test-rmse:1.696919+0.112701
## [38] train-rmse:1.588520+0.026953 test-rmse:1.685880+0.114312
## [39] train-rmse:1.570805+0.026751 test-rmse:1.667205+0.112686
## [40] train-rmse:1.555486+0.026422 test-rmse:1.653238+0.114802
## [41] train-rmse:1.542205+0.026227 test-rmse:1.642403+0.113323
## [42] train-rmse:1.530322+0.026066 test-rmse:1.635853+0.112786
## [43] train-rmse:1.519565+0.025789 test-rmse:1.621275+0.109310
## [44] train-rmse:1.509839+0.025426 test-rmse:1.614943+0.109258
## [45] train-rmse:1.500902+0.025202 test-rmse:1.614325+0.112044
## [46] train-rmse:1.492574+0.024884 test-rmse:1.610099+0.114982
## [47] train-rmse:1.484668+0.024543 test-rmse:1.602810+0.115702
## [48] train-rmse:1.477315+0.024364 test-rmse:1.598561+0.114682
## [49] train-rmse:1.470218+0.024213 test-rmse:1.593933+0.114351
## [50] train-rmse:1.463332+0.024032 test-rmse:1.589450+0.113864
## [51] train-rmse:1.456678+0.023961 test-rmse:1.578855+0.112700
## [52] train-rmse:1.450130+0.023849 test-rmse:1.576253+0.111375
## [53] train-rmse:1.443891+0.023759 test-rmse:1.569805+0.111388
## [54] train-rmse:1.437757+0.023635 test-rmse:1.567564+0.109677
## [55] train-rmse:1.431768+0.023655 test-rmse:1.565946+0.109212
## [56] train-rmse:1.426097+0.023489 test-rmse:1.560800+0.106283
## [57] train-rmse:1.420460+0.023342 test-rmse:1.558206+0.106322
## [58] train-rmse:1.414846+0.023218 test-rmse:1.554934+0.107369
## [59] train-rmse:1.409320+0.023087 test-rmse:1.550126+0.108250
## [60] train-rmse:1.403928+0.023009 test-rmse:1.544589+0.112510
## [61] train-rmse:1.398578+0.022984 test-rmse:1.541425+0.112918
## [62] train-rmse:1.393302+0.022986 test-rmse:1.533888+0.109650
## [63] train-rmse:1.388239+0.022860 test-rmse:1.529321+0.109615
## [64] train-rmse:1.383210+0.022807 test-rmse:1.527069+0.107632
## [65] train-rmse:1.378272+0.022750 test-rmse:1.523428+0.110008
## [66] train-rmse:1.373393+0.022689 test-rmse:1.519254+0.108652
## [67] train-rmse:1.368617+0.022677 test-rmse:1.515987+0.109069
## [68] train-rmse:1.364072+0.022524 test-rmse:1.512571+0.111627
## [69] train-rmse:1.359466+0.022443 test-rmse:1.512036+0.112983
## [70] train-rmse:1.354968+0.022316 test-rmse:1.511478+0.109268
## [71] train-rmse:1.350489+0.022241 test-rmse:1.507670+0.107294
## [72] train-rmse:1.346079+0.022082 test-rmse:1.503250+0.101790
## [73] train-rmse:1.341759+0.021997 test-rmse:1.500888+0.100328
## [74] train-rmse:1.337472+0.021912 test-rmse:1.498902+0.101475
## [75] train-rmse:1.333184+0.021821 test-rmse:1.494531+0.099808
## [76] train-rmse:1.328979+0.021752 test-rmse:1.494313+0.102370
## [77] train-rmse:1.324881+0.021713 test-rmse:1.491226+0.102053
## [78] train-rmse:1.320840+0.021631 test-rmse:1.487990+0.102041
## [79] train-rmse:1.316833+0.021568 test-rmse:1.485206+0.099994
## [80] train-rmse:1.312794+0.021423 test-rmse:1.484212+0.096645
## [81] train-rmse:1.308895+0.021274 test-rmse:1.478935+0.098615
## [82] train-rmse:1.305122+0.021199 test-rmse:1.475322+0.098768
## [83] train-rmse:1.301398+0.021122 test-rmse:1.473464+0.098023
## [84] train-rmse:1.297698+0.021039 test-rmse:1.469955+0.099021
## [85] train-rmse:1.294050+0.020953 test-rmse:1.468750+0.098587
## [86] train-rmse:1.290434+0.020873 test-rmse:1.466176+0.098471
## [87] train-rmse:1.286891+0.020824 test-rmse:1.462206+0.093198
## [88] train-rmse:1.283376+0.020749 test-rmse:1.454604+0.093002
## [89] train-rmse:1.279894+0.020725 test-rmse:1.455462+0.093631
## [90] train-rmse:1.276346+0.020649 test-rmse:1.456480+0.091085
## [91] train-rmse:1.272866+0.020646 test-rmse:1.455133+0.091518
## [92] train-rmse:1.269471+0.020639 test-rmse:1.453102+0.092322
## [93] train-rmse:1.266166+0.020624 test-rmse:1.452664+0.092615
## [94] train-rmse:1.262838+0.020605 test-rmse:1.452097+0.092453
## [95] train-rmse:1.259607+0.020564 test-rmse:1.450279+0.094332
## [96] train-rmse:1.256417+0.020553 test-rmse:1.447688+0.095005
## [97] train-rmse:1.253272+0.020500 test-rmse:1.445333+0.094525
## [98] train-rmse:1.250164+0.020480 test-rmse:1.443841+0.094429
## [99] train-rmse:1.247138+0.020452 test-rmse:1.441503+0.093427
## [100] train-rmse:1.244145+0.020437 test-rmse:1.436200+0.092952
## [101] train-rmse:1.241157+0.020418 test-rmse:1.436242+0.090376
## [102] train-rmse:1.238204+0.020409 test-rmse:1.433761+0.092880
## [103] train-rmse:1.235275+0.020404 test-rmse:1.433269+0.090065
## [104] train-rmse:1.232387+0.020336 test-rmse:1.430735+0.090133
## [105] train-rmse:1.229480+0.020357 test-rmse:1.428813+0.090191
## [106] train-rmse:1.226661+0.020349 test-rmse:1.424107+0.090643
## [107] train-rmse:1.223889+0.020321 test-rmse:1.422719+0.089205
## [108] train-rmse:1.221137+0.020284 test-rmse:1.419579+0.087595
## [109] train-rmse:1.218383+0.020259 test-rmse:1.417030+0.088368
## [110] train-rmse:1.215673+0.020257 test-rmse:1.414860+0.087855
## [111] train-rmse:1.213021+0.020243 test-rmse:1.414187+0.085714
## [112] train-rmse:1.210355+0.020208 test-rmse:1.412948+0.084817
## [113] train-rmse:1.207727+0.020212 test-rmse:1.410856+0.089416
## [114] train-rmse:1.205110+0.020211 test-rmse:1.408900+0.089662
## [115] train-rmse:1.202570+0.020234 test-rmse:1.405436+0.090440
## [116] train-rmse:1.200053+0.020278 test-rmse:1.404901+0.092161
## [117] train-rmse:1.197570+0.020288 test-rmse:1.403068+0.089751
## [118] train-rmse:1.195115+0.020267 test-rmse:1.400472+0.089998
## [119] train-rmse:1.192703+0.020250 test-rmse:1.398272+0.090802
## [120] train-rmse:1.190291+0.020211 test-rmse:1.395939+0.091317
## [121] train-rmse:1.187928+0.020188 test-rmse:1.393197+0.090510
## [122] train-rmse:1.185523+0.020156 test-rmse:1.392823+0.090474
## [123] train-rmse:1.183181+0.020137 test-rmse:1.389955+0.091380
## [124] train-rmse:1.180883+0.020083 test-rmse:1.390101+0.089339
## [125] train-rmse:1.178637+0.020049 test-rmse:1.388832+0.088766
## [126] train-rmse:1.176393+0.020006 test-rmse:1.388004+0.084955
## [127] train-rmse:1.174118+0.019985 test-rmse:1.385624+0.085283
## [128] train-rmse:1.171935+0.019949 test-rmse:1.383619+0.086436
## [129] train-rmse:1.169765+0.019908 test-rmse:1.382693+0.085542
## [130] train-rmse:1.167636+0.019912 test-rmse:1.380658+0.085801
## [131] train-rmse:1.165508+0.019915 test-rmse:1.380234+0.087212
## [132] train-rmse:1.163366+0.019933 test-rmse:1.378540+0.087584
## [133] train-rmse:1.161272+0.019939 test-rmse:1.376775+0.087992
## [134] train-rmse:1.159210+0.019960 test-rmse:1.375215+0.087923
## [135] train-rmse:1.157176+0.019979 test-rmse:1.372799+0.089754
## [136] train-rmse:1.155157+0.019983 test-rmse:1.371479+0.088388
## [137] train-rmse:1.153184+0.019982 test-rmse:1.371815+0.085759
## [138] train-rmse:1.151190+0.019924 test-rmse:1.370676+0.086256
## [139] train-rmse:1.149273+0.019890 test-rmse:1.369081+0.085947
## [140] train-rmse:1.147351+0.019855 test-rmse:1.367862+0.084769
## [141] train-rmse:1.145423+0.019808 test-rmse:1.366303+0.085729
## [142] train-rmse:1.143516+0.019766 test-rmse:1.363859+0.086190
## [143] train-rmse:1.141629+0.019727 test-rmse:1.361357+0.086541
## [144] train-rmse:1.139728+0.019712 test-rmse:1.360741+0.085241
## [145] train-rmse:1.137884+0.019676 test-rmse:1.360798+0.087139
## [146] train-rmse:1.136030+0.019658 test-rmse:1.359401+0.087388
## [147] train-rmse:1.134196+0.019587 test-rmse:1.358508+0.087082
## [148] train-rmse:1.132406+0.019558 test-rmse:1.356480+0.086617
## [149] train-rmse:1.130635+0.019524 test-rmse:1.353741+0.086813
## [150] train-rmse:1.128825+0.019485 test-rmse:1.351843+0.085974
## [151] train-rmse:1.127075+0.019453 test-rmse:1.348363+0.087071
## [152] train-rmse:1.125363+0.019445 test-rmse:1.345551+0.087548
## [153] train-rmse:1.123653+0.019434 test-rmse:1.346567+0.088013
## [154] train-rmse:1.121971+0.019430 test-rmse:1.345328+0.089183
## [155] train-rmse:1.120312+0.019429 test-rmse:1.345729+0.086512
## [156] train-rmse:1.118656+0.019416 test-rmse:1.344192+0.085052
## [157] train-rmse:1.117024+0.019400 test-rmse:1.343546+0.084917
## [158] train-rmse:1.115413+0.019375 test-rmse:1.342344+0.084301
## [159] train-rmse:1.113814+0.019349 test-rmse:1.341543+0.083989
## [160] train-rmse:1.112219+0.019332 test-rmse:1.339992+0.085848
## [161] train-rmse:1.110616+0.019330 test-rmse:1.338746+0.086668
## [162] train-rmse:1.109046+0.019312 test-rmse:1.337204+0.086807
## [163] train-rmse:1.107497+0.019292 test-rmse:1.336805+0.084500
## [164] train-rmse:1.105962+0.019272 test-rmse:1.335253+0.085596
## [165] train-rmse:1.104445+0.019255 test-rmse:1.333942+0.086537
## [166] train-rmse:1.102933+0.019242 test-rmse:1.333979+0.086938
## [167] train-rmse:1.101441+0.019220 test-rmse:1.332712+0.085861
## [168] train-rmse:1.099957+0.019191 test-rmse:1.331614+0.085721
## [169] train-rmse:1.098487+0.019160 test-rmse:1.330521+0.085872
## [170] train-rmse:1.097038+0.019133 test-rmse:1.328711+0.086492
## [171] train-rmse:1.095594+0.019109 test-rmse:1.326643+0.086999
## [172] train-rmse:1.094161+0.019083 test-rmse:1.325479+0.088088
## [173] train-rmse:1.092760+0.019072 test-rmse:1.323997+0.087901
## [174] train-rmse:1.091374+0.019052 test-rmse:1.324484+0.087968
## [175] train-rmse:1.089968+0.019018 test-rmse:1.323258+0.087682
## [176] train-rmse:1.088591+0.019007 test-rmse:1.321704+0.087989
## [177] train-rmse:1.087170+0.018955 test-rmse:1.319785+0.088035
## [178] train-rmse:1.085819+0.018937 test-rmse:1.318758+0.088753
## [179] train-rmse:1.084476+0.018917 test-rmse:1.317839+0.088949
## [180] train-rmse:1.083144+0.018897 test-rmse:1.317963+0.089225
## [181] train-rmse:1.081837+0.018882 test-rmse:1.318529+0.088990
## [182] train-rmse:1.080541+0.018867 test-rmse:1.316667+0.088528
## [183] train-rmse:1.079255+0.018844 test-rmse:1.315571+0.088449
## [184] train-rmse:1.077959+0.018828 test-rmse:1.314920+0.089607
## [185] train-rmse:1.076674+0.018811 test-rmse:1.313633+0.090431
## [186] train-rmse:1.075422+0.018785 test-rmse:1.312147+0.090476
## [187] train-rmse:1.074178+0.018753 test-rmse:1.310828+0.090712
## [188] train-rmse:1.072942+0.018727 test-rmse:1.310055+0.088560
## [189] train-rmse:1.071715+0.018706 test-rmse:1.309789+0.087485
## [190] train-rmse:1.070501+0.018686 test-rmse:1.310188+0.085793
## [191] train-rmse:1.069274+0.018654 test-rmse:1.309456+0.085924
## [192] train-rmse:1.068051+0.018607 test-rmse:1.308216+0.085229
## [193] train-rmse:1.066840+0.018551 test-rmse:1.307511+0.084650
## [194] train-rmse:1.065648+0.018533 test-rmse:1.306636+0.084909
## [195] train-rmse:1.064470+0.018537 test-rmse:1.306416+0.085732
## [196] train-rmse:1.063331+0.018522 test-rmse:1.305090+0.085887
## [197] train-rmse:1.062190+0.018502 test-rmse:1.304271+0.087041
## [198] train-rmse:1.061064+0.018487 test-rmse:1.302680+0.087430
## [199] train-rmse:1.059951+0.018467 test-rmse:1.302685+0.087204
## [200] train-rmse:1.058842+0.018438 test-rmse:1.301591+0.088907
## [201] train-rmse:1.057727+0.018426 test-rmse:1.300673+0.089150
## [202] train-rmse:1.056643+0.018399 test-rmse:1.299826+0.089125
## [203] train-rmse:1.055555+0.018370 test-rmse:1.299325+0.089240
## [204] train-rmse:1.054481+0.018350 test-rmse:1.298998+0.089221
## [205] train-rmse:1.053407+0.018332 test-rmse:1.298395+0.089015
## [206] train-rmse:1.052338+0.018301 test-rmse:1.297538+0.088813
## [207] train-rmse:1.051286+0.018272 test-rmse:1.296691+0.088513
## [208] train-rmse:1.050229+0.018241 test-rmse:1.294342+0.089443
## [209] train-rmse:1.049163+0.018220 test-rmse:1.293635+0.089794
## [210] train-rmse:1.048110+0.018185 test-rmse:1.290359+0.091082
## [211] train-rmse:1.047076+0.018149 test-rmse:1.289089+0.089729
## [212] train-rmse:1.046048+0.018113 test-rmse:1.288792+0.091212
## [213] train-rmse:1.045024+0.018098 test-rmse:1.288899+0.091139
## [214] train-rmse:1.044005+0.018051 test-rmse:1.288087+0.091928
## [215] train-rmse:1.042991+0.018014 test-rmse:1.287956+0.090535
## [216] train-rmse:1.042001+0.017994 test-rmse:1.287929+0.090763
## [217] train-rmse:1.040996+0.017968 test-rmse:1.288762+0.089972
## [218] train-rmse:1.040025+0.017953 test-rmse:1.288841+0.089804
## [219] train-rmse:1.039069+0.017933 test-rmse:1.287865+0.090252
## [220] train-rmse:1.038118+0.017917 test-rmse:1.286760+0.090512
## [221] train-rmse:1.037176+0.017897 test-rmse:1.286599+0.091211
## [222] train-rmse:1.036236+0.017877 test-rmse:1.285145+0.091641
## [223] train-rmse:1.035307+0.017860 test-rmse:1.284895+0.090810
## [224] train-rmse:1.034389+0.017838 test-rmse:1.282750+0.088605
## [225] train-rmse:1.033474+0.017814 test-rmse:1.282169+0.088468
## [226] train-rmse:1.032570+0.017790 test-rmse:1.281051+0.088403
## [227] train-rmse:1.031663+0.017765 test-rmse:1.280298+0.088118
## [228] train-rmse:1.030761+0.017737 test-rmse:1.279943+0.088850
## [229] train-rmse:1.029862+0.017725 test-rmse:1.278342+0.089420
## [230] train-rmse:1.028974+0.017710 test-rmse:1.277578+0.089875
## [231] train-rmse:1.028082+0.017697 test-rmse:1.277390+0.089675
## [232] train-rmse:1.027192+0.017691 test-rmse:1.277342+0.088659
## [233] train-rmse:1.026320+0.017689 test-rmse:1.277272+0.089597
## [234] train-rmse:1.025455+0.017681 test-rmse:1.277893+0.089377
## [235] train-rmse:1.024588+0.017676 test-rmse:1.278279+0.089208
## [236] train-rmse:1.023725+0.017658 test-rmse:1.277064+0.088683
## [237] train-rmse:1.022876+0.017645 test-rmse:1.276625+0.088222
## [238] train-rmse:1.022033+0.017633 test-rmse:1.275645+0.088431
## [239] train-rmse:1.021206+0.017619 test-rmse:1.276872+0.089805
## [240] train-rmse:1.020373+0.017612 test-rmse:1.275506+0.090267
## [241] train-rmse:1.019534+0.017599 test-rmse:1.275104+0.089434
## [242] train-rmse:1.018708+0.017583 test-rmse:1.272860+0.089204
## [243] train-rmse:1.017878+0.017569 test-rmse:1.272268+0.089654
## [244] train-rmse:1.017048+0.017541 test-rmse:1.271275+0.089137
## [245] train-rmse:1.016233+0.017523 test-rmse:1.271853+0.087762
## [246] train-rmse:1.015423+0.017513 test-rmse:1.269756+0.088431
## [247] train-rmse:1.014617+0.017505 test-rmse:1.268532+0.089154
## [248] train-rmse:1.013822+0.017503 test-rmse:1.268074+0.088982
## [249] train-rmse:1.013039+0.017500 test-rmse:1.266803+0.088572
## [250] train-rmse:1.012262+0.017494 test-rmse:1.266498+0.088784
## [251] train-rmse:1.011494+0.017490 test-rmse:1.266433+0.088340
## [252] train-rmse:1.010725+0.017486 test-rmse:1.266397+0.088515
## [253] train-rmse:1.009953+0.017483 test-rmse:1.264920+0.088325
## [254] train-rmse:1.009175+0.017479 test-rmse:1.263130+0.086313
## [255] train-rmse:1.008418+0.017471 test-rmse:1.263113+0.086474
## [256] train-rmse:1.007660+0.017475 test-rmse:1.263086+0.086750
## [257] train-rmse:1.006910+0.017476 test-rmse:1.262938+0.087282
## [258] train-rmse:1.006157+0.017478 test-rmse:1.262127+0.087740
## [259] train-rmse:1.005416+0.017476 test-rmse:1.261422+0.087595
## [260] train-rmse:1.004665+0.017467 test-rmse:1.260590+0.088212
## [261] train-rmse:1.003913+0.017457 test-rmse:1.260554+0.087547
## [262] train-rmse:1.003167+0.017448 test-rmse:1.259690+0.087593
## [263] train-rmse:1.002440+0.017450 test-rmse:1.260052+0.086907
## [264] train-rmse:1.001721+0.017439 test-rmse:1.260013+0.087480
## [265] train-rmse:1.000995+0.017428 test-rmse:1.258971+0.086580
## [266] train-rmse:1.000278+0.017430 test-rmse:1.259211+0.087173
## [267] train-rmse:0.999561+0.017437 test-rmse:1.259338+0.087045
## [268] train-rmse:0.998850+0.017441 test-rmse:1.260053+0.086140
## [269] train-rmse:0.998151+0.017446 test-rmse:1.259612+0.085780
## [270] train-rmse:0.997459+0.017452 test-rmse:1.259053+0.085281
## [271] train-rmse:0.996763+0.017441 test-rmse:1.257874+0.085525
## [272] train-rmse:0.996073+0.017450 test-rmse:1.257367+0.085691
## [273] train-rmse:0.995388+0.017451 test-rmse:1.258473+0.086047
## [274] train-rmse:0.994697+0.017456 test-rmse:1.258676+0.086618
## [275] train-rmse:0.994021+0.017458 test-rmse:1.258584+0.086231
## [276] train-rmse:0.993352+0.017461 test-rmse:1.256478+0.084914
## [277] train-rmse:0.992682+0.017471 test-rmse:1.256487+0.084967
## [278] train-rmse:0.991999+0.017450 test-rmse:1.256491+0.085059
## [279] train-rmse:0.991330+0.017456 test-rmse:1.256672+0.085843
## [280] train-rmse:0.990669+0.017466 test-rmse:1.255885+0.086310
## [281] train-rmse:0.990010+0.017463 test-rmse:1.255773+0.085590
## [282] train-rmse:0.989355+0.017456 test-rmse:1.254946+0.086087
## [283] train-rmse:0.988706+0.017468 test-rmse:1.254173+0.086689
## [284] train-rmse:0.988059+0.017472 test-rmse:1.253488+0.086678
## [285] train-rmse:0.987426+0.017472 test-rmse:1.253069+0.085882
## [286] train-rmse:0.986792+0.017467 test-rmse:1.252961+0.086329
## [287] train-rmse:0.986157+0.017461 test-rmse:1.252498+0.086251
## [288] train-rmse:0.985517+0.017471 test-rmse:1.251159+0.085863
## [289] train-rmse:0.984891+0.017467 test-rmse:1.251190+0.084798
## [290] train-rmse:0.984262+0.017450 test-rmse:1.250660+0.084340
## [291] train-rmse:0.983649+0.017449 test-rmse:1.250237+0.084917
## [292] train-rmse:0.983034+0.017446 test-rmse:1.249430+0.086351
## [293] train-rmse:0.982423+0.017441 test-rmse:1.248965+0.086287
## [294] train-rmse:0.981810+0.017438 test-rmse:1.247725+0.086598
## [295] train-rmse:0.981203+0.017443 test-rmse:1.248005+0.086729
## [296] train-rmse:0.980596+0.017453 test-rmse:1.247365+0.086364
## [297] train-rmse:0.979995+0.017457 test-rmse:1.247779+0.085820
## [298] train-rmse:0.979389+0.017465 test-rmse:1.249410+0.084768
## [299] train-rmse:0.978786+0.017473 test-rmse:1.249008+0.084931
## [300] train-rmse:0.978192+0.017478 test-rmse:1.249396+0.084425
## [301] train-rmse:0.977599+0.017465 test-rmse:1.249580+0.083852
## [302] train-rmse:0.977008+0.017465 test-rmse:1.249139+0.084336
## Stopping. Best iteration:
## [296] train-rmse:0.980596+0.017453 test-rmse:1.247365+0.086364
##
## 15
## [1] train-rmse:84.742499+0.085026 test-rmse:84.743227+0.408848
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.927655+0.073048 test-rmse:72.928244+0.420446
## [3] train-rmse:62.762302+0.062739 test-rmse:62.762725+0.430333
## [4] train-rmse:54.016514+0.053817 test-rmse:54.016744+0.438737
## [5] train-rmse:46.492472+0.046117 test-rmse:46.492479+0.445831
## [6] train-rmse:40.019990+0.039459 test-rmse:40.019755+0.451772
## [7] train-rmse:34.452692+0.033687 test-rmse:34.452158+0.456684
## [8] train-rmse:29.664635+0.028683 test-rmse:29.663766+0.460663
## [9] train-rmse:25.547519+0.024346 test-rmse:25.546270+0.463776
## [10] train-rmse:22.008217+0.020588 test-rmse:22.006533+0.466070
## [11] train-rmse:18.966660+0.017350 test-rmse:18.964487+0.467562
## [12] train-rmse:16.354035+0.014591 test-rmse:16.351313+0.468250
## [13] train-rmse:14.110245+0.012261 test-rmse:14.110562+0.451996
## [14] train-rmse:12.183608+0.010200 test-rmse:12.178845+0.439003
## [15] train-rmse:10.529751+0.008399 test-rmse:10.522686+0.433285
## [16] train-rmse:9.109590+0.006667 test-rmse:9.091692+0.429292
## [17] train-rmse:7.891496+0.006230 test-rmse:7.884601+0.409841
## [18] train-rmse:6.847586+0.006614 test-rmse:6.834415+0.405290
## [19] train-rmse:5.953083+0.006086 test-rmse:5.946275+0.384672
## [20] train-rmse:5.187721+0.007833 test-rmse:5.181589+0.375376
## [21] train-rmse:4.536197+0.007469 test-rmse:4.528135+0.370759
## [22] train-rmse:3.981189+0.009029 test-rmse:3.976836+0.352790
## [23] train-rmse:3.510541+0.009615 test-rmse:3.512352+0.330383
## [24] train-rmse:3.111550+0.010470 test-rmse:3.125699+0.317216
## [25] train-rmse:2.778005+0.011393 test-rmse:2.800493+0.298096
## [26] train-rmse:2.498968+0.011814 test-rmse:2.537104+0.277763
## [27] train-rmse:2.266053+0.012710 test-rmse:2.318601+0.256296
## [28] train-rmse:2.075625+0.012995 test-rmse:2.139332+0.235257
## [29] train-rmse:1.919071+0.013721 test-rmse:1.992278+0.214006
## [30] train-rmse:1.790805+0.014085 test-rmse:1.879562+0.193275
## [31] train-rmse:1.686474+0.015496 test-rmse:1.790345+0.175664
## [32] train-rmse:1.602386+0.014340 test-rmse:1.721444+0.165667
## [33] train-rmse:1.534133+0.014345 test-rmse:1.663030+0.156485
## [34] train-rmse:1.478708+0.014751 test-rmse:1.615864+0.147485
## [35] train-rmse:1.432309+0.017241 test-rmse:1.582915+0.136776
## [36] train-rmse:1.394082+0.016608 test-rmse:1.557991+0.125688
## [37] train-rmse:1.363524+0.015615 test-rmse:1.539460+0.120451
## [38] train-rmse:1.337619+0.016373 test-rmse:1.518778+0.114736
## [39] train-rmse:1.316414+0.016527 test-rmse:1.504307+0.108003
## [40] train-rmse:1.298126+0.017179 test-rmse:1.493207+0.104260
## [41] train-rmse:1.280951+0.017908 test-rmse:1.478162+0.105657
## [42] train-rmse:1.266853+0.018032 test-rmse:1.469129+0.100092
## [43] train-rmse:1.252794+0.019478 test-rmse:1.459971+0.098195
## [44] train-rmse:1.241049+0.019193 test-rmse:1.452511+0.095836
## [45] train-rmse:1.226717+0.018937 test-rmse:1.448980+0.093148
## [46] train-rmse:1.215128+0.019455 test-rmse:1.441758+0.091602
## [47] train-rmse:1.205153+0.018760 test-rmse:1.436087+0.091906
## [48] train-rmse:1.195743+0.019209 test-rmse:1.423480+0.089894
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## [50] train-rmse:1.177404+0.019865 test-rmse:1.408529+0.083807
## [51] train-rmse:1.168797+0.018911 test-rmse:1.404350+0.080955
## [52] train-rmse:1.160232+0.019634 test-rmse:1.398319+0.082145
## [53] train-rmse:1.152347+0.019824 test-rmse:1.394886+0.082490
## [54] train-rmse:1.144309+0.021169 test-rmse:1.389960+0.080195
## [55] train-rmse:1.135569+0.021053 test-rmse:1.385027+0.079838
## [56] train-rmse:1.128343+0.021850 test-rmse:1.379707+0.078623
## [57] train-rmse:1.121549+0.022302 test-rmse:1.372619+0.080389
## [58] train-rmse:1.114392+0.022471 test-rmse:1.369979+0.081308
## [59] train-rmse:1.106431+0.022922 test-rmse:1.359601+0.085827
## [60] train-rmse:1.099826+0.023339 test-rmse:1.355413+0.086357
## [61] train-rmse:1.092283+0.022353 test-rmse:1.352054+0.085863
## [62] train-rmse:1.085795+0.021818 test-rmse:1.348585+0.083706
## [63] train-rmse:1.080100+0.022329 test-rmse:1.341265+0.083553
## [64] train-rmse:1.073772+0.021309 test-rmse:1.334865+0.082710
## [65] train-rmse:1.068184+0.021436 test-rmse:1.332886+0.083379
## [66] train-rmse:1.060043+0.021501 test-rmse:1.323005+0.081996
## [67] train-rmse:1.054207+0.021366 test-rmse:1.317823+0.086852
## [68] train-rmse:1.047984+0.022415 test-rmse:1.315624+0.084222
## [69] train-rmse:1.042742+0.022543 test-rmse:1.312307+0.082303
## [70] train-rmse:1.036961+0.021771 test-rmse:1.308460+0.081804
## [71] train-rmse:1.031824+0.022103 test-rmse:1.303321+0.083554
## [72] train-rmse:1.024872+0.020920 test-rmse:1.302584+0.084781
## [73] train-rmse:1.019982+0.021270 test-rmse:1.298967+0.085715
## [74] train-rmse:1.014805+0.021687 test-rmse:1.294944+0.085059
## [75] train-rmse:1.008319+0.022349 test-rmse:1.292510+0.085034
## [76] train-rmse:1.003607+0.023178 test-rmse:1.285918+0.089405
## [77] train-rmse:0.998521+0.023130 test-rmse:1.281429+0.090554
## [78] train-rmse:0.993511+0.023049 test-rmse:1.280538+0.087094
## [79] train-rmse:0.989029+0.023281 test-rmse:1.276460+0.086912
## [80] train-rmse:0.984602+0.023354 test-rmse:1.273245+0.086913
## [81] train-rmse:0.977529+0.023200 test-rmse:1.266222+0.085252
## [82] train-rmse:0.972723+0.021886 test-rmse:1.264423+0.087143
## [83] train-rmse:0.968324+0.021997 test-rmse:1.261821+0.086222
## [84] train-rmse:0.962969+0.022743 test-rmse:1.257321+0.088019
## [85] train-rmse:0.957317+0.023564 test-rmse:1.253990+0.088772
## [86] train-rmse:0.952828+0.023300 test-rmse:1.248907+0.089440
## [87] train-rmse:0.948796+0.023507 test-rmse:1.246904+0.091211
## [88] train-rmse:0.945319+0.023517 test-rmse:1.244152+0.090266
## [89] train-rmse:0.941023+0.024010 test-rmse:1.242455+0.089081
## [90] train-rmse:0.935723+0.022106 test-rmse:1.241593+0.087786
## [91] train-rmse:0.930672+0.022542 test-rmse:1.238409+0.087475
## [92] train-rmse:0.926963+0.022949 test-rmse:1.234227+0.088488
## [93] train-rmse:0.923479+0.022765 test-rmse:1.230091+0.091206
## [94] train-rmse:0.919568+0.023244 test-rmse:1.227641+0.090212
## [95] train-rmse:0.915758+0.023637 test-rmse:1.222773+0.089220
## [96] train-rmse:0.911790+0.024389 test-rmse:1.219792+0.090052
## [97] train-rmse:0.907683+0.025129 test-rmse:1.216137+0.088366
## [98] train-rmse:0.903829+0.024241 test-rmse:1.214820+0.088068
## [99] train-rmse:0.900575+0.024091 test-rmse:1.213117+0.089358
## [100] train-rmse:0.896369+0.023162 test-rmse:1.210547+0.089658
## [101] train-rmse:0.893371+0.022972 test-rmse:1.210319+0.090943
## [102] train-rmse:0.889189+0.021998 test-rmse:1.205004+0.091811
## [103] train-rmse:0.884372+0.020842 test-rmse:1.202092+0.091275
## [104] train-rmse:0.881400+0.020693 test-rmse:1.201260+0.091112
## [105] train-rmse:0.877298+0.021073 test-rmse:1.200002+0.091438
## [106] train-rmse:0.872856+0.020055 test-rmse:1.199264+0.091439
## [107] train-rmse:0.868706+0.019396 test-rmse:1.198721+0.091921
## [108] train-rmse:0.864891+0.019458 test-rmse:1.195938+0.089666
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## [110] train-rmse:0.858559+0.019642 test-rmse:1.192038+0.091188
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## [112] train-rmse:0.851013+0.017975 test-rmse:1.183803+0.099008
## [113] train-rmse:0.847868+0.017843 test-rmse:1.183075+0.099551
## [114] train-rmse:0.844501+0.017941 test-rmse:1.180887+0.099428
## [115] train-rmse:0.840683+0.016537 test-rmse:1.179006+0.099887
## [116] train-rmse:0.837829+0.016589 test-rmse:1.178713+0.098549
## [117] train-rmse:0.835443+0.016765 test-rmse:1.176665+0.099472
## [118] train-rmse:0.832398+0.018000 test-rmse:1.172677+0.097098
## [119] train-rmse:0.828409+0.018585 test-rmse:1.171773+0.098197
## [120] train-rmse:0.825423+0.017681 test-rmse:1.168831+0.098726
## [121] train-rmse:0.822156+0.019351 test-rmse:1.166923+0.099163
## [122] train-rmse:0.818920+0.018931 test-rmse:1.165382+0.100314
## [123] train-rmse:0.816352+0.018985 test-rmse:1.163449+0.099460
## [124] train-rmse:0.813843+0.018716 test-rmse:1.160678+0.099373
## [125] train-rmse:0.811409+0.018754 test-rmse:1.160714+0.099557
## [126] train-rmse:0.808068+0.018765 test-rmse:1.158884+0.097183
## [127] train-rmse:0.805510+0.018932 test-rmse:1.157354+0.099754
## [128] train-rmse:0.803124+0.019166 test-rmse:1.156068+0.100139
## [129] train-rmse:0.800125+0.019216 test-rmse:1.155927+0.098347
## [130] train-rmse:0.797283+0.018846 test-rmse:1.154132+0.100971
## [131] train-rmse:0.795352+0.018955 test-rmse:1.153766+0.102208
## [132] train-rmse:0.792658+0.018394 test-rmse:1.152406+0.102046
## [133] train-rmse:0.790571+0.017807 test-rmse:1.151248+0.103771
## [134] train-rmse:0.788348+0.017719 test-rmse:1.150380+0.104072
## [135] train-rmse:0.785747+0.018692 test-rmse:1.147660+0.103734
## [136] train-rmse:0.783776+0.018468 test-rmse:1.146428+0.101088
## [137] train-rmse:0.780924+0.018187 test-rmse:1.145176+0.100940
## [138] train-rmse:0.779189+0.018142 test-rmse:1.144064+0.100140
## [139] train-rmse:0.776347+0.018811 test-rmse:1.140395+0.100267
## [140] train-rmse:0.773645+0.019023 test-rmse:1.139224+0.101993
## [141] train-rmse:0.770730+0.018018 test-rmse:1.136408+0.103997
## [142] train-rmse:0.767443+0.019440 test-rmse:1.134977+0.103328
## [143] train-rmse:0.764864+0.019415 test-rmse:1.134288+0.102544
## [144] train-rmse:0.761557+0.018434 test-rmse:1.134087+0.102178
## [145] train-rmse:0.758764+0.019461 test-rmse:1.133062+0.102563
## [146] train-rmse:0.755744+0.019184 test-rmse:1.132946+0.103127
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## [149] train-rmse:0.749103+0.018701 test-rmse:1.129095+0.104510
## [150] train-rmse:0.746501+0.018906 test-rmse:1.129708+0.103697
## [151] train-rmse:0.743621+0.018168 test-rmse:1.127601+0.105215
## [152] train-rmse:0.741823+0.018021 test-rmse:1.126911+0.105322
## [153] train-rmse:0.738874+0.018228 test-rmse:1.125391+0.103641
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## [156] train-rmse:0.732677+0.018490 test-rmse:1.121390+0.106072
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## [158] train-rmse:0.727923+0.019394 test-rmse:1.120532+0.105049
## [159] train-rmse:0.726162+0.019516 test-rmse:1.119505+0.105463
## [160] train-rmse:0.723399+0.017734 test-rmse:1.118893+0.104613
## [161] train-rmse:0.721101+0.016589 test-rmse:1.117272+0.106229
## [162] train-rmse:0.719104+0.017190 test-rmse:1.115475+0.105065
## [163] train-rmse:0.716344+0.015928 test-rmse:1.114494+0.103132
## [164] train-rmse:0.713907+0.015301 test-rmse:1.112890+0.104399
## [165] train-rmse:0.711654+0.015109 test-rmse:1.112017+0.104426
## [166] train-rmse:0.709113+0.015106 test-rmse:1.110193+0.102113
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## [168] train-rmse:0.703599+0.016552 test-rmse:1.105845+0.102528
## [169] train-rmse:0.700621+0.018675 test-rmse:1.102680+0.100872
## [170] train-rmse:0.698940+0.019252 test-rmse:1.103441+0.100857
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## [172] train-rmse:0.695694+0.019453 test-rmse:1.100676+0.102271
## [173] train-rmse:0.694054+0.019515 test-rmse:1.100239+0.102243
## [174] train-rmse:0.692532+0.019516 test-rmse:1.098986+0.102516
## [175] train-rmse:0.690797+0.019390 test-rmse:1.099345+0.102561
## [176] train-rmse:0.688045+0.018119 test-rmse:1.098684+0.102995
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## [178] train-rmse:0.683432+0.017311 test-rmse:1.095730+0.100489
## [179] train-rmse:0.681241+0.017476 test-rmse:1.094753+0.100813
## [180] train-rmse:0.679208+0.016462 test-rmse:1.094541+0.100933
## [181] train-rmse:0.677887+0.016369 test-rmse:1.094520+0.101999
## [182] train-rmse:0.675548+0.016201 test-rmse:1.094381+0.101625
## [183] train-rmse:0.673677+0.016301 test-rmse:1.093305+0.100796
## [184] train-rmse:0.671871+0.016566 test-rmse:1.090774+0.099030
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## [186] train-rmse:0.668686+0.017267 test-rmse:1.089235+0.098678
## [187] train-rmse:0.667401+0.017025 test-rmse:1.089546+0.099074
## [188] train-rmse:0.666124+0.017066 test-rmse:1.089838+0.098319
## [189] train-rmse:0.664173+0.017787 test-rmse:1.088851+0.096870
## [190] train-rmse:0.661582+0.016985 test-rmse:1.089389+0.095972
## [191] train-rmse:0.659443+0.016804 test-rmse:1.087834+0.097178
## [192] train-rmse:0.657271+0.017604 test-rmse:1.086487+0.096880
## [193] train-rmse:0.655812+0.018222 test-rmse:1.086244+0.097656
## [194] train-rmse:0.654577+0.018242 test-rmse:1.084246+0.098427
## [195] train-rmse:0.653122+0.018061 test-rmse:1.083393+0.098679
## [196] train-rmse:0.650873+0.018143 test-rmse:1.081331+0.098566
## [197] train-rmse:0.649103+0.018194 test-rmse:1.081252+0.099001
## [198] train-rmse:0.648099+0.018280 test-rmse:1.080877+0.098566
## [199] train-rmse:0.646364+0.018268 test-rmse:1.081298+0.099096
## [200] train-rmse:0.644817+0.018377 test-rmse:1.080305+0.099974
## [201] train-rmse:0.643309+0.019050 test-rmse:1.077981+0.098854
## [202] train-rmse:0.642063+0.019233 test-rmse:1.077584+0.099197
## [203] train-rmse:0.640459+0.019698 test-rmse:1.076391+0.099152
## [204] train-rmse:0.637974+0.018197 test-rmse:1.076506+0.098815
## [205] train-rmse:0.636490+0.017748 test-rmse:1.075304+0.100168
## [206] train-rmse:0.633853+0.017008 test-rmse:1.075067+0.099418
## [207] train-rmse:0.632012+0.016814 test-rmse:1.074319+0.099728
## [208] train-rmse:0.630348+0.017302 test-rmse:1.072889+0.099209
## [209] train-rmse:0.629172+0.017159 test-rmse:1.072049+0.099506
## [210] train-rmse:0.626974+0.017165 test-rmse:1.071883+0.100788
## [211] train-rmse:0.625435+0.017601 test-rmse:1.070919+0.100841
## [212] train-rmse:0.624046+0.017559 test-rmse:1.070241+0.100500
## [213] train-rmse:0.623137+0.017529 test-rmse:1.069554+0.100312
## [214] train-rmse:0.620998+0.016500 test-rmse:1.068353+0.101729
## [215] train-rmse:0.619650+0.017122 test-rmse:1.068646+0.101289
## [216] train-rmse:0.618167+0.017370 test-rmse:1.068644+0.100393
## [217] train-rmse:0.617190+0.017559 test-rmse:1.068035+0.100612
## [218] train-rmse:0.615928+0.017875 test-rmse:1.066151+0.100590
## [219] train-rmse:0.614553+0.017579 test-rmse:1.065625+0.101119
## [220] train-rmse:0.612841+0.018182 test-rmse:1.063545+0.102113
## [221] train-rmse:0.611943+0.018202 test-rmse:1.064458+0.102803
## [222] train-rmse:0.611031+0.018340 test-rmse:1.064544+0.102874
## [223] train-rmse:0.609869+0.018409 test-rmse:1.063494+0.103358
## [224] train-rmse:0.608342+0.017817 test-rmse:1.062882+0.102511
## [225] train-rmse:0.606302+0.017452 test-rmse:1.061799+0.102874
## [226] train-rmse:0.604344+0.017062 test-rmse:1.061420+0.103471
## [227] train-rmse:0.603280+0.017135 test-rmse:1.061000+0.104202
## [228] train-rmse:0.601943+0.016852 test-rmse:1.060545+0.104461
## [229] train-rmse:0.600871+0.016972 test-rmse:1.059912+0.103748
## [230] train-rmse:0.599682+0.017613 test-rmse:1.060037+0.103909
## [231] train-rmse:0.598520+0.017991 test-rmse:1.059406+0.104287
## [232] train-rmse:0.596925+0.017192 test-rmse:1.058340+0.105422
## [233] train-rmse:0.594753+0.018374 test-rmse:1.058859+0.105187
## [234] train-rmse:0.593966+0.018480 test-rmse:1.058280+0.105510
## [235] train-rmse:0.592981+0.018649 test-rmse:1.057244+0.105420
## [236] train-rmse:0.591261+0.017986 test-rmse:1.056873+0.105652
## [237] train-rmse:0.589937+0.017469 test-rmse:1.056278+0.106187
## [238] train-rmse:0.588165+0.017362 test-rmse:1.055084+0.105978
## [239] train-rmse:0.586929+0.017331 test-rmse:1.054838+0.106086
## [240] train-rmse:0.585783+0.017291 test-rmse:1.054798+0.106485
## [241] train-rmse:0.584560+0.017474 test-rmse:1.054225+0.105224
## [242] train-rmse:0.583249+0.017690 test-rmse:1.054518+0.105028
## [243] train-rmse:0.582520+0.017828 test-rmse:1.054591+0.104705
## [244] train-rmse:0.581301+0.017386 test-rmse:1.053559+0.104800
## [245] train-rmse:0.579655+0.016685 test-rmse:1.052925+0.104702
## [246] train-rmse:0.578151+0.017189 test-rmse:1.052318+0.104308
## [247] train-rmse:0.577010+0.016918 test-rmse:1.051811+0.104902
## [248] train-rmse:0.575929+0.017093 test-rmse:1.052021+0.104564
## [249] train-rmse:0.574956+0.017487 test-rmse:1.051810+0.104990
## [250] train-rmse:0.573701+0.017469 test-rmse:1.051677+0.106163
## [251] train-rmse:0.572802+0.017495 test-rmse:1.051673+0.106712
## [252] train-rmse:0.571594+0.017648 test-rmse:1.051765+0.106679
## [253] train-rmse:0.570740+0.018084 test-rmse:1.051939+0.106513
## [254] train-rmse:0.569434+0.018276 test-rmse:1.050431+0.106458
## [255] train-rmse:0.568797+0.018299 test-rmse:1.049445+0.105832
## [256] train-rmse:0.567925+0.018340 test-rmse:1.048137+0.105666
## [257] train-rmse:0.566980+0.018149 test-rmse:1.048182+0.106110
## [258] train-rmse:0.565082+0.017611 test-rmse:1.047331+0.105997
## [259] train-rmse:0.564438+0.017573 test-rmse:1.046642+0.106183
## [260] train-rmse:0.562549+0.017492 test-rmse:1.046146+0.106670
## [261] train-rmse:0.561359+0.017258 test-rmse:1.045944+0.107593
## [262] train-rmse:0.559855+0.017479 test-rmse:1.044379+0.106825
## [263] train-rmse:0.558433+0.017101 test-rmse:1.044557+0.105738
## [264] train-rmse:0.557591+0.017021 test-rmse:1.044175+0.106251
## [265] train-rmse:0.556152+0.016868 test-rmse:1.043879+0.106952
## [266] train-rmse:0.554524+0.016262 test-rmse:1.043919+0.106782
## [267] train-rmse:0.553581+0.016722 test-rmse:1.043612+0.107606
## [268] train-rmse:0.551457+0.016824 test-rmse:1.042226+0.107603
## [269] train-rmse:0.550508+0.016523 test-rmse:1.042310+0.107405
## [270] train-rmse:0.549393+0.016995 test-rmse:1.041295+0.106527
## [271] train-rmse:0.547660+0.016697 test-rmse:1.040956+0.106559
## [272] train-rmse:0.546353+0.016351 test-rmse:1.040728+0.107408
## [273] train-rmse:0.544161+0.015872 test-rmse:1.039466+0.107872
## [274] train-rmse:0.542496+0.015568 test-rmse:1.039578+0.107995
## [275] train-rmse:0.541866+0.015604 test-rmse:1.039850+0.108548
## [276] train-rmse:0.540973+0.015580 test-rmse:1.039722+0.108051
## [277] train-rmse:0.540020+0.015513 test-rmse:1.040168+0.107509
## [278] train-rmse:0.538890+0.015485 test-rmse:1.039177+0.106026
## [279] train-rmse:0.537052+0.015307 test-rmse:1.037990+0.106125
## [280] train-rmse:0.535486+0.015562 test-rmse:1.036850+0.105872
## [281] train-rmse:0.534774+0.015601 test-rmse:1.036912+0.105634
## [282] train-rmse:0.534096+0.015528 test-rmse:1.035995+0.105286
## [283] train-rmse:0.533130+0.015310 test-rmse:1.035987+0.105852
## [284] train-rmse:0.532125+0.014965 test-rmse:1.035296+0.105533
## [285] train-rmse:0.530775+0.014986 test-rmse:1.033978+0.104618
## [286] train-rmse:0.530132+0.015013 test-rmse:1.033913+0.104878
## [287] train-rmse:0.529087+0.014870 test-rmse:1.033919+0.105253
## [288] train-rmse:0.528176+0.014744 test-rmse:1.034185+0.105660
## [289] train-rmse:0.526877+0.015299 test-rmse:1.033801+0.105632
## [290] train-rmse:0.526273+0.015251 test-rmse:1.033360+0.105354
## [291] train-rmse:0.525466+0.015244 test-rmse:1.032226+0.104059
## [292] train-rmse:0.523841+0.014601 test-rmse:1.031590+0.103880
## [293] train-rmse:0.522978+0.014645 test-rmse:1.032154+0.103941
## [294] train-rmse:0.521435+0.013997 test-rmse:1.031687+0.104550
## [295] train-rmse:0.520777+0.014248 test-rmse:1.031841+0.104649
## [296] train-rmse:0.519550+0.014577 test-rmse:1.031869+0.105325
## [297] train-rmse:0.518124+0.014301 test-rmse:1.031696+0.105397
## [298] train-rmse:0.517276+0.014196 test-rmse:1.031195+0.106636
## [299] train-rmse:0.516507+0.014104 test-rmse:1.031506+0.107292
## [300] train-rmse:0.515032+0.013930 test-rmse:1.031505+0.107686
## [301] train-rmse:0.514199+0.014425 test-rmse:1.030620+0.107692
## [302] train-rmse:0.513405+0.014482 test-rmse:1.029545+0.107583
## [303] train-rmse:0.511888+0.014408 test-rmse:1.029066+0.107212
## [304] train-rmse:0.510720+0.014085 test-rmse:1.029094+0.106967
## [305] train-rmse:0.509900+0.014001 test-rmse:1.028860+0.107148
## [306] train-rmse:0.509271+0.014181 test-rmse:1.028750+0.107488
## [307] train-rmse:0.508614+0.014106 test-rmse:1.029317+0.107331
## [308] train-rmse:0.507549+0.014531 test-rmse:1.028743+0.106972
## [309] train-rmse:0.506767+0.014251 test-rmse:1.028433+0.107036
## [310] train-rmse:0.505664+0.014031 test-rmse:1.028555+0.107328
## [311] train-rmse:0.504349+0.014269 test-rmse:1.028510+0.106613
## [312] train-rmse:0.503555+0.014523 test-rmse:1.027988+0.106592
## [313] train-rmse:0.502799+0.014422 test-rmse:1.027244+0.107449
## [314] train-rmse:0.501751+0.014265 test-rmse:1.027227+0.107156
## [315] train-rmse:0.500780+0.014402 test-rmse:1.026958+0.107305
## [316] train-rmse:0.499888+0.014608 test-rmse:1.026835+0.106925
## [317] train-rmse:0.499292+0.014501 test-rmse:1.026361+0.107040
## [318] train-rmse:0.497997+0.014627 test-rmse:1.025384+0.107238
## [319] train-rmse:0.494986+0.013631 test-rmse:1.025676+0.107781
## [320] train-rmse:0.493956+0.013339 test-rmse:1.024807+0.106949
## [321] train-rmse:0.492731+0.012903 test-rmse:1.022938+0.105212
## [322] train-rmse:0.491108+0.012514 test-rmse:1.022535+0.105502
## [323] train-rmse:0.490076+0.012316 test-rmse:1.022137+0.105757
## [324] train-rmse:0.489328+0.012670 test-rmse:1.021062+0.105455
## [325] train-rmse:0.488092+0.013805 test-rmse:1.021838+0.105692
## [326] train-rmse:0.486673+0.013665 test-rmse:1.021834+0.105967
## [327] train-rmse:0.485423+0.013797 test-rmse:1.021982+0.106242
## [328] train-rmse:0.484681+0.014065 test-rmse:1.021979+0.106466
## [329] train-rmse:0.483896+0.014324 test-rmse:1.021607+0.106438
## [330] train-rmse:0.481885+0.014860 test-rmse:1.021239+0.106354
## Stopping. Best iteration:
## [324] train-rmse:0.489328+0.012670 test-rmse:1.021062+0.105455
##
## 16
## [1] train-rmse:84.742499+0.085026 test-rmse:84.743227+0.408848
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.927655+0.073048 test-rmse:72.928244+0.420446
## [3] train-rmse:62.762302+0.062739 test-rmse:62.762725+0.430333
## [4] train-rmse:54.016514+0.053817 test-rmse:54.016744+0.438737
## [5] train-rmse:46.492472+0.046117 test-rmse:46.492479+0.445831
## [6] train-rmse:40.019990+0.039459 test-rmse:40.019755+0.451772
## [7] train-rmse:34.452692+0.033687 test-rmse:34.452158+0.456684
## [8] train-rmse:29.664635+0.028683 test-rmse:29.663766+0.460663
## [9] train-rmse:25.547519+0.024346 test-rmse:25.546270+0.463776
## [10] train-rmse:22.008217+0.020588 test-rmse:22.006533+0.466070
## [11] train-rmse:18.966660+0.017350 test-rmse:18.964487+0.467562
## [12] train-rmse:16.354035+0.014591 test-rmse:16.351313+0.468250
## [13] train-rmse:14.110245+0.012261 test-rmse:14.110562+0.451996
## [14] train-rmse:12.183608+0.010200 test-rmse:12.178845+0.439003
## [15] train-rmse:10.529500+0.008613 test-rmse:10.520877+0.432219
## [16] train-rmse:9.108732+0.006536 test-rmse:9.099234+0.420248
## [17] train-rmse:7.888832+0.005903 test-rmse:7.872335+0.411980
## [18] train-rmse:6.841837+0.004860 test-rmse:6.831770+0.391848
## [19] train-rmse:5.943652+0.004959 test-rmse:5.940949+0.374660
## [20] train-rmse:5.174116+0.004837 test-rmse:5.167166+0.357896
## [21] train-rmse:4.515074+0.005982 test-rmse:4.507784+0.340483
## [22] train-rmse:3.952779+0.006823 test-rmse:3.954761+0.321665
## [23] train-rmse:3.471854+0.007874 test-rmse:3.479646+0.306723
## [24] train-rmse:3.064675+0.010112 test-rmse:3.080165+0.297719
## [25] train-rmse:2.719896+0.010735 test-rmse:2.740312+0.289242
## [26] train-rmse:2.427605+0.010884 test-rmse:2.456641+0.276002
## [27] train-rmse:2.180805+0.012943 test-rmse:2.228802+0.254658
## [28] train-rmse:1.974533+0.014426 test-rmse:2.040599+0.236029
## [29] train-rmse:1.805105+0.014818 test-rmse:1.888947+0.224124
## [30] train-rmse:1.664772+0.016179 test-rmse:1.764780+0.210371
## [31] train-rmse:1.546558+0.015255 test-rmse:1.663197+0.197674
## [32] train-rmse:1.451736+0.017977 test-rmse:1.584781+0.182725
## [33] train-rmse:1.371689+0.018426 test-rmse:1.519945+0.168108
## [34] train-rmse:1.307162+0.018764 test-rmse:1.473080+0.156219
## [35] train-rmse:1.256094+0.018852 test-rmse:1.434261+0.151055
## [36] train-rmse:1.211007+0.021083 test-rmse:1.405616+0.136003
## [37] train-rmse:1.175696+0.021527 test-rmse:1.374127+0.134638
## [38] train-rmse:1.144391+0.023036 test-rmse:1.358640+0.128231
## [39] train-rmse:1.116870+0.022685 test-rmse:1.341094+0.121863
## [40] train-rmse:1.091544+0.021756 test-rmse:1.328125+0.114535
## [41] train-rmse:1.073055+0.021532 test-rmse:1.315118+0.112638
## [42] train-rmse:1.055353+0.021472 test-rmse:1.308723+0.112081
## [43] train-rmse:1.039619+0.021168 test-rmse:1.297001+0.109073
## [44] train-rmse:1.025172+0.022085 test-rmse:1.284590+0.105442
## [45] train-rmse:1.011260+0.020760 test-rmse:1.276446+0.102147
## [46] train-rmse:0.998476+0.021244 test-rmse:1.267383+0.099821
## [47] train-rmse:0.986413+0.020967 test-rmse:1.260959+0.099009
## [48] train-rmse:0.974996+0.019140 test-rmse:1.254188+0.099167
## [49] train-rmse:0.964550+0.017238 test-rmse:1.243079+0.100397
## [50] train-rmse:0.952061+0.019977 test-rmse:1.236344+0.100163
## [51] train-rmse:0.942490+0.020365 test-rmse:1.231778+0.096068
## [52] train-rmse:0.932237+0.019387 test-rmse:1.227072+0.096821
## [53] train-rmse:0.920930+0.017642 test-rmse:1.222339+0.094844
## [54] train-rmse:0.912613+0.017566 test-rmse:1.213994+0.098127
## [55] train-rmse:0.903476+0.018612 test-rmse:1.204151+0.092648
## [56] train-rmse:0.896096+0.018649 test-rmse:1.198629+0.091370
## [57] train-rmse:0.888294+0.018822 test-rmse:1.191000+0.092877
## [58] train-rmse:0.881140+0.019961 test-rmse:1.181705+0.091824
## [59] train-rmse:0.873047+0.020024 test-rmse:1.177356+0.091516
## [60] train-rmse:0.865549+0.019478 test-rmse:1.174646+0.091952
## [61] train-rmse:0.858973+0.019025 test-rmse:1.172473+0.092426
## [62] train-rmse:0.850799+0.020910 test-rmse:1.165133+0.088573
## [63] train-rmse:0.841250+0.017443 test-rmse:1.157883+0.092123
## [64] train-rmse:0.832286+0.018383 test-rmse:1.152567+0.096403
## [65] train-rmse:0.823152+0.019070 test-rmse:1.146494+0.096973
## [66] train-rmse:0.814411+0.019161 test-rmse:1.142951+0.094460
## [67] train-rmse:0.809520+0.019505 test-rmse:1.138339+0.097752
## [68] train-rmse:0.803853+0.019321 test-rmse:1.133760+0.095942
## [69] train-rmse:0.797768+0.018020 test-rmse:1.130861+0.095890
## [70] train-rmse:0.791605+0.017506 test-rmse:1.126756+0.097091
## [71] train-rmse:0.785916+0.015943 test-rmse:1.120737+0.097745
## [72] train-rmse:0.779041+0.016647 test-rmse:1.119511+0.095148
## [73] train-rmse:0.773012+0.016450 test-rmse:1.115402+0.094820
## [74] train-rmse:0.767296+0.015425 test-rmse:1.111303+0.096202
## [75] train-rmse:0.762009+0.015981 test-rmse:1.108696+0.093444
## [76] train-rmse:0.754397+0.017753 test-rmse:1.104557+0.091863
## [77] train-rmse:0.749097+0.016857 test-rmse:1.098624+0.090300
## [78] train-rmse:0.744217+0.017516 test-rmse:1.096252+0.091140
## [79] train-rmse:0.739928+0.017218 test-rmse:1.095249+0.091984
## [80] train-rmse:0.733797+0.016521 test-rmse:1.092996+0.092220
## [81] train-rmse:0.727187+0.015676 test-rmse:1.089705+0.095234
## [82] train-rmse:0.722561+0.016180 test-rmse:1.086039+0.095406
## [83] train-rmse:0.717510+0.016480 test-rmse:1.081590+0.091376
## [84] train-rmse:0.713123+0.017300 test-rmse:1.077385+0.091602
## [85] train-rmse:0.708487+0.017232 test-rmse:1.074505+0.092252
## [86] train-rmse:0.701433+0.018452 test-rmse:1.074495+0.091639
## [87] train-rmse:0.696926+0.019393 test-rmse:1.072220+0.091646
## [88] train-rmse:0.692830+0.020504 test-rmse:1.071003+0.091231
## [89] train-rmse:0.687636+0.020277 test-rmse:1.068733+0.094752
## [90] train-rmse:0.683077+0.020311 test-rmse:1.065795+0.094456
## [91] train-rmse:0.676775+0.016960 test-rmse:1.061939+0.096793
## [92] train-rmse:0.672384+0.017177 test-rmse:1.056908+0.097059
## [93] train-rmse:0.669110+0.017490 test-rmse:1.056504+0.097423
## [94] train-rmse:0.666094+0.017891 test-rmse:1.055040+0.096861
## [95] train-rmse:0.662455+0.018597 test-rmse:1.052722+0.097959
## [96] train-rmse:0.656613+0.019197 test-rmse:1.046651+0.093952
## [97] train-rmse:0.651519+0.019569 test-rmse:1.044869+0.095763
## [98] train-rmse:0.646446+0.015742 test-rmse:1.040795+0.097680
## [99] train-rmse:0.642174+0.016306 test-rmse:1.041288+0.098184
## [100] train-rmse:0.637897+0.016118 test-rmse:1.037497+0.097019
## [101] train-rmse:0.634033+0.014809 test-rmse:1.035547+0.098304
## [102] train-rmse:0.630954+0.015047 test-rmse:1.033833+0.099374
## [103] train-rmse:0.627289+0.015869 test-rmse:1.031289+0.099956
## [104] train-rmse:0.624703+0.015979 test-rmse:1.030097+0.101264
## [105] train-rmse:0.621249+0.016070 test-rmse:1.030471+0.101254
## [106] train-rmse:0.617357+0.016710 test-rmse:1.029947+0.101830
## [107] train-rmse:0.613837+0.016314 test-rmse:1.028253+0.100337
## [108] train-rmse:0.609814+0.016953 test-rmse:1.024141+0.097321
## [109] train-rmse:0.605598+0.016006 test-rmse:1.022750+0.098342
## [110] train-rmse:0.602326+0.014727 test-rmse:1.019676+0.097920
## [111] train-rmse:0.598518+0.015370 test-rmse:1.018112+0.097465
## [112] train-rmse:0.594605+0.015955 test-rmse:1.016652+0.099449
## [113] train-rmse:0.590795+0.015762 test-rmse:1.015660+0.099395
## [114] train-rmse:0.588199+0.015153 test-rmse:1.014339+0.101161
## [115] train-rmse:0.585965+0.015327 test-rmse:1.013101+0.100981
## [116] train-rmse:0.582396+0.014916 test-rmse:1.011850+0.102582
## [117] train-rmse:0.579651+0.014420 test-rmse:1.010574+0.101719
## [118] train-rmse:0.577473+0.014276 test-rmse:1.009688+0.102400
## [119] train-rmse:0.574141+0.013842 test-rmse:1.007658+0.102719
## [120] train-rmse:0.570792+0.014510 test-rmse:1.004704+0.103069
## [121] train-rmse:0.568121+0.013603 test-rmse:1.004906+0.103592
## [122] train-rmse:0.565211+0.013808 test-rmse:1.004841+0.104461
## [123] train-rmse:0.561876+0.014510 test-rmse:1.003295+0.103105
## [124] train-rmse:0.558863+0.014452 test-rmse:0.999100+0.101070
## [125] train-rmse:0.555615+0.015281 test-rmse:0.998645+0.101178
## [126] train-rmse:0.552317+0.015718 test-rmse:0.995210+0.098855
## [127] train-rmse:0.547766+0.016064 test-rmse:0.992588+0.098394
## [128] train-rmse:0.543812+0.016492 test-rmse:0.991996+0.099611
## [129] train-rmse:0.540970+0.017044 test-rmse:0.989672+0.098729
## [130] train-rmse:0.537743+0.016011 test-rmse:0.988652+0.098751
## [131] train-rmse:0.534934+0.016983 test-rmse:0.986459+0.097739
## [132] train-rmse:0.532276+0.017085 test-rmse:0.984047+0.096449
## [133] train-rmse:0.529492+0.017120 test-rmse:0.984031+0.098134
## [134] train-rmse:0.526839+0.017845 test-rmse:0.984178+0.097877
## [135] train-rmse:0.524826+0.018489 test-rmse:0.984460+0.096672
## [136] train-rmse:0.521801+0.018930 test-rmse:0.986333+0.097890
## [137] train-rmse:0.518868+0.019079 test-rmse:0.986216+0.096825
## [138] train-rmse:0.516109+0.018467 test-rmse:0.986103+0.096529
## [139] train-rmse:0.513801+0.018122 test-rmse:0.985390+0.095914
## Stopping. Best iteration:
## [133] train-rmse:0.529492+0.017120 test-rmse:0.984031+0.098134
##
## 17
## [1] train-rmse:84.742499+0.085026 test-rmse:84.743227+0.408848
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.927655+0.073048 test-rmse:72.928244+0.420446
## [3] train-rmse:62.762302+0.062739 test-rmse:62.762725+0.430333
## [4] train-rmse:54.016514+0.053817 test-rmse:54.016744+0.438737
## [5] train-rmse:46.492472+0.046117 test-rmse:46.492479+0.445831
## [6] train-rmse:40.019990+0.039459 test-rmse:40.019755+0.451772
## [7] train-rmse:34.452692+0.033687 test-rmse:34.452158+0.456684
## [8] train-rmse:29.664635+0.028683 test-rmse:29.663766+0.460663
## [9] train-rmse:25.547519+0.024346 test-rmse:25.546270+0.463776
## [10] train-rmse:22.008217+0.020588 test-rmse:22.006533+0.466070
## [11] train-rmse:18.966660+0.017350 test-rmse:18.964487+0.467562
## [12] train-rmse:16.354035+0.014591 test-rmse:16.351313+0.468250
## [13] train-rmse:14.110245+0.012261 test-rmse:14.110562+0.451996
## [14] train-rmse:12.183608+0.010200 test-rmse:12.178845+0.439003
## [15] train-rmse:10.529442+0.008666 test-rmse:10.519318+0.431322
## [16] train-rmse:9.108445+0.006302 test-rmse:9.096704+0.419157
## [17] train-rmse:7.887358+0.005490 test-rmse:7.870960+0.406401
## [18] train-rmse:6.838835+0.004988 test-rmse:6.827381+0.387692
## [19] train-rmse:5.939252+0.004959 test-rmse:5.930642+0.367666
## [20] train-rmse:5.166632+0.003907 test-rmse:5.159530+0.348570
## [21] train-rmse:4.504876+0.004369 test-rmse:4.507363+0.332362
## [22] train-rmse:3.938223+0.004859 test-rmse:3.942135+0.317176
## [23] train-rmse:3.451273+0.006832 test-rmse:3.466023+0.304600
## [24] train-rmse:3.037006+0.007458 test-rmse:3.058632+0.294864
## [25] train-rmse:2.685042+0.007897 test-rmse:2.714573+0.278351
## [26] train-rmse:2.383725+0.009935 test-rmse:2.430817+0.261542
## [27] train-rmse:2.130621+0.010371 test-rmse:2.190791+0.251144
## [28] train-rmse:1.917250+0.013645 test-rmse:1.998533+0.231080
## [29] train-rmse:1.733970+0.010164 test-rmse:1.837451+0.214249
## [30] train-rmse:1.584181+0.011187 test-rmse:1.709089+0.202414
## [31] train-rmse:1.458544+0.013966 test-rmse:1.609323+0.185834
## [32] train-rmse:1.354215+0.013072 test-rmse:1.525740+0.168842
## [33] train-rmse:1.266559+0.013719 test-rmse:1.456761+0.159797
## [34] train-rmse:1.196098+0.012956 test-rmse:1.403260+0.151493
## [35] train-rmse:1.136615+0.013676 test-rmse:1.360957+0.139734
## [36] train-rmse:1.083677+0.013557 test-rmse:1.325985+0.132107
## [37] train-rmse:1.037183+0.014181 test-rmse:1.297012+0.129672
## [38] train-rmse:1.000467+0.013704 test-rmse:1.275664+0.121923
## [39] train-rmse:0.967132+0.013974 test-rmse:1.255788+0.117925
## [40] train-rmse:0.940800+0.011979 test-rmse:1.243341+0.115709
## [41] train-rmse:0.917435+0.014406 test-rmse:1.227356+0.112278
## [42] train-rmse:0.894079+0.012072 test-rmse:1.213755+0.109065
## [43] train-rmse:0.874671+0.014169 test-rmse:1.199624+0.105693
## [44] train-rmse:0.858780+0.014495 test-rmse:1.190709+0.103238
## [45] train-rmse:0.842306+0.014614 test-rmse:1.181318+0.100319
## [46] train-rmse:0.825071+0.014804 test-rmse:1.174946+0.097469
## [47] train-rmse:0.811010+0.015839 test-rmse:1.163443+0.094401
## [48] train-rmse:0.799041+0.015901 test-rmse:1.157855+0.094984
## [49] train-rmse:0.788058+0.016941 test-rmse:1.149179+0.096578
## [50] train-rmse:0.776608+0.015087 test-rmse:1.140283+0.096118
## [51] train-rmse:0.766131+0.016348 test-rmse:1.133298+0.091786
## [52] train-rmse:0.755658+0.017332 test-rmse:1.128637+0.089405
## [53] train-rmse:0.746050+0.016407 test-rmse:1.122751+0.088741
## [54] train-rmse:0.735420+0.017696 test-rmse:1.114312+0.089285
## [55] train-rmse:0.725020+0.018234 test-rmse:1.109196+0.091090
## [56] train-rmse:0.715494+0.020320 test-rmse:1.105368+0.090362
## [57] train-rmse:0.706462+0.019172 test-rmse:1.100411+0.090865
## [58] train-rmse:0.698835+0.019929 test-rmse:1.096810+0.091680
## [59] train-rmse:0.689974+0.021019 test-rmse:1.094864+0.091579
## [60] train-rmse:0.682973+0.020951 test-rmse:1.092718+0.090420
## [61] train-rmse:0.674308+0.019126 test-rmse:1.087928+0.089293
## [62] train-rmse:0.665497+0.019351 test-rmse:1.079495+0.084700
## [63] train-rmse:0.658209+0.020532 test-rmse:1.074828+0.083479
## [64] train-rmse:0.651915+0.020448 test-rmse:1.072834+0.087583
## [65] train-rmse:0.644442+0.021097 test-rmse:1.070043+0.088212
## [66] train-rmse:0.638834+0.021076 test-rmse:1.065021+0.089856
## [67] train-rmse:0.629584+0.022122 test-rmse:1.060459+0.089028
## [68] train-rmse:0.623211+0.021269 test-rmse:1.052600+0.085970
## [69] train-rmse:0.616935+0.021520 test-rmse:1.048792+0.085562
## [70] train-rmse:0.611251+0.019925 test-rmse:1.043238+0.082583
## [71] train-rmse:0.603924+0.019113 test-rmse:1.039341+0.081007
## [72] train-rmse:0.597320+0.018401 test-rmse:1.036438+0.080684
## [73] train-rmse:0.591123+0.017978 test-rmse:1.034048+0.078723
## [74] train-rmse:0.585373+0.017779 test-rmse:1.030438+0.076138
## [75] train-rmse:0.581201+0.017483 test-rmse:1.028949+0.077338
## [76] train-rmse:0.575232+0.018174 test-rmse:1.024719+0.076847
## [77] train-rmse:0.570199+0.017610 test-rmse:1.022755+0.076556
## [78] train-rmse:0.564836+0.017611 test-rmse:1.018007+0.072651
## [79] train-rmse:0.558530+0.016537 test-rmse:1.018822+0.075272
## [80] train-rmse:0.552307+0.018444 test-rmse:1.017794+0.074770
## [81] train-rmse:0.546808+0.018592 test-rmse:1.015303+0.075619
## [82] train-rmse:0.541249+0.019772 test-rmse:1.014169+0.075425
## [83] train-rmse:0.535092+0.017339 test-rmse:1.011421+0.077406
## [84] train-rmse:0.531177+0.016781 test-rmse:1.009339+0.077592
## [85] train-rmse:0.526325+0.016846 test-rmse:1.007357+0.075144
## [86] train-rmse:0.522063+0.016935 test-rmse:1.004769+0.074655
## [87] train-rmse:0.517476+0.016217 test-rmse:1.002106+0.076650
## [88] train-rmse:0.514137+0.016055 test-rmse:1.000067+0.076142
## [89] train-rmse:0.509547+0.015333 test-rmse:0.999300+0.076341
## [90] train-rmse:0.505311+0.015538 test-rmse:0.998167+0.076608
## [91] train-rmse:0.500240+0.015338 test-rmse:0.999141+0.079811
## [92] train-rmse:0.494178+0.014728 test-rmse:0.995675+0.079724
## [93] train-rmse:0.490258+0.015758 test-rmse:0.994715+0.080561
## [94] train-rmse:0.487062+0.016387 test-rmse:0.993842+0.080887
## [95] train-rmse:0.482309+0.015387 test-rmse:0.993753+0.080927
## [96] train-rmse:0.477155+0.016193 test-rmse:0.993038+0.082024
## [97] train-rmse:0.473334+0.017128 test-rmse:0.990631+0.080413
## [98] train-rmse:0.467727+0.017704 test-rmse:0.989837+0.081534
## [99] train-rmse:0.463919+0.017494 test-rmse:0.988701+0.080367
## [100] train-rmse:0.459467+0.018368 test-rmse:0.987893+0.079652
## [101] train-rmse:0.455186+0.017155 test-rmse:0.986645+0.081307
## [102] train-rmse:0.450772+0.017454 test-rmse:0.983468+0.080002
## [103] train-rmse:0.446542+0.015522 test-rmse:0.983161+0.081755
## [104] train-rmse:0.443261+0.013741 test-rmse:0.980406+0.081484
## [105] train-rmse:0.439041+0.013244 test-rmse:0.979557+0.080236
## [106] train-rmse:0.434989+0.012731 test-rmse:0.979059+0.082002
## [107] train-rmse:0.430177+0.012499 test-rmse:0.977506+0.081419
## [108] train-rmse:0.425499+0.011688 test-rmse:0.975449+0.080046
## [109] train-rmse:0.421455+0.011394 test-rmse:0.974887+0.081374
## [110] train-rmse:0.417684+0.010864 test-rmse:0.974186+0.081252
## [111] train-rmse:0.413624+0.011087 test-rmse:0.972958+0.082279
## [112] train-rmse:0.410494+0.010897 test-rmse:0.972723+0.083312
## [113] train-rmse:0.407367+0.011520 test-rmse:0.971839+0.082379
## [114] train-rmse:0.405195+0.012011 test-rmse:0.971632+0.081142
## [115] train-rmse:0.401270+0.011257 test-rmse:0.970493+0.081419
## [116] train-rmse:0.397551+0.009821 test-rmse:0.969364+0.080907
## [117] train-rmse:0.395246+0.009708 test-rmse:0.968810+0.080379
## [118] train-rmse:0.393056+0.009284 test-rmse:0.967589+0.081453
## [119] train-rmse:0.390244+0.010664 test-rmse:0.967856+0.082826
## [120] train-rmse:0.387283+0.011295 test-rmse:0.965476+0.080115
## [121] train-rmse:0.383119+0.010900 test-rmse:0.966882+0.082487
## [122] train-rmse:0.380677+0.010558 test-rmse:0.966409+0.079643
## [123] train-rmse:0.377113+0.010690 test-rmse:0.965416+0.079552
## [124] train-rmse:0.373855+0.011564 test-rmse:0.965812+0.080398
## [125] train-rmse:0.370835+0.011762 test-rmse:0.965340+0.080168
## [126] train-rmse:0.367849+0.011467 test-rmse:0.963447+0.082318
## [127] train-rmse:0.364309+0.011652 test-rmse:0.962911+0.082361
## [128] train-rmse:0.362182+0.011466 test-rmse:0.963793+0.083202
## [129] train-rmse:0.359723+0.011368 test-rmse:0.963338+0.083149
## [130] train-rmse:0.357011+0.011930 test-rmse:0.964258+0.083841
## [131] train-rmse:0.353601+0.011000 test-rmse:0.961992+0.084555
## [132] train-rmse:0.351232+0.011047 test-rmse:0.962085+0.084338
## [133] train-rmse:0.348433+0.011513 test-rmse:0.961161+0.083424
## [134] train-rmse:0.345603+0.011248 test-rmse:0.960767+0.081951
## [135] train-rmse:0.343440+0.011493 test-rmse:0.960549+0.081846
## [136] train-rmse:0.342083+0.011502 test-rmse:0.960614+0.081913
## [137] train-rmse:0.340156+0.011240 test-rmse:0.959765+0.081493
## [138] train-rmse:0.338443+0.011532 test-rmse:0.958703+0.082010
## [139] train-rmse:0.335981+0.012025 test-rmse:0.958901+0.081096
## [140] train-rmse:0.333440+0.011380 test-rmse:0.958834+0.081195
## [141] train-rmse:0.331294+0.011860 test-rmse:0.958418+0.081403
## [142] train-rmse:0.328577+0.012675 test-rmse:0.956853+0.080939
## [143] train-rmse:0.326041+0.012786 test-rmse:0.956297+0.080215
## [144] train-rmse:0.322436+0.012160 test-rmse:0.955939+0.082444
## [145] train-rmse:0.319639+0.011904 test-rmse:0.955838+0.082827
## [146] train-rmse:0.315915+0.010983 test-rmse:0.954614+0.082456
## [147] train-rmse:0.312954+0.010632 test-rmse:0.954737+0.082781
## [148] train-rmse:0.310878+0.011060 test-rmse:0.955487+0.084615
## [149] train-rmse:0.308554+0.010744 test-rmse:0.956174+0.083491
## [150] train-rmse:0.306354+0.009798 test-rmse:0.955782+0.084750
## [151] train-rmse:0.304220+0.009926 test-rmse:0.955631+0.086214
## [152] train-rmse:0.301555+0.010597 test-rmse:0.954637+0.085441
## Stopping. Best iteration:
## [146] train-rmse:0.315915+0.010983 test-rmse:0.954614+0.082456
##
## 18
## [1] train-rmse:84.742499+0.085026 test-rmse:84.743227+0.408848
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.927655+0.073048 test-rmse:72.928244+0.420446
## [3] train-rmse:62.762302+0.062739 test-rmse:62.762725+0.430333
## [4] train-rmse:54.016514+0.053817 test-rmse:54.016744+0.438737
## [5] train-rmse:46.492472+0.046117 test-rmse:46.492479+0.445831
## [6] train-rmse:40.019990+0.039459 test-rmse:40.019755+0.451772
## [7] train-rmse:34.452692+0.033687 test-rmse:34.452158+0.456684
## [8] train-rmse:29.664635+0.028683 test-rmse:29.663766+0.460663
## [9] train-rmse:25.547519+0.024346 test-rmse:25.546270+0.463776
## [10] train-rmse:22.008217+0.020588 test-rmse:22.006533+0.466070
## [11] train-rmse:18.966660+0.017350 test-rmse:18.964487+0.467562
## [12] train-rmse:16.354035+0.014591 test-rmse:16.351313+0.468250
## [13] train-rmse:14.110245+0.012261 test-rmse:14.110562+0.451996
## [14] train-rmse:12.183608+0.010200 test-rmse:12.178845+0.439003
## [15] train-rmse:10.529368+0.008735 test-rmse:10.521908+0.432823
## [16] train-rmse:9.107897+0.006711 test-rmse:9.094078+0.417845
## [17] train-rmse:7.887041+0.006472 test-rmse:7.868187+0.403657
## [18] train-rmse:6.837426+0.005736 test-rmse:6.822989+0.379714
## [19] train-rmse:5.936259+0.005709 test-rmse:5.925205+0.358837
## [20] train-rmse:5.162189+0.004734 test-rmse:5.152269+0.336979
## [21] train-rmse:4.498226+0.004320 test-rmse:4.494159+0.317391
## [22] train-rmse:3.927566+0.005445 test-rmse:3.929249+0.308873
## [23] train-rmse:3.439081+0.005593 test-rmse:3.444382+0.301607
## [24] train-rmse:3.018464+0.006935 test-rmse:3.042511+0.279953
## [25] train-rmse:2.657247+0.005705 test-rmse:2.701632+0.261497
## [26] train-rmse:2.351098+0.008408 test-rmse:2.408890+0.249851
## [27] train-rmse:2.087152+0.007696 test-rmse:2.170635+0.235374
## [28] train-rmse:1.865797+0.010464 test-rmse:1.973869+0.221434
## [29] train-rmse:1.676695+0.007365 test-rmse:1.810089+0.217425
## [30] train-rmse:1.517725+0.007649 test-rmse:1.677287+0.205923
## [31] train-rmse:1.382621+0.008076 test-rmse:1.566855+0.189237
## [32] train-rmse:1.269454+0.012462 test-rmse:1.476536+0.173441
## [33] train-rmse:1.172357+0.012679 test-rmse:1.408435+0.163126
## [34] train-rmse:1.094294+0.014234 test-rmse:1.353170+0.158812
## [35] train-rmse:1.028713+0.014171 test-rmse:1.303206+0.148840
## [36] train-rmse:0.972514+0.013534 test-rmse:1.270894+0.140050
## [37] train-rmse:0.924734+0.012094 test-rmse:1.241331+0.134170
## [38] train-rmse:0.884228+0.013593 test-rmse:1.220713+0.125220
## [39] train-rmse:0.848976+0.011668 test-rmse:1.201746+0.121165
## [40] train-rmse:0.816050+0.010540 test-rmse:1.180745+0.112430
## [41] train-rmse:0.789184+0.011259 test-rmse:1.165819+0.107546
## [42] train-rmse:0.764367+0.009681 test-rmse:1.151919+0.105453
## [43] train-rmse:0.742275+0.006017 test-rmse:1.140148+0.104427
## [44] train-rmse:0.722584+0.009467 test-rmse:1.126387+0.099265
## [45] train-rmse:0.703395+0.011328 test-rmse:1.116276+0.094115
## [46] train-rmse:0.687798+0.013699 test-rmse:1.110772+0.095265
## [47] train-rmse:0.674663+0.012884 test-rmse:1.103732+0.092957
## [48] train-rmse:0.659393+0.012981 test-rmse:1.095017+0.090288
## [49] train-rmse:0.649071+0.013811 test-rmse:1.087246+0.090196
## [50] train-rmse:0.635788+0.014516 test-rmse:1.081302+0.089930
## [51] train-rmse:0.623811+0.013130 test-rmse:1.078098+0.090137
## [52] train-rmse:0.613099+0.015208 test-rmse:1.072382+0.091629
## [53] train-rmse:0.603616+0.014368 test-rmse:1.065029+0.091229
## [54] train-rmse:0.595118+0.014861 test-rmse:1.061888+0.092432
## [55] train-rmse:0.584349+0.015055 test-rmse:1.056616+0.092953
## [56] train-rmse:0.575058+0.016023 test-rmse:1.053404+0.092133
## [57] train-rmse:0.565646+0.016829 test-rmse:1.051762+0.091077
## [58] train-rmse:0.557443+0.017556 test-rmse:1.045018+0.085840
## [59] train-rmse:0.549406+0.016870 test-rmse:1.038775+0.086615
## [60] train-rmse:0.541405+0.016920 test-rmse:1.036194+0.087221
## [61] train-rmse:0.533247+0.018695 test-rmse:1.031834+0.084859
## [62] train-rmse:0.523282+0.018695 test-rmse:1.028043+0.084951
## [63] train-rmse:0.514658+0.017089 test-rmse:1.023450+0.087223
## [64] train-rmse:0.507699+0.015434 test-rmse:1.018856+0.085641
## [65] train-rmse:0.501668+0.016575 test-rmse:1.015202+0.087971
## [66] train-rmse:0.495018+0.016419 test-rmse:1.012358+0.086729
## [67] train-rmse:0.487433+0.015916 test-rmse:1.012377+0.086206
## [68] train-rmse:0.482061+0.016415 test-rmse:1.009002+0.088904
## [69] train-rmse:0.476906+0.015726 test-rmse:1.004900+0.088835
## [70] train-rmse:0.469211+0.015213 test-rmse:1.003885+0.089782
## [71] train-rmse:0.462099+0.015664 test-rmse:1.001619+0.089841
## [72] train-rmse:0.456328+0.015470 test-rmse:0.999267+0.089371
## [73] train-rmse:0.452321+0.015711 test-rmse:0.997599+0.088403
## [74] train-rmse:0.444806+0.014441 test-rmse:0.994691+0.089286
## [75] train-rmse:0.438402+0.014545 test-rmse:0.991905+0.088333
## [76] train-rmse:0.433189+0.015669 test-rmse:0.990779+0.086620
## [77] train-rmse:0.427911+0.015225 test-rmse:0.987652+0.086815
## [78] train-rmse:0.422310+0.015024 test-rmse:0.988007+0.086781
## [79] train-rmse:0.416153+0.015256 test-rmse:0.984600+0.084992
## [80] train-rmse:0.412261+0.015084 test-rmse:0.982566+0.085901
## [81] train-rmse:0.408417+0.015379 test-rmse:0.980986+0.085287
## [82] train-rmse:0.401952+0.015554 test-rmse:0.979041+0.084863
## [83] train-rmse:0.397255+0.015119 test-rmse:0.979317+0.086275
## [84] train-rmse:0.392814+0.015249 test-rmse:0.976665+0.086705
## [85] train-rmse:0.387620+0.014368 test-rmse:0.975912+0.085769
## [86] train-rmse:0.384050+0.014380 test-rmse:0.975047+0.085497
## [87] train-rmse:0.380844+0.013798 test-rmse:0.973600+0.084685
## [88] train-rmse:0.376325+0.014650 test-rmse:0.972527+0.084053
## [89] train-rmse:0.371295+0.014999 test-rmse:0.971814+0.084975
## [90] train-rmse:0.366995+0.014646 test-rmse:0.970177+0.085525
## [91] train-rmse:0.361924+0.013201 test-rmse:0.966812+0.084506
## [92] train-rmse:0.357616+0.013518 test-rmse:0.965848+0.083597
## [93] train-rmse:0.353825+0.013143 test-rmse:0.964150+0.083826
## [94] train-rmse:0.349842+0.013342 test-rmse:0.964725+0.083831
## [95] train-rmse:0.346289+0.012028 test-rmse:0.964030+0.084278
## [96] train-rmse:0.342316+0.011350 test-rmse:0.963815+0.083547
## [97] train-rmse:0.338684+0.010008 test-rmse:0.963359+0.084377
## [98] train-rmse:0.333736+0.010929 test-rmse:0.963776+0.084461
## [99] train-rmse:0.330403+0.009956 test-rmse:0.962881+0.084291
## [100] train-rmse:0.327483+0.009984 test-rmse:0.963064+0.083936
## [101] train-rmse:0.324695+0.010353 test-rmse:0.961554+0.083390
## [102] train-rmse:0.319607+0.011655 test-rmse:0.962351+0.082681
## [103] train-rmse:0.315396+0.009590 test-rmse:0.963428+0.084170
## [104] train-rmse:0.310887+0.007879 test-rmse:0.962441+0.085735
## [105] train-rmse:0.308144+0.008946 test-rmse:0.960690+0.085896
## [106] train-rmse:0.304083+0.009987 test-rmse:0.959661+0.085194
## [107] train-rmse:0.299646+0.008153 test-rmse:0.959895+0.084777
## [108] train-rmse:0.296614+0.007180 test-rmse:0.960406+0.085027
## [109] train-rmse:0.293229+0.006332 test-rmse:0.959799+0.084727
## [110] train-rmse:0.288789+0.005000 test-rmse:0.957490+0.085116
## [111] train-rmse:0.286713+0.005023 test-rmse:0.957427+0.085856
## [112] train-rmse:0.283054+0.005406 test-rmse:0.957037+0.085020
## [113] train-rmse:0.279919+0.005709 test-rmse:0.956981+0.085026
## [114] train-rmse:0.277417+0.004637 test-rmse:0.958186+0.085138
## [115] train-rmse:0.274520+0.005179 test-rmse:0.958838+0.085061
## [116] train-rmse:0.271268+0.006008 test-rmse:0.957520+0.083980
## [117] train-rmse:0.269363+0.006270 test-rmse:0.957111+0.084174
## [118] train-rmse:0.266810+0.005797 test-rmse:0.956660+0.084115
## [119] train-rmse:0.262731+0.006427 test-rmse:0.956505+0.084336
## [120] train-rmse:0.260116+0.005479 test-rmse:0.955443+0.084110
## [121] train-rmse:0.257513+0.005002 test-rmse:0.955343+0.084221
## [122] train-rmse:0.253932+0.005391 test-rmse:0.953705+0.084567
## [123] train-rmse:0.251808+0.005345 test-rmse:0.953162+0.085398
## [124] train-rmse:0.248549+0.005953 test-rmse:0.953901+0.085179
## [125] train-rmse:0.246106+0.005481 test-rmse:0.953717+0.085913
## [126] train-rmse:0.243384+0.005812 test-rmse:0.952168+0.085176
## [127] train-rmse:0.241150+0.005466 test-rmse:0.951995+0.085584
## [128] train-rmse:0.239060+0.005305 test-rmse:0.952238+0.085027
## [129] train-rmse:0.236491+0.005168 test-rmse:0.952416+0.084958
## [130] train-rmse:0.233634+0.004753 test-rmse:0.952566+0.085011
## [131] train-rmse:0.230632+0.005148 test-rmse:0.951818+0.085271
## [132] train-rmse:0.228204+0.005590 test-rmse:0.951875+0.085418
## [133] train-rmse:0.225451+0.006739 test-rmse:0.951088+0.084634
## [134] train-rmse:0.223146+0.007438 test-rmse:0.951266+0.084415
## [135] train-rmse:0.220476+0.007307 test-rmse:0.950481+0.085015
## [136] train-rmse:0.218586+0.007597 test-rmse:0.949701+0.085480
## [137] train-rmse:0.215349+0.007455 test-rmse:0.949367+0.085941
## [138] train-rmse:0.212980+0.007083 test-rmse:0.949373+0.086091
## [139] train-rmse:0.210215+0.007534 test-rmse:0.949672+0.085954
## [140] train-rmse:0.207827+0.007782 test-rmse:0.949557+0.085995
## [141] train-rmse:0.206281+0.007939 test-rmse:0.948882+0.086463
## [142] train-rmse:0.204644+0.007426 test-rmse:0.949145+0.086813
## [143] train-rmse:0.203236+0.007206 test-rmse:0.948910+0.086520
## [144] train-rmse:0.201250+0.007350 test-rmse:0.948692+0.086414
## [145] train-rmse:0.199173+0.007464 test-rmse:0.948788+0.086818
## [146] train-rmse:0.196546+0.006533 test-rmse:0.948880+0.086628
## [147] train-rmse:0.195115+0.006493 test-rmse:0.948600+0.086480
## [148] train-rmse:0.193175+0.006309 test-rmse:0.948080+0.086293
## [149] train-rmse:0.191273+0.006209 test-rmse:0.947784+0.085896
## [150] train-rmse:0.189210+0.006896 test-rmse:0.947595+0.085801
## [151] train-rmse:0.187042+0.006835 test-rmse:0.946839+0.085892
## [152] train-rmse:0.185704+0.007179 test-rmse:0.946556+0.085460
## [153] train-rmse:0.183777+0.007239 test-rmse:0.946292+0.085515
## [154] train-rmse:0.182458+0.007397 test-rmse:0.945582+0.085923
## [155] train-rmse:0.180457+0.008084 test-rmse:0.945626+0.086058
## [156] train-rmse:0.178881+0.008424 test-rmse:0.945392+0.086016
## [157] train-rmse:0.176001+0.008196 test-rmse:0.945512+0.086073
## [158] train-rmse:0.174260+0.006995 test-rmse:0.945322+0.085959
## [159] train-rmse:0.172210+0.006359 test-rmse:0.945083+0.085749
## [160] train-rmse:0.170749+0.006161 test-rmse:0.944547+0.085321
## [161] train-rmse:0.169148+0.006047 test-rmse:0.944798+0.085381
## [162] train-rmse:0.167680+0.006333 test-rmse:0.944935+0.085759
## [163] train-rmse:0.166583+0.006658 test-rmse:0.944976+0.085793
## [164] train-rmse:0.164454+0.005900 test-rmse:0.944870+0.085773
## [165] train-rmse:0.162772+0.005449 test-rmse:0.944624+0.084959
## [166] train-rmse:0.161183+0.005310 test-rmse:0.944007+0.084796
## [167] train-rmse:0.159912+0.005252 test-rmse:0.944271+0.085042
## [168] train-rmse:0.158593+0.005829 test-rmse:0.944262+0.084934
## [169] train-rmse:0.156439+0.005375 test-rmse:0.944359+0.085480
## [170] train-rmse:0.154471+0.005834 test-rmse:0.944059+0.085390
## [171] train-rmse:0.152277+0.006303 test-rmse:0.943921+0.085488
## [172] train-rmse:0.150446+0.006101 test-rmse:0.943209+0.085441
## [173] train-rmse:0.148672+0.005774 test-rmse:0.942830+0.085348
## [174] train-rmse:0.146868+0.005279 test-rmse:0.942734+0.084939
## [175] train-rmse:0.145058+0.004912 test-rmse:0.942776+0.085550
## [176] train-rmse:0.143988+0.005132 test-rmse:0.942731+0.085662
## [177] train-rmse:0.142212+0.005640 test-rmse:0.942405+0.085983
## [178] train-rmse:0.140434+0.005634 test-rmse:0.942636+0.086088
## [179] train-rmse:0.139071+0.005454 test-rmse:0.942426+0.085890
## [180] train-rmse:0.137592+0.005883 test-rmse:0.942245+0.086556
## [181] train-rmse:0.136158+0.005814 test-rmse:0.942388+0.086741
## [182] train-rmse:0.134663+0.006378 test-rmse:0.942195+0.087224
## [183] train-rmse:0.133788+0.006579 test-rmse:0.942379+0.087593
## [184] train-rmse:0.132233+0.006389 test-rmse:0.942066+0.087755
## [185] train-rmse:0.130370+0.006100 test-rmse:0.941489+0.087452
## [186] train-rmse:0.128801+0.006317 test-rmse:0.941612+0.087692
## [187] train-rmse:0.127547+0.006182 test-rmse:0.941304+0.087487
## [188] train-rmse:0.126071+0.006116 test-rmse:0.941375+0.087819
## [189] train-rmse:0.124718+0.005856 test-rmse:0.941535+0.087852
## [190] train-rmse:0.123626+0.006103 test-rmse:0.941553+0.088157
## [191] train-rmse:0.122100+0.005694 test-rmse:0.942109+0.088894
## [192] train-rmse:0.120836+0.005872 test-rmse:0.941837+0.088738
## [193] train-rmse:0.119789+0.005979 test-rmse:0.941520+0.088268
## Stopping. Best iteration:
## [187] train-rmse:0.127547+0.006182 test-rmse:0.941304+0.087487
##
## 19
## [1] train-rmse:84.742499+0.085026 test-rmse:84.743227+0.408848
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.927655+0.073048 test-rmse:72.928244+0.420446
## [3] train-rmse:62.762302+0.062739 test-rmse:62.762725+0.430333
## [4] train-rmse:54.016514+0.053817 test-rmse:54.016744+0.438737
## [5] train-rmse:46.492472+0.046117 test-rmse:46.492479+0.445831
## [6] train-rmse:40.019990+0.039459 test-rmse:40.019755+0.451772
## [7] train-rmse:34.452692+0.033687 test-rmse:34.452158+0.456684
## [8] train-rmse:29.664635+0.028683 test-rmse:29.663766+0.460663
## [9] train-rmse:25.547519+0.024346 test-rmse:25.546270+0.463776
## [10] train-rmse:22.008217+0.020588 test-rmse:22.006533+0.466070
## [11] train-rmse:18.966660+0.017350 test-rmse:18.964487+0.467562
## [12] train-rmse:16.354035+0.014591 test-rmse:16.351313+0.468250
## [13] train-rmse:14.110245+0.012261 test-rmse:14.110562+0.451996
## [14] train-rmse:12.183608+0.010200 test-rmse:12.178845+0.439003
## [15] train-rmse:10.529368+0.008735 test-rmse:10.521908+0.432823
## [16] train-rmse:9.107897+0.006711 test-rmse:9.094078+0.417845
## [17] train-rmse:7.886734+0.006698 test-rmse:7.868809+0.405459
## [18] train-rmse:6.837121+0.006171 test-rmse:6.818811+0.376242
## [19] train-rmse:5.935532+0.006036 test-rmse:5.918674+0.356092
## [20] train-rmse:5.160097+0.004683 test-rmse:5.148081+0.338896
## [21] train-rmse:4.494517+0.004738 test-rmse:4.489799+0.318715
## [22] train-rmse:3.921800+0.003250 test-rmse:3.927604+0.310032
## [23] train-rmse:3.429640+0.004183 test-rmse:3.449076+0.284133
## [24] train-rmse:3.007066+0.002810 test-rmse:3.037118+0.272157
## [25] train-rmse:2.644465+0.005156 test-rmse:2.693464+0.251524
## [26] train-rmse:2.334633+0.006324 test-rmse:2.397529+0.235960
## [27] train-rmse:2.065875+0.006102 test-rmse:2.155835+0.221377
## [28] train-rmse:1.839178+0.003785 test-rmse:1.954665+0.216159
## [29] train-rmse:1.641644+0.002590 test-rmse:1.784317+0.197189
## [30] train-rmse:1.478259+0.002059 test-rmse:1.644288+0.183122
## [31] train-rmse:1.336618+0.005224 test-rmse:1.533203+0.168049
## [32] train-rmse:1.212153+0.005938 test-rmse:1.439769+0.157384
## [33] train-rmse:1.110277+0.006040 test-rmse:1.369639+0.148010
## [34] train-rmse:1.025883+0.006715 test-rmse:1.312197+0.137109
## [35] train-rmse:0.953991+0.006805 test-rmse:1.263586+0.131643
## [36] train-rmse:0.890717+0.006975 test-rmse:1.227849+0.120509
## [37] train-rmse:0.837245+0.007149 test-rmse:1.197090+0.116561
## [38] train-rmse:0.793249+0.010820 test-rmse:1.171232+0.119285
## [39] train-rmse:0.754220+0.014411 test-rmse:1.148950+0.118164
## [40] train-rmse:0.721033+0.015834 test-rmse:1.129580+0.113938
## [41] train-rmse:0.693793+0.017120 test-rmse:1.114594+0.115444
## [42] train-rmse:0.665271+0.017532 test-rmse:1.104649+0.112787
## [43] train-rmse:0.642266+0.016752 test-rmse:1.096668+0.112014
## [44] train-rmse:0.620277+0.014744 test-rmse:1.087312+0.109790
## [45] train-rmse:0.601380+0.015852 test-rmse:1.079940+0.108188
## [46] train-rmse:0.582180+0.017163 test-rmse:1.070107+0.104721
## [47] train-rmse:0.568328+0.018394 test-rmse:1.065246+0.102709
## [48] train-rmse:0.553844+0.017226 test-rmse:1.061494+0.099765
## [49] train-rmse:0.539470+0.017651 test-rmse:1.055354+0.099111
## [50] train-rmse:0.528166+0.018416 test-rmse:1.051412+0.098083
## [51] train-rmse:0.517432+0.021036 test-rmse:1.045468+0.097580
## [52] train-rmse:0.507569+0.019918 test-rmse:1.038733+0.095064
## [53] train-rmse:0.497338+0.019795 test-rmse:1.035576+0.096039
## [54] train-rmse:0.488884+0.019145 test-rmse:1.032391+0.095930
## [55] train-rmse:0.476811+0.017254 test-rmse:1.025160+0.091376
## [56] train-rmse:0.466917+0.018772 test-rmse:1.020591+0.090459
## [57] train-rmse:0.458519+0.017509 test-rmse:1.016930+0.090700
## [58] train-rmse:0.450780+0.017110 test-rmse:1.013115+0.091663
## [59] train-rmse:0.442917+0.016404 test-rmse:1.011072+0.089406
## [60] train-rmse:0.436273+0.017093 test-rmse:1.008180+0.089500
## [61] train-rmse:0.426542+0.015472 test-rmse:1.006332+0.090336
## [62] train-rmse:0.418016+0.015345 test-rmse:1.005159+0.089876
## [63] train-rmse:0.410608+0.015145 test-rmse:1.001520+0.089409
## [64] train-rmse:0.405240+0.013800 test-rmse:1.000953+0.092216
## [65] train-rmse:0.399241+0.014038 test-rmse:0.999054+0.091744
## [66] train-rmse:0.391658+0.015401 test-rmse:0.996590+0.091261
## [67] train-rmse:0.383507+0.016247 test-rmse:0.995241+0.092786
## [68] train-rmse:0.377530+0.015473 test-rmse:0.990087+0.091361
## [69] train-rmse:0.372808+0.016462 test-rmse:0.987712+0.091687
## [70] train-rmse:0.367508+0.015436 test-rmse:0.984390+0.090267
## [71] train-rmse:0.359568+0.015370 test-rmse:0.982330+0.088416
## [72] train-rmse:0.353453+0.017045 test-rmse:0.980615+0.089495
## [73] train-rmse:0.348726+0.017496 test-rmse:0.981365+0.089240
## [74] train-rmse:0.343052+0.017852 test-rmse:0.979451+0.088025
## [75] train-rmse:0.338804+0.017246 test-rmse:0.978195+0.088015
## [76] train-rmse:0.333102+0.016800 test-rmse:0.977567+0.087241
## [77] train-rmse:0.327132+0.017247 test-rmse:0.978089+0.085197
## [78] train-rmse:0.321309+0.016057 test-rmse:0.974985+0.085339
## [79] train-rmse:0.317252+0.015591 test-rmse:0.974806+0.085627
## [80] train-rmse:0.311427+0.015139 test-rmse:0.971412+0.085283
## [81] train-rmse:0.306374+0.014673 test-rmse:0.969845+0.085107
## [82] train-rmse:0.302205+0.014968 test-rmse:0.970400+0.085865
## [83] train-rmse:0.297368+0.013785 test-rmse:0.967878+0.086221
## [84] train-rmse:0.292358+0.013601 test-rmse:0.967182+0.085555
## [85] train-rmse:0.288639+0.012818 test-rmse:0.967362+0.086290
## [86] train-rmse:0.284570+0.013738 test-rmse:0.966608+0.088049
## [87] train-rmse:0.279602+0.012544 test-rmse:0.964688+0.087566
## [88] train-rmse:0.276188+0.012364 test-rmse:0.964527+0.087991
## [89] train-rmse:0.273072+0.012198 test-rmse:0.963221+0.088623
## [90] train-rmse:0.267928+0.013016 test-rmse:0.963012+0.086924
## [91] train-rmse:0.265086+0.012631 test-rmse:0.963234+0.086528
## [92] train-rmse:0.259824+0.011724 test-rmse:0.962424+0.086269
## [93] train-rmse:0.255849+0.011679 test-rmse:0.961371+0.086088
## [94] train-rmse:0.252798+0.011602 test-rmse:0.960302+0.085946
## [95] train-rmse:0.249165+0.011926 test-rmse:0.959405+0.085464
## [96] train-rmse:0.244891+0.012201 test-rmse:0.959818+0.084916
## [97] train-rmse:0.241685+0.012763 test-rmse:0.959034+0.084920
## [98] train-rmse:0.237101+0.012501 test-rmse:0.959082+0.086229
## [99] train-rmse:0.233586+0.012941 test-rmse:0.959069+0.084631
## [100] train-rmse:0.229367+0.013367 test-rmse:0.958113+0.084446
## [101] train-rmse:0.225291+0.013199 test-rmse:0.956331+0.084332
## [102] train-rmse:0.221929+0.012751 test-rmse:0.956647+0.084441
## [103] train-rmse:0.219553+0.013244 test-rmse:0.956157+0.084564
## [104] train-rmse:0.216658+0.012213 test-rmse:0.955769+0.085206
## [105] train-rmse:0.213283+0.012136 test-rmse:0.955816+0.085274
## [106] train-rmse:0.210926+0.011644 test-rmse:0.954950+0.085270
## [107] train-rmse:0.207773+0.012678 test-rmse:0.954597+0.085534
## [108] train-rmse:0.204570+0.012884 test-rmse:0.952970+0.084715
## [109] train-rmse:0.200452+0.011688 test-rmse:0.952417+0.085592
## [110] train-rmse:0.198094+0.012079 test-rmse:0.952522+0.086196
## [111] train-rmse:0.195793+0.012069 test-rmse:0.952779+0.086120
## [112] train-rmse:0.192509+0.010964 test-rmse:0.952669+0.085706
## [113] train-rmse:0.190100+0.010784 test-rmse:0.952221+0.085586
## [114] train-rmse:0.186849+0.010354 test-rmse:0.951126+0.084283
## [115] train-rmse:0.184576+0.010711 test-rmse:0.951439+0.084714
## [116] train-rmse:0.182503+0.010316 test-rmse:0.951319+0.084493
## [117] train-rmse:0.179964+0.009810 test-rmse:0.951315+0.084810
## [118] train-rmse:0.178358+0.010433 test-rmse:0.951248+0.084702
## [119] train-rmse:0.176508+0.010525 test-rmse:0.950740+0.085200
## [120] train-rmse:0.173463+0.010968 test-rmse:0.950609+0.084932
## [121] train-rmse:0.170043+0.010387 test-rmse:0.950727+0.084682
## [122] train-rmse:0.166772+0.010091 test-rmse:0.951214+0.085206
## [123] train-rmse:0.164294+0.009851 test-rmse:0.951223+0.085120
## [124] train-rmse:0.162186+0.009141 test-rmse:0.950344+0.084854
## [125] train-rmse:0.159747+0.009048 test-rmse:0.951122+0.084720
## [126] train-rmse:0.157444+0.009316 test-rmse:0.951746+0.084393
## [127] train-rmse:0.155335+0.008593 test-rmse:0.951005+0.084770
## [128] train-rmse:0.152726+0.008556 test-rmse:0.950850+0.085031
## [129] train-rmse:0.150234+0.009156 test-rmse:0.950615+0.085010
## [130] train-rmse:0.148433+0.009292 test-rmse:0.951233+0.085124
## Stopping. Best iteration:
## [124] train-rmse:0.162186+0.009141 test-rmse:0.950344+0.084854
##
## 20
## [1] train-rmse:84.742499+0.085026 test-rmse:84.743227+0.408848
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.927655+0.073048 test-rmse:72.928244+0.420446
## [3] train-rmse:62.762302+0.062739 test-rmse:62.762725+0.430333
## [4] train-rmse:54.016514+0.053817 test-rmse:54.016744+0.438737
## [5] train-rmse:46.492472+0.046117 test-rmse:46.492479+0.445831
## [6] train-rmse:40.019990+0.039459 test-rmse:40.019755+0.451772
## [7] train-rmse:34.452692+0.033687 test-rmse:34.452158+0.456684
## [8] train-rmse:29.664635+0.028683 test-rmse:29.663766+0.460663
## [9] train-rmse:25.547519+0.024346 test-rmse:25.546270+0.463776
## [10] train-rmse:22.008217+0.020588 test-rmse:22.006533+0.466070
## [11] train-rmse:18.966660+0.017350 test-rmse:18.964487+0.467562
## [12] train-rmse:16.354035+0.014591 test-rmse:16.351313+0.468250
## [13] train-rmse:14.110245+0.012261 test-rmse:14.110562+0.451996
## [14] train-rmse:12.183608+0.010200 test-rmse:12.178845+0.439003
## [15] train-rmse:10.529368+0.008735 test-rmse:10.521908+0.432823
## [16] train-rmse:9.107897+0.006711 test-rmse:9.094078+0.417845
## [17] train-rmse:7.886734+0.006698 test-rmse:7.868809+0.405459
## [18] train-rmse:6.836921+0.006146 test-rmse:6.822795+0.376246
## [19] train-rmse:5.935075+0.005890 test-rmse:5.924490+0.351750
## [20] train-rmse:5.159242+0.004461 test-rmse:5.149406+0.336462
## [21] train-rmse:4.492881+0.005183 test-rmse:4.482794+0.313941
## [22] train-rmse:3.919514+0.004773 test-rmse:3.915231+0.296337
## [23] train-rmse:3.426008+0.004326 test-rmse:3.438009+0.280984
## [24] train-rmse:3.001721+0.004814 test-rmse:3.023674+0.264227
## [25] train-rmse:2.636305+0.006881 test-rmse:2.677080+0.244242
## [26] train-rmse:2.321738+0.006237 test-rmse:2.376434+0.234384
## [27] train-rmse:2.048668+0.006523 test-rmse:2.122488+0.215488
## [28] train-rmse:1.818568+0.005555 test-rmse:1.910601+0.202885
## [29] train-rmse:1.618649+0.004959 test-rmse:1.743101+0.186453
## [30] train-rmse:1.448233+0.004785 test-rmse:1.609576+0.171016
## [31] train-rmse:1.303468+0.005015 test-rmse:1.494944+0.161892
## [32] train-rmse:1.178555+0.005748 test-rmse:1.404649+0.155009
## [33] train-rmse:1.071652+0.005033 test-rmse:1.325493+0.146896
## [34] train-rmse:0.981546+0.008577 test-rmse:1.265439+0.138573
## [35] train-rmse:0.901539+0.009556 test-rmse:1.215936+0.132898
## [36] train-rmse:0.829996+0.007177 test-rmse:1.177237+0.128106
## [37] train-rmse:0.768829+0.010801 test-rmse:1.145176+0.122155
## [38] train-rmse:0.719834+0.011635 test-rmse:1.121900+0.120353
## [39] train-rmse:0.677115+0.013752 test-rmse:1.101752+0.115800
## [40] train-rmse:0.639756+0.015664 test-rmse:1.085095+0.112605
## [41] train-rmse:0.608021+0.016976 test-rmse:1.067660+0.106640
## [42] train-rmse:0.580286+0.018194 test-rmse:1.058029+0.103055
## [43] train-rmse:0.553392+0.019773 test-rmse:1.050400+0.101450
## [44] train-rmse:0.528929+0.017941 test-rmse:1.040949+0.101417
## [45] train-rmse:0.504786+0.016247 test-rmse:1.032962+0.092489
## [46] train-rmse:0.485500+0.014028 test-rmse:1.028677+0.091069
## [47] train-rmse:0.469264+0.015995 test-rmse:1.021354+0.089071
## [48] train-rmse:0.453688+0.015689 test-rmse:1.013222+0.084155
## [49] train-rmse:0.438323+0.018302 test-rmse:1.009640+0.082538
## [50] train-rmse:0.427268+0.019058 test-rmse:1.007262+0.083638
## [51] train-rmse:0.415109+0.016957 test-rmse:1.004390+0.083248
## [52] train-rmse:0.400287+0.015970 test-rmse:0.999314+0.083477
## [53] train-rmse:0.389240+0.015187 test-rmse:0.993970+0.086437
## [54] train-rmse:0.378711+0.013757 test-rmse:0.991418+0.085156
## [55] train-rmse:0.367191+0.015603 test-rmse:0.989741+0.084824
## [56] train-rmse:0.358650+0.017769 test-rmse:0.987702+0.083538
## [57] train-rmse:0.350763+0.017484 test-rmse:0.985846+0.083749
## [58] train-rmse:0.343929+0.015767 test-rmse:0.982579+0.080854
## [59] train-rmse:0.335462+0.015339 test-rmse:0.981505+0.080138
## [60] train-rmse:0.328575+0.016009 test-rmse:0.981122+0.082032
## [61] train-rmse:0.322678+0.015612 test-rmse:0.978515+0.080326
## [62] train-rmse:0.313641+0.013822 test-rmse:0.977643+0.079909
## [63] train-rmse:0.307120+0.014160 test-rmse:0.976378+0.080431
## [64] train-rmse:0.298379+0.014860 test-rmse:0.974172+0.077271
## [65] train-rmse:0.291249+0.014393 test-rmse:0.971502+0.076754
## [66] train-rmse:0.285376+0.013458 test-rmse:0.970753+0.077140
## [67] train-rmse:0.278528+0.012876 test-rmse:0.967472+0.075307
## [68] train-rmse:0.272962+0.014263 test-rmse:0.966646+0.074464
## [69] train-rmse:0.268465+0.014915 test-rmse:0.964794+0.073264
## [70] train-rmse:0.262693+0.014688 test-rmse:0.964453+0.072852
## [71] train-rmse:0.257492+0.013445 test-rmse:0.962350+0.071804
## [72] train-rmse:0.251716+0.013451 test-rmse:0.960590+0.071013
## [73] train-rmse:0.246648+0.013399 test-rmse:0.959683+0.072171
## [74] train-rmse:0.242524+0.013298 test-rmse:0.958282+0.071635
## [75] train-rmse:0.236917+0.013315 test-rmse:0.958299+0.071467
## [76] train-rmse:0.232257+0.014027 test-rmse:0.957538+0.071194
## [77] train-rmse:0.227196+0.013971 test-rmse:0.956576+0.071364
## [78] train-rmse:0.223625+0.013709 test-rmse:0.955747+0.070390
## [79] train-rmse:0.218320+0.013071 test-rmse:0.956312+0.070236
## [80] train-rmse:0.215081+0.013068 test-rmse:0.956295+0.071015
## [81] train-rmse:0.211757+0.013199 test-rmse:0.954986+0.071364
## [82] train-rmse:0.208393+0.013167 test-rmse:0.954225+0.070411
## [83] train-rmse:0.205490+0.011940 test-rmse:0.953123+0.069921
## [84] train-rmse:0.202461+0.012471 test-rmse:0.952335+0.070082
## [85] train-rmse:0.197692+0.012506 test-rmse:0.951580+0.070123
## [86] train-rmse:0.194607+0.011880 test-rmse:0.950608+0.069740
## [87] train-rmse:0.191390+0.011385 test-rmse:0.950143+0.070115
## [88] train-rmse:0.187213+0.011527 test-rmse:0.949427+0.069957
## [89] train-rmse:0.183551+0.011047 test-rmse:0.949125+0.069765
## [90] train-rmse:0.181118+0.011634 test-rmse:0.949092+0.070434
## [91] train-rmse:0.176731+0.011040 test-rmse:0.947141+0.070183
## [92] train-rmse:0.172126+0.010076 test-rmse:0.947183+0.071104
## [93] train-rmse:0.169433+0.010646 test-rmse:0.946336+0.070799
## [94] train-rmse:0.165690+0.009328 test-rmse:0.945934+0.069819
## [95] train-rmse:0.162511+0.009224 test-rmse:0.944553+0.069430
## [96] train-rmse:0.159945+0.008701 test-rmse:0.944438+0.068901
## [97] train-rmse:0.157685+0.009307 test-rmse:0.943744+0.068534
## [98] train-rmse:0.154587+0.009294 test-rmse:0.943524+0.068204
## [99] train-rmse:0.151382+0.008451 test-rmse:0.942394+0.068183
## [100] train-rmse:0.148210+0.008071 test-rmse:0.942043+0.068107
## [101] train-rmse:0.145588+0.009079 test-rmse:0.941315+0.068001
## [102] train-rmse:0.142664+0.009093 test-rmse:0.940939+0.068000
## [103] train-rmse:0.139005+0.008269 test-rmse:0.939715+0.068174
## [104] train-rmse:0.136560+0.007994 test-rmse:0.939014+0.067872
## [105] train-rmse:0.133958+0.008096 test-rmse:0.938964+0.068452
## [106] train-rmse:0.131501+0.007573 test-rmse:0.939002+0.069158
## [107] train-rmse:0.128693+0.006780 test-rmse:0.937861+0.068920
## [108] train-rmse:0.127182+0.006617 test-rmse:0.937342+0.069053
## [109] train-rmse:0.125000+0.006591 test-rmse:0.936855+0.068666
## [110] train-rmse:0.123433+0.006879 test-rmse:0.936798+0.068134
## [111] train-rmse:0.121448+0.006648 test-rmse:0.936344+0.067555
## [112] train-rmse:0.118950+0.007415 test-rmse:0.936013+0.067347
## [113] train-rmse:0.116179+0.007071 test-rmse:0.934573+0.067085
## [114] train-rmse:0.113693+0.006682 test-rmse:0.934653+0.066624
## [115] train-rmse:0.111897+0.006958 test-rmse:0.934394+0.066409
## [116] train-rmse:0.109511+0.006556 test-rmse:0.933814+0.065885
## [117] train-rmse:0.107952+0.006592 test-rmse:0.934030+0.066034
## [118] train-rmse:0.106485+0.006851 test-rmse:0.933955+0.066115
## [119] train-rmse:0.104257+0.006636 test-rmse:0.933558+0.065956
## [120] train-rmse:0.102558+0.006807 test-rmse:0.932983+0.065972
## [121] train-rmse:0.100509+0.006775 test-rmse:0.932799+0.066473
## [122] train-rmse:0.098498+0.006759 test-rmse:0.932421+0.066099
## [123] train-rmse:0.096890+0.006839 test-rmse:0.931919+0.066181
## [124] train-rmse:0.095048+0.007210 test-rmse:0.931290+0.066044
## [125] train-rmse:0.093458+0.006590 test-rmse:0.931197+0.066331
## [126] train-rmse:0.091808+0.006293 test-rmse:0.931209+0.066817
## [127] train-rmse:0.090440+0.005964 test-rmse:0.930916+0.066836
## [128] train-rmse:0.088857+0.006052 test-rmse:0.930896+0.066866
## [129] train-rmse:0.087137+0.005942 test-rmse:0.930661+0.066955
## [130] train-rmse:0.085578+0.005501 test-rmse:0.930338+0.066816
## [131] train-rmse:0.084087+0.005877 test-rmse:0.929856+0.066801
## [132] train-rmse:0.082573+0.006444 test-rmse:0.929941+0.066707
## [133] train-rmse:0.080752+0.006486 test-rmse:0.929389+0.066996
## [134] train-rmse:0.079685+0.006359 test-rmse:0.929394+0.067252
## [135] train-rmse:0.078123+0.006481 test-rmse:0.928768+0.067803
## [136] train-rmse:0.076988+0.006495 test-rmse:0.928717+0.067455
## [137] train-rmse:0.075333+0.006094 test-rmse:0.928764+0.067595
## [138] train-rmse:0.073613+0.006132 test-rmse:0.928714+0.067761
## [139] train-rmse:0.071750+0.005862 test-rmse:0.928691+0.067780
## [140] train-rmse:0.070553+0.005662 test-rmse:0.928826+0.067910
## [141] train-rmse:0.069649+0.005729 test-rmse:0.928848+0.067845
## [142] train-rmse:0.068473+0.005516 test-rmse:0.928469+0.067798
## [143] train-rmse:0.067170+0.005224 test-rmse:0.928177+0.068160
## [144] train-rmse:0.065862+0.005299 test-rmse:0.928185+0.068231
## [145] train-rmse:0.065049+0.005207 test-rmse:0.928040+0.068500
## [146] train-rmse:0.063759+0.005012 test-rmse:0.927976+0.068656
## [147] train-rmse:0.062583+0.004598 test-rmse:0.927759+0.068859
## [148] train-rmse:0.061491+0.004343 test-rmse:0.927421+0.069052
## [149] train-rmse:0.060204+0.004242 test-rmse:0.927113+0.069428
## [150] train-rmse:0.058952+0.004210 test-rmse:0.926661+0.069222
## [151] train-rmse:0.057704+0.004093 test-rmse:0.926466+0.069190
## [152] train-rmse:0.056794+0.003971 test-rmse:0.926517+0.069211
## [153] train-rmse:0.055472+0.003759 test-rmse:0.926600+0.069283
## [154] train-rmse:0.054345+0.003532 test-rmse:0.926334+0.069044
## [155] train-rmse:0.053596+0.003278 test-rmse:0.926194+0.069140
## [156] train-rmse:0.052500+0.003186 test-rmse:0.925882+0.069039
## [157] train-rmse:0.051621+0.002703 test-rmse:0.925959+0.069350
## [158] train-rmse:0.050609+0.002504 test-rmse:0.925754+0.069494
## [159] train-rmse:0.049704+0.002556 test-rmse:0.925945+0.069615
## [160] train-rmse:0.048926+0.002530 test-rmse:0.925936+0.069552
## [161] train-rmse:0.048051+0.002571 test-rmse:0.925800+0.069734
## [162] train-rmse:0.047265+0.002346 test-rmse:0.925890+0.069799
## [163] train-rmse:0.046515+0.002382 test-rmse:0.925762+0.069907
## [164] train-rmse:0.045651+0.002212 test-rmse:0.925628+0.070008
## [165] train-rmse:0.044879+0.002277 test-rmse:0.925676+0.070049
## [166] train-rmse:0.044054+0.002279 test-rmse:0.925514+0.069995
## [167] train-rmse:0.043299+0.002364 test-rmse:0.925228+0.069818
## [168] train-rmse:0.042783+0.002450 test-rmse:0.925186+0.069875
## [169] train-rmse:0.041966+0.002364 test-rmse:0.925086+0.069964
## [170] train-rmse:0.041195+0.002410 test-rmse:0.925279+0.070023
## [171] train-rmse:0.040407+0.002105 test-rmse:0.925029+0.069966
## [172] train-rmse:0.039656+0.002012 test-rmse:0.924758+0.070215
## [173] train-rmse:0.039108+0.001936 test-rmse:0.924577+0.070102
## [174] train-rmse:0.038460+0.001920 test-rmse:0.924608+0.070222
## [175] train-rmse:0.037913+0.001993 test-rmse:0.924468+0.070195
## [176] train-rmse:0.037149+0.002170 test-rmse:0.924316+0.070169
## [177] train-rmse:0.036361+0.002036 test-rmse:0.924370+0.070209
## [178] train-rmse:0.035833+0.002107 test-rmse:0.924088+0.070200
## [179] train-rmse:0.035279+0.002032 test-rmse:0.923978+0.070213
## [180] train-rmse:0.034752+0.002101 test-rmse:0.924022+0.070222
## [181] train-rmse:0.034159+0.001984 test-rmse:0.924071+0.070281
## [182] train-rmse:0.033531+0.001993 test-rmse:0.924065+0.070359
## [183] train-rmse:0.033041+0.001832 test-rmse:0.924001+0.070422
## [184] train-rmse:0.032254+0.001810 test-rmse:0.924012+0.070542
## [185] train-rmse:0.031732+0.001756 test-rmse:0.923989+0.070659
## Stopping. Best iteration:
## [179] train-rmse:0.035279+0.002032 test-rmse:0.923978+0.070213
##
## 21
## [1] train-rmse:82.780940+0.083029 test-rmse:82.781650+0.410772
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.591332+0.069678 test-rmse:69.591866+0.423700
## [3] train-rmse:58.506326+0.058401 test-rmse:58.506664+0.434438
## [4] train-rmse:49.190703+0.048890 test-rmse:49.190803+0.443303
## [5] train-rmse:41.362720+0.040844 test-rmse:41.362536+0.450558
## [6] train-rmse:34.785638+0.034034 test-rmse:34.785128+0.456398
## [7] train-rmse:29.260552+0.028262 test-rmse:29.259653+0.460980
## [8] train-rmse:24.620358+0.023369 test-rmse:24.619006+0.464415
## [9] train-rmse:20.724704+0.019222 test-rmse:20.722832+0.466766
## [10] train-rmse:17.455756+0.015748 test-rmse:17.453284+0.468046
## [11] train-rmse:14.714250+0.013047 test-rmse:14.705223+0.464215
## [12] train-rmse:12.415611+0.010599 test-rmse:12.409716+0.446841
## [13] train-rmse:10.489682+0.008431 test-rmse:10.475633+0.448375
## [14] train-rmse:8.876798+0.006610 test-rmse:8.862031+0.441172
## [15] train-rmse:7.528642+0.005909 test-rmse:7.514037+0.428347
## [16] train-rmse:6.404087+0.005637 test-rmse:6.387192+0.418038
## [17] train-rmse:5.469022+0.006274 test-rmse:5.451500+0.412101
## [18] train-rmse:4.694578+0.007116 test-rmse:4.682648+0.396899
## [19] train-rmse:4.056292+0.008787 test-rmse:4.052763+0.381816
## [20] train-rmse:3.533026+0.009848 test-rmse:3.536434+0.360898
## [21] train-rmse:3.107438+0.011942 test-rmse:3.112390+0.334544
## [22] train-rmse:2.764123+0.013582 test-rmse:2.779130+0.307737
## [23] train-rmse:2.489793+0.015024 test-rmse:2.514287+0.279045
## [24] train-rmse:2.272413+0.016626 test-rmse:2.306339+0.247036
## [25] train-rmse:2.102116+0.018124 test-rmse:2.145368+0.216356
## [26] train-rmse:1.970162+0.019452 test-rmse:2.014307+0.195740
## [27] train-rmse:1.868051+0.020384 test-rmse:1.921799+0.171585
## [28] train-rmse:1.789008+0.021182 test-rmse:1.852984+0.152326
## [29] train-rmse:1.727974+0.021890 test-rmse:1.795820+0.136733
## [30] train-rmse:1.680749+0.022357 test-rmse:1.754544+0.125633
## [31] train-rmse:1.643333+0.022539 test-rmse:1.724513+0.118809
## [32] train-rmse:1.613511+0.022210 test-rmse:1.711525+0.118848
## [33] train-rmse:1.589502+0.022014 test-rmse:1.692927+0.119020
## [34] train-rmse:1.569324+0.021577 test-rmse:1.671661+0.121152
## [35] train-rmse:1.552707+0.021582 test-rmse:1.649427+0.115711
## [36] train-rmse:1.538179+0.021709 test-rmse:1.638440+0.114447
## [37] train-rmse:1.525444+0.021726 test-rmse:1.625827+0.113176
## [38] train-rmse:1.513835+0.021650 test-rmse:1.619423+0.112932
## [39] train-rmse:1.503207+0.021593 test-rmse:1.614285+0.111111
## [40] train-rmse:1.493476+0.021209 test-rmse:1.607992+0.113519
## [41] train-rmse:1.484561+0.021242 test-rmse:1.602532+0.115949
## [42] train-rmse:1.476175+0.021098 test-rmse:1.596492+0.119514
## [43] train-rmse:1.468111+0.020947 test-rmse:1.591992+0.118571
## [44] train-rmse:1.460268+0.020831 test-rmse:1.583863+0.115491
## [45] train-rmse:1.452718+0.020682 test-rmse:1.570573+0.108583
## [46] train-rmse:1.445486+0.020590 test-rmse:1.565881+0.109571
## [47] train-rmse:1.438412+0.020532 test-rmse:1.560456+0.108750
## [48] train-rmse:1.431588+0.020690 test-rmse:1.557692+0.106717
## [49] train-rmse:1.424819+0.020759 test-rmse:1.559508+0.106905
## [50] train-rmse:1.418338+0.020734 test-rmse:1.554626+0.108968
## [51] train-rmse:1.412069+0.020725 test-rmse:1.551227+0.108023
## [52] train-rmse:1.405839+0.020759 test-rmse:1.548964+0.107672
## [53] train-rmse:1.399838+0.020684 test-rmse:1.542884+0.110363
## [54] train-rmse:1.393957+0.020668 test-rmse:1.538535+0.109759
## [55] train-rmse:1.388047+0.020651 test-rmse:1.533206+0.109777
## [56] train-rmse:1.382253+0.020701 test-rmse:1.521542+0.103821
## [57] train-rmse:1.376523+0.020710 test-rmse:1.518067+0.105083
## [58] train-rmse:1.370879+0.020684 test-rmse:1.513213+0.108029
## [59] train-rmse:1.365425+0.020645 test-rmse:1.510918+0.106949
## [60] train-rmse:1.359995+0.020565 test-rmse:1.510248+0.106433
## [61] train-rmse:1.354740+0.020532 test-rmse:1.507278+0.101706
## [62] train-rmse:1.349625+0.020389 test-rmse:1.505257+0.104295
## [63] train-rmse:1.344469+0.020321 test-rmse:1.503435+0.105864
## [64] train-rmse:1.339434+0.020236 test-rmse:1.501626+0.104658
## [65] train-rmse:1.334409+0.019997 test-rmse:1.497892+0.100214
## [66] train-rmse:1.329581+0.019781 test-rmse:1.492883+0.099100
## [67] train-rmse:1.324873+0.019703 test-rmse:1.488122+0.098634
## [68] train-rmse:1.320259+0.019676 test-rmse:1.485749+0.097345
## [69] train-rmse:1.315681+0.019614 test-rmse:1.481501+0.096206
## [70] train-rmse:1.311110+0.019559 test-rmse:1.479614+0.095363
## [71] train-rmse:1.306659+0.019562 test-rmse:1.478878+0.092116
## [72] train-rmse:1.302233+0.019538 test-rmse:1.474405+0.095110
## [73] train-rmse:1.297933+0.019541 test-rmse:1.475206+0.093689
## [74] train-rmse:1.293701+0.019555 test-rmse:1.473793+0.093517
## [75] train-rmse:1.289506+0.019554 test-rmse:1.470365+0.093905
## [76] train-rmse:1.285347+0.019512 test-rmse:1.465395+0.095213
## [77] train-rmse:1.281248+0.019483 test-rmse:1.461452+0.095438
## [78] train-rmse:1.277240+0.019405 test-rmse:1.457553+0.095798
## [79] train-rmse:1.273271+0.019294 test-rmse:1.455765+0.091788
## [80] train-rmse:1.269324+0.019178 test-rmse:1.451550+0.092160
## [81] train-rmse:1.265459+0.019168 test-rmse:1.449947+0.091601
## [82] train-rmse:1.261674+0.019143 test-rmse:1.449388+0.090787
## [83] train-rmse:1.258003+0.019057 test-rmse:1.448859+0.085584
## [84] train-rmse:1.254387+0.019011 test-rmse:1.440704+0.084656
## [85] train-rmse:1.250811+0.018974 test-rmse:1.438978+0.085250
## [86] train-rmse:1.247256+0.018981 test-rmse:1.436556+0.086994
## [87] train-rmse:1.243700+0.018982 test-rmse:1.435556+0.088502
## [88] train-rmse:1.240266+0.019027 test-rmse:1.433075+0.088556
## [89] train-rmse:1.236918+0.019017 test-rmse:1.431684+0.089070
## [90] train-rmse:1.233528+0.019058 test-rmse:1.431067+0.085958
## [91] train-rmse:1.230212+0.019086 test-rmse:1.429253+0.087315
## [92] train-rmse:1.226905+0.019101 test-rmse:1.425528+0.088709
## [93] train-rmse:1.223688+0.019121 test-rmse:1.423092+0.089162
## [94] train-rmse:1.220561+0.019138 test-rmse:1.420966+0.090051
## [95] train-rmse:1.217417+0.019135 test-rmse:1.418267+0.089453
## [96] train-rmse:1.214287+0.019144 test-rmse:1.415055+0.087459
## [97] train-rmse:1.211149+0.019162 test-rmse:1.412254+0.086763
## [98] train-rmse:1.208093+0.019175 test-rmse:1.411289+0.086575
## [99] train-rmse:1.205131+0.019168 test-rmse:1.408608+0.081239
## [100] train-rmse:1.202192+0.019190 test-rmse:1.406853+0.080121
## [101] train-rmse:1.199223+0.019179 test-rmse:1.401811+0.081660
## [102] train-rmse:1.196393+0.019181 test-rmse:1.398137+0.084814
## [103] train-rmse:1.193579+0.019156 test-rmse:1.397498+0.084349
## [104] train-rmse:1.190798+0.019144 test-rmse:1.394805+0.082705
## [105] train-rmse:1.188018+0.019154 test-rmse:1.394253+0.082494
## [106] train-rmse:1.185305+0.019143 test-rmse:1.391665+0.083158
## [107] train-rmse:1.182591+0.019170 test-rmse:1.388495+0.084289
## [108] train-rmse:1.179929+0.019167 test-rmse:1.388400+0.086123
## [109] train-rmse:1.177208+0.019200 test-rmse:1.385283+0.086592
## [110] train-rmse:1.174539+0.019195 test-rmse:1.382414+0.086832
## [111] train-rmse:1.171981+0.019145 test-rmse:1.380591+0.086661
## [112] train-rmse:1.169467+0.019105 test-rmse:1.379412+0.086516
## [113] train-rmse:1.167013+0.019058 test-rmse:1.377666+0.086681
## [114] train-rmse:1.164563+0.019026 test-rmse:1.375829+0.082686
## [115] train-rmse:1.162115+0.018983 test-rmse:1.374701+0.083565
## [116] train-rmse:1.159709+0.018948 test-rmse:1.374472+0.085311
## [117] train-rmse:1.157307+0.018936 test-rmse:1.372049+0.080767
## [118] train-rmse:1.154972+0.018935 test-rmse:1.371632+0.080356
## [119] train-rmse:1.152631+0.018931 test-rmse:1.370171+0.081181
## [120] train-rmse:1.150339+0.018948 test-rmse:1.370296+0.082296
## [121] train-rmse:1.148117+0.018950 test-rmse:1.368705+0.082597
## [122] train-rmse:1.145902+0.018942 test-rmse:1.365859+0.084107
## [123] train-rmse:1.143714+0.018954 test-rmse:1.363920+0.082534
## [124] train-rmse:1.141534+0.018976 test-rmse:1.362060+0.082081
## [125] train-rmse:1.139369+0.019017 test-rmse:1.362863+0.080164
## [126] train-rmse:1.137259+0.019043 test-rmse:1.360887+0.079078
## [127] train-rmse:1.135182+0.019053 test-rmse:1.357176+0.079385
## [128] train-rmse:1.133091+0.019032 test-rmse:1.355260+0.079080
## [129] train-rmse:1.131049+0.019022 test-rmse:1.353851+0.078529
## [130] train-rmse:1.129047+0.018993 test-rmse:1.351029+0.077299
## [131] train-rmse:1.127053+0.018955 test-rmse:1.348217+0.078248
## [132] train-rmse:1.125075+0.018924 test-rmse:1.346442+0.081195
## [133] train-rmse:1.123122+0.018879 test-rmse:1.344938+0.080144
## [134] train-rmse:1.121179+0.018825 test-rmse:1.343742+0.080163
## [135] train-rmse:1.119244+0.018772 test-rmse:1.342808+0.081416
## [136] train-rmse:1.117309+0.018765 test-rmse:1.340932+0.081762
## [137] train-rmse:1.115422+0.018747 test-rmse:1.336540+0.081459
## [138] train-rmse:1.113538+0.018728 test-rmse:1.335309+0.082298
## [139] train-rmse:1.111682+0.018718 test-rmse:1.333408+0.082420
## [140] train-rmse:1.109840+0.018687 test-rmse:1.332682+0.080673
## [141] train-rmse:1.108011+0.018646 test-rmse:1.332729+0.081860
## [142] train-rmse:1.106207+0.018603 test-rmse:1.330436+0.082335
## [143] train-rmse:1.104405+0.018588 test-rmse:1.329034+0.082563
## [144] train-rmse:1.102603+0.018565 test-rmse:1.328391+0.082900
## [145] train-rmse:1.100831+0.018558 test-rmse:1.326180+0.082810
## [146] train-rmse:1.099102+0.018519 test-rmse:1.325825+0.082428
## [147] train-rmse:1.097388+0.018510 test-rmse:1.324535+0.081790
## [148] train-rmse:1.095693+0.018485 test-rmse:1.324033+0.081572
## [149] train-rmse:1.094040+0.018483 test-rmse:1.323608+0.082247
## [150] train-rmse:1.092420+0.018462 test-rmse:1.321957+0.083592
## [151] train-rmse:1.090819+0.018437 test-rmse:1.320917+0.081737
## [152] train-rmse:1.089227+0.018429 test-rmse:1.321282+0.082117
## [153] train-rmse:1.087650+0.018415 test-rmse:1.319190+0.081600
## [154] train-rmse:1.086074+0.018382 test-rmse:1.318329+0.081580
## [155] train-rmse:1.084514+0.018344 test-rmse:1.315359+0.079388
## [156] train-rmse:1.082969+0.018310 test-rmse:1.314892+0.081096
## [157] train-rmse:1.081447+0.018279 test-rmse:1.312070+0.083952
## [158] train-rmse:1.079935+0.018259 test-rmse:1.310549+0.084283
## [159] train-rmse:1.078440+0.018268 test-rmse:1.310065+0.084846
## [160] train-rmse:1.076955+0.018271 test-rmse:1.311156+0.085104
## [161] train-rmse:1.075480+0.018267 test-rmse:1.309629+0.086141
## [162] train-rmse:1.074029+0.018270 test-rmse:1.309350+0.085530
## [163] train-rmse:1.072582+0.018257 test-rmse:1.308182+0.085145
## [164] train-rmse:1.071133+0.018247 test-rmse:1.306176+0.085059
## [165] train-rmse:1.069731+0.018225 test-rmse:1.306145+0.084840
## [166] train-rmse:1.068320+0.018171 test-rmse:1.303986+0.084549
## [167] train-rmse:1.066944+0.018127 test-rmse:1.303132+0.085047
## [168] train-rmse:1.065593+0.018101 test-rmse:1.303978+0.085374
## [169] train-rmse:1.064246+0.018085 test-rmse:1.303660+0.085455
## [170] train-rmse:1.062926+0.018051 test-rmse:1.304575+0.084935
## [171] train-rmse:1.061599+0.018016 test-rmse:1.303537+0.084928
## [172] train-rmse:1.060244+0.017972 test-rmse:1.302647+0.082615
## [173] train-rmse:1.058939+0.017943 test-rmse:1.301325+0.082184
## [174] train-rmse:1.057645+0.017917 test-rmse:1.299657+0.082957
## [175] train-rmse:1.056369+0.017891 test-rmse:1.298914+0.084615
## [176] train-rmse:1.055097+0.017857 test-rmse:1.297578+0.085104
## [177] train-rmse:1.053849+0.017836 test-rmse:1.294070+0.084806
## [178] train-rmse:1.052615+0.017806 test-rmse:1.291617+0.085308
## [179] train-rmse:1.051398+0.017784 test-rmse:1.291678+0.087107
## [180] train-rmse:1.050186+0.017751 test-rmse:1.290964+0.088051
## [181] train-rmse:1.048991+0.017731 test-rmse:1.289767+0.088103
## [182] train-rmse:1.047791+0.017726 test-rmse:1.289171+0.087587
## [183] train-rmse:1.046604+0.017721 test-rmse:1.288394+0.087420
## [184] train-rmse:1.045435+0.017700 test-rmse:1.286362+0.087165
## [185] train-rmse:1.044267+0.017661 test-rmse:1.286914+0.086962
## [186] train-rmse:1.043095+0.017626 test-rmse:1.286157+0.087713
## [187] train-rmse:1.041956+0.017580 test-rmse:1.285611+0.087562
## [188] train-rmse:1.040812+0.017569 test-rmse:1.284803+0.088862
## [189] train-rmse:1.039685+0.017536 test-rmse:1.282970+0.089759
## [190] train-rmse:1.038566+0.017512 test-rmse:1.282075+0.090336
## [191] train-rmse:1.037458+0.017499 test-rmse:1.280548+0.090225
## [192] train-rmse:1.036352+0.017479 test-rmse:1.278943+0.090031
## [193] train-rmse:1.035224+0.017470 test-rmse:1.278252+0.088822
## [194] train-rmse:1.034110+0.017447 test-rmse:1.278265+0.088093
## [195] train-rmse:1.033036+0.017428 test-rmse:1.279669+0.088721
## [196] train-rmse:1.031950+0.017402 test-rmse:1.279789+0.086180
## [197] train-rmse:1.030911+0.017388 test-rmse:1.279522+0.086414
## [198] train-rmse:1.029880+0.017369 test-rmse:1.276415+0.083629
## [199] train-rmse:1.028855+0.017356 test-rmse:1.275627+0.082467
## [200] train-rmse:1.027813+0.017321 test-rmse:1.274858+0.082309
## [201] train-rmse:1.026793+0.017310 test-rmse:1.273606+0.081691
## [202] train-rmse:1.025794+0.017289 test-rmse:1.273178+0.082379
## [203] train-rmse:1.024782+0.017283 test-rmse:1.272367+0.082899
## [204] train-rmse:1.023796+0.017273 test-rmse:1.270108+0.083435
## [205] train-rmse:1.022817+0.017259 test-rmse:1.270021+0.083103
## [206] train-rmse:1.021837+0.017250 test-rmse:1.270305+0.082968
## [207] train-rmse:1.020870+0.017238 test-rmse:1.270446+0.083317
## [208] train-rmse:1.019912+0.017220 test-rmse:1.270016+0.083202
## [209] train-rmse:1.018948+0.017195 test-rmse:1.271081+0.082850
## [210] train-rmse:1.017990+0.017180 test-rmse:1.270808+0.082572
## [211] train-rmse:1.017043+0.017165 test-rmse:1.269751+0.081934
## [212] train-rmse:1.016105+0.017157 test-rmse:1.268790+0.083012
## [213] train-rmse:1.015154+0.017171 test-rmse:1.266901+0.083406
## [214] train-rmse:1.014217+0.017171 test-rmse:1.264951+0.084135
## [215] train-rmse:1.013286+0.017158 test-rmse:1.263761+0.085172
## [216] train-rmse:1.012371+0.017143 test-rmse:1.263969+0.084288
## [217] train-rmse:1.011467+0.017119 test-rmse:1.263175+0.084768
## [218] train-rmse:1.010572+0.017097 test-rmse:1.261849+0.085248
## [219] train-rmse:1.009668+0.017071 test-rmse:1.262154+0.084652
## [220] train-rmse:1.008790+0.017058 test-rmse:1.261378+0.085153
## [221] train-rmse:1.007922+0.017059 test-rmse:1.261211+0.085188
## [222] train-rmse:1.007028+0.017055 test-rmse:1.262016+0.084266
## [223] train-rmse:1.006165+0.017055 test-rmse:1.262161+0.083621
## [224] train-rmse:1.005300+0.017052 test-rmse:1.262881+0.083384
## [225] train-rmse:1.004450+0.017047 test-rmse:1.262958+0.083374
## [226] train-rmse:1.003598+0.017051 test-rmse:1.261469+0.084922
## [227] train-rmse:1.002741+0.017051 test-rmse:1.259751+0.081757
## [228] train-rmse:1.001894+0.017044 test-rmse:1.259457+0.081949
## [229] train-rmse:1.001053+0.017051 test-rmse:1.259180+0.082221
## [230] train-rmse:1.000217+0.017063 test-rmse:1.258061+0.082499
## [231] train-rmse:0.999396+0.017065 test-rmse:1.256606+0.082376
## [232] train-rmse:0.998587+0.017073 test-rmse:1.255921+0.081663
## [233] train-rmse:0.997774+0.017089 test-rmse:1.254818+0.082785
## [234] train-rmse:0.996974+0.017096 test-rmse:1.254517+0.083274
## [235] train-rmse:0.996171+0.017085 test-rmse:1.253717+0.083415
## [236] train-rmse:0.995358+0.017086 test-rmse:1.253325+0.083171
## [237] train-rmse:0.994566+0.017094 test-rmse:1.253145+0.083183
## [238] train-rmse:0.993775+0.017083 test-rmse:1.251875+0.083584
## [239] train-rmse:0.992999+0.017086 test-rmse:1.251907+0.083329
## [240] train-rmse:0.992233+0.017083 test-rmse:1.251230+0.083227
## [241] train-rmse:0.991457+0.017096 test-rmse:1.250270+0.083511
## [242] train-rmse:0.990690+0.017101 test-rmse:1.250625+0.082794
## [243] train-rmse:0.989935+0.017108 test-rmse:1.249719+0.083254
## [244] train-rmse:0.989182+0.017123 test-rmse:1.249648+0.082885
## [245] train-rmse:0.988436+0.017131 test-rmse:1.248331+0.082949
## [246] train-rmse:0.987687+0.017128 test-rmse:1.248318+0.083005
## [247] train-rmse:0.986940+0.017129 test-rmse:1.250239+0.083160
## [248] train-rmse:0.986196+0.017122 test-rmse:1.249811+0.083357
## [249] train-rmse:0.985463+0.017127 test-rmse:1.248734+0.083291
## [250] train-rmse:0.984721+0.017136 test-rmse:1.248371+0.084758
## [251] train-rmse:0.983989+0.017145 test-rmse:1.248508+0.084935
## [252] train-rmse:0.983253+0.017153 test-rmse:1.247531+0.084857
## [253] train-rmse:0.982513+0.017153 test-rmse:1.246160+0.082592
## [254] train-rmse:0.981784+0.017168 test-rmse:1.245951+0.083575
## [255] train-rmse:0.981087+0.017172 test-rmse:1.246678+0.082266
## [256] train-rmse:0.980384+0.017171 test-rmse:1.246507+0.081421
## [257] train-rmse:0.979665+0.017176 test-rmse:1.245651+0.081091
## [258] train-rmse:0.978969+0.017172 test-rmse:1.246092+0.080547
## [259] train-rmse:0.978268+0.017157 test-rmse:1.246627+0.080194
## [260] train-rmse:0.977579+0.017155 test-rmse:1.246793+0.080109
## [261] train-rmse:0.976892+0.017167 test-rmse:1.245926+0.080055
## [262] train-rmse:0.976220+0.017163 test-rmse:1.244810+0.080850
## [263] train-rmse:0.975554+0.017167 test-rmse:1.244741+0.080893
## [264] train-rmse:0.974877+0.017176 test-rmse:1.244221+0.081372
## [265] train-rmse:0.974215+0.017176 test-rmse:1.242690+0.080412
## [266] train-rmse:0.973555+0.017173 test-rmse:1.242766+0.080485
## [267] train-rmse:0.972911+0.017174 test-rmse:1.243238+0.080753
## [268] train-rmse:0.972269+0.017174 test-rmse:1.243096+0.080946
## [269] train-rmse:0.971624+0.017183 test-rmse:1.241884+0.081532
## [270] train-rmse:0.970967+0.017197 test-rmse:1.241706+0.081365
## [271] train-rmse:0.970333+0.017207 test-rmse:1.241114+0.080769
## [272] train-rmse:0.969688+0.017195 test-rmse:1.240704+0.080569
## [273] train-rmse:0.969057+0.017192 test-rmse:1.240051+0.079823
## [274] train-rmse:0.968431+0.017192 test-rmse:1.240617+0.079605
## [275] train-rmse:0.967799+0.017200 test-rmse:1.239362+0.079377
## [276] train-rmse:0.967176+0.017212 test-rmse:1.240172+0.078848
## [277] train-rmse:0.966556+0.017220 test-rmse:1.239506+0.079353
## [278] train-rmse:0.965937+0.017226 test-rmse:1.239681+0.079050
## [279] train-rmse:0.965298+0.017243 test-rmse:1.238654+0.080744
## [280] train-rmse:0.964683+0.017253 test-rmse:1.238114+0.081201
## [281] train-rmse:0.964074+0.017255 test-rmse:1.237541+0.081526
## [282] train-rmse:0.963468+0.017263 test-rmse:1.238330+0.081922
## [283] train-rmse:0.962873+0.017263 test-rmse:1.237581+0.082592
## [284] train-rmse:0.962266+0.017270 test-rmse:1.237284+0.082732
## [285] train-rmse:0.961658+0.017279 test-rmse:1.236398+0.082566
## [286] train-rmse:0.961080+0.017292 test-rmse:1.236561+0.082329
## [287] train-rmse:0.960503+0.017308 test-rmse:1.236322+0.081345
## [288] train-rmse:0.959899+0.017313 test-rmse:1.236671+0.080906
## [289] train-rmse:0.959305+0.017314 test-rmse:1.235740+0.081977
## [290] train-rmse:0.958725+0.017329 test-rmse:1.235412+0.082059
## [291] train-rmse:0.958153+0.017338 test-rmse:1.234985+0.082572
## [292] train-rmse:0.957584+0.017348 test-rmse:1.234943+0.082132
## [293] train-rmse:0.957007+0.017360 test-rmse:1.235650+0.081967
## [294] train-rmse:0.956435+0.017365 test-rmse:1.233742+0.080496
## [295] train-rmse:0.955858+0.017380 test-rmse:1.233132+0.080278
## [296] train-rmse:0.955290+0.017374 test-rmse:1.232641+0.079597
## [297] train-rmse:0.954713+0.017393 test-rmse:1.232615+0.079945
## [298] train-rmse:0.954154+0.017406 test-rmse:1.232625+0.080056
## [299] train-rmse:0.953603+0.017415 test-rmse:1.232879+0.079499
## [300] train-rmse:0.953052+0.017424 test-rmse:1.232857+0.079122
## [301] train-rmse:0.952495+0.017408 test-rmse:1.233037+0.079659
## [302] train-rmse:0.951948+0.017413 test-rmse:1.233145+0.079468
## [303] train-rmse:0.951407+0.017407 test-rmse:1.232769+0.079335
## Stopping. Best iteration:
## [297] train-rmse:0.954713+0.017393 test-rmse:1.232615+0.079945
##
## 22
## [1] train-rmse:82.780940+0.083029 test-rmse:82.781650+0.410772
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.591332+0.069678 test-rmse:69.591866+0.423700
## [3] train-rmse:58.506326+0.058401 test-rmse:58.506664+0.434438
## [4] train-rmse:49.190703+0.048890 test-rmse:49.190803+0.443303
## [5] train-rmse:41.362720+0.040844 test-rmse:41.362536+0.450558
## [6] train-rmse:34.785638+0.034034 test-rmse:34.785128+0.456398
## [7] train-rmse:29.260552+0.028262 test-rmse:29.259653+0.460980
## [8] train-rmse:24.620358+0.023369 test-rmse:24.619006+0.464415
## [9] train-rmse:20.724704+0.019222 test-rmse:20.722832+0.466766
## [10] train-rmse:17.455756+0.015748 test-rmse:17.453284+0.468046
## [11] train-rmse:14.714250+0.013047 test-rmse:14.705223+0.464215
## [12] train-rmse:12.415248+0.010202 test-rmse:12.403659+0.451383
## [13] train-rmse:10.488003+0.008204 test-rmse:10.477141+0.435665
## [14] train-rmse:8.872284+0.006744 test-rmse:8.856665+0.424004
## [15] train-rmse:7.518793+0.005725 test-rmse:7.508668+0.409615
## [16] train-rmse:6.386627+0.005217 test-rmse:6.375390+0.399833
## [17] train-rmse:5.441035+0.005189 test-rmse:5.430448+0.379198
## [18] train-rmse:4.652171+0.005393 test-rmse:4.644028+0.369327
## [19] train-rmse:3.998137+0.005127 test-rmse:4.004705+0.351566
## [20] train-rmse:3.457549+0.006013 test-rmse:3.463198+0.329429
## [21] train-rmse:3.011018+0.006237 test-rmse:3.031272+0.312075
## [22] train-rmse:2.647909+0.007554 test-rmse:2.685518+0.289283
## [23] train-rmse:2.353257+0.007212 test-rmse:2.407068+0.268967
## [24] train-rmse:2.116270+0.008782 test-rmse:2.187212+0.243296
## [25] train-rmse:1.928027+0.008274 test-rmse:2.006154+0.222251
## [26] train-rmse:1.779012+0.008611 test-rmse:1.873540+0.203308
## [27] train-rmse:1.660105+0.010267 test-rmse:1.772369+0.183466
## [28] train-rmse:1.568783+0.011328 test-rmse:1.693896+0.163638
## [29] train-rmse:1.496386+0.011982 test-rmse:1.635492+0.142906
## [30] train-rmse:1.439630+0.013132 test-rmse:1.589352+0.132532
## [31] train-rmse:1.393388+0.010744 test-rmse:1.556478+0.117981
## [32] train-rmse:1.356886+0.011090 test-rmse:1.533213+0.107674
## [33] train-rmse:1.328427+0.011156 test-rmse:1.503316+0.108785
## [34] train-rmse:1.303575+0.010797 test-rmse:1.488530+0.105219
## [35] train-rmse:1.283688+0.011153 test-rmse:1.473674+0.106099
## [36] train-rmse:1.263558+0.012779 test-rmse:1.462104+0.105405
## [37] train-rmse:1.247143+0.012667 test-rmse:1.451316+0.101445
## [38] train-rmse:1.233178+0.012627 test-rmse:1.440135+0.098596
## [39] train-rmse:1.219742+0.010813 test-rmse:1.434842+0.096369
## [40] train-rmse:1.208449+0.010828 test-rmse:1.429004+0.093150
## [41] train-rmse:1.197662+0.010983 test-rmse:1.422462+0.092018
## [42] train-rmse:1.187520+0.011138 test-rmse:1.412897+0.094169
## [43] train-rmse:1.175919+0.010429 test-rmse:1.405048+0.091884
## [44] train-rmse:1.163913+0.013608 test-rmse:1.399103+0.090532
## [45] train-rmse:1.154393+0.013779 test-rmse:1.394177+0.089702
## [46] train-rmse:1.145290+0.012658 test-rmse:1.388726+0.088413
## [47] train-rmse:1.136276+0.012084 test-rmse:1.372170+0.095016
## [48] train-rmse:1.128248+0.012351 test-rmse:1.369135+0.094336
## [49] train-rmse:1.119426+0.013079 test-rmse:1.366269+0.096087
## [50] train-rmse:1.110112+0.014724 test-rmse:1.354513+0.093965
## [51] train-rmse:1.100996+0.014456 test-rmse:1.352720+0.095284
## [52] train-rmse:1.094175+0.014716 test-rmse:1.350813+0.092796
## [53] train-rmse:1.087208+0.014801 test-rmse:1.344265+0.092468
## [54] train-rmse:1.078622+0.013609 test-rmse:1.340181+0.089325
## [55] train-rmse:1.071382+0.013917 test-rmse:1.331416+0.088716
## [56] train-rmse:1.064388+0.015298 test-rmse:1.326457+0.091423
## [57] train-rmse:1.057513+0.015636 test-rmse:1.324247+0.092075
## [58] train-rmse:1.049437+0.015138 test-rmse:1.320981+0.092427
## [59] train-rmse:1.041515+0.015452 test-rmse:1.313514+0.097925
## [60] train-rmse:1.035286+0.016561 test-rmse:1.311188+0.100393
## [61] train-rmse:1.028578+0.016965 test-rmse:1.307459+0.100032
## [62] train-rmse:1.021710+0.017329 test-rmse:1.300428+0.098081
## [63] train-rmse:1.016125+0.018258 test-rmse:1.292472+0.100181
## [64] train-rmse:1.007181+0.018888 test-rmse:1.289086+0.098079
## [65] train-rmse:1.001169+0.018624 test-rmse:1.286823+0.099133
## [66] train-rmse:0.996294+0.018644 test-rmse:1.282488+0.099456
## [67] train-rmse:0.991173+0.019100 test-rmse:1.282442+0.100589
## [68] train-rmse:0.985967+0.019185 test-rmse:1.278008+0.100213
## [69] train-rmse:0.979583+0.019671 test-rmse:1.272953+0.097629
## [70] train-rmse:0.974404+0.019989 test-rmse:1.270732+0.097649
## [71] train-rmse:0.967891+0.020404 test-rmse:1.268866+0.101161
## [72] train-rmse:0.961105+0.020992 test-rmse:1.266648+0.101947
## [73] train-rmse:0.956464+0.021434 test-rmse:1.261373+0.103170
## [74] train-rmse:0.951798+0.021642 test-rmse:1.256712+0.103782
## [75] train-rmse:0.944503+0.019687 test-rmse:1.251180+0.106617
## [76] train-rmse:0.939829+0.020002 test-rmse:1.249468+0.107107
## [77] train-rmse:0.935410+0.020616 test-rmse:1.245117+0.108187
## [78] train-rmse:0.931412+0.020688 test-rmse:1.241996+0.109850
## [79] train-rmse:0.926045+0.021821 test-rmse:1.239105+0.105365
## [80] train-rmse:0.920594+0.020503 test-rmse:1.233937+0.107664
## [81] train-rmse:0.914743+0.021027 test-rmse:1.232071+0.105664
## [82] train-rmse:0.910267+0.021278 test-rmse:1.229112+0.103003
## [83] train-rmse:0.905981+0.021468 test-rmse:1.226781+0.106349
## [84] train-rmse:0.901020+0.019894 test-rmse:1.222779+0.107670
## [85] train-rmse:0.897424+0.019669 test-rmse:1.222725+0.109274
## [86] train-rmse:0.891004+0.017976 test-rmse:1.222215+0.109321
## [87] train-rmse:0.886129+0.017950 test-rmse:1.217804+0.109629
## [88] train-rmse:0.881837+0.018626 test-rmse:1.215410+0.108628
## [89] train-rmse:0.878200+0.018878 test-rmse:1.212348+0.109266
## [90] train-rmse:0.873770+0.020144 test-rmse:1.210380+0.107979
## [91] train-rmse:0.870741+0.020476 test-rmse:1.207770+0.108535
## [92] train-rmse:0.865960+0.019487 test-rmse:1.201852+0.110110
## [93] train-rmse:0.861960+0.020172 test-rmse:1.202018+0.108811
## [94] train-rmse:0.858633+0.019472 test-rmse:1.201135+0.109796
## [95] train-rmse:0.855274+0.019551 test-rmse:1.198779+0.109649
## [96] train-rmse:0.851144+0.018112 test-rmse:1.197244+0.109691
## [97] train-rmse:0.846930+0.018573 test-rmse:1.193921+0.109431
## [98] train-rmse:0.841903+0.016167 test-rmse:1.192091+0.109375
## [99] train-rmse:0.839063+0.016279 test-rmse:1.191423+0.109445
## [100] train-rmse:0.835821+0.016204 test-rmse:1.188712+0.110522
## [101] train-rmse:0.831964+0.014392 test-rmse:1.186292+0.113258
## [102] train-rmse:0.828419+0.014177 test-rmse:1.185253+0.113568
## [103] train-rmse:0.825278+0.014875 test-rmse:1.180704+0.116389
## [104] train-rmse:0.820991+0.014771 test-rmse:1.177869+0.117780
## [105] train-rmse:0.818307+0.014990 test-rmse:1.177310+0.118452
## [106] train-rmse:0.815034+0.015701 test-rmse:1.172894+0.116442
## [107] train-rmse:0.810943+0.014444 test-rmse:1.172200+0.114923
## [108] train-rmse:0.807960+0.013974 test-rmse:1.170681+0.116337
## [109] train-rmse:0.804569+0.013470 test-rmse:1.166953+0.117356
## [110] train-rmse:0.801316+0.014028 test-rmse:1.166131+0.117172
## [111] train-rmse:0.796471+0.014293 test-rmse:1.164749+0.115331
## [112] train-rmse:0.792812+0.014564 test-rmse:1.161866+0.113620
## [113] train-rmse:0.788829+0.012461 test-rmse:1.160019+0.113414
## [114] train-rmse:0.786171+0.012061 test-rmse:1.160234+0.112709
## [115] train-rmse:0.782625+0.013673 test-rmse:1.157886+0.112151
## [116] train-rmse:0.779511+0.012487 test-rmse:1.155091+0.113073
## [117] train-rmse:0.775880+0.013031 test-rmse:1.153101+0.113245
## [118] train-rmse:0.773067+0.013578 test-rmse:1.152389+0.113283
## [119] train-rmse:0.770756+0.013646 test-rmse:1.151282+0.115207
## [120] train-rmse:0.768250+0.013507 test-rmse:1.150591+0.116740
## [121] train-rmse:0.764367+0.014219 test-rmse:1.145447+0.111981
## [122] train-rmse:0.761537+0.015156 test-rmse:1.144289+0.111984
## [123] train-rmse:0.757783+0.014228 test-rmse:1.140936+0.113928
## [124] train-rmse:0.754308+0.015752 test-rmse:1.139821+0.113985
## [125] train-rmse:0.752002+0.015401 test-rmse:1.136586+0.116275
## [126] train-rmse:0.750091+0.015344 test-rmse:1.134492+0.115064
## [127] train-rmse:0.747628+0.015616 test-rmse:1.134149+0.115948
## [128] train-rmse:0.744881+0.014745 test-rmse:1.132740+0.117621
## [129] train-rmse:0.741330+0.014799 test-rmse:1.129737+0.116036
## [130] train-rmse:0.737902+0.013984 test-rmse:1.129149+0.115853
## [131] train-rmse:0.733656+0.014771 test-rmse:1.123991+0.110788
## [132] train-rmse:0.731241+0.014864 test-rmse:1.122816+0.110059
## [133] train-rmse:0.729013+0.014866 test-rmse:1.121824+0.110633
## [134] train-rmse:0.725823+0.015805 test-rmse:1.120724+0.110367
## [135] train-rmse:0.722719+0.015885 test-rmse:1.117974+0.111026
## [136] train-rmse:0.720119+0.016217 test-rmse:1.115199+0.111717
## [137] train-rmse:0.718392+0.016386 test-rmse:1.113953+0.110339
## [138] train-rmse:0.716118+0.016004 test-rmse:1.113368+0.110004
## [139] train-rmse:0.713343+0.017153 test-rmse:1.111927+0.109373
## [140] train-rmse:0.711489+0.017080 test-rmse:1.111038+0.108383
## [141] train-rmse:0.709585+0.016921 test-rmse:1.108518+0.108232
## [142] train-rmse:0.707140+0.016888 test-rmse:1.107038+0.106716
## [143] train-rmse:0.705270+0.017441 test-rmse:1.106669+0.107017
## [144] train-rmse:0.703300+0.017398 test-rmse:1.106633+0.108387
## [145] train-rmse:0.700578+0.017319 test-rmse:1.107117+0.106950
## [146] train-rmse:0.698770+0.017006 test-rmse:1.106544+0.107989
## [147] train-rmse:0.696259+0.016652 test-rmse:1.106781+0.106988
## [148] train-rmse:0.693965+0.016703 test-rmse:1.105673+0.107687
## [149] train-rmse:0.691438+0.016043 test-rmse:1.105180+0.107577
## [150] train-rmse:0.688380+0.015503 test-rmse:1.104373+0.107149
## [151] train-rmse:0.686867+0.015774 test-rmse:1.104052+0.107329
## [152] train-rmse:0.685375+0.015726 test-rmse:1.102789+0.107539
## [153] train-rmse:0.683602+0.015470 test-rmse:1.101855+0.107805
## [154] train-rmse:0.680567+0.014456 test-rmse:1.101018+0.109885
## [155] train-rmse:0.679240+0.014503 test-rmse:1.100378+0.110087
## [156] train-rmse:0.677341+0.014348 test-rmse:1.097575+0.107989
## [157] train-rmse:0.675125+0.013703 test-rmse:1.097708+0.106265
## [158] train-rmse:0.672813+0.013282 test-rmse:1.097335+0.105844
## [159] train-rmse:0.671375+0.013629 test-rmse:1.095822+0.104772
## [160] train-rmse:0.668259+0.013590 test-rmse:1.095002+0.106127
## [161] train-rmse:0.666226+0.013799 test-rmse:1.096062+0.105264
## [162] train-rmse:0.664810+0.013928 test-rmse:1.095369+0.104798
## [163] train-rmse:0.662710+0.014243 test-rmse:1.095266+0.104303
## [164] train-rmse:0.661203+0.014472 test-rmse:1.094467+0.104170
## [165] train-rmse:0.659604+0.014893 test-rmse:1.093620+0.105274
## [166] train-rmse:0.657111+0.014549 test-rmse:1.093275+0.103973
## [167] train-rmse:0.655248+0.014585 test-rmse:1.092432+0.103794
## [168] train-rmse:0.653734+0.014357 test-rmse:1.090981+0.103618
## [169] train-rmse:0.651628+0.014881 test-rmse:1.089655+0.103439
## [170] train-rmse:0.649004+0.013952 test-rmse:1.089524+0.103227
## [171] train-rmse:0.647505+0.013876 test-rmse:1.089274+0.102776
## [172] train-rmse:0.645552+0.014349 test-rmse:1.087100+0.100348
## [173] train-rmse:0.643804+0.013701 test-rmse:1.085573+0.099207
## [174] train-rmse:0.642181+0.013605 test-rmse:1.083425+0.098423
## [175] train-rmse:0.638649+0.013729 test-rmse:1.081033+0.098892
## [176] train-rmse:0.637062+0.013599 test-rmse:1.080699+0.098896
## [177] train-rmse:0.635626+0.013585 test-rmse:1.080462+0.098431
## [178] train-rmse:0.633830+0.013317 test-rmse:1.078664+0.097883
## [179] train-rmse:0.632504+0.013147 test-rmse:1.076485+0.097246
## [180] train-rmse:0.631018+0.013321 test-rmse:1.076315+0.097500
## [181] train-rmse:0.629676+0.013329 test-rmse:1.076417+0.098132
## [182] train-rmse:0.628445+0.013096 test-rmse:1.076239+0.098042
## [183] train-rmse:0.626914+0.013058 test-rmse:1.076470+0.097947
## [184] train-rmse:0.625536+0.013204 test-rmse:1.077029+0.097389
## [185] train-rmse:0.623502+0.012293 test-rmse:1.076705+0.097380
## [186] train-rmse:0.621864+0.012702 test-rmse:1.076999+0.096374
## [187] train-rmse:0.620303+0.012199 test-rmse:1.076498+0.096569
## [188] train-rmse:0.618623+0.012680 test-rmse:1.076673+0.097629
## Stopping. Best iteration:
## [182] train-rmse:0.628445+0.013096 test-rmse:1.076239+0.098042
##
## 23
## [1] train-rmse:82.780940+0.083029 test-rmse:82.781650+0.410772
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.591332+0.069678 test-rmse:69.591866+0.423700
## [3] train-rmse:58.506326+0.058401 test-rmse:58.506664+0.434438
## [4] train-rmse:49.190703+0.048890 test-rmse:49.190803+0.443303
## [5] train-rmse:41.362720+0.040844 test-rmse:41.362536+0.450558
## [6] train-rmse:34.785638+0.034034 test-rmse:34.785128+0.456398
## [7] train-rmse:29.260552+0.028262 test-rmse:29.259653+0.460980
## [8] train-rmse:24.620358+0.023369 test-rmse:24.619006+0.464415
## [9] train-rmse:20.724704+0.019222 test-rmse:20.722832+0.466766
## [10] train-rmse:17.455756+0.015748 test-rmse:17.453284+0.468046
## [11] train-rmse:14.714250+0.013047 test-rmse:14.705223+0.464215
## [12] train-rmse:12.415248+0.010202 test-rmse:12.403659+0.451383
## [13] train-rmse:10.487754+0.008421 test-rmse:10.475114+0.434470
## [14] train-rmse:8.871626+0.006995 test-rmse:8.854390+0.425077
## [15] train-rmse:7.515582+0.005130 test-rmse:7.501034+0.404315
## [16] train-rmse:6.380096+0.005329 test-rmse:6.367459+0.392690
## [17] train-rmse:5.429276+0.005767 test-rmse:5.423361+0.363959
## [18] train-rmse:4.633891+0.005466 test-rmse:4.630494+0.341878
## [19] train-rmse:3.970904+0.006778 test-rmse:3.975269+0.323101
## [20] train-rmse:3.419328+0.006168 test-rmse:3.430206+0.307103
## [21] train-rmse:2.962253+0.005113 test-rmse:2.985760+0.286154
## [22] train-rmse:2.584684+0.007475 test-rmse:2.620999+0.272101
## [23] train-rmse:2.275609+0.007202 test-rmse:2.331899+0.255124
## [24] train-rmse:2.024278+0.009534 test-rmse:2.095104+0.237285
## [25] train-rmse:1.819430+0.012130 test-rmse:1.911041+0.221581
## [26] train-rmse:1.656484+0.013330 test-rmse:1.769291+0.200873
## [27] train-rmse:1.524889+0.015961 test-rmse:1.660966+0.181339
## [28] train-rmse:1.421829+0.017684 test-rmse:1.578777+0.163657
## [29] train-rmse:1.335870+0.012745 test-rmse:1.517872+0.149215
## [30] train-rmse:1.270453+0.014080 test-rmse:1.464671+0.140681
## [31] train-rmse:1.218165+0.015353 test-rmse:1.425980+0.136570
## [32] train-rmse:1.172082+0.011938 test-rmse:1.391281+0.129434
## [33] train-rmse:1.132556+0.011024 test-rmse:1.366760+0.123933
## [34] train-rmse:1.100882+0.009929 test-rmse:1.347917+0.114153
## [35] train-rmse:1.075918+0.011103 test-rmse:1.335013+0.111123
## [36] train-rmse:1.053859+0.010667 test-rmse:1.325799+0.110887
## [37] train-rmse:1.034670+0.009365 test-rmse:1.318307+0.108249
## [38] train-rmse:1.017376+0.010989 test-rmse:1.303374+0.108471
## [39] train-rmse:1.001803+0.009869 test-rmse:1.294981+0.101454
## [40] train-rmse:0.989088+0.009351 test-rmse:1.283627+0.099558
## [41] train-rmse:0.974139+0.011900 test-rmse:1.275442+0.096480
## [42] train-rmse:0.960657+0.008691 test-rmse:1.263112+0.101810
## [43] train-rmse:0.948254+0.007386 test-rmse:1.257721+0.100643
## [44] train-rmse:0.938103+0.008048 test-rmse:1.248372+0.098863
## [45] train-rmse:0.926126+0.007914 test-rmse:1.242229+0.098518
## [46] train-rmse:0.915333+0.009092 test-rmse:1.234643+0.099841
## [47] train-rmse:0.906294+0.008801 test-rmse:1.227887+0.097871
## [48] train-rmse:0.896197+0.008344 test-rmse:1.225428+0.100955
## [49] train-rmse:0.882155+0.008939 test-rmse:1.217495+0.102186
## [50] train-rmse:0.873918+0.009791 test-rmse:1.209979+0.102042
## [51] train-rmse:0.865099+0.009117 test-rmse:1.204982+0.102331
## [52] train-rmse:0.857153+0.009757 test-rmse:1.201755+0.100636
## [53] train-rmse:0.848373+0.011654 test-rmse:1.194284+0.099925
## [54] train-rmse:0.840901+0.012343 test-rmse:1.187499+0.105130
## [55] train-rmse:0.832164+0.010895 test-rmse:1.183279+0.104318
## [56] train-rmse:0.823969+0.010778 test-rmse:1.180723+0.103150
## [57] train-rmse:0.817667+0.010516 test-rmse:1.176264+0.101100
## [58] train-rmse:0.810314+0.012527 test-rmse:1.169930+0.102696
## [59] train-rmse:0.800249+0.014883 test-rmse:1.162631+0.105215
## [60] train-rmse:0.794305+0.015137 test-rmse:1.158230+0.107279
## [61] train-rmse:0.788025+0.015171 test-rmse:1.156994+0.105345
## [62] train-rmse:0.781002+0.013740 test-rmse:1.151106+0.104494
## [63] train-rmse:0.775076+0.013492 test-rmse:1.146973+0.105662
## [64] train-rmse:0.765146+0.014245 test-rmse:1.137855+0.104590
## [65] train-rmse:0.758881+0.014935 test-rmse:1.134884+0.103934
## [66] train-rmse:0.751009+0.015339 test-rmse:1.125643+0.096358
## [67] train-rmse:0.744769+0.016811 test-rmse:1.123762+0.096702
## [68] train-rmse:0.739051+0.016299 test-rmse:1.118893+0.096165
## [69] train-rmse:0.731484+0.016047 test-rmse:1.114813+0.097739
## [70] train-rmse:0.723881+0.014993 test-rmse:1.111140+0.097955
## [71] train-rmse:0.719172+0.016004 test-rmse:1.109408+0.099627
## [72] train-rmse:0.710671+0.017693 test-rmse:1.104641+0.097501
## [73] train-rmse:0.705522+0.017657 test-rmse:1.100378+0.094556
## [74] train-rmse:0.700815+0.017448 test-rmse:1.098055+0.094893
## [75] train-rmse:0.695948+0.018045 test-rmse:1.095690+0.095000
## [76] train-rmse:0.688010+0.015699 test-rmse:1.094599+0.097094
## [77] train-rmse:0.683797+0.015864 test-rmse:1.091750+0.099041
## [78] train-rmse:0.676030+0.012742 test-rmse:1.090032+0.098533
## [79] train-rmse:0.671515+0.012436 test-rmse:1.087557+0.100280
## [80] train-rmse:0.665271+0.011147 test-rmse:1.082443+0.102992
## [81] train-rmse:0.660840+0.011187 test-rmse:1.079930+0.102606
## [82] train-rmse:0.655921+0.011247 test-rmse:1.076892+0.100883
## [83] train-rmse:0.650987+0.010924 test-rmse:1.074584+0.101702
## [84] train-rmse:0.646735+0.010780 test-rmse:1.072523+0.102101
## [85] train-rmse:0.640662+0.010154 test-rmse:1.071386+0.101960
## [86] train-rmse:0.635924+0.010321 test-rmse:1.070427+0.102650
## [87] train-rmse:0.632006+0.010490 test-rmse:1.068110+0.102484
## [88] train-rmse:0.628604+0.011101 test-rmse:1.068218+0.102966
## [89] train-rmse:0.624755+0.010140 test-rmse:1.064786+0.103520
## [90] train-rmse:0.620778+0.010875 test-rmse:1.061049+0.103054
## [91] train-rmse:0.615345+0.011735 test-rmse:1.058401+0.099574
## [92] train-rmse:0.611618+0.012150 test-rmse:1.058168+0.098615
## [93] train-rmse:0.608153+0.012885 test-rmse:1.055767+0.098005
## [94] train-rmse:0.604380+0.013307 test-rmse:1.056075+0.098272
## [95] train-rmse:0.600144+0.013463 test-rmse:1.055971+0.098411
## [96] train-rmse:0.596179+0.013925 test-rmse:1.053258+0.100076
## [97] train-rmse:0.593518+0.013727 test-rmse:1.051573+0.100227
## [98] train-rmse:0.588744+0.012265 test-rmse:1.048189+0.099765
## [99] train-rmse:0.584478+0.013068 test-rmse:1.046345+0.099634
## [100] train-rmse:0.581702+0.012897 test-rmse:1.044013+0.099732
## [101] train-rmse:0.577276+0.014663 test-rmse:1.041522+0.098870
## [102] train-rmse:0.573221+0.013842 test-rmse:1.039565+0.097451
## [103] train-rmse:0.570460+0.013752 test-rmse:1.039312+0.096752
## [104] train-rmse:0.567231+0.013873 test-rmse:1.037018+0.095398
## [105] train-rmse:0.562002+0.011657 test-rmse:1.033529+0.094629
## [106] train-rmse:0.558123+0.011571 test-rmse:1.028876+0.089704
## [107] train-rmse:0.554633+0.012740 test-rmse:1.026714+0.089618
## [108] train-rmse:0.551222+0.012611 test-rmse:1.024926+0.090254
## [109] train-rmse:0.547528+0.011121 test-rmse:1.024511+0.090524
## [110] train-rmse:0.544955+0.011255 test-rmse:1.023488+0.090539
## [111] train-rmse:0.541903+0.011498 test-rmse:1.022952+0.088694
## [112] train-rmse:0.536897+0.012037 test-rmse:1.023395+0.087497
## [113] train-rmse:0.534353+0.011831 test-rmse:1.022057+0.088908
## [114] train-rmse:0.529614+0.009109 test-rmse:1.019714+0.090207
## [115] train-rmse:0.527318+0.009004 test-rmse:1.018495+0.089661
## [116] train-rmse:0.524952+0.008844 test-rmse:1.018832+0.090207
## [117] train-rmse:0.522542+0.008199 test-rmse:1.018157+0.090445
## [118] train-rmse:0.519472+0.009449 test-rmse:1.017270+0.089130
## [119] train-rmse:0.516841+0.009000 test-rmse:1.013510+0.089118
## [120] train-rmse:0.513598+0.009708 test-rmse:1.011734+0.090301
## [121] train-rmse:0.510596+0.008430 test-rmse:1.010702+0.090974
## [122] train-rmse:0.507278+0.007930 test-rmse:1.007919+0.088425
## [123] train-rmse:0.504210+0.008502 test-rmse:1.007099+0.088607
## [124] train-rmse:0.500785+0.010306 test-rmse:1.006511+0.089073
## [125] train-rmse:0.498979+0.010530 test-rmse:1.006538+0.088105
## [126] train-rmse:0.495318+0.011187 test-rmse:1.004898+0.087924
## [127] train-rmse:0.492199+0.010266 test-rmse:1.002657+0.088671
## [128] train-rmse:0.488322+0.010859 test-rmse:1.001284+0.086468
## [129] train-rmse:0.484060+0.011996 test-rmse:1.001017+0.086376
## [130] train-rmse:0.481413+0.012096 test-rmse:1.000115+0.085818
## [131] train-rmse:0.478238+0.012703 test-rmse:0.997099+0.082008
## [132] train-rmse:0.476010+0.012924 test-rmse:0.995801+0.081814
## [133] train-rmse:0.472901+0.012595 test-rmse:0.996448+0.081422
## [134] train-rmse:0.470188+0.013133 test-rmse:0.994562+0.080416
## [135] train-rmse:0.467035+0.012699 test-rmse:0.994172+0.080247
## [136] train-rmse:0.464332+0.013005 test-rmse:0.992785+0.080765
## [137] train-rmse:0.461219+0.013557 test-rmse:0.993005+0.079753
## [138] train-rmse:0.457989+0.013280 test-rmse:0.991787+0.078622
## [139] train-rmse:0.456376+0.013222 test-rmse:0.990314+0.080173
## [140] train-rmse:0.453535+0.012168 test-rmse:0.987912+0.081087
## [141] train-rmse:0.451278+0.012037 test-rmse:0.988536+0.080157
## [142] train-rmse:0.448053+0.011681 test-rmse:0.988419+0.081328
## [143] train-rmse:0.445390+0.012081 test-rmse:0.988063+0.081073
## [144] train-rmse:0.443623+0.012214 test-rmse:0.987901+0.080642
## [145] train-rmse:0.441558+0.012622 test-rmse:0.987295+0.079610
## [146] train-rmse:0.439808+0.012105 test-rmse:0.985061+0.078824
## [147] train-rmse:0.436794+0.011550 test-rmse:0.983676+0.079165
## [148] train-rmse:0.434343+0.011398 test-rmse:0.983769+0.080710
## [149] train-rmse:0.431978+0.011736 test-rmse:0.984135+0.081580
## [150] train-rmse:0.429820+0.011532 test-rmse:0.984366+0.081441
## [151] train-rmse:0.425900+0.010164 test-rmse:0.983272+0.082567
## [152] train-rmse:0.423721+0.010721 test-rmse:0.983359+0.082610
## [153] train-rmse:0.420637+0.011863 test-rmse:0.983069+0.083088
## [154] train-rmse:0.418394+0.012155 test-rmse:0.983125+0.083410
## [155] train-rmse:0.416556+0.012366 test-rmse:0.982525+0.083027
## [156] train-rmse:0.414437+0.012994 test-rmse:0.981733+0.082212
## [157] train-rmse:0.411390+0.012086 test-rmse:0.981024+0.082336
## [158] train-rmse:0.409206+0.012643 test-rmse:0.980269+0.082493
## [159] train-rmse:0.407124+0.012560 test-rmse:0.980041+0.082606
## [160] train-rmse:0.405209+0.012823 test-rmse:0.979490+0.082543
## [161] train-rmse:0.403160+0.011839 test-rmse:0.978712+0.083782
## [162] train-rmse:0.400752+0.012439 test-rmse:0.979964+0.085466
## [163] train-rmse:0.397966+0.012814 test-rmse:0.978140+0.083601
## [164] train-rmse:0.394711+0.012392 test-rmse:0.978602+0.083748
## [165] train-rmse:0.393100+0.012617 test-rmse:0.978289+0.083924
## [166] train-rmse:0.391007+0.012479 test-rmse:0.978235+0.084309
## [167] train-rmse:0.389410+0.012410 test-rmse:0.978072+0.082815
## [168] train-rmse:0.385948+0.012963 test-rmse:0.977592+0.082917
## [169] train-rmse:0.384320+0.012366 test-rmse:0.977804+0.082997
## [170] train-rmse:0.381626+0.010281 test-rmse:0.976431+0.085159
## [171] train-rmse:0.380193+0.009906 test-rmse:0.975234+0.084926
## [172] train-rmse:0.377454+0.009898 test-rmse:0.974307+0.085570
## [173] train-rmse:0.375519+0.009755 test-rmse:0.972962+0.083801
## [174] train-rmse:0.374315+0.010083 test-rmse:0.972429+0.083553
## [175] train-rmse:0.372209+0.009288 test-rmse:0.970359+0.084281
## [176] train-rmse:0.370268+0.009517 test-rmse:0.970525+0.082799
## [177] train-rmse:0.369152+0.009598 test-rmse:0.971057+0.082235
## [178] train-rmse:0.368130+0.009799 test-rmse:0.970949+0.081843
## [179] train-rmse:0.366439+0.010072 test-rmse:0.969409+0.081206
## [180] train-rmse:0.363855+0.009891 test-rmse:0.969531+0.081299
## [181] train-rmse:0.361190+0.010136 test-rmse:0.969424+0.081392
## [182] train-rmse:0.357889+0.009926 test-rmse:0.966203+0.080556
## [183] train-rmse:0.356144+0.009682 test-rmse:0.964510+0.080393
## [184] train-rmse:0.354455+0.010030 test-rmse:0.964773+0.080542
## [185] train-rmse:0.352241+0.009951 test-rmse:0.963076+0.079727
## [186] train-rmse:0.350087+0.010162 test-rmse:0.962517+0.080064
## [187] train-rmse:0.348338+0.010326 test-rmse:0.962215+0.079768
## [188] train-rmse:0.345846+0.011287 test-rmse:0.961325+0.078961
## [189] train-rmse:0.343762+0.011598 test-rmse:0.962733+0.079976
## [190] train-rmse:0.342366+0.011440 test-rmse:0.962333+0.080235
## [191] train-rmse:0.339755+0.011636 test-rmse:0.963162+0.079466
## [192] train-rmse:0.338502+0.011052 test-rmse:0.963276+0.079871
## [193] train-rmse:0.336566+0.011650 test-rmse:0.963524+0.079459
## [194] train-rmse:0.334448+0.011903 test-rmse:0.961991+0.079963
## Stopping. Best iteration:
## [188] train-rmse:0.345846+0.011287 test-rmse:0.961325+0.078961
##
## 24
## [1] train-rmse:82.780940+0.083029 test-rmse:82.781650+0.410772
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.591332+0.069678 test-rmse:69.591866+0.423700
## [3] train-rmse:58.506326+0.058401 test-rmse:58.506664+0.434438
## [4] train-rmse:49.190703+0.048890 test-rmse:49.190803+0.443303
## [5] train-rmse:41.362720+0.040844 test-rmse:41.362536+0.450558
## [6] train-rmse:34.785638+0.034034 test-rmse:34.785128+0.456398
## [7] train-rmse:29.260552+0.028262 test-rmse:29.259653+0.460980
## [8] train-rmse:24.620358+0.023369 test-rmse:24.619006+0.464415
## [9] train-rmse:20.724704+0.019222 test-rmse:20.722832+0.466766
## [10] train-rmse:17.455756+0.015748 test-rmse:17.453284+0.468046
## [11] train-rmse:14.714250+0.013047 test-rmse:14.705223+0.464215
## [12] train-rmse:12.415248+0.010202 test-rmse:12.403659+0.451383
## [13] train-rmse:10.487754+0.008421 test-rmse:10.475114+0.434470
## [14] train-rmse:8.871507+0.006889 test-rmse:8.851975+0.424152
## [15] train-rmse:7.514893+0.005074 test-rmse:7.500034+0.397897
## [16] train-rmse:6.377349+0.005123 test-rmse:6.364151+0.382597
## [17] train-rmse:5.424938+0.004873 test-rmse:5.416162+0.359040
## [18] train-rmse:4.625447+0.003727 test-rmse:4.624434+0.329523
## [19] train-rmse:3.957230+0.003817 test-rmse:3.968668+0.309600
## [20] train-rmse:3.399020+0.004954 test-rmse:3.415935+0.289789
## [21] train-rmse:2.933592+0.005362 test-rmse:2.953484+0.274130
## [22] train-rmse:2.545290+0.004864 test-rmse:2.581756+0.260607
## [23] train-rmse:2.227797+0.005490 test-rmse:2.283701+0.246382
## [24] train-rmse:1.963770+0.007263 test-rmse:2.041434+0.232958
## [25] train-rmse:1.747861+0.008370 test-rmse:1.848228+0.215749
## [26] train-rmse:1.570019+0.009462 test-rmse:1.693706+0.200552
## [27] train-rmse:1.430925+0.009254 test-rmse:1.577966+0.186859
## [28] train-rmse:1.316409+0.008121 test-rmse:1.485420+0.176644
## [29] train-rmse:1.221807+0.011644 test-rmse:1.416319+0.160057
## [30] train-rmse:1.146208+0.012850 test-rmse:1.368133+0.145661
## [31] train-rmse:1.085474+0.016641 test-rmse:1.327805+0.140517
## [32] train-rmse:1.035397+0.019575 test-rmse:1.299244+0.129969
## [33] train-rmse:0.992874+0.020925 test-rmse:1.271994+0.121181
## [34] train-rmse:0.959404+0.022609 test-rmse:1.250548+0.113957
## [35] train-rmse:0.927927+0.020571 test-rmse:1.231331+0.109051
## [36] train-rmse:0.904483+0.022682 test-rmse:1.219875+0.105914
## [37] train-rmse:0.880331+0.020087 test-rmse:1.211319+0.101165
## [38] train-rmse:0.858370+0.019055 test-rmse:1.195509+0.099380
## [39] train-rmse:0.840774+0.015607 test-rmse:1.185837+0.101718
## [40] train-rmse:0.825512+0.014991 test-rmse:1.177551+0.098718
## [41] train-rmse:0.809817+0.015067 test-rmse:1.170384+0.096804
## [42] train-rmse:0.792406+0.014611 test-rmse:1.159083+0.093603
## [43] train-rmse:0.779269+0.014942 test-rmse:1.149926+0.094530
## [44] train-rmse:0.766449+0.014590 test-rmse:1.141777+0.093371
## [45] train-rmse:0.755570+0.012631 test-rmse:1.136969+0.086997
## [46] train-rmse:0.743581+0.015199 test-rmse:1.130145+0.086496
## [47] train-rmse:0.731120+0.016627 test-rmse:1.122497+0.081822
## [48] train-rmse:0.722285+0.018185 test-rmse:1.117547+0.082293
## [49] train-rmse:0.709187+0.017344 test-rmse:1.113414+0.082551
## [50] train-rmse:0.699768+0.016448 test-rmse:1.105331+0.082870
## [51] train-rmse:0.689251+0.013360 test-rmse:1.100887+0.084463
## [52] train-rmse:0.680927+0.014102 test-rmse:1.096506+0.080214
## [53] train-rmse:0.672358+0.016132 test-rmse:1.091098+0.082793
## [54] train-rmse:0.661500+0.017212 test-rmse:1.078269+0.077200
## [55] train-rmse:0.652425+0.017557 test-rmse:1.073365+0.079362
## [56] train-rmse:0.645321+0.016801 test-rmse:1.069328+0.079882
## [57] train-rmse:0.634924+0.014740 test-rmse:1.064048+0.079006
## [58] train-rmse:0.627951+0.013590 test-rmse:1.061372+0.081713
## [59] train-rmse:0.617923+0.013994 test-rmse:1.056347+0.079067
## [60] train-rmse:0.611422+0.014366 test-rmse:1.049532+0.077106
## [61] train-rmse:0.604792+0.013332 test-rmse:1.047786+0.083629
## [62] train-rmse:0.598539+0.011770 test-rmse:1.046125+0.085994
## [63] train-rmse:0.592774+0.011443 test-rmse:1.044118+0.086318
## [64] train-rmse:0.584974+0.013161 test-rmse:1.036573+0.084794
## [65] train-rmse:0.578946+0.013438 test-rmse:1.029233+0.081566
## [66] train-rmse:0.572671+0.014850 test-rmse:1.027554+0.080955
## [67] train-rmse:0.567526+0.013909 test-rmse:1.025426+0.079801
## [68] train-rmse:0.560021+0.015676 test-rmse:1.026141+0.082398
## [69] train-rmse:0.554910+0.015198 test-rmse:1.024406+0.081196
## [70] train-rmse:0.549161+0.015297 test-rmse:1.022001+0.080534
## [71] train-rmse:0.543185+0.013542 test-rmse:1.021042+0.079969
## [72] train-rmse:0.534721+0.011152 test-rmse:1.017825+0.076423
## [73] train-rmse:0.528655+0.012236 test-rmse:1.013873+0.072930
## [74] train-rmse:0.523609+0.011713 test-rmse:1.012985+0.076540
## [75] train-rmse:0.518344+0.011174 test-rmse:1.010386+0.076937
## [76] train-rmse:0.512842+0.012597 test-rmse:1.009447+0.077146
## [77] train-rmse:0.508919+0.011908 test-rmse:1.008302+0.077844
## [78] train-rmse:0.501867+0.011090 test-rmse:1.007988+0.078522
## [79] train-rmse:0.497656+0.010519 test-rmse:1.008693+0.082166
## [80] train-rmse:0.492486+0.009855 test-rmse:1.005702+0.082220
## [81] train-rmse:0.487464+0.011464 test-rmse:1.004301+0.083225
## [82] train-rmse:0.482924+0.011967 test-rmse:1.006533+0.083833
## [83] train-rmse:0.477399+0.010070 test-rmse:1.002564+0.084595
## [84] train-rmse:0.471769+0.008828 test-rmse:0.998595+0.085112
## [85] train-rmse:0.465401+0.010200 test-rmse:0.994651+0.083066
## [86] train-rmse:0.460939+0.011114 test-rmse:0.996669+0.086324
## [87] train-rmse:0.456348+0.009504 test-rmse:0.994173+0.087590
## [88] train-rmse:0.452751+0.010052 test-rmse:0.993927+0.087810
## [89] train-rmse:0.449703+0.009853 test-rmse:0.992418+0.088132
## [90] train-rmse:0.443436+0.008269 test-rmse:0.989449+0.085243
## [91] train-rmse:0.438977+0.009638 test-rmse:0.989065+0.084808
## [92] train-rmse:0.435452+0.010488 test-rmse:0.988013+0.085673
## [93] train-rmse:0.431678+0.011925 test-rmse:0.987357+0.086470
## [94] train-rmse:0.427149+0.010184 test-rmse:0.986241+0.086369
## [95] train-rmse:0.423737+0.010117 test-rmse:0.986119+0.085720
## [96] train-rmse:0.418991+0.009242 test-rmse:0.985011+0.085043
## [97] train-rmse:0.414805+0.010927 test-rmse:0.983910+0.083988
## [98] train-rmse:0.409976+0.012132 test-rmse:0.980337+0.083920
## [99] train-rmse:0.406359+0.012454 test-rmse:0.979937+0.085156
## [100] train-rmse:0.401584+0.011444 test-rmse:0.979892+0.085118
## [101] train-rmse:0.397640+0.011156 test-rmse:0.980116+0.085358
## [102] train-rmse:0.394147+0.010704 test-rmse:0.979539+0.086206
## [103] train-rmse:0.391010+0.011465 test-rmse:0.978441+0.086588
## [104] train-rmse:0.388086+0.011209 test-rmse:0.977280+0.086636
## [105] train-rmse:0.384844+0.010845 test-rmse:0.977260+0.086904
## [106] train-rmse:0.382169+0.010906 test-rmse:0.974857+0.086766
## [107] train-rmse:0.378795+0.011830 test-rmse:0.976441+0.087823
## [108] train-rmse:0.374444+0.011256 test-rmse:0.975290+0.089086
## [109] train-rmse:0.369069+0.010179 test-rmse:0.972684+0.087478
## [110] train-rmse:0.365212+0.011593 test-rmse:0.969034+0.085126
## [111] train-rmse:0.360839+0.009865 test-rmse:0.969221+0.085250
## [112] train-rmse:0.357304+0.009713 test-rmse:0.968357+0.086100
## [113] train-rmse:0.354104+0.009261 test-rmse:0.967638+0.085774
## [114] train-rmse:0.351206+0.009023 test-rmse:0.966709+0.086471
## [115] train-rmse:0.347811+0.009275 test-rmse:0.965234+0.085523
## [116] train-rmse:0.344277+0.009692 test-rmse:0.965024+0.085700
## [117] train-rmse:0.341587+0.009541 test-rmse:0.964441+0.085769
## [118] train-rmse:0.338080+0.010120 test-rmse:0.964100+0.086767
## [119] train-rmse:0.333500+0.010449 test-rmse:0.964096+0.086995
## [120] train-rmse:0.331319+0.010551 test-rmse:0.964108+0.087318
## [121] train-rmse:0.328158+0.010666 test-rmse:0.961823+0.086714
## [122] train-rmse:0.325489+0.011035 test-rmse:0.960824+0.086309
## [123] train-rmse:0.322090+0.012236 test-rmse:0.961246+0.087200
## [124] train-rmse:0.318380+0.012748 test-rmse:0.960644+0.087087
## [125] train-rmse:0.315153+0.013210 test-rmse:0.961144+0.088125
## [126] train-rmse:0.312964+0.012727 test-rmse:0.960221+0.089641
## [127] train-rmse:0.310262+0.012315 test-rmse:0.959747+0.088926
## [128] train-rmse:0.306611+0.011706 test-rmse:0.959649+0.089731
## [129] train-rmse:0.304860+0.012164 test-rmse:0.957698+0.088290
## [130] train-rmse:0.302176+0.011828 test-rmse:0.956427+0.087312
## [131] train-rmse:0.299593+0.010848 test-rmse:0.955054+0.087200
## [132] train-rmse:0.297156+0.010524 test-rmse:0.955740+0.088899
## [133] train-rmse:0.295063+0.010220 test-rmse:0.955597+0.088611
## [134] train-rmse:0.292598+0.010233 test-rmse:0.955250+0.088930
## [135] train-rmse:0.289405+0.009447 test-rmse:0.953750+0.088782
## [136] train-rmse:0.286026+0.008409 test-rmse:0.953252+0.088971
## [137] train-rmse:0.284113+0.008284 test-rmse:0.953423+0.089246
## [138] train-rmse:0.281973+0.008666 test-rmse:0.952969+0.088444
## [139] train-rmse:0.279455+0.008282 test-rmse:0.952095+0.086991
## [140] train-rmse:0.276956+0.008434 test-rmse:0.951585+0.085798
## [141] train-rmse:0.275460+0.008436 test-rmse:0.951239+0.085002
## [142] train-rmse:0.273745+0.008646 test-rmse:0.950810+0.085742
## [143] train-rmse:0.270956+0.009330 test-rmse:0.950814+0.086065
## [144] train-rmse:0.268192+0.008915 test-rmse:0.950271+0.086422
## [145] train-rmse:0.267083+0.009292 test-rmse:0.949578+0.086183
## [146] train-rmse:0.264141+0.009118 test-rmse:0.949349+0.086073
## [147] train-rmse:0.261188+0.008820 test-rmse:0.948978+0.086033
## [148] train-rmse:0.259345+0.009733 test-rmse:0.948056+0.085726
## [149] train-rmse:0.257092+0.009423 test-rmse:0.947557+0.085503
## [150] train-rmse:0.254505+0.009855 test-rmse:0.947485+0.085506
## [151] train-rmse:0.252708+0.009152 test-rmse:0.946972+0.085977
## [152] train-rmse:0.250417+0.008162 test-rmse:0.946711+0.086003
## [153] train-rmse:0.248666+0.008409 test-rmse:0.946271+0.085909
## [154] train-rmse:0.245957+0.008684 test-rmse:0.945682+0.084914
## [155] train-rmse:0.243892+0.008462 test-rmse:0.945130+0.085538
## [156] train-rmse:0.241105+0.009238 test-rmse:0.944802+0.084569
## [157] train-rmse:0.239394+0.009310 test-rmse:0.944750+0.084797
## [158] train-rmse:0.237723+0.009422 test-rmse:0.944729+0.084374
## [159] train-rmse:0.235364+0.009129 test-rmse:0.944341+0.083716
## [160] train-rmse:0.234047+0.008761 test-rmse:0.944238+0.084346
## [161] train-rmse:0.232230+0.008352 test-rmse:0.943986+0.083514
## [162] train-rmse:0.230610+0.008930 test-rmse:0.943499+0.083281
## [163] train-rmse:0.228919+0.009571 test-rmse:0.942834+0.082918
## [164] train-rmse:0.227379+0.009097 test-rmse:0.942477+0.083124
## [165] train-rmse:0.224622+0.008508 test-rmse:0.942902+0.083526
## [166] train-rmse:0.222978+0.008552 test-rmse:0.942442+0.084554
## [167] train-rmse:0.221358+0.008853 test-rmse:0.943266+0.084764
## [168] train-rmse:0.219469+0.008653 test-rmse:0.943150+0.085253
## [169] train-rmse:0.217777+0.008942 test-rmse:0.943682+0.085767
## [170] train-rmse:0.216387+0.009285 test-rmse:0.943519+0.085339
## [171] train-rmse:0.214832+0.009122 test-rmse:0.942894+0.085572
## [172] train-rmse:0.212275+0.010499 test-rmse:0.942721+0.086046
## Stopping. Best iteration:
## [166] train-rmse:0.222978+0.008552 test-rmse:0.942442+0.084554
##
## 25
## [1] train-rmse:82.780940+0.083029 test-rmse:82.781650+0.410772
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.591332+0.069678 test-rmse:69.591866+0.423700
## [3] train-rmse:58.506326+0.058401 test-rmse:58.506664+0.434438
## [4] train-rmse:49.190703+0.048890 test-rmse:49.190803+0.443303
## [5] train-rmse:41.362720+0.040844 test-rmse:41.362536+0.450558
## [6] train-rmse:34.785638+0.034034 test-rmse:34.785128+0.456398
## [7] train-rmse:29.260552+0.028262 test-rmse:29.259653+0.460980
## [8] train-rmse:24.620358+0.023369 test-rmse:24.619006+0.464415
## [9] train-rmse:20.724704+0.019222 test-rmse:20.722832+0.466766
## [10] train-rmse:17.455756+0.015748 test-rmse:17.453284+0.468046
## [11] train-rmse:14.714250+0.013047 test-rmse:14.705223+0.464215
## [12] train-rmse:12.415248+0.010202 test-rmse:12.403659+0.451383
## [13] train-rmse:10.487754+0.008421 test-rmse:10.475114+0.434470
## [14] train-rmse:8.871425+0.006820 test-rmse:8.850692+0.423682
## [15] train-rmse:7.514660+0.004858 test-rmse:7.499711+0.397806
## [16] train-rmse:6.376062+0.004903 test-rmse:6.361624+0.377460
## [17] train-rmse:5.421941+0.004202 test-rmse:5.417193+0.347324
## [18] train-rmse:4.620578+0.002343 test-rmse:4.612585+0.328409
## [19] train-rmse:3.951043+0.002998 test-rmse:3.952878+0.310064
## [20] train-rmse:3.388101+0.004365 test-rmse:3.400548+0.283840
## [21] train-rmse:2.917234+0.002847 test-rmse:2.941066+0.269057
## [22] train-rmse:2.522736+0.006667 test-rmse:2.567780+0.251415
## [23] train-rmse:2.195011+0.005133 test-rmse:2.267853+0.239615
## [24] train-rmse:1.923144+0.007552 test-rmse:2.012838+0.221401
## [25] train-rmse:1.699241+0.008523 test-rmse:1.814498+0.207688
## [26] train-rmse:1.513280+0.007395 test-rmse:1.656916+0.201281
## [27] train-rmse:1.362997+0.007847 test-rmse:1.535616+0.191927
## [28] train-rmse:1.237832+0.006415 test-rmse:1.440174+0.177816
## [29] train-rmse:1.131952+0.009355 test-rmse:1.360910+0.167461
## [30] train-rmse:1.050610+0.010663 test-rmse:1.308484+0.159199
## [31] train-rmse:0.977367+0.013316 test-rmse:1.262453+0.150574
## [32] train-rmse:0.920648+0.011554 test-rmse:1.231008+0.143220
## [33] train-rmse:0.874845+0.011943 test-rmse:1.202314+0.144547
## [34] train-rmse:0.833073+0.009841 test-rmse:1.173518+0.133217
## [35] train-rmse:0.796042+0.012008 test-rmse:1.155738+0.126545
## [36] train-rmse:0.765642+0.012030 test-rmse:1.143509+0.124311
## [37] train-rmse:0.740770+0.013348 test-rmse:1.130954+0.121682
## [38] train-rmse:0.720431+0.015166 test-rmse:1.121465+0.117424
## [39] train-rmse:0.701529+0.014201 test-rmse:1.108513+0.107317
## [40] train-rmse:0.682130+0.015655 test-rmse:1.099721+0.101449
## [41] train-rmse:0.666128+0.016653 test-rmse:1.085811+0.099913
## [42] train-rmse:0.652331+0.018099 test-rmse:1.079234+0.095551
## [43] train-rmse:0.637723+0.016582 test-rmse:1.069353+0.091240
## [44] train-rmse:0.623883+0.018380 test-rmse:1.063484+0.091878
## [45] train-rmse:0.609655+0.016332 test-rmse:1.053871+0.087192
## [46] train-rmse:0.597372+0.017570 test-rmse:1.050426+0.084961
## [47] train-rmse:0.583827+0.019158 test-rmse:1.038729+0.083397
## [48] train-rmse:0.573610+0.018111 test-rmse:1.034696+0.083996
## [49] train-rmse:0.563919+0.018097 test-rmse:1.029534+0.082895
## [50] train-rmse:0.552900+0.016769 test-rmse:1.020371+0.079818
## [51] train-rmse:0.543755+0.017266 test-rmse:1.017351+0.079146
## [52] train-rmse:0.536994+0.016134 test-rmse:1.012913+0.079370
## [53] train-rmse:0.526718+0.017078 test-rmse:1.010112+0.079933
## [54] train-rmse:0.518486+0.017247 test-rmse:1.010050+0.078591
## [55] train-rmse:0.508040+0.016454 test-rmse:1.003565+0.074177
## [56] train-rmse:0.499565+0.016958 test-rmse:1.001879+0.074602
## [57] train-rmse:0.491697+0.015713 test-rmse:1.000346+0.074829
## [58] train-rmse:0.485008+0.016201 test-rmse:0.998860+0.074882
## [59] train-rmse:0.475024+0.014998 test-rmse:0.991933+0.075034
## [60] train-rmse:0.466916+0.014706 test-rmse:0.990295+0.076217
## [61] train-rmse:0.460427+0.014455 test-rmse:0.988167+0.077640
## [62] train-rmse:0.454348+0.014228 test-rmse:0.987295+0.077666
## [63] train-rmse:0.446763+0.014395 test-rmse:0.982087+0.076234
## [64] train-rmse:0.439529+0.016912 test-rmse:0.980214+0.076025
## [65] train-rmse:0.433040+0.016072 test-rmse:0.978039+0.076250
## [66] train-rmse:0.427478+0.014612 test-rmse:0.976250+0.076047
## [67] train-rmse:0.421347+0.015126 test-rmse:0.974602+0.075286
## [68] train-rmse:0.415038+0.015439 test-rmse:0.973524+0.074117
## [69] train-rmse:0.408504+0.015354 test-rmse:0.971508+0.075205
## [70] train-rmse:0.404359+0.014894 test-rmse:0.969682+0.075780
## [71] train-rmse:0.396068+0.013783 test-rmse:0.968940+0.075841
## [72] train-rmse:0.389306+0.013412 test-rmse:0.967375+0.075624
## [73] train-rmse:0.384889+0.012568 test-rmse:0.967100+0.075179
## [74] train-rmse:0.379510+0.014261 test-rmse:0.963945+0.072079
## [75] train-rmse:0.375805+0.013847 test-rmse:0.962803+0.072702
## [76] train-rmse:0.368848+0.013457 test-rmse:0.962112+0.074400
## [77] train-rmse:0.362519+0.014469 test-rmse:0.960644+0.075447
## [78] train-rmse:0.358427+0.014079 test-rmse:0.959840+0.075864
## [79] train-rmse:0.353202+0.014443 test-rmse:0.959858+0.076758
## [80] train-rmse:0.347378+0.014000 test-rmse:0.958184+0.075920
## [81] train-rmse:0.343989+0.013760 test-rmse:0.957132+0.076273
## [82] train-rmse:0.337533+0.013337 test-rmse:0.954659+0.077854
## [83] train-rmse:0.333111+0.013113 test-rmse:0.954236+0.078170
## [84] train-rmse:0.328763+0.013402 test-rmse:0.952013+0.077188
## [85] train-rmse:0.324136+0.013168 test-rmse:0.950933+0.076807
## [86] train-rmse:0.319561+0.013414 test-rmse:0.951501+0.077416
## [87] train-rmse:0.314459+0.012795 test-rmse:0.950358+0.078110
## [88] train-rmse:0.310767+0.012798 test-rmse:0.948408+0.078906
## [89] train-rmse:0.307177+0.013174 test-rmse:0.947751+0.078218
## [90] train-rmse:0.303147+0.012205 test-rmse:0.946725+0.078508
## [91] train-rmse:0.300139+0.012689 test-rmse:0.946378+0.078497
## [92] train-rmse:0.295813+0.012578 test-rmse:0.945971+0.079921
## [93] train-rmse:0.291502+0.012814 test-rmse:0.944826+0.079338
## [94] train-rmse:0.288294+0.012380 test-rmse:0.943485+0.080591
## [95] train-rmse:0.284688+0.011519 test-rmse:0.941406+0.080707
## [96] train-rmse:0.280424+0.012222 test-rmse:0.941260+0.080984
## [97] train-rmse:0.277196+0.012830 test-rmse:0.940927+0.081204
## [98] train-rmse:0.273783+0.012705 test-rmse:0.940975+0.080906
## [99] train-rmse:0.270160+0.013570 test-rmse:0.939455+0.080529
## [100] train-rmse:0.266154+0.014763 test-rmse:0.939667+0.081102
## [101] train-rmse:0.263260+0.014806 test-rmse:0.939256+0.081851
## [102] train-rmse:0.259993+0.013549 test-rmse:0.937382+0.082620
## [103] train-rmse:0.256371+0.013819 test-rmse:0.938098+0.082997
## [104] train-rmse:0.252228+0.014081 test-rmse:0.937579+0.083159
## [105] train-rmse:0.249885+0.013735 test-rmse:0.936981+0.084029
## [106] train-rmse:0.247029+0.013401 test-rmse:0.936970+0.084875
## [107] train-rmse:0.243611+0.013042 test-rmse:0.935924+0.085226
## [108] train-rmse:0.239397+0.014076 test-rmse:0.934982+0.085558
## [109] train-rmse:0.236694+0.014131 test-rmse:0.934975+0.085887
## [110] train-rmse:0.233490+0.013434 test-rmse:0.935175+0.085644
## [111] train-rmse:0.231647+0.013339 test-rmse:0.934624+0.085271
## [112] train-rmse:0.228993+0.013587 test-rmse:0.933457+0.085454
## [113] train-rmse:0.225878+0.012424 test-rmse:0.932641+0.085631
## [114] train-rmse:0.223601+0.012266 test-rmse:0.932364+0.085755
## [115] train-rmse:0.221184+0.012194 test-rmse:0.931788+0.086415
## [116] train-rmse:0.218041+0.011959 test-rmse:0.931331+0.085645
## [117] train-rmse:0.213993+0.011414 test-rmse:0.931511+0.085325
## [118] train-rmse:0.211775+0.010614 test-rmse:0.931228+0.085271
## [119] train-rmse:0.209095+0.011940 test-rmse:0.930376+0.085765
## [120] train-rmse:0.206527+0.011621 test-rmse:0.930163+0.085322
## [121] train-rmse:0.203790+0.011741 test-rmse:0.930021+0.085299
## [122] train-rmse:0.201506+0.011198 test-rmse:0.929943+0.085413
## [123] train-rmse:0.198889+0.011421 test-rmse:0.929300+0.086386
## [124] train-rmse:0.196936+0.011239 test-rmse:0.928957+0.085874
## [125] train-rmse:0.195142+0.011014 test-rmse:0.929229+0.085778
## [126] train-rmse:0.193034+0.010236 test-rmse:0.928908+0.085546
## [127] train-rmse:0.190022+0.010382 test-rmse:0.927925+0.086594
## [128] train-rmse:0.187794+0.010060 test-rmse:0.927636+0.086328
## [129] train-rmse:0.186006+0.009379 test-rmse:0.927751+0.086256
## [130] train-rmse:0.183553+0.009764 test-rmse:0.927283+0.086664
## [131] train-rmse:0.181434+0.009481 test-rmse:0.926712+0.087239
## [132] train-rmse:0.179329+0.009798 test-rmse:0.926224+0.087006
## [133] train-rmse:0.177110+0.009445 test-rmse:0.925847+0.086510
## [134] train-rmse:0.174993+0.009535 test-rmse:0.925598+0.086173
## [135] train-rmse:0.173057+0.009152 test-rmse:0.925448+0.085659
## [136] train-rmse:0.170246+0.009479 test-rmse:0.924941+0.085750
## [137] train-rmse:0.167973+0.009445 test-rmse:0.924782+0.085623
## [138] train-rmse:0.165763+0.009827 test-rmse:0.925292+0.085891
## [139] train-rmse:0.163472+0.009228 test-rmse:0.924311+0.084897
## [140] train-rmse:0.160954+0.009448 test-rmse:0.924220+0.085163
## [141] train-rmse:0.159192+0.009520 test-rmse:0.923452+0.085479
## [142] train-rmse:0.157127+0.009521 test-rmse:0.923168+0.085332
## [143] train-rmse:0.155687+0.009744 test-rmse:0.922934+0.086019
## [144] train-rmse:0.154080+0.009661 test-rmse:0.922427+0.086163
## [145] train-rmse:0.151437+0.009174 test-rmse:0.922208+0.086321
## [146] train-rmse:0.150030+0.008968 test-rmse:0.921893+0.086589
## [147] train-rmse:0.148131+0.009795 test-rmse:0.921447+0.086722
## [148] train-rmse:0.146005+0.009830 test-rmse:0.920948+0.086812
## [149] train-rmse:0.144879+0.009553 test-rmse:0.920756+0.086883
## [150] train-rmse:0.142897+0.009516 test-rmse:0.920600+0.086741
## [151] train-rmse:0.141252+0.009757 test-rmse:0.921069+0.086833
## [152] train-rmse:0.139733+0.009901 test-rmse:0.920945+0.086732
## [153] train-rmse:0.136747+0.009729 test-rmse:0.920360+0.086476
## [154] train-rmse:0.135716+0.009427 test-rmse:0.920315+0.086596
## [155] train-rmse:0.134663+0.009318 test-rmse:0.920091+0.086526
## [156] train-rmse:0.133176+0.009235 test-rmse:0.919493+0.086597
## [157] train-rmse:0.131788+0.009423 test-rmse:0.919821+0.086488
## [158] train-rmse:0.130375+0.009147 test-rmse:0.919479+0.086395
## [159] train-rmse:0.128176+0.009095 test-rmse:0.919301+0.086337
## [160] train-rmse:0.127417+0.009065 test-rmse:0.918882+0.086343
## [161] train-rmse:0.125281+0.008292 test-rmse:0.918115+0.086177
## [162] train-rmse:0.122954+0.007895 test-rmse:0.917861+0.086706
## [163] train-rmse:0.121936+0.007957 test-rmse:0.918097+0.086316
## [164] train-rmse:0.120568+0.007529 test-rmse:0.918260+0.086252
## [165] train-rmse:0.118795+0.007250 test-rmse:0.918172+0.086222
## [166] train-rmse:0.117190+0.007465 test-rmse:0.918244+0.085799
## [167] train-rmse:0.115487+0.007234 test-rmse:0.918343+0.085657
## [168] train-rmse:0.114482+0.007070 test-rmse:0.918170+0.085647
## Stopping. Best iteration:
## [162] train-rmse:0.122954+0.007895 test-rmse:0.917861+0.086706
##
## 26
## [1] train-rmse:82.780940+0.083029 test-rmse:82.781650+0.410772
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.591332+0.069678 test-rmse:69.591866+0.423700
## [3] train-rmse:58.506326+0.058401 test-rmse:58.506664+0.434438
## [4] train-rmse:49.190703+0.048890 test-rmse:49.190803+0.443303
## [5] train-rmse:41.362720+0.040844 test-rmse:41.362536+0.450558
## [6] train-rmse:34.785638+0.034034 test-rmse:34.785128+0.456398
## [7] train-rmse:29.260552+0.028262 test-rmse:29.259653+0.460980
## [8] train-rmse:24.620358+0.023369 test-rmse:24.619006+0.464415
## [9] train-rmse:20.724704+0.019222 test-rmse:20.722832+0.466766
## [10] train-rmse:17.455756+0.015748 test-rmse:17.453284+0.468046
## [11] train-rmse:14.714250+0.013047 test-rmse:14.705223+0.464215
## [12] train-rmse:12.415248+0.010202 test-rmse:12.403659+0.451383
## [13] train-rmse:10.487754+0.008421 test-rmse:10.475114+0.434470
## [14] train-rmse:8.871425+0.006820 test-rmse:8.850692+0.423682
## [15] train-rmse:7.514562+0.004778 test-rmse:7.497542+0.397228
## [16] train-rmse:6.375451+0.004812 test-rmse:6.363512+0.372509
## [17] train-rmse:5.421326+0.004005 test-rmse:5.414337+0.340739
## [18] train-rmse:4.618878+0.002482 test-rmse:4.609710+0.319742
## [19] train-rmse:3.946483+0.002030 test-rmse:3.946499+0.296266
## [20] train-rmse:3.380894+0.003843 test-rmse:3.395102+0.277658
## [21] train-rmse:2.906696+0.002064 test-rmse:2.923854+0.266557
## [22] train-rmse:2.510315+0.003258 test-rmse:2.549455+0.249310
## [23] train-rmse:2.177122+0.005756 test-rmse:2.245776+0.228780
## [24] train-rmse:1.900323+0.004076 test-rmse:1.990432+0.212736
## [25] train-rmse:1.666189+0.004931 test-rmse:1.787286+0.195793
## [26] train-rmse:1.472894+0.007233 test-rmse:1.635482+0.179546
## [27] train-rmse:1.311724+0.007487 test-rmse:1.501022+0.165022
## [28] train-rmse:1.178065+0.007997 test-rmse:1.409110+0.149272
## [29] train-rmse:1.068670+0.009911 test-rmse:1.329159+0.140532
## [30] train-rmse:0.976106+0.008521 test-rmse:1.266034+0.131248
## [31] train-rmse:0.900204+0.010700 test-rmse:1.220736+0.122532
## [32] train-rmse:0.836750+0.006920 test-rmse:1.184938+0.120341
## [33] train-rmse:0.777009+0.007669 test-rmse:1.154582+0.110448
## [34] train-rmse:0.732504+0.007253 test-rmse:1.134899+0.108141
## [35] train-rmse:0.696188+0.009051 test-rmse:1.115361+0.106341
## [36] train-rmse:0.666294+0.010510 test-rmse:1.101450+0.106178
## [37] train-rmse:0.635729+0.012715 test-rmse:1.088074+0.101241
## [38] train-rmse:0.610762+0.013652 test-rmse:1.080398+0.097450
## [39] train-rmse:0.590070+0.014083 test-rmse:1.070815+0.097941
## [40] train-rmse:0.572044+0.012897 test-rmse:1.063427+0.095591
## [41] train-rmse:0.554913+0.013483 test-rmse:1.058048+0.093610
## [42] train-rmse:0.539911+0.012547 test-rmse:1.048586+0.093561
## [43] train-rmse:0.523811+0.016297 test-rmse:1.044740+0.093484
## [44] train-rmse:0.512555+0.014667 test-rmse:1.039516+0.093997
## [45] train-rmse:0.497826+0.015878 test-rmse:1.031736+0.088139
## [46] train-rmse:0.488124+0.016432 test-rmse:1.027867+0.085739
## [47] train-rmse:0.476411+0.017601 test-rmse:1.022286+0.080990
## [48] train-rmse:0.464771+0.018149 test-rmse:1.020996+0.081899
## [49] train-rmse:0.450399+0.018474 test-rmse:1.014853+0.084046
## [50] train-rmse:0.441233+0.015797 test-rmse:1.012340+0.083185
## [51] train-rmse:0.430152+0.015368 test-rmse:1.007296+0.082357
## [52] train-rmse:0.420899+0.014685 test-rmse:1.005045+0.083343
## [53] train-rmse:0.412204+0.014374 test-rmse:1.001864+0.081514
## [54] train-rmse:0.405220+0.015711 test-rmse:0.996533+0.080615
## [55] train-rmse:0.397115+0.014266 test-rmse:0.993778+0.079053
## [56] train-rmse:0.387006+0.013518 test-rmse:0.988441+0.074691
## [57] train-rmse:0.380354+0.011490 test-rmse:0.985677+0.075512
## [58] train-rmse:0.371945+0.012047 test-rmse:0.982645+0.073688
## [59] train-rmse:0.363430+0.011353 test-rmse:0.980515+0.073577
## [60] train-rmse:0.355722+0.010566 test-rmse:0.979555+0.072236
## [61] train-rmse:0.348471+0.011751 test-rmse:0.980223+0.072227
## [62] train-rmse:0.340029+0.012575 test-rmse:0.976331+0.069269
## [63] train-rmse:0.333961+0.014498 test-rmse:0.974428+0.069767
## [64] train-rmse:0.327732+0.014123 test-rmse:0.971513+0.068732
## [65] train-rmse:0.320733+0.014596 test-rmse:0.970405+0.068479
## [66] train-rmse:0.315040+0.014195 test-rmse:0.970408+0.068948
## [67] train-rmse:0.310093+0.013874 test-rmse:0.967601+0.069268
## [68] train-rmse:0.304564+0.012663 test-rmse:0.966227+0.068123
## [69] train-rmse:0.298252+0.012700 test-rmse:0.966162+0.067424
## [70] train-rmse:0.292978+0.012700 test-rmse:0.963660+0.067085
## [71] train-rmse:0.287959+0.013699 test-rmse:0.963505+0.066062
## [72] train-rmse:0.283131+0.014379 test-rmse:0.964455+0.066757
## [73] train-rmse:0.278635+0.013931 test-rmse:0.964292+0.066660
## [74] train-rmse:0.273841+0.013209 test-rmse:0.964082+0.066917
## [75] train-rmse:0.268725+0.013593 test-rmse:0.962587+0.067052
## [76] train-rmse:0.264484+0.012921 test-rmse:0.962830+0.065122
## [77] train-rmse:0.260270+0.012893 test-rmse:0.961172+0.066095
## [78] train-rmse:0.256811+0.012504 test-rmse:0.959038+0.067315
## [79] train-rmse:0.251673+0.011449 test-rmse:0.958358+0.068355
## [80] train-rmse:0.246498+0.010158 test-rmse:0.957521+0.069467
## [81] train-rmse:0.240343+0.010693 test-rmse:0.955238+0.069010
## [82] train-rmse:0.234747+0.010157 test-rmse:0.954953+0.068881
## [83] train-rmse:0.231159+0.009723 test-rmse:0.954392+0.068755
## [84] train-rmse:0.227536+0.008889 test-rmse:0.953452+0.068666
## [85] train-rmse:0.223306+0.009423 test-rmse:0.953364+0.068379
## [86] train-rmse:0.219535+0.008562 test-rmse:0.952926+0.068495
## [87] train-rmse:0.215248+0.009758 test-rmse:0.952720+0.067800
## [88] train-rmse:0.212300+0.009240 test-rmse:0.951064+0.067588
## [89] train-rmse:0.209599+0.009988 test-rmse:0.950840+0.067561
## [90] train-rmse:0.206589+0.009641 test-rmse:0.949321+0.067932
## [91] train-rmse:0.204024+0.010031 test-rmse:0.948013+0.067387
## [92] train-rmse:0.200927+0.010162 test-rmse:0.947949+0.067834
## [93] train-rmse:0.196393+0.010427 test-rmse:0.948707+0.068442
## [94] train-rmse:0.191632+0.009428 test-rmse:0.948986+0.067256
## [95] train-rmse:0.189515+0.009809 test-rmse:0.948784+0.066690
## [96] train-rmse:0.186031+0.009732 test-rmse:0.947824+0.066333
## [97] train-rmse:0.182569+0.010976 test-rmse:0.947141+0.066421
## [98] train-rmse:0.179271+0.010768 test-rmse:0.947122+0.066708
## [99] train-rmse:0.176170+0.010564 test-rmse:0.945979+0.067010
## [100] train-rmse:0.173336+0.009943 test-rmse:0.945627+0.066941
## [101] train-rmse:0.171031+0.009884 test-rmse:0.945033+0.065803
## [102] train-rmse:0.167821+0.009127 test-rmse:0.944770+0.065974
## [103] train-rmse:0.166279+0.009325 test-rmse:0.944055+0.065899
## [104] train-rmse:0.163829+0.008794 test-rmse:0.943933+0.066073
## [105] train-rmse:0.160594+0.009035 test-rmse:0.943215+0.065708
## [106] train-rmse:0.157083+0.008873 test-rmse:0.942046+0.066392
## [107] train-rmse:0.155350+0.008697 test-rmse:0.942107+0.066315
## [108] train-rmse:0.152987+0.008148 test-rmse:0.942049+0.067118
## [109] train-rmse:0.151248+0.008072 test-rmse:0.941479+0.067272
## [110] train-rmse:0.148503+0.008096 test-rmse:0.941465+0.067153
## [111] train-rmse:0.146393+0.007710 test-rmse:0.942001+0.067312
## [112] train-rmse:0.144478+0.007137 test-rmse:0.941729+0.067674
## [113] train-rmse:0.141384+0.006736 test-rmse:0.940708+0.068074
## [114] train-rmse:0.139701+0.007278 test-rmse:0.940304+0.067702
## [115] train-rmse:0.137308+0.007917 test-rmse:0.939945+0.067168
## [116] train-rmse:0.135491+0.007975 test-rmse:0.939498+0.067183
## [117] train-rmse:0.132961+0.008385 test-rmse:0.939971+0.067221
## [118] train-rmse:0.131238+0.008770 test-rmse:0.939619+0.066739
## [119] train-rmse:0.128662+0.008256 test-rmse:0.938821+0.066616
## [120] train-rmse:0.126688+0.007713 test-rmse:0.938715+0.066028
## [121] train-rmse:0.124732+0.008149 test-rmse:0.938413+0.066090
## [122] train-rmse:0.122794+0.007514 test-rmse:0.938289+0.066502
## [123] train-rmse:0.120868+0.007771 test-rmse:0.938068+0.066519
## [124] train-rmse:0.118954+0.007331 test-rmse:0.938283+0.066809
## [125] train-rmse:0.116902+0.007243 test-rmse:0.938688+0.067606
## [126] train-rmse:0.114549+0.006967 test-rmse:0.938296+0.067893
## [127] train-rmse:0.112964+0.006967 test-rmse:0.937515+0.068344
## [128] train-rmse:0.110326+0.007561 test-rmse:0.937231+0.069054
## [129] train-rmse:0.107927+0.007790 test-rmse:0.937262+0.069311
## [130] train-rmse:0.105615+0.007133 test-rmse:0.936791+0.069195
## [131] train-rmse:0.103837+0.007046 test-rmse:0.936136+0.069934
## [132] train-rmse:0.102509+0.006966 test-rmse:0.935823+0.070208
## [133] train-rmse:0.100624+0.007213 test-rmse:0.936048+0.070917
## [134] train-rmse:0.099285+0.007472 test-rmse:0.935974+0.070482
## [135] train-rmse:0.097266+0.007331 test-rmse:0.936151+0.070887
## [136] train-rmse:0.095917+0.007413 test-rmse:0.935688+0.070627
## [137] train-rmse:0.094385+0.006835 test-rmse:0.936035+0.071373
## [138] train-rmse:0.092854+0.006903 test-rmse:0.936022+0.071799
## [139] train-rmse:0.090687+0.006540 test-rmse:0.935780+0.071252
## [140] train-rmse:0.089187+0.006342 test-rmse:0.935122+0.071058
## [141] train-rmse:0.087242+0.006058 test-rmse:0.934905+0.070960
## [142] train-rmse:0.086148+0.006245 test-rmse:0.935138+0.071225
## [143] train-rmse:0.085209+0.006076 test-rmse:0.934985+0.071102
## [144] train-rmse:0.083669+0.006291 test-rmse:0.935065+0.070983
## [145] train-rmse:0.082319+0.006690 test-rmse:0.934246+0.070863
## [146] train-rmse:0.081055+0.006956 test-rmse:0.934039+0.071296
## [147] train-rmse:0.079546+0.006859 test-rmse:0.933397+0.071084
## [148] train-rmse:0.078486+0.006871 test-rmse:0.933283+0.071256
## [149] train-rmse:0.077336+0.006660 test-rmse:0.932994+0.071304
## [150] train-rmse:0.075828+0.006214 test-rmse:0.933303+0.070829
## [151] train-rmse:0.074887+0.006422 test-rmse:0.933009+0.070460
## [152] train-rmse:0.073920+0.006304 test-rmse:0.932563+0.070759
## [153] train-rmse:0.072745+0.005838 test-rmse:0.932781+0.070982
## [154] train-rmse:0.072000+0.005618 test-rmse:0.932754+0.071195
## [155] train-rmse:0.070959+0.005703 test-rmse:0.932320+0.071734
## [156] train-rmse:0.070181+0.005921 test-rmse:0.932130+0.072027
## [157] train-rmse:0.068992+0.005962 test-rmse:0.931783+0.071905
## [158] train-rmse:0.067984+0.005837 test-rmse:0.931749+0.072101
## [159] train-rmse:0.066605+0.005590 test-rmse:0.931989+0.072257
## [160] train-rmse:0.065784+0.005616 test-rmse:0.931803+0.072449
## [161] train-rmse:0.064979+0.005903 test-rmse:0.931884+0.072663
## [162] train-rmse:0.063828+0.005873 test-rmse:0.931680+0.072820
## [163] train-rmse:0.062986+0.005890 test-rmse:0.931531+0.072784
## [164] train-rmse:0.061695+0.005322 test-rmse:0.931428+0.072697
## [165] train-rmse:0.060666+0.004625 test-rmse:0.931611+0.072857
## [166] train-rmse:0.059876+0.004659 test-rmse:0.931358+0.072924
## [167] train-rmse:0.059156+0.004442 test-rmse:0.931152+0.072842
## [168] train-rmse:0.058318+0.004027 test-rmse:0.931293+0.072884
## [169] train-rmse:0.057531+0.004162 test-rmse:0.931196+0.072948
## [170] train-rmse:0.056268+0.003924 test-rmse:0.931120+0.073218
## [171] train-rmse:0.055613+0.003946 test-rmse:0.931107+0.073243
## [172] train-rmse:0.054912+0.003969 test-rmse:0.931047+0.073249
## [173] train-rmse:0.054211+0.003899 test-rmse:0.931278+0.073377
## [174] train-rmse:0.052865+0.003917 test-rmse:0.931578+0.073516
## [175] train-rmse:0.052032+0.003701 test-rmse:0.931574+0.073793
## [176] train-rmse:0.051454+0.003654 test-rmse:0.931495+0.073863
## [177] train-rmse:0.050791+0.003603 test-rmse:0.931251+0.074018
## [178] train-rmse:0.050061+0.003892 test-rmse:0.931060+0.074214
## Stopping. Best iteration:
## [172] train-rmse:0.054912+0.003969 test-rmse:0.931047+0.073249
##
## 27
## [1] train-rmse:82.780940+0.083029 test-rmse:82.781650+0.410772
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.591332+0.069678 test-rmse:69.591866+0.423700
## [3] train-rmse:58.506326+0.058401 test-rmse:58.506664+0.434438
## [4] train-rmse:49.190703+0.048890 test-rmse:49.190803+0.443303
## [5] train-rmse:41.362720+0.040844 test-rmse:41.362536+0.450558
## [6] train-rmse:34.785638+0.034034 test-rmse:34.785128+0.456398
## [7] train-rmse:29.260552+0.028262 test-rmse:29.259653+0.460980
## [8] train-rmse:24.620358+0.023369 test-rmse:24.619006+0.464415
## [9] train-rmse:20.724704+0.019222 test-rmse:20.722832+0.466766
## [10] train-rmse:17.455756+0.015748 test-rmse:17.453284+0.468046
## [11] train-rmse:14.714250+0.013047 test-rmse:14.705223+0.464215
## [12] train-rmse:12.415248+0.010202 test-rmse:12.403659+0.451383
## [13] train-rmse:10.487754+0.008421 test-rmse:10.475114+0.434470
## [14] train-rmse:8.871425+0.006820 test-rmse:8.850692+0.423682
## [15] train-rmse:7.514562+0.004778 test-rmse:7.497542+0.397228
## [16] train-rmse:6.375011+0.004761 test-rmse:6.363278+0.369040
## [17] train-rmse:5.420127+0.003752 test-rmse:5.412896+0.337337
## [18] train-rmse:4.617428+0.002545 test-rmse:4.609104+0.314990
## [19] train-rmse:3.943628+0.003812 test-rmse:3.946012+0.290654
## [20] train-rmse:3.375995+0.003474 test-rmse:3.385593+0.276752
## [21] train-rmse:2.899824+0.001982 test-rmse:2.918255+0.262397
## [22] train-rmse:2.499791+0.002879 test-rmse:2.540666+0.247461
## [23] train-rmse:2.162015+0.004656 test-rmse:2.226820+0.230076
## [24] train-rmse:1.880966+0.004871 test-rmse:1.972173+0.221078
## [25] train-rmse:1.642736+0.003633 test-rmse:1.763213+0.207939
## [26] train-rmse:1.445809+0.007201 test-rmse:1.600646+0.196332
## [27] train-rmse:1.282820+0.009772 test-rmse:1.472293+0.181594
## [28] train-rmse:1.143423+0.007551 test-rmse:1.371600+0.174375
## [29] train-rmse:1.025242+0.007844 test-rmse:1.292708+0.159081
## [30] train-rmse:0.931788+0.009468 test-rmse:1.231903+0.151610
## [31] train-rmse:0.849289+0.008054 test-rmse:1.184502+0.141219
## [32] train-rmse:0.779330+0.008863 test-rmse:1.147847+0.137148
## [33] train-rmse:0.722442+0.007968 test-rmse:1.117241+0.131729
## [34] train-rmse:0.676253+0.008289 test-rmse:1.096750+0.125778
## [35] train-rmse:0.634501+0.007375 test-rmse:1.077847+0.113372
## [36] train-rmse:0.601432+0.006732 test-rmse:1.060590+0.114471
## [37] train-rmse:0.572735+0.005558 test-rmse:1.048096+0.105075
## [38] train-rmse:0.545528+0.007041 test-rmse:1.037497+0.105901
## [39] train-rmse:0.522201+0.007927 test-rmse:1.029370+0.102739
## [40] train-rmse:0.500440+0.005856 test-rmse:1.020384+0.096330
## [41] train-rmse:0.478978+0.008473 test-rmse:1.012377+0.092011
## [42] train-rmse:0.459904+0.010329 test-rmse:1.005350+0.087585
## [43] train-rmse:0.445771+0.010072 test-rmse:1.003254+0.086854
## [44] train-rmse:0.434238+0.011647 test-rmse:0.998182+0.087835
## [45] train-rmse:0.421605+0.015395 test-rmse:0.993052+0.085472
## [46] train-rmse:0.409913+0.014785 test-rmse:0.990535+0.088418
## [47] train-rmse:0.398512+0.014597 test-rmse:0.989664+0.087835
## [48] train-rmse:0.385187+0.016707 test-rmse:0.986277+0.086111
## [49] train-rmse:0.374177+0.017072 test-rmse:0.984871+0.083713
## [50] train-rmse:0.365129+0.017207 test-rmse:0.984019+0.084937
## [51] train-rmse:0.354567+0.017789 test-rmse:0.981638+0.086777
## [52] train-rmse:0.346384+0.017092 test-rmse:0.979126+0.087189
## [53] train-rmse:0.335302+0.015465 test-rmse:0.976285+0.086887
## [54] train-rmse:0.327976+0.015767 test-rmse:0.975134+0.084959
## [55] train-rmse:0.317668+0.014441 test-rmse:0.972588+0.084506
## [56] train-rmse:0.309488+0.016097 test-rmse:0.969751+0.085162
## [57] train-rmse:0.302068+0.015021 test-rmse:0.968193+0.083888
## [58] train-rmse:0.293167+0.015544 test-rmse:0.965925+0.084357
## [59] train-rmse:0.287837+0.015726 test-rmse:0.962070+0.084383
## [60] train-rmse:0.281141+0.013815 test-rmse:0.959818+0.081820
## [61] train-rmse:0.274744+0.012999 test-rmse:0.956075+0.082053
## [62] train-rmse:0.267693+0.013764 test-rmse:0.954490+0.083563
## [63] train-rmse:0.261733+0.013222 test-rmse:0.952891+0.082745
## [64] train-rmse:0.253448+0.013455 test-rmse:0.950760+0.080389
## [65] train-rmse:0.247657+0.015282 test-rmse:0.950598+0.080448
## [66] train-rmse:0.242280+0.016109 test-rmse:0.950746+0.080059
## [67] train-rmse:0.235533+0.015150 test-rmse:0.949315+0.079382
## [68] train-rmse:0.231155+0.012735 test-rmse:0.948814+0.078156
## [69] train-rmse:0.227227+0.011633 test-rmse:0.947314+0.079114
## [70] train-rmse:0.221396+0.012245 test-rmse:0.947680+0.082136
## [71] train-rmse:0.216239+0.013171 test-rmse:0.947169+0.081621
## [72] train-rmse:0.212763+0.013588 test-rmse:0.946509+0.080144
## [73] train-rmse:0.207642+0.014270 test-rmse:0.945932+0.080361
## [74] train-rmse:0.201947+0.013597 test-rmse:0.943346+0.080631
## [75] train-rmse:0.198719+0.013706 test-rmse:0.943283+0.081101
## [76] train-rmse:0.193781+0.012839 test-rmse:0.943308+0.081385
## [77] train-rmse:0.189261+0.012050 test-rmse:0.942539+0.081050
## [78] train-rmse:0.184781+0.011314 test-rmse:0.941594+0.081159
## [79] train-rmse:0.180778+0.011581 test-rmse:0.941622+0.080966
## [80] train-rmse:0.177902+0.012398 test-rmse:0.941017+0.081242
## [81] train-rmse:0.174377+0.012118 test-rmse:0.940262+0.081427
## [82] train-rmse:0.169915+0.010277 test-rmse:0.939246+0.082434
## [83] train-rmse:0.166159+0.011079 test-rmse:0.939229+0.083064
## [84] train-rmse:0.162580+0.011171 test-rmse:0.938381+0.082996
## [85] train-rmse:0.159440+0.011343 test-rmse:0.937126+0.083138
## [86] train-rmse:0.155738+0.011340 test-rmse:0.937084+0.082951
## [87] train-rmse:0.151194+0.010895 test-rmse:0.936594+0.082808
## [88] train-rmse:0.149077+0.010922 test-rmse:0.935934+0.082721
## [89] train-rmse:0.146477+0.010716 test-rmse:0.936075+0.082772
## [90] train-rmse:0.143722+0.009853 test-rmse:0.935549+0.082043
## [91] train-rmse:0.141019+0.010741 test-rmse:0.935326+0.081990
## [92] train-rmse:0.136625+0.010118 test-rmse:0.934045+0.081318
## [93] train-rmse:0.133863+0.010226 test-rmse:0.933749+0.081440
## [94] train-rmse:0.130860+0.010956 test-rmse:0.933155+0.081178
## [95] train-rmse:0.127592+0.010681 test-rmse:0.933190+0.080942
## [96] train-rmse:0.125295+0.010884 test-rmse:0.932880+0.080700
## [97] train-rmse:0.122890+0.010178 test-rmse:0.932247+0.079956
## [98] train-rmse:0.121049+0.009917 test-rmse:0.932446+0.079743
## [99] train-rmse:0.118734+0.009608 test-rmse:0.932337+0.079270
## [100] train-rmse:0.116291+0.009837 test-rmse:0.932212+0.078906
## [101] train-rmse:0.113426+0.008973 test-rmse:0.931727+0.077937
## [102] train-rmse:0.110537+0.008500 test-rmse:0.931582+0.077263
## [103] train-rmse:0.108279+0.008546 test-rmse:0.930535+0.076951
## [104] train-rmse:0.105892+0.008342 test-rmse:0.930887+0.077378
## [105] train-rmse:0.103895+0.008210 test-rmse:0.930271+0.076924
## [106] train-rmse:0.101550+0.008413 test-rmse:0.929345+0.076651
## [107] train-rmse:0.099834+0.008355 test-rmse:0.929031+0.076756
## [108] train-rmse:0.097798+0.008816 test-rmse:0.929122+0.076624
## [109] train-rmse:0.095737+0.008414 test-rmse:0.929005+0.076395
## [110] train-rmse:0.093869+0.008567 test-rmse:0.928719+0.075868
## [111] train-rmse:0.091785+0.009012 test-rmse:0.928701+0.076037
## [112] train-rmse:0.090173+0.008577 test-rmse:0.928401+0.075961
## [113] train-rmse:0.087927+0.008121 test-rmse:0.928280+0.076229
## [114] train-rmse:0.086345+0.007730 test-rmse:0.928267+0.076107
## [115] train-rmse:0.085212+0.007884 test-rmse:0.927990+0.076112
## [116] train-rmse:0.083652+0.007653 test-rmse:0.927548+0.076053
## [117] train-rmse:0.081418+0.007615 test-rmse:0.928155+0.075748
## [118] train-rmse:0.079792+0.007116 test-rmse:0.927812+0.075986
## [119] train-rmse:0.077583+0.006756 test-rmse:0.927685+0.076142
## [120] train-rmse:0.075829+0.007424 test-rmse:0.927629+0.076337
## [121] train-rmse:0.074158+0.007288 test-rmse:0.927687+0.076279
## [122] train-rmse:0.072730+0.007393 test-rmse:0.927405+0.076173
## [123] train-rmse:0.071794+0.007714 test-rmse:0.927495+0.076383
## [124] train-rmse:0.069673+0.007052 test-rmse:0.927270+0.076436
## [125] train-rmse:0.068397+0.006706 test-rmse:0.927086+0.076179
## [126] train-rmse:0.067145+0.006683 test-rmse:0.926934+0.076062
## [127] train-rmse:0.065250+0.006260 test-rmse:0.926608+0.076594
## [128] train-rmse:0.063962+0.006398 test-rmse:0.926577+0.076585
## [129] train-rmse:0.062365+0.005953 test-rmse:0.926142+0.076812
## [130] train-rmse:0.060895+0.005965 test-rmse:0.925688+0.077129
## [131] train-rmse:0.059125+0.005621 test-rmse:0.925580+0.077253
## [132] train-rmse:0.057941+0.005937 test-rmse:0.925618+0.077255
## [133] train-rmse:0.056970+0.006132 test-rmse:0.925824+0.077290
## [134] train-rmse:0.055815+0.006174 test-rmse:0.925648+0.077181
## [135] train-rmse:0.054604+0.005878 test-rmse:0.925632+0.077531
## [136] train-rmse:0.053678+0.005866 test-rmse:0.925712+0.077811
## [137] train-rmse:0.052638+0.005920 test-rmse:0.925692+0.077734
## Stopping. Best iteration:
## [131] train-rmse:0.059125+0.005621 test-rmse:0.925580+0.077253
##
## 28
## [1] train-rmse:80.819420+0.081048 test-rmse:80.820097+0.412709
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:66.333314+0.066366 test-rmse:66.333795+0.426872
## [3] train-rmse:54.447817+0.054272 test-rmse:54.448058+0.438320
## [4] train-rmse:44.696953+0.044278 test-rmse:44.696904+0.447496
## [5] train-rmse:36.698441+0.036026 test-rmse:36.698036+0.454731
## [6] train-rmse:30.138679+0.029183 test-rmse:30.137853+0.460281
## [7] train-rmse:24.760486+0.023514 test-rmse:24.759153+0.464320
## [8] train-rmse:20.352989+0.018825 test-rmse:20.351057+0.466952
## [9] train-rmse:16.743348+0.014997 test-rmse:16.740718+0.468195
## [10] train-rmse:13.788904+0.011956 test-rmse:13.790291+0.447132
## [11] train-rmse:11.372036+0.009360 test-rmse:11.362891+0.448084
## [12] train-rmse:9.396648+0.007374 test-rmse:9.384920+0.440194
## [13] train-rmse:7.783930+0.006672 test-rmse:7.776361+0.421633
## [14] train-rmse:6.471550+0.006810 test-rmse:6.463241+0.413743
## [15] train-rmse:5.407175+0.008658 test-rmse:5.393536+0.405405
## [16] train-rmse:4.548814+0.010388 test-rmse:4.538528+0.392710
## [17] train-rmse:3.860529+0.012604 test-rmse:3.862296+0.374399
## [18] train-rmse:3.313258+0.015092 test-rmse:3.316063+0.347203
## [19] train-rmse:2.882221+0.017704 test-rmse:2.893749+0.318056
## [20] train-rmse:2.547503+0.020207 test-rmse:2.568695+0.290207
## [21] train-rmse:2.290470+0.022492 test-rmse:2.322391+0.259012
## [22] train-rmse:2.094814+0.024025 test-rmse:2.137274+0.220411
## [23] train-rmse:1.947854+0.025494 test-rmse:2.004964+0.190195
## [24] train-rmse:1.838455+0.027248 test-rmse:1.905651+0.166674
## [25] train-rmse:1.757096+0.027760 test-rmse:1.821294+0.143429
## [26] train-rmse:1.696584+0.028113 test-rmse:1.770602+0.128485
## [27] train-rmse:1.650527+0.028364 test-rmse:1.729482+0.120977
## [28] train-rmse:1.615340+0.028383 test-rmse:1.700039+0.112378
## [29] train-rmse:1.587896+0.028199 test-rmse:1.688420+0.116047
## [30] train-rmse:1.565568+0.027807 test-rmse:1.666054+0.117322
## [31] train-rmse:1.547122+0.027484 test-rmse:1.652016+0.116269
## [32] train-rmse:1.531135+0.027058 test-rmse:1.638994+0.118884
## [33] train-rmse:1.517224+0.026859 test-rmse:1.619370+0.112322
## [34] train-rmse:1.505253+0.026678 test-rmse:1.607825+0.109804
## [35] train-rmse:1.494327+0.026364 test-rmse:1.601272+0.111411
## [36] train-rmse:1.484097+0.026003 test-rmse:1.596605+0.112956
## [37] train-rmse:1.474353+0.025642 test-rmse:1.593324+0.111636
## [38] train-rmse:1.465102+0.025344 test-rmse:1.590608+0.114552
## [39] train-rmse:1.456425+0.025064 test-rmse:1.581138+0.108603
## [40] train-rmse:1.448301+0.024959 test-rmse:1.575268+0.109411
## [41] train-rmse:1.440277+0.024825 test-rmse:1.574786+0.110778
## [42] train-rmse:1.432654+0.024782 test-rmse:1.571290+0.111167
## [43] train-rmse:1.425017+0.024854 test-rmse:1.558402+0.103580
## [44] train-rmse:1.417709+0.024624 test-rmse:1.551703+0.101153
## [45] train-rmse:1.410434+0.024450 test-rmse:1.549689+0.101221
## [46] train-rmse:1.403325+0.024333 test-rmse:1.544850+0.101637
## [47] train-rmse:1.396309+0.023989 test-rmse:1.537829+0.101683
## [48] train-rmse:1.389529+0.023790 test-rmse:1.534673+0.102257
## [49] train-rmse:1.382943+0.023553 test-rmse:1.529425+0.107686
## [50] train-rmse:1.376626+0.023556 test-rmse:1.525265+0.108177
## [51] train-rmse:1.370334+0.023489 test-rmse:1.518658+0.109323
## [52] train-rmse:1.364058+0.023509 test-rmse:1.515164+0.109217
## [53] train-rmse:1.357975+0.023461 test-rmse:1.513241+0.105471
## [54] train-rmse:1.351936+0.023345 test-rmse:1.502852+0.100343
## [55] train-rmse:1.346294+0.023163 test-rmse:1.498318+0.098644
## [56] train-rmse:1.340606+0.022975 test-rmse:1.496994+0.094653
## [57] train-rmse:1.334935+0.022955 test-rmse:1.493410+0.097001
## [58] train-rmse:1.329431+0.022874 test-rmse:1.491708+0.097565
## [59] train-rmse:1.324044+0.022758 test-rmse:1.486477+0.096588
## [60] train-rmse:1.318822+0.022540 test-rmse:1.486299+0.099572
## [61] train-rmse:1.313706+0.022323 test-rmse:1.484239+0.098826
## [62] train-rmse:1.308594+0.022196 test-rmse:1.479689+0.097189
## [63] train-rmse:1.303591+0.021964 test-rmse:1.476913+0.095993
## [64] train-rmse:1.298696+0.021833 test-rmse:1.475082+0.095288
## [65] train-rmse:1.293922+0.021698 test-rmse:1.473592+0.090083
## [66] train-rmse:1.289161+0.021480 test-rmse:1.469720+0.087414
## [67] train-rmse:1.284495+0.021435 test-rmse:1.461746+0.086543
## [68] train-rmse:1.279874+0.021408 test-rmse:1.457666+0.087292
## [69] train-rmse:1.275315+0.021430 test-rmse:1.452515+0.090614
## [70] train-rmse:1.270804+0.021486 test-rmse:1.447812+0.092119
## [71] train-rmse:1.266509+0.021418 test-rmse:1.447670+0.092979
## [72] train-rmse:1.262210+0.021474 test-rmse:1.447569+0.090176
## [73] train-rmse:1.257994+0.021509 test-rmse:1.443646+0.089285
## [74] train-rmse:1.253821+0.021499 test-rmse:1.439496+0.090275
## [75] train-rmse:1.249751+0.021487 test-rmse:1.437564+0.088107
## [76] train-rmse:1.245761+0.021489 test-rmse:1.433998+0.088584
## [77] train-rmse:1.241781+0.021432 test-rmse:1.436426+0.089326
## [78] train-rmse:1.237887+0.021304 test-rmse:1.431564+0.087903
## [79] train-rmse:1.234083+0.021254 test-rmse:1.427325+0.089741
## [80] train-rmse:1.230379+0.021168 test-rmse:1.425535+0.088818
## [81] train-rmse:1.226634+0.020968 test-rmse:1.423276+0.082490
## [82] train-rmse:1.222988+0.020914 test-rmse:1.421292+0.083446
## [83] train-rmse:1.219367+0.020894 test-rmse:1.418282+0.083699
## [84] train-rmse:1.215814+0.020905 test-rmse:1.415884+0.085850
## [85] train-rmse:1.212285+0.020969 test-rmse:1.413257+0.084255
## [86] train-rmse:1.208889+0.021005 test-rmse:1.410699+0.084025
## [87] train-rmse:1.205487+0.020979 test-rmse:1.406085+0.081296
## [88] train-rmse:1.202146+0.020968 test-rmse:1.403067+0.080440
## [89] train-rmse:1.198855+0.020994 test-rmse:1.402469+0.080116
## [90] train-rmse:1.195610+0.021061 test-rmse:1.399947+0.079028
## [91] train-rmse:1.192429+0.021097 test-rmse:1.392881+0.082701
## [92] train-rmse:1.189293+0.021082 test-rmse:1.390753+0.084094
## [93] train-rmse:1.186235+0.021020 test-rmse:1.389693+0.083500
## [94] train-rmse:1.183125+0.020995 test-rmse:1.389563+0.086014
## [95] train-rmse:1.180063+0.020965 test-rmse:1.388023+0.082041
## [96] train-rmse:1.177113+0.020851 test-rmse:1.386721+0.081080
## [97] train-rmse:1.174154+0.020807 test-rmse:1.385802+0.080859
## [98] train-rmse:1.171263+0.020715 test-rmse:1.384249+0.081049
## [99] train-rmse:1.168406+0.020672 test-rmse:1.380382+0.082252
## [100] train-rmse:1.165637+0.020620 test-rmse:1.377891+0.082482
## [101] train-rmse:1.162877+0.020591 test-rmse:1.376374+0.082818
## [102] train-rmse:1.160148+0.020559 test-rmse:1.374301+0.081598
## [103] train-rmse:1.157454+0.020504 test-rmse:1.373070+0.083220
## [104] train-rmse:1.154791+0.020477 test-rmse:1.371658+0.082860
## [105] train-rmse:1.152174+0.020430 test-rmse:1.369242+0.082320
## [106] train-rmse:1.149598+0.020385 test-rmse:1.367959+0.083552
## [107] train-rmse:1.147047+0.020294 test-rmse:1.366990+0.081329
## [108] train-rmse:1.144479+0.020260 test-rmse:1.363043+0.081556
## [109] train-rmse:1.141963+0.020186 test-rmse:1.362069+0.076554
## [110] train-rmse:1.139551+0.020136 test-rmse:1.360738+0.075627
## [111] train-rmse:1.137110+0.020075 test-rmse:1.357833+0.077776
## [112] train-rmse:1.134815+0.020083 test-rmse:1.356667+0.078242
## [113] train-rmse:1.132486+0.020072 test-rmse:1.354589+0.077403
## [114] train-rmse:1.130174+0.020084 test-rmse:1.352598+0.078310
## [115] train-rmse:1.127893+0.020124 test-rmse:1.349676+0.078140
## [116] train-rmse:1.125612+0.020173 test-rmse:1.350440+0.080093
## [117] train-rmse:1.123381+0.020209 test-rmse:1.347989+0.079742
## [118] train-rmse:1.121205+0.020218 test-rmse:1.345390+0.080194
## [119] train-rmse:1.119062+0.020218 test-rmse:1.343127+0.082292
## [120] train-rmse:1.116940+0.020163 test-rmse:1.343923+0.082253
## [121] train-rmse:1.114815+0.020138 test-rmse:1.342579+0.081773
## [122] train-rmse:1.112729+0.020121 test-rmse:1.340777+0.082339
## [123] train-rmse:1.110654+0.020070 test-rmse:1.339508+0.083831
## [124] train-rmse:1.108630+0.019993 test-rmse:1.337538+0.078965
## [125] train-rmse:1.106639+0.019917 test-rmse:1.335357+0.080146
## [126] train-rmse:1.104651+0.019856 test-rmse:1.334731+0.081058
## [127] train-rmse:1.102665+0.019817 test-rmse:1.333143+0.081879
## [128] train-rmse:1.100704+0.019741 test-rmse:1.333888+0.080940
## [129] train-rmse:1.098747+0.019711 test-rmse:1.332047+0.081876
## [130] train-rmse:1.096828+0.019682 test-rmse:1.329197+0.080280
## [131] train-rmse:1.094936+0.019631 test-rmse:1.327071+0.079966
## [132] train-rmse:1.093077+0.019575 test-rmse:1.326674+0.080080
## [133] train-rmse:1.091216+0.019553 test-rmse:1.325367+0.079978
## [134] train-rmse:1.089365+0.019517 test-rmse:1.324856+0.080245
## [135] train-rmse:1.087566+0.019500 test-rmse:1.322706+0.081814
## [136] train-rmse:1.085769+0.019478 test-rmse:1.319284+0.082654
## [137] train-rmse:1.083988+0.019459 test-rmse:1.317945+0.082717
## [138] train-rmse:1.082240+0.019434 test-rmse:1.316147+0.082685
## [139] train-rmse:1.080549+0.019404 test-rmse:1.315063+0.082522
## [140] train-rmse:1.078858+0.019352 test-rmse:1.314497+0.079964
## [141] train-rmse:1.077207+0.019330 test-rmse:1.312785+0.080264
## [142] train-rmse:1.075552+0.019302 test-rmse:1.310996+0.080974
## [143] train-rmse:1.073890+0.019270 test-rmse:1.309302+0.080684
## [144] train-rmse:1.072259+0.019215 test-rmse:1.305571+0.081810
## [145] train-rmse:1.070649+0.019185 test-rmse:1.305113+0.081740
## [146] train-rmse:1.069075+0.019147 test-rmse:1.303013+0.082797
## [147] train-rmse:1.067487+0.019127 test-rmse:1.303697+0.082874
## [148] train-rmse:1.065950+0.019071 test-rmse:1.301934+0.083967
## [149] train-rmse:1.064420+0.019008 test-rmse:1.300599+0.085423
## [150] train-rmse:1.062912+0.018959 test-rmse:1.299501+0.084569
## [151] train-rmse:1.061395+0.018906 test-rmse:1.300883+0.080827
## [152] train-rmse:1.059915+0.018835 test-rmse:1.299422+0.080522
## [153] train-rmse:1.058456+0.018771 test-rmse:1.298853+0.081536
## [154] train-rmse:1.057025+0.018711 test-rmse:1.297204+0.082409
## [155] train-rmse:1.055617+0.018642 test-rmse:1.296054+0.083090
## [156] train-rmse:1.054222+0.018606 test-rmse:1.295893+0.083611
## [157] train-rmse:1.052843+0.018587 test-rmse:1.294966+0.082703
## [158] train-rmse:1.051467+0.018548 test-rmse:1.294451+0.082133
## [159] train-rmse:1.050104+0.018525 test-rmse:1.293964+0.082069
## [160] train-rmse:1.048743+0.018516 test-rmse:1.294028+0.081992
## [161] train-rmse:1.047409+0.018511 test-rmse:1.293081+0.082071
## [162] train-rmse:1.046078+0.018504 test-rmse:1.295603+0.080377
## [163] train-rmse:1.044753+0.018502 test-rmse:1.294148+0.082153
## [164] train-rmse:1.043438+0.018502 test-rmse:1.293383+0.084645
## [165] train-rmse:1.042136+0.018494 test-rmse:1.291165+0.084866
## [166] train-rmse:1.040867+0.018461 test-rmse:1.289719+0.083051
## [167] train-rmse:1.039606+0.018428 test-rmse:1.287453+0.083818
## [168] train-rmse:1.038358+0.018373 test-rmse:1.285665+0.083761
## [169] train-rmse:1.037115+0.018306 test-rmse:1.283221+0.085355
## [170] train-rmse:1.035876+0.018240 test-rmse:1.282403+0.085304
## [171] train-rmse:1.034665+0.018201 test-rmse:1.281760+0.084448
## [172] train-rmse:1.033430+0.018150 test-rmse:1.279929+0.084414
## [173] train-rmse:1.032239+0.018087 test-rmse:1.279847+0.085195
## [174] train-rmse:1.031071+0.018045 test-rmse:1.277517+0.082968
## [175] train-rmse:1.029902+0.017994 test-rmse:1.276141+0.082403
## [176] train-rmse:1.028742+0.017958 test-rmse:1.274746+0.083419
## [177] train-rmse:1.027568+0.017915 test-rmse:1.274216+0.082999
## [178] train-rmse:1.026407+0.017896 test-rmse:1.274369+0.082801
## [179] train-rmse:1.025236+0.017880 test-rmse:1.275512+0.082103
## [180] train-rmse:1.024092+0.017846 test-rmse:1.274126+0.082668
## [181] train-rmse:1.022948+0.017815 test-rmse:1.273634+0.081942
## [182] train-rmse:1.021845+0.017797 test-rmse:1.272409+0.082266
## [183] train-rmse:1.020757+0.017772 test-rmse:1.271707+0.082146
## [184] train-rmse:1.019690+0.017759 test-rmse:1.270126+0.081811
## [185] train-rmse:1.018621+0.017744 test-rmse:1.270056+0.081218
## [186] train-rmse:1.017552+0.017724 test-rmse:1.269368+0.081122
## [187] train-rmse:1.016502+0.017731 test-rmse:1.268198+0.081756
## [188] train-rmse:1.015454+0.017734 test-rmse:1.268387+0.081165
## [189] train-rmse:1.014388+0.017723 test-rmse:1.267323+0.081021
## [190] train-rmse:1.013364+0.017721 test-rmse:1.266783+0.080635
## [191] train-rmse:1.012320+0.017751 test-rmse:1.266563+0.083029
## [192] train-rmse:1.011282+0.017748 test-rmse:1.267162+0.082334
## [193] train-rmse:1.010277+0.017734 test-rmse:1.266666+0.082582
## [194] train-rmse:1.009243+0.017697 test-rmse:1.268241+0.082278
## [195] train-rmse:1.008231+0.017682 test-rmse:1.267365+0.083784
## [196] train-rmse:1.007250+0.017662 test-rmse:1.266486+0.083282
## [197] train-rmse:1.006282+0.017645 test-rmse:1.265381+0.083435
## [198] train-rmse:1.005322+0.017637 test-rmse:1.263407+0.085016
## [199] train-rmse:1.004375+0.017634 test-rmse:1.263821+0.084823
## [200] train-rmse:1.003440+0.017621 test-rmse:1.259848+0.082105
## [201] train-rmse:1.002488+0.017622 test-rmse:1.258698+0.081647
## [202] train-rmse:1.001555+0.017604 test-rmse:1.257934+0.081736
## [203] train-rmse:1.000642+0.017612 test-rmse:1.257111+0.082403
## [204] train-rmse:0.999677+0.017577 test-rmse:1.258180+0.081197
## [205] train-rmse:0.998743+0.017602 test-rmse:1.256911+0.082518
## [206] train-rmse:0.997826+0.017612 test-rmse:1.256664+0.082811
## [207] train-rmse:0.996906+0.017589 test-rmse:1.256399+0.082390
## [208] train-rmse:0.996016+0.017584 test-rmse:1.254893+0.082808
## [209] train-rmse:0.995114+0.017583 test-rmse:1.255344+0.082595
## [210] train-rmse:0.994212+0.017561 test-rmse:1.253266+0.083121
## [211] train-rmse:0.993302+0.017572 test-rmse:1.252878+0.082605
## [212] train-rmse:0.992402+0.017579 test-rmse:1.252803+0.082395
## [213] train-rmse:0.991529+0.017558 test-rmse:1.252429+0.082276
## [214] train-rmse:0.990666+0.017550 test-rmse:1.250863+0.082240
## [215] train-rmse:0.989812+0.017551 test-rmse:1.250513+0.082341
## [216] train-rmse:0.988965+0.017546 test-rmse:1.251774+0.080690
## [217] train-rmse:0.988107+0.017534 test-rmse:1.251808+0.081051
## [218] train-rmse:0.987276+0.017525 test-rmse:1.251166+0.080039
## [219] train-rmse:0.986434+0.017518 test-rmse:1.250084+0.080141
## [220] train-rmse:0.985606+0.017517 test-rmse:1.250809+0.079814
## [221] train-rmse:0.984800+0.017516 test-rmse:1.250201+0.080339
## [222] train-rmse:0.983976+0.017525 test-rmse:1.251141+0.080560
## [223] train-rmse:0.983174+0.017527 test-rmse:1.250247+0.080762
## [224] train-rmse:0.982371+0.017539 test-rmse:1.250148+0.080451
## [225] train-rmse:0.981573+0.017558 test-rmse:1.249939+0.081184
## [226] train-rmse:0.980772+0.017554 test-rmse:1.248006+0.081133
## [227] train-rmse:0.979989+0.017558 test-rmse:1.247310+0.081844
## [228] train-rmse:0.979186+0.017577 test-rmse:1.246591+0.081956
## [229] train-rmse:0.978408+0.017582 test-rmse:1.247351+0.082497
## [230] train-rmse:0.977622+0.017593 test-rmse:1.247179+0.082478
## [231] train-rmse:0.976855+0.017601 test-rmse:1.246493+0.081152
## [232] train-rmse:0.976085+0.017598 test-rmse:1.245641+0.082578
## [233] train-rmse:0.975328+0.017610 test-rmse:1.244535+0.082397
## [234] train-rmse:0.974579+0.017621 test-rmse:1.244845+0.082014
## [235] train-rmse:0.973829+0.017603 test-rmse:1.244406+0.078504
## [236] train-rmse:0.973089+0.017590 test-rmse:1.245193+0.078691
## [237] train-rmse:0.972358+0.017591 test-rmse:1.244556+0.078293
## [238] train-rmse:0.971628+0.017583 test-rmse:1.244632+0.078886
## [239] train-rmse:0.970893+0.017582 test-rmse:1.244278+0.078796
## [240] train-rmse:0.970164+0.017586 test-rmse:1.243881+0.078785
## [241] train-rmse:0.969436+0.017598 test-rmse:1.243677+0.079296
## [242] train-rmse:0.968728+0.017599 test-rmse:1.242138+0.079071
## [243] train-rmse:0.968007+0.017614 test-rmse:1.243673+0.078304
## [244] train-rmse:0.967301+0.017613 test-rmse:1.242960+0.078450
## [245] train-rmse:0.966614+0.017612 test-rmse:1.242452+0.078300
## [246] train-rmse:0.965930+0.017606 test-rmse:1.241310+0.078359
## [247] train-rmse:0.965245+0.017604 test-rmse:1.240663+0.078560
## [248] train-rmse:0.964533+0.017624 test-rmse:1.240344+0.079076
## [249] train-rmse:0.963844+0.017631 test-rmse:1.239338+0.078757
## [250] train-rmse:0.963158+0.017630 test-rmse:1.238943+0.078394
## [251] train-rmse:0.962476+0.017629 test-rmse:1.237548+0.078865
## [252] train-rmse:0.961818+0.017637 test-rmse:1.236887+0.078704
## [253] train-rmse:0.961136+0.017661 test-rmse:1.237217+0.079676
## [254] train-rmse:0.960476+0.017668 test-rmse:1.237212+0.079425
## [255] train-rmse:0.959803+0.017666 test-rmse:1.237063+0.078770
## [256] train-rmse:0.959135+0.017675 test-rmse:1.236906+0.079639
## [257] train-rmse:0.958475+0.017681 test-rmse:1.236630+0.079390
## [258] train-rmse:0.957819+0.017690 test-rmse:1.235978+0.079718
## [259] train-rmse:0.957176+0.017706 test-rmse:1.235338+0.079808
## [260] train-rmse:0.956536+0.017716 test-rmse:1.234997+0.079208
## [261] train-rmse:0.955888+0.017735 test-rmse:1.234410+0.079456
## [262] train-rmse:0.955246+0.017741 test-rmse:1.232956+0.079922
## [263] train-rmse:0.954618+0.017752 test-rmse:1.233347+0.080154
## [264] train-rmse:0.953997+0.017761 test-rmse:1.232478+0.081622
## [265] train-rmse:0.953366+0.017764 test-rmse:1.232535+0.080699
## [266] train-rmse:0.952748+0.017767 test-rmse:1.233169+0.080869
## [267] train-rmse:0.952134+0.017756 test-rmse:1.230876+0.079836
## [268] train-rmse:0.951534+0.017757 test-rmse:1.230997+0.080224
## [269] train-rmse:0.950911+0.017773 test-rmse:1.230208+0.079314
## [270] train-rmse:0.950312+0.017776 test-rmse:1.231504+0.078680
## [271] train-rmse:0.949710+0.017781 test-rmse:1.231447+0.078396
## [272] train-rmse:0.949098+0.017786 test-rmse:1.231239+0.078441
## [273] train-rmse:0.948491+0.017804 test-rmse:1.230694+0.078088
## [274] train-rmse:0.947902+0.017807 test-rmse:1.230967+0.077710
## [275] train-rmse:0.947320+0.017807 test-rmse:1.229335+0.077655
## [276] train-rmse:0.946741+0.017812 test-rmse:1.229978+0.078358
## [277] train-rmse:0.946165+0.017823 test-rmse:1.230931+0.078109
## [278] train-rmse:0.945590+0.017835 test-rmse:1.230237+0.077705
## [279] train-rmse:0.945015+0.017853 test-rmse:1.229954+0.077780
## [280] train-rmse:0.944435+0.017862 test-rmse:1.229792+0.078129
## [281] train-rmse:0.943865+0.017873 test-rmse:1.229797+0.077130
## Stopping. Best iteration:
## [275] train-rmse:0.947320+0.017807 test-rmse:1.229335+0.077655
##
## 29
## [1] train-rmse:80.819420+0.081048 test-rmse:80.820097+0.412709
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:66.333314+0.066366 test-rmse:66.333795+0.426872
## [3] train-rmse:54.447817+0.054272 test-rmse:54.448058+0.438320
## [4] train-rmse:44.696953+0.044278 test-rmse:44.696904+0.447496
## [5] train-rmse:36.698441+0.036026 test-rmse:36.698036+0.454731
## [6] train-rmse:30.138679+0.029183 test-rmse:30.137853+0.460281
## [7] train-rmse:24.760486+0.023514 test-rmse:24.759153+0.464320
## [8] train-rmse:20.352989+0.018825 test-rmse:20.351057+0.466952
## [9] train-rmse:16.743348+0.014997 test-rmse:16.740718+0.468195
## [10] train-rmse:13.788904+0.011956 test-rmse:13.790291+0.447132
## [11] train-rmse:11.371515+0.009503 test-rmse:11.356805+0.444507
## [12] train-rmse:9.393765+0.008274 test-rmse:9.376567+0.422516
## [13] train-rmse:7.776440+0.007230 test-rmse:7.756271+0.416519
## [14] train-rmse:6.456077+0.007160 test-rmse:6.440817+0.398866
## [15] train-rmse:5.380383+0.006929 test-rmse:5.365981+0.377202
## [16] train-rmse:4.505246+0.007005 test-rmse:4.496731+0.363044
## [17] train-rmse:3.799239+0.008998 test-rmse:3.791541+0.339049
## [18] train-rmse:3.231170+0.008353 test-rmse:3.236381+0.318229
## [19] train-rmse:2.778242+0.010260 test-rmse:2.790408+0.293808
## [20] train-rmse:2.422526+0.011151 test-rmse:2.448310+0.270822
## [21] train-rmse:2.143502+0.013600 test-rmse:2.190067+0.241831
## [22] train-rmse:1.926873+0.013498 test-rmse:2.000454+0.218455
## [23] train-rmse:1.759794+0.015133 test-rmse:1.855684+0.191838
## [24] train-rmse:1.633537+0.016386 test-rmse:1.742014+0.170413
## [25] train-rmse:1.537277+0.015807 test-rmse:1.658970+0.148181
## [26] train-rmse:1.465359+0.016877 test-rmse:1.600960+0.131365
## [27] train-rmse:1.410498+0.019096 test-rmse:1.561388+0.124521
## [28] train-rmse:1.368350+0.018887 test-rmse:1.530568+0.118342
## [29] train-rmse:1.335620+0.018787 test-rmse:1.507970+0.110262
## [30] train-rmse:1.307077+0.018452 test-rmse:1.489664+0.114363
## [31] train-rmse:1.282380+0.020958 test-rmse:1.474761+0.113226
## [32] train-rmse:1.262075+0.019412 test-rmse:1.456040+0.109626
## [33] train-rmse:1.245870+0.019934 test-rmse:1.444064+0.106747
## [34] train-rmse:1.229732+0.020493 test-rmse:1.428477+0.099170
## [35] train-rmse:1.216175+0.020522 test-rmse:1.423147+0.102855
## [36] train-rmse:1.202666+0.019298 test-rmse:1.414580+0.100917
## [37] train-rmse:1.190243+0.019712 test-rmse:1.409708+0.104103
## [38] train-rmse:1.177915+0.020404 test-rmse:1.401532+0.101467
## [39] train-rmse:1.164573+0.017203 test-rmse:1.388263+0.095712
## [40] train-rmse:1.152427+0.019976 test-rmse:1.386518+0.094379
## [41] train-rmse:1.142055+0.019108 test-rmse:1.372165+0.104250
## [42] train-rmse:1.132576+0.019976 test-rmse:1.364603+0.102373
## [43] train-rmse:1.122488+0.020915 test-rmse:1.349007+0.098757
## [44] train-rmse:1.114000+0.020847 test-rmse:1.343416+0.095915
## [45] train-rmse:1.104597+0.020957 test-rmse:1.337735+0.093669
## [46] train-rmse:1.096633+0.021672 test-rmse:1.333783+0.095490
## [47] train-rmse:1.087612+0.023972 test-rmse:1.329608+0.091441
## [48] train-rmse:1.078801+0.022523 test-rmse:1.321653+0.093408
## [49] train-rmse:1.067915+0.021175 test-rmse:1.317700+0.092854
## [50] train-rmse:1.058315+0.020217 test-rmse:1.312273+0.094140
## [51] train-rmse:1.051811+0.020075 test-rmse:1.307016+0.091510
## [52] train-rmse:1.043784+0.019735 test-rmse:1.304154+0.096561
## [53] train-rmse:1.034812+0.018142 test-rmse:1.297694+0.092919
## [54] train-rmse:1.028616+0.017927 test-rmse:1.293363+0.094769
## [55] train-rmse:1.021434+0.017451 test-rmse:1.284945+0.095343
## [56] train-rmse:1.013891+0.020085 test-rmse:1.277878+0.096196
## [57] train-rmse:1.006343+0.020226 test-rmse:1.270991+0.096500
## [58] train-rmse:0.997429+0.019720 test-rmse:1.265085+0.096900
## [59] train-rmse:0.989553+0.020730 test-rmse:1.261231+0.098497
## [60] train-rmse:0.983783+0.020585 test-rmse:1.257636+0.099209
## [61] train-rmse:0.977142+0.020592 test-rmse:1.251446+0.097974
## [62] train-rmse:0.969507+0.020488 test-rmse:1.251065+0.100026
## [63] train-rmse:0.962998+0.019845 test-rmse:1.248213+0.098162
## [64] train-rmse:0.957250+0.020095 test-rmse:1.244875+0.098422
## [65] train-rmse:0.951907+0.020190 test-rmse:1.241049+0.096492
## [66] train-rmse:0.946235+0.019051 test-rmse:1.238053+0.096485
## [67] train-rmse:0.940895+0.019390 test-rmse:1.236706+0.097695
## [68] train-rmse:0.935259+0.018631 test-rmse:1.231060+0.098891
## [69] train-rmse:0.930946+0.018281 test-rmse:1.228141+0.098862
## [70] train-rmse:0.925936+0.018389 test-rmse:1.224865+0.100128
## [71] train-rmse:0.919019+0.018865 test-rmse:1.222702+0.101989
## [72] train-rmse:0.912793+0.018007 test-rmse:1.219284+0.101052
## [73] train-rmse:0.907935+0.018980 test-rmse:1.214205+0.102497
## [74] train-rmse:0.903312+0.018629 test-rmse:1.211959+0.101647
## [75] train-rmse:0.898485+0.017526 test-rmse:1.208319+0.102717
## [76] train-rmse:0.893325+0.018725 test-rmse:1.207450+0.100859
## [77] train-rmse:0.889268+0.019147 test-rmse:1.205654+0.102516
## [78] train-rmse:0.885015+0.019911 test-rmse:1.199452+0.105483
## [79] train-rmse:0.879941+0.019549 test-rmse:1.198607+0.105596
## [80] train-rmse:0.875694+0.019293 test-rmse:1.196195+0.104259
## [81] train-rmse:0.871758+0.018850 test-rmse:1.193674+0.104422
## [82] train-rmse:0.868500+0.018641 test-rmse:1.191318+0.104401
## [83] train-rmse:0.863275+0.018070 test-rmse:1.186999+0.107497
## [84] train-rmse:0.858085+0.016671 test-rmse:1.180532+0.107703
## [85] train-rmse:0.854650+0.016938 test-rmse:1.176461+0.108589
## [86] train-rmse:0.849637+0.018383 test-rmse:1.175313+0.108811
## [87] train-rmse:0.844344+0.016981 test-rmse:1.175281+0.109465
## [88] train-rmse:0.841280+0.016874 test-rmse:1.171637+0.111336
## [89] train-rmse:0.833947+0.014875 test-rmse:1.170757+0.112345
## [90] train-rmse:0.830013+0.014528 test-rmse:1.171346+0.113507
## [91] train-rmse:0.825718+0.012920 test-rmse:1.167931+0.113266
## [92] train-rmse:0.821700+0.012906 test-rmse:1.167046+0.114409
## [93] train-rmse:0.818618+0.012997 test-rmse:1.163473+0.113346
## [94] train-rmse:0.815348+0.013719 test-rmse:1.161275+0.115729
## [95] train-rmse:0.811808+0.013892 test-rmse:1.158452+0.115980
## [96] train-rmse:0.807207+0.013233 test-rmse:1.156617+0.117690
## [97] train-rmse:0.803497+0.012748 test-rmse:1.156831+0.118186
## [98] train-rmse:0.800543+0.012809 test-rmse:1.156809+0.118081
## [99] train-rmse:0.796178+0.012350 test-rmse:1.156749+0.118071
## [100] train-rmse:0.792385+0.011249 test-rmse:1.156443+0.117051
## [101] train-rmse:0.788180+0.013309 test-rmse:1.150682+0.110765
## [102] train-rmse:0.784225+0.012418 test-rmse:1.151102+0.111017
## [103] train-rmse:0.780134+0.012315 test-rmse:1.147814+0.112509
## [104] train-rmse:0.777641+0.012419 test-rmse:1.144133+0.115843
## [105] train-rmse:0.774959+0.012570 test-rmse:1.141661+0.115632
## [106] train-rmse:0.771955+0.012592 test-rmse:1.141093+0.115042
## [107] train-rmse:0.768911+0.011902 test-rmse:1.140223+0.116152
## [108] train-rmse:0.765877+0.011538 test-rmse:1.138990+0.116022
## [109] train-rmse:0.763242+0.010945 test-rmse:1.137904+0.116690
## [110] train-rmse:0.760232+0.010139 test-rmse:1.135302+0.116625
## [111] train-rmse:0.757679+0.010076 test-rmse:1.134874+0.117519
## [112] train-rmse:0.753971+0.008387 test-rmse:1.130385+0.116618
## [113] train-rmse:0.751308+0.008545 test-rmse:1.129973+0.116028
## [114] train-rmse:0.747899+0.008111 test-rmse:1.128092+0.115586
## [115] train-rmse:0.744269+0.007507 test-rmse:1.126969+0.115510
## [116] train-rmse:0.741176+0.007864 test-rmse:1.124745+0.114399
## [117] train-rmse:0.738620+0.008445 test-rmse:1.124747+0.113959
## [118] train-rmse:0.735817+0.008234 test-rmse:1.123515+0.116137
## [119] train-rmse:0.732489+0.008953 test-rmse:1.122639+0.114865
## [120] train-rmse:0.730193+0.009115 test-rmse:1.120645+0.117092
## [121] train-rmse:0.727667+0.009356 test-rmse:1.120377+0.116596
## [122] train-rmse:0.724871+0.009334 test-rmse:1.119690+0.116974
## [123] train-rmse:0.723046+0.009417 test-rmse:1.118784+0.118144
## [124] train-rmse:0.721130+0.009570 test-rmse:1.117819+0.116360
## [125] train-rmse:0.717597+0.010313 test-rmse:1.116696+0.116494
## [126] train-rmse:0.715495+0.010403 test-rmse:1.115615+0.116362
## [127] train-rmse:0.712977+0.010805 test-rmse:1.115436+0.116053
## [128] train-rmse:0.711157+0.010807 test-rmse:1.115753+0.114979
## [129] train-rmse:0.707876+0.012151 test-rmse:1.113857+0.114189
## [130] train-rmse:0.705744+0.011972 test-rmse:1.113107+0.114851
## [131] train-rmse:0.702976+0.010941 test-rmse:1.110749+0.115656
## [132] train-rmse:0.698974+0.008108 test-rmse:1.107858+0.115486
## [133] train-rmse:0.695377+0.007160 test-rmse:1.107482+0.115563
## [134] train-rmse:0.691048+0.007828 test-rmse:1.107360+0.117740
## [135] train-rmse:0.687916+0.007138 test-rmse:1.104802+0.114256
## [136] train-rmse:0.685799+0.006788 test-rmse:1.103198+0.114710
## [137] train-rmse:0.683681+0.007210 test-rmse:1.101242+0.115569
## [138] train-rmse:0.680261+0.006022 test-rmse:1.099385+0.114029
## [139] train-rmse:0.677618+0.005675 test-rmse:1.097988+0.112928
## [140] train-rmse:0.675130+0.006431 test-rmse:1.097453+0.113740
## [141] train-rmse:0.673639+0.006603 test-rmse:1.096903+0.114280
## [142] train-rmse:0.671677+0.006118 test-rmse:1.095587+0.115541
## [143] train-rmse:0.668594+0.006465 test-rmse:1.094012+0.115720
## [144] train-rmse:0.666307+0.007311 test-rmse:1.092388+0.116155
## [145] train-rmse:0.663933+0.007810 test-rmse:1.091320+0.116133
## [146] train-rmse:0.661215+0.008695 test-rmse:1.091509+0.115564
## [147] train-rmse:0.658465+0.008363 test-rmse:1.091761+0.114161
## [148] train-rmse:0.654842+0.009006 test-rmse:1.086505+0.115693
## [149] train-rmse:0.651726+0.010292 test-rmse:1.084320+0.115991
## [150] train-rmse:0.649736+0.009979 test-rmse:1.083852+0.116955
## [151] train-rmse:0.647561+0.009787 test-rmse:1.083313+0.117182
## [152] train-rmse:0.646418+0.009811 test-rmse:1.084019+0.116608
## [153] train-rmse:0.644946+0.010021 test-rmse:1.083894+0.116113
## [154] train-rmse:0.643289+0.010058 test-rmse:1.082954+0.117661
## [155] train-rmse:0.639157+0.006734 test-rmse:1.081165+0.120236
## [156] train-rmse:0.637128+0.007601 test-rmse:1.080107+0.119879
## [157] train-rmse:0.634707+0.007828 test-rmse:1.077235+0.121006
## [158] train-rmse:0.631620+0.006854 test-rmse:1.074600+0.119882
## [159] train-rmse:0.629262+0.006860 test-rmse:1.072800+0.119714
## [160] train-rmse:0.627257+0.006185 test-rmse:1.070639+0.121301
## [161] train-rmse:0.625828+0.005963 test-rmse:1.070526+0.120879
## [162] train-rmse:0.623569+0.005666 test-rmse:1.069593+0.121295
## [163] train-rmse:0.621556+0.006428 test-rmse:1.069709+0.120312
## [164] train-rmse:0.619987+0.006036 test-rmse:1.069426+0.120902
## [165] train-rmse:0.618468+0.006390 test-rmse:1.068707+0.121277
## [166] train-rmse:0.615222+0.006284 test-rmse:1.068571+0.119911
## [167] train-rmse:0.612604+0.006846 test-rmse:1.068948+0.118952
## [168] train-rmse:0.610457+0.006539 test-rmse:1.068279+0.118345
## [169] train-rmse:0.607239+0.006367 test-rmse:1.068837+0.121227
## [170] train-rmse:0.605896+0.006435 test-rmse:1.068065+0.121977
## [171] train-rmse:0.602776+0.005501 test-rmse:1.066937+0.119449
## [172] train-rmse:0.600779+0.004098 test-rmse:1.065732+0.118691
## [173] train-rmse:0.599541+0.004306 test-rmse:1.065580+0.119303
## [174] train-rmse:0.598082+0.004103 test-rmse:1.065993+0.117689
## [175] train-rmse:0.597047+0.004124 test-rmse:1.065958+0.116863
## [176] train-rmse:0.594968+0.004601 test-rmse:1.064611+0.115717
## [177] train-rmse:0.592683+0.005655 test-rmse:1.062032+0.116961
## [178] train-rmse:0.590248+0.005695 test-rmse:1.062418+0.114783
## [179] train-rmse:0.588839+0.005193 test-rmse:1.061416+0.114643
## [180] train-rmse:0.587338+0.005596 test-rmse:1.061650+0.114767
## [181] train-rmse:0.583578+0.004269 test-rmse:1.058426+0.112814
## [182] train-rmse:0.581129+0.004614 test-rmse:1.058847+0.111673
## [183] train-rmse:0.579463+0.004815 test-rmse:1.057752+0.111357
## [184] train-rmse:0.577799+0.005336 test-rmse:1.056646+0.111324
## [185] train-rmse:0.576290+0.005819 test-rmse:1.056545+0.111096
## [186] train-rmse:0.574374+0.004287 test-rmse:1.056000+0.110559
## [187] train-rmse:0.572551+0.004257 test-rmse:1.056379+0.109954
## [188] train-rmse:0.571191+0.004526 test-rmse:1.056008+0.110923
## [189] train-rmse:0.568653+0.003280 test-rmse:1.055247+0.110110
## [190] train-rmse:0.566710+0.002968 test-rmse:1.054477+0.110172
## [191] train-rmse:0.564912+0.003558 test-rmse:1.050996+0.112407
## [192] train-rmse:0.563567+0.003751 test-rmse:1.051289+0.111824
## [193] train-rmse:0.562259+0.003798 test-rmse:1.050671+0.111754
## [194] train-rmse:0.560756+0.003996 test-rmse:1.049932+0.111144
## [195] train-rmse:0.559140+0.004368 test-rmse:1.050156+0.110969
## [196] train-rmse:0.558103+0.004521 test-rmse:1.049855+0.111798
## [197] train-rmse:0.556629+0.003733 test-rmse:1.048797+0.111438
## [198] train-rmse:0.554630+0.004551 test-rmse:1.048348+0.111889
## [199] train-rmse:0.553224+0.004561 test-rmse:1.048557+0.111321
## [200] train-rmse:0.551367+0.004828 test-rmse:1.047598+0.111552
## [201] train-rmse:0.549911+0.004993 test-rmse:1.047415+0.112215
## [202] train-rmse:0.547711+0.004690 test-rmse:1.046014+0.112562
## [203] train-rmse:0.546898+0.004740 test-rmse:1.045144+0.113257
## [204] train-rmse:0.545850+0.004774 test-rmse:1.044445+0.113328
## [205] train-rmse:0.544668+0.004432 test-rmse:1.044088+0.112851
## [206] train-rmse:0.543637+0.004891 test-rmse:1.044697+0.112858
## [207] train-rmse:0.542256+0.004635 test-rmse:1.045091+0.112541
## [208] train-rmse:0.541427+0.004831 test-rmse:1.044442+0.112601
## [209] train-rmse:0.539883+0.004043 test-rmse:1.044454+0.111886
## [210] train-rmse:0.537862+0.003808 test-rmse:1.044093+0.111855
## [211] train-rmse:0.536470+0.004251 test-rmse:1.044343+0.112645
## Stopping. Best iteration:
## [205] train-rmse:0.544668+0.004432 test-rmse:1.044088+0.112851
##
## 30
## [1] train-rmse:80.819420+0.081048 test-rmse:80.820097+0.412709
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:66.333314+0.066366 test-rmse:66.333795+0.426872
## [3] train-rmse:54.447817+0.054272 test-rmse:54.448058+0.438320
## [4] train-rmse:44.696953+0.044278 test-rmse:44.696904+0.447496
## [5] train-rmse:36.698441+0.036026 test-rmse:36.698036+0.454731
## [6] train-rmse:30.138679+0.029183 test-rmse:30.137853+0.460281
## [7] train-rmse:24.760486+0.023514 test-rmse:24.759153+0.464320
## [8] train-rmse:20.352989+0.018825 test-rmse:20.351057+0.466952
## [9] train-rmse:16.743348+0.014997 test-rmse:16.740718+0.468195
## [10] train-rmse:13.788904+0.011956 test-rmse:13.790291+0.447132
## [11] train-rmse:11.371515+0.009503 test-rmse:11.356805+0.444507
## [12] train-rmse:9.393542+0.008409 test-rmse:9.381904+0.422309
## [13] train-rmse:7.774136+0.007840 test-rmse:7.754848+0.407089
## [14] train-rmse:6.448810+0.007115 test-rmse:6.437024+0.391278
## [15] train-rmse:5.367030+0.007351 test-rmse:5.358054+0.366154
## [16] train-rmse:4.484466+0.007099 test-rmse:4.481248+0.342888
## [17] train-rmse:3.767561+0.007657 test-rmse:3.774301+0.323370
## [18] train-rmse:3.186880+0.007726 test-rmse:3.198243+0.302540
## [19] train-rmse:2.719341+0.009974 test-rmse:2.743260+0.287408
## [20] train-rmse:2.347259+0.011960 test-rmse:2.391610+0.262809
## [21] train-rmse:2.050320+0.011809 test-rmse:2.115811+0.249164
## [22] train-rmse:1.815791+0.014440 test-rmse:1.911394+0.223008
## [23] train-rmse:1.631817+0.010672 test-rmse:1.753333+0.199748
## [24] train-rmse:1.492226+0.012053 test-rmse:1.636823+0.175206
## [25] train-rmse:1.386101+0.012831 test-rmse:1.549425+0.156111
## [26] train-rmse:1.301992+0.016778 test-rmse:1.483831+0.138412
## [27] train-rmse:1.238505+0.017048 test-rmse:1.437241+0.128334
## [28] train-rmse:1.188113+0.016401 test-rmse:1.404866+0.119542
## [29] train-rmse:1.144197+0.012397 test-rmse:1.374383+0.110867
## [30] train-rmse:1.107346+0.013158 test-rmse:1.350940+0.102683
## [31] train-rmse:1.079381+0.014076 test-rmse:1.334519+0.095235
## [32] train-rmse:1.055793+0.014075 test-rmse:1.324199+0.095185
## [33] train-rmse:1.032947+0.016227 test-rmse:1.306130+0.093103
## [34] train-rmse:1.011987+0.017515 test-rmse:1.294782+0.089816
## [35] train-rmse:0.996994+0.017640 test-rmse:1.279958+0.090009
## [36] train-rmse:0.980671+0.016208 test-rmse:1.270208+0.089004
## [37] train-rmse:0.966545+0.014685 test-rmse:1.265282+0.086375
## [38] train-rmse:0.953876+0.014561 test-rmse:1.256064+0.080829
## [39] train-rmse:0.939666+0.013637 test-rmse:1.244335+0.087703
## [40] train-rmse:0.927459+0.014629 test-rmse:1.233588+0.089465
## [41] train-rmse:0.915681+0.014304 test-rmse:1.223565+0.091131
## [42] train-rmse:0.899832+0.015430 test-rmse:1.211683+0.088697
## [43] train-rmse:0.887519+0.015718 test-rmse:1.206956+0.086900
## [44] train-rmse:0.877798+0.015202 test-rmse:1.200334+0.087366
## [45] train-rmse:0.868638+0.016144 test-rmse:1.197526+0.084919
## [46] train-rmse:0.861120+0.016375 test-rmse:1.190770+0.085223
## [47] train-rmse:0.851653+0.016304 test-rmse:1.188952+0.083872
## [48] train-rmse:0.840720+0.018651 test-rmse:1.182164+0.079005
## [49] train-rmse:0.832016+0.018725 test-rmse:1.176420+0.080028
## [50] train-rmse:0.820416+0.019274 test-rmse:1.160586+0.076866
## [51] train-rmse:0.811085+0.019925 test-rmse:1.158317+0.075410
## [52] train-rmse:0.803373+0.018826 test-rmse:1.155635+0.076747
## [53] train-rmse:0.796033+0.019180 test-rmse:1.150632+0.078199
## [54] train-rmse:0.789014+0.019357 test-rmse:1.147224+0.077472
## [55] train-rmse:0.780246+0.021704 test-rmse:1.141093+0.079646
## [56] train-rmse:0.767854+0.021461 test-rmse:1.134028+0.077191
## [57] train-rmse:0.761535+0.022010 test-rmse:1.132623+0.076081
## [58] train-rmse:0.755271+0.021610 test-rmse:1.128469+0.074254
## [59] train-rmse:0.749825+0.021822 test-rmse:1.127107+0.073971
## [60] train-rmse:0.742708+0.022669 test-rmse:1.123475+0.076306
## [61] train-rmse:0.733806+0.021121 test-rmse:1.117443+0.074397
## [62] train-rmse:0.724821+0.020337 test-rmse:1.114643+0.074808
## [63] train-rmse:0.716636+0.020444 test-rmse:1.109868+0.074328
## [64] train-rmse:0.710570+0.020960 test-rmse:1.106600+0.074731
## [65] train-rmse:0.704910+0.021780 test-rmse:1.102600+0.074579
## [66] train-rmse:0.698029+0.021114 test-rmse:1.096043+0.074996
## [67] train-rmse:0.691923+0.019500 test-rmse:1.092597+0.072676
## [68] train-rmse:0.686826+0.017984 test-rmse:1.088286+0.074002
## [69] train-rmse:0.677756+0.018900 test-rmse:1.086347+0.072441
## [70] train-rmse:0.671695+0.019002 test-rmse:1.081570+0.074302
## [71] train-rmse:0.666529+0.019348 test-rmse:1.078479+0.073976
## [72] train-rmse:0.660946+0.019755 test-rmse:1.075438+0.073687
## [73] train-rmse:0.655616+0.020640 test-rmse:1.074822+0.073268
## [74] train-rmse:0.651405+0.021010 test-rmse:1.074245+0.073040
## [75] train-rmse:0.646682+0.020431 test-rmse:1.068973+0.074769
## [76] train-rmse:0.640068+0.019041 test-rmse:1.067590+0.074489
## [77] train-rmse:0.632474+0.020209 test-rmse:1.067506+0.074284
## [78] train-rmse:0.628656+0.019630 test-rmse:1.063668+0.075995
## [79] train-rmse:0.624935+0.019655 test-rmse:1.064703+0.074871
## [80] train-rmse:0.619654+0.019273 test-rmse:1.061819+0.076288
## [81] train-rmse:0.614319+0.019808 test-rmse:1.059853+0.075973
## [82] train-rmse:0.609041+0.018623 test-rmse:1.058884+0.073808
## [83] train-rmse:0.604436+0.017296 test-rmse:1.056298+0.075162
## [84] train-rmse:0.600444+0.018595 test-rmse:1.053111+0.073351
## [85] train-rmse:0.594036+0.017758 test-rmse:1.048157+0.070124
## [86] train-rmse:0.589946+0.018871 test-rmse:1.047318+0.071022
## [87] train-rmse:0.586492+0.018996 test-rmse:1.047899+0.070193
## [88] train-rmse:0.582768+0.018426 test-rmse:1.045211+0.069014
## [89] train-rmse:0.578962+0.017953 test-rmse:1.043718+0.070337
## [90] train-rmse:0.574808+0.017614 test-rmse:1.042981+0.068064
## [91] train-rmse:0.570511+0.018527 test-rmse:1.042060+0.068375
## [92] train-rmse:0.566411+0.018251 test-rmse:1.043105+0.071457
## [93] train-rmse:0.562329+0.015842 test-rmse:1.041095+0.074297
## [94] train-rmse:0.557682+0.016614 test-rmse:1.038577+0.074271
## [95] train-rmse:0.553876+0.016744 test-rmse:1.033714+0.073743
## [96] train-rmse:0.550316+0.016657 test-rmse:1.031256+0.072371
## [97] train-rmse:0.544119+0.014703 test-rmse:1.027744+0.070491
## [98] train-rmse:0.539740+0.014845 test-rmse:1.025594+0.070068
## [99] train-rmse:0.535436+0.015863 test-rmse:1.024571+0.069542
## [100] train-rmse:0.531604+0.016303 test-rmse:1.023528+0.067337
## [101] train-rmse:0.526194+0.015607 test-rmse:1.023153+0.063991
## [102] train-rmse:0.520114+0.014882 test-rmse:1.023170+0.064803
## [103] train-rmse:0.516161+0.016588 test-rmse:1.019835+0.062595
## [104] train-rmse:0.511187+0.017390 test-rmse:1.016104+0.063449
## [105] train-rmse:0.507552+0.016283 test-rmse:1.015088+0.064233
## [106] train-rmse:0.505442+0.016529 test-rmse:1.013184+0.065446
## [107] train-rmse:0.502201+0.017129 test-rmse:1.012413+0.065027
## [108] train-rmse:0.499381+0.016687 test-rmse:1.013822+0.067684
## [109] train-rmse:0.495757+0.016612 test-rmse:1.012760+0.066863
## [110] train-rmse:0.492345+0.016479 test-rmse:1.012145+0.068010
## [111] train-rmse:0.489902+0.016463 test-rmse:1.010925+0.068334
## [112] train-rmse:0.485118+0.014498 test-rmse:1.012061+0.071278
## [113] train-rmse:0.480386+0.016232 test-rmse:1.010162+0.069652
## [114] train-rmse:0.477827+0.016620 test-rmse:1.008748+0.070597
## [115] train-rmse:0.475139+0.016317 test-rmse:1.009314+0.071968
## [116] train-rmse:0.470864+0.015780 test-rmse:1.010595+0.073873
## [117] train-rmse:0.468289+0.015546 test-rmse:1.009140+0.074677
## [118] train-rmse:0.464994+0.017150 test-rmse:1.008187+0.073751
## [119] train-rmse:0.461649+0.017127 test-rmse:1.007492+0.072232
## [120] train-rmse:0.459160+0.017528 test-rmse:1.007350+0.072818
## [121] train-rmse:0.456517+0.015823 test-rmse:1.006481+0.073410
## [122] train-rmse:0.453318+0.015728 test-rmse:1.004489+0.074142
## [123] train-rmse:0.450352+0.016354 test-rmse:1.002917+0.072859
## [124] train-rmse:0.447275+0.016442 test-rmse:1.004445+0.073974
## [125] train-rmse:0.444516+0.016073 test-rmse:1.001715+0.071792
## [126] train-rmse:0.442427+0.015065 test-rmse:1.000403+0.072721
## [127] train-rmse:0.439231+0.015274 test-rmse:1.000069+0.072640
## [128] train-rmse:0.436706+0.015028 test-rmse:0.999831+0.072061
## [129] train-rmse:0.434106+0.015587 test-rmse:0.998310+0.071845
## [130] train-rmse:0.430536+0.016845 test-rmse:0.997747+0.071499
## [131] train-rmse:0.428302+0.017041 test-rmse:0.997723+0.072634
## [132] train-rmse:0.426000+0.016989 test-rmse:0.996447+0.072685
## [133] train-rmse:0.421921+0.016965 test-rmse:0.993571+0.071335
## [134] train-rmse:0.419501+0.016885 test-rmse:0.993121+0.069559
## [135] train-rmse:0.416680+0.016791 test-rmse:0.992149+0.069976
## [136] train-rmse:0.414158+0.017418 test-rmse:0.991345+0.071159
## [137] train-rmse:0.412637+0.017388 test-rmse:0.990293+0.071664
## [138] train-rmse:0.410666+0.017761 test-rmse:0.988646+0.070956
## [139] train-rmse:0.409113+0.017682 test-rmse:0.987086+0.070263
## [140] train-rmse:0.405594+0.018204 test-rmse:0.988773+0.071397
## [141] train-rmse:0.402999+0.017393 test-rmse:0.987616+0.072087
## [142] train-rmse:0.399300+0.017951 test-rmse:0.987005+0.071036
## [143] train-rmse:0.396536+0.018013 test-rmse:0.986206+0.069702
## [144] train-rmse:0.393347+0.016969 test-rmse:0.986376+0.069786
## [145] train-rmse:0.390781+0.017563 test-rmse:0.985586+0.070232
## [146] train-rmse:0.388442+0.016527 test-rmse:0.985367+0.071210
## [147] train-rmse:0.386497+0.016928 test-rmse:0.985953+0.070239
## [148] train-rmse:0.383956+0.017440 test-rmse:0.985753+0.070416
## [149] train-rmse:0.382102+0.017457 test-rmse:0.986208+0.070757
## [150] train-rmse:0.378994+0.017080 test-rmse:0.984949+0.069436
## [151] train-rmse:0.376861+0.017303 test-rmse:0.985219+0.071606
## [152] train-rmse:0.373725+0.017192 test-rmse:0.985406+0.072192
## [153] train-rmse:0.371129+0.016663 test-rmse:0.984211+0.072903
## [154] train-rmse:0.369165+0.017211 test-rmse:0.984527+0.073211
## [155] train-rmse:0.366817+0.017593 test-rmse:0.984451+0.073013
## [156] train-rmse:0.365072+0.017125 test-rmse:0.983496+0.072297
## [157] train-rmse:0.363569+0.017004 test-rmse:0.984256+0.071605
## [158] train-rmse:0.361842+0.016905 test-rmse:0.983841+0.071617
## [159] train-rmse:0.359014+0.017669 test-rmse:0.982128+0.070996
## [160] train-rmse:0.357232+0.017567 test-rmse:0.980797+0.070753
## [161] train-rmse:0.354781+0.016943 test-rmse:0.979392+0.070415
## [162] train-rmse:0.352207+0.017367 test-rmse:0.979461+0.070634
## [163] train-rmse:0.348965+0.016822 test-rmse:0.976901+0.067855
## [164] train-rmse:0.347014+0.016426 test-rmse:0.977557+0.066691
## [165] train-rmse:0.345467+0.016729 test-rmse:0.978142+0.067503
## [166] train-rmse:0.343448+0.015451 test-rmse:0.977375+0.067328
## [167] train-rmse:0.340660+0.014957 test-rmse:0.977382+0.067262
## [168] train-rmse:0.338934+0.015275 test-rmse:0.977535+0.066952
## [169] train-rmse:0.336889+0.015245 test-rmse:0.977835+0.068893
## Stopping. Best iteration:
## [163] train-rmse:0.348965+0.016822 test-rmse:0.976901+0.067855
##
## 31
## [1] train-rmse:80.819420+0.081048 test-rmse:80.820097+0.412709
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:66.333314+0.066366 test-rmse:66.333795+0.426872
## [3] train-rmse:54.447817+0.054272 test-rmse:54.448058+0.438320
## [4] train-rmse:44.696953+0.044278 test-rmse:44.696904+0.447496
## [5] train-rmse:36.698441+0.036026 test-rmse:36.698036+0.454731
## [6] train-rmse:30.138679+0.029183 test-rmse:30.137853+0.460281
## [7] train-rmse:24.760486+0.023514 test-rmse:24.759153+0.464320
## [8] train-rmse:20.352989+0.018825 test-rmse:20.351057+0.466952
## [9] train-rmse:16.743348+0.014997 test-rmse:16.740718+0.468195
## [10] train-rmse:13.788904+0.011956 test-rmse:13.790291+0.447132
## [11] train-rmse:11.371515+0.009503 test-rmse:11.356805+0.444507
## [12] train-rmse:9.393542+0.008409 test-rmse:9.381904+0.422309
## [13] train-rmse:7.773823+0.007827 test-rmse:7.757796+0.396597
## [14] train-rmse:6.446837+0.007422 test-rmse:6.435029+0.387149
## [15] train-rmse:5.363486+0.004965 test-rmse:5.360439+0.362808
## [16] train-rmse:4.477504+0.005641 test-rmse:4.478461+0.334759
## [17] train-rmse:3.754570+0.006462 test-rmse:3.763430+0.309555
## [18] train-rmse:3.164846+0.006729 test-rmse:3.189729+0.280894
## [19] train-rmse:2.687296+0.008417 test-rmse:2.719001+0.271051
## [20] train-rmse:2.303158+0.007993 test-rmse:2.348730+0.251600
## [21] train-rmse:1.994930+0.007649 test-rmse:2.072259+0.232271
## [22] train-rmse:1.745704+0.011605 test-rmse:1.844879+0.208659
## [23] train-rmse:1.548974+0.011111 test-rmse:1.680931+0.193823
## [24] train-rmse:1.394682+0.009385 test-rmse:1.560479+0.177264
## [25] train-rmse:1.273841+0.011648 test-rmse:1.466850+0.157179
## [26] train-rmse:1.180597+0.013476 test-rmse:1.395910+0.139417
## [27] train-rmse:1.105822+0.016287 test-rmse:1.343702+0.135826
## [28] train-rmse:1.041784+0.012265 test-rmse:1.305198+0.130898
## [29] train-rmse:0.988975+0.012476 test-rmse:1.271933+0.120315
## [30] train-rmse:0.950339+0.013837 test-rmse:1.251354+0.114337
## [31] train-rmse:0.921127+0.012983 test-rmse:1.225992+0.114610
## [32] train-rmse:0.892691+0.008973 test-rmse:1.213364+0.112364
## [33] train-rmse:0.869407+0.010299 test-rmse:1.197881+0.108860
## [34] train-rmse:0.847251+0.011737 test-rmse:1.185006+0.109741
## [35] train-rmse:0.825359+0.012348 test-rmse:1.169502+0.105633
## [36] train-rmse:0.806408+0.010568 test-rmse:1.159816+0.102014
## [37] train-rmse:0.792171+0.011603 test-rmse:1.151228+0.103510
## [38] train-rmse:0.778103+0.013725 test-rmse:1.141889+0.098526
## [39] train-rmse:0.763701+0.012088 test-rmse:1.136403+0.096330
## [40] train-rmse:0.749319+0.012805 test-rmse:1.127953+0.095664
## [41] train-rmse:0.738145+0.012141 test-rmse:1.120149+0.096767
## [42] train-rmse:0.722831+0.014215 test-rmse:1.114422+0.094776
## [43] train-rmse:0.712837+0.015491 test-rmse:1.107534+0.093152
## [44] train-rmse:0.702999+0.016642 test-rmse:1.100097+0.089671
## [45] train-rmse:0.686120+0.015045 test-rmse:1.089840+0.086119
## [46] train-rmse:0.675066+0.016080 test-rmse:1.085558+0.086057
## [47] train-rmse:0.664936+0.015499 test-rmse:1.077003+0.082518
## [48] train-rmse:0.652816+0.017394 test-rmse:1.069411+0.083066
## [49] train-rmse:0.642633+0.016443 test-rmse:1.060838+0.078411
## [50] train-rmse:0.632370+0.017122 test-rmse:1.056071+0.079016
## [51] train-rmse:0.624665+0.017738 test-rmse:1.051077+0.077664
## [52] train-rmse:0.616183+0.017361 test-rmse:1.048758+0.079697
## [53] train-rmse:0.609433+0.016532 test-rmse:1.042744+0.078599
## [54] train-rmse:0.599103+0.016966 test-rmse:1.035975+0.077608
## [55] train-rmse:0.590569+0.015193 test-rmse:1.032669+0.079929
## [56] train-rmse:0.582647+0.016117 test-rmse:1.030946+0.082290
## [57] train-rmse:0.575082+0.016208 test-rmse:1.032445+0.085772
## [58] train-rmse:0.568436+0.017200 test-rmse:1.029552+0.082664
## [59] train-rmse:0.561989+0.017202 test-rmse:1.023905+0.077876
## [60] train-rmse:0.555485+0.015750 test-rmse:1.022451+0.076117
## [61] train-rmse:0.548729+0.013860 test-rmse:1.022311+0.076254
## [62] train-rmse:0.541761+0.015301 test-rmse:1.022181+0.076071
## [63] train-rmse:0.536742+0.015852 test-rmse:1.018957+0.076771
## [64] train-rmse:0.528852+0.015517 test-rmse:1.016692+0.076175
## [65] train-rmse:0.523079+0.014999 test-rmse:1.015286+0.077262
## [66] train-rmse:0.517444+0.014894 test-rmse:1.013351+0.075355
## [67] train-rmse:0.510309+0.015210 test-rmse:1.012087+0.076337
## [68] train-rmse:0.501222+0.015979 test-rmse:1.010380+0.077203
## [69] train-rmse:0.495931+0.016671 test-rmse:1.008078+0.076816
## [70] train-rmse:0.490235+0.017854 test-rmse:1.007123+0.075794
## [71] train-rmse:0.485026+0.017816 test-rmse:1.005544+0.075587
## [72] train-rmse:0.479710+0.018041 test-rmse:1.006659+0.075604
## [73] train-rmse:0.475315+0.017855 test-rmse:1.004410+0.076868
## [74] train-rmse:0.470731+0.019395 test-rmse:1.003300+0.076974
## [75] train-rmse:0.463590+0.020784 test-rmse:0.998799+0.077503
## [76] train-rmse:0.457741+0.019881 test-rmse:0.997462+0.077110
## [77] train-rmse:0.452403+0.017854 test-rmse:0.992198+0.074167
## [78] train-rmse:0.447784+0.016362 test-rmse:0.989730+0.073008
## [79] train-rmse:0.443247+0.016859 test-rmse:0.989149+0.074384
## [80] train-rmse:0.439536+0.016739 test-rmse:0.988533+0.074239
## [81] train-rmse:0.436247+0.016327 test-rmse:0.988317+0.073362
## [82] train-rmse:0.429171+0.016715 test-rmse:0.986384+0.075412
## [83] train-rmse:0.424611+0.014981 test-rmse:0.983103+0.076028
## [84] train-rmse:0.420211+0.014584 test-rmse:0.981474+0.075582
## [85] train-rmse:0.415772+0.015360 test-rmse:0.980587+0.075565
## [86] train-rmse:0.412361+0.015134 test-rmse:0.979740+0.075547
## [87] train-rmse:0.407209+0.014538 test-rmse:0.977452+0.075978
## [88] train-rmse:0.402688+0.014006 test-rmse:0.979678+0.075598
## [89] train-rmse:0.397964+0.014335 test-rmse:0.980021+0.075843
## [90] train-rmse:0.392912+0.013345 test-rmse:0.979747+0.075742
## [91] train-rmse:0.389395+0.014718 test-rmse:0.977221+0.074738
## [92] train-rmse:0.385357+0.014473 test-rmse:0.976774+0.074077
## [93] train-rmse:0.380419+0.013757 test-rmse:0.974191+0.074110
## [94] train-rmse:0.377601+0.013310 test-rmse:0.973376+0.075174
## [95] train-rmse:0.374119+0.013560 test-rmse:0.973045+0.075286
## [96] train-rmse:0.371300+0.014053 test-rmse:0.972824+0.075198
## [97] train-rmse:0.367068+0.013187 test-rmse:0.971194+0.075003
## [98] train-rmse:0.361868+0.011776 test-rmse:0.969852+0.075411
## [99] train-rmse:0.358290+0.011353 test-rmse:0.971318+0.077178
## [100] train-rmse:0.353616+0.011549 test-rmse:0.969360+0.076757
## [101] train-rmse:0.350435+0.011415 test-rmse:0.968436+0.074853
## [102] train-rmse:0.345481+0.013594 test-rmse:0.968312+0.075910
## [103] train-rmse:0.341975+0.014176 test-rmse:0.968623+0.073904
## [104] train-rmse:0.338914+0.014703 test-rmse:0.967426+0.073543
## [105] train-rmse:0.335289+0.014607 test-rmse:0.968297+0.072313
## [106] train-rmse:0.331827+0.015046 test-rmse:0.968098+0.072904
## [107] train-rmse:0.328113+0.013947 test-rmse:0.967834+0.074005
## [108] train-rmse:0.325005+0.013922 test-rmse:0.968214+0.073271
## [109] train-rmse:0.321896+0.014704 test-rmse:0.968587+0.073180
## [110] train-rmse:0.319035+0.015307 test-rmse:0.966789+0.071733
## [111] train-rmse:0.315908+0.016063 test-rmse:0.967595+0.070561
## [112] train-rmse:0.312895+0.016862 test-rmse:0.966794+0.070576
## [113] train-rmse:0.309628+0.015454 test-rmse:0.966644+0.073083
## [114] train-rmse:0.307460+0.015531 test-rmse:0.965846+0.072821
## [115] train-rmse:0.303845+0.014273 test-rmse:0.964865+0.072829
## [116] train-rmse:0.299324+0.012626 test-rmse:0.964237+0.072142
## [117] train-rmse:0.295375+0.010987 test-rmse:0.962991+0.072894
## [118] train-rmse:0.290911+0.010497 test-rmse:0.962627+0.072500
## [119] train-rmse:0.287661+0.009598 test-rmse:0.961276+0.072658
## [120] train-rmse:0.285707+0.009478 test-rmse:0.960917+0.072447
## [121] train-rmse:0.283695+0.010102 test-rmse:0.960384+0.071863
## [122] train-rmse:0.280617+0.010339 test-rmse:0.960174+0.072231
## [123] train-rmse:0.277617+0.010771 test-rmse:0.959509+0.071885
## [124] train-rmse:0.275790+0.010796 test-rmse:0.959402+0.072126
## [125] train-rmse:0.273856+0.010601 test-rmse:0.959617+0.073112
## [126] train-rmse:0.271232+0.010070 test-rmse:0.958938+0.073768
## [127] train-rmse:0.268355+0.009319 test-rmse:0.958811+0.073900
## [128] train-rmse:0.265899+0.008878 test-rmse:0.957287+0.073584
## [129] train-rmse:0.263924+0.008123 test-rmse:0.958188+0.074511
## [130] train-rmse:0.262197+0.007557 test-rmse:0.957733+0.074329
## [131] train-rmse:0.260798+0.007483 test-rmse:0.957038+0.074176
## [132] train-rmse:0.259085+0.007449 test-rmse:0.956692+0.074761
## [133] train-rmse:0.256794+0.007351 test-rmse:0.954459+0.072549
## [134] train-rmse:0.253898+0.007432 test-rmse:0.954355+0.072096
## [135] train-rmse:0.252048+0.007356 test-rmse:0.954530+0.071660
## [136] train-rmse:0.249673+0.007879 test-rmse:0.953857+0.071475
## [137] train-rmse:0.248043+0.008023 test-rmse:0.953676+0.072379
## [138] train-rmse:0.246205+0.008087 test-rmse:0.954153+0.073093
## [139] train-rmse:0.243856+0.007326 test-rmse:0.954100+0.072772
## [140] train-rmse:0.241698+0.007660 test-rmse:0.953606+0.073412
## [141] train-rmse:0.239222+0.007642 test-rmse:0.954291+0.073415
## [142] train-rmse:0.236901+0.008193 test-rmse:0.954006+0.073737
## [143] train-rmse:0.235332+0.008048 test-rmse:0.953705+0.073633
## [144] train-rmse:0.232302+0.007268 test-rmse:0.952542+0.073129
## [145] train-rmse:0.230135+0.006439 test-rmse:0.952441+0.072772
## [146] train-rmse:0.228450+0.006395 test-rmse:0.951473+0.070922
## [147] train-rmse:0.226768+0.006788 test-rmse:0.951795+0.072197
## [148] train-rmse:0.225618+0.006618 test-rmse:0.951427+0.072361
## [149] train-rmse:0.224207+0.006935 test-rmse:0.951604+0.072078
## [150] train-rmse:0.221801+0.007811 test-rmse:0.951641+0.072755
## [151] train-rmse:0.218745+0.007975 test-rmse:0.951819+0.072135
## [152] train-rmse:0.217115+0.007314 test-rmse:0.951356+0.072437
## [153] train-rmse:0.216028+0.007307 test-rmse:0.950963+0.073001
## [154] train-rmse:0.213679+0.007505 test-rmse:0.951027+0.073321
## [155] train-rmse:0.211297+0.007120 test-rmse:0.950704+0.073605
## [156] train-rmse:0.208704+0.006819 test-rmse:0.950475+0.074591
## [157] train-rmse:0.207083+0.006812 test-rmse:0.950226+0.074694
## [158] train-rmse:0.204638+0.007435 test-rmse:0.949752+0.074291
## [159] train-rmse:0.202018+0.005934 test-rmse:0.950013+0.073850
## [160] train-rmse:0.200623+0.005998 test-rmse:0.950104+0.073543
## [161] train-rmse:0.198693+0.006304 test-rmse:0.949401+0.073063
## [162] train-rmse:0.196851+0.006116 test-rmse:0.949066+0.073573
## [163] train-rmse:0.194773+0.006552 test-rmse:0.947923+0.072946
## [164] train-rmse:0.192944+0.006611 test-rmse:0.948097+0.073356
## [165] train-rmse:0.191532+0.007292 test-rmse:0.948208+0.073859
## [166] train-rmse:0.189623+0.007215 test-rmse:0.947940+0.072870
## [167] train-rmse:0.188161+0.006759 test-rmse:0.947776+0.073240
## [168] train-rmse:0.186572+0.007215 test-rmse:0.947728+0.073464
## [169] train-rmse:0.184826+0.007208 test-rmse:0.948264+0.073384
## [170] train-rmse:0.183646+0.007741 test-rmse:0.948215+0.073149
## [171] train-rmse:0.181242+0.008039 test-rmse:0.948099+0.073173
## [172] train-rmse:0.179638+0.008601 test-rmse:0.948625+0.073005
## [173] train-rmse:0.178358+0.008917 test-rmse:0.948409+0.072298
## [174] train-rmse:0.177295+0.009220 test-rmse:0.948222+0.071920
## Stopping. Best iteration:
## [168] train-rmse:0.186572+0.007215 test-rmse:0.947728+0.073464
##
## 32
## [1] train-rmse:80.819420+0.081048 test-rmse:80.820097+0.412709
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:66.333314+0.066366 test-rmse:66.333795+0.426872
## [3] train-rmse:54.447817+0.054272 test-rmse:54.448058+0.438320
## [4] train-rmse:44.696953+0.044278 test-rmse:44.696904+0.447496
## [5] train-rmse:36.698441+0.036026 test-rmse:36.698036+0.454731
## [6] train-rmse:30.138679+0.029183 test-rmse:30.137853+0.460281
## [7] train-rmse:24.760486+0.023514 test-rmse:24.759153+0.464320
## [8] train-rmse:20.352989+0.018825 test-rmse:20.351057+0.466952
## [9] train-rmse:16.743348+0.014997 test-rmse:16.740718+0.468195
## [10] train-rmse:13.788904+0.011956 test-rmse:13.790291+0.447132
## [11] train-rmse:11.371515+0.009503 test-rmse:11.356805+0.444507
## [12] train-rmse:9.393542+0.008409 test-rmse:9.381904+0.422309
## [13] train-rmse:7.773669+0.007693 test-rmse:7.756012+0.396089
## [14] train-rmse:6.445780+0.007520 test-rmse:6.426400+0.378108
## [15] train-rmse:5.360090+0.006689 test-rmse:5.349834+0.346391
## [16] train-rmse:4.470425+0.005588 test-rmse:4.472068+0.319541
## [17] train-rmse:3.745166+0.005033 test-rmse:3.751271+0.302699
## [18] train-rmse:3.150287+0.002838 test-rmse:3.164235+0.282641
## [19] train-rmse:2.667812+0.003652 test-rmse:2.686908+0.265602
## [20] train-rmse:2.273107+0.002879 test-rmse:2.322022+0.238910
## [21] train-rmse:1.956172+0.006963 test-rmse:2.039410+0.219757
## [22] train-rmse:1.697738+0.004602 test-rmse:1.807777+0.198578
## [23] train-rmse:1.488913+0.006462 test-rmse:1.632750+0.181726
## [24] train-rmse:1.327057+0.009986 test-rmse:1.500673+0.169537
## [25] train-rmse:1.193992+0.009662 test-rmse:1.407268+0.145727
## [26] train-rmse:1.092179+0.008992 test-rmse:1.334706+0.130173
## [27] train-rmse:1.008116+0.009928 test-rmse:1.282571+0.123284
## [28] train-rmse:0.937455+0.009162 test-rmse:1.244492+0.115596
## [29] train-rmse:0.883188+0.010385 test-rmse:1.214061+0.106346
## [30] train-rmse:0.839849+0.008718 test-rmse:1.189425+0.101802
## [31] train-rmse:0.799488+0.010497 test-rmse:1.167847+0.097386
## [32] train-rmse:0.765916+0.012686 test-rmse:1.150637+0.096231
## [33] train-rmse:0.737449+0.014545 test-rmse:1.138562+0.092856
## [34] train-rmse:0.713997+0.014708 test-rmse:1.123761+0.086504
## [35] train-rmse:0.692246+0.013755 test-rmse:1.113322+0.084509
## [36] train-rmse:0.673225+0.013833 test-rmse:1.100233+0.082652
## [37] train-rmse:0.652670+0.011768 test-rmse:1.091449+0.082318
## [38] train-rmse:0.635979+0.014143 test-rmse:1.083911+0.081187
## [39] train-rmse:0.622289+0.013882 test-rmse:1.078142+0.081406
## [40] train-rmse:0.605206+0.013750 test-rmse:1.073324+0.077567
## [41] train-rmse:0.593753+0.014143 test-rmse:1.067923+0.078180
## [42] train-rmse:0.579143+0.014235 test-rmse:1.060188+0.073396
## [43] train-rmse:0.567767+0.014393 test-rmse:1.056166+0.071562
## [44] train-rmse:0.556856+0.014763 test-rmse:1.049009+0.073455
## [45] train-rmse:0.545778+0.014654 test-rmse:1.044560+0.070404
## [46] train-rmse:0.534792+0.011880 test-rmse:1.042861+0.073159
## [47] train-rmse:0.524140+0.012325 test-rmse:1.040327+0.072826
## [48] train-rmse:0.514502+0.012410 test-rmse:1.033843+0.073478
## [49] train-rmse:0.504174+0.014610 test-rmse:1.030586+0.073545
## [50] train-rmse:0.497437+0.015398 test-rmse:1.028740+0.072957
## [51] train-rmse:0.489882+0.015753 test-rmse:1.025313+0.073856
## [52] train-rmse:0.482291+0.015680 test-rmse:1.022258+0.076853
## [53] train-rmse:0.473704+0.014892 test-rmse:1.016478+0.075350
## [54] train-rmse:0.464789+0.012844 test-rmse:1.014333+0.076117
## [55] train-rmse:0.456411+0.011277 test-rmse:1.011871+0.075033
## [56] train-rmse:0.450950+0.011097 test-rmse:1.010551+0.073879
## [57] train-rmse:0.444583+0.011785 test-rmse:1.006401+0.073284
## [58] train-rmse:0.437559+0.012178 test-rmse:1.005416+0.073884
## [59] train-rmse:0.430236+0.015076 test-rmse:1.001390+0.072457
## [60] train-rmse:0.422289+0.014972 test-rmse:1.000009+0.068856
## [61] train-rmse:0.415228+0.015421 test-rmse:0.998106+0.069464
## [62] train-rmse:0.408082+0.015486 test-rmse:0.992464+0.065899
## [63] train-rmse:0.398682+0.014165 test-rmse:0.989383+0.067004
## [64] train-rmse:0.393983+0.013340 test-rmse:0.990469+0.067389
## [65] train-rmse:0.387585+0.012976 test-rmse:0.988318+0.068528
## [66] train-rmse:0.382951+0.013438 test-rmse:0.986119+0.069927
## [67] train-rmse:0.376417+0.013953 test-rmse:0.987025+0.071328
## [68] train-rmse:0.372165+0.013832 test-rmse:0.986636+0.072744
## [69] train-rmse:0.367868+0.013358 test-rmse:0.986449+0.071425
## [70] train-rmse:0.360010+0.013611 test-rmse:0.985793+0.072548
## [71] train-rmse:0.355900+0.012764 test-rmse:0.984032+0.071658
## [72] train-rmse:0.348023+0.013490 test-rmse:0.978078+0.071508
## [73] train-rmse:0.341930+0.012800 test-rmse:0.977422+0.073146
## [74] train-rmse:0.335362+0.014225 test-rmse:0.975356+0.073710
## [75] train-rmse:0.330377+0.015279 test-rmse:0.974149+0.075204
## [76] train-rmse:0.324460+0.013544 test-rmse:0.972201+0.076625
## [77] train-rmse:0.320232+0.012779 test-rmse:0.970977+0.075778
## [78] train-rmse:0.316864+0.012793 test-rmse:0.970672+0.075545
## [79] train-rmse:0.312165+0.012423 test-rmse:0.970620+0.075616
## [80] train-rmse:0.307841+0.011789 test-rmse:0.970887+0.075514
## [81] train-rmse:0.303942+0.011532 test-rmse:0.969708+0.077447
## [82] train-rmse:0.299892+0.012356 test-rmse:0.968528+0.077671
## [83] train-rmse:0.296382+0.011756 test-rmse:0.965620+0.076782
## [84] train-rmse:0.290993+0.012684 test-rmse:0.964614+0.076692
## [85] train-rmse:0.287632+0.011923 test-rmse:0.963254+0.076136
## [86] train-rmse:0.283997+0.011737 test-rmse:0.963072+0.076856
## [87] train-rmse:0.280458+0.011843 test-rmse:0.963312+0.077616
## [88] train-rmse:0.274668+0.010445 test-rmse:0.961765+0.077722
## [89] train-rmse:0.270930+0.009762 test-rmse:0.961845+0.077975
## [90] train-rmse:0.266981+0.009018 test-rmse:0.960558+0.077295
## [91] train-rmse:0.264830+0.009885 test-rmse:0.958876+0.077404
## [92] train-rmse:0.260391+0.010531 test-rmse:0.958852+0.078028
## [93] train-rmse:0.256933+0.011647 test-rmse:0.958091+0.078951
## [94] train-rmse:0.253517+0.011501 test-rmse:0.957709+0.078480
## [95] train-rmse:0.248114+0.011582 test-rmse:0.957735+0.079527
## [96] train-rmse:0.245618+0.011511 test-rmse:0.958081+0.080540
## [97] train-rmse:0.242688+0.010466 test-rmse:0.957218+0.080256
## [98] train-rmse:0.238995+0.011882 test-rmse:0.955990+0.079000
## [99] train-rmse:0.235121+0.014042 test-rmse:0.956379+0.081081
## [100] train-rmse:0.231652+0.014700 test-rmse:0.954061+0.082241
## [101] train-rmse:0.228739+0.013038 test-rmse:0.952405+0.082070
## [102] train-rmse:0.225466+0.012310 test-rmse:0.952519+0.082683
## [103] train-rmse:0.220876+0.010407 test-rmse:0.951516+0.083440
## [104] train-rmse:0.217393+0.010598 test-rmse:0.949461+0.082133
## [105] train-rmse:0.214765+0.010761 test-rmse:0.949073+0.082048
## [106] train-rmse:0.210464+0.011796 test-rmse:0.950092+0.083377
## [107] train-rmse:0.207709+0.010511 test-rmse:0.950042+0.083656
## [108] train-rmse:0.205231+0.009890 test-rmse:0.949369+0.084135
## [109] train-rmse:0.203134+0.009735 test-rmse:0.948640+0.083836
## [110] train-rmse:0.200271+0.010641 test-rmse:0.948554+0.083627
## [111] train-rmse:0.197199+0.011082 test-rmse:0.948528+0.084326
## [112] train-rmse:0.194159+0.012224 test-rmse:0.947259+0.083707
## [113] train-rmse:0.191727+0.011628 test-rmse:0.946875+0.084216
## [114] train-rmse:0.189430+0.011613 test-rmse:0.946748+0.084584
## [115] train-rmse:0.187102+0.011457 test-rmse:0.946925+0.084146
## [116] train-rmse:0.184461+0.010997 test-rmse:0.946499+0.083401
## [117] train-rmse:0.181829+0.010918 test-rmse:0.946074+0.083475
## [118] train-rmse:0.178754+0.011708 test-rmse:0.946216+0.083757
## [119] train-rmse:0.177015+0.011633 test-rmse:0.946181+0.083858
## [120] train-rmse:0.173885+0.010332 test-rmse:0.946398+0.084231
## [121] train-rmse:0.171355+0.010830 test-rmse:0.946008+0.084372
## [122] train-rmse:0.168853+0.010176 test-rmse:0.945558+0.084651
## [123] train-rmse:0.166235+0.010681 test-rmse:0.945920+0.084875
## [124] train-rmse:0.164107+0.010447 test-rmse:0.945905+0.085125
## [125] train-rmse:0.162123+0.010301 test-rmse:0.945298+0.084709
## [126] train-rmse:0.159471+0.010000 test-rmse:0.944167+0.084049
## [127] train-rmse:0.156995+0.009685 test-rmse:0.944131+0.083929
## [128] train-rmse:0.155154+0.009481 test-rmse:0.944152+0.083885
## [129] train-rmse:0.152923+0.009390 test-rmse:0.943819+0.083302
## [130] train-rmse:0.150864+0.009154 test-rmse:0.942885+0.083557
## [131] train-rmse:0.148271+0.009081 test-rmse:0.942597+0.083743
## [132] train-rmse:0.145983+0.009273 test-rmse:0.942603+0.083376
## [133] train-rmse:0.143567+0.008557 test-rmse:0.942289+0.082851
## [134] train-rmse:0.141948+0.008251 test-rmse:0.941639+0.083262
## [135] train-rmse:0.139921+0.008195 test-rmse:0.941361+0.083454
## [136] train-rmse:0.137312+0.007574 test-rmse:0.941576+0.083177
## [137] train-rmse:0.135708+0.007403 test-rmse:0.941652+0.083405
## [138] train-rmse:0.134289+0.006954 test-rmse:0.941503+0.083503
## [139] train-rmse:0.132222+0.007451 test-rmse:0.941059+0.083624
## [140] train-rmse:0.130162+0.008156 test-rmse:0.940245+0.083731
## [141] train-rmse:0.128197+0.008225 test-rmse:0.939988+0.083584
## [142] train-rmse:0.126309+0.008265 test-rmse:0.940034+0.083193
## [143] train-rmse:0.125315+0.008172 test-rmse:0.940207+0.083146
## [144] train-rmse:0.123712+0.008422 test-rmse:0.939812+0.083580
## [145] train-rmse:0.122628+0.008540 test-rmse:0.939851+0.083606
## [146] train-rmse:0.121246+0.008492 test-rmse:0.940107+0.083937
## [147] train-rmse:0.119332+0.008782 test-rmse:0.939678+0.083753
## [148] train-rmse:0.117769+0.008652 test-rmse:0.939431+0.083498
## [149] train-rmse:0.116410+0.008672 test-rmse:0.939351+0.083406
## [150] train-rmse:0.114777+0.008603 test-rmse:0.938938+0.083237
## [151] train-rmse:0.112720+0.008183 test-rmse:0.939076+0.082910
## [152] train-rmse:0.111180+0.008441 test-rmse:0.938718+0.082600
## [153] train-rmse:0.109707+0.008647 test-rmse:0.938748+0.082887
## [154] train-rmse:0.108313+0.008486 test-rmse:0.938519+0.083026
## [155] train-rmse:0.107190+0.008759 test-rmse:0.938533+0.083339
## [156] train-rmse:0.106109+0.008949 test-rmse:0.938534+0.083118
## [157] train-rmse:0.104529+0.008144 test-rmse:0.938912+0.083226
## [158] train-rmse:0.103278+0.008060 test-rmse:0.938846+0.082998
## [159] train-rmse:0.102119+0.008178 test-rmse:0.938986+0.082921
## [160] train-rmse:0.100506+0.007689 test-rmse:0.938901+0.082794
## Stopping. Best iteration:
## [154] train-rmse:0.108313+0.008486 test-rmse:0.938519+0.083026
##
## 33
## [1] train-rmse:80.819420+0.081048 test-rmse:80.820097+0.412709
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:66.333314+0.066366 test-rmse:66.333795+0.426872
## [3] train-rmse:54.447817+0.054272 test-rmse:54.448058+0.438320
## [4] train-rmse:44.696953+0.044278 test-rmse:44.696904+0.447496
## [5] train-rmse:36.698441+0.036026 test-rmse:36.698036+0.454731
## [6] train-rmse:30.138679+0.029183 test-rmse:30.137853+0.460281
## [7] train-rmse:24.760486+0.023514 test-rmse:24.759153+0.464320
## [8] train-rmse:20.352989+0.018825 test-rmse:20.351057+0.466952
## [9] train-rmse:16.743348+0.014997 test-rmse:16.740718+0.468195
## [10] train-rmse:13.788904+0.011956 test-rmse:13.790291+0.447132
## [11] train-rmse:11.371515+0.009503 test-rmse:11.356805+0.444507
## [12] train-rmse:9.393542+0.008409 test-rmse:9.381904+0.422309
## [13] train-rmse:7.773535+0.007585 test-rmse:7.755639+0.395987
## [14] train-rmse:6.445268+0.007575 test-rmse:6.428719+0.370497
## [15] train-rmse:5.359345+0.006554 test-rmse:5.347835+0.338583
## [16] train-rmse:4.467974+0.005467 test-rmse:4.466988+0.305928
## [17] train-rmse:3.739305+0.003977 test-rmse:3.748250+0.296789
## [18] train-rmse:3.140834+0.004315 test-rmse:3.171692+0.271045
## [19] train-rmse:2.653176+0.004880 test-rmse:2.705858+0.246839
## [20] train-rmse:2.254940+0.007708 test-rmse:2.320668+0.229771
## [21] train-rmse:1.928765+0.007854 test-rmse:2.030222+0.208594
## [22] train-rmse:1.666461+0.010621 test-rmse:1.801403+0.187537
## [23] train-rmse:1.451935+0.008300 test-rmse:1.619086+0.180809
## [24] train-rmse:1.276395+0.007753 test-rmse:1.490536+0.165259
## [25] train-rmse:1.140052+0.009629 test-rmse:1.390468+0.159227
## [26] train-rmse:1.029220+0.009171 test-rmse:1.318902+0.148053
## [27] train-rmse:0.934817+0.008710 test-rmse:1.251324+0.144817
## [28] train-rmse:0.858545+0.010843 test-rmse:1.207491+0.134884
## [29] train-rmse:0.799191+0.013350 test-rmse:1.176845+0.129329
## [30] train-rmse:0.752631+0.013990 test-rmse:1.150284+0.121774
## [31] train-rmse:0.712843+0.011727 test-rmse:1.130602+0.122466
## [32] train-rmse:0.671918+0.016003 test-rmse:1.112494+0.111020
## [33] train-rmse:0.638628+0.016305 test-rmse:1.096713+0.106731
## [34] train-rmse:0.616317+0.018508 test-rmse:1.087265+0.102570
## [35] train-rmse:0.589914+0.017040 test-rmse:1.076822+0.095331
## [36] train-rmse:0.570089+0.017263 test-rmse:1.068650+0.097314
## [37] train-rmse:0.549030+0.018102 test-rmse:1.060712+0.094376
## [38] train-rmse:0.532026+0.021208 test-rmse:1.057269+0.094151
## [39] train-rmse:0.515905+0.020746 test-rmse:1.050011+0.095465
## [40] train-rmse:0.502283+0.021191 test-rmse:1.044343+0.092841
## [41] train-rmse:0.485029+0.018655 test-rmse:1.038837+0.092682
## [42] train-rmse:0.472996+0.020129 test-rmse:1.032057+0.090157
## [43] train-rmse:0.462481+0.021055 test-rmse:1.029289+0.089117
## [44] train-rmse:0.447982+0.021069 test-rmse:1.027935+0.088943
## [45] train-rmse:0.437472+0.018474 test-rmse:1.025263+0.089205
## [46] train-rmse:0.428767+0.017812 test-rmse:1.016618+0.086405
## [47] train-rmse:0.420707+0.017178 test-rmse:1.009852+0.083725
## [48] train-rmse:0.407085+0.016045 test-rmse:1.009326+0.083508
## [49] train-rmse:0.397562+0.018202 test-rmse:1.008506+0.084639
## [50] train-rmse:0.386453+0.016757 test-rmse:1.007813+0.086171
## [51] train-rmse:0.378929+0.016922 test-rmse:1.006931+0.085195
## [52] train-rmse:0.371578+0.019646 test-rmse:1.003286+0.084899
## [53] train-rmse:0.361735+0.017520 test-rmse:0.999982+0.081632
## [54] train-rmse:0.354173+0.017196 test-rmse:0.997029+0.083340
## [55] train-rmse:0.346961+0.015591 test-rmse:0.996972+0.082327
## [56] train-rmse:0.341174+0.017825 test-rmse:0.995201+0.082659
## [57] train-rmse:0.332665+0.014414 test-rmse:0.997084+0.084358
## [58] train-rmse:0.324176+0.014570 test-rmse:0.996939+0.083142
## [59] train-rmse:0.318277+0.015174 test-rmse:0.995944+0.083192
## [60] train-rmse:0.310778+0.012026 test-rmse:0.994765+0.085180
## [61] train-rmse:0.305899+0.011551 test-rmse:0.993150+0.085876
## [62] train-rmse:0.301081+0.010739 test-rmse:0.991951+0.085896
## [63] train-rmse:0.294229+0.010933 test-rmse:0.990866+0.087484
## [64] train-rmse:0.287719+0.011313 test-rmse:0.989077+0.087107
## [65] train-rmse:0.282239+0.012048 test-rmse:0.987646+0.086777
## [66] train-rmse:0.275074+0.013133 test-rmse:0.987802+0.089053
## [67] train-rmse:0.268366+0.013514 test-rmse:0.986646+0.089422
## [68] train-rmse:0.262883+0.013102 test-rmse:0.987139+0.087995
## [69] train-rmse:0.258160+0.014030 test-rmse:0.987181+0.087804
## [70] train-rmse:0.253875+0.013268 test-rmse:0.984902+0.087792
## [71] train-rmse:0.247591+0.013822 test-rmse:0.985882+0.087580
## [72] train-rmse:0.243424+0.012270 test-rmse:0.985521+0.086292
## [73] train-rmse:0.239348+0.013094 test-rmse:0.985155+0.087139
## [74] train-rmse:0.233011+0.014247 test-rmse:0.983273+0.085813
## [75] train-rmse:0.229987+0.014111 test-rmse:0.983169+0.086397
## [76] train-rmse:0.226294+0.012988 test-rmse:0.982464+0.086798
## [77] train-rmse:0.220911+0.012254 test-rmse:0.982030+0.086038
## [78] train-rmse:0.217344+0.013603 test-rmse:0.982051+0.086339
## [79] train-rmse:0.214051+0.012528 test-rmse:0.980997+0.085981
## [80] train-rmse:0.209766+0.013927 test-rmse:0.980847+0.086357
## [81] train-rmse:0.206163+0.014329 test-rmse:0.982496+0.086659
## [82] train-rmse:0.201255+0.013003 test-rmse:0.980785+0.086491
## [83] train-rmse:0.198117+0.013470 test-rmse:0.981233+0.086651
## [84] train-rmse:0.195118+0.014056 test-rmse:0.981293+0.087131
## [85] train-rmse:0.190516+0.013191 test-rmse:0.980023+0.085706
## [86] train-rmse:0.186892+0.014180 test-rmse:0.980638+0.085424
## [87] train-rmse:0.182814+0.014126 test-rmse:0.980122+0.085044
## [88] train-rmse:0.179230+0.013613 test-rmse:0.978767+0.084474
## [89] train-rmse:0.175679+0.012940 test-rmse:0.977990+0.083629
## [90] train-rmse:0.172445+0.013278 test-rmse:0.977408+0.083854
## [91] train-rmse:0.169301+0.012673 test-rmse:0.977037+0.083034
## [92] train-rmse:0.164155+0.013486 test-rmse:0.976629+0.083784
## [93] train-rmse:0.160358+0.011498 test-rmse:0.976336+0.083781
## [94] train-rmse:0.155409+0.010103 test-rmse:0.974879+0.084551
## [95] train-rmse:0.152609+0.009766 test-rmse:0.975290+0.084460
## [96] train-rmse:0.150026+0.009975 test-rmse:0.974474+0.084464
## [97] train-rmse:0.147564+0.010353 test-rmse:0.974447+0.084876
## [98] train-rmse:0.145572+0.010367 test-rmse:0.974174+0.084990
## [99] train-rmse:0.143211+0.010347 test-rmse:0.974176+0.085173
## [100] train-rmse:0.140425+0.010584 test-rmse:0.973849+0.084855
## [101] train-rmse:0.138731+0.010192 test-rmse:0.973361+0.085146
## [102] train-rmse:0.135966+0.010075 test-rmse:0.973652+0.085620
## [103] train-rmse:0.132527+0.008091 test-rmse:0.972822+0.085956
## [104] train-rmse:0.130380+0.008391 test-rmse:0.971980+0.085863
## [105] train-rmse:0.126108+0.008393 test-rmse:0.971951+0.086504
## [106] train-rmse:0.123494+0.009007 test-rmse:0.971991+0.086730
## [107] train-rmse:0.120835+0.008787 test-rmse:0.971458+0.086915
## [108] train-rmse:0.118684+0.009409 test-rmse:0.971198+0.086946
## [109] train-rmse:0.115621+0.008444 test-rmse:0.969992+0.086191
## [110] train-rmse:0.112442+0.006661 test-rmse:0.969065+0.086439
## [111] train-rmse:0.110767+0.006149 test-rmse:0.968312+0.086426
## [112] train-rmse:0.107483+0.006847 test-rmse:0.967830+0.086699
## [113] train-rmse:0.105585+0.006547 test-rmse:0.966961+0.086309
## [114] train-rmse:0.103442+0.006519 test-rmse:0.967024+0.086176
## [115] train-rmse:0.101389+0.006221 test-rmse:0.967020+0.085835
## [116] train-rmse:0.099685+0.006040 test-rmse:0.966952+0.085689
## [117] train-rmse:0.098206+0.006345 test-rmse:0.967341+0.086078
## [118] train-rmse:0.096494+0.005932 test-rmse:0.966978+0.085509
## [119] train-rmse:0.094943+0.005859 test-rmse:0.966961+0.085635
## [120] train-rmse:0.093329+0.006010 test-rmse:0.967099+0.085482
## [121] train-rmse:0.091625+0.005932 test-rmse:0.967192+0.085313
## [122] train-rmse:0.089981+0.005982 test-rmse:0.966805+0.085191
## [123] train-rmse:0.088708+0.005686 test-rmse:0.966684+0.085336
## [124] train-rmse:0.087160+0.005889 test-rmse:0.966733+0.085121
## [125] train-rmse:0.085683+0.006440 test-rmse:0.966417+0.085022
## [126] train-rmse:0.084259+0.006469 test-rmse:0.965705+0.085244
## [127] train-rmse:0.082300+0.006707 test-rmse:0.965728+0.085551
## [128] train-rmse:0.080594+0.006826 test-rmse:0.965317+0.085392
## [129] train-rmse:0.079158+0.006627 test-rmse:0.965421+0.085666
## [130] train-rmse:0.077572+0.006344 test-rmse:0.965432+0.085970
## [131] train-rmse:0.075994+0.006351 test-rmse:0.965163+0.085802
## [132] train-rmse:0.074490+0.006218 test-rmse:0.965070+0.085636
## [133] train-rmse:0.073135+0.006033 test-rmse:0.964840+0.086345
## [134] train-rmse:0.072028+0.006243 test-rmse:0.964851+0.086456
## [135] train-rmse:0.070891+0.005838 test-rmse:0.964919+0.086700
## [136] train-rmse:0.069712+0.005591 test-rmse:0.964447+0.086062
## [137] train-rmse:0.068263+0.005791 test-rmse:0.964357+0.086215
## [138] train-rmse:0.067225+0.005574 test-rmse:0.964293+0.086282
## [139] train-rmse:0.065658+0.005602 test-rmse:0.964550+0.086527
## [140] train-rmse:0.064611+0.005739 test-rmse:0.964618+0.086673
## [141] train-rmse:0.063474+0.005817 test-rmse:0.964904+0.087066
## [142] train-rmse:0.062158+0.006040 test-rmse:0.965019+0.087240
## [143] train-rmse:0.060880+0.005852 test-rmse:0.964915+0.087664
## [144] train-rmse:0.060082+0.005643 test-rmse:0.964519+0.087792
## Stopping. Best iteration:
## [138] train-rmse:0.067225+0.005574 test-rmse:0.964293+0.086282
##
## 34
## [1] train-rmse:80.819420+0.081048 test-rmse:80.820097+0.412709
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:66.333314+0.066366 test-rmse:66.333795+0.426872
## [3] train-rmse:54.447817+0.054272 test-rmse:54.448058+0.438320
## [4] train-rmse:44.696953+0.044278 test-rmse:44.696904+0.447496
## [5] train-rmse:36.698441+0.036026 test-rmse:36.698036+0.454731
## [6] train-rmse:30.138679+0.029183 test-rmse:30.137853+0.460281
## [7] train-rmse:24.760486+0.023514 test-rmse:24.759153+0.464320
## [8] train-rmse:20.352989+0.018825 test-rmse:20.351057+0.466952
## [9] train-rmse:16.743348+0.014997 test-rmse:16.740718+0.468195
## [10] train-rmse:13.788904+0.011956 test-rmse:13.790291+0.447132
## [11] train-rmse:11.371515+0.009503 test-rmse:11.356805+0.444507
## [12] train-rmse:9.393542+0.008409 test-rmse:9.381904+0.422309
## [13] train-rmse:7.773535+0.007585 test-rmse:7.755639+0.395987
## [14] train-rmse:6.445014+0.007635 test-rmse:6.432822+0.370739
## [15] train-rmse:5.359024+0.006186 test-rmse:5.353262+0.340785
## [16] train-rmse:4.467431+0.004738 test-rmse:4.470474+0.307119
## [17] train-rmse:3.736851+0.004093 test-rmse:3.748738+0.295580
## [18] train-rmse:3.135057+0.003928 test-rmse:3.172904+0.269596
## [19] train-rmse:2.643281+0.002209 test-rmse:2.699248+0.252877
## [20] train-rmse:2.242118+0.004098 test-rmse:2.316569+0.242920
## [21] train-rmse:1.915653+0.007624 test-rmse:2.020117+0.226067
## [22] train-rmse:1.643430+0.008715 test-rmse:1.784824+0.202783
## [23] train-rmse:1.422283+0.008704 test-rmse:1.601837+0.188423
## [24] train-rmse:1.242880+0.009529 test-rmse:1.461869+0.172190
## [25] train-rmse:1.099078+0.008365 test-rmse:1.352903+0.158954
## [26] train-rmse:0.975534+0.010952 test-rmse:1.271310+0.141467
## [27] train-rmse:0.876338+0.011461 test-rmse:1.215834+0.133791
## [28] train-rmse:0.797167+0.013144 test-rmse:1.174435+0.121516
## [29] train-rmse:0.732709+0.012019 test-rmse:1.138948+0.115968
## [30] train-rmse:0.679938+0.007179 test-rmse:1.120062+0.108524
## [31] train-rmse:0.635040+0.009004 test-rmse:1.103248+0.101276
## [32] train-rmse:0.594089+0.011796 test-rmse:1.091791+0.099154
## [33] train-rmse:0.562493+0.011293 test-rmse:1.078215+0.098921
## [34] train-rmse:0.534605+0.014406 test-rmse:1.072696+0.097775
## [35] train-rmse:0.511712+0.015817 test-rmse:1.061724+0.088869
## [36] train-rmse:0.493251+0.021040 test-rmse:1.056920+0.088441
## [37] train-rmse:0.473853+0.020340 test-rmse:1.051905+0.085423
## [38] train-rmse:0.457637+0.017869 test-rmse:1.046455+0.086130
## [39] train-rmse:0.442693+0.018019 test-rmse:1.043356+0.085574
## [40] train-rmse:0.424653+0.017720 test-rmse:1.039530+0.084252
## [41] train-rmse:0.410108+0.021278 test-rmse:1.035783+0.085916
## [42] train-rmse:0.393004+0.020310 test-rmse:1.033155+0.087196
## [43] train-rmse:0.379723+0.019006 test-rmse:1.031714+0.090894
## [44] train-rmse:0.368132+0.019099 test-rmse:1.032093+0.091237
## [45] train-rmse:0.356188+0.017625 test-rmse:1.028424+0.092747
## [46] train-rmse:0.344747+0.017548 test-rmse:1.024932+0.093379
## [47] train-rmse:0.335513+0.013977 test-rmse:1.022803+0.095222
## [48] train-rmse:0.325796+0.017098 test-rmse:1.022589+0.093844
## [49] train-rmse:0.318141+0.018088 test-rmse:1.019855+0.094658
## [50] train-rmse:0.308925+0.017412 test-rmse:1.016129+0.091360
## [51] train-rmse:0.299095+0.019786 test-rmse:1.015726+0.091252
## [52] train-rmse:0.288034+0.017912 test-rmse:1.013814+0.090938
## [53] train-rmse:0.278204+0.017578 test-rmse:1.009948+0.086163
## [54] train-rmse:0.273187+0.016303 test-rmse:1.008605+0.086592
## [55] train-rmse:0.266803+0.016062 test-rmse:1.008120+0.086737
## [56] train-rmse:0.258796+0.014037 test-rmse:1.006264+0.087406
## [57] train-rmse:0.251861+0.013964 test-rmse:1.006041+0.086658
## [58] train-rmse:0.245687+0.014914 test-rmse:1.005825+0.086308
## [59] train-rmse:0.240009+0.015018 test-rmse:1.005038+0.088506
## [60] train-rmse:0.231478+0.015980 test-rmse:1.004580+0.088062
## [61] train-rmse:0.222556+0.013583 test-rmse:1.005519+0.088535
## [62] train-rmse:0.217745+0.014058 test-rmse:1.003840+0.089202
## [63] train-rmse:0.211493+0.014192 test-rmse:1.001571+0.088074
## [64] train-rmse:0.207824+0.013478 test-rmse:1.000684+0.088347
## [65] train-rmse:0.201122+0.013887 test-rmse:1.000841+0.088433
## [66] train-rmse:0.193464+0.013519 test-rmse:0.998984+0.088312
## [67] train-rmse:0.189188+0.013481 test-rmse:0.999916+0.088810
## [68] train-rmse:0.184970+0.013407 test-rmse:0.999365+0.087544
## [69] train-rmse:0.180790+0.013667 test-rmse:0.998478+0.088209
## [70] train-rmse:0.176319+0.013453 test-rmse:0.997590+0.088542
## [71] train-rmse:0.172514+0.013340 test-rmse:0.998061+0.088757
## [72] train-rmse:0.169031+0.013013 test-rmse:0.997631+0.088829
## [73] train-rmse:0.164212+0.012771 test-rmse:0.998001+0.087318
## [74] train-rmse:0.159197+0.012950 test-rmse:0.998036+0.087210
## [75] train-rmse:0.155992+0.011903 test-rmse:0.997615+0.086825
## [76] train-rmse:0.152264+0.011364 test-rmse:0.997036+0.086447
## [77] train-rmse:0.148919+0.010910 test-rmse:0.996005+0.087218
## [78] train-rmse:0.145356+0.010966 test-rmse:0.994933+0.087867
## [79] train-rmse:0.141634+0.010377 test-rmse:0.994047+0.087831
## [80] train-rmse:0.137900+0.010235 test-rmse:0.992937+0.087508
## [81] train-rmse:0.134795+0.010341 test-rmse:0.991983+0.088522
## [82] train-rmse:0.131403+0.009955 test-rmse:0.991912+0.087703
## [83] train-rmse:0.128776+0.010001 test-rmse:0.991412+0.087451
## [84] train-rmse:0.124833+0.010165 test-rmse:0.989687+0.086270
## [85] train-rmse:0.122327+0.010247 test-rmse:0.988826+0.086921
## [86] train-rmse:0.119437+0.010202 test-rmse:0.987495+0.086203
## [87] train-rmse:0.115871+0.010585 test-rmse:0.986829+0.086078
## [88] train-rmse:0.113322+0.010848 test-rmse:0.986569+0.086385
## [89] train-rmse:0.110479+0.009732 test-rmse:0.986333+0.087235
## [90] train-rmse:0.107978+0.009517 test-rmse:0.985336+0.086678
## [91] train-rmse:0.105547+0.009700 test-rmse:0.984614+0.086596
## [92] train-rmse:0.102162+0.008855 test-rmse:0.984585+0.086468
## [93] train-rmse:0.099626+0.007917 test-rmse:0.984193+0.086782
## [94] train-rmse:0.097405+0.007276 test-rmse:0.984278+0.087375
## [95] train-rmse:0.095467+0.007408 test-rmse:0.983836+0.087534
## [96] train-rmse:0.093460+0.007525 test-rmse:0.983126+0.087386
## [97] train-rmse:0.091164+0.007672 test-rmse:0.982775+0.087058
## [98] train-rmse:0.089548+0.007633 test-rmse:0.982568+0.087086
## [99] train-rmse:0.086911+0.006722 test-rmse:0.982127+0.087443
## [100] train-rmse:0.085053+0.005980 test-rmse:0.981976+0.087354
## [101] train-rmse:0.082709+0.006286 test-rmse:0.981226+0.087687
## [102] train-rmse:0.081284+0.006660 test-rmse:0.980800+0.087706
## [103] train-rmse:0.079442+0.006878 test-rmse:0.980859+0.087586
## [104] train-rmse:0.077734+0.006471 test-rmse:0.980335+0.087364
## [105] train-rmse:0.075929+0.006488 test-rmse:0.980350+0.087752
## [106] train-rmse:0.074316+0.006375 test-rmse:0.980209+0.087806
## [107] train-rmse:0.072253+0.005853 test-rmse:0.980025+0.087955
## [108] train-rmse:0.069714+0.005644 test-rmse:0.979996+0.088718
## [109] train-rmse:0.068503+0.005928 test-rmse:0.979996+0.088602
## [110] train-rmse:0.066804+0.006037 test-rmse:0.979990+0.088868
## [111] train-rmse:0.065169+0.006147 test-rmse:0.979875+0.089188
## [112] train-rmse:0.063082+0.005864 test-rmse:0.979540+0.089245
## [113] train-rmse:0.061846+0.005759 test-rmse:0.979269+0.089076
## [114] train-rmse:0.060915+0.005724 test-rmse:0.979425+0.089397
## [115] train-rmse:0.059302+0.005182 test-rmse:0.979308+0.089123
## [116] train-rmse:0.057948+0.005265 test-rmse:0.979098+0.089329
## [117] train-rmse:0.056529+0.005265 test-rmse:0.979375+0.089524
## [118] train-rmse:0.055301+0.005075 test-rmse:0.979095+0.089328
## [119] train-rmse:0.054190+0.004730 test-rmse:0.979253+0.089094
## [120] train-rmse:0.053136+0.004809 test-rmse:0.979030+0.088801
## [121] train-rmse:0.051722+0.004642 test-rmse:0.978944+0.088794
## [122] train-rmse:0.050276+0.004386 test-rmse:0.978906+0.088770
## [123] train-rmse:0.049053+0.004250 test-rmse:0.978602+0.088869
## [124] train-rmse:0.048130+0.004248 test-rmse:0.978680+0.088607
## [125] train-rmse:0.047023+0.003811 test-rmse:0.978582+0.088733
## [126] train-rmse:0.046108+0.003951 test-rmse:0.978704+0.089001
## [127] train-rmse:0.045285+0.003880 test-rmse:0.978506+0.089057
## [128] train-rmse:0.043978+0.003819 test-rmse:0.978194+0.088907
## [129] train-rmse:0.042937+0.003858 test-rmse:0.978114+0.089170
## [130] train-rmse:0.042107+0.003633 test-rmse:0.978082+0.089319
## [131] train-rmse:0.041243+0.003762 test-rmse:0.977866+0.089024
## [132] train-rmse:0.040265+0.003697 test-rmse:0.977929+0.088918
## [133] train-rmse:0.039247+0.003691 test-rmse:0.977954+0.089042
## [134] train-rmse:0.038139+0.003327 test-rmse:0.977849+0.089461
## [135] train-rmse:0.037184+0.003071 test-rmse:0.977596+0.089073
## [136] train-rmse:0.036465+0.003250 test-rmse:0.977681+0.089240
## [137] train-rmse:0.035800+0.003094 test-rmse:0.977554+0.089255
## [138] train-rmse:0.034905+0.002970 test-rmse:0.977539+0.089277
## [139] train-rmse:0.033803+0.003029 test-rmse:0.977506+0.089054
## [140] train-rmse:0.033081+0.003079 test-rmse:0.977473+0.089089
## [141] train-rmse:0.032463+0.003104 test-rmse:0.977454+0.089068
## [142] train-rmse:0.031481+0.002683 test-rmse:0.977443+0.089145
## [143] train-rmse:0.030846+0.002631 test-rmse:0.977436+0.089223
## [144] train-rmse:0.030082+0.002397 test-rmse:0.977452+0.089320
## [145] train-rmse:0.029382+0.002196 test-rmse:0.977368+0.089388
## [146] train-rmse:0.028869+0.002341 test-rmse:0.977350+0.089573
## [147] train-rmse:0.028291+0.002336 test-rmse:0.977309+0.089570
## [148] train-rmse:0.027506+0.002134 test-rmse:0.977109+0.089607
## [149] train-rmse:0.026876+0.001953 test-rmse:0.977025+0.089604
## [150] train-rmse:0.026236+0.002077 test-rmse:0.977042+0.089581
## [151] train-rmse:0.025800+0.001973 test-rmse:0.977010+0.089621
## [152] train-rmse:0.025298+0.001945 test-rmse:0.976951+0.089799
## [153] train-rmse:0.024546+0.002074 test-rmse:0.976975+0.089862
## [154] train-rmse:0.024094+0.001951 test-rmse:0.976721+0.090002
## [155] train-rmse:0.023479+0.001943 test-rmse:0.976734+0.090062
## [156] train-rmse:0.023131+0.001914 test-rmse:0.976796+0.090262
## [157] train-rmse:0.022529+0.001879 test-rmse:0.976840+0.090321
## [158] train-rmse:0.022146+0.001828 test-rmse:0.976834+0.090380
## [159] train-rmse:0.021702+0.001773 test-rmse:0.976780+0.090308
## [160] train-rmse:0.021148+0.001656 test-rmse:0.976789+0.090402
## Stopping. Best iteration:
## [154] train-rmse:0.024094+0.001951 test-rmse:0.976721+0.090002
##
## 35
## [1] train-rmse:78.857910+0.079069 test-rmse:78.858575+0.414633
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:63.153612+0.063143 test-rmse:63.154042+0.429953
## [3] train-rmse:50.582123+0.050320 test-rmse:50.582268+0.441991
## [4] train-rmse:40.519815+0.039977 test-rmse:40.519597+0.451317
## [5] train-rmse:32.467526+0.031623 test-rmse:32.466867+0.458362
## [6] train-rmse:26.025803+0.024854 test-rmse:26.024606+0.463429
## [7] train-rmse:20.875072+0.019383 test-rmse:20.873221+0.466687
## [8] train-rmse:16.759770+0.015015 test-rmse:16.757143+0.468190
## [9] train-rmse:13.474406+0.011635 test-rmse:13.476270+0.444762
## [10] train-rmse:10.853386+0.008865 test-rmse:10.843712+0.445509
## [11] train-rmse:8.765037+0.007065 test-rmse:8.752229+0.435732
## [12] train-rmse:7.104254+0.006597 test-rmse:7.093096+0.420411
## [13] train-rmse:5.789055+0.007417 test-rmse:5.778875+0.409606
## [14] train-rmse:4.754012+0.010013 test-rmse:4.740514+0.395594
## [15] train-rmse:3.945171+0.012308 test-rmse:3.941774+0.373329
## [16] train-rmse:3.318506+0.014985 test-rmse:3.318884+0.343306
## [17] train-rmse:2.840826+0.018138 test-rmse:2.854816+0.312217
## [18] train-rmse:2.480765+0.021118 test-rmse:2.507821+0.275452
## [19] train-rmse:2.213968+0.023683 test-rmse:2.254453+0.233909
## [20] train-rmse:2.019322+0.025758 test-rmse:2.076611+0.199651
## [21] train-rmse:1.878894+0.026507 test-rmse:1.945525+0.173039
## [22] train-rmse:1.777949+0.027535 test-rmse:1.854114+0.153811
## [23] train-rmse:1.704840+0.029147 test-rmse:1.779650+0.136732
## [24] train-rmse:1.652708+0.029209 test-rmse:1.733624+0.121661
## [25] train-rmse:1.613709+0.028814 test-rmse:1.699818+0.116516
## [26] train-rmse:1.583657+0.028271 test-rmse:1.682481+0.116167
## [27] train-rmse:1.560006+0.027839 test-rmse:1.660964+0.114434
## [28] train-rmse:1.540504+0.027517 test-rmse:1.651773+0.116838
## [29] train-rmse:1.524056+0.027372 test-rmse:1.638167+0.120415
## [30] train-rmse:1.509581+0.027131 test-rmse:1.625305+0.124114
## [31] train-rmse:1.496618+0.026863 test-rmse:1.602925+0.114487
## [32] train-rmse:1.485111+0.026457 test-rmse:1.593056+0.114352
## [33] train-rmse:1.474473+0.026308 test-rmse:1.589836+0.114537
## [34] train-rmse:1.464384+0.025995 test-rmse:1.587951+0.111852
## [35] train-rmse:1.454971+0.025530 test-rmse:1.578757+0.109685
## [36] train-rmse:1.445767+0.025243 test-rmse:1.566633+0.107842
## [37] train-rmse:1.436789+0.025018 test-rmse:1.563568+0.110852
## [38] train-rmse:1.428164+0.024803 test-rmse:1.558981+0.109244
## [39] train-rmse:1.419869+0.024710 test-rmse:1.556212+0.109430
## [40] train-rmse:1.411603+0.024622 test-rmse:1.551075+0.107280
## [41] train-rmse:1.403698+0.024494 test-rmse:1.544982+0.107677
## [42] train-rmse:1.396103+0.024381 test-rmse:1.536105+0.112232
## [43] train-rmse:1.388596+0.024306 test-rmse:1.524757+0.107919
## [44] train-rmse:1.381193+0.024145 test-rmse:1.521287+0.106296
## [45] train-rmse:1.374201+0.024037 test-rmse:1.519783+0.109538
## [46] train-rmse:1.367340+0.023899 test-rmse:1.515285+0.109465
## [47] train-rmse:1.360548+0.023819 test-rmse:1.513337+0.102997
## [48] train-rmse:1.353837+0.023797 test-rmse:1.509301+0.096041
## [49] train-rmse:1.347179+0.023615 test-rmse:1.506412+0.094383
## [50] train-rmse:1.340702+0.023460 test-rmse:1.502285+0.094716
## [51] train-rmse:1.334351+0.023314 test-rmse:1.499114+0.094216
## [52] train-rmse:1.328296+0.023038 test-rmse:1.495438+0.094311
## [53] train-rmse:1.322298+0.022833 test-rmse:1.492477+0.094615
## [54] train-rmse:1.316389+0.022581 test-rmse:1.487709+0.094640
## [55] train-rmse:1.310606+0.022282 test-rmse:1.488744+0.098319
## [56] train-rmse:1.304937+0.022085 test-rmse:1.478738+0.097785
## [57] train-rmse:1.299313+0.021831 test-rmse:1.473300+0.100140
## [58] train-rmse:1.293789+0.021749 test-rmse:1.472908+0.099890
## [59] train-rmse:1.288557+0.021598 test-rmse:1.469126+0.101545
## [60] train-rmse:1.283530+0.021586 test-rmse:1.465207+0.095793
## [61] train-rmse:1.278515+0.021639 test-rmse:1.461904+0.096105
## [62] train-rmse:1.273616+0.021547 test-rmse:1.458796+0.094365
## [63] train-rmse:1.268710+0.021459 test-rmse:1.459466+0.090707
## [64] train-rmse:1.263762+0.021228 test-rmse:1.456206+0.089462
## [65] train-rmse:1.258954+0.021196 test-rmse:1.448790+0.089722
## [66] train-rmse:1.254187+0.021248 test-rmse:1.444976+0.090907
## [67] train-rmse:1.249551+0.021285 test-rmse:1.443552+0.092897
## [68] train-rmse:1.245049+0.021276 test-rmse:1.440593+0.092333
## [69] train-rmse:1.240665+0.021202 test-rmse:1.436995+0.091778
## [70] train-rmse:1.236326+0.021181 test-rmse:1.432632+0.091768
## [71] train-rmse:1.232055+0.021135 test-rmse:1.425860+0.092760
## [72] train-rmse:1.227891+0.021059 test-rmse:1.424436+0.091433
## [73] train-rmse:1.223745+0.020986 test-rmse:1.420209+0.091372
## [74] train-rmse:1.219667+0.020822 test-rmse:1.418307+0.095107
## [75] train-rmse:1.215599+0.020629 test-rmse:1.410859+0.094437
## [76] train-rmse:1.211615+0.020661 test-rmse:1.408748+0.093504
## [77] train-rmse:1.207701+0.020679 test-rmse:1.408080+0.087754
## [78] train-rmse:1.203889+0.020503 test-rmse:1.405832+0.086759
## [79] train-rmse:1.200178+0.020483 test-rmse:1.403656+0.086215
## [80] train-rmse:1.196548+0.020430 test-rmse:1.400120+0.090547
## [81] train-rmse:1.192994+0.020465 test-rmse:1.394558+0.089774
## [82] train-rmse:1.189466+0.020546 test-rmse:1.396325+0.088550
## [83] train-rmse:1.185883+0.020598 test-rmse:1.394230+0.089672
## [84] train-rmse:1.182465+0.020545 test-rmse:1.391738+0.091224
## [85] train-rmse:1.179096+0.020515 test-rmse:1.389413+0.091936
## [86] train-rmse:1.175727+0.020463 test-rmse:1.389518+0.086942
## [87] train-rmse:1.172476+0.020395 test-rmse:1.387551+0.086641
## [88] train-rmse:1.169282+0.020324 test-rmse:1.383800+0.087940
## [89] train-rmse:1.166122+0.020263 test-rmse:1.379868+0.087080
## [90] train-rmse:1.163045+0.020268 test-rmse:1.378449+0.087090
## [91] train-rmse:1.159989+0.020273 test-rmse:1.374857+0.088142
## [92] train-rmse:1.157024+0.020254 test-rmse:1.372173+0.087315
## [93] train-rmse:1.154086+0.020201 test-rmse:1.367960+0.087447
## [94] train-rmse:1.151238+0.020073 test-rmse:1.365916+0.085461
## [95] train-rmse:1.148427+0.019962 test-rmse:1.365131+0.086328
## [96] train-rmse:1.145638+0.019866 test-rmse:1.364209+0.087090
## [97] train-rmse:1.142847+0.019761 test-rmse:1.359874+0.086824
## [98] train-rmse:1.140107+0.019681 test-rmse:1.357831+0.087569
## [99] train-rmse:1.137409+0.019616 test-rmse:1.356735+0.090039
## [100] train-rmse:1.134715+0.019581 test-rmse:1.357023+0.088245
## [101] train-rmse:1.132085+0.019573 test-rmse:1.355021+0.089186
## [102] train-rmse:1.129469+0.019524 test-rmse:1.353501+0.087680
## [103] train-rmse:1.126919+0.019496 test-rmse:1.352443+0.086244
## [104] train-rmse:1.124348+0.019440 test-rmse:1.353265+0.082207
## [105] train-rmse:1.121784+0.019377 test-rmse:1.351823+0.081171
## [106] train-rmse:1.119363+0.019372 test-rmse:1.349077+0.081908
## [107] train-rmse:1.116964+0.019306 test-rmse:1.344529+0.082817
## [108] train-rmse:1.114524+0.019227 test-rmse:1.342813+0.084257
## [109] train-rmse:1.112206+0.019260 test-rmse:1.340808+0.085410
## [110] train-rmse:1.109921+0.019237 test-rmse:1.339251+0.084459
## [111] train-rmse:1.107599+0.019172 test-rmse:1.338722+0.085357
## [112] train-rmse:1.105379+0.019166 test-rmse:1.336174+0.084948
## [113] train-rmse:1.103172+0.019151 test-rmse:1.330225+0.088533
## [114] train-rmse:1.100995+0.019131 test-rmse:1.329034+0.090433
## [115] train-rmse:1.098848+0.019081 test-rmse:1.328149+0.091842
## [116] train-rmse:1.096691+0.019053 test-rmse:1.325335+0.091974
## [117] train-rmse:1.094588+0.019021 test-rmse:1.322748+0.090786
## [118] train-rmse:1.092491+0.019012 test-rmse:1.321281+0.090182
## [119] train-rmse:1.090435+0.018989 test-rmse:1.318522+0.089056
## [120] train-rmse:1.088418+0.018983 test-rmse:1.317627+0.090163
## [121] train-rmse:1.086462+0.018939 test-rmse:1.316622+0.089742
## [122] train-rmse:1.084458+0.018870 test-rmse:1.317165+0.086721
## [123] train-rmse:1.082520+0.018845 test-rmse:1.316029+0.087195
## [124] train-rmse:1.080609+0.018791 test-rmse:1.316322+0.088422
## [125] train-rmse:1.078736+0.018731 test-rmse:1.314691+0.088842
## [126] train-rmse:1.076875+0.018667 test-rmse:1.312880+0.089090
## [127] train-rmse:1.075022+0.018616 test-rmse:1.311541+0.090202
## [128] train-rmse:1.073199+0.018550 test-rmse:1.310357+0.087662
## [129] train-rmse:1.071384+0.018490 test-rmse:1.309509+0.088076
## [130] train-rmse:1.069607+0.018428 test-rmse:1.307846+0.088003
## [131] train-rmse:1.067833+0.018367 test-rmse:1.307060+0.087532
## [132] train-rmse:1.066089+0.018308 test-rmse:1.306343+0.089877
## [133] train-rmse:1.064320+0.018322 test-rmse:1.307475+0.089680
## [134] train-rmse:1.062623+0.018300 test-rmse:1.307123+0.090266
## [135] train-rmse:1.060940+0.018274 test-rmse:1.305771+0.091949
## [136] train-rmse:1.059317+0.018236 test-rmse:1.304332+0.091215
## [137] train-rmse:1.057728+0.018185 test-rmse:1.299562+0.092946
## [138] train-rmse:1.056142+0.018133 test-rmse:1.297683+0.092370
## [139] train-rmse:1.054558+0.018082 test-rmse:1.296185+0.092334
## [140] train-rmse:1.052997+0.018039 test-rmse:1.296122+0.093025
## [141] train-rmse:1.051467+0.017975 test-rmse:1.294265+0.094365
## [142] train-rmse:1.049934+0.017946 test-rmse:1.292512+0.095332
## [143] train-rmse:1.048397+0.017904 test-rmse:1.291619+0.093963
## [144] train-rmse:1.046899+0.017886 test-rmse:1.290585+0.094623
## [145] train-rmse:1.045410+0.017824 test-rmse:1.292104+0.091256
## [146] train-rmse:1.043949+0.017771 test-rmse:1.291087+0.090756
## [147] train-rmse:1.042501+0.017721 test-rmse:1.289300+0.090302
## [148] train-rmse:1.041061+0.017645 test-rmse:1.290050+0.090346
## [149] train-rmse:1.039642+0.017613 test-rmse:1.288920+0.090545
## [150] train-rmse:1.038249+0.017559 test-rmse:1.287060+0.090779
## [151] train-rmse:1.036860+0.017516 test-rmse:1.286235+0.090445
## [152] train-rmse:1.035464+0.017487 test-rmse:1.284237+0.091164
## [153] train-rmse:1.034093+0.017469 test-rmse:1.282329+0.092811
## [154] train-rmse:1.032759+0.017447 test-rmse:1.281839+0.091741
## [155] train-rmse:1.031430+0.017416 test-rmse:1.279707+0.091387
## [156] train-rmse:1.030104+0.017381 test-rmse:1.279612+0.092673
## [157] train-rmse:1.028756+0.017334 test-rmse:1.280115+0.092620
## [158] train-rmse:1.027452+0.017297 test-rmse:1.279717+0.092028
## [159] train-rmse:1.026167+0.017264 test-rmse:1.279537+0.091498
## [160] train-rmse:1.024880+0.017253 test-rmse:1.275555+0.088979
## [161] train-rmse:1.023606+0.017229 test-rmse:1.274472+0.090025
## [162] train-rmse:1.022386+0.017198 test-rmse:1.273328+0.088927
## [163] train-rmse:1.021144+0.017145 test-rmse:1.273637+0.089763
## [164] train-rmse:1.019924+0.017113 test-rmse:1.272542+0.089972
## [165] train-rmse:1.018679+0.017120 test-rmse:1.271811+0.090075
## [166] train-rmse:1.017482+0.017107 test-rmse:1.271031+0.089368
## [167] train-rmse:1.016297+0.017091 test-rmse:1.270718+0.090128
## [168] train-rmse:1.015130+0.017075 test-rmse:1.271977+0.087996
## [169] train-rmse:1.013983+0.017064 test-rmse:1.270701+0.088260
## [170] train-rmse:1.012831+0.017071 test-rmse:1.269554+0.088447
## [171] train-rmse:1.011703+0.017074 test-rmse:1.267913+0.088060
## [172] train-rmse:1.010580+0.017073 test-rmse:1.267580+0.090043
## [173] train-rmse:1.009441+0.017080 test-rmse:1.267179+0.089764
## [174] train-rmse:1.008334+0.017078 test-rmse:1.267006+0.088880
## [175] train-rmse:1.007241+0.017049 test-rmse:1.267221+0.088621
## [176] train-rmse:1.006127+0.017044 test-rmse:1.265363+0.089352
## [177] train-rmse:1.005072+0.017028 test-rmse:1.265085+0.088800
## [178] train-rmse:1.003967+0.017008 test-rmse:1.266202+0.089401
## [179] train-rmse:1.002897+0.016981 test-rmse:1.264934+0.089696
## [180] train-rmse:1.001850+0.016961 test-rmse:1.264350+0.089863
## [181] train-rmse:1.000780+0.016977 test-rmse:1.263263+0.089753
## [182] train-rmse:0.999727+0.016971 test-rmse:1.261813+0.090329
## [183] train-rmse:0.998670+0.016955 test-rmse:1.262054+0.089752
## [184] train-rmse:0.997622+0.016945 test-rmse:1.260338+0.090641
## [185] train-rmse:0.996596+0.016942 test-rmse:1.257710+0.091117
## [186] train-rmse:0.995565+0.016925 test-rmse:1.257238+0.090489
## [187] train-rmse:0.994555+0.016901 test-rmse:1.257199+0.090219
## [188] train-rmse:0.993552+0.016912 test-rmse:1.257159+0.090056
## [189] train-rmse:0.992537+0.016926 test-rmse:1.255645+0.086078
## [190] train-rmse:0.991560+0.016941 test-rmse:1.254704+0.086426
## [191] train-rmse:0.990597+0.016942 test-rmse:1.254024+0.086520
## [192] train-rmse:0.989629+0.016965 test-rmse:1.254425+0.088307
## [193] train-rmse:0.988676+0.016975 test-rmse:1.252924+0.089989
## [194] train-rmse:0.987726+0.016982 test-rmse:1.251759+0.090992
## [195] train-rmse:0.986795+0.016978 test-rmse:1.251618+0.090854
## [196] train-rmse:0.985863+0.016961 test-rmse:1.250855+0.090769
## [197] train-rmse:0.984957+0.016965 test-rmse:1.251553+0.090274
## [198] train-rmse:0.984028+0.016972 test-rmse:1.252462+0.089975
## [199] train-rmse:0.983106+0.016980 test-rmse:1.251505+0.090653
## [200] train-rmse:0.982217+0.016993 test-rmse:1.251202+0.090916
## [201] train-rmse:0.981309+0.016964 test-rmse:1.250288+0.089929
## [202] train-rmse:0.980429+0.016965 test-rmse:1.249451+0.089982
## [203] train-rmse:0.979530+0.016988 test-rmse:1.249531+0.087646
## [204] train-rmse:0.978643+0.016993 test-rmse:1.248638+0.088701
## [205] train-rmse:0.977784+0.017009 test-rmse:1.247558+0.089207
## [206] train-rmse:0.976917+0.017029 test-rmse:1.246998+0.089093
## [207] train-rmse:0.976080+0.017039 test-rmse:1.246121+0.089706
## [208] train-rmse:0.975248+0.017048 test-rmse:1.246693+0.089010
## [209] train-rmse:0.974419+0.017048 test-rmse:1.246010+0.089019
## [210] train-rmse:0.973558+0.017071 test-rmse:1.245481+0.088985
## [211] train-rmse:0.972729+0.017075 test-rmse:1.245360+0.088253
## [212] train-rmse:0.971875+0.017069 test-rmse:1.244487+0.087665
## [213] train-rmse:0.971054+0.017073 test-rmse:1.244751+0.088158
## [214] train-rmse:0.970241+0.017082 test-rmse:1.243614+0.088008
## [215] train-rmse:0.969438+0.017088 test-rmse:1.244843+0.087132
## [216] train-rmse:0.968648+0.017081 test-rmse:1.244034+0.086594
## [217] train-rmse:0.967825+0.017107 test-rmse:1.241810+0.085008
## [218] train-rmse:0.967055+0.017117 test-rmse:1.240940+0.086593
## [219] train-rmse:0.966270+0.017143 test-rmse:1.239857+0.086032
## [220] train-rmse:0.965496+0.017157 test-rmse:1.240526+0.084949
## [221] train-rmse:0.964735+0.017163 test-rmse:1.240228+0.084843
## [222] train-rmse:0.963960+0.017180 test-rmse:1.239931+0.085018
## [223] train-rmse:0.963200+0.017187 test-rmse:1.240133+0.085801
## [224] train-rmse:0.962446+0.017195 test-rmse:1.239088+0.086656
## [225] train-rmse:0.961708+0.017212 test-rmse:1.237667+0.087218
## [226] train-rmse:0.960973+0.017220 test-rmse:1.238059+0.086687
## [227] train-rmse:0.960233+0.017234 test-rmse:1.237809+0.087726
## [228] train-rmse:0.959499+0.017252 test-rmse:1.238453+0.088085
## [229] train-rmse:0.958732+0.017283 test-rmse:1.237478+0.088094
## [230] train-rmse:0.958016+0.017295 test-rmse:1.236069+0.088213
## [231] train-rmse:0.957298+0.017303 test-rmse:1.235179+0.087679
## [232] train-rmse:0.956584+0.017329 test-rmse:1.236106+0.087788
## [233] train-rmse:0.955858+0.017334 test-rmse:1.236025+0.088645
## [234] train-rmse:0.955144+0.017334 test-rmse:1.234923+0.088890
## [235] train-rmse:0.954450+0.017336 test-rmse:1.234350+0.088426
## [236] train-rmse:0.953737+0.017318 test-rmse:1.233108+0.087144
## [237] train-rmse:0.953048+0.017321 test-rmse:1.232361+0.088877
## [238] train-rmse:0.952367+0.017320 test-rmse:1.232118+0.088746
## [239] train-rmse:0.951661+0.017343 test-rmse:1.231743+0.088363
## [240] train-rmse:0.950971+0.017355 test-rmse:1.231396+0.087816
## [241] train-rmse:0.950277+0.017349 test-rmse:1.230243+0.088491
## [242] train-rmse:0.949599+0.017347 test-rmse:1.229885+0.088129
## [243] train-rmse:0.948902+0.017346 test-rmse:1.229398+0.088770
## [244] train-rmse:0.948234+0.017365 test-rmse:1.230903+0.087353
## [245] train-rmse:0.947572+0.017375 test-rmse:1.230583+0.087374
## [246] train-rmse:0.946922+0.017385 test-rmse:1.230162+0.086113
## [247] train-rmse:0.946278+0.017385 test-rmse:1.229234+0.086852
## [248] train-rmse:0.945628+0.017393 test-rmse:1.229915+0.087231
## [249] train-rmse:0.944976+0.017413 test-rmse:1.230398+0.086521
## [250] train-rmse:0.944321+0.017427 test-rmse:1.230234+0.086740
## [251] train-rmse:0.943675+0.017440 test-rmse:1.229576+0.087542
## [252] train-rmse:0.943057+0.017456 test-rmse:1.229149+0.087770
## [253] train-rmse:0.942452+0.017469 test-rmse:1.229724+0.087382
## [254] train-rmse:0.941838+0.017475 test-rmse:1.229854+0.086232
## [255] train-rmse:0.941219+0.017496 test-rmse:1.229590+0.085988
## [256] train-rmse:0.940542+0.017487 test-rmse:1.229236+0.085529
## [257] train-rmse:0.939921+0.017503 test-rmse:1.228894+0.085992
## [258] train-rmse:0.939304+0.017520 test-rmse:1.227731+0.085925
## [259] train-rmse:0.938693+0.017532 test-rmse:1.227929+0.085507
## [260] train-rmse:0.938088+0.017542 test-rmse:1.227302+0.086027
## [261] train-rmse:0.937466+0.017546 test-rmse:1.228016+0.086251
## [262] train-rmse:0.936856+0.017558 test-rmse:1.227718+0.087413
## [263] train-rmse:0.936240+0.017577 test-rmse:1.227523+0.086711
## [264] train-rmse:0.935628+0.017587 test-rmse:1.227159+0.087264
## [265] train-rmse:0.935031+0.017605 test-rmse:1.226053+0.087267
## [266] train-rmse:0.934435+0.017624 test-rmse:1.224299+0.085722
## [267] train-rmse:0.933849+0.017643 test-rmse:1.222889+0.085140
## [268] train-rmse:0.933245+0.017619 test-rmse:1.223249+0.084529
## [269] train-rmse:0.932646+0.017620 test-rmse:1.223280+0.085194
## [270] train-rmse:0.932076+0.017638 test-rmse:1.222906+0.085660
## [271] train-rmse:0.931506+0.017662 test-rmse:1.222760+0.085743
## [272] train-rmse:0.930947+0.017670 test-rmse:1.222985+0.085746
## [273] train-rmse:0.930391+0.017691 test-rmse:1.222951+0.086273
## [274] train-rmse:0.929838+0.017686 test-rmse:1.222864+0.085292
## [275] train-rmse:0.929252+0.017693 test-rmse:1.223596+0.085240
## [276] train-rmse:0.928693+0.017673 test-rmse:1.223223+0.085532
## [277] train-rmse:0.928152+0.017679 test-rmse:1.222223+0.084849
## [278] train-rmse:0.927607+0.017678 test-rmse:1.223337+0.085559
## [279] train-rmse:0.927080+0.017680 test-rmse:1.222959+0.085808
## [280] train-rmse:0.926555+0.017684 test-rmse:1.222065+0.085602
## [281] train-rmse:0.926016+0.017688 test-rmse:1.222234+0.085034
## [282] train-rmse:0.925469+0.017710 test-rmse:1.222135+0.084824
## [283] train-rmse:0.924943+0.017716 test-rmse:1.222062+0.085085
## [284] train-rmse:0.924422+0.017732 test-rmse:1.220637+0.084555
## [285] train-rmse:0.923896+0.017746 test-rmse:1.220719+0.085252
## [286] train-rmse:0.923378+0.017755 test-rmse:1.220914+0.086274
## [287] train-rmse:0.922864+0.017768 test-rmse:1.221086+0.085406
## [288] train-rmse:0.922340+0.017761 test-rmse:1.220457+0.086183
## [289] train-rmse:0.921830+0.017773 test-rmse:1.219695+0.086200
## [290] train-rmse:0.921325+0.017786 test-rmse:1.219212+0.086508
## [291] train-rmse:0.920809+0.017800 test-rmse:1.218861+0.086653
## [292] train-rmse:0.920292+0.017823 test-rmse:1.219467+0.086897
## [293] train-rmse:0.919801+0.017832 test-rmse:1.219455+0.087039
## [294] train-rmse:0.919301+0.017834 test-rmse:1.218015+0.085405
## [295] train-rmse:0.918796+0.017840 test-rmse:1.218103+0.084617
## [296] train-rmse:0.918295+0.017844 test-rmse:1.217608+0.086240
## [297] train-rmse:0.917793+0.017851 test-rmse:1.216708+0.086770
## [298] train-rmse:0.917302+0.017854 test-rmse:1.217151+0.085772
## [299] train-rmse:0.916816+0.017854 test-rmse:1.217528+0.085280
## [300] train-rmse:0.916326+0.017854 test-rmse:1.216764+0.084649
## [301] train-rmse:0.915833+0.017857 test-rmse:1.216521+0.085980
## [302] train-rmse:0.915355+0.017863 test-rmse:1.216735+0.085697
## [303] train-rmse:0.914883+0.017874 test-rmse:1.216156+0.086165
## [304] train-rmse:0.914410+0.017887 test-rmse:1.216518+0.085878
## [305] train-rmse:0.913910+0.017884 test-rmse:1.216612+0.085639
## [306] train-rmse:0.913436+0.017898 test-rmse:1.217503+0.085492
## [307] train-rmse:0.912966+0.017912 test-rmse:1.216968+0.085430
## [308] train-rmse:0.912493+0.017904 test-rmse:1.217213+0.085167
## [309] train-rmse:0.912009+0.017928 test-rmse:1.216637+0.085533
## Stopping. Best iteration:
## [303] train-rmse:0.914883+0.017874 test-rmse:1.216156+0.086165
##
## 36
## [1] train-rmse:78.857910+0.079069 test-rmse:78.858575+0.414633
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:63.153612+0.063143 test-rmse:63.154042+0.429953
## [3] train-rmse:50.582123+0.050320 test-rmse:50.582268+0.441991
## [4] train-rmse:40.519815+0.039977 test-rmse:40.519597+0.451317
## [5] train-rmse:32.467526+0.031623 test-rmse:32.466867+0.458362
## [6] train-rmse:26.025803+0.024854 test-rmse:26.024606+0.463429
## [7] train-rmse:20.875072+0.019383 test-rmse:20.873221+0.466687
## [8] train-rmse:16.759770+0.015015 test-rmse:16.757143+0.468190
## [9] train-rmse:13.474406+0.011635 test-rmse:13.476270+0.444762
## [10] train-rmse:10.852426+0.008666 test-rmse:10.838746+0.439334
## [11] train-rmse:8.760172+0.007617 test-rmse:8.745579+0.415025
## [12] train-rmse:7.092363+0.006688 test-rmse:7.074333+0.407666
## [13] train-rmse:5.766406+0.007399 test-rmse:5.751751+0.398661
## [14] train-rmse:4.713473+0.007583 test-rmse:4.707145+0.367522
## [15] train-rmse:3.885132+0.011202 test-rmse:3.888046+0.346253
## [16] train-rmse:3.235925+0.012777 test-rmse:3.246648+0.332867
## [17] train-rmse:2.733982+0.015483 test-rmse:2.754968+0.317623
## [18] train-rmse:2.349385+0.017672 test-rmse:2.386950+0.281270
## [19] train-rmse:2.056709+0.019943 test-rmse:2.113266+0.244227
## [20] train-rmse:1.838839+0.020825 test-rmse:1.918523+0.213870
## [21] train-rmse:1.680965+0.022861 test-rmse:1.778909+0.194384
## [22] train-rmse:1.562397+0.024143 test-rmse:1.676191+0.168318
## [23] train-rmse:1.477719+0.024673 test-rmse:1.606477+0.151562
## [24] train-rmse:1.412742+0.025595 test-rmse:1.554448+0.132031
## [25] train-rmse:1.365451+0.027220 test-rmse:1.522529+0.125914
## [26] train-rmse:1.325749+0.024964 test-rmse:1.495197+0.116072
## [27] train-rmse:1.295675+0.024716 test-rmse:1.474354+0.108786
## [28] train-rmse:1.272020+0.025710 test-rmse:1.463045+0.099642
## [29] train-rmse:1.253275+0.025995 test-rmse:1.447622+0.099377
## [30] train-rmse:1.236278+0.025596 test-rmse:1.435190+0.100108
## [31] train-rmse:1.217552+0.026381 test-rmse:1.428096+0.095632
## [32] train-rmse:1.202741+0.027612 test-rmse:1.419748+0.094230
## [33] train-rmse:1.188663+0.025906 test-rmse:1.409838+0.098252
## [34] train-rmse:1.176712+0.025999 test-rmse:1.403694+0.100467
## [35] train-rmse:1.164134+0.027639 test-rmse:1.393243+0.093624
## [36] train-rmse:1.153417+0.028158 test-rmse:1.383543+0.089509
## [37] train-rmse:1.142160+0.027523 test-rmse:1.374212+0.088990
## [38] train-rmse:1.129911+0.028283 test-rmse:1.370898+0.085196
## [39] train-rmse:1.118827+0.029403 test-rmse:1.357102+0.093548
## [40] train-rmse:1.108382+0.028502 test-rmse:1.348600+0.094982
## [41] train-rmse:1.099521+0.028176 test-rmse:1.345062+0.093798
## [42] train-rmse:1.090137+0.028342 test-rmse:1.343727+0.090590
## [43] train-rmse:1.079438+0.027457 test-rmse:1.339040+0.089758
## [44] train-rmse:1.070243+0.027724 test-rmse:1.331831+0.090854
## [45] train-rmse:1.060713+0.028998 test-rmse:1.323062+0.091661
## [46] train-rmse:1.053166+0.029637 test-rmse:1.313649+0.087112
## [47] train-rmse:1.045567+0.029511 test-rmse:1.305405+0.090179
## [48] train-rmse:1.035056+0.031208 test-rmse:1.298961+0.088359
## [49] train-rmse:1.027003+0.030979 test-rmse:1.290900+0.095296
## [50] train-rmse:1.019762+0.031366 test-rmse:1.286535+0.092632
## [51] train-rmse:1.012983+0.031503 test-rmse:1.283750+0.088886
## [52] train-rmse:1.003575+0.028772 test-rmse:1.277492+0.085198
## [53] train-rmse:0.997589+0.028867 test-rmse:1.272514+0.086420
## [54] train-rmse:0.990260+0.027639 test-rmse:1.268136+0.086127
## [55] train-rmse:0.984012+0.027761 test-rmse:1.266085+0.084227
## [56] train-rmse:0.974322+0.027332 test-rmse:1.258498+0.082211
## [57] train-rmse:0.966999+0.027084 test-rmse:1.252365+0.085253
## [58] train-rmse:0.961407+0.027265 test-rmse:1.249727+0.087800
## [59] train-rmse:0.956131+0.027303 test-rmse:1.245025+0.086313
## [60] train-rmse:0.947875+0.026645 test-rmse:1.236823+0.093022
## [61] train-rmse:0.939675+0.027262 test-rmse:1.232393+0.092777
## [62] train-rmse:0.933225+0.025048 test-rmse:1.226946+0.095339
## [63] train-rmse:0.927022+0.026153 test-rmse:1.223311+0.096760
## [64] train-rmse:0.918913+0.024604 test-rmse:1.219713+0.098978
## [65] train-rmse:0.912058+0.024905 test-rmse:1.217304+0.096292
## [66] train-rmse:0.907322+0.024658 test-rmse:1.213900+0.095353
## [67] train-rmse:0.902364+0.025046 test-rmse:1.210923+0.095606
## [68] train-rmse:0.895354+0.026419 test-rmse:1.208210+0.094637
## [69] train-rmse:0.890163+0.027577 test-rmse:1.206775+0.096373
## [70] train-rmse:0.883346+0.023967 test-rmse:1.202909+0.099201
## [71] train-rmse:0.878873+0.023754 test-rmse:1.196712+0.098920
## [72] train-rmse:0.872217+0.023453 test-rmse:1.195995+0.098776
## [73] train-rmse:0.866550+0.025572 test-rmse:1.190948+0.097842
## [74] train-rmse:0.859684+0.023099 test-rmse:1.188863+0.099564
## [75] train-rmse:0.855627+0.022968 test-rmse:1.186011+0.100472
## [76] train-rmse:0.851652+0.022916 test-rmse:1.184896+0.096668
## [77] train-rmse:0.845360+0.022253 test-rmse:1.181745+0.093486
## [78] train-rmse:0.841394+0.022266 test-rmse:1.177780+0.092968
## [79] train-rmse:0.835815+0.022864 test-rmse:1.174031+0.092544
## [80] train-rmse:0.831372+0.023245 test-rmse:1.172061+0.092576
## [81] train-rmse:0.826387+0.023449 test-rmse:1.170792+0.092505
## [82] train-rmse:0.822556+0.023622 test-rmse:1.169378+0.092841
## [83] train-rmse:0.818633+0.022275 test-rmse:1.166298+0.095858
## [84] train-rmse:0.812846+0.023967 test-rmse:1.160148+0.092444
## [85] train-rmse:0.808887+0.023920 test-rmse:1.156404+0.091057
## [86] train-rmse:0.804064+0.024755 test-rmse:1.153873+0.092019
## [87] train-rmse:0.798319+0.023924 test-rmse:1.150407+0.095174
## [88] train-rmse:0.794452+0.023952 test-rmse:1.149823+0.094382
## [89] train-rmse:0.790182+0.024064 test-rmse:1.146881+0.092440
## [90] train-rmse:0.786727+0.024795 test-rmse:1.143779+0.092679
## [91] train-rmse:0.782677+0.025182 test-rmse:1.142337+0.091075
## [92] train-rmse:0.779046+0.025529 test-rmse:1.141979+0.093406
## [93] train-rmse:0.775251+0.024512 test-rmse:1.136795+0.094304
## [94] train-rmse:0.771017+0.025210 test-rmse:1.135418+0.095206
## [95] train-rmse:0.766837+0.026019 test-rmse:1.130469+0.094478
## [96] train-rmse:0.762556+0.027352 test-rmse:1.128320+0.093087
## [97] train-rmse:0.758461+0.025728 test-rmse:1.126166+0.094408
## [98] train-rmse:0.755240+0.026447 test-rmse:1.124178+0.093094
## [99] train-rmse:0.750577+0.027448 test-rmse:1.122774+0.091232
## [100] train-rmse:0.747319+0.027592 test-rmse:1.123112+0.090157
## [101] train-rmse:0.744807+0.027699 test-rmse:1.122359+0.091252
## [102] train-rmse:0.740779+0.027587 test-rmse:1.119831+0.092169
## [103] train-rmse:0.738205+0.027694 test-rmse:1.118345+0.092748
## [104] train-rmse:0.735466+0.027999 test-rmse:1.116356+0.093591
## [105] train-rmse:0.732992+0.028154 test-rmse:1.115723+0.094678
## [106] train-rmse:0.727852+0.025553 test-rmse:1.112718+0.095801
## [107] train-rmse:0.724578+0.024190 test-rmse:1.108730+0.097521
## [108] train-rmse:0.721122+0.023306 test-rmse:1.105458+0.100143
## [109] train-rmse:0.716649+0.022798 test-rmse:1.104510+0.099047
## [110] train-rmse:0.713402+0.022555 test-rmse:1.101726+0.097441
## [111] train-rmse:0.709746+0.023488 test-rmse:1.099922+0.095865
## [112] train-rmse:0.707401+0.023279 test-rmse:1.098669+0.097312
## [113] train-rmse:0.703992+0.024823 test-rmse:1.096287+0.096759
## [114] train-rmse:0.699909+0.023606 test-rmse:1.095364+0.098128
## [115] train-rmse:0.697949+0.023812 test-rmse:1.093862+0.098835
## [116] train-rmse:0.694577+0.023166 test-rmse:1.093248+0.096204
## [117] train-rmse:0.691989+0.022456 test-rmse:1.090134+0.098097
## [118] train-rmse:0.689762+0.022224 test-rmse:1.090382+0.100138
## [119] train-rmse:0.686312+0.021418 test-rmse:1.089447+0.101414
## [120] train-rmse:0.682888+0.020469 test-rmse:1.087507+0.102057
## [121] train-rmse:0.679645+0.021110 test-rmse:1.083885+0.101084
## [122] train-rmse:0.677020+0.021394 test-rmse:1.083291+0.101860
## [123] train-rmse:0.674135+0.021902 test-rmse:1.082528+0.100241
## [124] train-rmse:0.671157+0.022711 test-rmse:1.081373+0.100269
## [125] train-rmse:0.669526+0.022879 test-rmse:1.081710+0.100929
## [126] train-rmse:0.666782+0.021006 test-rmse:1.080819+0.101064
## [127] train-rmse:0.664978+0.021399 test-rmse:1.080172+0.100480
## [128] train-rmse:0.663243+0.021314 test-rmse:1.079240+0.100245
## [129] train-rmse:0.660138+0.020090 test-rmse:1.078861+0.100049
## [130] train-rmse:0.658188+0.019919 test-rmse:1.077372+0.099610
## [131] train-rmse:0.655107+0.021091 test-rmse:1.076769+0.099122
## [132] train-rmse:0.653378+0.021394 test-rmse:1.076789+0.098195
## [133] train-rmse:0.649837+0.019400 test-rmse:1.078112+0.097398
## [134] train-rmse:0.647838+0.019774 test-rmse:1.078116+0.096664
## [135] train-rmse:0.645626+0.020451 test-rmse:1.077071+0.096445
## [136] train-rmse:0.643808+0.020736 test-rmse:1.077040+0.097078
## [137] train-rmse:0.641730+0.020788 test-rmse:1.075775+0.097766
## [138] train-rmse:0.638946+0.020726 test-rmse:1.073637+0.097282
## [139] train-rmse:0.637444+0.020547 test-rmse:1.071171+0.096241
## [140] train-rmse:0.633896+0.019621 test-rmse:1.069498+0.096252
## [141] train-rmse:0.632083+0.019894 test-rmse:1.068126+0.096190
## [142] train-rmse:0.630036+0.020533 test-rmse:1.067463+0.096006
## [143] train-rmse:0.625490+0.021068 test-rmse:1.064049+0.096011
## [144] train-rmse:0.623981+0.021091 test-rmse:1.063560+0.095918
## [145] train-rmse:0.622384+0.020879 test-rmse:1.061765+0.096360
## [146] train-rmse:0.620468+0.020676 test-rmse:1.061738+0.096979
## [147] train-rmse:0.618512+0.020015 test-rmse:1.060182+0.097512
## [148] train-rmse:0.616650+0.019821 test-rmse:1.059807+0.097246
## [149] train-rmse:0.613195+0.017539 test-rmse:1.057429+0.099482
## [150] train-rmse:0.610854+0.017314 test-rmse:1.056893+0.097828
## [151] train-rmse:0.608356+0.017925 test-rmse:1.056540+0.097050
## [152] train-rmse:0.605525+0.016777 test-rmse:1.053186+0.097469
## [153] train-rmse:0.603900+0.016428 test-rmse:1.053139+0.098292
## [154] train-rmse:0.601129+0.016915 test-rmse:1.053494+0.098009
## [155] train-rmse:0.599258+0.016594 test-rmse:1.054473+0.097160
## [156] train-rmse:0.596055+0.016963 test-rmse:1.052120+0.094909
## [157] train-rmse:0.594592+0.017045 test-rmse:1.050943+0.095826
## [158] train-rmse:0.592739+0.018086 test-rmse:1.050471+0.096528
## [159] train-rmse:0.591337+0.017567 test-rmse:1.050133+0.098302
## [160] train-rmse:0.589109+0.017588 test-rmse:1.049290+0.099262
## [161] train-rmse:0.586789+0.017323 test-rmse:1.046516+0.099546
## [162] train-rmse:0.585211+0.017353 test-rmse:1.045684+0.100662
## [163] train-rmse:0.581245+0.015901 test-rmse:1.045493+0.100067
## [164] train-rmse:0.579575+0.015607 test-rmse:1.044059+0.099611
## [165] train-rmse:0.578228+0.015815 test-rmse:1.041714+0.099383
## [166] train-rmse:0.575594+0.016074 test-rmse:1.041625+0.100352
## [167] train-rmse:0.573811+0.014914 test-rmse:1.041488+0.099958
## [168] train-rmse:0.572662+0.014878 test-rmse:1.041834+0.100631
## [169] train-rmse:0.571302+0.015042 test-rmse:1.041054+0.100595
## [170] train-rmse:0.568622+0.016546 test-rmse:1.042452+0.101632
## [171] train-rmse:0.565232+0.015210 test-rmse:1.042767+0.103675
## [172] train-rmse:0.561336+0.015565 test-rmse:1.042967+0.104527
## [173] train-rmse:0.559072+0.016162 test-rmse:1.040569+0.105219
## [174] train-rmse:0.558008+0.016248 test-rmse:1.039456+0.105028
## [175] train-rmse:0.556974+0.016208 test-rmse:1.039212+0.104334
## [176] train-rmse:0.555650+0.016137 test-rmse:1.039774+0.104219
## [177] train-rmse:0.554088+0.016612 test-rmse:1.039492+0.103732
## [178] train-rmse:0.550795+0.015156 test-rmse:1.038642+0.104000
## [179] train-rmse:0.549859+0.015049 test-rmse:1.039047+0.103182
## [180] train-rmse:0.547309+0.015286 test-rmse:1.036765+0.102230
## [181] train-rmse:0.546262+0.015607 test-rmse:1.035978+0.102959
## [182] train-rmse:0.542705+0.016817 test-rmse:1.030712+0.100251
## [183] train-rmse:0.541538+0.016535 test-rmse:1.031423+0.099329
## [184] train-rmse:0.540257+0.016426 test-rmse:1.031296+0.099532
## [185] train-rmse:0.539188+0.016522 test-rmse:1.031276+0.099939
## [186] train-rmse:0.537468+0.015412 test-rmse:1.031945+0.099633
## [187] train-rmse:0.535899+0.015010 test-rmse:1.030909+0.099748
## [188] train-rmse:0.534851+0.015065 test-rmse:1.029611+0.099773
## [189] train-rmse:0.532651+0.015021 test-rmse:1.030022+0.099866
## [190] train-rmse:0.531448+0.015426 test-rmse:1.029799+0.099357
## [191] train-rmse:0.528916+0.014717 test-rmse:1.028402+0.099874
## [192] train-rmse:0.526591+0.014637 test-rmse:1.027353+0.100830
## [193] train-rmse:0.525156+0.014773 test-rmse:1.026694+0.100182
## [194] train-rmse:0.523643+0.013949 test-rmse:1.026660+0.100676
## [195] train-rmse:0.521934+0.013478 test-rmse:1.024611+0.101326
## [196] train-rmse:0.519597+0.014248 test-rmse:1.023134+0.100550
## [197] train-rmse:0.517717+0.013737 test-rmse:1.023704+0.100370
## [198] train-rmse:0.516156+0.014067 test-rmse:1.022906+0.100585
## [199] train-rmse:0.514237+0.013800 test-rmse:1.022683+0.101672
## [200] train-rmse:0.513387+0.013783 test-rmse:1.023109+0.100747
## [201] train-rmse:0.512158+0.013869 test-rmse:1.022123+0.100014
## [202] train-rmse:0.509406+0.013292 test-rmse:1.021459+0.100361
## [203] train-rmse:0.506346+0.012135 test-rmse:1.021085+0.102044
## [204] train-rmse:0.504909+0.011981 test-rmse:1.020590+0.101157
## [205] train-rmse:0.502582+0.012944 test-rmse:1.020679+0.101142
## [206] train-rmse:0.501195+0.012216 test-rmse:1.020198+0.101558
## [207] train-rmse:0.499664+0.012711 test-rmse:1.020316+0.102297
## [208] train-rmse:0.498667+0.012934 test-rmse:1.020075+0.102217
## [209] train-rmse:0.497315+0.013466 test-rmse:1.019281+0.101931
## [210] train-rmse:0.495369+0.013696 test-rmse:1.018929+0.102980
## [211] train-rmse:0.493988+0.013978 test-rmse:1.019098+0.102163
## [212] train-rmse:0.492585+0.014042 test-rmse:1.018498+0.102486
## [213] train-rmse:0.491277+0.013624 test-rmse:1.017619+0.102005
## [214] train-rmse:0.488905+0.012941 test-rmse:1.017902+0.101875
## [215] train-rmse:0.487911+0.012885 test-rmse:1.017375+0.101645
## [216] train-rmse:0.486762+0.013464 test-rmse:1.017081+0.101474
## [217] train-rmse:0.484885+0.013737 test-rmse:1.015462+0.101100
## [218] train-rmse:0.483660+0.013456 test-rmse:1.015561+0.101184
## [219] train-rmse:0.482241+0.013486 test-rmse:1.016333+0.100089
## [220] train-rmse:0.481193+0.013543 test-rmse:1.016955+0.100071
## [221] train-rmse:0.480244+0.013348 test-rmse:1.017603+0.099113
## [222] train-rmse:0.479172+0.012870 test-rmse:1.017498+0.099710
## [223] train-rmse:0.477660+0.012281 test-rmse:1.016640+0.099796
## Stopping. Best iteration:
## [217] train-rmse:0.484885+0.013737 test-rmse:1.015462+0.101100
##
## 37
## [1] train-rmse:78.857910+0.079069 test-rmse:78.858575+0.414633
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:63.153612+0.063143 test-rmse:63.154042+0.429953
## [3] train-rmse:50.582123+0.050320 test-rmse:50.582268+0.441991
## [4] train-rmse:40.519815+0.039977 test-rmse:40.519597+0.451317
## [5] train-rmse:32.467526+0.031623 test-rmse:32.466867+0.458362
## [6] train-rmse:26.025803+0.024854 test-rmse:26.024606+0.463429
## [7] train-rmse:20.875072+0.019383 test-rmse:20.873221+0.466687
## [8] train-rmse:16.759770+0.015015 test-rmse:16.757143+0.468190
## [9] train-rmse:13.474406+0.011635 test-rmse:13.476270+0.444762
## [10] train-rmse:10.852426+0.008666 test-rmse:10.838746+0.439334
## [11] train-rmse:8.759242+0.007449 test-rmse:8.745693+0.414592
## [12] train-rmse:7.088593+0.006045 test-rmse:7.080451+0.399357
## [13] train-rmse:5.756763+0.006210 test-rmse:5.760522+0.372936
## [14] train-rmse:4.697592+0.006788 test-rmse:4.704601+0.345775
## [15] train-rmse:3.857221+0.009025 test-rmse:3.873337+0.322704
## [16] train-rmse:3.194791+0.010580 test-rmse:3.211338+0.307144
## [17] train-rmse:2.676304+0.012846 test-rmse:2.709172+0.283114
## [18] train-rmse:2.271337+0.015507 test-rmse:2.322867+0.257837
## [19] train-rmse:1.961739+0.014953 test-rmse:2.041723+0.237554
## [20] train-rmse:1.725658+0.016507 test-rmse:1.820227+0.212935
## [21] train-rmse:1.548601+0.017094 test-rmse:1.669126+0.188374
## [22] train-rmse:1.415140+0.017843 test-rmse:1.563355+0.165173
## [23] train-rmse:1.319247+0.017349 test-rmse:1.491538+0.148319
## [24] train-rmse:1.244937+0.020385 test-rmse:1.430465+0.139231
## [25] train-rmse:1.189363+0.020445 test-rmse:1.393451+0.127977
## [26] train-rmse:1.145466+0.022232 test-rmse:1.368714+0.118917
## [27] train-rmse:1.107728+0.021084 test-rmse:1.343357+0.110976
## [28] train-rmse:1.078415+0.021026 test-rmse:1.323957+0.103595
## [29] train-rmse:1.052614+0.018400 test-rmse:1.309853+0.105316
## [30] train-rmse:1.026906+0.017818 test-rmse:1.293232+0.105362
## [31] train-rmse:1.008753+0.019260 test-rmse:1.284770+0.103958
## [32] train-rmse:0.989132+0.015657 test-rmse:1.269847+0.099130
## [33] train-rmse:0.969486+0.016291 test-rmse:1.255264+0.103737
## [34] train-rmse:0.954508+0.015892 test-rmse:1.244952+0.104540
## [35] train-rmse:0.938714+0.017113 test-rmse:1.235829+0.101926
## [36] train-rmse:0.920494+0.014781 test-rmse:1.228411+0.103007
## [37] train-rmse:0.908385+0.014755 test-rmse:1.222384+0.102559
## [38] train-rmse:0.895220+0.017573 test-rmse:1.212128+0.102865
## [39] train-rmse:0.882244+0.015908 test-rmse:1.206803+0.098762
## [40] train-rmse:0.872037+0.016918 test-rmse:1.202307+0.099379
## [41] train-rmse:0.861223+0.015052 test-rmse:1.195409+0.100548
## [42] train-rmse:0.847465+0.014286 test-rmse:1.184438+0.110748
## [43] train-rmse:0.838063+0.014087 test-rmse:1.179897+0.110477
## [44] train-rmse:0.827410+0.014947 test-rmse:1.171977+0.111134
## [45] train-rmse:0.819009+0.015260 test-rmse:1.167254+0.112775
## [46] train-rmse:0.808400+0.014681 test-rmse:1.162705+0.112414
## [47] train-rmse:0.798853+0.015159 test-rmse:1.153449+0.106412
## [48] train-rmse:0.787990+0.013201 test-rmse:1.149849+0.107877
## [49] train-rmse:0.778315+0.013364 test-rmse:1.147370+0.107220
## [50] train-rmse:0.770113+0.013017 test-rmse:1.135315+0.100175
## [51] train-rmse:0.762206+0.014888 test-rmse:1.128961+0.102357
## [52] train-rmse:0.755300+0.015898 test-rmse:1.123240+0.102164
## [53] train-rmse:0.745369+0.016184 test-rmse:1.119161+0.101512
## [54] train-rmse:0.737736+0.017730 test-rmse:1.112223+0.099521
## [55] train-rmse:0.730821+0.017119 test-rmse:1.109690+0.099342
## [56] train-rmse:0.722992+0.018412 test-rmse:1.107390+0.099504
## [57] train-rmse:0.716445+0.017159 test-rmse:1.103256+0.101235
## [58] train-rmse:0.709016+0.015476 test-rmse:1.100502+0.101588
## [59] train-rmse:0.701251+0.016350 test-rmse:1.092794+0.099945
## [60] train-rmse:0.694865+0.017361 test-rmse:1.091900+0.099811
## [61] train-rmse:0.687242+0.019524 test-rmse:1.088508+0.098823
## [62] train-rmse:0.678829+0.019528 test-rmse:1.081948+0.099161
## [63] train-rmse:0.673479+0.019657 test-rmse:1.079968+0.097346
## [64] train-rmse:0.665371+0.020159 test-rmse:1.076970+0.093997
## [65] train-rmse:0.659251+0.019848 test-rmse:1.072466+0.096266
## [66] train-rmse:0.652251+0.018612 test-rmse:1.070068+0.101047
## [67] train-rmse:0.647179+0.018824 test-rmse:1.067930+0.102004
## [68] train-rmse:0.642146+0.019530 test-rmse:1.065502+0.102322
## [69] train-rmse:0.636865+0.021167 test-rmse:1.062818+0.102097
## [70] train-rmse:0.632675+0.021120 test-rmse:1.062146+0.103176
## [71] train-rmse:0.624912+0.022172 test-rmse:1.058648+0.102279
## [72] train-rmse:0.619100+0.022366 test-rmse:1.054328+0.103777
## [73] train-rmse:0.615039+0.021680 test-rmse:1.049280+0.105898
## [74] train-rmse:0.609387+0.019794 test-rmse:1.047042+0.106730
## [75] train-rmse:0.603407+0.021278 test-rmse:1.047355+0.106622
## [76] train-rmse:0.599016+0.020542 test-rmse:1.046862+0.108116
## [77] train-rmse:0.594252+0.019680 test-rmse:1.043768+0.107714
## [78] train-rmse:0.588657+0.018992 test-rmse:1.042939+0.105199
## [79] train-rmse:0.581219+0.019705 test-rmse:1.039777+0.109688
## [80] train-rmse:0.575261+0.019334 test-rmse:1.041155+0.106820
## [81] train-rmse:0.569752+0.018879 test-rmse:1.043472+0.106549
## [82] train-rmse:0.565279+0.018563 test-rmse:1.042437+0.105924
## [83] train-rmse:0.561337+0.017876 test-rmse:1.039048+0.105022
## [84] train-rmse:0.556608+0.017706 test-rmse:1.039363+0.104116
## [85] train-rmse:0.552767+0.016530 test-rmse:1.037094+0.104401
## [86] train-rmse:0.549316+0.016433 test-rmse:1.036256+0.105804
## [87] train-rmse:0.544709+0.015852 test-rmse:1.035748+0.105317
## [88] train-rmse:0.540082+0.016207 test-rmse:1.033585+0.105429
## [89] train-rmse:0.537117+0.016851 test-rmse:1.030789+0.105568
## [90] train-rmse:0.534482+0.016821 test-rmse:1.030583+0.105333
## [91] train-rmse:0.531012+0.017552 test-rmse:1.030483+0.105040
## [92] train-rmse:0.527880+0.018385 test-rmse:1.029049+0.105521
## [93] train-rmse:0.523352+0.018254 test-rmse:1.025939+0.102577
## [94] train-rmse:0.519171+0.017111 test-rmse:1.023580+0.103639
## [95] train-rmse:0.516281+0.016292 test-rmse:1.022277+0.104055
## [96] train-rmse:0.512977+0.016822 test-rmse:1.021054+0.104220
## [97] train-rmse:0.509637+0.015977 test-rmse:1.020442+0.104461
## [98] train-rmse:0.505821+0.016953 test-rmse:1.017959+0.102101
## [99] train-rmse:0.502894+0.017176 test-rmse:1.015626+0.103713
## [100] train-rmse:0.497667+0.018168 test-rmse:1.013229+0.099082
## [101] train-rmse:0.493520+0.017918 test-rmse:1.012449+0.098701
## [102] train-rmse:0.489121+0.016645 test-rmse:1.010555+0.099250
## [103] train-rmse:0.483981+0.018731 test-rmse:1.008374+0.096744
## [104] train-rmse:0.480862+0.018240 test-rmse:1.007916+0.095808
## [105] train-rmse:0.477546+0.017118 test-rmse:1.006142+0.097570
## [106] train-rmse:0.473830+0.016799 test-rmse:1.005064+0.097508
## [107] train-rmse:0.470137+0.016509 test-rmse:1.003379+0.097563
## [108] train-rmse:0.467460+0.016618 test-rmse:1.002534+0.098025
## [109] train-rmse:0.464456+0.016991 test-rmse:1.000007+0.097290
## [110] train-rmse:0.460445+0.017562 test-rmse:0.999988+0.097674
## [111] train-rmse:0.456788+0.017581 test-rmse:0.997802+0.099212
## [112] train-rmse:0.452578+0.018579 test-rmse:0.994273+0.097249
## [113] train-rmse:0.449633+0.017455 test-rmse:0.992494+0.098004
## [114] train-rmse:0.446067+0.015589 test-rmse:0.991647+0.097413
## [115] train-rmse:0.443220+0.016822 test-rmse:0.991110+0.095370
## [116] train-rmse:0.440429+0.016323 test-rmse:0.990583+0.094544
## [117] train-rmse:0.437484+0.016188 test-rmse:0.989242+0.093796
## [118] train-rmse:0.434017+0.016737 test-rmse:0.990432+0.094196
## [119] train-rmse:0.431399+0.017336 test-rmse:0.989126+0.093998
## [120] train-rmse:0.428128+0.017137 test-rmse:0.988131+0.094179
## [121] train-rmse:0.424050+0.017428 test-rmse:0.991085+0.097345
## [122] train-rmse:0.420381+0.018227 test-rmse:0.989704+0.097487
## [123] train-rmse:0.417928+0.018419 test-rmse:0.987942+0.098122
## [124] train-rmse:0.415580+0.018901 test-rmse:0.986537+0.096907
## [125] train-rmse:0.413855+0.018588 test-rmse:0.985520+0.096245
## [126] train-rmse:0.411641+0.018449 test-rmse:0.986461+0.095750
## [127] train-rmse:0.409638+0.017726 test-rmse:0.985377+0.096124
## [128] train-rmse:0.407202+0.017940 test-rmse:0.984313+0.095616
## [129] train-rmse:0.405328+0.018309 test-rmse:0.983993+0.095575
## [130] train-rmse:0.401378+0.016313 test-rmse:0.984392+0.098252
## [131] train-rmse:0.398265+0.016900 test-rmse:0.986005+0.098738
## [132] train-rmse:0.395536+0.018464 test-rmse:0.986516+0.099265
## [133] train-rmse:0.392936+0.019521 test-rmse:0.984268+0.099452
## [134] train-rmse:0.390318+0.019635 test-rmse:0.983431+0.098445
## [135] train-rmse:0.386899+0.020507 test-rmse:0.984804+0.099276
## [136] train-rmse:0.381874+0.021246 test-rmse:0.984143+0.099307
## [137] train-rmse:0.378938+0.019691 test-rmse:0.983118+0.098274
## [138] train-rmse:0.375886+0.020618 test-rmse:0.981958+0.097350
## [139] train-rmse:0.372631+0.020745 test-rmse:0.981970+0.096607
## [140] train-rmse:0.370038+0.020100 test-rmse:0.980595+0.097176
## [141] train-rmse:0.367171+0.021117 test-rmse:0.981836+0.097693
## [142] train-rmse:0.364333+0.020088 test-rmse:0.982530+0.097344
## [143] train-rmse:0.362086+0.020433 test-rmse:0.982420+0.097929
## [144] train-rmse:0.359202+0.021383 test-rmse:0.982224+0.098547
## [145] train-rmse:0.356871+0.021522 test-rmse:0.980504+0.099594
## [146] train-rmse:0.354864+0.020537 test-rmse:0.982430+0.100683
## [147] train-rmse:0.352594+0.020919 test-rmse:0.984794+0.101469
## [148] train-rmse:0.349855+0.021000 test-rmse:0.980449+0.099430
## [149] train-rmse:0.347679+0.020717 test-rmse:0.979716+0.099143
## [150] train-rmse:0.345960+0.020256 test-rmse:0.979542+0.098037
## [151] train-rmse:0.344130+0.020848 test-rmse:0.979157+0.097842
## [152] train-rmse:0.341068+0.021966 test-rmse:0.978816+0.096492
## [153] train-rmse:0.338279+0.022777 test-rmse:0.978147+0.096369
## [154] train-rmse:0.335075+0.023418 test-rmse:0.974999+0.093651
## [155] train-rmse:0.333757+0.023808 test-rmse:0.975481+0.093755
## [156] train-rmse:0.331084+0.022224 test-rmse:0.971863+0.091546
## [157] train-rmse:0.329328+0.021845 test-rmse:0.971895+0.091877
## [158] train-rmse:0.327642+0.022368 test-rmse:0.971537+0.091304
## [159] train-rmse:0.326317+0.022414 test-rmse:0.971678+0.091324
## [160] train-rmse:0.323635+0.020839 test-rmse:0.972660+0.092860
## [161] train-rmse:0.321359+0.020870 test-rmse:0.973585+0.093334
## [162] train-rmse:0.318719+0.021238 test-rmse:0.972710+0.092582
## [163] train-rmse:0.317388+0.021753 test-rmse:0.972435+0.092147
## [164] train-rmse:0.315143+0.021602 test-rmse:0.969236+0.091163
## [165] train-rmse:0.311735+0.021086 test-rmse:0.969445+0.090735
## [166] train-rmse:0.309642+0.021506 test-rmse:0.969535+0.089803
## [167] train-rmse:0.308085+0.021350 test-rmse:0.969315+0.089194
## [168] train-rmse:0.306410+0.021485 test-rmse:0.969708+0.089532
## [169] train-rmse:0.304932+0.021294 test-rmse:0.969063+0.089621
## [170] train-rmse:0.303375+0.020815 test-rmse:0.968213+0.090505
## [171] train-rmse:0.301539+0.021347 test-rmse:0.969009+0.090656
## [172] train-rmse:0.299291+0.021488 test-rmse:0.968464+0.088977
## [173] train-rmse:0.297604+0.021606 test-rmse:0.967906+0.088996
## [174] train-rmse:0.295649+0.021715 test-rmse:0.965179+0.088436
## [175] train-rmse:0.294433+0.021653 test-rmse:0.965381+0.088998
## [176] train-rmse:0.293392+0.021762 test-rmse:0.965807+0.089787
## [177] train-rmse:0.290723+0.021847 test-rmse:0.966328+0.089288
## [178] train-rmse:0.289093+0.022085 test-rmse:0.966432+0.089248
## [179] train-rmse:0.286501+0.022543 test-rmse:0.964524+0.088161
## [180] train-rmse:0.285647+0.022774 test-rmse:0.964702+0.087405
## [181] train-rmse:0.283377+0.023092 test-rmse:0.964047+0.087142
## [182] train-rmse:0.282038+0.022753 test-rmse:0.965034+0.087677
## [183] train-rmse:0.280862+0.022734 test-rmse:0.965352+0.087825
## [184] train-rmse:0.278779+0.022579 test-rmse:0.965072+0.088388
## [185] train-rmse:0.277310+0.022430 test-rmse:0.964563+0.088209
## [186] train-rmse:0.275864+0.022226 test-rmse:0.963964+0.089696
## [187] train-rmse:0.274508+0.022570 test-rmse:0.964278+0.089629
## [188] train-rmse:0.272877+0.022447 test-rmse:0.963696+0.089505
## [189] train-rmse:0.271030+0.022285 test-rmse:0.962822+0.089383
## [190] train-rmse:0.269513+0.021142 test-rmse:0.962859+0.089270
## [191] train-rmse:0.267783+0.019963 test-rmse:0.962665+0.089141
## [192] train-rmse:0.266019+0.020552 test-rmse:0.962032+0.088938
## [193] train-rmse:0.264754+0.020722 test-rmse:0.962055+0.088368
## [194] train-rmse:0.262866+0.018927 test-rmse:0.960423+0.089192
## [195] train-rmse:0.261815+0.019032 test-rmse:0.960824+0.088663
## [196] train-rmse:0.260784+0.018719 test-rmse:0.959579+0.089721
## [197] train-rmse:0.259509+0.018938 test-rmse:0.959818+0.089661
## [198] train-rmse:0.258427+0.019268 test-rmse:0.959933+0.089929
## [199] train-rmse:0.256808+0.018984 test-rmse:0.961101+0.091040
## [200] train-rmse:0.255528+0.019170 test-rmse:0.960974+0.090937
## [201] train-rmse:0.253569+0.020159 test-rmse:0.960867+0.091177
## [202] train-rmse:0.252453+0.020565 test-rmse:0.960124+0.090519
## Stopping. Best iteration:
## [196] train-rmse:0.260784+0.018719 test-rmse:0.959579+0.089721
##
## 38
## [1] train-rmse:78.857910+0.079069 test-rmse:78.858575+0.414633
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:63.153612+0.063143 test-rmse:63.154042+0.429953
## [3] train-rmse:50.582123+0.050320 test-rmse:50.582268+0.441991
## [4] train-rmse:40.519815+0.039977 test-rmse:40.519597+0.451317
## [5] train-rmse:32.467526+0.031623 test-rmse:32.466867+0.458362
## [6] train-rmse:26.025803+0.024854 test-rmse:26.024606+0.463429
## [7] train-rmse:20.875072+0.019383 test-rmse:20.873221+0.466687
## [8] train-rmse:16.759770+0.015015 test-rmse:16.757143+0.468190
## [9] train-rmse:13.474406+0.011635 test-rmse:13.476270+0.444762
## [10] train-rmse:10.852426+0.008666 test-rmse:10.838746+0.439334
## [11] train-rmse:8.759135+0.007336 test-rmse:8.743939+0.416209
## [12] train-rmse:7.088188+0.006208 test-rmse:7.075411+0.397595
## [13] train-rmse:5.754667+0.008005 test-rmse:5.756630+0.371827
## [14] train-rmse:4.691234+0.006763 test-rmse:4.695342+0.340636
## [15] train-rmse:3.846405+0.007943 test-rmse:3.851380+0.316140
## [16] train-rmse:3.174847+0.010724 test-rmse:3.188920+0.284109
## [17] train-rmse:2.644962+0.012145 test-rmse:2.667269+0.264712
## [18] train-rmse:2.225875+0.010746 test-rmse:2.279051+0.235077
## [19] train-rmse:1.901342+0.010404 test-rmse:1.988449+0.221348
## [20] train-rmse:1.648107+0.008932 test-rmse:1.763134+0.198580
## [21] train-rmse:1.458843+0.009537 test-rmse:1.604870+0.178974
## [22] train-rmse:1.310042+0.010949 test-rmse:1.485528+0.158244
## [23] train-rmse:1.200180+0.008647 test-rmse:1.405071+0.142676
## [24] train-rmse:1.116047+0.006273 test-rmse:1.343319+0.134687
## [25] train-rmse:1.048030+0.008233 test-rmse:1.301351+0.125086
## [26] train-rmse:0.994486+0.012543 test-rmse:1.274755+0.116649
## [27] train-rmse:0.950414+0.012186 test-rmse:1.251115+0.111983
## [28] train-rmse:0.913315+0.009481 test-rmse:1.224838+0.108050
## [29] train-rmse:0.883467+0.013909 test-rmse:1.210012+0.108184
## [30] train-rmse:0.858994+0.015547 test-rmse:1.194299+0.097852
## [31] train-rmse:0.836185+0.015187 test-rmse:1.179476+0.098491
## [32] train-rmse:0.818231+0.013814 test-rmse:1.170292+0.097820
## [33] train-rmse:0.802173+0.016109 test-rmse:1.161961+0.094020
## [34] train-rmse:0.785951+0.020469 test-rmse:1.153989+0.097513
## [35] train-rmse:0.769144+0.019442 test-rmse:1.147453+0.098380
## [36] train-rmse:0.754276+0.017597 test-rmse:1.140717+0.093189
## [37] train-rmse:0.739086+0.016931 test-rmse:1.130044+0.091483
## [38] train-rmse:0.723494+0.014384 test-rmse:1.118396+0.090510
## [39] train-rmse:0.710397+0.017084 test-rmse:1.110088+0.093818
## [40] train-rmse:0.693659+0.018838 test-rmse:1.100236+0.095914
## [41] train-rmse:0.682775+0.018769 test-rmse:1.094581+0.094149
## [42] train-rmse:0.673394+0.018680 test-rmse:1.083776+0.090512
## [43] train-rmse:0.663549+0.018179 test-rmse:1.077072+0.090707
## [44] train-rmse:0.648498+0.015664 test-rmse:1.066304+0.086876
## [45] train-rmse:0.639039+0.016426 test-rmse:1.063815+0.087232
## [46] train-rmse:0.628388+0.017706 test-rmse:1.057757+0.084772
## [47] train-rmse:0.619912+0.017356 test-rmse:1.052184+0.087248
## [48] train-rmse:0.612665+0.016576 test-rmse:1.049191+0.087510
## [49] train-rmse:0.601051+0.014732 test-rmse:1.043668+0.088273
## [50] train-rmse:0.590403+0.017137 test-rmse:1.038913+0.087405
## [51] train-rmse:0.582229+0.016103 test-rmse:1.032996+0.086140
## [52] train-rmse:0.575133+0.018064 test-rmse:1.028372+0.088202
## [53] train-rmse:0.567166+0.017394 test-rmse:1.025985+0.088044
## [54] train-rmse:0.560557+0.018755 test-rmse:1.020697+0.084267
## [55] train-rmse:0.554620+0.018422 test-rmse:1.017236+0.084048
## [56] train-rmse:0.543921+0.015316 test-rmse:1.016306+0.085288
## [57] train-rmse:0.537917+0.014551 test-rmse:1.013471+0.086986
## [58] train-rmse:0.530797+0.016233 test-rmse:1.011995+0.084898
## [59] train-rmse:0.522922+0.015084 test-rmse:1.010561+0.085363
## [60] train-rmse:0.517250+0.014664 test-rmse:1.007892+0.084717
## [61] train-rmse:0.511326+0.013798 test-rmse:1.008629+0.082970
## [62] train-rmse:0.503670+0.014605 test-rmse:1.005189+0.083867
## [63] train-rmse:0.495872+0.011591 test-rmse:1.002202+0.082282
## [64] train-rmse:0.488232+0.009512 test-rmse:0.998214+0.082213
## [65] train-rmse:0.482396+0.008513 test-rmse:0.999208+0.083063
## [66] train-rmse:0.477238+0.009540 test-rmse:0.997965+0.083131
## [67] train-rmse:0.473142+0.009599 test-rmse:0.996658+0.083023
## [68] train-rmse:0.468054+0.008722 test-rmse:0.997164+0.081179
## [69] train-rmse:0.463332+0.008686 test-rmse:0.994139+0.083488
## [70] train-rmse:0.458127+0.008916 test-rmse:0.995859+0.082617
## [71] train-rmse:0.452882+0.009724 test-rmse:0.993472+0.083032
## [72] train-rmse:0.448576+0.010873 test-rmse:0.992094+0.083667
## [73] train-rmse:0.442598+0.014121 test-rmse:0.989392+0.081478
## [74] train-rmse:0.437472+0.013401 test-rmse:0.990247+0.081857
## [75] train-rmse:0.433049+0.013492 test-rmse:0.987462+0.082165
## [76] train-rmse:0.428657+0.013461 test-rmse:0.986597+0.083161
## [77] train-rmse:0.422023+0.011832 test-rmse:0.989145+0.084088
## [78] train-rmse:0.416334+0.010725 test-rmse:0.988515+0.086672
## [79] train-rmse:0.412128+0.009778 test-rmse:0.986894+0.088130
## [80] train-rmse:0.406730+0.009257 test-rmse:0.984976+0.088091
## [81] train-rmse:0.403385+0.008778 test-rmse:0.985634+0.087870
## [82] train-rmse:0.398326+0.009388 test-rmse:0.986004+0.088677
## [83] train-rmse:0.394374+0.009305 test-rmse:0.987622+0.090123
## [84] train-rmse:0.388958+0.009332 test-rmse:0.985630+0.091536
## [85] train-rmse:0.385012+0.009895 test-rmse:0.988352+0.092418
## [86] train-rmse:0.380834+0.009004 test-rmse:0.988012+0.093027
## Stopping. Best iteration:
## [80] train-rmse:0.406730+0.009257 test-rmse:0.984976+0.088091
##
## 39
## [1] train-rmse:78.857910+0.079069 test-rmse:78.858575+0.414633
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:63.153612+0.063143 test-rmse:63.154042+0.429953
## [3] train-rmse:50.582123+0.050320 test-rmse:50.582268+0.441991
## [4] train-rmse:40.519815+0.039977 test-rmse:40.519597+0.451317
## [5] train-rmse:32.467526+0.031623 test-rmse:32.466867+0.458362
## [6] train-rmse:26.025803+0.024854 test-rmse:26.024606+0.463429
## [7] train-rmse:20.875072+0.019383 test-rmse:20.873221+0.466687
## [8] train-rmse:16.759770+0.015015 test-rmse:16.757143+0.468190
## [9] train-rmse:13.474406+0.011635 test-rmse:13.476270+0.444762
## [10] train-rmse:10.852426+0.008666 test-rmse:10.838746+0.439334
## [11] train-rmse:8.759135+0.007336 test-rmse:8.743939+0.416209
## [12] train-rmse:7.088128+0.006268 test-rmse:7.074247+0.396839
## [13] train-rmse:5.752654+0.008641 test-rmse:5.748869+0.358116
## [14] train-rmse:4.687146+0.007543 test-rmse:4.690633+0.327726
## [15] train-rmse:3.838498+0.009425 test-rmse:3.839707+0.307485
## [16] train-rmse:3.160484+0.009091 test-rmse:3.165089+0.286072
## [17] train-rmse:2.624630+0.012915 test-rmse:2.643750+0.252214
## [18] train-rmse:2.196287+0.010853 test-rmse:2.242631+0.224153
## [19] train-rmse:1.856291+0.011662 test-rmse:1.940439+0.207572
## [20] train-rmse:1.596164+0.012112 test-rmse:1.710245+0.192884
## [21] train-rmse:1.393509+0.008430 test-rmse:1.549738+0.177933
## [22] train-rmse:1.233851+0.008873 test-rmse:1.428295+0.161981
## [23] train-rmse:1.112598+0.006305 test-rmse:1.348428+0.146972
## [24] train-rmse:1.016959+0.009331 test-rmse:1.285660+0.140241
## [25] train-rmse:0.932699+0.011284 test-rmse:1.242975+0.128963
## [26] train-rmse:0.876148+0.011305 test-rmse:1.211713+0.125818
## [27] train-rmse:0.823744+0.011078 test-rmse:1.178630+0.110488
## [28] train-rmse:0.786522+0.009850 test-rmse:1.154305+0.111688
## [29] train-rmse:0.753734+0.009553 test-rmse:1.138748+0.103734
## [30] train-rmse:0.724689+0.013701 test-rmse:1.127850+0.103552
## [31] train-rmse:0.701096+0.015486 test-rmse:1.109592+0.101369
## [32] train-rmse:0.682026+0.015794 test-rmse:1.098884+0.102093
## [33] train-rmse:0.661543+0.013192 test-rmse:1.092875+0.100733
## [34] train-rmse:0.643208+0.013442 test-rmse:1.083665+0.100410
## [35] train-rmse:0.622298+0.016946 test-rmse:1.078159+0.094832
## [36] train-rmse:0.609020+0.015675 test-rmse:1.072923+0.092068
## [37] train-rmse:0.595831+0.016274 test-rmse:1.066094+0.095681
## [38] train-rmse:0.582903+0.014364 test-rmse:1.061376+0.093634
## [39] train-rmse:0.568390+0.012051 test-rmse:1.050592+0.088986
## [40] train-rmse:0.555058+0.015470 test-rmse:1.045350+0.089034
## [41] train-rmse:0.542878+0.015431 test-rmse:1.038232+0.088791
## [42] train-rmse:0.529929+0.016938 test-rmse:1.036016+0.089394
## [43] train-rmse:0.519479+0.017610 test-rmse:1.034393+0.090707
## [44] train-rmse:0.507735+0.018555 test-rmse:1.032348+0.089608
## [45] train-rmse:0.498851+0.017128 test-rmse:1.029136+0.092492
## [46] train-rmse:0.486755+0.019613 test-rmse:1.026517+0.094882
## [47] train-rmse:0.476638+0.016743 test-rmse:1.021914+0.095519
## [48] train-rmse:0.467546+0.018746 test-rmse:1.019482+0.096710
## [49] train-rmse:0.458744+0.018104 test-rmse:1.013582+0.096376
## [50] train-rmse:0.448744+0.014578 test-rmse:1.007350+0.093868
## [51] train-rmse:0.442622+0.013134 test-rmse:1.004380+0.093624
## [52] train-rmse:0.435057+0.013891 test-rmse:1.002677+0.090431
## [53] train-rmse:0.424405+0.014846 test-rmse:0.999483+0.092054
## [54] train-rmse:0.417455+0.014646 test-rmse:0.996141+0.091466
## [55] train-rmse:0.410655+0.016942 test-rmse:0.992723+0.092593
## [56] train-rmse:0.405278+0.016269 test-rmse:0.990453+0.093774
## [57] train-rmse:0.399904+0.016524 test-rmse:0.991776+0.093381
## [58] train-rmse:0.395246+0.016120 test-rmse:0.989600+0.094189
## [59] train-rmse:0.386022+0.014167 test-rmse:0.988758+0.096195
## [60] train-rmse:0.378094+0.012975 test-rmse:0.986270+0.096651
## [61] train-rmse:0.371079+0.014672 test-rmse:0.983626+0.098018
## [62] train-rmse:0.362668+0.014944 test-rmse:0.982200+0.099607
## [63] train-rmse:0.355399+0.012947 test-rmse:0.980353+0.099084
## [64] train-rmse:0.350101+0.011930 test-rmse:0.981822+0.099466
## [65] train-rmse:0.342872+0.013021 test-rmse:0.980316+0.098028
## [66] train-rmse:0.335310+0.011976 test-rmse:0.979954+0.095474
## [67] train-rmse:0.331229+0.010929 test-rmse:0.977856+0.097875
## [68] train-rmse:0.326018+0.010522 test-rmse:0.977872+0.095283
## [69] train-rmse:0.319782+0.010024 test-rmse:0.975974+0.095127
## [70] train-rmse:0.314911+0.009638 test-rmse:0.975068+0.095907
## [71] train-rmse:0.309653+0.010959 test-rmse:0.972505+0.094413
## [72] train-rmse:0.305136+0.010173 test-rmse:0.971425+0.093700
## [73] train-rmse:0.300146+0.011164 test-rmse:0.971418+0.094385
## [74] train-rmse:0.294874+0.011324 test-rmse:0.972708+0.095366
## [75] train-rmse:0.289198+0.010695 test-rmse:0.972739+0.095531
## [76] train-rmse:0.284783+0.012365 test-rmse:0.971585+0.094244
## [77] train-rmse:0.280805+0.013351 test-rmse:0.969262+0.095192
## [78] train-rmse:0.276274+0.012303 test-rmse:0.967980+0.096338
## [79] train-rmse:0.270950+0.011268 test-rmse:0.967391+0.096335
## [80] train-rmse:0.266125+0.009798 test-rmse:0.966201+0.096679
## [81] train-rmse:0.262226+0.009070 test-rmse:0.965917+0.096192
## [82] train-rmse:0.258167+0.010773 test-rmse:0.964005+0.096097
## [83] train-rmse:0.253119+0.008885 test-rmse:0.963093+0.096175
## [84] train-rmse:0.249231+0.008028 test-rmse:0.962565+0.096348
## [85] train-rmse:0.245057+0.007719 test-rmse:0.961218+0.095959
## [86] train-rmse:0.241973+0.008431 test-rmse:0.960672+0.096853
## [87] train-rmse:0.238247+0.009220 test-rmse:0.959936+0.096486
## [88] train-rmse:0.232967+0.007692 test-rmse:0.957680+0.095949
## [89] train-rmse:0.228484+0.007564 test-rmse:0.957784+0.096538
## [90] train-rmse:0.223671+0.007842 test-rmse:0.956665+0.097554
## [91] train-rmse:0.219993+0.008499 test-rmse:0.956124+0.099082
## [92] train-rmse:0.216290+0.009483 test-rmse:0.954982+0.099761
## [93] train-rmse:0.212325+0.009075 test-rmse:0.954984+0.100300
## [94] train-rmse:0.207664+0.008554 test-rmse:0.954083+0.099803
## [95] train-rmse:0.204782+0.008094 test-rmse:0.953626+0.100991
## [96] train-rmse:0.201440+0.009076 test-rmse:0.952858+0.101971
## [97] train-rmse:0.198478+0.009252 test-rmse:0.953296+0.101499
## [98] train-rmse:0.194800+0.009043 test-rmse:0.952004+0.102125
## [99] train-rmse:0.191975+0.008511 test-rmse:0.951525+0.101760
## [100] train-rmse:0.188270+0.007545 test-rmse:0.950583+0.101351
## [101] train-rmse:0.184843+0.008512 test-rmse:0.950061+0.100386
## [102] train-rmse:0.181355+0.007707 test-rmse:0.949102+0.099555
## [103] train-rmse:0.178169+0.008598 test-rmse:0.948785+0.100018
## [104] train-rmse:0.175911+0.008424 test-rmse:0.948859+0.100507
## [105] train-rmse:0.173857+0.008065 test-rmse:0.947818+0.100527
## [106] train-rmse:0.171135+0.008163 test-rmse:0.947000+0.100774
## [107] train-rmse:0.169268+0.008028 test-rmse:0.948402+0.102031
## [108] train-rmse:0.167408+0.008024 test-rmse:0.948130+0.101903
## [109] train-rmse:0.165291+0.008133 test-rmse:0.947441+0.102239
## [110] train-rmse:0.163003+0.007889 test-rmse:0.946681+0.101374
## [111] train-rmse:0.159807+0.007017 test-rmse:0.946637+0.102302
## [112] train-rmse:0.157444+0.007487 test-rmse:0.945984+0.101651
## [113] train-rmse:0.155895+0.007662 test-rmse:0.945881+0.102219
## [114] train-rmse:0.153726+0.007605 test-rmse:0.945756+0.102386
## [115] train-rmse:0.151474+0.007887 test-rmse:0.946439+0.102559
## [116] train-rmse:0.148486+0.008228 test-rmse:0.946016+0.102300
## [117] train-rmse:0.145294+0.007467 test-rmse:0.946198+0.101862
## [118] train-rmse:0.143477+0.007219 test-rmse:0.945669+0.101577
## [119] train-rmse:0.141310+0.007707 test-rmse:0.946334+0.101658
## [120] train-rmse:0.138109+0.007124 test-rmse:0.946656+0.101139
## [121] train-rmse:0.135954+0.007323 test-rmse:0.947147+0.101675
## [122] train-rmse:0.134249+0.007277 test-rmse:0.946523+0.102096
## [123] train-rmse:0.132589+0.006936 test-rmse:0.946751+0.102408
## [124] train-rmse:0.130955+0.006679 test-rmse:0.946007+0.103017
## Stopping. Best iteration:
## [118] train-rmse:0.143477+0.007219 test-rmse:0.945669+0.101577
##
## 40
## [1] train-rmse:78.857910+0.079069 test-rmse:78.858575+0.414633
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:63.153612+0.063143 test-rmse:63.154042+0.429953
## [3] train-rmse:50.582123+0.050320 test-rmse:50.582268+0.441991
## [4] train-rmse:40.519815+0.039977 test-rmse:40.519597+0.451317
## [5] train-rmse:32.467526+0.031623 test-rmse:32.466867+0.458362
## [6] train-rmse:26.025803+0.024854 test-rmse:26.024606+0.463429
## [7] train-rmse:20.875072+0.019383 test-rmse:20.873221+0.466687
## [8] train-rmse:16.759770+0.015015 test-rmse:16.757143+0.468190
## [9] train-rmse:13.474406+0.011635 test-rmse:13.476270+0.444762
## [10] train-rmse:10.852426+0.008666 test-rmse:10.838746+0.439334
## [11] train-rmse:8.759135+0.007336 test-rmse:8.743939+0.416209
## [12] train-rmse:7.088128+0.006268 test-rmse:7.074247+0.396839
## [13] train-rmse:5.751411+0.008302 test-rmse:5.741578+0.357091
## [14] train-rmse:4.684732+0.006363 test-rmse:4.679960+0.325245
## [15] train-rmse:3.833123+0.007578 test-rmse:3.829197+0.292860
## [16] train-rmse:3.150947+0.005766 test-rmse:3.156574+0.279847
## [17] train-rmse:2.607791+0.006664 test-rmse:2.637946+0.254843
## [18] train-rmse:2.172666+0.007642 test-rmse:2.234521+0.228476
## [19] train-rmse:1.828573+0.010415 test-rmse:1.939658+0.211039
## [20] train-rmse:1.555231+0.014794 test-rmse:1.709795+0.193682
## [21] train-rmse:1.343123+0.014555 test-rmse:1.542797+0.171717
## [22] train-rmse:1.177717+0.011444 test-rmse:1.431141+0.158918
## [23] train-rmse:1.041746+0.013599 test-rmse:1.336225+0.147629
## [24] train-rmse:0.935808+0.013913 test-rmse:1.263592+0.142382
## [25] train-rmse:0.854287+0.016791 test-rmse:1.214794+0.135788
## [26] train-rmse:0.790626+0.014036 test-rmse:1.186393+0.135561
## [27] train-rmse:0.735342+0.013399 test-rmse:1.163713+0.127475
## [28] train-rmse:0.691350+0.011434 test-rmse:1.137136+0.119936
## [29] train-rmse:0.654125+0.013430 test-rmse:1.125680+0.120408
## [30] train-rmse:0.626602+0.014198 test-rmse:1.109689+0.118218
## [31] train-rmse:0.599810+0.016539 test-rmse:1.097224+0.118862
## [32] train-rmse:0.578053+0.016357 test-rmse:1.078242+0.112353
## [33] train-rmse:0.557172+0.014068 test-rmse:1.073121+0.114379
## [34] train-rmse:0.539670+0.015200 test-rmse:1.061608+0.106545
## [35] train-rmse:0.522693+0.009881 test-rmse:1.055335+0.106592
## [36] train-rmse:0.505465+0.008828 test-rmse:1.052320+0.109544
## [37] train-rmse:0.489998+0.009192 test-rmse:1.044505+0.106948
## [38] train-rmse:0.473277+0.014022 test-rmse:1.040501+0.106749
## [39] train-rmse:0.459716+0.015516 test-rmse:1.035040+0.104072
## [40] train-rmse:0.448458+0.015515 test-rmse:1.031074+0.102458
## [41] train-rmse:0.438073+0.015066 test-rmse:1.025360+0.103946
## [42] train-rmse:0.423887+0.012940 test-rmse:1.020171+0.103827
## [43] train-rmse:0.408405+0.010340 test-rmse:1.013618+0.102221
## [44] train-rmse:0.397216+0.010223 test-rmse:1.010500+0.101545
## [45] train-rmse:0.388541+0.009224 test-rmse:1.007536+0.101876
## [46] train-rmse:0.377652+0.010911 test-rmse:1.005196+0.102947
## [47] train-rmse:0.368635+0.011698 test-rmse:1.002262+0.104180
## [48] train-rmse:0.362499+0.011302 test-rmse:1.000990+0.104806
## [49] train-rmse:0.353992+0.010698 test-rmse:0.999509+0.103590
## [50] train-rmse:0.345039+0.009104 test-rmse:0.998049+0.104708
## [51] train-rmse:0.336439+0.010140 test-rmse:0.994523+0.104343
## [52] train-rmse:0.329096+0.011186 test-rmse:0.991832+0.105979
## [53] train-rmse:0.323809+0.011155 test-rmse:0.990057+0.104345
## [54] train-rmse:0.317606+0.010986 test-rmse:0.988162+0.103258
## [55] train-rmse:0.309401+0.009794 test-rmse:0.987043+0.101786
## [56] train-rmse:0.302110+0.009491 test-rmse:0.983525+0.103051
## [57] train-rmse:0.295215+0.009289 test-rmse:0.982574+0.102953
## [58] train-rmse:0.287274+0.009208 test-rmse:0.980267+0.101433
## [59] train-rmse:0.280023+0.011501 test-rmse:0.979967+0.101593
## [60] train-rmse:0.272917+0.012135 test-rmse:0.979076+0.101742
## [61] train-rmse:0.266955+0.011022 test-rmse:0.980194+0.100567
## [62] train-rmse:0.262680+0.011131 test-rmse:0.978025+0.098980
## [63] train-rmse:0.257134+0.011475 test-rmse:0.977595+0.098136
## [64] train-rmse:0.249397+0.009823 test-rmse:0.976604+0.096652
## [65] train-rmse:0.242615+0.009327 test-rmse:0.974859+0.094303
## [66] train-rmse:0.238588+0.010250 test-rmse:0.974622+0.095033
## [67] train-rmse:0.232684+0.010642 test-rmse:0.974069+0.095238
## [68] train-rmse:0.227472+0.009245 test-rmse:0.972638+0.094535
## [69] train-rmse:0.222164+0.009064 test-rmse:0.971671+0.094173
## [70] train-rmse:0.216830+0.009422 test-rmse:0.971612+0.095469
## [71] train-rmse:0.212507+0.009656 test-rmse:0.970913+0.094990
## [72] train-rmse:0.208139+0.010151 test-rmse:0.970495+0.095637
## [73] train-rmse:0.202761+0.011687 test-rmse:0.970657+0.096136
## [74] train-rmse:0.197694+0.010627 test-rmse:0.969591+0.095888
## [75] train-rmse:0.193883+0.010517 test-rmse:0.969301+0.095259
## [76] train-rmse:0.189434+0.011496 test-rmse:0.970582+0.095383
## [77] train-rmse:0.184438+0.009831 test-rmse:0.970160+0.095511
## [78] train-rmse:0.180414+0.010153 test-rmse:0.969747+0.095417
## [79] train-rmse:0.175465+0.009490 test-rmse:0.969639+0.096219
## [80] train-rmse:0.172295+0.009085 test-rmse:0.968610+0.095599
## [81] train-rmse:0.169668+0.009239 test-rmse:0.967960+0.096000
## [82] train-rmse:0.165668+0.008449 test-rmse:0.967116+0.095101
## [83] train-rmse:0.162068+0.007831 test-rmse:0.967903+0.095268
## [84] train-rmse:0.158978+0.007874 test-rmse:0.967281+0.095368
## [85] train-rmse:0.155153+0.008519 test-rmse:0.966554+0.095458
## [86] train-rmse:0.151791+0.008313 test-rmse:0.966550+0.095926
## [87] train-rmse:0.149399+0.007815 test-rmse:0.964699+0.096839
## [88] train-rmse:0.145606+0.008049 test-rmse:0.963707+0.097497
## [89] train-rmse:0.142986+0.008631 test-rmse:0.963615+0.096861
## [90] train-rmse:0.139451+0.008608 test-rmse:0.963770+0.097302
## [91] train-rmse:0.135619+0.008731 test-rmse:0.963224+0.097126
## [92] train-rmse:0.133127+0.009042 test-rmse:0.962523+0.098198
## [93] train-rmse:0.130185+0.008984 test-rmse:0.963005+0.098153
## [94] train-rmse:0.127394+0.007830 test-rmse:0.963538+0.098031
## [95] train-rmse:0.124426+0.008079 test-rmse:0.963855+0.098336
## [96] train-rmse:0.121369+0.008087 test-rmse:0.963462+0.099437
## [97] train-rmse:0.119275+0.008106 test-rmse:0.963247+0.099566
## [98] train-rmse:0.116888+0.007902 test-rmse:0.963089+0.100252
## Stopping. Best iteration:
## [92] train-rmse:0.133127+0.009042 test-rmse:0.962523+0.098198
##
## 41
## [1] train-rmse:78.857910+0.079069 test-rmse:78.858575+0.414633
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:63.153612+0.063143 test-rmse:63.154042+0.429953
## [3] train-rmse:50.582123+0.050320 test-rmse:50.582268+0.441991
## [4] train-rmse:40.519815+0.039977 test-rmse:40.519597+0.451317
## [5] train-rmse:32.467526+0.031623 test-rmse:32.466867+0.458362
## [6] train-rmse:26.025803+0.024854 test-rmse:26.024606+0.463429
## [7] train-rmse:20.875072+0.019383 test-rmse:20.873221+0.466687
## [8] train-rmse:16.759770+0.015015 test-rmse:16.757143+0.468190
## [9] train-rmse:13.474406+0.011635 test-rmse:13.476270+0.444762
## [10] train-rmse:10.852426+0.008666 test-rmse:10.838746+0.439334
## [11] train-rmse:8.759135+0.007336 test-rmse:8.743939+0.416209
## [12] train-rmse:7.088128+0.006268 test-rmse:7.074247+0.396839
## [13] train-rmse:5.750586+0.007736 test-rmse:5.739268+0.353021
## [14] train-rmse:4.683024+0.006483 test-rmse:4.684312+0.320164
## [15] train-rmse:3.828848+0.006601 test-rmse:3.832846+0.293336
## [16] train-rmse:3.144562+0.005502 test-rmse:3.157162+0.275440
## [17] train-rmse:2.598045+0.005033 test-rmse:2.635319+0.255638
## [18] train-rmse:2.157342+0.003564 test-rmse:2.230373+0.232386
## [19] train-rmse:1.812300+0.005812 test-rmse:1.924762+0.209328
## [20] train-rmse:1.530198+0.003553 test-rmse:1.686053+0.186685
## [21] train-rmse:1.310598+0.004464 test-rmse:1.510553+0.167365
## [22] train-rmse:1.135846+0.010400 test-rmse:1.385806+0.153874
## [23] train-rmse:0.990994+0.006306 test-rmse:1.290105+0.147833
## [24] train-rmse:0.879758+0.007254 test-rmse:1.221196+0.137522
## [25] train-rmse:0.789806+0.002945 test-rmse:1.167073+0.134782
## [26] train-rmse:0.719412+0.006114 test-rmse:1.130746+0.136801
## [27] train-rmse:0.662046+0.011476 test-rmse:1.101146+0.128637
## [28] train-rmse:0.614421+0.015704 test-rmse:1.076293+0.119783
## [29] train-rmse:0.574653+0.015335 test-rmse:1.056972+0.109112
## [30] train-rmse:0.543114+0.018825 test-rmse:1.043055+0.108732
## [31] train-rmse:0.514943+0.020272 test-rmse:1.035800+0.106138
## [32] train-rmse:0.493972+0.019509 test-rmse:1.027119+0.107835
## [33] train-rmse:0.469206+0.022059 test-rmse:1.019310+0.111685
## [34] train-rmse:0.447174+0.017736 test-rmse:1.010226+0.103399
## [35] train-rmse:0.429909+0.017576 test-rmse:1.001382+0.099593
## [36] train-rmse:0.412067+0.015685 test-rmse:0.992462+0.099594
## [37] train-rmse:0.397346+0.015973 test-rmse:0.989338+0.100488
## [38] train-rmse:0.380808+0.012532 test-rmse:0.983482+0.099805
## [39] train-rmse:0.370250+0.013670 test-rmse:0.978417+0.102265
## [40] train-rmse:0.359480+0.012755 test-rmse:0.973799+0.103031
## [41] train-rmse:0.346573+0.013179 test-rmse:0.970567+0.101332
## [42] train-rmse:0.333772+0.012920 test-rmse:0.971717+0.100454
## [43] train-rmse:0.321059+0.013992 test-rmse:0.967538+0.098947
## [44] train-rmse:0.309351+0.016983 test-rmse:0.963293+0.097865
## [45] train-rmse:0.301214+0.014843 test-rmse:0.960254+0.097654
## [46] train-rmse:0.292636+0.015888 test-rmse:0.958798+0.098305
## [47] train-rmse:0.284675+0.013963 test-rmse:0.958068+0.097696
## [48] train-rmse:0.277378+0.013766 test-rmse:0.957815+0.097042
## [49] train-rmse:0.268993+0.013639 test-rmse:0.957722+0.099466
## [50] train-rmse:0.256852+0.011086 test-rmse:0.956213+0.099138
## [51] train-rmse:0.250717+0.011880 test-rmse:0.954983+0.100218
## [52] train-rmse:0.243782+0.009536 test-rmse:0.951626+0.100250
## [53] train-rmse:0.234025+0.007062 test-rmse:0.948600+0.097911
## [54] train-rmse:0.228391+0.007346 test-rmse:0.946768+0.097105
## [55] train-rmse:0.223501+0.008770 test-rmse:0.947201+0.096873
## [56] train-rmse:0.216817+0.007889 test-rmse:0.943728+0.095549
## [57] train-rmse:0.210915+0.007132 test-rmse:0.942199+0.097210
## [58] train-rmse:0.204394+0.007860 test-rmse:0.942448+0.097064
## [59] train-rmse:0.198175+0.009438 test-rmse:0.941538+0.097346
## [60] train-rmse:0.194119+0.008808 test-rmse:0.940567+0.097758
## [61] train-rmse:0.187940+0.007234 test-rmse:0.938852+0.098203
## [62] train-rmse:0.181416+0.006400 test-rmse:0.937859+0.097467
## [63] train-rmse:0.177012+0.005270 test-rmse:0.936883+0.096926
## [64] train-rmse:0.173411+0.005261 test-rmse:0.935956+0.096873
## [65] train-rmse:0.167746+0.004906 test-rmse:0.935939+0.097220
## [66] train-rmse:0.163397+0.005430 test-rmse:0.935191+0.097323
## [67] train-rmse:0.157724+0.004754 test-rmse:0.933754+0.097154
## [68] train-rmse:0.152440+0.004662 test-rmse:0.934610+0.097325
## [69] train-rmse:0.148094+0.003456 test-rmse:0.934567+0.095864
## [70] train-rmse:0.144077+0.005139 test-rmse:0.933165+0.094051
## [71] train-rmse:0.140238+0.005936 test-rmse:0.932622+0.094006
## [72] train-rmse:0.136367+0.004451 test-rmse:0.931605+0.095268
## [73] train-rmse:0.133859+0.005935 test-rmse:0.931995+0.096429
## [74] train-rmse:0.129924+0.005590 test-rmse:0.931040+0.095130
## [75] train-rmse:0.126033+0.004360 test-rmse:0.930593+0.094412
## [76] train-rmse:0.122633+0.004131 test-rmse:0.929899+0.093998
## [77] train-rmse:0.118808+0.004566 test-rmse:0.929934+0.094111
## [78] train-rmse:0.116337+0.004469 test-rmse:0.929677+0.094028
## [79] train-rmse:0.113275+0.005026 test-rmse:0.929065+0.094364
## [80] train-rmse:0.111082+0.005505 test-rmse:0.928648+0.094349
## [81] train-rmse:0.108586+0.005013 test-rmse:0.927502+0.094772
## [82] train-rmse:0.104670+0.005529 test-rmse:0.927410+0.095108
## [83] train-rmse:0.101312+0.005526 test-rmse:0.927328+0.095032
## [84] train-rmse:0.099019+0.005886 test-rmse:0.927350+0.095196
## [85] train-rmse:0.095984+0.006118 test-rmse:0.927186+0.094345
## [86] train-rmse:0.093052+0.006029 test-rmse:0.926670+0.094813
## [87] train-rmse:0.090258+0.005555 test-rmse:0.926279+0.095523
## [88] train-rmse:0.087928+0.005595 test-rmse:0.926022+0.095658
## [89] train-rmse:0.085711+0.005139 test-rmse:0.926100+0.095291
## [90] train-rmse:0.083669+0.004782 test-rmse:0.926167+0.095420
## [91] train-rmse:0.081317+0.003535 test-rmse:0.925842+0.094990
## [92] train-rmse:0.078236+0.003336 test-rmse:0.925533+0.095089
## [93] train-rmse:0.076485+0.003201 test-rmse:0.925178+0.094584
## [94] train-rmse:0.075281+0.003288 test-rmse:0.924931+0.094579
## [95] train-rmse:0.073045+0.003512 test-rmse:0.924773+0.094578
## [96] train-rmse:0.070718+0.003332 test-rmse:0.924447+0.094368
## [97] train-rmse:0.068936+0.003489 test-rmse:0.924279+0.094107
## [98] train-rmse:0.067164+0.003242 test-rmse:0.924102+0.094027
## [99] train-rmse:0.064939+0.002869 test-rmse:0.923844+0.093990
## [100] train-rmse:0.063397+0.002446 test-rmse:0.923988+0.093924
## [101] train-rmse:0.061784+0.002631 test-rmse:0.923991+0.093660
## [102] train-rmse:0.059640+0.002764 test-rmse:0.923643+0.094154
## [103] train-rmse:0.057163+0.002874 test-rmse:0.923700+0.094194
## [104] train-rmse:0.055862+0.002888 test-rmse:0.923426+0.094278
## [105] train-rmse:0.054860+0.002501 test-rmse:0.923145+0.094289
## [106] train-rmse:0.053184+0.002285 test-rmse:0.923182+0.094452
## [107] train-rmse:0.052186+0.002189 test-rmse:0.923250+0.094525
## [108] train-rmse:0.050776+0.002264 test-rmse:0.922982+0.094405
## [109] train-rmse:0.049170+0.001764 test-rmse:0.922885+0.094635
## [110] train-rmse:0.047735+0.001360 test-rmse:0.922435+0.095273
## [111] train-rmse:0.046798+0.001452 test-rmse:0.922313+0.095230
## [112] train-rmse:0.045522+0.001469 test-rmse:0.921858+0.095577
## [113] train-rmse:0.044213+0.001360 test-rmse:0.922101+0.095736
## [114] train-rmse:0.043093+0.001459 test-rmse:0.922121+0.095819
## [115] train-rmse:0.041874+0.001367 test-rmse:0.922313+0.095810
## [116] train-rmse:0.040902+0.001233 test-rmse:0.922194+0.095664
## [117] train-rmse:0.040153+0.001075 test-rmse:0.922266+0.095668
## [118] train-rmse:0.039082+0.001226 test-rmse:0.922449+0.095562
## Stopping. Best iteration:
## [112] train-rmse:0.045522+0.001469 test-rmse:0.921858+0.095577
##
## 42
## [1] train-rmse:76.896455+0.077079 test-rmse:76.897090+0.416550
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:60.052252+0.059983 test-rmse:60.052622+0.432945
## [3] train-rmse:46.904609+0.046553 test-rmse:46.904639+0.445440
## [4] train-rmse:36.644199+0.035970 test-rmse:36.643796+0.454772
## [5] train-rmse:28.639430+0.027617 test-rmse:28.638477+0.461462
## [6] train-rmse:22.397527+0.021009 test-rmse:22.395899+0.465834
## [7] train-rmse:17.534218+0.015835 test-rmse:17.531762+0.468024
## [8] train-rmse:13.749436+0.012173 test-rmse:13.738001+0.462524
## [9] train-rmse:10.805906+0.009591 test-rmse:10.799528+0.436844
## [10] train-rmse:8.520625+0.007969 test-rmse:8.506156+0.435495
## [11] train-rmse:6.750453+0.007086 test-rmse:6.731134+0.421727
## [12] train-rmse:5.387451+0.007276 test-rmse:5.370603+0.408660
## [13] train-rmse:4.346145+0.009686 test-rmse:4.342020+0.382658
## [14] train-rmse:3.558936+0.010907 test-rmse:3.565225+0.358475
## [15] train-rmse:2.971066+0.013697 test-rmse:2.978190+0.319563
## [16] train-rmse:2.541632+0.016073 test-rmse:2.568087+0.281587
## [17] train-rmse:2.233041+0.017586 test-rmse:2.272055+0.241215
## [18] train-rmse:2.015123+0.019303 test-rmse:2.065885+0.195050
## [19] train-rmse:1.863276+0.021045 test-rmse:1.931342+0.161071
## [20] train-rmse:1.758274+0.022065 test-rmse:1.838130+0.139910
## [21] train-rmse:1.685860+0.022744 test-rmse:1.764093+0.130603
## [22] train-rmse:1.634244+0.023415 test-rmse:1.715123+0.122936
## [23] train-rmse:1.596191+0.023872 test-rmse:1.687203+0.120962
## [24] train-rmse:1.567964+0.023911 test-rmse:1.664607+0.117727
## [25] train-rmse:1.545505+0.023061 test-rmse:1.651629+0.119992
## [26] train-rmse:1.527019+0.022405 test-rmse:1.637546+0.119110
## [27] train-rmse:1.510939+0.022311 test-rmse:1.625066+0.118863
## [28] train-rmse:1.497074+0.022108 test-rmse:1.612915+0.122684
## [29] train-rmse:1.484309+0.022042 test-rmse:1.602839+0.125130
## [30] train-rmse:1.472477+0.022100 test-rmse:1.587914+0.118223
## [31] train-rmse:1.461251+0.022193 test-rmse:1.577046+0.115896
## [32] train-rmse:1.450801+0.022046 test-rmse:1.573253+0.115434
## [33] train-rmse:1.441085+0.021714 test-rmse:1.565504+0.115475
## [34] train-rmse:1.431597+0.021309 test-rmse:1.563440+0.114222
## [35] train-rmse:1.422385+0.021227 test-rmse:1.564279+0.113422
## [36] train-rmse:1.413407+0.021122 test-rmse:1.555124+0.108096
## [37] train-rmse:1.404520+0.021054 test-rmse:1.549663+0.110972
## [38] train-rmse:1.395881+0.021165 test-rmse:1.541237+0.115521
## [39] train-rmse:1.387643+0.021066 test-rmse:1.532674+0.115491
## [40] train-rmse:1.379480+0.021344 test-rmse:1.525606+0.112728
## [41] train-rmse:1.371396+0.021240 test-rmse:1.522447+0.112768
## [42] train-rmse:1.363679+0.021230 test-rmse:1.514196+0.100152
## [43] train-rmse:1.356291+0.021056 test-rmse:1.509152+0.099261
## [44] train-rmse:1.349128+0.021028 test-rmse:1.506621+0.104261
## [45] train-rmse:1.341944+0.021123 test-rmse:1.503523+0.104458
## [46] train-rmse:1.334882+0.021014 test-rmse:1.499045+0.103101
## [47] train-rmse:1.328073+0.020947 test-rmse:1.491173+0.103963
## [48] train-rmse:1.321458+0.020806 test-rmse:1.480278+0.100511
## [49] train-rmse:1.314967+0.020624 test-rmse:1.476384+0.100985
## [50] train-rmse:1.308700+0.020500 test-rmse:1.473398+0.094959
## [51] train-rmse:1.302290+0.020127 test-rmse:1.474934+0.095334
## [52] train-rmse:1.296236+0.020051 test-rmse:1.471957+0.095102
## [53] train-rmse:1.290415+0.019876 test-rmse:1.466993+0.093341
## [54] train-rmse:1.284700+0.019757 test-rmse:1.464126+0.095966
## [55] train-rmse:1.279089+0.019588 test-rmse:1.453689+0.095701
## [56] train-rmse:1.273597+0.019608 test-rmse:1.451878+0.091031
## [57] train-rmse:1.268125+0.019581 test-rmse:1.452674+0.089601
## [58] train-rmse:1.262791+0.019625 test-rmse:1.455202+0.086267
## [59] train-rmse:1.257525+0.019574 test-rmse:1.450827+0.090088
## [60] train-rmse:1.252582+0.019596 test-rmse:1.448251+0.090973
## [61] train-rmse:1.247573+0.019730 test-rmse:1.444175+0.094475
## [62] train-rmse:1.242695+0.019860 test-rmse:1.440157+0.093992
## [63] train-rmse:1.237793+0.020039 test-rmse:1.434572+0.093620
## [64] train-rmse:1.232882+0.019999 test-rmse:1.430743+0.094156
## [65] train-rmse:1.228239+0.019972 test-rmse:1.424936+0.089781
## [66] train-rmse:1.223751+0.019868 test-rmse:1.420951+0.087781
## [67] train-rmse:1.219268+0.019738 test-rmse:1.417404+0.091563
## [68] train-rmse:1.214795+0.019714 test-rmse:1.415552+0.091388
## [69] train-rmse:1.210376+0.019706 test-rmse:1.415575+0.091058
## [70] train-rmse:1.206094+0.019701 test-rmse:1.407581+0.089535
## [71] train-rmse:1.202006+0.019724 test-rmse:1.399802+0.092693
## [72] train-rmse:1.197989+0.019687 test-rmse:1.396941+0.088526
## [73] train-rmse:1.193868+0.019557 test-rmse:1.395345+0.089557
## [74] train-rmse:1.189988+0.019511 test-rmse:1.393261+0.088686
## [75] train-rmse:1.186228+0.019479 test-rmse:1.391630+0.091243
## [76] train-rmse:1.182466+0.019437 test-rmse:1.389009+0.093073
## [77] train-rmse:1.178809+0.019442 test-rmse:1.389618+0.090132
## [78] train-rmse:1.175190+0.019424 test-rmse:1.385951+0.089783
## [79] train-rmse:1.171604+0.019361 test-rmse:1.382724+0.089785
## [80] train-rmse:1.168017+0.019283 test-rmse:1.378652+0.091125
## [81] train-rmse:1.164625+0.019334 test-rmse:1.377794+0.091476
## [82] train-rmse:1.161207+0.019404 test-rmse:1.377138+0.091988
## [83] train-rmse:1.157853+0.019440 test-rmse:1.374745+0.090458
## [84] train-rmse:1.154535+0.019424 test-rmse:1.372919+0.091517
## [85] train-rmse:1.151424+0.019382 test-rmse:1.373663+0.088560
## [86] train-rmse:1.148320+0.019266 test-rmse:1.371300+0.087640
## [87] train-rmse:1.145269+0.019215 test-rmse:1.366598+0.085485
## [88] train-rmse:1.142242+0.019140 test-rmse:1.362172+0.085629
## [89] train-rmse:1.139284+0.019067 test-rmse:1.360004+0.087623
## [90] train-rmse:1.136390+0.019068 test-rmse:1.357064+0.088229
## [91] train-rmse:1.133489+0.019053 test-rmse:1.353080+0.087235
## [92] train-rmse:1.130617+0.019039 test-rmse:1.351632+0.086877
## [93] train-rmse:1.127773+0.019073 test-rmse:1.348601+0.085683
## [94] train-rmse:1.124988+0.019056 test-rmse:1.346842+0.085296
## [95] train-rmse:1.122278+0.019000 test-rmse:1.345019+0.084893
## [96] train-rmse:1.119594+0.018951 test-rmse:1.342984+0.085150
## [97] train-rmse:1.116923+0.018858 test-rmse:1.340475+0.086434
## [98] train-rmse:1.114277+0.018790 test-rmse:1.337632+0.088178
## [99] train-rmse:1.111718+0.018747 test-rmse:1.338513+0.086246
## [100] train-rmse:1.109157+0.018710 test-rmse:1.340281+0.082710
## [101] train-rmse:1.106690+0.018696 test-rmse:1.339078+0.082255
## [102] train-rmse:1.104218+0.018687 test-rmse:1.338902+0.085601
## [103] train-rmse:1.101776+0.018699 test-rmse:1.336605+0.088682
## [104] train-rmse:1.099374+0.018686 test-rmse:1.333790+0.085771
## [105] train-rmse:1.097076+0.018700 test-rmse:1.331137+0.086642
## [106] train-rmse:1.094720+0.018601 test-rmse:1.328976+0.085780
## [107] train-rmse:1.092423+0.018598 test-rmse:1.325074+0.085324
## [108] train-rmse:1.090100+0.018541 test-rmse:1.323093+0.085432
## [109] train-rmse:1.087812+0.018501 test-rmse:1.319354+0.085660
## [110] train-rmse:1.085632+0.018475 test-rmse:1.318210+0.085208
## [111] train-rmse:1.083485+0.018453 test-rmse:1.317449+0.088305
## [112] train-rmse:1.081379+0.018414 test-rmse:1.315689+0.089217
## [113] train-rmse:1.079323+0.018338 test-rmse:1.314559+0.090947
## [114] train-rmse:1.077301+0.018285 test-rmse:1.312050+0.089770
## [115] train-rmse:1.075298+0.018255 test-rmse:1.310374+0.089548
## [116] train-rmse:1.073308+0.018245 test-rmse:1.310372+0.089554
## [117] train-rmse:1.071369+0.018216 test-rmse:1.309732+0.090067
## [118] train-rmse:1.069449+0.018174 test-rmse:1.308533+0.090628
## [119] train-rmse:1.067520+0.018112 test-rmse:1.307072+0.091880
## [120] train-rmse:1.065620+0.018095 test-rmse:1.305881+0.093431
## [121] train-rmse:1.063746+0.018037 test-rmse:1.306041+0.092346
## [122] train-rmse:1.061886+0.017993 test-rmse:1.305069+0.092001
## [123] train-rmse:1.060038+0.017995 test-rmse:1.299077+0.091420
## [124] train-rmse:1.058172+0.017947 test-rmse:1.300259+0.088456
## [125] train-rmse:1.056348+0.017926 test-rmse:1.298800+0.087812
## [126] train-rmse:1.054577+0.017883 test-rmse:1.296158+0.088168
## [127] train-rmse:1.052841+0.017824 test-rmse:1.295106+0.087882
## [128] train-rmse:1.051123+0.017792 test-rmse:1.293674+0.089073
## [129] train-rmse:1.049469+0.017757 test-rmse:1.292342+0.091518
## [130] train-rmse:1.047856+0.017724 test-rmse:1.291116+0.092048
## [131] train-rmse:1.046246+0.017685 test-rmse:1.289945+0.092032
## [132] train-rmse:1.044652+0.017642 test-rmse:1.288354+0.091019
## [133] train-rmse:1.043009+0.017562 test-rmse:1.289203+0.091173
## [134] train-rmse:1.041423+0.017539 test-rmse:1.287281+0.092660
## [135] train-rmse:1.039835+0.017488 test-rmse:1.286691+0.092771
## [136] train-rmse:1.038324+0.017465 test-rmse:1.286666+0.091097
## [137] train-rmse:1.036815+0.017416 test-rmse:1.285565+0.090887
## [138] train-rmse:1.035312+0.017388 test-rmse:1.284278+0.091467
## [139] train-rmse:1.033857+0.017398 test-rmse:1.283381+0.089925
## [140] train-rmse:1.032423+0.017397 test-rmse:1.281791+0.090128
## [141] train-rmse:1.030983+0.017426 test-rmse:1.280748+0.090451
## [142] train-rmse:1.029586+0.017414 test-rmse:1.279984+0.089012
## [143] train-rmse:1.028181+0.017396 test-rmse:1.276805+0.085330
## [144] train-rmse:1.026789+0.017373 test-rmse:1.276616+0.084898
## [145] train-rmse:1.025406+0.017346 test-rmse:1.276966+0.085442
## [146] train-rmse:1.024029+0.017314 test-rmse:1.276658+0.086136
## [147] train-rmse:1.022682+0.017279 test-rmse:1.275267+0.086489
## [148] train-rmse:1.021333+0.017243 test-rmse:1.274303+0.087402
## [149] train-rmse:1.019990+0.017203 test-rmse:1.274419+0.089193
## [150] train-rmse:1.018644+0.017181 test-rmse:1.272992+0.087802
## [151] train-rmse:1.017279+0.017120 test-rmse:1.273807+0.085549
## [152] train-rmse:1.015981+0.017131 test-rmse:1.272625+0.086689
## [153] train-rmse:1.014699+0.017147 test-rmse:1.272087+0.087142
## [154] train-rmse:1.013416+0.017156 test-rmse:1.272001+0.087932
## [155] train-rmse:1.012177+0.017148 test-rmse:1.269974+0.088709
## [156] train-rmse:1.010947+0.017139 test-rmse:1.270040+0.088520
## [157] train-rmse:1.009711+0.017105 test-rmse:1.269940+0.087628
## [158] train-rmse:1.008505+0.017088 test-rmse:1.269773+0.087863
## [159] train-rmse:1.007292+0.017060 test-rmse:1.266839+0.089256
## [160] train-rmse:1.006054+0.017026 test-rmse:1.266104+0.089973
## [161] train-rmse:1.004854+0.017012 test-rmse:1.265782+0.089703
## [162] train-rmse:1.003647+0.017019 test-rmse:1.264252+0.089160
## [163] train-rmse:1.002456+0.017005 test-rmse:1.264391+0.088708
## [164] train-rmse:1.001277+0.016989 test-rmse:1.264425+0.090625
## [165] train-rmse:1.000144+0.017000 test-rmse:1.262932+0.087466
## [166] train-rmse:0.998999+0.016997 test-rmse:1.261882+0.086170
## [167] train-rmse:0.997868+0.017010 test-rmse:1.261219+0.087466
## [168] train-rmse:0.996758+0.017017 test-rmse:1.259778+0.087471
## [169] train-rmse:0.995639+0.016997 test-rmse:1.258192+0.086186
## [170] train-rmse:0.994539+0.017016 test-rmse:1.258923+0.083760
## [171] train-rmse:0.993469+0.017026 test-rmse:1.258372+0.084174
## [172] train-rmse:0.992403+0.017038 test-rmse:1.260619+0.083788
## [173] train-rmse:0.991318+0.016997 test-rmse:1.258051+0.084183
## [174] train-rmse:0.990256+0.017004 test-rmse:1.256242+0.084367
## [175] train-rmse:0.989207+0.017028 test-rmse:1.255164+0.085067
## [176] train-rmse:0.988175+0.017032 test-rmse:1.253928+0.086247
## [177] train-rmse:0.987117+0.017074 test-rmse:1.251989+0.087014
## [178] train-rmse:0.986107+0.017080 test-rmse:1.252659+0.086114
## [179] train-rmse:0.985081+0.017089 test-rmse:1.251571+0.085719
## [180] train-rmse:0.984080+0.017107 test-rmse:1.251037+0.085375
## [181] train-rmse:0.983103+0.017116 test-rmse:1.251404+0.085881
## [182] train-rmse:0.982079+0.017127 test-rmse:1.251020+0.086099
## [183] train-rmse:0.981109+0.017138 test-rmse:1.251959+0.085751
## [184] train-rmse:0.980151+0.017154 test-rmse:1.251405+0.085075
## [185] train-rmse:0.979163+0.017187 test-rmse:1.251091+0.085717
## [186] train-rmse:0.978188+0.017194 test-rmse:1.250243+0.085984
## [187] train-rmse:0.977224+0.017175 test-rmse:1.249147+0.084842
## [188] train-rmse:0.976281+0.017205 test-rmse:1.248428+0.085174
## [189] train-rmse:0.975320+0.017238 test-rmse:1.247733+0.084711
## [190] train-rmse:0.974381+0.017245 test-rmse:1.247560+0.084465
## [191] train-rmse:0.973453+0.017218 test-rmse:1.246577+0.084115
## [192] train-rmse:0.972534+0.017226 test-rmse:1.246256+0.084229
## [193] train-rmse:0.971590+0.017250 test-rmse:1.244361+0.085009
## [194] train-rmse:0.970675+0.017255 test-rmse:1.244383+0.084710
## [195] train-rmse:0.969755+0.017266 test-rmse:1.243726+0.085235
## [196] train-rmse:0.968868+0.017277 test-rmse:1.244107+0.087480
## [197] train-rmse:0.968005+0.017294 test-rmse:1.244492+0.086143
## [198] train-rmse:0.967142+0.017312 test-rmse:1.243241+0.085238
## [199] train-rmse:0.966265+0.017307 test-rmse:1.243073+0.085701
## [200] train-rmse:0.965409+0.017318 test-rmse:1.240188+0.085140
## [201] train-rmse:0.964552+0.017333 test-rmse:1.240566+0.085664
## [202] train-rmse:0.963693+0.017348 test-rmse:1.240578+0.086104
## [203] train-rmse:0.962875+0.017343 test-rmse:1.240454+0.086439
## [204] train-rmse:0.962063+0.017345 test-rmse:1.239781+0.086343
## [205] train-rmse:0.961220+0.017369 test-rmse:1.240027+0.086036
## [206] train-rmse:0.960405+0.017389 test-rmse:1.237031+0.083822
## [207] train-rmse:0.959617+0.017408 test-rmse:1.236286+0.084021
## [208] train-rmse:0.958788+0.017425 test-rmse:1.236025+0.083605
## [209] train-rmse:0.958011+0.017448 test-rmse:1.236285+0.083926
## [210] train-rmse:0.957207+0.017426 test-rmse:1.237540+0.084863
## [211] train-rmse:0.956421+0.017450 test-rmse:1.236301+0.085136
## [212] train-rmse:0.955654+0.017469 test-rmse:1.237318+0.085328
## [213] train-rmse:0.954873+0.017494 test-rmse:1.236998+0.084395
## [214] train-rmse:0.954078+0.017504 test-rmse:1.237970+0.085086
## Stopping. Best iteration:
## [208] train-rmse:0.958788+0.017425 test-rmse:1.236025+0.083605
##
## 43
## [1] train-rmse:76.896455+0.077079 test-rmse:76.897090+0.416550
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:60.052252+0.059983 test-rmse:60.052622+0.432945
## [3] train-rmse:46.904609+0.046553 test-rmse:46.904639+0.445440
## [4] train-rmse:36.644199+0.035970 test-rmse:36.643796+0.454772
## [5] train-rmse:28.639430+0.027617 test-rmse:28.638477+0.461462
## [6] train-rmse:22.397527+0.021009 test-rmse:22.395899+0.465834
## [7] train-rmse:17.534218+0.015835 test-rmse:17.531762+0.468024
## [8] train-rmse:13.749436+0.012173 test-rmse:13.738001+0.462524
## [9] train-rmse:10.804913+0.008776 test-rmse:10.788056+0.441575
## [10] train-rmse:8.515530+0.006917 test-rmse:8.500487+0.411150
## [11] train-rmse:6.736689+0.006281 test-rmse:6.717966+0.403835
## [12] train-rmse:5.360953+0.007973 test-rmse:5.350553+0.381451
## [13] train-rmse:4.298171+0.008735 test-rmse:4.300034+0.362988
## [14] train-rmse:3.486509+0.012234 test-rmse:3.489929+0.332223
## [15] train-rmse:2.872144+0.012687 test-rmse:2.877938+0.303592
## [16] train-rmse:2.414804+0.015389 test-rmse:2.447518+0.266372
## [17] train-rmse:2.075482+0.014838 test-rmse:2.139145+0.229220
## [18] train-rmse:1.831749+0.013976 test-rmse:1.916506+0.195245
## [19] train-rmse:1.656901+0.016862 test-rmse:1.753624+0.169555
## [20] train-rmse:1.533837+0.015165 test-rmse:1.653067+0.150688
## [21] train-rmse:1.448478+0.014794 test-rmse:1.584506+0.127604
## [22] train-rmse:1.385631+0.016975 test-rmse:1.533386+0.118473
## [23] train-rmse:1.340671+0.017713 test-rmse:1.504677+0.105621
## [24] train-rmse:1.303057+0.014600 test-rmse:1.483400+0.097439
## [25] train-rmse:1.276015+0.015236 test-rmse:1.459993+0.098653
## [26] train-rmse:1.253126+0.014756 test-rmse:1.440055+0.100620
## [27] train-rmse:1.231256+0.015646 test-rmse:1.430348+0.100359
## [28] train-rmse:1.213981+0.016144 test-rmse:1.417800+0.097591
## [29] train-rmse:1.196649+0.014329 test-rmse:1.409616+0.093038
## [30] train-rmse:1.182331+0.013510 test-rmse:1.399810+0.092772
## [31] train-rmse:1.168732+0.013596 test-rmse:1.390749+0.091128
## [32] train-rmse:1.154670+0.014732 test-rmse:1.383779+0.088786
## [33] train-rmse:1.137778+0.013248 test-rmse:1.369275+0.098931
## [34] train-rmse:1.126383+0.013827 test-rmse:1.353383+0.102507
## [35] train-rmse:1.112774+0.015724 test-rmse:1.346151+0.095970
## [36] train-rmse:1.102322+0.015815 test-rmse:1.345649+0.094224
## [37] train-rmse:1.091775+0.015193 test-rmse:1.340439+0.087788
## [38] train-rmse:1.080964+0.016909 test-rmse:1.333046+0.089982
## [39] train-rmse:1.066740+0.015602 test-rmse:1.327736+0.090350
## [40] train-rmse:1.057373+0.015654 test-rmse:1.321690+0.089695
## [41] train-rmse:1.048601+0.015936 test-rmse:1.314425+0.092473
## [42] train-rmse:1.040404+0.016263 test-rmse:1.307431+0.089821
## [43] train-rmse:1.032229+0.016604 test-rmse:1.301352+0.095919
## [44] train-rmse:1.020977+0.017192 test-rmse:1.287187+0.091455
## [45] train-rmse:1.012302+0.017774 test-rmse:1.278845+0.091592
## [46] train-rmse:1.004203+0.018363 test-rmse:1.267839+0.096683
## [47] train-rmse:0.996937+0.018598 test-rmse:1.266249+0.092504
## [48] train-rmse:0.989194+0.017572 test-rmse:1.263433+0.090876
## [49] train-rmse:0.982152+0.017390 test-rmse:1.259843+0.090624
## [50] train-rmse:0.972621+0.017961 test-rmse:1.256629+0.090206
## [51] train-rmse:0.966216+0.018068 test-rmse:1.256220+0.090147
## [52] train-rmse:0.957188+0.018157 test-rmse:1.246141+0.089801
## [53] train-rmse:0.949513+0.015841 test-rmse:1.234537+0.096070
## [54] train-rmse:0.942143+0.016598 test-rmse:1.227220+0.093489
## [55] train-rmse:0.935907+0.017283 test-rmse:1.223858+0.094287
## [56] train-rmse:0.928446+0.019061 test-rmse:1.223123+0.096749
## [57] train-rmse:0.922509+0.019237 test-rmse:1.220498+0.095185
## [58] train-rmse:0.915015+0.019410 test-rmse:1.215392+0.095153
## [59] train-rmse:0.908827+0.019759 test-rmse:1.210263+0.095297
## [60] train-rmse:0.902342+0.020710 test-rmse:1.205757+0.098360
## [61] train-rmse:0.896036+0.021692 test-rmse:1.203157+0.097884
## [62] train-rmse:0.889180+0.021994 test-rmse:1.197058+0.096313
## [63] train-rmse:0.884166+0.021260 test-rmse:1.193614+0.096038
## [64] train-rmse:0.877260+0.020542 test-rmse:1.189816+0.094457
## [65] train-rmse:0.872307+0.021097 test-rmse:1.187519+0.094683
## [66] train-rmse:0.864070+0.018682 test-rmse:1.179374+0.095936
## [67] train-rmse:0.859261+0.019129 test-rmse:1.176551+0.094210
## [68] train-rmse:0.853584+0.020492 test-rmse:1.176108+0.093833
## [69] train-rmse:0.849057+0.020688 test-rmse:1.173560+0.093832
## [70] train-rmse:0.843174+0.022590 test-rmse:1.170489+0.092917
## [71] train-rmse:0.839214+0.022552 test-rmse:1.167563+0.094292
## [72] train-rmse:0.834597+0.022034 test-rmse:1.166843+0.094047
## [73] train-rmse:0.828845+0.020250 test-rmse:1.164318+0.093714
## [74] train-rmse:0.823668+0.020986 test-rmse:1.159696+0.092331
## [75] train-rmse:0.818527+0.020742 test-rmse:1.157636+0.092531
## [76] train-rmse:0.813715+0.021268 test-rmse:1.153134+0.096548
## [77] train-rmse:0.808957+0.021816 test-rmse:1.151882+0.096589
## [78] train-rmse:0.805192+0.022100 test-rmse:1.148687+0.097722
## [79] train-rmse:0.801212+0.021903 test-rmse:1.146091+0.096716
## [80] train-rmse:0.794078+0.022364 test-rmse:1.144308+0.096455
## [81] train-rmse:0.787042+0.018118 test-rmse:1.135093+0.101277
## [82] train-rmse:0.782876+0.016293 test-rmse:1.132883+0.101131
## [83] train-rmse:0.779106+0.016208 test-rmse:1.131344+0.100088
## [84] train-rmse:0.775017+0.017418 test-rmse:1.129574+0.100854
## [85] train-rmse:0.771951+0.017745 test-rmse:1.128280+0.101523
## [86] train-rmse:0.767290+0.015134 test-rmse:1.126649+0.103225
## [87] train-rmse:0.762799+0.015150 test-rmse:1.124387+0.103723
## [88] train-rmse:0.757049+0.015132 test-rmse:1.118692+0.102369
## [89] train-rmse:0.752002+0.014698 test-rmse:1.116787+0.104137
## [90] train-rmse:0.748524+0.013813 test-rmse:1.117193+0.104162
## [91] train-rmse:0.745379+0.013725 test-rmse:1.116502+0.103009
## [92] train-rmse:0.742159+0.014425 test-rmse:1.113599+0.102204
## [93] train-rmse:0.737806+0.013075 test-rmse:1.108573+0.102024
## [94] train-rmse:0.733652+0.013738 test-rmse:1.104179+0.101479
## [95] train-rmse:0.730455+0.013350 test-rmse:1.104047+0.102693
## [96] train-rmse:0.727587+0.013552 test-rmse:1.104315+0.102198
## [97] train-rmse:0.723227+0.015302 test-rmse:1.103167+0.101314
## [98] train-rmse:0.719937+0.015402 test-rmse:1.100155+0.099417
## [99] train-rmse:0.713063+0.016708 test-rmse:1.091626+0.089074
## [100] train-rmse:0.709530+0.016101 test-rmse:1.089437+0.090419
## [101] train-rmse:0.706871+0.015932 test-rmse:1.087330+0.090883
## [102] train-rmse:0.703599+0.016800 test-rmse:1.085358+0.088092
## [103] train-rmse:0.698929+0.016660 test-rmse:1.084593+0.089574
## [104] train-rmse:0.696682+0.016910 test-rmse:1.083222+0.089153
## [105] train-rmse:0.693504+0.015047 test-rmse:1.077591+0.090566
## [106] train-rmse:0.690006+0.016596 test-rmse:1.072631+0.087881
## [107] train-rmse:0.687816+0.016641 test-rmse:1.072990+0.086302
## [108] train-rmse:0.685640+0.016699 test-rmse:1.072210+0.087185
## [109] train-rmse:0.682460+0.015115 test-rmse:1.072933+0.085466
## [110] train-rmse:0.679296+0.014413 test-rmse:1.072693+0.085933
## [111] train-rmse:0.675408+0.013611 test-rmse:1.071585+0.088803
## [112] train-rmse:0.672365+0.013646 test-rmse:1.070091+0.087568
## [113] train-rmse:0.667613+0.014280 test-rmse:1.069036+0.087426
## [114] train-rmse:0.664465+0.014250 test-rmse:1.067411+0.087780
## [115] train-rmse:0.661848+0.014439 test-rmse:1.064312+0.086796
## [116] train-rmse:0.657709+0.013539 test-rmse:1.060548+0.088035
## [117] train-rmse:0.655543+0.013282 test-rmse:1.057744+0.088715
## [118] train-rmse:0.653771+0.013329 test-rmse:1.058602+0.089682
## [119] train-rmse:0.650193+0.014267 test-rmse:1.062999+0.091758
## [120] train-rmse:0.648226+0.014725 test-rmse:1.061926+0.091221
## [121] train-rmse:0.642698+0.017108 test-rmse:1.059256+0.089560
## [122] train-rmse:0.640161+0.017777 test-rmse:1.056778+0.089168
## [123] train-rmse:0.638213+0.017531 test-rmse:1.056093+0.089084
## [124] train-rmse:0.635337+0.016807 test-rmse:1.054863+0.089961
## [125] train-rmse:0.632852+0.016136 test-rmse:1.054831+0.089910
## [126] train-rmse:0.631123+0.016340 test-rmse:1.053751+0.089076
## [127] train-rmse:0.627985+0.016394 test-rmse:1.051657+0.088836
## [128] train-rmse:0.625340+0.015642 test-rmse:1.050482+0.089529
## [129] train-rmse:0.623835+0.015649 test-rmse:1.049490+0.089500
## [130] train-rmse:0.621711+0.014860 test-rmse:1.047933+0.091170
## [131] train-rmse:0.619775+0.015067 test-rmse:1.046553+0.092697
## [132] train-rmse:0.615901+0.014073 test-rmse:1.047324+0.092829
## [133] train-rmse:0.613880+0.014287 test-rmse:1.045835+0.092345
## [134] train-rmse:0.611034+0.013653 test-rmse:1.044585+0.091834
## [135] train-rmse:0.609733+0.013725 test-rmse:1.043729+0.092818
## [136] train-rmse:0.606223+0.011170 test-rmse:1.040947+0.093724
## [137] train-rmse:0.603870+0.011657 test-rmse:1.042750+0.092742
## [138] train-rmse:0.601869+0.011818 test-rmse:1.043076+0.092837
## [139] train-rmse:0.598776+0.012939 test-rmse:1.041587+0.093104
## [140] train-rmse:0.596919+0.012782 test-rmse:1.040523+0.091330
## [141] train-rmse:0.594052+0.010792 test-rmse:1.038798+0.092336
## [142] train-rmse:0.592734+0.010923 test-rmse:1.038845+0.091786
## [143] train-rmse:0.590290+0.010252 test-rmse:1.039046+0.090711
## [144] train-rmse:0.587609+0.010154 test-rmse:1.037316+0.089203
## [145] train-rmse:0.585484+0.009449 test-rmse:1.037423+0.088909
## [146] train-rmse:0.583074+0.009568 test-rmse:1.037229+0.089182
## [147] train-rmse:0.580326+0.009704 test-rmse:1.036380+0.089613
## [148] train-rmse:0.578721+0.009562 test-rmse:1.035430+0.090481
## [149] train-rmse:0.576229+0.010408 test-rmse:1.034338+0.089537
## [150] train-rmse:0.574195+0.011202 test-rmse:1.034731+0.088751
## [151] train-rmse:0.572810+0.011059 test-rmse:1.034228+0.088703
## [152] train-rmse:0.571063+0.010220 test-rmse:1.032443+0.087950
## [153] train-rmse:0.568610+0.010645 test-rmse:1.033172+0.089072
## [154] train-rmse:0.566834+0.011404 test-rmse:1.032452+0.090796
## [155] train-rmse:0.563896+0.009996 test-rmse:1.029946+0.091229
## [156] train-rmse:0.561340+0.009279 test-rmse:1.028866+0.091801
## [157] train-rmse:0.559251+0.009060 test-rmse:1.028490+0.091813
## [158] train-rmse:0.557661+0.009060 test-rmse:1.028792+0.091168
## [159] train-rmse:0.556100+0.009419 test-rmse:1.029621+0.090733
## [160] train-rmse:0.554368+0.009950 test-rmse:1.032364+0.090026
## [161] train-rmse:0.551471+0.008411 test-rmse:1.030291+0.090852
## [162] train-rmse:0.548909+0.007078 test-rmse:1.029249+0.090646
## [163] train-rmse:0.547088+0.006991 test-rmse:1.028320+0.090155
## [164] train-rmse:0.543124+0.007231 test-rmse:1.029504+0.090505
## [165] train-rmse:0.540725+0.007683 test-rmse:1.032572+0.092704
## [166] train-rmse:0.539001+0.008547 test-rmse:1.030379+0.091425
## [167] train-rmse:0.537372+0.007706 test-rmse:1.030065+0.091930
## [168] train-rmse:0.535764+0.008010 test-rmse:1.029772+0.093572
## [169] train-rmse:0.533904+0.008226 test-rmse:1.029161+0.093272
## Stopping. Best iteration:
## [163] train-rmse:0.547088+0.006991 test-rmse:1.028320+0.090155
##
## 44
## [1] train-rmse:76.896455+0.077079 test-rmse:76.897090+0.416550
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:60.052252+0.059983 test-rmse:60.052622+0.432945
## [3] train-rmse:46.904609+0.046553 test-rmse:46.904639+0.445440
## [4] train-rmse:36.644199+0.035970 test-rmse:36.643796+0.454772
## [5] train-rmse:28.639430+0.027617 test-rmse:28.638477+0.461462
## [6] train-rmse:22.397527+0.021009 test-rmse:22.395899+0.465834
## [7] train-rmse:17.534218+0.015835 test-rmse:17.531762+0.468024
## [8] train-rmse:13.749436+0.012173 test-rmse:13.738001+0.462524
## [9] train-rmse:10.804913+0.008776 test-rmse:10.788056+0.441575
## [10] train-rmse:8.514516+0.007377 test-rmse:8.495779+0.409644
## [11] train-rmse:6.732652+0.006112 test-rmse:6.720037+0.393282
## [12] train-rmse:5.350131+0.006080 test-rmse:5.353168+0.361359
## [13] train-rmse:4.278890+0.007523 test-rmse:4.281658+0.332328
## [14] train-rmse:3.451165+0.008825 test-rmse:3.463964+0.310787
## [15] train-rmse:2.817305+0.008576 test-rmse:2.858522+0.286480
## [16] train-rmse:2.340357+0.011068 test-rmse:2.398580+0.265362
## [17] train-rmse:1.984555+0.012848 test-rmse:2.068355+0.231438
## [18] train-rmse:1.717021+0.011798 test-rmse:1.822433+0.204692
## [19] train-rmse:1.520393+0.009900 test-rmse:1.650863+0.179030
## [20] train-rmse:1.384575+0.009172 test-rmse:1.539478+0.157507
## [21] train-rmse:1.285569+0.010894 test-rmse:1.463729+0.147479
## [22] train-rmse:1.214097+0.011009 test-rmse:1.408845+0.131961
## [23] train-rmse:1.160926+0.011252 test-rmse:1.370460+0.119535
## [24] train-rmse:1.115528+0.010843 test-rmse:1.345494+0.108228
## [25] train-rmse:1.080657+0.010818 test-rmse:1.322740+0.104876
## [26] train-rmse:1.048981+0.012306 test-rmse:1.313233+0.098370
## [27] train-rmse:1.024552+0.010007 test-rmse:1.302197+0.096595
## [28] train-rmse:1.004104+0.008001 test-rmse:1.283109+0.097688
## [29] train-rmse:0.985585+0.007460 test-rmse:1.269511+0.095349
## [30] train-rmse:0.961861+0.014339 test-rmse:1.258746+0.087929
## [31] train-rmse:0.945407+0.012567 test-rmse:1.251799+0.090061
## [32] train-rmse:0.929225+0.013912 test-rmse:1.246444+0.085335
## [33] train-rmse:0.913197+0.015773 test-rmse:1.235742+0.090946
## [34] train-rmse:0.899525+0.015994 test-rmse:1.229403+0.092043
## [35] train-rmse:0.883919+0.014369 test-rmse:1.211965+0.088235
## [36] train-rmse:0.872830+0.013551 test-rmse:1.205876+0.087553
## [37] train-rmse:0.859941+0.015584 test-rmse:1.199719+0.084791
## [38] train-rmse:0.845719+0.017527 test-rmse:1.186335+0.087472
## [39] train-rmse:0.834169+0.016203 test-rmse:1.178189+0.085238
## [40] train-rmse:0.823019+0.014560 test-rmse:1.173485+0.092269
## [41] train-rmse:0.814117+0.015156 test-rmse:1.171622+0.093019
## [42] train-rmse:0.802839+0.018008 test-rmse:1.167327+0.092011
## [43] train-rmse:0.791521+0.016419 test-rmse:1.155335+0.093074
## [44] train-rmse:0.780238+0.016843 test-rmse:1.148096+0.090400
## [45] train-rmse:0.770725+0.016979 test-rmse:1.144067+0.090471
## [46] train-rmse:0.759857+0.017639 test-rmse:1.136808+0.090478
## [47] train-rmse:0.750288+0.017930 test-rmse:1.133707+0.090612
## [48] train-rmse:0.741910+0.017844 test-rmse:1.129774+0.094729
## [49] train-rmse:0.736055+0.018204 test-rmse:1.124353+0.091460
## [50] train-rmse:0.726695+0.018814 test-rmse:1.118105+0.088369
## [51] train-rmse:0.717365+0.018570 test-rmse:1.117124+0.088737
## [52] train-rmse:0.709800+0.017994 test-rmse:1.114133+0.090819
## [53] train-rmse:0.704279+0.018813 test-rmse:1.110681+0.092681
## [54] train-rmse:0.695890+0.019598 test-rmse:1.102515+0.092337
## [55] train-rmse:0.685553+0.021500 test-rmse:1.100258+0.090886
## [56] train-rmse:0.680274+0.021695 test-rmse:1.097814+0.092457
## [57] train-rmse:0.673355+0.021282 test-rmse:1.097774+0.094626
## [58] train-rmse:0.664443+0.018933 test-rmse:1.094078+0.096556
## [59] train-rmse:0.657648+0.018154 test-rmse:1.086220+0.089989
## [60] train-rmse:0.651063+0.017770 test-rmse:1.083454+0.090565
## [61] train-rmse:0.642273+0.019044 test-rmse:1.080145+0.094421
## [62] train-rmse:0.636635+0.017978 test-rmse:1.078573+0.094583
## [63] train-rmse:0.627988+0.018160 test-rmse:1.079778+0.095388
## [64] train-rmse:0.622104+0.017651 test-rmse:1.079297+0.099473
## [65] train-rmse:0.614479+0.017314 test-rmse:1.076441+0.099587
## [66] train-rmse:0.609085+0.017919 test-rmse:1.071497+0.102454
## [67] train-rmse:0.602700+0.020186 test-rmse:1.067667+0.102761
## [68] train-rmse:0.597315+0.017743 test-rmse:1.066488+0.104377
## [69] train-rmse:0.591682+0.016421 test-rmse:1.065306+0.104373
## [70] train-rmse:0.587739+0.016249 test-rmse:1.064965+0.103779
## [71] train-rmse:0.583047+0.017136 test-rmse:1.064249+0.103512
## [72] train-rmse:0.577487+0.016545 test-rmse:1.056367+0.101067
## [73] train-rmse:0.571512+0.015167 test-rmse:1.055310+0.102186
## [74] train-rmse:0.563575+0.012955 test-rmse:1.056568+0.104298
## [75] train-rmse:0.559035+0.012377 test-rmse:1.053285+0.102426
## [76] train-rmse:0.554174+0.011469 test-rmse:1.053005+0.104055
## [77] train-rmse:0.545658+0.012498 test-rmse:1.051295+0.103000
## [78] train-rmse:0.539877+0.011441 test-rmse:1.052898+0.104838
## [79] train-rmse:0.534587+0.011791 test-rmse:1.051485+0.106612
## [80] train-rmse:0.529919+0.013198 test-rmse:1.050027+0.105577
## [81] train-rmse:0.526060+0.013247 test-rmse:1.050025+0.106252
## [82] train-rmse:0.519860+0.013324 test-rmse:1.045635+0.105193
## [83] train-rmse:0.515184+0.014287 test-rmse:1.045348+0.106084
## [84] train-rmse:0.512031+0.013271 test-rmse:1.045132+0.103501
## [85] train-rmse:0.508000+0.012520 test-rmse:1.044497+0.102141
## [86] train-rmse:0.503848+0.012574 test-rmse:1.042985+0.103050
## [87] train-rmse:0.499630+0.013462 test-rmse:1.041206+0.102799
## [88] train-rmse:0.495822+0.014648 test-rmse:1.039335+0.102135
## [89] train-rmse:0.491741+0.014684 test-rmse:1.036502+0.099069
## [90] train-rmse:0.488318+0.014320 test-rmse:1.036790+0.099291
## [91] train-rmse:0.483515+0.015379 test-rmse:1.034501+0.096813
## [92] train-rmse:0.480051+0.015118 test-rmse:1.031793+0.098066
## [93] train-rmse:0.476184+0.015397 test-rmse:1.031041+0.098920
## [94] train-rmse:0.472716+0.014440 test-rmse:1.029433+0.098918
## [95] train-rmse:0.468632+0.014048 test-rmse:1.028923+0.098868
## [96] train-rmse:0.464909+0.014470 test-rmse:1.025526+0.095981
## [97] train-rmse:0.460892+0.015563 test-rmse:1.023737+0.095976
## [98] train-rmse:0.457871+0.015330 test-rmse:1.023260+0.096876
## [99] train-rmse:0.454651+0.016276 test-rmse:1.022760+0.098054
## [100] train-rmse:0.451508+0.016588 test-rmse:1.022282+0.098841
## [101] train-rmse:0.448484+0.015601 test-rmse:1.018862+0.098478
## [102] train-rmse:0.444585+0.016906 test-rmse:1.015842+0.095251
## [103] train-rmse:0.440460+0.017257 test-rmse:1.017131+0.096330
## [104] train-rmse:0.437277+0.017136 test-rmse:1.016974+0.096172
## [105] train-rmse:0.434606+0.016696 test-rmse:1.015970+0.095422
## [106] train-rmse:0.429794+0.016872 test-rmse:1.012591+0.093320
## [107] train-rmse:0.425370+0.015742 test-rmse:1.010576+0.091340
## [108] train-rmse:0.421343+0.015341 test-rmse:1.010780+0.091248
## [109] train-rmse:0.416032+0.012165 test-rmse:1.009591+0.093372
## [110] train-rmse:0.413010+0.012566 test-rmse:1.008605+0.093290
## [111] train-rmse:0.410550+0.013272 test-rmse:1.006949+0.093100
## [112] train-rmse:0.408196+0.012927 test-rmse:1.006313+0.092908
## [113] train-rmse:0.406221+0.012848 test-rmse:1.004275+0.092456
## [114] train-rmse:0.402689+0.011066 test-rmse:1.004839+0.091282
## [115] train-rmse:0.399618+0.012073 test-rmse:1.006084+0.092507
## [116] train-rmse:0.395948+0.012454 test-rmse:1.004998+0.091763
## [117] train-rmse:0.390914+0.014410 test-rmse:1.003970+0.090616
## [118] train-rmse:0.388319+0.014925 test-rmse:1.003628+0.091972
## [119] train-rmse:0.385366+0.015021 test-rmse:1.002865+0.092486
## [120] train-rmse:0.381859+0.014830 test-rmse:1.003035+0.092209
## [121] train-rmse:0.378298+0.015446 test-rmse:1.001452+0.091377
## [122] train-rmse:0.375497+0.015136 test-rmse:1.002883+0.092934
## [123] train-rmse:0.371726+0.016492 test-rmse:1.002086+0.093302
## [124] train-rmse:0.369498+0.017401 test-rmse:1.002761+0.092283
## [125] train-rmse:0.366043+0.015904 test-rmse:0.999224+0.091978
## [126] train-rmse:0.362373+0.016543 test-rmse:0.998010+0.090897
## [127] train-rmse:0.359646+0.017242 test-rmse:0.996402+0.090413
## [128] train-rmse:0.357287+0.017537 test-rmse:0.996525+0.090402
## [129] train-rmse:0.355452+0.017295 test-rmse:0.995629+0.090203
## [130] train-rmse:0.352366+0.017506 test-rmse:0.991565+0.090834
## [131] train-rmse:0.349709+0.017209 test-rmse:0.991739+0.090199
## [132] train-rmse:0.346578+0.016114 test-rmse:0.990620+0.091296
## [133] train-rmse:0.344203+0.016052 test-rmse:0.990552+0.091451
## [134] train-rmse:0.340040+0.013450 test-rmse:0.990195+0.091351
## [135] train-rmse:0.337647+0.013299 test-rmse:0.988719+0.091894
## [136] train-rmse:0.334008+0.013630 test-rmse:0.988165+0.091113
## [137] train-rmse:0.332445+0.014094 test-rmse:0.988122+0.091270
## [138] train-rmse:0.331046+0.014334 test-rmse:0.986904+0.091175
## [139] train-rmse:0.329454+0.014542 test-rmse:0.987497+0.091168
## [140] train-rmse:0.327337+0.015164 test-rmse:0.986269+0.090797
## [141] train-rmse:0.324819+0.014269 test-rmse:0.985753+0.091661
## [142] train-rmse:0.321889+0.013862 test-rmse:0.986023+0.091540
## [143] train-rmse:0.319885+0.014529 test-rmse:0.987203+0.092824
## [144] train-rmse:0.317740+0.013773 test-rmse:0.986606+0.093402
## [145] train-rmse:0.314718+0.012936 test-rmse:0.984602+0.093296
## [146] train-rmse:0.312057+0.013076 test-rmse:0.984883+0.093186
## [147] train-rmse:0.310686+0.012587 test-rmse:0.985228+0.092306
## [148] train-rmse:0.308727+0.012446 test-rmse:0.986279+0.094142
## [149] train-rmse:0.305869+0.011276 test-rmse:0.985556+0.093159
## [150] train-rmse:0.302646+0.011353 test-rmse:0.985383+0.093360
## [151] train-rmse:0.300529+0.011774 test-rmse:0.984960+0.094652
## Stopping. Best iteration:
## [145] train-rmse:0.314718+0.012936 test-rmse:0.984602+0.093296
##
## 45
## [1] train-rmse:76.896455+0.077079 test-rmse:76.897090+0.416550
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:60.052252+0.059983 test-rmse:60.052622+0.432945
## [3] train-rmse:46.904609+0.046553 test-rmse:46.904639+0.445440
## [4] train-rmse:36.644199+0.035970 test-rmse:36.643796+0.454772
## [5] train-rmse:28.639430+0.027617 test-rmse:28.638477+0.461462
## [6] train-rmse:22.397527+0.021009 test-rmse:22.395899+0.465834
## [7] train-rmse:17.534218+0.015835 test-rmse:17.531762+0.468024
## [8] train-rmse:13.749436+0.012173 test-rmse:13.738001+0.462524
## [9] train-rmse:10.804913+0.008776 test-rmse:10.788056+0.441575
## [10] train-rmse:8.514278+0.007378 test-rmse:8.492002+0.409061
## [11] train-rmse:6.731914+0.006375 test-rmse:6.720350+0.384020
## [12] train-rmse:5.345361+0.007477 test-rmse:5.341201+0.365062
## [13] train-rmse:4.270096+0.008152 test-rmse:4.270712+0.338442
## [14] train-rmse:3.435314+0.009720 test-rmse:3.440555+0.312358
## [15] train-rmse:2.789173+0.009758 test-rmse:2.818763+0.271837
## [16] train-rmse:2.297521+0.010113 test-rmse:2.350539+0.238722
## [17] train-rmse:1.924136+0.014636 test-rmse:2.005824+0.220531
## [18] train-rmse:1.643423+0.014540 test-rmse:1.763966+0.196018
## [19] train-rmse:1.437299+0.012528 test-rmse:1.595382+0.170247
## [20] train-rmse:1.284594+0.013694 test-rmse:1.481403+0.147742
## [21] train-rmse:1.170286+0.016003 test-rmse:1.402556+0.140265
## [22] train-rmse:1.080363+0.016527 test-rmse:1.338482+0.124748
## [23] train-rmse:1.021300+0.017998 test-rmse:1.303308+0.114008
## [24] train-rmse:0.973583+0.015964 test-rmse:1.268169+0.104348
## [25] train-rmse:0.930666+0.014741 test-rmse:1.240754+0.104498
## [26] train-rmse:0.899980+0.013149 test-rmse:1.217586+0.106313
## [27] train-rmse:0.866860+0.011676 test-rmse:1.203414+0.101186
## [28] train-rmse:0.841494+0.015935 test-rmse:1.186392+0.105321
## [29] train-rmse:0.819782+0.016713 test-rmse:1.177443+0.101627
## [30] train-rmse:0.801431+0.020139 test-rmse:1.166370+0.091598
## [31] train-rmse:0.782234+0.020006 test-rmse:1.151170+0.081672
## [32] train-rmse:0.762138+0.019307 test-rmse:1.137594+0.079998
## [33] train-rmse:0.746536+0.015702 test-rmse:1.124938+0.084391
## [34] train-rmse:0.731106+0.016834 test-rmse:1.118932+0.086046
## [35] train-rmse:0.717340+0.016103 test-rmse:1.103787+0.086588
## [36] train-rmse:0.700374+0.016797 test-rmse:1.096787+0.085156
## [37] train-rmse:0.688341+0.017903 test-rmse:1.088022+0.085525
## [38] train-rmse:0.675938+0.020645 test-rmse:1.082677+0.085734
## [39] train-rmse:0.662479+0.019195 test-rmse:1.076324+0.079780
## [40] train-rmse:0.647993+0.022094 test-rmse:1.067738+0.076889
## [41] train-rmse:0.636731+0.021809 test-rmse:1.064396+0.076669
## [42] train-rmse:0.624063+0.020839 test-rmse:1.057954+0.076453
## [43] train-rmse:0.613446+0.019250 test-rmse:1.049621+0.074441
## [44] train-rmse:0.605716+0.018654 test-rmse:1.044381+0.076068
## [45] train-rmse:0.597946+0.017411 test-rmse:1.038981+0.077130
## [46] train-rmse:0.585755+0.016739 test-rmse:1.038243+0.076573
## [47] train-rmse:0.572304+0.016458 test-rmse:1.033957+0.076419
## [48] train-rmse:0.561702+0.018218 test-rmse:1.028592+0.076318
## [49] train-rmse:0.553124+0.016742 test-rmse:1.024943+0.074508
## [50] train-rmse:0.546002+0.015719 test-rmse:1.021003+0.074288
## [51] train-rmse:0.536868+0.015813 test-rmse:1.012464+0.070353
## [52] train-rmse:0.528657+0.018558 test-rmse:1.008884+0.069663
## [53] train-rmse:0.522474+0.018166 test-rmse:1.005174+0.070170
## [54] train-rmse:0.514151+0.015973 test-rmse:1.004076+0.070942
## [55] train-rmse:0.504746+0.014955 test-rmse:1.002369+0.071813
## [56] train-rmse:0.496530+0.015052 test-rmse:0.999825+0.070486
## [57] train-rmse:0.489550+0.015568 test-rmse:0.997062+0.070925
## [58] train-rmse:0.483942+0.017809 test-rmse:0.994392+0.071482
## [59] train-rmse:0.477666+0.016473 test-rmse:0.991795+0.070158
## [60] train-rmse:0.472004+0.015794 test-rmse:0.993499+0.068736
## [61] train-rmse:0.466873+0.016174 test-rmse:0.991179+0.068308
## [62] train-rmse:0.461102+0.013906 test-rmse:0.991136+0.066860
## [63] train-rmse:0.455466+0.013820 test-rmse:0.988842+0.065591
## [64] train-rmse:0.448760+0.014135 test-rmse:0.986096+0.069685
## [65] train-rmse:0.443918+0.014137 test-rmse:0.986692+0.070962
## [66] train-rmse:0.438637+0.015109 test-rmse:0.986219+0.070290
## [67] train-rmse:0.430859+0.015290 test-rmse:0.979229+0.068750
## [68] train-rmse:0.423923+0.015653 test-rmse:0.979762+0.068806
## [69] train-rmse:0.420296+0.016261 test-rmse:0.978848+0.068849
## [70] train-rmse:0.413799+0.014298 test-rmse:0.977120+0.068681
## [71] train-rmse:0.409051+0.013438 test-rmse:0.978973+0.067883
## [72] train-rmse:0.402774+0.013482 test-rmse:0.981598+0.069911
## [73] train-rmse:0.396791+0.014957 test-rmse:0.980034+0.070229
## [74] train-rmse:0.389706+0.011695 test-rmse:0.976389+0.070573
## [75] train-rmse:0.384215+0.012451 test-rmse:0.972010+0.069560
## [76] train-rmse:0.379144+0.012163 test-rmse:0.971506+0.069121
## [77] train-rmse:0.371723+0.011537 test-rmse:0.970947+0.067950
## [78] train-rmse:0.366163+0.011890 test-rmse:0.973150+0.069395
## [79] train-rmse:0.362009+0.012401 test-rmse:0.970970+0.068665
## [80] train-rmse:0.354856+0.012637 test-rmse:0.970979+0.070165
## [81] train-rmse:0.350655+0.010419 test-rmse:0.970932+0.068524
## [82] train-rmse:0.346362+0.012186 test-rmse:0.970355+0.067827
## [83] train-rmse:0.341391+0.012523 test-rmse:0.969781+0.067230
## [84] train-rmse:0.336248+0.011188 test-rmse:0.969116+0.067319
## [85] train-rmse:0.332853+0.010152 test-rmse:0.967021+0.066733
## [86] train-rmse:0.328323+0.010607 test-rmse:0.965546+0.064757
## [87] train-rmse:0.323994+0.011918 test-rmse:0.965026+0.063635
## [88] train-rmse:0.318588+0.012172 test-rmse:0.963986+0.064184
## [89] train-rmse:0.312546+0.009993 test-rmse:0.963605+0.064222
## [90] train-rmse:0.309202+0.010244 test-rmse:0.962101+0.064876
## [91] train-rmse:0.305930+0.010065 test-rmse:0.960913+0.065985
## [92] train-rmse:0.300645+0.011572 test-rmse:0.960536+0.066135
## [93] train-rmse:0.297723+0.011326 test-rmse:0.961526+0.065818
## [94] train-rmse:0.293980+0.013252 test-rmse:0.962741+0.066644
## [95] train-rmse:0.290109+0.013057 test-rmse:0.963735+0.066398
## [96] train-rmse:0.286020+0.013693 test-rmse:0.961482+0.065266
## [97] train-rmse:0.283428+0.013536 test-rmse:0.959933+0.067711
## [98] train-rmse:0.279723+0.013083 test-rmse:0.959001+0.068237
## [99] train-rmse:0.276569+0.013769 test-rmse:0.958432+0.068417
## [100] train-rmse:0.273926+0.014233 test-rmse:0.958636+0.068465
## [101] train-rmse:0.268185+0.014310 test-rmse:0.956616+0.068110
## [102] train-rmse:0.265732+0.014538 test-rmse:0.954134+0.066605
## [103] train-rmse:0.262536+0.014215 test-rmse:0.953752+0.066330
## [104] train-rmse:0.259713+0.013857 test-rmse:0.952544+0.065333
## [105] train-rmse:0.256888+0.012764 test-rmse:0.952384+0.065362
## [106] train-rmse:0.254026+0.012361 test-rmse:0.950627+0.064891
## [107] train-rmse:0.251622+0.013089 test-rmse:0.949601+0.065538
## [108] train-rmse:0.249033+0.012959 test-rmse:0.950387+0.066516
## [109] train-rmse:0.246831+0.013329 test-rmse:0.949529+0.065797
## [110] train-rmse:0.243737+0.012523 test-rmse:0.949893+0.065115
## [111] train-rmse:0.240240+0.012810 test-rmse:0.948097+0.066665
## [112] train-rmse:0.238206+0.013331 test-rmse:0.946612+0.066393
## [113] train-rmse:0.235768+0.013350 test-rmse:0.945656+0.065832
## [114] train-rmse:0.234062+0.013619 test-rmse:0.945523+0.066522
## [115] train-rmse:0.231930+0.013614 test-rmse:0.945569+0.065579
## [116] train-rmse:0.228971+0.014837 test-rmse:0.945598+0.065704
## [117] train-rmse:0.226087+0.014535 test-rmse:0.945476+0.065568
## [118] train-rmse:0.222643+0.014809 test-rmse:0.945132+0.066184
## [119] train-rmse:0.220248+0.014906 test-rmse:0.944603+0.065636
## [120] train-rmse:0.218682+0.015046 test-rmse:0.944121+0.065991
## [121] train-rmse:0.216444+0.014282 test-rmse:0.944315+0.065497
## [122] train-rmse:0.213932+0.015759 test-rmse:0.942989+0.064483
## [123] train-rmse:0.209878+0.014921 test-rmse:0.943317+0.063528
## [124] train-rmse:0.206145+0.013678 test-rmse:0.943102+0.063884
## [125] train-rmse:0.204831+0.013940 test-rmse:0.943161+0.063442
## [126] train-rmse:0.202250+0.013483 test-rmse:0.942587+0.063057
## [127] train-rmse:0.199302+0.014123 test-rmse:0.942184+0.063609
## [128] train-rmse:0.196941+0.014554 test-rmse:0.942469+0.064308
## [129] train-rmse:0.194685+0.013781 test-rmse:0.942019+0.064927
## [130] train-rmse:0.192711+0.014518 test-rmse:0.941799+0.064093
## [131] train-rmse:0.190860+0.014438 test-rmse:0.942261+0.064200
## [132] train-rmse:0.189381+0.014899 test-rmse:0.942452+0.064292
## [133] train-rmse:0.187526+0.014701 test-rmse:0.942915+0.064732
## [134] train-rmse:0.184806+0.014484 test-rmse:0.941802+0.064534
## [135] train-rmse:0.183119+0.015103 test-rmse:0.941607+0.064851
## [136] train-rmse:0.181509+0.015158 test-rmse:0.940549+0.064673
## [137] train-rmse:0.179893+0.014762 test-rmse:0.940337+0.064671
## [138] train-rmse:0.177906+0.015126 test-rmse:0.940682+0.064717
## [139] train-rmse:0.176074+0.014844 test-rmse:0.940465+0.064006
## [140] train-rmse:0.173902+0.013630 test-rmse:0.939629+0.063794
## [141] train-rmse:0.171047+0.013046 test-rmse:0.939242+0.063680
## [142] train-rmse:0.168340+0.012686 test-rmse:0.939397+0.063792
## [143] train-rmse:0.167052+0.012514 test-rmse:0.940107+0.064788
## [144] train-rmse:0.164140+0.010973 test-rmse:0.939279+0.065749
## [145] train-rmse:0.161455+0.011058 test-rmse:0.938675+0.065488
## [146] train-rmse:0.159707+0.011381 test-rmse:0.937763+0.065326
## [147] train-rmse:0.158000+0.012105 test-rmse:0.937670+0.065446
## [148] train-rmse:0.156596+0.011574 test-rmse:0.937852+0.065211
## [149] train-rmse:0.155016+0.011471 test-rmse:0.938445+0.065144
## [150] train-rmse:0.153158+0.011517 test-rmse:0.938166+0.065025
## [151] train-rmse:0.151505+0.012034 test-rmse:0.938555+0.065697
## [152] train-rmse:0.149281+0.011552 test-rmse:0.938911+0.065927
## [153] train-rmse:0.147504+0.011047 test-rmse:0.938128+0.065256
## Stopping. Best iteration:
## [147] train-rmse:0.158000+0.012105 test-rmse:0.937670+0.065446
##
## 46
## [1] train-rmse:76.896455+0.077079 test-rmse:76.897090+0.416550
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:60.052252+0.059983 test-rmse:60.052622+0.432945
## [3] train-rmse:46.904609+0.046553 test-rmse:46.904639+0.445440
## [4] train-rmse:36.644199+0.035970 test-rmse:36.643796+0.454772
## [5] train-rmse:28.639430+0.027617 test-rmse:28.638477+0.461462
## [6] train-rmse:22.397527+0.021009 test-rmse:22.395899+0.465834
## [7] train-rmse:17.534218+0.015835 test-rmse:17.531762+0.468024
## [8] train-rmse:13.749436+0.012173 test-rmse:13.738001+0.462524
## [9] train-rmse:10.804913+0.008776 test-rmse:10.788056+0.441575
## [10] train-rmse:8.514278+0.007378 test-rmse:8.492002+0.409061
## [11] train-rmse:6.731825+0.006492 test-rmse:6.718663+0.382900
## [12] train-rmse:5.342437+0.008647 test-rmse:5.330328+0.353701
## [13] train-rmse:4.263768+0.006998 test-rmse:4.267382+0.326484
## [14] train-rmse:3.422952+0.008331 test-rmse:3.425732+0.310146
## [15] train-rmse:2.772603+0.012154 test-rmse:2.793116+0.273267
## [16] train-rmse:2.269429+0.010037 test-rmse:2.315988+0.254329
## [17] train-rmse:1.887198+0.015606 test-rmse:1.973568+0.218795
## [18] train-rmse:1.589601+0.013774 test-rmse:1.718819+0.192429
## [19] train-rmse:1.370235+0.014299 test-rmse:1.539014+0.171252
## [20] train-rmse:1.208974+0.016674 test-rmse:1.407789+0.152988
## [21] train-rmse:1.080774+0.011602 test-rmse:1.329509+0.144786
## [22] train-rmse:0.982299+0.015400 test-rmse:1.266628+0.136276
## [23] train-rmse:0.911736+0.019415 test-rmse:1.217775+0.133514
## [24] train-rmse:0.851812+0.021977 test-rmse:1.184027+0.123478
## [25] train-rmse:0.806365+0.020390 test-rmse:1.160304+0.122775
## [26] train-rmse:0.768690+0.024131 test-rmse:1.138447+0.116995
## [27] train-rmse:0.738330+0.023494 test-rmse:1.127567+0.117346
## [28] train-rmse:0.709931+0.020396 test-rmse:1.114765+0.118634
## [29] train-rmse:0.684629+0.024752 test-rmse:1.102121+0.114679
## [30] train-rmse:0.663191+0.025503 test-rmse:1.087151+0.115782
## [31] train-rmse:0.644227+0.026702 test-rmse:1.077250+0.116081
## [32] train-rmse:0.627956+0.026658 test-rmse:1.071187+0.118599
## [33] train-rmse:0.610023+0.028754 test-rmse:1.065057+0.116394
## [34] train-rmse:0.592878+0.028943 test-rmse:1.056763+0.116711
## [35] train-rmse:0.572465+0.022805 test-rmse:1.051548+0.111339
## [36] train-rmse:0.558030+0.021360 test-rmse:1.048526+0.114096
## [37] train-rmse:0.545338+0.021704 test-rmse:1.040584+0.107410
## [38] train-rmse:0.535392+0.021580 test-rmse:1.034892+0.112183
## [39] train-rmse:0.525626+0.022481 test-rmse:1.031908+0.111925
## [40] train-rmse:0.513639+0.019829 test-rmse:1.026384+0.111384
## [41] train-rmse:0.500233+0.016401 test-rmse:1.015962+0.105620
## [42] train-rmse:0.490277+0.017077 test-rmse:1.017993+0.104756
## [43] train-rmse:0.479145+0.019539 test-rmse:1.011353+0.104496
## [44] train-rmse:0.467592+0.019549 test-rmse:1.008933+0.109173
## [45] train-rmse:0.459178+0.017729 test-rmse:1.005957+0.109534
## [46] train-rmse:0.449985+0.014927 test-rmse:1.004217+0.113436
## [47] train-rmse:0.440332+0.012403 test-rmse:1.001005+0.113143
## [48] train-rmse:0.429117+0.010701 test-rmse:1.000334+0.114983
## [49] train-rmse:0.421790+0.010888 test-rmse:0.997538+0.112601
## [50] train-rmse:0.413478+0.010987 test-rmse:0.994951+0.112724
## [51] train-rmse:0.405232+0.011203 test-rmse:0.995839+0.111445
## [52] train-rmse:0.396702+0.008579 test-rmse:0.994128+0.112189
## [53] train-rmse:0.389082+0.009573 test-rmse:0.996502+0.113666
## [54] train-rmse:0.381799+0.010590 test-rmse:0.991831+0.112318
## [55] train-rmse:0.376520+0.011577 test-rmse:0.989461+0.112385
## [56] train-rmse:0.368826+0.013646 test-rmse:0.985138+0.110704
## [57] train-rmse:0.361875+0.013769 test-rmse:0.984325+0.109038
## [58] train-rmse:0.353536+0.012259 test-rmse:0.982581+0.107371
## [59] train-rmse:0.345166+0.011678 test-rmse:0.980330+0.110060
## [60] train-rmse:0.338215+0.009889 test-rmse:0.977952+0.109251
## [61] train-rmse:0.331283+0.009599 test-rmse:0.978264+0.108029
## [62] train-rmse:0.324821+0.009124 test-rmse:0.977620+0.109561
## [63] train-rmse:0.318340+0.010606 test-rmse:0.975708+0.109479
## [64] train-rmse:0.313464+0.011644 test-rmse:0.974588+0.109809
## [65] train-rmse:0.307852+0.011513 test-rmse:0.973166+0.108695
## [66] train-rmse:0.302282+0.009818 test-rmse:0.972363+0.109770
## [67] train-rmse:0.297285+0.009925 test-rmse:0.970819+0.110449
## [68] train-rmse:0.291619+0.009124 test-rmse:0.968165+0.110448
## [69] train-rmse:0.285454+0.007375 test-rmse:0.965104+0.106776
## [70] train-rmse:0.280360+0.006827 test-rmse:0.962406+0.108870
## [71] train-rmse:0.274962+0.007114 test-rmse:0.961860+0.108169
## [72] train-rmse:0.269216+0.008072 test-rmse:0.960261+0.105113
## [73] train-rmse:0.265677+0.007311 test-rmse:0.960168+0.106930
## [74] train-rmse:0.261130+0.006975 test-rmse:0.961473+0.107850
## [75] train-rmse:0.257102+0.007413 test-rmse:0.961589+0.108738
## [76] train-rmse:0.251556+0.005623 test-rmse:0.961053+0.109216
## [77] train-rmse:0.247628+0.006425 test-rmse:0.959816+0.109795
## [78] train-rmse:0.243899+0.006752 test-rmse:0.961454+0.111423
## [79] train-rmse:0.239423+0.008506 test-rmse:0.960375+0.111846
## [80] train-rmse:0.235622+0.009766 test-rmse:0.959713+0.111029
## [81] train-rmse:0.231505+0.008767 test-rmse:0.957350+0.109541
## [82] train-rmse:0.225414+0.009482 test-rmse:0.956885+0.109450
## [83] train-rmse:0.221646+0.008590 test-rmse:0.956435+0.109038
## [84] train-rmse:0.218046+0.007204 test-rmse:0.956377+0.109721
## [85] train-rmse:0.212916+0.008069 test-rmse:0.957906+0.110733
## [86] train-rmse:0.209502+0.008284 test-rmse:0.957996+0.111178
## [87] train-rmse:0.206834+0.007790 test-rmse:0.957944+0.111257
## [88] train-rmse:0.201828+0.009000 test-rmse:0.958549+0.111119
## [89] train-rmse:0.199213+0.009209 test-rmse:0.958341+0.110759
## [90] train-rmse:0.194913+0.009662 test-rmse:0.956409+0.109030
## Stopping. Best iteration:
## [84] train-rmse:0.218046+0.007204 test-rmse:0.956377+0.109721
##
## 47
## [1] train-rmse:76.896455+0.077079 test-rmse:76.897090+0.416550
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:60.052252+0.059983 test-rmse:60.052622+0.432945
## [3] train-rmse:46.904609+0.046553 test-rmse:46.904639+0.445440
## [4] train-rmse:36.644199+0.035970 test-rmse:36.643796+0.454772
## [5] train-rmse:28.639430+0.027617 test-rmse:28.638477+0.461462
## [6] train-rmse:22.397527+0.021009 test-rmse:22.395899+0.465834
## [7] train-rmse:17.534218+0.015835 test-rmse:17.531762+0.468024
## [8] train-rmse:13.749436+0.012173 test-rmse:13.738001+0.462524
## [9] train-rmse:10.804913+0.008776 test-rmse:10.788056+0.441575
## [10] train-rmse:8.514278+0.007378 test-rmse:8.492002+0.409061
## [11] train-rmse:6.731743+0.006603 test-rmse:6.716073+0.381233
## [12] train-rmse:5.340679+0.008389 test-rmse:5.321861+0.351220
## [13] train-rmse:4.260448+0.009109 test-rmse:4.251978+0.316453
## [14] train-rmse:3.413410+0.008295 test-rmse:3.418892+0.304478
## [15] train-rmse:2.755259+0.008858 test-rmse:2.790929+0.268782
## [16] train-rmse:2.246507+0.009694 test-rmse:2.317167+0.249859
## [17] train-rmse:1.853000+0.008344 test-rmse:1.952723+0.217413
## [18] train-rmse:1.547897+0.011987 test-rmse:1.700737+0.192923
## [19] train-rmse:1.317425+0.012376 test-rmse:1.513192+0.186246
## [20] train-rmse:1.137973+0.014568 test-rmse:1.379605+0.167934
## [21] train-rmse:1.003701+0.013908 test-rmse:1.297206+0.150412
## [22] train-rmse:0.904643+0.020242 test-rmse:1.229107+0.132901
## [23] train-rmse:0.823781+0.023489 test-rmse:1.185738+0.124618
## [24] train-rmse:0.760005+0.021356 test-rmse:1.150482+0.114071
## [25] train-rmse:0.712398+0.022440 test-rmse:1.126148+0.111416
## [26] train-rmse:0.670427+0.017503 test-rmse:1.101765+0.106193
## [27] train-rmse:0.637509+0.015839 test-rmse:1.083744+0.105135
## [28] train-rmse:0.604530+0.018737 test-rmse:1.074194+0.102970
## [29] train-rmse:0.578565+0.021037 test-rmse:1.065657+0.101977
## [30] train-rmse:0.558184+0.019883 test-rmse:1.049548+0.096530
## [31] train-rmse:0.533959+0.023842 test-rmse:1.042449+0.094846
## [32] train-rmse:0.513861+0.021520 test-rmse:1.037104+0.089961
## [33] train-rmse:0.491910+0.019414 test-rmse:1.032744+0.091968
## [34] train-rmse:0.478511+0.019688 test-rmse:1.024424+0.090275
## [35] train-rmse:0.458901+0.021097 test-rmse:1.017935+0.087071
## [36] train-rmse:0.445768+0.021117 test-rmse:1.015206+0.088419
## [37] train-rmse:0.431800+0.022547 test-rmse:1.005403+0.085772
## [38] train-rmse:0.420184+0.020920 test-rmse:1.002687+0.089094
## [39] train-rmse:0.407434+0.016779 test-rmse:0.995362+0.088582
## [40] train-rmse:0.395226+0.020082 test-rmse:0.993631+0.088677
## [41] train-rmse:0.384347+0.019172 test-rmse:0.989706+0.088093
## [42] train-rmse:0.374824+0.020148 test-rmse:0.987263+0.089054
## [43] train-rmse:0.364358+0.018931 test-rmse:0.981532+0.086939
## [44] train-rmse:0.353606+0.019962 test-rmse:0.979991+0.086891
## [45] train-rmse:0.346068+0.018766 test-rmse:0.980789+0.084875
## [46] train-rmse:0.336389+0.017500 test-rmse:0.980440+0.085234
## [47] train-rmse:0.327612+0.018739 test-rmse:0.979050+0.084938
## [48] train-rmse:0.319609+0.016161 test-rmse:0.977536+0.085444
## [49] train-rmse:0.311929+0.014293 test-rmse:0.973582+0.084443
## [50] train-rmse:0.304127+0.013036 test-rmse:0.972708+0.084173
## [51] train-rmse:0.296458+0.012006 test-rmse:0.969050+0.085039
## [52] train-rmse:0.288113+0.012593 test-rmse:0.965259+0.085343
## [53] train-rmse:0.281399+0.014710 test-rmse:0.965421+0.087605
## [54] train-rmse:0.275815+0.016137 test-rmse:0.965604+0.087130
## [55] train-rmse:0.270323+0.015152 test-rmse:0.965087+0.085108
## [56] train-rmse:0.261869+0.012150 test-rmse:0.963766+0.085622
## [57] train-rmse:0.255763+0.011413 test-rmse:0.963245+0.086735
## [58] train-rmse:0.249574+0.010768 test-rmse:0.962888+0.085440
## [59] train-rmse:0.243846+0.010921 test-rmse:0.961769+0.085622
## [60] train-rmse:0.238403+0.011756 test-rmse:0.961304+0.085122
## [61] train-rmse:0.231983+0.011942 test-rmse:0.959910+0.085878
## [62] train-rmse:0.225797+0.011532 test-rmse:0.957904+0.085329
## [63] train-rmse:0.220381+0.009582 test-rmse:0.957519+0.085157
## [64] train-rmse:0.216031+0.009590 test-rmse:0.956395+0.084411
## [65] train-rmse:0.211534+0.010122 test-rmse:0.955559+0.085062
## [66] train-rmse:0.207053+0.009385 test-rmse:0.956176+0.084064
## [67] train-rmse:0.202867+0.009660 test-rmse:0.956062+0.083508
## [68] train-rmse:0.198025+0.008943 test-rmse:0.954550+0.083197
## [69] train-rmse:0.194267+0.009020 test-rmse:0.954243+0.083275
## [70] train-rmse:0.188934+0.008016 test-rmse:0.953583+0.081531
## [71] train-rmse:0.184849+0.006797 test-rmse:0.953858+0.081031
## [72] train-rmse:0.180559+0.006297 test-rmse:0.952059+0.080352
## [73] train-rmse:0.174996+0.005649 test-rmse:0.952461+0.078785
## [74] train-rmse:0.168017+0.005670 test-rmse:0.953513+0.078079
## [75] train-rmse:0.164592+0.006744 test-rmse:0.952503+0.078038
## [76] train-rmse:0.161131+0.007155 test-rmse:0.951432+0.078451
## [77] train-rmse:0.158514+0.007335 test-rmse:0.950112+0.078247
## [78] train-rmse:0.153281+0.007425 test-rmse:0.949866+0.077529
## [79] train-rmse:0.150820+0.007099 test-rmse:0.949190+0.077452
## [80] train-rmse:0.147321+0.007343 test-rmse:0.949316+0.077517
## [81] train-rmse:0.144070+0.006255 test-rmse:0.948812+0.078009
## [82] train-rmse:0.140938+0.007026 test-rmse:0.947659+0.077955
## [83] train-rmse:0.138810+0.007170 test-rmse:0.947082+0.076911
## [84] train-rmse:0.136437+0.006888 test-rmse:0.946649+0.076396
## [85] train-rmse:0.133849+0.006240 test-rmse:0.946691+0.076117
## [86] train-rmse:0.130824+0.006964 test-rmse:0.946423+0.076420
## [87] train-rmse:0.128370+0.006694 test-rmse:0.946064+0.076262
## [88] train-rmse:0.126176+0.006798 test-rmse:0.945711+0.076035
## [89] train-rmse:0.122301+0.006174 test-rmse:0.945333+0.076152
## [90] train-rmse:0.118958+0.006684 test-rmse:0.945077+0.076162
## [91] train-rmse:0.115724+0.005709 test-rmse:0.943704+0.075703
## [92] train-rmse:0.112262+0.005949 test-rmse:0.942937+0.075414
## [93] train-rmse:0.109467+0.005711 test-rmse:0.942192+0.074452
## [94] train-rmse:0.107452+0.005954 test-rmse:0.942328+0.074740
## [95] train-rmse:0.105262+0.005921 test-rmse:0.942387+0.074491
## [96] train-rmse:0.103666+0.006187 test-rmse:0.942355+0.074748
## [97] train-rmse:0.101737+0.006669 test-rmse:0.941990+0.075091
## [98] train-rmse:0.098593+0.005895 test-rmse:0.942018+0.075150
## [99] train-rmse:0.095835+0.006702 test-rmse:0.941790+0.075819
## [100] train-rmse:0.092901+0.006272 test-rmse:0.942685+0.075616
## [101] train-rmse:0.090815+0.005254 test-rmse:0.942654+0.075631
## [102] train-rmse:0.089254+0.005618 test-rmse:0.942471+0.075919
## [103] train-rmse:0.087281+0.005488 test-rmse:0.942219+0.076395
## [104] train-rmse:0.085377+0.005594 test-rmse:0.942062+0.076316
## [105] train-rmse:0.082674+0.005462 test-rmse:0.942363+0.076843
## Stopping. Best iteration:
## [99] train-rmse:0.095835+0.006702 test-rmse:0.941790+0.075819
##
## 48
## [1] train-rmse:76.896455+0.077079 test-rmse:76.897090+0.416550
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:60.052252+0.059983 test-rmse:60.052622+0.432945
## [3] train-rmse:46.904609+0.046553 test-rmse:46.904639+0.445440
## [4] train-rmse:36.644199+0.035970 test-rmse:36.643796+0.454772
## [5] train-rmse:28.639430+0.027617 test-rmse:28.638477+0.461462
## [6] train-rmse:22.397527+0.021009 test-rmse:22.395899+0.465834
## [7] train-rmse:17.534218+0.015835 test-rmse:17.531762+0.468024
## [8] train-rmse:13.749436+0.012173 test-rmse:13.738001+0.462524
## [9] train-rmse:10.804913+0.008776 test-rmse:10.788056+0.441575
## [10] train-rmse:8.514278+0.007378 test-rmse:8.492002+0.409061
## [11] train-rmse:6.731659+0.006717 test-rmse:6.716589+0.381560
## [12] train-rmse:5.339480+0.007778 test-rmse:5.317118+0.347855
## [13] train-rmse:4.258057+0.007489 test-rmse:4.245498+0.318131
## [14] train-rmse:3.409498+0.006037 test-rmse:3.419650+0.291134
## [15] train-rmse:2.748231+0.008470 test-rmse:2.779001+0.255160
## [16] train-rmse:2.232388+0.007418 test-rmse:2.299972+0.226047
## [17] train-rmse:1.832645+0.008668 test-rmse:1.934604+0.196661
## [18] train-rmse:1.515130+0.007885 test-rmse:1.674570+0.165560
## [19] train-rmse:1.273429+0.006509 test-rmse:1.482773+0.144616
## [20] train-rmse:1.085668+0.002917 test-rmse:1.348618+0.132379
## [21] train-rmse:0.935338+0.006628 test-rmse:1.253050+0.109949
## [22] train-rmse:0.826954+0.004923 test-rmse:1.186072+0.102095
## [23] train-rmse:0.738197+0.004949 test-rmse:1.130504+0.101030
## [24] train-rmse:0.666418+0.008133 test-rmse:1.094800+0.101765
## [25] train-rmse:0.612941+0.012465 test-rmse:1.071036+0.096712
## [26] train-rmse:0.571206+0.013215 test-rmse:1.057725+0.093005
## [27] train-rmse:0.532006+0.011181 test-rmse:1.032859+0.090675
## [28] train-rmse:0.493967+0.016124 test-rmse:1.025345+0.080755
## [29] train-rmse:0.467998+0.017233 test-rmse:1.016510+0.083467
## [30] train-rmse:0.445291+0.013267 test-rmse:1.004785+0.083098
## [31] train-rmse:0.428354+0.013416 test-rmse:0.999999+0.080752
## [32] train-rmse:0.403108+0.012133 test-rmse:0.994156+0.079428
## [33] train-rmse:0.388992+0.014322 test-rmse:0.993058+0.083018
## [34] train-rmse:0.370334+0.010803 test-rmse:0.983726+0.080539
## [35] train-rmse:0.354851+0.008208 test-rmse:0.981751+0.083623
## [36] train-rmse:0.341870+0.007795 test-rmse:0.981128+0.083482
## [37] train-rmse:0.329151+0.009055 test-rmse:0.978348+0.081105
## [38] train-rmse:0.318080+0.007234 test-rmse:0.974758+0.081900
## [39] train-rmse:0.306142+0.007378 test-rmse:0.972070+0.081336
## [40] train-rmse:0.292470+0.009483 test-rmse:0.968888+0.080757
## [41] train-rmse:0.280107+0.010517 test-rmse:0.967708+0.080398
## [42] train-rmse:0.272271+0.012182 test-rmse:0.965499+0.080218
## [43] train-rmse:0.265539+0.013126 test-rmse:0.962591+0.080348
## [44] train-rmse:0.255281+0.012358 test-rmse:0.959612+0.078969
## [45] train-rmse:0.247361+0.010521 test-rmse:0.959106+0.078327
## [46] train-rmse:0.238909+0.012028 test-rmse:0.960106+0.079403
## [47] train-rmse:0.233557+0.011961 test-rmse:0.957552+0.080233
## [48] train-rmse:0.225601+0.010213 test-rmse:0.956190+0.081882
## [49] train-rmse:0.219393+0.010512 test-rmse:0.956271+0.081192
## [50] train-rmse:0.211328+0.010677 test-rmse:0.956903+0.083117
## [51] train-rmse:0.204882+0.011400 test-rmse:0.956186+0.084224
## [52] train-rmse:0.197201+0.013692 test-rmse:0.954867+0.083873
## [53] train-rmse:0.188903+0.013160 test-rmse:0.953559+0.082571
## [54] train-rmse:0.182969+0.013273 test-rmse:0.953194+0.082168
## [55] train-rmse:0.178153+0.014300 test-rmse:0.952094+0.081415
## [56] train-rmse:0.170710+0.012670 test-rmse:0.952239+0.081412
## [57] train-rmse:0.165860+0.014196 test-rmse:0.951710+0.081868
## [58] train-rmse:0.162084+0.014093 test-rmse:0.952443+0.081479
## [59] train-rmse:0.156050+0.012528 test-rmse:0.951417+0.081357
## [60] train-rmse:0.150197+0.010801 test-rmse:0.952473+0.080814
## [61] train-rmse:0.145823+0.011158 test-rmse:0.951401+0.080809
## [62] train-rmse:0.143087+0.010997 test-rmse:0.949987+0.080904
## [63] train-rmse:0.138952+0.009850 test-rmse:0.949820+0.080727
## [64] train-rmse:0.134436+0.009977 test-rmse:0.950975+0.080724
## [65] train-rmse:0.130184+0.010840 test-rmse:0.951349+0.080816
## [66] train-rmse:0.126506+0.011038 test-rmse:0.951416+0.082045
## [67] train-rmse:0.123170+0.010966 test-rmse:0.951633+0.081673
## [68] train-rmse:0.119422+0.011606 test-rmse:0.951448+0.081549
## [69] train-rmse:0.115806+0.011076 test-rmse:0.950413+0.081593
## Stopping. Best iteration:
## [63] train-rmse:0.138952+0.009850 test-rmse:0.949820+0.080727
##
## 49
## [1] train-rmse:74.935020+0.075103 test-rmse:74.935632+0.418470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:57.029236+0.056907 test-rmse:57.029542+0.435851
## [3] train-rmse:43.410643+0.042960 test-rmse:43.410540+0.448676
## [4] train-rmse:33.055426+0.032244 test-rmse:33.054807+0.457865
## [5] train-rmse:25.185130+0.023974 test-rmse:25.183843+0.464019
## [6] train-rmse:19.208041+0.017613 test-rmse:19.205912+0.467458
## [7] train-rmse:14.674725+0.012875 test-rmse:14.671561+0.468210
## [8] train-rmse:11.239684+0.009540 test-rmse:11.239101+0.439296
## [9] train-rmse:8.642351+0.007677 test-rmse:8.631867+0.438326
## [10] train-rmse:6.682843+0.007924 test-rmse:6.669499+0.424188
## [11] train-rmse:5.215550+0.009488 test-rmse:5.199744+0.406549
## [12] train-rmse:4.126593+0.011388 test-rmse:4.122066+0.375559
## [13] train-rmse:3.330459+0.014980 test-rmse:3.332867+0.349905
## [14] train-rmse:2.757762+0.018063 test-rmse:2.767284+0.302645
## [15] train-rmse:2.356120+0.022009 test-rmse:2.383882+0.257325
## [16] train-rmse:2.081772+0.024216 test-rmse:2.124629+0.208428
## [17] train-rmse:1.896119+0.026045 test-rmse:1.954976+0.173216
## [18] train-rmse:1.772374+0.026797 test-rmse:1.845467+0.140368
## [19] train-rmse:1.689648+0.027088 test-rmse:1.776784+0.122007
## [20] train-rmse:1.632914+0.027034 test-rmse:1.712518+0.112980
## [21] train-rmse:1.593556+0.026761 test-rmse:1.674075+0.111435
## [22] train-rmse:1.563809+0.026171 test-rmse:1.657001+0.115151
## [23] train-rmse:1.540315+0.025459 test-rmse:1.640314+0.114869
## [24] train-rmse:1.520678+0.025163 test-rmse:1.629391+0.113721
## [25] train-rmse:1.504467+0.024422 test-rmse:1.613442+0.110285
## [26] train-rmse:1.489585+0.023901 test-rmse:1.601941+0.115872
## [27] train-rmse:1.476215+0.023518 test-rmse:1.578980+0.106867
## [28] train-rmse:1.464156+0.023374 test-rmse:1.577524+0.110048
## [29] train-rmse:1.452589+0.023228 test-rmse:1.571269+0.111697
## [30] train-rmse:1.441092+0.022882 test-rmse:1.570171+0.115633
## [31] train-rmse:1.430417+0.022819 test-rmse:1.565089+0.114195
## [32] train-rmse:1.420445+0.022722 test-rmse:1.559852+0.112981
## [33] train-rmse:1.410634+0.022479 test-rmse:1.551819+0.113239
## [34] train-rmse:1.401260+0.022377 test-rmse:1.550884+0.113607
## [35] train-rmse:1.392024+0.022360 test-rmse:1.540397+0.107310
## [36] train-rmse:1.383117+0.022482 test-rmse:1.531274+0.106471
## [37] train-rmse:1.374416+0.022335 test-rmse:1.523039+0.113682
## [38] train-rmse:1.366121+0.022225 test-rmse:1.519469+0.109271
## [39] train-rmse:1.357822+0.022327 test-rmse:1.508454+0.106146
## [40] train-rmse:1.350014+0.022061 test-rmse:1.503197+0.105378
## [41] train-rmse:1.342318+0.021965 test-rmse:1.503788+0.099313
## [42] train-rmse:1.334700+0.021576 test-rmse:1.500424+0.100188
## [43] train-rmse:1.327288+0.021372 test-rmse:1.492691+0.101672
## [44] train-rmse:1.319955+0.021168 test-rmse:1.484968+0.100479
## [45] train-rmse:1.312612+0.021064 test-rmse:1.476745+0.102291
## [46] train-rmse:1.305603+0.020927 test-rmse:1.474376+0.102836
## [47] train-rmse:1.298827+0.020769 test-rmse:1.470577+0.101140
## [48] train-rmse:1.292180+0.020663 test-rmse:1.465951+0.099364
## [49] train-rmse:1.285624+0.020517 test-rmse:1.461403+0.099841
## [50] train-rmse:1.279320+0.020303 test-rmse:1.456461+0.095557
## [51] train-rmse:1.273199+0.020108 test-rmse:1.454277+0.095393
## [52] train-rmse:1.267239+0.020037 test-rmse:1.449857+0.097276
## [53] train-rmse:1.261388+0.019907 test-rmse:1.450388+0.092261
## [54] train-rmse:1.255705+0.019789 test-rmse:1.441092+0.090194
## [55] train-rmse:1.250034+0.019847 test-rmse:1.436643+0.090189
## [56] train-rmse:1.244616+0.019863 test-rmse:1.437245+0.093168
## [57] train-rmse:1.239302+0.019947 test-rmse:1.431495+0.094531
## [58] train-rmse:1.234132+0.019995 test-rmse:1.427033+0.097982
## [59] train-rmse:1.229126+0.019921 test-rmse:1.429018+0.096794
## [60] train-rmse:1.224059+0.019895 test-rmse:1.425888+0.092393
## [61] train-rmse:1.219048+0.019862 test-rmse:1.421340+0.090113
## [62] train-rmse:1.214127+0.019888 test-rmse:1.415824+0.090151
## [63] train-rmse:1.209393+0.019888 test-rmse:1.412322+0.089915
## [64] train-rmse:1.204834+0.019859 test-rmse:1.408401+0.090058
## [65] train-rmse:1.200376+0.019813 test-rmse:1.405277+0.091430
## [66] train-rmse:1.195935+0.019840 test-rmse:1.398207+0.088725
## [67] train-rmse:1.191577+0.019792 test-rmse:1.394143+0.086034
## [68] train-rmse:1.187278+0.019732 test-rmse:1.389674+0.085404
## [69] train-rmse:1.183103+0.019697 test-rmse:1.383808+0.090767
## [70] train-rmse:1.178993+0.019632 test-rmse:1.382426+0.091227
## [71] train-rmse:1.174902+0.019575 test-rmse:1.377884+0.089020
## [72] train-rmse:1.171006+0.019491 test-rmse:1.374176+0.087958
## [73] train-rmse:1.167094+0.019281 test-rmse:1.376894+0.083899
## [74] train-rmse:1.163300+0.019242 test-rmse:1.372532+0.084217
## [75] train-rmse:1.159567+0.019258 test-rmse:1.370970+0.084056
## [76] train-rmse:1.155804+0.019158 test-rmse:1.368969+0.085040
## [77] train-rmse:1.152219+0.019160 test-rmse:1.362887+0.088781
## [78] train-rmse:1.148690+0.019155 test-rmse:1.360628+0.093388
## [79] train-rmse:1.145177+0.019205 test-rmse:1.359198+0.092481
## [80] train-rmse:1.141808+0.019138 test-rmse:1.359363+0.089838
## [81] train-rmse:1.138467+0.019062 test-rmse:1.355728+0.090336
## [82] train-rmse:1.135170+0.019103 test-rmse:1.355506+0.088538
## [83] train-rmse:1.132001+0.019064 test-rmse:1.353938+0.089548
## [84] train-rmse:1.128895+0.019067 test-rmse:1.350688+0.088248
## [85] train-rmse:1.125872+0.019118 test-rmse:1.347364+0.081442
## [86] train-rmse:1.122766+0.019051 test-rmse:1.345054+0.079263
## [87] train-rmse:1.119822+0.019002 test-rmse:1.343080+0.077802
## [88] train-rmse:1.116923+0.018941 test-rmse:1.337054+0.077620
## [89] train-rmse:1.113989+0.018863 test-rmse:1.334389+0.078780
## [90] train-rmse:1.111158+0.018828 test-rmse:1.334883+0.081185
## [91] train-rmse:1.108318+0.018797 test-rmse:1.331690+0.081433
## [92] train-rmse:1.105587+0.018698 test-rmse:1.331253+0.082589
## [93] train-rmse:1.102883+0.018553 test-rmse:1.330565+0.084394
## [94] train-rmse:1.100285+0.018419 test-rmse:1.327535+0.088306
## [95] train-rmse:1.097697+0.018322 test-rmse:1.325767+0.088282
## [96] train-rmse:1.095097+0.018226 test-rmse:1.324300+0.087874
## [97] train-rmse:1.092603+0.018216 test-rmse:1.321332+0.087429
## [98] train-rmse:1.090073+0.018172 test-rmse:1.317092+0.088039
## [99] train-rmse:1.087665+0.018179 test-rmse:1.315312+0.088470
## [100] train-rmse:1.085311+0.018170 test-rmse:1.312714+0.087069
## [101] train-rmse:1.082973+0.018158 test-rmse:1.313428+0.088162
## [102] train-rmse:1.080597+0.018138 test-rmse:1.314409+0.083616
## [103] train-rmse:1.078269+0.018174 test-rmse:1.311490+0.085575
## [104] train-rmse:1.076032+0.018109 test-rmse:1.309613+0.085687
## [105] train-rmse:1.073840+0.018025 test-rmse:1.305775+0.086259
## [106] train-rmse:1.071620+0.017951 test-rmse:1.304225+0.087964
## [107] train-rmse:1.069483+0.017896 test-rmse:1.306695+0.086718
## [108] train-rmse:1.067353+0.017842 test-rmse:1.304284+0.087421
## [109] train-rmse:1.065240+0.017782 test-rmse:1.302301+0.086944
## [110] train-rmse:1.063156+0.017731 test-rmse:1.300328+0.085778
## [111] train-rmse:1.061108+0.017667 test-rmse:1.300305+0.086659
## [112] train-rmse:1.059159+0.017671 test-rmse:1.299592+0.089275
## [113] train-rmse:1.057232+0.017670 test-rmse:1.298662+0.088553
## [114] train-rmse:1.055291+0.017614 test-rmse:1.295527+0.089121
## [115] train-rmse:1.053394+0.017592 test-rmse:1.294448+0.090743
## [116] train-rmse:1.051415+0.017475 test-rmse:1.292391+0.090212
## [117] train-rmse:1.049572+0.017397 test-rmse:1.290760+0.090896
## [118] train-rmse:1.047662+0.017368 test-rmse:1.289111+0.090218
## [119] train-rmse:1.045813+0.017262 test-rmse:1.287394+0.089920
## [120] train-rmse:1.044011+0.017230 test-rmse:1.286287+0.090512
## [121] train-rmse:1.042220+0.017198 test-rmse:1.286676+0.090569
## [122] train-rmse:1.040430+0.017177 test-rmse:1.290108+0.088203
## [123] train-rmse:1.038712+0.017121 test-rmse:1.287840+0.089935
## [124] train-rmse:1.037006+0.017049 test-rmse:1.286121+0.091782
## [125] train-rmse:1.035341+0.017012 test-rmse:1.283155+0.092169
## [126] train-rmse:1.033721+0.016979 test-rmse:1.283404+0.089417
## [127] train-rmse:1.032072+0.016898 test-rmse:1.281247+0.090998
## [128] train-rmse:1.030441+0.016850 test-rmse:1.277434+0.086801
## [129] train-rmse:1.028887+0.016802 test-rmse:1.277170+0.085418
## [130] train-rmse:1.027317+0.016763 test-rmse:1.274846+0.084146
## [131] train-rmse:1.025781+0.016680 test-rmse:1.274554+0.083605
## [132] train-rmse:1.024217+0.016645 test-rmse:1.271229+0.083598
## [133] train-rmse:1.022699+0.016620 test-rmse:1.270014+0.085741
## [134] train-rmse:1.021196+0.016584 test-rmse:1.269039+0.085650
## [135] train-rmse:1.019717+0.016575 test-rmse:1.269023+0.084878
## [136] train-rmse:1.018242+0.016541 test-rmse:1.267060+0.084965
## [137] train-rmse:1.016815+0.016545 test-rmse:1.267298+0.086321
## [138] train-rmse:1.015385+0.016540 test-rmse:1.266651+0.084792
## [139] train-rmse:1.013927+0.016523 test-rmse:1.264585+0.085164
## [140] train-rmse:1.012493+0.016550 test-rmse:1.264438+0.086486
## [141] train-rmse:1.011115+0.016547 test-rmse:1.262737+0.086972
## [142] train-rmse:1.009754+0.016552 test-rmse:1.263032+0.086859
## [143] train-rmse:1.008433+0.016547 test-rmse:1.264237+0.086031
## [144] train-rmse:1.007072+0.016471 test-rmse:1.262815+0.085793
## [145] train-rmse:1.005752+0.016476 test-rmse:1.261909+0.086304
## [146] train-rmse:1.004433+0.016438 test-rmse:1.260824+0.086596
## [147] train-rmse:1.003142+0.016425 test-rmse:1.260624+0.086873
## [148] train-rmse:1.001862+0.016414 test-rmse:1.260159+0.086281
## [149] train-rmse:1.000589+0.016400 test-rmse:1.257107+0.086200
## [150] train-rmse:0.999353+0.016380 test-rmse:1.255396+0.086730
## [151] train-rmse:0.998104+0.016361 test-rmse:1.257835+0.084822
## [152] train-rmse:0.996837+0.016365 test-rmse:1.256809+0.084371
## [153] train-rmse:0.995591+0.016378 test-rmse:1.256048+0.083834
## [154] train-rmse:0.994354+0.016408 test-rmse:1.255116+0.083576
## [155] train-rmse:0.993154+0.016411 test-rmse:1.254197+0.082834
## [156] train-rmse:0.991956+0.016411 test-rmse:1.251920+0.080087
## [157] train-rmse:0.990804+0.016427 test-rmse:1.251545+0.079575
## [158] train-rmse:0.989638+0.016426 test-rmse:1.250453+0.077888
## [159] train-rmse:0.988482+0.016430 test-rmse:1.252389+0.078985
## [160] train-rmse:0.987300+0.016443 test-rmse:1.251502+0.078949
## [161] train-rmse:0.986143+0.016451 test-rmse:1.251107+0.079377
## [162] train-rmse:0.985005+0.016438 test-rmse:1.251258+0.078952
## [163] train-rmse:0.983874+0.016433 test-rmse:1.250350+0.079105
## [164] train-rmse:0.982752+0.016429 test-rmse:1.249968+0.078201
## [165] train-rmse:0.981615+0.016447 test-rmse:1.250266+0.079238
## [166] train-rmse:0.980517+0.016424 test-rmse:1.248901+0.079960
## [167] train-rmse:0.979447+0.016429 test-rmse:1.247355+0.079852
## [168] train-rmse:0.978396+0.016445 test-rmse:1.245545+0.078802
## [169] train-rmse:0.977356+0.016440 test-rmse:1.243998+0.080584
## [170] train-rmse:0.976337+0.016448 test-rmse:1.242893+0.080731
## [171] train-rmse:0.975297+0.016395 test-rmse:1.241249+0.080282
## [172] train-rmse:0.974286+0.016367 test-rmse:1.239508+0.081138
## [173] train-rmse:0.973278+0.016357 test-rmse:1.239021+0.081892
## [174] train-rmse:0.972296+0.016363 test-rmse:1.239498+0.081579
## [175] train-rmse:0.971274+0.016381 test-rmse:1.238127+0.082074
## [176] train-rmse:0.970286+0.016402 test-rmse:1.237848+0.083085
## [177] train-rmse:0.969328+0.016409 test-rmse:1.237413+0.083782
## [178] train-rmse:0.968366+0.016419 test-rmse:1.237250+0.084832
## [179] train-rmse:0.967398+0.016416 test-rmse:1.236130+0.083732
## [180] train-rmse:0.966416+0.016363 test-rmse:1.236514+0.083331
## [181] train-rmse:0.965499+0.016361 test-rmse:1.235908+0.085800
## [182] train-rmse:0.964559+0.016388 test-rmse:1.236593+0.085697
## [183] train-rmse:0.963650+0.016397 test-rmse:1.236524+0.086034
## [184] train-rmse:0.962676+0.016436 test-rmse:1.235197+0.084574
## [185] train-rmse:0.961762+0.016451 test-rmse:1.235192+0.084199
## [186] train-rmse:0.960856+0.016468 test-rmse:1.234064+0.084662
## [187] train-rmse:0.959948+0.016506 test-rmse:1.232306+0.084300
## [188] train-rmse:0.959054+0.016545 test-rmse:1.231685+0.085234
## [189] train-rmse:0.958150+0.016542 test-rmse:1.230921+0.084706
## [190] train-rmse:0.957250+0.016577 test-rmse:1.231317+0.083042
## [191] train-rmse:0.956372+0.016585 test-rmse:1.230968+0.082570
## [192] train-rmse:0.955518+0.016616 test-rmse:1.231253+0.082512
## [193] train-rmse:0.954639+0.016610 test-rmse:1.230093+0.082993
## [194] train-rmse:0.953797+0.016647 test-rmse:1.229971+0.082431
## [195] train-rmse:0.952956+0.016673 test-rmse:1.230282+0.082415
## [196] train-rmse:0.952123+0.016681 test-rmse:1.231063+0.081630
## [197] train-rmse:0.951275+0.016688 test-rmse:1.230911+0.082268
## [198] train-rmse:0.950437+0.016683 test-rmse:1.229667+0.083389
## [199] train-rmse:0.949602+0.016701 test-rmse:1.231226+0.082316
## [200] train-rmse:0.948793+0.016712 test-rmse:1.229880+0.082583
## [201] train-rmse:0.947972+0.016729 test-rmse:1.229174+0.083023
## [202] train-rmse:0.947181+0.016722 test-rmse:1.229642+0.081833
## [203] train-rmse:0.946359+0.016708 test-rmse:1.228277+0.083123
## [204] train-rmse:0.945575+0.016707 test-rmse:1.225472+0.081726
## [205] train-rmse:0.944778+0.016725 test-rmse:1.225672+0.081168
## [206] train-rmse:0.944004+0.016727 test-rmse:1.227262+0.079816
## [207] train-rmse:0.943249+0.016736 test-rmse:1.226217+0.079177
## [208] train-rmse:0.942488+0.016765 test-rmse:1.226009+0.079249
## [209] train-rmse:0.941750+0.016785 test-rmse:1.225359+0.079726
## [210] train-rmse:0.940976+0.016788 test-rmse:1.226230+0.080866
## [211] train-rmse:0.940233+0.016791 test-rmse:1.226487+0.080226
## [212] train-rmse:0.939443+0.016830 test-rmse:1.227248+0.080971
## [213] train-rmse:0.938690+0.016822 test-rmse:1.227230+0.081146
## [214] train-rmse:0.937927+0.016831 test-rmse:1.226621+0.081019
## [215] train-rmse:0.937199+0.016836 test-rmse:1.225799+0.081695
## Stopping. Best iteration:
## [209] train-rmse:0.941750+0.016785 test-rmse:1.225359+0.079726
##
## 50
## [1] train-rmse:74.935020+0.075103 test-rmse:74.935632+0.418470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:57.029236+0.056907 test-rmse:57.029542+0.435851
## [3] train-rmse:43.410643+0.042960 test-rmse:43.410540+0.448676
## [4] train-rmse:33.055426+0.032244 test-rmse:33.054807+0.457865
## [5] train-rmse:25.185130+0.023974 test-rmse:25.183843+0.464019
## [6] train-rmse:19.208041+0.017613 test-rmse:19.205912+0.467458
## [7] train-rmse:14.674725+0.012875 test-rmse:14.671561+0.468210
## [8] train-rmse:11.238713+0.009134 test-rmse:11.227100+0.442238
## [9] train-rmse:8.637307+0.007415 test-rmse:8.617466+0.425233
## [10] train-rmse:6.668908+0.006979 test-rmse:6.649441+0.403020
## [11] train-rmse:5.189688+0.009079 test-rmse:5.180838+0.375972
## [12] train-rmse:4.079013+0.010891 test-rmse:4.079147+0.355457
## [13] train-rmse:3.256189+0.016107 test-rmse:3.264118+0.326310
## [14] train-rmse:2.656253+0.017865 test-rmse:2.676228+0.285568
## [15] train-rmse:2.224352+0.020547 test-rmse:2.271785+0.243380
## [16] train-rmse:1.916230+0.022320 test-rmse:1.995965+0.209779
## [17] train-rmse:1.703852+0.022787 test-rmse:1.813962+0.173971
## [18] train-rmse:1.560290+0.025170 test-rmse:1.689839+0.145537
## [19] train-rmse:1.463990+0.025241 test-rmse:1.608006+0.115946
## [20] train-rmse:1.394078+0.022260 test-rmse:1.550141+0.097680
## [21] train-rmse:1.344312+0.022392 test-rmse:1.515296+0.092964
## [22] train-rmse:1.306479+0.024941 test-rmse:1.489482+0.085542
## [23] train-rmse:1.275759+0.021584 test-rmse:1.476465+0.080392
## [24] train-rmse:1.252866+0.021138 test-rmse:1.454103+0.080621
## [25] train-rmse:1.232191+0.021053 test-rmse:1.444030+0.076643
## [26] train-rmse:1.213757+0.022210 test-rmse:1.439232+0.077279
## [27] train-rmse:1.192823+0.023064 test-rmse:1.431902+0.074877
## [28] train-rmse:1.178392+0.022596 test-rmse:1.412090+0.074403
## [29] train-rmse:1.164047+0.022555 test-rmse:1.400925+0.077809
## [30] train-rmse:1.149467+0.024167 test-rmse:1.384290+0.073845
## [31] train-rmse:1.135691+0.024976 test-rmse:1.379129+0.067837
## [32] train-rmse:1.123327+0.024229 test-rmse:1.368127+0.071006
## [33] train-rmse:1.111179+0.024920 test-rmse:1.359739+0.072053
## [34] train-rmse:1.096417+0.023181 test-rmse:1.341576+0.081635
## [35] train-rmse:1.086184+0.023714 test-rmse:1.328716+0.079851
## [36] train-rmse:1.075141+0.024763 test-rmse:1.324817+0.076057
## [37] train-rmse:1.062043+0.021166 test-rmse:1.318224+0.072820
## [38] train-rmse:1.050829+0.021919 test-rmse:1.313072+0.071447
## [39] train-rmse:1.040726+0.021639 test-rmse:1.310335+0.068794
## [40] train-rmse:1.030090+0.023498 test-rmse:1.301451+0.068553
## [41] train-rmse:1.020981+0.023043 test-rmse:1.295620+0.069519
## [42] train-rmse:1.012160+0.023176 test-rmse:1.288605+0.073320
## [43] train-rmse:1.003179+0.023742 test-rmse:1.277872+0.075805
## [44] train-rmse:0.993485+0.022792 test-rmse:1.268779+0.074045
## [45] train-rmse:0.985397+0.023537 test-rmse:1.261165+0.083608
## [46] train-rmse:0.977073+0.024553 test-rmse:1.253772+0.080360
## [47] train-rmse:0.966087+0.022270 test-rmse:1.254608+0.077577
## [48] train-rmse:0.959498+0.021908 test-rmse:1.251433+0.076943
## [49] train-rmse:0.948924+0.022919 test-rmse:1.249026+0.075543
## [50] train-rmse:0.939145+0.024563 test-rmse:1.243509+0.075539
## [51] train-rmse:0.932552+0.024919 test-rmse:1.241127+0.074510
## [52] train-rmse:0.924951+0.022145 test-rmse:1.233312+0.078345
## [53] train-rmse:0.916931+0.022386 test-rmse:1.223628+0.078219
## [54] train-rmse:0.911043+0.022579 test-rmse:1.222060+0.078692
## [55] train-rmse:0.905027+0.022851 test-rmse:1.218820+0.078962
## [56] train-rmse:0.897042+0.023063 test-rmse:1.212774+0.081474
## [57] train-rmse:0.891145+0.023645 test-rmse:1.208657+0.081452
## [58] train-rmse:0.883180+0.022792 test-rmse:1.205073+0.086327
## [59] train-rmse:0.877091+0.023289 test-rmse:1.201736+0.089132
## [60] train-rmse:0.871684+0.023149 test-rmse:1.198521+0.087683
## [61] train-rmse:0.863432+0.020799 test-rmse:1.194484+0.084783
## [62] train-rmse:0.854338+0.022888 test-rmse:1.187960+0.083027
## [63] train-rmse:0.848069+0.023449 test-rmse:1.186408+0.083454
## [64] train-rmse:0.843759+0.023785 test-rmse:1.179385+0.079807
## [65] train-rmse:0.837638+0.019275 test-rmse:1.173295+0.084158
## [66] train-rmse:0.832733+0.019539 test-rmse:1.171360+0.086710
## [67] train-rmse:0.826754+0.021121 test-rmse:1.165697+0.088032
## [68] train-rmse:0.819695+0.022469 test-rmse:1.164161+0.083898
## [69] train-rmse:0.815455+0.022406 test-rmse:1.162988+0.083434
## [70] train-rmse:0.810863+0.022947 test-rmse:1.159298+0.083821
## [71] train-rmse:0.804866+0.024345 test-rmse:1.156025+0.082191
## [72] train-rmse:0.800265+0.024609 test-rmse:1.152573+0.081998
## [73] train-rmse:0.792869+0.024596 test-rmse:1.153344+0.082198
## [74] train-rmse:0.788497+0.023827 test-rmse:1.148367+0.087607
## [75] train-rmse:0.782764+0.021952 test-rmse:1.144282+0.092491
## [76] train-rmse:0.778300+0.022082 test-rmse:1.142391+0.090704
## [77] train-rmse:0.772143+0.021560 test-rmse:1.142913+0.089659
## [78] train-rmse:0.765476+0.021463 test-rmse:1.140171+0.089994
## [79] train-rmse:0.762087+0.021084 test-rmse:1.136182+0.088410
## [80] train-rmse:0.759037+0.020995 test-rmse:1.135396+0.088052
## [81] train-rmse:0.754522+0.021740 test-rmse:1.130956+0.090068
## [82] train-rmse:0.750017+0.020969 test-rmse:1.129090+0.091300
## [83] train-rmse:0.746141+0.021659 test-rmse:1.127565+0.091430
## [84] train-rmse:0.740922+0.021555 test-rmse:1.124604+0.093473
## [85] train-rmse:0.737710+0.021920 test-rmse:1.121736+0.094093
## [86] train-rmse:0.734740+0.022057 test-rmse:1.119826+0.093061
## [87] train-rmse:0.730888+0.022306 test-rmse:1.116837+0.094670
## [88] train-rmse:0.728186+0.022408 test-rmse:1.116084+0.094089
## [89] train-rmse:0.723504+0.023923 test-rmse:1.114346+0.091822
## [90] train-rmse:0.720911+0.024063 test-rmse:1.112000+0.092935
## [91] train-rmse:0.718010+0.023931 test-rmse:1.111321+0.094298
## [92] train-rmse:0.714984+0.023885 test-rmse:1.109641+0.093214
## [93] train-rmse:0.711587+0.025165 test-rmse:1.109464+0.091271
## [94] train-rmse:0.705574+0.022247 test-rmse:1.105546+0.091135
## [95] train-rmse:0.701933+0.020198 test-rmse:1.105745+0.092682
## [96] train-rmse:0.698928+0.019647 test-rmse:1.104676+0.092486
## [97] train-rmse:0.694577+0.019377 test-rmse:1.101678+0.091298
## [98] train-rmse:0.689625+0.021224 test-rmse:1.096392+0.088164
## [99] train-rmse:0.685774+0.020734 test-rmse:1.091989+0.090846
## [100] train-rmse:0.681858+0.021725 test-rmse:1.093096+0.089168
## [101] train-rmse:0.675505+0.021617 test-rmse:1.090454+0.091193
## [102] train-rmse:0.672830+0.021554 test-rmse:1.089253+0.091981
## [103] train-rmse:0.668991+0.021519 test-rmse:1.087504+0.092759
## [104] train-rmse:0.666779+0.021626 test-rmse:1.086752+0.091321
## [105] train-rmse:0.664354+0.021500 test-rmse:1.085974+0.092415
## [106] train-rmse:0.660920+0.020738 test-rmse:1.084986+0.092830
## [107] train-rmse:0.657776+0.019850 test-rmse:1.083514+0.093189
## [108] train-rmse:0.655035+0.019902 test-rmse:1.083567+0.092814
## [109] train-rmse:0.651297+0.022503 test-rmse:1.082792+0.089845
## [110] train-rmse:0.647279+0.021848 test-rmse:1.083172+0.089902
## [111] train-rmse:0.644060+0.021614 test-rmse:1.080915+0.091353
## [112] train-rmse:0.641009+0.022099 test-rmse:1.077135+0.094557
## [113] train-rmse:0.637208+0.022871 test-rmse:1.074869+0.093491
## [114] train-rmse:0.635386+0.023037 test-rmse:1.073353+0.093468
## [115] train-rmse:0.632368+0.022709 test-rmse:1.071774+0.093549
## [116] train-rmse:0.629523+0.023658 test-rmse:1.071733+0.095252
## [117] train-rmse:0.626568+0.024291 test-rmse:1.071067+0.094348
## [118] train-rmse:0.623936+0.023237 test-rmse:1.071247+0.093775
## [119] train-rmse:0.621314+0.022732 test-rmse:1.072651+0.093079
## [120] train-rmse:0.616978+0.020638 test-rmse:1.069853+0.094334
## [121] train-rmse:0.614674+0.021544 test-rmse:1.069850+0.093472
## [122] train-rmse:0.610423+0.023553 test-rmse:1.066468+0.094750
## [123] train-rmse:0.606653+0.022463 test-rmse:1.065970+0.094300
## [124] train-rmse:0.604147+0.022596 test-rmse:1.063159+0.094761
## [125] train-rmse:0.599603+0.022857 test-rmse:1.057865+0.090155
## [126] train-rmse:0.596414+0.021324 test-rmse:1.057077+0.093882
## [127] train-rmse:0.594412+0.022048 test-rmse:1.057104+0.094205
## [128] train-rmse:0.591158+0.021836 test-rmse:1.055765+0.093455
## [129] train-rmse:0.588099+0.021536 test-rmse:1.054383+0.094585
## [130] train-rmse:0.586293+0.021793 test-rmse:1.054201+0.095214
## [131] train-rmse:0.583448+0.021099 test-rmse:1.053208+0.097316
## [132] train-rmse:0.579772+0.020350 test-rmse:1.053237+0.094875
## [133] train-rmse:0.577267+0.021087 test-rmse:1.053919+0.096213
## [134] train-rmse:0.575295+0.021170 test-rmse:1.053011+0.096368
## [135] train-rmse:0.571985+0.020162 test-rmse:1.051390+0.096290
## [136] train-rmse:0.568666+0.017904 test-rmse:1.050494+0.097849
## [137] train-rmse:0.565859+0.016339 test-rmse:1.048617+0.097197
## [138] train-rmse:0.563106+0.016139 test-rmse:1.048995+0.098617
## [139] train-rmse:0.560083+0.016302 test-rmse:1.048665+0.098440
## [140] train-rmse:0.558539+0.016609 test-rmse:1.048736+0.097531
## [141] train-rmse:0.556709+0.017156 test-rmse:1.047948+0.097659
## [142] train-rmse:0.555295+0.017233 test-rmse:1.048148+0.096718
## [143] train-rmse:0.553382+0.017849 test-rmse:1.046258+0.097100
## [144] train-rmse:0.551941+0.017663 test-rmse:1.046892+0.096164
## [145] train-rmse:0.549129+0.016020 test-rmse:1.047408+0.095312
## [146] train-rmse:0.547544+0.015944 test-rmse:1.047636+0.096355
## [147] train-rmse:0.545607+0.015341 test-rmse:1.045421+0.096766
## [148] train-rmse:0.543874+0.014787 test-rmse:1.044194+0.097835
## [149] train-rmse:0.541646+0.014150 test-rmse:1.044268+0.097306
## [150] train-rmse:0.539596+0.013768 test-rmse:1.042997+0.098112
## [151] train-rmse:0.537473+0.012911 test-rmse:1.043332+0.096836
## [152] train-rmse:0.535629+0.012852 test-rmse:1.043215+0.098754
## [153] train-rmse:0.533907+0.012778 test-rmse:1.042486+0.098875
## [154] train-rmse:0.531973+0.013780 test-rmse:1.041516+0.096747
## [155] train-rmse:0.530157+0.014130 test-rmse:1.040928+0.096093
## [156] train-rmse:0.527907+0.014184 test-rmse:1.038284+0.095336
## [157] train-rmse:0.525384+0.014183 test-rmse:1.037653+0.095731
## [158] train-rmse:0.523666+0.014271 test-rmse:1.036638+0.095481
## [159] train-rmse:0.522736+0.014069 test-rmse:1.036244+0.096070
## [160] train-rmse:0.520210+0.013803 test-rmse:1.035438+0.094896
## [161] train-rmse:0.517944+0.014568 test-rmse:1.032302+0.092855
## [162] train-rmse:0.516585+0.014721 test-rmse:1.032343+0.091838
## [163] train-rmse:0.512815+0.013337 test-rmse:1.028263+0.095389
## [164] train-rmse:0.511276+0.013339 test-rmse:1.025973+0.096125
## [165] train-rmse:0.509287+0.012877 test-rmse:1.024610+0.094994
## [166] train-rmse:0.507667+0.013665 test-rmse:1.024562+0.095420
## [167] train-rmse:0.505776+0.014305 test-rmse:1.022932+0.097068
## [168] train-rmse:0.504391+0.014259 test-rmse:1.022619+0.097474
## [169] train-rmse:0.502646+0.013515 test-rmse:1.023682+0.097734
## [170] train-rmse:0.501187+0.013927 test-rmse:1.023515+0.099161
## [171] train-rmse:0.498790+0.013819 test-rmse:1.026233+0.098488
## [172] train-rmse:0.497749+0.014040 test-rmse:1.024996+0.098704
## [173] train-rmse:0.496720+0.013883 test-rmse:1.025218+0.098495
## [174] train-rmse:0.494277+0.012714 test-rmse:1.023563+0.098745
## Stopping. Best iteration:
## [168] train-rmse:0.504391+0.014259 test-rmse:1.022619+0.097474
##
## 51
## [1] train-rmse:74.935020+0.075103 test-rmse:74.935632+0.418470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:57.029236+0.056907 test-rmse:57.029542+0.435851
## [3] train-rmse:43.410643+0.042960 test-rmse:43.410540+0.448676
## [4] train-rmse:33.055426+0.032244 test-rmse:33.054807+0.457865
## [5] train-rmse:25.185130+0.023974 test-rmse:25.183843+0.464019
## [6] train-rmse:19.208041+0.017613 test-rmse:19.205912+0.467458
## [7] train-rmse:14.674725+0.012875 test-rmse:14.671561+0.468210
## [8] train-rmse:11.238713+0.009134 test-rmse:11.227100+0.442238
## [9] train-rmse:8.636649+0.007690 test-rmse:8.608432+0.418295
## [10] train-rmse:6.663610+0.006629 test-rmse:6.639250+0.395611
## [11] train-rmse:5.174001+0.005237 test-rmse:5.158836+0.369689
## [12] train-rmse:4.050766+0.008634 test-rmse:4.050448+0.322760
## [13] train-rmse:3.208322+0.010715 test-rmse:3.225224+0.296392
## [14] train-rmse:2.587312+0.014571 test-rmse:2.615970+0.269692
## [15] train-rmse:2.133344+0.016521 test-rmse:2.193103+0.236814
## [16] train-rmse:1.807538+0.020095 test-rmse:1.897527+0.197924
## [17] train-rmse:1.572903+0.017177 test-rmse:1.687371+0.170391
## [18] train-rmse:1.410968+0.019551 test-rmse:1.558236+0.136622
## [19] train-rmse:1.295715+0.025199 test-rmse:1.474469+0.111808
## [20] train-rmse:1.216686+0.025422 test-rmse:1.423358+0.106217
## [21] train-rmse:1.157880+0.024658 test-rmse:1.388283+0.099073
## [22] train-rmse:1.109782+0.020951 test-rmse:1.358232+0.093351
## [23] train-rmse:1.074192+0.022328 test-rmse:1.345084+0.086293
## [24] train-rmse:1.047274+0.022411 test-rmse:1.320673+0.084308
## [25] train-rmse:1.022595+0.025489 test-rmse:1.299892+0.082185
## [26] train-rmse:0.999313+0.025056 test-rmse:1.287032+0.084025
## [27] train-rmse:0.976347+0.025808 test-rmse:1.273389+0.076935
## [28] train-rmse:0.959697+0.025921 test-rmse:1.262542+0.075769
## [29] train-rmse:0.938351+0.023359 test-rmse:1.247648+0.087636
## [30] train-rmse:0.921158+0.025669 test-rmse:1.230332+0.089045
## [31] train-rmse:0.901824+0.025726 test-rmse:1.224148+0.088278
## [32] train-rmse:0.882126+0.029522 test-rmse:1.217398+0.092797
## [33] train-rmse:0.869287+0.028308 test-rmse:1.210741+0.094738
## [34] train-rmse:0.854341+0.024579 test-rmse:1.202162+0.091718
## [35] train-rmse:0.843250+0.025076 test-rmse:1.193923+0.088293
## [36] train-rmse:0.830147+0.022708 test-rmse:1.183838+0.088980
## [37] train-rmse:0.815554+0.019221 test-rmse:1.178396+0.092408
## [38] train-rmse:0.802844+0.020606 test-rmse:1.171203+0.088713
## [39] train-rmse:0.789666+0.021283 test-rmse:1.165683+0.087903
## [40] train-rmse:0.780969+0.022415 test-rmse:1.158051+0.088810
## [41] train-rmse:0.769130+0.022839 test-rmse:1.152356+0.086882
## [42] train-rmse:0.759722+0.022757 test-rmse:1.148866+0.087820
## [43] train-rmse:0.749531+0.020791 test-rmse:1.138584+0.083950
## [44] train-rmse:0.737566+0.021796 test-rmse:1.131360+0.084764
## [45] train-rmse:0.729671+0.021799 test-rmse:1.128220+0.087527
## [46] train-rmse:0.721810+0.023149 test-rmse:1.124310+0.087477
## [47] train-rmse:0.713780+0.022412 test-rmse:1.117738+0.087556
## [48] train-rmse:0.707082+0.021233 test-rmse:1.116312+0.087613
## [49] train-rmse:0.699182+0.020659 test-rmse:1.111302+0.091215
## [50] train-rmse:0.691945+0.022288 test-rmse:1.109458+0.091885
## [51] train-rmse:0.684419+0.022844 test-rmse:1.106147+0.091093
## [52] train-rmse:0.677477+0.022228 test-rmse:1.104718+0.092139
## [53] train-rmse:0.668667+0.023121 test-rmse:1.099788+0.092747
## [54] train-rmse:0.662273+0.022917 test-rmse:1.094006+0.093696
## [55] train-rmse:0.654267+0.025718 test-rmse:1.092823+0.092116
## [56] train-rmse:0.645442+0.027595 test-rmse:1.084762+0.082799
## [57] train-rmse:0.640119+0.027766 test-rmse:1.083624+0.083532
## [58] train-rmse:0.634024+0.027512 test-rmse:1.079382+0.082676
## [59] train-rmse:0.626376+0.027039 test-rmse:1.076564+0.082182
## [60] train-rmse:0.618690+0.025717 test-rmse:1.074280+0.082567
## [61] train-rmse:0.612936+0.023727 test-rmse:1.072346+0.083838
## [62] train-rmse:0.604973+0.027323 test-rmse:1.062310+0.080060
## [63] train-rmse:0.599904+0.027599 test-rmse:1.058928+0.080113
## [64] train-rmse:0.593197+0.028369 test-rmse:1.058914+0.080407
## [65] train-rmse:0.588994+0.027658 test-rmse:1.057483+0.078632
## [66] train-rmse:0.580198+0.027899 test-rmse:1.054035+0.073981
## [67] train-rmse:0.573339+0.028362 test-rmse:1.048837+0.077276
## [68] train-rmse:0.567052+0.026611 test-rmse:1.047743+0.076531
## [69] train-rmse:0.562449+0.026752 test-rmse:1.047750+0.078505
## [70] train-rmse:0.557604+0.025942 test-rmse:1.047042+0.077574
## [71] train-rmse:0.553730+0.025810 test-rmse:1.047022+0.076721
## [72] train-rmse:0.547516+0.027348 test-rmse:1.047603+0.080238
## [73] train-rmse:0.543775+0.026342 test-rmse:1.046576+0.079822
## [74] train-rmse:0.539685+0.026490 test-rmse:1.045327+0.079241
## [75] train-rmse:0.532762+0.023966 test-rmse:1.042894+0.080919
## [76] train-rmse:0.527856+0.024835 test-rmse:1.042227+0.079816
## [77] train-rmse:0.521586+0.025673 test-rmse:1.040262+0.079390
## [78] train-rmse:0.513737+0.027019 test-rmse:1.033380+0.072667
## [79] train-rmse:0.508486+0.024004 test-rmse:1.030443+0.073329
## [80] train-rmse:0.504801+0.024806 test-rmse:1.029608+0.073800
## [81] train-rmse:0.500674+0.026302 test-rmse:1.033324+0.074355
## [82] train-rmse:0.496426+0.026815 test-rmse:1.033788+0.074781
## [83] train-rmse:0.492133+0.027029 test-rmse:1.034715+0.076317
## [84] train-rmse:0.484970+0.025001 test-rmse:1.030392+0.074591
## [85] train-rmse:0.481135+0.025607 test-rmse:1.028944+0.072869
## [86] train-rmse:0.478155+0.025911 test-rmse:1.029264+0.072339
## [87] train-rmse:0.473360+0.024237 test-rmse:1.026348+0.073901
## [88] train-rmse:0.470107+0.024573 test-rmse:1.023392+0.073491
## [89] train-rmse:0.465459+0.024332 test-rmse:1.025401+0.074166
## [90] train-rmse:0.461537+0.023076 test-rmse:1.025711+0.076502
## [91] train-rmse:0.457034+0.021855 test-rmse:1.023334+0.075045
## [92] train-rmse:0.453284+0.021348 test-rmse:1.020513+0.074040
## [93] train-rmse:0.448774+0.021625 test-rmse:1.020397+0.073657
## [94] train-rmse:0.444366+0.021408 test-rmse:1.020338+0.071564
## [95] train-rmse:0.440968+0.020458 test-rmse:1.019873+0.072908
## [96] train-rmse:0.437526+0.020843 test-rmse:1.019519+0.073626
## [97] train-rmse:0.433581+0.021520 test-rmse:1.018818+0.072706
## [98] train-rmse:0.430127+0.021298 test-rmse:1.018280+0.074467
## [99] train-rmse:0.426659+0.022616 test-rmse:1.016776+0.074665
## [100] train-rmse:0.423260+0.023195 test-rmse:1.015829+0.074957
## [101] train-rmse:0.420230+0.023634 test-rmse:1.016685+0.073840
## [102] train-rmse:0.415707+0.022073 test-rmse:1.014907+0.072968
## [103] train-rmse:0.412562+0.021848 test-rmse:1.014803+0.072083
## [104] train-rmse:0.408739+0.020713 test-rmse:1.014750+0.072201
## [105] train-rmse:0.404463+0.021686 test-rmse:1.015612+0.076429
## [106] train-rmse:0.400985+0.021411 test-rmse:1.015770+0.076591
## [107] train-rmse:0.397967+0.021332 test-rmse:1.014001+0.076570
## [108] train-rmse:0.394798+0.021646 test-rmse:1.013238+0.075463
## [109] train-rmse:0.392011+0.022052 test-rmse:1.012346+0.075538
## [110] train-rmse:0.388606+0.021786 test-rmse:1.011387+0.076590
## [111] train-rmse:0.385347+0.021842 test-rmse:1.010227+0.074638
## [112] train-rmse:0.381429+0.019069 test-rmse:1.010030+0.075713
## [113] train-rmse:0.378803+0.019082 test-rmse:1.010514+0.075766
## [114] train-rmse:0.376474+0.018101 test-rmse:1.010380+0.077305
## [115] train-rmse:0.372942+0.018245 test-rmse:1.010208+0.076735
## [116] train-rmse:0.371035+0.017637 test-rmse:1.010693+0.075906
## [117] train-rmse:0.366694+0.015128 test-rmse:1.009349+0.075477
## [118] train-rmse:0.362871+0.015899 test-rmse:1.004267+0.072867
## [119] train-rmse:0.359155+0.016183 test-rmse:1.003616+0.073250
## [120] train-rmse:0.356365+0.016489 test-rmse:1.003241+0.073303
## [121] train-rmse:0.354065+0.016996 test-rmse:1.003924+0.072558
## [122] train-rmse:0.350494+0.017799 test-rmse:1.002997+0.072202
## [123] train-rmse:0.348012+0.018688 test-rmse:1.002934+0.073318
## [124] train-rmse:0.345623+0.018960 test-rmse:1.002819+0.073174
## [125] train-rmse:0.342727+0.019643 test-rmse:1.003435+0.074459
## [126] train-rmse:0.340960+0.019559 test-rmse:1.003213+0.073426
## [127] train-rmse:0.337712+0.020344 test-rmse:1.002591+0.073275
## [128] train-rmse:0.335119+0.020089 test-rmse:1.002950+0.074688
## [129] train-rmse:0.332676+0.019351 test-rmse:1.002201+0.075008
## [130] train-rmse:0.330432+0.019377 test-rmse:1.002404+0.074832
## [131] train-rmse:0.327772+0.019732 test-rmse:1.002595+0.073245
## [132] train-rmse:0.325179+0.019272 test-rmse:1.002036+0.073363
## [133] train-rmse:0.322297+0.019742 test-rmse:1.000581+0.073318
## [134] train-rmse:0.319285+0.020277 test-rmse:0.998118+0.071178
## [135] train-rmse:0.317141+0.020716 test-rmse:0.998378+0.070507
## [136] train-rmse:0.315824+0.020429 test-rmse:0.999092+0.070698
## [137] train-rmse:0.313340+0.019226 test-rmse:0.998717+0.070586
## [138] train-rmse:0.310775+0.019770 test-rmse:0.999197+0.070199
## [139] train-rmse:0.308534+0.019698 test-rmse:0.996819+0.069550
## [140] train-rmse:0.305745+0.021116 test-rmse:0.996704+0.069731
## [141] train-rmse:0.304263+0.021463 test-rmse:0.996653+0.070124
## [142] train-rmse:0.302030+0.021954 test-rmse:0.996126+0.069944
## [143] train-rmse:0.298716+0.021615 test-rmse:0.995843+0.069033
## [144] train-rmse:0.295502+0.020455 test-rmse:0.995318+0.069094
## [145] train-rmse:0.293162+0.020801 test-rmse:0.995183+0.069061
## [146] train-rmse:0.291529+0.019980 test-rmse:0.994388+0.069793
## [147] train-rmse:0.288881+0.020019 test-rmse:0.993684+0.068760
## [148] train-rmse:0.285803+0.020205 test-rmse:0.994871+0.069715
## [149] train-rmse:0.284339+0.020156 test-rmse:0.995568+0.069900
## [150] train-rmse:0.282639+0.020096 test-rmse:0.995048+0.070211
## [151] train-rmse:0.280612+0.019788 test-rmse:0.994677+0.070530
## [152] train-rmse:0.278658+0.020081 test-rmse:0.995123+0.070688
## [153] train-rmse:0.276853+0.020492 test-rmse:0.995283+0.070402
## Stopping. Best iteration:
## [147] train-rmse:0.288881+0.020019 test-rmse:0.993684+0.068760
##
## 52
## [1] train-rmse:74.935020+0.075103 test-rmse:74.935632+0.418470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:57.029236+0.056907 test-rmse:57.029542+0.435851
## [3] train-rmse:43.410643+0.042960 test-rmse:43.410540+0.448676
## [4] train-rmse:33.055426+0.032244 test-rmse:33.054807+0.457865
## [5] train-rmse:25.185130+0.023974 test-rmse:25.183843+0.464019
## [6] train-rmse:19.208041+0.017613 test-rmse:19.205912+0.467458
## [7] train-rmse:14.674725+0.012875 test-rmse:14.671561+0.468210
## [8] train-rmse:11.238713+0.009134 test-rmse:11.227100+0.442238
## [9] train-rmse:8.636015+0.007803 test-rmse:8.608589+0.419396
## [10] train-rmse:6.661101+0.007070 test-rmse:6.642772+0.386531
## [11] train-rmse:5.166514+0.006650 test-rmse:5.166150+0.357397
## [12] train-rmse:4.038740+0.007711 test-rmse:4.036264+0.330414
## [13] train-rmse:3.188585+0.008339 test-rmse:3.190353+0.304602
## [14] train-rmse:2.547782+0.007514 test-rmse:2.571887+0.271421
## [15] train-rmse:2.077108+0.013777 test-rmse:2.136527+0.230384
## [16] train-rmse:1.730713+0.015900 test-rmse:1.834734+0.207958
## [17] train-rmse:1.478562+0.014143 test-rmse:1.623053+0.175780
## [18] train-rmse:1.297641+0.015222 test-rmse:1.487384+0.145946
## [19] train-rmse:1.167802+0.012056 test-rmse:1.400614+0.130524
## [20] train-rmse:1.081958+0.012990 test-rmse:1.334916+0.114997
## [21] train-rmse:1.013480+0.013336 test-rmse:1.294695+0.106721
## [22] train-rmse:0.959440+0.015303 test-rmse:1.266572+0.095564
## [23] train-rmse:0.918078+0.015909 test-rmse:1.239053+0.094356
## [24] train-rmse:0.884193+0.011837 test-rmse:1.220639+0.093586
## [25] train-rmse:0.851228+0.012522 test-rmse:1.207051+0.086000
## [26] train-rmse:0.825303+0.014577 test-rmse:1.189782+0.085207
## [27] train-rmse:0.806625+0.016066 test-rmse:1.181086+0.085882
## [28] train-rmse:0.784928+0.016357 test-rmse:1.163685+0.091221
## [29] train-rmse:0.765009+0.019901 test-rmse:1.157306+0.093566
## [30] train-rmse:0.745316+0.019180 test-rmse:1.142150+0.085501
## [31] train-rmse:0.728653+0.018560 test-rmse:1.129643+0.086090
## [32] train-rmse:0.712512+0.022892 test-rmse:1.123438+0.079547
## [33] train-rmse:0.694314+0.025482 test-rmse:1.114666+0.083949
## [34] train-rmse:0.682266+0.023974 test-rmse:1.104518+0.086559
## [35] train-rmse:0.664948+0.021517 test-rmse:1.094724+0.092855
## [36] train-rmse:0.649406+0.020694 test-rmse:1.091678+0.094731
## [37] train-rmse:0.636184+0.017576 test-rmse:1.082051+0.100124
## [38] train-rmse:0.622993+0.018145 test-rmse:1.073132+0.104980
## [39] train-rmse:0.611551+0.014157 test-rmse:1.065172+0.107868
## [40] train-rmse:0.600226+0.014523 test-rmse:1.057655+0.105099
## [41] train-rmse:0.588709+0.016761 test-rmse:1.055268+0.105672
## [42] train-rmse:0.578819+0.016213 test-rmse:1.051356+0.106443
## [43] train-rmse:0.565858+0.016064 test-rmse:1.053175+0.103862
## [44] train-rmse:0.557532+0.016702 test-rmse:1.047612+0.104303
## [45] train-rmse:0.545621+0.017554 test-rmse:1.038985+0.099137
## [46] train-rmse:0.538301+0.019198 test-rmse:1.034830+0.101197
## [47] train-rmse:0.527470+0.016474 test-rmse:1.029408+0.102716
## [48] train-rmse:0.520349+0.016658 test-rmse:1.027851+0.103426
## [49] train-rmse:0.513548+0.016665 test-rmse:1.021956+0.100954
## [50] train-rmse:0.502507+0.015717 test-rmse:1.018465+0.105216
## [51] train-rmse:0.494602+0.017279 test-rmse:1.015199+0.106173
## [52] train-rmse:0.486069+0.017698 test-rmse:1.011490+0.107404
## [53] train-rmse:0.475988+0.015540 test-rmse:1.008609+0.106688
## [54] train-rmse:0.469098+0.013666 test-rmse:1.006585+0.106654
## [55] train-rmse:0.462860+0.013813 test-rmse:1.008070+0.105246
## [56] train-rmse:0.457526+0.013928 test-rmse:1.008364+0.103300
## [57] train-rmse:0.451238+0.014537 test-rmse:1.003654+0.106226
## [58] train-rmse:0.445447+0.015420 test-rmse:0.999280+0.104829
## [59] train-rmse:0.439351+0.014980 test-rmse:0.998780+0.106624
## [60] train-rmse:0.431624+0.017396 test-rmse:0.999140+0.107689
## [61] train-rmse:0.424462+0.015852 test-rmse:0.993928+0.107033
## [62] train-rmse:0.415961+0.016558 test-rmse:0.993713+0.108141
## [63] train-rmse:0.410653+0.016569 test-rmse:0.992201+0.108280
## [64] train-rmse:0.405039+0.016863 test-rmse:0.991387+0.107661
## [65] train-rmse:0.398964+0.018494 test-rmse:0.990202+0.108175
## [66] train-rmse:0.394825+0.016895 test-rmse:0.989858+0.109290
## [67] train-rmse:0.388673+0.018615 test-rmse:0.986325+0.110181
## [68] train-rmse:0.383570+0.019675 test-rmse:0.985550+0.110240
## [69] train-rmse:0.377306+0.020782 test-rmse:0.984589+0.109796
## [70] train-rmse:0.373355+0.021556 test-rmse:0.983829+0.109199
## [71] train-rmse:0.369145+0.022358 test-rmse:0.985226+0.109282
## [72] train-rmse:0.363256+0.021641 test-rmse:0.983668+0.109429
## [73] train-rmse:0.357923+0.020577 test-rmse:0.982547+0.110867
## [74] train-rmse:0.354381+0.020929 test-rmse:0.979378+0.109737
## [75] train-rmse:0.348315+0.021878 test-rmse:0.978193+0.108978
## [76] train-rmse:0.342154+0.020430 test-rmse:0.976839+0.111447
## [77] train-rmse:0.336562+0.019319 test-rmse:0.977306+0.110164
## [78] train-rmse:0.329966+0.020372 test-rmse:0.974743+0.108568
## [79] train-rmse:0.325308+0.019881 test-rmse:0.973421+0.107899
## [80] train-rmse:0.320260+0.018784 test-rmse:0.973757+0.107482
## [81] train-rmse:0.316668+0.019060 test-rmse:0.973779+0.107102
## [82] train-rmse:0.310906+0.018253 test-rmse:0.972159+0.106231
## [83] train-rmse:0.306728+0.017779 test-rmse:0.972145+0.105377
## [84] train-rmse:0.298350+0.015805 test-rmse:0.970718+0.106385
## [85] train-rmse:0.294220+0.016021 test-rmse:0.968465+0.108060
## [86] train-rmse:0.291194+0.015792 test-rmse:0.967681+0.108187
## [87] train-rmse:0.287058+0.015137 test-rmse:0.966800+0.108975
## [88] train-rmse:0.283051+0.015170 test-rmse:0.966107+0.109369
## [89] train-rmse:0.279712+0.015728 test-rmse:0.968467+0.110331
## [90] train-rmse:0.275543+0.015696 test-rmse:0.968531+0.110024
## [91] train-rmse:0.271292+0.015682 test-rmse:0.967190+0.108504
## [92] train-rmse:0.268669+0.015603 test-rmse:0.967138+0.108755
## [93] train-rmse:0.265320+0.016188 test-rmse:0.965673+0.107968
## [94] train-rmse:0.261933+0.016412 test-rmse:0.964000+0.107707
## [95] train-rmse:0.257799+0.016087 test-rmse:0.962964+0.107786
## [96] train-rmse:0.254532+0.016527 test-rmse:0.963139+0.106516
## [97] train-rmse:0.251154+0.016091 test-rmse:0.962671+0.106044
## [98] train-rmse:0.248196+0.016626 test-rmse:0.961414+0.106295
## [99] train-rmse:0.243388+0.016009 test-rmse:0.960722+0.107109
## [100] train-rmse:0.241640+0.016377 test-rmse:0.959719+0.107783
## [101] train-rmse:0.238014+0.016861 test-rmse:0.960198+0.109389
## [102] train-rmse:0.234891+0.017105 test-rmse:0.958902+0.109959
## [103] train-rmse:0.230226+0.016612 test-rmse:0.956081+0.109050
## [104] train-rmse:0.226316+0.016668 test-rmse:0.954975+0.107619
## [105] train-rmse:0.223304+0.016627 test-rmse:0.955205+0.106394
## [106] train-rmse:0.220841+0.016684 test-rmse:0.954665+0.106353
## [107] train-rmse:0.217025+0.015429 test-rmse:0.955133+0.107557
## [108] train-rmse:0.214179+0.015643 test-rmse:0.954707+0.106745
## [109] train-rmse:0.211307+0.015957 test-rmse:0.953680+0.107261
## [110] train-rmse:0.207986+0.015649 test-rmse:0.953296+0.106688
## [111] train-rmse:0.206104+0.015814 test-rmse:0.952742+0.106926
## [112] train-rmse:0.203795+0.015562 test-rmse:0.951239+0.107077
## [113] train-rmse:0.202142+0.015634 test-rmse:0.951279+0.107174
## [114] train-rmse:0.200145+0.015424 test-rmse:0.952003+0.107041
## [115] train-rmse:0.196988+0.015486 test-rmse:0.951118+0.106512
## [116] train-rmse:0.195302+0.015428 test-rmse:0.952554+0.106734
## [117] train-rmse:0.192756+0.016215 test-rmse:0.952038+0.107207
## [118] train-rmse:0.190486+0.014438 test-rmse:0.952065+0.106132
## [119] train-rmse:0.188166+0.014598 test-rmse:0.951589+0.105602
## [120] train-rmse:0.185924+0.013521 test-rmse:0.952351+0.106237
## [121] train-rmse:0.182613+0.012867 test-rmse:0.950960+0.105602
## [122] train-rmse:0.181205+0.012537 test-rmse:0.950826+0.106608
## [123] train-rmse:0.178137+0.011142 test-rmse:0.950002+0.107455
## [124] train-rmse:0.175228+0.010442 test-rmse:0.950090+0.108254
## [125] train-rmse:0.173595+0.010683 test-rmse:0.948334+0.108528
## [126] train-rmse:0.170364+0.011606 test-rmse:0.947475+0.108345
## [127] train-rmse:0.167669+0.011770 test-rmse:0.948173+0.108934
## [128] train-rmse:0.164747+0.011654 test-rmse:0.947315+0.108066
## [129] train-rmse:0.161904+0.011023 test-rmse:0.946984+0.108051
## [130] train-rmse:0.160189+0.011439 test-rmse:0.946366+0.108390
## [131] train-rmse:0.157089+0.012158 test-rmse:0.946394+0.109335
## [132] train-rmse:0.154851+0.012015 test-rmse:0.946164+0.109529
## [133] train-rmse:0.153050+0.012048 test-rmse:0.946323+0.109237
## [134] train-rmse:0.151348+0.012433 test-rmse:0.946431+0.109410
## [135] train-rmse:0.149250+0.012536 test-rmse:0.946549+0.109598
## [136] train-rmse:0.147594+0.012376 test-rmse:0.946545+0.109886
## [137] train-rmse:0.146257+0.011679 test-rmse:0.946671+0.110282
## [138] train-rmse:0.144880+0.011802 test-rmse:0.945922+0.110401
## [139] train-rmse:0.143282+0.011943 test-rmse:0.945654+0.110817
## [140] train-rmse:0.141886+0.012288 test-rmse:0.946217+0.110577
## [141] train-rmse:0.140778+0.011693 test-rmse:0.947199+0.110266
## [142] train-rmse:0.138820+0.010582 test-rmse:0.946872+0.110723
## [143] train-rmse:0.137196+0.010134 test-rmse:0.946182+0.110500
## [144] train-rmse:0.135858+0.010146 test-rmse:0.946127+0.110915
## [145] train-rmse:0.133962+0.010435 test-rmse:0.946176+0.111114
## Stopping. Best iteration:
## [139] train-rmse:0.143282+0.011943 test-rmse:0.945654+0.110817
##
## 53
## [1] train-rmse:74.935020+0.075103 test-rmse:74.935632+0.418470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:57.029236+0.056907 test-rmse:57.029542+0.435851
## [3] train-rmse:43.410643+0.042960 test-rmse:43.410540+0.448676
## [4] train-rmse:33.055426+0.032244 test-rmse:33.054807+0.457865
## [5] train-rmse:25.185130+0.023974 test-rmse:25.183843+0.464019
## [6] train-rmse:19.208041+0.017613 test-rmse:19.205912+0.467458
## [7] train-rmse:14.674725+0.012875 test-rmse:14.671561+0.468210
## [8] train-rmse:11.238713+0.009134 test-rmse:11.227100+0.442238
## [9] train-rmse:8.636015+0.007803 test-rmse:8.608589+0.419396
## [10] train-rmse:6.660518+0.006648 test-rmse:6.642849+0.386460
## [11] train-rmse:5.162739+0.006744 test-rmse:5.149284+0.350638
## [12] train-rmse:4.031857+0.006012 test-rmse:4.021761+0.326130
## [13] train-rmse:3.170431+0.003914 test-rmse:3.174890+0.285096
## [14] train-rmse:2.524245+0.008130 test-rmse:2.539529+0.253394
## [15] train-rmse:2.040536+0.008570 test-rmse:2.102637+0.227543
## [16] train-rmse:1.679775+0.008056 test-rmse:1.786546+0.189456
## [17] train-rmse:1.415806+0.015572 test-rmse:1.566267+0.166746
## [18] train-rmse:1.222588+0.015373 test-rmse:1.415305+0.146753
## [19] train-rmse:1.085599+0.013906 test-rmse:1.316235+0.125936
## [20] train-rmse:0.978497+0.015714 test-rmse:1.250910+0.115497
## [21] train-rmse:0.901399+0.015430 test-rmse:1.210679+0.100409
## [22] train-rmse:0.840758+0.016294 test-rmse:1.171675+0.089423
## [23] train-rmse:0.796773+0.017100 test-rmse:1.146971+0.082961
## [24] train-rmse:0.762086+0.015401 test-rmse:1.127306+0.084764
## [25] train-rmse:0.723250+0.015201 test-rmse:1.109577+0.089575
## [26] train-rmse:0.696481+0.015745 test-rmse:1.092804+0.091907
## [27] train-rmse:0.664863+0.019510 test-rmse:1.074074+0.085905
## [28] train-rmse:0.644913+0.019082 test-rmse:1.065361+0.084426
## [29] train-rmse:0.624588+0.021563 test-rmse:1.059645+0.081240
## [30] train-rmse:0.608574+0.023025 test-rmse:1.054271+0.084928
## [31] train-rmse:0.586221+0.019619 test-rmse:1.046918+0.083781
## [32] train-rmse:0.570304+0.017449 test-rmse:1.040940+0.080418
## [33] train-rmse:0.556767+0.018267 test-rmse:1.037591+0.077106
## [34] train-rmse:0.545259+0.019902 test-rmse:1.033473+0.080664
## [35] train-rmse:0.530597+0.016543 test-rmse:1.031330+0.075308
## [36] train-rmse:0.520009+0.017851 test-rmse:1.027003+0.072559
## [37] train-rmse:0.499789+0.018266 test-rmse:1.022652+0.073746
## [38] train-rmse:0.489954+0.020638 test-rmse:1.016131+0.078366
## [39] train-rmse:0.478797+0.018260 test-rmse:1.008529+0.074618
## [40] train-rmse:0.468087+0.017321 test-rmse:1.004998+0.076739
## [41] train-rmse:0.456416+0.018681 test-rmse:1.001889+0.074498
## [42] train-rmse:0.447217+0.018620 test-rmse:1.000625+0.072229
## [43] train-rmse:0.436666+0.016845 test-rmse:0.998193+0.076557
## [44] train-rmse:0.426736+0.017028 test-rmse:0.995130+0.074863
## [45] train-rmse:0.416958+0.018442 test-rmse:0.991681+0.074709
## [46] train-rmse:0.407579+0.018140 test-rmse:0.987173+0.071774
## [47] train-rmse:0.398552+0.013193 test-rmse:0.986875+0.072425
## [48] train-rmse:0.388813+0.015103 test-rmse:0.988279+0.073720
## [49] train-rmse:0.377330+0.015053 test-rmse:0.985938+0.073279
## [50] train-rmse:0.370046+0.017595 test-rmse:0.985151+0.073857
## [51] train-rmse:0.361811+0.015038 test-rmse:0.980636+0.076278
## [52] train-rmse:0.355334+0.012919 test-rmse:0.979871+0.076721
## [53] train-rmse:0.345285+0.013987 test-rmse:0.976941+0.078403
## [54] train-rmse:0.336173+0.012134 test-rmse:0.975406+0.077320
## [55] train-rmse:0.327075+0.015172 test-rmse:0.975883+0.075241
## [56] train-rmse:0.321228+0.016385 test-rmse:0.976325+0.076194
## [57] train-rmse:0.314467+0.017816 test-rmse:0.974626+0.077311
## [58] train-rmse:0.308379+0.016092 test-rmse:0.973919+0.077272
## [59] train-rmse:0.303429+0.015698 test-rmse:0.973200+0.077534
## [60] train-rmse:0.295816+0.014867 test-rmse:0.972466+0.078176
## [61] train-rmse:0.291153+0.014412 test-rmse:0.969604+0.077773
## [62] train-rmse:0.285602+0.014807 test-rmse:0.968890+0.078866
## [63] train-rmse:0.277910+0.014999 test-rmse:0.966014+0.078928
## [64] train-rmse:0.272893+0.015350 test-rmse:0.962133+0.079714
## [65] train-rmse:0.267919+0.014198 test-rmse:0.960352+0.080287
## [66] train-rmse:0.263257+0.013170 test-rmse:0.961626+0.081157
## [67] train-rmse:0.259432+0.012903 test-rmse:0.959015+0.082643
## [68] train-rmse:0.255249+0.012545 test-rmse:0.958361+0.081032
## [69] train-rmse:0.249414+0.010983 test-rmse:0.955615+0.079998
## [70] train-rmse:0.244683+0.011947 test-rmse:0.954317+0.081839
## [71] train-rmse:0.240744+0.011909 test-rmse:0.953480+0.080323
## [72] train-rmse:0.235938+0.012954 test-rmse:0.953428+0.082026
## [73] train-rmse:0.229280+0.011876 test-rmse:0.951312+0.081829
## [74] train-rmse:0.226156+0.011180 test-rmse:0.950218+0.083014
## [75] train-rmse:0.221067+0.011865 test-rmse:0.949071+0.083870
## [76] train-rmse:0.216186+0.013169 test-rmse:0.948551+0.084103
## [77] train-rmse:0.211688+0.012465 test-rmse:0.948002+0.083207
## [78] train-rmse:0.205929+0.013109 test-rmse:0.946791+0.083800
## [79] train-rmse:0.202565+0.013303 test-rmse:0.946889+0.085349
## [80] train-rmse:0.198046+0.011930 test-rmse:0.946088+0.086053
## [81] train-rmse:0.193705+0.010401 test-rmse:0.945241+0.085206
## [82] train-rmse:0.188766+0.008764 test-rmse:0.943319+0.085831
## [83] train-rmse:0.185760+0.009203 test-rmse:0.943091+0.085763
## [84] train-rmse:0.181601+0.007759 test-rmse:0.941256+0.085585
## [85] train-rmse:0.176296+0.007541 test-rmse:0.941112+0.086108
## [86] train-rmse:0.173382+0.008071 test-rmse:0.940306+0.086476
## [87] train-rmse:0.170376+0.007088 test-rmse:0.941155+0.086218
## [88] train-rmse:0.166658+0.007636 test-rmse:0.939465+0.086097
## [89] train-rmse:0.163630+0.007972 test-rmse:0.939474+0.086228
## [90] train-rmse:0.161397+0.008201 test-rmse:0.939892+0.086256
## [91] train-rmse:0.158469+0.008984 test-rmse:0.939530+0.086313
## [92] train-rmse:0.156116+0.008512 test-rmse:0.938907+0.086806
## [93] train-rmse:0.153925+0.007454 test-rmse:0.937502+0.085916
## [94] train-rmse:0.151227+0.008118 test-rmse:0.936664+0.085084
## [95] train-rmse:0.148221+0.007231 test-rmse:0.935342+0.085040
## [96] train-rmse:0.145252+0.007904 test-rmse:0.934603+0.085774
## [97] train-rmse:0.142828+0.008403 test-rmse:0.934015+0.086129
## [98] train-rmse:0.140609+0.008499 test-rmse:0.933978+0.086758
## [99] train-rmse:0.137772+0.008219 test-rmse:0.933185+0.085790
## [100] train-rmse:0.135288+0.008199 test-rmse:0.932878+0.085472
## [101] train-rmse:0.133825+0.007786 test-rmse:0.932643+0.085568
## [102] train-rmse:0.131710+0.007754 test-rmse:0.932231+0.085848
## [103] train-rmse:0.129174+0.008103 test-rmse:0.932234+0.086327
## [104] train-rmse:0.127416+0.008576 test-rmse:0.932113+0.086344
## [105] train-rmse:0.125186+0.008266 test-rmse:0.932261+0.086502
## [106] train-rmse:0.122721+0.007519 test-rmse:0.931979+0.087422
## [107] train-rmse:0.121098+0.006233 test-rmse:0.931919+0.087486
## [108] train-rmse:0.119067+0.006922 test-rmse:0.931629+0.087705
## [109] train-rmse:0.115737+0.007158 test-rmse:0.930498+0.087620
## [110] train-rmse:0.113571+0.007438 test-rmse:0.930212+0.087659
## [111] train-rmse:0.111264+0.006829 test-rmse:0.929676+0.088710
## [112] train-rmse:0.109233+0.006023 test-rmse:0.929345+0.088749
## [113] train-rmse:0.107413+0.006316 test-rmse:0.928931+0.089094
## [114] train-rmse:0.105932+0.006228 test-rmse:0.928860+0.088770
## [115] train-rmse:0.103174+0.005271 test-rmse:0.928772+0.089430
## [116] train-rmse:0.101117+0.005336 test-rmse:0.928439+0.089741
## [117] train-rmse:0.099340+0.004820 test-rmse:0.928376+0.089232
## [118] train-rmse:0.097634+0.005221 test-rmse:0.928878+0.089666
## [119] train-rmse:0.095522+0.004654 test-rmse:0.928782+0.089963
## [120] train-rmse:0.093565+0.004695 test-rmse:0.928787+0.089634
## [121] train-rmse:0.092347+0.004999 test-rmse:0.929040+0.090260
## [122] train-rmse:0.090802+0.005341 test-rmse:0.929049+0.090322
## [123] train-rmse:0.089331+0.004829 test-rmse:0.928858+0.090152
## Stopping. Best iteration:
## [117] train-rmse:0.099340+0.004820 test-rmse:0.928376+0.089232
##
## 54
## [1] train-rmse:74.935020+0.075103 test-rmse:74.935632+0.418470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:57.029236+0.056907 test-rmse:57.029542+0.435851
## [3] train-rmse:43.410643+0.042960 test-rmse:43.410540+0.448676
## [4] train-rmse:33.055426+0.032244 test-rmse:33.054807+0.457865
## [5] train-rmse:25.185130+0.023974 test-rmse:25.183843+0.464019
## [6] train-rmse:19.208041+0.017613 test-rmse:19.205912+0.467458
## [7] train-rmse:14.674725+0.012875 test-rmse:14.671561+0.468210
## [8] train-rmse:11.238713+0.009134 test-rmse:11.227100+0.442238
## [9] train-rmse:8.636015+0.007803 test-rmse:8.608589+0.419396
## [10] train-rmse:6.660398+0.006583 test-rmse:6.644245+0.385203
## [11] train-rmse:5.160363+0.006611 test-rmse:5.142799+0.344526
## [12] train-rmse:4.025490+0.005701 test-rmse:4.015275+0.314266
## [13] train-rmse:3.161160+0.003525 test-rmse:3.170353+0.270909
## [14] train-rmse:2.503890+0.006299 test-rmse:2.541269+0.236272
## [15] train-rmse:2.012984+0.011058 test-rmse:2.069990+0.206994
## [16] train-rmse:1.640012+0.008932 test-rmse:1.749368+0.179027
## [17] train-rmse:1.363394+0.008662 test-rmse:1.537284+0.159505
## [18] train-rmse:1.155899+0.007150 test-rmse:1.384478+0.143114
## [19] train-rmse:1.001021+0.008769 test-rmse:1.281201+0.125973
## [20] train-rmse:0.885007+0.010870 test-rmse:1.205282+0.124716
## [21] train-rmse:0.802414+0.015040 test-rmse:1.157217+0.110275
## [22] train-rmse:0.739346+0.016014 test-rmse:1.124501+0.107889
## [23] train-rmse:0.688970+0.017213 test-rmse:1.098641+0.103888
## [24] train-rmse:0.650074+0.018255 test-rmse:1.082187+0.104890
## [25] train-rmse:0.620865+0.017360 test-rmse:1.072592+0.104082
## [26] train-rmse:0.588640+0.016119 test-rmse:1.047952+0.099693
## [27] train-rmse:0.559927+0.018775 test-rmse:1.040366+0.093349
## [28] train-rmse:0.538700+0.018499 test-rmse:1.034207+0.096583
## [29] train-rmse:0.519160+0.020154 test-rmse:1.026941+0.098036
## [30] train-rmse:0.503057+0.022174 test-rmse:1.017276+0.096979
## [31] train-rmse:0.488283+0.019972 test-rmse:1.018090+0.100552
## [32] train-rmse:0.467448+0.019453 test-rmse:1.013002+0.105366
## [33] train-rmse:0.450951+0.016293 test-rmse:1.007143+0.102638
## [34] train-rmse:0.436814+0.014756 test-rmse:0.997902+0.102857
## [35] train-rmse:0.423661+0.015284 test-rmse:0.994177+0.100852
## [36] train-rmse:0.411648+0.012179 test-rmse:0.992274+0.107637
## [37] train-rmse:0.398914+0.015340 test-rmse:0.988695+0.107272
## [38] train-rmse:0.386675+0.013366 test-rmse:0.987378+0.103938
## [39] train-rmse:0.372051+0.013101 test-rmse:0.985293+0.098216
## [40] train-rmse:0.361013+0.011275 test-rmse:0.983077+0.096345
## [41] train-rmse:0.349165+0.015250 test-rmse:0.979292+0.095586
## [42] train-rmse:0.339010+0.014082 test-rmse:0.979450+0.101517
## [43] train-rmse:0.329648+0.012855 test-rmse:0.977999+0.100698
## [44] train-rmse:0.321014+0.012869 test-rmse:0.976417+0.102799
## [45] train-rmse:0.311451+0.012749 test-rmse:0.978216+0.106309
## [46] train-rmse:0.304436+0.012477 test-rmse:0.976406+0.105563
## [47] train-rmse:0.295845+0.012325 test-rmse:0.973395+0.103019
## [48] train-rmse:0.288325+0.010807 test-rmse:0.971194+0.103228
## [49] train-rmse:0.280505+0.013420 test-rmse:0.971379+0.101827
## [50] train-rmse:0.273602+0.012242 test-rmse:0.970834+0.102927
## [51] train-rmse:0.264446+0.011282 test-rmse:0.968092+0.100891
## [52] train-rmse:0.256371+0.009885 test-rmse:0.965602+0.100973
## [53] train-rmse:0.248191+0.010608 test-rmse:0.967592+0.103822
## [54] train-rmse:0.243567+0.012172 test-rmse:0.965630+0.104436
## [55] train-rmse:0.235819+0.010870 test-rmse:0.964569+0.103988
## [56] train-rmse:0.231270+0.010688 test-rmse:0.964102+0.104706
## [57] train-rmse:0.224927+0.009901 test-rmse:0.965166+0.105499
## [58] train-rmse:0.217383+0.012184 test-rmse:0.964231+0.106035
## [59] train-rmse:0.210368+0.011667 test-rmse:0.963049+0.105178
## [60] train-rmse:0.206629+0.012189 test-rmse:0.962002+0.104110
## [61] train-rmse:0.200566+0.010217 test-rmse:0.959712+0.104002
## [62] train-rmse:0.193993+0.011268 test-rmse:0.961909+0.104571
## [63] train-rmse:0.188364+0.010981 test-rmse:0.961580+0.105485
## [64] train-rmse:0.183152+0.008854 test-rmse:0.961390+0.106957
## [65] train-rmse:0.177378+0.010971 test-rmse:0.959674+0.108356
## [66] train-rmse:0.172512+0.012166 test-rmse:0.960066+0.106121
## [67] train-rmse:0.168456+0.013176 test-rmse:0.959979+0.105330
## [68] train-rmse:0.161134+0.011144 test-rmse:0.959030+0.104763
## [69] train-rmse:0.157304+0.012026 test-rmse:0.958891+0.104981
## [70] train-rmse:0.154579+0.011346 test-rmse:0.958419+0.105822
## [71] train-rmse:0.150687+0.010452 test-rmse:0.958112+0.107049
## [72] train-rmse:0.146261+0.009631 test-rmse:0.957284+0.106575
## [73] train-rmse:0.142692+0.010206 test-rmse:0.957577+0.106455
## [74] train-rmse:0.139207+0.010912 test-rmse:0.957172+0.106204
## [75] train-rmse:0.135585+0.011215 test-rmse:0.957028+0.106945
## [76] train-rmse:0.132083+0.011591 test-rmse:0.956199+0.107424
## [77] train-rmse:0.128221+0.011509 test-rmse:0.955249+0.106860
## [78] train-rmse:0.125335+0.011858 test-rmse:0.954720+0.106734
## [79] train-rmse:0.122763+0.011767 test-rmse:0.954491+0.106746
## [80] train-rmse:0.119742+0.011595 test-rmse:0.953879+0.106206
## [81] train-rmse:0.117183+0.010780 test-rmse:0.954111+0.106628
## [82] train-rmse:0.112971+0.009601 test-rmse:0.953832+0.106219
## [83] train-rmse:0.110171+0.008533 test-rmse:0.954599+0.106251
## [84] train-rmse:0.107325+0.008151 test-rmse:0.954421+0.105949
## [85] train-rmse:0.104300+0.008163 test-rmse:0.953423+0.106815
## [86] train-rmse:0.102285+0.007996 test-rmse:0.952927+0.107104
## [87] train-rmse:0.099953+0.007561 test-rmse:0.951443+0.106542
## [88] train-rmse:0.097625+0.007029 test-rmse:0.951438+0.106366
## [89] train-rmse:0.094712+0.006893 test-rmse:0.951586+0.106264
## [90] train-rmse:0.091809+0.007245 test-rmse:0.951602+0.106174
## [91] train-rmse:0.089439+0.006569 test-rmse:0.951468+0.106352
## [92] train-rmse:0.087347+0.006608 test-rmse:0.950734+0.106493
## [93] train-rmse:0.085627+0.006178 test-rmse:0.950601+0.106469
## [94] train-rmse:0.083711+0.006471 test-rmse:0.950517+0.106043
## [95] train-rmse:0.081677+0.006193 test-rmse:0.950133+0.106142
## [96] train-rmse:0.079471+0.006099 test-rmse:0.950657+0.106458
## [97] train-rmse:0.077771+0.006247 test-rmse:0.950951+0.106422
## [98] train-rmse:0.075551+0.006548 test-rmse:0.950886+0.106530
## [99] train-rmse:0.073691+0.006245 test-rmse:0.950506+0.106364
## [100] train-rmse:0.071686+0.007361 test-rmse:0.949939+0.106777
## [101] train-rmse:0.069959+0.007230 test-rmse:0.949596+0.106927
## [102] train-rmse:0.068348+0.007055 test-rmse:0.949095+0.106868
## [103] train-rmse:0.066652+0.006558 test-rmse:0.949163+0.107030
## [104] train-rmse:0.065348+0.006221 test-rmse:0.949269+0.107352
## [105] train-rmse:0.063820+0.006735 test-rmse:0.948880+0.107119
## [106] train-rmse:0.062699+0.006817 test-rmse:0.948585+0.106978
## [107] train-rmse:0.061227+0.007066 test-rmse:0.948280+0.107218
## [108] train-rmse:0.059982+0.006942 test-rmse:0.948492+0.107609
## [109] train-rmse:0.058363+0.007075 test-rmse:0.948147+0.107734
## [110] train-rmse:0.056741+0.006546 test-rmse:0.948047+0.107594
## [111] train-rmse:0.054732+0.005939 test-rmse:0.948234+0.107650
## [112] train-rmse:0.053465+0.006216 test-rmse:0.948556+0.107477
## [113] train-rmse:0.052314+0.006269 test-rmse:0.949041+0.107475
## [114] train-rmse:0.050864+0.005835 test-rmse:0.948813+0.107743
## [115] train-rmse:0.049690+0.005755 test-rmse:0.948592+0.108106
## [116] train-rmse:0.048234+0.005549 test-rmse:0.948634+0.107855
## Stopping. Best iteration:
## [110] train-rmse:0.056741+0.006546 test-rmse:0.948047+0.107594
##
## 55
## [1] train-rmse:74.935020+0.075103 test-rmse:74.935632+0.418470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:57.029236+0.056907 test-rmse:57.029542+0.435851
## [3] train-rmse:43.410643+0.042960 test-rmse:43.410540+0.448676
## [4] train-rmse:33.055426+0.032244 test-rmse:33.054807+0.457865
## [5] train-rmse:25.185130+0.023974 test-rmse:25.183843+0.464019
## [6] train-rmse:19.208041+0.017613 test-rmse:19.205912+0.467458
## [7] train-rmse:14.674725+0.012875 test-rmse:14.671561+0.468210
## [8] train-rmse:11.238713+0.009134 test-rmse:11.227100+0.442238
## [9] train-rmse:8.636015+0.007803 test-rmse:8.608589+0.419396
## [10] train-rmse:6.660398+0.006583 test-rmse:6.644245+0.385203
## [11] train-rmse:5.159152+0.006123 test-rmse:5.144778+0.346228
## [12] train-rmse:4.024053+0.004692 test-rmse:4.019185+0.314474
## [13] train-rmse:3.157202+0.003696 test-rmse:3.168115+0.273580
## [14] train-rmse:2.500809+0.005587 test-rmse:2.531928+0.252475
## [15] train-rmse:2.001572+0.005372 test-rmse:2.083514+0.214700
## [16] train-rmse:1.617225+0.005919 test-rmse:1.753884+0.177185
## [17] train-rmse:1.333152+0.008202 test-rmse:1.508060+0.168761
## [18] train-rmse:1.117422+0.013866 test-rmse:1.353566+0.159002
## [19] train-rmse:0.949758+0.014047 test-rmse:1.246327+0.156165
## [20] train-rmse:0.829371+0.018327 test-rmse:1.180750+0.149868
## [21] train-rmse:0.737908+0.017727 test-rmse:1.132681+0.147446
## [22] train-rmse:0.666289+0.024301 test-rmse:1.101511+0.142637
## [23] train-rmse:0.605825+0.022685 test-rmse:1.081187+0.136142
## [24] train-rmse:0.555896+0.027568 test-rmse:1.065627+0.125809
## [25] train-rmse:0.521711+0.032205 test-rmse:1.051083+0.125148
## [26] train-rmse:0.491863+0.029039 test-rmse:1.044756+0.124402
## [27] train-rmse:0.466115+0.032050 test-rmse:1.034374+0.123631
## [28] train-rmse:0.443330+0.030303 test-rmse:1.029024+0.126743
## [29] train-rmse:0.419147+0.026529 test-rmse:1.018507+0.115545
## [30] train-rmse:0.401129+0.029628 test-rmse:1.018732+0.110465
## [31] train-rmse:0.384971+0.029741 test-rmse:1.018258+0.115010
## [32] train-rmse:0.368436+0.031589 test-rmse:1.013635+0.114983
## [33] train-rmse:0.353437+0.030070 test-rmse:1.008166+0.111807
## [34] train-rmse:0.339691+0.031208 test-rmse:1.005819+0.115956
## [35] train-rmse:0.325674+0.025338 test-rmse:1.002565+0.115419
## [36] train-rmse:0.314481+0.025651 test-rmse:0.999704+0.113922
## [37] train-rmse:0.303328+0.025063 test-rmse:0.996950+0.116790
## [38] train-rmse:0.293681+0.023380 test-rmse:0.998182+0.119016
## [39] train-rmse:0.279569+0.025703 test-rmse:0.989916+0.112077
## [40] train-rmse:0.268903+0.022890 test-rmse:0.991273+0.114364
## [41] train-rmse:0.257791+0.023430 test-rmse:0.990868+0.114484
## [42] train-rmse:0.250981+0.022108 test-rmse:0.989694+0.116010
## [43] train-rmse:0.244997+0.021960 test-rmse:0.988912+0.114517
## [44] train-rmse:0.234514+0.021504 test-rmse:0.984064+0.112197
## [45] train-rmse:0.225443+0.020613 test-rmse:0.983865+0.112140
## [46] train-rmse:0.218577+0.019308 test-rmse:0.984023+0.111462
## [47] train-rmse:0.212729+0.019434 test-rmse:0.982189+0.109886
## [48] train-rmse:0.204226+0.017377 test-rmse:0.982375+0.112060
## [49] train-rmse:0.197077+0.017525 test-rmse:0.980772+0.112713
## [50] train-rmse:0.191373+0.016966 test-rmse:0.982099+0.112571
## [51] train-rmse:0.182531+0.018458 test-rmse:0.981107+0.113124
## [52] train-rmse:0.176892+0.019347 test-rmse:0.979862+0.113612
## [53] train-rmse:0.169851+0.020221 test-rmse:0.979359+0.113587
## [54] train-rmse:0.165584+0.019336 test-rmse:0.979842+0.113711
## [55] train-rmse:0.159210+0.018921 test-rmse:0.978688+0.114539
## [56] train-rmse:0.154760+0.018349 test-rmse:0.978907+0.113700
## [57] train-rmse:0.150953+0.019783 test-rmse:0.978560+0.114488
## [58] train-rmse:0.145895+0.018556 test-rmse:0.978554+0.114809
## [59] train-rmse:0.139689+0.015706 test-rmse:0.978040+0.115928
## [60] train-rmse:0.134947+0.014862 test-rmse:0.978131+0.116647
## [61] train-rmse:0.130295+0.013322 test-rmse:0.977184+0.116432
## [62] train-rmse:0.127067+0.013005 test-rmse:0.976707+0.116499
## [63] train-rmse:0.124427+0.013312 test-rmse:0.976389+0.116217
## [64] train-rmse:0.120559+0.014135 test-rmse:0.976607+0.116948
## [65] train-rmse:0.116931+0.012893 test-rmse:0.975833+0.116479
## [66] train-rmse:0.113864+0.012481 test-rmse:0.976231+0.117270
## [67] train-rmse:0.109099+0.010514 test-rmse:0.975982+0.117023
## [68] train-rmse:0.105457+0.010069 test-rmse:0.976150+0.116617
## [69] train-rmse:0.101886+0.008838 test-rmse:0.974807+0.115915
## [70] train-rmse:0.099023+0.008882 test-rmse:0.975199+0.116238
## [71] train-rmse:0.094936+0.009304 test-rmse:0.975095+0.116167
## [72] train-rmse:0.092027+0.008978 test-rmse:0.974888+0.116695
## [73] train-rmse:0.089777+0.008543 test-rmse:0.974268+0.116100
## [74] train-rmse:0.086881+0.008824 test-rmse:0.974049+0.116326
## [75] train-rmse:0.084473+0.008875 test-rmse:0.974021+0.115595
## [76] train-rmse:0.081166+0.009161 test-rmse:0.973391+0.115303
## [77] train-rmse:0.078618+0.008948 test-rmse:0.973143+0.115298
## [78] train-rmse:0.076184+0.008867 test-rmse:0.973111+0.115548
## [79] train-rmse:0.073569+0.008490 test-rmse:0.972208+0.115219
## [80] train-rmse:0.071407+0.007695 test-rmse:0.971572+0.114136
## [81] train-rmse:0.068937+0.007181 test-rmse:0.971776+0.114316
## [82] train-rmse:0.065634+0.006665 test-rmse:0.971835+0.114044
## [83] train-rmse:0.064095+0.006434 test-rmse:0.971948+0.114020
## [84] train-rmse:0.062341+0.006352 test-rmse:0.972176+0.114301
## [85] train-rmse:0.060892+0.006300 test-rmse:0.972318+0.114412
## [86] train-rmse:0.058806+0.006120 test-rmse:0.972105+0.114833
## Stopping. Best iteration:
## [80] train-rmse:0.071407+0.007695 test-rmse:0.971572+0.114136
##
## 56
## [1] train-rmse:72.973630+0.073108 test-rmse:72.974214+0.420392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:54.084589+0.053899 test-rmse:54.084826+0.438661
## [3] train-rmse:40.095586+0.039547 test-rmse:40.095350+0.451697
## [4] train-rmse:29.739208+0.028775 test-rmse:29.738348+0.460589
## [5] train-rmse:22.077126+0.020671 test-rmse:22.075455+0.466019
## [6] train-rmse:16.415070+0.014660 test-rmse:16.412361+0.468234
## [7] train-rmse:12.237465+0.010317 test-rmse:12.240607+0.437841
## [8] train-rmse:9.159632+0.007283 test-rmse:9.149301+0.436970
## [9] train-rmse:6.898798+0.006999 test-rmse:6.883439+0.422561
## [10] train-rmse:5.250659+0.009739 test-rmse:5.233542+0.405578
## [11] train-rmse:4.061358+0.012394 test-rmse:4.056931+0.377645
## [12] train-rmse:3.219060+0.016570 test-rmse:3.219210+0.347440
## [13] train-rmse:2.634353+0.019967 test-rmse:2.645930+0.297148
## [14] train-rmse:2.239980+0.024141 test-rmse:2.272309+0.242974
## [15] train-rmse:1.983030+0.026624 test-rmse:2.031633+0.192651
## [16] train-rmse:1.817444+0.029112 test-rmse:1.870761+0.165300
## [17] train-rmse:1.711025+0.029461 test-rmse:1.775197+0.137429
## [18] train-rmse:1.642144+0.029382 test-rmse:1.715303+0.118613
## [19] train-rmse:1.595444+0.029381 test-rmse:1.679700+0.114742
## [20] train-rmse:1.562368+0.028358 test-rmse:1.643086+0.110186
## [21] train-rmse:1.537445+0.027714 test-rmse:1.621119+0.114362
## [22] train-rmse:1.516575+0.026849 test-rmse:1.615248+0.122625
## [23] train-rmse:1.498212+0.026748 test-rmse:1.600867+0.119241
## [24] train-rmse:1.482716+0.025897 test-rmse:1.596272+0.120051
## [25] train-rmse:1.468465+0.025056 test-rmse:1.577072+0.109651
## [26] train-rmse:1.455567+0.025006 test-rmse:1.569000+0.109049
## [27] train-rmse:1.443815+0.024726 test-rmse:1.565964+0.110352
## [28] train-rmse:1.432093+0.024456 test-rmse:1.553771+0.110213
## [29] train-rmse:1.420810+0.024044 test-rmse:1.544614+0.112083
## [30] train-rmse:1.409927+0.023808 test-rmse:1.545894+0.112737
## [31] train-rmse:1.399462+0.023402 test-rmse:1.545415+0.113169
## [32] train-rmse:1.389362+0.022990 test-rmse:1.541511+0.112687
## [33] train-rmse:1.379532+0.022837 test-rmse:1.533389+0.101031
## [34] train-rmse:1.369996+0.022849 test-rmse:1.526946+0.102916
## [35] train-rmse:1.360800+0.022920 test-rmse:1.516653+0.106752
## [36] train-rmse:1.351811+0.023166 test-rmse:1.503930+0.095811
## [37] train-rmse:1.343433+0.022997 test-rmse:1.501093+0.098949
## [38] train-rmse:1.335236+0.022775 test-rmse:1.495320+0.102245
## [39] train-rmse:1.327195+0.022560 test-rmse:1.488570+0.099328
## [40] train-rmse:1.319221+0.022476 test-rmse:1.482956+0.101179
## [41] train-rmse:1.311501+0.022128 test-rmse:1.473541+0.100167
## [42] train-rmse:1.303985+0.021797 test-rmse:1.472904+0.098832
## [43] train-rmse:1.296621+0.021512 test-rmse:1.466409+0.099781
## [44] train-rmse:1.289365+0.021052 test-rmse:1.464541+0.101540
## [45] train-rmse:1.282254+0.020744 test-rmse:1.461203+0.104556
## [46] train-rmse:1.275369+0.020491 test-rmse:1.456788+0.102548
## [47] train-rmse:1.268720+0.020365 test-rmse:1.451585+0.097517
## [48] train-rmse:1.262216+0.020181 test-rmse:1.449846+0.092843
## [49] train-rmse:1.255884+0.020221 test-rmse:1.445492+0.091823
## [50] train-rmse:1.249678+0.020327 test-rmse:1.438944+0.092024
## [51] train-rmse:1.243613+0.020391 test-rmse:1.436694+0.095503
## [52] train-rmse:1.237886+0.020473 test-rmse:1.438902+0.091675
## [53] train-rmse:1.232114+0.020622 test-rmse:1.433530+0.086937
## [54] train-rmse:1.226452+0.020492 test-rmse:1.431834+0.087507
## [55] train-rmse:1.221083+0.020549 test-rmse:1.426004+0.089475
## [56] train-rmse:1.215789+0.020599 test-rmse:1.421500+0.092147
## [57] train-rmse:1.210539+0.020696 test-rmse:1.420218+0.090010
## [58] train-rmse:1.205452+0.020820 test-rmse:1.418927+0.093445
## [59] train-rmse:1.200413+0.020991 test-rmse:1.413584+0.091132
## [60] train-rmse:1.195423+0.021025 test-rmse:1.407494+0.091755
## [61] train-rmse:1.190707+0.020964 test-rmse:1.396272+0.095616
## [62] train-rmse:1.186098+0.020945 test-rmse:1.393758+0.097336
## [63] train-rmse:1.181347+0.021053 test-rmse:1.387711+0.096665
## [64] train-rmse:1.176974+0.020976 test-rmse:1.384878+0.093889
## [65] train-rmse:1.172646+0.020775 test-rmse:1.382143+0.091788
## [66] train-rmse:1.168382+0.020585 test-rmse:1.376954+0.093257
## [67] train-rmse:1.164158+0.020431 test-rmse:1.373545+0.093064
## [68] train-rmse:1.160087+0.020349 test-rmse:1.373817+0.094530
## [69] train-rmse:1.156012+0.020247 test-rmse:1.371235+0.094511
## [70] train-rmse:1.152028+0.020186 test-rmse:1.369722+0.094849
## [71] train-rmse:1.148009+0.020000 test-rmse:1.372605+0.089938
## [72] train-rmse:1.144251+0.019902 test-rmse:1.369078+0.090785
## [73] train-rmse:1.140612+0.019888 test-rmse:1.365381+0.091383
## [74] train-rmse:1.137056+0.019820 test-rmse:1.365473+0.089692
## [75] train-rmse:1.133573+0.019790 test-rmse:1.364924+0.090284
## [76] train-rmse:1.130063+0.019729 test-rmse:1.359597+0.092051
## [77] train-rmse:1.126653+0.019627 test-rmse:1.352722+0.092446
## [78] train-rmse:1.123359+0.019546 test-rmse:1.349677+0.091726
## [79] train-rmse:1.120085+0.019535 test-rmse:1.345873+0.093210
## [80] train-rmse:1.116905+0.019536 test-rmse:1.340881+0.089980
## [81] train-rmse:1.113747+0.019525 test-rmse:1.338909+0.093137
## [82] train-rmse:1.110689+0.019588 test-rmse:1.332140+0.090758
## [83] train-rmse:1.107695+0.019677 test-rmse:1.332294+0.091118
## [84] train-rmse:1.104739+0.019754 test-rmse:1.330217+0.090460
## [85] train-rmse:1.101808+0.019790 test-rmse:1.325510+0.089770
## [86] train-rmse:1.098901+0.019785 test-rmse:1.326375+0.085318
## [87] train-rmse:1.096015+0.019681 test-rmse:1.325546+0.083802
## [88] train-rmse:1.093293+0.019636 test-rmse:1.325066+0.081602
## [89] train-rmse:1.090615+0.019577 test-rmse:1.322293+0.083835
## [90] train-rmse:1.087963+0.019510 test-rmse:1.322616+0.086067
## [91] train-rmse:1.085339+0.019496 test-rmse:1.320807+0.084929
## [92] train-rmse:1.082720+0.019489 test-rmse:1.318767+0.086066
## [93] train-rmse:1.080153+0.019414 test-rmse:1.317159+0.086133
## [94] train-rmse:1.077593+0.019382 test-rmse:1.316707+0.087167
## [95] train-rmse:1.075051+0.019362 test-rmse:1.315563+0.090092
## [96] train-rmse:1.072664+0.019367 test-rmse:1.313961+0.090892
## [97] train-rmse:1.070336+0.019357 test-rmse:1.311389+0.090234
## [98] train-rmse:1.068069+0.019335 test-rmse:1.307997+0.092748
## [99] train-rmse:1.065784+0.019312 test-rmse:1.305830+0.093699
## [100] train-rmse:1.063523+0.019228 test-rmse:1.305746+0.095480
## [101] train-rmse:1.061257+0.019156 test-rmse:1.304157+0.095663
## [102] train-rmse:1.059004+0.019103 test-rmse:1.300927+0.093249
## [103] train-rmse:1.056782+0.018987 test-rmse:1.298494+0.092991
## [104] train-rmse:1.054647+0.018917 test-rmse:1.297555+0.089185
## [105] train-rmse:1.052494+0.018784 test-rmse:1.297135+0.089595
## [106] train-rmse:1.050429+0.018700 test-rmse:1.296424+0.085810
## [107] train-rmse:1.048342+0.018592 test-rmse:1.296612+0.089258
## [108] train-rmse:1.046327+0.018497 test-rmse:1.295699+0.089074
## [109] train-rmse:1.044361+0.018369 test-rmse:1.296072+0.088435
## [110] train-rmse:1.042423+0.018305 test-rmse:1.292307+0.090058
## [111] train-rmse:1.040537+0.018232 test-rmse:1.290887+0.088929
## [112] train-rmse:1.038703+0.018197 test-rmse:1.290551+0.088989
## [113] train-rmse:1.036906+0.018155 test-rmse:1.290267+0.090538
## [114] train-rmse:1.035119+0.018108 test-rmse:1.289214+0.090629
## [115] train-rmse:1.033343+0.018047 test-rmse:1.287613+0.092843
## [116] train-rmse:1.031556+0.018008 test-rmse:1.285527+0.094105
## [117] train-rmse:1.029824+0.017984 test-rmse:1.283989+0.093109
## [118] train-rmse:1.028081+0.017956 test-rmse:1.282722+0.095344
## [119] train-rmse:1.026402+0.017951 test-rmse:1.282596+0.094424
## [120] train-rmse:1.024704+0.017894 test-rmse:1.281655+0.094046
## [121] train-rmse:1.022966+0.017889 test-rmse:1.281288+0.092928
## [122] train-rmse:1.021285+0.017850 test-rmse:1.276261+0.088993
## [123] train-rmse:1.019653+0.017801 test-rmse:1.274919+0.090830
## [124] train-rmse:1.018037+0.017751 test-rmse:1.275910+0.089687
## [125] train-rmse:1.016482+0.017718 test-rmse:1.273418+0.091760
## [126] train-rmse:1.014943+0.017694 test-rmse:1.273753+0.091828
## [127] train-rmse:1.013389+0.017655 test-rmse:1.271814+0.091660
## [128] train-rmse:1.011863+0.017665 test-rmse:1.270020+0.092295
## [129] train-rmse:1.010356+0.017677 test-rmse:1.269342+0.092384
## [130] train-rmse:1.008849+0.017648 test-rmse:1.270756+0.088239
## [131] train-rmse:1.007400+0.017624 test-rmse:1.269356+0.089606
## [132] train-rmse:1.005945+0.017597 test-rmse:1.266166+0.089238
## [133] train-rmse:1.004486+0.017561 test-rmse:1.266673+0.088398
## [134] train-rmse:1.003042+0.017551 test-rmse:1.267771+0.088177
## [135] train-rmse:1.001646+0.017553 test-rmse:1.266596+0.088810
## [136] train-rmse:1.000277+0.017566 test-rmse:1.265353+0.088761
## [137] train-rmse:0.998934+0.017568 test-rmse:1.263601+0.088411
## [138] train-rmse:0.997620+0.017566 test-rmse:1.262629+0.089272
## [139] train-rmse:0.996274+0.017597 test-rmse:1.260933+0.089754
## [140] train-rmse:0.994907+0.017649 test-rmse:1.260474+0.089258
## [141] train-rmse:0.993560+0.017627 test-rmse:1.260791+0.089786
## [142] train-rmse:0.992256+0.017611 test-rmse:1.260988+0.088912
## [143] train-rmse:0.990987+0.017598 test-rmse:1.260172+0.089766
## [144] train-rmse:0.989690+0.017588 test-rmse:1.255735+0.087450
## [145] train-rmse:0.988430+0.017582 test-rmse:1.253875+0.086279
## [146] train-rmse:0.987212+0.017588 test-rmse:1.253572+0.085561
## [147] train-rmse:0.985991+0.017603 test-rmse:1.254821+0.083133
## [148] train-rmse:0.984799+0.017570 test-rmse:1.255466+0.082385
## [149] train-rmse:0.983599+0.017572 test-rmse:1.254773+0.082462
## [150] train-rmse:0.982385+0.017593 test-rmse:1.253464+0.081456
## [151] train-rmse:0.981197+0.017587 test-rmse:1.253609+0.082198
## [152] train-rmse:0.980007+0.017603 test-rmse:1.252331+0.081710
## [153] train-rmse:0.978844+0.017618 test-rmse:1.251457+0.082392
## [154] train-rmse:0.977684+0.017632 test-rmse:1.252159+0.081753
## [155] train-rmse:0.976498+0.017643 test-rmse:1.251490+0.082678
## [156] train-rmse:0.975354+0.017652 test-rmse:1.249336+0.084208
## [157] train-rmse:0.974206+0.017650 test-rmse:1.249483+0.083568
## [158] train-rmse:0.973028+0.017690 test-rmse:1.248260+0.084506
## [159] train-rmse:0.971956+0.017696 test-rmse:1.247708+0.085043
## [160] train-rmse:0.970890+0.017688 test-rmse:1.247548+0.084669
## [161] train-rmse:0.969821+0.017680 test-rmse:1.249832+0.086221
## [162] train-rmse:0.968763+0.017675 test-rmse:1.247873+0.086733
## [163] train-rmse:0.967723+0.017656 test-rmse:1.246645+0.086495
## [164] train-rmse:0.966688+0.017661 test-rmse:1.246624+0.085794
## [165] train-rmse:0.965597+0.017665 test-rmse:1.243460+0.087108
## [166] train-rmse:0.964580+0.017681 test-rmse:1.244090+0.084329
## [167] train-rmse:0.963513+0.017734 test-rmse:1.242831+0.085814
## [168] train-rmse:0.962491+0.017702 test-rmse:1.242393+0.085403
## [169] train-rmse:0.961512+0.017709 test-rmse:1.241549+0.085171
## [170] train-rmse:0.960479+0.017742 test-rmse:1.239215+0.084687
## [171] train-rmse:0.959489+0.017741 test-rmse:1.238846+0.083102
## [172] train-rmse:0.958509+0.017750 test-rmse:1.238517+0.082865
## [173] train-rmse:0.957524+0.017801 test-rmse:1.239652+0.082014
## [174] train-rmse:0.956534+0.017786 test-rmse:1.238351+0.083063
## [175] train-rmse:0.955568+0.017782 test-rmse:1.236940+0.084677
## [176] train-rmse:0.954608+0.017819 test-rmse:1.236711+0.086683
## [177] train-rmse:0.953626+0.017861 test-rmse:1.235013+0.086500
## [178] train-rmse:0.952687+0.017886 test-rmse:1.234226+0.087117
## [179] train-rmse:0.951783+0.017913 test-rmse:1.234059+0.087484
## [180] train-rmse:0.950845+0.017948 test-rmse:1.233350+0.087957
## [181] train-rmse:0.949906+0.017984 test-rmse:1.232426+0.086668
## [182] train-rmse:0.949024+0.017996 test-rmse:1.234170+0.087436
## [183] train-rmse:0.948134+0.017987 test-rmse:1.234070+0.087215
## [184] train-rmse:0.947259+0.018012 test-rmse:1.232964+0.087112
## [185] train-rmse:0.946326+0.018036 test-rmse:1.234186+0.086748
## [186] train-rmse:0.945454+0.018065 test-rmse:1.233266+0.087016
## [187] train-rmse:0.944594+0.018077 test-rmse:1.231131+0.086859
## [188] train-rmse:0.943739+0.018085 test-rmse:1.232694+0.087572
## [189] train-rmse:0.942889+0.018100 test-rmse:1.231906+0.087248
## [190] train-rmse:0.942035+0.018120 test-rmse:1.233614+0.086077
## [191] train-rmse:0.941214+0.018127 test-rmse:1.230877+0.084473
## [192] train-rmse:0.940401+0.018135 test-rmse:1.230294+0.086147
## [193] train-rmse:0.939532+0.018149 test-rmse:1.230413+0.086310
## [194] train-rmse:0.938751+0.018174 test-rmse:1.229920+0.085587
## [195] train-rmse:0.937943+0.018183 test-rmse:1.230094+0.085050
## [196] train-rmse:0.937122+0.018220 test-rmse:1.229314+0.085812
## [197] train-rmse:0.936331+0.018244 test-rmse:1.229183+0.084468
## [198] train-rmse:0.935544+0.018230 test-rmse:1.229043+0.086638
## [199] train-rmse:0.934729+0.018225 test-rmse:1.228431+0.085192
## [200] train-rmse:0.933924+0.018242 test-rmse:1.228353+0.084820
## [201] train-rmse:0.933161+0.018250 test-rmse:1.227678+0.084490
## [202] train-rmse:0.932406+0.018249 test-rmse:1.227228+0.083698
## [203] train-rmse:0.931590+0.018257 test-rmse:1.227731+0.083559
## [204] train-rmse:0.930809+0.018275 test-rmse:1.227584+0.083811
## [205] train-rmse:0.930043+0.018298 test-rmse:1.227330+0.084362
## [206] train-rmse:0.929280+0.018334 test-rmse:1.227434+0.083070
## [207] train-rmse:0.928503+0.018307 test-rmse:1.228073+0.083014
## [208] train-rmse:0.927774+0.018342 test-rmse:1.228391+0.084046
## Stopping. Best iteration:
## [202] train-rmse:0.932406+0.018249 test-rmse:1.227228+0.083698
##
## 57
## [1] train-rmse:72.973630+0.073108 test-rmse:72.974214+0.420392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:54.084589+0.053899 test-rmse:54.084826+0.438661
## [3] train-rmse:40.095586+0.039547 test-rmse:40.095350+0.451697
## [4] train-rmse:29.739208+0.028775 test-rmse:29.738348+0.460589
## [5] train-rmse:22.077126+0.020671 test-rmse:22.075455+0.466019
## [6] train-rmse:16.415070+0.014660 test-rmse:16.412361+0.468234
## [7] train-rmse:12.237465+0.010317 test-rmse:12.240607+0.437841
## [8] train-rmse:9.156411+0.006799 test-rmse:9.143914+0.430245
## [9] train-rmse:6.887993+0.006220 test-rmse:6.871933+0.407301
## [10] train-rmse:5.224180+0.010063 test-rmse:5.213276+0.384102
## [11] train-rmse:4.011316+0.012293 test-rmse:4.007571+0.356028
## [12] train-rmse:3.141297+0.017120 test-rmse:3.149305+0.325292
## [13] train-rmse:2.523336+0.019784 test-rmse:2.542827+0.283536
## [14] train-rmse:2.097613+0.022876 test-rmse:2.143921+0.239107
## [15] train-rmse:1.805832+0.021361 test-rmse:1.882381+0.208533
## [16] train-rmse:1.617358+0.023826 test-rmse:1.713227+0.184478
## [17] train-rmse:1.490755+0.025618 test-rmse:1.616309+0.147115
## [18] train-rmse:1.408316+0.029656 test-rmse:1.564903+0.121343
## [19] train-rmse:1.353707+0.030193 test-rmse:1.512231+0.112636
## [20] train-rmse:1.312934+0.029157 test-rmse:1.483818+0.108866
## [21] train-rmse:1.277292+0.028771 test-rmse:1.462809+0.103921
## [22] train-rmse:1.253455+0.028757 test-rmse:1.442158+0.089322
## [23] train-rmse:1.232087+0.026953 test-rmse:1.425547+0.087289
## [24] train-rmse:1.213040+0.027987 test-rmse:1.417745+0.080473
## [25] train-rmse:1.194523+0.030221 test-rmse:1.410213+0.079661
## [26] train-rmse:1.179137+0.028998 test-rmse:1.399001+0.083387
## [27] train-rmse:1.164583+0.029399 test-rmse:1.388615+0.079316
## [28] train-rmse:1.148668+0.026762 test-rmse:1.390766+0.074902
## [29] train-rmse:1.132970+0.027713 test-rmse:1.373202+0.074496
## [30] train-rmse:1.120745+0.026956 test-rmse:1.365111+0.068586
## [31] train-rmse:1.106522+0.025002 test-rmse:1.356266+0.069287
## [32] train-rmse:1.091251+0.025632 test-rmse:1.347727+0.069113
## [33] train-rmse:1.074429+0.027555 test-rmse:1.335337+0.080332
## [34] train-rmse:1.064554+0.027239 test-rmse:1.321011+0.085132
## [35] train-rmse:1.053507+0.027837 test-rmse:1.318938+0.081271
## [36] train-rmse:1.044299+0.027663 test-rmse:1.315815+0.077985
## [37] train-rmse:1.034043+0.027935 test-rmse:1.308831+0.082203
## [38] train-rmse:1.022946+0.025131 test-rmse:1.306299+0.082758
## [39] train-rmse:1.010986+0.026564 test-rmse:1.300606+0.078144
## [40] train-rmse:1.002747+0.026599 test-rmse:1.289854+0.081817
## [41] train-rmse:0.993007+0.026085 test-rmse:1.287458+0.083669
## [42] train-rmse:0.982165+0.023877 test-rmse:1.283487+0.078253
## [43] train-rmse:0.972138+0.024978 test-rmse:1.272894+0.079815
## [44] train-rmse:0.964182+0.024224 test-rmse:1.264627+0.076932
## [45] train-rmse:0.953469+0.021074 test-rmse:1.263081+0.076687
## [46] train-rmse:0.944074+0.020524 test-rmse:1.252712+0.081183
## [47] train-rmse:0.936355+0.018753 test-rmse:1.249364+0.084409
## [48] train-rmse:0.926785+0.019617 test-rmse:1.246183+0.084419
## [49] train-rmse:0.918633+0.016723 test-rmse:1.241487+0.086222
## [50] train-rmse:0.911529+0.015978 test-rmse:1.237596+0.086754
## [51] train-rmse:0.905247+0.015884 test-rmse:1.228755+0.085912
## [52] train-rmse:0.899113+0.014766 test-rmse:1.224717+0.086288
## [53] train-rmse:0.891919+0.014444 test-rmse:1.223118+0.082485
## [54] train-rmse:0.885542+0.015217 test-rmse:1.217364+0.082056
## [55] train-rmse:0.877563+0.014337 test-rmse:1.215005+0.086209
## [56] train-rmse:0.868236+0.012461 test-rmse:1.211310+0.087413
## [57] train-rmse:0.858556+0.017949 test-rmse:1.199555+0.081428
## [58] train-rmse:0.850423+0.017679 test-rmse:1.194759+0.084807
## [59] train-rmse:0.844885+0.017183 test-rmse:1.195593+0.086986
## [60] train-rmse:0.837416+0.017800 test-rmse:1.195634+0.085735
## [61] train-rmse:0.832381+0.017254 test-rmse:1.195489+0.087221
## [62] train-rmse:0.827356+0.015957 test-rmse:1.187125+0.091143
## [63] train-rmse:0.820048+0.014493 test-rmse:1.180830+0.088876
## [64] train-rmse:0.815619+0.014015 test-rmse:1.177139+0.089400
## [65] train-rmse:0.811009+0.013677 test-rmse:1.173046+0.090995
## [66] train-rmse:0.804056+0.011678 test-rmse:1.169250+0.093541
## [67] train-rmse:0.799612+0.011809 test-rmse:1.165258+0.092420
## [68] train-rmse:0.794866+0.010666 test-rmse:1.163068+0.092542
## [69] train-rmse:0.790050+0.011194 test-rmse:1.156674+0.092800
## [70] train-rmse:0.781916+0.011098 test-rmse:1.155194+0.097737
## [71] train-rmse:0.775826+0.011746 test-rmse:1.154591+0.097291
## [72] train-rmse:0.771935+0.011322 test-rmse:1.155103+0.093189
## [73] train-rmse:0.767006+0.012049 test-rmse:1.151435+0.094397
## [74] train-rmse:0.761765+0.010650 test-rmse:1.144133+0.092659
## [75] train-rmse:0.758620+0.010630 test-rmse:1.142616+0.092805
## [76] train-rmse:0.754549+0.010593 test-rmse:1.140062+0.092545
## [77] train-rmse:0.748963+0.011967 test-rmse:1.134733+0.096573
## [78] train-rmse:0.744780+0.012569 test-rmse:1.134482+0.095882
## [79] train-rmse:0.739900+0.010296 test-rmse:1.135018+0.101680
## [80] train-rmse:0.735814+0.010410 test-rmse:1.132114+0.100246
## [81] train-rmse:0.731440+0.009807 test-rmse:1.126785+0.105588
## [82] train-rmse:0.727094+0.009370 test-rmse:1.128847+0.103562
## [83] train-rmse:0.721463+0.009861 test-rmse:1.123489+0.100347
## [84] train-rmse:0.717594+0.010274 test-rmse:1.120284+0.101822
## [85] train-rmse:0.713063+0.012155 test-rmse:1.119077+0.100065
## [86] train-rmse:0.709646+0.012471 test-rmse:1.116437+0.099548
## [87] train-rmse:0.702281+0.011678 test-rmse:1.109869+0.100753
## [88] train-rmse:0.698777+0.011612 test-rmse:1.109345+0.099930
## [89] train-rmse:0.693450+0.010982 test-rmse:1.108738+0.103563
## [90] train-rmse:0.689310+0.011149 test-rmse:1.106444+0.107805
## [91] train-rmse:0.684880+0.011522 test-rmse:1.105143+0.108044
## [92] train-rmse:0.681795+0.011079 test-rmse:1.102976+0.107504
## [93] train-rmse:0.678572+0.009768 test-rmse:1.105308+0.111663
## [94] train-rmse:0.674631+0.009341 test-rmse:1.103170+0.111998
## [95] train-rmse:0.671561+0.009562 test-rmse:1.099243+0.115552
## [96] train-rmse:0.666023+0.008346 test-rmse:1.097582+0.115138
## [97] train-rmse:0.662361+0.007204 test-rmse:1.097782+0.114237
## [98] train-rmse:0.658380+0.008086 test-rmse:1.097711+0.112586
## [99] train-rmse:0.654865+0.008863 test-rmse:1.096816+0.113625
## [100] train-rmse:0.651965+0.009578 test-rmse:1.094303+0.116105
## [101] train-rmse:0.646782+0.008730 test-rmse:1.096074+0.117530
## [102] train-rmse:0.643289+0.010170 test-rmse:1.096440+0.118661
## [103] train-rmse:0.639345+0.008383 test-rmse:1.094270+0.116221
## [104] train-rmse:0.635261+0.007947 test-rmse:1.091730+0.116748
## [105] train-rmse:0.633191+0.008005 test-rmse:1.091761+0.118703
## [106] train-rmse:0.630357+0.007741 test-rmse:1.087991+0.117858
## [107] train-rmse:0.627658+0.008704 test-rmse:1.085760+0.115485
## [108] train-rmse:0.624724+0.008722 test-rmse:1.084588+0.115303
## [109] train-rmse:0.621997+0.008170 test-rmse:1.084988+0.116494
## [110] train-rmse:0.618602+0.005739 test-rmse:1.082628+0.117548
## [111] train-rmse:0.615668+0.006115 test-rmse:1.081023+0.118864
## [112] train-rmse:0.612541+0.005973 test-rmse:1.077279+0.120742
## [113] train-rmse:0.610079+0.005884 test-rmse:1.076986+0.120070
## [114] train-rmse:0.607023+0.005930 test-rmse:1.076006+0.119603
## [115] train-rmse:0.603524+0.006194 test-rmse:1.074731+0.120786
## [116] train-rmse:0.601584+0.006313 test-rmse:1.073923+0.121094
## [117] train-rmse:0.600005+0.006354 test-rmse:1.073749+0.123833
## [118] train-rmse:0.597135+0.005636 test-rmse:1.073174+0.125388
## [119] train-rmse:0.593991+0.005511 test-rmse:1.071950+0.123522
## [120] train-rmse:0.590576+0.005827 test-rmse:1.070704+0.122988
## [121] train-rmse:0.585888+0.009571 test-rmse:1.070927+0.123759
## [122] train-rmse:0.583712+0.010505 test-rmse:1.070844+0.123607
## [123] train-rmse:0.580272+0.010400 test-rmse:1.067814+0.121368
## [124] train-rmse:0.578576+0.009929 test-rmse:1.066019+0.119018
## [125] train-rmse:0.574276+0.009572 test-rmse:1.064125+0.121154
## [126] train-rmse:0.570809+0.008499 test-rmse:1.063509+0.120454
## [127] train-rmse:0.567326+0.008436 test-rmse:1.065097+0.120316
## [128] train-rmse:0.563252+0.006667 test-rmse:1.061794+0.121484
## [129] train-rmse:0.561618+0.006796 test-rmse:1.062549+0.122477
## [130] train-rmse:0.558549+0.007043 test-rmse:1.060133+0.123014
## [131] train-rmse:0.553732+0.006238 test-rmse:1.056961+0.120717
## [132] train-rmse:0.551379+0.007041 test-rmse:1.055834+0.120096
## [133] train-rmse:0.549890+0.007153 test-rmse:1.055356+0.120297
## [134] train-rmse:0.547856+0.007171 test-rmse:1.054917+0.121169
## [135] train-rmse:0.546259+0.006869 test-rmse:1.053066+0.121610
## [136] train-rmse:0.544299+0.007605 test-rmse:1.052929+0.121955
## [137] train-rmse:0.541028+0.008598 test-rmse:1.051204+0.123731
## [138] train-rmse:0.538431+0.008506 test-rmse:1.051203+0.124014
## [139] train-rmse:0.535480+0.009752 test-rmse:1.050426+0.124906
## [140] train-rmse:0.532739+0.009542 test-rmse:1.050099+0.126243
## [141] train-rmse:0.530718+0.009407 test-rmse:1.048868+0.125713
## [142] train-rmse:0.529561+0.009507 test-rmse:1.048395+0.125576
## [143] train-rmse:0.525838+0.008390 test-rmse:1.047718+0.125228
## [144] train-rmse:0.522078+0.009811 test-rmse:1.044507+0.125144
## [145] train-rmse:0.519433+0.010108 test-rmse:1.042551+0.123983
## [146] train-rmse:0.517438+0.009836 test-rmse:1.042371+0.122907
## [147] train-rmse:0.514391+0.009800 test-rmse:1.041725+0.123927
## [148] train-rmse:0.512221+0.010197 test-rmse:1.041576+0.122567
## [149] train-rmse:0.510155+0.009104 test-rmse:1.042162+0.123598
## [150] train-rmse:0.508385+0.009383 test-rmse:1.040886+0.123447
## [151] train-rmse:0.507013+0.009710 test-rmse:1.041073+0.124121
## [152] train-rmse:0.504844+0.010409 test-rmse:1.040773+0.123632
## [153] train-rmse:0.501317+0.009440 test-rmse:1.039382+0.126004
## [154] train-rmse:0.499477+0.009355 test-rmse:1.039063+0.125343
## [155] train-rmse:0.498103+0.009478 test-rmse:1.037705+0.125729
## [156] train-rmse:0.496196+0.009024 test-rmse:1.037765+0.126374
## [157] train-rmse:0.494843+0.008753 test-rmse:1.037076+0.126087
## [158] train-rmse:0.493269+0.008711 test-rmse:1.037631+0.126137
## [159] train-rmse:0.491703+0.008382 test-rmse:1.036961+0.125954
## [160] train-rmse:0.489951+0.008539 test-rmse:1.037828+0.125759
## [161] train-rmse:0.487127+0.008193 test-rmse:1.037363+0.126628
## [162] train-rmse:0.485326+0.007860 test-rmse:1.037456+0.128027
## [163] train-rmse:0.484114+0.007661 test-rmse:1.036308+0.127975
## [164] train-rmse:0.482853+0.007761 test-rmse:1.036142+0.128766
## [165] train-rmse:0.479604+0.007624 test-rmse:1.034626+0.128091
## [166] train-rmse:0.476693+0.008910 test-rmse:1.034301+0.128265
## [167] train-rmse:0.474250+0.009090 test-rmse:1.034450+0.129236
## [168] train-rmse:0.471533+0.008739 test-rmse:1.032864+0.128636
## [169] train-rmse:0.470407+0.008954 test-rmse:1.031946+0.128834
## [170] train-rmse:0.468697+0.009049 test-rmse:1.032096+0.128328
## [171] train-rmse:0.466080+0.009718 test-rmse:1.029616+0.127969
## [172] train-rmse:0.464237+0.008990 test-rmse:1.030362+0.128485
## [173] train-rmse:0.461868+0.008055 test-rmse:1.031599+0.127704
## [174] train-rmse:0.459969+0.008016 test-rmse:1.030702+0.126770
## [175] train-rmse:0.458372+0.008021 test-rmse:1.029362+0.128396
## [176] train-rmse:0.456869+0.007525 test-rmse:1.029824+0.128631
## [177] train-rmse:0.454343+0.007517 test-rmse:1.027208+0.127322
## [178] train-rmse:0.452644+0.007743 test-rmse:1.028301+0.128057
## [179] train-rmse:0.451130+0.008283 test-rmse:1.027255+0.127149
## [180] train-rmse:0.450101+0.008428 test-rmse:1.027322+0.127076
## [181] train-rmse:0.446885+0.006812 test-rmse:1.025776+0.128461
## [182] train-rmse:0.444554+0.005398 test-rmse:1.023957+0.129762
## [183] train-rmse:0.443143+0.005516 test-rmse:1.023608+0.129504
## [184] train-rmse:0.441032+0.005227 test-rmse:1.024018+0.129798
## [185] train-rmse:0.439298+0.005158 test-rmse:1.020449+0.128335
## [186] train-rmse:0.438024+0.004915 test-rmse:1.020560+0.128090
## [187] train-rmse:0.436925+0.004987 test-rmse:1.021347+0.127528
## [188] train-rmse:0.435319+0.005149 test-rmse:1.021374+0.127057
## [189] train-rmse:0.433467+0.004833 test-rmse:1.021712+0.127377
## [190] train-rmse:0.431800+0.004420 test-rmse:1.021371+0.127662
## [191] train-rmse:0.430205+0.004628 test-rmse:1.021511+0.127193
## Stopping. Best iteration:
## [185] train-rmse:0.439298+0.005158 test-rmse:1.020449+0.128335
##
## 58
## [1] train-rmse:72.973630+0.073108 test-rmse:72.974214+0.420392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:54.084589+0.053899 test-rmse:54.084826+0.438661
## [3] train-rmse:40.095586+0.039547 test-rmse:40.095350+0.451697
## [4] train-rmse:29.739208+0.028775 test-rmse:29.738348+0.460589
## [5] train-rmse:22.077126+0.020671 test-rmse:22.075455+0.466019
## [6] train-rmse:16.415070+0.014660 test-rmse:16.412361+0.468234
## [7] train-rmse:12.237465+0.010317 test-rmse:12.240607+0.437841
## [8] train-rmse:9.155728+0.007342 test-rmse:9.140358+0.428084
## [9] train-rmse:6.882654+0.006442 test-rmse:6.863594+0.396710
## [10] train-rmse:5.212489+0.006415 test-rmse:5.210272+0.366685
## [11] train-rmse:3.987073+0.008676 test-rmse:3.987200+0.330467
## [12] train-rmse:3.097813+0.010924 test-rmse:3.114515+0.303933
## [13] train-rmse:2.461255+0.013317 test-rmse:2.495023+0.267409
## [14] train-rmse:2.011895+0.015347 test-rmse:2.073607+0.236143
## [15] train-rmse:1.700970+0.016456 test-rmse:1.794139+0.201720
## [16] train-rmse:1.488530+0.014076 test-rmse:1.606072+0.179130
## [17] train-rmse:1.345403+0.016698 test-rmse:1.500503+0.145191
## [18] train-rmse:1.248004+0.016783 test-rmse:1.438207+0.128927
## [19] train-rmse:1.176389+0.018901 test-rmse:1.385250+0.117952
## [20] train-rmse:1.125674+0.021611 test-rmse:1.351014+0.102923
## [21] train-rmse:1.086659+0.022195 test-rmse:1.325577+0.097626
## [22] train-rmse:1.051614+0.025405 test-rmse:1.309471+0.097724
## [23] train-rmse:1.022938+0.023545 test-rmse:1.292705+0.090293
## [24] train-rmse:1.001944+0.022116 test-rmse:1.279923+0.089563
## [25] train-rmse:0.980343+0.022457 test-rmse:1.261601+0.088158
## [26] train-rmse:0.960252+0.021736 test-rmse:1.248521+0.098394
## [27] train-rmse:0.938635+0.017369 test-rmse:1.230653+0.099541
## [28] train-rmse:0.917890+0.014929 test-rmse:1.212411+0.105084
## [29] train-rmse:0.902406+0.013677 test-rmse:1.201370+0.108026
## [30] train-rmse:0.886618+0.014359 test-rmse:1.194768+0.108371
## [31] train-rmse:0.872413+0.013989 test-rmse:1.179951+0.104845
## [32] train-rmse:0.855522+0.014612 test-rmse:1.173968+0.103207
## [33] train-rmse:0.839460+0.011914 test-rmse:1.167652+0.101507
## [34] train-rmse:0.825673+0.012132 test-rmse:1.157850+0.095400
## [35] train-rmse:0.812796+0.008490 test-rmse:1.148926+0.096306
## [36] train-rmse:0.797448+0.013501 test-rmse:1.141912+0.093429
## [37] train-rmse:0.786560+0.014625 test-rmse:1.137657+0.092621
## [38] train-rmse:0.774251+0.014089 test-rmse:1.129089+0.100102
## [39] train-rmse:0.762353+0.013396 test-rmse:1.124718+0.100627
## [40] train-rmse:0.754156+0.013909 test-rmse:1.121436+0.098899
## [41] train-rmse:0.745092+0.013153 test-rmse:1.112663+0.100240
## [42] train-rmse:0.735490+0.012263 test-rmse:1.099555+0.090952
## [43] train-rmse:0.721773+0.006930 test-rmse:1.092263+0.092413
## [44] train-rmse:0.711528+0.005559 test-rmse:1.088117+0.094988
## [45] train-rmse:0.700069+0.007355 test-rmse:1.080354+0.095455
## [46] train-rmse:0.687461+0.011057 test-rmse:1.075216+0.092905
## [47] train-rmse:0.678345+0.013126 test-rmse:1.062520+0.085601
## [48] train-rmse:0.671134+0.011657 test-rmse:1.057250+0.091565
## [49] train-rmse:0.661850+0.010910 test-rmse:1.052651+0.087304
## [50] train-rmse:0.654720+0.011474 test-rmse:1.049388+0.088127
## [51] train-rmse:0.648136+0.011097 test-rmse:1.045344+0.088218
## [52] train-rmse:0.641885+0.012232 test-rmse:1.045688+0.092041
## [53] train-rmse:0.633143+0.011124 test-rmse:1.044310+0.093705
## [54] train-rmse:0.623260+0.012722 test-rmse:1.040455+0.093199
## [55] train-rmse:0.614228+0.015908 test-rmse:1.041055+0.098777
## [56] train-rmse:0.605682+0.018307 test-rmse:1.038453+0.094703
## [57] train-rmse:0.596221+0.017625 test-rmse:1.030287+0.092146
## [58] train-rmse:0.588874+0.016618 test-rmse:1.026446+0.090795
## [59] train-rmse:0.583327+0.016075 test-rmse:1.021325+0.090867
## [60] train-rmse:0.576733+0.014728 test-rmse:1.022734+0.091349
## [61] train-rmse:0.572639+0.013924 test-rmse:1.020223+0.090705
## [62] train-rmse:0.566538+0.014953 test-rmse:1.024330+0.095151
## [63] train-rmse:0.560241+0.015509 test-rmse:1.024228+0.094680
## [64] train-rmse:0.554699+0.016992 test-rmse:1.022870+0.094276
## [65] train-rmse:0.550678+0.015818 test-rmse:1.021730+0.093037
## [66] train-rmse:0.544860+0.016352 test-rmse:1.018609+0.090783
## [67] train-rmse:0.540325+0.015217 test-rmse:1.016561+0.090976
## [68] train-rmse:0.533813+0.015191 test-rmse:1.016578+0.092201
## [69] train-rmse:0.525041+0.017412 test-rmse:1.015018+0.087683
## [70] train-rmse:0.519745+0.017988 test-rmse:1.014663+0.086698
## [71] train-rmse:0.513353+0.015872 test-rmse:1.012887+0.087694
## [72] train-rmse:0.509088+0.016201 test-rmse:1.011528+0.087538
## [73] train-rmse:0.506387+0.016171 test-rmse:1.009543+0.086814
## [74] train-rmse:0.499471+0.019701 test-rmse:1.008725+0.085536
## [75] train-rmse:0.491664+0.016805 test-rmse:1.007696+0.086627
## [76] train-rmse:0.486192+0.017299 test-rmse:1.007750+0.087200
## [77] train-rmse:0.481039+0.018151 test-rmse:1.005480+0.085017
## [78] train-rmse:0.476811+0.017711 test-rmse:1.005375+0.083377
## [79] train-rmse:0.472603+0.019001 test-rmse:1.003008+0.079731
## [80] train-rmse:0.466958+0.017921 test-rmse:1.002009+0.080593
## [81] train-rmse:0.463069+0.018576 test-rmse:1.000718+0.080283
## [82] train-rmse:0.459500+0.018884 test-rmse:1.000679+0.079397
## [83] train-rmse:0.456517+0.018930 test-rmse:1.000400+0.079540
## [84] train-rmse:0.452315+0.018040 test-rmse:0.998802+0.078222
## [85] train-rmse:0.447141+0.017473 test-rmse:0.998758+0.080051
## [86] train-rmse:0.444042+0.016905 test-rmse:0.998869+0.079293
## [87] train-rmse:0.439257+0.017150 test-rmse:0.998051+0.077150
## [88] train-rmse:0.433972+0.015671 test-rmse:0.995494+0.076211
## [89] train-rmse:0.429028+0.017010 test-rmse:0.993836+0.078118
## [90] train-rmse:0.420693+0.018509 test-rmse:0.990869+0.079825
## [91] train-rmse:0.416360+0.016925 test-rmse:0.990298+0.080427
## [92] train-rmse:0.412690+0.018249 test-rmse:0.990030+0.081920
## [93] train-rmse:0.408225+0.018501 test-rmse:0.986455+0.085437
## [94] train-rmse:0.403652+0.016812 test-rmse:0.986454+0.086301
## [95] train-rmse:0.400063+0.016293 test-rmse:0.985555+0.085291
## [96] train-rmse:0.395785+0.015799 test-rmse:0.985574+0.085615
## [97] train-rmse:0.393191+0.015848 test-rmse:0.983931+0.086210
## [98] train-rmse:0.390577+0.016228 test-rmse:0.982572+0.086794
## [99] train-rmse:0.386333+0.015561 test-rmse:0.982137+0.086724
## [100] train-rmse:0.383860+0.015810 test-rmse:0.981069+0.086557
## [101] train-rmse:0.380332+0.016132 test-rmse:0.981493+0.085806
## [102] train-rmse:0.377079+0.015951 test-rmse:0.981728+0.085520
## [103] train-rmse:0.372240+0.015881 test-rmse:0.983510+0.087446
## [104] train-rmse:0.367046+0.012667 test-rmse:0.981614+0.087799
## [105] train-rmse:0.363296+0.011910 test-rmse:0.982322+0.087509
## [106] train-rmse:0.360432+0.010610 test-rmse:0.981737+0.087978
## Stopping. Best iteration:
## [100] train-rmse:0.383860+0.015810 test-rmse:0.981069+0.086557
##
## 59
## [1] train-rmse:72.973630+0.073108 test-rmse:72.974214+0.420392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:54.084589+0.053899 test-rmse:54.084826+0.438661
## [3] train-rmse:40.095586+0.039547 test-rmse:40.095350+0.451697
## [4] train-rmse:29.739208+0.028775 test-rmse:29.738348+0.460589
## [5] train-rmse:22.077126+0.020671 test-rmse:22.075455+0.466019
## [6] train-rmse:16.415070+0.014660 test-rmse:16.412361+0.468234
## [7] train-rmse:12.237465+0.010317 test-rmse:12.240607+0.437841
## [8] train-rmse:9.155564+0.007504 test-rmse:9.137473+0.426410
## [9] train-rmse:6.879157+0.007055 test-rmse:6.867291+0.385217
## [10] train-rmse:5.205387+0.007009 test-rmse:5.208089+0.358468
## [11] train-rmse:3.973393+0.006980 test-rmse:3.976760+0.325585
## [12] train-rmse:3.072351+0.008815 test-rmse:3.090943+0.299116
## [13] train-rmse:2.414133+0.011016 test-rmse:2.445845+0.257389
## [14] train-rmse:1.943794+0.010864 test-rmse:2.004950+0.233428
## [15] train-rmse:1.613237+0.011681 test-rmse:1.711723+0.196162
## [16] train-rmse:1.378179+0.006328 test-rmse:1.533324+0.168753
## [17] train-rmse:1.215671+0.013205 test-rmse:1.408210+0.149481
## [18] train-rmse:1.105901+0.014251 test-rmse:1.335548+0.137089
## [19] train-rmse:1.028808+0.012265 test-rmse:1.278465+0.134593
## [20] train-rmse:0.968699+0.014232 test-rmse:1.237509+0.126268
## [21] train-rmse:0.923862+0.011493 test-rmse:1.208611+0.128935
## [22] train-rmse:0.889061+0.010631 test-rmse:1.191663+0.125267
## [23] train-rmse:0.854393+0.016148 test-rmse:1.174381+0.122850
## [24] train-rmse:0.828953+0.018445 test-rmse:1.160471+0.119887
## [25] train-rmse:0.807440+0.018036 test-rmse:1.139431+0.114725
## [26] train-rmse:0.784102+0.019350 test-rmse:1.124941+0.104662
## [27] train-rmse:0.764906+0.018082 test-rmse:1.115838+0.104131
## [28] train-rmse:0.744183+0.017348 test-rmse:1.108275+0.103483
## [29] train-rmse:0.726802+0.019991 test-rmse:1.099507+0.103857
## [30] train-rmse:0.710870+0.022574 test-rmse:1.092487+0.103647
## [31] train-rmse:0.696080+0.022766 test-rmse:1.084234+0.105879
## [32] train-rmse:0.680561+0.017813 test-rmse:1.073838+0.104190
## [33] train-rmse:0.665686+0.019121 test-rmse:1.065138+0.101041
## [34] train-rmse:0.651104+0.014534 test-rmse:1.054086+0.098888
## [35] train-rmse:0.637818+0.017989 test-rmse:1.050463+0.099448
## [36] train-rmse:0.619447+0.017513 test-rmse:1.042467+0.102211
## [37] train-rmse:0.607359+0.019874 test-rmse:1.031268+0.104394
## [38] train-rmse:0.594443+0.018340 test-rmse:1.025614+0.104568
## [39] train-rmse:0.582455+0.016287 test-rmse:1.020819+0.107104
## [40] train-rmse:0.572130+0.014370 test-rmse:1.020053+0.112534
## [41] train-rmse:0.562370+0.011560 test-rmse:1.015781+0.108956
## [42] train-rmse:0.551205+0.011800 test-rmse:1.017215+0.108965
## [43] train-rmse:0.539786+0.012829 test-rmse:1.016565+0.109349
## [44] train-rmse:0.531084+0.012383 test-rmse:1.013486+0.111194
## [45] train-rmse:0.522938+0.011392 test-rmse:1.008485+0.109546
## [46] train-rmse:0.512974+0.011398 test-rmse:1.008270+0.110840
## [47] train-rmse:0.506875+0.010700 test-rmse:1.006999+0.110949
## [48] train-rmse:0.500811+0.013112 test-rmse:1.004037+0.109966
## [49] train-rmse:0.491687+0.013618 test-rmse:0.999949+0.108638
## [50] train-rmse:0.481815+0.012469 test-rmse:0.999340+0.108284
## [51] train-rmse:0.475163+0.011129 test-rmse:0.997877+0.107042
## [52] train-rmse:0.467890+0.010722 test-rmse:1.000277+0.111841
## [53] train-rmse:0.459042+0.007367 test-rmse:0.999903+0.109985
## [54] train-rmse:0.450502+0.008938 test-rmse:0.996505+0.109623
## [55] train-rmse:0.444861+0.008947 test-rmse:0.990102+0.104112
## [56] train-rmse:0.436301+0.010516 test-rmse:0.984568+0.099334
## [57] train-rmse:0.425298+0.011181 test-rmse:0.987119+0.099696
## [58] train-rmse:0.419779+0.011267 test-rmse:0.988044+0.100398
## [59] train-rmse:0.411473+0.011253 test-rmse:0.986297+0.100294
## [60] train-rmse:0.406634+0.010835 test-rmse:0.984680+0.100762
## [61] train-rmse:0.401932+0.009955 test-rmse:0.984244+0.099833
## [62] train-rmse:0.395470+0.010520 test-rmse:0.982265+0.100455
## [63] train-rmse:0.388708+0.011464 test-rmse:0.977268+0.099236
## [64] train-rmse:0.381457+0.012854 test-rmse:0.977437+0.099516
## [65] train-rmse:0.374374+0.012780 test-rmse:0.974447+0.099206
## [66] train-rmse:0.367352+0.013532 test-rmse:0.972302+0.095308
## [67] train-rmse:0.360906+0.014683 test-rmse:0.969780+0.092511
## [68] train-rmse:0.356972+0.014357 test-rmse:0.967014+0.091057
## [69] train-rmse:0.351764+0.015223 test-rmse:0.965358+0.089829
## [70] train-rmse:0.347511+0.016592 test-rmse:0.966664+0.090488
## [71] train-rmse:0.339715+0.015063 test-rmse:0.961200+0.085955
## [72] train-rmse:0.333414+0.015244 test-rmse:0.958061+0.085648
## [73] train-rmse:0.324584+0.012456 test-rmse:0.953754+0.083040
## [74] train-rmse:0.318243+0.011491 test-rmse:0.952301+0.082127
## [75] train-rmse:0.313397+0.012352 test-rmse:0.950485+0.081595
## [76] train-rmse:0.307697+0.011189 test-rmse:0.949348+0.082447
## [77] train-rmse:0.304072+0.011743 test-rmse:0.948223+0.082521
## [78] train-rmse:0.300026+0.011382 test-rmse:0.947237+0.082752
## [79] train-rmse:0.296562+0.011590 test-rmse:0.946450+0.082284
## [80] train-rmse:0.291763+0.011648 test-rmse:0.942505+0.082668
## [81] train-rmse:0.285861+0.011065 test-rmse:0.942286+0.081605
## [82] train-rmse:0.280957+0.012614 test-rmse:0.942128+0.082336
## [83] train-rmse:0.276977+0.014002 test-rmse:0.941773+0.083014
## [84] train-rmse:0.270622+0.015157 test-rmse:0.939687+0.082871
## [85] train-rmse:0.267358+0.016400 test-rmse:0.938822+0.083026
## [86] train-rmse:0.262655+0.015732 test-rmse:0.937104+0.083784
## [87] train-rmse:0.259462+0.016652 test-rmse:0.935747+0.082588
## [88] train-rmse:0.256171+0.017464 test-rmse:0.935646+0.082326
## [89] train-rmse:0.251724+0.018170 test-rmse:0.935071+0.081486
## [90] train-rmse:0.245455+0.017274 test-rmse:0.932456+0.080530
## [91] train-rmse:0.241679+0.017324 test-rmse:0.931528+0.080354
## [92] train-rmse:0.238397+0.018528 test-rmse:0.932083+0.079377
## [93] train-rmse:0.234995+0.018600 test-rmse:0.930671+0.079430
## [94] train-rmse:0.231243+0.018414 test-rmse:0.930879+0.080597
## [95] train-rmse:0.227887+0.018642 test-rmse:0.929290+0.079634
## [96] train-rmse:0.225315+0.018712 test-rmse:0.928694+0.080178
## [97] train-rmse:0.222222+0.018044 test-rmse:0.928376+0.079993
## [98] train-rmse:0.220353+0.018327 test-rmse:0.928298+0.080097
## [99] train-rmse:0.216909+0.019044 test-rmse:0.928269+0.079600
## [100] train-rmse:0.214353+0.019544 test-rmse:0.927403+0.080458
## [101] train-rmse:0.211838+0.019251 test-rmse:0.926364+0.081109
## [102] train-rmse:0.208952+0.019197 test-rmse:0.925474+0.079808
## [103] train-rmse:0.206665+0.019848 test-rmse:0.923619+0.078547
## [104] train-rmse:0.203250+0.018964 test-rmse:0.924814+0.078197
## [105] train-rmse:0.199885+0.017813 test-rmse:0.924714+0.077026
## [106] train-rmse:0.197200+0.017776 test-rmse:0.924518+0.076676
## [107] train-rmse:0.193794+0.016767 test-rmse:0.924800+0.077209
## [108] train-rmse:0.191344+0.016705 test-rmse:0.924705+0.077724
## [109] train-rmse:0.189084+0.015915 test-rmse:0.923902+0.078370
## Stopping. Best iteration:
## [103] train-rmse:0.206665+0.019848 test-rmse:0.923619+0.078547
##
## 60
## [1] train-rmse:72.973630+0.073108 test-rmse:72.974214+0.420392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:54.084589+0.053899 test-rmse:54.084826+0.438661
## [3] train-rmse:40.095586+0.039547 test-rmse:40.095350+0.451697
## [4] train-rmse:29.739208+0.028775 test-rmse:29.738348+0.460589
## [5] train-rmse:22.077126+0.020671 test-rmse:22.075455+0.466019
## [6] train-rmse:16.415070+0.014660 test-rmse:16.412361+0.468234
## [7] train-rmse:12.237465+0.010317 test-rmse:12.240607+0.437841
## [8] train-rmse:9.155361+0.007719 test-rmse:9.142366+0.429291
## [9] train-rmse:6.877293+0.007322 test-rmse:6.875144+0.382179
## [10] train-rmse:5.198431+0.006320 test-rmse:5.199193+0.337700
## [11] train-rmse:3.962500+0.007430 test-rmse:3.973835+0.306252
## [12] train-rmse:3.053235+0.010408 test-rmse:3.072072+0.276186
## [13] train-rmse:2.381977+0.006934 test-rmse:2.436991+0.251740
## [14] train-rmse:1.897714+0.013897 test-rmse:1.995047+0.214167
## [15] train-rmse:1.543975+0.009682 test-rmse:1.694245+0.176336
## [16] train-rmse:1.294722+0.011563 test-rmse:1.491682+0.166057
## [17] train-rmse:1.118951+0.015624 test-rmse:1.365647+0.155846
## [18] train-rmse:0.988465+0.014531 test-rmse:1.281125+0.146541
## [19] train-rmse:0.903860+0.013958 test-rmse:1.231157+0.137614
## [20] train-rmse:0.834542+0.014177 test-rmse:1.192333+0.122163
## [21] train-rmse:0.785759+0.017877 test-rmse:1.169892+0.110418
## [22] train-rmse:0.746146+0.012668 test-rmse:1.146286+0.109238
## [23] train-rmse:0.713834+0.008315 test-rmse:1.123142+0.102294
## [24] train-rmse:0.686540+0.010710 test-rmse:1.113448+0.096743
## [25] train-rmse:0.659379+0.006767 test-rmse:1.104722+0.093107
## [26] train-rmse:0.638288+0.007910 test-rmse:1.099062+0.092561
## [27] train-rmse:0.613399+0.006372 test-rmse:1.079381+0.080481
## [28] train-rmse:0.595079+0.008767 test-rmse:1.067370+0.083204
## [29] train-rmse:0.576086+0.009097 test-rmse:1.058600+0.077608
## [30] train-rmse:0.557572+0.008745 test-rmse:1.047449+0.065919
## [31] train-rmse:0.542042+0.008321 test-rmse:1.042526+0.064809
## [32] train-rmse:0.524208+0.007719 test-rmse:1.031395+0.060220
## [33] train-rmse:0.507940+0.006175 test-rmse:1.026598+0.057190
## [34] train-rmse:0.496060+0.006723 test-rmse:1.025077+0.056013
## [35] train-rmse:0.484544+0.006274 test-rmse:1.023633+0.056277
## [36] train-rmse:0.472981+0.004103 test-rmse:1.017902+0.056039
## [37] train-rmse:0.461765+0.004014 test-rmse:1.012833+0.053257
## [38] train-rmse:0.450563+0.003215 test-rmse:1.011880+0.055347
## [39] train-rmse:0.441452+0.005069 test-rmse:1.008287+0.056370
## [40] train-rmse:0.428872+0.008023 test-rmse:1.004780+0.053170
## [41] train-rmse:0.419812+0.007677 test-rmse:1.000843+0.055070
## [42] train-rmse:0.410402+0.009487 test-rmse:0.998429+0.052752
## [43] train-rmse:0.398953+0.010534 test-rmse:0.999105+0.048901
## [44] train-rmse:0.387825+0.008922 test-rmse:0.996355+0.049198
## [45] train-rmse:0.377270+0.009726 test-rmse:0.992854+0.052519
## [46] train-rmse:0.368458+0.008559 test-rmse:0.992287+0.052597
## [47] train-rmse:0.361683+0.007200 test-rmse:0.991581+0.052022
## [48] train-rmse:0.353102+0.008904 test-rmse:0.990499+0.051491
## [49] train-rmse:0.342884+0.010090 test-rmse:0.990050+0.053777
## [50] train-rmse:0.333863+0.011985 test-rmse:0.989323+0.052575
## [51] train-rmse:0.326952+0.011592 test-rmse:0.988100+0.051503
## [52] train-rmse:0.318657+0.009710 test-rmse:0.986053+0.051149
## [53] train-rmse:0.311416+0.008375 test-rmse:0.987077+0.049834
## [54] train-rmse:0.303157+0.009570 test-rmse:0.984781+0.048959
## [55] train-rmse:0.297958+0.008390 test-rmse:0.983087+0.048773
## [56] train-rmse:0.290547+0.009510 test-rmse:0.981393+0.048466
## [57] train-rmse:0.284118+0.009297 test-rmse:0.981990+0.049728
## [58] train-rmse:0.279089+0.008667 test-rmse:0.981022+0.049596
## [59] train-rmse:0.272438+0.007534 test-rmse:0.979511+0.047326
## [60] train-rmse:0.268143+0.007577 test-rmse:0.981334+0.047949
## [61] train-rmse:0.262192+0.006868 test-rmse:0.982245+0.047561
## [62] train-rmse:0.255746+0.006634 test-rmse:0.980415+0.048804
## [63] train-rmse:0.250304+0.006763 test-rmse:0.980162+0.050729
## [64] train-rmse:0.245536+0.008906 test-rmse:0.979879+0.050258
## [65] train-rmse:0.240516+0.007717 test-rmse:0.979095+0.050439
## [66] train-rmse:0.234967+0.007294 test-rmse:0.977313+0.052415
## [67] train-rmse:0.229892+0.008578 test-rmse:0.976040+0.052597
## [68] train-rmse:0.225883+0.007421 test-rmse:0.975804+0.053479
## [69] train-rmse:0.221288+0.008992 test-rmse:0.976025+0.055507
## [70] train-rmse:0.217897+0.009810 test-rmse:0.975277+0.054050
## [71] train-rmse:0.212289+0.008934 test-rmse:0.974956+0.055491
## [72] train-rmse:0.207624+0.008267 test-rmse:0.974302+0.054081
## [73] train-rmse:0.204414+0.008038 test-rmse:0.973942+0.054643
## [74] train-rmse:0.199037+0.008452 test-rmse:0.973183+0.054769
## [75] train-rmse:0.194607+0.008019 test-rmse:0.973427+0.054497
## [76] train-rmse:0.189660+0.007497 test-rmse:0.972666+0.053396
## [77] train-rmse:0.185808+0.007337 test-rmse:0.970124+0.052292
## [78] train-rmse:0.182329+0.009018 test-rmse:0.969710+0.052886
## [79] train-rmse:0.178782+0.009048 test-rmse:0.968686+0.053593
## [80] train-rmse:0.174581+0.009014 test-rmse:0.968684+0.052954
## [81] train-rmse:0.171215+0.008270 test-rmse:0.969837+0.053603
## [82] train-rmse:0.167666+0.007455 test-rmse:0.968124+0.051606
## [83] train-rmse:0.163438+0.006329 test-rmse:0.967950+0.050166
## [84] train-rmse:0.160840+0.006091 test-rmse:0.968372+0.049764
## [85] train-rmse:0.157258+0.007865 test-rmse:0.968543+0.049235
## [86] train-rmse:0.154543+0.008031 test-rmse:0.968252+0.049128
## [87] train-rmse:0.151760+0.007732 test-rmse:0.967984+0.049459
## [88] train-rmse:0.147924+0.008440 test-rmse:0.967053+0.050314
## [89] train-rmse:0.144550+0.007612 test-rmse:0.966415+0.050032
## [90] train-rmse:0.141204+0.006511 test-rmse:0.966456+0.049389
## [91] train-rmse:0.138127+0.007854 test-rmse:0.965891+0.049517
## [92] train-rmse:0.134902+0.006168 test-rmse:0.964621+0.048364
## [93] train-rmse:0.132089+0.006235 test-rmse:0.964972+0.048473
## [94] train-rmse:0.129859+0.005572 test-rmse:0.965733+0.049180
## [95] train-rmse:0.127773+0.005371 test-rmse:0.965768+0.049480
## [96] train-rmse:0.126062+0.006254 test-rmse:0.964972+0.049479
## [97] train-rmse:0.122911+0.005834 test-rmse:0.964152+0.049244
## [98] train-rmse:0.120693+0.005356 test-rmse:0.963414+0.048638
## [99] train-rmse:0.117923+0.006357 test-rmse:0.963488+0.049055
## [100] train-rmse:0.116083+0.006283 test-rmse:0.963642+0.049194
## [101] train-rmse:0.114876+0.006659 test-rmse:0.963160+0.048890
## [102] train-rmse:0.113620+0.006313 test-rmse:0.963240+0.048965
## [103] train-rmse:0.112036+0.006846 test-rmse:0.963312+0.049798
## [104] train-rmse:0.109414+0.006474 test-rmse:0.963022+0.049380
## [105] train-rmse:0.106673+0.006660 test-rmse:0.962803+0.049325
## [106] train-rmse:0.104987+0.007220 test-rmse:0.962582+0.049277
## [107] train-rmse:0.103202+0.006648 test-rmse:0.963587+0.049437
## [108] train-rmse:0.101147+0.006477 test-rmse:0.964021+0.049799
## [109] train-rmse:0.099301+0.005897 test-rmse:0.962637+0.049300
## [110] train-rmse:0.096737+0.005419 test-rmse:0.962209+0.048929
## [111] train-rmse:0.094933+0.005445 test-rmse:0.961905+0.049066
## [112] train-rmse:0.091845+0.004699 test-rmse:0.961731+0.047952
## [113] train-rmse:0.090148+0.004633 test-rmse:0.960926+0.047842
## [114] train-rmse:0.088074+0.004194 test-rmse:0.961099+0.047565
## [115] train-rmse:0.086457+0.003823 test-rmse:0.961541+0.047924
## [116] train-rmse:0.084859+0.004454 test-rmse:0.961271+0.047803
## [117] train-rmse:0.083807+0.004538 test-rmse:0.961075+0.046797
## [118] train-rmse:0.082674+0.004732 test-rmse:0.961006+0.046883
## [119] train-rmse:0.081177+0.004192 test-rmse:0.961096+0.046823
## Stopping. Best iteration:
## [113] train-rmse:0.090148+0.004633 test-rmse:0.960926+0.047842
##
## 61
## [1] train-rmse:72.973630+0.073108 test-rmse:72.974214+0.420392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:54.084589+0.053899 test-rmse:54.084826+0.438661
## [3] train-rmse:40.095586+0.039547 test-rmse:40.095350+0.451697
## [4] train-rmse:29.739208+0.028775 test-rmse:29.738348+0.460589
## [5] train-rmse:22.077126+0.020671 test-rmse:22.075455+0.466019
## [6] train-rmse:16.415070+0.014660 test-rmse:16.412361+0.468234
## [7] train-rmse:12.237465+0.010317 test-rmse:12.240607+0.437841
## [8] train-rmse:9.155361+0.007719 test-rmse:9.142366+0.429291
## [9] train-rmse:6.876760+0.007375 test-rmse:6.871432+0.379394
## [10] train-rmse:5.196428+0.006609 test-rmse:5.194213+0.339542
## [11] train-rmse:3.957937+0.005722 test-rmse:3.980494+0.302889
## [12] train-rmse:3.041883+0.008263 test-rmse:3.074372+0.263195
## [13] train-rmse:2.366863+0.008128 test-rmse:2.438697+0.232456
## [14] train-rmse:1.874855+0.012074 test-rmse:1.968381+0.210792
## [15] train-rmse:1.505423+0.014224 test-rmse:1.646157+0.178191
## [16] train-rmse:1.241383+0.008842 test-rmse:1.452040+0.151515
## [17] train-rmse:1.054920+0.012045 test-rmse:1.322563+0.123226
## [18] train-rmse:0.915674+0.015636 test-rmse:1.244093+0.103876
## [19] train-rmse:0.818278+0.020616 test-rmse:1.191036+0.099374
## [20] train-rmse:0.744068+0.025735 test-rmse:1.148260+0.087330
## [21] train-rmse:0.684276+0.019076 test-rmse:1.114193+0.085892
## [22] train-rmse:0.636383+0.022706 test-rmse:1.086956+0.077759
## [23] train-rmse:0.602993+0.018825 test-rmse:1.069505+0.072608
## [24] train-rmse:0.573211+0.022877 test-rmse:1.058274+0.069654
## [25] train-rmse:0.543839+0.017983 test-rmse:1.045138+0.072025
## [26] train-rmse:0.520002+0.021367 test-rmse:1.037126+0.073419
## [27] train-rmse:0.496109+0.016967 test-rmse:1.030223+0.071798
## [28] train-rmse:0.473767+0.024244 test-rmse:1.022047+0.069197
## [29] train-rmse:0.452690+0.022091 test-rmse:1.015176+0.066381
## [30] train-rmse:0.435810+0.023262 test-rmse:1.015923+0.073817
## [31] train-rmse:0.418434+0.024443 test-rmse:1.008036+0.077347
## [32] train-rmse:0.404274+0.022242 test-rmse:1.001293+0.080078
## [33] train-rmse:0.387664+0.020951 test-rmse:0.999151+0.075570
## [34] train-rmse:0.375638+0.018073 test-rmse:1.000815+0.078488
## [35] train-rmse:0.364253+0.015876 test-rmse:0.996042+0.078826
## [36] train-rmse:0.351926+0.012866 test-rmse:0.992466+0.076265
## [37] train-rmse:0.341078+0.014618 test-rmse:0.989027+0.078243
## [38] train-rmse:0.331844+0.017044 test-rmse:0.985463+0.080725
## [39] train-rmse:0.320734+0.014505 test-rmse:0.983561+0.080813
## [40] train-rmse:0.308427+0.014360 test-rmse:0.980038+0.079890
## [41] train-rmse:0.301112+0.015137 test-rmse:0.980295+0.079994
## [42] train-rmse:0.292978+0.014395 test-rmse:0.978485+0.080205
## [43] train-rmse:0.280856+0.014309 test-rmse:0.977279+0.078820
## [44] train-rmse:0.272890+0.014555 test-rmse:0.976201+0.080045
## [45] train-rmse:0.265188+0.012403 test-rmse:0.976059+0.083716
## [46] train-rmse:0.259536+0.012260 test-rmse:0.975367+0.083705
## [47] train-rmse:0.251579+0.012250 test-rmse:0.974735+0.081155
## [48] train-rmse:0.244698+0.012056 test-rmse:0.974862+0.080805
## [49] train-rmse:0.235547+0.013205 test-rmse:0.972258+0.080572
## [50] train-rmse:0.227559+0.013066 test-rmse:0.971796+0.080280
## [51] train-rmse:0.220593+0.011601 test-rmse:0.971539+0.081010
## [52] train-rmse:0.215801+0.011938 test-rmse:0.970680+0.080601
## [53] train-rmse:0.210679+0.011401 test-rmse:0.966881+0.078790
## [54] train-rmse:0.201867+0.010805 test-rmse:0.966473+0.079578
## [55] train-rmse:0.196787+0.010848 test-rmse:0.965525+0.079884
## [56] train-rmse:0.190520+0.010831 test-rmse:0.966050+0.080669
## [57] train-rmse:0.184933+0.010346 test-rmse:0.965656+0.081420
## [58] train-rmse:0.179832+0.010120 test-rmse:0.965321+0.080411
## [59] train-rmse:0.174394+0.009437 test-rmse:0.965216+0.081082
## [60] train-rmse:0.170401+0.008469 test-rmse:0.965296+0.082175
## [61] train-rmse:0.165938+0.008865 test-rmse:0.964495+0.082184
## [62] train-rmse:0.160258+0.007588 test-rmse:0.963836+0.081775
## [63] train-rmse:0.156074+0.007268 test-rmse:0.963298+0.082118
## [64] train-rmse:0.151530+0.007079 test-rmse:0.963278+0.081941
## [65] train-rmse:0.147261+0.006067 test-rmse:0.961901+0.083020
## [66] train-rmse:0.142068+0.007510 test-rmse:0.961452+0.084104
## [67] train-rmse:0.138140+0.007659 test-rmse:0.960880+0.084094
## [68] train-rmse:0.134696+0.007636 test-rmse:0.960836+0.085481
## [69] train-rmse:0.131013+0.007453 test-rmse:0.960844+0.085918
## [70] train-rmse:0.127716+0.007908 test-rmse:0.961102+0.085245
## [71] train-rmse:0.124056+0.008618 test-rmse:0.960814+0.085726
## [72] train-rmse:0.120570+0.007526 test-rmse:0.959885+0.085444
## [73] train-rmse:0.116958+0.007301 test-rmse:0.958612+0.082664
## [74] train-rmse:0.112998+0.006239 test-rmse:0.958531+0.082875
## [75] train-rmse:0.109632+0.006292 test-rmse:0.958307+0.082896
## [76] train-rmse:0.106902+0.006736 test-rmse:0.958856+0.083335
## [77] train-rmse:0.103552+0.006238 test-rmse:0.959133+0.083341
## [78] train-rmse:0.101204+0.006828 test-rmse:0.958336+0.083608
## [79] train-rmse:0.098365+0.006526 test-rmse:0.958102+0.083371
## [80] train-rmse:0.094595+0.005161 test-rmse:0.957593+0.083349
## [81] train-rmse:0.091938+0.005755 test-rmse:0.957639+0.083502
## [82] train-rmse:0.089400+0.005332 test-rmse:0.957100+0.082994
## [83] train-rmse:0.087365+0.005922 test-rmse:0.956010+0.083647
## [84] train-rmse:0.085144+0.005807 test-rmse:0.955545+0.083550
## [85] train-rmse:0.083294+0.005341 test-rmse:0.955765+0.083763
## [86] train-rmse:0.080436+0.005301 test-rmse:0.955137+0.084309
## [87] train-rmse:0.077917+0.004622 test-rmse:0.955254+0.084233
## [88] train-rmse:0.076281+0.004936 test-rmse:0.954860+0.084392
## [89] train-rmse:0.074107+0.004963 test-rmse:0.955047+0.083678
## [90] train-rmse:0.072603+0.004849 test-rmse:0.954901+0.083731
## [91] train-rmse:0.070856+0.004453 test-rmse:0.954889+0.083572
## [92] train-rmse:0.068930+0.004304 test-rmse:0.954675+0.083438
## [93] train-rmse:0.067265+0.004143 test-rmse:0.954153+0.083346
## [94] train-rmse:0.065664+0.003682 test-rmse:0.953795+0.083301
## [95] train-rmse:0.063539+0.003116 test-rmse:0.953767+0.083493
## [96] train-rmse:0.061740+0.002728 test-rmse:0.953497+0.083158
## [97] train-rmse:0.059738+0.002266 test-rmse:0.952906+0.083449
## [98] train-rmse:0.057874+0.002105 test-rmse:0.952443+0.083137
## [99] train-rmse:0.056467+0.002049 test-rmse:0.952919+0.082893
## [100] train-rmse:0.055404+0.002257 test-rmse:0.952480+0.082851
## [101] train-rmse:0.053536+0.001895 test-rmse:0.952755+0.083254
## [102] train-rmse:0.052246+0.001772 test-rmse:0.953050+0.083305
## [103] train-rmse:0.050429+0.001903 test-rmse:0.952642+0.082877
## [104] train-rmse:0.049569+0.001808 test-rmse:0.952443+0.082846
## Stopping. Best iteration:
## [98] train-rmse:0.057874+0.002105 test-rmse:0.952443+0.083137
##
## 62
## [1] train-rmse:72.973630+0.073108 test-rmse:72.974214+0.420392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:54.084589+0.053899 test-rmse:54.084826+0.438661
## [3] train-rmse:40.095586+0.039547 test-rmse:40.095350+0.451697
## [4] train-rmse:29.739208+0.028775 test-rmse:29.738348+0.460589
## [5] train-rmse:22.077126+0.020671 test-rmse:22.075455+0.466019
## [6] train-rmse:16.415070+0.014660 test-rmse:16.412361+0.468234
## [7] train-rmse:12.237465+0.010317 test-rmse:12.240607+0.437841
## [8] train-rmse:9.155361+0.007719 test-rmse:9.142366+0.429291
## [9] train-rmse:6.876760+0.007375 test-rmse:6.871432+0.379394
## [10] train-rmse:5.196267+0.006432 test-rmse:5.196129+0.337892
## [11] train-rmse:3.956837+0.005706 test-rmse:3.984291+0.296929
## [12] train-rmse:3.035722+0.003814 test-rmse:3.071126+0.261048
## [13] train-rmse:2.356468+0.006097 test-rmse:2.428076+0.224582
## [14] train-rmse:1.854293+0.009393 test-rmse:1.959793+0.192101
## [15] train-rmse:1.485353+0.009129 test-rmse:1.623821+0.176258
## [16] train-rmse:1.212614+0.005812 test-rmse:1.410961+0.153199
## [17] train-rmse:1.012880+0.009153 test-rmse:1.287023+0.143409
## [18] train-rmse:0.869788+0.016097 test-rmse:1.203840+0.134159
## [19] train-rmse:0.762472+0.013805 test-rmse:1.130893+0.130654
## [20] train-rmse:0.681796+0.014903 test-rmse:1.091936+0.127890
## [21] train-rmse:0.624782+0.012924 test-rmse:1.062411+0.129353
## [22] train-rmse:0.572218+0.021034 test-rmse:1.046368+0.125450
## [23] train-rmse:0.529003+0.020629 test-rmse:1.026881+0.108340
## [24] train-rmse:0.492073+0.024258 test-rmse:1.006276+0.092300
## [25] train-rmse:0.461088+0.024474 test-rmse:0.999428+0.093010
## [26] train-rmse:0.430711+0.021890 test-rmse:0.989586+0.094189
## [27] train-rmse:0.407381+0.025238 test-rmse:0.984587+0.092845
## [28] train-rmse:0.384880+0.020439 test-rmse:0.974364+0.086327
## [29] train-rmse:0.365295+0.019167 test-rmse:0.969375+0.084685
## [30] train-rmse:0.348438+0.016468 test-rmse:0.962917+0.084412
## [31] train-rmse:0.332892+0.017203 test-rmse:0.962661+0.082821
## [32] train-rmse:0.321296+0.016746 test-rmse:0.958820+0.084132
## [33] train-rmse:0.307360+0.019646 test-rmse:0.955162+0.084249
## [34] train-rmse:0.294788+0.018805 test-rmse:0.949384+0.081915
## [35] train-rmse:0.279448+0.016046 test-rmse:0.946405+0.078014
## [36] train-rmse:0.267340+0.016010 test-rmse:0.942548+0.077271
## [37] train-rmse:0.257937+0.016855 test-rmse:0.941639+0.077647
## [38] train-rmse:0.246434+0.016694 test-rmse:0.938682+0.078546
## [39] train-rmse:0.236761+0.020385 test-rmse:0.936399+0.080826
## [40] train-rmse:0.227884+0.018745 test-rmse:0.936936+0.079912
## [41] train-rmse:0.218300+0.019016 test-rmse:0.934741+0.080188
## [42] train-rmse:0.208394+0.017743 test-rmse:0.932778+0.083083
## [43] train-rmse:0.201435+0.015168 test-rmse:0.931106+0.079955
## [44] train-rmse:0.193939+0.014488 test-rmse:0.928982+0.080215
## [45] train-rmse:0.188670+0.013098 test-rmse:0.928229+0.081458
## [46] train-rmse:0.183584+0.014351 test-rmse:0.924768+0.082031
## [47] train-rmse:0.176794+0.013872 test-rmse:0.923820+0.081095
## [48] train-rmse:0.169551+0.015369 test-rmse:0.924586+0.081165
## [49] train-rmse:0.164010+0.016424 test-rmse:0.924201+0.080737
## [50] train-rmse:0.158563+0.016441 test-rmse:0.924013+0.080541
## [51] train-rmse:0.154546+0.015489 test-rmse:0.923481+0.080087
## [52] train-rmse:0.148177+0.016274 test-rmse:0.921365+0.079353
## [53] train-rmse:0.141293+0.017219 test-rmse:0.920686+0.079345
## [54] train-rmse:0.136253+0.014578 test-rmse:0.920657+0.079260
## [55] train-rmse:0.131411+0.015137 test-rmse:0.920091+0.078591
## [56] train-rmse:0.126331+0.014152 test-rmse:0.919759+0.078082
## [57] train-rmse:0.121725+0.014420 test-rmse:0.920526+0.078300
## [58] train-rmse:0.118527+0.014315 test-rmse:0.919478+0.078311
## [59] train-rmse:0.113639+0.012877 test-rmse:0.918356+0.078250
## [60] train-rmse:0.108688+0.012304 test-rmse:0.917742+0.078009
## [61] train-rmse:0.105163+0.011895 test-rmse:0.916763+0.079173
## [62] train-rmse:0.100760+0.010743 test-rmse:0.916751+0.079253
## [63] train-rmse:0.096458+0.009759 test-rmse:0.915900+0.079485
## [64] train-rmse:0.093566+0.009873 test-rmse:0.916098+0.079602
## [65] train-rmse:0.090962+0.009346 test-rmse:0.916463+0.079370
## [66] train-rmse:0.087760+0.009024 test-rmse:0.915845+0.079728
## [67] train-rmse:0.084655+0.009542 test-rmse:0.915881+0.079407
## [68] train-rmse:0.081301+0.008501 test-rmse:0.916221+0.079595
## [69] train-rmse:0.078758+0.008873 test-rmse:0.916617+0.079674
## [70] train-rmse:0.075959+0.008594 test-rmse:0.916304+0.079777
## [71] train-rmse:0.074042+0.008720 test-rmse:0.916599+0.080205
## [72] train-rmse:0.071463+0.008571 test-rmse:0.915284+0.080274
## [73] train-rmse:0.068007+0.007779 test-rmse:0.915866+0.079856
## [74] train-rmse:0.065062+0.008085 test-rmse:0.914738+0.079724
## [75] train-rmse:0.061975+0.007887 test-rmse:0.914280+0.079630
## [76] train-rmse:0.059965+0.008182 test-rmse:0.913730+0.079764
## [77] train-rmse:0.056818+0.008435 test-rmse:0.914048+0.079816
## [78] train-rmse:0.054410+0.008153 test-rmse:0.913967+0.079635
## [79] train-rmse:0.052096+0.008570 test-rmse:0.913686+0.079373
## [80] train-rmse:0.050368+0.008824 test-rmse:0.913833+0.079476
## [81] train-rmse:0.048666+0.007676 test-rmse:0.913560+0.079411
## [82] train-rmse:0.046970+0.007272 test-rmse:0.913279+0.079404
## [83] train-rmse:0.045431+0.007161 test-rmse:0.913269+0.079372
## [84] train-rmse:0.043921+0.007533 test-rmse:0.912798+0.079479
## [85] train-rmse:0.042736+0.007271 test-rmse:0.912715+0.079499
## [86] train-rmse:0.041634+0.007089 test-rmse:0.912617+0.079677
## [87] train-rmse:0.040108+0.006494 test-rmse:0.912776+0.079889
## [88] train-rmse:0.038829+0.006067 test-rmse:0.913066+0.080202
## [89] train-rmse:0.037510+0.005545 test-rmse:0.912815+0.080267
## [90] train-rmse:0.036227+0.005581 test-rmse:0.912845+0.080228
## [91] train-rmse:0.034794+0.005436 test-rmse:0.913144+0.080628
## [92] train-rmse:0.033633+0.005356 test-rmse:0.913205+0.080613
## Stopping. Best iteration:
## [86] train-rmse:0.041634+0.007089 test-rmse:0.912617+0.079677
##
## 63
## [1] train-rmse:71.012277+0.071119 test-rmse:71.012837+0.422304
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:51.218313+0.050969 test-rmse:51.218473+0.441381
## [3] train-rmse:36.954831+0.036295 test-rmse:36.954435+0.454495
## [4] train-rmse:26.681634+0.025555 test-rmse:26.680503+0.462943
## [5] train-rmse:19.289380+0.017699 test-rmse:19.287267+0.467424
## [6] train-rmse:13.979685+0.012191 test-rmse:13.976312+0.468039
## [7] train-rmse:10.171997+0.008739 test-rmse:10.171457+0.434201
## [8] train-rmse:7.450598+0.007508 test-rmse:7.438130+0.429725
## [9] train-rmse:5.516366+0.009325 test-rmse:5.503641+0.410081
## [10] train-rmse:4.160364+0.013054 test-rmse:4.148558+0.384124
## [11] train-rmse:3.225769+0.016056 test-rmse:3.232590+0.344427
## [12] train-rmse:2.599935+0.019835 test-rmse:2.609445+0.291587
## [13] train-rmse:2.193887+0.022586 test-rmse:2.224136+0.234764
## [14] train-rmse:1.939006+0.025861 test-rmse:1.993414+0.181519
## [15] train-rmse:1.782560+0.027651 test-rmse:1.850735+0.141718
## [16] train-rmse:1.685900+0.029675 test-rmse:1.746834+0.122956
## [17] train-rmse:1.624023+0.029173 test-rmse:1.692415+0.114841
## [18] train-rmse:1.582816+0.028032 test-rmse:1.660570+0.113489
## [19] train-rmse:1.551868+0.027169 test-rmse:1.647506+0.117608
## [20] train-rmse:1.527974+0.026034 test-rmse:1.626408+0.117422
## [21] train-rmse:1.508268+0.025417 test-rmse:1.617338+0.120765
## [22] train-rmse:1.490398+0.025023 test-rmse:1.598495+0.121851
## [23] train-rmse:1.474053+0.024861 test-rmse:1.581589+0.116209
## [24] train-rmse:1.459820+0.024520 test-rmse:1.578852+0.117128
## [25] train-rmse:1.446610+0.023802 test-rmse:1.571705+0.115985
## [26] train-rmse:1.433911+0.023386 test-rmse:1.557670+0.104555
## [27] train-rmse:1.421908+0.023534 test-rmse:1.559270+0.109388
## [28] train-rmse:1.410379+0.023262 test-rmse:1.554256+0.109671
## [29] train-rmse:1.399238+0.022959 test-rmse:1.545607+0.105937
## [30] train-rmse:1.388590+0.022822 test-rmse:1.538915+0.104171
## [31] train-rmse:1.378081+0.022598 test-rmse:1.530276+0.107627
## [32] train-rmse:1.367967+0.022568 test-rmse:1.517293+0.109704
## [33] train-rmse:1.358192+0.022573 test-rmse:1.507859+0.110514
## [34] train-rmse:1.348614+0.022616 test-rmse:1.505888+0.101618
## [35] train-rmse:1.339259+0.022465 test-rmse:1.503240+0.094094
## [36] train-rmse:1.330152+0.022395 test-rmse:1.502841+0.095743
## [37] train-rmse:1.321698+0.022201 test-rmse:1.497795+0.098943
## [38] train-rmse:1.313216+0.022139 test-rmse:1.480001+0.099134
## [39] train-rmse:1.304674+0.022213 test-rmse:1.474650+0.099554
## [40] train-rmse:1.296580+0.021963 test-rmse:1.469158+0.097109
## [41] train-rmse:1.288807+0.021667 test-rmse:1.465120+0.094922
## [42] train-rmse:1.281025+0.021389 test-rmse:1.464639+0.094454
## [43] train-rmse:1.273681+0.020926 test-rmse:1.456245+0.095000
## [44] train-rmse:1.266581+0.020596 test-rmse:1.449875+0.095006
## [45] train-rmse:1.259761+0.020474 test-rmse:1.443933+0.096502
## [46] train-rmse:1.253267+0.020384 test-rmse:1.439474+0.099853
## [47] train-rmse:1.246882+0.020225 test-rmse:1.433990+0.096376
## [48] train-rmse:1.240577+0.020061 test-rmse:1.430215+0.093747
## [49] train-rmse:1.234329+0.019936 test-rmse:1.428976+0.086495
## [50] train-rmse:1.228238+0.019917 test-rmse:1.426755+0.085896
## [51] train-rmse:1.222291+0.020026 test-rmse:1.419925+0.087421
## [52] train-rmse:1.216507+0.020115 test-rmse:1.411139+0.086627
## [53] train-rmse:1.210871+0.020320 test-rmse:1.411976+0.082226
## [54] train-rmse:1.205335+0.020446 test-rmse:1.405876+0.084012
## [55] train-rmse:1.200089+0.020579 test-rmse:1.406432+0.086461
## [56] train-rmse:1.194949+0.020540 test-rmse:1.399265+0.087354
## [57] train-rmse:1.189758+0.020751 test-rmse:1.398477+0.093681
## [58] train-rmse:1.184707+0.020839 test-rmse:1.390664+0.089747
## [59] train-rmse:1.179821+0.020931 test-rmse:1.387525+0.093000
## [60] train-rmse:1.174904+0.020988 test-rmse:1.380083+0.094433
## [61] train-rmse:1.170190+0.021062 test-rmse:1.378302+0.094744
## [62] train-rmse:1.165655+0.021051 test-rmse:1.374265+0.095804
## [63] train-rmse:1.161233+0.020975 test-rmse:1.370231+0.096937
## [64] train-rmse:1.156904+0.020867 test-rmse:1.371524+0.088799
## [65] train-rmse:1.152614+0.020747 test-rmse:1.366172+0.088456
## [66] train-rmse:1.148320+0.020547 test-rmse:1.362858+0.088368
## [67] train-rmse:1.144183+0.020461 test-rmse:1.361421+0.090062
## [68] train-rmse:1.140172+0.020344 test-rmse:1.361860+0.088386
## [69] train-rmse:1.136296+0.020208 test-rmse:1.358760+0.086520
## [70] train-rmse:1.132494+0.020100 test-rmse:1.356796+0.087138
## [71] train-rmse:1.128825+0.020020 test-rmse:1.354814+0.085342
## [72] train-rmse:1.125214+0.019924 test-rmse:1.355251+0.086053
## [73] train-rmse:1.121703+0.019817 test-rmse:1.351695+0.084895
## [74] train-rmse:1.118279+0.019772 test-rmse:1.348776+0.083845
## [75] train-rmse:1.114889+0.019725 test-rmse:1.349295+0.078044
## [76] train-rmse:1.111507+0.019697 test-rmse:1.348556+0.082326
## [77] train-rmse:1.108142+0.019734 test-rmse:1.344542+0.082818
## [78] train-rmse:1.104899+0.019633 test-rmse:1.340938+0.083053
## [79] train-rmse:1.101756+0.019555 test-rmse:1.335043+0.082554
## [80] train-rmse:1.098601+0.019503 test-rmse:1.327415+0.083383
## [81] train-rmse:1.095498+0.019428 test-rmse:1.322677+0.083795
## [82] train-rmse:1.092459+0.019409 test-rmse:1.320069+0.082994
## [83] train-rmse:1.089523+0.019447 test-rmse:1.317506+0.083292
## [84] train-rmse:1.086683+0.019458 test-rmse:1.312863+0.086671
## [85] train-rmse:1.083856+0.019440 test-rmse:1.314445+0.086344
## [86] train-rmse:1.081087+0.019370 test-rmse:1.313630+0.085939
## [87] train-rmse:1.078358+0.019350 test-rmse:1.314057+0.087224
## [88] train-rmse:1.075677+0.019266 test-rmse:1.312263+0.087169
## [89] train-rmse:1.073122+0.019217 test-rmse:1.313591+0.087774
## [90] train-rmse:1.070482+0.019144 test-rmse:1.311224+0.087293
## [91] train-rmse:1.067973+0.019135 test-rmse:1.309299+0.088749
## [92] train-rmse:1.065442+0.019147 test-rmse:1.306731+0.087342
## [93] train-rmse:1.063018+0.019160 test-rmse:1.306587+0.088638
## [94] train-rmse:1.060681+0.019120 test-rmse:1.303203+0.090248
## [95] train-rmse:1.058322+0.019166 test-rmse:1.303252+0.091195
## [96] train-rmse:1.056039+0.019045 test-rmse:1.299934+0.091876
## [97] train-rmse:1.053733+0.018925 test-rmse:1.298887+0.091874
## [98] train-rmse:1.051493+0.018827 test-rmse:1.292900+0.091775
## [99] train-rmse:1.049298+0.018743 test-rmse:1.292223+0.089426
## [100] train-rmse:1.047073+0.018629 test-rmse:1.290392+0.089206
## [101] train-rmse:1.044964+0.018584 test-rmse:1.289704+0.089583
## [102] train-rmse:1.042865+0.018551 test-rmse:1.292203+0.088596
## [103] train-rmse:1.040785+0.018497 test-rmse:1.290132+0.088802
## [104] train-rmse:1.038745+0.018500 test-rmse:1.290384+0.088215
## [105] train-rmse:1.036759+0.018461 test-rmse:1.291530+0.088006
## [106] train-rmse:1.034796+0.018383 test-rmse:1.289073+0.083620
## [107] train-rmse:1.032866+0.018254 test-rmse:1.287962+0.083369
## [108] train-rmse:1.031005+0.018194 test-rmse:1.285326+0.084268
## [109] train-rmse:1.029175+0.018138 test-rmse:1.278888+0.080317
## [110] train-rmse:1.027383+0.018107 test-rmse:1.278312+0.080123
## [111] train-rmse:1.025599+0.018066 test-rmse:1.277816+0.080916
## [112] train-rmse:1.023789+0.017976 test-rmse:1.277253+0.081167
## [113] train-rmse:1.022048+0.017914 test-rmse:1.276564+0.084553
## [114] train-rmse:1.020278+0.017870 test-rmse:1.274018+0.085856
## [115] train-rmse:1.018582+0.017841 test-rmse:1.270975+0.086604
## [116] train-rmse:1.016903+0.017827 test-rmse:1.270593+0.087049
## [117] train-rmse:1.015241+0.017824 test-rmse:1.269062+0.087613
## [118] train-rmse:1.013581+0.017816 test-rmse:1.268315+0.087721
## [119] train-rmse:1.011956+0.017771 test-rmse:1.267794+0.087654
## [120] train-rmse:1.010311+0.017745 test-rmse:1.265567+0.089584
## [121] train-rmse:1.008654+0.017735 test-rmse:1.266353+0.090201
## [122] train-rmse:1.007069+0.017734 test-rmse:1.264324+0.090059
## [123] train-rmse:1.005460+0.017663 test-rmse:1.264646+0.088879
## [124] train-rmse:1.003933+0.017658 test-rmse:1.262699+0.089279
## [125] train-rmse:1.002364+0.017595 test-rmse:1.265567+0.089404
## [126] train-rmse:1.000782+0.017540 test-rmse:1.262399+0.089878
## [127] train-rmse:0.999282+0.017489 test-rmse:1.262561+0.089320
## [128] train-rmse:0.997811+0.017475 test-rmse:1.262785+0.087658
## [129] train-rmse:0.996399+0.017432 test-rmse:1.260441+0.088470
## [130] train-rmse:0.994948+0.017420 test-rmse:1.257903+0.087949
## [131] train-rmse:0.993588+0.017407 test-rmse:1.258283+0.088055
## [132] train-rmse:0.992224+0.017402 test-rmse:1.257655+0.089046
## [133] train-rmse:0.990846+0.017399 test-rmse:1.256958+0.089972
## [134] train-rmse:0.989506+0.017365 test-rmse:1.257025+0.088632
## [135] train-rmse:0.988121+0.017328 test-rmse:1.255365+0.088223
## [136] train-rmse:0.986769+0.017336 test-rmse:1.254530+0.088104
## [137] train-rmse:0.985446+0.017345 test-rmse:1.253261+0.089259
## [138] train-rmse:0.984138+0.017386 test-rmse:1.252822+0.089422
## [139] train-rmse:0.982776+0.017401 test-rmse:1.253864+0.087846
## [140] train-rmse:0.981466+0.017412 test-rmse:1.249574+0.087574
## [141] train-rmse:0.980177+0.017433 test-rmse:1.248501+0.087360
## [142] train-rmse:0.978945+0.017450 test-rmse:1.247761+0.086876
## [143] train-rmse:0.977633+0.017481 test-rmse:1.250913+0.085487
## [144] train-rmse:0.976415+0.017495 test-rmse:1.249997+0.083709
## [145] train-rmse:0.975204+0.017488 test-rmse:1.250217+0.081461
## [146] train-rmse:0.973952+0.017456 test-rmse:1.249788+0.081265
## [147] train-rmse:0.972782+0.017444 test-rmse:1.248348+0.077167
## [148] train-rmse:0.971626+0.017420 test-rmse:1.247269+0.077970
## [149] train-rmse:0.970413+0.017444 test-rmse:1.247222+0.080132
## [150] train-rmse:0.969257+0.017433 test-rmse:1.245324+0.079912
## [151] train-rmse:0.968127+0.017412 test-rmse:1.245919+0.081866
## [152] train-rmse:0.966990+0.017432 test-rmse:1.243795+0.083350
## [153] train-rmse:0.965848+0.017468 test-rmse:1.242612+0.084641
## [154] train-rmse:0.964764+0.017464 test-rmse:1.243851+0.084117
## [155] train-rmse:0.963682+0.017455 test-rmse:1.243073+0.083537
## [156] train-rmse:0.962594+0.017429 test-rmse:1.242455+0.084723
## [157] train-rmse:0.961460+0.017419 test-rmse:1.242137+0.084282
## [158] train-rmse:0.960399+0.017447 test-rmse:1.239710+0.085434
## [159] train-rmse:0.959322+0.017420 test-rmse:1.240929+0.084888
## [160] train-rmse:0.958251+0.017422 test-rmse:1.239531+0.083848
## [161] train-rmse:0.957149+0.017344 test-rmse:1.239385+0.083063
## [162] train-rmse:0.956083+0.017373 test-rmse:1.239322+0.082829
## [163] train-rmse:0.955042+0.017405 test-rmse:1.238627+0.083167
## [164] train-rmse:0.954035+0.017442 test-rmse:1.236909+0.083382
## [165] train-rmse:0.953025+0.017484 test-rmse:1.236647+0.082712
## [166] train-rmse:0.952051+0.017524 test-rmse:1.236955+0.083200
## [167] train-rmse:0.951042+0.017571 test-rmse:1.239271+0.083750
## [168] train-rmse:0.950074+0.017602 test-rmse:1.236162+0.079841
## [169] train-rmse:0.949114+0.017618 test-rmse:1.237401+0.079709
## [170] train-rmse:0.948187+0.017631 test-rmse:1.235672+0.080261
## [171] train-rmse:0.947253+0.017641 test-rmse:1.236058+0.079751
## [172] train-rmse:0.946267+0.017639 test-rmse:1.235507+0.079419
## [173] train-rmse:0.945345+0.017653 test-rmse:1.235369+0.078140
## [174] train-rmse:0.944441+0.017661 test-rmse:1.235966+0.079205
## [175] train-rmse:0.943500+0.017683 test-rmse:1.236357+0.079203
## [176] train-rmse:0.942615+0.017697 test-rmse:1.235828+0.079402
## [177] train-rmse:0.941693+0.017718 test-rmse:1.235963+0.081386
## [178] train-rmse:0.940780+0.017721 test-rmse:1.233946+0.081750
## [179] train-rmse:0.939896+0.017730 test-rmse:1.232272+0.081543
## [180] train-rmse:0.939019+0.017741 test-rmse:1.232197+0.081153
## [181] train-rmse:0.938163+0.017735 test-rmse:1.230738+0.082779
## [182] train-rmse:0.937290+0.017717 test-rmse:1.231286+0.082697
## [183] train-rmse:0.936445+0.017711 test-rmse:1.230802+0.082726
## [184] train-rmse:0.935541+0.017749 test-rmse:1.229958+0.082434
## [185] train-rmse:0.934675+0.017769 test-rmse:1.231055+0.082279
## [186] train-rmse:0.933840+0.017770 test-rmse:1.229862+0.082185
## [187] train-rmse:0.933013+0.017794 test-rmse:1.230302+0.081724
## [188] train-rmse:0.932143+0.017804 test-rmse:1.230012+0.081522
## [189] train-rmse:0.931308+0.017844 test-rmse:1.229895+0.082091
## [190] train-rmse:0.930516+0.017856 test-rmse:1.227673+0.080544
## [191] train-rmse:0.929715+0.017883 test-rmse:1.230245+0.081584
## [192] train-rmse:0.928945+0.017897 test-rmse:1.230363+0.081936
## [193] train-rmse:0.928154+0.017927 test-rmse:1.230121+0.080352
## [194] train-rmse:0.927392+0.017933 test-rmse:1.228089+0.080942
## [195] train-rmse:0.926635+0.017957 test-rmse:1.227103+0.081402
## [196] train-rmse:0.925843+0.017971 test-rmse:1.226765+0.082123
## [197] train-rmse:0.925088+0.018002 test-rmse:1.225558+0.082135
## [198] train-rmse:0.924350+0.018014 test-rmse:1.225660+0.081265
## [199] train-rmse:0.923628+0.017996 test-rmse:1.226696+0.083146
## [200] train-rmse:0.922862+0.018007 test-rmse:1.225980+0.083374
## [201] train-rmse:0.922111+0.018008 test-rmse:1.225161+0.083521
## [202] train-rmse:0.921368+0.018022 test-rmse:1.224277+0.084119
## [203] train-rmse:0.920653+0.018035 test-rmse:1.224241+0.084032
## [204] train-rmse:0.919937+0.018046 test-rmse:1.224789+0.083849
## [205] train-rmse:0.919208+0.018063 test-rmse:1.223929+0.084547
## [206] train-rmse:0.918504+0.018087 test-rmse:1.223230+0.085405
## [207] train-rmse:0.917813+0.018117 test-rmse:1.223719+0.085137
## [208] train-rmse:0.917110+0.018139 test-rmse:1.223166+0.085559
## [209] train-rmse:0.916417+0.018154 test-rmse:1.220769+0.083282
## [210] train-rmse:0.915731+0.018175 test-rmse:1.221186+0.083343
## [211] train-rmse:0.915027+0.018169 test-rmse:1.221608+0.083278
## [212] train-rmse:0.914336+0.018130 test-rmse:1.221293+0.082339
## [213] train-rmse:0.913653+0.018127 test-rmse:1.222614+0.083033
## [214] train-rmse:0.912995+0.018148 test-rmse:1.221587+0.082099
## [215] train-rmse:0.912296+0.018141 test-rmse:1.221915+0.080638
## Stopping. Best iteration:
## [209] train-rmse:0.916417+0.018154 test-rmse:1.220769+0.083282
##
## 64
## [1] train-rmse:71.012277+0.071119 test-rmse:71.012837+0.422304
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:51.218313+0.050969 test-rmse:51.218473+0.441381
## [3] train-rmse:36.954831+0.036295 test-rmse:36.954435+0.454495
## [4] train-rmse:26.681634+0.025555 test-rmse:26.680503+0.462943
## [5] train-rmse:19.289380+0.017699 test-rmse:19.287267+0.467424
## [6] train-rmse:13.979685+0.012191 test-rmse:13.976312+0.468039
## [7] train-rmse:10.170048+0.008156 test-rmse:10.157296+0.437450
## [8] train-rmse:7.441018+0.007129 test-rmse:7.434018+0.393737
## [9] train-rmse:5.490967+0.007118 test-rmse:5.483415+0.384501
## [10] train-rmse:4.109676+0.009871 test-rmse:4.116099+0.356537
## [11] train-rmse:3.141007+0.013569 test-rmse:3.155103+0.318665
## [12] train-rmse:2.480066+0.013818 test-rmse:2.509807+0.281035
## [13] train-rmse:2.038811+0.015797 test-rmse:2.099317+0.245454
## [14] train-rmse:1.751689+0.018755 test-rmse:1.834901+0.200935
## [15] train-rmse:1.569318+0.021082 test-rmse:1.684946+0.174611
## [16] train-rmse:1.452350+0.019007 test-rmse:1.595816+0.146714
## [17] train-rmse:1.378174+0.019404 test-rmse:1.529655+0.135719
## [18] train-rmse:1.330199+0.019410 test-rmse:1.498342+0.126552
## [19] train-rmse:1.293076+0.021989 test-rmse:1.474579+0.121377
## [20] train-rmse:1.265574+0.022770 test-rmse:1.450416+0.123687
## [21] train-rmse:1.239626+0.022258 test-rmse:1.437372+0.113850
## [22] train-rmse:1.209369+0.020905 test-rmse:1.425513+0.114273
## [23] train-rmse:1.189206+0.019330 test-rmse:1.418510+0.102349
## [24] train-rmse:1.172627+0.018479 test-rmse:1.400557+0.103686
## [25] train-rmse:1.156401+0.018161 test-rmse:1.390734+0.103233
## [26] train-rmse:1.140375+0.019932 test-rmse:1.381126+0.100739
## [27] train-rmse:1.123386+0.018016 test-rmse:1.367073+0.104132
## [28] train-rmse:1.107566+0.016271 test-rmse:1.349525+0.097475
## [29] train-rmse:1.094461+0.016504 test-rmse:1.340884+0.102255
## [30] train-rmse:1.082426+0.016799 test-rmse:1.332705+0.101536
## [31] train-rmse:1.070433+0.017016 test-rmse:1.320026+0.101731
## [32] train-rmse:1.058184+0.018447 test-rmse:1.320258+0.104342
## [33] train-rmse:1.042951+0.018883 test-rmse:1.313011+0.108713
## [34] train-rmse:1.030969+0.018941 test-rmse:1.304973+0.108978
## [35] train-rmse:1.016467+0.019943 test-rmse:1.300682+0.111074
## [36] train-rmse:1.006541+0.019523 test-rmse:1.293832+0.108725
## [37] train-rmse:0.997868+0.019055 test-rmse:1.288439+0.107258
## [38] train-rmse:0.988296+0.017387 test-rmse:1.285378+0.105403
## [39] train-rmse:0.976416+0.012974 test-rmse:1.272945+0.112417
## [40] train-rmse:0.965758+0.013855 test-rmse:1.265561+0.111994
## [41] train-rmse:0.956825+0.014567 test-rmse:1.261009+0.112670
## [42] train-rmse:0.945461+0.017588 test-rmse:1.258246+0.112558
## [43] train-rmse:0.935909+0.017375 test-rmse:1.252013+0.112424
## [44] train-rmse:0.928541+0.017683 test-rmse:1.244889+0.114648
## [45] train-rmse:0.918112+0.019444 test-rmse:1.243625+0.118281
## [46] train-rmse:0.911468+0.019168 test-rmse:1.239992+0.117835
## [47] train-rmse:0.903837+0.018450 test-rmse:1.235226+0.117092
## [48] train-rmse:0.895079+0.021190 test-rmse:1.231027+0.118043
## [49] train-rmse:0.887489+0.022497 test-rmse:1.224120+0.117237
## [50] train-rmse:0.878858+0.021823 test-rmse:1.218059+0.119181
## [51] train-rmse:0.873179+0.022871 test-rmse:1.211265+0.117724
## [52] train-rmse:0.868113+0.022545 test-rmse:1.206184+0.116518
## [53] train-rmse:0.862833+0.022946 test-rmse:1.201797+0.118178
## [54] train-rmse:0.854955+0.023541 test-rmse:1.197478+0.119499
## [55] train-rmse:0.849649+0.024158 test-rmse:1.195248+0.118778
## [56] train-rmse:0.842917+0.021567 test-rmse:1.192535+0.122087
## [57] train-rmse:0.832836+0.024891 test-rmse:1.187692+0.123623
## [58] train-rmse:0.826253+0.025443 test-rmse:1.191000+0.121280
## [59] train-rmse:0.821638+0.025615 test-rmse:1.184724+0.118585
## [60] train-rmse:0.815209+0.026953 test-rmse:1.179678+0.121481
## [61] train-rmse:0.808303+0.024591 test-rmse:1.180643+0.120886
## [62] train-rmse:0.801264+0.020916 test-rmse:1.178034+0.121405
## [63] train-rmse:0.794425+0.020586 test-rmse:1.177507+0.120991
## [64] train-rmse:0.787884+0.018319 test-rmse:1.172473+0.121925
## [65] train-rmse:0.783616+0.019014 test-rmse:1.171027+0.123175
## [66] train-rmse:0.778467+0.016566 test-rmse:1.167258+0.130663
## [67] train-rmse:0.770282+0.018902 test-rmse:1.156679+0.126911
## [68] train-rmse:0.766206+0.019768 test-rmse:1.154470+0.126437
## [69] train-rmse:0.761034+0.019701 test-rmse:1.148913+0.124700
## [70] train-rmse:0.757599+0.019843 test-rmse:1.145855+0.126587
## [71] train-rmse:0.753228+0.021184 test-rmse:1.144395+0.125029
## [72] train-rmse:0.747473+0.019347 test-rmse:1.143868+0.125310
## [73] train-rmse:0.741358+0.019192 test-rmse:1.145490+0.124497
## [74] train-rmse:0.735892+0.020210 test-rmse:1.136045+0.124420
## [75] train-rmse:0.731404+0.019996 test-rmse:1.130973+0.126119
## [76] train-rmse:0.725323+0.019331 test-rmse:1.127843+0.125897
## [77] train-rmse:0.720277+0.021178 test-rmse:1.126766+0.125905
## [78] train-rmse:0.715002+0.022716 test-rmse:1.126968+0.122558
## [79] train-rmse:0.709486+0.022163 test-rmse:1.123375+0.123745
## [80] train-rmse:0.704950+0.023921 test-rmse:1.121715+0.122683
## [81] train-rmse:0.701297+0.025271 test-rmse:1.121054+0.119266
## [82] train-rmse:0.696914+0.023831 test-rmse:1.119183+0.118658
## [83] train-rmse:0.692722+0.022503 test-rmse:1.119075+0.117562
## [84] train-rmse:0.687963+0.021428 test-rmse:1.116888+0.119387
## [85] train-rmse:0.683850+0.021840 test-rmse:1.113883+0.118922
## [86] train-rmse:0.679259+0.022379 test-rmse:1.107669+0.118194
## [87] train-rmse:0.672343+0.022423 test-rmse:1.103082+0.114990
## [88] train-rmse:0.667426+0.021204 test-rmse:1.101440+0.116814
## [89] train-rmse:0.664412+0.020575 test-rmse:1.099183+0.118194
## [90] train-rmse:0.661678+0.020577 test-rmse:1.098834+0.119082
## [91] train-rmse:0.656538+0.018508 test-rmse:1.094927+0.116028
## [92] train-rmse:0.652095+0.016877 test-rmse:1.095572+0.114392
## [93] train-rmse:0.647986+0.016600 test-rmse:1.090694+0.113559
## [94] train-rmse:0.645594+0.016599 test-rmse:1.087552+0.111786
## [95] train-rmse:0.642464+0.017012 test-rmse:1.084484+0.113001
## [96] train-rmse:0.637571+0.018967 test-rmse:1.081285+0.112794
## [97] train-rmse:0.634860+0.018351 test-rmse:1.079070+0.110728
## [98] train-rmse:0.629294+0.015860 test-rmse:1.076641+0.109768
## [99] train-rmse:0.625320+0.014296 test-rmse:1.077188+0.109803
## [100] train-rmse:0.620690+0.013423 test-rmse:1.076046+0.109250
## [101] train-rmse:0.618612+0.013484 test-rmse:1.076313+0.109042
## [102] train-rmse:0.615491+0.014586 test-rmse:1.075181+0.108834
## [103] train-rmse:0.612345+0.015020 test-rmse:1.072350+0.110001
## [104] train-rmse:0.608357+0.013694 test-rmse:1.071381+0.108848
## [105] train-rmse:0.606331+0.014307 test-rmse:1.071571+0.108329
## [106] train-rmse:0.602012+0.013404 test-rmse:1.069062+0.106766
## [107] train-rmse:0.599466+0.013523 test-rmse:1.070428+0.105303
## [108] train-rmse:0.596464+0.013110 test-rmse:1.071339+0.105435
## [109] train-rmse:0.594464+0.012783 test-rmse:1.070608+0.104762
## [110] train-rmse:0.591627+0.014581 test-rmse:1.070210+0.104925
## [111] train-rmse:0.587429+0.013626 test-rmse:1.069308+0.105214
## [112] train-rmse:0.584638+0.013190 test-rmse:1.067608+0.103095
## [113] train-rmse:0.582595+0.012999 test-rmse:1.066710+0.104146
## [114] train-rmse:0.580716+0.013226 test-rmse:1.065863+0.104128
## [115] train-rmse:0.578204+0.013723 test-rmse:1.064287+0.102567
## [116] train-rmse:0.574919+0.013790 test-rmse:1.063335+0.100924
## [117] train-rmse:0.570305+0.013398 test-rmse:1.060216+0.101418
## [118] train-rmse:0.566922+0.012004 test-rmse:1.059260+0.102767
## [119] train-rmse:0.564251+0.011565 test-rmse:1.058857+0.102834
## [120] train-rmse:0.561603+0.011957 test-rmse:1.058705+0.101806
## [121] train-rmse:0.558946+0.012417 test-rmse:1.058827+0.100834
## [122] train-rmse:0.554624+0.013507 test-rmse:1.056851+0.101667
## [123] train-rmse:0.552820+0.013357 test-rmse:1.057977+0.101143
## [124] train-rmse:0.550824+0.013033 test-rmse:1.056627+0.100513
## [125] train-rmse:0.547579+0.011468 test-rmse:1.055507+0.100384
## [126] train-rmse:0.545598+0.011946 test-rmse:1.054477+0.101069
## [127] train-rmse:0.542834+0.012231 test-rmse:1.053598+0.101481
## [128] train-rmse:0.538577+0.009693 test-rmse:1.053398+0.101633
## [129] train-rmse:0.535415+0.009929 test-rmse:1.054320+0.103154
## [130] train-rmse:0.532996+0.010566 test-rmse:1.053620+0.102307
## [131] train-rmse:0.529629+0.008223 test-rmse:1.053479+0.104198
## [132] train-rmse:0.527619+0.008610 test-rmse:1.055416+0.103696
## [133] train-rmse:0.525148+0.009107 test-rmse:1.056026+0.104656
## [134] train-rmse:0.523334+0.009188 test-rmse:1.055282+0.105028
## Stopping. Best iteration:
## [128] train-rmse:0.538577+0.009693 test-rmse:1.053398+0.101633
##
## 65
## [1] train-rmse:71.012277+0.071119 test-rmse:71.012837+0.422304
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:51.218313+0.050969 test-rmse:51.218473+0.441381
## [3] train-rmse:36.954831+0.036295 test-rmse:36.954435+0.454495
## [4] train-rmse:26.681634+0.025555 test-rmse:26.680503+0.462943
## [5] train-rmse:19.289380+0.017699 test-rmse:19.287267+0.467424
## [6] train-rmse:13.979685+0.012191 test-rmse:13.976312+0.468039
## [7] train-rmse:10.170048+0.008156 test-rmse:10.157296+0.437450
## [8] train-rmse:7.439169+0.007163 test-rmse:7.428380+0.392670
## [9] train-rmse:5.478573+0.004028 test-rmse:5.478081+0.366618
## [10] train-rmse:4.084144+0.004614 test-rmse:4.084038+0.336601
## [11] train-rmse:3.098640+0.004108 test-rmse:3.106681+0.305281
## [12] train-rmse:2.406223+0.010361 test-rmse:2.431456+0.271317
## [13] train-rmse:1.935586+0.006031 test-rmse:1.989484+0.241208
## [14] train-rmse:1.623749+0.009544 test-rmse:1.716385+0.206856
## [15] train-rmse:1.410843+0.015110 test-rmse:1.554967+0.167700
## [16] train-rmse:1.279634+0.017087 test-rmse:1.467748+0.146637
## [17] train-rmse:1.193682+0.016322 test-rmse:1.412275+0.135016
## [18] train-rmse:1.124449+0.015878 test-rmse:1.368784+0.116071
## [19] train-rmse:1.082941+0.015349 test-rmse:1.341063+0.108611
## [20] train-rmse:1.050507+0.015294 test-rmse:1.322574+0.108965
## [21] train-rmse:1.021377+0.014776 test-rmse:1.307045+0.101832
## [22] train-rmse:0.991845+0.012481 test-rmse:1.281254+0.098836
## [23] train-rmse:0.971931+0.012867 test-rmse:1.264992+0.088154
## [24] train-rmse:0.951927+0.012295 test-rmse:1.259859+0.085703
## [25] train-rmse:0.925773+0.017893 test-rmse:1.243937+0.078364
## [26] train-rmse:0.909923+0.018733 test-rmse:1.236638+0.078129
## [27] train-rmse:0.889084+0.016125 test-rmse:1.222819+0.077879
## [28] train-rmse:0.869800+0.019537 test-rmse:1.216375+0.076390
## [29] train-rmse:0.850126+0.017928 test-rmse:1.208246+0.077099
## [30] train-rmse:0.837482+0.018082 test-rmse:1.199845+0.077612
## [31] train-rmse:0.824786+0.017694 test-rmse:1.193485+0.076818
## [32] train-rmse:0.811864+0.020385 test-rmse:1.182192+0.078601
## [33] train-rmse:0.799370+0.022574 test-rmse:1.169322+0.079901
## [34] train-rmse:0.784744+0.023296 test-rmse:1.154895+0.077381
## [35] train-rmse:0.772776+0.026017 test-rmse:1.143520+0.076102
## [36] train-rmse:0.760745+0.024184 test-rmse:1.136900+0.081360
## [37] train-rmse:0.747157+0.022412 test-rmse:1.129801+0.079370
## [38] train-rmse:0.731307+0.021504 test-rmse:1.125015+0.081035
## [39] train-rmse:0.720192+0.023700 test-rmse:1.117198+0.080296
## [40] train-rmse:0.708832+0.021557 test-rmse:1.099040+0.066971
## [41] train-rmse:0.700748+0.021120 test-rmse:1.093293+0.063307
## [42] train-rmse:0.685227+0.022251 test-rmse:1.086416+0.069884
## [43] train-rmse:0.676482+0.022942 test-rmse:1.083065+0.066742
## [44] train-rmse:0.666825+0.025369 test-rmse:1.075794+0.066276
## [45] train-rmse:0.658490+0.022966 test-rmse:1.070875+0.067102
## [46] train-rmse:0.651079+0.021825 test-rmse:1.069724+0.066671
## [47] train-rmse:0.642689+0.020668 test-rmse:1.065983+0.069252
## [48] train-rmse:0.636948+0.020558 test-rmse:1.061232+0.069960
## [49] train-rmse:0.626845+0.019410 test-rmse:1.061749+0.072918
## [50] train-rmse:0.616837+0.018175 test-rmse:1.059654+0.071259
## [51] train-rmse:0.607460+0.018801 test-rmse:1.051269+0.074272
## [52] train-rmse:0.601501+0.019348 test-rmse:1.049525+0.073808
## [53] train-rmse:0.594407+0.017047 test-rmse:1.047850+0.073260
## [54] train-rmse:0.589515+0.017827 test-rmse:1.045018+0.072286
## [55] train-rmse:0.583328+0.018498 test-rmse:1.044731+0.070211
## [56] train-rmse:0.576006+0.020141 test-rmse:1.037358+0.073280
## [57] train-rmse:0.567316+0.016102 test-rmse:1.032043+0.073051
## [58] train-rmse:0.561267+0.013896 test-rmse:1.033057+0.071482
## [59] train-rmse:0.550609+0.013492 test-rmse:1.029750+0.068522
## [60] train-rmse:0.543822+0.013658 test-rmse:1.028186+0.070357
## [61] train-rmse:0.536344+0.014602 test-rmse:1.020557+0.065652
## [62] train-rmse:0.532603+0.013706 test-rmse:1.018354+0.064940
## [63] train-rmse:0.524799+0.018125 test-rmse:1.018325+0.062357
## [64] train-rmse:0.518057+0.020104 test-rmse:1.015942+0.062838
## [65] train-rmse:0.512396+0.019825 test-rmse:1.015500+0.061136
## [66] train-rmse:0.507463+0.018960 test-rmse:1.012864+0.062942
## [67] train-rmse:0.498592+0.021042 test-rmse:1.015558+0.064709
## [68] train-rmse:0.494108+0.021576 test-rmse:1.014687+0.063024
## [69] train-rmse:0.488940+0.022609 test-rmse:1.011176+0.061429
## [70] train-rmse:0.485796+0.022539 test-rmse:1.008696+0.063838
## [71] train-rmse:0.478494+0.019493 test-rmse:1.008825+0.062794
## [72] train-rmse:0.472971+0.017961 test-rmse:1.004158+0.061032
## [73] train-rmse:0.469278+0.017847 test-rmse:1.003684+0.059957
## [74] train-rmse:0.463127+0.015345 test-rmse:0.999343+0.059902
## [75] train-rmse:0.459065+0.014781 test-rmse:0.998869+0.059580
## [76] train-rmse:0.455133+0.015622 test-rmse:0.999052+0.060738
## [77] train-rmse:0.449916+0.016052 test-rmse:0.996709+0.062213
## [78] train-rmse:0.444395+0.015276 test-rmse:0.995900+0.061828
## [79] train-rmse:0.438237+0.014716 test-rmse:0.994280+0.061429
## [80] train-rmse:0.435691+0.014716 test-rmse:0.994173+0.060803
## [81] train-rmse:0.429932+0.014472 test-rmse:0.988593+0.056735
## [82] train-rmse:0.425294+0.012690 test-rmse:0.988041+0.056751
## [83] train-rmse:0.418106+0.013459 test-rmse:0.989624+0.059181
## [84] train-rmse:0.414695+0.013142 test-rmse:0.990660+0.059657
## [85] train-rmse:0.410839+0.014715 test-rmse:0.990109+0.058202
## [86] train-rmse:0.406020+0.014134 test-rmse:0.987966+0.058438
## [87] train-rmse:0.401514+0.013580 test-rmse:0.987426+0.058289
## [88] train-rmse:0.396885+0.013161 test-rmse:0.987040+0.058223
## [89] train-rmse:0.393882+0.012829 test-rmse:0.986591+0.059270
## [90] train-rmse:0.388860+0.014852 test-rmse:0.985498+0.060077
## [91] train-rmse:0.384975+0.015931 test-rmse:0.983959+0.058098
## [92] train-rmse:0.382228+0.016806 test-rmse:0.984007+0.057447
## [93] train-rmse:0.377531+0.014663 test-rmse:0.979043+0.055020
## [94] train-rmse:0.373625+0.014581 test-rmse:0.979142+0.054475
## [95] train-rmse:0.369847+0.014511 test-rmse:0.980512+0.055633
## [96] train-rmse:0.365434+0.014112 test-rmse:0.978882+0.057321
## [97] train-rmse:0.361541+0.015165 test-rmse:0.980243+0.057104
## [98] train-rmse:0.357378+0.014763 test-rmse:0.980170+0.056486
## [99] train-rmse:0.353337+0.015552 test-rmse:0.980777+0.058687
## [100] train-rmse:0.349286+0.017013 test-rmse:0.978837+0.057492
## [101] train-rmse:0.346994+0.017331 test-rmse:0.976548+0.056261
## [102] train-rmse:0.343888+0.017366 test-rmse:0.975207+0.056729
## [103] train-rmse:0.341462+0.017013 test-rmse:0.975734+0.055804
## [104] train-rmse:0.337249+0.015164 test-rmse:0.974478+0.057501
## [105] train-rmse:0.334478+0.015863 test-rmse:0.977280+0.059914
## [106] train-rmse:0.331000+0.015635 test-rmse:0.975718+0.058096
## [107] train-rmse:0.327259+0.016301 test-rmse:0.973230+0.058710
## [108] train-rmse:0.323372+0.016207 test-rmse:0.973764+0.058348
## [109] train-rmse:0.320606+0.016464 test-rmse:0.973260+0.058794
## [110] train-rmse:0.317174+0.015612 test-rmse:0.973884+0.057322
## [111] train-rmse:0.312201+0.012833 test-rmse:0.974306+0.061671
## [112] train-rmse:0.308932+0.011924 test-rmse:0.977201+0.060096
## [113] train-rmse:0.307215+0.012727 test-rmse:0.976833+0.060391
## Stopping. Best iteration:
## [107] train-rmse:0.327259+0.016301 test-rmse:0.973230+0.058710
##
## 66
## [1] train-rmse:71.012277+0.071119 test-rmse:71.012837+0.422304
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:51.218313+0.050969 test-rmse:51.218473+0.441381
## [3] train-rmse:36.954831+0.036295 test-rmse:36.954435+0.454495
## [4] train-rmse:26.681634+0.025555 test-rmse:26.680503+0.462943
## [5] train-rmse:19.289380+0.017699 test-rmse:19.287267+0.467424
## [6] train-rmse:13.979685+0.012191 test-rmse:13.976312+0.468039
## [7] train-rmse:10.170048+0.008156 test-rmse:10.157296+0.437450
## [8] train-rmse:7.437512+0.007594 test-rmse:7.418994+0.389481
## [9] train-rmse:5.475252+0.006010 test-rmse:5.458251+0.353602
## [10] train-rmse:4.072230+0.006118 test-rmse:4.070478+0.333730
## [11] train-rmse:3.070557+0.006010 test-rmse:3.088122+0.308058
## [12] train-rmse:2.363172+0.003590 test-rmse:2.404569+0.263449
## [13] train-rmse:1.876769+0.006319 test-rmse:1.953727+0.226589
## [14] train-rmse:1.535387+0.008401 test-rmse:1.678883+0.202472
## [15] train-rmse:1.313288+0.008403 test-rmse:1.505460+0.188607
## [16] train-rmse:1.163416+0.009332 test-rmse:1.383978+0.166802
## [17] train-rmse:1.061005+0.019153 test-rmse:1.331822+0.160501
## [18] train-rmse:0.987337+0.016146 test-rmse:1.292507+0.143125
## [19] train-rmse:0.926327+0.022761 test-rmse:1.268512+0.131940
## [20] train-rmse:0.886497+0.025142 test-rmse:1.244996+0.127046
## [21] train-rmse:0.846856+0.024729 test-rmse:1.213822+0.131587
## [22] train-rmse:0.819303+0.025133 test-rmse:1.191210+0.127346
## [23] train-rmse:0.794449+0.024575 test-rmse:1.177676+0.125326
## [24] train-rmse:0.769836+0.023957 test-rmse:1.156248+0.123672
## [25] train-rmse:0.746989+0.021969 test-rmse:1.146747+0.122236
## [26] train-rmse:0.728224+0.021907 test-rmse:1.131430+0.120729
## [27] train-rmse:0.709590+0.025003 test-rmse:1.117078+0.114193
## [28] train-rmse:0.691067+0.022417 test-rmse:1.109480+0.110726
## [29] train-rmse:0.676429+0.022646 test-rmse:1.098926+0.107969
## [30] train-rmse:0.658691+0.025581 test-rmse:1.087712+0.112509
## [31] train-rmse:0.641549+0.024010 test-rmse:1.081305+0.111983
## [32] train-rmse:0.630386+0.022417 test-rmse:1.074232+0.109741
## [33] train-rmse:0.618712+0.021810 test-rmse:1.063689+0.103204
## [34] train-rmse:0.604864+0.022778 test-rmse:1.062214+0.103251
## [35] train-rmse:0.591487+0.022084 test-rmse:1.057243+0.103348
## [36] train-rmse:0.579988+0.020660 test-rmse:1.053265+0.098463
## [37] train-rmse:0.561168+0.018208 test-rmse:1.045887+0.094130
## [38] train-rmse:0.552765+0.017169 test-rmse:1.040312+0.095702
## [39] train-rmse:0.544838+0.018269 test-rmse:1.040877+0.099349
## [40] train-rmse:0.531413+0.018048 test-rmse:1.033666+0.096368
## [41] train-rmse:0.517624+0.018362 test-rmse:1.028414+0.094165
## [42] train-rmse:0.505341+0.019231 test-rmse:1.025430+0.094104
## [43] train-rmse:0.495674+0.019635 test-rmse:1.022388+0.092027
## [44] train-rmse:0.486319+0.018378 test-rmse:1.022336+0.090800
## [45] train-rmse:0.478584+0.016725 test-rmse:1.018111+0.091134
## [46] train-rmse:0.472512+0.016111 test-rmse:1.014664+0.092685
## [47] train-rmse:0.464727+0.015195 test-rmse:1.013955+0.090367
## [48] train-rmse:0.456819+0.015877 test-rmse:1.014068+0.091626
## [49] train-rmse:0.449363+0.015685 test-rmse:1.009045+0.091172
## [50] train-rmse:0.440161+0.017058 test-rmse:1.003892+0.092593
## [51] train-rmse:0.433560+0.015230 test-rmse:1.002514+0.090798
## [52] train-rmse:0.421281+0.015093 test-rmse:0.999271+0.091492
## [53] train-rmse:0.414040+0.016127 test-rmse:0.994846+0.091056
## [54] train-rmse:0.408756+0.016584 test-rmse:0.991619+0.091491
## [55] train-rmse:0.403407+0.016354 test-rmse:0.990226+0.090505
## [56] train-rmse:0.396750+0.014210 test-rmse:0.992873+0.088893
## [57] train-rmse:0.387909+0.013703 test-rmse:0.988177+0.085044
## [58] train-rmse:0.382638+0.013768 test-rmse:0.987626+0.082828
## [59] train-rmse:0.376880+0.011941 test-rmse:0.983948+0.082409
## [60] train-rmse:0.371068+0.013876 test-rmse:0.982336+0.082865
## [61] train-rmse:0.364507+0.014294 test-rmse:0.981009+0.079872
## [62] train-rmse:0.357128+0.014480 test-rmse:0.976427+0.075508
## [63] train-rmse:0.348927+0.015408 test-rmse:0.974691+0.075714
## [64] train-rmse:0.344071+0.015026 test-rmse:0.971934+0.077071
## [65] train-rmse:0.339757+0.014469 test-rmse:0.968962+0.076442
## [66] train-rmse:0.333965+0.014098 test-rmse:0.967957+0.077707
## [67] train-rmse:0.329384+0.014128 test-rmse:0.966998+0.078039
## [68] train-rmse:0.325820+0.012910 test-rmse:0.965483+0.076239
## [69] train-rmse:0.320650+0.012147 test-rmse:0.964542+0.077316
## [70] train-rmse:0.314459+0.012938 test-rmse:0.964828+0.074865
## [71] train-rmse:0.309522+0.013554 test-rmse:0.964260+0.074854
## [72] train-rmse:0.305322+0.014481 test-rmse:0.964024+0.074744
## [73] train-rmse:0.301185+0.013713 test-rmse:0.963630+0.073747
## [74] train-rmse:0.295470+0.013014 test-rmse:0.964881+0.076886
## [75] train-rmse:0.289218+0.015266 test-rmse:0.962659+0.076769
## [76] train-rmse:0.285034+0.014904 test-rmse:0.961882+0.076276
## [77] train-rmse:0.281014+0.015974 test-rmse:0.961774+0.075764
## [78] train-rmse:0.275737+0.016466 test-rmse:0.961343+0.075884
## [79] train-rmse:0.271743+0.016971 test-rmse:0.960409+0.076274
## [80] train-rmse:0.265729+0.014402 test-rmse:0.959791+0.075991
## [81] train-rmse:0.261263+0.014186 test-rmse:0.959098+0.076446
## [82] train-rmse:0.258835+0.014318 test-rmse:0.958608+0.075135
## [83] train-rmse:0.254422+0.012329 test-rmse:0.957747+0.075151
## [84] train-rmse:0.248491+0.012204 test-rmse:0.958419+0.077562
## [85] train-rmse:0.245749+0.013007 test-rmse:0.956774+0.077833
## [86] train-rmse:0.242026+0.013436 test-rmse:0.955657+0.076943
## [87] train-rmse:0.238979+0.013878 test-rmse:0.955451+0.076654
## [88] train-rmse:0.235559+0.012628 test-rmse:0.953850+0.076145
## [89] train-rmse:0.231566+0.013347 test-rmse:0.952183+0.076570
## [90] train-rmse:0.227895+0.013195 test-rmse:0.951364+0.076070
## [91] train-rmse:0.222183+0.013445 test-rmse:0.951749+0.076989
## [92] train-rmse:0.219217+0.013096 test-rmse:0.950685+0.078160
## [93] train-rmse:0.216615+0.013802 test-rmse:0.949935+0.077439
## [94] train-rmse:0.214587+0.014178 test-rmse:0.949719+0.076639
## [95] train-rmse:0.212158+0.014294 test-rmse:0.950620+0.076814
## [96] train-rmse:0.210345+0.014116 test-rmse:0.950326+0.076869
## [97] train-rmse:0.206881+0.013617 test-rmse:0.949309+0.075232
## [98] train-rmse:0.203777+0.013348 test-rmse:0.949917+0.076880
## [99] train-rmse:0.199759+0.011246 test-rmse:0.948771+0.077851
## [100] train-rmse:0.196998+0.012104 test-rmse:0.948338+0.077424
## [101] train-rmse:0.193779+0.011446 test-rmse:0.948474+0.077303
## [102] train-rmse:0.190434+0.011104 test-rmse:0.947532+0.077470
## [103] train-rmse:0.187430+0.011297 test-rmse:0.947859+0.077917
## [104] train-rmse:0.185195+0.011485 test-rmse:0.948272+0.078083
## [105] train-rmse:0.181906+0.012854 test-rmse:0.947935+0.078804
## [106] train-rmse:0.179750+0.012798 test-rmse:0.947503+0.079433
## [107] train-rmse:0.176873+0.012309 test-rmse:0.948938+0.079568
## [108] train-rmse:0.174823+0.012333 test-rmse:0.947944+0.079048
## [109] train-rmse:0.171854+0.012373 test-rmse:0.947892+0.078794
## [110] train-rmse:0.169287+0.012871 test-rmse:0.949162+0.080064
## [111] train-rmse:0.166240+0.012326 test-rmse:0.949318+0.079590
## [112] train-rmse:0.163408+0.012738 test-rmse:0.948407+0.078780
## Stopping. Best iteration:
## [106] train-rmse:0.179750+0.012798 test-rmse:0.947503+0.079433
##
## 67
## [1] train-rmse:71.012277+0.071119 test-rmse:71.012837+0.422304
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:51.218313+0.050969 test-rmse:51.218473+0.441381
## [3] train-rmse:36.954831+0.036295 test-rmse:36.954435+0.454495
## [4] train-rmse:26.681634+0.025555 test-rmse:26.680503+0.462943
## [5] train-rmse:19.289380+0.017699 test-rmse:19.287267+0.467424
## [6] train-rmse:13.979685+0.012191 test-rmse:13.976312+0.468039
## [7] train-rmse:10.170048+0.008156 test-rmse:10.157296+0.437450
## [8] train-rmse:7.437276+0.007927 test-rmse:7.420348+0.391249
## [9] train-rmse:5.473658+0.006767 test-rmse:5.458193+0.352705
## [10] train-rmse:4.066124+0.006158 test-rmse:4.058031+0.320367
## [11] train-rmse:3.057552+0.006737 test-rmse:3.064833+0.289573
## [12] train-rmse:2.335579+0.006335 test-rmse:2.373450+0.259513
## [13] train-rmse:1.827084+0.007926 test-rmse:1.917101+0.219591
## [14] train-rmse:1.471440+0.007532 test-rmse:1.630888+0.180684
## [15] train-rmse:1.238705+0.010871 test-rmse:1.439712+0.168553
## [16] train-rmse:1.072491+0.004429 test-rmse:1.322173+0.145441
## [17] train-rmse:0.956113+0.009082 test-rmse:1.258598+0.134480
## [18] train-rmse:0.863132+0.008409 test-rmse:1.202700+0.119975
## [19] train-rmse:0.802485+0.013400 test-rmse:1.169790+0.110761
## [20] train-rmse:0.753704+0.015021 test-rmse:1.152251+0.110310
## [21] train-rmse:0.710454+0.009726 test-rmse:1.124978+0.112750
## [22] train-rmse:0.682172+0.011022 test-rmse:1.111564+0.111085
## [23] train-rmse:0.654280+0.012410 test-rmse:1.098979+0.105147
## [24] train-rmse:0.630403+0.014147 test-rmse:1.084464+0.102850
## [25] train-rmse:0.610843+0.018266 test-rmse:1.076083+0.104927
## [26] train-rmse:0.592957+0.017253 test-rmse:1.061792+0.098756
## [27] train-rmse:0.571170+0.023132 test-rmse:1.056365+0.095413
## [28] train-rmse:0.553513+0.021072 test-rmse:1.044397+0.088302
## [29] train-rmse:0.536134+0.020131 test-rmse:1.038005+0.089748
## [30] train-rmse:0.523019+0.023305 test-rmse:1.032788+0.088437
## [31] train-rmse:0.505760+0.023524 test-rmse:1.030690+0.087965
## [32] train-rmse:0.492420+0.017911 test-rmse:1.020100+0.085564
## [33] train-rmse:0.476457+0.022450 test-rmse:1.018988+0.082070
## [34] train-rmse:0.461680+0.019861 test-rmse:1.021236+0.076441
## [35] train-rmse:0.452824+0.021759 test-rmse:1.020599+0.076809
## [36] train-rmse:0.444285+0.020793 test-rmse:1.014830+0.074098
## [37] train-rmse:0.430459+0.020049 test-rmse:1.006316+0.074370
## [38] train-rmse:0.418761+0.017898 test-rmse:1.006972+0.074189
## [39] train-rmse:0.407295+0.019284 test-rmse:1.006201+0.074084
## [40] train-rmse:0.396020+0.020031 test-rmse:1.001683+0.073922
## [41] train-rmse:0.387222+0.016766 test-rmse:0.999775+0.073579
## [42] train-rmse:0.377003+0.017363 test-rmse:0.997048+0.076022
## [43] train-rmse:0.365315+0.017950 test-rmse:0.994198+0.076241
## [44] train-rmse:0.358466+0.016605 test-rmse:0.993362+0.076763
## [45] train-rmse:0.350573+0.017885 test-rmse:0.988857+0.073614
## [46] train-rmse:0.341570+0.018619 test-rmse:0.986734+0.075472
## [47] train-rmse:0.333115+0.016649 test-rmse:0.988211+0.075507
## [48] train-rmse:0.326041+0.016396 test-rmse:0.986738+0.073108
## [49] train-rmse:0.319590+0.017061 test-rmse:0.985267+0.070379
## [50] train-rmse:0.310745+0.016152 test-rmse:0.983624+0.067322
## [51] train-rmse:0.304989+0.014448 test-rmse:0.982101+0.067894
## [52] train-rmse:0.297103+0.013727 test-rmse:0.982797+0.068313
## [53] train-rmse:0.289960+0.014501 test-rmse:0.982248+0.069821
## [54] train-rmse:0.281976+0.012913 test-rmse:0.983740+0.069510
## [55] train-rmse:0.275206+0.013413 test-rmse:0.982651+0.071332
## [56] train-rmse:0.269609+0.012564 test-rmse:0.981153+0.070564
## [57] train-rmse:0.264462+0.012672 test-rmse:0.979268+0.071019
## [58] train-rmse:0.258675+0.012299 test-rmse:0.980934+0.069867
## [59] train-rmse:0.252426+0.010947 test-rmse:0.977356+0.069551
## [60] train-rmse:0.245705+0.010690 test-rmse:0.975598+0.070342
## [61] train-rmse:0.241192+0.008990 test-rmse:0.978354+0.070622
## [62] train-rmse:0.235909+0.010160 test-rmse:0.976280+0.069355
## [63] train-rmse:0.229207+0.010923 test-rmse:0.974469+0.069875
## [64] train-rmse:0.223050+0.012964 test-rmse:0.974864+0.070450
## [65] train-rmse:0.217833+0.010903 test-rmse:0.975573+0.071725
## [66] train-rmse:0.210532+0.012164 test-rmse:0.976549+0.070678
## [67] train-rmse:0.206259+0.011368 test-rmse:0.976342+0.072317
## [68] train-rmse:0.201046+0.011979 test-rmse:0.975390+0.072825
## [69] train-rmse:0.198569+0.012057 test-rmse:0.974856+0.075194
## Stopping. Best iteration:
## [63] train-rmse:0.229207+0.010923 test-rmse:0.974469+0.069875
##
## 68
## [1] train-rmse:71.012277+0.071119 test-rmse:71.012837+0.422304
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:51.218313+0.050969 test-rmse:51.218473+0.441381
## [3] train-rmse:36.954831+0.036295 test-rmse:36.954435+0.454495
## [4] train-rmse:26.681634+0.025555 test-rmse:26.680503+0.462943
## [5] train-rmse:19.289380+0.017699 test-rmse:19.287267+0.467424
## [6] train-rmse:13.979685+0.012191 test-rmse:13.976312+0.468039
## [7] train-rmse:10.170048+0.008156 test-rmse:10.157296+0.437450
## [8] train-rmse:7.437276+0.007927 test-rmse:7.420348+0.391249
## [9] train-rmse:5.472792+0.006635 test-rmse:5.453734+0.352232
## [10] train-rmse:4.062338+0.006368 test-rmse:4.058126+0.306594
## [11] train-rmse:3.048901+0.005638 test-rmse:3.056859+0.288898
## [12] train-rmse:2.320676+0.007329 test-rmse:2.362782+0.279943
## [13] train-rmse:1.802639+0.006990 test-rmse:1.905803+0.238018
## [14] train-rmse:1.433201+0.016683 test-rmse:1.602175+0.206894
## [15] train-rmse:1.171445+0.017737 test-rmse:1.405243+0.185818
## [16] train-rmse:0.994569+0.021781 test-rmse:1.289781+0.167055
## [17] train-rmse:0.866938+0.025597 test-rmse:1.223424+0.159744
## [18] train-rmse:0.772165+0.020176 test-rmse:1.168381+0.140452
## [19] train-rmse:0.708045+0.021047 test-rmse:1.143182+0.142856
## [20] train-rmse:0.658497+0.022858 test-rmse:1.121540+0.138393
## [21] train-rmse:0.611157+0.026914 test-rmse:1.110419+0.132136
## [22] train-rmse:0.578705+0.027182 test-rmse:1.098812+0.136750
## [23] train-rmse:0.550929+0.032048 test-rmse:1.085531+0.129805
## [24] train-rmse:0.524624+0.030018 test-rmse:1.068957+0.113684
## [25] train-rmse:0.500487+0.028997 test-rmse:1.064548+0.113852
## [26] train-rmse:0.479717+0.030299 test-rmse:1.057480+0.115231
## [27] train-rmse:0.456218+0.033784 test-rmse:1.056594+0.118608
## [28] train-rmse:0.439946+0.033058 test-rmse:1.048553+0.119766
## [29] train-rmse:0.424989+0.030414 test-rmse:1.044369+0.120875
## [30] train-rmse:0.408401+0.028136 test-rmse:1.041696+0.125722
## [31] train-rmse:0.393884+0.030185 test-rmse:1.035044+0.121534
## [32] train-rmse:0.378184+0.027579 test-rmse:1.033896+0.121562
## [33] train-rmse:0.362711+0.030699 test-rmse:1.034320+0.119773
## [34] train-rmse:0.347900+0.033224 test-rmse:1.030189+0.118911
## [35] train-rmse:0.338648+0.032704 test-rmse:1.029963+0.123492
## [36] train-rmse:0.328238+0.029325 test-rmse:1.025201+0.121078
## [37] train-rmse:0.319174+0.028263 test-rmse:1.020672+0.118130
## [38] train-rmse:0.308738+0.032145 test-rmse:1.018426+0.116539
## [39] train-rmse:0.296445+0.026493 test-rmse:1.014156+0.114323
## [40] train-rmse:0.287064+0.026126 test-rmse:1.009196+0.112683
## [41] train-rmse:0.273712+0.027541 test-rmse:1.007655+0.117470
## [42] train-rmse:0.265727+0.027094 test-rmse:1.006011+0.119572
## [43] train-rmse:0.256238+0.027094 test-rmse:1.003362+0.118284
## [44] train-rmse:0.249694+0.025260 test-rmse:1.001881+0.120076
## [45] train-rmse:0.243443+0.024973 test-rmse:1.001898+0.121346
## [46] train-rmse:0.236280+0.023736 test-rmse:1.001541+0.121198
## [47] train-rmse:0.230426+0.024186 test-rmse:1.001095+0.121667
## [48] train-rmse:0.224696+0.024630 test-rmse:0.998730+0.122281
## [49] train-rmse:0.218089+0.023772 test-rmse:0.997840+0.123028
## [50] train-rmse:0.209163+0.021069 test-rmse:0.996396+0.121951
## [51] train-rmse:0.203362+0.020860 test-rmse:0.995253+0.123162
## [52] train-rmse:0.196792+0.022660 test-rmse:0.993863+0.123515
## [53] train-rmse:0.188558+0.020878 test-rmse:0.993403+0.124167
## [54] train-rmse:0.180948+0.020013 test-rmse:0.989990+0.123795
## [55] train-rmse:0.174725+0.018559 test-rmse:0.991242+0.124774
## [56] train-rmse:0.170315+0.017652 test-rmse:0.989072+0.124692
## [57] train-rmse:0.165159+0.017556 test-rmse:0.989234+0.124630
## [58] train-rmse:0.157727+0.015039 test-rmse:0.990244+0.126666
## [59] train-rmse:0.152954+0.015498 test-rmse:0.988110+0.127637
## [60] train-rmse:0.148267+0.016206 test-rmse:0.987575+0.127527
## [61] train-rmse:0.144361+0.015000 test-rmse:0.987064+0.128022
## [62] train-rmse:0.141219+0.015718 test-rmse:0.986862+0.128416
## [63] train-rmse:0.136215+0.017680 test-rmse:0.985685+0.126856
## [64] train-rmse:0.132265+0.017258 test-rmse:0.986217+0.126195
## [65] train-rmse:0.127899+0.016201 test-rmse:0.986183+0.126377
## [66] train-rmse:0.124477+0.015749 test-rmse:0.985632+0.128443
## [67] train-rmse:0.120287+0.016017 test-rmse:0.985290+0.129377
## [68] train-rmse:0.116350+0.015825 test-rmse:0.984593+0.128451
## [69] train-rmse:0.113031+0.015231 test-rmse:0.984578+0.128889
## [70] train-rmse:0.109740+0.015114 test-rmse:0.984586+0.128833
## [71] train-rmse:0.106496+0.014406 test-rmse:0.983709+0.129072
## [72] train-rmse:0.103518+0.014172 test-rmse:0.983482+0.129218
## [73] train-rmse:0.099926+0.014009 test-rmse:0.984284+0.129948
## [74] train-rmse:0.097647+0.014479 test-rmse:0.984066+0.129602
## [75] train-rmse:0.095390+0.014422 test-rmse:0.984097+0.128978
## [76] train-rmse:0.092057+0.013119 test-rmse:0.983664+0.129444
## [77] train-rmse:0.089244+0.013945 test-rmse:0.983783+0.129604
## [78] train-rmse:0.086733+0.013010 test-rmse:0.983444+0.128974
## [79] train-rmse:0.083993+0.012181 test-rmse:0.983742+0.128958
## [80] train-rmse:0.081790+0.012142 test-rmse:0.983676+0.129268
## [81] train-rmse:0.079392+0.011538 test-rmse:0.984035+0.129078
## [82] train-rmse:0.077905+0.011049 test-rmse:0.983766+0.128986
## [83] train-rmse:0.075798+0.010915 test-rmse:0.983735+0.129373
## [84] train-rmse:0.073453+0.010876 test-rmse:0.983467+0.129584
## Stopping. Best iteration:
## [78] train-rmse:0.086733+0.013010 test-rmse:0.983444+0.128974
##
## 69
## [1] train-rmse:71.012277+0.071119 test-rmse:71.012837+0.422304
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:51.218313+0.050969 test-rmse:51.218473+0.441381
## [3] train-rmse:36.954831+0.036295 test-rmse:36.954435+0.454495
## [4] train-rmse:26.681634+0.025555 test-rmse:26.680503+0.462943
## [5] train-rmse:19.289380+0.017699 test-rmse:19.287267+0.467424
## [6] train-rmse:13.979685+0.012191 test-rmse:13.976312+0.468039
## [7] train-rmse:10.170048+0.008156 test-rmse:10.157296+0.437450
## [8] train-rmse:7.437276+0.007927 test-rmse:7.420348+0.391249
## [9] train-rmse:5.471940+0.006566 test-rmse:5.449733+0.350528
## [10] train-rmse:4.059608+0.006214 test-rmse:4.054649+0.297683
## [11] train-rmse:3.041809+0.008453 test-rmse:3.046802+0.279105
## [12] train-rmse:2.312454+0.006919 test-rmse:2.348750+0.267493
## [13] train-rmse:1.784525+0.003180 test-rmse:1.879647+0.226991
## [14] train-rmse:1.399430+0.006235 test-rmse:1.567198+0.191346
## [15] train-rmse:1.128842+0.007618 test-rmse:1.385443+0.172339
## [16] train-rmse:0.932953+0.010335 test-rmse:1.262540+0.160460
## [17] train-rmse:0.797706+0.009780 test-rmse:1.194373+0.148086
## [18] train-rmse:0.697093+0.012270 test-rmse:1.145983+0.134817
## [19] train-rmse:0.625067+0.009644 test-rmse:1.116499+0.135385
## [20] train-rmse:0.568582+0.013486 test-rmse:1.099894+0.132093
## [21] train-rmse:0.527745+0.012283 test-rmse:1.092086+0.130900
## [22] train-rmse:0.489926+0.010164 test-rmse:1.075318+0.118939
## [23] train-rmse:0.462278+0.012607 test-rmse:1.070882+0.115211
## [24] train-rmse:0.435182+0.010352 test-rmse:1.068405+0.110094
## [25] train-rmse:0.409446+0.003615 test-rmse:1.057059+0.106242
## [26] train-rmse:0.387354+0.009125 test-rmse:1.051025+0.110332
## [27] train-rmse:0.369917+0.010025 test-rmse:1.045130+0.104813
## [28] train-rmse:0.349184+0.012856 test-rmse:1.043385+0.101203
## [29] train-rmse:0.330369+0.014317 test-rmse:1.041795+0.105484
## [30] train-rmse:0.313901+0.010390 test-rmse:1.035035+0.101819
## [31] train-rmse:0.296310+0.009260 test-rmse:1.031529+0.099917
## [32] train-rmse:0.283242+0.006746 test-rmse:1.026871+0.100314
## [33] train-rmse:0.268818+0.006808 test-rmse:1.020720+0.094965
## [34] train-rmse:0.258594+0.008673 test-rmse:1.020987+0.099349
## [35] train-rmse:0.244651+0.008101 test-rmse:1.020965+0.099872
## [36] train-rmse:0.233147+0.009463 test-rmse:1.018643+0.099201
## [37] train-rmse:0.223544+0.011393 test-rmse:1.015997+0.098723
## [38] train-rmse:0.215343+0.010774 test-rmse:1.016138+0.099194
## [39] train-rmse:0.209298+0.010306 test-rmse:1.012782+0.097549
## [40] train-rmse:0.197113+0.008628 test-rmse:1.011067+0.096383
## [41] train-rmse:0.189220+0.006651 test-rmse:1.009615+0.097949
## [42] train-rmse:0.182927+0.007703 test-rmse:1.006226+0.097118
## [43] train-rmse:0.174814+0.008818 test-rmse:1.003253+0.097696
## [44] train-rmse:0.166539+0.007517 test-rmse:1.003147+0.097957
## [45] train-rmse:0.161238+0.008949 test-rmse:1.001885+0.096463
## [46] train-rmse:0.153009+0.010553 test-rmse:1.000420+0.095228
## [47] train-rmse:0.147659+0.011125 test-rmse:0.999741+0.096272
## [48] train-rmse:0.142322+0.011945 test-rmse:0.999608+0.098037
## [49] train-rmse:0.137953+0.011135 test-rmse:0.998249+0.097814
## [50] train-rmse:0.130798+0.009671 test-rmse:0.997443+0.098232
## [51] train-rmse:0.126355+0.009009 test-rmse:0.996702+0.098353
## [52] train-rmse:0.121124+0.008218 test-rmse:0.996572+0.098880
## [53] train-rmse:0.116503+0.007878 test-rmse:0.995773+0.098784
## [54] train-rmse:0.110911+0.006234 test-rmse:0.994881+0.098347
## [55] train-rmse:0.105968+0.006243 test-rmse:0.993239+0.098956
## [56] train-rmse:0.102557+0.006274 test-rmse:0.993927+0.100053
## [57] train-rmse:0.099821+0.005628 test-rmse:0.994273+0.100141
## [58] train-rmse:0.094885+0.004782 test-rmse:0.993263+0.099935
## [59] train-rmse:0.089948+0.004523 test-rmse:0.993984+0.099498
## [60] train-rmse:0.085874+0.003816 test-rmse:0.993720+0.099115
## [61] train-rmse:0.082850+0.003599 test-rmse:0.993171+0.099652
## [62] train-rmse:0.080374+0.004466 test-rmse:0.992758+0.100525
## [63] train-rmse:0.078009+0.004736 test-rmse:0.992645+0.101713
## [64] train-rmse:0.074440+0.003664 test-rmse:0.991612+0.101350
## [65] train-rmse:0.071548+0.003809 test-rmse:0.992042+0.100801
## [66] train-rmse:0.068200+0.004446 test-rmse:0.991811+0.100994
## [67] train-rmse:0.065301+0.004352 test-rmse:0.992404+0.100921
## [68] train-rmse:0.063670+0.004145 test-rmse:0.991719+0.100466
## [69] train-rmse:0.060892+0.004320 test-rmse:0.991287+0.100343
## [70] train-rmse:0.058061+0.003643 test-rmse:0.990548+0.101421
## [71] train-rmse:0.055190+0.004259 test-rmse:0.990467+0.101489
## [72] train-rmse:0.053503+0.004055 test-rmse:0.990947+0.101293
## [73] train-rmse:0.051898+0.003914 test-rmse:0.990618+0.101395
## [74] train-rmse:0.050281+0.004489 test-rmse:0.989928+0.101376
## [75] train-rmse:0.048206+0.004099 test-rmse:0.990026+0.101311
## [76] train-rmse:0.046467+0.003723 test-rmse:0.989273+0.101158
## [77] train-rmse:0.044580+0.003904 test-rmse:0.989285+0.101008
## [78] train-rmse:0.042844+0.004230 test-rmse:0.989448+0.100985
## [79] train-rmse:0.040985+0.004248 test-rmse:0.989410+0.101643
## [80] train-rmse:0.039416+0.004036 test-rmse:0.989418+0.101907
## [81] train-rmse:0.038508+0.004058 test-rmse:0.989377+0.102083
## [82] train-rmse:0.037011+0.003767 test-rmse:0.989417+0.102063
## Stopping. Best iteration:
## [76] train-rmse:0.046467+0.003723 test-rmse:0.989273+0.101158
##
## 70
## [1] train-rmse:69.050973+0.069138 test-rmse:69.051505+0.424216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:48.430438+0.048124 test-rmse:48.430518+0.444003
## [3] train-rmse:33.983752+0.033210 test-rmse:33.983192+0.457073
## [4] train-rmse:23.869216+0.022582 test-rmse:23.867778+0.464907
## [5] train-rmse:16.797328+0.015059 test-rmse:16.794708+0.468178
## [6] train-rmse:11.863720+0.009987 test-rmse:11.868057+0.432780
## [7] train-rmse:8.429082+0.006884 test-rmse:8.418294+0.431156
## [8] train-rmse:6.049948+0.008000 test-rmse:6.038449+0.413768
## [9] train-rmse:4.423552+0.012513 test-rmse:4.412873+0.390475
## [10] train-rmse:3.330880+0.017256 test-rmse:3.331439+0.354611
## [11] train-rmse:2.619719+0.021351 test-rmse:2.631955+0.298284
## [12] train-rmse:2.174924+0.024751 test-rmse:2.208733+0.237482
## [13] train-rmse:1.906422+0.027786 test-rmse:1.964113+0.179014
## [14] train-rmse:1.748558+0.029713 test-rmse:1.822861+0.139936
## [15] train-rmse:1.653591+0.031006 test-rmse:1.720749+0.122533
## [16] train-rmse:1.596365+0.030950 test-rmse:1.676778+0.112201
## [17] train-rmse:1.557923+0.029832 test-rmse:1.634716+0.102656
## [18] train-rmse:1.529840+0.028431 test-rmse:1.620144+0.107093
## [19] train-rmse:1.507683+0.027925 test-rmse:1.615213+0.119034
## [20] train-rmse:1.488697+0.027203 test-rmse:1.603535+0.116307
## [21] train-rmse:1.470624+0.027280 test-rmse:1.588309+0.108348
## [22] train-rmse:1.454887+0.026406 test-rmse:1.580705+0.113256
## [23] train-rmse:1.440829+0.026137 test-rmse:1.570358+0.110121
## [24] train-rmse:1.427884+0.025697 test-rmse:1.559791+0.106714
## [25] train-rmse:1.415073+0.025104 test-rmse:1.542006+0.102294
## [26] train-rmse:1.402536+0.024645 test-rmse:1.539613+0.101606
## [27] train-rmse:1.390895+0.023940 test-rmse:1.541009+0.104144
## [28] train-rmse:1.379494+0.023438 test-rmse:1.533480+0.102018
## [29] train-rmse:1.368789+0.023182 test-rmse:1.523345+0.098328
## [30] train-rmse:1.358188+0.023066 test-rmse:1.516733+0.097012
## [31] train-rmse:1.347889+0.022919 test-rmse:1.501620+0.099727
## [32] train-rmse:1.337903+0.022831 test-rmse:1.497793+0.106296
## [33] train-rmse:1.328392+0.022878 test-rmse:1.484280+0.106470
## [34] train-rmse:1.319148+0.022740 test-rmse:1.479832+0.097744
## [35] train-rmse:1.310114+0.022322 test-rmse:1.478094+0.089042
## [36] train-rmse:1.301034+0.022007 test-rmse:1.476850+0.091653
## [37] train-rmse:1.292135+0.022052 test-rmse:1.473935+0.095153
## [38] train-rmse:1.283724+0.021703 test-rmse:1.466334+0.091974
## [39] train-rmse:1.275421+0.021386 test-rmse:1.450584+0.093996
## [40] train-rmse:1.267500+0.021098 test-rmse:1.450908+0.095262
## [41] train-rmse:1.260048+0.021046 test-rmse:1.442651+0.094943
## [42] train-rmse:1.252716+0.020964 test-rmse:1.436728+0.096391
## [43] train-rmse:1.245562+0.020976 test-rmse:1.434272+0.095733
## [44] train-rmse:1.238887+0.020956 test-rmse:1.430362+0.093393
## [45] train-rmse:1.232265+0.020864 test-rmse:1.425192+0.088950
## [46] train-rmse:1.225704+0.020759 test-rmse:1.422505+0.087808
## [47] train-rmse:1.219518+0.020776 test-rmse:1.414806+0.089543
## [48] train-rmse:1.213461+0.020831 test-rmse:1.412296+0.094830
## [49] train-rmse:1.207470+0.020845 test-rmse:1.413194+0.088347
## [50] train-rmse:1.201510+0.020811 test-rmse:1.405852+0.084052
## [51] train-rmse:1.195906+0.020742 test-rmse:1.407263+0.078006
## [52] train-rmse:1.190418+0.020718 test-rmse:1.397641+0.077820
## [53] train-rmse:1.184999+0.020744 test-rmse:1.393665+0.078079
## [54] train-rmse:1.179541+0.020774 test-rmse:1.382247+0.083931
## [55] train-rmse:1.174137+0.020837 test-rmse:1.379506+0.085567
## [56] train-rmse:1.169056+0.020765 test-rmse:1.372307+0.086175
## [57] train-rmse:1.164059+0.020682 test-rmse:1.370321+0.087737
## [58] train-rmse:1.159086+0.020617 test-rmse:1.366753+0.082510
## [59] train-rmse:1.154378+0.020531 test-rmse:1.361413+0.081537
## [60] train-rmse:1.149716+0.020361 test-rmse:1.356275+0.084092
## [61] train-rmse:1.145379+0.020267 test-rmse:1.355076+0.085727
## [62] train-rmse:1.141093+0.020128 test-rmse:1.356524+0.086495
## [63] train-rmse:1.136960+0.020138 test-rmse:1.350671+0.082844
## [64] train-rmse:1.132831+0.020029 test-rmse:1.351274+0.084792
## [65] train-rmse:1.128847+0.019765 test-rmse:1.351000+0.084819
## [66] train-rmse:1.124983+0.019533 test-rmse:1.347062+0.082272
## [67] train-rmse:1.121022+0.019378 test-rmse:1.347039+0.084992
## [68] train-rmse:1.117138+0.019370 test-rmse:1.344735+0.087675
## [69] train-rmse:1.113473+0.019288 test-rmse:1.341173+0.088027
## [70] train-rmse:1.109910+0.019287 test-rmse:1.338750+0.085437
## [71] train-rmse:1.106483+0.019267 test-rmse:1.338580+0.082577
## [72] train-rmse:1.103142+0.019224 test-rmse:1.333180+0.084575
## [73] train-rmse:1.099876+0.019203 test-rmse:1.328796+0.084999
## [74] train-rmse:1.096565+0.019069 test-rmse:1.324921+0.086721
## [75] train-rmse:1.093313+0.019038 test-rmse:1.322644+0.087468
## [76] train-rmse:1.089999+0.019027 test-rmse:1.316965+0.085329
## [77] train-rmse:1.086691+0.018957 test-rmse:1.313481+0.086423
## [78] train-rmse:1.083557+0.018959 test-rmse:1.310443+0.086208
## [79] train-rmse:1.080498+0.019031 test-rmse:1.307124+0.086531
## [80] train-rmse:1.077600+0.019063 test-rmse:1.310319+0.081386
## [81] train-rmse:1.074731+0.019038 test-rmse:1.308306+0.080552
## [82] train-rmse:1.071945+0.018996 test-rmse:1.305687+0.081820
## [83] train-rmse:1.069165+0.018950 test-rmse:1.304395+0.083330
## [84] train-rmse:1.066479+0.018950 test-rmse:1.300217+0.086030
## [85] train-rmse:1.063811+0.018974 test-rmse:1.300384+0.086971
## [86] train-rmse:1.061243+0.018921 test-rmse:1.297661+0.087070
## [87] train-rmse:1.058742+0.018869 test-rmse:1.295357+0.084537
## [88] train-rmse:1.056182+0.018722 test-rmse:1.294793+0.083691
## [89] train-rmse:1.053706+0.018597 test-rmse:1.289916+0.086128
## [90] train-rmse:1.051248+0.018458 test-rmse:1.290449+0.085638
## [91] train-rmse:1.048856+0.018309 test-rmse:1.288417+0.086522
## [92] train-rmse:1.046549+0.018221 test-rmse:1.287908+0.087369
## [93] train-rmse:1.044281+0.018174 test-rmse:1.286883+0.089608
## [94] train-rmse:1.042010+0.018150 test-rmse:1.287105+0.092002
## [95] train-rmse:1.039746+0.018138 test-rmse:1.283666+0.089797
## [96] train-rmse:1.037575+0.018093 test-rmse:1.285353+0.087920
## [97] train-rmse:1.035491+0.018006 test-rmse:1.284038+0.089719
## [98] train-rmse:1.033427+0.017977 test-rmse:1.283837+0.090905
## [99] train-rmse:1.031407+0.017914 test-rmse:1.280880+0.092046
## [100] train-rmse:1.029353+0.017863 test-rmse:1.279109+0.091004
## [101] train-rmse:1.027237+0.017865 test-rmse:1.274168+0.090503
## [102] train-rmse:1.025260+0.017884 test-rmse:1.272338+0.088609
## [103] train-rmse:1.023343+0.017916 test-rmse:1.270613+0.089556
## [104] train-rmse:1.021470+0.017906 test-rmse:1.270835+0.089172
## [105] train-rmse:1.019614+0.017922 test-rmse:1.269422+0.090339
## [106] train-rmse:1.017776+0.017977 test-rmse:1.264882+0.085184
## [107] train-rmse:1.015924+0.017931 test-rmse:1.266600+0.081608
## [108] train-rmse:1.014151+0.017896 test-rmse:1.265398+0.080834
## [109] train-rmse:1.012319+0.017773 test-rmse:1.264024+0.080549
## [110] train-rmse:1.010533+0.017732 test-rmse:1.264514+0.079161
## [111] train-rmse:1.008768+0.017656 test-rmse:1.262949+0.080797
## [112] train-rmse:1.006994+0.017620 test-rmse:1.259266+0.080213
## [113] train-rmse:1.005336+0.017581 test-rmse:1.257776+0.081005
## [114] train-rmse:1.003694+0.017571 test-rmse:1.255677+0.084402
## [115] train-rmse:1.002087+0.017542 test-rmse:1.254739+0.083404
## [116] train-rmse:1.000416+0.017452 test-rmse:1.254937+0.081936
## [117] train-rmse:0.998771+0.017432 test-rmse:1.252912+0.081232
## [118] train-rmse:0.997188+0.017395 test-rmse:1.248542+0.083212
## [119] train-rmse:0.995579+0.017380 test-rmse:1.249336+0.082592
## [120] train-rmse:0.994026+0.017321 test-rmse:1.248509+0.081208
## [121] train-rmse:0.992499+0.017259 test-rmse:1.249642+0.083705
## [122] train-rmse:0.990987+0.017247 test-rmse:1.247994+0.083005
## [123] train-rmse:0.989514+0.017231 test-rmse:1.247811+0.082440
## [124] train-rmse:0.988061+0.017225 test-rmse:1.246392+0.083249
## [125] train-rmse:0.986634+0.017251 test-rmse:1.248510+0.083536
## [126] train-rmse:0.985142+0.017285 test-rmse:1.247455+0.083716
## [127] train-rmse:0.983750+0.017310 test-rmse:1.246616+0.083893
## [128] train-rmse:0.982379+0.017343 test-rmse:1.245770+0.085955
## [129] train-rmse:0.980998+0.017354 test-rmse:1.244030+0.086359
## [130] train-rmse:0.979650+0.017388 test-rmse:1.242848+0.087730
## [131] train-rmse:0.978288+0.017444 test-rmse:1.244343+0.087058
## [132] train-rmse:0.976892+0.017484 test-rmse:1.244266+0.085643
## [133] train-rmse:0.975544+0.017478 test-rmse:1.243038+0.085093
## [134] train-rmse:0.974232+0.017474 test-rmse:1.242582+0.084859
## [135] train-rmse:0.972947+0.017472 test-rmse:1.240634+0.085881
## [136] train-rmse:0.971663+0.017506 test-rmse:1.242390+0.085125
## [137] train-rmse:0.970348+0.017457 test-rmse:1.241633+0.085554
## [138] train-rmse:0.969051+0.017419 test-rmse:1.242139+0.085846
## [139] train-rmse:0.967785+0.017405 test-rmse:1.240339+0.085341
## [140] train-rmse:0.966555+0.017424 test-rmse:1.239503+0.088546
## [141] train-rmse:0.965354+0.017437 test-rmse:1.238574+0.089412
## [142] train-rmse:0.964171+0.017459 test-rmse:1.242387+0.089095
## [143] train-rmse:0.962988+0.017483 test-rmse:1.239633+0.090382
## [144] train-rmse:0.961816+0.017518 test-rmse:1.240532+0.083951
## [145] train-rmse:0.960621+0.017524 test-rmse:1.240176+0.083879
## [146] train-rmse:0.959486+0.017520 test-rmse:1.240321+0.083476
## [147] train-rmse:0.958389+0.017528 test-rmse:1.238893+0.082616
## Stopping. Best iteration:
## [141] train-rmse:0.965354+0.017437 test-rmse:1.238574+0.089412
##
## 71
## [1] train-rmse:69.050973+0.069138 test-rmse:69.051505+0.424216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:48.430438+0.048124 test-rmse:48.430518+0.444003
## [3] train-rmse:33.983752+0.033210 test-rmse:33.983192+0.457073
## [4] train-rmse:23.869216+0.022582 test-rmse:23.867778+0.464907
## [5] train-rmse:16.797328+0.015059 test-rmse:16.794708+0.468178
## [6] train-rmse:11.863720+0.009987 test-rmse:11.868057+0.432780
## [7] train-rmse:8.424224+0.006312 test-rmse:8.411042+0.423267
## [8] train-rmse:6.034749+0.006969 test-rmse:6.008414+0.398843
## [9] train-rmse:4.384879+0.011842 test-rmse:4.373720+0.361940
## [10] train-rmse:3.260753+0.015530 test-rmse:3.264289+0.317248
## [11] train-rmse:2.514409+0.022465 test-rmse:2.545968+0.270126
## [12] train-rmse:2.033088+0.024959 test-rmse:2.087428+0.219100
## [13] train-rmse:1.731607+0.030585 test-rmse:1.814687+0.178127
## [14] train-rmse:1.548073+0.032210 test-rmse:1.672455+0.137808
## [15] train-rmse:1.429408+0.025615 test-rmse:1.581070+0.118265
## [16] train-rmse:1.358756+0.023436 test-rmse:1.521415+0.103970
## [17] train-rmse:1.306793+0.024727 test-rmse:1.481702+0.089397
## [18] train-rmse:1.272175+0.024300 test-rmse:1.463159+0.086131
## [19] train-rmse:1.245761+0.025104 test-rmse:1.442652+0.095407
## [20] train-rmse:1.221929+0.024948 test-rmse:1.431227+0.091318
## [21] train-rmse:1.198351+0.026134 test-rmse:1.421183+0.086780
## [22] train-rmse:1.181019+0.025472 test-rmse:1.404348+0.086129
## [23] train-rmse:1.161925+0.026366 test-rmse:1.394392+0.083270
## [24] train-rmse:1.143733+0.026167 test-rmse:1.387934+0.081963
## [25] train-rmse:1.128700+0.024963 test-rmse:1.377959+0.083827
## [26] train-rmse:1.113603+0.025357 test-rmse:1.351075+0.097028
## [27] train-rmse:1.097676+0.026502 test-rmse:1.339931+0.098740
## [28] train-rmse:1.085779+0.026655 test-rmse:1.332696+0.099339
## [29] train-rmse:1.071238+0.027647 test-rmse:1.326344+0.091828
## [30] train-rmse:1.055310+0.029395 test-rmse:1.319155+0.089887
## [31] train-rmse:1.043131+0.029827 test-rmse:1.313891+0.085824
## [32] train-rmse:1.030178+0.032037 test-rmse:1.305996+0.085807
## [33] train-rmse:1.017864+0.033243 test-rmse:1.300477+0.089099
## [34] train-rmse:1.006227+0.029734 test-rmse:1.291600+0.090673
## [35] train-rmse:0.994233+0.031720 test-rmse:1.277376+0.095142
## [36] train-rmse:0.984141+0.031331 test-rmse:1.270762+0.095926
## [37] train-rmse:0.974102+0.032002 test-rmse:1.263872+0.098941
## [38] train-rmse:0.957815+0.029909 test-rmse:1.259518+0.099829
## [39] train-rmse:0.946440+0.032436 test-rmse:1.252270+0.103337
## [40] train-rmse:0.936733+0.029399 test-rmse:1.246200+0.103894
## [41] train-rmse:0.924863+0.029387 test-rmse:1.237242+0.103652
## [42] train-rmse:0.917417+0.029488 test-rmse:1.231737+0.104812
## [43] train-rmse:0.908357+0.030655 test-rmse:1.223979+0.102820
## [44] train-rmse:0.900807+0.031535 test-rmse:1.215874+0.098352
## [45] train-rmse:0.891187+0.027279 test-rmse:1.212022+0.095971
## [46] train-rmse:0.884874+0.028123 test-rmse:1.206836+0.098777
## [47] train-rmse:0.875276+0.025072 test-rmse:1.202038+0.103422
## [48] train-rmse:0.869576+0.024660 test-rmse:1.197613+0.105463
## [49] train-rmse:0.861938+0.021566 test-rmse:1.193858+0.108627
## [50] train-rmse:0.851454+0.019533 test-rmse:1.191041+0.109099
## [51] train-rmse:0.840607+0.023852 test-rmse:1.184871+0.100714
## [52] train-rmse:0.835540+0.023883 test-rmse:1.184285+0.097097
## [53] train-rmse:0.826659+0.020407 test-rmse:1.179988+0.100987
## [54] train-rmse:0.820504+0.018846 test-rmse:1.175365+0.104758
## [55] train-rmse:0.814722+0.020040 test-rmse:1.169682+0.104369
## [56] train-rmse:0.807780+0.019746 test-rmse:1.167427+0.104587
## [57] train-rmse:0.802953+0.019801 test-rmse:1.165221+0.102676
## [58] train-rmse:0.796620+0.019488 test-rmse:1.159619+0.099923
## [59] train-rmse:0.789982+0.019364 test-rmse:1.154235+0.101054
## [60] train-rmse:0.784163+0.020009 test-rmse:1.149334+0.101228
## [61] train-rmse:0.778047+0.018064 test-rmse:1.149313+0.101059
## [62] train-rmse:0.771371+0.017942 test-rmse:1.144605+0.103106
## [63] train-rmse:0.764695+0.021464 test-rmse:1.139057+0.095373
## [64] train-rmse:0.758878+0.020240 test-rmse:1.136565+0.097465
## [65] train-rmse:0.754025+0.020388 test-rmse:1.132279+0.093243
## [66] train-rmse:0.747706+0.019569 test-rmse:1.135909+0.099432
## [67] train-rmse:0.744102+0.019770 test-rmse:1.137016+0.096822
## [68] train-rmse:0.739315+0.020659 test-rmse:1.137923+0.095733
## [69] train-rmse:0.735073+0.020756 test-rmse:1.133177+0.096542
## [70] train-rmse:0.728995+0.018605 test-rmse:1.129581+0.101041
## [71] train-rmse:0.725380+0.017688 test-rmse:1.123591+0.101965
## [72] train-rmse:0.721400+0.018340 test-rmse:1.118195+0.105604
## [73] train-rmse:0.715457+0.019308 test-rmse:1.111242+0.107351
## [74] train-rmse:0.711126+0.019259 test-rmse:1.110792+0.106745
## [75] train-rmse:0.706422+0.017289 test-rmse:1.110942+0.104222
## [76] train-rmse:0.700687+0.017166 test-rmse:1.116458+0.103655
## [77] train-rmse:0.697770+0.016865 test-rmse:1.115059+0.103756
## [78] train-rmse:0.692518+0.014800 test-rmse:1.112247+0.106641
## [79] train-rmse:0.689249+0.015894 test-rmse:1.112441+0.107278
## [80] train-rmse:0.681633+0.016365 test-rmse:1.108908+0.106607
## [81] train-rmse:0.678325+0.016632 test-rmse:1.110455+0.107929
## [82] train-rmse:0.674760+0.015742 test-rmse:1.105736+0.110812
## [83] train-rmse:0.671353+0.015292 test-rmse:1.106236+0.110348
## [84] train-rmse:0.667580+0.014280 test-rmse:1.103087+0.110395
## [85] train-rmse:0.661858+0.015501 test-rmse:1.102241+0.111436
## [86] train-rmse:0.657537+0.014235 test-rmse:1.100682+0.112753
## [87] train-rmse:0.654286+0.014748 test-rmse:1.098381+0.113537
## [88] train-rmse:0.649062+0.015794 test-rmse:1.098008+0.113264
## [89] train-rmse:0.644695+0.015882 test-rmse:1.099647+0.109755
## [90] train-rmse:0.642033+0.016029 test-rmse:1.098168+0.108218
## [91] train-rmse:0.637764+0.017515 test-rmse:1.097534+0.108735
## [92] train-rmse:0.633893+0.017891 test-rmse:1.095922+0.107995
## [93] train-rmse:0.628728+0.017218 test-rmse:1.090360+0.106384
## [94] train-rmse:0.626369+0.016959 test-rmse:1.089892+0.107868
## [95] train-rmse:0.623171+0.016634 test-rmse:1.089949+0.105768
## [96] train-rmse:0.620151+0.015825 test-rmse:1.089135+0.105886
## [97] train-rmse:0.617611+0.016290 test-rmse:1.088267+0.106856
## [98] train-rmse:0.613143+0.014795 test-rmse:1.088279+0.105300
## [99] train-rmse:0.610225+0.014475 test-rmse:1.085939+0.106837
## [100] train-rmse:0.604891+0.015014 test-rmse:1.084418+0.105413
## [101] train-rmse:0.602237+0.015759 test-rmse:1.082836+0.106504
## [102] train-rmse:0.598970+0.017070 test-rmse:1.081574+0.108292
## [103] train-rmse:0.596614+0.017694 test-rmse:1.082129+0.109505
## [104] train-rmse:0.594168+0.017788 test-rmse:1.082658+0.109618
## [105] train-rmse:0.591854+0.017249 test-rmse:1.081102+0.107616
## [106] train-rmse:0.588947+0.016954 test-rmse:1.080960+0.105369
## [107] train-rmse:0.584825+0.015381 test-rmse:1.080293+0.105257
## [108] train-rmse:0.580694+0.015593 test-rmse:1.081350+0.103880
## [109] train-rmse:0.577157+0.015893 test-rmse:1.075357+0.101681
## [110] train-rmse:0.573710+0.015981 test-rmse:1.075198+0.102137
## [111] train-rmse:0.571147+0.016130 test-rmse:1.074440+0.102747
## [112] train-rmse:0.568073+0.016830 test-rmse:1.073335+0.102766
## [113] train-rmse:0.565430+0.017177 test-rmse:1.073427+0.102969
## [114] train-rmse:0.562654+0.017678 test-rmse:1.072059+0.101849
## [115] train-rmse:0.560018+0.017051 test-rmse:1.074480+0.102913
## [116] train-rmse:0.554271+0.015963 test-rmse:1.071629+0.103118
## [117] train-rmse:0.551573+0.016242 test-rmse:1.068762+0.102198
## [118] train-rmse:0.548898+0.017090 test-rmse:1.068311+0.100691
## [119] train-rmse:0.546463+0.017476 test-rmse:1.066319+0.101257
## [120] train-rmse:0.542073+0.016487 test-rmse:1.065233+0.102563
## [121] train-rmse:0.539896+0.016288 test-rmse:1.064865+0.103087
## [122] train-rmse:0.537112+0.017541 test-rmse:1.062981+0.100177
## [123] train-rmse:0.534116+0.018993 test-rmse:1.063664+0.100547
## [124] train-rmse:0.531816+0.018702 test-rmse:1.063428+0.100241
## [125] train-rmse:0.529723+0.018793 test-rmse:1.060607+0.100104
## [126] train-rmse:0.527041+0.019331 test-rmse:1.061102+0.099869
## [127] train-rmse:0.524710+0.019128 test-rmse:1.060943+0.099013
## [128] train-rmse:0.521671+0.018841 test-rmse:1.059875+0.098242
## [129] train-rmse:0.518475+0.018361 test-rmse:1.058886+0.099151
## [130] train-rmse:0.514450+0.017653 test-rmse:1.058527+0.100696
## [131] train-rmse:0.511069+0.016692 test-rmse:1.054926+0.101157
## [132] train-rmse:0.508879+0.016657 test-rmse:1.054008+0.100700
## [133] train-rmse:0.506286+0.015501 test-rmse:1.053491+0.100849
## [134] train-rmse:0.503766+0.015308 test-rmse:1.054132+0.102600
## [135] train-rmse:0.500024+0.013706 test-rmse:1.051186+0.102951
## [136] train-rmse:0.497535+0.014094 test-rmse:1.049921+0.103676
## [137] train-rmse:0.496079+0.014050 test-rmse:1.048260+0.105017
## [138] train-rmse:0.493438+0.012776 test-rmse:1.046810+0.102174
## [139] train-rmse:0.491552+0.012686 test-rmse:1.045525+0.101695
## [140] train-rmse:0.489636+0.012257 test-rmse:1.044378+0.101701
## [141] train-rmse:0.486246+0.011298 test-rmse:1.043234+0.103376
## [142] train-rmse:0.483285+0.011455 test-rmse:1.044264+0.106361
## [143] train-rmse:0.481575+0.010772 test-rmse:1.044578+0.105958
## [144] train-rmse:0.479713+0.010921 test-rmse:1.043382+0.105848
## [145] train-rmse:0.478136+0.010541 test-rmse:1.043923+0.105460
## [146] train-rmse:0.476017+0.011261 test-rmse:1.044310+0.105124
## [147] train-rmse:0.472810+0.011529 test-rmse:1.041865+0.102806
## [148] train-rmse:0.468870+0.010080 test-rmse:1.038627+0.104613
## [149] train-rmse:0.466287+0.009573 test-rmse:1.037185+0.105244
## [150] train-rmse:0.464998+0.009326 test-rmse:1.036472+0.106019
## [151] train-rmse:0.463704+0.009318 test-rmse:1.037226+0.106043
## [152] train-rmse:0.461992+0.009420 test-rmse:1.036798+0.105577
## [153] train-rmse:0.459089+0.008961 test-rmse:1.036039+0.106419
## [154] train-rmse:0.457820+0.009084 test-rmse:1.036345+0.106454
## [155] train-rmse:0.456152+0.009424 test-rmse:1.033633+0.104698
## [156] train-rmse:0.454182+0.009603 test-rmse:1.031927+0.104812
## [157] train-rmse:0.451813+0.008967 test-rmse:1.031305+0.104029
## [158] train-rmse:0.449996+0.009347 test-rmse:1.031549+0.103635
## [159] train-rmse:0.448952+0.009289 test-rmse:1.030959+0.103720
## [160] train-rmse:0.446475+0.010960 test-rmse:1.029734+0.103780
## [161] train-rmse:0.444404+0.011940 test-rmse:1.029621+0.105843
## [162] train-rmse:0.442207+0.011843 test-rmse:1.029725+0.105459
## [163] train-rmse:0.441046+0.012237 test-rmse:1.029972+0.105313
## [164] train-rmse:0.438672+0.012349 test-rmse:1.031439+0.104609
## [165] train-rmse:0.437625+0.012210 test-rmse:1.032366+0.104817
## [166] train-rmse:0.435994+0.011538 test-rmse:1.032556+0.105741
## [167] train-rmse:0.433805+0.011319 test-rmse:1.030957+0.105628
## Stopping. Best iteration:
## [161] train-rmse:0.444404+0.011940 test-rmse:1.029621+0.105843
##
## 72
## [1] train-rmse:69.050973+0.069138 test-rmse:69.051505+0.424216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:48.430438+0.048124 test-rmse:48.430518+0.444003
## [3] train-rmse:33.983752+0.033210 test-rmse:33.983192+0.457073
## [4] train-rmse:23.869216+0.022582 test-rmse:23.867778+0.464907
## [5] train-rmse:16.797328+0.015059 test-rmse:16.794708+0.468178
## [6] train-rmse:11.863720+0.009987 test-rmse:11.868057+0.432780
## [7] train-rmse:8.422620+0.007025 test-rmse:8.400161+0.415016
## [8] train-rmse:6.027573+0.003569 test-rmse:6.005910+0.381006
## [9] train-rmse:4.365732+0.008304 test-rmse:4.357714+0.328537
## [10] train-rmse:3.219781+0.008011 test-rmse:3.229160+0.288109
## [11] train-rmse:2.449275+0.007237 test-rmse:2.475848+0.262684
## [12] train-rmse:1.932423+0.009021 test-rmse:1.994884+0.219672
## [13] train-rmse:1.600686+0.011609 test-rmse:1.713805+0.174458
## [14] train-rmse:1.394251+0.013776 test-rmse:1.546422+0.143141
## [15] train-rmse:1.257608+0.011355 test-rmse:1.441379+0.117864
## [16] train-rmse:1.174274+0.012408 test-rmse:1.391762+0.100167
## [17] train-rmse:1.118882+0.015058 test-rmse:1.345837+0.094506
## [18] train-rmse:1.066737+0.010941 test-rmse:1.318723+0.090887
## [19] train-rmse:1.032784+0.014794 test-rmse:1.290251+0.086413
## [20] train-rmse:1.000832+0.017023 test-rmse:1.276924+0.089611
## [21] train-rmse:0.976652+0.016109 test-rmse:1.259255+0.083849
## [22] train-rmse:0.949799+0.016821 test-rmse:1.241272+0.089016
## [23] train-rmse:0.930175+0.017396 test-rmse:1.228320+0.093754
## [24] train-rmse:0.903795+0.013443 test-rmse:1.206525+0.093376
## [25] train-rmse:0.887491+0.012644 test-rmse:1.196739+0.096168
## [26] train-rmse:0.871725+0.010560 test-rmse:1.187049+0.100018
## [27] train-rmse:0.857637+0.010806 test-rmse:1.179045+0.098188
## [28] train-rmse:0.843169+0.010168 test-rmse:1.162898+0.097811
## [29] train-rmse:0.825873+0.011206 test-rmse:1.155826+0.100102
## [30] train-rmse:0.809039+0.010161 test-rmse:1.149839+0.101119
## [31] train-rmse:0.797534+0.009615 test-rmse:1.148354+0.101516
## [32] train-rmse:0.783912+0.010688 test-rmse:1.139760+0.103623
## [33] train-rmse:0.766916+0.007733 test-rmse:1.134763+0.108496
## [34] train-rmse:0.756116+0.006527 test-rmse:1.128934+0.108805
## [35] train-rmse:0.739207+0.009933 test-rmse:1.117201+0.111320
## [36] train-rmse:0.727698+0.012001 test-rmse:1.113535+0.115297
## [37] train-rmse:0.714979+0.009736 test-rmse:1.111120+0.111996
## [38] train-rmse:0.701995+0.009691 test-rmse:1.114871+0.112239
## [39] train-rmse:0.690978+0.012740 test-rmse:1.104460+0.102188
## [40] train-rmse:0.679917+0.011807 test-rmse:1.099002+0.102892
## [41] train-rmse:0.669941+0.009380 test-rmse:1.094832+0.109236
## [42] train-rmse:0.661354+0.009990 test-rmse:1.092471+0.115615
## [43] train-rmse:0.651354+0.008193 test-rmse:1.091049+0.115813
## [44] train-rmse:0.643127+0.007271 test-rmse:1.088320+0.117136
## [45] train-rmse:0.633657+0.010767 test-rmse:1.083812+0.120233
## [46] train-rmse:0.621223+0.008183 test-rmse:1.075041+0.121616
## [47] train-rmse:0.613601+0.007597 test-rmse:1.071266+0.121239
## [48] train-rmse:0.605797+0.011379 test-rmse:1.064078+0.116220
## [49] train-rmse:0.597284+0.012378 test-rmse:1.063040+0.113915
## [50] train-rmse:0.588816+0.011860 test-rmse:1.059592+0.114248
## [51] train-rmse:0.582416+0.010004 test-rmse:1.055725+0.113706
## [52] train-rmse:0.575619+0.011460 test-rmse:1.054359+0.115534
## [53] train-rmse:0.566209+0.010964 test-rmse:1.049208+0.113435
## [54] train-rmse:0.559229+0.011502 test-rmse:1.048946+0.113269
## [55] train-rmse:0.553487+0.012207 test-rmse:1.045969+0.113951
## [56] train-rmse:0.548035+0.012561 test-rmse:1.045428+0.116527
## [57] train-rmse:0.543638+0.012663 test-rmse:1.045328+0.116821
## [58] train-rmse:0.532027+0.015011 test-rmse:1.046857+0.116347
## [59] train-rmse:0.522622+0.014261 test-rmse:1.042624+0.120776
## [60] train-rmse:0.512158+0.015734 test-rmse:1.045864+0.121844
## [61] train-rmse:0.506744+0.015382 test-rmse:1.044491+0.121835
## [62] train-rmse:0.499784+0.013234 test-rmse:1.039339+0.121783
## [63] train-rmse:0.495199+0.012653 test-rmse:1.037266+0.118493
## [64] train-rmse:0.489537+0.008697 test-rmse:1.035460+0.121227
## [65] train-rmse:0.486383+0.009058 test-rmse:1.036356+0.120371
## [66] train-rmse:0.481091+0.007777 test-rmse:1.036092+0.120790
## [67] train-rmse:0.474535+0.008747 test-rmse:1.033896+0.118772
## [68] train-rmse:0.466826+0.007495 test-rmse:1.029402+0.123189
## [69] train-rmse:0.461082+0.006949 test-rmse:1.028842+0.121131
## [70] train-rmse:0.456333+0.006287 test-rmse:1.027154+0.122731
## [71] train-rmse:0.450822+0.005639 test-rmse:1.026109+0.124728
## [72] train-rmse:0.446134+0.005792 test-rmse:1.023662+0.123959
## [73] train-rmse:0.440549+0.007140 test-rmse:1.027842+0.124492
## [74] train-rmse:0.434875+0.007809 test-rmse:1.025662+0.125210
## [75] train-rmse:0.428952+0.009132 test-rmse:1.020354+0.121619
## [76] train-rmse:0.424309+0.008559 test-rmse:1.019195+0.121201
## [77] train-rmse:0.420320+0.008108 test-rmse:1.017772+0.119275
## [78] train-rmse:0.415457+0.008351 test-rmse:1.009866+0.118131
## [79] train-rmse:0.410880+0.008136 test-rmse:1.008654+0.118758
## [80] train-rmse:0.406574+0.009947 test-rmse:1.008967+0.116992
## [81] train-rmse:0.402085+0.010608 test-rmse:1.007647+0.116568
## [82] train-rmse:0.396695+0.013616 test-rmse:1.006788+0.116183
## [83] train-rmse:0.392734+0.013965 test-rmse:1.004850+0.116637
## [84] train-rmse:0.388206+0.015581 test-rmse:1.003986+0.118488
## [85] train-rmse:0.384724+0.016618 test-rmse:1.004060+0.119689
## [86] train-rmse:0.380949+0.016061 test-rmse:1.000693+0.120798
## [87] train-rmse:0.377210+0.017315 test-rmse:0.996755+0.121427
## [88] train-rmse:0.371683+0.014588 test-rmse:0.995418+0.121645
## [89] train-rmse:0.369132+0.013807 test-rmse:0.994654+0.121847
## [90] train-rmse:0.365291+0.014146 test-rmse:0.994937+0.120357
## [91] train-rmse:0.361493+0.014205 test-rmse:0.993828+0.120712
## [92] train-rmse:0.357830+0.015045 test-rmse:0.994142+0.121133
## [93] train-rmse:0.353663+0.013548 test-rmse:0.992878+0.122383
## [94] train-rmse:0.349498+0.013288 test-rmse:0.993138+0.121416
## [95] train-rmse:0.345443+0.012629 test-rmse:0.991046+0.122297
## [96] train-rmse:0.342422+0.012670 test-rmse:0.989055+0.123966
## [97] train-rmse:0.339821+0.012594 test-rmse:0.988962+0.125175
## [98] train-rmse:0.333735+0.013322 test-rmse:0.989510+0.126824
## [99] train-rmse:0.331532+0.013323 test-rmse:0.987667+0.127416
## [100] train-rmse:0.328108+0.012855 test-rmse:0.988073+0.126659
## [101] train-rmse:0.323450+0.013827 test-rmse:0.987386+0.125193
## [102] train-rmse:0.319321+0.012884 test-rmse:0.987308+0.124882
## [103] train-rmse:0.315388+0.011583 test-rmse:0.986382+0.126490
## [104] train-rmse:0.311869+0.013062 test-rmse:0.987087+0.125516
## [105] train-rmse:0.309621+0.012966 test-rmse:0.986184+0.125843
## [106] train-rmse:0.305559+0.014535 test-rmse:0.985983+0.125963
## [107] train-rmse:0.302722+0.014547 test-rmse:0.986193+0.125069
## [108] train-rmse:0.298261+0.013754 test-rmse:0.984082+0.124366
## [109] train-rmse:0.292413+0.015278 test-rmse:0.982539+0.124225
## [110] train-rmse:0.288870+0.016863 test-rmse:0.982756+0.122657
## [111] train-rmse:0.285723+0.016707 test-rmse:0.982702+0.121801
## [112] train-rmse:0.283658+0.016279 test-rmse:0.981990+0.123143
## [113] train-rmse:0.280341+0.016517 test-rmse:0.980886+0.123605
## [114] train-rmse:0.277103+0.017288 test-rmse:0.981469+0.123457
## [115] train-rmse:0.274059+0.017017 test-rmse:0.979835+0.123428
## [116] train-rmse:0.271448+0.017476 test-rmse:0.980044+0.125431
## [117] train-rmse:0.269270+0.018106 test-rmse:0.981706+0.124352
## [118] train-rmse:0.266671+0.018502 test-rmse:0.981564+0.124253
## [119] train-rmse:0.264142+0.017923 test-rmse:0.982192+0.125312
## [120] train-rmse:0.263003+0.017669 test-rmse:0.981853+0.125495
## [121] train-rmse:0.260008+0.017410 test-rmse:0.979941+0.126067
## Stopping. Best iteration:
## [115] train-rmse:0.274059+0.017017 test-rmse:0.979835+0.123428
##
## 73
## [1] train-rmse:69.050973+0.069138 test-rmse:69.051505+0.424216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:48.430438+0.048124 test-rmse:48.430518+0.444003
## [3] train-rmse:33.983752+0.033210 test-rmse:33.983192+0.457073
## [4] train-rmse:23.869216+0.022582 test-rmse:23.867778+0.464907
## [5] train-rmse:16.797328+0.015059 test-rmse:16.794708+0.468178
## [6] train-rmse:11.863720+0.009987 test-rmse:11.868057+0.432780
## [7] train-rmse:8.422296+0.007322 test-rmse:8.396843+0.412998
## [8] train-rmse:6.023561+0.003611 test-rmse:6.008353+0.370402
## [9] train-rmse:4.353975+0.007001 test-rmse:4.345759+0.324619
## [10] train-rmse:3.198622+0.009282 test-rmse:3.204965+0.274877
## [11] train-rmse:2.407831+0.014468 test-rmse:2.434740+0.251771
## [12] train-rmse:1.867000+0.020369 test-rmse:1.933744+0.207380
## [13] train-rmse:1.518229+0.021792 test-rmse:1.640208+0.178152
## [14] train-rmse:1.287069+0.022655 test-rmse:1.465636+0.148404
## [15] train-rmse:1.134755+0.023096 test-rmse:1.354259+0.116162
## [16] train-rmse:1.035463+0.027581 test-rmse:1.296111+0.103375
## [17] train-rmse:0.967455+0.030304 test-rmse:1.257116+0.097028
## [18] train-rmse:0.915606+0.029587 test-rmse:1.231326+0.090709
## [19] train-rmse:0.870559+0.028324 test-rmse:1.212730+0.083503
## [20] train-rmse:0.836999+0.027404 test-rmse:1.191819+0.081235
## [21] train-rmse:0.804985+0.032563 test-rmse:1.180850+0.079826
## [22] train-rmse:0.783142+0.032086 test-rmse:1.167352+0.078251
## [23] train-rmse:0.757191+0.030764 test-rmse:1.156199+0.079030
## [24] train-rmse:0.734877+0.032405 test-rmse:1.142926+0.075687
## [25] train-rmse:0.716653+0.033029 test-rmse:1.136343+0.073938
## [26] train-rmse:0.697729+0.028084 test-rmse:1.124016+0.077628
## [27] train-rmse:0.673485+0.031614 test-rmse:1.113135+0.083275
## [28] train-rmse:0.654788+0.030778 test-rmse:1.107826+0.083866
## [29] train-rmse:0.642162+0.029425 test-rmse:1.096469+0.079677
## [30] train-rmse:0.621788+0.025598 test-rmse:1.084129+0.080684
## [31] train-rmse:0.606321+0.023256 test-rmse:1.073520+0.080705
## [32] train-rmse:0.591664+0.027171 test-rmse:1.064766+0.077075
## [33] train-rmse:0.578267+0.023917 test-rmse:1.057456+0.076382
## [34] train-rmse:0.561966+0.025658 test-rmse:1.056224+0.081263
## [35] train-rmse:0.551848+0.025091 test-rmse:1.053457+0.083965
## [36] train-rmse:0.542202+0.025018 test-rmse:1.047449+0.085009
## [37] train-rmse:0.529639+0.026471 test-rmse:1.046464+0.086358
## [38] train-rmse:0.517078+0.024298 test-rmse:1.042962+0.081503
## [39] train-rmse:0.506886+0.028088 test-rmse:1.043490+0.078789
## [40] train-rmse:0.496461+0.023979 test-rmse:1.040012+0.080891
## [41] train-rmse:0.489012+0.023445 test-rmse:1.037533+0.083023
## [42] train-rmse:0.482607+0.022722 test-rmse:1.037219+0.081900
## [43] train-rmse:0.473715+0.024284 test-rmse:1.031415+0.081604
## [44] train-rmse:0.460946+0.021365 test-rmse:1.025033+0.080319
## [45] train-rmse:0.450796+0.023067 test-rmse:1.022648+0.079973
## [46] train-rmse:0.441986+0.019263 test-rmse:1.017813+0.078799
## [47] train-rmse:0.435737+0.019920 test-rmse:1.015344+0.078537
## [48] train-rmse:0.428139+0.019474 test-rmse:1.019362+0.080974
## [49] train-rmse:0.421872+0.020940 test-rmse:1.018116+0.081165
## [50] train-rmse:0.416361+0.020251 test-rmse:1.018874+0.080798
## [51] train-rmse:0.404824+0.019979 test-rmse:1.015401+0.083387
## [52] train-rmse:0.395116+0.019393 test-rmse:1.014203+0.083704
## [53] train-rmse:0.389106+0.018735 test-rmse:1.012656+0.083840
## [54] train-rmse:0.381517+0.018718 test-rmse:1.012718+0.084294
## [55] train-rmse:0.373398+0.019750 test-rmse:1.015604+0.089825
## [56] train-rmse:0.364895+0.017746 test-rmse:1.013905+0.088075
## [57] train-rmse:0.358071+0.015375 test-rmse:1.010172+0.087734
## [58] train-rmse:0.352445+0.016017 test-rmse:1.008130+0.087764
## [59] train-rmse:0.346111+0.014392 test-rmse:1.005191+0.085624
## [60] train-rmse:0.340810+0.012500 test-rmse:1.005002+0.086380
## [61] train-rmse:0.333881+0.010271 test-rmse:1.003767+0.087613
## [62] train-rmse:0.330037+0.009870 test-rmse:1.001992+0.087694
## [63] train-rmse:0.320915+0.014385 test-rmse:1.000283+0.089536
## [64] train-rmse:0.314252+0.014294 test-rmse:1.001457+0.091182
## [65] train-rmse:0.310383+0.014022 test-rmse:1.001270+0.090147
## [66] train-rmse:0.306451+0.013693 test-rmse:0.999817+0.089013
## [67] train-rmse:0.301335+0.014760 test-rmse:1.001058+0.092219
## [68] train-rmse:0.293539+0.014296 test-rmse:0.995598+0.088325
## [69] train-rmse:0.290629+0.014396 test-rmse:0.996344+0.088021
## [70] train-rmse:0.286104+0.014656 test-rmse:0.994247+0.088551
## [71] train-rmse:0.280046+0.014996 test-rmse:0.994176+0.085062
## [72] train-rmse:0.273416+0.014853 test-rmse:0.989925+0.084623
## [73] train-rmse:0.268938+0.012240 test-rmse:0.990228+0.085039
## [74] train-rmse:0.262503+0.013410 test-rmse:0.990139+0.084878
## [75] train-rmse:0.257216+0.013377 test-rmse:0.989031+0.083918
## [76] train-rmse:0.253823+0.015008 test-rmse:0.987452+0.084298
## [77] train-rmse:0.251020+0.014700 test-rmse:0.986785+0.085242
## [78] train-rmse:0.245883+0.014460 test-rmse:0.986772+0.083938
## [79] train-rmse:0.241839+0.016163 test-rmse:0.987289+0.083284
## [80] train-rmse:0.236603+0.015701 test-rmse:0.985246+0.081199
## [81] train-rmse:0.232151+0.014631 test-rmse:0.984915+0.080452
## [82] train-rmse:0.226801+0.014337 test-rmse:0.984643+0.080401
## [83] train-rmse:0.223544+0.013917 test-rmse:0.985057+0.079984
## [84] train-rmse:0.219341+0.014656 test-rmse:0.984484+0.079914
## [85] train-rmse:0.214702+0.015740 test-rmse:0.985799+0.080263
## [86] train-rmse:0.211338+0.015737 test-rmse:0.983495+0.079813
## [87] train-rmse:0.206570+0.014578 test-rmse:0.983598+0.080116
## [88] train-rmse:0.202950+0.015219 test-rmse:0.982931+0.080708
## [89] train-rmse:0.199547+0.013571 test-rmse:0.982366+0.081907
## [90] train-rmse:0.196507+0.013480 test-rmse:0.982791+0.082336
## [91] train-rmse:0.192424+0.014219 test-rmse:0.982643+0.080998
## [92] train-rmse:0.187929+0.014103 test-rmse:0.983236+0.081949
## [93] train-rmse:0.185017+0.013728 test-rmse:0.982824+0.082530
## [94] train-rmse:0.181997+0.014401 test-rmse:0.982609+0.083134
## [95] train-rmse:0.178102+0.014814 test-rmse:0.981088+0.083218
## [96] train-rmse:0.175593+0.014497 test-rmse:0.980334+0.083288
## [97] train-rmse:0.172292+0.014177 test-rmse:0.981600+0.084609
## [98] train-rmse:0.169294+0.014390 test-rmse:0.981417+0.085026
## [99] train-rmse:0.165918+0.014753 test-rmse:0.981142+0.085699
## [100] train-rmse:0.163628+0.014422 test-rmse:0.980781+0.085014
## [101] train-rmse:0.161134+0.014928 test-rmse:0.979831+0.084758
## [102] train-rmse:0.158717+0.015299 test-rmse:0.980235+0.086231
## [103] train-rmse:0.156468+0.016027 test-rmse:0.980398+0.086092
## [104] train-rmse:0.154175+0.016801 test-rmse:0.979522+0.085788
## [105] train-rmse:0.151043+0.015409 test-rmse:0.978968+0.086118
## [106] train-rmse:0.148378+0.015017 test-rmse:0.980080+0.086366
## [107] train-rmse:0.146180+0.015040 test-rmse:0.979677+0.086336
## [108] train-rmse:0.143999+0.014831 test-rmse:0.979326+0.085662
## [109] train-rmse:0.141282+0.015043 test-rmse:0.980104+0.086257
## [110] train-rmse:0.138415+0.015078 test-rmse:0.979901+0.087004
## [111] train-rmse:0.137131+0.014910 test-rmse:0.980074+0.086933
## Stopping. Best iteration:
## [105] train-rmse:0.151043+0.015409 test-rmse:0.978968+0.086118
##
## 74
## [1] train-rmse:69.050973+0.069138 test-rmse:69.051505+0.424216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:48.430438+0.048124 test-rmse:48.430518+0.444003
## [3] train-rmse:33.983752+0.033210 test-rmse:33.983192+0.457073
## [4] train-rmse:23.869216+0.022582 test-rmse:23.867778+0.464907
## [5] train-rmse:16.797328+0.015059 test-rmse:16.794708+0.468178
## [6] train-rmse:11.863720+0.009987 test-rmse:11.868057+0.432780
## [7] train-rmse:8.421914+0.007726 test-rmse:8.402332+0.416388
## [8] train-rmse:6.020259+0.006925 test-rmse:6.006833+0.371065
## [9] train-rmse:4.348825+0.006797 test-rmse:4.338621+0.318428
## [10] train-rmse:3.181702+0.008314 test-rmse:3.188730+0.275117
## [11] train-rmse:2.373846+0.014241 test-rmse:2.409102+0.245717
## [12] train-rmse:1.827958+0.013096 test-rmse:1.903134+0.218724
## [13] train-rmse:1.456549+0.027733 test-rmse:1.596820+0.170109
## [14] train-rmse:1.205318+0.026193 test-rmse:1.419493+0.127960
## [15] train-rmse:1.046383+0.025483 test-rmse:1.303151+0.112401
## [16] train-rmse:0.939962+0.027885 test-rmse:1.242938+0.101731
## [17] train-rmse:0.857582+0.031275 test-rmse:1.191404+0.094580
## [18] train-rmse:0.799014+0.027784 test-rmse:1.154115+0.086355
## [19] train-rmse:0.751809+0.028314 test-rmse:1.129885+0.090800
## [20] train-rmse:0.711343+0.027646 test-rmse:1.107622+0.090636
## [21] train-rmse:0.680460+0.031644 test-rmse:1.097867+0.090598
## [22] train-rmse:0.650425+0.029611 test-rmse:1.088321+0.086544
## [23] train-rmse:0.622748+0.028854 test-rmse:1.083559+0.096287
## [24] train-rmse:0.602676+0.029053 test-rmse:1.064186+0.097288
## [25] train-rmse:0.580161+0.035033 test-rmse:1.057280+0.102309
## [26] train-rmse:0.555178+0.030859 test-rmse:1.042244+0.099783
## [27] train-rmse:0.534297+0.032847 test-rmse:1.029557+0.101715
## [28] train-rmse:0.517516+0.032496 test-rmse:1.022878+0.098846
## [29] train-rmse:0.501231+0.027389 test-rmse:1.020082+0.101771
## [30] train-rmse:0.488672+0.028380 test-rmse:1.014008+0.103082
## [31] train-rmse:0.473958+0.026674 test-rmse:1.006803+0.103979
## [32] train-rmse:0.460839+0.029587 test-rmse:1.002405+0.100801
## [33] train-rmse:0.444416+0.026214 test-rmse:0.998224+0.103433
## [34] train-rmse:0.430470+0.021884 test-rmse:0.999466+0.102002
## [35] train-rmse:0.419339+0.018836 test-rmse:0.995730+0.103909
## [36] train-rmse:0.403875+0.024224 test-rmse:0.992807+0.104361
## [37] train-rmse:0.391903+0.023681 test-rmse:0.986549+0.104913
## [38] train-rmse:0.381122+0.023298 test-rmse:0.986195+0.102194
## [39] train-rmse:0.369875+0.021129 test-rmse:0.984919+0.100248
## [40] train-rmse:0.362633+0.019513 test-rmse:0.986075+0.099109
## [41] train-rmse:0.350688+0.017496 test-rmse:0.983852+0.100944
## [42] train-rmse:0.341994+0.018321 test-rmse:0.982439+0.101891
## [43] train-rmse:0.333645+0.017909 test-rmse:0.981283+0.101443
## [44] train-rmse:0.323973+0.019433 test-rmse:0.976011+0.103241
## [45] train-rmse:0.312580+0.016078 test-rmse:0.974071+0.100173
## [46] train-rmse:0.306970+0.015227 test-rmse:0.972218+0.100726
## [47] train-rmse:0.299537+0.014915 test-rmse:0.971931+0.103412
## [48] train-rmse:0.290801+0.014415 test-rmse:0.971419+0.104587
## [49] train-rmse:0.283896+0.014950 test-rmse:0.972036+0.104319
## [50] train-rmse:0.274970+0.015305 test-rmse:0.971304+0.103405
## [51] train-rmse:0.269602+0.014228 test-rmse:0.970437+0.102572
## [52] train-rmse:0.264043+0.016767 test-rmse:0.968669+0.101217
## [53] train-rmse:0.257836+0.017266 test-rmse:0.969505+0.102281
## [54] train-rmse:0.250359+0.019323 test-rmse:0.968167+0.102099
## [55] train-rmse:0.243094+0.019109 test-rmse:0.969513+0.101597
## [56] train-rmse:0.236385+0.017866 test-rmse:0.968822+0.103925
## [57] train-rmse:0.229041+0.017571 test-rmse:0.969672+0.101824
## [58] train-rmse:0.222433+0.017026 test-rmse:0.969193+0.101564
## [59] train-rmse:0.214963+0.016935 test-rmse:0.967457+0.101677
## [60] train-rmse:0.208329+0.014097 test-rmse:0.967080+0.101594
## [61] train-rmse:0.203328+0.013801 test-rmse:0.966916+0.102026
## [62] train-rmse:0.199697+0.013996 test-rmse:0.966968+0.101672
## [63] train-rmse:0.194814+0.012854 test-rmse:0.967858+0.101280
## [64] train-rmse:0.188431+0.013816 test-rmse:0.966986+0.100607
## [65] train-rmse:0.183082+0.014003 test-rmse:0.967263+0.101584
## [66] train-rmse:0.178884+0.012665 test-rmse:0.966007+0.102147
## [67] train-rmse:0.175078+0.013251 test-rmse:0.964584+0.101817
## [68] train-rmse:0.170638+0.013483 test-rmse:0.963810+0.101154
## [69] train-rmse:0.165995+0.013855 test-rmse:0.963112+0.101792
## [70] train-rmse:0.162784+0.013252 test-rmse:0.963815+0.101663
## [71] train-rmse:0.159114+0.013358 test-rmse:0.963525+0.103320
## [72] train-rmse:0.155234+0.013024 test-rmse:0.962840+0.103550
## [73] train-rmse:0.151316+0.012880 test-rmse:0.962394+0.103747
## [74] train-rmse:0.148411+0.013294 test-rmse:0.962768+0.104977
## [75] train-rmse:0.145553+0.012953 test-rmse:0.963218+0.104431
## [76] train-rmse:0.141411+0.012366 test-rmse:0.963121+0.102177
## [77] train-rmse:0.138407+0.012779 test-rmse:0.962038+0.101456
## [78] train-rmse:0.136161+0.012024 test-rmse:0.960412+0.101913
## [79] train-rmse:0.132560+0.012150 test-rmse:0.959760+0.103033
## [80] train-rmse:0.129832+0.011695 test-rmse:0.960531+0.103442
## [81] train-rmse:0.126022+0.011507 test-rmse:0.960381+0.103024
## [82] train-rmse:0.123358+0.011384 test-rmse:0.960859+0.102317
## [83] train-rmse:0.121024+0.011351 test-rmse:0.960409+0.102400
## [84] train-rmse:0.119347+0.011234 test-rmse:0.960561+0.102409
## [85] train-rmse:0.115960+0.010619 test-rmse:0.961963+0.102799
## Stopping. Best iteration:
## [79] train-rmse:0.132560+0.012150 test-rmse:0.959760+0.103033
##
## 75
## [1] train-rmse:69.050973+0.069138 test-rmse:69.051505+0.424216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:48.430438+0.048124 test-rmse:48.430518+0.444003
## [3] train-rmse:33.983752+0.033210 test-rmse:33.983192+0.457073
## [4] train-rmse:23.869216+0.022582 test-rmse:23.867778+0.464907
## [5] train-rmse:16.797328+0.015059 test-rmse:16.794708+0.468178
## [6] train-rmse:11.863720+0.009987 test-rmse:11.868057+0.432780
## [7] train-rmse:8.421914+0.007726 test-rmse:8.402332+0.416388
## [8] train-rmse:6.020005+0.006779 test-rmse:6.011603+0.366924
## [9] train-rmse:4.344490+0.005714 test-rmse:4.343985+0.310398
## [10] train-rmse:3.176032+0.008185 test-rmse:3.184665+0.268561
## [11] train-rmse:2.360933+0.011537 test-rmse:2.406966+0.240298
## [12] train-rmse:1.790304+0.009778 test-rmse:1.887393+0.183872
## [13] train-rmse:1.406366+0.012901 test-rmse:1.559486+0.159783
## [14] train-rmse:1.145912+0.008057 test-rmse:1.353288+0.151562
## [15] train-rmse:0.963783+0.023931 test-rmse:1.242465+0.138017
## [16] train-rmse:0.845963+0.020179 test-rmse:1.175803+0.119155
## [17] train-rmse:0.756645+0.027632 test-rmse:1.134532+0.115003
## [18] train-rmse:0.685541+0.025135 test-rmse:1.106541+0.103737
## [19] train-rmse:0.640833+0.032456 test-rmse:1.076971+0.091481
## [20] train-rmse:0.597820+0.034171 test-rmse:1.065658+0.088250
## [21] train-rmse:0.560555+0.028165 test-rmse:1.054880+0.081973
## [22] train-rmse:0.531712+0.027879 test-rmse:1.051068+0.083874
## [23] train-rmse:0.498524+0.031506 test-rmse:1.041435+0.082613
## [24] train-rmse:0.475041+0.031874 test-rmse:1.032153+0.087213
## [25] train-rmse:0.453521+0.026129 test-rmse:1.025471+0.089163
## [26] train-rmse:0.434093+0.024030 test-rmse:1.021070+0.085682
## [27] train-rmse:0.417407+0.029181 test-rmse:1.019555+0.091234
## [28] train-rmse:0.402731+0.027170 test-rmse:1.007078+0.083422
## [29] train-rmse:0.385556+0.021885 test-rmse:1.001447+0.088357
## [30] train-rmse:0.372312+0.023193 test-rmse:0.997021+0.091982
## [31] train-rmse:0.358919+0.021919 test-rmse:0.992728+0.094321
## [32] train-rmse:0.348062+0.022187 test-rmse:0.993776+0.094992
## [33] train-rmse:0.335172+0.022610 test-rmse:0.990825+0.093780
## [34] train-rmse:0.324074+0.022171 test-rmse:0.984498+0.097515
## [35] train-rmse:0.313646+0.024759 test-rmse:0.984330+0.097653
## [36] train-rmse:0.300467+0.023184 test-rmse:0.979984+0.098288
## [37] train-rmse:0.290832+0.023129 test-rmse:0.979628+0.093904
## [38] train-rmse:0.281245+0.023623 test-rmse:0.978700+0.097299
## [39] train-rmse:0.269718+0.018140 test-rmse:0.976325+0.098820
## [40] train-rmse:0.262221+0.016970 test-rmse:0.973487+0.101448
## [41] train-rmse:0.252926+0.016162 test-rmse:0.972788+0.102762
## [42] train-rmse:0.243280+0.016068 test-rmse:0.970177+0.102589
## [43] train-rmse:0.234726+0.014761 test-rmse:0.969789+0.100607
## [44] train-rmse:0.228112+0.014120 test-rmse:0.967610+0.099691
## [45] train-rmse:0.222226+0.013748 test-rmse:0.966478+0.100031
## [46] train-rmse:0.212428+0.010226 test-rmse:0.967449+0.103864
## [47] train-rmse:0.202447+0.010652 test-rmse:0.965098+0.104476
## [48] train-rmse:0.196874+0.010758 test-rmse:0.965163+0.104921
## [49] train-rmse:0.190362+0.009634 test-rmse:0.964105+0.104417
## [50] train-rmse:0.182647+0.007417 test-rmse:0.964539+0.107258
## [51] train-rmse:0.176543+0.007465 test-rmse:0.964315+0.106598
## [52] train-rmse:0.169709+0.007856 test-rmse:0.963943+0.106475
## [53] train-rmse:0.164099+0.009018 test-rmse:0.964781+0.106837
## [54] train-rmse:0.158377+0.009647 test-rmse:0.963449+0.105277
## [55] train-rmse:0.153653+0.011255 test-rmse:0.963472+0.105000
## [56] train-rmse:0.147938+0.009573 test-rmse:0.962553+0.103559
## [57] train-rmse:0.141924+0.010291 test-rmse:0.962075+0.103901
## [58] train-rmse:0.137825+0.009164 test-rmse:0.961393+0.102668
## [59] train-rmse:0.131186+0.008268 test-rmse:0.958968+0.101089
## [60] train-rmse:0.126965+0.007356 test-rmse:0.958162+0.101437
## [61] train-rmse:0.121297+0.008300 test-rmse:0.956926+0.101711
## [62] train-rmse:0.117682+0.008940 test-rmse:0.955935+0.101210
## [63] train-rmse:0.112626+0.008687 test-rmse:0.954983+0.102785
## [64] train-rmse:0.108685+0.007932 test-rmse:0.954159+0.102916
## [65] train-rmse:0.105736+0.007936 test-rmse:0.953094+0.103052
## [66] train-rmse:0.102259+0.008120 test-rmse:0.952880+0.103391
## [67] train-rmse:0.099836+0.007990 test-rmse:0.952590+0.103555
## [68] train-rmse:0.097170+0.008240 test-rmse:0.952272+0.102958
## [69] train-rmse:0.094132+0.007990 test-rmse:0.952554+0.103495
## [70] train-rmse:0.090500+0.008013 test-rmse:0.952552+0.104030
## [71] train-rmse:0.087314+0.008361 test-rmse:0.952851+0.104014
## [72] train-rmse:0.084632+0.007416 test-rmse:0.952933+0.103961
## [73] train-rmse:0.082633+0.007584 test-rmse:0.952888+0.103759
## [74] train-rmse:0.079968+0.007863 test-rmse:0.951677+0.103996
## [75] train-rmse:0.077909+0.008011 test-rmse:0.950568+0.103933
## [76] train-rmse:0.074631+0.007426 test-rmse:0.951625+0.104176
## [77] train-rmse:0.072006+0.007933 test-rmse:0.951605+0.104087
## [78] train-rmse:0.069417+0.008339 test-rmse:0.951257+0.104234
## [79] train-rmse:0.067562+0.007717 test-rmse:0.950640+0.103942
## [80] train-rmse:0.065987+0.007960 test-rmse:0.950028+0.103857
## [81] train-rmse:0.064166+0.008253 test-rmse:0.949807+0.103899
## [82] train-rmse:0.062178+0.007832 test-rmse:0.949328+0.104560
## [83] train-rmse:0.060560+0.007384 test-rmse:0.949059+0.104495
## [84] train-rmse:0.058654+0.006421 test-rmse:0.948085+0.104547
## [85] train-rmse:0.056145+0.007266 test-rmse:0.948389+0.104586
## [86] train-rmse:0.054266+0.006341 test-rmse:0.948876+0.104536
## [87] train-rmse:0.053022+0.006304 test-rmse:0.948617+0.105006
## [88] train-rmse:0.051243+0.006618 test-rmse:0.948738+0.105319
## [89] train-rmse:0.049324+0.005763 test-rmse:0.948974+0.105490
## [90] train-rmse:0.047935+0.005921 test-rmse:0.949210+0.105315
## Stopping. Best iteration:
## [84] train-rmse:0.058654+0.006421 test-rmse:0.948085+0.104547
##
## 76
## [1] train-rmse:69.050973+0.069138 test-rmse:69.051505+0.424216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:48.430438+0.048124 test-rmse:48.430518+0.444003
## [3] train-rmse:33.983752+0.033210 test-rmse:33.983192+0.457073
## [4] train-rmse:23.869216+0.022582 test-rmse:23.867778+0.464907
## [5] train-rmse:16.797328+0.015059 test-rmse:16.794708+0.468178
## [6] train-rmse:11.863720+0.009987 test-rmse:11.868057+0.432780
## [7] train-rmse:8.421914+0.007726 test-rmse:8.402332+0.416388
## [8] train-rmse:6.019871+0.006715 test-rmse:6.018442+0.361345
## [9] train-rmse:4.343651+0.006682 test-rmse:4.348947+0.297078
## [10] train-rmse:3.168806+0.005095 test-rmse:3.182927+0.257728
## [11] train-rmse:2.342072+0.013464 test-rmse:2.389965+0.218968
## [12] train-rmse:1.764748+0.015907 test-rmse:1.869661+0.166401
## [13] train-rmse:1.363945+0.014187 test-rmse:1.534824+0.140309
## [14] train-rmse:1.083733+0.017025 test-rmse:1.339141+0.125047
## [15] train-rmse:0.890555+0.020488 test-rmse:1.216112+0.115246
## [16] train-rmse:0.752790+0.027973 test-rmse:1.138593+0.102159
## [17] train-rmse:0.656654+0.032079 test-rmse:1.099628+0.096102
## [18] train-rmse:0.593114+0.034028 test-rmse:1.070868+0.098283
## [19] train-rmse:0.537789+0.041202 test-rmse:1.044629+0.090198
## [20] train-rmse:0.495312+0.046188 test-rmse:1.035363+0.087211
## [21] train-rmse:0.453614+0.036122 test-rmse:1.027970+0.082828
## [22] train-rmse:0.426603+0.033863 test-rmse:1.026023+0.083760
## [23] train-rmse:0.402506+0.033430 test-rmse:1.018778+0.082394
## [24] train-rmse:0.381683+0.037933 test-rmse:1.015053+0.082535
## [25] train-rmse:0.364039+0.035709 test-rmse:1.011818+0.082831
## [26] train-rmse:0.345981+0.037648 test-rmse:1.004353+0.084153
## [27] train-rmse:0.329892+0.037734 test-rmse:1.001890+0.087075
## [28] train-rmse:0.314319+0.037931 test-rmse:0.996699+0.088172
## [29] train-rmse:0.295617+0.031053 test-rmse:0.994624+0.088979
## [30] train-rmse:0.281915+0.025065 test-rmse:0.985990+0.086715
## [31] train-rmse:0.270566+0.026798 test-rmse:0.982848+0.085638
## [32] train-rmse:0.255533+0.022273 test-rmse:0.980079+0.084975
## [33] train-rmse:0.242118+0.023646 test-rmse:0.973345+0.086484
## [34] train-rmse:0.229361+0.019902 test-rmse:0.970485+0.088052
## [35] train-rmse:0.220864+0.020494 test-rmse:0.968403+0.089923
## [36] train-rmse:0.209974+0.019595 test-rmse:0.965711+0.089716
## [37] train-rmse:0.201088+0.019728 test-rmse:0.966111+0.087717
## [38] train-rmse:0.192069+0.018290 test-rmse:0.966746+0.087894
## [39] train-rmse:0.183375+0.018070 test-rmse:0.962069+0.090780
## [40] train-rmse:0.174511+0.016480 test-rmse:0.961040+0.090924
## [41] train-rmse:0.166015+0.016148 test-rmse:0.958010+0.092116
## [42] train-rmse:0.158049+0.016906 test-rmse:0.956993+0.092482
## [43] train-rmse:0.151338+0.018298 test-rmse:0.956120+0.093659
## [44] train-rmse:0.144647+0.017085 test-rmse:0.956113+0.093340
## [45] train-rmse:0.137993+0.016318 test-rmse:0.955992+0.093729
## [46] train-rmse:0.132047+0.015395 test-rmse:0.955162+0.093053
## [47] train-rmse:0.126943+0.015470 test-rmse:0.954493+0.093813
## [48] train-rmse:0.121188+0.014135 test-rmse:0.952647+0.094531
## [49] train-rmse:0.115731+0.015347 test-rmse:0.951321+0.095289
## [50] train-rmse:0.112201+0.014892 test-rmse:0.950530+0.095603
## [51] train-rmse:0.107060+0.014066 test-rmse:0.950768+0.095473
## [52] train-rmse:0.102058+0.013055 test-rmse:0.949292+0.095656
## [53] train-rmse:0.096963+0.012858 test-rmse:0.948612+0.095259
## [54] train-rmse:0.094166+0.013278 test-rmse:0.947930+0.095355
## [55] train-rmse:0.089740+0.012415 test-rmse:0.946755+0.096670
## [56] train-rmse:0.086998+0.011537 test-rmse:0.946373+0.095938
## [57] train-rmse:0.084327+0.011199 test-rmse:0.946635+0.095837
## [58] train-rmse:0.079590+0.010513 test-rmse:0.947326+0.095770
## [59] train-rmse:0.077086+0.009656 test-rmse:0.946692+0.095546
## [60] train-rmse:0.074038+0.009193 test-rmse:0.946156+0.095308
## [61] train-rmse:0.070391+0.008773 test-rmse:0.946369+0.095098
## [62] train-rmse:0.067440+0.008448 test-rmse:0.946375+0.094536
## [63] train-rmse:0.064099+0.008388 test-rmse:0.945952+0.095107
## [64] train-rmse:0.060949+0.008100 test-rmse:0.945960+0.095254
## [65] train-rmse:0.059049+0.007525 test-rmse:0.945255+0.095561
## [66] train-rmse:0.056051+0.006699 test-rmse:0.944903+0.095565
## [67] train-rmse:0.053705+0.006362 test-rmse:0.945048+0.095441
## [68] train-rmse:0.050738+0.005748 test-rmse:0.944732+0.095551
## [69] train-rmse:0.049056+0.005361 test-rmse:0.944945+0.095782
## [70] train-rmse:0.046997+0.005237 test-rmse:0.944514+0.095780
## [71] train-rmse:0.045518+0.005190 test-rmse:0.944579+0.096119
## [72] train-rmse:0.044193+0.005169 test-rmse:0.944038+0.096155
## [73] train-rmse:0.041894+0.005065 test-rmse:0.943185+0.096597
## [74] train-rmse:0.040346+0.004900 test-rmse:0.943067+0.097181
## [75] train-rmse:0.038742+0.004985 test-rmse:0.942983+0.097453
## [76] train-rmse:0.037162+0.004283 test-rmse:0.942623+0.097461
## [77] train-rmse:0.036132+0.003940 test-rmse:0.942620+0.097260
## [78] train-rmse:0.034381+0.003691 test-rmse:0.942545+0.097102
## [79] train-rmse:0.032860+0.003857 test-rmse:0.942803+0.097285
## [80] train-rmse:0.031461+0.003714 test-rmse:0.942787+0.097088
## [81] train-rmse:0.030253+0.003642 test-rmse:0.942737+0.097100
## [82] train-rmse:0.028979+0.003166 test-rmse:0.942736+0.096715
## [83] train-rmse:0.027841+0.003052 test-rmse:0.942752+0.096403
## [84] train-rmse:0.026618+0.003179 test-rmse:0.942736+0.096619
## Stopping. Best iteration:
## [78] train-rmse:0.034381+0.003691 test-rmse:0.942545+0.097102
##
## 77
## [1] train-rmse:67.089722+0.067155 test-rmse:67.090222+0.426122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:45.720995+0.045347 test-rmse:45.720976+0.446533
## [3] train-rmse:31.177771+0.030287 test-rmse:31.177018+0.459422
## [4] train-rmse:21.288879+0.019834 test-rmse:21.287091+0.466458
## [5] train-rmse:14.577831+0.012778 test-rmse:14.574641+0.468189
## [6] train-rmse:10.035147+0.008594 test-rmse:10.035415+0.429019
## [7] train-rmse:6.972645+0.007444 test-rmse:6.959648+0.423008
## [8] train-rmse:4.925980+0.010311 test-rmse:4.914400+0.397346
## [9] train-rmse:3.587957+0.015052 test-rmse:3.586788+0.357965
## [10] train-rmse:2.737162+0.020655 test-rmse:2.750996+0.315122
## [11] train-rmse:2.220138+0.023745 test-rmse:2.244753+0.241066
## [12] train-rmse:1.920317+0.025530 test-rmse:1.968043+0.180942
## [13] train-rmse:1.749796+0.027180 test-rmse:1.822636+0.135432
## [14] train-rmse:1.652111+0.028203 test-rmse:1.737535+0.117227
## [15] train-rmse:1.592832+0.029314 test-rmse:1.668086+0.107415
## [16] train-rmse:1.555096+0.028349 test-rmse:1.644565+0.109735
## [17] train-rmse:1.526478+0.026995 test-rmse:1.625208+0.109081
## [18] train-rmse:1.503427+0.025305 test-rmse:1.609643+0.111229
## [19] train-rmse:1.484162+0.025497 test-rmse:1.594021+0.117232
## [20] train-rmse:1.466949+0.024989 test-rmse:1.583322+0.113872
## [21] train-rmse:1.450607+0.025352 test-rmse:1.574973+0.109928
## [22] train-rmse:1.434865+0.024904 test-rmse:1.566116+0.112959
## [23] train-rmse:1.420755+0.024906 test-rmse:1.559472+0.116094
## [24] train-rmse:1.406981+0.024941 test-rmse:1.538411+0.104964
## [25] train-rmse:1.394080+0.024909 test-rmse:1.542677+0.104912
## [26] train-rmse:1.382167+0.024290 test-rmse:1.535312+0.105024
## [27] train-rmse:1.370496+0.023658 test-rmse:1.532179+0.104209
## [28] train-rmse:1.359375+0.023499 test-rmse:1.526732+0.100928
## [29] train-rmse:1.348797+0.023148 test-rmse:1.516656+0.106640
## [30] train-rmse:1.338269+0.023097 test-rmse:1.504216+0.111599
## [31] train-rmse:1.327827+0.023032 test-rmse:1.491791+0.108117
## [32] train-rmse:1.317850+0.022720 test-rmse:1.488276+0.107420
## [33] train-rmse:1.308176+0.022861 test-rmse:1.484443+0.096106
## [34] train-rmse:1.298921+0.022583 test-rmse:1.474930+0.093804
## [35] train-rmse:1.289973+0.022158 test-rmse:1.464839+0.095738
## [36] train-rmse:1.280880+0.021755 test-rmse:1.463830+0.088254
## [37] train-rmse:1.271818+0.021449 test-rmse:1.460171+0.088737
## [38] train-rmse:1.262743+0.021445 test-rmse:1.457282+0.088674
## [39] train-rmse:1.254495+0.021074 test-rmse:1.440884+0.090750
## [40] train-rmse:1.246639+0.021030 test-rmse:1.433945+0.092499
## [41] train-rmse:1.239034+0.020970 test-rmse:1.433043+0.094757
## [42] train-rmse:1.231783+0.021095 test-rmse:1.428980+0.095082
## [43] train-rmse:1.224861+0.021132 test-rmse:1.424003+0.092781
## [44] train-rmse:1.218257+0.021119 test-rmse:1.420865+0.090678
## [45] train-rmse:1.211799+0.021393 test-rmse:1.415397+0.093550
## [46] train-rmse:1.205463+0.021502 test-rmse:1.409726+0.093702
## [47] train-rmse:1.199270+0.021553 test-rmse:1.402113+0.093718
## [48] train-rmse:1.193370+0.021688 test-rmse:1.401221+0.085385
## [49] train-rmse:1.187655+0.021586 test-rmse:1.394846+0.082662
## [50] train-rmse:1.181968+0.021503 test-rmse:1.388511+0.077996
## [51] train-rmse:1.176425+0.021436 test-rmse:1.392271+0.076562
## [52] train-rmse:1.170938+0.021546 test-rmse:1.390822+0.075554
## [53] train-rmse:1.165544+0.021537 test-rmse:1.382378+0.073654
## [54] train-rmse:1.160149+0.021455 test-rmse:1.375647+0.081736
## [55] train-rmse:1.155075+0.021452 test-rmse:1.375415+0.086114
## [56] train-rmse:1.150067+0.021367 test-rmse:1.369256+0.087023
## [57] train-rmse:1.145307+0.021223 test-rmse:1.364862+0.084894
## [58] train-rmse:1.140692+0.021009 test-rmse:1.362713+0.087384
## [59] train-rmse:1.136134+0.020782 test-rmse:1.356179+0.086630
## [60] train-rmse:1.131603+0.020624 test-rmse:1.353246+0.082524
## [61] train-rmse:1.127344+0.020561 test-rmse:1.347776+0.081805
## [62] train-rmse:1.123025+0.020486 test-rmse:1.345907+0.081751
## [63] train-rmse:1.118739+0.020470 test-rmse:1.345105+0.086272
## [64] train-rmse:1.114626+0.020533 test-rmse:1.342092+0.085052
## [65] train-rmse:1.110768+0.020456 test-rmse:1.340583+0.083995
## [66] train-rmse:1.106927+0.020312 test-rmse:1.338597+0.084220
## [67] train-rmse:1.103242+0.020123 test-rmse:1.335889+0.082527
## [68] train-rmse:1.099672+0.019979 test-rmse:1.332145+0.086352
## [69] train-rmse:1.096011+0.019743 test-rmse:1.328456+0.087105
## [70] train-rmse:1.092545+0.019611 test-rmse:1.322684+0.086721
## [71] train-rmse:1.089055+0.019516 test-rmse:1.317140+0.087982
## [72] train-rmse:1.085671+0.019468 test-rmse:1.319599+0.086716
## [73] train-rmse:1.082312+0.019537 test-rmse:1.318744+0.084619
## [74] train-rmse:1.079109+0.019557 test-rmse:1.318711+0.085145
## [75] train-rmse:1.075986+0.019589 test-rmse:1.316596+0.084768
## [76] train-rmse:1.073023+0.019585 test-rmse:1.314321+0.085667
## [77] train-rmse:1.070058+0.019660 test-rmse:1.309851+0.089972
## [78] train-rmse:1.067103+0.019716 test-rmse:1.311776+0.087339
## [79] train-rmse:1.064203+0.019703 test-rmse:1.308515+0.086147
## [80] train-rmse:1.061283+0.019642 test-rmse:1.306457+0.088079
## [81] train-rmse:1.058512+0.019682 test-rmse:1.303963+0.087905
## [82] train-rmse:1.055770+0.019670 test-rmse:1.301085+0.089456
## [83] train-rmse:1.053160+0.019584 test-rmse:1.302633+0.086913
## [84] train-rmse:1.050602+0.019471 test-rmse:1.299921+0.085651
## [85] train-rmse:1.048077+0.019370 test-rmse:1.297766+0.085934
## [86] train-rmse:1.045625+0.019322 test-rmse:1.297101+0.084835
## [87] train-rmse:1.043199+0.019291 test-rmse:1.292026+0.086897
## [88] train-rmse:1.040816+0.019255 test-rmse:1.287916+0.086997
## [89] train-rmse:1.038507+0.019154 test-rmse:1.287485+0.085387
## [90] train-rmse:1.036238+0.019049 test-rmse:1.285498+0.086638
## [91] train-rmse:1.033946+0.018935 test-rmse:1.282688+0.086842
## [92] train-rmse:1.031665+0.018924 test-rmse:1.277761+0.087121
## [93] train-rmse:1.029378+0.018877 test-rmse:1.277284+0.088260
## [94] train-rmse:1.027189+0.018864 test-rmse:1.277524+0.090447
## [95] train-rmse:1.025060+0.018837 test-rmse:1.276164+0.087643
## [96] train-rmse:1.022959+0.018800 test-rmse:1.274460+0.091426
## [97] train-rmse:1.020838+0.018742 test-rmse:1.274954+0.090422
## [98] train-rmse:1.018753+0.018733 test-rmse:1.273044+0.090609
## [99] train-rmse:1.016759+0.018709 test-rmse:1.268972+0.083962
## [100] train-rmse:1.014793+0.018666 test-rmse:1.270372+0.081160
## [101] train-rmse:1.012826+0.018694 test-rmse:1.268747+0.081968
## [102] train-rmse:1.010944+0.018683 test-rmse:1.267969+0.081579
## [103] train-rmse:1.009091+0.018582 test-rmse:1.268468+0.080083
## [104] train-rmse:1.007196+0.018507 test-rmse:1.270440+0.080507
## [105] train-rmse:1.005393+0.018451 test-rmse:1.269411+0.082776
## [106] train-rmse:1.003548+0.018539 test-rmse:1.266711+0.083119
## [107] train-rmse:1.001817+0.018536 test-rmse:1.265250+0.082228
## [108] train-rmse:1.000157+0.018566 test-rmse:1.263119+0.081195
## [109] train-rmse:0.998515+0.018580 test-rmse:1.263378+0.079358
## [110] train-rmse:0.996839+0.018574 test-rmse:1.260337+0.083414
## [111] train-rmse:0.995159+0.018539 test-rmse:1.260376+0.081713
## [112] train-rmse:0.993495+0.018489 test-rmse:1.258048+0.084029
## [113] train-rmse:0.991827+0.018461 test-rmse:1.256855+0.084849
## [114] train-rmse:0.990131+0.018326 test-rmse:1.257476+0.084209
## [115] train-rmse:0.988546+0.018293 test-rmse:1.256526+0.082737
## [116] train-rmse:0.986999+0.018254 test-rmse:1.256946+0.084074
## [117] train-rmse:0.985463+0.018200 test-rmse:1.256707+0.084386
## [118] train-rmse:0.983876+0.018140 test-rmse:1.256928+0.085356
## [119] train-rmse:0.982356+0.018140 test-rmse:1.256890+0.085003
## [120] train-rmse:0.980804+0.018099 test-rmse:1.256867+0.085155
## [121] train-rmse:0.979361+0.018117 test-rmse:1.256438+0.085783
## [122] train-rmse:0.977950+0.018140 test-rmse:1.255511+0.085198
## [123] train-rmse:0.976504+0.018168 test-rmse:1.254216+0.083758
## [124] train-rmse:0.975140+0.018165 test-rmse:1.254174+0.084714
## [125] train-rmse:0.973687+0.018181 test-rmse:1.252732+0.084022
## [126] train-rmse:0.972288+0.018187 test-rmse:1.251163+0.083257
## [127] train-rmse:0.970909+0.018200 test-rmse:1.249653+0.083139
## [128] train-rmse:0.969585+0.018223 test-rmse:1.244851+0.081901
## [129] train-rmse:0.968281+0.018254 test-rmse:1.239935+0.079054
## [130] train-rmse:0.966955+0.018293 test-rmse:1.241376+0.078397
## [131] train-rmse:0.965656+0.018299 test-rmse:1.241401+0.079077
## [132] train-rmse:0.964388+0.018298 test-rmse:1.242247+0.078424
## [133] train-rmse:0.963149+0.018262 test-rmse:1.242149+0.078571
## [134] train-rmse:0.961868+0.018292 test-rmse:1.240370+0.079239
## [135] train-rmse:0.960579+0.018339 test-rmse:1.240869+0.081542
## Stopping. Best iteration:
## [129] train-rmse:0.968281+0.018254 test-rmse:1.239935+0.079054
##
## 78
## [1] train-rmse:67.089722+0.067155 test-rmse:67.090222+0.426122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:45.720995+0.045347 test-rmse:45.720976+0.446533
## [3] train-rmse:31.177771+0.030287 test-rmse:31.177018+0.459422
## [4] train-rmse:21.288879+0.019834 test-rmse:21.287091+0.466458
## [5] train-rmse:14.577831+0.012778 test-rmse:14.574641+0.468189
## [6] train-rmse:10.033350+0.007985 test-rmse:10.019403+0.432742
## [7] train-rmse:6.960861+0.007028 test-rmse:6.943355+0.397670
## [8] train-rmse:4.894359+0.006876 test-rmse:4.881230+0.373566
## [9] train-rmse:3.518489+0.012354 test-rmse:3.520147+0.320042
## [10] train-rmse:2.627537+0.012463 test-rmse:2.661704+0.276642
## [11] train-rmse:2.068578+0.014812 test-rmse:2.132296+0.222587
## [12] train-rmse:1.730319+0.015588 test-rmse:1.833462+0.168407
## [13] train-rmse:1.532963+0.013786 test-rmse:1.664094+0.127202
## [14] train-rmse:1.415704+0.015160 test-rmse:1.580893+0.102509
## [15] train-rmse:1.343711+0.016516 test-rmse:1.519277+0.111611
## [16] train-rmse:1.293835+0.013492 test-rmse:1.484210+0.112397
## [17] train-rmse:1.258596+0.012955 test-rmse:1.456795+0.116635
## [18] train-rmse:1.232493+0.013178 test-rmse:1.435607+0.118706
## [19] train-rmse:1.206117+0.018663 test-rmse:1.424911+0.116737
## [20] train-rmse:1.186224+0.017856 test-rmse:1.413820+0.111777
## [21] train-rmse:1.163204+0.012263 test-rmse:1.384037+0.102288
## [22] train-rmse:1.143312+0.014946 test-rmse:1.363997+0.110169
## [23] train-rmse:1.126491+0.014411 test-rmse:1.353524+0.106650
## [24] train-rmse:1.109524+0.014975 test-rmse:1.338468+0.109669
## [25] train-rmse:1.091166+0.010868 test-rmse:1.330766+0.111162
## [26] train-rmse:1.069556+0.011341 test-rmse:1.324362+0.114198
## [27] train-rmse:1.054306+0.015313 test-rmse:1.314666+0.111129
## [28] train-rmse:1.039464+0.016217 test-rmse:1.306954+0.108593
## [29] train-rmse:1.026651+0.017334 test-rmse:1.299843+0.105854
## [30] train-rmse:1.012946+0.016938 test-rmse:1.287827+0.098558
## [31] train-rmse:0.997953+0.016038 test-rmse:1.282833+0.098334
## [32] train-rmse:0.987661+0.015865 test-rmse:1.278806+0.102459
## [33] train-rmse:0.977263+0.014307 test-rmse:1.270549+0.105846
## [34] train-rmse:0.964969+0.012507 test-rmse:1.261183+0.105827
## [35] train-rmse:0.953988+0.010127 test-rmse:1.255920+0.103875
## [36] train-rmse:0.944109+0.008598 test-rmse:1.246447+0.101856
## [37] train-rmse:0.936139+0.007969 test-rmse:1.245863+0.099637
## [38] train-rmse:0.926914+0.007143 test-rmse:1.232682+0.106512
## [39] train-rmse:0.917488+0.005120 test-rmse:1.223310+0.103988
## [40] train-rmse:0.907333+0.004568 test-rmse:1.218337+0.101488
## [41] train-rmse:0.898454+0.007469 test-rmse:1.218606+0.101560
## [42] train-rmse:0.888497+0.005355 test-rmse:1.207852+0.104002
## [43] train-rmse:0.877463+0.004949 test-rmse:1.200880+0.103229
## [44] train-rmse:0.870598+0.005859 test-rmse:1.198687+0.100703
## [45] train-rmse:0.861049+0.004959 test-rmse:1.199513+0.103911
## [46] train-rmse:0.852395+0.006137 test-rmse:1.193725+0.107184
## [47] train-rmse:0.842334+0.005225 test-rmse:1.189827+0.107047
## [48] train-rmse:0.836952+0.005465 test-rmse:1.188210+0.109899
## [49] train-rmse:0.828792+0.005526 test-rmse:1.182177+0.113868
## [50] train-rmse:0.822616+0.005557 test-rmse:1.181603+0.117574
## [51] train-rmse:0.815382+0.005552 test-rmse:1.173815+0.118658
## [52] train-rmse:0.806953+0.006054 test-rmse:1.173307+0.118065
## [53] train-rmse:0.800129+0.004992 test-rmse:1.164103+0.107288
## [54] train-rmse:0.794052+0.005646 test-rmse:1.158231+0.104444
## [55] train-rmse:0.784943+0.008535 test-rmse:1.158576+0.106331
## [56] train-rmse:0.780891+0.008633 test-rmse:1.160132+0.102041
## [57] train-rmse:0.774470+0.010586 test-rmse:1.160854+0.104166
## [58] train-rmse:0.765210+0.010894 test-rmse:1.155606+0.105102
## [59] train-rmse:0.759226+0.010543 test-rmse:1.152995+0.108823
## [60] train-rmse:0.751816+0.008719 test-rmse:1.147183+0.108407
## [61] train-rmse:0.745067+0.010813 test-rmse:1.143995+0.105632
## [62] train-rmse:0.738072+0.008746 test-rmse:1.142485+0.106067
## [63] train-rmse:0.731533+0.007549 test-rmse:1.133123+0.099934
## [64] train-rmse:0.727663+0.007231 test-rmse:1.132975+0.099952
## [65] train-rmse:0.721074+0.007777 test-rmse:1.128217+0.095942
## [66] train-rmse:0.716009+0.008708 test-rmse:1.124771+0.094034
## [67] train-rmse:0.708488+0.006125 test-rmse:1.125453+0.091995
## [68] train-rmse:0.703128+0.006430 test-rmse:1.124437+0.093020
## [69] train-rmse:0.699343+0.007275 test-rmse:1.123163+0.090656
## [70] train-rmse:0.692877+0.011489 test-rmse:1.122285+0.092986
## [71] train-rmse:0.685643+0.011073 test-rmse:1.123154+0.093671
## [72] train-rmse:0.681829+0.010515 test-rmse:1.123738+0.094538
## [73] train-rmse:0.675239+0.013011 test-rmse:1.119279+0.095024
## [74] train-rmse:0.669884+0.014312 test-rmse:1.119611+0.091837
## [75] train-rmse:0.664557+0.014737 test-rmse:1.110242+0.089834
## [76] train-rmse:0.657124+0.012618 test-rmse:1.106655+0.091170
## [77] train-rmse:0.653596+0.011799 test-rmse:1.106083+0.089823
## [78] train-rmse:0.648497+0.013236 test-rmse:1.104557+0.089833
## [79] train-rmse:0.644644+0.013874 test-rmse:1.102149+0.091242
## [80] train-rmse:0.639394+0.014503 test-rmse:1.099765+0.093683
## [81] train-rmse:0.636618+0.014703 test-rmse:1.100051+0.094781
## [82] train-rmse:0.633974+0.015131 test-rmse:1.100023+0.095213
## [83] train-rmse:0.630039+0.014977 test-rmse:1.097221+0.096064
## [84] train-rmse:0.625225+0.015880 test-rmse:1.092101+0.089163
## [85] train-rmse:0.622306+0.016324 test-rmse:1.090475+0.087131
## [86] train-rmse:0.619378+0.015849 test-rmse:1.089792+0.087882
## [87] train-rmse:0.615283+0.015520 test-rmse:1.088426+0.088721
## [88] train-rmse:0.610116+0.014203 test-rmse:1.085193+0.090321
## [89] train-rmse:0.606470+0.013495 test-rmse:1.083396+0.089041
## [90] train-rmse:0.603465+0.014722 test-rmse:1.081525+0.087903
## [91] train-rmse:0.598855+0.014720 test-rmse:1.079340+0.089618
## [92] train-rmse:0.595070+0.013519 test-rmse:1.078882+0.089423
## [93] train-rmse:0.591043+0.011589 test-rmse:1.075463+0.089522
## [94] train-rmse:0.588236+0.011169 test-rmse:1.074087+0.090148
## [95] train-rmse:0.584973+0.010096 test-rmse:1.074700+0.087764
## [96] train-rmse:0.580252+0.008251 test-rmse:1.071044+0.087650
## [97] train-rmse:0.577306+0.008030 test-rmse:1.068821+0.088070
## [98] train-rmse:0.573545+0.007600 test-rmse:1.065043+0.089798
## [99] train-rmse:0.570377+0.008028 test-rmse:1.064138+0.090316
## [100] train-rmse:0.566304+0.009134 test-rmse:1.063642+0.090791
## [101] train-rmse:0.563226+0.009983 test-rmse:1.061445+0.089031
## [102] train-rmse:0.560372+0.011583 test-rmse:1.060815+0.088721
## [103] train-rmse:0.558915+0.011538 test-rmse:1.059712+0.088923
## [104] train-rmse:0.554473+0.010420 test-rmse:1.057993+0.089142
## [105] train-rmse:0.551988+0.010027 test-rmse:1.057301+0.089743
## [106] train-rmse:0.548088+0.009352 test-rmse:1.056751+0.089928
## [107] train-rmse:0.545378+0.009568 test-rmse:1.056323+0.089038
## [108] train-rmse:0.542422+0.011100 test-rmse:1.055792+0.089581
## [109] train-rmse:0.538814+0.011666 test-rmse:1.055305+0.089214
## [110] train-rmse:0.536019+0.012095 test-rmse:1.053970+0.090149
## [111] train-rmse:0.531131+0.010420 test-rmse:1.054976+0.087285
## [112] train-rmse:0.528407+0.010950 test-rmse:1.052720+0.087522
## [113] train-rmse:0.526220+0.010462 test-rmse:1.050248+0.088839
## [114] train-rmse:0.522477+0.010580 test-rmse:1.047945+0.090070
## [115] train-rmse:0.520161+0.010755 test-rmse:1.049079+0.091305
## [116] train-rmse:0.516031+0.012154 test-rmse:1.046531+0.091836
## [117] train-rmse:0.513508+0.012522 test-rmse:1.047855+0.091593
## [118] train-rmse:0.510832+0.013380 test-rmse:1.046645+0.090252
## [119] train-rmse:0.507953+0.013242 test-rmse:1.047975+0.090643
## [120] train-rmse:0.505244+0.013772 test-rmse:1.047685+0.090962
## [121] train-rmse:0.503003+0.012642 test-rmse:1.046099+0.091132
## [122] train-rmse:0.498219+0.009097 test-rmse:1.046139+0.091385
## [123] train-rmse:0.495625+0.007931 test-rmse:1.044903+0.091156
## [124] train-rmse:0.492802+0.006135 test-rmse:1.047728+0.090760
## [125] train-rmse:0.490357+0.005843 test-rmse:1.048268+0.091402
## [126] train-rmse:0.488035+0.006516 test-rmse:1.047249+0.090171
## [127] train-rmse:0.486909+0.006634 test-rmse:1.047139+0.090273
## [128] train-rmse:0.483447+0.005408 test-rmse:1.042244+0.091284
## [129] train-rmse:0.480526+0.004922 test-rmse:1.042217+0.091002
## [130] train-rmse:0.478050+0.004648 test-rmse:1.041434+0.090415
## [131] train-rmse:0.476158+0.004142 test-rmse:1.042275+0.090903
## [132] train-rmse:0.474216+0.004169 test-rmse:1.041110+0.090886
## [133] train-rmse:0.472292+0.004306 test-rmse:1.039173+0.090916
## [134] train-rmse:0.470841+0.004738 test-rmse:1.037889+0.092103
## [135] train-rmse:0.467492+0.005533 test-rmse:1.035814+0.090102
## [136] train-rmse:0.464650+0.005324 test-rmse:1.036369+0.090973
## [137] train-rmse:0.462893+0.005879 test-rmse:1.034580+0.089774
## [138] train-rmse:0.460144+0.004968 test-rmse:1.034392+0.089892
## [139] train-rmse:0.457893+0.004622 test-rmse:1.032983+0.089591
## [140] train-rmse:0.455592+0.004791 test-rmse:1.032411+0.090474
## [141] train-rmse:0.453575+0.005325 test-rmse:1.033382+0.090217
## [142] train-rmse:0.450667+0.006050 test-rmse:1.030813+0.086168
## [143] train-rmse:0.447757+0.005113 test-rmse:1.030111+0.085442
## [144] train-rmse:0.444649+0.005158 test-rmse:1.028962+0.086054
## [145] train-rmse:0.442295+0.005030 test-rmse:1.027547+0.085366
## [146] train-rmse:0.439881+0.005046 test-rmse:1.024776+0.083942
## [147] train-rmse:0.437484+0.005057 test-rmse:1.023248+0.084244
## [148] train-rmse:0.435281+0.004609 test-rmse:1.024259+0.084530
## [149] train-rmse:0.434071+0.004598 test-rmse:1.023962+0.084810
## [150] train-rmse:0.430792+0.005171 test-rmse:1.023714+0.082900
## [151] train-rmse:0.428251+0.005473 test-rmse:1.018158+0.082875
## [152] train-rmse:0.425916+0.005619 test-rmse:1.017358+0.083237
## [153] train-rmse:0.424639+0.005875 test-rmse:1.016172+0.083064
## [154] train-rmse:0.422999+0.006214 test-rmse:1.015005+0.083002
## [155] train-rmse:0.421804+0.006742 test-rmse:1.015044+0.082933
## [156] train-rmse:0.419179+0.005308 test-rmse:1.014951+0.082559
## [157] train-rmse:0.416542+0.005006 test-rmse:1.014580+0.083748
## [158] train-rmse:0.414766+0.004716 test-rmse:1.013724+0.085715
## [159] train-rmse:0.412468+0.004701 test-rmse:1.012200+0.085779
## [160] train-rmse:0.410976+0.004426 test-rmse:1.012216+0.085420
## [161] train-rmse:0.409357+0.003655 test-rmse:1.011959+0.086254
## [162] train-rmse:0.407300+0.003956 test-rmse:1.011604+0.086477
## [163] train-rmse:0.405280+0.004976 test-rmse:1.011877+0.086201
## [164] train-rmse:0.403320+0.005280 test-rmse:1.011225+0.086920
## [165] train-rmse:0.401262+0.005130 test-rmse:1.010520+0.086047
## [166] train-rmse:0.399847+0.005128 test-rmse:1.010520+0.086352
## [167] train-rmse:0.397421+0.005411 test-rmse:1.012315+0.086435
## [168] train-rmse:0.395841+0.005043 test-rmse:1.013934+0.086230
## [169] train-rmse:0.393999+0.005901 test-rmse:1.013673+0.086017
## [170] train-rmse:0.392433+0.005646 test-rmse:1.011953+0.086519
## [171] train-rmse:0.391001+0.005512 test-rmse:1.012757+0.086056
## [172] train-rmse:0.389565+0.005706 test-rmse:1.012395+0.085268
## Stopping. Best iteration:
## [166] train-rmse:0.399847+0.005128 test-rmse:1.010520+0.086352
##
## 79
## [1] train-rmse:67.089722+0.067155 test-rmse:67.090222+0.426122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:45.720995+0.045347 test-rmse:45.720976+0.446533
## [3] train-rmse:31.177771+0.030287 test-rmse:31.177018+0.459422
## [4] train-rmse:21.288879+0.019834 test-rmse:21.287091+0.466458
## [5] train-rmse:14.577831+0.012778 test-rmse:14.574641+0.468189
## [6] train-rmse:10.033350+0.007985 test-rmse:10.019403+0.432742
## [7] train-rmse:6.958338+0.006971 test-rmse:6.938001+0.398116
## [8] train-rmse:4.877566+0.006298 test-rmse:4.864121+0.355916
## [9] train-rmse:3.490456+0.008463 test-rmse:3.493121+0.302810
## [10] train-rmse:2.570018+0.009349 test-rmse:2.601317+0.263719
## [11] train-rmse:1.983004+0.014944 test-rmse:2.039573+0.217836
## [12] train-rmse:1.605373+0.019850 test-rmse:1.717158+0.184751
## [13] train-rmse:1.378243+0.012586 test-rmse:1.543584+0.141911
## [14] train-rmse:1.243240+0.014777 test-rmse:1.442030+0.129568
## [15] train-rmse:1.156878+0.011653 test-rmse:1.385019+0.121799
## [16] train-rmse:1.096515+0.014297 test-rmse:1.356316+0.114353
## [17] train-rmse:1.048629+0.009632 test-rmse:1.329313+0.106042
## [18] train-rmse:1.013323+0.007005 test-rmse:1.314982+0.107757
## [19] train-rmse:0.987291+0.005681 test-rmse:1.302156+0.109527
## [20] train-rmse:0.961461+0.006620 test-rmse:1.280936+0.112325
## [21] train-rmse:0.938157+0.005347 test-rmse:1.268361+0.099527
## [22] train-rmse:0.915478+0.006057 test-rmse:1.254266+0.096260
## [23] train-rmse:0.892866+0.006967 test-rmse:1.235327+0.088898
## [24] train-rmse:0.873414+0.008587 test-rmse:1.230346+0.089670
## [25] train-rmse:0.847432+0.007787 test-rmse:1.212680+0.086580
## [26] train-rmse:0.829949+0.005155 test-rmse:1.200854+0.088529
## [27] train-rmse:0.812621+0.005254 test-rmse:1.187869+0.084004
## [28] train-rmse:0.793851+0.007396 test-rmse:1.184283+0.078247
## [29] train-rmse:0.778049+0.007604 test-rmse:1.171812+0.076398
## [30] train-rmse:0.762417+0.006708 test-rmse:1.162485+0.079924
## [31] train-rmse:0.747558+0.005884 test-rmse:1.150395+0.073638
## [32] train-rmse:0.728644+0.007688 test-rmse:1.140578+0.077568
## [33] train-rmse:0.718190+0.007064 test-rmse:1.136365+0.079276
## [34] train-rmse:0.708911+0.008147 test-rmse:1.132377+0.075984
## [35] train-rmse:0.697488+0.011424 test-rmse:1.120901+0.075705
## [36] train-rmse:0.687205+0.010680 test-rmse:1.117676+0.071153
## [37] train-rmse:0.676052+0.011412 test-rmse:1.119964+0.066995
## [38] train-rmse:0.665553+0.011264 test-rmse:1.110253+0.066127
## [39] train-rmse:0.655460+0.010218 test-rmse:1.104836+0.065845
## [40] train-rmse:0.643975+0.010712 test-rmse:1.096191+0.067086
## [41] train-rmse:0.634813+0.010890 test-rmse:1.091468+0.070804
## [42] train-rmse:0.623594+0.011306 test-rmse:1.082792+0.059628
## [43] train-rmse:0.615242+0.010503 test-rmse:1.083395+0.058907
## [44] train-rmse:0.607082+0.010515 test-rmse:1.081752+0.062839
## [45] train-rmse:0.599239+0.013639 test-rmse:1.075658+0.063968
## [46] train-rmse:0.591033+0.009843 test-rmse:1.072939+0.058745
## [47] train-rmse:0.582987+0.008845 test-rmse:1.071689+0.057676
## [48] train-rmse:0.575150+0.009110 test-rmse:1.070819+0.058989
## [49] train-rmse:0.570804+0.009141 test-rmse:1.067875+0.056947
## [50] train-rmse:0.561607+0.011220 test-rmse:1.070190+0.055859
## [51] train-rmse:0.550354+0.010054 test-rmse:1.073555+0.061348
## [52] train-rmse:0.542371+0.009506 test-rmse:1.071443+0.060377
## [53] train-rmse:0.534986+0.009593 test-rmse:1.067056+0.058248
## [54] train-rmse:0.527658+0.010489 test-rmse:1.066448+0.052703
## [55] train-rmse:0.520015+0.008472 test-rmse:1.066050+0.052033
## [56] train-rmse:0.512345+0.006834 test-rmse:1.057877+0.045071
## [57] train-rmse:0.503860+0.007244 test-rmse:1.053517+0.046755
## [58] train-rmse:0.496350+0.007513 test-rmse:1.051809+0.045817
## [59] train-rmse:0.490005+0.007402 test-rmse:1.048732+0.044130
## [60] train-rmse:0.483549+0.010170 test-rmse:1.051268+0.049120
## [61] train-rmse:0.477833+0.008955 test-rmse:1.049583+0.048505
## [62] train-rmse:0.472420+0.009854 test-rmse:1.049248+0.047036
## [63] train-rmse:0.467185+0.010625 test-rmse:1.049530+0.047627
## [64] train-rmse:0.463522+0.010143 test-rmse:1.048648+0.047274
## [65] train-rmse:0.458097+0.011121 test-rmse:1.045774+0.046355
## [66] train-rmse:0.453061+0.010086 test-rmse:1.043331+0.044206
## [67] train-rmse:0.446750+0.007077 test-rmse:1.042582+0.043573
## [68] train-rmse:0.440814+0.007699 test-rmse:1.040355+0.044781
## [69] train-rmse:0.433314+0.008466 test-rmse:1.040283+0.043035
## [70] train-rmse:0.424903+0.009937 test-rmse:1.040819+0.046852
## [71] train-rmse:0.419872+0.008894 test-rmse:1.039487+0.044516
## [72] train-rmse:0.414878+0.010479 test-rmse:1.038972+0.043282
## [73] train-rmse:0.409205+0.007960 test-rmse:1.040400+0.043087
## [74] train-rmse:0.404770+0.010444 test-rmse:1.040498+0.043367
## [75] train-rmse:0.398828+0.010901 test-rmse:1.040846+0.040784
## [76] train-rmse:0.393285+0.011220 test-rmse:1.041349+0.043285
## [77] train-rmse:0.387509+0.013459 test-rmse:1.041531+0.044517
## [78] train-rmse:0.381919+0.012771 test-rmse:1.038287+0.045380
## [79] train-rmse:0.378489+0.012778 test-rmse:1.037101+0.044753
## [80] train-rmse:0.375285+0.012287 test-rmse:1.036898+0.043731
## [81] train-rmse:0.369743+0.012470 test-rmse:1.033973+0.041294
## [82] train-rmse:0.365684+0.012543 test-rmse:1.030732+0.039911
## [83] train-rmse:0.362040+0.011570 test-rmse:1.030173+0.040157
## [84] train-rmse:0.358501+0.010798 test-rmse:1.030518+0.040588
## [85] train-rmse:0.354259+0.011305 test-rmse:1.026861+0.041297
## [86] train-rmse:0.349861+0.011458 test-rmse:1.027640+0.039925
## [87] train-rmse:0.347543+0.010943 test-rmse:1.027021+0.040395
## [88] train-rmse:0.343805+0.012107 test-rmse:1.023347+0.037625
## [89] train-rmse:0.340473+0.012152 test-rmse:1.022638+0.038025
## [90] train-rmse:0.335395+0.011463 test-rmse:1.022528+0.036481
## [91] train-rmse:0.330161+0.010115 test-rmse:1.022579+0.035044
## [92] train-rmse:0.326060+0.007922 test-rmse:1.023146+0.034720
## [93] train-rmse:0.323116+0.008492 test-rmse:1.022458+0.033632
## [94] train-rmse:0.320255+0.008947 test-rmse:1.021556+0.034690
## [95] train-rmse:0.316761+0.007761 test-rmse:1.022527+0.035287
## [96] train-rmse:0.314122+0.007517 test-rmse:1.021714+0.033664
## [97] train-rmse:0.311361+0.008063 test-rmse:1.021430+0.032784
## [98] train-rmse:0.305525+0.009790 test-rmse:1.020884+0.033017
## [99] train-rmse:0.302845+0.009701 test-rmse:1.021763+0.034259
## [100] train-rmse:0.299774+0.010728 test-rmse:1.021411+0.032726
## [101] train-rmse:0.297342+0.011244 test-rmse:1.019748+0.033893
## [102] train-rmse:0.294561+0.010832 test-rmse:1.021899+0.035113
## [103] train-rmse:0.290426+0.011654 test-rmse:1.020375+0.035899
## [104] train-rmse:0.288807+0.011676 test-rmse:1.021152+0.036242
## [105] train-rmse:0.286299+0.010960 test-rmse:1.019068+0.037980
## [106] train-rmse:0.283225+0.010605 test-rmse:1.019792+0.036509
## [107] train-rmse:0.280606+0.011439 test-rmse:1.019614+0.036872
## [108] train-rmse:0.276950+0.013238 test-rmse:1.019821+0.036873
## [109] train-rmse:0.272663+0.012709 test-rmse:1.019210+0.036423
## [110] train-rmse:0.270315+0.012345 test-rmse:1.018986+0.035290
## [111] train-rmse:0.267776+0.012646 test-rmse:1.019998+0.036739
## [112] train-rmse:0.265206+0.012859 test-rmse:1.018748+0.035561
## [113] train-rmse:0.262922+0.012442 test-rmse:1.018486+0.036028
## [114] train-rmse:0.258486+0.013099 test-rmse:1.016109+0.036321
## [115] train-rmse:0.255313+0.013215 test-rmse:1.016655+0.037733
## [116] train-rmse:0.253445+0.012805 test-rmse:1.016207+0.037185
## [117] train-rmse:0.250038+0.011590 test-rmse:1.015969+0.038141
## [118] train-rmse:0.247560+0.011278 test-rmse:1.015887+0.038486
## [119] train-rmse:0.245002+0.010387 test-rmse:1.014430+0.039771
## [120] train-rmse:0.242515+0.009914 test-rmse:1.013139+0.039937
## [121] train-rmse:0.240322+0.008917 test-rmse:1.012867+0.039912
## [122] train-rmse:0.238060+0.009160 test-rmse:1.010202+0.043261
## [123] train-rmse:0.235970+0.009191 test-rmse:1.009213+0.044334
## [124] train-rmse:0.233422+0.009313 test-rmse:1.007799+0.044537
## [125] train-rmse:0.230901+0.008935 test-rmse:1.007340+0.043624
## [126] train-rmse:0.227700+0.009703 test-rmse:1.005869+0.045534
## [127] train-rmse:0.226051+0.009816 test-rmse:1.006058+0.045981
## [128] train-rmse:0.224316+0.009632 test-rmse:1.005579+0.046493
## [129] train-rmse:0.222636+0.010000 test-rmse:1.004700+0.046430
## [130] train-rmse:0.219738+0.010332 test-rmse:1.003977+0.047121
## [131] train-rmse:0.217457+0.010449 test-rmse:1.003630+0.044884
## [132] train-rmse:0.215235+0.010954 test-rmse:1.003873+0.046200
## [133] train-rmse:0.212324+0.011867 test-rmse:1.003565+0.046127
## [134] train-rmse:0.210504+0.011584 test-rmse:1.004178+0.047192
## [135] train-rmse:0.207833+0.012532 test-rmse:1.002681+0.046130
## [136] train-rmse:0.205332+0.013630 test-rmse:1.002328+0.044888
## [137] train-rmse:0.203175+0.013867 test-rmse:1.002070+0.044738
## [138] train-rmse:0.201722+0.013761 test-rmse:1.002076+0.045134
## [139] train-rmse:0.198556+0.012386 test-rmse:1.002441+0.046254
## [140] train-rmse:0.196918+0.011789 test-rmse:1.002156+0.046390
## [141] train-rmse:0.194627+0.011625 test-rmse:1.002440+0.046779
## [142] train-rmse:0.192388+0.011870 test-rmse:1.001857+0.046967
## [143] train-rmse:0.190415+0.011542 test-rmse:1.000497+0.046484
## [144] train-rmse:0.188774+0.011400 test-rmse:1.000185+0.046308
## [145] train-rmse:0.186427+0.011416 test-rmse:0.999967+0.046827
## [146] train-rmse:0.184977+0.011377 test-rmse:1.001287+0.047367
## [147] train-rmse:0.183364+0.011568 test-rmse:1.000360+0.048623
## [148] train-rmse:0.181268+0.011322 test-rmse:0.999693+0.047906
## [149] train-rmse:0.179814+0.011600 test-rmse:1.000200+0.048710
## [150] train-rmse:0.177711+0.010514 test-rmse:0.999379+0.049410
## [151] train-rmse:0.175822+0.010326 test-rmse:0.998310+0.048860
## [152] train-rmse:0.174220+0.010669 test-rmse:0.998687+0.048343
## [153] train-rmse:0.172620+0.010393 test-rmse:0.999129+0.048508
## [154] train-rmse:0.171493+0.010286 test-rmse:0.999546+0.048483
## [155] train-rmse:0.170249+0.010799 test-rmse:0.999633+0.048878
## [156] train-rmse:0.168969+0.010567 test-rmse:0.999503+0.049439
## [157] train-rmse:0.167268+0.010224 test-rmse:0.999624+0.050190
## Stopping. Best iteration:
## [151] train-rmse:0.175822+0.010326 test-rmse:0.998310+0.048860
##
## 80
## [1] train-rmse:67.089722+0.067155 test-rmse:67.090222+0.426122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:45.720995+0.045347 test-rmse:45.720976+0.446533
## [3] train-rmse:31.177771+0.030287 test-rmse:31.177018+0.459422
## [4] train-rmse:21.288879+0.019834 test-rmse:21.287091+0.466458
## [5] train-rmse:14.577831+0.012778 test-rmse:14.574641+0.468189
## [6] train-rmse:10.033350+0.007985 test-rmse:10.019403+0.432742
## [7] train-rmse:6.956906+0.007680 test-rmse:6.934376+0.398368
## [8] train-rmse:4.872694+0.007449 test-rmse:4.858835+0.348119
## [9] train-rmse:3.470061+0.006883 test-rmse:3.473220+0.299592
## [10] train-rmse:2.536244+0.006255 test-rmse:2.576310+0.250312
## [11] train-rmse:1.915540+0.007966 test-rmse:2.004242+0.230139
## [12] train-rmse:1.523008+0.011436 test-rmse:1.684663+0.202591
## [13] train-rmse:1.277627+0.018611 test-rmse:1.478431+0.185683
## [14] train-rmse:1.116616+0.009198 test-rmse:1.372157+0.158453
## [15] train-rmse:1.020066+0.005357 test-rmse:1.318178+0.151066
## [16] train-rmse:0.952997+0.007605 test-rmse:1.276803+0.143651
## [17] train-rmse:0.906487+0.007915 test-rmse:1.241829+0.139844
## [18] train-rmse:0.866357+0.007435 test-rmse:1.216814+0.138375
## [19] train-rmse:0.823058+0.009540 test-rmse:1.192293+0.126413
## [20] train-rmse:0.794079+0.011791 test-rmse:1.171485+0.128949
## [21] train-rmse:0.767563+0.013932 test-rmse:1.162257+0.127317
## [22] train-rmse:0.745397+0.012557 test-rmse:1.147047+0.122275
## [23] train-rmse:0.716064+0.009671 test-rmse:1.138226+0.125883
## [24] train-rmse:0.689699+0.014119 test-rmse:1.128800+0.125474
## [25] train-rmse:0.671358+0.013642 test-rmse:1.123582+0.123098
## [26] train-rmse:0.650340+0.013934 test-rmse:1.108509+0.118763
## [27] train-rmse:0.633929+0.012353 test-rmse:1.103678+0.121629
## [28] train-rmse:0.613539+0.010762 test-rmse:1.087659+0.113910
## [29] train-rmse:0.599436+0.015197 test-rmse:1.084613+0.113490
## [30] train-rmse:0.580552+0.011276 test-rmse:1.081559+0.111294
## [31] train-rmse:0.563293+0.016187 test-rmse:1.073960+0.114482
## [32] train-rmse:0.548889+0.015232 test-rmse:1.066204+0.112507
## [33] train-rmse:0.538207+0.016614 test-rmse:1.064426+0.111093
## [34] train-rmse:0.523241+0.017142 test-rmse:1.061011+0.113211
## [35] train-rmse:0.509455+0.013928 test-rmse:1.052798+0.110273
## [36] train-rmse:0.496333+0.017387 test-rmse:1.049917+0.106823
## [37] train-rmse:0.481142+0.017796 test-rmse:1.051090+0.107123
## [38] train-rmse:0.467526+0.017820 test-rmse:1.049128+0.109862
## [39] train-rmse:0.460105+0.018802 test-rmse:1.043863+0.107420
## [40] train-rmse:0.450015+0.017715 test-rmse:1.042174+0.112168
## [41] train-rmse:0.437499+0.014596 test-rmse:1.041963+0.109711
## [42] train-rmse:0.428084+0.012049 test-rmse:1.039659+0.106040
## [43] train-rmse:0.421240+0.012268 test-rmse:1.035800+0.103347
## [44] train-rmse:0.413745+0.012974 test-rmse:1.032662+0.105035
## [45] train-rmse:0.406211+0.012264 test-rmse:1.029006+0.104973
## [46] train-rmse:0.397959+0.012351 test-rmse:1.030096+0.108316
## [47] train-rmse:0.387438+0.010594 test-rmse:1.029119+0.109283
## [48] train-rmse:0.379576+0.009851 test-rmse:1.029674+0.111040
## [49] train-rmse:0.372512+0.011261 test-rmse:1.026187+0.111051
## [50] train-rmse:0.362431+0.013090 test-rmse:1.022809+0.107627
## [51] train-rmse:0.353614+0.016554 test-rmse:1.023423+0.107849
## [52] train-rmse:0.347168+0.015210 test-rmse:1.019415+0.104823
## [53] train-rmse:0.340412+0.013127 test-rmse:1.016040+0.103817
## [54] train-rmse:0.332437+0.011228 test-rmse:1.012835+0.101892
## [55] train-rmse:0.326642+0.010561 test-rmse:1.008421+0.101820
## [56] train-rmse:0.321813+0.010319 test-rmse:1.005549+0.102796
## [57] train-rmse:0.315600+0.010078 test-rmse:1.007709+0.103728
## [58] train-rmse:0.310795+0.012421 test-rmse:1.007622+0.103939
## [59] train-rmse:0.304346+0.011681 test-rmse:1.005196+0.102405
## [60] train-rmse:0.297620+0.012022 test-rmse:1.003783+0.101053
## [61] train-rmse:0.292933+0.011351 test-rmse:1.004030+0.098981
## [62] train-rmse:0.287287+0.009464 test-rmse:1.001175+0.098498
## [63] train-rmse:0.282982+0.009734 test-rmse:1.002573+0.099139
## [64] train-rmse:0.275170+0.009098 test-rmse:1.001157+0.099718
## [65] train-rmse:0.269767+0.010164 test-rmse:1.000761+0.099760
## [66] train-rmse:0.266844+0.009838 test-rmse:0.998719+0.100444
## [67] train-rmse:0.263086+0.009851 test-rmse:0.997860+0.101307
## [68] train-rmse:0.258617+0.011796 test-rmse:0.997753+0.099765
## [69] train-rmse:0.253306+0.011185 test-rmse:0.998878+0.098948
## [70] train-rmse:0.250082+0.012212 test-rmse:0.998238+0.099704
## [71] train-rmse:0.245170+0.012182 test-rmse:0.997912+0.099890
## [72] train-rmse:0.239390+0.011235 test-rmse:0.997557+0.100097
## [73] train-rmse:0.232674+0.010275 test-rmse:0.997575+0.100935
## [74] train-rmse:0.228959+0.010916 test-rmse:0.996451+0.099538
## [75] train-rmse:0.225378+0.011774 test-rmse:0.996794+0.100509
## [76] train-rmse:0.223161+0.011250 test-rmse:0.995358+0.101477
## [77] train-rmse:0.219252+0.010478 test-rmse:0.993581+0.101900
## [78] train-rmse:0.214842+0.011953 test-rmse:0.991804+0.102585
## [79] train-rmse:0.211119+0.013828 test-rmse:0.991408+0.102134
## [80] train-rmse:0.206116+0.014963 test-rmse:0.992010+0.103446
## [81] train-rmse:0.201936+0.015371 test-rmse:0.990555+0.103897
## [82] train-rmse:0.197373+0.015740 test-rmse:0.992538+0.104442
## [83] train-rmse:0.193040+0.015941 test-rmse:0.992060+0.104332
## [84] train-rmse:0.190183+0.015459 test-rmse:0.990611+0.105028
## [85] train-rmse:0.186215+0.015256 test-rmse:0.991446+0.105204
## [86] train-rmse:0.182848+0.016572 test-rmse:0.991072+0.104160
## [87] train-rmse:0.180259+0.015561 test-rmse:0.990036+0.104072
## [88] train-rmse:0.176741+0.016061 test-rmse:0.989997+0.104096
## [89] train-rmse:0.173491+0.014900 test-rmse:0.989473+0.103842
## [90] train-rmse:0.170741+0.013336 test-rmse:0.989815+0.103167
## [91] train-rmse:0.167623+0.013757 test-rmse:0.990749+0.103063
## [92] train-rmse:0.165567+0.013787 test-rmse:0.990327+0.103426
## [93] train-rmse:0.163603+0.013916 test-rmse:0.989393+0.102838
## [94] train-rmse:0.160820+0.014381 test-rmse:0.989583+0.102723
## [95] train-rmse:0.158273+0.013511 test-rmse:0.989214+0.103594
## [96] train-rmse:0.155806+0.013274 test-rmse:0.989096+0.103789
## [97] train-rmse:0.152438+0.012603 test-rmse:0.988312+0.104589
## [98] train-rmse:0.149402+0.012266 test-rmse:0.987211+0.105118
## [99] train-rmse:0.146172+0.012245 test-rmse:0.987487+0.105425
## [100] train-rmse:0.143426+0.012775 test-rmse:0.987538+0.105934
## [101] train-rmse:0.139545+0.012514 test-rmse:0.986602+0.105910
## [102] train-rmse:0.137441+0.012083 test-rmse:0.987620+0.105310
## [103] train-rmse:0.136018+0.012057 test-rmse:0.987601+0.105175
## [104] train-rmse:0.133491+0.011383 test-rmse:0.987779+0.105221
## [105] train-rmse:0.131804+0.011523 test-rmse:0.986916+0.104670
## [106] train-rmse:0.129218+0.011037 test-rmse:0.985951+0.105238
## [107] train-rmse:0.127478+0.010853 test-rmse:0.986051+0.105517
## [108] train-rmse:0.125741+0.010656 test-rmse:0.985447+0.105557
## [109] train-rmse:0.122389+0.010240 test-rmse:0.984915+0.104893
## [110] train-rmse:0.120697+0.009718 test-rmse:0.984833+0.105023
## [111] train-rmse:0.118600+0.009544 test-rmse:0.983963+0.105664
## [112] train-rmse:0.116699+0.009009 test-rmse:0.983164+0.105158
## [113] train-rmse:0.114976+0.009021 test-rmse:0.982418+0.104456
## [114] train-rmse:0.113000+0.009078 test-rmse:0.982274+0.103610
## [115] train-rmse:0.110841+0.008901 test-rmse:0.982522+0.103167
## [116] train-rmse:0.108397+0.007685 test-rmse:0.983378+0.103569
## [117] train-rmse:0.106646+0.007849 test-rmse:0.983455+0.104021
## [118] train-rmse:0.104197+0.008665 test-rmse:0.983395+0.103882
## [119] train-rmse:0.102206+0.008606 test-rmse:0.983466+0.104231
## [120] train-rmse:0.100153+0.008380 test-rmse:0.983040+0.104789
## Stopping. Best iteration:
## [114] train-rmse:0.113000+0.009078 test-rmse:0.982274+0.103610
##
## 81
## [1] train-rmse:67.089722+0.067155 test-rmse:67.090222+0.426122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:45.720995+0.045347 test-rmse:45.720976+0.446533
## [3] train-rmse:31.177771+0.030287 test-rmse:31.177018+0.459422
## [4] train-rmse:21.288879+0.019834 test-rmse:21.287091+0.466458
## [5] train-rmse:14.577831+0.012778 test-rmse:14.574641+0.468189
## [6] train-rmse:10.033350+0.007985 test-rmse:10.019403+0.432742
## [7] train-rmse:6.956287+0.007933 test-rmse:6.928342+0.392669
## [8] train-rmse:4.869886+0.007605 test-rmse:4.849505+0.342202
## [9] train-rmse:3.462509+0.006692 test-rmse:3.458477+0.295323
## [10] train-rmse:2.515476+0.007873 test-rmse:2.548650+0.250993
## [11] train-rmse:1.875970+0.013144 test-rmse:1.954929+0.217260
## [12] train-rmse:1.459934+0.013508 test-rmse:1.618167+0.186677
## [13] train-rmse:1.200365+0.015045 test-rmse:1.423315+0.175581
## [14] train-rmse:1.033313+0.024410 test-rmse:1.305405+0.164056
## [15] train-rmse:0.915805+0.021380 test-rmse:1.246094+0.149270
## [16] train-rmse:0.842514+0.025823 test-rmse:1.204662+0.144297
## [17] train-rmse:0.784503+0.027133 test-rmse:1.179407+0.138032
## [18] train-rmse:0.738357+0.017792 test-rmse:1.141910+0.127976
## [19] train-rmse:0.699127+0.025338 test-rmse:1.118456+0.124614
## [20] train-rmse:0.665714+0.024472 test-rmse:1.106991+0.116689
## [21] train-rmse:0.639457+0.026286 test-rmse:1.100314+0.118245
## [22] train-rmse:0.613614+0.027377 test-rmse:1.090065+0.114892
## [23] train-rmse:0.589479+0.024453 test-rmse:1.081126+0.116300
## [24] train-rmse:0.564804+0.019093 test-rmse:1.071142+0.118434
## [25] train-rmse:0.549740+0.018065 test-rmse:1.063216+0.117917
## [26] train-rmse:0.527894+0.019154 test-rmse:1.051483+0.114410
## [27] train-rmse:0.514392+0.020640 test-rmse:1.046118+0.114377
## [28] train-rmse:0.493985+0.023146 test-rmse:1.035025+0.103106
## [29] train-rmse:0.478914+0.021962 test-rmse:1.031011+0.102450
## [30] train-rmse:0.465420+0.021302 test-rmse:1.030964+0.110415
## [31] train-rmse:0.453726+0.022778 test-rmse:1.028855+0.108367
## [32] train-rmse:0.436057+0.019812 test-rmse:1.016645+0.104890
## [33] train-rmse:0.423081+0.021640 test-rmse:1.015287+0.100308
## [34] train-rmse:0.411886+0.025068 test-rmse:1.010778+0.098583
## [35] train-rmse:0.394388+0.022186 test-rmse:1.002986+0.094754
## [36] train-rmse:0.379713+0.021448 test-rmse:1.001716+0.094934
## [37] train-rmse:0.368488+0.020075 test-rmse:1.002049+0.093945
## [38] train-rmse:0.360448+0.019309 test-rmse:1.000359+0.093266
## [39] train-rmse:0.348100+0.017689 test-rmse:1.000034+0.095049
## [40] train-rmse:0.336232+0.019795 test-rmse:0.998471+0.094074
## [41] train-rmse:0.324132+0.016384 test-rmse:0.994577+0.093983
## [42] train-rmse:0.312723+0.012564 test-rmse:0.995130+0.090275
## [43] train-rmse:0.302672+0.015240 test-rmse:0.995199+0.092293
## [44] train-rmse:0.293621+0.016198 test-rmse:0.990665+0.093443
## [45] train-rmse:0.285876+0.017651 test-rmse:0.987182+0.091207
## [46] train-rmse:0.279311+0.016977 test-rmse:0.982211+0.091787
## [47] train-rmse:0.272470+0.016869 test-rmse:0.983680+0.092725
## [48] train-rmse:0.260527+0.017400 test-rmse:0.983156+0.091477
## [49] train-rmse:0.254399+0.016140 test-rmse:0.982817+0.091475
## [50] train-rmse:0.246937+0.014072 test-rmse:0.980851+0.092321
## [51] train-rmse:0.241149+0.012287 test-rmse:0.981009+0.091892
## [52] train-rmse:0.231355+0.012657 test-rmse:0.978459+0.092378
## [53] train-rmse:0.224220+0.015434 test-rmse:0.977695+0.092143
## [54] train-rmse:0.217320+0.013000 test-rmse:0.973743+0.093266
## [55] train-rmse:0.212106+0.012908 test-rmse:0.972848+0.092897
## [56] train-rmse:0.207660+0.013040 test-rmse:0.972600+0.092268
## [57] train-rmse:0.200845+0.013372 test-rmse:0.972285+0.094202
## [58] train-rmse:0.196084+0.013073 test-rmse:0.972436+0.094881
## [59] train-rmse:0.187903+0.015106 test-rmse:0.972234+0.093727
## [60] train-rmse:0.182731+0.015776 test-rmse:0.971376+0.093096
## [61] train-rmse:0.178853+0.016017 test-rmse:0.971463+0.093416
## [62] train-rmse:0.172521+0.014070 test-rmse:0.972798+0.094657
## [63] train-rmse:0.167674+0.014099 test-rmse:0.969996+0.094277
## [64] train-rmse:0.164553+0.013714 test-rmse:0.970190+0.094745
## [65] train-rmse:0.160690+0.014260 test-rmse:0.968263+0.096309
## [66] train-rmse:0.156842+0.014719 test-rmse:0.967872+0.097124
## [67] train-rmse:0.153822+0.015277 test-rmse:0.967724+0.097592
## [68] train-rmse:0.151073+0.015498 test-rmse:0.965345+0.098959
## [69] train-rmse:0.145202+0.014280 test-rmse:0.964776+0.099020
## [70] train-rmse:0.140617+0.012677 test-rmse:0.964346+0.099886
## [71] train-rmse:0.138330+0.012741 test-rmse:0.963747+0.099403
## [72] train-rmse:0.133610+0.011255 test-rmse:0.964483+0.100219
## [73] train-rmse:0.130181+0.011536 test-rmse:0.964024+0.099744
## [74] train-rmse:0.128549+0.011968 test-rmse:0.964636+0.100130
## [75] train-rmse:0.123525+0.013178 test-rmse:0.964205+0.100890
## [76] train-rmse:0.119933+0.012340 test-rmse:0.963413+0.100006
## [77] train-rmse:0.116451+0.010609 test-rmse:0.963531+0.100550
## [78] train-rmse:0.113575+0.011097 test-rmse:0.963244+0.100523
## [79] train-rmse:0.111594+0.010410 test-rmse:0.963220+0.100090
## [80] train-rmse:0.108063+0.010964 test-rmse:0.963110+0.100017
## [81] train-rmse:0.104696+0.010423 test-rmse:0.963006+0.100318
## [82] train-rmse:0.101196+0.009148 test-rmse:0.963251+0.100337
## [83] train-rmse:0.098515+0.008977 test-rmse:0.962491+0.100597
## [84] train-rmse:0.095263+0.008656 test-rmse:0.962582+0.100796
## [85] train-rmse:0.092855+0.009262 test-rmse:0.962612+0.100727
## [86] train-rmse:0.090992+0.008989 test-rmse:0.962813+0.100907
## [87] train-rmse:0.089385+0.008878 test-rmse:0.962680+0.101223
## [88] train-rmse:0.086615+0.008094 test-rmse:0.962631+0.100868
## [89] train-rmse:0.084549+0.008536 test-rmse:0.962501+0.101142
## Stopping. Best iteration:
## [83] train-rmse:0.098515+0.008977 test-rmse:0.962491+0.100597
##
## 82
## [1] train-rmse:67.089722+0.067155 test-rmse:67.090222+0.426122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:45.720995+0.045347 test-rmse:45.720976+0.446533
## [3] train-rmse:31.177771+0.030287 test-rmse:31.177018+0.459422
## [4] train-rmse:21.288879+0.019834 test-rmse:21.287091+0.466458
## [5] train-rmse:14.577831+0.012778 test-rmse:14.574641+0.468189
## [6] train-rmse:10.033350+0.007985 test-rmse:10.019403+0.432742
## [7] train-rmse:6.956287+0.007933 test-rmse:6.928342+0.392669
## [8] train-rmse:4.868580+0.007211 test-rmse:4.845417+0.341855
## [9] train-rmse:3.456576+0.007332 test-rmse:3.450492+0.280140
## [10] train-rmse:2.497755+0.008618 test-rmse:2.533760+0.254205
## [11] train-rmse:1.850610+0.008693 test-rmse:1.949656+0.231075
## [12] train-rmse:1.413716+0.005851 test-rmse:1.598175+0.201705
## [13] train-rmse:1.132529+0.013211 test-rmse:1.398451+0.166158
## [14] train-rmse:0.945690+0.013688 test-rmse:1.269640+0.152285
## [15] train-rmse:0.818157+0.016349 test-rmse:1.189824+0.144827
## [16] train-rmse:0.731902+0.020714 test-rmse:1.152963+0.132263
## [17] train-rmse:0.667798+0.020090 test-rmse:1.126590+0.133157
## [18] train-rmse:0.621693+0.020676 test-rmse:1.106770+0.129230
## [19] train-rmse:0.586626+0.026359 test-rmse:1.090716+0.134020
## [20] train-rmse:0.552378+0.025679 test-rmse:1.077178+0.134767
## [21] train-rmse:0.520590+0.020066 test-rmse:1.059872+0.125874
## [22] train-rmse:0.495942+0.019082 test-rmse:1.051975+0.128329
## [23] train-rmse:0.469992+0.019538 test-rmse:1.041908+0.133380
## [24] train-rmse:0.452201+0.020366 test-rmse:1.029210+0.127647
## [25] train-rmse:0.436874+0.019212 test-rmse:1.019212+0.121968
## [26] train-rmse:0.419404+0.013421 test-rmse:1.011079+0.118360
## [27] train-rmse:0.401320+0.016166 test-rmse:1.006988+0.119895
## [28] train-rmse:0.383231+0.013822 test-rmse:1.001242+0.119779
## [29] train-rmse:0.365749+0.013692 test-rmse:0.999472+0.116311
## [30] train-rmse:0.349158+0.013758 test-rmse:0.998948+0.116781
## [31] train-rmse:0.337948+0.014351 test-rmse:0.997234+0.118858
## [32] train-rmse:0.322119+0.014740 test-rmse:0.987515+0.118133
## [33] train-rmse:0.309504+0.019932 test-rmse:0.985648+0.118667
## [34] train-rmse:0.300242+0.017502 test-rmse:0.984679+0.119432
## [35] train-rmse:0.289527+0.017272 test-rmse:0.982615+0.119984
## [36] train-rmse:0.277690+0.016121 test-rmse:0.981911+0.119982
## [37] train-rmse:0.265858+0.015814 test-rmse:0.979149+0.121243
## [38] train-rmse:0.252781+0.013739 test-rmse:0.973372+0.117986
## [39] train-rmse:0.244644+0.010520 test-rmse:0.973961+0.120213
## [40] train-rmse:0.234698+0.010214 test-rmse:0.974919+0.123117
## [41] train-rmse:0.227114+0.012821 test-rmse:0.972680+0.119220
## [42] train-rmse:0.220655+0.012546 test-rmse:0.970708+0.121181
## [43] train-rmse:0.214774+0.012173 test-rmse:0.972655+0.121757
## [44] train-rmse:0.205000+0.011713 test-rmse:0.969938+0.124761
## [45] train-rmse:0.196736+0.013520 test-rmse:0.966750+0.125753
## [46] train-rmse:0.190302+0.011504 test-rmse:0.964893+0.127115
## [47] train-rmse:0.184833+0.011123 test-rmse:0.964585+0.126579
## [48] train-rmse:0.175915+0.012143 test-rmse:0.963254+0.127250
## [49] train-rmse:0.170024+0.014291 test-rmse:0.962588+0.127713
## [50] train-rmse:0.161440+0.014198 test-rmse:0.962476+0.128023
## [51] train-rmse:0.157331+0.013682 test-rmse:0.961754+0.128068
## [52] train-rmse:0.153396+0.012364 test-rmse:0.960640+0.128203
## [53] train-rmse:0.147368+0.014537 test-rmse:0.959936+0.125502
## [54] train-rmse:0.141831+0.015118 test-rmse:0.959843+0.125969
## [55] train-rmse:0.136721+0.013477 test-rmse:0.957831+0.127700
## [56] train-rmse:0.130331+0.014048 test-rmse:0.955823+0.129182
## [57] train-rmse:0.124948+0.013962 test-rmse:0.955032+0.129304
## [58] train-rmse:0.120835+0.013602 test-rmse:0.955127+0.130845
## [59] train-rmse:0.117040+0.011839 test-rmse:0.953341+0.131629
## [60] train-rmse:0.113607+0.012044 test-rmse:0.953550+0.132040
## [61] train-rmse:0.108584+0.012008 test-rmse:0.953124+0.131240
## [62] train-rmse:0.105726+0.012132 test-rmse:0.953400+0.130512
## [63] train-rmse:0.099611+0.010550 test-rmse:0.952382+0.130313
## [64] train-rmse:0.095994+0.010703 test-rmse:0.952107+0.130542
## [65] train-rmse:0.092989+0.011659 test-rmse:0.951703+0.130036
## [66] train-rmse:0.089284+0.010742 test-rmse:0.951825+0.130521
## [67] train-rmse:0.085392+0.010324 test-rmse:0.951764+0.129942
## [68] train-rmse:0.083276+0.010281 test-rmse:0.952017+0.129650
## [69] train-rmse:0.081678+0.010343 test-rmse:0.951680+0.129890
## [70] train-rmse:0.079139+0.009962 test-rmse:0.951601+0.129883
## [71] train-rmse:0.076058+0.009364 test-rmse:0.951851+0.130056
## [72] train-rmse:0.073380+0.009086 test-rmse:0.952281+0.130363
## [73] train-rmse:0.070717+0.007952 test-rmse:0.952218+0.129978
## [74] train-rmse:0.068611+0.008069 test-rmse:0.951719+0.130181
## [75] train-rmse:0.065854+0.007744 test-rmse:0.951470+0.130360
## [76] train-rmse:0.064033+0.007620 test-rmse:0.951567+0.130199
## [77] train-rmse:0.061637+0.007728 test-rmse:0.951592+0.130011
## [78] train-rmse:0.058643+0.007119 test-rmse:0.951454+0.129783
## [79] train-rmse:0.056493+0.007175 test-rmse:0.951058+0.130093
## [80] train-rmse:0.054696+0.006624 test-rmse:0.950935+0.130190
## [81] train-rmse:0.052298+0.006978 test-rmse:0.951362+0.130595
## [82] train-rmse:0.050780+0.006870 test-rmse:0.951030+0.130705
## [83] train-rmse:0.049415+0.006959 test-rmse:0.951088+0.130517
## [84] train-rmse:0.047574+0.006701 test-rmse:0.950773+0.130456
## [85] train-rmse:0.045595+0.006084 test-rmse:0.951026+0.130728
## [86] train-rmse:0.043447+0.006004 test-rmse:0.950941+0.130908
## [87] train-rmse:0.041917+0.005868 test-rmse:0.950643+0.130974
## [88] train-rmse:0.040708+0.005957 test-rmse:0.950786+0.130723
## [89] train-rmse:0.038939+0.005647 test-rmse:0.950416+0.130645
## [90] train-rmse:0.037714+0.005915 test-rmse:0.950424+0.130691
## [91] train-rmse:0.036124+0.005988 test-rmse:0.950629+0.130559
## [92] train-rmse:0.034998+0.005862 test-rmse:0.950718+0.130806
## [93] train-rmse:0.033765+0.005713 test-rmse:0.950535+0.131140
## [94] train-rmse:0.032983+0.005857 test-rmse:0.950398+0.131235
## [95] train-rmse:0.032205+0.005677 test-rmse:0.950289+0.131341
## [96] train-rmse:0.031254+0.005789 test-rmse:0.950303+0.131321
## [97] train-rmse:0.029769+0.005949 test-rmse:0.950255+0.131200
## [98] train-rmse:0.028715+0.005545 test-rmse:0.950336+0.131353
## [99] train-rmse:0.027992+0.005419 test-rmse:0.950094+0.131453
## [100] train-rmse:0.027273+0.005403 test-rmse:0.950412+0.131308
## [101] train-rmse:0.026681+0.005614 test-rmse:0.950355+0.131293
## [102] train-rmse:0.025738+0.005663 test-rmse:0.950345+0.131220
## [103] train-rmse:0.024835+0.005451 test-rmse:0.950424+0.131086
## [104] train-rmse:0.023828+0.005053 test-rmse:0.950657+0.131064
## [105] train-rmse:0.022934+0.005134 test-rmse:0.950646+0.131018
## Stopping. Best iteration:
## [99] train-rmse:0.027992+0.005419 test-rmse:0.950094+0.131453
##
## 83
## [1] train-rmse:67.089722+0.067155 test-rmse:67.090222+0.426122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:45.720995+0.045347 test-rmse:45.720976+0.446533
## [3] train-rmse:31.177771+0.030287 test-rmse:31.177018+0.459422
## [4] train-rmse:21.288879+0.019834 test-rmse:21.287091+0.466458
## [5] train-rmse:14.577831+0.012778 test-rmse:14.574641+0.468189
## [6] train-rmse:10.033350+0.007985 test-rmse:10.019403+0.432742
## [7] train-rmse:6.956287+0.007933 test-rmse:6.928342+0.392669
## [8] train-rmse:4.867821+0.007179 test-rmse:4.840103+0.344260
## [9] train-rmse:3.452738+0.008047 test-rmse:3.444994+0.277931
## [10] train-rmse:2.488319+0.009252 test-rmse:2.526181+0.252209
## [11] train-rmse:1.836971+0.014895 test-rmse:1.938387+0.222542
## [12] train-rmse:1.387315+0.010646 test-rmse:1.574310+0.195619
## [13] train-rmse:1.092997+0.017196 test-rmse:1.374158+0.174826
## [14] train-rmse:0.888691+0.016086 test-rmse:1.247996+0.155203
## [15] train-rmse:0.746396+0.018556 test-rmse:1.166628+0.123272
## [16] train-rmse:0.650449+0.015990 test-rmse:1.116639+0.118748
## [17] train-rmse:0.583005+0.018383 test-rmse:1.086660+0.104937
## [18] train-rmse:0.532540+0.025126 test-rmse:1.071279+0.111787
## [19] train-rmse:0.487477+0.034467 test-rmse:1.063139+0.114961
## [20] train-rmse:0.457492+0.032546 test-rmse:1.050534+0.111757
## [21] train-rmse:0.417279+0.023760 test-rmse:1.037835+0.108237
## [22] train-rmse:0.393038+0.022467 test-rmse:1.029022+0.108360
## [23] train-rmse:0.365392+0.021834 test-rmse:1.021121+0.109435
## [24] train-rmse:0.346151+0.021397 test-rmse:1.018517+0.111291
## [25] train-rmse:0.326435+0.022573 test-rmse:1.012978+0.116293
## [26] train-rmse:0.310040+0.021828 test-rmse:1.010569+0.118110
## [27] train-rmse:0.292313+0.020338 test-rmse:1.007744+0.118600
## [28] train-rmse:0.275945+0.021799 test-rmse:1.001396+0.120437
## [29] train-rmse:0.263505+0.022512 test-rmse:0.995314+0.120283
## [30] train-rmse:0.249335+0.023577 test-rmse:0.992613+0.119644
## [31] train-rmse:0.238702+0.022662 test-rmse:0.989592+0.118595
## [32] train-rmse:0.225614+0.020034 test-rmse:0.987463+0.120719
## [33] train-rmse:0.218123+0.019387 test-rmse:0.984709+0.122533
## [34] train-rmse:0.207470+0.020446 test-rmse:0.985475+0.122770
## [35] train-rmse:0.197229+0.020864 test-rmse:0.984610+0.122683
## [36] train-rmse:0.189198+0.017501 test-rmse:0.983307+0.123988
## [37] train-rmse:0.179543+0.018753 test-rmse:0.981133+0.124503
## [38] train-rmse:0.171671+0.019359 test-rmse:0.980061+0.124913
## [39] train-rmse:0.166234+0.019556 test-rmse:0.976861+0.124929
## [40] train-rmse:0.157958+0.019946 test-rmse:0.975696+0.123368
## [41] train-rmse:0.149835+0.017068 test-rmse:0.975003+0.125449
## [42] train-rmse:0.142294+0.013992 test-rmse:0.973754+0.124970
## [43] train-rmse:0.133784+0.013182 test-rmse:0.973170+0.125125
## [44] train-rmse:0.128105+0.013303 test-rmse:0.972571+0.125260
## [45] train-rmse:0.122302+0.011970 test-rmse:0.972574+0.123808
## [46] train-rmse:0.117873+0.011184 test-rmse:0.971893+0.123623
## [47] train-rmse:0.111348+0.009955 test-rmse:0.973099+0.122377
## [48] train-rmse:0.107038+0.010762 test-rmse:0.970540+0.123424
## [49] train-rmse:0.101366+0.011409 test-rmse:0.969891+0.124874
## [50] train-rmse:0.097624+0.011415 test-rmse:0.968927+0.125543
## [51] train-rmse:0.093178+0.012039 test-rmse:0.968521+0.124885
## [52] train-rmse:0.088851+0.011049 test-rmse:0.968707+0.124914
## [53] train-rmse:0.084979+0.009914 test-rmse:0.968110+0.124885
## [54] train-rmse:0.082122+0.009328 test-rmse:0.967014+0.124645
## [55] train-rmse:0.079430+0.008921 test-rmse:0.966761+0.124507
## [56] train-rmse:0.074952+0.008717 test-rmse:0.966839+0.125575
## [57] train-rmse:0.070989+0.007048 test-rmse:0.967105+0.124832
## [58] train-rmse:0.068446+0.007537 test-rmse:0.966635+0.124947
## [59] train-rmse:0.065160+0.006939 test-rmse:0.967213+0.126039
## [60] train-rmse:0.063064+0.006237 test-rmse:0.966833+0.125570
## [61] train-rmse:0.058857+0.005502 test-rmse:0.967858+0.125441
## [62] train-rmse:0.056732+0.005992 test-rmse:0.968105+0.125737
## [63] train-rmse:0.054330+0.005292 test-rmse:0.967559+0.125580
## [64] train-rmse:0.051801+0.004104 test-rmse:0.967717+0.125612
## Stopping. Best iteration:
## [58] train-rmse:0.068446+0.007537 test-rmse:0.966635+0.124947
##
## 84
## [1] train-rmse:65.128522+0.065151 test-rmse:65.128984+0.428029
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:43.089980+0.042641 test-rmse:43.089869+0.448962
## [3] train-rmse:28.532279+0.027510 test-rmse:28.531320+0.461535
## [4] train-rmse:18.927944+0.017318 test-rmse:18.925764+0.467564
## [5] train-rmse:12.609187+0.010926 test-rmse:12.605351+0.467328
## [6] train-rmse:8.465691+0.007995 test-rmse:8.464860+0.425645
## [7] train-rmse:5.768139+0.008530 test-rmse:5.765397+0.404039
## [8] train-rmse:4.039267+0.012868 test-rmse:4.032792+0.378724
## [9] train-rmse:2.967184+0.018449 test-rmse:2.974938+0.326507
## [10] train-rmse:2.328054+0.023721 test-rmse:2.355448+0.270681
## [11] train-rmse:1.968015+0.026199 test-rmse:2.008427+0.192188
## [12] train-rmse:1.773680+0.026876 test-rmse:1.834205+0.146352
## [13] train-rmse:1.666251+0.026661 test-rmse:1.749280+0.123601
## [14] train-rmse:1.602783+0.026666 test-rmse:1.698691+0.121517
## [15] train-rmse:1.560992+0.028581 test-rmse:1.644681+0.109782
## [16] train-rmse:1.531632+0.027736 test-rmse:1.630104+0.114997
## [17] train-rmse:1.507780+0.026228 test-rmse:1.612489+0.111091
## [18] train-rmse:1.486238+0.024173 test-rmse:1.598828+0.112324
## [19] train-rmse:1.467742+0.024640 test-rmse:1.582284+0.117589
## [20] train-rmse:1.450937+0.023898 test-rmse:1.571867+0.113546
## [21] train-rmse:1.435051+0.023938 test-rmse:1.569471+0.117913
## [22] train-rmse:1.419363+0.024041 test-rmse:1.566913+0.113601
## [23] train-rmse:1.404865+0.024066 test-rmse:1.555006+0.114824
## [24] train-rmse:1.391161+0.023928 test-rmse:1.530228+0.103088
## [25] train-rmse:1.378189+0.023949 test-rmse:1.534257+0.103182
## [26] train-rmse:1.365964+0.023803 test-rmse:1.526954+0.103043
## [27] train-rmse:1.354394+0.023297 test-rmse:1.514704+0.097936
## [28] train-rmse:1.342697+0.022928 test-rmse:1.500785+0.098217
## [29] train-rmse:1.331495+0.022954 test-rmse:1.493688+0.108424
## [30] train-rmse:1.320692+0.022901 test-rmse:1.491054+0.098335
## [31] train-rmse:1.310168+0.022856 test-rmse:1.485991+0.096375
## [32] train-rmse:1.300268+0.022841 test-rmse:1.483012+0.096813
## [33] train-rmse:1.290638+0.022525 test-rmse:1.475828+0.086476
## [34] train-rmse:1.281418+0.022223 test-rmse:1.472043+0.088529
## [35] train-rmse:1.272152+0.022051 test-rmse:1.463163+0.091139
## [36] train-rmse:1.263070+0.021895 test-rmse:1.455360+0.085765
## [37] train-rmse:1.254197+0.021675 test-rmse:1.453839+0.087269
## [38] train-rmse:1.245507+0.021316 test-rmse:1.438188+0.091075
## [39] train-rmse:1.237112+0.020884 test-rmse:1.428659+0.093226
## [40] train-rmse:1.229168+0.020705 test-rmse:1.421684+0.094785
## [41] train-rmse:1.221320+0.020715 test-rmse:1.417245+0.096834
## [42] train-rmse:1.214231+0.020980 test-rmse:1.411018+0.094911
## [43] train-rmse:1.207431+0.020879 test-rmse:1.407154+0.098625
## [44] train-rmse:1.200623+0.020913 test-rmse:1.410117+0.089416
## [45] train-rmse:1.194322+0.020972 test-rmse:1.408983+0.083445
## [46] train-rmse:1.188107+0.021263 test-rmse:1.403239+0.089194
## [47] train-rmse:1.182108+0.021378 test-rmse:1.397397+0.084583
## [48] train-rmse:1.176189+0.021460 test-rmse:1.387906+0.076843
## [49] train-rmse:1.170253+0.021418 test-rmse:1.383165+0.078129
## [50] train-rmse:1.164591+0.021530 test-rmse:1.379683+0.076285
## [51] train-rmse:1.159129+0.021368 test-rmse:1.374172+0.081452
## [52] train-rmse:1.153887+0.021191 test-rmse:1.367962+0.083346
## [53] train-rmse:1.148693+0.021151 test-rmse:1.370467+0.085547
## [54] train-rmse:1.143550+0.021268 test-rmse:1.366183+0.086644
## [55] train-rmse:1.138591+0.021234 test-rmse:1.366199+0.087966
## [56] train-rmse:1.133826+0.021156 test-rmse:1.360390+0.089010
## [57] train-rmse:1.129260+0.020952 test-rmse:1.355135+0.089782
## [58] train-rmse:1.124752+0.020778 test-rmse:1.349934+0.088618
## [59] train-rmse:1.120090+0.020595 test-rmse:1.345750+0.087406
## [60] train-rmse:1.115642+0.020351 test-rmse:1.343127+0.089294
## [61] train-rmse:1.111203+0.020271 test-rmse:1.338977+0.091939
## [62] train-rmse:1.107039+0.020123 test-rmse:1.335366+0.087440
## [63] train-rmse:1.103030+0.020091 test-rmse:1.330095+0.087779
## [64] train-rmse:1.099181+0.020076 test-rmse:1.328796+0.088927
## [65] train-rmse:1.095172+0.019881 test-rmse:1.325013+0.085108
## [66] train-rmse:1.091471+0.019732 test-rmse:1.325174+0.086217
## [67] train-rmse:1.087871+0.019568 test-rmse:1.323701+0.085865
## [68] train-rmse:1.084366+0.019316 test-rmse:1.318236+0.084686
## [69] train-rmse:1.080877+0.019172 test-rmse:1.312551+0.083967
## [70] train-rmse:1.077423+0.019091 test-rmse:1.313319+0.089697
## [71] train-rmse:1.074060+0.018928 test-rmse:1.311286+0.089989
## [72] train-rmse:1.070961+0.018945 test-rmse:1.311485+0.087420
## [73] train-rmse:1.067891+0.018959 test-rmse:1.310523+0.087916
## [74] train-rmse:1.064889+0.018966 test-rmse:1.308462+0.091342
## [75] train-rmse:1.061819+0.019059 test-rmse:1.308008+0.086325
## [76] train-rmse:1.058852+0.019096 test-rmse:1.304786+0.086391
## [77] train-rmse:1.055901+0.019048 test-rmse:1.306028+0.085790
## [78] train-rmse:1.052944+0.019035 test-rmse:1.305732+0.085620
## [79] train-rmse:1.050193+0.018960 test-rmse:1.302854+0.085115
## [80] train-rmse:1.047548+0.018913 test-rmse:1.299787+0.089795
## [81] train-rmse:1.044830+0.018712 test-rmse:1.298795+0.090359
## [82] train-rmse:1.042343+0.018608 test-rmse:1.297048+0.090217
## [83] train-rmse:1.039819+0.018562 test-rmse:1.294209+0.089197
## [84] train-rmse:1.037360+0.018463 test-rmse:1.292088+0.088418
## [85] train-rmse:1.034946+0.018412 test-rmse:1.292074+0.088119
## [86] train-rmse:1.032537+0.018305 test-rmse:1.287380+0.088516
## [87] train-rmse:1.030228+0.018232 test-rmse:1.284064+0.087803
## [88] train-rmse:1.027946+0.018176 test-rmse:1.283356+0.086452
## [89] train-rmse:1.025656+0.018181 test-rmse:1.281584+0.088844
## [90] train-rmse:1.023407+0.018208 test-rmse:1.279532+0.087755
## [91] train-rmse:1.021230+0.018137 test-rmse:1.275806+0.085888
## [92] train-rmse:1.019085+0.018084 test-rmse:1.272125+0.085941
## [93] train-rmse:1.016976+0.018061 test-rmse:1.271929+0.087991
## [94] train-rmse:1.014917+0.018061 test-rmse:1.273394+0.087255
## [95] train-rmse:1.012846+0.018080 test-rmse:1.267529+0.082836
## [96] train-rmse:1.010838+0.018054 test-rmse:1.266103+0.085849
## [97] train-rmse:1.008802+0.018091 test-rmse:1.265922+0.087391
## [98] train-rmse:1.006827+0.018026 test-rmse:1.266178+0.088390
## [99] train-rmse:1.004849+0.018005 test-rmse:1.264789+0.087551
## [100] train-rmse:1.002921+0.017953 test-rmse:1.263989+0.088534
## [101] train-rmse:1.000970+0.018057 test-rmse:1.262453+0.086870
## [102] train-rmse:0.998963+0.018094 test-rmse:1.262919+0.085632
## [103] train-rmse:0.997063+0.018064 test-rmse:1.260790+0.086832
## [104] train-rmse:0.995271+0.018102 test-rmse:1.259645+0.087250
## [105] train-rmse:0.993489+0.018112 test-rmse:1.256704+0.087644
## [106] train-rmse:0.991775+0.018080 test-rmse:1.254388+0.087715
## [107] train-rmse:0.990019+0.018050 test-rmse:1.253950+0.087281
## [108] train-rmse:0.988306+0.018068 test-rmse:1.252236+0.085806
## [109] train-rmse:0.986608+0.018099 test-rmse:1.253403+0.086489
## [110] train-rmse:0.985004+0.018093 test-rmse:1.254525+0.087247
## [111] train-rmse:0.983395+0.018067 test-rmse:1.254069+0.088686
## [112] train-rmse:0.981733+0.018047 test-rmse:1.253877+0.087905
## [113] train-rmse:0.980178+0.018015 test-rmse:1.251165+0.088711
## [114] train-rmse:0.978646+0.017980 test-rmse:1.247095+0.090155
## [115] train-rmse:0.977172+0.017950 test-rmse:1.245128+0.090149
## [116] train-rmse:0.975691+0.017938 test-rmse:1.246651+0.087617
## [117] train-rmse:0.974174+0.017931 test-rmse:1.249386+0.088641
## [118] train-rmse:0.972682+0.017913 test-rmse:1.247787+0.089957
## [119] train-rmse:0.971203+0.017909 test-rmse:1.248535+0.087516
## [120] train-rmse:0.969714+0.017836 test-rmse:1.246636+0.086870
## [121] train-rmse:0.968306+0.017815 test-rmse:1.246033+0.085073
## Stopping. Best iteration:
## [115] train-rmse:0.977172+0.017950 test-rmse:1.245128+0.090149
##
## 85
## [1] train-rmse:65.128522+0.065151 test-rmse:65.128984+0.428029
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:43.089980+0.042641 test-rmse:43.089869+0.448962
## [3] train-rmse:28.532279+0.027510 test-rmse:28.531320+0.461535
## [4] train-rmse:18.927944+0.017318 test-rmse:18.925764+0.467564
## [5] train-rmse:12.609187+0.010926 test-rmse:12.605351+0.467328
## [6] train-rmse:8.460439+0.007583 test-rmse:8.445984+0.428384
## [7] train-rmse:5.744850+0.008552 test-rmse:5.714723+0.395340
## [8] train-rmse:3.979996+0.010019 test-rmse:3.971292+0.364271
## [9] train-rmse:2.859986+0.014353 test-rmse:2.874296+0.300106
## [10] train-rmse:2.173353+0.019284 test-rmse:2.229326+0.246594
## [11] train-rmse:1.770674+0.021479 test-rmse:1.871746+0.193060
## [12] train-rmse:1.544911+0.018980 test-rmse:1.690000+0.140128
## [13] train-rmse:1.415208+0.025213 test-rmse:1.580073+0.109067
## [14] train-rmse:1.342219+0.025725 test-rmse:1.525668+0.098071
## [15] train-rmse:1.294784+0.025276 test-rmse:1.490578+0.089998
## [16] train-rmse:1.261005+0.024718 test-rmse:1.478200+0.088940
## [17] train-rmse:1.234049+0.025961 test-rmse:1.455296+0.095371
## [18] train-rmse:1.206680+0.022876 test-rmse:1.435248+0.091469
## [19] train-rmse:1.183617+0.020678 test-rmse:1.420515+0.095012
## [20] train-rmse:1.161179+0.016900 test-rmse:1.402755+0.097517
## [21] train-rmse:1.141060+0.018726 test-rmse:1.383427+0.111694
## [22] train-rmse:1.122417+0.017094 test-rmse:1.371142+0.108032
## [23] train-rmse:1.106618+0.017851 test-rmse:1.351100+0.108066
## [24] train-rmse:1.087129+0.013421 test-rmse:1.333235+0.109685
## [25] train-rmse:1.067154+0.017776 test-rmse:1.335767+0.106496
## [26] train-rmse:1.047368+0.017184 test-rmse:1.316433+0.095380
## [27] train-rmse:1.030545+0.020206 test-rmse:1.305039+0.087849
## [28] train-rmse:1.015904+0.018096 test-rmse:1.297201+0.086225
## [29] train-rmse:1.002214+0.017059 test-rmse:1.292300+0.085366
## [30] train-rmse:0.989427+0.015558 test-rmse:1.287143+0.085556
## [31] train-rmse:0.976963+0.016146 test-rmse:1.280757+0.087267
## [32] train-rmse:0.964628+0.016948 test-rmse:1.267686+0.096550
## [33] train-rmse:0.952071+0.018997 test-rmse:1.260276+0.096982
## [34] train-rmse:0.941045+0.020960 test-rmse:1.255108+0.093666
## [35] train-rmse:0.929648+0.020368 test-rmse:1.251324+0.094110
## [36] train-rmse:0.921150+0.020725 test-rmse:1.243674+0.095272
## [37] train-rmse:0.912471+0.021637 test-rmse:1.237736+0.095891
## [38] train-rmse:0.898801+0.019918 test-rmse:1.235739+0.099550
## [39] train-rmse:0.889041+0.019975 test-rmse:1.226946+0.106113
## [40] train-rmse:0.881781+0.019790 test-rmse:1.223212+0.105617
## [41] train-rmse:0.870389+0.020298 test-rmse:1.212521+0.106473
## [42] train-rmse:0.858882+0.017511 test-rmse:1.201731+0.100397
## [43] train-rmse:0.851900+0.016354 test-rmse:1.203855+0.099643
## [44] train-rmse:0.843474+0.017258 test-rmse:1.200345+0.100457
## [45] train-rmse:0.835331+0.019594 test-rmse:1.202061+0.099753
## [46] train-rmse:0.829464+0.019560 test-rmse:1.197547+0.102962
## [47] train-rmse:0.822970+0.019514 test-rmse:1.192263+0.106756
## [48] train-rmse:0.815629+0.017157 test-rmse:1.188570+0.109221
## [49] train-rmse:0.808374+0.017433 test-rmse:1.183242+0.113892
## [50] train-rmse:0.801518+0.015442 test-rmse:1.177177+0.115783
## [51] train-rmse:0.796273+0.014736 test-rmse:1.174805+0.113157
## [52] train-rmse:0.789478+0.016138 test-rmse:1.168784+0.116618
## [53] train-rmse:0.781826+0.017723 test-rmse:1.160890+0.111886
## [54] train-rmse:0.774990+0.015538 test-rmse:1.158941+0.108456
## [55] train-rmse:0.767917+0.017870 test-rmse:1.156816+0.111228
## [56] train-rmse:0.760454+0.019856 test-rmse:1.154355+0.112691
## [57] train-rmse:0.753098+0.016935 test-rmse:1.149542+0.119510
## [58] train-rmse:0.743637+0.017663 test-rmse:1.148058+0.120164
## [59] train-rmse:0.737797+0.020336 test-rmse:1.142931+0.111768
## [60] train-rmse:0.730482+0.018289 test-rmse:1.138639+0.110434
## [61] train-rmse:0.724320+0.016696 test-rmse:1.134605+0.114005
## [62] train-rmse:0.719762+0.016511 test-rmse:1.132774+0.110315
## [63] train-rmse:0.715137+0.016467 test-rmse:1.133328+0.111071
## [64] train-rmse:0.710842+0.015764 test-rmse:1.127866+0.113190
## [65] train-rmse:0.706800+0.015751 test-rmse:1.125907+0.114582
## [66] train-rmse:0.699706+0.015736 test-rmse:1.123741+0.109440
## [67] train-rmse:0.692769+0.014191 test-rmse:1.117530+0.110042
## [68] train-rmse:0.684575+0.014877 test-rmse:1.117901+0.110382
## [69] train-rmse:0.677820+0.015799 test-rmse:1.116740+0.109271
## [70] train-rmse:0.672927+0.015809 test-rmse:1.117887+0.113109
## [71] train-rmse:0.667032+0.017054 test-rmse:1.118182+0.111000
## [72] train-rmse:0.663071+0.017856 test-rmse:1.116969+0.113906
## [73] train-rmse:0.657485+0.019243 test-rmse:1.117337+0.110263
## [74] train-rmse:0.653125+0.018420 test-rmse:1.115433+0.112105
## [75] train-rmse:0.649372+0.018838 test-rmse:1.111473+0.108546
## [76] train-rmse:0.644418+0.018206 test-rmse:1.110713+0.108280
## [77] train-rmse:0.638851+0.019344 test-rmse:1.109253+0.110279
## [78] train-rmse:0.634149+0.021118 test-rmse:1.104571+0.106510
## [79] train-rmse:0.629793+0.020923 test-rmse:1.102476+0.109026
## [80] train-rmse:0.627010+0.020647 test-rmse:1.100830+0.107494
## [81] train-rmse:0.622696+0.021392 test-rmse:1.099249+0.105704
## [82] train-rmse:0.618937+0.020974 test-rmse:1.096940+0.106350
## [83] train-rmse:0.614393+0.021484 test-rmse:1.098752+0.107014
## [84] train-rmse:0.611324+0.020829 test-rmse:1.098543+0.106413
## [85] train-rmse:0.606672+0.020873 test-rmse:1.098818+0.106016
## [86] train-rmse:0.601992+0.020794 test-rmse:1.098823+0.104599
## [87] train-rmse:0.597446+0.021985 test-rmse:1.094927+0.101153
## [88] train-rmse:0.592849+0.020263 test-rmse:1.095773+0.099884
## [89] train-rmse:0.589523+0.019485 test-rmse:1.096360+0.101536
## [90] train-rmse:0.586253+0.019567 test-rmse:1.096325+0.103444
## [91] train-rmse:0.581371+0.019843 test-rmse:1.095480+0.103398
## [92] train-rmse:0.575359+0.016420 test-rmse:1.093253+0.105271
## [93] train-rmse:0.570625+0.015617 test-rmse:1.093020+0.107663
## [94] train-rmse:0.568352+0.015004 test-rmse:1.092258+0.109143
## [95] train-rmse:0.565511+0.014919 test-rmse:1.092213+0.106499
## [96] train-rmse:0.561765+0.014617 test-rmse:1.093040+0.104396
## [97] train-rmse:0.559067+0.014470 test-rmse:1.091311+0.105181
## [98] train-rmse:0.556366+0.014495 test-rmse:1.090417+0.104984
## [99] train-rmse:0.554220+0.014424 test-rmse:1.089745+0.103747
## [100] train-rmse:0.551160+0.015555 test-rmse:1.086692+0.101453
## [101] train-rmse:0.548321+0.015362 test-rmse:1.087295+0.102343
## [102] train-rmse:0.543449+0.017176 test-rmse:1.087572+0.100528
## [103] train-rmse:0.539729+0.015507 test-rmse:1.085498+0.101870
## [104] train-rmse:0.536920+0.016224 test-rmse:1.085351+0.102690
## [105] train-rmse:0.530830+0.015377 test-rmse:1.075902+0.099079
## [106] train-rmse:0.528429+0.016319 test-rmse:1.075420+0.099820
## [107] train-rmse:0.526119+0.016071 test-rmse:1.074131+0.100195
## [108] train-rmse:0.522466+0.015105 test-rmse:1.069109+0.101306
## [109] train-rmse:0.518517+0.013629 test-rmse:1.069734+0.101706
## [110] train-rmse:0.514311+0.012623 test-rmse:1.069062+0.101551
## [111] train-rmse:0.511543+0.012400 test-rmse:1.066563+0.098274
## [112] train-rmse:0.508821+0.012701 test-rmse:1.065726+0.098889
## [113] train-rmse:0.505815+0.012382 test-rmse:1.063610+0.098226
## [114] train-rmse:0.503390+0.011684 test-rmse:1.062156+0.098732
## [115] train-rmse:0.501861+0.011736 test-rmse:1.060929+0.099093
## [116] train-rmse:0.498868+0.012575 test-rmse:1.061792+0.098452
## [117] train-rmse:0.496835+0.012924 test-rmse:1.059464+0.098500
## [118] train-rmse:0.494474+0.012384 test-rmse:1.059296+0.098768
## [119] train-rmse:0.492212+0.013386 test-rmse:1.059431+0.100490
## [120] train-rmse:0.489029+0.013783 test-rmse:1.059431+0.100023
## [121] train-rmse:0.486867+0.013751 test-rmse:1.059181+0.100079
## [122] train-rmse:0.483743+0.014031 test-rmse:1.055780+0.102199
## [123] train-rmse:0.481693+0.014612 test-rmse:1.054647+0.102732
## [124] train-rmse:0.479621+0.014572 test-rmse:1.054650+0.103051
## [125] train-rmse:0.476264+0.013808 test-rmse:1.051530+0.103105
## [126] train-rmse:0.474378+0.013705 test-rmse:1.048955+0.103324
## [127] train-rmse:0.471687+0.013164 test-rmse:1.048135+0.102753
## [128] train-rmse:0.468604+0.014097 test-rmse:1.048739+0.103152
## [129] train-rmse:0.465759+0.015901 test-rmse:1.051949+0.104795
## [130] train-rmse:0.464278+0.015987 test-rmse:1.052349+0.104400
## [131] train-rmse:0.462170+0.016018 test-rmse:1.051228+0.104961
## [132] train-rmse:0.460474+0.016811 test-rmse:1.050806+0.105279
## [133] train-rmse:0.457668+0.016826 test-rmse:1.048218+0.106109
## Stopping. Best iteration:
## [127] train-rmse:0.471687+0.013164 test-rmse:1.048135+0.102753
##
## 86
## [1] train-rmse:65.128522+0.065151 test-rmse:65.128984+0.428029
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:43.089980+0.042641 test-rmse:43.089869+0.448962
## [3] train-rmse:28.532279+0.027510 test-rmse:28.531320+0.461535
## [4] train-rmse:18.927944+0.017318 test-rmse:18.925764+0.467564
## [5] train-rmse:12.609187+0.010926 test-rmse:12.605351+0.467328
## [6] train-rmse:8.456805+0.006678 test-rmse:8.441080+0.420318
## [7] train-rmse:5.736973+0.007885 test-rmse:5.719805+0.374520
## [8] train-rmse:3.954219+0.008727 test-rmse:3.954740+0.319372
## [9] train-rmse:2.813389+0.016074 test-rmse:2.830636+0.269047
## [10] train-rmse:2.096177+0.011308 test-rmse:2.153867+0.230326
## [11] train-rmse:1.654753+0.006092 test-rmse:1.773606+0.182035
## [12] train-rmse:1.401861+0.008930 test-rmse:1.576046+0.146730
## [13] train-rmse:1.257049+0.008939 test-rmse:1.480697+0.123631
## [14] train-rmse:1.164658+0.011656 test-rmse:1.420311+0.119311
## [15] train-rmse:1.103522+0.009256 test-rmse:1.382678+0.110912
## [16] train-rmse:1.058265+0.006769 test-rmse:1.353882+0.108574
## [17] train-rmse:1.023860+0.005625 test-rmse:1.334327+0.106751
## [18] train-rmse:0.994223+0.007645 test-rmse:1.311624+0.106606
## [19] train-rmse:0.968054+0.010682 test-rmse:1.299929+0.112611
## [20] train-rmse:0.943521+0.012396 test-rmse:1.289886+0.105795
## [21] train-rmse:0.915054+0.012573 test-rmse:1.271625+0.107094
## [22] train-rmse:0.888634+0.011765 test-rmse:1.257902+0.105941
## [23] train-rmse:0.866651+0.019277 test-rmse:1.247333+0.101381
## [24] train-rmse:0.847588+0.021501 test-rmse:1.236136+0.105267
## [25] train-rmse:0.830151+0.018554 test-rmse:1.232070+0.100285
## [26] train-rmse:0.813659+0.018256 test-rmse:1.220104+0.096658
## [27] train-rmse:0.799044+0.020165 test-rmse:1.214860+0.093914
## [28] train-rmse:0.782006+0.023323 test-rmse:1.210170+0.089609
## [29] train-rmse:0.763952+0.019635 test-rmse:1.205173+0.091919
## [30] train-rmse:0.748087+0.020570 test-rmse:1.191837+0.091142
## [31] train-rmse:0.734693+0.020588 test-rmse:1.195672+0.096946
## [32] train-rmse:0.717789+0.012194 test-rmse:1.183833+0.097229
## [33] train-rmse:0.702172+0.015718 test-rmse:1.171365+0.097617
## [34] train-rmse:0.693480+0.014816 test-rmse:1.163003+0.098866
## [35] train-rmse:0.682386+0.016534 test-rmse:1.158014+0.097083
## [36] train-rmse:0.670756+0.020321 test-rmse:1.157314+0.099737
## [37] train-rmse:0.660088+0.019962 test-rmse:1.151243+0.098267
## [38] train-rmse:0.649537+0.020162 test-rmse:1.148104+0.100350
## [39] train-rmse:0.639141+0.021943 test-rmse:1.147401+0.107093
## [40] train-rmse:0.624124+0.018751 test-rmse:1.143439+0.110460
## [41] train-rmse:0.615408+0.021519 test-rmse:1.144342+0.107241
## [42] train-rmse:0.605505+0.025392 test-rmse:1.142884+0.104590
## [43] train-rmse:0.598350+0.024525 test-rmse:1.136519+0.106188
## [44] train-rmse:0.585989+0.017366 test-rmse:1.138123+0.111045
## [45] train-rmse:0.577745+0.018744 test-rmse:1.132203+0.110188
## [46] train-rmse:0.569240+0.016679 test-rmse:1.131111+0.108725
## [47] train-rmse:0.560173+0.015492 test-rmse:1.128757+0.107133
## [48] train-rmse:0.551898+0.012652 test-rmse:1.124938+0.104356
## [49] train-rmse:0.541510+0.013083 test-rmse:1.123304+0.101329
## [50] train-rmse:0.533761+0.014872 test-rmse:1.120688+0.100169
## [51] train-rmse:0.525436+0.016363 test-rmse:1.122406+0.107828
## [52] train-rmse:0.516278+0.017568 test-rmse:1.124006+0.109359
## [53] train-rmse:0.506744+0.018112 test-rmse:1.113190+0.106059
## [54] train-rmse:0.501177+0.018410 test-rmse:1.112218+0.103999
## [55] train-rmse:0.494664+0.018549 test-rmse:1.111391+0.105148
## [56] train-rmse:0.488298+0.018834 test-rmse:1.114332+0.109094
## [57] train-rmse:0.484516+0.018196 test-rmse:1.110965+0.109336
## [58] train-rmse:0.476226+0.017716 test-rmse:1.107444+0.110610
## [59] train-rmse:0.471426+0.018252 test-rmse:1.109308+0.108806
## [60] train-rmse:0.464818+0.019230 test-rmse:1.108685+0.109040
## [61] train-rmse:0.458148+0.018689 test-rmse:1.107945+0.109787
## [62] train-rmse:0.452405+0.020694 test-rmse:1.105858+0.109669
## [63] train-rmse:0.447038+0.020549 test-rmse:1.105420+0.110764
## [64] train-rmse:0.440330+0.019570 test-rmse:1.101685+0.113093
## [65] train-rmse:0.434125+0.020829 test-rmse:1.098815+0.112174
## [66] train-rmse:0.426683+0.022445 test-rmse:1.096566+0.112350
## [67] train-rmse:0.420346+0.021032 test-rmse:1.093628+0.113865
## [68] train-rmse:0.414357+0.019399 test-rmse:1.095022+0.117468
## [69] train-rmse:0.407959+0.019804 test-rmse:1.090877+0.115218
## [70] train-rmse:0.401373+0.020168 test-rmse:1.090914+0.113746
## [71] train-rmse:0.396959+0.020880 test-rmse:1.089441+0.110185
## [72] train-rmse:0.391124+0.016979 test-rmse:1.087869+0.108147
## [73] train-rmse:0.385767+0.017325 test-rmse:1.083124+0.107220
## [74] train-rmse:0.383058+0.017462 test-rmse:1.082169+0.107071
## [75] train-rmse:0.378813+0.017579 test-rmse:1.082045+0.107813
## [76] train-rmse:0.375223+0.018072 test-rmse:1.079798+0.110179
## [77] train-rmse:0.371268+0.018403 test-rmse:1.081355+0.110414
## [78] train-rmse:0.366174+0.019254 test-rmse:1.079428+0.109489
## [79] train-rmse:0.362158+0.019472 test-rmse:1.078168+0.108026
## [80] train-rmse:0.357438+0.016242 test-rmse:1.077019+0.108140
## [81] train-rmse:0.352781+0.016937 test-rmse:1.075878+0.107988
## [82] train-rmse:0.348474+0.015511 test-rmse:1.076585+0.110232
## [83] train-rmse:0.343748+0.016162 test-rmse:1.075319+0.109957
## [84] train-rmse:0.339095+0.017430 test-rmse:1.074936+0.109563
## [85] train-rmse:0.334183+0.015668 test-rmse:1.076600+0.111515
## [86] train-rmse:0.329776+0.016676 test-rmse:1.075999+0.113329
## [87] train-rmse:0.326439+0.016475 test-rmse:1.076066+0.114343
## [88] train-rmse:0.321270+0.014153 test-rmse:1.075541+0.114128
## [89] train-rmse:0.317732+0.015147 test-rmse:1.075685+0.113742
## [90] train-rmse:0.313522+0.013201 test-rmse:1.074797+0.114335
## [91] train-rmse:0.310473+0.011656 test-rmse:1.074361+0.115202
## [92] train-rmse:0.307500+0.011561 test-rmse:1.072468+0.116598
## [93] train-rmse:0.302368+0.013090 test-rmse:1.067012+0.109186
## [94] train-rmse:0.298288+0.012585 test-rmse:1.065440+0.110055
## [95] train-rmse:0.294483+0.013540 test-rmse:1.065141+0.109611
## [96] train-rmse:0.291749+0.013435 test-rmse:1.064917+0.110198
## [97] train-rmse:0.288155+0.013522 test-rmse:1.064880+0.110507
## [98] train-rmse:0.283407+0.013519 test-rmse:1.063353+0.111630
## [99] train-rmse:0.280233+0.013520 test-rmse:1.063878+0.111919
## [100] train-rmse:0.277921+0.014011 test-rmse:1.064380+0.111637
## [101] train-rmse:0.275444+0.013335 test-rmse:1.064138+0.113741
## [102] train-rmse:0.272131+0.014050 test-rmse:1.063683+0.115344
## [103] train-rmse:0.268968+0.013814 test-rmse:1.062520+0.116220
## [104] train-rmse:0.265378+0.013778 test-rmse:1.059016+0.113918
## [105] train-rmse:0.263152+0.014649 test-rmse:1.059419+0.116091
## [106] train-rmse:0.258929+0.014195 test-rmse:1.058637+0.115971
## [107] train-rmse:0.255440+0.013797 test-rmse:1.058031+0.115063
## [108] train-rmse:0.252827+0.013489 test-rmse:1.058198+0.115121
## [109] train-rmse:0.250109+0.013346 test-rmse:1.058875+0.115921
## [110] train-rmse:0.247390+0.014530 test-rmse:1.058519+0.115983
## [111] train-rmse:0.244397+0.015314 test-rmse:1.058654+0.115764
## [112] train-rmse:0.241722+0.014825 test-rmse:1.059067+0.116321
## [113] train-rmse:0.238005+0.012865 test-rmse:1.057961+0.116248
## [114] train-rmse:0.235007+0.011382 test-rmse:1.057771+0.116251
## [115] train-rmse:0.233111+0.010480 test-rmse:1.056383+0.117455
## [116] train-rmse:0.230473+0.010972 test-rmse:1.053653+0.115092
## [117] train-rmse:0.227397+0.010207 test-rmse:1.052644+0.115398
## [118] train-rmse:0.225099+0.010329 test-rmse:1.052454+0.115525
## [119] train-rmse:0.222882+0.010997 test-rmse:1.051864+0.115314
## [120] train-rmse:0.220756+0.010876 test-rmse:1.050947+0.116022
## [121] train-rmse:0.218288+0.011612 test-rmse:1.049737+0.115743
## [122] train-rmse:0.215394+0.012054 test-rmse:1.049286+0.115582
## [123] train-rmse:0.213401+0.012829 test-rmse:1.049079+0.116069
## [124] train-rmse:0.210204+0.012772 test-rmse:1.048409+0.116672
## [125] train-rmse:0.207678+0.013375 test-rmse:1.046854+0.115722
## [126] train-rmse:0.206191+0.013934 test-rmse:1.047759+0.117380
## [127] train-rmse:0.204150+0.014193 test-rmse:1.046119+0.117501
## [128] train-rmse:0.201715+0.013658 test-rmse:1.046752+0.118029
## [129] train-rmse:0.198133+0.014227 test-rmse:1.046389+0.118400
## [130] train-rmse:0.195708+0.014803 test-rmse:1.046967+0.118705
## [131] train-rmse:0.194135+0.015207 test-rmse:1.047087+0.118057
## [132] train-rmse:0.192838+0.015549 test-rmse:1.047553+0.117973
## [133] train-rmse:0.190631+0.014855 test-rmse:1.046669+0.118126
## Stopping. Best iteration:
## [127] train-rmse:0.204150+0.014193 test-rmse:1.046119+0.117501
##
## 87
## [1] train-rmse:65.128522+0.065151 test-rmse:65.128984+0.428029
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:43.089980+0.042641 test-rmse:43.089869+0.448962
## [3] train-rmse:28.532279+0.027510 test-rmse:28.531320+0.461535
## [4] train-rmse:18.927944+0.017318 test-rmse:18.925764+0.467564
## [5] train-rmse:12.609187+0.010926 test-rmse:12.605351+0.467328
## [6] train-rmse:8.456477+0.006423 test-rmse:8.436758+0.418648
## [7] train-rmse:5.731794+0.006942 test-rmse:5.721747+0.374621
## [8] train-rmse:3.941269+0.007399 test-rmse:3.948083+0.327056
## [9] train-rmse:2.781966+0.010492 test-rmse:2.802684+0.275992
## [10] train-rmse:2.031557+0.005323 test-rmse:2.096557+0.217198
## [11] train-rmse:1.565414+0.017722 test-rmse:1.695085+0.168777
## [12] train-rmse:1.285343+0.021237 test-rmse:1.498880+0.135082
## [13] train-rmse:1.118656+0.023424 test-rmse:1.393472+0.116944
## [14] train-rmse:1.013624+0.024029 test-rmse:1.326587+0.112592
## [15] train-rmse:0.944530+0.020381 test-rmse:1.279855+0.104336
## [16] train-rmse:0.885623+0.020654 test-rmse:1.247476+0.093867
## [17] train-rmse:0.841032+0.021189 test-rmse:1.224027+0.090078
## [18] train-rmse:0.808243+0.019929 test-rmse:1.195770+0.086041
## [19] train-rmse:0.779467+0.022765 test-rmse:1.180681+0.084573
## [20] train-rmse:0.753867+0.022744 test-rmse:1.168504+0.079976
## [21] train-rmse:0.730130+0.021382 test-rmse:1.152878+0.085025
## [22] train-rmse:0.705698+0.020101 test-rmse:1.139178+0.081963
## [23] train-rmse:0.682106+0.012892 test-rmse:1.126461+0.081881
## [24] train-rmse:0.666910+0.013313 test-rmse:1.124710+0.086730
## [25] train-rmse:0.643875+0.005883 test-rmse:1.111357+0.083081
## [26] train-rmse:0.626373+0.006382 test-rmse:1.096856+0.081906
## [27] train-rmse:0.606762+0.013116 test-rmse:1.089537+0.080201
## [28] train-rmse:0.593972+0.014973 test-rmse:1.084795+0.083543
## [29] train-rmse:0.579135+0.013897 test-rmse:1.083497+0.089663
## [30] train-rmse:0.567197+0.016850 test-rmse:1.074595+0.090479
## [31] train-rmse:0.549067+0.016246 test-rmse:1.071021+0.086944
## [32] train-rmse:0.539221+0.018853 test-rmse:1.069531+0.086886
## [33] train-rmse:0.525797+0.023266 test-rmse:1.059197+0.075109
## [34] train-rmse:0.513818+0.019780 test-rmse:1.055491+0.076614
## [35] train-rmse:0.498580+0.016464 test-rmse:1.044190+0.076422
## [36] train-rmse:0.488600+0.014448 test-rmse:1.043274+0.078992
## [37] train-rmse:0.472431+0.013345 test-rmse:1.037466+0.082567
## [38] train-rmse:0.461381+0.012823 test-rmse:1.029293+0.082141
## [39] train-rmse:0.448364+0.013954 test-rmse:1.025989+0.087163
## [40] train-rmse:0.438385+0.015951 test-rmse:1.021693+0.093893
## [41] train-rmse:0.428412+0.018523 test-rmse:1.018686+0.093785
## [42] train-rmse:0.416139+0.015246 test-rmse:1.017371+0.096313
## [43] train-rmse:0.409181+0.015625 test-rmse:1.011386+0.100708
## [44] train-rmse:0.399943+0.014559 test-rmse:1.006540+0.097520
## [45] train-rmse:0.388592+0.014147 test-rmse:1.007066+0.097109
## [46] train-rmse:0.379752+0.013242 test-rmse:1.006496+0.096237
## [47] train-rmse:0.370593+0.011377 test-rmse:1.004974+0.095256
## [48] train-rmse:0.364541+0.011270 test-rmse:1.003569+0.096162
## [49] train-rmse:0.356039+0.011676 test-rmse:1.003318+0.095311
## [50] train-rmse:0.346360+0.009019 test-rmse:1.002202+0.099142
## [51] train-rmse:0.339249+0.010425 test-rmse:1.000632+0.097185
## [52] train-rmse:0.331665+0.010523 test-rmse:1.000527+0.097854
## [53] train-rmse:0.325280+0.011183 test-rmse:0.999603+0.098228
## [54] train-rmse:0.315855+0.012467 test-rmse:1.002244+0.098712
## [55] train-rmse:0.308015+0.010392 test-rmse:0.999486+0.100205
## [56] train-rmse:0.301855+0.009355 test-rmse:0.997327+0.098373
## [57] train-rmse:0.294850+0.010475 test-rmse:0.994954+0.097801
## [58] train-rmse:0.287286+0.010663 test-rmse:0.993162+0.096902
## [59] train-rmse:0.281370+0.008473 test-rmse:0.991901+0.097178
## [60] train-rmse:0.274485+0.006344 test-rmse:0.989871+0.096447
## [61] train-rmse:0.268612+0.007114 test-rmse:0.989452+0.095471
## [62] train-rmse:0.264585+0.007545 test-rmse:0.990622+0.095403
## [63] train-rmse:0.257866+0.008424 test-rmse:0.989971+0.092762
## [64] train-rmse:0.255130+0.008737 test-rmse:0.988923+0.094307
## [65] train-rmse:0.249999+0.010092 test-rmse:0.987597+0.092734
## [66] train-rmse:0.243511+0.008648 test-rmse:0.986971+0.093350
## [67] train-rmse:0.238208+0.007368 test-rmse:0.985238+0.092383
## [68] train-rmse:0.233919+0.007252 test-rmse:0.984320+0.092001
## [69] train-rmse:0.230065+0.006195 test-rmse:0.984316+0.092042
## [70] train-rmse:0.225577+0.005805 test-rmse:0.983321+0.091856
## [71] train-rmse:0.220826+0.005227 test-rmse:0.980615+0.092117
## [72] train-rmse:0.216121+0.006805 test-rmse:0.980788+0.093070
## [73] train-rmse:0.210186+0.007654 test-rmse:0.980972+0.093197
## [74] train-rmse:0.206651+0.006877 test-rmse:0.981385+0.092631
## [75] train-rmse:0.202815+0.007251 test-rmse:0.979281+0.092243
## [76] train-rmse:0.198095+0.006047 test-rmse:0.977968+0.092573
## [77] train-rmse:0.194376+0.005648 test-rmse:0.976668+0.093480
## [78] train-rmse:0.190297+0.005265 test-rmse:0.976557+0.093060
## [79] train-rmse:0.187204+0.004985 test-rmse:0.974836+0.093756
## [80] train-rmse:0.183513+0.005814 test-rmse:0.974341+0.093949
## [81] train-rmse:0.180927+0.006061 test-rmse:0.973835+0.094045
## [82] train-rmse:0.177661+0.005436 test-rmse:0.973270+0.093593
## [83] train-rmse:0.173773+0.005451 test-rmse:0.972975+0.094101
## [84] train-rmse:0.169841+0.005556 test-rmse:0.973472+0.094414
## [85] train-rmse:0.167090+0.005180 test-rmse:0.972887+0.094027
## [86] train-rmse:0.163499+0.004980 test-rmse:0.971955+0.094381
## [87] train-rmse:0.160244+0.004721 test-rmse:0.971521+0.094501
## [88] train-rmse:0.156268+0.004989 test-rmse:0.971239+0.096039
## [89] train-rmse:0.154042+0.004602 test-rmse:0.972799+0.096391
## [90] train-rmse:0.151864+0.005156 test-rmse:0.972601+0.096052
## [91] train-rmse:0.149224+0.005716 test-rmse:0.972755+0.095280
## [92] train-rmse:0.147171+0.005778 test-rmse:0.972187+0.095732
## [93] train-rmse:0.144059+0.005338 test-rmse:0.971687+0.095793
## [94] train-rmse:0.141498+0.004851 test-rmse:0.971316+0.095333
## Stopping. Best iteration:
## [88] train-rmse:0.156268+0.004989 test-rmse:0.971239+0.096039
##
## 88
## [1] train-rmse:65.128522+0.065151 test-rmse:65.128984+0.428029
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:43.089980+0.042641 test-rmse:43.089869+0.448962
## [3] train-rmse:28.532279+0.027510 test-rmse:28.531320+0.461535
## [4] train-rmse:18.927944+0.017318 test-rmse:18.925764+0.467564
## [5] train-rmse:12.609187+0.010926 test-rmse:12.605351+0.467328
## [6] train-rmse:8.456477+0.006423 test-rmse:8.436758+0.418648
## [7] train-rmse:5.729794+0.006249 test-rmse:5.716134+0.369583
## [8] train-rmse:3.935685+0.007207 test-rmse:3.948879+0.309717
## [9] train-rmse:2.762046+0.010375 test-rmse:2.797214+0.254629
## [10] train-rmse:1.996407+0.006365 test-rmse:2.083439+0.227091
## [11] train-rmse:1.513624+0.011994 test-rmse:1.666029+0.175350
## [12] train-rmse:1.215888+0.011368 test-rmse:1.434886+0.142019
## [13] train-rmse:1.018687+0.019588 test-rmse:1.311294+0.120045
## [14] train-rmse:0.899905+0.016744 test-rmse:1.248309+0.110211
## [15] train-rmse:0.824034+0.018719 test-rmse:1.200279+0.101228
## [16] train-rmse:0.761427+0.020642 test-rmse:1.167397+0.096943
## [17] train-rmse:0.721866+0.019939 test-rmse:1.146198+0.092884
## [18] train-rmse:0.681485+0.023085 test-rmse:1.129741+0.085434
## [19] train-rmse:0.647806+0.023855 test-rmse:1.111845+0.086879
## [20] train-rmse:0.618133+0.019569 test-rmse:1.103531+0.089420
## [21] train-rmse:0.589335+0.016135 test-rmse:1.092935+0.096912
## [22] train-rmse:0.564785+0.019980 test-rmse:1.081005+0.103045
## [23] train-rmse:0.539887+0.022514 test-rmse:1.073387+0.102453
## [24] train-rmse:0.520340+0.021600 test-rmse:1.063112+0.106047
## [25] train-rmse:0.495563+0.018118 test-rmse:1.056638+0.104264
## [26] train-rmse:0.477120+0.014207 test-rmse:1.049546+0.101675
## [27] train-rmse:0.464791+0.013480 test-rmse:1.042585+0.106752
## [28] train-rmse:0.451125+0.011378 test-rmse:1.038550+0.105639
## [29] train-rmse:0.439106+0.011173 test-rmse:1.032267+0.102460
## [30] train-rmse:0.422910+0.014941 test-rmse:1.027885+0.106010
## [31] train-rmse:0.405657+0.013160 test-rmse:1.028468+0.109164
## [32] train-rmse:0.394089+0.015951 test-rmse:1.026148+0.114297
## [33] train-rmse:0.381659+0.014933 test-rmse:1.021551+0.110140
## [34] train-rmse:0.370520+0.014974 test-rmse:1.021165+0.110426
## [35] train-rmse:0.360634+0.012010 test-rmse:1.022687+0.109250
## [36] train-rmse:0.348740+0.014426 test-rmse:1.025438+0.113281
## [37] train-rmse:0.337545+0.013364 test-rmse:1.023552+0.113385
## [38] train-rmse:0.325506+0.009890 test-rmse:1.021728+0.112904
## [39] train-rmse:0.314001+0.011330 test-rmse:1.023229+0.114464
## [40] train-rmse:0.303476+0.012056 test-rmse:1.021751+0.114099
## Stopping. Best iteration:
## [34] train-rmse:0.370520+0.014974 test-rmse:1.021165+0.110426
##
## 89
## [1] train-rmse:65.128522+0.065151 test-rmse:65.128984+0.428029
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:43.089980+0.042641 test-rmse:43.089869+0.448962
## [3] train-rmse:28.532279+0.027510 test-rmse:28.531320+0.461535
## [4] train-rmse:18.927944+0.017318 test-rmse:18.925764+0.467564
## [5] train-rmse:12.609187+0.010926 test-rmse:12.605351+0.467328
## [6] train-rmse:8.456477+0.006423 test-rmse:8.436758+0.418648
## [7] train-rmse:5.729794+0.006249 test-rmse:5.716134+0.369583
## [8] train-rmse:3.933380+0.007138 test-rmse:3.940611+0.312459
## [9] train-rmse:2.755998+0.011000 test-rmse:2.788011+0.258246
## [10] train-rmse:1.974774+0.007483 test-rmse:2.074872+0.225849
## [11] train-rmse:1.475134+0.017680 test-rmse:1.660618+0.193689
## [12] train-rmse:1.157109+0.010332 test-rmse:1.421439+0.156018
## [13] train-rmse:0.946331+0.017679 test-rmse:1.285673+0.142913
## [14] train-rmse:0.808836+0.023608 test-rmse:1.211103+0.134573
## [15] train-rmse:0.721768+0.025865 test-rmse:1.161835+0.134315
## [16] train-rmse:0.652842+0.022927 test-rmse:1.136157+0.124310
## [17] train-rmse:0.612110+0.024798 test-rmse:1.119858+0.121396
## [18] train-rmse:0.574349+0.030156 test-rmse:1.098470+0.121149
## [19] train-rmse:0.541366+0.024671 test-rmse:1.086095+0.122691
## [20] train-rmse:0.518444+0.026806 test-rmse:1.076554+0.119706
## [21] train-rmse:0.482400+0.021993 test-rmse:1.059558+0.105363
## [22] train-rmse:0.461784+0.024375 test-rmse:1.052687+0.104087
## [23] train-rmse:0.440039+0.027350 test-rmse:1.039748+0.104707
## [24] train-rmse:0.416751+0.030357 test-rmse:1.043093+0.103681
## [25] train-rmse:0.396978+0.028602 test-rmse:1.036609+0.102453
## [26] train-rmse:0.378307+0.023023 test-rmse:1.029436+0.102521
## [27] train-rmse:0.364016+0.023911 test-rmse:1.029187+0.101911
## [28] train-rmse:0.344842+0.019082 test-rmse:1.017718+0.094467
## [29] train-rmse:0.330635+0.023393 test-rmse:1.013348+0.095115
## [30] train-rmse:0.316048+0.023214 test-rmse:1.009928+0.095750
## [31] train-rmse:0.305659+0.024416 test-rmse:1.008285+0.100016
## [32] train-rmse:0.290112+0.024755 test-rmse:1.006563+0.099474
## [33] train-rmse:0.275987+0.023421 test-rmse:1.002566+0.095194
## [34] train-rmse:0.260375+0.022908 test-rmse:1.004300+0.095015
## [35] train-rmse:0.252851+0.022779 test-rmse:1.002760+0.097270
## [36] train-rmse:0.246707+0.022016 test-rmse:1.001907+0.098947
## [37] train-rmse:0.236542+0.019628 test-rmse:0.999845+0.100852
## [38] train-rmse:0.227724+0.021844 test-rmse:1.001520+0.097968
## [39] train-rmse:0.220581+0.021525 test-rmse:1.002784+0.096342
## [40] train-rmse:0.211745+0.018494 test-rmse:1.001012+0.098198
## [41] train-rmse:0.204953+0.017083 test-rmse:0.999003+0.099350
## [42] train-rmse:0.196098+0.018572 test-rmse:0.999316+0.098196
## [43] train-rmse:0.189304+0.019344 test-rmse:0.998219+0.098856
## [44] train-rmse:0.183825+0.017811 test-rmse:0.996390+0.098283
## [45] train-rmse:0.177798+0.018420 test-rmse:0.995702+0.098760
## [46] train-rmse:0.172901+0.019923 test-rmse:0.992601+0.099074
## [47] train-rmse:0.164291+0.016554 test-rmse:0.990216+0.099968
## [48] train-rmse:0.159528+0.017686 test-rmse:0.990531+0.100710
## [49] train-rmse:0.152966+0.016499 test-rmse:0.989354+0.098013
## [50] train-rmse:0.147255+0.016674 test-rmse:0.989182+0.098276
## [51] train-rmse:0.142763+0.015700 test-rmse:0.988604+0.097927
## [52] train-rmse:0.137633+0.016083 test-rmse:0.989831+0.097106
## [53] train-rmse:0.131996+0.015180 test-rmse:0.989328+0.095726
## [54] train-rmse:0.128448+0.014805 test-rmse:0.987757+0.094494
## [55] train-rmse:0.124317+0.015286 test-rmse:0.987864+0.094588
## [56] train-rmse:0.118839+0.014445 test-rmse:0.988040+0.094621
## [57] train-rmse:0.112971+0.011994 test-rmse:0.987158+0.094767
## [58] train-rmse:0.108073+0.012177 test-rmse:0.985734+0.094936
## [59] train-rmse:0.102831+0.011333 test-rmse:0.985821+0.094265
## [60] train-rmse:0.099385+0.010940 test-rmse:0.984731+0.096524
## [61] train-rmse:0.096066+0.011628 test-rmse:0.983306+0.095206
## [62] train-rmse:0.092346+0.010919 test-rmse:0.983223+0.095084
## [63] train-rmse:0.088704+0.010372 test-rmse:0.982140+0.094430
## [64] train-rmse:0.084940+0.009646 test-rmse:0.982068+0.094799
## [65] train-rmse:0.081371+0.008650 test-rmse:0.980755+0.093917
## [66] train-rmse:0.078531+0.009178 test-rmse:0.979955+0.094762
## [67] train-rmse:0.074612+0.008319 test-rmse:0.979136+0.094533
## [68] train-rmse:0.072082+0.008325 test-rmse:0.979011+0.094629
## [69] train-rmse:0.069586+0.008229 test-rmse:0.978332+0.094524
## [70] train-rmse:0.067844+0.008622 test-rmse:0.977756+0.094389
## [71] train-rmse:0.065909+0.008591 test-rmse:0.977763+0.094445
## [72] train-rmse:0.063586+0.008235 test-rmse:0.977423+0.094502
## [73] train-rmse:0.060755+0.007755 test-rmse:0.976871+0.095536
## [74] train-rmse:0.058835+0.007479 test-rmse:0.977050+0.095294
## [75] train-rmse:0.056410+0.006938 test-rmse:0.977350+0.095200
## [76] train-rmse:0.054587+0.006564 test-rmse:0.977435+0.095478
## [77] train-rmse:0.051965+0.006219 test-rmse:0.977348+0.095898
## [78] train-rmse:0.050019+0.006225 test-rmse:0.977071+0.095563
## [79] train-rmse:0.047831+0.005596 test-rmse:0.976684+0.095998
## [80] train-rmse:0.046364+0.005522 test-rmse:0.976513+0.095944
## [81] train-rmse:0.044829+0.005339 test-rmse:0.976248+0.096112
## [82] train-rmse:0.043718+0.005676 test-rmse:0.975615+0.096360
## [83] train-rmse:0.042122+0.005876 test-rmse:0.975598+0.096225
## [84] train-rmse:0.040737+0.005413 test-rmse:0.975491+0.095798
## [85] train-rmse:0.038898+0.004905 test-rmse:0.975484+0.095714
## [86] train-rmse:0.037363+0.004370 test-rmse:0.975377+0.095524
## [87] train-rmse:0.036078+0.004188 test-rmse:0.975208+0.095666
## [88] train-rmse:0.034903+0.003837 test-rmse:0.975056+0.095981
## [89] train-rmse:0.033636+0.003316 test-rmse:0.974785+0.095509
## [90] train-rmse:0.032629+0.003314 test-rmse:0.974531+0.095567
## [91] train-rmse:0.031202+0.003243 test-rmse:0.974552+0.095438
## [92] train-rmse:0.029556+0.002918 test-rmse:0.974644+0.095310
## [93] train-rmse:0.028696+0.002685 test-rmse:0.974842+0.095268
## [94] train-rmse:0.027812+0.002493 test-rmse:0.974584+0.095403
## [95] train-rmse:0.026790+0.002098 test-rmse:0.974387+0.095400
## [96] train-rmse:0.025690+0.001820 test-rmse:0.974128+0.095451
## [97] train-rmse:0.024662+0.001531 test-rmse:0.973939+0.095851
## [98] train-rmse:0.023836+0.001287 test-rmse:0.973774+0.095939
## [99] train-rmse:0.023125+0.001324 test-rmse:0.973826+0.095891
## [100] train-rmse:0.022158+0.001483 test-rmse:0.973835+0.096016
## [101] train-rmse:0.021369+0.001816 test-rmse:0.973705+0.096095
## [102] train-rmse:0.020571+0.001768 test-rmse:0.973553+0.096222
## [103] train-rmse:0.020033+0.001963 test-rmse:0.973421+0.096129
## [104] train-rmse:0.019227+0.001764 test-rmse:0.973263+0.096221
## [105] train-rmse:0.018481+0.001660 test-rmse:0.973223+0.096232
## [106] train-rmse:0.018090+0.001627 test-rmse:0.973176+0.096249
## [107] train-rmse:0.017444+0.001643 test-rmse:0.973138+0.096082
## [108] train-rmse:0.016831+0.001444 test-rmse:0.973149+0.096001
## [109] train-rmse:0.016205+0.001584 test-rmse:0.973339+0.096150
## [110] train-rmse:0.015836+0.001476 test-rmse:0.973212+0.096184
## [111] train-rmse:0.015323+0.001366 test-rmse:0.973127+0.096276
## [112] train-rmse:0.014793+0.001376 test-rmse:0.973100+0.096323
## [113] train-rmse:0.014199+0.001310 test-rmse:0.972995+0.096287
## [114] train-rmse:0.013770+0.001365 test-rmse:0.972849+0.096322
## [115] train-rmse:0.013216+0.001320 test-rmse:0.972866+0.096302
## [116] train-rmse:0.012647+0.001253 test-rmse:0.972876+0.096316
## [117] train-rmse:0.012210+0.001233 test-rmse:0.972780+0.096225
## [118] train-rmse:0.011826+0.001114 test-rmse:0.972709+0.096239
## [119] train-rmse:0.011317+0.000924 test-rmse:0.972773+0.096341
## [120] train-rmse:0.011009+0.000912 test-rmse:0.972799+0.096392
## [121] train-rmse:0.010680+0.000935 test-rmse:0.972713+0.096441
## [122] train-rmse:0.010287+0.000903 test-rmse:0.972624+0.096449
## [123] train-rmse:0.009864+0.000899 test-rmse:0.972640+0.096470
## [124] train-rmse:0.009437+0.000906 test-rmse:0.972621+0.096494
## [125] train-rmse:0.009064+0.000814 test-rmse:0.972544+0.096555
## [126] train-rmse:0.008738+0.000694 test-rmse:0.972544+0.096658
## [127] train-rmse:0.008527+0.000716 test-rmse:0.972518+0.096640
## [128] train-rmse:0.008222+0.000732 test-rmse:0.972521+0.096623
## [129] train-rmse:0.007954+0.000697 test-rmse:0.972548+0.096626
## [130] train-rmse:0.007579+0.000654 test-rmse:0.972473+0.096729
## [131] train-rmse:0.007288+0.000690 test-rmse:0.972500+0.096785
## [132] train-rmse:0.007138+0.000670 test-rmse:0.972485+0.096837
## [133] train-rmse:0.006900+0.000616 test-rmse:0.972516+0.096882
## [134] train-rmse:0.006681+0.000581 test-rmse:0.972553+0.096920
## [135] train-rmse:0.006531+0.000587 test-rmse:0.972561+0.096941
## [136] train-rmse:0.006340+0.000571 test-rmse:0.972605+0.096984
## Stopping. Best iteration:
## [130] train-rmse:0.007579+0.000654 test-rmse:0.972473+0.096729
##
## 90
## [1] train-rmse:65.128522+0.065151 test-rmse:65.128984+0.428029
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:43.089980+0.042641 test-rmse:43.089869+0.448962
## [3] train-rmse:28.532279+0.027510 test-rmse:28.531320+0.461535
## [4] train-rmse:18.927944+0.017318 test-rmse:18.925764+0.467564
## [5] train-rmse:12.609187+0.010926 test-rmse:12.605351+0.467328
## [6] train-rmse:8.456477+0.006423 test-rmse:8.436758+0.418648
## [7] train-rmse:5.729794+0.006249 test-rmse:5.716134+0.369583
## [8] train-rmse:3.931654+0.006881 test-rmse:3.937356+0.307082
## [9] train-rmse:2.749839+0.008635 test-rmse:2.787607+0.245075
## [10] train-rmse:1.962017+0.008346 test-rmse:2.053653+0.217761
## [11] train-rmse:1.449139+0.007638 test-rmse:1.617486+0.179987
## [12] train-rmse:1.116789+0.013939 test-rmse:1.376983+0.149298
## [13] train-rmse:0.900163+0.013266 test-rmse:1.248227+0.123912
## [14] train-rmse:0.751772+0.009670 test-rmse:1.158289+0.102223
## [15] train-rmse:0.653226+0.019083 test-rmse:1.115684+0.090110
## [16] train-rmse:0.583291+0.018341 test-rmse:1.084450+0.087553
## [17] train-rmse:0.526927+0.012263 test-rmse:1.067269+0.071192
## [18] train-rmse:0.487917+0.012830 test-rmse:1.045325+0.067576
## [19] train-rmse:0.446217+0.019407 test-rmse:1.038956+0.073396
## [20] train-rmse:0.416409+0.015185 test-rmse:1.031565+0.073506
## [21] train-rmse:0.391069+0.019229 test-rmse:1.027450+0.075515
## [22] train-rmse:0.365039+0.018247 test-rmse:1.016286+0.066982
## [23] train-rmse:0.344793+0.019976 test-rmse:1.011241+0.064104
## [24] train-rmse:0.328298+0.018500 test-rmse:1.009442+0.066903
## [25] train-rmse:0.312724+0.018908 test-rmse:1.006122+0.068464
## [26] train-rmse:0.295205+0.019237 test-rmse:1.001634+0.063608
## [27] train-rmse:0.274537+0.015824 test-rmse:0.995719+0.062584
## [28] train-rmse:0.261430+0.014886 test-rmse:0.998070+0.065439
## [29] train-rmse:0.241963+0.015494 test-rmse:0.993217+0.066125
## [30] train-rmse:0.230928+0.011838 test-rmse:0.989127+0.067564
## [31] train-rmse:0.219212+0.014316 test-rmse:0.989453+0.068719
## [32] train-rmse:0.211551+0.016920 test-rmse:0.986823+0.067891
## [33] train-rmse:0.201144+0.017037 test-rmse:0.985573+0.067552
## [34] train-rmse:0.189157+0.014130 test-rmse:0.984756+0.067646
## [35] train-rmse:0.179512+0.013011 test-rmse:0.984136+0.067108
## [36] train-rmse:0.172138+0.012100 test-rmse:0.980900+0.065801
## [37] train-rmse:0.164891+0.010314 test-rmse:0.980337+0.066645
## [38] train-rmse:0.154820+0.009741 test-rmse:0.979125+0.065394
## [39] train-rmse:0.147071+0.009011 test-rmse:0.977689+0.063915
## [40] train-rmse:0.138717+0.008305 test-rmse:0.974209+0.065176
## [41] train-rmse:0.133364+0.007704 test-rmse:0.973894+0.066201
## [42] train-rmse:0.127510+0.007052 test-rmse:0.974329+0.066410
## [43] train-rmse:0.117762+0.005737 test-rmse:0.974098+0.066729
## [44] train-rmse:0.112575+0.005832 test-rmse:0.975157+0.067158
## [45] train-rmse:0.108418+0.006015 test-rmse:0.974847+0.068853
## [46] train-rmse:0.102752+0.005153 test-rmse:0.974630+0.069315
## [47] train-rmse:0.097523+0.004816 test-rmse:0.973256+0.068308
## [48] train-rmse:0.091882+0.004919 test-rmse:0.973502+0.068177
## [49] train-rmse:0.086759+0.003642 test-rmse:0.973134+0.068022
## [50] train-rmse:0.083487+0.003295 test-rmse:0.972749+0.068205
## [51] train-rmse:0.080231+0.004885 test-rmse:0.972923+0.068660
## [52] train-rmse:0.075510+0.004879 test-rmse:0.972100+0.067841
## [53] train-rmse:0.072077+0.003893 test-rmse:0.971740+0.067947
## [54] train-rmse:0.068003+0.003518 test-rmse:0.971914+0.068157
## [55] train-rmse:0.065061+0.003252 test-rmse:0.971851+0.068263
## [56] train-rmse:0.061561+0.003382 test-rmse:0.971507+0.069103
## [57] train-rmse:0.058912+0.003481 test-rmse:0.971310+0.068561
## [58] train-rmse:0.055952+0.003970 test-rmse:0.970835+0.068805
## [59] train-rmse:0.053624+0.003330 test-rmse:0.971343+0.068459
## [60] train-rmse:0.050157+0.004342 test-rmse:0.971024+0.068410
## [61] train-rmse:0.047844+0.004492 test-rmse:0.970508+0.067882
## [62] train-rmse:0.045851+0.004311 test-rmse:0.969945+0.067560
## [63] train-rmse:0.043566+0.003262 test-rmse:0.969837+0.068137
## [64] train-rmse:0.041712+0.002663 test-rmse:0.969637+0.068238
## [65] train-rmse:0.039497+0.002328 test-rmse:0.970197+0.068437
## [66] train-rmse:0.037755+0.002784 test-rmse:0.970502+0.068779
## [67] train-rmse:0.036094+0.002537 test-rmse:0.970436+0.068599
## [68] train-rmse:0.034383+0.002590 test-rmse:0.970556+0.068861
## [69] train-rmse:0.031932+0.002076 test-rmse:0.970379+0.069068
## [70] train-rmse:0.030522+0.002001 test-rmse:0.970608+0.069197
## Stopping. Best iteration:
## [64] train-rmse:0.041712+0.002663 test-rmse:0.969637+0.068238
##
## 91
## [1] train-rmse:63.167377+0.063167 test-rmse:63.167806+0.429924
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:40.537449+0.040014 test-rmse:40.537229+0.451282
## [3] train-rmse:26.042731+0.024892 test-rmse:26.041535+0.463398
## [4] train-rmse:16.774179+0.015039 test-rmse:16.771557+0.468173
## [5] train-rmse:10.867463+0.008994 test-rmse:10.873367+0.425402
## [6] train-rmse:7.118900+0.007397 test-rmse:7.108045+0.421111
## [7] train-rmse:4.764347+0.011628 test-rmse:4.756193+0.392741
## [8] train-rmse:3.328396+0.018694 test-rmse:3.338913+0.354373
## [9] train-rmse:2.487714+0.026405 test-rmse:2.512488+0.299437
## [10] train-rmse:2.022803+0.030115 test-rmse:2.067346+0.217651
## [11] train-rmse:1.782618+0.032111 test-rmse:1.846605+0.160528
## [12] train-rmse:1.657092+0.032434 test-rmse:1.745003+0.131462
## [13] train-rmse:1.587646+0.032171 test-rmse:1.689104+0.125140
## [14] train-rmse:1.543321+0.031505 test-rmse:1.632660+0.108990
## [15] train-rmse:1.513898+0.030900 test-rmse:1.605572+0.111804
## [16] train-rmse:1.490483+0.029751 test-rmse:1.592937+0.124634
## [17] train-rmse:1.469684+0.028659 test-rmse:1.579261+0.120899
## [18] train-rmse:1.450165+0.028455 test-rmse:1.569438+0.116273
## [19] train-rmse:1.432927+0.027744 test-rmse:1.559773+0.113072
## [20] train-rmse:1.417346+0.027243 test-rmse:1.553118+0.115824
## [21] train-rmse:1.402598+0.026699 test-rmse:1.544881+0.116623
## [22] train-rmse:1.388181+0.026192 test-rmse:1.537092+0.113370
## [23] train-rmse:1.373845+0.025341 test-rmse:1.511353+0.102574
## [24] train-rmse:1.360449+0.024764 test-rmse:1.513409+0.110346
## [25] train-rmse:1.347828+0.024531 test-rmse:1.502897+0.119814
## [26] train-rmse:1.336114+0.024267 test-rmse:1.498907+0.108056
## [27] train-rmse:1.324571+0.023585 test-rmse:1.491644+0.094134
## [28] train-rmse:1.313094+0.023084 test-rmse:1.482651+0.095098
## [29] train-rmse:1.302246+0.022845 test-rmse:1.472871+0.091665
## [30] train-rmse:1.291439+0.022460 test-rmse:1.465865+0.098868
## [31] train-rmse:1.280435+0.022956 test-rmse:1.457908+0.098749
## [32] train-rmse:1.270304+0.022787 test-rmse:1.449608+0.100625
## [33] train-rmse:1.260982+0.022502 test-rmse:1.445648+0.099889
## [34] train-rmse:1.252040+0.022297 test-rmse:1.441879+0.097733
## [35] train-rmse:1.243195+0.022265 test-rmse:1.435705+0.095738
## [36] train-rmse:1.234425+0.022087 test-rmse:1.431075+0.092112
## [37] train-rmse:1.226190+0.022108 test-rmse:1.426219+0.085078
## [38] train-rmse:1.218182+0.022004 test-rmse:1.422267+0.088515
## [39] train-rmse:1.210518+0.021719 test-rmse:1.411903+0.094321
## [40] train-rmse:1.203173+0.021406 test-rmse:1.402180+0.096691
## [41] train-rmse:1.196089+0.021085 test-rmse:1.395394+0.094562
## [42] train-rmse:1.189422+0.020977 test-rmse:1.389780+0.100058
## [43] train-rmse:1.182864+0.020835 test-rmse:1.396138+0.092189
## [44] train-rmse:1.176437+0.020781 test-rmse:1.390836+0.086275
## [45] train-rmse:1.170282+0.020872 test-rmse:1.379107+0.094560
## [46] train-rmse:1.164272+0.021000 test-rmse:1.373708+0.083982
## [47] train-rmse:1.158460+0.021090 test-rmse:1.370067+0.081921
## [48] train-rmse:1.152552+0.021182 test-rmse:1.366534+0.083623
## [49] train-rmse:1.146837+0.021077 test-rmse:1.363879+0.082831
## [50] train-rmse:1.141489+0.020943 test-rmse:1.358710+0.081032
## [51] train-rmse:1.136382+0.020845 test-rmse:1.362748+0.088672
## [52] train-rmse:1.131367+0.020810 test-rmse:1.357592+0.089566
## [53] train-rmse:1.126488+0.020567 test-rmse:1.351649+0.086591
## [54] train-rmse:1.121633+0.020349 test-rmse:1.346830+0.087881
## [55] train-rmse:1.117067+0.020134 test-rmse:1.343243+0.089387
## [56] train-rmse:1.112462+0.019905 test-rmse:1.341426+0.089776
## [57] train-rmse:1.108006+0.019709 test-rmse:1.337100+0.092701
## [58] train-rmse:1.103665+0.019751 test-rmse:1.333402+0.094067
## [59] train-rmse:1.099139+0.019666 test-rmse:1.327803+0.097872
## [60] train-rmse:1.094813+0.019638 test-rmse:1.325917+0.096212
## [61] train-rmse:1.090873+0.019480 test-rmse:1.327377+0.088829
## [62] train-rmse:1.087090+0.019335 test-rmse:1.322321+0.095937
## [63] train-rmse:1.083323+0.019314 test-rmse:1.316849+0.092526
## [64] train-rmse:1.079624+0.019243 test-rmse:1.312460+0.095261
## [65] train-rmse:1.076097+0.019087 test-rmse:1.306194+0.095037
## [66] train-rmse:1.072624+0.018956 test-rmse:1.305558+0.093932
## [67] train-rmse:1.069286+0.018886 test-rmse:1.302934+0.094019
## [68] train-rmse:1.065910+0.018846 test-rmse:1.306312+0.093832
## [69] train-rmse:1.062707+0.018862 test-rmse:1.302433+0.093247
## [70] train-rmse:1.059493+0.018827 test-rmse:1.301884+0.096033
## [71] train-rmse:1.056356+0.018820 test-rmse:1.302894+0.095549
## [72] train-rmse:1.053265+0.018804 test-rmse:1.301380+0.094599
## [73] train-rmse:1.050344+0.018800 test-rmse:1.297606+0.097074
## [74] train-rmse:1.047529+0.018762 test-rmse:1.298170+0.093880
## [75] train-rmse:1.044758+0.018769 test-rmse:1.296090+0.092696
## [76] train-rmse:1.042043+0.018706 test-rmse:1.294729+0.093343
## [77] train-rmse:1.039415+0.018660 test-rmse:1.294290+0.093760
## [78] train-rmse:1.036834+0.018563 test-rmse:1.292220+0.097245
## [79] train-rmse:1.034180+0.018481 test-rmse:1.289580+0.095457
## [80] train-rmse:1.031690+0.018458 test-rmse:1.286535+0.096215
## [81] train-rmse:1.029294+0.018327 test-rmse:1.288442+0.092636
## [82] train-rmse:1.026874+0.018176 test-rmse:1.285998+0.092006
## [83] train-rmse:1.024526+0.018107 test-rmse:1.281776+0.089937
## [84] train-rmse:1.022190+0.018049 test-rmse:1.281103+0.091598
## [85] train-rmse:1.019820+0.017967 test-rmse:1.277218+0.092782
## [86] train-rmse:1.017468+0.017911 test-rmse:1.276021+0.095146
## [87] train-rmse:1.015192+0.017797 test-rmse:1.270412+0.089972
## [88] train-rmse:1.012960+0.017667 test-rmse:1.265684+0.091410
## [89] train-rmse:1.010760+0.017561 test-rmse:1.262255+0.089228
## [90] train-rmse:1.008506+0.017512 test-rmse:1.260580+0.088399
## [91] train-rmse:1.006292+0.017490 test-rmse:1.262824+0.086071
## [92] train-rmse:1.004144+0.017473 test-rmse:1.263925+0.086292
## [93] train-rmse:1.002071+0.017512 test-rmse:1.262654+0.087729
## [94] train-rmse:1.000062+0.017588 test-rmse:1.260378+0.089254
## [95] train-rmse:0.998116+0.017638 test-rmse:1.257990+0.088063
## [96] train-rmse:0.996187+0.017645 test-rmse:1.257731+0.088711
## [97] train-rmse:0.994273+0.017670 test-rmse:1.256342+0.090508
## [98] train-rmse:0.992397+0.017645 test-rmse:1.256676+0.089633
## [99] train-rmse:0.990359+0.017611 test-rmse:1.254221+0.093454
## [100] train-rmse:0.988516+0.017596 test-rmse:1.253489+0.092830
## [101] train-rmse:0.986685+0.017542 test-rmse:1.251943+0.091937
## [102] train-rmse:0.984861+0.017456 test-rmse:1.252437+0.088654
## [103] train-rmse:0.983071+0.017441 test-rmse:1.251378+0.088592
## [104] train-rmse:0.981350+0.017420 test-rmse:1.250581+0.086525
## [105] train-rmse:0.979577+0.017450 test-rmse:1.248701+0.086270
## [106] train-rmse:0.977828+0.017448 test-rmse:1.250662+0.084747
## [107] train-rmse:0.976149+0.017455 test-rmse:1.250366+0.086375
## [108] train-rmse:0.974552+0.017469 test-rmse:1.249588+0.086669
## [109] train-rmse:0.972961+0.017506 test-rmse:1.247808+0.085292
## [110] train-rmse:0.971426+0.017576 test-rmse:1.248719+0.086380
## [111] train-rmse:0.969878+0.017666 test-rmse:1.246183+0.085009
## [112] train-rmse:0.968318+0.017645 test-rmse:1.245321+0.089794
## [113] train-rmse:0.966790+0.017725 test-rmse:1.245235+0.087927
## [114] train-rmse:0.965277+0.017690 test-rmse:1.245152+0.088412
## [115] train-rmse:0.963832+0.017692 test-rmse:1.241154+0.084408
## [116] train-rmse:0.962421+0.017709 test-rmse:1.240247+0.084686
## [117] train-rmse:0.961007+0.017730 test-rmse:1.240209+0.083227
## [118] train-rmse:0.959512+0.017725 test-rmse:1.240677+0.082660
## [119] train-rmse:0.958089+0.017794 test-rmse:1.240791+0.081938
## [120] train-rmse:0.956701+0.017858 test-rmse:1.238970+0.083839
## [121] train-rmse:0.955282+0.017858 test-rmse:1.237122+0.083820
## [122] train-rmse:0.953852+0.017780 test-rmse:1.236597+0.084720
## [123] train-rmse:0.952528+0.017790 test-rmse:1.238527+0.084290
## [124] train-rmse:0.951179+0.017773 test-rmse:1.237690+0.085654
## [125] train-rmse:0.949861+0.017759 test-rmse:1.238109+0.087537
## [126] train-rmse:0.948536+0.017747 test-rmse:1.237075+0.084313
## [127] train-rmse:0.947274+0.017731 test-rmse:1.236566+0.084948
## [128] train-rmse:0.945994+0.017775 test-rmse:1.235212+0.087155
## [129] train-rmse:0.944812+0.017807 test-rmse:1.233984+0.085491
## [130] train-rmse:0.943604+0.017885 test-rmse:1.234285+0.085873
## [131] train-rmse:0.942340+0.017944 test-rmse:1.233408+0.085350
## [132] train-rmse:0.941120+0.017937 test-rmse:1.234237+0.084653
## [133] train-rmse:0.939923+0.017917 test-rmse:1.232784+0.085348
## [134] train-rmse:0.938645+0.018004 test-rmse:1.233612+0.084413
## [135] train-rmse:0.937413+0.018052 test-rmse:1.233321+0.083810
## [136] train-rmse:0.936226+0.018087 test-rmse:1.233160+0.083308
## [137] train-rmse:0.935098+0.018107 test-rmse:1.231631+0.085283
## [138] train-rmse:0.933923+0.018159 test-rmse:1.230444+0.083131
## [139] train-rmse:0.932772+0.018106 test-rmse:1.228305+0.083640
## [140] train-rmse:0.931649+0.018124 test-rmse:1.228719+0.085283
## [141] train-rmse:0.930549+0.018163 test-rmse:1.226896+0.085739
## [142] train-rmse:0.929483+0.018200 test-rmse:1.226810+0.085504
## [143] train-rmse:0.928425+0.018198 test-rmse:1.224676+0.084915
## [144] train-rmse:0.927358+0.018246 test-rmse:1.226828+0.087243
## [145] train-rmse:0.926310+0.018268 test-rmse:1.224430+0.088353
## [146] train-rmse:0.925320+0.018281 test-rmse:1.224171+0.088290
## [147] train-rmse:0.924327+0.018307 test-rmse:1.222388+0.087831
## [148] train-rmse:0.923365+0.018302 test-rmse:1.220601+0.086161
## [149] train-rmse:0.922377+0.018323 test-rmse:1.219249+0.086984
## [150] train-rmse:0.921299+0.018298 test-rmse:1.220390+0.084981
## [151] train-rmse:0.920321+0.018326 test-rmse:1.219103+0.086782
## [152] train-rmse:0.919346+0.018413 test-rmse:1.218526+0.086476
## [153] train-rmse:0.918411+0.018471 test-rmse:1.217552+0.087564
## [154] train-rmse:0.917467+0.018501 test-rmse:1.215865+0.086270
## [155] train-rmse:0.916556+0.018541 test-rmse:1.215706+0.087165
## [156] train-rmse:0.915659+0.018574 test-rmse:1.215963+0.087054
## [157] train-rmse:0.914712+0.018648 test-rmse:1.216393+0.085848
## [158] train-rmse:0.913728+0.018682 test-rmse:1.215183+0.085787
## [159] train-rmse:0.912837+0.018673 test-rmse:1.217085+0.083291
## [160] train-rmse:0.911915+0.018741 test-rmse:1.215417+0.085499
## [161] train-rmse:0.911008+0.018775 test-rmse:1.215753+0.084537
## [162] train-rmse:0.910132+0.018796 test-rmse:1.215105+0.085186
## [163] train-rmse:0.909273+0.018834 test-rmse:1.218792+0.086373
## [164] train-rmse:0.908417+0.018828 test-rmse:1.219193+0.084061
## [165] train-rmse:0.907527+0.018844 test-rmse:1.219017+0.084083
## [166] train-rmse:0.906692+0.018866 test-rmse:1.219889+0.087156
## [167] train-rmse:0.905844+0.018868 test-rmse:1.219964+0.087609
## [168] train-rmse:0.905040+0.018856 test-rmse:1.221664+0.086923
## Stopping. Best iteration:
## [162] train-rmse:0.910132+0.018796 test-rmse:1.215105+0.085186
##
## 92
## [1] train-rmse:63.167377+0.063167 test-rmse:63.167806+0.429924
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:40.537449+0.040014 test-rmse:40.537229+0.451282
## [3] train-rmse:26.042731+0.024892 test-rmse:26.041535+0.463398
## [4] train-rmse:16.774179+0.015039 test-rmse:16.771557+0.468173
## [5] train-rmse:10.867463+0.008994 test-rmse:10.873367+0.425402
## [6] train-rmse:7.108491+0.006411 test-rmse:7.096666+0.405457
## [7] train-rmse:4.734394+0.009254 test-rmse:4.730032+0.377466
## [8] train-rmse:3.259567+0.016598 test-rmse:3.269057+0.314256
## [9] train-rmse:2.375473+0.025802 test-rmse:2.408750+0.271933
## [10] train-rmse:1.869481+0.027837 test-rmse:1.942971+0.221006
## [11] train-rmse:1.587394+0.022911 test-rmse:1.699738+0.168216
## [12] train-rmse:1.436747+0.023297 test-rmse:1.584953+0.141997
## [13] train-rmse:1.351293+0.022012 test-rmse:1.523033+0.118289
## [14] train-rmse:1.296467+0.019080 test-rmse:1.491602+0.105978
## [15] train-rmse:1.260937+0.019268 test-rmse:1.468681+0.112418
## [16] train-rmse:1.226273+0.018484 test-rmse:1.450827+0.118968
## [17] train-rmse:1.199066+0.016913 test-rmse:1.442599+0.107962
## [18] train-rmse:1.172837+0.015378 test-rmse:1.421935+0.105547
## [19] train-rmse:1.148475+0.017910 test-rmse:1.415694+0.109305
## [20] train-rmse:1.129101+0.017903 test-rmse:1.386319+0.109335
## [21] train-rmse:1.109104+0.016528 test-rmse:1.369576+0.114167
## [22] train-rmse:1.088868+0.015314 test-rmse:1.359001+0.107122
## [23] train-rmse:1.065609+0.005994 test-rmse:1.351858+0.109414
## [24] train-rmse:1.046845+0.009112 test-rmse:1.337379+0.115991
## [25] train-rmse:1.033670+0.008675 test-rmse:1.329351+0.113096
## [26] train-rmse:1.015194+0.011530 test-rmse:1.306127+0.096187
## [27] train-rmse:1.000221+0.013956 test-rmse:1.290095+0.093881
## [28] train-rmse:0.983869+0.014311 test-rmse:1.277630+0.099181
## [29] train-rmse:0.971826+0.014934 test-rmse:1.269127+0.105064
## [30] train-rmse:0.958296+0.012367 test-rmse:1.262438+0.102925
## [31] train-rmse:0.947986+0.012530 test-rmse:1.256515+0.104884
## [32] train-rmse:0.937061+0.014134 test-rmse:1.248041+0.105025
## [33] train-rmse:0.926285+0.013665 test-rmse:1.246844+0.100878
## [34] train-rmse:0.914933+0.014346 test-rmse:1.241364+0.099507
## [35] train-rmse:0.903923+0.012466 test-rmse:1.231602+0.091060
## [36] train-rmse:0.890521+0.010135 test-rmse:1.224561+0.091376
## [37] train-rmse:0.881080+0.011932 test-rmse:1.218883+0.094190
## [38] train-rmse:0.869193+0.011913 test-rmse:1.204825+0.102207
## [39] train-rmse:0.854327+0.016471 test-rmse:1.202052+0.104635
## [40] train-rmse:0.842363+0.013973 test-rmse:1.198893+0.106215
## [41] train-rmse:0.833319+0.011196 test-rmse:1.194088+0.105722
## [42] train-rmse:0.818638+0.007780 test-rmse:1.188931+0.102749
## [43] train-rmse:0.811820+0.007192 test-rmse:1.187441+0.104964
## [44] train-rmse:0.805030+0.006598 test-rmse:1.187242+0.109776
## [45] train-rmse:0.796334+0.007684 test-rmse:1.178715+0.103936
## [46] train-rmse:0.788874+0.007577 test-rmse:1.179007+0.105907
## [47] train-rmse:0.782326+0.008608 test-rmse:1.177070+0.107149
## [48] train-rmse:0.773677+0.009340 test-rmse:1.168441+0.105724
## [49] train-rmse:0.762827+0.009887 test-rmse:1.163475+0.104394
## [50] train-rmse:0.754346+0.010095 test-rmse:1.162550+0.095470
## [51] train-rmse:0.747824+0.009079 test-rmse:1.157630+0.095312
## [52] train-rmse:0.740446+0.005793 test-rmse:1.154770+0.098143
## [53] train-rmse:0.733420+0.008052 test-rmse:1.149705+0.090616
## [54] train-rmse:0.727422+0.010578 test-rmse:1.146811+0.088466
## [55] train-rmse:0.720318+0.009321 test-rmse:1.142689+0.094316
## [56] train-rmse:0.715138+0.008920 test-rmse:1.138633+0.097852
## [57] train-rmse:0.710660+0.009880 test-rmse:1.134126+0.100046
## [58] train-rmse:0.702496+0.010617 test-rmse:1.127523+0.097366
## [59] train-rmse:0.696647+0.010937 test-rmse:1.121610+0.096636
## [60] train-rmse:0.690161+0.009342 test-rmse:1.122812+0.096005
## [61] train-rmse:0.685873+0.009342 test-rmse:1.121678+0.097906
## [62] train-rmse:0.679213+0.010796 test-rmse:1.118851+0.093436
## [63] train-rmse:0.674618+0.011646 test-rmse:1.120750+0.093828
## [64] train-rmse:0.670117+0.010447 test-rmse:1.118936+0.097086
## [65] train-rmse:0.664020+0.009177 test-rmse:1.122641+0.100493
## [66] train-rmse:0.658062+0.010053 test-rmse:1.117826+0.099120
## [67] train-rmse:0.653774+0.011537 test-rmse:1.117174+0.098510
## [68] train-rmse:0.648860+0.012603 test-rmse:1.116330+0.097989
## [69] train-rmse:0.643245+0.012994 test-rmse:1.115340+0.097528
## [70] train-rmse:0.635804+0.013136 test-rmse:1.108637+0.103699
## [71] train-rmse:0.629743+0.013477 test-rmse:1.107247+0.104417
## [72] train-rmse:0.624686+0.013236 test-rmse:1.107500+0.106158
## [73] train-rmse:0.620692+0.013869 test-rmse:1.106724+0.106219
## [74] train-rmse:0.613624+0.014666 test-rmse:1.096584+0.097349
## [75] train-rmse:0.610488+0.014964 test-rmse:1.096361+0.098359
## [76] train-rmse:0.607192+0.015228 test-rmse:1.095644+0.097894
## [77] train-rmse:0.601642+0.014903 test-rmse:1.091939+0.097968
## [78] train-rmse:0.597154+0.013990 test-rmse:1.090526+0.098309
## [79] train-rmse:0.593340+0.014804 test-rmse:1.087015+0.096733
## [80] train-rmse:0.588846+0.014290 test-rmse:1.086265+0.097835
## [81] train-rmse:0.583305+0.012279 test-rmse:1.082901+0.099291
## [82] train-rmse:0.578981+0.011313 test-rmse:1.080008+0.103845
## [83] train-rmse:0.574969+0.012357 test-rmse:1.079236+0.100565
## [84] train-rmse:0.571612+0.012504 test-rmse:1.080505+0.100815
## [85] train-rmse:0.567838+0.011676 test-rmse:1.080114+0.099564
## [86] train-rmse:0.565348+0.011214 test-rmse:1.076975+0.098769
## [87] train-rmse:0.562581+0.012212 test-rmse:1.074094+0.098042
## [88] train-rmse:0.558559+0.010276 test-rmse:1.072595+0.098522
## [89] train-rmse:0.551765+0.010697 test-rmse:1.069659+0.099723
## [90] train-rmse:0.548713+0.010953 test-rmse:1.069298+0.100218
## [91] train-rmse:0.545886+0.011383 test-rmse:1.069528+0.099531
## [92] train-rmse:0.541982+0.012281 test-rmse:1.069118+0.099003
## [93] train-rmse:0.538557+0.013187 test-rmse:1.067774+0.103874
## [94] train-rmse:0.534325+0.010338 test-rmse:1.066242+0.103889
## [95] train-rmse:0.530149+0.008159 test-rmse:1.063464+0.105316
## [96] train-rmse:0.526437+0.008610 test-rmse:1.064441+0.105329
## [97] train-rmse:0.524052+0.008666 test-rmse:1.063898+0.105355
## [98] train-rmse:0.520573+0.008672 test-rmse:1.063632+0.107083
## [99] train-rmse:0.517488+0.008812 test-rmse:1.061758+0.106044
## [100] train-rmse:0.515436+0.009396 test-rmse:1.059427+0.104433
## [101] train-rmse:0.511041+0.009465 test-rmse:1.058884+0.105604
## [102] train-rmse:0.507592+0.010069 test-rmse:1.058445+0.104326
## [103] train-rmse:0.503987+0.010407 test-rmse:1.058616+0.104382
## [104] train-rmse:0.500991+0.011022 test-rmse:1.059593+0.103267
## [105] train-rmse:0.498289+0.011226 test-rmse:1.057912+0.102740
## [106] train-rmse:0.495143+0.011714 test-rmse:1.057375+0.102131
## [107] train-rmse:0.492338+0.013230 test-rmse:1.055302+0.100570
## [108] train-rmse:0.489458+0.013394 test-rmse:1.053935+0.100378
## [109] train-rmse:0.487377+0.013839 test-rmse:1.053428+0.098543
## [110] train-rmse:0.484818+0.013708 test-rmse:1.053044+0.098955
## [111] train-rmse:0.482491+0.014204 test-rmse:1.053921+0.099470
## [112] train-rmse:0.479342+0.014837 test-rmse:1.046998+0.097609
## [113] train-rmse:0.476178+0.015242 test-rmse:1.045718+0.098770
## [114] train-rmse:0.474606+0.015770 test-rmse:1.045069+0.097371
## [115] train-rmse:0.471407+0.014067 test-rmse:1.045188+0.097237
## [116] train-rmse:0.469161+0.014195 test-rmse:1.046179+0.099842
## [117] train-rmse:0.466212+0.015042 test-rmse:1.045191+0.101289
## [118] train-rmse:0.464430+0.015254 test-rmse:1.045081+0.101265
## [119] train-rmse:0.463036+0.015497 test-rmse:1.043885+0.101769
## [120] train-rmse:0.461055+0.014682 test-rmse:1.043349+0.101228
## [121] train-rmse:0.457237+0.014828 test-rmse:1.044385+0.101490
## [122] train-rmse:0.455056+0.014807 test-rmse:1.044680+0.098662
## [123] train-rmse:0.452490+0.013946 test-rmse:1.043923+0.098430
## [124] train-rmse:0.449593+0.013287 test-rmse:1.044197+0.097668
## [125] train-rmse:0.446979+0.014048 test-rmse:1.042465+0.096486
## [126] train-rmse:0.445003+0.014081 test-rmse:1.043049+0.096323
## [127] train-rmse:0.442263+0.013789 test-rmse:1.042272+0.095753
## [128] train-rmse:0.440933+0.013516 test-rmse:1.042149+0.096899
## [129] train-rmse:0.437748+0.013547 test-rmse:1.043044+0.098693
## [130] train-rmse:0.435140+0.013766 test-rmse:1.037414+0.096905
## [131] train-rmse:0.433452+0.014725 test-rmse:1.036450+0.096620
## [132] train-rmse:0.430454+0.015284 test-rmse:1.035866+0.096711
## [133] train-rmse:0.428424+0.014811 test-rmse:1.034271+0.097263
## [134] train-rmse:0.426500+0.014996 test-rmse:1.032416+0.098587
## [135] train-rmse:0.424737+0.014561 test-rmse:1.030423+0.098737
## [136] train-rmse:0.422827+0.015380 test-rmse:1.029381+0.099183
## [137] train-rmse:0.421142+0.014953 test-rmse:1.029024+0.098492
## [138] train-rmse:0.417193+0.013649 test-rmse:1.025849+0.098907
## [139] train-rmse:0.414939+0.013807 test-rmse:1.024735+0.099717
## [140] train-rmse:0.412000+0.013546 test-rmse:1.023393+0.097449
## [141] train-rmse:0.410029+0.013160 test-rmse:1.024035+0.097749
## [142] train-rmse:0.406872+0.011320 test-rmse:1.022447+0.099900
## [143] train-rmse:0.404123+0.010685 test-rmse:1.022882+0.100510
## [144] train-rmse:0.401953+0.011813 test-rmse:1.021880+0.100782
## [145] train-rmse:0.400951+0.011656 test-rmse:1.021174+0.101643
## [146] train-rmse:0.399263+0.012122 test-rmse:1.020198+0.100354
## [147] train-rmse:0.396768+0.012635 test-rmse:1.021256+0.100320
## [148] train-rmse:0.394811+0.013093 test-rmse:1.022011+0.100777
## [149] train-rmse:0.393560+0.013135 test-rmse:1.021621+0.101614
## [150] train-rmse:0.390256+0.013529 test-rmse:1.017144+0.098261
## [151] train-rmse:0.386938+0.013970 test-rmse:1.014204+0.099818
## [152] train-rmse:0.384793+0.013167 test-rmse:1.014056+0.100081
## [153] train-rmse:0.382851+0.013933 test-rmse:1.014427+0.099948
## [154] train-rmse:0.380759+0.013737 test-rmse:1.015286+0.099317
## [155] train-rmse:0.379005+0.013587 test-rmse:1.015983+0.100537
## [156] train-rmse:0.377035+0.013111 test-rmse:1.015920+0.100862
## [157] train-rmse:0.375717+0.012829 test-rmse:1.014934+0.101144
## [158] train-rmse:0.373660+0.013303 test-rmse:1.015428+0.100156
## Stopping. Best iteration:
## [152] train-rmse:0.384793+0.013167 test-rmse:1.014056+0.100081
##
## 93
## [1] train-rmse:63.167377+0.063167 test-rmse:63.167806+0.429924
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:40.537449+0.040014 test-rmse:40.537229+0.451282
## [3] train-rmse:26.042731+0.024892 test-rmse:26.041535+0.463398
## [4] train-rmse:16.774179+0.015039 test-rmse:16.771557+0.468173
## [5] train-rmse:10.867463+0.008994 test-rmse:10.873367+0.425402
## [6] train-rmse:7.106011+0.006748 test-rmse:7.085501+0.396855
## [7] train-rmse:4.718623+0.003599 test-rmse:4.721924+0.364739
## [8] train-rmse:3.219054+0.011312 test-rmse:3.242144+0.299721
## [9] train-rmse:2.296407+0.010827 test-rmse:2.337673+0.257433
## [10] train-rmse:1.749960+0.014075 test-rmse:1.837462+0.213316
## [11] train-rmse:1.436272+0.021982 test-rmse:1.589656+0.174545
## [12] train-rmse:1.261480+0.032824 test-rmse:1.470713+0.151604
## [13] train-rmse:1.166567+0.035756 test-rmse:1.394704+0.146870
## [14] train-rmse:1.104997+0.034148 test-rmse:1.360821+0.132846
## [15] train-rmse:1.057456+0.031930 test-rmse:1.330234+0.120436
## [16] train-rmse:1.015283+0.037196 test-rmse:1.310682+0.107988
## [17] train-rmse:0.974894+0.031290 test-rmse:1.286566+0.113084
## [18] train-rmse:0.944280+0.030166 test-rmse:1.275443+0.105746
## [19] train-rmse:0.919084+0.027171 test-rmse:1.261440+0.109036
## [20] train-rmse:0.892745+0.024284 test-rmse:1.235746+0.115190
## [21] train-rmse:0.870071+0.025195 test-rmse:1.223269+0.114105
## [22] train-rmse:0.848050+0.021418 test-rmse:1.215429+0.117143
## [23] train-rmse:0.824919+0.019795 test-rmse:1.200408+0.111956
## [24] train-rmse:0.806454+0.018662 test-rmse:1.181236+0.114052
## [25] train-rmse:0.790335+0.017952 test-rmse:1.179714+0.113176
## [26] train-rmse:0.775530+0.019562 test-rmse:1.163392+0.117558
## [27] train-rmse:0.758444+0.018290 test-rmse:1.154461+0.117703
## [28] train-rmse:0.742228+0.017957 test-rmse:1.146174+0.119689
## [29] train-rmse:0.727151+0.021996 test-rmse:1.139388+0.122588
## [30] train-rmse:0.711517+0.023698 test-rmse:1.140212+0.128371
## [31] train-rmse:0.699411+0.023290 test-rmse:1.132836+0.128583
## [32] train-rmse:0.681491+0.021290 test-rmse:1.126097+0.130657
## [33] train-rmse:0.665383+0.023959 test-rmse:1.118019+0.128580
## [34] train-rmse:0.650251+0.021809 test-rmse:1.103953+0.113816
## [35] train-rmse:0.639287+0.021395 test-rmse:1.095532+0.115536
## [36] train-rmse:0.628640+0.021887 test-rmse:1.092120+0.118089
## [37] train-rmse:0.613337+0.016641 test-rmse:1.095119+0.121958
## [38] train-rmse:0.603367+0.015927 test-rmse:1.090623+0.118936
## [39] train-rmse:0.592920+0.015406 test-rmse:1.086236+0.122642
## [40] train-rmse:0.585662+0.015617 test-rmse:1.086520+0.120722
## [41] train-rmse:0.574181+0.013149 test-rmse:1.081874+0.118673
## [42] train-rmse:0.560533+0.015406 test-rmse:1.076753+0.121080
## [43] train-rmse:0.550968+0.011611 test-rmse:1.069214+0.109372
## [44] train-rmse:0.543551+0.009899 test-rmse:1.067598+0.110782
## [45] train-rmse:0.536559+0.009341 test-rmse:1.065334+0.112095
## [46] train-rmse:0.527828+0.008157 test-rmse:1.064278+0.111738
## [47] train-rmse:0.519689+0.011156 test-rmse:1.068185+0.112643
## [48] train-rmse:0.510899+0.013405 test-rmse:1.063437+0.113773
## [49] train-rmse:0.503978+0.012892 test-rmse:1.062615+0.115106
## [50] train-rmse:0.497211+0.013848 test-rmse:1.059741+0.116462
## [51] train-rmse:0.483750+0.013251 test-rmse:1.054210+0.118278
## [52] train-rmse:0.474420+0.013190 test-rmse:1.045363+0.113534
## [53] train-rmse:0.466811+0.010790 test-rmse:1.037868+0.118869
## [54] train-rmse:0.460032+0.009607 test-rmse:1.036064+0.118088
## [55] train-rmse:0.454981+0.010820 test-rmse:1.035240+0.118398
## [56] train-rmse:0.449109+0.009259 test-rmse:1.033481+0.116219
## [57] train-rmse:0.441593+0.011888 test-rmse:1.032282+0.116721
## [58] train-rmse:0.435539+0.011486 test-rmse:1.034613+0.117152
## [59] train-rmse:0.429501+0.013844 test-rmse:1.032562+0.117570
## [60] train-rmse:0.425123+0.012751 test-rmse:1.035119+0.115319
## [61] train-rmse:0.417453+0.013974 test-rmse:1.033429+0.115662
## [62] train-rmse:0.413456+0.014287 test-rmse:1.035224+0.115730
## [63] train-rmse:0.406575+0.015874 test-rmse:1.035770+0.114234
## Stopping. Best iteration:
## [57] train-rmse:0.441593+0.011888 test-rmse:1.032282+0.116721
##
## 94
## [1] train-rmse:63.167377+0.063167 test-rmse:63.167806+0.429924
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:40.537449+0.040014 test-rmse:40.537229+0.451282
## [3] train-rmse:26.042731+0.024892 test-rmse:26.041535+0.463398
## [4] train-rmse:16.774179+0.015039 test-rmse:16.771557+0.468173
## [5] train-rmse:10.867463+0.008994 test-rmse:10.873367+0.425402
## [6] train-rmse:7.104368+0.006686 test-rmse:7.075519+0.392453
## [7] train-rmse:4.711631+0.004317 test-rmse:4.721595+0.349681
## [8] train-rmse:3.202509+0.007727 test-rmse:3.222562+0.290372
## [9] train-rmse:2.260776+0.005711 test-rmse:2.310395+0.244498
## [10] train-rmse:1.691476+0.013508 test-rmse:1.798696+0.202549
## [11] train-rmse:1.352608+0.012953 test-rmse:1.529242+0.166038
## [12] train-rmse:1.161231+0.011602 test-rmse:1.401128+0.155664
## [13] train-rmse:1.052712+0.009545 test-rmse:1.340649+0.149266
## [14] train-rmse:0.983347+0.013649 test-rmse:1.287543+0.149372
## [15] train-rmse:0.927558+0.015405 test-rmse:1.246587+0.144054
## [16] train-rmse:0.879551+0.017920 test-rmse:1.223701+0.139919
## [17] train-rmse:0.843123+0.019838 test-rmse:1.202138+0.141553
## [18] train-rmse:0.805561+0.018850 test-rmse:1.189004+0.142281
## [19] train-rmse:0.765485+0.017744 test-rmse:1.165430+0.129040
## [20] train-rmse:0.740331+0.018758 test-rmse:1.150370+0.126282
## [21] train-rmse:0.719131+0.019083 test-rmse:1.148420+0.117290
## [22] train-rmse:0.691068+0.019484 test-rmse:1.135770+0.129060
## [23] train-rmse:0.670045+0.022854 test-rmse:1.125401+0.133947
## [24] train-rmse:0.649326+0.027531 test-rmse:1.117921+0.132073
## [25] train-rmse:0.634481+0.023557 test-rmse:1.101460+0.133631
## [26] train-rmse:0.612969+0.024514 test-rmse:1.099151+0.135326
## [27] train-rmse:0.595334+0.026441 test-rmse:1.086868+0.139072
## [28] train-rmse:0.573313+0.027573 test-rmse:1.079789+0.131139
## [29] train-rmse:0.560162+0.023787 test-rmse:1.074383+0.130288
## [30] train-rmse:0.545629+0.022631 test-rmse:1.071372+0.123386
## [31] train-rmse:0.534888+0.022394 test-rmse:1.063125+0.126968
## [32] train-rmse:0.519840+0.022888 test-rmse:1.059356+0.128131
## [33] train-rmse:0.504689+0.026964 test-rmse:1.060561+0.131202
## [34] train-rmse:0.492964+0.026679 test-rmse:1.058889+0.133170
## [35] train-rmse:0.479230+0.026822 test-rmse:1.050792+0.134050
## [36] train-rmse:0.469620+0.026905 test-rmse:1.051531+0.136016
## [37] train-rmse:0.455629+0.027779 test-rmse:1.050387+0.136863
## [38] train-rmse:0.445602+0.027037 test-rmse:1.048136+0.135975
## [39] train-rmse:0.436244+0.027759 test-rmse:1.040033+0.134453
## [40] train-rmse:0.428696+0.025857 test-rmse:1.037051+0.137689
## [41] train-rmse:0.418282+0.025328 test-rmse:1.038410+0.138315
## [42] train-rmse:0.407506+0.025890 test-rmse:1.042075+0.140107
## [43] train-rmse:0.400490+0.027928 test-rmse:1.042227+0.139007
## [44] train-rmse:0.391405+0.026825 test-rmse:1.040440+0.136969
## [45] train-rmse:0.382303+0.026450 test-rmse:1.039313+0.135493
## [46] train-rmse:0.371946+0.029761 test-rmse:1.033474+0.135163
## [47] train-rmse:0.362761+0.030970 test-rmse:1.029115+0.137925
## [48] train-rmse:0.354389+0.032660 test-rmse:1.025747+0.139503
## [49] train-rmse:0.345161+0.029501 test-rmse:1.021144+0.139441
## [50] train-rmse:0.340246+0.029230 test-rmse:1.015670+0.137106
## [51] train-rmse:0.334376+0.030398 test-rmse:1.013279+0.137636
## [52] train-rmse:0.326351+0.029074 test-rmse:1.010488+0.138153
## [53] train-rmse:0.319265+0.029901 test-rmse:1.005825+0.140542
## [54] train-rmse:0.312014+0.031563 test-rmse:1.007133+0.139813
## [55] train-rmse:0.303131+0.030380 test-rmse:1.009516+0.137698
## [56] train-rmse:0.297303+0.031913 test-rmse:1.011900+0.137334
## [57] train-rmse:0.287660+0.027921 test-rmse:1.009514+0.135773
## [58] train-rmse:0.281193+0.029713 test-rmse:1.009517+0.135923
## [59] train-rmse:0.274125+0.029357 test-rmse:1.006674+0.136438
## Stopping. Best iteration:
## [53] train-rmse:0.319265+0.029901 test-rmse:1.005825+0.140542
##
## 95
## [1] train-rmse:63.167377+0.063167 test-rmse:63.167806+0.429924
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:40.537449+0.040014 test-rmse:40.537229+0.451282
## [3] train-rmse:26.042731+0.024892 test-rmse:26.041535+0.463398
## [4] train-rmse:16.774179+0.015039 test-rmse:16.771557+0.468173
## [5] train-rmse:10.867463+0.008994 test-rmse:10.873367+0.425402
## [6] train-rmse:7.103617+0.007464 test-rmse:7.079943+0.395225
## [7] train-rmse:4.707817+0.005542 test-rmse:4.712634+0.330122
## [8] train-rmse:3.190933+0.006557 test-rmse:3.196629+0.269146
## [9] train-rmse:2.231872+0.012791 test-rmse:2.268306+0.227356
## [10] train-rmse:1.642334+0.009334 test-rmse:1.746908+0.199394
## [11] train-rmse:1.279783+0.011757 test-rmse:1.477860+0.155072
## [12] train-rmse:1.058515+0.016501 test-rmse:1.334176+0.140169
## [13] train-rmse:0.923932+0.015905 test-rmse:1.260219+0.119980
## [14] train-rmse:0.841174+0.018054 test-rmse:1.220252+0.116870
## [15] train-rmse:0.784275+0.020576 test-rmse:1.186415+0.119831
## [16] train-rmse:0.731049+0.022182 test-rmse:1.157089+0.129610
## [17] train-rmse:0.684391+0.035777 test-rmse:1.135481+0.132520
## [18] train-rmse:0.650668+0.032225 test-rmse:1.106988+0.115423
## [19] train-rmse:0.623194+0.030062 test-rmse:1.096983+0.118982
## [20] train-rmse:0.593706+0.028840 test-rmse:1.090429+0.117429
## [21] train-rmse:0.570963+0.030261 test-rmse:1.083840+0.116519
## [22] train-rmse:0.548708+0.024460 test-rmse:1.081699+0.113771
## [23] train-rmse:0.523959+0.026129 test-rmse:1.073680+0.119082
## [24] train-rmse:0.507403+0.026602 test-rmse:1.064521+0.119898
## [25] train-rmse:0.487800+0.032631 test-rmse:1.063767+0.117058
## [26] train-rmse:0.469130+0.035500 test-rmse:1.062055+0.114155
## [27] train-rmse:0.448915+0.033477 test-rmse:1.055657+0.112107
## [28] train-rmse:0.432938+0.034647 test-rmse:1.050785+0.109930
## [29] train-rmse:0.419940+0.036026 test-rmse:1.038215+0.099862
## [30] train-rmse:0.401269+0.033809 test-rmse:1.043181+0.105417
## [31] train-rmse:0.389848+0.030074 test-rmse:1.044885+0.106011
## [32] train-rmse:0.374060+0.030151 test-rmse:1.040248+0.102540
## [33] train-rmse:0.359063+0.028021 test-rmse:1.038191+0.102182
## [34] train-rmse:0.345060+0.031746 test-rmse:1.037238+0.101189
## [35] train-rmse:0.330694+0.032002 test-rmse:1.033166+0.102733
## [36] train-rmse:0.322138+0.030049 test-rmse:1.033820+0.100422
## [37] train-rmse:0.312443+0.029869 test-rmse:1.031550+0.095776
## [38] train-rmse:0.303733+0.029298 test-rmse:1.028773+0.097788
## [39] train-rmse:0.293391+0.027639 test-rmse:1.025482+0.098238
## [40] train-rmse:0.280543+0.024040 test-rmse:1.023781+0.100590
## [41] train-rmse:0.274291+0.025239 test-rmse:1.022693+0.099115
## [42] train-rmse:0.265468+0.023636 test-rmse:1.019636+0.098592
## [43] train-rmse:0.257754+0.020594 test-rmse:1.018615+0.098798
## [44] train-rmse:0.247482+0.021421 test-rmse:1.015467+0.096588
## [45] train-rmse:0.238765+0.016980 test-rmse:1.011646+0.098524
## [46] train-rmse:0.229141+0.013482 test-rmse:1.010080+0.097802
## [47] train-rmse:0.222693+0.014094 test-rmse:1.010287+0.098587
## [48] train-rmse:0.214648+0.015391 test-rmse:1.006601+0.099447
## [49] train-rmse:0.208413+0.014955 test-rmse:1.005256+0.097762
## [50] train-rmse:0.200885+0.017749 test-rmse:1.003339+0.096914
## [51] train-rmse:0.195915+0.017496 test-rmse:1.001936+0.095574
## [52] train-rmse:0.190775+0.017943 test-rmse:0.999576+0.095419
## [53] train-rmse:0.184595+0.018541 test-rmse:0.998191+0.094886
## [54] train-rmse:0.178124+0.016750 test-rmse:0.994747+0.092551
## [55] train-rmse:0.171961+0.014889 test-rmse:0.994883+0.090883
## [56] train-rmse:0.167779+0.015605 test-rmse:0.993977+0.091188
## [57] train-rmse:0.162093+0.014089 test-rmse:0.993162+0.091059
## [58] train-rmse:0.157675+0.014345 test-rmse:0.993902+0.091536
## [59] train-rmse:0.151863+0.012597 test-rmse:0.993183+0.090569
## [60] train-rmse:0.147824+0.011289 test-rmse:0.993452+0.090776
## [61] train-rmse:0.144177+0.011854 test-rmse:0.992321+0.089915
## [62] train-rmse:0.139479+0.011334 test-rmse:0.992430+0.089436
## [63] train-rmse:0.135292+0.009990 test-rmse:0.992294+0.089857
## [64] train-rmse:0.130611+0.009643 test-rmse:0.992138+0.089811
## [65] train-rmse:0.127335+0.009664 test-rmse:0.993492+0.089642
## [66] train-rmse:0.122935+0.007796 test-rmse:0.994273+0.090952
## [67] train-rmse:0.119372+0.006792 test-rmse:0.996417+0.091463
## [68] train-rmse:0.116260+0.007408 test-rmse:0.995924+0.089652
## [69] train-rmse:0.112651+0.006638 test-rmse:0.995291+0.089448
## [70] train-rmse:0.108541+0.006697 test-rmse:0.994897+0.089223
## Stopping. Best iteration:
## [64] train-rmse:0.130611+0.009643 test-rmse:0.992138+0.089811
##
## 96
## [1] train-rmse:63.167377+0.063167 test-rmse:63.167806+0.429924
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:40.537449+0.040014 test-rmse:40.537229+0.451282
## [3] train-rmse:26.042731+0.024892 test-rmse:26.041535+0.463398
## [4] train-rmse:16.774179+0.015039 test-rmse:16.771557+0.468173
## [5] train-rmse:10.867463+0.008994 test-rmse:10.873367+0.425402
## [6] train-rmse:7.103617+0.007464 test-rmse:7.079943+0.395225
## [7] train-rmse:4.706805+0.004449 test-rmse:4.709556+0.333135
## [8] train-rmse:3.186273+0.007021 test-rmse:3.206045+0.261238
## [9] train-rmse:2.215385+0.005774 test-rmse:2.272518+0.222085
## [10] train-rmse:1.603987+0.017996 test-rmse:1.717170+0.200457
## [11] train-rmse:1.220525+0.014496 test-rmse:1.435018+0.156794
## [12] train-rmse:0.983356+0.013033 test-rmse:1.268876+0.150402
## [13] train-rmse:0.840171+0.010302 test-rmse:1.189044+0.128932
## [14] train-rmse:0.747081+0.009192 test-rmse:1.135267+0.119651
## [15] train-rmse:0.677027+0.017346 test-rmse:1.105563+0.107775
## [16] train-rmse:0.630166+0.016125 test-rmse:1.085191+0.099111
## [17] train-rmse:0.590651+0.010665 test-rmse:1.072080+0.087596
## [18] train-rmse:0.542438+0.011533 test-rmse:1.046286+0.084501
## [19] train-rmse:0.509522+0.017448 test-rmse:1.042674+0.093513
## [20] train-rmse:0.481831+0.024569 test-rmse:1.030894+0.086647
## [21] train-rmse:0.458981+0.022273 test-rmse:1.025916+0.083189
## [22] train-rmse:0.432797+0.023657 test-rmse:1.018443+0.082212
## [23] train-rmse:0.411347+0.023047 test-rmse:1.012634+0.078892
## [24] train-rmse:0.390131+0.023044 test-rmse:0.999023+0.081197
## [25] train-rmse:0.374573+0.027378 test-rmse:0.996433+0.078265
## [26] train-rmse:0.357718+0.025222 test-rmse:0.991919+0.087065
## [27] train-rmse:0.342376+0.034247 test-rmse:0.987835+0.086638
## [28] train-rmse:0.323997+0.029897 test-rmse:0.988475+0.087137
## [29] train-rmse:0.310439+0.033172 test-rmse:0.986466+0.090525
## [30] train-rmse:0.295836+0.029914 test-rmse:0.985868+0.090835
## [31] train-rmse:0.282594+0.026935 test-rmse:0.980555+0.091203
## [32] train-rmse:0.266223+0.026211 test-rmse:0.982685+0.092307
## [33] train-rmse:0.253296+0.025899 test-rmse:0.984085+0.090503
## [34] train-rmse:0.241184+0.026216 test-rmse:0.982026+0.088775
## [35] train-rmse:0.228830+0.024185 test-rmse:0.979167+0.089638
## [36] train-rmse:0.219904+0.023915 test-rmse:0.974837+0.090548
## [37] train-rmse:0.211429+0.024501 test-rmse:0.974415+0.089828
## [38] train-rmse:0.201848+0.023163 test-rmse:0.973161+0.090125
## [39] train-rmse:0.192410+0.020887 test-rmse:0.971652+0.090559
## [40] train-rmse:0.183217+0.021134 test-rmse:0.969818+0.090103
## [41] train-rmse:0.175607+0.021756 test-rmse:0.967811+0.089940
## [42] train-rmse:0.168928+0.020645 test-rmse:0.964137+0.091693
## [43] train-rmse:0.162134+0.018535 test-rmse:0.965365+0.093483
## [44] train-rmse:0.156771+0.019570 test-rmse:0.964405+0.093307
## [45] train-rmse:0.149164+0.019888 test-rmse:0.963198+0.093524
## [46] train-rmse:0.143407+0.018784 test-rmse:0.961079+0.095305
## [47] train-rmse:0.139265+0.018549 test-rmse:0.960373+0.095247
## [48] train-rmse:0.132549+0.019144 test-rmse:0.960741+0.095826
## [49] train-rmse:0.126678+0.019636 test-rmse:0.960359+0.095603
## [50] train-rmse:0.119062+0.015132 test-rmse:0.959994+0.095859
## [51] train-rmse:0.115155+0.015389 test-rmse:0.959302+0.094926
## [52] train-rmse:0.109359+0.013784 test-rmse:0.958126+0.095061
## [53] train-rmse:0.104917+0.012570 test-rmse:0.958384+0.096116
## [54] train-rmse:0.101697+0.012346 test-rmse:0.957728+0.096987
## [55] train-rmse:0.097539+0.012404 test-rmse:0.958332+0.098909
## [56] train-rmse:0.094085+0.012203 test-rmse:0.958565+0.099749
## [57] train-rmse:0.090312+0.011981 test-rmse:0.958506+0.099960
## [58] train-rmse:0.085950+0.010456 test-rmse:0.959064+0.099419
## [59] train-rmse:0.083216+0.010410 test-rmse:0.958174+0.099894
## [60] train-rmse:0.080609+0.010414 test-rmse:0.958056+0.100043
## Stopping. Best iteration:
## [54] train-rmse:0.101697+0.012346 test-rmse:0.957728+0.096987
##
## 97
## [1] train-rmse:63.167377+0.063167 test-rmse:63.167806+0.429924
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:40.537449+0.040014 test-rmse:40.537229+0.451282
## [3] train-rmse:26.042731+0.024892 test-rmse:26.041535+0.463398
## [4] train-rmse:16.774179+0.015039 test-rmse:16.771557+0.468173
## [5] train-rmse:10.867463+0.008994 test-rmse:10.873367+0.425402
## [6] train-rmse:7.103617+0.007464 test-rmse:7.079943+0.395225
## [7] train-rmse:4.705812+0.003392 test-rmse:4.706798+0.336177
## [8] train-rmse:3.178809+0.002953 test-rmse:3.190824+0.265549
## [9] train-rmse:2.200409+0.011126 test-rmse:2.274945+0.217438
## [10] train-rmse:1.581109+0.012675 test-rmse:1.725791+0.185168
## [11] train-rmse:1.180676+0.014045 test-rmse:1.425697+0.156041
## [12] train-rmse:0.935345+0.017815 test-rmse:1.259583+0.151866
## [13] train-rmse:0.782418+0.019617 test-rmse:1.188382+0.141068
## [14] train-rmse:0.674153+0.030224 test-rmse:1.145269+0.132585
## [15] train-rmse:0.600860+0.040058 test-rmse:1.117354+0.114493
## [16] train-rmse:0.543412+0.033446 test-rmse:1.091657+0.106647
## [17] train-rmse:0.495893+0.033821 test-rmse:1.076450+0.108362
## [18] train-rmse:0.448394+0.043272 test-rmse:1.066799+0.105082
## [19] train-rmse:0.414982+0.040699 test-rmse:1.052229+0.103850
## [20] train-rmse:0.387445+0.036660 test-rmse:1.049510+0.108687
## [21] train-rmse:0.363852+0.037943 test-rmse:1.039916+0.105038
## [22] train-rmse:0.340627+0.036171 test-rmse:1.035780+0.102330
## [23] train-rmse:0.320414+0.036438 test-rmse:1.033726+0.105221
## [24] train-rmse:0.304537+0.034133 test-rmse:1.031532+0.105162
## [25] train-rmse:0.286266+0.035894 test-rmse:1.027430+0.104335
## [26] train-rmse:0.264446+0.031542 test-rmse:1.027487+0.103224
## [27] train-rmse:0.249825+0.027453 test-rmse:1.023954+0.103966
## [28] train-rmse:0.236958+0.025776 test-rmse:1.022770+0.107848
## [29] train-rmse:0.223543+0.028715 test-rmse:1.020345+0.109746
## [30] train-rmse:0.209439+0.021964 test-rmse:1.022090+0.108915
## [31] train-rmse:0.194936+0.021254 test-rmse:1.023307+0.111975
## [32] train-rmse:0.184636+0.021860 test-rmse:1.021858+0.111252
## [33] train-rmse:0.175376+0.020706 test-rmse:1.019251+0.110610
## [34] train-rmse:0.168291+0.020368 test-rmse:1.020044+0.108614
## [35] train-rmse:0.161753+0.021832 test-rmse:1.019632+0.107779
## [36] train-rmse:0.151544+0.020609 test-rmse:1.018068+0.106707
## [37] train-rmse:0.143846+0.019801 test-rmse:1.013974+0.105769
## [38] train-rmse:0.133919+0.017211 test-rmse:1.013835+0.107899
## [39] train-rmse:0.126633+0.014332 test-rmse:1.011506+0.106783
## [40] train-rmse:0.119541+0.013687 test-rmse:1.009367+0.106522
## [41] train-rmse:0.114548+0.013288 test-rmse:1.008039+0.107858
## [42] train-rmse:0.107831+0.012403 test-rmse:1.007230+0.108960
## [43] train-rmse:0.102671+0.011493 test-rmse:1.007503+0.108056
## [44] train-rmse:0.097764+0.011080 test-rmse:1.007312+0.107203
## [45] train-rmse:0.092515+0.009174 test-rmse:1.005474+0.106967
## [46] train-rmse:0.087564+0.011823 test-rmse:1.005254+0.106471
## [47] train-rmse:0.083761+0.011387 test-rmse:1.004473+0.106184
## [48] train-rmse:0.078146+0.010118 test-rmse:1.003654+0.107121
## [49] train-rmse:0.074159+0.010936 test-rmse:1.004838+0.106565
## [50] train-rmse:0.069244+0.010497 test-rmse:1.004652+0.107387
## [51] train-rmse:0.065053+0.009835 test-rmse:1.004205+0.108034
## [52] train-rmse:0.062231+0.009191 test-rmse:1.003092+0.108708
## [53] train-rmse:0.059557+0.008718 test-rmse:1.002363+0.109054
## [54] train-rmse:0.056845+0.008741 test-rmse:1.001113+0.108774
## [55] train-rmse:0.054721+0.008155 test-rmse:1.001251+0.109316
## [56] train-rmse:0.051986+0.007773 test-rmse:1.001719+0.109371
## [57] train-rmse:0.050236+0.007394 test-rmse:1.002199+0.109553
## [58] train-rmse:0.047743+0.007091 test-rmse:1.001603+0.109039
## [59] train-rmse:0.044984+0.006015 test-rmse:1.001253+0.108493
## [60] train-rmse:0.042590+0.006110 test-rmse:1.001294+0.109165
## Stopping. Best iteration:
## [54] train-rmse:0.056845+0.008741 test-rmse:1.001113+0.108774
##
## 98
## [1] train-rmse:61.206300+0.061172 test-rmse:61.206691+0.431814
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:38.063428+0.037460 test-rmse:38.063095+0.453499
## [3] train-rmse:23.704581+0.022414 test-rmse:23.703120+0.465001
## [4] train-rmse:14.815793+0.013021 test-rmse:14.812671+0.468221
## [5] train-rmse:9.336035+0.008127 test-rmse:9.337172+0.420874
## [6] train-rmse:5.980202+0.007938 test-rmse:5.969196+0.411210
## [7] train-rmse:3.961538+0.012777 test-rmse:3.955978+0.371040
## [8] train-rmse:2.798525+0.019652 test-rmse:2.815248+0.316911
## [9] train-rmse:2.162510+0.025445 test-rmse:2.202568+0.252381
## [10] train-rmse:1.840861+0.030190 test-rmse:1.903047+0.170753
## [11] train-rmse:1.683423+0.030311 test-rmse:1.764420+0.132421
## [12] train-rmse:1.600581+0.028836 test-rmse:1.701198+0.120824
## [13] train-rmse:1.551937+0.027363 test-rmse:1.659438+0.124240
## [14] train-rmse:1.517298+0.026956 test-rmse:1.611966+0.107623
## [15] train-rmse:1.491412+0.026330 test-rmse:1.600330+0.109984
## [16] train-rmse:1.469360+0.024899 test-rmse:1.582148+0.113442
## [17] train-rmse:1.449292+0.024252 test-rmse:1.563663+0.105406
## [18] train-rmse:1.431214+0.024985 test-rmse:1.558559+0.106518
## [19] train-rmse:1.414177+0.025236 test-rmse:1.551344+0.110605
## [20] train-rmse:1.398572+0.025069 test-rmse:1.545082+0.107640
## [21] train-rmse:1.383665+0.024523 test-rmse:1.533115+0.109514
## [22] train-rmse:1.369892+0.024145 test-rmse:1.526258+0.109787
## [23] train-rmse:1.356057+0.023537 test-rmse:1.519825+0.107015
## [24] train-rmse:1.342142+0.022311 test-rmse:1.511347+0.090781
## [25] train-rmse:1.329335+0.022039 test-rmse:1.501247+0.097749
## [26] train-rmse:1.317170+0.021966 test-rmse:1.492495+0.093802
## [27] train-rmse:1.305540+0.021767 test-rmse:1.476604+0.096205
## [28] train-rmse:1.294402+0.021737 test-rmse:1.468270+0.097720
## [29] train-rmse:1.283473+0.021521 test-rmse:1.459113+0.099244
## [30] train-rmse:1.272932+0.021161 test-rmse:1.460128+0.105812
## [31] train-rmse:1.261682+0.021356 test-rmse:1.450207+0.092900
## [32] train-rmse:1.251482+0.021640 test-rmse:1.439736+0.092946
## [33] train-rmse:1.241682+0.020950 test-rmse:1.434428+0.093599
## [34] train-rmse:1.232904+0.020725 test-rmse:1.429840+0.094708
## [35] train-rmse:1.224371+0.020625 test-rmse:1.423278+0.092858
## [36] train-rmse:1.216085+0.020568 test-rmse:1.420536+0.091154
## [37] train-rmse:1.207836+0.020733 test-rmse:1.417695+0.085380
## [38] train-rmse:1.199994+0.020575 test-rmse:1.415534+0.085463
## [39] train-rmse:1.192631+0.020553 test-rmse:1.404687+0.087978
## [40] train-rmse:1.185512+0.020414 test-rmse:1.395402+0.089704
## [41] train-rmse:1.178499+0.020261 test-rmse:1.389689+0.087640
## [42] train-rmse:1.172024+0.020058 test-rmse:1.386194+0.088052
## [43] train-rmse:1.165633+0.020086 test-rmse:1.384960+0.079004
## [44] train-rmse:1.159139+0.019885 test-rmse:1.380598+0.073242
## [45] train-rmse:1.153081+0.019813 test-rmse:1.371966+0.072341
## [46] train-rmse:1.147226+0.019670 test-rmse:1.365375+0.077134
## [47] train-rmse:1.141591+0.019488 test-rmse:1.363694+0.079994
## [48] train-rmse:1.135958+0.019330 test-rmse:1.357134+0.082982
## [49] train-rmse:1.130429+0.019215 test-rmse:1.355031+0.075777
## [50] train-rmse:1.125151+0.019028 test-rmse:1.350017+0.073549
## [51] train-rmse:1.119942+0.018895 test-rmse:1.350446+0.078449
## [52] train-rmse:1.114746+0.019025 test-rmse:1.347145+0.079049
## [53] train-rmse:1.109802+0.018956 test-rmse:1.342443+0.079123
## [54] train-rmse:1.105154+0.018871 test-rmse:1.339224+0.078949
## [55] train-rmse:1.100713+0.018718 test-rmse:1.334377+0.080060
## [56] train-rmse:1.096332+0.018662 test-rmse:1.328965+0.085450
## [57] train-rmse:1.091977+0.018713 test-rmse:1.327976+0.089802
## [58] train-rmse:1.087827+0.018615 test-rmse:1.328594+0.083467
## [59] train-rmse:1.083714+0.018595 test-rmse:1.325541+0.088396
## [60] train-rmse:1.079840+0.018569 test-rmse:1.321972+0.088979
## [61] train-rmse:1.075892+0.018376 test-rmse:1.316977+0.089169
## [62] train-rmse:1.072132+0.018147 test-rmse:1.314730+0.091053
## [63] train-rmse:1.068462+0.017999 test-rmse:1.311649+0.090509
## [64] train-rmse:1.064862+0.017851 test-rmse:1.302575+0.089995
## [65] train-rmse:1.061447+0.017715 test-rmse:1.300546+0.089649
## [66] train-rmse:1.058090+0.017694 test-rmse:1.295591+0.089541
## [67] train-rmse:1.054831+0.017647 test-rmse:1.294973+0.089082
## [68] train-rmse:1.051699+0.017573 test-rmse:1.293381+0.089244
## [69] train-rmse:1.048402+0.017480 test-rmse:1.289356+0.090133
## [70] train-rmse:1.045282+0.017501 test-rmse:1.287803+0.091392
## [71] train-rmse:1.042362+0.017589 test-rmse:1.290780+0.090916
## [72] train-rmse:1.039445+0.017595 test-rmse:1.291017+0.091319
## [73] train-rmse:1.036574+0.017607 test-rmse:1.291299+0.091924
## [74] train-rmse:1.033847+0.017447 test-rmse:1.289400+0.089612
## [75] train-rmse:1.031137+0.017311 test-rmse:1.284529+0.087928
## [76] train-rmse:1.028496+0.017190 test-rmse:1.284912+0.085871
## [77] train-rmse:1.025915+0.017101 test-rmse:1.282329+0.086713
## [78] train-rmse:1.023343+0.017166 test-rmse:1.279688+0.085944
## [79] train-rmse:1.020840+0.017194 test-rmse:1.274882+0.086883
## [80] train-rmse:1.018454+0.017148 test-rmse:1.272123+0.087876
## [81] train-rmse:1.016065+0.017115 test-rmse:1.273654+0.090668
## [82] train-rmse:1.013667+0.017016 test-rmse:1.273124+0.089594
## [83] train-rmse:1.011363+0.016964 test-rmse:1.274055+0.088585
## [84] train-rmse:1.009107+0.017001 test-rmse:1.275403+0.086017
## [85] train-rmse:1.006741+0.017019 test-rmse:1.269510+0.080815
## [86] train-rmse:1.004524+0.017014 test-rmse:1.270934+0.081176
## [87] train-rmse:1.002332+0.016985 test-rmse:1.268227+0.084087
## [88] train-rmse:1.000223+0.016935 test-rmse:1.265089+0.086376
## [89] train-rmse:0.998078+0.016896 test-rmse:1.265150+0.088439
## [90] train-rmse:0.996062+0.016836 test-rmse:1.265658+0.087188
## [91] train-rmse:0.993972+0.016839 test-rmse:1.265885+0.088443
## [92] train-rmse:0.991883+0.016763 test-rmse:1.259139+0.088218
## [93] train-rmse:0.989892+0.016825 test-rmse:1.258214+0.087892
## [94] train-rmse:0.987966+0.016874 test-rmse:1.261103+0.086137
## [95] train-rmse:0.986074+0.016919 test-rmse:1.262200+0.084951
## [96] train-rmse:0.984180+0.016910 test-rmse:1.258539+0.086109
## [97] train-rmse:0.982359+0.016928 test-rmse:1.257397+0.087808
## [98] train-rmse:0.980403+0.016786 test-rmse:1.255355+0.084605
## [99] train-rmse:0.978624+0.016826 test-rmse:1.250066+0.084735
## [100] train-rmse:0.976774+0.016896 test-rmse:1.248974+0.084851
## [101] train-rmse:0.974862+0.017025 test-rmse:1.248827+0.082334
## [102] train-rmse:0.973095+0.017113 test-rmse:1.247150+0.081318
## [103] train-rmse:0.971334+0.017071 test-rmse:1.248839+0.081075
## [104] train-rmse:0.969625+0.017149 test-rmse:1.249846+0.083660
## [105] train-rmse:0.967945+0.017201 test-rmse:1.248456+0.082952
## [106] train-rmse:0.966374+0.017246 test-rmse:1.246976+0.083296
## [107] train-rmse:0.964739+0.017289 test-rmse:1.245511+0.084412
## [108] train-rmse:0.963110+0.017366 test-rmse:1.243821+0.084596
## [109] train-rmse:0.961566+0.017471 test-rmse:1.243737+0.086062
## [110] train-rmse:0.960086+0.017506 test-rmse:1.242749+0.083178
## [111] train-rmse:0.958456+0.017605 test-rmse:1.242900+0.086453
## [112] train-rmse:0.956866+0.017661 test-rmse:1.242070+0.084703
## [113] train-rmse:0.955420+0.017655 test-rmse:1.242123+0.082057
## [114] train-rmse:0.953980+0.017572 test-rmse:1.240963+0.082005
## [115] train-rmse:0.952564+0.017590 test-rmse:1.240392+0.079738
## [116] train-rmse:0.951136+0.017609 test-rmse:1.238597+0.079228
## [117] train-rmse:0.949776+0.017639 test-rmse:1.236196+0.082103
## [118] train-rmse:0.948377+0.017702 test-rmse:1.234832+0.082106
## [119] train-rmse:0.947068+0.017736 test-rmse:1.234394+0.082115
## [120] train-rmse:0.945678+0.017740 test-rmse:1.232235+0.078538
## [121] train-rmse:0.944354+0.017753 test-rmse:1.234582+0.076826
## [122] train-rmse:0.943024+0.017697 test-rmse:1.236370+0.077714
## [123] train-rmse:0.941696+0.017695 test-rmse:1.236973+0.079391
## [124] train-rmse:0.940346+0.017713 test-rmse:1.235054+0.079903
## [125] train-rmse:0.939056+0.017720 test-rmse:1.235698+0.079931
## [126] train-rmse:0.937769+0.017748 test-rmse:1.233342+0.080856
## Stopping. Best iteration:
## [120] train-rmse:0.945678+0.017740 test-rmse:1.232235+0.078538
##
## 99
## [1] train-rmse:61.206300+0.061172 test-rmse:61.206691+0.431814
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:38.063428+0.037460 test-rmse:38.063095+0.453499
## [3] train-rmse:23.704581+0.022414 test-rmse:23.703120+0.465001
## [4] train-rmse:14.815793+0.013021 test-rmse:14.812671+0.468221
## [5] train-rmse:9.334342+0.007963 test-rmse:9.323402+0.427411
## [6] train-rmse:5.964672+0.010702 test-rmse:5.963430+0.378448
## [7] train-rmse:3.911708+0.013264 test-rmse:3.928905+0.365325
## [8] train-rmse:2.698953+0.018620 test-rmse:2.729413+0.301356
## [9] train-rmse:2.013266+0.026951 test-rmse:2.093623+0.253492
## [10] train-rmse:1.649253+0.030973 test-rmse:1.758732+0.197313
## [11] train-rmse:1.464292+0.034330 test-rmse:1.612720+0.160453
## [12] train-rmse:1.366563+0.036411 test-rmse:1.547933+0.136549
## [13] train-rmse:1.303465+0.032348 test-rmse:1.521655+0.122212
## [14] train-rmse:1.258863+0.020856 test-rmse:1.478727+0.114049
## [15] train-rmse:1.222949+0.020409 test-rmse:1.440246+0.112913
## [16] train-rmse:1.193012+0.024532 test-rmse:1.431345+0.112592
## [17] train-rmse:1.165409+0.027481 test-rmse:1.410106+0.096261
## [18] train-rmse:1.139458+0.025365 test-rmse:1.390989+0.097194
## [19] train-rmse:1.119852+0.026324 test-rmse:1.375419+0.097881
## [20] train-rmse:1.098685+0.025248 test-rmse:1.369428+0.099171
## [21] train-rmse:1.076780+0.023154 test-rmse:1.353042+0.098568
## [22] train-rmse:1.059098+0.022019 test-rmse:1.340419+0.102346
## [23] train-rmse:1.043423+0.022754 test-rmse:1.328036+0.101680
## [24] train-rmse:1.027498+0.023481 test-rmse:1.317190+0.096933
## [25] train-rmse:1.010446+0.027677 test-rmse:1.306704+0.087867
## [26] train-rmse:0.993733+0.028119 test-rmse:1.296445+0.082198
## [27] train-rmse:0.979146+0.029818 test-rmse:1.290919+0.085611
## [28] train-rmse:0.966542+0.030955 test-rmse:1.284183+0.081986
## [29] train-rmse:0.954346+0.032650 test-rmse:1.282332+0.083568
## [30] train-rmse:0.941642+0.033926 test-rmse:1.273982+0.080183
## [31] train-rmse:0.929511+0.031731 test-rmse:1.261332+0.089009
## [32] train-rmse:0.918655+0.031363 test-rmse:1.252632+0.091345
## [33] train-rmse:0.905206+0.030042 test-rmse:1.245952+0.092779
## [34] train-rmse:0.894502+0.029187 test-rmse:1.245215+0.091459
## [35] train-rmse:0.885474+0.028020 test-rmse:1.238303+0.090067
## [36] train-rmse:0.876049+0.030294 test-rmse:1.233947+0.093407
## [37] train-rmse:0.863362+0.032808 test-rmse:1.223194+0.093201
## [38] train-rmse:0.854751+0.031683 test-rmse:1.221084+0.093813
## [39] train-rmse:0.846841+0.031029 test-rmse:1.221806+0.095141
## [40] train-rmse:0.835574+0.029173 test-rmse:1.215674+0.096757
## [41] train-rmse:0.825394+0.031597 test-rmse:1.211786+0.093141
## [42] train-rmse:0.815776+0.028920 test-rmse:1.203188+0.091357
## [43] train-rmse:0.808830+0.028468 test-rmse:1.202864+0.096016
## [44] train-rmse:0.801276+0.028130 test-rmse:1.197791+0.091435
## [45] train-rmse:0.790110+0.027906 test-rmse:1.190224+0.091090
## [46] train-rmse:0.784984+0.027534 test-rmse:1.181671+0.088582
## [47] train-rmse:0.779257+0.028406 test-rmse:1.181621+0.090757
## [48] train-rmse:0.773865+0.028251 test-rmse:1.176860+0.087096
## [49] train-rmse:0.764500+0.022889 test-rmse:1.174953+0.083205
## [50] train-rmse:0.756852+0.020254 test-rmse:1.172395+0.082940
## [51] train-rmse:0.747504+0.019570 test-rmse:1.173248+0.080972
## [52] train-rmse:0.741761+0.017462 test-rmse:1.165649+0.087944
## [53] train-rmse:0.733069+0.015869 test-rmse:1.159903+0.086910
## [54] train-rmse:0.724655+0.014864 test-rmse:1.157789+0.088092
## [55] train-rmse:0.720265+0.015807 test-rmse:1.158506+0.088257
## [56] train-rmse:0.712082+0.017719 test-rmse:1.156543+0.086716
## [57] train-rmse:0.704480+0.017556 test-rmse:1.152026+0.089041
## [58] train-rmse:0.696870+0.020765 test-rmse:1.145605+0.091815
## [59] train-rmse:0.687681+0.020035 test-rmse:1.143628+0.089465
## [60] train-rmse:0.681634+0.019719 test-rmse:1.139496+0.087705
## [61] train-rmse:0.675987+0.019932 test-rmse:1.133024+0.084993
## [62] train-rmse:0.669686+0.020175 test-rmse:1.133339+0.086995
## [63] train-rmse:0.663155+0.018753 test-rmse:1.132797+0.084184
## [64] train-rmse:0.656930+0.018637 test-rmse:1.126184+0.087571
## [65] train-rmse:0.652892+0.017943 test-rmse:1.126027+0.087426
## [66] train-rmse:0.649472+0.017562 test-rmse:1.126563+0.085319
## [67] train-rmse:0.644924+0.017444 test-rmse:1.125477+0.086193
## [68] train-rmse:0.638834+0.015265 test-rmse:1.119882+0.086528
## [69] train-rmse:0.634092+0.016170 test-rmse:1.117278+0.084507
## [70] train-rmse:0.629990+0.017656 test-rmse:1.113135+0.082203
## [71] train-rmse:0.626669+0.018960 test-rmse:1.108904+0.082856
## [72] train-rmse:0.622598+0.019577 test-rmse:1.110095+0.080915
## [73] train-rmse:0.616712+0.018755 test-rmse:1.110688+0.082482
## [74] train-rmse:0.611751+0.018660 test-rmse:1.109290+0.082342
## [75] train-rmse:0.605280+0.018267 test-rmse:1.106920+0.079815
## [76] train-rmse:0.596512+0.017858 test-rmse:1.105344+0.083256
## [77] train-rmse:0.593823+0.017922 test-rmse:1.102519+0.080453
## [78] train-rmse:0.588390+0.017421 test-rmse:1.100698+0.080840
## [79] train-rmse:0.583682+0.019288 test-rmse:1.094794+0.076698
## [80] train-rmse:0.580198+0.019560 test-rmse:1.096702+0.074891
## [81] train-rmse:0.574497+0.018324 test-rmse:1.095227+0.079300
## [82] train-rmse:0.571526+0.018253 test-rmse:1.095554+0.077636
## [83] train-rmse:0.567427+0.016461 test-rmse:1.095333+0.074885
## [84] train-rmse:0.563904+0.017147 test-rmse:1.093309+0.074774
## [85] train-rmse:0.561128+0.017559 test-rmse:1.093406+0.075921
## [86] train-rmse:0.557348+0.016169 test-rmse:1.090028+0.076526
## [87] train-rmse:0.553728+0.015362 test-rmse:1.089182+0.075317
## [88] train-rmse:0.551209+0.014736 test-rmse:1.088793+0.075095
## [89] train-rmse:0.547232+0.014771 test-rmse:1.085828+0.073058
## [90] train-rmse:0.542895+0.013281 test-rmse:1.082649+0.075115
## [91] train-rmse:0.538309+0.015348 test-rmse:1.078216+0.075418
## [92] train-rmse:0.534311+0.015781 test-rmse:1.077753+0.078860
## [93] train-rmse:0.530850+0.016962 test-rmse:1.074954+0.079903
## [94] train-rmse:0.526008+0.018468 test-rmse:1.073812+0.076248
## [95] train-rmse:0.522430+0.018904 test-rmse:1.072596+0.075439
## [96] train-rmse:0.520417+0.019042 test-rmse:1.071897+0.076318
## [97] train-rmse:0.517368+0.020012 test-rmse:1.071947+0.076527
## [98] train-rmse:0.512720+0.021528 test-rmse:1.069145+0.076664
## [99] train-rmse:0.510256+0.021560 test-rmse:1.069282+0.076439
## [100] train-rmse:0.508245+0.021210 test-rmse:1.068689+0.076792
## [101] train-rmse:0.505053+0.020719 test-rmse:1.065957+0.077333
## [102] train-rmse:0.501814+0.019683 test-rmse:1.067525+0.080122
## [103] train-rmse:0.498741+0.018936 test-rmse:1.065525+0.079115
## [104] train-rmse:0.495783+0.019508 test-rmse:1.066356+0.081780
## [105] train-rmse:0.492195+0.018847 test-rmse:1.065391+0.080386
## [106] train-rmse:0.488826+0.019013 test-rmse:1.064816+0.078919
## [107] train-rmse:0.483173+0.017562 test-rmse:1.060634+0.077297
## [108] train-rmse:0.480467+0.017279 test-rmse:1.058793+0.076277
## [109] train-rmse:0.476736+0.015142 test-rmse:1.057151+0.077824
## [110] train-rmse:0.473505+0.016520 test-rmse:1.058039+0.076234
## [111] train-rmse:0.469844+0.017542 test-rmse:1.057874+0.076976
## [112] train-rmse:0.466034+0.018303 test-rmse:1.057010+0.076091
## [113] train-rmse:0.464501+0.018263 test-rmse:1.056553+0.077184
## [114] train-rmse:0.461772+0.018610 test-rmse:1.056782+0.077721
## [115] train-rmse:0.458295+0.018664 test-rmse:1.054569+0.076203
## [116] train-rmse:0.456457+0.018636 test-rmse:1.055212+0.076705
## [117] train-rmse:0.454517+0.018567 test-rmse:1.054625+0.076528
## [118] train-rmse:0.451930+0.018495 test-rmse:1.056620+0.076320
## [119] train-rmse:0.449998+0.018112 test-rmse:1.057327+0.076008
## [120] train-rmse:0.447690+0.017380 test-rmse:1.055023+0.076355
## [121] train-rmse:0.444377+0.018896 test-rmse:1.053288+0.074830
## [122] train-rmse:0.440503+0.017858 test-rmse:1.050944+0.078872
## [123] train-rmse:0.436571+0.017766 test-rmse:1.050343+0.078967
## [124] train-rmse:0.433677+0.018452 test-rmse:1.050260+0.080884
## [125] train-rmse:0.430266+0.016708 test-rmse:1.049625+0.079143
## [126] train-rmse:0.427137+0.016926 test-rmse:1.050588+0.079809
## [127] train-rmse:0.423862+0.015390 test-rmse:1.049695+0.081517
## [128] train-rmse:0.420676+0.016247 test-rmse:1.051637+0.083406
## [129] train-rmse:0.418771+0.016213 test-rmse:1.049759+0.081840
## [130] train-rmse:0.416735+0.016520 test-rmse:1.052013+0.081811
## [131] train-rmse:0.413216+0.015680 test-rmse:1.049768+0.082395
## Stopping. Best iteration:
## [125] train-rmse:0.430266+0.016708 test-rmse:1.049625+0.079143
##
## 100
## [1] train-rmse:61.206300+0.061172 test-rmse:61.206691+0.431814
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:38.063428+0.037460 test-rmse:38.063095+0.453499
## [3] train-rmse:23.704581+0.022414 test-rmse:23.703120+0.465001
## [4] train-rmse:14.815793+0.013021 test-rmse:14.812671+0.468221
## [5] train-rmse:9.334342+0.007963 test-rmse:9.323402+0.427411
## [6] train-rmse:5.959580+0.009265 test-rmse:5.947716+0.372978
## [7] train-rmse:3.887371+0.010622 test-rmse:3.889910+0.336216
## [8] train-rmse:2.647127+0.015181 test-rmse:2.669477+0.284725
## [9] train-rmse:1.922413+0.017145 test-rmse:2.003553+0.250023
## [10] train-rmse:1.529429+0.017036 test-rmse:1.650112+0.186618
## [11] train-rmse:1.312724+0.012719 test-rmse:1.480860+0.158129
## [12] train-rmse:1.192451+0.014783 test-rmse:1.407844+0.135609
## [13] train-rmse:1.117744+0.020673 test-rmse:1.367564+0.111202
## [14] train-rmse:1.056168+0.020456 test-rmse:1.336417+0.098564
## [15] train-rmse:1.014632+0.015373 test-rmse:1.313155+0.093901
## [16] train-rmse:0.980207+0.019747 test-rmse:1.288570+0.090379
## [17] train-rmse:0.952346+0.018425 test-rmse:1.272707+0.094985
## [18] train-rmse:0.919852+0.022856 test-rmse:1.257086+0.093940
## [19] train-rmse:0.894709+0.024654 test-rmse:1.245359+0.097554
## [20] train-rmse:0.871331+0.027620 test-rmse:1.227915+0.101618
## [21] train-rmse:0.844766+0.023743 test-rmse:1.217436+0.099851
## [22] train-rmse:0.822870+0.024367 test-rmse:1.201368+0.085708
## [23] train-rmse:0.802510+0.026910 test-rmse:1.183122+0.076703
## [24] train-rmse:0.785654+0.027860 test-rmse:1.169261+0.085263
## [25] train-rmse:0.771822+0.028755 test-rmse:1.164189+0.083455
## [26] train-rmse:0.754571+0.026089 test-rmse:1.152781+0.078961
## [27] train-rmse:0.735599+0.028363 test-rmse:1.148465+0.081003
## [28] train-rmse:0.719974+0.026499 test-rmse:1.145792+0.081799
## [29] train-rmse:0.706573+0.028151 test-rmse:1.137968+0.077330
## [30] train-rmse:0.694275+0.026041 test-rmse:1.129430+0.083094
## [31] train-rmse:0.684637+0.027065 test-rmse:1.116724+0.079053
## [32] train-rmse:0.669482+0.030349 test-rmse:1.111502+0.081623
## [33] train-rmse:0.655290+0.026898 test-rmse:1.103304+0.083145
## [34] train-rmse:0.642277+0.026204 test-rmse:1.092884+0.076799
## [35] train-rmse:0.630545+0.028942 test-rmse:1.088336+0.077733
## [36] train-rmse:0.620939+0.030372 test-rmse:1.073426+0.064361
## [37] train-rmse:0.610749+0.029787 test-rmse:1.072825+0.063262
## [38] train-rmse:0.601182+0.033218 test-rmse:1.064988+0.057650
## [39] train-rmse:0.593137+0.034935 test-rmse:1.059589+0.058699
## [40] train-rmse:0.582988+0.035027 test-rmse:1.053449+0.057747
## [41] train-rmse:0.572598+0.032909 test-rmse:1.052265+0.057925
## [42] train-rmse:0.561277+0.029792 test-rmse:1.041810+0.055606
## [43] train-rmse:0.552195+0.026961 test-rmse:1.038877+0.058190
## [44] train-rmse:0.544291+0.026015 test-rmse:1.033256+0.061590
## [45] train-rmse:0.536932+0.027323 test-rmse:1.022584+0.062471
## [46] train-rmse:0.528980+0.025383 test-rmse:1.019349+0.065027
## [47] train-rmse:0.518711+0.024144 test-rmse:1.015731+0.062468
## [48] train-rmse:0.512383+0.026391 test-rmse:1.010942+0.063859
## [49] train-rmse:0.503375+0.028477 test-rmse:1.009235+0.065284
## [50] train-rmse:0.496976+0.026269 test-rmse:1.005536+0.064312
## [51] train-rmse:0.491767+0.026633 test-rmse:1.004247+0.064114
## [52] train-rmse:0.483037+0.027483 test-rmse:1.006096+0.063189
## [53] train-rmse:0.472446+0.027953 test-rmse:1.002720+0.059216
## [54] train-rmse:0.463104+0.025692 test-rmse:1.001247+0.058640
## [55] train-rmse:0.454614+0.025365 test-rmse:1.005686+0.061611
## [56] train-rmse:0.448104+0.024450 test-rmse:1.002979+0.060037
## [57] train-rmse:0.443059+0.024027 test-rmse:1.001003+0.061402
## [58] train-rmse:0.436662+0.022619 test-rmse:0.996464+0.058660
## [59] train-rmse:0.430432+0.022097 test-rmse:0.996127+0.060150
## [60] train-rmse:0.424381+0.023772 test-rmse:0.999711+0.063297
## [61] train-rmse:0.419901+0.026435 test-rmse:0.998438+0.062146
## [62] train-rmse:0.412113+0.026353 test-rmse:0.998353+0.066466
## [63] train-rmse:0.403951+0.025838 test-rmse:0.999008+0.063860
## [64] train-rmse:0.398654+0.025187 test-rmse:1.000616+0.066246
## [65] train-rmse:0.393666+0.022596 test-rmse:0.999619+0.064474
## Stopping. Best iteration:
## [59] train-rmse:0.430432+0.022097 test-rmse:0.996127+0.060150
##
## 101
## [1] train-rmse:61.206300+0.061172 test-rmse:61.206691+0.431814
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:38.063428+0.037460 test-rmse:38.063095+0.453499
## [3] train-rmse:23.704581+0.022414 test-rmse:23.703120+0.465001
## [4] train-rmse:14.815793+0.013021 test-rmse:14.812671+0.468221
## [5] train-rmse:9.334342+0.007963 test-rmse:9.323402+0.427411
## [6] train-rmse:5.956052+0.008980 test-rmse:5.946320+0.375514
## [7] train-rmse:3.876816+0.008401 test-rmse:3.871795+0.332488
## [8] train-rmse:2.615169+0.013751 test-rmse:2.633736+0.263273
## [9] train-rmse:1.859091+0.023967 test-rmse:1.937477+0.205648
## [10] train-rmse:1.426907+0.023415 test-rmse:1.559973+0.159525
## [11] train-rmse:1.193325+0.028425 test-rmse:1.390498+0.135620
## [12] train-rmse:1.052858+0.041994 test-rmse:1.318621+0.110268
## [13] train-rmse:0.967708+0.042092 test-rmse:1.277211+0.095002
## [14] train-rmse:0.901471+0.038562 test-rmse:1.248531+0.085173
## [15] train-rmse:0.857331+0.036262 test-rmse:1.213814+0.078263
## [16] train-rmse:0.816093+0.040678 test-rmse:1.188488+0.073972
## [17] train-rmse:0.782813+0.035771 test-rmse:1.175635+0.071430
## [18] train-rmse:0.752870+0.036396 test-rmse:1.155688+0.069682
## [19] train-rmse:0.721247+0.030488 test-rmse:1.129783+0.069258
## [20] train-rmse:0.692574+0.032079 test-rmse:1.105371+0.052823
## [21] train-rmse:0.668434+0.032055 test-rmse:1.096803+0.052387
## [22] train-rmse:0.651403+0.031080 test-rmse:1.090184+0.050271
## [23] train-rmse:0.631539+0.033403 test-rmse:1.087087+0.055281
## [24] train-rmse:0.609102+0.033162 test-rmse:1.075835+0.048238
## [25] train-rmse:0.589047+0.032740 test-rmse:1.069960+0.043674
## [26] train-rmse:0.565483+0.035445 test-rmse:1.064098+0.048215
## [27] train-rmse:0.550540+0.031964 test-rmse:1.059101+0.051997
## [28] train-rmse:0.532543+0.032164 test-rmse:1.050733+0.050195
## [29] train-rmse:0.516935+0.036814 test-rmse:1.043970+0.045193
## [30] train-rmse:0.502472+0.034714 test-rmse:1.044540+0.049700
## [31] train-rmse:0.491952+0.033561 test-rmse:1.041597+0.052390
## [32] train-rmse:0.478301+0.029062 test-rmse:1.043385+0.049546
## [33] train-rmse:0.466166+0.028356 test-rmse:1.036575+0.049119
## [34] train-rmse:0.455704+0.029802 test-rmse:1.034304+0.048566
## [35] train-rmse:0.447537+0.031437 test-rmse:1.034424+0.051085
## [36] train-rmse:0.437341+0.030033 test-rmse:1.033735+0.046523
## [37] train-rmse:0.423895+0.029379 test-rmse:1.026532+0.049810
## [38] train-rmse:0.413709+0.029958 test-rmse:1.015294+0.047573
## [39] train-rmse:0.403938+0.032378 test-rmse:1.017696+0.047416
## [40] train-rmse:0.393554+0.031155 test-rmse:1.019313+0.050074
## [41] train-rmse:0.384452+0.030317 test-rmse:1.017084+0.047986
## [42] train-rmse:0.378859+0.031292 test-rmse:1.013506+0.047907
## [43] train-rmse:0.370147+0.033967 test-rmse:1.013418+0.050751
## [44] train-rmse:0.360376+0.030534 test-rmse:1.010550+0.053398
## [45] train-rmse:0.351531+0.027787 test-rmse:1.005223+0.052209
## [46] train-rmse:0.342972+0.028669 test-rmse:1.004350+0.051209
## [47] train-rmse:0.335841+0.028453 test-rmse:1.002860+0.052535
## [48] train-rmse:0.324429+0.025866 test-rmse:0.996107+0.053354
## [49] train-rmse:0.313311+0.022154 test-rmse:0.996512+0.054201
## [50] train-rmse:0.306586+0.021506 test-rmse:0.995630+0.054430
## [51] train-rmse:0.299520+0.023480 test-rmse:0.992994+0.053675
## [52] train-rmse:0.293287+0.024176 test-rmse:0.991318+0.053943
## [53] train-rmse:0.288510+0.023497 test-rmse:0.992197+0.054791
## [54] train-rmse:0.282673+0.024521 test-rmse:0.992257+0.051932
## [55] train-rmse:0.275155+0.026179 test-rmse:0.990374+0.050235
## [56] train-rmse:0.266189+0.025361 test-rmse:0.989159+0.053939
## [57] train-rmse:0.257615+0.025159 test-rmse:0.988024+0.053586
## [58] train-rmse:0.252302+0.023090 test-rmse:0.988909+0.051223
## [59] train-rmse:0.246521+0.022094 test-rmse:0.988942+0.052008
## [60] train-rmse:0.239882+0.020393 test-rmse:0.985029+0.049705
## [61] train-rmse:0.233045+0.019835 test-rmse:0.985690+0.049739
## [62] train-rmse:0.228907+0.017874 test-rmse:0.984438+0.050134
## [63] train-rmse:0.223925+0.018006 test-rmse:0.983665+0.050672
## [64] train-rmse:0.218446+0.017682 test-rmse:0.984220+0.053465
## [65] train-rmse:0.212931+0.018125 test-rmse:0.984785+0.053973
## [66] train-rmse:0.207961+0.018226 test-rmse:0.985896+0.055155
## [67] train-rmse:0.204162+0.018408 test-rmse:0.985898+0.055501
## [68] train-rmse:0.198589+0.017424 test-rmse:0.986322+0.056162
## [69] train-rmse:0.194298+0.016612 test-rmse:0.985853+0.055750
## Stopping. Best iteration:
## [63] train-rmse:0.223925+0.018006 test-rmse:0.983665+0.050672
##
## 102
## [1] train-rmse:61.206300+0.061172 test-rmse:61.206691+0.431814
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:38.063428+0.037460 test-rmse:38.063095+0.453499
## [3] train-rmse:23.704581+0.022414 test-rmse:23.703120+0.465001
## [4] train-rmse:14.815793+0.013021 test-rmse:14.812671+0.468221
## [5] train-rmse:9.334342+0.007963 test-rmse:9.323402+0.427411
## [6] train-rmse:5.954602+0.008652 test-rmse:5.940598+0.372931
## [7] train-rmse:3.871042+0.006881 test-rmse:3.874928+0.326396
## [8] train-rmse:2.597212+0.008252 test-rmse:2.625575+0.264568
## [9] train-rmse:1.816630+0.013650 test-rmse:1.905601+0.215494
## [10] train-rmse:1.357399+0.024623 test-rmse:1.550566+0.155210
## [11] train-rmse:1.093482+0.022295 test-rmse:1.365776+0.129504
## [12] train-rmse:0.935583+0.028714 test-rmse:1.261058+0.103611
## [13] train-rmse:0.848413+0.033954 test-rmse:1.213906+0.091588
## [14] train-rmse:0.772816+0.026976 test-rmse:1.175449+0.080873
## [15] train-rmse:0.720443+0.031332 test-rmse:1.153618+0.091297
## [16] train-rmse:0.670526+0.034161 test-rmse:1.137670+0.089578
## [17] train-rmse:0.638438+0.033252 test-rmse:1.122583+0.085342
## [18] train-rmse:0.609356+0.034696 test-rmse:1.117775+0.093041
## [19] train-rmse:0.588540+0.035855 test-rmse:1.097949+0.093141
## [20] train-rmse:0.569082+0.035208 test-rmse:1.088615+0.088113
## [21] train-rmse:0.540009+0.039343 test-rmse:1.068154+0.080536
## [22] train-rmse:0.519388+0.036871 test-rmse:1.067038+0.078395
## [23] train-rmse:0.496491+0.038691 test-rmse:1.063822+0.080704
## [24] train-rmse:0.480660+0.038866 test-rmse:1.057783+0.078406
## [25] train-rmse:0.458094+0.032866 test-rmse:1.048519+0.076879
## [26] train-rmse:0.443261+0.031124 test-rmse:1.041520+0.080290
## [27] train-rmse:0.426313+0.030179 test-rmse:1.039356+0.078130
## [28] train-rmse:0.409380+0.025721 test-rmse:1.032058+0.078070
## [29] train-rmse:0.395068+0.026080 test-rmse:1.034913+0.085419
## [30] train-rmse:0.379836+0.023167 test-rmse:1.029322+0.087342
## [31] train-rmse:0.366693+0.023772 test-rmse:1.025707+0.086022
## [32] train-rmse:0.353563+0.024152 test-rmse:1.027050+0.086419
## [33] train-rmse:0.341055+0.023539 test-rmse:1.026268+0.085745
## [34] train-rmse:0.330301+0.027911 test-rmse:1.020799+0.090718
## [35] train-rmse:0.318609+0.030669 test-rmse:1.020290+0.091172
## [36] train-rmse:0.311437+0.030116 test-rmse:1.020087+0.088812
## [37] train-rmse:0.302289+0.027984 test-rmse:1.019790+0.091417
## [38] train-rmse:0.291737+0.029528 test-rmse:1.013245+0.087973
## [39] train-rmse:0.283956+0.028477 test-rmse:1.013895+0.088675
## [40] train-rmse:0.275150+0.028387 test-rmse:1.015105+0.089383
## [41] train-rmse:0.268032+0.025029 test-rmse:1.012693+0.092255
## [42] train-rmse:0.260058+0.023564 test-rmse:1.009310+0.089307
## [43] train-rmse:0.251355+0.021619 test-rmse:1.009284+0.093396
## [44] train-rmse:0.244132+0.023759 test-rmse:1.009285+0.092496
## [45] train-rmse:0.234851+0.022539 test-rmse:1.008857+0.091813
## [46] train-rmse:0.224435+0.018442 test-rmse:1.006112+0.093206
## [47] train-rmse:0.214799+0.018784 test-rmse:1.001019+0.090465
## [48] train-rmse:0.207143+0.019120 test-rmse:0.997478+0.096216
## [49] train-rmse:0.199233+0.016796 test-rmse:0.997196+0.093814
## [50] train-rmse:0.194333+0.017819 test-rmse:0.995663+0.095120
## [51] train-rmse:0.186042+0.017710 test-rmse:0.997601+0.097215
## [52] train-rmse:0.181457+0.017506 test-rmse:0.997509+0.099210
## [53] train-rmse:0.175237+0.017281 test-rmse:0.995207+0.099153
## [54] train-rmse:0.170043+0.016992 test-rmse:0.995727+0.097884
## [55] train-rmse:0.162098+0.015381 test-rmse:0.996350+0.100242
## [56] train-rmse:0.157647+0.014200 test-rmse:0.996496+0.098460
## [57] train-rmse:0.152984+0.012263 test-rmse:0.995633+0.098633
## [58] train-rmse:0.147891+0.011542 test-rmse:0.995279+0.098912
## [59] train-rmse:0.144802+0.011669 test-rmse:0.995253+0.099868
## Stopping. Best iteration:
## [53] train-rmse:0.175237+0.017281 test-rmse:0.995207+0.099153
##
## 103
## [1] train-rmse:61.206300+0.061172 test-rmse:61.206691+0.431814
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:38.063428+0.037460 test-rmse:38.063095+0.453499
## [3] train-rmse:23.704581+0.022414 test-rmse:23.703120+0.465001
## [4] train-rmse:14.815793+0.013021 test-rmse:14.812671+0.468221
## [5] train-rmse:9.334342+0.007963 test-rmse:9.323402+0.427411
## [6] train-rmse:5.953824+0.007985 test-rmse:5.934971+0.371769
## [7] train-rmse:3.866855+0.006520 test-rmse:3.866282+0.327001
## [8] train-rmse:2.585452+0.008821 test-rmse:2.622243+0.256336
## [9] train-rmse:1.788230+0.007985 test-rmse:1.899666+0.208489
## [10] train-rmse:1.313849+0.014863 test-rmse:1.520799+0.176661
## [11] train-rmse:1.036072+0.030977 test-rmse:1.331825+0.157503
## [12] train-rmse:0.868894+0.030416 test-rmse:1.228308+0.137842
## [13] train-rmse:0.766524+0.035703 test-rmse:1.189324+0.129373
## [14] train-rmse:0.687238+0.036331 test-rmse:1.155337+0.119975
## [15] train-rmse:0.631808+0.036890 test-rmse:1.131803+0.115000
## [16] train-rmse:0.585091+0.031720 test-rmse:1.113451+0.114785
## [17] train-rmse:0.538242+0.029412 test-rmse:1.095162+0.107216
## [18] train-rmse:0.512665+0.028148 test-rmse:1.085863+0.106001
## [19] train-rmse:0.487769+0.029550 test-rmse:1.075304+0.099075
## [20] train-rmse:0.457842+0.030557 test-rmse:1.072002+0.106771
## [21] train-rmse:0.437605+0.032188 test-rmse:1.069125+0.108587
## [22] train-rmse:0.411253+0.022711 test-rmse:1.061725+0.104071
## [23] train-rmse:0.394121+0.023355 test-rmse:1.060973+0.107604
## [24] train-rmse:0.372562+0.019953 test-rmse:1.055414+0.111842
## [25] train-rmse:0.355266+0.018348 test-rmse:1.052932+0.107247
## [26] train-rmse:0.337838+0.017398 test-rmse:1.053007+0.105094
## [27] train-rmse:0.320543+0.018581 test-rmse:1.046381+0.107699
## [28] train-rmse:0.304611+0.017256 test-rmse:1.040036+0.108603
## [29] train-rmse:0.288729+0.013751 test-rmse:1.034578+0.108081
## [30] train-rmse:0.279890+0.013522 test-rmse:1.033108+0.107599
## [31] train-rmse:0.266317+0.016762 test-rmse:1.031944+0.108426
## [32] train-rmse:0.256497+0.015275 test-rmse:1.024986+0.107423
## [33] train-rmse:0.243057+0.015896 test-rmse:1.022914+0.111055
## [34] train-rmse:0.230832+0.015712 test-rmse:1.020091+0.112978
## [35] train-rmse:0.219017+0.013039 test-rmse:1.019382+0.111989
## [36] train-rmse:0.209170+0.013580 test-rmse:1.016708+0.111425
## [37] train-rmse:0.199875+0.017004 test-rmse:1.016271+0.112603
## [38] train-rmse:0.192837+0.015615 test-rmse:1.017011+0.113535
## [39] train-rmse:0.183804+0.018593 test-rmse:1.015163+0.112832
## [40] train-rmse:0.176023+0.017314 test-rmse:1.011827+0.113505
## [41] train-rmse:0.170065+0.018140 test-rmse:1.010099+0.115508
## [42] train-rmse:0.162370+0.016583 test-rmse:1.011121+0.116512
## [43] train-rmse:0.158019+0.016776 test-rmse:1.009699+0.116594
## [44] train-rmse:0.149731+0.018190 test-rmse:1.009149+0.116437
## [45] train-rmse:0.143798+0.018808 test-rmse:1.007334+0.116687
## [46] train-rmse:0.137081+0.018022 test-rmse:1.007812+0.114395
## [47] train-rmse:0.130243+0.016949 test-rmse:1.007574+0.115375
## [48] train-rmse:0.124785+0.016392 test-rmse:1.008310+0.116072
## [49] train-rmse:0.118529+0.014480 test-rmse:1.007769+0.116770
## [50] train-rmse:0.112738+0.012800 test-rmse:1.007966+0.116779
## [51] train-rmse:0.108671+0.014334 test-rmse:1.006768+0.116982
## [52] train-rmse:0.103140+0.013437 test-rmse:1.007970+0.117790
## [53] train-rmse:0.098117+0.012652 test-rmse:1.009689+0.117277
## [54] train-rmse:0.093792+0.012445 test-rmse:1.009941+0.117620
## [55] train-rmse:0.091018+0.012147 test-rmse:1.010331+0.118477
## [56] train-rmse:0.086867+0.011380 test-rmse:1.009150+0.119032
## [57] train-rmse:0.082954+0.011044 test-rmse:1.008641+0.118982
## Stopping. Best iteration:
## [51] train-rmse:0.108671+0.014334 test-rmse:1.006768+0.116982
##
## 104
## [1] train-rmse:61.206300+0.061172 test-rmse:61.206691+0.431814
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:38.063428+0.037460 test-rmse:38.063095+0.453499
## [3] train-rmse:23.704581+0.022414 test-rmse:23.703120+0.465001
## [4] train-rmse:14.815793+0.013021 test-rmse:14.812671+0.468221
## [5] train-rmse:9.334342+0.007963 test-rmse:9.323402+0.427411
## [6] train-rmse:5.953824+0.007985 test-rmse:5.934971+0.371769
## [7] train-rmse:3.864373+0.005977 test-rmse:3.863765+0.321895
## [8] train-rmse:2.578048+0.008099 test-rmse:2.629716+0.250392
## [9] train-rmse:1.767350+0.008561 test-rmse:1.895938+0.198284
## [10] train-rmse:1.286898+0.016758 test-rmse:1.500295+0.168409
## [11] train-rmse:0.975282+0.022017 test-rmse:1.305683+0.127989
## [12] train-rmse:0.797926+0.034461 test-rmse:1.195842+0.114084
## [13] train-rmse:0.677562+0.039606 test-rmse:1.145878+0.093093
## [14] train-rmse:0.597819+0.041050 test-rmse:1.113286+0.081332
## [15] train-rmse:0.538172+0.046386 test-rmse:1.097950+0.078027
## [16] train-rmse:0.499039+0.043019 test-rmse:1.082584+0.079449
## [17] train-rmse:0.462622+0.055602 test-rmse:1.075678+0.077304
## [18] train-rmse:0.419633+0.041898 test-rmse:1.063736+0.088866
## [19] train-rmse:0.397018+0.038547 test-rmse:1.054048+0.096899
## [20] train-rmse:0.376696+0.040447 test-rmse:1.041786+0.096601
## [21] train-rmse:0.347612+0.038709 test-rmse:1.036885+0.103155
## [22] train-rmse:0.323915+0.033255 test-rmse:1.035033+0.104025
## [23] train-rmse:0.307088+0.032832 test-rmse:1.031728+0.106769
## [24] train-rmse:0.284231+0.027239 test-rmse:1.031695+0.110947
## [25] train-rmse:0.265712+0.026552 test-rmse:1.032078+0.110373
## [26] train-rmse:0.248385+0.030753 test-rmse:1.030510+0.111007
## [27] train-rmse:0.235763+0.028729 test-rmse:1.026588+0.113871
## [28] train-rmse:0.221521+0.030030 test-rmse:1.025215+0.116632
## [29] train-rmse:0.205813+0.030605 test-rmse:1.022037+0.113232
## [30] train-rmse:0.195094+0.030210 test-rmse:1.021904+0.112551
## [31] train-rmse:0.180665+0.024981 test-rmse:1.018480+0.112022
## [32] train-rmse:0.169819+0.023446 test-rmse:1.018050+0.111419
## [33] train-rmse:0.161108+0.024257 test-rmse:1.017440+0.110848
## [34] train-rmse:0.153745+0.022032 test-rmse:1.015949+0.111296
## [35] train-rmse:0.147013+0.022294 test-rmse:1.014709+0.112222
## [36] train-rmse:0.137607+0.022847 test-rmse:1.014057+0.115007
## [37] train-rmse:0.129083+0.020966 test-rmse:1.014157+0.111978
## [38] train-rmse:0.121718+0.018641 test-rmse:1.010925+0.113255
## [39] train-rmse:0.116694+0.018229 test-rmse:1.009527+0.113825
## [40] train-rmse:0.111895+0.016748 test-rmse:1.009198+0.112540
## [41] train-rmse:0.105071+0.016630 test-rmse:1.009891+0.114032
## [42] train-rmse:0.097475+0.015051 test-rmse:1.010812+0.114516
## [43] train-rmse:0.092353+0.015191 test-rmse:1.010304+0.113934
## [44] train-rmse:0.088516+0.015572 test-rmse:1.009982+0.113762
## [45] train-rmse:0.081408+0.014324 test-rmse:1.010359+0.112567
## [46] train-rmse:0.077189+0.014783 test-rmse:1.010639+0.112833
## Stopping. Best iteration:
## [40] train-rmse:0.111895+0.016748 test-rmse:1.009198+0.112540
##
## 105
## [1] train-rmse:59.245290+0.059177 test-rmse:59.245643+0.433703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:35.667956+0.034978 test-rmse:35.667497+0.455608
## [3] train-rmse:21.513287+0.020080 test-rmse:21.511532+0.466322
## [4] train-rmse:13.041444+0.011310 test-rmse:13.037761+0.467604
## [5] train-rmse:7.998222+0.007843 test-rmse:7.998328+0.417416
## [6] train-rmse:5.027708+0.009628 test-rmse:5.030977+0.393844
## [7] train-rmse:3.329908+0.015255 test-rmse:3.334289+0.351356
## [8] train-rmse:2.415650+0.022642 test-rmse:2.447139+0.283287
## [9] train-rmse:1.952082+0.026684 test-rmse:2.006050+0.212866
## [10] train-rmse:1.733217+0.030236 test-rmse:1.809564+0.145537
## [11] train-rmse:1.629524+0.029410 test-rmse:1.717783+0.124389
## [12] train-rmse:1.571849+0.026819 test-rmse:1.677169+0.121622
## [13] train-rmse:1.533831+0.024745 test-rmse:1.621557+0.109307
## [14] train-rmse:1.503744+0.024040 test-rmse:1.599529+0.109819
## [15] train-rmse:1.479170+0.023349 test-rmse:1.590049+0.110163
## [16] train-rmse:1.457765+0.022485 test-rmse:1.576332+0.113421
## [17] train-rmse:1.437602+0.022251 test-rmse:1.571652+0.113718
## [18] train-rmse:1.419087+0.023416 test-rmse:1.553950+0.105347
## [19] train-rmse:1.402029+0.023702 test-rmse:1.545092+0.107669
## [20] train-rmse:1.385517+0.023961 test-rmse:1.535283+0.104598
## [21] train-rmse:1.370357+0.023605 test-rmse:1.529963+0.111903
## [22] train-rmse:1.356322+0.022772 test-rmse:1.520344+0.107026
## [23] train-rmse:1.342071+0.021692 test-rmse:1.513982+0.089696
## [24] train-rmse:1.328626+0.021020 test-rmse:1.502288+0.087149
## [25] train-rmse:1.315764+0.020436 test-rmse:1.493301+0.093189
## [26] train-rmse:1.303801+0.020315 test-rmse:1.481693+0.102339
## [27] train-rmse:1.292196+0.019954 test-rmse:1.477807+0.101942
## [28] train-rmse:1.280920+0.019479 test-rmse:1.463875+0.103457
## [29] train-rmse:1.269716+0.019395 test-rmse:1.454616+0.088206
## [30] train-rmse:1.258843+0.019003 test-rmse:1.453382+0.094591
## [31] train-rmse:1.248241+0.019022 test-rmse:1.441289+0.090614
## [32] train-rmse:1.237887+0.019151 test-rmse:1.427158+0.089168
## [33] train-rmse:1.228431+0.018817 test-rmse:1.420009+0.092632
## [34] train-rmse:1.219396+0.018757 test-rmse:1.413411+0.092293
## [35] train-rmse:1.210702+0.018749 test-rmse:1.415884+0.092483
## [36] train-rmse:1.202352+0.018953 test-rmse:1.408081+0.094976
## [37] train-rmse:1.194430+0.019018 test-rmse:1.408870+0.087314
## [38] train-rmse:1.186866+0.019045 test-rmse:1.406238+0.085767
## [39] train-rmse:1.179413+0.018971 test-rmse:1.397468+0.081849
## [40] train-rmse:1.172245+0.018839 test-rmse:1.383015+0.079966
## [41] train-rmse:1.165646+0.018848 test-rmse:1.383649+0.078085
## [42] train-rmse:1.159148+0.018992 test-rmse:1.383742+0.078489
## [43] train-rmse:1.152934+0.018850 test-rmse:1.372594+0.088444
## [44] train-rmse:1.146871+0.018775 test-rmse:1.368485+0.092235
## [45] train-rmse:1.140832+0.018561 test-rmse:1.362434+0.087400
## [46] train-rmse:1.135018+0.018349 test-rmse:1.354009+0.089978
## [47] train-rmse:1.129395+0.018247 test-rmse:1.350404+0.091755
## [48] train-rmse:1.123841+0.018139 test-rmse:1.346289+0.091656
## [49] train-rmse:1.118499+0.018038 test-rmse:1.343690+0.084858
## [50] train-rmse:1.113147+0.018046 test-rmse:1.338143+0.083469
## [51] train-rmse:1.108027+0.017828 test-rmse:1.341726+0.083544
## [52] train-rmse:1.103043+0.017831 test-rmse:1.336081+0.082109
## [53] train-rmse:1.098155+0.017718 test-rmse:1.330457+0.081228
## [54] train-rmse:1.093539+0.017602 test-rmse:1.327323+0.082892
## [55] train-rmse:1.089034+0.017534 test-rmse:1.326605+0.084475
## [56] train-rmse:1.084874+0.017383 test-rmse:1.320929+0.086973
## [57] train-rmse:1.080672+0.017260 test-rmse:1.318088+0.089111
## [58] train-rmse:1.076689+0.017219 test-rmse:1.313173+0.089172
## [59] train-rmse:1.072521+0.017204 test-rmse:1.311130+0.092718
## [60] train-rmse:1.068699+0.017013 test-rmse:1.308090+0.094626
## [61] train-rmse:1.064987+0.016707 test-rmse:1.301415+0.092845
## [62] train-rmse:1.061460+0.016662 test-rmse:1.299352+0.093066
## [63] train-rmse:1.057979+0.016595 test-rmse:1.294934+0.095058
## [64] train-rmse:1.054613+0.016587 test-rmse:1.291807+0.094091
## [65] train-rmse:1.051364+0.016585 test-rmse:1.292513+0.091345
## [66] train-rmse:1.048102+0.016529 test-rmse:1.289971+0.086587
## [67] train-rmse:1.044850+0.016460 test-rmse:1.291453+0.087871
## [68] train-rmse:1.041675+0.016307 test-rmse:1.288277+0.088462
## [69] train-rmse:1.038638+0.016274 test-rmse:1.281949+0.088205
## [70] train-rmse:1.035627+0.016210 test-rmse:1.282005+0.092086
## [71] train-rmse:1.032716+0.016255 test-rmse:1.281422+0.090338
## [72] train-rmse:1.029867+0.016258 test-rmse:1.277972+0.087728
## [73] train-rmse:1.027156+0.016208 test-rmse:1.277152+0.087601
## [74] train-rmse:1.024541+0.016146 test-rmse:1.274104+0.088192
## [75] train-rmse:1.021891+0.016172 test-rmse:1.269962+0.088059
## [76] train-rmse:1.019293+0.016263 test-rmse:1.270135+0.090474
## [77] train-rmse:1.016645+0.016007 test-rmse:1.269116+0.089488
## [78] train-rmse:1.014071+0.015889 test-rmse:1.268275+0.089119
## [79] train-rmse:1.011582+0.015823 test-rmse:1.260438+0.083553
## [80] train-rmse:1.009208+0.015838 test-rmse:1.263525+0.079648
## [81] train-rmse:1.006838+0.015809 test-rmse:1.265497+0.079776
## [82] train-rmse:1.004489+0.015695 test-rmse:1.263393+0.079977
## [83] train-rmse:1.002273+0.015687 test-rmse:1.261157+0.080729
## [84] train-rmse:1.000079+0.015680 test-rmse:1.264524+0.077112
## [85] train-rmse:0.997818+0.015714 test-rmse:1.261374+0.079745
## Stopping. Best iteration:
## [79] train-rmse:1.011582+0.015823 test-rmse:1.260438+0.083553
##
## 106
## [1] train-rmse:59.245290+0.059177 test-rmse:59.245643+0.433703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:35.667956+0.034978 test-rmse:35.667497+0.455608
## [3] train-rmse:21.513287+0.020080 test-rmse:21.511532+0.466322
## [4] train-rmse:13.041444+0.011310 test-rmse:13.037761+0.467604
## [5] train-rmse:7.992215+0.007328 test-rmse:7.976335+0.420551
## [6] train-rmse:4.996688+0.009735 test-rmse:4.977064+0.381281
## [7] train-rmse:3.247068+0.009899 test-rmse:3.268571+0.345926
## [8] train-rmse:2.272548+0.015085 test-rmse:2.333002+0.264931
## [9] train-rmse:1.762441+0.021221 test-rmse:1.878030+0.184807
## [10] train-rmse:1.502267+0.017421 test-rmse:1.663084+0.131381
## [11] train-rmse:1.379054+0.020156 test-rmse:1.574759+0.104553
## [12] train-rmse:1.304261+0.015342 test-rmse:1.515768+0.098075
## [13] train-rmse:1.262375+0.014490 test-rmse:1.499029+0.095753
## [14] train-rmse:1.224506+0.015886 test-rmse:1.458244+0.108503
## [15] train-rmse:1.191232+0.016049 test-rmse:1.441120+0.107187
## [16] train-rmse:1.164090+0.016208 test-rmse:1.428724+0.108628
## [17] train-rmse:1.137076+0.019811 test-rmse:1.401846+0.098725
## [18] train-rmse:1.115024+0.020039 test-rmse:1.388015+0.096325
## [19] train-rmse:1.094147+0.020896 test-rmse:1.361024+0.106296
## [20] train-rmse:1.074463+0.021155 test-rmse:1.348134+0.099898
## [21] train-rmse:1.056761+0.022198 test-rmse:1.341788+0.104189
## [22] train-rmse:1.037780+0.022463 test-rmse:1.331502+0.101849
## [23] train-rmse:1.015602+0.026318 test-rmse:1.321570+0.106558
## [24] train-rmse:1.001847+0.025832 test-rmse:1.313030+0.112496
## [25] train-rmse:0.981979+0.022244 test-rmse:1.304360+0.116176
## [26] train-rmse:0.961579+0.022796 test-rmse:1.290487+0.117040
## [27] train-rmse:0.947117+0.023590 test-rmse:1.279909+0.109113
## [28] train-rmse:0.929456+0.025918 test-rmse:1.270911+0.106917
## [29] train-rmse:0.918701+0.026558 test-rmse:1.262204+0.110976
## [30] train-rmse:0.907244+0.028031 test-rmse:1.262453+0.112326
## [31] train-rmse:0.895317+0.029015 test-rmse:1.255173+0.114246
## [32] train-rmse:0.883632+0.030534 test-rmse:1.250440+0.119150
## [33] train-rmse:0.873772+0.030123 test-rmse:1.246801+0.118481
## [34] train-rmse:0.863347+0.031706 test-rmse:1.238737+0.122021
## [35] train-rmse:0.852542+0.032636 test-rmse:1.222228+0.122949
## [36] train-rmse:0.844768+0.032491 test-rmse:1.220834+0.123228
## [37] train-rmse:0.834364+0.034081 test-rmse:1.216475+0.126179
## [38] train-rmse:0.821469+0.035914 test-rmse:1.201034+0.121090
## [39] train-rmse:0.809534+0.031373 test-rmse:1.198866+0.115330
## [40] train-rmse:0.802600+0.031775 test-rmse:1.200596+0.116028
## [41] train-rmse:0.795546+0.030805 test-rmse:1.199619+0.114706
## [42] train-rmse:0.781065+0.028987 test-rmse:1.191932+0.120797
## [43] train-rmse:0.770553+0.032044 test-rmse:1.181616+0.112170
## [44] train-rmse:0.763263+0.033595 test-rmse:1.179541+0.115279
## [45] train-rmse:0.757287+0.034347 test-rmse:1.175011+0.120978
## [46] train-rmse:0.750101+0.034022 test-rmse:1.169887+0.121396
## [47] train-rmse:0.742234+0.030199 test-rmse:1.168533+0.120379
## [48] train-rmse:0.735287+0.028275 test-rmse:1.165307+0.120586
## [49] train-rmse:0.729020+0.028111 test-rmse:1.158029+0.122208
## [50] train-rmse:0.721673+0.027093 test-rmse:1.161059+0.114587
## [51] train-rmse:0.713554+0.028736 test-rmse:1.157983+0.111042
## [52] train-rmse:0.703220+0.024154 test-rmse:1.148183+0.111240
## [53] train-rmse:0.696727+0.025241 test-rmse:1.154746+0.116974
## [54] train-rmse:0.687348+0.024731 test-rmse:1.145983+0.105014
## [55] train-rmse:0.677662+0.025922 test-rmse:1.143643+0.111782
## [56] train-rmse:0.670116+0.026962 test-rmse:1.139812+0.113251
## [57] train-rmse:0.665011+0.028194 test-rmse:1.136592+0.111643
## [58] train-rmse:0.659082+0.030053 test-rmse:1.136604+0.110601
## [59] train-rmse:0.652654+0.032166 test-rmse:1.128800+0.100375
## [60] train-rmse:0.645428+0.029971 test-rmse:1.126653+0.104843
## [61] train-rmse:0.641110+0.030908 test-rmse:1.127091+0.108836
## [62] train-rmse:0.636079+0.028487 test-rmse:1.126372+0.109472
## [63] train-rmse:0.631046+0.028318 test-rmse:1.123100+0.109597
## [64] train-rmse:0.627605+0.028231 test-rmse:1.120386+0.110618
## [65] train-rmse:0.622441+0.028944 test-rmse:1.118975+0.108909
## [66] train-rmse:0.615437+0.024526 test-rmse:1.119130+0.112912
## [67] train-rmse:0.610162+0.023670 test-rmse:1.111278+0.115037
## [68] train-rmse:0.604722+0.024231 test-rmse:1.108087+0.112438
## [69] train-rmse:0.599401+0.026001 test-rmse:1.104543+0.110338
## [70] train-rmse:0.595658+0.026414 test-rmse:1.103906+0.110352
## [71] train-rmse:0.591177+0.027937 test-rmse:1.100873+0.112146
## [72] train-rmse:0.587283+0.027409 test-rmse:1.100925+0.112919
## [73] train-rmse:0.584898+0.027926 test-rmse:1.098821+0.113275
## [74] train-rmse:0.579342+0.026276 test-rmse:1.096459+0.112457
## [75] train-rmse:0.577008+0.026958 test-rmse:1.096367+0.111446
## [76] train-rmse:0.574727+0.027307 test-rmse:1.093532+0.112640
## [77] train-rmse:0.571451+0.026949 test-rmse:1.091840+0.113477
## [78] train-rmse:0.569127+0.026941 test-rmse:1.090606+0.111321
## [79] train-rmse:0.564954+0.025908 test-rmse:1.090603+0.112634
## [80] train-rmse:0.559634+0.024927 test-rmse:1.088702+0.110916
## [81] train-rmse:0.555318+0.025050 test-rmse:1.086313+0.108540
## [82] train-rmse:0.551328+0.025537 test-rmse:1.087320+0.109433
## [83] train-rmse:0.546615+0.026448 test-rmse:1.080195+0.104318
## [84] train-rmse:0.542280+0.026630 test-rmse:1.077492+0.104163
## [85] train-rmse:0.537021+0.028633 test-rmse:1.079473+0.106926
## [86] train-rmse:0.533084+0.029545 test-rmse:1.077862+0.105405
## [87] train-rmse:0.530218+0.030548 test-rmse:1.076930+0.104530
## [88] train-rmse:0.524354+0.031225 test-rmse:1.075332+0.105223
## [89] train-rmse:0.519707+0.032594 test-rmse:1.070299+0.102160
## [90] train-rmse:0.514675+0.033791 test-rmse:1.071858+0.102412
## [91] train-rmse:0.510682+0.030018 test-rmse:1.069616+0.102529
## [92] train-rmse:0.504053+0.026905 test-rmse:1.072025+0.111580
## [93] train-rmse:0.501222+0.027164 test-rmse:1.071013+0.111178
## [94] train-rmse:0.498989+0.027362 test-rmse:1.070947+0.113297
## [95] train-rmse:0.497031+0.027049 test-rmse:1.068806+0.115198
## [96] train-rmse:0.494636+0.027093 test-rmse:1.072388+0.113118
## [97] train-rmse:0.491068+0.028595 test-rmse:1.068565+0.110038
## [98] train-rmse:0.487383+0.029065 test-rmse:1.069090+0.110717
## [99] train-rmse:0.484841+0.029744 test-rmse:1.068463+0.111412
## [100] train-rmse:0.481462+0.029508 test-rmse:1.067944+0.110019
## [101] train-rmse:0.477002+0.028085 test-rmse:1.065918+0.106289
## [102] train-rmse:0.474410+0.027733 test-rmse:1.064578+0.106853
## [103] train-rmse:0.472297+0.028189 test-rmse:1.064215+0.105806
## [104] train-rmse:0.468311+0.027428 test-rmse:1.061320+0.107639
## [105] train-rmse:0.465969+0.027633 test-rmse:1.059430+0.109309
## [106] train-rmse:0.463341+0.028294 test-rmse:1.059449+0.109953
## [107] train-rmse:0.461386+0.028184 test-rmse:1.057088+0.109956
## [108] train-rmse:0.456819+0.024499 test-rmse:1.053991+0.110547
## [109] train-rmse:0.453871+0.023618 test-rmse:1.052515+0.108090
## [110] train-rmse:0.450939+0.024376 test-rmse:1.051714+0.107435
## [111] train-rmse:0.448369+0.024065 test-rmse:1.053127+0.106008
## [112] train-rmse:0.445419+0.023728 test-rmse:1.053104+0.106461
## [113] train-rmse:0.442365+0.023348 test-rmse:1.050824+0.106495
## [114] train-rmse:0.440453+0.023851 test-rmse:1.051552+0.107760
## [115] train-rmse:0.437713+0.023480 test-rmse:1.050134+0.104974
## [116] train-rmse:0.435315+0.022717 test-rmse:1.049474+0.106149
## [117] train-rmse:0.431256+0.023712 test-rmse:1.040010+0.094570
## [118] train-rmse:0.429498+0.023122 test-rmse:1.038685+0.094329
## [119] train-rmse:0.426565+0.024359 test-rmse:1.031637+0.088877
## [120] train-rmse:0.423005+0.023840 test-rmse:1.031045+0.088473
## [121] train-rmse:0.419510+0.023352 test-rmse:1.030391+0.087843
## [122] train-rmse:0.417452+0.023884 test-rmse:1.030306+0.087800
## [123] train-rmse:0.414942+0.024587 test-rmse:1.028719+0.089216
## [124] train-rmse:0.412810+0.025402 test-rmse:1.027430+0.088523
## [125] train-rmse:0.410455+0.024479 test-rmse:1.025366+0.088155
## [126] train-rmse:0.408574+0.023991 test-rmse:1.026540+0.087735
## [127] train-rmse:0.406455+0.024133 test-rmse:1.027147+0.089849
## [128] train-rmse:0.403929+0.025007 test-rmse:1.019995+0.087121
## [129] train-rmse:0.402123+0.025494 test-rmse:1.020003+0.087822
## [130] train-rmse:0.398556+0.026224 test-rmse:1.017153+0.087116
## [131] train-rmse:0.395573+0.025046 test-rmse:1.016555+0.088247
## [132] train-rmse:0.391302+0.022524 test-rmse:1.015372+0.090354
## [133] train-rmse:0.388957+0.022392 test-rmse:1.015094+0.089947
## [134] train-rmse:0.386451+0.022942 test-rmse:1.013700+0.088102
## [135] train-rmse:0.384829+0.023019 test-rmse:1.013327+0.089169
## [136] train-rmse:0.383031+0.023019 test-rmse:1.012568+0.090324
## [137] train-rmse:0.381064+0.022117 test-rmse:1.011616+0.091412
## [138] train-rmse:0.379138+0.021758 test-rmse:1.011762+0.092133
## [139] train-rmse:0.377553+0.021778 test-rmse:1.010116+0.093585
## [140] train-rmse:0.374318+0.021575 test-rmse:1.005994+0.093720
## [141] train-rmse:0.372325+0.021427 test-rmse:1.005963+0.095577
## [142] train-rmse:0.369728+0.019466 test-rmse:1.004693+0.099107
## [143] train-rmse:0.366410+0.019497 test-rmse:1.003397+0.098262
## [144] train-rmse:0.363808+0.018744 test-rmse:1.002025+0.097463
## [145] train-rmse:0.362186+0.018151 test-rmse:1.001284+0.098346
## [146] train-rmse:0.360881+0.018506 test-rmse:1.000107+0.098318
## [147] train-rmse:0.358765+0.017920 test-rmse:0.999529+0.097846
## [148] train-rmse:0.357042+0.018544 test-rmse:0.999172+0.098053
## [149] train-rmse:0.355181+0.018016 test-rmse:0.998683+0.099720
## [150] train-rmse:0.353828+0.018293 test-rmse:0.997602+0.099392
## [151] train-rmse:0.352262+0.018449 test-rmse:0.997374+0.099003
## [152] train-rmse:0.350631+0.018099 test-rmse:0.994357+0.100038
## [153] train-rmse:0.349355+0.017964 test-rmse:0.994277+0.101871
## [154] train-rmse:0.347421+0.017711 test-rmse:0.993849+0.102569
## [155] train-rmse:0.346123+0.017799 test-rmse:0.993842+0.103732
## [156] train-rmse:0.344631+0.017970 test-rmse:0.992821+0.102807
## [157] train-rmse:0.342386+0.018101 test-rmse:0.992662+0.102264
## [158] train-rmse:0.339750+0.018158 test-rmse:0.993334+0.102877
## [159] train-rmse:0.337616+0.017555 test-rmse:0.992723+0.102956
## [160] train-rmse:0.335298+0.017679 test-rmse:0.991226+0.102330
## [161] train-rmse:0.333787+0.018128 test-rmse:0.991564+0.102682
## [162] train-rmse:0.331360+0.018893 test-rmse:0.991059+0.102864
## [163] train-rmse:0.329373+0.018929 test-rmse:0.991001+0.101355
## [164] train-rmse:0.328002+0.019031 test-rmse:0.991031+0.102176
## [165] train-rmse:0.325839+0.019352 test-rmse:0.990915+0.101896
## [166] train-rmse:0.323075+0.020866 test-rmse:0.990728+0.102025
## [167] train-rmse:0.322291+0.020996 test-rmse:0.989382+0.102331
## [168] train-rmse:0.320433+0.020124 test-rmse:0.988710+0.101366
## [169] train-rmse:0.318376+0.020049 test-rmse:0.988742+0.099952
## [170] train-rmse:0.316782+0.020058 test-rmse:0.988534+0.100681
## [171] train-rmse:0.314963+0.019920 test-rmse:0.988891+0.100892
## [172] train-rmse:0.313211+0.018690 test-rmse:0.987400+0.100758
## [173] train-rmse:0.310648+0.017270 test-rmse:0.985467+0.103298
## [174] train-rmse:0.309419+0.017455 test-rmse:0.985046+0.103737
## [175] train-rmse:0.307840+0.017202 test-rmse:0.984085+0.103571
## [176] train-rmse:0.306228+0.017218 test-rmse:0.985534+0.103794
## [177] train-rmse:0.305056+0.017311 test-rmse:0.985572+0.104021
## [178] train-rmse:0.303417+0.016600 test-rmse:0.985173+0.104274
## [179] train-rmse:0.301962+0.017130 test-rmse:0.984631+0.104358
## [180] train-rmse:0.300776+0.017002 test-rmse:0.985568+0.104410
## [181] train-rmse:0.299520+0.017246 test-rmse:0.986184+0.104247
## Stopping. Best iteration:
## [175] train-rmse:0.307840+0.017202 test-rmse:0.984085+0.103571
##
## 107
## [1] train-rmse:59.245290+0.059177 test-rmse:59.245643+0.433703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:35.667956+0.034978 test-rmse:35.667497+0.455608
## [3] train-rmse:21.513287+0.020080 test-rmse:21.511532+0.466322
## [4] train-rmse:13.041444+0.011310 test-rmse:13.037761+0.467604
## [5] train-rmse:7.989149+0.005702 test-rmse:7.970775+0.412728
## [6] train-rmse:4.985493+0.010897 test-rmse:4.965905+0.362435
## [7] train-rmse:3.207324+0.012162 test-rmse:3.234830+0.290773
## [8] train-rmse:2.204240+0.017924 test-rmse:2.246474+0.248913
## [9] train-rmse:1.649812+0.016210 test-rmse:1.755868+0.197723
## [10] train-rmse:1.356822+0.018566 test-rmse:1.526433+0.141656
## [11] train-rmse:1.207466+0.022972 test-rmse:1.413869+0.118898
## [12] train-rmse:1.125866+0.024381 test-rmse:1.354361+0.117052
## [13] train-rmse:1.064997+0.025053 test-rmse:1.323968+0.125704
## [14] train-rmse:1.021806+0.022902 test-rmse:1.291193+0.122466
## [15] train-rmse:0.982917+0.023178 test-rmse:1.267402+0.113798
## [16] train-rmse:0.957048+0.025561 test-rmse:1.251984+0.115374
## [17] train-rmse:0.921109+0.033240 test-rmse:1.242249+0.114997
## [18] train-rmse:0.898583+0.033736 test-rmse:1.223506+0.117732
## [19] train-rmse:0.866529+0.023217 test-rmse:1.213486+0.108584
## [20] train-rmse:0.844560+0.020559 test-rmse:1.196884+0.113014
## [21] train-rmse:0.824005+0.022383 test-rmse:1.180055+0.106663
## [22] train-rmse:0.808461+0.023972 test-rmse:1.169038+0.109082
## [23] train-rmse:0.788102+0.024221 test-rmse:1.141977+0.104298
## [24] train-rmse:0.765152+0.028331 test-rmse:1.130313+0.102395
## [25] train-rmse:0.750583+0.026463 test-rmse:1.119731+0.100460
## [26] train-rmse:0.737247+0.025375 test-rmse:1.113780+0.095649
## [27] train-rmse:0.721287+0.027158 test-rmse:1.109287+0.098180
## [28] train-rmse:0.701149+0.028856 test-rmse:1.109030+0.112256
## [29] train-rmse:0.686720+0.028725 test-rmse:1.097636+0.117331
## [30] train-rmse:0.677741+0.029498 test-rmse:1.093538+0.116717
## [31] train-rmse:0.661762+0.023644 test-rmse:1.087420+0.114159
## [32] train-rmse:0.642753+0.023400 test-rmse:1.080921+0.114950
## [33] train-rmse:0.633767+0.024188 test-rmse:1.077242+0.118569
## [34] train-rmse:0.622424+0.023871 test-rmse:1.073181+0.117218
## [35] train-rmse:0.611884+0.026298 test-rmse:1.072548+0.111915
## [36] train-rmse:0.601600+0.023597 test-rmse:1.068622+0.113688
## [37] train-rmse:0.590711+0.023617 test-rmse:1.063811+0.118622
## [38] train-rmse:0.580640+0.024053 test-rmse:1.064538+0.116467
## [39] train-rmse:0.572486+0.023243 test-rmse:1.056600+0.120178
## [40] train-rmse:0.561766+0.019726 test-rmse:1.051133+0.120823
## [41] train-rmse:0.546344+0.021300 test-rmse:1.045951+0.118331
## [42] train-rmse:0.537146+0.022442 test-rmse:1.042443+0.116569
## [43] train-rmse:0.521900+0.018233 test-rmse:1.037901+0.121797
## [44] train-rmse:0.514822+0.018006 test-rmse:1.035724+0.120116
## [45] train-rmse:0.508927+0.016670 test-rmse:1.033825+0.119874
## [46] train-rmse:0.499728+0.018807 test-rmse:1.032598+0.119944
## [47] train-rmse:0.487214+0.022837 test-rmse:1.027182+0.116853
## [48] train-rmse:0.478883+0.021235 test-rmse:1.024645+0.116467
## [49] train-rmse:0.469125+0.022624 test-rmse:1.027829+0.116101
## [50] train-rmse:0.463328+0.022373 test-rmse:1.025419+0.115997
## [51] train-rmse:0.456568+0.023959 test-rmse:1.023499+0.113771
## [52] train-rmse:0.449506+0.023265 test-rmse:1.021550+0.115123
## [53] train-rmse:0.442981+0.022846 test-rmse:1.018936+0.116238
## [54] train-rmse:0.436600+0.022288 test-rmse:1.017470+0.116762
## [55] train-rmse:0.427790+0.018786 test-rmse:1.012563+0.119567
## [56] train-rmse:0.419140+0.021080 test-rmse:1.009981+0.118356
## [57] train-rmse:0.410020+0.022480 test-rmse:1.006174+0.120824
## [58] train-rmse:0.403102+0.020003 test-rmse:1.006337+0.123727
## [59] train-rmse:0.395351+0.020893 test-rmse:1.001937+0.126599
## [60] train-rmse:0.389978+0.020457 test-rmse:1.001098+0.125346
## [61] train-rmse:0.382654+0.021102 test-rmse:0.998296+0.126462
## [62] train-rmse:0.377974+0.019871 test-rmse:0.995556+0.126598
## [63] train-rmse:0.372585+0.021928 test-rmse:0.994821+0.126599
## [64] train-rmse:0.367131+0.020666 test-rmse:0.992754+0.128726
## [65] train-rmse:0.361442+0.019156 test-rmse:0.990425+0.128227
## [66] train-rmse:0.357242+0.019261 test-rmse:0.990858+0.127126
## [67] train-rmse:0.353230+0.021418 test-rmse:0.990361+0.128222
## [68] train-rmse:0.347288+0.022505 test-rmse:0.989358+0.127430
## [69] train-rmse:0.339995+0.020296 test-rmse:0.989383+0.126805
## [70] train-rmse:0.336427+0.020885 test-rmse:0.988493+0.125364
## [71] train-rmse:0.330580+0.019663 test-rmse:0.985804+0.126182
## [72] train-rmse:0.326548+0.020127 test-rmse:0.985427+0.128220
## [73] train-rmse:0.323015+0.018600 test-rmse:0.983559+0.129279
## [74] train-rmse:0.317908+0.018220 test-rmse:0.983623+0.129348
## [75] train-rmse:0.313241+0.019908 test-rmse:0.981323+0.129151
## [76] train-rmse:0.306774+0.017877 test-rmse:0.980679+0.129175
## [77] train-rmse:0.303592+0.018490 test-rmse:0.979335+0.128568
## [78] train-rmse:0.298456+0.018050 test-rmse:0.976112+0.130882
## [79] train-rmse:0.292319+0.015176 test-rmse:0.977395+0.131100
## [80] train-rmse:0.287838+0.013301 test-rmse:0.976877+0.130592
## [81] train-rmse:0.283354+0.013946 test-rmse:0.976343+0.129502
## [82] train-rmse:0.279489+0.012592 test-rmse:0.975377+0.130021
## [83] train-rmse:0.274939+0.012695 test-rmse:0.973832+0.130236
## [84] train-rmse:0.269991+0.011694 test-rmse:0.973141+0.130111
## [85] train-rmse:0.265295+0.011880 test-rmse:0.974440+0.130977
## [86] train-rmse:0.261937+0.013168 test-rmse:0.973348+0.131424
## [87] train-rmse:0.258572+0.013203 test-rmse:0.971440+0.131455
## [88] train-rmse:0.254129+0.012756 test-rmse:0.970675+0.131938
## [89] train-rmse:0.251838+0.013554 test-rmse:0.970772+0.130718
## [90] train-rmse:0.249487+0.013555 test-rmse:0.970667+0.131586
## [91] train-rmse:0.246305+0.012994 test-rmse:0.971349+0.132368
## [92] train-rmse:0.242462+0.012089 test-rmse:0.972618+0.132131
## [93] train-rmse:0.240155+0.011539 test-rmse:0.971803+0.132569
## [94] train-rmse:0.236113+0.011258 test-rmse:0.972123+0.132358
## [95] train-rmse:0.234138+0.011584 test-rmse:0.970884+0.133380
## [96] train-rmse:0.230883+0.011112 test-rmse:0.970998+0.134054
## Stopping. Best iteration:
## [90] train-rmse:0.249487+0.013555 test-rmse:0.970667+0.131586
##
## 108
## [1] train-rmse:59.245290+0.059177 test-rmse:59.245643+0.433703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:35.667956+0.034978 test-rmse:35.667497+0.455608
## [3] train-rmse:21.513287+0.020080 test-rmse:21.511532+0.466322
## [4] train-rmse:13.041444+0.011310 test-rmse:13.037761+0.467604
## [5] train-rmse:7.989149+0.005702 test-rmse:7.970775+0.412728
## [6] train-rmse:4.979993+0.009478 test-rmse:4.968226+0.353445
## [7] train-rmse:3.193258+0.013836 test-rmse:3.207039+0.288137
## [8] train-rmse:2.159060+0.020770 test-rmse:2.201037+0.232272
## [9] train-rmse:1.580682+0.026144 test-rmse:1.692577+0.167235
## [10] train-rmse:1.265316+0.034873 test-rmse:1.469643+0.123929
## [11] train-rmse:1.103308+0.041038 test-rmse:1.359073+0.126128
## [12] train-rmse:0.993168+0.025125 test-rmse:1.300962+0.114321
## [13] train-rmse:0.935717+0.024434 test-rmse:1.279592+0.095795
## [14] train-rmse:0.884898+0.029714 test-rmse:1.258860+0.086387
## [15] train-rmse:0.835896+0.031718 test-rmse:1.240327+0.081697
## [16] train-rmse:0.799074+0.033108 test-rmse:1.218187+0.081836
## [17] train-rmse:0.771207+0.040466 test-rmse:1.201679+0.077359
## [18] train-rmse:0.744875+0.045267 test-rmse:1.187385+0.075032
## [19] train-rmse:0.713686+0.030329 test-rmse:1.171900+0.076014
## [20] train-rmse:0.693296+0.027926 test-rmse:1.164927+0.077681
## [21] train-rmse:0.663536+0.027960 test-rmse:1.153443+0.086766
## [22] train-rmse:0.642765+0.031231 test-rmse:1.129702+0.065178
## [23] train-rmse:0.618320+0.033026 test-rmse:1.128982+0.058487
## [24] train-rmse:0.596288+0.030987 test-rmse:1.119405+0.054856
## [25] train-rmse:0.572091+0.031338 test-rmse:1.115052+0.049598
## [26] train-rmse:0.552191+0.032044 test-rmse:1.106913+0.046587
## [27] train-rmse:0.540821+0.031323 test-rmse:1.100196+0.051414
## [28] train-rmse:0.525818+0.035152 test-rmse:1.088368+0.039573
## [29] train-rmse:0.507770+0.025906 test-rmse:1.081191+0.041767
## [30] train-rmse:0.495623+0.024225 test-rmse:1.078951+0.040460
## [31] train-rmse:0.480408+0.026088 test-rmse:1.074036+0.046243
## [32] train-rmse:0.465476+0.030138 test-rmse:1.070655+0.047353
## [33] train-rmse:0.455755+0.029913 test-rmse:1.064355+0.043703
## [34] train-rmse:0.437394+0.029101 test-rmse:1.057722+0.043749
## [35] train-rmse:0.420420+0.024928 test-rmse:1.049106+0.045137
## [36] train-rmse:0.406601+0.023398 test-rmse:1.053078+0.048018
## [37] train-rmse:0.395923+0.020885 test-rmse:1.050455+0.047732
## [38] train-rmse:0.383989+0.019726 test-rmse:1.053792+0.046165
## [39] train-rmse:0.372694+0.023538 test-rmse:1.047984+0.048235
## [40] train-rmse:0.364940+0.025065 test-rmse:1.047236+0.049333
## [41] train-rmse:0.355609+0.025702 test-rmse:1.042139+0.050232
## [42] train-rmse:0.344360+0.027560 test-rmse:1.040643+0.050323
## [43] train-rmse:0.335129+0.026919 test-rmse:1.040162+0.050828
## [44] train-rmse:0.326247+0.024736 test-rmse:1.041012+0.053146
## [45] train-rmse:0.317523+0.026765 test-rmse:1.040715+0.053101
## [46] train-rmse:0.309620+0.027536 test-rmse:1.037839+0.052828
## [47] train-rmse:0.301871+0.026003 test-rmse:1.036169+0.051291
## [48] train-rmse:0.295466+0.026599 test-rmse:1.034248+0.051360
## [49] train-rmse:0.288179+0.023965 test-rmse:1.030779+0.054800
## [50] train-rmse:0.282296+0.022650 test-rmse:1.030304+0.056626
## [51] train-rmse:0.275712+0.021672 test-rmse:1.028789+0.055999
## [52] train-rmse:0.269101+0.022151 test-rmse:1.026669+0.056246
## [53] train-rmse:0.261132+0.019352 test-rmse:1.026137+0.056028
## [54] train-rmse:0.255708+0.017627 test-rmse:1.024499+0.057178
## [55] train-rmse:0.249765+0.016626 test-rmse:1.025480+0.059170
## [56] train-rmse:0.243874+0.017336 test-rmse:1.024501+0.060687
## [57] train-rmse:0.238394+0.017444 test-rmse:1.022393+0.060806
## [58] train-rmse:0.231489+0.018427 test-rmse:1.021399+0.060796
## [59] train-rmse:0.226481+0.018155 test-rmse:1.020631+0.060671
## [60] train-rmse:0.218036+0.016010 test-rmse:1.020516+0.060316
## [61] train-rmse:0.212694+0.014701 test-rmse:1.019152+0.061353
## [62] train-rmse:0.208017+0.016410 test-rmse:1.018908+0.062262
## [63] train-rmse:0.204232+0.015543 test-rmse:1.019611+0.061183
## [64] train-rmse:0.200253+0.015195 test-rmse:1.019320+0.061437
## [65] train-rmse:0.194885+0.014955 test-rmse:1.020368+0.060946
## [66] train-rmse:0.188110+0.013109 test-rmse:1.018580+0.060321
## [67] train-rmse:0.183154+0.013288 test-rmse:1.017534+0.060051
## [68] train-rmse:0.178559+0.012750 test-rmse:1.016373+0.059136
## [69] train-rmse:0.173131+0.012546 test-rmse:1.014869+0.059104
## [70] train-rmse:0.168828+0.013248 test-rmse:1.015998+0.058355
## [71] train-rmse:0.164754+0.013265 test-rmse:1.014898+0.057707
## [72] train-rmse:0.161621+0.013486 test-rmse:1.015439+0.057479
## [73] train-rmse:0.158102+0.013714 test-rmse:1.014554+0.058286
## [74] train-rmse:0.154714+0.013852 test-rmse:1.014137+0.059659
## [75] train-rmse:0.150873+0.012063 test-rmse:1.013941+0.057567
## [76] train-rmse:0.148049+0.011903 test-rmse:1.013128+0.058357
## [77] train-rmse:0.144795+0.011170 test-rmse:1.012091+0.058538
## [78] train-rmse:0.141696+0.010361 test-rmse:1.010469+0.058030
## [79] train-rmse:0.138923+0.009548 test-rmse:1.010041+0.058008
## [80] train-rmse:0.136320+0.008038 test-rmse:1.010056+0.058412
## [81] train-rmse:0.132713+0.008447 test-rmse:1.009868+0.058543
## [82] train-rmse:0.129596+0.008899 test-rmse:1.008421+0.059487
## [83] train-rmse:0.127634+0.009182 test-rmse:1.008574+0.059860
## [84] train-rmse:0.125435+0.008677 test-rmse:1.008310+0.060302
## [85] train-rmse:0.123148+0.008117 test-rmse:1.007797+0.060622
## [86] train-rmse:0.121395+0.008220 test-rmse:1.007924+0.060456
## [87] train-rmse:0.118397+0.007479 test-rmse:1.008225+0.061658
## [88] train-rmse:0.115346+0.006270 test-rmse:1.008103+0.061733
## [89] train-rmse:0.112396+0.006491 test-rmse:1.008023+0.060761
## [90] train-rmse:0.109765+0.007525 test-rmse:1.007210+0.061452
## [91] train-rmse:0.107645+0.006503 test-rmse:1.006947+0.061360
## [92] train-rmse:0.105685+0.006449 test-rmse:1.006772+0.060763
## [93] train-rmse:0.102586+0.006755 test-rmse:1.006206+0.061172
## [94] train-rmse:0.100402+0.006717 test-rmse:1.005856+0.061556
## [95] train-rmse:0.098326+0.006697 test-rmse:1.004515+0.062507
## [96] train-rmse:0.096455+0.006565 test-rmse:1.004242+0.061156
## [97] train-rmse:0.094075+0.006332 test-rmse:1.004463+0.061127
## [98] train-rmse:0.092155+0.006503 test-rmse:1.003997+0.061438
## [99] train-rmse:0.090277+0.006295 test-rmse:1.004472+0.060600
## [100] train-rmse:0.087587+0.006148 test-rmse:1.004318+0.060334
## [101] train-rmse:0.086395+0.006402 test-rmse:1.003934+0.059971
## [102] train-rmse:0.084196+0.006359 test-rmse:1.003695+0.060518
## [103] train-rmse:0.082036+0.006702 test-rmse:1.004171+0.059757
## [104] train-rmse:0.079706+0.005483 test-rmse:1.003109+0.059292
## [105] train-rmse:0.078233+0.004938 test-rmse:1.002651+0.059664
## [106] train-rmse:0.076403+0.004580 test-rmse:1.003297+0.059703
## [107] train-rmse:0.074777+0.004541 test-rmse:1.003332+0.058913
## [108] train-rmse:0.072916+0.004406 test-rmse:1.003368+0.058696
## [109] train-rmse:0.071076+0.004096 test-rmse:1.003354+0.058133
## [110] train-rmse:0.069816+0.003956 test-rmse:1.003476+0.058351
## [111] train-rmse:0.068837+0.004006 test-rmse:1.003086+0.058336
## Stopping. Best iteration:
## [105] train-rmse:0.078233+0.004938 test-rmse:1.002651+0.059664
##
## 109
## [1] train-rmse:59.245290+0.059177 test-rmse:59.245643+0.433703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:35.667956+0.034978 test-rmse:35.667497+0.455608
## [3] train-rmse:21.513287+0.020080 test-rmse:21.511532+0.466322
## [4] train-rmse:13.041444+0.011310 test-rmse:13.037761+0.467604
## [5] train-rmse:7.989149+0.005702 test-rmse:7.970775+0.412728
## [6] train-rmse:4.976391+0.007664 test-rmse:4.964721+0.342478
## [7] train-rmse:3.184545+0.010114 test-rmse:3.188088+0.279668
## [8] train-rmse:2.131958+0.012685 test-rmse:2.180726+0.221207
## [9] train-rmse:1.516723+0.009640 test-rmse:1.649733+0.170722
## [10] train-rmse:1.172230+0.016259 test-rmse:1.391573+0.128836
## [11] train-rmse:0.990639+0.020340 test-rmse:1.270521+0.115012
## [12] train-rmse:0.882073+0.016787 test-rmse:1.215870+0.095836
## [13] train-rmse:0.813372+0.015261 test-rmse:1.187841+0.098459
## [14] train-rmse:0.759028+0.014644 test-rmse:1.165200+0.098117
## [15] train-rmse:0.710631+0.015465 test-rmse:1.145620+0.102919
## [16] train-rmse:0.666382+0.014330 test-rmse:1.127009+0.095319
## [17] train-rmse:0.637245+0.015774 test-rmse:1.108588+0.097978
## [18] train-rmse:0.604486+0.022511 test-rmse:1.094599+0.098929
## [19] train-rmse:0.570025+0.019133 test-rmse:1.087188+0.087569
## [20] train-rmse:0.543033+0.016928 test-rmse:1.078696+0.087052
## [21] train-rmse:0.523031+0.014271 test-rmse:1.062897+0.087247
## [22] train-rmse:0.503589+0.014715 test-rmse:1.065971+0.088226
## [23] train-rmse:0.481741+0.015828 test-rmse:1.059954+0.090231
## [24] train-rmse:0.460057+0.019803 test-rmse:1.049542+0.090531
## [25] train-rmse:0.441225+0.018587 test-rmse:1.043223+0.088273
## [26] train-rmse:0.419388+0.013946 test-rmse:1.043303+0.087851
## [27] train-rmse:0.403921+0.019034 test-rmse:1.036031+0.088286
## [28] train-rmse:0.387898+0.013287 test-rmse:1.030135+0.088953
## [29] train-rmse:0.376645+0.011036 test-rmse:1.021603+0.084646
## [30] train-rmse:0.361981+0.009527 test-rmse:1.017138+0.085497
## [31] train-rmse:0.344149+0.015347 test-rmse:1.013087+0.088455
## [32] train-rmse:0.333822+0.014346 test-rmse:1.014165+0.085845
## [33] train-rmse:0.321260+0.016061 test-rmse:1.012293+0.084070
## [34] train-rmse:0.305386+0.013432 test-rmse:1.012854+0.085997
## [35] train-rmse:0.293085+0.016330 test-rmse:1.011640+0.084882
## [36] train-rmse:0.283248+0.014669 test-rmse:1.008288+0.083485
## [37] train-rmse:0.274783+0.014253 test-rmse:1.005805+0.082253
## [38] train-rmse:0.263088+0.014111 test-rmse:1.000718+0.085501
## [39] train-rmse:0.251439+0.014831 test-rmse:0.999493+0.081957
## [40] train-rmse:0.241963+0.012765 test-rmse:0.994602+0.082382
## [41] train-rmse:0.231211+0.014750 test-rmse:0.994751+0.084689
## [42] train-rmse:0.222667+0.012528 test-rmse:0.990879+0.083867
## [43] train-rmse:0.216303+0.014366 test-rmse:0.987587+0.083423
## [44] train-rmse:0.208978+0.011462 test-rmse:0.984625+0.084454
## [45] train-rmse:0.203814+0.011586 test-rmse:0.984815+0.084651
## [46] train-rmse:0.196857+0.011642 test-rmse:0.982541+0.082595
## [47] train-rmse:0.190358+0.013002 test-rmse:0.984109+0.081766
## [48] train-rmse:0.185731+0.013066 test-rmse:0.983326+0.081867
## [49] train-rmse:0.176994+0.015039 test-rmse:0.984780+0.081685
## [50] train-rmse:0.170720+0.011552 test-rmse:0.984932+0.082689
## [51] train-rmse:0.165220+0.012400 test-rmse:0.983581+0.082607
## [52] train-rmse:0.161072+0.012124 test-rmse:0.981423+0.082281
## [53] train-rmse:0.155014+0.009698 test-rmse:0.983176+0.082352
## [54] train-rmse:0.151600+0.009840 test-rmse:0.983495+0.081396
## [55] train-rmse:0.147567+0.009282 test-rmse:0.981696+0.082027
## [56] train-rmse:0.141702+0.010993 test-rmse:0.983447+0.083929
## [57] train-rmse:0.136264+0.010806 test-rmse:0.981576+0.084312
## [58] train-rmse:0.130950+0.012063 test-rmse:0.981079+0.085105
## [59] train-rmse:0.127094+0.010952 test-rmse:0.981099+0.085711
## [60] train-rmse:0.120796+0.010887 test-rmse:0.980127+0.085983
## [61] train-rmse:0.117597+0.010271 test-rmse:0.979651+0.086961
## [62] train-rmse:0.115100+0.010310 test-rmse:0.980350+0.087767
## [63] train-rmse:0.110830+0.011253 test-rmse:0.978691+0.088096
## [64] train-rmse:0.106078+0.011295 test-rmse:0.978222+0.088133
## [65] train-rmse:0.101327+0.010094 test-rmse:0.977219+0.088086
## [66] train-rmse:0.097990+0.010489 test-rmse:0.977949+0.088261
## [67] train-rmse:0.095198+0.010486 test-rmse:0.977036+0.088071
## [68] train-rmse:0.092170+0.010632 test-rmse:0.976712+0.088013
## [69] train-rmse:0.088696+0.009948 test-rmse:0.975710+0.088110
## [70] train-rmse:0.086840+0.009984 test-rmse:0.975546+0.088992
## [71] train-rmse:0.084388+0.009399 test-rmse:0.974516+0.088664
## [72] train-rmse:0.081799+0.009600 test-rmse:0.974553+0.088027
## [73] train-rmse:0.078499+0.009240 test-rmse:0.974588+0.088646
## [74] train-rmse:0.075454+0.009227 test-rmse:0.974486+0.088859
## [75] train-rmse:0.073551+0.008383 test-rmse:0.974307+0.089228
## [76] train-rmse:0.071212+0.007796 test-rmse:0.973612+0.090540
## [77] train-rmse:0.068700+0.007591 test-rmse:0.973694+0.090248
## [78] train-rmse:0.066457+0.007534 test-rmse:0.973453+0.090459
## [79] train-rmse:0.064799+0.007002 test-rmse:0.973347+0.090184
## [80] train-rmse:0.062188+0.006574 test-rmse:0.973405+0.090667
## [81] train-rmse:0.059694+0.006122 test-rmse:0.972748+0.090428
## [82] train-rmse:0.057572+0.005773 test-rmse:0.972793+0.090467
## [83] train-rmse:0.054940+0.005461 test-rmse:0.972412+0.090394
## [84] train-rmse:0.052953+0.005937 test-rmse:0.971996+0.090594
## [85] train-rmse:0.051083+0.006162 test-rmse:0.971527+0.091322
## [86] train-rmse:0.050013+0.006406 test-rmse:0.971249+0.091243
## [87] train-rmse:0.048262+0.005618 test-rmse:0.970514+0.091008
## [88] train-rmse:0.047308+0.005323 test-rmse:0.970261+0.090774
## [89] train-rmse:0.046576+0.005313 test-rmse:0.970174+0.091057
## [90] train-rmse:0.044800+0.005549 test-rmse:0.970031+0.090858
## [91] train-rmse:0.042348+0.005324 test-rmse:0.970227+0.090545
## [92] train-rmse:0.040613+0.005147 test-rmse:0.969892+0.091010
## [93] train-rmse:0.039469+0.004840 test-rmse:0.969823+0.090957
## [94] train-rmse:0.038258+0.004673 test-rmse:0.970002+0.091121
## [95] train-rmse:0.037311+0.004642 test-rmse:0.969652+0.091040
## [96] train-rmse:0.036508+0.004577 test-rmse:0.970031+0.091002
## [97] train-rmse:0.035568+0.004244 test-rmse:0.970256+0.091071
## [98] train-rmse:0.034471+0.003867 test-rmse:0.969999+0.091200
## [99] train-rmse:0.033538+0.003508 test-rmse:0.970179+0.091298
## [100] train-rmse:0.032346+0.003572 test-rmse:0.969903+0.091014
## [101] train-rmse:0.031570+0.003330 test-rmse:0.970014+0.091107
## Stopping. Best iteration:
## [95] train-rmse:0.037311+0.004642 test-rmse:0.969652+0.091040
##
## 110
## [1] train-rmse:59.245290+0.059177 test-rmse:59.245643+0.433703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:35.667956+0.034978 test-rmse:35.667497+0.455608
## [3] train-rmse:21.513287+0.020080 test-rmse:21.511532+0.466322
## [4] train-rmse:13.041444+0.011310 test-rmse:13.037761+0.467604
## [5] train-rmse:7.989149+0.005702 test-rmse:7.970775+0.412728
## [6] train-rmse:4.974738+0.007389 test-rmse:4.968379+0.340208
## [7] train-rmse:3.177615+0.006767 test-rmse:3.182698+0.286292
## [8] train-rmse:2.115333+0.010135 test-rmse:2.168740+0.251418
## [9] train-rmse:1.486553+0.022376 test-rmse:1.615388+0.185004
## [10] train-rmse:1.117040+0.012471 test-rmse:1.358315+0.169594
## [11] train-rmse:0.916025+0.023526 test-rmse:1.239783+0.143830
## [12] train-rmse:0.776697+0.018818 test-rmse:1.168682+0.144612
## [13] train-rmse:0.697067+0.011972 test-rmse:1.136428+0.145083
## [14] train-rmse:0.637729+0.019503 test-rmse:1.109534+0.130994
## [15] train-rmse:0.583299+0.016578 test-rmse:1.081892+0.104458
## [16] train-rmse:0.540363+0.021833 test-rmse:1.071440+0.093826
## [17] train-rmse:0.514185+0.024136 test-rmse:1.052918+0.096640
## [18] train-rmse:0.475351+0.007393 test-rmse:1.044139+0.098963
## [19] train-rmse:0.445932+0.010484 test-rmse:1.034844+0.098770
## [20] train-rmse:0.422721+0.014290 test-rmse:1.027603+0.098330
## [21] train-rmse:0.397086+0.014736 test-rmse:1.027807+0.098680
## [22] train-rmse:0.380357+0.012038 test-rmse:1.022759+0.100749
## [23] train-rmse:0.355935+0.013175 test-rmse:1.019591+0.101852
## [24] train-rmse:0.334903+0.016607 test-rmse:1.020415+0.100400
## [25] train-rmse:0.322806+0.018004 test-rmse:1.015379+0.097303
## [26] train-rmse:0.302318+0.015191 test-rmse:1.012514+0.095638
## [27] train-rmse:0.288269+0.013637 test-rmse:1.009261+0.095771
## [28] train-rmse:0.274184+0.013448 test-rmse:1.008239+0.099869
## [29] train-rmse:0.263122+0.010476 test-rmse:1.006514+0.101739
## [30] train-rmse:0.251556+0.010847 test-rmse:1.006238+0.100341
## [31] train-rmse:0.238650+0.010437 test-rmse:1.002681+0.097116
## [32] train-rmse:0.227517+0.011688 test-rmse:1.001470+0.097943
## [33] train-rmse:0.216156+0.009893 test-rmse:0.997693+0.101039
## [34] train-rmse:0.205002+0.009224 test-rmse:0.997069+0.101493
## [35] train-rmse:0.197229+0.008597 test-rmse:0.993752+0.102920
## [36] train-rmse:0.187324+0.006907 test-rmse:0.990993+0.099776
## [37] train-rmse:0.177061+0.007018 test-rmse:0.986507+0.102056
## [38] train-rmse:0.168725+0.007774 test-rmse:0.986542+0.103554
## [39] train-rmse:0.161422+0.006639 test-rmse:0.986551+0.104901
## [40] train-rmse:0.151891+0.005538 test-rmse:0.985341+0.106791
## [41] train-rmse:0.144403+0.007542 test-rmse:0.982658+0.104002
## [42] train-rmse:0.138478+0.008171 test-rmse:0.981136+0.103275
## [43] train-rmse:0.132182+0.006670 test-rmse:0.981971+0.104837
## [44] train-rmse:0.126274+0.005349 test-rmse:0.980797+0.105162
## [45] train-rmse:0.121281+0.005053 test-rmse:0.979686+0.107447
## [46] train-rmse:0.115401+0.004762 test-rmse:0.980267+0.106738
## [47] train-rmse:0.109273+0.002669 test-rmse:0.979534+0.106563
## [48] train-rmse:0.104639+0.003396 test-rmse:0.979451+0.106592
## [49] train-rmse:0.101286+0.002742 test-rmse:0.978098+0.106296
## [50] train-rmse:0.096312+0.002215 test-rmse:0.977168+0.105332
## [51] train-rmse:0.091829+0.002716 test-rmse:0.977002+0.105762
## [52] train-rmse:0.087833+0.002427 test-rmse:0.976914+0.105224
## [53] train-rmse:0.082889+0.003227 test-rmse:0.976913+0.104587
## [54] train-rmse:0.079323+0.002642 test-rmse:0.975835+0.106080
## [55] train-rmse:0.075573+0.002095 test-rmse:0.975210+0.106199
## [56] train-rmse:0.071297+0.002407 test-rmse:0.975930+0.107424
## [57] train-rmse:0.067984+0.001928 test-rmse:0.974895+0.107409
## [58] train-rmse:0.064698+0.002554 test-rmse:0.974187+0.107346
## [59] train-rmse:0.062765+0.003069 test-rmse:0.974306+0.107544
## [60] train-rmse:0.060296+0.002370 test-rmse:0.973758+0.107937
## [61] train-rmse:0.057139+0.002740 test-rmse:0.973445+0.107429
## [62] train-rmse:0.053999+0.002613 test-rmse:0.974009+0.107736
## [63] train-rmse:0.051525+0.002767 test-rmse:0.972611+0.108493
## [64] train-rmse:0.049119+0.002393 test-rmse:0.972046+0.107920
## [65] train-rmse:0.046341+0.002415 test-rmse:0.972283+0.107888
## [66] train-rmse:0.044923+0.002773 test-rmse:0.972453+0.107833
## [67] train-rmse:0.042885+0.002034 test-rmse:0.972235+0.107764
## [68] train-rmse:0.041000+0.002159 test-rmse:0.972618+0.107604
## [69] train-rmse:0.039401+0.002160 test-rmse:0.972311+0.107861
## [70] train-rmse:0.037749+0.001900 test-rmse:0.972309+0.108086
## Stopping. Best iteration:
## [64] train-rmse:0.049119+0.002393 test-rmse:0.972046+0.107920
##
## 111
## [1] train-rmse:59.245290+0.059177 test-rmse:59.245643+0.433703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:35.667956+0.034978 test-rmse:35.667497+0.455608
## [3] train-rmse:21.513287+0.020080 test-rmse:21.511532+0.466322
## [4] train-rmse:13.041444+0.011310 test-rmse:13.037761+0.467604
## [5] train-rmse:7.989149+0.005702 test-rmse:7.970775+0.412728
## [6] train-rmse:4.974033+0.007000 test-rmse:4.964287+0.340916
## [7] train-rmse:3.171186+0.004632 test-rmse:3.176656+0.280722
## [8] train-rmse:2.101106+0.009282 test-rmse:2.140290+0.231008
## [9] train-rmse:1.454380+0.026098 test-rmse:1.591939+0.182822
## [10] train-rmse:1.070309+0.016361 test-rmse:1.347266+0.142547
## [11] train-rmse:0.849865+0.025751 test-rmse:1.220017+0.102769
## [12] train-rmse:0.708743+0.029210 test-rmse:1.164335+0.092221
## [13] train-rmse:0.615929+0.028800 test-rmse:1.129369+0.071930
## [14] train-rmse:0.553579+0.032115 test-rmse:1.102359+0.070547
## [15] train-rmse:0.500903+0.028548 test-rmse:1.093246+0.072871
## [16] train-rmse:0.470936+0.028501 test-rmse:1.074189+0.076167
## [17] train-rmse:0.439394+0.028249 test-rmse:1.066455+0.069947
## [18] train-rmse:0.410293+0.030757 test-rmse:1.055017+0.065837
## [19] train-rmse:0.382777+0.026387 test-rmse:1.051513+0.066292
## [20] train-rmse:0.352991+0.026051 test-rmse:1.045673+0.066964
## [21] train-rmse:0.326541+0.024720 test-rmse:1.041511+0.070161
## [22] train-rmse:0.304190+0.024799 test-rmse:1.037956+0.067912
## [23] train-rmse:0.286573+0.025108 test-rmse:1.035378+0.069291
## [24] train-rmse:0.269483+0.024144 test-rmse:1.034004+0.064451
## [25] train-rmse:0.251426+0.020443 test-rmse:1.028465+0.068566
## [26] train-rmse:0.230873+0.020595 test-rmse:1.027642+0.063020
## [27] train-rmse:0.217123+0.019806 test-rmse:1.026490+0.066485
## [28] train-rmse:0.206651+0.018142 test-rmse:1.020268+0.065688
## [29] train-rmse:0.195682+0.018390 test-rmse:1.018249+0.063253
## [30] train-rmse:0.183001+0.018345 test-rmse:1.016344+0.062573
## [31] train-rmse:0.171982+0.015299 test-rmse:1.015498+0.064276
## [32] train-rmse:0.157113+0.015358 test-rmse:1.014430+0.063293
## [33] train-rmse:0.148741+0.012606 test-rmse:1.012426+0.063467
## [34] train-rmse:0.140448+0.013093 test-rmse:1.013896+0.064062
## [35] train-rmse:0.133458+0.012038 test-rmse:1.013061+0.061906
## [36] train-rmse:0.124907+0.012900 test-rmse:1.013635+0.062292
## [37] train-rmse:0.117205+0.012691 test-rmse:1.013062+0.063246
## [38] train-rmse:0.110685+0.011959 test-rmse:1.012320+0.063719
## [39] train-rmse:0.105131+0.010604 test-rmse:1.011198+0.063309
## [40] train-rmse:0.097541+0.011550 test-rmse:1.010232+0.063289
## [41] train-rmse:0.091927+0.008769 test-rmse:1.010349+0.062693
## [42] train-rmse:0.086329+0.007594 test-rmse:1.008501+0.062882
## [43] train-rmse:0.081542+0.007212 test-rmse:1.007040+0.061559
## [44] train-rmse:0.076030+0.007896 test-rmse:1.006475+0.061283
## [45] train-rmse:0.072817+0.008649 test-rmse:1.006449+0.060446
## [46] train-rmse:0.068749+0.008516 test-rmse:1.006926+0.060989
## [47] train-rmse:0.065803+0.007862 test-rmse:1.006732+0.061274
## [48] train-rmse:0.062938+0.007796 test-rmse:1.006044+0.061513
## [49] train-rmse:0.059286+0.007388 test-rmse:1.005574+0.061430
## [50] train-rmse:0.054532+0.007015 test-rmse:1.006467+0.059243
## [51] train-rmse:0.051310+0.006475 test-rmse:1.006166+0.058933
## [52] train-rmse:0.048218+0.004857 test-rmse:1.005682+0.059603
## [53] train-rmse:0.045585+0.004122 test-rmse:1.005878+0.059530
## [54] train-rmse:0.042939+0.004327 test-rmse:1.005947+0.059445
## [55] train-rmse:0.039929+0.004049 test-rmse:1.005973+0.059628
## Stopping. Best iteration:
## [49] train-rmse:0.059286+0.007388 test-rmse:1.005574+0.061430
##
## 112## max_depth eta
## 63 7 0.26## [1] train-rmse:96.903592+0.106998 test-rmse:96.903929+0.480417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.282645+0.098508 test-rmse:87.282849+0.494309
## [3] train-rmse:78.624342+0.091095 test-rmse:78.624364+0.507694
## [4] train-rmse:70.833145+0.084703 test-rmse:70.832979+0.520704
## [5] train-rmse:63.823124+0.079254 test-rmse:63.822745+0.533458
## [6] train-rmse:57.516975+0.074698 test-rmse:57.516330+0.546089
## [7] train-rmse:51.845108+0.070990 test-rmse:51.844165+0.558710
## [8] train-rmse:46.744962+0.068087 test-rmse:46.743658+0.571428
## [9] train-rmse:42.160251+0.065957 test-rmse:42.158527+0.584358
## [10] train-rmse:38.040358+0.064573 test-rmse:38.038154+0.597586
## [11] train-rmse:34.337589+0.063141 test-rmse:34.344373+0.598399
## [12] train-rmse:31.009436+0.061320 test-rmse:31.036346+0.600886
## [13] train-rmse:28.018613+0.059096 test-rmse:28.051969+0.611933
## [14] train-rmse:25.331300+0.055997 test-rmse:25.385387+0.619286
## [15] train-rmse:22.917261+0.053241 test-rmse:22.964550+0.616728
## [16] train-rmse:20.750379+0.050484 test-rmse:20.810078+0.610616
## [17] train-rmse:18.806157+0.047987 test-rmse:18.857919+0.606590
## [18] train-rmse:17.063534+0.045831 test-rmse:17.127583+0.600467
## [19] train-rmse:15.502573+0.043972 test-rmse:15.558737+0.596481
## [20] train-rmse:14.106478+0.042550 test-rmse:14.191969+0.616710
## [21] train-rmse:12.858692+0.041691 test-rmse:12.938224+0.614787
## [22] train-rmse:11.745963+0.041384 test-rmse:11.835577+0.604483
## [23] train-rmse:10.754624+0.041085 test-rmse:10.836637+0.603875
## [24] train-rmse:9.873867+0.042059 test-rmse:9.972510+0.616216
## [25] train-rmse:9.091924+0.042606 test-rmse:9.193679+0.612675
## [26] train-rmse:8.400282+0.044121 test-rmse:8.494716+0.607543
## [27] train-rmse:7.790210+0.045483 test-rmse:7.890323+0.615830
## [28] train-rmse:7.252296+0.046687 test-rmse:7.385747+0.634971
## [29] train-rmse:6.779517+0.047894 test-rmse:6.897802+0.622580
## [30] train-rmse:6.366342+0.049495 test-rmse:6.487876+0.604393
## [31] train-rmse:6.005059+0.051310 test-rmse:6.118924+0.586602
## [32] train-rmse:5.689480+0.052852 test-rmse:5.818287+0.606057
## [33] train-rmse:5.415944+0.054295 test-rmse:5.549357+0.581365
## [34] train-rmse:5.178796+0.055855 test-rmse:5.297457+0.558788
## [35] train-rmse:4.972802+0.056846 test-rmse:5.104747+0.573445
## [36] train-rmse:4.794287+0.057711 test-rmse:4.921263+0.552506
## [37] train-rmse:4.640263+0.058945 test-rmse:4.772928+0.523762
## [38] train-rmse:4.506117+0.060137 test-rmse:4.636273+0.517995
## [39] train-rmse:4.389116+0.060343 test-rmse:4.530475+0.530665
## [40] train-rmse:4.287646+0.060738 test-rmse:4.444312+0.520585
## [41] train-rmse:4.198071+0.061035 test-rmse:4.346438+0.495953
## [42] train-rmse:4.120055+0.061210 test-rmse:4.274099+0.489672
## [43] train-rmse:4.050620+0.061200 test-rmse:4.201074+0.473330
## [44] train-rmse:3.988714+0.060388 test-rmse:4.142445+0.475686
## [45] train-rmse:3.933689+0.059885 test-rmse:4.103595+0.464808
## [46] train-rmse:3.883860+0.059826 test-rmse:4.067777+0.474075
## [47] train-rmse:3.838262+0.060116 test-rmse:4.025269+0.458694
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## [66] train-rmse:3.333316+0.050907 test-rmse:3.553415+0.350842
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## [74] train-rmse:3.187461+0.046894 test-rmse:3.401838+0.325588
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## [80] train-rmse:3.088973+0.044420 test-rmse:3.326879+0.333415
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## [86] train-rmse:2.997534+0.042074 test-rmse:3.233806+0.309283
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## [93] train-rmse:2.897717+0.040595 test-rmse:3.145950+0.325037
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## [110] train-rmse:2.682819+0.038608 test-rmse:2.922822+0.305337
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## [120] train-rmse:2.570716+0.037194 test-rmse:2.830575+0.313109
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## [122] train-rmse:2.549330+0.036926 test-rmse:2.802569+0.308902
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## [124] train-rmse:2.528330+0.036627 test-rmse:2.785126+0.299892
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## [126] train-rmse:2.507618+0.036251 test-rmse:2.764632+0.298431
## [127] train-rmse:2.497323+0.036056 test-rmse:2.754003+0.296722
## [128] train-rmse:2.487041+0.035842 test-rmse:2.739810+0.299630
## [129] train-rmse:2.476851+0.035715 test-rmse:2.729712+0.301894
## [130] train-rmse:2.466849+0.035554 test-rmse:2.721241+0.302179
## [131] train-rmse:2.456933+0.035398 test-rmse:2.716356+0.299890
## [132] train-rmse:2.447148+0.035232 test-rmse:2.705139+0.304041
## [133] train-rmse:2.437409+0.035071 test-rmse:2.700301+0.307161
## [134] train-rmse:2.427752+0.034913 test-rmse:2.692033+0.307256
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## [136] train-rmse:2.408545+0.034615 test-rmse:2.677580+0.305624
## [137] train-rmse:2.399037+0.034342 test-rmse:2.666868+0.310443
## [138] train-rmse:2.389536+0.034038 test-rmse:2.647162+0.303025
## [139] train-rmse:2.380175+0.033795 test-rmse:2.639702+0.301825
## [140] train-rmse:2.370907+0.033608 test-rmse:2.632964+0.300067
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## [144] train-rmse:2.334469+0.032851 test-rmse:2.591358+0.286880
## [145] train-rmse:2.325443+0.032758 test-rmse:2.583797+0.285826
## [146] train-rmse:2.316454+0.032671 test-rmse:2.575507+0.287959
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## [159] train-rmse:2.204998+0.030377 test-rmse:2.462540+0.279160
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## [169] train-rmse:2.125348+0.028814 test-rmse:2.389766+0.278427
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## [173] train-rmse:2.094890+0.028102 test-rmse:2.358853+0.279001
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## [175] train-rmse:2.079889+0.027900 test-rmse:2.346699+0.279148
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## [190] train-rmse:1.972902+0.025621 test-rmse:2.233526+0.268725
## [191] train-rmse:1.966034+0.025501 test-rmse:2.225412+0.264562
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## [195] train-rmse:1.939022+0.024865 test-rmse:2.207501+0.265239
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## [197] train-rmse:1.925802+0.024561 test-rmse:2.195392+0.259892
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## [201] train-rmse:1.899848+0.023837 test-rmse:2.168722+0.261791
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## [203] train-rmse:1.887172+0.023485 test-rmse:2.146510+0.250457
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## [207] train-rmse:1.861977+0.022960 test-rmse:2.125421+0.253512
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## [209] train-rmse:1.849674+0.022757 test-rmse:2.115972+0.256163
## [210] train-rmse:1.843597+0.022649 test-rmse:2.112640+0.257491
## [211] train-rmse:1.837539+0.022530 test-rmse:2.110040+0.256588
## [212] train-rmse:1.831443+0.022408 test-rmse:2.104986+0.252039
## [213] train-rmse:1.825365+0.022256 test-rmse:2.096965+0.251903
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## [219] train-rmse:1.789869+0.021579 test-rmse:2.060631+0.258512
## [220] train-rmse:1.784076+0.021425 test-rmse:2.056212+0.255387
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## [222] train-rmse:1.772641+0.021252 test-rmse:2.048257+0.255509
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## [224] train-rmse:1.761356+0.021053 test-rmse:2.038046+0.256407
## [225] train-rmse:1.755736+0.020979 test-rmse:2.030654+0.249462
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## [228] train-rmse:1.738994+0.020647 test-rmse:2.012655+0.243908
## [229] train-rmse:1.733487+0.020512 test-rmse:2.005790+0.239954
## [230] train-rmse:1.727969+0.020376 test-rmse:2.003267+0.240915
## [231] train-rmse:1.722529+0.020258 test-rmse:1.996157+0.243086
## [232] train-rmse:1.717125+0.020130 test-rmse:1.990482+0.243211
## [233] train-rmse:1.711803+0.020017 test-rmse:1.987698+0.244793
## [234] train-rmse:1.706456+0.019907 test-rmse:1.983971+0.245040
## [235] train-rmse:1.701124+0.019773 test-rmse:1.980447+0.246194
## [236] train-rmse:1.695828+0.019645 test-rmse:1.976299+0.248435
## [237] train-rmse:1.690607+0.019519 test-rmse:1.971382+0.250056
## [238] train-rmse:1.685399+0.019405 test-rmse:1.967211+0.252427
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## [240] train-rmse:1.675078+0.019136 test-rmse:1.955889+0.254523
## [241] train-rmse:1.669963+0.019012 test-rmse:1.950254+0.251164
## [242] train-rmse:1.664856+0.018910 test-rmse:1.941432+0.244690
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## [244] train-rmse:1.654601+0.018667 test-rmse:1.928657+0.239332
## [245] train-rmse:1.649537+0.018623 test-rmse:1.922720+0.238137
## [246] train-rmse:1.644493+0.018618 test-rmse:1.918863+0.235969
## [247] train-rmse:1.639486+0.018614 test-rmse:1.915446+0.236220
## [248] train-rmse:1.634504+0.018580 test-rmse:1.909585+0.236249
## [249] train-rmse:1.629494+0.018561 test-rmse:1.905179+0.237869
## [250] train-rmse:1.624602+0.018466 test-rmse:1.901512+0.236223
## [251] train-rmse:1.619739+0.018348 test-rmse:1.896093+0.233936
## [252] train-rmse:1.614912+0.018205 test-rmse:1.890527+0.233350
## [253] train-rmse:1.610120+0.018066 test-rmse:1.884722+0.235769
## [254] train-rmse:1.605358+0.017970 test-rmse:1.882329+0.234759
## [255] train-rmse:1.600610+0.017905 test-rmse:1.879126+0.235293
## [256] train-rmse:1.595953+0.017798 test-rmse:1.876894+0.234777
## [257] train-rmse:1.591307+0.017697 test-rmse:1.872104+0.235438
## [258] train-rmse:1.586683+0.017601 test-rmse:1.869511+0.235314
## [259] train-rmse:1.582066+0.017532 test-rmse:1.865889+0.237553
## [260] train-rmse:1.577442+0.017462 test-rmse:1.862995+0.238693
## [261] train-rmse:1.572837+0.017383 test-rmse:1.859741+0.240730
## [262] train-rmse:1.568219+0.017319 test-rmse:1.855487+0.241772
## [263] train-rmse:1.563637+0.017223 test-rmse:1.852357+0.241657
## [264] train-rmse:1.559078+0.017083 test-rmse:1.843428+0.234976
## [265] train-rmse:1.554487+0.016963 test-rmse:1.834266+0.231427
## [266] train-rmse:1.549929+0.016887 test-rmse:1.829378+0.228804
## [267] train-rmse:1.545432+0.016811 test-rmse:1.824890+0.225336
## [268] train-rmse:1.540942+0.016748 test-rmse:1.820036+0.225447
## [269] train-rmse:1.536492+0.016720 test-rmse:1.816396+0.226943
## [270] train-rmse:1.532058+0.016723 test-rmse:1.811570+0.226882
## [271] train-rmse:1.527652+0.016746 test-rmse:1.808075+0.228238
## [272] train-rmse:1.523293+0.016748 test-rmse:1.803394+0.228801
## [273] train-rmse:1.518950+0.016711 test-rmse:1.800428+0.229220
## [274] train-rmse:1.514649+0.016638 test-rmse:1.796142+0.226732
## [275] train-rmse:1.510388+0.016545 test-rmse:1.794943+0.227634
## [276] train-rmse:1.506163+0.016449 test-rmse:1.788273+0.229171
## [277] train-rmse:1.501962+0.016343 test-rmse:1.784916+0.226222
## [278] train-rmse:1.497779+0.016236 test-rmse:1.778667+0.227145
## [279] train-rmse:1.493614+0.016151 test-rmse:1.772925+0.227527
## [280] train-rmse:1.489462+0.016073 test-rmse:1.771230+0.226903
## [281] train-rmse:1.485309+0.016002 test-rmse:1.768158+0.227014
## [282] train-rmse:1.481206+0.015954 test-rmse:1.764162+0.224157
## [283] train-rmse:1.477117+0.015903 test-rmse:1.760725+0.224321
## [284] train-rmse:1.473008+0.015881 test-rmse:1.756375+0.225048
## [285] train-rmse:1.468904+0.015893 test-rmse:1.754207+0.223495
## [286] train-rmse:1.464824+0.015906 test-rmse:1.750669+0.224854
## [287] train-rmse:1.460796+0.015850 test-rmse:1.746191+0.223023
## [288] train-rmse:1.456774+0.015777 test-rmse:1.743186+0.223253
## [289] train-rmse:1.452759+0.015706 test-rmse:1.739478+0.221934
## [290] train-rmse:1.448818+0.015626 test-rmse:1.736615+0.222358
## [291] train-rmse:1.444868+0.015538 test-rmse:1.732005+0.222924
## [292] train-rmse:1.440940+0.015447 test-rmse:1.728580+0.224234
## [293] train-rmse:1.437050+0.015366 test-rmse:1.724983+0.226582
## [294] train-rmse:1.433168+0.015282 test-rmse:1.720364+0.222874
## [295] train-rmse:1.429329+0.015247 test-rmse:1.714857+0.217690
## [296] train-rmse:1.425508+0.015219 test-rmse:1.711998+0.218069
## [297] train-rmse:1.421696+0.015217 test-rmse:1.706364+0.219835
## [298] train-rmse:1.417932+0.015192 test-rmse:1.699994+0.221849
## [299] train-rmse:1.414207+0.015148 test-rmse:1.698097+0.220742
## [300] train-rmse:1.410518+0.015121 test-rmse:1.696076+0.220828
## [301] train-rmse:1.406808+0.015108 test-rmse:1.694045+0.220679
## [302] train-rmse:1.403116+0.015070 test-rmse:1.690151+0.218561
## [303] train-rmse:1.399444+0.015023 test-rmse:1.685476+0.219060
## [304] train-rmse:1.395772+0.014970 test-rmse:1.681300+0.218337
## [305] train-rmse:1.392100+0.014930 test-rmse:1.678308+0.218242
## [306] train-rmse:1.388451+0.014894 test-rmse:1.673644+0.218146
## [307] train-rmse:1.384801+0.014850 test-rmse:1.670484+0.218304
## [308] train-rmse:1.381157+0.014794 test-rmse:1.669155+0.218149
## [309] train-rmse:1.377537+0.014805 test-rmse:1.666180+0.217616
## [310] train-rmse:1.373956+0.014775 test-rmse:1.664040+0.218025
## [311] train-rmse:1.370437+0.014762 test-rmse:1.659437+0.218925
## [312] train-rmse:1.366936+0.014738 test-rmse:1.656982+0.216078
## [313] train-rmse:1.363451+0.014693 test-rmse:1.653346+0.216991
## [314] train-rmse:1.359986+0.014626 test-rmse:1.652205+0.217058
## [315] train-rmse:1.356531+0.014536 test-rmse:1.649178+0.218692
## [316] train-rmse:1.353082+0.014444 test-rmse:1.644644+0.216281
## [317] train-rmse:1.349639+0.014369 test-rmse:1.638308+0.211330
## [318] train-rmse:1.346244+0.014337 test-rmse:1.636589+0.212182
## [319] train-rmse:1.342869+0.014324 test-rmse:1.631892+0.213597
## [320] train-rmse:1.339513+0.014309 test-rmse:1.628533+0.214962
## [321] train-rmse:1.336178+0.014287 test-rmse:1.626642+0.214554
## [322] train-rmse:1.332860+0.014269 test-rmse:1.624620+0.215751
## [323] train-rmse:1.329562+0.014257 test-rmse:1.620090+0.215064
## [324] train-rmse:1.326310+0.014247 test-rmse:1.615560+0.214132
## [325] train-rmse:1.323060+0.014227 test-rmse:1.613232+0.213251
## [326] train-rmse:1.319824+0.014190 test-rmse:1.610605+0.213342
## [327] train-rmse:1.316587+0.014161 test-rmse:1.607124+0.213179
## [328] train-rmse:1.313349+0.014123 test-rmse:1.603752+0.212891
## [329] train-rmse:1.310123+0.014079 test-rmse:1.599860+0.212162
## [330] train-rmse:1.306930+0.014049 test-rmse:1.597570+0.212245
## [331] train-rmse:1.303753+0.014056 test-rmse:1.592238+0.212654
## [332] train-rmse:1.300567+0.014052 test-rmse:1.590286+0.213178
## [333] train-rmse:1.297385+0.014048 test-rmse:1.586518+0.212871
## [334] train-rmse:1.294232+0.014014 test-rmse:1.584933+0.211558
## [335] train-rmse:1.291098+0.013981 test-rmse:1.582832+0.211442
## [336] train-rmse:1.288002+0.013963 test-rmse:1.578826+0.210425
## [337] train-rmse:1.284946+0.013920 test-rmse:1.573638+0.208769
## [338] train-rmse:1.281917+0.013906 test-rmse:1.565952+0.204391
## [339] train-rmse:1.278884+0.013903 test-rmse:1.563182+0.204221
## [340] train-rmse:1.275844+0.013903 test-rmse:1.558983+0.204745
## [341] train-rmse:1.272799+0.013921 test-rmse:1.557131+0.204615
## [342] train-rmse:1.269779+0.013919 test-rmse:1.555328+0.204617
## [343] train-rmse:1.266774+0.013910 test-rmse:1.553918+0.204592
## [344] train-rmse:1.263801+0.013893 test-rmse:1.551572+0.204786
## [345] train-rmse:1.260837+0.013888 test-rmse:1.549855+0.206304
## [346] train-rmse:1.257919+0.013881 test-rmse:1.548469+0.207197
## [347] train-rmse:1.255019+0.013882 test-rmse:1.546371+0.207477
## [348] train-rmse:1.252134+0.013888 test-rmse:1.542709+0.207942
## [349] train-rmse:1.249299+0.013877 test-rmse:1.541220+0.207702
## [350] train-rmse:1.246425+0.013836 test-rmse:1.538619+0.206815
## [351] train-rmse:1.243598+0.013822 test-rmse:1.537554+0.207489
## [352] train-rmse:1.240771+0.013807 test-rmse:1.535551+0.207346
## [353] train-rmse:1.237987+0.013786 test-rmse:1.532120+0.207337
## [354] train-rmse:1.235199+0.013765 test-rmse:1.530093+0.207413
## [355] train-rmse:1.232415+0.013753 test-rmse:1.526737+0.206289
## [356] train-rmse:1.229645+0.013724 test-rmse:1.523131+0.206734
## [357] train-rmse:1.226881+0.013695 test-rmse:1.520073+0.206281
## [358] train-rmse:1.224133+0.013671 test-rmse:1.516805+0.205972
## [359] train-rmse:1.221385+0.013652 test-rmse:1.512968+0.204172
## [360] train-rmse:1.218653+0.013636 test-rmse:1.510120+0.203996
## [361] train-rmse:1.215922+0.013622 test-rmse:1.508733+0.204566
## [362] train-rmse:1.213217+0.013589 test-rmse:1.506027+0.201121
## [363] train-rmse:1.210549+0.013581 test-rmse:1.500010+0.196772
## [364] train-rmse:1.207883+0.013590 test-rmse:1.495189+0.197781
## [365] train-rmse:1.205225+0.013605 test-rmse:1.492809+0.199304
## [366] train-rmse:1.202567+0.013613 test-rmse:1.491528+0.199690
## [367] train-rmse:1.199957+0.013616 test-rmse:1.490241+0.199447
## [368] train-rmse:1.197357+0.013608 test-rmse:1.489707+0.198912
## [369] train-rmse:1.194786+0.013590 test-rmse:1.488395+0.199952
## [370] train-rmse:1.192211+0.013588 test-rmse:1.486567+0.200430
## [371] train-rmse:1.189662+0.013588 test-rmse:1.483756+0.199672
## [372] train-rmse:1.187120+0.013588 test-rmse:1.480414+0.201110
## [373] train-rmse:1.184583+0.013594 test-rmse:1.478092+0.201271
## [374] train-rmse:1.182080+0.013595 test-rmse:1.475694+0.199447
## [375] train-rmse:1.179573+0.013598 test-rmse:1.472205+0.197514
## [376] train-rmse:1.177098+0.013599 test-rmse:1.467219+0.192702
## [377] train-rmse:1.174634+0.013604 test-rmse:1.465348+0.192308
## [378] train-rmse:1.172176+0.013611 test-rmse:1.464090+0.191381
## [379] train-rmse:1.169738+0.013631 test-rmse:1.461272+0.193005
## [380] train-rmse:1.167320+0.013641 test-rmse:1.458523+0.192269
## [381] train-rmse:1.164917+0.013642 test-rmse:1.458267+0.192596
## [382] train-rmse:1.162512+0.013635 test-rmse:1.458071+0.193300
## [383] train-rmse:1.160114+0.013617 test-rmse:1.456761+0.194722
## [384] train-rmse:1.157733+0.013598 test-rmse:1.453366+0.195354
## [385] train-rmse:1.155350+0.013581 test-rmse:1.450950+0.196262
## [386] train-rmse:1.152983+0.013567 test-rmse:1.447782+0.196288
## [387] train-rmse:1.150634+0.013532 test-rmse:1.446481+0.196196
## [388] train-rmse:1.148313+0.013516 test-rmse:1.443540+0.197417
## [389] train-rmse:1.145996+0.013509 test-rmse:1.440955+0.199373
## [390] train-rmse:1.143685+0.013515 test-rmse:1.437269+0.199296
## [391] train-rmse:1.141401+0.013503 test-rmse:1.433985+0.198186
## [392] train-rmse:1.139123+0.013509 test-rmse:1.430955+0.195688
## [393] train-rmse:1.136855+0.013507 test-rmse:1.426697+0.191220
## [394] train-rmse:1.134613+0.013507 test-rmse:1.424271+0.190010
## [395] train-rmse:1.132377+0.013504 test-rmse:1.423238+0.189695
## [396] train-rmse:1.130155+0.013507 test-rmse:1.422459+0.189770
## [397] train-rmse:1.127949+0.013516 test-rmse:1.420028+0.189105
## [398] train-rmse:1.125743+0.013520 test-rmse:1.418708+0.189342
## [399] train-rmse:1.123546+0.013521 test-rmse:1.417274+0.189163
## [400] train-rmse:1.121351+0.013524 test-rmse:1.415750+0.187655
## [401] train-rmse:1.119159+0.013540 test-rmse:1.412349+0.186879
## [402] train-rmse:1.116983+0.013557 test-rmse:1.411212+0.186855
## [403] train-rmse:1.114829+0.013542 test-rmse:1.408651+0.187646
## [404] train-rmse:1.112682+0.013522 test-rmse:1.406081+0.189292
## [405] train-rmse:1.110564+0.013509 test-rmse:1.404344+0.188932
## [406] train-rmse:1.108468+0.013481 test-rmse:1.403221+0.188778
## [407] train-rmse:1.106360+0.013449 test-rmse:1.401497+0.188723
## [408] train-rmse:1.104282+0.013444 test-rmse:1.400342+0.187627
## [409] train-rmse:1.102231+0.013448 test-rmse:1.399458+0.188384
## [410] train-rmse:1.100196+0.013464 test-rmse:1.396443+0.187334
## [411] train-rmse:1.098167+0.013477 test-rmse:1.394883+0.187062
## [412] train-rmse:1.096139+0.013485 test-rmse:1.392124+0.185745
## [413] train-rmse:1.094114+0.013483 test-rmse:1.390405+0.186510
## [414] train-rmse:1.092095+0.013489 test-rmse:1.389317+0.187767
## [415] train-rmse:1.090101+0.013495 test-rmse:1.389003+0.187984
## [416] train-rmse:1.088119+0.013494 test-rmse:1.387001+0.188809
## [417] train-rmse:1.086139+0.013499 test-rmse:1.383250+0.186496
## [418] train-rmse:1.084166+0.013499 test-rmse:1.382209+0.187059
## [419] train-rmse:1.082187+0.013497 test-rmse:1.379141+0.188752
## [420] train-rmse:1.080228+0.013496 test-rmse:1.374652+0.185588
## [421] train-rmse:1.078279+0.013484 test-rmse:1.373583+0.185554
## [422] train-rmse:1.076349+0.013485 test-rmse:1.371417+0.186298
## [423] train-rmse:1.074407+0.013481 test-rmse:1.371002+0.184954
## [424] train-rmse:1.072495+0.013476 test-rmse:1.369037+0.185094
## [425] train-rmse:1.070596+0.013474 test-rmse:1.367526+0.185347
## [426] train-rmse:1.068701+0.013483 test-rmse:1.364933+0.185409
## [427] train-rmse:1.066817+0.013481 test-rmse:1.364533+0.185367
## [428] train-rmse:1.064918+0.013494 test-rmse:1.361981+0.184652
## [429] train-rmse:1.063053+0.013494 test-rmse:1.358419+0.183499
## [430] train-rmse:1.061193+0.013482 test-rmse:1.355287+0.185422
## [431] train-rmse:1.059340+0.013465 test-rmse:1.352987+0.186003
## [432] train-rmse:1.057502+0.013451 test-rmse:1.351756+0.185694
## [433] train-rmse:1.055672+0.013451 test-rmse:1.351465+0.184392
## [434] train-rmse:1.053862+0.013443 test-rmse:1.350339+0.183830
## [435] train-rmse:1.052069+0.013450 test-rmse:1.347930+0.185018
## [436] train-rmse:1.050297+0.013454 test-rmse:1.348023+0.183827
## [437] train-rmse:1.048536+0.013454 test-rmse:1.346722+0.183768
## [438] train-rmse:1.046771+0.013442 test-rmse:1.346276+0.183940
## [439] train-rmse:1.045016+0.013437 test-rmse:1.344402+0.184450
## [440] train-rmse:1.043265+0.013425 test-rmse:1.341466+0.182746
## [441] train-rmse:1.041518+0.013409 test-rmse:1.340383+0.183324
## [442] train-rmse:1.039781+0.013418 test-rmse:1.340100+0.183420
## [443] train-rmse:1.038058+0.013427 test-rmse:1.339055+0.182131
## [444] train-rmse:1.036345+0.013442 test-rmse:1.334401+0.179021
## [445] train-rmse:1.034647+0.013451 test-rmse:1.332815+0.180081
## [446] train-rmse:1.032966+0.013463 test-rmse:1.330584+0.181606
## [447] train-rmse:1.031287+0.013475 test-rmse:1.330126+0.180860
## [448] train-rmse:1.029612+0.013489 test-rmse:1.327861+0.180291
## [449] train-rmse:1.027944+0.013493 test-rmse:1.325868+0.181370
## [450] train-rmse:1.026291+0.013498 test-rmse:1.324019+0.181791
## [451] train-rmse:1.024640+0.013504 test-rmse:1.321383+0.182448
## [452] train-rmse:1.023000+0.013497 test-rmse:1.320550+0.182426
## [453] train-rmse:1.021365+0.013488 test-rmse:1.320008+0.181426
## [454] train-rmse:1.019735+0.013482 test-rmse:1.319928+0.181438
## [455] train-rmse:1.018121+0.013480 test-rmse:1.318324+0.180741
## [456] train-rmse:1.016521+0.013473 test-rmse:1.315381+0.180625
## [457] train-rmse:1.014925+0.013458 test-rmse:1.313671+0.181145
## [458] train-rmse:1.013341+0.013456 test-rmse:1.312300+0.179804
## [459] train-rmse:1.011763+0.013457 test-rmse:1.310973+0.180452
## [460] train-rmse:1.010184+0.013462 test-rmse:1.309607+0.180154
## [461] train-rmse:1.008628+0.013461 test-rmse:1.308109+0.180414
## [462] train-rmse:1.007067+0.013463 test-rmse:1.307572+0.179905
## [463] train-rmse:1.005536+0.013474 test-rmse:1.305661+0.179381
## [464] train-rmse:1.004018+0.013476 test-rmse:1.304834+0.180109
## [465] train-rmse:1.002504+0.013471 test-rmse:1.302314+0.178440
## [466] train-rmse:1.000983+0.013466 test-rmse:1.300772+0.176958
## [467] train-rmse:0.999475+0.013477 test-rmse:1.300041+0.177116
## [468] train-rmse:0.997977+0.013486 test-rmse:1.296192+0.174323
## [469] train-rmse:0.996480+0.013487 test-rmse:1.294778+0.175455
## [470] train-rmse:0.994991+0.013492 test-rmse:1.292711+0.175685
## [471] train-rmse:0.993506+0.013495 test-rmse:1.291444+0.176610
## [472] train-rmse:0.992028+0.013495 test-rmse:1.291110+0.177044
## [473] train-rmse:0.990569+0.013501 test-rmse:1.289951+0.178155
## [474] train-rmse:0.989118+0.013509 test-rmse:1.289040+0.177758
## [475] train-rmse:0.987668+0.013515 test-rmse:1.287323+0.178572
## [476] train-rmse:0.986221+0.013520 test-rmse:1.285469+0.178288
## [477] train-rmse:0.984774+0.013524 test-rmse:1.284452+0.178353
## [478] train-rmse:0.983333+0.013528 test-rmse:1.283069+0.177695
## [479] train-rmse:0.981905+0.013523 test-rmse:1.281945+0.177719
## [480] train-rmse:0.980505+0.013531 test-rmse:1.281976+0.177660
## [481] train-rmse:0.979114+0.013532 test-rmse:1.281002+0.178036
## [482] train-rmse:0.977729+0.013536 test-rmse:1.280964+0.177397
## [483] train-rmse:0.976338+0.013536 test-rmse:1.279452+0.176638
## [484] train-rmse:0.974959+0.013544 test-rmse:1.279298+0.176096
## [485] train-rmse:0.973583+0.013549 test-rmse:1.277589+0.176556
## [486] train-rmse:0.972215+0.013550 test-rmse:1.276971+0.177281
## [487] train-rmse:0.970870+0.013558 test-rmse:1.275125+0.176879
## [488] train-rmse:0.969521+0.013569 test-rmse:1.274196+0.177052
## [489] train-rmse:0.968168+0.013575 test-rmse:1.273559+0.177184
## [490] train-rmse:0.966828+0.013582 test-rmse:1.272340+0.177663
## [491] train-rmse:0.965492+0.013588 test-rmse:1.270730+0.178066
## [492] train-rmse:0.964166+0.013594 test-rmse:1.269409+0.178861
## [493] train-rmse:0.962848+0.013609 test-rmse:1.267479+0.178035
## [494] train-rmse:0.961536+0.013622 test-rmse:1.264794+0.177437
## [495] train-rmse:0.960224+0.013638 test-rmse:1.261896+0.173767
## [496] train-rmse:0.958929+0.013656 test-rmse:1.261709+0.173827
## [497] train-rmse:0.957636+0.013671 test-rmse:1.261487+0.173971
## [498] train-rmse:0.956349+0.013695 test-rmse:1.258856+0.173668
## [499] train-rmse:0.955075+0.013711 test-rmse:1.258299+0.172709
## [500] train-rmse:0.953818+0.013723 test-rmse:1.257477+0.172442
## [501] train-rmse:0.952565+0.013718 test-rmse:1.256250+0.172859
## [502] train-rmse:0.951318+0.013718 test-rmse:1.255478+0.172792
## [503] train-rmse:0.950075+0.013717 test-rmse:1.253607+0.172752
## [504] train-rmse:0.948836+0.013715 test-rmse:1.253433+0.172703
## [505] train-rmse:0.947598+0.013715 test-rmse:1.252305+0.172124
## [506] train-rmse:0.946377+0.013727 test-rmse:1.252309+0.172086
## [507] train-rmse:0.945156+0.013734 test-rmse:1.251633+0.172608
## [508] train-rmse:0.943945+0.013750 test-rmse:1.250551+0.172467
## [509] train-rmse:0.942740+0.013769 test-rmse:1.248163+0.173575
## [510] train-rmse:0.941544+0.013781 test-rmse:1.246905+0.173216
## [511] train-rmse:0.940355+0.013793 test-rmse:1.246415+0.172031
## [512] train-rmse:0.939166+0.013803 test-rmse:1.245768+0.172230
## [513] train-rmse:0.937965+0.013805 test-rmse:1.245100+0.172655
## [514] train-rmse:0.936770+0.013822 test-rmse:1.243725+0.172893
## [515] train-rmse:0.935595+0.013827 test-rmse:1.242829+0.172665
## [516] train-rmse:0.934434+0.013847 test-rmse:1.241409+0.172387
## [517] train-rmse:0.933278+0.013865 test-rmse:1.240130+0.169951
## [518] train-rmse:0.932129+0.013885 test-rmse:1.237688+0.169766
## [519] train-rmse:0.930992+0.013892 test-rmse:1.236567+0.169983
## [520] train-rmse:0.929855+0.013895 test-rmse:1.235984+0.169808
## [521] train-rmse:0.928731+0.013903 test-rmse:1.233690+0.169084
## [522] train-rmse:0.927614+0.013913 test-rmse:1.231875+0.169636
## [523] train-rmse:0.926502+0.013924 test-rmse:1.231862+0.169797
## [524] train-rmse:0.925392+0.013941 test-rmse:1.230542+0.170165
## [525] train-rmse:0.924281+0.013955 test-rmse:1.230551+0.170104
## [526] train-rmse:0.923173+0.013972 test-rmse:1.229820+0.170040
## [527] train-rmse:0.922075+0.013982 test-rmse:1.228020+0.166425
## [528] train-rmse:0.920991+0.014002 test-rmse:1.226720+0.166593
## [529] train-rmse:0.919915+0.014016 test-rmse:1.225906+0.167088
## [530] train-rmse:0.918842+0.014038 test-rmse:1.225146+0.167714
## [531] train-rmse:0.917776+0.014049 test-rmse:1.224038+0.166205
## [532] train-rmse:0.916714+0.014060 test-rmse:1.222949+0.166119
## [533] train-rmse:0.915669+0.014076 test-rmse:1.222492+0.166222
## [534] train-rmse:0.914621+0.014088 test-rmse:1.220904+0.166452
## [535] train-rmse:0.913578+0.014100 test-rmse:1.220882+0.165554
## [536] train-rmse:0.912525+0.014106 test-rmse:1.219864+0.164170
## [537] train-rmse:0.911475+0.014118 test-rmse:1.219364+0.164466
## [538] train-rmse:0.910430+0.014130 test-rmse:1.219477+0.164408
## [539] train-rmse:0.909392+0.014141 test-rmse:1.218408+0.164959
## [540] train-rmse:0.908362+0.014152 test-rmse:1.216976+0.165579
## [541] train-rmse:0.907342+0.014155 test-rmse:1.215736+0.165883
## [542] train-rmse:0.906330+0.014164 test-rmse:1.214937+0.165844
## [543] train-rmse:0.905321+0.014175 test-rmse:1.214421+0.165415
## [544] train-rmse:0.904305+0.014181 test-rmse:1.213456+0.165307
## [545] train-rmse:0.903309+0.014183 test-rmse:1.211273+0.165291
## [546] train-rmse:0.902315+0.014190 test-rmse:1.210750+0.165322
## [547] train-rmse:0.901337+0.014196 test-rmse:1.209598+0.165910
## [548] train-rmse:0.900361+0.014206 test-rmse:1.207911+0.166363
## [549] train-rmse:0.899392+0.014210 test-rmse:1.207293+0.166809
## [550] train-rmse:0.898416+0.014230 test-rmse:1.206207+0.167157
## [551] train-rmse:0.897446+0.014244 test-rmse:1.205144+0.167045
## [552] train-rmse:0.896481+0.014259 test-rmse:1.204641+0.167419
## [553] train-rmse:0.895533+0.014277 test-rmse:1.204927+0.167140
## [554] train-rmse:0.894583+0.014284 test-rmse:1.204265+0.167903
## [555] train-rmse:0.893643+0.014305 test-rmse:1.203497+0.166321
## [556] train-rmse:0.892716+0.014319 test-rmse:1.202553+0.166013
## [557] train-rmse:0.891787+0.014335 test-rmse:1.202508+0.166329
## [558] train-rmse:0.890864+0.014354 test-rmse:1.202354+0.166492
## [559] train-rmse:0.889940+0.014372 test-rmse:1.201734+0.166603
## [560] train-rmse:0.889022+0.014388 test-rmse:1.199800+0.163765
## [561] train-rmse:0.888104+0.014406 test-rmse:1.199275+0.164109
## [562] train-rmse:0.887188+0.014428 test-rmse:1.198368+0.164793
## [563] train-rmse:0.886278+0.014439 test-rmse:1.196892+0.163846
## [564] train-rmse:0.885364+0.014459 test-rmse:1.196307+0.164206
## [565] train-rmse:0.884470+0.014478 test-rmse:1.196408+0.163941
## [566] train-rmse:0.883580+0.014497 test-rmse:1.194961+0.164048
## [567] train-rmse:0.882692+0.014510 test-rmse:1.194131+0.164111
## [568] train-rmse:0.881816+0.014525 test-rmse:1.194178+0.164375
## [569] train-rmse:0.880944+0.014535 test-rmse:1.192224+0.164671
## [570] train-rmse:0.880083+0.014546 test-rmse:1.191769+0.164605
## [571] train-rmse:0.879218+0.014554 test-rmse:1.190919+0.164460
## [572] train-rmse:0.878356+0.014570 test-rmse:1.190613+0.163441
## [573] train-rmse:0.877502+0.014584 test-rmse:1.190554+0.162856
## [574] train-rmse:0.876654+0.014598 test-rmse:1.190162+0.163059
## [575] train-rmse:0.875813+0.014611 test-rmse:1.189870+0.163557
## [576] train-rmse:0.874977+0.014622 test-rmse:1.188804+0.163210
## [577] train-rmse:0.874141+0.014634 test-rmse:1.188233+0.163822
## [578] train-rmse:0.873313+0.014645 test-rmse:1.187632+0.163527
## [579] train-rmse:0.872485+0.014661 test-rmse:1.185887+0.162481
## [580] train-rmse:0.871658+0.014667 test-rmse:1.185257+0.163043
## [581] train-rmse:0.870836+0.014677 test-rmse:1.184419+0.163090
## [582] train-rmse:0.870018+0.014684 test-rmse:1.183405+0.162340
## [583] train-rmse:0.869201+0.014698 test-rmse:1.183367+0.162102
## [584] train-rmse:0.868385+0.014707 test-rmse:1.181895+0.159868
## [585] train-rmse:0.867574+0.014713 test-rmse:1.181125+0.159535
## [586] train-rmse:0.866777+0.014726 test-rmse:1.180736+0.159653
## [587] train-rmse:0.865984+0.014741 test-rmse:1.180130+0.160406
## [588] train-rmse:0.865194+0.014756 test-rmse:1.179578+0.160393
## [589] train-rmse:0.864406+0.014773 test-rmse:1.178557+0.161350
## [590] train-rmse:0.863623+0.014790 test-rmse:1.177647+0.161903
## [591] train-rmse:0.862849+0.014804 test-rmse:1.176272+0.161406
## [592] train-rmse:0.862070+0.014826 test-rmse:1.175164+0.162355
## [593] train-rmse:0.861297+0.014843 test-rmse:1.174379+0.162344
## [594] train-rmse:0.860526+0.014856 test-rmse:1.173292+0.163055
## [595] train-rmse:0.859760+0.014866 test-rmse:1.173159+0.162788
## [596] train-rmse:0.858999+0.014884 test-rmse:1.171813+0.161754
## [597] train-rmse:0.858236+0.014897 test-rmse:1.171341+0.161545
## [598] train-rmse:0.857482+0.014902 test-rmse:1.170360+0.161438
## [599] train-rmse:0.856737+0.014912 test-rmse:1.169982+0.161666
## [600] train-rmse:0.855999+0.014920 test-rmse:1.169348+0.161219
## [601] train-rmse:0.855259+0.014922 test-rmse:1.168856+0.160011
## [602] train-rmse:0.854522+0.014926 test-rmse:1.167768+0.160303
## [603] train-rmse:0.853793+0.014938 test-rmse:1.167005+0.160287
## [604] train-rmse:0.853067+0.014950 test-rmse:1.164746+0.158462
## [605] train-rmse:0.852351+0.014967 test-rmse:1.164894+0.158685
## [606] train-rmse:0.851625+0.014974 test-rmse:1.164430+0.158877
## [607] train-rmse:0.850907+0.014978 test-rmse:1.164437+0.159091
## [608] train-rmse:0.850192+0.014993 test-rmse:1.163374+0.159889
## [609] train-rmse:0.849480+0.015005 test-rmse:1.163006+0.159886
## [610] train-rmse:0.848774+0.015018 test-rmse:1.163380+0.159979
## [611] train-rmse:0.848072+0.015027 test-rmse:1.162847+0.159989
## [612] train-rmse:0.847378+0.015036 test-rmse:1.162003+0.160522
## [613] train-rmse:0.846681+0.015050 test-rmse:1.160849+0.160444
## [614] train-rmse:0.845991+0.015066 test-rmse:1.160063+0.160274
## [615] train-rmse:0.845307+0.015083 test-rmse:1.159483+0.159779
## [616] train-rmse:0.844628+0.015104 test-rmse:1.158959+0.159499
## [617] train-rmse:0.843953+0.015123 test-rmse:1.158712+0.159711
## [618] train-rmse:0.843279+0.015141 test-rmse:1.158653+0.159883
## [619] train-rmse:0.842606+0.015156 test-rmse:1.157657+0.158584
## [620] train-rmse:0.841934+0.015168 test-rmse:1.156306+0.158700
## [621] train-rmse:0.841270+0.015183 test-rmse:1.156015+0.159088
## [622] train-rmse:0.840609+0.015195 test-rmse:1.155661+0.159930
## [623] train-rmse:0.839951+0.015208 test-rmse:1.154580+0.160443
## [624] train-rmse:0.839296+0.015224 test-rmse:1.154471+0.160225
## [625] train-rmse:0.838643+0.015236 test-rmse:1.153807+0.160672
## [626] train-rmse:0.837991+0.015237 test-rmse:1.153231+0.159949
## [627] train-rmse:0.837348+0.015251 test-rmse:1.153009+0.159869
## [628] train-rmse:0.836706+0.015265 test-rmse:1.150964+0.158168
## [629] train-rmse:0.836069+0.015278 test-rmse:1.151363+0.158013
## [630] train-rmse:0.835435+0.015285 test-rmse:1.150577+0.158555
## [631] train-rmse:0.834804+0.015303 test-rmse:1.150938+0.158035
## [632] train-rmse:0.834182+0.015317 test-rmse:1.150207+0.158312
## [633] train-rmse:0.833563+0.015331 test-rmse:1.149541+0.158319
## [634] train-rmse:0.832941+0.015338 test-rmse:1.147907+0.157249
## [635] train-rmse:0.832322+0.015355 test-rmse:1.146949+0.156806
## [636] train-rmse:0.831711+0.015370 test-rmse:1.146528+0.157008
## [637] train-rmse:0.831104+0.015384 test-rmse:1.145908+0.157557
## [638] train-rmse:0.830500+0.015397 test-rmse:1.145689+0.157278
## [639] train-rmse:0.829900+0.015405 test-rmse:1.145645+0.158016
## [640] train-rmse:0.829302+0.015417 test-rmse:1.145256+0.158004
## [641] train-rmse:0.828705+0.015425 test-rmse:1.145096+0.157869
## [642] train-rmse:0.828111+0.015434 test-rmse:1.144942+0.157563
## [643] train-rmse:0.827518+0.015449 test-rmse:1.144436+0.157410
## [644] train-rmse:0.826927+0.015463 test-rmse:1.143893+0.158100
## [645] train-rmse:0.826342+0.015475 test-rmse:1.143949+0.157689
## [646] train-rmse:0.825756+0.015483 test-rmse:1.143477+0.157641
## [647] train-rmse:0.825175+0.015497 test-rmse:1.142908+0.157051
## [648] train-rmse:0.824595+0.015511 test-rmse:1.142843+0.157005
## [649] train-rmse:0.824022+0.015522 test-rmse:1.142311+0.156765
## [650] train-rmse:0.823449+0.015537 test-rmse:1.141616+0.156648
## [651] train-rmse:0.822877+0.015556 test-rmse:1.141625+0.156401
## [652] train-rmse:0.822313+0.015570 test-rmse:1.140852+0.156119
## [653] train-rmse:0.821757+0.015583 test-rmse:1.140476+0.156368
## [654] train-rmse:0.821198+0.015596 test-rmse:1.139444+0.156657
## [655] train-rmse:0.820643+0.015611 test-rmse:1.139101+0.156555
## [656] train-rmse:0.820092+0.015622 test-rmse:1.136798+0.154852
## [657] train-rmse:0.819539+0.015634 test-rmse:1.135932+0.155391
## [658] train-rmse:0.818994+0.015649 test-rmse:1.134679+0.155047
## [659] train-rmse:0.818444+0.015662 test-rmse:1.134689+0.154849
## [660] train-rmse:0.817904+0.015667 test-rmse:1.134648+0.154783
## [661] train-rmse:0.817368+0.015673 test-rmse:1.133961+0.155311
## [662] train-rmse:0.816833+0.015681 test-rmse:1.134116+0.155130
## [663] train-rmse:0.816299+0.015688 test-rmse:1.133867+0.155800
## [664] train-rmse:0.815769+0.015700 test-rmse:1.132846+0.156125
## [665] train-rmse:0.815245+0.015711 test-rmse:1.132742+0.156327
## [666] train-rmse:0.814715+0.015726 test-rmse:1.132037+0.156272
## [667] train-rmse:0.814191+0.015737 test-rmse:1.131881+0.155771
## [668] train-rmse:0.813672+0.015746 test-rmse:1.130468+0.155435
## [669] train-rmse:0.813153+0.015755 test-rmse:1.129604+0.155794
## [670] train-rmse:0.812638+0.015767 test-rmse:1.129318+0.156161
## [671] train-rmse:0.812131+0.015782 test-rmse:1.128850+0.156388
## [672] train-rmse:0.811622+0.015799 test-rmse:1.127894+0.156063
## [673] train-rmse:0.811115+0.015814 test-rmse:1.127738+0.156449
## [674] train-rmse:0.810611+0.015826 test-rmse:1.127446+0.156475
## [675] train-rmse:0.810114+0.015838 test-rmse:1.126858+0.156670
## [676] train-rmse:0.809620+0.015853 test-rmse:1.126869+0.156298
## [677] train-rmse:0.809131+0.015866 test-rmse:1.126468+0.156034
## [678] train-rmse:0.808638+0.015880 test-rmse:1.126020+0.155545
## [679] train-rmse:0.808143+0.015900 test-rmse:1.125953+0.155687
## [680] train-rmse:0.807652+0.015920 test-rmse:1.125277+0.155517
## [681] train-rmse:0.807168+0.015935 test-rmse:1.124904+0.155095
## [682] train-rmse:0.806686+0.015942 test-rmse:1.124763+0.154966
## [683] train-rmse:0.806208+0.015951 test-rmse:1.124195+0.154945
## [684] train-rmse:0.805733+0.015958 test-rmse:1.124369+0.154606
## [685] train-rmse:0.805260+0.015971 test-rmse:1.124051+0.154756
## [686] train-rmse:0.804782+0.015987 test-rmse:1.123868+0.154872
## [687] train-rmse:0.804312+0.016001 test-rmse:1.123712+0.154092
## [688] train-rmse:0.803845+0.016017 test-rmse:1.123314+0.154430
## [689] train-rmse:0.803380+0.016033 test-rmse:1.123372+0.153994
## [690] train-rmse:0.802914+0.016048 test-rmse:1.122061+0.154166
## [691] train-rmse:0.802447+0.016060 test-rmse:1.121335+0.154642
## [692] train-rmse:0.801981+0.016075 test-rmse:1.120520+0.155054
## [693] train-rmse:0.801523+0.016089 test-rmse:1.120150+0.155322
## [694] train-rmse:0.801068+0.016103 test-rmse:1.119852+0.155523
## [695] train-rmse:0.800615+0.016117 test-rmse:1.119318+0.155414
## [696] train-rmse:0.800162+0.016128 test-rmse:1.118805+0.155539
## [697] train-rmse:0.799713+0.016139 test-rmse:1.118676+0.155016
## [698] train-rmse:0.799271+0.016155 test-rmse:1.118563+0.155339
## [699] train-rmse:0.798831+0.016170 test-rmse:1.118342+0.155429
## [700] train-rmse:0.798393+0.016185 test-rmse:1.117489+0.155272
## [701] train-rmse:0.797955+0.016203 test-rmse:1.117531+0.155765
## [702] train-rmse:0.797515+0.016223 test-rmse:1.116364+0.154280
## [703] train-rmse:0.797082+0.016241 test-rmse:1.115889+0.154021
## [704] train-rmse:0.796649+0.016259 test-rmse:1.115070+0.153661
## [705] train-rmse:0.796221+0.016278 test-rmse:1.114800+0.152950
## [706] train-rmse:0.795794+0.016292 test-rmse:1.114618+0.152754
## [707] train-rmse:0.795366+0.016307 test-rmse:1.114336+0.152629
## [708] train-rmse:0.794937+0.016327 test-rmse:1.114113+0.153140
## [709] train-rmse:0.794510+0.016343 test-rmse:1.113688+0.153260
## [710] train-rmse:0.794088+0.016356 test-rmse:1.112963+0.153640
## [711] train-rmse:0.793668+0.016371 test-rmse:1.112871+0.154011
## [712] train-rmse:0.793253+0.016380 test-rmse:1.112022+0.154005
## [713] train-rmse:0.792842+0.016391 test-rmse:1.112005+0.153603
## [714] train-rmse:0.792426+0.016405 test-rmse:1.111465+0.153918
## [715] train-rmse:0.792004+0.016422 test-rmse:1.111337+0.154027
## [716] train-rmse:0.791595+0.016437 test-rmse:1.111353+0.153849
## [717] train-rmse:0.791191+0.016452 test-rmse:1.110999+0.153823
## [718] train-rmse:0.790788+0.016465 test-rmse:1.110966+0.153671
## [719] train-rmse:0.790386+0.016478 test-rmse:1.110254+0.153435
## [720] train-rmse:0.789987+0.016491 test-rmse:1.110120+0.153275
## [721] train-rmse:0.789589+0.016506 test-rmse:1.108420+0.153894
## [722] train-rmse:0.789195+0.016522 test-rmse:1.108561+0.154016
## [723] train-rmse:0.788796+0.016541 test-rmse:1.108645+0.153842
## [724] train-rmse:0.788395+0.016547 test-rmse:1.107754+0.154046
## [725] train-rmse:0.788001+0.016565 test-rmse:1.107643+0.154056
## [726] train-rmse:0.787609+0.016580 test-rmse:1.107274+0.154236
## [727] train-rmse:0.787218+0.016597 test-rmse:1.106478+0.153971
## [728] train-rmse:0.786828+0.016610 test-rmse:1.106022+0.154147
## [729] train-rmse:0.786442+0.016621 test-rmse:1.105740+0.153451
## [730] train-rmse:0.786055+0.016635 test-rmse:1.105624+0.153575
## [731] train-rmse:0.785669+0.016650 test-rmse:1.105363+0.153344
## [732] train-rmse:0.785288+0.016664 test-rmse:1.105323+0.152247
## [733] train-rmse:0.784910+0.016680 test-rmse:1.105245+0.152092
## [734] train-rmse:0.784527+0.016700 test-rmse:1.103430+0.150735
## [735] train-rmse:0.784152+0.016715 test-rmse:1.102932+0.151138
## [736] train-rmse:0.783779+0.016730 test-rmse:1.102340+0.151273
## [737] train-rmse:0.783406+0.016745 test-rmse:1.102184+0.151119
## [738] train-rmse:0.783033+0.016756 test-rmse:1.101605+0.151591
## [739] train-rmse:0.782666+0.016771 test-rmse:1.100831+0.151681
## [740] train-rmse:0.782299+0.016784 test-rmse:1.100577+0.150918
## [741] train-rmse:0.781935+0.016799 test-rmse:1.100383+0.151217
## [742] train-rmse:0.781570+0.016815 test-rmse:1.100060+0.151225
## [743] train-rmse:0.781202+0.016834 test-rmse:1.099669+0.151014
## [744] train-rmse:0.780839+0.016848 test-rmse:1.099970+0.151186
## [745] train-rmse:0.780480+0.016863 test-rmse:1.099798+0.151350
## [746] train-rmse:0.780121+0.016872 test-rmse:1.099928+0.151303
## [747] train-rmse:0.779766+0.016882 test-rmse:1.100024+0.151204
## [748] train-rmse:0.779408+0.016894 test-rmse:1.099749+0.151436
## [749] train-rmse:0.779055+0.016906 test-rmse:1.099233+0.151471
## [750] train-rmse:0.778703+0.016918 test-rmse:1.099097+0.151218
## [751] train-rmse:0.778353+0.016931 test-rmse:1.098502+0.151002
## [752] train-rmse:0.778000+0.016941 test-rmse:1.098439+0.151027
## [753] train-rmse:0.777652+0.016952 test-rmse:1.097894+0.150925
## [754] train-rmse:0.777305+0.016963 test-rmse:1.097103+0.151151
## [755] train-rmse:0.776961+0.016979 test-rmse:1.096959+0.150912
## [756] train-rmse:0.776616+0.016996 test-rmse:1.096852+0.151018
## [757] train-rmse:0.776274+0.017010 test-rmse:1.096602+0.151140
## [758] train-rmse:0.775931+0.017026 test-rmse:1.095849+0.150760
## [759] train-rmse:0.775589+0.017038 test-rmse:1.095490+0.150394
## [760] train-rmse:0.775253+0.017052 test-rmse:1.095667+0.150524
## [761] train-rmse:0.774918+0.017062 test-rmse:1.094988+0.151229
## [762] train-rmse:0.774585+0.017075 test-rmse:1.094562+0.151390
## [763] train-rmse:0.774253+0.017090 test-rmse:1.094352+0.151586
## [764] train-rmse:0.773919+0.017102 test-rmse:1.094192+0.151478
## [765] train-rmse:0.773588+0.017112 test-rmse:1.094031+0.151309
## [766] train-rmse:0.773256+0.017127 test-rmse:1.093969+0.150978
## [767] train-rmse:0.772927+0.017140 test-rmse:1.093316+0.150876
## [768] train-rmse:0.772602+0.017152 test-rmse:1.093059+0.150821
## [769] train-rmse:0.772277+0.017165 test-rmse:1.092814+0.150987
## [770] train-rmse:0.771955+0.017178 test-rmse:1.092731+0.151450
## [771] train-rmse:0.771636+0.017191 test-rmse:1.092366+0.151545
## [772] train-rmse:0.771314+0.017201 test-rmse:1.091984+0.151155
## [773] train-rmse:0.770995+0.017215 test-rmse:1.091602+0.151243
## [774] train-rmse:0.770677+0.017231 test-rmse:1.091917+0.151085
## [775] train-rmse:0.770362+0.017246 test-rmse:1.090975+0.151770
## [776] train-rmse:0.770048+0.017258 test-rmse:1.090965+0.151478
## [777] train-rmse:0.769736+0.017272 test-rmse:1.090559+0.151756
## [778] train-rmse:0.769424+0.017285 test-rmse:1.090764+0.151674
## [779] train-rmse:0.769108+0.017294 test-rmse:1.090267+0.151997
## [780] train-rmse:0.768798+0.017307 test-rmse:1.089621+0.151693
## [781] train-rmse:0.768487+0.017320 test-rmse:1.089068+0.151790
## [782] train-rmse:0.768177+0.017334 test-rmse:1.087365+0.150612
## [783] train-rmse:0.767868+0.017345 test-rmse:1.087235+0.150599
## [784] train-rmse:0.767559+0.017359 test-rmse:1.087573+0.150178
## [785] train-rmse:0.767249+0.017369 test-rmse:1.086903+0.150719
## [786] train-rmse:0.766945+0.017381 test-rmse:1.086516+0.150059
## [787] train-rmse:0.766642+0.017392 test-rmse:1.086466+0.150385
## [788] train-rmse:0.766341+0.017405 test-rmse:1.086083+0.150401
## [789] train-rmse:0.766035+0.017413 test-rmse:1.085833+0.150014
## [790] train-rmse:0.765731+0.017426 test-rmse:1.085446+0.150199
## [791] train-rmse:0.765432+0.017435 test-rmse:1.085384+0.150054
## [792] train-rmse:0.765131+0.017441 test-rmse:1.085055+0.149481
## [793] train-rmse:0.764832+0.017453 test-rmse:1.084672+0.149612
## [794] train-rmse:0.764534+0.017468 test-rmse:1.084115+0.149650
## [795] train-rmse:0.764241+0.017479 test-rmse:1.083807+0.149507
## [796] train-rmse:0.763946+0.017493 test-rmse:1.083911+0.149555
## [797] train-rmse:0.763656+0.017503 test-rmse:1.084081+0.149427
## [798] train-rmse:0.763369+0.017516 test-rmse:1.084164+0.149442
## [799] train-rmse:0.763081+0.017528 test-rmse:1.083896+0.149611
## [800] train-rmse:0.762793+0.017541 test-rmse:1.083920+0.149563
## [801] train-rmse:0.762507+0.017553 test-rmse:1.083792+0.149705
## [802] train-rmse:0.762220+0.017559 test-rmse:1.083429+0.149429
## [803] train-rmse:0.761936+0.017569 test-rmse:1.082810+0.149623
## [804] train-rmse:0.761653+0.017574 test-rmse:1.082625+0.149943
## [805] train-rmse:0.761366+0.017579 test-rmse:1.082434+0.150083
## [806] train-rmse:0.761084+0.017590 test-rmse:1.082825+0.149914
## [807] train-rmse:0.760799+0.017593 test-rmse:1.082326+0.150278
## [808] train-rmse:0.760519+0.017600 test-rmse:1.082151+0.150312
## [809] train-rmse:0.760242+0.017609 test-rmse:1.082224+0.149972
## [810] train-rmse:0.759964+0.017612 test-rmse:1.082050+0.149798
## [811] train-rmse:0.759684+0.017625 test-rmse:1.081975+0.150131
## [812] train-rmse:0.759407+0.017630 test-rmse:1.081779+0.150233
## [813] train-rmse:0.759135+0.017640 test-rmse:1.081031+0.149860
## [814] train-rmse:0.758860+0.017649 test-rmse:1.080049+0.149747
## [815] train-rmse:0.758586+0.017656 test-rmse:1.080276+0.149715
## [816] train-rmse:0.758311+0.017660 test-rmse:1.079974+0.150510
## [817] train-rmse:0.758040+0.017669 test-rmse:1.079414+0.150699
## [818] train-rmse:0.757771+0.017675 test-rmse:1.079244+0.150767
## [819] train-rmse:0.757503+0.017682 test-rmse:1.079234+0.150773
## [820] train-rmse:0.757236+0.017689 test-rmse:1.078434+0.151380
## [821] train-rmse:0.756970+0.017695 test-rmse:1.077973+0.151181
## [822] train-rmse:0.756703+0.017702 test-rmse:1.078074+0.151070
## [823] train-rmse:0.756432+0.017707 test-rmse:1.077155+0.151322
## [824] train-rmse:0.756166+0.017713 test-rmse:1.076501+0.150706
## [825] train-rmse:0.755897+0.017720 test-rmse:1.075911+0.150362
## [826] train-rmse:0.755632+0.017727 test-rmse:1.075417+0.148899
## [827] train-rmse:0.755370+0.017732 test-rmse:1.075281+0.148946
## [828] train-rmse:0.755110+0.017740 test-rmse:1.075581+0.148710
## [829] train-rmse:0.754847+0.017749 test-rmse:1.075066+0.148580
## [830] train-rmse:0.754585+0.017751 test-rmse:1.075011+0.148879
## [831] train-rmse:0.754326+0.017760 test-rmse:1.075138+0.148524
## [832] train-rmse:0.754067+0.017770 test-rmse:1.074997+0.148725
## [833] train-rmse:0.753804+0.017779 test-rmse:1.074880+0.148920
## [834] train-rmse:0.753545+0.017790 test-rmse:1.074592+0.149073
## [835] train-rmse:0.753290+0.017796 test-rmse:1.074230+0.148658
## [836] train-rmse:0.753034+0.017803 test-rmse:1.074170+0.148762
## [837] train-rmse:0.752781+0.017806 test-rmse:1.074466+0.148780
## [838] train-rmse:0.752527+0.017813 test-rmse:1.074817+0.148317
## [839] train-rmse:0.752270+0.017816 test-rmse:1.074333+0.148915
## [840] train-rmse:0.752016+0.017819 test-rmse:1.074366+0.148707
## [841] train-rmse:0.751767+0.017825 test-rmse:1.073755+0.148573
## [842] train-rmse:0.751518+0.017831 test-rmse:1.073457+0.148710
## [843] train-rmse:0.751266+0.017833 test-rmse:1.073318+0.148485
## [844] train-rmse:0.751018+0.017839 test-rmse:1.073100+0.147887
## [845] train-rmse:0.750770+0.017841 test-rmse:1.072952+0.147781
## [846] train-rmse:0.750525+0.017845 test-rmse:1.072755+0.147581
## [847] train-rmse:0.750281+0.017849 test-rmse:1.072535+0.147833
## [848] train-rmse:0.750036+0.017853 test-rmse:1.072183+0.148008
## [849] train-rmse:0.749791+0.017857 test-rmse:1.072261+0.148353
## [850] train-rmse:0.749548+0.017862 test-rmse:1.071916+0.148601
## [851] train-rmse:0.749307+0.017867 test-rmse:1.071674+0.148654
## [852] train-rmse:0.749067+0.017871 test-rmse:1.071653+0.148468
## [853] train-rmse:0.748823+0.017878 test-rmse:1.071021+0.148196
## [854] train-rmse:0.748583+0.017882 test-rmse:1.070751+0.148419
## [855] train-rmse:0.748345+0.017883 test-rmse:1.070813+0.148354
## [856] train-rmse:0.748104+0.017882 test-rmse:1.070987+0.148156
## [857] train-rmse:0.747867+0.017885 test-rmse:1.070888+0.148162
## [858] train-rmse:0.747626+0.017893 test-rmse:1.070862+0.148435
## [859] train-rmse:0.747391+0.017894 test-rmse:1.070740+0.148339
## [860] train-rmse:0.747154+0.017895 test-rmse:1.069146+0.148677
## [861] train-rmse:0.746920+0.017898 test-rmse:1.069287+0.148927
## [862] train-rmse:0.746689+0.017901 test-rmse:1.069005+0.148499
## [863] train-rmse:0.746455+0.017905 test-rmse:1.069529+0.148339
## [864] train-rmse:0.746219+0.017906 test-rmse:1.069390+0.148497
## [865] train-rmse:0.745989+0.017909 test-rmse:1.069186+0.148631
## [866] train-rmse:0.745760+0.017913 test-rmse:1.069030+0.148437
## [867] train-rmse:0.745532+0.017917 test-rmse:1.068813+0.148437
## [868] train-rmse:0.745304+0.017920 test-rmse:1.068600+0.148319
## [869] train-rmse:0.745075+0.017923 test-rmse:1.068685+0.148409
## [870] train-rmse:0.744845+0.017924 test-rmse:1.068917+0.148235
## [871] train-rmse:0.744609+0.017924 test-rmse:1.068227+0.148248
## [872] train-rmse:0.744381+0.017926 test-rmse:1.068161+0.148408
## [873] train-rmse:0.744155+0.017932 test-rmse:1.068407+0.148355
## [874] train-rmse:0.743931+0.017933 test-rmse:1.068331+0.148195
## [875] train-rmse:0.743705+0.017932 test-rmse:1.067589+0.148101
## [876] train-rmse:0.743478+0.017935 test-rmse:1.067882+0.148189
## [877] train-rmse:0.743257+0.017937 test-rmse:1.067537+0.147678
## [878] train-rmse:0.743031+0.017938 test-rmse:1.067311+0.148035
## [879] train-rmse:0.742810+0.017940 test-rmse:1.066739+0.147646
## [880] train-rmse:0.742587+0.017938 test-rmse:1.066209+0.147699
## [881] train-rmse:0.742365+0.017939 test-rmse:1.066472+0.147811
## [882] train-rmse:0.742146+0.017939 test-rmse:1.066279+0.147890
## [883] train-rmse:0.741928+0.017939 test-rmse:1.066029+0.148054
## [884] train-rmse:0.741710+0.017937 test-rmse:1.066295+0.147681
## [885] train-rmse:0.741487+0.017933 test-rmse:1.066041+0.147763
## [886] train-rmse:0.741272+0.017934 test-rmse:1.065272+0.146621
## [887] train-rmse:0.741052+0.017934 test-rmse:1.065239+0.146741
## [888] train-rmse:0.740838+0.017934 test-rmse:1.065092+0.146628
## [889] train-rmse:0.740625+0.017934 test-rmse:1.064752+0.146320
## [890] train-rmse:0.740407+0.017936 test-rmse:1.064753+0.146624
## [891] train-rmse:0.740194+0.017935 test-rmse:1.064655+0.146815
## [892] train-rmse:0.739980+0.017936 test-rmse:1.064738+0.146291
## [893] train-rmse:0.739764+0.017934 test-rmse:1.064696+0.146380
## [894] train-rmse:0.739551+0.017934 test-rmse:1.064588+0.146260
## [895] train-rmse:0.739339+0.017933 test-rmse:1.064056+0.146447
## [896] train-rmse:0.739129+0.017932 test-rmse:1.063958+0.146411
## [897] train-rmse:0.738920+0.017932 test-rmse:1.064182+0.146537
## [898] train-rmse:0.738707+0.017926 test-rmse:1.063925+0.146915
## [899] train-rmse:0.738497+0.017929 test-rmse:1.063887+0.147000
## [900] train-rmse:0.738289+0.017927 test-rmse:1.063900+0.147008
## [901] train-rmse:0.738083+0.017925 test-rmse:1.063911+0.146602
## [902] train-rmse:0.737875+0.017924 test-rmse:1.063755+0.146669
## [903] train-rmse:0.737670+0.017920 test-rmse:1.063263+0.146434
## [904] train-rmse:0.737464+0.017918 test-rmse:1.063367+0.146555
## [905] train-rmse:0.737258+0.017916 test-rmse:1.062932+0.146053
## [906] train-rmse:0.737050+0.017916 test-rmse:1.063307+0.145573
## [907] train-rmse:0.736844+0.017914 test-rmse:1.063131+0.146052
## [908] train-rmse:0.736641+0.017911 test-rmse:1.062858+0.146034
## [909] train-rmse:0.736437+0.017910 test-rmse:1.063036+0.146387
## [910] train-rmse:0.736234+0.017908 test-rmse:1.063546+0.146186
## [911] train-rmse:0.736030+0.017903 test-rmse:1.063157+0.146542
## [912] train-rmse:0.735827+0.017900 test-rmse:1.062527+0.146775
## [913] train-rmse:0.735627+0.017900 test-rmse:1.062439+0.146587
## [914] train-rmse:0.735427+0.017898 test-rmse:1.062170+0.146898
## [915] train-rmse:0.735229+0.017898 test-rmse:1.062208+0.146912
## [916] train-rmse:0.735031+0.017897 test-rmse:1.062001+0.146809
## [917] train-rmse:0.734833+0.017895 test-rmse:1.061860+0.147259
## [918] train-rmse:0.734631+0.017893 test-rmse:1.062049+0.146843
## [919] train-rmse:0.734433+0.017889 test-rmse:1.061841+0.147219
## [920] train-rmse:0.734238+0.017888 test-rmse:1.061259+0.146890
## [921] train-rmse:0.734041+0.017890 test-rmse:1.061374+0.146006
## [922] train-rmse:0.733845+0.017885 test-rmse:1.061080+0.145893
## [923] train-rmse:0.733650+0.017881 test-rmse:1.060941+0.145829
## [924] train-rmse:0.733455+0.017879 test-rmse:1.060721+0.145976
## [925] train-rmse:0.733262+0.017876 test-rmse:1.060577+0.146166
## [926] train-rmse:0.733070+0.017873 test-rmse:1.060225+0.146092
## [927] train-rmse:0.732876+0.017868 test-rmse:1.059538+0.146654
## [928] train-rmse:0.732684+0.017864 test-rmse:1.058707+0.145490
## [929] train-rmse:0.732492+0.017859 test-rmse:1.059009+0.145525
## [930] train-rmse:0.732302+0.017855 test-rmse:1.059044+0.145674
## [931] train-rmse:0.732113+0.017850 test-rmse:1.059063+0.145554
## [932] train-rmse:0.731924+0.017847 test-rmse:1.059376+0.145418
## [933] train-rmse:0.731733+0.017845 test-rmse:1.059018+0.145856
## [934] train-rmse:0.731545+0.017839 test-rmse:1.058967+0.145881
## Stopping. Best iteration:
## [928] train-rmse:0.732684+0.017864 test-rmse:1.058707+0.145490
##
## 1
## [1] train-rmse:96.903592+0.106998 test-rmse:96.903929+0.480417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.282645+0.098508 test-rmse:87.282849+0.494309
## [3] train-rmse:78.624342+0.091095 test-rmse:78.624364+0.507694
## [4] train-rmse:70.833145+0.084703 test-rmse:70.832979+0.520704
## [5] train-rmse:63.823124+0.079254 test-rmse:63.822745+0.533458
## [6] train-rmse:57.516975+0.074698 test-rmse:57.516330+0.546089
## [7] train-rmse:51.845108+0.070990 test-rmse:51.844165+0.558710
## [8] train-rmse:46.744962+0.068087 test-rmse:46.743658+0.571428
## [9] train-rmse:42.160251+0.065957 test-rmse:42.158527+0.584358
## [10] train-rmse:38.040358+0.064573 test-rmse:38.038154+0.597586
## [11] train-rmse:34.337210+0.063071 test-rmse:34.350626+0.598556
## [12] train-rmse:31.005955+0.060200 test-rmse:31.042059+0.599867
## [13] train-rmse:28.009255+0.055404 test-rmse:28.072289+0.607604
## [14] train-rmse:25.314196+0.049445 test-rmse:25.390021+0.600070
## [15] train-rmse:22.890372+0.044958 test-rmse:22.965470+0.576291
## [16] train-rmse:20.711772+0.039023 test-rmse:20.796414+0.575351
## [17] train-rmse:18.753540+0.035875 test-rmse:18.835082+0.556648
## [18] train-rmse:16.994682+0.033591 test-rmse:17.089226+0.560594
## [19] train-rmse:15.416491+0.032215 test-rmse:15.503699+0.550490
## [20] train-rmse:14.001346+0.031400 test-rmse:14.086849+0.552402
## [21] train-rmse:12.732217+0.031486 test-rmse:12.836036+0.558618
## [22] train-rmse:11.596165+0.031514 test-rmse:11.708262+0.574927
## [23] train-rmse:10.580631+0.032645 test-rmse:10.674199+0.554746
## [24] train-rmse:9.673749+0.033271 test-rmse:9.782996+0.560681
## [25] train-rmse:8.864457+0.034644 test-rmse:8.985166+0.566239
## [26] train-rmse:8.145084+0.036482 test-rmse:8.256056+0.552985
## [27] train-rmse:7.505425+0.038456 test-rmse:7.622562+0.554320
## [28] train-rmse:6.938516+0.039990 test-rmse:7.071684+0.551833
## [29] train-rmse:6.436337+0.041666 test-rmse:6.584945+0.564107
## [30] train-rmse:5.992657+0.043146 test-rmse:6.132801+0.543434
## [31] train-rmse:5.599359+0.041883 test-rmse:5.746200+0.544379
## [32] train-rmse:5.255394+0.043138 test-rmse:5.434934+0.545927
## [33] train-rmse:4.952521+0.044036 test-rmse:5.128967+0.525312
## [34] train-rmse:4.687117+0.044381 test-rmse:4.875904+0.526018
## [35] train-rmse:4.454935+0.045175 test-rmse:4.646489+0.508077
## [36] train-rmse:4.251331+0.045322 test-rmse:4.449914+0.497761
## [37] train-rmse:4.073334+0.046035 test-rmse:4.280028+0.487697
## [38] train-rmse:3.917132+0.045833 test-rmse:4.131685+0.471516
## [39] train-rmse:3.780072+0.045524 test-rmse:4.004770+0.472105
## [40] train-rmse:3.657792+0.044667 test-rmse:3.896157+0.448417
## [41] train-rmse:3.550741+0.044948 test-rmse:3.800533+0.439780
## [42] train-rmse:3.456389+0.044801 test-rmse:3.699295+0.424059
## [43] train-rmse:3.372716+0.044682 test-rmse:3.631084+0.420264
## [44] train-rmse:3.297363+0.043517 test-rmse:3.547196+0.389610
## [45] train-rmse:3.230409+0.042676 test-rmse:3.493195+0.386518
## [46] train-rmse:3.170498+0.042351 test-rmse:3.440386+0.380181
## [47] train-rmse:3.114505+0.041358 test-rmse:3.385934+0.389243
## [48] train-rmse:3.063113+0.042380 test-rmse:3.349741+0.392263
## [49] train-rmse:3.013688+0.039554 test-rmse:3.297581+0.374530
## [50] train-rmse:2.970185+0.038830 test-rmse:3.260338+0.363578
## [51] train-rmse:2.928829+0.038117 test-rmse:3.218999+0.349595
## [52] train-rmse:2.891077+0.038308 test-rmse:3.189837+0.348906
## [53] train-rmse:2.855265+0.037460 test-rmse:3.159027+0.346956
## [54] train-rmse:2.821757+0.036800 test-rmse:3.126200+0.340613
## [55] train-rmse:2.790148+0.036493 test-rmse:3.095666+0.347363
## [56] train-rmse:2.758153+0.036958 test-rmse:3.075680+0.346777
## [57] train-rmse:2.728014+0.035848 test-rmse:3.053263+0.342571
## [58] train-rmse:2.698781+0.034103 test-rmse:3.023863+0.327888
## [59] train-rmse:2.671520+0.033896 test-rmse:3.001965+0.323873
## [60] train-rmse:2.644417+0.033326 test-rmse:2.978071+0.330616
## [61] train-rmse:2.617995+0.032143 test-rmse:2.953800+0.321751
## [62] train-rmse:2.592088+0.030681 test-rmse:2.936543+0.321686
## [63] train-rmse:2.567522+0.030192 test-rmse:2.917478+0.323878
## [64] train-rmse:2.543110+0.029797 test-rmse:2.899063+0.322537
## [65] train-rmse:2.518494+0.028454 test-rmse:2.871226+0.316812
## [66] train-rmse:2.495353+0.027529 test-rmse:2.851102+0.309918
## [67] train-rmse:2.473253+0.027109 test-rmse:2.828504+0.300830
## [68] train-rmse:2.450428+0.026959 test-rmse:2.812867+0.306010
## [69] train-rmse:2.428450+0.025834 test-rmse:2.796345+0.311279
## [70] train-rmse:2.405915+0.025244 test-rmse:2.777141+0.311361
## [71] train-rmse:2.384275+0.024448 test-rmse:2.759391+0.307141
## [72] train-rmse:2.363316+0.023832 test-rmse:2.738882+0.311051
## [73] train-rmse:2.343199+0.023461 test-rmse:2.724545+0.312446
## [74] train-rmse:2.323428+0.022953 test-rmse:2.704173+0.301245
## [75] train-rmse:2.303517+0.022460 test-rmse:2.691909+0.297497
## [76] train-rmse:2.284273+0.022127 test-rmse:2.666253+0.293056
## [77] train-rmse:2.264605+0.021559 test-rmse:2.646537+0.296157
## [78] train-rmse:2.244519+0.021168 test-rmse:2.634618+0.294553
## [79] train-rmse:2.225779+0.020927 test-rmse:2.617140+0.296813
## [80] train-rmse:2.207266+0.020979 test-rmse:2.602281+0.289520
## [81] train-rmse:2.188771+0.020991 test-rmse:2.583908+0.287384
## [82] train-rmse:2.170850+0.020502 test-rmse:2.560650+0.293416
## [83] train-rmse:2.153246+0.020045 test-rmse:2.543946+0.284298
## [84] train-rmse:2.136200+0.019745 test-rmse:2.524607+0.282740
## [85] train-rmse:2.118832+0.019326 test-rmse:2.511680+0.282851
## [86] train-rmse:2.102110+0.019008 test-rmse:2.498890+0.282130
## [87] train-rmse:2.085137+0.018581 test-rmse:2.487189+0.284486
## [88] train-rmse:2.067816+0.019155 test-rmse:2.474048+0.285322
## [89] train-rmse:2.051373+0.018733 test-rmse:2.462911+0.283801
## [90] train-rmse:2.034893+0.018621 test-rmse:2.443560+0.281345
## [91] train-rmse:2.018908+0.018593 test-rmse:2.427170+0.279420
## [92] train-rmse:2.002918+0.018043 test-rmse:2.411539+0.274876
## [93] train-rmse:1.987355+0.017915 test-rmse:2.397809+0.279991
## [94] train-rmse:1.971693+0.017131 test-rmse:2.386819+0.278944
## [95] train-rmse:1.956479+0.016861 test-rmse:2.370896+0.283631
## [96] train-rmse:1.941229+0.016119 test-rmse:2.354609+0.276198
## [97] train-rmse:1.925771+0.016264 test-rmse:2.341434+0.273060
## [98] train-rmse:1.911131+0.016125 test-rmse:2.326584+0.272796
## [99] train-rmse:1.896270+0.015789 test-rmse:2.322678+0.271345
## [100] train-rmse:1.882030+0.015644 test-rmse:2.303847+0.271833
## [101] train-rmse:1.866661+0.015573 test-rmse:2.293366+0.273826
## [102] train-rmse:1.852421+0.015209 test-rmse:2.283741+0.272162
## [103] train-rmse:1.838406+0.014976 test-rmse:2.273086+0.273254
## [104] train-rmse:1.824779+0.014728 test-rmse:2.261181+0.276057
## [105] train-rmse:1.811187+0.014586 test-rmse:2.245065+0.275264
## [106] train-rmse:1.797672+0.014384 test-rmse:2.229132+0.272661
## [107] train-rmse:1.784318+0.014305 test-rmse:2.215784+0.275457
## [108] train-rmse:1.771001+0.014037 test-rmse:2.201090+0.268053
## [109] train-rmse:1.757999+0.013770 test-rmse:2.190097+0.268286
## [110] train-rmse:1.745126+0.013473 test-rmse:2.173695+0.262031
## [111] train-rmse:1.732274+0.012858 test-rmse:2.165490+0.262344
## [112] train-rmse:1.719885+0.012775 test-rmse:2.152237+0.264736
## [113] train-rmse:1.707483+0.012655 test-rmse:2.143244+0.264831
## [114] train-rmse:1.694282+0.011628 test-rmse:2.134399+0.270257
## [115] train-rmse:1.681553+0.011673 test-rmse:2.117653+0.268367
## [116] train-rmse:1.669361+0.011377 test-rmse:2.106793+0.266011
## [117] train-rmse:1.657426+0.011349 test-rmse:2.100528+0.264376
## [118] train-rmse:1.645802+0.011176 test-rmse:2.097308+0.264801
## [119] train-rmse:1.633558+0.011105 test-rmse:2.086915+0.258842
## [120] train-rmse:1.621967+0.010572 test-rmse:2.074351+0.260120
## [121] train-rmse:1.610235+0.010282 test-rmse:2.062573+0.264257
## [122] train-rmse:1.599007+0.010117 test-rmse:2.051115+0.263751
## [123] train-rmse:1.587746+0.009740 test-rmse:2.041220+0.267092
## [124] train-rmse:1.576520+0.009836 test-rmse:2.029643+0.261723
## [125] train-rmse:1.564994+0.010080 test-rmse:2.017223+0.262869
## [126] train-rmse:1.553977+0.009799 test-rmse:2.010119+0.264941
## [127] train-rmse:1.543246+0.009873 test-rmse:1.997727+0.255952
## [128] train-rmse:1.532340+0.009181 test-rmse:1.992044+0.259457
## [129] train-rmse:1.521974+0.009184 test-rmse:1.986086+0.261415
## [130] train-rmse:1.511460+0.009134 test-rmse:1.974616+0.258318
## [131] train-rmse:1.499958+0.010429 test-rmse:1.965112+0.258271
## [132] train-rmse:1.489294+0.009940 test-rmse:1.950557+0.257224
## [133] train-rmse:1.478877+0.010248 test-rmse:1.944028+0.257350
## [134] train-rmse:1.469115+0.010254 test-rmse:1.936921+0.258736
## [135] train-rmse:1.459153+0.010669 test-rmse:1.928157+0.253796
## [136] train-rmse:1.449372+0.010812 test-rmse:1.922667+0.256647
## [137] train-rmse:1.439380+0.010697 test-rmse:1.913348+0.257972
## [138] train-rmse:1.429580+0.010438 test-rmse:1.905724+0.262494
## [139] train-rmse:1.420136+0.010577 test-rmse:1.898883+0.260725
## [140] train-rmse:1.410754+0.010626 test-rmse:1.892410+0.259282
## [141] train-rmse:1.401603+0.010637 test-rmse:1.886125+0.258770
## [142] train-rmse:1.392042+0.010637 test-rmse:1.875085+0.258349
## [143] train-rmse:1.382722+0.010169 test-rmse:1.866551+0.257013
## [144] train-rmse:1.373546+0.009995 test-rmse:1.855902+0.256088
## [145] train-rmse:1.364765+0.010062 test-rmse:1.847603+0.253063
## [146] train-rmse:1.356021+0.010124 test-rmse:1.839949+0.253654
## [147] train-rmse:1.347286+0.010141 test-rmse:1.830954+0.256962
## [148] train-rmse:1.338449+0.010448 test-rmse:1.825051+0.257063
## [149] train-rmse:1.329887+0.010565 test-rmse:1.817862+0.254655
## [150] train-rmse:1.321360+0.010504 test-rmse:1.811424+0.255010
## [151] train-rmse:1.313152+0.010431 test-rmse:1.804433+0.257253
## [152] train-rmse:1.304677+0.010396 test-rmse:1.798590+0.257170
## [153] train-rmse:1.296459+0.010547 test-rmse:1.789004+0.255523
## [154] train-rmse:1.287998+0.010938 test-rmse:1.778922+0.249423
## [155] train-rmse:1.279989+0.010929 test-rmse:1.770021+0.251782
## [156] train-rmse:1.272011+0.011308 test-rmse:1.766465+0.250348
## [157] train-rmse:1.264098+0.011379 test-rmse:1.758351+0.249977
## [158] train-rmse:1.256025+0.011442 test-rmse:1.752294+0.251360
## [159] train-rmse:1.248079+0.011745 test-rmse:1.744829+0.253363
## [160] train-rmse:1.240202+0.012089 test-rmse:1.740263+0.252306
## [161] train-rmse:1.232541+0.012300 test-rmse:1.734055+0.252651
## [162] train-rmse:1.224798+0.012504 test-rmse:1.722983+0.248937
## [163] train-rmse:1.217347+0.012549 test-rmse:1.717133+0.246839
## [164] train-rmse:1.209765+0.012763 test-rmse:1.713070+0.245930
## [165] train-rmse:1.202536+0.012887 test-rmse:1.707224+0.248306
## [166] train-rmse:1.195136+0.013016 test-rmse:1.698027+0.248272
## [167] train-rmse:1.187972+0.013306 test-rmse:1.692567+0.249034
## [168] train-rmse:1.181007+0.013258 test-rmse:1.685654+0.249949
## [169] train-rmse:1.174045+0.013517 test-rmse:1.679261+0.250214
## [170] train-rmse:1.166601+0.013660 test-rmse:1.674769+0.247489
## [171] train-rmse:1.159866+0.013623 test-rmse:1.669290+0.246907
## [172] train-rmse:1.152891+0.013650 test-rmse:1.663727+0.246056
## [173] train-rmse:1.145617+0.013882 test-rmse:1.658392+0.246807
## [174] train-rmse:1.138456+0.013921 test-rmse:1.654854+0.245190
## [175] train-rmse:1.131560+0.013556 test-rmse:1.650953+0.247440
## [176] train-rmse:1.125043+0.013700 test-rmse:1.646637+0.247284
## [177] train-rmse:1.118289+0.013974 test-rmse:1.637793+0.248945
## [178] train-rmse:1.111607+0.014085 test-rmse:1.631629+0.248747
## [179] train-rmse:1.105058+0.014161 test-rmse:1.628482+0.248615
## [180] train-rmse:1.098692+0.014359 test-rmse:1.623094+0.247339
## [181] train-rmse:1.092520+0.014603 test-rmse:1.616904+0.248157
## [182] train-rmse:1.086267+0.014294 test-rmse:1.614131+0.248479
## [183] train-rmse:1.080026+0.014233 test-rmse:1.609011+0.248844
## [184] train-rmse:1.073910+0.014375 test-rmse:1.604997+0.249606
## [185] train-rmse:1.067548+0.013971 test-rmse:1.600934+0.251807
## [186] train-rmse:1.061329+0.014074 test-rmse:1.590486+0.248044
## [187] train-rmse:1.054967+0.014343 test-rmse:1.586990+0.248522
## [188] train-rmse:1.048953+0.014664 test-rmse:1.580947+0.248550
## [189] train-rmse:1.043242+0.014774 test-rmse:1.574277+0.249775
## [190] train-rmse:1.037193+0.014803 test-rmse:1.570348+0.249188
## [191] train-rmse:1.031402+0.015064 test-rmse:1.567219+0.248622
## [192] train-rmse:1.025318+0.015143 test-rmse:1.558356+0.246565
## [193] train-rmse:1.019769+0.015438 test-rmse:1.554245+0.247946
## [194] train-rmse:1.014122+0.015223 test-rmse:1.549808+0.245872
## [195] train-rmse:1.008896+0.015300 test-rmse:1.545958+0.245863
## [196] train-rmse:1.003373+0.015376 test-rmse:1.539927+0.247256
## [197] train-rmse:0.998241+0.015450 test-rmse:1.535582+0.247362
## [198] train-rmse:0.993023+0.015452 test-rmse:1.530976+0.247063
## [199] train-rmse:0.987666+0.015351 test-rmse:1.525121+0.248376
## [200] train-rmse:0.982421+0.015442 test-rmse:1.521286+0.248996
## [201] train-rmse:0.977086+0.015398 test-rmse:1.518621+0.250807
## [202] train-rmse:0.971992+0.015370 test-rmse:1.515171+0.249578
## [203] train-rmse:0.966777+0.015301 test-rmse:1.512957+0.249738
## [204] train-rmse:0.961841+0.015391 test-rmse:1.510956+0.249441
## [205] train-rmse:0.956748+0.015279 test-rmse:1.506553+0.249425
## [206] train-rmse:0.951763+0.015427 test-rmse:1.502455+0.248339
## [207] train-rmse:0.946692+0.015585 test-rmse:1.496976+0.249949
## [208] train-rmse:0.941464+0.015850 test-rmse:1.492329+0.250450
## [209] train-rmse:0.936179+0.015772 test-rmse:1.489017+0.249720
## [210] train-rmse:0.931476+0.015921 test-rmse:1.483649+0.252417
## [211] train-rmse:0.926850+0.016001 test-rmse:1.478952+0.252127
## [212] train-rmse:0.922269+0.016128 test-rmse:1.477074+0.253534
## [213] train-rmse:0.917737+0.015946 test-rmse:1.474035+0.254733
## [214] train-rmse:0.913057+0.015917 test-rmse:1.470684+0.254138
## [215] train-rmse:0.908522+0.016060 test-rmse:1.466791+0.254726
## [216] train-rmse:0.903878+0.016455 test-rmse:1.460176+0.255378
## [217] train-rmse:0.899412+0.016584 test-rmse:1.456761+0.252774
## [218] train-rmse:0.895215+0.016548 test-rmse:1.452752+0.249776
## [219] train-rmse:0.890947+0.016412 test-rmse:1.451746+0.250495
## [220] train-rmse:0.886517+0.016562 test-rmse:1.449682+0.250473
## [221] train-rmse:0.882186+0.016660 test-rmse:1.445611+0.251778
## [222] train-rmse:0.877903+0.016674 test-rmse:1.441113+0.253819
## [223] train-rmse:0.873618+0.016813 test-rmse:1.438673+0.254011
## [224] train-rmse:0.869091+0.017288 test-rmse:1.435359+0.256491
## [225] train-rmse:0.864807+0.017329 test-rmse:1.432449+0.255509
## [226] train-rmse:0.860706+0.017195 test-rmse:1.428925+0.255592
## [227] train-rmse:0.856746+0.017267 test-rmse:1.422882+0.252335
## [228] train-rmse:0.852642+0.017261 test-rmse:1.418771+0.250315
## [229] train-rmse:0.848809+0.017323 test-rmse:1.416665+0.249705
## [230] train-rmse:0.844989+0.017445 test-rmse:1.413599+0.247168
## [231] train-rmse:0.841205+0.017495 test-rmse:1.410780+0.247047
## [232] train-rmse:0.837441+0.017552 test-rmse:1.408269+0.248301
## [233] train-rmse:0.833364+0.017145 test-rmse:1.405649+0.248580
## [234] train-rmse:0.829604+0.017243 test-rmse:1.401333+0.249649
## [235] train-rmse:0.825652+0.017099 test-rmse:1.396343+0.249980
## [236] train-rmse:0.821549+0.017099 test-rmse:1.393410+0.249131
## [237] train-rmse:0.817919+0.017041 test-rmse:1.389413+0.249494
## [238] train-rmse:0.814055+0.017192 test-rmse:1.387060+0.250532
## [239] train-rmse:0.810456+0.017380 test-rmse:1.384573+0.249657
## [240] train-rmse:0.806755+0.017298 test-rmse:1.382854+0.249597
## [241] train-rmse:0.803070+0.017340 test-rmse:1.378989+0.249519
## [242] train-rmse:0.799334+0.017829 test-rmse:1.377745+0.249687
## [243] train-rmse:0.795905+0.017797 test-rmse:1.376014+0.249489
## [244] train-rmse:0.792298+0.017730 test-rmse:1.373608+0.247890
## [245] train-rmse:0.788797+0.017995 test-rmse:1.370780+0.249213
## [246] train-rmse:0.785509+0.018005 test-rmse:1.365014+0.246747
## [247] train-rmse:0.781988+0.017878 test-rmse:1.364274+0.247563
## [248] train-rmse:0.778719+0.017899 test-rmse:1.361985+0.247371
## [249] train-rmse:0.775491+0.017969 test-rmse:1.360053+0.247921
## [250] train-rmse:0.772263+0.018028 test-rmse:1.356718+0.248882
## [251] train-rmse:0.768583+0.018169 test-rmse:1.356086+0.249413
## [252] train-rmse:0.765262+0.018026 test-rmse:1.353316+0.250333
## [253] train-rmse:0.761202+0.017737 test-rmse:1.351301+0.251631
## [254] train-rmse:0.758126+0.017657 test-rmse:1.348770+0.251943
## [255] train-rmse:0.755227+0.017718 test-rmse:1.347029+0.251798
## [256] train-rmse:0.752062+0.017584 test-rmse:1.344512+0.251810
## [257] train-rmse:0.749078+0.017710 test-rmse:1.340778+0.250402
## [258] train-rmse:0.746097+0.017787 test-rmse:1.337965+0.251187
## [259] train-rmse:0.743075+0.017555 test-rmse:1.335164+0.249942
## [260] train-rmse:0.740154+0.017458 test-rmse:1.332963+0.249726
## [261] train-rmse:0.737229+0.017676 test-rmse:1.331186+0.249523
## [262] train-rmse:0.734395+0.017699 test-rmse:1.329228+0.250792
## [263] train-rmse:0.731410+0.017575 test-rmse:1.326896+0.250165
## [264] train-rmse:0.728468+0.017456 test-rmse:1.325944+0.251255
## [265] train-rmse:0.725171+0.017221 test-rmse:1.325333+0.251837
## [266] train-rmse:0.722337+0.017047 test-rmse:1.322328+0.252236
## [267] train-rmse:0.719660+0.017027 test-rmse:1.320051+0.253876
## [268] train-rmse:0.716977+0.016997 test-rmse:1.319005+0.253994
## [269] train-rmse:0.714209+0.016950 test-rmse:1.315949+0.253156
## [270] train-rmse:0.711402+0.016871 test-rmse:1.314230+0.253502
## [271] train-rmse:0.708523+0.016771 test-rmse:1.312086+0.252966
## [272] train-rmse:0.705669+0.017171 test-rmse:1.310207+0.253814
## [273] train-rmse:0.702362+0.017260 test-rmse:1.307555+0.253839
## [274] train-rmse:0.699392+0.017288 test-rmse:1.305747+0.253253
## [275] train-rmse:0.696534+0.017201 test-rmse:1.304877+0.251421
## [276] train-rmse:0.694034+0.017209 test-rmse:1.303126+0.251572
## [277] train-rmse:0.691698+0.017297 test-rmse:1.300920+0.252153
## [278] train-rmse:0.689238+0.017300 test-rmse:1.299534+0.252885
## [279] train-rmse:0.686677+0.017226 test-rmse:1.297868+0.251999
## [280] train-rmse:0.684229+0.017364 test-rmse:1.295939+0.251630
## [281] train-rmse:0.681996+0.017349 test-rmse:1.293911+0.251941
## [282] train-rmse:0.679596+0.017423 test-rmse:1.292749+0.252157
## [283] train-rmse:0.677179+0.017409 test-rmse:1.290529+0.252876
## [284] train-rmse:0.674837+0.017275 test-rmse:1.289832+0.252611
## [285] train-rmse:0.672515+0.017400 test-rmse:1.287873+0.254272
## [286] train-rmse:0.670318+0.017347 test-rmse:1.286314+0.255511
## [287] train-rmse:0.667557+0.017278 test-rmse:1.284245+0.257758
## [288] train-rmse:0.665042+0.017628 test-rmse:1.282384+0.258460
## [289] train-rmse:0.662522+0.017563 test-rmse:1.280224+0.257714
## [290] train-rmse:0.659873+0.017710 test-rmse:1.279097+0.257151
## [291] train-rmse:0.657137+0.017791 test-rmse:1.277504+0.258087
## [292] train-rmse:0.654911+0.017604 test-rmse:1.275867+0.258159
## [293] train-rmse:0.652362+0.017033 test-rmse:1.273567+0.257416
## [294] train-rmse:0.650034+0.017065 test-rmse:1.271884+0.257817
## [295] train-rmse:0.647618+0.017578 test-rmse:1.269430+0.259134
## [296] train-rmse:0.645320+0.018011 test-rmse:1.267786+0.259021
## [297] train-rmse:0.642972+0.017781 test-rmse:1.266987+0.258847
## [298] train-rmse:0.640746+0.017698 test-rmse:1.266055+0.259677
## [299] train-rmse:0.638385+0.017218 test-rmse:1.265314+0.260399
## [300] train-rmse:0.636087+0.017339 test-rmse:1.265131+0.260925
## [301] train-rmse:0.634010+0.017308 test-rmse:1.261396+0.256729
## [302] train-rmse:0.631946+0.017385 test-rmse:1.260531+0.256754
## [303] train-rmse:0.629949+0.017436 test-rmse:1.258932+0.256950
## [304] train-rmse:0.627677+0.017527 test-rmse:1.257293+0.257087
## [305] train-rmse:0.625847+0.017452 test-rmse:1.256083+0.257043
## [306] train-rmse:0.623877+0.017576 test-rmse:1.254529+0.256835
## [307] train-rmse:0.622071+0.017553 test-rmse:1.252943+0.257616
## [308] train-rmse:0.620037+0.017455 test-rmse:1.250879+0.256665
## [309] train-rmse:0.618275+0.017539 test-rmse:1.248881+0.256752
## [310] train-rmse:0.616401+0.017481 test-rmse:1.248552+0.257266
## [311] train-rmse:0.613925+0.017471 test-rmse:1.247280+0.258547
## [312] train-rmse:0.611635+0.017528 test-rmse:1.246725+0.259348
## [313] train-rmse:0.609673+0.017315 test-rmse:1.245483+0.259608
## [314] train-rmse:0.607808+0.017199 test-rmse:1.242904+0.260481
## [315] train-rmse:0.605903+0.017380 test-rmse:1.241420+0.260694
## [316] train-rmse:0.603542+0.017448 test-rmse:1.240450+0.261958
## [317] train-rmse:0.601669+0.017410 test-rmse:1.236975+0.257622
## [318] train-rmse:0.599584+0.017609 test-rmse:1.236454+0.258086
## [319] train-rmse:0.597795+0.017707 test-rmse:1.235210+0.258806
## [320] train-rmse:0.596096+0.017527 test-rmse:1.232574+0.258178
## [321] train-rmse:0.594284+0.017590 test-rmse:1.231639+0.257835
## [322] train-rmse:0.592315+0.017173 test-rmse:1.230655+0.256702
## [323] train-rmse:0.590486+0.017217 test-rmse:1.230067+0.257785
## [324] train-rmse:0.588830+0.017227 test-rmse:1.228742+0.257590
## [325] train-rmse:0.587123+0.017244 test-rmse:1.227684+0.258427
## [326] train-rmse:0.585468+0.017422 test-rmse:1.226819+0.258890
## [327] train-rmse:0.583590+0.017233 test-rmse:1.226364+0.259720
## [328] train-rmse:0.581504+0.017099 test-rmse:1.225389+0.259363
## [329] train-rmse:0.579783+0.017085 test-rmse:1.223556+0.259893
## [330] train-rmse:0.577873+0.016925 test-rmse:1.221290+0.259861
## [331] train-rmse:0.576159+0.016945 test-rmse:1.220373+0.260512
## [332] train-rmse:0.574562+0.016937 test-rmse:1.218781+0.260739
## [333] train-rmse:0.572887+0.017061 test-rmse:1.218646+0.260297
## [334] train-rmse:0.571361+0.017144 test-rmse:1.218588+0.260653
## [335] train-rmse:0.569909+0.017112 test-rmse:1.217496+0.260799
## [336] train-rmse:0.568264+0.017038 test-rmse:1.215204+0.261250
## [337] train-rmse:0.566479+0.017441 test-rmse:1.214666+0.261347
## [338] train-rmse:0.564924+0.017472 test-rmse:1.214109+0.261070
## [339] train-rmse:0.563482+0.017376 test-rmse:1.213403+0.261865
## [340] train-rmse:0.562099+0.017296 test-rmse:1.211131+0.257993
## [341] train-rmse:0.560607+0.017287 test-rmse:1.210484+0.258140
## [342] train-rmse:0.559099+0.017359 test-rmse:1.207627+0.259823
## [343] train-rmse:0.557514+0.017192 test-rmse:1.206243+0.258886
## [344] train-rmse:0.556013+0.017176 test-rmse:1.204947+0.258949
## [345] train-rmse:0.554421+0.017582 test-rmse:1.204914+0.259248
## [346] train-rmse:0.552918+0.017528 test-rmse:1.203777+0.259232
## [347] train-rmse:0.551476+0.017623 test-rmse:1.202056+0.260011
## [348] train-rmse:0.549918+0.018028 test-rmse:1.201005+0.258951
## [349] train-rmse:0.548362+0.018143 test-rmse:1.200482+0.259285
## [350] train-rmse:0.546659+0.018106 test-rmse:1.199983+0.259112
## [351] train-rmse:0.545214+0.017979 test-rmse:1.199443+0.259399
## [352] train-rmse:0.543514+0.018090 test-rmse:1.199122+0.259778
## [353] train-rmse:0.542072+0.017906 test-rmse:1.197840+0.260062
## [354] train-rmse:0.540561+0.017819 test-rmse:1.196930+0.260341
## [355] train-rmse:0.539079+0.017830 test-rmse:1.196868+0.259964
## [356] train-rmse:0.537448+0.018088 test-rmse:1.195813+0.259778
## [357] train-rmse:0.536048+0.018046 test-rmse:1.195025+0.259822
## [358] train-rmse:0.534618+0.018128 test-rmse:1.193680+0.260072
## [359] train-rmse:0.533469+0.018190 test-rmse:1.193563+0.259957
## [360] train-rmse:0.532178+0.018188 test-rmse:1.192973+0.260105
## [361] train-rmse:0.530754+0.018026 test-rmse:1.192274+0.260880
## [362] train-rmse:0.529297+0.018039 test-rmse:1.191545+0.260281
## [363] train-rmse:0.528039+0.018043 test-rmse:1.190794+0.260426
## [364] train-rmse:0.526628+0.018097 test-rmse:1.190453+0.261577
## [365] train-rmse:0.525348+0.018153 test-rmse:1.190247+0.261239
## [366] train-rmse:0.524085+0.018120 test-rmse:1.188933+0.261344
## [367] train-rmse:0.522350+0.018241 test-rmse:1.186891+0.261204
## [368] train-rmse:0.521180+0.018275 test-rmse:1.185713+0.261407
## [369] train-rmse:0.520042+0.018233 test-rmse:1.184533+0.261998
## [370] train-rmse:0.518788+0.018175 test-rmse:1.183730+0.262075
## [371] train-rmse:0.517423+0.018182 test-rmse:1.183297+0.262668
## [372] train-rmse:0.516341+0.018123 test-rmse:1.181594+0.262441
## [373] train-rmse:0.515165+0.018124 test-rmse:1.181768+0.262658
## [374] train-rmse:0.513986+0.018139 test-rmse:1.180430+0.263252
## [375] train-rmse:0.512892+0.018204 test-rmse:1.180250+0.263068
## [376] train-rmse:0.511604+0.018573 test-rmse:1.180163+0.262682
## [377] train-rmse:0.510228+0.018676 test-rmse:1.178842+0.263472
## [378] train-rmse:0.509105+0.018617 test-rmse:1.178197+0.263503
## [379] train-rmse:0.508111+0.018663 test-rmse:1.177448+0.263436
## [380] train-rmse:0.506809+0.018792 test-rmse:1.177887+0.263539
## [381] train-rmse:0.505704+0.018780 test-rmse:1.176997+0.263822
## [382] train-rmse:0.504687+0.018763 test-rmse:1.176342+0.263610
## [383] train-rmse:0.503633+0.018749 test-rmse:1.176000+0.264324
## [384] train-rmse:0.502507+0.018819 test-rmse:1.175668+0.263988
## [385] train-rmse:0.501439+0.018786 test-rmse:1.175116+0.264189
## [386] train-rmse:0.500036+0.018960 test-rmse:1.173771+0.263605
## [387] train-rmse:0.499009+0.018949 test-rmse:1.171419+0.264136
## [388] train-rmse:0.497942+0.018895 test-rmse:1.169181+0.261691
## [389] train-rmse:0.496818+0.018945 test-rmse:1.168855+0.262561
## [390] train-rmse:0.495636+0.018891 test-rmse:1.167410+0.262590
## [391] train-rmse:0.494648+0.018760 test-rmse:1.166573+0.262961
## [392] train-rmse:0.493498+0.018776 test-rmse:1.166044+0.262687
## [393] train-rmse:0.492494+0.018733 test-rmse:1.165586+0.262596
## [394] train-rmse:0.491282+0.018492 test-rmse:1.164629+0.262792
## [395] train-rmse:0.490103+0.018273 test-rmse:1.164710+0.263434
## [396] train-rmse:0.489112+0.018392 test-rmse:1.164397+0.263804
## [397] train-rmse:0.488087+0.018289 test-rmse:1.164445+0.263974
## [398] train-rmse:0.486984+0.018322 test-rmse:1.162580+0.264457
## [399] train-rmse:0.485471+0.018369 test-rmse:1.160566+0.264168
## [400] train-rmse:0.484383+0.018309 test-rmse:1.160304+0.264380
## [401] train-rmse:0.483210+0.018153 test-rmse:1.160018+0.264312
## [402] train-rmse:0.482163+0.018192 test-rmse:1.159518+0.264089
## [403] train-rmse:0.481217+0.018079 test-rmse:1.159435+0.264715
## [404] train-rmse:0.480178+0.018455 test-rmse:1.158033+0.264855
## [405] train-rmse:0.479201+0.018419 test-rmse:1.156995+0.265004
## [406] train-rmse:0.478364+0.018353 test-rmse:1.156151+0.265711
## [407] train-rmse:0.477060+0.018002 test-rmse:1.155721+0.264840
## [408] train-rmse:0.476101+0.018046 test-rmse:1.155052+0.264959
## [409] train-rmse:0.475176+0.018033 test-rmse:1.153609+0.264580
## [410] train-rmse:0.474103+0.018326 test-rmse:1.153413+0.264706
## [411] train-rmse:0.473287+0.018257 test-rmse:1.152889+0.264090
## [412] train-rmse:0.472345+0.018255 test-rmse:1.152533+0.263978
## [413] train-rmse:0.471474+0.018306 test-rmse:1.151894+0.264539
## [414] train-rmse:0.470588+0.018316 test-rmse:1.151234+0.264406
## [415] train-rmse:0.469019+0.017981 test-rmse:1.150488+0.263665
## [416] train-rmse:0.468069+0.018008 test-rmse:1.148716+0.262439
## [417] train-rmse:0.466967+0.018087 test-rmse:1.147080+0.263662
## [418] train-rmse:0.466034+0.017905 test-rmse:1.146642+0.264139
## [419] train-rmse:0.465240+0.017909 test-rmse:1.146365+0.264330
## [420] train-rmse:0.464292+0.017947 test-rmse:1.146182+0.264448
## [421] train-rmse:0.463383+0.017897 test-rmse:1.145567+0.264480
## [422] train-rmse:0.462397+0.018221 test-rmse:1.145052+0.264988
## [423] train-rmse:0.461613+0.018155 test-rmse:1.144556+0.265663
## [424] train-rmse:0.460844+0.018104 test-rmse:1.144388+0.265467
## [425] train-rmse:0.459985+0.017967 test-rmse:1.143658+0.265222
## [426] train-rmse:0.458979+0.017920 test-rmse:1.142389+0.265625
## [427] train-rmse:0.458133+0.018004 test-rmse:1.142533+0.265204
## [428] train-rmse:0.457236+0.018270 test-rmse:1.141852+0.265556
## [429] train-rmse:0.456353+0.018256 test-rmse:1.141216+0.265699
## [430] train-rmse:0.455554+0.018201 test-rmse:1.139509+0.263185
## [431] train-rmse:0.454466+0.018401 test-rmse:1.139122+0.263025
## [432] train-rmse:0.453696+0.018425 test-rmse:1.138550+0.263211
## [433] train-rmse:0.452860+0.018522 test-rmse:1.138095+0.263280
## [434] train-rmse:0.452012+0.018568 test-rmse:1.138005+0.263374
## [435] train-rmse:0.451223+0.018498 test-rmse:1.137335+0.263725
## [436] train-rmse:0.450470+0.018518 test-rmse:1.135977+0.264262
## [437] train-rmse:0.449410+0.018708 test-rmse:1.135755+0.264327
## [438] train-rmse:0.448433+0.018752 test-rmse:1.134122+0.265292
## [439] train-rmse:0.447582+0.018746 test-rmse:1.133905+0.265572
## [440] train-rmse:0.446753+0.018598 test-rmse:1.133470+0.265317
## [441] train-rmse:0.446040+0.018538 test-rmse:1.133396+0.265137
## [442] train-rmse:0.444853+0.018629 test-rmse:1.133160+0.265186
## [443] train-rmse:0.444092+0.018682 test-rmse:1.132914+0.265398
## [444] train-rmse:0.443218+0.018557 test-rmse:1.132004+0.265243
## [445] train-rmse:0.442345+0.018744 test-rmse:1.132125+0.265695
## [446] train-rmse:0.441409+0.018946 test-rmse:1.132014+0.265933
## [447] train-rmse:0.440441+0.018654 test-rmse:1.131506+0.265307
## [448] train-rmse:0.439791+0.018740 test-rmse:1.130659+0.265450
## [449] train-rmse:0.439108+0.018699 test-rmse:1.129975+0.265463
## [450] train-rmse:0.438386+0.018678 test-rmse:1.130074+0.265320
## [451] train-rmse:0.437704+0.018698 test-rmse:1.129748+0.265257
## [452] train-rmse:0.437022+0.018645 test-rmse:1.129322+0.264867
## [453] train-rmse:0.436279+0.018826 test-rmse:1.129108+0.265262
## [454] train-rmse:0.435506+0.018760 test-rmse:1.128983+0.265635
## [455] train-rmse:0.434473+0.018599 test-rmse:1.128338+0.266229
## [456] train-rmse:0.433758+0.018451 test-rmse:1.127932+0.266410
## [457] train-rmse:0.433060+0.018472 test-rmse:1.126945+0.267086
## [458] train-rmse:0.432101+0.018358 test-rmse:1.125805+0.266292
## [459] train-rmse:0.431242+0.018479 test-rmse:1.125902+0.266302
## [460] train-rmse:0.430582+0.018551 test-rmse:1.125670+0.266164
## [461] train-rmse:0.429977+0.018507 test-rmse:1.125369+0.266171
## [462] train-rmse:0.429203+0.018464 test-rmse:1.124648+0.265839
## [463] train-rmse:0.428351+0.018675 test-rmse:1.123301+0.266634
## [464] train-rmse:0.427205+0.018959 test-rmse:1.122885+0.266658
## [465] train-rmse:0.426659+0.018930 test-rmse:1.122458+0.266418
## [466] train-rmse:0.425973+0.018919 test-rmse:1.122060+0.266527
## [467] train-rmse:0.425381+0.018962 test-rmse:1.121658+0.267097
## [468] train-rmse:0.424791+0.019056 test-rmse:1.121153+0.267335
## [469] train-rmse:0.424117+0.018997 test-rmse:1.121115+0.267553
## [470] train-rmse:0.423311+0.019032 test-rmse:1.121589+0.268005
## [471] train-rmse:0.422699+0.018954 test-rmse:1.121462+0.268031
## [472] train-rmse:0.421882+0.018858 test-rmse:1.121228+0.267820
## [473] train-rmse:0.421054+0.018666 test-rmse:1.120000+0.268003
## [474] train-rmse:0.420385+0.018611 test-rmse:1.119796+0.267972
## [475] train-rmse:0.419668+0.018582 test-rmse:1.118500+0.267248
## [476] train-rmse:0.418967+0.018697 test-rmse:1.118431+0.266830
## [477] train-rmse:0.418364+0.018660 test-rmse:1.118246+0.266758
## [478] train-rmse:0.417746+0.018688 test-rmse:1.117283+0.267396
## [479] train-rmse:0.417047+0.018638 test-rmse:1.116692+0.267230
## [480] train-rmse:0.416380+0.018601 test-rmse:1.115814+0.267450
## [481] train-rmse:0.415746+0.018632 test-rmse:1.115111+0.267431
## [482] train-rmse:0.415181+0.018704 test-rmse:1.114643+0.267575
## [483] train-rmse:0.414472+0.018630 test-rmse:1.114065+0.267645
## [484] train-rmse:0.413978+0.018604 test-rmse:1.114185+0.267436
## [485] train-rmse:0.413479+0.018574 test-rmse:1.113969+0.267542
## [486] train-rmse:0.412767+0.018492 test-rmse:1.114065+0.267633
## [487] train-rmse:0.411985+0.018315 test-rmse:1.113563+0.267634
## [488] train-rmse:0.411164+0.018251 test-rmse:1.113444+0.267704
## [489] train-rmse:0.410309+0.018073 test-rmse:1.113625+0.268089
## [490] train-rmse:0.409800+0.018035 test-rmse:1.113434+0.268641
## [491] train-rmse:0.408877+0.017739 test-rmse:1.112872+0.268385
## [492] train-rmse:0.408292+0.017878 test-rmse:1.112687+0.268450
## [493] train-rmse:0.407835+0.017883 test-rmse:1.111412+0.268953
## [494] train-rmse:0.407282+0.017901 test-rmse:1.111322+0.269153
## [495] train-rmse:0.406158+0.018105 test-rmse:1.111255+0.268734
## [496] train-rmse:0.405552+0.018132 test-rmse:1.110724+0.268942
## [497] train-rmse:0.404885+0.018229 test-rmse:1.110291+0.268806
## [498] train-rmse:0.404351+0.018268 test-rmse:1.110041+0.268448
## [499] train-rmse:0.403819+0.018179 test-rmse:1.109789+0.268716
## [500] train-rmse:0.402888+0.018244 test-rmse:1.109860+0.269291
## [501] train-rmse:0.402366+0.018185 test-rmse:1.109026+0.268765
## [502] train-rmse:0.401736+0.018263 test-rmse:1.108519+0.269206
## [503] train-rmse:0.400999+0.018135 test-rmse:1.108911+0.269368
## [504] train-rmse:0.400420+0.018079 test-rmse:1.107062+0.269873
## [505] train-rmse:0.399837+0.018193 test-rmse:1.107081+0.269752
## [506] train-rmse:0.399203+0.018163 test-rmse:1.105500+0.267730
## [507] train-rmse:0.398724+0.018150 test-rmse:1.105220+0.267881
## [508] train-rmse:0.398207+0.018300 test-rmse:1.104922+0.268134
## [509] train-rmse:0.397641+0.018287 test-rmse:1.104346+0.267686
## [510] train-rmse:0.397140+0.018345 test-rmse:1.103880+0.267244
## [511] train-rmse:0.396448+0.018214 test-rmse:1.103314+0.267466
## [512] train-rmse:0.395761+0.018164 test-rmse:1.102807+0.267557
## [513] train-rmse:0.395216+0.018088 test-rmse:1.102520+0.267729
## [514] train-rmse:0.394615+0.017977 test-rmse:1.102905+0.267283
## [515] train-rmse:0.394123+0.017894 test-rmse:1.102606+0.267296
## [516] train-rmse:0.393440+0.017720 test-rmse:1.102209+0.267370
## [517] train-rmse:0.392471+0.017783 test-rmse:1.101557+0.267714
## [518] train-rmse:0.391637+0.017591 test-rmse:1.101365+0.267128
## [519] train-rmse:0.390920+0.017925 test-rmse:1.100964+0.267173
## [520] train-rmse:0.390342+0.017916 test-rmse:1.100369+0.267432
## [521] train-rmse:0.389857+0.017928 test-rmse:1.099952+0.267836
## [522] train-rmse:0.389292+0.018021 test-rmse:1.100125+0.267974
## [523] train-rmse:0.388876+0.018004 test-rmse:1.098994+0.268527
## [524] train-rmse:0.388361+0.018124 test-rmse:1.098404+0.268156
## [525] train-rmse:0.387936+0.018164 test-rmse:1.098097+0.267678
## [526] train-rmse:0.387361+0.018071 test-rmse:1.097663+0.267453
## [527] train-rmse:0.386800+0.017977 test-rmse:1.097896+0.267345
## [528] train-rmse:0.386384+0.017974 test-rmse:1.097327+0.267073
## [529] train-rmse:0.385369+0.018040 test-rmse:1.097062+0.267230
## [530] train-rmse:0.384551+0.018117 test-rmse:1.096930+0.267683
## [531] train-rmse:0.384051+0.018073 test-rmse:1.096724+0.267787
## [532] train-rmse:0.382930+0.018321 test-rmse:1.096790+0.268262
## [533] train-rmse:0.382332+0.018367 test-rmse:1.096342+0.268673
## [534] train-rmse:0.381889+0.018404 test-rmse:1.096067+0.268540
## [535] train-rmse:0.381439+0.018336 test-rmse:1.095783+0.268657
## [536] train-rmse:0.381019+0.018360 test-rmse:1.095123+0.268452
## [537] train-rmse:0.380476+0.018308 test-rmse:1.094638+0.268436
## [538] train-rmse:0.379946+0.018286 test-rmse:1.094436+0.268492
## [539] train-rmse:0.379433+0.018425 test-rmse:1.094028+0.268579
## [540] train-rmse:0.378989+0.018388 test-rmse:1.093861+0.268639
## [541] train-rmse:0.378536+0.018440 test-rmse:1.094119+0.268362
## [542] train-rmse:0.377977+0.018550 test-rmse:1.093033+0.268907
## [543] train-rmse:0.377390+0.018443 test-rmse:1.092895+0.268866
## [544] train-rmse:0.376668+0.018327 test-rmse:1.092653+0.268388
## [545] train-rmse:0.376194+0.018455 test-rmse:1.092307+0.268468
## [546] train-rmse:0.375574+0.018395 test-rmse:1.091998+0.268638
## [547] train-rmse:0.375059+0.018455 test-rmse:1.092092+0.268390
## [548] train-rmse:0.374414+0.018379 test-rmse:1.092149+0.268594
## [549] train-rmse:0.373796+0.018218 test-rmse:1.091967+0.269208
## [550] train-rmse:0.373391+0.018264 test-rmse:1.091763+0.268915
## [551] train-rmse:0.372553+0.018193 test-rmse:1.091800+0.269255
## [552] train-rmse:0.372151+0.018162 test-rmse:1.091383+0.269481
## [553] train-rmse:0.371521+0.018259 test-rmse:1.091259+0.269818
## [554] train-rmse:0.370975+0.018340 test-rmse:1.091203+0.269915
## [555] train-rmse:0.370444+0.018263 test-rmse:1.091132+0.269722
## [556] train-rmse:0.369786+0.018199 test-rmse:1.091384+0.269856
## [557] train-rmse:0.368981+0.018067 test-rmse:1.091212+0.270000
## [558] train-rmse:0.368617+0.018112 test-rmse:1.090788+0.269939
## [559] train-rmse:0.368228+0.018054 test-rmse:1.090372+0.270079
## [560] train-rmse:0.367812+0.018012 test-rmse:1.090079+0.270075
## [561] train-rmse:0.367452+0.017937 test-rmse:1.090035+0.270055
## [562] train-rmse:0.367008+0.017876 test-rmse:1.089878+0.269985
## [563] train-rmse:0.366627+0.017902 test-rmse:1.089677+0.269981
## [564] train-rmse:0.366153+0.017847 test-rmse:1.089873+0.270206
## [565] train-rmse:0.365619+0.017781 test-rmse:1.089974+0.270384
## [566] train-rmse:0.364990+0.017653 test-rmse:1.089635+0.270222
## [567] train-rmse:0.364293+0.017614 test-rmse:1.090069+0.270238
## [568] train-rmse:0.363422+0.017250 test-rmse:1.089523+0.270365
## [569] train-rmse:0.363023+0.017262 test-rmse:1.089105+0.270694
## [570] train-rmse:0.362380+0.017240 test-rmse:1.089083+0.270564
## [571] train-rmse:0.361799+0.017407 test-rmse:1.088833+0.270930
## [572] train-rmse:0.361410+0.017434 test-rmse:1.088764+0.270639
## [573] train-rmse:0.360604+0.017078 test-rmse:1.087995+0.270710
## [574] train-rmse:0.359980+0.016921 test-rmse:1.088031+0.270694
## [575] train-rmse:0.359653+0.016947 test-rmse:1.087896+0.270668
## [576] train-rmse:0.359247+0.016948 test-rmse:1.087831+0.270529
## [577] train-rmse:0.358698+0.016977 test-rmse:1.087627+0.270539
## [578] train-rmse:0.358280+0.016945 test-rmse:1.087004+0.270484
## [579] train-rmse:0.357887+0.016878 test-rmse:1.087172+0.270516
## [580] train-rmse:0.357513+0.017001 test-rmse:1.087169+0.270464
## [581] train-rmse:0.357131+0.016981 test-rmse:1.086589+0.270488
## [582] train-rmse:0.356334+0.016780 test-rmse:1.086638+0.269811
## [583] train-rmse:0.355249+0.017145 test-rmse:1.086752+0.269777
## [584] train-rmse:0.354761+0.017387 test-rmse:1.086911+0.269691
## [585] train-rmse:0.354310+0.017387 test-rmse:1.086301+0.269383
## [586] train-rmse:0.353910+0.017406 test-rmse:1.086332+0.269567
## [587] train-rmse:0.353495+0.017333 test-rmse:1.086216+0.269781
## [588] train-rmse:0.352583+0.017048 test-rmse:1.085841+0.269602
## [589] train-rmse:0.352161+0.017019 test-rmse:1.085764+0.269626
## [590] train-rmse:0.351842+0.016975 test-rmse:1.085629+0.269705
## [591] train-rmse:0.351460+0.016972 test-rmse:1.084739+0.269797
## [592] train-rmse:0.350988+0.017087 test-rmse:1.084731+0.269660
## [593] train-rmse:0.350528+0.017166 test-rmse:1.084880+0.269408
## [594] train-rmse:0.350050+0.017155 test-rmse:1.084763+0.269397
## [595] train-rmse:0.349469+0.016981 test-rmse:1.084225+0.268619
## [596] train-rmse:0.349192+0.016961 test-rmse:1.084033+0.268498
## [597] train-rmse:0.348808+0.016940 test-rmse:1.083935+0.268620
## [598] train-rmse:0.348280+0.016910 test-rmse:1.083599+0.268796
## [599] train-rmse:0.347805+0.016789 test-rmse:1.083598+0.268902
## [600] train-rmse:0.347462+0.016762 test-rmse:1.083697+0.269085
## [601] train-rmse:0.346678+0.016874 test-rmse:1.083749+0.269154
## [602] train-rmse:0.345971+0.016941 test-rmse:1.083683+0.269674
## [603] train-rmse:0.345658+0.017000 test-rmse:1.083341+0.269583
## [604] train-rmse:0.345233+0.016942 test-rmse:1.083105+0.269735
## [605] train-rmse:0.344761+0.016787 test-rmse:1.083239+0.270104
## [606] train-rmse:0.344420+0.016738 test-rmse:1.082859+0.270123
## [607] train-rmse:0.343843+0.016636 test-rmse:1.082609+0.269978
## [608] train-rmse:0.343232+0.016519 test-rmse:1.082234+0.270124
## [609] train-rmse:0.342774+0.016438 test-rmse:1.081775+0.270317
## [610] train-rmse:0.342449+0.016453 test-rmse:1.081538+0.270511
## [611] train-rmse:0.341871+0.016684 test-rmse:1.081244+0.270354
## [612] train-rmse:0.341222+0.016579 test-rmse:1.081123+0.269890
## [613] train-rmse:0.340924+0.016565 test-rmse:1.081193+0.270121
## [614] train-rmse:0.340537+0.016532 test-rmse:1.081337+0.270117
## [615] train-rmse:0.340196+0.016558 test-rmse:1.081201+0.270073
## [616] train-rmse:0.339563+0.017024 test-rmse:1.080859+0.269769
## [617] train-rmse:0.338874+0.017002 test-rmse:1.080645+0.268809
## [618] train-rmse:0.338512+0.016985 test-rmse:1.080563+0.268824
## [619] train-rmse:0.338138+0.016924 test-rmse:1.080474+0.269095
## [620] train-rmse:0.337585+0.017265 test-rmse:1.079927+0.268748
## [621] train-rmse:0.337141+0.017291 test-rmse:1.079876+0.268859
## [622] train-rmse:0.336787+0.017256 test-rmse:1.079993+0.268904
## [623] train-rmse:0.336190+0.017469 test-rmse:1.079832+0.268951
## [624] train-rmse:0.335671+0.017319 test-rmse:1.080115+0.268937
## [625] train-rmse:0.335205+0.017245 test-rmse:1.080404+0.268985
## [626] train-rmse:0.334628+0.017362 test-rmse:1.080417+0.269341
## [627] train-rmse:0.334278+0.017367 test-rmse:1.080341+0.269243
## [628] train-rmse:0.333817+0.017256 test-rmse:1.080038+0.268901
## [629] train-rmse:0.333528+0.017279 test-rmse:1.079416+0.268867
## [630] train-rmse:0.333197+0.017267 test-rmse:1.079284+0.268739
## [631] train-rmse:0.332806+0.017250 test-rmse:1.078857+0.268764
## [632] train-rmse:0.332382+0.017416 test-rmse:1.078749+0.269164
## [633] train-rmse:0.332002+0.017392 test-rmse:1.078742+0.269160
## [634] train-rmse:0.331473+0.017408 test-rmse:1.078970+0.269337
## [635] train-rmse:0.331197+0.017399 test-rmse:1.078847+0.269386
## [636] train-rmse:0.330594+0.017209 test-rmse:1.078560+0.269017
## [637] train-rmse:0.330311+0.017297 test-rmse:1.078182+0.268705
## [638] train-rmse:0.329959+0.017315 test-rmse:1.078071+0.268398
## [639] train-rmse:0.329550+0.017333 test-rmse:1.077979+0.268307
## [640] train-rmse:0.329240+0.017419 test-rmse:1.077920+0.268549
## [641] train-rmse:0.328970+0.017425 test-rmse:1.077819+0.268285
## [642] train-rmse:0.328525+0.017372 test-rmse:1.077867+0.268195
## [643] train-rmse:0.328002+0.017485 test-rmse:1.077753+0.268139
## [644] train-rmse:0.327547+0.017488 test-rmse:1.077969+0.267826
## [645] train-rmse:0.327289+0.017481 test-rmse:1.078035+0.267593
## [646] train-rmse:0.326650+0.017204 test-rmse:1.077873+0.267375
## [647] train-rmse:0.326020+0.016954 test-rmse:1.077919+0.267336
## [648] train-rmse:0.325546+0.016721 test-rmse:1.077507+0.267324
## [649] train-rmse:0.324778+0.017014 test-rmse:1.077295+0.266913
## [650] train-rmse:0.324275+0.016955 test-rmse:1.077411+0.266617
## [651] train-rmse:0.323691+0.016915 test-rmse:1.077273+0.266987
## [652] train-rmse:0.323154+0.017039 test-rmse:1.077130+0.266705
## [653] train-rmse:0.322952+0.017031 test-rmse:1.076783+0.266562
## [654] train-rmse:0.322660+0.017068 test-rmse:1.076765+0.266461
## [655] train-rmse:0.322116+0.017052 test-rmse:1.076904+0.266285
## [656] train-rmse:0.321722+0.017321 test-rmse:1.076750+0.266311
## [657] train-rmse:0.321298+0.017515 test-rmse:1.076321+0.266148
## [658] train-rmse:0.321026+0.017514 test-rmse:1.075979+0.265757
## [659] train-rmse:0.320593+0.017405 test-rmse:1.075891+0.265798
## [660] train-rmse:0.320282+0.017389 test-rmse:1.075844+0.265781
## [661] train-rmse:0.319980+0.017404 test-rmse:1.075851+0.265723
## [662] train-rmse:0.319469+0.017526 test-rmse:1.075624+0.265572
## [663] train-rmse:0.319142+0.017488 test-rmse:1.075622+0.265610
## [664] train-rmse:0.318708+0.017540 test-rmse:1.075658+0.265558
## [665] train-rmse:0.318335+0.017525 test-rmse:1.075623+0.265620
## [666] train-rmse:0.317887+0.017696 test-rmse:1.075797+0.265792
## [667] train-rmse:0.317352+0.017503 test-rmse:1.075588+0.265818
## [668] train-rmse:0.317124+0.017503 test-rmse:1.075174+0.265345
## [669] train-rmse:0.316648+0.017369 test-rmse:1.074586+0.265301
## [670] train-rmse:0.316190+0.017725 test-rmse:1.074530+0.265274
## [671] train-rmse:0.315724+0.018084 test-rmse:1.074866+0.265199
## [672] train-rmse:0.315354+0.018092 test-rmse:1.074840+0.265402
## [673] train-rmse:0.315009+0.018248 test-rmse:1.074743+0.265557
## [674] train-rmse:0.314421+0.018304 test-rmse:1.074420+0.265624
## [675] train-rmse:0.314097+0.018330 test-rmse:1.074088+0.265574
## [676] train-rmse:0.313690+0.018252 test-rmse:1.074021+0.265546
## [677] train-rmse:0.313251+0.018310 test-rmse:1.074079+0.265991
## [678] train-rmse:0.312822+0.018305 test-rmse:1.074129+0.265960
## [679] train-rmse:0.312329+0.018092 test-rmse:1.074074+0.266029
## [680] train-rmse:0.312067+0.018111 test-rmse:1.073782+0.265876
## [681] train-rmse:0.311700+0.018028 test-rmse:1.073589+0.265678
## [682] train-rmse:0.311167+0.017955 test-rmse:1.073932+0.266052
## [683] train-rmse:0.310924+0.017913 test-rmse:1.073894+0.265841
## [684] train-rmse:0.310462+0.017818 test-rmse:1.073656+0.265944
## [685] train-rmse:0.310208+0.017815 test-rmse:1.073500+0.265717
## [686] train-rmse:0.309861+0.018006 test-rmse:1.073639+0.265661
## [687] train-rmse:0.309509+0.018167 test-rmse:1.073401+0.265451
## [688] train-rmse:0.309308+0.018185 test-rmse:1.072958+0.265633
## [689] train-rmse:0.308843+0.018121 test-rmse:1.073046+0.265626
## [690] train-rmse:0.308415+0.018135 test-rmse:1.073127+0.265585
## [691] train-rmse:0.308069+0.018302 test-rmse:1.072559+0.266115
## [692] train-rmse:0.307676+0.018239 test-rmse:1.072363+0.266122
## [693] train-rmse:0.307383+0.018293 test-rmse:1.072479+0.266343
## [694] train-rmse:0.306795+0.018175 test-rmse:1.072501+0.266089
## [695] train-rmse:0.306522+0.018211 test-rmse:1.072430+0.265855
## [696] train-rmse:0.306152+0.018512 test-rmse:1.072227+0.265854
## [697] train-rmse:0.305800+0.018521 test-rmse:1.072215+0.265947
## [698] train-rmse:0.305441+0.018467 test-rmse:1.072328+0.265907
## [699] train-rmse:0.305084+0.018405 test-rmse:1.072009+0.266148
## [700] train-rmse:0.304769+0.018319 test-rmse:1.072021+0.266331
## [701] train-rmse:0.304539+0.018352 test-rmse:1.071799+0.266048
## [702] train-rmse:0.304246+0.018362 test-rmse:1.071001+0.266471
## [703] train-rmse:0.303809+0.018277 test-rmse:1.071281+0.266263
## [704] train-rmse:0.303327+0.018072 test-rmse:1.071301+0.266155
## [705] train-rmse:0.302652+0.017937 test-rmse:1.071296+0.266141
## [706] train-rmse:0.302326+0.018116 test-rmse:1.071194+0.266042
## [707] train-rmse:0.301862+0.018128 test-rmse:1.071414+0.266470
## [708] train-rmse:0.301362+0.018118 test-rmse:1.071251+0.266249
## Stopping. Best iteration:
## [702] train-rmse:0.304246+0.018362 test-rmse:1.071001+0.266471
##
## 2
## [1] train-rmse:96.903592+0.106998 test-rmse:96.903929+0.480417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.282645+0.098508 test-rmse:87.282849+0.494309
## [3] train-rmse:78.624342+0.091095 test-rmse:78.624364+0.507694
## [4] train-rmse:70.833145+0.084703 test-rmse:70.832979+0.520704
## [5] train-rmse:63.823124+0.079254 test-rmse:63.822745+0.533458
## [6] train-rmse:57.516975+0.074698 test-rmse:57.516330+0.546089
## [7] train-rmse:51.845108+0.070990 test-rmse:51.844165+0.558710
## [8] train-rmse:46.744962+0.068087 test-rmse:46.743658+0.571428
## [9] train-rmse:42.160251+0.065957 test-rmse:42.158527+0.584358
## [10] train-rmse:38.040358+0.064573 test-rmse:38.038154+0.597586
## [11] train-rmse:34.337210+0.063071 test-rmse:34.350626+0.598556
## [12] train-rmse:31.005821+0.060048 test-rmse:31.044042+0.597332
## [13] train-rmse:28.008132+0.054748 test-rmse:28.071744+0.610449
## [14] train-rmse:25.311016+0.048086 test-rmse:25.390260+0.601052
## [15] train-rmse:22.884224+0.042327 test-rmse:22.971323+0.589327
## [16] train-rmse:20.701371+0.037871 test-rmse:20.785068+0.568733
## [17] train-rmse:18.738375+0.033563 test-rmse:18.835100+0.571964
## [18] train-rmse:16.971872+0.031783 test-rmse:17.066980+0.561134
## [19] train-rmse:15.384260+0.030548 test-rmse:15.478114+0.557996
## [20] train-rmse:13.957898+0.029638 test-rmse:14.042649+0.557835
## [21] train-rmse:12.677213+0.028364 test-rmse:12.778338+0.562113
## [22] train-rmse:11.527256+0.028784 test-rmse:11.633744+0.553770
## [23] train-rmse:10.495689+0.029370 test-rmse:10.612807+0.562064
## [24] train-rmse:9.572374+0.029971 test-rmse:9.695868+0.550398
## [25] train-rmse:8.743929+0.028937 test-rmse:8.849699+0.540415
## [26] train-rmse:8.004857+0.029804 test-rmse:8.130794+0.543777
## [27] train-rmse:7.345813+0.030585 test-rmse:7.482567+0.533334
## [28] train-rmse:6.755814+0.032033 test-rmse:6.893641+0.518299
## [29] train-rmse:6.229023+0.029540 test-rmse:6.384279+0.507222
## [30] train-rmse:5.759412+0.031546 test-rmse:5.922803+0.509892
## [31] train-rmse:5.344315+0.032235 test-rmse:5.514337+0.505877
## [32] train-rmse:4.975162+0.031747 test-rmse:5.165829+0.510527
## [33] train-rmse:4.650118+0.032892 test-rmse:4.838814+0.492203
## [34] train-rmse:4.363351+0.034567 test-rmse:4.561770+0.484460
## [35] train-rmse:4.108070+0.033875 test-rmse:4.315639+0.473393
## [36] train-rmse:3.882311+0.033429 test-rmse:4.101073+0.452401
## [37] train-rmse:3.685141+0.033598 test-rmse:3.911038+0.448415
## [38] train-rmse:3.508515+0.033490 test-rmse:3.747826+0.433565
## [39] train-rmse:3.350417+0.030687 test-rmse:3.608910+0.423742
## [40] train-rmse:3.213553+0.031283 test-rmse:3.494155+0.407783
## [41] train-rmse:3.091098+0.030877 test-rmse:3.389190+0.401497
## [42] train-rmse:2.979849+0.026216 test-rmse:3.275588+0.381273
## [43] train-rmse:2.884033+0.026533 test-rmse:3.194915+0.383231
## [44] train-rmse:2.798796+0.026348 test-rmse:3.116867+0.377812
## [45] train-rmse:2.719700+0.026421 test-rmse:3.043164+0.351006
## [46] train-rmse:2.649731+0.024950 test-rmse:2.991760+0.342303
## [47] train-rmse:2.586577+0.025698 test-rmse:2.934555+0.339942
## [48] train-rmse:2.526526+0.025498 test-rmse:2.884629+0.343305
## [49] train-rmse:2.472533+0.024096 test-rmse:2.829507+0.319778
## [50] train-rmse:2.424282+0.024094 test-rmse:2.793675+0.312961
## [51] train-rmse:2.375536+0.025040 test-rmse:2.758217+0.303034
## [52] train-rmse:2.333454+0.025205 test-rmse:2.725695+0.295124
## [53] train-rmse:2.292683+0.024996 test-rmse:2.693239+0.307182
## [54] train-rmse:2.255788+0.024462 test-rmse:2.666601+0.308425
## [55] train-rmse:2.219617+0.024176 test-rmse:2.641296+0.306462
## [56] train-rmse:2.182849+0.025536 test-rmse:2.612987+0.298060
## [57] train-rmse:2.150143+0.024507 test-rmse:2.588918+0.289460
## [58] train-rmse:2.116657+0.025669 test-rmse:2.561536+0.281016
## [59] train-rmse:2.087312+0.025129 test-rmse:2.535829+0.277106
## [60] train-rmse:2.058541+0.024382 test-rmse:2.507661+0.273682
## [61] train-rmse:2.029610+0.024237 test-rmse:2.479990+0.268914
## [62] train-rmse:2.001742+0.024147 test-rmse:2.460402+0.266293
## [63] train-rmse:1.975201+0.023934 test-rmse:2.440250+0.258861
## [64] train-rmse:1.949534+0.023198 test-rmse:2.417282+0.260614
## [65] train-rmse:1.924036+0.022079 test-rmse:2.387024+0.255067
## [66] train-rmse:1.899453+0.021873 test-rmse:2.361898+0.256676
## [67] train-rmse:1.875271+0.021109 test-rmse:2.345415+0.257878
## [68] train-rmse:1.851507+0.020796 test-rmse:2.327594+0.250987
## [69] train-rmse:1.829417+0.020409 test-rmse:2.309421+0.245577
## [70] train-rmse:1.806714+0.019564 test-rmse:2.290039+0.246706
## [71] train-rmse:1.784406+0.019613 test-rmse:2.270980+0.254781
## [72] train-rmse:1.763078+0.019050 test-rmse:2.257684+0.247921
## [73] train-rmse:1.742121+0.018421 test-rmse:2.236868+0.246796
## [74] train-rmse:1.721717+0.017802 test-rmse:2.217871+0.243398
## [75] train-rmse:1.700607+0.017264 test-rmse:2.204715+0.249051
## [76] train-rmse:1.680621+0.018057 test-rmse:2.184071+0.246844
## [77] train-rmse:1.661626+0.017733 test-rmse:2.170901+0.250446
## [78] train-rmse:1.642222+0.017159 test-rmse:2.150408+0.242492
## [79] train-rmse:1.623694+0.016990 test-rmse:2.137242+0.245361
## [80] train-rmse:1.604965+0.016349 test-rmse:2.124931+0.245435
## [81] train-rmse:1.587128+0.016321 test-rmse:2.104764+0.244285
## [82] train-rmse:1.569691+0.015807 test-rmse:2.085683+0.244183
## [83] train-rmse:1.551302+0.014431 test-rmse:2.070711+0.245473
## [84] train-rmse:1.533813+0.014606 test-rmse:2.058394+0.249921
## [85] train-rmse:1.516013+0.014103 test-rmse:2.043800+0.247432
## [86] train-rmse:1.496976+0.016374 test-rmse:2.033882+0.244091
## [87] train-rmse:1.479359+0.014947 test-rmse:2.018201+0.244769
## [88] train-rmse:1.463447+0.014570 test-rmse:2.007119+0.245113
## [89] train-rmse:1.447509+0.014517 test-rmse:1.989501+0.246643
## [90] train-rmse:1.432259+0.014339 test-rmse:1.977473+0.248920
## [91] train-rmse:1.415645+0.014313 test-rmse:1.963090+0.246674
## [92] train-rmse:1.399829+0.015237 test-rmse:1.953849+0.245409
## [93] train-rmse:1.385246+0.015082 test-rmse:1.941619+0.242928
## [94] train-rmse:1.369812+0.013210 test-rmse:1.927496+0.243206
## [95] train-rmse:1.355254+0.012970 test-rmse:1.918435+0.242440
## [96] train-rmse:1.340821+0.012438 test-rmse:1.906441+0.246024
## [97] train-rmse:1.326442+0.011994 test-rmse:1.896638+0.251337
## [98] train-rmse:1.313196+0.011759 test-rmse:1.881718+0.250331
## [99] train-rmse:1.299696+0.011019 test-rmse:1.869127+0.246965
## [100] train-rmse:1.286556+0.010647 test-rmse:1.855851+0.240834
## [101] train-rmse:1.273213+0.010424 test-rmse:1.848254+0.243112
## [102] train-rmse:1.257594+0.012365 test-rmse:1.835447+0.242621
## [103] train-rmse:1.244479+0.012499 test-rmse:1.826766+0.242272
## [104] train-rmse:1.231748+0.011902 test-rmse:1.819227+0.247286
## [105] train-rmse:1.219535+0.011800 test-rmse:1.810168+0.244075
## [106] train-rmse:1.206731+0.012440 test-rmse:1.798558+0.246603
## [107] train-rmse:1.194615+0.012212 test-rmse:1.784182+0.248236
## [108] train-rmse:1.183190+0.012268 test-rmse:1.771883+0.249021
## [109] train-rmse:1.171204+0.011913 test-rmse:1.764920+0.247194
## [110] train-rmse:1.158337+0.012072 test-rmse:1.756620+0.247600
## [111] train-rmse:1.146330+0.011554 test-rmse:1.745850+0.248103
## [112] train-rmse:1.135189+0.011512 test-rmse:1.739679+0.249819
## [113] train-rmse:1.123415+0.010648 test-rmse:1.730883+0.249553
## [114] train-rmse:1.111460+0.010976 test-rmse:1.724260+0.250055
## [115] train-rmse:1.100974+0.010887 test-rmse:1.711868+0.249483
## [116] train-rmse:1.090631+0.010725 test-rmse:1.702592+0.251122
## [117] train-rmse:1.080183+0.010524 test-rmse:1.692196+0.250705
## [118] train-rmse:1.069826+0.010185 test-rmse:1.686030+0.253367
## [119] train-rmse:1.059226+0.009815 test-rmse:1.681119+0.255662
## [120] train-rmse:1.049402+0.009678 test-rmse:1.673920+0.256199
## [121] train-rmse:1.039158+0.009515 test-rmse:1.667199+0.256811
## [122] train-rmse:1.029656+0.009296 test-rmse:1.658630+0.257627
## [123] train-rmse:1.019512+0.009914 test-rmse:1.652224+0.258241
## [124] train-rmse:1.010347+0.009657 test-rmse:1.645830+0.258448
## [125] train-rmse:1.001202+0.009809 test-rmse:1.636329+0.261243
## [126] train-rmse:0.991644+0.009447 test-rmse:1.629005+0.259847
## [127] train-rmse:0.982690+0.009474 test-rmse:1.619063+0.261220
## [128] train-rmse:0.973060+0.009289 test-rmse:1.610814+0.259828
## [129] train-rmse:0.964136+0.008670 test-rmse:1.602560+0.262765
## [130] train-rmse:0.954598+0.007785 test-rmse:1.594702+0.265521
## [131] train-rmse:0.945696+0.008386 test-rmse:1.589669+0.266315
## [132] train-rmse:0.937389+0.008377 test-rmse:1.583448+0.267336
## [133] train-rmse:0.929256+0.008366 test-rmse:1.575776+0.268741
## [134] train-rmse:0.919870+0.009865 test-rmse:1.572914+0.268754
## [135] train-rmse:0.912052+0.009729 test-rmse:1.567937+0.271249
## [136] train-rmse:0.903756+0.010363 test-rmse:1.560852+0.272053
## [137] train-rmse:0.895723+0.010372 test-rmse:1.556918+0.270041
## [138] train-rmse:0.887716+0.010295 test-rmse:1.550990+0.270602
## [139] train-rmse:0.879189+0.011057 test-rmse:1.544469+0.270900
## [140] train-rmse:0.871843+0.010864 test-rmse:1.538197+0.271677
## [141] train-rmse:0.864804+0.010782 test-rmse:1.531964+0.273067
## [142] train-rmse:0.856788+0.010941 test-rmse:1.523477+0.273453
## [143] train-rmse:0.849180+0.011793 test-rmse:1.517657+0.273658
## [144] train-rmse:0.841112+0.011202 test-rmse:1.513010+0.273499
## [145] train-rmse:0.833697+0.010775 test-rmse:1.510299+0.275377
## [146] train-rmse:0.826107+0.010552 test-rmse:1.502720+0.277743
## [147] train-rmse:0.819113+0.010852 test-rmse:1.496734+0.269304
## [148] train-rmse:0.812475+0.010624 test-rmse:1.492196+0.270897
## [149] train-rmse:0.806033+0.010247 test-rmse:1.485880+0.270464
## [150] train-rmse:0.799809+0.010162 test-rmse:1.479898+0.269418
## [151] train-rmse:0.793041+0.011148 test-rmse:1.476792+0.268121
## [152] train-rmse:0.785880+0.011297 test-rmse:1.473403+0.267106
## [153] train-rmse:0.779515+0.010746 test-rmse:1.468665+0.270415
## [154] train-rmse:0.773218+0.010834 test-rmse:1.458597+0.264854
## [155] train-rmse:0.766947+0.010927 test-rmse:1.456305+0.266484
## [156] train-rmse:0.760829+0.011399 test-rmse:1.451065+0.265484
## [157] train-rmse:0.754934+0.011454 test-rmse:1.448464+0.268093
## [158] train-rmse:0.748620+0.011968 test-rmse:1.445372+0.268126
## [159] train-rmse:0.743058+0.011740 test-rmse:1.441670+0.268286
## [160] train-rmse:0.737281+0.011420 test-rmse:1.439747+0.268091
## [161] train-rmse:0.731854+0.011296 test-rmse:1.433157+0.268957
## [162] train-rmse:0.725912+0.011839 test-rmse:1.429183+0.269420
## [163] train-rmse:0.720301+0.011692 test-rmse:1.421880+0.263030
## [164] train-rmse:0.714475+0.012476 test-rmse:1.417235+0.263291
## [165] train-rmse:0.709355+0.012625 test-rmse:1.414070+0.263513
## [166] train-rmse:0.704158+0.013147 test-rmse:1.410137+0.264265
## [167] train-rmse:0.698923+0.013043 test-rmse:1.406091+0.264903
## [168] train-rmse:0.693389+0.013077 test-rmse:1.400890+0.265742
## [169] train-rmse:0.688095+0.013452 test-rmse:1.396469+0.266059
## [170] train-rmse:0.683140+0.013572 test-rmse:1.393657+0.266951
## [171] train-rmse:0.677922+0.013506 test-rmse:1.390095+0.268700
## [172] train-rmse:0.673060+0.013226 test-rmse:1.387772+0.269032
## [173] train-rmse:0.667500+0.014169 test-rmse:1.385303+0.268556
## [174] train-rmse:0.662954+0.014049 test-rmse:1.380749+0.269180
## [175] train-rmse:0.657635+0.013783 test-rmse:1.373226+0.263774
## [176] train-rmse:0.652173+0.015040 test-rmse:1.369698+0.263942
## [177] train-rmse:0.647891+0.015003 test-rmse:1.367279+0.264328
## [178] train-rmse:0.643552+0.015045 test-rmse:1.365357+0.264369
## [179] train-rmse:0.639325+0.014796 test-rmse:1.361243+0.265006
## [180] train-rmse:0.634806+0.014981 test-rmse:1.357677+0.266005
## [181] train-rmse:0.630285+0.015155 test-rmse:1.355717+0.266479
## [182] train-rmse:0.625819+0.014965 test-rmse:1.352034+0.266592
## [183] train-rmse:0.621425+0.015227 test-rmse:1.348262+0.267235
## [184] train-rmse:0.617388+0.015239 test-rmse:1.346739+0.267572
## [185] train-rmse:0.613294+0.014982 test-rmse:1.343507+0.267305
## [186] train-rmse:0.608885+0.014701 test-rmse:1.337318+0.261604
## [187] train-rmse:0.604844+0.014863 test-rmse:1.336268+0.261933
## [188] train-rmse:0.600790+0.014829 test-rmse:1.334166+0.262158
## [189] train-rmse:0.596792+0.014947 test-rmse:1.332107+0.263168
## [190] train-rmse:0.593011+0.014724 test-rmse:1.330616+0.264971
## [191] train-rmse:0.588680+0.014141 test-rmse:1.327222+0.265226
## [192] train-rmse:0.584814+0.013996 test-rmse:1.325939+0.265170
## [193] train-rmse:0.580805+0.014055 test-rmse:1.322939+0.265405
## [194] train-rmse:0.576598+0.014219 test-rmse:1.320381+0.265624
## [195] train-rmse:0.572742+0.014099 test-rmse:1.317321+0.267100
## [196] train-rmse:0.568954+0.013957 test-rmse:1.311816+0.263663
## [197] train-rmse:0.565047+0.013949 test-rmse:1.309921+0.261930
## [198] train-rmse:0.560837+0.014310 test-rmse:1.308220+0.262216
## [199] train-rmse:0.557343+0.014466 test-rmse:1.306678+0.263090
## [200] train-rmse:0.554093+0.014434 test-rmse:1.305119+0.264665
## [201] train-rmse:0.550407+0.014124 test-rmse:1.301502+0.264208
## [202] train-rmse:0.546456+0.014281 test-rmse:1.300493+0.264910
## [203] train-rmse:0.543073+0.014225 test-rmse:1.300400+0.265524
## [204] train-rmse:0.539818+0.013808 test-rmse:1.295349+0.265717
## [205] train-rmse:0.536572+0.013799 test-rmse:1.292229+0.262259
## [206] train-rmse:0.533170+0.014059 test-rmse:1.290633+0.262706
## [207] train-rmse:0.529931+0.014077 test-rmse:1.289322+0.263765
## [208] train-rmse:0.526752+0.014071 test-rmse:1.286035+0.263218
## [209] train-rmse:0.523891+0.013878 test-rmse:1.284750+0.263566
## [210] train-rmse:0.521031+0.013904 test-rmse:1.283386+0.263816
## [211] train-rmse:0.518256+0.013918 test-rmse:1.281434+0.263501
## [212] train-rmse:0.515276+0.013692 test-rmse:1.280476+0.264076
## [213] train-rmse:0.512486+0.013464 test-rmse:1.278409+0.264800
## [214] train-rmse:0.509077+0.013233 test-rmse:1.277908+0.266064
## [215] train-rmse:0.505957+0.013238 test-rmse:1.273609+0.262380
## [216] train-rmse:0.503252+0.013177 test-rmse:1.272891+0.262845
## [217] train-rmse:0.500451+0.013333 test-rmse:1.269332+0.263479
## [218] train-rmse:0.497776+0.013509 test-rmse:1.268613+0.263767
## [219] train-rmse:0.495017+0.013323 test-rmse:1.267345+0.264306
## [220] train-rmse:0.491921+0.013083 test-rmse:1.265822+0.264992
## [221] train-rmse:0.489277+0.012929 test-rmse:1.263303+0.265535
## [222] train-rmse:0.486225+0.012468 test-rmse:1.262508+0.266656
## [223] train-rmse:0.483796+0.012601 test-rmse:1.261148+0.266444
## [224] train-rmse:0.481554+0.012496 test-rmse:1.260145+0.266375
## [225] train-rmse:0.478482+0.012022 test-rmse:1.258978+0.266233
## [226] train-rmse:0.475888+0.012173 test-rmse:1.258235+0.266726
## [227] train-rmse:0.473424+0.011941 test-rmse:1.257585+0.267117
## [228] train-rmse:0.470458+0.012195 test-rmse:1.256843+0.266885
## [229] train-rmse:0.467958+0.012526 test-rmse:1.255528+0.266981
## [230] train-rmse:0.465477+0.012428 test-rmse:1.254185+0.267520
## [231] train-rmse:0.462545+0.012628 test-rmse:1.253514+0.266926
## [232] train-rmse:0.460233+0.012571 test-rmse:1.251813+0.266878
## [233] train-rmse:0.457746+0.012104 test-rmse:1.250866+0.266579
## [234] train-rmse:0.455471+0.012363 test-rmse:1.250292+0.266749
## [235] train-rmse:0.453176+0.012145 test-rmse:1.246048+0.267475
## [236] train-rmse:0.450918+0.011866 test-rmse:1.245263+0.267191
## [237] train-rmse:0.448497+0.012039 test-rmse:1.243429+0.268402
## [238] train-rmse:0.446557+0.011906 test-rmse:1.242547+0.268985
## [239] train-rmse:0.444418+0.012026 test-rmse:1.241140+0.269315
## [240] train-rmse:0.442476+0.011959 test-rmse:1.239652+0.269227
## [241] train-rmse:0.440309+0.012108 test-rmse:1.237183+0.269934
## [242] train-rmse:0.437787+0.011922 test-rmse:1.235789+0.270577
## [243] train-rmse:0.435554+0.011673 test-rmse:1.233878+0.270559
## [244] train-rmse:0.433424+0.011747 test-rmse:1.233325+0.271293
## [245] train-rmse:0.430913+0.011387 test-rmse:1.231606+0.271967
## [246] train-rmse:0.429070+0.011375 test-rmse:1.231363+0.272843
## [247] train-rmse:0.426847+0.011287 test-rmse:1.230627+0.272766
## [248] train-rmse:0.424496+0.011473 test-rmse:1.230347+0.271662
## [249] train-rmse:0.422369+0.011770 test-rmse:1.228635+0.272406
## [250] train-rmse:0.420335+0.011938 test-rmse:1.228129+0.273296
## [251] train-rmse:0.418295+0.011782 test-rmse:1.226851+0.273344
## [252] train-rmse:0.416563+0.011820 test-rmse:1.226157+0.273614
## [253] train-rmse:0.414449+0.012410 test-rmse:1.225381+0.273376
## [254] train-rmse:0.412560+0.011730 test-rmse:1.223457+0.274100
## [255] train-rmse:0.410476+0.011630 test-rmse:1.223268+0.273992
## [256] train-rmse:0.408595+0.011595 test-rmse:1.221129+0.275550
## [257] train-rmse:0.406577+0.011442 test-rmse:1.217866+0.277229
## [258] train-rmse:0.404613+0.011561 test-rmse:1.217672+0.277317
## [259] train-rmse:0.402831+0.011613 test-rmse:1.216763+0.277175
## [260] train-rmse:0.401269+0.011761 test-rmse:1.215868+0.277053
## [261] train-rmse:0.399520+0.011394 test-rmse:1.215018+0.276568
## [262] train-rmse:0.397308+0.010983 test-rmse:1.212790+0.276781
## [263] train-rmse:0.395551+0.011189 test-rmse:1.212734+0.276953
## [264] train-rmse:0.394036+0.011115 test-rmse:1.211249+0.277558
## [265] train-rmse:0.392347+0.011097 test-rmse:1.211141+0.278415
## [266] train-rmse:0.390344+0.011236 test-rmse:1.210898+0.278830
## [267] train-rmse:0.388775+0.011066 test-rmse:1.210092+0.279185
## [268] train-rmse:0.387219+0.010764 test-rmse:1.208675+0.278871
## [269] train-rmse:0.385669+0.010844 test-rmse:1.207821+0.278910
## [270] train-rmse:0.383651+0.010375 test-rmse:1.207176+0.277717
## [271] train-rmse:0.381810+0.010634 test-rmse:1.206993+0.277726
## [272] train-rmse:0.380326+0.010779 test-rmse:1.206261+0.277825
## [273] train-rmse:0.378791+0.010646 test-rmse:1.204812+0.277652
## [274] train-rmse:0.377213+0.010709 test-rmse:1.204866+0.277945
## [275] train-rmse:0.375816+0.010782 test-rmse:1.204120+0.278282
## [276] train-rmse:0.374460+0.010566 test-rmse:1.204472+0.278386
## [277] train-rmse:0.372926+0.010336 test-rmse:1.202386+0.278637
## [278] train-rmse:0.371385+0.010481 test-rmse:1.200296+0.279318
## [279] train-rmse:0.370033+0.010441 test-rmse:1.199026+0.279539
## [280] train-rmse:0.368713+0.010533 test-rmse:1.198711+0.279555
## [281] train-rmse:0.366977+0.010736 test-rmse:1.198388+0.279668
## [282] train-rmse:0.365161+0.010507 test-rmse:1.198101+0.280250
## [283] train-rmse:0.363400+0.010339 test-rmse:1.198070+0.280473
## [284] train-rmse:0.362017+0.010427 test-rmse:1.196283+0.282030
## [285] train-rmse:0.360472+0.010027 test-rmse:1.195217+0.282952
## [286] train-rmse:0.358913+0.009620 test-rmse:1.194817+0.282963
## [287] train-rmse:0.357345+0.009780 test-rmse:1.194525+0.283217
## [288] train-rmse:0.356031+0.009606 test-rmse:1.194033+0.283283
## [289] train-rmse:0.354826+0.009452 test-rmse:1.191864+0.283695
## [290] train-rmse:0.353088+0.009054 test-rmse:1.190703+0.284120
## [291] train-rmse:0.351337+0.009444 test-rmse:1.190239+0.284276
## [292] train-rmse:0.350101+0.009463 test-rmse:1.189997+0.284105
## [293] train-rmse:0.348526+0.009270 test-rmse:1.188990+0.284782
## [294] train-rmse:0.347341+0.009462 test-rmse:1.188063+0.285044
## [295] train-rmse:0.346168+0.009484 test-rmse:1.187465+0.284949
## [296] train-rmse:0.344782+0.009277 test-rmse:1.187378+0.285188
## [297] train-rmse:0.343407+0.009082 test-rmse:1.186149+0.285084
## [298] train-rmse:0.342123+0.009181 test-rmse:1.186097+0.285091
## [299] train-rmse:0.340989+0.009280 test-rmse:1.185229+0.284774
## [300] train-rmse:0.339975+0.009226 test-rmse:1.183637+0.286352
## [301] train-rmse:0.338276+0.009164 test-rmse:1.182881+0.285894
## [302] train-rmse:0.336767+0.009309 test-rmse:1.182320+0.285873
## [303] train-rmse:0.335380+0.009364 test-rmse:1.181418+0.284971
## [304] train-rmse:0.334006+0.009669 test-rmse:1.180897+0.284948
## [305] train-rmse:0.332947+0.009574 test-rmse:1.179880+0.285333
## [306] train-rmse:0.331940+0.009488 test-rmse:1.178840+0.285928
## [307] train-rmse:0.330927+0.009501 test-rmse:1.178765+0.285812
## [308] train-rmse:0.329887+0.009538 test-rmse:1.178406+0.286054
## [309] train-rmse:0.328829+0.009811 test-rmse:1.178017+0.285753
## [310] train-rmse:0.326966+0.009432 test-rmse:1.177197+0.285255
## [311] train-rmse:0.325506+0.009626 test-rmse:1.176986+0.285122
## [312] train-rmse:0.324302+0.009412 test-rmse:1.176642+0.285991
## [313] train-rmse:0.322888+0.009573 test-rmse:1.175913+0.286265
## [314] train-rmse:0.321814+0.009162 test-rmse:1.175383+0.286311
## [315] train-rmse:0.320716+0.009230 test-rmse:1.175238+0.286164
## [316] train-rmse:0.319652+0.009113 test-rmse:1.174569+0.286661
## [317] train-rmse:0.318502+0.008905 test-rmse:1.174006+0.286772
## [318] train-rmse:0.317455+0.008998 test-rmse:1.174016+0.286332
## [319] train-rmse:0.316474+0.009038 test-rmse:1.173411+0.287476
## [320] train-rmse:0.315289+0.008955 test-rmse:1.172338+0.287816
## [321] train-rmse:0.314212+0.008724 test-rmse:1.171578+0.287618
## [322] train-rmse:0.313281+0.008765 test-rmse:1.171053+0.287845
## [323] train-rmse:0.311917+0.008603 test-rmse:1.170776+0.288086
## [324] train-rmse:0.310944+0.008391 test-rmse:1.170558+0.288292
## [325] train-rmse:0.309857+0.008292 test-rmse:1.170173+0.288507
## [326] train-rmse:0.308623+0.008517 test-rmse:1.169729+0.288924
## [327] train-rmse:0.307562+0.008747 test-rmse:1.169624+0.289425
## [328] train-rmse:0.306709+0.008736 test-rmse:1.168085+0.289213
## [329] train-rmse:0.305638+0.008494 test-rmse:1.167396+0.290195
## [330] train-rmse:0.304719+0.008446 test-rmse:1.166802+0.290351
## [331] train-rmse:0.303443+0.008253 test-rmse:1.166473+0.290573
## [332] train-rmse:0.302499+0.007997 test-rmse:1.166423+0.290792
## [333] train-rmse:0.301498+0.008069 test-rmse:1.166455+0.290630
## [334] train-rmse:0.300603+0.008079 test-rmse:1.165241+0.291142
## [335] train-rmse:0.299420+0.008308 test-rmse:1.164833+0.291310
## [336] train-rmse:0.298054+0.008276 test-rmse:1.163970+0.291220
## [337] train-rmse:0.297117+0.008222 test-rmse:1.163446+0.291238
## [338] train-rmse:0.296162+0.008133 test-rmse:1.162253+0.291166
## [339] train-rmse:0.295117+0.008201 test-rmse:1.161662+0.290542
## [340] train-rmse:0.294210+0.008320 test-rmse:1.161382+0.290655
## [341] train-rmse:0.293484+0.008428 test-rmse:1.161101+0.290504
## [342] train-rmse:0.292574+0.008437 test-rmse:1.160321+0.291112
## [343] train-rmse:0.291223+0.009115 test-rmse:1.160500+0.290995
## [344] train-rmse:0.290068+0.008959 test-rmse:1.159793+0.291436
## [345] train-rmse:0.289183+0.008844 test-rmse:1.159705+0.291621
## [346] train-rmse:0.288263+0.008837 test-rmse:1.159015+0.291616
## [347] train-rmse:0.287260+0.008854 test-rmse:1.158132+0.291376
## [348] train-rmse:0.286284+0.008941 test-rmse:1.157243+0.291832
## [349] train-rmse:0.285427+0.009094 test-rmse:1.157239+0.292022
## [350] train-rmse:0.284141+0.009034 test-rmse:1.156713+0.292224
## [351] train-rmse:0.283024+0.009064 test-rmse:1.156128+0.293025
## [352] train-rmse:0.282248+0.009006 test-rmse:1.155941+0.292993
## [353] train-rmse:0.281495+0.009054 test-rmse:1.155726+0.292966
## [354] train-rmse:0.280559+0.009042 test-rmse:1.155635+0.292741
## [355] train-rmse:0.279453+0.009488 test-rmse:1.155471+0.292819
## [356] train-rmse:0.278640+0.009275 test-rmse:1.155439+0.293107
## [357] train-rmse:0.277770+0.009669 test-rmse:1.155041+0.292921
## [358] train-rmse:0.276788+0.009861 test-rmse:1.154554+0.293378
## [359] train-rmse:0.275829+0.010027 test-rmse:1.154460+0.293336
## [360] train-rmse:0.274597+0.010645 test-rmse:1.154070+0.292678
## [361] train-rmse:0.273906+0.010641 test-rmse:1.153731+0.293102
## [362] train-rmse:0.273201+0.010491 test-rmse:1.153419+0.293110
## [363] train-rmse:0.272533+0.010530 test-rmse:1.153394+0.293306
## [364] train-rmse:0.271544+0.010600 test-rmse:1.153445+0.293268
## [365] train-rmse:0.270717+0.010534 test-rmse:1.153438+0.293702
## [366] train-rmse:0.269553+0.011065 test-rmse:1.152254+0.293713
## [367] train-rmse:0.268542+0.010936 test-rmse:1.152008+0.293989
## [368] train-rmse:0.267851+0.010856 test-rmse:1.151884+0.294034
## [369] train-rmse:0.266835+0.011050 test-rmse:1.151385+0.294257
## [370] train-rmse:0.265964+0.011108 test-rmse:1.150957+0.294420
## [371] train-rmse:0.265009+0.010995 test-rmse:1.150275+0.294780
## [372] train-rmse:0.264065+0.010747 test-rmse:1.149885+0.294365
## [373] train-rmse:0.263053+0.011023 test-rmse:1.149352+0.294454
## [374] train-rmse:0.262310+0.010962 test-rmse:1.148877+0.294641
## [375] train-rmse:0.261620+0.010920 test-rmse:1.149062+0.294804
## [376] train-rmse:0.260696+0.011187 test-rmse:1.148958+0.294814
## [377] train-rmse:0.260137+0.011209 test-rmse:1.148307+0.294694
## [378] train-rmse:0.259324+0.011209 test-rmse:1.148067+0.294927
## [379] train-rmse:0.258506+0.010850 test-rmse:1.147856+0.295404
## [380] train-rmse:0.257778+0.010790 test-rmse:1.147619+0.295571
## [381] train-rmse:0.256692+0.010721 test-rmse:1.147846+0.295366
## [382] train-rmse:0.255905+0.010566 test-rmse:1.147329+0.295724
## [383] train-rmse:0.255215+0.010410 test-rmse:1.147185+0.295704
## [384] train-rmse:0.254559+0.010393 test-rmse:1.146828+0.295363
## [385] train-rmse:0.253811+0.010632 test-rmse:1.146802+0.295332
## [386] train-rmse:0.252863+0.010453 test-rmse:1.146625+0.295911
## [387] train-rmse:0.252005+0.010589 test-rmse:1.146245+0.296295
## [388] train-rmse:0.251163+0.010578 test-rmse:1.145963+0.295770
## [389] train-rmse:0.250541+0.010609 test-rmse:1.145650+0.295844
## [390] train-rmse:0.249998+0.010685 test-rmse:1.145395+0.295860
## [391] train-rmse:0.249377+0.010608 test-rmse:1.145027+0.296033
## [392] train-rmse:0.248381+0.010754 test-rmse:1.144018+0.297094
## [393] train-rmse:0.247833+0.010696 test-rmse:1.143843+0.297220
## [394] train-rmse:0.247108+0.010760 test-rmse:1.143680+0.297171
## [395] train-rmse:0.246192+0.010984 test-rmse:1.143781+0.297490
## [396] train-rmse:0.245360+0.011198 test-rmse:1.143852+0.297096
## [397] train-rmse:0.244713+0.011168 test-rmse:1.143740+0.297105
## [398] train-rmse:0.243723+0.011141 test-rmse:1.143754+0.297334
## [399] train-rmse:0.243110+0.011089 test-rmse:1.142802+0.298180
## [400] train-rmse:0.242200+0.011009 test-rmse:1.142558+0.297886
## [401] train-rmse:0.241756+0.011134 test-rmse:1.142356+0.297795
## [402] train-rmse:0.241117+0.011042 test-rmse:1.142104+0.297348
## [403] train-rmse:0.240481+0.010993 test-rmse:1.141914+0.297230
## [404] train-rmse:0.239829+0.011075 test-rmse:1.141742+0.297359
## [405] train-rmse:0.238697+0.011613 test-rmse:1.141614+0.297723
## [406] train-rmse:0.237728+0.011704 test-rmse:1.141743+0.297763
## [407] train-rmse:0.236909+0.011466 test-rmse:1.141480+0.297379
## [408] train-rmse:0.236199+0.011375 test-rmse:1.141307+0.297586
## [409] train-rmse:0.235387+0.011217 test-rmse:1.140922+0.297446
## [410] train-rmse:0.234769+0.011401 test-rmse:1.140406+0.297775
## [411] train-rmse:0.234167+0.011276 test-rmse:1.140370+0.297612
## [412] train-rmse:0.233285+0.011452 test-rmse:1.140524+0.297532
## [413] train-rmse:0.232469+0.011643 test-rmse:1.140389+0.297387
## [414] train-rmse:0.231781+0.011510 test-rmse:1.139594+0.297823
## [415] train-rmse:0.231149+0.011578 test-rmse:1.139357+0.297699
## [416] train-rmse:0.230538+0.011808 test-rmse:1.139089+0.297744
## [417] train-rmse:0.229663+0.011625 test-rmse:1.138767+0.298032
## [418] train-rmse:0.228622+0.011524 test-rmse:1.138792+0.297770
## [419] train-rmse:0.227940+0.011371 test-rmse:1.138936+0.297658
## [420] train-rmse:0.227303+0.011333 test-rmse:1.138965+0.298023
## [421] train-rmse:0.226701+0.011408 test-rmse:1.138929+0.297965
## [422] train-rmse:0.226189+0.011324 test-rmse:1.138940+0.298280
## [423] train-rmse:0.225682+0.011325 test-rmse:1.138543+0.298340
## [424] train-rmse:0.225024+0.011542 test-rmse:1.138560+0.298033
## [425] train-rmse:0.224136+0.011628 test-rmse:1.138556+0.298427
## [426] train-rmse:0.223678+0.011546 test-rmse:1.138316+0.298496
## [427] train-rmse:0.222835+0.011492 test-rmse:1.138144+0.298653
## [428] train-rmse:0.222156+0.011287 test-rmse:1.137954+0.298791
## [429] train-rmse:0.221632+0.011213 test-rmse:1.137773+0.299071
## [430] train-rmse:0.221233+0.011178 test-rmse:1.137335+0.298728
## [431] train-rmse:0.220738+0.011166 test-rmse:1.137077+0.298897
## [432] train-rmse:0.220097+0.011130 test-rmse:1.136990+0.299001
## [433] train-rmse:0.219596+0.011121 test-rmse:1.136760+0.298981
## [434] train-rmse:0.219025+0.011351 test-rmse:1.136713+0.298893
## [435] train-rmse:0.218323+0.011239 test-rmse:1.136403+0.298770
## [436] train-rmse:0.217374+0.011029 test-rmse:1.136552+0.298979
## [437] train-rmse:0.216825+0.010839 test-rmse:1.136202+0.299273
## [438] train-rmse:0.216145+0.011065 test-rmse:1.135736+0.299492
## [439] train-rmse:0.215575+0.010837 test-rmse:1.135470+0.300334
## [440] train-rmse:0.214856+0.011335 test-rmse:1.135470+0.300170
## [441] train-rmse:0.214416+0.011361 test-rmse:1.135279+0.300101
## [442] train-rmse:0.213757+0.011161 test-rmse:1.135478+0.300109
## [443] train-rmse:0.213112+0.011136 test-rmse:1.135287+0.299884
## [444] train-rmse:0.212480+0.011240 test-rmse:1.134806+0.300326
## [445] train-rmse:0.211817+0.011341 test-rmse:1.134527+0.300613
## [446] train-rmse:0.211257+0.011435 test-rmse:1.134557+0.300419
## [447] train-rmse:0.210674+0.011417 test-rmse:1.134596+0.300525
## [448] train-rmse:0.210086+0.011233 test-rmse:1.134486+0.300934
## [449] train-rmse:0.209508+0.011244 test-rmse:1.134598+0.301105
## [450] train-rmse:0.208825+0.011160 test-rmse:1.134544+0.301205
## [451] train-rmse:0.208177+0.011044 test-rmse:1.134197+0.301171
## [452] train-rmse:0.207900+0.011031 test-rmse:1.133741+0.301625
## [453] train-rmse:0.207534+0.011024 test-rmse:1.133573+0.301559
## [454] train-rmse:0.206664+0.010956 test-rmse:1.133537+0.301469
## [455] train-rmse:0.206134+0.011127 test-rmse:1.133365+0.301568
## [456] train-rmse:0.205404+0.011017 test-rmse:1.133740+0.301300
## [457] train-rmse:0.204990+0.010925 test-rmse:1.133497+0.301382
## [458] train-rmse:0.204426+0.010912 test-rmse:1.133635+0.301615
## [459] train-rmse:0.203910+0.010833 test-rmse:1.133660+0.301456
## [460] train-rmse:0.203483+0.010930 test-rmse:1.133748+0.301593
## [461] train-rmse:0.203037+0.010884 test-rmse:1.133338+0.301693
## [462] train-rmse:0.202663+0.010924 test-rmse:1.133074+0.301836
## [463] train-rmse:0.202104+0.011031 test-rmse:1.132940+0.301723
## [464] train-rmse:0.201568+0.011072 test-rmse:1.132662+0.301930
## [465] train-rmse:0.200849+0.011024 test-rmse:1.132565+0.302061
## [466] train-rmse:0.200221+0.011018 test-rmse:1.132613+0.302095
## [467] train-rmse:0.199749+0.011013 test-rmse:1.132781+0.302163
## [468] train-rmse:0.199080+0.011050 test-rmse:1.132886+0.302202
## [469] train-rmse:0.198565+0.011107 test-rmse:1.132676+0.301990
## [470] train-rmse:0.198237+0.011014 test-rmse:1.132604+0.301999
## [471] train-rmse:0.197799+0.010926 test-rmse:1.132438+0.302102
## [472] train-rmse:0.197259+0.011012 test-rmse:1.132364+0.302268
## [473] train-rmse:0.196906+0.011115 test-rmse:1.132442+0.302286
## [474] train-rmse:0.196416+0.010985 test-rmse:1.131869+0.302146
## [475] train-rmse:0.195916+0.010845 test-rmse:1.131708+0.302258
## [476] train-rmse:0.195391+0.010784 test-rmse:1.131467+0.302209
## [477] train-rmse:0.194901+0.010838 test-rmse:1.131460+0.302255
## [478] train-rmse:0.194351+0.010962 test-rmse:1.131383+0.302391
## [479] train-rmse:0.193833+0.011124 test-rmse:1.131312+0.302304
## [480] train-rmse:0.193229+0.011332 test-rmse:1.131453+0.302377
## [481] train-rmse:0.192672+0.011353 test-rmse:1.131092+0.302482
## [482] train-rmse:0.192107+0.011475 test-rmse:1.131194+0.302600
## [483] train-rmse:0.191715+0.011393 test-rmse:1.131236+0.302901
## [484] train-rmse:0.191360+0.011289 test-rmse:1.130957+0.303019
## [485] train-rmse:0.190852+0.011258 test-rmse:1.130870+0.303038
## [486] train-rmse:0.190365+0.011292 test-rmse:1.130639+0.303254
## [487] train-rmse:0.189745+0.011334 test-rmse:1.130612+0.303008
## [488] train-rmse:0.189106+0.011249 test-rmse:1.130522+0.303146
## [489] train-rmse:0.188355+0.011308 test-rmse:1.130731+0.303128
## [490] train-rmse:0.187868+0.011207 test-rmse:1.130687+0.303186
## [491] train-rmse:0.187330+0.011178 test-rmse:1.130573+0.303410
## [492] train-rmse:0.186882+0.011214 test-rmse:1.130649+0.303444
## [493] train-rmse:0.186452+0.011167 test-rmse:1.130772+0.303442
## [494] train-rmse:0.185982+0.011242 test-rmse:1.130669+0.303458
## Stopping. Best iteration:
## [488] train-rmse:0.189106+0.011249 test-rmse:1.130522+0.303146
##
## 3
## [1] train-rmse:96.903592+0.106998 test-rmse:96.903929+0.480417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.282645+0.098508 test-rmse:87.282849+0.494309
## [3] train-rmse:78.624342+0.091095 test-rmse:78.624364+0.507694
## [4] train-rmse:70.833145+0.084703 test-rmse:70.832979+0.520704
## [5] train-rmse:63.823124+0.079254 test-rmse:63.822745+0.533458
## [6] train-rmse:57.516975+0.074698 test-rmse:57.516330+0.546089
## [7] train-rmse:51.845108+0.070990 test-rmse:51.844165+0.558710
## [8] train-rmse:46.744962+0.068087 test-rmse:46.743658+0.571428
## [9] train-rmse:42.160251+0.065957 test-rmse:42.158527+0.584358
## [10] train-rmse:38.040358+0.064573 test-rmse:38.038154+0.597586
## [11] train-rmse:34.337210+0.063071 test-rmse:34.350626+0.598556
## [12] train-rmse:31.005821+0.060048 test-rmse:31.044042+0.597332
## [13] train-rmse:28.008132+0.054748 test-rmse:28.071744+0.610449
## [14] train-rmse:25.311016+0.048086 test-rmse:25.390260+0.601052
## [15] train-rmse:22.884224+0.042327 test-rmse:22.971323+0.589327
## [16] train-rmse:20.700483+0.038078 test-rmse:20.782687+0.564264
## [17] train-rmse:18.736104+0.033770 test-rmse:18.827295+0.567654
## [18] train-rmse:16.966929+0.032027 test-rmse:17.060569+0.556279
## [19] train-rmse:15.374780+0.030624 test-rmse:15.465001+0.549492
## [20] train-rmse:13.942235+0.028910 test-rmse:14.035342+0.540714
## [21] train-rmse:12.654367+0.027510 test-rmse:12.748046+0.529373
## [22] train-rmse:11.496546+0.027583 test-rmse:11.606951+0.539584
## [23] train-rmse:10.453759+0.025810 test-rmse:10.552590+0.541009
## [24] train-rmse:9.519689+0.026060 test-rmse:9.622914+0.533528
## [25] train-rmse:8.679701+0.024064 test-rmse:8.794529+0.560827
## [26] train-rmse:7.926602+0.025737 test-rmse:8.038001+0.540362
## [27] train-rmse:7.252198+0.026198 test-rmse:7.353726+0.520842
## [28] train-rmse:6.648564+0.026714 test-rmse:6.762973+0.530259
## [29] train-rmse:6.105827+0.025679 test-rmse:6.235793+0.508695
## [30] train-rmse:5.623034+0.026529 test-rmse:5.746186+0.497288
## [31] train-rmse:5.190838+0.027496 test-rmse:5.341873+0.493737
## [32] train-rmse:4.801772+0.024996 test-rmse:4.965796+0.489566
## [33] train-rmse:4.458838+0.025350 test-rmse:4.630845+0.472616
## [34] train-rmse:4.152818+0.025026 test-rmse:4.338804+0.466934
## [35] train-rmse:3.877183+0.026667 test-rmse:4.085898+0.469876
## [36] train-rmse:3.636001+0.028157 test-rmse:3.860339+0.468396
## [37] train-rmse:3.419039+0.027924 test-rmse:3.658858+0.455913
## [38] train-rmse:3.228188+0.028970 test-rmse:3.481032+0.446157
## [39] train-rmse:3.058397+0.030489 test-rmse:3.337229+0.431669
## [40] train-rmse:2.903593+0.027416 test-rmse:3.190352+0.406235
## [41] train-rmse:2.769421+0.027654 test-rmse:3.073312+0.402438
## [42] train-rmse:2.649114+0.027862 test-rmse:2.973549+0.386669
## [43] train-rmse:2.541394+0.027832 test-rmse:2.883240+0.369306
## [44] train-rmse:2.445686+0.027970 test-rmse:2.802418+0.368262
## [45] train-rmse:2.358421+0.028853 test-rmse:2.729399+0.369333
## [46] train-rmse:2.280267+0.028303 test-rmse:2.661520+0.343710
## [47] train-rmse:2.209564+0.028417 test-rmse:2.605615+0.332642
## [48] train-rmse:2.145625+0.029241 test-rmse:2.553902+0.328658
## [49] train-rmse:2.086887+0.029653 test-rmse:2.504333+0.319988
## [50] train-rmse:2.032135+0.027744 test-rmse:2.453161+0.304228
## [51] train-rmse:1.981325+0.027743 test-rmse:2.423234+0.303755
## [52] train-rmse:1.935006+0.028006 test-rmse:2.383931+0.300244
## [53] train-rmse:1.890053+0.026021 test-rmse:2.345159+0.284860
## [54] train-rmse:1.846184+0.023441 test-rmse:2.307485+0.278394
## [55] train-rmse:1.808109+0.023603 test-rmse:2.280474+0.273104
## [56] train-rmse:1.769440+0.024854 test-rmse:2.254789+0.273173
## [57] train-rmse:1.734500+0.024363 test-rmse:2.223066+0.271658
## [58] train-rmse:1.697390+0.027237 test-rmse:2.199058+0.265533
## [59] train-rmse:1.666238+0.026607 test-rmse:2.173754+0.262998
## [60] train-rmse:1.636324+0.026570 test-rmse:2.149478+0.263205
## [61] train-rmse:1.607752+0.026730 test-rmse:2.126721+0.255685
## [62] train-rmse:1.578643+0.025259 test-rmse:2.099942+0.251567
## [63] train-rmse:1.549086+0.028969 test-rmse:2.080003+0.250084
## [64] train-rmse:1.523846+0.028459 test-rmse:2.056242+0.246061
## [65] train-rmse:1.497083+0.027808 test-rmse:2.042523+0.251329
## [66] train-rmse:1.472907+0.027471 test-rmse:2.026683+0.249550
## [67] train-rmse:1.449172+0.027718 test-rmse:2.002898+0.247503
## [68] train-rmse:1.420988+0.025970 test-rmse:1.986222+0.238729
## [69] train-rmse:1.398490+0.025683 test-rmse:1.967102+0.245180
## [70] train-rmse:1.376615+0.025444 test-rmse:1.953882+0.244249
## [71] train-rmse:1.356103+0.025414 test-rmse:1.934058+0.242862
## [72] train-rmse:1.333878+0.025379 test-rmse:1.915151+0.245720
## [73] train-rmse:1.313837+0.024937 test-rmse:1.902176+0.241435
## [74] train-rmse:1.291606+0.025316 test-rmse:1.881730+0.237436
## [75] train-rmse:1.272377+0.025283 test-rmse:1.863983+0.238270
## [76] train-rmse:1.252038+0.023408 test-rmse:1.850452+0.238345
## [77] train-rmse:1.234160+0.023093 test-rmse:1.838326+0.238799
## [78] train-rmse:1.214411+0.022648 test-rmse:1.821860+0.237007
## [79] train-rmse:1.196087+0.023493 test-rmse:1.803190+0.238962
## [80] train-rmse:1.178382+0.022418 test-rmse:1.792489+0.243001
## [81] train-rmse:1.160766+0.022456 test-rmse:1.780261+0.243947
## [82] train-rmse:1.140807+0.023191 test-rmse:1.767845+0.241006
## [83] train-rmse:1.124011+0.023812 test-rmse:1.756335+0.244113
## [84] train-rmse:1.106786+0.022235 test-rmse:1.743578+0.245425
## [85] train-rmse:1.090249+0.022079 test-rmse:1.732577+0.247463
## [86] train-rmse:1.075206+0.021732 test-rmse:1.721578+0.247818
## [87] train-rmse:1.057975+0.022961 test-rmse:1.709214+0.249843
## [88] train-rmse:1.043267+0.023138 test-rmse:1.696931+0.250241
## [89] train-rmse:1.029316+0.022803 test-rmse:1.688577+0.252262
## [90] train-rmse:1.013801+0.022371 test-rmse:1.675930+0.253234
## [91] train-rmse:1.000715+0.022109 test-rmse:1.662351+0.253152
## [92] train-rmse:0.987033+0.022017 test-rmse:1.652577+0.254956
## [93] train-rmse:0.973606+0.022222 test-rmse:1.643001+0.256112
## [94] train-rmse:0.960244+0.022339 test-rmse:1.632743+0.259543
## [95] train-rmse:0.946181+0.023838 test-rmse:1.617831+0.253668
## [96] train-rmse:0.933436+0.023830 test-rmse:1.610599+0.253750
## [97] train-rmse:0.921712+0.023693 test-rmse:1.600531+0.253001
## [98] train-rmse:0.909326+0.023013 test-rmse:1.594244+0.252592
## [99] train-rmse:0.897928+0.022947 test-rmse:1.578558+0.246719
## [100] train-rmse:0.885456+0.023257 test-rmse:1.570441+0.246262
## [101] train-rmse:0.873453+0.022776 test-rmse:1.562890+0.245832
## [102] train-rmse:0.861987+0.022056 test-rmse:1.550823+0.249653
## [103] train-rmse:0.851366+0.022097 test-rmse:1.541686+0.249869
## [104] train-rmse:0.839440+0.023526 test-rmse:1.536269+0.251635
## [105] train-rmse:0.827946+0.022872 test-rmse:1.521524+0.251637
## [106] train-rmse:0.815705+0.024339 test-rmse:1.516384+0.249437
## [107] train-rmse:0.805447+0.024231 test-rmse:1.509278+0.252074
## [108] train-rmse:0.795549+0.024193 test-rmse:1.497993+0.243701
## [109] train-rmse:0.785988+0.024019 test-rmse:1.490171+0.245959
## [110] train-rmse:0.776664+0.023677 test-rmse:1.480548+0.245029
## [111] train-rmse:0.767067+0.024041 test-rmse:1.475295+0.243924
## [112] train-rmse:0.757595+0.023334 test-rmse:1.468347+0.245507
## [113] train-rmse:0.748397+0.023709 test-rmse:1.462601+0.244658
## [114] train-rmse:0.737169+0.025252 test-rmse:1.455137+0.248055
## [115] train-rmse:0.728488+0.024794 test-rmse:1.446354+0.243194
## [116] train-rmse:0.720038+0.025038 test-rmse:1.441708+0.241292
## [117] train-rmse:0.711637+0.025183 test-rmse:1.434921+0.242374
## [118] train-rmse:0.702818+0.026446 test-rmse:1.427572+0.242346
## [119] train-rmse:0.694453+0.025576 test-rmse:1.416390+0.237563
## [120] train-rmse:0.686343+0.025526 test-rmse:1.411368+0.238639
## [121] train-rmse:0.678469+0.025848 test-rmse:1.405135+0.239622
## [122] train-rmse:0.670859+0.025624 test-rmse:1.400493+0.241050
## [123] train-rmse:0.663863+0.025301 test-rmse:1.395125+0.241349
## [124] train-rmse:0.656291+0.024596 test-rmse:1.391483+0.240409
## [125] train-rmse:0.648607+0.024871 test-rmse:1.384986+0.233709
## [126] train-rmse:0.641169+0.024940 test-rmse:1.379436+0.234988
## [127] train-rmse:0.634697+0.024546 test-rmse:1.372742+0.238586
## [128] train-rmse:0.628035+0.024267 test-rmse:1.371045+0.237266
## [129] train-rmse:0.620886+0.023612 test-rmse:1.365329+0.240247
## [130] train-rmse:0.614350+0.023380 test-rmse:1.358305+0.234609
## [131] train-rmse:0.607197+0.024132 test-rmse:1.353549+0.235087
## [132] train-rmse:0.600604+0.024159 test-rmse:1.348844+0.236758
## [133] train-rmse:0.594488+0.024040 test-rmse:1.344875+0.237468
## [134] train-rmse:0.588699+0.023977 test-rmse:1.340064+0.238077
## [135] train-rmse:0.582437+0.023314 test-rmse:1.332230+0.234272
## [136] train-rmse:0.575837+0.023980 test-rmse:1.329198+0.232271
## [137] train-rmse:0.570257+0.024084 test-rmse:1.325982+0.232015
## [138] train-rmse:0.564194+0.023665 test-rmse:1.322096+0.232958
## [139] train-rmse:0.558310+0.022684 test-rmse:1.319865+0.235373
## [140] train-rmse:0.552761+0.022785 test-rmse:1.315714+0.236588
## [141] train-rmse:0.547049+0.022552 test-rmse:1.311820+0.236878
## [142] train-rmse:0.542318+0.022430 test-rmse:1.308677+0.236236
## [143] train-rmse:0.537545+0.022349 test-rmse:1.305601+0.237128
## [144] train-rmse:0.532658+0.022228 test-rmse:1.301588+0.235933
## [145] train-rmse:0.525840+0.021798 test-rmse:1.298901+0.234864
## [146] train-rmse:0.520279+0.021858 test-rmse:1.296057+0.237141
## [147] train-rmse:0.515350+0.021704 test-rmse:1.292881+0.238980
## [148] train-rmse:0.510718+0.021756 test-rmse:1.288504+0.239485
## [149] train-rmse:0.506233+0.021691 test-rmse:1.285632+0.239983
## [150] train-rmse:0.501142+0.021163 test-rmse:1.282259+0.239423
## [151] train-rmse:0.495925+0.021460 test-rmse:1.279829+0.240887
## [152] train-rmse:0.490953+0.021015 test-rmse:1.278661+0.241826
## [153] train-rmse:0.486117+0.020808 test-rmse:1.276460+0.240043
## [154] train-rmse:0.481808+0.021125 test-rmse:1.274101+0.240811
## [155] train-rmse:0.477068+0.020759 test-rmse:1.270505+0.241705
## [156] train-rmse:0.472906+0.020427 test-rmse:1.267712+0.243792
## [157] train-rmse:0.468971+0.020334 test-rmse:1.263928+0.244258
## [158] train-rmse:0.465285+0.020306 test-rmse:1.262533+0.243415
## [159] train-rmse:0.460897+0.019468 test-rmse:1.259915+0.245377
## [160] train-rmse:0.456325+0.019359 test-rmse:1.258677+0.245187
## [161] train-rmse:0.452435+0.019437 test-rmse:1.256906+0.245815
## [162] train-rmse:0.448519+0.019519 test-rmse:1.255583+0.246694
## [163] train-rmse:0.444986+0.019664 test-rmse:1.252596+0.247018
## [164] train-rmse:0.441536+0.019503 test-rmse:1.249113+0.246206
## [165] train-rmse:0.438081+0.019087 test-rmse:1.247261+0.247552
## [166] train-rmse:0.434779+0.018503 test-rmse:1.245322+0.248525
## [167] train-rmse:0.431040+0.018918 test-rmse:1.244376+0.249065
## [168] train-rmse:0.427096+0.019473 test-rmse:1.241984+0.249260
## [169] train-rmse:0.423909+0.019152 test-rmse:1.240362+0.249003
## [170] train-rmse:0.420521+0.018577 test-rmse:1.237806+0.245788
## [171] train-rmse:0.416709+0.017602 test-rmse:1.237076+0.246519
## [172] train-rmse:0.413238+0.017458 test-rmse:1.235888+0.246859
## [173] train-rmse:0.409774+0.017480 test-rmse:1.232969+0.247634
## [174] train-rmse:0.406124+0.017492 test-rmse:1.230945+0.248790
## [175] train-rmse:0.402937+0.017276 test-rmse:1.229470+0.248654
## [176] train-rmse:0.400100+0.016921 test-rmse:1.227515+0.248781
## [177] train-rmse:0.396737+0.016457 test-rmse:1.225801+0.250331
## [178] train-rmse:0.393653+0.016583 test-rmse:1.225129+0.249989
## [179] train-rmse:0.390894+0.016690 test-rmse:1.223704+0.250016
## [180] train-rmse:0.387625+0.016724 test-rmse:1.222554+0.250224
## [181] train-rmse:0.384391+0.017106 test-rmse:1.220761+0.250869
## [182] train-rmse:0.381653+0.016870 test-rmse:1.219692+0.251077
## [183] train-rmse:0.378838+0.017247 test-rmse:1.217968+0.251942
## [184] train-rmse:0.376521+0.017312 test-rmse:1.215777+0.252609
## [185] train-rmse:0.373481+0.017011 test-rmse:1.214659+0.253419
## [186] train-rmse:0.370356+0.016957 test-rmse:1.212795+0.253641
## [187] train-rmse:0.367823+0.016802 test-rmse:1.211970+0.254639
## [188] train-rmse:0.365703+0.016531 test-rmse:1.210308+0.254622
## [189] train-rmse:0.363271+0.016581 test-rmse:1.208831+0.254355
## [190] train-rmse:0.360462+0.016948 test-rmse:1.208072+0.254932
## [191] train-rmse:0.358243+0.016935 test-rmse:1.206781+0.255282
## [192] train-rmse:0.355670+0.016936 test-rmse:1.206074+0.255129
## [193] train-rmse:0.353387+0.017088 test-rmse:1.205308+0.255435
## [194] train-rmse:0.350304+0.016667 test-rmse:1.204998+0.254971
## [195] train-rmse:0.347593+0.016166 test-rmse:1.204503+0.254790
## [196] train-rmse:0.345170+0.015883 test-rmse:1.202712+0.255647
## [197] train-rmse:0.342705+0.016065 test-rmse:1.202496+0.256060
## [198] train-rmse:0.340429+0.015974 test-rmse:1.200599+0.256546
## [199] train-rmse:0.337986+0.016409 test-rmse:1.200047+0.257167
## [200] train-rmse:0.336116+0.016081 test-rmse:1.199006+0.256874
## [201] train-rmse:0.333456+0.016485 test-rmse:1.198878+0.257164
## [202] train-rmse:0.331215+0.016372 test-rmse:1.197772+0.258057
## [203] train-rmse:0.328879+0.016078 test-rmse:1.196909+0.258732
## [204] train-rmse:0.326648+0.015751 test-rmse:1.195596+0.259504
## [205] train-rmse:0.324811+0.015595 test-rmse:1.193777+0.259987
## [206] train-rmse:0.322678+0.016168 test-rmse:1.193459+0.259710
## [207] train-rmse:0.320636+0.016175 test-rmse:1.192821+0.259897
## [208] train-rmse:0.318951+0.016082 test-rmse:1.191941+0.259691
## [209] train-rmse:0.316288+0.017011 test-rmse:1.190590+0.261421
## [210] train-rmse:0.314001+0.016534 test-rmse:1.189963+0.262963
## [211] train-rmse:0.312064+0.016573 test-rmse:1.190050+0.262854
## [212] train-rmse:0.310381+0.016172 test-rmse:1.188854+0.263518
## [213] train-rmse:0.308199+0.016240 test-rmse:1.188749+0.263507
## [214] train-rmse:0.306555+0.016024 test-rmse:1.187794+0.263597
## [215] train-rmse:0.304930+0.016085 test-rmse:1.186684+0.263242
## [216] train-rmse:0.302704+0.015935 test-rmse:1.185461+0.263264
## [217] train-rmse:0.300174+0.016067 test-rmse:1.183262+0.264232
## [218] train-rmse:0.298677+0.015901 test-rmse:1.183087+0.264836
## [219] train-rmse:0.297051+0.015804 test-rmse:1.183136+0.265520
## [220] train-rmse:0.294739+0.016192 test-rmse:1.181789+0.266427
## [221] train-rmse:0.292787+0.015975 test-rmse:1.181647+0.266444
## [222] train-rmse:0.290909+0.015808 test-rmse:1.181377+0.267155
## [223] train-rmse:0.288847+0.015781 test-rmse:1.180604+0.267670
## [224] train-rmse:0.286681+0.015532 test-rmse:1.180454+0.267689
## [225] train-rmse:0.284327+0.014876 test-rmse:1.179599+0.268336
## [226] train-rmse:0.282800+0.014935 test-rmse:1.179194+0.268051
## [227] train-rmse:0.281400+0.014774 test-rmse:1.178309+0.268446
## [228] train-rmse:0.279694+0.014758 test-rmse:1.177852+0.268543
## [229] train-rmse:0.277492+0.015261 test-rmse:1.177143+0.268528
## [230] train-rmse:0.275493+0.015735 test-rmse:1.177150+0.268578
## [231] train-rmse:0.273951+0.015404 test-rmse:1.176093+0.268949
## [232] train-rmse:0.272771+0.015476 test-rmse:1.175480+0.269526
## [233] train-rmse:0.271322+0.015462 test-rmse:1.175015+0.269507
## [234] train-rmse:0.269619+0.015222 test-rmse:1.174384+0.269340
## [235] train-rmse:0.268256+0.015161 test-rmse:1.173793+0.270309
## [236] train-rmse:0.266342+0.015407 test-rmse:1.172751+0.270994
## [237] train-rmse:0.264863+0.015541 test-rmse:1.172207+0.271397
## [238] train-rmse:0.263318+0.015790 test-rmse:1.172118+0.271634
## [239] train-rmse:0.261698+0.015343 test-rmse:1.171668+0.272515
## [240] train-rmse:0.260286+0.015320 test-rmse:1.170602+0.273292
## [241] train-rmse:0.258977+0.015194 test-rmse:1.170645+0.273202
## [242] train-rmse:0.257367+0.015239 test-rmse:1.169223+0.272193
## [243] train-rmse:0.255723+0.015120 test-rmse:1.168246+0.272514
## [244] train-rmse:0.254267+0.015235 test-rmse:1.167327+0.272891
## [245] train-rmse:0.253131+0.015013 test-rmse:1.167130+0.273083
## [246] train-rmse:0.251841+0.015224 test-rmse:1.167185+0.273343
## [247] train-rmse:0.250438+0.015555 test-rmse:1.166686+0.273524
## [248] train-rmse:0.249394+0.015573 test-rmse:1.165955+0.273603
## [249] train-rmse:0.247965+0.015438 test-rmse:1.165721+0.273991
## [250] train-rmse:0.246643+0.015730 test-rmse:1.165111+0.274359
## [251] train-rmse:0.245026+0.016177 test-rmse:1.164360+0.274948
## [252] train-rmse:0.243636+0.015792 test-rmse:1.163813+0.274760
## [253] train-rmse:0.242168+0.015888 test-rmse:1.163254+0.274995
## [254] train-rmse:0.240695+0.015882 test-rmse:1.162972+0.274990
## [255] train-rmse:0.239461+0.015377 test-rmse:1.162459+0.275450
## [256] train-rmse:0.238283+0.015040 test-rmse:1.162188+0.276251
## [257] train-rmse:0.236939+0.014721 test-rmse:1.161749+0.276471
## [258] train-rmse:0.235370+0.014908 test-rmse:1.161822+0.276767
## [259] train-rmse:0.234149+0.014870 test-rmse:1.161863+0.276907
## [260] train-rmse:0.233024+0.015008 test-rmse:1.161601+0.276863
## [261] train-rmse:0.231787+0.015183 test-rmse:1.161292+0.276644
## [262] train-rmse:0.230221+0.015014 test-rmse:1.161132+0.276572
## [263] train-rmse:0.228856+0.014466 test-rmse:1.160851+0.276837
## [264] train-rmse:0.227714+0.014715 test-rmse:1.160186+0.277034
## [265] train-rmse:0.226278+0.014652 test-rmse:1.160126+0.277274
## [266] train-rmse:0.224938+0.014492 test-rmse:1.160098+0.277343
## [267] train-rmse:0.223338+0.014105 test-rmse:1.159506+0.277468
## [268] train-rmse:0.222157+0.014071 test-rmse:1.159026+0.277723
## [269] train-rmse:0.221138+0.013963 test-rmse:1.158677+0.277866
## [270] train-rmse:0.220224+0.013945 test-rmse:1.158702+0.277827
## [271] train-rmse:0.219020+0.013694 test-rmse:1.158095+0.277684
## [272] train-rmse:0.218194+0.013638 test-rmse:1.158116+0.278020
## [273] train-rmse:0.217317+0.013419 test-rmse:1.157926+0.278228
## [274] train-rmse:0.216077+0.013547 test-rmse:1.157834+0.278029
## [275] train-rmse:0.215058+0.013408 test-rmse:1.157564+0.278172
## [276] train-rmse:0.214108+0.013254 test-rmse:1.157306+0.278294
## [277] train-rmse:0.213101+0.013548 test-rmse:1.157261+0.278661
## [278] train-rmse:0.211669+0.013572 test-rmse:1.157084+0.278270
## [279] train-rmse:0.210534+0.013203 test-rmse:1.156923+0.278326
## [280] train-rmse:0.209166+0.013292 test-rmse:1.156491+0.278337
## [281] train-rmse:0.207540+0.013027 test-rmse:1.156421+0.278574
## [282] train-rmse:0.206387+0.012962 test-rmse:1.156315+0.278760
## [283] train-rmse:0.205438+0.012878 test-rmse:1.156314+0.278679
## [284] train-rmse:0.204383+0.012424 test-rmse:1.155842+0.279059
## [285] train-rmse:0.203163+0.012302 test-rmse:1.156079+0.279310
## [286] train-rmse:0.201963+0.012069 test-rmse:1.155984+0.279490
## [287] train-rmse:0.200989+0.012011 test-rmse:1.155509+0.279822
## [288] train-rmse:0.200056+0.011712 test-rmse:1.155401+0.279872
## [289] train-rmse:0.198757+0.011576 test-rmse:1.155147+0.279971
## [290] train-rmse:0.197601+0.011499 test-rmse:1.154943+0.280443
## [291] train-rmse:0.196484+0.011515 test-rmse:1.154874+0.280475
## [292] train-rmse:0.195544+0.011481 test-rmse:1.154761+0.280425
## [293] train-rmse:0.194422+0.011219 test-rmse:1.154605+0.280513
## [294] train-rmse:0.193333+0.011424 test-rmse:1.154588+0.280565
## [295] train-rmse:0.192168+0.011508 test-rmse:1.154430+0.280661
## [296] train-rmse:0.191404+0.011413 test-rmse:1.154077+0.280901
## [297] train-rmse:0.190235+0.011743 test-rmse:1.153884+0.280892
## [298] train-rmse:0.189417+0.011655 test-rmse:1.153663+0.280872
## [299] train-rmse:0.188327+0.011431 test-rmse:1.153353+0.281313
## [300] train-rmse:0.187439+0.011274 test-rmse:1.152903+0.281562
## [301] train-rmse:0.186613+0.011517 test-rmse:1.152828+0.281618
## [302] train-rmse:0.185411+0.011083 test-rmse:1.152641+0.281528
## [303] train-rmse:0.184765+0.011111 test-rmse:1.152104+0.281511
## [304] train-rmse:0.184014+0.011191 test-rmse:1.151463+0.281900
## [305] train-rmse:0.183132+0.010948 test-rmse:1.151474+0.282382
## [306] train-rmse:0.182374+0.010822 test-rmse:1.151537+0.282668
## [307] train-rmse:0.181554+0.010698 test-rmse:1.151433+0.282531
## [308] train-rmse:0.180549+0.010873 test-rmse:1.151262+0.282755
## [309] train-rmse:0.179787+0.010803 test-rmse:1.151196+0.282677
## [310] train-rmse:0.178953+0.010844 test-rmse:1.151141+0.282751
## [311] train-rmse:0.178306+0.010910 test-rmse:1.151057+0.282693
## [312] train-rmse:0.177310+0.010872 test-rmse:1.150446+0.283125
## [313] train-rmse:0.176344+0.010232 test-rmse:1.150511+0.283474
## [314] train-rmse:0.175557+0.009972 test-rmse:1.150604+0.283636
## [315] train-rmse:0.174792+0.010069 test-rmse:1.150536+0.283742
## [316] train-rmse:0.174188+0.009813 test-rmse:1.150281+0.283817
## [317] train-rmse:0.173072+0.009691 test-rmse:1.150349+0.284017
## [318] train-rmse:0.172198+0.009670 test-rmse:1.150385+0.284086
## [319] train-rmse:0.171090+0.009510 test-rmse:1.150267+0.284015
## [320] train-rmse:0.170400+0.009478 test-rmse:1.150017+0.284135
## [321] train-rmse:0.169641+0.009782 test-rmse:1.150476+0.283871
## [322] train-rmse:0.168577+0.009761 test-rmse:1.150738+0.283724
## [323] train-rmse:0.167405+0.009498 test-rmse:1.150619+0.283899
## [324] train-rmse:0.166294+0.009536 test-rmse:1.150123+0.284024
## [325] train-rmse:0.165559+0.009665 test-rmse:1.150124+0.284140
## [326] train-rmse:0.164672+0.009599 test-rmse:1.149867+0.284175
## [327] train-rmse:0.164021+0.009560 test-rmse:1.149702+0.284291
## [328] train-rmse:0.162847+0.009751 test-rmse:1.149910+0.284265
## [329] train-rmse:0.162210+0.009605 test-rmse:1.149690+0.284266
## [330] train-rmse:0.161404+0.009721 test-rmse:1.149100+0.284602
## [331] train-rmse:0.160499+0.009766 test-rmse:1.149041+0.284653
## [332] train-rmse:0.159831+0.009798 test-rmse:1.148610+0.284766
## [333] train-rmse:0.159105+0.009731 test-rmse:1.148790+0.284881
## [334] train-rmse:0.158428+0.009644 test-rmse:1.148610+0.284854
## [335] train-rmse:0.157782+0.009722 test-rmse:1.148727+0.285038
## [336] train-rmse:0.156794+0.009451 test-rmse:1.149049+0.284899
## [337] train-rmse:0.155957+0.009519 test-rmse:1.148837+0.285038
## [338] train-rmse:0.155093+0.009376 test-rmse:1.148834+0.285087
## [339] train-rmse:0.154322+0.009337 test-rmse:1.148594+0.285443
## [340] train-rmse:0.153512+0.009315 test-rmse:1.148638+0.285319
## [341] train-rmse:0.152883+0.009460 test-rmse:1.148464+0.285529
## [342] train-rmse:0.152102+0.009543 test-rmse:1.148295+0.285833
## [343] train-rmse:0.151425+0.009685 test-rmse:1.148218+0.285777
## [344] train-rmse:0.150874+0.009681 test-rmse:1.147820+0.285971
## [345] train-rmse:0.150127+0.009776 test-rmse:1.147770+0.285913
## [346] train-rmse:0.149333+0.009640 test-rmse:1.147529+0.285779
## [347] train-rmse:0.148477+0.009487 test-rmse:1.147714+0.285833
## [348] train-rmse:0.147729+0.009647 test-rmse:1.147639+0.285786
## [349] train-rmse:0.147062+0.009758 test-rmse:1.147155+0.286046
## [350] train-rmse:0.146205+0.010091 test-rmse:1.147313+0.286022
## [351] train-rmse:0.145596+0.010078 test-rmse:1.147171+0.285915
## [352] train-rmse:0.145152+0.009958 test-rmse:1.147172+0.286133
## [353] train-rmse:0.144555+0.009653 test-rmse:1.147086+0.286340
## [354] train-rmse:0.143866+0.009824 test-rmse:1.146810+0.286557
## [355] train-rmse:0.143112+0.009704 test-rmse:1.146960+0.286395
## [356] train-rmse:0.142207+0.009568 test-rmse:1.146911+0.286644
## [357] train-rmse:0.141450+0.009632 test-rmse:1.146976+0.286708
## [358] train-rmse:0.140935+0.009550 test-rmse:1.146943+0.286875
## [359] train-rmse:0.140454+0.009400 test-rmse:1.146738+0.286895
## [360] train-rmse:0.139626+0.009076 test-rmse:1.146451+0.286973
## [361] train-rmse:0.138945+0.008882 test-rmse:1.146428+0.286937
## [362] train-rmse:0.138209+0.009069 test-rmse:1.146473+0.286956
## [363] train-rmse:0.137626+0.009116 test-rmse:1.146220+0.287094
## [364] train-rmse:0.137184+0.009115 test-rmse:1.146069+0.286929
## [365] train-rmse:0.136451+0.008655 test-rmse:1.145965+0.286955
## [366] train-rmse:0.135570+0.008029 test-rmse:1.146107+0.286728
## [367] train-rmse:0.134805+0.007759 test-rmse:1.145532+0.286958
## [368] train-rmse:0.133962+0.007746 test-rmse:1.145411+0.287122
## [369] train-rmse:0.133302+0.007385 test-rmse:1.145407+0.287219
## [370] train-rmse:0.132828+0.007173 test-rmse:1.145245+0.287269
## [371] train-rmse:0.132135+0.007093 test-rmse:1.145248+0.287215
## [372] train-rmse:0.131567+0.007080 test-rmse:1.145071+0.287199
## [373] train-rmse:0.131029+0.007125 test-rmse:1.144974+0.287187
## [374] train-rmse:0.130659+0.007109 test-rmse:1.144921+0.287023
## [375] train-rmse:0.130178+0.007225 test-rmse:1.144880+0.287045
## [376] train-rmse:0.129672+0.007352 test-rmse:1.144580+0.287024
## [377] train-rmse:0.129096+0.007429 test-rmse:1.144654+0.287050
## [378] train-rmse:0.128401+0.007480 test-rmse:1.144592+0.287005
## [379] train-rmse:0.127758+0.007225 test-rmse:1.144499+0.287089
## [380] train-rmse:0.127200+0.007386 test-rmse:1.144305+0.287143
## [381] train-rmse:0.126571+0.007335 test-rmse:1.143980+0.287369
## [382] train-rmse:0.126105+0.007311 test-rmse:1.143988+0.287479
## [383] train-rmse:0.125550+0.007236 test-rmse:1.143839+0.287613
## [384] train-rmse:0.124970+0.007325 test-rmse:1.143649+0.287700
## [385] train-rmse:0.124566+0.007259 test-rmse:1.143806+0.287781
## [386] train-rmse:0.123969+0.007096 test-rmse:1.143636+0.287702
## [387] train-rmse:0.123398+0.007189 test-rmse:1.143713+0.287706
## [388] train-rmse:0.122776+0.007193 test-rmse:1.143910+0.287615
## [389] train-rmse:0.122354+0.007244 test-rmse:1.143827+0.287501
## [390] train-rmse:0.121952+0.007238 test-rmse:1.143756+0.287432
## [391] train-rmse:0.121549+0.007345 test-rmse:1.143825+0.287591
## [392] train-rmse:0.120985+0.007120 test-rmse:1.143855+0.287656
## Stopping. Best iteration:
## [386] train-rmse:0.123969+0.007096 test-rmse:1.143636+0.287702
##
## 4
## [1] train-rmse:96.903592+0.106998 test-rmse:96.903929+0.480417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.282645+0.098508 test-rmse:87.282849+0.494309
## [3] train-rmse:78.624342+0.091095 test-rmse:78.624364+0.507694
## [4] train-rmse:70.833145+0.084703 test-rmse:70.832979+0.520704
## [5] train-rmse:63.823124+0.079254 test-rmse:63.822745+0.533458
## [6] train-rmse:57.516975+0.074698 test-rmse:57.516330+0.546089
## [7] train-rmse:51.845108+0.070990 test-rmse:51.844165+0.558710
## [8] train-rmse:46.744962+0.068087 test-rmse:46.743658+0.571428
## [9] train-rmse:42.160251+0.065957 test-rmse:42.158527+0.584358
## [10] train-rmse:38.040358+0.064573 test-rmse:38.038154+0.597586
## [11] train-rmse:34.337210+0.063071 test-rmse:34.350626+0.598556
## [12] train-rmse:31.005821+0.060048 test-rmse:31.044042+0.597332
## [13] train-rmse:28.008132+0.054748 test-rmse:28.071744+0.610449
## [14] train-rmse:25.311016+0.048086 test-rmse:25.390260+0.601052
## [15] train-rmse:22.884224+0.042327 test-rmse:22.971323+0.589327
## [16] train-rmse:20.699700+0.038362 test-rmse:20.782865+0.565840
## [17] train-rmse:18.734460+0.034159 test-rmse:18.824948+0.561699
## [18] train-rmse:16.963329+0.032186 test-rmse:17.055491+0.547623
## [19] train-rmse:15.368465+0.030162 test-rmse:15.461173+0.540547
## [20] train-rmse:13.932268+0.028128 test-rmse:14.028353+0.533924
## [21] train-rmse:12.639703+0.026371 test-rmse:12.736051+0.520671
## [22] train-rmse:11.476635+0.024693 test-rmse:11.580206+0.516285
## [23] train-rmse:10.426596+0.023228 test-rmse:10.539773+0.536950
## [24] train-rmse:9.482156+0.021908 test-rmse:9.591154+0.523666
## [25] train-rmse:8.633551+0.019821 test-rmse:8.733526+0.523319
## [26] train-rmse:7.869128+0.020533 test-rmse:7.986303+0.526708
## [27] train-rmse:7.182900+0.019147 test-rmse:7.295854+0.530794
## [28] train-rmse:6.566392+0.018017 test-rmse:6.680100+0.513088
## [29] train-rmse:6.013836+0.018919 test-rmse:6.143164+0.508353
## [30] train-rmse:5.513999+0.017053 test-rmse:5.652347+0.489139
## [31] train-rmse:5.067886+0.017827 test-rmse:5.216839+0.478874
## [32] train-rmse:4.667098+0.018197 test-rmse:4.844472+0.478200
## [33] train-rmse:4.309964+0.018821 test-rmse:4.499325+0.464482
## [34] train-rmse:3.986878+0.017744 test-rmse:4.193132+0.446282
## [35] train-rmse:3.698471+0.017958 test-rmse:3.925855+0.437626
## [36] train-rmse:3.443495+0.019932 test-rmse:3.687417+0.422269
## [37] train-rmse:3.213454+0.023743 test-rmse:3.477290+0.419771
## [38] train-rmse:3.007678+0.024786 test-rmse:3.280186+0.394904
## [39] train-rmse:2.816959+0.022828 test-rmse:3.110194+0.380425
## [40] train-rmse:2.652534+0.025694 test-rmse:2.964076+0.376494
## [41] train-rmse:2.505734+0.026277 test-rmse:2.837358+0.369247
## [42] train-rmse:2.375023+0.028401 test-rmse:2.716156+0.354777
## [43] train-rmse:2.256770+0.030219 test-rmse:2.615709+0.353980
## [44] train-rmse:2.150164+0.031895 test-rmse:2.532646+0.341226
## [45] train-rmse:2.055365+0.033083 test-rmse:2.450775+0.322188
## [46] train-rmse:1.966370+0.031956 test-rmse:2.382960+0.314859
## [47] train-rmse:1.885901+0.037561 test-rmse:2.325225+0.312266
## [48] train-rmse:1.815731+0.037979 test-rmse:2.266220+0.296277
## [49] train-rmse:1.749418+0.038024 test-rmse:2.217639+0.293201
## [50] train-rmse:1.690438+0.036317 test-rmse:2.172166+0.282692
## [51] train-rmse:1.634682+0.036548 test-rmse:2.132887+0.280583
## [52] train-rmse:1.581546+0.038209 test-rmse:2.091524+0.277827
## [53] train-rmse:1.531213+0.041435 test-rmse:2.063877+0.270672
## [54] train-rmse:1.489683+0.042067 test-rmse:2.025278+0.271871
## [55] train-rmse:1.449629+0.041845 test-rmse:1.999578+0.269677
## [56] train-rmse:1.412571+0.040857 test-rmse:1.978213+0.258881
## [57] train-rmse:1.378179+0.040258 test-rmse:1.948633+0.262679
## [58] train-rmse:1.341751+0.042375 test-rmse:1.924473+0.258425
## [59] train-rmse:1.304019+0.040700 test-rmse:1.901107+0.248581
## [60] train-rmse:1.270714+0.042636 test-rmse:1.880424+0.244226
## [61] train-rmse:1.242459+0.042154 test-rmse:1.862686+0.244706
## [62] train-rmse:1.215016+0.041125 test-rmse:1.837689+0.241960
## [63] train-rmse:1.189895+0.040188 test-rmse:1.817514+0.244969
## [64] train-rmse:1.165141+0.040186 test-rmse:1.799251+0.241337
## [65] train-rmse:1.138550+0.040730 test-rmse:1.785826+0.238063
## [66] train-rmse:1.113167+0.037774 test-rmse:1.763967+0.237183
## [67] train-rmse:1.089425+0.038728 test-rmse:1.750505+0.242244
## [68] train-rmse:1.068337+0.038603 test-rmse:1.734823+0.236377
## [69] train-rmse:1.047564+0.037250 test-rmse:1.719139+0.236399
## [70] train-rmse:1.027774+0.036975 test-rmse:1.706148+0.236273
## [71] train-rmse:1.006206+0.037450 test-rmse:1.694827+0.235989
## [72] train-rmse:0.986006+0.035453 test-rmse:1.679829+0.237180
## [73] train-rmse:0.965625+0.033541 test-rmse:1.664528+0.240042
## [74] train-rmse:0.947695+0.032413 test-rmse:1.651046+0.239534
## [75] train-rmse:0.928196+0.034650 test-rmse:1.637819+0.242917
## [76] train-rmse:0.910062+0.034710 test-rmse:1.625333+0.241638
## [77] train-rmse:0.893707+0.034002 test-rmse:1.615088+0.244080
## [78] train-rmse:0.876332+0.032898 test-rmse:1.599936+0.242384
## [79] train-rmse:0.860506+0.032912 test-rmse:1.591479+0.244443
## [80] train-rmse:0.844505+0.032211 test-rmse:1.576663+0.245172
## [81] train-rmse:0.830048+0.031549 test-rmse:1.565051+0.246045
## [82] train-rmse:0.816064+0.031133 test-rmse:1.556077+0.245606
## [83] train-rmse:0.802184+0.029360 test-rmse:1.545776+0.245797
## [84] train-rmse:0.789188+0.028659 test-rmse:1.536408+0.250504
## [85] train-rmse:0.775707+0.028845 test-rmse:1.527237+0.249593
## [86] train-rmse:0.763128+0.028591 test-rmse:1.514399+0.244251
## [87] train-rmse:0.749896+0.029373 test-rmse:1.508597+0.243172
## [88] train-rmse:0.738054+0.029034 test-rmse:1.499889+0.243358
## [89] train-rmse:0.724829+0.028642 test-rmse:1.490592+0.245267
## [90] train-rmse:0.713235+0.028545 test-rmse:1.482510+0.244148
## [91] train-rmse:0.702033+0.028238 test-rmse:1.476963+0.244871
## [92] train-rmse:0.689936+0.027567 test-rmse:1.466961+0.235655
## [93] train-rmse:0.678178+0.029223 test-rmse:1.456916+0.239021
## [94] train-rmse:0.667957+0.028701 test-rmse:1.448678+0.237508
## [95] train-rmse:0.657851+0.028470 test-rmse:1.440952+0.233469
## [96] train-rmse:0.648274+0.027974 test-rmse:1.431722+0.235054
## [97] train-rmse:0.638337+0.027664 test-rmse:1.425181+0.234471
## [98] train-rmse:0.628699+0.027480 test-rmse:1.418942+0.234073
## [99] train-rmse:0.619284+0.026544 test-rmse:1.412553+0.229921
## [100] train-rmse:0.609246+0.027170 test-rmse:1.406538+0.232317
## [101] train-rmse:0.600010+0.027635 test-rmse:1.399666+0.232838
## [102] train-rmse:0.591817+0.027391 test-rmse:1.393330+0.234088
## [103] train-rmse:0.582704+0.026035 test-rmse:1.387857+0.233525
## [104] train-rmse:0.574103+0.026157 test-rmse:1.384167+0.236263
## [105] train-rmse:0.564956+0.026136 test-rmse:1.377012+0.231371
## [106] train-rmse:0.557295+0.025186 test-rmse:1.368392+0.232062
## [107] train-rmse:0.549484+0.024643 test-rmse:1.366547+0.234150
## [108] train-rmse:0.542338+0.024222 test-rmse:1.358964+0.229328
## [109] train-rmse:0.534527+0.023894 test-rmse:1.355342+0.229647
## [110] train-rmse:0.527639+0.023931 test-rmse:1.350651+0.229294
## [111] train-rmse:0.520576+0.024073 test-rmse:1.347438+0.230613
## [112] train-rmse:0.513601+0.023241 test-rmse:1.342108+0.229435
## [113] train-rmse:0.506604+0.023430 test-rmse:1.339851+0.228779
## [114] train-rmse:0.499119+0.023813 test-rmse:1.337596+0.229553
## [115] train-rmse:0.492914+0.023466 test-rmse:1.332166+0.230606
## [116] train-rmse:0.485751+0.024168 test-rmse:1.327691+0.232446
## [117] train-rmse:0.479410+0.023448 test-rmse:1.323268+0.233067
## [118] train-rmse:0.473759+0.023469 test-rmse:1.321473+0.233425
## [119] train-rmse:0.467314+0.022758 test-rmse:1.319077+0.233014
## [120] train-rmse:0.462146+0.022179 test-rmse:1.315979+0.232939
## [121] train-rmse:0.456676+0.022147 test-rmse:1.311925+0.235538
## [122] train-rmse:0.450854+0.022462 test-rmse:1.307702+0.236628
## [123] train-rmse:0.445741+0.021727 test-rmse:1.304230+0.238117
## [124] train-rmse:0.440027+0.021391 test-rmse:1.300545+0.236846
## [125] train-rmse:0.434951+0.021546 test-rmse:1.298191+0.237070
## [126] train-rmse:0.429769+0.021340 test-rmse:1.296335+0.237057
## [127] train-rmse:0.424489+0.021453 test-rmse:1.293868+0.236813
## [128] train-rmse:0.419433+0.020688 test-rmse:1.292201+0.237056
## [129] train-rmse:0.414693+0.020191 test-rmse:1.288396+0.236522
## [130] train-rmse:0.409712+0.019672 test-rmse:1.286895+0.237040
## [131] train-rmse:0.404203+0.020522 test-rmse:1.282972+0.237536
## [132] train-rmse:0.400020+0.020081 test-rmse:1.281754+0.238619
## [133] train-rmse:0.395452+0.020467 test-rmse:1.279638+0.239818
## [134] train-rmse:0.390781+0.019841 test-rmse:1.277213+0.239498
## [135] train-rmse:0.386398+0.019603 test-rmse:1.275360+0.240995
## [136] train-rmse:0.382545+0.019788 test-rmse:1.273108+0.241788
## [137] train-rmse:0.378436+0.019884 test-rmse:1.271663+0.241701
## [138] train-rmse:0.374470+0.019293 test-rmse:1.269453+0.240990
## [139] train-rmse:0.370751+0.018991 test-rmse:1.267147+0.241837
## [140] train-rmse:0.367393+0.018818 test-rmse:1.264719+0.242067
## [141] train-rmse:0.362998+0.018251 test-rmse:1.264986+0.242724
## [142] train-rmse:0.358640+0.018698 test-rmse:1.262934+0.242421
## [143] train-rmse:0.355058+0.018517 test-rmse:1.260579+0.243230
## [144] train-rmse:0.351902+0.018200 test-rmse:1.258440+0.243243
## [145] train-rmse:0.348192+0.018013 test-rmse:1.256975+0.242980
## [146] train-rmse:0.344595+0.018197 test-rmse:1.254425+0.242351
## [147] train-rmse:0.341272+0.018185 test-rmse:1.251858+0.243261
## [148] train-rmse:0.338307+0.018001 test-rmse:1.249469+0.244667
## [149] train-rmse:0.335005+0.018258 test-rmse:1.248110+0.244234
## [150] train-rmse:0.331720+0.017443 test-rmse:1.246899+0.244559
## [151] train-rmse:0.328858+0.017614 test-rmse:1.244885+0.245595
## [152] train-rmse:0.326025+0.017513 test-rmse:1.243079+0.245837
## [153] train-rmse:0.322409+0.016491 test-rmse:1.241515+0.245918
## [154] train-rmse:0.319111+0.016428 test-rmse:1.240173+0.246066
## [155] train-rmse:0.316188+0.016699 test-rmse:1.238426+0.246967
## [156] train-rmse:0.312105+0.015752 test-rmse:1.238013+0.247302
## [157] train-rmse:0.308797+0.015664 test-rmse:1.237356+0.248229
## [158] train-rmse:0.306255+0.015648 test-rmse:1.236296+0.248277
## [159] train-rmse:0.303268+0.015325 test-rmse:1.233570+0.249148
## [160] train-rmse:0.300701+0.015199 test-rmse:1.232436+0.250057
## [161] train-rmse:0.297388+0.014863 test-rmse:1.231059+0.250206
## [162] train-rmse:0.294767+0.014385 test-rmse:1.230273+0.250428
## [163] train-rmse:0.292195+0.014194 test-rmse:1.229293+0.250527
## [164] train-rmse:0.288537+0.013211 test-rmse:1.229038+0.250988
## [165] train-rmse:0.285974+0.013237 test-rmse:1.227310+0.252029
## [166] train-rmse:0.283360+0.012870 test-rmse:1.225292+0.252009
## [167] train-rmse:0.280947+0.012605 test-rmse:1.224778+0.251567
## [168] train-rmse:0.278265+0.012519 test-rmse:1.224542+0.251662
## [169] train-rmse:0.275821+0.012664 test-rmse:1.222911+0.252510
## [170] train-rmse:0.272918+0.012088 test-rmse:1.222112+0.253119
## [171] train-rmse:0.270705+0.011970 test-rmse:1.221067+0.253021
## [172] train-rmse:0.268556+0.011509 test-rmse:1.220128+0.253105
## [173] train-rmse:0.265925+0.011446 test-rmse:1.220049+0.253295
## [174] train-rmse:0.262882+0.010542 test-rmse:1.218784+0.253804
## [175] train-rmse:0.260686+0.009821 test-rmse:1.217477+0.253957
## [176] train-rmse:0.257550+0.008985 test-rmse:1.217005+0.253348
## [177] train-rmse:0.255820+0.008875 test-rmse:1.215755+0.253882
## [178] train-rmse:0.253762+0.008932 test-rmse:1.215328+0.254492
## [179] train-rmse:0.251311+0.009645 test-rmse:1.214553+0.254412
## [180] train-rmse:0.248974+0.009162 test-rmse:1.214038+0.254596
## [181] train-rmse:0.246218+0.009297 test-rmse:1.212486+0.254363
## [182] train-rmse:0.243764+0.008358 test-rmse:1.211355+0.253883
## [183] train-rmse:0.242235+0.008309 test-rmse:1.211152+0.253929
## [184] train-rmse:0.240505+0.008316 test-rmse:1.210015+0.254491
## [185] train-rmse:0.237607+0.007541 test-rmse:1.210215+0.254534
## [186] train-rmse:0.235775+0.007490 test-rmse:1.209427+0.254310
## [187] train-rmse:0.233676+0.007910 test-rmse:1.209263+0.254189
## [188] train-rmse:0.231992+0.007693 test-rmse:1.208296+0.254355
## [189] train-rmse:0.229685+0.007153 test-rmse:1.207559+0.254391
## [190] train-rmse:0.227831+0.006818 test-rmse:1.207219+0.254279
## [191] train-rmse:0.225697+0.006295 test-rmse:1.206489+0.254834
## [192] train-rmse:0.224081+0.006305 test-rmse:1.206462+0.254431
## [193] train-rmse:0.221895+0.006192 test-rmse:1.205203+0.253211
## [194] train-rmse:0.220163+0.006247 test-rmse:1.205154+0.252841
## [195] train-rmse:0.218650+0.006204 test-rmse:1.205172+0.252963
## [196] train-rmse:0.216999+0.005909 test-rmse:1.204710+0.252745
## [197] train-rmse:0.214853+0.005376 test-rmse:1.204009+0.252949
## [198] train-rmse:0.213059+0.004871 test-rmse:1.202667+0.253538
## [199] train-rmse:0.210987+0.004406 test-rmse:1.202358+0.252825
## [200] train-rmse:0.209240+0.004807 test-rmse:1.201602+0.253548
## [201] train-rmse:0.207073+0.004996 test-rmse:1.201386+0.253656
## [202] train-rmse:0.205109+0.005065 test-rmse:1.200831+0.253381
## [203] train-rmse:0.203802+0.004843 test-rmse:1.199517+0.252185
## [204] train-rmse:0.202331+0.004946 test-rmse:1.199470+0.252393
## [205] train-rmse:0.200377+0.005083 test-rmse:1.198145+0.252300
## [206] train-rmse:0.198945+0.004988 test-rmse:1.197779+0.252383
## [207] train-rmse:0.196619+0.004025 test-rmse:1.197207+0.252134
## [208] train-rmse:0.195095+0.003996 test-rmse:1.196570+0.251726
## [209] train-rmse:0.193525+0.004076 test-rmse:1.196420+0.252232
## [210] train-rmse:0.191893+0.004300 test-rmse:1.196139+0.251910
## [211] train-rmse:0.190177+0.004439 test-rmse:1.196175+0.251960
## [212] train-rmse:0.188559+0.003956 test-rmse:1.196002+0.252319
## [213] train-rmse:0.187289+0.003731 test-rmse:1.195189+0.252786
## [214] train-rmse:0.185955+0.003558 test-rmse:1.194947+0.253015
## [215] train-rmse:0.184589+0.003093 test-rmse:1.194793+0.253140
## [216] train-rmse:0.183050+0.003238 test-rmse:1.194746+0.252741
## [217] train-rmse:0.181514+0.003561 test-rmse:1.194573+0.252635
## [218] train-rmse:0.180448+0.003563 test-rmse:1.194190+0.252378
## [219] train-rmse:0.178753+0.003740 test-rmse:1.193749+0.251604
## [220] train-rmse:0.177414+0.003414 test-rmse:1.193763+0.251415
## [221] train-rmse:0.175938+0.003413 test-rmse:1.193212+0.251497
## [222] train-rmse:0.174381+0.003255 test-rmse:1.192331+0.251547
## [223] train-rmse:0.172863+0.003736 test-rmse:1.192082+0.251642
## [224] train-rmse:0.171252+0.003931 test-rmse:1.191320+0.250911
## [225] train-rmse:0.170133+0.003748 test-rmse:1.190685+0.251043
## [226] train-rmse:0.169001+0.003742 test-rmse:1.190344+0.250929
## [227] train-rmse:0.167628+0.003850 test-rmse:1.190057+0.250634
## [228] train-rmse:0.165910+0.003993 test-rmse:1.190209+0.250755
## [229] train-rmse:0.164657+0.004068 test-rmse:1.190119+0.250863
## [230] train-rmse:0.162875+0.004070 test-rmse:1.190296+0.250778
## [231] train-rmse:0.161705+0.004298 test-rmse:1.189815+0.250808
## [232] train-rmse:0.160266+0.004528 test-rmse:1.189124+0.250798
## [233] train-rmse:0.159378+0.004477 test-rmse:1.188938+0.250952
## [234] train-rmse:0.158099+0.004549 test-rmse:1.189073+0.251458
## [235] train-rmse:0.157053+0.004561 test-rmse:1.188792+0.251573
## [236] train-rmse:0.156005+0.004605 test-rmse:1.188526+0.251678
## [237] train-rmse:0.154981+0.004682 test-rmse:1.188213+0.252013
## [238] train-rmse:0.153904+0.004178 test-rmse:1.188047+0.251976
## [239] train-rmse:0.152780+0.004129 test-rmse:1.187501+0.251968
## [240] train-rmse:0.151948+0.004046 test-rmse:1.187562+0.252090
## [241] train-rmse:0.151011+0.003864 test-rmse:1.187383+0.252382
## [242] train-rmse:0.149862+0.003579 test-rmse:1.186942+0.252050
## [243] train-rmse:0.148444+0.003699 test-rmse:1.186667+0.252134
## [244] train-rmse:0.147327+0.003444 test-rmse:1.186784+0.252110
## [245] train-rmse:0.145610+0.003179 test-rmse:1.186453+0.252017
## [246] train-rmse:0.144712+0.003195 test-rmse:1.186195+0.251823
## [247] train-rmse:0.143938+0.003316 test-rmse:1.185722+0.252114
## [248] train-rmse:0.142968+0.003120 test-rmse:1.185421+0.252382
## [249] train-rmse:0.141815+0.003517 test-rmse:1.185328+0.252313
## [250] train-rmse:0.140855+0.003301 test-rmse:1.185321+0.252511
## [251] train-rmse:0.139423+0.003357 test-rmse:1.185240+0.252473
## [252] train-rmse:0.138152+0.003729 test-rmse:1.184882+0.251856
## [253] train-rmse:0.137558+0.003719 test-rmse:1.184642+0.252142
## [254] train-rmse:0.136542+0.003707 test-rmse:1.184465+0.252238
## [255] train-rmse:0.135568+0.003500 test-rmse:1.184077+0.252533
## [256] train-rmse:0.134253+0.003192 test-rmse:1.183841+0.252431
## [257] train-rmse:0.133301+0.003201 test-rmse:1.183221+0.252994
## [258] train-rmse:0.132338+0.003554 test-rmse:1.182987+0.252916
## [259] train-rmse:0.131402+0.003390 test-rmse:1.182923+0.252907
## [260] train-rmse:0.130219+0.003633 test-rmse:1.183082+0.252798
## [261] train-rmse:0.129175+0.003873 test-rmse:1.182822+0.252912
## [262] train-rmse:0.128222+0.003785 test-rmse:1.182488+0.252612
## [263] train-rmse:0.127622+0.003665 test-rmse:1.182318+0.252574
## [264] train-rmse:0.126735+0.003736 test-rmse:1.182487+0.252449
## [265] train-rmse:0.125406+0.003485 test-rmse:1.183212+0.252245
## [266] train-rmse:0.124531+0.003728 test-rmse:1.182801+0.252405
## [267] train-rmse:0.123859+0.003690 test-rmse:1.182817+0.252380
## [268] train-rmse:0.122753+0.003567 test-rmse:1.182735+0.252385
## [269] train-rmse:0.121618+0.003624 test-rmse:1.182635+0.252406
## Stopping. Best iteration:
## [263] train-rmse:0.127622+0.003665 test-rmse:1.182318+0.252574
##
## 5
## [1] train-rmse:96.903592+0.106998 test-rmse:96.903929+0.480417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.282645+0.098508 test-rmse:87.282849+0.494309
## [3] train-rmse:78.624342+0.091095 test-rmse:78.624364+0.507694
## [4] train-rmse:70.833145+0.084703 test-rmse:70.832979+0.520704
## [5] train-rmse:63.823124+0.079254 test-rmse:63.822745+0.533458
## [6] train-rmse:57.516975+0.074698 test-rmse:57.516330+0.546089
## [7] train-rmse:51.845108+0.070990 test-rmse:51.844165+0.558710
## [8] train-rmse:46.744962+0.068087 test-rmse:46.743658+0.571428
## [9] train-rmse:42.160251+0.065957 test-rmse:42.158527+0.584358
## [10] train-rmse:38.040358+0.064573 test-rmse:38.038154+0.597586
## [11] train-rmse:34.337210+0.063071 test-rmse:34.350626+0.598556
## [12] train-rmse:31.005821+0.060048 test-rmse:31.044042+0.597332
## [13] train-rmse:28.008132+0.054748 test-rmse:28.071744+0.610449
## [14] train-rmse:25.311016+0.048086 test-rmse:25.390260+0.601052
## [15] train-rmse:22.884224+0.042327 test-rmse:22.971323+0.589327
## [16] train-rmse:20.699214+0.038558 test-rmse:20.779786+0.566136
## [17] train-rmse:18.732640+0.035501 test-rmse:18.809895+0.548494
## [18] train-rmse:16.961778+0.032157 test-rmse:17.050837+0.549521
## [19] train-rmse:15.365906+0.030230 test-rmse:15.456689+0.542893
## [20] train-rmse:13.928139+0.028072 test-rmse:14.024890+0.538734
## [21] train-rmse:12.632778+0.026272 test-rmse:12.727751+0.521288
## [22] train-rmse:11.466344+0.024935 test-rmse:11.565012+0.514069
## [23] train-rmse:10.413596+0.020772 test-rmse:10.517577+0.535645
## [24] train-rmse:9.463138+0.022247 test-rmse:9.572681+0.524821
## [25] train-rmse:8.610215+0.021615 test-rmse:8.719829+0.515855
## [26] train-rmse:7.840771+0.019867 test-rmse:7.962845+0.532539
## [27] train-rmse:7.147232+0.021635 test-rmse:7.276132+0.514949
## [28] train-rmse:6.524273+0.019741 test-rmse:6.652128+0.504456
## [29] train-rmse:5.963593+0.018418 test-rmse:6.099874+0.494550
## [30] train-rmse:5.457976+0.020633 test-rmse:5.604529+0.502882
## [31] train-rmse:5.002970+0.017012 test-rmse:5.162404+0.487746
## [32] train-rmse:4.594770+0.017220 test-rmse:4.769087+0.468035
## [33] train-rmse:4.227106+0.017223 test-rmse:4.425534+0.473939
## [34] train-rmse:3.898614+0.016758 test-rmse:4.111958+0.457947
## [35] train-rmse:3.602056+0.019763 test-rmse:3.820944+0.442582
## [36] train-rmse:3.335971+0.021901 test-rmse:3.565324+0.429736
## [37] train-rmse:3.094387+0.024820 test-rmse:3.346400+0.414779
## [38] train-rmse:2.878213+0.024383 test-rmse:3.154377+0.407571
## [39] train-rmse:2.681442+0.022533 test-rmse:2.985375+0.396473
## [40] train-rmse:2.506811+0.029625 test-rmse:2.826628+0.380868
## [41] train-rmse:2.349030+0.031148 test-rmse:2.697128+0.378217
## [42] train-rmse:2.208151+0.031636 test-rmse:2.576620+0.376687
## [43] train-rmse:2.078746+0.039001 test-rmse:2.476051+0.360403
## [44] train-rmse:1.963920+0.041806 test-rmse:2.380741+0.352011
## [45] train-rmse:1.854647+0.044382 test-rmse:2.295589+0.338518
## [46] train-rmse:1.763569+0.045591 test-rmse:2.227750+0.329889
## [47] train-rmse:1.677277+0.049558 test-rmse:2.167187+0.324836
## [48] train-rmse:1.599060+0.049080 test-rmse:2.110247+0.315758
## [49] train-rmse:1.529008+0.050307 test-rmse:2.056606+0.306483
## [50] train-rmse:1.461772+0.053289 test-rmse:2.015686+0.301743
## [51] train-rmse:1.402007+0.053433 test-rmse:1.981012+0.296968
## [52] train-rmse:1.346771+0.052253 test-rmse:1.943169+0.284261
## [53] train-rmse:1.297314+0.053890 test-rmse:1.914223+0.283852
## [54] train-rmse:1.252922+0.053918 test-rmse:1.886825+0.272234
## [55] train-rmse:1.208042+0.051536 test-rmse:1.857006+0.265848
## [56] train-rmse:1.169023+0.051105 test-rmse:1.827363+0.265396
## [57] train-rmse:1.131584+0.050248 test-rmse:1.806749+0.259060
## [58] train-rmse:1.098067+0.050103 test-rmse:1.783080+0.259119
## [59] train-rmse:1.062950+0.048713 test-rmse:1.763860+0.253046
## [60] train-rmse:1.033644+0.048141 test-rmse:1.741747+0.249647
## [61] train-rmse:1.005274+0.047371 test-rmse:1.717432+0.252832
## [62] train-rmse:0.979046+0.047440 test-rmse:1.697538+0.251771
## [63] train-rmse:0.950940+0.049120 test-rmse:1.684476+0.248590
## [64] train-rmse:0.925769+0.047494 test-rmse:1.670457+0.250401
## [65] train-rmse:0.902198+0.046417 test-rmse:1.653356+0.247043
## [66] train-rmse:0.881092+0.045308 test-rmse:1.634769+0.247647
## [67] train-rmse:0.858658+0.043357 test-rmse:1.616204+0.246047
## [68] train-rmse:0.835035+0.043560 test-rmse:1.601609+0.251664
## [69] train-rmse:0.815638+0.042759 test-rmse:1.592387+0.250551
## [70] train-rmse:0.794662+0.043181 test-rmse:1.572212+0.253767
## [71] train-rmse:0.776025+0.041558 test-rmse:1.557254+0.248159
## [72] train-rmse:0.757651+0.042529 test-rmse:1.544349+0.248063
## [73] train-rmse:0.738613+0.042237 test-rmse:1.529150+0.250937
## [74] train-rmse:0.722363+0.041104 test-rmse:1.519494+0.250127
## [75] train-rmse:0.706800+0.041001 test-rmse:1.512424+0.253673
## [76] train-rmse:0.691681+0.041641 test-rmse:1.503133+0.254362
## [77] train-rmse:0.677671+0.040774 test-rmse:1.492978+0.254472
## [78] train-rmse:0.662581+0.038655 test-rmse:1.481230+0.254087
## [79] train-rmse:0.648072+0.037138 test-rmse:1.473033+0.254505
## [80] train-rmse:0.634800+0.036798 test-rmse:1.469482+0.256567
## [81] train-rmse:0.622153+0.036703 test-rmse:1.457238+0.252266
## [82] train-rmse:0.607037+0.037154 test-rmse:1.448234+0.251116
## [83] train-rmse:0.595336+0.036594 test-rmse:1.441387+0.251581
## [84] train-rmse:0.584005+0.037128 test-rmse:1.435770+0.251858
## [85] train-rmse:0.572932+0.036265 test-rmse:1.425418+0.248404
## [86] train-rmse:0.561885+0.036553 test-rmse:1.418768+0.248361
## [87] train-rmse:0.551605+0.035794 test-rmse:1.411102+0.250616
## [88] train-rmse:0.541118+0.035757 test-rmse:1.401813+0.252825
## [89] train-rmse:0.531442+0.035944 test-rmse:1.397073+0.252382
## [90] train-rmse:0.521641+0.034499 test-rmse:1.387563+0.249721
## [91] train-rmse:0.511062+0.035028 test-rmse:1.383686+0.251624
## [92] train-rmse:0.502460+0.034534 test-rmse:1.376429+0.248456
## [93] train-rmse:0.493087+0.033768 test-rmse:1.370455+0.247343
## [94] train-rmse:0.484444+0.033436 test-rmse:1.364160+0.248611
## [95] train-rmse:0.476820+0.033303 test-rmse:1.361433+0.248315
## [96] train-rmse:0.469244+0.032812 test-rmse:1.351878+0.246671
## [97] train-rmse:0.461757+0.031807 test-rmse:1.348243+0.245659
## [98] train-rmse:0.453301+0.032380 test-rmse:1.344142+0.246312
## [99] train-rmse:0.444972+0.030469 test-rmse:1.338619+0.243416
## [100] train-rmse:0.437927+0.029251 test-rmse:1.334821+0.245566
## [101] train-rmse:0.431187+0.029208 test-rmse:1.330351+0.245793
## [102] train-rmse:0.423375+0.029863 test-rmse:1.326989+0.247850
## [103] train-rmse:0.416181+0.029917 test-rmse:1.324188+0.246415
## [104] train-rmse:0.409635+0.029824 test-rmse:1.320241+0.246018
## [105] train-rmse:0.403538+0.029767 test-rmse:1.316547+0.247813
## [106] train-rmse:0.397074+0.028787 test-rmse:1.313011+0.248842
## [107] train-rmse:0.391120+0.029096 test-rmse:1.309233+0.249122
## [108] train-rmse:0.384363+0.030368 test-rmse:1.307633+0.248683
## [109] train-rmse:0.378301+0.029082 test-rmse:1.303133+0.245997
## [110] train-rmse:0.372191+0.029558 test-rmse:1.300700+0.246379
## [111] train-rmse:0.366620+0.028336 test-rmse:1.296042+0.243207
## [112] train-rmse:0.361630+0.027929 test-rmse:1.292400+0.241062
## [113] train-rmse:0.356477+0.028016 test-rmse:1.290964+0.241134
## [114] train-rmse:0.351178+0.028376 test-rmse:1.288292+0.242376
## [115] train-rmse:0.346085+0.028029 test-rmse:1.284555+0.241241
## [116] train-rmse:0.340727+0.027961 test-rmse:1.283024+0.242667
## [117] train-rmse:0.335780+0.027166 test-rmse:1.280618+0.240869
## [118] train-rmse:0.331494+0.026828 test-rmse:1.279179+0.242046
## [119] train-rmse:0.326711+0.026628 test-rmse:1.277205+0.243573
## [120] train-rmse:0.321615+0.025205 test-rmse:1.273660+0.240513
## [121] train-rmse:0.317994+0.025221 test-rmse:1.271764+0.241039
## [122] train-rmse:0.312508+0.023304 test-rmse:1.270580+0.240728
## [123] train-rmse:0.308431+0.022845 test-rmse:1.269762+0.241339
## [124] train-rmse:0.303980+0.021850 test-rmse:1.268961+0.242416
## [125] train-rmse:0.300433+0.021705 test-rmse:1.267265+0.242871
## [126] train-rmse:0.296554+0.020407 test-rmse:1.265817+0.242774
## [127] train-rmse:0.292300+0.020908 test-rmse:1.263650+0.243359
## [128] train-rmse:0.288726+0.020209 test-rmse:1.260817+0.241495
## [129] train-rmse:0.284627+0.020792 test-rmse:1.260294+0.242122
## [130] train-rmse:0.280781+0.020478 test-rmse:1.257875+0.240386
## [131] train-rmse:0.277573+0.020199 test-rmse:1.256148+0.241622
## [132] train-rmse:0.274602+0.020436 test-rmse:1.255212+0.241683
## [133] train-rmse:0.270878+0.019892 test-rmse:1.253310+0.240837
## [134] train-rmse:0.267904+0.019324 test-rmse:1.251982+0.240880
## [135] train-rmse:0.264169+0.019584 test-rmse:1.251634+0.240555
## [136] train-rmse:0.261170+0.020116 test-rmse:1.250272+0.241026
## [137] train-rmse:0.257551+0.019114 test-rmse:1.249342+0.241745
## [138] train-rmse:0.253078+0.018510 test-rmse:1.248620+0.242290
## [139] train-rmse:0.250326+0.018615 test-rmse:1.247628+0.242466
## [140] train-rmse:0.247423+0.018372 test-rmse:1.246732+0.242170
## [141] train-rmse:0.244275+0.018036 test-rmse:1.245261+0.243681
## [142] train-rmse:0.241549+0.017722 test-rmse:1.244745+0.244085
## [143] train-rmse:0.238305+0.017903 test-rmse:1.244053+0.243994
## [144] train-rmse:0.235429+0.018042 test-rmse:1.243404+0.244993
## [145] train-rmse:0.232189+0.017150 test-rmse:1.243493+0.245016
## [146] train-rmse:0.229563+0.016883 test-rmse:1.241042+0.244376
## [147] train-rmse:0.226792+0.017119 test-rmse:1.239900+0.243796
## [148] train-rmse:0.223896+0.016568 test-rmse:1.238756+0.243102
## [149] train-rmse:0.221550+0.016853 test-rmse:1.237939+0.242876
## [150] train-rmse:0.218538+0.016508 test-rmse:1.237460+0.241577
## [151] train-rmse:0.216275+0.016447 test-rmse:1.237384+0.242155
## [152] train-rmse:0.214164+0.016556 test-rmse:1.236302+0.242178
## [153] train-rmse:0.211180+0.016389 test-rmse:1.235098+0.241695
## [154] train-rmse:0.208308+0.016315 test-rmse:1.234856+0.241573
## [155] train-rmse:0.206196+0.016068 test-rmse:1.234410+0.241336
## [156] train-rmse:0.203989+0.015993 test-rmse:1.233891+0.241439
## [157] train-rmse:0.201639+0.015937 test-rmse:1.232643+0.241908
## [158] train-rmse:0.199207+0.015727 test-rmse:1.232489+0.242254
## [159] train-rmse:0.197018+0.015582 test-rmse:1.231549+0.242640
## [160] train-rmse:0.194879+0.015583 test-rmse:1.231934+0.243074
## [161] train-rmse:0.192726+0.015704 test-rmse:1.232011+0.243276
## [162] train-rmse:0.190422+0.015021 test-rmse:1.230897+0.243512
## [163] train-rmse:0.188795+0.015032 test-rmse:1.230555+0.243754
## [164] train-rmse:0.186866+0.015378 test-rmse:1.229562+0.244544
## [165] train-rmse:0.184839+0.015207 test-rmse:1.229021+0.244811
## [166] train-rmse:0.182481+0.014706 test-rmse:1.228614+0.244923
## [167] train-rmse:0.179845+0.014038 test-rmse:1.227929+0.245415
## [168] train-rmse:0.177933+0.013884 test-rmse:1.227806+0.245856
## [169] train-rmse:0.176162+0.013663 test-rmse:1.227371+0.246261
## [170] train-rmse:0.174267+0.013140 test-rmse:1.227245+0.245967
## [171] train-rmse:0.172182+0.012844 test-rmse:1.226107+0.246640
## [172] train-rmse:0.170099+0.012566 test-rmse:1.225762+0.246516
## [173] train-rmse:0.168516+0.012778 test-rmse:1.225329+0.246491
## [174] train-rmse:0.166544+0.012457 test-rmse:1.224528+0.246698
## [175] train-rmse:0.164702+0.012120 test-rmse:1.224175+0.246254
## [176] train-rmse:0.162787+0.011732 test-rmse:1.223810+0.246174
## [177] train-rmse:0.160985+0.011416 test-rmse:1.223842+0.246681
## [178] train-rmse:0.159247+0.011194 test-rmse:1.223454+0.246463
## [179] train-rmse:0.157488+0.011015 test-rmse:1.222741+0.246528
## [180] train-rmse:0.155473+0.010607 test-rmse:1.222904+0.246281
## [181] train-rmse:0.153876+0.010404 test-rmse:1.222527+0.246681
## [182] train-rmse:0.152417+0.010387 test-rmse:1.222298+0.246651
## [183] train-rmse:0.150522+0.010002 test-rmse:1.222408+0.246632
## [184] train-rmse:0.149266+0.009724 test-rmse:1.222217+0.246910
## [185] train-rmse:0.147932+0.009833 test-rmse:1.222049+0.246968
## [186] train-rmse:0.146481+0.009700 test-rmse:1.221733+0.247132
## [187] train-rmse:0.144978+0.009413 test-rmse:1.221309+0.246969
## [188] train-rmse:0.143503+0.009677 test-rmse:1.221329+0.247153
## [189] train-rmse:0.142305+0.009525 test-rmse:1.220982+0.247482
## [190] train-rmse:0.140492+0.009819 test-rmse:1.220881+0.247680
## [191] train-rmse:0.138782+0.009602 test-rmse:1.221200+0.247937
## [192] train-rmse:0.137298+0.009706 test-rmse:1.220597+0.248086
## [193] train-rmse:0.136009+0.009691 test-rmse:1.220629+0.248247
## [194] train-rmse:0.134715+0.009607 test-rmse:1.220623+0.248097
## [195] train-rmse:0.133025+0.009511 test-rmse:1.220227+0.247654
## [196] train-rmse:0.131661+0.009268 test-rmse:1.219804+0.247990
## [197] train-rmse:0.130505+0.009296 test-rmse:1.219645+0.248007
## [198] train-rmse:0.129076+0.009291 test-rmse:1.219510+0.247862
## [199] train-rmse:0.127327+0.008940 test-rmse:1.220058+0.247773
## [200] train-rmse:0.125770+0.008794 test-rmse:1.219747+0.247619
## [201] train-rmse:0.124556+0.008815 test-rmse:1.219602+0.247629
## [202] train-rmse:0.123113+0.008674 test-rmse:1.219539+0.247740
## [203] train-rmse:0.122017+0.008684 test-rmse:1.219385+0.248359
## [204] train-rmse:0.120715+0.008585 test-rmse:1.219225+0.248369
## [205] train-rmse:0.119654+0.008490 test-rmse:1.218933+0.248197
## [206] train-rmse:0.118388+0.008682 test-rmse:1.218386+0.247844
## [207] train-rmse:0.117381+0.008317 test-rmse:1.218053+0.248188
## [208] train-rmse:0.116209+0.008428 test-rmse:1.218002+0.248113
## [209] train-rmse:0.115094+0.008068 test-rmse:1.217996+0.247962
## [210] train-rmse:0.114139+0.008137 test-rmse:1.217953+0.248032
## [211] train-rmse:0.113100+0.008204 test-rmse:1.217697+0.248144
## [212] train-rmse:0.112115+0.008201 test-rmse:1.217417+0.248351
## [213] train-rmse:0.110527+0.008099 test-rmse:1.217583+0.248345
## [214] train-rmse:0.109260+0.007672 test-rmse:1.217740+0.248414
## [215] train-rmse:0.108061+0.007470 test-rmse:1.217903+0.248331
## [216] train-rmse:0.106922+0.007299 test-rmse:1.217622+0.248136
## [217] train-rmse:0.106065+0.007168 test-rmse:1.217378+0.248177
## [218] train-rmse:0.105026+0.007114 test-rmse:1.217311+0.248231
## [219] train-rmse:0.103937+0.007169 test-rmse:1.217235+0.248325
## [220] train-rmse:0.102628+0.006853 test-rmse:1.216836+0.248365
## [221] train-rmse:0.101610+0.006796 test-rmse:1.216557+0.248758
## [222] train-rmse:0.100617+0.006681 test-rmse:1.216501+0.248705
## [223] train-rmse:0.099097+0.006870 test-rmse:1.216124+0.248776
## [224] train-rmse:0.097878+0.007022 test-rmse:1.215839+0.248531
## [225] train-rmse:0.096798+0.007130 test-rmse:1.215662+0.248587
## [226] train-rmse:0.095905+0.007114 test-rmse:1.215627+0.248756
## [227] train-rmse:0.094820+0.006777 test-rmse:1.215543+0.248601
## [228] train-rmse:0.094085+0.006905 test-rmse:1.215485+0.248601
## [229] train-rmse:0.092688+0.006648 test-rmse:1.215494+0.248749
## [230] train-rmse:0.091890+0.006557 test-rmse:1.215307+0.248549
## [231] train-rmse:0.090895+0.006245 test-rmse:1.215425+0.248487
## [232] train-rmse:0.089806+0.006180 test-rmse:1.215220+0.248375
## [233] train-rmse:0.088719+0.006051 test-rmse:1.214944+0.248414
## [234] train-rmse:0.087937+0.006183 test-rmse:1.215078+0.248367
## [235] train-rmse:0.087011+0.005982 test-rmse:1.214988+0.248572
## [236] train-rmse:0.086323+0.005879 test-rmse:1.214684+0.248541
## [237] train-rmse:0.085518+0.005898 test-rmse:1.214739+0.248561
## [238] train-rmse:0.084585+0.005941 test-rmse:1.214648+0.248676
## [239] train-rmse:0.083975+0.005966 test-rmse:1.214352+0.248688
## [240] train-rmse:0.083247+0.006081 test-rmse:1.214137+0.248671
## [241] train-rmse:0.082364+0.005986 test-rmse:1.214287+0.248599
## [242] train-rmse:0.081212+0.005943 test-rmse:1.214271+0.248359
## [243] train-rmse:0.080267+0.005951 test-rmse:1.214276+0.248261
## [244] train-rmse:0.079558+0.005797 test-rmse:1.214262+0.248162
## [245] train-rmse:0.078763+0.005759 test-rmse:1.214018+0.248245
## [246] train-rmse:0.077974+0.005752 test-rmse:1.213863+0.248288
## [247] train-rmse:0.077329+0.005792 test-rmse:1.213862+0.248324
## [248] train-rmse:0.076801+0.005764 test-rmse:1.213643+0.248320
## [249] train-rmse:0.076013+0.005841 test-rmse:1.213776+0.248520
## [250] train-rmse:0.075251+0.005782 test-rmse:1.213841+0.248740
## [251] train-rmse:0.074586+0.005584 test-rmse:1.213691+0.248725
## [252] train-rmse:0.073792+0.005858 test-rmse:1.213538+0.248887
## [253] train-rmse:0.073095+0.005679 test-rmse:1.213308+0.248938
## [254] train-rmse:0.072254+0.005565 test-rmse:1.213424+0.249050
## [255] train-rmse:0.071416+0.005547 test-rmse:1.213091+0.248774
## [256] train-rmse:0.070631+0.005666 test-rmse:1.212953+0.248501
## [257] train-rmse:0.069955+0.005443 test-rmse:1.212792+0.248570
## [258] train-rmse:0.069347+0.005411 test-rmse:1.212824+0.248770
## [259] train-rmse:0.068445+0.005140 test-rmse:1.212811+0.248671
## [260] train-rmse:0.067943+0.005085 test-rmse:1.212902+0.248645
## [261] train-rmse:0.067361+0.004845 test-rmse:1.212833+0.248570
## [262] train-rmse:0.066791+0.004716 test-rmse:1.212693+0.248594
## [263] train-rmse:0.066147+0.004941 test-rmse:1.212611+0.248587
## [264] train-rmse:0.065311+0.005049 test-rmse:1.212626+0.248506
## [265] train-rmse:0.064751+0.005103 test-rmse:1.212696+0.248631
## [266] train-rmse:0.064151+0.005077 test-rmse:1.212614+0.248625
## [267] train-rmse:0.063498+0.005040 test-rmse:1.212426+0.248447
## [268] train-rmse:0.062709+0.004901 test-rmse:1.212267+0.248468
## [269] train-rmse:0.062293+0.004901 test-rmse:1.212241+0.248379
## [270] train-rmse:0.061671+0.004988 test-rmse:1.212245+0.248155
## [271] train-rmse:0.061002+0.005106 test-rmse:1.212249+0.248099
## [272] train-rmse:0.060459+0.005219 test-rmse:1.212239+0.248044
## [273] train-rmse:0.059724+0.004808 test-rmse:1.212245+0.247992
## [274] train-rmse:0.059164+0.004741 test-rmse:1.212087+0.247848
## [275] train-rmse:0.058608+0.004716 test-rmse:1.211961+0.247756
## [276] train-rmse:0.058156+0.004792 test-rmse:1.212006+0.247822
## [277] train-rmse:0.057752+0.004727 test-rmse:1.211988+0.247764
## [278] train-rmse:0.057318+0.004622 test-rmse:1.211907+0.247709
## [279] train-rmse:0.056952+0.004534 test-rmse:1.211936+0.247788
## [280] train-rmse:0.056397+0.004523 test-rmse:1.211974+0.247692
## [281] train-rmse:0.055680+0.004509 test-rmse:1.211761+0.247767
## [282] train-rmse:0.055181+0.004473 test-rmse:1.211628+0.247909
## [283] train-rmse:0.054639+0.004293 test-rmse:1.211506+0.247919
## [284] train-rmse:0.054158+0.004368 test-rmse:1.211497+0.247825
## [285] train-rmse:0.053630+0.004305 test-rmse:1.211407+0.247720
## [286] train-rmse:0.053171+0.004423 test-rmse:1.211211+0.247796
## [287] train-rmse:0.052620+0.004245 test-rmse:1.211242+0.247878
## [288] train-rmse:0.051954+0.004214 test-rmse:1.211051+0.247714
## [289] train-rmse:0.051444+0.004116 test-rmse:1.210976+0.247765
## [290] train-rmse:0.050968+0.004185 test-rmse:1.210967+0.247605
## [291] train-rmse:0.050536+0.004031 test-rmse:1.211056+0.247645
## [292] train-rmse:0.050122+0.004159 test-rmse:1.211075+0.247612
## [293] train-rmse:0.049566+0.004047 test-rmse:1.211000+0.247427
## [294] train-rmse:0.049127+0.004111 test-rmse:1.210903+0.247367
## [295] train-rmse:0.048754+0.004104 test-rmse:1.210747+0.247326
## [296] train-rmse:0.048311+0.004007 test-rmse:1.210566+0.247312
## [297] train-rmse:0.047687+0.003919 test-rmse:1.210565+0.247311
## [298] train-rmse:0.047164+0.003678 test-rmse:1.210546+0.247386
## [299] train-rmse:0.046731+0.003580 test-rmse:1.210460+0.247416
## [300] train-rmse:0.046177+0.003582 test-rmse:1.210410+0.247475
## [301] train-rmse:0.045851+0.003684 test-rmse:1.210358+0.247429
## [302] train-rmse:0.045210+0.003621 test-rmse:1.210401+0.247506
## [303] train-rmse:0.044885+0.003584 test-rmse:1.210426+0.247423
## [304] train-rmse:0.044384+0.003440 test-rmse:1.210390+0.247387
## [305] train-rmse:0.043975+0.003322 test-rmse:1.210407+0.247363
## [306] train-rmse:0.043519+0.003149 test-rmse:1.210330+0.247369
## [307] train-rmse:0.043228+0.003150 test-rmse:1.210290+0.247420
## [308] train-rmse:0.042930+0.003098 test-rmse:1.210273+0.247362
## [309] train-rmse:0.042585+0.002923 test-rmse:1.210229+0.247435
## [310] train-rmse:0.042179+0.003010 test-rmse:1.210210+0.247549
## [311] train-rmse:0.041737+0.002994 test-rmse:1.210082+0.247519
## [312] train-rmse:0.041409+0.003088 test-rmse:1.210122+0.247490
## [313] train-rmse:0.040927+0.002947 test-rmse:1.210026+0.247417
## [314] train-rmse:0.040493+0.002952 test-rmse:1.209934+0.247436
## [315] train-rmse:0.040042+0.003008 test-rmse:1.209863+0.247461
## [316] train-rmse:0.039764+0.002929 test-rmse:1.209846+0.247474
## [317] train-rmse:0.039466+0.002958 test-rmse:1.209807+0.247476
## [318] train-rmse:0.039054+0.002924 test-rmse:1.209866+0.247423
## [319] train-rmse:0.038730+0.002874 test-rmse:1.209821+0.247384
## [320] train-rmse:0.038336+0.002900 test-rmse:1.209625+0.247151
## [321] train-rmse:0.038075+0.002812 test-rmse:1.209714+0.247181
## [322] train-rmse:0.037710+0.002812 test-rmse:1.209678+0.247205
## [323] train-rmse:0.037351+0.002709 test-rmse:1.209631+0.247140
## [324] train-rmse:0.037069+0.002698 test-rmse:1.209652+0.247124
## [325] train-rmse:0.036692+0.002708 test-rmse:1.209492+0.247147
## [326] train-rmse:0.036362+0.002778 test-rmse:1.209541+0.247118
## [327] train-rmse:0.036069+0.002784 test-rmse:1.209532+0.247140
## [328] train-rmse:0.035804+0.002710 test-rmse:1.209395+0.247175
## [329] train-rmse:0.035279+0.002646 test-rmse:1.209380+0.247147
## [330] train-rmse:0.035013+0.002630 test-rmse:1.209311+0.247113
## [331] train-rmse:0.034783+0.002685 test-rmse:1.209318+0.247094
## [332] train-rmse:0.034378+0.002593 test-rmse:1.209375+0.247085
## [333] train-rmse:0.034139+0.002569 test-rmse:1.209396+0.247073
## [334] train-rmse:0.033754+0.002605 test-rmse:1.209464+0.247110
## [335] train-rmse:0.033438+0.002525 test-rmse:1.209453+0.247097
## [336] train-rmse:0.033075+0.002415 test-rmse:1.209494+0.247032
## Stopping. Best iteration:
## [330] train-rmse:0.035013+0.002630 test-rmse:1.209311+0.247113
##
## 6
## [1] train-rmse:96.903592+0.106998 test-rmse:96.903929+0.480417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:87.282645+0.098508 test-rmse:87.282849+0.494309
## [3] train-rmse:78.624342+0.091095 test-rmse:78.624364+0.507694
## [4] train-rmse:70.833145+0.084703 test-rmse:70.832979+0.520704
## [5] train-rmse:63.823124+0.079254 test-rmse:63.822745+0.533458
## [6] train-rmse:57.516975+0.074698 test-rmse:57.516330+0.546089
## [7] train-rmse:51.845108+0.070990 test-rmse:51.844165+0.558710
## [8] train-rmse:46.744962+0.068087 test-rmse:46.743658+0.571428
## [9] train-rmse:42.160251+0.065957 test-rmse:42.158527+0.584358
## [10] train-rmse:38.040358+0.064573 test-rmse:38.038154+0.597586
## [11] train-rmse:34.337210+0.063071 test-rmse:34.350626+0.598556
## [12] train-rmse:31.005821+0.060048 test-rmse:31.044042+0.597332
## [13] train-rmse:28.008132+0.054748 test-rmse:28.071744+0.610449
## [14] train-rmse:25.311016+0.048086 test-rmse:25.390260+0.601052
## [15] train-rmse:22.884224+0.042327 test-rmse:22.971323+0.589327
## [16] train-rmse:20.699120+0.038556 test-rmse:20.776404+0.565117
## [17] train-rmse:18.732227+0.035347 test-rmse:18.806225+0.551060
## [18] train-rmse:16.960527+0.032078 test-rmse:17.051561+0.548661
## [19] train-rmse:15.363785+0.030173 test-rmse:15.453523+0.547278
## [20] train-rmse:13.925187+0.027859 test-rmse:14.016791+0.538118
## [21] train-rmse:12.628441+0.026397 test-rmse:12.719716+0.523291
## [22] train-rmse:11.460509+0.024725 test-rmse:11.555876+0.517683
## [23] train-rmse:10.405973+0.023184 test-rmse:10.513737+0.530469
## [24] train-rmse:9.450404+0.021873 test-rmse:9.562061+0.527873
## [25] train-rmse:8.593586+0.020524 test-rmse:8.699985+0.515564
## [26] train-rmse:7.819321+0.018185 test-rmse:7.942457+0.526541
## [27] train-rmse:7.119594+0.019717 test-rmse:7.244492+0.513656
## [28] train-rmse:6.491688+0.018154 test-rmse:6.616384+0.500985
## [29] train-rmse:5.922559+0.013914 test-rmse:6.067761+0.505958
## [30] train-rmse:5.409595+0.014938 test-rmse:5.563262+0.486892
## [31] train-rmse:4.945688+0.017245 test-rmse:5.107922+0.474043
## [32] train-rmse:4.529206+0.016881 test-rmse:4.707647+0.459525
## [33] train-rmse:4.154580+0.015890 test-rmse:4.353662+0.457948
## [34] train-rmse:3.817614+0.016413 test-rmse:4.020540+0.453289
## [35] train-rmse:3.511798+0.015001 test-rmse:3.735819+0.436369
## [36] train-rmse:3.237723+0.014854 test-rmse:3.479325+0.426283
## [37] train-rmse:2.990964+0.016874 test-rmse:3.251292+0.410163
## [38] train-rmse:2.768075+0.016523 test-rmse:3.059122+0.401471
## [39] train-rmse:2.568963+0.016977 test-rmse:2.881828+0.395944
## [40] train-rmse:2.387283+0.020207 test-rmse:2.716601+0.385618
## [41] train-rmse:2.228047+0.020761 test-rmse:2.577999+0.375698
## [42] train-rmse:2.080347+0.020191 test-rmse:2.461563+0.369516
## [43] train-rmse:1.947399+0.018072 test-rmse:2.351671+0.352871
## [44] train-rmse:1.824270+0.020653 test-rmse:2.252942+0.347317
## [45] train-rmse:1.711438+0.022935 test-rmse:2.173382+0.335905
## [46] train-rmse:1.610031+0.023591 test-rmse:2.099643+0.319942
## [47] train-rmse:1.520385+0.025886 test-rmse:2.034813+0.318073
## [48] train-rmse:1.436894+0.023609 test-rmse:1.972896+0.301116
## [49] train-rmse:1.359636+0.029695 test-rmse:1.925192+0.291040
## [50] train-rmse:1.289203+0.030152 test-rmse:1.875411+0.284089
## [51] train-rmse:1.223412+0.029692 test-rmse:1.830740+0.280501
## [52] train-rmse:1.164917+0.029138 test-rmse:1.791876+0.272738
## [53] train-rmse:1.111661+0.032644 test-rmse:1.759923+0.275050
## [54] train-rmse:1.066081+0.032944 test-rmse:1.730979+0.272665
## [55] train-rmse:1.021644+0.033897 test-rmse:1.704071+0.265895
## [56] train-rmse:0.981832+0.035657 test-rmse:1.682905+0.261600
## [57] train-rmse:0.944074+0.035785 test-rmse:1.661272+0.259075
## [58] train-rmse:0.907958+0.034833 test-rmse:1.637381+0.255850
## [59] train-rmse:0.873163+0.035855 test-rmse:1.615099+0.256005
## [60] train-rmse:0.842191+0.037772 test-rmse:1.597590+0.255652
## [61] train-rmse:0.814739+0.036335 test-rmse:1.578486+0.258930
## [62] train-rmse:0.786970+0.036819 test-rmse:1.554846+0.262054
## [63] train-rmse:0.762222+0.037636 test-rmse:1.541225+0.259383
## [64] train-rmse:0.739707+0.037105 test-rmse:1.525889+0.260196
## [65] train-rmse:0.717641+0.036817 test-rmse:1.516474+0.263383
## [66] train-rmse:0.697421+0.037477 test-rmse:1.507365+0.260252
## [67] train-rmse:0.676396+0.035998 test-rmse:1.494669+0.261080
## [68] train-rmse:0.659020+0.035811 test-rmse:1.482273+0.254677
## [69] train-rmse:0.640118+0.035985 test-rmse:1.471215+0.254907
## [70] train-rmse:0.622171+0.037659 test-rmse:1.460511+0.250348
## [71] train-rmse:0.605557+0.037423 test-rmse:1.450496+0.251393
## [72] train-rmse:0.590287+0.036887 test-rmse:1.439117+0.254208
## [73] train-rmse:0.574305+0.036979 test-rmse:1.429032+0.253361
## [74] train-rmse:0.559085+0.033178 test-rmse:1.418036+0.248544
## [75] train-rmse:0.545246+0.033137 test-rmse:1.414635+0.250243
## [76] train-rmse:0.532492+0.033695 test-rmse:1.404688+0.251886
## [77] train-rmse:0.518860+0.031999 test-rmse:1.395494+0.247605
## [78] train-rmse:0.507176+0.032067 test-rmse:1.389294+0.245387
## [79] train-rmse:0.494961+0.032088 test-rmse:1.381248+0.243380
## [80] train-rmse:0.484869+0.030783 test-rmse:1.373514+0.247845
## [81] train-rmse:0.473937+0.031166 test-rmse:1.369038+0.248159
## [82] train-rmse:0.463482+0.031138 test-rmse:1.362944+0.246230
## [83] train-rmse:0.454085+0.031284 test-rmse:1.358079+0.245779
## [84] train-rmse:0.445567+0.031167 test-rmse:1.352343+0.247117
## [85] train-rmse:0.435850+0.030425 test-rmse:1.348883+0.247468
## [86] train-rmse:0.426999+0.030024 test-rmse:1.342698+0.250709
## [87] train-rmse:0.418416+0.029268 test-rmse:1.339383+0.252203
## [88] train-rmse:0.410361+0.029576 test-rmse:1.336131+0.252390
## [89] train-rmse:0.402820+0.029604 test-rmse:1.331422+0.251962
## [90] train-rmse:0.394924+0.029334 test-rmse:1.328206+0.252958
## [91] train-rmse:0.385952+0.026935 test-rmse:1.324968+0.251452
## [92] train-rmse:0.377670+0.026195 test-rmse:1.321500+0.253228
## [93] train-rmse:0.370458+0.025594 test-rmse:1.319633+0.254545
## [94] train-rmse:0.363654+0.025131 test-rmse:1.316978+0.254882
## [95] train-rmse:0.356270+0.025488 test-rmse:1.315782+0.255790
## [96] train-rmse:0.350045+0.025504 test-rmse:1.312682+0.256837
## [97] train-rmse:0.344147+0.023895 test-rmse:1.308600+0.257574
## [98] train-rmse:0.338003+0.023900 test-rmse:1.305068+0.257706
## [99] train-rmse:0.332077+0.023660 test-rmse:1.304204+0.257508
## [100] train-rmse:0.325646+0.023448 test-rmse:1.300819+0.257649
## [101] train-rmse:0.320053+0.023614 test-rmse:1.297990+0.259335
## [102] train-rmse:0.314433+0.023697 test-rmse:1.295668+0.259495
## [103] train-rmse:0.309610+0.022906 test-rmse:1.293477+0.261109
## [104] train-rmse:0.305229+0.022362 test-rmse:1.292247+0.262367
## [105] train-rmse:0.299732+0.022068 test-rmse:1.290408+0.263506
## [106] train-rmse:0.295239+0.021602 test-rmse:1.288195+0.262610
## [107] train-rmse:0.289549+0.020993 test-rmse:1.286291+0.263683
## [108] train-rmse:0.283817+0.020548 test-rmse:1.285945+0.264243
## [109] train-rmse:0.279563+0.020644 test-rmse:1.284485+0.264237
## [110] train-rmse:0.275401+0.020884 test-rmse:1.282713+0.264777
## [111] train-rmse:0.271322+0.020918 test-rmse:1.281359+0.265803
## [112] train-rmse:0.267363+0.020155 test-rmse:1.280789+0.265254
## [113] train-rmse:0.264035+0.020135 test-rmse:1.278386+0.265683
## [114] train-rmse:0.259217+0.020447 test-rmse:1.276975+0.266538
## [115] train-rmse:0.255305+0.020377 test-rmse:1.274727+0.267612
## [116] train-rmse:0.250683+0.019609 test-rmse:1.273777+0.267870
## [117] train-rmse:0.246829+0.019358 test-rmse:1.272315+0.268348
## [118] train-rmse:0.243722+0.019306 test-rmse:1.271884+0.267721
## [119] train-rmse:0.240175+0.019574 test-rmse:1.270856+0.268471
## [120] train-rmse:0.236642+0.018894 test-rmse:1.268569+0.269243
## [121] train-rmse:0.232612+0.019223 test-rmse:1.267131+0.269383
## [122] train-rmse:0.229103+0.019080 test-rmse:1.266821+0.269820
## [123] train-rmse:0.225729+0.019573 test-rmse:1.266207+0.270188
## [124] train-rmse:0.221587+0.018123 test-rmse:1.263636+0.268981
## [125] train-rmse:0.217668+0.017071 test-rmse:1.262615+0.268706
## [126] train-rmse:0.215044+0.017258 test-rmse:1.261031+0.269493
## [127] train-rmse:0.212033+0.016935 test-rmse:1.260701+0.269829
## [128] train-rmse:0.208629+0.016447 test-rmse:1.260269+0.270555
## [129] train-rmse:0.205026+0.016128 test-rmse:1.260273+0.271052
## [130] train-rmse:0.201569+0.015338 test-rmse:1.259222+0.270686
## [131] train-rmse:0.198709+0.014486 test-rmse:1.258547+0.270815
## [132] train-rmse:0.195584+0.014589 test-rmse:1.258870+0.270469
## [133] train-rmse:0.193077+0.014565 test-rmse:1.258614+0.270217
## [134] train-rmse:0.189869+0.014081 test-rmse:1.257470+0.269862
## [135] train-rmse:0.186646+0.014451 test-rmse:1.257296+0.269885
## [136] train-rmse:0.184339+0.014631 test-rmse:1.256736+0.269987
## [137] train-rmse:0.181060+0.014898 test-rmse:1.256457+0.270077
## [138] train-rmse:0.178481+0.014738 test-rmse:1.255773+0.270964
## [139] train-rmse:0.176332+0.014329 test-rmse:1.255462+0.271413
## [140] train-rmse:0.173984+0.013023 test-rmse:1.254506+0.271904
## [141] train-rmse:0.171895+0.013281 test-rmse:1.254193+0.272017
## [142] train-rmse:0.168671+0.012658 test-rmse:1.253811+0.271674
## [143] train-rmse:0.166364+0.012856 test-rmse:1.253297+0.271981
## [144] train-rmse:0.164105+0.013233 test-rmse:1.253041+0.272457
## [145] train-rmse:0.161466+0.012815 test-rmse:1.252535+0.272568
## [146] train-rmse:0.159265+0.012840 test-rmse:1.252224+0.272869
## [147] train-rmse:0.156132+0.012287 test-rmse:1.251834+0.272951
## [148] train-rmse:0.154179+0.012193 test-rmse:1.251606+0.273127
## [149] train-rmse:0.151629+0.011666 test-rmse:1.251398+0.273425
## [150] train-rmse:0.149652+0.011087 test-rmse:1.250804+0.273697
## [151] train-rmse:0.146940+0.010298 test-rmse:1.250390+0.273755
## [152] train-rmse:0.144715+0.010188 test-rmse:1.249973+0.273503
## [153] train-rmse:0.143247+0.009923 test-rmse:1.249699+0.273559
## [154] train-rmse:0.141020+0.009755 test-rmse:1.249469+0.273307
## [155] train-rmse:0.138761+0.009652 test-rmse:1.249125+0.273856
## [156] train-rmse:0.137336+0.009435 test-rmse:1.248307+0.273636
## [157] train-rmse:0.135687+0.009207 test-rmse:1.248024+0.273358
## [158] train-rmse:0.133575+0.009057 test-rmse:1.247615+0.273366
## [159] train-rmse:0.131577+0.009335 test-rmse:1.247449+0.273437
## [160] train-rmse:0.129809+0.009293 test-rmse:1.247399+0.273588
## [161] train-rmse:0.127798+0.008592 test-rmse:1.247264+0.273514
## [162] train-rmse:0.125989+0.008988 test-rmse:1.247080+0.273416
## [163] train-rmse:0.124197+0.009200 test-rmse:1.246953+0.273375
## [164] train-rmse:0.122647+0.008835 test-rmse:1.246946+0.273285
## [165] train-rmse:0.120915+0.008907 test-rmse:1.246557+0.273037
## [166] train-rmse:0.119128+0.008439 test-rmse:1.245743+0.273612
## [167] train-rmse:0.117459+0.008705 test-rmse:1.245127+0.273810
## [168] train-rmse:0.115793+0.008248 test-rmse:1.244669+0.273935
## [169] train-rmse:0.114211+0.008399 test-rmse:1.244893+0.273796
## [170] train-rmse:0.112675+0.008213 test-rmse:1.244501+0.273573
## [171] train-rmse:0.111072+0.008496 test-rmse:1.244730+0.273507
## [172] train-rmse:0.109566+0.008720 test-rmse:1.244919+0.273703
## [173] train-rmse:0.108810+0.008828 test-rmse:1.244556+0.273695
## [174] train-rmse:0.107443+0.008650 test-rmse:1.244393+0.273709
## [175] train-rmse:0.106387+0.008513 test-rmse:1.244302+0.273453
## [176] train-rmse:0.104890+0.008503 test-rmse:1.244220+0.273105
## [177] train-rmse:0.103862+0.008564 test-rmse:1.244019+0.273375
## [178] train-rmse:0.102919+0.008868 test-rmse:1.243595+0.273471
## [179] train-rmse:0.101228+0.008481 test-rmse:1.243170+0.273764
## [180] train-rmse:0.099942+0.008477 test-rmse:1.242513+0.273494
## [181] train-rmse:0.098768+0.008601 test-rmse:1.242269+0.273518
## [182] train-rmse:0.097345+0.008250 test-rmse:1.242053+0.273682
## [183] train-rmse:0.096283+0.008486 test-rmse:1.241993+0.273797
## [184] train-rmse:0.095226+0.008528 test-rmse:1.242088+0.273478
## [185] train-rmse:0.093916+0.008634 test-rmse:1.241361+0.273454
## [186] train-rmse:0.092864+0.008663 test-rmse:1.240936+0.273366
## [187] train-rmse:0.091351+0.008591 test-rmse:1.240764+0.273515
## [188] train-rmse:0.090548+0.008842 test-rmse:1.240674+0.273754
## [189] train-rmse:0.089372+0.008863 test-rmse:1.240534+0.273671
## [190] train-rmse:0.087980+0.008715 test-rmse:1.240472+0.273669
## [191] train-rmse:0.086418+0.008465 test-rmse:1.240286+0.273732
## [192] train-rmse:0.084876+0.008305 test-rmse:1.239730+0.273670
## [193] train-rmse:0.083833+0.008582 test-rmse:1.239712+0.273815
## [194] train-rmse:0.083025+0.008478 test-rmse:1.239605+0.273932
## [195] train-rmse:0.082003+0.008840 test-rmse:1.239267+0.273980
## [196] train-rmse:0.080692+0.008508 test-rmse:1.239305+0.273919
## [197] train-rmse:0.079500+0.008342 test-rmse:1.239275+0.273880
## [198] train-rmse:0.078578+0.008355 test-rmse:1.238881+0.273293
## [199] train-rmse:0.077473+0.008778 test-rmse:1.238544+0.273116
## [200] train-rmse:0.076577+0.008451 test-rmse:1.238623+0.273319
## [201] train-rmse:0.075450+0.008518 test-rmse:1.238115+0.272737
## [202] train-rmse:0.074568+0.008351 test-rmse:1.238116+0.272791
## [203] train-rmse:0.073706+0.008046 test-rmse:1.238038+0.272763
## [204] train-rmse:0.072835+0.008268 test-rmse:1.237921+0.272720
## [205] train-rmse:0.072170+0.008181 test-rmse:1.237903+0.272743
## [206] train-rmse:0.071296+0.007885 test-rmse:1.237710+0.272356
## [207] train-rmse:0.070366+0.007507 test-rmse:1.237461+0.272412
## [208] train-rmse:0.069678+0.007669 test-rmse:1.237228+0.272565
## [209] train-rmse:0.068508+0.007709 test-rmse:1.237243+0.272626
## [210] train-rmse:0.067705+0.007903 test-rmse:1.237095+0.272585
## [211] train-rmse:0.066560+0.007699 test-rmse:1.237203+0.272439
## [212] train-rmse:0.065770+0.007517 test-rmse:1.237046+0.272527
## [213] train-rmse:0.064901+0.007493 test-rmse:1.236986+0.272350
## [214] train-rmse:0.063906+0.007078 test-rmse:1.236889+0.272335
## [215] train-rmse:0.063116+0.006887 test-rmse:1.236672+0.272136
## [216] train-rmse:0.062369+0.006870 test-rmse:1.236877+0.272133
## [217] train-rmse:0.061790+0.006808 test-rmse:1.236754+0.272164
## [218] train-rmse:0.060969+0.006814 test-rmse:1.236743+0.272292
## [219] train-rmse:0.060091+0.006609 test-rmse:1.236765+0.272197
## [220] train-rmse:0.059248+0.006403 test-rmse:1.236577+0.272223
## [221] train-rmse:0.058604+0.006411 test-rmse:1.236470+0.272306
## [222] train-rmse:0.057669+0.006204 test-rmse:1.236467+0.272395
## [223] train-rmse:0.057038+0.006061 test-rmse:1.236415+0.272462
## [224] train-rmse:0.056312+0.005958 test-rmse:1.236309+0.272541
## [225] train-rmse:0.055785+0.005909 test-rmse:1.236252+0.272552
## [226] train-rmse:0.055240+0.005782 test-rmse:1.236114+0.272682
## [227] train-rmse:0.054642+0.005744 test-rmse:1.235956+0.272606
## [228] train-rmse:0.053926+0.005770 test-rmse:1.235905+0.272533
## [229] train-rmse:0.052989+0.005818 test-rmse:1.236235+0.272454
## [230] train-rmse:0.052256+0.005723 test-rmse:1.236124+0.272499
## [231] train-rmse:0.051587+0.005620 test-rmse:1.236071+0.272501
## [232] train-rmse:0.050959+0.005545 test-rmse:1.235927+0.272445
## [233] train-rmse:0.050444+0.005460 test-rmse:1.235855+0.272306
## [234] train-rmse:0.049873+0.005658 test-rmse:1.235781+0.272327
## [235] train-rmse:0.049271+0.005406 test-rmse:1.235700+0.272366
## [236] train-rmse:0.048696+0.005477 test-rmse:1.235708+0.272464
## [237] train-rmse:0.048046+0.005125 test-rmse:1.235647+0.272486
## [238] train-rmse:0.047534+0.005082 test-rmse:1.235611+0.272586
## [239] train-rmse:0.046932+0.004966 test-rmse:1.235534+0.272607
## [240] train-rmse:0.046319+0.005072 test-rmse:1.235445+0.272566
## [241] train-rmse:0.045749+0.004760 test-rmse:1.235492+0.272742
## [242] train-rmse:0.045277+0.004759 test-rmse:1.235382+0.272706
## [243] train-rmse:0.044571+0.004765 test-rmse:1.235379+0.272709
## [244] train-rmse:0.044104+0.004769 test-rmse:1.235262+0.272761
## [245] train-rmse:0.043597+0.004847 test-rmse:1.235160+0.272867
## [246] train-rmse:0.043102+0.004871 test-rmse:1.234977+0.272786
## [247] train-rmse:0.042667+0.004861 test-rmse:1.234883+0.272753
## [248] train-rmse:0.042048+0.004999 test-rmse:1.234813+0.272746
## [249] train-rmse:0.041629+0.004749 test-rmse:1.234759+0.272767
## [250] train-rmse:0.041148+0.004821 test-rmse:1.234848+0.272814
## [251] train-rmse:0.040462+0.004915 test-rmse:1.234655+0.272536
## [252] train-rmse:0.039962+0.004881 test-rmse:1.234735+0.272683
## [253] train-rmse:0.039553+0.004884 test-rmse:1.234671+0.272680
## [254] train-rmse:0.039082+0.004891 test-rmse:1.234648+0.272679
## [255] train-rmse:0.038437+0.004761 test-rmse:1.234755+0.272725
## [256] train-rmse:0.038073+0.004809 test-rmse:1.234718+0.272803
## [257] train-rmse:0.037652+0.004858 test-rmse:1.234692+0.272727
## [258] train-rmse:0.037117+0.004780 test-rmse:1.234709+0.272783
## [259] train-rmse:0.036536+0.004590 test-rmse:1.234638+0.272882
## [260] train-rmse:0.036065+0.004442 test-rmse:1.234594+0.272966
## [261] train-rmse:0.035560+0.004489 test-rmse:1.234519+0.272998
## [262] train-rmse:0.035233+0.004304 test-rmse:1.234502+0.273018
## [263] train-rmse:0.034891+0.004393 test-rmse:1.234499+0.273050
## [264] train-rmse:0.034492+0.004251 test-rmse:1.234510+0.273031
## [265] train-rmse:0.033993+0.003960 test-rmse:1.234525+0.272944
## [266] train-rmse:0.033602+0.003878 test-rmse:1.234551+0.272976
## [267] train-rmse:0.033233+0.003992 test-rmse:1.234462+0.273045
## [268] train-rmse:0.032800+0.003820 test-rmse:1.234505+0.273154
## [269] train-rmse:0.032488+0.003831 test-rmse:1.234456+0.273169
## [270] train-rmse:0.032011+0.003922 test-rmse:1.234533+0.273176
## [271] train-rmse:0.031644+0.003729 test-rmse:1.234497+0.273254
## [272] train-rmse:0.031137+0.003691 test-rmse:1.234522+0.273179
## [273] train-rmse:0.030792+0.003706 test-rmse:1.234582+0.273160
## [274] train-rmse:0.030446+0.003704 test-rmse:1.234569+0.273210
## [275] train-rmse:0.030136+0.003655 test-rmse:1.234618+0.273181
## Stopping. Best iteration:
## [269] train-rmse:0.032488+0.003831 test-rmse:1.234456+0.273169
##
## 7
## [1] train-rmse:94.766116+0.105094 test-rmse:94.766423+0.483432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:83.478061+0.095217 test-rmse:83.478186+0.500072
## [3] train-rmse:73.545764+0.086897 test-rmse:73.545670+0.516055
## [4] train-rmse:64.807895+0.080011 test-rmse:64.807548+0.531590
## [5] train-rmse:57.122527+0.074434 test-rmse:57.121858+0.546922
## [6] train-rmse:50.364803+0.070103 test-rmse:50.363757+0.562250
## [7] train-rmse:44.424928+0.066945 test-rmse:44.423422+0.577769
## [8] train-rmse:39.206363+0.064904 test-rmse:39.204310+0.593657
## [9] train-rmse:34.622293+0.063135 test-rmse:34.630677+0.594785
## [10] train-rmse:30.594723+0.060722 test-rmse:30.630489+0.604196
## [11] train-rmse:27.056456+0.057873 test-rmse:27.095997+0.613561
## [12] train-rmse:23.949549+0.054320 test-rmse:23.995303+0.615775
## [13] train-rmse:21.222715+0.050457 test-rmse:21.269670+0.604507
## [14] train-rmse:18.832115+0.047483 test-rmse:18.883423+0.604515
## [15] train-rmse:16.738141+0.044457 test-rmse:16.790695+0.593163
## [16] train-rmse:14.907185+0.042292 test-rmse:14.963802+0.593355
## [17] train-rmse:13.307986+0.040939 test-rmse:13.386424+0.612319
## [18] train-rmse:11.915518+0.039770 test-rmse:11.992147+0.611774
## [19] train-rmse:10.704840+0.039595 test-rmse:10.781294+0.594755
## [20] train-rmse:9.655175+0.040497 test-rmse:9.750042+0.623974
## [21] train-rmse:8.748829+0.041598 test-rmse:8.856167+0.612053
## [22] train-rmse:7.967544+0.043582 test-rmse:8.063941+0.603644
## [23] train-rmse:7.298479+0.045003 test-rmse:7.402483+0.613350
## [24] train-rmse:6.725725+0.046385 test-rmse:6.869280+0.634522
## [25] train-rmse:6.238510+0.048091 test-rmse:6.361015+0.615094
## [26] train-rmse:5.825917+0.050446 test-rmse:5.933825+0.593497
## [27] train-rmse:5.477669+0.052186 test-rmse:5.605874+0.605462
## [28] train-rmse:5.183190+0.053479 test-rmse:5.315651+0.573886
## [29] train-rmse:4.936568+0.055848 test-rmse:5.075822+0.581816
## [30] train-rmse:4.728897+0.057870 test-rmse:4.860011+0.562708
## [31] train-rmse:4.554202+0.058722 test-rmse:4.685291+0.533175
## [32] train-rmse:4.406881+0.058998 test-rmse:4.561367+0.533926
## [33] train-rmse:4.281675+0.059823 test-rmse:4.422080+0.504101
## [34] train-rmse:4.175976+0.060859 test-rmse:4.321895+0.502744
## [35] train-rmse:4.085136+0.060910 test-rmse:4.236879+0.506586
## [36] train-rmse:4.006612+0.060149 test-rmse:4.150903+0.467718
## [37] train-rmse:3.938293+0.059631 test-rmse:4.102550+0.473081
## [38] train-rmse:3.877959+0.059648 test-rmse:4.039883+0.436625
## [39] train-rmse:3.824328+0.059555 test-rmse:3.994279+0.435802
## [40] train-rmse:3.776243+0.059393 test-rmse:3.951819+0.437385
## [41] train-rmse:3.732029+0.058969 test-rmse:3.910642+0.436886
## [42] train-rmse:3.691358+0.058459 test-rmse:3.887112+0.427359
## [43] train-rmse:3.653409+0.057759 test-rmse:3.849881+0.427098
## [44] train-rmse:3.618822+0.057420 test-rmse:3.821198+0.401793
## [45] train-rmse:3.586348+0.057267 test-rmse:3.779050+0.381496
## [46] train-rmse:3.555542+0.056969 test-rmse:3.754748+0.391545
## [47] train-rmse:3.525970+0.056124 test-rmse:3.728019+0.387419
## [48] train-rmse:3.497606+0.055057 test-rmse:3.695316+0.392074
## [49] train-rmse:3.469982+0.054110 test-rmse:3.675901+0.386560
## [50] train-rmse:3.443124+0.053374 test-rmse:3.662275+0.373258
## [51] train-rmse:3.416811+0.052758 test-rmse:3.634631+0.379196
## [52] train-rmse:3.391136+0.052163 test-rmse:3.617841+0.377881
## [53] train-rmse:3.366028+0.051610 test-rmse:3.587519+0.363524
## [54] train-rmse:3.342311+0.051285 test-rmse:3.564214+0.350329
## [55] train-rmse:3.319122+0.050930 test-rmse:3.542951+0.356820
## [56] train-rmse:3.296104+0.050593 test-rmse:3.521122+0.355594
## [57] train-rmse:3.273719+0.050104 test-rmse:3.501817+0.357790
## [58] train-rmse:3.251579+0.049395 test-rmse:3.464170+0.343679
## [59] train-rmse:3.229884+0.048764 test-rmse:3.440221+0.343694
## [60] train-rmse:3.208474+0.047991 test-rmse:3.425516+0.343185
## [61] train-rmse:3.187311+0.047335 test-rmse:3.408892+0.340170
## [62] train-rmse:3.166658+0.046698 test-rmse:3.397014+0.344714
## [63] train-rmse:3.146309+0.046207 test-rmse:3.379760+0.334851
## [64] train-rmse:3.126258+0.045461 test-rmse:3.356166+0.318556
## [65] train-rmse:3.106896+0.045121 test-rmse:3.341214+0.328238
## [66] train-rmse:3.087715+0.044846 test-rmse:3.318751+0.328035
## [67] train-rmse:3.068877+0.044232 test-rmse:3.308646+0.331519
## [68] train-rmse:3.050239+0.043716 test-rmse:3.279506+0.318850
## [69] train-rmse:3.031692+0.043239 test-rmse:3.262915+0.321418
## [70] train-rmse:3.013485+0.042738 test-rmse:3.235063+0.310994
## [71] train-rmse:2.995650+0.042399 test-rmse:3.217766+0.311133
## [72] train-rmse:2.977853+0.041925 test-rmse:3.196720+0.312941
## [73] train-rmse:2.960309+0.041729 test-rmse:3.183120+0.316217
## [74] train-rmse:2.942912+0.041604 test-rmse:3.167007+0.316777
## [75] train-rmse:2.925803+0.041484 test-rmse:3.157465+0.316244
## [76] train-rmse:2.908966+0.041365 test-rmse:3.139078+0.309315
## [77] train-rmse:2.892168+0.041301 test-rmse:3.126406+0.310663
## [78] train-rmse:2.875678+0.041174 test-rmse:3.118964+0.313998
## [79] train-rmse:2.859456+0.040967 test-rmse:3.100724+0.313807
## [80] train-rmse:2.843318+0.040664 test-rmse:3.088933+0.318687
## [81] train-rmse:2.827551+0.040381 test-rmse:3.076002+0.321455
## [82] train-rmse:2.811992+0.040038 test-rmse:3.065432+0.326999
## [83] train-rmse:2.796430+0.039724 test-rmse:3.050329+0.331446
## [84] train-rmse:2.781111+0.039452 test-rmse:3.034502+0.327882
## [85] train-rmse:2.766115+0.039369 test-rmse:3.021771+0.328506
## [86] train-rmse:2.751163+0.039486 test-rmse:3.003059+0.323376
## [87] train-rmse:2.736360+0.039508 test-rmse:2.985837+0.323292
## [88] train-rmse:2.721704+0.039462 test-rmse:2.974367+0.330859
## [89] train-rmse:2.707014+0.039507 test-rmse:2.947522+0.318986
## [90] train-rmse:2.692566+0.039330 test-rmse:2.937948+0.317518
## [91] train-rmse:2.678204+0.039231 test-rmse:2.921951+0.305261
## [92] train-rmse:2.664053+0.039200 test-rmse:2.914937+0.306954
## [93] train-rmse:2.650177+0.039170 test-rmse:2.905672+0.308340
## [94] train-rmse:2.636434+0.039057 test-rmse:2.891467+0.309542
## [95] train-rmse:2.622955+0.038936 test-rmse:2.878142+0.314874
## [96] train-rmse:2.609680+0.038702 test-rmse:2.867878+0.310649
## [97] train-rmse:2.596369+0.038489 test-rmse:2.859884+0.311408
## [98] train-rmse:2.583111+0.038206 test-rmse:2.849674+0.311116
## [99] train-rmse:2.570096+0.037967 test-rmse:2.834406+0.313350
## [100] train-rmse:2.557125+0.037620 test-rmse:2.821091+0.311535
## [101] train-rmse:2.544390+0.037543 test-rmse:2.807691+0.319138
## [102] train-rmse:2.531694+0.037344 test-rmse:2.788644+0.326941
## [103] train-rmse:2.518893+0.037088 test-rmse:2.771507+0.311464
## [104] train-rmse:2.506304+0.036877 test-rmse:2.756091+0.302488
## [105] train-rmse:2.493777+0.036649 test-rmse:2.748220+0.304900
## [106] train-rmse:2.481343+0.036324 test-rmse:2.739901+0.305028
## [107] train-rmse:2.468914+0.036141 test-rmse:2.725454+0.302887
## [108] train-rmse:2.456625+0.035980 test-rmse:2.716745+0.305003
## [109] train-rmse:2.444775+0.035944 test-rmse:2.706300+0.309505
## [110] train-rmse:2.433212+0.035855 test-rmse:2.693004+0.310564
## [111] train-rmse:2.421697+0.035815 test-rmse:2.682687+0.314703
## [112] train-rmse:2.410250+0.035722 test-rmse:2.670822+0.310986
## [113] train-rmse:2.398803+0.035544 test-rmse:2.659878+0.312571
## [114] train-rmse:2.387401+0.035252 test-rmse:2.653937+0.314120
## [115] train-rmse:2.376103+0.034869 test-rmse:2.645432+0.314620
## [116] train-rmse:2.364879+0.034537 test-rmse:2.636337+0.318987
## [117] train-rmse:2.353672+0.034195 test-rmse:2.621417+0.323520
## [118] train-rmse:2.342577+0.033958 test-rmse:2.611687+0.314784
## [119] train-rmse:2.331587+0.033792 test-rmse:2.601762+0.318058
## [120] train-rmse:2.320629+0.033621 test-rmse:2.593339+0.315844
## [121] train-rmse:2.309663+0.033414 test-rmse:2.574618+0.307881
## [122] train-rmse:2.298895+0.033134 test-rmse:2.564875+0.303102
## [123] train-rmse:2.288124+0.032957 test-rmse:2.553236+0.293841
## [124] train-rmse:2.277461+0.032735 test-rmse:2.545357+0.295104
## [125] train-rmse:2.267026+0.032496 test-rmse:2.530596+0.298129
## [126] train-rmse:2.256724+0.032324 test-rmse:2.521716+0.293176
## [127] train-rmse:2.246367+0.032121 test-rmse:2.512166+0.290174
## [128] train-rmse:2.236206+0.032074 test-rmse:2.498886+0.292459
## [129] train-rmse:2.226155+0.031963 test-rmse:2.487673+0.294983
## [130] train-rmse:2.216162+0.031856 test-rmse:2.482881+0.293125
## [131] train-rmse:2.206097+0.031626 test-rmse:2.469962+0.293442
## [132] train-rmse:2.196147+0.031443 test-rmse:2.466030+0.295595
## [133] train-rmse:2.186213+0.031168 test-rmse:2.451652+0.294499
## [134] train-rmse:2.176331+0.031007 test-rmse:2.440614+0.295177
## [135] train-rmse:2.166491+0.030814 test-rmse:2.433831+0.295848
## [136] train-rmse:2.156791+0.030572 test-rmse:2.427376+0.297591
## [137] train-rmse:2.147226+0.030444 test-rmse:2.421414+0.296394
## [138] train-rmse:2.137764+0.030245 test-rmse:2.409338+0.284685
## [139] train-rmse:2.128405+0.030062 test-rmse:2.398506+0.275466
## [140] train-rmse:2.119097+0.029902 test-rmse:2.385816+0.273302
## [141] train-rmse:2.109846+0.029643 test-rmse:2.372010+0.272006
## [142] train-rmse:2.100729+0.029462 test-rmse:2.363465+0.274881
## [143] train-rmse:2.091669+0.029233 test-rmse:2.356818+0.273320
## [144] train-rmse:2.082694+0.029064 test-rmse:2.352745+0.275617
## [145] train-rmse:2.073775+0.028892 test-rmse:2.344855+0.277006
## [146] train-rmse:2.064877+0.028714 test-rmse:2.336404+0.277100
## [147] train-rmse:2.056048+0.028477 test-rmse:2.327039+0.278142
## [148] train-rmse:2.047229+0.028249 test-rmse:2.318670+0.279127
## [149] train-rmse:2.038397+0.027960 test-rmse:2.307108+0.280897
## [150] train-rmse:2.029627+0.027766 test-rmse:2.300385+0.280920
## [151] train-rmse:2.020890+0.027556 test-rmse:2.288640+0.278272
## [152] train-rmse:2.012138+0.027350 test-rmse:2.276518+0.282267
## [153] train-rmse:2.003447+0.027124 test-rmse:2.271039+0.282089
## [154] train-rmse:1.994887+0.026958 test-rmse:2.263348+0.281025
## [155] train-rmse:1.986515+0.026814 test-rmse:2.259098+0.275936
## [156] train-rmse:1.978180+0.026677 test-rmse:2.253117+0.275557
## [157] train-rmse:1.969911+0.026537 test-rmse:2.248471+0.277425
## [158] train-rmse:1.961662+0.026355 test-rmse:2.241787+0.273599
## [159] train-rmse:1.953468+0.026281 test-rmse:2.229481+0.276760
## [160] train-rmse:1.945445+0.026192 test-rmse:2.223289+0.275327
## [161] train-rmse:1.937462+0.026089 test-rmse:2.216395+0.275392
## [162] train-rmse:1.929546+0.025982 test-rmse:2.210069+0.275560
## [163] train-rmse:1.921606+0.025829 test-rmse:2.198752+0.277495
## [164] train-rmse:1.913729+0.025622 test-rmse:2.192482+0.273546
## [165] train-rmse:1.905861+0.025398 test-rmse:2.178998+0.267183
## [166] train-rmse:1.898022+0.025179 test-rmse:2.161131+0.261927
## [167] train-rmse:1.890211+0.024960 test-rmse:2.155139+0.265174
## [168] train-rmse:1.882402+0.024753 test-rmse:2.147876+0.264977
## [169] train-rmse:1.874709+0.024498 test-rmse:2.146029+0.265120
## [170] train-rmse:1.867089+0.024276 test-rmse:2.136544+0.262734
## [171] train-rmse:1.859510+0.024119 test-rmse:2.133072+0.262114
## [172] train-rmse:1.852034+0.023927 test-rmse:2.126888+0.257689
## [173] train-rmse:1.844563+0.023763 test-rmse:2.118792+0.256232
## [174] train-rmse:1.837112+0.023533 test-rmse:2.113339+0.259727
## [175] train-rmse:1.829843+0.023382 test-rmse:2.105555+0.261248
## [176] train-rmse:1.822575+0.023218 test-rmse:2.099822+0.261432
## [177] train-rmse:1.815405+0.023113 test-rmse:2.092695+0.261138
## [178] train-rmse:1.808214+0.023011 test-rmse:2.088209+0.264038
## [179] train-rmse:1.801136+0.022944 test-rmse:2.085561+0.267123
## [180] train-rmse:1.794073+0.022868 test-rmse:2.074062+0.260373
## [181] train-rmse:1.787082+0.022689 test-rmse:2.063790+0.253856
## [182] train-rmse:1.780136+0.022454 test-rmse:2.056403+0.256549
## [183] train-rmse:1.773180+0.022231 test-rmse:2.047799+0.254729
## [184] train-rmse:1.766305+0.022012 test-rmse:2.039149+0.257134
## [185] train-rmse:1.759432+0.021813 test-rmse:2.035616+0.257404
## [186] train-rmse:1.752570+0.021667 test-rmse:2.026290+0.256387
## [187] train-rmse:1.745725+0.021541 test-rmse:2.018337+0.253518
## [188] train-rmse:1.738953+0.021367 test-rmse:2.015236+0.251608
## [189] train-rmse:1.732285+0.021230 test-rmse:2.008651+0.248470
## [190] train-rmse:1.725674+0.021111 test-rmse:2.005538+0.249334
## [191] train-rmse:1.719063+0.021007 test-rmse:1.998127+0.244362
## [192] train-rmse:1.712535+0.020889 test-rmse:1.995789+0.242274
## [193] train-rmse:1.706094+0.020780 test-rmse:1.989811+0.242937
## [194] train-rmse:1.699678+0.020625 test-rmse:1.981055+0.241532
## [195] train-rmse:1.693305+0.020458 test-rmse:1.976053+0.244074
## [196] train-rmse:1.686943+0.020325 test-rmse:1.972646+0.244315
## [197] train-rmse:1.680615+0.020192 test-rmse:1.965545+0.248246
## [198] train-rmse:1.674334+0.020123 test-rmse:1.957949+0.251478
## [199] train-rmse:1.668164+0.020008 test-rmse:1.953652+0.252829
## [200] train-rmse:1.662038+0.019918 test-rmse:1.944464+0.255449
## [201] train-rmse:1.655884+0.019835 test-rmse:1.941151+0.255459
## [202] train-rmse:1.649749+0.019697 test-rmse:1.939390+0.256106
## [203] train-rmse:1.643712+0.019491 test-rmse:1.932201+0.251038
## [204] train-rmse:1.637655+0.019311 test-rmse:1.920565+0.241963
## [205] train-rmse:1.631618+0.019169 test-rmse:1.918264+0.243535
## [206] train-rmse:1.625611+0.019015 test-rmse:1.910530+0.245158
## [207] train-rmse:1.619646+0.018874 test-rmse:1.902426+0.245429
## [208] train-rmse:1.613787+0.018744 test-rmse:1.895817+0.247846
## [209] train-rmse:1.607958+0.018577 test-rmse:1.893054+0.246263
## [210] train-rmse:1.602199+0.018471 test-rmse:1.886833+0.246091
## [211] train-rmse:1.596443+0.018373 test-rmse:1.880512+0.242772
## [212] train-rmse:1.590723+0.018263 test-rmse:1.877666+0.242541
## [213] train-rmse:1.585070+0.018124 test-rmse:1.872871+0.242603
## [214] train-rmse:1.579419+0.018029 test-rmse:1.866729+0.240267
## [215] train-rmse:1.573791+0.017943 test-rmse:1.861774+0.237685
## [216] train-rmse:1.568199+0.017879 test-rmse:1.855898+0.238010
## [217] train-rmse:1.562700+0.017810 test-rmse:1.853433+0.237227
## [218] train-rmse:1.557217+0.017718 test-rmse:1.848812+0.238141
## [219] train-rmse:1.551763+0.017625 test-rmse:1.843922+0.239823
## [220] train-rmse:1.546324+0.017519 test-rmse:1.839153+0.243514
## [221] train-rmse:1.540919+0.017448 test-rmse:1.834412+0.245873
## [222] train-rmse:1.535550+0.017324 test-rmse:1.829587+0.248362
## [223] train-rmse:1.530167+0.017212 test-rmse:1.824589+0.246299
## [224] train-rmse:1.524874+0.017136 test-rmse:1.818302+0.240998
## [225] train-rmse:1.519602+0.017067 test-rmse:1.809846+0.234427
## [226] train-rmse:1.514429+0.016936 test-rmse:1.802201+0.233297
## [227] train-rmse:1.509234+0.016815 test-rmse:1.797278+0.232290
## [228] train-rmse:1.504020+0.016694 test-rmse:1.792296+0.233271
## [229] train-rmse:1.498825+0.016538 test-rmse:1.785395+0.236186
## [230] train-rmse:1.493700+0.016374 test-rmse:1.780600+0.238645
## [231] train-rmse:1.488676+0.016204 test-rmse:1.775475+0.238130
## [232] train-rmse:1.483667+0.016081 test-rmse:1.771361+0.236776
## [233] train-rmse:1.478703+0.016026 test-rmse:1.768239+0.236008
## [234] train-rmse:1.473706+0.015938 test-rmse:1.763849+0.233883
## [235] train-rmse:1.468787+0.015869 test-rmse:1.756996+0.231784
## [236] train-rmse:1.463873+0.015810 test-rmse:1.755074+0.230632
## [237] train-rmse:1.459001+0.015729 test-rmse:1.750584+0.229883
## [238] train-rmse:1.454188+0.015686 test-rmse:1.746317+0.229639
## [239] train-rmse:1.449393+0.015637 test-rmse:1.740863+0.227314
## [240] train-rmse:1.444581+0.015578 test-rmse:1.736057+0.228043
## [241] train-rmse:1.439805+0.015530 test-rmse:1.732842+0.227218
## [242] train-rmse:1.435149+0.015453 test-rmse:1.728328+0.228486
## [243] train-rmse:1.430506+0.015368 test-rmse:1.722653+0.231161
## [244] train-rmse:1.425867+0.015243 test-rmse:1.718224+0.232873
## [245] train-rmse:1.421236+0.015145 test-rmse:1.715261+0.232874
## [246] train-rmse:1.416606+0.015022 test-rmse:1.713139+0.234245
## [247] train-rmse:1.412066+0.014925 test-rmse:1.711861+0.234760
## [248] train-rmse:1.407566+0.014866 test-rmse:1.705860+0.230722
## [249] train-rmse:1.403088+0.014807 test-rmse:1.700245+0.232725
## [250] train-rmse:1.398639+0.014716 test-rmse:1.696482+0.233299
## [251] train-rmse:1.394171+0.014645 test-rmse:1.689514+0.230932
## [252] train-rmse:1.389688+0.014567 test-rmse:1.680454+0.224980
## [253] train-rmse:1.385277+0.014474 test-rmse:1.676344+0.226200
## [254] train-rmse:1.380913+0.014410 test-rmse:1.675271+0.227267
## [255] train-rmse:1.376563+0.014282 test-rmse:1.669804+0.226892
## [256] train-rmse:1.372230+0.014216 test-rmse:1.668429+0.226190
## [257] train-rmse:1.367919+0.014185 test-rmse:1.663823+0.225880
## [258] train-rmse:1.363615+0.014146 test-rmse:1.657223+0.226232
## [259] train-rmse:1.359378+0.014100 test-rmse:1.652163+0.224648
## [260] train-rmse:1.355202+0.014037 test-rmse:1.648614+0.222694
## [261] train-rmse:1.351057+0.013975 test-rmse:1.643688+0.221435
## [262] train-rmse:1.346939+0.013897 test-rmse:1.640114+0.218775
## [263] train-rmse:1.342844+0.013823 test-rmse:1.633010+0.217894
## [264] train-rmse:1.338789+0.013773 test-rmse:1.628167+0.217606
## [265] train-rmse:1.334748+0.013682 test-rmse:1.625190+0.218281
## [266] train-rmse:1.330713+0.013580 test-rmse:1.620153+0.220023
## [267] train-rmse:1.326698+0.013449 test-rmse:1.618162+0.218341
## [268] train-rmse:1.322755+0.013367 test-rmse:1.613923+0.218646
## [269] train-rmse:1.318814+0.013293 test-rmse:1.612434+0.218524
## [270] train-rmse:1.314926+0.013239 test-rmse:1.610926+0.217798
## [271] train-rmse:1.311063+0.013176 test-rmse:1.606218+0.219829
## [272] train-rmse:1.307187+0.013141 test-rmse:1.601813+0.219815
## [273] train-rmse:1.303310+0.013109 test-rmse:1.598795+0.218999
## [274] train-rmse:1.299486+0.013088 test-rmse:1.594569+0.218699
## [275] train-rmse:1.295708+0.013049 test-rmse:1.589658+0.218038
## [276] train-rmse:1.291943+0.013009 test-rmse:1.584416+0.219334
## [277] train-rmse:1.288200+0.012977 test-rmse:1.578079+0.217051
## [278] train-rmse:1.284470+0.012935 test-rmse:1.574743+0.217453
## [279] train-rmse:1.280748+0.012874 test-rmse:1.572582+0.217779
## [280] train-rmse:1.277037+0.012831 test-rmse:1.571462+0.217765
## [281] train-rmse:1.273335+0.012771 test-rmse:1.563794+0.210941
## [282] train-rmse:1.269717+0.012691 test-rmse:1.559982+0.208279
## [283] train-rmse:1.266106+0.012658 test-rmse:1.557499+0.207578
## [284] train-rmse:1.262502+0.012639 test-rmse:1.555776+0.207670
## [285] train-rmse:1.258956+0.012607 test-rmse:1.555311+0.207777
## [286] train-rmse:1.255425+0.012573 test-rmse:1.553340+0.208862
## [287] train-rmse:1.251905+0.012514 test-rmse:1.548610+0.207027
## [288] train-rmse:1.248407+0.012479 test-rmse:1.545827+0.206680
## [289] train-rmse:1.244961+0.012415 test-rmse:1.541585+0.205162
## [290] train-rmse:1.241540+0.012359 test-rmse:1.538317+0.207283
## [291] train-rmse:1.238082+0.012319 test-rmse:1.534697+0.208578
## [292] train-rmse:1.234646+0.012268 test-rmse:1.530583+0.204732
## [293] train-rmse:1.231230+0.012183 test-rmse:1.528505+0.205859
## [294] train-rmse:1.227838+0.012091 test-rmse:1.519925+0.201910
## [295] train-rmse:1.224533+0.012038 test-rmse:1.517020+0.201509
## [296] train-rmse:1.221229+0.012002 test-rmse:1.514723+0.203534
## [297] train-rmse:1.217929+0.011977 test-rmse:1.512058+0.205489
## [298] train-rmse:1.214609+0.011900 test-rmse:1.510622+0.206498
## [299] train-rmse:1.211380+0.011887 test-rmse:1.509278+0.207424
## [300] train-rmse:1.208162+0.011877 test-rmse:1.503794+0.208805
## [301] train-rmse:1.204961+0.011860 test-rmse:1.501518+0.209381
## [302] train-rmse:1.201779+0.011817 test-rmse:1.498948+0.210674
## [303] train-rmse:1.198612+0.011764 test-rmse:1.494455+0.210143
## [304] train-rmse:1.195459+0.011727 test-rmse:1.492118+0.211067
## [305] train-rmse:1.192343+0.011695 test-rmse:1.488479+0.210173
## [306] train-rmse:1.189265+0.011668 test-rmse:1.485630+0.210279
## [307] train-rmse:1.186163+0.011592 test-rmse:1.482687+0.208164
## [308] train-rmse:1.183131+0.011562 test-rmse:1.480576+0.207976
## [309] train-rmse:1.180097+0.011536 test-rmse:1.478623+0.207485
## [310] train-rmse:1.177104+0.011525 test-rmse:1.473151+0.206847
## [311] train-rmse:1.174140+0.011515 test-rmse:1.472437+0.206758
## [312] train-rmse:1.171152+0.011498 test-rmse:1.469168+0.200741
## [313] train-rmse:1.168180+0.011455 test-rmse:1.467031+0.200826
## [314] train-rmse:1.165235+0.011411 test-rmse:1.462936+0.200048
## [315] train-rmse:1.162292+0.011382 test-rmse:1.459272+0.199956
## [316] train-rmse:1.159350+0.011337 test-rmse:1.457194+0.200150
## [317] train-rmse:1.156439+0.011289 test-rmse:1.454047+0.199750
## [318] train-rmse:1.153544+0.011250 test-rmse:1.450110+0.198752
## [319] train-rmse:1.150667+0.011215 test-rmse:1.448184+0.199978
## [320] train-rmse:1.147826+0.011169 test-rmse:1.447534+0.200068
## [321] train-rmse:1.145002+0.011106 test-rmse:1.440741+0.202217
## [322] train-rmse:1.142211+0.011052 test-rmse:1.436482+0.203071
## [323] train-rmse:1.139458+0.011036 test-rmse:1.433842+0.204612
## [324] train-rmse:1.136720+0.011016 test-rmse:1.432034+0.204070
## [325] train-rmse:1.134005+0.010994 test-rmse:1.432328+0.204183
## [326] train-rmse:1.131316+0.010984 test-rmse:1.430833+0.203706
## [327] train-rmse:1.128629+0.010950 test-rmse:1.428179+0.204248
## [328] train-rmse:1.125937+0.010916 test-rmse:1.426443+0.206150
## [329] train-rmse:1.123255+0.010867 test-rmse:1.422657+0.202454
## [330] train-rmse:1.120581+0.010815 test-rmse:1.419698+0.201441
## [331] train-rmse:1.117942+0.010752 test-rmse:1.417046+0.200581
## [332] train-rmse:1.115355+0.010726 test-rmse:1.414824+0.201375
## [333] train-rmse:1.112773+0.010694 test-rmse:1.413177+0.201902
## [334] train-rmse:1.110221+0.010684 test-rmse:1.410363+0.199945
## [335] train-rmse:1.107671+0.010696 test-rmse:1.406172+0.194187
## [336] train-rmse:1.105127+0.010693 test-rmse:1.403544+0.195380
## [337] train-rmse:1.102604+0.010653 test-rmse:1.401135+0.196001
## [338] train-rmse:1.100118+0.010627 test-rmse:1.399751+0.195930
## [339] train-rmse:1.097638+0.010609 test-rmse:1.396600+0.196766
## [340] train-rmse:1.095170+0.010597 test-rmse:1.394233+0.196949
## [341] train-rmse:1.092697+0.010593 test-rmse:1.391035+0.196785
## [342] train-rmse:1.090240+0.010560 test-rmse:1.388599+0.197090
## [343] train-rmse:1.087808+0.010534 test-rmse:1.385842+0.195983
## [344] train-rmse:1.085385+0.010503 test-rmse:1.384219+0.197603
## [345] train-rmse:1.082991+0.010470 test-rmse:1.382963+0.195514
## [346] train-rmse:1.080619+0.010398 test-rmse:1.377074+0.197848
## [347] train-rmse:1.078238+0.010329 test-rmse:1.373879+0.198103
## [348] train-rmse:1.075888+0.010288 test-rmse:1.371366+0.197797
## [349] train-rmse:1.073554+0.010246 test-rmse:1.370434+0.196007
## [350] train-rmse:1.071241+0.010202 test-rmse:1.369479+0.194990
## [351] train-rmse:1.068952+0.010142 test-rmse:1.369377+0.194937
## [352] train-rmse:1.066663+0.010099 test-rmse:1.368225+0.195171
## [353] train-rmse:1.064388+0.010049 test-rmse:1.366730+0.194624
## [354] train-rmse:1.062113+0.010014 test-rmse:1.363584+0.195125
## [355] train-rmse:1.059864+0.009997 test-rmse:1.362510+0.196260
## [356] train-rmse:1.057634+0.009982 test-rmse:1.359751+0.192841
## [357] train-rmse:1.055438+0.009952 test-rmse:1.358220+0.193704
## [358] train-rmse:1.053260+0.009956 test-rmse:1.356784+0.193892
## [359] train-rmse:1.051075+0.009961 test-rmse:1.353156+0.188842
## [360] train-rmse:1.048881+0.009967 test-rmse:1.350016+0.188183
## [361] train-rmse:1.046685+0.010021 test-rmse:1.349934+0.188101
## [362] train-rmse:1.044572+0.009999 test-rmse:1.347722+0.189491
## [363] train-rmse:1.042457+0.009988 test-rmse:1.342662+0.191009
## [364] train-rmse:1.040352+0.009999 test-rmse:1.340151+0.191593
## [365] train-rmse:1.038246+0.009983 test-rmse:1.339130+0.191511
## [366] train-rmse:1.036190+0.009966 test-rmse:1.336925+0.191961
## [367] train-rmse:1.034139+0.009970 test-rmse:1.335344+0.191607
## [368] train-rmse:1.032083+0.009965 test-rmse:1.332750+0.191343
## [369] train-rmse:1.030042+0.009956 test-rmse:1.331324+0.190446
## [370] train-rmse:1.028026+0.009946 test-rmse:1.329999+0.191235
## [371] train-rmse:1.026012+0.009947 test-rmse:1.329469+0.190910
## [372] train-rmse:1.024011+0.009927 test-rmse:1.327834+0.189880
## [373] train-rmse:1.022021+0.009905 test-rmse:1.325417+0.189785
## [374] train-rmse:1.020054+0.009879 test-rmse:1.322847+0.188597
## [375] train-rmse:1.018088+0.009860 test-rmse:1.320601+0.188424
## [376] train-rmse:1.016137+0.009821 test-rmse:1.317669+0.189796
## [377] train-rmse:1.014217+0.009798 test-rmse:1.317129+0.189939
## [378] train-rmse:1.012310+0.009782 test-rmse:1.314516+0.187654
## [379] train-rmse:1.010413+0.009755 test-rmse:1.312361+0.188034
## [380] train-rmse:1.008527+0.009741 test-rmse:1.310558+0.189369
## [381] train-rmse:1.006643+0.009725 test-rmse:1.308530+0.189236
## [382] train-rmse:1.004750+0.009707 test-rmse:1.307729+0.188505
## [383] train-rmse:1.002863+0.009687 test-rmse:1.306648+0.188624
## [384] train-rmse:1.001013+0.009655 test-rmse:1.303661+0.188049
## [385] train-rmse:0.999193+0.009642 test-rmse:1.302964+0.187669
## [386] train-rmse:0.997379+0.009630 test-rmse:1.301898+0.187904
## [387] train-rmse:0.995575+0.009650 test-rmse:1.300416+0.188064
## [388] train-rmse:0.993759+0.009660 test-rmse:1.300575+0.187281
## [389] train-rmse:0.991974+0.009659 test-rmse:1.298107+0.187950
## [390] train-rmse:0.990202+0.009654 test-rmse:1.296457+0.188766
## [391] train-rmse:0.988448+0.009660 test-rmse:1.291113+0.185749
## [392] train-rmse:0.986691+0.009671 test-rmse:1.291792+0.185492
## [393] train-rmse:0.984937+0.009675 test-rmse:1.291191+0.185274
## [394] train-rmse:0.983198+0.009684 test-rmse:1.288966+0.183679
## [395] train-rmse:0.981463+0.009696 test-rmse:1.288886+0.184165
## [396] train-rmse:0.979730+0.009688 test-rmse:1.287506+0.184322
## [397] train-rmse:0.978021+0.009689 test-rmse:1.285747+0.185430
## [398] train-rmse:0.976337+0.009693 test-rmse:1.284162+0.185962
## [399] train-rmse:0.974668+0.009709 test-rmse:1.281237+0.185960
## [400] train-rmse:0.973006+0.009720 test-rmse:1.279301+0.186533
## [401] train-rmse:0.971346+0.009725 test-rmse:1.278141+0.186563
## [402] train-rmse:0.969694+0.009735 test-rmse:1.276261+0.186330
## [403] train-rmse:0.968067+0.009732 test-rmse:1.274032+0.184615
## [404] train-rmse:0.966448+0.009725 test-rmse:1.271877+0.184738
## [405] train-rmse:0.964833+0.009714 test-rmse:1.270529+0.183961
## [406] train-rmse:0.963243+0.009725 test-rmse:1.269192+0.184175
## [407] train-rmse:0.961660+0.009734 test-rmse:1.267673+0.184922
## [408] train-rmse:0.960083+0.009739 test-rmse:1.267381+0.184357
## [409] train-rmse:0.958522+0.009743 test-rmse:1.266104+0.184161
## [410] train-rmse:0.956969+0.009737 test-rmse:1.264437+0.183783
## [411] train-rmse:0.955423+0.009745 test-rmse:1.263090+0.183813
## [412] train-rmse:0.953887+0.009747 test-rmse:1.261748+0.183289
## [413] train-rmse:0.952364+0.009765 test-rmse:1.261694+0.182483
## [414] train-rmse:0.950838+0.009785 test-rmse:1.259494+0.182734
## [415] train-rmse:0.949323+0.009791 test-rmse:1.256916+0.178905
## [416] train-rmse:0.947808+0.009796 test-rmse:1.256369+0.179824
## [417] train-rmse:0.946301+0.009818 test-rmse:1.256397+0.178725
## [418] train-rmse:0.944805+0.009842 test-rmse:1.254447+0.179330
## [419] train-rmse:0.943330+0.009849 test-rmse:1.252698+0.179725
## [420] train-rmse:0.941858+0.009855 test-rmse:1.252337+0.180333
## [421] train-rmse:0.940405+0.009853 test-rmse:1.251119+0.180335
## [422] train-rmse:0.938967+0.009873 test-rmse:1.249839+0.181208
## [423] train-rmse:0.937525+0.009900 test-rmse:1.249178+0.181170
## [424] train-rmse:0.936085+0.009915 test-rmse:1.246912+0.180279
## [425] train-rmse:0.934662+0.009943 test-rmse:1.244819+0.180342
## [426] train-rmse:0.933257+0.009970 test-rmse:1.244148+0.180431
## [427] train-rmse:0.931862+0.009981 test-rmse:1.242935+0.180366
## [428] train-rmse:0.930478+0.009999 test-rmse:1.240754+0.181765
## [429] train-rmse:0.929117+0.010018 test-rmse:1.239357+0.181552
## [430] train-rmse:0.927766+0.010044 test-rmse:1.238458+0.181591
## [431] train-rmse:0.926419+0.010071 test-rmse:1.236271+0.182145
## [432] train-rmse:0.925073+0.010091 test-rmse:1.234934+0.182134
## [433] train-rmse:0.923728+0.010111 test-rmse:1.233943+0.182487
## [434] train-rmse:0.922385+0.010126 test-rmse:1.233176+0.182852
## [435] train-rmse:0.921057+0.010143 test-rmse:1.231992+0.182989
## [436] train-rmse:0.919738+0.010147 test-rmse:1.230746+0.183076
## [437] train-rmse:0.918432+0.010167 test-rmse:1.229712+0.183038
## [438] train-rmse:0.917135+0.010196 test-rmse:1.228634+0.181831
## [439] train-rmse:0.915852+0.010221 test-rmse:1.227194+0.181482
## [440] train-rmse:0.914577+0.010245 test-rmse:1.226441+0.180610
## [441] train-rmse:0.913305+0.010261 test-rmse:1.222846+0.179096
## [442] train-rmse:0.912045+0.010279 test-rmse:1.222403+0.178436
## [443] train-rmse:0.910791+0.010283 test-rmse:1.221557+0.178722
## [444] train-rmse:0.909545+0.010295 test-rmse:1.220701+0.177009
## [445] train-rmse:0.908312+0.010309 test-rmse:1.220455+0.177223
## [446] train-rmse:0.907077+0.010326 test-rmse:1.217488+0.174032
## [447] train-rmse:0.905841+0.010355 test-rmse:1.217657+0.174281
## [448] train-rmse:0.904603+0.010376 test-rmse:1.215865+0.174186
## [449] train-rmse:0.903394+0.010403 test-rmse:1.215307+0.174043
## [450] train-rmse:0.902196+0.010423 test-rmse:1.213224+0.174367
## [451] train-rmse:0.901003+0.010450 test-rmse:1.212557+0.174904
## [452] train-rmse:0.899810+0.010479 test-rmse:1.211669+0.174961
## [453] train-rmse:0.898624+0.010507 test-rmse:1.210815+0.174774
## [454] train-rmse:0.897442+0.010522 test-rmse:1.210590+0.175644
## [455] train-rmse:0.896283+0.010550 test-rmse:1.210195+0.175440
## [456] train-rmse:0.895132+0.010570 test-rmse:1.209453+0.174833
## [457] train-rmse:0.893986+0.010588 test-rmse:1.207823+0.176069
## [458] train-rmse:0.892845+0.010611 test-rmse:1.207240+0.175716
## [459] train-rmse:0.891699+0.010628 test-rmse:1.205141+0.176481
## [460] train-rmse:0.890565+0.010646 test-rmse:1.202835+0.176407
## [461] train-rmse:0.889428+0.010664 test-rmse:1.202464+0.176881
## [462] train-rmse:0.888305+0.010673 test-rmse:1.201490+0.176851
## [463] train-rmse:0.887185+0.010694 test-rmse:1.201102+0.176612
## [464] train-rmse:0.886071+0.010728 test-rmse:1.199545+0.176595
## [465] train-rmse:0.884976+0.010755 test-rmse:1.199387+0.175941
## [466] train-rmse:0.883899+0.010779 test-rmse:1.197735+0.175019
## [467] train-rmse:0.882826+0.010811 test-rmse:1.195648+0.173265
## [468] train-rmse:0.881763+0.010845 test-rmse:1.194944+0.174358
## [469] train-rmse:0.880704+0.010880 test-rmse:1.194124+0.174484
## [470] train-rmse:0.879660+0.010912 test-rmse:1.193092+0.174986
## [471] train-rmse:0.878614+0.010941 test-rmse:1.192267+0.175557
## [472] train-rmse:0.877577+0.010965 test-rmse:1.192292+0.175633
## [473] train-rmse:0.876539+0.010995 test-rmse:1.189867+0.172778
## [474] train-rmse:0.875505+0.011017 test-rmse:1.189145+0.171683
## [475] train-rmse:0.874485+0.011043 test-rmse:1.189035+0.171862
## [476] train-rmse:0.873472+0.011067 test-rmse:1.187474+0.171812
## [477] train-rmse:0.872456+0.011091 test-rmse:1.187690+0.171924
## [478] train-rmse:0.871445+0.011118 test-rmse:1.188107+0.171586
## [479] train-rmse:0.870432+0.011141 test-rmse:1.187052+0.172329
## [480] train-rmse:0.869429+0.011156 test-rmse:1.185893+0.172688
## [481] train-rmse:0.868429+0.011169 test-rmse:1.185107+0.173096
## [482] train-rmse:0.867432+0.011191 test-rmse:1.183630+0.173950
## [483] train-rmse:0.866448+0.011217 test-rmse:1.181939+0.173304
## [484] train-rmse:0.865466+0.011235 test-rmse:1.180336+0.172014
## [485] train-rmse:0.864499+0.011268 test-rmse:1.179486+0.171927
## [486] train-rmse:0.863552+0.011290 test-rmse:1.177436+0.172941
## [487] train-rmse:0.862602+0.011313 test-rmse:1.175418+0.169758
## [488] train-rmse:0.861664+0.011345 test-rmse:1.174939+0.170188
## [489] train-rmse:0.860735+0.011373 test-rmse:1.174524+0.170380
## [490] train-rmse:0.859794+0.011390 test-rmse:1.174586+0.169891
## [491] train-rmse:0.858874+0.011413 test-rmse:1.174035+0.169594
## [492] train-rmse:0.857961+0.011435 test-rmse:1.172519+0.170023
## [493] train-rmse:0.857053+0.011462 test-rmse:1.172727+0.169850
## [494] train-rmse:0.856151+0.011488 test-rmse:1.172455+0.170159
## [495] train-rmse:0.855248+0.011511 test-rmse:1.171752+0.169348
## [496] train-rmse:0.854349+0.011533 test-rmse:1.170601+0.169863
## [497] train-rmse:0.853457+0.011559 test-rmse:1.169355+0.170483
## [498] train-rmse:0.852563+0.011589 test-rmse:1.170007+0.170496
## [499] train-rmse:0.851673+0.011623 test-rmse:1.169189+0.170088
## [500] train-rmse:0.850800+0.011652 test-rmse:1.168197+0.170182
## [501] train-rmse:0.849930+0.011684 test-rmse:1.167154+0.170811
## [502] train-rmse:0.849069+0.011718 test-rmse:1.167755+0.170526
## [503] train-rmse:0.848208+0.011753 test-rmse:1.167144+0.170513
## [504] train-rmse:0.847348+0.011794 test-rmse:1.166675+0.170670
## [505] train-rmse:0.846498+0.011822 test-rmse:1.165120+0.169723
## [506] train-rmse:0.845660+0.011841 test-rmse:1.164082+0.168390
## [507] train-rmse:0.844823+0.011855 test-rmse:1.162666+0.168439
## [508] train-rmse:0.843998+0.011872 test-rmse:1.162596+0.167495
## [509] train-rmse:0.843179+0.011891 test-rmse:1.161584+0.167977
## [510] train-rmse:0.842360+0.011906 test-rmse:1.161083+0.168114
## [511] train-rmse:0.841550+0.011917 test-rmse:1.160153+0.168721
## [512] train-rmse:0.840751+0.011948 test-rmse:1.159259+0.168934
## [513] train-rmse:0.839952+0.011978 test-rmse:1.159073+0.169170
## [514] train-rmse:0.839153+0.012009 test-rmse:1.158652+0.169115
## [515] train-rmse:0.838363+0.012043 test-rmse:1.156406+0.166344
## [516] train-rmse:0.837580+0.012065 test-rmse:1.155394+0.166362
## [517] train-rmse:0.836799+0.012081 test-rmse:1.154783+0.166435
## [518] train-rmse:0.836024+0.012106 test-rmse:1.154344+0.166576
## [519] train-rmse:0.835251+0.012136 test-rmse:1.153010+0.167319
## [520] train-rmse:0.834486+0.012168 test-rmse:1.152050+0.167061
## [521] train-rmse:0.833722+0.012202 test-rmse:1.150986+0.166920
## [522] train-rmse:0.832958+0.012236 test-rmse:1.150990+0.167579
## [523] train-rmse:0.832199+0.012268 test-rmse:1.150649+0.167731
## [524] train-rmse:0.831447+0.012294 test-rmse:1.150042+0.167881
## [525] train-rmse:0.830701+0.012319 test-rmse:1.148755+0.168489
## [526] train-rmse:0.829959+0.012341 test-rmse:1.148660+0.167621
## [527] train-rmse:0.829221+0.012359 test-rmse:1.147585+0.166935
## [528] train-rmse:0.828491+0.012374 test-rmse:1.147182+0.167503
## [529] train-rmse:0.827766+0.012392 test-rmse:1.145797+0.167291
## [530] train-rmse:0.827052+0.012415 test-rmse:1.145420+0.167574
## [531] train-rmse:0.826337+0.012440 test-rmse:1.144038+0.166582
## [532] train-rmse:0.825631+0.012459 test-rmse:1.143581+0.166664
## [533] train-rmse:0.824918+0.012492 test-rmse:1.143005+0.166651
## [534] train-rmse:0.824211+0.012525 test-rmse:1.143284+0.166577
## [535] train-rmse:0.823510+0.012550 test-rmse:1.143075+0.167315
## [536] train-rmse:0.822815+0.012574 test-rmse:1.142489+0.167855
## [537] train-rmse:0.822123+0.012594 test-rmse:1.142228+0.168781
## [538] train-rmse:0.821443+0.012622 test-rmse:1.141732+0.168622
## [539] train-rmse:0.820766+0.012655 test-rmse:1.141017+0.168627
## [540] train-rmse:0.820091+0.012683 test-rmse:1.141504+0.168388
## [541] train-rmse:0.819409+0.012701 test-rmse:1.141531+0.167668
## [542] train-rmse:0.818743+0.012727 test-rmse:1.141440+0.167285
## [543] train-rmse:0.818083+0.012746 test-rmse:1.141536+0.166881
## [544] train-rmse:0.817429+0.012772 test-rmse:1.140868+0.167590
## [545] train-rmse:0.816774+0.012797 test-rmse:1.139095+0.167855
## [546] train-rmse:0.816113+0.012840 test-rmse:1.139135+0.167674
## [547] train-rmse:0.815464+0.012861 test-rmse:1.138420+0.167979
## [548] train-rmse:0.814820+0.012888 test-rmse:1.137261+0.167070
## [549] train-rmse:0.814179+0.012916 test-rmse:1.136603+0.166884
## [550] train-rmse:0.813538+0.012944 test-rmse:1.135641+0.167290
## [551] train-rmse:0.812909+0.012966 test-rmse:1.133443+0.165143
## [552] train-rmse:0.812278+0.012991 test-rmse:1.133092+0.165557
## [553] train-rmse:0.811652+0.013014 test-rmse:1.132884+0.165814
## [554] train-rmse:0.811026+0.013041 test-rmse:1.131987+0.165322
## [555] train-rmse:0.810401+0.013066 test-rmse:1.130967+0.165770
## [556] train-rmse:0.809784+0.013093 test-rmse:1.131119+0.165560
## [557] train-rmse:0.809175+0.013116 test-rmse:1.130701+0.165490
## [558] train-rmse:0.808571+0.013141 test-rmse:1.130530+0.165280
## [559] train-rmse:0.807964+0.013161 test-rmse:1.129572+0.164239
## [560] train-rmse:0.807366+0.013188 test-rmse:1.129332+0.164622
## [561] train-rmse:0.806770+0.013220 test-rmse:1.128047+0.164652
## [562] train-rmse:0.806183+0.013254 test-rmse:1.127587+0.165089
## [563] train-rmse:0.805596+0.013280 test-rmse:1.127583+0.165127
## [564] train-rmse:0.805015+0.013304 test-rmse:1.126905+0.165318
## [565] train-rmse:0.804435+0.013327 test-rmse:1.126103+0.164577
## [566] train-rmse:0.803862+0.013346 test-rmse:1.125612+0.164928
## [567] train-rmse:0.803295+0.013365 test-rmse:1.124856+0.164517
## [568] train-rmse:0.802732+0.013383 test-rmse:1.124663+0.164878
## [569] train-rmse:0.802169+0.013402 test-rmse:1.123212+0.162817
## [570] train-rmse:0.801617+0.013424 test-rmse:1.123081+0.162864
## [571] train-rmse:0.801061+0.013444 test-rmse:1.121650+0.163048
## [572] train-rmse:0.800500+0.013478 test-rmse:1.120611+0.163701
## [573] train-rmse:0.799952+0.013504 test-rmse:1.120334+0.163513
## [574] train-rmse:0.799405+0.013532 test-rmse:1.119722+0.163639
## [575] train-rmse:0.798857+0.013560 test-rmse:1.119897+0.164117
## [576] train-rmse:0.798311+0.013590 test-rmse:1.119704+0.163772
## [577] train-rmse:0.797769+0.013618 test-rmse:1.119261+0.163886
## [578] train-rmse:0.797229+0.013648 test-rmse:1.117775+0.164025
## [579] train-rmse:0.796687+0.013672 test-rmse:1.117641+0.164017
## [580] train-rmse:0.796154+0.013701 test-rmse:1.117390+0.163653
## [581] train-rmse:0.795622+0.013727 test-rmse:1.116748+0.162807
## [582] train-rmse:0.795093+0.013751 test-rmse:1.115428+0.162002
## [583] train-rmse:0.794572+0.013774 test-rmse:1.114707+0.161934
## [584] train-rmse:0.794055+0.013800 test-rmse:1.114002+0.162047
## [585] train-rmse:0.793541+0.013826 test-rmse:1.113691+0.162143
## [586] train-rmse:0.793031+0.013847 test-rmse:1.113605+0.161917
## [587] train-rmse:0.792517+0.013874 test-rmse:1.113581+0.162111
## [588] train-rmse:0.792010+0.013894 test-rmse:1.113210+0.162162
## [589] train-rmse:0.791508+0.013912 test-rmse:1.112745+0.162656
## [590] train-rmse:0.791015+0.013935 test-rmse:1.112780+0.162733
## [591] train-rmse:0.790528+0.013953 test-rmse:1.111909+0.162615
## [592] train-rmse:0.790041+0.013971 test-rmse:1.112074+0.162988
## [593] train-rmse:0.789555+0.013991 test-rmse:1.112437+0.162540
## [594] train-rmse:0.789070+0.014010 test-rmse:1.112525+0.162902
## [595] train-rmse:0.788590+0.014034 test-rmse:1.112209+0.163372
## [596] train-rmse:0.788105+0.014048 test-rmse:1.111914+0.163224
## [597] train-rmse:0.787630+0.014072 test-rmse:1.109922+0.161424
## [598] train-rmse:0.787156+0.014097 test-rmse:1.108954+0.160701
## [599] train-rmse:0.786684+0.014122 test-rmse:1.109115+0.160628
## [600] train-rmse:0.786212+0.014151 test-rmse:1.107940+0.160344
## [601] train-rmse:0.785742+0.014179 test-rmse:1.107867+0.160448
## [602] train-rmse:0.785274+0.014202 test-rmse:1.107378+0.160799
## [603] train-rmse:0.784813+0.014224 test-rmse:1.107346+0.160669
## [604] train-rmse:0.784349+0.014245 test-rmse:1.106196+0.160268
## [605] train-rmse:0.783892+0.014268 test-rmse:1.105289+0.160531
## [606] train-rmse:0.783441+0.014289 test-rmse:1.104810+0.160485
## [607] train-rmse:0.782988+0.014308 test-rmse:1.105102+0.159679
## [608] train-rmse:0.782534+0.014327 test-rmse:1.104234+0.159845
## [609] train-rmse:0.782080+0.014348 test-rmse:1.103963+0.159622
## [610] train-rmse:0.781633+0.014371 test-rmse:1.103288+0.159832
## [611] train-rmse:0.781183+0.014387 test-rmse:1.102789+0.159966
## [612] train-rmse:0.780738+0.014410 test-rmse:1.102532+0.159826
## [613] train-rmse:0.780292+0.014435 test-rmse:1.101964+0.159798
## [614] train-rmse:0.779850+0.014461 test-rmse:1.101891+0.160412
## [615] train-rmse:0.779413+0.014488 test-rmse:1.101568+0.160675
## [616] train-rmse:0.778979+0.014515 test-rmse:1.101209+0.161111
## [617] train-rmse:0.778537+0.014537 test-rmse:1.100571+0.160264
## [618] train-rmse:0.778106+0.014555 test-rmse:1.100100+0.161194
## [619] train-rmse:0.777681+0.014571 test-rmse:1.100397+0.161389
## [620] train-rmse:0.777262+0.014591 test-rmse:1.099339+0.161356
## [621] train-rmse:0.776843+0.014613 test-rmse:1.098949+0.161549
## [622] train-rmse:0.776427+0.014634 test-rmse:1.098999+0.161502
## [623] train-rmse:0.776007+0.014658 test-rmse:1.098589+0.161307
## [624] train-rmse:0.775597+0.014679 test-rmse:1.098003+0.161598
## [625] train-rmse:0.775189+0.014695 test-rmse:1.097220+0.161250
## [626] train-rmse:0.774780+0.014710 test-rmse:1.097055+0.161040
## [627] train-rmse:0.774373+0.014724 test-rmse:1.096311+0.160661
## [628] train-rmse:0.773965+0.014739 test-rmse:1.095957+0.160666
## [629] train-rmse:0.773558+0.014757 test-rmse:1.095310+0.160971
## [630] train-rmse:0.773148+0.014773 test-rmse:1.095154+0.160596
## [631] train-rmse:0.772746+0.014794 test-rmse:1.095197+0.160111
## [632] train-rmse:0.772347+0.014821 test-rmse:1.095067+0.160198
## [633] train-rmse:0.771939+0.014834 test-rmse:1.094715+0.160127
## [634] train-rmse:0.771537+0.014860 test-rmse:1.094504+0.160319
## [635] train-rmse:0.771151+0.014880 test-rmse:1.093889+0.159926
## [636] train-rmse:0.770763+0.014900 test-rmse:1.094067+0.160081
## [637] train-rmse:0.770376+0.014922 test-rmse:1.093301+0.159576
## [638] train-rmse:0.769988+0.014943 test-rmse:1.093067+0.159775
## [639] train-rmse:0.769600+0.014965 test-rmse:1.091620+0.158392
## [640] train-rmse:0.769214+0.014983 test-rmse:1.091285+0.158677
## [641] train-rmse:0.768833+0.014998 test-rmse:1.090517+0.158802
## [642] train-rmse:0.768454+0.015010 test-rmse:1.090983+0.158930
## [643] train-rmse:0.768075+0.015022 test-rmse:1.091172+0.158595
## [644] train-rmse:0.767699+0.015039 test-rmse:1.090349+0.159092
## [645] train-rmse:0.767316+0.015061 test-rmse:1.089952+0.158982
## [646] train-rmse:0.766942+0.015075 test-rmse:1.089900+0.159132
## [647] train-rmse:0.766570+0.015089 test-rmse:1.089707+0.159190
## [648] train-rmse:0.766201+0.015106 test-rmse:1.090105+0.159105
## [649] train-rmse:0.765833+0.015122 test-rmse:1.090002+0.159138
## [650] train-rmse:0.765465+0.015138 test-rmse:1.089551+0.159217
## [651] train-rmse:0.765102+0.015158 test-rmse:1.089251+0.159388
## [652] train-rmse:0.764741+0.015181 test-rmse:1.089206+0.159301
## [653] train-rmse:0.764378+0.015201 test-rmse:1.088642+0.159514
## [654] train-rmse:0.764020+0.015216 test-rmse:1.088060+0.159120
## [655] train-rmse:0.763665+0.015230 test-rmse:1.087437+0.159147
## [656] train-rmse:0.763309+0.015244 test-rmse:1.087261+0.158506
## [657] train-rmse:0.762957+0.015263 test-rmse:1.087162+0.158308
## [658] train-rmse:0.762604+0.015283 test-rmse:1.086343+0.158183
## [659] train-rmse:0.762251+0.015300 test-rmse:1.086021+0.158222
## [660] train-rmse:0.761902+0.015317 test-rmse:1.086280+0.158069
## [661] train-rmse:0.761558+0.015333 test-rmse:1.085803+0.157897
## [662] train-rmse:0.761215+0.015348 test-rmse:1.084872+0.158043
## [663] train-rmse:0.760873+0.015362 test-rmse:1.085128+0.158228
## [664] train-rmse:0.760530+0.015382 test-rmse:1.084919+0.158116
## [665] train-rmse:0.760192+0.015397 test-rmse:1.084643+0.158426
## [666] train-rmse:0.759859+0.015412 test-rmse:1.084089+0.158588
## [667] train-rmse:0.759509+0.015420 test-rmse:1.084402+0.158162
## [668] train-rmse:0.759175+0.015432 test-rmse:1.084247+0.158348
## [669] train-rmse:0.758836+0.015444 test-rmse:1.083507+0.157826
## [670] train-rmse:0.758504+0.015457 test-rmse:1.082715+0.156203
## [671] train-rmse:0.758171+0.015465 test-rmse:1.082383+0.156708
## [672] train-rmse:0.757840+0.015484 test-rmse:1.082188+0.156320
## [673] train-rmse:0.757512+0.015499 test-rmse:1.081193+0.156712
## [674] train-rmse:0.757186+0.015514 test-rmse:1.081015+0.156589
## [675] train-rmse:0.756859+0.015532 test-rmse:1.080431+0.156531
## [676] train-rmse:0.756532+0.015550 test-rmse:1.079969+0.156702
## [677] train-rmse:0.756207+0.015564 test-rmse:1.080137+0.156625
## [678] train-rmse:0.755886+0.015582 test-rmse:1.079949+0.156948
## [679] train-rmse:0.755564+0.015596 test-rmse:1.079438+0.156994
## [680] train-rmse:0.755245+0.015610 test-rmse:1.079362+0.156589
## [681] train-rmse:0.754922+0.015620 test-rmse:1.079162+0.156723
## [682] train-rmse:0.754601+0.015629 test-rmse:1.078878+0.156738
## [683] train-rmse:0.754280+0.015637 test-rmse:1.079105+0.156816
## [684] train-rmse:0.753969+0.015647 test-rmse:1.078463+0.156762
## [685] train-rmse:0.753655+0.015658 test-rmse:1.078054+0.156097
## [686] train-rmse:0.753344+0.015671 test-rmse:1.078124+0.155572
## [687] train-rmse:0.753036+0.015685 test-rmse:1.078240+0.155669
## [688] train-rmse:0.752727+0.015695 test-rmse:1.078126+0.155756
## [689] train-rmse:0.752417+0.015702 test-rmse:1.077820+0.155390
## [690] train-rmse:0.752111+0.015714 test-rmse:1.077423+0.155406
## [691] train-rmse:0.751801+0.015731 test-rmse:1.077565+0.155119
## [692] train-rmse:0.751493+0.015742 test-rmse:1.077323+0.155136
## [693] train-rmse:0.751189+0.015752 test-rmse:1.077752+0.155607
## [694] train-rmse:0.750887+0.015764 test-rmse:1.077262+0.155806
## [695] train-rmse:0.750586+0.015772 test-rmse:1.076671+0.155483
## [696] train-rmse:0.750289+0.015783 test-rmse:1.076145+0.155648
## [697] train-rmse:0.749994+0.015795 test-rmse:1.075902+0.155376
## [698] train-rmse:0.749697+0.015809 test-rmse:1.075757+0.155298
## [699] train-rmse:0.749393+0.015816 test-rmse:1.075674+0.155607
## [700] train-rmse:0.749100+0.015827 test-rmse:1.074865+0.155564
## [701] train-rmse:0.748807+0.015834 test-rmse:1.074732+0.155845
## [702] train-rmse:0.748511+0.015845 test-rmse:1.075168+0.155722
## [703] train-rmse:0.748216+0.015856 test-rmse:1.074802+0.155765
## [704] train-rmse:0.747928+0.015866 test-rmse:1.075055+0.155749
## [705] train-rmse:0.747638+0.015875 test-rmse:1.074619+0.155985
## [706] train-rmse:0.747354+0.015887 test-rmse:1.073895+0.156062
## [707] train-rmse:0.747070+0.015898 test-rmse:1.073377+0.156124
## [708] train-rmse:0.746784+0.015910 test-rmse:1.072739+0.155641
## [709] train-rmse:0.746505+0.015920 test-rmse:1.072577+0.155715
## [710] train-rmse:0.746225+0.015928 test-rmse:1.072093+0.156096
## [711] train-rmse:0.745933+0.015935 test-rmse:1.071737+0.156336
## [712] train-rmse:0.745651+0.015938 test-rmse:1.071063+0.156566
## [713] train-rmse:0.745374+0.015946 test-rmse:1.071469+0.156304
## [714] train-rmse:0.745095+0.015950 test-rmse:1.070944+0.155948
## [715] train-rmse:0.744817+0.015955 test-rmse:1.071065+0.155737
## [716] train-rmse:0.744539+0.015959 test-rmse:1.070477+0.156229
## [717] train-rmse:0.744266+0.015965 test-rmse:1.070280+0.156106
## [718] train-rmse:0.743984+0.015968 test-rmse:1.070452+0.155798
## [719] train-rmse:0.743710+0.015977 test-rmse:1.069963+0.155930
## [720] train-rmse:0.743431+0.015981 test-rmse:1.069579+0.156296
## [721] train-rmse:0.743157+0.015985 test-rmse:1.069680+0.156308
## [722] train-rmse:0.742886+0.015990 test-rmse:1.069520+0.156917
## [723] train-rmse:0.742616+0.015998 test-rmse:1.069460+0.156708
## [724] train-rmse:0.742346+0.016006 test-rmse:1.068877+0.156975
## [725] train-rmse:0.742079+0.016015 test-rmse:1.068548+0.156363
## [726] train-rmse:0.741815+0.016022 test-rmse:1.068655+0.156101
## [727] train-rmse:0.741549+0.016030 test-rmse:1.067833+0.156113
## [728] train-rmse:0.741288+0.016035 test-rmse:1.067537+0.156184
## [729] train-rmse:0.741029+0.016041 test-rmse:1.067686+0.156422
## [730] train-rmse:0.740770+0.016046 test-rmse:1.067812+0.155686
## [731] train-rmse:0.740506+0.016050 test-rmse:1.067654+0.156340
## [732] train-rmse:0.740245+0.016053 test-rmse:1.067179+0.156355
## [733] train-rmse:0.739988+0.016054 test-rmse:1.067534+0.155977
## [734] train-rmse:0.739731+0.016056 test-rmse:1.067411+0.156270
## [735] train-rmse:0.739473+0.016056 test-rmse:1.067442+0.156072
## [736] train-rmse:0.739218+0.016060 test-rmse:1.066962+0.155650
## [737] train-rmse:0.738965+0.016062 test-rmse:1.066818+0.154999
## [738] train-rmse:0.738711+0.016066 test-rmse:1.066163+0.155331
## [739] train-rmse:0.738460+0.016071 test-rmse:1.065087+0.154006
## [740] train-rmse:0.738209+0.016076 test-rmse:1.065134+0.154088
## [741] train-rmse:0.737959+0.016080 test-rmse:1.064583+0.153992
## [742] train-rmse:0.737706+0.016083 test-rmse:1.064445+0.153718
## [743] train-rmse:0.737452+0.016086 test-rmse:1.064759+0.153674
## [744] train-rmse:0.737205+0.016088 test-rmse:1.065018+0.153629
## [745] train-rmse:0.736957+0.016089 test-rmse:1.064496+0.154014
## [746] train-rmse:0.736710+0.016090 test-rmse:1.064593+0.153978
## [747] train-rmse:0.736461+0.016091 test-rmse:1.064378+0.154083
## [748] train-rmse:0.736210+0.016091 test-rmse:1.064523+0.153854
## [749] train-rmse:0.735965+0.016096 test-rmse:1.064609+0.154161
## [750] train-rmse:0.735719+0.016094 test-rmse:1.064259+0.153926
## [751] train-rmse:0.735479+0.016095 test-rmse:1.064137+0.153930
## [752] train-rmse:0.735234+0.016094 test-rmse:1.063894+0.154185
## [753] train-rmse:0.734990+0.016092 test-rmse:1.063875+0.154042
## [754] train-rmse:0.734753+0.016090 test-rmse:1.064010+0.153912
## [755] train-rmse:0.734515+0.016091 test-rmse:1.063491+0.154128
## [756] train-rmse:0.734275+0.016090 test-rmse:1.063419+0.153752
## [757] train-rmse:0.734041+0.016091 test-rmse:1.063334+0.153527
## [758] train-rmse:0.733807+0.016089 test-rmse:1.062778+0.153668
## [759] train-rmse:0.733574+0.016086 test-rmse:1.062387+0.153339
## [760] train-rmse:0.733336+0.016082 test-rmse:1.062688+0.152911
## [761] train-rmse:0.733105+0.016083 test-rmse:1.063183+0.152952
## [762] train-rmse:0.732871+0.016083 test-rmse:1.062612+0.153245
## [763] train-rmse:0.732641+0.016082 test-rmse:1.062775+0.152953
## [764] train-rmse:0.732405+0.016082 test-rmse:1.062707+0.153099
## [765] train-rmse:0.732173+0.016083 test-rmse:1.062338+0.153331
## [766] train-rmse:0.731943+0.016081 test-rmse:1.062306+0.153339
## [767] train-rmse:0.731715+0.016079 test-rmse:1.062155+0.153566
## [768] train-rmse:0.731490+0.016079 test-rmse:1.061808+0.153861
## [769] train-rmse:0.731266+0.016077 test-rmse:1.061365+0.153758
## [770] train-rmse:0.731042+0.016077 test-rmse:1.061480+0.153783
## [771] train-rmse:0.730818+0.016075 test-rmse:1.060960+0.154266
## [772] train-rmse:0.730594+0.016073 test-rmse:1.060489+0.154090
## [773] train-rmse:0.730375+0.016070 test-rmse:1.059902+0.154318
## [774] train-rmse:0.730155+0.016067 test-rmse:1.059960+0.154693
## [775] train-rmse:0.729932+0.016067 test-rmse:1.059736+0.154014
## [776] train-rmse:0.729712+0.016064 test-rmse:1.059064+0.153938
## [777] train-rmse:0.729486+0.016050 test-rmse:1.058769+0.153824
## [778] train-rmse:0.729264+0.016046 test-rmse:1.058803+0.153339
## [779] train-rmse:0.729042+0.016042 test-rmse:1.058373+0.153326
## [780] train-rmse:0.728820+0.016031 test-rmse:1.057997+0.153218
## [781] train-rmse:0.728605+0.016027 test-rmse:1.057208+0.152756
## [782] train-rmse:0.728383+0.016020 test-rmse:1.056926+0.152935
## [783] train-rmse:0.728169+0.016018 test-rmse:1.055725+0.151417
## [784] train-rmse:0.727951+0.016010 test-rmse:1.055265+0.151412
## [785] train-rmse:0.727732+0.016006 test-rmse:1.054916+0.151418
## [786] train-rmse:0.727517+0.016001 test-rmse:1.054991+0.151143
## [787] train-rmse:0.727298+0.015993 test-rmse:1.055231+0.151024
## [788] train-rmse:0.727086+0.015986 test-rmse:1.054430+0.151000
## [789] train-rmse:0.726876+0.015981 test-rmse:1.053941+0.151002
## [790] train-rmse:0.726666+0.015977 test-rmse:1.053647+0.151054
## [791] train-rmse:0.726455+0.015972 test-rmse:1.053450+0.150594
## [792] train-rmse:0.726245+0.015967 test-rmse:1.053171+0.150450
## [793] train-rmse:0.726034+0.015962 test-rmse:1.053328+0.150138
## [794] train-rmse:0.725823+0.015956 test-rmse:1.053330+0.149881
## [795] train-rmse:0.725610+0.015949 test-rmse:1.053001+0.150221
## [796] train-rmse:0.725400+0.015940 test-rmse:1.052814+0.149818
## [797] train-rmse:0.725188+0.015935 test-rmse:1.052750+0.150072
## [798] train-rmse:0.724983+0.015924 test-rmse:1.052524+0.149994
## [799] train-rmse:0.724778+0.015916 test-rmse:1.051822+0.149917
## [800] train-rmse:0.724572+0.015906 test-rmse:1.052313+0.150018
## [801] train-rmse:0.724370+0.015897 test-rmse:1.052277+0.149691
## [802] train-rmse:0.724166+0.015888 test-rmse:1.051972+0.149579
## [803] train-rmse:0.723961+0.015881 test-rmse:1.051864+0.149583
## [804] train-rmse:0.723759+0.015873 test-rmse:1.051271+0.149664
## [805] train-rmse:0.723559+0.015864 test-rmse:1.050937+0.149233
## [806] train-rmse:0.723359+0.015856 test-rmse:1.050573+0.149403
## [807] train-rmse:0.723161+0.015848 test-rmse:1.051010+0.149207
## [808] train-rmse:0.722961+0.015839 test-rmse:1.050593+0.149554
## [809] train-rmse:0.722761+0.015830 test-rmse:1.050004+0.149532
## [810] train-rmse:0.722563+0.015821 test-rmse:1.049531+0.149136
## [811] train-rmse:0.722360+0.015810 test-rmse:1.049681+0.149002
## [812] train-rmse:0.722161+0.015800 test-rmse:1.049449+0.149339
## [813] train-rmse:0.721960+0.015789 test-rmse:1.049789+0.149079
## [814] train-rmse:0.721765+0.015781 test-rmse:1.049411+0.149155
## [815] train-rmse:0.721570+0.015768 test-rmse:1.048661+0.148678
## [816] train-rmse:0.721376+0.015757 test-rmse:1.048919+0.148644
## [817] train-rmse:0.721179+0.015750 test-rmse:1.048406+0.148530
## [818] train-rmse:0.720987+0.015739 test-rmse:1.048386+0.148376
## [819] train-rmse:0.720793+0.015728 test-rmse:1.048283+0.147946
## [820] train-rmse:0.720603+0.015717 test-rmse:1.048082+0.148064
## [821] train-rmse:0.720404+0.015706 test-rmse:1.048345+0.148142
## [822] train-rmse:0.720215+0.015695 test-rmse:1.048091+0.147443
## [823] train-rmse:0.720018+0.015683 test-rmse:1.047599+0.147703
## [824] train-rmse:0.719830+0.015674 test-rmse:1.046525+0.146537
## [825] train-rmse:0.719642+0.015664 test-rmse:1.046117+0.146587
## [826] train-rmse:0.719453+0.015652 test-rmse:1.046160+0.146465
## [827] train-rmse:0.719266+0.015640 test-rmse:1.045639+0.146968
## [828] train-rmse:0.719080+0.015632 test-rmse:1.045536+0.147068
## [829] train-rmse:0.718894+0.015622 test-rmse:1.045511+0.147030
## [830] train-rmse:0.718706+0.015609 test-rmse:1.045775+0.146930
## [831] train-rmse:0.718520+0.015595 test-rmse:1.046004+0.146759
## [832] train-rmse:0.718334+0.015582 test-rmse:1.046043+0.146981
## [833] train-rmse:0.718146+0.015568 test-rmse:1.045814+0.146623
## [834] train-rmse:0.717960+0.015557 test-rmse:1.045794+0.145971
## [835] train-rmse:0.717772+0.015543 test-rmse:1.045633+0.145502
## Stopping. Best iteration:
## [829] train-rmse:0.718894+0.015622 test-rmse:1.045511+0.147030
##
## 8
## [1] train-rmse:94.766116+0.105094 test-rmse:94.766423+0.483432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:83.478061+0.095217 test-rmse:83.478186+0.500072
## [3] train-rmse:73.545764+0.086897 test-rmse:73.545670+0.516055
## [4] train-rmse:64.807895+0.080011 test-rmse:64.807548+0.531590
## [5] train-rmse:57.122527+0.074434 test-rmse:57.121858+0.546922
## [6] train-rmse:50.364803+0.070103 test-rmse:50.363757+0.562250
## [7] train-rmse:44.424928+0.066945 test-rmse:44.423422+0.577769
## [8] train-rmse:39.206363+0.064904 test-rmse:39.204310+0.593657
## [9] train-rmse:34.622293+0.063135 test-rmse:34.630677+0.594785
## [10] train-rmse:30.591222+0.059482 test-rmse:30.630729+0.602525
## [11] train-rmse:27.046633+0.053406 test-rmse:27.109487+0.606996
## [12] train-rmse:23.929207+0.045920 test-rmse:24.000442+0.580116
## [13] train-rmse:21.189291+0.040281 test-rmse:21.265156+0.581461
## [14] train-rmse:18.782400+0.033623 test-rmse:18.877167+0.584945
## [15] train-rmse:16.669828+0.030754 test-rmse:16.754118+0.563482
## [16] train-rmse:14.816258+0.029109 test-rmse:14.915749+0.566890
## [17] train-rmse:13.192993+0.028337 test-rmse:13.299956+0.582644
## [18] train-rmse:11.772369+0.028212 test-rmse:11.866388+0.551522
## [19] train-rmse:10.532035+0.028831 test-rmse:10.641003+0.557631
## [20] train-rmse:9.451512+0.029733 test-rmse:9.582640+0.569439
## [21] train-rmse:8.511694+0.031360 test-rmse:8.645439+0.583578
## [22] train-rmse:7.695953+0.033340 test-rmse:7.825109+0.558207
## [23] train-rmse:6.990903+0.035285 test-rmse:7.141761+0.549602
## [24] train-rmse:6.381549+0.037432 test-rmse:6.558248+0.557563
## [25] train-rmse:5.855398+0.036452 test-rmse:6.019081+0.540192
## [26] train-rmse:5.406448+0.038580 test-rmse:5.585065+0.542725
## [27] train-rmse:5.022503+0.039907 test-rmse:5.204402+0.518089
## [28] train-rmse:4.694001+0.040314 test-rmse:4.883385+0.518065
## [29] train-rmse:4.412902+0.039738 test-rmse:4.638226+0.519981
## [30] train-rmse:4.173967+0.040546 test-rmse:4.378171+0.491890
## [31] train-rmse:3.971348+0.039758 test-rmse:4.201956+0.490766
## [32] train-rmse:3.798811+0.039949 test-rmse:4.049231+0.475419
## [33] train-rmse:3.650476+0.039560 test-rmse:3.906412+0.447988
## [34] train-rmse:3.523801+0.039527 test-rmse:3.783196+0.438369
## [35] train-rmse:3.414176+0.040930 test-rmse:3.682786+0.436392
## [36] train-rmse:3.317690+0.039459 test-rmse:3.596762+0.424499
## [37] train-rmse:3.234079+0.039003 test-rmse:3.501574+0.391774
## [38] train-rmse:3.160492+0.038832 test-rmse:3.439888+0.380744
## [39] train-rmse:3.090999+0.034231 test-rmse:3.397616+0.381123
## [40] train-rmse:3.032294+0.034604 test-rmse:3.339730+0.373057
## [41] train-rmse:2.978680+0.034096 test-rmse:3.291501+0.372030
## [42] train-rmse:2.929123+0.034896 test-rmse:3.238516+0.362613
## [43] train-rmse:2.883536+0.034495 test-rmse:3.185558+0.340520
## [44] train-rmse:2.839349+0.033727 test-rmse:3.153715+0.345419
## [45] train-rmse:2.796578+0.030197 test-rmse:3.121506+0.338315
## [46] train-rmse:2.758005+0.031026 test-rmse:3.099283+0.344197
## [47] train-rmse:2.722105+0.030384 test-rmse:3.054430+0.319845
## [48] train-rmse:2.687866+0.029540 test-rmse:3.021460+0.319865
## [49] train-rmse:2.655206+0.029340 test-rmse:2.996221+0.307268
## [50] train-rmse:2.621266+0.029133 test-rmse:2.973288+0.313774
## [51] train-rmse:2.590715+0.028645 test-rmse:2.943425+0.317500
## [52] train-rmse:2.560634+0.027598 test-rmse:2.918515+0.317844
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## [54] train-rmse:2.501892+0.026490 test-rmse:2.869777+0.295579
## [55] train-rmse:2.474875+0.025955 test-rmse:2.842185+0.301409
## [56] train-rmse:2.447003+0.025092 test-rmse:2.817418+0.295264
## [57] train-rmse:2.419785+0.023897 test-rmse:2.795675+0.287105
## [58] train-rmse:2.393858+0.024172 test-rmse:2.777074+0.292829
## [59] train-rmse:2.367000+0.024643 test-rmse:2.763482+0.294832
## [60] train-rmse:2.342787+0.024269 test-rmse:2.729130+0.275141
## [61] train-rmse:2.318562+0.023762 test-rmse:2.700914+0.276099
## [62] train-rmse:2.294430+0.022425 test-rmse:2.683863+0.272957
## [63] train-rmse:2.269907+0.022619 test-rmse:2.652857+0.275957
## [64] train-rmse:2.246380+0.021834 test-rmse:2.643622+0.276739
## [65] train-rmse:2.221955+0.019875 test-rmse:2.623680+0.265328
## [66] train-rmse:2.200239+0.019549 test-rmse:2.606271+0.266657
## [67] train-rmse:2.177671+0.020026 test-rmse:2.592163+0.264254
## [68] train-rmse:2.156747+0.019597 test-rmse:2.571740+0.269749
## [69] train-rmse:2.134652+0.019360 test-rmse:2.552505+0.266601
## [70] train-rmse:2.113583+0.019045 test-rmse:2.534218+0.265147
## [71] train-rmse:2.093020+0.018967 test-rmse:2.511239+0.268927
## [72] train-rmse:2.071789+0.018299 test-rmse:2.494173+0.258889
## [73] train-rmse:2.051619+0.017872 test-rmse:2.480035+0.262967
## [74] train-rmse:2.031588+0.016894 test-rmse:2.469166+0.263922
## [75] train-rmse:2.012615+0.016494 test-rmse:2.448805+0.266812
## [76] train-rmse:1.993592+0.015368 test-rmse:2.432633+0.265514
## [77] train-rmse:1.975055+0.015179 test-rmse:2.421154+0.263308
## [78] train-rmse:1.956678+0.014741 test-rmse:2.390761+0.259549
## [79] train-rmse:1.937212+0.014664 test-rmse:2.371700+0.257875
## [80] train-rmse:1.918824+0.013980 test-rmse:2.360433+0.254648
## [81] train-rmse:1.901237+0.013596 test-rmse:2.347834+0.259273
## [82] train-rmse:1.884075+0.013274 test-rmse:2.326786+0.255350
## [83] train-rmse:1.866069+0.012714 test-rmse:2.319575+0.257489
## [84] train-rmse:1.848641+0.012368 test-rmse:2.304911+0.263187
## [85] train-rmse:1.831688+0.011925 test-rmse:2.289220+0.257483
## [86] train-rmse:1.815077+0.011303 test-rmse:2.274496+0.255499
## [87] train-rmse:1.798341+0.010551 test-rmse:2.259965+0.258934
## [88] train-rmse:1.781853+0.010041 test-rmse:2.242714+0.256117
## [89] train-rmse:1.764975+0.009435 test-rmse:2.230112+0.259138
## [90] train-rmse:1.749402+0.008964 test-rmse:2.218319+0.262777
## [91] train-rmse:1.733880+0.009090 test-rmse:2.203511+0.255311
## [92] train-rmse:1.718898+0.008890 test-rmse:2.187171+0.249950
## [93] train-rmse:1.703895+0.008650 test-rmse:2.170961+0.249084
## [94] train-rmse:1.688317+0.007952 test-rmse:2.155731+0.246938
## [95] train-rmse:1.673549+0.007949 test-rmse:2.145410+0.247018
## [96] train-rmse:1.659022+0.007654 test-rmse:2.133947+0.250005
## [97] train-rmse:1.644848+0.007478 test-rmse:2.116389+0.252146
## [98] train-rmse:1.630218+0.007273 test-rmse:2.111290+0.251380
## [99] train-rmse:1.616281+0.007237 test-rmse:2.107750+0.250957
## [100] train-rmse:1.601351+0.006645 test-rmse:2.096617+0.248019
## [101] train-rmse:1.587503+0.006125 test-rmse:2.081166+0.249065
## [102] train-rmse:1.574036+0.005957 test-rmse:2.062210+0.249173
## [103] train-rmse:1.560912+0.006288 test-rmse:2.048003+0.244194
## [104] train-rmse:1.547906+0.006134 test-rmse:2.037545+0.242729
## [105] train-rmse:1.534564+0.006131 test-rmse:2.030440+0.244951
## [106] train-rmse:1.521680+0.006524 test-rmse:2.019237+0.249017
## [107] train-rmse:1.509188+0.006307 test-rmse:2.003407+0.246679
## [108] train-rmse:1.496732+0.006332 test-rmse:1.992062+0.242400
## [109] train-rmse:1.484445+0.006857 test-rmse:1.976398+0.243605
## [110] train-rmse:1.471864+0.006339 test-rmse:1.974325+0.245439
## [111] train-rmse:1.459494+0.005958 test-rmse:1.966076+0.245015
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## [113] train-rmse:1.435862+0.005760 test-rmse:1.944539+0.240403
## [114] train-rmse:1.424379+0.005849 test-rmse:1.933447+0.241470
## [115] train-rmse:1.412532+0.006028 test-rmse:1.922230+0.236002
## [116] train-rmse:1.401591+0.006059 test-rmse:1.910511+0.234596
## [117] train-rmse:1.390436+0.006490 test-rmse:1.902573+0.240025
## [118] train-rmse:1.379422+0.006514 test-rmse:1.891715+0.240170
## [119] train-rmse:1.368180+0.006755 test-rmse:1.883163+0.242792
## [120] train-rmse:1.356780+0.007211 test-rmse:1.875431+0.246906
## [121] train-rmse:1.345482+0.007191 test-rmse:1.870579+0.245117
## [122] train-rmse:1.334723+0.007116 test-rmse:1.863831+0.244104
## [123] train-rmse:1.324013+0.006916 test-rmse:1.850742+0.244442
## [124] train-rmse:1.313355+0.006801 test-rmse:1.844662+0.244741
## [125] train-rmse:1.303357+0.007015 test-rmse:1.831995+0.240882
## [126] train-rmse:1.293262+0.006916 test-rmse:1.824312+0.242528
## [127] train-rmse:1.283319+0.007099 test-rmse:1.814900+0.239099
## [128] train-rmse:1.273695+0.007439 test-rmse:1.805359+0.239259
## [129] train-rmse:1.263670+0.007135 test-rmse:1.797467+0.238043
## [130] train-rmse:1.254125+0.007318 test-rmse:1.789310+0.237630
## [131] train-rmse:1.244739+0.007485 test-rmse:1.780180+0.238561
## [132] train-rmse:1.235369+0.007783 test-rmse:1.773997+0.239593
## [133] train-rmse:1.226050+0.007603 test-rmse:1.759637+0.235797
## [134] train-rmse:1.216986+0.007475 test-rmse:1.755517+0.235631
## [135] train-rmse:1.207562+0.007461 test-rmse:1.750186+0.239192
## [136] train-rmse:1.198715+0.007567 test-rmse:1.739553+0.238653
## [137] train-rmse:1.189803+0.007738 test-rmse:1.733502+0.242187
## [138] train-rmse:1.181019+0.007692 test-rmse:1.726535+0.242743
## [139] train-rmse:1.172246+0.008257 test-rmse:1.720859+0.240903
## [140] train-rmse:1.163590+0.008337 test-rmse:1.716353+0.239687
## [141] train-rmse:1.155168+0.008577 test-rmse:1.709735+0.240217
## [142] train-rmse:1.146890+0.008727 test-rmse:1.702787+0.241350
## [143] train-rmse:1.138598+0.008857 test-rmse:1.694795+0.241699
## [144] train-rmse:1.130669+0.009048 test-rmse:1.684145+0.235745
## [145] train-rmse:1.122131+0.009067 test-rmse:1.676595+0.236997
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## [147] train-rmse:1.105139+0.009338 test-rmse:1.661756+0.239380
## [148] train-rmse:1.096992+0.009917 test-rmse:1.658944+0.240103
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## [150] train-rmse:1.081916+0.009781 test-rmse:1.647583+0.242397
## [151] train-rmse:1.073026+0.010289 test-rmse:1.642724+0.241871
## [152] train-rmse:1.065764+0.010442 test-rmse:1.636027+0.241031
## [153] train-rmse:1.058092+0.011029 test-rmse:1.629954+0.240636
## [154] train-rmse:1.051069+0.011137 test-rmse:1.624877+0.242592
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## [156] train-rmse:1.037312+0.011370 test-rmse:1.607205+0.239050
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## [158] train-rmse:1.022934+0.011794 test-rmse:1.597483+0.241377
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## [160] train-rmse:1.009115+0.011215 test-rmse:1.588277+0.242418
## [161] train-rmse:1.002654+0.011457 test-rmse:1.582200+0.241608
## [162] train-rmse:0.996102+0.011669 test-rmse:1.576813+0.244389
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## [165] train-rmse:0.976972+0.012060 test-rmse:1.563311+0.242137
## [166] train-rmse:0.970664+0.012553 test-rmse:1.560709+0.242049
## [167] train-rmse:0.964583+0.012539 test-rmse:1.553220+0.242937
## [168] train-rmse:0.958669+0.012598 test-rmse:1.545781+0.243592
## [169] train-rmse:0.951989+0.013054 test-rmse:1.542730+0.242550
## [170] train-rmse:0.946160+0.013299 test-rmse:1.536793+0.242300
## [171] train-rmse:0.939796+0.012983 test-rmse:1.534799+0.243186
## [172] train-rmse:0.933885+0.012877 test-rmse:1.530696+0.246755
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## [174] train-rmse:0.922346+0.012915 test-rmse:1.517180+0.245570
## [175] train-rmse:0.916736+0.012993 test-rmse:1.512660+0.243193
## [176] train-rmse:0.910903+0.013627 test-rmse:1.510171+0.243873
## [177] train-rmse:0.905250+0.013885 test-rmse:1.506089+0.243453
## [178] train-rmse:0.899259+0.013831 test-rmse:1.499750+0.244243
## [179] train-rmse:0.893641+0.014025 test-rmse:1.493605+0.246924
## [180] train-rmse:0.887492+0.014840 test-rmse:1.490471+0.245440
## [181] train-rmse:0.882036+0.014518 test-rmse:1.487296+0.244860
## [182] train-rmse:0.876640+0.014357 test-rmse:1.484602+0.246690
## [183] train-rmse:0.871831+0.014371 test-rmse:1.478646+0.243655
## [184] train-rmse:0.866730+0.014334 test-rmse:1.477247+0.244500
## [185] train-rmse:0.861714+0.014325 test-rmse:1.472335+0.244788
## [186] train-rmse:0.856807+0.014176 test-rmse:1.466885+0.245885
## [187] train-rmse:0.851621+0.013990 test-rmse:1.465597+0.246535
## [188] train-rmse:0.846722+0.014045 test-rmse:1.461857+0.246883
## [189] train-rmse:0.842124+0.014178 test-rmse:1.456939+0.247939
## [190] train-rmse:0.837611+0.014193 test-rmse:1.452098+0.247101
## [191] train-rmse:0.832660+0.013916 test-rmse:1.447403+0.247516
## [192] train-rmse:0.827503+0.013553 test-rmse:1.444106+0.248597
## [193] train-rmse:0.822920+0.013586 test-rmse:1.440756+0.250864
## [194] train-rmse:0.818176+0.014035 test-rmse:1.438448+0.249586
## [195] train-rmse:0.813929+0.014171 test-rmse:1.434725+0.251012
## [196] train-rmse:0.809131+0.013868 test-rmse:1.431166+0.250778
## [197] train-rmse:0.804402+0.014079 test-rmse:1.427316+0.251503
## [198] train-rmse:0.800080+0.013759 test-rmse:1.423613+0.252549
## [199] train-rmse:0.795900+0.013796 test-rmse:1.420380+0.251250
## [200] train-rmse:0.791695+0.013839 test-rmse:1.417368+0.251898
## [201] train-rmse:0.787448+0.014057 test-rmse:1.415805+0.252216
## [202] train-rmse:0.782591+0.014457 test-rmse:1.412254+0.251109
## [203] train-rmse:0.777684+0.014823 test-rmse:1.406929+0.252196
## [204] train-rmse:0.773811+0.014735 test-rmse:1.404195+0.251958
## [205] train-rmse:0.769622+0.014898 test-rmse:1.399543+0.252739
## [206] train-rmse:0.765675+0.015087 test-rmse:1.397824+0.253098
## [207] train-rmse:0.761565+0.015388 test-rmse:1.394737+0.255697
## [208] train-rmse:0.757337+0.015203 test-rmse:1.393696+0.255771
## [209] train-rmse:0.753166+0.015192 test-rmse:1.390387+0.256050
## [210] train-rmse:0.749238+0.014977 test-rmse:1.388328+0.255609
## [211] train-rmse:0.745202+0.015138 test-rmse:1.383671+0.256510
## [212] train-rmse:0.741465+0.015165 test-rmse:1.380059+0.257195
## [213] train-rmse:0.737868+0.015042 test-rmse:1.377966+0.256428
## [214] train-rmse:0.734394+0.015157 test-rmse:1.375247+0.257216
## [215] train-rmse:0.730396+0.015545 test-rmse:1.374046+0.258995
## [216] train-rmse:0.726824+0.015536 test-rmse:1.369710+0.256971
## [217] train-rmse:0.723201+0.015489 test-rmse:1.368350+0.256030
## [218] train-rmse:0.719584+0.015356 test-rmse:1.366721+0.256925
## [219] train-rmse:0.716261+0.015179 test-rmse:1.363437+0.258940
## [220] train-rmse:0.713052+0.015129 test-rmse:1.360658+0.259453
## [221] train-rmse:0.709515+0.015236 test-rmse:1.359659+0.258906
## [222] train-rmse:0.706157+0.015356 test-rmse:1.358300+0.259224
## [223] train-rmse:0.702918+0.015316 test-rmse:1.357036+0.260013
## [224] train-rmse:0.699795+0.015036 test-rmse:1.354776+0.258721
## [225] train-rmse:0.696317+0.014825 test-rmse:1.353274+0.258900
## [226] train-rmse:0.692882+0.015003 test-rmse:1.352147+0.259322
## [227] train-rmse:0.689837+0.014837 test-rmse:1.351256+0.260045
## [228] train-rmse:0.686752+0.014841 test-rmse:1.347496+0.257549
## [229] train-rmse:0.683810+0.014839 test-rmse:1.345730+0.258231
## [230] train-rmse:0.680864+0.014901 test-rmse:1.338078+0.259925
## [231] train-rmse:0.677663+0.014984 test-rmse:1.336336+0.259269
## [232] train-rmse:0.674757+0.014881 test-rmse:1.333649+0.259981
## [233] train-rmse:0.671731+0.014891 test-rmse:1.331969+0.260189
## [234] train-rmse:0.668538+0.014778 test-rmse:1.331119+0.261117
## [235] train-rmse:0.665533+0.014679 test-rmse:1.326926+0.260682
## [236] train-rmse:0.662815+0.014704 test-rmse:1.324725+0.260747
## [237] train-rmse:0.659264+0.014355 test-rmse:1.321134+0.261931
## [238] train-rmse:0.656088+0.014425 test-rmse:1.319158+0.262031
## [239] train-rmse:0.653474+0.014298 test-rmse:1.317611+0.262156
## [240] train-rmse:0.650898+0.014077 test-rmse:1.316777+0.263051
## [241] train-rmse:0.648363+0.014100 test-rmse:1.316693+0.263171
## [242] train-rmse:0.645676+0.014205 test-rmse:1.314210+0.263277
## [243] train-rmse:0.642947+0.013978 test-rmse:1.312661+0.264886
## [244] train-rmse:0.639721+0.013964 test-rmse:1.311248+0.263638
## [245] train-rmse:0.637025+0.014143 test-rmse:1.310925+0.264004
## [246] train-rmse:0.633937+0.014334 test-rmse:1.308249+0.264209
## [247] train-rmse:0.631121+0.014017 test-rmse:1.305793+0.265233
## [248] train-rmse:0.628566+0.013970 test-rmse:1.304690+0.265869
## [249] train-rmse:0.626024+0.014041 test-rmse:1.301649+0.267836
## [250] train-rmse:0.623349+0.014013 test-rmse:1.300844+0.269028
## [251] train-rmse:0.621016+0.013894 test-rmse:1.297240+0.267361
## [252] train-rmse:0.618413+0.013862 test-rmse:1.296965+0.267254
## [253] train-rmse:0.616206+0.013881 test-rmse:1.296115+0.268312
## [254] train-rmse:0.614024+0.013776 test-rmse:1.293959+0.268422
## [255] train-rmse:0.611367+0.013747 test-rmse:1.291259+0.268931
## [256] train-rmse:0.609077+0.013892 test-rmse:1.290688+0.268625
## [257] train-rmse:0.606828+0.013702 test-rmse:1.289913+0.269131
## [258] train-rmse:0.604523+0.013557 test-rmse:1.286645+0.269918
## [259] train-rmse:0.602205+0.013846 test-rmse:1.285282+0.270959
## [260] train-rmse:0.599861+0.013944 test-rmse:1.283907+0.270762
## [261] train-rmse:0.597779+0.013933 test-rmse:1.283274+0.271897
## [262] train-rmse:0.595564+0.013938 test-rmse:1.280777+0.270955
## [263] train-rmse:0.593388+0.013780 test-rmse:1.278652+0.271141
## [264] train-rmse:0.591209+0.013775 test-rmse:1.275184+0.271942
## [265] train-rmse:0.589096+0.013641 test-rmse:1.274121+0.271624
## [266] train-rmse:0.587039+0.013657 test-rmse:1.274078+0.271676
## [267] train-rmse:0.584681+0.013749 test-rmse:1.272981+0.272570
## [268] train-rmse:0.582502+0.013739 test-rmse:1.271236+0.273292
## [269] train-rmse:0.580440+0.013395 test-rmse:1.266912+0.269304
## [270] train-rmse:0.578587+0.013325 test-rmse:1.265903+0.269046
## [271] train-rmse:0.576351+0.013553 test-rmse:1.265256+0.270227
## [272] train-rmse:0.574227+0.013406 test-rmse:1.264899+0.270093
## [273] train-rmse:0.572338+0.013260 test-rmse:1.264238+0.269928
## [274] train-rmse:0.569919+0.012912 test-rmse:1.262449+0.269128
## [275] train-rmse:0.568088+0.012702 test-rmse:1.259431+0.269893
## [276] train-rmse:0.565805+0.013308 test-rmse:1.257607+0.269824
## [277] train-rmse:0.563872+0.013512 test-rmse:1.256114+0.269794
## [278] train-rmse:0.561886+0.013473 test-rmse:1.255006+0.269958
## [279] train-rmse:0.560053+0.013520 test-rmse:1.254548+0.270799
## [280] train-rmse:0.558497+0.013532 test-rmse:1.252179+0.272784
## [281] train-rmse:0.556560+0.013452 test-rmse:1.251315+0.272246
## [282] train-rmse:0.554697+0.013379 test-rmse:1.249438+0.272251
## [283] train-rmse:0.552889+0.013218 test-rmse:1.248256+0.271372
## [284] train-rmse:0.550726+0.013707 test-rmse:1.247279+0.271075
## [285] train-rmse:0.548795+0.013654 test-rmse:1.245966+0.270657
## [286] train-rmse:0.546857+0.013697 test-rmse:1.242856+0.266809
## [287] train-rmse:0.545014+0.013555 test-rmse:1.241546+0.268202
## [288] train-rmse:0.543341+0.013589 test-rmse:1.240900+0.268020
## [289] train-rmse:0.541537+0.013691 test-rmse:1.239869+0.267700
## [290] train-rmse:0.539588+0.013916 test-rmse:1.239302+0.268332
## [291] train-rmse:0.538118+0.013895 test-rmse:1.238262+0.268343
## [292] train-rmse:0.536080+0.013727 test-rmse:1.235735+0.270167
## [293] train-rmse:0.534559+0.013920 test-rmse:1.234219+0.270617
## [294] train-rmse:0.532463+0.013918 test-rmse:1.233450+0.271114
## [295] train-rmse:0.530798+0.013801 test-rmse:1.232602+0.272394
## [296] train-rmse:0.529006+0.013738 test-rmse:1.231654+0.272598
## [297] train-rmse:0.527008+0.014043 test-rmse:1.230967+0.273104
## [298] train-rmse:0.525447+0.014017 test-rmse:1.229729+0.273363
## [299] train-rmse:0.523839+0.013874 test-rmse:1.228173+0.271694
## [300] train-rmse:0.522333+0.013921 test-rmse:1.226662+0.272652
## [301] train-rmse:0.520835+0.013778 test-rmse:1.226199+0.273395
## [302] train-rmse:0.519025+0.013522 test-rmse:1.224734+0.274312
## [303] train-rmse:0.517482+0.013706 test-rmse:1.223937+0.273435
## [304] train-rmse:0.515826+0.014065 test-rmse:1.223782+0.273212
## [305] train-rmse:0.514200+0.014282 test-rmse:1.222438+0.273634
## [306] train-rmse:0.512732+0.014106 test-rmse:1.222161+0.273482
## [307] train-rmse:0.511192+0.014108 test-rmse:1.221531+0.273891
## [308] train-rmse:0.509811+0.014052 test-rmse:1.220541+0.274056
## [309] train-rmse:0.508549+0.014025 test-rmse:1.217409+0.275471
## [310] train-rmse:0.507199+0.013863 test-rmse:1.216117+0.274862
## [311] train-rmse:0.505853+0.013988 test-rmse:1.215342+0.274675
## [312] train-rmse:0.504576+0.013889 test-rmse:1.214721+0.274771
## [313] train-rmse:0.502891+0.013567 test-rmse:1.213526+0.274545
## [314] train-rmse:0.501591+0.013690 test-rmse:1.212066+0.274621
## [315] train-rmse:0.500250+0.013586 test-rmse:1.211425+0.274854
## [316] train-rmse:0.498880+0.013546 test-rmse:1.210684+0.275103
## [317] train-rmse:0.497253+0.013921 test-rmse:1.209113+0.276030
## [318] train-rmse:0.495441+0.013934 test-rmse:1.208062+0.277173
## [319] train-rmse:0.494290+0.013892 test-rmse:1.207507+0.277563
## [320] train-rmse:0.492998+0.013823 test-rmse:1.207201+0.277182
## [321] train-rmse:0.491518+0.013808 test-rmse:1.206695+0.277641
## [322] train-rmse:0.490110+0.013976 test-rmse:1.205506+0.277806
## [323] train-rmse:0.488476+0.013611 test-rmse:1.204674+0.277584
## [324] train-rmse:0.487136+0.013775 test-rmse:1.203910+0.277112
## [325] train-rmse:0.485694+0.013776 test-rmse:1.202720+0.277229
## [326] train-rmse:0.483985+0.013911 test-rmse:1.202144+0.277426
## [327] train-rmse:0.482904+0.013865 test-rmse:1.201610+0.277322
## [328] train-rmse:0.481556+0.013989 test-rmse:1.201651+0.277257
## [329] train-rmse:0.480320+0.013903 test-rmse:1.201829+0.277524
## [330] train-rmse:0.479121+0.014169 test-rmse:1.201620+0.278378
## [331] train-rmse:0.477851+0.014133 test-rmse:1.200862+0.277518
## [332] train-rmse:0.476758+0.014113 test-rmse:1.200061+0.277483
## [333] train-rmse:0.475523+0.014214 test-rmse:1.197754+0.279651
## [334] train-rmse:0.473828+0.014166 test-rmse:1.194968+0.276943
## [335] train-rmse:0.472726+0.014035 test-rmse:1.193812+0.276800
## [336] train-rmse:0.471688+0.013919 test-rmse:1.192357+0.277154
## [337] train-rmse:0.470469+0.014071 test-rmse:1.192186+0.277865
## [338] train-rmse:0.469356+0.013968 test-rmse:1.191948+0.277683
## [339] train-rmse:0.468239+0.014031 test-rmse:1.190767+0.277794
## [340] train-rmse:0.467169+0.014039 test-rmse:1.189633+0.278106
## [341] train-rmse:0.465913+0.014267 test-rmse:1.188936+0.277771
## [342] train-rmse:0.464914+0.014189 test-rmse:1.187842+0.277931
## [343] train-rmse:0.463932+0.014251 test-rmse:1.187806+0.278313
## [344] train-rmse:0.462709+0.014202 test-rmse:1.187134+0.278396
## [345] train-rmse:0.461590+0.014189 test-rmse:1.185918+0.278755
## [346] train-rmse:0.460576+0.014174 test-rmse:1.185491+0.278699
## [347] train-rmse:0.459454+0.014296 test-rmse:1.185252+0.279087
## [348] train-rmse:0.458473+0.014379 test-rmse:1.183348+0.278841
## [349] train-rmse:0.457306+0.014140 test-rmse:1.182624+0.278417
## [350] train-rmse:0.456284+0.014246 test-rmse:1.182252+0.278667
## [351] train-rmse:0.454813+0.013976 test-rmse:1.181238+0.280691
## [352] train-rmse:0.453810+0.014108 test-rmse:1.179200+0.278406
## [353] train-rmse:0.452900+0.014092 test-rmse:1.178152+0.277452
## [354] train-rmse:0.451938+0.013994 test-rmse:1.177316+0.278381
## [355] train-rmse:0.450949+0.013894 test-rmse:1.175406+0.279392
## [356] train-rmse:0.450051+0.013864 test-rmse:1.174753+0.280095
## [357] train-rmse:0.449077+0.014030 test-rmse:1.174717+0.280756
## [358] train-rmse:0.448081+0.014021 test-rmse:1.172885+0.280876
## [359] train-rmse:0.447057+0.014133 test-rmse:1.172873+0.280641
## [360] train-rmse:0.446219+0.014115 test-rmse:1.173059+0.281083
## [361] train-rmse:0.445298+0.014105 test-rmse:1.172457+0.281303
## [362] train-rmse:0.444554+0.014055 test-rmse:1.172041+0.281320
## [363] train-rmse:0.443732+0.014054 test-rmse:1.171548+0.280991
## [364] train-rmse:0.442402+0.014042 test-rmse:1.170202+0.281819
## [365] train-rmse:0.441432+0.014108 test-rmse:1.170254+0.282107
## [366] train-rmse:0.440327+0.013796 test-rmse:1.169272+0.281574
## [367] train-rmse:0.439097+0.014255 test-rmse:1.168839+0.281693
## [368] train-rmse:0.438137+0.014377 test-rmse:1.167927+0.281500
## [369] train-rmse:0.437267+0.014322 test-rmse:1.167125+0.281967
## [370] train-rmse:0.436428+0.014226 test-rmse:1.166574+0.281389
## [371] train-rmse:0.435429+0.014224 test-rmse:1.165194+0.280771
## [372] train-rmse:0.434345+0.014609 test-rmse:1.164928+0.280339
## [373] train-rmse:0.433544+0.014614 test-rmse:1.164613+0.281123
## [374] train-rmse:0.432667+0.014888 test-rmse:1.164206+0.281055
## [375] train-rmse:0.431933+0.014845 test-rmse:1.163613+0.281296
## [376] train-rmse:0.430903+0.014876 test-rmse:1.163889+0.281374
## [377] train-rmse:0.430017+0.014818 test-rmse:1.163735+0.281181
## [378] train-rmse:0.429054+0.014877 test-rmse:1.163357+0.280855
## [379] train-rmse:0.428002+0.014896 test-rmse:1.162117+0.281757
## [380] train-rmse:0.427268+0.014864 test-rmse:1.161872+0.281572
## [381] train-rmse:0.426217+0.014789 test-rmse:1.162938+0.281163
## [382] train-rmse:0.425521+0.014738 test-rmse:1.161664+0.280737
## [383] train-rmse:0.424771+0.014644 test-rmse:1.160998+0.280567
## [384] train-rmse:0.423905+0.014666 test-rmse:1.160612+0.280877
## [385] train-rmse:0.423175+0.014672 test-rmse:1.160336+0.280905
## [386] train-rmse:0.422210+0.014443 test-rmse:1.160211+0.281186
## [387] train-rmse:0.421371+0.014504 test-rmse:1.159470+0.281332
## [388] train-rmse:0.420389+0.014218 test-rmse:1.158900+0.281731
## [389] train-rmse:0.419614+0.014261 test-rmse:1.157564+0.282290
## [390] train-rmse:0.418853+0.014331 test-rmse:1.156858+0.282398
## [391] train-rmse:0.417989+0.014584 test-rmse:1.156706+0.282544
## [392] train-rmse:0.417218+0.014413 test-rmse:1.156233+0.282009
## [393] train-rmse:0.416598+0.014359 test-rmse:1.156183+0.282426
## [394] train-rmse:0.415867+0.014301 test-rmse:1.155974+0.282966
## [395] train-rmse:0.415059+0.014191 test-rmse:1.155481+0.283786
## [396] train-rmse:0.414183+0.014207 test-rmse:1.154815+0.282882
## [397] train-rmse:0.413460+0.014324 test-rmse:1.154485+0.282871
## [398] train-rmse:0.412587+0.014352 test-rmse:1.154566+0.282741
## [399] train-rmse:0.411805+0.014399 test-rmse:1.153683+0.282091
## [400] train-rmse:0.411184+0.014384 test-rmse:1.153448+0.282008
## [401] train-rmse:0.410465+0.014349 test-rmse:1.153096+0.281919
## [402] train-rmse:0.409708+0.014325 test-rmse:1.153271+0.281671
## [403] train-rmse:0.408760+0.014162 test-rmse:1.152366+0.281408
## [404] train-rmse:0.407613+0.013784 test-rmse:1.153349+0.281478
## [405] train-rmse:0.406969+0.013689 test-rmse:1.152603+0.281795
## [406] train-rmse:0.406007+0.014092 test-rmse:1.151868+0.281738
## [407] train-rmse:0.405314+0.014092 test-rmse:1.151353+0.282359
## [408] train-rmse:0.404687+0.014005 test-rmse:1.150817+0.282222
## [409] train-rmse:0.403541+0.014029 test-rmse:1.151053+0.282755
## [410] train-rmse:0.402866+0.014076 test-rmse:1.150422+0.283123
## [411] train-rmse:0.401870+0.013962 test-rmse:1.150254+0.283247
## [412] train-rmse:0.401060+0.013890 test-rmse:1.149549+0.282830
## [413] train-rmse:0.400169+0.013732 test-rmse:1.148692+0.283193
## [414] train-rmse:0.399434+0.013678 test-rmse:1.148709+0.282952
## [415] train-rmse:0.398837+0.013720 test-rmse:1.148077+0.282316
## [416] train-rmse:0.397886+0.013782 test-rmse:1.147646+0.282062
## [417] train-rmse:0.396863+0.013753 test-rmse:1.147206+0.282142
## [418] train-rmse:0.395982+0.013919 test-rmse:1.146948+0.282702
## [419] train-rmse:0.394892+0.013704 test-rmse:1.146628+0.283738
## [420] train-rmse:0.394190+0.013742 test-rmse:1.146416+0.283985
## [421] train-rmse:0.393505+0.013722 test-rmse:1.146122+0.284312
## [422] train-rmse:0.392705+0.013575 test-rmse:1.146110+0.283833
## [423] train-rmse:0.392106+0.013577 test-rmse:1.145944+0.283897
## [424] train-rmse:0.391459+0.013495 test-rmse:1.145896+0.284009
## [425] train-rmse:0.390829+0.013433 test-rmse:1.145649+0.283663
## [426] train-rmse:0.389979+0.013467 test-rmse:1.145976+0.283769
## [427] train-rmse:0.389348+0.013415 test-rmse:1.146073+0.283715
## [428] train-rmse:0.388748+0.013434 test-rmse:1.146171+0.283502
## [429] train-rmse:0.388150+0.013369 test-rmse:1.145379+0.283282
## [430] train-rmse:0.387535+0.013352 test-rmse:1.144910+0.283118
## [431] train-rmse:0.386987+0.013417 test-rmse:1.144750+0.283593
## [432] train-rmse:0.386297+0.013521 test-rmse:1.144151+0.284035
## [433] train-rmse:0.385599+0.013677 test-rmse:1.143746+0.284102
## [434] train-rmse:0.384799+0.013637 test-rmse:1.143494+0.284457
## [435] train-rmse:0.384102+0.013976 test-rmse:1.142876+0.284631
## [436] train-rmse:0.383390+0.013977 test-rmse:1.143173+0.284601
## [437] train-rmse:0.382689+0.013945 test-rmse:1.141419+0.282992
## [438] train-rmse:0.382070+0.013949 test-rmse:1.141325+0.283355
## [439] train-rmse:0.381205+0.013977 test-rmse:1.140889+0.283748
## [440] train-rmse:0.380364+0.014204 test-rmse:1.140259+0.282867
## [441] train-rmse:0.379592+0.014341 test-rmse:1.139891+0.282888
## [442] train-rmse:0.379083+0.014284 test-rmse:1.139729+0.282517
## [443] train-rmse:0.378495+0.014338 test-rmse:1.138837+0.282988
## [444] train-rmse:0.377657+0.014193 test-rmse:1.139049+0.282355
## [445] train-rmse:0.377152+0.014190 test-rmse:1.139029+0.282682
## [446] train-rmse:0.376542+0.014317 test-rmse:1.138448+0.282867
## [447] train-rmse:0.375933+0.014366 test-rmse:1.138019+0.282982
## [448] train-rmse:0.375423+0.014416 test-rmse:1.137605+0.283016
## [449] train-rmse:0.374872+0.014443 test-rmse:1.137425+0.283045
## [450] train-rmse:0.374424+0.014423 test-rmse:1.137442+0.282816
## [451] train-rmse:0.373932+0.014362 test-rmse:1.137259+0.283047
## [452] train-rmse:0.373373+0.014232 test-rmse:1.136795+0.282798
## [453] train-rmse:0.372887+0.014295 test-rmse:1.136537+0.282444
## [454] train-rmse:0.372037+0.014211 test-rmse:1.136667+0.283032
## [455] train-rmse:0.370751+0.014127 test-rmse:1.136768+0.282841
## [456] train-rmse:0.369814+0.013995 test-rmse:1.136256+0.282291
## [457] train-rmse:0.369018+0.013886 test-rmse:1.135861+0.281881
## [458] train-rmse:0.368076+0.013891 test-rmse:1.135918+0.282371
## [459] train-rmse:0.367629+0.013917 test-rmse:1.135645+0.282398
## [460] train-rmse:0.366944+0.013922 test-rmse:1.135451+0.283069
## [461] train-rmse:0.366290+0.013855 test-rmse:1.134665+0.283431
## [462] train-rmse:0.365750+0.013752 test-rmse:1.134473+0.283416
## [463] train-rmse:0.365119+0.013716 test-rmse:1.134343+0.283406
## [464] train-rmse:0.364323+0.013750 test-rmse:1.134209+0.283710
## [465] train-rmse:0.363459+0.013458 test-rmse:1.134669+0.284218
## [466] train-rmse:0.362867+0.013713 test-rmse:1.134243+0.284260
## [467] train-rmse:0.362351+0.013717 test-rmse:1.134196+0.284508
## [468] train-rmse:0.361634+0.013747 test-rmse:1.134122+0.284586
## [469] train-rmse:0.360874+0.013545 test-rmse:1.133641+0.284604
## [470] train-rmse:0.360427+0.013534 test-rmse:1.133596+0.285168
## [471] train-rmse:0.359805+0.013562 test-rmse:1.133776+0.285193
## [472] train-rmse:0.359227+0.013424 test-rmse:1.132952+0.284263
## [473] train-rmse:0.358691+0.013567 test-rmse:1.132882+0.284329
## [474] train-rmse:0.358092+0.013816 test-rmse:1.132123+0.284222
## [475] train-rmse:0.357633+0.013963 test-rmse:1.131844+0.284193
## [476] train-rmse:0.356943+0.013833 test-rmse:1.131435+0.284453
## [477] train-rmse:0.356324+0.013701 test-rmse:1.131263+0.284340
## [478] train-rmse:0.355713+0.013605 test-rmse:1.131150+0.283902
## [479] train-rmse:0.355290+0.013566 test-rmse:1.130514+0.284171
## [480] train-rmse:0.354568+0.013506 test-rmse:1.130186+0.284261
## [481] train-rmse:0.354124+0.013501 test-rmse:1.130044+0.284229
## [482] train-rmse:0.353667+0.013410 test-rmse:1.129623+0.283447
## [483] train-rmse:0.352754+0.013380 test-rmse:1.128638+0.283133
## [484] train-rmse:0.352295+0.013405 test-rmse:1.129100+0.283426
## [485] train-rmse:0.351595+0.013172 test-rmse:1.128593+0.283774
## [486] train-rmse:0.350878+0.013452 test-rmse:1.129006+0.284133
## [487] train-rmse:0.350363+0.013498 test-rmse:1.129021+0.283996
## [488] train-rmse:0.349457+0.013704 test-rmse:1.128911+0.284526
## [489] train-rmse:0.348601+0.014297 test-rmse:1.128532+0.284734
## [490] train-rmse:0.347744+0.014381 test-rmse:1.128268+0.284798
## [491] train-rmse:0.347267+0.014597 test-rmse:1.127806+0.284617
## [492] train-rmse:0.346698+0.014523 test-rmse:1.128395+0.284166
## [493] train-rmse:0.345951+0.014303 test-rmse:1.128019+0.283537
## [494] train-rmse:0.345361+0.014090 test-rmse:1.128451+0.283471
## [495] train-rmse:0.344750+0.013895 test-rmse:1.127913+0.283534
## [496] train-rmse:0.344267+0.013947 test-rmse:1.127845+0.283453
## [497] train-rmse:0.343890+0.013916 test-rmse:1.127599+0.283721
## [498] train-rmse:0.343478+0.014002 test-rmse:1.127291+0.283873
## [499] train-rmse:0.342828+0.014244 test-rmse:1.127232+0.283669
## [500] train-rmse:0.342300+0.014327 test-rmse:1.126990+0.284144
## [501] train-rmse:0.341658+0.014280 test-rmse:1.127039+0.284039
## [502] train-rmse:0.341017+0.014273 test-rmse:1.126969+0.284098
## [503] train-rmse:0.340614+0.014280 test-rmse:1.126586+0.284156
## [504] train-rmse:0.340242+0.014266 test-rmse:1.126814+0.284107
## [505] train-rmse:0.339404+0.014484 test-rmse:1.126095+0.283945
## [506] train-rmse:0.338521+0.014235 test-rmse:1.125637+0.283495
## [507] train-rmse:0.337829+0.014117 test-rmse:1.125518+0.283454
## [508] train-rmse:0.337242+0.014008 test-rmse:1.125361+0.282843
## [509] train-rmse:0.336755+0.014002 test-rmse:1.123791+0.283197
## [510] train-rmse:0.336250+0.014019 test-rmse:1.123696+0.282987
## [511] train-rmse:0.335782+0.014342 test-rmse:1.123081+0.282499
## [512] train-rmse:0.335308+0.014235 test-rmse:1.122913+0.282317
## [513] train-rmse:0.334954+0.014211 test-rmse:1.122116+0.282099
## [514] train-rmse:0.334461+0.014475 test-rmse:1.122255+0.282315
## [515] train-rmse:0.333936+0.014687 test-rmse:1.121744+0.281965
## [516] train-rmse:0.333505+0.014888 test-rmse:1.121472+0.281971
## [517] train-rmse:0.333025+0.014957 test-rmse:1.121736+0.281980
## [518] train-rmse:0.332432+0.015010 test-rmse:1.121728+0.282042
## [519] train-rmse:0.331919+0.015077 test-rmse:1.121332+0.282411
## [520] train-rmse:0.331395+0.014921 test-rmse:1.122028+0.281844
## [521] train-rmse:0.330964+0.014933 test-rmse:1.121624+0.282099
## [522] train-rmse:0.330414+0.014809 test-rmse:1.121985+0.282379
## [523] train-rmse:0.329709+0.014983 test-rmse:1.121799+0.281841
## [524] train-rmse:0.328860+0.015360 test-rmse:1.121189+0.281499
## [525] train-rmse:0.328117+0.015554 test-rmse:1.120672+0.281732
## [526] train-rmse:0.327474+0.015944 test-rmse:1.120649+0.281698
## [527] train-rmse:0.327005+0.016167 test-rmse:1.120541+0.281879
## [528] train-rmse:0.326282+0.015986 test-rmse:1.120238+0.281960
## [529] train-rmse:0.325946+0.016028 test-rmse:1.120256+0.281796
## [530] train-rmse:0.325580+0.015978 test-rmse:1.120170+0.281813
## [531] train-rmse:0.325107+0.016061 test-rmse:1.120066+0.281719
## [532] train-rmse:0.324592+0.015914 test-rmse:1.119396+0.281775
## [533] train-rmse:0.324218+0.015867 test-rmse:1.119120+0.281499
## [534] train-rmse:0.323810+0.015843 test-rmse:1.119101+0.281650
## [535] train-rmse:0.323270+0.015730 test-rmse:1.118464+0.282071
## [536] train-rmse:0.322685+0.015508 test-rmse:1.118601+0.282305
## [537] train-rmse:0.321901+0.015551 test-rmse:1.118687+0.282381
## [538] train-rmse:0.321394+0.015434 test-rmse:1.118889+0.282158
## [539] train-rmse:0.320848+0.015500 test-rmse:1.118099+0.282469
## [540] train-rmse:0.320196+0.015280 test-rmse:1.118276+0.282870
## [541] train-rmse:0.319816+0.015289 test-rmse:1.118252+0.282944
## [542] train-rmse:0.319491+0.015233 test-rmse:1.118141+0.282578
## [543] train-rmse:0.319193+0.015202 test-rmse:1.117899+0.282380
## [544] train-rmse:0.318639+0.014951 test-rmse:1.117537+0.282233
## [545] train-rmse:0.318357+0.014921 test-rmse:1.117646+0.281882
## [546] train-rmse:0.318011+0.015053 test-rmse:1.117559+0.281770
## [547] train-rmse:0.317162+0.015047 test-rmse:1.116249+0.282836
## [548] train-rmse:0.316631+0.014876 test-rmse:1.116200+0.282827
## [549] train-rmse:0.315965+0.015052 test-rmse:1.116365+0.282616
## [550] train-rmse:0.315570+0.015222 test-rmse:1.116035+0.283017
## [551] train-rmse:0.314893+0.015191 test-rmse:1.116148+0.282866
## [552] train-rmse:0.314468+0.015113 test-rmse:1.116071+0.283419
## [553] train-rmse:0.313903+0.014983 test-rmse:1.116375+0.283803
## [554] train-rmse:0.313397+0.014928 test-rmse:1.116016+0.283490
## [555] train-rmse:0.312508+0.015128 test-rmse:1.115402+0.282677
## [556] train-rmse:0.311527+0.015289 test-rmse:1.115134+0.282740
## [557] train-rmse:0.311182+0.015265 test-rmse:1.114819+0.282951
## [558] train-rmse:0.310744+0.015611 test-rmse:1.114648+0.282524
## [559] train-rmse:0.310264+0.015762 test-rmse:1.114690+0.282193
## [560] train-rmse:0.309612+0.015680 test-rmse:1.114906+0.282508
## [561] train-rmse:0.308993+0.015549 test-rmse:1.114481+0.282218
## [562] train-rmse:0.308478+0.015527 test-rmse:1.113697+0.282737
## [563] train-rmse:0.308078+0.015463 test-rmse:1.113619+0.282688
## [564] train-rmse:0.307721+0.015418 test-rmse:1.113537+0.282509
## [565] train-rmse:0.307311+0.015460 test-rmse:1.113491+0.282644
## [566] train-rmse:0.306632+0.015672 test-rmse:1.113391+0.282975
## [567] train-rmse:0.306157+0.015677 test-rmse:1.113437+0.283176
## [568] train-rmse:0.305579+0.016076 test-rmse:1.113645+0.282897
## [569] train-rmse:0.304973+0.015976 test-rmse:1.113413+0.282965
## [570] train-rmse:0.304620+0.015947 test-rmse:1.113077+0.283624
## [571] train-rmse:0.304065+0.015896 test-rmse:1.113147+0.284101
## [572] train-rmse:0.303661+0.015886 test-rmse:1.113025+0.283918
## [573] train-rmse:0.303324+0.015833 test-rmse:1.113224+0.283989
## [574] train-rmse:0.302763+0.015731 test-rmse:1.113542+0.284202
## [575] train-rmse:0.302043+0.015435 test-rmse:1.113805+0.284063
## [576] train-rmse:0.301562+0.015331 test-rmse:1.113594+0.284053
## [577] train-rmse:0.301191+0.015307 test-rmse:1.113634+0.283570
## [578] train-rmse:0.300933+0.015346 test-rmse:1.113307+0.283338
## Stopping. Best iteration:
## [572] train-rmse:0.303661+0.015886 test-rmse:1.113025+0.283918
##
## 9
## [1] train-rmse:94.766116+0.105094 test-rmse:94.766423+0.483432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:83.478061+0.095217 test-rmse:83.478186+0.500072
## [3] train-rmse:73.545764+0.086897 test-rmse:73.545670+0.516055
## [4] train-rmse:64.807895+0.080011 test-rmse:64.807548+0.531590
## [5] train-rmse:57.122527+0.074434 test-rmse:57.121858+0.546922
## [6] train-rmse:50.364803+0.070103 test-rmse:50.363757+0.562250
## [7] train-rmse:44.424928+0.066945 test-rmse:44.423422+0.577769
## [8] train-rmse:39.206363+0.064904 test-rmse:39.204310+0.593657
## [9] train-rmse:34.622293+0.063135 test-rmse:34.630677+0.594785
## [10] train-rmse:30.591081+0.059323 test-rmse:30.633139+0.599495
## [11] train-rmse:27.044432+0.052606 test-rmse:27.116927+0.608458
## [12] train-rmse:23.924558+0.044243 test-rmse:24.000367+0.581464
## [13] train-rmse:21.180680+0.037427 test-rmse:21.262963+0.569043
## [14] train-rmse:18.769037+0.030462 test-rmse:18.868901+0.580277
## [15] train-rmse:16.646216+0.028802 test-rmse:16.744065+0.566385
## [16] train-rmse:14.781228+0.027676 test-rmse:14.877556+0.562284
## [17] train-rmse:13.144516+0.025905 test-rmse:13.240838+0.558682
## [18] train-rmse:11.708013+0.025421 test-rmse:11.817619+0.566114
## [19] train-rmse:10.449898+0.027004 test-rmse:10.565119+0.556238
## [20] train-rmse:9.349082+0.027036 test-rmse:9.467515+0.540108
## [21] train-rmse:8.384510+0.026125 test-rmse:8.518904+0.556707
## [22] train-rmse:7.544330+0.026955 test-rmse:7.676664+0.547996
## [23] train-rmse:6.811611+0.026988 test-rmse:6.965998+0.559498
## [24] train-rmse:6.169861+0.025357 test-rmse:6.325715+0.531711
## [25] train-rmse:5.618868+0.025744 test-rmse:5.783679+0.534846
## [26] train-rmse:5.142511+0.026903 test-rmse:5.304263+0.511496
## [27] train-rmse:4.729720+0.027540 test-rmse:4.923034+0.501924
## [28] train-rmse:4.372540+0.029575 test-rmse:4.576643+0.480154
## [29] train-rmse:4.064365+0.028080 test-rmse:4.297324+0.485290
## [30] train-rmse:3.798282+0.025136 test-rmse:4.037570+0.458516
## [31] train-rmse:3.572058+0.025330 test-rmse:3.819568+0.445247
## [32] train-rmse:3.375524+0.022065 test-rmse:3.627331+0.436148
## [33] train-rmse:3.207947+0.021133 test-rmse:3.471546+0.419714
## [34] train-rmse:3.063050+0.021231 test-rmse:3.349843+0.418248
## [35] train-rmse:2.935828+0.025476 test-rmse:3.250824+0.410694
## [36] train-rmse:2.823274+0.022691 test-rmse:3.158177+0.399002
## [37] train-rmse:2.724622+0.021346 test-rmse:3.077488+0.376727
## [38] train-rmse:2.640115+0.021912 test-rmse:3.010983+0.366407
## [39] train-rmse:2.564328+0.020107 test-rmse:2.942058+0.357326
## [40] train-rmse:2.496710+0.020753 test-rmse:2.878821+0.352020
## [41] train-rmse:2.434770+0.020278 test-rmse:2.819786+0.342647
## [42] train-rmse:2.378122+0.019649 test-rmse:2.770066+0.324367
## [43] train-rmse:2.326586+0.019253 test-rmse:2.737963+0.325026
## [44] train-rmse:2.275161+0.021400 test-rmse:2.688385+0.321581
## [45] train-rmse:2.229104+0.018526 test-rmse:2.634666+0.294136
## [46] train-rmse:2.187026+0.017785 test-rmse:2.605209+0.286797
## [47] train-rmse:2.147196+0.017552 test-rmse:2.573551+0.288386
## [48] train-rmse:2.109804+0.017762 test-rmse:2.542914+0.284184
## [49] train-rmse:2.072802+0.018742 test-rmse:2.513946+0.278775
## [50] train-rmse:2.038266+0.017073 test-rmse:2.483258+0.271675
## [51] train-rmse:2.005229+0.016514 test-rmse:2.451280+0.276788
## [52] train-rmse:1.973634+0.016449 test-rmse:2.427045+0.274476
## [53] train-rmse:1.943410+0.015952 test-rmse:2.406586+0.274349
## [54] train-rmse:1.911532+0.014085 test-rmse:2.384350+0.262042
## [55] train-rmse:1.882938+0.013588 test-rmse:2.344396+0.259806
## [56] train-rmse:1.851637+0.015979 test-rmse:2.330886+0.260342
## [57] train-rmse:1.822498+0.018350 test-rmse:2.303040+0.266713
## [58] train-rmse:1.795389+0.017645 test-rmse:2.284219+0.268178
## [59] train-rmse:1.768698+0.016904 test-rmse:2.261195+0.262715
## [60] train-rmse:1.742761+0.015577 test-rmse:2.242259+0.262833
## [61] train-rmse:1.718078+0.015367 test-rmse:2.218057+0.256612
## [62] train-rmse:1.693009+0.016129 test-rmse:2.198022+0.263977
## [63] train-rmse:1.669871+0.015802 test-rmse:2.172909+0.257297
## [64] train-rmse:1.645181+0.015300 test-rmse:2.156074+0.250034
## [65] train-rmse:1.622905+0.015303 test-rmse:2.138266+0.249819
## [66] train-rmse:1.600465+0.015518 test-rmse:2.121834+0.249472
## [67] train-rmse:1.578593+0.015107 test-rmse:2.102598+0.253874
## [68] train-rmse:1.555993+0.016993 test-rmse:2.087659+0.249219
## [69] train-rmse:1.534641+0.016049 test-rmse:2.060076+0.247424
## [70] train-rmse:1.513063+0.016213 test-rmse:2.045443+0.247732
## [71] train-rmse:1.493281+0.015999 test-rmse:2.028319+0.243842
## [72] train-rmse:1.473850+0.015459 test-rmse:2.012606+0.248843
## [73] train-rmse:1.454694+0.015549 test-rmse:1.997327+0.249966
## [74] train-rmse:1.434820+0.015276 test-rmse:1.986840+0.252473
## [75] train-rmse:1.415986+0.014636 test-rmse:1.963347+0.249517
## [76] train-rmse:1.398104+0.014131 test-rmse:1.943463+0.250342
## [77] train-rmse:1.380152+0.015334 test-rmse:1.928311+0.247990
## [78] train-rmse:1.363011+0.015424 test-rmse:1.910620+0.242822
## [79] train-rmse:1.344939+0.015339 test-rmse:1.896330+0.245007
## [80] train-rmse:1.328385+0.015162 test-rmse:1.885932+0.244295
## [81] train-rmse:1.311351+0.014438 test-rmse:1.867754+0.241059
## [82] train-rmse:1.294669+0.015434 test-rmse:1.855176+0.240640
## [83] train-rmse:1.277381+0.015880 test-rmse:1.843488+0.240787
## [84] train-rmse:1.257646+0.014799 test-rmse:1.828344+0.241494
## [85] train-rmse:1.241806+0.014878 test-rmse:1.817898+0.242599
## [86] train-rmse:1.227087+0.015461 test-rmse:1.806913+0.242104
## [87] train-rmse:1.212126+0.015180 test-rmse:1.795720+0.237298
## [88] train-rmse:1.196816+0.014259 test-rmse:1.775276+0.237974
## [89] train-rmse:1.181982+0.013908 test-rmse:1.764759+0.238278
## [90] train-rmse:1.168062+0.013638 test-rmse:1.750770+0.237084
## [91] train-rmse:1.153298+0.013281 test-rmse:1.737937+0.238183
## [92] train-rmse:1.140081+0.013141 test-rmse:1.727680+0.239890
## [93] train-rmse:1.123371+0.017139 test-rmse:1.720286+0.242884
## [94] train-rmse:1.109494+0.016864 test-rmse:1.712341+0.243919
## [95] train-rmse:1.096101+0.016859 test-rmse:1.700593+0.246386
## [96] train-rmse:1.083070+0.017138 test-rmse:1.689979+0.245444
## [97] train-rmse:1.070193+0.016559 test-rmse:1.677337+0.248055
## [98] train-rmse:1.058087+0.015972 test-rmse:1.663513+0.250414
## [99] train-rmse:1.046417+0.015926 test-rmse:1.654357+0.250385
## [100] train-rmse:1.034286+0.016305 test-rmse:1.645292+0.252174
## [101] train-rmse:1.022014+0.015477 test-rmse:1.634126+0.255159
## [102] train-rmse:1.009695+0.015413 test-rmse:1.628862+0.253767
## [103] train-rmse:0.998190+0.015858 test-rmse:1.621879+0.252990
## [104] train-rmse:0.987363+0.015547 test-rmse:1.611921+0.251288
## [105] train-rmse:0.976707+0.015875 test-rmse:1.603269+0.252503
## [106] train-rmse:0.965882+0.015679 test-rmse:1.594983+0.252962
## [107] train-rmse:0.955177+0.015046 test-rmse:1.588796+0.252218
## [108] train-rmse:0.944615+0.014414 test-rmse:1.578585+0.256763
## [109] train-rmse:0.934645+0.014225 test-rmse:1.569258+0.257098
## [110] train-rmse:0.924071+0.014606 test-rmse:1.561995+0.258762
## [111] train-rmse:0.914174+0.014865 test-rmse:1.554668+0.260907
## [112] train-rmse:0.904169+0.015681 test-rmse:1.547426+0.260530
## [113] train-rmse:0.893907+0.016239 test-rmse:1.540747+0.260571
## [114] train-rmse:0.884603+0.016379 test-rmse:1.534812+0.263640
## [115] train-rmse:0.875715+0.016346 test-rmse:1.528542+0.264341
## [116] train-rmse:0.867115+0.016028 test-rmse:1.521286+0.265748
## [117] train-rmse:0.858546+0.015981 test-rmse:1.512480+0.266824
## [118] train-rmse:0.848847+0.015142 test-rmse:1.504480+0.264747
## [119] train-rmse:0.839394+0.015382 test-rmse:1.493549+0.256098
## [120] train-rmse:0.830784+0.014784 test-rmse:1.490966+0.257123
## [121] train-rmse:0.822715+0.015014 test-rmse:1.483745+0.258046
## [122] train-rmse:0.814130+0.014702 test-rmse:1.481035+0.261702
## [123] train-rmse:0.806050+0.014467 test-rmse:1.472662+0.254111
## [124] train-rmse:0.798407+0.014014 test-rmse:1.464134+0.255338
## [125] train-rmse:0.790664+0.013617 test-rmse:1.456870+0.256050
## [126] train-rmse:0.783151+0.013960 test-rmse:1.451938+0.257322
## [127] train-rmse:0.775378+0.013600 test-rmse:1.443917+0.257532
## [128] train-rmse:0.767717+0.014359 test-rmse:1.439745+0.258793
## [129] train-rmse:0.759949+0.014137 test-rmse:1.432421+0.256453
## [130] train-rmse:0.752254+0.013536 test-rmse:1.427507+0.257529
## [131] train-rmse:0.743945+0.014109 test-rmse:1.420703+0.258812
## [132] train-rmse:0.737331+0.013843 test-rmse:1.415357+0.258202
## [133] train-rmse:0.730593+0.014087 test-rmse:1.411532+0.258954
## [134] train-rmse:0.723203+0.014968 test-rmse:1.406519+0.259486
## [135] train-rmse:0.716654+0.014520 test-rmse:1.401914+0.260244
## [136] train-rmse:0.709463+0.014195 test-rmse:1.395094+0.261181
## [137] train-rmse:0.703198+0.014270 test-rmse:1.387684+0.254267
## [138] train-rmse:0.696639+0.014959 test-rmse:1.384480+0.254816
## [139] train-rmse:0.689036+0.014843 test-rmse:1.380293+0.252394
## [140] train-rmse:0.682300+0.014878 test-rmse:1.372991+0.255127
## [141] train-rmse:0.676042+0.014951 test-rmse:1.369404+0.254424
## [142] train-rmse:0.670657+0.015046 test-rmse:1.364429+0.254856
## [143] train-rmse:0.664868+0.014726 test-rmse:1.357486+0.256201
## [144] train-rmse:0.658870+0.015018 test-rmse:1.353280+0.256657
## [145] train-rmse:0.653310+0.014849 test-rmse:1.351108+0.256099
## [146] train-rmse:0.647135+0.014325 test-rmse:1.347546+0.255055
## [147] train-rmse:0.641986+0.014397 test-rmse:1.343889+0.255604
## [148] train-rmse:0.636207+0.014138 test-rmse:1.341204+0.255444
## [149] train-rmse:0.629964+0.013372 test-rmse:1.330219+0.249945
## [150] train-rmse:0.625130+0.012945 test-rmse:1.325926+0.250581
## [151] train-rmse:0.620192+0.013087 test-rmse:1.323723+0.251274
## [152] train-rmse:0.614722+0.013215 test-rmse:1.319866+0.250205
## [153] train-rmse:0.609666+0.013594 test-rmse:1.316514+0.251541
## [154] train-rmse:0.604480+0.013666 test-rmse:1.314958+0.252382
## [155] train-rmse:0.599979+0.013408 test-rmse:1.312355+0.254183
## [156] train-rmse:0.594562+0.014378 test-rmse:1.305553+0.251150
## [157] train-rmse:0.589145+0.013718 test-rmse:1.303152+0.253270
## [158] train-rmse:0.584751+0.013971 test-rmse:1.298781+0.252280
## [159] train-rmse:0.580042+0.013496 test-rmse:1.296078+0.252518
## [160] train-rmse:0.575531+0.013238 test-rmse:1.292631+0.254358
## [161] train-rmse:0.571260+0.013195 test-rmse:1.289846+0.254520
## [162] train-rmse:0.567279+0.012776 test-rmse:1.287457+0.254454
## [163] train-rmse:0.562968+0.012632 test-rmse:1.285939+0.253995
## [164] train-rmse:0.558669+0.012327 test-rmse:1.284331+0.254326
## [165] train-rmse:0.553704+0.011568 test-rmse:1.278284+0.248894
## [166] train-rmse:0.549798+0.011551 test-rmse:1.275663+0.249868
## [167] train-rmse:0.545369+0.011476 test-rmse:1.273912+0.251775
## [168] train-rmse:0.541463+0.011874 test-rmse:1.271019+0.252590
## [169] train-rmse:0.537538+0.011635 test-rmse:1.268993+0.253279
## [170] train-rmse:0.533753+0.011383 test-rmse:1.264205+0.254157
## [171] train-rmse:0.530227+0.011271 test-rmse:1.264086+0.254409
## [172] train-rmse:0.525992+0.010811 test-rmse:1.259857+0.254887
## [173] train-rmse:0.522101+0.010881 test-rmse:1.255633+0.249472
## [174] train-rmse:0.518501+0.010684 test-rmse:1.253582+0.249748
## [175] train-rmse:0.515243+0.010610 test-rmse:1.248913+0.251312
## [176] train-rmse:0.511756+0.010568 test-rmse:1.248034+0.250554
## [177] train-rmse:0.508015+0.010421 test-rmse:1.245440+0.250732
## [178] train-rmse:0.504930+0.010406 test-rmse:1.243322+0.251710
## [179] train-rmse:0.501594+0.010023 test-rmse:1.241454+0.252512
## [180] train-rmse:0.498008+0.010172 test-rmse:1.240935+0.253345
## [181] train-rmse:0.494310+0.008994 test-rmse:1.237731+0.253929
## [182] train-rmse:0.491057+0.009087 test-rmse:1.232642+0.250651
## [183] train-rmse:0.487112+0.009442 test-rmse:1.232162+0.251882
## [184] train-rmse:0.484415+0.009506 test-rmse:1.226176+0.253543
## [185] train-rmse:0.481334+0.009363 test-rmse:1.224395+0.254064
## [186] train-rmse:0.478104+0.009526 test-rmse:1.221698+0.254835
## [187] train-rmse:0.474796+0.009025 test-rmse:1.218069+0.251604
## [188] train-rmse:0.471972+0.009152 test-rmse:1.215706+0.251867
## [189] train-rmse:0.468738+0.009961 test-rmse:1.213541+0.252758
## [190] train-rmse:0.465380+0.009790 test-rmse:1.212474+0.252693
## [191] train-rmse:0.462569+0.009846 test-rmse:1.210640+0.252253
## [192] train-rmse:0.459984+0.009977 test-rmse:1.209067+0.252438
## [193] train-rmse:0.456656+0.009717 test-rmse:1.208162+0.251700
## [194] train-rmse:0.453741+0.009795 test-rmse:1.207516+0.251713
## [195] train-rmse:0.450888+0.009657 test-rmse:1.205814+0.252156
## [196] train-rmse:0.447663+0.009350 test-rmse:1.202502+0.248566
## [197] train-rmse:0.444551+0.009258 test-rmse:1.201518+0.249663
## [198] train-rmse:0.441794+0.009040 test-rmse:1.201090+0.250921
## [199] train-rmse:0.439054+0.008936 test-rmse:1.199179+0.249858
## [200] train-rmse:0.436847+0.008777 test-rmse:1.195100+0.250743
## [201] train-rmse:0.434408+0.008680 test-rmse:1.194529+0.250309
## [202] train-rmse:0.431811+0.008830 test-rmse:1.192977+0.250305
## [203] train-rmse:0.428933+0.008092 test-rmse:1.189691+0.251634
## [204] train-rmse:0.426533+0.008265 test-rmse:1.188872+0.251902
## [205] train-rmse:0.423970+0.008547 test-rmse:1.188133+0.252776
## [206] train-rmse:0.421481+0.008829 test-rmse:1.186309+0.252328
## [207] train-rmse:0.418828+0.009325 test-rmse:1.184358+0.252921
## [208] train-rmse:0.416144+0.010165 test-rmse:1.179483+0.255049
## [209] train-rmse:0.413688+0.009759 test-rmse:1.177348+0.254862
## [210] train-rmse:0.411791+0.009700 test-rmse:1.176211+0.254801
## [211] train-rmse:0.409392+0.009698 test-rmse:1.174937+0.255533
## [212] train-rmse:0.406917+0.009521 test-rmse:1.174284+0.256345
## [213] train-rmse:0.404751+0.009221 test-rmse:1.173201+0.256234
## [214] train-rmse:0.402532+0.008987 test-rmse:1.172015+0.255924
## [215] train-rmse:0.400552+0.008966 test-rmse:1.169794+0.257589
## [216] train-rmse:0.398626+0.009091 test-rmse:1.168719+0.257617
## [217] train-rmse:0.396154+0.009106 test-rmse:1.168466+0.256885
## [218] train-rmse:0.393645+0.009183 test-rmse:1.167712+0.259275
## [219] train-rmse:0.391296+0.009398 test-rmse:1.165883+0.260142
## [220] train-rmse:0.389638+0.009244 test-rmse:1.165389+0.260805
## [221] train-rmse:0.387613+0.008892 test-rmse:1.164543+0.260703
## [222] train-rmse:0.385121+0.009402 test-rmse:1.163193+0.261411
## [223] train-rmse:0.382541+0.008841 test-rmse:1.161729+0.260811
## [224] train-rmse:0.380487+0.008699 test-rmse:1.160171+0.261029
## [225] train-rmse:0.378427+0.008586 test-rmse:1.157813+0.263216
## [226] train-rmse:0.376628+0.008665 test-rmse:1.156806+0.263735
## [227] train-rmse:0.374726+0.008775 test-rmse:1.156557+0.263474
## [228] train-rmse:0.372906+0.008990 test-rmse:1.155384+0.263522
## [229] train-rmse:0.370916+0.008857 test-rmse:1.153499+0.262619
## [230] train-rmse:0.369428+0.008929 test-rmse:1.152543+0.263725
## [231] train-rmse:0.367174+0.009539 test-rmse:1.151550+0.263885
## [232] train-rmse:0.365743+0.009659 test-rmse:1.149628+0.263922
## [233] train-rmse:0.364047+0.009527 test-rmse:1.149315+0.263996
## [234] train-rmse:0.362448+0.009249 test-rmse:1.148650+0.263596
## [235] train-rmse:0.360365+0.009473 test-rmse:1.148345+0.263465
## [236] train-rmse:0.358425+0.008994 test-rmse:1.147924+0.264413
## [237] train-rmse:0.356561+0.008846 test-rmse:1.147849+0.264727
## [238] train-rmse:0.355069+0.008734 test-rmse:1.145120+0.265610
## [239] train-rmse:0.353246+0.008894 test-rmse:1.144872+0.266670
## [240] train-rmse:0.351749+0.008889 test-rmse:1.143656+0.267063
## [241] train-rmse:0.350075+0.008833 test-rmse:1.143102+0.267165
## [242] train-rmse:0.348222+0.009209 test-rmse:1.142361+0.267404
## [243] train-rmse:0.346223+0.008843 test-rmse:1.141596+0.267541
## [244] train-rmse:0.344873+0.008766 test-rmse:1.140138+0.266431
## [245] train-rmse:0.343078+0.008864 test-rmse:1.139802+0.266233
## [246] train-rmse:0.341345+0.008832 test-rmse:1.139097+0.266913
## [247] train-rmse:0.339999+0.008701 test-rmse:1.136090+0.268163
## [248] train-rmse:0.338227+0.008474 test-rmse:1.136426+0.268377
## [249] train-rmse:0.336802+0.008205 test-rmse:1.135545+0.268278
## [250] train-rmse:0.335118+0.008413 test-rmse:1.135363+0.268963
## [251] train-rmse:0.333485+0.008614 test-rmse:1.134258+0.269182
## [252] train-rmse:0.332176+0.008414 test-rmse:1.134317+0.269552
## [253] train-rmse:0.330786+0.008463 test-rmse:1.133552+0.269998
## [254] train-rmse:0.329312+0.008654 test-rmse:1.133449+0.269732
## [255] train-rmse:0.327521+0.008705 test-rmse:1.133513+0.270037
## [256] train-rmse:0.325843+0.008525 test-rmse:1.132244+0.269724
## [257] train-rmse:0.324646+0.008312 test-rmse:1.132235+0.269826
## [258] train-rmse:0.323104+0.008621 test-rmse:1.132342+0.270344
## [259] train-rmse:0.321862+0.008721 test-rmse:1.130966+0.269383
## [260] train-rmse:0.320098+0.008844 test-rmse:1.130013+0.269609
## [261] train-rmse:0.319052+0.008914 test-rmse:1.128203+0.271402
## [262] train-rmse:0.317542+0.008890 test-rmse:1.127326+0.271642
## [263] train-rmse:0.316369+0.008887 test-rmse:1.126894+0.271788
## [264] train-rmse:0.314909+0.008911 test-rmse:1.126505+0.272145
## [265] train-rmse:0.313755+0.008894 test-rmse:1.125929+0.272086
## [266] train-rmse:0.312714+0.009109 test-rmse:1.124991+0.271894
## [267] train-rmse:0.310978+0.009023 test-rmse:1.124049+0.272463
## [268] train-rmse:0.309585+0.009170 test-rmse:1.123529+0.272909
## [269] train-rmse:0.308267+0.009141 test-rmse:1.122563+0.274158
## [270] train-rmse:0.307224+0.009187 test-rmse:1.121978+0.273852
## [271] train-rmse:0.305648+0.009277 test-rmse:1.121022+0.274009
## [272] train-rmse:0.304309+0.009133 test-rmse:1.119091+0.274217
## [273] train-rmse:0.303046+0.009096 test-rmse:1.118428+0.274500
## [274] train-rmse:0.301985+0.009460 test-rmse:1.117743+0.274500
## [275] train-rmse:0.300518+0.009228 test-rmse:1.116976+0.274786
## [276] train-rmse:0.299319+0.009324 test-rmse:1.116828+0.275054
## [277] train-rmse:0.298207+0.009267 test-rmse:1.116526+0.275544
## [278] train-rmse:0.296911+0.009000 test-rmse:1.116155+0.275877
## [279] train-rmse:0.295779+0.009238 test-rmse:1.116305+0.276225
## [280] train-rmse:0.294565+0.009300 test-rmse:1.116875+0.277484
## [281] train-rmse:0.293178+0.009450 test-rmse:1.115428+0.278836
## [282] train-rmse:0.291961+0.009377 test-rmse:1.114826+0.278036
## [283] train-rmse:0.290168+0.009014 test-rmse:1.113761+0.277656
## [284] train-rmse:0.288856+0.009154 test-rmse:1.112860+0.278056
## [285] train-rmse:0.287631+0.009022 test-rmse:1.112453+0.277829
## [286] train-rmse:0.286306+0.008443 test-rmse:1.112094+0.278349
## [287] train-rmse:0.285370+0.008625 test-rmse:1.111462+0.278054
## [288] train-rmse:0.284151+0.008599 test-rmse:1.111353+0.278353
## [289] train-rmse:0.283248+0.008509 test-rmse:1.110743+0.278501
## [290] train-rmse:0.281882+0.008601 test-rmse:1.110818+0.278600
## [291] train-rmse:0.280605+0.008632 test-rmse:1.110303+0.278197
## [292] train-rmse:0.279685+0.008645 test-rmse:1.110006+0.278569
## [293] train-rmse:0.278633+0.008149 test-rmse:1.109223+0.279067
## [294] train-rmse:0.277128+0.008844 test-rmse:1.109186+0.279127
## [295] train-rmse:0.275924+0.008932 test-rmse:1.109021+0.279058
## [296] train-rmse:0.274804+0.008967 test-rmse:1.108846+0.278694
## [297] train-rmse:0.273787+0.009404 test-rmse:1.107487+0.279689
## [298] train-rmse:0.272475+0.009480 test-rmse:1.107001+0.279763
## [299] train-rmse:0.271533+0.009288 test-rmse:1.106161+0.279324
## [300] train-rmse:0.270724+0.009232 test-rmse:1.106490+0.279599
## [301] train-rmse:0.269535+0.009322 test-rmse:1.105351+0.279517
## [302] train-rmse:0.268504+0.009545 test-rmse:1.105053+0.279881
## [303] train-rmse:0.267508+0.009800 test-rmse:1.104379+0.280534
## [304] train-rmse:0.266440+0.009678 test-rmse:1.104458+0.280557
## [305] train-rmse:0.265477+0.009699 test-rmse:1.104372+0.280424
## [306] train-rmse:0.264193+0.009692 test-rmse:1.104221+0.280567
## [307] train-rmse:0.263077+0.009412 test-rmse:1.103498+0.280603
## [308] train-rmse:0.261856+0.009726 test-rmse:1.103371+0.280167
## [309] train-rmse:0.260746+0.009649 test-rmse:1.103363+0.280170
## [310] train-rmse:0.259800+0.009556 test-rmse:1.103483+0.280459
## [311] train-rmse:0.258495+0.009274 test-rmse:1.103681+0.280475
## [312] train-rmse:0.257624+0.009094 test-rmse:1.103542+0.280113
## [313] train-rmse:0.256325+0.009011 test-rmse:1.102949+0.279880
## [314] train-rmse:0.255581+0.008987 test-rmse:1.102451+0.280021
## [315] train-rmse:0.254757+0.009020 test-rmse:1.101253+0.280745
## [316] train-rmse:0.253727+0.009122 test-rmse:1.101215+0.280781
## [317] train-rmse:0.252710+0.009065 test-rmse:1.101250+0.281666
## [318] train-rmse:0.251613+0.008929 test-rmse:1.100659+0.281560
## [319] train-rmse:0.250776+0.009024 test-rmse:1.100442+0.281578
## [320] train-rmse:0.249988+0.009039 test-rmse:1.099927+0.281549
## [321] train-rmse:0.248919+0.009432 test-rmse:1.099454+0.281995
## [322] train-rmse:0.248090+0.009346 test-rmse:1.098975+0.282206
## [323] train-rmse:0.247183+0.009561 test-rmse:1.099108+0.282628
## [324] train-rmse:0.246365+0.009757 test-rmse:1.099190+0.282548
## [325] train-rmse:0.245370+0.009569 test-rmse:1.098588+0.282493
## [326] train-rmse:0.244531+0.009644 test-rmse:1.098476+0.282759
## [327] train-rmse:0.243806+0.009506 test-rmse:1.098008+0.282512
## [328] train-rmse:0.242994+0.009354 test-rmse:1.097377+0.282989
## [329] train-rmse:0.242068+0.009062 test-rmse:1.096930+0.283223
## [330] train-rmse:0.241199+0.009197 test-rmse:1.096710+0.283050
## [331] train-rmse:0.240194+0.009478 test-rmse:1.096797+0.283181
## [332] train-rmse:0.239359+0.009348 test-rmse:1.096980+0.283400
## [333] train-rmse:0.238305+0.009110 test-rmse:1.096580+0.283457
## [334] train-rmse:0.237306+0.009226 test-rmse:1.096504+0.283883
## [335] train-rmse:0.236806+0.009242 test-rmse:1.096307+0.283590
## [336] train-rmse:0.236259+0.009173 test-rmse:1.096109+0.283725
## [337] train-rmse:0.235216+0.008751 test-rmse:1.096034+0.284076
## [338] train-rmse:0.234298+0.008768 test-rmse:1.096169+0.284062
## [339] train-rmse:0.233623+0.008902 test-rmse:1.094902+0.284902
## [340] train-rmse:0.232822+0.008896 test-rmse:1.094885+0.284871
## [341] train-rmse:0.232017+0.009128 test-rmse:1.094620+0.284663
## [342] train-rmse:0.230605+0.008914 test-rmse:1.094651+0.284583
## [343] train-rmse:0.229974+0.008789 test-rmse:1.094004+0.285136
## [344] train-rmse:0.229199+0.008650 test-rmse:1.093395+0.285597
## [345] train-rmse:0.228414+0.008502 test-rmse:1.093020+0.285177
## [346] train-rmse:0.227813+0.008404 test-rmse:1.092593+0.285088
## [347] train-rmse:0.226586+0.008517 test-rmse:1.092447+0.285210
## [348] train-rmse:0.225683+0.008591 test-rmse:1.092746+0.285350
## [349] train-rmse:0.224797+0.008646 test-rmse:1.092485+0.285417
## [350] train-rmse:0.224099+0.008750 test-rmse:1.092000+0.285713
## [351] train-rmse:0.223384+0.008685 test-rmse:1.091617+0.285499
## [352] train-rmse:0.222592+0.009004 test-rmse:1.092057+0.285331
## [353] train-rmse:0.221996+0.008968 test-rmse:1.091721+0.285496
## [354] train-rmse:0.221108+0.008936 test-rmse:1.091526+0.285474
## [355] train-rmse:0.220441+0.008992 test-rmse:1.091088+0.285579
## [356] train-rmse:0.219512+0.008912 test-rmse:1.090760+0.286131
## [357] train-rmse:0.218865+0.008770 test-rmse:1.090750+0.286351
## [358] train-rmse:0.217876+0.008719 test-rmse:1.090605+0.286723
## [359] train-rmse:0.217286+0.008689 test-rmse:1.090237+0.286690
## [360] train-rmse:0.216584+0.008695 test-rmse:1.090208+0.286509
## [361] train-rmse:0.215885+0.008553 test-rmse:1.090243+0.286872
## [362] train-rmse:0.215198+0.008566 test-rmse:1.090117+0.286604
## [363] train-rmse:0.214503+0.008564 test-rmse:1.090472+0.286631
## [364] train-rmse:0.214000+0.008566 test-rmse:1.089875+0.287175
## [365] train-rmse:0.213215+0.008371 test-rmse:1.089768+0.287185
## [366] train-rmse:0.212684+0.008308 test-rmse:1.089592+0.287084
## [367] train-rmse:0.211571+0.008647 test-rmse:1.089563+0.287142
## [368] train-rmse:0.210651+0.008597 test-rmse:1.089557+0.286791
## [369] train-rmse:0.209980+0.008545 test-rmse:1.089367+0.286734
## [370] train-rmse:0.209424+0.008813 test-rmse:1.089180+0.286853
## [371] train-rmse:0.208732+0.008635 test-rmse:1.089258+0.286993
## [372] train-rmse:0.207689+0.008859 test-rmse:1.089846+0.288394
## [373] train-rmse:0.206770+0.008829 test-rmse:1.089640+0.289028
## [374] train-rmse:0.205956+0.008717 test-rmse:1.089321+0.289699
## [375] train-rmse:0.205420+0.008761 test-rmse:1.089313+0.289619
## [376] train-rmse:0.204713+0.008627 test-rmse:1.089209+0.289898
## Stopping. Best iteration:
## [370] train-rmse:0.209424+0.008813 test-rmse:1.089180+0.286853
##
## 10
## [1] train-rmse:94.766116+0.105094 test-rmse:94.766423+0.483432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:83.478061+0.095217 test-rmse:83.478186+0.500072
## [3] train-rmse:73.545764+0.086897 test-rmse:73.545670+0.516055
## [4] train-rmse:64.807895+0.080011 test-rmse:64.807548+0.531590
## [5] train-rmse:57.122527+0.074434 test-rmse:57.121858+0.546922
## [6] train-rmse:50.364803+0.070103 test-rmse:50.363757+0.562250
## [7] train-rmse:44.424928+0.066945 test-rmse:44.423422+0.577769
## [8] train-rmse:39.206363+0.064904 test-rmse:39.204310+0.593657
## [9] train-rmse:34.622293+0.063135 test-rmse:34.630677+0.594785
## [10] train-rmse:30.591081+0.059323 test-rmse:30.633139+0.599495
## [11] train-rmse:27.044432+0.052606 test-rmse:27.116927+0.608458
## [12] train-rmse:23.924558+0.044243 test-rmse:24.000367+0.581464
## [13] train-rmse:21.179843+0.037983 test-rmse:21.260459+0.568265
## [14] train-rmse:18.765142+0.033113 test-rmse:18.845933+0.566596
## [15] train-rmse:16.641043+0.027979 test-rmse:16.735798+0.565393
## [16] train-rmse:14.769916+0.026410 test-rmse:14.858769+0.559388
## [17] train-rmse:13.124335+0.024421 test-rmse:13.212948+0.543444
## [18] train-rmse:11.678605+0.023895 test-rmse:11.768421+0.533339
## [19] train-rmse:10.406373+0.021848 test-rmse:10.490011+0.527254
## [20] train-rmse:9.291683+0.020976 test-rmse:9.396597+0.543450
## [21] train-rmse:8.312294+0.020685 test-rmse:8.426048+0.526347
## [22] train-rmse:7.455733+0.020218 test-rmse:7.568301+0.524141
## [23] train-rmse:6.706077+0.019020 test-rmse:6.840491+0.526798
## [24] train-rmse:6.049048+0.019995 test-rmse:6.181267+0.513519
## [25] train-rmse:5.478027+0.020484 test-rmse:5.615741+0.495991
## [26] train-rmse:4.977582+0.022875 test-rmse:5.142073+0.486243
## [27] train-rmse:4.544526+0.022719 test-rmse:4.725749+0.490618
## [28] train-rmse:4.166672+0.027342 test-rmse:4.353921+0.471481
## [29] train-rmse:3.836347+0.023318 test-rmse:4.050294+0.477598
## [30] train-rmse:3.546311+0.030549 test-rmse:3.786223+0.439024
## [31] train-rmse:3.295700+0.030374 test-rmse:3.557617+0.434266
## [32] train-rmse:3.082782+0.031078 test-rmse:3.360198+0.420410
## [33] train-rmse:2.894734+0.035328 test-rmse:3.211101+0.401690
## [34] train-rmse:2.732664+0.037979 test-rmse:3.061364+0.380391
## [35] train-rmse:2.593546+0.039142 test-rmse:2.916688+0.367966
## [36] train-rmse:2.472514+0.040309 test-rmse:2.824840+0.348979
## [37] train-rmse:2.364889+0.041997 test-rmse:2.738479+0.331113
## [38] train-rmse:2.263053+0.040206 test-rmse:2.668750+0.326780
## [39] train-rmse:2.179393+0.040679 test-rmse:2.584484+0.315298
## [40] train-rmse:2.103974+0.041084 test-rmse:2.524366+0.311480
## [41] train-rmse:2.032187+0.041071 test-rmse:2.471273+0.293841
## [42] train-rmse:1.968513+0.037210 test-rmse:2.411283+0.278940
## [43] train-rmse:1.909954+0.035559 test-rmse:2.366178+0.271433
## [44] train-rmse:1.858124+0.036614 test-rmse:2.325584+0.267207
## [45] train-rmse:1.809513+0.036295 test-rmse:2.294975+0.260998
## [46] train-rmse:1.764954+0.035661 test-rmse:2.249833+0.251488
## [47] train-rmse:1.721570+0.032697 test-rmse:2.212988+0.247703
## [48] train-rmse:1.681073+0.032419 test-rmse:2.183125+0.249856
## [49] train-rmse:1.638695+0.038736 test-rmse:2.158639+0.252946
## [50] train-rmse:1.603962+0.037550 test-rmse:2.128837+0.249920
## [51] train-rmse:1.567104+0.037887 test-rmse:2.099652+0.247464
## [52] train-rmse:1.534981+0.037297 test-rmse:2.077305+0.239922
## [53] train-rmse:1.500359+0.036570 test-rmse:2.046379+0.241335
## [54] train-rmse:1.470272+0.036232 test-rmse:2.024745+0.242127
## [55] train-rmse:1.441158+0.034954 test-rmse:2.000875+0.240998
## [56] train-rmse:1.412802+0.034438 test-rmse:1.975775+0.234938
## [57] train-rmse:1.385888+0.032572 test-rmse:1.951375+0.230038
## [58] train-rmse:1.359340+0.030738 test-rmse:1.936601+0.229596
## [59] train-rmse:1.333503+0.030611 test-rmse:1.918165+0.232401
## [60] train-rmse:1.308760+0.030558 test-rmse:1.897306+0.234402
## [61] train-rmse:1.281424+0.033040 test-rmse:1.878919+0.231419
## [62] train-rmse:1.257777+0.031951 test-rmse:1.858079+0.235654
## [63] train-rmse:1.234251+0.032075 test-rmse:1.842517+0.232445
## [64] train-rmse:1.210899+0.032789 test-rmse:1.828265+0.229888
## [65] train-rmse:1.187698+0.030176 test-rmse:1.804302+0.221624
## [66] train-rmse:1.166668+0.029884 test-rmse:1.784033+0.224742
## [67] train-rmse:1.143717+0.032161 test-rmse:1.769163+0.219564
## [68] train-rmse:1.123249+0.031931 test-rmse:1.754949+0.224881
## [69] train-rmse:1.103281+0.031467 test-rmse:1.739764+0.224306
## [70] train-rmse:1.083888+0.031054 test-rmse:1.729112+0.220502
## [71] train-rmse:1.064651+0.031925 test-rmse:1.714494+0.222607
## [72] train-rmse:1.046649+0.031299 test-rmse:1.696541+0.224134
## [73] train-rmse:1.028506+0.030658 test-rmse:1.678625+0.228301
## [74] train-rmse:1.010997+0.030468 test-rmse:1.665150+0.228946
## [75] train-rmse:0.994701+0.030296 test-rmse:1.651672+0.228930
## [76] train-rmse:0.978261+0.029610 test-rmse:1.639404+0.231983
## [77] train-rmse:0.962003+0.029442 test-rmse:1.627078+0.234561
## [78] train-rmse:0.946678+0.028757 test-rmse:1.616591+0.234014
## [79] train-rmse:0.929988+0.029530 test-rmse:1.605796+0.233613
## [80] train-rmse:0.914986+0.028403 test-rmse:1.596868+0.236982
## [81] train-rmse:0.900009+0.028014 test-rmse:1.584142+0.233934
## [82] train-rmse:0.885682+0.028284 test-rmse:1.572630+0.234728
## [83] train-rmse:0.872228+0.027880 test-rmse:1.562483+0.234829
## [84] train-rmse:0.857438+0.026564 test-rmse:1.551501+0.231617
## [85] train-rmse:0.842751+0.026613 test-rmse:1.541719+0.235737
## [86] train-rmse:0.829518+0.026764 test-rmse:1.533355+0.237421
## [87] train-rmse:0.816259+0.026615 test-rmse:1.526072+0.241927
## [88] train-rmse:0.803339+0.025724 test-rmse:1.511196+0.242760
## [89] train-rmse:0.791312+0.025626 test-rmse:1.500480+0.243408
## [90] train-rmse:0.778798+0.025913 test-rmse:1.494884+0.239767
## [91] train-rmse:0.766700+0.026051 test-rmse:1.487791+0.242083
## [92] train-rmse:0.754470+0.026243 test-rmse:1.481476+0.241415
## [93] train-rmse:0.743284+0.025181 test-rmse:1.472540+0.241333
## [94] train-rmse:0.731830+0.026091 test-rmse:1.463317+0.242040
## [95] train-rmse:0.721468+0.026041 test-rmse:1.458543+0.240542
## [96] train-rmse:0.710793+0.026129 test-rmse:1.449789+0.242157
## [97] train-rmse:0.700806+0.026218 test-rmse:1.443784+0.245655
## [98] train-rmse:0.690855+0.025704 test-rmse:1.438389+0.247380
## [99] train-rmse:0.680749+0.025542 test-rmse:1.432242+0.245537
## [100] train-rmse:0.671303+0.026084 test-rmse:1.423416+0.247261
## [101] train-rmse:0.660813+0.025386 test-rmse:1.417634+0.248168
## [102] train-rmse:0.652048+0.025037 test-rmse:1.409377+0.243820
## [103] train-rmse:0.643088+0.024298 test-rmse:1.402431+0.244299
## [104] train-rmse:0.634312+0.023822 test-rmse:1.397956+0.242880
## [105] train-rmse:0.626185+0.023575 test-rmse:1.393174+0.244954
## [106] train-rmse:0.617696+0.021958 test-rmse:1.386206+0.242152
## [107] train-rmse:0.610042+0.021609 test-rmse:1.377721+0.245738
## [108] train-rmse:0.602689+0.021245 test-rmse:1.372807+0.246072
## [109] train-rmse:0.594303+0.020714 test-rmse:1.368646+0.245893
## [110] train-rmse:0.585766+0.020828 test-rmse:1.364244+0.244574
## [111] train-rmse:0.578689+0.020530 test-rmse:1.359447+0.245557
## [112] train-rmse:0.572038+0.020489 test-rmse:1.351848+0.245910
## [113] train-rmse:0.564883+0.021172 test-rmse:1.346981+0.247555
## [114] train-rmse:0.558319+0.020846 test-rmse:1.341973+0.248669
## [115] train-rmse:0.551691+0.020560 test-rmse:1.336433+0.246672
## [116] train-rmse:0.543868+0.021137 test-rmse:1.333395+0.247893
## [117] train-rmse:0.536810+0.020918 test-rmse:1.326587+0.249043
## [118] train-rmse:0.530043+0.020247 test-rmse:1.323987+0.250299
## [119] train-rmse:0.524095+0.019676 test-rmse:1.320443+0.252146
## [120] train-rmse:0.518256+0.019715 test-rmse:1.316431+0.254225
## [121] train-rmse:0.512600+0.019787 test-rmse:1.313782+0.256860
## [122] train-rmse:0.506096+0.019533 test-rmse:1.307671+0.252278
## [123] train-rmse:0.499977+0.020174 test-rmse:1.305435+0.253734
## [124] train-rmse:0.494060+0.019444 test-rmse:1.302316+0.255187
## [125] train-rmse:0.488474+0.019541 test-rmse:1.298283+0.255272
## [126] train-rmse:0.483423+0.019520 test-rmse:1.294397+0.255986
## [127] train-rmse:0.478563+0.019469 test-rmse:1.292368+0.257445
## [128] train-rmse:0.473416+0.019546 test-rmse:1.287871+0.252910
## [129] train-rmse:0.468299+0.019070 test-rmse:1.284734+0.253719
## [130] train-rmse:0.462920+0.019105 test-rmse:1.281211+0.254339
## [131] train-rmse:0.457469+0.019253 test-rmse:1.279915+0.254585
## [132] train-rmse:0.452738+0.019505 test-rmse:1.274985+0.256421
## [133] train-rmse:0.448308+0.019608 test-rmse:1.272415+0.257485
## [134] train-rmse:0.442522+0.018553 test-rmse:1.268803+0.254451
## [135] train-rmse:0.437535+0.018379 test-rmse:1.264038+0.256731
## [136] train-rmse:0.433286+0.017995 test-rmse:1.262069+0.257309
## [137] train-rmse:0.428784+0.017635 test-rmse:1.260413+0.257603
## [138] train-rmse:0.424690+0.017596 test-rmse:1.257106+0.258311
## [139] train-rmse:0.419953+0.018209 test-rmse:1.256043+0.258329
## [140] train-rmse:0.414768+0.018328 test-rmse:1.253679+0.259712
## [141] train-rmse:0.410605+0.018092 test-rmse:1.252530+0.260743
## [142] train-rmse:0.406420+0.017682 test-rmse:1.250753+0.261636
## [143] train-rmse:0.402465+0.017647 test-rmse:1.249199+0.260879
## [144] train-rmse:0.398664+0.017319 test-rmse:1.245497+0.262709
## [145] train-rmse:0.394940+0.017486 test-rmse:1.244379+0.263030
## [146] train-rmse:0.390838+0.017227 test-rmse:1.243028+0.264100
## [147] train-rmse:0.387548+0.017050 test-rmse:1.241234+0.263402
## [148] train-rmse:0.383949+0.017072 test-rmse:1.238777+0.264064
## [149] train-rmse:0.380624+0.017262 test-rmse:1.237615+0.264619
## [150] train-rmse:0.376645+0.017393 test-rmse:1.235685+0.264128
## [151] train-rmse:0.373005+0.017774 test-rmse:1.232076+0.265742
## [152] train-rmse:0.369644+0.017597 test-rmse:1.229922+0.267004
## [153] train-rmse:0.366495+0.017731 test-rmse:1.226607+0.267263
## [154] train-rmse:0.363131+0.017785 test-rmse:1.223451+0.266790
## [155] train-rmse:0.359436+0.017619 test-rmse:1.221045+0.268174
## [156] train-rmse:0.356162+0.017525 test-rmse:1.220509+0.269042
## [157] train-rmse:0.353221+0.017745 test-rmse:1.218835+0.269505
## [158] train-rmse:0.350146+0.018034 test-rmse:1.218253+0.270243
## [159] train-rmse:0.347030+0.018215 test-rmse:1.215986+0.271598
## [160] train-rmse:0.343964+0.017573 test-rmse:1.214186+0.272394
## [161] train-rmse:0.341270+0.017497 test-rmse:1.212329+0.272478
## [162] train-rmse:0.338346+0.016813 test-rmse:1.211465+0.273018
## [163] train-rmse:0.334920+0.015919 test-rmse:1.210240+0.271428
## [164] train-rmse:0.331803+0.015699 test-rmse:1.208653+0.272477
## [165] train-rmse:0.328518+0.015887 test-rmse:1.207330+0.273449
## [166] train-rmse:0.326105+0.015607 test-rmse:1.206582+0.273305
## [167] train-rmse:0.323343+0.015856 test-rmse:1.205491+0.273514
## [168] train-rmse:0.320198+0.015235 test-rmse:1.204847+0.273502
## [169] train-rmse:0.317740+0.015238 test-rmse:1.203966+0.274369
## [170] train-rmse:0.315200+0.015190 test-rmse:1.202952+0.274928
## [171] train-rmse:0.312738+0.015034 test-rmse:1.202498+0.275881
## [172] train-rmse:0.309805+0.015716 test-rmse:1.201323+0.276077
## [173] train-rmse:0.306546+0.014830 test-rmse:1.199677+0.275911
## [174] train-rmse:0.304052+0.014327 test-rmse:1.199403+0.276611
## [175] train-rmse:0.301017+0.014823 test-rmse:1.198565+0.276714
## [176] train-rmse:0.298503+0.014839 test-rmse:1.197421+0.277425
## [177] train-rmse:0.296580+0.014856 test-rmse:1.197089+0.277053
## [178] train-rmse:0.294592+0.014388 test-rmse:1.195872+0.277490
## [179] train-rmse:0.292428+0.014095 test-rmse:1.195499+0.276905
## [180] train-rmse:0.290191+0.013949 test-rmse:1.194411+0.276577
## [181] train-rmse:0.288186+0.013896 test-rmse:1.192736+0.278454
## [182] train-rmse:0.285803+0.013763 test-rmse:1.192579+0.278793
## [183] train-rmse:0.283833+0.013593 test-rmse:1.192087+0.278737
## [184] train-rmse:0.281747+0.013594 test-rmse:1.190233+0.276786
## [185] train-rmse:0.279623+0.013496 test-rmse:1.189233+0.277232
## [186] train-rmse:0.277356+0.013139 test-rmse:1.189154+0.277533
## [187] train-rmse:0.274977+0.012831 test-rmse:1.188851+0.277621
## [188] train-rmse:0.272894+0.012387 test-rmse:1.188277+0.277260
## [189] train-rmse:0.271159+0.012275 test-rmse:1.187576+0.277000
## [190] train-rmse:0.268870+0.013206 test-rmse:1.186257+0.277325
## [191] train-rmse:0.266890+0.013304 test-rmse:1.185969+0.276954
## [192] train-rmse:0.264488+0.013430 test-rmse:1.184939+0.277319
## [193] train-rmse:0.262808+0.012893 test-rmse:1.183871+0.277508
## [194] train-rmse:0.260703+0.013015 test-rmse:1.183825+0.278195
## [195] train-rmse:0.258918+0.013088 test-rmse:1.183066+0.278522
## [196] train-rmse:0.257298+0.013173 test-rmse:1.182625+0.278245
## [197] train-rmse:0.255663+0.013107 test-rmse:1.182073+0.277063
## [198] train-rmse:0.254222+0.013090 test-rmse:1.181278+0.277495
## [199] train-rmse:0.252570+0.013219 test-rmse:1.180005+0.277460
## [200] train-rmse:0.250361+0.012455 test-rmse:1.179330+0.277533
## [201] train-rmse:0.248536+0.012431 test-rmse:1.178745+0.277703
## [202] train-rmse:0.246476+0.012068 test-rmse:1.178133+0.277575
## [203] train-rmse:0.245055+0.011902 test-rmse:1.178541+0.277638
## [204] train-rmse:0.243685+0.012036 test-rmse:1.177946+0.278182
## [205] train-rmse:0.242361+0.012465 test-rmse:1.177513+0.278113
## [206] train-rmse:0.240335+0.012742 test-rmse:1.177314+0.278395
## [207] train-rmse:0.239071+0.012806 test-rmse:1.176318+0.279216
## [208] train-rmse:0.237669+0.013044 test-rmse:1.175705+0.279270
## [209] train-rmse:0.235832+0.012536 test-rmse:1.174497+0.279515
## [210] train-rmse:0.233600+0.012263 test-rmse:1.173089+0.280835
## [211] train-rmse:0.232224+0.012232 test-rmse:1.172719+0.280851
## [212] train-rmse:0.230412+0.011858 test-rmse:1.172141+0.281184
## [213] train-rmse:0.228458+0.011644 test-rmse:1.171681+0.281160
## [214] train-rmse:0.226666+0.011129 test-rmse:1.171530+0.281365
## [215] train-rmse:0.225128+0.010863 test-rmse:1.171512+0.281282
## [216] train-rmse:0.223294+0.010673 test-rmse:1.171635+0.280791
## [217] train-rmse:0.221406+0.010536 test-rmse:1.171199+0.281030
## [218] train-rmse:0.219757+0.011034 test-rmse:1.171309+0.280777
## [219] train-rmse:0.218282+0.011302 test-rmse:1.171087+0.281256
## [220] train-rmse:0.217073+0.011151 test-rmse:1.170266+0.281703
## [221] train-rmse:0.215707+0.011259 test-rmse:1.169342+0.281938
## [222] train-rmse:0.214321+0.011360 test-rmse:1.169026+0.282138
## [223] train-rmse:0.212771+0.011729 test-rmse:1.168412+0.282125
## [224] train-rmse:0.211520+0.011446 test-rmse:1.167891+0.282368
## [225] train-rmse:0.210304+0.011376 test-rmse:1.168125+0.282203
## [226] train-rmse:0.208674+0.011151 test-rmse:1.167131+0.280807
## [227] train-rmse:0.207255+0.010787 test-rmse:1.166312+0.280910
## [228] train-rmse:0.206286+0.010919 test-rmse:1.165993+0.280803
## [229] train-rmse:0.205066+0.010947 test-rmse:1.166040+0.280922
## [230] train-rmse:0.203788+0.011318 test-rmse:1.165773+0.280826
## [231] train-rmse:0.202452+0.011251 test-rmse:1.165764+0.280733
## [232] train-rmse:0.201084+0.011102 test-rmse:1.165489+0.281118
## [233] train-rmse:0.199690+0.010597 test-rmse:1.164785+0.281311
## [234] train-rmse:0.198550+0.010677 test-rmse:1.164277+0.281690
## [235] train-rmse:0.197279+0.010584 test-rmse:1.164062+0.281895
## [236] train-rmse:0.195617+0.010285 test-rmse:1.163665+0.282179
## [237] train-rmse:0.194726+0.010304 test-rmse:1.163277+0.282241
## [238] train-rmse:0.193527+0.010361 test-rmse:1.163088+0.282442
## [239] train-rmse:0.192576+0.010214 test-rmse:1.162796+0.282495
## [240] train-rmse:0.191291+0.010085 test-rmse:1.162532+0.282372
## [241] train-rmse:0.190127+0.010438 test-rmse:1.161834+0.282803
## [242] train-rmse:0.189001+0.010362 test-rmse:1.161159+0.282590
## [243] train-rmse:0.187462+0.010048 test-rmse:1.160866+0.282881
## [244] train-rmse:0.186232+0.009787 test-rmse:1.160275+0.282725
## [245] train-rmse:0.185010+0.009265 test-rmse:1.160197+0.282983
## [246] train-rmse:0.184116+0.009256 test-rmse:1.159396+0.283458
## [247] train-rmse:0.183184+0.009346 test-rmse:1.159234+0.283487
## [248] train-rmse:0.182105+0.009045 test-rmse:1.159143+0.283708
## [249] train-rmse:0.181143+0.008946 test-rmse:1.159247+0.283979
## [250] train-rmse:0.179818+0.008574 test-rmse:1.158954+0.284278
## [251] train-rmse:0.178827+0.008672 test-rmse:1.158881+0.284137
## [252] train-rmse:0.177834+0.008323 test-rmse:1.158612+0.284131
## [253] train-rmse:0.176656+0.008344 test-rmse:1.157668+0.284109
## [254] train-rmse:0.175582+0.008762 test-rmse:1.157461+0.284359
## [255] train-rmse:0.174304+0.009093 test-rmse:1.156498+0.284583
## [256] train-rmse:0.172913+0.009252 test-rmse:1.156452+0.284176
## [257] train-rmse:0.171874+0.009312 test-rmse:1.156125+0.284375
## [258] train-rmse:0.170658+0.009349 test-rmse:1.155466+0.284948
## [259] train-rmse:0.169292+0.009220 test-rmse:1.155397+0.284972
## [260] train-rmse:0.168206+0.009270 test-rmse:1.155381+0.284977
## [261] train-rmse:0.167429+0.009099 test-rmse:1.154782+0.285229
## [262] train-rmse:0.166662+0.009107 test-rmse:1.154742+0.284915
## [263] train-rmse:0.165593+0.009284 test-rmse:1.154552+0.285024
## [264] train-rmse:0.164656+0.009092 test-rmse:1.154230+0.285008
## [265] train-rmse:0.163822+0.008957 test-rmse:1.153473+0.284818
## [266] train-rmse:0.162506+0.008687 test-rmse:1.153569+0.285433
## [267] train-rmse:0.161517+0.008673 test-rmse:1.153542+0.285314
## [268] train-rmse:0.160470+0.008625 test-rmse:1.153545+0.285495
## [269] train-rmse:0.159406+0.008499 test-rmse:1.153568+0.285856
## [270] train-rmse:0.158257+0.008828 test-rmse:1.153720+0.286221
## [271] train-rmse:0.157533+0.008915 test-rmse:1.153630+0.286091
## Stopping. Best iteration:
## [265] train-rmse:0.163822+0.008957 test-rmse:1.153473+0.284818
##
## 11
## [1] train-rmse:94.766116+0.105094 test-rmse:94.766423+0.483432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:83.478061+0.095217 test-rmse:83.478186+0.500072
## [3] train-rmse:73.545764+0.086897 test-rmse:73.545670+0.516055
## [4] train-rmse:64.807895+0.080011 test-rmse:64.807548+0.531590
## [5] train-rmse:57.122527+0.074434 test-rmse:57.121858+0.546922
## [6] train-rmse:50.364803+0.070103 test-rmse:50.363757+0.562250
## [7] train-rmse:44.424928+0.066945 test-rmse:44.423422+0.577769
## [8] train-rmse:39.206363+0.064904 test-rmse:39.204310+0.593657
## [9] train-rmse:34.622293+0.063135 test-rmse:34.630677+0.594785
## [10] train-rmse:30.591081+0.059323 test-rmse:30.633139+0.599495
## [11] train-rmse:27.044432+0.052606 test-rmse:27.116927+0.608458
## [12] train-rmse:23.924558+0.044243 test-rmse:24.000367+0.581464
## [13] train-rmse:21.179292+0.038293 test-rmse:21.256309+0.567112
## [14] train-rmse:18.763729+0.033512 test-rmse:18.846606+0.553622
## [15] train-rmse:16.635810+0.030452 test-rmse:16.714029+0.545435
## [16] train-rmse:14.763425+0.026037 test-rmse:14.859881+0.563238
## [17] train-rmse:13.112334+0.024482 test-rmse:13.218849+0.547795
## [18] train-rmse:11.659372+0.022727 test-rmse:11.769373+0.535353
## [19] train-rmse:10.377710+0.021390 test-rmse:10.481675+0.538021
## [20] train-rmse:9.252015+0.019227 test-rmse:9.371911+0.544917
## [21] train-rmse:8.260731+0.017732 test-rmse:8.370771+0.535472
## [22] train-rmse:7.387540+0.018301 test-rmse:7.501691+0.523754
## [23] train-rmse:6.624639+0.017662 test-rmse:6.754430+0.531739
## [24] train-rmse:5.952468+0.017521 test-rmse:6.089171+0.513031
## [25] train-rmse:5.363936+0.018444 test-rmse:5.518246+0.499198
## [26] train-rmse:4.847252+0.023244 test-rmse:5.010321+0.471890
## [27] train-rmse:4.390979+0.018833 test-rmse:4.574961+0.478059
## [28] train-rmse:3.996956+0.019195 test-rmse:4.192845+0.460193
## [29] train-rmse:3.647203+0.016162 test-rmse:3.873672+0.445782
## [30] train-rmse:3.343811+0.019817 test-rmse:3.592677+0.424085
## [31] train-rmse:3.080003+0.020058 test-rmse:3.354422+0.417743
## [32] train-rmse:2.853310+0.020742 test-rmse:3.150157+0.405749
## [33] train-rmse:2.652145+0.021954 test-rmse:2.967490+0.380995
## [34] train-rmse:2.478271+0.021275 test-rmse:2.810214+0.369633
## [35] train-rmse:2.322253+0.028088 test-rmse:2.682745+0.353102
## [36] train-rmse:2.181247+0.026653 test-rmse:2.564531+0.333046
## [37] train-rmse:2.063408+0.029940 test-rmse:2.463990+0.321731
## [38] train-rmse:1.956376+0.030114 test-rmse:2.389258+0.305024
## [39] train-rmse:1.864952+0.030690 test-rmse:2.319180+0.308247
## [40] train-rmse:1.785654+0.031229 test-rmse:2.252531+0.300180
## [41] train-rmse:1.712445+0.032381 test-rmse:2.205995+0.296163
## [42] train-rmse:1.644534+0.040502 test-rmse:2.150766+0.277110
## [43] train-rmse:1.583027+0.039398 test-rmse:2.103278+0.273955
## [44] train-rmse:1.527952+0.038686 test-rmse:2.074138+0.276846
## [45] train-rmse:1.472383+0.037236 test-rmse:2.031433+0.271434
## [46] train-rmse:1.423954+0.034157 test-rmse:1.995403+0.262118
## [47] train-rmse:1.374815+0.040741 test-rmse:1.966648+0.254629
## [48] train-rmse:1.332587+0.038715 test-rmse:1.938385+0.252669
## [49] train-rmse:1.295548+0.038282 test-rmse:1.907571+0.250618
## [50] train-rmse:1.259944+0.039310 test-rmse:1.886847+0.251343
## [51] train-rmse:1.226550+0.038010 test-rmse:1.866558+0.246322
## [52] train-rmse:1.195045+0.037261 test-rmse:1.842712+0.254234
## [53] train-rmse:1.164036+0.036304 test-rmse:1.816936+0.253298
## [54] train-rmse:1.135287+0.033967 test-rmse:1.792764+0.247378
## [55] train-rmse:1.108092+0.033525 test-rmse:1.774698+0.241359
## [56] train-rmse:1.081958+0.033121 test-rmse:1.748049+0.243934
## [57] train-rmse:1.055138+0.033966 test-rmse:1.734406+0.243092
## [58] train-rmse:1.030870+0.033774 test-rmse:1.718989+0.241204
## [59] train-rmse:1.005124+0.038119 test-rmse:1.697670+0.244139
## [60] train-rmse:0.981761+0.040725 test-rmse:1.688197+0.247412
## [61] train-rmse:0.960542+0.040066 test-rmse:1.670020+0.245238
## [62] train-rmse:0.937997+0.038919 test-rmse:1.653821+0.245963
## [63] train-rmse:0.915999+0.038733 test-rmse:1.640444+0.244509
## [64] train-rmse:0.896091+0.037996 test-rmse:1.624731+0.252383
## [65] train-rmse:0.877535+0.037249 test-rmse:1.609208+0.242521
## [66] train-rmse:0.859337+0.036698 test-rmse:1.591475+0.249789
## [67] train-rmse:0.841671+0.035798 test-rmse:1.574217+0.251430
## [68] train-rmse:0.824260+0.034427 test-rmse:1.562767+0.252540
## [69] train-rmse:0.807836+0.034229 test-rmse:1.548778+0.246673
## [70] train-rmse:0.791855+0.033454 test-rmse:1.540067+0.247316
## [71] train-rmse:0.775953+0.031870 test-rmse:1.526339+0.249459
## [72] train-rmse:0.761250+0.032169 test-rmse:1.512251+0.251105
## [73] train-rmse:0.746440+0.032244 test-rmse:1.503270+0.251628
## [74] train-rmse:0.731638+0.031198 test-rmse:1.487983+0.247564
## [75] train-rmse:0.715911+0.032686 test-rmse:1.478697+0.247421
## [76] train-rmse:0.701430+0.032668 test-rmse:1.469743+0.246276
## [77] train-rmse:0.688690+0.032142 test-rmse:1.462200+0.250247
## [78] train-rmse:0.676931+0.032131 test-rmse:1.449443+0.245096
## [79] train-rmse:0.664581+0.032720 test-rmse:1.436439+0.248150
## [80] train-rmse:0.651010+0.032893 test-rmse:1.432002+0.248506
## [81] train-rmse:0.640085+0.032300 test-rmse:1.423289+0.247754
## [82] train-rmse:0.628600+0.032011 test-rmse:1.415018+0.250097
## [83] train-rmse:0.618256+0.031376 test-rmse:1.409087+0.250724
## [84] train-rmse:0.606879+0.029756 test-rmse:1.402365+0.253694
## [85] train-rmse:0.595871+0.028900 test-rmse:1.390010+0.250661
## [86] train-rmse:0.584452+0.030145 test-rmse:1.387186+0.252931
## [87] train-rmse:0.575532+0.029253 test-rmse:1.377004+0.247628
## [88] train-rmse:0.565472+0.028443 test-rmse:1.373514+0.248215
## [89] train-rmse:0.555541+0.027652 test-rmse:1.368732+0.247688
## [90] train-rmse:0.546029+0.027137 test-rmse:1.363214+0.249193
## [91] train-rmse:0.537784+0.026937 test-rmse:1.355635+0.246818
## [92] train-rmse:0.528745+0.026176 test-rmse:1.351565+0.245869
## [93] train-rmse:0.519497+0.026678 test-rmse:1.346487+0.247139
## [94] train-rmse:0.512283+0.026691 test-rmse:1.342220+0.250350
## [95] train-rmse:0.504061+0.026389 test-rmse:1.339077+0.250011
## [96] train-rmse:0.495300+0.026740 test-rmse:1.333184+0.251200
## [97] train-rmse:0.488135+0.026153 test-rmse:1.328971+0.250880
## [98] train-rmse:0.481121+0.025983 test-rmse:1.325380+0.252071
## [99] train-rmse:0.472725+0.026028 test-rmse:1.324071+0.254150
## [100] train-rmse:0.464815+0.024948 test-rmse:1.321146+0.254694
## [101] train-rmse:0.458254+0.025403 test-rmse:1.314372+0.256212
## [102] train-rmse:0.451588+0.025663 test-rmse:1.312609+0.257525
## [103] train-rmse:0.444413+0.024104 test-rmse:1.309875+0.257476
## [104] train-rmse:0.438464+0.023663 test-rmse:1.305880+0.256882
## [105] train-rmse:0.431665+0.024278 test-rmse:1.300370+0.260916
## [106] train-rmse:0.424816+0.022897 test-rmse:1.296996+0.260082
## [107] train-rmse:0.418023+0.022378 test-rmse:1.293509+0.263441
## [108] train-rmse:0.412011+0.022284 test-rmse:1.290475+0.264262
## [109] train-rmse:0.406737+0.022217 test-rmse:1.288960+0.265262
## [110] train-rmse:0.401658+0.021711 test-rmse:1.286671+0.264970
## [111] train-rmse:0.396309+0.021511 test-rmse:1.284209+0.265303
## [112] train-rmse:0.391358+0.021754 test-rmse:1.281756+0.266462
## [113] train-rmse:0.386034+0.020889 test-rmse:1.280520+0.267177
## [114] train-rmse:0.380767+0.020474 test-rmse:1.276983+0.269824
## [115] train-rmse:0.375853+0.020861 test-rmse:1.275907+0.270951
## [116] train-rmse:0.371039+0.020411 test-rmse:1.275133+0.271430
## [117] train-rmse:0.367163+0.020340 test-rmse:1.273406+0.271063
## [118] train-rmse:0.363232+0.020823 test-rmse:1.270886+0.271508
## [119] train-rmse:0.358196+0.020988 test-rmse:1.270194+0.271345
## [120] train-rmse:0.354774+0.020938 test-rmse:1.268372+0.272262
## [121] train-rmse:0.349822+0.019978 test-rmse:1.265931+0.273755
## [122] train-rmse:0.346000+0.019811 test-rmse:1.265371+0.274543
## [123] train-rmse:0.340710+0.020894 test-rmse:1.262766+0.273227
## [124] train-rmse:0.336827+0.020588 test-rmse:1.262212+0.274467
## [125] train-rmse:0.332375+0.019802 test-rmse:1.259991+0.273890
## [126] train-rmse:0.328641+0.019376 test-rmse:1.259687+0.274237
## [127] train-rmse:0.323879+0.019419 test-rmse:1.255862+0.270321
## [128] train-rmse:0.319900+0.018186 test-rmse:1.254687+0.271011
## [129] train-rmse:0.316110+0.018593 test-rmse:1.253621+0.270970
## [130] train-rmse:0.312847+0.018586 test-rmse:1.252510+0.271558
## [131] train-rmse:0.310032+0.018963 test-rmse:1.250021+0.272352
## [132] train-rmse:0.306061+0.018987 test-rmse:1.249687+0.273711
## [133] train-rmse:0.302335+0.018477 test-rmse:1.248791+0.274195
## [134] train-rmse:0.299408+0.018964 test-rmse:1.246630+0.274694
## [135] train-rmse:0.296296+0.019043 test-rmse:1.246959+0.275391
## [136] train-rmse:0.293364+0.018461 test-rmse:1.245127+0.276742
## [137] train-rmse:0.289649+0.017366 test-rmse:1.243772+0.277416
## [138] train-rmse:0.286593+0.016766 test-rmse:1.242903+0.277022
## [139] train-rmse:0.283240+0.017101 test-rmse:1.242820+0.277329
## [140] train-rmse:0.280172+0.016099 test-rmse:1.242353+0.277955
## [141] train-rmse:0.277216+0.015812 test-rmse:1.240850+0.277179
## [142] train-rmse:0.273628+0.016706 test-rmse:1.241121+0.276694
## [143] train-rmse:0.270856+0.016105 test-rmse:1.239698+0.276073
## [144] train-rmse:0.267869+0.016400 test-rmse:1.239528+0.276451
## [145] train-rmse:0.265466+0.016139 test-rmse:1.239294+0.277657
## [146] train-rmse:0.262048+0.015834 test-rmse:1.239180+0.276481
## [147] train-rmse:0.259192+0.015628 test-rmse:1.239324+0.276625
## [148] train-rmse:0.256024+0.015335 test-rmse:1.239002+0.276429
## [149] train-rmse:0.253395+0.015895 test-rmse:1.238422+0.276323
## [150] train-rmse:0.251067+0.015808 test-rmse:1.237204+0.277494
## [151] train-rmse:0.248131+0.015135 test-rmse:1.236637+0.276968
## [152] train-rmse:0.245640+0.014755 test-rmse:1.235130+0.275219
## [153] train-rmse:0.243330+0.014542 test-rmse:1.234574+0.274987
## [154] train-rmse:0.240603+0.014242 test-rmse:1.233443+0.276139
## [155] train-rmse:0.238742+0.014196 test-rmse:1.232664+0.276672
## [156] train-rmse:0.236476+0.014278 test-rmse:1.232208+0.276393
## [157] train-rmse:0.234297+0.014180 test-rmse:1.231699+0.275721
## [158] train-rmse:0.232130+0.014388 test-rmse:1.231323+0.275489
## [159] train-rmse:0.229874+0.014348 test-rmse:1.230992+0.275452
## [160] train-rmse:0.227469+0.014137 test-rmse:1.229173+0.276478
## [161] train-rmse:0.225251+0.013978 test-rmse:1.229381+0.277477
## [162] train-rmse:0.222566+0.013673 test-rmse:1.229551+0.277770
## [163] train-rmse:0.220654+0.013634 test-rmse:1.228253+0.278155
## [164] train-rmse:0.218715+0.014069 test-rmse:1.228125+0.278357
## [165] train-rmse:0.216471+0.014124 test-rmse:1.227874+0.278002
## [166] train-rmse:0.213591+0.012888 test-rmse:1.226425+0.276906
## [167] train-rmse:0.211419+0.013246 test-rmse:1.225468+0.276782
## [168] train-rmse:0.209287+0.013219 test-rmse:1.225350+0.277721
## [169] train-rmse:0.207237+0.013558 test-rmse:1.224706+0.277275
## [170] train-rmse:0.205339+0.013573 test-rmse:1.224222+0.277124
## [171] train-rmse:0.202771+0.012978 test-rmse:1.224226+0.276336
## [172] train-rmse:0.201103+0.013028 test-rmse:1.223996+0.276765
## [173] train-rmse:0.198761+0.013187 test-rmse:1.223876+0.277757
## [174] train-rmse:0.197493+0.013402 test-rmse:1.223769+0.277917
## [175] train-rmse:0.195594+0.013134 test-rmse:1.222449+0.278253
## [176] train-rmse:0.193295+0.013027 test-rmse:1.222620+0.277065
## [177] train-rmse:0.191902+0.012869 test-rmse:1.222494+0.276990
## [178] train-rmse:0.190375+0.012930 test-rmse:1.222389+0.276872
## [179] train-rmse:0.188325+0.012761 test-rmse:1.222219+0.277273
## [180] train-rmse:0.186694+0.012592 test-rmse:1.222438+0.277943
## [181] train-rmse:0.184353+0.012255 test-rmse:1.221881+0.277619
## [182] train-rmse:0.182958+0.012356 test-rmse:1.221187+0.277852
## [183] train-rmse:0.181627+0.012318 test-rmse:1.220737+0.276734
## [184] train-rmse:0.179593+0.011588 test-rmse:1.220614+0.276846
## [185] train-rmse:0.177848+0.011201 test-rmse:1.221025+0.277139
## [186] train-rmse:0.176062+0.011401 test-rmse:1.220983+0.277039
## [187] train-rmse:0.174914+0.011375 test-rmse:1.220215+0.276832
## [188] train-rmse:0.173738+0.011390 test-rmse:1.220032+0.276528
## [189] train-rmse:0.172197+0.011113 test-rmse:1.220093+0.276765
## [190] train-rmse:0.170644+0.010849 test-rmse:1.220173+0.277115
## [191] train-rmse:0.168962+0.010651 test-rmse:1.220443+0.277490
## [192] train-rmse:0.167038+0.010456 test-rmse:1.219938+0.277096
## [193] train-rmse:0.165231+0.009559 test-rmse:1.219674+0.277075
## [194] train-rmse:0.163611+0.010080 test-rmse:1.219604+0.276942
## [195] train-rmse:0.161515+0.010046 test-rmse:1.218864+0.277014
## [196] train-rmse:0.160390+0.009886 test-rmse:1.218784+0.276934
## [197] train-rmse:0.158691+0.010112 test-rmse:1.218925+0.276820
## [198] train-rmse:0.157329+0.010106 test-rmse:1.218783+0.276810
## [199] train-rmse:0.156122+0.010106 test-rmse:1.218841+0.277110
## [200] train-rmse:0.154778+0.009902 test-rmse:1.218795+0.277194
## [201] train-rmse:0.153684+0.009862 test-rmse:1.218507+0.276995
## [202] train-rmse:0.152200+0.010300 test-rmse:1.218267+0.277133
## [203] train-rmse:0.150818+0.010294 test-rmse:1.217726+0.277263
## [204] train-rmse:0.149391+0.010257 test-rmse:1.217739+0.277481
## [205] train-rmse:0.148108+0.009874 test-rmse:1.217676+0.277513
## [206] train-rmse:0.146698+0.010030 test-rmse:1.217153+0.277924
## [207] train-rmse:0.144895+0.009774 test-rmse:1.217371+0.278087
## [208] train-rmse:0.143400+0.009591 test-rmse:1.217204+0.278492
## [209] train-rmse:0.142199+0.009816 test-rmse:1.217059+0.278133
## [210] train-rmse:0.140980+0.009735 test-rmse:1.216899+0.278640
## [211] train-rmse:0.139754+0.010001 test-rmse:1.216848+0.279008
## [212] train-rmse:0.138782+0.009872 test-rmse:1.216606+0.278797
## [213] train-rmse:0.137826+0.009833 test-rmse:1.216188+0.278667
## [214] train-rmse:0.136780+0.009659 test-rmse:1.216014+0.278907
## [215] train-rmse:0.135185+0.009458 test-rmse:1.215636+0.278755
## [216] train-rmse:0.133991+0.009117 test-rmse:1.215199+0.278824
## [217] train-rmse:0.132833+0.009041 test-rmse:1.215017+0.278778
## [218] train-rmse:0.131494+0.008751 test-rmse:1.215113+0.278770
## [219] train-rmse:0.129771+0.008852 test-rmse:1.215003+0.278585
## [220] train-rmse:0.128580+0.008816 test-rmse:1.215196+0.278387
## [221] train-rmse:0.127465+0.008599 test-rmse:1.215047+0.278299
## [222] train-rmse:0.126439+0.008600 test-rmse:1.215163+0.278501
## [223] train-rmse:0.125310+0.008157 test-rmse:1.215189+0.278808
## [224] train-rmse:0.124539+0.008103 test-rmse:1.214931+0.279003
## [225] train-rmse:0.123306+0.008062 test-rmse:1.214882+0.279033
## [226] train-rmse:0.122683+0.008012 test-rmse:1.214769+0.278881
## [227] train-rmse:0.121923+0.007794 test-rmse:1.214792+0.279135
## [228] train-rmse:0.120612+0.007055 test-rmse:1.214566+0.279024
## [229] train-rmse:0.119543+0.007330 test-rmse:1.214852+0.279021
## [230] train-rmse:0.118171+0.007488 test-rmse:1.214719+0.278998
## [231] train-rmse:0.117226+0.007198 test-rmse:1.214727+0.278928
## [232] train-rmse:0.116554+0.007224 test-rmse:1.214766+0.278984
## [233] train-rmse:0.115311+0.007071 test-rmse:1.214624+0.278879
## [234] train-rmse:0.114412+0.007196 test-rmse:1.214506+0.279190
## [235] train-rmse:0.113465+0.006645 test-rmse:1.214290+0.279019
## [236] train-rmse:0.112206+0.006574 test-rmse:1.214201+0.279081
## [237] train-rmse:0.111198+0.006239 test-rmse:1.213839+0.279282
## [238] train-rmse:0.110240+0.006363 test-rmse:1.213945+0.279194
## [239] train-rmse:0.109069+0.006168 test-rmse:1.213739+0.279363
## [240] train-rmse:0.108174+0.006286 test-rmse:1.213800+0.279514
## [241] train-rmse:0.106966+0.006223 test-rmse:1.213570+0.279577
## [242] train-rmse:0.105940+0.006090 test-rmse:1.213694+0.279552
## [243] train-rmse:0.104890+0.006102 test-rmse:1.213682+0.279646
## [244] train-rmse:0.103625+0.005907 test-rmse:1.213631+0.279673
## [245] train-rmse:0.102761+0.006368 test-rmse:1.213473+0.279860
## [246] train-rmse:0.102200+0.006239 test-rmse:1.213371+0.279941
## [247] train-rmse:0.101274+0.006164 test-rmse:1.213351+0.280233
## [248] train-rmse:0.100213+0.005842 test-rmse:1.213089+0.279973
## [249] train-rmse:0.099342+0.005880 test-rmse:1.212908+0.279669
## [250] train-rmse:0.098466+0.005736 test-rmse:1.212807+0.279597
## [251] train-rmse:0.097577+0.005873 test-rmse:1.212667+0.279597
## [252] train-rmse:0.096681+0.005683 test-rmse:1.212831+0.279528
## [253] train-rmse:0.095834+0.005808 test-rmse:1.212947+0.279368
## [254] train-rmse:0.095096+0.005732 test-rmse:1.212851+0.279385
## [255] train-rmse:0.093909+0.005740 test-rmse:1.213342+0.279327
## [256] train-rmse:0.093039+0.005697 test-rmse:1.212960+0.279214
## [257] train-rmse:0.092080+0.005558 test-rmse:1.213027+0.279256
## Stopping. Best iteration:
## [251] train-rmse:0.097577+0.005873 test-rmse:1.212667+0.279597
##
## 12
## [1] train-rmse:94.766116+0.105094 test-rmse:94.766423+0.483432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:83.478061+0.095217 test-rmse:83.478186+0.500072
## [3] train-rmse:73.545764+0.086897 test-rmse:73.545670+0.516055
## [4] train-rmse:64.807895+0.080011 test-rmse:64.807548+0.531590
## [5] train-rmse:57.122527+0.074434 test-rmse:57.121858+0.546922
## [6] train-rmse:50.364803+0.070103 test-rmse:50.363757+0.562250
## [7] train-rmse:44.424928+0.066945 test-rmse:44.423422+0.577769
## [8] train-rmse:39.206363+0.064904 test-rmse:39.204310+0.593657
## [9] train-rmse:34.622293+0.063135 test-rmse:34.630677+0.594785
## [10] train-rmse:30.591081+0.059323 test-rmse:30.633139+0.599495
## [11] train-rmse:27.044432+0.052606 test-rmse:27.116927+0.608458
## [12] train-rmse:23.924558+0.044243 test-rmse:24.000367+0.581464
## [13] train-rmse:21.179186+0.038168 test-rmse:21.254095+0.569931
## [14] train-rmse:18.763117+0.033214 test-rmse:18.846948+0.554102
## [15] train-rmse:16.633769+0.029552 test-rmse:16.718301+0.537407
## [16] train-rmse:14.759743+0.024871 test-rmse:14.865453+0.554510
## [17] train-rmse:13.106433+0.023083 test-rmse:13.213154+0.550035
## [18] train-rmse:11.649263+0.022642 test-rmse:11.754813+0.531973
## [19] train-rmse:10.363184+0.020328 test-rmse:10.455913+0.532696
## [20] train-rmse:9.231174+0.018751 test-rmse:9.346748+0.545403
## [21] train-rmse:8.234509+0.018663 test-rmse:8.358545+0.530804
## [22] train-rmse:7.355970+0.017605 test-rmse:7.470321+0.527576
## [23] train-rmse:6.583057+0.017371 test-rmse:6.703227+0.506742
## [24] train-rmse:5.902712+0.017313 test-rmse:6.038969+0.522256
## [25] train-rmse:5.303167+0.016856 test-rmse:5.444801+0.502666
## [26] train-rmse:4.774540+0.011522 test-rmse:4.938081+0.514155
## [27] train-rmse:4.310325+0.012062 test-rmse:4.501785+0.490404
## [28] train-rmse:3.903009+0.012535 test-rmse:4.117182+0.464566
## [29] train-rmse:3.541850+0.013060 test-rmse:3.780119+0.439052
## [30] train-rmse:3.228953+0.015855 test-rmse:3.490742+0.415358
## [31] train-rmse:2.944791+0.021266 test-rmse:3.239411+0.415041
## [32] train-rmse:2.698317+0.020821 test-rmse:3.026283+0.395378
## [33] train-rmse:2.487551+0.023420 test-rmse:2.846586+0.386577
## [34] train-rmse:2.301964+0.024751 test-rmse:2.673977+0.362545
## [35] train-rmse:2.134016+0.025926 test-rmse:2.534117+0.343824
## [36] train-rmse:1.989028+0.024635 test-rmse:2.421753+0.338408
## [37] train-rmse:1.863416+0.028760 test-rmse:2.312954+0.316013
## [38] train-rmse:1.748818+0.029301 test-rmse:2.223962+0.310499
## [39] train-rmse:1.650628+0.028585 test-rmse:2.150311+0.308472
## [40] train-rmse:1.555914+0.034733 test-rmse:2.089170+0.298411
## [41] train-rmse:1.477830+0.033070 test-rmse:2.030426+0.285116
## [42] train-rmse:1.402782+0.032547 test-rmse:1.973685+0.271119
## [43] train-rmse:1.338031+0.034974 test-rmse:1.932324+0.268160
## [44] train-rmse:1.278539+0.034069 test-rmse:1.888461+0.262692
## [45] train-rmse:1.221516+0.034048 test-rmse:1.852421+0.254257
## [46] train-rmse:1.174382+0.033113 test-rmse:1.824720+0.257167
## [47] train-rmse:1.131473+0.031951 test-rmse:1.798117+0.251463
## [48] train-rmse:1.086617+0.033529 test-rmse:1.767787+0.245196
## [49] train-rmse:1.046298+0.035641 test-rmse:1.740198+0.244140
## [50] train-rmse:1.012750+0.035114 test-rmse:1.711977+0.244244
## [51] train-rmse:0.979244+0.037334 test-rmse:1.694344+0.241326
## [52] train-rmse:0.946322+0.036137 test-rmse:1.661793+0.242149
## [53] train-rmse:0.917903+0.035254 test-rmse:1.644892+0.243838
## [54] train-rmse:0.891422+0.033801 test-rmse:1.626912+0.246044
## [55] train-rmse:0.861855+0.035214 test-rmse:1.609171+0.247209
## [56] train-rmse:0.836060+0.034811 test-rmse:1.595088+0.246181
## [57] train-rmse:0.813230+0.035037 test-rmse:1.578776+0.247924
## [58] train-rmse:0.791398+0.035115 test-rmse:1.568263+0.254398
## [59] train-rmse:0.770203+0.034056 test-rmse:1.542789+0.246693
## [60] train-rmse:0.749630+0.032875 test-rmse:1.529651+0.246386
## [61] train-rmse:0.730123+0.032711 test-rmse:1.514970+0.249522
## [62] train-rmse:0.711720+0.032274 test-rmse:1.497211+0.245601
## [63] train-rmse:0.691270+0.031532 test-rmse:1.485434+0.247018
## [64] train-rmse:0.674734+0.030472 test-rmse:1.472180+0.242000
## [65] train-rmse:0.658638+0.030240 test-rmse:1.464777+0.244849
## [66] train-rmse:0.640444+0.029343 test-rmse:1.448869+0.247804
## [67] train-rmse:0.623643+0.028859 test-rmse:1.445952+0.247136
## [68] train-rmse:0.608406+0.030654 test-rmse:1.438148+0.247603
## [69] train-rmse:0.594244+0.030252 test-rmse:1.430188+0.247989
## [70] train-rmse:0.580658+0.030171 test-rmse:1.422839+0.247060
## [71] train-rmse:0.567929+0.030617 test-rmse:1.414334+0.248791
## [72] train-rmse:0.555924+0.029667 test-rmse:1.405194+0.254303
## [73] train-rmse:0.539409+0.029793 test-rmse:1.396233+0.250334
## [74] train-rmse:0.528390+0.029821 test-rmse:1.388087+0.246315
## [75] train-rmse:0.515470+0.029133 test-rmse:1.382877+0.248330
## [76] train-rmse:0.505067+0.029281 test-rmse:1.376754+0.247854
## [77] train-rmse:0.495028+0.029530 test-rmse:1.368047+0.245531
## [78] train-rmse:0.484631+0.028223 test-rmse:1.363108+0.247683
## [79] train-rmse:0.475530+0.028284 test-rmse:1.357823+0.247800
## [80] train-rmse:0.465648+0.027625 test-rmse:1.352665+0.248470
## [81] train-rmse:0.456583+0.027378 test-rmse:1.347032+0.249173
## [82] train-rmse:0.446224+0.025867 test-rmse:1.344835+0.249528
## [83] train-rmse:0.436529+0.025593 test-rmse:1.340284+0.249449
## [84] train-rmse:0.428596+0.025594 test-rmse:1.335953+0.250026
## [85] train-rmse:0.419666+0.024503 test-rmse:1.328778+0.247088
## [86] train-rmse:0.410948+0.025295 test-rmse:1.324393+0.248304
## [87] train-rmse:0.403306+0.023824 test-rmse:1.319131+0.245648
## [88] train-rmse:0.396069+0.023103 test-rmse:1.317244+0.246247
## [89] train-rmse:0.389468+0.022983 test-rmse:1.313944+0.247488
## [90] train-rmse:0.382502+0.022632 test-rmse:1.309655+0.247954
## [91] train-rmse:0.375891+0.021574 test-rmse:1.305041+0.245916
## [92] train-rmse:0.369044+0.021673 test-rmse:1.303296+0.247629
## [93] train-rmse:0.363009+0.021390 test-rmse:1.300025+0.247341
## [94] train-rmse:0.356756+0.021048 test-rmse:1.293177+0.243888
## [95] train-rmse:0.350312+0.019920 test-rmse:1.290950+0.243738
## [96] train-rmse:0.344265+0.019711 test-rmse:1.289109+0.245806
## [97] train-rmse:0.338233+0.020466 test-rmse:1.285937+0.247264
## [98] train-rmse:0.332809+0.020644 test-rmse:1.283669+0.249816
## [99] train-rmse:0.326957+0.019383 test-rmse:1.279224+0.249795
## [100] train-rmse:0.321785+0.019224 test-rmse:1.276684+0.249542
## [101] train-rmse:0.316847+0.018109 test-rmse:1.273464+0.249083
## [102] train-rmse:0.311230+0.017777 test-rmse:1.272679+0.249619
## [103] train-rmse:0.307002+0.017481 test-rmse:1.269490+0.250171
## [104] train-rmse:0.301912+0.016117 test-rmse:1.267448+0.251076
## [105] train-rmse:0.297042+0.014951 test-rmse:1.266783+0.252707
## [106] train-rmse:0.290827+0.012914 test-rmse:1.264588+0.253841
## [107] train-rmse:0.286443+0.012110 test-rmse:1.264108+0.254733
## [108] train-rmse:0.281733+0.012246 test-rmse:1.263107+0.254605
## [109] train-rmse:0.277446+0.011825 test-rmse:1.260973+0.255394
## [110] train-rmse:0.273333+0.010940 test-rmse:1.260108+0.256858
## [111] train-rmse:0.269778+0.010882 test-rmse:1.258627+0.257284
## [112] train-rmse:0.266234+0.010437 test-rmse:1.257074+0.256735
## [113] train-rmse:0.262495+0.010872 test-rmse:1.256520+0.256237
## [114] train-rmse:0.258166+0.011188 test-rmse:1.254873+0.257519
## [115] train-rmse:0.252917+0.010536 test-rmse:1.253049+0.258247
## [116] train-rmse:0.249196+0.010405 test-rmse:1.253019+0.259306
## [117] train-rmse:0.244802+0.009996 test-rmse:1.251496+0.259304
## [118] train-rmse:0.241158+0.008793 test-rmse:1.250186+0.259690
## [119] train-rmse:0.237973+0.008389 test-rmse:1.249598+0.260201
## [120] train-rmse:0.234063+0.008127 test-rmse:1.247899+0.260762
## [121] train-rmse:0.231213+0.007371 test-rmse:1.247759+0.261109
## [122] train-rmse:0.227918+0.006758 test-rmse:1.246617+0.261440
## [123] train-rmse:0.224974+0.007165 test-rmse:1.244725+0.261166
## [124] train-rmse:0.221285+0.007286 test-rmse:1.243736+0.261884
## [125] train-rmse:0.218692+0.007325 test-rmse:1.243097+0.261971
## [126] train-rmse:0.215503+0.007554 test-rmse:1.242325+0.262368
## [127] train-rmse:0.211730+0.007362 test-rmse:1.241037+0.261864
## [128] train-rmse:0.209050+0.007011 test-rmse:1.239977+0.262532
## [129] train-rmse:0.206685+0.006958 test-rmse:1.239924+0.263240
## [130] train-rmse:0.204189+0.006790 test-rmse:1.239183+0.263567
## [131] train-rmse:0.202143+0.006720 test-rmse:1.237585+0.264277
## [132] train-rmse:0.198873+0.006693 test-rmse:1.236859+0.264947
## [133] train-rmse:0.195282+0.006871 test-rmse:1.236780+0.266183
## [134] train-rmse:0.192047+0.005463 test-rmse:1.235806+0.266199
## [135] train-rmse:0.188932+0.006070 test-rmse:1.235104+0.266463
## [136] train-rmse:0.186626+0.006188 test-rmse:1.234378+0.266902
## [137] train-rmse:0.184211+0.005693 test-rmse:1.233033+0.267553
## [138] train-rmse:0.181551+0.006295 test-rmse:1.231986+0.267628
## [139] train-rmse:0.178963+0.005476 test-rmse:1.231586+0.267814
## [140] train-rmse:0.176205+0.006056 test-rmse:1.231357+0.267802
## [141] train-rmse:0.173772+0.006039 test-rmse:1.231153+0.268540
## [142] train-rmse:0.171388+0.005797 test-rmse:1.231132+0.268234
## [143] train-rmse:0.169107+0.006200 test-rmse:1.230886+0.267881
## [144] train-rmse:0.166174+0.006195 test-rmse:1.230582+0.268769
## [145] train-rmse:0.164544+0.006078 test-rmse:1.230508+0.269125
## [146] train-rmse:0.162333+0.005991 test-rmse:1.229972+0.268695
## [147] train-rmse:0.160379+0.006525 test-rmse:1.229544+0.268498
## [148] train-rmse:0.157663+0.006613 test-rmse:1.228964+0.268775
## [149] train-rmse:0.155448+0.006635 test-rmse:1.228689+0.268204
## [150] train-rmse:0.153848+0.006605 test-rmse:1.228105+0.268445
## [151] train-rmse:0.152304+0.006224 test-rmse:1.227279+0.268597
## [152] train-rmse:0.150510+0.006083 test-rmse:1.226938+0.268221
## [153] train-rmse:0.148184+0.005675 test-rmse:1.226875+0.268880
## [154] train-rmse:0.145767+0.005865 test-rmse:1.226480+0.269557
## [155] train-rmse:0.143348+0.006086 test-rmse:1.225906+0.269944
## [156] train-rmse:0.141487+0.005950 test-rmse:1.225196+0.270339
## [157] train-rmse:0.139494+0.006197 test-rmse:1.224901+0.270513
## [158] train-rmse:0.137478+0.005752 test-rmse:1.224534+0.270281
## [159] train-rmse:0.136050+0.005970 test-rmse:1.223701+0.270544
## [160] train-rmse:0.134410+0.006217 test-rmse:1.223295+0.270110
## [161] train-rmse:0.132886+0.006341 test-rmse:1.223325+0.270368
## [162] train-rmse:0.130932+0.006376 test-rmse:1.223345+0.271092
## [163] train-rmse:0.129156+0.006388 test-rmse:1.222934+0.271715
## [164] train-rmse:0.127044+0.005998 test-rmse:1.222750+0.271955
## [165] train-rmse:0.125538+0.005474 test-rmse:1.222548+0.272270
## [166] train-rmse:0.123555+0.005489 test-rmse:1.222401+0.272648
## [167] train-rmse:0.122260+0.005778 test-rmse:1.221747+0.273120
## [168] train-rmse:0.120409+0.005241 test-rmse:1.221882+0.272887
## [169] train-rmse:0.119335+0.005362 test-rmse:1.221195+0.272783
## [170] train-rmse:0.117817+0.005007 test-rmse:1.220587+0.272433
## [171] train-rmse:0.116546+0.004551 test-rmse:1.220246+0.272559
## [172] train-rmse:0.115059+0.004729 test-rmse:1.220127+0.272721
## [173] train-rmse:0.112915+0.004457 test-rmse:1.220195+0.273126
## [174] train-rmse:0.111193+0.004417 test-rmse:1.220361+0.272920
## [175] train-rmse:0.109606+0.004363 test-rmse:1.219872+0.272744
## [176] train-rmse:0.108445+0.004659 test-rmse:1.219375+0.272933
## [177] train-rmse:0.107081+0.004968 test-rmse:1.219123+0.273484
## [178] train-rmse:0.105437+0.005080 test-rmse:1.218933+0.273281
## [179] train-rmse:0.104332+0.004778 test-rmse:1.218852+0.273624
## [180] train-rmse:0.103353+0.004834 test-rmse:1.218566+0.273857
## [181] train-rmse:0.102265+0.004550 test-rmse:1.218684+0.274135
## [182] train-rmse:0.101252+0.004739 test-rmse:1.218031+0.274309
## [183] train-rmse:0.099836+0.004786 test-rmse:1.218003+0.274472
## [184] train-rmse:0.098428+0.004692 test-rmse:1.217396+0.274648
## [185] train-rmse:0.097019+0.004749 test-rmse:1.217529+0.274769
## [186] train-rmse:0.095962+0.004668 test-rmse:1.217249+0.274613
## [187] train-rmse:0.094786+0.004620 test-rmse:1.217085+0.274923
## [188] train-rmse:0.093181+0.004599 test-rmse:1.217329+0.274948
## [189] train-rmse:0.092011+0.004655 test-rmse:1.217294+0.275059
## [190] train-rmse:0.090657+0.004542 test-rmse:1.217352+0.274912
## [191] train-rmse:0.089387+0.004667 test-rmse:1.217242+0.274966
## [192] train-rmse:0.088489+0.004471 test-rmse:1.216931+0.275366
## [193] train-rmse:0.087759+0.004344 test-rmse:1.216851+0.275363
## [194] train-rmse:0.086318+0.003904 test-rmse:1.216951+0.275142
## [195] train-rmse:0.085310+0.003841 test-rmse:1.216744+0.275236
## [196] train-rmse:0.084536+0.003941 test-rmse:1.216414+0.275514
## [197] train-rmse:0.083457+0.004057 test-rmse:1.216337+0.275262
## [198] train-rmse:0.082385+0.004121 test-rmse:1.216166+0.275499
## [199] train-rmse:0.081633+0.003950 test-rmse:1.216145+0.275551
## [200] train-rmse:0.080788+0.003813 test-rmse:1.215658+0.275884
## [201] train-rmse:0.079851+0.003765 test-rmse:1.215596+0.276116
## [202] train-rmse:0.078484+0.003322 test-rmse:1.215289+0.276254
## [203] train-rmse:0.077414+0.003132 test-rmse:1.215268+0.276459
## [204] train-rmse:0.076356+0.002985 test-rmse:1.215243+0.276692
## [205] train-rmse:0.075417+0.003057 test-rmse:1.215437+0.276770
## [206] train-rmse:0.074824+0.003248 test-rmse:1.215185+0.276734
## [207] train-rmse:0.074023+0.003122 test-rmse:1.215122+0.276898
## [208] train-rmse:0.073330+0.003215 test-rmse:1.215023+0.276846
## [209] train-rmse:0.072312+0.003168 test-rmse:1.215229+0.276737
## [210] train-rmse:0.071284+0.003486 test-rmse:1.214983+0.277000
## [211] train-rmse:0.070410+0.003398 test-rmse:1.215044+0.277011
## [212] train-rmse:0.069761+0.003496 test-rmse:1.214950+0.277049
## [213] train-rmse:0.068972+0.003534 test-rmse:1.215118+0.276993
## [214] train-rmse:0.067975+0.003486 test-rmse:1.214924+0.276660
## [215] train-rmse:0.066892+0.003482 test-rmse:1.214819+0.276936
## [216] train-rmse:0.066263+0.003428 test-rmse:1.214633+0.277076
## [217] train-rmse:0.065632+0.003608 test-rmse:1.214512+0.277037
## [218] train-rmse:0.064708+0.003441 test-rmse:1.214266+0.277231
## [219] train-rmse:0.064026+0.003462 test-rmse:1.214236+0.277341
## [220] train-rmse:0.063207+0.003415 test-rmse:1.214051+0.277351
## [221] train-rmse:0.062534+0.003436 test-rmse:1.213720+0.277529
## [222] train-rmse:0.061803+0.003352 test-rmse:1.213631+0.277665
## [223] train-rmse:0.061094+0.003324 test-rmse:1.213604+0.277684
## [224] train-rmse:0.060025+0.003438 test-rmse:1.213541+0.277603
## [225] train-rmse:0.059205+0.003540 test-rmse:1.213646+0.277921
## [226] train-rmse:0.058689+0.003482 test-rmse:1.213350+0.277936
## [227] train-rmse:0.058001+0.003586 test-rmse:1.213287+0.277879
## [228] train-rmse:0.057278+0.003721 test-rmse:1.213161+0.277981
## [229] train-rmse:0.056722+0.003719 test-rmse:1.213265+0.277721
## [230] train-rmse:0.056208+0.003676 test-rmse:1.213184+0.277958
## [231] train-rmse:0.055618+0.003599 test-rmse:1.213181+0.277708
## [232] train-rmse:0.054750+0.003507 test-rmse:1.212867+0.277641
## [233] train-rmse:0.054144+0.003572 test-rmse:1.212829+0.277746
## [234] train-rmse:0.053542+0.003468 test-rmse:1.212705+0.277710
## [235] train-rmse:0.052797+0.003605 test-rmse:1.212617+0.277620
## [236] train-rmse:0.052386+0.003588 test-rmse:1.212534+0.277744
## [237] train-rmse:0.051645+0.003517 test-rmse:1.212588+0.277690
## [238] train-rmse:0.050993+0.003601 test-rmse:1.212640+0.277760
## [239] train-rmse:0.050384+0.003500 test-rmse:1.212600+0.277837
## [240] train-rmse:0.049925+0.003483 test-rmse:1.212603+0.277754
## [241] train-rmse:0.049294+0.003598 test-rmse:1.212520+0.277885
## [242] train-rmse:0.048804+0.003469 test-rmse:1.212583+0.278009
## [243] train-rmse:0.048112+0.003322 test-rmse:1.212612+0.278086
## [244] train-rmse:0.047742+0.003371 test-rmse:1.212654+0.278202
## [245] train-rmse:0.047243+0.003246 test-rmse:1.212595+0.278326
## [246] train-rmse:0.046681+0.003192 test-rmse:1.212535+0.278357
## [247] train-rmse:0.046201+0.003307 test-rmse:1.212554+0.278465
## Stopping. Best iteration:
## [241] train-rmse:0.049294+0.003598 test-rmse:1.212520+0.277885
##
## 13
## [1] train-rmse:94.766116+0.105094 test-rmse:94.766423+0.483432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:83.478061+0.095217 test-rmse:83.478186+0.500072
## [3] train-rmse:73.545764+0.086897 test-rmse:73.545670+0.516055
## [4] train-rmse:64.807895+0.080011 test-rmse:64.807548+0.531590
## [5] train-rmse:57.122527+0.074434 test-rmse:57.121858+0.546922
## [6] train-rmse:50.364803+0.070103 test-rmse:50.363757+0.562250
## [7] train-rmse:44.424928+0.066945 test-rmse:44.423422+0.577769
## [8] train-rmse:39.206363+0.064904 test-rmse:39.204310+0.593657
## [9] train-rmse:34.622293+0.063135 test-rmse:34.630677+0.594785
## [10] train-rmse:30.591081+0.059323 test-rmse:30.633139+0.599495
## [11] train-rmse:27.044432+0.052606 test-rmse:27.116927+0.608458
## [12] train-rmse:23.924558+0.044243 test-rmse:24.000367+0.581464
## [13] train-rmse:21.179186+0.038168 test-rmse:21.254095+0.569931
## [14] train-rmse:18.762979+0.033086 test-rmse:18.843525+0.557408
## [15] train-rmse:16.632680+0.028932 test-rmse:16.718552+0.544604
## [16] train-rmse:14.757554+0.024911 test-rmse:14.865136+0.560231
## [17] train-rmse:13.102790+0.023009 test-rmse:13.202217+0.552515
## [18] train-rmse:11.643877+0.021336 test-rmse:11.750754+0.538480
## [19] train-rmse:10.355913+0.020557 test-rmse:10.464216+0.543100
## [20] train-rmse:9.218523+0.018455 test-rmse:9.346267+0.549347
## [21] train-rmse:8.217220+0.018132 test-rmse:8.347294+0.530971
## [22] train-rmse:7.333927+0.016836 test-rmse:7.463266+0.532799
## [23] train-rmse:6.552563+0.018974 test-rmse:6.682205+0.509463
## [24] train-rmse:5.864949+0.019722 test-rmse:6.012543+0.508536
## [25] train-rmse:5.258397+0.021594 test-rmse:5.411270+0.493331
## [26] train-rmse:4.722021+0.018879 test-rmse:4.885467+0.478523
## [27] train-rmse:4.250854+0.020659 test-rmse:4.431355+0.489662
## [28] train-rmse:3.837302+0.022213 test-rmse:4.033625+0.471267
## [29] train-rmse:3.469695+0.020687 test-rmse:3.686606+0.449447
## [30] train-rmse:3.145822+0.020978 test-rmse:3.397280+0.450598
## [31] train-rmse:2.862378+0.021459 test-rmse:3.138633+0.431523
## [32] train-rmse:2.612560+0.022417 test-rmse:2.919195+0.411351
## [33] train-rmse:2.390129+0.022528 test-rmse:2.720612+0.403131
## [34] train-rmse:2.191950+0.025833 test-rmse:2.562663+0.394256
## [35] train-rmse:2.020805+0.024631 test-rmse:2.415637+0.380515
## [36] train-rmse:1.868489+0.027085 test-rmse:2.287722+0.360976
## [37] train-rmse:1.731521+0.027047 test-rmse:2.186001+0.342348
## [38] train-rmse:1.606885+0.029319 test-rmse:2.096015+0.323283
## [39] train-rmse:1.503576+0.028755 test-rmse:2.013728+0.306714
## [40] train-rmse:1.405592+0.028462 test-rmse:1.952164+0.293355
## [41] train-rmse:1.322797+0.026667 test-rmse:1.889183+0.283278
## [42] train-rmse:1.247702+0.027196 test-rmse:1.839083+0.279111
## [43] train-rmse:1.178622+0.030939 test-rmse:1.792909+0.262313
## [44] train-rmse:1.113680+0.033517 test-rmse:1.752779+0.257383
## [45] train-rmse:1.056234+0.033284 test-rmse:1.714562+0.247096
## [46] train-rmse:1.007645+0.031703 test-rmse:1.674651+0.244106
## [47] train-rmse:0.959439+0.034449 test-rmse:1.649825+0.242261
## [48] train-rmse:0.917371+0.035758 test-rmse:1.620708+0.239042
## [49] train-rmse:0.875850+0.034963 test-rmse:1.596263+0.243363
## [50] train-rmse:0.840890+0.035597 test-rmse:1.577667+0.241462
## [51] train-rmse:0.808761+0.035124 test-rmse:1.559133+0.241214
## [52] train-rmse:0.780028+0.035103 test-rmse:1.540922+0.241719
## [53] train-rmse:0.748018+0.033336 test-rmse:1.530475+0.237721
## [54] train-rmse:0.719514+0.034224 test-rmse:1.513829+0.243915
## [55] train-rmse:0.693554+0.034531 test-rmse:1.497194+0.243097
## [56] train-rmse:0.670193+0.034754 test-rmse:1.484927+0.240240
## [57] train-rmse:0.648733+0.035009 test-rmse:1.472748+0.240937
## [58] train-rmse:0.627698+0.031873 test-rmse:1.453543+0.234412
## [59] train-rmse:0.606004+0.032087 test-rmse:1.437280+0.230069
## [60] train-rmse:0.587359+0.032613 test-rmse:1.428356+0.228952
## [61] train-rmse:0.569310+0.033034 test-rmse:1.421497+0.233517
## [62] train-rmse:0.552546+0.031331 test-rmse:1.409101+0.228061
## [63] train-rmse:0.536202+0.031625 test-rmse:1.401383+0.229478
## [64] train-rmse:0.520277+0.032536 test-rmse:1.390616+0.225699
## [65] train-rmse:0.505365+0.031543 test-rmse:1.382635+0.225121
## [66] train-rmse:0.489942+0.032409 test-rmse:1.376451+0.227388
## [67] train-rmse:0.478311+0.031543 test-rmse:1.370357+0.230933
## [68] train-rmse:0.466757+0.030564 test-rmse:1.364303+0.225084
## [69] train-rmse:0.455400+0.030793 test-rmse:1.358774+0.225043
## [70] train-rmse:0.444080+0.029612 test-rmse:1.353659+0.224933
## [71] train-rmse:0.433692+0.029624 test-rmse:1.348458+0.224968
## [72] train-rmse:0.422932+0.029305 test-rmse:1.343679+0.227171
## [73] train-rmse:0.413376+0.029390 test-rmse:1.336890+0.227720
## [74] train-rmse:0.402857+0.029134 test-rmse:1.336719+0.227804
## [75] train-rmse:0.393698+0.028401 test-rmse:1.332070+0.226744
## [76] train-rmse:0.384855+0.026223 test-rmse:1.325308+0.223922
## [77] train-rmse:0.375395+0.027111 test-rmse:1.320748+0.221336
## [78] train-rmse:0.366922+0.027251 test-rmse:1.316730+0.222160
## [79] train-rmse:0.358023+0.025669 test-rmse:1.313345+0.221381
## [80] train-rmse:0.351107+0.025674 test-rmse:1.308541+0.222988
## [81] train-rmse:0.343708+0.025718 test-rmse:1.306119+0.225719
## [82] train-rmse:0.336218+0.024662 test-rmse:1.300935+0.223847
## [83] train-rmse:0.328380+0.025244 test-rmse:1.298408+0.223763
## [84] train-rmse:0.320870+0.023829 test-rmse:1.295740+0.225820
## [85] train-rmse:0.313020+0.024223 test-rmse:1.292435+0.227372
## [86] train-rmse:0.306983+0.023717 test-rmse:1.289607+0.227920
## [87] train-rmse:0.300711+0.024033 test-rmse:1.288137+0.228517
## [88] train-rmse:0.294143+0.022703 test-rmse:1.284098+0.227203
## [89] train-rmse:0.288392+0.023031 test-rmse:1.281763+0.230038
## [90] train-rmse:0.281938+0.022000 test-rmse:1.280250+0.230745
## [91] train-rmse:0.276233+0.022299 test-rmse:1.277438+0.230647
## [92] train-rmse:0.270434+0.020662 test-rmse:1.272981+0.230311
## [93] train-rmse:0.264093+0.021803 test-rmse:1.271576+0.230275
## [94] train-rmse:0.259324+0.022255 test-rmse:1.269741+0.231091
## [95] train-rmse:0.254548+0.021721 test-rmse:1.267902+0.231831
## [96] train-rmse:0.250547+0.022050 test-rmse:1.264945+0.232322
## [97] train-rmse:0.245454+0.022109 test-rmse:1.263707+0.233908
## [98] train-rmse:0.240055+0.018987 test-rmse:1.263156+0.234902
## [99] train-rmse:0.235418+0.018542 test-rmse:1.262011+0.235928
## [100] train-rmse:0.231290+0.018807 test-rmse:1.260219+0.235873
## [101] train-rmse:0.226685+0.018885 test-rmse:1.259724+0.237335
## [102] train-rmse:0.222065+0.019336 test-rmse:1.259184+0.237376
## [103] train-rmse:0.217910+0.019367 test-rmse:1.259251+0.237448
## [104] train-rmse:0.213785+0.020103 test-rmse:1.258012+0.237665
## [105] train-rmse:0.210401+0.020545 test-rmse:1.256111+0.237462
## [106] train-rmse:0.207112+0.020404 test-rmse:1.255716+0.238166
## [107] train-rmse:0.203450+0.020338 test-rmse:1.254164+0.239872
## [108] train-rmse:0.199139+0.018909 test-rmse:1.253157+0.240757
## [109] train-rmse:0.195451+0.017716 test-rmse:1.253332+0.240987
## [110] train-rmse:0.191983+0.016646 test-rmse:1.252961+0.241208
## [111] train-rmse:0.188731+0.016336 test-rmse:1.252688+0.241406
## [112] train-rmse:0.185807+0.016460 test-rmse:1.252303+0.242155
## [113] train-rmse:0.182434+0.015854 test-rmse:1.252133+0.242846
## [114] train-rmse:0.179681+0.015200 test-rmse:1.250712+0.243944
## [115] train-rmse:0.176334+0.015665 test-rmse:1.250267+0.244321
## [116] train-rmse:0.172500+0.014684 test-rmse:1.250269+0.245044
## [117] train-rmse:0.170240+0.014683 test-rmse:1.249910+0.245391
## [118] train-rmse:0.167358+0.014921 test-rmse:1.249178+0.245207
## [119] train-rmse:0.164394+0.014285 test-rmse:1.248344+0.246212
## [120] train-rmse:0.161169+0.013423 test-rmse:1.247893+0.245622
## [121] train-rmse:0.158762+0.013608 test-rmse:1.247603+0.245433
## [122] train-rmse:0.155987+0.013866 test-rmse:1.247401+0.244912
## [123] train-rmse:0.152987+0.013738 test-rmse:1.246773+0.245320
## [124] train-rmse:0.150041+0.013555 test-rmse:1.246845+0.246548
## [125] train-rmse:0.147349+0.012578 test-rmse:1.246468+0.246713
## [126] train-rmse:0.145371+0.012370 test-rmse:1.246549+0.247536
## [127] train-rmse:0.142514+0.011379 test-rmse:1.245672+0.248369
## [128] train-rmse:0.139800+0.010819 test-rmse:1.244979+0.248694
## [129] train-rmse:0.136335+0.009767 test-rmse:1.245502+0.249296
## [130] train-rmse:0.134316+0.010119 test-rmse:1.245385+0.249095
## [131] train-rmse:0.132378+0.009940 test-rmse:1.244981+0.249659
## [132] train-rmse:0.130273+0.009723 test-rmse:1.244798+0.249837
## [133] train-rmse:0.127764+0.009773 test-rmse:1.244397+0.249864
## [134] train-rmse:0.126053+0.009655 test-rmse:1.243445+0.250554
## [135] train-rmse:0.124070+0.009801 test-rmse:1.243260+0.250644
## [136] train-rmse:0.122293+0.010229 test-rmse:1.242803+0.250769
## [137] train-rmse:0.120384+0.010061 test-rmse:1.242707+0.250634
## [138] train-rmse:0.118250+0.010177 test-rmse:1.242780+0.250765
## [139] train-rmse:0.116312+0.010197 test-rmse:1.242572+0.251469
## [140] train-rmse:0.114439+0.010250 test-rmse:1.242557+0.251124
## [141] train-rmse:0.112576+0.009783 test-rmse:1.242584+0.251685
## [142] train-rmse:0.111160+0.009525 test-rmse:1.242237+0.251883
## [143] train-rmse:0.109414+0.009252 test-rmse:1.241767+0.252071
## [144] train-rmse:0.107594+0.009846 test-rmse:1.241623+0.251943
## [145] train-rmse:0.105886+0.009810 test-rmse:1.242112+0.251870
## [146] train-rmse:0.103848+0.009210 test-rmse:1.242249+0.252087
## [147] train-rmse:0.102124+0.009492 test-rmse:1.242187+0.252440
## [148] train-rmse:0.100658+0.009263 test-rmse:1.241830+0.252568
## [149] train-rmse:0.099023+0.008854 test-rmse:1.241307+0.253035
## [150] train-rmse:0.097362+0.008958 test-rmse:1.240925+0.252954
## [151] train-rmse:0.096011+0.008780 test-rmse:1.240917+0.253026
## [152] train-rmse:0.094528+0.008419 test-rmse:1.240884+0.253353
## [153] train-rmse:0.092968+0.008090 test-rmse:1.240634+0.253335
## [154] train-rmse:0.091428+0.007646 test-rmse:1.240752+0.253483
## [155] train-rmse:0.090314+0.007330 test-rmse:1.240361+0.253883
## [156] train-rmse:0.088662+0.006949 test-rmse:1.240063+0.254375
## [157] train-rmse:0.087280+0.006726 test-rmse:1.240044+0.254437
## [158] train-rmse:0.085644+0.006669 test-rmse:1.239969+0.254690
## [159] train-rmse:0.084400+0.006540 test-rmse:1.239845+0.254532
## [160] train-rmse:0.083132+0.006316 test-rmse:1.239865+0.254400
## [161] train-rmse:0.082287+0.006317 test-rmse:1.239689+0.254355
## [162] train-rmse:0.080762+0.005806 test-rmse:1.239306+0.254207
## [163] train-rmse:0.079824+0.005897 test-rmse:1.238887+0.254451
## [164] train-rmse:0.078417+0.005855 test-rmse:1.238673+0.254656
## [165] train-rmse:0.077193+0.005747 test-rmse:1.238600+0.254787
## [166] train-rmse:0.075847+0.005610 test-rmse:1.238569+0.254640
## [167] train-rmse:0.074945+0.005430 test-rmse:1.238334+0.254702
## [168] train-rmse:0.073794+0.005312 test-rmse:1.238294+0.254715
## [169] train-rmse:0.072578+0.005242 test-rmse:1.238293+0.254981
## [170] train-rmse:0.071662+0.005242 test-rmse:1.238345+0.255067
## [171] train-rmse:0.070440+0.005369 test-rmse:1.238237+0.254886
## [172] train-rmse:0.069449+0.005580 test-rmse:1.238216+0.254719
## [173] train-rmse:0.068638+0.005453 test-rmse:1.238347+0.254930
## [174] train-rmse:0.067525+0.005005 test-rmse:1.238452+0.254931
## [175] train-rmse:0.066727+0.005164 test-rmse:1.238406+0.255033
## [176] train-rmse:0.065975+0.005127 test-rmse:1.238210+0.255042
## [177] train-rmse:0.064857+0.005447 test-rmse:1.238382+0.254930
## [178] train-rmse:0.063831+0.005474 test-rmse:1.238271+0.255225
## [179] train-rmse:0.063085+0.005301 test-rmse:1.238292+0.255140
## [180] train-rmse:0.062069+0.005398 test-rmse:1.238245+0.255243
## [181] train-rmse:0.061198+0.005284 test-rmse:1.238367+0.255493
## [182] train-rmse:0.060243+0.005003 test-rmse:1.238091+0.255238
## [183] train-rmse:0.059368+0.005102 test-rmse:1.238130+0.255401
## [184] train-rmse:0.058423+0.004925 test-rmse:1.237960+0.255461
## [185] train-rmse:0.057685+0.004900 test-rmse:1.237815+0.255489
## [186] train-rmse:0.057057+0.004811 test-rmse:1.237799+0.255458
## [187] train-rmse:0.056179+0.004762 test-rmse:1.237659+0.255335
## [188] train-rmse:0.055398+0.004668 test-rmse:1.237505+0.255089
## [189] train-rmse:0.054549+0.004491 test-rmse:1.237519+0.255093
## [190] train-rmse:0.053624+0.004262 test-rmse:1.237560+0.255128
## [191] train-rmse:0.052818+0.004317 test-rmse:1.237577+0.255236
## [192] train-rmse:0.051966+0.003874 test-rmse:1.237606+0.255446
## [193] train-rmse:0.050866+0.003917 test-rmse:1.237737+0.255442
## [194] train-rmse:0.050303+0.003890 test-rmse:1.237562+0.255524
## Stopping. Best iteration:
## [188] train-rmse:0.055398+0.004668 test-rmse:1.237505+0.255089
##
## 14
## [1] train-rmse:92.628808+0.103203 test-rmse:92.629086+0.486485
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.759407+0.092047 test-rmse:79.759461+0.505884
## [3] train-rmse:68.693979+0.083013 test-rmse:68.693753+0.524472
## [4] train-rmse:59.182194+0.075874 test-rmse:59.181623+0.542625
## [5] train-rmse:51.008879+0.070487 test-rmse:51.007880+0.560691
## [6] train-rmse:43.989113+0.066746 test-rmse:43.987572+0.578998
## [7] train-rmse:37.964006+0.064561 test-rmse:37.961789+0.597840
## [8] train-rmse:32.793479+0.062609 test-rmse:32.804148+0.599778
## [9] train-rmse:28.355840+0.059727 test-rmse:28.394154+0.605770
## [10] train-rmse:24.547662+0.056449 test-rmse:24.577396+0.602940
## [11] train-rmse:21.283196+0.052833 test-rmse:21.331104+0.594859
## [12] train-rmse:18.487003+0.050689 test-rmse:18.524194+0.588696
## [13] train-rmse:16.097431+0.048114 test-rmse:16.159480+0.591071
## [14] train-rmse:14.058384+0.046750 test-rmse:14.115977+0.588073
## [15] train-rmse:12.322882+0.046188 test-rmse:12.384434+0.587699
## [16] train-rmse:10.849941+0.047199 test-rmse:10.940862+0.609699
## [17] train-rmse:9.606294+0.048004 test-rmse:9.685658+0.603839
## [18] train-rmse:8.559377+0.050329 test-rmse:8.650224+0.586773
## [19] train-rmse:7.681997+0.052965 test-rmse:7.792186+0.616601
## [20] train-rmse:6.951996+0.055043 test-rmse:7.055595+0.606184
## [21] train-rmse:6.346683+0.057353 test-rmse:6.466274+0.582588
## [22] train-rmse:5.847920+0.059772 test-rmse:5.974706+0.600619
## [23] train-rmse:5.440075+0.061542 test-rmse:5.551264+0.575458
## [24] train-rmse:5.106299+0.063485 test-rmse:5.218925+0.556433
## [25] train-rmse:4.833225+0.064562 test-rmse:4.978670+0.561681
## [26] train-rmse:4.612543+0.066525 test-rmse:4.733785+0.528780
## [27] train-rmse:4.432452+0.067487 test-rmse:4.552582+0.499697
## [28] train-rmse:4.283432+0.067850 test-rmse:4.429581+0.493118
## [29] train-rmse:4.159952+0.067827 test-rmse:4.298094+0.481846
## [30] train-rmse:4.057023+0.066921 test-rmse:4.214193+0.485508
## [31] train-rmse:3.971433+0.066729 test-rmse:4.124089+0.461181
## [32] train-rmse:3.897616+0.066581 test-rmse:4.051681+0.438929
## [33] train-rmse:3.832328+0.066193 test-rmse:3.991574+0.420961
## [34] train-rmse:3.774209+0.065372 test-rmse:3.948151+0.410040
## [35] train-rmse:3.722405+0.064571 test-rmse:3.905919+0.407543
## [36] train-rmse:3.677078+0.063322 test-rmse:3.865971+0.412688
## [37] train-rmse:3.635250+0.062598 test-rmse:3.837397+0.411279
## [38] train-rmse:3.596220+0.062428 test-rmse:3.789863+0.395193
## [39] train-rmse:3.559458+0.061975 test-rmse:3.740172+0.373843
## [40] train-rmse:3.524423+0.061438 test-rmse:3.710604+0.371944
## [41] train-rmse:3.490956+0.060637 test-rmse:3.678137+0.366203
## [42] train-rmse:3.458851+0.059681 test-rmse:3.651878+0.359232
## [43] train-rmse:3.427960+0.058475 test-rmse:3.633640+0.347220
## [44] train-rmse:3.398064+0.057351 test-rmse:3.606522+0.357253
## [45] train-rmse:3.369306+0.056548 test-rmse:3.594849+0.356982
## [46] train-rmse:3.340722+0.056098 test-rmse:3.563774+0.359184
## [47] train-rmse:3.312832+0.055479 test-rmse:3.549550+0.369767
## [48] train-rmse:3.285970+0.054688 test-rmse:3.505894+0.351938
## [49] train-rmse:3.259703+0.053941 test-rmse:3.470456+0.339144
## [50] train-rmse:3.234303+0.053394 test-rmse:3.445721+0.336129
## [51] train-rmse:3.209070+0.052788 test-rmse:3.424100+0.333702
## [52] train-rmse:3.184415+0.052139 test-rmse:3.400719+0.323716
## [53] train-rmse:3.160211+0.051257 test-rmse:3.387402+0.322376
## [54] train-rmse:3.136323+0.050288 test-rmse:3.364107+0.320974
## [55] train-rmse:3.113122+0.049243 test-rmse:3.351343+0.330419
## [56] train-rmse:3.090305+0.048856 test-rmse:3.326410+0.331083
## [57] train-rmse:3.068134+0.048317 test-rmse:3.315565+0.337560
## [58] train-rmse:3.046122+0.047532 test-rmse:3.279419+0.321239
## [59] train-rmse:3.024574+0.046938 test-rmse:3.244388+0.310056
## [60] train-rmse:3.003517+0.046475 test-rmse:3.229552+0.317407
## [61] train-rmse:2.982543+0.045840 test-rmse:3.213800+0.316487
## [62] train-rmse:2.961963+0.045511 test-rmse:3.206205+0.309078
## [63] train-rmse:2.941740+0.045145 test-rmse:3.178193+0.316613
## [64] train-rmse:2.921411+0.044768 test-rmse:3.164965+0.315406
## [65] train-rmse:2.901655+0.044316 test-rmse:3.148111+0.318147
## [66] train-rmse:2.882110+0.044190 test-rmse:3.123792+0.311605
## [67] train-rmse:2.862795+0.044151 test-rmse:3.106331+0.313575
## [68] train-rmse:2.843499+0.043989 test-rmse:3.090525+0.316713
## [69] train-rmse:2.824581+0.044086 test-rmse:3.067362+0.320214
## [70] train-rmse:2.806202+0.044126 test-rmse:3.053240+0.321660
## [71] train-rmse:2.788175+0.043861 test-rmse:3.031178+0.306954
## [72] train-rmse:2.770396+0.043519 test-rmse:3.007328+0.296552
## [73] train-rmse:2.752929+0.043123 test-rmse:2.992202+0.303984
## [74] train-rmse:2.735537+0.042831 test-rmse:2.970887+0.306345
## [75] train-rmse:2.718538+0.042643 test-rmse:2.958802+0.308604
## [76] train-rmse:2.701692+0.042311 test-rmse:2.938917+0.301725
## [77] train-rmse:2.684957+0.042038 test-rmse:2.924760+0.307081
## [78] train-rmse:2.668281+0.041937 test-rmse:2.910433+0.307987
## [79] train-rmse:2.651886+0.041934 test-rmse:2.894194+0.314270
## [80] train-rmse:2.635629+0.042086 test-rmse:2.879393+0.315079
## [81] train-rmse:2.619442+0.042006 test-rmse:2.870276+0.311490
## [82] train-rmse:2.603442+0.041816 test-rmse:2.866100+0.311223
## [83] train-rmse:2.587641+0.041742 test-rmse:2.846589+0.313760
## [84] train-rmse:2.572088+0.041586 test-rmse:2.828795+0.314879
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## [88] train-rmse:2.512256+0.039880 test-rmse:2.766904+0.286940
## [89] train-rmse:2.497766+0.039517 test-rmse:2.749730+0.293433
## [90] train-rmse:2.483282+0.039219 test-rmse:2.740643+0.303397
## [91] train-rmse:2.468920+0.038855 test-rmse:2.724283+0.298793
## [92] train-rmse:2.454653+0.038732 test-rmse:2.702117+0.303900
## [93] train-rmse:2.440610+0.038598 test-rmse:2.694355+0.304043
## [94] train-rmse:2.426637+0.038481 test-rmse:2.686241+0.302456
## [95] train-rmse:2.412800+0.038342 test-rmse:2.670662+0.305408
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## [97] train-rmse:2.385849+0.037483 test-rmse:2.649277+0.306538
## [98] train-rmse:2.372713+0.037049 test-rmse:2.641622+0.308983
## [99] train-rmse:2.359591+0.036658 test-rmse:2.631745+0.309656
## [100] train-rmse:2.346625+0.036368 test-rmse:2.623197+0.312123
## [101] train-rmse:2.333784+0.036128 test-rmse:2.607379+0.302241
## [102] train-rmse:2.321104+0.035727 test-rmse:2.592702+0.287815
## [103] train-rmse:2.308437+0.035375 test-rmse:2.568550+0.284910
## [104] train-rmse:2.295750+0.035006 test-rmse:2.554230+0.290126
## [105] train-rmse:2.283309+0.034800 test-rmse:2.536733+0.293937
## [106] train-rmse:2.271080+0.034650 test-rmse:2.525985+0.291929
## [107] train-rmse:2.258934+0.034472 test-rmse:2.513541+0.286318
## [108] train-rmse:2.246730+0.034371 test-rmse:2.499880+0.281792
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## [112] train-rmse:2.198426+0.033874 test-rmse:2.457711+0.286915
## [113] train-rmse:2.186891+0.033597 test-rmse:2.452376+0.286187
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## [115] train-rmse:2.164190+0.032841 test-rmse:2.431088+0.285279
## [116] train-rmse:2.153067+0.032600 test-rmse:2.421220+0.286316
## [117] train-rmse:2.142019+0.032412 test-rmse:2.413888+0.285605
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## [119] train-rmse:2.119963+0.031820 test-rmse:2.386901+0.285259
## [120] train-rmse:2.109132+0.031487 test-rmse:2.383386+0.282937
## [121] train-rmse:2.098239+0.031107 test-rmse:2.372398+0.280711
## [122] train-rmse:2.087369+0.030749 test-rmse:2.357995+0.285521
## [123] train-rmse:2.076549+0.030436 test-rmse:2.346043+0.292062
## [124] train-rmse:2.065847+0.030052 test-rmse:2.335380+0.292781
## [125] train-rmse:2.055357+0.029854 test-rmse:2.320648+0.290001
## [126] train-rmse:2.045064+0.029612 test-rmse:2.311919+0.284980
## [127] train-rmse:2.034735+0.029332 test-rmse:2.296235+0.277128
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## [131] train-rmse:1.994616+0.028138 test-rmse:2.259840+0.264178
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## [133] train-rmse:1.975018+0.027666 test-rmse:2.242771+0.271236
## [134] train-rmse:1.965277+0.027469 test-rmse:2.236990+0.272776
## [135] train-rmse:1.955563+0.027306 test-rmse:2.225821+0.268046
## [136] train-rmse:1.945870+0.027147 test-rmse:2.215068+0.266671
## [137] train-rmse:1.936229+0.027022 test-rmse:2.204113+0.269991
## [138] train-rmse:1.926787+0.026923 test-rmse:2.195652+0.264236
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## [140] train-rmse:1.908096+0.026503 test-rmse:2.176745+0.269110
## [141] train-rmse:1.898905+0.026283 test-rmse:2.164161+0.273246
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## [144] train-rmse:1.871887+0.025698 test-rmse:2.144488+0.272846
## [145] train-rmse:1.863016+0.025405 test-rmse:2.141244+0.272991
## [146] train-rmse:1.854234+0.025052 test-rmse:2.136134+0.269016
## [147] train-rmse:1.845600+0.024771 test-rmse:2.125475+0.263145
## [148] train-rmse:1.837062+0.024452 test-rmse:2.114375+0.256338
## [149] train-rmse:1.828457+0.024196 test-rmse:2.100403+0.258192
## [150] train-rmse:1.819916+0.023912 test-rmse:2.087377+0.256582
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## [152] train-rmse:1.803076+0.023489 test-rmse:2.066248+0.243135
## [153] train-rmse:1.794767+0.023424 test-rmse:2.057271+0.244762
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## [155] train-rmse:1.778153+0.023221 test-rmse:2.047303+0.242058
## [156] train-rmse:1.769905+0.023025 test-rmse:2.034402+0.243644
## [157] train-rmse:1.761685+0.022822 test-rmse:2.028479+0.245306
## [158] train-rmse:1.753556+0.022519 test-rmse:2.023351+0.247769
## [159] train-rmse:1.745527+0.022383 test-rmse:2.016310+0.249345
## [160] train-rmse:1.737575+0.022186 test-rmse:2.013173+0.249008
## [161] train-rmse:1.729738+0.021939 test-rmse:2.005136+0.248640
## [162] train-rmse:1.721969+0.021670 test-rmse:1.998719+0.249373
## [163] train-rmse:1.714345+0.021439 test-rmse:1.993104+0.254085
## [164] train-rmse:1.706733+0.021289 test-rmse:1.986721+0.256903
## [165] train-rmse:1.699182+0.021188 test-rmse:1.976156+0.250494
## [166] train-rmse:1.691756+0.020960 test-rmse:1.962242+0.242430
## [167] train-rmse:1.684396+0.020722 test-rmse:1.956526+0.241063
## [168] train-rmse:1.677038+0.020481 test-rmse:1.948090+0.243287
## [169] train-rmse:1.669782+0.020211 test-rmse:1.940855+0.242811
## [170] train-rmse:1.662520+0.019988 test-rmse:1.936444+0.239235
## [171] train-rmse:1.655283+0.019858 test-rmse:1.930097+0.242144
## [172] train-rmse:1.648109+0.019630 test-rmse:1.922573+0.238218
## [173] train-rmse:1.641004+0.019471 test-rmse:1.913343+0.239521
## [174] train-rmse:1.633903+0.019288 test-rmse:1.906117+0.233477
## [175] train-rmse:1.626846+0.019149 test-rmse:1.897888+0.231600
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## [178] train-rmse:1.606164+0.018833 test-rmse:1.874302+0.236578
## [179] train-rmse:1.599396+0.018754 test-rmse:1.872340+0.237252
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## [181] train-rmse:1.586068+0.018562 test-rmse:1.861274+0.240884
## [182] train-rmse:1.579418+0.018431 test-rmse:1.856520+0.240067
## [183] train-rmse:1.572803+0.018311 test-rmse:1.854188+0.240206
## [184] train-rmse:1.566250+0.018245 test-rmse:1.849831+0.236451
## [185] train-rmse:1.559807+0.018145 test-rmse:1.840650+0.239317
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## [187] train-rmse:1.547124+0.017944 test-rmse:1.823974+0.237398
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## [190] train-rmse:1.528409+0.017639 test-rmse:1.802505+0.231907
## [191] train-rmse:1.522261+0.017538 test-rmse:1.796792+0.233151
## [192] train-rmse:1.516121+0.017386 test-rmse:1.796885+0.233481
## [193] train-rmse:1.509978+0.017216 test-rmse:1.793431+0.234422
## [194] train-rmse:1.503881+0.017014 test-rmse:1.790007+0.234180
## [195] train-rmse:1.497766+0.016811 test-rmse:1.783234+0.237599
## [196] train-rmse:1.491768+0.016638 test-rmse:1.774535+0.238081
## [197] train-rmse:1.485858+0.016431 test-rmse:1.769890+0.237253
## [198] train-rmse:1.479989+0.016228 test-rmse:1.764219+0.235581
## [199] train-rmse:1.474099+0.016015 test-rmse:1.758820+0.236724
## [200] train-rmse:1.468278+0.015796 test-rmse:1.752644+0.238429
## [201] train-rmse:1.462473+0.015605 test-rmse:1.749208+0.238649
## [202] train-rmse:1.456666+0.015391 test-rmse:1.745709+0.236114
## [203] train-rmse:1.451044+0.015382 test-rmse:1.739261+0.232990
## [204] train-rmse:1.445407+0.015317 test-rmse:1.734314+0.236429
## [205] train-rmse:1.439820+0.015256 test-rmse:1.732721+0.235408
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## [207] train-rmse:1.428795+0.015105 test-rmse:1.723102+0.230463
## [208] train-rmse:1.423334+0.014962 test-rmse:1.714800+0.231470
## [209] train-rmse:1.417943+0.014874 test-rmse:1.706578+0.228074
## [210] train-rmse:1.412518+0.014837 test-rmse:1.694121+0.225307
## [211] train-rmse:1.407136+0.014761 test-rmse:1.689464+0.225100
## [212] train-rmse:1.401831+0.014647 test-rmse:1.680643+0.215668
## [213] train-rmse:1.396547+0.014516 test-rmse:1.676200+0.216723
## [214] train-rmse:1.391322+0.014382 test-rmse:1.670271+0.219381
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## [217] train-rmse:1.376105+0.014014 test-rmse:1.659831+0.219571
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## [224] train-rmse:1.341630+0.013648 test-rmse:1.627908+0.220855
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## [226] train-rmse:1.332128+0.013456 test-rmse:1.618228+0.221400
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## [228] train-rmse:1.322731+0.013296 test-rmse:1.609567+0.221256
## [229] train-rmse:1.318062+0.013196 test-rmse:1.606296+0.223271
## [230] train-rmse:1.313472+0.013145 test-rmse:1.606256+0.222385
## [231] train-rmse:1.308897+0.013061 test-rmse:1.605529+0.221911
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## [233] train-rmse:1.299865+0.012966 test-rmse:1.590810+0.218839
## [234] train-rmse:1.295399+0.012903 test-rmse:1.585110+0.217667
## [235] train-rmse:1.290950+0.012866 test-rmse:1.579299+0.214511
## [236] train-rmse:1.286552+0.012828 test-rmse:1.570852+0.206187
## [237] train-rmse:1.282210+0.012760 test-rmse:1.567600+0.205648
## [238] train-rmse:1.277902+0.012705 test-rmse:1.565396+0.204587
## [239] train-rmse:1.273633+0.012600 test-rmse:1.559930+0.206386
## [240] train-rmse:1.269436+0.012557 test-rmse:1.556278+0.208817
## [241] train-rmse:1.265272+0.012524 test-rmse:1.554505+0.209910
## [242] train-rmse:1.261104+0.012493 test-rmse:1.550339+0.209129
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## [244] train-rmse:1.252816+0.012438 test-rmse:1.542929+0.210425
## [245] train-rmse:1.248698+0.012385 test-rmse:1.535217+0.209468
## [246] train-rmse:1.244593+0.012350 test-rmse:1.531625+0.211013
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## [248] train-rmse:1.236470+0.012254 test-rmse:1.524335+0.211300
## [249] train-rmse:1.232485+0.012201 test-rmse:1.520311+0.208959
## [250] train-rmse:1.228532+0.012172 test-rmse:1.517492+0.209514
## [251] train-rmse:1.224592+0.012101 test-rmse:1.515666+0.210388
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## [253] train-rmse:1.216865+0.011995 test-rmse:1.506652+0.206516
## [254] train-rmse:1.213021+0.011941 test-rmse:1.502130+0.208691
## [255] train-rmse:1.209244+0.011908 test-rmse:1.499497+0.210095
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## [257] train-rmse:1.201737+0.011783 test-rmse:1.493342+0.206802
## [258] train-rmse:1.198045+0.011755 test-rmse:1.490350+0.208658
## [259] train-rmse:1.194346+0.011741 test-rmse:1.483587+0.209395
## [260] train-rmse:1.190688+0.011729 test-rmse:1.480743+0.208985
## [261] train-rmse:1.187060+0.011724 test-rmse:1.472701+0.202012
## [262] train-rmse:1.183433+0.011728 test-rmse:1.469318+0.204061
## [263] train-rmse:1.179835+0.011738 test-rmse:1.464891+0.203045
## [264] train-rmse:1.176323+0.011702 test-rmse:1.463081+0.203314
## [265] train-rmse:1.172820+0.011653 test-rmse:1.460262+0.201363
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## [267] train-rmse:1.165908+0.011585 test-rmse:1.456810+0.202609
## [268] train-rmse:1.162484+0.011544 test-rmse:1.452877+0.202959
## [269] train-rmse:1.159076+0.011530 test-rmse:1.450280+0.198587
## [270] train-rmse:1.155707+0.011507 test-rmse:1.445251+0.200781
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## [273] train-rmse:1.145826+0.011437 test-rmse:1.437138+0.198940
## [274] train-rmse:1.142593+0.011416 test-rmse:1.433769+0.200515
## [275] train-rmse:1.139383+0.011387 test-rmse:1.430340+0.198853
## [276] train-rmse:1.136154+0.011345 test-rmse:1.427712+0.199899
## [277] train-rmse:1.132956+0.011323 test-rmse:1.422619+0.196655
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## [279] train-rmse:1.126677+0.011218 test-rmse:1.416912+0.198987
## [280] train-rmse:1.123536+0.011178 test-rmse:1.413726+0.201191
## [281] train-rmse:1.120406+0.011141 test-rmse:1.410506+0.200503
## [282] train-rmse:1.117320+0.011143 test-rmse:1.409215+0.200573
## [283] train-rmse:1.114263+0.011162 test-rmse:1.402450+0.195193
## [284] train-rmse:1.111254+0.011172 test-rmse:1.401079+0.195919
## [285] train-rmse:1.108258+0.011155 test-rmse:1.397524+0.195814
## [286] train-rmse:1.105260+0.011149 test-rmse:1.396361+0.196804
## [287] train-rmse:1.102294+0.011123 test-rmse:1.393622+0.196914
## [288] train-rmse:1.099402+0.011117 test-rmse:1.391617+0.196763
## [289] train-rmse:1.096548+0.011092 test-rmse:1.390958+0.196567
## [290] train-rmse:1.093702+0.011073 test-rmse:1.385620+0.194771
## [291] train-rmse:1.090875+0.011041 test-rmse:1.381938+0.194444
## [292] train-rmse:1.088071+0.011030 test-rmse:1.381460+0.193197
## [293] train-rmse:1.085254+0.011014 test-rmse:1.378515+0.193221
## [294] train-rmse:1.082460+0.011005 test-rmse:1.376783+0.192807
## [295] train-rmse:1.079696+0.010957 test-rmse:1.374096+0.192284
## [296] train-rmse:1.076973+0.010952 test-rmse:1.369939+0.188815
## [297] train-rmse:1.074248+0.010940 test-rmse:1.367676+0.189431
## [298] train-rmse:1.071545+0.010908 test-rmse:1.366117+0.189425
## [299] train-rmse:1.068843+0.010862 test-rmse:1.361560+0.190466
## [300] train-rmse:1.066200+0.010820 test-rmse:1.359841+0.190848
## [301] train-rmse:1.063576+0.010795 test-rmse:1.356959+0.192854
## [302] train-rmse:1.060933+0.010758 test-rmse:1.352767+0.186606
## [303] train-rmse:1.058346+0.010734 test-rmse:1.351005+0.187374
## [304] train-rmse:1.055772+0.010715 test-rmse:1.348915+0.188591
## [305] train-rmse:1.053199+0.010681 test-rmse:1.346664+0.189848
## [306] train-rmse:1.050627+0.010666 test-rmse:1.341191+0.190704
## [307] train-rmse:1.048079+0.010667 test-rmse:1.339516+0.190230
## [308] train-rmse:1.045562+0.010709 test-rmse:1.339343+0.190179
## [309] train-rmse:1.043061+0.010748 test-rmse:1.337718+0.191456
## [310] train-rmse:1.040565+0.010737 test-rmse:1.337612+0.192022
## [311] train-rmse:1.038095+0.010749 test-rmse:1.335796+0.191905
## [312] train-rmse:1.035703+0.010731 test-rmse:1.334004+0.189069
## [313] train-rmse:1.033323+0.010719 test-rmse:1.331036+0.190554
## [314] train-rmse:1.030966+0.010709 test-rmse:1.328156+0.187815
## [315] train-rmse:1.028605+0.010704 test-rmse:1.324730+0.187887
## [316] train-rmse:1.026275+0.010705 test-rmse:1.322367+0.186834
## [317] train-rmse:1.023954+0.010719 test-rmse:1.321177+0.186487
## [318] train-rmse:1.021647+0.010729 test-rmse:1.320681+0.185764
## [319] train-rmse:1.019345+0.010737 test-rmse:1.319987+0.184949
## [320] train-rmse:1.017030+0.010724 test-rmse:1.317586+0.184169
## [321] train-rmse:1.014765+0.010708 test-rmse:1.313955+0.183558
## [322] train-rmse:1.012508+0.010706 test-rmse:1.312029+0.183227
## [323] train-rmse:1.010255+0.010691 test-rmse:1.308713+0.185200
## [324] train-rmse:1.008013+0.010656 test-rmse:1.307472+0.185843
## [325] train-rmse:1.005821+0.010638 test-rmse:1.300023+0.181558
## [326] train-rmse:1.003689+0.010629 test-rmse:1.299524+0.181038
## [327] train-rmse:1.001554+0.010627 test-rmse:1.299099+0.181153
## [328] train-rmse:0.999439+0.010634 test-rmse:1.297728+0.182300
## [329] train-rmse:0.997342+0.010635 test-rmse:1.296705+0.182590
## [330] train-rmse:0.995234+0.010630 test-rmse:1.293111+0.182644
## [331] train-rmse:0.993150+0.010625 test-rmse:1.291606+0.183258
## [332] train-rmse:0.991097+0.010628 test-rmse:1.288369+0.183401
## [333] train-rmse:0.989037+0.010626 test-rmse:1.285881+0.181510
## [334] train-rmse:0.986994+0.010620 test-rmse:1.283136+0.180902
## [335] train-rmse:0.984968+0.010618 test-rmse:1.280146+0.181959
## [336] train-rmse:0.982946+0.010636 test-rmse:1.276497+0.181915
## [337] train-rmse:0.980945+0.010666 test-rmse:1.275372+0.181600
## [338] train-rmse:0.978942+0.010677 test-rmse:1.273780+0.181657
## [339] train-rmse:0.976963+0.010700 test-rmse:1.271992+0.181666
## [340] train-rmse:0.974986+0.010708 test-rmse:1.271167+0.181347
## [341] train-rmse:0.973077+0.010697 test-rmse:1.269714+0.181178
## [342] train-rmse:0.971147+0.010663 test-rmse:1.269969+0.180873
## [343] train-rmse:0.969205+0.010663 test-rmse:1.269014+0.180717
## [344] train-rmse:0.967296+0.010660 test-rmse:1.267946+0.180627
## [345] train-rmse:0.965413+0.010671 test-rmse:1.266594+0.182400
## [346] train-rmse:0.963546+0.010675 test-rmse:1.264995+0.182798
## [347] train-rmse:0.961697+0.010680 test-rmse:1.262986+0.183719
## [348] train-rmse:0.959849+0.010687 test-rmse:1.260985+0.184744
## [349] train-rmse:0.958012+0.010709 test-rmse:1.258787+0.181944
## [350] train-rmse:0.956190+0.010720 test-rmse:1.257414+0.180167
## [351] train-rmse:0.954375+0.010714 test-rmse:1.253613+0.175522
## [352] train-rmse:0.952594+0.010713 test-rmse:1.251167+0.177664
## [353] train-rmse:0.950837+0.010741 test-rmse:1.249327+0.178548
## [354] train-rmse:0.949102+0.010764 test-rmse:1.248435+0.178362
## [355] train-rmse:0.947370+0.010801 test-rmse:1.247223+0.178767
## [356] train-rmse:0.945656+0.010847 test-rmse:1.247182+0.178436
## [357] train-rmse:0.943964+0.010855 test-rmse:1.245503+0.176974
## [358] train-rmse:0.942277+0.010870 test-rmse:1.242431+0.175954
## [359] train-rmse:0.940579+0.010888 test-rmse:1.241049+0.175970
## [360] train-rmse:0.938891+0.010901 test-rmse:1.238480+0.176450
## [361] train-rmse:0.937234+0.010934 test-rmse:1.237276+0.177473
## [362] train-rmse:0.935605+0.010961 test-rmse:1.234971+0.176060
## [363] train-rmse:0.933964+0.010981 test-rmse:1.233685+0.175818
## [364] train-rmse:0.932347+0.010979 test-rmse:1.232362+0.176771
## [365] train-rmse:0.930741+0.010996 test-rmse:1.231232+0.174516
## [366] train-rmse:0.929155+0.010998 test-rmse:1.229182+0.175628
## [367] train-rmse:0.927574+0.011011 test-rmse:1.228137+0.175276
## [368] train-rmse:0.925989+0.011019 test-rmse:1.226818+0.175550
## [369] train-rmse:0.924436+0.011027 test-rmse:1.225815+0.174825
## [370] train-rmse:0.922873+0.011015 test-rmse:1.225485+0.174810
## [371] train-rmse:0.921343+0.011011 test-rmse:1.224683+0.175026
## [372] train-rmse:0.919822+0.011022 test-rmse:1.223598+0.174864
## [373] train-rmse:0.918296+0.011038 test-rmse:1.222332+0.175755
## [374] train-rmse:0.916786+0.011068 test-rmse:1.221831+0.175576
## [375] train-rmse:0.915283+0.011100 test-rmse:1.219990+0.176626
## [376] train-rmse:0.913789+0.011148 test-rmse:1.218400+0.174438
## [377] train-rmse:0.912313+0.011179 test-rmse:1.217887+0.175700
## [378] train-rmse:0.910847+0.011197 test-rmse:1.215716+0.176606
## [379] train-rmse:0.909392+0.011206 test-rmse:1.214937+0.177289
## [380] train-rmse:0.907939+0.011218 test-rmse:1.212177+0.176617
## [381] train-rmse:0.906519+0.011251 test-rmse:1.211725+0.176523
## [382] train-rmse:0.905104+0.011291 test-rmse:1.212182+0.176159
## [383] train-rmse:0.903694+0.011303 test-rmse:1.208909+0.172245
## [384] train-rmse:0.902295+0.011310 test-rmse:1.208045+0.172658
## [385] train-rmse:0.900922+0.011337 test-rmse:1.208481+0.172518
## [386] train-rmse:0.899555+0.011376 test-rmse:1.208075+0.172986
## [387] train-rmse:0.898193+0.011420 test-rmse:1.206309+0.173719
## [388] train-rmse:0.896843+0.011443 test-rmse:1.203771+0.172285
## [389] train-rmse:0.895514+0.011472 test-rmse:1.202963+0.172957
## [390] train-rmse:0.894192+0.011494 test-rmse:1.200506+0.172705
## [391] train-rmse:0.892867+0.011504 test-rmse:1.200352+0.172234
## [392] train-rmse:0.891545+0.011526 test-rmse:1.198512+0.171502
## [393] train-rmse:0.890248+0.011555 test-rmse:1.197525+0.172018
## [394] train-rmse:0.888956+0.011589 test-rmse:1.196520+0.172444
## [395] train-rmse:0.887678+0.011607 test-rmse:1.194541+0.171046
## [396] train-rmse:0.886409+0.011630 test-rmse:1.192859+0.170857
## [397] train-rmse:0.885130+0.011643 test-rmse:1.191708+0.172196
## [398] train-rmse:0.883860+0.011655 test-rmse:1.190232+0.172594
## [399] train-rmse:0.882601+0.011665 test-rmse:1.190254+0.171754
## [400] train-rmse:0.881357+0.011686 test-rmse:1.190054+0.172196
## [401] train-rmse:0.880125+0.011690 test-rmse:1.189774+0.173409
## [402] train-rmse:0.878920+0.011723 test-rmse:1.186474+0.169857
## [403] train-rmse:0.877713+0.011740 test-rmse:1.186692+0.170541
## [404] train-rmse:0.876516+0.011775 test-rmse:1.184513+0.169726
## [405] train-rmse:0.875329+0.011809 test-rmse:1.182847+0.169780
## [406] train-rmse:0.874149+0.011840 test-rmse:1.182870+0.169306
## [407] train-rmse:0.872971+0.011861 test-rmse:1.181363+0.168171
## [408] train-rmse:0.871781+0.011883 test-rmse:1.180451+0.168514
## [409] train-rmse:0.870621+0.011915 test-rmse:1.179763+0.168320
## [410] train-rmse:0.869466+0.011952 test-rmse:1.177778+0.167893
## [411] train-rmse:0.868317+0.011983 test-rmse:1.175264+0.168726
## [412] train-rmse:0.867186+0.012003 test-rmse:1.173185+0.168571
## [413] train-rmse:0.866057+0.012030 test-rmse:1.172616+0.168141
## [414] train-rmse:0.864942+0.012072 test-rmse:1.171656+0.168096
## [415] train-rmse:0.863842+0.012112 test-rmse:1.172018+0.166595
## [416] train-rmse:0.862747+0.012152 test-rmse:1.170762+0.165948
## [417] train-rmse:0.861665+0.012175 test-rmse:1.170228+0.166329
## [418] train-rmse:0.860602+0.012187 test-rmse:1.169979+0.166650
## [419] train-rmse:0.859541+0.012197 test-rmse:1.169711+0.166949
## [420] train-rmse:0.858483+0.012215 test-rmse:1.169283+0.167615
## [421] train-rmse:0.857436+0.012240 test-rmse:1.168195+0.167870
## [422] train-rmse:0.856379+0.012258 test-rmse:1.167914+0.168235
## [423] train-rmse:0.855330+0.012280 test-rmse:1.165110+0.167249
## [424] train-rmse:0.854290+0.012302 test-rmse:1.164720+0.168079
## [425] train-rmse:0.853258+0.012331 test-rmse:1.164018+0.168560
## [426] train-rmse:0.852235+0.012360 test-rmse:1.163722+0.168280
## [427] train-rmse:0.851218+0.012372 test-rmse:1.163515+0.168792
## [428] train-rmse:0.850205+0.012392 test-rmse:1.162287+0.168360
## [429] train-rmse:0.849212+0.012420 test-rmse:1.160421+0.164884
## [430] train-rmse:0.848223+0.012448 test-rmse:1.158648+0.164953
## [431] train-rmse:0.847241+0.012476 test-rmse:1.156924+0.165987
## [432] train-rmse:0.846275+0.012499 test-rmse:1.155856+0.166627
## [433] train-rmse:0.845310+0.012524 test-rmse:1.155749+0.165770
## [434] train-rmse:0.844358+0.012548 test-rmse:1.154161+0.165963
## [435] train-rmse:0.843416+0.012569 test-rmse:1.153423+0.166442
## [436] train-rmse:0.842472+0.012598 test-rmse:1.152955+0.166114
## [437] train-rmse:0.841551+0.012630 test-rmse:1.152129+0.166490
## [438] train-rmse:0.840620+0.012642 test-rmse:1.151187+0.166569
## [439] train-rmse:0.839710+0.012657 test-rmse:1.150671+0.165551
## [440] train-rmse:0.838810+0.012677 test-rmse:1.149351+0.165911
## [441] train-rmse:0.837900+0.012696 test-rmse:1.148692+0.165875
## [442] train-rmse:0.837005+0.012716 test-rmse:1.147466+0.166593
## [443] train-rmse:0.836125+0.012728 test-rmse:1.146308+0.165823
## [444] train-rmse:0.835250+0.012744 test-rmse:1.146305+0.166169
## [445] train-rmse:0.834377+0.012761 test-rmse:1.146305+0.165951
## [446] train-rmse:0.833511+0.012780 test-rmse:1.145003+0.165836
## [447] train-rmse:0.832644+0.012805 test-rmse:1.144821+0.165380
## [448] train-rmse:0.831775+0.012818 test-rmse:1.143558+0.164190
## [449] train-rmse:0.830918+0.012825 test-rmse:1.142589+0.164853
## [450] train-rmse:0.830070+0.012829 test-rmse:1.143001+0.164917
## [451] train-rmse:0.829226+0.012838 test-rmse:1.142065+0.165732
## [452] train-rmse:0.828383+0.012853 test-rmse:1.141244+0.165837
## [453] train-rmse:0.827548+0.012873 test-rmse:1.140678+0.165796
## [454] train-rmse:0.826723+0.012901 test-rmse:1.140073+0.166212
## [455] train-rmse:0.825902+0.012929 test-rmse:1.139586+0.166968
## [456] train-rmse:0.825093+0.012965 test-rmse:1.138242+0.165694
## [457] train-rmse:0.824276+0.013011 test-rmse:1.138315+0.164675
## [458] train-rmse:0.823470+0.013052 test-rmse:1.137598+0.164534
## [459] train-rmse:0.822673+0.013094 test-rmse:1.134934+0.162085
## [460] train-rmse:0.821895+0.013126 test-rmse:1.134432+0.162126
## [461] train-rmse:0.821120+0.013166 test-rmse:1.133314+0.162031
## [462] train-rmse:0.820333+0.013191 test-rmse:1.133707+0.162083
## [463] train-rmse:0.819565+0.013213 test-rmse:1.132789+0.162795
## [464] train-rmse:0.818803+0.013241 test-rmse:1.132473+0.163405
## [465] train-rmse:0.818039+0.013279 test-rmse:1.131011+0.164734
## [466] train-rmse:0.817292+0.013305 test-rmse:1.130408+0.165058
## [467] train-rmse:0.816544+0.013319 test-rmse:1.129938+0.163987
## [468] train-rmse:0.815804+0.013334 test-rmse:1.129132+0.162496
## [469] train-rmse:0.815066+0.013347 test-rmse:1.128728+0.162468
## [470] train-rmse:0.814337+0.013363 test-rmse:1.128756+0.162647
## [471] train-rmse:0.813609+0.013371 test-rmse:1.127911+0.162438
## [472] train-rmse:0.812886+0.013389 test-rmse:1.126433+0.163194
## [473] train-rmse:0.812171+0.013408 test-rmse:1.126308+0.163625
## [474] train-rmse:0.811461+0.013425 test-rmse:1.125890+0.163787
## [475] train-rmse:0.810760+0.013454 test-rmse:1.124783+0.163964
## [476] train-rmse:0.810066+0.013482 test-rmse:1.124529+0.164031
## [477] train-rmse:0.809372+0.013509 test-rmse:1.123712+0.163561
## [478] train-rmse:0.808677+0.013522 test-rmse:1.123327+0.163090
## [479] train-rmse:0.807992+0.013549 test-rmse:1.122867+0.162914
## [480] train-rmse:0.807312+0.013574 test-rmse:1.121643+0.163451
## [481] train-rmse:0.806632+0.013613 test-rmse:1.120781+0.162979
## [482] train-rmse:0.805959+0.013645 test-rmse:1.120118+0.163169
## [483] train-rmse:0.805293+0.013683 test-rmse:1.120152+0.163219
## [484] train-rmse:0.804637+0.013712 test-rmse:1.119091+0.162667
## [485] train-rmse:0.803980+0.013741 test-rmse:1.117938+0.163286
## [486] train-rmse:0.803334+0.013774 test-rmse:1.117217+0.163402
## [487] train-rmse:0.802696+0.013799 test-rmse:1.116071+0.162571
## [488] train-rmse:0.802052+0.013832 test-rmse:1.115464+0.162705
## [489] train-rmse:0.801414+0.013858 test-rmse:1.115146+0.162640
## [490] train-rmse:0.800788+0.013880 test-rmse:1.113688+0.163000
## [491] train-rmse:0.800165+0.013907 test-rmse:1.113662+0.163026
## [492] train-rmse:0.799540+0.013932 test-rmse:1.113411+0.163151
## [493] train-rmse:0.798928+0.013969 test-rmse:1.112706+0.162670
## [494] train-rmse:0.798318+0.014009 test-rmse:1.111910+0.162952
## [495] train-rmse:0.797718+0.014039 test-rmse:1.111394+0.162952
## [496] train-rmse:0.797116+0.014063 test-rmse:1.111387+0.163448
## [497] train-rmse:0.796526+0.014084 test-rmse:1.110760+0.163131
## [498] train-rmse:0.795940+0.014108 test-rmse:1.109391+0.163693
## [499] train-rmse:0.795353+0.014133 test-rmse:1.109953+0.163418
## [500] train-rmse:0.794764+0.014158 test-rmse:1.109908+0.162826
## [501] train-rmse:0.794172+0.014182 test-rmse:1.109420+0.162898
## [502] train-rmse:0.793588+0.014204 test-rmse:1.109021+0.163450
## [503] train-rmse:0.793000+0.014229 test-rmse:1.108169+0.162696
## [504] train-rmse:0.792419+0.014253 test-rmse:1.107197+0.162270
## [505] train-rmse:0.791840+0.014278 test-rmse:1.107364+0.162080
## [506] train-rmse:0.791267+0.014301 test-rmse:1.105410+0.159656
## [507] train-rmse:0.790697+0.014312 test-rmse:1.105307+0.158260
## [508] train-rmse:0.790119+0.014327 test-rmse:1.104835+0.158936
## [509] train-rmse:0.789556+0.014348 test-rmse:1.104203+0.157572
## [510] train-rmse:0.788992+0.014365 test-rmse:1.103820+0.158029
## [511] train-rmse:0.788439+0.014388 test-rmse:1.103707+0.158381
## [512] train-rmse:0.787890+0.014417 test-rmse:1.103792+0.157912
## [513] train-rmse:0.787327+0.014453 test-rmse:1.102693+0.158368
## [514] train-rmse:0.786787+0.014474 test-rmse:1.102551+0.157936
## [515] train-rmse:0.786255+0.014489 test-rmse:1.101608+0.158307
## [516] train-rmse:0.785727+0.014504 test-rmse:1.100836+0.158484
## [517] train-rmse:0.785190+0.014524 test-rmse:1.099681+0.158866
## [518] train-rmse:0.784666+0.014543 test-rmse:1.099244+0.159342
## [519] train-rmse:0.784145+0.014563 test-rmse:1.099043+0.159512
## [520] train-rmse:0.783629+0.014586 test-rmse:1.098768+0.159476
## [521] train-rmse:0.783113+0.014611 test-rmse:1.098980+0.159197
## [522] train-rmse:0.782593+0.014633 test-rmse:1.097872+0.159188
## [523] train-rmse:0.782075+0.014648 test-rmse:1.097366+0.159629
## [524] train-rmse:0.781570+0.014665 test-rmse:1.096550+0.159501
## [525] train-rmse:0.781071+0.014683 test-rmse:1.096027+0.159644
## [526] train-rmse:0.780563+0.014690 test-rmse:1.095542+0.159439
## [527] train-rmse:0.780057+0.014710 test-rmse:1.095713+0.159202
## [528] train-rmse:0.779552+0.014733 test-rmse:1.095857+0.159395
## [529] train-rmse:0.779054+0.014753 test-rmse:1.095601+0.160274
## [530] train-rmse:0.778563+0.014774 test-rmse:1.094668+0.160078
## [531] train-rmse:0.778071+0.014786 test-rmse:1.094382+0.160007
## [532] train-rmse:0.777586+0.014813 test-rmse:1.093279+0.157265
## [533] train-rmse:0.777102+0.014832 test-rmse:1.092672+0.155928
## [534] train-rmse:0.776623+0.014846 test-rmse:1.092044+0.156601
## [535] train-rmse:0.776141+0.014854 test-rmse:1.091857+0.156634
## [536] train-rmse:0.775669+0.014870 test-rmse:1.090956+0.156578
## [537] train-rmse:0.775195+0.014887 test-rmse:1.090229+0.156992
## [538] train-rmse:0.774723+0.014902 test-rmse:1.089988+0.156987
## [539] train-rmse:0.774257+0.014916 test-rmse:1.089836+0.157040
## [540] train-rmse:0.773790+0.014933 test-rmse:1.089974+0.157303
## [541] train-rmse:0.773326+0.014951 test-rmse:1.089817+0.157771
## [542] train-rmse:0.772874+0.014970 test-rmse:1.089678+0.157709
## [543] train-rmse:0.772412+0.015000 test-rmse:1.089571+0.157864
## [544] train-rmse:0.771964+0.015013 test-rmse:1.088932+0.158654
## [545] train-rmse:0.771513+0.015031 test-rmse:1.088658+0.158733
## [546] train-rmse:0.771061+0.015041 test-rmse:1.088889+0.158961
## [547] train-rmse:0.770603+0.015050 test-rmse:1.088197+0.159220
## [548] train-rmse:0.770162+0.015071 test-rmse:1.088171+0.159790
## [549] train-rmse:0.769719+0.015091 test-rmse:1.088164+0.159670
## [550] train-rmse:0.769283+0.015108 test-rmse:1.087796+0.159637
## [551] train-rmse:0.768848+0.015125 test-rmse:1.087436+0.159676
## [552] train-rmse:0.768414+0.015136 test-rmse:1.086855+0.159618
## [553] train-rmse:0.767978+0.015151 test-rmse:1.086123+0.159944
## [554] train-rmse:0.767541+0.015162 test-rmse:1.085734+0.159593
## [555] train-rmse:0.767112+0.015178 test-rmse:1.084581+0.159915
## [556] train-rmse:0.766672+0.015189 test-rmse:1.084738+0.159958
## [557] train-rmse:0.766233+0.015196 test-rmse:1.082805+0.157441
## [558] train-rmse:0.765812+0.015205 test-rmse:1.081707+0.156355
## [559] train-rmse:0.765386+0.015212 test-rmse:1.081003+0.155821
## [560] train-rmse:0.764955+0.015221 test-rmse:1.081273+0.155754
## [561] train-rmse:0.764540+0.015231 test-rmse:1.081113+0.156002
## [562] train-rmse:0.764136+0.015241 test-rmse:1.081306+0.155843
## [563] train-rmse:0.763728+0.015251 test-rmse:1.080951+0.155813
## [564] train-rmse:0.763311+0.015269 test-rmse:1.081079+0.155714
## [565] train-rmse:0.762904+0.015276 test-rmse:1.081012+0.155593
## [566] train-rmse:0.762501+0.015292 test-rmse:1.080681+0.155346
## [567] train-rmse:0.762097+0.015311 test-rmse:1.080052+0.155496
## [568] train-rmse:0.761699+0.015317 test-rmse:1.079870+0.155821
## [569] train-rmse:0.761299+0.015329 test-rmse:1.079806+0.156028
## [570] train-rmse:0.760893+0.015336 test-rmse:1.079978+0.155359
## [571] train-rmse:0.760498+0.015350 test-rmse:1.079851+0.155204
## [572] train-rmse:0.760113+0.015362 test-rmse:1.079700+0.154843
## [573] train-rmse:0.759712+0.015362 test-rmse:1.079034+0.155100
## [574] train-rmse:0.759322+0.015374 test-rmse:1.078067+0.155135
## [575] train-rmse:0.758931+0.015381 test-rmse:1.078329+0.154839
## [576] train-rmse:0.758545+0.015389 test-rmse:1.077577+0.155059
## [577] train-rmse:0.758159+0.015399 test-rmse:1.077362+0.154976
## [578] train-rmse:0.757777+0.015412 test-rmse:1.076587+0.155104
## [579] train-rmse:0.757398+0.015423 test-rmse:1.075524+0.155256
## [580] train-rmse:0.757016+0.015437 test-rmse:1.075539+0.154156
## [581] train-rmse:0.756637+0.015443 test-rmse:1.074527+0.154783
## [582] train-rmse:0.756257+0.015444 test-rmse:1.075326+0.154725
## [583] train-rmse:0.755885+0.015452 test-rmse:1.075364+0.154612
## [584] train-rmse:0.755507+0.015456 test-rmse:1.075410+0.154884
## [585] train-rmse:0.755137+0.015462 test-rmse:1.075106+0.155690
## [586] train-rmse:0.754768+0.015468 test-rmse:1.075011+0.155860
## [587] train-rmse:0.754402+0.015477 test-rmse:1.075149+0.155567
## Stopping. Best iteration:
## [581] train-rmse:0.756637+0.015443 test-rmse:1.074527+0.154783
##
## 15
## [1] train-rmse:92.628808+0.103203 test-rmse:92.629086+0.486485
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.759407+0.092047 test-rmse:79.759461+0.505884
## [3] train-rmse:68.693979+0.083013 test-rmse:68.693753+0.524472
## [4] train-rmse:59.182194+0.075874 test-rmse:59.181623+0.542625
## [5] train-rmse:51.008879+0.070487 test-rmse:51.007880+0.560691
## [6] train-rmse:43.989113+0.066746 test-rmse:43.987572+0.578998
## [7] train-rmse:37.964006+0.064561 test-rmse:37.961789+0.597840
## [8] train-rmse:32.792771+0.062486 test-rmse:32.812824+0.600191
## [9] train-rmse:28.348289+0.057070 test-rmse:28.400813+0.605839
## [10] train-rmse:24.529821+0.049480 test-rmse:24.598526+0.594332
## [11] train-rmse:21.249653+0.042361 test-rmse:21.327018+0.565084
## [12] train-rmse:18.435195+0.036069 test-rmse:18.530101+0.571643
## [13] train-rmse:16.022484+0.033409 test-rmse:16.127978+0.582419
## [14] train-rmse:13.956643+0.033214 test-rmse:14.051034+0.557772
## [15] train-rmse:12.189947+0.033130 test-rmse:12.290844+0.560556
## [16] train-rmse:10.684749+0.033781 test-rmse:10.793786+0.574657
## [17] train-rmse:9.403027+0.036683 test-rmse:9.506849+0.547340
## [18] train-rmse:8.316173+0.038990 test-rmse:8.451922+0.547666
## [19] train-rmse:7.397939+0.041192 test-rmse:7.544376+0.561493
## [20] train-rmse:6.625793+0.043480 test-rmse:6.760919+0.542466
## [21] train-rmse:5.979619+0.045728 test-rmse:6.129418+0.542723
## [22] train-rmse:5.437990+0.047738 test-rmse:5.627482+0.541603
## [23] train-rmse:4.985261+0.046209 test-rmse:5.160653+0.512791
## [24] train-rmse:4.613228+0.047104 test-rmse:4.807307+0.522268
## [25] train-rmse:4.297025+0.042824 test-rmse:4.522742+0.516395
## [26] train-rmse:4.041135+0.042023 test-rmse:4.253515+0.474533
## [27] train-rmse:3.830546+0.040913 test-rmse:4.062612+0.463171
## [28] train-rmse:3.654722+0.040499 test-rmse:3.890591+0.463423
## [29] train-rmse:3.503339+0.038792 test-rmse:3.763626+0.436524
## [30] train-rmse:3.379332+0.038489 test-rmse:3.643699+0.407765
## [31] train-rmse:3.272816+0.035998 test-rmse:3.559215+0.393888
## [32] train-rmse:3.182411+0.035907 test-rmse:3.475124+0.404545
## [33] train-rmse:3.104259+0.034747 test-rmse:3.410206+0.411955
## [34] train-rmse:3.034785+0.034487 test-rmse:3.328467+0.370048
## [35] train-rmse:2.969529+0.033639 test-rmse:3.278747+0.365274
## [36] train-rmse:2.912711+0.033209 test-rmse:3.228880+0.351440
## [37] train-rmse:2.861708+0.033835 test-rmse:3.188498+0.351523
## [38] train-rmse:2.811957+0.030985 test-rmse:3.143469+0.346617
## [39] train-rmse:2.767679+0.030597 test-rmse:3.100885+0.335508
## [40] train-rmse:2.725281+0.030508 test-rmse:3.049835+0.318568
## [41] train-rmse:2.681515+0.029203 test-rmse:3.016280+0.325163
## [42] train-rmse:2.643170+0.028911 test-rmse:2.986726+0.327882
## [43] train-rmse:2.606529+0.028123 test-rmse:2.960445+0.322327
## [44] train-rmse:2.570585+0.027538 test-rmse:2.921680+0.320492
## [45] train-rmse:2.534560+0.027293 test-rmse:2.886485+0.312341
## [46] train-rmse:2.501862+0.026678 test-rmse:2.861588+0.294186
## [47] train-rmse:2.470374+0.026340 test-rmse:2.829932+0.302536
## [48] train-rmse:2.438204+0.027110 test-rmse:2.804389+0.311366
## [49] train-rmse:2.406773+0.025717 test-rmse:2.779125+0.308187
## [50] train-rmse:2.375930+0.023030 test-rmse:2.753207+0.305190
## [51] train-rmse:2.346908+0.022281 test-rmse:2.725464+0.301712
## [52] train-rmse:2.315946+0.022105 test-rmse:2.702956+0.289706
## [53] train-rmse:2.287425+0.021150 test-rmse:2.682599+0.285256
## [54] train-rmse:2.259278+0.020233 test-rmse:2.662835+0.294835
## [55] train-rmse:2.232614+0.019692 test-rmse:2.642617+0.292446
## [56] train-rmse:2.206153+0.019484 test-rmse:2.613635+0.287029
## [57] train-rmse:2.180431+0.019492 test-rmse:2.591387+0.295765
## [58] train-rmse:2.155299+0.019190 test-rmse:2.567255+0.292223
## [59] train-rmse:2.130395+0.019084 test-rmse:2.546360+0.286557
## [60] train-rmse:2.105023+0.018212 test-rmse:2.527044+0.277301
## [61] train-rmse:2.079956+0.018045 test-rmse:2.513123+0.282160
## [62] train-rmse:2.057103+0.017684 test-rmse:2.494133+0.286899
## [63] train-rmse:2.033851+0.015754 test-rmse:2.474370+0.278546
## [64] train-rmse:2.011358+0.015404 test-rmse:2.449775+0.276404
## [65] train-rmse:1.988585+0.015116 test-rmse:2.435292+0.277430
## [66] train-rmse:1.966704+0.014495 test-rmse:2.415898+0.277086
## [67] train-rmse:1.944805+0.014132 test-rmse:2.394639+0.272694
## [68] train-rmse:1.923648+0.013525 test-rmse:2.371795+0.272127
## [69] train-rmse:1.902518+0.012820 test-rmse:2.354337+0.276708
## [70] train-rmse:1.881863+0.012428 test-rmse:2.336547+0.277703
## [71] train-rmse:1.861356+0.012259 test-rmse:2.322202+0.277616
## [72] train-rmse:1.841925+0.011709 test-rmse:2.300112+0.277995
## [73] train-rmse:1.822686+0.011483 test-rmse:2.285231+0.277378
## [74] train-rmse:1.802655+0.010882 test-rmse:2.265583+0.278170
## [75] train-rmse:1.781877+0.009408 test-rmse:2.251731+0.268996
## [76] train-rmse:1.763311+0.009082 test-rmse:2.229091+0.267263
## [77] train-rmse:1.744832+0.009021 test-rmse:2.215070+0.265310
## [78] train-rmse:1.726816+0.008794 test-rmse:2.201691+0.266013
## [79] train-rmse:1.708747+0.008084 test-rmse:2.190412+0.261963
## [80] train-rmse:1.690620+0.008655 test-rmse:2.176095+0.268568
## [81] train-rmse:1.673361+0.008095 test-rmse:2.163307+0.273893
## [82] train-rmse:1.656307+0.007825 test-rmse:2.144432+0.272714
## [83] train-rmse:1.639235+0.007613 test-rmse:2.125949+0.263921
## [84] train-rmse:1.622455+0.007216 test-rmse:2.105747+0.258822
## [85] train-rmse:1.606497+0.006961 test-rmse:2.092747+0.255976
## [86] train-rmse:1.590523+0.007106 test-rmse:2.076224+0.252221
## [87] train-rmse:1.574023+0.006765 test-rmse:2.061382+0.246637
## [88] train-rmse:1.558793+0.006488 test-rmse:2.047991+0.246542
## [89] train-rmse:1.543882+0.006304 test-rmse:2.032054+0.249244
## [90] train-rmse:1.529146+0.005903 test-rmse:2.013974+0.248879
## [91] train-rmse:1.514421+0.005910 test-rmse:2.003584+0.256165
## [92] train-rmse:1.499362+0.006804 test-rmse:1.992011+0.255747
## [93] train-rmse:1.485169+0.006923 test-rmse:1.981602+0.253770
## [94] train-rmse:1.469863+0.007034 test-rmse:1.975081+0.257125
## [95] train-rmse:1.454097+0.009714 test-rmse:1.966680+0.260261
## [96] train-rmse:1.439529+0.009750 test-rmse:1.955309+0.258577
## [97] train-rmse:1.426123+0.009951 test-rmse:1.944925+0.252231
## [98] train-rmse:1.412814+0.010115 test-rmse:1.931860+0.250854
## [99] train-rmse:1.399701+0.010341 test-rmse:1.918292+0.249027
## [100] train-rmse:1.387184+0.010306 test-rmse:1.901454+0.252540
## [101] train-rmse:1.374765+0.009925 test-rmse:1.894115+0.252890
## [102] train-rmse:1.362302+0.010019 test-rmse:1.884876+0.251871
## [103] train-rmse:1.349948+0.009924 test-rmse:1.876625+0.253689
## [104] train-rmse:1.337463+0.010073 test-rmse:1.871214+0.253132
## [105] train-rmse:1.325150+0.010336 test-rmse:1.855049+0.250042
## [106] train-rmse:1.313296+0.010316 test-rmse:1.846428+0.250718
## [107] train-rmse:1.301268+0.009765 test-rmse:1.834467+0.249313
## [108] train-rmse:1.289053+0.010226 test-rmse:1.824648+0.252199
## [109] train-rmse:1.277349+0.010306 test-rmse:1.818912+0.250420
## [110] train-rmse:1.266485+0.010432 test-rmse:1.804789+0.253391
## [111] train-rmse:1.255024+0.010514 test-rmse:1.798357+0.254271
## [112] train-rmse:1.244286+0.010433 test-rmse:1.786353+0.252707
## [113] train-rmse:1.232649+0.012071 test-rmse:1.778507+0.252441
## [114] train-rmse:1.222371+0.011892 test-rmse:1.771372+0.252847
## [115] train-rmse:1.211032+0.011604 test-rmse:1.767096+0.254524
## [116] train-rmse:1.200405+0.012405 test-rmse:1.754590+0.252152
## [117] train-rmse:1.189763+0.011983 test-rmse:1.745319+0.247451
## [118] train-rmse:1.179737+0.012124 test-rmse:1.731869+0.246339
## [119] train-rmse:1.169403+0.011931 test-rmse:1.728189+0.245490
## [120] train-rmse:1.159683+0.011712 test-rmse:1.718940+0.247604
## [121] train-rmse:1.150387+0.011711 test-rmse:1.712237+0.247542
## [122] train-rmse:1.141204+0.011738 test-rmse:1.705103+0.250132
## [123] train-rmse:1.131166+0.011455 test-rmse:1.702974+0.249556
## [124] train-rmse:1.121974+0.011253 test-rmse:1.695017+0.253152
## [125] train-rmse:1.112242+0.011344 test-rmse:1.684751+0.253169
## [126] train-rmse:1.103355+0.011317 test-rmse:1.675096+0.253161
## [127] train-rmse:1.093731+0.012041 test-rmse:1.663926+0.242833
## [128] train-rmse:1.085197+0.012230 test-rmse:1.656335+0.245579
## [129] train-rmse:1.076389+0.012490 test-rmse:1.652684+0.247198
## [130] train-rmse:1.067969+0.012629 test-rmse:1.644123+0.246352
## [131] train-rmse:1.059070+0.012060 test-rmse:1.635765+0.248611
## [132] train-rmse:1.050547+0.011786 test-rmse:1.626441+0.248408
## [133] train-rmse:1.042220+0.011037 test-rmse:1.619132+0.252302
## [134] train-rmse:1.034137+0.011201 test-rmse:1.613835+0.251668
## [135] train-rmse:1.025211+0.011741 test-rmse:1.607440+0.253662
## [136] train-rmse:1.017134+0.012162 test-rmse:1.599819+0.255783
## [137] train-rmse:1.009383+0.012046 test-rmse:1.592930+0.251967
## [138] train-rmse:1.001676+0.011847 test-rmse:1.588743+0.250443
## [139] train-rmse:0.993816+0.011927 test-rmse:1.586245+0.251596
## [140] train-rmse:0.986540+0.012026 test-rmse:1.579997+0.249766
## [141] train-rmse:0.979178+0.012072 test-rmse:1.574610+0.252227
## [142] train-rmse:0.972103+0.012252 test-rmse:1.569236+0.250411
## [143] train-rmse:0.964788+0.011989 test-rmse:1.562963+0.252896
## [144] train-rmse:0.956815+0.011666 test-rmse:1.556531+0.255397
## [145] train-rmse:0.950185+0.011696 test-rmse:1.549188+0.260530
## [146] train-rmse:0.943175+0.012206 test-rmse:1.544678+0.258498
## [147] train-rmse:0.936241+0.011931 test-rmse:1.537873+0.254814
## [148] train-rmse:0.929601+0.011713 test-rmse:1.533829+0.255883
## [149] train-rmse:0.923210+0.011611 test-rmse:1.530388+0.256833
## [150] train-rmse:0.916889+0.011531 test-rmse:1.524391+0.258180
## [151] train-rmse:0.910105+0.011879 test-rmse:1.520763+0.256292
## [152] train-rmse:0.903632+0.012277 test-rmse:1.515072+0.257876
## [153] train-rmse:0.897542+0.012529 test-rmse:1.510447+0.256401
## [154] train-rmse:0.890575+0.012860 test-rmse:1.506979+0.257506
## [155] train-rmse:0.883812+0.013774 test-rmse:1.500751+0.257396
## [156] train-rmse:0.877843+0.013555 test-rmse:1.497190+0.258285
## [157] train-rmse:0.872131+0.013335 test-rmse:1.490114+0.263121
## [158] train-rmse:0.866128+0.013206 test-rmse:1.482473+0.258710
## [159] train-rmse:0.860033+0.013366 test-rmse:1.478286+0.258327
## [160] train-rmse:0.854695+0.013284 test-rmse:1.474746+0.258420
## [161] train-rmse:0.849017+0.013389 test-rmse:1.471110+0.257159
## [162] train-rmse:0.843485+0.013479 test-rmse:1.467712+0.257772
## [163] train-rmse:0.838154+0.013667 test-rmse:1.465894+0.257089
## [164] train-rmse:0.832914+0.013575 test-rmse:1.460723+0.257965
## [165] train-rmse:0.827580+0.013774 test-rmse:1.457088+0.256815
## [166] train-rmse:0.821927+0.013929 test-rmse:1.451122+0.255234
## [167] train-rmse:0.816662+0.013893 test-rmse:1.449154+0.255975
## [168] train-rmse:0.811224+0.013211 test-rmse:1.447688+0.254236
## [169] train-rmse:0.805881+0.013514 test-rmse:1.442383+0.256660
## [170] train-rmse:0.801007+0.013768 test-rmse:1.437781+0.256762
## [171] train-rmse:0.796474+0.013776 test-rmse:1.433415+0.258149
## [172] train-rmse:0.791975+0.013814 test-rmse:1.429855+0.258018
## [173] train-rmse:0.787383+0.013906 test-rmse:1.425797+0.256581
## [174] train-rmse:0.782332+0.013705 test-rmse:1.421981+0.258035
## [175] train-rmse:0.777394+0.013851 test-rmse:1.416144+0.257347
## [176] train-rmse:0.772810+0.013854 test-rmse:1.409492+0.259106
## [177] train-rmse:0.768141+0.013917 test-rmse:1.408572+0.260322
## [178] train-rmse:0.763785+0.013552 test-rmse:1.406625+0.260630
## [179] train-rmse:0.759052+0.012758 test-rmse:1.405208+0.259356
## [180] train-rmse:0.754254+0.013281 test-rmse:1.401651+0.259264
## [181] train-rmse:0.749916+0.013050 test-rmse:1.398449+0.261221
## [182] train-rmse:0.745673+0.013213 test-rmse:1.395135+0.261773
## [183] train-rmse:0.741517+0.013438 test-rmse:1.392850+0.262604
## [184] train-rmse:0.737344+0.013550 test-rmse:1.391056+0.262671
## [185] train-rmse:0.733269+0.013754 test-rmse:1.385136+0.264572
## [186] train-rmse:0.729163+0.014053 test-rmse:1.381858+0.265348
## [187] train-rmse:0.724500+0.014750 test-rmse:1.379444+0.264717
## [188] train-rmse:0.720777+0.014601 test-rmse:1.375539+0.266292
## [189] train-rmse:0.717197+0.014475 test-rmse:1.371494+0.264934
## [190] train-rmse:0.713061+0.014563 test-rmse:1.369295+0.264957
## [191] train-rmse:0.709130+0.014331 test-rmse:1.368563+0.263278
## [192] train-rmse:0.705278+0.013647 test-rmse:1.366802+0.263082
## [193] train-rmse:0.701678+0.013500 test-rmse:1.364068+0.265175
## [194] train-rmse:0.697674+0.013846 test-rmse:1.361097+0.266801
## [195] train-rmse:0.694067+0.013955 test-rmse:1.358565+0.267121
## [196] train-rmse:0.690511+0.014033 test-rmse:1.354167+0.268144
## [197] train-rmse:0.686357+0.014325 test-rmse:1.352915+0.268354
## [198] train-rmse:0.682977+0.014368 test-rmse:1.351938+0.268758
## [199] train-rmse:0.679256+0.014391 test-rmse:1.349062+0.269656
## [200] train-rmse:0.675806+0.014431 test-rmse:1.349342+0.270987
## [201] train-rmse:0.672677+0.014390 test-rmse:1.346472+0.270759
## [202] train-rmse:0.669284+0.014330 test-rmse:1.345994+0.271256
## [203] train-rmse:0.665788+0.013891 test-rmse:1.342930+0.270185
## [204] train-rmse:0.662324+0.014198 test-rmse:1.337904+0.269517
## [205] train-rmse:0.659328+0.014083 test-rmse:1.333446+0.269697
## [206] train-rmse:0.656275+0.014028 test-rmse:1.331325+0.270445
## [207] train-rmse:0.652997+0.013914 test-rmse:1.327347+0.271177
## [208] train-rmse:0.649977+0.013659 test-rmse:1.325016+0.272024
## [209] train-rmse:0.646780+0.013773 test-rmse:1.323335+0.271988
## [210] train-rmse:0.643241+0.014041 test-rmse:1.321056+0.270510
## [211] train-rmse:0.640180+0.013832 test-rmse:1.317321+0.271908
## [212] train-rmse:0.637321+0.013775 test-rmse:1.316530+0.271023
## [213] train-rmse:0.634113+0.014341 test-rmse:1.314776+0.270767
## [214] train-rmse:0.631459+0.014278 test-rmse:1.313579+0.271178
## [215] train-rmse:0.628685+0.014059 test-rmse:1.311501+0.272513
## [216] train-rmse:0.625443+0.013703 test-rmse:1.311391+0.272549
## [217] train-rmse:0.622058+0.013615 test-rmse:1.311456+0.273296
## [218] train-rmse:0.619057+0.013837 test-rmse:1.306779+0.268433
## [219] train-rmse:0.616439+0.013769 test-rmse:1.304753+0.267553
## [220] train-rmse:0.613729+0.013673 test-rmse:1.303640+0.268692
## [221] train-rmse:0.611113+0.013467 test-rmse:1.301770+0.269317
## [222] train-rmse:0.608858+0.013394 test-rmse:1.299908+0.268525
## [223] train-rmse:0.606127+0.013027 test-rmse:1.297691+0.269093
## [224] train-rmse:0.603461+0.013180 test-rmse:1.296582+0.270173
## [225] train-rmse:0.600932+0.013167 test-rmse:1.296306+0.270978
## [226] train-rmse:0.597963+0.013498 test-rmse:1.293294+0.273545
## [227] train-rmse:0.595482+0.013504 test-rmse:1.291019+0.273104
## [228] train-rmse:0.592739+0.013015 test-rmse:1.289350+0.271787
## [229] train-rmse:0.590300+0.013151 test-rmse:1.289254+0.271545
## [230] train-rmse:0.588041+0.012965 test-rmse:1.288160+0.272013
## [231] train-rmse:0.585662+0.012812 test-rmse:1.287770+0.272647
## [232] train-rmse:0.583425+0.012774 test-rmse:1.286078+0.272927
## [233] train-rmse:0.580939+0.012779 test-rmse:1.283878+0.272889
## [234] train-rmse:0.578762+0.012811 test-rmse:1.282463+0.272410
## [235] train-rmse:0.576468+0.012875 test-rmse:1.280243+0.271570
## [236] train-rmse:0.574224+0.013021 test-rmse:1.279271+0.272502
## [237] train-rmse:0.571324+0.013141 test-rmse:1.279307+0.272845
## [238] train-rmse:0.568990+0.012704 test-rmse:1.278927+0.274848
## [239] train-rmse:0.567021+0.012464 test-rmse:1.276746+0.276618
## [240] train-rmse:0.565027+0.012555 test-rmse:1.274686+0.273402
## [241] train-rmse:0.562771+0.012552 test-rmse:1.274063+0.274377
## [242] train-rmse:0.560476+0.012823 test-rmse:1.272916+0.273631
## [243] train-rmse:0.557882+0.013075 test-rmse:1.270905+0.275087
## [244] train-rmse:0.555452+0.013522 test-rmse:1.267969+0.274608
## [245] train-rmse:0.553385+0.013619 test-rmse:1.266482+0.275205
## [246] train-rmse:0.550856+0.012986 test-rmse:1.265003+0.274398
## [247] train-rmse:0.548802+0.012934 test-rmse:1.264097+0.275206
## [248] train-rmse:0.546621+0.012687 test-rmse:1.263859+0.276136
## [249] train-rmse:0.544456+0.012351 test-rmse:1.263564+0.276124
## [250] train-rmse:0.542672+0.012397 test-rmse:1.262184+0.276202
## [251] train-rmse:0.540392+0.011958 test-rmse:1.261778+0.277322
## [252] train-rmse:0.538388+0.012189 test-rmse:1.258134+0.275851
## [253] train-rmse:0.536608+0.012044 test-rmse:1.256005+0.275048
## [254] train-rmse:0.533930+0.012616 test-rmse:1.253926+0.274863
## [255] train-rmse:0.532124+0.012671 test-rmse:1.252724+0.275986
## [256] train-rmse:0.530137+0.012742 test-rmse:1.251763+0.276245
## [257] train-rmse:0.528268+0.012850 test-rmse:1.251592+0.276322
## [258] train-rmse:0.526605+0.012601 test-rmse:1.249640+0.276467
## [259] train-rmse:0.524785+0.012556 test-rmse:1.249157+0.276238
## [260] train-rmse:0.522663+0.012250 test-rmse:1.247980+0.275237
## [261] train-rmse:0.521049+0.012193 test-rmse:1.245205+0.276306
## [262] train-rmse:0.519305+0.012135 test-rmse:1.244602+0.276374
## [263] train-rmse:0.517540+0.011935 test-rmse:1.244213+0.276800
## [264] train-rmse:0.515976+0.012014 test-rmse:1.243527+0.277043
## [265] train-rmse:0.514431+0.011987 test-rmse:1.242212+0.276847
## [266] train-rmse:0.512889+0.011870 test-rmse:1.241087+0.277513
## [267] train-rmse:0.511072+0.012047 test-rmse:1.240210+0.277321
## [268] train-rmse:0.509490+0.012175 test-rmse:1.238864+0.277461
## [269] train-rmse:0.507714+0.012061 test-rmse:1.238350+0.278305
## [270] train-rmse:0.506199+0.011951 test-rmse:1.237891+0.278606
## [271] train-rmse:0.503909+0.011235 test-rmse:1.237487+0.277949
## [272] train-rmse:0.502425+0.011344 test-rmse:1.237280+0.277533
## [273] train-rmse:0.500509+0.010893 test-rmse:1.236107+0.277537
## [274] train-rmse:0.498755+0.011116 test-rmse:1.233337+0.278550
## [275] train-rmse:0.497372+0.011155 test-rmse:1.232796+0.279471
## [276] train-rmse:0.495924+0.011120 test-rmse:1.229858+0.280889
## [277] train-rmse:0.494665+0.011012 test-rmse:1.228489+0.281765
## [278] train-rmse:0.493203+0.010984 test-rmse:1.228151+0.282903
## [279] train-rmse:0.491768+0.011062 test-rmse:1.228568+0.282554
## [280] train-rmse:0.490024+0.011094 test-rmse:1.225070+0.279316
## [281] train-rmse:0.488196+0.011025 test-rmse:1.223919+0.278674
## [282] train-rmse:0.486796+0.010801 test-rmse:1.223522+0.278734
## [283] train-rmse:0.485499+0.010799 test-rmse:1.222275+0.278302
## [284] train-rmse:0.484321+0.010675 test-rmse:1.220999+0.279033
## [285] train-rmse:0.482916+0.010754 test-rmse:1.219177+0.279174
## [286] train-rmse:0.481293+0.010258 test-rmse:1.218845+0.279709
## [287] train-rmse:0.479898+0.010509 test-rmse:1.218527+0.279710
## [288] train-rmse:0.478649+0.010459 test-rmse:1.218470+0.280353
## [289] train-rmse:0.477116+0.010110 test-rmse:1.217763+0.280804
## [290] train-rmse:0.475145+0.010543 test-rmse:1.217218+0.281147
## [291] train-rmse:0.473963+0.010544 test-rmse:1.215750+0.281589
## [292] train-rmse:0.472248+0.010937 test-rmse:1.215496+0.281754
## [293] train-rmse:0.470839+0.010642 test-rmse:1.215492+0.281976
## [294] train-rmse:0.469397+0.010624 test-rmse:1.215336+0.282814
## [295] train-rmse:0.468284+0.010559 test-rmse:1.214703+0.283553
## [296] train-rmse:0.466609+0.009841 test-rmse:1.213817+0.283768
## [297] train-rmse:0.465308+0.010124 test-rmse:1.211949+0.283701
## [298] train-rmse:0.463799+0.010160 test-rmse:1.212122+0.283601
## [299] train-rmse:0.462477+0.010119 test-rmse:1.210575+0.283901
## [300] train-rmse:0.461183+0.009869 test-rmse:1.210917+0.283901
## [301] train-rmse:0.459881+0.009634 test-rmse:1.210774+0.285068
## [302] train-rmse:0.458726+0.009645 test-rmse:1.210427+0.284131
## [303] train-rmse:0.457689+0.009483 test-rmse:1.209673+0.283701
## [304] train-rmse:0.456511+0.009104 test-rmse:1.206608+0.281359
## [305] train-rmse:0.455221+0.008852 test-rmse:1.205864+0.281386
## [306] train-rmse:0.453896+0.008719 test-rmse:1.205193+0.281156
## [307] train-rmse:0.452834+0.008702 test-rmse:1.203003+0.280823
## [308] train-rmse:0.451225+0.008840 test-rmse:1.200547+0.281200
## [309] train-rmse:0.450128+0.008850 test-rmse:1.200029+0.281261
## [310] train-rmse:0.449157+0.008854 test-rmse:1.199582+0.281364
## [311] train-rmse:0.448208+0.008813 test-rmse:1.199000+0.281473
## [312] train-rmse:0.447210+0.008864 test-rmse:1.198252+0.282592
## [313] train-rmse:0.446247+0.008647 test-rmse:1.198130+0.282707
## [314] train-rmse:0.445261+0.008369 test-rmse:1.197221+0.283259
## [315] train-rmse:0.444041+0.008063 test-rmse:1.197249+0.282601
## [316] train-rmse:0.443111+0.008078 test-rmse:1.196486+0.282520
## [317] train-rmse:0.441988+0.008003 test-rmse:1.196718+0.283041
## [318] train-rmse:0.440974+0.008085 test-rmse:1.196309+0.282708
## [319] train-rmse:0.439944+0.007997 test-rmse:1.195289+0.282104
## [320] train-rmse:0.438904+0.007968 test-rmse:1.195587+0.282953
## [321] train-rmse:0.437880+0.008017 test-rmse:1.195443+0.282717
## [322] train-rmse:0.436126+0.008563 test-rmse:1.193795+0.283466
## [323] train-rmse:0.435267+0.008510 test-rmse:1.193056+0.283744
## [324] train-rmse:0.434304+0.008518 test-rmse:1.192193+0.284037
## [325] train-rmse:0.433412+0.008578 test-rmse:1.191378+0.283919
## [326] train-rmse:0.432559+0.008411 test-rmse:1.190316+0.285063
## [327] train-rmse:0.431449+0.008756 test-rmse:1.187507+0.286753
## [328] train-rmse:0.430673+0.008621 test-rmse:1.186459+0.286726
## [329] train-rmse:0.429597+0.008458 test-rmse:1.185038+0.286636
## [330] train-rmse:0.428118+0.008734 test-rmse:1.184752+0.287369
## [331] train-rmse:0.427284+0.008733 test-rmse:1.184255+0.287276
## [332] train-rmse:0.426254+0.008790 test-rmse:1.183171+0.286429
## [333] train-rmse:0.425467+0.008784 test-rmse:1.182730+0.286323
## [334] train-rmse:0.424597+0.008868 test-rmse:1.182162+0.286433
## [335] train-rmse:0.423765+0.008731 test-rmse:1.181976+0.286985
## [336] train-rmse:0.422893+0.008615 test-rmse:1.181873+0.286191
## [337] train-rmse:0.421960+0.008465 test-rmse:1.181478+0.287333
## [338] train-rmse:0.420869+0.008908 test-rmse:1.180449+0.287118
## [339] train-rmse:0.420101+0.008930 test-rmse:1.180471+0.287178
## [340] train-rmse:0.419219+0.008871 test-rmse:1.179569+0.287021
## [341] train-rmse:0.418287+0.008980 test-rmse:1.179534+0.286922
## [342] train-rmse:0.417478+0.009003 test-rmse:1.178096+0.286780
## [343] train-rmse:0.416518+0.008921 test-rmse:1.178286+0.286422
## [344] train-rmse:0.415106+0.009460 test-rmse:1.177276+0.286955
## [345] train-rmse:0.413967+0.009143 test-rmse:1.176366+0.287250
## [346] train-rmse:0.413147+0.009103 test-rmse:1.176548+0.288040
## [347] train-rmse:0.412408+0.009138 test-rmse:1.175996+0.288080
## [348] train-rmse:0.411652+0.009093 test-rmse:1.175170+0.288798
## [349] train-rmse:0.410599+0.009443 test-rmse:1.174723+0.288738
## [350] train-rmse:0.409747+0.009588 test-rmse:1.174163+0.288073
## [351] train-rmse:0.408573+0.009249 test-rmse:1.174019+0.288943
## [352] train-rmse:0.407859+0.009165 test-rmse:1.172318+0.289874
## [353] train-rmse:0.407206+0.009035 test-rmse:1.171409+0.289530
## [354] train-rmse:0.406200+0.009520 test-rmse:1.171424+0.289672
## [355] train-rmse:0.405268+0.009660 test-rmse:1.170523+0.290397
## [356] train-rmse:0.404493+0.009664 test-rmse:1.169818+0.290683
## [357] train-rmse:0.403577+0.009648 test-rmse:1.169218+0.291699
## [358] train-rmse:0.402961+0.009670 test-rmse:1.168516+0.291282
## [359] train-rmse:0.402220+0.009664 test-rmse:1.168232+0.291405
## [360] train-rmse:0.401433+0.009661 test-rmse:1.167880+0.291535
## [361] train-rmse:0.400129+0.010128 test-rmse:1.167197+0.291668
## [362] train-rmse:0.399197+0.010501 test-rmse:1.166417+0.291778
## [363] train-rmse:0.397711+0.010944 test-rmse:1.165669+0.292206
## [364] train-rmse:0.397053+0.010951 test-rmse:1.165355+0.291707
## [365] train-rmse:0.395809+0.011373 test-rmse:1.163629+0.292455
## [366] train-rmse:0.394470+0.011524 test-rmse:1.163799+0.292827
## [367] train-rmse:0.393457+0.011189 test-rmse:1.163733+0.292672
## [368] train-rmse:0.392797+0.011102 test-rmse:1.163470+0.292873
## [369] train-rmse:0.391843+0.010824 test-rmse:1.162732+0.293911
## [370] train-rmse:0.391026+0.011013 test-rmse:1.162658+0.293301
## [371] train-rmse:0.389857+0.011198 test-rmse:1.162304+0.293525
## [372] train-rmse:0.388762+0.011321 test-rmse:1.162785+0.293463
## [373] train-rmse:0.387456+0.010807 test-rmse:1.162591+0.293527
## [374] train-rmse:0.386237+0.010424 test-rmse:1.161260+0.293329
## [375] train-rmse:0.385070+0.010472 test-rmse:1.161038+0.293740
## [376] train-rmse:0.384449+0.010428 test-rmse:1.161040+0.293959
## [377] train-rmse:0.383603+0.010169 test-rmse:1.160482+0.294630
## [378] train-rmse:0.383009+0.010166 test-rmse:1.160193+0.294535
## [379] train-rmse:0.382430+0.010044 test-rmse:1.159612+0.294906
## [380] train-rmse:0.381715+0.009889 test-rmse:1.158282+0.295895
## [381] train-rmse:0.381089+0.009853 test-rmse:1.157532+0.295136
## [382] train-rmse:0.379685+0.009980 test-rmse:1.157296+0.294921
## [383] train-rmse:0.378792+0.010261 test-rmse:1.157033+0.294803
## [384] train-rmse:0.378174+0.010290 test-rmse:1.156571+0.295014
## [385] train-rmse:0.377596+0.010270 test-rmse:1.156182+0.295136
## [386] train-rmse:0.376341+0.010638 test-rmse:1.156679+0.295031
## [387] train-rmse:0.375568+0.010397 test-rmse:1.156376+0.295405
## [388] train-rmse:0.374724+0.010196 test-rmse:1.156487+0.294964
## [389] train-rmse:0.373520+0.010611 test-rmse:1.156138+0.294944
## [390] train-rmse:0.372949+0.010553 test-rmse:1.155866+0.295204
## [391] train-rmse:0.372183+0.010772 test-rmse:1.155322+0.294872
## [392] train-rmse:0.371184+0.010921 test-rmse:1.155235+0.295268
## [393] train-rmse:0.370572+0.010827 test-rmse:1.155279+0.295383
## [394] train-rmse:0.369790+0.010758 test-rmse:1.154750+0.295316
## [395] train-rmse:0.369126+0.010697 test-rmse:1.154557+0.295702
## [396] train-rmse:0.368407+0.010776 test-rmse:1.153396+0.295768
## [397] train-rmse:0.367941+0.010796 test-rmse:1.152929+0.295040
## [398] train-rmse:0.366974+0.010788 test-rmse:1.153801+0.294741
## [399] train-rmse:0.366273+0.010979 test-rmse:1.153754+0.294890
## [400] train-rmse:0.365744+0.010957 test-rmse:1.153322+0.294953
## [401] train-rmse:0.365195+0.010947 test-rmse:1.153014+0.295012
## [402] train-rmse:0.364533+0.010981 test-rmse:1.153002+0.295287
## [403] train-rmse:0.363446+0.010566 test-rmse:1.152697+0.295428
## [404] train-rmse:0.362705+0.010539 test-rmse:1.153000+0.295887
## [405] train-rmse:0.362298+0.010562 test-rmse:1.151765+0.295326
## [406] train-rmse:0.361198+0.010876 test-rmse:1.151634+0.295214
## [407] train-rmse:0.360067+0.011148 test-rmse:1.152231+0.294850
## [408] train-rmse:0.359476+0.010974 test-rmse:1.151600+0.295001
## [409] train-rmse:0.358684+0.010693 test-rmse:1.151281+0.294437
## [410] train-rmse:0.357881+0.010599 test-rmse:1.151238+0.294270
## [411] train-rmse:0.357285+0.010769 test-rmse:1.150961+0.293700
## [412] train-rmse:0.356474+0.010551 test-rmse:1.150835+0.294001
## [413] train-rmse:0.355680+0.010439 test-rmse:1.149939+0.294596
## [414] train-rmse:0.354613+0.010673 test-rmse:1.149545+0.294883
## [415] train-rmse:0.354085+0.010555 test-rmse:1.149659+0.294718
## [416] train-rmse:0.353348+0.010356 test-rmse:1.149608+0.294985
## [417] train-rmse:0.352717+0.010523 test-rmse:1.149274+0.295574
## [418] train-rmse:0.352035+0.010377 test-rmse:1.149109+0.295754
## [419] train-rmse:0.351273+0.010681 test-rmse:1.148892+0.295597
## [420] train-rmse:0.350803+0.010665 test-rmse:1.148266+0.295675
## [421] train-rmse:0.350145+0.010562 test-rmse:1.147056+0.296030
## [422] train-rmse:0.349427+0.010280 test-rmse:1.146950+0.295823
## [423] train-rmse:0.348321+0.010540 test-rmse:1.146870+0.296256
## [424] train-rmse:0.347400+0.010225 test-rmse:1.146184+0.296449
## [425] train-rmse:0.346755+0.010481 test-rmse:1.145861+0.296561
## [426] train-rmse:0.345630+0.011158 test-rmse:1.145673+0.296222
## [427] train-rmse:0.344932+0.011605 test-rmse:1.145142+0.296328
## [428] train-rmse:0.344276+0.011724 test-rmse:1.144901+0.295898
## [429] train-rmse:0.343615+0.011925 test-rmse:1.144665+0.296372
## [430] train-rmse:0.342771+0.012271 test-rmse:1.144623+0.295678
## [431] train-rmse:0.342090+0.012046 test-rmse:1.144651+0.296495
## [432] train-rmse:0.341219+0.011728 test-rmse:1.144587+0.296309
## [433] train-rmse:0.340790+0.011741 test-rmse:1.144559+0.295941
## [434] train-rmse:0.340100+0.011610 test-rmse:1.144560+0.296003
## [435] train-rmse:0.339534+0.011786 test-rmse:1.144511+0.295719
## [436] train-rmse:0.338696+0.011982 test-rmse:1.144174+0.295805
## [437] train-rmse:0.338145+0.012202 test-rmse:1.144273+0.295519
## [438] train-rmse:0.337527+0.012324 test-rmse:1.144118+0.295626
## [439] train-rmse:0.336883+0.012208 test-rmse:1.143403+0.295428
## [440] train-rmse:0.336309+0.012248 test-rmse:1.143335+0.295157
## [441] train-rmse:0.335765+0.012163 test-rmse:1.143310+0.295041
## [442] train-rmse:0.335148+0.012278 test-rmse:1.142932+0.295434
## [443] train-rmse:0.334363+0.012453 test-rmse:1.143592+0.296020
## [444] train-rmse:0.333934+0.012471 test-rmse:1.143507+0.296127
## [445] train-rmse:0.333589+0.012486 test-rmse:1.142821+0.295945
## [446] train-rmse:0.332959+0.012407 test-rmse:1.142598+0.295654
## [447] train-rmse:0.332156+0.012388 test-rmse:1.142336+0.295499
## [448] train-rmse:0.331476+0.012324 test-rmse:1.141079+0.296120
## [449] train-rmse:0.330791+0.012451 test-rmse:1.140522+0.296173
## [450] train-rmse:0.330332+0.012473 test-rmse:1.140558+0.296124
## [451] train-rmse:0.329922+0.012531 test-rmse:1.140658+0.295855
## [452] train-rmse:0.329302+0.012775 test-rmse:1.140482+0.295729
## [453] train-rmse:0.328558+0.012657 test-rmse:1.139975+0.295529
## [454] train-rmse:0.328143+0.012663 test-rmse:1.139458+0.295989
## [455] train-rmse:0.327389+0.012399 test-rmse:1.139227+0.295703
## [456] train-rmse:0.326997+0.012347 test-rmse:1.139045+0.295859
## [457] train-rmse:0.326487+0.012302 test-rmse:1.139050+0.295907
## [458] train-rmse:0.325478+0.012716 test-rmse:1.138709+0.296314
## [459] train-rmse:0.324618+0.013009 test-rmse:1.138764+0.296920
## [460] train-rmse:0.323924+0.012710 test-rmse:1.137734+0.296693
## [461] train-rmse:0.322977+0.012294 test-rmse:1.137095+0.297179
## [462] train-rmse:0.322602+0.012280 test-rmse:1.137046+0.297076
## [463] train-rmse:0.321989+0.012426 test-rmse:1.136548+0.296964
## [464] train-rmse:0.321558+0.012324 test-rmse:1.136151+0.298340
## [465] train-rmse:0.321069+0.012508 test-rmse:1.136341+0.298062
## [466] train-rmse:0.320277+0.012570 test-rmse:1.135894+0.297528
## [467] train-rmse:0.319398+0.012640 test-rmse:1.135809+0.297514
## [468] train-rmse:0.318802+0.012520 test-rmse:1.134889+0.298100
## [469] train-rmse:0.318478+0.012474 test-rmse:1.134569+0.298393
## [470] train-rmse:0.317986+0.012653 test-rmse:1.134873+0.298001
## [471] train-rmse:0.317125+0.013161 test-rmse:1.134710+0.298074
## [472] train-rmse:0.316386+0.012890 test-rmse:1.134475+0.298566
## [473] train-rmse:0.315739+0.012919 test-rmse:1.134246+0.298852
## [474] train-rmse:0.315387+0.013019 test-rmse:1.134138+0.298771
## [475] train-rmse:0.314847+0.013035 test-rmse:1.133968+0.298707
## [476] train-rmse:0.314266+0.013033 test-rmse:1.133903+0.299489
## [477] train-rmse:0.313701+0.012870 test-rmse:1.134085+0.299904
## [478] train-rmse:0.312836+0.013072 test-rmse:1.133881+0.299254
## [479] train-rmse:0.312343+0.013036 test-rmse:1.133777+0.299223
## [480] train-rmse:0.311793+0.012840 test-rmse:1.133537+0.299325
## [481] train-rmse:0.311394+0.012927 test-rmse:1.133367+0.299176
## [482] train-rmse:0.310924+0.013125 test-rmse:1.132633+0.299104
## [483] train-rmse:0.310531+0.013168 test-rmse:1.132688+0.299099
## [484] train-rmse:0.309957+0.013028 test-rmse:1.133041+0.299806
## [485] train-rmse:0.309151+0.012969 test-rmse:1.133078+0.299527
## [486] train-rmse:0.308584+0.012820 test-rmse:1.133127+0.299555
## [487] train-rmse:0.307827+0.012970 test-rmse:1.133273+0.299419
## [488] train-rmse:0.307133+0.012917 test-rmse:1.133338+0.299316
## Stopping. Best iteration:
## [482] train-rmse:0.310924+0.013125 test-rmse:1.132633+0.299104
##
## 16
## [1] train-rmse:92.628808+0.103203 test-rmse:92.629086+0.486485
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.759407+0.092047 test-rmse:79.759461+0.505884
## [3] train-rmse:68.693979+0.083013 test-rmse:68.693753+0.524472
## [4] train-rmse:59.182194+0.075874 test-rmse:59.181623+0.542625
## [5] train-rmse:51.008879+0.070487 test-rmse:51.007880+0.560691
## [6] train-rmse:43.989113+0.066746 test-rmse:43.987572+0.578998
## [7] train-rmse:37.964006+0.064561 test-rmse:37.961789+0.597840
## [8] train-rmse:32.792771+0.062486 test-rmse:32.812824+0.600191
## [9] train-rmse:28.347628+0.056546 test-rmse:28.405012+0.603047
## [10] train-rmse:24.526212+0.047562 test-rmse:24.604208+0.597355
## [11] train-rmse:21.241542+0.040416 test-rmse:21.330120+0.584586
## [12] train-rmse:18.420442+0.033843 test-rmse:18.524625+0.584364
## [13] train-rmse:15.995581+0.031368 test-rmse:16.096637+0.568124
## [14] train-rmse:13.915861+0.030179 test-rmse:14.005689+0.571934
## [15] train-rmse:12.132837+0.027478 test-rmse:12.239895+0.575865
## [16] train-rmse:10.605318+0.027634 test-rmse:10.716857+0.553933
## [17] train-rmse:9.302092+0.027405 test-rmse:9.412489+0.532183
## [18] train-rmse:8.188368+0.030193 test-rmse:8.303805+0.526104
## [19] train-rmse:7.238961+0.030166 test-rmse:7.370027+0.546766
## [20] train-rmse:6.433768+0.032662 test-rmse:6.591290+0.541301
## [21] train-rmse:5.751019+0.034573 test-rmse:5.912105+0.540102
## [22] train-rmse:5.172746+0.031415 test-rmse:5.336810+0.513995
## [23] train-rmse:4.688737+0.031903 test-rmse:4.879754+0.517789
## [24] train-rmse:4.282114+0.033487 test-rmse:4.502809+0.511387
## [25] train-rmse:3.942267+0.035933 test-rmse:4.172514+0.465181
## [26] train-rmse:3.655424+0.040266 test-rmse:3.909865+0.459659
## [27] train-rmse:3.413934+0.038489 test-rmse:3.698667+0.450476
## [28] train-rmse:3.214332+0.038927 test-rmse:3.492458+0.433132
## [29] train-rmse:3.045497+0.039632 test-rmse:3.339020+0.410841
## [30] train-rmse:2.903797+0.038975 test-rmse:3.221544+0.397068
## [31] train-rmse:2.782187+0.039236 test-rmse:3.118725+0.402439
## [32] train-rmse:2.671534+0.036999 test-rmse:3.034277+0.374924
## [33] train-rmse:2.577829+0.037071 test-rmse:2.954087+0.357534
## [34] train-rmse:2.499145+0.037057 test-rmse:2.882853+0.353547
## [35] train-rmse:2.428784+0.036801 test-rmse:2.823288+0.350158
## [36] train-rmse:2.366095+0.036078 test-rmse:2.765769+0.334934
## [37] train-rmse:2.305613+0.036094 test-rmse:2.727075+0.331560
## [38] train-rmse:2.252655+0.036320 test-rmse:2.673830+0.338230
## [39] train-rmse:2.199143+0.036343 test-rmse:2.636575+0.329572
## [40] train-rmse:2.150645+0.032506 test-rmse:2.587223+0.312432
## [41] train-rmse:2.106068+0.030921 test-rmse:2.549829+0.296527
## [42] train-rmse:2.064198+0.028942 test-rmse:2.519928+0.293085
## [43] train-rmse:2.024535+0.028484 test-rmse:2.480527+0.293350
## [44] train-rmse:1.986333+0.027449 test-rmse:2.459694+0.286656
## [45] train-rmse:1.947686+0.027721 test-rmse:2.438860+0.290949
## [46] train-rmse:1.912729+0.026469 test-rmse:2.409987+0.302643
## [47] train-rmse:1.878424+0.024960 test-rmse:2.377100+0.296227
## [48] train-rmse:1.845839+0.024371 test-rmse:2.347507+0.292668
## [49] train-rmse:1.812299+0.025089 test-rmse:2.320634+0.284265
## [50] train-rmse:1.779229+0.028222 test-rmse:2.285463+0.284274
## [51] train-rmse:1.749561+0.027727 test-rmse:2.263956+0.288556
## [52] train-rmse:1.720344+0.027383 test-rmse:2.238049+0.288538
## [53] train-rmse:1.691353+0.025907 test-rmse:2.216105+0.273568
## [54] train-rmse:1.663460+0.025890 test-rmse:2.193439+0.280663
## [55] train-rmse:1.636001+0.025302 test-rmse:2.173336+0.282040
## [56] train-rmse:1.608830+0.024465 test-rmse:2.154349+0.275687
## [57] train-rmse:1.581359+0.027106 test-rmse:2.138143+0.275296
## [58] train-rmse:1.554050+0.026750 test-rmse:2.112117+0.272556
## [59] train-rmse:1.529599+0.026475 test-rmse:2.090383+0.276229
## [60] train-rmse:1.505450+0.025829 test-rmse:2.071432+0.262245
## [61] train-rmse:1.482357+0.024824 test-rmse:2.047227+0.267703
## [62] train-rmse:1.459393+0.024398 test-rmse:2.034258+0.262260
## [63] train-rmse:1.437467+0.024017 test-rmse:2.014616+0.270319
## [64] train-rmse:1.415256+0.024607 test-rmse:1.990780+0.259347
## [65] train-rmse:1.391793+0.023978 test-rmse:1.970963+0.256790
## [66] train-rmse:1.370431+0.023283 test-rmse:1.953395+0.256259
## [67] train-rmse:1.347862+0.020861 test-rmse:1.935076+0.254926
## [68] train-rmse:1.328147+0.020923 test-rmse:1.914576+0.253732
## [69] train-rmse:1.307662+0.019679 test-rmse:1.893852+0.260944
## [70] train-rmse:1.288998+0.019416 test-rmse:1.876384+0.261539
## [71] train-rmse:1.269592+0.018498 test-rmse:1.862373+0.262711
## [72] train-rmse:1.250279+0.018222 test-rmse:1.846284+0.259624
## [73] train-rmse:1.232810+0.017880 test-rmse:1.834468+0.256254
## [74] train-rmse:1.214774+0.017559 test-rmse:1.818080+0.256584
## [75] train-rmse:1.196938+0.018851 test-rmse:1.805853+0.258726
## [76] train-rmse:1.179790+0.018913 test-rmse:1.792201+0.265422
## [77] train-rmse:1.163653+0.018936 test-rmse:1.777992+0.265211
## [78] train-rmse:1.146184+0.016765 test-rmse:1.763878+0.261840
## [79] train-rmse:1.130176+0.016303 test-rmse:1.750570+0.261315
## [80] train-rmse:1.115217+0.015816 test-rmse:1.738939+0.264841
## [81] train-rmse:1.100148+0.015600 test-rmse:1.725906+0.268309
## [82] train-rmse:1.085251+0.015317 test-rmse:1.714902+0.267995
## [83] train-rmse:1.068581+0.014679 test-rmse:1.706341+0.273003
## [84] train-rmse:1.054380+0.013997 test-rmse:1.683373+0.266174
## [85] train-rmse:1.039009+0.013893 test-rmse:1.668364+0.262364
## [86] train-rmse:1.024742+0.014746 test-rmse:1.661008+0.261985
## [87] train-rmse:1.011574+0.014834 test-rmse:1.651693+0.264016
## [88] train-rmse:0.998252+0.015316 test-rmse:1.644026+0.264740
## [89] train-rmse:0.984896+0.015613 test-rmse:1.630992+0.261197
## [90] train-rmse:0.972653+0.015677 test-rmse:1.622384+0.263343
## [91] train-rmse:0.958772+0.016424 test-rmse:1.611097+0.268501
## [92] train-rmse:0.946584+0.016540 test-rmse:1.599233+0.269609
## [93] train-rmse:0.932689+0.015761 test-rmse:1.589686+0.266245
## [94] train-rmse:0.921092+0.015772 test-rmse:1.583766+0.265506
## [95] train-rmse:0.909216+0.015937 test-rmse:1.574744+0.267680
## [96] train-rmse:0.897861+0.016255 test-rmse:1.567768+0.270301
## [97] train-rmse:0.886795+0.015491 test-rmse:1.556839+0.273253
## [98] train-rmse:0.875948+0.015041 test-rmse:1.549013+0.276591
## [99] train-rmse:0.864568+0.015652 test-rmse:1.539146+0.269037
## [100] train-rmse:0.853355+0.015506 test-rmse:1.528483+0.269776
## [101] train-rmse:0.842992+0.014876 test-rmse:1.522524+0.269029
## [102] train-rmse:0.830809+0.016498 test-rmse:1.519140+0.270010
## [103] train-rmse:0.821155+0.016505 test-rmse:1.513130+0.266597
## [104] train-rmse:0.811773+0.016262 test-rmse:1.501469+0.267506
## [105] train-rmse:0.801953+0.016654 test-rmse:1.495998+0.268101
## [106] train-rmse:0.792624+0.017077 test-rmse:1.491286+0.269688
## [107] train-rmse:0.783718+0.017014 test-rmse:1.482112+0.270432
## [108] train-rmse:0.774914+0.017297 test-rmse:1.473903+0.270721
## [109] train-rmse:0.764901+0.016986 test-rmse:1.464596+0.267943
## [110] train-rmse:0.755818+0.015777 test-rmse:1.460493+0.269259
## [111] train-rmse:0.747890+0.015717 test-rmse:1.454504+0.266991
## [112] train-rmse:0.739975+0.015640 test-rmse:1.449879+0.267377
## [113] train-rmse:0.731911+0.015889 test-rmse:1.442768+0.269984
## [114] train-rmse:0.724299+0.016008 test-rmse:1.438345+0.268932
## [115] train-rmse:0.716586+0.016088 test-rmse:1.429546+0.271883
## [116] train-rmse:0.708839+0.015644 test-rmse:1.424905+0.270161
## [117] train-rmse:0.701476+0.015589 test-rmse:1.419480+0.269921
## [118] train-rmse:0.694288+0.015629 test-rmse:1.412456+0.270972
## [119] train-rmse:0.687622+0.015595 test-rmse:1.407645+0.273177
## [120] train-rmse:0.679858+0.016026 test-rmse:1.401894+0.275284
## [121] train-rmse:0.672606+0.015763 test-rmse:1.394797+0.275272
## [122] train-rmse:0.664146+0.016778 test-rmse:1.391843+0.274910
## [123] train-rmse:0.657619+0.016149 test-rmse:1.387747+0.276524
## [124] train-rmse:0.651457+0.016002 test-rmse:1.384292+0.276351
## [125] train-rmse:0.644357+0.016466 test-rmse:1.378274+0.278213
## [126] train-rmse:0.636364+0.016568 test-rmse:1.371997+0.277569
## [127] train-rmse:0.629927+0.016418 test-rmse:1.367485+0.279947
## [128] train-rmse:0.623815+0.016808 test-rmse:1.364628+0.278949
## [129] train-rmse:0.618220+0.016805 test-rmse:1.360882+0.280154
## [130] train-rmse:0.612179+0.016112 test-rmse:1.356492+0.279115
## [131] train-rmse:0.606365+0.016235 test-rmse:1.353833+0.279989
## [132] train-rmse:0.600351+0.016040 test-rmse:1.346841+0.272014
## [133] train-rmse:0.595248+0.016191 test-rmse:1.342993+0.271889
## [134] train-rmse:0.589303+0.016020 test-rmse:1.340071+0.272426
## [135] train-rmse:0.584100+0.016221 test-rmse:1.337973+0.272279
## [136] train-rmse:0.578699+0.015864 test-rmse:1.331556+0.273028
## [137] train-rmse:0.573144+0.014835 test-rmse:1.328523+0.275335
## [138] train-rmse:0.567748+0.015344 test-rmse:1.326631+0.276480
## [139] train-rmse:0.562772+0.015276 test-rmse:1.323712+0.278006
## [140] train-rmse:0.557863+0.015456 test-rmse:1.321486+0.278627
## [141] train-rmse:0.552772+0.015721 test-rmse:1.315574+0.272382
## [142] train-rmse:0.548107+0.015490 test-rmse:1.313793+0.273604
## [143] train-rmse:0.543152+0.015718 test-rmse:1.310352+0.273991
## [144] train-rmse:0.539077+0.015662 test-rmse:1.306903+0.274006
## [145] train-rmse:0.534654+0.015664 test-rmse:1.302722+0.274615
## [146] train-rmse:0.528283+0.015487 test-rmse:1.300390+0.276473
## [147] train-rmse:0.523858+0.015671 test-rmse:1.299713+0.276294
## [148] train-rmse:0.519947+0.015486 test-rmse:1.298354+0.277406
## [149] train-rmse:0.515725+0.015351 test-rmse:1.292532+0.271210
## [150] train-rmse:0.510656+0.015743 test-rmse:1.290659+0.273362
## [151] train-rmse:0.506426+0.015952 test-rmse:1.288984+0.274531
## [152] train-rmse:0.502114+0.016102 test-rmse:1.286388+0.275149
## [153] train-rmse:0.497883+0.016022 test-rmse:1.283843+0.274964
## [154] train-rmse:0.493912+0.016027 test-rmse:1.280659+0.277101
## [155] train-rmse:0.490120+0.014779 test-rmse:1.278585+0.276802
## [156] train-rmse:0.486350+0.014523 test-rmse:1.277555+0.278003
## [157] train-rmse:0.482788+0.014367 test-rmse:1.275332+0.278027
## [158] train-rmse:0.478862+0.014720 test-rmse:1.271322+0.272351
## [159] train-rmse:0.475106+0.014155 test-rmse:1.268986+0.272087
## [160] train-rmse:0.471892+0.013711 test-rmse:1.265996+0.272223
## [161] train-rmse:0.468227+0.013962 test-rmse:1.264213+0.273691
## [162] train-rmse:0.463745+0.014937 test-rmse:1.262433+0.274898
## [163] train-rmse:0.460537+0.015021 test-rmse:1.259218+0.276683
## [164] train-rmse:0.457565+0.014957 test-rmse:1.257328+0.275659
## [165] train-rmse:0.454264+0.014805 test-rmse:1.255235+0.276042
## [166] train-rmse:0.450798+0.015067 test-rmse:1.253726+0.277045
## [167] train-rmse:0.447794+0.015016 test-rmse:1.252631+0.277988
## [168] train-rmse:0.444689+0.014930 test-rmse:1.250185+0.278250
## [169] train-rmse:0.440817+0.015482 test-rmse:1.249899+0.278976
## [170] train-rmse:0.438256+0.015635 test-rmse:1.248015+0.278332
## [171] train-rmse:0.434838+0.014653 test-rmse:1.245552+0.279851
## [172] train-rmse:0.431817+0.014465 test-rmse:1.244626+0.280189
## [173] train-rmse:0.428291+0.014147 test-rmse:1.242456+0.280691
## [174] train-rmse:0.425104+0.014642 test-rmse:1.241831+0.279337
## [175] train-rmse:0.422204+0.014479 test-rmse:1.240859+0.279719
## [176] train-rmse:0.419027+0.014329 test-rmse:1.240429+0.278724
## [177] train-rmse:0.416269+0.014535 test-rmse:1.237277+0.279462
## [178] train-rmse:0.413217+0.014712 test-rmse:1.234739+0.280983
## [179] train-rmse:0.410377+0.014778 test-rmse:1.232673+0.283000
## [180] train-rmse:0.407944+0.014703 test-rmse:1.231678+0.282623
## [181] train-rmse:0.405459+0.014703 test-rmse:1.230978+0.283577
## [182] train-rmse:0.402729+0.014703 test-rmse:1.230628+0.283966
## [183] train-rmse:0.400639+0.014694 test-rmse:1.229471+0.283493
## [184] train-rmse:0.397909+0.014734 test-rmse:1.228474+0.283488
## [185] train-rmse:0.395552+0.014642 test-rmse:1.227798+0.283118
## [186] train-rmse:0.393466+0.014645 test-rmse:1.227340+0.283465
## [187] train-rmse:0.390638+0.014359 test-rmse:1.225479+0.284681
## [188] train-rmse:0.388311+0.014539 test-rmse:1.223794+0.285132
## [189] train-rmse:0.386160+0.014108 test-rmse:1.223101+0.285608
## [190] train-rmse:0.383929+0.013930 test-rmse:1.222877+0.285297
## [191] train-rmse:0.381195+0.014114 test-rmse:1.221776+0.286620
## [192] train-rmse:0.379136+0.013900 test-rmse:1.221749+0.286275
## [193] train-rmse:0.377143+0.013933 test-rmse:1.218815+0.287368
## [194] train-rmse:0.375039+0.013955 test-rmse:1.218618+0.287148
## [195] train-rmse:0.372939+0.013782 test-rmse:1.218061+0.287315
## [196] train-rmse:0.370611+0.013311 test-rmse:1.217377+0.287668
## [197] train-rmse:0.368528+0.013467 test-rmse:1.215787+0.287810
## [198] train-rmse:0.366471+0.013523 test-rmse:1.214154+0.288312
## [199] train-rmse:0.364566+0.013472 test-rmse:1.213675+0.288785
## [200] train-rmse:0.362747+0.013445 test-rmse:1.213895+0.288489
## [201] train-rmse:0.360976+0.013386 test-rmse:1.213697+0.288409
## [202] train-rmse:0.359228+0.013203 test-rmse:1.212272+0.288741
## [203] train-rmse:0.357037+0.013709 test-rmse:1.210065+0.287402
## [204] train-rmse:0.355271+0.013272 test-rmse:1.208359+0.288417
## [205] train-rmse:0.353595+0.013261 test-rmse:1.207977+0.288338
## [206] train-rmse:0.351465+0.013363 test-rmse:1.207604+0.288333
## [207] train-rmse:0.349427+0.012790 test-rmse:1.206403+0.289000
## [208] train-rmse:0.346330+0.012057 test-rmse:1.206148+0.289257
## [209] train-rmse:0.344333+0.011964 test-rmse:1.205916+0.287946
## [210] train-rmse:0.342256+0.012013 test-rmse:1.205000+0.288792
## [211] train-rmse:0.340127+0.012192 test-rmse:1.204656+0.288587
## [212] train-rmse:0.337951+0.012489 test-rmse:1.202990+0.288117
## [213] train-rmse:0.336377+0.012661 test-rmse:1.202163+0.288039
## [214] train-rmse:0.334989+0.012571 test-rmse:1.201415+0.287934
## [215] train-rmse:0.333171+0.012631 test-rmse:1.200804+0.289065
## [216] train-rmse:0.331639+0.012722 test-rmse:1.200640+0.289104
## [217] train-rmse:0.329798+0.012854 test-rmse:1.200654+0.289157
## [218] train-rmse:0.328080+0.012838 test-rmse:1.200111+0.289288
## [219] train-rmse:0.326343+0.013124 test-rmse:1.198624+0.289205
## [220] train-rmse:0.324649+0.013132 test-rmse:1.198099+0.289772
## [221] train-rmse:0.322576+0.012028 test-rmse:1.197393+0.290853
## [222] train-rmse:0.321073+0.012062 test-rmse:1.196811+0.290940
## [223] train-rmse:0.319568+0.012072 test-rmse:1.195410+0.291467
## [224] train-rmse:0.317804+0.012462 test-rmse:1.195269+0.292307
## [225] train-rmse:0.316461+0.012397 test-rmse:1.194239+0.292397
## [226] train-rmse:0.314881+0.012233 test-rmse:1.193889+0.293105
## [227] train-rmse:0.313104+0.012170 test-rmse:1.191992+0.294330
## [228] train-rmse:0.311911+0.011995 test-rmse:1.191385+0.294419
## [229] train-rmse:0.309726+0.011824 test-rmse:1.190526+0.293712
## [230] train-rmse:0.307549+0.011575 test-rmse:1.190593+0.293334
## [231] train-rmse:0.306247+0.011361 test-rmse:1.190290+0.293082
## [232] train-rmse:0.304797+0.011262 test-rmse:1.189952+0.293005
## [233] train-rmse:0.303208+0.011118 test-rmse:1.190279+0.293276
## [234] train-rmse:0.301654+0.011742 test-rmse:1.189653+0.293569
## [235] train-rmse:0.300550+0.011751 test-rmse:1.189112+0.293633
## [236] train-rmse:0.299425+0.011648 test-rmse:1.188636+0.293571
## [237] train-rmse:0.297178+0.011872 test-rmse:1.187326+0.294054
## [238] train-rmse:0.295385+0.012013 test-rmse:1.185980+0.293788
## [239] train-rmse:0.294227+0.012239 test-rmse:1.185547+0.293907
## [240] train-rmse:0.292883+0.012068 test-rmse:1.184910+0.294020
## [241] train-rmse:0.291298+0.012045 test-rmse:1.185403+0.294197
## [242] train-rmse:0.289575+0.011810 test-rmse:1.185755+0.293877
## [243] train-rmse:0.287988+0.011996 test-rmse:1.185255+0.294037
## [244] train-rmse:0.286887+0.011897 test-rmse:1.183686+0.294377
## [245] train-rmse:0.285792+0.011763 test-rmse:1.183239+0.294643
## [246] train-rmse:0.283793+0.010912 test-rmse:1.182686+0.294909
## [247] train-rmse:0.282760+0.010818 test-rmse:1.182247+0.294848
## [248] train-rmse:0.281689+0.010702 test-rmse:1.181328+0.294656
## [249] train-rmse:0.280352+0.010577 test-rmse:1.180647+0.294890
## [250] train-rmse:0.278674+0.010387 test-rmse:1.180953+0.294725
## [251] train-rmse:0.277383+0.010478 test-rmse:1.180371+0.294674
## [252] train-rmse:0.275982+0.010796 test-rmse:1.180413+0.294533
## [253] train-rmse:0.274199+0.010821 test-rmse:1.179484+0.295073
## [254] train-rmse:0.272857+0.010974 test-rmse:1.178528+0.294288
## [255] train-rmse:0.271570+0.011095 test-rmse:1.178127+0.294414
## [256] train-rmse:0.270403+0.010943 test-rmse:1.178128+0.294523
## [257] train-rmse:0.268492+0.011302 test-rmse:1.177900+0.294488
## [258] train-rmse:0.267245+0.011263 test-rmse:1.177985+0.294634
## [259] train-rmse:0.266220+0.011081 test-rmse:1.177013+0.294746
## [260] train-rmse:0.265057+0.011442 test-rmse:1.176769+0.294743
## [261] train-rmse:0.263692+0.011389 test-rmse:1.175435+0.295030
## [262] train-rmse:0.262436+0.011504 test-rmse:1.174605+0.294637
## [263] train-rmse:0.261513+0.011479 test-rmse:1.174153+0.295256
## [264] train-rmse:0.260316+0.011457 test-rmse:1.173990+0.295505
## [265] train-rmse:0.259142+0.011072 test-rmse:1.172877+0.294941
## [266] train-rmse:0.257646+0.011083 test-rmse:1.172639+0.294485
## [267] train-rmse:0.256673+0.011331 test-rmse:1.172518+0.294467
## [268] train-rmse:0.255461+0.011246 test-rmse:1.172718+0.294937
## [269] train-rmse:0.254427+0.011480 test-rmse:1.172852+0.294825
## [270] train-rmse:0.253373+0.011629 test-rmse:1.171960+0.295490
## [271] train-rmse:0.252385+0.011649 test-rmse:1.171604+0.295691
## [272] train-rmse:0.251233+0.011888 test-rmse:1.171232+0.296002
## [273] train-rmse:0.249936+0.011909 test-rmse:1.170967+0.295324
## [274] train-rmse:0.248588+0.011711 test-rmse:1.170316+0.295908
## [275] train-rmse:0.247609+0.011592 test-rmse:1.169633+0.295727
## [276] train-rmse:0.246723+0.011739 test-rmse:1.169591+0.295686
## [277] train-rmse:0.245612+0.011601 test-rmse:1.169248+0.296079
## [278] train-rmse:0.244734+0.011392 test-rmse:1.169340+0.295814
## [279] train-rmse:0.243210+0.011437 test-rmse:1.169177+0.295561
## [280] train-rmse:0.242068+0.011430 test-rmse:1.169063+0.296201
## [281] train-rmse:0.241090+0.011514 test-rmse:1.169050+0.295801
## [282] train-rmse:0.240308+0.011697 test-rmse:1.168927+0.295875
## [283] train-rmse:0.239155+0.011779 test-rmse:1.168927+0.296571
## [284] train-rmse:0.238017+0.011448 test-rmse:1.167814+0.296025
## [285] train-rmse:0.236866+0.011440 test-rmse:1.167585+0.296227
## [286] train-rmse:0.235919+0.011191 test-rmse:1.167567+0.295789
## [287] train-rmse:0.235026+0.011247 test-rmse:1.166772+0.296495
## [288] train-rmse:0.233846+0.011248 test-rmse:1.167069+0.296285
## [289] train-rmse:0.233163+0.011190 test-rmse:1.166811+0.296229
## [290] train-rmse:0.232200+0.011299 test-rmse:1.166388+0.296104
## [291] train-rmse:0.231418+0.011190 test-rmse:1.165972+0.296268
## [292] train-rmse:0.230305+0.011114 test-rmse:1.166019+0.296399
## [293] train-rmse:0.228987+0.011372 test-rmse:1.165367+0.296918
## [294] train-rmse:0.227933+0.011226 test-rmse:1.164877+0.296783
## [295] train-rmse:0.226991+0.011211 test-rmse:1.164467+0.296943
## [296] train-rmse:0.225764+0.011803 test-rmse:1.164949+0.296697
## [297] train-rmse:0.225060+0.011788 test-rmse:1.164650+0.296519
## [298] train-rmse:0.224334+0.011847 test-rmse:1.164336+0.296730
## [299] train-rmse:0.223749+0.011744 test-rmse:1.163957+0.296737
## [300] train-rmse:0.222976+0.011804 test-rmse:1.163793+0.296560
## [301] train-rmse:0.222432+0.011794 test-rmse:1.163696+0.296047
## [302] train-rmse:0.221380+0.011694 test-rmse:1.163404+0.296185
## [303] train-rmse:0.220340+0.011512 test-rmse:1.163264+0.296413
## [304] train-rmse:0.219627+0.011584 test-rmse:1.163181+0.296242
## [305] train-rmse:0.218804+0.011556 test-rmse:1.162546+0.296545
## [306] train-rmse:0.217884+0.011552 test-rmse:1.162224+0.296925
## [307] train-rmse:0.216996+0.011563 test-rmse:1.161790+0.297239
## [308] train-rmse:0.216384+0.011501 test-rmse:1.161546+0.297335
## [309] train-rmse:0.215530+0.011551 test-rmse:1.161737+0.297541
## [310] train-rmse:0.214648+0.011474 test-rmse:1.161406+0.297701
## [311] train-rmse:0.213739+0.011208 test-rmse:1.161610+0.297734
## [312] train-rmse:0.212923+0.011082 test-rmse:1.160631+0.297524
## [313] train-rmse:0.212202+0.011088 test-rmse:1.160660+0.297370
## [314] train-rmse:0.211284+0.011209 test-rmse:1.159198+0.298133
## [315] train-rmse:0.210427+0.011490 test-rmse:1.159029+0.298306
## [316] train-rmse:0.209754+0.011557 test-rmse:1.158946+0.298157
## [317] train-rmse:0.208994+0.011421 test-rmse:1.158505+0.298202
## [318] train-rmse:0.208415+0.011418 test-rmse:1.158132+0.298197
## [319] train-rmse:0.207640+0.011334 test-rmse:1.158101+0.298355
## [320] train-rmse:0.206824+0.010995 test-rmse:1.157808+0.298490
## [321] train-rmse:0.206277+0.010911 test-rmse:1.157618+0.298741
## [322] train-rmse:0.205369+0.011173 test-rmse:1.157359+0.298678
## [323] train-rmse:0.204655+0.011254 test-rmse:1.156790+0.298026
## [324] train-rmse:0.203955+0.011287 test-rmse:1.156838+0.298136
## [325] train-rmse:0.203171+0.011175 test-rmse:1.156902+0.298173
## [326] train-rmse:0.202264+0.011289 test-rmse:1.156346+0.298036
## [327] train-rmse:0.201637+0.011580 test-rmse:1.156224+0.298300
## [328] train-rmse:0.201086+0.011672 test-rmse:1.156071+0.298298
## [329] train-rmse:0.200299+0.011444 test-rmse:1.156172+0.298418
## [330] train-rmse:0.199599+0.011520 test-rmse:1.155868+0.298548
## [331] train-rmse:0.198922+0.011367 test-rmse:1.155472+0.298367
## [332] train-rmse:0.198194+0.011396 test-rmse:1.155332+0.298312
## [333] train-rmse:0.197330+0.011170 test-rmse:1.154741+0.298482
## [334] train-rmse:0.196698+0.011174 test-rmse:1.154768+0.298618
## [335] train-rmse:0.195982+0.011029 test-rmse:1.154750+0.298855
## [336] train-rmse:0.195129+0.010927 test-rmse:1.154003+0.298884
## [337] train-rmse:0.194563+0.010971 test-rmse:1.153705+0.298973
## [338] train-rmse:0.193990+0.010932 test-rmse:1.153450+0.298817
## [339] train-rmse:0.193239+0.010728 test-rmse:1.153469+0.298861
## [340] train-rmse:0.192651+0.010651 test-rmse:1.153414+0.299119
## [341] train-rmse:0.191591+0.010354 test-rmse:1.153344+0.299085
## [342] train-rmse:0.190772+0.010176 test-rmse:1.152360+0.297235
## [343] train-rmse:0.190075+0.010155 test-rmse:1.152063+0.297500
## [344] train-rmse:0.188946+0.010152 test-rmse:1.152297+0.297893
## [345] train-rmse:0.187717+0.009491 test-rmse:1.152108+0.297993
## [346] train-rmse:0.187087+0.009367 test-rmse:1.151909+0.298155
## [347] train-rmse:0.186405+0.009292 test-rmse:1.151340+0.298591
## [348] train-rmse:0.185636+0.009298 test-rmse:1.151506+0.298388
## [349] train-rmse:0.184930+0.009173 test-rmse:1.151722+0.298477
## [350] train-rmse:0.184178+0.009303 test-rmse:1.151665+0.298431
## [351] train-rmse:0.183233+0.009774 test-rmse:1.151525+0.299496
## [352] train-rmse:0.182611+0.009807 test-rmse:1.151652+0.299522
## [353] train-rmse:0.181664+0.010266 test-rmse:1.151436+0.298758
## Stopping. Best iteration:
## [347] train-rmse:0.186405+0.009292 test-rmse:1.151340+0.298591
##
## 17
## [1] train-rmse:92.628808+0.103203 test-rmse:92.629086+0.486485
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.759407+0.092047 test-rmse:79.759461+0.505884
## [3] train-rmse:68.693979+0.083013 test-rmse:68.693753+0.524472
## [4] train-rmse:59.182194+0.075874 test-rmse:59.181623+0.542625
## [5] train-rmse:51.008879+0.070487 test-rmse:51.007880+0.560691
## [6] train-rmse:43.989113+0.066746 test-rmse:43.987572+0.578998
## [7] train-rmse:37.964006+0.064561 test-rmse:37.961789+0.597840
## [8] train-rmse:32.792771+0.062486 test-rmse:32.812824+0.600191
## [9] train-rmse:28.347628+0.056546 test-rmse:28.405012+0.603047
## [10] train-rmse:24.526212+0.047562 test-rmse:24.604208+0.597355
## [11] train-rmse:21.241343+0.040405 test-rmse:21.326121+0.583443
## [12] train-rmse:18.417935+0.034250 test-rmse:18.525899+0.581104
## [13] train-rmse:15.987990+0.031400 test-rmse:16.097872+0.562048
## [14] train-rmse:13.899948+0.029311 test-rmse:14.004335+0.548106
## [15] train-rmse:12.106548+0.026770 test-rmse:12.200467+0.539913
## [16] train-rmse:10.566577+0.025728 test-rmse:10.654600+0.525526
## [17] train-rmse:9.245970+0.024571 test-rmse:9.318455+0.530479
## [18] train-rmse:8.113030+0.022106 test-rmse:8.220829+0.549446
## [19] train-rmse:7.145043+0.020269 test-rmse:7.251480+0.552039
## [20] train-rmse:6.318365+0.021331 test-rmse:6.439033+0.545423
## [21] train-rmse:5.615859+0.021277 test-rmse:5.741820+0.516183
## [22] train-rmse:5.014909+0.025425 test-rmse:5.164890+0.528896
## [23] train-rmse:4.499328+0.026528 test-rmse:4.663292+0.498491
## [24] train-rmse:4.067427+0.031250 test-rmse:4.260924+0.480041
## [25] train-rmse:3.700867+0.033557 test-rmse:3.929454+0.448814
## [26] train-rmse:3.391949+0.037551 test-rmse:3.651677+0.436393
## [27] train-rmse:3.130254+0.038607 test-rmse:3.416798+0.420749
## [28] train-rmse:2.910513+0.042151 test-rmse:3.216516+0.394886
## [29] train-rmse:2.722758+0.040492 test-rmse:3.057391+0.385395
## [30] train-rmse:2.562062+0.040565 test-rmse:2.906973+0.364539
## [31] train-rmse:2.425791+0.039594 test-rmse:2.793578+0.352945
## [32] train-rmse:2.309401+0.040177 test-rmse:2.697833+0.343470
## [33] train-rmse:2.209442+0.040588 test-rmse:2.607066+0.336313
## [34] train-rmse:2.118800+0.036569 test-rmse:2.543287+0.315626
## [35] train-rmse:2.036311+0.035313 test-rmse:2.478330+0.306889
## [36] train-rmse:1.966546+0.035187 test-rmse:2.425188+0.302620
## [37] train-rmse:1.901708+0.034231 test-rmse:2.379828+0.299966
## [38] train-rmse:1.841937+0.038279 test-rmse:2.329353+0.297985
## [39] train-rmse:1.788312+0.040255 test-rmse:2.278325+0.298999
## [40] train-rmse:1.734776+0.038901 test-rmse:2.237533+0.288436
## [41] train-rmse:1.688591+0.038298 test-rmse:2.203624+0.277849
## [42] train-rmse:1.642625+0.041805 test-rmse:2.170823+0.281256
## [43] train-rmse:1.602968+0.041083 test-rmse:2.135278+0.279941
## [44] train-rmse:1.559047+0.037155 test-rmse:2.101594+0.273562
## [45] train-rmse:1.521753+0.035633 test-rmse:2.075482+0.262629
## [46] train-rmse:1.487321+0.034085 test-rmse:2.044639+0.267221
## [47] train-rmse:1.448860+0.033730 test-rmse:2.010192+0.270319
## [48] train-rmse:1.414449+0.030986 test-rmse:1.983332+0.262608
## [49] train-rmse:1.383427+0.030459 test-rmse:1.959689+0.258600
## [50] train-rmse:1.353096+0.030606 test-rmse:1.937165+0.252270
## [51] train-rmse:1.317339+0.038883 test-rmse:1.916070+0.255689
## [52] train-rmse:1.289024+0.038041 test-rmse:1.893005+0.264215
## [53] train-rmse:1.260365+0.036573 test-rmse:1.866290+0.262819
## [54] train-rmse:1.233526+0.035413 test-rmse:1.849035+0.257198
## [55] train-rmse:1.207988+0.034933 test-rmse:1.829824+0.253823
## [56] train-rmse:1.178216+0.035792 test-rmse:1.814126+0.256777
## [57] train-rmse:1.154237+0.036667 test-rmse:1.791999+0.259621
## [58] train-rmse:1.130658+0.035165 test-rmse:1.770380+0.267604
## [59] train-rmse:1.108143+0.034504 test-rmse:1.751134+0.266851
## [60] train-rmse:1.084954+0.034082 test-rmse:1.737515+0.267527
## [61] train-rmse:1.062460+0.031703 test-rmse:1.719709+0.266688
## [62] train-rmse:1.037890+0.031664 test-rmse:1.694823+0.270496
## [63] train-rmse:1.017582+0.031477 test-rmse:1.682751+0.271250
## [64] train-rmse:0.996420+0.030182 test-rmse:1.666283+0.272670
## [65] train-rmse:0.978059+0.030362 test-rmse:1.648989+0.271968
## [66] train-rmse:0.958396+0.029011 test-rmse:1.630865+0.269798
## [67] train-rmse:0.940411+0.028233 test-rmse:1.620585+0.270448
## [68] train-rmse:0.923131+0.027486 test-rmse:1.606645+0.276087
## [69] train-rmse:0.903489+0.029087 test-rmse:1.590664+0.281437
## [70] train-rmse:0.887215+0.028122 test-rmse:1.575050+0.280705
## [71] train-rmse:0.868708+0.026310 test-rmse:1.562451+0.278872
## [72] train-rmse:0.852425+0.025542 test-rmse:1.550088+0.283032
## [73] train-rmse:0.837043+0.024506 test-rmse:1.540096+0.282773
## [74] train-rmse:0.821659+0.025669 test-rmse:1.523682+0.276027
## [75] train-rmse:0.807449+0.025266 test-rmse:1.513999+0.275437
## [76] train-rmse:0.791779+0.025180 test-rmse:1.500579+0.279358
## [77] train-rmse:0.778611+0.024653 test-rmse:1.489202+0.278272
## [78] train-rmse:0.761959+0.024294 test-rmse:1.482267+0.279012
## [79] train-rmse:0.748409+0.023253 test-rmse:1.467942+0.273725
## [80] train-rmse:0.734879+0.020591 test-rmse:1.457676+0.271737
## [81] train-rmse:0.722100+0.021584 test-rmse:1.447751+0.276969
## [82] train-rmse:0.710063+0.021153 test-rmse:1.438219+0.276593
## [83] train-rmse:0.697233+0.020777 test-rmse:1.427705+0.265874
## [84] train-rmse:0.684813+0.020926 test-rmse:1.416711+0.267658
## [85] train-rmse:0.673918+0.020404 test-rmse:1.407837+0.268698
## [86] train-rmse:0.663161+0.019269 test-rmse:1.402704+0.271555
## [87] train-rmse:0.653186+0.019044 test-rmse:1.393690+0.269983
## [88] train-rmse:0.643568+0.018903 test-rmse:1.381817+0.263402
## [89] train-rmse:0.633112+0.018083 test-rmse:1.372095+0.265016
## [90] train-rmse:0.623452+0.017934 test-rmse:1.367043+0.267144
## [91] train-rmse:0.613031+0.017213 test-rmse:1.362792+0.266841
## [92] train-rmse:0.602994+0.016844 test-rmse:1.353080+0.267927
## [93] train-rmse:0.592508+0.016678 test-rmse:1.348744+0.270875
## [94] train-rmse:0.582081+0.018307 test-rmse:1.338098+0.266353
## [95] train-rmse:0.574257+0.017773 test-rmse:1.333723+0.267411
## [96] train-rmse:0.566133+0.017163 test-rmse:1.330152+0.268328
## [97] train-rmse:0.558327+0.016882 test-rmse:1.320019+0.269692
## [98] train-rmse:0.550934+0.016762 test-rmse:1.313082+0.269742
## [99] train-rmse:0.541672+0.017810 test-rmse:1.304445+0.264394
## [100] train-rmse:0.533891+0.018127 test-rmse:1.300872+0.266476
## [101] train-rmse:0.526544+0.017716 test-rmse:1.299776+0.267357
## [102] train-rmse:0.520348+0.017325 test-rmse:1.293355+0.267296
## [103] train-rmse:0.513372+0.016483 test-rmse:1.289422+0.267958
## [104] train-rmse:0.506756+0.015662 test-rmse:1.284730+0.269254
## [105] train-rmse:0.499646+0.015752 test-rmse:1.277779+0.263825
## [106] train-rmse:0.492435+0.016399 test-rmse:1.276007+0.265669
## [107] train-rmse:0.486179+0.016484 test-rmse:1.271881+0.266568
## [108] train-rmse:0.480394+0.016553 test-rmse:1.267856+0.266785
## [109] train-rmse:0.474442+0.015983 test-rmse:1.263800+0.265953
## [110] train-rmse:0.468563+0.016005 test-rmse:1.260984+0.267044
## [111] train-rmse:0.462390+0.015424 test-rmse:1.254544+0.261586
## [112] train-rmse:0.456913+0.015709 test-rmse:1.251015+0.261707
## [113] train-rmse:0.451272+0.015149 test-rmse:1.248885+0.261415
## [114] train-rmse:0.444765+0.014334 test-rmse:1.247636+0.263477
## [115] train-rmse:0.438974+0.014845 test-rmse:1.244646+0.264358
## [116] train-rmse:0.433821+0.014776 test-rmse:1.241339+0.264221
## [117] train-rmse:0.428737+0.014374 test-rmse:1.239087+0.266253
## [118] train-rmse:0.423516+0.015218 test-rmse:1.235305+0.266852
## [119] train-rmse:0.416671+0.013718 test-rmse:1.233069+0.265648
## [120] train-rmse:0.412105+0.013902 test-rmse:1.229125+0.267048
## [121] train-rmse:0.406561+0.013955 test-rmse:1.225133+0.268221
## [122] train-rmse:0.401692+0.013686 test-rmse:1.224086+0.269395
## [123] train-rmse:0.397694+0.013361 test-rmse:1.221452+0.270831
## [124] train-rmse:0.393265+0.012959 test-rmse:1.217379+0.270408
## [125] train-rmse:0.387838+0.012116 test-rmse:1.215333+0.271567
## [126] train-rmse:0.383695+0.011853 test-rmse:1.214627+0.272559
## [127] train-rmse:0.379248+0.012431 test-rmse:1.212463+0.272603
## [128] train-rmse:0.375110+0.012086 test-rmse:1.211008+0.274101
## [129] train-rmse:0.371120+0.011981 test-rmse:1.207551+0.274475
## [130] train-rmse:0.367182+0.011693 test-rmse:1.204633+0.273175
## [131] train-rmse:0.363239+0.011301 test-rmse:1.202594+0.273927
## [132] train-rmse:0.359121+0.011271 test-rmse:1.202374+0.273736
## [133] train-rmse:0.355527+0.010437 test-rmse:1.198164+0.274189
## [134] train-rmse:0.352289+0.010947 test-rmse:1.196325+0.275625
## [135] train-rmse:0.347912+0.010789 test-rmse:1.194410+0.276773
## [136] train-rmse:0.344764+0.010595 test-rmse:1.192574+0.276384
## [137] train-rmse:0.341362+0.010138 test-rmse:1.189847+0.276145
## [138] train-rmse:0.336883+0.010873 test-rmse:1.187482+0.277884
## [139] train-rmse:0.333329+0.011366 test-rmse:1.185471+0.277114
## [140] train-rmse:0.330036+0.010942 test-rmse:1.184508+0.277342
## [141] train-rmse:0.325984+0.010887 test-rmse:1.183494+0.277767
## [142] train-rmse:0.322710+0.011337 test-rmse:1.182082+0.278997
## [143] train-rmse:0.319608+0.011110 test-rmse:1.179817+0.281253
## [144] train-rmse:0.316888+0.010792 test-rmse:1.178281+0.279467
## [145] train-rmse:0.313192+0.010093 test-rmse:1.176682+0.280878
## [146] train-rmse:0.309278+0.009775 test-rmse:1.176469+0.281026
## [147] train-rmse:0.306551+0.010045 test-rmse:1.175410+0.281207
## [148] train-rmse:0.304339+0.009814 test-rmse:1.173479+0.281555
## [149] train-rmse:0.301371+0.008864 test-rmse:1.171551+0.281646
## [150] train-rmse:0.298574+0.008842 test-rmse:1.171273+0.282423
## [151] train-rmse:0.296068+0.008444 test-rmse:1.170372+0.282522
## [152] train-rmse:0.292720+0.008118 test-rmse:1.168951+0.281997
## [153] train-rmse:0.290101+0.008902 test-rmse:1.168230+0.282510
## [154] train-rmse:0.287426+0.009500 test-rmse:1.167416+0.282661
## [155] train-rmse:0.284388+0.009571 test-rmse:1.167718+0.282536
## [156] train-rmse:0.281553+0.009233 test-rmse:1.167290+0.282205
## [157] train-rmse:0.279282+0.009364 test-rmse:1.166861+0.282745
## [158] train-rmse:0.276083+0.008533 test-rmse:1.165079+0.283513
## [159] train-rmse:0.273576+0.008843 test-rmse:1.164047+0.284379
## [160] train-rmse:0.271377+0.009376 test-rmse:1.162106+0.286014
## [161] train-rmse:0.269043+0.009184 test-rmse:1.161680+0.285752
## [162] train-rmse:0.266886+0.009176 test-rmse:1.160610+0.285984
## [163] train-rmse:0.264300+0.008857 test-rmse:1.159834+0.286528
## [164] train-rmse:0.261921+0.008578 test-rmse:1.158590+0.285888
## [165] train-rmse:0.259764+0.008667 test-rmse:1.157670+0.286137
## [166] train-rmse:0.257828+0.008463 test-rmse:1.157267+0.287530
## [167] train-rmse:0.255811+0.008582 test-rmse:1.156533+0.287545
## [168] train-rmse:0.253875+0.008237 test-rmse:1.154967+0.287974
## [169] train-rmse:0.251436+0.007770 test-rmse:1.155254+0.288272
## [170] train-rmse:0.249821+0.007375 test-rmse:1.154128+0.288894
## [171] train-rmse:0.247829+0.007580 test-rmse:1.153206+0.290239
## [172] train-rmse:0.245988+0.007590 test-rmse:1.153483+0.290291
## [173] train-rmse:0.243595+0.007858 test-rmse:1.153138+0.291060
## [174] train-rmse:0.241963+0.007763 test-rmse:1.151828+0.288962
## [175] train-rmse:0.239438+0.007752 test-rmse:1.150950+0.288436
## [176] train-rmse:0.237645+0.007416 test-rmse:1.150244+0.288628
## [177] train-rmse:0.235460+0.006955 test-rmse:1.147786+0.290257
## [178] train-rmse:0.233348+0.007079 test-rmse:1.147561+0.290245
## [179] train-rmse:0.231460+0.007073 test-rmse:1.147153+0.291246
## [180] train-rmse:0.229905+0.007072 test-rmse:1.145390+0.290734
## [181] train-rmse:0.227486+0.006988 test-rmse:1.145769+0.290644
## [182] train-rmse:0.226011+0.006644 test-rmse:1.145194+0.290933
## [183] train-rmse:0.224316+0.006527 test-rmse:1.144818+0.290888
## [184] train-rmse:0.222755+0.006288 test-rmse:1.144312+0.291156
## [185] train-rmse:0.220503+0.006432 test-rmse:1.144231+0.291325
## [186] train-rmse:0.218761+0.006898 test-rmse:1.143020+0.292139
## [187] train-rmse:0.217371+0.007171 test-rmse:1.141643+0.293075
## [188] train-rmse:0.215149+0.006733 test-rmse:1.142130+0.293040
## [189] train-rmse:0.213284+0.006609 test-rmse:1.141833+0.292512
## [190] train-rmse:0.211499+0.006263 test-rmse:1.141158+0.293351
## [191] train-rmse:0.209932+0.006314 test-rmse:1.140993+0.293121
## [192] train-rmse:0.208570+0.006411 test-rmse:1.141059+0.293489
## [193] train-rmse:0.207415+0.006456 test-rmse:1.140372+0.293403
## [194] train-rmse:0.205865+0.006326 test-rmse:1.140277+0.293026
## [195] train-rmse:0.204400+0.006386 test-rmse:1.140264+0.293272
## [196] train-rmse:0.202724+0.006246 test-rmse:1.139909+0.293140
## [197] train-rmse:0.201105+0.005944 test-rmse:1.139442+0.293321
## [198] train-rmse:0.199142+0.006140 test-rmse:1.139028+0.293859
## [199] train-rmse:0.197528+0.006110 test-rmse:1.138527+0.294090
## [200] train-rmse:0.195812+0.006382 test-rmse:1.138187+0.294130
## [201] train-rmse:0.194048+0.006274 test-rmse:1.137896+0.294085
## [202] train-rmse:0.192419+0.006635 test-rmse:1.137136+0.294753
## [203] train-rmse:0.190809+0.006475 test-rmse:1.137112+0.294529
## [204] train-rmse:0.189549+0.006431 test-rmse:1.136739+0.294526
## [205] train-rmse:0.187568+0.006424 test-rmse:1.137066+0.294895
## [206] train-rmse:0.185722+0.005824 test-rmse:1.135545+0.295575
## [207] train-rmse:0.184701+0.005867 test-rmse:1.135207+0.295777
## [208] train-rmse:0.183136+0.005600 test-rmse:1.135072+0.295839
## [209] train-rmse:0.181326+0.005723 test-rmse:1.134190+0.295005
## [210] train-rmse:0.180067+0.005610 test-rmse:1.133905+0.295130
## [211] train-rmse:0.178682+0.005521 test-rmse:1.133809+0.295267
## [212] train-rmse:0.177009+0.005595 test-rmse:1.133517+0.295400
## [213] train-rmse:0.175695+0.005235 test-rmse:1.132965+0.296086
## [214] train-rmse:0.174790+0.005113 test-rmse:1.132335+0.296210
## [215] train-rmse:0.173140+0.005431 test-rmse:1.132237+0.295985
## [216] train-rmse:0.171794+0.005745 test-rmse:1.132344+0.296016
## [217] train-rmse:0.170351+0.006489 test-rmse:1.132257+0.295639
## [218] train-rmse:0.169126+0.006235 test-rmse:1.131955+0.295955
## [219] train-rmse:0.167518+0.006331 test-rmse:1.131444+0.295877
## [220] train-rmse:0.166347+0.006339 test-rmse:1.130354+0.296423
## [221] train-rmse:0.165258+0.006059 test-rmse:1.129892+0.296633
## [222] train-rmse:0.164309+0.006119 test-rmse:1.129478+0.296801
## [223] train-rmse:0.163155+0.006213 test-rmse:1.128846+0.296728
## [224] train-rmse:0.162163+0.006194 test-rmse:1.128320+0.297291
## [225] train-rmse:0.161143+0.006332 test-rmse:1.128012+0.297579
## [226] train-rmse:0.160247+0.006509 test-rmse:1.127888+0.297372
## [227] train-rmse:0.159271+0.006513 test-rmse:1.127276+0.297121
## [228] train-rmse:0.158310+0.006487 test-rmse:1.126894+0.297099
## [229] train-rmse:0.157291+0.006610 test-rmse:1.126507+0.297348
## [230] train-rmse:0.155486+0.006923 test-rmse:1.126668+0.297470
## [231] train-rmse:0.154235+0.006489 test-rmse:1.126544+0.297592
## [232] train-rmse:0.153277+0.006230 test-rmse:1.126338+0.297744
## [233] train-rmse:0.152542+0.006131 test-rmse:1.126133+0.297921
## [234] train-rmse:0.151388+0.006043 test-rmse:1.126118+0.297800
## [235] train-rmse:0.150333+0.006435 test-rmse:1.125554+0.298042
## [236] train-rmse:0.149266+0.006347 test-rmse:1.125252+0.297003
## [237] train-rmse:0.148043+0.006695 test-rmse:1.125537+0.296715
## [238] train-rmse:0.147169+0.006456 test-rmse:1.125381+0.297169
## [239] train-rmse:0.146158+0.006737 test-rmse:1.125389+0.297198
## [240] train-rmse:0.145471+0.006651 test-rmse:1.125307+0.297398
## [241] train-rmse:0.144772+0.006669 test-rmse:1.125112+0.297731
## [242] train-rmse:0.143702+0.006788 test-rmse:1.124612+0.297868
## [243] train-rmse:0.142360+0.006853 test-rmse:1.124406+0.298015
## [244] train-rmse:0.141308+0.006617 test-rmse:1.124246+0.298336
## [245] train-rmse:0.140603+0.006499 test-rmse:1.123794+0.298473
## [246] train-rmse:0.139604+0.006223 test-rmse:1.123622+0.298748
## [247] train-rmse:0.138934+0.006476 test-rmse:1.123598+0.298723
## [248] train-rmse:0.137895+0.006401 test-rmse:1.123548+0.298446
## [249] train-rmse:0.137048+0.006557 test-rmse:1.123601+0.298449
## [250] train-rmse:0.136034+0.007059 test-rmse:1.123513+0.298369
## [251] train-rmse:0.135355+0.006978 test-rmse:1.123695+0.298536
## [252] train-rmse:0.134356+0.006599 test-rmse:1.123684+0.298509
## [253] train-rmse:0.133391+0.006863 test-rmse:1.123688+0.298605
## [254] train-rmse:0.132591+0.006893 test-rmse:1.123417+0.298861
## [255] train-rmse:0.131524+0.007071 test-rmse:1.123670+0.298719
## [256] train-rmse:0.130825+0.007058 test-rmse:1.123335+0.298854
## [257] train-rmse:0.130035+0.007144 test-rmse:1.123382+0.299093
## [258] train-rmse:0.129322+0.006982 test-rmse:1.123243+0.299075
## [259] train-rmse:0.128314+0.006476 test-rmse:1.122734+0.299212
## [260] train-rmse:0.127658+0.006594 test-rmse:1.122672+0.299339
## [261] train-rmse:0.126581+0.006382 test-rmse:1.122449+0.299222
## [262] train-rmse:0.125626+0.006197 test-rmse:1.122136+0.299165
## [263] train-rmse:0.124950+0.006160 test-rmse:1.121815+0.298990
## [264] train-rmse:0.124087+0.006560 test-rmse:1.121780+0.298963
## [265] train-rmse:0.122891+0.006130 test-rmse:1.121925+0.299077
## [266] train-rmse:0.122090+0.005997 test-rmse:1.121696+0.299361
## [267] train-rmse:0.121232+0.005645 test-rmse:1.121668+0.299434
## [268] train-rmse:0.120407+0.006041 test-rmse:1.121793+0.299135
## [269] train-rmse:0.119636+0.005990 test-rmse:1.121765+0.299024
## [270] train-rmse:0.118833+0.005836 test-rmse:1.121845+0.299006
## [271] train-rmse:0.118157+0.005825 test-rmse:1.121675+0.299132
## [272] train-rmse:0.117313+0.005807 test-rmse:1.121378+0.299425
## [273] train-rmse:0.116261+0.005807 test-rmse:1.121351+0.299456
## [274] train-rmse:0.115508+0.005791 test-rmse:1.121079+0.299462
## [275] train-rmse:0.114724+0.005833 test-rmse:1.120739+0.299239
## [276] train-rmse:0.113926+0.005996 test-rmse:1.120305+0.298994
## [277] train-rmse:0.113132+0.006067 test-rmse:1.120351+0.298755
## [278] train-rmse:0.112335+0.005951 test-rmse:1.120075+0.298834
## [279] train-rmse:0.111764+0.005987 test-rmse:1.120046+0.298797
## [280] train-rmse:0.110797+0.006097 test-rmse:1.119738+0.298438
## [281] train-rmse:0.110131+0.006344 test-rmse:1.119601+0.298493
## [282] train-rmse:0.109311+0.006323 test-rmse:1.119690+0.298555
## [283] train-rmse:0.108377+0.006611 test-rmse:1.119558+0.298478
## [284] train-rmse:0.107718+0.006438 test-rmse:1.119499+0.298620
## [285] train-rmse:0.107079+0.006181 test-rmse:1.119246+0.298711
## [286] train-rmse:0.106348+0.006259 test-rmse:1.119354+0.298579
## [287] train-rmse:0.105516+0.006324 test-rmse:1.119378+0.298315
## [288] train-rmse:0.104858+0.006187 test-rmse:1.119510+0.298219
## [289] train-rmse:0.104302+0.006225 test-rmse:1.119383+0.298011
## [290] train-rmse:0.103801+0.006265 test-rmse:1.119395+0.298130
## [291] train-rmse:0.103274+0.006231 test-rmse:1.119465+0.298176
## Stopping. Best iteration:
## [285] train-rmse:0.107079+0.006181 test-rmse:1.119246+0.298711
##
## 18
## [1] train-rmse:92.628808+0.103203 test-rmse:92.629086+0.486485
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.759407+0.092047 test-rmse:79.759461+0.505884
## [3] train-rmse:68.693979+0.083013 test-rmse:68.693753+0.524472
## [4] train-rmse:59.182194+0.075874 test-rmse:59.181623+0.542625
## [5] train-rmse:51.008879+0.070487 test-rmse:51.007880+0.560691
## [6] train-rmse:43.989113+0.066746 test-rmse:43.987572+0.578998
## [7] train-rmse:37.964006+0.064561 test-rmse:37.961789+0.597840
## [8] train-rmse:32.792771+0.062486 test-rmse:32.812824+0.600191
## [9] train-rmse:28.347628+0.056546 test-rmse:28.405012+0.603047
## [10] train-rmse:24.526212+0.047562 test-rmse:24.604208+0.597355
## [11] train-rmse:21.241343+0.040405 test-rmse:21.326121+0.583443
## [12] train-rmse:18.415967+0.034544 test-rmse:18.527125+0.579424
## [13] train-rmse:15.983780+0.031428 test-rmse:16.089447+0.551215
## [14] train-rmse:13.890473+0.028814 test-rmse:14.001859+0.546493
## [15] train-rmse:12.089009+0.026069 test-rmse:12.199322+0.540545
## [16] train-rmse:10.538138+0.023684 test-rmse:10.626556+0.548689
## [17] train-rmse:9.207120+0.024305 test-rmse:9.300124+0.524380
## [18] train-rmse:8.059525+0.022565 test-rmse:8.171307+0.545520
## [19] train-rmse:7.076084+0.019516 test-rmse:7.201030+0.537516
## [20] train-rmse:6.233626+0.019041 test-rmse:6.360407+0.513603
## [21] train-rmse:5.507929+0.018915 test-rmse:5.648438+0.508370
## [22] train-rmse:4.890658+0.019760 test-rmse:5.039857+0.491070
## [23] train-rmse:4.357581+0.019375 test-rmse:4.520526+0.471837
## [24] train-rmse:3.901884+0.025466 test-rmse:4.090338+0.469460
## [25] train-rmse:3.515432+0.028141 test-rmse:3.728666+0.450209
## [26] train-rmse:3.189928+0.033211 test-rmse:3.440098+0.439700
## [27] train-rmse:2.904858+0.036309 test-rmse:3.186586+0.403608
## [28] train-rmse:2.666581+0.042399 test-rmse:2.966299+0.393865
## [29] train-rmse:2.458811+0.040437 test-rmse:2.792751+0.367025
## [30] train-rmse:2.282203+0.042723 test-rmse:2.645800+0.350057
## [31] train-rmse:2.134697+0.045338 test-rmse:2.529281+0.331596
## [32] train-rmse:2.003862+0.044479 test-rmse:2.430332+0.303059
## [33] train-rmse:1.890703+0.044821 test-rmse:2.354167+0.297641
## [34] train-rmse:1.794785+0.045176 test-rmse:2.272879+0.287327
## [35] train-rmse:1.707384+0.044817 test-rmse:2.208685+0.262659
## [36] train-rmse:1.630441+0.046289 test-rmse:2.156159+0.243206
## [37] train-rmse:1.562038+0.047108 test-rmse:2.100206+0.245081
## [38] train-rmse:1.501329+0.044239 test-rmse:2.052522+0.234414
## [39] train-rmse:1.447227+0.043572 test-rmse:2.013938+0.225810
## [40] train-rmse:1.397460+0.041573 test-rmse:1.972698+0.209255
## [41] train-rmse:1.347161+0.045499 test-rmse:1.946615+0.209568
## [42] train-rmse:1.300459+0.044677 test-rmse:1.915868+0.200629
## [43] train-rmse:1.257741+0.043102 test-rmse:1.884895+0.204649
## [44] train-rmse:1.219892+0.041763 test-rmse:1.849334+0.201563
## [45] train-rmse:1.184778+0.040873 test-rmse:1.818692+0.202044
## [46] train-rmse:1.142539+0.043893 test-rmse:1.795602+0.194271
## [47] train-rmse:1.110219+0.043203 test-rmse:1.769443+0.195867
## [48] train-rmse:1.079772+0.042282 test-rmse:1.747855+0.195905
## [49] train-rmse:1.048071+0.045047 test-rmse:1.723307+0.195096
## [50] train-rmse:1.014865+0.042467 test-rmse:1.703307+0.192071
## [51] train-rmse:0.980308+0.041566 test-rmse:1.686309+0.196053
## [52] train-rmse:0.955157+0.040383 test-rmse:1.667789+0.201479
## [53] train-rmse:0.930742+0.038035 test-rmse:1.646599+0.198185
## [54] train-rmse:0.907047+0.037440 test-rmse:1.626072+0.196141
## [55] train-rmse:0.881750+0.037133 test-rmse:1.612698+0.203141
## [56] train-rmse:0.858792+0.035767 test-rmse:1.593074+0.202943
## [57] train-rmse:0.835931+0.034872 test-rmse:1.572181+0.210214
## [58] train-rmse:0.815943+0.033622 test-rmse:1.565838+0.207437
## [59] train-rmse:0.794679+0.031713 test-rmse:1.551327+0.204282
## [60] train-rmse:0.774195+0.033001 test-rmse:1.537966+0.203626
## [61] train-rmse:0.756367+0.031329 test-rmse:1.518938+0.207373
## [62] train-rmse:0.739558+0.031040 test-rmse:1.505554+0.206576
## [63] train-rmse:0.722979+0.030801 test-rmse:1.492616+0.209552
## [64] train-rmse:0.706306+0.030779 test-rmse:1.483812+0.206374
## [65] train-rmse:0.691290+0.029528 test-rmse:1.474007+0.209145
## [66] train-rmse:0.673252+0.030606 test-rmse:1.462405+0.213164
## [67] train-rmse:0.655321+0.030212 test-rmse:1.453033+0.215823
## [68] train-rmse:0.641961+0.029103 test-rmse:1.444718+0.216271
## [69] train-rmse:0.628899+0.028193 test-rmse:1.436064+0.218266
## [70] train-rmse:0.616068+0.027956 test-rmse:1.430055+0.220309
## [71] train-rmse:0.603594+0.026870 test-rmse:1.421996+0.220909
## [72] train-rmse:0.592228+0.026660 test-rmse:1.412847+0.221706
## [73] train-rmse:0.578655+0.023792 test-rmse:1.400295+0.213344
## [74] train-rmse:0.564817+0.023934 test-rmse:1.393845+0.213099
## [75] train-rmse:0.553835+0.022996 test-rmse:1.388325+0.210717
## [76] train-rmse:0.542980+0.021425 test-rmse:1.379603+0.216466
## [77] train-rmse:0.532767+0.021738 test-rmse:1.371610+0.211132
## [78] train-rmse:0.523461+0.021428 test-rmse:1.365299+0.210224
## [79] train-rmse:0.514207+0.021035 test-rmse:1.362637+0.210422
## [80] train-rmse:0.504366+0.020991 test-rmse:1.357366+0.210041
## [81] train-rmse:0.495838+0.020515 test-rmse:1.349390+0.211397
## [82] train-rmse:0.484853+0.018309 test-rmse:1.345246+0.213274
## [83] train-rmse:0.476123+0.016626 test-rmse:1.340418+0.214050
## [84] train-rmse:0.467788+0.016652 test-rmse:1.336089+0.214444
## [85] train-rmse:0.460534+0.016199 test-rmse:1.331874+0.214548
## [86] train-rmse:0.452076+0.015892 test-rmse:1.329027+0.216906
## [87] train-rmse:0.442455+0.016433 test-rmse:1.327552+0.219520
## [88] train-rmse:0.435300+0.016016 test-rmse:1.322627+0.219630
## [89] train-rmse:0.427422+0.014350 test-rmse:1.319034+0.219796
## [90] train-rmse:0.420829+0.013940 test-rmse:1.316207+0.219103
## [91] train-rmse:0.414380+0.013038 test-rmse:1.313995+0.220411
## [92] train-rmse:0.407319+0.013223 test-rmse:1.312287+0.223108
## [93] train-rmse:0.401477+0.012777 test-rmse:1.307538+0.224879
## [94] train-rmse:0.395187+0.012277 test-rmse:1.304306+0.225895
## [95] train-rmse:0.389344+0.011242 test-rmse:1.300582+0.225759
## [96] train-rmse:0.382391+0.010420 test-rmse:1.298948+0.227430
## [97] train-rmse:0.375975+0.010586 test-rmse:1.297582+0.228140
## [98] train-rmse:0.369653+0.010549 test-rmse:1.294964+0.228307
## [99] train-rmse:0.364645+0.010199 test-rmse:1.292091+0.228678
## [100] train-rmse:0.358792+0.010118 test-rmse:1.289126+0.229076
## [101] train-rmse:0.354525+0.010728 test-rmse:1.286305+0.230272
## [102] train-rmse:0.348868+0.009866 test-rmse:1.285858+0.228296
## [103] train-rmse:0.343477+0.007844 test-rmse:1.284087+0.228215
## [104] train-rmse:0.337998+0.007127 test-rmse:1.279914+0.228112
## [105] train-rmse:0.333727+0.007256 test-rmse:1.277861+0.229145
## [106] train-rmse:0.328150+0.007803 test-rmse:1.275470+0.230719
## [107] train-rmse:0.323263+0.007484 test-rmse:1.275066+0.229928
## [108] train-rmse:0.319046+0.007318 test-rmse:1.271901+0.231614
## [109] train-rmse:0.314526+0.007541 test-rmse:1.272429+0.232987
## [110] train-rmse:0.310703+0.007032 test-rmse:1.271295+0.234107
## [111] train-rmse:0.307467+0.006830 test-rmse:1.270621+0.233042
## [112] train-rmse:0.303021+0.006271 test-rmse:1.268157+0.232792
## [113] train-rmse:0.298822+0.006213 test-rmse:1.267963+0.233898
## [114] train-rmse:0.295077+0.006745 test-rmse:1.265729+0.234029
## [115] train-rmse:0.291712+0.006677 test-rmse:1.264050+0.233860
## [116] train-rmse:0.287341+0.006850 test-rmse:1.262432+0.234022
## [117] train-rmse:0.283363+0.006261 test-rmse:1.262508+0.234070
## [118] train-rmse:0.280054+0.005961 test-rmse:1.261944+0.233716
## [119] train-rmse:0.277221+0.005847 test-rmse:1.259624+0.235365
## [120] train-rmse:0.274070+0.005872 test-rmse:1.258927+0.234720
## [121] train-rmse:0.269453+0.006271 test-rmse:1.257347+0.235863
## [122] train-rmse:0.266241+0.006004 test-rmse:1.256688+0.236576
## [123] train-rmse:0.263180+0.006199 test-rmse:1.256018+0.236416
## [124] train-rmse:0.260029+0.005826 test-rmse:1.254940+0.236486
## [125] train-rmse:0.256541+0.005057 test-rmse:1.254626+0.236873
## [126] train-rmse:0.252672+0.004392 test-rmse:1.253520+0.236232
## [127] train-rmse:0.249118+0.004685 test-rmse:1.252535+0.235810
## [128] train-rmse:0.245390+0.004943 test-rmse:1.251923+0.236392
## [129] train-rmse:0.242219+0.003996 test-rmse:1.251654+0.236294
## [130] train-rmse:0.240166+0.003985 test-rmse:1.250475+0.237224
## [131] train-rmse:0.237035+0.003764 test-rmse:1.249436+0.236395
## [132] train-rmse:0.234386+0.003649 test-rmse:1.249622+0.234288
## [133] train-rmse:0.231407+0.003555 test-rmse:1.248603+0.234572
## [134] train-rmse:0.229135+0.003469 test-rmse:1.248213+0.234999
## [135] train-rmse:0.226566+0.003276 test-rmse:1.247959+0.235594
## [136] train-rmse:0.224266+0.003438 test-rmse:1.247350+0.235320
## [137] train-rmse:0.221272+0.003152 test-rmse:1.247440+0.235183
## [138] train-rmse:0.218743+0.002689 test-rmse:1.246557+0.235745
## [139] train-rmse:0.215136+0.002884 test-rmse:1.245825+0.236066
## [140] train-rmse:0.212277+0.002237 test-rmse:1.244412+0.236562
## [141] train-rmse:0.210243+0.002177 test-rmse:1.242870+0.238147
## [142] train-rmse:0.207654+0.002741 test-rmse:1.242494+0.238070
## [143] train-rmse:0.205716+0.002971 test-rmse:1.242286+0.238542
## [144] train-rmse:0.203583+0.003177 test-rmse:1.241392+0.239433
## [145] train-rmse:0.200894+0.003910 test-rmse:1.240336+0.239863
## [146] train-rmse:0.199102+0.003999 test-rmse:1.239676+0.239131
## [147] train-rmse:0.195725+0.003293 test-rmse:1.239557+0.238745
## [148] train-rmse:0.194142+0.003183 test-rmse:1.239453+0.239129
## [149] train-rmse:0.191921+0.003882 test-rmse:1.239418+0.239325
## [150] train-rmse:0.189720+0.003915 test-rmse:1.239542+0.239639
## [151] train-rmse:0.187519+0.003360 test-rmse:1.239169+0.239712
## [152] train-rmse:0.185857+0.003279 test-rmse:1.238792+0.240167
## [153] train-rmse:0.183475+0.002795 test-rmse:1.238237+0.239538
## [154] train-rmse:0.181507+0.002687 test-rmse:1.238085+0.238984
## [155] train-rmse:0.178834+0.002889 test-rmse:1.237744+0.238807
## [156] train-rmse:0.176333+0.002790 test-rmse:1.236996+0.238203
## [157] train-rmse:0.174609+0.002195 test-rmse:1.236812+0.238045
## [158] train-rmse:0.173014+0.002307 test-rmse:1.236413+0.238447
## [159] train-rmse:0.171316+0.002356 test-rmse:1.235433+0.239068
## [160] train-rmse:0.168982+0.001979 test-rmse:1.236321+0.238508
## [161] train-rmse:0.166135+0.002215 test-rmse:1.235735+0.238731
## [162] train-rmse:0.164642+0.002149 test-rmse:1.235243+0.238594
## [163] train-rmse:0.163089+0.001986 test-rmse:1.235528+0.238611
## [164] train-rmse:0.161153+0.001978 test-rmse:1.235055+0.239019
## [165] train-rmse:0.159363+0.002295 test-rmse:1.234835+0.239171
## [166] train-rmse:0.157852+0.002422 test-rmse:1.234483+0.239284
## [167] train-rmse:0.156409+0.002122 test-rmse:1.233077+0.239251
## [168] train-rmse:0.154670+0.002257 test-rmse:1.233466+0.239182
## [169] train-rmse:0.153290+0.002065 test-rmse:1.233385+0.239618
## [170] train-rmse:0.151357+0.002790 test-rmse:1.233111+0.239474
## [171] train-rmse:0.150154+0.002718 test-rmse:1.232798+0.239672
## [172] train-rmse:0.148816+0.002476 test-rmse:1.232806+0.239599
## [173] train-rmse:0.147396+0.002554 test-rmse:1.232611+0.239719
## [174] train-rmse:0.146301+0.002525 test-rmse:1.231751+0.240094
## [175] train-rmse:0.144943+0.002208 test-rmse:1.230972+0.239414
## [176] train-rmse:0.143120+0.002605 test-rmse:1.231018+0.239550
## [177] train-rmse:0.141388+0.002610 test-rmse:1.230668+0.239756
## [178] train-rmse:0.139882+0.002584 test-rmse:1.230188+0.240148
## [179] train-rmse:0.138702+0.002497 test-rmse:1.229980+0.240302
## [180] train-rmse:0.137710+0.002443 test-rmse:1.229621+0.240131
## [181] train-rmse:0.136522+0.002850 test-rmse:1.229372+0.240111
## [182] train-rmse:0.134382+0.002587 test-rmse:1.228965+0.240095
## [183] train-rmse:0.132883+0.002548 test-rmse:1.228732+0.240244
## [184] train-rmse:0.131656+0.003046 test-rmse:1.228780+0.240318
## [185] train-rmse:0.130571+0.002884 test-rmse:1.228669+0.240370
## [186] train-rmse:0.129025+0.003253 test-rmse:1.228214+0.240129
## [187] train-rmse:0.127844+0.003301 test-rmse:1.228248+0.240070
## [188] train-rmse:0.126735+0.003359 test-rmse:1.228356+0.240277
## [189] train-rmse:0.125666+0.003342 test-rmse:1.227393+0.240454
## [190] train-rmse:0.124824+0.003686 test-rmse:1.227133+0.240331
## [191] train-rmse:0.123809+0.003715 test-rmse:1.227173+0.240335
## [192] train-rmse:0.122254+0.003575 test-rmse:1.227250+0.240390
## [193] train-rmse:0.121098+0.003611 test-rmse:1.227344+0.240437
## [194] train-rmse:0.119467+0.003625 test-rmse:1.227562+0.240640
## [195] train-rmse:0.117741+0.003677 test-rmse:1.227462+0.240468
## [196] train-rmse:0.116371+0.003407 test-rmse:1.227541+0.240288
## Stopping. Best iteration:
## [190] train-rmse:0.124824+0.003686 test-rmse:1.227133+0.240331
##
## 19
## [1] train-rmse:92.628808+0.103203 test-rmse:92.629086+0.486485
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.759407+0.092047 test-rmse:79.759461+0.505884
## [3] train-rmse:68.693979+0.083013 test-rmse:68.693753+0.524472
## [4] train-rmse:59.182194+0.075874 test-rmse:59.181623+0.542625
## [5] train-rmse:51.008879+0.070487 test-rmse:51.007880+0.560691
## [6] train-rmse:43.989113+0.066746 test-rmse:43.987572+0.578998
## [7] train-rmse:37.964006+0.064561 test-rmse:37.961789+0.597840
## [8] train-rmse:32.792771+0.062486 test-rmse:32.812824+0.600191
## [9] train-rmse:28.347628+0.056546 test-rmse:28.405012+0.603047
## [10] train-rmse:24.526212+0.047562 test-rmse:24.604208+0.597355
## [11] train-rmse:21.241343+0.040405 test-rmse:21.326121+0.583443
## [12] train-rmse:18.414927+0.034101 test-rmse:18.520150+0.581977
## [13] train-rmse:15.981665+0.030714 test-rmse:16.083777+0.552404
## [14] train-rmse:13.885936+0.027627 test-rmse:13.991632+0.554503
## [15] train-rmse:12.080579+0.023917 test-rmse:12.179302+0.547038
## [16] train-rmse:10.522182+0.020020 test-rmse:10.612564+0.547292
## [17] train-rmse:9.183809+0.019721 test-rmse:9.275141+0.533896
## [18] train-rmse:8.027399+0.017655 test-rmse:8.140117+0.538313
## [19] train-rmse:7.034784+0.016064 test-rmse:7.150319+0.535187
## [20] train-rmse:6.179970+0.017560 test-rmse:6.303632+0.509384
## [21] train-rmse:5.441886+0.014972 test-rmse:5.579309+0.519260
## [22] train-rmse:4.806141+0.012229 test-rmse:4.966443+0.483660
## [23] train-rmse:4.263855+0.015642 test-rmse:4.439594+0.460046
## [24] train-rmse:3.794540+0.019936 test-rmse:3.995575+0.447081
## [25] train-rmse:3.392017+0.025451 test-rmse:3.620750+0.425271
## [26] train-rmse:3.045492+0.024152 test-rmse:3.322166+0.418018
## [27] train-rmse:2.749623+0.026686 test-rmse:3.055042+0.415737
## [28] train-rmse:2.494835+0.031156 test-rmse:2.829554+0.376248
## [29] train-rmse:2.280093+0.032507 test-rmse:2.632071+0.363469
## [30] train-rmse:2.094578+0.032295 test-rmse:2.477372+0.347401
## [31] train-rmse:1.934402+0.037078 test-rmse:2.352338+0.328524
## [32] train-rmse:1.794763+0.037208 test-rmse:2.243624+0.324666
## [33] train-rmse:1.671803+0.043997 test-rmse:2.151537+0.303918
## [34] train-rmse:1.564354+0.047482 test-rmse:2.074438+0.282509
## [35] train-rmse:1.473247+0.046765 test-rmse:2.005517+0.284614
## [36] train-rmse:1.393900+0.047181 test-rmse:1.940063+0.278011
## [37] train-rmse:1.324410+0.047831 test-rmse:1.887873+0.276064
## [38] train-rmse:1.253463+0.047729 test-rmse:1.855050+0.273390
## [39] train-rmse:1.194889+0.047663 test-rmse:1.816118+0.266012
## [40] train-rmse:1.140423+0.045617 test-rmse:1.780549+0.264626
## [41] train-rmse:1.093394+0.044648 test-rmse:1.745305+0.260010
## [42] train-rmse:1.047818+0.044893 test-rmse:1.722373+0.261210
## [43] train-rmse:1.006418+0.043427 test-rmse:1.690966+0.259115
## [44] train-rmse:0.970213+0.042565 test-rmse:1.666465+0.262518
## [45] train-rmse:0.935481+0.040894 test-rmse:1.642895+0.268958
## [46] train-rmse:0.902928+0.040542 test-rmse:1.622011+0.263715
## [47] train-rmse:0.870148+0.036583 test-rmse:1.597407+0.265530
## [48] train-rmse:0.837452+0.038504 test-rmse:1.580838+0.272487
## [49] train-rmse:0.810654+0.037732 test-rmse:1.561056+0.274160
## [50] train-rmse:0.784257+0.034747 test-rmse:1.539165+0.263360
## [51] train-rmse:0.756611+0.033818 test-rmse:1.513612+0.264885
## [52] train-rmse:0.735290+0.033132 test-rmse:1.492252+0.260415
## [53] train-rmse:0.707012+0.034582 test-rmse:1.474192+0.253949
## [54] train-rmse:0.685732+0.034170 test-rmse:1.463029+0.258299
## [55] train-rmse:0.666632+0.033473 test-rmse:1.453149+0.260034
## [56] train-rmse:0.647383+0.032149 test-rmse:1.437604+0.260364
## [57] train-rmse:0.629596+0.031768 test-rmse:1.427358+0.259276
## [58] train-rmse:0.610685+0.030872 test-rmse:1.411980+0.252763
## [59] train-rmse:0.594765+0.030892 test-rmse:1.395769+0.254300
## [60] train-rmse:0.577509+0.032127 test-rmse:1.387702+0.255940
## [61] train-rmse:0.561821+0.033284 test-rmse:1.378830+0.253983
## [62] train-rmse:0.547392+0.032840 test-rmse:1.371321+0.254294
## [63] train-rmse:0.532022+0.031058 test-rmse:1.363819+0.253917
## [64] train-rmse:0.519890+0.030643 test-rmse:1.357392+0.253865
## [65] train-rmse:0.506890+0.030673 test-rmse:1.346990+0.248414
## [66] train-rmse:0.494471+0.030678 test-rmse:1.339486+0.249064
## [67] train-rmse:0.483706+0.030238 test-rmse:1.333717+0.248655
## [68] train-rmse:0.471601+0.030194 test-rmse:1.322778+0.251787
## [69] train-rmse:0.460878+0.031018 test-rmse:1.320343+0.251374
## [70] train-rmse:0.449916+0.030757 test-rmse:1.315858+0.254470
## [71] train-rmse:0.438590+0.030906 test-rmse:1.310088+0.257377
## [72] train-rmse:0.428285+0.028802 test-rmse:1.304447+0.258094
## [73] train-rmse:0.419538+0.028761 test-rmse:1.297357+0.261047
## [74] train-rmse:0.411161+0.028804 test-rmse:1.293728+0.260898
## [75] train-rmse:0.402697+0.027840 test-rmse:1.288562+0.262416
## [76] train-rmse:0.394545+0.027006 test-rmse:1.286629+0.263049
## [77] train-rmse:0.383708+0.022846 test-rmse:1.282305+0.263153
## [78] train-rmse:0.376896+0.022456 test-rmse:1.279006+0.263578
## [79] train-rmse:0.368656+0.021818 test-rmse:1.275591+0.263945
## [80] train-rmse:0.361759+0.021974 test-rmse:1.272736+0.263907
## [81] train-rmse:0.354413+0.022177 test-rmse:1.266621+0.267144
## [82] train-rmse:0.348225+0.020688 test-rmse:1.264798+0.267956
## [83] train-rmse:0.341662+0.021315 test-rmse:1.263192+0.267680
## [84] train-rmse:0.335718+0.021187 test-rmse:1.260332+0.267306
## [85] train-rmse:0.328855+0.020389 test-rmse:1.255881+0.268934
## [86] train-rmse:0.321112+0.016842 test-rmse:1.253431+0.267250
## [87] train-rmse:0.315102+0.017524 test-rmse:1.252255+0.265214
## [88] train-rmse:0.308814+0.017218 test-rmse:1.247829+0.266506
## [89] train-rmse:0.302050+0.016745 test-rmse:1.244899+0.266053
## [90] train-rmse:0.297371+0.017267 test-rmse:1.240727+0.267898
## [91] train-rmse:0.291944+0.017144 test-rmse:1.241168+0.268712
## [92] train-rmse:0.285490+0.017526 test-rmse:1.239772+0.269455
## [93] train-rmse:0.280585+0.017175 test-rmse:1.237098+0.270063
## [94] train-rmse:0.276567+0.016708 test-rmse:1.234456+0.267674
## [95] train-rmse:0.271446+0.016258 test-rmse:1.231941+0.269009
## [96] train-rmse:0.267977+0.016295 test-rmse:1.230002+0.268876
## [97] train-rmse:0.261854+0.017498 test-rmse:1.229032+0.270069
## [98] train-rmse:0.257182+0.017186 test-rmse:1.227934+0.272055
## [99] train-rmse:0.251701+0.016751 test-rmse:1.225698+0.271794
## [100] train-rmse:0.247148+0.017400 test-rmse:1.225543+0.271375
## [101] train-rmse:0.242976+0.017053 test-rmse:1.223510+0.272194
## [102] train-rmse:0.238424+0.015805 test-rmse:1.221236+0.272237
## [103] train-rmse:0.234411+0.015574 test-rmse:1.220371+0.272403
## [104] train-rmse:0.231390+0.015270 test-rmse:1.219855+0.272887
## [105] train-rmse:0.226377+0.014482 test-rmse:1.217148+0.273436
## [106] train-rmse:0.222267+0.014388 test-rmse:1.217221+0.273664
## [107] train-rmse:0.219624+0.014213 test-rmse:1.215625+0.273936
## [108] train-rmse:0.215609+0.013619 test-rmse:1.215091+0.272977
## [109] train-rmse:0.211767+0.013879 test-rmse:1.215385+0.272981
## [110] train-rmse:0.208398+0.012838 test-rmse:1.213772+0.273605
## [111] train-rmse:0.205324+0.012629 test-rmse:1.213198+0.274148
## [112] train-rmse:0.201943+0.012281 test-rmse:1.212108+0.273472
## [113] train-rmse:0.198080+0.012103 test-rmse:1.211352+0.273721
## [114] train-rmse:0.194386+0.012860 test-rmse:1.210700+0.274114
## [115] train-rmse:0.191115+0.013056 test-rmse:1.209936+0.275040
## [116] train-rmse:0.188367+0.012977 test-rmse:1.208875+0.274395
## [117] train-rmse:0.184864+0.012899 test-rmse:1.207990+0.273678
## [118] train-rmse:0.181809+0.013070 test-rmse:1.207502+0.273617
## [119] train-rmse:0.178800+0.013078 test-rmse:1.207087+0.273932
## [120] train-rmse:0.176092+0.012950 test-rmse:1.207171+0.273963
## [121] train-rmse:0.173507+0.013114 test-rmse:1.205890+0.274151
## [122] train-rmse:0.170769+0.013263 test-rmse:1.205571+0.273740
## [123] train-rmse:0.167950+0.013448 test-rmse:1.205502+0.273592
## [124] train-rmse:0.165430+0.013438 test-rmse:1.204771+0.274053
## [125] train-rmse:0.162229+0.013686 test-rmse:1.204835+0.274252
## [126] train-rmse:0.159688+0.013302 test-rmse:1.204256+0.274924
## [127] train-rmse:0.157059+0.013839 test-rmse:1.203762+0.275171
## [128] train-rmse:0.154770+0.013866 test-rmse:1.202718+0.275299
## [129] train-rmse:0.152987+0.013535 test-rmse:1.201453+0.276210
## [130] train-rmse:0.150621+0.013500 test-rmse:1.201766+0.275989
## [131] train-rmse:0.147488+0.012277 test-rmse:1.200648+0.274986
## [132] train-rmse:0.145815+0.012410 test-rmse:1.200820+0.275012
## [133] train-rmse:0.143360+0.012739 test-rmse:1.201039+0.274544
## [134] train-rmse:0.141472+0.012163 test-rmse:1.199706+0.273335
## [135] train-rmse:0.139085+0.011414 test-rmse:1.198806+0.274072
## [136] train-rmse:0.137350+0.011508 test-rmse:1.198226+0.273941
## [137] train-rmse:0.135165+0.011295 test-rmse:1.198116+0.274025
## [138] train-rmse:0.132893+0.011035 test-rmse:1.197589+0.274376
## [139] train-rmse:0.131383+0.010804 test-rmse:1.197137+0.274621
## [140] train-rmse:0.128987+0.010627 test-rmse:1.196303+0.273553
## [141] train-rmse:0.127225+0.010021 test-rmse:1.195957+0.273817
## [142] train-rmse:0.125590+0.010077 test-rmse:1.196001+0.273304
## [143] train-rmse:0.123970+0.009714 test-rmse:1.196079+0.273630
## [144] train-rmse:0.121709+0.009540 test-rmse:1.195456+0.274128
## [145] train-rmse:0.119289+0.009152 test-rmse:1.194780+0.274500
## [146] train-rmse:0.117837+0.008999 test-rmse:1.194534+0.274198
## [147] train-rmse:0.116153+0.009148 test-rmse:1.194642+0.274388
## [148] train-rmse:0.114355+0.008874 test-rmse:1.194013+0.273601
## [149] train-rmse:0.113199+0.009070 test-rmse:1.193641+0.274133
## [150] train-rmse:0.111823+0.009034 test-rmse:1.193091+0.274029
## [151] train-rmse:0.110338+0.009394 test-rmse:1.192691+0.274040
## [152] train-rmse:0.108635+0.009262 test-rmse:1.192803+0.274223
## [153] train-rmse:0.107032+0.008782 test-rmse:1.192885+0.274251
## [154] train-rmse:0.105105+0.008838 test-rmse:1.192850+0.274491
## [155] train-rmse:0.102946+0.008461 test-rmse:1.193017+0.274642
## [156] train-rmse:0.101076+0.009056 test-rmse:1.192795+0.274764
## [157] train-rmse:0.099447+0.008602 test-rmse:1.192503+0.274746
## [158] train-rmse:0.098302+0.008312 test-rmse:1.192336+0.275177
## [159] train-rmse:0.096366+0.007856 test-rmse:1.192138+0.275328
## [160] train-rmse:0.095418+0.008170 test-rmse:1.192183+0.275387
## [161] train-rmse:0.094213+0.007810 test-rmse:1.191912+0.275408
## [162] train-rmse:0.092594+0.007448 test-rmse:1.191782+0.275523
## [163] train-rmse:0.091201+0.007523 test-rmse:1.191391+0.275740
## [164] train-rmse:0.089600+0.007186 test-rmse:1.191168+0.276024
## [165] train-rmse:0.088010+0.006795 test-rmse:1.191095+0.276107
## [166] train-rmse:0.086599+0.006498 test-rmse:1.191134+0.276169
## [167] train-rmse:0.085749+0.006437 test-rmse:1.191334+0.276612
## [168] train-rmse:0.084229+0.006369 test-rmse:1.191354+0.276756
## [169] train-rmse:0.082891+0.005707 test-rmse:1.190453+0.276087
## [170] train-rmse:0.081711+0.005766 test-rmse:1.190004+0.275807
## [171] train-rmse:0.080712+0.005772 test-rmse:1.189870+0.275814
## [172] train-rmse:0.079598+0.005974 test-rmse:1.189590+0.275699
## [173] train-rmse:0.078589+0.005875 test-rmse:1.189739+0.275931
## [174] train-rmse:0.077412+0.005802 test-rmse:1.189591+0.275752
## [175] train-rmse:0.075973+0.005663 test-rmse:1.189594+0.275916
## [176] train-rmse:0.075005+0.005585 test-rmse:1.189088+0.275784
## [177] train-rmse:0.073721+0.005379 test-rmse:1.189208+0.275830
## [178] train-rmse:0.072778+0.005222 test-rmse:1.189240+0.275696
## [179] train-rmse:0.072085+0.005246 test-rmse:1.189102+0.275749
## [180] train-rmse:0.070982+0.005568 test-rmse:1.189061+0.275597
## [181] train-rmse:0.070230+0.005464 test-rmse:1.189099+0.275640
## [182] train-rmse:0.069073+0.005300 test-rmse:1.189205+0.275580
## [183] train-rmse:0.068008+0.005355 test-rmse:1.188799+0.275601
## [184] train-rmse:0.067007+0.005450 test-rmse:1.188704+0.275775
## [185] train-rmse:0.066117+0.005391 test-rmse:1.188565+0.275865
## [186] train-rmse:0.064924+0.005335 test-rmse:1.188700+0.275918
## [187] train-rmse:0.064232+0.005135 test-rmse:1.188551+0.276002
## [188] train-rmse:0.063408+0.005245 test-rmse:1.188633+0.276044
## [189] train-rmse:0.062461+0.005062 test-rmse:1.188458+0.276029
## [190] train-rmse:0.061612+0.005082 test-rmse:1.188704+0.276457
## [191] train-rmse:0.060764+0.005027 test-rmse:1.188593+0.276477
## [192] train-rmse:0.059881+0.005095 test-rmse:1.188763+0.276270
## [193] train-rmse:0.059026+0.005087 test-rmse:1.188526+0.276455
## [194] train-rmse:0.058219+0.004970 test-rmse:1.188416+0.276494
## [195] train-rmse:0.057220+0.004891 test-rmse:1.188405+0.276620
## [196] train-rmse:0.056557+0.004887 test-rmse:1.188181+0.276431
## [197] train-rmse:0.055664+0.004563 test-rmse:1.187723+0.276435
## [198] train-rmse:0.054941+0.004716 test-rmse:1.187639+0.276408
## [199] train-rmse:0.054016+0.004660 test-rmse:1.187578+0.276342
## [200] train-rmse:0.053223+0.004669 test-rmse:1.187516+0.276492
## [201] train-rmse:0.052130+0.004445 test-rmse:1.187592+0.276302
## [202] train-rmse:0.051239+0.004106 test-rmse:1.187898+0.276751
## [203] train-rmse:0.050444+0.004067 test-rmse:1.187893+0.276685
## [204] train-rmse:0.049631+0.003950 test-rmse:1.188063+0.276529
## [205] train-rmse:0.048821+0.004037 test-rmse:1.188371+0.276308
## [206] train-rmse:0.048193+0.004079 test-rmse:1.188259+0.276391
## Stopping. Best iteration:
## [200] train-rmse:0.053223+0.004669 test-rmse:1.187516+0.276492
##
## 20
## [1] train-rmse:92.628808+0.103203 test-rmse:92.629086+0.486485
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:79.759407+0.092047 test-rmse:79.759461+0.505884
## [3] train-rmse:68.693979+0.083013 test-rmse:68.693753+0.524472
## [4] train-rmse:59.182194+0.075874 test-rmse:59.181623+0.542625
## [5] train-rmse:51.008879+0.070487 test-rmse:51.007880+0.560691
## [6] train-rmse:43.989113+0.066746 test-rmse:43.987572+0.578998
## [7] train-rmse:37.964006+0.064561 test-rmse:37.961789+0.597840
## [8] train-rmse:32.792771+0.062486 test-rmse:32.812824+0.600191
## [9] train-rmse:28.347628+0.056546 test-rmse:28.405012+0.603047
## [10] train-rmse:24.526212+0.047562 test-rmse:24.604208+0.597355
## [11] train-rmse:21.241343+0.040405 test-rmse:21.326121+0.583443
## [12] train-rmse:18.414287+0.033773 test-rmse:18.518230+0.578389
## [13] train-rmse:15.979761+0.029789 test-rmse:16.078246+0.563472
## [14] train-rmse:13.882815+0.027581 test-rmse:13.987163+0.543768
## [15] train-rmse:12.074928+0.023745 test-rmse:12.176397+0.532731
## [16] train-rmse:10.518055+0.021118 test-rmse:10.621855+0.524769
## [17] train-rmse:9.171425+0.019747 test-rmse:9.275629+0.527784
## [18] train-rmse:8.011062+0.019951 test-rmse:8.128771+0.537786
## [19] train-rmse:7.010764+0.015221 test-rmse:7.129887+0.515363
## [20] train-rmse:6.148265+0.013362 test-rmse:6.279884+0.499722
## [21] train-rmse:5.400888+0.009871 test-rmse:5.543436+0.478121
## [22] train-rmse:4.759112+0.011125 test-rmse:4.912639+0.465521
## [23] train-rmse:4.202014+0.011775 test-rmse:4.369683+0.465114
## [24] train-rmse:3.725125+0.009861 test-rmse:3.916361+0.458159
## [25] train-rmse:3.314427+0.012040 test-rmse:3.534912+0.439006
## [26] train-rmse:2.959787+0.015255 test-rmse:3.215922+0.411766
## [27] train-rmse:2.657719+0.019670 test-rmse:2.958186+0.387959
## [28] train-rmse:2.392989+0.020023 test-rmse:2.736462+0.383405
## [29] train-rmse:2.166363+0.022185 test-rmse:2.542173+0.377744
## [30] train-rmse:1.965084+0.023520 test-rmse:2.375177+0.366483
## [31] train-rmse:1.794347+0.031018 test-rmse:2.248976+0.354729
## [32] train-rmse:1.645176+0.034919 test-rmse:2.145884+0.339242
## [33] train-rmse:1.514419+0.035790 test-rmse:2.054989+0.326594
## [34] train-rmse:1.400753+0.037878 test-rmse:1.969232+0.316107
## [35] train-rmse:1.300646+0.040879 test-rmse:1.902877+0.311757
## [36] train-rmse:1.213459+0.040080 test-rmse:1.846693+0.303502
## [37] train-rmse:1.135276+0.037200 test-rmse:1.791972+0.293508
## [38] train-rmse:1.067429+0.037880 test-rmse:1.743405+0.291123
## [39] train-rmse:1.006715+0.037609 test-rmse:1.701767+0.287720
## [40] train-rmse:0.954485+0.037809 test-rmse:1.666566+0.284140
## [41] train-rmse:0.903766+0.036001 test-rmse:1.630396+0.278569
## [42] train-rmse:0.859188+0.036467 test-rmse:1.603991+0.278506
## [43] train-rmse:0.816912+0.035001 test-rmse:1.584111+0.277567
## [44] train-rmse:0.779089+0.036418 test-rmse:1.559079+0.278939
## [45] train-rmse:0.746254+0.035606 test-rmse:1.535959+0.279086
## [46] train-rmse:0.712989+0.036023 test-rmse:1.517036+0.278879
## [47] train-rmse:0.685821+0.035614 test-rmse:1.498857+0.280517
## [48] train-rmse:0.656579+0.030632 test-rmse:1.482794+0.278931
## [49] train-rmse:0.629080+0.030965 test-rmse:1.465499+0.287585
## [50] train-rmse:0.605157+0.032968 test-rmse:1.453162+0.287294
## [51] train-rmse:0.581778+0.030853 test-rmse:1.435254+0.280789
## [52] train-rmse:0.559398+0.031674 test-rmse:1.422646+0.275605
## [53] train-rmse:0.539659+0.032297 test-rmse:1.410523+0.274421
## [54] train-rmse:0.521001+0.033384 test-rmse:1.406704+0.271601
## [55] train-rmse:0.502590+0.032754 test-rmse:1.396503+0.276299
## [56] train-rmse:0.487253+0.032398 test-rmse:1.389032+0.274313
## [57] train-rmse:0.472966+0.032594 test-rmse:1.380783+0.275677
## [58] train-rmse:0.456110+0.030003 test-rmse:1.367127+0.272252
## [59] train-rmse:0.441301+0.030768 test-rmse:1.358893+0.269119
## [60] train-rmse:0.429268+0.030170 test-rmse:1.351423+0.271240
## [61] train-rmse:0.417836+0.030033 test-rmse:1.345700+0.270074
## [62] train-rmse:0.403600+0.030083 test-rmse:1.340638+0.270857
## [63] train-rmse:0.393467+0.030674 test-rmse:1.336619+0.270680
## [64] train-rmse:0.380555+0.028689 test-rmse:1.328341+0.268275
## [65] train-rmse:0.370706+0.027776 test-rmse:1.326895+0.271120
## [66] train-rmse:0.360972+0.026823 test-rmse:1.323261+0.271202
## [67] train-rmse:0.351977+0.026555 test-rmse:1.320478+0.271939
## [68] train-rmse:0.343710+0.026147 test-rmse:1.316646+0.272034
## [69] train-rmse:0.334249+0.024864 test-rmse:1.315070+0.272704
## [70] train-rmse:0.326016+0.024150 test-rmse:1.309217+0.274817
## [71] train-rmse:0.318050+0.023972 test-rmse:1.304483+0.274629
## [72] train-rmse:0.308366+0.023817 test-rmse:1.303186+0.276963
## [73] train-rmse:0.300585+0.021790 test-rmse:1.298678+0.273564
## [74] train-rmse:0.293728+0.020600 test-rmse:1.294505+0.270968
## [75] train-rmse:0.285849+0.019175 test-rmse:1.290344+0.272913
## [76] train-rmse:0.280004+0.018924 test-rmse:1.286455+0.272651
## [77] train-rmse:0.273406+0.018395 test-rmse:1.284542+0.273338
## [78] train-rmse:0.267037+0.016325 test-rmse:1.283796+0.274569
## [79] train-rmse:0.260734+0.016387 test-rmse:1.283447+0.274029
## [80] train-rmse:0.254737+0.016875 test-rmse:1.280340+0.275327
## [81] train-rmse:0.248774+0.016417 test-rmse:1.277649+0.272586
## [82] train-rmse:0.243998+0.016440 test-rmse:1.275950+0.273335
## [83] train-rmse:0.238874+0.015631 test-rmse:1.274099+0.273551
## [84] train-rmse:0.233429+0.016331 test-rmse:1.272724+0.274739
## [85] train-rmse:0.228045+0.016936 test-rmse:1.269821+0.275915
## [86] train-rmse:0.222497+0.015979 test-rmse:1.268936+0.276228
## [87] train-rmse:0.217532+0.015145 test-rmse:1.266897+0.277012
## [88] train-rmse:0.211986+0.015019 test-rmse:1.266681+0.276831
## [89] train-rmse:0.206713+0.015431 test-rmse:1.266574+0.275488
## [90] train-rmse:0.203306+0.015645 test-rmse:1.264488+0.275940
## [91] train-rmse:0.199127+0.014420 test-rmse:1.263488+0.277232
## [92] train-rmse:0.194735+0.014148 test-rmse:1.262345+0.278450
## [93] train-rmse:0.190894+0.013668 test-rmse:1.260852+0.279214
## [94] train-rmse:0.187178+0.013642 test-rmse:1.260101+0.279784
## [95] train-rmse:0.182527+0.013973 test-rmse:1.260532+0.279368
## [96] train-rmse:0.178737+0.013967 test-rmse:1.259497+0.278961
## [97] train-rmse:0.175504+0.013626 test-rmse:1.258317+0.278804
## [98] train-rmse:0.171643+0.013076 test-rmse:1.256676+0.277744
## [99] train-rmse:0.168790+0.013323 test-rmse:1.255847+0.278666
## [100] train-rmse:0.164935+0.012905 test-rmse:1.255172+0.278472
## [101] train-rmse:0.161612+0.013851 test-rmse:1.255302+0.277319
## [102] train-rmse:0.158243+0.013684 test-rmse:1.254423+0.277190
## [103] train-rmse:0.153977+0.012911 test-rmse:1.253688+0.276483
## [104] train-rmse:0.150662+0.012334 test-rmse:1.252428+0.275424
## [105] train-rmse:0.147647+0.011510 test-rmse:1.251838+0.276727
## [106] train-rmse:0.143990+0.011003 test-rmse:1.251180+0.276273
## [107] train-rmse:0.140791+0.010533 test-rmse:1.250295+0.276419
## [108] train-rmse:0.138114+0.010485 test-rmse:1.248855+0.275404
## [109] train-rmse:0.135692+0.010971 test-rmse:1.248566+0.275276
## [110] train-rmse:0.133578+0.010866 test-rmse:1.247146+0.275264
## [111] train-rmse:0.131213+0.011298 test-rmse:1.246786+0.275295
## [112] train-rmse:0.128677+0.011162 test-rmse:1.246457+0.275553
## [113] train-rmse:0.126002+0.010663 test-rmse:1.245729+0.275745
## [114] train-rmse:0.123710+0.010714 test-rmse:1.245316+0.275745
## [115] train-rmse:0.121729+0.010674 test-rmse:1.244913+0.276357
## [116] train-rmse:0.119043+0.011507 test-rmse:1.244420+0.276052
## [117] train-rmse:0.116842+0.011446 test-rmse:1.244507+0.275688
## [118] train-rmse:0.114993+0.011170 test-rmse:1.244812+0.276208
## [119] train-rmse:0.112714+0.010616 test-rmse:1.244135+0.275695
## [120] train-rmse:0.110525+0.009827 test-rmse:1.244183+0.276101
## [121] train-rmse:0.107525+0.009164 test-rmse:1.244209+0.275776
## [122] train-rmse:0.105335+0.009108 test-rmse:1.243932+0.275501
## [123] train-rmse:0.103116+0.009164 test-rmse:1.243976+0.275798
## [124] train-rmse:0.100840+0.008883 test-rmse:1.243760+0.275591
## [125] train-rmse:0.099461+0.008957 test-rmse:1.243125+0.275517
## [126] train-rmse:0.097555+0.008854 test-rmse:1.242881+0.275510
## [127] train-rmse:0.095443+0.008472 test-rmse:1.242645+0.275411
## [128] train-rmse:0.093446+0.008596 test-rmse:1.243136+0.275172
## [129] train-rmse:0.091608+0.008324 test-rmse:1.242517+0.274978
## [130] train-rmse:0.089808+0.008327 test-rmse:1.242282+0.274988
## [131] train-rmse:0.088257+0.008405 test-rmse:1.242519+0.274832
## [132] train-rmse:0.086590+0.008107 test-rmse:1.241929+0.274957
## [133] train-rmse:0.084513+0.007703 test-rmse:1.241958+0.273873
## [134] train-rmse:0.082686+0.007061 test-rmse:1.241853+0.274033
## [135] train-rmse:0.081251+0.007037 test-rmse:1.241537+0.274197
## [136] train-rmse:0.080277+0.007028 test-rmse:1.241456+0.274286
## [137] train-rmse:0.077906+0.006655 test-rmse:1.241675+0.273708
## [138] train-rmse:0.076527+0.006635 test-rmse:1.241359+0.273600
## [139] train-rmse:0.075346+0.006587 test-rmse:1.241532+0.273968
## [140] train-rmse:0.074104+0.006564 test-rmse:1.241191+0.273883
## [141] train-rmse:0.072515+0.006267 test-rmse:1.241033+0.273831
## [142] train-rmse:0.071261+0.005948 test-rmse:1.240880+0.273750
## [143] train-rmse:0.069974+0.006023 test-rmse:1.241244+0.274039
## [144] train-rmse:0.068447+0.005623 test-rmse:1.240800+0.273015
## [145] train-rmse:0.067657+0.005352 test-rmse:1.240696+0.273057
## [146] train-rmse:0.066482+0.005209 test-rmse:1.240579+0.272895
## [147] train-rmse:0.065352+0.005587 test-rmse:1.240356+0.273047
## [148] train-rmse:0.064086+0.005489 test-rmse:1.240477+0.273322
## [149] train-rmse:0.063185+0.005455 test-rmse:1.240705+0.273127
## [150] train-rmse:0.062008+0.005477 test-rmse:1.240920+0.272813
## [151] train-rmse:0.060788+0.005568 test-rmse:1.240832+0.272781
## [152] train-rmse:0.059798+0.005779 test-rmse:1.240836+0.272705
## [153] train-rmse:0.058765+0.005625 test-rmse:1.240925+0.272459
## Stopping. Best iteration:
## [147] train-rmse:0.065352+0.005587 test-rmse:1.240356+0.273047
##
## 21
## [1] train-rmse:90.491687+0.101310 test-rmse:90.491937+0.489579
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.126776+0.089017 test-rmse:76.126749+0.511751
## [3] train-rmse:64.064040+0.079442 test-rmse:64.063658+0.532994
## [4] train-rmse:53.938632+0.072315 test-rmse:53.937809+0.553884
## [5] train-rmse:45.444263+0.067432 test-rmse:45.442850+0.574932
## [6] train-rmse:38.323881+0.064650 test-rmse:38.321713+0.596609
## [7] train-rmse:32.357844+0.062426 test-rmse:32.370365+0.599210
## [8] train-rmse:27.358503+0.058615 test-rmse:27.407408+0.614574
## [9] train-rmse:23.170743+0.052849 test-rmse:23.224391+0.637225
## [10] train-rmse:19.667929+0.048399 test-rmse:19.740667+0.625096
## [11] train-rmse:16.742503+0.043954 test-rmse:16.803793+0.620919
## [12] train-rmse:14.306709+0.042070 test-rmse:14.377406+0.619376
## [13] train-rmse:12.283999+0.040262 test-rmse:12.387327+0.648636
## [14] train-rmse:10.611733+0.039515 test-rmse:10.719909+0.646098
## [15] train-rmse:9.235868+0.041705 test-rmse:9.346363+0.623531
## [16] train-rmse:8.111741+0.043445 test-rmse:8.204748+0.608984
## [17] train-rmse:7.199134+0.046579 test-rmse:7.294492+0.601291
## [18] train-rmse:6.462629+0.050242 test-rmse:6.598453+0.624610
## [19] train-rmse:5.872721+0.051575 test-rmse:6.002141+0.624286
## [20] train-rmse:5.405293+0.054498 test-rmse:5.538347+0.600798
## [21] train-rmse:5.033612+0.057069 test-rmse:5.168147+0.561662
## [22] train-rmse:4.740029+0.059030 test-rmse:4.891245+0.574892
## [23] train-rmse:4.509976+0.061202 test-rmse:4.635351+0.534952
## [24] train-rmse:4.327411+0.063208 test-rmse:4.459016+0.485014
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## [27] train-rmse:3.960876+0.061372 test-rmse:4.111519+0.463799
## [28] train-rmse:3.879916+0.061372 test-rmse:4.018334+0.421420
## [29] train-rmse:3.809761+0.061427 test-rmse:3.973112+0.412768
## [30] train-rmse:3.747749+0.061541 test-rmse:3.923940+0.400307
## [31] train-rmse:3.691604+0.060279 test-rmse:3.885414+0.409655
## [32] train-rmse:3.641962+0.059215 test-rmse:3.831084+0.412890
## [33] train-rmse:3.596686+0.057934 test-rmse:3.794003+0.401146
## [34] train-rmse:3.554404+0.057106 test-rmse:3.749558+0.398107
## [35] train-rmse:3.514885+0.056873 test-rmse:3.708760+0.372835
## [36] train-rmse:3.477558+0.056297 test-rmse:3.665133+0.353343
## [37] train-rmse:3.441546+0.055316 test-rmse:3.635282+0.350642
## [38] train-rmse:3.406470+0.054404 test-rmse:3.604503+0.355388
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## [40] train-rmse:3.341013+0.052711 test-rmse:3.554999+0.345100
## [41] train-rmse:3.308954+0.051941 test-rmse:3.532639+0.348564
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## [44] train-rmse:3.218564+0.049667 test-rmse:3.446994+0.362562
## [45] train-rmse:3.189886+0.049271 test-rmse:3.410206+0.348295
## [46] train-rmse:3.161797+0.048589 test-rmse:3.378059+0.338935
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## [48] train-rmse:3.107594+0.047107 test-rmse:3.324157+0.316392
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## [50] train-rmse:3.056283+0.045644 test-rmse:3.284962+0.314984
## [51] train-rmse:3.031076+0.044819 test-rmse:3.258885+0.320411
## [52] train-rmse:3.006901+0.044031 test-rmse:3.246054+0.326569
## [53] train-rmse:2.983017+0.043254 test-rmse:3.223984+0.326907
## [54] train-rmse:2.959649+0.042934 test-rmse:3.200305+0.331821
## [55] train-rmse:2.936290+0.042438 test-rmse:3.179442+0.337457
## [56] train-rmse:2.913165+0.042207 test-rmse:3.152873+0.341150
## [57] train-rmse:2.890428+0.042016 test-rmse:3.126373+0.324213
## [58] train-rmse:2.868086+0.041972 test-rmse:3.104460+0.331539
## [59] train-rmse:2.845725+0.041722 test-rmse:3.092960+0.332985
## [60] train-rmse:2.823853+0.041709 test-rmse:3.067622+0.323395
## [61] train-rmse:2.802682+0.041935 test-rmse:3.047628+0.301163
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## [64] train-rmse:2.740967+0.041360 test-rmse:2.989614+0.305876
## [65] train-rmse:2.720963+0.040889 test-rmse:2.970443+0.308017
## [66] train-rmse:2.701600+0.040459 test-rmse:2.956668+0.307800
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## [68] train-rmse:2.663745+0.040105 test-rmse:2.915645+0.316904
## [69] train-rmse:2.645068+0.039952 test-rmse:2.895035+0.326200
## [70] train-rmse:2.626554+0.039807 test-rmse:2.884789+0.321686
## [71] train-rmse:2.608080+0.039730 test-rmse:2.859263+0.309785
## [72] train-rmse:2.589723+0.039677 test-rmse:2.832817+0.304272
## [73] train-rmse:2.571909+0.039451 test-rmse:2.814269+0.312480
## [74] train-rmse:2.554201+0.039276 test-rmse:2.793250+0.292762
## [75] train-rmse:2.536653+0.039119 test-rmse:2.779994+0.297790
## [76] train-rmse:2.519423+0.038888 test-rmse:2.767445+0.301979
## [77] train-rmse:2.502365+0.038647 test-rmse:2.753523+0.310582
## [78] train-rmse:2.485731+0.038295 test-rmse:2.732561+0.308361
## [79] train-rmse:2.469307+0.037696 test-rmse:2.724065+0.305890
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## [86] train-rmse:2.358955+0.035261 test-rmse:2.629127+0.321136
## [87] train-rmse:2.343958+0.035092 test-rmse:2.606375+0.319166
## [88] train-rmse:2.329076+0.034955 test-rmse:2.585518+0.311582
## [89] train-rmse:2.314337+0.034770 test-rmse:2.573013+0.298971
## [90] train-rmse:2.299768+0.034550 test-rmse:2.553195+0.290187
## [91] train-rmse:2.285417+0.034093 test-rmse:2.534181+0.274445
## [92] train-rmse:2.271204+0.033565 test-rmse:2.527269+0.281779
## [93] train-rmse:2.257108+0.033107 test-rmse:2.512535+0.286058
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## [95] train-rmse:2.229248+0.032432 test-rmse:2.489407+0.285454
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## [97] train-rmse:2.202090+0.031943 test-rmse:2.460758+0.282624
## [98] train-rmse:2.188820+0.031774 test-rmse:2.448302+0.289855
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## [100] train-rmse:2.162226+0.031361 test-rmse:2.428798+0.291739
## [101] train-rmse:2.149028+0.031163 test-rmse:2.416360+0.286655
## [102] train-rmse:2.136053+0.030830 test-rmse:2.404649+0.291440
## [103] train-rmse:2.123280+0.030462 test-rmse:2.383873+0.289755
## [104] train-rmse:2.110775+0.030115 test-rmse:2.370163+0.279724
## [105] train-rmse:2.098465+0.029858 test-rmse:2.359545+0.283193
## [106] train-rmse:2.086273+0.029614 test-rmse:2.352037+0.284660
## [107] train-rmse:2.073977+0.029321 test-rmse:2.338790+0.290199
## [108] train-rmse:2.061892+0.028988 test-rmse:2.327364+0.287788
## [109] train-rmse:2.049968+0.028633 test-rmse:2.318353+0.285764
## [110] train-rmse:2.038095+0.028199 test-rmse:2.305235+0.285788
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## [112] train-rmse:2.014693+0.027442 test-rmse:2.283449+0.269992
## [113] train-rmse:2.003187+0.027084 test-rmse:2.267574+0.268996
## [114] train-rmse:1.991724+0.026683 test-rmse:2.254395+0.269928
## [115] train-rmse:1.980414+0.026477 test-rmse:2.240424+0.271100
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## [117] train-rmse:1.957886+0.026160 test-rmse:2.215161+0.268674
## [118] train-rmse:1.946724+0.025980 test-rmse:2.203933+0.268860
## [119] train-rmse:1.935602+0.025924 test-rmse:2.190283+0.272223
## [120] train-rmse:1.924557+0.025775 test-rmse:2.180108+0.268238
## [121] train-rmse:1.913761+0.025424 test-rmse:2.172151+0.268969
## [122] train-rmse:1.903181+0.025112 test-rmse:2.159411+0.266923
## [123] train-rmse:1.892677+0.024694 test-rmse:2.150806+0.264599
## [124] train-rmse:1.882329+0.024334 test-rmse:2.140317+0.267571
## [125] train-rmse:1.872077+0.023915 test-rmse:2.135072+0.264924
## [126] train-rmse:1.861954+0.023613 test-rmse:2.124229+0.256104
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## [128] train-rmse:1.841690+0.023126 test-rmse:2.107499+0.260603
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## [130] train-rmse:1.821979+0.022769 test-rmse:2.089252+0.262542
## [131] train-rmse:1.812173+0.022531 test-rmse:2.079899+0.268034
## [132] train-rmse:1.802501+0.022345 test-rmse:2.070700+0.270931
## [133] train-rmse:1.792913+0.022054 test-rmse:2.065938+0.269544
## [134] train-rmse:1.783336+0.021702 test-rmse:2.054645+0.256786
## [135] train-rmse:1.773935+0.021386 test-rmse:2.040233+0.253733
## [136] train-rmse:1.764623+0.021164 test-rmse:2.030535+0.253117
## [137] train-rmse:1.755488+0.020911 test-rmse:2.024162+0.254631
## [138] train-rmse:1.746374+0.020648 test-rmse:2.022708+0.254341
## [139] train-rmse:1.737386+0.020504 test-rmse:2.014522+0.252466
## [140] train-rmse:1.728421+0.020368 test-rmse:2.007000+0.250098
## [141] train-rmse:1.719485+0.020229 test-rmse:1.998562+0.246507
## [142] train-rmse:1.710598+0.020079 test-rmse:1.990975+0.245538
## [143] train-rmse:1.701737+0.019900 test-rmse:1.982845+0.247056
## [144] train-rmse:1.693113+0.019690 test-rmse:1.977392+0.251928
## [145] train-rmse:1.684515+0.019586 test-rmse:1.965716+0.247476
## [146] train-rmse:1.676016+0.019362 test-rmse:1.956703+0.245856
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## [148] train-rmse:1.659097+0.019030 test-rmse:1.936635+0.241414
## [149] train-rmse:1.650685+0.018920 test-rmse:1.925529+0.245308
## [150] train-rmse:1.642440+0.018713 test-rmse:1.918315+0.247152
## [151] train-rmse:1.634230+0.018370 test-rmse:1.910855+0.247876
## [152] train-rmse:1.626112+0.017984 test-rmse:1.902849+0.246418
## [153] train-rmse:1.618170+0.017722 test-rmse:1.893191+0.246725
## [154] train-rmse:1.610298+0.017557 test-rmse:1.888155+0.244512
## [155] train-rmse:1.602457+0.017308 test-rmse:1.874682+0.246335
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## [158] train-rmse:1.579513+0.016773 test-rmse:1.862483+0.247075
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## [162] train-rmse:1.549682+0.015999 test-rmse:1.830335+0.236593
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## [165] train-rmse:1.527928+0.015906 test-rmse:1.807569+0.237062
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## [168] train-rmse:1.506709+0.015896 test-rmse:1.787295+0.231259
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## [171] train-rmse:1.486187+0.015479 test-rmse:1.774399+0.224377
## [172] train-rmse:1.479347+0.015321 test-rmse:1.769674+0.226034
## [173] train-rmse:1.472478+0.015131 test-rmse:1.765165+0.226642
## [174] train-rmse:1.465766+0.014986 test-rmse:1.755578+0.229320
## [175] train-rmse:1.459109+0.014810 test-rmse:1.744158+0.232668
## [176] train-rmse:1.452567+0.014458 test-rmse:1.736970+0.234142
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## [178] train-rmse:1.439670+0.013989 test-rmse:1.725939+0.231508
## [179] train-rmse:1.433325+0.013812 test-rmse:1.715018+0.230771
## [180] train-rmse:1.426952+0.013721 test-rmse:1.706270+0.232124
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## [182] train-rmse:1.414476+0.013707 test-rmse:1.697569+0.230964
## [183] train-rmse:1.408305+0.013664 test-rmse:1.697275+0.231071
## [184] train-rmse:1.402128+0.013612 test-rmse:1.688874+0.229144
## [185] train-rmse:1.396084+0.013573 test-rmse:1.684493+0.227494
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## [187] train-rmse:1.384230+0.013365 test-rmse:1.670295+0.218016
## [188] train-rmse:1.378455+0.013260 test-rmse:1.666733+0.218669
## [189] train-rmse:1.372682+0.013170 test-rmse:1.661456+0.220041
## [190] train-rmse:1.366908+0.013119 test-rmse:1.655560+0.222807
## [191] train-rmse:1.361222+0.013056 test-rmse:1.649134+0.219722
## [192] train-rmse:1.355567+0.013011 test-rmse:1.644608+0.222238
## [193] train-rmse:1.350025+0.012955 test-rmse:1.640421+0.222011
## [194] train-rmse:1.344495+0.012844 test-rmse:1.632702+0.218866
## [195] train-rmse:1.339007+0.012762 test-rmse:1.626074+0.217990
## [196] train-rmse:1.333525+0.012624 test-rmse:1.621331+0.218055
## [197] train-rmse:1.328096+0.012450 test-rmse:1.612349+0.219624
## [198] train-rmse:1.322683+0.012314 test-rmse:1.607199+0.218405
## [199] train-rmse:1.317303+0.012195 test-rmse:1.601790+0.221049
## [200] train-rmse:1.311964+0.012101 test-rmse:1.592024+0.223633
## [201] train-rmse:1.306737+0.012065 test-rmse:1.589065+0.222879
## [202] train-rmse:1.301519+0.012041 test-rmse:1.579052+0.212618
## [203] train-rmse:1.296394+0.012023 test-rmse:1.578490+0.213479
## [204] train-rmse:1.291310+0.011937 test-rmse:1.573678+0.215721
## [205] train-rmse:1.286234+0.011847 test-rmse:1.570052+0.216411
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## [207] train-rmse:1.276169+0.011836 test-rmse:1.566255+0.216744
## [208] train-rmse:1.271226+0.011801 test-rmse:1.563001+0.215532
## [209] train-rmse:1.266335+0.011684 test-rmse:1.555415+0.211074
## [210] train-rmse:1.261493+0.011606 test-rmse:1.551779+0.206281
## [211] train-rmse:1.256690+0.011531 test-rmse:1.547570+0.205671
## [212] train-rmse:1.251961+0.011476 test-rmse:1.541688+0.205014
## [213] train-rmse:1.247280+0.011426 test-rmse:1.536273+0.206381
## [214] train-rmse:1.242659+0.011385 test-rmse:1.528924+0.206847
## [215] train-rmse:1.238081+0.011408 test-rmse:1.524466+0.205666
## [216] train-rmse:1.233526+0.011432 test-rmse:1.520835+0.206991
## [217] train-rmse:1.228981+0.011442 test-rmse:1.515614+0.205424
## [218] train-rmse:1.224499+0.011434 test-rmse:1.509281+0.206199
## [219] train-rmse:1.220014+0.011478 test-rmse:1.504279+0.206022
## [220] train-rmse:1.215551+0.011469 test-rmse:1.497050+0.208909
## [221] train-rmse:1.211137+0.011410 test-rmse:1.493220+0.209635
## [222] train-rmse:1.206787+0.011310 test-rmse:1.486745+0.200915
## [223] train-rmse:1.202506+0.011213 test-rmse:1.482683+0.202051
## [224] train-rmse:1.198239+0.011175 test-rmse:1.478508+0.205709
## [225] train-rmse:1.194005+0.011177 test-rmse:1.475281+0.206829
## [226] train-rmse:1.189820+0.011196 test-rmse:1.472198+0.205418
## [227] train-rmse:1.185723+0.011185 test-rmse:1.470442+0.205296
## [228] train-rmse:1.181641+0.011156 test-rmse:1.465332+0.206417
## [229] train-rmse:1.177560+0.011115 test-rmse:1.461882+0.205269
## [230] train-rmse:1.173539+0.011127 test-rmse:1.461994+0.205095
## [231] train-rmse:1.169585+0.011094 test-rmse:1.459190+0.204336
## [232] train-rmse:1.165679+0.011094 test-rmse:1.454385+0.206981
## [233] train-rmse:1.161727+0.011095 test-rmse:1.451554+0.208419
## [234] train-rmse:1.157778+0.011116 test-rmse:1.446665+0.203354
## [235] train-rmse:1.153871+0.011106 test-rmse:1.445382+0.199102
## [236] train-rmse:1.150045+0.011099 test-rmse:1.442101+0.199446
## [237] train-rmse:1.146223+0.011047 test-rmse:1.437263+0.199682
## [238] train-rmse:1.142460+0.010990 test-rmse:1.435611+0.198240
## [239] train-rmse:1.138748+0.010935 test-rmse:1.430153+0.200206
## [240] train-rmse:1.135031+0.010851 test-rmse:1.425836+0.201601
## [241] train-rmse:1.131341+0.010824 test-rmse:1.420799+0.201622
## [242] train-rmse:1.127662+0.010797 test-rmse:1.418631+0.201401
## [243] train-rmse:1.124037+0.010807 test-rmse:1.414095+0.200639
## [244] train-rmse:1.120448+0.010810 test-rmse:1.410707+0.200121
## [245] train-rmse:1.116903+0.010825 test-rmse:1.408538+0.199103
## [246] train-rmse:1.113423+0.010791 test-rmse:1.401640+0.192671
## [247] train-rmse:1.109969+0.010776 test-rmse:1.398328+0.192764
## [248] train-rmse:1.106590+0.010772 test-rmse:1.396829+0.193052
## [249] train-rmse:1.103241+0.010794 test-rmse:1.395159+0.192249
## [250] train-rmse:1.099922+0.010820 test-rmse:1.390610+0.189090
## [251] train-rmse:1.096598+0.010816 test-rmse:1.387864+0.189068
## [252] train-rmse:1.093334+0.010828 test-rmse:1.385176+0.190233
## [253] train-rmse:1.090048+0.010841 test-rmse:1.383472+0.191466
## [254] train-rmse:1.086787+0.010894 test-rmse:1.379645+0.188241
## [255] train-rmse:1.083585+0.010912 test-rmse:1.377856+0.189833
## [256] train-rmse:1.080410+0.010956 test-rmse:1.376458+0.191272
## [257] train-rmse:1.077244+0.010991 test-rmse:1.373328+0.189727
## [258] train-rmse:1.074065+0.011015 test-rmse:1.369481+0.191664
## [259] train-rmse:1.070988+0.010995 test-rmse:1.366138+0.193507
## [260] train-rmse:1.067914+0.010969 test-rmse:1.362493+0.194057
## [261] train-rmse:1.064860+0.010917 test-rmse:1.359280+0.191968
## [262] train-rmse:1.061819+0.010856 test-rmse:1.354649+0.192998
## [263] train-rmse:1.058813+0.010844 test-rmse:1.346460+0.187783
## [264] train-rmse:1.055810+0.010836 test-rmse:1.343448+0.185084
## [265] train-rmse:1.052820+0.010830 test-rmse:1.339418+0.186154
## [266] train-rmse:1.049845+0.010834 test-rmse:1.336765+0.186772
## [267] train-rmse:1.046942+0.010800 test-rmse:1.331803+0.189778
## [268] train-rmse:1.044060+0.010788 test-rmse:1.329609+0.188289
## [269] train-rmse:1.041199+0.010750 test-rmse:1.327076+0.188425
## [270] train-rmse:1.038371+0.010719 test-rmse:1.324638+0.187483
## [271] train-rmse:1.035593+0.010706 test-rmse:1.322071+0.187026
## [272] train-rmse:1.032834+0.010708 test-rmse:1.318158+0.187746
## [273] train-rmse:1.030116+0.010689 test-rmse:1.315154+0.186409
## [274] train-rmse:1.027363+0.010671 test-rmse:1.313538+0.186049
## [275] train-rmse:1.024681+0.010655 test-rmse:1.311457+0.183435
## [276] train-rmse:1.022039+0.010688 test-rmse:1.307501+0.180990
## [277] train-rmse:1.019391+0.010723 test-rmse:1.307635+0.180978
## [278] train-rmse:1.016785+0.010753 test-rmse:1.305388+0.182642
## [279] train-rmse:1.014232+0.010789 test-rmse:1.303522+0.182489
## [280] train-rmse:1.011664+0.010798 test-rmse:1.303023+0.182471
## [281] train-rmse:1.009148+0.010807 test-rmse:1.301755+0.182518
## [282] train-rmse:1.006627+0.010795 test-rmse:1.300027+0.181430
## [283] train-rmse:1.004101+0.010795 test-rmse:1.299725+0.181046
## [284] train-rmse:1.001592+0.010808 test-rmse:1.297359+0.182432
## [285] train-rmse:0.999115+0.010808 test-rmse:1.292895+0.183062
## [286] train-rmse:0.996663+0.010805 test-rmse:1.292278+0.184224
## [287] train-rmse:0.994251+0.010812 test-rmse:1.291129+0.185237
## [288] train-rmse:0.991889+0.010788 test-rmse:1.285120+0.179975
## [289] train-rmse:0.989544+0.010772 test-rmse:1.283488+0.178786
## [290] train-rmse:0.987210+0.010747 test-rmse:1.279441+0.180697
## [291] train-rmse:0.984897+0.010741 test-rmse:1.275699+0.180106
## [292] train-rmse:0.982602+0.010743 test-rmse:1.273889+0.180652
## [293] train-rmse:0.980341+0.010755 test-rmse:1.272655+0.181736
## [294] train-rmse:0.978090+0.010743 test-rmse:1.269577+0.181302
## [295] train-rmse:0.975848+0.010722 test-rmse:1.266475+0.178733
## [296] train-rmse:0.973587+0.010707 test-rmse:1.265977+0.177628
## [297] train-rmse:0.971383+0.010738 test-rmse:1.263517+0.178029
## [298] train-rmse:0.969196+0.010799 test-rmse:1.259705+0.179961
## [299] train-rmse:0.967015+0.010859 test-rmse:1.258035+0.179901
## [300] train-rmse:0.964865+0.010898 test-rmse:1.256878+0.179696
## [301] train-rmse:0.962739+0.010931 test-rmse:1.255221+0.180087
## [302] train-rmse:0.960632+0.010936 test-rmse:1.251812+0.177201
## [303] train-rmse:0.958514+0.010953 test-rmse:1.250947+0.177403
## [304] train-rmse:0.956394+0.010958 test-rmse:1.249775+0.177789
## [305] train-rmse:0.954313+0.010965 test-rmse:1.249778+0.176868
## [306] train-rmse:0.952268+0.010999 test-rmse:1.247782+0.175352
## [307] train-rmse:0.950250+0.011016 test-rmse:1.245332+0.175289
## [308] train-rmse:0.948238+0.011037 test-rmse:1.243369+0.174222
## [309] train-rmse:0.946255+0.011060 test-rmse:1.241017+0.174904
## [310] train-rmse:0.944292+0.011102 test-rmse:1.240280+0.174275
## [311] train-rmse:0.942357+0.011152 test-rmse:1.238225+0.175200
## [312] train-rmse:0.940444+0.011189 test-rmse:1.235544+0.174050
## [313] train-rmse:0.938563+0.011223 test-rmse:1.234285+0.174251
## [314] train-rmse:0.936679+0.011264 test-rmse:1.232747+0.175108
## [315] train-rmse:0.934783+0.011276 test-rmse:1.232239+0.175539
## [316] train-rmse:0.932921+0.011290 test-rmse:1.231372+0.175804
## [317] train-rmse:0.931069+0.011330 test-rmse:1.230218+0.175769
## [318] train-rmse:0.929231+0.011350 test-rmse:1.227869+0.176916
## [319] train-rmse:0.927406+0.011371 test-rmse:1.225640+0.178006
## [320] train-rmse:0.925606+0.011418 test-rmse:1.222933+0.178050
## [321] train-rmse:0.923830+0.011424 test-rmse:1.219781+0.172561
## [322] train-rmse:0.922067+0.011457 test-rmse:1.217634+0.170868
## [323] train-rmse:0.920297+0.011484 test-rmse:1.216335+0.170127
## [324] train-rmse:0.918544+0.011496 test-rmse:1.213546+0.170939
## [325] train-rmse:0.916810+0.011498 test-rmse:1.212230+0.170184
## [326] train-rmse:0.915083+0.011505 test-rmse:1.211504+0.168415
## [327] train-rmse:0.913372+0.011518 test-rmse:1.208149+0.170606
## [328] train-rmse:0.911691+0.011531 test-rmse:1.205659+0.169824
## [329] train-rmse:0.910026+0.011549 test-rmse:1.205137+0.169377
## [330] train-rmse:0.908377+0.011561 test-rmse:1.203860+0.169933
## [331] train-rmse:0.906737+0.011563 test-rmse:1.203784+0.170359
## [332] train-rmse:0.905111+0.011561 test-rmse:1.202934+0.170521
## [333] train-rmse:0.903495+0.011580 test-rmse:1.203059+0.169109
## [334] train-rmse:0.901884+0.011588 test-rmse:1.202416+0.169825
## [335] train-rmse:0.900281+0.011589 test-rmse:1.201463+0.167359
## [336] train-rmse:0.898691+0.011600 test-rmse:1.199140+0.165066
## [337] train-rmse:0.897118+0.011630 test-rmse:1.197311+0.166467
## [338] train-rmse:0.895567+0.011655 test-rmse:1.196812+0.166103
## [339] train-rmse:0.894050+0.011693 test-rmse:1.194686+0.166656
## [340] train-rmse:0.892546+0.011729 test-rmse:1.194119+0.167133
## [341] train-rmse:0.891050+0.011768 test-rmse:1.193121+0.168124
## [342] train-rmse:0.889546+0.011787 test-rmse:1.193003+0.168951
## [343] train-rmse:0.888085+0.011818 test-rmse:1.191094+0.169386
## [344] train-rmse:0.886634+0.011859 test-rmse:1.189198+0.169736
## [345] train-rmse:0.885194+0.011898 test-rmse:1.183690+0.165368
## [346] train-rmse:0.883761+0.011937 test-rmse:1.182131+0.164453
## [347] train-rmse:0.882360+0.011958 test-rmse:1.181641+0.164420
## [348] train-rmse:0.880943+0.011978 test-rmse:1.180533+0.164264
## [349] train-rmse:0.879533+0.011980 test-rmse:1.180040+0.163356
## [350] train-rmse:0.878140+0.012023 test-rmse:1.179130+0.163242
## [351] train-rmse:0.876736+0.012038 test-rmse:1.178168+0.162638
## [352] train-rmse:0.875374+0.012057 test-rmse:1.178531+0.162917
## [353] train-rmse:0.874020+0.012088 test-rmse:1.176708+0.164756
## [354] train-rmse:0.872682+0.012112 test-rmse:1.176565+0.164016
## [355] train-rmse:0.871350+0.012132 test-rmse:1.173691+0.162769
## [356] train-rmse:0.870021+0.012156 test-rmse:1.173536+0.162191
## [357] train-rmse:0.868701+0.012176 test-rmse:1.171772+0.164221
## [358] train-rmse:0.867409+0.012206 test-rmse:1.170726+0.164498
## [359] train-rmse:0.866135+0.012248 test-rmse:1.169925+0.164862
## [360] train-rmse:0.864871+0.012284 test-rmse:1.168354+0.164968
## [361] train-rmse:0.863605+0.012307 test-rmse:1.166118+0.164802
## [362] train-rmse:0.862349+0.012345 test-rmse:1.164331+0.164855
## [363] train-rmse:0.861103+0.012369 test-rmse:1.162914+0.164160
## [364] train-rmse:0.859863+0.012389 test-rmse:1.161571+0.163637
## [365] train-rmse:0.858625+0.012408 test-rmse:1.160503+0.164281
## [366] train-rmse:0.857412+0.012425 test-rmse:1.158499+0.163149
## [367] train-rmse:0.856218+0.012457 test-rmse:1.159013+0.163477
## [368] train-rmse:0.855039+0.012492 test-rmse:1.156365+0.163568
## [369] train-rmse:0.853862+0.012513 test-rmse:1.156235+0.163087
## [370] train-rmse:0.852693+0.012551 test-rmse:1.154315+0.163125
## [371] train-rmse:0.851540+0.012590 test-rmse:1.152768+0.164116
## [372] train-rmse:0.850398+0.012630 test-rmse:1.152900+0.164221
## [373] train-rmse:0.849258+0.012677 test-rmse:1.152274+0.164190
## [374] train-rmse:0.848117+0.012708 test-rmse:1.151900+0.163796
## [375] train-rmse:0.847002+0.012740 test-rmse:1.151972+0.163902
## [376] train-rmse:0.845890+0.012747 test-rmse:1.148895+0.159934
## [377] train-rmse:0.844795+0.012761 test-rmse:1.147946+0.160594
## [378] train-rmse:0.843708+0.012780 test-rmse:1.148124+0.160903
## [379] train-rmse:0.842633+0.012819 test-rmse:1.147321+0.161239
## [380] train-rmse:0.841561+0.012845 test-rmse:1.147713+0.161483
## [381] train-rmse:0.840500+0.012871 test-rmse:1.145544+0.160622
## [382] train-rmse:0.839452+0.012906 test-rmse:1.145506+0.160774
## [383] train-rmse:0.838402+0.012938 test-rmse:1.144208+0.161421
## [384] train-rmse:0.837371+0.012955 test-rmse:1.143370+0.161966
## [385] train-rmse:0.836362+0.012980 test-rmse:1.142734+0.160972
## [386] train-rmse:0.835355+0.013006 test-rmse:1.141383+0.159601
## [387] train-rmse:0.834354+0.013023 test-rmse:1.139596+0.159419
## [388] train-rmse:0.833360+0.013055 test-rmse:1.138567+0.159370
## [389] train-rmse:0.832354+0.013066 test-rmse:1.138913+0.158893
## [390] train-rmse:0.831373+0.013087 test-rmse:1.139011+0.158246
## [391] train-rmse:0.830394+0.013112 test-rmse:1.136912+0.158617
## [392] train-rmse:0.829429+0.013149 test-rmse:1.135717+0.158874
## [393] train-rmse:0.828474+0.013198 test-rmse:1.135229+0.159133
## [394] train-rmse:0.827532+0.013241 test-rmse:1.134879+0.159639
## [395] train-rmse:0.826566+0.013273 test-rmse:1.134072+0.160713
## [396] train-rmse:0.825637+0.013310 test-rmse:1.134115+0.160938
## [397] train-rmse:0.824721+0.013343 test-rmse:1.133564+0.160725
## [398] train-rmse:0.823816+0.013381 test-rmse:1.133129+0.160789
## [399] train-rmse:0.822912+0.013405 test-rmse:1.131870+0.160217
## [400] train-rmse:0.822017+0.013439 test-rmse:1.128320+0.157014
## [401] train-rmse:0.821142+0.013450 test-rmse:1.126343+0.156953
## [402] train-rmse:0.820260+0.013473 test-rmse:1.124512+0.155859
## [403] train-rmse:0.819385+0.013489 test-rmse:1.122277+0.154445
## [404] train-rmse:0.818518+0.013514 test-rmse:1.122823+0.154240
## [405] train-rmse:0.817638+0.013534 test-rmse:1.121893+0.154802
## [406] train-rmse:0.816779+0.013564 test-rmse:1.121776+0.154979
## [407] train-rmse:0.815930+0.013583 test-rmse:1.121351+0.154847
## [408] train-rmse:0.815092+0.013608 test-rmse:1.121503+0.154488
## [409] train-rmse:0.814249+0.013624 test-rmse:1.120717+0.155684
## [410] train-rmse:0.813413+0.013634 test-rmse:1.119396+0.156215
## [411] train-rmse:0.812592+0.013660 test-rmse:1.118374+0.157043
## [412] train-rmse:0.811769+0.013686 test-rmse:1.117758+0.156671
## [413] train-rmse:0.810958+0.013710 test-rmse:1.117346+0.156692
## [414] train-rmse:0.810155+0.013736 test-rmse:1.117133+0.157314
## [415] train-rmse:0.809352+0.013769 test-rmse:1.116092+0.158522
## [416] train-rmse:0.808558+0.013793 test-rmse:1.115619+0.157997
## [417] train-rmse:0.807770+0.013830 test-rmse:1.115009+0.158569
## [418] train-rmse:0.806971+0.013851 test-rmse:1.115265+0.157462
## [419] train-rmse:0.806202+0.013876 test-rmse:1.115243+0.157456
## [420] train-rmse:0.805448+0.013905 test-rmse:1.114898+0.158052
## [421] train-rmse:0.804704+0.013926 test-rmse:1.115186+0.157955
## [422] train-rmse:0.803951+0.013946 test-rmse:1.113915+0.157993
## [423] train-rmse:0.803187+0.013957 test-rmse:1.111867+0.158428
## [424] train-rmse:0.802436+0.013978 test-rmse:1.111103+0.157863
## [425] train-rmse:0.801703+0.014004 test-rmse:1.110934+0.157778
## [426] train-rmse:0.800976+0.014034 test-rmse:1.110444+0.158192
## [427] train-rmse:0.800248+0.014055 test-rmse:1.109551+0.158363
## [428] train-rmse:0.799519+0.014063 test-rmse:1.110091+0.157853
## [429] train-rmse:0.798809+0.014081 test-rmse:1.109338+0.158432
## [430] train-rmse:0.798082+0.014084 test-rmse:1.109449+0.157425
## [431] train-rmse:0.797365+0.014097 test-rmse:1.106492+0.154540
## [432] train-rmse:0.796664+0.014122 test-rmse:1.105096+0.153582
## [433] train-rmse:0.795969+0.014146 test-rmse:1.104180+0.154053
## [434] train-rmse:0.795265+0.014163 test-rmse:1.102237+0.152825
## [435] train-rmse:0.794573+0.014176 test-rmse:1.101995+0.153061
## [436] train-rmse:0.793896+0.014190 test-rmse:1.101675+0.153062
## [437] train-rmse:0.793210+0.014209 test-rmse:1.101272+0.153551
## [438] train-rmse:0.792528+0.014240 test-rmse:1.101476+0.153011
## [439] train-rmse:0.791860+0.014268 test-rmse:1.100802+0.153677
## [440] train-rmse:0.791200+0.014280 test-rmse:1.100553+0.153757
## [441] train-rmse:0.790548+0.014294 test-rmse:1.100332+0.153290
## [442] train-rmse:0.789899+0.014314 test-rmse:1.099342+0.153353
## [443] train-rmse:0.789254+0.014331 test-rmse:1.098304+0.153940
## [444] train-rmse:0.788612+0.014344 test-rmse:1.097504+0.154175
## [445] train-rmse:0.787966+0.014349 test-rmse:1.097873+0.153689
## [446] train-rmse:0.787322+0.014354 test-rmse:1.097357+0.153491
## [447] train-rmse:0.786699+0.014365 test-rmse:1.095316+0.153462
## [448] train-rmse:0.786084+0.014381 test-rmse:1.095220+0.153077
## [449] train-rmse:0.785473+0.014395 test-rmse:1.094666+0.153436
## [450] train-rmse:0.784846+0.014407 test-rmse:1.094205+0.153534
## [451] train-rmse:0.784232+0.014426 test-rmse:1.092910+0.152755
## [452] train-rmse:0.783627+0.014447 test-rmse:1.091884+0.153253
## [453] train-rmse:0.783027+0.014453 test-rmse:1.092084+0.152922
## [454] train-rmse:0.782419+0.014454 test-rmse:1.091099+0.152714
## [455] train-rmse:0.781825+0.014464 test-rmse:1.089749+0.152634
## [456] train-rmse:0.781237+0.014471 test-rmse:1.089165+0.153031
## [457] train-rmse:0.780646+0.014476 test-rmse:1.086572+0.153516
## [458] train-rmse:0.780060+0.014482 test-rmse:1.086894+0.153001
## [459] train-rmse:0.779479+0.014492 test-rmse:1.086272+0.152987
## [460] train-rmse:0.778902+0.014507 test-rmse:1.085470+0.153515
## [461] train-rmse:0.778313+0.014531 test-rmse:1.086042+0.153438
## [462] train-rmse:0.777744+0.014546 test-rmse:1.086178+0.153112
## [463] train-rmse:0.777170+0.014555 test-rmse:1.085137+0.153718
## [464] train-rmse:0.776605+0.014569 test-rmse:1.084732+0.153988
## [465] train-rmse:0.776046+0.014570 test-rmse:1.084466+0.153787
## [466] train-rmse:0.775499+0.014582 test-rmse:1.084565+0.153104
## [467] train-rmse:0.774943+0.014591 test-rmse:1.083948+0.153745
## [468] train-rmse:0.774395+0.014592 test-rmse:1.083805+0.153947
## [469] train-rmse:0.773853+0.014599 test-rmse:1.084536+0.153226
## [470] train-rmse:0.773307+0.014606 test-rmse:1.083747+0.153458
## [471] train-rmse:0.772767+0.014615 test-rmse:1.082568+0.153761
## [472] train-rmse:0.772231+0.014622 test-rmse:1.082964+0.153436
## [473] train-rmse:0.771692+0.014633 test-rmse:1.081458+0.152649
## [474] train-rmse:0.771159+0.014638 test-rmse:1.080052+0.151944
## [475] train-rmse:0.770630+0.014647 test-rmse:1.078101+0.149380
## [476] train-rmse:0.770102+0.014655 test-rmse:1.076457+0.149884
## [477] train-rmse:0.769578+0.014664 test-rmse:1.076139+0.150187
## [478] train-rmse:0.769060+0.014676 test-rmse:1.075939+0.151053
## [479] train-rmse:0.768551+0.014683 test-rmse:1.076116+0.151083
## [480] train-rmse:0.768040+0.014684 test-rmse:1.075045+0.151706
## [481] train-rmse:0.767535+0.014676 test-rmse:1.075006+0.151414
## [482] train-rmse:0.767028+0.014679 test-rmse:1.075337+0.151041
## [483] train-rmse:0.766533+0.014680 test-rmse:1.075032+0.151759
## [484] train-rmse:0.766028+0.014695 test-rmse:1.074527+0.151223
## [485] train-rmse:0.765532+0.014706 test-rmse:1.074637+0.150746
## [486] train-rmse:0.765040+0.014714 test-rmse:1.073912+0.150968
## [487] train-rmse:0.764548+0.014716 test-rmse:1.073322+0.151017
## [488] train-rmse:0.764063+0.014723 test-rmse:1.073403+0.150564
## [489] train-rmse:0.763577+0.014735 test-rmse:1.073250+0.150964
## [490] train-rmse:0.763096+0.014745 test-rmse:1.073098+0.150745
## [491] train-rmse:0.762613+0.014745 test-rmse:1.072659+0.150475
## [492] train-rmse:0.762142+0.014746 test-rmse:1.072745+0.150828
## [493] train-rmse:0.761679+0.014753 test-rmse:1.072471+0.150760
## [494] train-rmse:0.761217+0.014756 test-rmse:1.071612+0.151171
## [495] train-rmse:0.760753+0.014757 test-rmse:1.072013+0.151181
## [496] train-rmse:0.760292+0.014762 test-rmse:1.071205+0.150621
## [497] train-rmse:0.759833+0.014765 test-rmse:1.070442+0.151082
## [498] train-rmse:0.759373+0.014774 test-rmse:1.070737+0.150885
## [499] train-rmse:0.758913+0.014770 test-rmse:1.070314+0.151281
## [500] train-rmse:0.758459+0.014783 test-rmse:1.069544+0.151277
## [501] train-rmse:0.758008+0.014780 test-rmse:1.069206+0.151177
## [502] train-rmse:0.757564+0.014782 test-rmse:1.069394+0.151045
## [503] train-rmse:0.757122+0.014785 test-rmse:1.069634+0.151169
## [504] train-rmse:0.756681+0.014787 test-rmse:1.069261+0.151447
## [505] train-rmse:0.756229+0.014773 test-rmse:1.068001+0.152634
## [506] train-rmse:0.755799+0.014778 test-rmse:1.067951+0.152605
## [507] train-rmse:0.755371+0.014778 test-rmse:1.067638+0.152438
## [508] train-rmse:0.754941+0.014776 test-rmse:1.067859+0.151983
## [509] train-rmse:0.754520+0.014778 test-rmse:1.066773+0.152043
## [510] train-rmse:0.754102+0.014784 test-rmse:1.066337+0.151722
## [511] train-rmse:0.753674+0.014795 test-rmse:1.065189+0.151450
## [512] train-rmse:0.753250+0.014801 test-rmse:1.064933+0.151636
## [513] train-rmse:0.752835+0.014802 test-rmse:1.064932+0.151933
## [514] train-rmse:0.752421+0.014803 test-rmse:1.063776+0.151234
## [515] train-rmse:0.752008+0.014805 test-rmse:1.064099+0.151112
## [516] train-rmse:0.751583+0.014790 test-rmse:1.062311+0.149366
## [517] train-rmse:0.751172+0.014782 test-rmse:1.062145+0.149693
## [518] train-rmse:0.750768+0.014783 test-rmse:1.062009+0.150149
## [519] train-rmse:0.750362+0.014781 test-rmse:1.061033+0.149796
## [520] train-rmse:0.749947+0.014775 test-rmse:1.061097+0.149417
## [521] train-rmse:0.749545+0.014778 test-rmse:1.061165+0.148550
## [522] train-rmse:0.749134+0.014777 test-rmse:1.060834+0.149329
## [523] train-rmse:0.748726+0.014776 test-rmse:1.060928+0.149677
## [524] train-rmse:0.748326+0.014772 test-rmse:1.060427+0.149888
## [525] train-rmse:0.747936+0.014772 test-rmse:1.060244+0.149370
## [526] train-rmse:0.747544+0.014769 test-rmse:1.059995+0.149490
## [527] train-rmse:0.747152+0.014759 test-rmse:1.059727+0.149474
## [528] train-rmse:0.746763+0.014752 test-rmse:1.060464+0.148992
## [529] train-rmse:0.746366+0.014749 test-rmse:1.059315+0.149440
## [530] train-rmse:0.745978+0.014743 test-rmse:1.059443+0.149133
## [531] train-rmse:0.745601+0.014735 test-rmse:1.058752+0.149260
## [532] train-rmse:0.745225+0.014734 test-rmse:1.058567+0.149313
## [533] train-rmse:0.744854+0.014729 test-rmse:1.057517+0.148848
## [534] train-rmse:0.744485+0.014725 test-rmse:1.057077+0.148983
## [535] train-rmse:0.744110+0.014723 test-rmse:1.057389+0.148711
## [536] train-rmse:0.743744+0.014717 test-rmse:1.056652+0.149131
## [537] train-rmse:0.743380+0.014710 test-rmse:1.055868+0.148839
## [538] train-rmse:0.743018+0.014704 test-rmse:1.055764+0.148886
## [539] train-rmse:0.742642+0.014701 test-rmse:1.055599+0.148790
## [540] train-rmse:0.742277+0.014697 test-rmse:1.056218+0.148154
## [541] train-rmse:0.741917+0.014686 test-rmse:1.055691+0.147484
## [542] train-rmse:0.741552+0.014666 test-rmse:1.056337+0.147527
## [543] train-rmse:0.741190+0.014662 test-rmse:1.056267+0.147434
## [544] train-rmse:0.740841+0.014659 test-rmse:1.055860+0.147581
## [545] train-rmse:0.740487+0.014649 test-rmse:1.054491+0.147167
## [546] train-rmse:0.740130+0.014633 test-rmse:1.054744+0.147298
## [547] train-rmse:0.739776+0.014618 test-rmse:1.054235+0.146892
## [548] train-rmse:0.739430+0.014606 test-rmse:1.054707+0.147042
## [549] train-rmse:0.739070+0.014581 test-rmse:1.053797+0.147715
## [550] train-rmse:0.738726+0.014571 test-rmse:1.053216+0.148088
## [551] train-rmse:0.738378+0.014556 test-rmse:1.053074+0.148537
## [552] train-rmse:0.738042+0.014549 test-rmse:1.052697+0.148655
## [553] train-rmse:0.737708+0.014542 test-rmse:1.052859+0.148342
## [554] train-rmse:0.737372+0.014534 test-rmse:1.052849+0.148399
## [555] train-rmse:0.737030+0.014522 test-rmse:1.052172+0.148810
## [556] train-rmse:0.736696+0.014515 test-rmse:1.052599+0.148970
## [557] train-rmse:0.736363+0.014507 test-rmse:1.052847+0.148273
## [558] train-rmse:0.736024+0.014496 test-rmse:1.052304+0.149158
## [559] train-rmse:0.735702+0.014491 test-rmse:1.051821+0.149431
## [560] train-rmse:0.735376+0.014483 test-rmse:1.051991+0.149228
## [561] train-rmse:0.735042+0.014469 test-rmse:1.051149+0.148744
## [562] train-rmse:0.734713+0.014455 test-rmse:1.050553+0.147939
## [563] train-rmse:0.734391+0.014445 test-rmse:1.050932+0.148045
## [564] train-rmse:0.734065+0.014426 test-rmse:1.050224+0.148104
## [565] train-rmse:0.733748+0.014416 test-rmse:1.050279+0.148470
## [566] train-rmse:0.733421+0.014404 test-rmse:1.050001+0.148840
## [567] train-rmse:0.733095+0.014398 test-rmse:1.050222+0.148565
## [568] train-rmse:0.732771+0.014381 test-rmse:1.049975+0.148704
## [569] train-rmse:0.732457+0.014366 test-rmse:1.049951+0.148767
## [570] train-rmse:0.732142+0.014359 test-rmse:1.049551+0.149239
## [571] train-rmse:0.731832+0.014347 test-rmse:1.048534+0.147265
## [572] train-rmse:0.731519+0.014331 test-rmse:1.048020+0.147446
## [573] train-rmse:0.731203+0.014315 test-rmse:1.047801+0.147900
## [574] train-rmse:0.730899+0.014300 test-rmse:1.047250+0.148053
## [575] train-rmse:0.730588+0.014283 test-rmse:1.047207+0.147909
## [576] train-rmse:0.730284+0.014272 test-rmse:1.047022+0.147849
## [577] train-rmse:0.729990+0.014257 test-rmse:1.046094+0.147924
## [578] train-rmse:0.729695+0.014245 test-rmse:1.046505+0.148081
## [579] train-rmse:0.729395+0.014233 test-rmse:1.046326+0.147257
## [580] train-rmse:0.729095+0.014214 test-rmse:1.046683+0.147018
## [581] train-rmse:0.728793+0.014196 test-rmse:1.046554+0.146373
## [582] train-rmse:0.728497+0.014175 test-rmse:1.045430+0.146673
## [583] train-rmse:0.728204+0.014156 test-rmse:1.044928+0.146774
## [584] train-rmse:0.727915+0.014140 test-rmse:1.044574+0.146929
## [585] train-rmse:0.727629+0.014125 test-rmse:1.043912+0.147108
## [586] train-rmse:0.727345+0.014110 test-rmse:1.043940+0.147045
## [587] train-rmse:0.727055+0.014086 test-rmse:1.043363+0.147402
## [588] train-rmse:0.726765+0.014069 test-rmse:1.043390+0.147460
## [589] train-rmse:0.726469+0.014048 test-rmse:1.043099+0.147756
## [590] train-rmse:0.726184+0.014033 test-rmse:1.042845+0.147975
## [591] train-rmse:0.725890+0.014011 test-rmse:1.043200+0.147559
## [592] train-rmse:0.725604+0.013992 test-rmse:1.043766+0.147297
## [593] train-rmse:0.725324+0.013976 test-rmse:1.043625+0.147241
## [594] train-rmse:0.725044+0.013959 test-rmse:1.043712+0.147275
## [595] train-rmse:0.724762+0.013943 test-rmse:1.043745+0.147444
## [596] train-rmse:0.724487+0.013925 test-rmse:1.044016+0.146746
## Stopping. Best iteration:
## [590] train-rmse:0.726184+0.014033 test-rmse:1.042845+0.147975
##
## 22
## [1] train-rmse:90.491687+0.101310 test-rmse:90.491937+0.489579
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.126776+0.089017 test-rmse:76.126749+0.511751
## [3] train-rmse:64.064040+0.079442 test-rmse:64.063658+0.532994
## [4] train-rmse:53.938632+0.072315 test-rmse:53.937809+0.553884
## [5] train-rmse:45.444263+0.067432 test-rmse:45.442850+0.574932
## [6] train-rmse:38.323881+0.064650 test-rmse:38.321713+0.596609
## [7] train-rmse:32.357049+0.062292 test-rmse:32.380286+0.599805
## [8] train-rmse:27.349028+0.055196 test-rmse:27.413797+0.615298
## [9] train-rmse:23.147529+0.046101 test-rmse:23.226967+0.598006
## [10] train-rmse:19.623934+0.038763 test-rmse:19.711613+0.564451
## [11] train-rmse:16.674452+0.033225 test-rmse:16.782382+0.573776
## [12] train-rmse:14.208644+0.032145 test-rmse:14.317282+0.580789
## [13] train-rmse:12.149940+0.034348 test-rmse:12.251966+0.547746
## [14] train-rmse:10.436074+0.036068 test-rmse:10.552537+0.560775
## [15] train-rmse:9.018139+0.038012 test-rmse:9.155961+0.589833
## [16] train-rmse:7.847373+0.041476 test-rmse:7.961882+0.548147
## [17] train-rmse:6.884117+0.043745 test-rmse:7.027852+0.558354
## [18] train-rmse:6.100016+0.046284 test-rmse:6.249827+0.543626
## [19] train-rmse:5.461676+0.049190 test-rmse:5.621882+0.532285
## [20] train-rmse:4.945171+0.051543 test-rmse:5.109951+0.503055
## [21] train-rmse:4.531039+0.051524 test-rmse:4.728603+0.489762
## [22] train-rmse:4.190742+0.047651 test-rmse:4.425835+0.499028
## [23] train-rmse:3.921890+0.047731 test-rmse:4.129078+0.440438
## [24] train-rmse:3.705702+0.047054 test-rmse:3.927053+0.437467
## [25] train-rmse:3.530664+0.047114 test-rmse:3.772518+0.433361
## [26] train-rmse:3.366821+0.038626 test-rmse:3.636475+0.435691
## [27] train-rmse:3.245789+0.038681 test-rmse:3.506495+0.379611
## [28] train-rmse:3.146206+0.038254 test-rmse:3.413056+0.371867
## [29] train-rmse:3.060468+0.037558 test-rmse:3.331509+0.359848
## [30] train-rmse:2.983176+0.037077 test-rmse:3.282703+0.367776
## [31] train-rmse:2.916563+0.037083 test-rmse:3.190757+0.326154
## [32] train-rmse:2.854361+0.035998 test-rmse:3.138829+0.335776
## [33] train-rmse:2.798994+0.034674 test-rmse:3.091708+0.331384
## [34] train-rmse:2.745161+0.034132 test-rmse:3.063597+0.313991
## [35] train-rmse:2.698107+0.034119 test-rmse:3.026549+0.315817
## [36] train-rmse:2.652673+0.033570 test-rmse:2.986135+0.318285
## [37] train-rmse:2.609624+0.031913 test-rmse:2.946655+0.330503
## [38] train-rmse:2.568761+0.031454 test-rmse:2.896550+0.290021
## [39] train-rmse:2.528192+0.031224 test-rmse:2.871639+0.294539
## [40] train-rmse:2.489654+0.030631 test-rmse:2.850185+0.293170
## [41] train-rmse:2.452128+0.030418 test-rmse:2.804601+0.287896
## [42] train-rmse:2.416684+0.030170 test-rmse:2.770143+0.295215
## [43] train-rmse:2.381557+0.028094 test-rmse:2.755613+0.296611
## [44] train-rmse:2.345848+0.027293 test-rmse:2.720604+0.281854
## [45] train-rmse:2.312782+0.026394 test-rmse:2.691562+0.290720
## [46] train-rmse:2.280385+0.025215 test-rmse:2.656658+0.284181
## [47] train-rmse:2.249087+0.024685 test-rmse:2.629030+0.286986
## [48] train-rmse:2.217004+0.023649 test-rmse:2.608859+0.284595
## [49] train-rmse:2.186020+0.022976 test-rmse:2.576362+0.270042
## [50] train-rmse:2.156163+0.022593 test-rmse:2.553350+0.266320
## [51] train-rmse:2.127536+0.021821 test-rmse:2.529997+0.262376
## [52] train-rmse:2.098348+0.022860 test-rmse:2.511495+0.270710
## [53] train-rmse:2.071036+0.022635 test-rmse:2.485520+0.274132
## [54] train-rmse:2.044524+0.022027 test-rmse:2.466961+0.266137
## [55] train-rmse:2.018373+0.021750 test-rmse:2.441084+0.263081
## [56] train-rmse:1.992172+0.021094 test-rmse:2.416462+0.254274
## [57] train-rmse:1.966410+0.020613 test-rmse:2.382053+0.251877
## [58] train-rmse:1.941208+0.021042 test-rmse:2.365548+0.250166
## [59] train-rmse:1.916944+0.020540 test-rmse:2.336865+0.254745
## [60] train-rmse:1.893086+0.020268 test-rmse:2.311304+0.250755
## [61] train-rmse:1.870091+0.019812 test-rmse:2.299047+0.250823
## [62] train-rmse:1.847209+0.019511 test-rmse:2.279076+0.240341
## [63] train-rmse:1.823188+0.019030 test-rmse:2.254221+0.237320
## [64] train-rmse:1.800516+0.018470 test-rmse:2.237898+0.239067
## [65] train-rmse:1.777229+0.018090 test-rmse:2.216892+0.237358
## [66] train-rmse:1.755539+0.017455 test-rmse:2.201178+0.243042
## [67] train-rmse:1.733742+0.017496 test-rmse:2.186801+0.245008
## [68] train-rmse:1.712988+0.017152 test-rmse:2.174515+0.245994
## [69] train-rmse:1.692628+0.017067 test-rmse:2.156325+0.256774
## [70] train-rmse:1.672919+0.016660 test-rmse:2.137013+0.258319
## [71] train-rmse:1.653914+0.016334 test-rmse:2.117702+0.256823
## [72] train-rmse:1.634480+0.016105 test-rmse:2.106852+0.255795
## [73] train-rmse:1.615390+0.015602 test-rmse:2.091371+0.254940
## [74] train-rmse:1.595791+0.014204 test-rmse:2.076327+0.252693
## [75] train-rmse:1.577648+0.014209 test-rmse:2.062652+0.253600
## [76] train-rmse:1.559295+0.014786 test-rmse:2.038278+0.247149
## [77] train-rmse:1.541874+0.014673 test-rmse:2.012678+0.233838
## [78] train-rmse:1.524605+0.014269 test-rmse:2.002513+0.236702
## [79] train-rmse:1.507158+0.013956 test-rmse:1.990640+0.239903
## [80] train-rmse:1.490240+0.013572 test-rmse:1.978906+0.237575
## [81] train-rmse:1.473897+0.013392 test-rmse:1.961161+0.236238
## [82] train-rmse:1.458043+0.013489 test-rmse:1.950181+0.237465
## [83] train-rmse:1.442592+0.013569 test-rmse:1.938483+0.234360
## [84] train-rmse:1.427294+0.013308 test-rmse:1.924628+0.231687
## [85] train-rmse:1.411099+0.013492 test-rmse:1.915609+0.234485
## [86] train-rmse:1.395963+0.013269 test-rmse:1.902893+0.235441
## [87] train-rmse:1.380893+0.013122 test-rmse:1.892611+0.231996
## [88] train-rmse:1.366193+0.012809 test-rmse:1.877033+0.232695
## [89] train-rmse:1.352148+0.012810 test-rmse:1.863051+0.229992
## [90] train-rmse:1.337752+0.013476 test-rmse:1.852606+0.232079
## [91] train-rmse:1.323988+0.013451 test-rmse:1.846747+0.233225
## [92] train-rmse:1.309803+0.013514 test-rmse:1.835211+0.235058
## [93] train-rmse:1.296577+0.013751 test-rmse:1.819771+0.236777
## [94] train-rmse:1.282601+0.014240 test-rmse:1.809028+0.235359
## [95] train-rmse:1.269219+0.014715 test-rmse:1.796415+0.232288
## [96] train-rmse:1.256222+0.014588 test-rmse:1.786627+0.238247
## [97] train-rmse:1.243503+0.014985 test-rmse:1.776545+0.235872
## [98] train-rmse:1.231028+0.014894 test-rmse:1.771388+0.233717
## [99] train-rmse:1.218727+0.015149 test-rmse:1.759366+0.235627
## [100] train-rmse:1.206469+0.014935 test-rmse:1.750498+0.238072
## [101] train-rmse:1.194814+0.014689 test-rmse:1.736089+0.235652
## [102] train-rmse:1.183365+0.014845 test-rmse:1.721164+0.237297
## [103] train-rmse:1.170609+0.013934 test-rmse:1.712575+0.235138
## [104] train-rmse:1.158723+0.013389 test-rmse:1.706151+0.237250
## [105] train-rmse:1.147080+0.013003 test-rmse:1.695769+0.239811
## [106] train-rmse:1.136335+0.012922 test-rmse:1.674916+0.234643
## [107] train-rmse:1.125471+0.013206 test-rmse:1.665307+0.237121
## [108] train-rmse:1.114824+0.013072 test-rmse:1.659196+0.237296
## [109] train-rmse:1.104505+0.013147 test-rmse:1.651820+0.235282
## [110] train-rmse:1.094200+0.013614 test-rmse:1.645336+0.237078
## [111] train-rmse:1.083843+0.013583 test-rmse:1.636055+0.240756
## [112] train-rmse:1.073873+0.013731 test-rmse:1.629218+0.238991
## [113] train-rmse:1.064146+0.013421 test-rmse:1.623039+0.237435
## [114] train-rmse:1.054909+0.013498 test-rmse:1.614348+0.238438
## [115] train-rmse:1.044788+0.012837 test-rmse:1.602917+0.240048
## [116] train-rmse:1.035136+0.012949 test-rmse:1.597565+0.244204
## [117] train-rmse:1.025533+0.013419 test-rmse:1.587982+0.240056
## [118] train-rmse:1.016350+0.013620 test-rmse:1.582294+0.240699
## [119] train-rmse:1.007091+0.014229 test-rmse:1.575546+0.237898
## [120] train-rmse:0.998489+0.014219 test-rmse:1.569714+0.237701
## [121] train-rmse:0.989706+0.014313 test-rmse:1.562559+0.238419
## [122] train-rmse:0.980982+0.014477 test-rmse:1.556062+0.239411
## [123] train-rmse:0.972534+0.014484 test-rmse:1.550440+0.240427
## [124] train-rmse:0.964632+0.014569 test-rmse:1.541534+0.243883
## [125] train-rmse:0.956977+0.014708 test-rmse:1.537334+0.244520
## [126] train-rmse:0.949339+0.015068 test-rmse:1.532571+0.241739
## [127] train-rmse:0.940711+0.015582 test-rmse:1.525242+0.240113
## [128] train-rmse:0.932533+0.016335 test-rmse:1.520492+0.241244
## [129] train-rmse:0.925142+0.016275 test-rmse:1.513201+0.243769
## [130] train-rmse:0.916717+0.017308 test-rmse:1.505411+0.240801
## [131] train-rmse:0.909814+0.017413 test-rmse:1.501193+0.239973
## [132] train-rmse:0.902622+0.017289 test-rmse:1.496197+0.238919
## [133] train-rmse:0.895771+0.017412 test-rmse:1.488712+0.238680
## [134] train-rmse:0.888968+0.017346 test-rmse:1.484817+0.238723
## [135] train-rmse:0.882166+0.017623 test-rmse:1.479253+0.241466
## [136] train-rmse:0.875431+0.017670 test-rmse:1.473610+0.243508
## [137] train-rmse:0.868731+0.017487 test-rmse:1.470695+0.245149
## [138] train-rmse:0.861961+0.017711 test-rmse:1.467662+0.247996
## [139] train-rmse:0.855697+0.017859 test-rmse:1.462422+0.244424
## [140] train-rmse:0.849380+0.017651 test-rmse:1.457663+0.245115
## [141] train-rmse:0.842441+0.017822 test-rmse:1.452026+0.244141
## [142] train-rmse:0.836216+0.018176 test-rmse:1.446916+0.245452
## [143] train-rmse:0.830361+0.018215 test-rmse:1.442063+0.243725
## [144] train-rmse:0.824336+0.018023 test-rmse:1.437949+0.245185
## [145] train-rmse:0.818692+0.018240 test-rmse:1.434340+0.245439
## [146] train-rmse:0.812828+0.018335 test-rmse:1.425681+0.246678
## [147] train-rmse:0.807134+0.017949 test-rmse:1.421146+0.246548
## [148] train-rmse:0.801430+0.017854 test-rmse:1.417603+0.247047
## [149] train-rmse:0.795840+0.017890 test-rmse:1.414586+0.246194
## [150] train-rmse:0.790421+0.018085 test-rmse:1.411450+0.247346
## [151] train-rmse:0.785297+0.018369 test-rmse:1.407994+0.248035
## [152] train-rmse:0.780192+0.018170 test-rmse:1.400769+0.248766
## [153] train-rmse:0.774478+0.018224 test-rmse:1.394262+0.249141
## [154] train-rmse:0.767165+0.015977 test-rmse:1.391002+0.248977
## [155] train-rmse:0.762158+0.015799 test-rmse:1.389044+0.249738
## [156] train-rmse:0.757350+0.015503 test-rmse:1.386284+0.248501
## [157] train-rmse:0.752583+0.015422 test-rmse:1.383238+0.249626
## [158] train-rmse:0.747971+0.015569 test-rmse:1.378821+0.248844
## [159] train-rmse:0.743278+0.015678 test-rmse:1.375427+0.248970
## [160] train-rmse:0.738422+0.015618 test-rmse:1.371874+0.251559
## [161] train-rmse:0.733947+0.015556 test-rmse:1.369964+0.251535
## [162] train-rmse:0.729563+0.015776 test-rmse:1.366759+0.252419
## [163] train-rmse:0.724012+0.015445 test-rmse:1.366068+0.250036
## [164] train-rmse:0.719282+0.014978 test-rmse:1.363034+0.252120
## [165] train-rmse:0.714219+0.014228 test-rmse:1.356740+0.248348
## [166] train-rmse:0.709986+0.014189 test-rmse:1.352570+0.251005
## [167] train-rmse:0.705682+0.014609 test-rmse:1.350302+0.251373
## [168] train-rmse:0.701528+0.014880 test-rmse:1.345163+0.250580
## [169] train-rmse:0.696937+0.014658 test-rmse:1.345668+0.250581
## [170] train-rmse:0.692978+0.014445 test-rmse:1.343814+0.253014
## [171] train-rmse:0.687467+0.013483 test-rmse:1.341475+0.253852
## [172] train-rmse:0.682779+0.012480 test-rmse:1.337977+0.251235
## [173] train-rmse:0.678873+0.012045 test-rmse:1.334939+0.252615
## [174] train-rmse:0.675134+0.012125 test-rmse:1.328284+0.248820
## [175] train-rmse:0.671113+0.012726 test-rmse:1.326267+0.249083
## [176] train-rmse:0.666985+0.013000 test-rmse:1.325400+0.249877
## [177] train-rmse:0.662814+0.012906 test-rmse:1.322888+0.249941
## [178] train-rmse:0.658674+0.012767 test-rmse:1.318102+0.251095
## [179] train-rmse:0.654943+0.012658 test-rmse:1.314627+0.252023
## [180] train-rmse:0.649902+0.011917 test-rmse:1.311802+0.250850
## [181] train-rmse:0.646285+0.012231 test-rmse:1.309470+0.252422
## [182] train-rmse:0.642263+0.011983 test-rmse:1.305841+0.251384
## [183] train-rmse:0.638539+0.011455 test-rmse:1.303507+0.253916
## [184] train-rmse:0.635288+0.011508 test-rmse:1.301968+0.254303
## [185] train-rmse:0.631929+0.011776 test-rmse:1.301285+0.255130
## [186] train-rmse:0.628797+0.011933 test-rmse:1.300209+0.255022
## [187] train-rmse:0.625412+0.011706 test-rmse:1.296241+0.250203
## [188] train-rmse:0.622321+0.011733 test-rmse:1.295060+0.250586
## [189] train-rmse:0.618989+0.012060 test-rmse:1.294473+0.251461
## [190] train-rmse:0.615191+0.011738 test-rmse:1.292606+0.254751
## [191] train-rmse:0.612310+0.011621 test-rmse:1.289840+0.253977
## [192] train-rmse:0.609372+0.011614 test-rmse:1.286316+0.254874
## [193] train-rmse:0.606361+0.011870 test-rmse:1.285807+0.254242
## [194] train-rmse:0.603449+0.011794 test-rmse:1.283913+0.253199
## [195] train-rmse:0.600570+0.011771 test-rmse:1.282477+0.252860
## [196] train-rmse:0.597927+0.011992 test-rmse:1.281514+0.253503
## [197] train-rmse:0.594639+0.012216 test-rmse:1.280176+0.254583
## [198] train-rmse:0.591640+0.012416 test-rmse:1.278630+0.253894
## [199] train-rmse:0.587756+0.012474 test-rmse:1.277585+0.254965
## [200] train-rmse:0.584488+0.012053 test-rmse:1.274798+0.257302
## [201] train-rmse:0.581937+0.011797 test-rmse:1.272569+0.257289
## [202] train-rmse:0.579221+0.011746 test-rmse:1.270475+0.259270
## [203] train-rmse:0.576702+0.011621 test-rmse:1.268840+0.260836
## [204] train-rmse:0.574219+0.011651 test-rmse:1.264413+0.261498
## [205] train-rmse:0.571511+0.011867 test-rmse:1.261439+0.262907
## [206] train-rmse:0.568655+0.011603 test-rmse:1.260475+0.262776
## [207] train-rmse:0.564961+0.011272 test-rmse:1.258675+0.263111
## [208] train-rmse:0.562497+0.011387 test-rmse:1.256189+0.263092
## [209] train-rmse:0.560118+0.011525 test-rmse:1.255687+0.262823
## [210] train-rmse:0.557356+0.011523 test-rmse:1.255524+0.262857
## [211] train-rmse:0.554754+0.011619 test-rmse:1.254024+0.262680
## [212] train-rmse:0.552559+0.011793 test-rmse:1.251735+0.263906
## [213] train-rmse:0.550133+0.011983 test-rmse:1.249696+0.265619
## [214] train-rmse:0.547864+0.011787 test-rmse:1.249002+0.265312
## [215] train-rmse:0.545695+0.011993 test-rmse:1.245659+0.262904
## [216] train-rmse:0.543383+0.012462 test-rmse:1.244910+0.263207
## [217] train-rmse:0.541475+0.012435 test-rmse:1.242817+0.263022
## [218] train-rmse:0.539269+0.012517 test-rmse:1.241260+0.264869
## [219] train-rmse:0.536484+0.012989 test-rmse:1.240591+0.264663
## [220] train-rmse:0.534035+0.013472 test-rmse:1.238314+0.265874
## [221] train-rmse:0.531148+0.012986 test-rmse:1.237094+0.266294
## [222] train-rmse:0.529073+0.012811 test-rmse:1.236413+0.267169
## [223] train-rmse:0.526856+0.012943 test-rmse:1.234792+0.266794
## [224] train-rmse:0.524947+0.012934 test-rmse:1.233429+0.266931
## [225] train-rmse:0.522686+0.012512 test-rmse:1.232097+0.267178
## [226] train-rmse:0.520765+0.012373 test-rmse:1.231423+0.267695
## [227] train-rmse:0.518831+0.012600 test-rmse:1.228432+0.269082
## [228] train-rmse:0.516705+0.012284 test-rmse:1.226955+0.268044
## [229] train-rmse:0.514884+0.012246 test-rmse:1.226495+0.267641
## [230] train-rmse:0.512979+0.012236 test-rmse:1.222059+0.263848
## [231] train-rmse:0.510116+0.012671 test-rmse:1.221104+0.265177
## [232] train-rmse:0.508384+0.012545 test-rmse:1.220145+0.263166
## [233] train-rmse:0.506905+0.012485 test-rmse:1.217846+0.263928
## [234] train-rmse:0.505228+0.012617 test-rmse:1.216961+0.264995
## [235] train-rmse:0.503135+0.013296 test-rmse:1.216216+0.265169
## [236] train-rmse:0.501226+0.012743 test-rmse:1.215326+0.264814
## [237] train-rmse:0.499635+0.012617 test-rmse:1.212903+0.265523
## [238] train-rmse:0.497765+0.012293 test-rmse:1.209498+0.265941
## [239] train-rmse:0.496046+0.012495 test-rmse:1.207266+0.264981
## [240] train-rmse:0.494335+0.012412 test-rmse:1.206417+0.265233
## [241] train-rmse:0.491966+0.012188 test-rmse:1.205892+0.266258
## [242] train-rmse:0.490388+0.012354 test-rmse:1.204784+0.267206
## [243] train-rmse:0.488088+0.012138 test-rmse:1.205482+0.266738
## [244] train-rmse:0.486451+0.012121 test-rmse:1.205089+0.267044
## [245] train-rmse:0.484940+0.012025 test-rmse:1.204141+0.266917
## [246] train-rmse:0.483437+0.012112 test-rmse:1.203365+0.267332
## [247] train-rmse:0.481838+0.012099 test-rmse:1.201939+0.266574
## [248] train-rmse:0.480110+0.012159 test-rmse:1.199483+0.265189
## [249] train-rmse:0.478849+0.012223 test-rmse:1.196696+0.264411
## [250] train-rmse:0.477381+0.012077 test-rmse:1.196140+0.265511
## [251] train-rmse:0.475881+0.012266 test-rmse:1.196479+0.264912
## [252] train-rmse:0.473785+0.011741 test-rmse:1.196512+0.265423
## [253] train-rmse:0.471655+0.011431 test-rmse:1.195666+0.264952
## [254] train-rmse:0.470352+0.011288 test-rmse:1.195014+0.264755
## [255] train-rmse:0.468933+0.011354 test-rmse:1.193120+0.265496
## [256] train-rmse:0.467335+0.011872 test-rmse:1.192427+0.265596
## [257] train-rmse:0.465957+0.011964 test-rmse:1.192538+0.266519
## [258] train-rmse:0.464638+0.011894 test-rmse:1.190430+0.266538
## [259] train-rmse:0.463285+0.011829 test-rmse:1.189853+0.266421
## [260] train-rmse:0.462054+0.011764 test-rmse:1.188860+0.266379
## [261] train-rmse:0.460555+0.012022 test-rmse:1.189090+0.267139
## [262] train-rmse:0.459232+0.011988 test-rmse:1.186547+0.265872
## [263] train-rmse:0.457554+0.011860 test-rmse:1.185332+0.265332
## [264] train-rmse:0.456228+0.011666 test-rmse:1.184696+0.265180
## [265] train-rmse:0.454037+0.011679 test-rmse:1.184288+0.266108
## [266] train-rmse:0.452946+0.011622 test-rmse:1.183185+0.266542
## [267] train-rmse:0.451733+0.011543 test-rmse:1.183115+0.266908
## [268] train-rmse:0.450260+0.011439 test-rmse:1.183540+0.267176
## [269] train-rmse:0.449104+0.011314 test-rmse:1.182130+0.268060
## [270] train-rmse:0.447772+0.011371 test-rmse:1.180670+0.266438
## [271] train-rmse:0.446111+0.011175 test-rmse:1.180556+0.267253
## [272] train-rmse:0.445089+0.011190 test-rmse:1.178062+0.268142
## [273] train-rmse:0.444042+0.011130 test-rmse:1.177435+0.267709
## [274] train-rmse:0.442014+0.012049 test-rmse:1.177038+0.267894
## [275] train-rmse:0.440777+0.011868 test-rmse:1.175607+0.268524
## [276] train-rmse:0.439478+0.012098 test-rmse:1.175343+0.268848
## [277] train-rmse:0.438149+0.012308 test-rmse:1.174632+0.269089
## [278] train-rmse:0.437094+0.012369 test-rmse:1.174669+0.268496
## [279] train-rmse:0.435952+0.012748 test-rmse:1.174341+0.268927
## [280] train-rmse:0.434814+0.012934 test-rmse:1.173316+0.269030
## [281] train-rmse:0.433429+0.013179 test-rmse:1.172907+0.268550
## [282] train-rmse:0.432257+0.013136 test-rmse:1.172312+0.268991
## [283] train-rmse:0.430921+0.013090 test-rmse:1.172247+0.268978
## [284] train-rmse:0.429317+0.013084 test-rmse:1.171900+0.269411
## [285] train-rmse:0.427976+0.012914 test-rmse:1.171470+0.270435
## [286] train-rmse:0.426594+0.012833 test-rmse:1.172059+0.271152
## [287] train-rmse:0.425616+0.012870 test-rmse:1.171020+0.270118
## [288] train-rmse:0.424363+0.012773 test-rmse:1.169030+0.269181
## [289] train-rmse:0.423463+0.012901 test-rmse:1.168852+0.269038
## [290] train-rmse:0.422617+0.012966 test-rmse:1.168874+0.268945
## [291] train-rmse:0.421526+0.012919 test-rmse:1.168890+0.269659
## [292] train-rmse:0.420445+0.012738 test-rmse:1.169180+0.270634
## [293] train-rmse:0.419559+0.012546 test-rmse:1.167678+0.269479
## [294] train-rmse:0.418499+0.012515 test-rmse:1.167280+0.269712
## [295] train-rmse:0.417122+0.012593 test-rmse:1.166088+0.270216
## [296] train-rmse:0.415913+0.012550 test-rmse:1.165258+0.271247
## [297] train-rmse:0.415058+0.012640 test-rmse:1.164826+0.271807
## [298] train-rmse:0.413954+0.012648 test-rmse:1.164452+0.271494
## [299] train-rmse:0.412826+0.012467 test-rmse:1.163713+0.272037
## [300] train-rmse:0.411879+0.012449 test-rmse:1.163385+0.272050
## [301] train-rmse:0.410531+0.012752 test-rmse:1.161763+0.273696
## [302] train-rmse:0.409559+0.012817 test-rmse:1.161562+0.274210
## [303] train-rmse:0.408480+0.012770 test-rmse:1.161001+0.274191
## [304] train-rmse:0.407391+0.012471 test-rmse:1.161125+0.274556
## [305] train-rmse:0.406520+0.012311 test-rmse:1.160884+0.274890
## [306] train-rmse:0.405056+0.013148 test-rmse:1.160320+0.274922
## [307] train-rmse:0.404157+0.013095 test-rmse:1.159615+0.274528
## [308] train-rmse:0.403173+0.012917 test-rmse:1.158629+0.275308
## [309] train-rmse:0.401395+0.012301 test-rmse:1.155737+0.272749
## [310] train-rmse:0.400250+0.012089 test-rmse:1.155672+0.273394
## [311] train-rmse:0.399185+0.012271 test-rmse:1.155879+0.273486
## [312] train-rmse:0.398325+0.012471 test-rmse:1.155690+0.272892
## [313] train-rmse:0.397545+0.012357 test-rmse:1.154920+0.273312
## [314] train-rmse:0.396942+0.012323 test-rmse:1.153863+0.273326
## [315] train-rmse:0.396283+0.012246 test-rmse:1.153258+0.272899
## [316] train-rmse:0.394751+0.012783 test-rmse:1.152483+0.272989
## [317] train-rmse:0.393988+0.012787 test-rmse:1.152488+0.273179
## [318] train-rmse:0.393280+0.012690 test-rmse:1.151618+0.272809
## [319] train-rmse:0.392196+0.012747 test-rmse:1.151555+0.272439
## [320] train-rmse:0.390896+0.013131 test-rmse:1.150541+0.273267
## [321] train-rmse:0.390031+0.012990 test-rmse:1.150242+0.273667
## [322] train-rmse:0.388554+0.013056 test-rmse:1.148462+0.275696
## [323] train-rmse:0.387630+0.013037 test-rmse:1.148111+0.275367
## [324] train-rmse:0.386496+0.012819 test-rmse:1.147832+0.274971
## [325] train-rmse:0.385689+0.012785 test-rmse:1.146691+0.275599
## [326] train-rmse:0.383951+0.012948 test-rmse:1.146852+0.275917
## [327] train-rmse:0.382731+0.012306 test-rmse:1.146914+0.275759
## [328] train-rmse:0.381669+0.011844 test-rmse:1.146379+0.275746
## [329] train-rmse:0.380424+0.011841 test-rmse:1.145174+0.276562
## [330] train-rmse:0.379381+0.012204 test-rmse:1.142683+0.275872
## [331] train-rmse:0.378663+0.012142 test-rmse:1.142136+0.275955
## [332] train-rmse:0.377977+0.012192 test-rmse:1.141891+0.275913
## [333] train-rmse:0.376927+0.012153 test-rmse:1.142102+0.275683
## [334] train-rmse:0.376261+0.012228 test-rmse:1.141765+0.275989
## [335] train-rmse:0.375151+0.012538 test-rmse:1.141046+0.275629
## [336] train-rmse:0.374152+0.012364 test-rmse:1.139106+0.275294
## [337] train-rmse:0.373379+0.012176 test-rmse:1.139071+0.275901
## [338] train-rmse:0.372407+0.012394 test-rmse:1.139347+0.276212
## [339] train-rmse:0.371578+0.012191 test-rmse:1.139160+0.276263
## [340] train-rmse:0.370576+0.012253 test-rmse:1.139051+0.276768
## [341] train-rmse:0.369898+0.012289 test-rmse:1.138851+0.276672
## [342] train-rmse:0.368849+0.012643 test-rmse:1.138331+0.276493
## [343] train-rmse:0.367900+0.012736 test-rmse:1.137310+0.276855
## [344] train-rmse:0.366946+0.012461 test-rmse:1.136684+0.276660
## [345] train-rmse:0.365701+0.012918 test-rmse:1.136439+0.276552
## [346] train-rmse:0.364791+0.012752 test-rmse:1.135291+0.277921
## [347] train-rmse:0.363755+0.012749 test-rmse:1.135361+0.278595
## [348] train-rmse:0.362815+0.013015 test-rmse:1.135297+0.278576
## [349] train-rmse:0.362162+0.013050 test-rmse:1.135177+0.279220
## [350] train-rmse:0.361189+0.012890 test-rmse:1.135424+0.279083
## [351] train-rmse:0.360492+0.013118 test-rmse:1.135311+0.278753
## [352] train-rmse:0.359549+0.013543 test-rmse:1.135114+0.278638
## [353] train-rmse:0.358416+0.013337 test-rmse:1.134783+0.280164
## [354] train-rmse:0.357575+0.013213 test-rmse:1.134642+0.280190
## [355] train-rmse:0.356722+0.013320 test-rmse:1.134733+0.280453
## [356] train-rmse:0.355588+0.013063 test-rmse:1.134922+0.279981
## [357] train-rmse:0.354790+0.012878 test-rmse:1.134455+0.279941
## [358] train-rmse:0.353935+0.012801 test-rmse:1.134687+0.280158
## [359] train-rmse:0.353151+0.012663 test-rmse:1.134172+0.280305
## [360] train-rmse:0.352127+0.013004 test-rmse:1.134025+0.280408
## [361] train-rmse:0.351587+0.013008 test-rmse:1.133841+0.280541
## [362] train-rmse:0.350850+0.013252 test-rmse:1.133472+0.280283
## [363] train-rmse:0.350352+0.013216 test-rmse:1.132862+0.280471
## [364] train-rmse:0.349121+0.012744 test-rmse:1.131726+0.280656
## [365] train-rmse:0.348533+0.012833 test-rmse:1.131821+0.281103
## [366] train-rmse:0.347891+0.012801 test-rmse:1.131674+0.281987
## [367] train-rmse:0.347148+0.012603 test-rmse:1.132495+0.281565
## [368] train-rmse:0.346633+0.012555 test-rmse:1.132914+0.281609
## [369] train-rmse:0.345084+0.012804 test-rmse:1.131994+0.281078
## [370] train-rmse:0.344512+0.012668 test-rmse:1.132085+0.280834
## [371] train-rmse:0.343797+0.012678 test-rmse:1.131370+0.280870
## [372] train-rmse:0.342829+0.012713 test-rmse:1.130354+0.280416
## [373] train-rmse:0.341848+0.012480 test-rmse:1.130225+0.279819
## [374] train-rmse:0.340687+0.012718 test-rmse:1.129623+0.279674
## [375] train-rmse:0.339953+0.012654 test-rmse:1.129853+0.280176
## [376] train-rmse:0.339084+0.012919 test-rmse:1.129598+0.280362
## [377] train-rmse:0.338181+0.012940 test-rmse:1.128965+0.280066
## [378] train-rmse:0.337296+0.013323 test-rmse:1.128512+0.280692
## [379] train-rmse:0.336795+0.013313 test-rmse:1.127949+0.280543
## [380] train-rmse:0.336277+0.013238 test-rmse:1.127391+0.280640
## [381] train-rmse:0.335747+0.013188 test-rmse:1.127510+0.280574
## [382] train-rmse:0.335228+0.013152 test-rmse:1.127403+0.280583
## [383] train-rmse:0.334113+0.013203 test-rmse:1.125519+0.281603
## [384] train-rmse:0.333384+0.013323 test-rmse:1.126329+0.281906
## [385] train-rmse:0.332646+0.013710 test-rmse:1.126248+0.281554
## [386] train-rmse:0.332028+0.013833 test-rmse:1.125907+0.281711
## [387] train-rmse:0.331364+0.013798 test-rmse:1.126425+0.281985
## [388] train-rmse:0.330276+0.014190 test-rmse:1.125890+0.281495
## [389] train-rmse:0.329677+0.014083 test-rmse:1.125686+0.281651
## Stopping. Best iteration:
## [383] train-rmse:0.334113+0.013203 test-rmse:1.125519+0.281603
##
## 23
## [1] train-rmse:90.491687+0.101310 test-rmse:90.491937+0.489579
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.126776+0.089017 test-rmse:76.126749+0.511751
## [3] train-rmse:64.064040+0.079442 test-rmse:64.063658+0.532994
## [4] train-rmse:53.938632+0.072315 test-rmse:53.937809+0.553884
## [5] train-rmse:45.444263+0.067432 test-rmse:45.442850+0.574932
## [6] train-rmse:38.323881+0.064650 test-rmse:38.321713+0.596609
## [7] train-rmse:32.357049+0.062292 test-rmse:32.380286+0.599805
## [8] train-rmse:27.347881+0.054482 test-rmse:27.420350+0.612889
## [9] train-rmse:23.142614+0.043845 test-rmse:23.227096+0.593161
## [10] train-rmse:19.611997+0.037776 test-rmse:19.706151+0.580679
## [11] train-rmse:16.651396+0.030630 test-rmse:16.767246+0.591673
## [12] train-rmse:14.167588+0.030039 test-rmse:14.270114+0.572736
## [13] train-rmse:12.089057+0.030663 test-rmse:12.179702+0.567446
## [14] train-rmse:10.353299+0.030622 test-rmse:10.432156+0.533161
## [15] train-rmse:8.907558+0.030730 test-rmse:9.004630+0.509209
## [16] train-rmse:7.701161+0.032188 test-rmse:7.821867+0.524842
## [17] train-rmse:6.703909+0.035334 test-rmse:6.830093+0.494633
## [18] train-rmse:5.881015+0.038111 test-rmse:6.038751+0.503424
## [19] train-rmse:5.195771+0.033648 test-rmse:5.400219+0.517045
## [20] train-rmse:4.638617+0.035798 test-rmse:4.839467+0.496035
## [21] train-rmse:4.188560+0.036161 test-rmse:4.376245+0.462619
## [22] train-rmse:3.819552+0.036793 test-rmse:4.042189+0.461901
## [23] train-rmse:3.521521+0.038637 test-rmse:3.755845+0.433760
## [24] train-rmse:3.277710+0.038342 test-rmse:3.543826+0.426294
## [25] train-rmse:3.075690+0.036744 test-rmse:3.360617+0.418248
## [26] train-rmse:2.908592+0.038050 test-rmse:3.206487+0.390463
## [27] train-rmse:2.772706+0.036619 test-rmse:3.098846+0.377681
## [28] train-rmse:2.653330+0.032184 test-rmse:2.998277+0.357334
## [29] train-rmse:2.556178+0.032559 test-rmse:2.898095+0.346243
## [30] train-rmse:2.468814+0.034103 test-rmse:2.827607+0.333614
## [31] train-rmse:2.388296+0.031391 test-rmse:2.773312+0.316409
## [32] train-rmse:2.319519+0.029312 test-rmse:2.727025+0.318330
## [33] train-rmse:2.258380+0.029014 test-rmse:2.677041+0.320729
## [34] train-rmse:2.202527+0.027795 test-rmse:2.626609+0.308875
## [35] train-rmse:2.149187+0.028457 test-rmse:2.582302+0.308667
## [36] train-rmse:2.099757+0.029865 test-rmse:2.528266+0.296102
## [37] train-rmse:2.051885+0.028917 test-rmse:2.488359+0.300452
## [38] train-rmse:2.007129+0.026379 test-rmse:2.459535+0.292517
## [39] train-rmse:1.963686+0.026033 test-rmse:2.421023+0.279966
## [40] train-rmse:1.923075+0.024958 test-rmse:2.390459+0.278678
## [41] train-rmse:1.884113+0.024798 test-rmse:2.354354+0.281032
## [42] train-rmse:1.844200+0.027480 test-rmse:2.313241+0.293863
## [43] train-rmse:1.805113+0.025525 test-rmse:2.295412+0.289056
## [44] train-rmse:1.768535+0.022358 test-rmse:2.267334+0.280770
## [45] train-rmse:1.731184+0.016102 test-rmse:2.237600+0.276495
## [46] train-rmse:1.698893+0.015917 test-rmse:2.204862+0.274134
## [47] train-rmse:1.666808+0.016772 test-rmse:2.167818+0.278385
## [48] train-rmse:1.635210+0.016218 test-rmse:2.135258+0.275417
## [49] train-rmse:1.602724+0.014945 test-rmse:2.123468+0.276002
## [50] train-rmse:1.572864+0.013783 test-rmse:2.099934+0.273013
## [51] train-rmse:1.541996+0.017965 test-rmse:2.077904+0.271803
## [52] train-rmse:1.514303+0.017925 test-rmse:2.044962+0.266649
## [53] train-rmse:1.487196+0.017197 test-rmse:2.026108+0.274262
## [54] train-rmse:1.460451+0.017552 test-rmse:2.004882+0.273477
## [55] train-rmse:1.434970+0.016875 test-rmse:1.983723+0.277711
## [56] train-rmse:1.407565+0.018829 test-rmse:1.960044+0.269998
## [57] train-rmse:1.383120+0.017327 test-rmse:1.938318+0.272809
## [58] train-rmse:1.359607+0.016932 test-rmse:1.915643+0.268924
## [59] train-rmse:1.336597+0.016701 test-rmse:1.900095+0.269774
## [60] train-rmse:1.312930+0.016587 test-rmse:1.886782+0.266562
## [61] train-rmse:1.290999+0.015998 test-rmse:1.864494+0.271941
## [62] train-rmse:1.268540+0.013696 test-rmse:1.839264+0.273589
## [63] train-rmse:1.246824+0.014374 test-rmse:1.825169+0.271813
## [64] train-rmse:1.225127+0.016212 test-rmse:1.811246+0.270851
## [65] train-rmse:1.205670+0.016360 test-rmse:1.793584+0.266798
## [66] train-rmse:1.186120+0.015815 test-rmse:1.775227+0.269888
## [67] train-rmse:1.167350+0.015335 test-rmse:1.758692+0.269707
## [68] train-rmse:1.148202+0.015884 test-rmse:1.747024+0.271103
## [69] train-rmse:1.128973+0.014660 test-rmse:1.725006+0.264522
## [70] train-rmse:1.111376+0.014147 test-rmse:1.711865+0.264594
## [71] train-rmse:1.094548+0.013933 test-rmse:1.699217+0.268610
## [72] train-rmse:1.076954+0.013304 test-rmse:1.686362+0.272469
## [73] train-rmse:1.059026+0.010730 test-rmse:1.672575+0.268978
## [74] train-rmse:1.041352+0.012704 test-rmse:1.659071+0.269885
## [75] train-rmse:1.026224+0.012631 test-rmse:1.646367+0.274682
## [76] train-rmse:1.010248+0.012746 test-rmse:1.633550+0.274047
## [77] train-rmse:0.994408+0.014762 test-rmse:1.622897+0.272112
## [78] train-rmse:0.979677+0.014980 test-rmse:1.610654+0.277823
## [79] train-rmse:0.964258+0.013819 test-rmse:1.604203+0.279123
## [80] train-rmse:0.949938+0.013723 test-rmse:1.588260+0.279027
## [81] train-rmse:0.936057+0.013282 test-rmse:1.578893+0.279477
## [82] train-rmse:0.921151+0.012542 test-rmse:1.572337+0.278386
## [83] train-rmse:0.908552+0.012123 test-rmse:1.563287+0.281624
## [84] train-rmse:0.894941+0.011199 test-rmse:1.552766+0.281924
## [85] train-rmse:0.881620+0.012891 test-rmse:1.545873+0.281508
## [86] train-rmse:0.869982+0.013130 test-rmse:1.537571+0.282409
## [87] train-rmse:0.858121+0.012929 test-rmse:1.521997+0.272608
## [88] train-rmse:0.846772+0.013558 test-rmse:1.507965+0.271336
## [89] train-rmse:0.835264+0.012799 test-rmse:1.504839+0.272544
## [90] train-rmse:0.824147+0.012408 test-rmse:1.497095+0.271390
## [91] train-rmse:0.814143+0.012430 test-rmse:1.491010+0.271988
## [92] train-rmse:0.802554+0.012289 test-rmse:1.480748+0.273028
## [93] train-rmse:0.792263+0.012496 test-rmse:1.468547+0.271557
## [94] train-rmse:0.781581+0.012683 test-rmse:1.463981+0.275816
## [95] train-rmse:0.771024+0.013900 test-rmse:1.457859+0.278185
## [96] train-rmse:0.761076+0.012967 test-rmse:1.452995+0.281839
## [97] train-rmse:0.751050+0.012845 test-rmse:1.447148+0.280297
## [98] train-rmse:0.740736+0.013030 test-rmse:1.442254+0.278706
## [99] train-rmse:0.730740+0.014077 test-rmse:1.427296+0.268003
## [100] train-rmse:0.721634+0.014777 test-rmse:1.421526+0.267448
## [101] train-rmse:0.711879+0.017076 test-rmse:1.416579+0.269507
## [102] train-rmse:0.701540+0.016070 test-rmse:1.415001+0.271976
## [103] train-rmse:0.692925+0.015689 test-rmse:1.411555+0.272113
## [104] train-rmse:0.684198+0.015595 test-rmse:1.404627+0.273800
## [105] train-rmse:0.676060+0.014262 test-rmse:1.392956+0.264673
## [106] train-rmse:0.667767+0.014931 test-rmse:1.388199+0.265401
## [107] train-rmse:0.659738+0.015057 test-rmse:1.385413+0.265789
## [108] train-rmse:0.652518+0.015420 test-rmse:1.381146+0.268105
## [109] train-rmse:0.645476+0.015441 test-rmse:1.376334+0.270224
## [110] train-rmse:0.638566+0.015691 test-rmse:1.369038+0.272177
## [111] train-rmse:0.631826+0.015770 test-rmse:1.365288+0.271545
## [112] train-rmse:0.625045+0.014776 test-rmse:1.361725+0.273747
## [113] train-rmse:0.618417+0.014904 test-rmse:1.354388+0.268837
## [114] train-rmse:0.611836+0.015091 test-rmse:1.348523+0.270758
## [115] train-rmse:0.605779+0.014904 test-rmse:1.345390+0.271070
## [116] train-rmse:0.598230+0.014925 test-rmse:1.341055+0.272576
## [117] train-rmse:0.591608+0.014520 test-rmse:1.339144+0.273374
## [118] train-rmse:0.584911+0.015399 test-rmse:1.335637+0.270849
## [119] train-rmse:0.577788+0.016888 test-rmse:1.327910+0.270882
## [120] train-rmse:0.572176+0.016084 test-rmse:1.323992+0.271111
## [121] train-rmse:0.566615+0.015960 test-rmse:1.320931+0.271651
## [122] train-rmse:0.560007+0.016821 test-rmse:1.318169+0.271677
## [123] train-rmse:0.554499+0.016473 test-rmse:1.312023+0.272259
## [124] train-rmse:0.548671+0.015518 test-rmse:1.308795+0.271213
## [125] train-rmse:0.543780+0.015592 test-rmse:1.306374+0.273083
## [126] train-rmse:0.538789+0.015428 test-rmse:1.303946+0.274587
## [127] train-rmse:0.533110+0.016012 test-rmse:1.303694+0.275998
## [128] train-rmse:0.528364+0.015720 test-rmse:1.296826+0.279332
## [129] train-rmse:0.522963+0.015116 test-rmse:1.295569+0.280704
## [130] train-rmse:0.517460+0.015699 test-rmse:1.288189+0.273367
## [131] train-rmse:0.512780+0.015588 test-rmse:1.284770+0.274362
## [132] train-rmse:0.508243+0.015885 test-rmse:1.283082+0.276035
## [133] train-rmse:0.502600+0.016272 test-rmse:1.278774+0.277126
## [134] train-rmse:0.498144+0.015580 test-rmse:1.278260+0.276506
## [135] train-rmse:0.493480+0.016095 test-rmse:1.271515+0.270598
## [136] train-rmse:0.488990+0.016945 test-rmse:1.267084+0.271716
## [137] train-rmse:0.484701+0.016791 test-rmse:1.263642+0.272809
## [138] train-rmse:0.480148+0.016258 test-rmse:1.261332+0.272888
## [139] train-rmse:0.475673+0.016398 test-rmse:1.259071+0.273642
## [140] train-rmse:0.471758+0.016987 test-rmse:1.255148+0.274316
## [141] train-rmse:0.466784+0.016254 test-rmse:1.253343+0.275677
## [142] train-rmse:0.462757+0.016698 test-rmse:1.251620+0.273914
## [143] train-rmse:0.458600+0.016522 test-rmse:1.250023+0.275088
## [144] train-rmse:0.454309+0.016223 test-rmse:1.248941+0.275452
## [145] train-rmse:0.450454+0.016173 test-rmse:1.242799+0.269999
## [146] train-rmse:0.446745+0.016432 test-rmse:1.241181+0.271446
## [147] train-rmse:0.443447+0.016598 test-rmse:1.240587+0.271921
## [148] train-rmse:0.439714+0.016916 test-rmse:1.239803+0.272454
## [149] train-rmse:0.436181+0.016746 test-rmse:1.238171+0.272697
## [150] train-rmse:0.432408+0.016897 test-rmse:1.237135+0.271897
## [151] train-rmse:0.427647+0.015414 test-rmse:1.236062+0.272341
## [152] train-rmse:0.424445+0.015504 test-rmse:1.234654+0.271908
## [153] train-rmse:0.420736+0.015131 test-rmse:1.232947+0.272598
## [154] train-rmse:0.417757+0.015302 test-rmse:1.231598+0.272454
## [155] train-rmse:0.414849+0.015570 test-rmse:1.229324+0.274129
## [156] train-rmse:0.411800+0.015704 test-rmse:1.229386+0.273477
## [157] train-rmse:0.408649+0.015896 test-rmse:1.227817+0.274783
## [158] train-rmse:0.405814+0.015841 test-rmse:1.226308+0.274425
## [159] train-rmse:0.402979+0.015541 test-rmse:1.223589+0.274684
## [160] train-rmse:0.400062+0.015858 test-rmse:1.223172+0.274600
## [161] train-rmse:0.396484+0.015044 test-rmse:1.222207+0.275227
## [162] train-rmse:0.393820+0.015191 test-rmse:1.222793+0.275787
## [163] train-rmse:0.390897+0.014582 test-rmse:1.222059+0.276315
## [164] train-rmse:0.388204+0.014620 test-rmse:1.217957+0.278203
## [165] train-rmse:0.385662+0.014432 test-rmse:1.216522+0.277891
## [166] train-rmse:0.382702+0.015028 test-rmse:1.215132+0.279045
## [167] train-rmse:0.378802+0.015390 test-rmse:1.214288+0.278937
## [168] train-rmse:0.376623+0.015349 test-rmse:1.213897+0.279523
## [169] train-rmse:0.373909+0.015461 test-rmse:1.209766+0.278443
## [170] train-rmse:0.371252+0.015512 test-rmse:1.209150+0.279833
## [171] train-rmse:0.368387+0.015831 test-rmse:1.208191+0.280262
## [172] train-rmse:0.366184+0.015985 test-rmse:1.207643+0.281121
## [173] train-rmse:0.363447+0.016458 test-rmse:1.205298+0.281781
## [174] train-rmse:0.361216+0.015806 test-rmse:1.202869+0.281992
## [175] train-rmse:0.358818+0.015321 test-rmse:1.202128+0.282830
## [176] train-rmse:0.356441+0.015115 test-rmse:1.201757+0.283292
## [177] train-rmse:0.354498+0.015083 test-rmse:1.201275+0.284222
## [178] train-rmse:0.352574+0.015180 test-rmse:1.201132+0.284771
## [179] train-rmse:0.349537+0.014985 test-rmse:1.199589+0.284633
## [180] train-rmse:0.347091+0.015064 test-rmse:1.199796+0.284852
## [181] train-rmse:0.344518+0.015642 test-rmse:1.199385+0.285344
## [182] train-rmse:0.342239+0.015705 test-rmse:1.196917+0.286912
## [183] train-rmse:0.340003+0.015713 test-rmse:1.196728+0.287140
## [184] train-rmse:0.338142+0.015527 test-rmse:1.193808+0.287099
## [185] train-rmse:0.335662+0.015294 test-rmse:1.193111+0.286995
## [186] train-rmse:0.333244+0.015742 test-rmse:1.191259+0.285829
## [187] train-rmse:0.331272+0.016001 test-rmse:1.189893+0.287081
## [188] train-rmse:0.329487+0.015781 test-rmse:1.189219+0.287270
## [189] train-rmse:0.328038+0.015571 test-rmse:1.188363+0.287384
## [190] train-rmse:0.326441+0.015768 test-rmse:1.185741+0.287107
## [191] train-rmse:0.324854+0.015711 test-rmse:1.185263+0.287997
## [192] train-rmse:0.322865+0.015875 test-rmse:1.183292+0.288864
## [193] train-rmse:0.320942+0.016008 test-rmse:1.183168+0.288899
## [194] train-rmse:0.319009+0.016373 test-rmse:1.182657+0.289039
## [195] train-rmse:0.316372+0.015959 test-rmse:1.182483+0.289202
## [196] train-rmse:0.314683+0.015676 test-rmse:1.181827+0.289146
## [197] train-rmse:0.312367+0.015722 test-rmse:1.180619+0.288617
## [198] train-rmse:0.310918+0.015703 test-rmse:1.179971+0.288623
## [199] train-rmse:0.308429+0.014657 test-rmse:1.179714+0.287106
## [200] train-rmse:0.306791+0.014502 test-rmse:1.178971+0.287160
## [201] train-rmse:0.305161+0.014846 test-rmse:1.178796+0.288059
## [202] train-rmse:0.303437+0.014295 test-rmse:1.178208+0.288234
## [203] train-rmse:0.302040+0.014226 test-rmse:1.177390+0.288685
## [204] train-rmse:0.300417+0.014124 test-rmse:1.176415+0.288326
## [205] train-rmse:0.298243+0.013893 test-rmse:1.175612+0.287423
## [206] train-rmse:0.296630+0.013465 test-rmse:1.173645+0.287827
## [207] train-rmse:0.295173+0.013529 test-rmse:1.172630+0.287729
## [208] train-rmse:0.293635+0.013057 test-rmse:1.172127+0.288290
## [209] train-rmse:0.292085+0.013372 test-rmse:1.171789+0.289670
## [210] train-rmse:0.290482+0.013322 test-rmse:1.171107+0.289480
## [211] train-rmse:0.288635+0.013278 test-rmse:1.170837+0.289878
## [212] train-rmse:0.286893+0.013539 test-rmse:1.169781+0.291284
## [213] train-rmse:0.285469+0.013599 test-rmse:1.170338+0.291177
## [214] train-rmse:0.283758+0.013937 test-rmse:1.169435+0.291009
## [215] train-rmse:0.281982+0.014550 test-rmse:1.168744+0.291326
## [216] train-rmse:0.280566+0.014612 test-rmse:1.166896+0.291875
## [217] train-rmse:0.279157+0.014608 test-rmse:1.165928+0.290966
## [218] train-rmse:0.277948+0.014338 test-rmse:1.165830+0.291113
## [219] train-rmse:0.276536+0.013988 test-rmse:1.164845+0.291855
## [220] train-rmse:0.275023+0.014244 test-rmse:1.164947+0.291809
## [221] train-rmse:0.273140+0.014135 test-rmse:1.164228+0.292413
## [222] train-rmse:0.271366+0.014098 test-rmse:1.163285+0.293113
## [223] train-rmse:0.269857+0.013924 test-rmse:1.162560+0.293077
## [224] train-rmse:0.268123+0.014453 test-rmse:1.162065+0.293507
## [225] train-rmse:0.266379+0.013581 test-rmse:1.162328+0.293592
## [226] train-rmse:0.265204+0.013507 test-rmse:1.162146+0.293670
## [227] train-rmse:0.263618+0.012981 test-rmse:1.161120+0.293702
## [228] train-rmse:0.262290+0.013117 test-rmse:1.160586+0.293700
## [229] train-rmse:0.261357+0.013131 test-rmse:1.159531+0.293056
## [230] train-rmse:0.259434+0.013202 test-rmse:1.159009+0.293199
## [231] train-rmse:0.258518+0.013194 test-rmse:1.157451+0.293372
## [232] train-rmse:0.257364+0.012992 test-rmse:1.157179+0.293300
## [233] train-rmse:0.256555+0.013006 test-rmse:1.156746+0.292889
## [234] train-rmse:0.255007+0.013251 test-rmse:1.156023+0.293205
## [235] train-rmse:0.253098+0.012896 test-rmse:1.154439+0.293038
## [236] train-rmse:0.251640+0.013027 test-rmse:1.155189+0.293678
## [237] train-rmse:0.250420+0.013296 test-rmse:1.154699+0.293414
## [238] train-rmse:0.248638+0.012449 test-rmse:1.154584+0.293822
## [239] train-rmse:0.247459+0.012829 test-rmse:1.154652+0.294263
## [240] train-rmse:0.246215+0.013083 test-rmse:1.154313+0.294445
## [241] train-rmse:0.244603+0.012936 test-rmse:1.154176+0.294574
## [242] train-rmse:0.243700+0.012952 test-rmse:1.153613+0.294387
## [243] train-rmse:0.242471+0.012993 test-rmse:1.153449+0.294657
## [244] train-rmse:0.241601+0.012934 test-rmse:1.152955+0.294436
## [245] train-rmse:0.240723+0.013026 test-rmse:1.151506+0.294580
## [246] train-rmse:0.239113+0.012999 test-rmse:1.151078+0.294709
## [247] train-rmse:0.237968+0.013258 test-rmse:1.151155+0.294771
## [248] train-rmse:0.236850+0.012782 test-rmse:1.151108+0.294885
## [249] train-rmse:0.235767+0.012447 test-rmse:1.151378+0.294972
## [250] train-rmse:0.234661+0.012472 test-rmse:1.150661+0.294760
## [251] train-rmse:0.233229+0.012498 test-rmse:1.149901+0.294885
## [252] train-rmse:0.232049+0.012520 test-rmse:1.149802+0.295142
## [253] train-rmse:0.231235+0.012353 test-rmse:1.149570+0.295396
## [254] train-rmse:0.229979+0.012273 test-rmse:1.149647+0.295194
## [255] train-rmse:0.228993+0.012380 test-rmse:1.149907+0.295692
## [256] train-rmse:0.227903+0.012382 test-rmse:1.149572+0.295849
## [257] train-rmse:0.226732+0.012160 test-rmse:1.149220+0.296030
## [258] train-rmse:0.225702+0.012132 test-rmse:1.149525+0.296108
## [259] train-rmse:0.224454+0.012089 test-rmse:1.149598+0.296704
## [260] train-rmse:0.223480+0.012343 test-rmse:1.149797+0.296626
## [261] train-rmse:0.222395+0.012447 test-rmse:1.149410+0.296724
## [262] train-rmse:0.221282+0.012537 test-rmse:1.149621+0.296435
## [263] train-rmse:0.220206+0.012499 test-rmse:1.149172+0.295405
## [264] train-rmse:0.219378+0.012666 test-rmse:1.148038+0.295704
## [265] train-rmse:0.218407+0.012760 test-rmse:1.148133+0.296052
## [266] train-rmse:0.217465+0.012970 test-rmse:1.147877+0.295965
## [267] train-rmse:0.216519+0.013068 test-rmse:1.147970+0.295609
## [268] train-rmse:0.215641+0.013072 test-rmse:1.148191+0.295453
## [269] train-rmse:0.214789+0.013057 test-rmse:1.147711+0.295944
## [270] train-rmse:0.213854+0.013097 test-rmse:1.147253+0.296280
## [271] train-rmse:0.212897+0.013296 test-rmse:1.144962+0.293996
## [272] train-rmse:0.211640+0.012786 test-rmse:1.144587+0.293986
## [273] train-rmse:0.210617+0.012578 test-rmse:1.145575+0.293409
## [274] train-rmse:0.209369+0.012677 test-rmse:1.145929+0.293613
## [275] train-rmse:0.208374+0.012608 test-rmse:1.146023+0.294308
## [276] train-rmse:0.207338+0.012519 test-rmse:1.145867+0.293926
## [277] train-rmse:0.205948+0.012500 test-rmse:1.145512+0.294153
## [278] train-rmse:0.204643+0.013124 test-rmse:1.145506+0.294385
## Stopping. Best iteration:
## [272] train-rmse:0.211640+0.012786 test-rmse:1.144587+0.293986
##
## 24
## [1] train-rmse:90.491687+0.101310 test-rmse:90.491937+0.489579
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.126776+0.089017 test-rmse:76.126749+0.511751
## [3] train-rmse:64.064040+0.079442 test-rmse:64.063658+0.532994
## [4] train-rmse:53.938632+0.072315 test-rmse:53.937809+0.553884
## [5] train-rmse:45.444263+0.067432 test-rmse:45.442850+0.574932
## [6] train-rmse:38.323881+0.064650 test-rmse:38.321713+0.596609
## [7] train-rmse:32.357049+0.062292 test-rmse:32.380286+0.599805
## [8] train-rmse:27.347881+0.054482 test-rmse:27.420350+0.612889
## [9] train-rmse:23.142614+0.043845 test-rmse:23.227096+0.593161
## [10] train-rmse:19.611012+0.037429 test-rmse:19.700200+0.582908
## [11] train-rmse:16.646035+0.030843 test-rmse:16.760352+0.587068
## [12] train-rmse:14.154684+0.028559 test-rmse:14.259100+0.558952
## [13] train-rmse:12.062907+0.026808 test-rmse:12.162189+0.547296
## [14] train-rmse:10.309098+0.024499 test-rmse:10.397156+0.551498
## [15] train-rmse:8.842632+0.026958 test-rmse:8.921511+0.528925
## [16] train-rmse:7.617306+0.028478 test-rmse:7.703463+0.524612
## [17] train-rmse:6.592495+0.026597 test-rmse:6.705683+0.533850
## [18] train-rmse:5.743550+0.028846 test-rmse:5.885709+0.529533
## [19] train-rmse:5.034208+0.026301 test-rmse:5.177221+0.497896
## [20] train-rmse:4.447442+0.028633 test-rmse:4.606941+0.477093
## [21] train-rmse:3.966193+0.032774 test-rmse:4.174609+0.456850
## [22] train-rmse:3.567169+0.031533 test-rmse:3.805248+0.441233
## [23] train-rmse:3.240059+0.037816 test-rmse:3.493340+0.425490
## [24] train-rmse:2.963663+0.044280 test-rmse:3.268147+0.401403
## [25] train-rmse:2.742812+0.044574 test-rmse:3.046287+0.369113
## [26] train-rmse:2.557539+0.043530 test-rmse:2.900777+0.338487
## [27] train-rmse:2.403897+0.045031 test-rmse:2.750604+0.308441
## [28] train-rmse:2.271459+0.044232 test-rmse:2.663076+0.297432
## [29] train-rmse:2.160621+0.046944 test-rmse:2.572861+0.275154
## [30] train-rmse:2.064611+0.048068 test-rmse:2.487781+0.257613
## [31] train-rmse:1.978280+0.046708 test-rmse:2.407052+0.249760
## [32] train-rmse:1.905271+0.044628 test-rmse:2.335887+0.249578
## [33] train-rmse:1.831861+0.040033 test-rmse:2.291313+0.237120
## [34] train-rmse:1.771489+0.037845 test-rmse:2.243072+0.232075
## [35] train-rmse:1.715998+0.037131 test-rmse:2.197152+0.239506
## [36] train-rmse:1.665384+0.036649 test-rmse:2.155272+0.240312
## [37] train-rmse:1.613263+0.031269 test-rmse:2.109532+0.226551
## [38] train-rmse:1.560703+0.028705 test-rmse:2.072122+0.208360
## [39] train-rmse:1.510612+0.034265 test-rmse:2.046922+0.210808
## [40] train-rmse:1.469225+0.033822 test-rmse:2.022417+0.216269
## [41] train-rmse:1.430866+0.031706 test-rmse:1.990089+0.202115
## [42] train-rmse:1.392162+0.032079 test-rmse:1.953599+0.198640
## [43] train-rmse:1.357030+0.032230 test-rmse:1.920543+0.193606
## [44] train-rmse:1.323115+0.031459 test-rmse:1.894428+0.192768
## [45] train-rmse:1.290132+0.031034 test-rmse:1.865998+0.201641
## [46] train-rmse:1.258778+0.031736 test-rmse:1.840430+0.195281
## [47] train-rmse:1.223949+0.029256 test-rmse:1.822329+0.198324
## [48] train-rmse:1.193977+0.029175 test-rmse:1.795200+0.195671
## [49] train-rmse:1.165747+0.028708 test-rmse:1.772113+0.198332
## [50] train-rmse:1.135219+0.031615 test-rmse:1.746779+0.205005
## [51] train-rmse:1.109349+0.030619 test-rmse:1.718146+0.198326
## [52] train-rmse:1.083256+0.029795 test-rmse:1.697928+0.198791
## [53] train-rmse:1.057198+0.029633 test-rmse:1.674025+0.198017
## [54] train-rmse:1.033781+0.029218 test-rmse:1.656989+0.201153
## [55] train-rmse:1.009793+0.029950 test-rmse:1.641736+0.201896
## [56] train-rmse:0.987464+0.030512 test-rmse:1.627214+0.201597
## [57] train-rmse:0.963804+0.029383 test-rmse:1.611437+0.197171
## [58] train-rmse:0.942968+0.029347 test-rmse:1.596480+0.198378
## [59] train-rmse:0.921847+0.028170 test-rmse:1.578499+0.203363
## [60] train-rmse:0.897770+0.024554 test-rmse:1.567357+0.197379
## [61] train-rmse:0.879090+0.024440 test-rmse:1.554066+0.195967
## [62] train-rmse:0.860938+0.024636 test-rmse:1.540723+0.197382
## [63] train-rmse:0.843970+0.024035 test-rmse:1.525811+0.204563
## [64] train-rmse:0.826230+0.026068 test-rmse:1.511890+0.203602
## [65] train-rmse:0.809702+0.025212 test-rmse:1.499835+0.204912
## [66] train-rmse:0.791931+0.024320 test-rmse:1.483625+0.205079
## [67] train-rmse:0.775772+0.022275 test-rmse:1.469672+0.202661
## [68] train-rmse:0.758976+0.022660 test-rmse:1.465026+0.204666
## [69] train-rmse:0.742470+0.022874 test-rmse:1.457951+0.203550
## [70] train-rmse:0.727677+0.022662 test-rmse:1.445451+0.202791
## [71] train-rmse:0.713950+0.022157 test-rmse:1.439182+0.205517
## [72] train-rmse:0.700695+0.021837 test-rmse:1.425112+0.203472
## [73] train-rmse:0.688245+0.021809 test-rmse:1.417600+0.202968
## [74] train-rmse:0.675123+0.021176 test-rmse:1.404309+0.203999
## [75] train-rmse:0.663160+0.020744 test-rmse:1.396936+0.205381
## [76] train-rmse:0.649567+0.019931 test-rmse:1.386632+0.203908
## [77] train-rmse:0.637952+0.019730 test-rmse:1.380493+0.206653
## [78] train-rmse:0.626662+0.019126 test-rmse:1.375299+0.210071
## [79] train-rmse:0.615142+0.019151 test-rmse:1.364951+0.212573
## [80] train-rmse:0.604852+0.019318 test-rmse:1.359448+0.211935
## [81] train-rmse:0.594004+0.018744 test-rmse:1.350500+0.213324
## [82] train-rmse:0.582630+0.017842 test-rmse:1.344759+0.215650
## [83] train-rmse:0.573492+0.017648 test-rmse:1.337378+0.219431
## [84] train-rmse:0.563670+0.018370 test-rmse:1.329688+0.219004
## [85] train-rmse:0.554951+0.017949 test-rmse:1.323867+0.219373
## [86] train-rmse:0.546838+0.018025 test-rmse:1.313627+0.221886
## [87] train-rmse:0.536787+0.020257 test-rmse:1.305364+0.215432
## [88] train-rmse:0.528044+0.020367 test-rmse:1.299729+0.217429
## [89] train-rmse:0.518774+0.019834 test-rmse:1.295952+0.215694
## [90] train-rmse:0.510517+0.019232 test-rmse:1.289097+0.219262
## [91] train-rmse:0.502695+0.019535 test-rmse:1.287002+0.220079
## [92] train-rmse:0.494443+0.019013 test-rmse:1.279295+0.213104
## [93] train-rmse:0.487351+0.018999 test-rmse:1.276960+0.215283
## [94] train-rmse:0.479389+0.019781 test-rmse:1.272341+0.217821
## [95] train-rmse:0.472059+0.019768 test-rmse:1.267593+0.218217
## [96] train-rmse:0.464840+0.021353 test-rmse:1.264907+0.218614
## [97] train-rmse:0.458587+0.020640 test-rmse:1.258947+0.221347
## [98] train-rmse:0.451733+0.019234 test-rmse:1.253262+0.216069
## [99] train-rmse:0.445384+0.019396 test-rmse:1.248277+0.215926
## [100] train-rmse:0.439489+0.019412 test-rmse:1.245634+0.215932
## [101] train-rmse:0.433940+0.019153 test-rmse:1.242882+0.216051
## [102] train-rmse:0.428351+0.019952 test-rmse:1.239641+0.216505
## [103] train-rmse:0.421474+0.018786 test-rmse:1.236190+0.218072
## [104] train-rmse:0.415282+0.018892 test-rmse:1.233421+0.217688
## [105] train-rmse:0.409567+0.018745 test-rmse:1.231581+0.218815
## [106] train-rmse:0.404320+0.019094 test-rmse:1.230185+0.219218
## [107] train-rmse:0.398071+0.019532 test-rmse:1.228295+0.220757
## [108] train-rmse:0.392921+0.018979 test-rmse:1.222277+0.215489
## [109] train-rmse:0.388811+0.018973 test-rmse:1.220220+0.216241
## [110] train-rmse:0.382924+0.019126 test-rmse:1.216355+0.217894
## [111] train-rmse:0.377280+0.018668 test-rmse:1.214898+0.217941
## [112] train-rmse:0.371898+0.017365 test-rmse:1.213011+0.218093
## [113] train-rmse:0.367337+0.016706 test-rmse:1.211158+0.218699
## [114] train-rmse:0.363043+0.017158 test-rmse:1.209926+0.219261
## [115] train-rmse:0.358059+0.016827 test-rmse:1.208405+0.221347
## [116] train-rmse:0.353819+0.016790 test-rmse:1.206569+0.223571
## [117] train-rmse:0.349917+0.017137 test-rmse:1.205151+0.223886
## [118] train-rmse:0.346339+0.017188 test-rmse:1.201604+0.224988
## [119] train-rmse:0.341953+0.017151 test-rmse:1.199128+0.227075
## [120] train-rmse:0.337653+0.016981 test-rmse:1.197560+0.229457
## [121] train-rmse:0.334016+0.017052 test-rmse:1.196435+0.228710
## [122] train-rmse:0.330261+0.017985 test-rmse:1.195360+0.228798
## [123] train-rmse:0.325418+0.017347 test-rmse:1.194118+0.228404
## [124] train-rmse:0.321457+0.016653 test-rmse:1.193092+0.228420
## [125] train-rmse:0.318496+0.016411 test-rmse:1.191175+0.229726
## [126] train-rmse:0.314481+0.016076 test-rmse:1.189778+0.229636
## [127] train-rmse:0.311136+0.016341 test-rmse:1.189004+0.230006
## [128] train-rmse:0.306653+0.015129 test-rmse:1.187369+0.231421
## [129] train-rmse:0.302742+0.015412 test-rmse:1.186307+0.231261
## [130] train-rmse:0.298906+0.014538 test-rmse:1.184326+0.231455
## [131] train-rmse:0.296172+0.014295 test-rmse:1.184151+0.231271
## [132] train-rmse:0.293238+0.014231 test-rmse:1.183587+0.232087
## [133] train-rmse:0.290489+0.014467 test-rmse:1.182047+0.232524
## [134] train-rmse:0.287481+0.014389 test-rmse:1.180363+0.232674
## [135] train-rmse:0.284654+0.014170 test-rmse:1.179781+0.233098
## [136] train-rmse:0.282087+0.014587 test-rmse:1.178837+0.232824
## [137] train-rmse:0.278933+0.014889 test-rmse:1.177066+0.234386
## [138] train-rmse:0.276010+0.015117 test-rmse:1.175070+0.235084
## [139] train-rmse:0.272769+0.013723 test-rmse:1.174703+0.234942
## [140] train-rmse:0.268936+0.012811 test-rmse:1.174914+0.235714
## [141] train-rmse:0.266379+0.012776 test-rmse:1.174619+0.235556
## [142] train-rmse:0.263963+0.013289 test-rmse:1.173855+0.236764
## [143] train-rmse:0.261449+0.012685 test-rmse:1.173767+0.235756
## [144] train-rmse:0.258478+0.013068 test-rmse:1.173026+0.236429
## [145] train-rmse:0.255661+0.013187 test-rmse:1.173036+0.236971
## [146] train-rmse:0.252496+0.012496 test-rmse:1.171584+0.238171
## [147] train-rmse:0.250225+0.012061 test-rmse:1.170747+0.237502
## [148] train-rmse:0.247856+0.012561 test-rmse:1.169859+0.237797
## [149] train-rmse:0.245704+0.012730 test-rmse:1.169410+0.237703
## [150] train-rmse:0.243497+0.013525 test-rmse:1.168471+0.237556
## [151] train-rmse:0.240997+0.013337 test-rmse:1.168400+0.237914
## [152] train-rmse:0.238328+0.012495 test-rmse:1.166886+0.238768
## [153] train-rmse:0.236345+0.012669 test-rmse:1.165409+0.239517
## [154] train-rmse:0.234397+0.013007 test-rmse:1.163909+0.239805
## [155] train-rmse:0.232498+0.012788 test-rmse:1.163775+0.240628
## [156] train-rmse:0.230565+0.013179 test-rmse:1.163605+0.240536
## [157] train-rmse:0.228834+0.012724 test-rmse:1.163361+0.240412
## [158] train-rmse:0.227285+0.013004 test-rmse:1.162750+0.240922
## [159] train-rmse:0.225206+0.013182 test-rmse:1.162122+0.240976
## [160] train-rmse:0.222570+0.013362 test-rmse:1.161569+0.239976
## [161] train-rmse:0.220736+0.013677 test-rmse:1.160836+0.240541
## [162] train-rmse:0.219236+0.013580 test-rmse:1.159454+0.241299
## [163] train-rmse:0.216990+0.013823 test-rmse:1.159403+0.241534
## [164] train-rmse:0.214807+0.013350 test-rmse:1.158829+0.241637
## [165] train-rmse:0.212116+0.012275 test-rmse:1.159325+0.241359
## [166] train-rmse:0.210173+0.012091 test-rmse:1.157708+0.242412
## [167] train-rmse:0.208804+0.012064 test-rmse:1.157710+0.242919
## [168] train-rmse:0.206212+0.012087 test-rmse:1.157592+0.242972
## [169] train-rmse:0.204333+0.012473 test-rmse:1.157890+0.242982
## [170] train-rmse:0.202040+0.012135 test-rmse:1.156337+0.243662
## [171] train-rmse:0.200240+0.011619 test-rmse:1.155256+0.244212
## [172] train-rmse:0.198587+0.011210 test-rmse:1.154748+0.244179
## [173] train-rmse:0.197191+0.011310 test-rmse:1.154862+0.243919
## [174] train-rmse:0.195216+0.011499 test-rmse:1.154752+0.243898
## [175] train-rmse:0.193350+0.011213 test-rmse:1.154772+0.244103
## [176] train-rmse:0.191677+0.011462 test-rmse:1.154622+0.243906
## [177] train-rmse:0.189934+0.011074 test-rmse:1.153631+0.244427
## [178] train-rmse:0.188708+0.011110 test-rmse:1.154118+0.243944
## [179] train-rmse:0.186968+0.011012 test-rmse:1.153401+0.244492
## [180] train-rmse:0.185212+0.010657 test-rmse:1.153438+0.244490
## [181] train-rmse:0.183482+0.010838 test-rmse:1.153606+0.244422
## [182] train-rmse:0.180950+0.010407 test-rmse:1.153052+0.244542
## [183] train-rmse:0.179385+0.010418 test-rmse:1.152805+0.245295
## [184] train-rmse:0.178023+0.010046 test-rmse:1.152833+0.245611
## [185] train-rmse:0.176598+0.010356 test-rmse:1.152291+0.245775
## [186] train-rmse:0.175272+0.010788 test-rmse:1.151682+0.245668
## [187] train-rmse:0.174264+0.011146 test-rmse:1.150856+0.245654
## [188] train-rmse:0.172513+0.010797 test-rmse:1.150685+0.245736
## [189] train-rmse:0.171141+0.010696 test-rmse:1.150560+0.245756
## [190] train-rmse:0.169299+0.010021 test-rmse:1.150121+0.246059
## [191] train-rmse:0.167834+0.010202 test-rmse:1.149649+0.246281
## [192] train-rmse:0.166781+0.010220 test-rmse:1.149315+0.246467
## [193] train-rmse:0.165592+0.010342 test-rmse:1.148463+0.246575
## [194] train-rmse:0.164545+0.010361 test-rmse:1.148355+0.246780
## [195] train-rmse:0.163248+0.010399 test-rmse:1.147919+0.246763
## [196] train-rmse:0.161770+0.010337 test-rmse:1.147813+0.246980
## [197] train-rmse:0.160110+0.010382 test-rmse:1.147493+0.247237
## [198] train-rmse:0.158540+0.010406 test-rmse:1.147136+0.247266
## [199] train-rmse:0.157385+0.010478 test-rmse:1.146841+0.248050
## [200] train-rmse:0.156212+0.010578 test-rmse:1.146844+0.248103
## [201] train-rmse:0.154829+0.010309 test-rmse:1.147457+0.247738
## [202] train-rmse:0.153813+0.010207 test-rmse:1.147087+0.247707
## [203] train-rmse:0.152385+0.010241 test-rmse:1.147450+0.247728
## [204] train-rmse:0.150567+0.009515 test-rmse:1.147420+0.247810
## [205] train-rmse:0.149686+0.009628 test-rmse:1.147386+0.248093
## Stopping. Best iteration:
## [199] train-rmse:0.157385+0.010478 test-rmse:1.146841+0.248050
##
## 25
## [1] train-rmse:90.491687+0.101310 test-rmse:90.491937+0.489579
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.126776+0.089017 test-rmse:76.126749+0.511751
## [3] train-rmse:64.064040+0.079442 test-rmse:64.063658+0.532994
## [4] train-rmse:53.938632+0.072315 test-rmse:53.937809+0.553884
## [5] train-rmse:45.444263+0.067432 test-rmse:45.442850+0.574932
## [6] train-rmse:38.323881+0.064650 test-rmse:38.321713+0.596609
## [7] train-rmse:32.357049+0.062292 test-rmse:32.380286+0.599805
## [8] train-rmse:27.347881+0.054482 test-rmse:27.420350+0.612889
## [9] train-rmse:23.142614+0.043845 test-rmse:23.227096+0.593161
## [10] train-rmse:19.610397+0.037134 test-rmse:19.706818+0.582337
## [11] train-rmse:16.642647+0.030876 test-rmse:16.754448+0.578970
## [12] train-rmse:14.146475+0.029134 test-rmse:14.255316+0.558178
## [13] train-rmse:12.047483+0.026878 test-rmse:12.152864+0.549636
## [14] train-rmse:10.282251+0.022133 test-rmse:10.368125+0.560137
## [15] train-rmse:8.800834+0.024085 test-rmse:8.882288+0.542207
## [16] train-rmse:7.557792+0.022823 test-rmse:7.638580+0.530966
## [17] train-rmse:6.516109+0.024624 test-rmse:6.609978+0.528065
## [18] train-rmse:5.643814+0.025692 test-rmse:5.755417+0.506203
## [19] train-rmse:4.915964+0.029467 test-rmse:5.050901+0.474536
## [20] train-rmse:4.302048+0.026658 test-rmse:4.470345+0.476621
## [21] train-rmse:3.795513+0.031403 test-rmse:4.000362+0.446758
## [22] train-rmse:3.371460+0.035674 test-rmse:3.614480+0.435628
## [23] train-rmse:3.018717+0.034558 test-rmse:3.301068+0.392178
## [24] train-rmse:2.730403+0.038704 test-rmse:3.059644+0.361830
## [25] train-rmse:2.488238+0.039523 test-rmse:2.837257+0.338981
## [26] train-rmse:2.277696+0.036074 test-rmse:2.664395+0.310878
## [27] train-rmse:2.108968+0.037094 test-rmse:2.514939+0.290200
## [28] train-rmse:1.964397+0.038432 test-rmse:2.401671+0.276939
## [29] train-rmse:1.834571+0.047451 test-rmse:2.309616+0.269668
## [30] train-rmse:1.731628+0.048200 test-rmse:2.228800+0.254096
## [31] train-rmse:1.643333+0.048063 test-rmse:2.156324+0.238189
## [32] train-rmse:1.560722+0.045882 test-rmse:2.093652+0.219220
## [33] train-rmse:1.490061+0.053305 test-rmse:2.051681+0.213471
## [34] train-rmse:1.423838+0.052579 test-rmse:2.013682+0.219036
## [35] train-rmse:1.365556+0.047382 test-rmse:1.965362+0.206069
## [36] train-rmse:1.315041+0.045824 test-rmse:1.922681+0.205279
## [37] train-rmse:1.264295+0.045972 test-rmse:1.893587+0.200103
## [38] train-rmse:1.212711+0.044133 test-rmse:1.852171+0.196074
## [39] train-rmse:1.171039+0.042442 test-rmse:1.816606+0.195290
## [40] train-rmse:1.131067+0.039328 test-rmse:1.784764+0.190276
## [41] train-rmse:1.094584+0.038563 test-rmse:1.757174+0.190440
## [42] train-rmse:1.058378+0.038644 test-rmse:1.723592+0.197728
## [43] train-rmse:1.020302+0.044719 test-rmse:1.690458+0.200854
## [44] train-rmse:0.988736+0.042548 test-rmse:1.668748+0.201688
## [45] train-rmse:0.957981+0.041155 test-rmse:1.644216+0.202019
## [46] train-rmse:0.929760+0.040225 test-rmse:1.619538+0.200979
## [47] train-rmse:0.903648+0.040050 test-rmse:1.600528+0.199100
## [48] train-rmse:0.874915+0.043614 test-rmse:1.581455+0.202474
## [49] train-rmse:0.849713+0.042029 test-rmse:1.562102+0.209246
## [50] train-rmse:0.825876+0.040632 test-rmse:1.545796+0.213378
## [51] train-rmse:0.800712+0.040248 test-rmse:1.525959+0.215919
## [52] train-rmse:0.779012+0.038879 test-rmse:1.509945+0.217982
## [53] train-rmse:0.757086+0.037818 test-rmse:1.496758+0.215806
## [54] train-rmse:0.736202+0.035977 test-rmse:1.481466+0.223673
## [55] train-rmse:0.716610+0.035079 test-rmse:1.466249+0.226816
## [56] train-rmse:0.697161+0.033779 test-rmse:1.456510+0.226085
## [57] train-rmse:0.678804+0.031924 test-rmse:1.440590+0.226167
## [58] train-rmse:0.661327+0.031052 test-rmse:1.432730+0.225141
## [59] train-rmse:0.644308+0.030599 test-rmse:1.424585+0.228976
## [60] train-rmse:0.628675+0.029623 test-rmse:1.409533+0.232329
## [61] train-rmse:0.613823+0.029262 test-rmse:1.400173+0.236232
## [62] train-rmse:0.599399+0.029452 test-rmse:1.389559+0.236791
## [63] train-rmse:0.585458+0.028601 test-rmse:1.377397+0.238427
## [64] train-rmse:0.570399+0.028277 test-rmse:1.372848+0.237448
## [65] train-rmse:0.557028+0.027272 test-rmse:1.366429+0.236558
## [66] train-rmse:0.545453+0.026522 test-rmse:1.355786+0.230613
## [67] train-rmse:0.533944+0.025686 test-rmse:1.345816+0.234643
## [68] train-rmse:0.522398+0.024291 test-rmse:1.338804+0.234982
## [69] train-rmse:0.511982+0.023765 test-rmse:1.332528+0.235857
## [70] train-rmse:0.500175+0.023782 test-rmse:1.327565+0.239062
## [71] train-rmse:0.488199+0.022330 test-rmse:1.320978+0.236387
## [72] train-rmse:0.479137+0.021584 test-rmse:1.315979+0.237136
## [73] train-rmse:0.469965+0.021378 test-rmse:1.308302+0.239138
## [74] train-rmse:0.461156+0.020427 test-rmse:1.301834+0.238827
## [75] train-rmse:0.451510+0.020202 test-rmse:1.295455+0.240891
## [76] train-rmse:0.442504+0.019338 test-rmse:1.288606+0.235252
## [77] train-rmse:0.433267+0.018653 test-rmse:1.285771+0.237631
## [78] train-rmse:0.425283+0.018600 test-rmse:1.283249+0.238370
## [79] train-rmse:0.417021+0.017706 test-rmse:1.276194+0.235512
## [80] train-rmse:0.409741+0.016691 test-rmse:1.271712+0.234489
## [81] train-rmse:0.402161+0.016809 test-rmse:1.268050+0.235084
## [82] train-rmse:0.396014+0.016485 test-rmse:1.262377+0.236896
## [83] train-rmse:0.387080+0.015647 test-rmse:1.259909+0.238302
## [84] train-rmse:0.379749+0.015409 test-rmse:1.257724+0.240882
## [85] train-rmse:0.373121+0.015402 test-rmse:1.255383+0.241168
## [86] train-rmse:0.367005+0.014595 test-rmse:1.251973+0.241014
## [87] train-rmse:0.359755+0.014806 test-rmse:1.249986+0.241092
## [88] train-rmse:0.353370+0.015263 test-rmse:1.247637+0.241169
## [89] train-rmse:0.346299+0.012990 test-rmse:1.243869+0.240232
## [90] train-rmse:0.340773+0.012690 test-rmse:1.242174+0.239271
## [91] train-rmse:0.335758+0.012312 test-rmse:1.239906+0.240463
## [92] train-rmse:0.329868+0.011213 test-rmse:1.239085+0.242624
## [93] train-rmse:0.322852+0.011848 test-rmse:1.238018+0.242938
## [94] train-rmse:0.317707+0.011872 test-rmse:1.235151+0.245200
## [95] train-rmse:0.311642+0.011156 test-rmse:1.233783+0.244913
## [96] train-rmse:0.307035+0.011033 test-rmse:1.228967+0.246636
## [97] train-rmse:0.302838+0.010529 test-rmse:1.228543+0.247438
## [98] train-rmse:0.298660+0.010963 test-rmse:1.227049+0.248817
## [99] train-rmse:0.292465+0.009416 test-rmse:1.224543+0.250109
## [100] train-rmse:0.287442+0.008941 test-rmse:1.222727+0.250241
## [101] train-rmse:0.282553+0.009839 test-rmse:1.223939+0.249418
## [102] train-rmse:0.278610+0.009680 test-rmse:1.223353+0.250842
## [103] train-rmse:0.274750+0.009643 test-rmse:1.222206+0.251641
## [104] train-rmse:0.271500+0.009724 test-rmse:1.219027+0.252057
## [105] train-rmse:0.267184+0.009977 test-rmse:1.217204+0.252303
## [106] train-rmse:0.263234+0.009477 test-rmse:1.216348+0.251986
## [107] train-rmse:0.259271+0.010142 test-rmse:1.215100+0.250909
## [108] train-rmse:0.255370+0.009939 test-rmse:1.214152+0.252169
## [109] train-rmse:0.251078+0.010263 test-rmse:1.213980+0.251858
## [110] train-rmse:0.246855+0.009855 test-rmse:1.213153+0.252362
## [111] train-rmse:0.244021+0.009915 test-rmse:1.212923+0.252186
## [112] train-rmse:0.240685+0.009982 test-rmse:1.211883+0.251487
## [113] train-rmse:0.236475+0.010904 test-rmse:1.211590+0.251273
## [114] train-rmse:0.232480+0.010836 test-rmse:1.210096+0.251307
## [115] train-rmse:0.229107+0.010359 test-rmse:1.209795+0.252152
## [116] train-rmse:0.226007+0.009321 test-rmse:1.209509+0.253127
## [117] train-rmse:0.222917+0.008928 test-rmse:1.208514+0.253371
## [118] train-rmse:0.219177+0.008351 test-rmse:1.207683+0.254045
## [119] train-rmse:0.214889+0.008362 test-rmse:1.206756+0.253056
## [120] train-rmse:0.211205+0.008131 test-rmse:1.205716+0.254538
## [121] train-rmse:0.208460+0.008217 test-rmse:1.205939+0.254157
## [122] train-rmse:0.204778+0.007418 test-rmse:1.204697+0.253269
## [123] train-rmse:0.201794+0.006730 test-rmse:1.203916+0.252813
## [124] train-rmse:0.199641+0.007350 test-rmse:1.203220+0.252835
## [125] train-rmse:0.196268+0.007156 test-rmse:1.202486+0.253543
## [126] train-rmse:0.193551+0.006629 test-rmse:1.202050+0.253303
## [127] train-rmse:0.191206+0.006771 test-rmse:1.201874+0.253114
## [128] train-rmse:0.188898+0.006746 test-rmse:1.201816+0.253207
## [129] train-rmse:0.186885+0.006961 test-rmse:1.201172+0.253256
## [130] train-rmse:0.184143+0.006705 test-rmse:1.201172+0.253422
## [131] train-rmse:0.181358+0.005798 test-rmse:1.199692+0.254142
## [132] train-rmse:0.178778+0.006863 test-rmse:1.199017+0.254641
## [133] train-rmse:0.176240+0.006288 test-rmse:1.198619+0.254534
## [134] train-rmse:0.173987+0.006005 test-rmse:1.198515+0.254943
## [135] train-rmse:0.172024+0.005581 test-rmse:1.198029+0.254638
## [136] train-rmse:0.169221+0.005215 test-rmse:1.197724+0.255344
## [137] train-rmse:0.166900+0.005167 test-rmse:1.197185+0.255110
## [138] train-rmse:0.164442+0.004788 test-rmse:1.195793+0.255124
## [139] train-rmse:0.162373+0.004839 test-rmse:1.195228+0.254813
## [140] train-rmse:0.159980+0.004592 test-rmse:1.194290+0.254688
## [141] train-rmse:0.158194+0.004595 test-rmse:1.193916+0.255023
## [142] train-rmse:0.156392+0.004798 test-rmse:1.193640+0.255186
## [143] train-rmse:0.154696+0.004967 test-rmse:1.193680+0.255061
## [144] train-rmse:0.153167+0.005362 test-rmse:1.192854+0.256065
## [145] train-rmse:0.151281+0.005327 test-rmse:1.191583+0.254814
## [146] train-rmse:0.149204+0.004973 test-rmse:1.191149+0.255119
## [147] train-rmse:0.147030+0.004610 test-rmse:1.191037+0.255027
## [148] train-rmse:0.145414+0.004354 test-rmse:1.190608+0.255245
## [149] train-rmse:0.143184+0.003505 test-rmse:1.190491+0.254840
## [150] train-rmse:0.141555+0.003561 test-rmse:1.189949+0.254744
## [151] train-rmse:0.139859+0.003430 test-rmse:1.189414+0.254533
## [152] train-rmse:0.138923+0.003512 test-rmse:1.189095+0.254212
## [153] train-rmse:0.136535+0.003302 test-rmse:1.188507+0.254391
## [154] train-rmse:0.135182+0.003217 test-rmse:1.187648+0.254849
## [155] train-rmse:0.132903+0.004043 test-rmse:1.187323+0.255030
## [156] train-rmse:0.131577+0.004266 test-rmse:1.186986+0.255238
## [157] train-rmse:0.130291+0.004221 test-rmse:1.186696+0.255264
## [158] train-rmse:0.128845+0.003939 test-rmse:1.186597+0.255032
## [159] train-rmse:0.127687+0.003874 test-rmse:1.186413+0.255047
## [160] train-rmse:0.125968+0.004116 test-rmse:1.185910+0.255117
## [161] train-rmse:0.124685+0.003937 test-rmse:1.185019+0.254315
## [162] train-rmse:0.122819+0.004003 test-rmse:1.184630+0.254042
## [163] train-rmse:0.121314+0.004067 test-rmse:1.185181+0.254086
## [164] train-rmse:0.119990+0.004048 test-rmse:1.184764+0.253933
## [165] train-rmse:0.118364+0.004294 test-rmse:1.184269+0.253369
## [166] train-rmse:0.117069+0.003906 test-rmse:1.183793+0.253618
## [167] train-rmse:0.115475+0.004368 test-rmse:1.183459+0.254078
## [168] train-rmse:0.114318+0.004079 test-rmse:1.183530+0.253631
## [169] train-rmse:0.112564+0.003699 test-rmse:1.182740+0.252060
## [170] train-rmse:0.111318+0.003836 test-rmse:1.182833+0.252134
## [171] train-rmse:0.110298+0.003999 test-rmse:1.182022+0.252089
## [172] train-rmse:0.109028+0.004050 test-rmse:1.181681+0.251867
## [173] train-rmse:0.107287+0.004207 test-rmse:1.181615+0.251804
## [174] train-rmse:0.106259+0.004140 test-rmse:1.181843+0.251670
## [175] train-rmse:0.104599+0.003467 test-rmse:1.181716+0.251529
## [176] train-rmse:0.103515+0.003548 test-rmse:1.181453+0.251544
## [177] train-rmse:0.102429+0.003443 test-rmse:1.181281+0.251414
## [178] train-rmse:0.100969+0.003108 test-rmse:1.181393+0.251314
## [179] train-rmse:0.099853+0.002817 test-rmse:1.181119+0.251539
## [180] train-rmse:0.098917+0.002776 test-rmse:1.180693+0.251747
## [181] train-rmse:0.097761+0.003120 test-rmse:1.180366+0.251694
## [182] train-rmse:0.096705+0.003028 test-rmse:1.180183+0.251567
## [183] train-rmse:0.095656+0.003018 test-rmse:1.180024+0.251145
## [184] train-rmse:0.094308+0.003481 test-rmse:1.180145+0.251080
## [185] train-rmse:0.093254+0.003902 test-rmse:1.180143+0.251434
## [186] train-rmse:0.092335+0.003552 test-rmse:1.179800+0.251389
## [187] train-rmse:0.091327+0.003589 test-rmse:1.179648+0.251139
## [188] train-rmse:0.090329+0.003789 test-rmse:1.179206+0.251032
## [189] train-rmse:0.089306+0.003526 test-rmse:1.179130+0.250915
## [190] train-rmse:0.088050+0.003041 test-rmse:1.178861+0.250929
## [191] train-rmse:0.086962+0.003034 test-rmse:1.179307+0.250766
## [192] train-rmse:0.085970+0.003063 test-rmse:1.179094+0.250832
## [193] train-rmse:0.084880+0.002989 test-rmse:1.178685+0.250820
## [194] train-rmse:0.083215+0.002699 test-rmse:1.178824+0.250935
## [195] train-rmse:0.082250+0.002524 test-rmse:1.178448+0.250726
## [196] train-rmse:0.081269+0.002985 test-rmse:1.178366+0.250681
## [197] train-rmse:0.080596+0.003010 test-rmse:1.178184+0.250825
## [198] train-rmse:0.079602+0.002717 test-rmse:1.178164+0.251128
## [199] train-rmse:0.078825+0.002795 test-rmse:1.178134+0.250847
## [200] train-rmse:0.078059+0.002735 test-rmse:1.178131+0.250681
## [201] train-rmse:0.077218+0.003077 test-rmse:1.178039+0.250882
## [202] train-rmse:0.076268+0.003255 test-rmse:1.177428+0.251077
## [203] train-rmse:0.075686+0.003276 test-rmse:1.177268+0.251340
## [204] train-rmse:0.075061+0.003197 test-rmse:1.177101+0.251502
## [205] train-rmse:0.074219+0.003134 test-rmse:1.176968+0.251381
## [206] train-rmse:0.073233+0.003216 test-rmse:1.176854+0.251665
## [207] train-rmse:0.072335+0.003248 test-rmse:1.176849+0.251621
## [208] train-rmse:0.071412+0.003230 test-rmse:1.176799+0.251486
## [209] train-rmse:0.070760+0.002981 test-rmse:1.176565+0.251202
## [210] train-rmse:0.069955+0.002861 test-rmse:1.176488+0.251050
## [211] train-rmse:0.069064+0.002944 test-rmse:1.176490+0.250892
## [212] train-rmse:0.068309+0.002976 test-rmse:1.176388+0.250950
## [213] train-rmse:0.067665+0.002965 test-rmse:1.176490+0.250848
## [214] train-rmse:0.066788+0.002712 test-rmse:1.176496+0.251010
## [215] train-rmse:0.065942+0.002714 test-rmse:1.176437+0.250924
## [216] train-rmse:0.065172+0.002955 test-rmse:1.176435+0.250877
## [217] train-rmse:0.064306+0.003026 test-rmse:1.176167+0.251204
## [218] train-rmse:0.063596+0.002820 test-rmse:1.175845+0.251272
## [219] train-rmse:0.063004+0.002698 test-rmse:1.175804+0.251315
## [220] train-rmse:0.062412+0.002517 test-rmse:1.175674+0.250840
## [221] train-rmse:0.061687+0.002488 test-rmse:1.175679+0.250723
## [222] train-rmse:0.061127+0.002432 test-rmse:1.175535+0.250816
## [223] train-rmse:0.060337+0.002371 test-rmse:1.175481+0.250708
## [224] train-rmse:0.059674+0.002196 test-rmse:1.175370+0.250647
## [225] train-rmse:0.059206+0.002021 test-rmse:1.175356+0.250623
## [226] train-rmse:0.058645+0.002146 test-rmse:1.175055+0.250607
## [227] train-rmse:0.057772+0.002089 test-rmse:1.174882+0.250663
## [228] train-rmse:0.057081+0.001944 test-rmse:1.174668+0.250248
## [229] train-rmse:0.056618+0.001856 test-rmse:1.174715+0.250268
## [230] train-rmse:0.056040+0.001704 test-rmse:1.174498+0.250300
## [231] train-rmse:0.055667+0.001712 test-rmse:1.174470+0.250276
## [232] train-rmse:0.054823+0.001933 test-rmse:1.174633+0.250046
## [233] train-rmse:0.054316+0.001877 test-rmse:1.174503+0.250017
## [234] train-rmse:0.053711+0.001704 test-rmse:1.174689+0.250204
## [235] train-rmse:0.053019+0.001473 test-rmse:1.174625+0.249939
## [236] train-rmse:0.052610+0.001446 test-rmse:1.174625+0.250009
## [237] train-rmse:0.051769+0.001881 test-rmse:1.174627+0.250092
## Stopping. Best iteration:
## [231] train-rmse:0.055667+0.001712 test-rmse:1.174470+0.250276
##
## 26
## [1] train-rmse:90.491687+0.101310 test-rmse:90.491937+0.489579
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.126776+0.089017 test-rmse:76.126749+0.511751
## [3] train-rmse:64.064040+0.079442 test-rmse:64.063658+0.532994
## [4] train-rmse:53.938632+0.072315 test-rmse:53.937809+0.553884
## [5] train-rmse:45.444263+0.067432 test-rmse:45.442850+0.574932
## [6] train-rmse:38.323881+0.064650 test-rmse:38.321713+0.596609
## [7] train-rmse:32.357049+0.062292 test-rmse:32.380286+0.599805
## [8] train-rmse:27.347881+0.054482 test-rmse:27.420350+0.612889
## [9] train-rmse:23.142614+0.043845 test-rmse:23.227096+0.593161
## [10] train-rmse:19.610094+0.036791 test-rmse:19.703792+0.586145
## [11] train-rmse:16.641395+0.030207 test-rmse:16.745301+0.583392
## [12] train-rmse:14.142440+0.028373 test-rmse:14.246529+0.559704
## [13] train-rmse:12.039050+0.026215 test-rmse:12.150034+0.548388
## [14] train-rmse:10.270221+0.023820 test-rmse:10.386441+0.534831
## [15] train-rmse:8.778229+0.023154 test-rmse:8.883696+0.513424
## [16] train-rmse:7.526449+0.022603 test-rmse:7.640949+0.493221
## [17] train-rmse:6.474282+0.023214 test-rmse:6.601322+0.513513
## [18] train-rmse:5.587813+0.023537 test-rmse:5.722885+0.497660
## [19] train-rmse:4.844880+0.025248 test-rmse:4.993995+0.487410
## [20] train-rmse:4.215217+0.023024 test-rmse:4.390858+0.455253
## [21] train-rmse:3.694864+0.022909 test-rmse:3.918911+0.444046
## [22] train-rmse:3.257291+0.024194 test-rmse:3.502098+0.430216
## [23] train-rmse:2.891112+0.024719 test-rmse:3.176954+0.396478
## [24] train-rmse:2.580362+0.027083 test-rmse:2.919586+0.362780
## [25] train-rmse:2.321712+0.029340 test-rmse:2.698569+0.355269
## [26] train-rmse:2.100181+0.032959 test-rmse:2.525763+0.340667
## [27] train-rmse:1.914004+0.040719 test-rmse:2.378226+0.329643
## [28] train-rmse:1.760109+0.040593 test-rmse:2.263155+0.323877
## [29] train-rmse:1.628957+0.041529 test-rmse:2.165142+0.314448
## [30] train-rmse:1.516870+0.044325 test-rmse:2.089349+0.307635
## [31] train-rmse:1.413608+0.050334 test-rmse:2.025223+0.291372
## [32] train-rmse:1.327010+0.046696 test-rmse:1.963090+0.284345
## [33] train-rmse:1.249300+0.043234 test-rmse:1.916630+0.268297
## [34] train-rmse:1.179596+0.040531 test-rmse:1.868423+0.258992
## [35] train-rmse:1.116107+0.035827 test-rmse:1.823107+0.253290
## [36] train-rmse:1.063200+0.038612 test-rmse:1.787580+0.247315
## [37] train-rmse:1.016543+0.036843 test-rmse:1.755233+0.250447
## [38] train-rmse:0.968619+0.034122 test-rmse:1.720290+0.255574
## [39] train-rmse:0.930324+0.032594 test-rmse:1.695138+0.257174
## [40] train-rmse:0.888469+0.036994 test-rmse:1.662235+0.254836
## [41] train-rmse:0.854031+0.033894 test-rmse:1.636739+0.259840
## [42] train-rmse:0.819305+0.033354 test-rmse:1.611147+0.263372
## [43] train-rmse:0.785075+0.031273 test-rmse:1.587407+0.265978
## [44] train-rmse:0.753135+0.031404 test-rmse:1.569267+0.274294
## [45] train-rmse:0.727447+0.030617 test-rmse:1.542670+0.265261
## [46] train-rmse:0.700230+0.026208 test-rmse:1.527223+0.269810
## [47] train-rmse:0.677237+0.024888 test-rmse:1.509106+0.271303
## [48] train-rmse:0.656402+0.024147 test-rmse:1.496595+0.277724
## [49] train-rmse:0.631241+0.026411 test-rmse:1.484773+0.278756
## [50] train-rmse:0.612375+0.025877 test-rmse:1.472779+0.278258
## [51] train-rmse:0.593035+0.025871 test-rmse:1.461015+0.280790
## [52] train-rmse:0.574761+0.024674 test-rmse:1.446289+0.277959
## [53] train-rmse:0.555060+0.022907 test-rmse:1.437759+0.274096
## [54] train-rmse:0.537888+0.022094 test-rmse:1.428895+0.278412
## [55] train-rmse:0.519717+0.023160 test-rmse:1.424446+0.281930
## [56] train-rmse:0.505463+0.022674 test-rmse:1.416187+0.282310
## [57] train-rmse:0.491883+0.022687 test-rmse:1.409396+0.281768
## [58] train-rmse:0.477460+0.021134 test-rmse:1.401914+0.280375
## [59] train-rmse:0.464643+0.020687 test-rmse:1.395601+0.285395
## [60] train-rmse:0.452338+0.020831 test-rmse:1.388228+0.286623
## [61] train-rmse:0.441331+0.019642 test-rmse:1.382247+0.288753
## [62] train-rmse:0.427861+0.020551 test-rmse:1.375533+0.290595
## [63] train-rmse:0.415108+0.021528 test-rmse:1.372823+0.290263
## [64] train-rmse:0.404081+0.019095 test-rmse:1.366684+0.291838
## [65] train-rmse:0.394267+0.018092 test-rmse:1.360834+0.290330
## [66] train-rmse:0.384778+0.018459 test-rmse:1.355286+0.291718
## [67] train-rmse:0.374703+0.018851 test-rmse:1.351409+0.291821
## [68] train-rmse:0.364222+0.019941 test-rmse:1.349716+0.294590
## [69] train-rmse:0.356682+0.019642 test-rmse:1.345385+0.293954
## [70] train-rmse:0.348636+0.019843 test-rmse:1.341040+0.295204
## [71] train-rmse:0.338611+0.016220 test-rmse:1.336187+0.295928
## [72] train-rmse:0.330747+0.014739 test-rmse:1.332917+0.292699
## [73] train-rmse:0.322817+0.015672 test-rmse:1.331252+0.294897
## [74] train-rmse:0.315253+0.014868 test-rmse:1.323653+0.290739
## [75] train-rmse:0.307681+0.014018 test-rmse:1.320057+0.293774
## [76] train-rmse:0.301390+0.013861 test-rmse:1.318435+0.295147
## [77] train-rmse:0.295380+0.013808 test-rmse:1.316075+0.295796
## [78] train-rmse:0.288474+0.013939 test-rmse:1.314124+0.296064
## [79] train-rmse:0.281268+0.012971 test-rmse:1.312444+0.297820
## [80] train-rmse:0.276196+0.012649 test-rmse:1.309283+0.297159
## [81] train-rmse:0.271108+0.012138 test-rmse:1.308178+0.298285
## [82] train-rmse:0.265712+0.012129 test-rmse:1.306312+0.298556
## [83] train-rmse:0.260919+0.012552 test-rmse:1.304054+0.298630
## [84] train-rmse:0.254736+0.011196 test-rmse:1.301844+0.300253
## [85] train-rmse:0.249356+0.011906 test-rmse:1.300878+0.299576
## [86] train-rmse:0.242530+0.011633 test-rmse:1.298560+0.300057
## [87] train-rmse:0.237460+0.010557 test-rmse:1.295484+0.299041
## [88] train-rmse:0.233384+0.011167 test-rmse:1.294721+0.300138
## [89] train-rmse:0.229435+0.010238 test-rmse:1.293888+0.300884
## [90] train-rmse:0.225618+0.010414 test-rmse:1.292768+0.300400
## [91] train-rmse:0.221488+0.009836 test-rmse:1.290922+0.302100
## [92] train-rmse:0.215662+0.011081 test-rmse:1.289751+0.302541
## [93] train-rmse:0.211374+0.010945 test-rmse:1.289831+0.302637
## [94] train-rmse:0.207797+0.011264 test-rmse:1.288842+0.301911
## [95] train-rmse:0.203168+0.011108 test-rmse:1.288706+0.301804
## [96] train-rmse:0.198774+0.010547 test-rmse:1.287539+0.302634
## [97] train-rmse:0.195057+0.010058 test-rmse:1.287657+0.303045
## [98] train-rmse:0.191191+0.009659 test-rmse:1.287539+0.302761
## [99] train-rmse:0.188286+0.010170 test-rmse:1.286687+0.302365
## [100] train-rmse:0.184351+0.010132 test-rmse:1.286770+0.302372
## [101] train-rmse:0.181060+0.009167 test-rmse:1.287258+0.302922
## [102] train-rmse:0.177605+0.009838 test-rmse:1.286181+0.304151
## [103] train-rmse:0.174369+0.009838 test-rmse:1.285395+0.304226
## [104] train-rmse:0.170937+0.009392 test-rmse:1.284581+0.304306
## [105] train-rmse:0.168281+0.009399 test-rmse:1.283721+0.305156
## [106] train-rmse:0.164623+0.008742 test-rmse:1.282333+0.303880
## [107] train-rmse:0.160808+0.009164 test-rmse:1.281053+0.303157
## [108] train-rmse:0.158184+0.009240 test-rmse:1.279658+0.303375
## [109] train-rmse:0.154805+0.009768 test-rmse:1.279933+0.302492
## [110] train-rmse:0.151878+0.009920 test-rmse:1.278936+0.303329
## [111] train-rmse:0.148961+0.009421 test-rmse:1.278407+0.303996
## [112] train-rmse:0.146175+0.008983 test-rmse:1.277630+0.304021
## [113] train-rmse:0.144323+0.008683 test-rmse:1.277453+0.303959
## [114] train-rmse:0.141825+0.008772 test-rmse:1.276955+0.304133
## [115] train-rmse:0.139507+0.008991 test-rmse:1.276818+0.304722
## [116] train-rmse:0.137127+0.008872 test-rmse:1.277109+0.304301
## [117] train-rmse:0.134894+0.008370 test-rmse:1.277029+0.304812
## [118] train-rmse:0.132444+0.008474 test-rmse:1.275850+0.304791
## [119] train-rmse:0.129684+0.008676 test-rmse:1.276243+0.304578
## [120] train-rmse:0.127370+0.009072 test-rmse:1.276061+0.304393
## [121] train-rmse:0.125093+0.008925 test-rmse:1.276462+0.304005
## [122] train-rmse:0.123036+0.009242 test-rmse:1.275837+0.303972
## [123] train-rmse:0.120882+0.008891 test-rmse:1.276393+0.304133
## [124] train-rmse:0.119051+0.009060 test-rmse:1.276543+0.303768
## [125] train-rmse:0.117422+0.008961 test-rmse:1.276314+0.303936
## [126] train-rmse:0.115676+0.009049 test-rmse:1.276349+0.304222
## [127] train-rmse:0.113758+0.009493 test-rmse:1.276198+0.304077
## [128] train-rmse:0.111656+0.009345 test-rmse:1.276203+0.304251
## Stopping. Best iteration:
## [122] train-rmse:0.123036+0.009242 test-rmse:1.275837+0.303972
##
## 27
## [1] train-rmse:90.491687+0.101310 test-rmse:90.491937+0.489579
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:76.126776+0.089017 test-rmse:76.126749+0.511751
## [3] train-rmse:64.064040+0.079442 test-rmse:64.063658+0.532994
## [4] train-rmse:53.938632+0.072315 test-rmse:53.937809+0.553884
## [5] train-rmse:45.444263+0.067432 test-rmse:45.442850+0.574932
## [6] train-rmse:38.323881+0.064650 test-rmse:38.321713+0.596609
## [7] train-rmse:32.357049+0.062292 test-rmse:32.380286+0.599805
## [8] train-rmse:27.347881+0.054482 test-rmse:27.420350+0.612889
## [9] train-rmse:23.142614+0.043845 test-rmse:23.227096+0.593161
## [10] train-rmse:19.610094+0.036791 test-rmse:19.703792+0.586145
## [11] train-rmse:16.640550+0.030101 test-rmse:16.744168+0.579503
## [12] train-rmse:14.139348+0.028534 test-rmse:14.255473+0.564351
## [13] train-rmse:12.033090+0.025776 test-rmse:12.134680+0.555104
## [14] train-rmse:10.260358+0.022901 test-rmse:10.360152+0.540845
## [15] train-rmse:8.760678+0.021462 test-rmse:8.847631+0.532347
## [16] train-rmse:7.502907+0.022787 test-rmse:7.594884+0.517849
## [17] train-rmse:6.438340+0.020465 test-rmse:6.559697+0.529041
## [18] train-rmse:5.541810+0.018999 test-rmse:5.671659+0.495632
## [19] train-rmse:4.786810+0.016905 test-rmse:4.940214+0.477249
## [20] train-rmse:4.153128+0.017113 test-rmse:4.330265+0.447208
## [21] train-rmse:3.617587+0.016057 test-rmse:3.839933+0.442076
## [22] train-rmse:3.167512+0.017769 test-rmse:3.407156+0.412359
## [23] train-rmse:2.791443+0.019810 test-rmse:3.067735+0.390249
## [24] train-rmse:2.473084+0.023583 test-rmse:2.791110+0.365581
## [25] train-rmse:2.192799+0.030016 test-rmse:2.551064+0.357304
## [26] train-rmse:1.962941+0.030562 test-rmse:2.359100+0.334306
## [27] train-rmse:1.767521+0.029188 test-rmse:2.205886+0.302148
## [28] train-rmse:1.604362+0.032527 test-rmse:2.076234+0.276655
## [29] train-rmse:1.459207+0.032204 test-rmse:1.971579+0.253365
## [30] train-rmse:1.338357+0.032577 test-rmse:1.887390+0.245276
## [31] train-rmse:1.234029+0.028029 test-rmse:1.812015+0.232954
## [32] train-rmse:1.138560+0.034412 test-rmse:1.758618+0.222570
## [33] train-rmse:1.060524+0.036307 test-rmse:1.708025+0.216917
## [34] train-rmse:0.995652+0.036596 test-rmse:1.665162+0.215091
## [35] train-rmse:0.931661+0.037640 test-rmse:1.632586+0.210219
## [36] train-rmse:0.876204+0.041000 test-rmse:1.591881+0.209055
## [37] train-rmse:0.828557+0.037420 test-rmse:1.563314+0.211605
## [38] train-rmse:0.783131+0.037669 test-rmse:1.537547+0.216673
## [39] train-rmse:0.745291+0.039333 test-rmse:1.512925+0.218829
## [40] train-rmse:0.708433+0.038226 test-rmse:1.487464+0.221897
## [41] train-rmse:0.675667+0.033008 test-rmse:1.472031+0.216708
## [42] train-rmse:0.648182+0.032641 test-rmse:1.450918+0.220914
## [43] train-rmse:0.617280+0.032791 test-rmse:1.436117+0.221226
## [44] train-rmse:0.589578+0.034444 test-rmse:1.420265+0.226409
## [45] train-rmse:0.567228+0.033946 test-rmse:1.405023+0.225934
## [46] train-rmse:0.545373+0.033817 test-rmse:1.394005+0.225618
## [47] train-rmse:0.521603+0.031443 test-rmse:1.383021+0.229259
## [48] train-rmse:0.502668+0.032761 test-rmse:1.376235+0.231528
## [49] train-rmse:0.485723+0.030408 test-rmse:1.364576+0.232633
## [50] train-rmse:0.469481+0.029924 test-rmse:1.356213+0.231898
## [51] train-rmse:0.452063+0.029685 test-rmse:1.345957+0.230028
## [52] train-rmse:0.436371+0.026810 test-rmse:1.334940+0.229376
## [53] train-rmse:0.421010+0.026944 test-rmse:1.328382+0.229398
## [54] train-rmse:0.406807+0.026355 test-rmse:1.326130+0.232855
## [55] train-rmse:0.394442+0.024679 test-rmse:1.320212+0.232179
## [56] train-rmse:0.382399+0.024577 test-rmse:1.315816+0.232857
## [57] train-rmse:0.371013+0.022938 test-rmse:1.308341+0.238439
## [58] train-rmse:0.360400+0.022013 test-rmse:1.305012+0.238162
## [59] train-rmse:0.351165+0.021103 test-rmse:1.299187+0.239119
## [60] train-rmse:0.341420+0.021534 test-rmse:1.295823+0.239151
## [61] train-rmse:0.332266+0.021703 test-rmse:1.290351+0.240183
## [62] train-rmse:0.321151+0.018803 test-rmse:1.288060+0.241197
## [63] train-rmse:0.312487+0.017379 test-rmse:1.284554+0.243375
## [64] train-rmse:0.303341+0.017924 test-rmse:1.281034+0.243410
## [65] train-rmse:0.295428+0.016513 test-rmse:1.277818+0.243255
## [66] train-rmse:0.287922+0.015826 test-rmse:1.274941+0.245288
## [67] train-rmse:0.279148+0.015863 test-rmse:1.269902+0.241850
## [68] train-rmse:0.269432+0.012852 test-rmse:1.269208+0.241896
## [69] train-rmse:0.261822+0.012989 test-rmse:1.267003+0.242660
## [70] train-rmse:0.253846+0.012714 test-rmse:1.264220+0.241762
## [71] train-rmse:0.247464+0.012544 test-rmse:1.263131+0.244124
## [72] train-rmse:0.242088+0.012044 test-rmse:1.261176+0.243674
## [73] train-rmse:0.236344+0.011214 test-rmse:1.258899+0.243549
## [74] train-rmse:0.229193+0.011525 test-rmse:1.257325+0.243059
## [75] train-rmse:0.222796+0.010296 test-rmse:1.254034+0.243768
## [76] train-rmse:0.218100+0.009836 test-rmse:1.252194+0.244531
## [77] train-rmse:0.213399+0.008975 test-rmse:1.250173+0.246168
## [78] train-rmse:0.207653+0.008925 test-rmse:1.248293+0.246989
## [79] train-rmse:0.201373+0.009461 test-rmse:1.248076+0.246690
## [80] train-rmse:0.196628+0.008574 test-rmse:1.246957+0.245919
## [81] train-rmse:0.192215+0.009429 test-rmse:1.245758+0.247181
## [82] train-rmse:0.187920+0.008382 test-rmse:1.244677+0.246501
## [83] train-rmse:0.183536+0.008886 test-rmse:1.244189+0.246853
## [84] train-rmse:0.179114+0.008457 test-rmse:1.242792+0.247697
## [85] train-rmse:0.173585+0.009102 test-rmse:1.242105+0.248025
## [86] train-rmse:0.169555+0.009266 test-rmse:1.241486+0.248468
## [87] train-rmse:0.165373+0.008617 test-rmse:1.241127+0.248170
## [88] train-rmse:0.161822+0.007903 test-rmse:1.240298+0.247805
## [89] train-rmse:0.158253+0.008169 test-rmse:1.240256+0.248093
## [90] train-rmse:0.155232+0.007936 test-rmse:1.239361+0.247904
## [91] train-rmse:0.151885+0.007370 test-rmse:1.237927+0.248755
## [92] train-rmse:0.147625+0.006241 test-rmse:1.237379+0.248511
## [93] train-rmse:0.144311+0.006286 test-rmse:1.236554+0.247908
## [94] train-rmse:0.140910+0.006660 test-rmse:1.237133+0.247136
## [95] train-rmse:0.138286+0.006545 test-rmse:1.236461+0.246909
## [96] train-rmse:0.135193+0.006837 test-rmse:1.235718+0.246754
## [97] train-rmse:0.132401+0.006823 test-rmse:1.234661+0.246967
## [98] train-rmse:0.129163+0.006002 test-rmse:1.234465+0.247145
## [99] train-rmse:0.127048+0.005673 test-rmse:1.233923+0.246996
## [100] train-rmse:0.124202+0.006013 test-rmse:1.232829+0.247009
## [101] train-rmse:0.121725+0.005791 test-rmse:1.232478+0.247048
## [102] train-rmse:0.117289+0.005327 test-rmse:1.231241+0.246198
## [103] train-rmse:0.114797+0.005662 test-rmse:1.230964+0.246195
## [104] train-rmse:0.112254+0.005200 test-rmse:1.231198+0.246144
## [105] train-rmse:0.109763+0.004647 test-rmse:1.230893+0.246381
## [106] train-rmse:0.106970+0.004455 test-rmse:1.230543+0.246825
## [107] train-rmse:0.104036+0.003410 test-rmse:1.230448+0.246688
## [108] train-rmse:0.101752+0.003522 test-rmse:1.230471+0.246389
## [109] train-rmse:0.098990+0.004009 test-rmse:1.230064+0.246371
## [110] train-rmse:0.096246+0.003974 test-rmse:1.229039+0.245972
## [111] train-rmse:0.094160+0.004332 test-rmse:1.228122+0.246691
## [112] train-rmse:0.092057+0.004026 test-rmse:1.228050+0.246557
## [113] train-rmse:0.089708+0.003892 test-rmse:1.227781+0.246988
## [114] train-rmse:0.087918+0.004350 test-rmse:1.227693+0.246418
## [115] train-rmse:0.085812+0.004327 test-rmse:1.227438+0.246347
## [116] train-rmse:0.084049+0.004621 test-rmse:1.227462+0.246436
## [117] train-rmse:0.082399+0.004414 test-rmse:1.226447+0.246511
## [118] train-rmse:0.080654+0.004791 test-rmse:1.225893+0.246613
## [119] train-rmse:0.078780+0.004990 test-rmse:1.225440+0.246487
## [120] train-rmse:0.076668+0.004811 test-rmse:1.225432+0.246500
## [121] train-rmse:0.075017+0.004609 test-rmse:1.225416+0.246510
## [122] train-rmse:0.073126+0.004376 test-rmse:1.225446+0.246654
## [123] train-rmse:0.071276+0.004157 test-rmse:1.225854+0.247118
## [124] train-rmse:0.069578+0.003505 test-rmse:1.225631+0.247233
## [125] train-rmse:0.068037+0.003218 test-rmse:1.225427+0.247381
## [126] train-rmse:0.066627+0.003140 test-rmse:1.225412+0.247198
## [127] train-rmse:0.065443+0.003093 test-rmse:1.225225+0.247287
## [128] train-rmse:0.063638+0.003287 test-rmse:1.225246+0.247797
## [129] train-rmse:0.062206+0.003464 test-rmse:1.225188+0.247971
## [130] train-rmse:0.061028+0.003570 test-rmse:1.225105+0.248032
## [131] train-rmse:0.059720+0.003371 test-rmse:1.225064+0.248029
## [132] train-rmse:0.058322+0.003243 test-rmse:1.224408+0.247963
## [133] train-rmse:0.057127+0.003584 test-rmse:1.223780+0.247611
## [134] train-rmse:0.055715+0.003230 test-rmse:1.223512+0.247812
## [135] train-rmse:0.054517+0.002960 test-rmse:1.223501+0.247774
## [136] train-rmse:0.053714+0.003067 test-rmse:1.223371+0.247847
## [137] train-rmse:0.052710+0.002894 test-rmse:1.223230+0.247785
## [138] train-rmse:0.051229+0.002569 test-rmse:1.222729+0.247740
## [139] train-rmse:0.050417+0.002764 test-rmse:1.222321+0.247871
## [140] train-rmse:0.049285+0.002355 test-rmse:1.221555+0.247546
## [141] train-rmse:0.048207+0.002470 test-rmse:1.221609+0.247707
## [142] train-rmse:0.047154+0.002346 test-rmse:1.221615+0.247714
## [143] train-rmse:0.046373+0.002194 test-rmse:1.221634+0.247774
## [144] train-rmse:0.045666+0.002286 test-rmse:1.221446+0.247903
## [145] train-rmse:0.044634+0.002443 test-rmse:1.221169+0.247820
## [146] train-rmse:0.043867+0.002262 test-rmse:1.221151+0.247666
## [147] train-rmse:0.043207+0.002287 test-rmse:1.221049+0.247869
## [148] train-rmse:0.042469+0.002174 test-rmse:1.221091+0.247710
## [149] train-rmse:0.041734+0.002182 test-rmse:1.220783+0.247575
## [150] train-rmse:0.040707+0.002046 test-rmse:1.220787+0.247551
## [151] train-rmse:0.039693+0.002177 test-rmse:1.220619+0.247561
## [152] train-rmse:0.038923+0.002372 test-rmse:1.220685+0.247359
## [153] train-rmse:0.038275+0.002457 test-rmse:1.220609+0.247552
## [154] train-rmse:0.037537+0.002311 test-rmse:1.220742+0.247377
## [155] train-rmse:0.036718+0.002021 test-rmse:1.220667+0.247403
## [156] train-rmse:0.035788+0.001915 test-rmse:1.220547+0.247401
## [157] train-rmse:0.035110+0.001720 test-rmse:1.220333+0.247415
## [158] train-rmse:0.034368+0.001686 test-rmse:1.220291+0.247358
## [159] train-rmse:0.033707+0.001973 test-rmse:1.220251+0.247435
## [160] train-rmse:0.032915+0.001906 test-rmse:1.219991+0.247336
## [161] train-rmse:0.032176+0.001994 test-rmse:1.220125+0.247499
## [162] train-rmse:0.031455+0.001812 test-rmse:1.220115+0.247525
## [163] train-rmse:0.030836+0.001725 test-rmse:1.220026+0.247528
## [164] train-rmse:0.030197+0.001719 test-rmse:1.219958+0.247478
## [165] train-rmse:0.029671+0.001558 test-rmse:1.219882+0.247516
## [166] train-rmse:0.029030+0.001831 test-rmse:1.219677+0.247408
## [167] train-rmse:0.028493+0.001738 test-rmse:1.219531+0.247365
## [168] train-rmse:0.027724+0.001879 test-rmse:1.219395+0.247599
## [169] train-rmse:0.027116+0.001829 test-rmse:1.219215+0.247670
## [170] train-rmse:0.026486+0.001996 test-rmse:1.219141+0.247569
## [171] train-rmse:0.025756+0.002202 test-rmse:1.219057+0.247667
## [172] train-rmse:0.025142+0.002206 test-rmse:1.218891+0.247480
## [173] train-rmse:0.024700+0.002147 test-rmse:1.218851+0.247438
## [174] train-rmse:0.023956+0.002113 test-rmse:1.218790+0.247442
## [175] train-rmse:0.023532+0.002047 test-rmse:1.218726+0.247353
## [176] train-rmse:0.023117+0.001910 test-rmse:1.218612+0.247314
## [177] train-rmse:0.022507+0.001930 test-rmse:1.218449+0.247287
## [178] train-rmse:0.022036+0.001981 test-rmse:1.218420+0.247256
## [179] train-rmse:0.021731+0.001994 test-rmse:1.218409+0.247296
## [180] train-rmse:0.021355+0.002032 test-rmse:1.218396+0.247297
## [181] train-rmse:0.020985+0.001860 test-rmse:1.218428+0.247340
## [182] train-rmse:0.020558+0.001868 test-rmse:1.218426+0.247354
## [183] train-rmse:0.020261+0.001839 test-rmse:1.218417+0.247372
## [184] train-rmse:0.019982+0.001724 test-rmse:1.218355+0.247400
## [185] train-rmse:0.019608+0.001844 test-rmse:1.218332+0.247376
## [186] train-rmse:0.019154+0.001733 test-rmse:1.218281+0.247380
## [187] train-rmse:0.018822+0.001776 test-rmse:1.218246+0.247409
## [188] train-rmse:0.018505+0.001720 test-rmse:1.218083+0.247320
## [189] train-rmse:0.018116+0.001727 test-rmse:1.218122+0.247341
## [190] train-rmse:0.017894+0.001660 test-rmse:1.218103+0.247327
## [191] train-rmse:0.017557+0.001741 test-rmse:1.218066+0.247319
## [192] train-rmse:0.017206+0.001597 test-rmse:1.218026+0.247415
## [193] train-rmse:0.016898+0.001537 test-rmse:1.217988+0.247441
## [194] train-rmse:0.016591+0.001511 test-rmse:1.217931+0.247376
## [195] train-rmse:0.016309+0.001546 test-rmse:1.217984+0.247349
## [196] train-rmse:0.015956+0.001393 test-rmse:1.217971+0.247354
## [197] train-rmse:0.015618+0.001433 test-rmse:1.217969+0.247298
## [198] train-rmse:0.015321+0.001381 test-rmse:1.217911+0.247269
## [199] train-rmse:0.015058+0.001349 test-rmse:1.217850+0.247265
## [200] train-rmse:0.014799+0.001431 test-rmse:1.217811+0.247248
## [201] train-rmse:0.014536+0.001401 test-rmse:1.217838+0.247264
## [202] train-rmse:0.014217+0.001430 test-rmse:1.217811+0.247255
## [203] train-rmse:0.013926+0.001324 test-rmse:1.217845+0.247179
## [204] train-rmse:0.013610+0.001379 test-rmse:1.217855+0.247197
## [205] train-rmse:0.013333+0.001311 test-rmse:1.217849+0.247156
## [206] train-rmse:0.013092+0.001311 test-rmse:1.217811+0.247181
## [207] train-rmse:0.012847+0.001238 test-rmse:1.217791+0.247179
## [208] train-rmse:0.012661+0.001152 test-rmse:1.217796+0.247223
## [209] train-rmse:0.012452+0.001233 test-rmse:1.217789+0.247214
## [210] train-rmse:0.012192+0.001220 test-rmse:1.217741+0.247194
## [211] train-rmse:0.012027+0.001211 test-rmse:1.217736+0.247205
## [212] train-rmse:0.011847+0.001180 test-rmse:1.217740+0.247226
## [213] train-rmse:0.011551+0.001147 test-rmse:1.217729+0.247217
## [214] train-rmse:0.011324+0.001023 test-rmse:1.217712+0.247219
## [215] train-rmse:0.011118+0.001091 test-rmse:1.217666+0.247260
## [216] train-rmse:0.010946+0.001036 test-rmse:1.217677+0.247296
## [217] train-rmse:0.010773+0.000973 test-rmse:1.217683+0.247328
## [218] train-rmse:0.010550+0.001013 test-rmse:1.217720+0.247325
## [219] train-rmse:0.010366+0.001057 test-rmse:1.217700+0.247325
## [220] train-rmse:0.010123+0.001063 test-rmse:1.217672+0.247361
## [221] train-rmse:0.009972+0.001056 test-rmse:1.217652+0.247366
## [222] train-rmse:0.009760+0.001097 test-rmse:1.217652+0.247345
## [223] train-rmse:0.009585+0.001092 test-rmse:1.217618+0.247379
## [224] train-rmse:0.009416+0.001088 test-rmse:1.217598+0.247343
## [225] train-rmse:0.009288+0.001120 test-rmse:1.217584+0.247344
## [226] train-rmse:0.009097+0.001069 test-rmse:1.217610+0.247364
## [227] train-rmse:0.008957+0.001031 test-rmse:1.217575+0.247352
## [228] train-rmse:0.008837+0.000999 test-rmse:1.217542+0.247362
## [229] train-rmse:0.008688+0.001041 test-rmse:1.217480+0.247353
## [230] train-rmse:0.008554+0.001023 test-rmse:1.217461+0.247360
## [231] train-rmse:0.008391+0.000978 test-rmse:1.217416+0.247357
## [232] train-rmse:0.008266+0.000951 test-rmse:1.217427+0.247393
## [233] train-rmse:0.008117+0.000989 test-rmse:1.217377+0.247401
## [234] train-rmse:0.007980+0.000976 test-rmse:1.217362+0.247423
## [235] train-rmse:0.007856+0.000978 test-rmse:1.217336+0.247402
## [236] train-rmse:0.007697+0.000970 test-rmse:1.217361+0.247378
## [237] train-rmse:0.007577+0.001000 test-rmse:1.217343+0.247403
## [238] train-rmse:0.007436+0.000942 test-rmse:1.217327+0.247400
## [239] train-rmse:0.007312+0.000926 test-rmse:1.217327+0.247418
## [240] train-rmse:0.007166+0.000881 test-rmse:1.217329+0.247418
## [241] train-rmse:0.007025+0.000829 test-rmse:1.217297+0.247444
## [242] train-rmse:0.006887+0.000835 test-rmse:1.217278+0.247438
## [243] train-rmse:0.006744+0.000770 test-rmse:1.217307+0.247452
## [244] train-rmse:0.006616+0.000770 test-rmse:1.217309+0.247442
## [245] train-rmse:0.006509+0.000728 test-rmse:1.217306+0.247458
## [246] train-rmse:0.006368+0.000758 test-rmse:1.217315+0.247441
## [247] train-rmse:0.006232+0.000784 test-rmse:1.217304+0.247443
## [248] train-rmse:0.006122+0.000755 test-rmse:1.217311+0.247462
## Stopping. Best iteration:
## [242] train-rmse:0.006887+0.000835 test-rmse:1.217278+0.247438
##
## 28
## [1] train-rmse:88.354776+0.099451 test-rmse:88.354991+0.492721
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.580200+0.086121 test-rmse:72.580084+0.517683
## [3] train-rmse:59.651027+0.076207 test-rmse:59.650480+0.541664
## [4] train-rmse:49.060283+0.069346 test-rmse:49.059156+0.565463
## [5] train-rmse:40.392603+0.065298 test-rmse:40.390688+0.589811
## [6] train-rmse:33.305450+0.062294 test-rmse:33.325024+0.602958
## [7] train-rmse:27.508679+0.058860 test-rmse:27.554566+0.620972
## [8] train-rmse:22.770783+0.052111 test-rmse:22.835533+0.630949
## [9] train-rmse:18.903834+0.046090 test-rmse:18.976729+0.635782
## [10] train-rmse:15.756250+0.041559 test-rmse:15.829300+0.614686
## [11] train-rmse:13.202671+0.038209 test-rmse:13.283407+0.619139
## [12] train-rmse:11.141500+0.037056 test-rmse:11.259582+0.653336
## [13] train-rmse:9.487753+0.037294 test-rmse:9.599920+0.644895
## [14] train-rmse:8.170487+0.041202 test-rmse:8.280559+0.618018
## [15] train-rmse:7.130328+0.044029 test-rmse:7.233652+0.605272
## [16] train-rmse:6.316209+0.046702 test-rmse:6.451578+0.650261
## [17] train-rmse:5.686757+0.050673 test-rmse:5.825238+0.592969
## [18] train-rmse:5.201585+0.054575 test-rmse:5.354011+0.596740
## [19] train-rmse:4.833377+0.056455 test-rmse:4.964974+0.558675
## [20] train-rmse:4.552054+0.058826 test-rmse:4.709401+0.580361
## [21] train-rmse:4.332538+0.060598 test-rmse:4.485790+0.517057
## [22] train-rmse:4.162644+0.062126 test-rmse:4.324848+0.478652
## [23] train-rmse:4.030247+0.060889 test-rmse:4.192762+0.476440
## [24] train-rmse:3.924270+0.060278 test-rmse:4.092720+0.483915
## [25] train-rmse:3.836081+0.060291 test-rmse:3.994037+0.451272
## [26] train-rmse:3.761173+0.060593 test-rmse:3.937055+0.423560
## [27] train-rmse:3.696754+0.059158 test-rmse:3.909680+0.420039
## [28] train-rmse:3.640555+0.057655 test-rmse:3.837839+0.390707
## [29] train-rmse:3.590137+0.056377 test-rmse:3.802343+0.382483
## [30] train-rmse:3.542737+0.055282 test-rmse:3.763977+0.382153
## [31] train-rmse:3.498500+0.054505 test-rmse:3.719457+0.384227
## [32] train-rmse:3.455911+0.053867 test-rmse:3.647723+0.351571
## [33] train-rmse:3.414998+0.053394 test-rmse:3.613521+0.371913
## [34] train-rmse:3.375688+0.052771 test-rmse:3.588664+0.358605
## [35] train-rmse:3.338638+0.052095 test-rmse:3.538701+0.334134
## [36] train-rmse:3.302863+0.050327 test-rmse:3.513398+0.332699
## [37] train-rmse:3.268821+0.048858 test-rmse:3.491195+0.336209
## [38] train-rmse:3.235354+0.047960 test-rmse:3.461427+0.335507
## [39] train-rmse:3.202406+0.046915 test-rmse:3.441848+0.336438
## [40] train-rmse:3.170376+0.045651 test-rmse:3.414826+0.347883
## [41] train-rmse:3.139339+0.045092 test-rmse:3.378871+0.337009
## [42] train-rmse:3.108593+0.044517 test-rmse:3.328404+0.314520
## [43] train-rmse:3.078644+0.044210 test-rmse:3.297090+0.320145
## [44] train-rmse:3.049492+0.043772 test-rmse:3.294551+0.324082
## [45] train-rmse:3.020278+0.042906 test-rmse:3.261720+0.324378
## [46] train-rmse:2.992573+0.042398 test-rmse:3.238803+0.318829
## [47] train-rmse:2.965773+0.041581 test-rmse:3.218809+0.321168
## [48] train-rmse:2.939748+0.040748 test-rmse:3.196153+0.330196
## [49] train-rmse:2.914021+0.040104 test-rmse:3.163465+0.313626
## [50] train-rmse:2.888573+0.039700 test-rmse:3.137511+0.314046
## [51] train-rmse:2.863348+0.039372 test-rmse:3.116422+0.321134
## [52] train-rmse:2.838581+0.039203 test-rmse:3.098934+0.326517
## [53] train-rmse:2.814428+0.038891 test-rmse:3.074495+0.327502
## [54] train-rmse:2.790275+0.038617 test-rmse:3.052695+0.328372
## [55] train-rmse:2.766607+0.038667 test-rmse:3.018165+0.323456
## [56] train-rmse:2.743227+0.038882 test-rmse:2.981754+0.304698
## [57] train-rmse:2.720113+0.039096 test-rmse:2.957214+0.307508
## [58] train-rmse:2.697934+0.038984 test-rmse:2.945052+0.309659
## [59] train-rmse:2.676099+0.038844 test-rmse:2.925038+0.319406
## [60] train-rmse:2.654651+0.038828 test-rmse:2.910090+0.321736
## [61] train-rmse:2.633650+0.038529 test-rmse:2.905263+0.314411
## [62] train-rmse:2.612728+0.038214 test-rmse:2.880835+0.298281
## [63] train-rmse:2.592502+0.037772 test-rmse:2.868216+0.304699
## [64] train-rmse:2.572053+0.037583 test-rmse:2.847792+0.309117
## [65] train-rmse:2.552209+0.037554 test-rmse:2.833647+0.306230
## [66] train-rmse:2.532728+0.037141 test-rmse:2.811809+0.309316
## [67] train-rmse:2.513213+0.036895 test-rmse:2.793742+0.310122
## [68] train-rmse:2.493889+0.036478 test-rmse:2.766936+0.304892
## [69] train-rmse:2.474990+0.036105 test-rmse:2.751485+0.314692
## [70] train-rmse:2.456238+0.035787 test-rmse:2.723656+0.293843
## [71] train-rmse:2.437690+0.035064 test-rmse:2.701153+0.288752
## [72] train-rmse:2.419593+0.034700 test-rmse:2.686823+0.295221
## [73] train-rmse:2.401555+0.034379 test-rmse:2.671405+0.299691
## [74] train-rmse:2.383796+0.033956 test-rmse:2.651234+0.308420
## [75] train-rmse:2.366308+0.033603 test-rmse:2.640647+0.315073
## [76] train-rmse:2.349043+0.033170 test-rmse:2.619194+0.316792
## [77] train-rmse:2.332365+0.032803 test-rmse:2.607481+0.300476
## [78] train-rmse:2.315730+0.032486 test-rmse:2.588751+0.289747
## [79] train-rmse:2.299380+0.032258 test-rmse:2.575058+0.286864
## [80] train-rmse:2.283098+0.032203 test-rmse:2.559220+0.291224
## [81] train-rmse:2.267115+0.031999 test-rmse:2.539968+0.290121
## [82] train-rmse:2.251110+0.031818 test-rmse:2.517953+0.281562
## [83] train-rmse:2.235078+0.031426 test-rmse:2.507936+0.283210
## [84] train-rmse:2.219481+0.031065 test-rmse:2.495868+0.290949
## [85] train-rmse:2.203865+0.030674 test-rmse:2.481093+0.286313
## [86] train-rmse:2.188219+0.030421 test-rmse:2.466408+0.282141
## [87] train-rmse:2.173101+0.030324 test-rmse:2.447541+0.289644
## [88] train-rmse:2.158077+0.029984 test-rmse:2.437271+0.292857
## [89] train-rmse:2.143415+0.029577 test-rmse:2.427928+0.291936
## [90] train-rmse:2.128721+0.029162 test-rmse:2.409289+0.272943
## [91] train-rmse:2.114310+0.028668 test-rmse:2.393881+0.280248
## [92] train-rmse:2.100185+0.028314 test-rmse:2.380846+0.285672
## [93] train-rmse:2.086388+0.028082 test-rmse:2.365719+0.279541
## [94] train-rmse:2.072586+0.027844 test-rmse:2.353176+0.277702
## [95] train-rmse:2.059015+0.027534 test-rmse:2.339427+0.280666
## [96] train-rmse:2.045459+0.027186 test-rmse:2.321109+0.275261
## [97] train-rmse:2.032058+0.026843 test-rmse:2.307884+0.272459
## [98] train-rmse:2.018798+0.026435 test-rmse:2.295791+0.262962
## [99] train-rmse:2.005475+0.026132 test-rmse:2.278371+0.254167
## [100] train-rmse:1.992281+0.025850 test-rmse:2.252606+0.255487
## [101] train-rmse:1.979233+0.025632 test-rmse:2.244127+0.257798
## [102] train-rmse:1.966227+0.025300 test-rmse:2.226949+0.258788
## [103] train-rmse:1.953382+0.025049 test-rmse:2.213304+0.255341
## [104] train-rmse:1.940805+0.024846 test-rmse:2.203781+0.256239
## [105] train-rmse:1.928423+0.024772 test-rmse:2.198071+0.257491
## [106] train-rmse:1.916245+0.024430 test-rmse:2.183981+0.266002
## [107] train-rmse:1.904135+0.024188 test-rmse:2.173656+0.268022
## [108] train-rmse:1.892442+0.023919 test-rmse:2.166145+0.266329
## [109] train-rmse:1.880748+0.023736 test-rmse:2.155259+0.261218
## [110] train-rmse:1.869172+0.023405 test-rmse:2.146470+0.260180
## [111] train-rmse:1.857552+0.023108 test-rmse:2.131926+0.256475
## [112] train-rmse:1.846225+0.022834 test-rmse:2.126431+0.259220
## [113] train-rmse:1.834940+0.022585 test-rmse:2.113743+0.264203
## [114] train-rmse:1.823689+0.022298 test-rmse:2.110979+0.260243
## [115] train-rmse:1.812578+0.022100 test-rmse:2.102247+0.261836
## [116] train-rmse:1.801557+0.021848 test-rmse:2.088968+0.246127
## [117] train-rmse:1.790687+0.021692 test-rmse:2.081031+0.244223
## [118] train-rmse:1.779807+0.021495 test-rmse:2.066845+0.246847
## [119] train-rmse:1.768861+0.021377 test-rmse:2.054955+0.249362
## [120] train-rmse:1.758075+0.021189 test-rmse:2.047121+0.250146
## [121] train-rmse:1.747465+0.020804 test-rmse:2.034331+0.249155
## [122] train-rmse:1.737052+0.020603 test-rmse:2.022781+0.240493
## [123] train-rmse:1.726703+0.020522 test-rmse:2.014978+0.247153
## [124] train-rmse:1.716390+0.020357 test-rmse:2.005596+0.248161
## [125] train-rmse:1.706372+0.020232 test-rmse:1.992461+0.248546
## [126] train-rmse:1.696455+0.020005 test-rmse:1.984733+0.246470
## [127] train-rmse:1.686609+0.019825 test-rmse:1.974231+0.249632
## [128] train-rmse:1.677038+0.019616 test-rmse:1.968484+0.248128
## [129] train-rmse:1.667570+0.019431 test-rmse:1.966031+0.250378
## [130] train-rmse:1.658179+0.019322 test-rmse:1.955194+0.254780
## [131] train-rmse:1.648903+0.019248 test-rmse:1.947865+0.254285
## [132] train-rmse:1.639710+0.019017 test-rmse:1.938088+0.253876
## [133] train-rmse:1.630544+0.018727 test-rmse:1.930067+0.250185
## [134] train-rmse:1.621357+0.018415 test-rmse:1.919773+0.252206
## [135] train-rmse:1.612174+0.018230 test-rmse:1.908771+0.248698
## [136] train-rmse:1.603289+0.018044 test-rmse:1.894286+0.245765
## [137] train-rmse:1.594327+0.017856 test-rmse:1.879918+0.232715
## [138] train-rmse:1.585421+0.017663 test-rmse:1.873755+0.232980
## [139] train-rmse:1.576698+0.017590 test-rmse:1.866733+0.238113
## [140] train-rmse:1.568086+0.017448 test-rmse:1.861172+0.238388
## [141] train-rmse:1.559578+0.017419 test-rmse:1.854457+0.239197
## [142] train-rmse:1.551182+0.017365 test-rmse:1.845275+0.236738
## [143] train-rmse:1.542866+0.017345 test-rmse:1.834232+0.239763
## [144] train-rmse:1.534597+0.017299 test-rmse:1.829261+0.240909
## [145] train-rmse:1.526269+0.017382 test-rmse:1.815488+0.236682
## [146] train-rmse:1.518137+0.017374 test-rmse:1.811692+0.231149
## [147] train-rmse:1.510018+0.017302 test-rmse:1.802157+0.233512
## [148] train-rmse:1.501905+0.017208 test-rmse:1.790597+0.233182
## [149] train-rmse:1.493990+0.016999 test-rmse:1.783753+0.234961
## [150] train-rmse:1.486222+0.016737 test-rmse:1.775182+0.234637
## [151] train-rmse:1.478578+0.016528 test-rmse:1.768579+0.237586
## [152] train-rmse:1.470966+0.016381 test-rmse:1.765883+0.237726
## [153] train-rmse:1.463441+0.016188 test-rmse:1.751388+0.226022
## [154] train-rmse:1.456120+0.016119 test-rmse:1.751728+0.225151
## [155] train-rmse:1.448832+0.016047 test-rmse:1.748916+0.226727
## [156] train-rmse:1.441557+0.016107 test-rmse:1.742912+0.228346
## [157] train-rmse:1.434401+0.016033 test-rmse:1.736463+0.226254
## [158] train-rmse:1.427203+0.015915 test-rmse:1.728728+0.224589
## [159] train-rmse:1.420052+0.015793 test-rmse:1.722809+0.227521
## [160] train-rmse:1.412950+0.015805 test-rmse:1.716099+0.226702
## [161] train-rmse:1.405924+0.015799 test-rmse:1.706912+0.223861
## [162] train-rmse:1.398995+0.015818 test-rmse:1.698156+0.224671
## [163] train-rmse:1.392155+0.015794 test-rmse:1.688766+0.226181
## [164] train-rmse:1.385349+0.015713 test-rmse:1.683412+0.219988
## [165] train-rmse:1.378619+0.015578 test-rmse:1.673403+0.219039
## [166] train-rmse:1.372072+0.015518 test-rmse:1.668900+0.220241
## [167] train-rmse:1.365508+0.015449 test-rmse:1.659012+0.215727
## [168] train-rmse:1.359006+0.015353 test-rmse:1.653570+0.214295
## [169] train-rmse:1.352444+0.015292 test-rmse:1.648228+0.215946
## [170] train-rmse:1.346027+0.015308 test-rmse:1.640406+0.216928
## [171] train-rmse:1.339636+0.015234 test-rmse:1.636952+0.217545
## [172] train-rmse:1.333387+0.015147 test-rmse:1.632817+0.220452
## [173] train-rmse:1.327176+0.015144 test-rmse:1.626501+0.220042
## [174] train-rmse:1.321094+0.015144 test-rmse:1.620986+0.221781
## [175] train-rmse:1.315132+0.015103 test-rmse:1.614872+0.221632
## [176] train-rmse:1.309200+0.015091 test-rmse:1.604702+0.211697
## [177] train-rmse:1.303304+0.015061 test-rmse:1.599260+0.212160
## [178] train-rmse:1.297465+0.015021 test-rmse:1.597752+0.212437
## [179] train-rmse:1.291709+0.014991 test-rmse:1.593304+0.208093
## [180] train-rmse:1.286052+0.014979 test-rmse:1.586847+0.206633
## [181] train-rmse:1.280401+0.014966 test-rmse:1.576560+0.201784
## [182] train-rmse:1.274786+0.014954 test-rmse:1.571862+0.201594
## [183] train-rmse:1.269190+0.014911 test-rmse:1.570552+0.202015
## [184] train-rmse:1.263588+0.014891 test-rmse:1.564851+0.198777
## [185] train-rmse:1.258112+0.014863 test-rmse:1.559345+0.199655
## [186] train-rmse:1.252697+0.014943 test-rmse:1.550084+0.201165
## [187] train-rmse:1.247360+0.014955 test-rmse:1.544554+0.199450
## [188] train-rmse:1.242026+0.014932 test-rmse:1.541001+0.199712
## [189] train-rmse:1.236677+0.014953 test-rmse:1.536968+0.202002
## [190] train-rmse:1.231382+0.014956 test-rmse:1.530545+0.198577
## [191] train-rmse:1.226197+0.014937 test-rmse:1.525863+0.196818
## [192] train-rmse:1.221061+0.014931 test-rmse:1.521832+0.197496
## [193] train-rmse:1.215980+0.014937 test-rmse:1.517962+0.198357
## [194] train-rmse:1.210980+0.014995 test-rmse:1.514056+0.199832
## [195] train-rmse:1.206017+0.015012 test-rmse:1.508742+0.201500
## [196] train-rmse:1.201119+0.015019 test-rmse:1.503353+0.203258
## [197] train-rmse:1.196271+0.015076 test-rmse:1.497416+0.203650
## [198] train-rmse:1.191467+0.015132 test-rmse:1.495886+0.204066
## [199] train-rmse:1.186681+0.015127 test-rmse:1.484829+0.197387
## [200] train-rmse:1.182002+0.015051 test-rmse:1.478596+0.193748
## [201] train-rmse:1.177380+0.015060 test-rmse:1.474684+0.194256
## [202] train-rmse:1.172775+0.015023 test-rmse:1.470661+0.196252
## [203] train-rmse:1.168293+0.015028 test-rmse:1.466363+0.198207
## [204] train-rmse:1.163863+0.015026 test-rmse:1.462157+0.192108
## [205] train-rmse:1.159439+0.014997 test-rmse:1.457000+0.192795
## [206] train-rmse:1.155056+0.014983 test-rmse:1.454548+0.192919
## [207] train-rmse:1.150621+0.014919 test-rmse:1.448949+0.191874
## [208] train-rmse:1.146323+0.014942 test-rmse:1.449051+0.192733
## [209] train-rmse:1.142112+0.014952 test-rmse:1.445209+0.192744
## [210] train-rmse:1.137902+0.014931 test-rmse:1.439692+0.192733
## [211] train-rmse:1.133692+0.014944 test-rmse:1.437781+0.192760
## [212] train-rmse:1.129497+0.014930 test-rmse:1.435604+0.194209
## [213] train-rmse:1.125374+0.014903 test-rmse:1.429136+0.185370
## [214] train-rmse:1.121282+0.014916 test-rmse:1.423021+0.187244
## [215] train-rmse:1.117241+0.014881 test-rmse:1.419726+0.188633
## [216] train-rmse:1.113278+0.014856 test-rmse:1.418375+0.187941
## [217] train-rmse:1.109341+0.014849 test-rmse:1.414179+0.187090
## [218] train-rmse:1.105455+0.014868 test-rmse:1.408728+0.188337
## [219] train-rmse:1.101642+0.014862 test-rmse:1.405863+0.189171
## [220] train-rmse:1.097839+0.014821 test-rmse:1.399254+0.188004
## [221] train-rmse:1.094034+0.014799 test-rmse:1.394898+0.188814
## [222] train-rmse:1.090265+0.014800 test-rmse:1.389975+0.186764
## [223] train-rmse:1.086530+0.014748 test-rmse:1.386715+0.187519
## [224] train-rmse:1.082831+0.014714 test-rmse:1.383775+0.186946
## [225] train-rmse:1.079136+0.014694 test-rmse:1.379991+0.187184
## [226] train-rmse:1.075504+0.014697 test-rmse:1.377825+0.186393
## [227] train-rmse:1.071941+0.014724 test-rmse:1.375399+0.184836
## [228] train-rmse:1.068375+0.014745 test-rmse:1.371143+0.187371
## [229] train-rmse:1.064876+0.014778 test-rmse:1.365744+0.183430
## [230] train-rmse:1.061448+0.014818 test-rmse:1.364206+0.183601
## [231] train-rmse:1.058046+0.014810 test-rmse:1.363117+0.180164
## [232] train-rmse:1.054638+0.014755 test-rmse:1.360994+0.181070
## [233] train-rmse:1.051324+0.014726 test-rmse:1.358442+0.183815
## [234] train-rmse:1.048020+0.014715 test-rmse:1.357143+0.184508
## [235] train-rmse:1.044746+0.014687 test-rmse:1.353638+0.184537
## [236] train-rmse:1.041507+0.014642 test-rmse:1.347751+0.175861
## [237] train-rmse:1.038287+0.014610 test-rmse:1.345312+0.176586
## [238] train-rmse:1.035056+0.014621 test-rmse:1.342481+0.176026
## [239] train-rmse:1.031942+0.014576 test-rmse:1.340030+0.174966
## [240] train-rmse:1.028857+0.014570 test-rmse:1.338138+0.171980
## [241] train-rmse:1.025768+0.014587 test-rmse:1.335038+0.174236
## [242] train-rmse:1.022721+0.014607 test-rmse:1.329157+0.171738
## [243] train-rmse:1.019726+0.014652 test-rmse:1.327220+0.172514
## [244] train-rmse:1.016779+0.014690 test-rmse:1.326026+0.173380
## [245] train-rmse:1.013830+0.014722 test-rmse:1.322037+0.174533
## [246] train-rmse:1.010900+0.014723 test-rmse:1.322496+0.174341
## [247] train-rmse:1.008013+0.014728 test-rmse:1.320755+0.174802
## [248] train-rmse:1.005123+0.014725 test-rmse:1.319612+0.174529
## [249] train-rmse:1.002243+0.014721 test-rmse:1.314563+0.174130
## [250] train-rmse:0.999395+0.014703 test-rmse:1.309758+0.176099
## [251] train-rmse:0.996610+0.014757 test-rmse:1.307990+0.175972
## [252] train-rmse:0.993811+0.014710 test-rmse:1.305017+0.175455
## [253] train-rmse:0.991088+0.014741 test-rmse:1.301958+0.176520
## [254] train-rmse:0.988358+0.014786 test-rmse:1.298989+0.176751
## [255] train-rmse:0.985640+0.014807 test-rmse:1.298611+0.176043
## [256] train-rmse:0.982918+0.014831 test-rmse:1.296612+0.174660
## [257] train-rmse:0.980288+0.014848 test-rmse:1.293638+0.173733
## [258] train-rmse:0.977696+0.014888 test-rmse:1.292861+0.174720
## [259] train-rmse:0.975089+0.014908 test-rmse:1.290440+0.174331
## [260] train-rmse:0.972558+0.014905 test-rmse:1.287615+0.174308
## [261] train-rmse:0.970057+0.014866 test-rmse:1.280962+0.168536
## [262] train-rmse:0.967585+0.014808 test-rmse:1.277366+0.170003
## [263] train-rmse:0.965174+0.014802 test-rmse:1.274164+0.170159
## [264] train-rmse:0.962762+0.014777 test-rmse:1.272548+0.171393
## [265] train-rmse:0.960385+0.014783 test-rmse:1.270506+0.171080
## [266] train-rmse:0.958034+0.014765 test-rmse:1.265667+0.170577
## [267] train-rmse:0.955712+0.014785 test-rmse:1.264161+0.171315
## [268] train-rmse:0.953412+0.014790 test-rmse:1.262421+0.170048
## [269] train-rmse:0.951103+0.014793 test-rmse:1.259824+0.168829
## [270] train-rmse:0.948822+0.014828 test-rmse:1.256287+0.170198
## [271] train-rmse:0.946567+0.014871 test-rmse:1.252808+0.171413
## [272] train-rmse:0.944332+0.014907 test-rmse:1.251226+0.170428
## [273] train-rmse:0.942088+0.014925 test-rmse:1.249444+0.170110
## [274] train-rmse:0.939851+0.014930 test-rmse:1.245072+0.172064
## [275] train-rmse:0.937621+0.014934 test-rmse:1.244805+0.171140
## [276] train-rmse:0.935422+0.014951 test-rmse:1.244115+0.170166
## [277] train-rmse:0.933220+0.014972 test-rmse:1.242952+0.168068
## [278] train-rmse:0.931115+0.015000 test-rmse:1.240187+0.164913
## [279] train-rmse:0.929036+0.015008 test-rmse:1.237729+0.165497
## [280] train-rmse:0.926975+0.015044 test-rmse:1.237289+0.164922
## [281] train-rmse:0.924874+0.015083 test-rmse:1.235869+0.164716
## [282] train-rmse:0.922851+0.015099 test-rmse:1.235943+0.164763
## [283] train-rmse:0.920853+0.015113 test-rmse:1.235758+0.164852
## [284] train-rmse:0.918857+0.015136 test-rmse:1.233196+0.165887
## [285] train-rmse:0.916886+0.015123 test-rmse:1.233159+0.164584
## [286] train-rmse:0.914926+0.015138 test-rmse:1.232764+0.164901
## [287] train-rmse:0.912984+0.015146 test-rmse:1.231529+0.166311
## [288] train-rmse:0.911070+0.015179 test-rmse:1.230953+0.166543
## [289] train-rmse:0.909154+0.015187 test-rmse:1.230080+0.166660
## [290] train-rmse:0.907255+0.015194 test-rmse:1.225835+0.161237
## [291] train-rmse:0.905384+0.015191 test-rmse:1.223681+0.159120
## [292] train-rmse:0.903495+0.015190 test-rmse:1.223631+0.158531
## [293] train-rmse:0.901659+0.015177 test-rmse:1.222814+0.158344
## [294] train-rmse:0.899799+0.015187 test-rmse:1.220005+0.157335
## [295] train-rmse:0.897972+0.015186 test-rmse:1.216947+0.158327
## [296] train-rmse:0.896200+0.015181 test-rmse:1.214918+0.158978
## [297] train-rmse:0.894468+0.015195 test-rmse:1.214191+0.158807
## [298] train-rmse:0.892747+0.015206 test-rmse:1.212073+0.158092
## [299] train-rmse:0.891028+0.015234 test-rmse:1.209361+0.158103
## [300] train-rmse:0.889312+0.015279 test-rmse:1.204992+0.159462
## [301] train-rmse:0.887609+0.015326 test-rmse:1.204353+0.159251
## [302] train-rmse:0.885949+0.015341 test-rmse:1.202761+0.159505
## [303] train-rmse:0.884309+0.015362 test-rmse:1.203571+0.159076
## [304] train-rmse:0.882669+0.015370 test-rmse:1.202690+0.156316
## [305] train-rmse:0.881041+0.015383 test-rmse:1.200996+0.156385
## [306] train-rmse:0.879458+0.015395 test-rmse:1.200619+0.156612
## [307] train-rmse:0.877855+0.015436 test-rmse:1.197649+0.157368
## [308] train-rmse:0.876283+0.015456 test-rmse:1.195782+0.158397
## [309] train-rmse:0.874728+0.015475 test-rmse:1.194828+0.158867
## [310] train-rmse:0.873191+0.015480 test-rmse:1.194059+0.158251
## [311] train-rmse:0.871668+0.015515 test-rmse:1.193300+0.158999
## [312] train-rmse:0.870181+0.015537 test-rmse:1.190994+0.159667
## [313] train-rmse:0.868709+0.015555 test-rmse:1.189756+0.159522
## [314] train-rmse:0.867251+0.015541 test-rmse:1.189712+0.155929
## [315] train-rmse:0.865781+0.015544 test-rmse:1.189439+0.155589
## [316] train-rmse:0.864317+0.015566 test-rmse:1.188924+0.155755
## [317] train-rmse:0.862880+0.015571 test-rmse:1.188390+0.156518
## [318] train-rmse:0.861454+0.015581 test-rmse:1.186461+0.157788
## [319] train-rmse:0.860055+0.015577 test-rmse:1.184992+0.158466
## [320] train-rmse:0.858676+0.015565 test-rmse:1.184206+0.157828
## [321] train-rmse:0.857255+0.015589 test-rmse:1.182828+0.157681
## [322] train-rmse:0.855880+0.015571 test-rmse:1.180180+0.156870
## [323] train-rmse:0.854516+0.015575 test-rmse:1.178621+0.157495
## [324] train-rmse:0.853163+0.015569 test-rmse:1.177569+0.155881
## [325] train-rmse:0.851823+0.015575 test-rmse:1.177638+0.155626
## [326] train-rmse:0.850503+0.015592 test-rmse:1.176566+0.156299
## [327] train-rmse:0.849189+0.015600 test-rmse:1.173445+0.151879
## [328] train-rmse:0.847879+0.015609 test-rmse:1.169932+0.152744
## [329] train-rmse:0.846593+0.015607 test-rmse:1.168486+0.152672
## [330] train-rmse:0.845321+0.015619 test-rmse:1.169012+0.152601
## [331] train-rmse:0.844054+0.015641 test-rmse:1.167812+0.149830
## [332] train-rmse:0.842809+0.015648 test-rmse:1.166318+0.150364
## [333] train-rmse:0.841578+0.015666 test-rmse:1.165431+0.150791
## [334] train-rmse:0.840374+0.015678 test-rmse:1.164706+0.151907
## [335] train-rmse:0.839156+0.015670 test-rmse:1.161700+0.153555
## [336] train-rmse:0.837974+0.015672 test-rmse:1.162417+0.153943
## [337] train-rmse:0.836771+0.015695 test-rmse:1.160620+0.152558
## [338] train-rmse:0.835618+0.015705 test-rmse:1.159154+0.153331
## [339] train-rmse:0.834477+0.015721 test-rmse:1.158924+0.153739
## [340] train-rmse:0.833356+0.015737 test-rmse:1.157905+0.153367
## [341] train-rmse:0.832238+0.015761 test-rmse:1.158147+0.152922
## [342] train-rmse:0.831086+0.015737 test-rmse:1.155196+0.153230
## [343] train-rmse:0.829977+0.015744 test-rmse:1.154084+0.153473
## [344] train-rmse:0.828892+0.015747 test-rmse:1.153054+0.153646
## [345] train-rmse:0.827789+0.015749 test-rmse:1.150960+0.154406
## [346] train-rmse:0.826705+0.015764 test-rmse:1.150066+0.155078
## [347] train-rmse:0.825623+0.015786 test-rmse:1.148694+0.155118
## [348] train-rmse:0.824562+0.015802 test-rmse:1.148607+0.154823
## [349] train-rmse:0.823505+0.015823 test-rmse:1.147383+0.154485
## [350] train-rmse:0.822453+0.015832 test-rmse:1.146540+0.154457
## [351] train-rmse:0.821417+0.015837 test-rmse:1.144489+0.153736
## [352] train-rmse:0.820368+0.015787 test-rmse:1.144280+0.153352
## [353] train-rmse:0.819365+0.015792 test-rmse:1.144659+0.153868
## [354] train-rmse:0.818348+0.015791 test-rmse:1.143957+0.154156
## [355] train-rmse:0.817372+0.015800 test-rmse:1.143593+0.152145
## [356] train-rmse:0.816391+0.015789 test-rmse:1.140574+0.148599
## [357] train-rmse:0.815419+0.015813 test-rmse:1.138032+0.150439
## [358] train-rmse:0.814441+0.015796 test-rmse:1.137925+0.150113
## [359] train-rmse:0.813500+0.015805 test-rmse:1.136541+0.150944
## [360] train-rmse:0.812559+0.015826 test-rmse:1.135972+0.150715
## [361] train-rmse:0.811605+0.015843 test-rmse:1.135124+0.151361
## [362] train-rmse:0.810667+0.015868 test-rmse:1.134860+0.150524
## [363] train-rmse:0.809720+0.015891 test-rmse:1.132863+0.150577
## [364] train-rmse:0.808806+0.015893 test-rmse:1.131851+0.151579
## [365] train-rmse:0.807905+0.015891 test-rmse:1.131416+0.150891
## [366] train-rmse:0.807002+0.015887 test-rmse:1.131126+0.151349
## [367] train-rmse:0.806117+0.015886 test-rmse:1.130032+0.150848
## [368] train-rmse:0.805231+0.015884 test-rmse:1.129016+0.151330
## [369] train-rmse:0.804355+0.015888 test-rmse:1.128541+0.151390
## [370] train-rmse:0.803481+0.015892 test-rmse:1.127698+0.151181
## [371] train-rmse:0.802618+0.015890 test-rmse:1.124636+0.150178
## [372] train-rmse:0.801770+0.015893 test-rmse:1.125098+0.149958
## [373] train-rmse:0.800927+0.015886 test-rmse:1.125431+0.150264
## [374] train-rmse:0.800080+0.015896 test-rmse:1.125473+0.149510
## [375] train-rmse:0.799224+0.015850 test-rmse:1.123462+0.150771
## [376] train-rmse:0.798408+0.015845 test-rmse:1.122989+0.150196
## [377] train-rmse:0.797593+0.015841 test-rmse:1.122820+0.150051
## [378] train-rmse:0.796784+0.015841 test-rmse:1.122389+0.150372
## [379] train-rmse:0.795986+0.015848 test-rmse:1.121176+0.150512
## [380] train-rmse:0.795193+0.015857 test-rmse:1.120515+0.150841
## [381] train-rmse:0.794394+0.015845 test-rmse:1.119428+0.150110
## [382] train-rmse:0.793609+0.015847 test-rmse:1.117381+0.150331
## [383] train-rmse:0.792823+0.015857 test-rmse:1.116949+0.150260
## [384] train-rmse:0.792055+0.015865 test-rmse:1.116472+0.150434
## [385] train-rmse:0.791288+0.015868 test-rmse:1.116213+0.150727
## [386] train-rmse:0.790494+0.015816 test-rmse:1.113168+0.150742
## [387] train-rmse:0.789746+0.015822 test-rmse:1.112627+0.150372
## [388] train-rmse:0.789001+0.015822 test-rmse:1.110750+0.149207
## [389] train-rmse:0.788268+0.015818 test-rmse:1.110934+0.149606
## [390] train-rmse:0.787540+0.015812 test-rmse:1.111539+0.149605
## [391] train-rmse:0.786821+0.015809 test-rmse:1.112270+0.149574
## [392] train-rmse:0.786097+0.015782 test-rmse:1.109395+0.147028
## [393] train-rmse:0.785400+0.015775 test-rmse:1.108882+0.147344
## [394] train-rmse:0.784702+0.015788 test-rmse:1.109048+0.147279
## [395] train-rmse:0.783999+0.015778 test-rmse:1.109137+0.148011
## [396] train-rmse:0.783306+0.015767 test-rmse:1.109255+0.147322
## [397] train-rmse:0.782624+0.015766 test-rmse:1.108439+0.147338
## [398] train-rmse:0.781937+0.015763 test-rmse:1.107224+0.147146
## [399] train-rmse:0.781258+0.015761 test-rmse:1.107131+0.146762
## [400] train-rmse:0.780588+0.015758 test-rmse:1.106826+0.147558
## [401] train-rmse:0.779926+0.015764 test-rmse:1.105034+0.148133
## [402] train-rmse:0.779266+0.015770 test-rmse:1.103698+0.148306
## [403] train-rmse:0.778610+0.015773 test-rmse:1.103173+0.148075
## [404] train-rmse:0.777958+0.015779 test-rmse:1.101836+0.148184
## [405] train-rmse:0.777305+0.015782 test-rmse:1.101317+0.148205
## [406] train-rmse:0.776669+0.015784 test-rmse:1.101499+0.147572
## [407] train-rmse:0.776034+0.015767 test-rmse:1.100931+0.147724
## [408] train-rmse:0.775410+0.015752 test-rmse:1.100221+0.148049
## [409] train-rmse:0.774784+0.015743 test-rmse:1.100861+0.147306
## [410] train-rmse:0.774139+0.015753 test-rmse:1.100072+0.147831
## [411] train-rmse:0.773524+0.015739 test-rmse:1.098262+0.147246
## [412] train-rmse:0.772916+0.015730 test-rmse:1.097696+0.147440
## [413] train-rmse:0.772319+0.015723 test-rmse:1.098036+0.147595
## [414] train-rmse:0.771716+0.015720 test-rmse:1.096185+0.148243
## [415] train-rmse:0.771123+0.015712 test-rmse:1.095422+0.149069
## [416] train-rmse:0.770534+0.015703 test-rmse:1.095330+0.149326
## [417] train-rmse:0.769942+0.015704 test-rmse:1.094378+0.149862
## [418] train-rmse:0.769356+0.015686 test-rmse:1.093626+0.150902
## [419] train-rmse:0.768768+0.015664 test-rmse:1.092724+0.149728
## [420] train-rmse:0.768200+0.015653 test-rmse:1.092689+0.149872
## [421] train-rmse:0.767636+0.015642 test-rmse:1.091989+0.150286
## [422] train-rmse:0.767048+0.015649 test-rmse:1.091839+0.150386
## [423] train-rmse:0.766474+0.015648 test-rmse:1.091111+0.149788
## [424] train-rmse:0.765896+0.015648 test-rmse:1.089696+0.150916
## [425] train-rmse:0.765340+0.015633 test-rmse:1.090564+0.150792
## [426] train-rmse:0.764790+0.015617 test-rmse:1.089039+0.147857
## [427] train-rmse:0.764236+0.015610 test-rmse:1.088814+0.148442
## [428] train-rmse:0.763684+0.015601 test-rmse:1.088808+0.148965
## [429] train-rmse:0.763151+0.015604 test-rmse:1.088007+0.147984
## [430] train-rmse:0.762621+0.015601 test-rmse:1.086058+0.148685
## [431] train-rmse:0.762097+0.015593 test-rmse:1.085598+0.148553
## [432] train-rmse:0.761574+0.015584 test-rmse:1.085054+0.148829
## [433] train-rmse:0.761058+0.015583 test-rmse:1.084906+0.149567
## [434] train-rmse:0.760542+0.015581 test-rmse:1.084642+0.149453
## [435] train-rmse:0.760027+0.015574 test-rmse:1.084814+0.149256
## [436] train-rmse:0.759514+0.015566 test-rmse:1.083916+0.149374
## [437] train-rmse:0.759010+0.015565 test-rmse:1.084339+0.149072
## [438] train-rmse:0.758506+0.015570 test-rmse:1.083621+0.149100
## [439] train-rmse:0.758006+0.015564 test-rmse:1.083429+0.149751
## [440] train-rmse:0.757495+0.015548 test-rmse:1.083507+0.149844
## [441] train-rmse:0.756989+0.015549 test-rmse:1.082518+0.149914
## [442] train-rmse:0.756495+0.015549 test-rmse:1.081388+0.149814
## [443] train-rmse:0.756007+0.015543 test-rmse:1.081177+0.149376
## [444] train-rmse:0.755521+0.015541 test-rmse:1.081140+0.149374
## [445] train-rmse:0.755036+0.015541 test-rmse:1.080446+0.149468
## [446] train-rmse:0.754555+0.015538 test-rmse:1.079923+0.148733
## [447] train-rmse:0.754084+0.015529 test-rmse:1.080436+0.148692
## [448] train-rmse:0.753609+0.015517 test-rmse:1.080044+0.148670
## [449] train-rmse:0.753140+0.015518 test-rmse:1.079456+0.147635
## [450] train-rmse:0.752675+0.015504 test-rmse:1.078448+0.147870
## [451] train-rmse:0.752213+0.015495 test-rmse:1.078577+0.147918
## [452] train-rmse:0.751752+0.015488 test-rmse:1.078769+0.147839
## [453] train-rmse:0.751274+0.015461 test-rmse:1.078881+0.147598
## [454] train-rmse:0.750822+0.015458 test-rmse:1.078493+0.147669
## [455] train-rmse:0.750378+0.015449 test-rmse:1.077431+0.147604
## [456] train-rmse:0.749933+0.015444 test-rmse:1.076806+0.147173
## [457] train-rmse:0.749494+0.015439 test-rmse:1.076394+0.148156
## [458] train-rmse:0.749052+0.015435 test-rmse:1.077017+0.147529
## [459] train-rmse:0.748618+0.015434 test-rmse:1.075931+0.148037
## [460] train-rmse:0.748174+0.015429 test-rmse:1.076034+0.147799
## [461] train-rmse:0.747734+0.015413 test-rmse:1.075280+0.147482
## [462] train-rmse:0.747294+0.015398 test-rmse:1.074478+0.147850
## [463] train-rmse:0.746868+0.015387 test-rmse:1.074164+0.147877
## [464] train-rmse:0.746439+0.015374 test-rmse:1.073873+0.147798
## [465] train-rmse:0.746012+0.015363 test-rmse:1.072893+0.148073
## [466] train-rmse:0.745584+0.015351 test-rmse:1.073040+0.147860
## [467] train-rmse:0.745172+0.015345 test-rmse:1.072836+0.147878
## [468] train-rmse:0.744765+0.015337 test-rmse:1.072559+0.148090
## [469] train-rmse:0.744362+0.015333 test-rmse:1.071626+0.149117
## [470] train-rmse:0.743946+0.015339 test-rmse:1.071704+0.148047
## [471] train-rmse:0.743539+0.015337 test-rmse:1.070397+0.147509
## [472] train-rmse:0.743140+0.015337 test-rmse:1.070346+0.147455
## [473] train-rmse:0.742731+0.015335 test-rmse:1.069725+0.147410
## [474] train-rmse:0.742324+0.015317 test-rmse:1.068946+0.147525
## [475] train-rmse:0.741929+0.015317 test-rmse:1.068781+0.148065
## [476] train-rmse:0.741542+0.015306 test-rmse:1.068546+0.148110
## [477] train-rmse:0.741150+0.015291 test-rmse:1.068065+0.147820
## [478] train-rmse:0.740757+0.015277 test-rmse:1.068428+0.147464
## [479] train-rmse:0.740361+0.015255 test-rmse:1.067420+0.145093
## [480] train-rmse:0.739966+0.015246 test-rmse:1.067336+0.145248
## [481] train-rmse:0.739576+0.015244 test-rmse:1.067154+0.145404
## [482] train-rmse:0.739194+0.015229 test-rmse:1.066469+0.145135
## [483] train-rmse:0.738809+0.015222 test-rmse:1.066157+0.145404
## [484] train-rmse:0.738430+0.015208 test-rmse:1.065822+0.145719
## [485] train-rmse:0.738056+0.015201 test-rmse:1.065544+0.145446
## [486] train-rmse:0.737676+0.015194 test-rmse:1.065076+0.145904
## [487] train-rmse:0.737312+0.015179 test-rmse:1.064555+0.146235
## [488] train-rmse:0.736945+0.015164 test-rmse:1.064136+0.146266
## [489] train-rmse:0.736580+0.015149 test-rmse:1.064457+0.146621
## [490] train-rmse:0.736215+0.015137 test-rmse:1.064708+0.146865
## [491] train-rmse:0.735842+0.015119 test-rmse:1.064706+0.146260
## [492] train-rmse:0.735479+0.015106 test-rmse:1.064002+0.145795
## [493] train-rmse:0.735115+0.015094 test-rmse:1.063554+0.146153
## [494] train-rmse:0.734739+0.015079 test-rmse:1.063863+0.145137
## [495] train-rmse:0.734373+0.015058 test-rmse:1.064018+0.145243
## [496] train-rmse:0.734009+0.015030 test-rmse:1.062155+0.145079
## [497] train-rmse:0.733660+0.015011 test-rmse:1.062177+0.144642
## [498] train-rmse:0.733301+0.015002 test-rmse:1.062519+0.144775
## [499] train-rmse:0.732948+0.014984 test-rmse:1.062614+0.145153
## [500] train-rmse:0.732599+0.014966 test-rmse:1.062426+0.145264
## [501] train-rmse:0.732244+0.014948 test-rmse:1.061589+0.145598
## [502] train-rmse:0.731897+0.014931 test-rmse:1.061971+0.145289
## [503] train-rmse:0.731542+0.014927 test-rmse:1.061551+0.144637
## [504] train-rmse:0.731200+0.014918 test-rmse:1.061292+0.144055
## [505] train-rmse:0.730859+0.014901 test-rmse:1.060485+0.143118
## [506] train-rmse:0.730523+0.014883 test-rmse:1.060220+0.142990
## [507] train-rmse:0.730189+0.014864 test-rmse:1.059461+0.143035
## [508] train-rmse:0.729855+0.014842 test-rmse:1.059122+0.143410
## [509] train-rmse:0.729529+0.014824 test-rmse:1.058669+0.142694
## [510] train-rmse:0.729201+0.014811 test-rmse:1.059099+0.142972
## [511] train-rmse:0.728874+0.014801 test-rmse:1.058242+0.142976
## [512] train-rmse:0.728547+0.014784 test-rmse:1.058097+0.143383
## [513] train-rmse:0.728221+0.014771 test-rmse:1.058674+0.143043
## [514] train-rmse:0.727881+0.014776 test-rmse:1.058747+0.143307
## [515] train-rmse:0.727554+0.014756 test-rmse:1.058688+0.143791
## [516] train-rmse:0.727232+0.014742 test-rmse:1.057986+0.141233
## [517] train-rmse:0.726915+0.014724 test-rmse:1.057897+0.140751
## [518] train-rmse:0.726588+0.014718 test-rmse:1.057916+0.141171
## [519] train-rmse:0.726273+0.014703 test-rmse:1.057020+0.140436
## [520] train-rmse:0.725962+0.014686 test-rmse:1.057032+0.140532
## [521] train-rmse:0.725639+0.014668 test-rmse:1.056094+0.140463
## [522] train-rmse:0.725323+0.014657 test-rmse:1.055434+0.140016
## [523] train-rmse:0.725011+0.014643 test-rmse:1.055343+0.140415
## [524] train-rmse:0.724703+0.014630 test-rmse:1.054482+0.139866
## [525] train-rmse:0.724386+0.014607 test-rmse:1.054247+0.139822
## [526] train-rmse:0.724085+0.014596 test-rmse:1.053657+0.139529
## [527] train-rmse:0.723780+0.014580 test-rmse:1.053188+0.139450
## [528] train-rmse:0.723474+0.014569 test-rmse:1.053261+0.139270
## [529] train-rmse:0.723171+0.014547 test-rmse:1.053346+0.139245
## [530] train-rmse:0.722871+0.014534 test-rmse:1.053196+0.139777
## [531] train-rmse:0.722561+0.014506 test-rmse:1.052498+0.139305
## [532] train-rmse:0.722259+0.014492 test-rmse:1.052608+0.138620
## [533] train-rmse:0.721959+0.014476 test-rmse:1.053146+0.138021
## [534] train-rmse:0.721659+0.014461 test-rmse:1.052612+0.138140
## [535] train-rmse:0.721364+0.014440 test-rmse:1.052500+0.138524
## [536] train-rmse:0.721072+0.014425 test-rmse:1.051945+0.138620
## [537] train-rmse:0.720786+0.014404 test-rmse:1.050820+0.138700
## [538] train-rmse:0.720498+0.014391 test-rmse:1.050923+0.139115
## [539] train-rmse:0.720209+0.014375 test-rmse:1.050245+0.137914
## [540] train-rmse:0.719918+0.014365 test-rmse:1.049902+0.137754
## [541] train-rmse:0.719632+0.014353 test-rmse:1.049179+0.137438
## [542] train-rmse:0.719343+0.014344 test-rmse:1.049065+0.136616
## [543] train-rmse:0.719051+0.014324 test-rmse:1.047689+0.136709
## [544] train-rmse:0.718763+0.014309 test-rmse:1.047449+0.136986
## [545] train-rmse:0.718482+0.014294 test-rmse:1.046678+0.137023
## [546] train-rmse:0.718206+0.014276 test-rmse:1.046851+0.136906
## [547] train-rmse:0.717921+0.014257 test-rmse:1.046416+0.137090
## [548] train-rmse:0.717649+0.014237 test-rmse:1.046320+0.136410
## [549] train-rmse:0.717365+0.014210 test-rmse:1.046171+0.136536
## [550] train-rmse:0.717089+0.014190 test-rmse:1.046341+0.136795
## [551] train-rmse:0.716812+0.014179 test-rmse:1.046030+0.136641
## [552] train-rmse:0.716529+0.014161 test-rmse:1.046218+0.136583
## [553] train-rmse:0.716255+0.014143 test-rmse:1.044902+0.135022
## [554] train-rmse:0.715979+0.014123 test-rmse:1.044924+0.134937
## [555] train-rmse:0.715702+0.014113 test-rmse:1.045297+0.134804
## [556] train-rmse:0.715429+0.014088 test-rmse:1.044750+0.135541
## [557] train-rmse:0.715163+0.014073 test-rmse:1.044991+0.135854
## [558] train-rmse:0.714894+0.014053 test-rmse:1.044831+0.135423
## [559] train-rmse:0.714624+0.014030 test-rmse:1.044343+0.134875
## [560] train-rmse:0.714357+0.014015 test-rmse:1.044660+0.134835
## [561] train-rmse:0.714088+0.014000 test-rmse:1.044014+0.134688
## [562] train-rmse:0.713822+0.013971 test-rmse:1.043868+0.134693
## [563] train-rmse:0.713562+0.013954 test-rmse:1.043508+0.135565
## [564] train-rmse:0.713306+0.013936 test-rmse:1.042475+0.134768
## [565] train-rmse:0.713039+0.013917 test-rmse:1.042452+0.134173
## [566] train-rmse:0.712766+0.013896 test-rmse:1.042796+0.134165
## [567] train-rmse:0.712507+0.013882 test-rmse:1.042632+0.133742
## [568] train-rmse:0.712239+0.013869 test-rmse:1.042345+0.134087
## [569] train-rmse:0.711981+0.013853 test-rmse:1.040918+0.133704
## [570] train-rmse:0.711724+0.013835 test-rmse:1.040277+0.133460
## [571] train-rmse:0.711475+0.013818 test-rmse:1.039804+0.133065
## [572] train-rmse:0.711214+0.013803 test-rmse:1.039339+0.132966
## [573] train-rmse:0.710960+0.013790 test-rmse:1.039358+0.133120
## [574] train-rmse:0.710704+0.013771 test-rmse:1.039331+0.132733
## [575] train-rmse:0.710457+0.013754 test-rmse:1.039197+0.132654
## [576] train-rmse:0.710205+0.013732 test-rmse:1.039827+0.132069
## [577] train-rmse:0.709956+0.013717 test-rmse:1.039942+0.132197
## [578] train-rmse:0.709712+0.013703 test-rmse:1.040158+0.131995
## [579] train-rmse:0.709460+0.013688 test-rmse:1.039870+0.131282
## [580] train-rmse:0.709212+0.013671 test-rmse:1.040000+0.130861
## [581] train-rmse:0.708959+0.013654 test-rmse:1.039806+0.129946
## Stopping. Best iteration:
## [575] train-rmse:0.710457+0.013754 test-rmse:1.039197+0.132654
##
## 29
## [1] train-rmse:88.354776+0.099451 test-rmse:88.354991+0.492721
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.580200+0.086121 test-rmse:72.580084+0.517683
## [3] train-rmse:59.651027+0.076207 test-rmse:59.650480+0.541664
## [4] train-rmse:49.060283+0.069346 test-rmse:49.059156+0.565463
## [5] train-rmse:40.392603+0.065298 test-rmse:40.390688+0.589811
## [6] train-rmse:33.305450+0.062294 test-rmse:33.325024+0.602958
## [7] train-rmse:27.501240+0.056303 test-rmse:27.565675+0.605247
## [8] train-rmse:22.748923+0.043983 test-rmse:22.834604+0.595306
## [9] train-rmse:18.857207+0.034942 test-rmse:18.942480+0.554897
## [10] train-rmse:15.680107+0.030189 test-rmse:15.792283+0.570148
## [11] train-rmse:13.091815+0.028666 test-rmse:13.211942+0.589982
## [12] train-rmse:10.988389+0.029503 test-rmse:11.109339+0.600467
## [13] train-rmse:9.284139+0.030401 test-rmse:9.403491+0.561410
## [14] train-rmse:7.915690+0.032548 test-rmse:8.052855+0.573083
## [15] train-rmse:6.820068+0.036483 test-rmse:6.947404+0.549529
## [16] train-rmse:5.951501+0.039115 test-rmse:6.089870+0.546498
## [17] train-rmse:5.265249+0.041469 test-rmse:5.423926+0.508121
## [18] train-rmse:4.729290+0.046590 test-rmse:4.896954+0.504263
## [19] train-rmse:4.312518+0.046447 test-rmse:4.499525+0.509782
## [20] train-rmse:3.984436+0.046445 test-rmse:4.193286+0.474173
## [21] train-rmse:3.726576+0.048184 test-rmse:3.969123+0.471700
## [22] train-rmse:3.523119+0.048990 test-rmse:3.776852+0.453719
## [23] train-rmse:3.354244+0.043740 test-rmse:3.644185+0.449980
## [24] train-rmse:3.223950+0.043715 test-rmse:3.513074+0.419787
## [25] train-rmse:3.114803+0.040811 test-rmse:3.392081+0.386838
## [26] train-rmse:3.023158+0.040075 test-rmse:3.325596+0.376176
## [27] train-rmse:2.945873+0.039198 test-rmse:3.240113+0.360405
## [28] train-rmse:2.874294+0.035007 test-rmse:3.178414+0.337242
## [29] train-rmse:2.809829+0.035283 test-rmse:3.127522+0.353776
## [30] train-rmse:2.752241+0.034650 test-rmse:3.066547+0.330498
## [31] train-rmse:2.696467+0.035282 test-rmse:3.020176+0.330344
## [32] train-rmse:2.646891+0.033870 test-rmse:2.977808+0.326633
## [33] train-rmse:2.599772+0.033213 test-rmse:2.932623+0.314249
## [34] train-rmse:2.552564+0.032144 test-rmse:2.894003+0.306721
## [35] train-rmse:2.507859+0.029122 test-rmse:2.850493+0.311785
## [36] train-rmse:2.465164+0.028412 test-rmse:2.811457+0.293933
## [37] train-rmse:2.422934+0.028904 test-rmse:2.776210+0.307306
## [38] train-rmse:2.383384+0.028312 test-rmse:2.739430+0.299264
## [39] train-rmse:2.344935+0.027318 test-rmse:2.701950+0.298541
## [40] train-rmse:2.308398+0.026565 test-rmse:2.670362+0.283024
## [41] train-rmse:2.270663+0.024163 test-rmse:2.651344+0.284238
## [42] train-rmse:2.234721+0.022722 test-rmse:2.604272+0.281601
## [43] train-rmse:2.200480+0.022284 test-rmse:2.581229+0.284662
## [44] train-rmse:2.167258+0.021871 test-rmse:2.549736+0.281936
## [45] train-rmse:2.134353+0.021607 test-rmse:2.513220+0.277506
## [46] train-rmse:2.101975+0.021406 test-rmse:2.482495+0.267558
## [47] train-rmse:2.070065+0.021706 test-rmse:2.458103+0.265417
## [48] train-rmse:2.039868+0.020980 test-rmse:2.430024+0.252010
## [49] train-rmse:2.010596+0.020378 test-rmse:2.405600+0.258449
## [50] train-rmse:1.980836+0.019309 test-rmse:2.383979+0.265010
## [51] train-rmse:1.952731+0.018669 test-rmse:2.347554+0.258450
## [52] train-rmse:1.923951+0.017156 test-rmse:2.315165+0.258385
## [53] train-rmse:1.896824+0.016641 test-rmse:2.286675+0.256380
## [54] train-rmse:1.870243+0.015960 test-rmse:2.267987+0.256060
## [55] train-rmse:1.842488+0.014014 test-rmse:2.251363+0.254020
## [56] train-rmse:1.815610+0.012721 test-rmse:2.222551+0.240465
## [57] train-rmse:1.790428+0.011483 test-rmse:2.198938+0.247726
## [58] train-rmse:1.766061+0.009976 test-rmse:2.189796+0.248188
## [59] train-rmse:1.741615+0.009899 test-rmse:2.171895+0.250531
## [60] train-rmse:1.718405+0.009515 test-rmse:2.147085+0.248611
## [61] train-rmse:1.694615+0.009040 test-rmse:2.129032+0.237498
## [62] train-rmse:1.669440+0.009687 test-rmse:2.105237+0.242892
## [63] train-rmse:1.646894+0.009501 test-rmse:2.092453+0.243439
## [64] train-rmse:1.625275+0.009041 test-rmse:2.076485+0.248900
## [65] train-rmse:1.603930+0.008601 test-rmse:2.056785+0.251123
## [66] train-rmse:1.582787+0.008632 test-rmse:2.038523+0.247078
## [67] train-rmse:1.562254+0.007929 test-rmse:2.017499+0.243799
## [68] train-rmse:1.542704+0.007057 test-rmse:2.000550+0.249751
## [69] train-rmse:1.523218+0.006402 test-rmse:1.976692+0.248244
## [70] train-rmse:1.504139+0.006067 test-rmse:1.965080+0.250880
## [71] train-rmse:1.485071+0.005779 test-rmse:1.947909+0.237707
## [72] train-rmse:1.466679+0.005003 test-rmse:1.935800+0.245203
## [73] train-rmse:1.446616+0.007634 test-rmse:1.926907+0.244001
## [74] train-rmse:1.428509+0.006945 test-rmse:1.914659+0.240568
## [75] train-rmse:1.410975+0.007099 test-rmse:1.894520+0.243572
## [76] train-rmse:1.393649+0.007042 test-rmse:1.881825+0.237420
## [77] train-rmse:1.376794+0.006905 test-rmse:1.866773+0.235850
## [78] train-rmse:1.358657+0.007039 test-rmse:1.843515+0.231741
## [79] train-rmse:1.341998+0.006476 test-rmse:1.821258+0.229005
## [80] train-rmse:1.326079+0.006693 test-rmse:1.808397+0.229920
## [81] train-rmse:1.310639+0.006222 test-rmse:1.795737+0.231766
## [82] train-rmse:1.295556+0.005959 test-rmse:1.779291+0.228098
## [83] train-rmse:1.279371+0.005763 test-rmse:1.769112+0.230331
## [84] train-rmse:1.264131+0.005821 test-rmse:1.758783+0.228932
## [85] train-rmse:1.249129+0.006578 test-rmse:1.745655+0.232259
## [86] train-rmse:1.235153+0.007079 test-rmse:1.736314+0.227592
## [87] train-rmse:1.221157+0.007365 test-rmse:1.729048+0.231988
## [88] train-rmse:1.207424+0.007976 test-rmse:1.719965+0.239097
## [89] train-rmse:1.193762+0.008409 test-rmse:1.701142+0.230412
## [90] train-rmse:1.178893+0.006540 test-rmse:1.689677+0.225960
## [91] train-rmse:1.165895+0.006789 test-rmse:1.678592+0.229716
## [92] train-rmse:1.152603+0.007169 test-rmse:1.667281+0.232987
## [93] train-rmse:1.139760+0.007028 test-rmse:1.654003+0.232709
## [94] train-rmse:1.127460+0.007192 test-rmse:1.645444+0.227533
## [95] train-rmse:1.115472+0.007260 test-rmse:1.636014+0.227052
## [96] train-rmse:1.103773+0.007174 test-rmse:1.624495+0.232335
## [97] train-rmse:1.091213+0.006575 test-rmse:1.616107+0.231078
## [98] train-rmse:1.079924+0.006658 test-rmse:1.605198+0.232806
## [99] train-rmse:1.068134+0.006905 test-rmse:1.595931+0.238437
## [100] train-rmse:1.055596+0.007622 test-rmse:1.587633+0.236316
## [101] train-rmse:1.044238+0.007547 test-rmse:1.579739+0.234900
## [102] train-rmse:1.033547+0.008267 test-rmse:1.570386+0.238584
## [103] train-rmse:1.022473+0.008490 test-rmse:1.568024+0.241276
## [104] train-rmse:1.012438+0.008698 test-rmse:1.554768+0.239903
## [105] train-rmse:1.001504+0.008583 test-rmse:1.548684+0.241972
## [106] train-rmse:0.991775+0.008967 test-rmse:1.543464+0.244418
## [107] train-rmse:0.982288+0.009103 test-rmse:1.535521+0.244000
## [108] train-rmse:0.972827+0.009049 test-rmse:1.527179+0.241386
## [109] train-rmse:0.963824+0.009479 test-rmse:1.518975+0.241558
## [110] train-rmse:0.954801+0.009906 test-rmse:1.512743+0.236955
## [111] train-rmse:0.945101+0.011247 test-rmse:1.499604+0.235204
## [112] train-rmse:0.936208+0.011439 test-rmse:1.489522+0.233138
## [113] train-rmse:0.926727+0.011617 test-rmse:1.486163+0.236179
## [114] train-rmse:0.918118+0.011955 test-rmse:1.480734+0.240918
## [115] train-rmse:0.907929+0.011287 test-rmse:1.477175+0.242069
## [116] train-rmse:0.898864+0.011117 test-rmse:1.471630+0.242332
## [117] train-rmse:0.890878+0.011343 test-rmse:1.465929+0.242418
## [118] train-rmse:0.882839+0.011251 test-rmse:1.457834+0.240976
## [119] train-rmse:0.874887+0.011471 test-rmse:1.451032+0.241866
## [120] train-rmse:0.867292+0.011672 test-rmse:1.442560+0.241769
## [121] train-rmse:0.859571+0.011553 test-rmse:1.435464+0.241136
## [122] train-rmse:0.851902+0.011681 test-rmse:1.428856+0.243975
## [123] train-rmse:0.844502+0.011873 test-rmse:1.419518+0.239947
## [124] train-rmse:0.837530+0.011865 test-rmse:1.413588+0.240518
## [125] train-rmse:0.830352+0.011842 test-rmse:1.411413+0.241600
## [126] train-rmse:0.823319+0.011741 test-rmse:1.406877+0.239001
## [127] train-rmse:0.815809+0.011736 test-rmse:1.404574+0.240465
## [128] train-rmse:0.808657+0.012846 test-rmse:1.402404+0.242149
## [129] train-rmse:0.801803+0.013123 test-rmse:1.398882+0.241557
## [130] train-rmse:0.795773+0.013535 test-rmse:1.397682+0.241574
## [131] train-rmse:0.788918+0.014269 test-rmse:1.394585+0.241873
## [132] train-rmse:0.782127+0.013897 test-rmse:1.388189+0.242235
## [133] train-rmse:0.775449+0.014963 test-rmse:1.383632+0.240349
## [134] train-rmse:0.768515+0.014939 test-rmse:1.375608+0.242663
## [135] train-rmse:0.761742+0.014858 test-rmse:1.371461+0.242452
## [136] train-rmse:0.756209+0.014811 test-rmse:1.365652+0.239839
## [137] train-rmse:0.750277+0.015086 test-rmse:1.361300+0.241689
## [138] train-rmse:0.745098+0.015084 test-rmse:1.356512+0.244622
## [139] train-rmse:0.739083+0.015769 test-rmse:1.355540+0.245241
## [140] train-rmse:0.733764+0.016013 test-rmse:1.352551+0.246823
## [141] train-rmse:0.728219+0.015956 test-rmse:1.347313+0.244027
## [142] train-rmse:0.722830+0.016226 test-rmse:1.345190+0.242961
## [143] train-rmse:0.717413+0.016469 test-rmse:1.340901+0.241231
## [144] train-rmse:0.710729+0.014871 test-rmse:1.337554+0.241760
## [145] train-rmse:0.705319+0.015130 test-rmse:1.334210+0.244242
## [146] train-rmse:0.700082+0.014754 test-rmse:1.331829+0.244813
## [147] train-rmse:0.695070+0.014714 test-rmse:1.326351+0.240803
## [148] train-rmse:0.690272+0.014666 test-rmse:1.320493+0.240576
## [149] train-rmse:0.685625+0.014966 test-rmse:1.317064+0.241864
## [150] train-rmse:0.681344+0.014885 test-rmse:1.313800+0.243488
## [151] train-rmse:0.677136+0.015018 test-rmse:1.311002+0.244433
## [152] train-rmse:0.672316+0.015015 test-rmse:1.309907+0.243783
## [153] train-rmse:0.667637+0.014549 test-rmse:1.307554+0.240815
## [154] train-rmse:0.663528+0.014523 test-rmse:1.305904+0.242057
## [155] train-rmse:0.659048+0.014409 test-rmse:1.300036+0.243215
## [156] train-rmse:0.654778+0.014450 test-rmse:1.295985+0.243839
## [157] train-rmse:0.649574+0.013192 test-rmse:1.292917+0.244439
## [158] train-rmse:0.644200+0.014207 test-rmse:1.292176+0.245011
## [159] train-rmse:0.639106+0.014801 test-rmse:1.290862+0.245163
## [160] train-rmse:0.635114+0.015302 test-rmse:1.290830+0.244033
## [161] train-rmse:0.631278+0.015345 test-rmse:1.285170+0.242448
## [162] train-rmse:0.627548+0.015123 test-rmse:1.281468+0.243276
## [163] train-rmse:0.623593+0.014975 test-rmse:1.277931+0.246303
## [164] train-rmse:0.619642+0.014787 test-rmse:1.272952+0.248009
## [165] train-rmse:0.615844+0.014362 test-rmse:1.268710+0.248875
## [166] train-rmse:0.610952+0.014241 test-rmse:1.268214+0.249324
## [167] train-rmse:0.605974+0.015215 test-rmse:1.264699+0.250069
## [168] train-rmse:0.602572+0.015366 test-rmse:1.263839+0.250360
## [169] train-rmse:0.599071+0.015432 test-rmse:1.261737+0.253242
## [170] train-rmse:0.594391+0.015941 test-rmse:1.259706+0.252858
## [171] train-rmse:0.591028+0.016285 test-rmse:1.258640+0.252755
## [172] train-rmse:0.587853+0.016267 test-rmse:1.256813+0.253049
## [173] train-rmse:0.584768+0.016030 test-rmse:1.255589+0.249996
## [174] train-rmse:0.581561+0.016049 test-rmse:1.253472+0.248936
## [175] train-rmse:0.578037+0.015672 test-rmse:1.252224+0.249736
## [176] train-rmse:0.575217+0.016104 test-rmse:1.248788+0.250593
## [177] train-rmse:0.572463+0.016117 test-rmse:1.247912+0.251144
## [178] train-rmse:0.569607+0.016048 test-rmse:1.245566+0.252851
## [179] train-rmse:0.566220+0.015411 test-rmse:1.241525+0.252142
## [180] train-rmse:0.563303+0.015151 test-rmse:1.239541+0.252274
## [181] train-rmse:0.560292+0.015743 test-rmse:1.235824+0.250342
## [182] train-rmse:0.557219+0.016624 test-rmse:1.232112+0.252810
## [183] train-rmse:0.554660+0.016582 test-rmse:1.230761+0.252996
## [184] train-rmse:0.551661+0.016572 test-rmse:1.228863+0.253382
## [185] train-rmse:0.548558+0.016336 test-rmse:1.228189+0.254305
## [186] train-rmse:0.545694+0.016647 test-rmse:1.227018+0.253587
## [187] train-rmse:0.543211+0.016577 test-rmse:1.225635+0.253252
## [188] train-rmse:0.540883+0.016481 test-rmse:1.223193+0.254425
## [189] train-rmse:0.537262+0.016710 test-rmse:1.221930+0.256116
## [190] train-rmse:0.534394+0.017390 test-rmse:1.221627+0.257004
## [191] train-rmse:0.531888+0.017203 test-rmse:1.219954+0.257323
## [192] train-rmse:0.529581+0.017464 test-rmse:1.218913+0.258098
## [193] train-rmse:0.526957+0.017539 test-rmse:1.216458+0.258893
## [194] train-rmse:0.524721+0.017611 test-rmse:1.215083+0.258465
## [195] train-rmse:0.522134+0.017469 test-rmse:1.215673+0.258521
## [196] train-rmse:0.520018+0.017573 test-rmse:1.211301+0.258679
## [197] train-rmse:0.517625+0.017864 test-rmse:1.209112+0.259020
## [198] train-rmse:0.515382+0.017909 test-rmse:1.207531+0.259574
## [199] train-rmse:0.513187+0.017852 test-rmse:1.206402+0.260401
## [200] train-rmse:0.510284+0.017874 test-rmse:1.204919+0.261807
## [201] train-rmse:0.508191+0.017728 test-rmse:1.203060+0.261614
## [202] train-rmse:0.506094+0.017535 test-rmse:1.202246+0.261384
## [203] train-rmse:0.504012+0.017454 test-rmse:1.202782+0.262711
## [204] train-rmse:0.501621+0.017205 test-rmse:1.200948+0.261589
## [205] train-rmse:0.499445+0.017258 test-rmse:1.199084+0.262025
## [206] train-rmse:0.496533+0.016435 test-rmse:1.196466+0.262377
## [207] train-rmse:0.494380+0.016762 test-rmse:1.195771+0.262330
## [208] train-rmse:0.492276+0.016906 test-rmse:1.194794+0.262474
## [209] train-rmse:0.490019+0.017008 test-rmse:1.193101+0.262910
## [210] train-rmse:0.487945+0.017375 test-rmse:1.192022+0.263836
## [211] train-rmse:0.486007+0.017647 test-rmse:1.192455+0.264084
## [212] train-rmse:0.483560+0.017450 test-rmse:1.191550+0.264046
## [213] train-rmse:0.481677+0.017408 test-rmse:1.190126+0.264244
## [214] train-rmse:0.479802+0.017337 test-rmse:1.189377+0.264487
## [215] train-rmse:0.478050+0.017295 test-rmse:1.188563+0.264983
## [216] train-rmse:0.475692+0.017797 test-rmse:1.187757+0.264704
## [217] train-rmse:0.474072+0.017817 test-rmse:1.185984+0.265007
## [218] train-rmse:0.472377+0.017853 test-rmse:1.185228+0.264938
## [219] train-rmse:0.470150+0.017596 test-rmse:1.183379+0.265804
## [220] train-rmse:0.468436+0.017431 test-rmse:1.180743+0.266066
## [221] train-rmse:0.466750+0.017538 test-rmse:1.180904+0.266020
## [222] train-rmse:0.465087+0.017840 test-rmse:1.181260+0.265401
## [223] train-rmse:0.463562+0.018016 test-rmse:1.180647+0.267059
## [224] train-rmse:0.462015+0.018049 test-rmse:1.179569+0.267221
## [225] train-rmse:0.460341+0.018256 test-rmse:1.176695+0.268874
## [226] train-rmse:0.458653+0.018832 test-rmse:1.175750+0.268446
## [227] train-rmse:0.457310+0.018859 test-rmse:1.173337+0.269033
## [228] train-rmse:0.455966+0.018798 test-rmse:1.172733+0.268823
## [229] train-rmse:0.454239+0.018790 test-rmse:1.172584+0.269591
## [230] train-rmse:0.452493+0.019275 test-rmse:1.171203+0.270212
## [231] train-rmse:0.451175+0.019423 test-rmse:1.169791+0.270644
## [232] train-rmse:0.449376+0.019152 test-rmse:1.168303+0.270336
## [233] train-rmse:0.447816+0.019100 test-rmse:1.168289+0.271362
## [234] train-rmse:0.446374+0.018947 test-rmse:1.166012+0.272252
## [235] train-rmse:0.444897+0.018762 test-rmse:1.165773+0.271959
## [236] train-rmse:0.443548+0.018747 test-rmse:1.165084+0.270902
## [237] train-rmse:0.441718+0.018686 test-rmse:1.161791+0.265989
## [238] train-rmse:0.440141+0.018671 test-rmse:1.162150+0.265767
## [239] train-rmse:0.438550+0.018483 test-rmse:1.161948+0.266379
## [240] train-rmse:0.437222+0.018757 test-rmse:1.161646+0.265815
## [241] train-rmse:0.435799+0.018969 test-rmse:1.161159+0.266413
## [242] train-rmse:0.434605+0.019065 test-rmse:1.159237+0.265881
## [243] train-rmse:0.433440+0.018956 test-rmse:1.157034+0.266877
## [244] train-rmse:0.432067+0.019098 test-rmse:1.156223+0.267240
## [245] train-rmse:0.430628+0.018944 test-rmse:1.156579+0.267192
## [246] train-rmse:0.429547+0.019171 test-rmse:1.155089+0.268266
## [247] train-rmse:0.428228+0.019290 test-rmse:1.154950+0.269074
## [248] train-rmse:0.426800+0.019735 test-rmse:1.154404+0.268694
## [249] train-rmse:0.425364+0.019606 test-rmse:1.153598+0.268672
## [250] train-rmse:0.424150+0.019533 test-rmse:1.153368+0.268451
## [251] train-rmse:0.422917+0.019340 test-rmse:1.153340+0.269257
## [252] train-rmse:0.421479+0.019225 test-rmse:1.153964+0.269154
## [253] train-rmse:0.419640+0.019296 test-rmse:1.153294+0.269065
## [254] train-rmse:0.418533+0.019366 test-rmse:1.152149+0.269634
## [255] train-rmse:0.417502+0.019588 test-rmse:1.151007+0.268376
## [256] train-rmse:0.416354+0.019719 test-rmse:1.151303+0.268802
## [257] train-rmse:0.414605+0.019693 test-rmse:1.148960+0.268775
## [258] train-rmse:0.413222+0.019581 test-rmse:1.147954+0.269993
## [259] train-rmse:0.411966+0.019399 test-rmse:1.148854+0.268770
## [260] train-rmse:0.410523+0.019085 test-rmse:1.147507+0.269928
## [261] train-rmse:0.409443+0.018937 test-rmse:1.146298+0.269495
## [262] train-rmse:0.407713+0.019119 test-rmse:1.144716+0.270831
## [263] train-rmse:0.406142+0.019120 test-rmse:1.144966+0.271548
## [264] train-rmse:0.404662+0.018857 test-rmse:1.143642+0.270896
## [265] train-rmse:0.403595+0.018876 test-rmse:1.143400+0.271192
## [266] train-rmse:0.402347+0.019157 test-rmse:1.142790+0.270894
## [267] train-rmse:0.401491+0.019187 test-rmse:1.142120+0.271124
## [268] train-rmse:0.400506+0.019203 test-rmse:1.140121+0.270178
## [269] train-rmse:0.399694+0.019389 test-rmse:1.139224+0.270640
## [270] train-rmse:0.398768+0.019296 test-rmse:1.138616+0.270699
## [271] train-rmse:0.397496+0.018895 test-rmse:1.137675+0.270536
## [272] train-rmse:0.396383+0.018861 test-rmse:1.137222+0.270428
## [273] train-rmse:0.395287+0.018718 test-rmse:1.136004+0.271553
## [274] train-rmse:0.394061+0.018420 test-rmse:1.136186+0.272212
## [275] train-rmse:0.392737+0.018253 test-rmse:1.135286+0.272887
## [276] train-rmse:0.391746+0.018244 test-rmse:1.132446+0.269247
## [277] train-rmse:0.390328+0.018410 test-rmse:1.131978+0.268558
## [278] train-rmse:0.389502+0.018427 test-rmse:1.132208+0.269054
## [279] train-rmse:0.388428+0.018345 test-rmse:1.132688+0.269162
## [280] train-rmse:0.387534+0.018499 test-rmse:1.131207+0.269318
## [281] train-rmse:0.385863+0.018899 test-rmse:1.129397+0.270018
## [282] train-rmse:0.384836+0.019060 test-rmse:1.129736+0.269796
## [283] train-rmse:0.384034+0.019121 test-rmse:1.129186+0.270174
## [284] train-rmse:0.383006+0.019253 test-rmse:1.129069+0.270265
## [285] train-rmse:0.381867+0.019590 test-rmse:1.129468+0.269835
## [286] train-rmse:0.380948+0.019515 test-rmse:1.129068+0.269587
## [287] train-rmse:0.379459+0.019226 test-rmse:1.128726+0.270074
## [288] train-rmse:0.378560+0.019136 test-rmse:1.128170+0.269651
## [289] train-rmse:0.377712+0.019065 test-rmse:1.128419+0.270308
## [290] train-rmse:0.377044+0.018989 test-rmse:1.126739+0.270423
## [291] train-rmse:0.376069+0.018884 test-rmse:1.126407+0.270381
## [292] train-rmse:0.375260+0.019056 test-rmse:1.126217+0.270470
## [293] train-rmse:0.373421+0.018408 test-rmse:1.126240+0.271019
## [294] train-rmse:0.372663+0.018351 test-rmse:1.125429+0.271650
## [295] train-rmse:0.371806+0.018282 test-rmse:1.125485+0.272674
## [296] train-rmse:0.371010+0.018315 test-rmse:1.125127+0.272519
## [297] train-rmse:0.369821+0.018882 test-rmse:1.124474+0.271863
## [298] train-rmse:0.369071+0.018986 test-rmse:1.124752+0.272037
## [299] train-rmse:0.368180+0.018906 test-rmse:1.124435+0.272181
## [300] train-rmse:0.367464+0.018919 test-rmse:1.124359+0.272087
## [301] train-rmse:0.366153+0.018643 test-rmse:1.123097+0.271627
## [302] train-rmse:0.365094+0.018306 test-rmse:1.123386+0.272301
## [303] train-rmse:0.364361+0.018229 test-rmse:1.121478+0.272234
## [304] train-rmse:0.363457+0.018149 test-rmse:1.121413+0.271962
## [305] train-rmse:0.361681+0.018190 test-rmse:1.120871+0.271670
## [306] train-rmse:0.360355+0.018095 test-rmse:1.120589+0.271676
## [307] train-rmse:0.358465+0.018175 test-rmse:1.119868+0.271625
## [308] train-rmse:0.357722+0.018469 test-rmse:1.118612+0.271194
## [309] train-rmse:0.356742+0.018278 test-rmse:1.119048+0.271050
## [310] train-rmse:0.355914+0.018234 test-rmse:1.118842+0.271204
## [311] train-rmse:0.355356+0.018243 test-rmse:1.118794+0.271618
## [312] train-rmse:0.354299+0.018226 test-rmse:1.118425+0.271778
## [313] train-rmse:0.353147+0.018288 test-rmse:1.117416+0.271297
## [314] train-rmse:0.352431+0.018292 test-rmse:1.117124+0.272092
## [315] train-rmse:0.351509+0.018459 test-rmse:1.116747+0.271715
## [316] train-rmse:0.350358+0.018336 test-rmse:1.116191+0.271480
## [317] train-rmse:0.349501+0.018089 test-rmse:1.115979+0.271797
## [318] train-rmse:0.348505+0.017857 test-rmse:1.115993+0.272550
## [319] train-rmse:0.347575+0.017601 test-rmse:1.116116+0.273113
## [320] train-rmse:0.346740+0.017566 test-rmse:1.116203+0.273596
## [321] train-rmse:0.345954+0.017731 test-rmse:1.115204+0.272505
## [322] train-rmse:0.345351+0.017752 test-rmse:1.115058+0.272344
## [323] train-rmse:0.344522+0.017576 test-rmse:1.113693+0.272515
## [324] train-rmse:0.343479+0.017481 test-rmse:1.113354+0.272515
## [325] train-rmse:0.342814+0.017473 test-rmse:1.113486+0.272175
## [326] train-rmse:0.341445+0.017225 test-rmse:1.113702+0.272819
## [327] train-rmse:0.340640+0.017007 test-rmse:1.112229+0.273631
## [328] train-rmse:0.339998+0.016922 test-rmse:1.112022+0.273486
## [329] train-rmse:0.339312+0.016920 test-rmse:1.111903+0.273615
## [330] train-rmse:0.337966+0.016678 test-rmse:1.111898+0.272842
## [331] train-rmse:0.336972+0.016597 test-rmse:1.111039+0.272780
## [332] train-rmse:0.336129+0.017041 test-rmse:1.110857+0.272371
## [333] train-rmse:0.334967+0.016892 test-rmse:1.110637+0.273308
## [334] train-rmse:0.334380+0.016957 test-rmse:1.110074+0.273703
## [335] train-rmse:0.333305+0.016671 test-rmse:1.110043+0.273714
## [336] train-rmse:0.332799+0.016700 test-rmse:1.109101+0.273636
## [337] train-rmse:0.331906+0.016441 test-rmse:1.109092+0.273456
## [338] train-rmse:0.331100+0.016259 test-rmse:1.109425+0.273166
## [339] train-rmse:0.330483+0.016334 test-rmse:1.109096+0.273016
## [340] train-rmse:0.330012+0.016321 test-rmse:1.108831+0.272949
## [341] train-rmse:0.329057+0.016730 test-rmse:1.108927+0.272294
## [342] train-rmse:0.327858+0.016624 test-rmse:1.108261+0.272507
## [343] train-rmse:0.327029+0.016613 test-rmse:1.107395+0.273057
## [344] train-rmse:0.326432+0.016767 test-rmse:1.107047+0.272750
## [345] train-rmse:0.325807+0.016750 test-rmse:1.106953+0.272977
## [346] train-rmse:0.324892+0.017219 test-rmse:1.107092+0.273716
## [347] train-rmse:0.324194+0.017158 test-rmse:1.107565+0.273251
## [348] train-rmse:0.323457+0.016903 test-rmse:1.107450+0.274264
## [349] train-rmse:0.322191+0.016852 test-rmse:1.106605+0.273943
## [350] train-rmse:0.321345+0.017065 test-rmse:1.106126+0.274146
## [351] train-rmse:0.320700+0.016999 test-rmse:1.106696+0.274066
## [352] train-rmse:0.319843+0.016672 test-rmse:1.106311+0.274055
## [353] train-rmse:0.319241+0.016704 test-rmse:1.106137+0.273716
## [354] train-rmse:0.318109+0.016802 test-rmse:1.105304+0.273542
## [355] train-rmse:0.317390+0.016793 test-rmse:1.105189+0.273765
## [356] train-rmse:0.316841+0.016739 test-rmse:1.105136+0.273343
## [357] train-rmse:0.315606+0.016562 test-rmse:1.104314+0.273799
## [358] train-rmse:0.314860+0.016660 test-rmse:1.103437+0.274693
## [359] train-rmse:0.313887+0.016317 test-rmse:1.103559+0.274946
## [360] train-rmse:0.312789+0.016143 test-rmse:1.103040+0.274994
## [361] train-rmse:0.311868+0.016039 test-rmse:1.102902+0.274725
## [362] train-rmse:0.311387+0.016089 test-rmse:1.102949+0.274607
## [363] train-rmse:0.310938+0.016057 test-rmse:1.102935+0.273804
## [364] train-rmse:0.310206+0.015998 test-rmse:1.102951+0.273671
## [365] train-rmse:0.309247+0.016230 test-rmse:1.102855+0.273628
## [366] train-rmse:0.308412+0.016562 test-rmse:1.102780+0.273987
## [367] train-rmse:0.307966+0.016550 test-rmse:1.102736+0.274309
## [368] train-rmse:0.307288+0.016862 test-rmse:1.102633+0.273656
## [369] train-rmse:0.306325+0.016610 test-rmse:1.102467+0.273500
## [370] train-rmse:0.305388+0.016452 test-rmse:1.101242+0.274052
## [371] train-rmse:0.304745+0.016267 test-rmse:1.101551+0.273668
## [372] train-rmse:0.303801+0.016151 test-rmse:1.101207+0.273442
## [373] train-rmse:0.303155+0.016010 test-rmse:1.100830+0.272971
## [374] train-rmse:0.302744+0.015983 test-rmse:1.100630+0.273088
## [375] train-rmse:0.302024+0.015702 test-rmse:1.100186+0.272935
## [376] train-rmse:0.301104+0.015643 test-rmse:1.101004+0.272632
## [377] train-rmse:0.300403+0.015541 test-rmse:1.101583+0.272814
## [378] train-rmse:0.299539+0.015348 test-rmse:1.101545+0.273140
## [379] train-rmse:0.298824+0.015484 test-rmse:1.101345+0.273199
## [380] train-rmse:0.298363+0.015370 test-rmse:1.101260+0.273685
## [381] train-rmse:0.297711+0.015484 test-rmse:1.102198+0.274189
## Stopping. Best iteration:
## [375] train-rmse:0.302024+0.015702 test-rmse:1.100186+0.272935
##
## 30
## [1] train-rmse:88.354776+0.099451 test-rmse:88.354991+0.492721
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.580200+0.086121 test-rmse:72.580084+0.517683
## [3] train-rmse:59.651027+0.076207 test-rmse:59.650480+0.541664
## [4] train-rmse:49.060283+0.069346 test-rmse:49.059156+0.565463
## [5] train-rmse:40.392603+0.065298 test-rmse:40.390688+0.589811
## [6] train-rmse:33.305450+0.062294 test-rmse:33.325024+0.602958
## [7] train-rmse:27.499631+0.054718 test-rmse:27.567201+0.606506
## [8] train-rmse:22.742521+0.042423 test-rmse:22.827392+0.586102
## [9] train-rmse:18.844127+0.035821 test-rmse:18.944245+0.567467
## [10] train-rmse:15.655837+0.030143 test-rmse:15.772980+0.580372
## [11] train-rmse:13.045770+0.030218 test-rmse:13.161424+0.577854
## [12] train-rmse:10.916797+0.029511 test-rmse:11.042306+0.563497
## [13] train-rmse:9.184949+0.031596 test-rmse:9.303873+0.537073
## [14] train-rmse:7.782081+0.032437 test-rmse:7.893612+0.497883
## [15] train-rmse:6.650337+0.033765 test-rmse:6.799557+0.519137
## [16] train-rmse:5.742958+0.035483 test-rmse:5.927860+0.527346
## [17] train-rmse:5.014527+0.035904 test-rmse:5.194970+0.497376
## [18] train-rmse:4.428476+0.030274 test-rmse:4.651711+0.465674
## [19] train-rmse:3.969247+0.031642 test-rmse:4.216757+0.453661
## [20] train-rmse:3.604374+0.032167 test-rmse:3.895411+0.434965
## [21] train-rmse:3.316318+0.031981 test-rmse:3.620239+0.398874
## [22] train-rmse:3.089347+0.031727 test-rmse:3.420059+0.394112
## [23] train-rmse:2.904711+0.032087 test-rmse:3.246001+0.374138
## [24] train-rmse:2.753156+0.028050 test-rmse:3.120859+0.350856
## [25] train-rmse:2.630628+0.027160 test-rmse:3.011106+0.330538
## [26] train-rmse:2.522051+0.027301 test-rmse:2.917670+0.311054
## [27] train-rmse:2.433128+0.025556 test-rmse:2.842542+0.309584
## [28] train-rmse:2.354186+0.025549 test-rmse:2.773059+0.311495
## [29] train-rmse:2.279863+0.020255 test-rmse:2.718762+0.290765
## [30] train-rmse:2.205109+0.020918 test-rmse:2.663625+0.248074
## [31] train-rmse:2.148069+0.020199 test-rmse:2.610134+0.247277
## [32] train-rmse:2.090926+0.020720 test-rmse:2.566875+0.255003
## [33] train-rmse:2.038438+0.019519 test-rmse:2.522859+0.263028
## [34] train-rmse:1.986624+0.019610 test-rmse:2.469702+0.261483
## [35] train-rmse:1.937226+0.020806 test-rmse:2.429064+0.251930
## [36] train-rmse:1.892482+0.020009 test-rmse:2.379215+0.233664
## [37] train-rmse:1.847614+0.018067 test-rmse:2.351953+0.235871
## [38] train-rmse:1.806151+0.017741 test-rmse:2.322679+0.246247
## [39] train-rmse:1.767271+0.016616 test-rmse:2.291414+0.247721
## [40] train-rmse:1.726941+0.017586 test-rmse:2.263771+0.247713
## [41] train-rmse:1.688846+0.015646 test-rmse:2.222185+0.235154
## [42] train-rmse:1.651066+0.014257 test-rmse:2.181652+0.225617
## [43] train-rmse:1.616168+0.013171 test-rmse:2.148092+0.222977
## [44] train-rmse:1.582360+0.013233 test-rmse:2.119085+0.225099
## [45] train-rmse:1.548959+0.010616 test-rmse:2.100504+0.224974
## [46] train-rmse:1.515945+0.011616 test-rmse:2.081021+0.228670
## [47] train-rmse:1.485299+0.011117 test-rmse:2.046717+0.228403
## [48] train-rmse:1.455782+0.010955 test-rmse:2.022526+0.224492
## [49] train-rmse:1.426020+0.010190 test-rmse:1.995267+0.214043
## [50] train-rmse:1.397077+0.010633 test-rmse:1.969251+0.223302
## [51] train-rmse:1.368177+0.011243 test-rmse:1.951902+0.219014
## [52] train-rmse:1.341063+0.010512 test-rmse:1.936571+0.218106
## [53] train-rmse:1.313508+0.011630 test-rmse:1.909412+0.224692
## [54] train-rmse:1.289015+0.011596 test-rmse:1.894929+0.222136
## [55] train-rmse:1.262988+0.009765 test-rmse:1.863196+0.211479
## [56] train-rmse:1.239362+0.009976 test-rmse:1.843078+0.211775
## [57] train-rmse:1.217114+0.009901 test-rmse:1.822482+0.212702
## [58] train-rmse:1.194828+0.010224 test-rmse:1.806715+0.211595
## [59] train-rmse:1.171755+0.010234 test-rmse:1.793199+0.210073
## [60] train-rmse:1.151337+0.009164 test-rmse:1.778525+0.213718
## [61] train-rmse:1.131519+0.009567 test-rmse:1.760612+0.217303
## [62] train-rmse:1.110275+0.009082 test-rmse:1.746604+0.216878
## [63] train-rmse:1.090703+0.008884 test-rmse:1.723569+0.215173
## [64] train-rmse:1.071172+0.008531 test-rmse:1.711148+0.215685
## [65] train-rmse:1.052987+0.008086 test-rmse:1.694789+0.219957
## [66] train-rmse:1.033610+0.008632 test-rmse:1.683858+0.218856
## [67] train-rmse:1.016552+0.009020 test-rmse:1.669742+0.221590
## [68] train-rmse:0.999848+0.009482 test-rmse:1.654494+0.226350
## [69] train-rmse:0.982154+0.010362 test-rmse:1.638473+0.223970
## [70] train-rmse:0.962439+0.012092 test-rmse:1.624970+0.216840
## [71] train-rmse:0.946522+0.012208 test-rmse:1.613912+0.220911
## [72] train-rmse:0.931159+0.013389 test-rmse:1.601234+0.225690
## [73] train-rmse:0.915652+0.012945 test-rmse:1.587479+0.229359
## [74] train-rmse:0.901163+0.012603 test-rmse:1.575399+0.234000
## [75] train-rmse:0.886501+0.013471 test-rmse:1.568485+0.236635
## [76] train-rmse:0.872714+0.013175 test-rmse:1.556881+0.232355
## [77] train-rmse:0.858539+0.012985 test-rmse:1.548682+0.237126
## [78] train-rmse:0.844864+0.012671 test-rmse:1.541764+0.234422
## [79] train-rmse:0.832138+0.012731 test-rmse:1.529167+0.232267
## [80] train-rmse:0.818933+0.011435 test-rmse:1.518041+0.233852
## [81] train-rmse:0.806231+0.011297 test-rmse:1.508636+0.239101
## [82] train-rmse:0.793055+0.011031 test-rmse:1.503121+0.242079
## [83] train-rmse:0.781003+0.010542 test-rmse:1.491953+0.243142
## [84] train-rmse:0.768480+0.012052 test-rmse:1.478178+0.236129
## [85] train-rmse:0.757239+0.011615 test-rmse:1.472636+0.236679
## [86] train-rmse:0.746176+0.010903 test-rmse:1.466297+0.243214
## [87] train-rmse:0.735203+0.011199 test-rmse:1.460757+0.246494
## [88] train-rmse:0.725714+0.011161 test-rmse:1.456030+0.245707
## [89] train-rmse:0.715779+0.011744 test-rmse:1.444717+0.248366
## [90] train-rmse:0.706325+0.012356 test-rmse:1.439741+0.248098
## [91] train-rmse:0.697152+0.012400 test-rmse:1.434806+0.248253
## [92] train-rmse:0.688493+0.012514 test-rmse:1.428708+0.250444
## [93] train-rmse:0.677514+0.012260 test-rmse:1.417355+0.246577
## [94] train-rmse:0.667899+0.011218 test-rmse:1.410257+0.243243
## [95] train-rmse:0.657543+0.012987 test-rmse:1.403007+0.243558
## [96] train-rmse:0.649904+0.012910 test-rmse:1.398112+0.248718
## [97] train-rmse:0.641021+0.012621 test-rmse:1.393271+0.242249
## [98] train-rmse:0.633779+0.012455 test-rmse:1.388155+0.246888
## [99] train-rmse:0.624715+0.012053 test-rmse:1.378575+0.246625
## [100] train-rmse:0.616829+0.011856 test-rmse:1.376178+0.245976
## [101] train-rmse:0.608668+0.013154 test-rmse:1.371703+0.248786
## [102] train-rmse:0.601082+0.012974 test-rmse:1.367286+0.250975
## [103] train-rmse:0.594044+0.012970 test-rmse:1.363419+0.250174
## [104] train-rmse:0.586686+0.013834 test-rmse:1.357298+0.251916
## [105] train-rmse:0.579487+0.012944 test-rmse:1.351690+0.254266
## [106] train-rmse:0.571874+0.014337 test-rmse:1.346144+0.256265
## [107] train-rmse:0.565136+0.014885 test-rmse:1.343188+0.256240
## [108] train-rmse:0.559367+0.014487 test-rmse:1.340480+0.256013
## [109] train-rmse:0.552282+0.013812 test-rmse:1.337621+0.256581
## [110] train-rmse:0.546317+0.013573 test-rmse:1.332339+0.258785
## [111] train-rmse:0.540177+0.013337 test-rmse:1.330356+0.260699
## [112] train-rmse:0.534653+0.013467 test-rmse:1.323110+0.262970
## [113] train-rmse:0.529535+0.013364 test-rmse:1.321291+0.262945
## [114] train-rmse:0.522813+0.012790 test-rmse:1.317023+0.261019
## [115] train-rmse:0.516621+0.011940 test-rmse:1.308042+0.254110
## [116] train-rmse:0.510249+0.011294 test-rmse:1.307928+0.253612
## [117] train-rmse:0.505322+0.011934 test-rmse:1.303769+0.255281
## [118] train-rmse:0.500874+0.011464 test-rmse:1.300091+0.256327
## [119] train-rmse:0.495441+0.011065 test-rmse:1.298035+0.257563
## [120] train-rmse:0.491149+0.011101 test-rmse:1.293339+0.260378
## [121] train-rmse:0.486216+0.011170 test-rmse:1.286784+0.255102
## [122] train-rmse:0.481031+0.010817 test-rmse:1.285499+0.255795
## [123] train-rmse:0.476254+0.010989 test-rmse:1.283009+0.256260
## [124] train-rmse:0.471777+0.010414 test-rmse:1.279709+0.257637
## [125] train-rmse:0.467733+0.010349 test-rmse:1.277806+0.257526
## [126] train-rmse:0.463179+0.010027 test-rmse:1.276737+0.258421
## [127] train-rmse:0.458078+0.009982 test-rmse:1.276153+0.258798
## [128] train-rmse:0.454408+0.010059 test-rmse:1.271961+0.260188
## [129] train-rmse:0.450354+0.009795 test-rmse:1.272247+0.259642
## [130] train-rmse:0.445638+0.009783 test-rmse:1.271561+0.261043
## [131] train-rmse:0.441870+0.009661 test-rmse:1.268547+0.261347
## [132] train-rmse:0.437335+0.009818 test-rmse:1.266905+0.263476
## [133] train-rmse:0.433515+0.010076 test-rmse:1.261638+0.268949
## [134] train-rmse:0.429613+0.010619 test-rmse:1.259674+0.270264
## [135] train-rmse:0.426299+0.010483 test-rmse:1.254655+0.268972
## [136] train-rmse:0.422830+0.010204 test-rmse:1.255061+0.268174
## [137] train-rmse:0.419813+0.010096 test-rmse:1.253726+0.268891
## [138] train-rmse:0.416133+0.009677 test-rmse:1.251346+0.270348
## [139] train-rmse:0.412692+0.009308 test-rmse:1.250450+0.269736
## [140] train-rmse:0.408201+0.009925 test-rmse:1.248452+0.268987
## [141] train-rmse:0.404388+0.009666 test-rmse:1.243611+0.273505
## [142] train-rmse:0.401039+0.010095 test-rmse:1.243192+0.274924
## [143] train-rmse:0.398485+0.010120 test-rmse:1.241625+0.274553
## [144] train-rmse:0.395434+0.009877 test-rmse:1.241164+0.275693
## [145] train-rmse:0.392406+0.010027 test-rmse:1.239541+0.276303
## [146] train-rmse:0.388229+0.009386 test-rmse:1.237276+0.276334
## [147] train-rmse:0.385055+0.009678 test-rmse:1.235806+0.276718
## [148] train-rmse:0.382359+0.010336 test-rmse:1.232529+0.279916
## [149] train-rmse:0.379589+0.011205 test-rmse:1.230690+0.280665
## [150] train-rmse:0.376804+0.011435 test-rmse:1.227924+0.279121
## [151] train-rmse:0.373639+0.011232 test-rmse:1.227697+0.279481
## [152] train-rmse:0.370696+0.010921 test-rmse:1.226434+0.280625
## [153] train-rmse:0.367641+0.010407 test-rmse:1.226342+0.280417
## [154] train-rmse:0.364005+0.011124 test-rmse:1.224030+0.281857
## [155] train-rmse:0.361700+0.011035 test-rmse:1.223000+0.281892
## [156] train-rmse:0.359302+0.010813 test-rmse:1.217129+0.280270
## [157] train-rmse:0.356289+0.010672 test-rmse:1.216832+0.281149
## [158] train-rmse:0.353528+0.010816 test-rmse:1.216602+0.282302
## [159] train-rmse:0.350849+0.010461 test-rmse:1.215250+0.281585
## [160] train-rmse:0.348269+0.010601 test-rmse:1.213909+0.282394
## [161] train-rmse:0.345323+0.010014 test-rmse:1.214224+0.281477
## [162] train-rmse:0.343439+0.010143 test-rmse:1.212942+0.281718
## [163] train-rmse:0.340874+0.010259 test-rmse:1.212338+0.281717
## [164] train-rmse:0.338580+0.010993 test-rmse:1.209376+0.280108
## [165] train-rmse:0.336512+0.010912 test-rmse:1.209000+0.279898
## [166] train-rmse:0.333581+0.010326 test-rmse:1.207271+0.280262
## [167] train-rmse:0.329753+0.010616 test-rmse:1.207217+0.282808
## [168] train-rmse:0.328135+0.010493 test-rmse:1.206074+0.283153
## [169] train-rmse:0.325244+0.009915 test-rmse:1.205338+0.283226
## [170] train-rmse:0.322923+0.009913 test-rmse:1.204480+0.283884
## [171] train-rmse:0.320777+0.010101 test-rmse:1.202920+0.284112
## [172] train-rmse:0.318993+0.010246 test-rmse:1.202377+0.283891
## [173] train-rmse:0.316685+0.010206 test-rmse:1.198953+0.281868
## [174] train-rmse:0.314671+0.010737 test-rmse:1.199258+0.281201
## [175] train-rmse:0.312799+0.010825 test-rmse:1.199362+0.280256
## [176] train-rmse:0.310358+0.010853 test-rmse:1.199013+0.281576
## [177] train-rmse:0.308432+0.010677 test-rmse:1.197480+0.282662
## [178] train-rmse:0.306111+0.010571 test-rmse:1.194989+0.281950
## [179] train-rmse:0.304359+0.010812 test-rmse:1.192369+0.284845
## [180] train-rmse:0.302632+0.010689 test-rmse:1.192162+0.285671
## [181] train-rmse:0.299565+0.010082 test-rmse:1.190744+0.286449
## [182] train-rmse:0.297789+0.009635 test-rmse:1.190174+0.286072
## [183] train-rmse:0.295620+0.009922 test-rmse:1.189353+0.286445
## [184] train-rmse:0.293539+0.010000 test-rmse:1.187931+0.285721
## [185] train-rmse:0.292109+0.009952 test-rmse:1.187664+0.285736
## [186] train-rmse:0.290506+0.009848 test-rmse:1.187031+0.285971
## [187] train-rmse:0.289077+0.009612 test-rmse:1.186100+0.285872
## [188] train-rmse:0.287325+0.009854 test-rmse:1.185995+0.285962
## [189] train-rmse:0.285663+0.009804 test-rmse:1.185390+0.287574
## [190] train-rmse:0.284258+0.009522 test-rmse:1.184620+0.288195
## [191] train-rmse:0.282440+0.009895 test-rmse:1.184050+0.288784
## [192] train-rmse:0.280452+0.010942 test-rmse:1.181222+0.290910
## [193] train-rmse:0.278811+0.011462 test-rmse:1.180607+0.291322
## [194] train-rmse:0.277179+0.011736 test-rmse:1.179367+0.291711
## [195] train-rmse:0.275890+0.011619 test-rmse:1.178700+0.292744
## [196] train-rmse:0.274119+0.011416 test-rmse:1.178969+0.292770
## [197] train-rmse:0.272065+0.011461 test-rmse:1.177881+0.291881
## [198] train-rmse:0.270220+0.011126 test-rmse:1.177824+0.292315
## [199] train-rmse:0.268635+0.011611 test-rmse:1.176354+0.291663
## [200] train-rmse:0.266775+0.012063 test-rmse:1.175584+0.292377
## [201] train-rmse:0.265054+0.012313 test-rmse:1.175094+0.292508
## [202] train-rmse:0.263597+0.012194 test-rmse:1.175039+0.293059
## [203] train-rmse:0.262331+0.012432 test-rmse:1.175184+0.293399
## [204] train-rmse:0.260365+0.012550 test-rmse:1.173973+0.293876
## [205] train-rmse:0.258649+0.012439 test-rmse:1.174087+0.293853
## [206] train-rmse:0.257079+0.012335 test-rmse:1.173437+0.294102
## [207] train-rmse:0.255413+0.011773 test-rmse:1.172888+0.293780
## [208] train-rmse:0.254480+0.011803 test-rmse:1.171957+0.292963
## [209] train-rmse:0.252927+0.012226 test-rmse:1.170533+0.292658
## [210] train-rmse:0.251625+0.012230 test-rmse:1.170158+0.292459
## [211] train-rmse:0.250275+0.011809 test-rmse:1.169854+0.292849
## [212] train-rmse:0.248739+0.011823 test-rmse:1.169000+0.293167
## [213] train-rmse:0.246990+0.011441 test-rmse:1.168378+0.293246
## [214] train-rmse:0.245150+0.011408 test-rmse:1.165977+0.293449
## [215] train-rmse:0.243841+0.011557 test-rmse:1.165629+0.294311
## [216] train-rmse:0.242459+0.011198 test-rmse:1.165989+0.294765
## [217] train-rmse:0.241124+0.011551 test-rmse:1.166001+0.294845
## [218] train-rmse:0.239637+0.011244 test-rmse:1.165738+0.294917
## [219] train-rmse:0.237850+0.011663 test-rmse:1.166044+0.294624
## [220] train-rmse:0.236789+0.011432 test-rmse:1.166926+0.294639
## [221] train-rmse:0.235243+0.011516 test-rmse:1.164856+0.294854
## [222] train-rmse:0.234072+0.011197 test-rmse:1.164221+0.295142
## [223] train-rmse:0.232926+0.010949 test-rmse:1.164106+0.295533
## [224] train-rmse:0.231718+0.010827 test-rmse:1.163730+0.296406
## [225] train-rmse:0.230410+0.010555 test-rmse:1.162586+0.296893
## [226] train-rmse:0.229078+0.010321 test-rmse:1.162956+0.297005
## [227] train-rmse:0.227248+0.010334 test-rmse:1.161268+0.294050
## [228] train-rmse:0.225844+0.010247 test-rmse:1.161248+0.293464
## [229] train-rmse:0.224560+0.010031 test-rmse:1.160963+0.293273
## [230] train-rmse:0.223446+0.010221 test-rmse:1.160664+0.294075
## [231] train-rmse:0.222222+0.009879 test-rmse:1.159887+0.294160
## [232] train-rmse:0.221202+0.010179 test-rmse:1.159374+0.293522
## [233] train-rmse:0.219942+0.009903 test-rmse:1.159131+0.293720
## [234] train-rmse:0.218882+0.009964 test-rmse:1.158966+0.294155
## [235] train-rmse:0.217747+0.009906 test-rmse:1.158418+0.294508
## [236] train-rmse:0.217006+0.009945 test-rmse:1.158256+0.294959
## [237] train-rmse:0.215809+0.010002 test-rmse:1.158034+0.295094
## [238] train-rmse:0.213902+0.009565 test-rmse:1.158988+0.295119
## [239] train-rmse:0.212723+0.009107 test-rmse:1.158442+0.295517
## [240] train-rmse:0.211557+0.009000 test-rmse:1.158494+0.295264
## [241] train-rmse:0.210494+0.009207 test-rmse:1.158569+0.295954
## [242] train-rmse:0.209654+0.009155 test-rmse:1.158081+0.296495
## [243] train-rmse:0.208776+0.009153 test-rmse:1.158314+0.296465
## Stopping. Best iteration:
## [237] train-rmse:0.215809+0.010002 test-rmse:1.158034+0.295094
##
## 31
## [1] train-rmse:88.354776+0.099451 test-rmse:88.354991+0.492721
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.580200+0.086121 test-rmse:72.580084+0.517683
## [3] train-rmse:59.651027+0.076207 test-rmse:59.650480+0.541664
## [4] train-rmse:49.060283+0.069346 test-rmse:49.059156+0.565463
## [5] train-rmse:40.392603+0.065298 test-rmse:40.390688+0.589811
## [6] train-rmse:33.305450+0.062294 test-rmse:33.325024+0.602958
## [7] train-rmse:27.499631+0.054718 test-rmse:27.567201+0.606506
## [8] train-rmse:22.742521+0.042423 test-rmse:22.827392+0.586102
## [9] train-rmse:18.842251+0.035443 test-rmse:18.939813+0.563510
## [10] train-rmse:15.644787+0.031946 test-rmse:15.741622+0.546056
## [11] train-rmse:13.025645+0.027952 test-rmse:13.147386+0.573694
## [12] train-rmse:10.877414+0.025790 test-rmse:10.979798+0.557818
## [13] train-rmse:9.121586+0.025211 test-rmse:9.232568+0.543558
## [14] train-rmse:7.689630+0.023213 test-rmse:7.793666+0.537134
## [15] train-rmse:6.522987+0.020950 test-rmse:6.669151+0.540671
## [16] train-rmse:5.579319+0.021161 test-rmse:5.715168+0.522890
## [17] train-rmse:4.814496+0.018837 test-rmse:4.995788+0.506893
## [18] train-rmse:4.196805+0.017980 test-rmse:4.398920+0.481394
## [19] train-rmse:3.705448+0.018163 test-rmse:3.940366+0.451674
## [20] train-rmse:3.311872+0.016557 test-rmse:3.556118+0.414984
## [21] train-rmse:2.995890+0.018572 test-rmse:3.285905+0.408715
## [22] train-rmse:2.734809+0.023812 test-rmse:3.041479+0.368574
## [23] train-rmse:2.526822+0.025525 test-rmse:2.865014+0.352558
## [24] train-rmse:2.359924+0.024734 test-rmse:2.710076+0.329976
## [25] train-rmse:2.216305+0.029866 test-rmse:2.586653+0.289685
## [26] train-rmse:2.099520+0.032084 test-rmse:2.495176+0.272530
## [27] train-rmse:1.999319+0.029836 test-rmse:2.410047+0.283143
## [28] train-rmse:1.901764+0.032505 test-rmse:2.334843+0.268638
## [29] train-rmse:1.825347+0.030712 test-rmse:2.271738+0.252600
## [30] train-rmse:1.756897+0.026589 test-rmse:2.213991+0.241290
## [31] train-rmse:1.695756+0.026399 test-rmse:2.163303+0.240296
## [32] train-rmse:1.634951+0.023416 test-rmse:2.127759+0.242823
## [33] train-rmse:1.582133+0.023692 test-rmse:2.088775+0.236255
## [34] train-rmse:1.528979+0.017639 test-rmse:2.045184+0.232333
## [35] train-rmse:1.476290+0.021901 test-rmse:2.007545+0.229958
## [36] train-rmse:1.432885+0.021388 test-rmse:1.964505+0.228836
## [37] train-rmse:1.391731+0.021318 test-rmse:1.932514+0.221394
## [38] train-rmse:1.350602+0.017354 test-rmse:1.898130+0.221036
## [39] train-rmse:1.312542+0.016719 test-rmse:1.873587+0.213894
## [40] train-rmse:1.277370+0.016142 test-rmse:1.849197+0.221300
## [41] train-rmse:1.240726+0.017654 test-rmse:1.825618+0.225314
## [42] train-rmse:1.206309+0.016815 test-rmse:1.795923+0.225215
## [43] train-rmse:1.172337+0.020781 test-rmse:1.765740+0.225197
## [44] train-rmse:1.141344+0.019409 test-rmse:1.744854+0.217953
## [45] train-rmse:1.111007+0.020095 test-rmse:1.723179+0.219984
## [46] train-rmse:1.082077+0.020163 test-rmse:1.700156+0.220366
## [47] train-rmse:1.050906+0.019252 test-rmse:1.677650+0.225569
## [48] train-rmse:1.023700+0.017468 test-rmse:1.661920+0.234489
## [49] train-rmse:0.996142+0.019023 test-rmse:1.635615+0.239715
## [50] train-rmse:0.971186+0.019504 test-rmse:1.616564+0.247338
## [51] train-rmse:0.947003+0.019488 test-rmse:1.598650+0.243939
## [52] train-rmse:0.924058+0.019336 test-rmse:1.575426+0.252399
## [53] train-rmse:0.901789+0.017873 test-rmse:1.561300+0.249246
## [54] train-rmse:0.880235+0.016904 test-rmse:1.548622+0.247570
## [55] train-rmse:0.859662+0.016895 test-rmse:1.530511+0.253372
## [56] train-rmse:0.840226+0.017138 test-rmse:1.511096+0.240105
## [57] train-rmse:0.817057+0.014789 test-rmse:1.495461+0.247076
## [58] train-rmse:0.799175+0.014676 test-rmse:1.477824+0.250383
## [59] train-rmse:0.777856+0.014161 test-rmse:1.462465+0.241864
## [60] train-rmse:0.760344+0.013790 test-rmse:1.450669+0.245030
## [61] train-rmse:0.744568+0.013203 test-rmse:1.440056+0.244100
## [62] train-rmse:0.726642+0.013323 test-rmse:1.426069+0.245437
## [63] train-rmse:0.711530+0.013913 test-rmse:1.416891+0.248437
## [64] train-rmse:0.696350+0.015453 test-rmse:1.405077+0.249382
## [65] train-rmse:0.682828+0.016008 test-rmse:1.396673+0.250696
## [66] train-rmse:0.663449+0.018143 test-rmse:1.386196+0.253823
## [67] train-rmse:0.649272+0.015497 test-rmse:1.380975+0.255349
## [68] train-rmse:0.636266+0.015474 test-rmse:1.361752+0.254025
## [69] train-rmse:0.623288+0.015453 test-rmse:1.355886+0.259809
## [70] train-rmse:0.611623+0.015228 test-rmse:1.351917+0.260564
## [71] train-rmse:0.599669+0.015189 test-rmse:1.340426+0.261134
## [72] train-rmse:0.588254+0.015090 test-rmse:1.333211+0.263228
## [73] train-rmse:0.576944+0.015512 test-rmse:1.327637+0.265830
## [74] train-rmse:0.566022+0.015784 test-rmse:1.321149+0.266233
## [75] train-rmse:0.553932+0.013670 test-rmse:1.308165+0.254905
## [76] train-rmse:0.544243+0.013686 test-rmse:1.306771+0.256582
## [77] train-rmse:0.535219+0.013649 test-rmse:1.297479+0.256987
## [78] train-rmse:0.524469+0.015040 test-rmse:1.295315+0.255674
## [79] train-rmse:0.514942+0.014988 test-rmse:1.287033+0.260533
## [80] train-rmse:0.505852+0.015389 test-rmse:1.279788+0.262778
## [81] train-rmse:0.496923+0.013993 test-rmse:1.269985+0.254359
## [82] train-rmse:0.488033+0.014633 test-rmse:1.265024+0.257074
## [83] train-rmse:0.480552+0.014306 test-rmse:1.263319+0.258981
## [84] train-rmse:0.472838+0.014527 test-rmse:1.260026+0.260429
## [85] train-rmse:0.464871+0.014833 test-rmse:1.253851+0.264198
## [86] train-rmse:0.457739+0.015510 test-rmse:1.251205+0.264264
## [87] train-rmse:0.449572+0.014401 test-rmse:1.242432+0.256584
## [88] train-rmse:0.442268+0.014442 test-rmse:1.239428+0.258407
## [89] train-rmse:0.436143+0.014427 test-rmse:1.236415+0.257801
## [90] train-rmse:0.429397+0.014732 test-rmse:1.234471+0.260334
## [91] train-rmse:0.423284+0.014006 test-rmse:1.231974+0.260766
## [92] train-rmse:0.415980+0.014321 test-rmse:1.226005+0.253181
## [93] train-rmse:0.410263+0.014344 test-rmse:1.222088+0.256463
## [94] train-rmse:0.403576+0.014349 test-rmse:1.219103+0.258129
## [95] train-rmse:0.397656+0.013624 test-rmse:1.218108+0.261523
## [96] train-rmse:0.391106+0.014868 test-rmse:1.216217+0.265505
## [97] train-rmse:0.386475+0.014748 test-rmse:1.214375+0.266537
## [98] train-rmse:0.380678+0.013756 test-rmse:1.210361+0.268048
## [99] train-rmse:0.375366+0.013823 test-rmse:1.209768+0.270873
## [100] train-rmse:0.369662+0.013185 test-rmse:1.208577+0.269609
## [101] train-rmse:0.365625+0.013072 test-rmse:1.206750+0.270016
## [102] train-rmse:0.360802+0.013496 test-rmse:1.203258+0.270715
## [103] train-rmse:0.355371+0.014466 test-rmse:1.201377+0.272261
## [104] train-rmse:0.350998+0.014063 test-rmse:1.197462+0.272956
## [105] train-rmse:0.346438+0.013496 test-rmse:1.193731+0.266807
## [106] train-rmse:0.342802+0.013515 test-rmse:1.192410+0.266871
## [107] train-rmse:0.338682+0.013683 test-rmse:1.190285+0.267708
## [108] train-rmse:0.332446+0.011633 test-rmse:1.190103+0.267238
## [109] train-rmse:0.328594+0.011662 test-rmse:1.189747+0.266706
## [110] train-rmse:0.324571+0.012115 test-rmse:1.186288+0.269285
## [111] train-rmse:0.320013+0.012271 test-rmse:1.186332+0.270314
## [112] train-rmse:0.316686+0.012298 test-rmse:1.185233+0.270867
## [113] train-rmse:0.312920+0.011378 test-rmse:1.184720+0.272376
## [114] train-rmse:0.308936+0.011263 test-rmse:1.183725+0.273697
## [115] train-rmse:0.304778+0.011285 test-rmse:1.181365+0.276769
## [116] train-rmse:0.300864+0.010487 test-rmse:1.180654+0.277113
## [117] train-rmse:0.296918+0.010393 test-rmse:1.179876+0.276909
## [118] train-rmse:0.292865+0.010841 test-rmse:1.177720+0.277746
## [119] train-rmse:0.289296+0.010944 test-rmse:1.176903+0.278618
## [120] train-rmse:0.286824+0.010401 test-rmse:1.176355+0.279516
## [121] train-rmse:0.282675+0.010619 test-rmse:1.174522+0.280279
## [122] train-rmse:0.279217+0.010393 test-rmse:1.174185+0.280670
## [123] train-rmse:0.276949+0.009923 test-rmse:1.171942+0.282314
## [124] train-rmse:0.272916+0.010144 test-rmse:1.171349+0.282324
## [125] train-rmse:0.269582+0.009837 test-rmse:1.169030+0.279041
## [126] train-rmse:0.266229+0.010295 test-rmse:1.168387+0.279403
## [127] train-rmse:0.263302+0.010999 test-rmse:1.166629+0.280897
## [128] train-rmse:0.259065+0.009530 test-rmse:1.166173+0.281674
## [129] train-rmse:0.256622+0.009760 test-rmse:1.165921+0.281411
## [130] train-rmse:0.253131+0.009945 test-rmse:1.163916+0.282844
## [131] train-rmse:0.250829+0.009730 test-rmse:1.163189+0.283671
## [132] train-rmse:0.248300+0.009645 test-rmse:1.162494+0.283454
## [133] train-rmse:0.245718+0.010297 test-rmse:1.161883+0.283342
## [134] train-rmse:0.242731+0.010533 test-rmse:1.161133+0.283452
## [135] train-rmse:0.240691+0.010427 test-rmse:1.160310+0.283095
## [136] train-rmse:0.238502+0.010284 test-rmse:1.160226+0.285029
## [137] train-rmse:0.236705+0.010555 test-rmse:1.158592+0.285320
## [138] train-rmse:0.234432+0.010179 test-rmse:1.158022+0.285446
## [139] train-rmse:0.231279+0.009332 test-rmse:1.156610+0.285420
## [140] train-rmse:0.228117+0.009265 test-rmse:1.155977+0.285177
## [141] train-rmse:0.226171+0.009171 test-rmse:1.155599+0.285515
## [142] train-rmse:0.223937+0.009829 test-rmse:1.155973+0.286009
## [143] train-rmse:0.221521+0.010634 test-rmse:1.155460+0.286956
## [144] train-rmse:0.219512+0.010295 test-rmse:1.154634+0.287186
## [145] train-rmse:0.216743+0.011016 test-rmse:1.153563+0.287909
## [146] train-rmse:0.214714+0.010869 test-rmse:1.153444+0.287966
## [147] train-rmse:0.212763+0.011015 test-rmse:1.153649+0.287784
## [148] train-rmse:0.210392+0.011013 test-rmse:1.153371+0.286535
## [149] train-rmse:0.208306+0.011295 test-rmse:1.152964+0.288628
## [150] train-rmse:0.205906+0.011288 test-rmse:1.150720+0.288392
## [151] train-rmse:0.203247+0.012037 test-rmse:1.149840+0.289125
## [152] train-rmse:0.201334+0.012360 test-rmse:1.149351+0.289354
## [153] train-rmse:0.199248+0.011713 test-rmse:1.149116+0.289368
## [154] train-rmse:0.197493+0.011823 test-rmse:1.148496+0.289393
## [155] train-rmse:0.195007+0.011196 test-rmse:1.147983+0.289457
## [156] train-rmse:0.193110+0.011089 test-rmse:1.147946+0.289805
## [157] train-rmse:0.191029+0.010987 test-rmse:1.147649+0.290077
## [158] train-rmse:0.189661+0.010615 test-rmse:1.146994+0.290102
## [159] train-rmse:0.188293+0.010706 test-rmse:1.146730+0.289991
## [160] train-rmse:0.186378+0.010996 test-rmse:1.146508+0.289825
## [161] train-rmse:0.184624+0.011437 test-rmse:1.145734+0.289148
## [162] train-rmse:0.182646+0.011760 test-rmse:1.145405+0.289393
## [163] train-rmse:0.180310+0.012462 test-rmse:1.145762+0.289713
## [164] train-rmse:0.178574+0.012027 test-rmse:1.145473+0.289727
## [165] train-rmse:0.176586+0.011738 test-rmse:1.146228+0.289175
## [166] train-rmse:0.174632+0.011858 test-rmse:1.145584+0.289687
## [167] train-rmse:0.173165+0.012167 test-rmse:1.145329+0.289821
## [168] train-rmse:0.171461+0.011863 test-rmse:1.145305+0.290070
## [169] train-rmse:0.169997+0.011833 test-rmse:1.144729+0.290329
## [170] train-rmse:0.168925+0.011950 test-rmse:1.144178+0.290282
## [171] train-rmse:0.166930+0.011661 test-rmse:1.143543+0.290146
## [172] train-rmse:0.165636+0.011554 test-rmse:1.143270+0.289979
## [173] train-rmse:0.164220+0.011263 test-rmse:1.143251+0.290706
## [174] train-rmse:0.162771+0.011012 test-rmse:1.143155+0.290959
## [175] train-rmse:0.161095+0.011455 test-rmse:1.143114+0.290917
## [176] train-rmse:0.159613+0.011414 test-rmse:1.143272+0.291470
## [177] train-rmse:0.157178+0.011029 test-rmse:1.142689+0.291480
## [178] train-rmse:0.155540+0.011442 test-rmse:1.142132+0.292647
## [179] train-rmse:0.154319+0.011263 test-rmse:1.141635+0.293122
## [180] train-rmse:0.152549+0.010725 test-rmse:1.141723+0.293121
## [181] train-rmse:0.151164+0.010500 test-rmse:1.141392+0.293189
## [182] train-rmse:0.149905+0.010162 test-rmse:1.141231+0.293422
## [183] train-rmse:0.148770+0.010185 test-rmse:1.141577+0.294054
## [184] train-rmse:0.147391+0.010167 test-rmse:1.141619+0.294312
## [185] train-rmse:0.145756+0.009691 test-rmse:1.141899+0.294104
## [186] train-rmse:0.144747+0.009596 test-rmse:1.140960+0.294183
## [187] train-rmse:0.143537+0.009577 test-rmse:1.141109+0.294542
## [188] train-rmse:0.142371+0.009843 test-rmse:1.140891+0.294517
## [189] train-rmse:0.141037+0.009909 test-rmse:1.141073+0.294741
## [190] train-rmse:0.139751+0.009742 test-rmse:1.140394+0.294083
## [191] train-rmse:0.138562+0.009777 test-rmse:1.139659+0.294585
## [192] train-rmse:0.137348+0.009398 test-rmse:1.139519+0.294660
## [193] train-rmse:0.136268+0.009390 test-rmse:1.139192+0.294720
## [194] train-rmse:0.134602+0.009420 test-rmse:1.139360+0.294777
## [195] train-rmse:0.133358+0.009629 test-rmse:1.139226+0.294994
## [196] train-rmse:0.132335+0.009947 test-rmse:1.139198+0.295314
## [197] train-rmse:0.130911+0.009696 test-rmse:1.138824+0.294796
## [198] train-rmse:0.129380+0.009645 test-rmse:1.137605+0.292792
## [199] train-rmse:0.128611+0.009700 test-rmse:1.136334+0.291085
## [200] train-rmse:0.127392+0.009419 test-rmse:1.136574+0.290924
## [201] train-rmse:0.126065+0.009089 test-rmse:1.136543+0.290758
## [202] train-rmse:0.125216+0.009471 test-rmse:1.136507+0.290625
## [203] train-rmse:0.123913+0.009109 test-rmse:1.135375+0.289322
## [204] train-rmse:0.123020+0.009255 test-rmse:1.135531+0.289267
## [205] train-rmse:0.121885+0.009405 test-rmse:1.135339+0.289288
## [206] train-rmse:0.120725+0.008928 test-rmse:1.135382+0.289751
## [207] train-rmse:0.119442+0.009571 test-rmse:1.134581+0.288256
## [208] train-rmse:0.118126+0.009374 test-rmse:1.134616+0.288110
## [209] train-rmse:0.117482+0.009370 test-rmse:1.134585+0.288102
## [210] train-rmse:0.116668+0.009601 test-rmse:1.134567+0.288330
## [211] train-rmse:0.115544+0.009638 test-rmse:1.134612+0.288354
## [212] train-rmse:0.114125+0.009423 test-rmse:1.134615+0.288313
## [213] train-rmse:0.112890+0.009678 test-rmse:1.134553+0.288428
## [214] train-rmse:0.111874+0.009796 test-rmse:1.134419+0.287913
## [215] train-rmse:0.110852+0.009826 test-rmse:1.134216+0.288093
## [216] train-rmse:0.110084+0.009728 test-rmse:1.132937+0.287209
## [217] train-rmse:0.109144+0.009594 test-rmse:1.133174+0.287669
## [218] train-rmse:0.107982+0.009181 test-rmse:1.133246+0.288181
## [219] train-rmse:0.107390+0.009120 test-rmse:1.133130+0.287929
## [220] train-rmse:0.106518+0.009177 test-rmse:1.133096+0.287793
## [221] train-rmse:0.105745+0.009061 test-rmse:1.132691+0.287979
## [222] train-rmse:0.104753+0.008918 test-rmse:1.132669+0.288245
## [223] train-rmse:0.103355+0.008631 test-rmse:1.132267+0.288020
## [224] train-rmse:0.102491+0.008822 test-rmse:1.132371+0.288511
## [225] train-rmse:0.101580+0.008676 test-rmse:1.132306+0.288368
## [226] train-rmse:0.100618+0.008790 test-rmse:1.132489+0.288055
## [227] train-rmse:0.099800+0.008727 test-rmse:1.132237+0.288293
## [228] train-rmse:0.098800+0.008833 test-rmse:1.132240+0.288433
## [229] train-rmse:0.097681+0.008739 test-rmse:1.132114+0.288459
## [230] train-rmse:0.096937+0.008667 test-rmse:1.132004+0.288442
## [231] train-rmse:0.096056+0.008852 test-rmse:1.132057+0.288725
## [232] train-rmse:0.095157+0.008695 test-rmse:1.131501+0.288486
## [233] train-rmse:0.094565+0.008628 test-rmse:1.131541+0.288603
## [234] train-rmse:0.093861+0.008540 test-rmse:1.131174+0.288598
## [235] train-rmse:0.093014+0.008652 test-rmse:1.131678+0.288221
## [236] train-rmse:0.092373+0.008368 test-rmse:1.131522+0.288061
## [237] train-rmse:0.091590+0.008351 test-rmse:1.131596+0.287991
## [238] train-rmse:0.090284+0.008034 test-rmse:1.131393+0.288238
## [239] train-rmse:0.089073+0.007978 test-rmse:1.131326+0.288450
## [240] train-rmse:0.088316+0.007978 test-rmse:1.131250+0.288211
## Stopping. Best iteration:
## [234] train-rmse:0.093861+0.008540 test-rmse:1.131174+0.288598
##
## 32
## [1] train-rmse:88.354776+0.099451 test-rmse:88.354991+0.492721
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.580200+0.086121 test-rmse:72.580084+0.517683
## [3] train-rmse:59.651027+0.076207 test-rmse:59.650480+0.541664
## [4] train-rmse:49.060283+0.069346 test-rmse:49.059156+0.565463
## [5] train-rmse:40.392603+0.065298 test-rmse:40.390688+0.589811
## [6] train-rmse:33.305450+0.062294 test-rmse:33.325024+0.602958
## [7] train-rmse:27.499631+0.054718 test-rmse:27.567201+0.606506
## [8] train-rmse:22.742521+0.042423 test-rmse:22.827392+0.586102
## [9] train-rmse:18.841019+0.034764 test-rmse:18.946899+0.564596
## [10] train-rmse:15.638930+0.030528 test-rmse:15.729740+0.548585
## [11] train-rmse:13.012615+0.025019 test-rmse:13.141128+0.575844
## [12] train-rmse:10.852866+0.021930 test-rmse:10.960652+0.588384
## [13] train-rmse:9.079022+0.021885 test-rmse:9.178371+0.562475
## [14] train-rmse:7.630168+0.022282 test-rmse:7.724710+0.549960
## [15] train-rmse:6.441231+0.017252 test-rmse:6.582625+0.572170
## [16] train-rmse:5.474906+0.018851 test-rmse:5.608682+0.531231
## [17] train-rmse:4.684672+0.023730 test-rmse:4.858099+0.526226
## [18] train-rmse:4.036770+0.020664 test-rmse:4.242577+0.479451
## [19] train-rmse:3.510921+0.024447 test-rmse:3.746395+0.457844
## [20] train-rmse:3.083893+0.026959 test-rmse:3.388646+0.446422
## [21] train-rmse:2.745172+0.030389 test-rmse:3.058159+0.402310
## [22] train-rmse:2.466371+0.029226 test-rmse:2.812963+0.387304
## [23] train-rmse:2.243746+0.027398 test-rmse:2.649514+0.361389
## [24] train-rmse:2.061665+0.027664 test-rmse:2.495970+0.336585
## [25] train-rmse:1.907814+0.027212 test-rmse:2.360075+0.309364
## [26] train-rmse:1.779583+0.031090 test-rmse:2.246652+0.272894
## [27] train-rmse:1.671760+0.030203 test-rmse:2.168855+0.263900
## [28] train-rmse:1.576253+0.039794 test-rmse:2.112822+0.272138
## [29] train-rmse:1.496887+0.040200 test-rmse:2.042314+0.266297
## [30] train-rmse:1.425729+0.037673 test-rmse:1.992286+0.260996
## [31] train-rmse:1.362020+0.036215 test-rmse:1.947470+0.245883
## [32] train-rmse:1.298543+0.035860 test-rmse:1.891730+0.229172
## [33] train-rmse:1.244693+0.033178 test-rmse:1.850008+0.219147
## [34] train-rmse:1.197855+0.030814 test-rmse:1.813071+0.212447
## [35] train-rmse:1.150661+0.031944 test-rmse:1.772647+0.218479
## [36] train-rmse:1.101674+0.032988 test-rmse:1.752219+0.216403
## [37] train-rmse:1.063969+0.033049 test-rmse:1.725976+0.210195
## [38] train-rmse:1.021807+0.039212 test-rmse:1.695766+0.206251
## [39] train-rmse:0.987947+0.038270 test-rmse:1.666291+0.205713
## [40] train-rmse:0.949259+0.036425 test-rmse:1.645575+0.209435
## [41] train-rmse:0.916638+0.032300 test-rmse:1.623121+0.211000
## [42] train-rmse:0.886934+0.031926 test-rmse:1.599842+0.209175
## [43] train-rmse:0.859188+0.033037 test-rmse:1.586169+0.214335
## [44] train-rmse:0.832262+0.032836 test-rmse:1.564403+0.207289
## [45] train-rmse:0.806033+0.030063 test-rmse:1.546545+0.207792
## [46] train-rmse:0.783420+0.028893 test-rmse:1.528278+0.211007
## [47] train-rmse:0.753396+0.028316 test-rmse:1.513314+0.208594
## [48] train-rmse:0.730143+0.028503 test-rmse:1.502009+0.208116
## [49] train-rmse:0.709867+0.028441 test-rmse:1.486269+0.209528
## [50] train-rmse:0.691309+0.027380 test-rmse:1.475012+0.214861
## [51] train-rmse:0.667996+0.025217 test-rmse:1.458941+0.222982
## [52] train-rmse:0.649558+0.022620 test-rmse:1.450325+0.224324
## [53] train-rmse:0.629945+0.023585 test-rmse:1.438120+0.228026
## [54] train-rmse:0.613896+0.024005 test-rmse:1.424717+0.231980
## [55] train-rmse:0.594702+0.025410 test-rmse:1.419247+0.234251
## [56] train-rmse:0.580258+0.024568 test-rmse:1.412132+0.237100
## [57] train-rmse:0.565796+0.024688 test-rmse:1.397249+0.238616
## [58] train-rmse:0.551883+0.024650 test-rmse:1.387038+0.240388
## [59] train-rmse:0.536582+0.024746 test-rmse:1.380169+0.243639
## [60] train-rmse:0.522926+0.023255 test-rmse:1.375988+0.247340
## [61] train-rmse:0.509017+0.022657 test-rmse:1.366194+0.251826
## [62] train-rmse:0.494679+0.021306 test-rmse:1.356826+0.244447
## [63] train-rmse:0.482950+0.022328 test-rmse:1.350784+0.247212
## [64] train-rmse:0.472022+0.022383 test-rmse:1.342841+0.248934
## [65] train-rmse:0.461710+0.021551 test-rmse:1.336596+0.242953
## [66] train-rmse:0.451600+0.021680 test-rmse:1.330695+0.246441
## [67] train-rmse:0.441012+0.021882 test-rmse:1.321864+0.250437
## [68] train-rmse:0.431238+0.022035 test-rmse:1.318591+0.252670
## [69] train-rmse:0.422093+0.021737 test-rmse:1.312294+0.256620
## [70] train-rmse:0.413970+0.021163 test-rmse:1.307116+0.255866
## [71] train-rmse:0.404616+0.021394 test-rmse:1.302108+0.254496
## [72] train-rmse:0.395218+0.021711 test-rmse:1.299678+0.254972
## [73] train-rmse:0.386001+0.019862 test-rmse:1.295836+0.256042
## [74] train-rmse:0.378154+0.019525 test-rmse:1.290624+0.258325
## [75] train-rmse:0.370886+0.018828 test-rmse:1.286921+0.262180
## [76] train-rmse:0.364539+0.018567 test-rmse:1.282244+0.264047
## [77] train-rmse:0.357284+0.018978 test-rmse:1.280699+0.266246
## [78] train-rmse:0.349922+0.018129 test-rmse:1.275413+0.261875
## [79] train-rmse:0.344041+0.018014 test-rmse:1.272393+0.262333
## [80] train-rmse:0.338987+0.017495 test-rmse:1.269157+0.261540
## [81] train-rmse:0.331951+0.017892 test-rmse:1.264945+0.265639
## [82] train-rmse:0.325435+0.018476 test-rmse:1.262601+0.266871
## [83] train-rmse:0.319085+0.017625 test-rmse:1.263347+0.264718
## [84] train-rmse:0.313907+0.017885 test-rmse:1.262176+0.266223
## [85] train-rmse:0.308267+0.015864 test-rmse:1.260791+0.266599
## [86] train-rmse:0.301611+0.013774 test-rmse:1.258242+0.266855
## [87] train-rmse:0.295081+0.014409 test-rmse:1.256391+0.268018
## [88] train-rmse:0.289365+0.015542 test-rmse:1.255483+0.268446
## [89] train-rmse:0.284508+0.016003 test-rmse:1.251604+0.271865
## [90] train-rmse:0.280110+0.015000 test-rmse:1.250520+0.271804
## [91] train-rmse:0.274834+0.013902 test-rmse:1.249079+0.272408
## [92] train-rmse:0.270080+0.013483 test-rmse:1.247491+0.271404
## [93] train-rmse:0.264972+0.014840 test-rmse:1.246436+0.271132
## [94] train-rmse:0.259306+0.014529 test-rmse:1.245717+0.269051
## [95] train-rmse:0.255148+0.014887 test-rmse:1.242303+0.271209
## [96] train-rmse:0.250478+0.014945 test-rmse:1.241474+0.271896
## [97] train-rmse:0.246975+0.015214 test-rmse:1.240551+0.272947
## [98] train-rmse:0.243093+0.014996 test-rmse:1.239261+0.273035
## [99] train-rmse:0.237566+0.012525 test-rmse:1.237369+0.270928
## [100] train-rmse:0.234153+0.012599 test-rmse:1.236055+0.269807
## [101] train-rmse:0.230806+0.012984 test-rmse:1.232421+0.271582
## [102] train-rmse:0.226685+0.013056 test-rmse:1.231096+0.271312
## [103] train-rmse:0.223556+0.012826 test-rmse:1.230723+0.271004
## [104] train-rmse:0.218475+0.010912 test-rmse:1.229625+0.271157
## [105] train-rmse:0.214483+0.010732 test-rmse:1.229272+0.273164
## [106] train-rmse:0.211759+0.010652 test-rmse:1.229364+0.274999
## [107] train-rmse:0.208439+0.010638 test-rmse:1.228221+0.275459
## [108] train-rmse:0.204906+0.010333 test-rmse:1.227490+0.275640
## [109] train-rmse:0.201305+0.010152 test-rmse:1.226264+0.274360
## [110] train-rmse:0.198902+0.010354 test-rmse:1.226905+0.275279
## [111] train-rmse:0.196207+0.010225 test-rmse:1.226656+0.275808
## [112] train-rmse:0.193225+0.010164 test-rmse:1.225824+0.275427
## [113] train-rmse:0.190717+0.010416 test-rmse:1.224557+0.275605
## [114] train-rmse:0.187364+0.009134 test-rmse:1.224796+0.275102
## [115] train-rmse:0.184771+0.009571 test-rmse:1.223228+0.276174
## [116] train-rmse:0.180938+0.008523 test-rmse:1.222564+0.276437
## [117] train-rmse:0.178152+0.008113 test-rmse:1.221348+0.277280
## [118] train-rmse:0.175679+0.008233 test-rmse:1.221882+0.277339
## [119] train-rmse:0.173536+0.008521 test-rmse:1.221123+0.277805
## [120] train-rmse:0.170919+0.009063 test-rmse:1.220533+0.278792
## [121] train-rmse:0.168300+0.008727 test-rmse:1.219898+0.277696
## [122] train-rmse:0.165151+0.007442 test-rmse:1.219282+0.277156
## [123] train-rmse:0.163064+0.006959 test-rmse:1.218592+0.276839
## [124] train-rmse:0.161078+0.006611 test-rmse:1.217714+0.277049
## [125] train-rmse:0.159045+0.006967 test-rmse:1.218129+0.276251
## [126] train-rmse:0.155786+0.006475 test-rmse:1.218743+0.276016
## [127] train-rmse:0.153755+0.006803 test-rmse:1.217913+0.276643
## [128] train-rmse:0.151366+0.006156 test-rmse:1.217786+0.276582
## [129] train-rmse:0.149693+0.006335 test-rmse:1.217605+0.276725
## [130] train-rmse:0.147549+0.006367 test-rmse:1.217559+0.276404
## [131] train-rmse:0.145908+0.006626 test-rmse:1.216739+0.276455
## [132] train-rmse:0.143638+0.006579 test-rmse:1.216223+0.276715
## [133] train-rmse:0.141805+0.006425 test-rmse:1.215534+0.276922
## [134] train-rmse:0.139088+0.005784 test-rmse:1.214485+0.276992
## [135] train-rmse:0.136490+0.005376 test-rmse:1.214844+0.276824
## [136] train-rmse:0.135115+0.005136 test-rmse:1.214697+0.277194
## [137] train-rmse:0.132961+0.005299 test-rmse:1.214269+0.277349
## [138] train-rmse:0.130678+0.005352 test-rmse:1.213410+0.275376
## [139] train-rmse:0.129083+0.005364 test-rmse:1.212912+0.275638
## [140] train-rmse:0.126512+0.005423 test-rmse:1.212322+0.275666
## [141] train-rmse:0.125121+0.005650 test-rmse:1.212487+0.275788
## [142] train-rmse:0.122740+0.005944 test-rmse:1.211812+0.274428
## [143] train-rmse:0.120691+0.005848 test-rmse:1.211555+0.274780
## [144] train-rmse:0.119469+0.005642 test-rmse:1.211630+0.274592
## [145] train-rmse:0.117053+0.004679 test-rmse:1.211314+0.274935
## [146] train-rmse:0.115533+0.004460 test-rmse:1.210882+0.274879
## [147] train-rmse:0.113428+0.004058 test-rmse:1.211046+0.274926
## [148] train-rmse:0.111473+0.003438 test-rmse:1.211484+0.275137
## [149] train-rmse:0.110405+0.003570 test-rmse:1.211458+0.275094
## [150] train-rmse:0.108788+0.003899 test-rmse:1.212572+0.274603
## [151] train-rmse:0.107706+0.003975 test-rmse:1.212621+0.274771
## [152] train-rmse:0.105694+0.004575 test-rmse:1.212454+0.274687
## Stopping. Best iteration:
## [146] train-rmse:0.115533+0.004460 test-rmse:1.210882+0.274879
##
## 33
## [1] train-rmse:88.354776+0.099451 test-rmse:88.354991+0.492721
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.580200+0.086121 test-rmse:72.580084+0.517683
## [3] train-rmse:59.651027+0.076207 test-rmse:59.650480+0.541664
## [4] train-rmse:49.060283+0.069346 test-rmse:49.059156+0.565463
## [5] train-rmse:40.392603+0.065298 test-rmse:40.390688+0.589811
## [6] train-rmse:33.305450+0.062294 test-rmse:33.325024+0.602958
## [7] train-rmse:27.499631+0.054718 test-rmse:27.567201+0.606506
## [8] train-rmse:22.742521+0.042423 test-rmse:22.827392+0.586102
## [9] train-rmse:18.840151+0.034039 test-rmse:18.938184+0.574569
## [10] train-rmse:15.636043+0.029316 test-rmse:15.726484+0.547297
## [11] train-rmse:13.004530+0.022807 test-rmse:13.141543+0.576410
## [12] train-rmse:10.842777+0.024247 test-rmse:10.973904+0.566458
## [13] train-rmse:9.058053+0.021846 test-rmse:9.186889+0.556585
## [14] train-rmse:7.596620+0.021553 test-rmse:7.710081+0.532686
## [15] train-rmse:6.394477+0.018866 test-rmse:6.532602+0.545129
## [16] train-rmse:5.409853+0.014536 test-rmse:5.573584+0.524558
## [17] train-rmse:4.601075+0.017307 test-rmse:4.774124+0.493996
## [18] train-rmse:3.937223+0.013127 test-rmse:4.131430+0.466455
## [19] train-rmse:3.399393+0.016135 test-rmse:3.627981+0.463389
## [20] train-rmse:2.960402+0.014656 test-rmse:3.215964+0.438620
## [21] train-rmse:2.593486+0.028489 test-rmse:2.891126+0.404564
## [22] train-rmse:2.296057+0.029463 test-rmse:2.630954+0.376057
## [23] train-rmse:2.047519+0.041259 test-rmse:2.426057+0.374204
## [24] train-rmse:1.846067+0.043900 test-rmse:2.260523+0.344273
## [25] train-rmse:1.687405+0.045395 test-rmse:2.133172+0.323670
## [26] train-rmse:1.540249+0.042737 test-rmse:2.041258+0.287226
## [27] train-rmse:1.424480+0.047196 test-rmse:1.963719+0.277695
## [28] train-rmse:1.328239+0.046543 test-rmse:1.889818+0.262515
## [29] train-rmse:1.243423+0.044734 test-rmse:1.826042+0.256813
## [30] train-rmse:1.162013+0.045013 test-rmse:1.770095+0.254409
## [31] train-rmse:1.096321+0.046087 test-rmse:1.726992+0.240772
## [32] train-rmse:1.040388+0.044388 test-rmse:1.685653+0.239514
## [33] train-rmse:0.987358+0.041876 test-rmse:1.655270+0.233092
## [34] train-rmse:0.939718+0.038650 test-rmse:1.618175+0.243229
## [35] train-rmse:0.897945+0.037084 test-rmse:1.592260+0.238500
## [36] train-rmse:0.856894+0.034976 test-rmse:1.558087+0.240069
## [37] train-rmse:0.822103+0.035353 test-rmse:1.533173+0.226849
## [38] train-rmse:0.786105+0.036563 test-rmse:1.511146+0.226977
## [39] train-rmse:0.756116+0.036283 test-rmse:1.497431+0.227292
## [40] train-rmse:0.726692+0.038407 test-rmse:1.477980+0.227944
## [41] train-rmse:0.697145+0.041446 test-rmse:1.467907+0.224800
## [42] train-rmse:0.671428+0.037844 test-rmse:1.448856+0.230476
## [43] train-rmse:0.647094+0.037824 test-rmse:1.428625+0.222422
## [44] train-rmse:0.620734+0.036212 test-rmse:1.416983+0.219869
## [45] train-rmse:0.599643+0.036112 test-rmse:1.402331+0.219442
## [46] train-rmse:0.577185+0.037529 test-rmse:1.395626+0.221574
## [47] train-rmse:0.558790+0.037622 test-rmse:1.381587+0.224954
## [48] train-rmse:0.540878+0.035949 test-rmse:1.369848+0.215790
## [49] train-rmse:0.521859+0.036567 test-rmse:1.363273+0.220141
## [50] train-rmse:0.503676+0.032140 test-rmse:1.353733+0.224452
## [51] train-rmse:0.488324+0.031144 test-rmse:1.346330+0.228582
## [52] train-rmse:0.472706+0.031537 test-rmse:1.340072+0.228984
## [53] train-rmse:0.459417+0.031475 test-rmse:1.334334+0.233489
## [54] train-rmse:0.445570+0.028712 test-rmse:1.324439+0.234745
## [55] train-rmse:0.432812+0.027730 test-rmse:1.319717+0.238435
## [56] train-rmse:0.420772+0.027544 test-rmse:1.317467+0.241124
## [57] train-rmse:0.408359+0.025563 test-rmse:1.308190+0.232864
## [58] train-rmse:0.397235+0.025536 test-rmse:1.300782+0.232976
## [59] train-rmse:0.387608+0.025389 test-rmse:1.299127+0.236286
## [60] train-rmse:0.376227+0.025341 test-rmse:1.287850+0.232804
## [61] train-rmse:0.366511+0.025290 test-rmse:1.283405+0.234487
## [62] train-rmse:0.358604+0.024942 test-rmse:1.280020+0.233722
## [63] train-rmse:0.349349+0.024465 test-rmse:1.275602+0.237729
## [64] train-rmse:0.340124+0.024242 test-rmse:1.273712+0.240961
## [65] train-rmse:0.328982+0.022881 test-rmse:1.274075+0.241645
## [66] train-rmse:0.320511+0.022719 test-rmse:1.270456+0.242319
## [67] train-rmse:0.313026+0.022615 test-rmse:1.269726+0.243468
## [68] train-rmse:0.304862+0.022472 test-rmse:1.267873+0.246296
## [69] train-rmse:0.297705+0.021002 test-rmse:1.266471+0.247182
## [70] train-rmse:0.289518+0.017944 test-rmse:1.264024+0.249351
## [71] train-rmse:0.283109+0.017858 test-rmse:1.261882+0.248496
## [72] train-rmse:0.277287+0.017950 test-rmse:1.259019+0.251795
## [73] train-rmse:0.270503+0.017167 test-rmse:1.256930+0.252010
## [74] train-rmse:0.265244+0.016428 test-rmse:1.256352+0.253621
## [75] train-rmse:0.258909+0.015595 test-rmse:1.255916+0.254116
## [76] train-rmse:0.253943+0.015839 test-rmse:1.254239+0.253846
## [77] train-rmse:0.248766+0.016352 test-rmse:1.253625+0.253611
## [78] train-rmse:0.243464+0.016171 test-rmse:1.253333+0.255399
## [79] train-rmse:0.238441+0.015406 test-rmse:1.251981+0.256698
## [80] train-rmse:0.233460+0.014817 test-rmse:1.251037+0.258416
## [81] train-rmse:0.228942+0.014574 test-rmse:1.249417+0.257968
## [82] train-rmse:0.222533+0.014742 test-rmse:1.247765+0.259183
## [83] train-rmse:0.216992+0.013585 test-rmse:1.246081+0.258489
## [84] train-rmse:0.211293+0.012383 test-rmse:1.246232+0.259027
## [85] train-rmse:0.205862+0.011760 test-rmse:1.246891+0.258930
## [86] train-rmse:0.202476+0.012071 test-rmse:1.244504+0.260557
## [87] train-rmse:0.198537+0.010994 test-rmse:1.244351+0.260890
## [88] train-rmse:0.194376+0.011233 test-rmse:1.243791+0.260918
## [89] train-rmse:0.190165+0.011194 test-rmse:1.242446+0.260739
## [90] train-rmse:0.186059+0.011100 test-rmse:1.242417+0.261594
## [91] train-rmse:0.182236+0.010050 test-rmse:1.241216+0.262442
## [92] train-rmse:0.177891+0.009511 test-rmse:1.240342+0.262994
## [93] train-rmse:0.173579+0.009850 test-rmse:1.240510+0.263915
## [94] train-rmse:0.170756+0.009393 test-rmse:1.239595+0.263743
## [95] train-rmse:0.167807+0.009491 test-rmse:1.238774+0.264231
## [96] train-rmse:0.164787+0.010019 test-rmse:1.237201+0.264828
## [97] train-rmse:0.161050+0.010326 test-rmse:1.236694+0.266282
## [98] train-rmse:0.158713+0.010145 test-rmse:1.236483+0.267005
## [99] train-rmse:0.156011+0.010181 test-rmse:1.235202+0.265118
## [100] train-rmse:0.152372+0.010079 test-rmse:1.233631+0.265542
## [101] train-rmse:0.149816+0.009603 test-rmse:1.231867+0.265904
## [102] train-rmse:0.146551+0.009858 test-rmse:1.231202+0.265884
## [103] train-rmse:0.143133+0.009084 test-rmse:1.231040+0.265853
## [104] train-rmse:0.140376+0.009140 test-rmse:1.229539+0.266225
## [105] train-rmse:0.137883+0.008923 test-rmse:1.228040+0.266366
## [106] train-rmse:0.135627+0.009018 test-rmse:1.227936+0.266137
## [107] train-rmse:0.132665+0.008553 test-rmse:1.227126+0.264905
## [108] train-rmse:0.129803+0.009305 test-rmse:1.227063+0.264742
## [109] train-rmse:0.127168+0.009557 test-rmse:1.226894+0.265050
## [110] train-rmse:0.124855+0.009868 test-rmse:1.227080+0.265172
## [111] train-rmse:0.122180+0.010229 test-rmse:1.226694+0.265045
## [112] train-rmse:0.119798+0.009757 test-rmse:1.226258+0.264815
## [113] train-rmse:0.117731+0.009865 test-rmse:1.226192+0.265446
## [114] train-rmse:0.115664+0.009409 test-rmse:1.226265+0.265730
## [115] train-rmse:0.112947+0.008459 test-rmse:1.225581+0.265380
## [116] train-rmse:0.111382+0.008248 test-rmse:1.224727+0.265100
## [117] train-rmse:0.109409+0.008036 test-rmse:1.224674+0.264764
## [118] train-rmse:0.108109+0.008163 test-rmse:1.224347+0.264988
## [119] train-rmse:0.105466+0.008412 test-rmse:1.224068+0.265106
## [120] train-rmse:0.103441+0.008837 test-rmse:1.223483+0.265089
## [121] train-rmse:0.101520+0.008983 test-rmse:1.223290+0.264950
## [122] train-rmse:0.099562+0.009321 test-rmse:1.223220+0.265047
## [123] train-rmse:0.097718+0.009098 test-rmse:1.223220+0.265066
## [124] train-rmse:0.095261+0.009064 test-rmse:1.222584+0.265328
## [125] train-rmse:0.093389+0.008939 test-rmse:1.222043+0.265584
## [126] train-rmse:0.091195+0.008042 test-rmse:1.222483+0.265758
## [127] train-rmse:0.089407+0.008150 test-rmse:1.222292+0.265710
## [128] train-rmse:0.088009+0.008119 test-rmse:1.221679+0.265507
## [129] train-rmse:0.086113+0.007489 test-rmse:1.221096+0.265419
## [130] train-rmse:0.084720+0.007535 test-rmse:1.221042+0.265308
## [131] train-rmse:0.083381+0.007666 test-rmse:1.221211+0.265118
## [132] train-rmse:0.081756+0.007786 test-rmse:1.221259+0.265223
## [133] train-rmse:0.080114+0.007382 test-rmse:1.221342+0.265155
## [134] train-rmse:0.078579+0.007169 test-rmse:1.220639+0.265310
## [135] train-rmse:0.076956+0.006739 test-rmse:1.220299+0.265324
## [136] train-rmse:0.075772+0.006653 test-rmse:1.220351+0.265863
## [137] train-rmse:0.074795+0.006771 test-rmse:1.220446+0.265781
## [138] train-rmse:0.073543+0.006732 test-rmse:1.220357+0.265885
## [139] train-rmse:0.072465+0.006603 test-rmse:1.219796+0.264875
## [140] train-rmse:0.071231+0.006440 test-rmse:1.219412+0.264868
## [141] train-rmse:0.069846+0.006151 test-rmse:1.219459+0.264468
## [142] train-rmse:0.068056+0.005752 test-rmse:1.219490+0.264558
## [143] train-rmse:0.067052+0.006054 test-rmse:1.219647+0.264578
## [144] train-rmse:0.065606+0.005932 test-rmse:1.219461+0.264308
## [145] train-rmse:0.064408+0.006077 test-rmse:1.219382+0.264141
## [146] train-rmse:0.063013+0.005919 test-rmse:1.219441+0.264392
## [147] train-rmse:0.061976+0.005810 test-rmse:1.219186+0.264418
## [148] train-rmse:0.061133+0.005756 test-rmse:1.219218+0.264297
## [149] train-rmse:0.059922+0.005360 test-rmse:1.219198+0.264526
## [150] train-rmse:0.058727+0.005725 test-rmse:1.218791+0.263568
## [151] train-rmse:0.057828+0.005655 test-rmse:1.218803+0.263746
## [152] train-rmse:0.056994+0.005492 test-rmse:1.218420+0.263705
## [153] train-rmse:0.055862+0.005856 test-rmse:1.218176+0.263975
## [154] train-rmse:0.055042+0.005416 test-rmse:1.218260+0.263740
## [155] train-rmse:0.053878+0.005147 test-rmse:1.217772+0.262936
## [156] train-rmse:0.052977+0.005128 test-rmse:1.217510+0.262911
## [157] train-rmse:0.051883+0.005249 test-rmse:1.217640+0.262788
## [158] train-rmse:0.051332+0.005356 test-rmse:1.217577+0.262791
## [159] train-rmse:0.050363+0.005452 test-rmse:1.217319+0.262641
## [160] train-rmse:0.049222+0.004961 test-rmse:1.217396+0.262687
## [161] train-rmse:0.048612+0.004728 test-rmse:1.217289+0.262734
## [162] train-rmse:0.047612+0.004590 test-rmse:1.217020+0.262131
## [163] train-rmse:0.046776+0.004490 test-rmse:1.217394+0.261977
## [164] train-rmse:0.045763+0.004520 test-rmse:1.217308+0.261962
## [165] train-rmse:0.044801+0.004526 test-rmse:1.217463+0.262113
## [166] train-rmse:0.044040+0.004523 test-rmse:1.217384+0.262047
## [167] train-rmse:0.043394+0.004349 test-rmse:1.217344+0.261988
## [168] train-rmse:0.042716+0.004107 test-rmse:1.217243+0.261988
## Stopping. Best iteration:
## [162] train-rmse:0.047612+0.004590 test-rmse:1.217020+0.262131
##
## 34
## [1] train-rmse:88.354776+0.099451 test-rmse:88.354991+0.492721
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:72.580200+0.086121 test-rmse:72.580084+0.517683
## [3] train-rmse:59.651027+0.076207 test-rmse:59.650480+0.541664
## [4] train-rmse:49.060283+0.069346 test-rmse:49.059156+0.565463
## [5] train-rmse:40.392603+0.065298 test-rmse:40.390688+0.589811
## [6] train-rmse:33.305450+0.062294 test-rmse:33.325024+0.602958
## [7] train-rmse:27.499631+0.054718 test-rmse:27.567201+0.606506
## [8] train-rmse:22.742521+0.042423 test-rmse:22.827392+0.586102
## [9] train-rmse:18.839725+0.033893 test-rmse:18.936239+0.569217
## [10] train-rmse:15.635067+0.029561 test-rmse:15.716343+0.546027
## [11] train-rmse:13.001250+0.024985 test-rmse:13.099206+0.531626
## [12] train-rmse:10.835387+0.022667 test-rmse:10.975627+0.543076
## [13] train-rmse:9.047487+0.016770 test-rmse:9.167870+0.550670
## [14] train-rmse:7.576279+0.019151 test-rmse:7.685661+0.547533
## [15] train-rmse:6.364456+0.015178 test-rmse:6.497015+0.529417
## [16] train-rmse:5.367526+0.016078 test-rmse:5.530261+0.532106
## [17] train-rmse:4.549011+0.015244 test-rmse:4.721163+0.503008
## [18] train-rmse:3.879230+0.017351 test-rmse:4.102155+0.499748
## [19] train-rmse:3.322755+0.019451 test-rmse:3.559009+0.480683
## [20] train-rmse:2.870203+0.016937 test-rmse:3.139189+0.448459
## [21] train-rmse:2.490767+0.016106 test-rmse:2.792940+0.419527
## [22] train-rmse:2.179956+0.016903 test-rmse:2.531513+0.391975
## [23] train-rmse:1.921918+0.024814 test-rmse:2.331269+0.365084
## [24] train-rmse:1.713961+0.026514 test-rmse:2.169927+0.344775
## [25] train-rmse:1.531515+0.028100 test-rmse:2.036824+0.328138
## [26] train-rmse:1.379396+0.034154 test-rmse:1.941791+0.303299
## [27] train-rmse:1.261103+0.035519 test-rmse:1.856952+0.296315
## [28] train-rmse:1.149221+0.040049 test-rmse:1.793448+0.275472
## [29] train-rmse:1.061394+0.037572 test-rmse:1.734154+0.272244
## [30] train-rmse:0.981153+0.043003 test-rmse:1.689719+0.265052
## [31] train-rmse:0.913920+0.040335 test-rmse:1.648801+0.264519
## [32] train-rmse:0.855662+0.041270 test-rmse:1.607724+0.259591
## [33] train-rmse:0.802416+0.042299 test-rmse:1.579702+0.261996
## [34] train-rmse:0.753126+0.037709 test-rmse:1.554829+0.263919
## [35] train-rmse:0.712059+0.040441 test-rmse:1.533892+0.259476
## [36] train-rmse:0.676013+0.036720 test-rmse:1.500838+0.249468
## [37] train-rmse:0.640051+0.033827 test-rmse:1.470580+0.242865
## [38] train-rmse:0.611964+0.034270 test-rmse:1.453140+0.245991
## [39] train-rmse:0.584974+0.032568 test-rmse:1.435704+0.244907
## [40] train-rmse:0.555364+0.031831 test-rmse:1.420069+0.245924
## [41] train-rmse:0.529412+0.032200 test-rmse:1.402773+0.249811
## [42] train-rmse:0.507193+0.029762 test-rmse:1.386927+0.245381
## [43] train-rmse:0.485507+0.026424 test-rmse:1.376251+0.240975
## [44] train-rmse:0.467125+0.025606 test-rmse:1.364535+0.244138
## [45] train-rmse:0.446958+0.023202 test-rmse:1.352410+0.246812
## [46] train-rmse:0.428871+0.022797 test-rmse:1.346833+0.251286
## [47] train-rmse:0.413199+0.022799 test-rmse:1.340009+0.251778
## [48] train-rmse:0.398157+0.023053 test-rmse:1.329952+0.251842
## [49] train-rmse:0.383734+0.021381 test-rmse:1.323954+0.256829
## [50] train-rmse:0.368606+0.018582 test-rmse:1.317938+0.248926
## [51] train-rmse:0.355307+0.018182 test-rmse:1.312147+0.249484
## [52] train-rmse:0.344064+0.017547 test-rmse:1.307682+0.253276
## [53] train-rmse:0.331898+0.016506 test-rmse:1.301073+0.256847
## [54] train-rmse:0.320633+0.016951 test-rmse:1.295466+0.256434
## [55] train-rmse:0.309786+0.014695 test-rmse:1.293910+0.256830
## [56] train-rmse:0.300012+0.015078 test-rmse:1.291293+0.257236
## [57] train-rmse:0.290434+0.015671 test-rmse:1.289162+0.256975
## [58] train-rmse:0.279873+0.014183 test-rmse:1.286124+0.260034
## [59] train-rmse:0.272818+0.013786 test-rmse:1.281712+0.260137
## [60] train-rmse:0.262630+0.014396 test-rmse:1.280757+0.262835
## [61] train-rmse:0.254902+0.013491 test-rmse:1.278486+0.264915
## [62] train-rmse:0.247725+0.013312 test-rmse:1.276052+0.265245
## [63] train-rmse:0.239493+0.011531 test-rmse:1.273308+0.267029
## [64] train-rmse:0.230768+0.008762 test-rmse:1.272672+0.267675
## [65] train-rmse:0.225372+0.008715 test-rmse:1.270153+0.269530
## [66] train-rmse:0.219089+0.009147 test-rmse:1.269997+0.269667
## [67] train-rmse:0.212359+0.010133 test-rmse:1.267806+0.270655
## [68] train-rmse:0.206016+0.009669 test-rmse:1.265106+0.268703
## [69] train-rmse:0.201216+0.010041 test-rmse:1.263629+0.268686
## [70] train-rmse:0.195681+0.008878 test-rmse:1.262197+0.268226
## [71] train-rmse:0.189377+0.007158 test-rmse:1.263592+0.267540
## [72] train-rmse:0.184983+0.006436 test-rmse:1.263110+0.266687
## [73] train-rmse:0.179969+0.007358 test-rmse:1.261831+0.267100
## [74] train-rmse:0.175377+0.008112 test-rmse:1.261139+0.267561
## [75] train-rmse:0.171753+0.008226 test-rmse:1.260584+0.267742
## [76] train-rmse:0.166618+0.006949 test-rmse:1.259425+0.268071
## [77] train-rmse:0.162172+0.007717 test-rmse:1.259070+0.268023
## [78] train-rmse:0.157468+0.007503 test-rmse:1.258668+0.268751
## [79] train-rmse:0.154424+0.007725 test-rmse:1.257412+0.269693
## [80] train-rmse:0.150119+0.007572 test-rmse:1.257492+0.270275
## [81] train-rmse:0.145728+0.007652 test-rmse:1.257183+0.269874
## [82] train-rmse:0.142028+0.007338 test-rmse:1.257971+0.271617
## [83] train-rmse:0.139038+0.007739 test-rmse:1.257403+0.272181
## [84] train-rmse:0.135806+0.008062 test-rmse:1.257251+0.271637
## [85] train-rmse:0.131954+0.007555 test-rmse:1.256916+0.271542
## [86] train-rmse:0.128438+0.007356 test-rmse:1.257224+0.272319
## [87] train-rmse:0.125705+0.007450 test-rmse:1.256496+0.272702
## [88] train-rmse:0.121726+0.007196 test-rmse:1.256039+0.273057
## [89] train-rmse:0.118462+0.007345 test-rmse:1.255277+0.272561
## [90] train-rmse:0.115486+0.007114 test-rmse:1.254312+0.273482
## [91] train-rmse:0.112757+0.006301 test-rmse:1.254176+0.274223
## [92] train-rmse:0.110054+0.005872 test-rmse:1.254273+0.274185
## [93] train-rmse:0.107030+0.006085 test-rmse:1.254756+0.273522
## [94] train-rmse:0.103318+0.005106 test-rmse:1.254606+0.274492
## [95] train-rmse:0.101177+0.005203 test-rmse:1.253471+0.272371
## [96] train-rmse:0.098711+0.004984 test-rmse:1.253558+0.272623
## [97] train-rmse:0.096754+0.005387 test-rmse:1.253125+0.272595
## [98] train-rmse:0.094312+0.005346 test-rmse:1.253055+0.272695
## [99] train-rmse:0.092525+0.005235 test-rmse:1.251646+0.271345
## [100] train-rmse:0.090302+0.005719 test-rmse:1.251155+0.271376
## [101] train-rmse:0.088924+0.005738 test-rmse:1.251067+0.271452
## [102] train-rmse:0.086945+0.005239 test-rmse:1.251281+0.271437
## [103] train-rmse:0.084875+0.004357 test-rmse:1.251017+0.271143
## [104] train-rmse:0.082860+0.004650 test-rmse:1.250786+0.271673
## [105] train-rmse:0.079831+0.004379 test-rmse:1.250326+0.270255
## [106] train-rmse:0.077076+0.004022 test-rmse:1.250109+0.269397
## [107] train-rmse:0.074822+0.003727 test-rmse:1.250305+0.268823
## [108] train-rmse:0.073211+0.003701 test-rmse:1.250704+0.269139
## [109] train-rmse:0.071642+0.003493 test-rmse:1.250774+0.268942
## [110] train-rmse:0.069762+0.003993 test-rmse:1.250839+0.269121
## [111] train-rmse:0.068384+0.003657 test-rmse:1.250689+0.268777
## [112] train-rmse:0.067091+0.003573 test-rmse:1.250385+0.268562
## Stopping. Best iteration:
## [106] train-rmse:0.077076+0.004022 test-rmse:1.250109+0.269397
##
## 35
## [1] train-rmse:86.218085+0.097587 test-rmse:86.218257+0.495906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.119772+0.083348 test-rmse:69.119559+0.523711
## [3] train-rmse:55.450065+0.073313 test-rmse:55.449316+0.550523
## [4] train-rmse:44.530696+0.066998 test-rmse:44.529209+0.577462
## [5] train-rmse:35.819717+0.064113 test-rmse:35.817187+0.605511
## [6] train-rmse:28.873706+0.061822 test-rmse:28.904146+0.614944
## [7] train-rmse:23.336712+0.056588 test-rmse:23.387526+0.621094
## [8] train-rmse:18.928374+0.051429 test-rmse:18.990305+0.625622
## [9] train-rmse:15.431930+0.048481 test-rmse:15.493831+0.597266
## [10] train-rmse:12.669229+0.045690 test-rmse:12.742270+0.601259
## [11] train-rmse:10.501816+0.045914 test-rmse:10.604033+0.649982
## [12] train-rmse:8.815721+0.048505 test-rmse:8.927163+0.620500
## [13] train-rmse:7.514515+0.050123 test-rmse:7.618751+0.603155
## [14] train-rmse:6.524028+0.052757 test-rmse:6.652014+0.641810
## [15] train-rmse:5.777394+0.055370 test-rmse:5.910574+0.598407
## [16] train-rmse:5.220425+0.058246 test-rmse:5.336339+0.553171
## [17] train-rmse:4.810975+0.061909 test-rmse:4.934905+0.557642
## [18] train-rmse:4.508372+0.064293 test-rmse:4.682256+0.565118
## [19] train-rmse:4.282185+0.065402 test-rmse:4.442564+0.508581
## [20] train-rmse:4.109592+0.064963 test-rmse:4.246249+0.459739
## [21] train-rmse:3.976104+0.064884 test-rmse:4.132040+0.454648
## [22] train-rmse:3.870823+0.063387 test-rmse:4.055262+0.462062
## [23] train-rmse:3.784360+0.062307 test-rmse:3.946165+0.422955
## [24] train-rmse:3.709631+0.061285 test-rmse:3.886796+0.403979
## [25] train-rmse:3.645084+0.059759 test-rmse:3.850590+0.406598
## [26] train-rmse:3.588719+0.059298 test-rmse:3.791692+0.369599
## [27] train-rmse:3.535645+0.057971 test-rmse:3.739218+0.373724
## [28] train-rmse:3.485913+0.057234 test-rmse:3.701119+0.360517
## [29] train-rmse:3.440924+0.055801 test-rmse:3.659184+0.373136
## [30] train-rmse:3.398031+0.055024 test-rmse:3.624568+0.377294
## [31] train-rmse:3.355489+0.054011 test-rmse:3.565587+0.339375
## [32] train-rmse:3.314723+0.053083 test-rmse:3.531149+0.346737
## [33] train-rmse:3.274417+0.051574 test-rmse:3.466545+0.319726
## [34] train-rmse:3.236015+0.049898 test-rmse:3.440429+0.318641
## [35] train-rmse:3.199475+0.049112 test-rmse:3.417135+0.322469
## [36] train-rmse:3.162980+0.048468 test-rmse:3.391542+0.323862
## [37] train-rmse:3.128213+0.047941 test-rmse:3.364447+0.324509
## [38] train-rmse:3.094414+0.046125 test-rmse:3.320302+0.314838
## [39] train-rmse:3.061657+0.044855 test-rmse:3.298414+0.327793
## [40] train-rmse:3.030097+0.043880 test-rmse:3.267268+0.332706
## [41] train-rmse:2.999544+0.043105 test-rmse:3.247096+0.325556
## [42] train-rmse:2.969364+0.042384 test-rmse:3.210209+0.299150
## [43] train-rmse:2.939879+0.042052 test-rmse:3.196606+0.296713
## [44] train-rmse:2.910241+0.041700 test-rmse:3.157764+0.312227
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## [47] train-rmse:2.825721+0.040675 test-rmse:3.059525+0.305155
## [48] train-rmse:2.798717+0.040331 test-rmse:3.042005+0.312355
## [49] train-rmse:2.771636+0.039916 test-rmse:3.019322+0.318041
## [50] train-rmse:2.745194+0.039504 test-rmse:2.994363+0.324798
## [51] train-rmse:2.719699+0.039090 test-rmse:2.960506+0.328566
## [52] train-rmse:2.694419+0.038779 test-rmse:2.935939+0.326799
## [53] train-rmse:2.669667+0.039132 test-rmse:2.920622+0.310212
## [54] train-rmse:2.646250+0.038758 test-rmse:2.902270+0.293392
## [55] train-rmse:2.622906+0.038279 test-rmse:2.876555+0.303775
## [56] train-rmse:2.599714+0.037749 test-rmse:2.863495+0.298120
## [57] train-rmse:2.577125+0.037520 test-rmse:2.850918+0.300991
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## [59] train-rmse:2.532644+0.037277 test-rmse:2.814767+0.304514
## [60] train-rmse:2.510681+0.037161 test-rmse:2.791268+0.304927
## [61] train-rmse:2.489285+0.036558 test-rmse:2.772596+0.308003
## [62] train-rmse:2.468228+0.036209 test-rmse:2.745587+0.305731
## [63] train-rmse:2.447332+0.035938 test-rmse:2.719047+0.306870
## [64] train-rmse:2.426846+0.035659 test-rmse:2.680348+0.293122
## [65] train-rmse:2.406683+0.035292 test-rmse:2.670618+0.292618
## [66] train-rmse:2.386877+0.034867 test-rmse:2.658774+0.295694
## [67] train-rmse:2.367536+0.034449 test-rmse:2.640261+0.306142
## [68] train-rmse:2.348219+0.033927 test-rmse:2.625994+0.310927
## [69] train-rmse:2.329026+0.033406 test-rmse:2.610937+0.315361
## [70] train-rmse:2.309952+0.033058 test-rmse:2.591817+0.314083
## [71] train-rmse:2.291152+0.032664 test-rmse:2.565239+0.306264
## [72] train-rmse:2.272911+0.031906 test-rmse:2.551557+0.312891
## [73] train-rmse:2.254728+0.031141 test-rmse:2.529740+0.298557
## [74] train-rmse:2.237077+0.030741 test-rmse:2.512182+0.300064
## [75] train-rmse:2.219856+0.030537 test-rmse:2.505601+0.297933
## [76] train-rmse:2.202671+0.030292 test-rmse:2.479265+0.301712
## [77] train-rmse:2.185534+0.030171 test-rmse:2.458143+0.305023
## [78] train-rmse:2.168636+0.029788 test-rmse:2.438746+0.298451
## [79] train-rmse:2.151869+0.029529 test-rmse:2.423521+0.301170
## [80] train-rmse:2.135128+0.029301 test-rmse:2.405507+0.303217
## [81] train-rmse:2.118782+0.028711 test-rmse:2.391837+0.296819
## [82] train-rmse:2.102441+0.028145 test-rmse:2.362709+0.277395
## [83] train-rmse:2.086618+0.027757 test-rmse:2.348219+0.281991
## [84] train-rmse:2.071097+0.027289 test-rmse:2.330444+0.271418
## [85] train-rmse:2.055963+0.026972 test-rmse:2.324268+0.268049
## [86] train-rmse:2.040710+0.026505 test-rmse:2.316762+0.260803
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## [88] train-rmse:2.011064+0.026009 test-rmse:2.293797+0.270169
## [89] train-rmse:1.996335+0.025888 test-rmse:2.278997+0.275270
## [90] train-rmse:1.981941+0.025592 test-rmse:2.263124+0.271595
## [91] train-rmse:1.967617+0.025192 test-rmse:2.245970+0.275666
## [92] train-rmse:1.953482+0.024892 test-rmse:2.231597+0.271879
## [93] train-rmse:1.939543+0.024742 test-rmse:2.211124+0.269073
## [94] train-rmse:1.925611+0.024517 test-rmse:2.202589+0.267956
## [95] train-rmse:1.911851+0.024051 test-rmse:2.180753+0.270574
## [96] train-rmse:1.898345+0.023444 test-rmse:2.173583+0.274940
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## [99] train-rmse:1.858987+0.021918 test-rmse:2.139122+0.270197
## [100] train-rmse:1.846059+0.021454 test-rmse:2.127578+0.266446
## [101] train-rmse:1.833476+0.021011 test-rmse:2.103529+0.267701
## [102] train-rmse:1.820952+0.020649 test-rmse:2.097551+0.269572
## [103] train-rmse:1.808548+0.020433 test-rmse:2.089180+0.275390
## [104] train-rmse:1.796114+0.020018 test-rmse:2.080944+0.277437
## [105] train-rmse:1.783851+0.019737 test-rmse:2.077537+0.278678
## [106] train-rmse:1.771777+0.019303 test-rmse:2.057163+0.260424
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## [108] train-rmse:1.748475+0.019135 test-rmse:2.033204+0.259720
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## [111] train-rmse:1.714407+0.018874 test-rmse:2.000655+0.249253
## [112] train-rmse:1.703394+0.018589 test-rmse:1.991647+0.250879
## [113] train-rmse:1.692367+0.018192 test-rmse:1.977747+0.257935
## [114] train-rmse:1.681405+0.017771 test-rmse:1.967977+0.258552
## [115] train-rmse:1.670523+0.017301 test-rmse:1.955029+0.259019
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## [117] train-rmse:1.649094+0.016391 test-rmse:1.939036+0.253024
## [118] train-rmse:1.638572+0.016060 test-rmse:1.935494+0.249459
## [119] train-rmse:1.628055+0.015878 test-rmse:1.924262+0.246179
## [120] train-rmse:1.617710+0.015735 test-rmse:1.916897+0.243894
## [121] train-rmse:1.607541+0.015508 test-rmse:1.903308+0.247850
## [122] train-rmse:1.597589+0.015327 test-rmse:1.895527+0.247335
## [123] train-rmse:1.587800+0.015163 test-rmse:1.890033+0.249206
## [124] train-rmse:1.578007+0.015076 test-rmse:1.880832+0.245583
## [125] train-rmse:1.568333+0.015095 test-rmse:1.859078+0.235960
## [126] train-rmse:1.558858+0.014954 test-rmse:1.852672+0.229487
## [127] train-rmse:1.549598+0.014709 test-rmse:1.843562+0.231127
## [128] train-rmse:1.540384+0.014415 test-rmse:1.830161+0.232207
## [129] train-rmse:1.531110+0.014184 test-rmse:1.821603+0.236882
## [130] train-rmse:1.521934+0.014000 test-rmse:1.815146+0.238510
## [131] train-rmse:1.512964+0.013713 test-rmse:1.804483+0.241447
## [132] train-rmse:1.504198+0.013633 test-rmse:1.799674+0.244028
## [133] train-rmse:1.495535+0.013492 test-rmse:1.784928+0.235597
## [134] train-rmse:1.486949+0.013502 test-rmse:1.775055+0.235300
## [135] train-rmse:1.478432+0.013552 test-rmse:1.767678+0.232809
## [136] train-rmse:1.470021+0.013478 test-rmse:1.763902+0.236623
## [137] train-rmse:1.461653+0.013306 test-rmse:1.752451+0.223720
## [138] train-rmse:1.453370+0.013100 test-rmse:1.740320+0.226411
## [139] train-rmse:1.445102+0.012856 test-rmse:1.735590+0.224477
## [140] train-rmse:1.436911+0.012590 test-rmse:1.728333+0.226049
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## [142] train-rmse:1.420568+0.011946 test-rmse:1.713844+0.226327
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## [144] train-rmse:1.404609+0.011661 test-rmse:1.697341+0.227845
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## [146] train-rmse:1.389083+0.011541 test-rmse:1.680298+0.230640
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## [150] train-rmse:1.359259+0.011305 test-rmse:1.660887+0.221104
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## [152] train-rmse:1.345198+0.011082 test-rmse:1.650928+0.221115
## [153] train-rmse:1.338212+0.010961 test-rmse:1.639918+0.225660
## [154] train-rmse:1.331298+0.010783 test-rmse:1.633648+0.226666
## [155] train-rmse:1.324292+0.010573 test-rmse:1.621329+0.228522
## [156] train-rmse:1.317518+0.010448 test-rmse:1.617666+0.229092
## [157] train-rmse:1.310839+0.010378 test-rmse:1.611989+0.224644
## [158] train-rmse:1.304085+0.010349 test-rmse:1.601681+0.221015
## [159] train-rmse:1.297571+0.010316 test-rmse:1.595561+0.221157
## [160] train-rmse:1.291180+0.010264 test-rmse:1.594327+0.222507
## [161] train-rmse:1.284923+0.010182 test-rmse:1.584663+0.209730
## [162] train-rmse:1.278652+0.010079 test-rmse:1.578619+0.210867
## [163] train-rmse:1.272437+0.009987 test-rmse:1.574240+0.208036
## [164] train-rmse:1.266356+0.009905 test-rmse:1.569680+0.207973
## [165] train-rmse:1.260271+0.009834 test-rmse:1.560314+0.210552
## [166] train-rmse:1.254167+0.009771 test-rmse:1.559166+0.209678
## [167] train-rmse:1.248062+0.009710 test-rmse:1.553689+0.210894
## [168] train-rmse:1.242022+0.009712 test-rmse:1.545090+0.214532
## [169] train-rmse:1.236005+0.009636 test-rmse:1.537864+0.214900
## [170] train-rmse:1.230074+0.009483 test-rmse:1.530193+0.215367
## [171] train-rmse:1.224257+0.009445 test-rmse:1.525158+0.215791
## [172] train-rmse:1.218487+0.009308 test-rmse:1.519382+0.214696
## [173] train-rmse:1.212830+0.009181 test-rmse:1.512107+0.213328
## [174] train-rmse:1.207418+0.009154 test-rmse:1.504474+0.209012
## [175] train-rmse:1.201986+0.009184 test-rmse:1.499508+0.212188
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## [178] train-rmse:1.186144+0.008951 test-rmse:1.490932+0.208827
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## [180] train-rmse:1.175932+0.008757 test-rmse:1.482326+0.206567
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## [183] train-rmse:1.160710+0.008686 test-rmse:1.465880+0.211280
## [184] train-rmse:1.155800+0.008588 test-rmse:1.464406+0.213153
## [185] train-rmse:1.150806+0.008336 test-rmse:1.463597+0.213415
## [186] train-rmse:1.145959+0.008250 test-rmse:1.455311+0.206979
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## [188] train-rmse:1.136417+0.008070 test-rmse:1.449369+0.209320
## [189] train-rmse:1.131704+0.007974 test-rmse:1.446486+0.211394
## [190] train-rmse:1.127119+0.007926 test-rmse:1.442022+0.208407
## [191] train-rmse:1.122533+0.007899 test-rmse:1.439694+0.209528
## [192] train-rmse:1.117982+0.007875 test-rmse:1.432687+0.211076
## [193] train-rmse:1.113486+0.007872 test-rmse:1.427463+0.212506
## [194] train-rmse:1.109013+0.007815 test-rmse:1.423385+0.213169
## [195] train-rmse:1.104596+0.007751 test-rmse:1.416320+0.213188
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## [197] train-rmse:1.095945+0.007601 test-rmse:1.403028+0.217463
## [198] train-rmse:1.091728+0.007605 test-rmse:1.396512+0.208501
## [199] train-rmse:1.087555+0.007674 test-rmse:1.391443+0.209930
## [200] train-rmse:1.083566+0.007642 test-rmse:1.387106+0.207636
## [201] train-rmse:1.079566+0.007591 test-rmse:1.381361+0.207333
## [202] train-rmse:1.075552+0.007549 test-rmse:1.376074+0.207479
## [203] train-rmse:1.071603+0.007509 test-rmse:1.375199+0.206364
## [204] train-rmse:1.067651+0.007468 test-rmse:1.372014+0.205799
## [205] train-rmse:1.063769+0.007463 test-rmse:1.369271+0.204383
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## [207] train-rmse:1.056160+0.007386 test-rmse:1.362949+0.203973
## [208] train-rmse:1.052471+0.007384 test-rmse:1.363124+0.203072
## [209] train-rmse:1.048784+0.007338 test-rmse:1.359722+0.203179
## [210] train-rmse:1.045086+0.007298 test-rmse:1.357266+0.204221
## [211] train-rmse:1.041477+0.007339 test-rmse:1.355872+0.204794
## [212] train-rmse:1.037880+0.007390 test-rmse:1.351546+0.202758
## [213] train-rmse:1.034308+0.007459 test-rmse:1.346422+0.200594
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## [215] train-rmse:1.027329+0.007563 test-rmse:1.341470+0.202884
## [216] train-rmse:1.023893+0.007569 test-rmse:1.337470+0.203630
## [217] train-rmse:1.020478+0.007567 test-rmse:1.334459+0.206367
## [218] train-rmse:1.017128+0.007519 test-rmse:1.332183+0.206745
## [219] train-rmse:1.013796+0.007452 test-rmse:1.330110+0.207557
## [220] train-rmse:1.010463+0.007365 test-rmse:1.328172+0.206843
## [221] train-rmse:1.007211+0.007329 test-rmse:1.326009+0.207980
## [222] train-rmse:1.003992+0.007343 test-rmse:1.321422+0.210513
## [223] train-rmse:1.000842+0.007348 test-rmse:1.315651+0.201610
## [224] train-rmse:0.997695+0.007392 test-rmse:1.313249+0.201850
## [225] train-rmse:0.994631+0.007395 test-rmse:1.311130+0.201908
## [226] train-rmse:0.991611+0.007393 test-rmse:1.307519+0.200417
## [227] train-rmse:0.988635+0.007340 test-rmse:1.305261+0.198753
## [228] train-rmse:0.985696+0.007313 test-rmse:1.300835+0.200097
## [229] train-rmse:0.982773+0.007255 test-rmse:1.295336+0.203043
## [230] train-rmse:0.979929+0.007212 test-rmse:1.294994+0.203890
## [231] train-rmse:0.977087+0.007167 test-rmse:1.290148+0.200117
## [232] train-rmse:0.974233+0.007138 test-rmse:1.286757+0.197162
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## [234] train-rmse:0.968716+0.007190 test-rmse:1.283952+0.195216
## [235] train-rmse:0.965992+0.007176 test-rmse:1.279933+0.194968
## [236] train-rmse:0.963229+0.007219 test-rmse:1.278283+0.194621
## [237] train-rmse:0.960509+0.007265 test-rmse:1.277298+0.195164
## [238] train-rmse:0.957844+0.007323 test-rmse:1.273887+0.195843
## [239] train-rmse:0.955227+0.007366 test-rmse:1.270874+0.196453
## [240] train-rmse:0.952636+0.007409 test-rmse:1.267771+0.197288
## [241] train-rmse:0.950108+0.007410 test-rmse:1.268600+0.195557
## [242] train-rmse:0.947587+0.007429 test-rmse:1.265628+0.197611
## [243] train-rmse:0.945060+0.007435 test-rmse:1.263590+0.197716
## [244] train-rmse:0.942604+0.007496 test-rmse:1.260317+0.199146
## [245] train-rmse:0.940179+0.007467 test-rmse:1.260222+0.199754
## [246] train-rmse:0.937769+0.007467 test-rmse:1.260215+0.201017
## [247] train-rmse:0.935377+0.007492 test-rmse:1.256969+0.202285
## [248] train-rmse:0.932993+0.007523 test-rmse:1.252190+0.201784
## [249] train-rmse:0.930647+0.007519 test-rmse:1.247371+0.195225
## [250] train-rmse:0.928308+0.007527 test-rmse:1.246029+0.195337
## [251] train-rmse:0.925983+0.007592 test-rmse:1.243874+0.196846
## [252] train-rmse:0.923688+0.007639 test-rmse:1.244367+0.196501
## [253] train-rmse:0.921435+0.007665 test-rmse:1.241887+0.196711
## [254] train-rmse:0.919216+0.007686 test-rmse:1.238310+0.195567
## [255] train-rmse:0.916971+0.007776 test-rmse:1.237059+0.194868
## [256] train-rmse:0.914783+0.007841 test-rmse:1.236235+0.194713
## [257] train-rmse:0.912625+0.007876 test-rmse:1.234007+0.193723
## [258] train-rmse:0.910481+0.007926 test-rmse:1.232450+0.192662
## [259] train-rmse:0.908367+0.007975 test-rmse:1.230000+0.193105
## [260] train-rmse:0.906295+0.008029 test-rmse:1.227691+0.193860
## [261] train-rmse:0.904243+0.008083 test-rmse:1.223775+0.192606
## [262] train-rmse:0.902180+0.008073 test-rmse:1.218359+0.190482
## [263] train-rmse:0.900145+0.008142 test-rmse:1.219523+0.189198
## [264] train-rmse:0.898141+0.008230 test-rmse:1.217348+0.190434
## [265] train-rmse:0.896180+0.008336 test-rmse:1.216712+0.190731
## [266] train-rmse:0.894252+0.008408 test-rmse:1.217103+0.190799
## [267] train-rmse:0.892339+0.008471 test-rmse:1.216028+0.191319
## [268] train-rmse:0.890443+0.008523 test-rmse:1.213869+0.191367
## [269] train-rmse:0.888603+0.008564 test-rmse:1.212712+0.192290
## [270] train-rmse:0.886778+0.008594 test-rmse:1.211927+0.192478
## [271] train-rmse:0.884985+0.008658 test-rmse:1.211567+0.193205
## [272] train-rmse:0.883177+0.008740 test-rmse:1.209665+0.194015
## [273] train-rmse:0.881385+0.008794 test-rmse:1.206127+0.195473
## [274] train-rmse:0.879629+0.008802 test-rmse:1.203546+0.193559
## [275] train-rmse:0.877888+0.008841 test-rmse:1.198856+0.189054
## [276] train-rmse:0.876144+0.008905 test-rmse:1.194846+0.190418
## [277] train-rmse:0.874433+0.008957 test-rmse:1.192821+0.191432
## [278] train-rmse:0.872735+0.009001 test-rmse:1.191899+0.190341
## [279] train-rmse:0.871076+0.009036 test-rmse:1.191440+0.189617
## [280] train-rmse:0.869425+0.009094 test-rmse:1.191278+0.189307
## [281] train-rmse:0.867787+0.009157 test-rmse:1.191286+0.190164
## [282] train-rmse:0.866127+0.009133 test-rmse:1.190412+0.189665
## [283] train-rmse:0.864518+0.009192 test-rmse:1.188105+0.188153
## [284] train-rmse:0.862894+0.009236 test-rmse:1.187355+0.186678
## [285] train-rmse:0.861339+0.009296 test-rmse:1.185601+0.186486
## [286] train-rmse:0.859802+0.009327 test-rmse:1.183643+0.187890
## [287] train-rmse:0.858260+0.009400 test-rmse:1.180779+0.186565
## [288] train-rmse:0.856728+0.009471 test-rmse:1.179709+0.188845
## [289] train-rmse:0.855215+0.009534 test-rmse:1.176968+0.187149
## [290] train-rmse:0.853712+0.009578 test-rmse:1.176305+0.187573
## [291] train-rmse:0.852220+0.009611 test-rmse:1.175578+0.188190
## [292] train-rmse:0.850766+0.009645 test-rmse:1.175987+0.188368
## [293] train-rmse:0.849321+0.009682 test-rmse:1.175524+0.188895
## [294] train-rmse:0.847900+0.009722 test-rmse:1.175258+0.189248
## [295] train-rmse:0.846507+0.009754 test-rmse:1.174803+0.189767
## [296] train-rmse:0.845150+0.009796 test-rmse:1.173974+0.189639
## [297] train-rmse:0.843798+0.009849 test-rmse:1.171756+0.190436
## [298] train-rmse:0.842455+0.009882 test-rmse:1.170399+0.190405
## [299] train-rmse:0.841128+0.009931 test-rmse:1.169826+0.190958
## [300] train-rmse:0.839801+0.009962 test-rmse:1.168804+0.192305
## [301] train-rmse:0.838479+0.009995 test-rmse:1.167190+0.192695
## [302] train-rmse:0.837188+0.010042 test-rmse:1.163103+0.188543
## [303] train-rmse:0.835910+0.010080 test-rmse:1.161069+0.189837
## [304] train-rmse:0.834655+0.010135 test-rmse:1.158834+0.188540
## [305] train-rmse:0.833374+0.010198 test-rmse:1.157595+0.186463
## [306] train-rmse:0.832116+0.010276 test-rmse:1.157433+0.187361
## [307] train-rmse:0.830832+0.010333 test-rmse:1.157607+0.186714
## [308] train-rmse:0.829624+0.010384 test-rmse:1.155360+0.185347
## [309] train-rmse:0.828431+0.010428 test-rmse:1.155505+0.185487
## [310] train-rmse:0.827239+0.010477 test-rmse:1.154623+0.185766
## [311] train-rmse:0.826050+0.010529 test-rmse:1.152652+0.186895
## [312] train-rmse:0.824881+0.010569 test-rmse:1.151867+0.187246
## [313] train-rmse:0.823735+0.010614 test-rmse:1.152172+0.187840
## [314] train-rmse:0.822581+0.010674 test-rmse:1.151154+0.188097
## [315] train-rmse:0.821440+0.010725 test-rmse:1.149011+0.188464
## [316] train-rmse:0.820320+0.010753 test-rmse:1.149346+0.188589
## [317] train-rmse:0.819211+0.010777 test-rmse:1.148311+0.189437
## [318] train-rmse:0.818112+0.010812 test-rmse:1.143477+0.189984
## [319] train-rmse:0.817046+0.010836 test-rmse:1.142827+0.188292
## [320] train-rmse:0.815979+0.010864 test-rmse:1.140162+0.184024
## [321] train-rmse:0.814922+0.010899 test-rmse:1.139921+0.184636
## [322] train-rmse:0.813908+0.010930 test-rmse:1.139819+0.183836
## [323] train-rmse:0.812886+0.010972 test-rmse:1.138484+0.183613
## [324] train-rmse:0.811869+0.011011 test-rmse:1.138181+0.183583
## [325] train-rmse:0.810845+0.011058 test-rmse:1.137585+0.183160
## [326] train-rmse:0.809841+0.011109 test-rmse:1.138333+0.181888
## [327] train-rmse:0.808834+0.011165 test-rmse:1.136056+0.180614
## [328] train-rmse:0.807847+0.011204 test-rmse:1.134272+0.181798
## [329] train-rmse:0.806875+0.011245 test-rmse:1.133620+0.182349
## [330] train-rmse:0.805912+0.011274 test-rmse:1.133155+0.182576
## [331] train-rmse:0.804946+0.011293 test-rmse:1.132084+0.183833
## [332] train-rmse:0.803950+0.011284 test-rmse:1.131213+0.184725
## [333] train-rmse:0.803017+0.011304 test-rmse:1.131529+0.184906
## [334] train-rmse:0.802073+0.011339 test-rmse:1.130976+0.184773
## [335] train-rmse:0.801144+0.011387 test-rmse:1.130493+0.185836
## [336] train-rmse:0.800226+0.011436 test-rmse:1.129970+0.186700
## [337] train-rmse:0.799310+0.011497 test-rmse:1.128611+0.186680
## [338] train-rmse:0.798424+0.011530 test-rmse:1.128686+0.186002
## [339] train-rmse:0.797566+0.011575 test-rmse:1.128015+0.187096
## [340] train-rmse:0.796689+0.011601 test-rmse:1.128458+0.186934
## [341] train-rmse:0.795842+0.011643 test-rmse:1.128213+0.186732
## [342] train-rmse:0.794996+0.011681 test-rmse:1.127016+0.187012
## [343] train-rmse:0.794150+0.011727 test-rmse:1.126193+0.186805
## [344] train-rmse:0.793313+0.011779 test-rmse:1.124004+0.186355
## [345] train-rmse:0.792485+0.011819 test-rmse:1.122442+0.185214
## [346] train-rmse:0.791657+0.011854 test-rmse:1.122187+0.186176
## [347] train-rmse:0.790838+0.011899 test-rmse:1.121250+0.185586
## [348] train-rmse:0.790023+0.011939 test-rmse:1.120407+0.185429
## [349] train-rmse:0.789215+0.011977 test-rmse:1.120817+0.184506
## [350] train-rmse:0.788425+0.011997 test-rmse:1.121353+0.183808
## [351] train-rmse:0.787632+0.012017 test-rmse:1.120286+0.184142
## [352] train-rmse:0.786863+0.012047 test-rmse:1.119475+0.184640
## [353] train-rmse:0.786098+0.012076 test-rmse:1.117602+0.184721
## [354] train-rmse:0.785337+0.012091 test-rmse:1.116880+0.185357
## [355] train-rmse:0.784571+0.012129 test-rmse:1.115141+0.184490
## [356] train-rmse:0.783805+0.012161 test-rmse:1.115044+0.185044
## [357] train-rmse:0.783041+0.012184 test-rmse:1.114858+0.185474
## [358] train-rmse:0.782289+0.012229 test-rmse:1.115154+0.185882
## [359] train-rmse:0.781557+0.012259 test-rmse:1.114142+0.186781
## [360] train-rmse:0.780840+0.012293 test-rmse:1.114192+0.186238
## [361] train-rmse:0.780130+0.012328 test-rmse:1.112890+0.186567
## [362] train-rmse:0.779410+0.012352 test-rmse:1.111904+0.187105
## [363] train-rmse:0.778679+0.012395 test-rmse:1.111280+0.185340
## [364] train-rmse:0.777981+0.012437 test-rmse:1.110285+0.185363
## [365] train-rmse:0.777289+0.012461 test-rmse:1.109672+0.185143
## [366] train-rmse:0.776600+0.012474 test-rmse:1.109642+0.184948
## [367] train-rmse:0.775926+0.012489 test-rmse:1.109690+0.186150
## [368] train-rmse:0.775256+0.012504 test-rmse:1.109708+0.186249
## [369] train-rmse:0.774587+0.012530 test-rmse:1.109278+0.186109
## [370] train-rmse:0.773922+0.012550 test-rmse:1.108279+0.185846
## [371] train-rmse:0.773253+0.012574 test-rmse:1.107106+0.185316
## [372] train-rmse:0.772592+0.012590 test-rmse:1.106839+0.185392
## [373] train-rmse:0.771938+0.012603 test-rmse:1.106819+0.185323
## [374] train-rmse:0.771298+0.012627 test-rmse:1.106729+0.184740
## [375] train-rmse:0.770664+0.012653 test-rmse:1.106297+0.184914
## [376] train-rmse:0.770029+0.012683 test-rmse:1.106193+0.184858
## [377] train-rmse:0.769400+0.012719 test-rmse:1.105886+0.185430
## [378] train-rmse:0.768745+0.012735 test-rmse:1.105174+0.185463
## [379] train-rmse:0.768118+0.012758 test-rmse:1.102749+0.182580
## [380] train-rmse:0.767487+0.012784 test-rmse:1.102539+0.182242
## [381] train-rmse:0.766865+0.012809 test-rmse:1.101217+0.182530
## [382] train-rmse:0.766252+0.012838 test-rmse:1.101227+0.183019
## [383] train-rmse:0.765656+0.012867 test-rmse:1.100075+0.184118
## [384] train-rmse:0.765065+0.012889 test-rmse:1.099896+0.184472
## [385] train-rmse:0.764490+0.012912 test-rmse:1.099779+0.184850
## [386] train-rmse:0.763922+0.012936 test-rmse:1.099817+0.184725
## [387] train-rmse:0.763347+0.012960 test-rmse:1.098823+0.183855
## [388] train-rmse:0.762767+0.012985 test-rmse:1.099050+0.184254
## [389] train-rmse:0.762188+0.013015 test-rmse:1.098068+0.184878
## [390] train-rmse:0.761610+0.013014 test-rmse:1.097598+0.183870
## [391] train-rmse:0.761038+0.013031 test-rmse:1.096332+0.184661
## [392] train-rmse:0.760495+0.013041 test-rmse:1.095805+0.185115
## [393] train-rmse:0.759931+0.013041 test-rmse:1.095453+0.185243
## [394] train-rmse:0.759390+0.013052 test-rmse:1.094900+0.184866
## [395] train-rmse:0.758846+0.013061 test-rmse:1.094335+0.185023
## [396] train-rmse:0.758303+0.013090 test-rmse:1.094397+0.185458
## [397] train-rmse:0.757759+0.013096 test-rmse:1.094832+0.184899
## [398] train-rmse:0.757219+0.013109 test-rmse:1.094260+0.184859
## [399] train-rmse:0.756693+0.013123 test-rmse:1.093868+0.184857
## [400] train-rmse:0.756153+0.013138 test-rmse:1.093111+0.184815
## [401] train-rmse:0.755627+0.013156 test-rmse:1.092517+0.185511
## [402] train-rmse:0.755105+0.013177 test-rmse:1.092352+0.185468
## [403] train-rmse:0.754564+0.013179 test-rmse:1.091968+0.185807
## [404] train-rmse:0.754055+0.013187 test-rmse:1.092377+0.185476
## [405] train-rmse:0.753549+0.013204 test-rmse:1.093143+0.185046
## [406] train-rmse:0.753039+0.013214 test-rmse:1.091527+0.185587
## [407] train-rmse:0.752542+0.013217 test-rmse:1.091771+0.185768
## [408] train-rmse:0.752040+0.013228 test-rmse:1.091338+0.185593
## [409] train-rmse:0.751543+0.013239 test-rmse:1.091704+0.185498
## [410] train-rmse:0.751032+0.013252 test-rmse:1.089957+0.186102
## [411] train-rmse:0.750542+0.013260 test-rmse:1.089779+0.186326
## [412] train-rmse:0.750051+0.013270 test-rmse:1.089315+0.186576
## [413] train-rmse:0.749549+0.013303 test-rmse:1.088788+0.185365
## [414] train-rmse:0.749070+0.013321 test-rmse:1.087591+0.184054
## [415] train-rmse:0.748578+0.013327 test-rmse:1.087547+0.183445
## [416] train-rmse:0.748096+0.013342 test-rmse:1.086640+0.183768
## [417] train-rmse:0.747628+0.013349 test-rmse:1.086897+0.184112
## [418] train-rmse:0.747160+0.013350 test-rmse:1.085018+0.181311
## [419] train-rmse:0.746692+0.013355 test-rmse:1.085295+0.181592
## [420] train-rmse:0.746229+0.013365 test-rmse:1.085828+0.181424
## [421] train-rmse:0.745759+0.013382 test-rmse:1.085159+0.182065
## [422] train-rmse:0.745294+0.013401 test-rmse:1.085400+0.182624
## [423] train-rmse:0.744839+0.013408 test-rmse:1.085809+0.182196
## [424] train-rmse:0.744383+0.013415 test-rmse:1.085453+0.182107
## Stopping. Best iteration:
## [418] train-rmse:0.747160+0.013350 test-rmse:1.085018+0.181311
##
## 36
## [1] train-rmse:86.218085+0.097587 test-rmse:86.218257+0.495906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.119772+0.083348 test-rmse:69.119559+0.523711
## [3] train-rmse:55.450065+0.073313 test-rmse:55.449316+0.550523
## [4] train-rmse:44.530696+0.066998 test-rmse:44.529209+0.577462
## [5] train-rmse:35.819717+0.064113 test-rmse:35.817187+0.605511
## [6] train-rmse:28.868193+0.060253 test-rmse:28.910774+0.605132
## [7] train-rmse:23.313352+0.045540 test-rmse:23.415351+0.630046
## [8] train-rmse:18.877434+0.034820 test-rmse:18.968087+0.577209
## [9] train-rmse:15.345303+0.030199 test-rmse:15.463877+0.589790
## [10] train-rmse:12.541798+0.029245 test-rmse:12.630278+0.565191
## [11] train-rmse:10.324019+0.030061 test-rmse:10.421563+0.553936
## [12] train-rmse:8.576948+0.030183 test-rmse:8.715715+0.573531
## [13] train-rmse:7.215962+0.030788 test-rmse:7.357945+0.588687
## [14] train-rmse:6.163901+0.032520 test-rmse:6.315908+0.570193
## [15] train-rmse:5.356311+0.035271 test-rmse:5.536542+0.517035
## [16] train-rmse:4.741948+0.035781 test-rmse:4.926266+0.510589
## [17] train-rmse:4.275764+0.033822 test-rmse:4.491846+0.501499
## [18] train-rmse:3.923732+0.034852 test-rmse:4.163768+0.472551
## [19] train-rmse:3.656502+0.033676 test-rmse:3.875276+0.436680
## [20] train-rmse:3.450685+0.033489 test-rmse:3.682739+0.416965
## [21] train-rmse:3.287942+0.034668 test-rmse:3.542844+0.395608
## [22] train-rmse:3.159897+0.034985 test-rmse:3.452547+0.390182
## [23] train-rmse:3.053346+0.034238 test-rmse:3.347280+0.349068
## [24] train-rmse:2.964834+0.033394 test-rmse:3.269519+0.342367
## [25] train-rmse:2.886680+0.032282 test-rmse:3.194679+0.336490
## [26] train-rmse:2.814434+0.032696 test-rmse:3.121556+0.310406
## [27] train-rmse:2.747466+0.026438 test-rmse:3.072872+0.297847
## [28] train-rmse:2.687352+0.026735 test-rmse:3.031016+0.309229
## [29] train-rmse:2.631787+0.027045 test-rmse:2.985978+0.319562
## [30] train-rmse:2.579818+0.026598 test-rmse:2.932128+0.298502
## [31] train-rmse:2.522730+0.024119 test-rmse:2.883731+0.278007
## [32] train-rmse:2.475294+0.023876 test-rmse:2.843100+0.285840
## [33] train-rmse:2.429654+0.022483 test-rmse:2.781082+0.289105
## [34] train-rmse:2.385882+0.022161 test-rmse:2.747967+0.288312
## [35] train-rmse:2.343070+0.022035 test-rmse:2.713314+0.296870
## [36] train-rmse:2.301775+0.021245 test-rmse:2.673454+0.282416
## [37] train-rmse:2.261933+0.020435 test-rmse:2.637899+0.282528
## [38] train-rmse:2.221566+0.019816 test-rmse:2.606102+0.271064
## [39] train-rmse:2.182538+0.020330 test-rmse:2.583256+0.282848
## [40] train-rmse:2.142258+0.017242 test-rmse:2.552862+0.271146
## [41] train-rmse:2.106065+0.016370 test-rmse:2.529139+0.275057
## [42] train-rmse:2.071403+0.016111 test-rmse:2.486720+0.268249
## [43] train-rmse:2.036724+0.015171 test-rmse:2.456735+0.273107
## [44] train-rmse:2.002208+0.012455 test-rmse:2.428173+0.267585
## [45] train-rmse:1.969739+0.011796 test-rmse:2.383105+0.264149
## [46] train-rmse:1.938152+0.011525 test-rmse:2.349785+0.246655
## [47] train-rmse:1.906713+0.010287 test-rmse:2.330678+0.251695
## [48] train-rmse:1.875837+0.007503 test-rmse:2.299507+0.244335
## [49] train-rmse:1.845247+0.006949 test-rmse:2.264379+0.244014
## [50] train-rmse:1.816866+0.006540 test-rmse:2.247602+0.240841
## [51] train-rmse:1.788380+0.006375 test-rmse:2.223226+0.245311
## [52] train-rmse:1.760423+0.006797 test-rmse:2.190486+0.234343
## [53] train-rmse:1.733529+0.006791 test-rmse:2.171672+0.226303
## [54] train-rmse:1.706977+0.006430 test-rmse:2.156438+0.225727
## [55] train-rmse:1.680952+0.006266 test-rmse:2.131714+0.237805
## [56] train-rmse:1.655384+0.006223 test-rmse:2.110375+0.233397
## [57] train-rmse:1.630186+0.004929 test-rmse:2.098119+0.237248
## [58] train-rmse:1.605742+0.004130 test-rmse:2.072430+0.235692
## [59] train-rmse:1.582021+0.004738 test-rmse:2.053160+0.240065
## [60] train-rmse:1.559239+0.004932 test-rmse:2.029859+0.239073
## [61] train-rmse:1.536972+0.004962 test-rmse:2.000208+0.240103
## [62] train-rmse:1.515612+0.004887 test-rmse:1.991490+0.238148
## [63] train-rmse:1.494189+0.004891 test-rmse:1.965137+0.233352
## [64] train-rmse:1.473183+0.004689 test-rmse:1.940635+0.236181
## [65] train-rmse:1.452741+0.005391 test-rmse:1.919362+0.241590
## [66] train-rmse:1.431507+0.005862 test-rmse:1.912762+0.244916
## [67] train-rmse:1.410734+0.007043 test-rmse:1.894594+0.245280
## [68] train-rmse:1.391793+0.007461 test-rmse:1.872878+0.240364
## [69] train-rmse:1.372597+0.008372 test-rmse:1.858550+0.241857
## [70] train-rmse:1.352899+0.008639 test-rmse:1.838541+0.238757
## [71] train-rmse:1.334979+0.009339 test-rmse:1.825949+0.234117
## [72] train-rmse:1.315601+0.007417 test-rmse:1.807089+0.226962
## [73] train-rmse:1.297649+0.008668 test-rmse:1.792324+0.229050
## [74] train-rmse:1.280896+0.009147 test-rmse:1.778951+0.230001
## [75] train-rmse:1.264127+0.009122 test-rmse:1.765308+0.220329
## [76] train-rmse:1.247200+0.010981 test-rmse:1.753163+0.224138
## [77] train-rmse:1.228896+0.010733 test-rmse:1.738843+0.221003
## [78] train-rmse:1.213603+0.010873 test-rmse:1.724174+0.227250
## [79] train-rmse:1.198436+0.011413 test-rmse:1.707784+0.225108
## [80] train-rmse:1.182989+0.011433 test-rmse:1.697655+0.229482
## [81] train-rmse:1.168184+0.012199 test-rmse:1.688532+0.233987
## [82] train-rmse:1.152946+0.012994 test-rmse:1.676170+0.233409
## [83] train-rmse:1.138916+0.013066 test-rmse:1.656395+0.235188
## [84] train-rmse:1.123834+0.013258 test-rmse:1.646545+0.233053
## [85] train-rmse:1.110248+0.013276 test-rmse:1.640380+0.232415
## [86] train-rmse:1.096200+0.014121 test-rmse:1.629025+0.229983
## [87] train-rmse:1.083146+0.013983 test-rmse:1.614771+0.230247
## [88] train-rmse:1.070198+0.014059 test-rmse:1.604252+0.228414
## [89] train-rmse:1.058093+0.014418 test-rmse:1.588689+0.230304
## [90] train-rmse:1.045951+0.014729 test-rmse:1.576449+0.230848
## [91] train-rmse:1.032558+0.015587 test-rmse:1.567920+0.233474
## [92] train-rmse:1.020536+0.016052 test-rmse:1.560032+0.237895
## [93] train-rmse:1.008990+0.015696 test-rmse:1.556087+0.240733
## [94] train-rmse:0.997822+0.015718 test-rmse:1.547303+0.236983
## [95] train-rmse:0.986745+0.016316 test-rmse:1.536304+0.231561
## [96] train-rmse:0.975469+0.016477 test-rmse:1.527216+0.230482
## [97] train-rmse:0.964193+0.016506 test-rmse:1.518627+0.234549
## [98] train-rmse:0.952534+0.016845 test-rmse:1.514084+0.236460
## [99] train-rmse:0.942020+0.017825 test-rmse:1.507193+0.237012
## [100] train-rmse:0.931826+0.018031 test-rmse:1.500077+0.237048
## [101] train-rmse:0.922172+0.018285 test-rmse:1.490848+0.232499
## [102] train-rmse:0.912882+0.018438 test-rmse:1.482531+0.233993
## [103] train-rmse:0.903174+0.018884 test-rmse:1.471248+0.234039
## [104] train-rmse:0.894241+0.019323 test-rmse:1.462392+0.234372
## [105] train-rmse:0.884969+0.019539 test-rmse:1.449857+0.230499
## [106] train-rmse:0.875903+0.019935 test-rmse:1.446902+0.233025
## [107] train-rmse:0.866303+0.020344 test-rmse:1.441915+0.230924
## [108] train-rmse:0.857951+0.019969 test-rmse:1.435209+0.230675
## [109] train-rmse:0.849942+0.020035 test-rmse:1.428362+0.230107
## [110] train-rmse:0.841434+0.020475 test-rmse:1.420818+0.231053
## [111] train-rmse:0.833189+0.020643 test-rmse:1.409766+0.232620
## [112] train-rmse:0.825610+0.020937 test-rmse:1.405198+0.234887
## [113] train-rmse:0.818249+0.021103 test-rmse:1.401596+0.234965
## [114] train-rmse:0.810588+0.021061 test-rmse:1.395747+0.231348
## [115] train-rmse:0.802665+0.021979 test-rmse:1.390771+0.233628
## [116] train-rmse:0.795348+0.022307 test-rmse:1.386245+0.235739
## [117] train-rmse:0.788267+0.022194 test-rmse:1.378408+0.234912
## [118] train-rmse:0.780602+0.023587 test-rmse:1.369389+0.236839
## [119] train-rmse:0.772452+0.022765 test-rmse:1.368180+0.237234
## [120] train-rmse:0.764083+0.021786 test-rmse:1.362865+0.234901
## [121] train-rmse:0.756882+0.021524 test-rmse:1.360154+0.234351
## [122] train-rmse:0.750647+0.021744 test-rmse:1.350868+0.226225
## [123] train-rmse:0.743963+0.021147 test-rmse:1.345932+0.229144
## [124] train-rmse:0.737431+0.021774 test-rmse:1.338812+0.227987
## [125] train-rmse:0.731480+0.021826 test-rmse:1.334523+0.229041
## [126] train-rmse:0.724942+0.021622 test-rmse:1.330007+0.232122
## [127] train-rmse:0.719403+0.021849 test-rmse:1.326715+0.231575
## [128] train-rmse:0.713462+0.021349 test-rmse:1.324772+0.233391
## [129] train-rmse:0.708116+0.021420 test-rmse:1.319296+0.230123
## [130] train-rmse:0.702257+0.021940 test-rmse:1.314804+0.229174
## [131] train-rmse:0.697042+0.021975 test-rmse:1.312120+0.231726
## [132] train-rmse:0.691898+0.021984 test-rmse:1.311201+0.230613
## [133] train-rmse:0.685571+0.021813 test-rmse:1.302427+0.229279
## [134] train-rmse:0.680443+0.022147 test-rmse:1.296747+0.233191
## [135] train-rmse:0.675576+0.021874 test-rmse:1.287746+0.228883
## [136] train-rmse:0.670304+0.021896 test-rmse:1.284164+0.227473
## [137] train-rmse:0.665427+0.022242 test-rmse:1.280652+0.226973
## [138] train-rmse:0.660314+0.022424 test-rmse:1.276845+0.225215
## [139] train-rmse:0.654705+0.022606 test-rmse:1.273833+0.223021
## [140] train-rmse:0.650039+0.022548 test-rmse:1.271407+0.223478
## [141] train-rmse:0.645619+0.022507 test-rmse:1.269823+0.225514
## [142] train-rmse:0.641437+0.022367 test-rmse:1.268972+0.225052
## [143] train-rmse:0.637015+0.022852 test-rmse:1.267453+0.224971
## [144] train-rmse:0.632774+0.022737 test-rmse:1.267088+0.225524
## [145] train-rmse:0.628417+0.022639 test-rmse:1.265294+0.224852
## [146] train-rmse:0.623917+0.023082 test-rmse:1.264057+0.224214
## [147] train-rmse:0.619943+0.022927 test-rmse:1.259650+0.226783
## [148] train-rmse:0.616202+0.022898 test-rmse:1.254455+0.229880
## [149] train-rmse:0.611773+0.022788 test-rmse:1.252898+0.229257
## [150] train-rmse:0.607965+0.023157 test-rmse:1.247025+0.231427
## [151] train-rmse:0.603884+0.023046 test-rmse:1.245647+0.232346
## [152] train-rmse:0.599828+0.023500 test-rmse:1.240651+0.235280
## [153] train-rmse:0.595040+0.023823 test-rmse:1.238349+0.236150
## [154] train-rmse:0.591810+0.023785 test-rmse:1.236371+0.236565
## [155] train-rmse:0.588166+0.023556 test-rmse:1.234072+0.236181
## [156] train-rmse:0.584692+0.023550 test-rmse:1.231368+0.234446
## [157] train-rmse:0.581066+0.022935 test-rmse:1.230478+0.235482
## [158] train-rmse:0.577782+0.023059 test-rmse:1.230267+0.235541
## [159] train-rmse:0.574020+0.023344 test-rmse:1.227626+0.235024
## [160] train-rmse:0.569931+0.022544 test-rmse:1.223175+0.236265
## [161] train-rmse:0.566091+0.022468 test-rmse:1.217914+0.233320
## [162] train-rmse:0.562189+0.022487 test-rmse:1.215864+0.234957
## [163] train-rmse:0.558686+0.022101 test-rmse:1.213483+0.236692
## [164] train-rmse:0.555883+0.022160 test-rmse:1.211424+0.236777
## [165] train-rmse:0.552721+0.021962 test-rmse:1.209265+0.234940
## [166] train-rmse:0.549800+0.021879 test-rmse:1.208146+0.235417
## [167] train-rmse:0.546885+0.022024 test-rmse:1.206312+0.236161
## [168] train-rmse:0.543708+0.021828 test-rmse:1.204840+0.236993
## [169] train-rmse:0.540971+0.021784 test-rmse:1.203586+0.236236
## [170] train-rmse:0.538084+0.021968 test-rmse:1.201943+0.236343
## [171] train-rmse:0.535503+0.022269 test-rmse:1.200097+0.237941
## [172] train-rmse:0.532627+0.022381 test-rmse:1.199447+0.239141
## [173] train-rmse:0.530042+0.022614 test-rmse:1.196278+0.241335
## [174] train-rmse:0.527527+0.022484 test-rmse:1.195862+0.242443
## [175] train-rmse:0.525307+0.022426 test-rmse:1.195057+0.242718
## [176] train-rmse:0.522320+0.022603 test-rmse:1.191443+0.244512
## [177] train-rmse:0.518026+0.020423 test-rmse:1.190557+0.243937
## [178] train-rmse:0.515450+0.020412 test-rmse:1.188210+0.243791
## [179] train-rmse:0.513011+0.020685 test-rmse:1.186067+0.244752
## [180] train-rmse:0.508699+0.018941 test-rmse:1.186946+0.246602
## [181] train-rmse:0.506057+0.019040 test-rmse:1.186682+0.247511
## [182] train-rmse:0.503765+0.018981 test-rmse:1.183422+0.248920
## [183] train-rmse:0.500960+0.019180 test-rmse:1.182686+0.248090
## [184] train-rmse:0.498479+0.018788 test-rmse:1.182157+0.249029
## [185] train-rmse:0.495056+0.019477 test-rmse:1.180904+0.250184
## [186] train-rmse:0.492765+0.019730 test-rmse:1.177589+0.247411
## [187] train-rmse:0.490443+0.020319 test-rmse:1.176377+0.247780
## [188] train-rmse:0.487584+0.019916 test-rmse:1.176346+0.247656
## [189] train-rmse:0.485608+0.019838 test-rmse:1.175053+0.247635
## [190] train-rmse:0.483149+0.019787 test-rmse:1.173514+0.247985
## [191] train-rmse:0.481105+0.019777 test-rmse:1.171608+0.248555
## [192] train-rmse:0.479100+0.019207 test-rmse:1.170238+0.248019
## [193] train-rmse:0.477056+0.019156 test-rmse:1.169453+0.249310
## [194] train-rmse:0.475050+0.018840 test-rmse:1.168169+0.249312
## [195] train-rmse:0.472896+0.019046 test-rmse:1.166008+0.249557
## [196] train-rmse:0.470954+0.019380 test-rmse:1.164853+0.250157
## [197] train-rmse:0.468651+0.019238 test-rmse:1.164610+0.250121
## [198] train-rmse:0.466110+0.019110 test-rmse:1.160380+0.250639
## [199] train-rmse:0.463927+0.019312 test-rmse:1.160648+0.252202
## [200] train-rmse:0.462161+0.019314 test-rmse:1.160785+0.253151
## [201] train-rmse:0.460372+0.019524 test-rmse:1.160499+0.253745
## [202] train-rmse:0.458176+0.019513 test-rmse:1.158124+0.255339
## [203] train-rmse:0.456152+0.019826 test-rmse:1.154185+0.255898
## [204] train-rmse:0.454566+0.019839 test-rmse:1.150765+0.257208
## [205] train-rmse:0.452939+0.019894 test-rmse:1.149683+0.256677
## [206] train-rmse:0.451262+0.020214 test-rmse:1.148661+0.257031
## [207] train-rmse:0.449050+0.020247 test-rmse:1.147858+0.257345
## [208] train-rmse:0.447614+0.020237 test-rmse:1.146687+0.257832
## [209] train-rmse:0.445435+0.020389 test-rmse:1.145930+0.258835
## [210] train-rmse:0.443770+0.020478 test-rmse:1.144818+0.258743
## [211] train-rmse:0.442366+0.020316 test-rmse:1.144060+0.258991
## [212] train-rmse:0.440414+0.020170 test-rmse:1.141962+0.260499
## [213] train-rmse:0.438180+0.020529 test-rmse:1.141487+0.261013
## [214] train-rmse:0.436640+0.020332 test-rmse:1.140104+0.260831
## [215] train-rmse:0.435013+0.020346 test-rmse:1.139702+0.261835
## [216] train-rmse:0.433264+0.020001 test-rmse:1.139122+0.263367
## [217] train-rmse:0.431807+0.019969 test-rmse:1.138135+0.263011
## [218] train-rmse:0.430441+0.019848 test-rmse:1.137518+0.263382
## [219] train-rmse:0.429063+0.020137 test-rmse:1.136289+0.263261
## [220] train-rmse:0.427031+0.020639 test-rmse:1.134953+0.263457
## [221] train-rmse:0.425620+0.020633 test-rmse:1.133965+0.263528
## [222] train-rmse:0.424006+0.020482 test-rmse:1.133276+0.264650
## [223] train-rmse:0.422555+0.020342 test-rmse:1.132710+0.264589
## [224] train-rmse:0.421139+0.020287 test-rmse:1.132604+0.265026
## [225] train-rmse:0.419604+0.020273 test-rmse:1.132856+0.265874
## [226] train-rmse:0.418312+0.020072 test-rmse:1.132687+0.265707
## [227] train-rmse:0.417003+0.019918 test-rmse:1.130660+0.267263
## [228] train-rmse:0.415645+0.020166 test-rmse:1.129033+0.267724
## [229] train-rmse:0.414428+0.020253 test-rmse:1.128414+0.267786
## [230] train-rmse:0.413033+0.020821 test-rmse:1.127801+0.267190
## [231] train-rmse:0.411542+0.020748 test-rmse:1.127720+0.267770
## [232] train-rmse:0.410329+0.020822 test-rmse:1.127073+0.267579
## [233] train-rmse:0.409113+0.020902 test-rmse:1.126447+0.267746
## [234] train-rmse:0.408057+0.021056 test-rmse:1.124306+0.268150
## [235] train-rmse:0.406663+0.021109 test-rmse:1.124346+0.268142
## [236] train-rmse:0.404108+0.019712 test-rmse:1.124276+0.267350
## [237] train-rmse:0.401898+0.019633 test-rmse:1.123969+0.267622
## [238] train-rmse:0.400299+0.019992 test-rmse:1.123363+0.267669
## [239] train-rmse:0.399153+0.020245 test-rmse:1.122908+0.268232
## [240] train-rmse:0.397868+0.019942 test-rmse:1.123015+0.267516
## [241] train-rmse:0.396715+0.019634 test-rmse:1.122068+0.268094
## [242] train-rmse:0.395626+0.019649 test-rmse:1.121383+0.267297
## [243] train-rmse:0.394355+0.019500 test-rmse:1.121135+0.267020
## [244] train-rmse:0.393004+0.019477 test-rmse:1.120127+0.267628
## [245] train-rmse:0.391694+0.019529 test-rmse:1.120017+0.267615
## [246] train-rmse:0.390691+0.019441 test-rmse:1.119604+0.268132
## [247] train-rmse:0.389385+0.019589 test-rmse:1.117962+0.268949
## [248] train-rmse:0.387969+0.019705 test-rmse:1.117113+0.269469
## [249] train-rmse:0.386747+0.019604 test-rmse:1.116950+0.270300
## [250] train-rmse:0.384978+0.019310 test-rmse:1.116266+0.270608
## [251] train-rmse:0.383720+0.019263 test-rmse:1.115941+0.271365
## [252] train-rmse:0.382267+0.019387 test-rmse:1.115120+0.271236
## [253] train-rmse:0.380523+0.019248 test-rmse:1.114719+0.269728
## [254] train-rmse:0.379018+0.019291 test-rmse:1.114356+0.269805
## [255] train-rmse:0.377910+0.019408 test-rmse:1.113778+0.270327
## [256] train-rmse:0.376648+0.019356 test-rmse:1.111708+0.272497
## [257] train-rmse:0.375833+0.019366 test-rmse:1.111482+0.272862
## [258] train-rmse:0.375057+0.019330 test-rmse:1.110964+0.273065
## [259] train-rmse:0.373353+0.020170 test-rmse:1.110526+0.272262
## [260] train-rmse:0.371562+0.019447 test-rmse:1.109749+0.272523
## [261] train-rmse:0.369722+0.020074 test-rmse:1.110543+0.273488
## [262] train-rmse:0.367869+0.019689 test-rmse:1.111340+0.274632
## [263] train-rmse:0.367140+0.019788 test-rmse:1.110666+0.274362
## [264] train-rmse:0.365712+0.018998 test-rmse:1.109851+0.274352
## [265] train-rmse:0.364434+0.019645 test-rmse:1.109817+0.274293
## [266] train-rmse:0.362999+0.019863 test-rmse:1.109461+0.274234
## [267] train-rmse:0.361613+0.019623 test-rmse:1.109695+0.274346
## [268] train-rmse:0.360773+0.019663 test-rmse:1.109301+0.275094
## [269] train-rmse:0.359749+0.019844 test-rmse:1.108037+0.274197
## [270] train-rmse:0.357994+0.019855 test-rmse:1.107237+0.274957
## [271] train-rmse:0.357080+0.019915 test-rmse:1.107210+0.275390
## [272] train-rmse:0.355803+0.019390 test-rmse:1.105431+0.276029
## [273] train-rmse:0.355003+0.019618 test-rmse:1.104902+0.276098
## [274] train-rmse:0.354170+0.019624 test-rmse:1.104375+0.275061
## [275] train-rmse:0.353005+0.019613 test-rmse:1.103686+0.275045
## [276] train-rmse:0.351767+0.019513 test-rmse:1.103244+0.275003
## [277] train-rmse:0.350810+0.019491 test-rmse:1.102841+0.274415
## [278] train-rmse:0.349130+0.019671 test-rmse:1.102435+0.274166
## [279] train-rmse:0.348414+0.019625 test-rmse:1.102070+0.273890
## [280] train-rmse:0.347455+0.019760 test-rmse:1.101632+0.274263
## [281] train-rmse:0.346641+0.019739 test-rmse:1.101181+0.274114
## [282] train-rmse:0.345031+0.019700 test-rmse:1.099548+0.274273
## [283] train-rmse:0.343754+0.019865 test-rmse:1.099412+0.274421
## [284] train-rmse:0.342764+0.019923 test-rmse:1.099115+0.274829
## [285] train-rmse:0.341659+0.019743 test-rmse:1.099049+0.274619
## [286] train-rmse:0.340529+0.019353 test-rmse:1.097974+0.274815
## [287] train-rmse:0.339817+0.019345 test-rmse:1.097136+0.274329
## [288] train-rmse:0.339224+0.019352 test-rmse:1.096687+0.274345
## [289] train-rmse:0.338386+0.019299 test-rmse:1.097016+0.274883
## [290] train-rmse:0.337370+0.019510 test-rmse:1.096212+0.274172
## [291] train-rmse:0.336244+0.019674 test-rmse:1.095778+0.273548
## [292] train-rmse:0.335221+0.019635 test-rmse:1.095417+0.273810
## [293] train-rmse:0.334274+0.019708 test-rmse:1.094863+0.274333
## [294] train-rmse:0.333395+0.019649 test-rmse:1.094468+0.274653
## [295] train-rmse:0.332681+0.019846 test-rmse:1.094375+0.274980
## [296] train-rmse:0.330931+0.019214 test-rmse:1.093997+0.274970
## [297] train-rmse:0.329714+0.018523 test-rmse:1.094135+0.274550
## [298] train-rmse:0.329111+0.018538 test-rmse:1.093408+0.274513
## [299] train-rmse:0.328368+0.018894 test-rmse:1.093832+0.274967
## [300] train-rmse:0.327608+0.019043 test-rmse:1.093752+0.275495
## [301] train-rmse:0.326842+0.019077 test-rmse:1.093239+0.275846
## [302] train-rmse:0.326056+0.019218 test-rmse:1.092844+0.275273
## [303] train-rmse:0.324802+0.018660 test-rmse:1.092415+0.275339
## [304] train-rmse:0.324228+0.018644 test-rmse:1.092099+0.275082
## [305] train-rmse:0.323701+0.018589 test-rmse:1.091442+0.275081
## [306] train-rmse:0.322410+0.018902 test-rmse:1.090704+0.275459
## [307] train-rmse:0.321207+0.018896 test-rmse:1.090608+0.275280
## [308] train-rmse:0.319869+0.017920 test-rmse:1.090895+0.275087
## [309] train-rmse:0.319287+0.017943 test-rmse:1.090531+0.274645
## [310] train-rmse:0.318737+0.017919 test-rmse:1.090504+0.275084
## [311] train-rmse:0.317534+0.017945 test-rmse:1.090965+0.275284
## [312] train-rmse:0.316554+0.017737 test-rmse:1.090829+0.274979
## [313] train-rmse:0.315951+0.017681 test-rmse:1.089260+0.275448
## [314] train-rmse:0.315290+0.017781 test-rmse:1.088562+0.275102
## [315] train-rmse:0.313979+0.017218 test-rmse:1.089375+0.274693
## [316] train-rmse:0.313067+0.017392 test-rmse:1.089399+0.274413
## [317] train-rmse:0.312448+0.017362 test-rmse:1.089199+0.274877
## [318] train-rmse:0.311892+0.017418 test-rmse:1.089011+0.275043
## [319] train-rmse:0.310857+0.017421 test-rmse:1.088938+0.274807
## [320] train-rmse:0.309936+0.017968 test-rmse:1.088899+0.274410
## Stopping. Best iteration:
## [314] train-rmse:0.315290+0.017781 test-rmse:1.088562+0.275102
##
## 37
## [1] train-rmse:86.218085+0.097587 test-rmse:86.218257+0.495906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.119772+0.083348 test-rmse:69.119559+0.523711
## [3] train-rmse:55.450065+0.073313 test-rmse:55.449316+0.550523
## [4] train-rmse:44.530696+0.066998 test-rmse:44.529209+0.577462
## [5] train-rmse:35.819717+0.064113 test-rmse:35.817187+0.605511
## [6] train-rmse:28.868193+0.060253 test-rmse:28.910774+0.605132
## [7] train-rmse:23.310282+0.045896 test-rmse:23.406557+0.627461
## [8] train-rmse:18.867857+0.037343 test-rmse:18.979796+0.599473
## [9] train-rmse:15.320496+0.031877 test-rmse:15.416832+0.552494
## [10] train-rmse:12.495280+0.029742 test-rmse:12.622691+0.589008
## [11] train-rmse:10.249727+0.030474 test-rmse:10.357590+0.580554
## [12] train-rmse:8.469106+0.033116 test-rmse:8.581459+0.544196
## [13] train-rmse:7.067838+0.037151 test-rmse:7.223211+0.548161
## [14] train-rmse:5.965211+0.045675 test-rmse:6.097338+0.495274
## [15] train-rmse:5.110312+0.049227 test-rmse:5.273886+0.502933
## [16] train-rmse:4.446475+0.050509 test-rmse:4.635513+0.508720
## [17] train-rmse:3.929727+0.051423 test-rmse:4.176275+0.490657
## [18] train-rmse:3.530173+0.051804 test-rmse:3.808676+0.424984
## [19] train-rmse:3.226414+0.047610 test-rmse:3.520594+0.369592
## [20] train-rmse:2.979064+0.025503 test-rmse:3.313464+0.347687
## [21] train-rmse:2.794493+0.025309 test-rmse:3.154703+0.356407
## [22] train-rmse:2.645804+0.024712 test-rmse:3.032894+0.365605
## [23] train-rmse:2.522955+0.023611 test-rmse:2.912602+0.332101
## [24] train-rmse:2.416105+0.022665 test-rmse:2.838580+0.301444
## [25] train-rmse:2.331027+0.020790 test-rmse:2.766785+0.292535
## [26] train-rmse:2.247177+0.014072 test-rmse:2.718555+0.295144
## [27] train-rmse:2.177042+0.014143 test-rmse:2.639537+0.287034
## [28] train-rmse:2.111555+0.016866 test-rmse:2.577315+0.279900
## [29] train-rmse:2.046492+0.011060 test-rmse:2.530067+0.272708
## [30] train-rmse:1.991383+0.010206 test-rmse:2.490141+0.282484
## [31] train-rmse:1.939821+0.010608 test-rmse:2.443497+0.281249
## [32] train-rmse:1.890277+0.011569 test-rmse:2.396193+0.275094
## [33] train-rmse:1.841614+0.013109 test-rmse:2.350421+0.266005
## [34] train-rmse:1.795195+0.013309 test-rmse:2.306006+0.242627
## [35] train-rmse:1.749466+0.012582 test-rmse:2.266859+0.242224
## [36] train-rmse:1.707352+0.012688 test-rmse:2.240713+0.253119
## [37] train-rmse:1.667585+0.013658 test-rmse:2.207986+0.254870
## [38] train-rmse:1.626479+0.015783 test-rmse:2.165326+0.245010
## [39] train-rmse:1.589034+0.015297 test-rmse:2.135029+0.235116
## [40] train-rmse:1.552012+0.015419 test-rmse:2.098025+0.224196
## [41] train-rmse:1.516658+0.016026 test-rmse:2.069857+0.230202
## [42] train-rmse:1.480254+0.014729 test-rmse:2.047961+0.235476
## [43] train-rmse:1.446928+0.014844 test-rmse:2.027200+0.237010
## [44] train-rmse:1.411770+0.016230 test-rmse:1.994632+0.235586
## [45] train-rmse:1.382116+0.016030 test-rmse:1.971452+0.243921
## [46] train-rmse:1.352897+0.016774 test-rmse:1.945252+0.242433
## [47] train-rmse:1.322823+0.014340 test-rmse:1.916883+0.233120
## [48] train-rmse:1.294357+0.013421 test-rmse:1.898089+0.235149
## [49] train-rmse:1.266166+0.012941 test-rmse:1.865289+0.227943
## [50] train-rmse:1.238275+0.014042 test-rmse:1.848276+0.226408
## [51] train-rmse:1.211182+0.015544 test-rmse:1.833215+0.230915
## [52] train-rmse:1.186385+0.015749 test-rmse:1.816686+0.228186
## [53] train-rmse:1.162392+0.015206 test-rmse:1.791249+0.225396
## [54] train-rmse:1.138856+0.013574 test-rmse:1.765491+0.221103
## [55] train-rmse:1.116635+0.013538 test-rmse:1.748398+0.219160
## [56] train-rmse:1.094095+0.012878 test-rmse:1.735381+0.226489
## [57] train-rmse:1.073062+0.012225 test-rmse:1.715582+0.229782
## [58] train-rmse:1.051386+0.010883 test-rmse:1.705234+0.229679
## [59] train-rmse:1.028823+0.011918 test-rmse:1.687666+0.229031
## [60] train-rmse:1.008029+0.012413 test-rmse:1.667919+0.222529
## [61] train-rmse:0.989948+0.012554 test-rmse:1.654684+0.226704
## [62] train-rmse:0.972911+0.012225 test-rmse:1.642695+0.226391
## [63] train-rmse:0.955790+0.011915 test-rmse:1.624150+0.225998
## [64] train-rmse:0.937422+0.012273 test-rmse:1.613666+0.224736
## [65] train-rmse:0.920370+0.012278 test-rmse:1.600685+0.231221
## [66] train-rmse:0.902850+0.011287 test-rmse:1.589817+0.225198
## [67] train-rmse:0.886272+0.012304 test-rmse:1.577939+0.225831
## [68] train-rmse:0.871071+0.011740 test-rmse:1.568569+0.227074
## [69] train-rmse:0.854409+0.015629 test-rmse:1.555822+0.226153
## [70] train-rmse:0.840212+0.016067 test-rmse:1.542672+0.228121
## [71] train-rmse:0.824723+0.017606 test-rmse:1.528310+0.231083
## [72] train-rmse:0.810258+0.018028 test-rmse:1.520593+0.226342
## [73] train-rmse:0.795796+0.016770 test-rmse:1.513377+0.230321
## [74] train-rmse:0.783715+0.016879 test-rmse:1.503872+0.231239
## [75] train-rmse:0.771058+0.016730 test-rmse:1.499243+0.230216
## [76] train-rmse:0.757739+0.016611 test-rmse:1.491165+0.233399
## [77] train-rmse:0.744815+0.015308 test-rmse:1.469709+0.223441
## [78] train-rmse:0.732932+0.015140 test-rmse:1.457105+0.226109
## [79] train-rmse:0.721079+0.015839 test-rmse:1.451261+0.228477
## [80] train-rmse:0.707562+0.016161 test-rmse:1.448437+0.233584
## [81] train-rmse:0.697124+0.016368 test-rmse:1.440173+0.232952
## [82] train-rmse:0.685447+0.016118 test-rmse:1.433172+0.232485
## [83] train-rmse:0.675434+0.016526 test-rmse:1.421965+0.234681
## [84] train-rmse:0.666332+0.016534 test-rmse:1.416147+0.236127
## [85] train-rmse:0.655429+0.016308 test-rmse:1.404917+0.225163
## [86] train-rmse:0.646513+0.016102 test-rmse:1.397432+0.227815
## [87] train-rmse:0.636745+0.015610 test-rmse:1.391181+0.228484
## [88] train-rmse:0.627441+0.015780 test-rmse:1.387988+0.228267
## [89] train-rmse:0.617163+0.017370 test-rmse:1.383233+0.226689
## [90] train-rmse:0.607855+0.018100 test-rmse:1.379000+0.228835
## [91] train-rmse:0.598474+0.017392 test-rmse:1.374718+0.231132
## [92] train-rmse:0.590298+0.016903 test-rmse:1.367667+0.232239
## [93] train-rmse:0.583256+0.016407 test-rmse:1.363660+0.233067
## [94] train-rmse:0.575124+0.016839 test-rmse:1.355879+0.227596
## [95] train-rmse:0.567983+0.016688 test-rmse:1.350765+0.230369
## [96] train-rmse:0.559971+0.016807 test-rmse:1.346833+0.232306
## [97] train-rmse:0.552719+0.016591 test-rmse:1.344681+0.233092
## [98] train-rmse:0.546466+0.016402 test-rmse:1.332491+0.227854
## [99] train-rmse:0.539259+0.016588 test-rmse:1.326287+0.231868
## [100] train-rmse:0.533067+0.016666 test-rmse:1.326975+0.232141
## [101] train-rmse:0.526825+0.017056 test-rmse:1.322727+0.233315
## [102] train-rmse:0.520826+0.017172 test-rmse:1.315297+0.231413
## [103] train-rmse:0.515526+0.017308 test-rmse:1.309851+0.234179
## [104] train-rmse:0.509678+0.017301 test-rmse:1.308103+0.233034
## [105] train-rmse:0.502919+0.017172 test-rmse:1.304113+0.232026
## [106] train-rmse:0.498074+0.017184 test-rmse:1.296587+0.235577
## [107] train-rmse:0.493076+0.016534 test-rmse:1.294050+0.237308
## [108] train-rmse:0.487719+0.016779 test-rmse:1.292180+0.237344
## [109] train-rmse:0.482544+0.017096 test-rmse:1.290785+0.237438
## [110] train-rmse:0.476465+0.014189 test-rmse:1.288659+0.239182
## [111] train-rmse:0.471687+0.013881 test-rmse:1.285122+0.240555
## [112] train-rmse:0.465905+0.012835 test-rmse:1.279609+0.235471
## [113] train-rmse:0.461207+0.012359 test-rmse:1.280028+0.236076
## [114] train-rmse:0.455781+0.011361 test-rmse:1.276339+0.236583
## [115] train-rmse:0.451068+0.012037 test-rmse:1.271331+0.236309
## [116] train-rmse:0.447238+0.011865 test-rmse:1.268678+0.236953
## [117] train-rmse:0.442458+0.011962 test-rmse:1.267052+0.238211
## [118] train-rmse:0.438343+0.012242 test-rmse:1.267258+0.238247
## [119] train-rmse:0.434012+0.012541 test-rmse:1.266207+0.240452
## [120] train-rmse:0.428833+0.010894 test-rmse:1.261236+0.233847
## [121] train-rmse:0.425014+0.010538 test-rmse:1.260180+0.233374
## [122] train-rmse:0.420770+0.011371 test-rmse:1.258420+0.234541
## [123] train-rmse:0.416822+0.010799 test-rmse:1.255988+0.234896
## [124] train-rmse:0.412934+0.011452 test-rmse:1.254284+0.235212
## [125] train-rmse:0.408913+0.011928 test-rmse:1.251880+0.236412
## [126] train-rmse:0.405013+0.011958 test-rmse:1.251345+0.236865
## [127] train-rmse:0.401955+0.011739 test-rmse:1.248964+0.238345
## [128] train-rmse:0.397943+0.011174 test-rmse:1.243539+0.232523
## [129] train-rmse:0.394468+0.010637 test-rmse:1.242035+0.233363
## [130] train-rmse:0.391350+0.010735 test-rmse:1.241013+0.233420
## [131] train-rmse:0.388152+0.010724 test-rmse:1.238078+0.234195
## [132] train-rmse:0.384403+0.010630 test-rmse:1.235927+0.235539
## [133] train-rmse:0.380629+0.011472 test-rmse:1.235839+0.237467
## [134] train-rmse:0.377121+0.011740 test-rmse:1.235184+0.238562
## [135] train-rmse:0.374411+0.011747 test-rmse:1.234635+0.237890
## [136] train-rmse:0.371660+0.011846 test-rmse:1.233701+0.236724
## [137] train-rmse:0.366258+0.013477 test-rmse:1.233028+0.237872
## [138] train-rmse:0.363212+0.013389 test-rmse:1.228912+0.235220
## [139] train-rmse:0.360581+0.013242 test-rmse:1.226935+0.236371
## [140] train-rmse:0.358069+0.013624 test-rmse:1.226111+0.236706
## [141] train-rmse:0.355481+0.013864 test-rmse:1.225224+0.236784
## [142] train-rmse:0.352314+0.013269 test-rmse:1.219778+0.235295
## [143] train-rmse:0.350033+0.013142 test-rmse:1.219673+0.235163
## [144] train-rmse:0.347264+0.013570 test-rmse:1.217022+0.237423
## [145] train-rmse:0.344111+0.014935 test-rmse:1.215589+0.237608
## [146] train-rmse:0.341533+0.015035 test-rmse:1.212065+0.237289
## [147] train-rmse:0.339120+0.015049 test-rmse:1.211449+0.239414
## [148] train-rmse:0.336569+0.015145 test-rmse:1.211427+0.239699
## [149] train-rmse:0.332660+0.013934 test-rmse:1.210522+0.239319
## [150] train-rmse:0.329852+0.013585 test-rmse:1.208832+0.242306
## [151] train-rmse:0.326423+0.013631 test-rmse:1.207900+0.243941
## [152] train-rmse:0.324460+0.014043 test-rmse:1.204398+0.243672
## [153] train-rmse:0.322509+0.013760 test-rmse:1.202974+0.244806
## [154] train-rmse:0.319949+0.013287 test-rmse:1.202474+0.245078
## [155] train-rmse:0.317272+0.012676 test-rmse:1.201976+0.245507
## [156] train-rmse:0.314981+0.012655 test-rmse:1.201107+0.245729
## [157] train-rmse:0.312810+0.012578 test-rmse:1.200809+0.245370
## [158] train-rmse:0.310752+0.012625 test-rmse:1.199694+0.246038
## [159] train-rmse:0.307275+0.013779 test-rmse:1.198820+0.247805
## [160] train-rmse:0.305149+0.013835 test-rmse:1.198558+0.248621
## [161] train-rmse:0.302828+0.013968 test-rmse:1.198399+0.249399
## [162] train-rmse:0.300902+0.014238 test-rmse:1.196345+0.248596
## [163] train-rmse:0.298821+0.014223 test-rmse:1.196199+0.249062
## [164] train-rmse:0.296966+0.014578 test-rmse:1.194969+0.249379
## [165] train-rmse:0.294324+0.014146 test-rmse:1.194903+0.249514
## [166] train-rmse:0.292365+0.014295 test-rmse:1.194420+0.249325
## [167] train-rmse:0.290368+0.014587 test-rmse:1.193358+0.250015
## [168] train-rmse:0.288271+0.014565 test-rmse:1.192227+0.250035
## [169] train-rmse:0.286590+0.014389 test-rmse:1.191658+0.249835
## [170] train-rmse:0.284676+0.014837 test-rmse:1.190445+0.251194
## [171] train-rmse:0.282941+0.015480 test-rmse:1.190057+0.251907
## [172] train-rmse:0.281283+0.015295 test-rmse:1.189203+0.252052
## [173] train-rmse:0.279315+0.015095 test-rmse:1.190307+0.251100
## [174] train-rmse:0.277101+0.015027 test-rmse:1.188778+0.250163
## [175] train-rmse:0.274832+0.014619 test-rmse:1.188279+0.251287
## [176] train-rmse:0.273324+0.014483 test-rmse:1.187604+0.252405
## [177] train-rmse:0.270461+0.014602 test-rmse:1.186740+0.253087
## [178] train-rmse:0.268920+0.014625 test-rmse:1.186305+0.253319
## [179] train-rmse:0.266326+0.013905 test-rmse:1.185892+0.253495
## [180] train-rmse:0.264928+0.014414 test-rmse:1.185207+0.253394
## [181] train-rmse:0.263088+0.014519 test-rmse:1.185297+0.253672
## [182] train-rmse:0.261045+0.014438 test-rmse:1.185051+0.253917
## [183] train-rmse:0.259660+0.014416 test-rmse:1.184459+0.254079
## [184] train-rmse:0.257552+0.014782 test-rmse:1.184200+0.254084
## [185] train-rmse:0.255366+0.013962 test-rmse:1.184113+0.254097
## [186] train-rmse:0.253964+0.013908 test-rmse:1.183221+0.253800
## [187] train-rmse:0.252654+0.013680 test-rmse:1.182946+0.253510
## [188] train-rmse:0.251253+0.014213 test-rmse:1.182621+0.254430
## [189] train-rmse:0.249539+0.014180 test-rmse:1.182392+0.254909
## [190] train-rmse:0.246990+0.014087 test-rmse:1.182591+0.255435
## [191] train-rmse:0.245334+0.014313 test-rmse:1.182315+0.255208
## [192] train-rmse:0.243582+0.013891 test-rmse:1.182526+0.255043
## [193] train-rmse:0.241720+0.013827 test-rmse:1.181266+0.254936
## [194] train-rmse:0.240227+0.013609 test-rmse:1.180625+0.254845
## [195] train-rmse:0.238399+0.013560 test-rmse:1.179819+0.255875
## [196] train-rmse:0.237281+0.013567 test-rmse:1.179522+0.255978
## [197] train-rmse:0.236090+0.013946 test-rmse:1.179227+0.255945
## [198] train-rmse:0.234833+0.014033 test-rmse:1.179087+0.256213
## [199] train-rmse:0.234024+0.013977 test-rmse:1.178383+0.256076
## [200] train-rmse:0.232956+0.013937 test-rmse:1.177539+0.255557
## [201] train-rmse:0.231144+0.014828 test-rmse:1.177163+0.255126
## [202] train-rmse:0.228987+0.015045 test-rmse:1.177378+0.256911
## [203] train-rmse:0.227084+0.014708 test-rmse:1.176843+0.257295
## [204] train-rmse:0.225846+0.014958 test-rmse:1.176529+0.257436
## [205] train-rmse:0.223731+0.014531 test-rmse:1.176392+0.257031
## [206] train-rmse:0.222488+0.014462 test-rmse:1.175785+0.256707
## [207] train-rmse:0.221394+0.014419 test-rmse:1.175278+0.256928
## [208] train-rmse:0.219975+0.014171 test-rmse:1.174585+0.256428
## [209] train-rmse:0.218104+0.014235 test-rmse:1.174358+0.255982
## [210] train-rmse:0.216945+0.014365 test-rmse:1.173687+0.256538
## [211] train-rmse:0.215890+0.014527 test-rmse:1.173940+0.256871
## [212] train-rmse:0.215157+0.014592 test-rmse:1.173248+0.256954
## [213] train-rmse:0.213299+0.014557 test-rmse:1.173184+0.256805
## [214] train-rmse:0.212081+0.014971 test-rmse:1.172931+0.257379
## [215] train-rmse:0.211212+0.014852 test-rmse:1.172665+0.257484
## [216] train-rmse:0.210147+0.014881 test-rmse:1.172155+0.258047
## [217] train-rmse:0.209060+0.014776 test-rmse:1.171935+0.258099
## [218] train-rmse:0.208035+0.014837 test-rmse:1.170293+0.259171
## [219] train-rmse:0.206723+0.015298 test-rmse:1.169574+0.259138
## [220] train-rmse:0.205178+0.015124 test-rmse:1.169576+0.259399
## [221] train-rmse:0.203802+0.014943 test-rmse:1.168855+0.259150
## [222] train-rmse:0.202359+0.014694 test-rmse:1.168663+0.259090
## [223] train-rmse:0.200983+0.014454 test-rmse:1.168851+0.258899
## [224] train-rmse:0.199922+0.014727 test-rmse:1.168820+0.258855
## [225] train-rmse:0.198405+0.015580 test-rmse:1.167901+0.259499
## [226] train-rmse:0.197090+0.015397 test-rmse:1.167683+0.259383
## [227] train-rmse:0.195530+0.015060 test-rmse:1.167780+0.259686
## [228] train-rmse:0.194314+0.015053 test-rmse:1.167248+0.258927
## [229] train-rmse:0.193150+0.014893 test-rmse:1.166746+0.259168
## [230] train-rmse:0.192172+0.014681 test-rmse:1.166466+0.259477
## [231] train-rmse:0.191488+0.014628 test-rmse:1.166376+0.259777
## [232] train-rmse:0.190240+0.014200 test-rmse:1.166103+0.259705
## [233] train-rmse:0.189243+0.014158 test-rmse:1.166224+0.259793
## [234] train-rmse:0.188315+0.014067 test-rmse:1.165433+0.259377
## [235] train-rmse:0.187361+0.014352 test-rmse:1.165320+0.259525
## [236] train-rmse:0.186213+0.014431 test-rmse:1.164796+0.259421
## [237] train-rmse:0.185146+0.014452 test-rmse:1.164796+0.259480
## [238] train-rmse:0.184010+0.014506 test-rmse:1.164591+0.259845
## [239] train-rmse:0.182912+0.014402 test-rmse:1.164781+0.260303
## [240] train-rmse:0.181907+0.014061 test-rmse:1.164242+0.260288
## [241] train-rmse:0.181027+0.013947 test-rmse:1.163201+0.261384
## [242] train-rmse:0.180219+0.013992 test-rmse:1.163283+0.261784
## [243] train-rmse:0.178941+0.014488 test-rmse:1.163077+0.261796
## [244] train-rmse:0.177695+0.014443 test-rmse:1.163105+0.261943
## [245] train-rmse:0.177019+0.014330 test-rmse:1.162993+0.262310
## [246] train-rmse:0.175178+0.013268 test-rmse:1.162253+0.261599
## [247] train-rmse:0.173973+0.013711 test-rmse:1.162011+0.261606
## [248] train-rmse:0.173140+0.014034 test-rmse:1.161401+0.262000
## [249] train-rmse:0.172046+0.014586 test-rmse:1.161441+0.262146
## [250] train-rmse:0.171200+0.014672 test-rmse:1.161068+0.262259
## [251] train-rmse:0.170127+0.014467 test-rmse:1.161323+0.262409
## [252] train-rmse:0.168924+0.014258 test-rmse:1.161140+0.262760
## [253] train-rmse:0.168029+0.014416 test-rmse:1.160799+0.262748
## [254] train-rmse:0.167292+0.014383 test-rmse:1.160568+0.262938
## [255] train-rmse:0.166645+0.014455 test-rmse:1.160067+0.263614
## [256] train-rmse:0.165833+0.014497 test-rmse:1.159792+0.263510
## [257] train-rmse:0.165220+0.014438 test-rmse:1.159891+0.263807
## [258] train-rmse:0.164314+0.014163 test-rmse:1.160131+0.263888
## [259] train-rmse:0.163671+0.014311 test-rmse:1.160189+0.264102
## [260] train-rmse:0.162897+0.014324 test-rmse:1.160123+0.264045
## [261] train-rmse:0.161766+0.014387 test-rmse:1.159728+0.263701
## [262] train-rmse:0.160545+0.014456 test-rmse:1.159857+0.263730
## [263] train-rmse:0.159265+0.014023 test-rmse:1.159688+0.264041
## [264] train-rmse:0.158420+0.013854 test-rmse:1.159498+0.264071
## [265] train-rmse:0.157465+0.014173 test-rmse:1.159623+0.264026
## [266] train-rmse:0.156666+0.014188 test-rmse:1.159616+0.263840
## [267] train-rmse:0.156012+0.014054 test-rmse:1.159211+0.263613
## [268] train-rmse:0.155353+0.014122 test-rmse:1.159268+0.263968
## [269] train-rmse:0.154427+0.014252 test-rmse:1.159014+0.264087
## [270] train-rmse:0.153663+0.014042 test-rmse:1.158773+0.263288
## [271] train-rmse:0.152733+0.013872 test-rmse:1.158870+0.263565
## [272] train-rmse:0.151853+0.013598 test-rmse:1.158901+0.263566
## [273] train-rmse:0.150848+0.013701 test-rmse:1.158943+0.264051
## [274] train-rmse:0.149816+0.013421 test-rmse:1.157604+0.262095
## [275] train-rmse:0.147975+0.012354 test-rmse:1.157312+0.261926
## [276] train-rmse:0.146959+0.012248 test-rmse:1.157023+0.262097
## [277] train-rmse:0.146377+0.012521 test-rmse:1.156640+0.262359
## [278] train-rmse:0.145722+0.012468 test-rmse:1.156648+0.262166
## [279] train-rmse:0.144846+0.012339 test-rmse:1.157014+0.262167
## [280] train-rmse:0.143812+0.011879 test-rmse:1.156762+0.262371
## [281] train-rmse:0.142940+0.012114 test-rmse:1.156546+0.262167
## [282] train-rmse:0.142180+0.012049 test-rmse:1.156446+0.262252
## [283] train-rmse:0.141492+0.011873 test-rmse:1.156148+0.262343
## [284] train-rmse:0.140896+0.011854 test-rmse:1.155857+0.262322
## [285] train-rmse:0.139922+0.011551 test-rmse:1.156200+0.262509
## [286] train-rmse:0.138858+0.011582 test-rmse:1.155865+0.263176
## [287] train-rmse:0.138096+0.011532 test-rmse:1.156208+0.263293
## [288] train-rmse:0.137110+0.011601 test-rmse:1.156368+0.263226
## [289] train-rmse:0.136198+0.011500 test-rmse:1.156030+0.263247
## [290] train-rmse:0.135312+0.011277 test-rmse:1.156082+0.263169
## Stopping. Best iteration:
## [284] train-rmse:0.140896+0.011854 test-rmse:1.155857+0.262322
##
## 38
## [1] train-rmse:86.218085+0.097587 test-rmse:86.218257+0.495906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.119772+0.083348 test-rmse:69.119559+0.523711
## [3] train-rmse:55.450065+0.073313 test-rmse:55.449316+0.550523
## [4] train-rmse:44.530696+0.066998 test-rmse:44.529209+0.577462
## [5] train-rmse:35.819717+0.064113 test-rmse:35.817187+0.605511
## [6] train-rmse:28.868193+0.060253 test-rmse:28.910774+0.605132
## [7] train-rmse:23.310282+0.045896 test-rmse:23.406557+0.627461
## [8] train-rmse:18.866006+0.037435 test-rmse:18.967372+0.593530
## [9] train-rmse:15.310408+0.033279 test-rmse:15.412543+0.568451
## [10] train-rmse:12.469649+0.030121 test-rmse:12.553802+0.547333
## [11] train-rmse:10.201716+0.025635 test-rmse:10.315598+0.567040
## [12] train-rmse:8.392718+0.021801 test-rmse:8.486664+0.542367
## [13] train-rmse:6.956828+0.027223 test-rmse:7.070615+0.515208
## [14] train-rmse:5.823456+0.028633 test-rmse:5.965077+0.527750
## [15] train-rmse:4.931354+0.031885 test-rmse:5.076442+0.490182
## [16] train-rmse:4.232689+0.033822 test-rmse:4.415469+0.476963
## [17] train-rmse:3.676451+0.028771 test-rmse:3.877354+0.440170
## [18] train-rmse:3.245486+0.034802 test-rmse:3.501201+0.437606
## [19] train-rmse:2.905614+0.032326 test-rmse:3.194676+0.406835
## [20] train-rmse:2.638290+0.036327 test-rmse:2.960297+0.366572
## [21] train-rmse:2.432202+0.037506 test-rmse:2.789654+0.337009
## [22] train-rmse:2.265150+0.042802 test-rmse:2.651296+0.310619
## [23] train-rmse:2.127086+0.040069 test-rmse:2.538440+0.289776
## [24] train-rmse:2.014603+0.038954 test-rmse:2.448708+0.298286
## [25] train-rmse:1.912718+0.041912 test-rmse:2.365478+0.294881
## [26] train-rmse:1.824213+0.042572 test-rmse:2.295223+0.274256
## [27] train-rmse:1.751265+0.042173 test-rmse:2.225413+0.271267
## [28] train-rmse:1.682198+0.037851 test-rmse:2.164430+0.263222
## [29] train-rmse:1.621622+0.037248 test-rmse:2.113382+0.265547
## [30] train-rmse:1.553579+0.023052 test-rmse:2.068851+0.265307
## [31] train-rmse:1.501768+0.021349 test-rmse:2.034422+0.266447
## [32] train-rmse:1.452346+0.020311 test-rmse:1.993328+0.262706
## [33] train-rmse:1.407518+0.019259 test-rmse:1.957455+0.272803
## [34] train-rmse:1.357203+0.021014 test-rmse:1.908490+0.245073
## [35] train-rmse:1.313674+0.020872 test-rmse:1.867169+0.246047
## [36] train-rmse:1.271876+0.017279 test-rmse:1.845775+0.243175
## [37] train-rmse:1.232905+0.017033 test-rmse:1.824238+0.243374
## [38] train-rmse:1.192917+0.021756 test-rmse:1.802296+0.252524
## [39] train-rmse:1.158170+0.022243 test-rmse:1.776657+0.255054
## [40] train-rmse:1.117669+0.019533 test-rmse:1.752363+0.249964
## [41] train-rmse:1.084004+0.019090 test-rmse:1.728570+0.253049
## [42] train-rmse:1.052970+0.017986 test-rmse:1.693650+0.247926
## [43] train-rmse:1.023588+0.017526 test-rmse:1.674904+0.252205
## [44] train-rmse:0.994469+0.017166 test-rmse:1.644183+0.254878
## [45] train-rmse:0.962334+0.017064 test-rmse:1.624755+0.255962
## [46] train-rmse:0.936258+0.018449 test-rmse:1.601403+0.259215
## [47] train-rmse:0.908328+0.020998 test-rmse:1.583935+0.260341
## [48] train-rmse:0.883231+0.019437 test-rmse:1.567804+0.263714
## [49] train-rmse:0.858719+0.017926 test-rmse:1.543494+0.267655
## [50] train-rmse:0.836007+0.017725 test-rmse:1.533069+0.268552
## [51] train-rmse:0.815321+0.016445 test-rmse:1.516840+0.267250
## [52] train-rmse:0.794997+0.016553 test-rmse:1.502714+0.271180
## [53] train-rmse:0.771043+0.018321 test-rmse:1.485964+0.274375
## [54] train-rmse:0.752063+0.019184 test-rmse:1.467327+0.274971
## [55] train-rmse:0.734268+0.019500 test-rmse:1.460307+0.280412
## [56] train-rmse:0.714490+0.022633 test-rmse:1.439122+0.266890
## [57] train-rmse:0.696843+0.022806 test-rmse:1.427421+0.263116
## [58] train-rmse:0.680545+0.023755 test-rmse:1.411887+0.264519
## [59] train-rmse:0.665243+0.024359 test-rmse:1.401071+0.265723
## [60] train-rmse:0.649657+0.022689 test-rmse:1.395792+0.268858
## [61] train-rmse:0.633657+0.021168 test-rmse:1.371533+0.257673
## [62] train-rmse:0.619602+0.021792 test-rmse:1.364373+0.261781
## [63] train-rmse:0.606231+0.021783 test-rmse:1.352254+0.254356
## [64] train-rmse:0.593238+0.021401 test-rmse:1.342055+0.255958
## [65] train-rmse:0.582192+0.021878 test-rmse:1.327091+0.257485
## [66] train-rmse:0.569598+0.021126 test-rmse:1.321461+0.256216
## [67] train-rmse:0.558521+0.021275 test-rmse:1.317633+0.256032
## [68] train-rmse:0.547903+0.021383 test-rmse:1.314211+0.254827
## [69] train-rmse:0.535864+0.020959 test-rmse:1.306722+0.254896
## [70] train-rmse:0.525399+0.020499 test-rmse:1.298055+0.258392
## [71] train-rmse:0.515048+0.021237 test-rmse:1.289706+0.263841
## [72] train-rmse:0.504710+0.020652 test-rmse:1.279236+0.256278
## [73] train-rmse:0.495167+0.020956 test-rmse:1.273390+0.257464
## [74] train-rmse:0.485794+0.021569 test-rmse:1.271157+0.259830
## [75] train-rmse:0.477251+0.021059 test-rmse:1.263048+0.261687
## [76] train-rmse:0.467397+0.020728 test-rmse:1.258061+0.262616
## [77] train-rmse:0.459397+0.020880 test-rmse:1.252509+0.261160
## [78] train-rmse:0.450706+0.019602 test-rmse:1.245954+0.256389
## [79] train-rmse:0.441137+0.018582 test-rmse:1.240534+0.260267
## [80] train-rmse:0.434365+0.018369 test-rmse:1.238192+0.261236
## [81] train-rmse:0.426317+0.018932 test-rmse:1.238218+0.261640
## [82] train-rmse:0.418094+0.018371 test-rmse:1.234204+0.261426
## [83] train-rmse:0.412077+0.018688 test-rmse:1.230976+0.261449
## [84] train-rmse:0.405552+0.018867 test-rmse:1.230034+0.260764
## [85] train-rmse:0.399782+0.018296 test-rmse:1.227038+0.262076
## [86] train-rmse:0.392828+0.018349 test-rmse:1.221908+0.265095
## [87] train-rmse:0.386548+0.019316 test-rmse:1.218878+0.264845
## [88] train-rmse:0.381109+0.019394 test-rmse:1.215635+0.264792
## [89] train-rmse:0.374826+0.018987 test-rmse:1.213137+0.263597
## [90] train-rmse:0.369171+0.017902 test-rmse:1.208612+0.266292
## [91] train-rmse:0.363164+0.017928 test-rmse:1.207151+0.266646
## [92] train-rmse:0.358007+0.018105 test-rmse:1.205794+0.266767
## [93] train-rmse:0.352053+0.018546 test-rmse:1.202060+0.269837
## [94] train-rmse:0.346766+0.018384 test-rmse:1.197774+0.264956
## [95] train-rmse:0.341240+0.017775 test-rmse:1.196777+0.264203
## [96] train-rmse:0.336847+0.017811 test-rmse:1.193781+0.265162
## [97] train-rmse:0.332298+0.017945 test-rmse:1.193499+0.265212
## [98] train-rmse:0.328596+0.017888 test-rmse:1.191339+0.263737
## [99] train-rmse:0.323141+0.017443 test-rmse:1.187614+0.265488
## [100] train-rmse:0.318225+0.016931 test-rmse:1.186502+0.265730
## [101] train-rmse:0.314101+0.016930 test-rmse:1.185738+0.264929
## [102] train-rmse:0.310288+0.016665 test-rmse:1.183797+0.264800
## [103] train-rmse:0.306081+0.016146 test-rmse:1.180457+0.265202
## [104] train-rmse:0.302266+0.015687 test-rmse:1.180026+0.265610
## [105] train-rmse:0.298805+0.015500 test-rmse:1.179156+0.266234
## [106] train-rmse:0.295571+0.015400 test-rmse:1.177372+0.266721
## [107] train-rmse:0.291818+0.015407 test-rmse:1.175493+0.267338
## [108] train-rmse:0.287148+0.015256 test-rmse:1.175073+0.267753
## [109] train-rmse:0.283099+0.014223 test-rmse:1.173359+0.267685
## [110] train-rmse:0.278997+0.014706 test-rmse:1.171382+0.266977
## [111] train-rmse:0.275274+0.015481 test-rmse:1.169575+0.268741
## [112] train-rmse:0.271595+0.014283 test-rmse:1.168437+0.269876
## [113] train-rmse:0.268668+0.014148 test-rmse:1.168391+0.270610
## [114] train-rmse:0.265513+0.014292 test-rmse:1.167463+0.271018
## [115] train-rmse:0.261972+0.014097 test-rmse:1.166003+0.271585
## [116] train-rmse:0.258982+0.013898 test-rmse:1.165721+0.271804
## [117] train-rmse:0.255558+0.013733 test-rmse:1.164801+0.272037
## [118] train-rmse:0.253172+0.014035 test-rmse:1.164060+0.271455
## [119] train-rmse:0.250165+0.013632 test-rmse:1.163334+0.272272
## [120] train-rmse:0.246703+0.014832 test-rmse:1.162886+0.271988
## [121] train-rmse:0.244399+0.014674 test-rmse:1.162824+0.272734
## [122] train-rmse:0.241795+0.014586 test-rmse:1.159541+0.268529
## [123] train-rmse:0.239354+0.014683 test-rmse:1.158966+0.268959
## [124] train-rmse:0.236383+0.014260 test-rmse:1.158671+0.269207
## [125] train-rmse:0.233121+0.015360 test-rmse:1.157771+0.268658
## [126] train-rmse:0.230262+0.014634 test-rmse:1.157497+0.268584
## [127] train-rmse:0.227906+0.014175 test-rmse:1.156060+0.268999
## [128] train-rmse:0.225372+0.013963 test-rmse:1.155408+0.270038
## [129] train-rmse:0.222201+0.014608 test-rmse:1.154972+0.270002
## [130] train-rmse:0.219661+0.015010 test-rmse:1.154423+0.270934
## [131] train-rmse:0.217516+0.014832 test-rmse:1.153837+0.271280
## [132] train-rmse:0.215372+0.015094 test-rmse:1.153503+0.271751
## [133] train-rmse:0.213185+0.014958 test-rmse:1.152916+0.272603
## [134] train-rmse:0.209979+0.014651 test-rmse:1.153098+0.272575
## [135] train-rmse:0.207623+0.014759 test-rmse:1.152362+0.272838
## [136] train-rmse:0.204954+0.014387 test-rmse:1.151090+0.272495
## [137] train-rmse:0.203159+0.014247 test-rmse:1.150085+0.272915
## [138] train-rmse:0.199703+0.013823 test-rmse:1.148813+0.272332
## [139] train-rmse:0.197941+0.014352 test-rmse:1.149016+0.273565
## [140] train-rmse:0.195635+0.014442 test-rmse:1.147938+0.273269
## [141] train-rmse:0.193148+0.013958 test-rmse:1.147702+0.271914
## [142] train-rmse:0.191164+0.014536 test-rmse:1.147156+0.272126
## [143] train-rmse:0.188766+0.015223 test-rmse:1.146686+0.273029
## [144] train-rmse:0.186486+0.014784 test-rmse:1.145735+0.273628
## [145] train-rmse:0.184350+0.014422 test-rmse:1.144631+0.273898
## [146] train-rmse:0.182477+0.014535 test-rmse:1.144906+0.274186
## [147] train-rmse:0.181021+0.014203 test-rmse:1.145092+0.273755
## [148] train-rmse:0.178941+0.014168 test-rmse:1.145020+0.273390
## [149] train-rmse:0.177282+0.013906 test-rmse:1.144208+0.273870
## [150] train-rmse:0.175463+0.013207 test-rmse:1.143821+0.273634
## [151] train-rmse:0.173692+0.013400 test-rmse:1.143388+0.272639
## [152] train-rmse:0.171866+0.014200 test-rmse:1.143264+0.272216
## [153] train-rmse:0.169873+0.014365 test-rmse:1.142963+0.272382
## [154] train-rmse:0.168213+0.014161 test-rmse:1.143261+0.273342
## [155] train-rmse:0.166336+0.014005 test-rmse:1.143465+0.272896
## [156] train-rmse:0.164117+0.013526 test-rmse:1.142655+0.273026
## [157] train-rmse:0.162202+0.013091 test-rmse:1.142558+0.272871
## [158] train-rmse:0.160219+0.013477 test-rmse:1.141379+0.273745
## [159] train-rmse:0.159023+0.013298 test-rmse:1.141252+0.273850
## [160] train-rmse:0.157100+0.012715 test-rmse:1.139635+0.272246
## [161] train-rmse:0.155692+0.012365 test-rmse:1.140036+0.272393
## [162] train-rmse:0.154102+0.012353 test-rmse:1.139415+0.272775
## [163] train-rmse:0.152463+0.012223 test-rmse:1.139199+0.273387
## [164] train-rmse:0.151064+0.012392 test-rmse:1.138758+0.273933
## [165] train-rmse:0.149451+0.012049 test-rmse:1.137924+0.272957
## [166] train-rmse:0.147717+0.011695 test-rmse:1.137280+0.273570
## [167] train-rmse:0.146537+0.011756 test-rmse:1.137107+0.273775
## [168] train-rmse:0.144542+0.011576 test-rmse:1.137403+0.273357
## [169] train-rmse:0.143318+0.011717 test-rmse:1.137049+0.273558
## [170] train-rmse:0.142030+0.011514 test-rmse:1.136867+0.273947
## [171] train-rmse:0.140677+0.011341 test-rmse:1.136788+0.273033
## [172] train-rmse:0.139681+0.011315 test-rmse:1.136180+0.273155
## [173] train-rmse:0.138048+0.010974 test-rmse:1.135643+0.273211
## [174] train-rmse:0.136891+0.011062 test-rmse:1.135481+0.273129
## [175] train-rmse:0.135127+0.011258 test-rmse:1.135183+0.273333
## [176] train-rmse:0.133148+0.010947 test-rmse:1.134393+0.273378
## [177] train-rmse:0.132115+0.010766 test-rmse:1.134425+0.273757
## [178] train-rmse:0.130733+0.010663 test-rmse:1.133936+0.273503
## [179] train-rmse:0.129637+0.010442 test-rmse:1.134109+0.273949
## [180] train-rmse:0.128642+0.010381 test-rmse:1.133792+0.274201
## [181] train-rmse:0.127014+0.010029 test-rmse:1.134082+0.273755
## [182] train-rmse:0.125793+0.010044 test-rmse:1.133040+0.271987
## [183] train-rmse:0.124380+0.009791 test-rmse:1.132649+0.272014
## [184] train-rmse:0.123206+0.009811 test-rmse:1.132423+0.272315
## [185] train-rmse:0.122047+0.009795 test-rmse:1.131944+0.272334
## [186] train-rmse:0.119851+0.009854 test-rmse:1.132153+0.272395
## [187] train-rmse:0.119002+0.009876 test-rmse:1.131114+0.270976
## [188] train-rmse:0.118001+0.009813 test-rmse:1.131244+0.271134
## [189] train-rmse:0.116848+0.009401 test-rmse:1.130965+0.271237
## [190] train-rmse:0.115821+0.009329 test-rmse:1.130696+0.271790
## [191] train-rmse:0.114868+0.009081 test-rmse:1.130494+0.272144
## [192] train-rmse:0.114267+0.009023 test-rmse:1.130223+0.272111
## [193] train-rmse:0.113518+0.008981 test-rmse:1.129829+0.272202
## [194] train-rmse:0.111790+0.008475 test-rmse:1.130002+0.272360
## [195] train-rmse:0.109808+0.007527 test-rmse:1.129640+0.272547
## [196] train-rmse:0.107637+0.007051 test-rmse:1.130782+0.273329
## [197] train-rmse:0.106574+0.007250 test-rmse:1.131312+0.272951
## [198] train-rmse:0.105865+0.007052 test-rmse:1.130498+0.272750
## [199] train-rmse:0.104967+0.006856 test-rmse:1.129351+0.271740
## [200] train-rmse:0.104017+0.006691 test-rmse:1.129409+0.271632
## [201] train-rmse:0.102999+0.007007 test-rmse:1.129225+0.271755
## [202] train-rmse:0.101898+0.006848 test-rmse:1.129123+0.271547
## [203] train-rmse:0.101128+0.006923 test-rmse:1.129361+0.271436
## [204] train-rmse:0.100148+0.006433 test-rmse:1.129232+0.271541
## [205] train-rmse:0.099140+0.006860 test-rmse:1.128710+0.271585
## [206] train-rmse:0.098234+0.007093 test-rmse:1.127933+0.271832
## [207] train-rmse:0.097334+0.006615 test-rmse:1.128476+0.271830
## [208] train-rmse:0.096583+0.006774 test-rmse:1.128181+0.272506
## [209] train-rmse:0.095806+0.007091 test-rmse:1.128139+0.272475
## [210] train-rmse:0.095137+0.007299 test-rmse:1.128167+0.272932
## [211] train-rmse:0.094268+0.007174 test-rmse:1.128252+0.273068
## [212] train-rmse:0.093110+0.006659 test-rmse:1.128094+0.273098
## Stopping. Best iteration:
## [206] train-rmse:0.098234+0.007093 test-rmse:1.127933+0.271832
##
## 39
## [1] train-rmse:86.218085+0.097587 test-rmse:86.218257+0.495906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.119772+0.083348 test-rmse:69.119559+0.523711
## [3] train-rmse:55.450065+0.073313 test-rmse:55.449316+0.550523
## [4] train-rmse:44.530696+0.066998 test-rmse:44.529209+0.577462
## [5] train-rmse:35.819717+0.064113 test-rmse:35.817187+0.605511
## [6] train-rmse:28.868193+0.060253 test-rmse:28.910774+0.605132
## [7] train-rmse:23.310282+0.045896 test-rmse:23.406557+0.627461
## [8] train-rmse:18.864911+0.037248 test-rmse:18.966760+0.596436
## [9] train-rmse:15.305449+0.033583 test-rmse:15.411134+0.559722
## [10] train-rmse:12.456289+0.030877 test-rmse:12.561671+0.532906
## [11] train-rmse:10.175636+0.026978 test-rmse:10.264841+0.529039
## [12] train-rmse:8.346776+0.030590 test-rmse:8.467034+0.540278
## [13] train-rmse:6.889319+0.029617 test-rmse:7.001238+0.521320
## [14] train-rmse:5.724944+0.033015 test-rmse:5.873543+0.518644
## [15] train-rmse:4.802166+0.036944 test-rmse:4.975841+0.487872
## [16] train-rmse:4.069513+0.039333 test-rmse:4.276470+0.451544
## [17] train-rmse:3.482504+0.040276 test-rmse:3.718835+0.457488
## [18] train-rmse:3.023774+0.040293 test-rmse:3.274318+0.410695
## [19] train-rmse:2.659198+0.032426 test-rmse:2.982196+0.405317
## [20] train-rmse:2.362885+0.027158 test-rmse:2.706749+0.378938
## [21] train-rmse:2.125495+0.028959 test-rmse:2.511262+0.343606
## [22] train-rmse:1.943669+0.030828 test-rmse:2.379552+0.325694
## [23] train-rmse:1.791426+0.030396 test-rmse:2.241104+0.297300
## [24] train-rmse:1.670095+0.031209 test-rmse:2.155301+0.280638
## [25] train-rmse:1.563173+0.030774 test-rmse:2.074725+0.261604
## [26] train-rmse:1.478079+0.031576 test-rmse:2.005500+0.248021
## [27] train-rmse:1.401038+0.029938 test-rmse:1.928714+0.238354
## [28] train-rmse:1.332678+0.027751 test-rmse:1.889715+0.222907
## [29] train-rmse:1.267665+0.024348 test-rmse:1.834323+0.237841
## [30] train-rmse:1.210246+0.026990 test-rmse:1.792095+0.223701
## [31] train-rmse:1.155816+0.028851 test-rmse:1.759501+0.231556
## [32] train-rmse:1.109284+0.027045 test-rmse:1.730906+0.217315
## [33] train-rmse:1.064781+0.026562 test-rmse:1.694901+0.215674
## [34] train-rmse:1.023346+0.025178 test-rmse:1.654332+0.217055
## [35] train-rmse:0.981231+0.023291 test-rmse:1.621570+0.206846
## [36] train-rmse:0.943897+0.023915 test-rmse:1.595587+0.214168
## [37] train-rmse:0.909462+0.022899 test-rmse:1.567166+0.214898
## [38] train-rmse:0.871249+0.030864 test-rmse:1.543759+0.217626
## [39] train-rmse:0.838870+0.027619 test-rmse:1.518971+0.216098
## [40] train-rmse:0.810449+0.027653 test-rmse:1.496368+0.216232
## [41] train-rmse:0.778922+0.026789 test-rmse:1.476487+0.224888
## [42] train-rmse:0.753112+0.026109 test-rmse:1.458070+0.228862
## [43] train-rmse:0.729826+0.025546 test-rmse:1.439518+0.233019
## [44] train-rmse:0.706379+0.024891 test-rmse:1.423822+0.235898
## [45] train-rmse:0.675334+0.026669 test-rmse:1.401679+0.238591
## [46] train-rmse:0.654637+0.026639 test-rmse:1.389796+0.234554
## [47] train-rmse:0.635481+0.024835 test-rmse:1.378549+0.239723
## [48] train-rmse:0.611803+0.022531 test-rmse:1.368790+0.237075
## [49] train-rmse:0.592649+0.023704 test-rmse:1.354911+0.236949
## [50] train-rmse:0.575773+0.023658 test-rmse:1.344592+0.239648
## [51] train-rmse:0.559440+0.024628 test-rmse:1.332076+0.231859
## [52] train-rmse:0.543765+0.023860 test-rmse:1.325459+0.232316
## [53] train-rmse:0.528272+0.024000 test-rmse:1.316846+0.231445
## [54] train-rmse:0.511523+0.024730 test-rmse:1.305331+0.235173
## [55] train-rmse:0.498103+0.023383 test-rmse:1.298559+0.236954
## [56] train-rmse:0.485262+0.024106 test-rmse:1.291051+0.237910
## [57] train-rmse:0.470381+0.022513 test-rmse:1.286274+0.243617
## [58] train-rmse:0.458259+0.020469 test-rmse:1.275946+0.241374
## [59] train-rmse:0.447381+0.020639 test-rmse:1.271916+0.246314
## [60] train-rmse:0.436803+0.020336 test-rmse:1.264362+0.248667
## [61] train-rmse:0.427361+0.019988 test-rmse:1.260081+0.250666
## [62] train-rmse:0.416589+0.020077 test-rmse:1.254759+0.250755
## [63] train-rmse:0.404133+0.020472 test-rmse:1.250282+0.251273
## [64] train-rmse:0.394973+0.020653 test-rmse:1.247406+0.250369
## [65] train-rmse:0.384650+0.019402 test-rmse:1.243004+0.253369
## [66] train-rmse:0.374825+0.018058 test-rmse:1.236099+0.257135
## [67] train-rmse:0.366799+0.016826 test-rmse:1.233031+0.257675
## [68] train-rmse:0.359798+0.017331 test-rmse:1.231463+0.256895
## [69] train-rmse:0.352531+0.017877 test-rmse:1.228652+0.257492
## [70] train-rmse:0.343921+0.015679 test-rmse:1.228582+0.258157
## [71] train-rmse:0.337126+0.015669 test-rmse:1.226873+0.260579
## [72] train-rmse:0.329287+0.015760 test-rmse:1.225121+0.260836
## [73] train-rmse:0.322310+0.014527 test-rmse:1.222394+0.261493
## [74] train-rmse:0.315375+0.012644 test-rmse:1.221944+0.262162
## [75] train-rmse:0.309773+0.011782 test-rmse:1.217781+0.266196
## [76] train-rmse:0.303867+0.011356 test-rmse:1.216742+0.267069
## [77] train-rmse:0.297681+0.010646 test-rmse:1.214846+0.267430
## [78] train-rmse:0.290764+0.009598 test-rmse:1.210768+0.266588
## [79] train-rmse:0.285750+0.009989 test-rmse:1.207630+0.268137
## [80] train-rmse:0.279231+0.009308 test-rmse:1.205593+0.269207
## [81] train-rmse:0.272974+0.009974 test-rmse:1.204091+0.268946
## [82] train-rmse:0.268921+0.009689 test-rmse:1.202469+0.269971
## [83] train-rmse:0.264443+0.009217 test-rmse:1.202405+0.269597
## [84] train-rmse:0.259301+0.009430 test-rmse:1.201769+0.269665
## [85] train-rmse:0.254192+0.010030 test-rmse:1.201314+0.270227
## [86] train-rmse:0.249211+0.009613 test-rmse:1.201116+0.270680
## [87] train-rmse:0.244293+0.009558 test-rmse:1.199273+0.270262
## [88] train-rmse:0.238676+0.010562 test-rmse:1.195951+0.269117
## [89] train-rmse:0.234700+0.010708 test-rmse:1.194821+0.269268
## [90] train-rmse:0.230248+0.010226 test-rmse:1.193679+0.270245
## [91] train-rmse:0.225238+0.011167 test-rmse:1.191410+0.270697
## [92] train-rmse:0.221662+0.011103 test-rmse:1.190584+0.271203
## [93] train-rmse:0.217547+0.011978 test-rmse:1.189398+0.271099
## [94] train-rmse:0.213103+0.011374 test-rmse:1.189604+0.271204
## [95] train-rmse:0.209789+0.011908 test-rmse:1.189717+0.270655
## [96] train-rmse:0.204948+0.012029 test-rmse:1.188759+0.271537
## [97] train-rmse:0.201775+0.011863 test-rmse:1.188639+0.272788
## [98] train-rmse:0.198499+0.011225 test-rmse:1.187606+0.274064
## [99] train-rmse:0.195072+0.011322 test-rmse:1.187302+0.273937
## [100] train-rmse:0.191379+0.011813 test-rmse:1.187251+0.272903
## [101] train-rmse:0.187068+0.010120 test-rmse:1.186933+0.272306
## [102] train-rmse:0.183569+0.010071 test-rmse:1.186331+0.271513
## [103] train-rmse:0.181202+0.010208 test-rmse:1.185616+0.271790
## [104] train-rmse:0.177796+0.010274 test-rmse:1.184195+0.272031
## [105] train-rmse:0.174438+0.010579 test-rmse:1.183330+0.272418
## [106] train-rmse:0.171338+0.010889 test-rmse:1.182376+0.274022
## [107] train-rmse:0.167587+0.011258 test-rmse:1.182436+0.274128
## [108] train-rmse:0.164459+0.009783 test-rmse:1.182420+0.274872
## [109] train-rmse:0.162696+0.009210 test-rmse:1.180159+0.273540
## [110] train-rmse:0.160612+0.009317 test-rmse:1.179967+0.273269
## [111] train-rmse:0.157815+0.008726 test-rmse:1.179311+0.273841
## [112] train-rmse:0.155639+0.008776 test-rmse:1.179426+0.273829
## [113] train-rmse:0.152322+0.009586 test-rmse:1.179116+0.273925
## [114] train-rmse:0.150328+0.009904 test-rmse:1.178870+0.274011
## [115] train-rmse:0.147367+0.009594 test-rmse:1.178157+0.273546
## [116] train-rmse:0.145109+0.010122 test-rmse:1.177613+0.273293
## [117] train-rmse:0.141108+0.009003 test-rmse:1.177806+0.273322
## [118] train-rmse:0.139192+0.009384 test-rmse:1.178268+0.273634
## [119] train-rmse:0.137226+0.008933 test-rmse:1.177537+0.273970
## [120] train-rmse:0.134134+0.007972 test-rmse:1.177732+0.274194
## [121] train-rmse:0.131996+0.007940 test-rmse:1.176150+0.272598
## [122] train-rmse:0.129811+0.007884 test-rmse:1.175787+0.272521
## [123] train-rmse:0.128072+0.006886 test-rmse:1.176021+0.272614
## [124] train-rmse:0.126482+0.006895 test-rmse:1.176297+0.272467
## [125] train-rmse:0.123660+0.005721 test-rmse:1.176874+0.272657
## [126] train-rmse:0.121696+0.005343 test-rmse:1.176596+0.272771
## [127] train-rmse:0.119107+0.006244 test-rmse:1.176141+0.273194
## [128] train-rmse:0.117152+0.006120 test-rmse:1.176086+0.273611
## Stopping. Best iteration:
## [122] train-rmse:0.129811+0.007884 test-rmse:1.175787+0.272521
##
## 40
## [1] train-rmse:86.218085+0.097587 test-rmse:86.218257+0.495906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.119772+0.083348 test-rmse:69.119559+0.523711
## [3] train-rmse:55.450065+0.073313 test-rmse:55.449316+0.550523
## [4] train-rmse:44.530696+0.066998 test-rmse:44.529209+0.577462
## [5] train-rmse:35.819717+0.064113 test-rmse:35.817187+0.605511
## [6] train-rmse:28.868193+0.060253 test-rmse:28.910774+0.605132
## [7] train-rmse:23.310282+0.045896 test-rmse:23.406557+0.627461
## [8] train-rmse:18.864508+0.036756 test-rmse:18.962976+0.601228
## [9] train-rmse:15.303019+0.032641 test-rmse:15.401728+0.555499
## [10] train-rmse:12.448038+0.028808 test-rmse:12.556415+0.541169
## [11] train-rmse:10.158663+0.026132 test-rmse:10.303151+0.563010
## [12] train-rmse:8.323074+0.024827 test-rmse:8.475917+0.532949
## [13] train-rmse:6.842557+0.026020 test-rmse:6.991238+0.522417
## [14] train-rmse:5.665543+0.022189 test-rmse:5.836693+0.532774
## [15] train-rmse:4.722709+0.026120 test-rmse:4.901020+0.506425
## [16] train-rmse:3.961930+0.015887 test-rmse:4.165998+0.464783
## [17] train-rmse:3.361242+0.014950 test-rmse:3.632963+0.467426
## [18] train-rmse:2.879510+0.011112 test-rmse:3.194591+0.417171
## [19] train-rmse:2.503801+0.012158 test-rmse:2.857520+0.381168
## [20] train-rmse:2.199069+0.014432 test-rmse:2.598141+0.367128
## [21] train-rmse:1.952066+0.016376 test-rmse:2.369736+0.337458
## [22] train-rmse:1.750896+0.021453 test-rmse:2.212502+0.319904
## [23] train-rmse:1.584359+0.021647 test-rmse:2.093126+0.271008
## [24] train-rmse:1.457716+0.021829 test-rmse:1.981258+0.251610
## [25] train-rmse:1.345736+0.026388 test-rmse:1.909642+0.232505
## [26] train-rmse:1.251222+0.030929 test-rmse:1.850079+0.211057
## [27] train-rmse:1.171879+0.032670 test-rmse:1.794595+0.200517
## [28] train-rmse:1.096428+0.024878 test-rmse:1.749471+0.203395
## [29] train-rmse:1.023028+0.025165 test-rmse:1.700074+0.207921
## [30] train-rmse:0.967825+0.025789 test-rmse:1.656643+0.212888
## [31] train-rmse:0.914003+0.030271 test-rmse:1.621180+0.205887
## [32] train-rmse:0.866332+0.026267 test-rmse:1.595999+0.208228
## [33] train-rmse:0.817117+0.024649 test-rmse:1.564510+0.212695
## [34] train-rmse:0.780459+0.022982 test-rmse:1.541036+0.205651
## [35] train-rmse:0.746534+0.022574 test-rmse:1.509517+0.208074
## [36] train-rmse:0.711200+0.023871 test-rmse:1.489443+0.206509
## [37] train-rmse:0.679492+0.025133 test-rmse:1.477832+0.213524
## [38] train-rmse:0.652267+0.025000 test-rmse:1.453517+0.201660
## [39] train-rmse:0.626816+0.023245 test-rmse:1.433149+0.214013
## [40] train-rmse:0.601663+0.024054 test-rmse:1.418752+0.213614
## [41] train-rmse:0.576732+0.026285 test-rmse:1.403282+0.215345
## [42] train-rmse:0.554698+0.026305 test-rmse:1.388150+0.207509
## [43] train-rmse:0.536162+0.025973 test-rmse:1.376110+0.208229
## [44] train-rmse:0.516351+0.023952 test-rmse:1.365861+0.207206
## [45] train-rmse:0.499137+0.023982 test-rmse:1.352243+0.211187
## [46] train-rmse:0.477675+0.023743 test-rmse:1.345830+0.217322
## [47] train-rmse:0.461042+0.023745 test-rmse:1.338681+0.221715
## [48] train-rmse:0.446918+0.023215 test-rmse:1.329168+0.222965
## [49] train-rmse:0.432483+0.022118 test-rmse:1.321775+0.223380
## [50] train-rmse:0.416969+0.022025 test-rmse:1.314476+0.228809
## [51] train-rmse:0.401320+0.024111 test-rmse:1.310357+0.227921
## [52] train-rmse:0.389272+0.023922 test-rmse:1.303409+0.227307
## [53] train-rmse:0.377605+0.023375 test-rmse:1.296969+0.229266
## [54] train-rmse:0.364426+0.022718 test-rmse:1.293612+0.230678
## [55] train-rmse:0.351741+0.021755 test-rmse:1.290719+0.232657
## [56] train-rmse:0.340674+0.020684 test-rmse:1.286453+0.235214
## [57] train-rmse:0.331946+0.021080 test-rmse:1.280183+0.235927
## [58] train-rmse:0.320571+0.017363 test-rmse:1.278196+0.237766
## [59] train-rmse:0.312539+0.016842 test-rmse:1.270595+0.236708
## [60] train-rmse:0.303767+0.015445 test-rmse:1.268248+0.239362
## [61] train-rmse:0.293297+0.014731 test-rmse:1.266668+0.241027
## [62] train-rmse:0.285582+0.015183 test-rmse:1.263511+0.242387
## [63] train-rmse:0.277296+0.013635 test-rmse:1.260484+0.241055
## [64] train-rmse:0.270815+0.013866 test-rmse:1.260105+0.242271
## [65] train-rmse:0.263958+0.012836 test-rmse:1.257536+0.243718
## [66] train-rmse:0.256778+0.012166 test-rmse:1.254423+0.242858
## [67] train-rmse:0.249180+0.012751 test-rmse:1.254545+0.241537
## [68] train-rmse:0.243050+0.013052 test-rmse:1.253009+0.242546
## [69] train-rmse:0.237702+0.013346 test-rmse:1.250882+0.243866
## [70] train-rmse:0.230375+0.013659 test-rmse:1.248935+0.245311
## [71] train-rmse:0.225515+0.013704 test-rmse:1.248669+0.246018
## [72] train-rmse:0.218962+0.011310 test-rmse:1.245517+0.244805
## [73] train-rmse:0.214458+0.011694 test-rmse:1.243848+0.244820
## [74] train-rmse:0.210035+0.012053 test-rmse:1.242843+0.245725
## [75] train-rmse:0.204262+0.012826 test-rmse:1.240934+0.245002
## [76] train-rmse:0.199775+0.012411 test-rmse:1.240640+0.243694
## [77] train-rmse:0.196021+0.012177 test-rmse:1.240167+0.243597
## [78] train-rmse:0.191694+0.010993 test-rmse:1.238542+0.243048
## [79] train-rmse:0.187002+0.011720 test-rmse:1.237398+0.244520
## [80] train-rmse:0.181888+0.011827 test-rmse:1.236388+0.243874
## [81] train-rmse:0.177222+0.011783 test-rmse:1.236155+0.243591
## [82] train-rmse:0.172802+0.011726 test-rmse:1.235514+0.244018
## [83] train-rmse:0.169157+0.011755 test-rmse:1.234728+0.243900
## [84] train-rmse:0.166341+0.011968 test-rmse:1.233816+0.243807
## [85] train-rmse:0.162573+0.012333 test-rmse:1.233974+0.244123
## [86] train-rmse:0.159111+0.011318 test-rmse:1.234149+0.244368
## [87] train-rmse:0.155584+0.010643 test-rmse:1.231906+0.244389
## [88] train-rmse:0.152524+0.010588 test-rmse:1.230324+0.244285
## [89] train-rmse:0.149537+0.010755 test-rmse:1.230437+0.244949
## [90] train-rmse:0.147177+0.010520 test-rmse:1.231091+0.245220
## [91] train-rmse:0.144827+0.010835 test-rmse:1.230267+0.244805
## [92] train-rmse:0.141239+0.010931 test-rmse:1.229961+0.244898
## [93] train-rmse:0.138340+0.011308 test-rmse:1.228392+0.245127
## [94] train-rmse:0.136075+0.010947 test-rmse:1.227368+0.245264
## [95] train-rmse:0.131775+0.009162 test-rmse:1.227649+0.245275
## [96] train-rmse:0.128948+0.008326 test-rmse:1.226569+0.244992
## [97] train-rmse:0.126018+0.007477 test-rmse:1.226402+0.244963
## [98] train-rmse:0.123322+0.007745 test-rmse:1.225663+0.244246
## [99] train-rmse:0.120181+0.007188 test-rmse:1.225249+0.244104
## [100] train-rmse:0.117271+0.007603 test-rmse:1.225073+0.244209
## [101] train-rmse:0.113635+0.007389 test-rmse:1.224210+0.242853
## [102] train-rmse:0.110344+0.006476 test-rmse:1.223817+0.243363
## [103] train-rmse:0.107583+0.006701 test-rmse:1.223861+0.243571
## [104] train-rmse:0.105173+0.006933 test-rmse:1.222830+0.243249
## [105] train-rmse:0.103263+0.007471 test-rmse:1.222476+0.242924
## [106] train-rmse:0.101000+0.007940 test-rmse:1.221542+0.243499
## [107] train-rmse:0.098472+0.007367 test-rmse:1.221439+0.242533
## [108] train-rmse:0.096399+0.007824 test-rmse:1.220760+0.242249
## [109] train-rmse:0.094148+0.006456 test-rmse:1.220403+0.242797
## [110] train-rmse:0.092735+0.006640 test-rmse:1.220235+0.242969
## [111] train-rmse:0.090607+0.006604 test-rmse:1.219856+0.243215
## [112] train-rmse:0.088842+0.006942 test-rmse:1.219761+0.243393
## [113] train-rmse:0.086840+0.006274 test-rmse:1.218890+0.242446
## [114] train-rmse:0.085670+0.006326 test-rmse:1.218301+0.242815
## [115] train-rmse:0.083715+0.005771 test-rmse:1.218094+0.243192
## [116] train-rmse:0.082025+0.005860 test-rmse:1.217923+0.243014
## [117] train-rmse:0.080396+0.006092 test-rmse:1.217683+0.243564
## [118] train-rmse:0.079074+0.006295 test-rmse:1.217594+0.243472
## [119] train-rmse:0.076999+0.006494 test-rmse:1.217878+0.243495
## [120] train-rmse:0.075297+0.006270 test-rmse:1.217269+0.243370
## [121] train-rmse:0.074006+0.006187 test-rmse:1.217055+0.243523
## [122] train-rmse:0.072840+0.006311 test-rmse:1.216867+0.243833
## [123] train-rmse:0.070987+0.006200 test-rmse:1.217156+0.244396
## [124] train-rmse:0.069320+0.005982 test-rmse:1.216774+0.244377
## [125] train-rmse:0.067918+0.005968 test-rmse:1.216727+0.244350
## [126] train-rmse:0.066766+0.005850 test-rmse:1.216173+0.244277
## [127] train-rmse:0.064674+0.004728 test-rmse:1.216050+0.244842
## [128] train-rmse:0.063501+0.004531 test-rmse:1.215952+0.245076
## [129] train-rmse:0.062390+0.004775 test-rmse:1.215756+0.245132
## [130] train-rmse:0.061231+0.003911 test-rmse:1.215820+0.245442
## [131] train-rmse:0.060207+0.004097 test-rmse:1.215471+0.245538
## [132] train-rmse:0.058899+0.003799 test-rmse:1.215365+0.245163
## [133] train-rmse:0.057988+0.003871 test-rmse:1.215363+0.244875
## [134] train-rmse:0.056288+0.003777 test-rmse:1.215217+0.244952
## [135] train-rmse:0.055549+0.003909 test-rmse:1.215057+0.244857
## [136] train-rmse:0.054442+0.004268 test-rmse:1.214773+0.245079
## [137] train-rmse:0.053603+0.004189 test-rmse:1.214609+0.245016
## [138] train-rmse:0.052781+0.004294 test-rmse:1.214724+0.245219
## [139] train-rmse:0.051319+0.004118 test-rmse:1.214067+0.245135
## [140] train-rmse:0.050214+0.004082 test-rmse:1.213892+0.245113
## [141] train-rmse:0.049190+0.004279 test-rmse:1.213939+0.245303
## [142] train-rmse:0.048502+0.004030 test-rmse:1.213568+0.245003
## [143] train-rmse:0.047669+0.004193 test-rmse:1.213584+0.244927
## [144] train-rmse:0.046979+0.003975 test-rmse:1.213273+0.244925
## [145] train-rmse:0.045904+0.003621 test-rmse:1.213284+0.244894
## [146] train-rmse:0.044701+0.003838 test-rmse:1.213448+0.244765
## [147] train-rmse:0.043923+0.003925 test-rmse:1.213288+0.244756
## [148] train-rmse:0.042766+0.004028 test-rmse:1.213095+0.244514
## [149] train-rmse:0.042083+0.004075 test-rmse:1.212591+0.244001
## [150] train-rmse:0.041190+0.004019 test-rmse:1.212624+0.244010
## [151] train-rmse:0.040299+0.003748 test-rmse:1.212446+0.244125
## [152] train-rmse:0.039584+0.003905 test-rmse:1.212393+0.244092
## [153] train-rmse:0.039074+0.003938 test-rmse:1.212300+0.244023
## [154] train-rmse:0.038353+0.003940 test-rmse:1.212223+0.243967
## [155] train-rmse:0.037704+0.003685 test-rmse:1.212304+0.243878
## [156] train-rmse:0.036908+0.003456 test-rmse:1.212180+0.243864
## [157] train-rmse:0.036300+0.003605 test-rmse:1.212022+0.243852
## [158] train-rmse:0.035670+0.003647 test-rmse:1.212117+0.243819
## [159] train-rmse:0.035057+0.003420 test-rmse:1.211965+0.243927
## [160] train-rmse:0.034413+0.003585 test-rmse:1.211766+0.243974
## [161] train-rmse:0.033790+0.003546 test-rmse:1.211725+0.243974
## [162] train-rmse:0.033074+0.003380 test-rmse:1.211733+0.243894
## [163] train-rmse:0.032505+0.003162 test-rmse:1.211631+0.243941
## [164] train-rmse:0.031722+0.002819 test-rmse:1.211618+0.243797
## [165] train-rmse:0.031334+0.002796 test-rmse:1.211581+0.243858
## [166] train-rmse:0.030807+0.002862 test-rmse:1.211667+0.243934
## [167] train-rmse:0.030243+0.003053 test-rmse:1.211740+0.244007
## [168] train-rmse:0.029440+0.002880 test-rmse:1.211453+0.243607
## [169] train-rmse:0.028931+0.002711 test-rmse:1.211468+0.243571
## [170] train-rmse:0.028444+0.002621 test-rmse:1.211455+0.243553
## [171] train-rmse:0.027807+0.002548 test-rmse:1.211453+0.243497
## [172] train-rmse:0.027165+0.002563 test-rmse:1.211520+0.243681
## [173] train-rmse:0.026602+0.002486 test-rmse:1.211439+0.243646
## [174] train-rmse:0.026145+0.002474 test-rmse:1.211467+0.243727
## [175] train-rmse:0.025658+0.002564 test-rmse:1.211345+0.243716
## [176] train-rmse:0.024979+0.002322 test-rmse:1.211386+0.243602
## [177] train-rmse:0.024450+0.002184 test-rmse:1.211339+0.243688
## [178] train-rmse:0.024000+0.002145 test-rmse:1.211281+0.243723
## [179] train-rmse:0.023560+0.002005 test-rmse:1.211244+0.243696
## [180] train-rmse:0.022992+0.001889 test-rmse:1.211280+0.243797
## [181] train-rmse:0.022459+0.001825 test-rmse:1.211134+0.243793
## [182] train-rmse:0.022055+0.001871 test-rmse:1.211130+0.243916
## [183] train-rmse:0.021697+0.001782 test-rmse:1.211174+0.243911
## [184] train-rmse:0.021212+0.001797 test-rmse:1.211224+0.243887
## [185] train-rmse:0.020916+0.001833 test-rmse:1.211114+0.243907
## [186] train-rmse:0.020433+0.001793 test-rmse:1.211204+0.244036
## [187] train-rmse:0.020086+0.001671 test-rmse:1.211215+0.244040
## [188] train-rmse:0.019802+0.001680 test-rmse:1.211228+0.244074
## [189] train-rmse:0.019408+0.001705 test-rmse:1.211199+0.244098
## [190] train-rmse:0.019051+0.001761 test-rmse:1.211150+0.244101
## [191] train-rmse:0.018694+0.001749 test-rmse:1.211084+0.244014
## [192] train-rmse:0.018419+0.001648 test-rmse:1.211085+0.243998
## [193] train-rmse:0.018082+0.001631 test-rmse:1.211088+0.243964
## [194] train-rmse:0.017696+0.001601 test-rmse:1.211183+0.244238
## [195] train-rmse:0.017263+0.001498 test-rmse:1.211200+0.244216
## [196] train-rmse:0.016860+0.001432 test-rmse:1.211268+0.244204
## [197] train-rmse:0.016517+0.001359 test-rmse:1.211264+0.244194
## Stopping. Best iteration:
## [191] train-rmse:0.018694+0.001749 test-rmse:1.211084+0.244014
##
## 41
## [1] train-rmse:86.218085+0.097587 test-rmse:86.218257+0.495906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:69.119772+0.083348 test-rmse:69.119559+0.523711
## [3] train-rmse:55.450065+0.073313 test-rmse:55.449316+0.550523
## [4] train-rmse:44.530696+0.066998 test-rmse:44.529209+0.577462
## [5] train-rmse:35.819717+0.064113 test-rmse:35.817187+0.605511
## [6] train-rmse:28.868193+0.060253 test-rmse:28.910774+0.605132
## [7] train-rmse:23.310282+0.045896 test-rmse:23.406557+0.627461
## [8] train-rmse:18.864508+0.036756 test-rmse:18.962976+0.601228
## [9] train-rmse:15.301535+0.032064 test-rmse:15.394958+0.558768
## [10] train-rmse:12.443528+0.028864 test-rmse:12.545145+0.539702
## [11] train-rmse:10.149762+0.025610 test-rmse:10.258270+0.525967
## [12] train-rmse:8.306724+0.021494 test-rmse:8.439027+0.534927
## [13] train-rmse:6.816993+0.021466 test-rmse:6.958118+0.528510
## [14] train-rmse:5.627154+0.019552 test-rmse:5.771085+0.491517
## [15] train-rmse:4.664539+0.019977 test-rmse:4.857436+0.492940
## [16] train-rmse:3.893047+0.015855 test-rmse:4.114369+0.452195
## [17] train-rmse:3.280281+0.017422 test-rmse:3.518115+0.429761
## [18] train-rmse:2.787811+0.015588 test-rmse:3.046772+0.402898
## [19] train-rmse:2.386155+0.017808 test-rmse:2.735240+0.395151
## [20] train-rmse:2.064322+0.022050 test-rmse:2.455285+0.364409
## [21] train-rmse:1.802798+0.024300 test-rmse:2.244791+0.350233
## [22] train-rmse:1.595191+0.031092 test-rmse:2.077181+0.333235
## [23] train-rmse:1.427243+0.032893 test-rmse:1.954787+0.329157
## [24] train-rmse:1.285178+0.032119 test-rmse:1.851543+0.311873
## [25] train-rmse:1.165193+0.038460 test-rmse:1.783970+0.295759
## [26] train-rmse:1.064234+0.040630 test-rmse:1.713994+0.289953
## [27] train-rmse:0.983545+0.041683 test-rmse:1.662286+0.286935
## [28] train-rmse:0.912150+0.039011 test-rmse:1.618842+0.285246
## [29] train-rmse:0.850997+0.038620 test-rmse:1.577708+0.293522
## [30] train-rmse:0.799752+0.038160 test-rmse:1.546014+0.292070
## [31] train-rmse:0.749592+0.040222 test-rmse:1.517193+0.288828
## [32] train-rmse:0.709855+0.040589 test-rmse:1.489939+0.290350
## [33] train-rmse:0.670596+0.039708 test-rmse:1.456668+0.279572
## [34] train-rmse:0.635537+0.040179 test-rmse:1.433235+0.283797
## [35] train-rmse:0.603121+0.042774 test-rmse:1.413442+0.282225
## [36] train-rmse:0.572722+0.043169 test-rmse:1.394258+0.284372
## [37] train-rmse:0.544210+0.039307 test-rmse:1.372240+0.277375
## [38] train-rmse:0.514340+0.039182 test-rmse:1.357795+0.271019
## [39] train-rmse:0.489737+0.038949 test-rmse:1.347472+0.275347
## [40] train-rmse:0.470323+0.039364 test-rmse:1.336568+0.274228
## [41] train-rmse:0.450380+0.038645 test-rmse:1.326425+0.268053
## [42] train-rmse:0.432656+0.039892 test-rmse:1.316009+0.269917
## [43] train-rmse:0.413489+0.037036 test-rmse:1.309669+0.269562
## [44] train-rmse:0.397377+0.037107 test-rmse:1.306758+0.273514
## [45] train-rmse:0.380983+0.033687 test-rmse:1.299125+0.274965
## [46] train-rmse:0.365869+0.035549 test-rmse:1.289381+0.278758
## [47] train-rmse:0.353871+0.034066 test-rmse:1.282488+0.278965
## [48] train-rmse:0.342527+0.033923 test-rmse:1.278609+0.280621
## [49] train-rmse:0.329063+0.031570 test-rmse:1.276604+0.284438
## [50] train-rmse:0.315428+0.026978 test-rmse:1.270113+0.286260
## [51] train-rmse:0.301866+0.025077 test-rmse:1.265626+0.285899
## [52] train-rmse:0.292112+0.023777 test-rmse:1.263711+0.286333
## [53] train-rmse:0.282882+0.023428 test-rmse:1.259411+0.288468
## [54] train-rmse:0.273838+0.023682 test-rmse:1.256442+0.288856
## [55] train-rmse:0.265206+0.022758 test-rmse:1.255907+0.290640
## [56] train-rmse:0.257892+0.021875 test-rmse:1.251537+0.289061
## [57] train-rmse:0.250912+0.021644 test-rmse:1.246954+0.291026
## [58] train-rmse:0.243351+0.021554 test-rmse:1.243064+0.288168
## [59] train-rmse:0.235900+0.020677 test-rmse:1.241441+0.288193
## [60] train-rmse:0.228563+0.021511 test-rmse:1.238418+0.287866
## [61] train-rmse:0.221715+0.022528 test-rmse:1.237348+0.289136
## [62] train-rmse:0.214502+0.022214 test-rmse:1.236871+0.290612
## [63] train-rmse:0.207456+0.019404 test-rmse:1.236015+0.290111
## [64] train-rmse:0.200350+0.018266 test-rmse:1.233855+0.290832
## [65] train-rmse:0.193766+0.016455 test-rmse:1.231872+0.290889
## [66] train-rmse:0.187697+0.016567 test-rmse:1.232042+0.291928
## [67] train-rmse:0.181398+0.017787 test-rmse:1.230479+0.291303
## [68] train-rmse:0.175705+0.018289 test-rmse:1.230131+0.291009
## [69] train-rmse:0.170179+0.018541 test-rmse:1.228409+0.291402
## [70] train-rmse:0.164963+0.019504 test-rmse:1.227855+0.292190
## [71] train-rmse:0.159011+0.015921 test-rmse:1.226549+0.290712
## [72] train-rmse:0.154137+0.014749 test-rmse:1.225826+0.289659
## [73] train-rmse:0.149912+0.015386 test-rmse:1.224129+0.291324
## [74] train-rmse:0.143934+0.014932 test-rmse:1.223883+0.290824
## [75] train-rmse:0.140041+0.014596 test-rmse:1.223648+0.291505
## [76] train-rmse:0.137231+0.014877 test-rmse:1.223749+0.292179
## [77] train-rmse:0.132512+0.014259 test-rmse:1.223494+0.291609
## [78] train-rmse:0.128374+0.013169 test-rmse:1.222841+0.293247
## [79] train-rmse:0.124050+0.012747 test-rmse:1.221911+0.293400
## [80] train-rmse:0.120529+0.010512 test-rmse:1.221512+0.293709
## [81] train-rmse:0.116538+0.011357 test-rmse:1.220864+0.293648
## [82] train-rmse:0.113143+0.011595 test-rmse:1.220306+0.294149
## [83] train-rmse:0.109779+0.010694 test-rmse:1.219251+0.293654
## [84] train-rmse:0.106206+0.010029 test-rmse:1.218892+0.293613
## [85] train-rmse:0.103737+0.009420 test-rmse:1.218599+0.293172
## [86] train-rmse:0.100079+0.007995 test-rmse:1.218664+0.293481
## [87] train-rmse:0.096818+0.007062 test-rmse:1.218220+0.293331
## [88] train-rmse:0.094622+0.007082 test-rmse:1.218592+0.293650
## [89] train-rmse:0.092112+0.006904 test-rmse:1.217755+0.293127
## [90] train-rmse:0.090223+0.006645 test-rmse:1.217410+0.293566
## [91] train-rmse:0.087287+0.006673 test-rmse:1.217451+0.293778
## [92] train-rmse:0.085050+0.005776 test-rmse:1.217073+0.294567
## [93] train-rmse:0.082545+0.005637 test-rmse:1.216919+0.294552
## [94] train-rmse:0.080723+0.005532 test-rmse:1.216612+0.294295
## [95] train-rmse:0.078271+0.005235 test-rmse:1.215949+0.294244
## [96] train-rmse:0.076165+0.004702 test-rmse:1.216188+0.294657
## [97] train-rmse:0.073916+0.004356 test-rmse:1.216508+0.295323
## [98] train-rmse:0.071921+0.004716 test-rmse:1.216257+0.295482
## [99] train-rmse:0.069478+0.004067 test-rmse:1.215700+0.295481
## [100] train-rmse:0.067675+0.004096 test-rmse:1.215797+0.295423
## [101] train-rmse:0.065487+0.003376 test-rmse:1.215825+0.295596
## [102] train-rmse:0.063844+0.003160 test-rmse:1.215652+0.295457
## [103] train-rmse:0.061632+0.003578 test-rmse:1.215449+0.295339
## [104] train-rmse:0.059407+0.003155 test-rmse:1.214891+0.295306
## [105] train-rmse:0.057827+0.003692 test-rmse:1.214514+0.295937
## [106] train-rmse:0.055974+0.003715 test-rmse:1.214505+0.295969
## [107] train-rmse:0.054496+0.003499 test-rmse:1.214508+0.295845
## [108] train-rmse:0.052960+0.003151 test-rmse:1.213999+0.295213
## [109] train-rmse:0.051542+0.003277 test-rmse:1.213904+0.295760
## [110] train-rmse:0.049863+0.003066 test-rmse:1.213624+0.295520
## [111] train-rmse:0.048453+0.003130 test-rmse:1.213315+0.295506
## [112] train-rmse:0.047426+0.003250 test-rmse:1.213129+0.295831
## [113] train-rmse:0.046375+0.003100 test-rmse:1.212874+0.295993
## [114] train-rmse:0.045193+0.003434 test-rmse:1.212702+0.295921
## [115] train-rmse:0.044350+0.003338 test-rmse:1.212741+0.296115
## [116] train-rmse:0.043302+0.003519 test-rmse:1.212858+0.296863
## [117] train-rmse:0.042259+0.003368 test-rmse:1.212860+0.297013
## [118] train-rmse:0.041134+0.003026 test-rmse:1.212472+0.297230
## [119] train-rmse:0.039901+0.002876 test-rmse:1.212401+0.297366
## [120] train-rmse:0.038772+0.002444 test-rmse:1.212357+0.297457
## [121] train-rmse:0.037954+0.002492 test-rmse:1.212443+0.297284
## [122] train-rmse:0.036900+0.002362 test-rmse:1.212323+0.297351
## [123] train-rmse:0.035922+0.002245 test-rmse:1.212082+0.297217
## [124] train-rmse:0.034966+0.002302 test-rmse:1.211940+0.297176
## [125] train-rmse:0.033933+0.002171 test-rmse:1.212089+0.297142
## [126] train-rmse:0.033218+0.002216 test-rmse:1.211979+0.297276
## [127] train-rmse:0.032210+0.002304 test-rmse:1.212124+0.297036
## [128] train-rmse:0.031317+0.002167 test-rmse:1.212020+0.297028
## [129] train-rmse:0.030717+0.001916 test-rmse:1.211986+0.297066
## [130] train-rmse:0.029678+0.001730 test-rmse:1.211833+0.296772
## [131] train-rmse:0.028906+0.001515 test-rmse:1.211875+0.296922
## [132] train-rmse:0.028239+0.001543 test-rmse:1.211764+0.296777
## [133] train-rmse:0.027628+0.001465 test-rmse:1.211690+0.296765
## [134] train-rmse:0.026746+0.001288 test-rmse:1.211770+0.296545
## [135] train-rmse:0.026220+0.001411 test-rmse:1.211761+0.296532
## [136] train-rmse:0.025352+0.001361 test-rmse:1.211814+0.296513
## [137] train-rmse:0.024650+0.001589 test-rmse:1.211765+0.296507
## [138] train-rmse:0.024076+0.001544 test-rmse:1.211750+0.296526
## [139] train-rmse:0.023413+0.001258 test-rmse:1.211755+0.296571
## Stopping. Best iteration:
## [133] train-rmse:0.027628+0.001465 test-rmse:1.211690+0.296765
##
## 42
## [1] train-rmse:84.081620+0.095738 test-rmse:84.081755+0.499144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:65.745564+0.080728 test-rmse:65.745244+0.529833
## [3] train-rmse:51.456281+0.070760 test-rmse:51.455305+0.559619
## [4] train-rmse:40.333913+0.065280 test-rmse:40.331998+0.589993
## [5] train-rmse:31.689540+0.061479 test-rmse:31.714312+0.607511
## [6] train-rmse:24.970252+0.056849 test-rmse:25.029365+0.633135
## [7] train-rmse:19.754920+0.049853 test-rmse:19.798399+0.633426
## [8] train-rmse:15.720236+0.042300 test-rmse:15.790007+0.614441
## [9] train-rmse:12.615792+0.038547 test-rmse:12.670548+0.614834
## [10] train-rmse:10.245422+0.037236 test-rmse:10.369963+0.659447
## [11] train-rmse:8.455333+0.038142 test-rmse:8.546730+0.640634
## [12] train-rmse:7.119962+0.043697 test-rmse:7.218161+0.606465
## [13] train-rmse:6.138735+0.048589 test-rmse:6.250143+0.624065
## [14] train-rmse:5.429717+0.050998 test-rmse:5.600702+0.674514
## [15] train-rmse:4.915263+0.053617 test-rmse:5.058797+0.603845
## [16] train-rmse:4.553632+0.056731 test-rmse:4.679948+0.561406
## [17] train-rmse:4.294095+0.058540 test-rmse:4.429185+0.542903
## [18] train-rmse:4.101971+0.059061 test-rmse:4.301763+0.545234
## [19] train-rmse:3.956456+0.058702 test-rmse:4.138639+0.491650
## [20] train-rmse:3.845893+0.057742 test-rmse:4.010007+0.476791
## [21] train-rmse:3.755253+0.058087 test-rmse:3.914267+0.427814
## [22] train-rmse:3.678922+0.057797 test-rmse:3.830782+0.426185
## [23] train-rmse:3.611507+0.057684 test-rmse:3.802728+0.430768
## [24] train-rmse:3.551422+0.055776 test-rmse:3.769495+0.418760
## [25] train-rmse:3.495733+0.053244 test-rmse:3.719601+0.372572
## [26] train-rmse:3.443229+0.049542 test-rmse:3.679601+0.388349
## [27] train-rmse:3.394372+0.048256 test-rmse:3.627570+0.383475
## [28] train-rmse:3.347520+0.046979 test-rmse:3.563454+0.338718
## [29] train-rmse:3.303036+0.047434 test-rmse:3.521593+0.333630
## [30] train-rmse:3.260062+0.047774 test-rmse:3.487151+0.349810
## [31] train-rmse:3.219151+0.046681 test-rmse:3.441458+0.323274
## [32] train-rmse:3.179069+0.046601 test-rmse:3.405821+0.329017
## [33] train-rmse:3.140590+0.045814 test-rmse:3.395258+0.330508
## [34] train-rmse:3.102832+0.045440 test-rmse:3.352875+0.340494
## [35] train-rmse:3.065597+0.044798 test-rmse:3.322165+0.347883
## [36] train-rmse:3.030082+0.042983 test-rmse:3.274847+0.338624
## [37] train-rmse:2.994517+0.041213 test-rmse:3.229832+0.339269
## [38] train-rmse:2.959191+0.039082 test-rmse:3.183756+0.298569
## [39] train-rmse:2.926901+0.038687 test-rmse:3.166186+0.303982
## [40] train-rmse:2.895282+0.038491 test-rmse:3.137110+0.306385
## [41] train-rmse:2.864496+0.038919 test-rmse:3.108226+0.315855
## [42] train-rmse:2.834139+0.039469 test-rmse:3.081711+0.318316
## [43] train-rmse:2.804027+0.039668 test-rmse:3.062950+0.311535
## [44] train-rmse:2.774069+0.039862 test-rmse:3.042898+0.317225
## [45] train-rmse:2.744747+0.039763 test-rmse:3.008620+0.329865
## [46] train-rmse:2.715994+0.039845 test-rmse:2.970539+0.311976
## [47] train-rmse:2.688264+0.040006 test-rmse:2.935135+0.319441
## [48] train-rmse:2.661036+0.039612 test-rmse:2.917966+0.324675
## [49] train-rmse:2.634322+0.039107 test-rmse:2.891818+0.314544
## [50] train-rmse:2.608247+0.038127 test-rmse:2.873470+0.321340
## [51] train-rmse:2.582717+0.037201 test-rmse:2.822808+0.299498
## [52] train-rmse:2.557826+0.036798 test-rmse:2.812292+0.302120
## [53] train-rmse:2.533668+0.036372 test-rmse:2.802599+0.302309
## [54] train-rmse:2.509583+0.035995 test-rmse:2.774355+0.297515
## [55] train-rmse:2.485661+0.035175 test-rmse:2.758169+0.293739
## [56] train-rmse:2.462341+0.035124 test-rmse:2.737093+0.300555
## [57] train-rmse:2.439203+0.035201 test-rmse:2.711220+0.305600
## [58] train-rmse:2.416407+0.035460 test-rmse:2.690032+0.310711
## [59] train-rmse:2.393961+0.035538 test-rmse:2.666071+0.308235
## [60] train-rmse:2.371907+0.035188 test-rmse:2.638015+0.292491
## [61] train-rmse:2.350388+0.034735 test-rmse:2.610671+0.286891
## [62] train-rmse:2.329274+0.034466 test-rmse:2.593779+0.295615
## [63] train-rmse:2.308714+0.034231 test-rmse:2.581491+0.298565
## [64] train-rmse:2.288301+0.033732 test-rmse:2.560947+0.299192
## [65] train-rmse:2.268142+0.033192 test-rmse:2.548138+0.287748
## [66] train-rmse:2.248505+0.032643 test-rmse:2.527383+0.274053
## [67] train-rmse:2.229124+0.032176 test-rmse:2.501638+0.252447
## [68] train-rmse:2.210012+0.031589 test-rmse:2.487342+0.249316
## [69] train-rmse:2.191188+0.031083 test-rmse:2.470133+0.249308
## [70] train-rmse:2.172343+0.030464 test-rmse:2.452849+0.257406
## [71] train-rmse:2.153898+0.030199 test-rmse:2.439900+0.260858
## [72] train-rmse:2.135342+0.030043 test-rmse:2.416554+0.267152
## [73] train-rmse:2.117069+0.029931 test-rmse:2.392808+0.265287
## [74] train-rmse:2.098786+0.029786 test-rmse:2.376875+0.274515
## [75] train-rmse:2.080767+0.029623 test-rmse:2.360547+0.274422
## [76] train-rmse:2.063090+0.029385 test-rmse:2.341924+0.276445
## [77] train-rmse:2.045918+0.029412 test-rmse:2.317374+0.275039
## [78] train-rmse:2.028891+0.029100 test-rmse:2.299823+0.273662
## [79] train-rmse:2.012282+0.028519 test-rmse:2.277988+0.264853
## [80] train-rmse:1.995656+0.027859 test-rmse:2.265764+0.269221
## [81] train-rmse:1.979308+0.027108 test-rmse:2.254339+0.261727
## [82] train-rmse:1.963354+0.026712 test-rmse:2.241040+0.272673
## [83] train-rmse:1.948047+0.026512 test-rmse:2.224617+0.266577
## [84] train-rmse:1.932729+0.026395 test-rmse:2.204782+0.262297
## [85] train-rmse:1.917388+0.026136 test-rmse:2.194632+0.265175
## [86] train-rmse:1.902557+0.026030 test-rmse:2.178578+0.264746
## [87] train-rmse:1.887865+0.025931 test-rmse:2.168132+0.265890
## [88] train-rmse:1.873212+0.025678 test-rmse:2.154724+0.265185
## [89] train-rmse:1.858633+0.025576 test-rmse:2.143531+0.262081
## [90] train-rmse:1.844259+0.025373 test-rmse:2.125979+0.261466
## [91] train-rmse:1.830163+0.025296 test-rmse:2.112577+0.258759
## [92] train-rmse:1.816343+0.024934 test-rmse:2.097299+0.250048
## [93] train-rmse:1.802610+0.024573 test-rmse:2.081292+0.248678
## [94] train-rmse:1.789020+0.024149 test-rmse:2.073025+0.245277
## [95] train-rmse:1.775667+0.023575 test-rmse:2.058870+0.252075
## [96] train-rmse:1.762506+0.023174 test-rmse:2.038594+0.236219
## [97] train-rmse:1.749555+0.022905 test-rmse:2.028203+0.238061
## [98] train-rmse:1.736760+0.022479 test-rmse:2.012899+0.241139
## [99] train-rmse:1.724002+0.022104 test-rmse:2.001775+0.245559
## [100] train-rmse:1.711515+0.021635 test-rmse:1.989584+0.245968
## [101] train-rmse:1.699101+0.021126 test-rmse:1.975675+0.233235
## [102] train-rmse:1.686868+0.020697 test-rmse:1.966647+0.238804
## [103] train-rmse:1.674887+0.020400 test-rmse:1.955943+0.230967
## [104] train-rmse:1.663061+0.020205 test-rmse:1.944035+0.228353
## [105] train-rmse:1.651151+0.019993 test-rmse:1.936502+0.232080
## [106] train-rmse:1.639273+0.019905 test-rmse:1.927401+0.237521
## [107] train-rmse:1.627702+0.019759 test-rmse:1.917948+0.240477
## [108] train-rmse:1.616228+0.019686 test-rmse:1.899061+0.243529
## [109] train-rmse:1.604939+0.019649 test-rmse:1.892423+0.250332
## [110] train-rmse:1.593810+0.019591 test-rmse:1.883608+0.250743
## [111] train-rmse:1.582774+0.019454 test-rmse:1.867894+0.233655
## [112] train-rmse:1.572027+0.019012 test-rmse:1.856987+0.230844
## [113] train-rmse:1.561304+0.018685 test-rmse:1.850647+0.229702
## [114] train-rmse:1.550922+0.018486 test-rmse:1.834643+0.222873
## [115] train-rmse:1.540656+0.018183 test-rmse:1.827283+0.219454
## [116] train-rmse:1.530489+0.017910 test-rmse:1.820043+0.223218
## [117] train-rmse:1.520341+0.017680 test-rmse:1.809685+0.227065
## [118] train-rmse:1.510257+0.017363 test-rmse:1.805260+0.227420
## [119] train-rmse:1.500471+0.017269 test-rmse:1.792885+0.223992
## [120] train-rmse:1.490787+0.017066 test-rmse:1.786121+0.218892
## [121] train-rmse:1.480880+0.017045 test-rmse:1.777621+0.223356
## [122] train-rmse:1.471434+0.016784 test-rmse:1.765019+0.222846
## [123] train-rmse:1.462011+0.016502 test-rmse:1.757094+0.211491
## [124] train-rmse:1.452626+0.016081 test-rmse:1.751148+0.212239
## [125] train-rmse:1.443303+0.015728 test-rmse:1.745021+0.217890
## [126] train-rmse:1.434029+0.015502 test-rmse:1.737717+0.216980
## [127] train-rmse:1.424974+0.015397 test-rmse:1.728284+0.213350
## [128] train-rmse:1.416116+0.015216 test-rmse:1.718282+0.215227
## [129] train-rmse:1.407262+0.015114 test-rmse:1.712707+0.213210
## [130] train-rmse:1.398637+0.015143 test-rmse:1.702447+0.216504
## [131] train-rmse:1.390027+0.015176 test-rmse:1.694235+0.219262
## [132] train-rmse:1.381558+0.015077 test-rmse:1.684418+0.219539
## [133] train-rmse:1.373108+0.015023 test-rmse:1.673753+0.220348
## [134] train-rmse:1.364808+0.014933 test-rmse:1.666023+0.223684
## [135] train-rmse:1.356694+0.014882 test-rmse:1.657017+0.226677
## [136] train-rmse:1.348805+0.014843 test-rmse:1.652243+0.221793
## [137] train-rmse:1.341064+0.014751 test-rmse:1.648543+0.217178
## [138] train-rmse:1.333383+0.014633 test-rmse:1.641307+0.216807
## [139] train-rmse:1.325754+0.014526 test-rmse:1.630502+0.210792
## [140] train-rmse:1.318169+0.014412 test-rmse:1.613424+0.201291
## [141] train-rmse:1.310704+0.014401 test-rmse:1.610179+0.199313
## [142] train-rmse:1.303328+0.014373 test-rmse:1.600393+0.201444
## [143] train-rmse:1.295958+0.014272 test-rmse:1.593299+0.201663
## [144] train-rmse:1.288623+0.014142 test-rmse:1.587795+0.197751
## [145] train-rmse:1.281524+0.013955 test-rmse:1.586152+0.197203
## [146] train-rmse:1.274428+0.013742 test-rmse:1.574316+0.190198
## [147] train-rmse:1.267467+0.013605 test-rmse:1.571699+0.192306
## [148] train-rmse:1.260666+0.013479 test-rmse:1.566743+0.194720
## [149] train-rmse:1.253998+0.013395 test-rmse:1.559172+0.190797
## [150] train-rmse:1.247394+0.013285 test-rmse:1.551455+0.188813
## [151] train-rmse:1.240816+0.013093 test-rmse:1.539853+0.190999
## [152] train-rmse:1.234370+0.013027 test-rmse:1.534440+0.193743
## [153] train-rmse:1.227925+0.013024 test-rmse:1.531187+0.195351
## [154] train-rmse:1.221679+0.012996 test-rmse:1.526017+0.195774
## [155] train-rmse:1.215365+0.012985 test-rmse:1.519890+0.197165
## [156] train-rmse:1.209056+0.012974 test-rmse:1.516642+0.195499
## [157] train-rmse:1.202720+0.012929 test-rmse:1.511563+0.196419
## [158] train-rmse:1.196482+0.012746 test-rmse:1.509073+0.197903
## [159] train-rmse:1.190461+0.012733 test-rmse:1.504078+0.193060
## [160] train-rmse:1.184510+0.012808 test-rmse:1.499111+0.195526
## [161] train-rmse:1.178771+0.012661 test-rmse:1.486583+0.186705
## [162] train-rmse:1.173155+0.012572 test-rmse:1.483305+0.189809
## [163] train-rmse:1.167514+0.012457 test-rmse:1.476084+0.192421
## [164] train-rmse:1.161963+0.012419 test-rmse:1.467917+0.190586
## [165] train-rmse:1.156492+0.012406 test-rmse:1.462386+0.191218
## [166] train-rmse:1.150968+0.012390 test-rmse:1.455005+0.194505
## [167] train-rmse:1.145396+0.012387 test-rmse:1.452042+0.195192
## [168] train-rmse:1.139916+0.012387 test-rmse:1.444026+0.188638
## [169] train-rmse:1.134594+0.012299 test-rmse:1.435766+0.190428
## [170] train-rmse:1.129342+0.012207 test-rmse:1.435370+0.189353
## [171] train-rmse:1.124259+0.012120 test-rmse:1.431576+0.184198
## [172] train-rmse:1.119283+0.012067 test-rmse:1.424847+0.184510
## [173] train-rmse:1.114341+0.011970 test-rmse:1.419866+0.185153
## [174] train-rmse:1.109459+0.011877 test-rmse:1.415735+0.183469
## [175] train-rmse:1.104583+0.011757 test-rmse:1.409394+0.182306
## [176] train-rmse:1.099711+0.011607 test-rmse:1.405836+0.186067
## [177] train-rmse:1.094942+0.011485 test-rmse:1.400297+0.188595
## [178] train-rmse:1.090280+0.011406 test-rmse:1.393685+0.186541
## [179] train-rmse:1.085626+0.011381 test-rmse:1.389440+0.186418
## [180] train-rmse:1.081015+0.011392 test-rmse:1.380344+0.177278
## [181] train-rmse:1.076413+0.011381 test-rmse:1.376498+0.177577
## [182] train-rmse:1.071862+0.011339 test-rmse:1.373471+0.176437
## [183] train-rmse:1.067355+0.011292 test-rmse:1.368232+0.176868
## [184] train-rmse:1.062935+0.011356 test-rmse:1.367834+0.174390
## [185] train-rmse:1.058447+0.011358 test-rmse:1.364444+0.177020
## [186] train-rmse:1.054205+0.011312 test-rmse:1.362802+0.178389
## [187] train-rmse:1.050007+0.011293 test-rmse:1.361825+0.177869
## [188] train-rmse:1.045844+0.011291 test-rmse:1.358206+0.172222
## [189] train-rmse:1.041790+0.011231 test-rmse:1.354027+0.175507
## [190] train-rmse:1.037758+0.011190 test-rmse:1.348627+0.179710
## [191] train-rmse:1.033732+0.011131 test-rmse:1.345170+0.181368
## [192] train-rmse:1.029749+0.011165 test-rmse:1.339974+0.179047
## [193] train-rmse:1.025887+0.011180 test-rmse:1.337476+0.179104
## [194] train-rmse:1.022073+0.011178 test-rmse:1.330584+0.180356
## [195] train-rmse:1.018357+0.011167 test-rmse:1.328976+0.179551
## [196] train-rmse:1.014661+0.011201 test-rmse:1.326263+0.180020
## [197] train-rmse:1.010980+0.011203 test-rmse:1.323319+0.181304
## [198] train-rmse:1.007344+0.011262 test-rmse:1.317932+0.182005
## [199] train-rmse:1.003730+0.011315 test-rmse:1.313910+0.183466
## [200] train-rmse:1.000141+0.011346 test-rmse:1.311487+0.181616
## [201] train-rmse:0.996633+0.011364 test-rmse:1.309244+0.181525
## [202] train-rmse:0.993214+0.011322 test-rmse:1.307507+0.178590
## [203] train-rmse:0.989854+0.011297 test-rmse:1.303827+0.175848
## [204] train-rmse:0.986493+0.011303 test-rmse:1.299353+0.178363
## [205] train-rmse:0.983147+0.011353 test-rmse:1.297086+0.178761
## [206] train-rmse:0.979847+0.011419 test-rmse:1.293302+0.180882
## [207] train-rmse:0.976633+0.011462 test-rmse:1.293004+0.180591
## [208] train-rmse:0.973421+0.011489 test-rmse:1.288068+0.180726
## [209] train-rmse:0.970304+0.011523 test-rmse:1.287944+0.182000
## [210] train-rmse:0.967248+0.011524 test-rmse:1.284506+0.181770
## [211] train-rmse:0.964191+0.011523 test-rmse:1.275052+0.173964
## [212] train-rmse:0.961164+0.011544 test-rmse:1.271948+0.176031
## [213] train-rmse:0.958167+0.011548 test-rmse:1.268705+0.177512
## [214] train-rmse:0.955217+0.011572 test-rmse:1.264505+0.178239
## [215] train-rmse:0.952259+0.011582 test-rmse:1.263726+0.177459
## [216] train-rmse:0.949348+0.011540 test-rmse:1.258850+0.178267
## [217] train-rmse:0.946477+0.011534 test-rmse:1.257781+0.176444
## [218] train-rmse:0.943637+0.011571 test-rmse:1.254922+0.175570
## [219] train-rmse:0.940845+0.011580 test-rmse:1.252742+0.175309
## [220] train-rmse:0.938114+0.011594 test-rmse:1.250917+0.174198
## [221] train-rmse:0.935381+0.011625 test-rmse:1.247900+0.170491
## [222] train-rmse:0.932683+0.011671 test-rmse:1.243229+0.173702
## [223] train-rmse:0.929990+0.011700 test-rmse:1.242090+0.173668
## [224] train-rmse:0.927366+0.011740 test-rmse:1.240225+0.175397
## [225] train-rmse:0.924759+0.011817 test-rmse:1.238105+0.175882
## [226] train-rmse:0.922169+0.011889 test-rmse:1.235513+0.173620
## [227] train-rmse:0.919612+0.011918 test-rmse:1.234279+0.173057
## [228] train-rmse:0.917038+0.012000 test-rmse:1.232632+0.174097
## [229] train-rmse:0.914569+0.012082 test-rmse:1.231114+0.176013
## [230] train-rmse:0.912154+0.012171 test-rmse:1.229443+0.176859
## [231] train-rmse:0.909788+0.012242 test-rmse:1.227973+0.178125
## [232] train-rmse:0.907478+0.012302 test-rmse:1.227257+0.178740
## [233] train-rmse:0.905122+0.012327 test-rmse:1.227348+0.177533
## [234] train-rmse:0.902833+0.012358 test-rmse:1.220848+0.170068
## [235] train-rmse:0.900576+0.012289 test-rmse:1.217969+0.170464
## [236] train-rmse:0.898356+0.012289 test-rmse:1.214600+0.172087
## [237] train-rmse:0.896121+0.012302 test-rmse:1.212978+0.171637
## [238] train-rmse:0.893923+0.012325 test-rmse:1.211866+0.171801
## [239] train-rmse:0.891769+0.012369 test-rmse:1.207125+0.171840
## [240] train-rmse:0.889643+0.012430 test-rmse:1.207164+0.168809
## [241] train-rmse:0.887547+0.012494 test-rmse:1.205309+0.169354
## [242] train-rmse:0.885488+0.012524 test-rmse:1.203819+0.172118
## [243] train-rmse:0.883465+0.012599 test-rmse:1.201410+0.171429
## [244] train-rmse:0.881447+0.012640 test-rmse:1.200267+0.171022
## [245] train-rmse:0.879472+0.012687 test-rmse:1.198761+0.170862
## [246] train-rmse:0.877487+0.012720 test-rmse:1.197174+0.168071
## [247] train-rmse:0.875507+0.012792 test-rmse:1.194345+0.166194
## [248] train-rmse:0.873577+0.012885 test-rmse:1.191116+0.164319
## [249] train-rmse:0.871690+0.012978 test-rmse:1.189126+0.164518
## [250] train-rmse:0.869831+0.013033 test-rmse:1.189524+0.164251
## [251] train-rmse:0.867944+0.013093 test-rmse:1.186286+0.167469
## [252] train-rmse:0.866086+0.013159 test-rmse:1.185552+0.167810
## [253] train-rmse:0.864249+0.013255 test-rmse:1.185703+0.168202
## [254] train-rmse:0.862408+0.013326 test-rmse:1.184255+0.168170
## [255] train-rmse:0.860587+0.013374 test-rmse:1.181804+0.169446
## [256] train-rmse:0.858831+0.013457 test-rmse:1.180115+0.170190
## [257] train-rmse:0.857090+0.013490 test-rmse:1.178798+0.171871
## [258] train-rmse:0.855377+0.013564 test-rmse:1.177670+0.172416
## [259] train-rmse:0.853703+0.013612 test-rmse:1.178363+0.173213
## [260] train-rmse:0.852020+0.013660 test-rmse:1.176648+0.173262
## [261] train-rmse:0.850359+0.013679 test-rmse:1.173651+0.173789
## [262] train-rmse:0.848737+0.013706 test-rmse:1.168039+0.166664
## [263] train-rmse:0.847135+0.013730 test-rmse:1.167662+0.168038
## [264] train-rmse:0.845533+0.013749 test-rmse:1.164683+0.165672
## [265] train-rmse:0.843944+0.013776 test-rmse:1.160990+0.164626
## [266] train-rmse:0.842396+0.013775 test-rmse:1.160556+0.163980
## [267] train-rmse:0.840853+0.013746 test-rmse:1.158405+0.164510
## [268] train-rmse:0.839353+0.013769 test-rmse:1.158423+0.162823
## [269] train-rmse:0.837862+0.013800 test-rmse:1.156520+0.163047
## [270] train-rmse:0.836380+0.013830 test-rmse:1.155256+0.163635
## [271] train-rmse:0.834949+0.013897 test-rmse:1.154901+0.163770
## [272] train-rmse:0.833521+0.013956 test-rmse:1.154100+0.162749
## [273] train-rmse:0.832083+0.013997 test-rmse:1.152148+0.164265
## [274] train-rmse:0.830698+0.014066 test-rmse:1.151534+0.163869
## [275] train-rmse:0.829315+0.014125 test-rmse:1.150664+0.164124
## [276] train-rmse:0.827943+0.014190 test-rmse:1.149987+0.165146
## [277] train-rmse:0.826581+0.014252 test-rmse:1.145499+0.165545
## [278] train-rmse:0.825237+0.014311 test-rmse:1.144726+0.165991
## [279] train-rmse:0.823915+0.014343 test-rmse:1.145593+0.164791
## [280] train-rmse:0.822602+0.014365 test-rmse:1.144791+0.164491
## [281] train-rmse:0.821325+0.014395 test-rmse:1.143034+0.163944
## [282] train-rmse:0.820053+0.014412 test-rmse:1.142808+0.164836
## [283] train-rmse:0.818794+0.014418 test-rmse:1.141340+0.165080
## [284] train-rmse:0.817558+0.014415 test-rmse:1.140686+0.166371
## [285] train-rmse:0.816328+0.014441 test-rmse:1.139734+0.163936
## [286] train-rmse:0.815133+0.014484 test-rmse:1.139026+0.164290
## [287] train-rmse:0.813954+0.014519 test-rmse:1.138640+0.165068
## [288] train-rmse:0.812768+0.014563 test-rmse:1.138229+0.165354
## [289] train-rmse:0.811598+0.014597 test-rmse:1.138225+0.163575
## [290] train-rmse:0.810451+0.014620 test-rmse:1.136675+0.163621
## [291] train-rmse:0.809319+0.014666 test-rmse:1.134246+0.163368
## [292] train-rmse:0.808198+0.014713 test-rmse:1.133422+0.163949
## [293] train-rmse:0.807095+0.014762 test-rmse:1.132692+0.164688
## [294] train-rmse:0.806015+0.014809 test-rmse:1.131702+0.164696
## [295] train-rmse:0.804918+0.014859 test-rmse:1.131224+0.163756
## [296] train-rmse:0.803858+0.014901 test-rmse:1.129409+0.162595
## [297] train-rmse:0.802813+0.014948 test-rmse:1.128193+0.162197
## [298] train-rmse:0.801769+0.014993 test-rmse:1.127195+0.162158
## [299] train-rmse:0.800728+0.015046 test-rmse:1.126651+0.161519
## [300] train-rmse:0.799687+0.015086 test-rmse:1.123254+0.158799
## [301] train-rmse:0.798661+0.015130 test-rmse:1.119840+0.158761
## [302] train-rmse:0.797647+0.015166 test-rmse:1.120058+0.159211
## [303] train-rmse:0.796659+0.015196 test-rmse:1.118913+0.159553
## [304] train-rmse:0.795662+0.015227 test-rmse:1.119049+0.159640
## [305] train-rmse:0.794686+0.015261 test-rmse:1.118359+0.159631
## [306] train-rmse:0.793713+0.015321 test-rmse:1.118547+0.159142
## [307] train-rmse:0.792750+0.015377 test-rmse:1.116477+0.158526
## [308] train-rmse:0.791788+0.015420 test-rmse:1.116903+0.158504
## [309] train-rmse:0.790830+0.015475 test-rmse:1.116244+0.159247
## [310] train-rmse:0.789904+0.015526 test-rmse:1.114632+0.159729
## [311] train-rmse:0.788994+0.015578 test-rmse:1.114692+0.160698
## [312] train-rmse:0.788077+0.015629 test-rmse:1.114153+0.160021
## [313] train-rmse:0.787182+0.015668 test-rmse:1.112260+0.160319
## [314] train-rmse:0.786305+0.015708 test-rmse:1.112765+0.159957
## [315] train-rmse:0.785421+0.015756 test-rmse:1.111977+0.159349
## [316] train-rmse:0.784555+0.015790 test-rmse:1.111206+0.158632
## [317] train-rmse:0.783691+0.015808 test-rmse:1.108064+0.156297
## [318] train-rmse:0.782853+0.015831 test-rmse:1.107349+0.157055
## [319] train-rmse:0.782008+0.015847 test-rmse:1.106470+0.156644
## [320] train-rmse:0.781153+0.015847 test-rmse:1.104570+0.155746
## [321] train-rmse:0.780341+0.015882 test-rmse:1.104104+0.155865
## [322] train-rmse:0.779537+0.015920 test-rmse:1.102665+0.156732
## [323] train-rmse:0.778746+0.015948 test-rmse:1.101794+0.157050
## [324] train-rmse:0.777948+0.015996 test-rmse:1.101360+0.157995
## [325] train-rmse:0.777170+0.016035 test-rmse:1.100311+0.158078
## [326] train-rmse:0.776381+0.016060 test-rmse:1.101186+0.157745
## [327] train-rmse:0.775609+0.016099 test-rmse:1.098785+0.157480
## [328] train-rmse:0.774866+0.016137 test-rmse:1.098180+0.156999
## [329] train-rmse:0.774113+0.016155 test-rmse:1.097245+0.157267
## [330] train-rmse:0.773385+0.016181 test-rmse:1.096183+0.156969
## [331] train-rmse:0.772661+0.016218 test-rmse:1.096348+0.156957
## [332] train-rmse:0.771925+0.016255 test-rmse:1.095334+0.157065
## [333] train-rmse:0.771193+0.016293 test-rmse:1.094241+0.157310
## [334] train-rmse:0.770460+0.016330 test-rmse:1.093082+0.157542
## [335] train-rmse:0.769749+0.016347 test-rmse:1.092946+0.157182
## [336] train-rmse:0.769048+0.016389 test-rmse:1.091677+0.156587
## [337] train-rmse:0.768350+0.016424 test-rmse:1.090094+0.156486
## [338] train-rmse:0.767650+0.016434 test-rmse:1.089985+0.155851
## [339] train-rmse:0.766951+0.016460 test-rmse:1.090548+0.156126
## [340] train-rmse:0.766266+0.016483 test-rmse:1.089545+0.157248
## [341] train-rmse:0.765590+0.016504 test-rmse:1.089259+0.157793
## [342] train-rmse:0.764910+0.016522 test-rmse:1.088872+0.157865
## [343] train-rmse:0.764244+0.016542 test-rmse:1.088273+0.158157
## [344] train-rmse:0.763594+0.016558 test-rmse:1.087671+0.157900
## [345] train-rmse:0.762919+0.016594 test-rmse:1.086315+0.159149
## [346] train-rmse:0.762265+0.016626 test-rmse:1.086629+0.159530
## [347] train-rmse:0.761617+0.016650 test-rmse:1.086074+0.159745
## [348] train-rmse:0.760965+0.016665 test-rmse:1.085888+0.159725
## [349] train-rmse:0.760336+0.016691 test-rmse:1.085764+0.159924
## [350] train-rmse:0.759715+0.016719 test-rmse:1.085307+0.159980
## [351] train-rmse:0.759063+0.016723 test-rmse:1.085612+0.159712
## [352] train-rmse:0.758427+0.016750 test-rmse:1.085336+0.159863
## [353] train-rmse:0.757805+0.016768 test-rmse:1.084555+0.160003
## [354] train-rmse:0.757196+0.016781 test-rmse:1.083969+0.158830
## [355] train-rmse:0.756597+0.016814 test-rmse:1.083649+0.159073
## [356] train-rmse:0.755975+0.016857 test-rmse:1.083351+0.157519
## [357] train-rmse:0.755365+0.016879 test-rmse:1.083525+0.157679
## [358] train-rmse:0.754769+0.016904 test-rmse:1.082504+0.157341
## [359] train-rmse:0.754187+0.016915 test-rmse:1.079717+0.155798
## [360] train-rmse:0.753601+0.016916 test-rmse:1.079587+0.157227
## [361] train-rmse:0.753019+0.016946 test-rmse:1.080620+0.156209
## [362] train-rmse:0.752437+0.016966 test-rmse:1.080037+0.156020
## [363] train-rmse:0.751858+0.016985 test-rmse:1.080164+0.156455
## [364] train-rmse:0.751286+0.017008 test-rmse:1.078417+0.156780
## [365] train-rmse:0.750720+0.017026 test-rmse:1.077974+0.157487
## [366] train-rmse:0.750156+0.017028 test-rmse:1.077429+0.156252
## [367] train-rmse:0.749593+0.017044 test-rmse:1.077386+0.156228
## [368] train-rmse:0.749036+0.017053 test-rmse:1.077266+0.155912
## [369] train-rmse:0.748501+0.017065 test-rmse:1.076927+0.154978
## [370] train-rmse:0.747963+0.017084 test-rmse:1.077287+0.154672
## [371] train-rmse:0.747431+0.017099 test-rmse:1.076789+0.154891
## [372] train-rmse:0.746899+0.017104 test-rmse:1.076643+0.155738
## [373] train-rmse:0.746351+0.017119 test-rmse:1.077120+0.155628
## [374] train-rmse:0.745817+0.017120 test-rmse:1.076434+0.155932
## [375] train-rmse:0.745293+0.017130 test-rmse:1.075798+0.156244
## [376] train-rmse:0.744779+0.017144 test-rmse:1.074762+0.155852
## [377] train-rmse:0.744266+0.017155 test-rmse:1.074532+0.154928
## [378] train-rmse:0.743755+0.017172 test-rmse:1.075050+0.154378
## [379] train-rmse:0.743223+0.017175 test-rmse:1.074523+0.154229
## [380] train-rmse:0.742706+0.017183 test-rmse:1.073869+0.153936
## [381] train-rmse:0.742204+0.017195 test-rmse:1.073311+0.153929
## [382] train-rmse:0.741704+0.017197 test-rmse:1.072285+0.154596
## [383] train-rmse:0.741185+0.017194 test-rmse:1.072710+0.154445
## [384] train-rmse:0.740691+0.017200 test-rmse:1.072522+0.154429
## [385] train-rmse:0.740197+0.017218 test-rmse:1.071176+0.155452
## [386] train-rmse:0.739682+0.017206 test-rmse:1.071243+0.155376
## [387] train-rmse:0.739207+0.017225 test-rmse:1.071520+0.155606
## [388] train-rmse:0.738732+0.017230 test-rmse:1.070390+0.155685
## [389] train-rmse:0.738253+0.017239 test-rmse:1.069623+0.154291
## [390] train-rmse:0.737763+0.017213 test-rmse:1.069078+0.153986
## [391] train-rmse:0.737299+0.017218 test-rmse:1.069070+0.153840
## [392] train-rmse:0.736826+0.017223 test-rmse:1.069247+0.153720
## [393] train-rmse:0.736368+0.017232 test-rmse:1.068763+0.153709
## [394] train-rmse:0.735923+0.017240 test-rmse:1.067005+0.153350
## [395] train-rmse:0.735447+0.017202 test-rmse:1.066518+0.152684
## [396] train-rmse:0.735000+0.017209 test-rmse:1.066673+0.152042
## [397] train-rmse:0.734552+0.017219 test-rmse:1.063974+0.149053
## [398] train-rmse:0.734102+0.017191 test-rmse:1.063423+0.148791
## [399] train-rmse:0.733646+0.017192 test-rmse:1.063220+0.146677
## [400] train-rmse:0.733197+0.017180 test-rmse:1.062885+0.146636
## [401] train-rmse:0.732765+0.017183 test-rmse:1.062781+0.146781
## [402] train-rmse:0.732319+0.017173 test-rmse:1.062159+0.145932
## [403] train-rmse:0.731885+0.017168 test-rmse:1.061081+0.145080
## [404] train-rmse:0.731457+0.017168 test-rmse:1.061268+0.145118
## [405] train-rmse:0.731025+0.017170 test-rmse:1.061239+0.145247
## [406] train-rmse:0.730584+0.017179 test-rmse:1.061402+0.145363
## [407] train-rmse:0.730149+0.017164 test-rmse:1.060432+0.145752
## [408] train-rmse:0.729727+0.017140 test-rmse:1.060909+0.146440
## [409] train-rmse:0.729305+0.017133 test-rmse:1.060385+0.146854
## [410] train-rmse:0.728882+0.017125 test-rmse:1.059323+0.146752
## [411] train-rmse:0.728455+0.017115 test-rmse:1.057470+0.146582
## [412] train-rmse:0.728038+0.017114 test-rmse:1.057041+0.145236
## [413] train-rmse:0.727624+0.017104 test-rmse:1.056789+0.145806
## [414] train-rmse:0.727213+0.017097 test-rmse:1.056052+0.145570
## [415] train-rmse:0.726804+0.017073 test-rmse:1.055346+0.146406
## [416] train-rmse:0.726409+0.017053 test-rmse:1.054942+0.146874
## [417] train-rmse:0.726017+0.017040 test-rmse:1.054370+0.147245
## [418] train-rmse:0.725630+0.017025 test-rmse:1.054739+0.146799
## [419] train-rmse:0.725253+0.017007 test-rmse:1.053716+0.146099
## [420] train-rmse:0.724851+0.016996 test-rmse:1.054051+0.145737
## [421] train-rmse:0.724475+0.016986 test-rmse:1.053947+0.146430
## [422] train-rmse:0.724106+0.016969 test-rmse:1.054219+0.145795
## [423] train-rmse:0.723687+0.016960 test-rmse:1.054200+0.147023
## [424] train-rmse:0.723314+0.016933 test-rmse:1.053315+0.148118
## [425] train-rmse:0.722937+0.016901 test-rmse:1.053237+0.148171
## [426] train-rmse:0.722563+0.016876 test-rmse:1.052346+0.148827
## [427] train-rmse:0.722197+0.016855 test-rmse:1.052739+0.148506
## [428] train-rmse:0.721817+0.016831 test-rmse:1.051742+0.148493
## [429] train-rmse:0.721449+0.016814 test-rmse:1.051481+0.147677
## [430] train-rmse:0.721063+0.016793 test-rmse:1.051611+0.147779
## [431] train-rmse:0.720689+0.016787 test-rmse:1.051230+0.147730
## [432] train-rmse:0.720300+0.016753 test-rmse:1.050403+0.146821
## [433] train-rmse:0.719943+0.016737 test-rmse:1.049541+0.147146
## [434] train-rmse:0.719564+0.016709 test-rmse:1.049280+0.145774
## [435] train-rmse:0.719206+0.016684 test-rmse:1.049154+0.145487
## [436] train-rmse:0.718839+0.016661 test-rmse:1.048852+0.145395
## [437] train-rmse:0.718481+0.016641 test-rmse:1.049218+0.145507
## [438] train-rmse:0.718116+0.016631 test-rmse:1.048916+0.145109
## [439] train-rmse:0.717764+0.016604 test-rmse:1.049880+0.144571
## [440] train-rmse:0.717408+0.016585 test-rmse:1.049272+0.145232
## [441] train-rmse:0.717065+0.016572 test-rmse:1.049503+0.145401
## [442] train-rmse:0.716704+0.016562 test-rmse:1.048763+0.145798
## [443] train-rmse:0.716349+0.016554 test-rmse:1.048938+0.145430
## [444] train-rmse:0.716001+0.016522 test-rmse:1.048272+0.144753
## [445] train-rmse:0.715664+0.016501 test-rmse:1.047693+0.144030
## [446] train-rmse:0.715322+0.016489 test-rmse:1.046065+0.144347
## [447] train-rmse:0.714983+0.016470 test-rmse:1.044656+0.143075
## [448] train-rmse:0.714654+0.016456 test-rmse:1.044049+0.143243
## [449] train-rmse:0.714313+0.016448 test-rmse:1.045204+0.143304
## [450] train-rmse:0.713959+0.016421 test-rmse:1.045788+0.142833
## [451] train-rmse:0.713627+0.016411 test-rmse:1.044986+0.142435
## [452] train-rmse:0.713287+0.016388 test-rmse:1.044639+0.142299
## [453] train-rmse:0.712947+0.016374 test-rmse:1.044478+0.142569
## [454] train-rmse:0.712601+0.016348 test-rmse:1.045127+0.141672
## Stopping. Best iteration:
## [448] train-rmse:0.714654+0.016456 test-rmse:1.044049+0.143243
##
## 43
## [1] train-rmse:84.081620+0.095738 test-rmse:84.081755+0.499144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:65.745564+0.080728 test-rmse:65.745244+0.529833
## [3] train-rmse:51.456281+0.070760 test-rmse:51.455305+0.559619
## [4] train-rmse:40.333913+0.065280 test-rmse:40.331998+0.589993
## [5] train-rmse:31.689540+0.061479 test-rmse:31.714312+0.607511
## [6] train-rmse:24.957962+0.052538 test-rmse:25.039115+0.615759
## [7] train-rmse:19.717437+0.037545 test-rmse:19.787439+0.561703
## [8] train-rmse:15.648228+0.028053 test-rmse:15.753311+0.560288
## [9] train-rmse:12.500815+0.027719 test-rmse:12.628483+0.595321
## [10] train-rmse:10.073285+0.028941 test-rmse:10.197140+0.587130
## [11] train-rmse:8.221207+0.035252 test-rmse:8.369077+0.616239
## [12] train-rmse:6.817221+0.041172 test-rmse:6.956753+0.550897
## [13] train-rmse:5.767234+0.042342 test-rmse:5.926906+0.572645
## [14] train-rmse:4.987617+0.050615 test-rmse:5.185270+0.554882
## [15] train-rmse:4.412418+0.056959 test-rmse:4.617399+0.493994
## [16] train-rmse:3.992286+0.059297 test-rmse:4.206299+0.478053
## [17] train-rmse:3.681498+0.055484 test-rmse:3.930489+0.470373
## [18] train-rmse:3.450023+0.056217 test-rmse:3.708428+0.419877
## [19] train-rmse:3.275561+0.054657 test-rmse:3.555849+0.429959
## [20] train-rmse:3.135297+0.054724 test-rmse:3.426770+0.397267
## [21] train-rmse:3.022824+0.053705 test-rmse:3.321586+0.351889
## [22] train-rmse:2.920914+0.047114 test-rmse:3.254305+0.335403
## [23] train-rmse:2.839463+0.044230 test-rmse:3.177903+0.330682
## [24] train-rmse:2.767029+0.045540 test-rmse:3.119421+0.325373
## [25] train-rmse:2.700709+0.044671 test-rmse:3.077603+0.318244
## [26] train-rmse:2.638893+0.044930 test-rmse:2.999127+0.303642
## [27] train-rmse:2.578474+0.042047 test-rmse:2.948836+0.312639
## [28] train-rmse:2.520063+0.038520 test-rmse:2.911349+0.326823
## [29] train-rmse:2.466357+0.036295 test-rmse:2.855780+0.300759
## [30] train-rmse:2.415192+0.035780 test-rmse:2.813618+0.299459
## [31] train-rmse:2.364734+0.034989 test-rmse:2.770110+0.308051
## [32] train-rmse:2.320013+0.033767 test-rmse:2.708792+0.297370
## [33] train-rmse:2.276371+0.033571 test-rmse:2.665638+0.297880
## [34] train-rmse:2.233466+0.032995 test-rmse:2.642848+0.300773
## [35] train-rmse:2.191558+0.032262 test-rmse:2.605932+0.292073
## [36] train-rmse:2.150933+0.030981 test-rmse:2.572459+0.275019
## [37] train-rmse:2.109911+0.030052 test-rmse:2.531727+0.277200
## [38] train-rmse:2.068172+0.028552 test-rmse:2.483761+0.264594
## [39] train-rmse:2.029739+0.027739 test-rmse:2.442902+0.267616
## [40] train-rmse:1.992890+0.025881 test-rmse:2.410205+0.257123
## [41] train-rmse:1.956477+0.024104 test-rmse:2.380101+0.259705
## [42] train-rmse:1.921971+0.022925 test-rmse:2.351815+0.273575
## [43] train-rmse:1.886790+0.021403 test-rmse:2.322923+0.278539
## [44] train-rmse:1.854075+0.020375 test-rmse:2.300308+0.279481
## [45] train-rmse:1.822224+0.019305 test-rmse:2.276265+0.271720
## [46] train-rmse:1.791266+0.018672 test-rmse:2.252007+0.266670
## [47] train-rmse:1.760471+0.016079 test-rmse:2.214421+0.261899
## [48] train-rmse:1.729730+0.013984 test-rmse:2.187814+0.260035
## [49] train-rmse:1.699872+0.013912 test-rmse:2.158646+0.253941
## [50] train-rmse:1.670972+0.012180 test-rmse:2.129412+0.253848
## [51] train-rmse:1.643923+0.012412 test-rmse:2.107461+0.257450
## [52] train-rmse:1.617655+0.012250 test-rmse:2.087613+0.268942
## [53] train-rmse:1.591088+0.012023 test-rmse:2.063913+0.260149
## [54] train-rmse:1.565868+0.011963 test-rmse:2.044531+0.268117
## [55] train-rmse:1.541186+0.011368 test-rmse:2.022206+0.259445
## [56] train-rmse:1.517019+0.010735 test-rmse:1.998157+0.265318
## [57] train-rmse:1.493523+0.009726 test-rmse:1.974822+0.257993
## [58] train-rmse:1.469832+0.009166 test-rmse:1.954202+0.261812
## [59] train-rmse:1.447000+0.008857 test-rmse:1.938221+0.263246
## [60] train-rmse:1.424888+0.009124 test-rmse:1.922559+0.262566
## [61] train-rmse:1.402726+0.010070 test-rmse:1.900765+0.261557
## [62] train-rmse:1.381970+0.009444 test-rmse:1.883381+0.259055
## [63] train-rmse:1.361448+0.009618 test-rmse:1.863426+0.243496
## [64] train-rmse:1.341776+0.009333 test-rmse:1.843773+0.229997
## [65] train-rmse:1.322117+0.009610 test-rmse:1.834003+0.232485
## [66] train-rmse:1.302406+0.010117 test-rmse:1.814119+0.238269
## [67] train-rmse:1.283357+0.010906 test-rmse:1.796659+0.237635
## [68] train-rmse:1.265791+0.010793 test-rmse:1.775130+0.239049
## [69] train-rmse:1.247394+0.011170 test-rmse:1.756811+0.239718
## [70] train-rmse:1.230071+0.010998 test-rmse:1.744085+0.245663
## [71] train-rmse:1.212425+0.010532 test-rmse:1.728935+0.251287
## [72] train-rmse:1.195325+0.011358 test-rmse:1.713163+0.244073
## [73] train-rmse:1.178698+0.010460 test-rmse:1.702990+0.246740
## [74] train-rmse:1.160792+0.012112 test-rmse:1.688249+0.246705
## [75] train-rmse:1.145411+0.011576 test-rmse:1.675389+0.246069
## [76] train-rmse:1.129702+0.012132 test-rmse:1.669104+0.250786
## [77] train-rmse:1.113408+0.012271 test-rmse:1.657879+0.248620
## [78] train-rmse:1.098734+0.012768 test-rmse:1.644680+0.249861
## [79] train-rmse:1.084849+0.013059 test-rmse:1.622432+0.242172
## [80] train-rmse:1.070274+0.012306 test-rmse:1.611646+0.244577
## [81] train-rmse:1.056448+0.012288 test-rmse:1.603603+0.242845
## [82] train-rmse:1.043090+0.012500 test-rmse:1.584807+0.245831
## [83] train-rmse:1.030650+0.012609 test-rmse:1.570514+0.248898
## [84] train-rmse:1.018206+0.013077 test-rmse:1.565935+0.248717
## [85] train-rmse:1.006457+0.013052 test-rmse:1.557931+0.247440
## [86] train-rmse:0.994563+0.012484 test-rmse:1.546294+0.249523
## [87] train-rmse:0.981188+0.014338 test-rmse:1.536722+0.252506
## [88] train-rmse:0.968195+0.015760 test-rmse:1.530918+0.252686
## [89] train-rmse:0.956550+0.015289 test-rmse:1.521815+0.249220
## [90] train-rmse:0.945311+0.014961 test-rmse:1.511069+0.248348
## [91] train-rmse:0.933051+0.015446 test-rmse:1.505272+0.252743
## [92] train-rmse:0.922521+0.015473 test-rmse:1.497569+0.252861
## [93] train-rmse:0.911480+0.016673 test-rmse:1.488032+0.252094
## [94] train-rmse:0.901502+0.016651 test-rmse:1.480638+0.252059
## [95] train-rmse:0.891831+0.016305 test-rmse:1.472721+0.253462
## [96] train-rmse:0.882512+0.016573 test-rmse:1.467586+0.254974
## [97] train-rmse:0.872184+0.015619 test-rmse:1.456311+0.251164
## [98] train-rmse:0.862466+0.016238 test-rmse:1.446335+0.250282
## [99] train-rmse:0.854025+0.016094 test-rmse:1.433421+0.254872
## [100] train-rmse:0.843360+0.018355 test-rmse:1.425719+0.254041
## [101] train-rmse:0.834973+0.018639 test-rmse:1.421840+0.254348
## [102] train-rmse:0.826768+0.018769 test-rmse:1.416174+0.251997
## [103] train-rmse:0.818162+0.018980 test-rmse:1.412027+0.249984
## [104] train-rmse:0.808915+0.020265 test-rmse:1.408538+0.252282
## [105] train-rmse:0.799435+0.021030 test-rmse:1.406075+0.256754
## [106] train-rmse:0.789911+0.020324 test-rmse:1.401999+0.260358
## [107] train-rmse:0.782030+0.020568 test-rmse:1.396591+0.260774
## [108] train-rmse:0.774830+0.020702 test-rmse:1.392095+0.262924
## [109] train-rmse:0.767861+0.020584 test-rmse:1.385026+0.259418
## [110] train-rmse:0.759686+0.019895 test-rmse:1.377905+0.255438
## [111] train-rmse:0.753242+0.019991 test-rmse:1.371893+0.260602
## [112] train-rmse:0.745114+0.021336 test-rmse:1.367523+0.258293
## [113] train-rmse:0.738791+0.021383 test-rmse:1.363421+0.257704
## [114] train-rmse:0.731127+0.020829 test-rmse:1.360986+0.257411
## [115] train-rmse:0.724782+0.020916 test-rmse:1.358097+0.256661
## [116] train-rmse:0.717906+0.021290 test-rmse:1.353075+0.255662
## [117] train-rmse:0.711900+0.020835 test-rmse:1.347714+0.254676
## [118] train-rmse:0.705733+0.021136 test-rmse:1.344044+0.256261
## [119] train-rmse:0.700240+0.021113 test-rmse:1.340533+0.256969
## [120] train-rmse:0.694596+0.021394 test-rmse:1.338124+0.261048
## [121] train-rmse:0.689306+0.021399 test-rmse:1.330731+0.254073
## [122] train-rmse:0.683484+0.021506 test-rmse:1.327324+0.256647
## [123] train-rmse:0.677173+0.019959 test-rmse:1.322839+0.260391
## [124] train-rmse:0.671696+0.019553 test-rmse:1.320962+0.261079
## [125] train-rmse:0.666375+0.018868 test-rmse:1.316432+0.263752
## [126] train-rmse:0.661764+0.018701 test-rmse:1.312360+0.262988
## [127] train-rmse:0.655875+0.017178 test-rmse:1.308039+0.262524
## [128] train-rmse:0.650870+0.017473 test-rmse:1.306432+0.262803
## [129] train-rmse:0.646357+0.017732 test-rmse:1.303898+0.262760
## [130] train-rmse:0.641024+0.017493 test-rmse:1.297089+0.264803
## [131] train-rmse:0.636058+0.018867 test-rmse:1.291495+0.266991
## [132] train-rmse:0.631086+0.017996 test-rmse:1.288177+0.267794
## [133] train-rmse:0.625631+0.017268 test-rmse:1.282575+0.268871
## [134] train-rmse:0.619754+0.017941 test-rmse:1.280755+0.269828
## [135] train-rmse:0.615050+0.018178 test-rmse:1.281140+0.270790
## [136] train-rmse:0.610382+0.018626 test-rmse:1.280371+0.271512
## [137] train-rmse:0.605876+0.018676 test-rmse:1.277271+0.273159
## [138] train-rmse:0.602180+0.018463 test-rmse:1.274092+0.271736
## [139] train-rmse:0.598335+0.018389 test-rmse:1.271778+0.269510
## [140] train-rmse:0.594676+0.018144 test-rmse:1.269689+0.269677
## [141] train-rmse:0.590868+0.017935 test-rmse:1.269626+0.270629
## [142] train-rmse:0.586674+0.017276 test-rmse:1.267990+0.269287
## [143] train-rmse:0.582484+0.016900 test-rmse:1.266659+0.271008
## [144] train-rmse:0.578762+0.017336 test-rmse:1.264728+0.271112
## [145] train-rmse:0.574841+0.017080 test-rmse:1.262874+0.272437
## [146] train-rmse:0.570300+0.017222 test-rmse:1.261343+0.272664
## [147] train-rmse:0.566843+0.017632 test-rmse:1.259412+0.272520
## [148] train-rmse:0.563648+0.017682 test-rmse:1.255219+0.274156
## [149] train-rmse:0.560089+0.017486 test-rmse:1.252764+0.274994
## [150] train-rmse:0.556874+0.017758 test-rmse:1.251880+0.274149
## [151] train-rmse:0.552603+0.019225 test-rmse:1.251068+0.274332
## [152] train-rmse:0.549364+0.019677 test-rmse:1.248450+0.274198
## [153] train-rmse:0.545869+0.019324 test-rmse:1.244626+0.275210
## [154] train-rmse:0.543053+0.019704 test-rmse:1.243254+0.274513
## [155] train-rmse:0.538263+0.018436 test-rmse:1.242735+0.274186
## [156] train-rmse:0.535278+0.018666 test-rmse:1.240153+0.274724
## [157] train-rmse:0.532369+0.018310 test-rmse:1.237807+0.276358
## [158] train-rmse:0.529780+0.018234 test-rmse:1.237709+0.275493
## [159] train-rmse:0.527030+0.017980 test-rmse:1.236430+0.276604
## [160] train-rmse:0.523487+0.017709 test-rmse:1.232389+0.276928
## [161] train-rmse:0.520886+0.017752 test-rmse:1.226378+0.269130
## [162] train-rmse:0.517914+0.017747 test-rmse:1.223720+0.267812
## [163] train-rmse:0.515314+0.017646 test-rmse:1.221902+0.269396
## [164] train-rmse:0.512490+0.017805 test-rmse:1.220897+0.268838
## [165] train-rmse:0.509019+0.018402 test-rmse:1.220278+0.269020
## [166] train-rmse:0.506143+0.018216 test-rmse:1.219019+0.266924
## [167] train-rmse:0.503692+0.018148 test-rmse:1.218977+0.267957
## [168] train-rmse:0.500955+0.018032 test-rmse:1.218696+0.268996
## [169] train-rmse:0.498358+0.018146 test-rmse:1.217641+0.268387
## [170] train-rmse:0.495535+0.017588 test-rmse:1.213771+0.267875
## [171] train-rmse:0.493128+0.017228 test-rmse:1.209660+0.270016
## [172] train-rmse:0.490851+0.017293 test-rmse:1.208883+0.269858
## [173] train-rmse:0.488178+0.016736 test-rmse:1.208096+0.271487
## [174] train-rmse:0.485876+0.016176 test-rmse:1.207060+0.272135
## [175] train-rmse:0.483737+0.016165 test-rmse:1.205517+0.273830
## [176] train-rmse:0.481766+0.016064 test-rmse:1.204605+0.273993
## [177] train-rmse:0.479562+0.016416 test-rmse:1.202788+0.273205
## [178] train-rmse:0.477494+0.016310 test-rmse:1.202696+0.273935
## [179] train-rmse:0.475408+0.016131 test-rmse:1.200647+0.274229
## [180] train-rmse:0.471797+0.016573 test-rmse:1.199849+0.273349
## [181] train-rmse:0.469581+0.016630 test-rmse:1.199673+0.272955
## [182] train-rmse:0.467602+0.016569 test-rmse:1.195400+0.272224
## [183] train-rmse:0.465816+0.016614 test-rmse:1.194572+0.272792
## [184] train-rmse:0.463134+0.017198 test-rmse:1.191748+0.273853
## [185] train-rmse:0.461197+0.017211 test-rmse:1.190821+0.274233
## [186] train-rmse:0.459135+0.016766 test-rmse:1.190188+0.274806
## [187] train-rmse:0.457614+0.016779 test-rmse:1.189265+0.274673
## [188] train-rmse:0.455835+0.016796 test-rmse:1.187597+0.274741
## [189] train-rmse:0.454008+0.016846 test-rmse:1.182921+0.269772
## [190] train-rmse:0.452104+0.017307 test-rmse:1.183212+0.269460
## [191] train-rmse:0.450424+0.016908 test-rmse:1.181234+0.269503
## [192] train-rmse:0.448278+0.017241 test-rmse:1.180091+0.269151
## [193] train-rmse:0.446040+0.016674 test-rmse:1.177142+0.271904
## [194] train-rmse:0.444437+0.016687 test-rmse:1.177094+0.271200
## [195] train-rmse:0.442455+0.016608 test-rmse:1.175005+0.272073
## [196] train-rmse:0.440818+0.016543 test-rmse:1.175908+0.272780
## [197] train-rmse:0.439287+0.016732 test-rmse:1.175298+0.272891
## [198] train-rmse:0.437727+0.016835 test-rmse:1.173819+0.273285
## [199] train-rmse:0.436224+0.016632 test-rmse:1.170856+0.273345
## [200] train-rmse:0.434514+0.016563 test-rmse:1.169930+0.271658
## [201] train-rmse:0.432238+0.015912 test-rmse:1.168474+0.273058
## [202] train-rmse:0.430776+0.015582 test-rmse:1.166671+0.274536
## [203] train-rmse:0.429318+0.015694 test-rmse:1.166757+0.275358
## [204] train-rmse:0.427596+0.015509 test-rmse:1.166072+0.275029
## [205] train-rmse:0.425532+0.015344 test-rmse:1.166589+0.275314
## [206] train-rmse:0.423449+0.015857 test-rmse:1.165060+0.275818
## [207] train-rmse:0.422073+0.015873 test-rmse:1.163443+0.276922
## [208] train-rmse:0.420652+0.015795 test-rmse:1.163059+0.277546
## [209] train-rmse:0.418947+0.015238 test-rmse:1.163005+0.279573
## [210] train-rmse:0.417397+0.015202 test-rmse:1.160343+0.280272
## [211] train-rmse:0.415972+0.014944 test-rmse:1.159625+0.279390
## [212] train-rmse:0.414568+0.014822 test-rmse:1.159403+0.279140
## [213] train-rmse:0.413088+0.015236 test-rmse:1.159476+0.280058
## [214] train-rmse:0.411415+0.015089 test-rmse:1.158941+0.279737
## [215] train-rmse:0.409604+0.015436 test-rmse:1.158419+0.280944
## [216] train-rmse:0.407977+0.015791 test-rmse:1.157847+0.281331
## [217] train-rmse:0.406120+0.015947 test-rmse:1.157543+0.282278
## [218] train-rmse:0.404629+0.015790 test-rmse:1.155770+0.281225
## [219] train-rmse:0.402934+0.015660 test-rmse:1.155052+0.280878
## [220] train-rmse:0.401752+0.015453 test-rmse:1.155301+0.281500
## [221] train-rmse:0.399773+0.014774 test-rmse:1.152193+0.280585
## [222] train-rmse:0.398270+0.015061 test-rmse:1.151615+0.280065
## [223] train-rmse:0.396619+0.015689 test-rmse:1.151013+0.280137
## [224] train-rmse:0.395150+0.016376 test-rmse:1.150787+0.279805
## [225] train-rmse:0.393665+0.016048 test-rmse:1.150912+0.280050
## [226] train-rmse:0.392577+0.015862 test-rmse:1.150311+0.279635
## [227] train-rmse:0.391425+0.015958 test-rmse:1.150613+0.279374
## [228] train-rmse:0.390268+0.015713 test-rmse:1.149734+0.279288
## [229] train-rmse:0.388742+0.015855 test-rmse:1.147083+0.280593
## [230] train-rmse:0.387266+0.015672 test-rmse:1.146841+0.279639
## [231] train-rmse:0.386245+0.015614 test-rmse:1.146036+0.279515
## [232] train-rmse:0.385014+0.015664 test-rmse:1.144133+0.278565
## [233] train-rmse:0.383854+0.015528 test-rmse:1.142233+0.279199
## [234] train-rmse:0.382251+0.016102 test-rmse:1.142799+0.280179
## [235] train-rmse:0.380153+0.015964 test-rmse:1.142274+0.279958
## [236] train-rmse:0.378688+0.015824 test-rmse:1.142579+0.280934
## [237] train-rmse:0.376916+0.015637 test-rmse:1.143577+0.281324
## [238] train-rmse:0.375717+0.016052 test-rmse:1.143512+0.281138
## [239] train-rmse:0.374351+0.016634 test-rmse:1.143584+0.280622
## Stopping. Best iteration:
## [233] train-rmse:0.383854+0.015528 test-rmse:1.142233+0.279199
##
## 44
## [1] train-rmse:84.081620+0.095738 test-rmse:84.081755+0.499144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:65.745564+0.080728 test-rmse:65.745244+0.529833
## [3] train-rmse:51.456281+0.070760 test-rmse:51.455305+0.559619
## [4] train-rmse:40.333913+0.065280 test-rmse:40.331998+0.589993
## [5] train-rmse:31.689540+0.061479 test-rmse:31.714312+0.607511
## [6] train-rmse:24.953949+0.049329 test-rmse:25.041610+0.619070
## [7] train-rmse:19.706671+0.036005 test-rmse:19.808996+0.591268
## [8] train-rmse:15.621615+0.027126 test-rmse:15.737683+0.580544
## [9] train-rmse:12.447722+0.025853 test-rmse:12.537473+0.582635
## [10] train-rmse:9.988609+0.022876 test-rmse:10.101578+0.601499
## [11] train-rmse:8.090604+0.024929 test-rmse:8.224543+0.569874
## [12] train-rmse:6.640176+0.033135 test-rmse:6.758231+0.512123
## [13] train-rmse:5.535445+0.040268 test-rmse:5.700192+0.521364
## [14] train-rmse:4.702128+0.038646 test-rmse:4.917873+0.528464
## [15] train-rmse:4.081141+0.041958 test-rmse:4.276463+0.466363
## [16] train-rmse:3.606524+0.040842 test-rmse:3.843258+0.448298
## [17] train-rmse:3.260567+0.039679 test-rmse:3.548522+0.427203
## [18] train-rmse:3.000935+0.041847 test-rmse:3.302871+0.408704
## [19] train-rmse:2.796549+0.029419 test-rmse:3.101693+0.372966
## [20] train-rmse:2.633887+0.025568 test-rmse:2.986683+0.357589
## [21] train-rmse:2.505731+0.023749 test-rmse:2.870765+0.345030
## [22] train-rmse:2.399237+0.020470 test-rmse:2.773112+0.310459
## [23] train-rmse:2.307078+0.022180 test-rmse:2.695857+0.289475
## [24] train-rmse:2.218183+0.028319 test-rmse:2.633579+0.270892
## [25] train-rmse:2.142810+0.023492 test-rmse:2.553390+0.259275
## [26] train-rmse:2.075447+0.022302 test-rmse:2.488151+0.267092
## [27] train-rmse:2.011602+0.023326 test-rmse:2.444460+0.261704
## [28] train-rmse:1.953810+0.022936 test-rmse:2.389163+0.265753
## [29] train-rmse:1.898001+0.022218 test-rmse:2.338042+0.255781
## [30] train-rmse:1.834361+0.024906 test-rmse:2.295561+0.250633
## [31] train-rmse:1.782446+0.025765 test-rmse:2.261249+0.240815
## [32] train-rmse:1.732106+0.026542 test-rmse:2.213312+0.235002
## [33] train-rmse:1.684941+0.025270 test-rmse:2.162518+0.232948
## [34] train-rmse:1.641332+0.025493 test-rmse:2.119970+0.232808
## [35] train-rmse:1.597351+0.025901 test-rmse:2.091938+0.239046
## [36] train-rmse:1.554999+0.025841 test-rmse:2.067965+0.248978
## [37] train-rmse:1.514155+0.025190 test-rmse:2.038253+0.235847
## [38] train-rmse:1.474027+0.024374 test-rmse:2.008324+0.241028
## [39] train-rmse:1.437040+0.026065 test-rmse:1.969392+0.247338
## [40] train-rmse:1.401754+0.025917 test-rmse:1.940925+0.247292
## [41] train-rmse:1.366013+0.026553 test-rmse:1.911801+0.248948
## [42] train-rmse:1.333649+0.025731 test-rmse:1.876210+0.241584
## [43] train-rmse:1.301425+0.025606 test-rmse:1.860946+0.244975
## [44] train-rmse:1.269426+0.027056 test-rmse:1.834197+0.249550
## [45] train-rmse:1.240371+0.026886 test-rmse:1.813098+0.245607
## [46] train-rmse:1.209668+0.024895 test-rmse:1.793330+0.251897
## [47] train-rmse:1.179548+0.022489 test-rmse:1.768360+0.250372
## [48] train-rmse:1.152183+0.023236 test-rmse:1.743468+0.252092
## [49] train-rmse:1.127320+0.023023 test-rmse:1.719947+0.255337
## [50] train-rmse:1.100792+0.022942 test-rmse:1.700236+0.265776
## [51] train-rmse:1.076475+0.022727 test-rmse:1.680000+0.273955
## [52] train-rmse:1.052780+0.023763 test-rmse:1.662841+0.271493
## [53] train-rmse:1.028723+0.022875 test-rmse:1.647026+0.270092
## [54] train-rmse:1.006000+0.023694 test-rmse:1.629339+0.259062
## [55] train-rmse:0.985169+0.022621 test-rmse:1.611425+0.257852
## [56] train-rmse:0.964361+0.021751 test-rmse:1.591913+0.256349
## [57] train-rmse:0.944006+0.022991 test-rmse:1.574311+0.259077
## [58] train-rmse:0.924912+0.023427 test-rmse:1.560789+0.259032
## [59] train-rmse:0.904102+0.023715 test-rmse:1.550044+0.260595
## [60] train-rmse:0.886887+0.024010 test-rmse:1.537572+0.269564
## [61] train-rmse:0.868807+0.022988 test-rmse:1.530373+0.271847
## [62] train-rmse:0.852061+0.023089 test-rmse:1.499382+0.246275
## [63] train-rmse:0.834558+0.021676 test-rmse:1.491311+0.247321
## [64] train-rmse:0.819705+0.021671 test-rmse:1.475569+0.250332
## [65] train-rmse:0.804502+0.021947 test-rmse:1.467607+0.254573
## [66] train-rmse:0.788588+0.021146 test-rmse:1.455456+0.256339
## [67] train-rmse:0.775075+0.020829 test-rmse:1.449405+0.255984
## [68] train-rmse:0.759091+0.022881 test-rmse:1.438969+0.252002
## [69] train-rmse:0.744734+0.021557 test-rmse:1.427323+0.253063
## [70] train-rmse:0.731457+0.021256 test-rmse:1.410899+0.240787
## [71] train-rmse:0.717486+0.022612 test-rmse:1.402288+0.242019
## [72] train-rmse:0.704648+0.022968 test-rmse:1.394509+0.240716
## [73] train-rmse:0.692745+0.023052 test-rmse:1.383640+0.245029
## [74] train-rmse:0.681786+0.023469 test-rmse:1.377360+0.246201
## [75] train-rmse:0.669396+0.020888 test-rmse:1.364190+0.247998
## [76] train-rmse:0.657532+0.021043 test-rmse:1.360381+0.249063
## [77] train-rmse:0.647288+0.020941 test-rmse:1.350902+0.251128
## [78] train-rmse:0.633584+0.019132 test-rmse:1.341530+0.249384
## [79] train-rmse:0.623735+0.018750 test-rmse:1.338573+0.253529
## [80] train-rmse:0.614387+0.018640 test-rmse:1.328465+0.256691
## [81] train-rmse:0.605729+0.017694 test-rmse:1.319509+0.257635
## [82] train-rmse:0.596066+0.017180 test-rmse:1.313218+0.262558
## [83] train-rmse:0.586011+0.017187 test-rmse:1.302817+0.249356
## [84] train-rmse:0.577689+0.016655 test-rmse:1.296515+0.251701
## [85] train-rmse:0.569519+0.016753 test-rmse:1.292213+0.252587
## [86] train-rmse:0.560959+0.015817 test-rmse:1.287905+0.253029
## [87] train-rmse:0.552031+0.017113 test-rmse:1.279211+0.255816
## [88] train-rmse:0.544211+0.017066 test-rmse:1.276631+0.257108
## [89] train-rmse:0.537594+0.016872 test-rmse:1.271608+0.261320
## [90] train-rmse:0.530723+0.015957 test-rmse:1.265405+0.261602
## [91] train-rmse:0.523633+0.016230 test-rmse:1.262325+0.261733
## [92] train-rmse:0.516636+0.016817 test-rmse:1.256592+0.262662
## [93] train-rmse:0.510401+0.016402 test-rmse:1.251784+0.261472
## [94] train-rmse:0.503696+0.017247 test-rmse:1.247700+0.256405
## [95] train-rmse:0.497819+0.016425 test-rmse:1.245135+0.257820
## [96] train-rmse:0.491141+0.016405 test-rmse:1.242561+0.259048
## [97] train-rmse:0.485006+0.016136 test-rmse:1.240685+0.261421
## [98] train-rmse:0.479311+0.016085 test-rmse:1.236446+0.261575
## [99] train-rmse:0.474082+0.016229 test-rmse:1.234196+0.262361
## [100] train-rmse:0.468491+0.016483 test-rmse:1.229649+0.262868
## [101] train-rmse:0.463971+0.016194 test-rmse:1.226167+0.263702
## [102] train-rmse:0.458697+0.015883 test-rmse:1.223639+0.264340
## [103] train-rmse:0.452770+0.015664 test-rmse:1.221485+0.263103
## [104] train-rmse:0.446903+0.015754 test-rmse:1.212711+0.253927
## [105] train-rmse:0.442269+0.015915 test-rmse:1.207213+0.258673
## [106] train-rmse:0.437884+0.015846 test-rmse:1.205374+0.258848
## [107] train-rmse:0.434097+0.015920 test-rmse:1.202283+0.261526
## [108] train-rmse:0.428877+0.016516 test-rmse:1.199770+0.262404
## [109] train-rmse:0.424710+0.016987 test-rmse:1.194862+0.264190
## [110] train-rmse:0.420323+0.016390 test-rmse:1.191973+0.264203
## [111] train-rmse:0.416888+0.016439 test-rmse:1.189853+0.263855
## [112] train-rmse:0.412381+0.016805 test-rmse:1.187544+0.263191
## [113] train-rmse:0.408397+0.016838 test-rmse:1.187906+0.264550
## [114] train-rmse:0.402515+0.016350 test-rmse:1.184502+0.261525
## [115] train-rmse:0.398875+0.016935 test-rmse:1.183742+0.263361
## [116] train-rmse:0.394733+0.017643 test-rmse:1.181448+0.263775
## [117] train-rmse:0.390801+0.017196 test-rmse:1.177032+0.264014
## [118] train-rmse:0.387099+0.017722 test-rmse:1.175178+0.264153
## [119] train-rmse:0.383601+0.017527 test-rmse:1.175146+0.264334
## [120] train-rmse:0.379159+0.018230 test-rmse:1.173739+0.264477
## [121] train-rmse:0.374988+0.016952 test-rmse:1.171899+0.265879
## [122] train-rmse:0.371886+0.016853 test-rmse:1.171453+0.266075
## [123] train-rmse:0.368450+0.016201 test-rmse:1.170519+0.263965
## [124] train-rmse:0.364897+0.015484 test-rmse:1.169970+0.265340
## [125] train-rmse:0.361787+0.015723 test-rmse:1.168939+0.265640
## [126] train-rmse:0.357616+0.014909 test-rmse:1.166418+0.266835
## [127] train-rmse:0.353589+0.016143 test-rmse:1.161625+0.271000
## [128] train-rmse:0.351239+0.016300 test-rmse:1.160609+0.272020
## [129] train-rmse:0.348132+0.015930 test-rmse:1.159317+0.272864
## [130] train-rmse:0.345500+0.016221 test-rmse:1.158598+0.272653
## [131] train-rmse:0.342077+0.015771 test-rmse:1.157601+0.272439
## [132] train-rmse:0.338573+0.015136 test-rmse:1.155668+0.278005
## [133] train-rmse:0.335115+0.013578 test-rmse:1.154312+0.277568
## [134] train-rmse:0.332385+0.014368 test-rmse:1.154679+0.277230
## [135] train-rmse:0.329901+0.014501 test-rmse:1.154584+0.277912
## [136] train-rmse:0.327001+0.014164 test-rmse:1.154931+0.278493
## [137] train-rmse:0.323229+0.013664 test-rmse:1.155433+0.277633
## [138] train-rmse:0.320323+0.014321 test-rmse:1.154285+0.278234
## [139] train-rmse:0.317488+0.014103 test-rmse:1.153567+0.278123
## [140] train-rmse:0.315162+0.014239 test-rmse:1.153004+0.278029
## [141] train-rmse:0.313132+0.014254 test-rmse:1.152608+0.277799
## [142] train-rmse:0.309598+0.014047 test-rmse:1.151534+0.277749
## [143] train-rmse:0.307113+0.014381 test-rmse:1.151093+0.278679
## [144] train-rmse:0.304470+0.014042 test-rmse:1.149416+0.279390
## [145] train-rmse:0.302510+0.014234 test-rmse:1.149049+0.280747
## [146] train-rmse:0.300058+0.014542 test-rmse:1.147877+0.280753
## [147] train-rmse:0.298520+0.014583 test-rmse:1.145941+0.281689
## [148] train-rmse:0.296390+0.015042 test-rmse:1.145145+0.281619
## [149] train-rmse:0.294214+0.015201 test-rmse:1.143779+0.281763
## [150] train-rmse:0.291717+0.015691 test-rmse:1.141518+0.282075
## [151] train-rmse:0.289349+0.015588 test-rmse:1.141147+0.282853
## [152] train-rmse:0.286933+0.015224 test-rmse:1.142086+0.285613
## [153] train-rmse:0.284184+0.015681 test-rmse:1.140683+0.285809
## [154] train-rmse:0.282051+0.015944 test-rmse:1.140070+0.285861
## [155] train-rmse:0.278759+0.015569 test-rmse:1.139561+0.285924
## [156] train-rmse:0.276827+0.015341 test-rmse:1.138517+0.286755
## [157] train-rmse:0.274321+0.015259 test-rmse:1.138629+0.286939
## [158] train-rmse:0.272648+0.015485 test-rmse:1.137850+0.287919
## [159] train-rmse:0.270820+0.015679 test-rmse:1.136726+0.288545
## [160] train-rmse:0.269200+0.015460 test-rmse:1.138084+0.291473
## [161] train-rmse:0.266810+0.015101 test-rmse:1.137943+0.291614
## [162] train-rmse:0.265051+0.014710 test-rmse:1.138364+0.291571
## [163] train-rmse:0.262818+0.014154 test-rmse:1.137049+0.292386
## [164] train-rmse:0.260527+0.014184 test-rmse:1.136558+0.292318
## [165] train-rmse:0.258409+0.014472 test-rmse:1.136153+0.291932
## [166] train-rmse:0.256351+0.014642 test-rmse:1.136286+0.292112
## [167] train-rmse:0.254083+0.014660 test-rmse:1.135520+0.296199
## [168] train-rmse:0.252837+0.014445 test-rmse:1.134718+0.296238
## [169] train-rmse:0.251009+0.014379 test-rmse:1.134270+0.296008
## [170] train-rmse:0.248628+0.013883 test-rmse:1.133495+0.295799
## [171] train-rmse:0.246010+0.014193 test-rmse:1.133331+0.296183
## [172] train-rmse:0.244487+0.014105 test-rmse:1.132495+0.295231
## [173] train-rmse:0.243078+0.013956 test-rmse:1.132232+0.295305
## [174] train-rmse:0.240907+0.013837 test-rmse:1.132073+0.295228
## [175] train-rmse:0.239596+0.014094 test-rmse:1.132116+0.295892
## [176] train-rmse:0.237757+0.013668 test-rmse:1.131100+0.297313
## [177] train-rmse:0.236441+0.013881 test-rmse:1.130386+0.297430
## [178] train-rmse:0.234507+0.013919 test-rmse:1.130116+0.297158
## [179] train-rmse:0.232836+0.014469 test-rmse:1.129927+0.296706
## [180] train-rmse:0.231396+0.014501 test-rmse:1.129317+0.296465
## [181] train-rmse:0.229374+0.014022 test-rmse:1.128562+0.297134
## [182] train-rmse:0.228116+0.013948 test-rmse:1.128503+0.297069
## [183] train-rmse:0.226428+0.014639 test-rmse:1.127581+0.298178
## [184] train-rmse:0.224846+0.014788 test-rmse:1.128626+0.299476
## [185] train-rmse:0.223188+0.015305 test-rmse:1.127959+0.299406
## [186] train-rmse:0.221817+0.015902 test-rmse:1.127517+0.298639
## [187] train-rmse:0.220062+0.015273 test-rmse:1.127552+0.298578
## [188] train-rmse:0.217767+0.014523 test-rmse:1.127856+0.298640
## [189] train-rmse:0.216069+0.014876 test-rmse:1.127312+0.298896
## [190] train-rmse:0.214674+0.015148 test-rmse:1.127082+0.298762
## [191] train-rmse:0.212878+0.015381 test-rmse:1.126792+0.298534
## [192] train-rmse:0.211510+0.015196 test-rmse:1.126313+0.298764
## [193] train-rmse:0.210628+0.015098 test-rmse:1.126477+0.299203
## [194] train-rmse:0.209244+0.015127 test-rmse:1.126882+0.300008
## [195] train-rmse:0.208283+0.015446 test-rmse:1.126557+0.299891
## [196] train-rmse:0.207103+0.015248 test-rmse:1.125192+0.300221
## [197] train-rmse:0.205687+0.015231 test-rmse:1.124270+0.299592
## [198] train-rmse:0.204467+0.015420 test-rmse:1.124796+0.301877
## [199] train-rmse:0.203082+0.015700 test-rmse:1.124114+0.301806
## [200] train-rmse:0.201908+0.015414 test-rmse:1.123188+0.301688
## [201] train-rmse:0.200707+0.015464 test-rmse:1.122993+0.302068
## [202] train-rmse:0.199800+0.015546 test-rmse:1.121791+0.301957
## [203] train-rmse:0.198928+0.015485 test-rmse:1.121468+0.302760
## [204] train-rmse:0.197341+0.015423 test-rmse:1.120695+0.302889
## [205] train-rmse:0.195928+0.015431 test-rmse:1.120149+0.302987
## [206] train-rmse:0.194465+0.015282 test-rmse:1.119294+0.302064
## [207] train-rmse:0.193390+0.015434 test-rmse:1.119496+0.302242
## [208] train-rmse:0.191926+0.015310 test-rmse:1.119517+0.302645
## [209] train-rmse:0.190602+0.015855 test-rmse:1.119340+0.302859
## [210] train-rmse:0.189633+0.016056 test-rmse:1.119008+0.302582
## [211] train-rmse:0.187688+0.015353 test-rmse:1.119253+0.302534
## [212] train-rmse:0.186321+0.014931 test-rmse:1.119183+0.302553
## [213] train-rmse:0.185597+0.014842 test-rmse:1.119169+0.303067
## [214] train-rmse:0.183623+0.014355 test-rmse:1.119163+0.302770
## [215] train-rmse:0.182412+0.014220 test-rmse:1.117978+0.303056
## [216] train-rmse:0.181437+0.014581 test-rmse:1.118116+0.302905
## [217] train-rmse:0.180370+0.014414 test-rmse:1.118123+0.302874
## [218] train-rmse:0.179356+0.014215 test-rmse:1.117811+0.303346
## [219] train-rmse:0.178480+0.014383 test-rmse:1.117775+0.303793
## [220] train-rmse:0.177592+0.014300 test-rmse:1.117446+0.303863
## [221] train-rmse:0.176596+0.014432 test-rmse:1.117032+0.303155
## [222] train-rmse:0.174945+0.014218 test-rmse:1.117477+0.302850
## [223] train-rmse:0.173484+0.013651 test-rmse:1.117193+0.303153
## [224] train-rmse:0.172667+0.013606 test-rmse:1.117693+0.303259
## [225] train-rmse:0.171947+0.013527 test-rmse:1.116767+0.304041
## [226] train-rmse:0.171078+0.013337 test-rmse:1.116828+0.304022
## [227] train-rmse:0.170132+0.013558 test-rmse:1.116777+0.304126
## [228] train-rmse:0.168656+0.013028 test-rmse:1.116190+0.304186
## [229] train-rmse:0.167247+0.012582 test-rmse:1.117146+0.304686
## [230] train-rmse:0.165618+0.012611 test-rmse:1.116857+0.304356
## [231] train-rmse:0.164320+0.012358 test-rmse:1.116856+0.304433
## [232] train-rmse:0.162891+0.012161 test-rmse:1.116533+0.304623
## [233] train-rmse:0.162265+0.012109 test-rmse:1.116326+0.305206
## [234] train-rmse:0.161726+0.012201 test-rmse:1.116334+0.305635
## Stopping. Best iteration:
## [228] train-rmse:0.168656+0.013028 test-rmse:1.116190+0.304186
##
## 45
## [1] train-rmse:84.081620+0.095738 test-rmse:84.081755+0.499144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:65.745564+0.080728 test-rmse:65.745244+0.529833
## [3] train-rmse:51.456281+0.070760 test-rmse:51.455305+0.559619
## [4] train-rmse:40.333913+0.065280 test-rmse:40.331998+0.589993
## [5] train-rmse:31.689540+0.061479 test-rmse:31.714312+0.607511
## [6] train-rmse:24.953949+0.049329 test-rmse:25.041610+0.619070
## [7] train-rmse:19.706037+0.036258 test-rmse:19.802895+0.589006
## [8] train-rmse:15.613110+0.029067 test-rmse:15.705885+0.542770
## [9] train-rmse:12.425888+0.023358 test-rmse:12.554310+0.565264
## [10] train-rmse:9.943915+0.021968 test-rmse:10.060056+0.557322
## [11] train-rmse:8.018847+0.025852 test-rmse:8.131086+0.551784
## [12] train-rmse:6.531100+0.028117 test-rmse:6.646201+0.522701
## [13] train-rmse:5.388417+0.026464 test-rmse:5.551147+0.535090
## [14] train-rmse:4.514148+0.029276 test-rmse:4.714462+0.477970
## [15] train-rmse:3.842059+0.030385 test-rmse:4.052024+0.479891
## [16] train-rmse:3.326161+0.030269 test-rmse:3.582356+0.464007
## [17] train-rmse:2.943736+0.034928 test-rmse:3.218309+0.422861
## [18] train-rmse:2.655560+0.034291 test-rmse:2.986689+0.382133
## [19] train-rmse:2.431561+0.038573 test-rmse:2.777437+0.348375
## [20] train-rmse:2.255226+0.035630 test-rmse:2.636314+0.328869
## [21] train-rmse:2.099312+0.034171 test-rmse:2.508234+0.321136
## [22] train-rmse:1.984961+0.032298 test-rmse:2.410090+0.303500
## [23] train-rmse:1.880780+0.026758 test-rmse:2.323506+0.256767
## [24] train-rmse:1.784014+0.031595 test-rmse:2.246882+0.235495
## [25] train-rmse:1.702943+0.030734 test-rmse:2.172913+0.224115
## [26] train-rmse:1.625317+0.039194 test-rmse:2.124681+0.231973
## [27] train-rmse:1.560731+0.038036 test-rmse:2.080120+0.228086
## [28] train-rmse:1.503749+0.037317 test-rmse:2.036507+0.227903
## [29] train-rmse:1.446632+0.035998 test-rmse:1.996011+0.233262
## [30] train-rmse:1.389764+0.033684 test-rmse:1.945578+0.228934
## [31] train-rmse:1.339737+0.034201 test-rmse:1.896041+0.220762
## [32] train-rmse:1.291781+0.033385 test-rmse:1.862710+0.211967
## [33] train-rmse:1.249048+0.032930 test-rmse:1.834458+0.218933
## [34] train-rmse:1.208528+0.032207 test-rmse:1.798671+0.234307
## [35] train-rmse:1.169276+0.030574 test-rmse:1.778018+0.229849
## [36] train-rmse:1.130887+0.030173 test-rmse:1.753240+0.220578
## [37] train-rmse:1.090793+0.026254 test-rmse:1.715991+0.228873
## [38] train-rmse:1.054171+0.031238 test-rmse:1.698281+0.232803
## [39] train-rmse:1.016798+0.028485 test-rmse:1.663631+0.234380
## [40] train-rmse:0.981546+0.031609 test-rmse:1.635321+0.235446
## [41] train-rmse:0.951088+0.031605 test-rmse:1.610414+0.236715
## [42] train-rmse:0.922545+0.029063 test-rmse:1.594554+0.235014
## [43] train-rmse:0.896391+0.028713 test-rmse:1.578717+0.233639
## [44] train-rmse:0.870577+0.028698 test-rmse:1.557042+0.241300
## [45] train-rmse:0.843859+0.030926 test-rmse:1.536173+0.241643
## [46] train-rmse:0.821795+0.030933 test-rmse:1.522723+0.236630
## [47] train-rmse:0.799965+0.030241 test-rmse:1.507719+0.241434
## [48] train-rmse:0.776406+0.031301 test-rmse:1.494160+0.233697
## [49] train-rmse:0.755388+0.028767 test-rmse:1.479217+0.231591
## [50] train-rmse:0.734033+0.028177 test-rmse:1.462775+0.234061
## [51] train-rmse:0.715358+0.028867 test-rmse:1.451506+0.240748
## [52] train-rmse:0.696194+0.027870 test-rmse:1.442879+0.244286
## [53] train-rmse:0.679006+0.026750 test-rmse:1.421052+0.224012
## [54] train-rmse:0.662752+0.026151 test-rmse:1.405760+0.229997
## [55] train-rmse:0.647907+0.026023 test-rmse:1.391310+0.234455
## [56] train-rmse:0.633177+0.025888 test-rmse:1.382882+0.234721
## [57] train-rmse:0.615938+0.028251 test-rmse:1.377345+0.234908
## [58] train-rmse:0.602284+0.028774 test-rmse:1.369295+0.238148
## [59] train-rmse:0.589245+0.027678 test-rmse:1.340591+0.224176
## [60] train-rmse:0.575983+0.025744 test-rmse:1.335135+0.222769
## [61] train-rmse:0.563535+0.025845 test-rmse:1.324600+0.224609
## [62] train-rmse:0.551147+0.024846 test-rmse:1.315449+0.228800
## [63] train-rmse:0.540376+0.024318 test-rmse:1.311550+0.229481
## [64] train-rmse:0.526838+0.024859 test-rmse:1.303699+0.232721
## [65] train-rmse:0.517238+0.024775 test-rmse:1.285879+0.223608
## [66] train-rmse:0.506750+0.024512 test-rmse:1.281601+0.222978
## [67] train-rmse:0.496549+0.022557 test-rmse:1.272268+0.225889
## [68] train-rmse:0.485391+0.021055 test-rmse:1.265933+0.228052
## [69] train-rmse:0.476244+0.022128 test-rmse:1.262305+0.230241
## [70] train-rmse:0.467512+0.022658 test-rmse:1.260644+0.231968
## [71] train-rmse:0.459033+0.022334 test-rmse:1.255172+0.233950
## [72] train-rmse:0.449669+0.024624 test-rmse:1.243172+0.237921
## [73] train-rmse:0.440851+0.023369 test-rmse:1.232094+0.232416
## [74] train-rmse:0.432651+0.023061 test-rmse:1.230805+0.230889
## [75] train-rmse:0.424975+0.022694 test-rmse:1.229176+0.231234
## [76] train-rmse:0.417869+0.022209 test-rmse:1.227835+0.231377
## [77] train-rmse:0.411319+0.023139 test-rmse:1.225070+0.231843
## [78] train-rmse:0.401404+0.022161 test-rmse:1.216113+0.232562
## [79] train-rmse:0.395517+0.022041 test-rmse:1.210761+0.231322
## [80] train-rmse:0.388956+0.021874 test-rmse:1.206177+0.233187
## [81] train-rmse:0.382555+0.020213 test-rmse:1.200715+0.231660
## [82] train-rmse:0.376982+0.020692 test-rmse:1.199381+0.232519
## [83] train-rmse:0.369936+0.020837 test-rmse:1.196287+0.233022
## [84] train-rmse:0.364530+0.020958 test-rmse:1.194063+0.233824
## [85] train-rmse:0.359945+0.021129 test-rmse:1.191304+0.234342
## [86] train-rmse:0.354811+0.021005 test-rmse:1.191311+0.234018
## [87] train-rmse:0.348161+0.020555 test-rmse:1.189822+0.234508
## [88] train-rmse:0.342388+0.021295 test-rmse:1.186446+0.235378
## [89] train-rmse:0.337070+0.020847 test-rmse:1.180888+0.236532
## [90] train-rmse:0.332093+0.020333 test-rmse:1.179746+0.237611
## [91] train-rmse:0.326652+0.018685 test-rmse:1.178504+0.238819
## [92] train-rmse:0.320881+0.017977 test-rmse:1.176322+0.240696
## [93] train-rmse:0.315882+0.017668 test-rmse:1.173017+0.243276
## [94] train-rmse:0.311597+0.016698 test-rmse:1.169801+0.242550
## [95] train-rmse:0.307038+0.016336 test-rmse:1.168244+0.244479
## [96] train-rmse:0.303671+0.015724 test-rmse:1.166863+0.244846
## [97] train-rmse:0.299088+0.014499 test-rmse:1.166167+0.245126
## [98] train-rmse:0.295470+0.014746 test-rmse:1.162991+0.245934
## [99] train-rmse:0.290697+0.014510 test-rmse:1.161720+0.245555
## [100] train-rmse:0.286069+0.013284 test-rmse:1.160168+0.247652
## [101] train-rmse:0.282463+0.013295 test-rmse:1.157854+0.247330
## [102] train-rmse:0.279254+0.012509 test-rmse:1.154230+0.248837
## [103] train-rmse:0.275464+0.012186 test-rmse:1.152761+0.251335
## [104] train-rmse:0.271614+0.012489 test-rmse:1.151499+0.251539
## [105] train-rmse:0.268281+0.013196 test-rmse:1.150385+0.251346
## [106] train-rmse:0.263704+0.013051 test-rmse:1.148865+0.252866
## [107] train-rmse:0.260546+0.013643 test-rmse:1.146694+0.252207
## [108] train-rmse:0.256907+0.012852 test-rmse:1.145319+0.253495
## [109] train-rmse:0.253868+0.012586 test-rmse:1.143706+0.253326
## [110] train-rmse:0.250896+0.012188 test-rmse:1.143225+0.254406
## [111] train-rmse:0.248056+0.011990 test-rmse:1.141444+0.255061
## [112] train-rmse:0.245486+0.012308 test-rmse:1.140551+0.253784
## [113] train-rmse:0.241194+0.012997 test-rmse:1.137308+0.254193
## [114] train-rmse:0.237355+0.013709 test-rmse:1.137632+0.253532
## [115] train-rmse:0.234630+0.014469 test-rmse:1.136214+0.253387
## [116] train-rmse:0.232094+0.015190 test-rmse:1.135346+0.253804
## [117] train-rmse:0.229628+0.014889 test-rmse:1.133941+0.254514
## [118] train-rmse:0.225423+0.015006 test-rmse:1.133160+0.255223
## [119] train-rmse:0.222829+0.015507 test-rmse:1.132745+0.254851
## [120] train-rmse:0.220588+0.015327 test-rmse:1.131789+0.255210
## [121] train-rmse:0.217273+0.014863 test-rmse:1.131427+0.255489
## [122] train-rmse:0.213886+0.014648 test-rmse:1.130750+0.256402
## [123] train-rmse:0.211498+0.014634 test-rmse:1.130686+0.255048
## [124] train-rmse:0.209363+0.014700 test-rmse:1.129112+0.255793
## [125] train-rmse:0.207354+0.014613 test-rmse:1.128099+0.255674
## [126] train-rmse:0.204929+0.014667 test-rmse:1.125699+0.256750
## [127] train-rmse:0.202496+0.014676 test-rmse:1.125568+0.257122
## [128] train-rmse:0.199784+0.014331 test-rmse:1.125317+0.256410
## [129] train-rmse:0.197398+0.014440 test-rmse:1.124251+0.256857
## [130] train-rmse:0.194788+0.014362 test-rmse:1.124627+0.257697
## [131] train-rmse:0.192362+0.013873 test-rmse:1.123299+0.257862
## [132] train-rmse:0.190185+0.013812 test-rmse:1.122387+0.258194
## [133] train-rmse:0.187659+0.013789 test-rmse:1.121298+0.256378
## [134] train-rmse:0.185354+0.013779 test-rmse:1.120571+0.256729
## [135] train-rmse:0.183646+0.013846 test-rmse:1.120219+0.257298
## [136] train-rmse:0.181211+0.012738 test-rmse:1.119945+0.257363
## [137] train-rmse:0.178998+0.012476 test-rmse:1.119800+0.257911
## [138] train-rmse:0.176339+0.012308 test-rmse:1.118063+0.255769
## [139] train-rmse:0.174272+0.011783 test-rmse:1.117633+0.256558
## [140] train-rmse:0.172051+0.011778 test-rmse:1.117492+0.257615
## [141] train-rmse:0.170221+0.011467 test-rmse:1.116999+0.257638
## [142] train-rmse:0.168179+0.011799 test-rmse:1.116630+0.257877
## [143] train-rmse:0.166312+0.010947 test-rmse:1.116319+0.257716
## [144] train-rmse:0.163697+0.010714 test-rmse:1.115992+0.257563
## [145] train-rmse:0.162104+0.010828 test-rmse:1.115333+0.256932
## [146] train-rmse:0.160248+0.011071 test-rmse:1.114255+0.257938
## [147] train-rmse:0.158596+0.011442 test-rmse:1.114128+0.258039
## [148] train-rmse:0.156929+0.011264 test-rmse:1.114720+0.257797
## [149] train-rmse:0.155261+0.010597 test-rmse:1.114731+0.257787
## [150] train-rmse:0.153409+0.010324 test-rmse:1.114380+0.257839
## [151] train-rmse:0.152095+0.010220 test-rmse:1.114106+0.257633
## [152] train-rmse:0.150611+0.009896 test-rmse:1.113547+0.257333
## [153] train-rmse:0.148487+0.010047 test-rmse:1.113689+0.257234
## [154] train-rmse:0.147284+0.009988 test-rmse:1.113495+0.257833
## [155] train-rmse:0.145466+0.009633 test-rmse:1.112550+0.255133
## [156] train-rmse:0.144010+0.009721 test-rmse:1.112428+0.255498
## [157] train-rmse:0.141791+0.009465 test-rmse:1.111914+0.255545
## [158] train-rmse:0.140339+0.009527 test-rmse:1.111807+0.256135
## [159] train-rmse:0.138632+0.009363 test-rmse:1.111868+0.256022
## [160] train-rmse:0.136673+0.009432 test-rmse:1.111872+0.256203
## [161] train-rmse:0.134654+0.009529 test-rmse:1.111732+0.255970
## [162] train-rmse:0.133282+0.009255 test-rmse:1.111297+0.255556
## [163] train-rmse:0.131485+0.008069 test-rmse:1.110197+0.256745
## [164] train-rmse:0.130213+0.007737 test-rmse:1.110099+0.256353
## [165] train-rmse:0.128200+0.006466 test-rmse:1.109973+0.256585
## [166] train-rmse:0.127039+0.006312 test-rmse:1.109126+0.256106
## [167] train-rmse:0.125445+0.006865 test-rmse:1.109261+0.255943
## [168] train-rmse:0.124059+0.006583 test-rmse:1.109419+0.255694
## [169] train-rmse:0.122005+0.006275 test-rmse:1.109097+0.254926
## [170] train-rmse:0.120777+0.006030 test-rmse:1.108808+0.254949
## [171] train-rmse:0.119352+0.006177 test-rmse:1.108469+0.254587
## [172] train-rmse:0.118141+0.006336 test-rmse:1.108755+0.254399
## [173] train-rmse:0.116893+0.006140 test-rmse:1.108677+0.254650
## [174] train-rmse:0.115816+0.006124 test-rmse:1.108835+0.254738
## [175] train-rmse:0.114748+0.005964 test-rmse:1.108824+0.254514
## [176] train-rmse:0.113373+0.006250 test-rmse:1.108681+0.254428
## [177] train-rmse:0.111634+0.006527 test-rmse:1.108804+0.254268
## Stopping. Best iteration:
## [171] train-rmse:0.119352+0.006177 test-rmse:1.108469+0.254587
##
## 46
## [1] train-rmse:84.081620+0.095738 test-rmse:84.081755+0.499144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:65.745564+0.080728 test-rmse:65.745244+0.529833
## [3] train-rmse:51.456281+0.070760 test-rmse:51.455305+0.559619
## [4] train-rmse:40.333913+0.065280 test-rmse:40.331998+0.589993
## [5] train-rmse:31.689540+0.061479 test-rmse:31.714312+0.607511
## [6] train-rmse:24.953949+0.049329 test-rmse:25.041610+0.619070
## [7] train-rmse:19.705462+0.036373 test-rmse:19.793211+0.583360
## [8] train-rmse:15.607711+0.030525 test-rmse:15.702199+0.539144
## [9] train-rmse:12.409847+0.025609 test-rmse:12.514664+0.528006
## [10] train-rmse:9.914462+0.020587 test-rmse:10.051362+0.567605
## [11] train-rmse:7.962759+0.025247 test-rmse:8.084005+0.548263
## [12] train-rmse:6.444892+0.024938 test-rmse:6.579505+0.523065
## [13] train-rmse:5.258193+0.012877 test-rmse:5.431518+0.534114
## [14] train-rmse:4.346881+0.010188 test-rmse:4.565814+0.473969
## [15] train-rmse:3.647234+0.018033 test-rmse:3.885820+0.454851
## [16] train-rmse:3.101052+0.023369 test-rmse:3.378118+0.407172
## [17] train-rmse:2.685935+0.031946 test-rmse:3.020060+0.387355
## [18] train-rmse:2.368331+0.034207 test-rmse:2.730209+0.338711
## [19] train-rmse:2.123408+0.040926 test-rmse:2.517248+0.309516
## [20] train-rmse:1.929676+0.044834 test-rmse:2.347084+0.266359
## [21] train-rmse:1.774736+0.046201 test-rmse:2.221561+0.252004
## [22] train-rmse:1.647664+0.053469 test-rmse:2.125923+0.238121
## [23] train-rmse:1.531658+0.041936 test-rmse:2.034912+0.222050
## [24] train-rmse:1.436925+0.040347 test-rmse:1.953803+0.197764
## [25] train-rmse:1.360411+0.039315 test-rmse:1.915058+0.206289
## [26] train-rmse:1.292937+0.037912 test-rmse:1.857107+0.199417
## [27] train-rmse:1.228067+0.036636 test-rmse:1.805131+0.204865
## [28] train-rmse:1.171073+0.036642 test-rmse:1.768471+0.207724
## [29] train-rmse:1.112525+0.041198 test-rmse:1.724898+0.208111
## [30] train-rmse:1.063865+0.039070 test-rmse:1.692481+0.201429
## [31] train-rmse:1.018581+0.036370 test-rmse:1.663654+0.205269
## [32] train-rmse:0.972593+0.038807 test-rmse:1.630262+0.211932
## [33] train-rmse:0.934587+0.037388 test-rmse:1.598548+0.212445
## [34] train-rmse:0.897590+0.035904 test-rmse:1.574263+0.218151
## [35] train-rmse:0.861534+0.034915 test-rmse:1.539745+0.224104
## [36] train-rmse:0.827381+0.032606 test-rmse:1.525332+0.228572
## [37] train-rmse:0.796052+0.030990 test-rmse:1.503630+0.234734
## [38] train-rmse:0.768355+0.030525 test-rmse:1.478331+0.236287
## [39] train-rmse:0.734593+0.026476 test-rmse:1.465520+0.234462
## [40] train-rmse:0.707481+0.024952 test-rmse:1.452709+0.241952
## [41] train-rmse:0.684357+0.024093 test-rmse:1.436771+0.243302
## [42] train-rmse:0.662510+0.022613 test-rmse:1.421880+0.247061
## [43] train-rmse:0.639019+0.022031 test-rmse:1.397080+0.258835
## [44] train-rmse:0.615526+0.026085 test-rmse:1.390536+0.257533
## [45] train-rmse:0.596331+0.024927 test-rmse:1.378485+0.259267
## [46] train-rmse:0.577039+0.023403 test-rmse:1.367317+0.263248
## [47] train-rmse:0.558689+0.023321 test-rmse:1.359882+0.265897
## [48] train-rmse:0.542712+0.023178 test-rmse:1.349598+0.265182
## [49] train-rmse:0.522056+0.025977 test-rmse:1.338382+0.268901
## [50] train-rmse:0.504790+0.024655 test-rmse:1.335054+0.268728
## [51] train-rmse:0.485852+0.022428 test-rmse:1.326144+0.267608
## [52] train-rmse:0.473556+0.022404 test-rmse:1.319406+0.269918
## [53] train-rmse:0.460837+0.023646 test-rmse:1.312628+0.267656
## [54] train-rmse:0.449116+0.021847 test-rmse:1.308624+0.272514
## [55] train-rmse:0.436617+0.020188 test-rmse:1.300698+0.278127
## [56] train-rmse:0.425198+0.020375 test-rmse:1.293028+0.281380
## [57] train-rmse:0.412953+0.019777 test-rmse:1.289112+0.285930
## [58] train-rmse:0.402765+0.018118 test-rmse:1.280037+0.290789
## [59] train-rmse:0.391163+0.016916 test-rmse:1.268698+0.281385
## [60] train-rmse:0.380097+0.018183 test-rmse:1.265045+0.281511
## [61] train-rmse:0.371557+0.018490 test-rmse:1.261376+0.284588
## [62] train-rmse:0.362683+0.018884 test-rmse:1.256847+0.284478
## [63] train-rmse:0.354314+0.017947 test-rmse:1.253722+0.286990
## [64] train-rmse:0.345866+0.017082 test-rmse:1.247490+0.294126
## [65] train-rmse:0.337868+0.016589 test-rmse:1.247436+0.296268
## [66] train-rmse:0.330777+0.014932 test-rmse:1.243107+0.294218
## [67] train-rmse:0.322270+0.014763 test-rmse:1.241072+0.295925
## [68] train-rmse:0.314863+0.014865 test-rmse:1.238390+0.298324
## [69] train-rmse:0.309059+0.014890 test-rmse:1.237082+0.299808
## [70] train-rmse:0.302583+0.015407 test-rmse:1.233929+0.301833
## [71] train-rmse:0.295829+0.015842 test-rmse:1.233404+0.302910
## [72] train-rmse:0.289512+0.014716 test-rmse:1.229463+0.300393
## [73] train-rmse:0.281733+0.014276 test-rmse:1.227663+0.303300
## [74] train-rmse:0.276398+0.014390 test-rmse:1.227195+0.304427
## [75] train-rmse:0.269441+0.013579 test-rmse:1.225761+0.305360
## [76] train-rmse:0.263532+0.011888 test-rmse:1.221511+0.299874
## [77] train-rmse:0.259056+0.012342 test-rmse:1.220885+0.300203
## [78] train-rmse:0.253778+0.013075 test-rmse:1.219078+0.300363
## [79] train-rmse:0.249146+0.013803 test-rmse:1.218327+0.300540
## [80] train-rmse:0.243300+0.013621 test-rmse:1.217238+0.303519
## [81] train-rmse:0.238561+0.014215 test-rmse:1.216372+0.304439
## [82] train-rmse:0.233775+0.012941 test-rmse:1.212226+0.299692
## [83] train-rmse:0.229035+0.014226 test-rmse:1.210785+0.300114
## [84] train-rmse:0.224539+0.014473 test-rmse:1.210041+0.298338
## [85] train-rmse:0.220875+0.014354 test-rmse:1.210120+0.298446
## [86] train-rmse:0.215279+0.011935 test-rmse:1.209140+0.298992
## [87] train-rmse:0.210354+0.011168 test-rmse:1.207854+0.298363
## [88] train-rmse:0.205890+0.011587 test-rmse:1.207461+0.297726
## [89] train-rmse:0.201996+0.010971 test-rmse:1.206851+0.296048
## [90] train-rmse:0.197795+0.012152 test-rmse:1.206935+0.296451
## [91] train-rmse:0.194403+0.011993 test-rmse:1.206368+0.296095
## [92] train-rmse:0.190751+0.011324 test-rmse:1.205858+0.297254
## [93] train-rmse:0.188058+0.011117 test-rmse:1.204972+0.297909
## [94] train-rmse:0.184011+0.010973 test-rmse:1.206666+0.298628
## [95] train-rmse:0.180642+0.011865 test-rmse:1.206351+0.297960
## [96] train-rmse:0.177562+0.011937 test-rmse:1.203449+0.294216
## [97] train-rmse:0.174564+0.011933 test-rmse:1.202109+0.295012
## [98] train-rmse:0.171304+0.011462 test-rmse:1.201399+0.294760
## [99] train-rmse:0.167640+0.010162 test-rmse:1.201237+0.296158
## [100] train-rmse:0.165001+0.010492 test-rmse:1.200512+0.296363
## [101] train-rmse:0.161476+0.011512 test-rmse:1.200995+0.295678
## [102] train-rmse:0.158750+0.012033 test-rmse:1.200075+0.296166
## [103] train-rmse:0.155363+0.011739 test-rmse:1.199126+0.297192
## [104] train-rmse:0.151977+0.010293 test-rmse:1.198525+0.296729
## [105] train-rmse:0.149878+0.009755 test-rmse:1.197771+0.297188
## [106] train-rmse:0.147702+0.010052 test-rmse:1.197733+0.297185
## [107] train-rmse:0.145383+0.010195 test-rmse:1.196601+0.297790
## [108] train-rmse:0.142200+0.008839 test-rmse:1.196816+0.298003
## [109] train-rmse:0.139511+0.008490 test-rmse:1.196348+0.297440
## [110] train-rmse:0.137464+0.008830 test-rmse:1.196095+0.297373
## [111] train-rmse:0.134711+0.007957 test-rmse:1.195597+0.297257
## [112] train-rmse:0.132334+0.008027 test-rmse:1.195649+0.296957
## [113] train-rmse:0.129227+0.008977 test-rmse:1.195258+0.296846
## [114] train-rmse:0.127569+0.008770 test-rmse:1.194429+0.296971
## [115] train-rmse:0.124985+0.008979 test-rmse:1.194423+0.297314
## [116] train-rmse:0.123294+0.009175 test-rmse:1.193896+0.296953
## [117] train-rmse:0.121094+0.009000 test-rmse:1.193249+0.297122
## [118] train-rmse:0.118757+0.008864 test-rmse:1.193049+0.297248
## [119] train-rmse:0.117300+0.008556 test-rmse:1.192278+0.296595
## [120] train-rmse:0.114932+0.008112 test-rmse:1.191508+0.296403
## [121] train-rmse:0.111998+0.008434 test-rmse:1.191442+0.296801
## [122] train-rmse:0.110322+0.008402 test-rmse:1.190794+0.296965
## [123] train-rmse:0.108319+0.008457 test-rmse:1.190256+0.296900
## [124] train-rmse:0.106217+0.008298 test-rmse:1.189745+0.297007
## [125] train-rmse:0.104354+0.008548 test-rmse:1.189515+0.297045
## [126] train-rmse:0.101416+0.007939 test-rmse:1.189613+0.297022
## [127] train-rmse:0.099942+0.007870 test-rmse:1.189330+0.297160
## [128] train-rmse:0.098507+0.007464 test-rmse:1.188669+0.297301
## [129] train-rmse:0.096532+0.007423 test-rmse:1.188170+0.297310
## [130] train-rmse:0.095341+0.007645 test-rmse:1.187613+0.297355
## [131] train-rmse:0.094047+0.007596 test-rmse:1.186922+0.297487
## [132] train-rmse:0.092688+0.007857 test-rmse:1.186972+0.297671
## [133] train-rmse:0.091620+0.007843 test-rmse:1.187045+0.297606
## [134] train-rmse:0.089516+0.007915 test-rmse:1.186994+0.297692
## [135] train-rmse:0.087864+0.007244 test-rmse:1.186868+0.297648
## [136] train-rmse:0.086402+0.007058 test-rmse:1.186600+0.298167
## [137] train-rmse:0.085484+0.006850 test-rmse:1.186740+0.298394
## [138] train-rmse:0.084283+0.006650 test-rmse:1.185975+0.298722
## [139] train-rmse:0.083257+0.006568 test-rmse:1.185947+0.298940
## [140] train-rmse:0.082208+0.006770 test-rmse:1.185919+0.299034
## [141] train-rmse:0.080017+0.006437 test-rmse:1.185793+0.299213
## [142] train-rmse:0.078883+0.006358 test-rmse:1.185653+0.299297
## [143] train-rmse:0.077962+0.006368 test-rmse:1.185195+0.299581
## [144] train-rmse:0.076787+0.006266 test-rmse:1.184988+0.299140
## [145] train-rmse:0.075478+0.005841 test-rmse:1.184789+0.299222
## [146] train-rmse:0.074095+0.006004 test-rmse:1.184766+0.299010
## [147] train-rmse:0.072913+0.006113 test-rmse:1.184863+0.298822
## [148] train-rmse:0.071887+0.006008 test-rmse:1.184652+0.298722
## [149] train-rmse:0.070755+0.005958 test-rmse:1.184949+0.299022
## [150] train-rmse:0.069912+0.005773 test-rmse:1.185486+0.300101
## [151] train-rmse:0.069130+0.005624 test-rmse:1.185353+0.300203
## [152] train-rmse:0.068281+0.005538 test-rmse:1.185217+0.300176
## [153] train-rmse:0.067072+0.005732 test-rmse:1.184964+0.300295
## [154] train-rmse:0.065774+0.005767 test-rmse:1.184789+0.300135
## Stopping. Best iteration:
## [148] train-rmse:0.071887+0.006008 test-rmse:1.184652+0.298722
##
## 47
## [1] train-rmse:84.081620+0.095738 test-rmse:84.081755+0.499144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:65.745564+0.080728 test-rmse:65.745244+0.529833
## [3] train-rmse:51.456281+0.070760 test-rmse:51.455305+0.559619
## [4] train-rmse:40.333913+0.065280 test-rmse:40.331998+0.589993
## [5] train-rmse:31.689540+0.061479 test-rmse:31.714312+0.607511
## [6] train-rmse:24.953949+0.049329 test-rmse:25.041610+0.619070
## [7] train-rmse:19.705462+0.036373 test-rmse:19.793211+0.583360
## [8] train-rmse:15.605001+0.030877 test-rmse:15.690680+0.537880
## [9] train-rmse:12.401264+0.026182 test-rmse:12.500978+0.522424
## [10] train-rmse:9.897780+0.022328 test-rmse:10.041477+0.564657
## [11] train-rmse:7.934351+0.030769 test-rmse:8.083247+0.540704
## [12] train-rmse:6.395160+0.031510 test-rmse:6.543273+0.514415
## [13] train-rmse:5.198807+0.026439 test-rmse:5.383026+0.529193
## [14] train-rmse:4.265459+0.027533 test-rmse:4.476462+0.484545
## [15] train-rmse:3.537128+0.030327 test-rmse:3.779949+0.433896
## [16] train-rmse:2.967039+0.027086 test-rmse:3.241263+0.396838
## [17] train-rmse:2.523238+0.030952 test-rmse:2.832517+0.399873
## [18] train-rmse:2.180661+0.024114 test-rmse:2.551530+0.380491
## [19] train-rmse:1.913056+0.030437 test-rmse:2.323402+0.334402
## [20] train-rmse:1.700472+0.029251 test-rmse:2.156486+0.304580
## [21] train-rmse:1.531006+0.029290 test-rmse:2.020937+0.283738
## [22] train-rmse:1.387833+0.038067 test-rmse:1.914834+0.269539
## [23] train-rmse:1.271281+0.046710 test-rmse:1.837523+0.253537
## [24] train-rmse:1.172628+0.049182 test-rmse:1.745523+0.247836
## [25] train-rmse:1.086898+0.036883 test-rmse:1.687784+0.237411
## [26] train-rmse:1.017564+0.038896 test-rmse:1.641104+0.234006
## [27] train-rmse:0.957670+0.037240 test-rmse:1.596950+0.231481
## [28] train-rmse:0.899905+0.037396 test-rmse:1.545903+0.223132
## [29] train-rmse:0.852123+0.035580 test-rmse:1.513163+0.218280
## [30] train-rmse:0.809393+0.036542 test-rmse:1.481555+0.222506
## [31] train-rmse:0.766450+0.039089 test-rmse:1.464825+0.229471
## [32] train-rmse:0.726623+0.033613 test-rmse:1.442272+0.236551
## [33] train-rmse:0.688524+0.030508 test-rmse:1.421641+0.236589
## [34] train-rmse:0.655709+0.031804 test-rmse:1.404670+0.243033
## [35] train-rmse:0.625926+0.029885 test-rmse:1.373782+0.240120
## [36] train-rmse:0.597530+0.029875 test-rmse:1.357575+0.241504
## [37] train-rmse:0.571479+0.030362 test-rmse:1.347348+0.239355
## [38] train-rmse:0.549033+0.028751 test-rmse:1.335754+0.247769
## [39] train-rmse:0.522270+0.027736 test-rmse:1.320579+0.241484
## [40] train-rmse:0.503012+0.026933 test-rmse:1.310670+0.246417
## [41] train-rmse:0.484200+0.026788 test-rmse:1.297855+0.248862
## [42] train-rmse:0.466589+0.025321 test-rmse:1.285306+0.241588
## [43] train-rmse:0.451063+0.025132 test-rmse:1.281072+0.238872
## [44] train-rmse:0.433807+0.023020 test-rmse:1.273113+0.239678
## [45] train-rmse:0.418490+0.022218 test-rmse:1.265669+0.242320
## [46] train-rmse:0.403256+0.024939 test-rmse:1.258972+0.242026
## [47] train-rmse:0.391059+0.023959 test-rmse:1.250080+0.245067
## [48] train-rmse:0.376676+0.020833 test-rmse:1.245804+0.245564
## [49] train-rmse:0.363244+0.018791 test-rmse:1.237136+0.253047
## [50] train-rmse:0.352947+0.017354 test-rmse:1.234737+0.254486
## [51] train-rmse:0.339735+0.019533 test-rmse:1.230614+0.255692
## [52] train-rmse:0.328175+0.018504 test-rmse:1.223961+0.260008
## [53] train-rmse:0.318114+0.016855 test-rmse:1.219907+0.257257
## [54] train-rmse:0.310179+0.016265 test-rmse:1.215397+0.256705
## [55] train-rmse:0.301734+0.017317 test-rmse:1.212756+0.258205
## [56] train-rmse:0.293793+0.017399 test-rmse:1.212057+0.259125
## [57] train-rmse:0.285188+0.017873 test-rmse:1.210207+0.261063
## [58] train-rmse:0.277603+0.016744 test-rmse:1.206818+0.260495
## [59] train-rmse:0.266640+0.015834 test-rmse:1.201896+0.260012
## [60] train-rmse:0.259390+0.016194 test-rmse:1.199626+0.260568
## [61] train-rmse:0.252396+0.015683 test-rmse:1.198184+0.259377
## [62] train-rmse:0.245178+0.017058 test-rmse:1.196252+0.260871
## [63] train-rmse:0.235212+0.016900 test-rmse:1.194219+0.260229
## [64] train-rmse:0.228810+0.015618 test-rmse:1.192849+0.259473
## [65] train-rmse:0.222398+0.015781 test-rmse:1.192388+0.260567
## [66] train-rmse:0.216312+0.015334 test-rmse:1.190691+0.260463
## [67] train-rmse:0.210343+0.014529 test-rmse:1.189102+0.262504
## [68] train-rmse:0.205233+0.014156 test-rmse:1.185843+0.264046
## [69] train-rmse:0.199589+0.013560 test-rmse:1.184531+0.264236
## [70] train-rmse:0.193983+0.013376 test-rmse:1.182757+0.264300
## [71] train-rmse:0.188870+0.011751 test-rmse:1.183336+0.268203
## [72] train-rmse:0.184552+0.012112 test-rmse:1.182744+0.267869
## [73] train-rmse:0.179359+0.011440 test-rmse:1.182073+0.268032
## [74] train-rmse:0.175414+0.011126 test-rmse:1.180158+0.266851
## [75] train-rmse:0.170297+0.011291 test-rmse:1.179497+0.266689
## [76] train-rmse:0.165288+0.011257 test-rmse:1.179597+0.266303
## [77] train-rmse:0.162327+0.010871 test-rmse:1.178943+0.265779
## [78] train-rmse:0.158611+0.010750 test-rmse:1.179533+0.267614
## [79] train-rmse:0.154516+0.010520 test-rmse:1.180283+0.268127
## [80] train-rmse:0.150850+0.010977 test-rmse:1.179617+0.267600
## [81] train-rmse:0.147289+0.010426 test-rmse:1.178719+0.266869
## [82] train-rmse:0.143489+0.009814 test-rmse:1.177891+0.267551
## [83] train-rmse:0.139219+0.010193 test-rmse:1.178150+0.267709
## [84] train-rmse:0.135074+0.010089 test-rmse:1.178327+0.267544
## [85] train-rmse:0.131870+0.009972 test-rmse:1.177072+0.267922
## [86] train-rmse:0.129302+0.009733 test-rmse:1.176600+0.267249
## [87] train-rmse:0.126907+0.009972 test-rmse:1.176267+0.267106
## [88] train-rmse:0.123498+0.009969 test-rmse:1.175828+0.267333
## [89] train-rmse:0.120566+0.010170 test-rmse:1.175487+0.267546
## [90] train-rmse:0.117793+0.009925 test-rmse:1.175473+0.267283
## [91] train-rmse:0.114518+0.009978 test-rmse:1.175198+0.267630
## [92] train-rmse:0.111251+0.010315 test-rmse:1.175445+0.267316
## [93] train-rmse:0.108525+0.010180 test-rmse:1.175340+0.267299
## [94] train-rmse:0.105481+0.010388 test-rmse:1.175752+0.268133
## [95] train-rmse:0.102764+0.009265 test-rmse:1.175435+0.268391
## [96] train-rmse:0.100024+0.007590 test-rmse:1.174705+0.268742
## [97] train-rmse:0.096827+0.007431 test-rmse:1.174198+0.267206
## [98] train-rmse:0.094677+0.006896 test-rmse:1.174012+0.267148
## [99] train-rmse:0.091853+0.005868 test-rmse:1.172993+0.267050
## [100] train-rmse:0.089176+0.005150 test-rmse:1.172803+0.267782
## [101] train-rmse:0.086911+0.004837 test-rmse:1.173016+0.267481
## [102] train-rmse:0.085068+0.004823 test-rmse:1.172660+0.267859
## [103] train-rmse:0.083361+0.005147 test-rmse:1.172720+0.268388
## [104] train-rmse:0.081845+0.005013 test-rmse:1.172491+0.268716
## [105] train-rmse:0.079379+0.004841 test-rmse:1.172388+0.269094
## [106] train-rmse:0.077217+0.005272 test-rmse:1.172013+0.269492
## [107] train-rmse:0.075300+0.005085 test-rmse:1.172154+0.269779
## [108] train-rmse:0.073781+0.005132 test-rmse:1.171811+0.269717
## [109] train-rmse:0.072184+0.004789 test-rmse:1.171988+0.269478
## [110] train-rmse:0.069922+0.004405 test-rmse:1.171565+0.268452
## [111] train-rmse:0.068455+0.004525 test-rmse:1.171442+0.268763
## [112] train-rmse:0.066583+0.004265 test-rmse:1.171777+0.269182
## [113] train-rmse:0.065107+0.004249 test-rmse:1.171619+0.269387
## [114] train-rmse:0.063679+0.004534 test-rmse:1.171334+0.269802
## [115] train-rmse:0.062346+0.004513 test-rmse:1.171457+0.269885
## [116] train-rmse:0.061063+0.004631 test-rmse:1.171109+0.269782
## [117] train-rmse:0.060007+0.004301 test-rmse:1.170932+0.270082
## [118] train-rmse:0.058314+0.003958 test-rmse:1.171045+0.270180
## [119] train-rmse:0.057223+0.003747 test-rmse:1.171009+0.270116
## [120] train-rmse:0.055831+0.003335 test-rmse:1.170934+0.270251
## [121] train-rmse:0.054781+0.003316 test-rmse:1.170914+0.270073
## [122] train-rmse:0.053361+0.003146 test-rmse:1.170825+0.270214
## [123] train-rmse:0.052350+0.003248 test-rmse:1.170810+0.269908
## [124] train-rmse:0.051324+0.003239 test-rmse:1.170218+0.269807
## [125] train-rmse:0.050362+0.003463 test-rmse:1.170050+0.269789
## [126] train-rmse:0.049053+0.003289 test-rmse:1.169823+0.269423
## [127] train-rmse:0.048103+0.003288 test-rmse:1.169703+0.269534
## [128] train-rmse:0.047041+0.003464 test-rmse:1.169542+0.269385
## [129] train-rmse:0.046218+0.003199 test-rmse:1.169656+0.270266
## [130] train-rmse:0.044953+0.003299 test-rmse:1.169724+0.270420
## [131] train-rmse:0.043818+0.003014 test-rmse:1.169745+0.270482
## [132] train-rmse:0.042493+0.003024 test-rmse:1.169629+0.270599
## [133] train-rmse:0.041345+0.002558 test-rmse:1.169735+0.270389
## [134] train-rmse:0.040525+0.002581 test-rmse:1.169622+0.270338
## Stopping. Best iteration:
## [128] train-rmse:0.047041+0.003464 test-rmse:1.169542+0.269385
##
## 48
## [1] train-rmse:84.081620+0.095738 test-rmse:84.081755+0.499144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:65.745564+0.080728 test-rmse:65.745244+0.529833
## [3] train-rmse:51.456281+0.070760 test-rmse:51.455305+0.559619
## [4] train-rmse:40.333913+0.065280 test-rmse:40.331998+0.589993
## [5] train-rmse:31.689540+0.061479 test-rmse:31.714312+0.607511
## [6] train-rmse:24.953949+0.049329 test-rmse:25.041610+0.619070
## [7] train-rmse:19.705462+0.036373 test-rmse:19.793211+0.583360
## [8] train-rmse:15.602282+0.031192 test-rmse:15.695041+0.540999
## [9] train-rmse:12.394326+0.027930 test-rmse:12.499482+0.522623
## [10] train-rmse:9.884900+0.024095 test-rmse:10.020661+0.561586
## [11] train-rmse:7.910858+0.024049 test-rmse:8.053069+0.542563
## [12] train-rmse:6.357084+0.026270 test-rmse:6.513093+0.513524
## [13] train-rmse:5.147407+0.025174 test-rmse:5.308363+0.496852
## [14] train-rmse:4.198837+0.027177 test-rmse:4.411802+0.484336
## [15] train-rmse:3.450412+0.027015 test-rmse:3.685586+0.447219
## [16] train-rmse:2.868001+0.026804 test-rmse:3.144885+0.398283
## [17] train-rmse:2.413372+0.030439 test-rmse:2.757687+0.419668
## [18] train-rmse:2.054539+0.023356 test-rmse:2.433906+0.387548
## [19] train-rmse:1.766474+0.019117 test-rmse:2.193244+0.359996
## [20] train-rmse:1.541685+0.033665 test-rmse:2.029756+0.324657
## [21] train-rmse:1.361239+0.034085 test-rmse:1.897945+0.290899
## [22] train-rmse:1.211678+0.029340 test-rmse:1.800501+0.286125
## [23] train-rmse:1.095683+0.034197 test-rmse:1.721328+0.260803
## [24] train-rmse:1.002758+0.033813 test-rmse:1.649401+0.258370
## [25] train-rmse:0.918172+0.033671 test-rmse:1.605994+0.260599
## [26] train-rmse:0.851585+0.038448 test-rmse:1.556772+0.260314
## [27] train-rmse:0.791131+0.035478 test-rmse:1.520260+0.271965
## [28] train-rmse:0.736585+0.031317 test-rmse:1.488850+0.261995
## [29] train-rmse:0.684642+0.034490 test-rmse:1.457188+0.265806
## [30] train-rmse:0.645630+0.033635 test-rmse:1.434789+0.269474
## [31] train-rmse:0.609224+0.029601 test-rmse:1.409652+0.266260
## [32] train-rmse:0.574612+0.027660 test-rmse:1.391951+0.265100
## [33] train-rmse:0.543210+0.029678 test-rmse:1.373672+0.265552
## [34] train-rmse:0.517691+0.028066 test-rmse:1.353446+0.261853
## [35] train-rmse:0.493716+0.026424 test-rmse:1.338345+0.269617
## [36] train-rmse:0.471494+0.024631 test-rmse:1.326199+0.268374
## [37] train-rmse:0.448964+0.024004 test-rmse:1.317744+0.270182
## [38] train-rmse:0.429505+0.021606 test-rmse:1.305100+0.266219
## [39] train-rmse:0.408275+0.019209 test-rmse:1.300023+0.272475
## [40] train-rmse:0.391714+0.018535 test-rmse:1.290239+0.268219
## [41] train-rmse:0.377442+0.017534 test-rmse:1.282955+0.268704
## [42] train-rmse:0.359354+0.018426 test-rmse:1.276596+0.270995
## [43] train-rmse:0.344069+0.020930 test-rmse:1.273180+0.270406
## [44] train-rmse:0.332108+0.020193 test-rmse:1.266691+0.267606
## [45] train-rmse:0.319348+0.018411 test-rmse:1.259545+0.269156
## [46] train-rmse:0.306135+0.017248 test-rmse:1.255146+0.272254
## [47] train-rmse:0.289059+0.018529 test-rmse:1.252679+0.275577
## [48] train-rmse:0.277341+0.018617 test-rmse:1.245859+0.276847
## [49] train-rmse:0.268571+0.017421 test-rmse:1.242455+0.278164
## [50] train-rmse:0.255220+0.016534 test-rmse:1.242069+0.279171
## [51] train-rmse:0.247915+0.016864 test-rmse:1.238881+0.280887
## [52] train-rmse:0.239948+0.016983 test-rmse:1.233641+0.281787
## [53] train-rmse:0.230500+0.016319 test-rmse:1.231432+0.284590
## [54] train-rmse:0.222471+0.014386 test-rmse:1.228025+0.284231
## [55] train-rmse:0.215602+0.015512 test-rmse:1.225135+0.284622
## [56] train-rmse:0.206540+0.014740 test-rmse:1.222422+0.284072
## [57] train-rmse:0.198280+0.011220 test-rmse:1.221448+0.284574
## [58] train-rmse:0.191759+0.010826 test-rmse:1.220815+0.284591
## [59] train-rmse:0.185028+0.011629 test-rmse:1.219770+0.285566
## [60] train-rmse:0.177441+0.009455 test-rmse:1.218341+0.285376
## [61] train-rmse:0.171875+0.009745 test-rmse:1.216492+0.286777
## [62] train-rmse:0.166204+0.010242 test-rmse:1.214922+0.288023
## [63] train-rmse:0.160898+0.010842 test-rmse:1.215226+0.289282
## [64] train-rmse:0.154845+0.010832 test-rmse:1.212828+0.289945
## [65] train-rmse:0.150135+0.010139 test-rmse:1.212217+0.290703
## [66] train-rmse:0.145617+0.008023 test-rmse:1.210899+0.290280
## [67] train-rmse:0.140814+0.006892 test-rmse:1.209994+0.288617
## [68] train-rmse:0.135496+0.005796 test-rmse:1.208238+0.289616
## [69] train-rmse:0.131208+0.007197 test-rmse:1.207392+0.290226
## [70] train-rmse:0.126726+0.008155 test-rmse:1.205842+0.290758
## [71] train-rmse:0.122957+0.008614 test-rmse:1.204905+0.291745
## [72] train-rmse:0.118794+0.006947 test-rmse:1.204766+0.291865
## [73] train-rmse:0.115297+0.007443 test-rmse:1.203757+0.292623
## [74] train-rmse:0.111696+0.007528 test-rmse:1.201846+0.291962
## [75] train-rmse:0.108645+0.007039 test-rmse:1.201139+0.292614
## [76] train-rmse:0.104455+0.007762 test-rmse:1.200903+0.292657
## [77] train-rmse:0.101278+0.007805 test-rmse:1.200449+0.293249
## [78] train-rmse:0.098864+0.008375 test-rmse:1.200299+0.293374
## [79] train-rmse:0.096409+0.008772 test-rmse:1.200025+0.293120
## [80] train-rmse:0.093466+0.008735 test-rmse:1.199375+0.293600
## [81] train-rmse:0.091127+0.008307 test-rmse:1.199280+0.293039
## [82] train-rmse:0.088237+0.008728 test-rmse:1.198879+0.292943
## [83] train-rmse:0.085953+0.008595 test-rmse:1.198651+0.293626
## [84] train-rmse:0.083573+0.008114 test-rmse:1.198069+0.294162
## [85] train-rmse:0.081758+0.008280 test-rmse:1.197455+0.294386
## [86] train-rmse:0.079537+0.008396 test-rmse:1.197382+0.294726
## [87] train-rmse:0.077058+0.008217 test-rmse:1.196768+0.295061
## [88] train-rmse:0.074748+0.007779 test-rmse:1.196242+0.295336
## [89] train-rmse:0.072527+0.007902 test-rmse:1.196236+0.295313
## [90] train-rmse:0.070917+0.007734 test-rmse:1.196404+0.295511
## [91] train-rmse:0.068748+0.007034 test-rmse:1.196321+0.295894
## [92] train-rmse:0.066862+0.007154 test-rmse:1.196104+0.295878
## [93] train-rmse:0.064883+0.007159 test-rmse:1.196056+0.295824
## [94] train-rmse:0.063198+0.007232 test-rmse:1.195555+0.296408
## [95] train-rmse:0.061054+0.006790 test-rmse:1.195190+0.295812
## [96] train-rmse:0.059640+0.006784 test-rmse:1.195205+0.295498
## [97] train-rmse:0.057051+0.006330 test-rmse:1.194921+0.296002
## [98] train-rmse:0.055871+0.006443 test-rmse:1.194533+0.296179
## [99] train-rmse:0.053515+0.006160 test-rmse:1.194234+0.296342
## [100] train-rmse:0.052259+0.005844 test-rmse:1.194025+0.296211
## [101] train-rmse:0.050680+0.006330 test-rmse:1.194055+0.296350
## [102] train-rmse:0.048952+0.005836 test-rmse:1.193971+0.296770
## [103] train-rmse:0.047330+0.005921 test-rmse:1.194072+0.296675
## [104] train-rmse:0.046417+0.006055 test-rmse:1.193684+0.296900
## [105] train-rmse:0.045159+0.005634 test-rmse:1.193666+0.297092
## [106] train-rmse:0.043886+0.005551 test-rmse:1.193723+0.297121
## [107] train-rmse:0.042617+0.005551 test-rmse:1.193607+0.296993
## [108] train-rmse:0.041109+0.005123 test-rmse:1.193365+0.297012
## [109] train-rmse:0.040119+0.005128 test-rmse:1.193334+0.296965
## [110] train-rmse:0.038988+0.004894 test-rmse:1.193347+0.296672
## [111] train-rmse:0.037489+0.004570 test-rmse:1.193110+0.296507
## [112] train-rmse:0.036513+0.004366 test-rmse:1.193234+0.296633
## [113] train-rmse:0.035339+0.004348 test-rmse:1.192975+0.296314
## [114] train-rmse:0.034146+0.004148 test-rmse:1.192876+0.296253
## [115] train-rmse:0.033445+0.004249 test-rmse:1.192839+0.296354
## [116] train-rmse:0.032582+0.004339 test-rmse:1.192826+0.296423
## [117] train-rmse:0.031529+0.004322 test-rmse:1.192524+0.296524
## [118] train-rmse:0.030389+0.004402 test-rmse:1.192304+0.296597
## [119] train-rmse:0.029600+0.004200 test-rmse:1.192111+0.296682
## [120] train-rmse:0.028623+0.003745 test-rmse:1.191994+0.296560
## [121] train-rmse:0.027703+0.003792 test-rmse:1.192133+0.296688
## [122] train-rmse:0.027020+0.003625 test-rmse:1.191826+0.296565
## [123] train-rmse:0.026297+0.003527 test-rmse:1.191825+0.296600
## [124] train-rmse:0.025544+0.003442 test-rmse:1.191682+0.296653
## [125] train-rmse:0.024855+0.003596 test-rmse:1.191705+0.296772
## [126] train-rmse:0.024334+0.003604 test-rmse:1.191626+0.296738
## [127] train-rmse:0.023574+0.003342 test-rmse:1.191356+0.296754
## [128] train-rmse:0.022783+0.003435 test-rmse:1.191457+0.296689
## [129] train-rmse:0.022279+0.003549 test-rmse:1.191468+0.296681
## [130] train-rmse:0.021760+0.003550 test-rmse:1.191220+0.296466
## [131] train-rmse:0.021292+0.003604 test-rmse:1.191267+0.296504
## [132] train-rmse:0.020806+0.003685 test-rmse:1.191250+0.296469
## [133] train-rmse:0.020100+0.003585 test-rmse:1.191135+0.296315
## [134] train-rmse:0.019437+0.003651 test-rmse:1.190974+0.296301
## [135] train-rmse:0.018921+0.003532 test-rmse:1.190822+0.296373
## [136] train-rmse:0.018381+0.003356 test-rmse:1.190604+0.296306
## [137] train-rmse:0.017829+0.003344 test-rmse:1.190617+0.296348
## [138] train-rmse:0.017434+0.003343 test-rmse:1.190580+0.296291
## [139] train-rmse:0.017027+0.003272 test-rmse:1.190503+0.296256
## [140] train-rmse:0.016536+0.003143 test-rmse:1.190466+0.296372
## [141] train-rmse:0.016177+0.003159 test-rmse:1.190398+0.296380
## [142] train-rmse:0.015683+0.003058 test-rmse:1.190350+0.296415
## [143] train-rmse:0.015262+0.002863 test-rmse:1.190333+0.296398
## [144] train-rmse:0.014655+0.002742 test-rmse:1.190426+0.296461
## [145] train-rmse:0.014192+0.002692 test-rmse:1.190441+0.296418
## [146] train-rmse:0.013794+0.002637 test-rmse:1.190435+0.296402
## [147] train-rmse:0.013329+0.002445 test-rmse:1.190376+0.296392
## [148] train-rmse:0.013011+0.002394 test-rmse:1.190296+0.296367
## [149] train-rmse:0.012679+0.002469 test-rmse:1.190314+0.296392
## [150] train-rmse:0.012380+0.002484 test-rmse:1.190282+0.296414
## [151] train-rmse:0.012017+0.002422 test-rmse:1.190330+0.296427
## [152] train-rmse:0.011746+0.002442 test-rmse:1.190332+0.296467
## [153] train-rmse:0.011422+0.002424 test-rmse:1.190322+0.296497
## [154] train-rmse:0.011066+0.002386 test-rmse:1.190358+0.296453
## [155] train-rmse:0.010777+0.002325 test-rmse:1.190345+0.296426
## [156] train-rmse:0.010547+0.002286 test-rmse:1.190265+0.296409
## [157] train-rmse:0.010205+0.002263 test-rmse:1.190224+0.296452
## [158] train-rmse:0.009862+0.002257 test-rmse:1.190211+0.296504
## [159] train-rmse:0.009579+0.002125 test-rmse:1.190089+0.296508
## [160] train-rmse:0.009347+0.002110 test-rmse:1.190094+0.296511
## [161] train-rmse:0.009079+0.002079 test-rmse:1.190103+0.296484
## [162] train-rmse:0.008765+0.002073 test-rmse:1.190122+0.296461
## [163] train-rmse:0.008486+0.001887 test-rmse:1.190084+0.296494
## [164] train-rmse:0.008254+0.001804 test-rmse:1.189998+0.296464
## [165] train-rmse:0.008050+0.001707 test-rmse:1.189933+0.296490
## [166] train-rmse:0.007873+0.001701 test-rmse:1.189932+0.296534
## [167] train-rmse:0.007689+0.001648 test-rmse:1.189926+0.296549
## [168] train-rmse:0.007476+0.001611 test-rmse:1.189932+0.296584
## [169] train-rmse:0.007260+0.001613 test-rmse:1.189927+0.296558
## [170] train-rmse:0.007003+0.001527 test-rmse:1.189921+0.296569
## [171] train-rmse:0.006811+0.001477 test-rmse:1.189866+0.296593
## [172] train-rmse:0.006604+0.001413 test-rmse:1.189818+0.296554
## [173] train-rmse:0.006345+0.001283 test-rmse:1.189812+0.296601
## [174] train-rmse:0.006212+0.001248 test-rmse:1.189781+0.296585
## [175] train-rmse:0.006033+0.001217 test-rmse:1.189776+0.296608
## [176] train-rmse:0.005862+0.001093 test-rmse:1.189775+0.296588
## [177] train-rmse:0.005710+0.001020 test-rmse:1.189735+0.296585
## [178] train-rmse:0.005583+0.001007 test-rmse:1.189765+0.296589
## [179] train-rmse:0.005435+0.000989 test-rmse:1.189802+0.296624
## [180] train-rmse:0.005301+0.000983 test-rmse:1.189765+0.296627
## [181] train-rmse:0.005106+0.000956 test-rmse:1.189780+0.296644
## [182] train-rmse:0.004946+0.000888 test-rmse:1.189755+0.296589
## [183] train-rmse:0.004770+0.000822 test-rmse:1.189748+0.296604
## Stopping. Best iteration:
## [177] train-rmse:0.005710+0.001020 test-rmse:1.189735+0.296585
##
## 49
## [1] train-rmse:81.945409+0.093908 test-rmse:81.945506+0.502440
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:62.457670+0.078257 test-rmse:62.457233+0.536077
## [3] train-rmse:47.664860+0.068582 test-rmse:47.663622+0.569012
## [4] train-rmse:36.454471+0.064225 test-rmse:36.452043+0.603174
## [5] train-rmse:27.969937+0.061508 test-rmse:28.007095+0.615836
## [6] train-rmse:21.549896+0.056392 test-rmse:21.572466+0.610108
## [7] train-rmse:16.706704+0.050158 test-rmse:16.758855+0.589503
## [8] train-rmse:13.075257+0.047529 test-rmse:13.113197+0.583535
## [9] train-rmse:10.375291+0.046383 test-rmse:10.490280+0.629833
## [10] train-rmse:8.393507+0.048055 test-rmse:8.494915+0.610455
## [11] train-rmse:6.962017+0.050820 test-rmse:7.034570+0.584090
## [12] train-rmse:5.944969+0.053048 test-rmse:6.075599+0.621844
## [13] train-rmse:5.232127+0.053606 test-rmse:5.366875+0.587680
## [14] train-rmse:4.741363+0.057719 test-rmse:4.873162+0.564543
## [15] train-rmse:4.404529+0.061690 test-rmse:4.541155+0.500262
## [16] train-rmse:4.169462+0.061076 test-rmse:4.322474+0.482919
## [17] train-rmse:3.997986+0.059965 test-rmse:4.155512+0.484125
## [18] train-rmse:3.865744+0.058721 test-rmse:4.051967+0.495428
## [19] train-rmse:3.764081+0.059053 test-rmse:3.949823+0.451903
## [20] train-rmse:3.679791+0.059208 test-rmse:3.853639+0.373148
## [21] train-rmse:3.608367+0.058167 test-rmse:3.785030+0.362486
## [22] train-rmse:3.543241+0.056712 test-rmse:3.752032+0.377415
## [23] train-rmse:3.483056+0.055947 test-rmse:3.703800+0.371296
## [24] train-rmse:3.427825+0.053496 test-rmse:3.656058+0.367822
## [25] train-rmse:3.374272+0.051636 test-rmse:3.580830+0.332883
## [26] train-rmse:3.322942+0.050535 test-rmse:3.554553+0.340958
## [27] train-rmse:3.274668+0.049528 test-rmse:3.497638+0.348513
## [28] train-rmse:3.229207+0.049135 test-rmse:3.452332+0.314688
## [29] train-rmse:3.184866+0.047540 test-rmse:3.424331+0.317925
## [30] train-rmse:3.142288+0.045773 test-rmse:3.388358+0.311670
## [31] train-rmse:3.101166+0.044865 test-rmse:3.355507+0.324575
## [32] train-rmse:3.060458+0.044819 test-rmse:3.304386+0.318068
## [33] train-rmse:3.020681+0.044250 test-rmse:3.268771+0.317601
## [34] train-rmse:2.984133+0.043057 test-rmse:3.241430+0.312206
## [35] train-rmse:2.947466+0.041899 test-rmse:3.176598+0.296200
## [36] train-rmse:2.910920+0.040659 test-rmse:3.149057+0.304690
## [37] train-rmse:2.875730+0.039578 test-rmse:3.132214+0.295995
## [38] train-rmse:2.842200+0.039085 test-rmse:3.104769+0.302863
## [39] train-rmse:2.808772+0.038854 test-rmse:3.056280+0.307608
## [40] train-rmse:2.776230+0.038797 test-rmse:3.029255+0.318348
## [41] train-rmse:2.743966+0.039251 test-rmse:3.005164+0.323849
## [42] train-rmse:2.712214+0.039076 test-rmse:2.971701+0.293729
## [43] train-rmse:2.681738+0.038506 test-rmse:2.950165+0.288351
## [44] train-rmse:2.652044+0.038927 test-rmse:2.925963+0.299430
## [45] train-rmse:2.622748+0.039206 test-rmse:2.891648+0.296631
## [46] train-rmse:2.594406+0.038816 test-rmse:2.847379+0.306375
## [47] train-rmse:2.566962+0.038380 test-rmse:2.835777+0.307586
## [48] train-rmse:2.540356+0.038240 test-rmse:2.803360+0.285363
## [49] train-rmse:2.514231+0.037666 test-rmse:2.780167+0.279739
## [50] train-rmse:2.488318+0.037041 test-rmse:2.756944+0.293708
## [51] train-rmse:2.462842+0.036706 test-rmse:2.728132+0.294144
## [52] train-rmse:2.438017+0.036305 test-rmse:2.710246+0.290261
## [53] train-rmse:2.413126+0.036220 test-rmse:2.679698+0.301044
## [54] train-rmse:2.388428+0.035738 test-rmse:2.646954+0.301968
## [55] train-rmse:2.364119+0.034534 test-rmse:2.634812+0.306757
## [56] train-rmse:2.340782+0.034288 test-rmse:2.622614+0.309451
## [57] train-rmse:2.317526+0.033778 test-rmse:2.598629+0.305560
## [58] train-rmse:2.294827+0.032966 test-rmse:2.556305+0.287536
## [59] train-rmse:2.272658+0.032482 test-rmse:2.538690+0.290181
## [60] train-rmse:2.250597+0.032158 test-rmse:2.511180+0.271882
## [61] train-rmse:2.229020+0.031928 test-rmse:2.486303+0.282126
## [62] train-rmse:2.208111+0.031555 test-rmse:2.469395+0.269859
## [63] train-rmse:2.187372+0.031020 test-rmse:2.460361+0.272049
## [64] train-rmse:2.167059+0.030390 test-rmse:2.445949+0.274720
## [65] train-rmse:2.146952+0.030028 test-rmse:2.420849+0.265850
## [66] train-rmse:2.127067+0.029226 test-rmse:2.400030+0.261310
## [67] train-rmse:2.107400+0.028708 test-rmse:2.383994+0.261997
## [68] train-rmse:2.087797+0.027927 test-rmse:2.362811+0.261151
## [69] train-rmse:2.068367+0.027246 test-rmse:2.345765+0.257533
## [70] train-rmse:2.049440+0.026897 test-rmse:2.332638+0.257407
## [71] train-rmse:2.030792+0.026187 test-rmse:2.320916+0.261370
## [72] train-rmse:2.012067+0.025608 test-rmse:2.300347+0.270953
## [73] train-rmse:1.994209+0.024956 test-rmse:2.288683+0.269951
## [74] train-rmse:1.976659+0.024435 test-rmse:2.269095+0.262860
## [75] train-rmse:1.959237+0.023988 test-rmse:2.245170+0.263807
## [76] train-rmse:1.942098+0.023907 test-rmse:2.226142+0.256321
## [77] train-rmse:1.924990+0.023741 test-rmse:2.206574+0.265201
## [78] train-rmse:1.908218+0.023472 test-rmse:2.191992+0.264162
## [79] train-rmse:1.891903+0.023223 test-rmse:2.179009+0.260306
## [80] train-rmse:1.875703+0.023143 test-rmse:2.167486+0.260781
## [81] train-rmse:1.859570+0.022800 test-rmse:2.144140+0.240517
## [82] train-rmse:1.843789+0.022280 test-rmse:2.129544+0.238416
## [83] train-rmse:1.828166+0.022081 test-rmse:2.121564+0.243437
## [84] train-rmse:1.812606+0.021797 test-rmse:2.108075+0.246757
## [85] train-rmse:1.797752+0.021429 test-rmse:2.083368+0.244731
## [86] train-rmse:1.782992+0.021143 test-rmse:2.065401+0.252658
## [87] train-rmse:1.768324+0.020937 test-rmse:2.047364+0.240849
## [88] train-rmse:1.754045+0.020548 test-rmse:2.030606+0.241643
## [89] train-rmse:1.740099+0.020210 test-rmse:2.004631+0.232490
## [90] train-rmse:1.726376+0.019811 test-rmse:1.991532+0.232769
## [91] train-rmse:1.712771+0.019632 test-rmse:1.982681+0.239134
## [92] train-rmse:1.699366+0.019252 test-rmse:1.975723+0.241486
## [93] train-rmse:1.685800+0.018830 test-rmse:1.960120+0.246763
## [94] train-rmse:1.672473+0.018739 test-rmse:1.953127+0.254117
## [95] train-rmse:1.659532+0.018463 test-rmse:1.946382+0.254207
## [96] train-rmse:1.646719+0.018081 test-rmse:1.935032+0.248180
## [97] train-rmse:1.633973+0.017626 test-rmse:1.924042+0.250650
## [98] train-rmse:1.621343+0.017436 test-rmse:1.916317+0.247528
## [99] train-rmse:1.608847+0.017284 test-rmse:1.903749+0.240488
## [100] train-rmse:1.596503+0.017052 test-rmse:1.896841+0.242583
## [101] train-rmse:1.584435+0.016893 test-rmse:1.888228+0.242436
## [102] train-rmse:1.572481+0.016729 test-rmse:1.883939+0.239117
## [103] train-rmse:1.560668+0.016820 test-rmse:1.865528+0.221926
## [104] train-rmse:1.548824+0.016830 test-rmse:1.858571+0.218943
## [105] train-rmse:1.537470+0.016450 test-rmse:1.846252+0.224397
## [106] train-rmse:1.526023+0.016219 test-rmse:1.840478+0.226007
## [107] train-rmse:1.515199+0.016078 test-rmse:1.827218+0.226172
## [108] train-rmse:1.504310+0.015907 test-rmse:1.814465+0.221398
## [109] train-rmse:1.493565+0.015707 test-rmse:1.802693+0.222349
## [110] train-rmse:1.483006+0.015678 test-rmse:1.783900+0.226497
## [111] train-rmse:1.472521+0.015746 test-rmse:1.774098+0.228229
## [112] train-rmse:1.462230+0.015773 test-rmse:1.765291+0.233195
## [113] train-rmse:1.452146+0.015692 test-rmse:1.752506+0.224775
## [114] train-rmse:1.442247+0.015387 test-rmse:1.740476+0.218736
## [115] train-rmse:1.432380+0.015200 test-rmse:1.732010+0.214042
## [116] train-rmse:1.422831+0.015014 test-rmse:1.726341+0.217477
## [117] train-rmse:1.413330+0.014987 test-rmse:1.712400+0.219154
## [118] train-rmse:1.403802+0.014900 test-rmse:1.709124+0.220264
## [119] train-rmse:1.394403+0.014734 test-rmse:1.697442+0.216668
## [120] train-rmse:1.385148+0.014551 test-rmse:1.683509+0.214902
## [121] train-rmse:1.376089+0.014351 test-rmse:1.678295+0.212922
## [122] train-rmse:1.367112+0.014111 test-rmse:1.671177+0.213633
## [123] train-rmse:1.358207+0.013792 test-rmse:1.663423+0.213945
## [124] train-rmse:1.349450+0.013611 test-rmse:1.652027+0.215627
## [125] train-rmse:1.340866+0.013504 test-rmse:1.643392+0.218720
## [126] train-rmse:1.332434+0.013308 test-rmse:1.628543+0.204605
## [127] train-rmse:1.324155+0.013394 test-rmse:1.620601+0.202265
## [128] train-rmse:1.315821+0.013462 test-rmse:1.618153+0.203210
## [129] train-rmse:1.307359+0.013532 test-rmse:1.614262+0.203786
## [130] train-rmse:1.299240+0.013612 test-rmse:1.611547+0.203792
## [131] train-rmse:1.291153+0.013850 test-rmse:1.602031+0.201216
## [132] train-rmse:1.283319+0.013945 test-rmse:1.596727+0.204615
## [133] train-rmse:1.275549+0.014038 test-rmse:1.591157+0.194741
## [134] train-rmse:1.267925+0.013971 test-rmse:1.580022+0.193582
## [135] train-rmse:1.260317+0.013931 test-rmse:1.573576+0.195097
## [136] train-rmse:1.252814+0.013954 test-rmse:1.565144+0.197828
## [137] train-rmse:1.245424+0.014060 test-rmse:1.555071+0.198024
## [138] train-rmse:1.238198+0.014048 test-rmse:1.546879+0.201533
## [139] train-rmse:1.231150+0.014008 test-rmse:1.541618+0.204338
## [140] train-rmse:1.224299+0.013997 test-rmse:1.532690+0.201947
## [141] train-rmse:1.217494+0.013842 test-rmse:1.522328+0.191245
## [142] train-rmse:1.210853+0.013741 test-rmse:1.522010+0.190812
## [143] train-rmse:1.204244+0.013737 test-rmse:1.511192+0.189191
## [144] train-rmse:1.197667+0.013689 test-rmse:1.501269+0.193005
## [145] train-rmse:1.191201+0.013706 test-rmse:1.495491+0.193440
## [146] train-rmse:1.184861+0.013683 test-rmse:1.490974+0.192600
## [147] train-rmse:1.178481+0.013541 test-rmse:1.485867+0.192865
## [148] train-rmse:1.172251+0.013468 test-rmse:1.476735+0.188749
## [149] train-rmse:1.166156+0.013422 test-rmse:1.471510+0.191275
## [150] train-rmse:1.160188+0.013314 test-rmse:1.464155+0.194833
## [151] train-rmse:1.154348+0.013318 test-rmse:1.456610+0.197538
## [152] train-rmse:1.148399+0.013273 test-rmse:1.450873+0.196415
## [153] train-rmse:1.142541+0.013363 test-rmse:1.449043+0.197366
## [154] train-rmse:1.136694+0.013400 test-rmse:1.440030+0.194683
## [155] train-rmse:1.130957+0.013527 test-rmse:1.437663+0.191699
## [156] train-rmse:1.125328+0.013504 test-rmse:1.430617+0.189721
## [157] train-rmse:1.119762+0.013485 test-rmse:1.428006+0.189792
## [158] train-rmse:1.114190+0.013468 test-rmse:1.423993+0.190580
## [159] train-rmse:1.108656+0.013527 test-rmse:1.419616+0.188137
## [160] train-rmse:1.103311+0.013503 test-rmse:1.418010+0.187946
## [161] train-rmse:1.098033+0.013484 test-rmse:1.415008+0.189595
## [162] train-rmse:1.092915+0.013569 test-rmse:1.408218+0.195957
## [163] train-rmse:1.087947+0.013605 test-rmse:1.395588+0.187098
## [164] train-rmse:1.082932+0.013613 test-rmse:1.395439+0.186706
## [165] train-rmse:1.078070+0.013670 test-rmse:1.393548+0.186270
## [166] train-rmse:1.073309+0.013681 test-rmse:1.390232+0.187081
## [167] train-rmse:1.068683+0.013700 test-rmse:1.387600+0.187751
## [168] train-rmse:1.064062+0.013732 test-rmse:1.381688+0.187181
## [169] train-rmse:1.059466+0.013783 test-rmse:1.375330+0.187106
## [170] train-rmse:1.054907+0.013892 test-rmse:1.367136+0.187228
## [171] train-rmse:1.050500+0.013893 test-rmse:1.364140+0.184533
## [172] train-rmse:1.046168+0.013870 test-rmse:1.359978+0.185372
## [173] train-rmse:1.041827+0.013866 test-rmse:1.355590+0.183527
## [174] train-rmse:1.037554+0.013871 test-rmse:1.353661+0.183373
## [175] train-rmse:1.033323+0.013846 test-rmse:1.351037+0.183623
## [176] train-rmse:1.029146+0.013794 test-rmse:1.347103+0.186393
## [177] train-rmse:1.025012+0.013855 test-rmse:1.340387+0.184180
## [178] train-rmse:1.020950+0.013925 test-rmse:1.334478+0.182115
## [179] train-rmse:1.016896+0.013993 test-rmse:1.333916+0.183416
## [180] train-rmse:1.012860+0.014035 test-rmse:1.330021+0.185434
## [181] train-rmse:1.008815+0.014097 test-rmse:1.328501+0.186154
## [182] train-rmse:1.004882+0.014096 test-rmse:1.324599+0.183176
## [183] train-rmse:1.001018+0.014014 test-rmse:1.321869+0.182346
## [184] train-rmse:0.997259+0.014044 test-rmse:1.317468+0.184978
## [185] train-rmse:0.993427+0.013960 test-rmse:1.315353+0.184448
## [186] train-rmse:0.989725+0.014008 test-rmse:1.308186+0.184517
## [187] train-rmse:0.986108+0.014025 test-rmse:1.306989+0.185300
## [188] train-rmse:0.982525+0.014075 test-rmse:1.304207+0.185974
## [189] train-rmse:0.978994+0.014150 test-rmse:1.303326+0.186728
## [190] train-rmse:0.975525+0.014181 test-rmse:1.296175+0.187563
## [191] train-rmse:0.972091+0.014227 test-rmse:1.291560+0.180089
## [192] train-rmse:0.968710+0.014221 test-rmse:1.287912+0.181141
## [193] train-rmse:0.965480+0.014278 test-rmse:1.285029+0.181794
## [194] train-rmse:0.962263+0.014348 test-rmse:1.283632+0.180535
## [195] train-rmse:0.959117+0.014422 test-rmse:1.283271+0.181915
## [196] train-rmse:0.955892+0.014476 test-rmse:1.279977+0.180857
## [197] train-rmse:0.952707+0.014518 test-rmse:1.275627+0.177563
## [198] train-rmse:0.949599+0.014572 test-rmse:1.272954+0.179214
## [199] train-rmse:0.946621+0.014637 test-rmse:1.268451+0.175022
## [200] train-rmse:0.943668+0.014725 test-rmse:1.266539+0.175014
## [201] train-rmse:0.940760+0.014832 test-rmse:1.263001+0.172557
## [202] train-rmse:0.937921+0.014918 test-rmse:1.260473+0.171374
## [203] train-rmse:0.935075+0.014964 test-rmse:1.257966+0.171360
## [204] train-rmse:0.932257+0.014974 test-rmse:1.255371+0.173203
## [205] train-rmse:0.929457+0.015015 test-rmse:1.253748+0.174410
## [206] train-rmse:0.926684+0.015069 test-rmse:1.252088+0.174004
## [207] train-rmse:0.923917+0.015087 test-rmse:1.249474+0.174177
## [208] train-rmse:0.921266+0.015143 test-rmse:1.246921+0.174736
## [209] train-rmse:0.918648+0.015267 test-rmse:1.246628+0.173705
## [210] train-rmse:0.916023+0.015322 test-rmse:1.242900+0.173935
## [211] train-rmse:0.913414+0.015454 test-rmse:1.240512+0.174710
## [212] train-rmse:0.910850+0.015572 test-rmse:1.237525+0.176850
## [213] train-rmse:0.908372+0.015620 test-rmse:1.236176+0.177536
## [214] train-rmse:0.905892+0.015684 test-rmse:1.233525+0.179320
## [215] train-rmse:0.903468+0.015679 test-rmse:1.232247+0.178719
## [216] train-rmse:0.901043+0.015708 test-rmse:1.229280+0.177414
## [217] train-rmse:0.898695+0.015745 test-rmse:1.223940+0.170830
## [218] train-rmse:0.896365+0.015808 test-rmse:1.222297+0.174198
## [219] train-rmse:0.894116+0.015847 test-rmse:1.219510+0.177379
## [220] train-rmse:0.891891+0.015898 test-rmse:1.218840+0.177159
## [221] train-rmse:0.889679+0.015917 test-rmse:1.217603+0.178593
## [222] train-rmse:0.887484+0.015970 test-rmse:1.215276+0.175562
## [223] train-rmse:0.885302+0.016034 test-rmse:1.214223+0.175539
## [224] train-rmse:0.883163+0.016106 test-rmse:1.212071+0.176201
## [225] train-rmse:0.881026+0.016199 test-rmse:1.209168+0.178052
## [226] train-rmse:0.878955+0.016290 test-rmse:1.208232+0.179205
## [227] train-rmse:0.876891+0.016318 test-rmse:1.205341+0.177689
## [228] train-rmse:0.874867+0.016345 test-rmse:1.203050+0.178151
## [229] train-rmse:0.872825+0.016416 test-rmse:1.201413+0.177234
## [230] train-rmse:0.870824+0.016489 test-rmse:1.197596+0.178314
## [231] train-rmse:0.868897+0.016551 test-rmse:1.193577+0.175963
## [232] train-rmse:0.867020+0.016618 test-rmse:1.192798+0.177310
## [233] train-rmse:0.865158+0.016688 test-rmse:1.190791+0.174616
## [234] train-rmse:0.863302+0.016776 test-rmse:1.189879+0.175012
## [235] train-rmse:0.861462+0.016858 test-rmse:1.189663+0.175647
## [236] train-rmse:0.859632+0.016913 test-rmse:1.189228+0.176596
## [237] train-rmse:0.857791+0.016959 test-rmse:1.187912+0.175041
## [238] train-rmse:0.856033+0.017001 test-rmse:1.187512+0.177075
## [239] train-rmse:0.854285+0.017060 test-rmse:1.187659+0.177060
## [240] train-rmse:0.852544+0.017113 test-rmse:1.183627+0.171622
## [241] train-rmse:0.850820+0.017146 test-rmse:1.182249+0.171090
## [242] train-rmse:0.849107+0.017177 test-rmse:1.180846+0.172406
## [243] train-rmse:0.847405+0.017230 test-rmse:1.181167+0.171303
## [244] train-rmse:0.845742+0.017282 test-rmse:1.181389+0.170576
## [245] train-rmse:0.844083+0.017310 test-rmse:1.180333+0.170277
## [246] train-rmse:0.842444+0.017340 test-rmse:1.178531+0.171157
## [247] train-rmse:0.840841+0.017378 test-rmse:1.174782+0.173751
## [248] train-rmse:0.839280+0.017436 test-rmse:1.171558+0.173199
## [249] train-rmse:0.837776+0.017497 test-rmse:1.171059+0.173799
## [250] train-rmse:0.836264+0.017564 test-rmse:1.168653+0.171875
## [251] train-rmse:0.834779+0.017613 test-rmse:1.167918+0.171862
## [252] train-rmse:0.833334+0.017649 test-rmse:1.167132+0.170536
## [253] train-rmse:0.831917+0.017678 test-rmse:1.165580+0.171271
## [254] train-rmse:0.830486+0.017703 test-rmse:1.164792+0.171498
## [255] train-rmse:0.829084+0.017743 test-rmse:1.163928+0.170439
## [256] train-rmse:0.827710+0.017781 test-rmse:1.162705+0.170880
## [257] train-rmse:0.826361+0.017815 test-rmse:1.161729+0.171171
## [258] train-rmse:0.825017+0.017866 test-rmse:1.159047+0.170605
## [259] train-rmse:0.823697+0.017887 test-rmse:1.159345+0.169859
## [260] train-rmse:0.822380+0.017931 test-rmse:1.156232+0.169747
## [261] train-rmse:0.821098+0.017965 test-rmse:1.156822+0.168246
## [262] train-rmse:0.819821+0.018007 test-rmse:1.155203+0.168163
## [263] train-rmse:0.818587+0.018052 test-rmse:1.154209+0.169783
## [264] train-rmse:0.817381+0.018102 test-rmse:1.153280+0.169906
## [265] train-rmse:0.816198+0.018147 test-rmse:1.153118+0.169122
## [266] train-rmse:0.814996+0.018209 test-rmse:1.151646+0.168630
## [267] train-rmse:0.813766+0.018238 test-rmse:1.150176+0.169767
## [268] train-rmse:0.812592+0.018302 test-rmse:1.149958+0.170501
## [269] train-rmse:0.811425+0.018369 test-rmse:1.147422+0.167060
## [270] train-rmse:0.810282+0.018409 test-rmse:1.146895+0.166939
## [271] train-rmse:0.809145+0.018454 test-rmse:1.146739+0.166327
## [272] train-rmse:0.808022+0.018495 test-rmse:1.145526+0.166966
## [273] train-rmse:0.806908+0.018544 test-rmse:1.143872+0.167143
## [274] train-rmse:0.805811+0.018573 test-rmse:1.142270+0.167119
## [275] train-rmse:0.804694+0.018615 test-rmse:1.141769+0.168499
## [276] train-rmse:0.803618+0.018655 test-rmse:1.139268+0.167043
## [277] train-rmse:0.802548+0.018705 test-rmse:1.138558+0.166369
## [278] train-rmse:0.801480+0.018751 test-rmse:1.136337+0.165853
## [279] train-rmse:0.800461+0.018794 test-rmse:1.134296+0.165307
## [280] train-rmse:0.799438+0.018843 test-rmse:1.134697+0.163472
## [281] train-rmse:0.798447+0.018883 test-rmse:1.133772+0.163596
## [282] train-rmse:0.797464+0.018919 test-rmse:1.133704+0.163329
## [283] train-rmse:0.796487+0.018952 test-rmse:1.129861+0.165802
## [284] train-rmse:0.795489+0.018976 test-rmse:1.127994+0.164927
## [285] train-rmse:0.794515+0.019014 test-rmse:1.126486+0.163792
## [286] train-rmse:0.793558+0.019059 test-rmse:1.125614+0.164891
## [287] train-rmse:0.792611+0.019121 test-rmse:1.125291+0.164247
## [288] train-rmse:0.791693+0.019179 test-rmse:1.124761+0.164810
## [289] train-rmse:0.790755+0.019236 test-rmse:1.125031+0.165076
## [290] train-rmse:0.789860+0.019290 test-rmse:1.124808+0.166259
## [291] train-rmse:0.788976+0.019313 test-rmse:1.124272+0.165655
## [292] train-rmse:0.788092+0.019354 test-rmse:1.123738+0.165762
## [293] train-rmse:0.787221+0.019410 test-rmse:1.123107+0.165528
## [294] train-rmse:0.786348+0.019447 test-rmse:1.121795+0.165854
## [295] train-rmse:0.785470+0.019490 test-rmse:1.122018+0.165481
## [296] train-rmse:0.784588+0.019546 test-rmse:1.121454+0.165083
## [297] train-rmse:0.783725+0.019572 test-rmse:1.120936+0.165517
## [298] train-rmse:0.782871+0.019601 test-rmse:1.121139+0.164991
## [299] train-rmse:0.782068+0.019623 test-rmse:1.120874+0.163580
## [300] train-rmse:0.781251+0.019648 test-rmse:1.119856+0.164099
## [301] train-rmse:0.780447+0.019675 test-rmse:1.119180+0.164553
## [302] train-rmse:0.779654+0.019716 test-rmse:1.118504+0.164524
## [303] train-rmse:0.778858+0.019755 test-rmse:1.117091+0.163111
## [304] train-rmse:0.778074+0.019787 test-rmse:1.116892+0.163686
## [305] train-rmse:0.777301+0.019815 test-rmse:1.115255+0.163489
## [306] train-rmse:0.776483+0.019847 test-rmse:1.116309+0.164186
## [307] train-rmse:0.775729+0.019879 test-rmse:1.115123+0.163448
## [308] train-rmse:0.774956+0.019886 test-rmse:1.114959+0.163813
## [309] train-rmse:0.774182+0.019909 test-rmse:1.114668+0.164351
## [310] train-rmse:0.773430+0.019942 test-rmse:1.114965+0.163658
## [311] train-rmse:0.772709+0.019956 test-rmse:1.115139+0.164290
## [312] train-rmse:0.771975+0.019971 test-rmse:1.114038+0.163103
## [313] train-rmse:0.771255+0.019986 test-rmse:1.112530+0.163774
## [314] train-rmse:0.770537+0.020007 test-rmse:1.111867+0.164275
## [315] train-rmse:0.769839+0.020026 test-rmse:1.113316+0.163502
## [316] train-rmse:0.769120+0.020051 test-rmse:1.111191+0.162746
## [317] train-rmse:0.768414+0.020056 test-rmse:1.111380+0.163612
## [318] train-rmse:0.767727+0.020061 test-rmse:1.109039+0.160563
## [319] train-rmse:0.766997+0.020105 test-rmse:1.106375+0.161686
## [320] train-rmse:0.766329+0.020126 test-rmse:1.104649+0.161088
## [321] train-rmse:0.765659+0.020142 test-rmse:1.104916+0.160029
## [322] train-rmse:0.764977+0.020173 test-rmse:1.104381+0.160565
## [323] train-rmse:0.764294+0.020200 test-rmse:1.103904+0.160590
## [324] train-rmse:0.763633+0.020232 test-rmse:1.101987+0.160820
## [325] train-rmse:0.762964+0.020253 test-rmse:1.102225+0.161040
## [326] train-rmse:0.762300+0.020299 test-rmse:1.100874+0.162373
## [327] train-rmse:0.761648+0.020308 test-rmse:1.100492+0.162228
## [328] train-rmse:0.760992+0.020296 test-rmse:1.100983+0.162364
## [329] train-rmse:0.760346+0.020311 test-rmse:1.100253+0.161438
## [330] train-rmse:0.759714+0.020334 test-rmse:1.098213+0.161415
## [331] train-rmse:0.759096+0.020352 test-rmse:1.098053+0.160781
## [332] train-rmse:0.758464+0.020382 test-rmse:1.098000+0.160732
## [333] train-rmse:0.757852+0.020410 test-rmse:1.097950+0.160588
## [334] train-rmse:0.757234+0.020428 test-rmse:1.097084+0.160394
## [335] train-rmse:0.756626+0.020446 test-rmse:1.096735+0.160843
## [336] train-rmse:0.756026+0.020470 test-rmse:1.096390+0.160837
## [337] train-rmse:0.755440+0.020494 test-rmse:1.096209+0.161135
## [338] train-rmse:0.754843+0.020527 test-rmse:1.095687+0.160465
## [339] train-rmse:0.754249+0.020527 test-rmse:1.095600+0.160009
## [340] train-rmse:0.753692+0.020536 test-rmse:1.094840+0.159819
## [341] train-rmse:0.753113+0.020529 test-rmse:1.094265+0.160526
## [342] train-rmse:0.752529+0.020525 test-rmse:1.094347+0.160013
## [343] train-rmse:0.751959+0.020522 test-rmse:1.094208+0.159601
## [344] train-rmse:0.751381+0.020522 test-rmse:1.094190+0.160493
## [345] train-rmse:0.750818+0.020539 test-rmse:1.093432+0.160290
## [346] train-rmse:0.750251+0.020518 test-rmse:1.092598+0.160571
## [347] train-rmse:0.749680+0.020549 test-rmse:1.091506+0.161493
## [348] train-rmse:0.749142+0.020553 test-rmse:1.090831+0.161522
## [349] train-rmse:0.748572+0.020514 test-rmse:1.088725+0.160975
## [350] train-rmse:0.748039+0.020522 test-rmse:1.088443+0.159229
## [351] train-rmse:0.747478+0.020482 test-rmse:1.086795+0.157178
## [352] train-rmse:0.746945+0.020485 test-rmse:1.086010+0.156441
## [353] train-rmse:0.746412+0.020465 test-rmse:1.085401+0.154622
## [354] train-rmse:0.745883+0.020456 test-rmse:1.084853+0.155997
## [355] train-rmse:0.745360+0.020454 test-rmse:1.084448+0.155467
## [356] train-rmse:0.744832+0.020462 test-rmse:1.084421+0.155299
## [357] train-rmse:0.744309+0.020457 test-rmse:1.083666+0.155301
## [358] train-rmse:0.743794+0.020446 test-rmse:1.084833+0.155341
## [359] train-rmse:0.743283+0.020436 test-rmse:1.082347+0.153038
## [360] train-rmse:0.742787+0.020421 test-rmse:1.082625+0.153341
## [361] train-rmse:0.742284+0.020409 test-rmse:1.083474+0.153150
## [362] train-rmse:0.741793+0.020409 test-rmse:1.082324+0.152996
## [363] train-rmse:0.741306+0.020409 test-rmse:1.082432+0.152116
## [364] train-rmse:0.740807+0.020407 test-rmse:1.082059+0.153093
## [365] train-rmse:0.740310+0.020377 test-rmse:1.081360+0.152076
## [366] train-rmse:0.739812+0.020356 test-rmse:1.081188+0.151965
## [367] train-rmse:0.739331+0.020346 test-rmse:1.080411+0.152258
## [368] train-rmse:0.738829+0.020352 test-rmse:1.080375+0.152745
## [369] train-rmse:0.738362+0.020347 test-rmse:1.080129+0.153535
## [370] train-rmse:0.737902+0.020338 test-rmse:1.078082+0.153669
## [371] train-rmse:0.737420+0.020298 test-rmse:1.077276+0.152130
## [372] train-rmse:0.736950+0.020296 test-rmse:1.076167+0.151679
## [373] train-rmse:0.736475+0.020274 test-rmse:1.074643+0.151424
## [374] train-rmse:0.736030+0.020262 test-rmse:1.074500+0.151707
## [375] train-rmse:0.735584+0.020249 test-rmse:1.073639+0.151320
## [376] train-rmse:0.735116+0.020225 test-rmse:1.072737+0.151652
## [377] train-rmse:0.734655+0.020222 test-rmse:1.073074+0.151828
## [378] train-rmse:0.734201+0.020187 test-rmse:1.072684+0.150572
## [379] train-rmse:0.733738+0.020184 test-rmse:1.071858+0.149881
## [380] train-rmse:0.733306+0.020176 test-rmse:1.071270+0.150304
## [381] train-rmse:0.732867+0.020165 test-rmse:1.071411+0.150303
## [382] train-rmse:0.732434+0.020155 test-rmse:1.071709+0.150256
## [383] train-rmse:0.731955+0.020159 test-rmse:1.072341+0.150515
## [384] train-rmse:0.731537+0.020140 test-rmse:1.072586+0.150214
## [385] train-rmse:0.731118+0.020116 test-rmse:1.072158+0.150395
## [386] train-rmse:0.730696+0.020105 test-rmse:1.072709+0.150533
## Stopping. Best iteration:
## [380] train-rmse:0.733306+0.020176 test-rmse:1.071270+0.150304
##
## 50
## [1] train-rmse:81.945409+0.093908 test-rmse:81.945506+0.502440
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:62.457670+0.078257 test-rmse:62.457233+0.536077
## [3] train-rmse:47.664860+0.068582 test-rmse:47.663622+0.569012
## [4] train-rmse:36.454471+0.064225 test-rmse:36.452043+0.603174
## [5] train-rmse:27.964685+0.060685 test-rmse:28.013021+0.606000
## [6] train-rmse:21.521181+0.043421 test-rmse:21.618757+0.603288
## [7] train-rmse:16.640378+0.030921 test-rmse:16.724844+0.547950
## [8] train-rmse:12.962080+0.027932 test-rmse:13.094154+0.593607
## [9] train-rmse:10.202474+0.027471 test-rmse:10.308182+0.536817
## [10] train-rmse:8.152006+0.029800 test-rmse:8.272982+0.561366
## [11] train-rmse:6.646593+0.033471 test-rmse:6.782075+0.567231
## [12] train-rmse:5.550908+0.034022 test-rmse:5.702840+0.563062
## [13] train-rmse:4.766235+0.039063 test-rmse:4.980341+0.553072
## [14] train-rmse:4.210285+0.044273 test-rmse:4.403562+0.463663
## [15] train-rmse:3.815366+0.045338 test-rmse:4.030817+0.471455
## [16] train-rmse:3.528030+0.043597 test-rmse:3.792359+0.464442
## [17] train-rmse:3.318546+0.039466 test-rmse:3.566883+0.367523
## [18] train-rmse:3.163046+0.038860 test-rmse:3.426777+0.363554
## [19] train-rmse:3.031580+0.030755 test-rmse:3.343476+0.374238
## [20] train-rmse:2.928986+0.027567 test-rmse:3.246068+0.386229
## [21] train-rmse:2.841169+0.027219 test-rmse:3.172113+0.386044
## [22] train-rmse:2.760548+0.026613 test-rmse:3.118785+0.362876
## [23] train-rmse:2.684486+0.022042 test-rmse:3.051784+0.331197
## [24] train-rmse:2.616830+0.019598 test-rmse:2.972848+0.301820
## [25] train-rmse:2.551247+0.017699 test-rmse:2.932272+0.303475
## [26] train-rmse:2.487708+0.017133 test-rmse:2.865791+0.303270
## [27] train-rmse:2.432913+0.016792 test-rmse:2.835060+0.306689
## [28] train-rmse:2.378976+0.015781 test-rmse:2.774957+0.281159
## [29] train-rmse:2.326016+0.014222 test-rmse:2.734814+0.294325
## [30] train-rmse:2.275741+0.015172 test-rmse:2.682341+0.292616
## [31] train-rmse:2.228122+0.014359 test-rmse:2.639018+0.306456
## [32] train-rmse:2.181573+0.014471 test-rmse:2.596154+0.296203
## [33] train-rmse:2.135444+0.014896 test-rmse:2.562372+0.278132
## [34] train-rmse:2.092993+0.015781 test-rmse:2.524941+0.284559
## [35] train-rmse:2.049420+0.012541 test-rmse:2.490628+0.294440
## [36] train-rmse:2.007693+0.014245 test-rmse:2.454092+0.290164
## [37] train-rmse:1.968607+0.014035 test-rmse:2.419093+0.293885
## [38] train-rmse:1.930176+0.014186 test-rmse:2.369330+0.291749
## [39] train-rmse:1.891843+0.013829 test-rmse:2.329920+0.264140
## [40] train-rmse:1.855790+0.014190 test-rmse:2.289523+0.261308
## [41] train-rmse:1.819381+0.013317 test-rmse:2.257176+0.253454
## [42] train-rmse:1.785440+0.013426 test-rmse:2.226426+0.254182
## [43] train-rmse:1.751936+0.013848 test-rmse:2.214230+0.259938
## [44] train-rmse:1.720745+0.014034 test-rmse:2.180618+0.264613
## [45] train-rmse:1.686377+0.011481 test-rmse:2.157705+0.266260
## [46] train-rmse:1.655853+0.011236 test-rmse:2.131593+0.271966
## [47] train-rmse:1.625350+0.012855 test-rmse:2.100362+0.252805
## [48] train-rmse:1.596277+0.011994 test-rmse:2.082875+0.256763
## [49] train-rmse:1.567902+0.011947 test-rmse:2.058213+0.254532
## [50] train-rmse:1.540573+0.012437 test-rmse:2.030922+0.250720
## [51] train-rmse:1.515262+0.012323 test-rmse:2.003981+0.234598
## [52] train-rmse:1.489508+0.012065 test-rmse:1.983918+0.231551
## [53] train-rmse:1.463758+0.011792 test-rmse:1.971901+0.231946
## [54] train-rmse:1.439735+0.012178 test-rmse:1.944262+0.230427
## [55] train-rmse:1.416337+0.012321 test-rmse:1.926945+0.229355
## [56] train-rmse:1.393432+0.011849 test-rmse:1.905698+0.228330
## [57] train-rmse:1.370505+0.012874 test-rmse:1.880026+0.229300
## [58] train-rmse:1.346785+0.013406 test-rmse:1.851751+0.233446
## [59] train-rmse:1.325367+0.014478 test-rmse:1.835123+0.237311
## [60] train-rmse:1.302974+0.015745 test-rmse:1.821232+0.233544
## [61] train-rmse:1.281807+0.015625 test-rmse:1.802358+0.234663
## [62] train-rmse:1.260532+0.016231 test-rmse:1.790666+0.242916
## [63] train-rmse:1.239172+0.018105 test-rmse:1.769638+0.231511
## [64] train-rmse:1.220748+0.018699 test-rmse:1.751066+0.239999
## [65] train-rmse:1.203132+0.018818 test-rmse:1.734206+0.238532
## [66] train-rmse:1.185396+0.018351 test-rmse:1.716945+0.240057
## [67] train-rmse:1.167452+0.017854 test-rmse:1.701490+0.230303
## [68] train-rmse:1.150317+0.018061 test-rmse:1.689117+0.231004
## [69] train-rmse:1.133743+0.018035 test-rmse:1.675272+0.235213
## [70] train-rmse:1.117298+0.019034 test-rmse:1.656720+0.233677
## [71] train-rmse:1.101295+0.019586 test-rmse:1.646676+0.234520
## [72] train-rmse:1.083594+0.017743 test-rmse:1.636828+0.228477
## [73] train-rmse:1.067329+0.017830 test-rmse:1.624883+0.229614
## [74] train-rmse:1.053349+0.017876 test-rmse:1.604157+0.223064
## [75] train-rmse:1.039288+0.017935 test-rmse:1.594813+0.230635
## [76] train-rmse:1.024884+0.018181 test-rmse:1.576612+0.231022
## [77] train-rmse:1.011652+0.018058 test-rmse:1.566112+0.231361
## [78] train-rmse:0.997380+0.018930 test-rmse:1.556202+0.232069
## [79] train-rmse:0.983588+0.018283 test-rmse:1.545081+0.237476
## [80] train-rmse:0.970882+0.018525 test-rmse:1.533503+0.236622
## [81] train-rmse:0.956811+0.019178 test-rmse:1.524823+0.235343
## [82] train-rmse:0.943015+0.020827 test-rmse:1.519196+0.231215
## [83] train-rmse:0.930622+0.021317 test-rmse:1.507311+0.236371
## [84] train-rmse:0.919315+0.021645 test-rmse:1.499478+0.234103
## [85] train-rmse:0.908009+0.022476 test-rmse:1.493367+0.233024
## [86] train-rmse:0.896690+0.021339 test-rmse:1.484170+0.232573
## [87] train-rmse:0.884456+0.023292 test-rmse:1.477973+0.237830
## [88] train-rmse:0.874079+0.023484 test-rmse:1.466665+0.238109
## [89] train-rmse:0.863123+0.024239 test-rmse:1.460227+0.243138
## [90] train-rmse:0.853176+0.024209 test-rmse:1.454808+0.243300
## [91] train-rmse:0.841707+0.023394 test-rmse:1.444502+0.242796
## [92] train-rmse:0.832741+0.023379 test-rmse:1.431448+0.239942
## [93] train-rmse:0.823105+0.022975 test-rmse:1.423626+0.244388
## [94] train-rmse:0.814209+0.023111 test-rmse:1.414093+0.244874
## [95] train-rmse:0.805049+0.023844 test-rmse:1.411041+0.244830
## [96] train-rmse:0.796649+0.023992 test-rmse:1.405358+0.244874
## [97] train-rmse:0.787737+0.024063 test-rmse:1.399830+0.242050
## [98] train-rmse:0.779745+0.024587 test-rmse:1.396463+0.239140
## [99] train-rmse:0.769590+0.020994 test-rmse:1.395315+0.241645
## [100] train-rmse:0.760955+0.022497 test-rmse:1.391031+0.243116
## [101] train-rmse:0.753475+0.022439 test-rmse:1.385934+0.243422
## [102] train-rmse:0.746441+0.022426 test-rmse:1.382195+0.242633
## [103] train-rmse:0.738619+0.022961 test-rmse:1.381096+0.242501
## [104] train-rmse:0.731705+0.023046 test-rmse:1.374174+0.242975
## [105] train-rmse:0.724584+0.022587 test-rmse:1.370667+0.245538
## [106] train-rmse:0.717756+0.023062 test-rmse:1.365102+0.247785
## [107] train-rmse:0.709708+0.022889 test-rmse:1.359323+0.243566
## [108] train-rmse:0.704077+0.022913 test-rmse:1.353618+0.243968
## [109] train-rmse:0.698227+0.023123 test-rmse:1.350667+0.244352
## [110] train-rmse:0.692253+0.023182 test-rmse:1.346544+0.245309
## [111] train-rmse:0.686452+0.023320 test-rmse:1.338523+0.247542
## [112] train-rmse:0.679128+0.024070 test-rmse:1.334661+0.245728
## [113] train-rmse:0.673395+0.023583 test-rmse:1.327297+0.244681
## [114] train-rmse:0.667307+0.024447 test-rmse:1.327326+0.243208
## [115] train-rmse:0.661313+0.024903 test-rmse:1.323971+0.245544
## [116] train-rmse:0.655614+0.023669 test-rmse:1.321790+0.247573
## [117] train-rmse:0.649806+0.022720 test-rmse:1.316683+0.250476
## [118] train-rmse:0.644423+0.023200 test-rmse:1.312345+0.252846
## [119] train-rmse:0.639785+0.023423 test-rmse:1.306777+0.253114
## [120] train-rmse:0.635356+0.023392 test-rmse:1.305199+0.251918
## [121] train-rmse:0.630630+0.023421 test-rmse:1.304245+0.251845
## [122] train-rmse:0.625853+0.023789 test-rmse:1.303316+0.250811
## [123] train-rmse:0.620897+0.022949 test-rmse:1.297142+0.252144
## [124] train-rmse:0.615457+0.022968 test-rmse:1.285855+0.250591
## [125] train-rmse:0.610621+0.023593 test-rmse:1.282540+0.251820
## [126] train-rmse:0.605995+0.023706 test-rmse:1.280793+0.251161
## [127] train-rmse:0.601141+0.023696 test-rmse:1.278961+0.252273
## [128] train-rmse:0.597237+0.023669 test-rmse:1.276662+0.254634
## [129] train-rmse:0.591821+0.020408 test-rmse:1.274705+0.255132
## [130] train-rmse:0.586813+0.021188 test-rmse:1.270450+0.252670
## [131] train-rmse:0.582651+0.020986 test-rmse:1.266906+0.254574
## [132] train-rmse:0.576731+0.019100 test-rmse:1.264742+0.256067
## [133] train-rmse:0.573323+0.018846 test-rmse:1.261184+0.257526
## [134] train-rmse:0.569552+0.018873 test-rmse:1.257181+0.255473
## [135] train-rmse:0.566026+0.018942 test-rmse:1.253829+0.256061
## [136] train-rmse:0.562954+0.018876 test-rmse:1.251105+0.256713
## [137] train-rmse:0.558877+0.019102 test-rmse:1.249881+0.255656
## [138] train-rmse:0.554972+0.019467 test-rmse:1.245797+0.255422
## [139] train-rmse:0.549267+0.017913 test-rmse:1.245676+0.257477
## [140] train-rmse:0.545622+0.018018 test-rmse:1.243222+0.257031
## [141] train-rmse:0.542187+0.018714 test-rmse:1.241761+0.259713
## [142] train-rmse:0.539120+0.018112 test-rmse:1.240893+0.260409
## [143] train-rmse:0.535466+0.017313 test-rmse:1.235436+0.252500
## [144] train-rmse:0.532206+0.017294 test-rmse:1.234859+0.252971
## [145] train-rmse:0.528896+0.017248 test-rmse:1.230410+0.250821
## [146] train-rmse:0.525660+0.017650 test-rmse:1.228937+0.251651
## [147] train-rmse:0.523102+0.017665 test-rmse:1.225939+0.250972
## [148] train-rmse:0.519347+0.017232 test-rmse:1.224868+0.250664
## [149] train-rmse:0.515677+0.017884 test-rmse:1.222566+0.252868
## [150] train-rmse:0.512556+0.017767 test-rmse:1.220554+0.251450
## [151] train-rmse:0.509500+0.018048 test-rmse:1.219869+0.252199
## [152] train-rmse:0.506654+0.018436 test-rmse:1.217513+0.255018
## [153] train-rmse:0.504234+0.018384 test-rmse:1.213610+0.257382
## [154] train-rmse:0.501836+0.018341 test-rmse:1.213420+0.258349
## [155] train-rmse:0.499551+0.018098 test-rmse:1.211712+0.257500
## [156] train-rmse:0.496612+0.018352 test-rmse:1.210597+0.258563
## [157] train-rmse:0.494512+0.018265 test-rmse:1.210028+0.258711
## [158] train-rmse:0.492246+0.018187 test-rmse:1.207899+0.259643
## [159] train-rmse:0.489663+0.018024 test-rmse:1.202628+0.259001
## [160] train-rmse:0.486184+0.019083 test-rmse:1.203758+0.259734
## [161] train-rmse:0.484062+0.019088 test-rmse:1.202621+0.260315
## [162] train-rmse:0.481553+0.019131 test-rmse:1.202705+0.260501
## [163] train-rmse:0.479536+0.019162 test-rmse:1.201677+0.261410
## [164] train-rmse:0.476888+0.019401 test-rmse:1.201451+0.262496
## [165] train-rmse:0.474341+0.019901 test-rmse:1.200557+0.262894
## [166] train-rmse:0.472110+0.020106 test-rmse:1.200943+0.262374
## [167] train-rmse:0.469238+0.020621 test-rmse:1.197949+0.262501
## [168] train-rmse:0.467036+0.020549 test-rmse:1.195216+0.262155
## [169] train-rmse:0.464916+0.020520 test-rmse:1.194652+0.262697
## [170] train-rmse:0.461607+0.020930 test-rmse:1.192115+0.264125
## [171] train-rmse:0.459250+0.021385 test-rmse:1.190475+0.264633
## [172] train-rmse:0.457454+0.021438 test-rmse:1.189206+0.265651
## [173] train-rmse:0.455352+0.021106 test-rmse:1.186785+0.266452
## [174] train-rmse:0.453774+0.020908 test-rmse:1.185741+0.265814
## [175] train-rmse:0.451620+0.021523 test-rmse:1.184709+0.266545
## [176] train-rmse:0.449651+0.021564 test-rmse:1.184255+0.266167
## [177] train-rmse:0.447174+0.020794 test-rmse:1.182532+0.267153
## [178] train-rmse:0.444948+0.020577 test-rmse:1.180434+0.266443
## [179] train-rmse:0.442907+0.020647 test-rmse:1.179057+0.266250
## [180] train-rmse:0.439827+0.019035 test-rmse:1.178300+0.266824
## [181] train-rmse:0.437491+0.019405 test-rmse:1.177309+0.267139
## [182] train-rmse:0.435607+0.019793 test-rmse:1.172769+0.260948
## [183] train-rmse:0.433780+0.019983 test-rmse:1.172094+0.263197
## [184] train-rmse:0.432261+0.020007 test-rmse:1.171815+0.263536
## [185] train-rmse:0.430899+0.019834 test-rmse:1.170674+0.263748
## [186] train-rmse:0.428377+0.020724 test-rmse:1.169029+0.263960
## [187] train-rmse:0.426784+0.020718 test-rmse:1.168548+0.264085
## [188] train-rmse:0.424249+0.019721 test-rmse:1.170604+0.265032
## [189] train-rmse:0.422113+0.019785 test-rmse:1.169612+0.263901
## [190] train-rmse:0.420316+0.019963 test-rmse:1.168970+0.264052
## [191] train-rmse:0.418672+0.020102 test-rmse:1.168495+0.263923
## [192] train-rmse:0.416873+0.020096 test-rmse:1.168379+0.263998
## [193] train-rmse:0.415634+0.020068 test-rmse:1.167164+0.264127
## [194] train-rmse:0.413913+0.020421 test-rmse:1.165980+0.263919
## [195] train-rmse:0.411431+0.021424 test-rmse:1.164257+0.264064
## [196] train-rmse:0.409336+0.021072 test-rmse:1.163584+0.264560
## [197] train-rmse:0.407943+0.021173 test-rmse:1.162045+0.264266
## [198] train-rmse:0.406389+0.021692 test-rmse:1.160783+0.262458
## [199] train-rmse:0.403762+0.020962 test-rmse:1.160048+0.263045
## [200] train-rmse:0.401978+0.021163 test-rmse:1.160245+0.263023
## [201] train-rmse:0.400109+0.019750 test-rmse:1.160106+0.263148
## [202] train-rmse:0.398195+0.019669 test-rmse:1.159063+0.263722
## [203] train-rmse:0.395700+0.018830 test-rmse:1.158000+0.263409
## [204] train-rmse:0.394539+0.018790 test-rmse:1.157299+0.263691
## [205] train-rmse:0.393237+0.019179 test-rmse:1.155879+0.263628
## [206] train-rmse:0.390720+0.020188 test-rmse:1.155202+0.265189
## [207] train-rmse:0.389614+0.020308 test-rmse:1.154090+0.266170
## [208] train-rmse:0.387729+0.020213 test-rmse:1.154021+0.266659
## [209] train-rmse:0.386544+0.020077 test-rmse:1.152953+0.265715
## [210] train-rmse:0.384683+0.020698 test-rmse:1.151579+0.265845
## [211] train-rmse:0.383452+0.020863 test-rmse:1.149544+0.266781
## [212] train-rmse:0.382136+0.021006 test-rmse:1.148214+0.267415
## [213] train-rmse:0.381080+0.020996 test-rmse:1.146464+0.265333
## [214] train-rmse:0.379715+0.021062 test-rmse:1.146109+0.265223
## [215] train-rmse:0.378135+0.021560 test-rmse:1.145939+0.265589
## [216] train-rmse:0.376480+0.022308 test-rmse:1.144644+0.266252
## [217] train-rmse:0.375150+0.022626 test-rmse:1.144607+0.265975
## [218] train-rmse:0.373581+0.023166 test-rmse:1.143842+0.265579
## [219] train-rmse:0.371845+0.023214 test-rmse:1.142813+0.266390
## [220] train-rmse:0.369793+0.022755 test-rmse:1.142384+0.265456
## [221] train-rmse:0.367566+0.022642 test-rmse:1.142028+0.266267
## [222] train-rmse:0.366151+0.022073 test-rmse:1.142066+0.266427
## [223] train-rmse:0.364541+0.021960 test-rmse:1.140961+0.266946
## [224] train-rmse:0.363523+0.021808 test-rmse:1.140340+0.266400
## [225] train-rmse:0.362377+0.021869 test-rmse:1.139602+0.267069
## [226] train-rmse:0.360495+0.022548 test-rmse:1.138886+0.266987
## [227] train-rmse:0.358896+0.022745 test-rmse:1.137189+0.266915
## [228] train-rmse:0.357071+0.022133 test-rmse:1.136534+0.267446
## [229] train-rmse:0.355064+0.021084 test-rmse:1.136781+0.267091
## [230] train-rmse:0.353734+0.020616 test-rmse:1.136173+0.266472
## [231] train-rmse:0.352635+0.020305 test-rmse:1.133274+0.267974
## [232] train-rmse:0.350836+0.020379 test-rmse:1.133305+0.267325
## [233] train-rmse:0.349158+0.020605 test-rmse:1.133575+0.269347
## [234] train-rmse:0.348270+0.020620 test-rmse:1.132109+0.270657
## [235] train-rmse:0.347335+0.020534 test-rmse:1.131882+0.270470
## [236] train-rmse:0.345502+0.020309 test-rmse:1.130942+0.270547
## [237] train-rmse:0.344246+0.020350 test-rmse:1.131804+0.269671
## [238] train-rmse:0.342901+0.019873 test-rmse:1.130238+0.271419
## [239] train-rmse:0.342151+0.019927 test-rmse:1.129908+0.271320
## [240] train-rmse:0.339905+0.019782 test-rmse:1.130425+0.272840
## [241] train-rmse:0.338126+0.021039 test-rmse:1.129847+0.274613
## [242] train-rmse:0.336563+0.020952 test-rmse:1.130407+0.274019
## [243] train-rmse:0.335413+0.021550 test-rmse:1.130414+0.274878
## [244] train-rmse:0.333947+0.021139 test-rmse:1.129551+0.274650
## [245] train-rmse:0.333077+0.021086 test-rmse:1.129327+0.275514
## [246] train-rmse:0.331777+0.020600 test-rmse:1.129566+0.275284
## [247] train-rmse:0.330834+0.020623 test-rmse:1.129897+0.275329
## [248] train-rmse:0.329740+0.020679 test-rmse:1.130205+0.275826
## [249] train-rmse:0.329022+0.020564 test-rmse:1.130121+0.276047
## [250] train-rmse:0.327908+0.019735 test-rmse:1.129607+0.276281
## [251] train-rmse:0.326820+0.019859 test-rmse:1.128609+0.275749
## [252] train-rmse:0.325645+0.020179 test-rmse:1.128684+0.276054
## [253] train-rmse:0.324946+0.020142 test-rmse:1.127918+0.276656
## [254] train-rmse:0.324080+0.019983 test-rmse:1.127748+0.276701
## [255] train-rmse:0.322977+0.020271 test-rmse:1.127746+0.276307
## [256] train-rmse:0.322185+0.020613 test-rmse:1.127786+0.276502
## [257] train-rmse:0.320918+0.020093 test-rmse:1.127620+0.275926
## [258] train-rmse:0.320287+0.019921 test-rmse:1.127576+0.275903
## [259] train-rmse:0.318797+0.019896 test-rmse:1.127179+0.275730
## [260] train-rmse:0.317591+0.019173 test-rmse:1.125727+0.276219
## [261] train-rmse:0.316236+0.018933 test-rmse:1.124713+0.275912
## [262] train-rmse:0.314438+0.019223 test-rmse:1.124746+0.276106
## [263] train-rmse:0.313805+0.019241 test-rmse:1.124407+0.276720
## [264] train-rmse:0.312686+0.019501 test-rmse:1.125065+0.277506
## [265] train-rmse:0.311208+0.019292 test-rmse:1.124327+0.276322
## [266] train-rmse:0.309967+0.019592 test-rmse:1.123669+0.277014
## [267] train-rmse:0.309062+0.019549 test-rmse:1.122946+0.276336
## [268] train-rmse:0.308129+0.019549 test-rmse:1.122591+0.276687
## [269] train-rmse:0.307306+0.019461 test-rmse:1.121944+0.276461
## [270] train-rmse:0.306550+0.019650 test-rmse:1.121867+0.276217
## [271] train-rmse:0.305801+0.019751 test-rmse:1.121506+0.276001
## [272] train-rmse:0.304981+0.019653 test-rmse:1.121068+0.275497
## [273] train-rmse:0.304284+0.019837 test-rmse:1.121004+0.275458
## [274] train-rmse:0.303402+0.019861 test-rmse:1.118203+0.276163
## [275] train-rmse:0.302677+0.019766 test-rmse:1.118121+0.276140
## [276] train-rmse:0.301566+0.019303 test-rmse:1.117992+0.275772
## [277] train-rmse:0.300794+0.018847 test-rmse:1.117041+0.277126
## [278] train-rmse:0.299871+0.018389 test-rmse:1.116053+0.277709
## [279] train-rmse:0.299200+0.018340 test-rmse:1.116061+0.277778
## [280] train-rmse:0.298102+0.018443 test-rmse:1.115557+0.277927
## [281] train-rmse:0.297023+0.018286 test-rmse:1.114306+0.278841
## [282] train-rmse:0.296093+0.018399 test-rmse:1.114391+0.278951
## [283] train-rmse:0.295292+0.018274 test-rmse:1.113773+0.278833
## [284] train-rmse:0.293594+0.019091 test-rmse:1.114750+0.278604
## [285] train-rmse:0.292512+0.018972 test-rmse:1.114178+0.278532
## [286] train-rmse:0.291706+0.018994 test-rmse:1.114193+0.277787
## [287] train-rmse:0.290478+0.019196 test-rmse:1.113212+0.278711
## [288] train-rmse:0.289469+0.019270 test-rmse:1.113478+0.278324
## [289] train-rmse:0.288934+0.019266 test-rmse:1.112761+0.277621
## [290] train-rmse:0.288125+0.019245 test-rmse:1.112086+0.277798
## [291] train-rmse:0.287578+0.019298 test-rmse:1.111271+0.277676
## [292] train-rmse:0.286746+0.018848 test-rmse:1.111128+0.277949
## [293] train-rmse:0.285828+0.018054 test-rmse:1.110856+0.278128
## [294] train-rmse:0.284647+0.017637 test-rmse:1.110723+0.277825
## [295] train-rmse:0.283587+0.017619 test-rmse:1.110798+0.278302
## [296] train-rmse:0.282570+0.017699 test-rmse:1.110688+0.278114
## [297] train-rmse:0.281930+0.017670 test-rmse:1.111035+0.278396
## [298] train-rmse:0.281078+0.018075 test-rmse:1.110574+0.278317
## [299] train-rmse:0.279964+0.017641 test-rmse:1.111106+0.278324
## [300] train-rmse:0.279317+0.017660 test-rmse:1.111052+0.278002
## [301] train-rmse:0.278060+0.018121 test-rmse:1.110859+0.278642
## [302] train-rmse:0.277556+0.018035 test-rmse:1.110370+0.278888
## [303] train-rmse:0.276258+0.017962 test-rmse:1.110456+0.278751
## [304] train-rmse:0.275469+0.018228 test-rmse:1.110440+0.278820
## [305] train-rmse:0.274784+0.018399 test-rmse:1.110317+0.279194
## [306] train-rmse:0.273413+0.018130 test-rmse:1.109711+0.278334
## [307] train-rmse:0.272889+0.018070 test-rmse:1.109547+0.278291
## [308] train-rmse:0.271955+0.018177 test-rmse:1.110063+0.278188
## [309] train-rmse:0.271172+0.018367 test-rmse:1.109607+0.278226
## [310] train-rmse:0.270454+0.018460 test-rmse:1.109116+0.277887
## [311] train-rmse:0.269936+0.018557 test-rmse:1.108793+0.277999
## [312] train-rmse:0.269187+0.018615 test-rmse:1.108398+0.277524
## [313] train-rmse:0.267975+0.018756 test-rmse:1.108528+0.277939
## [314] train-rmse:0.266631+0.019094 test-rmse:1.108493+0.278831
## [315] train-rmse:0.265639+0.019236 test-rmse:1.108392+0.279021
## [316] train-rmse:0.265130+0.019088 test-rmse:1.107548+0.280015
## [317] train-rmse:0.264251+0.018695 test-rmse:1.107669+0.279748
## [318] train-rmse:0.263565+0.018680 test-rmse:1.107541+0.279712
## [319] train-rmse:0.263036+0.018827 test-rmse:1.106699+0.279153
## [320] train-rmse:0.262362+0.018320 test-rmse:1.106688+0.278625
## [321] train-rmse:0.261319+0.017380 test-rmse:1.106400+0.278302
## [322] train-rmse:0.260065+0.017642 test-rmse:1.106182+0.278058
## [323] train-rmse:0.259180+0.017732 test-rmse:1.105799+0.278119
## [324] train-rmse:0.258326+0.017716 test-rmse:1.105956+0.278085
## [325] train-rmse:0.257563+0.017782 test-rmse:1.105761+0.278358
## [326] train-rmse:0.256686+0.018213 test-rmse:1.106400+0.277978
## [327] train-rmse:0.256083+0.018290 test-rmse:1.106757+0.278024
## [328] train-rmse:0.255227+0.018583 test-rmse:1.107040+0.278500
## [329] train-rmse:0.254477+0.018874 test-rmse:1.107397+0.277772
## [330] train-rmse:0.253462+0.018467 test-rmse:1.107566+0.278415
## [331] train-rmse:0.252609+0.018460 test-rmse:1.108062+0.278606
## Stopping. Best iteration:
## [325] train-rmse:0.257563+0.017782 test-rmse:1.105761+0.278358
##
## 51
## [1] train-rmse:81.945409+0.093908 test-rmse:81.945506+0.502440
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:62.457670+0.078257 test-rmse:62.457233+0.536077
## [3] train-rmse:47.664860+0.068582 test-rmse:47.663622+0.569012
## [4] train-rmse:36.454471+0.064225 test-rmse:36.452043+0.603174
## [5] train-rmse:27.964685+0.060685 test-rmse:28.013021+0.606000
## [6] train-rmse:21.516921+0.044302 test-rmse:21.607782+0.599820
## [7] train-rmse:16.626704+0.036872 test-rmse:16.724599+0.578938
## [8] train-rmse:12.922753+0.032367 test-rmse:13.036829+0.588952
## [9] train-rmse:10.130322+0.034175 test-rmse:10.218783+0.571032
## [10] train-rmse:8.031383+0.034770 test-rmse:8.159022+0.557093
## [11] train-rmse:6.467814+0.037630 test-rmse:6.608135+0.533067
## [12] train-rmse:5.313037+0.043228 test-rmse:5.467592+0.488888
## [13] train-rmse:4.474999+0.047671 test-rmse:4.648073+0.497674
## [14] train-rmse:3.862779+0.051137 test-rmse:4.047443+0.473609
## [15] train-rmse:3.417908+0.049425 test-rmse:3.649137+0.411472
## [16] train-rmse:3.085204+0.036717 test-rmse:3.349706+0.396220
## [17] train-rmse:2.847310+0.036697 test-rmse:3.146167+0.347705
## [18] train-rmse:2.665618+0.034013 test-rmse:2.983192+0.342085
## [19] train-rmse:2.521713+0.031933 test-rmse:2.862768+0.325210
## [20] train-rmse:2.403688+0.033878 test-rmse:2.740151+0.309262
## [21] train-rmse:2.305880+0.033197 test-rmse:2.659825+0.298278
## [22] train-rmse:2.217405+0.028199 test-rmse:2.596221+0.274876
## [23] train-rmse:2.134482+0.030104 test-rmse:2.524109+0.305094
## [24] train-rmse:2.061073+0.029717 test-rmse:2.449683+0.288070
## [25] train-rmse:1.992637+0.029602 test-rmse:2.399160+0.275562
## [26] train-rmse:1.929547+0.027445 test-rmse:2.347323+0.283139
## [27] train-rmse:1.864657+0.023987 test-rmse:2.266543+0.283145
## [28] train-rmse:1.802867+0.021855 test-rmse:2.204268+0.261565
## [29] train-rmse:1.745465+0.021352 test-rmse:2.183488+0.243856
## [30] train-rmse:1.690344+0.024070 test-rmse:2.151576+0.233771
## [31] train-rmse:1.642063+0.024876 test-rmse:2.103540+0.232836
## [32] train-rmse:1.592420+0.024370 test-rmse:2.061766+0.246265
## [33] train-rmse:1.547023+0.020451 test-rmse:2.028832+0.249918
## [34] train-rmse:1.503310+0.021699 test-rmse:1.986471+0.249296
## [35] train-rmse:1.463866+0.020598 test-rmse:1.946623+0.243481
## [36] train-rmse:1.417800+0.023751 test-rmse:1.915485+0.234317
## [37] train-rmse:1.376906+0.023499 test-rmse:1.882398+0.234722
## [38] train-rmse:1.340809+0.022639 test-rmse:1.846507+0.230298
## [39] train-rmse:1.307066+0.021724 test-rmse:1.822508+0.230781
## [40] train-rmse:1.274739+0.020657 test-rmse:1.795759+0.221555
## [41] train-rmse:1.242033+0.020086 test-rmse:1.773729+0.232652
## [42] train-rmse:1.207857+0.019963 test-rmse:1.743168+0.229688
## [43] train-rmse:1.175961+0.019202 test-rmse:1.720872+0.235006
## [44] train-rmse:1.146555+0.018797 test-rmse:1.689020+0.236572
## [45] train-rmse:1.115702+0.017819 test-rmse:1.667302+0.232034
## [46] train-rmse:1.089314+0.017591 test-rmse:1.645594+0.232831
## [47] train-rmse:1.063264+0.018224 test-rmse:1.622519+0.234451
## [48] train-rmse:1.036292+0.019243 test-rmse:1.601025+0.234733
## [49] train-rmse:1.009552+0.021295 test-rmse:1.583182+0.236959
## [50] train-rmse:0.985187+0.021226 test-rmse:1.565880+0.233585
## [51] train-rmse:0.962186+0.019549 test-rmse:1.544019+0.235683
## [52] train-rmse:0.940407+0.019192 test-rmse:1.528552+0.238177
## [53] train-rmse:0.919385+0.019108 test-rmse:1.515391+0.243767
## [54] train-rmse:0.896745+0.019449 test-rmse:1.503114+0.251395
## [55] train-rmse:0.873046+0.023198 test-rmse:1.489057+0.248766
## [56] train-rmse:0.854017+0.020883 test-rmse:1.470094+0.242901
## [57] train-rmse:0.836662+0.020897 test-rmse:1.457291+0.241411
## [58] train-rmse:0.816976+0.023534 test-rmse:1.443902+0.241144
## [59] train-rmse:0.800456+0.023031 test-rmse:1.432293+0.246347
## [60] train-rmse:0.782227+0.022695 test-rmse:1.422877+0.246644
## [61] train-rmse:0.765379+0.022740 test-rmse:1.408447+0.246883
## [62] train-rmse:0.751003+0.022975 test-rmse:1.396107+0.248208
## [63] train-rmse:0.737076+0.022479 test-rmse:1.385844+0.248054
## [64] train-rmse:0.721411+0.022305 test-rmse:1.373036+0.253743
## [65] train-rmse:0.708285+0.022561 test-rmse:1.363592+0.254579
## [66] train-rmse:0.694531+0.022314 test-rmse:1.352584+0.256127
## [67] train-rmse:0.683177+0.021795 test-rmse:1.345288+0.254622
## [68] train-rmse:0.671802+0.021423 test-rmse:1.340162+0.252106
## [69] train-rmse:0.661291+0.021417 test-rmse:1.334052+0.252490
## [70] train-rmse:0.647597+0.022065 test-rmse:1.326596+0.247009
## [71] train-rmse:0.634195+0.022170 test-rmse:1.312021+0.251305
## [72] train-rmse:0.623908+0.022451 test-rmse:1.301683+0.250433
## [73] train-rmse:0.612037+0.021126 test-rmse:1.293530+0.253506
## [74] train-rmse:0.601860+0.021647 test-rmse:1.288159+0.254962
## [75] train-rmse:0.592366+0.022436 test-rmse:1.284255+0.256883
## [76] train-rmse:0.583654+0.022375 test-rmse:1.278853+0.259240
## [77] train-rmse:0.575071+0.021606 test-rmse:1.275241+0.262398
## [78] train-rmse:0.566494+0.021420 test-rmse:1.271062+0.264079
## [79] train-rmse:0.555376+0.020195 test-rmse:1.270628+0.266471
## [80] train-rmse:0.544871+0.019304 test-rmse:1.263398+0.263894
## [81] train-rmse:0.534386+0.018358 test-rmse:1.258333+0.264561
## [82] train-rmse:0.527111+0.017721 test-rmse:1.252973+0.264815
## [83] train-rmse:0.518491+0.017856 test-rmse:1.248545+0.266410
## [84] train-rmse:0.511493+0.018379 test-rmse:1.244031+0.267025
## [85] train-rmse:0.504262+0.018576 test-rmse:1.240385+0.268186
## [86] train-rmse:0.497540+0.018379 test-rmse:1.237459+0.269507
## [87] train-rmse:0.490491+0.018955 test-rmse:1.234370+0.270048
## [88] train-rmse:0.484131+0.019538 test-rmse:1.232255+0.272389
## [89] train-rmse:0.476925+0.020307 test-rmse:1.231027+0.272647
## [90] train-rmse:0.470524+0.020068 test-rmse:1.227118+0.275107
## [91] train-rmse:0.465349+0.019936 test-rmse:1.224771+0.274173
## [92] train-rmse:0.460271+0.019375 test-rmse:1.217931+0.269475
## [93] train-rmse:0.453461+0.020563 test-rmse:1.216901+0.268482
## [94] train-rmse:0.447476+0.021113 test-rmse:1.214571+0.266064
## [95] train-rmse:0.442814+0.021106 test-rmse:1.212178+0.266603
## [96] train-rmse:0.436815+0.020985 test-rmse:1.212352+0.266031
## [97] train-rmse:0.431538+0.021780 test-rmse:1.208185+0.265084
## [98] train-rmse:0.426208+0.022657 test-rmse:1.205892+0.269051
## [99] train-rmse:0.421328+0.022329 test-rmse:1.201205+0.271956
## [100] train-rmse:0.416174+0.021365 test-rmse:1.199148+0.273041
## [101] train-rmse:0.411793+0.021197 test-rmse:1.198472+0.275287
## [102] train-rmse:0.406026+0.022306 test-rmse:1.195547+0.276476
## [103] train-rmse:0.401305+0.022361 test-rmse:1.193780+0.276642
## [104] train-rmse:0.397123+0.022598 test-rmse:1.192897+0.277973
## [105] train-rmse:0.393293+0.022643 test-rmse:1.188803+0.276179
## [106] train-rmse:0.389033+0.022790 test-rmse:1.187412+0.277492
## [107] train-rmse:0.384949+0.022478 test-rmse:1.187009+0.277477
## [108] train-rmse:0.381099+0.022758 test-rmse:1.184639+0.275763
## [109] train-rmse:0.376850+0.022620 test-rmse:1.183798+0.277795
## [110] train-rmse:0.372840+0.022798 test-rmse:1.182624+0.278333
## [111] train-rmse:0.368188+0.021759 test-rmse:1.183800+0.279099
## [112] train-rmse:0.365323+0.021547 test-rmse:1.183072+0.280149
## [113] train-rmse:0.362074+0.022172 test-rmse:1.182792+0.279252
## [114] train-rmse:0.359309+0.022374 test-rmse:1.181612+0.278776
## [115] train-rmse:0.355580+0.021240 test-rmse:1.177003+0.279928
## [116] train-rmse:0.352644+0.021361 test-rmse:1.175318+0.281042
## [117] train-rmse:0.347713+0.020571 test-rmse:1.171827+0.274439
## [118] train-rmse:0.344536+0.021041 test-rmse:1.171307+0.275434
## [119] train-rmse:0.340562+0.020291 test-rmse:1.168524+0.276453
## [120] train-rmse:0.337632+0.020099 test-rmse:1.165890+0.277575
## [121] train-rmse:0.334358+0.019067 test-rmse:1.165029+0.276894
## [122] train-rmse:0.330909+0.019655 test-rmse:1.162450+0.275286
## [123] train-rmse:0.327817+0.019689 test-rmse:1.162249+0.275615
## [124] train-rmse:0.323951+0.019215 test-rmse:1.160272+0.276255
## [125] train-rmse:0.320891+0.020196 test-rmse:1.159810+0.276064
## [126] train-rmse:0.318173+0.020674 test-rmse:1.157993+0.277347
## [127] train-rmse:0.315624+0.020803 test-rmse:1.156428+0.278307
## [128] train-rmse:0.313380+0.020836 test-rmse:1.156611+0.279126
## [129] train-rmse:0.309795+0.019989 test-rmse:1.154903+0.279254
## [130] train-rmse:0.307396+0.020159 test-rmse:1.153457+0.278652
## [131] train-rmse:0.305035+0.019832 test-rmse:1.152281+0.278788
## [132] train-rmse:0.302413+0.019177 test-rmse:1.150433+0.279832
## [133] train-rmse:0.297763+0.017443 test-rmse:1.150094+0.278851
## [134] train-rmse:0.295094+0.017770 test-rmse:1.149372+0.279241
## [135] train-rmse:0.292332+0.017796 test-rmse:1.148372+0.279589
## [136] train-rmse:0.289543+0.017759 test-rmse:1.148706+0.279410
## [137] train-rmse:0.285448+0.015374 test-rmse:1.148869+0.279871
## [138] train-rmse:0.283314+0.015439 test-rmse:1.147139+0.279304
## [139] train-rmse:0.280981+0.014962 test-rmse:1.145290+0.279925
## [140] train-rmse:0.278624+0.014756 test-rmse:1.144504+0.280047
## [141] train-rmse:0.276421+0.014431 test-rmse:1.144105+0.280366
## [142] train-rmse:0.274148+0.015023 test-rmse:1.143101+0.279809
## [143] train-rmse:0.272083+0.014453 test-rmse:1.142280+0.279022
## [144] train-rmse:0.270043+0.014888 test-rmse:1.141700+0.278326
## [145] train-rmse:0.267432+0.015495 test-rmse:1.140317+0.278621
## [146] train-rmse:0.265121+0.015219 test-rmse:1.139558+0.279574
## [147] train-rmse:0.262608+0.015874 test-rmse:1.137508+0.281227
## [148] train-rmse:0.260720+0.016342 test-rmse:1.137380+0.281174
## [149] train-rmse:0.259104+0.016546 test-rmse:1.137059+0.282075
## [150] train-rmse:0.256701+0.015898 test-rmse:1.136862+0.281737
## [151] train-rmse:0.254237+0.015877 test-rmse:1.136374+0.282246
## [152] train-rmse:0.252136+0.015339 test-rmse:1.135871+0.282876
## [153] train-rmse:0.250333+0.014834 test-rmse:1.135963+0.284014
## [154] train-rmse:0.248978+0.014790 test-rmse:1.136070+0.284169
## [155] train-rmse:0.246692+0.014953 test-rmse:1.135518+0.284660
## [156] train-rmse:0.244797+0.015007 test-rmse:1.135551+0.284745
## [157] train-rmse:0.243436+0.014917 test-rmse:1.135258+0.284642
## [158] train-rmse:0.241740+0.015147 test-rmse:1.134025+0.283958
## [159] train-rmse:0.239708+0.015411 test-rmse:1.133530+0.283284
## [160] train-rmse:0.237843+0.016126 test-rmse:1.133433+0.283817
## [161] train-rmse:0.235649+0.016122 test-rmse:1.133512+0.284494
## [162] train-rmse:0.233912+0.016100 test-rmse:1.133449+0.284676
## [163] train-rmse:0.232074+0.015868 test-rmse:1.132717+0.284330
## [164] train-rmse:0.229531+0.016261 test-rmse:1.132819+0.284194
## [165] train-rmse:0.227745+0.016140 test-rmse:1.132971+0.284717
## [166] train-rmse:0.225833+0.015832 test-rmse:1.132986+0.285984
## [167] train-rmse:0.224347+0.015838 test-rmse:1.132005+0.286033
## [168] train-rmse:0.223124+0.015829 test-rmse:1.130941+0.286498
## [169] train-rmse:0.220529+0.014966 test-rmse:1.131060+0.286940
## [170] train-rmse:0.219251+0.014981 test-rmse:1.130049+0.287275
## [171] train-rmse:0.217530+0.015069 test-rmse:1.129325+0.286911
## [172] train-rmse:0.215438+0.014060 test-rmse:1.130366+0.286143
## [173] train-rmse:0.214198+0.013924 test-rmse:1.130292+0.286240
## [174] train-rmse:0.211924+0.013143 test-rmse:1.130427+0.286195
## [175] train-rmse:0.210999+0.013308 test-rmse:1.129551+0.286761
## [176] train-rmse:0.209602+0.013675 test-rmse:1.129071+0.286491
## [177] train-rmse:0.207981+0.013687 test-rmse:1.129506+0.286215
## [178] train-rmse:0.206113+0.012839 test-rmse:1.128650+0.286427
## [179] train-rmse:0.204816+0.013015 test-rmse:1.128458+0.286041
## [180] train-rmse:0.202390+0.012129 test-rmse:1.127046+0.284610
## [181] train-rmse:0.200895+0.012400 test-rmse:1.127380+0.285509
## [182] train-rmse:0.199405+0.011967 test-rmse:1.127226+0.286770
## [183] train-rmse:0.198013+0.012163 test-rmse:1.127513+0.287083
## [184] train-rmse:0.196365+0.012143 test-rmse:1.127209+0.287242
## [185] train-rmse:0.194808+0.011769 test-rmse:1.126731+0.287268
## [186] train-rmse:0.193221+0.012262 test-rmse:1.126225+0.287389
## [187] train-rmse:0.191994+0.011865 test-rmse:1.125682+0.288010
## [188] train-rmse:0.190547+0.011695 test-rmse:1.126076+0.288191
## [189] train-rmse:0.188115+0.011095 test-rmse:1.125519+0.287843
## [190] train-rmse:0.187191+0.011186 test-rmse:1.125074+0.287642
## [191] train-rmse:0.186286+0.011084 test-rmse:1.125087+0.288021
## [192] train-rmse:0.184768+0.011686 test-rmse:1.125193+0.287688
## [193] train-rmse:0.183020+0.011168 test-rmse:1.125483+0.287430
## [194] train-rmse:0.181276+0.011268 test-rmse:1.125094+0.286169
## [195] train-rmse:0.179818+0.011396 test-rmse:1.124214+0.285374
## [196] train-rmse:0.178261+0.011946 test-rmse:1.123885+0.284329
## [197] train-rmse:0.176725+0.012266 test-rmse:1.123297+0.284189
## [198] train-rmse:0.175610+0.012056 test-rmse:1.123212+0.284439
## [199] train-rmse:0.174757+0.011964 test-rmse:1.123056+0.284756
## [200] train-rmse:0.173564+0.011907 test-rmse:1.122536+0.284659
## [201] train-rmse:0.171960+0.011462 test-rmse:1.122329+0.284526
## [202] train-rmse:0.171465+0.011335 test-rmse:1.122074+0.284440
## [203] train-rmse:0.170271+0.011755 test-rmse:1.122041+0.284874
## [204] train-rmse:0.169043+0.011773 test-rmse:1.122248+0.284693
## [205] train-rmse:0.167595+0.011850 test-rmse:1.121903+0.285755
## [206] train-rmse:0.166624+0.012043 test-rmse:1.121330+0.286462
## [207] train-rmse:0.165549+0.012329 test-rmse:1.121640+0.286685
## [208] train-rmse:0.164677+0.012363 test-rmse:1.121157+0.286085
## [209] train-rmse:0.163708+0.012288 test-rmse:1.120379+0.286454
## [210] train-rmse:0.162435+0.012299 test-rmse:1.120632+0.286799
## [211] train-rmse:0.161300+0.012402 test-rmse:1.120791+0.286725
## [212] train-rmse:0.159867+0.012520 test-rmse:1.120431+0.286731
## [213] train-rmse:0.158938+0.012341 test-rmse:1.120230+0.286847
## [214] train-rmse:0.157386+0.011176 test-rmse:1.120852+0.286511
## [215] train-rmse:0.156539+0.011432 test-rmse:1.121034+0.286651
## [216] train-rmse:0.155588+0.011499 test-rmse:1.120299+0.287284
## [217] train-rmse:0.154581+0.011208 test-rmse:1.120356+0.287369
## [218] train-rmse:0.153361+0.011051 test-rmse:1.120206+0.287245
## [219] train-rmse:0.152601+0.011174 test-rmse:1.120363+0.287417
## [220] train-rmse:0.151427+0.010981 test-rmse:1.119760+0.286781
## [221] train-rmse:0.150390+0.010606 test-rmse:1.120331+0.286785
## [222] train-rmse:0.148994+0.010130 test-rmse:1.120542+0.286307
## [223] train-rmse:0.148213+0.010206 test-rmse:1.120820+0.286032
## [224] train-rmse:0.147187+0.010213 test-rmse:1.120837+0.286006
## [225] train-rmse:0.145964+0.009726 test-rmse:1.120447+0.285902
## [226] train-rmse:0.144977+0.009997 test-rmse:1.120335+0.285603
## Stopping. Best iteration:
## [220] train-rmse:0.151427+0.010981 test-rmse:1.119760+0.286781
##
## 52
## [1] train-rmse:81.945409+0.093908 test-rmse:81.945506+0.502440
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:62.457670+0.078257 test-rmse:62.457233+0.536077
## [3] train-rmse:47.664860+0.068582 test-rmse:47.663622+0.569012
## [4] train-rmse:36.454471+0.064225 test-rmse:36.452043+0.603174
## [5] train-rmse:27.964685+0.060685 test-rmse:28.013021+0.606000
## [6] train-rmse:21.516921+0.044302 test-rmse:21.607782+0.599820
## [7] train-rmse:16.622252+0.036724 test-rmse:16.726139+0.563579
## [8] train-rmse:12.901630+0.031627 test-rmse:13.028168+0.549716
## [9] train-rmse:10.087566+0.030662 test-rmse:10.202823+0.535852
## [10] train-rmse:7.951679+0.026526 test-rmse:8.094495+0.591939
## [11] train-rmse:6.351447+0.032111 test-rmse:6.522367+0.520072
## [12] train-rmse:5.148003+0.027712 test-rmse:5.337664+0.499825
## [13] train-rmse:4.252136+0.030293 test-rmse:4.512160+0.484762
## [14] train-rmse:3.592654+0.031720 test-rmse:3.890971+0.467608
## [15] train-rmse:3.101608+0.032978 test-rmse:3.431092+0.412827
## [16] train-rmse:2.744452+0.033244 test-rmse:3.107276+0.379444
## [17] train-rmse:2.478171+0.032639 test-rmse:2.877711+0.351197
## [18] train-rmse:2.271461+0.026734 test-rmse:2.710464+0.328276
## [19] train-rmse:2.111261+0.026331 test-rmse:2.545805+0.285741
## [20] train-rmse:1.984998+0.022932 test-rmse:2.457097+0.276445
## [21] train-rmse:1.874009+0.020667 test-rmse:2.375788+0.277695
## [22] train-rmse:1.779970+0.019070 test-rmse:2.303615+0.263862
## [23] train-rmse:1.693343+0.024082 test-rmse:2.219293+0.254508
## [24] train-rmse:1.619252+0.022267 test-rmse:2.147772+0.223864
## [25] train-rmse:1.549367+0.018182 test-rmse:2.096146+0.207885
## [26] train-rmse:1.485771+0.020650 test-rmse:2.036178+0.210860
## [27] train-rmse:1.425800+0.018231 test-rmse:1.995072+0.212057
## [28] train-rmse:1.372503+0.018174 test-rmse:1.954583+0.208373
## [29] train-rmse:1.322064+0.016543 test-rmse:1.916385+0.217967
## [30] train-rmse:1.271257+0.014310 test-rmse:1.868208+0.216360
## [31] train-rmse:1.223497+0.015807 test-rmse:1.829958+0.221936
## [32] train-rmse:1.176770+0.017845 test-rmse:1.802508+0.215611
## [33] train-rmse:1.137066+0.018768 test-rmse:1.763517+0.210081
## [34] train-rmse:1.093488+0.013191 test-rmse:1.727575+0.209704
## [35] train-rmse:1.049927+0.019137 test-rmse:1.700152+0.222903
## [36] train-rmse:1.011678+0.018985 test-rmse:1.659679+0.226012
## [37] train-rmse:0.976642+0.018742 test-rmse:1.636169+0.230963
## [38] train-rmse:0.944421+0.019895 test-rmse:1.615729+0.224483
## [39] train-rmse:0.911176+0.020484 test-rmse:1.601976+0.227371
## [40] train-rmse:0.883112+0.018764 test-rmse:1.572400+0.241386
## [41] train-rmse:0.857542+0.018487 test-rmse:1.549327+0.243139
## [42] train-rmse:0.828690+0.016751 test-rmse:1.516058+0.235666
## [43] train-rmse:0.800130+0.014475 test-rmse:1.491692+0.225334
## [44] train-rmse:0.776232+0.013600 test-rmse:1.468238+0.229530
## [45] train-rmse:0.748223+0.012480 test-rmse:1.457519+0.235618
## [46] train-rmse:0.727393+0.012353 test-rmse:1.444170+0.232812
## [47] train-rmse:0.705065+0.011901 test-rmse:1.430317+0.237034
## [48] train-rmse:0.683856+0.010850 test-rmse:1.420026+0.239797
## [49] train-rmse:0.663387+0.011240 test-rmse:1.402598+0.243222
## [50] train-rmse:0.644225+0.010441 test-rmse:1.395133+0.240492
## [51] train-rmse:0.626269+0.012249 test-rmse:1.385353+0.246259
## [52] train-rmse:0.609309+0.012507 test-rmse:1.370011+0.247091
## [53] train-rmse:0.591036+0.013590 test-rmse:1.355726+0.233389
## [54] train-rmse:0.575153+0.013574 test-rmse:1.348922+0.236238
## [55] train-rmse:0.560955+0.013928 test-rmse:1.343433+0.237617
## [56] train-rmse:0.547514+0.013766 test-rmse:1.332432+0.239598
## [57] train-rmse:0.533323+0.013769 test-rmse:1.328427+0.239447
## [58] train-rmse:0.521357+0.014037 test-rmse:1.324181+0.240633
## [59] train-rmse:0.506372+0.012656 test-rmse:1.308028+0.231556
## [60] train-rmse:0.495563+0.012557 test-rmse:1.303453+0.235370
## [61] train-rmse:0.484372+0.011826 test-rmse:1.301326+0.232918
## [62] train-rmse:0.472063+0.012305 test-rmse:1.298360+0.230294
## [63] train-rmse:0.462410+0.011983 test-rmse:1.292859+0.231637
## [64] train-rmse:0.453228+0.010983 test-rmse:1.291077+0.234777
## [65] train-rmse:0.441716+0.010149 test-rmse:1.279498+0.228177
## [66] train-rmse:0.432784+0.010061 test-rmse:1.271580+0.232876
## [67] train-rmse:0.424348+0.010064 test-rmse:1.266508+0.234184
## [68] train-rmse:0.416476+0.010216 test-rmse:1.262662+0.235200
## [69] train-rmse:0.408574+0.010510 test-rmse:1.257896+0.237369
## [70] train-rmse:0.401420+0.009988 test-rmse:1.251989+0.244325
## [71] train-rmse:0.393753+0.010463 test-rmse:1.251049+0.242611
## [72] train-rmse:0.387588+0.009885 test-rmse:1.246442+0.243946
## [73] train-rmse:0.379080+0.009172 test-rmse:1.242086+0.246780
## [74] train-rmse:0.371554+0.010373 test-rmse:1.238886+0.248948
## [75] train-rmse:0.363699+0.009542 test-rmse:1.232587+0.246949
## [76] train-rmse:0.358508+0.009314 test-rmse:1.224555+0.251419
## [77] train-rmse:0.351283+0.009858 test-rmse:1.221125+0.252165
## [78] train-rmse:0.345358+0.009312 test-rmse:1.217840+0.253033
## [79] train-rmse:0.339588+0.009121 test-rmse:1.215909+0.252940
## [80] train-rmse:0.334421+0.008654 test-rmse:1.211864+0.253258
## [81] train-rmse:0.329578+0.007789 test-rmse:1.209120+0.253498
## [82] train-rmse:0.324510+0.007657 test-rmse:1.208837+0.254901
## [83] train-rmse:0.319334+0.007793 test-rmse:1.204922+0.257211
## [84] train-rmse:0.313999+0.006968 test-rmse:1.202831+0.256774
## [85] train-rmse:0.308445+0.007897 test-rmse:1.199616+0.258729
## [86] train-rmse:0.304990+0.007143 test-rmse:1.196091+0.258932
## [87] train-rmse:0.300419+0.007446 test-rmse:1.193999+0.259578
## [88] train-rmse:0.295086+0.006353 test-rmse:1.190131+0.264509
## [89] train-rmse:0.290009+0.007039 test-rmse:1.188225+0.263820
## [90] train-rmse:0.285140+0.007415 test-rmse:1.187916+0.263668
## [91] train-rmse:0.281547+0.007158 test-rmse:1.186670+0.264308
## [92] train-rmse:0.278442+0.007144 test-rmse:1.185769+0.263850
## [93] train-rmse:0.274897+0.007181 test-rmse:1.185165+0.264085
## [94] train-rmse:0.270833+0.006427 test-rmse:1.184219+0.265726
## [95] train-rmse:0.266239+0.006346 test-rmse:1.181179+0.263003
## [96] train-rmse:0.262636+0.006100 test-rmse:1.178614+0.264591
## [97] train-rmse:0.259045+0.006903 test-rmse:1.177629+0.264571
## [98] train-rmse:0.256436+0.006822 test-rmse:1.175788+0.264605
## [99] train-rmse:0.252589+0.006266 test-rmse:1.174493+0.265592
## [100] train-rmse:0.248871+0.005692 test-rmse:1.173207+0.266664
## [101] train-rmse:0.245793+0.005778 test-rmse:1.171547+0.268461
## [102] train-rmse:0.242732+0.006280 test-rmse:1.170281+0.269092
## [103] train-rmse:0.239432+0.005256 test-rmse:1.166263+0.270731
## [104] train-rmse:0.236548+0.005136 test-rmse:1.165350+0.271735
## [105] train-rmse:0.234099+0.005813 test-rmse:1.164107+0.271344
## [106] train-rmse:0.230311+0.006931 test-rmse:1.164769+0.271092
## [107] train-rmse:0.226795+0.006512 test-rmse:1.162773+0.270656
## [108] train-rmse:0.224237+0.006486 test-rmse:1.161492+0.271246
## [109] train-rmse:0.220108+0.006117 test-rmse:1.161474+0.271832
## [110] train-rmse:0.215670+0.006947 test-rmse:1.159026+0.271955
## [111] train-rmse:0.212740+0.006043 test-rmse:1.159306+0.271718
## [112] train-rmse:0.209354+0.004648 test-rmse:1.158380+0.272324
## [113] train-rmse:0.204466+0.004169 test-rmse:1.158342+0.272562
## [114] train-rmse:0.201790+0.004577 test-rmse:1.157828+0.272398
## [115] train-rmse:0.199133+0.003778 test-rmse:1.156623+0.273618
## [116] train-rmse:0.196353+0.004475 test-rmse:1.155882+0.272883
## [117] train-rmse:0.193569+0.005125 test-rmse:1.154680+0.273179
## [118] train-rmse:0.191486+0.004997 test-rmse:1.154208+0.273366
## [119] train-rmse:0.188840+0.005945 test-rmse:1.154082+0.273385
## [120] train-rmse:0.186245+0.006008 test-rmse:1.153217+0.273816
## [121] train-rmse:0.184077+0.005933 test-rmse:1.152723+0.274268
## [122] train-rmse:0.180323+0.005938 test-rmse:1.150949+0.274241
## [123] train-rmse:0.178543+0.006052 test-rmse:1.150302+0.274929
## [124] train-rmse:0.175738+0.006253 test-rmse:1.149926+0.274725
## [125] train-rmse:0.173122+0.005962 test-rmse:1.150131+0.275023
## [126] train-rmse:0.169986+0.006018 test-rmse:1.150068+0.275113
## [127] train-rmse:0.167838+0.006570 test-rmse:1.149816+0.275443
## [128] train-rmse:0.165702+0.007152 test-rmse:1.147768+0.275110
## [129] train-rmse:0.163660+0.007181 test-rmse:1.148864+0.274729
## [130] train-rmse:0.161110+0.006889 test-rmse:1.147716+0.274391
## [131] train-rmse:0.159394+0.007089 test-rmse:1.146516+0.275211
## [132] train-rmse:0.157230+0.007284 test-rmse:1.147139+0.276064
## [133] train-rmse:0.154959+0.006762 test-rmse:1.146183+0.276359
## [134] train-rmse:0.152337+0.006874 test-rmse:1.145458+0.276449
## [135] train-rmse:0.149922+0.006749 test-rmse:1.144930+0.276981
## [136] train-rmse:0.148253+0.006178 test-rmse:1.144649+0.277781
## [137] train-rmse:0.145538+0.005868 test-rmse:1.144302+0.278205
## [138] train-rmse:0.143444+0.005626 test-rmse:1.143081+0.278956
## [139] train-rmse:0.141924+0.006033 test-rmse:1.142890+0.278893
## [140] train-rmse:0.140356+0.006100 test-rmse:1.142673+0.279114
## [141] train-rmse:0.138293+0.006553 test-rmse:1.142312+0.279199
## [142] train-rmse:0.137079+0.006648 test-rmse:1.142338+0.278102
## [143] train-rmse:0.135265+0.005908 test-rmse:1.142580+0.278463
## [144] train-rmse:0.132679+0.005886 test-rmse:1.141782+0.278995
## [145] train-rmse:0.131174+0.005959 test-rmse:1.141027+0.279308
## [146] train-rmse:0.129185+0.005698 test-rmse:1.141012+0.279839
## [147] train-rmse:0.127788+0.005847 test-rmse:1.140765+0.279876
## [148] train-rmse:0.125762+0.005781 test-rmse:1.140223+0.280180
## [149] train-rmse:0.124561+0.005549 test-rmse:1.140103+0.279924
## [150] train-rmse:0.122680+0.005761 test-rmse:1.139943+0.280273
## [151] train-rmse:0.121013+0.005528 test-rmse:1.140187+0.280670
## [152] train-rmse:0.119601+0.005231 test-rmse:1.140020+0.281317
## [153] train-rmse:0.117744+0.004746 test-rmse:1.139810+0.281672
## [154] train-rmse:0.116695+0.004943 test-rmse:1.139456+0.281907
## [155] train-rmse:0.115274+0.004591 test-rmse:1.139594+0.282489
## [156] train-rmse:0.114064+0.005061 test-rmse:1.139569+0.282487
## [157] train-rmse:0.113066+0.004984 test-rmse:1.139607+0.282526
## [158] train-rmse:0.111942+0.005195 test-rmse:1.139238+0.282474
## [159] train-rmse:0.110945+0.005774 test-rmse:1.138774+0.282721
## [160] train-rmse:0.109784+0.005760 test-rmse:1.138629+0.282592
## [161] train-rmse:0.107984+0.005557 test-rmse:1.137834+0.282728
## [162] train-rmse:0.106307+0.005452 test-rmse:1.137298+0.282839
## [163] train-rmse:0.105395+0.005244 test-rmse:1.137019+0.283504
## [164] train-rmse:0.103415+0.004752 test-rmse:1.137084+0.283378
## [165] train-rmse:0.102080+0.004535 test-rmse:1.136765+0.283017
## [166] train-rmse:0.100965+0.004996 test-rmse:1.136561+0.283209
## [167] train-rmse:0.099606+0.005113 test-rmse:1.135899+0.283455
## [168] train-rmse:0.098430+0.004899 test-rmse:1.136163+0.284253
## [169] train-rmse:0.096963+0.005024 test-rmse:1.135982+0.284632
## [170] train-rmse:0.095904+0.005257 test-rmse:1.135496+0.284994
## [171] train-rmse:0.094392+0.005227 test-rmse:1.135356+0.285172
## [172] train-rmse:0.093330+0.005320 test-rmse:1.134406+0.285166
## [173] train-rmse:0.092015+0.004929 test-rmse:1.134287+0.285328
## [174] train-rmse:0.090873+0.004947 test-rmse:1.134451+0.285350
## [175] train-rmse:0.089934+0.005135 test-rmse:1.134074+0.285564
## [176] train-rmse:0.089047+0.004829 test-rmse:1.134075+0.285919
## [177] train-rmse:0.087794+0.005211 test-rmse:1.133816+0.285917
## [178] train-rmse:0.086778+0.005484 test-rmse:1.133872+0.285591
## [179] train-rmse:0.085555+0.005743 test-rmse:1.133364+0.285252
## [180] train-rmse:0.084478+0.005909 test-rmse:1.133267+0.285101
## [181] train-rmse:0.083432+0.005794 test-rmse:1.132970+0.285057
## [182] train-rmse:0.082540+0.005955 test-rmse:1.132956+0.285107
## [183] train-rmse:0.081710+0.006178 test-rmse:1.132937+0.285003
## [184] train-rmse:0.080634+0.006228 test-rmse:1.133209+0.285246
## [185] train-rmse:0.079419+0.005730 test-rmse:1.133005+0.285477
## [186] train-rmse:0.078261+0.005584 test-rmse:1.132527+0.285282
## [187] train-rmse:0.077250+0.005411 test-rmse:1.132411+0.285558
## [188] train-rmse:0.076508+0.005289 test-rmse:1.132153+0.285774
## [189] train-rmse:0.075780+0.005318 test-rmse:1.132117+0.285667
## [190] train-rmse:0.074647+0.005293 test-rmse:1.132023+0.285870
## [191] train-rmse:0.073725+0.005173 test-rmse:1.132465+0.285830
## [192] train-rmse:0.073080+0.005018 test-rmse:1.132389+0.286078
## [193] train-rmse:0.072240+0.004935 test-rmse:1.131935+0.285791
## [194] train-rmse:0.071260+0.004632 test-rmse:1.131839+0.285827
## [195] train-rmse:0.070423+0.004897 test-rmse:1.131890+0.285764
## [196] train-rmse:0.069866+0.004813 test-rmse:1.131698+0.285682
## [197] train-rmse:0.069034+0.005057 test-rmse:1.131563+0.285915
## [198] train-rmse:0.068325+0.005258 test-rmse:1.131140+0.286159
## [199] train-rmse:0.067394+0.005004 test-rmse:1.131167+0.286215
## [200] train-rmse:0.066115+0.005471 test-rmse:1.130948+0.286806
## [201] train-rmse:0.064980+0.005780 test-rmse:1.131064+0.286825
## [202] train-rmse:0.064553+0.005917 test-rmse:1.130973+0.286754
## [203] train-rmse:0.063860+0.005754 test-rmse:1.131123+0.286711
## [204] train-rmse:0.063260+0.005668 test-rmse:1.131002+0.286773
## [205] train-rmse:0.062429+0.005565 test-rmse:1.130791+0.286776
## [206] train-rmse:0.061614+0.005601 test-rmse:1.130387+0.286983
## [207] train-rmse:0.060694+0.005912 test-rmse:1.130413+0.286997
## [208] train-rmse:0.059955+0.005848 test-rmse:1.130100+0.287070
## [209] train-rmse:0.059377+0.005759 test-rmse:1.130074+0.287070
## [210] train-rmse:0.058783+0.005953 test-rmse:1.130072+0.287005
## [211] train-rmse:0.058074+0.006098 test-rmse:1.130095+0.286854
## [212] train-rmse:0.057213+0.006176 test-rmse:1.130000+0.286987
## [213] train-rmse:0.056474+0.006047 test-rmse:1.130002+0.287006
## [214] train-rmse:0.055948+0.006030 test-rmse:1.130053+0.287108
## [215] train-rmse:0.055534+0.005981 test-rmse:1.129811+0.287000
## [216] train-rmse:0.054932+0.005792 test-rmse:1.129634+0.287035
## [217] train-rmse:0.054155+0.005614 test-rmse:1.129342+0.287101
## [218] train-rmse:0.053726+0.005719 test-rmse:1.129316+0.287533
## [219] train-rmse:0.053078+0.005440 test-rmse:1.129059+0.287540
## [220] train-rmse:0.052370+0.005130 test-rmse:1.129148+0.287819
## [221] train-rmse:0.051645+0.005125 test-rmse:1.129261+0.287809
## [222] train-rmse:0.050958+0.005119 test-rmse:1.129104+0.287781
## [223] train-rmse:0.050310+0.005008 test-rmse:1.129062+0.287607
## [224] train-rmse:0.049615+0.005063 test-rmse:1.129324+0.287764
## [225] train-rmse:0.049098+0.005098 test-rmse:1.129216+0.287781
## Stopping. Best iteration:
## [219] train-rmse:0.053078+0.005440 test-rmse:1.129059+0.287540
##
## 53
## [1] train-rmse:81.945409+0.093908 test-rmse:81.945506+0.502440
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:62.457670+0.078257 test-rmse:62.457233+0.536077
## [3] train-rmse:47.664860+0.068582 test-rmse:47.663622+0.569012
## [4] train-rmse:36.454471+0.064225 test-rmse:36.452043+0.603174
## [5] train-rmse:27.964685+0.060685 test-rmse:28.013021+0.606000
## [6] train-rmse:21.516921+0.044302 test-rmse:21.607782+0.599820
## [7] train-rmse:16.617937+0.036973 test-rmse:16.728899+0.559902
## [8] train-rmse:12.886534+0.032290 test-rmse:13.002651+0.537229
## [9] train-rmse:10.053380+0.030104 test-rmse:10.224078+0.575322
## [10] train-rmse:7.892023+0.030433 test-rmse:8.043042+0.556857
## [11] train-rmse:6.257841+0.027019 test-rmse:6.420309+0.527548
## [12] train-rmse:5.017586+0.026935 test-rmse:5.214201+0.534011
## [13] train-rmse:4.079962+0.016810 test-rmse:4.292396+0.489891
## [14] train-rmse:3.386739+0.016562 test-rmse:3.649147+0.453423
## [15] train-rmse:2.865897+0.020398 test-rmse:3.193246+0.437701
## [16] train-rmse:2.475899+0.022872 test-rmse:2.853624+0.363278
## [17] train-rmse:2.187099+0.020673 test-rmse:2.602668+0.335560
## [18] train-rmse:1.955952+0.028263 test-rmse:2.434040+0.301447
## [19] train-rmse:1.779072+0.037453 test-rmse:2.301157+0.298675
## [20] train-rmse:1.642555+0.042356 test-rmse:2.181042+0.267714
## [21] train-rmse:1.528542+0.045346 test-rmse:2.106417+0.248318
## [22] train-rmse:1.433369+0.037728 test-rmse:2.032524+0.235640
## [23] train-rmse:1.350830+0.034339 test-rmse:1.977086+0.222338
## [24] train-rmse:1.262866+0.038290 test-rmse:1.925010+0.208963
## [25] train-rmse:1.197746+0.036888 test-rmse:1.870319+0.195981
## [26] train-rmse:1.139780+0.034967 test-rmse:1.820931+0.193895
## [27] train-rmse:1.086892+0.033412 test-rmse:1.774885+0.199676
## [28] train-rmse:1.031090+0.032044 test-rmse:1.742878+0.204397
## [29] train-rmse:0.985016+0.031404 test-rmse:1.707585+0.200128
## [30] train-rmse:0.935875+0.035718 test-rmse:1.668296+0.193784
## [31] train-rmse:0.894294+0.034228 test-rmse:1.639061+0.199075
## [32] train-rmse:0.857655+0.032561 test-rmse:1.618180+0.204934
## [33] train-rmse:0.821792+0.030994 test-rmse:1.594882+0.208881
## [34] train-rmse:0.789366+0.029156 test-rmse:1.550792+0.211146
## [35] train-rmse:0.758799+0.027838 test-rmse:1.534925+0.216259
## [36] train-rmse:0.727813+0.026693 test-rmse:1.514387+0.214685
## [37] train-rmse:0.700567+0.026800 test-rmse:1.496685+0.216063
## [38] train-rmse:0.676507+0.024438 test-rmse:1.475577+0.208148
## [39] train-rmse:0.649828+0.025435 test-rmse:1.455127+0.213881
## [40] train-rmse:0.626796+0.026365 test-rmse:1.440286+0.207554
## [41] train-rmse:0.601506+0.028032 test-rmse:1.427741+0.207794
## [42] train-rmse:0.580209+0.027967 test-rmse:1.413333+0.200875
## [43] train-rmse:0.561028+0.021285 test-rmse:1.403065+0.200901
## [44] train-rmse:0.541858+0.021422 test-rmse:1.397725+0.206370
## [45] train-rmse:0.520753+0.023259 test-rmse:1.389216+0.204101
## [46] train-rmse:0.502609+0.019970 test-rmse:1.372606+0.205543
## [47] train-rmse:0.487406+0.020738 test-rmse:1.365351+0.206944
## [48] train-rmse:0.474033+0.020435 test-rmse:1.355475+0.211156
## [49] train-rmse:0.458421+0.021518 test-rmse:1.348639+0.212184
## [50] train-rmse:0.445472+0.023126 test-rmse:1.340454+0.211919
## [51] train-rmse:0.432396+0.022712 test-rmse:1.331901+0.209335
## [52] train-rmse:0.419443+0.021123 test-rmse:1.330420+0.211201
## [53] train-rmse:0.405379+0.019596 test-rmse:1.322879+0.214452
## [54] train-rmse:0.394998+0.019231 test-rmse:1.316695+0.213314
## [55] train-rmse:0.382367+0.019654 test-rmse:1.310397+0.211844
## [56] train-rmse:0.371689+0.015353 test-rmse:1.300562+0.210980
## [57] train-rmse:0.361787+0.015796 test-rmse:1.298880+0.213383
## [58] train-rmse:0.352192+0.016108 test-rmse:1.293740+0.214570
## [59] train-rmse:0.342837+0.016289 test-rmse:1.289051+0.210085
## [60] train-rmse:0.332629+0.016171 test-rmse:1.285455+0.210295
## [61] train-rmse:0.324742+0.015961 test-rmse:1.283588+0.213106
## [62] train-rmse:0.317707+0.015348 test-rmse:1.279486+0.216677
## [63] train-rmse:0.309975+0.015131 test-rmse:1.277135+0.214214
## [64] train-rmse:0.302951+0.014658 test-rmse:1.275024+0.215382
## [65] train-rmse:0.295667+0.014615 test-rmse:1.274590+0.212901
## [66] train-rmse:0.290109+0.014794 test-rmse:1.271319+0.213627
## [67] train-rmse:0.282979+0.013729 test-rmse:1.269077+0.214336
## [68] train-rmse:0.277400+0.014528 test-rmse:1.265802+0.217374
## [69] train-rmse:0.270736+0.013303 test-rmse:1.264884+0.216562
## [70] train-rmse:0.264738+0.013339 test-rmse:1.263207+0.215938
## [71] train-rmse:0.257945+0.014272 test-rmse:1.260744+0.212506
## [72] train-rmse:0.252573+0.013560 test-rmse:1.261384+0.213104
## [73] train-rmse:0.246412+0.014661 test-rmse:1.260852+0.213064
## [74] train-rmse:0.241312+0.015873 test-rmse:1.257647+0.215029
## [75] train-rmse:0.235902+0.015432 test-rmse:1.256128+0.214446
## [76] train-rmse:0.232548+0.014704 test-rmse:1.254376+0.215696
## [77] train-rmse:0.227342+0.016807 test-rmse:1.252764+0.218472
## [78] train-rmse:0.222906+0.016529 test-rmse:1.251533+0.218685
## [79] train-rmse:0.216417+0.015044 test-rmse:1.250099+0.218258
## [80] train-rmse:0.212901+0.015573 test-rmse:1.250698+0.218613
## [81] train-rmse:0.208258+0.015996 test-rmse:1.250443+0.219565
## [82] train-rmse:0.204374+0.016103 test-rmse:1.250462+0.220549
## [83] train-rmse:0.200688+0.015145 test-rmse:1.248485+0.219146
## [84] train-rmse:0.197029+0.015880 test-rmse:1.246524+0.220982
## [85] train-rmse:0.193841+0.015266 test-rmse:1.244857+0.220400
## [86] train-rmse:0.189414+0.015434 test-rmse:1.244306+0.220641
## [87] train-rmse:0.184092+0.014524 test-rmse:1.243932+0.221466
## [88] train-rmse:0.181196+0.015108 test-rmse:1.243297+0.221700
## [89] train-rmse:0.177726+0.014825 test-rmse:1.243701+0.221920
## [90] train-rmse:0.172222+0.014128 test-rmse:1.244566+0.222536
## [91] train-rmse:0.169049+0.013273 test-rmse:1.244322+0.224360
## [92] train-rmse:0.165726+0.012740 test-rmse:1.244077+0.224859
## [93] train-rmse:0.162556+0.012675 test-rmse:1.243038+0.226006
## [94] train-rmse:0.159916+0.012144 test-rmse:1.243570+0.226373
## [95] train-rmse:0.157646+0.011843 test-rmse:1.243049+0.226871
## [96] train-rmse:0.154364+0.012330 test-rmse:1.242683+0.226614
## [97] train-rmse:0.151208+0.011898 test-rmse:1.241509+0.224390
## [98] train-rmse:0.147706+0.011336 test-rmse:1.242285+0.224575
## [99] train-rmse:0.145532+0.010960 test-rmse:1.242000+0.224819
## [100] train-rmse:0.142997+0.011031 test-rmse:1.241547+0.224580
## [101] train-rmse:0.140733+0.010189 test-rmse:1.240438+0.224070
## [102] train-rmse:0.137783+0.011142 test-rmse:1.240297+0.224959
## [103] train-rmse:0.133725+0.011557 test-rmse:1.241080+0.225404
## [104] train-rmse:0.131740+0.011748 test-rmse:1.240013+0.226306
## [105] train-rmse:0.129431+0.012389 test-rmse:1.239693+0.226573
## [106] train-rmse:0.127280+0.012394 test-rmse:1.240223+0.227320
## [107] train-rmse:0.125422+0.013067 test-rmse:1.240051+0.227560
## [108] train-rmse:0.122835+0.012316 test-rmse:1.240223+0.227619
## [109] train-rmse:0.120620+0.011917 test-rmse:1.240018+0.228415
## [110] train-rmse:0.118762+0.012213 test-rmse:1.239522+0.229547
## [111] train-rmse:0.115560+0.011392 test-rmse:1.239336+0.229422
## [112] train-rmse:0.112867+0.010764 test-rmse:1.239125+0.229710
## [113] train-rmse:0.110524+0.011091 test-rmse:1.238902+0.230036
## [114] train-rmse:0.108342+0.010482 test-rmse:1.238555+0.230032
## [115] train-rmse:0.106486+0.010044 test-rmse:1.238048+0.229930
## [116] train-rmse:0.104258+0.010137 test-rmse:1.238028+0.229653
## [117] train-rmse:0.102649+0.010413 test-rmse:1.238289+0.229644
## [118] train-rmse:0.100078+0.011162 test-rmse:1.238294+0.230145
## [119] train-rmse:0.097652+0.011192 test-rmse:1.238285+0.230437
## [120] train-rmse:0.096263+0.011069 test-rmse:1.237443+0.230385
## [121] train-rmse:0.094784+0.010564 test-rmse:1.236978+0.230563
## [122] train-rmse:0.092916+0.010793 test-rmse:1.236598+0.230913
## [123] train-rmse:0.091274+0.010487 test-rmse:1.236448+0.231215
## [124] train-rmse:0.089508+0.010904 test-rmse:1.236233+0.231572
## [125] train-rmse:0.088066+0.010768 test-rmse:1.235799+0.231055
## [126] train-rmse:0.086319+0.011037 test-rmse:1.235915+0.231803
## [127] train-rmse:0.084690+0.011149 test-rmse:1.235949+0.231574
## [128] train-rmse:0.082596+0.010156 test-rmse:1.235479+0.231526
## [129] train-rmse:0.081259+0.009538 test-rmse:1.235161+0.231868
## [130] train-rmse:0.079830+0.009602 test-rmse:1.235184+0.231662
## [131] train-rmse:0.078567+0.009359 test-rmse:1.234928+0.232035
## [132] train-rmse:0.076678+0.009412 test-rmse:1.235091+0.232501
## [133] train-rmse:0.075524+0.009534 test-rmse:1.234732+0.232866
## [134] train-rmse:0.074143+0.009532 test-rmse:1.234328+0.233123
## [135] train-rmse:0.072199+0.009153 test-rmse:1.234225+0.232767
## [136] train-rmse:0.070866+0.009004 test-rmse:1.234445+0.232775
## [137] train-rmse:0.069265+0.009531 test-rmse:1.234377+0.232728
## [138] train-rmse:0.068505+0.009430 test-rmse:1.234277+0.232783
## [139] train-rmse:0.067304+0.009537 test-rmse:1.234229+0.232875
## [140] train-rmse:0.066235+0.009456 test-rmse:1.233897+0.233012
## [141] train-rmse:0.064641+0.008947 test-rmse:1.233618+0.232900
## [142] train-rmse:0.063560+0.008772 test-rmse:1.233300+0.232686
## [143] train-rmse:0.062450+0.008613 test-rmse:1.233292+0.232852
## [144] train-rmse:0.061373+0.008654 test-rmse:1.233172+0.233333
## [145] train-rmse:0.059802+0.008221 test-rmse:1.233134+0.233261
## [146] train-rmse:0.058461+0.008485 test-rmse:1.233114+0.233532
## [147] train-rmse:0.057614+0.008211 test-rmse:1.233122+0.233735
## [148] train-rmse:0.056647+0.007729 test-rmse:1.233319+0.233597
## [149] train-rmse:0.055318+0.007423 test-rmse:1.233299+0.233720
## [150] train-rmse:0.054370+0.007434 test-rmse:1.233159+0.233947
## [151] train-rmse:0.053637+0.007561 test-rmse:1.232949+0.234232
## [152] train-rmse:0.052478+0.007806 test-rmse:1.233009+0.234195
## [153] train-rmse:0.051507+0.007694 test-rmse:1.233067+0.234203
## [154] train-rmse:0.050670+0.007535 test-rmse:1.232927+0.234148
## [155] train-rmse:0.049840+0.007425 test-rmse:1.232869+0.234162
## [156] train-rmse:0.049225+0.007296 test-rmse:1.232777+0.234179
## [157] train-rmse:0.048439+0.007419 test-rmse:1.232816+0.234084
## [158] train-rmse:0.047424+0.007331 test-rmse:1.232661+0.234049
## [159] train-rmse:0.046468+0.007083 test-rmse:1.232329+0.233792
## [160] train-rmse:0.045743+0.006902 test-rmse:1.232234+0.233895
## [161] train-rmse:0.045091+0.006794 test-rmse:1.232050+0.233883
## [162] train-rmse:0.044608+0.006810 test-rmse:1.231920+0.233918
## [163] train-rmse:0.043641+0.006485 test-rmse:1.231867+0.234184
## [164] train-rmse:0.043192+0.006460 test-rmse:1.231759+0.234194
## [165] train-rmse:0.042373+0.006343 test-rmse:1.231753+0.234043
## [166] train-rmse:0.041600+0.006235 test-rmse:1.231923+0.234297
## [167] train-rmse:0.040784+0.005907 test-rmse:1.232008+0.234247
## [168] train-rmse:0.039874+0.005753 test-rmse:1.231927+0.233987
## [169] train-rmse:0.039147+0.005732 test-rmse:1.231869+0.233941
## [170] train-rmse:0.038469+0.005839 test-rmse:1.231706+0.233977
## [171] train-rmse:0.038000+0.006010 test-rmse:1.231746+0.234089
## [172] train-rmse:0.037154+0.005963 test-rmse:1.231765+0.234143
## [173] train-rmse:0.036410+0.005924 test-rmse:1.231328+0.233973
## [174] train-rmse:0.035632+0.005768 test-rmse:1.231308+0.233915
## [175] train-rmse:0.034761+0.005530 test-rmse:1.231250+0.233903
## [176] train-rmse:0.034264+0.005608 test-rmse:1.231096+0.233874
## [177] train-rmse:0.033521+0.005774 test-rmse:1.230935+0.233943
## [178] train-rmse:0.032789+0.005712 test-rmse:1.230964+0.233973
## [179] train-rmse:0.032292+0.005510 test-rmse:1.231102+0.234201
## [180] train-rmse:0.031598+0.005491 test-rmse:1.230948+0.234066
## [181] train-rmse:0.031045+0.005340 test-rmse:1.231002+0.234181
## [182] train-rmse:0.030518+0.005467 test-rmse:1.230954+0.234199
## [183] train-rmse:0.030085+0.005509 test-rmse:1.230646+0.233885
## [184] train-rmse:0.029480+0.005318 test-rmse:1.230489+0.233818
## [185] train-rmse:0.029061+0.005184 test-rmse:1.230422+0.233836
## [186] train-rmse:0.028415+0.004911 test-rmse:1.230346+0.233918
## [187] train-rmse:0.028024+0.004809 test-rmse:1.230428+0.233808
## [188] train-rmse:0.027544+0.004939 test-rmse:1.230464+0.233915
## [189] train-rmse:0.027044+0.004734 test-rmse:1.230493+0.233773
## [190] train-rmse:0.026770+0.004756 test-rmse:1.230561+0.233727
## [191] train-rmse:0.026383+0.004736 test-rmse:1.230466+0.233776
## [192] train-rmse:0.025883+0.004676 test-rmse:1.230320+0.233663
## [193] train-rmse:0.025382+0.004475 test-rmse:1.230264+0.233617
## [194] train-rmse:0.024881+0.004479 test-rmse:1.230395+0.233560
## [195] train-rmse:0.024456+0.004471 test-rmse:1.230513+0.233552
## [196] train-rmse:0.024048+0.004611 test-rmse:1.230397+0.233536
## [197] train-rmse:0.023681+0.004627 test-rmse:1.230555+0.233566
## [198] train-rmse:0.023362+0.004568 test-rmse:1.230509+0.233551
## [199] train-rmse:0.022822+0.004326 test-rmse:1.230596+0.233540
## Stopping. Best iteration:
## [193] train-rmse:0.025382+0.004475 test-rmse:1.230264+0.233617
##
## 54
## [1] train-rmse:81.945409+0.093908 test-rmse:81.945506+0.502440
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:62.457670+0.078257 test-rmse:62.457233+0.536077
## [3] train-rmse:47.664860+0.068582 test-rmse:47.663622+0.569012
## [4] train-rmse:36.454471+0.064225 test-rmse:36.452043+0.603174
## [5] train-rmse:27.964685+0.060685 test-rmse:28.013021+0.606000
## [6] train-rmse:21.516921+0.044302 test-rmse:21.607782+0.599820
## [7] train-rmse:16.613988+0.036793 test-rmse:16.713533+0.569142
## [8] train-rmse:12.878342+0.030916 test-rmse:12.997253+0.543238
## [9] train-rmse:10.035884+0.030378 test-rmse:10.191464+0.577035
## [10] train-rmse:7.864926+0.037195 test-rmse:8.020629+0.537540
## [11] train-rmse:6.203277+0.031683 test-rmse:6.352221+0.523988
## [12] train-rmse:4.943953+0.027639 test-rmse:5.129925+0.527131
## [13] train-rmse:3.984136+0.024721 test-rmse:4.217616+0.474127
## [14] train-rmse:3.254753+0.021480 test-rmse:3.505916+0.432766
## [15] train-rmse:2.707729+0.021706 test-rmse:3.015811+0.385520
## [16] train-rmse:2.300881+0.024272 test-rmse:2.644885+0.335157
## [17] train-rmse:1.982190+0.022968 test-rmse:2.410987+0.312198
## [18] train-rmse:1.731792+0.025799 test-rmse:2.220551+0.294522
## [19] train-rmse:1.540609+0.031831 test-rmse:2.076838+0.257758
## [20] train-rmse:1.385509+0.032026 test-rmse:1.954849+0.223139
## [21] train-rmse:1.265290+0.025842 test-rmse:1.870147+0.219271
## [22] train-rmse:1.161740+0.031733 test-rmse:1.818290+0.199714
## [23] train-rmse:1.079210+0.032874 test-rmse:1.760727+0.205325
## [24] train-rmse:1.009389+0.027364 test-rmse:1.716186+0.199216
## [25] train-rmse:0.938681+0.021767 test-rmse:1.666950+0.192549
## [26] train-rmse:0.881572+0.020902 test-rmse:1.622379+0.197672
## [27] train-rmse:0.828717+0.020255 test-rmse:1.593805+0.212260
## [28] train-rmse:0.784937+0.019124 test-rmse:1.562676+0.211103
## [29] train-rmse:0.745189+0.017771 test-rmse:1.532110+0.200311
## [30] train-rmse:0.704434+0.018417 test-rmse:1.505506+0.202307
## [31] train-rmse:0.668138+0.021196 test-rmse:1.494668+0.208115
## [32] train-rmse:0.635777+0.021850 test-rmse:1.471065+0.213435
## [33] train-rmse:0.602679+0.018303 test-rmse:1.449093+0.211828
## [34] train-rmse:0.578077+0.016691 test-rmse:1.435675+0.215496
## [35] train-rmse:0.552017+0.018047 test-rmse:1.422005+0.215731
## [36] train-rmse:0.527340+0.018034 test-rmse:1.408955+0.220066
## [37] train-rmse:0.507459+0.017578 test-rmse:1.399700+0.222566
## [38] train-rmse:0.486774+0.015390 test-rmse:1.378726+0.216578
## [39] train-rmse:0.466501+0.016330 test-rmse:1.372419+0.223887
## [40] train-rmse:0.449403+0.015678 test-rmse:1.365114+0.223724
## [41] train-rmse:0.432163+0.016611 test-rmse:1.351208+0.215061
## [42] train-rmse:0.417241+0.016451 test-rmse:1.344875+0.220101
## [43] train-rmse:0.398828+0.015397 test-rmse:1.340435+0.222858
## [44] train-rmse:0.384410+0.016065 test-rmse:1.340334+0.225464
## [45] train-rmse:0.369786+0.015630 test-rmse:1.330095+0.221307
## [46] train-rmse:0.356935+0.015031 test-rmse:1.324067+0.224042
## [47] train-rmse:0.345384+0.015261 test-rmse:1.322983+0.224778
## [48] train-rmse:0.334297+0.012289 test-rmse:1.319157+0.229891
## [49] train-rmse:0.322280+0.014084 test-rmse:1.317081+0.229212
## [50] train-rmse:0.310132+0.011325 test-rmse:1.308934+0.225941
## [51] train-rmse:0.302500+0.011307 test-rmse:1.307274+0.226583
## [52] train-rmse:0.294037+0.010738 test-rmse:1.305071+0.227876
## [53] train-rmse:0.284784+0.010740 test-rmse:1.302651+0.227577
## [54] train-rmse:0.275277+0.007712 test-rmse:1.302617+0.227379
## [55] train-rmse:0.268576+0.007350 test-rmse:1.300712+0.227026
## [56] train-rmse:0.258890+0.005800 test-rmse:1.300306+0.227082
## [57] train-rmse:0.252466+0.005997 test-rmse:1.298864+0.228214
## [58] train-rmse:0.245020+0.005710 test-rmse:1.297959+0.229618
## [59] train-rmse:0.237418+0.006340 test-rmse:1.296402+0.229750
## [60] train-rmse:0.229815+0.005375 test-rmse:1.296830+0.229876
## [61] train-rmse:0.222808+0.006648 test-rmse:1.293901+0.232185
## [62] train-rmse:0.215938+0.006809 test-rmse:1.292745+0.233276
## [63] train-rmse:0.210411+0.006754 test-rmse:1.293068+0.233664
## [64] train-rmse:0.205004+0.007031 test-rmse:1.290731+0.234062
## [65] train-rmse:0.198406+0.007128 test-rmse:1.289870+0.233622
## [66] train-rmse:0.192723+0.007596 test-rmse:1.290158+0.235221
## [67] train-rmse:0.185049+0.009679 test-rmse:1.288926+0.236365
## [68] train-rmse:0.180674+0.009937 test-rmse:1.287328+0.235275
## [69] train-rmse:0.176283+0.010022 test-rmse:1.288662+0.235327
## [70] train-rmse:0.171329+0.011162 test-rmse:1.288351+0.235323
## [71] train-rmse:0.166093+0.012499 test-rmse:1.286545+0.235927
## [72] train-rmse:0.161837+0.012508 test-rmse:1.287381+0.236116
## [73] train-rmse:0.158574+0.012013 test-rmse:1.285824+0.237282
## [74] train-rmse:0.154643+0.012300 test-rmse:1.285124+0.237929
## [75] train-rmse:0.151289+0.012007 test-rmse:1.284186+0.238646
## [76] train-rmse:0.147940+0.012085 test-rmse:1.283504+0.238836
## [77] train-rmse:0.144399+0.012708 test-rmse:1.283186+0.238898
## [78] train-rmse:0.141338+0.012816 test-rmse:1.282811+0.239307
## [79] train-rmse:0.138309+0.011880 test-rmse:1.283380+0.239301
## [80] train-rmse:0.133377+0.010567 test-rmse:1.283192+0.238867
## [81] train-rmse:0.128937+0.009960 test-rmse:1.283417+0.240995
## [82] train-rmse:0.124049+0.010725 test-rmse:1.283467+0.240629
## [83] train-rmse:0.121818+0.010527 test-rmse:1.283002+0.241528
## [84] train-rmse:0.118162+0.010547 test-rmse:1.282393+0.241789
## [85] train-rmse:0.114552+0.010470 test-rmse:1.282204+0.242516
## [86] train-rmse:0.111384+0.010761 test-rmse:1.280854+0.241665
## [87] train-rmse:0.108215+0.009555 test-rmse:1.281100+0.241290
## [88] train-rmse:0.105125+0.008823 test-rmse:1.281211+0.241222
## [89] train-rmse:0.102144+0.008525 test-rmse:1.281277+0.240855
## [90] train-rmse:0.099990+0.008829 test-rmse:1.281481+0.241520
## [91] train-rmse:0.097387+0.008240 test-rmse:1.281241+0.241810
## [92] train-rmse:0.094623+0.008787 test-rmse:1.281277+0.242090
## Stopping. Best iteration:
## [86] train-rmse:0.111384+0.010761 test-rmse:1.280854+0.241665
##
## 55
## [1] train-rmse:81.945409+0.093908 test-rmse:81.945506+0.502440
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:62.457670+0.078257 test-rmse:62.457233+0.536077
## [3] train-rmse:47.664860+0.068582 test-rmse:47.663622+0.569012
## [4] train-rmse:36.454471+0.064225 test-rmse:36.452043+0.603174
## [5] train-rmse:27.964685+0.060685 test-rmse:28.013021+0.606000
## [6] train-rmse:21.516921+0.044302 test-rmse:21.607782+0.599820
## [7] train-rmse:16.611249+0.036771 test-rmse:16.709704+0.552788
## [8] train-rmse:12.873152+0.031018 test-rmse:12.972887+0.531801
## [9] train-rmse:10.024280+0.029354 test-rmse:10.174735+0.575881
## [10] train-rmse:7.847697+0.031522 test-rmse:8.005752+0.544642
## [11] train-rmse:6.178972+0.036264 test-rmse:6.343513+0.503038
## [12] train-rmse:4.904946+0.032873 test-rmse:5.067323+0.458663
## [13] train-rmse:3.926009+0.024533 test-rmse:4.145320+0.465340
## [14] train-rmse:3.178020+0.017111 test-rmse:3.455237+0.418345
## [15] train-rmse:2.607629+0.011430 test-rmse:2.968947+0.441520
## [16] train-rmse:2.166919+0.006395 test-rmse:2.581184+0.412348
## [17] train-rmse:1.833274+0.004454 test-rmse:2.287488+0.388680
## [18] train-rmse:1.562058+0.019022 test-rmse:2.073139+0.338250
## [19] train-rmse:1.367637+0.020643 test-rmse:1.913477+0.309236
## [20] train-rmse:1.203408+0.026309 test-rmse:1.801469+0.289940
## [21] train-rmse:1.082884+0.027335 test-rmse:1.722078+0.270993
## [22] train-rmse:0.978031+0.034034 test-rmse:1.669556+0.255578
## [23] train-rmse:0.895748+0.032110 test-rmse:1.599569+0.255565
## [24] train-rmse:0.823677+0.029588 test-rmse:1.558410+0.259476
## [25] train-rmse:0.759777+0.029135 test-rmse:1.517646+0.250555
## [26] train-rmse:0.706476+0.025171 test-rmse:1.473135+0.241412
## [27] train-rmse:0.655306+0.023759 test-rmse:1.453494+0.248554
## [28] train-rmse:0.616694+0.021803 test-rmse:1.424815+0.247998
## [29] train-rmse:0.583292+0.021489 test-rmse:1.396845+0.238794
## [30] train-rmse:0.547148+0.020321 test-rmse:1.382000+0.230618
## [31] train-rmse:0.519330+0.019231 test-rmse:1.358210+0.237024
## [32] train-rmse:0.488721+0.016424 test-rmse:1.344832+0.231663
## [33] train-rmse:0.462032+0.018049 test-rmse:1.336076+0.239619
## [34] train-rmse:0.440698+0.016371 test-rmse:1.323593+0.231279
## [35] train-rmse:0.420156+0.017744 test-rmse:1.314293+0.227485
## [36] train-rmse:0.400764+0.016658 test-rmse:1.306180+0.233446
## [37] train-rmse:0.383260+0.016459 test-rmse:1.301393+0.230882
## [38] train-rmse:0.366165+0.013877 test-rmse:1.289661+0.226428
## [39] train-rmse:0.352945+0.013540 test-rmse:1.285003+0.225504
## [40] train-rmse:0.337309+0.013562 test-rmse:1.281943+0.227225
## [41] train-rmse:0.322840+0.014144 test-rmse:1.279579+0.228668
## [42] train-rmse:0.309373+0.015500 test-rmse:1.273662+0.228868
## [43] train-rmse:0.292646+0.017783 test-rmse:1.268317+0.229497
## [44] train-rmse:0.280343+0.016832 test-rmse:1.265745+0.231413
## [45] train-rmse:0.265971+0.014359 test-rmse:1.263123+0.229328
## [46] train-rmse:0.255761+0.015811 test-rmse:1.262353+0.228554
## [47] train-rmse:0.246514+0.014883 test-rmse:1.262737+0.229511
## [48] train-rmse:0.235286+0.014960 test-rmse:1.258308+0.225214
## [49] train-rmse:0.225621+0.014240 test-rmse:1.256343+0.224648
## [50] train-rmse:0.218789+0.014386 test-rmse:1.254725+0.226475
## [51] train-rmse:0.210227+0.013233 test-rmse:1.253538+0.226759
## [52] train-rmse:0.200562+0.014190 test-rmse:1.253670+0.226825
## [53] train-rmse:0.193579+0.013108 test-rmse:1.251897+0.228036
## [54] train-rmse:0.185822+0.012435 test-rmse:1.250636+0.230215
## [55] train-rmse:0.178894+0.012124 test-rmse:1.248596+0.230095
## [56] train-rmse:0.172932+0.010613 test-rmse:1.248024+0.230269
## [57] train-rmse:0.166789+0.010504 test-rmse:1.247404+0.230363
## [58] train-rmse:0.161079+0.011735 test-rmse:1.247533+0.231269
## [59] train-rmse:0.154992+0.009932 test-rmse:1.247417+0.231485
## [60] train-rmse:0.149981+0.010655 test-rmse:1.246755+0.231803
## [61] train-rmse:0.143679+0.009968 test-rmse:1.245323+0.232766
## [62] train-rmse:0.138953+0.009526 test-rmse:1.244951+0.233691
## [63] train-rmse:0.133542+0.008860 test-rmse:1.244077+0.234033
## [64] train-rmse:0.128200+0.008204 test-rmse:1.245347+0.234781
## [65] train-rmse:0.123493+0.007745 test-rmse:1.244816+0.234280
## [66] train-rmse:0.119628+0.007497 test-rmse:1.245085+0.234383
## [67] train-rmse:0.115093+0.007517 test-rmse:1.244525+0.234888
## [68] train-rmse:0.110724+0.007692 test-rmse:1.243213+0.234906
## [69] train-rmse:0.107110+0.007149 test-rmse:1.242201+0.235257
## [70] train-rmse:0.103651+0.006883 test-rmse:1.242187+0.234818
## [71] train-rmse:0.100878+0.006554 test-rmse:1.242206+0.235101
## [72] train-rmse:0.097894+0.007134 test-rmse:1.242448+0.235124
## [73] train-rmse:0.093607+0.007715 test-rmse:1.242185+0.235933
## [74] train-rmse:0.090707+0.008061 test-rmse:1.242074+0.236372
## [75] train-rmse:0.087651+0.007951 test-rmse:1.241650+0.235649
## [76] train-rmse:0.085270+0.007562 test-rmse:1.241825+0.235777
## [77] train-rmse:0.082628+0.008150 test-rmse:1.240999+0.236278
## [78] train-rmse:0.079949+0.006971 test-rmse:1.240597+0.236097
## [79] train-rmse:0.077235+0.007548 test-rmse:1.240986+0.236173
## [80] train-rmse:0.074578+0.007089 test-rmse:1.240968+0.235865
## [81] train-rmse:0.071770+0.007059 test-rmse:1.240785+0.235817
## [82] train-rmse:0.069822+0.007592 test-rmse:1.240120+0.235625
## [83] train-rmse:0.067238+0.007400 test-rmse:1.239605+0.236175
## [84] train-rmse:0.064595+0.007626 test-rmse:1.239429+0.235918
## [85] train-rmse:0.062118+0.007096 test-rmse:1.239705+0.236118
## [86] train-rmse:0.059404+0.006291 test-rmse:1.239866+0.236706
## [87] train-rmse:0.057909+0.006315 test-rmse:1.239834+0.236954
## [88] train-rmse:0.056275+0.005760 test-rmse:1.239908+0.237102
## [89] train-rmse:0.053624+0.005034 test-rmse:1.239834+0.237350
## [90] train-rmse:0.051997+0.005144 test-rmse:1.239192+0.237622
## [91] train-rmse:0.050597+0.005373 test-rmse:1.238785+0.237478
## [92] train-rmse:0.049051+0.005382 test-rmse:1.238467+0.237791
## [93] train-rmse:0.047086+0.005378 test-rmse:1.238936+0.237717
## [94] train-rmse:0.045561+0.005417 test-rmse:1.238638+0.237976
## [95] train-rmse:0.043914+0.005250 test-rmse:1.238464+0.238035
## [96] train-rmse:0.042331+0.005086 test-rmse:1.238581+0.238008
## [97] train-rmse:0.040968+0.004850 test-rmse:1.238564+0.238543
## [98] train-rmse:0.039350+0.004636 test-rmse:1.238456+0.238291
## [99] train-rmse:0.038088+0.004628 test-rmse:1.238350+0.238163
## [100] train-rmse:0.037003+0.004355 test-rmse:1.238369+0.238305
## [101] train-rmse:0.035840+0.003937 test-rmse:1.238261+0.237997
## [102] train-rmse:0.034747+0.004112 test-rmse:1.238016+0.238055
## [103] train-rmse:0.033617+0.003890 test-rmse:1.237546+0.238225
## [104] train-rmse:0.032422+0.003487 test-rmse:1.237583+0.238413
## [105] train-rmse:0.031109+0.003379 test-rmse:1.237166+0.237720
## [106] train-rmse:0.030192+0.003309 test-rmse:1.237159+0.237884
## [107] train-rmse:0.029419+0.003308 test-rmse:1.237014+0.237888
## [108] train-rmse:0.028556+0.003173 test-rmse:1.236870+0.238098
## [109] train-rmse:0.027427+0.003056 test-rmse:1.236771+0.238150
## [110] train-rmse:0.026667+0.002622 test-rmse:1.236863+0.237978
## [111] train-rmse:0.025912+0.002531 test-rmse:1.236751+0.238076
## [112] train-rmse:0.025353+0.002552 test-rmse:1.236855+0.238305
## [113] train-rmse:0.024828+0.002329 test-rmse:1.236746+0.238281
## [114] train-rmse:0.024098+0.002265 test-rmse:1.236662+0.238263
## [115] train-rmse:0.023594+0.002224 test-rmse:1.236621+0.238318
## [116] train-rmse:0.022963+0.002267 test-rmse:1.236489+0.238306
## [117] train-rmse:0.022172+0.002450 test-rmse:1.236203+0.238423
## [118] train-rmse:0.021435+0.002356 test-rmse:1.235987+0.238001
## [119] train-rmse:0.020796+0.002249 test-rmse:1.235861+0.237961
## [120] train-rmse:0.020210+0.002252 test-rmse:1.235833+0.237889
## [121] train-rmse:0.019278+0.002283 test-rmse:1.235661+0.237823
## [122] train-rmse:0.018720+0.002411 test-rmse:1.235695+0.237800
## [123] train-rmse:0.018102+0.002259 test-rmse:1.235642+0.237774
## [124] train-rmse:0.017434+0.002045 test-rmse:1.235509+0.237795
## [125] train-rmse:0.016905+0.002028 test-rmse:1.235409+0.237806
## [126] train-rmse:0.016265+0.002023 test-rmse:1.235363+0.237765
## [127] train-rmse:0.015626+0.001881 test-rmse:1.235460+0.237765
## [128] train-rmse:0.015055+0.001847 test-rmse:1.235377+0.237812
## [129] train-rmse:0.014470+0.001699 test-rmse:1.235406+0.237756
## [130] train-rmse:0.014142+0.001687 test-rmse:1.235384+0.237771
## [131] train-rmse:0.013608+0.001607 test-rmse:1.235324+0.237815
## [132] train-rmse:0.013248+0.001701 test-rmse:1.235334+0.237770
## [133] train-rmse:0.012972+0.001706 test-rmse:1.235257+0.237716
## [134] train-rmse:0.012588+0.001634 test-rmse:1.235166+0.237520
## [135] train-rmse:0.012292+0.001553 test-rmse:1.235119+0.237537
## [136] train-rmse:0.011967+0.001561 test-rmse:1.235116+0.237609
## [137] train-rmse:0.011572+0.001484 test-rmse:1.235084+0.237595
## [138] train-rmse:0.011177+0.001323 test-rmse:1.235050+0.237646
## [139] train-rmse:0.010789+0.001153 test-rmse:1.235070+0.237594
## [140] train-rmse:0.010433+0.001082 test-rmse:1.235060+0.237753
## [141] train-rmse:0.010187+0.001016 test-rmse:1.234944+0.237622
## [142] train-rmse:0.009867+0.000923 test-rmse:1.234997+0.237639
## [143] train-rmse:0.009560+0.000838 test-rmse:1.234945+0.237665
## [144] train-rmse:0.009279+0.000830 test-rmse:1.234954+0.237670
## [145] train-rmse:0.008955+0.000932 test-rmse:1.234994+0.237702
## [146] train-rmse:0.008659+0.000937 test-rmse:1.235026+0.237715
## [147] train-rmse:0.008355+0.000950 test-rmse:1.235048+0.237716
## Stopping. Best iteration:
## [141] train-rmse:0.010187+0.001016 test-rmse:1.234944+0.237622
##
## 56
## [1] train-rmse:79.809479+0.092102 test-rmse:79.809535+0.505799
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:59.256192+0.075941 test-rmse:59.255628+0.542464
## [3] train-rmse:44.071015+0.066791 test-rmse:44.069481+0.578759
## [4] train-rmse:32.877449+0.063877 test-rmse:32.874400+0.617127
## [5] train-rmse:24.634475+0.060742 test-rmse:24.690776+0.625206
## [6] train-rmse:18.573545+0.055391 test-rmse:18.609407+0.621394
## [7] train-rmse:14.143569+0.051891 test-rmse:14.205308+0.597702
## [8] train-rmse:10.931946+0.050383 test-rmse:10.978015+0.588382
## [9] train-rmse:8.636010+0.051580 test-rmse:8.769294+0.635027
## [10] train-rmse:7.025644+0.055247 test-rmse:7.142430+0.606061
## [11] train-rmse:5.919090+0.058471 test-rmse:6.003713+0.562276
## [12] train-rmse:5.171796+0.060088 test-rmse:5.299922+0.614464
## [13] train-rmse:4.671005+0.060374 test-rmse:4.833563+0.576035
## [14] train-rmse:4.338928+0.065031 test-rmse:4.479763+0.522340
## [15] train-rmse:4.113815+0.064835 test-rmse:4.252527+0.469725
## [16] train-rmse:3.948042+0.063418 test-rmse:4.098868+0.427799
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## [18] train-rmse:3.727605+0.058472 test-rmse:3.924465+0.447582
## [19] train-rmse:3.643206+0.059181 test-rmse:3.823848+0.384948
## [20] train-rmse:3.570969+0.057627 test-rmse:3.779028+0.385576
## [21] train-rmse:3.505118+0.057445 test-rmse:3.733223+0.391874
## [22] train-rmse:3.443301+0.057131 test-rmse:3.692211+0.383069
## [23] train-rmse:3.384345+0.054585 test-rmse:3.606337+0.335266
## [24] train-rmse:3.329655+0.052102 test-rmse:3.557780+0.335683
## [25] train-rmse:3.275569+0.050473 test-rmse:3.497089+0.342052
## [26] train-rmse:3.224459+0.047754 test-rmse:3.453455+0.349163
## [27] train-rmse:3.176153+0.046465 test-rmse:3.391236+0.313192
## [28] train-rmse:3.130627+0.045176 test-rmse:3.367875+0.322023
## [29] train-rmse:3.085913+0.043220 test-rmse:3.324723+0.309271
## [30] train-rmse:3.042128+0.042642 test-rmse:3.284526+0.315184
## [31] train-rmse:3.000215+0.042896 test-rmse:3.253518+0.323465
## [32] train-rmse:2.959851+0.042357 test-rmse:3.220669+0.330662
## [33] train-rmse:2.920841+0.042023 test-rmse:3.167170+0.294741
## [34] train-rmse:2.882607+0.041136 test-rmse:3.141497+0.310211
## [35] train-rmse:2.845701+0.041177 test-rmse:3.093701+0.309397
## [36] train-rmse:2.809753+0.041172 test-rmse:3.061944+0.313596
## [37] train-rmse:2.774004+0.041137 test-rmse:3.023282+0.309623
## [38] train-rmse:2.738782+0.040702 test-rmse:2.984594+0.311667
## [39] train-rmse:2.703897+0.041269 test-rmse:2.956680+0.317583
## [40] train-rmse:2.669960+0.041054 test-rmse:2.899883+0.294403
## [41] train-rmse:2.638000+0.040212 test-rmse:2.884652+0.297749
## [42] train-rmse:2.606767+0.039753 test-rmse:2.853462+0.287784
## [43] train-rmse:2.575377+0.039126 test-rmse:2.842830+0.290789
## [44] train-rmse:2.545046+0.038159 test-rmse:2.812230+0.297312
## [45] train-rmse:2.515315+0.037097 test-rmse:2.788086+0.308555
## [46] train-rmse:2.487626+0.036629 test-rmse:2.768432+0.310905
## [47] train-rmse:2.459873+0.036413 test-rmse:2.727722+0.281069
## [48] train-rmse:2.432531+0.035417 test-rmse:2.701580+0.284734
## [49] train-rmse:2.405908+0.035111 test-rmse:2.677022+0.284357
## [50] train-rmse:2.379617+0.034789 test-rmse:2.640748+0.291497
## [51] train-rmse:2.353399+0.033944 test-rmse:2.620551+0.292246
## [52] train-rmse:2.328024+0.033408 test-rmse:2.606364+0.298628
## [53] train-rmse:2.303504+0.033293 test-rmse:2.596157+0.305678
## [54] train-rmse:2.279462+0.032974 test-rmse:2.570384+0.302735
## [55] train-rmse:2.255495+0.032499 test-rmse:2.528953+0.295175
## [56] train-rmse:2.231617+0.031896 test-rmse:2.490076+0.283343
## [57] train-rmse:2.208006+0.030990 test-rmse:2.468546+0.265585
## [58] train-rmse:2.185175+0.030879 test-rmse:2.445148+0.261373
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## [60] train-rmse:2.140662+0.030238 test-rmse:2.411788+0.253667
## [61] train-rmse:2.118710+0.029696 test-rmse:2.387990+0.263672
## [62] train-rmse:2.096979+0.029127 test-rmse:2.380474+0.264291
## [63] train-rmse:2.075571+0.028676 test-rmse:2.356812+0.258958
## [64] train-rmse:2.054835+0.027800 test-rmse:2.338487+0.260357
## [65] train-rmse:2.034195+0.026815 test-rmse:2.313322+0.268361
## [66] train-rmse:2.013919+0.025923 test-rmse:2.288591+0.273806
## [67] train-rmse:1.994346+0.025384 test-rmse:2.277435+0.275860
## [68] train-rmse:1.974659+0.024591 test-rmse:2.252341+0.273510
## [69] train-rmse:1.955723+0.023586 test-rmse:2.241631+0.262662
## [70] train-rmse:1.936833+0.022406 test-rmse:2.212372+0.244564
## [71] train-rmse:1.918480+0.021735 test-rmse:2.189974+0.254913
## [72] train-rmse:1.900490+0.021123 test-rmse:2.172888+0.244981
## [73] train-rmse:1.882604+0.020743 test-rmse:2.160842+0.246438
## [74] train-rmse:1.865011+0.020740 test-rmse:2.132112+0.247987
## [75] train-rmse:1.847601+0.020512 test-rmse:2.125387+0.250951
## [76] train-rmse:1.830351+0.020401 test-rmse:2.112352+0.259194
## [77] train-rmse:1.813647+0.019956 test-rmse:2.093888+0.244087
## [78] train-rmse:1.797150+0.019354 test-rmse:2.079649+0.243054
## [79] train-rmse:1.780980+0.018642 test-rmse:2.065201+0.242805
## [80] train-rmse:1.764909+0.017885 test-rmse:2.055943+0.233342
## [81] train-rmse:1.748846+0.017492 test-rmse:2.039019+0.235519
## [82] train-rmse:1.733485+0.017170 test-rmse:2.025688+0.238217
## [83] train-rmse:1.718182+0.016630 test-rmse:2.012199+0.247671
## [84] train-rmse:1.703592+0.016118 test-rmse:1.998285+0.247093
## [85] train-rmse:1.689488+0.016095 test-rmse:1.985400+0.237948
## [86] train-rmse:1.675308+0.015984 test-rmse:1.967582+0.235698
## [87] train-rmse:1.661161+0.015949 test-rmse:1.957580+0.236556
## [88] train-rmse:1.646944+0.015819 test-rmse:1.944803+0.242410
## [89] train-rmse:1.633021+0.015623 test-rmse:1.902446+0.232818
## [90] train-rmse:1.619252+0.015436 test-rmse:1.897303+0.231609
## [91] train-rmse:1.605417+0.015315 test-rmse:1.891689+0.235265
## [92] train-rmse:1.592160+0.014930 test-rmse:1.879788+0.234758
## [93] train-rmse:1.579240+0.014444 test-rmse:1.863553+0.234180
## [94] train-rmse:1.566625+0.013977 test-rmse:1.855547+0.230631
## [95] train-rmse:1.553996+0.013705 test-rmse:1.853264+0.228533
## [96] train-rmse:1.541488+0.013436 test-rmse:1.828836+0.228316
## [97] train-rmse:1.529199+0.013066 test-rmse:1.813957+0.227532
## [98] train-rmse:1.516996+0.012714 test-rmse:1.797378+0.216332
## [99] train-rmse:1.504814+0.012246 test-rmse:1.785123+0.221790
## [100] train-rmse:1.492712+0.012173 test-rmse:1.780279+0.224470
## [101] train-rmse:1.480625+0.011956 test-rmse:1.771165+0.220361
## [102] train-rmse:1.468968+0.011471 test-rmse:1.764931+0.223863
## [103] train-rmse:1.457872+0.011100 test-rmse:1.760592+0.225116
## [104] train-rmse:1.446883+0.010673 test-rmse:1.752113+0.225315
## [105] train-rmse:1.436063+0.010221 test-rmse:1.737617+0.229658
## [106] train-rmse:1.425280+0.010036 test-rmse:1.727315+0.231488
## [107] train-rmse:1.414656+0.009753 test-rmse:1.712684+0.229816
## [108] train-rmse:1.404377+0.009672 test-rmse:1.703646+0.224829
## [109] train-rmse:1.394292+0.009630 test-rmse:1.692654+0.218779
## [110] train-rmse:1.384245+0.009584 test-rmse:1.672464+0.200574
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## [112] train-rmse:1.364349+0.009341 test-rmse:1.647809+0.204444
## [113] train-rmse:1.354574+0.009259 test-rmse:1.641566+0.202767
## [114] train-rmse:1.344991+0.009144 test-rmse:1.634869+0.207512
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## [116] train-rmse:1.326029+0.008915 test-rmse:1.620434+0.208050
## [117] train-rmse:1.316810+0.008949 test-rmse:1.613346+0.209565
## [118] train-rmse:1.307901+0.008849 test-rmse:1.605677+0.206947
## [119] train-rmse:1.298980+0.008667 test-rmse:1.593422+0.198874
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## [121] train-rmse:1.281615+0.008621 test-rmse:1.573359+0.204058
## [122] train-rmse:1.273300+0.008644 test-rmse:1.565469+0.201355
## [123] train-rmse:1.265137+0.008634 test-rmse:1.561021+0.201102
## [124] train-rmse:1.257066+0.008772 test-rmse:1.548482+0.200905
## [125] train-rmse:1.249093+0.008693 test-rmse:1.542710+0.203371
## [126] train-rmse:1.241156+0.008745 test-rmse:1.538180+0.203814
## [127] train-rmse:1.233335+0.008629 test-rmse:1.526687+0.207946
## [128] train-rmse:1.225800+0.008509 test-rmse:1.523401+0.207540
## [129] train-rmse:1.218352+0.008422 test-rmse:1.516421+0.209644
## [130] train-rmse:1.210998+0.008406 test-rmse:1.507512+0.208672
## [131] train-rmse:1.203528+0.008408 test-rmse:1.501995+0.206975
## [132] train-rmse:1.196183+0.008483 test-rmse:1.491135+0.201809
## [133] train-rmse:1.189015+0.008555 test-rmse:1.483138+0.186680
## [134] train-rmse:1.181924+0.008587 test-rmse:1.475697+0.189249
## [135] train-rmse:1.174920+0.008709 test-rmse:1.472460+0.181710
## [136] train-rmse:1.168067+0.008696 test-rmse:1.465941+0.183807
## [137] train-rmse:1.161380+0.008788 test-rmse:1.461813+0.181660
## [138] train-rmse:1.154774+0.008799 test-rmse:1.454882+0.185410
## [139] train-rmse:1.148355+0.008837 test-rmse:1.447546+0.182633
## [140] train-rmse:1.141982+0.008837 test-rmse:1.441857+0.186538
## [141] train-rmse:1.135590+0.008807 test-rmse:1.431941+0.186031
## [142] train-rmse:1.129324+0.008639 test-rmse:1.425572+0.191661
## [143] train-rmse:1.123121+0.008501 test-rmse:1.415493+0.185737
## [144] train-rmse:1.116996+0.008455 test-rmse:1.412255+0.188606
## [145] train-rmse:1.111193+0.008457 test-rmse:1.409298+0.187976
## [146] train-rmse:1.105452+0.008560 test-rmse:1.407153+0.190201
## [147] train-rmse:1.099683+0.008618 test-rmse:1.399076+0.186310
## [148] train-rmse:1.094047+0.008656 test-rmse:1.394235+0.188316
## [149] train-rmse:1.088599+0.008709 test-rmse:1.391107+0.186787
## [150] train-rmse:1.083222+0.008666 test-rmse:1.383989+0.184818
## [151] train-rmse:1.077905+0.008646 test-rmse:1.378124+0.188741
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## [153] train-rmse:1.067383+0.008629 test-rmse:1.367941+0.188226
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## [156] train-rmse:1.052027+0.008439 test-rmse:1.347100+0.178183
## [157] train-rmse:1.047055+0.008289 test-rmse:1.345887+0.180401
## [158] train-rmse:1.042124+0.008179 test-rmse:1.342704+0.180973
## [159] train-rmse:1.037257+0.008021 test-rmse:1.339986+0.180860
## [160] train-rmse:1.032419+0.007860 test-rmse:1.332501+0.180047
## [161] train-rmse:1.027720+0.007791 test-rmse:1.329182+0.180215
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## [163] train-rmse:1.018620+0.007818 test-rmse:1.318447+0.178003
## [164] train-rmse:1.014031+0.007838 test-rmse:1.316732+0.178559
## [165] train-rmse:1.009656+0.007861 test-rmse:1.314975+0.178946
## [166] train-rmse:1.005400+0.007798 test-rmse:1.306385+0.178898
## [167] train-rmse:1.001147+0.007720 test-rmse:1.303844+0.177847
## [168] train-rmse:0.996970+0.007666 test-rmse:1.300853+0.179016
## [169] train-rmse:0.992886+0.007666 test-rmse:1.298947+0.177210
## [170] train-rmse:0.988877+0.007654 test-rmse:1.295069+0.177938
## [171] train-rmse:0.984925+0.007659 test-rmse:1.291717+0.181767
## [172] train-rmse:0.981053+0.007755 test-rmse:1.288318+0.180147
## [173] train-rmse:0.977106+0.007749 test-rmse:1.283309+0.178109
## [174] train-rmse:0.973270+0.007732 test-rmse:1.279118+0.179637
## [175] train-rmse:0.969517+0.007740 test-rmse:1.275196+0.179816
## [176] train-rmse:0.965893+0.007769 test-rmse:1.272890+0.180131
## [177] train-rmse:0.962238+0.007837 test-rmse:1.271084+0.180787
## [178] train-rmse:0.958671+0.007933 test-rmse:1.268109+0.179417
## [179] train-rmse:0.955186+0.007993 test-rmse:1.259804+0.169887
## [180] train-rmse:0.951707+0.008044 test-rmse:1.253721+0.166540
## [181] train-rmse:0.948292+0.008100 test-rmse:1.249938+0.167714
## [182] train-rmse:0.944974+0.008168 test-rmse:1.247148+0.167819
## [183] train-rmse:0.941701+0.008206 test-rmse:1.245124+0.168056
## [184] train-rmse:0.938499+0.008155 test-rmse:1.241195+0.170667
## [185] train-rmse:0.935389+0.008138 test-rmse:1.240636+0.170279
## [186] train-rmse:0.932293+0.008159 test-rmse:1.237143+0.171607
## [187] train-rmse:0.929204+0.008137 test-rmse:1.235719+0.171531
## [188] train-rmse:0.926154+0.008135 test-rmse:1.235502+0.171637
## [189] train-rmse:0.923126+0.008116 test-rmse:1.228960+0.173961
## [190] train-rmse:0.920190+0.008131 test-rmse:1.225506+0.173826
## [191] train-rmse:0.917255+0.008177 test-rmse:1.218854+0.173150
## [192] train-rmse:0.914362+0.008250 test-rmse:1.217427+0.172430
## [193] train-rmse:0.911558+0.008262 test-rmse:1.214995+0.173872
## [194] train-rmse:0.908789+0.008277 test-rmse:1.211381+0.173868
## [195] train-rmse:0.906014+0.008281 test-rmse:1.207398+0.170623
## [196] train-rmse:0.903304+0.008290 test-rmse:1.205461+0.169096
## [197] train-rmse:0.900655+0.008266 test-rmse:1.203587+0.169249
## [198] train-rmse:0.898007+0.008264 test-rmse:1.199134+0.167890
## [199] train-rmse:0.895398+0.008263 test-rmse:1.198190+0.166793
## [200] train-rmse:0.892850+0.008239 test-rmse:1.196967+0.166625
## [201] train-rmse:0.890323+0.008263 test-rmse:1.193822+0.168024
## [202] train-rmse:0.887884+0.008289 test-rmse:1.193510+0.168800
## [203] train-rmse:0.885487+0.008296 test-rmse:1.192676+0.167748
## [204] train-rmse:0.883064+0.008314 test-rmse:1.189769+0.168088
## [205] train-rmse:0.880717+0.008303 test-rmse:1.189444+0.168650
## [206] train-rmse:0.878453+0.008339 test-rmse:1.187241+0.168772
## [207] train-rmse:0.876139+0.008406 test-rmse:1.185318+0.170745
## [208] train-rmse:0.873798+0.008405 test-rmse:1.187464+0.169826
## [209] train-rmse:0.871545+0.008425 test-rmse:1.186781+0.170019
## [210] train-rmse:0.869381+0.008479 test-rmse:1.186571+0.169987
## [211] train-rmse:0.867229+0.008534 test-rmse:1.184875+0.171083
## [212] train-rmse:0.865074+0.008596 test-rmse:1.178873+0.164601
## [213] train-rmse:0.862937+0.008612 test-rmse:1.176568+0.164589
## [214] train-rmse:0.860891+0.008656 test-rmse:1.170511+0.164554
## [215] train-rmse:0.858903+0.008751 test-rmse:1.167735+0.161519
## [216] train-rmse:0.856968+0.008816 test-rmse:1.168008+0.159929
## [217] train-rmse:0.855024+0.008877 test-rmse:1.166123+0.160461
## [218] train-rmse:0.853134+0.008935 test-rmse:1.162863+0.160607
## [219] train-rmse:0.851238+0.008976 test-rmse:1.162737+0.159900
## [220] train-rmse:0.849381+0.008995 test-rmse:1.161129+0.160187
## [221] train-rmse:0.847545+0.009028 test-rmse:1.161139+0.159660
## [222] train-rmse:0.845719+0.009040 test-rmse:1.156992+0.158303
## [223] train-rmse:0.843898+0.009062 test-rmse:1.156636+0.156315
## [224] train-rmse:0.842077+0.009076 test-rmse:1.156526+0.154389
## [225] train-rmse:0.840270+0.009083 test-rmse:1.155242+0.154393
## [226] train-rmse:0.838565+0.009090 test-rmse:1.153816+0.154395
## [227] train-rmse:0.836853+0.009087 test-rmse:1.151807+0.156727
## [228] train-rmse:0.835174+0.009122 test-rmse:1.147501+0.157918
## [229] train-rmse:0.833561+0.009135 test-rmse:1.148036+0.157040
## [230] train-rmse:0.831974+0.009138 test-rmse:1.147895+0.156465
## [231] train-rmse:0.830410+0.009168 test-rmse:1.148037+0.156195
## [232] train-rmse:0.828867+0.009168 test-rmse:1.146878+0.156481
## [233] train-rmse:0.827345+0.009173 test-rmse:1.147235+0.156447
## [234] train-rmse:0.825855+0.009163 test-rmse:1.144999+0.157532
## [235] train-rmse:0.824368+0.009167 test-rmse:1.142686+0.158449
## [236] train-rmse:0.822888+0.009180 test-rmse:1.140237+0.159332
## [237] train-rmse:0.821421+0.009222 test-rmse:1.139964+0.159091
## [238] train-rmse:0.819954+0.009229 test-rmse:1.137205+0.161103
## [239] train-rmse:0.818540+0.009244 test-rmse:1.136763+0.161823
## [240] train-rmse:0.817059+0.009375 test-rmse:1.135034+0.163313
## [241] train-rmse:0.815674+0.009412 test-rmse:1.133429+0.162978
## [242] train-rmse:0.814306+0.009427 test-rmse:1.131787+0.162471
## [243] train-rmse:0.812981+0.009476 test-rmse:1.131321+0.162087
## [244] train-rmse:0.811663+0.009555 test-rmse:1.127525+0.156564
## [245] train-rmse:0.810382+0.009629 test-rmse:1.124319+0.154682
## [246] train-rmse:0.809103+0.009686 test-rmse:1.123223+0.154338
## [247] train-rmse:0.807846+0.009733 test-rmse:1.121742+0.155333
## [248] train-rmse:0.806593+0.009787 test-rmse:1.119431+0.155068
## [249] train-rmse:0.805364+0.009813 test-rmse:1.119675+0.155392
## [250] train-rmse:0.804134+0.009843 test-rmse:1.118898+0.156047
## [251] train-rmse:0.802903+0.009897 test-rmse:1.118606+0.156826
## [252] train-rmse:0.801676+0.009947 test-rmse:1.118531+0.157281
## [253] train-rmse:0.800504+0.009968 test-rmse:1.118272+0.156976
## [254] train-rmse:0.799346+0.010029 test-rmse:1.118036+0.155877
## [255] train-rmse:0.798199+0.010068 test-rmse:1.117632+0.156259
## [256] train-rmse:0.797064+0.010104 test-rmse:1.115587+0.156546
## [257] train-rmse:0.795981+0.010145 test-rmse:1.113980+0.158112
## [258] train-rmse:0.794847+0.010174 test-rmse:1.112830+0.158230
## [259] train-rmse:0.793764+0.010206 test-rmse:1.112575+0.158438
## [260] train-rmse:0.792680+0.010255 test-rmse:1.110964+0.156135
## [261] train-rmse:0.791617+0.010305 test-rmse:1.111085+0.156475
## [262] train-rmse:0.790577+0.010337 test-rmse:1.111975+0.156567
## [263] train-rmse:0.789521+0.010360 test-rmse:1.110027+0.156424
## [264] train-rmse:0.788486+0.010388 test-rmse:1.107453+0.151814
## [265] train-rmse:0.787461+0.010425 test-rmse:1.104510+0.150850
## [266] train-rmse:0.786476+0.010462 test-rmse:1.104581+0.150106
## [267] train-rmse:0.785459+0.010451 test-rmse:1.103287+0.149959
## [268] train-rmse:0.784485+0.010509 test-rmse:1.100827+0.150261
## [269] train-rmse:0.783525+0.010553 test-rmse:1.100799+0.150013
## [270] train-rmse:0.782548+0.010606 test-rmse:1.100103+0.150251
## [271] train-rmse:0.781559+0.010655 test-rmse:1.098566+0.150987
## [272] train-rmse:0.780598+0.010675 test-rmse:1.099074+0.150038
## [273] train-rmse:0.779665+0.010698 test-rmse:1.098108+0.150846
## [274] train-rmse:0.778707+0.010722 test-rmse:1.098121+0.151323
## [275] train-rmse:0.777760+0.010705 test-rmse:1.095839+0.152783
## [276] train-rmse:0.776847+0.010720 test-rmse:1.093963+0.153692
## [277] train-rmse:0.775932+0.010767 test-rmse:1.092529+0.152798
## [278] train-rmse:0.775018+0.010818 test-rmse:1.092276+0.152088
## [279] train-rmse:0.774146+0.010858 test-rmse:1.090726+0.152719
## [280] train-rmse:0.773292+0.010892 test-rmse:1.090421+0.152144
## [281] train-rmse:0.772427+0.010917 test-rmse:1.089338+0.152787
## [282] train-rmse:0.771561+0.010970 test-rmse:1.088522+0.153351
## [283] train-rmse:0.770702+0.011006 test-rmse:1.087448+0.153007
## [284] train-rmse:0.769864+0.011039 test-rmse:1.085369+0.153274
## [285] train-rmse:0.769030+0.011086 test-rmse:1.086329+0.152056
## [286] train-rmse:0.768192+0.011138 test-rmse:1.085154+0.153408
## [287] train-rmse:0.767363+0.011170 test-rmse:1.085591+0.151994
## [288] train-rmse:0.766549+0.011183 test-rmse:1.085630+0.151993
## [289] train-rmse:0.765753+0.011201 test-rmse:1.086418+0.151741
## [290] train-rmse:0.764954+0.011238 test-rmse:1.084461+0.153363
## [291] train-rmse:0.764162+0.011271 test-rmse:1.084832+0.153133
## [292] train-rmse:0.763356+0.011258 test-rmse:1.084091+0.153083
## [293] train-rmse:0.762560+0.011266 test-rmse:1.083898+0.152690
## [294] train-rmse:0.761795+0.011272 test-rmse:1.083386+0.153757
## [295] train-rmse:0.761007+0.011274 test-rmse:1.082904+0.151750
## [296] train-rmse:0.760248+0.011306 test-rmse:1.083085+0.152619
## [297] train-rmse:0.759492+0.011332 test-rmse:1.082406+0.152820
## [298] train-rmse:0.758746+0.011340 test-rmse:1.081264+0.152096
## [299] train-rmse:0.758020+0.011352 test-rmse:1.079405+0.153291
## [300] train-rmse:0.757294+0.011357 test-rmse:1.077250+0.149911
## [301] train-rmse:0.756583+0.011370 test-rmse:1.078328+0.149680
## [302] train-rmse:0.755834+0.011360 test-rmse:1.077380+0.148230
## [303] train-rmse:0.755117+0.011363 test-rmse:1.077152+0.148436
## [304] train-rmse:0.754427+0.011381 test-rmse:1.077499+0.149237
## [305] train-rmse:0.753748+0.011401 test-rmse:1.077160+0.149711
## [306] train-rmse:0.753065+0.011421 test-rmse:1.076064+0.150052
## [307] train-rmse:0.752377+0.011421 test-rmse:1.075540+0.151226
## [308] train-rmse:0.751691+0.011423 test-rmse:1.075272+0.150071
## [309] train-rmse:0.751006+0.011439 test-rmse:1.075100+0.150803
## [310] train-rmse:0.750340+0.011442 test-rmse:1.074239+0.150592
## [311] train-rmse:0.749682+0.011443 test-rmse:1.074701+0.150330
## [312] train-rmse:0.749032+0.011463 test-rmse:1.073783+0.151007
## [313] train-rmse:0.748382+0.011481 test-rmse:1.071868+0.151228
## [314] train-rmse:0.747748+0.011492 test-rmse:1.070523+0.150472
## [315] train-rmse:0.747093+0.011498 test-rmse:1.071572+0.150242
## [316] train-rmse:0.746472+0.011522 test-rmse:1.071001+0.150264
## [317] train-rmse:0.745839+0.011532 test-rmse:1.069512+0.150559
## [318] train-rmse:0.745228+0.011552 test-rmse:1.068866+0.151022
## [319] train-rmse:0.744624+0.011566 test-rmse:1.069433+0.151014
## [320] train-rmse:0.744011+0.011578 test-rmse:1.068872+0.151921
## [321] train-rmse:0.743403+0.011579 test-rmse:1.068645+0.151577
## [322] train-rmse:0.742809+0.011588 test-rmse:1.068564+0.151454
## [323] train-rmse:0.742193+0.011582 test-rmse:1.067686+0.151802
## [324] train-rmse:0.741602+0.011569 test-rmse:1.068194+0.151635
## [325] train-rmse:0.741020+0.011570 test-rmse:1.067958+0.151921
## [326] train-rmse:0.740447+0.011595 test-rmse:1.068406+0.151499
## [327] train-rmse:0.739808+0.011594 test-rmse:1.068073+0.152287
## [328] train-rmse:0.739246+0.011601 test-rmse:1.067330+0.151934
## [329] train-rmse:0.738694+0.011615 test-rmse:1.066627+0.151750
## [330] train-rmse:0.738112+0.011620 test-rmse:1.065963+0.150171
## [331] train-rmse:0.737537+0.011628 test-rmse:1.064679+0.150221
## [332] train-rmse:0.736983+0.011649 test-rmse:1.064269+0.149757
## [333] train-rmse:0.736427+0.011653 test-rmse:1.063006+0.150730
## [334] train-rmse:0.735874+0.011658 test-rmse:1.063800+0.150302
## [335] train-rmse:0.735338+0.011660 test-rmse:1.063789+0.150666
## [336] train-rmse:0.734808+0.011651 test-rmse:1.061235+0.147756
## [337] train-rmse:0.734283+0.011654 test-rmse:1.061097+0.148218
## [338] train-rmse:0.733760+0.011657 test-rmse:1.060882+0.148087
## [339] train-rmse:0.733236+0.011670 test-rmse:1.061042+0.148182
## [340] train-rmse:0.732725+0.011673 test-rmse:1.060926+0.147928
## [341] train-rmse:0.732211+0.011679 test-rmse:1.060490+0.146358
## [342] train-rmse:0.731700+0.011661 test-rmse:1.060501+0.146586
## [343] train-rmse:0.731160+0.011687 test-rmse:1.060286+0.147058
## [344] train-rmse:0.730653+0.011659 test-rmse:1.058979+0.146845
## [345] train-rmse:0.730139+0.011659 test-rmse:1.060412+0.147090
## [346] train-rmse:0.729630+0.011657 test-rmse:1.059098+0.147565
## [347] train-rmse:0.729109+0.011649 test-rmse:1.058204+0.145903
## [348] train-rmse:0.728612+0.011647 test-rmse:1.058094+0.146049
## [349] train-rmse:0.728120+0.011637 test-rmse:1.057653+0.146310
## [350] train-rmse:0.727627+0.011616 test-rmse:1.056549+0.145902
## [351] train-rmse:0.727128+0.011628 test-rmse:1.056949+0.145370
## [352] train-rmse:0.726629+0.011628 test-rmse:1.057213+0.146001
## [353] train-rmse:0.726152+0.011598 test-rmse:1.056738+0.145877
## [354] train-rmse:0.725691+0.011577 test-rmse:1.057495+0.145905
## [355] train-rmse:0.725230+0.011569 test-rmse:1.058034+0.145435
## [356] train-rmse:0.724767+0.011559 test-rmse:1.056325+0.145039
## [357] train-rmse:0.724293+0.011568 test-rmse:1.055571+0.144412
## [358] train-rmse:0.723839+0.011560 test-rmse:1.056112+0.144705
## [359] train-rmse:0.723376+0.011580 test-rmse:1.056017+0.144873
## [360] train-rmse:0.722922+0.011573 test-rmse:1.055854+0.143609
## [361] train-rmse:0.722484+0.011568 test-rmse:1.054716+0.143463
## [362] train-rmse:0.722052+0.011551 test-rmse:1.054281+0.144666
## [363] train-rmse:0.721591+0.011526 test-rmse:1.053478+0.143198
## [364] train-rmse:0.721148+0.011532 test-rmse:1.053737+0.143579
## [365] train-rmse:0.720719+0.011545 test-rmse:1.052355+0.143997
## [366] train-rmse:0.720273+0.011536 test-rmse:1.052386+0.144311
## [367] train-rmse:0.719835+0.011537 test-rmse:1.053247+0.144019
## [368] train-rmse:0.719401+0.011524 test-rmse:1.052862+0.144766
## [369] train-rmse:0.718986+0.011507 test-rmse:1.052274+0.144167
## [370] train-rmse:0.718561+0.011499 test-rmse:1.051784+0.144191
## [371] train-rmse:0.718127+0.011487 test-rmse:1.052065+0.144439
## [372] train-rmse:0.717709+0.011477 test-rmse:1.050909+0.144639
## [373] train-rmse:0.717274+0.011428 test-rmse:1.051062+0.143704
## [374] train-rmse:0.716868+0.011412 test-rmse:1.049913+0.143328
## [375] train-rmse:0.716448+0.011376 test-rmse:1.050011+0.143540
## [376] train-rmse:0.716018+0.011366 test-rmse:1.050561+0.142086
## [377] train-rmse:0.715605+0.011353 test-rmse:1.050342+0.142605
## [378] train-rmse:0.715201+0.011342 test-rmse:1.050146+0.141226
## [379] train-rmse:0.714792+0.011331 test-rmse:1.048598+0.141271
## [380] train-rmse:0.714392+0.011325 test-rmse:1.047812+0.140994
## [381] train-rmse:0.713987+0.011291 test-rmse:1.047300+0.141411
## [382] train-rmse:0.713603+0.011284 test-rmse:1.047346+0.141082
## [383] train-rmse:0.713199+0.011265 test-rmse:1.046867+0.140905
## [384] train-rmse:0.712800+0.011257 test-rmse:1.046824+0.140854
## [385] train-rmse:0.712413+0.011244 test-rmse:1.047210+0.140260
## [386] train-rmse:0.712026+0.011231 test-rmse:1.045767+0.140168
## [387] train-rmse:0.711640+0.011219 test-rmse:1.045178+0.139160
## [388] train-rmse:0.711261+0.011205 test-rmse:1.043855+0.139211
## [389] train-rmse:0.710871+0.011200 test-rmse:1.044323+0.139809
## [390] train-rmse:0.710503+0.011187 test-rmse:1.043605+0.140758
## [391] train-rmse:0.710110+0.011163 test-rmse:1.043117+0.140094
## [392] train-rmse:0.709732+0.011151 test-rmse:1.042907+0.138858
## [393] train-rmse:0.709366+0.011138 test-rmse:1.042334+0.138284
## [394] train-rmse:0.708980+0.011130 test-rmse:1.043103+0.137706
## [395] train-rmse:0.708604+0.011118 test-rmse:1.043513+0.137910
## [396] train-rmse:0.708205+0.011139 test-rmse:1.043674+0.137860
## [397] train-rmse:0.707842+0.011135 test-rmse:1.042902+0.138134
## [398] train-rmse:0.707480+0.011141 test-rmse:1.043064+0.137721
## [399] train-rmse:0.707123+0.011138 test-rmse:1.044009+0.137111
## Stopping. Best iteration:
## [393] train-rmse:0.709366+0.011138 test-rmse:1.042334+0.138284
##
## 57
## [1] train-rmse:79.809479+0.092102 test-rmse:79.809535+0.505799
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:59.256192+0.075941 test-rmse:59.255628+0.542464
## [3] train-rmse:44.071015+0.066791 test-rmse:44.069481+0.578759
## [4] train-rmse:32.877449+0.063877 test-rmse:32.874400+0.617127
## [5] train-rmse:24.618845+0.054846 test-rmse:24.675687+0.630838
## [6] train-rmse:18.523824+0.040246 test-rmse:18.603025+0.566420
## [7] train-rmse:14.038760+0.031048 test-rmse:14.150045+0.573013
## [8] train-rmse:10.763609+0.029543 test-rmse:10.854733+0.526361
## [9] train-rmse:8.395366+0.029747 test-rmse:8.485099+0.537584
## [10] train-rmse:6.701759+0.037745 test-rmse:6.831168+0.560858
## [11] train-rmse:5.510709+0.040196 test-rmse:5.658718+0.587676
## [12] train-rmse:4.681621+0.036896 test-rmse:4.848968+0.523884
## [13] train-rmse:4.115300+0.042750 test-rmse:4.315836+0.490857
## [14] train-rmse:3.724777+0.037536 test-rmse:3.978559+0.477879
## [15] train-rmse:3.449985+0.037318 test-rmse:3.668437+0.427493
## [16] train-rmse:3.244994+0.046101 test-rmse:3.473936+0.384532
## [17] train-rmse:3.096177+0.047244 test-rmse:3.352070+0.378223
## [18] train-rmse:2.972492+0.043455 test-rmse:3.220401+0.350697
## [19] train-rmse:2.865677+0.034914 test-rmse:3.145087+0.347987
## [20] train-rmse:2.774897+0.033987 test-rmse:3.054364+0.331104
## [21] train-rmse:2.694661+0.032215 test-rmse:3.001736+0.326110
## [22] train-rmse:2.615632+0.030808 test-rmse:2.947552+0.309614
## [23] train-rmse:2.543806+0.026636 test-rmse:2.895282+0.292367
## [24] train-rmse:2.479995+0.025109 test-rmse:2.848669+0.305921
## [25] train-rmse:2.415582+0.020045 test-rmse:2.771881+0.290644
## [26] train-rmse:2.359060+0.020262 test-rmse:2.727940+0.287780
## [27] train-rmse:2.303012+0.018763 test-rmse:2.688346+0.279984
## [28] train-rmse:2.246595+0.017270 test-rmse:2.635403+0.280509
## [29] train-rmse:2.192423+0.018323 test-rmse:2.601972+0.290267
## [30] train-rmse:2.140963+0.015385 test-rmse:2.555196+0.279269
## [31] train-rmse:2.092893+0.014887 test-rmse:2.501933+0.258591
## [32] train-rmse:2.047490+0.013309 test-rmse:2.464498+0.267705
## [33] train-rmse:2.000641+0.012118 test-rmse:2.420678+0.270201
## [34] train-rmse:1.956874+0.009879 test-rmse:2.375642+0.267970
## [35] train-rmse:1.916078+0.009774 test-rmse:2.335662+0.279608
## [36] train-rmse:1.876192+0.008717 test-rmse:2.288222+0.285948
## [37] train-rmse:1.837415+0.008460 test-rmse:2.248697+0.273956
## [38] train-rmse:1.799231+0.007105 test-rmse:2.231597+0.270138
## [39] train-rmse:1.761242+0.006353 test-rmse:2.185656+0.252172
## [40] train-rmse:1.725182+0.003151 test-rmse:2.151969+0.254816
## [41] train-rmse:1.688205+0.005344 test-rmse:2.118046+0.258138
## [42] train-rmse:1.654100+0.004600 test-rmse:2.100135+0.261768
## [43] train-rmse:1.622078+0.004437 test-rmse:2.072166+0.268418
## [44] train-rmse:1.590829+0.005317 test-rmse:2.048466+0.250913
## [45] train-rmse:1.560053+0.005936 test-rmse:2.027683+0.254391
## [46] train-rmse:1.530726+0.006389 test-rmse:1.991671+0.254922
## [47] train-rmse:1.500978+0.006515 test-rmse:1.971972+0.261644
## [48] train-rmse:1.472486+0.006474 test-rmse:1.941537+0.259991
## [49] train-rmse:1.445639+0.007254 test-rmse:1.921949+0.253564
## [50] train-rmse:1.419524+0.007188 test-rmse:1.885935+0.245488
## [51] train-rmse:1.392181+0.006011 test-rmse:1.866855+0.248570
## [52] train-rmse:1.366065+0.006518 test-rmse:1.852972+0.250687
## [53] train-rmse:1.340468+0.006648 test-rmse:1.838942+0.245942
## [54] train-rmse:1.315693+0.006652 test-rmse:1.822805+0.254838
## [55] train-rmse:1.293365+0.007505 test-rmse:1.799294+0.259006
## [56] train-rmse:1.269975+0.009130 test-rmse:1.772896+0.262980
## [57] train-rmse:1.248643+0.009744 test-rmse:1.752850+0.253844
## [58] train-rmse:1.227687+0.010029 test-rmse:1.735310+0.252577
## [59] train-rmse:1.206682+0.010034 test-rmse:1.715497+0.258926
## [60] train-rmse:1.185912+0.010747 test-rmse:1.705567+0.253399
## [61] train-rmse:1.164728+0.011856 test-rmse:1.692500+0.250599
## [62] train-rmse:1.146157+0.011971 test-rmse:1.678937+0.245855
## [63] train-rmse:1.126239+0.013797 test-rmse:1.663849+0.250736
## [64] train-rmse:1.108678+0.013660 test-rmse:1.648684+0.254211
## [65] train-rmse:1.091439+0.013835 test-rmse:1.634951+0.248434
## [66] train-rmse:1.074522+0.014509 test-rmse:1.625371+0.252740
## [67] train-rmse:1.058831+0.015050 test-rmse:1.612850+0.247897
## [68] train-rmse:1.043086+0.014262 test-rmse:1.598380+0.250523
## [69] train-rmse:1.028281+0.014497 test-rmse:1.585079+0.248292
## [70] train-rmse:1.013238+0.014898 test-rmse:1.570629+0.255353
## [71] train-rmse:0.998363+0.014618 test-rmse:1.556169+0.250724
## [72] train-rmse:0.983866+0.016143 test-rmse:1.545816+0.257653
## [73] train-rmse:0.967890+0.016915 test-rmse:1.532229+0.255111
## [74] train-rmse:0.954796+0.017302 test-rmse:1.519934+0.257850
## [75] train-rmse:0.940909+0.017935 test-rmse:1.505471+0.255223
## [76] train-rmse:0.927772+0.017361 test-rmse:1.495486+0.257695
## [77] train-rmse:0.915151+0.016739 test-rmse:1.489795+0.264216
## [78] train-rmse:0.903260+0.017428 test-rmse:1.484092+0.261950
## [79] train-rmse:0.891878+0.017383 test-rmse:1.478563+0.264849
## [80] train-rmse:0.880124+0.018124 test-rmse:1.476140+0.266085
## [81] train-rmse:0.869641+0.018062 test-rmse:1.460086+0.264290
## [82] train-rmse:0.857144+0.019040 test-rmse:1.451690+0.262241
## [83] train-rmse:0.845922+0.020217 test-rmse:1.440155+0.262233
## [84] train-rmse:0.835317+0.020997 test-rmse:1.432471+0.262821
## [85] train-rmse:0.824890+0.021479 test-rmse:1.425156+0.264028
## [86] train-rmse:0.815277+0.021888 test-rmse:1.419965+0.264610
## [87] train-rmse:0.804335+0.022791 test-rmse:1.413419+0.265322
## [88] train-rmse:0.795245+0.023772 test-rmse:1.399059+0.257067
## [89] train-rmse:0.786325+0.023463 test-rmse:1.392480+0.254250
## [90] train-rmse:0.777414+0.023966 test-rmse:1.387723+0.254026
## [91] train-rmse:0.767145+0.020678 test-rmse:1.386183+0.254281
## [92] train-rmse:0.758676+0.020408 test-rmse:1.379914+0.251773
## [93] train-rmse:0.750774+0.019942 test-rmse:1.369887+0.254088
## [94] train-rmse:0.741433+0.020623 test-rmse:1.366462+0.253813
## [95] train-rmse:0.733206+0.020253 test-rmse:1.359006+0.253304
## [96] train-rmse:0.725304+0.020923 test-rmse:1.354280+0.256455
## [97] train-rmse:0.717914+0.021545 test-rmse:1.346315+0.258400
## [98] train-rmse:0.711570+0.021362 test-rmse:1.336604+0.259170
## [99] train-rmse:0.704820+0.021246 test-rmse:1.334758+0.260143
## [100] train-rmse:0.697190+0.021670 test-rmse:1.328012+0.257064
## [101] train-rmse:0.689436+0.021531 test-rmse:1.326268+0.258230
## [102] train-rmse:0.683273+0.021433 test-rmse:1.321747+0.261845
## [103] train-rmse:0.677493+0.021501 test-rmse:1.314314+0.255212
## [104] train-rmse:0.671088+0.021965 test-rmse:1.310485+0.258629
## [105] train-rmse:0.663616+0.020167 test-rmse:1.308256+0.256950
## [106] train-rmse:0.658091+0.019967 test-rmse:1.304879+0.258673
## [107] train-rmse:0.651242+0.020799 test-rmse:1.299043+0.261472
## [108] train-rmse:0.645282+0.020477 test-rmse:1.297115+0.264346
## [109] train-rmse:0.639999+0.020641 test-rmse:1.291014+0.266860
## [110] train-rmse:0.633723+0.020124 test-rmse:1.283345+0.268709
## [111] train-rmse:0.628679+0.020321 test-rmse:1.280023+0.268865
## [112] train-rmse:0.623490+0.019719 test-rmse:1.275926+0.267940
## [113] train-rmse:0.618249+0.020233 test-rmse:1.272196+0.264928
## [114] train-rmse:0.612913+0.021305 test-rmse:1.269353+0.266654
## [115] train-rmse:0.608405+0.021339 test-rmse:1.264044+0.267976
## [116] train-rmse:0.603136+0.021617 test-rmse:1.262660+0.270397
## [117] train-rmse:0.597040+0.021111 test-rmse:1.255187+0.270267
## [118] train-rmse:0.592283+0.020789 test-rmse:1.254462+0.272732
## [119] train-rmse:0.587665+0.021075 test-rmse:1.253172+0.274947
## [120] train-rmse:0.582495+0.021073 test-rmse:1.253094+0.275675
## [121] train-rmse:0.578168+0.020984 test-rmse:1.249382+0.275855
## [122] train-rmse:0.574122+0.020976 test-rmse:1.250154+0.276827
## [123] train-rmse:0.569533+0.020877 test-rmse:1.247937+0.277947
## [124] train-rmse:0.564223+0.020933 test-rmse:1.247834+0.281349
## [125] train-rmse:0.561045+0.020732 test-rmse:1.242408+0.283413
## [126] train-rmse:0.557382+0.020893 test-rmse:1.239840+0.280709
## [127] train-rmse:0.552912+0.020604 test-rmse:1.236279+0.275885
## [128] train-rmse:0.549635+0.020624 test-rmse:1.235791+0.278105
## [129] train-rmse:0.545934+0.020947 test-rmse:1.235096+0.278633
## [130] train-rmse:0.541287+0.021201 test-rmse:1.231694+0.278760
## [131] train-rmse:0.536569+0.022838 test-rmse:1.229861+0.278606
## [132] train-rmse:0.533610+0.023168 test-rmse:1.223431+0.281248
## [133] train-rmse:0.529773+0.023229 test-rmse:1.219501+0.282294
## [134] train-rmse:0.525418+0.022239 test-rmse:1.215762+0.281582
## [135] train-rmse:0.522182+0.022005 test-rmse:1.214782+0.283467
## [136] train-rmse:0.519286+0.022146 test-rmse:1.213337+0.283009
## [137] train-rmse:0.516536+0.022119 test-rmse:1.213258+0.283681
## [138] train-rmse:0.513357+0.022485 test-rmse:1.211821+0.283259
## [139] train-rmse:0.510442+0.022678 test-rmse:1.209970+0.284651
## [140] train-rmse:0.507392+0.022245 test-rmse:1.209720+0.286919
## [141] train-rmse:0.504763+0.021940 test-rmse:1.205392+0.287069
## [142] train-rmse:0.501362+0.022332 test-rmse:1.205294+0.287177
## [143] train-rmse:0.498077+0.022319 test-rmse:1.203920+0.289837
## [144] train-rmse:0.495731+0.022140 test-rmse:1.200885+0.290929
## [145] train-rmse:0.493449+0.021980 test-rmse:1.198639+0.290623
## [146] train-rmse:0.488654+0.020874 test-rmse:1.197036+0.294167
## [147] train-rmse:0.485681+0.020299 test-rmse:1.196461+0.291946
## [148] train-rmse:0.482496+0.021415 test-rmse:1.196308+0.291366
## [149] train-rmse:0.480063+0.021139 test-rmse:1.194376+0.290752
## [150] train-rmse:0.477874+0.021277 test-rmse:1.192956+0.291400
## [151] train-rmse:0.475827+0.021126 test-rmse:1.190246+0.290915
## [152] train-rmse:0.473469+0.021077 test-rmse:1.189726+0.290065
## [153] train-rmse:0.470993+0.020980 test-rmse:1.188467+0.291335
## [154] train-rmse:0.468662+0.020937 test-rmse:1.186967+0.291740
## [155] train-rmse:0.465856+0.020575 test-rmse:1.185294+0.292078
## [156] train-rmse:0.463231+0.020494 test-rmse:1.185225+0.292588
## [157] train-rmse:0.460646+0.020735 test-rmse:1.184055+0.293696
## [158] train-rmse:0.458782+0.020445 test-rmse:1.183304+0.293333
## [159] train-rmse:0.456825+0.020720 test-rmse:1.182148+0.292270
## [160] train-rmse:0.454991+0.020778 test-rmse:1.180047+0.292748
## [161] train-rmse:0.452911+0.020960 test-rmse:1.174233+0.285402
## [162] train-rmse:0.451279+0.021021 test-rmse:1.174109+0.284890
## [163] train-rmse:0.449238+0.021202 test-rmse:1.171434+0.286995
## [164] train-rmse:0.446113+0.020766 test-rmse:1.169179+0.287128
## [165] train-rmse:0.444429+0.020569 test-rmse:1.166853+0.286704
## [166] train-rmse:0.441955+0.021493 test-rmse:1.164599+0.287479
## [167] train-rmse:0.439570+0.021909 test-rmse:1.164188+0.287435
## [168] train-rmse:0.436810+0.021924 test-rmse:1.163973+0.288689
## [169] train-rmse:0.435147+0.021793 test-rmse:1.162632+0.289031
## [170] train-rmse:0.433562+0.021853 test-rmse:1.162708+0.289666
## [171] train-rmse:0.431226+0.022232 test-rmse:1.163365+0.289439
## [172] train-rmse:0.429664+0.022329 test-rmse:1.162074+0.289424
## [173] train-rmse:0.427919+0.022303 test-rmse:1.163097+0.288795
## [174] train-rmse:0.426008+0.022623 test-rmse:1.163992+0.289354
## [175] train-rmse:0.422597+0.020754 test-rmse:1.163123+0.290913
## [176] train-rmse:0.420810+0.020726 test-rmse:1.162750+0.289952
## [177] train-rmse:0.418883+0.019926 test-rmse:1.160292+0.289636
## [178] train-rmse:0.417256+0.020031 test-rmse:1.159548+0.290604
## [179] train-rmse:0.416037+0.020049 test-rmse:1.158583+0.291132
## [180] train-rmse:0.413716+0.019376 test-rmse:1.157732+0.291379
## [181] train-rmse:0.412233+0.019350 test-rmse:1.154965+0.288060
## [182] train-rmse:0.410434+0.019270 test-rmse:1.154313+0.287832
## [183] train-rmse:0.408088+0.019507 test-rmse:1.152615+0.287251
## [184] train-rmse:0.406418+0.019271 test-rmse:1.153318+0.287631
## [185] train-rmse:0.405047+0.019440 test-rmse:1.153198+0.287996
## [186] train-rmse:0.403625+0.019288 test-rmse:1.152960+0.288108
## [187] train-rmse:0.400780+0.018416 test-rmse:1.150773+0.286942
## [188] train-rmse:0.399271+0.018273 test-rmse:1.150407+0.288019
## [189] train-rmse:0.396878+0.017741 test-rmse:1.150319+0.287738
## [190] train-rmse:0.394944+0.016933 test-rmse:1.148738+0.288775
## [191] train-rmse:0.393497+0.016947 test-rmse:1.148184+0.289107
## [192] train-rmse:0.391267+0.016455 test-rmse:1.147790+0.288801
## [193] train-rmse:0.388591+0.015189 test-rmse:1.147081+0.289556
## [194] train-rmse:0.386647+0.015328 test-rmse:1.148219+0.291647
## [195] train-rmse:0.385279+0.015740 test-rmse:1.148160+0.290799
## [196] train-rmse:0.384055+0.015734 test-rmse:1.146851+0.290937
## [197] train-rmse:0.380929+0.014958 test-rmse:1.146044+0.290660
## [198] train-rmse:0.379343+0.015263 test-rmse:1.145665+0.290356
## [199] train-rmse:0.378111+0.015314 test-rmse:1.144366+0.289938
## [200] train-rmse:0.375947+0.014075 test-rmse:1.143096+0.288736
## [201] train-rmse:0.374599+0.013809 test-rmse:1.143024+0.288399
## [202] train-rmse:0.373426+0.013495 test-rmse:1.142150+0.287985
## [203] train-rmse:0.371923+0.012656 test-rmse:1.140387+0.288359
## [204] train-rmse:0.370299+0.012595 test-rmse:1.140123+0.288766
## [205] train-rmse:0.368291+0.012997 test-rmse:1.141146+0.289045
## [206] train-rmse:0.366378+0.013032 test-rmse:1.140877+0.289597
## [207] train-rmse:0.363885+0.013895 test-rmse:1.140365+0.287405
## [208] train-rmse:0.361869+0.013837 test-rmse:1.139846+0.286753
## [209] train-rmse:0.359688+0.014083 test-rmse:1.139482+0.286890
## [210] train-rmse:0.358316+0.013825 test-rmse:1.138764+0.286917
## [211] train-rmse:0.357130+0.013333 test-rmse:1.138631+0.287243
## [212] train-rmse:0.355539+0.013261 test-rmse:1.136944+0.285259
## [213] train-rmse:0.353267+0.013129 test-rmse:1.136255+0.285871
## [214] train-rmse:0.352298+0.013102 test-rmse:1.136128+0.285707
## [215] train-rmse:0.351007+0.013235 test-rmse:1.134697+0.286435
## [216] train-rmse:0.350002+0.013055 test-rmse:1.134389+0.287356
## [217] train-rmse:0.348598+0.013366 test-rmse:1.133480+0.286926
## [218] train-rmse:0.347237+0.013664 test-rmse:1.133746+0.287193
## [219] train-rmse:0.346176+0.013758 test-rmse:1.132796+0.287813
## [220] train-rmse:0.345378+0.013549 test-rmse:1.132111+0.287955
## [221] train-rmse:0.343329+0.013506 test-rmse:1.130890+0.287993
## [222] train-rmse:0.341841+0.013452 test-rmse:1.130369+0.288747
## [223] train-rmse:0.340152+0.013130 test-rmse:1.130114+0.288530
## [224] train-rmse:0.339239+0.012660 test-rmse:1.129468+0.288298
## [225] train-rmse:0.338353+0.012561 test-rmse:1.128938+0.288067
## [226] train-rmse:0.337496+0.012409 test-rmse:1.129129+0.288722
## [227] train-rmse:0.335448+0.012421 test-rmse:1.128128+0.287348
## [228] train-rmse:0.333734+0.012960 test-rmse:1.127051+0.285633
## [229] train-rmse:0.332296+0.013108 test-rmse:1.125777+0.285159
## [230] train-rmse:0.330971+0.012995 test-rmse:1.125983+0.286509
## [231] train-rmse:0.330099+0.012737 test-rmse:1.126748+0.286658
## [232] train-rmse:0.328102+0.012495 test-rmse:1.125641+0.287272
## [233] train-rmse:0.326889+0.012302 test-rmse:1.125750+0.286948
## [234] train-rmse:0.325585+0.011924 test-rmse:1.125435+0.286821
## [235] train-rmse:0.324499+0.011643 test-rmse:1.124466+0.287245
## [236] train-rmse:0.323615+0.011599 test-rmse:1.123926+0.287573
## [237] train-rmse:0.322378+0.012220 test-rmse:1.123282+0.288616
## [238] train-rmse:0.321191+0.011527 test-rmse:1.123292+0.287047
## [239] train-rmse:0.319854+0.011795 test-rmse:1.122780+0.287457
## [240] train-rmse:0.319276+0.011641 test-rmse:1.122365+0.288112
## [241] train-rmse:0.317706+0.010957 test-rmse:1.122240+0.288628
## [242] train-rmse:0.316688+0.011050 test-rmse:1.121318+0.287927
## [243] train-rmse:0.315514+0.011132 test-rmse:1.121388+0.287642
## [244] train-rmse:0.314428+0.011374 test-rmse:1.121904+0.287694
## [245] train-rmse:0.313649+0.011454 test-rmse:1.121918+0.287532
## [246] train-rmse:0.312858+0.011138 test-rmse:1.120997+0.286896
## [247] train-rmse:0.311455+0.011220 test-rmse:1.120527+0.287321
## [248] train-rmse:0.310497+0.011226 test-rmse:1.120499+0.287243
## [249] train-rmse:0.309231+0.011220 test-rmse:1.120266+0.287506
## [250] train-rmse:0.307567+0.011719 test-rmse:1.119713+0.287100
## [251] train-rmse:0.306612+0.011722 test-rmse:1.119279+0.287348
## [252] train-rmse:0.305414+0.011918 test-rmse:1.118701+0.287710
## [253] train-rmse:0.304732+0.011885 test-rmse:1.118614+0.287762
## [254] train-rmse:0.303012+0.011801 test-rmse:1.118778+0.287855
## [255] train-rmse:0.301751+0.012117 test-rmse:1.118048+0.287992
## [256] train-rmse:0.300619+0.012364 test-rmse:1.117373+0.288178
## [257] train-rmse:0.299171+0.012553 test-rmse:1.117482+0.288204
## [258] train-rmse:0.298088+0.012692 test-rmse:1.116833+0.288214
## [259] train-rmse:0.296828+0.012226 test-rmse:1.116202+0.288759
## [260] train-rmse:0.295631+0.012578 test-rmse:1.114852+0.289586
## [261] train-rmse:0.294724+0.012665 test-rmse:1.114791+0.290283
## [262] train-rmse:0.293282+0.012271 test-rmse:1.116280+0.290112
## [263] train-rmse:0.292726+0.012154 test-rmse:1.115618+0.289823
## [264] train-rmse:0.291482+0.012658 test-rmse:1.116137+0.289725
## [265] train-rmse:0.290112+0.012833 test-rmse:1.116982+0.290136
## [266] train-rmse:0.289286+0.012961 test-rmse:1.117491+0.289647
## [267] train-rmse:0.288678+0.012974 test-rmse:1.116567+0.289394
## Stopping. Best iteration:
## [261] train-rmse:0.294724+0.012665 test-rmse:1.114791+0.290283
##
## 58
## [1] train-rmse:79.809479+0.092102 test-rmse:79.809535+0.505799
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:59.256192+0.075941 test-rmse:59.255628+0.542464
## [3] train-rmse:44.071015+0.066791 test-rmse:44.069481+0.578759
## [4] train-rmse:32.877449+0.063877 test-rmse:32.874400+0.617127
## [5] train-rmse:24.614156+0.051485 test-rmse:24.681269+0.627135
## [6] train-rmse:18.506317+0.036734 test-rmse:18.594059+0.596130
## [7] train-rmse:14.002013+0.029239 test-rmse:14.074514+0.529601
## [8] train-rmse:10.693251+0.027448 test-rmse:10.798520+0.564686
## [9] train-rmse:8.273753+0.029730 test-rmse:8.358817+0.557105
## [10] train-rmse:6.514360+0.035823 test-rmse:6.644401+0.548786
## [11] train-rmse:5.258885+0.048743 test-rmse:5.404754+0.507402
## [12] train-rmse:4.364118+0.058693 test-rmse:4.498511+0.436104
## [13] train-rmse:3.744103+0.054990 test-rmse:3.924537+0.436294
## [14] train-rmse:3.299108+0.050096 test-rmse:3.532879+0.422723
## [15] train-rmse:2.988710+0.052487 test-rmse:3.258235+0.381828
## [16] train-rmse:2.758348+0.047292 test-rmse:3.038487+0.326751
## [17] train-rmse:2.584406+0.050466 test-rmse:2.887138+0.290905
## [18] train-rmse:2.441488+0.049342 test-rmse:2.764575+0.281589
## [19] train-rmse:2.327198+0.044394 test-rmse:2.655996+0.287138
## [20] train-rmse:2.231152+0.042987 test-rmse:2.572827+0.293801
## [21] train-rmse:2.139228+0.037538 test-rmse:2.514186+0.280575
## [22] train-rmse:2.056273+0.035608 test-rmse:2.439781+0.266171
## [23] train-rmse:1.981731+0.033937 test-rmse:2.372428+0.260549
## [24] train-rmse:1.910377+0.035367 test-rmse:2.317149+0.291686
## [25] train-rmse:1.844744+0.031660 test-rmse:2.266827+0.298508
## [26] train-rmse:1.781235+0.030497 test-rmse:2.203532+0.300823
## [27] train-rmse:1.721650+0.029095 test-rmse:2.164712+0.283426
## [28] train-rmse:1.666336+0.024908 test-rmse:2.124022+0.273105
## [29] train-rmse:1.612430+0.024291 test-rmse:2.071570+0.274195
## [30] train-rmse:1.560648+0.026156 test-rmse:2.027130+0.281178
## [31] train-rmse:1.513081+0.024782 test-rmse:1.974549+0.282878
## [32] train-rmse:1.467303+0.023088 test-rmse:1.941073+0.290210
## [33] train-rmse:1.423722+0.021889 test-rmse:1.897445+0.279042
## [34] train-rmse:1.381878+0.020950 test-rmse:1.857728+0.285803
## [35] train-rmse:1.339266+0.024483 test-rmse:1.825220+0.275765
## [36] train-rmse:1.300991+0.023568 test-rmse:1.791275+0.283012
## [37] train-rmse:1.260623+0.023050 test-rmse:1.761150+0.271335
## [38] train-rmse:1.224650+0.020216 test-rmse:1.734687+0.275572
## [39] train-rmse:1.190557+0.018513 test-rmse:1.712979+0.281662
## [40] train-rmse:1.157900+0.016855 test-rmse:1.682496+0.279131
## [41] train-rmse:1.123524+0.018059 test-rmse:1.649973+0.277178
## [42] train-rmse:1.093392+0.017625 test-rmse:1.618723+0.275178
## [43] train-rmse:1.064672+0.017158 test-rmse:1.591451+0.270729
## [44] train-rmse:1.036351+0.017184 test-rmse:1.566810+0.279177
## [45] train-rmse:1.010859+0.017563 test-rmse:1.549730+0.272851
## [46] train-rmse:0.985147+0.017630 test-rmse:1.535277+0.278462
## [47] train-rmse:0.962336+0.017330 test-rmse:1.514352+0.280747
## [48] train-rmse:0.938477+0.016209 test-rmse:1.503368+0.283314
## [49] train-rmse:0.914929+0.016132 test-rmse:1.486259+0.281551
## [50] train-rmse:0.893219+0.016048 test-rmse:1.469454+0.279990
## [51] train-rmse:0.871511+0.015371 test-rmse:1.442725+0.279442
## [52] train-rmse:0.850285+0.015961 test-rmse:1.429372+0.289364
## [53] train-rmse:0.828570+0.018237 test-rmse:1.414840+0.294961
## [54] train-rmse:0.810030+0.018200 test-rmse:1.405934+0.290783
## [55] train-rmse:0.792748+0.016753 test-rmse:1.392710+0.300281
## [56] train-rmse:0.775993+0.016568 test-rmse:1.379885+0.301844
## [57] train-rmse:0.757456+0.018519 test-rmse:1.371121+0.299794
## [58] train-rmse:0.739394+0.020496 test-rmse:1.357869+0.298942
## [59] train-rmse:0.723399+0.019742 test-rmse:1.345274+0.295427
## [60] train-rmse:0.707587+0.019114 test-rmse:1.332716+0.302018
## [61] train-rmse:0.692294+0.020260 test-rmse:1.326938+0.296491
## [62] train-rmse:0.677077+0.020924 test-rmse:1.321333+0.294959
## [63] train-rmse:0.664323+0.020690 test-rmse:1.312222+0.295761
## [64] train-rmse:0.652202+0.020920 test-rmse:1.302210+0.297726
## [65] train-rmse:0.640488+0.022572 test-rmse:1.296198+0.298559
## [66] train-rmse:0.628936+0.023125 test-rmse:1.283255+0.300537
## [67] train-rmse:0.618440+0.023297 test-rmse:1.278454+0.302627
## [68] train-rmse:0.606413+0.020542 test-rmse:1.271731+0.302731
## [69] train-rmse:0.596580+0.020603 test-rmse:1.266253+0.302137
## [70] train-rmse:0.585684+0.020320 test-rmse:1.261441+0.302218
## [71] train-rmse:0.573870+0.020397 test-rmse:1.256289+0.302620
## [72] train-rmse:0.563668+0.018420 test-rmse:1.248077+0.304356
## [73] train-rmse:0.555104+0.017683 test-rmse:1.237860+0.304615
## [74] train-rmse:0.546148+0.017202 test-rmse:1.234458+0.305431
## [75] train-rmse:0.536966+0.018157 test-rmse:1.227535+0.307093
## [76] train-rmse:0.527921+0.016036 test-rmse:1.224672+0.309836
## [77] train-rmse:0.519218+0.014432 test-rmse:1.224250+0.314249
## [78] train-rmse:0.510949+0.014921 test-rmse:1.222839+0.314961
## [79] train-rmse:0.502198+0.015250 test-rmse:1.218415+0.317486
## [80] train-rmse:0.493168+0.016803 test-rmse:1.215569+0.318687
## [81] train-rmse:0.486400+0.016031 test-rmse:1.212176+0.319078
## [82] train-rmse:0.480049+0.016447 test-rmse:1.205914+0.320915
## [83] train-rmse:0.473919+0.015831 test-rmse:1.200909+0.325934
## [84] train-rmse:0.466192+0.015385 test-rmse:1.198371+0.327360
## [85] train-rmse:0.458469+0.016286 test-rmse:1.196133+0.329575
## [86] train-rmse:0.453265+0.015940 test-rmse:1.189451+0.326547
## [87] train-rmse:0.446902+0.016519 test-rmse:1.186076+0.323738
## [88] train-rmse:0.441207+0.017162 test-rmse:1.183668+0.325577
## [89] train-rmse:0.436031+0.017169 test-rmse:1.175982+0.326289
## [90] train-rmse:0.429591+0.015974 test-rmse:1.174695+0.327139
## [91] train-rmse:0.424334+0.016706 test-rmse:1.173661+0.326774
## [92] train-rmse:0.419174+0.016637 test-rmse:1.173020+0.326699
## [93] train-rmse:0.414554+0.016773 test-rmse:1.167871+0.328084
## [94] train-rmse:0.408835+0.018394 test-rmse:1.163513+0.329184
## [95] train-rmse:0.403875+0.018806 test-rmse:1.162236+0.329947
## [96] train-rmse:0.398417+0.019752 test-rmse:1.161824+0.331943
## [97] train-rmse:0.393343+0.019213 test-rmse:1.160457+0.333195
## [98] train-rmse:0.387919+0.017936 test-rmse:1.158857+0.335774
## [99] train-rmse:0.383946+0.017656 test-rmse:1.156038+0.337922
## [100] train-rmse:0.378492+0.016723 test-rmse:1.152404+0.336480
## [101] train-rmse:0.373488+0.017040 test-rmse:1.150796+0.338346
## [102] train-rmse:0.368739+0.016699 test-rmse:1.150291+0.338208
## [103] train-rmse:0.364239+0.016182 test-rmse:1.149902+0.336082
## [104] train-rmse:0.359565+0.015642 test-rmse:1.150282+0.335753
## [105] train-rmse:0.355926+0.015131 test-rmse:1.149264+0.335069
## [106] train-rmse:0.351708+0.014588 test-rmse:1.148948+0.339906
## [107] train-rmse:0.348381+0.014458 test-rmse:1.147208+0.340111
## [108] train-rmse:0.345400+0.014490 test-rmse:1.145463+0.340661
## [109] train-rmse:0.342429+0.014582 test-rmse:1.143658+0.339463
## [110] train-rmse:0.339164+0.015028 test-rmse:1.141499+0.340530
## [111] train-rmse:0.335559+0.014035 test-rmse:1.139255+0.337756
## [112] train-rmse:0.332007+0.015322 test-rmse:1.136160+0.338157
## [113] train-rmse:0.327696+0.016901 test-rmse:1.134095+0.339689
## [114] train-rmse:0.324575+0.016476 test-rmse:1.131809+0.340083
## [115] train-rmse:0.320014+0.016493 test-rmse:1.131941+0.341327
## [116] train-rmse:0.316915+0.015369 test-rmse:1.130504+0.340055
## [117] train-rmse:0.314080+0.015733 test-rmse:1.130127+0.341089
## [118] train-rmse:0.310596+0.015443 test-rmse:1.129754+0.343009
## [119] train-rmse:0.305968+0.014348 test-rmse:1.127334+0.343728
## [120] train-rmse:0.302917+0.013814 test-rmse:1.125843+0.345529
## [121] train-rmse:0.299920+0.013441 test-rmse:1.123930+0.344228
## [122] train-rmse:0.296551+0.014881 test-rmse:1.122679+0.344678
## [123] train-rmse:0.293616+0.014541 test-rmse:1.122272+0.344947
## [124] train-rmse:0.291481+0.014889 test-rmse:1.119477+0.346645
## [125] train-rmse:0.289298+0.015102 test-rmse:1.118775+0.346677
## [126] train-rmse:0.286030+0.013823 test-rmse:1.118418+0.346896
## [127] train-rmse:0.282106+0.014555 test-rmse:1.117616+0.346370
## [128] train-rmse:0.279512+0.014298 test-rmse:1.116695+0.347329
## [129] train-rmse:0.277180+0.014254 test-rmse:1.115427+0.347992
## [130] train-rmse:0.274928+0.013855 test-rmse:1.114196+0.348202
## [131] train-rmse:0.272283+0.014231 test-rmse:1.113534+0.349038
## [132] train-rmse:0.270066+0.014490 test-rmse:1.113049+0.349768
## [133] train-rmse:0.266192+0.013596 test-rmse:1.112704+0.350765
## [134] train-rmse:0.263240+0.013518 test-rmse:1.112783+0.350925
## [135] train-rmse:0.260517+0.012518 test-rmse:1.112138+0.351644
## [136] train-rmse:0.258553+0.012305 test-rmse:1.110965+0.349903
## [137] train-rmse:0.255692+0.013226 test-rmse:1.112057+0.352657
## [138] train-rmse:0.253353+0.013213 test-rmse:1.112018+0.353437
## [139] train-rmse:0.251435+0.013076 test-rmse:1.111678+0.354490
## [140] train-rmse:0.248109+0.013720 test-rmse:1.111125+0.355363
## [141] train-rmse:0.246348+0.013805 test-rmse:1.111073+0.355038
## [142] train-rmse:0.244255+0.013691 test-rmse:1.110070+0.355494
## [143] train-rmse:0.241622+0.013861 test-rmse:1.109358+0.355537
## [144] train-rmse:0.240296+0.014095 test-rmse:1.106872+0.356870
## [145] train-rmse:0.237529+0.013829 test-rmse:1.106606+0.359075
## [146] train-rmse:0.235435+0.014036 test-rmse:1.105127+0.358816
## [147] train-rmse:0.232628+0.013886 test-rmse:1.105060+0.359803
## [148] train-rmse:0.230316+0.012924 test-rmse:1.105334+0.360152
## [149] train-rmse:0.228706+0.013208 test-rmse:1.104653+0.359265
## [150] train-rmse:0.226575+0.013170 test-rmse:1.105002+0.360367
## [151] train-rmse:0.223881+0.014657 test-rmse:1.104693+0.360525
## [152] train-rmse:0.222058+0.014335 test-rmse:1.104639+0.360609
## [153] train-rmse:0.220274+0.014225 test-rmse:1.103934+0.361619
## [154] train-rmse:0.217673+0.014880 test-rmse:1.103600+0.360201
## [155] train-rmse:0.216074+0.014780 test-rmse:1.103365+0.360512
## [156] train-rmse:0.214653+0.014713 test-rmse:1.102591+0.360430
## [157] train-rmse:0.212805+0.014255 test-rmse:1.101457+0.361374
## [158] train-rmse:0.211193+0.013882 test-rmse:1.101220+0.360837
## [159] train-rmse:0.209632+0.013742 test-rmse:1.101039+0.361013
## [160] train-rmse:0.207977+0.014027 test-rmse:1.100693+0.361217
## [161] train-rmse:0.206380+0.014053 test-rmse:1.100998+0.361936
## [162] train-rmse:0.204943+0.014386 test-rmse:1.100568+0.362537
## [163] train-rmse:0.202485+0.013211 test-rmse:1.099405+0.363667
## [164] train-rmse:0.200537+0.012721 test-rmse:1.099422+0.363346
## [165] train-rmse:0.198462+0.013072 test-rmse:1.099330+0.363229
## [166] train-rmse:0.197160+0.013101 test-rmse:1.099174+0.363299
## [167] train-rmse:0.195520+0.012882 test-rmse:1.099034+0.363385
## [168] train-rmse:0.193952+0.013115 test-rmse:1.098583+0.363504
## [169] train-rmse:0.192515+0.012533 test-rmse:1.097922+0.362159
## [170] train-rmse:0.191182+0.012257 test-rmse:1.097834+0.362029
## [171] train-rmse:0.189601+0.013110 test-rmse:1.097742+0.362211
## [172] train-rmse:0.188385+0.013162 test-rmse:1.096048+0.361453
## [173] train-rmse:0.186855+0.013730 test-rmse:1.096137+0.361372
## [174] train-rmse:0.185286+0.014399 test-rmse:1.096766+0.361171
## [175] train-rmse:0.183868+0.014570 test-rmse:1.096819+0.361219
## [176] train-rmse:0.182219+0.014534 test-rmse:1.096288+0.361560
## [177] train-rmse:0.180591+0.014858 test-rmse:1.097183+0.361223
## [178] train-rmse:0.178660+0.014257 test-rmse:1.097411+0.361383
## Stopping. Best iteration:
## [172] train-rmse:0.188385+0.013162 test-rmse:1.096048+0.361453
##
## 59
## [1] train-rmse:79.809479+0.092102 test-rmse:79.809535+0.505799
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:59.256192+0.075941 test-rmse:59.255628+0.542464
## [3] train-rmse:44.071015+0.066791 test-rmse:44.069481+0.578759
## [4] train-rmse:32.877449+0.063877 test-rmse:32.874400+0.617127
## [5] train-rmse:24.614156+0.051485 test-rmse:24.681269+0.627135
## [6] train-rmse:18.505762+0.036764 test-rmse:18.586601+0.594150
## [7] train-rmse:13.991944+0.030477 test-rmse:14.063530+0.521900
## [8] train-rmse:10.659516+0.026283 test-rmse:10.771836+0.541636
## [9] train-rmse:8.206978+0.023201 test-rmse:8.366505+0.576332
## [10] train-rmse:6.404964+0.024609 test-rmse:6.531586+0.529098
## [11] train-rmse:5.099514+0.033020 test-rmse:5.246194+0.497309
## [12] train-rmse:4.157289+0.038677 test-rmse:4.353924+0.493435
## [13] train-rmse:3.471049+0.034651 test-rmse:3.719858+0.430112
## [14] train-rmse:2.989294+0.029568 test-rmse:3.273025+0.377721
## [15] train-rmse:2.637283+0.042304 test-rmse:2.964900+0.377535
## [16] train-rmse:2.389280+0.041189 test-rmse:2.751946+0.347444
## [17] train-rmse:2.189467+0.048458 test-rmse:2.613825+0.292384
## [18] train-rmse:2.028198+0.049407 test-rmse:2.473158+0.250086
## [19] train-rmse:1.900346+0.041810 test-rmse:2.353399+0.222164
## [20] train-rmse:1.784591+0.035322 test-rmse:2.262904+0.205007
## [21] train-rmse:1.690691+0.032379 test-rmse:2.207786+0.199742
## [22] train-rmse:1.604263+0.040375 test-rmse:2.124023+0.196424
## [23] train-rmse:1.528976+0.038758 test-rmse:2.085101+0.200434
## [24] train-rmse:1.463242+0.039250 test-rmse:2.021041+0.186800
## [25] train-rmse:1.395346+0.038429 test-rmse:1.973152+0.175164
## [26] train-rmse:1.336650+0.039142 test-rmse:1.920606+0.180342
## [27] train-rmse:1.282160+0.037360 test-rmse:1.861067+0.174916
## [28] train-rmse:1.221782+0.040870 test-rmse:1.830181+0.167459
## [29] train-rmse:1.175684+0.041099 test-rmse:1.785170+0.159381
## [30] train-rmse:1.129000+0.038492 test-rmse:1.755504+0.165982
## [31] train-rmse:1.084232+0.037987 test-rmse:1.729840+0.163075
## [32] train-rmse:1.041403+0.030361 test-rmse:1.697426+0.165898
## [33] train-rmse:0.999985+0.030522 test-rmse:1.678007+0.161987
## [34] train-rmse:0.964101+0.031845 test-rmse:1.644123+0.164218
## [35] train-rmse:0.928768+0.031884 test-rmse:1.607884+0.173005
## [36] train-rmse:0.895105+0.030320 test-rmse:1.588723+0.170454
## [37] train-rmse:0.862395+0.027938 test-rmse:1.563290+0.182722
## [38] train-rmse:0.834567+0.028981 test-rmse:1.540058+0.183976
## [39] train-rmse:0.807107+0.028437 test-rmse:1.522223+0.186388
## [40] train-rmse:0.778493+0.027474 test-rmse:1.504352+0.188389
## [41] train-rmse:0.753994+0.026124 test-rmse:1.484436+0.191433
## [42] train-rmse:0.731394+0.025879 test-rmse:1.472876+0.198605
## [43] train-rmse:0.708147+0.024076 test-rmse:1.449761+0.201028
## [44] train-rmse:0.681893+0.027291 test-rmse:1.442959+0.206032
## [45] train-rmse:0.661689+0.026394 test-rmse:1.437653+0.210855
## [46] train-rmse:0.639457+0.026698 test-rmse:1.429321+0.210663
## [47] train-rmse:0.621103+0.026231 test-rmse:1.417218+0.212969
## [48] train-rmse:0.604187+0.027135 test-rmse:1.403644+0.213638
## [49] train-rmse:0.586049+0.026745 test-rmse:1.394297+0.216116
## [50] train-rmse:0.570069+0.024984 test-rmse:1.381271+0.220189
## [51] train-rmse:0.554544+0.023710 test-rmse:1.373722+0.224331
## [52] train-rmse:0.541207+0.024196 test-rmse:1.365996+0.225137
## [53] train-rmse:0.526532+0.022800 test-rmse:1.362397+0.226275
## [54] train-rmse:0.514104+0.022084 test-rmse:1.350083+0.226404
## [55] train-rmse:0.500592+0.022938 test-rmse:1.344753+0.232372
## [56] train-rmse:0.488805+0.023491 test-rmse:1.340654+0.232586
## [57] train-rmse:0.475623+0.021467 test-rmse:1.336338+0.233270
## [58] train-rmse:0.463456+0.023044 test-rmse:1.330088+0.235923
## [59] train-rmse:0.454115+0.023448 test-rmse:1.325317+0.228957
## [60] train-rmse:0.444582+0.022579 test-rmse:1.319533+0.230896
## [61] train-rmse:0.433541+0.023457 test-rmse:1.313689+0.237191
## [62] train-rmse:0.422250+0.023568 test-rmse:1.311031+0.240821
## [63] train-rmse:0.412840+0.023005 test-rmse:1.303962+0.242931
## [64] train-rmse:0.404253+0.023487 test-rmse:1.301127+0.245498
## [65] train-rmse:0.394620+0.024516 test-rmse:1.294895+0.235915
## [66] train-rmse:0.386251+0.023043 test-rmse:1.290084+0.241218
## [67] train-rmse:0.376645+0.021294 test-rmse:1.285468+0.241008
## [68] train-rmse:0.368825+0.021483 test-rmse:1.279824+0.240860
## [69] train-rmse:0.360530+0.019571 test-rmse:1.276638+0.242522
## [70] train-rmse:0.353712+0.018520 test-rmse:1.275368+0.242842
## [71] train-rmse:0.346361+0.018978 test-rmse:1.265073+0.237530
## [72] train-rmse:0.340233+0.018037 test-rmse:1.264546+0.236943
## [73] train-rmse:0.332897+0.017516 test-rmse:1.262678+0.239180
## [74] train-rmse:0.326306+0.016129 test-rmse:1.262435+0.237636
## [75] train-rmse:0.319661+0.016438 test-rmse:1.256822+0.238570
## [76] train-rmse:0.313437+0.016629 test-rmse:1.254371+0.238875
## [77] train-rmse:0.307391+0.014801 test-rmse:1.252957+0.238849
## [78] train-rmse:0.301620+0.014351 test-rmse:1.253846+0.240666
## [79] train-rmse:0.296685+0.014877 test-rmse:1.252017+0.240921
## [80] train-rmse:0.291592+0.015029 test-rmse:1.247013+0.245659
## [81] train-rmse:0.286847+0.015007 test-rmse:1.246903+0.245589
## [82] train-rmse:0.281580+0.016173 test-rmse:1.244723+0.246687
## [83] train-rmse:0.276125+0.016802 test-rmse:1.242925+0.248038
## [84] train-rmse:0.270717+0.016014 test-rmse:1.242300+0.248123
## [85] train-rmse:0.265403+0.014484 test-rmse:1.240050+0.249870
## [86] train-rmse:0.261274+0.013881 test-rmse:1.239323+0.250538
## [87] train-rmse:0.257434+0.013765 test-rmse:1.238345+0.252310
## [88] train-rmse:0.253692+0.013957 test-rmse:1.237295+0.252826
## [89] train-rmse:0.249406+0.013557 test-rmse:1.235311+0.253646
## [90] train-rmse:0.244182+0.013812 test-rmse:1.235085+0.253846
## [91] train-rmse:0.240012+0.012710 test-rmse:1.233590+0.255019
## [92] train-rmse:0.236414+0.013188 test-rmse:1.232791+0.255117
## [93] train-rmse:0.232677+0.012259 test-rmse:1.231045+0.257243
## [94] train-rmse:0.228711+0.013036 test-rmse:1.230598+0.259049
## [95] train-rmse:0.224694+0.012805 test-rmse:1.230811+0.260409
## [96] train-rmse:0.220931+0.011705 test-rmse:1.231152+0.261606
## [97] train-rmse:0.216061+0.011819 test-rmse:1.230117+0.261180
## [98] train-rmse:0.212366+0.010549 test-rmse:1.230082+0.261873
## [99] train-rmse:0.209258+0.010421 test-rmse:1.229073+0.262033
## [100] train-rmse:0.205501+0.009435 test-rmse:1.229450+0.262228
## [101] train-rmse:0.202128+0.009987 test-rmse:1.228959+0.262343
## [102] train-rmse:0.199333+0.008958 test-rmse:1.228723+0.262947
## [103] train-rmse:0.196153+0.009302 test-rmse:1.228206+0.261897
## [104] train-rmse:0.192937+0.009257 test-rmse:1.227074+0.262687
## [105] train-rmse:0.189739+0.010009 test-rmse:1.227522+0.262975
## [106] train-rmse:0.186639+0.010075 test-rmse:1.225743+0.263534
## [107] train-rmse:0.184805+0.009797 test-rmse:1.225582+0.263894
## [108] train-rmse:0.182509+0.008966 test-rmse:1.224314+0.264771
## [109] train-rmse:0.179452+0.009758 test-rmse:1.223366+0.263676
## [110] train-rmse:0.175765+0.009368 test-rmse:1.222556+0.263616
## [111] train-rmse:0.173119+0.009993 test-rmse:1.221416+0.262793
## [112] train-rmse:0.170758+0.010270 test-rmse:1.221199+0.262997
## [113] train-rmse:0.167755+0.010697 test-rmse:1.219989+0.263401
## [114] train-rmse:0.165156+0.010584 test-rmse:1.219315+0.263902
## [115] train-rmse:0.162884+0.010952 test-rmse:1.219958+0.264313
## [116] train-rmse:0.160787+0.010585 test-rmse:1.219775+0.264702
## [117] train-rmse:0.158256+0.010279 test-rmse:1.218743+0.264896
## [118] train-rmse:0.156335+0.010234 test-rmse:1.218063+0.264791
## [119] train-rmse:0.154027+0.009914 test-rmse:1.217689+0.264825
## [120] train-rmse:0.151670+0.010153 test-rmse:1.217890+0.265001
## [121] train-rmse:0.150023+0.010196 test-rmse:1.217827+0.265298
## [122] train-rmse:0.148386+0.009977 test-rmse:1.217823+0.265180
## [123] train-rmse:0.145467+0.008862 test-rmse:1.218410+0.265444
## [124] train-rmse:0.143296+0.008917 test-rmse:1.217707+0.265996
## [125] train-rmse:0.141604+0.008995 test-rmse:1.216995+0.265611
## [126] train-rmse:0.139429+0.009745 test-rmse:1.216901+0.266084
## [127] train-rmse:0.137781+0.008967 test-rmse:1.216306+0.266278
## [128] train-rmse:0.135735+0.008891 test-rmse:1.216152+0.266417
## [129] train-rmse:0.133830+0.008806 test-rmse:1.214463+0.266210
## [130] train-rmse:0.132014+0.008450 test-rmse:1.214441+0.266542
## [131] train-rmse:0.130507+0.008306 test-rmse:1.214158+0.266312
## [132] train-rmse:0.128364+0.008078 test-rmse:1.214352+0.266147
## [133] train-rmse:0.126052+0.007839 test-rmse:1.212309+0.264514
## [134] train-rmse:0.124052+0.007407 test-rmse:1.212003+0.264265
## [135] train-rmse:0.122100+0.008449 test-rmse:1.211673+0.265130
## [136] train-rmse:0.120268+0.008051 test-rmse:1.211617+0.264884
## [137] train-rmse:0.119284+0.008008 test-rmse:1.211380+0.265113
## [138] train-rmse:0.117198+0.007994 test-rmse:1.211847+0.264823
## [139] train-rmse:0.115838+0.008121 test-rmse:1.211658+0.265257
## [140] train-rmse:0.113892+0.008399 test-rmse:1.211483+0.265123
## [141] train-rmse:0.112361+0.008278 test-rmse:1.211014+0.265342
## [142] train-rmse:0.110919+0.008029 test-rmse:1.210700+0.265593
## [143] train-rmse:0.109135+0.007258 test-rmse:1.210354+0.265263
## [144] train-rmse:0.107264+0.007151 test-rmse:1.210835+0.264986
## [145] train-rmse:0.105479+0.006975 test-rmse:1.211338+0.264993
## [146] train-rmse:0.103778+0.006297 test-rmse:1.210642+0.264630
## [147] train-rmse:0.102435+0.005821 test-rmse:1.210080+0.264649
## [148] train-rmse:0.101297+0.006010 test-rmse:1.209529+0.264144
## [149] train-rmse:0.100497+0.006342 test-rmse:1.209123+0.263992
## [150] train-rmse:0.099014+0.006561 test-rmse:1.209058+0.264204
## [151] train-rmse:0.097187+0.006496 test-rmse:1.209004+0.264312
## [152] train-rmse:0.095830+0.006663 test-rmse:1.209072+0.264298
## [153] train-rmse:0.094659+0.006675 test-rmse:1.208881+0.264205
## [154] train-rmse:0.093275+0.006715 test-rmse:1.208399+0.264416
## [155] train-rmse:0.091876+0.006797 test-rmse:1.208557+0.264487
## [156] train-rmse:0.090559+0.006809 test-rmse:1.208890+0.264317
## [157] train-rmse:0.089778+0.006903 test-rmse:1.208721+0.264286
## [158] train-rmse:0.088889+0.007160 test-rmse:1.208159+0.264931
## [159] train-rmse:0.087758+0.007241 test-rmse:1.207835+0.264884
## [160] train-rmse:0.086880+0.007108 test-rmse:1.207855+0.265202
## [161] train-rmse:0.086103+0.007087 test-rmse:1.207397+0.265146
## [162] train-rmse:0.084954+0.007406 test-rmse:1.207587+0.265170
## [163] train-rmse:0.084089+0.007505 test-rmse:1.207334+0.264955
## [164] train-rmse:0.082455+0.007278 test-rmse:1.207150+0.265273
## [165] train-rmse:0.081514+0.007501 test-rmse:1.206864+0.264711
## [166] train-rmse:0.080794+0.007673 test-rmse:1.206886+0.264674
## [167] train-rmse:0.079789+0.007788 test-rmse:1.207241+0.264707
## [168] train-rmse:0.078459+0.007543 test-rmse:1.206155+0.263544
## [169] train-rmse:0.077447+0.007532 test-rmse:1.205726+0.263904
## [170] train-rmse:0.076717+0.007452 test-rmse:1.205596+0.263846
## [171] train-rmse:0.075692+0.007214 test-rmse:1.206291+0.264956
## [172] train-rmse:0.074624+0.006945 test-rmse:1.206466+0.266311
## [173] train-rmse:0.074100+0.006952 test-rmse:1.206159+0.266512
## [174] train-rmse:0.073314+0.006659 test-rmse:1.205853+0.266562
## [175] train-rmse:0.072506+0.006908 test-rmse:1.205372+0.266658
## [176] train-rmse:0.071599+0.006540 test-rmse:1.205107+0.266941
## [177] train-rmse:0.071009+0.006476 test-rmse:1.204459+0.265889
## [178] train-rmse:0.069984+0.006446 test-rmse:1.204505+0.265920
## [179] train-rmse:0.069287+0.006545 test-rmse:1.204240+0.265635
## [180] train-rmse:0.068013+0.006584 test-rmse:1.203958+0.265379
## [181] train-rmse:0.066900+0.006455 test-rmse:1.204168+0.265772
## [182] train-rmse:0.066317+0.006616 test-rmse:1.204094+0.265864
## [183] train-rmse:0.065562+0.006459 test-rmse:1.203863+0.266018
## [184] train-rmse:0.064587+0.006414 test-rmse:1.203765+0.266167
## [185] train-rmse:0.063783+0.006348 test-rmse:1.203198+0.265304
## [186] train-rmse:0.062940+0.006578 test-rmse:1.202903+0.264848
## [187] train-rmse:0.062218+0.006751 test-rmse:1.202983+0.264615
## [188] train-rmse:0.061469+0.006703 test-rmse:1.202912+0.264795
## [189] train-rmse:0.060329+0.006542 test-rmse:1.202868+0.264810
## [190] train-rmse:0.059770+0.006532 test-rmse:1.202830+0.264990
## [191] train-rmse:0.059258+0.006739 test-rmse:1.202705+0.265031
## [192] train-rmse:0.058483+0.006725 test-rmse:1.202568+0.265181
## [193] train-rmse:0.057560+0.006452 test-rmse:1.201569+0.264551
## [194] train-rmse:0.057004+0.006772 test-rmse:1.201484+0.264642
## [195] train-rmse:0.056251+0.006467 test-rmse:1.201482+0.264679
## [196] train-rmse:0.055691+0.006511 test-rmse:1.201199+0.264730
## [197] train-rmse:0.055025+0.006712 test-rmse:1.201326+0.264534
## [198] train-rmse:0.054329+0.006779 test-rmse:1.201414+0.264783
## [199] train-rmse:0.053749+0.006645 test-rmse:1.201395+0.264757
## [200] train-rmse:0.053155+0.006525 test-rmse:1.201378+0.265046
## [201] train-rmse:0.052645+0.006629 test-rmse:1.201378+0.265052
## [202] train-rmse:0.051989+0.006587 test-rmse:1.201267+0.265136
## Stopping. Best iteration:
## [196] train-rmse:0.055691+0.006511 test-rmse:1.201199+0.264730
##
## 60
## [1] train-rmse:79.809479+0.092102 test-rmse:79.809535+0.505799
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:59.256192+0.075941 test-rmse:59.255628+0.542464
## [3] train-rmse:44.071015+0.066791 test-rmse:44.069481+0.578759
## [4] train-rmse:32.877449+0.063877 test-rmse:32.874400+0.617127
## [5] train-rmse:24.614156+0.051485 test-rmse:24.681269+0.627135
## [6] train-rmse:18.505762+0.036764 test-rmse:18.586601+0.594150
## [7] train-rmse:13.983832+0.031561 test-rmse:14.052284+0.524473
## [8] train-rmse:10.637425+0.024293 test-rmse:10.766325+0.576050
## [9] train-rmse:8.158321+0.026011 test-rmse:8.287571+0.541326
## [10] train-rmse:6.321855+0.020681 test-rmse:6.447181+0.528021
## [11] train-rmse:4.975350+0.027693 test-rmse:5.132631+0.538519
## [12] train-rmse:3.983543+0.030135 test-rmse:4.185880+0.481579
## [13] train-rmse:3.272550+0.034439 test-rmse:3.515236+0.450243
## [14] train-rmse:2.737396+0.023057 test-rmse:3.029197+0.395248
## [15] train-rmse:2.359951+0.013946 test-rmse:2.701978+0.361824
## [16] train-rmse:2.072142+0.021409 test-rmse:2.495889+0.329945
## [17] train-rmse:1.867856+0.022464 test-rmse:2.314856+0.288424
## [18] train-rmse:1.707267+0.024998 test-rmse:2.182618+0.280317
## [19] train-rmse:1.569173+0.036006 test-rmse:2.081442+0.252271
## [20] train-rmse:1.464921+0.035836 test-rmse:2.019723+0.232268
## [21] train-rmse:1.375988+0.036855 test-rmse:1.944152+0.229297
## [22] train-rmse:1.294613+0.037838 test-rmse:1.875842+0.216432
## [23] train-rmse:1.211586+0.045513 test-rmse:1.831568+0.230929
## [24] train-rmse:1.143012+0.041921 test-rmse:1.773352+0.211269
## [25] train-rmse:1.082662+0.037386 test-rmse:1.722185+0.212685
## [26] train-rmse:1.028085+0.040252 test-rmse:1.686823+0.212724
## [27] train-rmse:0.978925+0.039744 test-rmse:1.643611+0.217596
## [28] train-rmse:0.932247+0.040757 test-rmse:1.614215+0.217373
## [29] train-rmse:0.885377+0.036656 test-rmse:1.578875+0.225640
## [30] train-rmse:0.846488+0.035548 test-rmse:1.562452+0.232931
## [31] train-rmse:0.806831+0.026896 test-rmse:1.521451+0.227064
## [32] train-rmse:0.773149+0.026479 test-rmse:1.497278+0.226477
## [33] train-rmse:0.739320+0.027182 test-rmse:1.479309+0.228972
## [34] train-rmse:0.706224+0.027013 test-rmse:1.450559+0.228967
## [35] train-rmse:0.674753+0.029676 test-rmse:1.432603+0.229272
## [36] train-rmse:0.648940+0.027843 test-rmse:1.415042+0.229980
## [37] train-rmse:0.624678+0.024044 test-rmse:1.387793+0.213316
## [38] train-rmse:0.603232+0.023072 test-rmse:1.376980+0.220814
## [39] train-rmse:0.580050+0.022822 test-rmse:1.363737+0.222395
## [40] train-rmse:0.559103+0.022034 test-rmse:1.355566+0.228981
## [41] train-rmse:0.537718+0.024105 test-rmse:1.344808+0.236536
## [42] train-rmse:0.522988+0.023468 test-rmse:1.332314+0.241182
## [43] train-rmse:0.506007+0.022775 test-rmse:1.323967+0.242304
## [44] train-rmse:0.486072+0.021053 test-rmse:1.311849+0.246543
## [45] train-rmse:0.469901+0.021013 test-rmse:1.307492+0.248927
## [46] train-rmse:0.453909+0.020757 test-rmse:1.301660+0.253601
## [47] train-rmse:0.440049+0.021810 test-rmse:1.289341+0.250208
## [48] train-rmse:0.425395+0.020400 test-rmse:1.286713+0.252270
## [49] train-rmse:0.412455+0.019560 test-rmse:1.276139+0.242189
## [50] train-rmse:0.400712+0.019027 test-rmse:1.269870+0.244243
## [51] train-rmse:0.388178+0.018991 test-rmse:1.262334+0.238411
## [52] train-rmse:0.378167+0.018851 test-rmse:1.259853+0.243999
## [53] train-rmse:0.366578+0.018343 test-rmse:1.256972+0.242890
## [54] train-rmse:0.356067+0.015986 test-rmse:1.253625+0.241722
## [55] train-rmse:0.348038+0.016947 test-rmse:1.251346+0.244207
## [56] train-rmse:0.336440+0.012941 test-rmse:1.242667+0.240070
## [57] train-rmse:0.328211+0.013377 test-rmse:1.241006+0.240072
## [58] train-rmse:0.319387+0.013736 test-rmse:1.236325+0.238935
## [59] train-rmse:0.309225+0.011681 test-rmse:1.235665+0.240869
## [60] train-rmse:0.301795+0.013429 test-rmse:1.232625+0.241036
## [61] train-rmse:0.293779+0.011957 test-rmse:1.227524+0.246209
## [62] train-rmse:0.287641+0.011340 test-rmse:1.225928+0.247459
## [63] train-rmse:0.281413+0.011225 test-rmse:1.220633+0.240942
## [64] train-rmse:0.275007+0.010576 test-rmse:1.218746+0.241352
## [65] train-rmse:0.269580+0.011117 test-rmse:1.217956+0.243002
## [66] train-rmse:0.263859+0.011020 test-rmse:1.216783+0.241600
## [67] train-rmse:0.257625+0.011661 test-rmse:1.214891+0.245493
## [68] train-rmse:0.250314+0.009598 test-rmse:1.211758+0.245125
## [69] train-rmse:0.244026+0.009199 test-rmse:1.210147+0.242841
## [70] train-rmse:0.238043+0.007246 test-rmse:1.210000+0.244139
## [71] train-rmse:0.233470+0.008069 test-rmse:1.208781+0.245189
## [72] train-rmse:0.228706+0.007841 test-rmse:1.209421+0.245810
## [73] train-rmse:0.222614+0.005762 test-rmse:1.208420+0.246840
## [74] train-rmse:0.216029+0.006782 test-rmse:1.207503+0.248069
## [75] train-rmse:0.210618+0.006227 test-rmse:1.206151+0.248692
## [76] train-rmse:0.205893+0.005528 test-rmse:1.204766+0.247693
## [77] train-rmse:0.202412+0.005477 test-rmse:1.204238+0.247727
## [78] train-rmse:0.197188+0.005078 test-rmse:1.204391+0.247144
## [79] train-rmse:0.191296+0.006033 test-rmse:1.205681+0.244495
## [80] train-rmse:0.187137+0.006723 test-rmse:1.205411+0.245347
## [81] train-rmse:0.182210+0.006731 test-rmse:1.204811+0.245402
## [82] train-rmse:0.177085+0.007054 test-rmse:1.205157+0.244806
## [83] train-rmse:0.172897+0.007286 test-rmse:1.204989+0.244708
## Stopping. Best iteration:
## [77] train-rmse:0.202412+0.005477 test-rmse:1.204238+0.247727
##
## 61
## [1] train-rmse:79.809479+0.092102 test-rmse:79.809535+0.505799
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:59.256192+0.075941 test-rmse:59.255628+0.542464
## [3] train-rmse:44.071015+0.066791 test-rmse:44.069481+0.578759
## [4] train-rmse:32.877449+0.063877 test-rmse:32.874400+0.617127
## [5] train-rmse:24.614156+0.051485 test-rmse:24.681269+0.627135
## [6] train-rmse:18.505762+0.036764 test-rmse:18.586601+0.594150
## [7] train-rmse:13.976202+0.032315 test-rmse:14.050951+0.523410
## [8] train-rmse:10.619082+0.022655 test-rmse:10.747934+0.574611
## [9] train-rmse:8.130783+0.024911 test-rmse:8.262527+0.537729
## [10] train-rmse:6.264807+0.021943 test-rmse:6.394779+0.530510
## [11] train-rmse:4.889850+0.013924 test-rmse:5.058619+0.500756
## [12] train-rmse:3.881220+0.008913 test-rmse:4.095274+0.497993
## [13] train-rmse:3.132597+0.018000 test-rmse:3.391050+0.443168
## [14] train-rmse:2.581711+0.024653 test-rmse:2.876187+0.408493
## [15] train-rmse:2.175788+0.032227 test-rmse:2.539935+0.371330
## [16] train-rmse:1.872813+0.037115 test-rmse:2.301511+0.339305
## [17] train-rmse:1.651579+0.036493 test-rmse:2.124641+0.324529
## [18] train-rmse:1.482054+0.035398 test-rmse:1.985246+0.319313
## [19] train-rmse:1.337175+0.029476 test-rmse:1.879206+0.306576
## [20] train-rmse:1.229528+0.024495 test-rmse:1.793061+0.288175
## [21] train-rmse:1.112706+0.034821 test-rmse:1.723280+0.270923
## [22] train-rmse:1.028813+0.029732 test-rmse:1.666871+0.268385
## [23] train-rmse:0.961495+0.030854 test-rmse:1.612169+0.264037
## [24] train-rmse:0.891983+0.036976 test-rmse:1.576199+0.270183
## [25] train-rmse:0.837400+0.033044 test-rmse:1.538207+0.261903
## [26] train-rmse:0.789640+0.033200 test-rmse:1.506504+0.263813
## [27] train-rmse:0.744108+0.028802 test-rmse:1.465335+0.272135
## [28] train-rmse:0.702256+0.025298 test-rmse:1.446292+0.273129
## [29] train-rmse:0.662246+0.028888 test-rmse:1.420446+0.286362
## [30] train-rmse:0.628745+0.027432 test-rmse:1.398876+0.266047
## [31] train-rmse:0.599152+0.026225 test-rmse:1.373657+0.264992
## [32] train-rmse:0.573738+0.025601 test-rmse:1.359618+0.267609
## [33] train-rmse:0.548658+0.025764 test-rmse:1.345666+0.268911
## [34] train-rmse:0.520256+0.018894 test-rmse:1.329941+0.252711
## [35] train-rmse:0.498569+0.018229 test-rmse:1.311214+0.246279
## [36] train-rmse:0.478292+0.018830 test-rmse:1.302822+0.249019
## [37] train-rmse:0.456557+0.017852 test-rmse:1.292277+0.249261
## [38] train-rmse:0.439305+0.016011 test-rmse:1.278470+0.242930
## [39] train-rmse:0.422756+0.016688 test-rmse:1.267350+0.245575
## [40] train-rmse:0.403787+0.011623 test-rmse:1.260648+0.243771
## [41] train-rmse:0.391411+0.012049 test-rmse:1.253055+0.243552
## [42] train-rmse:0.377507+0.012793 test-rmse:1.245400+0.245198
## [43] train-rmse:0.358103+0.010350 test-rmse:1.242190+0.248652
## [44] train-rmse:0.341853+0.009818 test-rmse:1.238028+0.251133
## [45] train-rmse:0.332074+0.010452 test-rmse:1.235771+0.251678
## [46] train-rmse:0.318565+0.009169 test-rmse:1.231742+0.250705
## [47] train-rmse:0.306835+0.010952 test-rmse:1.228605+0.250027
## [48] train-rmse:0.296854+0.009544 test-rmse:1.226442+0.251805
## [49] train-rmse:0.285646+0.007145 test-rmse:1.219592+0.244823
## [50] train-rmse:0.276984+0.006284 test-rmse:1.219032+0.246753
## [51] train-rmse:0.269009+0.006670 test-rmse:1.216587+0.241916
## [52] train-rmse:0.258533+0.005889 test-rmse:1.211641+0.245431
## [53] train-rmse:0.249442+0.004636 test-rmse:1.207502+0.239437
## [54] train-rmse:0.242876+0.005715 test-rmse:1.208554+0.240719
## [55] train-rmse:0.233157+0.005527 test-rmse:1.206532+0.242155
## [56] train-rmse:0.227583+0.005966 test-rmse:1.205860+0.242910
## [57] train-rmse:0.222099+0.005679 test-rmse:1.202600+0.241785
## [58] train-rmse:0.213999+0.006148 test-rmse:1.197744+0.241524
## [59] train-rmse:0.206618+0.007210 test-rmse:1.196440+0.241919
## [60] train-rmse:0.200899+0.007911 test-rmse:1.194064+0.241401
## [61] train-rmse:0.196233+0.007725 test-rmse:1.192437+0.241476
## [62] train-rmse:0.188806+0.005823 test-rmse:1.190177+0.244093
## [63] train-rmse:0.182690+0.007200 test-rmse:1.188980+0.243676
## [64] train-rmse:0.175451+0.008191 test-rmse:1.185510+0.242147
## [65] train-rmse:0.170139+0.007013 test-rmse:1.185610+0.242230
## [66] train-rmse:0.164791+0.006814 test-rmse:1.184431+0.242924
## [67] train-rmse:0.160119+0.006156 test-rmse:1.182241+0.242961
## [68] train-rmse:0.155742+0.006510 test-rmse:1.181394+0.243357
## [69] train-rmse:0.150682+0.007246 test-rmse:1.180035+0.242874
## [70] train-rmse:0.146604+0.008795 test-rmse:1.179259+0.244947
## [71] train-rmse:0.142069+0.008200 test-rmse:1.179030+0.245277
## [72] train-rmse:0.137186+0.007876 test-rmse:1.176778+0.246049
## [73] train-rmse:0.133793+0.008540 test-rmse:1.176603+0.246955
## [74] train-rmse:0.128342+0.008349 test-rmse:1.175871+0.246757
## [75] train-rmse:0.124613+0.007982 test-rmse:1.174111+0.246759
## [76] train-rmse:0.121740+0.008195 test-rmse:1.173811+0.246638
## [77] train-rmse:0.117279+0.007963 test-rmse:1.173372+0.246347
## [78] train-rmse:0.113675+0.008105 test-rmse:1.172400+0.246246
## [79] train-rmse:0.111220+0.008259 test-rmse:1.172374+0.246224
## [80] train-rmse:0.108155+0.007608 test-rmse:1.172028+0.246267
## [81] train-rmse:0.105122+0.007478 test-rmse:1.171341+0.246327
## [82] train-rmse:0.102536+0.007578 test-rmse:1.169931+0.245099
## [83] train-rmse:0.100274+0.007337 test-rmse:1.169699+0.245490
## [84] train-rmse:0.096900+0.007873 test-rmse:1.169624+0.244953
## [85] train-rmse:0.094072+0.008095 test-rmse:1.169324+0.245304
## [86] train-rmse:0.091532+0.007058 test-rmse:1.169396+0.245139
## [87] train-rmse:0.088602+0.007322 test-rmse:1.169315+0.245171
## [88] train-rmse:0.086761+0.007047 test-rmse:1.169640+0.245155
## [89] train-rmse:0.084807+0.007347 test-rmse:1.169169+0.244856
## [90] train-rmse:0.083260+0.007424 test-rmse:1.168884+0.244773
## [91] train-rmse:0.081036+0.007466 test-rmse:1.168778+0.244665
## [92] train-rmse:0.078766+0.007857 test-rmse:1.168803+0.245175
## [93] train-rmse:0.076683+0.008154 test-rmse:1.168386+0.245452
## [94] train-rmse:0.074059+0.008507 test-rmse:1.168368+0.245580
## [95] train-rmse:0.071038+0.008379 test-rmse:1.168741+0.245410
## [96] train-rmse:0.068983+0.008408 test-rmse:1.168214+0.245762
## [97] train-rmse:0.067263+0.009016 test-rmse:1.167713+0.246032
## [98] train-rmse:0.065394+0.009265 test-rmse:1.167554+0.246120
## [99] train-rmse:0.063889+0.008500 test-rmse:1.167447+0.246069
## [100] train-rmse:0.061868+0.007419 test-rmse:1.167361+0.246016
## [101] train-rmse:0.060329+0.007141 test-rmse:1.166642+0.246047
## [102] train-rmse:0.058124+0.007046 test-rmse:1.166712+0.245895
## [103] train-rmse:0.056974+0.007045 test-rmse:1.166038+0.246344
## [104] train-rmse:0.055658+0.007486 test-rmse:1.165885+0.245651
## [105] train-rmse:0.054005+0.007317 test-rmse:1.165936+0.245464
## [106] train-rmse:0.053134+0.007178 test-rmse:1.165716+0.245491
## [107] train-rmse:0.051610+0.007384 test-rmse:1.165807+0.245277
## [108] train-rmse:0.049551+0.005799 test-rmse:1.165635+0.244897
## [109] train-rmse:0.048117+0.005118 test-rmse:1.165830+0.244655
## [110] train-rmse:0.047081+0.005447 test-rmse:1.165792+0.244630
## [111] train-rmse:0.045987+0.005489 test-rmse:1.165379+0.244335
## [112] train-rmse:0.045064+0.005548 test-rmse:1.165294+0.244296
## [113] train-rmse:0.044279+0.005428 test-rmse:1.165507+0.244283
## [114] train-rmse:0.043067+0.005566 test-rmse:1.165359+0.244449
## [115] train-rmse:0.042066+0.005218 test-rmse:1.165406+0.244222
## [116] train-rmse:0.040989+0.004938 test-rmse:1.165231+0.244164
## [117] train-rmse:0.040039+0.004840 test-rmse:1.165133+0.244173
## [118] train-rmse:0.038928+0.004992 test-rmse:1.165078+0.244258
## [119] train-rmse:0.038059+0.004930 test-rmse:1.165185+0.244149
## [120] train-rmse:0.037321+0.005119 test-rmse:1.165145+0.244319
## [121] train-rmse:0.036305+0.005062 test-rmse:1.164939+0.244308
## [122] train-rmse:0.035491+0.005288 test-rmse:1.164938+0.244383
## [123] train-rmse:0.034370+0.005171 test-rmse:1.164855+0.244273
## [124] train-rmse:0.033589+0.004993 test-rmse:1.164700+0.244303
## [125] train-rmse:0.032407+0.004526 test-rmse:1.164686+0.244277
## [126] train-rmse:0.031477+0.004604 test-rmse:1.164541+0.244067
## [127] train-rmse:0.030571+0.004598 test-rmse:1.164481+0.243968
## [128] train-rmse:0.029912+0.004451 test-rmse:1.164383+0.243714
## [129] train-rmse:0.029204+0.004470 test-rmse:1.164333+0.243675
## [130] train-rmse:0.028280+0.004643 test-rmse:1.164164+0.243576
## [131] train-rmse:0.027607+0.004703 test-rmse:1.164141+0.243724
## [132] train-rmse:0.026919+0.004564 test-rmse:1.163921+0.243599
## [133] train-rmse:0.026096+0.004399 test-rmse:1.163746+0.243626
## [134] train-rmse:0.025341+0.004437 test-rmse:1.163673+0.243654
## [135] train-rmse:0.024462+0.004344 test-rmse:1.163580+0.243727
## [136] train-rmse:0.023859+0.004038 test-rmse:1.163591+0.243735
## [137] train-rmse:0.023398+0.003989 test-rmse:1.163499+0.243898
## [138] train-rmse:0.022788+0.003930 test-rmse:1.163438+0.243893
## [139] train-rmse:0.022074+0.003698 test-rmse:1.163428+0.244162
## [140] train-rmse:0.021606+0.003544 test-rmse:1.163329+0.243979
## [141] train-rmse:0.021158+0.003552 test-rmse:1.163231+0.243939
## [142] train-rmse:0.020471+0.003375 test-rmse:1.163282+0.243943
## [143] train-rmse:0.019715+0.003176 test-rmse:1.163397+0.243845
## [144] train-rmse:0.019028+0.003017 test-rmse:1.163260+0.243825
## [145] train-rmse:0.018611+0.003027 test-rmse:1.163229+0.243888
## [146] train-rmse:0.018200+0.003120 test-rmse:1.163112+0.243771
## [147] train-rmse:0.017544+0.003036 test-rmse:1.163027+0.243833
## [148] train-rmse:0.017096+0.003041 test-rmse:1.162903+0.243871
## [149] train-rmse:0.016437+0.002843 test-rmse:1.163079+0.243868
## [150] train-rmse:0.016053+0.002770 test-rmse:1.163116+0.243840
## [151] train-rmse:0.015713+0.002803 test-rmse:1.163043+0.243873
## [152] train-rmse:0.015320+0.002712 test-rmse:1.163006+0.243927
## [153] train-rmse:0.014918+0.002829 test-rmse:1.163080+0.243923
## [154] train-rmse:0.014591+0.002831 test-rmse:1.163057+0.243942
## Stopping. Best iteration:
## [148] train-rmse:0.017096+0.003041 test-rmse:1.162903+0.243871
##
## 62
## [1] train-rmse:79.809479+0.092102 test-rmse:79.809535+0.505799
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:59.256192+0.075941 test-rmse:59.255628+0.542464
## [3] train-rmse:44.071015+0.066791 test-rmse:44.069481+0.578759
## [4] train-rmse:32.877449+0.063877 test-rmse:32.874400+0.617127
## [5] train-rmse:24.614156+0.051485 test-rmse:24.681269+0.627135
## [6] train-rmse:18.505762+0.036764 test-rmse:18.586601+0.594150
## [7] train-rmse:13.970517+0.031727 test-rmse:14.038209+0.542316
## [8] train-rmse:10.607168+0.022797 test-rmse:10.730211+0.577112
## [9] train-rmse:8.107371+0.026487 test-rmse:8.253823+0.545305
## [10] train-rmse:6.231761+0.014967 test-rmse:6.365112+0.541642
## [11] train-rmse:4.845479+0.016390 test-rmse:5.001965+0.501427
## [12] train-rmse:3.808283+0.022923 test-rmse:4.032027+0.480626
## [13] train-rmse:3.045267+0.031563 test-rmse:3.319165+0.427932
## [14] train-rmse:2.466924+0.029217 test-rmse:2.795407+0.380491
## [15] train-rmse:2.046680+0.033731 test-rmse:2.432418+0.357601
## [16] train-rmse:1.720032+0.042996 test-rmse:2.168479+0.331452
## [17] train-rmse:1.468407+0.038476 test-rmse:1.988995+0.304032
## [18] train-rmse:1.283174+0.036979 test-rmse:1.844564+0.277773
## [19] train-rmse:1.132673+0.042599 test-rmse:1.771451+0.255048
## [20] train-rmse:1.012059+0.034718 test-rmse:1.679931+0.235921
## [21] train-rmse:0.914349+0.037310 test-rmse:1.617931+0.225747
## [22] train-rmse:0.837376+0.036893 test-rmse:1.581570+0.235197
## [23] train-rmse:0.775957+0.035742 test-rmse:1.538457+0.237032
## [24] train-rmse:0.714892+0.034431 test-rmse:1.508559+0.228325
## [25] train-rmse:0.663080+0.035329 test-rmse:1.470706+0.232324
## [26] train-rmse:0.615968+0.031475 test-rmse:1.441655+0.218866
## [27] train-rmse:0.576787+0.028881 test-rmse:1.413596+0.219881
## [28] train-rmse:0.537837+0.031067 test-rmse:1.410117+0.224335
## [29] train-rmse:0.508184+0.031970 test-rmse:1.393931+0.226073
## [30] train-rmse:0.478530+0.032311 test-rmse:1.373462+0.218612
## [31] train-rmse:0.451287+0.031445 test-rmse:1.356365+0.228635
## [32] train-rmse:0.427414+0.027673 test-rmse:1.347400+0.233936
## [33] train-rmse:0.405837+0.027339 test-rmse:1.338150+0.234445
## [34] train-rmse:0.385521+0.023217 test-rmse:1.330269+0.239062
## [35] train-rmse:0.366765+0.021119 test-rmse:1.320423+0.242512
## [36] train-rmse:0.349514+0.020552 test-rmse:1.314156+0.246368
## [37] train-rmse:0.331146+0.018925 test-rmse:1.307739+0.250132
## [38] train-rmse:0.316063+0.018494 test-rmse:1.304125+0.248745
## [39] train-rmse:0.301846+0.017958 test-rmse:1.300137+0.250870
## [40] train-rmse:0.291642+0.017759 test-rmse:1.295887+0.254136
## [41] train-rmse:0.277238+0.018966 test-rmse:1.289871+0.256156
## [42] train-rmse:0.263910+0.016155 test-rmse:1.286571+0.257849
## [43] train-rmse:0.252199+0.014425 test-rmse:1.284804+0.258369
## [44] train-rmse:0.243111+0.015908 test-rmse:1.282711+0.257470
## [45] train-rmse:0.233831+0.014124 test-rmse:1.279875+0.257419
## [46] train-rmse:0.224619+0.013896 test-rmse:1.279358+0.257243
## [47] train-rmse:0.214004+0.013228 test-rmse:1.274983+0.256341
## [48] train-rmse:0.204983+0.013388 test-rmse:1.272715+0.254320
## [49] train-rmse:0.197481+0.013375 test-rmse:1.269349+0.256132
## [50] train-rmse:0.188403+0.013154 test-rmse:1.268677+0.257831
## [51] train-rmse:0.181104+0.013587 test-rmse:1.270462+0.259095
## [52] train-rmse:0.174930+0.012034 test-rmse:1.269331+0.259548
## [53] train-rmse:0.167999+0.012643 test-rmse:1.268074+0.260441
## [54] train-rmse:0.159708+0.012314 test-rmse:1.266256+0.262183
## [55] train-rmse:0.153989+0.013993 test-rmse:1.265820+0.262618
## [56] train-rmse:0.147761+0.014734 test-rmse:1.264263+0.264584
## [57] train-rmse:0.140500+0.014225 test-rmse:1.262812+0.265999
## [58] train-rmse:0.134655+0.013742 test-rmse:1.261450+0.263002
## [59] train-rmse:0.128779+0.011764 test-rmse:1.259389+0.263278
## [60] train-rmse:0.124129+0.011053 test-rmse:1.259032+0.263313
## [61] train-rmse:0.119271+0.011496 test-rmse:1.258313+0.262337
## [62] train-rmse:0.116099+0.012237 test-rmse:1.255889+0.263522
## [63] train-rmse:0.111325+0.010886 test-rmse:1.253121+0.260480
## [64] train-rmse:0.107440+0.010324 test-rmse:1.253069+0.260792
## [65] train-rmse:0.102562+0.010083 test-rmse:1.252914+0.260749
## [66] train-rmse:0.098979+0.009156 test-rmse:1.251773+0.259768
## [67] train-rmse:0.094724+0.009035 test-rmse:1.251696+0.259840
## [68] train-rmse:0.092551+0.008496 test-rmse:1.251645+0.259527
## [69] train-rmse:0.089110+0.008948 test-rmse:1.252382+0.258976
## [70] train-rmse:0.085977+0.008923 test-rmse:1.252613+0.259370
## [71] train-rmse:0.081665+0.008194 test-rmse:1.251856+0.259717
## [72] train-rmse:0.078767+0.007848 test-rmse:1.251926+0.259636
## [73] train-rmse:0.075648+0.006984 test-rmse:1.250712+0.259937
## [74] train-rmse:0.072663+0.006225 test-rmse:1.250853+0.260283
## [75] train-rmse:0.069737+0.006370 test-rmse:1.249978+0.260330
## [76] train-rmse:0.067401+0.006046 test-rmse:1.249322+0.260169
## [77] train-rmse:0.065136+0.006031 test-rmse:1.248687+0.260711
## [78] train-rmse:0.063263+0.005787 test-rmse:1.248372+0.260224
## [79] train-rmse:0.061083+0.005524 test-rmse:1.248553+0.260132
## [80] train-rmse:0.059636+0.005731 test-rmse:1.248680+0.260184
## [81] train-rmse:0.057390+0.005302 test-rmse:1.248484+0.259916
## [82] train-rmse:0.055729+0.005184 test-rmse:1.247943+0.260424
## [83] train-rmse:0.053018+0.004996 test-rmse:1.247308+0.259971
## [84] train-rmse:0.050934+0.004935 test-rmse:1.247340+0.260108
## [85] train-rmse:0.049690+0.004894 test-rmse:1.247011+0.260565
## [86] train-rmse:0.047834+0.004624 test-rmse:1.246223+0.259599
## [87] train-rmse:0.045911+0.004246 test-rmse:1.245831+0.259535
## [88] train-rmse:0.044161+0.004420 test-rmse:1.245558+0.259547
## [89] train-rmse:0.042296+0.004096 test-rmse:1.246005+0.260441
## [90] train-rmse:0.041160+0.004235 test-rmse:1.245398+0.260210
## [91] train-rmse:0.040129+0.004027 test-rmse:1.245511+0.260046
## [92] train-rmse:0.038559+0.003830 test-rmse:1.244874+0.259772
## [93] train-rmse:0.037075+0.003974 test-rmse:1.244613+0.259821
## [94] train-rmse:0.035429+0.004090 test-rmse:1.244813+0.260230
## [95] train-rmse:0.034410+0.004498 test-rmse:1.244758+0.260072
## [96] train-rmse:0.033253+0.004131 test-rmse:1.244211+0.260227
## [97] train-rmse:0.032270+0.004072 test-rmse:1.244198+0.260373
## [98] train-rmse:0.031374+0.004086 test-rmse:1.244007+0.260168
## [99] train-rmse:0.030493+0.004115 test-rmse:1.243901+0.260259
## [100] train-rmse:0.028926+0.003862 test-rmse:1.243869+0.260024
## [101] train-rmse:0.027811+0.003480 test-rmse:1.244069+0.260139
## [102] train-rmse:0.026627+0.003265 test-rmse:1.244043+0.260344
## [103] train-rmse:0.025877+0.003225 test-rmse:1.243967+0.260336
## [104] train-rmse:0.025001+0.003371 test-rmse:1.244135+0.260330
## [105] train-rmse:0.024422+0.003416 test-rmse:1.244171+0.260475
## [106] train-rmse:0.023533+0.003227 test-rmse:1.244271+0.260473
## Stopping. Best iteration:
## [100] train-rmse:0.028926+0.003862 test-rmse:1.243869+0.260024
##
## 63
## [1] train-rmse:77.673825+0.090310 test-rmse:77.673837+0.509215
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:56.141246+0.073781 test-rmse:56.140539+0.549016
## [3] train-rmse:40.670034+0.065404 test-rmse:40.668152+0.588925
## [4] train-rmse:29.583382+0.061017 test-rmse:29.625472+0.605847
## [5] train-rmse:21.646907+0.057207 test-rmse:21.735936+0.643300
## [6] train-rmse:15.983337+0.051232 test-rmse:16.050024+0.645204
## [7] train-rmse:11.978003+0.051675 test-rmse:12.076230+0.612720
## [8] train-rmse:9.184355+0.053515 test-rmse:9.318300+0.698265
## [9] train-rmse:7.273962+0.061099 test-rmse:7.417010+0.646397
## [10] train-rmse:5.999996+0.071971 test-rmse:6.119492+0.603607
## [11] train-rmse:5.168102+0.072229 test-rmse:5.276947+0.567624
## [12] train-rmse:4.627800+0.077435 test-rmse:4.767812+0.558824
## [13] train-rmse:4.289076+0.079941 test-rmse:4.464644+0.556615
## [14] train-rmse:4.060899+0.077814 test-rmse:4.190719+0.488553
## [15] train-rmse:3.897036+0.074924 test-rmse:4.061487+0.478012
## [16] train-rmse:3.773423+0.069573 test-rmse:3.919488+0.390057
## [17] train-rmse:3.678410+0.067873 test-rmse:3.856132+0.391725
## [18] train-rmse:3.597593+0.065723 test-rmse:3.780941+0.407116
## [19] train-rmse:3.526715+0.063055 test-rmse:3.739173+0.393329
## [20] train-rmse:3.459155+0.059498 test-rmse:3.685062+0.381678
## [21] train-rmse:3.393745+0.056674 test-rmse:3.663849+0.399339
## [22] train-rmse:3.331230+0.053912 test-rmse:3.590382+0.356464
## [23] train-rmse:3.271891+0.050008 test-rmse:3.510515+0.369788
## [24] train-rmse:3.214746+0.047283 test-rmse:3.457933+0.368552
## [25] train-rmse:3.161659+0.046919 test-rmse:3.369284+0.320630
## [26] train-rmse:3.111678+0.045292 test-rmse:3.344943+0.321722
## [27] train-rmse:3.063851+0.043944 test-rmse:3.278756+0.315941
## [28] train-rmse:3.018711+0.043270 test-rmse:3.254675+0.321072
## [29] train-rmse:2.975718+0.041957 test-rmse:3.197086+0.291649
## [30] train-rmse:2.933236+0.040829 test-rmse:3.155788+0.300846
## [31] train-rmse:2.892198+0.040460 test-rmse:3.137363+0.309243
## [32] train-rmse:2.852188+0.039822 test-rmse:3.111884+0.310575
## [33] train-rmse:2.812072+0.039062 test-rmse:3.073699+0.306116
## [34] train-rmse:2.773507+0.038860 test-rmse:3.036285+0.311318
## [35] train-rmse:2.735861+0.039147 test-rmse:3.006152+0.315409
## [36] train-rmse:2.699062+0.039240 test-rmse:2.985197+0.320633
## [37] train-rmse:2.662474+0.039506 test-rmse:2.936649+0.331592
## [38] train-rmse:2.625769+0.038911 test-rmse:2.904053+0.336173
## [39] train-rmse:2.590994+0.038789 test-rmse:2.856039+0.293888
## [40] train-rmse:2.558277+0.038145 test-rmse:2.821006+0.288044
## [41] train-rmse:2.525595+0.037756 test-rmse:2.802160+0.289549
## [42] train-rmse:2.494160+0.036993 test-rmse:2.774853+0.309062
## [43] train-rmse:2.463797+0.035616 test-rmse:2.741448+0.306169
## [44] train-rmse:2.434654+0.034945 test-rmse:2.686588+0.292901
## [45] train-rmse:2.405650+0.034591 test-rmse:2.673165+0.282707
## [46] train-rmse:2.377180+0.033935 test-rmse:2.646631+0.280408
## [47] train-rmse:2.349579+0.033568 test-rmse:2.621803+0.282967
## [48] train-rmse:2.322012+0.033247 test-rmse:2.593642+0.289729
## [49] train-rmse:2.294778+0.032724 test-rmse:2.567674+0.298991
## [50] train-rmse:2.268723+0.032704 test-rmse:2.546359+0.299396
## [51] train-rmse:2.243220+0.031773 test-rmse:2.519124+0.283646
## [52] train-rmse:2.218117+0.031199 test-rmse:2.505986+0.289979
## [53] train-rmse:2.192959+0.030534 test-rmse:2.482088+0.306871
## [54] train-rmse:2.167860+0.029901 test-rmse:2.459933+0.295504
## [55] train-rmse:2.142882+0.028512 test-rmse:2.436658+0.292425
## [56] train-rmse:2.119516+0.027465 test-rmse:2.425163+0.294335
## [57] train-rmse:2.096064+0.026014 test-rmse:2.380408+0.270931
## [58] train-rmse:2.073447+0.025527 test-rmse:2.360859+0.270050
## [59] train-rmse:2.051420+0.025174 test-rmse:2.345926+0.281343
## [60] train-rmse:2.030025+0.024834 test-rmse:2.327318+0.287025
## [61] train-rmse:2.008544+0.024566 test-rmse:2.307361+0.293636
## [62] train-rmse:1.987133+0.024408 test-rmse:2.284290+0.290833
## [63] train-rmse:1.966248+0.023799 test-rmse:2.260699+0.278947
## [64] train-rmse:1.945554+0.023190 test-rmse:2.228410+0.269057
## [65] train-rmse:1.925277+0.022753 test-rmse:2.212894+0.269784
## [66] train-rmse:1.905014+0.022307 test-rmse:2.181723+0.267611
## [67] train-rmse:1.884852+0.021511 test-rmse:2.155400+0.271198
## [68] train-rmse:1.864838+0.021195 test-rmse:2.142540+0.263591
## [69] train-rmse:1.845521+0.020809 test-rmse:2.124884+0.264020
## [70] train-rmse:1.827060+0.020680 test-rmse:2.102875+0.250750
## [71] train-rmse:1.809195+0.020814 test-rmse:2.096424+0.250851
## [72] train-rmse:1.791367+0.020813 test-rmse:2.073622+0.249554
## [73] train-rmse:1.773533+0.020723 test-rmse:2.047958+0.260821
## [74] train-rmse:1.756374+0.020748 test-rmse:2.039087+0.258902
## [75] train-rmse:1.739751+0.020441 test-rmse:2.016701+0.263280
## [76] train-rmse:1.723345+0.020269 test-rmse:2.006436+0.266406
## [77] train-rmse:1.707022+0.020204 test-rmse:1.996618+0.255876
## [78] train-rmse:1.691074+0.020139 test-rmse:1.969599+0.237642
## [79] train-rmse:1.675561+0.019839 test-rmse:1.962112+0.237831
## [80] train-rmse:1.659905+0.019402 test-rmse:1.950932+0.242473
## [81] train-rmse:1.644019+0.018745 test-rmse:1.942062+0.241057
## [82] train-rmse:1.629171+0.018026 test-rmse:1.915894+0.233567
## [83] train-rmse:1.614757+0.017449 test-rmse:1.909395+0.237379
## [84] train-rmse:1.600594+0.016930 test-rmse:1.895151+0.238599
## [85] train-rmse:1.586504+0.016262 test-rmse:1.883410+0.239722
## [86] train-rmse:1.572499+0.015756 test-rmse:1.863616+0.240604
## [87] train-rmse:1.558671+0.015491 test-rmse:1.844559+0.229547
## [88] train-rmse:1.544868+0.015130 test-rmse:1.835243+0.235039
## [89] train-rmse:1.531512+0.014825 test-rmse:1.829038+0.232381
## [90] train-rmse:1.518406+0.014561 test-rmse:1.813459+0.229974
## [91] train-rmse:1.505220+0.014419 test-rmse:1.799344+0.229833
## [92] train-rmse:1.492122+0.014428 test-rmse:1.784499+0.233924
## [93] train-rmse:1.479310+0.014296 test-rmse:1.776463+0.226351
## [94] train-rmse:1.466624+0.014161 test-rmse:1.768444+0.227208
## [95] train-rmse:1.454179+0.014233 test-rmse:1.756106+0.230054
## [96] train-rmse:1.442121+0.013900 test-rmse:1.742958+0.237025
## [97] train-rmse:1.430290+0.013630 test-rmse:1.730617+0.232096
## [98] train-rmse:1.418517+0.013367 test-rmse:1.724459+0.232424
## [99] train-rmse:1.407401+0.013101 test-rmse:1.695119+0.217383
## [100] train-rmse:1.396210+0.012872 test-rmse:1.688497+0.218649
## [101] train-rmse:1.385231+0.012856 test-rmse:1.682865+0.220508
## [102] train-rmse:1.374729+0.012739 test-rmse:1.672359+0.219215
## [103] train-rmse:1.364302+0.012511 test-rmse:1.662973+0.216850
## [104] train-rmse:1.353841+0.012367 test-rmse:1.656245+0.213727
## [105] train-rmse:1.343474+0.012330 test-rmse:1.642272+0.217748
## [106] train-rmse:1.333577+0.012007 test-rmse:1.635918+0.217842
## [107] train-rmse:1.323731+0.011647 test-rmse:1.627389+0.215481
## [108] train-rmse:1.314011+0.011314 test-rmse:1.620386+0.216116
## [109] train-rmse:1.304261+0.010970 test-rmse:1.608670+0.209916
## [110] train-rmse:1.294604+0.010673 test-rmse:1.601963+0.208159
## [111] train-rmse:1.284987+0.010233 test-rmse:1.584698+0.202982
## [112] train-rmse:1.275790+0.010065 test-rmse:1.578181+0.208499
## [113] train-rmse:1.266772+0.009874 test-rmse:1.563872+0.209573
## [114] train-rmse:1.257931+0.009825 test-rmse:1.560750+0.209496
## [115] train-rmse:1.249111+0.009815 test-rmse:1.551265+0.208630
## [116] train-rmse:1.240313+0.009761 test-rmse:1.545277+0.206938
## [117] train-rmse:1.231738+0.009833 test-rmse:1.531310+0.207662
## [118] train-rmse:1.223438+0.009818 test-rmse:1.523625+0.210637
## [119] train-rmse:1.215166+0.009818 test-rmse:1.520329+0.212089
## [120] train-rmse:1.207089+0.009818 test-rmse:1.507176+0.196928
## [121] train-rmse:1.199263+0.009850 test-rmse:1.499774+0.199352
## [122] train-rmse:1.191277+0.009899 test-rmse:1.498201+0.199951
## [123] train-rmse:1.183684+0.009739 test-rmse:1.493141+0.202019
## [124] train-rmse:1.176460+0.009678 test-rmse:1.486956+0.206392
## [125] train-rmse:1.169221+0.009703 test-rmse:1.474933+0.198169
## [126] train-rmse:1.162097+0.009703 test-rmse:1.465288+0.197203
## [127] train-rmse:1.155132+0.009659 test-rmse:1.455939+0.198252
## [128] train-rmse:1.148207+0.009619 test-rmse:1.454266+0.197894
## [129] train-rmse:1.141268+0.009687 test-rmse:1.448085+0.200040
## [130] train-rmse:1.134554+0.009528 test-rmse:1.443222+0.201439
## [131] train-rmse:1.127882+0.009401 test-rmse:1.436324+0.198942
## [132] train-rmse:1.121346+0.009081 test-rmse:1.429811+0.202222
## [133] train-rmse:1.114837+0.008858 test-rmse:1.424314+0.200452
## [134] train-rmse:1.108431+0.008614 test-rmse:1.412244+0.197711
## [135] train-rmse:1.102211+0.008531 test-rmse:1.402284+0.198169
## [136] train-rmse:1.095977+0.008583 test-rmse:1.397587+0.198519
## [137] train-rmse:1.089636+0.008381 test-rmse:1.388084+0.199738
## [138] train-rmse:1.083715+0.008434 test-rmse:1.382135+0.199307
## [139] train-rmse:1.077989+0.008488 test-rmse:1.379339+0.195028
## [140] train-rmse:1.072339+0.008549 test-rmse:1.374237+0.197616
## [141] train-rmse:1.066627+0.008632 test-rmse:1.368247+0.196586
## [142] train-rmse:1.061020+0.008700 test-rmse:1.366403+0.195432
## [143] train-rmse:1.055461+0.008643 test-rmse:1.359036+0.196436
## [144] train-rmse:1.049922+0.008672 test-rmse:1.361123+0.195621
## [145] train-rmse:1.044446+0.008753 test-rmse:1.352141+0.184426
## [146] train-rmse:1.039295+0.008824 test-rmse:1.350885+0.183508
## [147] train-rmse:1.034255+0.008828 test-rmse:1.348927+0.185379
## [148] train-rmse:1.029246+0.008717 test-rmse:1.340494+0.185670
## [149] train-rmse:1.024329+0.008605 test-rmse:1.333648+0.180853
## [150] train-rmse:1.019535+0.008435 test-rmse:1.329212+0.184788
## [151] train-rmse:1.014772+0.008245 test-rmse:1.321397+0.181090
## [152] train-rmse:1.010027+0.008062 test-rmse:1.318684+0.180105
## [153] train-rmse:1.005463+0.007952 test-rmse:1.314399+0.180063
## [154] train-rmse:1.000958+0.007880 test-rmse:1.311870+0.181204
## [155] train-rmse:0.996486+0.007773 test-rmse:1.308721+0.182402
## [156] train-rmse:0.991973+0.007590 test-rmse:1.304471+0.184461
## [157] train-rmse:0.987552+0.007548 test-rmse:1.298881+0.186369
## [158] train-rmse:0.983179+0.007503 test-rmse:1.300566+0.185219
## [159] train-rmse:0.978906+0.007507 test-rmse:1.294814+0.187638
## [160] train-rmse:0.974726+0.007565 test-rmse:1.287511+0.189063
## [161] train-rmse:0.970683+0.007569 test-rmse:1.286096+0.185817
## [162] train-rmse:0.966822+0.007536 test-rmse:1.281385+0.187874
## [163] train-rmse:0.962945+0.007508 test-rmse:1.278142+0.186544
## [164] train-rmse:0.959155+0.007507 test-rmse:1.273898+0.188423
## [165] train-rmse:0.955381+0.007375 test-rmse:1.273799+0.186282
## [166] train-rmse:0.951694+0.007319 test-rmse:1.270824+0.188458
## [167] train-rmse:0.948061+0.007295 test-rmse:1.267194+0.189189
## [168] train-rmse:0.944605+0.007390 test-rmse:1.263617+0.191258
## [169] train-rmse:0.941226+0.007427 test-rmse:1.264005+0.187812
## [170] train-rmse:0.937783+0.007532 test-rmse:1.261351+0.190481
## [171] train-rmse:0.934351+0.007626 test-rmse:1.257043+0.190311
## [172] train-rmse:0.931012+0.007639 test-rmse:1.255578+0.190278
## [173] train-rmse:0.927738+0.007611 test-rmse:1.254211+0.188917
## [174] train-rmse:0.924551+0.007573 test-rmse:1.247270+0.188808
## [175] train-rmse:0.921398+0.007586 test-rmse:1.239533+0.179710
## [176] train-rmse:0.918275+0.007527 test-rmse:1.234541+0.178750
## [177] train-rmse:0.915175+0.007484 test-rmse:1.231827+0.180875
## [178] train-rmse:0.912194+0.007424 test-rmse:1.229588+0.181575
## [179] train-rmse:0.909229+0.007381 test-rmse:1.228552+0.183883
## [180] train-rmse:0.906299+0.007375 test-rmse:1.227978+0.185254
## [181] train-rmse:0.903418+0.007455 test-rmse:1.225601+0.185325
## [182] train-rmse:0.900577+0.007536 test-rmse:1.221004+0.181478
## [183] train-rmse:0.897751+0.007618 test-rmse:1.217204+0.181391
## [184] train-rmse:0.894998+0.007693 test-rmse:1.214648+0.178120
## [185] train-rmse:0.892191+0.007786 test-rmse:1.215456+0.177753
## [186] train-rmse:0.889511+0.007783 test-rmse:1.212742+0.179531
## [187] train-rmse:0.886872+0.007789 test-rmse:1.209622+0.180497
## [188] train-rmse:0.884310+0.007763 test-rmse:1.205547+0.179521
## [189] train-rmse:0.881811+0.007781 test-rmse:1.203719+0.180986
## [190] train-rmse:0.879294+0.007835 test-rmse:1.200813+0.181773
## [191] train-rmse:0.876868+0.007849 test-rmse:1.197790+0.179076
## [192] train-rmse:0.874541+0.007908 test-rmse:1.195450+0.180283
## [193] train-rmse:0.872186+0.007952 test-rmse:1.192661+0.181102
## [194] train-rmse:0.869870+0.008043 test-rmse:1.190308+0.183189
## [195] train-rmse:0.867661+0.008121 test-rmse:1.187686+0.183629
## [196] train-rmse:0.865423+0.008189 test-rmse:1.188266+0.183492
## [197] train-rmse:0.863176+0.008233 test-rmse:1.185640+0.183073
## [198] train-rmse:0.860958+0.008199 test-rmse:1.180060+0.179354
## [199] train-rmse:0.858745+0.008251 test-rmse:1.182087+0.178964
## [200] train-rmse:0.856581+0.008277 test-rmse:1.176428+0.172441
## [201] train-rmse:0.854478+0.008300 test-rmse:1.172656+0.174012
## [202] train-rmse:0.852390+0.008334 test-rmse:1.171175+0.176164
## [203] train-rmse:0.850349+0.008323 test-rmse:1.168785+0.177593
## [204] train-rmse:0.848315+0.008294 test-rmse:1.166433+0.173580
## [205] train-rmse:0.846374+0.008307 test-rmse:1.165145+0.174128
## [206] train-rmse:0.844429+0.008314 test-rmse:1.162432+0.175434
## [207] train-rmse:0.842527+0.008334 test-rmse:1.161410+0.176022
## [208] train-rmse:0.840677+0.008406 test-rmse:1.159953+0.175499
## [209] train-rmse:0.838900+0.008465 test-rmse:1.158984+0.175181
## [210] train-rmse:0.837075+0.008533 test-rmse:1.158529+0.175849
## [211] train-rmse:0.835349+0.008571 test-rmse:1.158529+0.176338
## [212] train-rmse:0.833651+0.008634 test-rmse:1.156981+0.176646
## [213] train-rmse:0.831954+0.008676 test-rmse:1.155138+0.174863
## [214] train-rmse:0.830256+0.008718 test-rmse:1.154789+0.175332
## [215] train-rmse:0.828636+0.008745 test-rmse:1.153779+0.171649
## [216] train-rmse:0.827031+0.008771 test-rmse:1.153564+0.172745
## [217] train-rmse:0.825450+0.008795 test-rmse:1.152382+0.170591
## [218] train-rmse:0.823899+0.008820 test-rmse:1.148574+0.164674
## [219] train-rmse:0.822363+0.008833 test-rmse:1.148412+0.166678
## [220] train-rmse:0.820855+0.008836 test-rmse:1.149016+0.166255
## [221] train-rmse:0.819272+0.008955 test-rmse:1.146277+0.168842
## [222] train-rmse:0.817686+0.009003 test-rmse:1.144615+0.165419
## [223] train-rmse:0.816210+0.009072 test-rmse:1.144197+0.166060
## [224] train-rmse:0.814787+0.009131 test-rmse:1.141560+0.164818
## [225] train-rmse:0.813395+0.009195 test-rmse:1.140860+0.163880
## [226] train-rmse:0.812009+0.009227 test-rmse:1.139938+0.165304
## [227] train-rmse:0.810644+0.009284 test-rmse:1.137611+0.166411
## [228] train-rmse:0.809274+0.009378 test-rmse:1.136374+0.167300
## [229] train-rmse:0.807855+0.009412 test-rmse:1.134985+0.167776
## [230] train-rmse:0.806531+0.009457 test-rmse:1.132880+0.168846
## [231] train-rmse:0.805152+0.009509 test-rmse:1.130895+0.168674
## [232] train-rmse:0.803875+0.009606 test-rmse:1.129808+0.167742
## [233] train-rmse:0.802570+0.009696 test-rmse:1.126771+0.168202
## [234] train-rmse:0.801288+0.009798 test-rmse:1.127195+0.167534
## [235] train-rmse:0.800043+0.009879 test-rmse:1.125334+0.167978
## [236] train-rmse:0.798777+0.009943 test-rmse:1.127011+0.167698
## [237] train-rmse:0.797570+0.009984 test-rmse:1.126291+0.169182
## [238] train-rmse:0.796361+0.010039 test-rmse:1.123407+0.171658
## [239] train-rmse:0.795109+0.010082 test-rmse:1.124938+0.170408
## [240] train-rmse:0.793861+0.010190 test-rmse:1.122409+0.169528
## [241] train-rmse:0.792686+0.010261 test-rmse:1.120328+0.171793
## [242] train-rmse:0.791552+0.010323 test-rmse:1.119609+0.172391
## [243] train-rmse:0.790445+0.010394 test-rmse:1.120349+0.172084
## [244] train-rmse:0.789316+0.010426 test-rmse:1.119369+0.172389
## [245] train-rmse:0.788200+0.010501 test-rmse:1.118154+0.173218
## [246] train-rmse:0.787108+0.010535 test-rmse:1.117315+0.173155
## [247] train-rmse:0.786026+0.010576 test-rmse:1.116807+0.173270
## [248] train-rmse:0.784957+0.010610 test-rmse:1.115094+0.173354
## [249] train-rmse:0.783880+0.010641 test-rmse:1.115315+0.172239
## [250] train-rmse:0.782865+0.010699 test-rmse:1.114263+0.169897
## [251] train-rmse:0.781764+0.010743 test-rmse:1.112190+0.170996
## [252] train-rmse:0.780760+0.010822 test-rmse:1.112101+0.170573
## [253] train-rmse:0.779772+0.010895 test-rmse:1.112135+0.170773
## [254] train-rmse:0.778792+0.010969 test-rmse:1.110191+0.171294
## [255] train-rmse:0.777826+0.011038 test-rmse:1.110194+0.171144
## [256] train-rmse:0.776846+0.011098 test-rmse:1.107945+0.170379
## [257] train-rmse:0.775872+0.011177 test-rmse:1.108562+0.171126
## [258] train-rmse:0.774878+0.011234 test-rmse:1.107288+0.171454
## [259] train-rmse:0.773944+0.011300 test-rmse:1.106718+0.171790
## [260] train-rmse:0.772990+0.011339 test-rmse:1.107435+0.172428
## [261] train-rmse:0.772043+0.011390 test-rmse:1.102592+0.168859
## [262] train-rmse:0.771129+0.011449 test-rmse:1.101453+0.169342
## [263] train-rmse:0.770211+0.011528 test-rmse:1.100758+0.170034
## [264] train-rmse:0.769307+0.011596 test-rmse:1.099116+0.168341
## [265] train-rmse:0.768396+0.011669 test-rmse:1.099165+0.167857
## [266] train-rmse:0.767480+0.011705 test-rmse:1.098572+0.168251
## [267] train-rmse:0.766602+0.011718 test-rmse:1.098466+0.168754
## [268] train-rmse:0.765728+0.011743 test-rmse:1.096131+0.169299
## [269] train-rmse:0.764880+0.011803 test-rmse:1.095769+0.169025
## [270] train-rmse:0.763982+0.011867 test-rmse:1.093712+0.170121
## [271] train-rmse:0.763147+0.011920 test-rmse:1.093776+0.169671
## [272] train-rmse:0.762333+0.011974 test-rmse:1.092570+0.170170
## [273] train-rmse:0.761522+0.012038 test-rmse:1.092162+0.169832
## [274] train-rmse:0.760706+0.012049 test-rmse:1.090604+0.170679
## [275] train-rmse:0.759888+0.012061 test-rmse:1.089916+0.171514
## [276] train-rmse:0.759075+0.012103 test-rmse:1.091726+0.171063
## [277] train-rmse:0.758241+0.012136 test-rmse:1.091075+0.171137
## [278] train-rmse:0.757471+0.012180 test-rmse:1.088494+0.171613
## [279] train-rmse:0.756658+0.012217 test-rmse:1.088897+0.171286
## [280] train-rmse:0.755900+0.012262 test-rmse:1.088322+0.171483
## [281] train-rmse:0.755144+0.012286 test-rmse:1.088010+0.171975
## [282] train-rmse:0.754403+0.012308 test-rmse:1.088834+0.170840
## [283] train-rmse:0.753655+0.012329 test-rmse:1.088445+0.171296
## [284] train-rmse:0.752906+0.012350 test-rmse:1.087627+0.171148
## [285] train-rmse:0.752155+0.012369 test-rmse:1.087773+0.171924
## [286] train-rmse:0.751434+0.012363 test-rmse:1.087061+0.171367
## [287] train-rmse:0.750707+0.012342 test-rmse:1.088058+0.168786
## [288] train-rmse:0.749941+0.012358 test-rmse:1.085926+0.169338
## [289] train-rmse:0.749238+0.012380 test-rmse:1.085246+0.168212
## [290] train-rmse:0.748548+0.012402 test-rmse:1.084408+0.168627
## [291] train-rmse:0.747860+0.012423 test-rmse:1.084637+0.169166
## [292] train-rmse:0.747190+0.012441 test-rmse:1.084704+0.169766
## [293] train-rmse:0.746517+0.012434 test-rmse:1.082986+0.168120
## [294] train-rmse:0.745829+0.012427 test-rmse:1.084231+0.167131
## [295] train-rmse:0.745139+0.012452 test-rmse:1.084406+0.167702
## [296] train-rmse:0.744473+0.012495 test-rmse:1.085501+0.168489
## [297] train-rmse:0.743818+0.012534 test-rmse:1.084555+0.167741
## [298] train-rmse:0.743171+0.012573 test-rmse:1.084089+0.168084
## [299] train-rmse:0.742547+0.012606 test-rmse:1.083239+0.168122
## Stopping. Best iteration:
## [293] train-rmse:0.746517+0.012434 test-rmse:1.082986+0.168120
##
## 64
## [1] train-rmse:77.673825+0.090310 test-rmse:77.673837+0.509215
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:56.141246+0.073781 test-rmse:56.140539+0.549016
## [3] train-rmse:40.670034+0.065404 test-rmse:40.668152+0.588925
## [4] train-rmse:29.583382+0.061017 test-rmse:29.625472+0.605847
## [5] train-rmse:21.623629+0.047512 test-rmse:21.720874+0.648633
## [6] train-rmse:15.918484+0.035098 test-rmse:16.005864+0.580751
## [7] train-rmse:11.848531+0.034455 test-rmse:11.984302+0.591692
## [8] train-rmse:8.978132+0.037325 test-rmse:9.063821+0.529134
## [9] train-rmse:6.988340+0.039910 test-rmse:7.096236+0.545274
## [10] train-rmse:5.618695+0.043090 test-rmse:5.753589+0.541540
## [11] train-rmse:4.701192+0.048269 test-rmse:4.891447+0.546311
## [12] train-rmse:4.095224+0.049868 test-rmse:4.285748+0.476164
## [13] train-rmse:3.686998+0.048687 test-rmse:3.954349+0.465924
## [14] train-rmse:3.406569+0.046027 test-rmse:3.671667+0.427515
## [15] train-rmse:3.208467+0.046093 test-rmse:3.458316+0.368388
## [16] train-rmse:3.050930+0.036466 test-rmse:3.315211+0.331076
## [17] train-rmse:2.932349+0.032639 test-rmse:3.227169+0.344653
## [18] train-rmse:2.834480+0.032115 test-rmse:3.153049+0.337092
## [19] train-rmse:2.744050+0.030052 test-rmse:3.059028+0.360969
## [20] train-rmse:2.662361+0.032143 test-rmse:2.995022+0.363269
## [21] train-rmse:2.586843+0.032949 test-rmse:2.917780+0.341633
## [22] train-rmse:2.506110+0.025704 test-rmse:2.845381+0.283204
## [23] train-rmse:2.438730+0.025007 test-rmse:2.773795+0.273568
## [24] train-rmse:2.371549+0.021045 test-rmse:2.734770+0.290539
## [25] train-rmse:2.310919+0.018185 test-rmse:2.694504+0.311607
## [26] train-rmse:2.252767+0.019330 test-rmse:2.646528+0.308379
## [27] train-rmse:2.194710+0.016328 test-rmse:2.605940+0.305629
## [28] train-rmse:2.141731+0.016858 test-rmse:2.566008+0.287962
## [29] train-rmse:2.091189+0.015527 test-rmse:2.517455+0.286037
## [30] train-rmse:2.039084+0.015687 test-rmse:2.467181+0.269044
## [31] train-rmse:1.991155+0.016466 test-rmse:2.418454+0.285548
## [32] train-rmse:1.941487+0.016018 test-rmse:2.372288+0.275515
## [33] train-rmse:1.893266+0.016053 test-rmse:2.336549+0.280049
## [34] train-rmse:1.850306+0.015335 test-rmse:2.294557+0.289330
## [35] train-rmse:1.806655+0.012062 test-rmse:2.259196+0.274588
## [36] train-rmse:1.766190+0.010223 test-rmse:2.220315+0.259091
## [37] train-rmse:1.727682+0.010129 test-rmse:2.191305+0.253671
## [38] train-rmse:1.691083+0.009269 test-rmse:2.151929+0.258582
## [39] train-rmse:1.655226+0.008405 test-rmse:2.133582+0.262065
## [40] train-rmse:1.620291+0.008471 test-rmse:2.091142+0.250930
## [41] train-rmse:1.586289+0.006358 test-rmse:2.071500+0.263276
## [42] train-rmse:1.553373+0.006917 test-rmse:2.055855+0.265993
## [43] train-rmse:1.516925+0.008104 test-rmse:2.030424+0.269530
## [44] train-rmse:1.486705+0.008021 test-rmse:2.001574+0.243451
## [45] train-rmse:1.457449+0.008919 test-rmse:1.977083+0.252215
## [46] train-rmse:1.428617+0.009716 test-rmse:1.947191+0.235471
## [47] train-rmse:1.400994+0.009431 test-rmse:1.916370+0.242472
## [48] train-rmse:1.373245+0.009993 test-rmse:1.888481+0.232624
## [49] train-rmse:1.345139+0.010469 test-rmse:1.858279+0.233219
## [50] train-rmse:1.318948+0.008987 test-rmse:1.841841+0.232298
## [51] train-rmse:1.292473+0.012789 test-rmse:1.825755+0.230381
## [52] train-rmse:1.269009+0.012915 test-rmse:1.810148+0.236820
## [53] train-rmse:1.245614+0.014664 test-rmse:1.801275+0.238835
## [54] train-rmse:1.223181+0.014582 test-rmse:1.784266+0.243026
## [55] train-rmse:1.200910+0.014863 test-rmse:1.769425+0.233505
## [56] train-rmse:1.179410+0.014386 test-rmse:1.749997+0.231323
## [57] train-rmse:1.156466+0.014821 test-rmse:1.726140+0.241366
## [58] train-rmse:1.136403+0.016592 test-rmse:1.701336+0.246595
## [59] train-rmse:1.114886+0.016362 test-rmse:1.688464+0.241360
## [60] train-rmse:1.094864+0.015928 test-rmse:1.673888+0.238308
## [61] train-rmse:1.076219+0.015015 test-rmse:1.654790+0.237310
## [62] train-rmse:1.058306+0.014919 test-rmse:1.639030+0.243080
## [63] train-rmse:1.041831+0.014234 test-rmse:1.625144+0.240617
## [64] train-rmse:1.026348+0.014607 test-rmse:1.609374+0.245705
## [65] train-rmse:1.010955+0.015079 test-rmse:1.593295+0.240502
## [66] train-rmse:0.996108+0.015206 test-rmse:1.578414+0.239559
## [67] train-rmse:0.979017+0.015218 test-rmse:1.563208+0.243040
## [68] train-rmse:0.963117+0.015480 test-rmse:1.553286+0.245460
## [69] train-rmse:0.946825+0.014863 test-rmse:1.543714+0.248542
## [70] train-rmse:0.932255+0.014900 test-rmse:1.533466+0.250112
## [71] train-rmse:0.917509+0.015928 test-rmse:1.523824+0.241835
## [72] train-rmse:0.904392+0.016141 test-rmse:1.518432+0.244733
## [73] train-rmse:0.892059+0.016356 test-rmse:1.512213+0.245021
## [74] train-rmse:0.880079+0.016106 test-rmse:1.502151+0.249489
## [75] train-rmse:0.866302+0.017824 test-rmse:1.490041+0.248994
## [76] train-rmse:0.855237+0.017842 test-rmse:1.483594+0.253935
## [77] train-rmse:0.843652+0.017242 test-rmse:1.477914+0.257547
## [78] train-rmse:0.830191+0.018518 test-rmse:1.465364+0.253487
## [79] train-rmse:0.819160+0.019138 test-rmse:1.453276+0.255059
## [80] train-rmse:0.807503+0.019065 test-rmse:1.447414+0.256261
## [81] train-rmse:0.796903+0.018961 test-rmse:1.437094+0.251901
## [82] train-rmse:0.785972+0.019066 test-rmse:1.432038+0.253299
## [83] train-rmse:0.776819+0.019144 test-rmse:1.421927+0.262395
## [84] train-rmse:0.767827+0.018767 test-rmse:1.418367+0.259989
## [85] train-rmse:0.759242+0.018295 test-rmse:1.413462+0.260115
## [86] train-rmse:0.750819+0.018296 test-rmse:1.404192+0.262069
## [87] train-rmse:0.739742+0.017189 test-rmse:1.400169+0.261291
## [88] train-rmse:0.731084+0.016766 test-rmse:1.395927+0.263847
## [89] train-rmse:0.722194+0.016531 test-rmse:1.389884+0.265645
## [90] train-rmse:0.712102+0.014601 test-rmse:1.380885+0.265796
## [91] train-rmse:0.702771+0.014683 test-rmse:1.379390+0.268328
## [92] train-rmse:0.693861+0.014519 test-rmse:1.374431+0.271209
## [93] train-rmse:0.686947+0.014151 test-rmse:1.370908+0.271085
## [94] train-rmse:0.679234+0.014336 test-rmse:1.362450+0.268901
## [95] train-rmse:0.672994+0.014269 test-rmse:1.356096+0.269996
## [96] train-rmse:0.664904+0.014645 test-rmse:1.350175+0.273673
## [97] train-rmse:0.658521+0.014729 test-rmse:1.344877+0.265835
## [98] train-rmse:0.652596+0.014573 test-rmse:1.345109+0.265240
## [99] train-rmse:0.644823+0.012981 test-rmse:1.343373+0.268374
## [100] train-rmse:0.638911+0.012189 test-rmse:1.333302+0.271480
## [101] train-rmse:0.631149+0.011615 test-rmse:1.328064+0.270802
## [102] train-rmse:0.624152+0.011747 test-rmse:1.323180+0.270738
## [103] train-rmse:0.617910+0.012449 test-rmse:1.318614+0.273912
## [104] train-rmse:0.612756+0.012260 test-rmse:1.315519+0.272933
## [105] train-rmse:0.606429+0.011229 test-rmse:1.315103+0.274922
## [106] train-rmse:0.601025+0.011748 test-rmse:1.308955+0.276278
## [107] train-rmse:0.594895+0.010453 test-rmse:1.307755+0.278766
## [108] train-rmse:0.590677+0.010310 test-rmse:1.305352+0.281431
## [109] train-rmse:0.585635+0.011049 test-rmse:1.304730+0.279330
## [110] train-rmse:0.579729+0.009977 test-rmse:1.303083+0.282376
## [111] train-rmse:0.574138+0.009909 test-rmse:1.298320+0.285275
## [112] train-rmse:0.570067+0.009333 test-rmse:1.293437+0.284192
## [113] train-rmse:0.565218+0.009150 test-rmse:1.288093+0.287659
## [114] train-rmse:0.560850+0.009272 test-rmse:1.284977+0.289368
## [115] train-rmse:0.556583+0.008886 test-rmse:1.285888+0.291854
## [116] train-rmse:0.552798+0.009469 test-rmse:1.281414+0.296461
## [117] train-rmse:0.548575+0.009323 test-rmse:1.277856+0.295466
## [118] train-rmse:0.544824+0.009159 test-rmse:1.271268+0.293193
## [119] train-rmse:0.540332+0.009040 test-rmse:1.269043+0.291301
## [120] train-rmse:0.536613+0.008496 test-rmse:1.269644+0.292401
## [121] train-rmse:0.532102+0.007578 test-rmse:1.268485+0.291194
## [122] train-rmse:0.528029+0.007614 test-rmse:1.266347+0.293318
## [123] train-rmse:0.524037+0.008638 test-rmse:1.265656+0.292426
## [124] train-rmse:0.520396+0.008523 test-rmse:1.262337+0.293318
## [125] train-rmse:0.517172+0.007844 test-rmse:1.259630+0.293546
## [126] train-rmse:0.513573+0.007210 test-rmse:1.257853+0.290801
## [127] train-rmse:0.509046+0.009414 test-rmse:1.256834+0.289933
## [128] train-rmse:0.505459+0.010289 test-rmse:1.255221+0.291009
## [129] train-rmse:0.502137+0.009958 test-rmse:1.254062+0.291922
## [130] train-rmse:0.499675+0.010278 test-rmse:1.254149+0.293110
## [131] train-rmse:0.495664+0.009463 test-rmse:1.248885+0.293944
## [132] train-rmse:0.490947+0.008336 test-rmse:1.246700+0.296111
## [133] train-rmse:0.488364+0.007947 test-rmse:1.245815+0.297031
## [134] train-rmse:0.485393+0.008274 test-rmse:1.242313+0.300997
## [135] train-rmse:0.481626+0.007635 test-rmse:1.241049+0.302144
## [136] train-rmse:0.477820+0.007154 test-rmse:1.238650+0.304242
## [137] train-rmse:0.475223+0.006854 test-rmse:1.236004+0.304998
## [138] train-rmse:0.471693+0.007832 test-rmse:1.232897+0.307768
## [139] train-rmse:0.469214+0.007594 test-rmse:1.228338+0.306044
## [140] train-rmse:0.465711+0.007751 test-rmse:1.227270+0.306020
## [141] train-rmse:0.463102+0.007414 test-rmse:1.224772+0.306934
## [142] train-rmse:0.460970+0.007241 test-rmse:1.221584+0.306245
## [143] train-rmse:0.458540+0.008132 test-rmse:1.221067+0.306549
## [144] train-rmse:0.455126+0.008411 test-rmse:1.221941+0.305941
## [145] train-rmse:0.452502+0.007985 test-rmse:1.219346+0.305893
## [146] train-rmse:0.450401+0.008452 test-rmse:1.219347+0.305333
## [147] train-rmse:0.447113+0.008792 test-rmse:1.220078+0.305770
## [148] train-rmse:0.444959+0.008758 test-rmse:1.220429+0.305346
## [149] train-rmse:0.442655+0.008513 test-rmse:1.219227+0.305236
## [150] train-rmse:0.439829+0.008343 test-rmse:1.213823+0.305733
## [151] train-rmse:0.436882+0.008004 test-rmse:1.212990+0.304752
## [152] train-rmse:0.434210+0.008318 test-rmse:1.211130+0.306317
## [153] train-rmse:0.432585+0.008511 test-rmse:1.209113+0.307340
## [154] train-rmse:0.430409+0.008729 test-rmse:1.204353+0.300177
## [155] train-rmse:0.428385+0.008709 test-rmse:1.203952+0.299966
## [156] train-rmse:0.425963+0.009018 test-rmse:1.203875+0.301469
## [157] train-rmse:0.423652+0.008964 test-rmse:1.203972+0.301852
## [158] train-rmse:0.421053+0.009184 test-rmse:1.201318+0.302139
## [159] train-rmse:0.418145+0.008580 test-rmse:1.199154+0.303742
## [160] train-rmse:0.415915+0.008012 test-rmse:1.197889+0.304243
## [161] train-rmse:0.413610+0.007868 test-rmse:1.197796+0.305409
## [162] train-rmse:0.411560+0.008957 test-rmse:1.196977+0.305460
## [163] train-rmse:0.409394+0.008614 test-rmse:1.197542+0.307547
## [164] train-rmse:0.407727+0.008591 test-rmse:1.196819+0.308654
## [165] train-rmse:0.405003+0.007921 test-rmse:1.196725+0.307019
## [166] train-rmse:0.402940+0.007305 test-rmse:1.196825+0.306470
## [167] train-rmse:0.400443+0.007941 test-rmse:1.197735+0.305192
## [168] train-rmse:0.398413+0.008479 test-rmse:1.197245+0.305942
## [169] train-rmse:0.397005+0.008639 test-rmse:1.195576+0.306491
## [170] train-rmse:0.395504+0.008747 test-rmse:1.194146+0.307024
## [171] train-rmse:0.393256+0.010133 test-rmse:1.193357+0.306829
## [172] train-rmse:0.391083+0.010978 test-rmse:1.194341+0.305194
## [173] train-rmse:0.388736+0.011188 test-rmse:1.192751+0.306122
## [174] train-rmse:0.385995+0.011417 test-rmse:1.192587+0.306360
## [175] train-rmse:0.383967+0.010846 test-rmse:1.193526+0.307062
## [176] train-rmse:0.382411+0.011434 test-rmse:1.193568+0.305103
## [177] train-rmse:0.379691+0.012601 test-rmse:1.193446+0.305722
## [178] train-rmse:0.377809+0.012589 test-rmse:1.188900+0.301525
## [179] train-rmse:0.375403+0.012130 test-rmse:1.188360+0.301500
## [180] train-rmse:0.373575+0.011971 test-rmse:1.186672+0.301616
## [181] train-rmse:0.372400+0.011872 test-rmse:1.186638+0.300820
## [182] train-rmse:0.371160+0.011956 test-rmse:1.186647+0.300764
## [183] train-rmse:0.369128+0.011573 test-rmse:1.186429+0.300432
## [184] train-rmse:0.366880+0.011637 test-rmse:1.185262+0.299826
## [185] train-rmse:0.365609+0.011777 test-rmse:1.184753+0.300620
## [186] train-rmse:0.362996+0.011586 test-rmse:1.184357+0.302055
## [187] train-rmse:0.361456+0.011420 test-rmse:1.182559+0.302164
## [188] train-rmse:0.359902+0.011263 test-rmse:1.181439+0.302838
## [189] train-rmse:0.358298+0.011121 test-rmse:1.180123+0.302500
## [190] train-rmse:0.355878+0.011974 test-rmse:1.181292+0.303230
## [191] train-rmse:0.353532+0.011341 test-rmse:1.181727+0.304130
## [192] train-rmse:0.351705+0.010968 test-rmse:1.181740+0.303307
## [193] train-rmse:0.350144+0.011696 test-rmse:1.180595+0.302186
## [194] train-rmse:0.348481+0.011501 test-rmse:1.179817+0.302244
## [195] train-rmse:0.346809+0.011002 test-rmse:1.178704+0.303525
## [196] train-rmse:0.345671+0.010817 test-rmse:1.177744+0.304199
## [197] train-rmse:0.344669+0.010671 test-rmse:1.176735+0.304548
## [198] train-rmse:0.342833+0.010429 test-rmse:1.173114+0.305389
## [199] train-rmse:0.341491+0.010350 test-rmse:1.172848+0.305565
## [200] train-rmse:0.339945+0.010494 test-rmse:1.172391+0.307416
## [201] train-rmse:0.338699+0.010328 test-rmse:1.170741+0.307887
## [202] train-rmse:0.336931+0.009772 test-rmse:1.170160+0.307585
## [203] train-rmse:0.334777+0.010039 test-rmse:1.170251+0.307123
## [204] train-rmse:0.333966+0.009994 test-rmse:1.169801+0.307688
## [205] train-rmse:0.332225+0.009768 test-rmse:1.168432+0.307774
## [206] train-rmse:0.330943+0.009567 test-rmse:1.167654+0.307420
## [207] train-rmse:0.329990+0.009853 test-rmse:1.166657+0.307654
## [208] train-rmse:0.328556+0.010769 test-rmse:1.165970+0.307840
## [209] train-rmse:0.326315+0.010554 test-rmse:1.166166+0.308022
## [210] train-rmse:0.325226+0.010587 test-rmse:1.164620+0.307376
## [211] train-rmse:0.324195+0.010634 test-rmse:1.164657+0.307463
## [212] train-rmse:0.322575+0.010445 test-rmse:1.163995+0.307212
## [213] train-rmse:0.321351+0.010123 test-rmse:1.162768+0.308590
## [214] train-rmse:0.318884+0.009859 test-rmse:1.161972+0.309108
## [215] train-rmse:0.318050+0.009871 test-rmse:1.162161+0.309222
## [216] train-rmse:0.316962+0.010083 test-rmse:1.161836+0.309350
## [217] train-rmse:0.316080+0.009906 test-rmse:1.160916+0.310127
## [218] train-rmse:0.314508+0.009632 test-rmse:1.159069+0.309315
## [219] train-rmse:0.313420+0.009512 test-rmse:1.157781+0.309021
## [220] train-rmse:0.311748+0.009497 test-rmse:1.158178+0.309879
## [221] train-rmse:0.310731+0.009712 test-rmse:1.156922+0.309504
## [222] train-rmse:0.309542+0.009304 test-rmse:1.157783+0.311329
## [223] train-rmse:0.307989+0.009352 test-rmse:1.157562+0.311179
## [224] train-rmse:0.307180+0.009324 test-rmse:1.157683+0.310827
## [225] train-rmse:0.306211+0.009061 test-rmse:1.156920+0.309223
## [226] train-rmse:0.305216+0.008780 test-rmse:1.156528+0.309553
## [227] train-rmse:0.303856+0.009526 test-rmse:1.156600+0.309617
## [228] train-rmse:0.302726+0.009634 test-rmse:1.156101+0.309420
## [229] train-rmse:0.301282+0.009630 test-rmse:1.155596+0.309589
## [230] train-rmse:0.299979+0.010230 test-rmse:1.155422+0.310467
## [231] train-rmse:0.298390+0.009536 test-rmse:1.155833+0.309878
## [232] train-rmse:0.297597+0.009574 test-rmse:1.155674+0.310474
## [233] train-rmse:0.296785+0.009377 test-rmse:1.156163+0.310488
## [234] train-rmse:0.296131+0.009218 test-rmse:1.155672+0.310903
## [235] train-rmse:0.295339+0.009430 test-rmse:1.155702+0.311299
## [236] train-rmse:0.294423+0.009351 test-rmse:1.155436+0.310951
## Stopping. Best iteration:
## [230] train-rmse:0.299979+0.010230 test-rmse:1.155422+0.310467
##
## 65
## [1] train-rmse:77.673825+0.090310 test-rmse:77.673837+0.509215
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:56.141246+0.073781 test-rmse:56.140539+0.549016
## [3] train-rmse:40.670034+0.065404 test-rmse:40.668152+0.588925
## [4] train-rmse:29.583382+0.061017 test-rmse:29.625472+0.605847
## [5] train-rmse:21.613113+0.040697 test-rmse:21.729475+0.638688
## [6] train-rmse:15.887792+0.030287 test-rmse:16.007352+0.606523
## [7] train-rmse:11.786035+0.028937 test-rmse:11.880795+0.541095
## [8] train-rmse:8.870852+0.032306 test-rmse:8.990332+0.571737
## [9] train-rmse:6.804078+0.036482 test-rmse:6.948308+0.542521
## [10] train-rmse:5.366765+0.046729 test-rmse:5.500766+0.523540
## [11] train-rmse:4.375623+0.056921 test-rmse:4.576805+0.508942
## [12] train-rmse:3.708178+0.057004 test-rmse:3.922250+0.459197
## [13] train-rmse:3.241259+0.049604 test-rmse:3.511211+0.410721
## [14] train-rmse:2.921435+0.047931 test-rmse:3.222807+0.402836
## [15] train-rmse:2.689569+0.047259 test-rmse:3.023026+0.402726
## [16] train-rmse:2.517431+0.039992 test-rmse:2.877716+0.354419
## [17] train-rmse:2.369729+0.040327 test-rmse:2.746548+0.274496
## [18] train-rmse:2.253569+0.042391 test-rmse:2.659640+0.284771
## [19] train-rmse:2.156036+0.043821 test-rmse:2.584510+0.282499
## [20] train-rmse:2.062524+0.035329 test-rmse:2.510377+0.294657
## [21] train-rmse:1.983611+0.033787 test-rmse:2.438100+0.298399
## [22] train-rmse:1.906531+0.029751 test-rmse:2.370640+0.272662
## [23] train-rmse:1.838699+0.028773 test-rmse:2.301009+0.284086
## [24] train-rmse:1.770419+0.031172 test-rmse:2.253767+0.290240
## [25] train-rmse:1.704743+0.031702 test-rmse:2.222048+0.303380
## [26] train-rmse:1.645842+0.033769 test-rmse:2.164273+0.287324
## [27] train-rmse:1.590011+0.033515 test-rmse:2.111505+0.280267
## [28] train-rmse:1.537366+0.031153 test-rmse:2.056874+0.267283
## [29] train-rmse:1.487204+0.028447 test-rmse:2.017841+0.264458
## [30] train-rmse:1.438912+0.031638 test-rmse:1.991436+0.257315
## [31] train-rmse:1.393752+0.031593 test-rmse:1.959000+0.267410
## [32] train-rmse:1.348822+0.030103 test-rmse:1.901364+0.265875
## [33] train-rmse:1.305194+0.030864 test-rmse:1.867005+0.265342
## [34] train-rmse:1.267397+0.030215 test-rmse:1.826239+0.263620
## [35] train-rmse:1.228975+0.031783 test-rmse:1.796036+0.242586
## [36] train-rmse:1.191061+0.027163 test-rmse:1.759678+0.238450
## [37] train-rmse:1.157096+0.026535 test-rmse:1.733456+0.238966
## [38] train-rmse:1.121495+0.027167 test-rmse:1.713592+0.230768
## [39] train-rmse:1.088284+0.027934 test-rmse:1.684177+0.229130
## [40] train-rmse:1.055561+0.027262 test-rmse:1.650964+0.237057
## [41] train-rmse:1.022802+0.029218 test-rmse:1.633470+0.249955
## [42] train-rmse:0.994104+0.026654 test-rmse:1.616071+0.255131
## [43] train-rmse:0.968154+0.025709 test-rmse:1.578426+0.249194
## [44] train-rmse:0.942024+0.026327 test-rmse:1.568041+0.250538
## [45] train-rmse:0.917953+0.026123 test-rmse:1.545512+0.256688
## [46] train-rmse:0.892362+0.023452 test-rmse:1.528740+0.263636
## [47] train-rmse:0.869261+0.025084 test-rmse:1.513402+0.264478
## [48] train-rmse:0.846246+0.023753 test-rmse:1.496662+0.254848
## [49] train-rmse:0.819406+0.023357 test-rmse:1.467170+0.257197
## [50] train-rmse:0.798826+0.022680 test-rmse:1.452944+0.262230
## [51] train-rmse:0.778389+0.023307 test-rmse:1.434213+0.262609
## [52] train-rmse:0.761701+0.023747 test-rmse:1.425636+0.260132
## [53] train-rmse:0.744065+0.023291 test-rmse:1.406960+0.267709
## [54] train-rmse:0.728134+0.024535 test-rmse:1.399669+0.267087
## [55] train-rmse:0.712561+0.023953 test-rmse:1.390601+0.269630
## [56] train-rmse:0.695703+0.024363 test-rmse:1.383400+0.267071
## [57] train-rmse:0.680322+0.023935 test-rmse:1.374823+0.263917
## [58] train-rmse:0.666066+0.023448 test-rmse:1.352251+0.243880
## [59] train-rmse:0.651814+0.021915 test-rmse:1.344135+0.249086
## [60] train-rmse:0.636168+0.022960 test-rmse:1.335044+0.254548
## [61] train-rmse:0.624385+0.022473 test-rmse:1.325325+0.257373
## [62] train-rmse:0.611742+0.022906 test-rmse:1.313538+0.253021
## [63] train-rmse:0.600440+0.023626 test-rmse:1.312005+0.253270
## [64] train-rmse:0.589755+0.023599 test-rmse:1.300471+0.254614
## [65] train-rmse:0.578598+0.021955 test-rmse:1.293563+0.252773
## [66] train-rmse:0.567481+0.022857 test-rmse:1.285718+0.253390
## [67] train-rmse:0.557174+0.023519 test-rmse:1.272079+0.262687
## [68] train-rmse:0.546854+0.021653 test-rmse:1.266751+0.259929
## [69] train-rmse:0.537761+0.021099 test-rmse:1.258898+0.260896
## [70] train-rmse:0.528787+0.021400 test-rmse:1.255482+0.264309
## [71] train-rmse:0.518889+0.019181 test-rmse:1.247685+0.259857
## [72] train-rmse:0.509252+0.017981 test-rmse:1.242829+0.261542
## [73] train-rmse:0.498951+0.017743 test-rmse:1.237112+0.263170
## [74] train-rmse:0.491143+0.018603 test-rmse:1.228863+0.266187
## [75] train-rmse:0.483891+0.018017 test-rmse:1.225462+0.268668
## [76] train-rmse:0.475401+0.016388 test-rmse:1.219279+0.268355
## [77] train-rmse:0.468762+0.016514 test-rmse:1.212502+0.269368
## [78] train-rmse:0.461147+0.016955 test-rmse:1.207953+0.269602
## [79] train-rmse:0.454635+0.017153 test-rmse:1.205829+0.269106
## [80] train-rmse:0.448678+0.017032 test-rmse:1.196743+0.260795
## [81] train-rmse:0.442375+0.016783 test-rmse:1.191431+0.263618
## [82] train-rmse:0.435725+0.016753 test-rmse:1.189496+0.261814
## [83] train-rmse:0.430070+0.017458 test-rmse:1.188217+0.261187
## [84] train-rmse:0.421207+0.017636 test-rmse:1.187372+0.264213
## [85] train-rmse:0.414727+0.015848 test-rmse:1.182027+0.267135
## [86] train-rmse:0.409799+0.016223 test-rmse:1.174251+0.272050
## [87] train-rmse:0.404297+0.016383 test-rmse:1.171933+0.271940
## [88] train-rmse:0.399554+0.016121 test-rmse:1.168985+0.268340
## [89] train-rmse:0.395021+0.015986 test-rmse:1.167488+0.269324
## [90] train-rmse:0.390192+0.015617 test-rmse:1.167793+0.270387
## [91] train-rmse:0.385292+0.014983 test-rmse:1.165318+0.270702
## [92] train-rmse:0.378250+0.014638 test-rmse:1.160807+0.268858
## [93] train-rmse:0.374239+0.014950 test-rmse:1.159218+0.268956
## [94] train-rmse:0.369270+0.015717 test-rmse:1.157023+0.268478
## [95] train-rmse:0.363644+0.015903 test-rmse:1.157056+0.269372
## [96] train-rmse:0.359611+0.015168 test-rmse:1.155000+0.269107
## [97] train-rmse:0.355759+0.015318 test-rmse:1.155530+0.268704
## [98] train-rmse:0.351247+0.014065 test-rmse:1.155415+0.271553
## [99] train-rmse:0.347461+0.014427 test-rmse:1.155026+0.272309
## [100] train-rmse:0.343206+0.014514 test-rmse:1.154018+0.273265
## [101] train-rmse:0.338870+0.015874 test-rmse:1.150133+0.272877
## [102] train-rmse:0.335120+0.015135 test-rmse:1.148754+0.273633
## [103] train-rmse:0.332010+0.015651 test-rmse:1.146160+0.275431
## [104] train-rmse:0.328532+0.015442 test-rmse:1.145203+0.275322
## [105] train-rmse:0.324469+0.014966 test-rmse:1.145041+0.275886
## [106] train-rmse:0.319845+0.016000 test-rmse:1.142812+0.279344
## [107] train-rmse:0.315953+0.014973 test-rmse:1.141648+0.280592
## [108] train-rmse:0.312688+0.014323 test-rmse:1.141350+0.281317
## [109] train-rmse:0.308327+0.014366 test-rmse:1.139462+0.280408
## [110] train-rmse:0.305141+0.014366 test-rmse:1.135630+0.284693
## [111] train-rmse:0.301837+0.014288 test-rmse:1.136783+0.287820
## [112] train-rmse:0.297565+0.013506 test-rmse:1.135021+0.288278
## [113] train-rmse:0.294953+0.013028 test-rmse:1.132990+0.288762
## [114] train-rmse:0.292042+0.012969 test-rmse:1.128994+0.291027
## [115] train-rmse:0.289277+0.013981 test-rmse:1.128772+0.291661
## [116] train-rmse:0.285902+0.014760 test-rmse:1.128770+0.291446
## [117] train-rmse:0.282325+0.013850 test-rmse:1.128204+0.292356
## [118] train-rmse:0.279842+0.013633 test-rmse:1.130365+0.294735
## [119] train-rmse:0.276669+0.014286 test-rmse:1.129010+0.294440
## [120] train-rmse:0.274470+0.014015 test-rmse:1.127218+0.294298
## [121] train-rmse:0.272028+0.014259 test-rmse:1.124543+0.295209
## [122] train-rmse:0.268835+0.013990 test-rmse:1.123982+0.294052
## [123] train-rmse:0.266128+0.013483 test-rmse:1.122715+0.294791
## [124] train-rmse:0.263096+0.012519 test-rmse:1.121529+0.295032
## [125] train-rmse:0.260305+0.012173 test-rmse:1.119811+0.295608
## [126] train-rmse:0.257516+0.011904 test-rmse:1.117273+0.295940
## [127] train-rmse:0.255632+0.011787 test-rmse:1.114521+0.297415
## [128] train-rmse:0.253807+0.012496 test-rmse:1.113746+0.296370
## [129] train-rmse:0.250762+0.012511 test-rmse:1.114286+0.296286
## [130] train-rmse:0.248163+0.012558 test-rmse:1.113822+0.296526
## [131] train-rmse:0.246219+0.012467 test-rmse:1.110485+0.293643
## [132] train-rmse:0.243276+0.013064 test-rmse:1.108620+0.295265
## [133] train-rmse:0.240420+0.013115 test-rmse:1.107682+0.296755
## [134] train-rmse:0.237782+0.013547 test-rmse:1.108482+0.296365
## [135] train-rmse:0.235624+0.013802 test-rmse:1.108341+0.296661
## [136] train-rmse:0.234353+0.013683 test-rmse:1.107266+0.296751
## [137] train-rmse:0.231757+0.013860 test-rmse:1.107514+0.295971
## [138] train-rmse:0.229494+0.013430 test-rmse:1.106663+0.297503
## [139] train-rmse:0.227544+0.013192 test-rmse:1.106594+0.298142
## [140] train-rmse:0.225651+0.013173 test-rmse:1.106353+0.296655
## [141] train-rmse:0.223687+0.013186 test-rmse:1.105591+0.297127
## [142] train-rmse:0.221915+0.013212 test-rmse:1.105745+0.296930
## [143] train-rmse:0.220219+0.012974 test-rmse:1.105999+0.296925
## [144] train-rmse:0.218316+0.012838 test-rmse:1.104068+0.298176
## [145] train-rmse:0.216318+0.012031 test-rmse:1.103080+0.299094
## [146] train-rmse:0.214082+0.012177 test-rmse:1.103774+0.298995
## [147] train-rmse:0.212383+0.011754 test-rmse:1.104294+0.299017
## [148] train-rmse:0.210892+0.011721 test-rmse:1.103911+0.297796
## [149] train-rmse:0.208941+0.011800 test-rmse:1.103027+0.298251
## [150] train-rmse:0.207309+0.011729 test-rmse:1.103041+0.298590
## [151] train-rmse:0.205980+0.011974 test-rmse:1.102322+0.298933
## [152] train-rmse:0.203302+0.011652 test-rmse:1.101470+0.298575
## [153] train-rmse:0.201932+0.011673 test-rmse:1.100538+0.298960
## [154] train-rmse:0.200216+0.011593 test-rmse:1.100067+0.298618
## [155] train-rmse:0.197862+0.011696 test-rmse:1.099613+0.299497
## [156] train-rmse:0.196876+0.011710 test-rmse:1.099360+0.300462
## [157] train-rmse:0.195293+0.011535 test-rmse:1.099106+0.301039
## [158] train-rmse:0.193684+0.011339 test-rmse:1.099442+0.301376
## [159] train-rmse:0.191790+0.011257 test-rmse:1.100051+0.301599
## [160] train-rmse:0.190574+0.011138 test-rmse:1.099845+0.301118
## [161] train-rmse:0.188528+0.010469 test-rmse:1.098018+0.300023
## [162] train-rmse:0.186363+0.010606 test-rmse:1.099553+0.299254
## [163] train-rmse:0.184808+0.010966 test-rmse:1.098694+0.297857
## [164] train-rmse:0.182777+0.010495 test-rmse:1.098784+0.298207
## [165] train-rmse:0.181444+0.010725 test-rmse:1.098822+0.297880
## [166] train-rmse:0.179721+0.011282 test-rmse:1.098514+0.297139
## [167] train-rmse:0.178271+0.011103 test-rmse:1.098110+0.297119
## Stopping. Best iteration:
## [161] train-rmse:0.188528+0.010469 test-rmse:1.098018+0.300023
##
## 66
## [1] train-rmse:77.673825+0.090310 test-rmse:77.673837+0.509215
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:56.141246+0.073781 test-rmse:56.140539+0.549016
## [3] train-rmse:40.670034+0.065404 test-rmse:40.668152+0.588925
## [4] train-rmse:29.583382+0.061017 test-rmse:29.625472+0.605847
## [5] train-rmse:21.613113+0.040697 test-rmse:21.729475+0.638688
## [6] train-rmse:15.883063+0.030328 test-rmse:16.009680+0.588402
## [7] train-rmse:11.759822+0.029706 test-rmse:11.858543+0.541627
## [8] train-rmse:8.807497+0.027265 test-rmse:8.943163+0.605963
## [9] train-rmse:6.692556+0.030060 test-rmse:6.812441+0.561395
## [10] train-rmse:5.204799+0.036345 test-rmse:5.357743+0.524792
## [11] train-rmse:4.158041+0.046026 test-rmse:4.370406+0.513374
## [12] train-rmse:3.418593+0.033325 test-rmse:3.694005+0.407150
## [13] train-rmse:2.915683+0.036401 test-rmse:3.221715+0.377693
## [14] train-rmse:2.565668+0.031488 test-rmse:2.874038+0.320201
## [15] train-rmse:2.294058+0.033645 test-rmse:2.656608+0.322368
## [16] train-rmse:2.100477+0.032603 test-rmse:2.526729+0.281883
## [17] train-rmse:1.951565+0.033202 test-rmse:2.391927+0.245147
## [18] train-rmse:1.831340+0.029682 test-rmse:2.299050+0.248022
## [19] train-rmse:1.730443+0.027680 test-rmse:2.218424+0.239313
## [20] train-rmse:1.637961+0.027071 test-rmse:2.117803+0.230995
## [21] train-rmse:1.556053+0.025178 test-rmse:2.064698+0.224053
## [22] train-rmse:1.482931+0.024953 test-rmse:2.007231+0.219411
## [23] train-rmse:1.413305+0.023003 test-rmse:1.950091+0.210235
## [24] train-rmse:1.345053+0.020412 test-rmse:1.893619+0.216992
## [25] train-rmse:1.286674+0.017766 test-rmse:1.836503+0.212325
## [26] train-rmse:1.220401+0.023147 test-rmse:1.794534+0.211344
## [27] train-rmse:1.163210+0.020160 test-rmse:1.767001+0.210432
## [28] train-rmse:1.114025+0.018593 test-rmse:1.745336+0.212573
## [29] train-rmse:1.071061+0.017467 test-rmse:1.710611+0.215424
## [30] train-rmse:1.029088+0.016961 test-rmse:1.668365+0.223395
## [31] train-rmse:0.989620+0.014136 test-rmse:1.623434+0.193804
## [32] train-rmse:0.944219+0.018079 test-rmse:1.595907+0.194351
## [33] train-rmse:0.908165+0.019973 test-rmse:1.564778+0.186868
## [34] train-rmse:0.873928+0.021216 test-rmse:1.538786+0.185171
## [35] train-rmse:0.841007+0.022047 test-rmse:1.505850+0.189704
## [36] train-rmse:0.809784+0.021767 test-rmse:1.483448+0.185717
## [37] train-rmse:0.777980+0.021679 test-rmse:1.463331+0.195755
## [38] train-rmse:0.747458+0.023466 test-rmse:1.443402+0.193398
## [39] train-rmse:0.722942+0.024757 test-rmse:1.424295+0.188947
## [40] train-rmse:0.700305+0.024909 test-rmse:1.404276+0.192687
## [41] train-rmse:0.678961+0.025749 test-rmse:1.393252+0.197825
## [42] train-rmse:0.656380+0.025110 test-rmse:1.380622+0.193443
## [43] train-rmse:0.637250+0.025083 test-rmse:1.365588+0.194574
## [44] train-rmse:0.619167+0.023972 test-rmse:1.352057+0.194913
## [45] train-rmse:0.600946+0.021181 test-rmse:1.340886+0.189239
## [46] train-rmse:0.579562+0.024363 test-rmse:1.335053+0.193077
## [47] train-rmse:0.560347+0.024017 test-rmse:1.319448+0.181068
## [48] train-rmse:0.545520+0.023787 test-rmse:1.309837+0.182906
## [49] train-rmse:0.529666+0.024343 test-rmse:1.296505+0.185429
## [50] train-rmse:0.513431+0.025194 test-rmse:1.282388+0.186724
## [51] train-rmse:0.498522+0.022579 test-rmse:1.280715+0.185765
## [52] train-rmse:0.485438+0.020739 test-rmse:1.276416+0.189280
## [53] train-rmse:0.472099+0.021658 test-rmse:1.262455+0.178028
## [54] train-rmse:0.460878+0.020966 test-rmse:1.249328+0.179672
## [55] train-rmse:0.450213+0.019604 test-rmse:1.245954+0.183209
## [56] train-rmse:0.440616+0.019115 test-rmse:1.239842+0.180032
## [57] train-rmse:0.429202+0.021683 test-rmse:1.237260+0.181316
## [58] train-rmse:0.419787+0.021695 test-rmse:1.232462+0.180666
## [59] train-rmse:0.410085+0.021381 test-rmse:1.223251+0.171895
## [60] train-rmse:0.399748+0.019287 test-rmse:1.217190+0.173091
## [61] train-rmse:0.387977+0.018853 test-rmse:1.209029+0.180242
## [62] train-rmse:0.379973+0.019606 test-rmse:1.206057+0.179324
## [63] train-rmse:0.371880+0.020488 test-rmse:1.200848+0.180473
## [64] train-rmse:0.364933+0.020517 test-rmse:1.194422+0.175235
## [65] train-rmse:0.356678+0.019606 test-rmse:1.192124+0.178280
## [66] train-rmse:0.348070+0.016919 test-rmse:1.192003+0.178367
## [67] train-rmse:0.341060+0.015690 test-rmse:1.189728+0.178422
## [68] train-rmse:0.335032+0.016256 test-rmse:1.186438+0.181088
## [69] train-rmse:0.328774+0.016766 test-rmse:1.179218+0.185167
## [70] train-rmse:0.323564+0.016608 test-rmse:1.178324+0.186213
## [71] train-rmse:0.314094+0.015970 test-rmse:1.176189+0.188721
## [72] train-rmse:0.307028+0.015058 test-rmse:1.174493+0.189446
## [73] train-rmse:0.302601+0.014772 test-rmse:1.173554+0.189860
## [74] train-rmse:0.297196+0.013682 test-rmse:1.171098+0.189482
## [75] train-rmse:0.291202+0.013027 test-rmse:1.169311+0.189960
## [76] train-rmse:0.286420+0.013314 test-rmse:1.166243+0.190047
## [77] train-rmse:0.279546+0.011393 test-rmse:1.165783+0.189178
## [78] train-rmse:0.274327+0.010425 test-rmse:1.163063+0.191111
## [79] train-rmse:0.269697+0.008613 test-rmse:1.160090+0.192289
## [80] train-rmse:0.264853+0.009044 test-rmse:1.158785+0.194022
## [81] train-rmse:0.259629+0.010557 test-rmse:1.157961+0.193731
## [82] train-rmse:0.255763+0.011726 test-rmse:1.155895+0.193626
## [83] train-rmse:0.251233+0.012187 test-rmse:1.155142+0.195004
## [84] train-rmse:0.245453+0.010438 test-rmse:1.153917+0.196369
## [85] train-rmse:0.240191+0.010672 test-rmse:1.153106+0.194915
## [86] train-rmse:0.234802+0.009912 test-rmse:1.152139+0.194157
## [87] train-rmse:0.230587+0.010207 test-rmse:1.151236+0.194126
## [88] train-rmse:0.226897+0.010649 test-rmse:1.148588+0.196214
## [89] train-rmse:0.222624+0.011783 test-rmse:1.147279+0.198034
## [90] train-rmse:0.219032+0.012513 test-rmse:1.146800+0.197937
## [91] train-rmse:0.215441+0.012222 test-rmse:1.146434+0.198006
## [92] train-rmse:0.211259+0.013325 test-rmse:1.146639+0.198438
## [93] train-rmse:0.207944+0.013429 test-rmse:1.146730+0.199274
## [94] train-rmse:0.204318+0.012881 test-rmse:1.146258+0.199215
## [95] train-rmse:0.201360+0.013538 test-rmse:1.145175+0.199029
## [96] train-rmse:0.198418+0.013167 test-rmse:1.144669+0.199280
## [97] train-rmse:0.195401+0.013021 test-rmse:1.144208+0.200193
## [98] train-rmse:0.192558+0.012289 test-rmse:1.143865+0.200286
## [99] train-rmse:0.189518+0.012684 test-rmse:1.144157+0.200120
## [100] train-rmse:0.187282+0.012726 test-rmse:1.143705+0.199251
## [101] train-rmse:0.184691+0.013193 test-rmse:1.142973+0.199051
## [102] train-rmse:0.182264+0.013156 test-rmse:1.140931+0.198849
## [103] train-rmse:0.179414+0.013436 test-rmse:1.141590+0.198296
## [104] train-rmse:0.175715+0.012815 test-rmse:1.139939+0.198743
## [105] train-rmse:0.171557+0.010528 test-rmse:1.140482+0.199136
## [106] train-rmse:0.168840+0.009248 test-rmse:1.140852+0.199117
## [107] train-rmse:0.166170+0.009486 test-rmse:1.140579+0.198364
## [108] train-rmse:0.163661+0.010046 test-rmse:1.140143+0.199273
## [109] train-rmse:0.159876+0.010339 test-rmse:1.139814+0.199383
## [110] train-rmse:0.156381+0.011092 test-rmse:1.139686+0.199412
## [111] train-rmse:0.154166+0.011709 test-rmse:1.139192+0.199706
## [112] train-rmse:0.152043+0.011715 test-rmse:1.138473+0.200316
## [113] train-rmse:0.150125+0.012271 test-rmse:1.138385+0.200463
## [114] train-rmse:0.147785+0.011057 test-rmse:1.138800+0.199452
## [115] train-rmse:0.146075+0.010500 test-rmse:1.138446+0.199377
## [116] train-rmse:0.143751+0.010476 test-rmse:1.138703+0.199002
## [117] train-rmse:0.140566+0.008869 test-rmse:1.137959+0.199144
## [118] train-rmse:0.139052+0.008939 test-rmse:1.137501+0.199378
## [119] train-rmse:0.137156+0.009119 test-rmse:1.136602+0.198695
## [120] train-rmse:0.134985+0.009304 test-rmse:1.136811+0.198111
## [121] train-rmse:0.133436+0.009749 test-rmse:1.137000+0.198208
## [122] train-rmse:0.131708+0.009885 test-rmse:1.137068+0.198573
## [123] train-rmse:0.129484+0.008978 test-rmse:1.136980+0.198607
## [124] train-rmse:0.127765+0.009212 test-rmse:1.136420+0.199053
## [125] train-rmse:0.126335+0.009069 test-rmse:1.136115+0.198748
## [126] train-rmse:0.123979+0.008842 test-rmse:1.135855+0.199546
## [127] train-rmse:0.121626+0.007772 test-rmse:1.135791+0.200021
## [128] train-rmse:0.120065+0.007587 test-rmse:1.134566+0.200447
## [129] train-rmse:0.118471+0.007704 test-rmse:1.134819+0.200436
## [130] train-rmse:0.116647+0.007568 test-rmse:1.135409+0.200938
## [131] train-rmse:0.114978+0.006999 test-rmse:1.135739+0.201210
## [132] train-rmse:0.113377+0.006709 test-rmse:1.135539+0.201376
## [133] train-rmse:0.111753+0.005771 test-rmse:1.134640+0.200729
## [134] train-rmse:0.110068+0.005286 test-rmse:1.134384+0.200763
## [135] train-rmse:0.108603+0.005242 test-rmse:1.134165+0.200889
## [136] train-rmse:0.106890+0.005517 test-rmse:1.134000+0.201316
## [137] train-rmse:0.105596+0.005548 test-rmse:1.133733+0.200322
## [138] train-rmse:0.103697+0.005180 test-rmse:1.134401+0.200655
## [139] train-rmse:0.102484+0.005190 test-rmse:1.134590+0.200776
## [140] train-rmse:0.100476+0.005641 test-rmse:1.134325+0.201191
## [141] train-rmse:0.099394+0.005847 test-rmse:1.134293+0.200921
## [142] train-rmse:0.098014+0.005809 test-rmse:1.134257+0.200903
## [143] train-rmse:0.096189+0.005288 test-rmse:1.135295+0.200585
## Stopping. Best iteration:
## [137] train-rmse:0.105596+0.005548 test-rmse:1.133733+0.200322
##
## 67
## [1] train-rmse:77.673825+0.090310 test-rmse:77.673837+0.509215
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:56.141246+0.073781 test-rmse:56.140539+0.549016
## [3] train-rmse:40.670034+0.065404 test-rmse:40.668152+0.588925
## [4] train-rmse:29.583382+0.061017 test-rmse:29.625472+0.605847
## [5] train-rmse:21.613113+0.040697 test-rmse:21.729475+0.638688
## [6] train-rmse:15.878204+0.030275 test-rmse:16.014299+0.585968
## [7] train-rmse:11.738701+0.029907 test-rmse:11.849867+0.561178
## [8] train-rmse:8.757393+0.024250 test-rmse:8.871273+0.531719
## [9] train-rmse:6.612885+0.028092 test-rmse:6.742540+0.558179
## [10] train-rmse:5.084540+0.028864 test-rmse:5.218537+0.510852
## [11] train-rmse:3.984251+0.034690 test-rmse:4.164467+0.506388
## [12] train-rmse:3.210263+0.040748 test-rmse:3.463201+0.424200
## [13] train-rmse:2.658179+0.040559 test-rmse:2.969774+0.365566
## [14] train-rmse:2.269429+0.046834 test-rmse:2.627798+0.359720
## [15] train-rmse:1.988852+0.044589 test-rmse:2.413566+0.339725
## [16] train-rmse:1.767291+0.056657 test-rmse:2.248707+0.277707
## [17] train-rmse:1.613559+0.061142 test-rmse:2.116618+0.289376
## [18] train-rmse:1.488201+0.052700 test-rmse:2.037728+0.268774
## [19] train-rmse:1.381245+0.046636 test-rmse:1.970725+0.275070
## [20] train-rmse:1.292870+0.045641 test-rmse:1.903966+0.269417
## [21] train-rmse:1.214134+0.043658 test-rmse:1.851200+0.259016
## [22] train-rmse:1.134790+0.045373 test-rmse:1.785104+0.244271
## [23] train-rmse:1.071854+0.042236 test-rmse:1.738441+0.253163
## [24] train-rmse:1.005227+0.043466 test-rmse:1.713611+0.249796
## [25] train-rmse:0.947445+0.046936 test-rmse:1.671602+0.243457
## [26] train-rmse:0.901398+0.044798 test-rmse:1.641804+0.243251
## [27] train-rmse:0.854320+0.044857 test-rmse:1.598925+0.251271
## [28] train-rmse:0.811253+0.038085 test-rmse:1.569700+0.246918
## [29] train-rmse:0.771380+0.039372 test-rmse:1.534586+0.229882
## [30] train-rmse:0.738070+0.038571 test-rmse:1.515872+0.227590
## [31] train-rmse:0.704065+0.040977 test-rmse:1.492403+0.228977
## [32] train-rmse:0.669843+0.033552 test-rmse:1.475409+0.242394
## [33] train-rmse:0.639096+0.028507 test-rmse:1.452845+0.243827
## [34] train-rmse:0.612717+0.030983 test-rmse:1.429179+0.235511
## [35] train-rmse:0.588567+0.030754 test-rmse:1.413755+0.235734
## [36] train-rmse:0.567881+0.029292 test-rmse:1.398248+0.236381
## [37] train-rmse:0.544841+0.027137 test-rmse:1.383421+0.233916
## [38] train-rmse:0.522606+0.028287 test-rmse:1.372465+0.233836
## [39] train-rmse:0.502618+0.027438 test-rmse:1.362451+0.222394
## [40] train-rmse:0.485970+0.027931 test-rmse:1.348358+0.224372
## [41] train-rmse:0.465715+0.025015 test-rmse:1.334748+0.226993
## [42] train-rmse:0.447766+0.024235 test-rmse:1.322722+0.229958
## [43] train-rmse:0.433649+0.021149 test-rmse:1.316684+0.231108
## [44] train-rmse:0.417285+0.022621 test-rmse:1.308414+0.231816
## [45] train-rmse:0.405449+0.023298 test-rmse:1.304111+0.227817
## [46] train-rmse:0.393957+0.023412 test-rmse:1.300969+0.228901
## [47] train-rmse:0.382624+0.022330 test-rmse:1.293644+0.232315
## [48] train-rmse:0.369402+0.021367 test-rmse:1.290384+0.236521
## [49] train-rmse:0.358531+0.020563 test-rmse:1.287588+0.239804
## [50] train-rmse:0.347579+0.016815 test-rmse:1.283935+0.241528
## [51] train-rmse:0.336916+0.016762 test-rmse:1.277235+0.242919
## [52] train-rmse:0.326626+0.016483 test-rmse:1.274713+0.246433
## [53] train-rmse:0.317516+0.015840 test-rmse:1.273248+0.248437
## [54] train-rmse:0.307959+0.014470 test-rmse:1.271684+0.249410
## [55] train-rmse:0.298398+0.017141 test-rmse:1.268915+0.249415
## [56] train-rmse:0.288469+0.015526 test-rmse:1.269077+0.251160
## [57] train-rmse:0.281777+0.014974 test-rmse:1.264157+0.253536
## [58] train-rmse:0.272597+0.015828 test-rmse:1.263786+0.255683
## [59] train-rmse:0.264925+0.013404 test-rmse:1.262051+0.258751
## [60] train-rmse:0.258278+0.012088 test-rmse:1.260887+0.260097
## [61] train-rmse:0.251084+0.010619 test-rmse:1.257729+0.259583
## [62] train-rmse:0.244715+0.009778 test-rmse:1.256543+0.259531
## [63] train-rmse:0.239254+0.010260 test-rmse:1.254704+0.258688
## [64] train-rmse:0.232789+0.009298 test-rmse:1.249312+0.260870
## [65] train-rmse:0.226492+0.009251 test-rmse:1.247970+0.261405
## [66] train-rmse:0.220282+0.007671 test-rmse:1.245920+0.263328
## [67] train-rmse:0.214240+0.007757 test-rmse:1.244839+0.262989
## [68] train-rmse:0.206797+0.011138 test-rmse:1.244831+0.263412
## [69] train-rmse:0.201628+0.010297 test-rmse:1.243725+0.264571
## [70] train-rmse:0.198023+0.010526 test-rmse:1.240605+0.262248
## [71] train-rmse:0.193546+0.010528 test-rmse:1.239236+0.261500
## [72] train-rmse:0.188060+0.009479 test-rmse:1.238119+0.261586
## [73] train-rmse:0.183066+0.010983 test-rmse:1.236295+0.262214
## [74] train-rmse:0.176924+0.009549 test-rmse:1.236427+0.263130
## [75] train-rmse:0.173177+0.008793 test-rmse:1.234183+0.265458
## [76] train-rmse:0.168891+0.007898 test-rmse:1.231714+0.267693
## [77] train-rmse:0.165217+0.008747 test-rmse:1.230822+0.268696
## [78] train-rmse:0.161813+0.008681 test-rmse:1.230254+0.267743
## [79] train-rmse:0.157899+0.008900 test-rmse:1.229439+0.267582
## [80] train-rmse:0.153959+0.008698 test-rmse:1.228169+0.267706
## [81] train-rmse:0.149693+0.008746 test-rmse:1.228363+0.268770
## [82] train-rmse:0.145102+0.007810 test-rmse:1.227551+0.267362
## [83] train-rmse:0.141142+0.006958 test-rmse:1.225715+0.268310
## [84] train-rmse:0.137438+0.005684 test-rmse:1.226192+0.268933
## [85] train-rmse:0.134349+0.006055 test-rmse:1.225046+0.268917
## [86] train-rmse:0.131221+0.005983 test-rmse:1.224473+0.269930
## [87] train-rmse:0.128811+0.005808 test-rmse:1.224444+0.269987
## [88] train-rmse:0.126358+0.005432 test-rmse:1.224359+0.269752
## [89] train-rmse:0.123851+0.005149 test-rmse:1.224081+0.269518
## [90] train-rmse:0.121663+0.005694 test-rmse:1.223818+0.270078
## [91] train-rmse:0.119101+0.004808 test-rmse:1.223044+0.270418
## [92] train-rmse:0.116862+0.004687 test-rmse:1.222283+0.270071
## [93] train-rmse:0.114450+0.005180 test-rmse:1.221951+0.269736
## [94] train-rmse:0.111312+0.006031 test-rmse:1.221276+0.269656
## [95] train-rmse:0.108361+0.006267 test-rmse:1.221273+0.269349
## [96] train-rmse:0.107240+0.006325 test-rmse:1.220666+0.269625
## [97] train-rmse:0.104295+0.006469 test-rmse:1.221287+0.269583
## [98] train-rmse:0.101844+0.006142 test-rmse:1.221081+0.269525
## [99] train-rmse:0.099908+0.006182 test-rmse:1.220762+0.269225
## [100] train-rmse:0.097310+0.006272 test-rmse:1.221307+0.268793
## [101] train-rmse:0.095562+0.006655 test-rmse:1.221586+0.268529
## [102] train-rmse:0.093248+0.006221 test-rmse:1.221368+0.267784
## Stopping. Best iteration:
## [96] train-rmse:0.107240+0.006325 test-rmse:1.220666+0.269625
##
## 68
## [1] train-rmse:77.673825+0.090310 test-rmse:77.673837+0.509215
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:56.141246+0.073781 test-rmse:56.140539+0.549016
## [3] train-rmse:40.670034+0.065404 test-rmse:40.668152+0.588925
## [4] train-rmse:29.583382+0.061017 test-rmse:29.625472+0.605847
## [5] train-rmse:21.613113+0.040697 test-rmse:21.729475+0.638688
## [6] train-rmse:15.873245+0.030267 test-rmse:15.994896+0.594448
## [7] train-rmse:11.725557+0.026684 test-rmse:11.854042+0.563792
## [8] train-rmse:8.739105+0.031412 test-rmse:8.893071+0.601722
## [9] train-rmse:6.559318+0.036595 test-rmse:6.709558+0.571321
## [10] train-rmse:4.997963+0.033527 test-rmse:5.187891+0.501544
## [11] train-rmse:3.883372+0.030382 test-rmse:4.107251+0.452266
## [12] train-rmse:3.074878+0.031209 test-rmse:3.377137+0.439925
## [13] train-rmse:2.500652+0.025727 test-rmse:2.852292+0.368856
## [14] train-rmse:2.084619+0.025118 test-rmse:2.512076+0.344768
## [15] train-rmse:1.770017+0.022155 test-rmse:2.291106+0.284344
## [16] train-rmse:1.553553+0.018854 test-rmse:2.124480+0.272201
## [17] train-rmse:1.384461+0.025006 test-rmse:1.979226+0.254906
## [18] train-rmse:1.254227+0.025828 test-rmse:1.890115+0.244814
## [19] train-rmse:1.144497+0.024967 test-rmse:1.796031+0.215531
## [20] train-rmse:1.048983+0.025090 test-rmse:1.711163+0.192111
## [21] train-rmse:0.973847+0.028803 test-rmse:1.670700+0.188622
## [22] train-rmse:0.911389+0.030071 test-rmse:1.606064+0.195728
## [23] train-rmse:0.851487+0.031029 test-rmse:1.568394+0.202559
## [24] train-rmse:0.785185+0.024436 test-rmse:1.548632+0.214727
## [25] train-rmse:0.731915+0.028642 test-rmse:1.520059+0.218679
## [26] train-rmse:0.689962+0.026488 test-rmse:1.481222+0.225216
## [27] train-rmse:0.649401+0.026083 test-rmse:1.453130+0.224776
## [28] train-rmse:0.615731+0.027273 test-rmse:1.432383+0.226178
## [29] train-rmse:0.581298+0.027684 test-rmse:1.418220+0.227355
## [30] train-rmse:0.551359+0.029120 test-rmse:1.405748+0.237744
## [31] train-rmse:0.524418+0.028976 test-rmse:1.385097+0.237371
## [32] train-rmse:0.498316+0.028381 test-rmse:1.373371+0.241228
## [33] train-rmse:0.477448+0.028057 test-rmse:1.360073+0.243833
## [34] train-rmse:0.453009+0.023723 test-rmse:1.350484+0.251026
## [35] train-rmse:0.432361+0.021920 test-rmse:1.341037+0.249518
## [36] train-rmse:0.415946+0.022894 test-rmse:1.335918+0.250068
## [37] train-rmse:0.400266+0.021585 test-rmse:1.331893+0.252364
## [38] train-rmse:0.383436+0.019859 test-rmse:1.327929+0.253602
## [39] train-rmse:0.366352+0.019462 test-rmse:1.324380+0.256006
## [40] train-rmse:0.349305+0.018124 test-rmse:1.319398+0.252682
## [41] train-rmse:0.333992+0.016873 test-rmse:1.319406+0.256428
## [42] train-rmse:0.321310+0.014435 test-rmse:1.316664+0.256694
## [43] train-rmse:0.310546+0.015021 test-rmse:1.312023+0.258208
## [44] train-rmse:0.300149+0.015037 test-rmse:1.306707+0.258921
## [45] train-rmse:0.288816+0.015206 test-rmse:1.305141+0.262969
## [46] train-rmse:0.277744+0.015689 test-rmse:1.302412+0.266588
## [47] train-rmse:0.267442+0.015732 test-rmse:1.302369+0.267611
## [48] train-rmse:0.257655+0.014225 test-rmse:1.301289+0.266592
## [49] train-rmse:0.248514+0.014978 test-rmse:1.301106+0.269080
## [50] train-rmse:0.241506+0.015399 test-rmse:1.299080+0.269484
## [51] train-rmse:0.229905+0.014085 test-rmse:1.296214+0.271966
## [52] train-rmse:0.218890+0.011145 test-rmse:1.294316+0.271237
## [53] train-rmse:0.209256+0.009409 test-rmse:1.291926+0.271873
## [54] train-rmse:0.203969+0.009770 test-rmse:1.291212+0.271804
## [55] train-rmse:0.195904+0.009568 test-rmse:1.290374+0.271582
## [56] train-rmse:0.189350+0.011291 test-rmse:1.289220+0.272275
## [57] train-rmse:0.183582+0.012345 test-rmse:1.287303+0.274013
## [58] train-rmse:0.176538+0.009872 test-rmse:1.287417+0.274800
## [59] train-rmse:0.169719+0.007689 test-rmse:1.288361+0.275398
## [60] train-rmse:0.165714+0.007528 test-rmse:1.287649+0.276132
## [61] train-rmse:0.159138+0.007465 test-rmse:1.287702+0.275044
## [62] train-rmse:0.154384+0.007351 test-rmse:1.287907+0.274793
## [63] train-rmse:0.147792+0.006312 test-rmse:1.286821+0.276539
## [64] train-rmse:0.142934+0.006943 test-rmse:1.287852+0.276314
## [65] train-rmse:0.137381+0.008124 test-rmse:1.286739+0.276919
## [66] train-rmse:0.132842+0.008106 test-rmse:1.286891+0.276855
## [67] train-rmse:0.128455+0.007142 test-rmse:1.286689+0.275157
## [68] train-rmse:0.123756+0.006917 test-rmse:1.285783+0.274829
## [69] train-rmse:0.119753+0.008038 test-rmse:1.283565+0.274740
## [70] train-rmse:0.116236+0.009003 test-rmse:1.282322+0.275372
## [71] train-rmse:0.113012+0.007874 test-rmse:1.281710+0.274784
## [72] train-rmse:0.110105+0.006845 test-rmse:1.281502+0.274365
## [73] train-rmse:0.107271+0.006567 test-rmse:1.280965+0.275529
## [74] train-rmse:0.104410+0.006371 test-rmse:1.279773+0.276214
## [75] train-rmse:0.100439+0.007073 test-rmse:1.278926+0.277196
## [76] train-rmse:0.098081+0.007319 test-rmse:1.278345+0.278250
## [77] train-rmse:0.095354+0.006505 test-rmse:1.277570+0.278422
## [78] train-rmse:0.091459+0.005687 test-rmse:1.276594+0.276949
## [79] train-rmse:0.088818+0.005898 test-rmse:1.275275+0.275950
## [80] train-rmse:0.085268+0.006145 test-rmse:1.274888+0.276073
## [81] train-rmse:0.082594+0.006610 test-rmse:1.274830+0.276236
## [82] train-rmse:0.079640+0.006727 test-rmse:1.274200+0.275582
## [83] train-rmse:0.076720+0.005358 test-rmse:1.273020+0.275550
## [84] train-rmse:0.074927+0.005837 test-rmse:1.272978+0.275779
## [85] train-rmse:0.072865+0.005717 test-rmse:1.272016+0.276371
## [86] train-rmse:0.070985+0.005801 test-rmse:1.272048+0.276607
## [87] train-rmse:0.068549+0.005178 test-rmse:1.271551+0.276816
## [88] train-rmse:0.066167+0.003918 test-rmse:1.271843+0.276896
## [89] train-rmse:0.064472+0.004594 test-rmse:1.271008+0.276355
## [90] train-rmse:0.062558+0.004085 test-rmse:1.270606+0.276642
## [91] train-rmse:0.060524+0.003724 test-rmse:1.269579+0.275611
## [92] train-rmse:0.058519+0.004035 test-rmse:1.269482+0.276135
## [93] train-rmse:0.057078+0.003740 test-rmse:1.269632+0.276074
## [94] train-rmse:0.055181+0.004114 test-rmse:1.269992+0.276069
## [95] train-rmse:0.053506+0.004303 test-rmse:1.269641+0.276390
## [96] train-rmse:0.052067+0.004393 test-rmse:1.269901+0.276171
## [97] train-rmse:0.050388+0.004597 test-rmse:1.269991+0.275693
## [98] train-rmse:0.048972+0.004422 test-rmse:1.270110+0.276250
## Stopping. Best iteration:
## [92] train-rmse:0.058519+0.004035 test-rmse:1.269482+0.276135
##
## 69
## [1] train-rmse:77.673825+0.090310 test-rmse:77.673837+0.509215
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:56.141246+0.073781 test-rmse:56.140539+0.549016
## [3] train-rmse:40.670034+0.065404 test-rmse:40.668152+0.588925
## [4] train-rmse:29.583382+0.061017 test-rmse:29.625472+0.605847
## [5] train-rmse:21.613113+0.040697 test-rmse:21.729475+0.638688
## [6] train-rmse:15.869784+0.030072 test-rmse:15.990444+0.575910
## [7] train-rmse:11.718226+0.025159 test-rmse:11.841952+0.558107
## [8] train-rmse:8.717026+0.027391 test-rmse:8.824311+0.532432
## [9] train-rmse:6.541765+0.021702 test-rmse:6.706908+0.556603
## [10] train-rmse:4.956433+0.014977 test-rmse:5.134424+0.501214
## [11] train-rmse:3.813202+0.011043 test-rmse:4.009276+0.463767
## [12] train-rmse:2.977259+0.007196 test-rmse:3.276266+0.473450
## [13] train-rmse:2.372147+0.006932 test-rmse:2.751477+0.416379
## [14] train-rmse:1.940573+0.014248 test-rmse:2.361619+0.390663
## [15] train-rmse:1.615307+0.024426 test-rmse:2.122904+0.345388
## [16] train-rmse:1.365023+0.035442 test-rmse:1.950396+0.301701
## [17] train-rmse:1.190933+0.036547 test-rmse:1.823231+0.293969
## [18] train-rmse:1.051956+0.044370 test-rmse:1.733759+0.295518
## [19] train-rmse:0.933387+0.055949 test-rmse:1.668875+0.288644
## [20] train-rmse:0.840838+0.050931 test-rmse:1.615531+0.283769
## [21] train-rmse:0.760493+0.040966 test-rmse:1.560905+0.278612
## [22] train-rmse:0.697429+0.040201 test-rmse:1.527616+0.276325
## [23] train-rmse:0.643420+0.035247 test-rmse:1.496283+0.275146
## [24] train-rmse:0.595228+0.030201 test-rmse:1.477024+0.275191
## [25] train-rmse:0.547095+0.031025 test-rmse:1.450678+0.285143
## [26] train-rmse:0.512943+0.030200 test-rmse:1.431754+0.290317
## [27] train-rmse:0.480268+0.026053 test-rmse:1.408280+0.295388
## [28] train-rmse:0.451021+0.025779 test-rmse:1.394313+0.300611
## [29] train-rmse:0.422679+0.026944 test-rmse:1.384894+0.294186
## [30] train-rmse:0.399576+0.025975 test-rmse:1.370406+0.296439
## [31] train-rmse:0.377604+0.024655 test-rmse:1.360937+0.299252
## [32] train-rmse:0.356937+0.021988 test-rmse:1.348628+0.302312
## [33] train-rmse:0.333206+0.022181 test-rmse:1.343053+0.302705
## [34] train-rmse:0.316456+0.023469 test-rmse:1.332913+0.301387
## [35] train-rmse:0.300281+0.021855 test-rmse:1.324410+0.308028
## [36] train-rmse:0.285341+0.020227 test-rmse:1.320612+0.307169
## [37] train-rmse:0.270761+0.015036 test-rmse:1.315965+0.307771
## [38] train-rmse:0.259929+0.015371 test-rmse:1.310362+0.310899
## [39] train-rmse:0.246541+0.013348 test-rmse:1.306659+0.310204
## [40] train-rmse:0.233020+0.016914 test-rmse:1.303326+0.311764
## [41] train-rmse:0.222630+0.017373 test-rmse:1.298060+0.312883
## [42] train-rmse:0.211284+0.015180 test-rmse:1.294311+0.310085
## [43] train-rmse:0.202936+0.015370 test-rmse:1.291613+0.310533
## [44] train-rmse:0.196327+0.016294 test-rmse:1.291445+0.310726
## [45] train-rmse:0.184276+0.013448 test-rmse:1.291747+0.309979
## [46] train-rmse:0.176777+0.012169 test-rmse:1.291752+0.311825
## [47] train-rmse:0.171758+0.011799 test-rmse:1.291219+0.315399
## [48] train-rmse:0.165613+0.009710 test-rmse:1.288904+0.316355
## [49] train-rmse:0.158030+0.010803 test-rmse:1.287328+0.316265
## [50] train-rmse:0.150597+0.007104 test-rmse:1.287557+0.317306
## [51] train-rmse:0.143754+0.006949 test-rmse:1.286454+0.317874
## [52] train-rmse:0.138010+0.007259 test-rmse:1.285770+0.317209
## [53] train-rmse:0.132139+0.008034 test-rmse:1.284774+0.316848
## [54] train-rmse:0.126125+0.007295 test-rmse:1.283148+0.317790
## [55] train-rmse:0.119796+0.005453 test-rmse:1.281805+0.318300
## [56] train-rmse:0.115572+0.005303 test-rmse:1.280424+0.317617
## [57] train-rmse:0.112059+0.004893 test-rmse:1.280067+0.317545
## [58] train-rmse:0.108297+0.004583 test-rmse:1.279122+0.317637
## [59] train-rmse:0.103652+0.005153 test-rmse:1.278221+0.318409
## [60] train-rmse:0.099652+0.005439 test-rmse:1.276927+0.318338
## [61] train-rmse:0.093688+0.004413 test-rmse:1.276871+0.318394
## [62] train-rmse:0.090091+0.004409 test-rmse:1.276789+0.318528
## [63] train-rmse:0.086078+0.003225 test-rmse:1.277444+0.319723
## [64] train-rmse:0.082947+0.003316 test-rmse:1.276746+0.319697
## [65] train-rmse:0.079920+0.003103 test-rmse:1.275817+0.319743
## [66] train-rmse:0.077247+0.003934 test-rmse:1.275005+0.320822
## [67] train-rmse:0.074988+0.003730 test-rmse:1.274647+0.320543
## [68] train-rmse:0.072442+0.003956 test-rmse:1.274501+0.320617
## [69] train-rmse:0.070334+0.003945 test-rmse:1.274223+0.320522
## [70] train-rmse:0.067335+0.004776 test-rmse:1.274368+0.320346
## [71] train-rmse:0.064856+0.004519 test-rmse:1.274288+0.320349
## [72] train-rmse:0.062336+0.004239 test-rmse:1.274250+0.320179
## [73] train-rmse:0.058685+0.002582 test-rmse:1.275229+0.319930
## [74] train-rmse:0.056384+0.001902 test-rmse:1.274583+0.320414
## [75] train-rmse:0.053770+0.001839 test-rmse:1.273901+0.319826
## [76] train-rmse:0.052034+0.002765 test-rmse:1.273407+0.319989
## [77] train-rmse:0.050145+0.002876 test-rmse:1.272909+0.319930
## [78] train-rmse:0.048138+0.002957 test-rmse:1.272786+0.319709
## [79] train-rmse:0.045991+0.002965 test-rmse:1.272309+0.319478
## [80] train-rmse:0.043427+0.002598 test-rmse:1.272866+0.319843
## [81] train-rmse:0.041864+0.002489 test-rmse:1.272800+0.319596
## [82] train-rmse:0.040718+0.002591 test-rmse:1.272635+0.319734
## [83] train-rmse:0.038914+0.002025 test-rmse:1.272714+0.320189
## [84] train-rmse:0.037551+0.001689 test-rmse:1.272080+0.320546
## [85] train-rmse:0.035919+0.001360 test-rmse:1.271847+0.320482
## [86] train-rmse:0.034693+0.002108 test-rmse:1.271779+0.320650
## [87] train-rmse:0.033512+0.002057 test-rmse:1.271571+0.320359
## [88] train-rmse:0.032458+0.001894 test-rmse:1.271401+0.320284
## [89] train-rmse:0.030946+0.001780 test-rmse:1.271257+0.320064
## [90] train-rmse:0.029538+0.001722 test-rmse:1.271275+0.320002
## [91] train-rmse:0.027831+0.001425 test-rmse:1.271351+0.320076
## [92] train-rmse:0.026780+0.001452 test-rmse:1.271412+0.320223
## [93] train-rmse:0.025713+0.001094 test-rmse:1.271089+0.320209
## [94] train-rmse:0.024808+0.001091 test-rmse:1.271092+0.320194
## [95] train-rmse:0.023889+0.001204 test-rmse:1.271201+0.320250
## [96] train-rmse:0.023080+0.001273 test-rmse:1.271330+0.320297
## [97] train-rmse:0.021818+0.000794 test-rmse:1.271259+0.320468
## [98] train-rmse:0.020666+0.000908 test-rmse:1.271098+0.320536
## [99] train-rmse:0.019627+0.001087 test-rmse:1.271177+0.320475
## Stopping. Best iteration:
## [93] train-rmse:0.025713+0.001094 test-rmse:1.271089+0.320209
##
## 70
## [1] train-rmse:75.538506+0.088543 test-rmse:75.538455+0.512708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:53.112974+0.071800 test-rmse:53.112100+0.555751
## [3] train-rmse:37.457246+0.064447 test-rmse:37.454956+0.599591
## [4] train-rmse:26.559660+0.061260 test-rmse:26.588342+0.611811
## [5] train-rmse:18.983199+0.057330 test-rmse:19.032940+0.624248
## [6] train-rmse:13.743705+0.053375 test-rmse:13.792476+0.581864
## [7] train-rmse:10.176078+0.056883 test-rmse:10.302429+0.672384
## [8] train-rmse:7.786917+0.062390 test-rmse:7.896483+0.650646
## [9] train-rmse:6.240562+0.072649 test-rmse:6.336027+0.572318
## [10] train-rmse:5.260618+0.078588 test-rmse:5.409231+0.594989
## [11] train-rmse:4.653557+0.077710 test-rmse:4.744061+0.520029
## [12] train-rmse:4.266949+0.074329 test-rmse:4.431429+0.527665
## [13] train-rmse:4.030357+0.076020 test-rmse:4.180580+0.477266
## [14] train-rmse:3.861458+0.076394 test-rmse:4.033381+0.440634
## [15] train-rmse:3.736975+0.069844 test-rmse:3.868390+0.370716
## [16] train-rmse:3.637661+0.066689 test-rmse:3.840988+0.363158
## [17] train-rmse:3.554124+0.065567 test-rmse:3.785790+0.351734
## [18] train-rmse:3.479425+0.062027 test-rmse:3.716459+0.352961
## [19] train-rmse:3.412139+0.061118 test-rmse:3.668886+0.367820
## [20] train-rmse:3.346780+0.060582 test-rmse:3.624966+0.362178
## [21] train-rmse:3.282739+0.060162 test-rmse:3.530953+0.326871
## [22] train-rmse:3.222001+0.059473 test-rmse:3.479056+0.332148
## [23] train-rmse:3.162812+0.057937 test-rmse:3.407530+0.336741
## [24] train-rmse:3.106758+0.055354 test-rmse:3.323623+0.287157
## [25] train-rmse:3.055088+0.052282 test-rmse:3.280863+0.281555
## [26] train-rmse:3.005235+0.049265 test-rmse:3.239230+0.301019
## [27] train-rmse:2.956962+0.048387 test-rmse:3.188709+0.299221
## [28] train-rmse:2.913136+0.046919 test-rmse:3.161897+0.311397
## [29] train-rmse:2.868509+0.044277 test-rmse:3.102401+0.286478
## [30] train-rmse:2.826156+0.043451 test-rmse:3.070192+0.295540
## [31] train-rmse:2.784955+0.043474 test-rmse:3.049468+0.302158
## [32] train-rmse:2.744250+0.044023 test-rmse:3.014845+0.303430
## [33] train-rmse:2.704754+0.044923 test-rmse:2.979617+0.294722
## [34] train-rmse:2.665689+0.044867 test-rmse:2.937940+0.304999
## [35] train-rmse:2.627304+0.045514 test-rmse:2.900734+0.305499
## [36] train-rmse:2.589733+0.045210 test-rmse:2.859396+0.315767
## [37] train-rmse:2.552974+0.045319 test-rmse:2.818818+0.322078
## [38] train-rmse:2.517677+0.044415 test-rmse:2.781441+0.275636
## [39] train-rmse:2.483161+0.044025 test-rmse:2.748490+0.276441
## [40] train-rmse:2.450268+0.043917 test-rmse:2.714765+0.292219
## [41] train-rmse:2.417744+0.042934 test-rmse:2.694399+0.294786
## [42] train-rmse:2.386592+0.041270 test-rmse:2.665045+0.304954
## [43] train-rmse:2.356969+0.040130 test-rmse:2.646674+0.300206
## [44] train-rmse:2.328274+0.040005 test-rmse:2.613043+0.302388
## [45] train-rmse:2.300205+0.039180 test-rmse:2.591563+0.308700
## [46] train-rmse:2.272031+0.038805 test-rmse:2.561616+0.309816
## [47] train-rmse:2.243403+0.038084 test-rmse:2.526966+0.288584
## [48] train-rmse:2.215807+0.037185 test-rmse:2.495718+0.288149
## [49] train-rmse:2.188496+0.036273 test-rmse:2.472480+0.302614
## [50] train-rmse:2.161956+0.036092 test-rmse:2.450261+0.296803
## [51] train-rmse:2.135876+0.035801 test-rmse:2.417279+0.276715
## [52] train-rmse:2.110279+0.035454 test-rmse:2.384592+0.292891
## [53] train-rmse:2.084957+0.034509 test-rmse:2.358047+0.282057
## [54] train-rmse:2.059867+0.033941 test-rmse:2.321574+0.275856
## [55] train-rmse:2.035550+0.033395 test-rmse:2.310122+0.277217
## [56] train-rmse:2.012535+0.032680 test-rmse:2.288039+0.280194
## [57] train-rmse:1.989957+0.031796 test-rmse:2.271389+0.279656
## [58] train-rmse:1.967881+0.030901 test-rmse:2.260787+0.277673
## [59] train-rmse:1.945345+0.029513 test-rmse:2.233721+0.275167
## [60] train-rmse:1.924012+0.028544 test-rmse:2.207011+0.277866
## [61] train-rmse:1.902512+0.027370 test-rmse:2.189809+0.277110
## [62] train-rmse:1.881715+0.026932 test-rmse:2.173103+0.266741
## [63] train-rmse:1.861499+0.026505 test-rmse:2.137903+0.245901
## [64] train-rmse:1.841439+0.026156 test-rmse:2.120739+0.241897
## [65] train-rmse:1.821220+0.024844 test-rmse:2.104386+0.241306
## [66] train-rmse:1.801810+0.024054 test-rmse:2.092797+0.245120
## [67] train-rmse:1.782784+0.023690 test-rmse:2.071601+0.247509
## [68] train-rmse:1.764083+0.023181 test-rmse:2.053822+0.250012
## [69] train-rmse:1.745149+0.022680 test-rmse:2.031500+0.251941
## [70] train-rmse:1.726664+0.022627 test-rmse:2.010798+0.243617
## [71] train-rmse:1.709039+0.022645 test-rmse:1.982332+0.238940
## [72] train-rmse:1.691634+0.022603 test-rmse:1.973013+0.239758
## [73] train-rmse:1.674512+0.022423 test-rmse:1.956667+0.245675
## [74] train-rmse:1.657680+0.021841 test-rmse:1.939736+0.248356
## [75] train-rmse:1.641533+0.021487 test-rmse:1.930950+0.247435
## [76] train-rmse:1.625310+0.021601 test-rmse:1.925722+0.247884
## [77] train-rmse:1.609434+0.021503 test-rmse:1.921126+0.245510
## [78] train-rmse:1.594024+0.020905 test-rmse:1.898406+0.248735
## [79] train-rmse:1.578845+0.020057 test-rmse:1.888113+0.250238
## [80] train-rmse:1.563972+0.019208 test-rmse:1.864741+0.229156
## [81] train-rmse:1.549672+0.018656 test-rmse:1.855362+0.219756
## [82] train-rmse:1.535366+0.018118 test-rmse:1.843881+0.216598
## [83] train-rmse:1.521038+0.017515 test-rmse:1.829431+0.218908
## [84] train-rmse:1.507024+0.017034 test-rmse:1.812403+0.220923
## [85] train-rmse:1.493049+0.016412 test-rmse:1.796679+0.217732
## [86] train-rmse:1.479665+0.016153 test-rmse:1.790544+0.217286
## [87] train-rmse:1.466206+0.015768 test-rmse:1.764673+0.207248
## [88] train-rmse:1.453125+0.015264 test-rmse:1.758072+0.208938
## [89] train-rmse:1.440286+0.014830 test-rmse:1.739217+0.217495
## [90] train-rmse:1.427497+0.014507 test-rmse:1.735910+0.215368
## [91] train-rmse:1.414843+0.014106 test-rmse:1.720269+0.214509
## [92] train-rmse:1.402209+0.014303 test-rmse:1.712762+0.215092
## [93] train-rmse:1.389774+0.014445 test-rmse:1.699081+0.215410
## [94] train-rmse:1.377775+0.014117 test-rmse:1.689845+0.211850
## [95] train-rmse:1.366062+0.013875 test-rmse:1.675860+0.216898
## [96] train-rmse:1.354858+0.013491 test-rmse:1.660789+0.222115
## [97] train-rmse:1.343651+0.013366 test-rmse:1.653015+0.225504
## [98] train-rmse:1.332771+0.012909 test-rmse:1.648113+0.226559
## [99] train-rmse:1.322147+0.012692 test-rmse:1.644746+0.222977
## [100] train-rmse:1.311744+0.012452 test-rmse:1.630095+0.222355
## [101] train-rmse:1.301587+0.012370 test-rmse:1.617592+0.224579
## [102] train-rmse:1.291552+0.012049 test-rmse:1.606460+0.223029
## [103] train-rmse:1.281773+0.011667 test-rmse:1.601222+0.224949
## [104] train-rmse:1.272235+0.011461 test-rmse:1.594340+0.229186
## [105] train-rmse:1.262670+0.011568 test-rmse:1.576818+0.211463
## [106] train-rmse:1.253457+0.011342 test-rmse:1.569535+0.212092
## [107] train-rmse:1.244204+0.011182 test-rmse:1.562931+0.210170
## [108] train-rmse:1.235170+0.011083 test-rmse:1.550865+0.208163
## [109] train-rmse:1.226427+0.010928 test-rmse:1.538275+0.206150
## [110] train-rmse:1.217579+0.010828 test-rmse:1.532651+0.205231
## [111] train-rmse:1.208746+0.010700 test-rmse:1.517231+0.204330
## [112] train-rmse:1.200270+0.010539 test-rmse:1.505706+0.194337
## [113] train-rmse:1.191840+0.010394 test-rmse:1.495083+0.195280
## [114] train-rmse:1.183626+0.010227 test-rmse:1.489956+0.197058
## [115] train-rmse:1.175391+0.010202 test-rmse:1.484113+0.200794
## [116] train-rmse:1.167410+0.010243 test-rmse:1.474420+0.203460
## [117] train-rmse:1.159501+0.010036 test-rmse:1.465822+0.206257
## [118] train-rmse:1.151937+0.009950 test-rmse:1.458858+0.206299
## [119] train-rmse:1.144499+0.009700 test-rmse:1.455752+0.202311
## [120] train-rmse:1.137272+0.009478 test-rmse:1.452698+0.201351
## [121] train-rmse:1.130064+0.009258 test-rmse:1.449171+0.203030
## [122] train-rmse:1.123007+0.009151 test-rmse:1.442235+0.207891
## [123] train-rmse:1.116143+0.008993 test-rmse:1.438339+0.210154
## [124] train-rmse:1.109367+0.008970 test-rmse:1.434485+0.211250
## [125] train-rmse:1.102501+0.008895 test-rmse:1.428006+0.209253
## [126] train-rmse:1.095873+0.008964 test-rmse:1.416714+0.195285
## [127] train-rmse:1.089447+0.008854 test-rmse:1.412014+0.197285
## [128] train-rmse:1.083088+0.008705 test-rmse:1.404923+0.200671
## [129] train-rmse:1.076932+0.008633 test-rmse:1.398531+0.198660
## [130] train-rmse:1.070818+0.008604 test-rmse:1.393987+0.193954
## [131] train-rmse:1.064824+0.008512 test-rmse:1.384403+0.200197
## [132] train-rmse:1.058919+0.008463 test-rmse:1.380703+0.196064
## [133] train-rmse:1.053195+0.008501 test-rmse:1.377356+0.196309
## [134] train-rmse:1.047531+0.008553 test-rmse:1.366318+0.191592
## [135] train-rmse:1.041939+0.008590 test-rmse:1.362498+0.187547
## [136] train-rmse:1.036424+0.008624 test-rmse:1.357500+0.187521
## [137] train-rmse:1.031148+0.008615 test-rmse:1.348511+0.187487
## [138] train-rmse:1.025982+0.008608 test-rmse:1.339205+0.191743
## [139] train-rmse:1.020811+0.008603 test-rmse:1.335263+0.191475
## [140] train-rmse:1.015730+0.008642 test-rmse:1.331857+0.192058
## [141] train-rmse:1.010738+0.008633 test-rmse:1.329575+0.191613
## [142] train-rmse:1.005777+0.008547 test-rmse:1.325447+0.193930
## [143] train-rmse:1.000852+0.008497 test-rmse:1.319007+0.197196
## [144] train-rmse:0.996041+0.008380 test-rmse:1.315700+0.199008
## [145] train-rmse:0.991356+0.008294 test-rmse:1.312195+0.200860
## [146] train-rmse:0.986866+0.008169 test-rmse:1.309350+0.201143
## [147] train-rmse:0.982441+0.008110 test-rmse:1.309641+0.199859
## [148] train-rmse:0.977979+0.007969 test-rmse:1.300941+0.196747
## [149] train-rmse:0.973648+0.007927 test-rmse:1.290328+0.189173
## [150] train-rmse:0.969443+0.007871 test-rmse:1.287130+0.188042
## [151] train-rmse:0.965358+0.007846 test-rmse:1.284885+0.186539
## [152] train-rmse:0.961262+0.007761 test-rmse:1.279556+0.184155
## [153] train-rmse:0.957185+0.007845 test-rmse:1.277231+0.184165
## [154] train-rmse:0.953197+0.007870 test-rmse:1.275447+0.184388
## [155] train-rmse:0.949355+0.007834 test-rmse:1.273706+0.184381
## [156] train-rmse:0.945550+0.007872 test-rmse:1.273019+0.183812
## [157] train-rmse:0.941704+0.007846 test-rmse:1.268168+0.177894
## [158] train-rmse:0.937871+0.007829 test-rmse:1.265516+0.178592
## [159] train-rmse:0.934196+0.007771 test-rmse:1.265004+0.178199
## [160] train-rmse:0.930636+0.007814 test-rmse:1.258296+0.180082
## [161] train-rmse:0.927060+0.007863 test-rmse:1.254390+0.178948
## [162] train-rmse:0.923655+0.007948 test-rmse:1.253130+0.174635
## [163] train-rmse:0.920350+0.008032 test-rmse:1.251374+0.175723
## [164] train-rmse:0.917064+0.008115 test-rmse:1.244305+0.177026
## [165] train-rmse:0.913891+0.008206 test-rmse:1.243084+0.176497
## [166] train-rmse:0.910789+0.008292 test-rmse:1.240367+0.176915
## [167] train-rmse:0.907702+0.008315 test-rmse:1.237205+0.178069
## [168] train-rmse:0.904653+0.008340 test-rmse:1.233574+0.179856
## [169] train-rmse:0.901700+0.008274 test-rmse:1.235399+0.179764
## [170] train-rmse:0.898794+0.008291 test-rmse:1.234774+0.181610
## [171] train-rmse:0.895873+0.008333 test-rmse:1.229594+0.184066
## [172] train-rmse:0.893074+0.008322 test-rmse:1.227823+0.184384
## [173] train-rmse:0.890237+0.008333 test-rmse:1.224794+0.183465
## [174] train-rmse:0.887477+0.008430 test-rmse:1.220659+0.180402
## [175] train-rmse:0.884749+0.008465 test-rmse:1.219138+0.178896
## [176] train-rmse:0.882040+0.008472 test-rmse:1.213941+0.181514
## [177] train-rmse:0.879377+0.008510 test-rmse:1.208890+0.182530
## [178] train-rmse:0.876803+0.008541 test-rmse:1.208576+0.181778
## [179] train-rmse:0.874261+0.008575 test-rmse:1.200826+0.175129
## [180] train-rmse:0.871794+0.008637 test-rmse:1.199137+0.177215
## [181] train-rmse:0.869309+0.008725 test-rmse:1.195680+0.179008
## [182] train-rmse:0.866884+0.008733 test-rmse:1.193999+0.179147
## [183] train-rmse:0.864530+0.008770 test-rmse:1.190839+0.176625
## [184] train-rmse:0.862215+0.008810 test-rmse:1.190941+0.176789
## [185] train-rmse:0.859957+0.008862 test-rmse:1.190363+0.177300
## [186] train-rmse:0.857740+0.008949 test-rmse:1.189503+0.176926
## [187] train-rmse:0.855598+0.008986 test-rmse:1.189616+0.176455
## [188] train-rmse:0.853436+0.008991 test-rmse:1.187563+0.175817
## [189] train-rmse:0.851368+0.009017 test-rmse:1.186708+0.176442
## [190] train-rmse:0.849261+0.009088 test-rmse:1.183447+0.175096
## [191] train-rmse:0.847276+0.009159 test-rmse:1.184266+0.173019
## [192] train-rmse:0.845235+0.009195 test-rmse:1.179188+0.174318
## [193] train-rmse:0.843263+0.009217 test-rmse:1.177751+0.175541
## [194] train-rmse:0.841234+0.009172 test-rmse:1.174267+0.174590
## [195] train-rmse:0.839351+0.009218 test-rmse:1.171243+0.175436
## [196] train-rmse:0.837532+0.009258 test-rmse:1.170599+0.176401
## [197] train-rmse:0.835603+0.009440 test-rmse:1.167648+0.179672
## [198] train-rmse:0.833705+0.009522 test-rmse:1.166936+0.178369
## [199] train-rmse:0.831907+0.009586 test-rmse:1.164651+0.179198
## [200] train-rmse:0.830141+0.009664 test-rmse:1.164950+0.181282
## [201] train-rmse:0.828404+0.009717 test-rmse:1.162843+0.184166
## [202] train-rmse:0.826626+0.009767 test-rmse:1.161810+0.184483
## [203] train-rmse:0.824948+0.009790 test-rmse:1.159022+0.185555
## [204] train-rmse:0.823315+0.009801 test-rmse:1.159097+0.186386
## [205] train-rmse:0.821670+0.009888 test-rmse:1.158457+0.186189
## [206] train-rmse:0.820084+0.009927 test-rmse:1.157684+0.186257
## [207] train-rmse:0.818490+0.009953 test-rmse:1.155859+0.187310
## [208] train-rmse:0.816877+0.009972 test-rmse:1.153945+0.184604
## [209] train-rmse:0.815370+0.010019 test-rmse:1.151988+0.181515
## [210] train-rmse:0.813872+0.010098 test-rmse:1.147519+0.181183
## [211] train-rmse:0.812379+0.010190 test-rmse:1.143045+0.174706
## [212] train-rmse:0.810839+0.010318 test-rmse:1.142283+0.174422
## [213] train-rmse:0.809374+0.010412 test-rmse:1.142533+0.173191
## [214] train-rmse:0.807933+0.010472 test-rmse:1.141223+0.172305
## [215] train-rmse:0.806533+0.010566 test-rmse:1.139532+0.171986
## [216] train-rmse:0.805108+0.010653 test-rmse:1.139181+0.172383
## [217] train-rmse:0.803707+0.010761 test-rmse:1.136805+0.173267
## [218] train-rmse:0.802304+0.010844 test-rmse:1.135590+0.171663
## [219] train-rmse:0.800960+0.010894 test-rmse:1.134354+0.172178
## [220] train-rmse:0.799626+0.010922 test-rmse:1.132555+0.173671
## [221] train-rmse:0.798318+0.010982 test-rmse:1.130841+0.174720
## [222] train-rmse:0.797052+0.011053 test-rmse:1.129973+0.175295
## [223] train-rmse:0.795758+0.011168 test-rmse:1.127397+0.175038
## [224] train-rmse:0.794504+0.011211 test-rmse:1.126454+0.173441
## [225] train-rmse:0.793285+0.011269 test-rmse:1.127808+0.173015
## [226] train-rmse:0.791984+0.011373 test-rmse:1.127882+0.173709
## [227] train-rmse:0.790787+0.011413 test-rmse:1.126429+0.174589
## [228] train-rmse:0.789598+0.011491 test-rmse:1.124577+0.176662
## [229] train-rmse:0.788363+0.011567 test-rmse:1.123598+0.173876
## [230] train-rmse:0.787206+0.011642 test-rmse:1.122953+0.174417
## [231] train-rmse:0.786055+0.011718 test-rmse:1.121939+0.174919
## [232] train-rmse:0.784939+0.011755 test-rmse:1.121369+0.176275
## [233] train-rmse:0.783835+0.011806 test-rmse:1.120730+0.177207
## [234] train-rmse:0.782724+0.011872 test-rmse:1.121528+0.176942
## [235] train-rmse:0.781565+0.011857 test-rmse:1.120633+0.176910
## [236] train-rmse:0.780486+0.011863 test-rmse:1.120276+0.178530
## [237] train-rmse:0.779289+0.011891 test-rmse:1.119668+0.174900
## [238] train-rmse:0.778197+0.011920 test-rmse:1.117511+0.174537
## [239] train-rmse:0.777152+0.011937 test-rmse:1.115542+0.173412
## [240] train-rmse:0.776004+0.012014 test-rmse:1.112900+0.175372
## [241] train-rmse:0.774965+0.012076 test-rmse:1.109006+0.171967
## [242] train-rmse:0.773888+0.012089 test-rmse:1.109679+0.169954
## [243] train-rmse:0.772917+0.012137 test-rmse:1.107267+0.170682
## [244] train-rmse:0.771949+0.012185 test-rmse:1.104885+0.172767
## [245] train-rmse:0.770964+0.012213 test-rmse:1.104075+0.171541
## [246] train-rmse:0.769925+0.012301 test-rmse:1.103712+0.170654
## [247] train-rmse:0.769010+0.012323 test-rmse:1.103160+0.170528
## [248] train-rmse:0.768118+0.012344 test-rmse:1.103522+0.171003
## [249] train-rmse:0.767224+0.012354 test-rmse:1.102526+0.172298
## [250] train-rmse:0.766333+0.012383 test-rmse:1.103242+0.172228
## [251] train-rmse:0.765386+0.012447 test-rmse:1.103425+0.171787
## [252] train-rmse:0.764438+0.012450 test-rmse:1.103508+0.172474
## [253] train-rmse:0.763547+0.012485 test-rmse:1.102552+0.174428
## [254] train-rmse:0.762658+0.012537 test-rmse:1.102584+0.172367
## [255] train-rmse:0.761775+0.012574 test-rmse:1.102138+0.173887
## [256] train-rmse:0.760900+0.012632 test-rmse:1.101764+0.174716
## [257] train-rmse:0.760053+0.012679 test-rmse:1.101836+0.175527
## [258] train-rmse:0.759215+0.012717 test-rmse:1.101803+0.174983
## [259] train-rmse:0.758397+0.012741 test-rmse:1.101378+0.174317
## [260] train-rmse:0.757532+0.012718 test-rmse:1.099437+0.175777
## [261] train-rmse:0.756702+0.012713 test-rmse:1.096935+0.176164
## [262] train-rmse:0.755881+0.012737 test-rmse:1.095867+0.176689
## [263] train-rmse:0.755073+0.012743 test-rmse:1.095826+0.176364
## [264] train-rmse:0.754273+0.012758 test-rmse:1.096159+0.176403
## [265] train-rmse:0.753453+0.012779 test-rmse:1.096210+0.177963
## [266] train-rmse:0.752632+0.012773 test-rmse:1.096307+0.178368
## [267] train-rmse:0.751860+0.012799 test-rmse:1.095211+0.176729
## [268] train-rmse:0.751090+0.012819 test-rmse:1.093950+0.175989
## [269] train-rmse:0.750315+0.012852 test-rmse:1.094738+0.175485
## [270] train-rmse:0.749540+0.012883 test-rmse:1.092749+0.174458
## [271] train-rmse:0.748780+0.012887 test-rmse:1.090207+0.171176
## [272] train-rmse:0.748054+0.012904 test-rmse:1.091404+0.170295
## [273] train-rmse:0.747319+0.012922 test-rmse:1.088985+0.172209
## [274] train-rmse:0.746594+0.012941 test-rmse:1.089252+0.171843
## [275] train-rmse:0.745880+0.012947 test-rmse:1.088440+0.170925
## [276] train-rmse:0.745176+0.012960 test-rmse:1.087706+0.171376
## [277] train-rmse:0.744492+0.012983 test-rmse:1.087047+0.171739
## [278] train-rmse:0.743816+0.013001 test-rmse:1.086515+0.171418
## [279] train-rmse:0.743141+0.013008 test-rmse:1.084861+0.170871
## [280] train-rmse:0.742408+0.012996 test-rmse:1.083612+0.169859
## [281] train-rmse:0.741711+0.013021 test-rmse:1.084097+0.169692
## [282] train-rmse:0.741037+0.013032 test-rmse:1.081888+0.168036
## [283] train-rmse:0.740353+0.013052 test-rmse:1.080870+0.166443
## [284] train-rmse:0.739715+0.013062 test-rmse:1.079073+0.167538
## [285] train-rmse:0.739083+0.013073 test-rmse:1.078550+0.167186
## [286] train-rmse:0.738457+0.013070 test-rmse:1.078008+0.167286
## [287] train-rmse:0.737810+0.013086 test-rmse:1.076593+0.168809
## [288] train-rmse:0.737143+0.013059 test-rmse:1.076486+0.167117
## [289] train-rmse:0.736525+0.013056 test-rmse:1.075992+0.165647
## [290] train-rmse:0.735899+0.013054 test-rmse:1.074448+0.166486
## [291] train-rmse:0.735270+0.013031 test-rmse:1.074914+0.165372
## [292] train-rmse:0.734626+0.013021 test-rmse:1.072236+0.163410
## [293] train-rmse:0.733981+0.013049 test-rmse:1.071104+0.164048
## [294] train-rmse:0.733358+0.013053 test-rmse:1.070253+0.162887
## [295] train-rmse:0.732751+0.013058 test-rmse:1.069701+0.163884
## [296] train-rmse:0.732129+0.013084 test-rmse:1.069094+0.163218
## [297] train-rmse:0.731531+0.013093 test-rmse:1.068790+0.163331
## [298] train-rmse:0.730920+0.013042 test-rmse:1.068680+0.162663
## [299] train-rmse:0.730328+0.013035 test-rmse:1.068219+0.161565
## [300] train-rmse:0.729750+0.013015 test-rmse:1.066736+0.159954
## [301] train-rmse:0.729181+0.013013 test-rmse:1.066013+0.160436
## [302] train-rmse:0.728616+0.013001 test-rmse:1.064539+0.161569
## [303] train-rmse:0.728053+0.012972 test-rmse:1.065588+0.159883
## [304] train-rmse:0.727468+0.012952 test-rmse:1.065156+0.160100
## [305] train-rmse:0.726891+0.012937 test-rmse:1.065370+0.159327
## [306] train-rmse:0.726341+0.012930 test-rmse:1.065997+0.158742
## [307] train-rmse:0.725748+0.012918 test-rmse:1.064277+0.160038
## [308] train-rmse:0.725213+0.012912 test-rmse:1.064554+0.160129
## [309] train-rmse:0.724669+0.012921 test-rmse:1.064737+0.159929
## [310] train-rmse:0.724094+0.012935 test-rmse:1.064186+0.160237
## [311] train-rmse:0.723557+0.012918 test-rmse:1.062548+0.160022
## [312] train-rmse:0.723027+0.012914 test-rmse:1.061654+0.160485
## [313] train-rmse:0.722511+0.012897 test-rmse:1.060971+0.160177
## [314] train-rmse:0.721957+0.012893 test-rmse:1.060887+0.158800
## [315] train-rmse:0.721413+0.012893 test-rmse:1.060130+0.158787
## [316] train-rmse:0.720899+0.012873 test-rmse:1.059560+0.159226
## [317] train-rmse:0.720372+0.012859 test-rmse:1.059720+0.159262
## [318] train-rmse:0.719861+0.012865 test-rmse:1.060445+0.160160
## [319] train-rmse:0.719360+0.012866 test-rmse:1.060716+0.160510
## [320] train-rmse:0.718825+0.012826 test-rmse:1.060349+0.160750
## [321] train-rmse:0.718347+0.012812 test-rmse:1.060722+0.160620
## [322] train-rmse:0.717847+0.012801 test-rmse:1.059851+0.161674
## Stopping. Best iteration:
## [316] train-rmse:0.720899+0.012873 test-rmse:1.059560+0.159226
##
## 71
## [1] train-rmse:75.538506+0.088543 test-rmse:75.538455+0.512708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:53.112974+0.071800 test-rmse:53.112100+0.555751
## [3] train-rmse:37.457246+0.064447 test-rmse:37.454956+0.599591
## [4] train-rmse:26.556096+0.061192 test-rmse:26.606015+0.614020
## [5] train-rmse:18.942103+0.046909 test-rmse:18.986064+0.540532
## [6] train-rmse:13.646918+0.045394 test-rmse:13.735835+0.573823
## [7] train-rmse:10.004129+0.049418 test-rmse:10.114857+0.561768
## [8] train-rmse:7.528265+0.053194 test-rmse:7.679244+0.553116
## [9] train-rmse:5.877478+0.059423 test-rmse:6.034324+0.555193
## [10] train-rmse:4.803570+0.065966 test-rmse:4.976334+0.506287
## [11] train-rmse:4.114395+0.067026 test-rmse:4.356855+0.483266
## [12] train-rmse:3.665615+0.079652 test-rmse:3.913226+0.439640
## [13] train-rmse:3.365690+0.078224 test-rmse:3.614524+0.377732
## [14] train-rmse:3.149371+0.065342 test-rmse:3.405066+0.322218
## [15] train-rmse:2.990945+0.059689 test-rmse:3.281745+0.325983
## [16] train-rmse:2.863230+0.054528 test-rmse:3.159545+0.318663
## [17] train-rmse:2.762389+0.051370 test-rmse:3.058411+0.309883
## [18] train-rmse:2.673248+0.049951 test-rmse:2.987907+0.313167
## [19] train-rmse:2.593184+0.048763 test-rmse:2.938979+0.300523
## [20] train-rmse:2.515754+0.048772 test-rmse:2.856545+0.302677
## [21] train-rmse:2.443304+0.048655 test-rmse:2.779923+0.278810
## [22] train-rmse:2.372921+0.047085 test-rmse:2.712657+0.286993
## [23] train-rmse:2.303868+0.043401 test-rmse:2.635758+0.258123
## [24] train-rmse:2.241488+0.041827 test-rmse:2.599585+0.273572
## [25] train-rmse:2.183169+0.037715 test-rmse:2.552713+0.276887
## [26] train-rmse:2.128716+0.035795 test-rmse:2.491779+0.264510
## [27] train-rmse:2.068655+0.034236 test-rmse:2.452295+0.231304
## [28] train-rmse:2.017329+0.033610 test-rmse:2.407018+0.224095
## [29] train-rmse:1.966323+0.032639 test-rmse:2.356679+0.227397
## [30] train-rmse:1.918773+0.031211 test-rmse:2.306620+0.224655
## [31] train-rmse:1.870807+0.029881 test-rmse:2.263328+0.239162
## [32] train-rmse:1.825197+0.028326 test-rmse:2.222933+0.223635
## [33] train-rmse:1.781851+0.027123 test-rmse:2.162307+0.221723
## [34] train-rmse:1.740592+0.023868 test-rmse:2.140151+0.224326
## [35] train-rmse:1.701011+0.022954 test-rmse:2.105405+0.215149
## [36] train-rmse:1.660662+0.022209 test-rmse:2.076085+0.227969
## [37] train-rmse:1.619862+0.016948 test-rmse:2.040059+0.216356
## [38] train-rmse:1.579924+0.016490 test-rmse:2.029615+0.221154
## [39] train-rmse:1.544720+0.016836 test-rmse:1.997560+0.235065
## [40] train-rmse:1.509178+0.015751 test-rmse:1.975852+0.239114
## [41] train-rmse:1.475657+0.016149 test-rmse:1.946556+0.238834
## [42] train-rmse:1.444427+0.015502 test-rmse:1.915269+0.239423
## [43] train-rmse:1.413273+0.015709 test-rmse:1.887001+0.235409
## [44] train-rmse:1.381386+0.016032 test-rmse:1.866852+0.231635
## [45] train-rmse:1.353513+0.015243 test-rmse:1.838827+0.233139
## [46] train-rmse:1.326298+0.015840 test-rmse:1.809209+0.218293
## [47] train-rmse:1.299396+0.015341 test-rmse:1.791146+0.225436
## [48] train-rmse:1.272501+0.014729 test-rmse:1.769763+0.224994
## [49] train-rmse:1.247702+0.014660 test-rmse:1.744634+0.218742
## [50] train-rmse:1.223865+0.015203 test-rmse:1.719460+0.222834
## [51] train-rmse:1.199989+0.015758 test-rmse:1.697203+0.229971
## [52] train-rmse:1.176748+0.016799 test-rmse:1.692373+0.233504
## [53] train-rmse:1.155087+0.018271 test-rmse:1.670887+0.216037
## [54] train-rmse:1.132096+0.017717 test-rmse:1.663699+0.212740
## [55] train-rmse:1.111185+0.017282 test-rmse:1.642958+0.209448
## [56] train-rmse:1.090984+0.017442 test-rmse:1.627138+0.213462
## [57] train-rmse:1.072485+0.017520 test-rmse:1.600746+0.208879
## [58] train-rmse:1.054900+0.017819 test-rmse:1.586102+0.216703
## [59] train-rmse:1.037573+0.017897 test-rmse:1.559647+0.218294
## [60] train-rmse:1.020307+0.017698 test-rmse:1.549989+0.212505
## [61] train-rmse:1.004557+0.017558 test-rmse:1.537650+0.211290
## [62] train-rmse:0.986290+0.017486 test-rmse:1.514180+0.194245
## [63] train-rmse:0.969797+0.017888 test-rmse:1.503868+0.197239
## [64] train-rmse:0.952221+0.015422 test-rmse:1.496652+0.201301
## [65] train-rmse:0.936495+0.015817 test-rmse:1.481172+0.209710
## [66] train-rmse:0.923087+0.016381 test-rmse:1.470327+0.218305
## [67] train-rmse:0.906464+0.016876 test-rmse:1.454653+0.217353
## [68] train-rmse:0.893055+0.017839 test-rmse:1.447848+0.218392
## [69] train-rmse:0.878558+0.020350 test-rmse:1.439144+0.215925
## [70] train-rmse:0.866116+0.019938 test-rmse:1.425196+0.216582
## [71] train-rmse:0.853781+0.019732 test-rmse:1.418545+0.216855
## [72] train-rmse:0.841603+0.019124 test-rmse:1.409513+0.220401
## [73] train-rmse:0.827517+0.021545 test-rmse:1.405476+0.220068
## [74] train-rmse:0.816777+0.021021 test-rmse:1.394004+0.207944
## [75] train-rmse:0.805560+0.019566 test-rmse:1.384037+0.206076
## [76] train-rmse:0.794547+0.019655 test-rmse:1.375665+0.207788
## [77] train-rmse:0.785007+0.020025 test-rmse:1.366335+0.212647
## [78] train-rmse:0.773717+0.017737 test-rmse:1.362853+0.213111
## [79] train-rmse:0.763843+0.018321 test-rmse:1.358828+0.208638
## [80] train-rmse:0.753958+0.020468 test-rmse:1.352958+0.211056
## [81] train-rmse:0.744220+0.022085 test-rmse:1.346705+0.211468
## [82] train-rmse:0.733807+0.021876 test-rmse:1.338741+0.207174
## [83] train-rmse:0.721774+0.021437 test-rmse:1.329808+0.204387
## [84] train-rmse:0.714366+0.021509 test-rmse:1.325601+0.207445
## [85] train-rmse:0.706234+0.021524 test-rmse:1.322029+0.209028
## [86] train-rmse:0.699160+0.021857 test-rmse:1.319191+0.206404
## [87] train-rmse:0.692033+0.022877 test-rmse:1.306873+0.205720
## [88] train-rmse:0.684481+0.022008 test-rmse:1.306278+0.207484
## [89] train-rmse:0.677542+0.022188 test-rmse:1.295096+0.203764
## [90] train-rmse:0.669913+0.022070 test-rmse:1.288416+0.207722
## [91] train-rmse:0.661209+0.019333 test-rmse:1.280555+0.205326
## [92] train-rmse:0.654042+0.019202 test-rmse:1.274081+0.204559
## [93] train-rmse:0.647255+0.019657 test-rmse:1.268738+0.208880
## [94] train-rmse:0.640466+0.019955 test-rmse:1.265913+0.209128
## [95] train-rmse:0.634474+0.019933 test-rmse:1.260622+0.211063
## [96] train-rmse:0.626936+0.019445 test-rmse:1.259401+0.212347
## [97] train-rmse:0.619108+0.019032 test-rmse:1.254596+0.214996
## [98] train-rmse:0.613524+0.019508 test-rmse:1.249825+0.214314
## [99] train-rmse:0.608491+0.019758 test-rmse:1.249550+0.215017
## [100] train-rmse:0.602900+0.019783 test-rmse:1.247644+0.217331
## [101] train-rmse:0.597017+0.020562 test-rmse:1.244606+0.217345
## [102] train-rmse:0.589904+0.021744 test-rmse:1.241589+0.218063
## [103] train-rmse:0.584619+0.021000 test-rmse:1.238508+0.222067
## [104] train-rmse:0.578706+0.020520 test-rmse:1.239522+0.229498
## [105] train-rmse:0.572820+0.019628 test-rmse:1.234315+0.227224
## [106] train-rmse:0.567109+0.019515 test-rmse:1.234430+0.226909
## [107] train-rmse:0.562733+0.018925 test-rmse:1.230605+0.227037
## [108] train-rmse:0.559068+0.018786 test-rmse:1.226887+0.227024
## [109] train-rmse:0.554527+0.019503 test-rmse:1.220307+0.229579
## [110] train-rmse:0.550074+0.019479 test-rmse:1.220260+0.230264
## [111] train-rmse:0.544647+0.018888 test-rmse:1.217992+0.231575
## [112] train-rmse:0.540483+0.019054 test-rmse:1.217153+0.233397
## [113] train-rmse:0.535421+0.019931 test-rmse:1.213084+0.233615
## [114] train-rmse:0.531832+0.020190 test-rmse:1.213112+0.233033
## [115] train-rmse:0.528536+0.020653 test-rmse:1.212296+0.233070
## [116] train-rmse:0.524537+0.020841 test-rmse:1.211614+0.232585
## [117] train-rmse:0.520322+0.020231 test-rmse:1.208113+0.235531
## [118] train-rmse:0.516491+0.020957 test-rmse:1.206237+0.237342
## [119] train-rmse:0.512479+0.020715 test-rmse:1.206382+0.237694
## [120] train-rmse:0.508709+0.020921 test-rmse:1.201582+0.237151
## [121] train-rmse:0.505931+0.020945 test-rmse:1.199426+0.237099
## [122] train-rmse:0.502559+0.021394 test-rmse:1.197455+0.238886
## [123] train-rmse:0.498639+0.021942 test-rmse:1.197270+0.240994
## [124] train-rmse:0.495532+0.021698 test-rmse:1.194983+0.240741
## [125] train-rmse:0.491784+0.019953 test-rmse:1.193835+0.241570
## [126] train-rmse:0.487591+0.018639 test-rmse:1.190784+0.241436
## [127] train-rmse:0.485330+0.018810 test-rmse:1.188715+0.242062
## [128] train-rmse:0.482138+0.018612 test-rmse:1.185785+0.242330
## [129] train-rmse:0.478578+0.018697 test-rmse:1.186215+0.242560
## [130] train-rmse:0.474436+0.017368 test-rmse:1.185782+0.243089
## [131] train-rmse:0.471620+0.017400 test-rmse:1.183249+0.241791
## [132] train-rmse:0.469482+0.017587 test-rmse:1.181754+0.242088
## [133] train-rmse:0.467032+0.017346 test-rmse:1.182341+0.240857
## [134] train-rmse:0.464747+0.017545 test-rmse:1.181092+0.241907
## [135] train-rmse:0.462447+0.017204 test-rmse:1.179501+0.243811
## [136] train-rmse:0.459970+0.017909 test-rmse:1.177944+0.244224
## [137] train-rmse:0.456721+0.017670 test-rmse:1.177847+0.243351
## [138] train-rmse:0.453511+0.017142 test-rmse:1.176680+0.242653
## [139] train-rmse:0.449424+0.017307 test-rmse:1.173189+0.243823
## [140] train-rmse:0.446651+0.017389 test-rmse:1.172310+0.243708
## [141] train-rmse:0.444708+0.017149 test-rmse:1.170995+0.243858
## [142] train-rmse:0.442417+0.017636 test-rmse:1.171353+0.244209
## [143] train-rmse:0.439088+0.017507 test-rmse:1.173482+0.252008
## [144] train-rmse:0.434974+0.016417 test-rmse:1.167912+0.253193
## [145] train-rmse:0.432342+0.016252 test-rmse:1.165253+0.252709
## [146] train-rmse:0.430264+0.016268 test-rmse:1.163764+0.254480
## [147] train-rmse:0.426806+0.017833 test-rmse:1.161316+0.256004
## [148] train-rmse:0.424079+0.017919 test-rmse:1.160200+0.255770
## [149] train-rmse:0.422017+0.017955 test-rmse:1.154939+0.258391
## [150] train-rmse:0.420056+0.018448 test-rmse:1.153515+0.257246
## [151] train-rmse:0.418089+0.018640 test-rmse:1.153233+0.257393
## [152] train-rmse:0.416475+0.018388 test-rmse:1.152308+0.257438
## [153] train-rmse:0.414147+0.018681 test-rmse:1.152083+0.257745
## [154] train-rmse:0.411594+0.019042 test-rmse:1.150755+0.258475
## [155] train-rmse:0.409399+0.019183 test-rmse:1.150858+0.257887
## [156] train-rmse:0.406977+0.019801 test-rmse:1.150756+0.257335
## [157] train-rmse:0.404427+0.018860 test-rmse:1.150283+0.256478
## [158] train-rmse:0.401885+0.019319 test-rmse:1.151008+0.256839
## [159] train-rmse:0.400211+0.019152 test-rmse:1.150166+0.257370
## [160] train-rmse:0.397714+0.019247 test-rmse:1.150029+0.256909
## [161] train-rmse:0.395569+0.018629 test-rmse:1.148923+0.257043
## [162] train-rmse:0.392999+0.017599 test-rmse:1.147008+0.257192
## [163] train-rmse:0.390854+0.017354 test-rmse:1.146378+0.256741
## [164] train-rmse:0.388362+0.016955 test-rmse:1.145259+0.257425
## [165] train-rmse:0.385922+0.016684 test-rmse:1.145775+0.258178
## [166] train-rmse:0.383852+0.016506 test-rmse:1.146035+0.257810
## [167] train-rmse:0.381655+0.016959 test-rmse:1.145615+0.257141
## [168] train-rmse:0.380405+0.017070 test-rmse:1.145471+0.257060
## [169] train-rmse:0.378491+0.017564 test-rmse:1.144219+0.258020
## [170] train-rmse:0.377056+0.018017 test-rmse:1.143693+0.258724
## [171] train-rmse:0.375530+0.017785 test-rmse:1.143567+0.257944
## [172] train-rmse:0.373766+0.018445 test-rmse:1.143696+0.257305
## [173] train-rmse:0.372039+0.018746 test-rmse:1.143253+0.257770
## [174] train-rmse:0.370490+0.019238 test-rmse:1.141450+0.257095
## [175] train-rmse:0.369106+0.019015 test-rmse:1.140690+0.258062
## [176] train-rmse:0.367929+0.019096 test-rmse:1.140302+0.257566
## [177] train-rmse:0.365742+0.018588 test-rmse:1.138271+0.258774
## [178] train-rmse:0.362923+0.018877 test-rmse:1.136465+0.258173
## [179] train-rmse:0.361261+0.019404 test-rmse:1.135608+0.258264
## [180] train-rmse:0.359466+0.019312 test-rmse:1.135197+0.258429
## [181] train-rmse:0.358282+0.019382 test-rmse:1.136072+0.258203
## [182] train-rmse:0.356405+0.019781 test-rmse:1.134919+0.257720
## [183] train-rmse:0.354309+0.019912 test-rmse:1.135395+0.257093
## [184] train-rmse:0.352614+0.019777 test-rmse:1.136312+0.259249
## [185] train-rmse:0.350992+0.019883 test-rmse:1.135669+0.259043
## [186] train-rmse:0.349862+0.019722 test-rmse:1.135975+0.259659
## [187] train-rmse:0.348654+0.019838 test-rmse:1.135578+0.260069
## [188] train-rmse:0.346984+0.019425 test-rmse:1.136174+0.261161
## Stopping. Best iteration:
## [182] train-rmse:0.356405+0.019781 test-rmse:1.134919+0.257720
##
## 72
## [1] train-rmse:75.538506+0.088543 test-rmse:75.538455+0.512708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:53.112974+0.071800 test-rmse:53.112100+0.555751
## [3] train-rmse:37.457246+0.064447 test-rmse:37.454956+0.599591
## [4] train-rmse:26.556096+0.061192 test-rmse:26.606015+0.614020
## [5] train-rmse:18.931370+0.044260 test-rmse:19.000555+0.544208
## [6] train-rmse:13.614586+0.037905 test-rmse:13.713583+0.552988
## [7] train-rmse:9.930088+0.037636 test-rmse:10.052425+0.540135
## [8] train-rmse:7.397129+0.044571 test-rmse:7.489797+0.529948
## [9] train-rmse:5.666951+0.051245 test-rmse:5.825230+0.518159
## [10] train-rmse:4.513191+0.068965 test-rmse:4.699179+0.469312
## [11] train-rmse:3.741623+0.055928 test-rmse:3.922214+0.394260
## [12] train-rmse:3.249059+0.052094 test-rmse:3.473606+0.378965
## [13] train-rmse:2.914551+0.042727 test-rmse:3.214684+0.386809
## [14] train-rmse:2.681178+0.041572 test-rmse:3.002193+0.362358
## [15] train-rmse:2.505173+0.039642 test-rmse:2.852254+0.367828
## [16] train-rmse:2.353460+0.029173 test-rmse:2.730949+0.318511
## [17] train-rmse:2.227183+0.031153 test-rmse:2.632187+0.290204
## [18] train-rmse:2.125291+0.027054 test-rmse:2.561479+0.284243
## [19] train-rmse:2.027995+0.020466 test-rmse:2.488755+0.260652
## [20] train-rmse:1.945732+0.017893 test-rmse:2.412426+0.253231
## [21] train-rmse:1.869947+0.017773 test-rmse:2.351217+0.265068
## [22] train-rmse:1.796482+0.019112 test-rmse:2.294620+0.277979
## [23] train-rmse:1.726553+0.018207 test-rmse:2.224915+0.278445
## [24] train-rmse:1.661887+0.016096 test-rmse:2.181856+0.260739
## [25] train-rmse:1.602101+0.016652 test-rmse:2.134091+0.270334
## [26] train-rmse:1.543417+0.017999 test-rmse:2.079893+0.260480
## [27] train-rmse:1.487606+0.016033 test-rmse:2.035601+0.266533
## [28] train-rmse:1.434833+0.012288 test-rmse:2.000902+0.267759
## [29] train-rmse:1.385709+0.011621 test-rmse:1.970511+0.263471
## [30] train-rmse:1.339013+0.011820 test-rmse:1.923985+0.253534
## [31] train-rmse:1.294968+0.013742 test-rmse:1.887111+0.246045
## [32] train-rmse:1.251192+0.015001 test-rmse:1.850316+0.246521
## [33] train-rmse:1.204121+0.015103 test-rmse:1.811310+0.228924
## [34] train-rmse:1.166198+0.015372 test-rmse:1.775464+0.242695
## [35] train-rmse:1.129881+0.015703 test-rmse:1.738450+0.238313
## [36] train-rmse:1.096185+0.016630 test-rmse:1.706439+0.242801
## [37] train-rmse:1.061245+0.015225 test-rmse:1.680262+0.250981
## [38] train-rmse:1.030718+0.015114 test-rmse:1.656002+0.248117
## [39] train-rmse:0.999655+0.014592 test-rmse:1.632771+0.251976
## [40] train-rmse:0.966522+0.016281 test-rmse:1.613738+0.258199
## [41] train-rmse:0.938367+0.018102 test-rmse:1.594126+0.255564
## [42] train-rmse:0.911158+0.017225 test-rmse:1.570373+0.263008
## [43] train-rmse:0.887465+0.018564 test-rmse:1.557109+0.264131
## [44] train-rmse:0.862175+0.019761 test-rmse:1.534091+0.256430
## [45] train-rmse:0.837634+0.017987 test-rmse:1.510171+0.259181
## [46] train-rmse:0.816312+0.017306 test-rmse:1.493172+0.259038
## [47] train-rmse:0.796233+0.017146 test-rmse:1.480027+0.263325
## [48] train-rmse:0.772250+0.017936 test-rmse:1.471762+0.265614
## [49] train-rmse:0.755124+0.018401 test-rmse:1.460493+0.265563
## [50] train-rmse:0.734616+0.019826 test-rmse:1.450115+0.263292
## [51] train-rmse:0.718154+0.020482 test-rmse:1.442893+0.264048
## [52] train-rmse:0.700977+0.020540 test-rmse:1.435293+0.264595
## [53] train-rmse:0.684932+0.019419 test-rmse:1.420703+0.266509
## [54] train-rmse:0.667981+0.021928 test-rmse:1.412562+0.271838
## [55] train-rmse:0.653865+0.021776 test-rmse:1.403010+0.275792
## [56] train-rmse:0.636738+0.021549 test-rmse:1.394147+0.281371
## [57] train-rmse:0.621876+0.023323 test-rmse:1.376270+0.276543
## [58] train-rmse:0.609103+0.022839 test-rmse:1.368512+0.280913
## [59] train-rmse:0.597451+0.022438 test-rmse:1.354525+0.285260
## [60] train-rmse:0.585324+0.021277 test-rmse:1.348860+0.284595
## [61] train-rmse:0.575147+0.020266 test-rmse:1.343874+0.284159
## [62] train-rmse:0.565269+0.020793 test-rmse:1.339629+0.281516
## [63] train-rmse:0.554957+0.020023 test-rmse:1.325434+0.286098
## [64] train-rmse:0.545172+0.019260 test-rmse:1.322480+0.288098
## [65] train-rmse:0.533874+0.020085 test-rmse:1.318429+0.288371
## [66] train-rmse:0.520138+0.015571 test-rmse:1.312623+0.291931
## [67] train-rmse:0.511193+0.015067 test-rmse:1.310831+0.294944
## [68] train-rmse:0.503325+0.014673 test-rmse:1.307807+0.294910
## [69] train-rmse:0.495882+0.013842 test-rmse:1.301839+0.295939
## [70] train-rmse:0.487859+0.014560 test-rmse:1.294150+0.299679
## [71] train-rmse:0.479087+0.013575 test-rmse:1.288325+0.300878
## [72] train-rmse:0.471540+0.014776 test-rmse:1.282081+0.302430
## [73] train-rmse:0.463568+0.014558 test-rmse:1.284258+0.307946
## [74] train-rmse:0.456287+0.016805 test-rmse:1.281299+0.308015
## [75] train-rmse:0.450272+0.017511 test-rmse:1.278052+0.307521
## [76] train-rmse:0.444771+0.017621 test-rmse:1.275893+0.309198
## [77] train-rmse:0.436912+0.014821 test-rmse:1.274417+0.310368
## [78] train-rmse:0.429977+0.014109 test-rmse:1.269973+0.310448
## [79] train-rmse:0.421732+0.011459 test-rmse:1.264492+0.311761
## [80] train-rmse:0.415494+0.011006 test-rmse:1.259856+0.313261
## [81] train-rmse:0.409233+0.010993 test-rmse:1.258961+0.312771
## [82] train-rmse:0.404883+0.011379 test-rmse:1.257938+0.311840
## [83] train-rmse:0.400108+0.011781 test-rmse:1.253687+0.312840
## [84] train-rmse:0.392806+0.011916 test-rmse:1.249013+0.309334
## [85] train-rmse:0.384947+0.009565 test-rmse:1.246016+0.313360
## [86] train-rmse:0.378796+0.010373 test-rmse:1.246735+0.314243
## [87] train-rmse:0.373149+0.010721 test-rmse:1.242654+0.316578
## [88] train-rmse:0.369342+0.010950 test-rmse:1.242403+0.318096
## [89] train-rmse:0.364695+0.009632 test-rmse:1.239071+0.318817
## [90] train-rmse:0.360770+0.009615 test-rmse:1.238481+0.318915
## [91] train-rmse:0.355345+0.010095 test-rmse:1.235601+0.319396
## [92] train-rmse:0.350721+0.010460 test-rmse:1.234324+0.319235
## [93] train-rmse:0.347084+0.011624 test-rmse:1.233354+0.320809
## [94] train-rmse:0.342907+0.010818 test-rmse:1.230186+0.322527
## [95] train-rmse:0.337038+0.010907 test-rmse:1.228691+0.323045
## [96] train-rmse:0.332196+0.009241 test-rmse:1.227603+0.325648
## [97] train-rmse:0.327368+0.009176 test-rmse:1.226640+0.327242
## [98] train-rmse:0.322978+0.009190 test-rmse:1.225943+0.327612
## [99] train-rmse:0.318855+0.010277 test-rmse:1.224665+0.328649
## [100] train-rmse:0.316308+0.010263 test-rmse:1.222353+0.328374
## [101] train-rmse:0.311710+0.010816 test-rmse:1.223867+0.328272
## [102] train-rmse:0.306951+0.011854 test-rmse:1.221865+0.328782
## [103] train-rmse:0.302774+0.014099 test-rmse:1.219879+0.329277
## [104] train-rmse:0.299613+0.013694 test-rmse:1.218955+0.330192
## [105] train-rmse:0.296875+0.013402 test-rmse:1.218809+0.330202
## [106] train-rmse:0.293584+0.013838 test-rmse:1.217031+0.331598
## [107] train-rmse:0.289782+0.014919 test-rmse:1.214773+0.331598
## [108] train-rmse:0.287196+0.014928 test-rmse:1.215348+0.331531
## [109] train-rmse:0.283655+0.014092 test-rmse:1.214549+0.331709
## [110] train-rmse:0.281067+0.014344 test-rmse:1.213342+0.332563
## [111] train-rmse:0.277479+0.014488 test-rmse:1.213778+0.333070
## [112] train-rmse:0.273253+0.014301 test-rmse:1.213191+0.333914
## [113] train-rmse:0.269817+0.014772 test-rmse:1.213146+0.334090
## [114] train-rmse:0.264540+0.013271 test-rmse:1.215450+0.340352
## [115] train-rmse:0.262213+0.013384 test-rmse:1.213937+0.341322
## [116] train-rmse:0.258408+0.013746 test-rmse:1.211025+0.342750
## [117] train-rmse:0.256509+0.013566 test-rmse:1.210626+0.343315
## [118] train-rmse:0.254036+0.013433 test-rmse:1.210190+0.343321
## [119] train-rmse:0.251299+0.013918 test-rmse:1.208978+0.344444
## [120] train-rmse:0.249553+0.013720 test-rmse:1.209389+0.344988
## [121] train-rmse:0.246174+0.012409 test-rmse:1.209342+0.349789
## [122] train-rmse:0.243018+0.012292 test-rmse:1.209253+0.349615
## [123] train-rmse:0.241214+0.012628 test-rmse:1.208982+0.350421
## [124] train-rmse:0.238918+0.012895 test-rmse:1.209081+0.349794
## [125] train-rmse:0.237231+0.012751 test-rmse:1.206197+0.348371
## [126] train-rmse:0.234697+0.012021 test-rmse:1.206945+0.347552
## [127] train-rmse:0.231497+0.011551 test-rmse:1.206398+0.348132
## [128] train-rmse:0.229545+0.012615 test-rmse:1.205743+0.348784
## [129] train-rmse:0.226877+0.013142 test-rmse:1.206845+0.349142
## [130] train-rmse:0.225230+0.013133 test-rmse:1.206172+0.349894
## [131] train-rmse:0.223282+0.013181 test-rmse:1.205750+0.350885
## [132] train-rmse:0.220129+0.012727 test-rmse:1.205952+0.351485
## [133] train-rmse:0.217750+0.012689 test-rmse:1.204442+0.352195
## [134] train-rmse:0.215637+0.012357 test-rmse:1.203401+0.353282
## [135] train-rmse:0.214172+0.012543 test-rmse:1.202521+0.353039
## [136] train-rmse:0.212497+0.012871 test-rmse:1.201224+0.351951
## [137] train-rmse:0.210364+0.012891 test-rmse:1.201214+0.352263
## [138] train-rmse:0.208081+0.012418 test-rmse:1.200634+0.352800
## [139] train-rmse:0.206386+0.011986 test-rmse:1.199977+0.352668
## [140] train-rmse:0.204735+0.012510 test-rmse:1.199072+0.352565
## [141] train-rmse:0.202525+0.012249 test-rmse:1.198534+0.351636
## [142] train-rmse:0.201013+0.012303 test-rmse:1.198091+0.351574
## [143] train-rmse:0.199662+0.012144 test-rmse:1.198263+0.352158
## [144] train-rmse:0.198104+0.011808 test-rmse:1.198536+0.351817
## [145] train-rmse:0.196125+0.012139 test-rmse:1.199638+0.351313
## [146] train-rmse:0.193245+0.011878 test-rmse:1.198673+0.352035
## [147] train-rmse:0.191150+0.012338 test-rmse:1.198856+0.351918
## [148] train-rmse:0.189216+0.011827 test-rmse:1.198745+0.352604
## Stopping. Best iteration:
## [142] train-rmse:0.201013+0.012303 test-rmse:1.198091+0.351574
##
## 73
## [1] train-rmse:75.538506+0.088543 test-rmse:75.538455+0.512708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:53.112974+0.071800 test-rmse:53.112100+0.555751
## [3] train-rmse:37.457246+0.064447 test-rmse:37.454956+0.599591
## [4] train-rmse:26.556096+0.061192 test-rmse:26.606015+0.614020
## [5] train-rmse:18.931370+0.044260 test-rmse:19.000555+0.544208
## [6] train-rmse:13.604119+0.037057 test-rmse:13.696023+0.539023
## [7] train-rmse:9.889688+0.037642 test-rmse:9.976141+0.506999
## [8] train-rmse:7.304746+0.043756 test-rmse:7.416002+0.562837
## [9] train-rmse:5.535881+0.049514 test-rmse:5.652638+0.493683
## [10] train-rmse:4.318052+0.064560 test-rmse:4.475064+0.465869
## [11] train-rmse:3.501731+0.069356 test-rmse:3.718452+0.455531
## [12] train-rmse:2.929605+0.047797 test-rmse:3.239643+0.353178
## [13] train-rmse:2.559046+0.047135 test-rmse:2.903768+0.311285
## [14] train-rmse:2.293669+0.054881 test-rmse:2.656271+0.271993
## [15] train-rmse:2.097714+0.045736 test-rmse:2.485404+0.246345
## [16] train-rmse:1.940867+0.041177 test-rmse:2.386935+0.230104
## [17] train-rmse:1.819803+0.040013 test-rmse:2.265001+0.201099
## [18] train-rmse:1.712004+0.036270 test-rmse:2.185793+0.184274
## [19] train-rmse:1.613788+0.035804 test-rmse:2.098105+0.182880
## [20] train-rmse:1.528602+0.030656 test-rmse:2.027174+0.211358
## [21] train-rmse:1.450072+0.031651 test-rmse:1.960085+0.189895
## [22] train-rmse:1.375892+0.032070 test-rmse:1.910046+0.170363
## [23] train-rmse:1.309562+0.033620 test-rmse:1.871900+0.176094
## [24] train-rmse:1.246976+0.032884 test-rmse:1.824361+0.179595
## [25] train-rmse:1.191421+0.030960 test-rmse:1.770097+0.174825
## [26] train-rmse:1.136426+0.026293 test-rmse:1.718417+0.194449
## [27] train-rmse:1.083565+0.033606 test-rmse:1.676716+0.192751
## [28] train-rmse:1.032663+0.032055 test-rmse:1.640297+0.182210
## [29] train-rmse:0.988544+0.032242 test-rmse:1.613002+0.185576
## [30] train-rmse:0.949504+0.031554 test-rmse:1.571728+0.196480
## [31] train-rmse:0.910949+0.029023 test-rmse:1.546836+0.200956
## [32] train-rmse:0.869568+0.021987 test-rmse:1.533606+0.204852
## [33] train-rmse:0.837817+0.021108 test-rmse:1.511465+0.200258
## [34] train-rmse:0.805166+0.023748 test-rmse:1.478181+0.209700
## [35] train-rmse:0.774288+0.024738 test-rmse:1.456122+0.208942
## [36] train-rmse:0.744265+0.023497 test-rmse:1.443651+0.208836
## [37] train-rmse:0.716189+0.025269 test-rmse:1.431981+0.208231
## [38] train-rmse:0.691955+0.024166 test-rmse:1.418645+0.208503
## [39] train-rmse:0.669337+0.020809 test-rmse:1.398177+0.214798
## [40] train-rmse:0.646264+0.021289 test-rmse:1.386123+0.213985
## [41] train-rmse:0.627144+0.020973 test-rmse:1.375728+0.213685
## [42] train-rmse:0.605448+0.022190 test-rmse:1.366396+0.217129
## [43] train-rmse:0.588300+0.021454 test-rmse:1.353019+0.223200
## [44] train-rmse:0.567044+0.022916 test-rmse:1.336262+0.228309
## [45] train-rmse:0.550274+0.021804 test-rmse:1.326495+0.229740
## [46] train-rmse:0.533338+0.021697 test-rmse:1.321243+0.231067
## [47] train-rmse:0.519049+0.021767 test-rmse:1.310236+0.232801
## [48] train-rmse:0.505839+0.022528 test-rmse:1.305802+0.236121
## [49] train-rmse:0.491111+0.024065 test-rmse:1.298671+0.239571
## [50] train-rmse:0.477318+0.020123 test-rmse:1.295767+0.241638
## [51] train-rmse:0.463664+0.022414 test-rmse:1.280136+0.236589
## [52] train-rmse:0.451526+0.023244 test-rmse:1.275470+0.235375
## [53] train-rmse:0.441324+0.021466 test-rmse:1.271011+0.241130
## [54] train-rmse:0.431179+0.022370 test-rmse:1.268233+0.241143
## [55] train-rmse:0.421376+0.022813 test-rmse:1.261747+0.243749
## [56] train-rmse:0.410210+0.023793 test-rmse:1.257180+0.243977
## [57] train-rmse:0.397646+0.022877 test-rmse:1.254833+0.240554
## [58] train-rmse:0.389392+0.022772 test-rmse:1.251829+0.241787
## [59] train-rmse:0.379918+0.021237 test-rmse:1.250871+0.242800
## [60] train-rmse:0.370275+0.020177 test-rmse:1.249587+0.242495
## [61] train-rmse:0.361290+0.019547 test-rmse:1.247042+0.244059
## [62] train-rmse:0.353662+0.020731 test-rmse:1.241747+0.246457
## [63] train-rmse:0.346061+0.019680 test-rmse:1.239869+0.249723
## [64] train-rmse:0.337575+0.019452 test-rmse:1.239340+0.252139
## [65] train-rmse:0.329795+0.020035 test-rmse:1.234689+0.250036
## [66] train-rmse:0.322786+0.021716 test-rmse:1.230999+0.251266
## [67] train-rmse:0.316675+0.021995 test-rmse:1.228368+0.253191
## [68] train-rmse:0.309789+0.022209 test-rmse:1.224936+0.250167
## [69] train-rmse:0.301540+0.021239 test-rmse:1.223702+0.251037
## [70] train-rmse:0.296318+0.021569 test-rmse:1.221433+0.251909
## [71] train-rmse:0.292104+0.021561 test-rmse:1.220798+0.252110
## [72] train-rmse:0.287187+0.019916 test-rmse:1.219834+0.252653
## [73] train-rmse:0.278713+0.018415 test-rmse:1.218642+0.256168
## [74] train-rmse:0.272575+0.015522 test-rmse:1.221142+0.256347
## [75] train-rmse:0.266931+0.016347 test-rmse:1.220049+0.255973
## [76] train-rmse:0.262520+0.016499 test-rmse:1.219340+0.256029
## [77] train-rmse:0.258581+0.015971 test-rmse:1.217106+0.256957
## [78] train-rmse:0.254226+0.015736 test-rmse:1.216453+0.258096
## [79] train-rmse:0.248052+0.016411 test-rmse:1.213603+0.259157
## [80] train-rmse:0.242260+0.015843 test-rmse:1.211266+0.261889
## [81] train-rmse:0.237621+0.016372 test-rmse:1.210416+0.263391
## [82] train-rmse:0.233402+0.015517 test-rmse:1.209815+0.264735
## [83] train-rmse:0.228918+0.014360 test-rmse:1.208312+0.266717
## [84] train-rmse:0.224653+0.014804 test-rmse:1.205349+0.267354
## [85] train-rmse:0.220953+0.015092 test-rmse:1.203425+0.267997
## [86] train-rmse:0.217168+0.015064 test-rmse:1.201796+0.268592
## [87] train-rmse:0.212858+0.015851 test-rmse:1.200512+0.268374
## [88] train-rmse:0.209738+0.015475 test-rmse:1.200617+0.268401
## [89] train-rmse:0.205711+0.015002 test-rmse:1.201283+0.268105
## [90] train-rmse:0.201928+0.012751 test-rmse:1.198990+0.265566
## [91] train-rmse:0.197669+0.013148 test-rmse:1.196943+0.266534
## [92] train-rmse:0.194079+0.012491 test-rmse:1.196642+0.267186
## [93] train-rmse:0.191451+0.012313 test-rmse:1.197483+0.267974
## [94] train-rmse:0.188954+0.010962 test-rmse:1.195252+0.266618
## [95] train-rmse:0.186514+0.011152 test-rmse:1.194695+0.267184
## [96] train-rmse:0.180393+0.009761 test-rmse:1.195786+0.266646
## [97] train-rmse:0.177151+0.010542 test-rmse:1.194829+0.267252
## [98] train-rmse:0.172291+0.010765 test-rmse:1.194156+0.265543
## [99] train-rmse:0.169097+0.008623 test-rmse:1.193541+0.265026
## [100] train-rmse:0.165441+0.007599 test-rmse:1.195186+0.267901
## [101] train-rmse:0.162893+0.007152 test-rmse:1.194872+0.267609
## [102] train-rmse:0.160922+0.007539 test-rmse:1.195298+0.268217
## [103] train-rmse:0.157507+0.007881 test-rmse:1.195003+0.267013
## [104] train-rmse:0.154459+0.006653 test-rmse:1.194985+0.267354
## [105] train-rmse:0.152600+0.006367 test-rmse:1.193189+0.268654
## [106] train-rmse:0.150725+0.007170 test-rmse:1.192164+0.269459
## [107] train-rmse:0.147997+0.007395 test-rmse:1.191289+0.269895
## [108] train-rmse:0.145252+0.007365 test-rmse:1.192165+0.270028
## [109] train-rmse:0.142452+0.007241 test-rmse:1.192597+0.270825
## [110] train-rmse:0.140147+0.007710 test-rmse:1.191353+0.272032
## [111] train-rmse:0.138555+0.007646 test-rmse:1.190852+0.271592
## [112] train-rmse:0.135692+0.007414 test-rmse:1.191821+0.271251
## [113] train-rmse:0.132466+0.006616 test-rmse:1.190914+0.271055
## [114] train-rmse:0.129695+0.006738 test-rmse:1.190864+0.271066
## [115] train-rmse:0.127156+0.006678 test-rmse:1.190606+0.270828
## [116] train-rmse:0.125052+0.007570 test-rmse:1.189965+0.271569
## [117] train-rmse:0.122746+0.007994 test-rmse:1.189472+0.271667
## [118] train-rmse:0.121039+0.007764 test-rmse:1.188354+0.271842
## [119] train-rmse:0.118382+0.007309 test-rmse:1.188018+0.272226
## [120] train-rmse:0.116194+0.006529 test-rmse:1.188939+0.271810
## [121] train-rmse:0.114859+0.006112 test-rmse:1.188606+0.272696
## [122] train-rmse:0.113205+0.005909 test-rmse:1.188630+0.272665
## [123] train-rmse:0.111338+0.005643 test-rmse:1.187349+0.273050
## [124] train-rmse:0.109517+0.005310 test-rmse:1.186587+0.273651
## [125] train-rmse:0.107739+0.005963 test-rmse:1.186109+0.273888
## [126] train-rmse:0.106257+0.005816 test-rmse:1.185693+0.273938
## [127] train-rmse:0.104242+0.005706 test-rmse:1.185382+0.274519
## [128] train-rmse:0.102944+0.006111 test-rmse:1.185566+0.274544
## [129] train-rmse:0.100322+0.005704 test-rmse:1.185429+0.274816
## [130] train-rmse:0.098395+0.005904 test-rmse:1.186298+0.273653
## [131] train-rmse:0.097316+0.005655 test-rmse:1.185904+0.274315
## [132] train-rmse:0.095884+0.005477 test-rmse:1.185079+0.274311
## [133] train-rmse:0.094486+0.005931 test-rmse:1.184877+0.274783
## [134] train-rmse:0.092991+0.005617 test-rmse:1.184151+0.275304
## [135] train-rmse:0.091469+0.005647 test-rmse:1.183535+0.275562
## [136] train-rmse:0.089322+0.005968 test-rmse:1.183151+0.275765
## [137] train-rmse:0.088226+0.005822 test-rmse:1.183256+0.275907
## [138] train-rmse:0.086571+0.005886 test-rmse:1.182853+0.276351
## [139] train-rmse:0.085191+0.006264 test-rmse:1.182874+0.276278
## [140] train-rmse:0.084246+0.006215 test-rmse:1.183199+0.276264
## [141] train-rmse:0.083174+0.006331 test-rmse:1.183441+0.276714
## [142] train-rmse:0.081697+0.006672 test-rmse:1.183581+0.276752
## [143] train-rmse:0.080390+0.006372 test-rmse:1.183065+0.276658
## [144] train-rmse:0.078913+0.006147 test-rmse:1.182917+0.277157
## Stopping. Best iteration:
## [138] train-rmse:0.086571+0.005886 test-rmse:1.182853+0.276351
##
## 74
## [1] train-rmse:75.538506+0.088543 test-rmse:75.538455+0.512708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:53.112974+0.071800 test-rmse:53.112100+0.555751
## [3] train-rmse:37.457246+0.064447 test-rmse:37.454956+0.599591
## [4] train-rmse:26.556096+0.061192 test-rmse:26.606015+0.614020
## [5] train-rmse:18.931370+0.044260 test-rmse:19.000555+0.544208
## [6] train-rmse:13.596779+0.038393 test-rmse:13.694289+0.534649
## [7] train-rmse:9.858964+0.037033 test-rmse:9.950282+0.507452
## [8] train-rmse:7.246851+0.038459 test-rmse:7.386817+0.542357
## [9] train-rmse:5.430962+0.033211 test-rmse:5.590116+0.486574
## [10] train-rmse:4.177342+0.032761 test-rmse:4.356490+0.430576
## [11] train-rmse:3.313640+0.022464 test-rmse:3.578235+0.408390
## [12] train-rmse:2.705888+0.039692 test-rmse:3.038435+0.337879
## [13] train-rmse:2.285222+0.024668 test-rmse:2.696574+0.293933
## [14] train-rmse:2.002178+0.021009 test-rmse:2.465433+0.295951
## [15] train-rmse:1.788309+0.031541 test-rmse:2.320457+0.252036
## [16] train-rmse:1.618505+0.048172 test-rmse:2.200497+0.241060
## [17] train-rmse:1.479215+0.043778 test-rmse:2.091740+0.245901
## [18] train-rmse:1.357420+0.030432 test-rmse:2.017016+0.234636
## [19] train-rmse:1.261398+0.032024 test-rmse:1.926019+0.218294
## [20] train-rmse:1.182077+0.026685 test-rmse:1.875795+0.220634
## [21] train-rmse:1.105832+0.022607 test-rmse:1.818136+0.240650
## [22] train-rmse:1.038762+0.015707 test-rmse:1.775026+0.251838
## [23] train-rmse:0.969323+0.022407 test-rmse:1.724490+0.245972
## [24] train-rmse:0.916327+0.020145 test-rmse:1.693081+0.261345
## [25] train-rmse:0.865362+0.022204 test-rmse:1.668398+0.252672
## [26] train-rmse:0.818308+0.023329 test-rmse:1.637092+0.259820
## [27] train-rmse:0.779833+0.022174 test-rmse:1.603781+0.254617
## [28] train-rmse:0.735732+0.020356 test-rmse:1.558686+0.240937
## [29] train-rmse:0.698134+0.020255 test-rmse:1.532031+0.246342
## [30] train-rmse:0.661819+0.017876 test-rmse:1.512576+0.240655
## [31] train-rmse:0.630238+0.016040 test-rmse:1.494122+0.242369
## [32] train-rmse:0.602303+0.014789 test-rmse:1.479932+0.240228
## [33] train-rmse:0.575911+0.016253 test-rmse:1.469433+0.247657
## [34] train-rmse:0.551384+0.018368 test-rmse:1.452457+0.250029
## [35] train-rmse:0.526096+0.019919 test-rmse:1.446345+0.247550
## [36] train-rmse:0.506293+0.021639 test-rmse:1.436865+0.247885
## [37] train-rmse:0.485988+0.019353 test-rmse:1.427158+0.251956
## [38] train-rmse:0.465668+0.017585 test-rmse:1.412757+0.260102
## [39] train-rmse:0.448122+0.017313 test-rmse:1.405536+0.259794
## [40] train-rmse:0.430807+0.016847 test-rmse:1.397974+0.262966
## [41] train-rmse:0.416388+0.015748 test-rmse:1.392285+0.265505
## [42] train-rmse:0.401897+0.015493 test-rmse:1.386749+0.262936
## [43] train-rmse:0.390141+0.014519 test-rmse:1.381189+0.267524
## [44] train-rmse:0.374000+0.015399 test-rmse:1.379139+0.263378
## [45] train-rmse:0.361311+0.015857 test-rmse:1.362710+0.269912
## [46] train-rmse:0.350675+0.017885 test-rmse:1.359026+0.270733
## [47] train-rmse:0.340061+0.016235 test-rmse:1.353046+0.273205
## [48] train-rmse:0.328742+0.014281 test-rmse:1.337962+0.269126
## [49] train-rmse:0.316591+0.011210 test-rmse:1.337297+0.270176
## [50] train-rmse:0.309447+0.010980 test-rmse:1.335508+0.273147
## [51] train-rmse:0.299919+0.010843 test-rmse:1.330877+0.273797
## [52] train-rmse:0.290396+0.008933 test-rmse:1.329308+0.277043
## [53] train-rmse:0.281816+0.010170 test-rmse:1.330053+0.277698
## [54] train-rmse:0.272657+0.010628 test-rmse:1.328493+0.279851
## [55] train-rmse:0.265561+0.008359 test-rmse:1.325281+0.280875
## [56] train-rmse:0.259173+0.006746 test-rmse:1.320520+0.283359
## [57] train-rmse:0.252117+0.006922 test-rmse:1.318093+0.281137
## [58] train-rmse:0.242979+0.006630 test-rmse:1.318681+0.281862
## [59] train-rmse:0.232421+0.006243 test-rmse:1.310916+0.282977
## [60] train-rmse:0.226763+0.007667 test-rmse:1.308847+0.285775
## [61] train-rmse:0.221476+0.007951 test-rmse:1.304634+0.286997
## [62] train-rmse:0.216033+0.006474 test-rmse:1.303483+0.287982
## [63] train-rmse:0.209826+0.006944 test-rmse:1.302085+0.290561
## [64] train-rmse:0.205649+0.007473 test-rmse:1.301864+0.291146
## [65] train-rmse:0.201069+0.007369 test-rmse:1.301104+0.291818
## [66] train-rmse:0.194829+0.006071 test-rmse:1.298619+0.292272
## [67] train-rmse:0.189869+0.005499 test-rmse:1.297147+0.292815
## [68] train-rmse:0.180620+0.006811 test-rmse:1.293227+0.286852
## [69] train-rmse:0.175792+0.006380 test-rmse:1.292579+0.286480
## [70] train-rmse:0.171077+0.007183 test-rmse:1.291050+0.286695
## [71] train-rmse:0.166088+0.006522 test-rmse:1.291192+0.289024
## [72] train-rmse:0.162014+0.005982 test-rmse:1.289710+0.288924
## [73] train-rmse:0.159171+0.005501 test-rmse:1.288323+0.290113
## [74] train-rmse:0.155742+0.005808 test-rmse:1.288088+0.290644
## [75] train-rmse:0.151006+0.005945 test-rmse:1.286952+0.291771
## [76] train-rmse:0.147608+0.005251 test-rmse:1.285342+0.292558
## [77] train-rmse:0.144429+0.005591 test-rmse:1.285046+0.292314
## [78] train-rmse:0.140933+0.006018 test-rmse:1.286368+0.293500
## [79] train-rmse:0.138209+0.006606 test-rmse:1.286326+0.294948
## [80] train-rmse:0.134059+0.005724 test-rmse:1.286461+0.297693
## [81] train-rmse:0.131229+0.006413 test-rmse:1.283911+0.294077
## [82] train-rmse:0.126838+0.005866 test-rmse:1.282415+0.293776
## [83] train-rmse:0.123576+0.005083 test-rmse:1.282031+0.293609
## [84] train-rmse:0.120457+0.005765 test-rmse:1.281590+0.293374
## [85] train-rmse:0.118165+0.005141 test-rmse:1.280391+0.294055
## [86] train-rmse:0.115118+0.005689 test-rmse:1.278143+0.294818
## [87] train-rmse:0.111678+0.005218 test-rmse:1.277665+0.294639
## [88] train-rmse:0.108950+0.004210 test-rmse:1.278146+0.295521
## [89] train-rmse:0.106936+0.004456 test-rmse:1.278129+0.295828
## [90] train-rmse:0.104175+0.004356 test-rmse:1.277756+0.296079
## [91] train-rmse:0.101522+0.004420 test-rmse:1.277972+0.296031
## [92] train-rmse:0.099420+0.004210 test-rmse:1.276348+0.293605
## [93] train-rmse:0.096480+0.003341 test-rmse:1.275732+0.294337
## [94] train-rmse:0.094516+0.002556 test-rmse:1.275639+0.294258
## [95] train-rmse:0.092071+0.002032 test-rmse:1.275340+0.292975
## [96] train-rmse:0.090188+0.001847 test-rmse:1.275324+0.293110
## [97] train-rmse:0.088562+0.001900 test-rmse:1.275039+0.292473
## [98] train-rmse:0.086564+0.001676 test-rmse:1.275401+0.292749
## [99] train-rmse:0.084540+0.001646 test-rmse:1.275120+0.293217
## [100] train-rmse:0.082371+0.001721 test-rmse:1.275178+0.293249
## [101] train-rmse:0.080313+0.001630 test-rmse:1.275197+0.293786
## [102] train-rmse:0.078424+0.001616 test-rmse:1.274356+0.294337
## [103] train-rmse:0.076553+0.001713 test-rmse:1.274063+0.294253
## [104] train-rmse:0.075035+0.001677 test-rmse:1.274030+0.293923
## [105] train-rmse:0.073429+0.001888 test-rmse:1.273796+0.294085
## [106] train-rmse:0.072066+0.002028 test-rmse:1.273824+0.294299
## [107] train-rmse:0.070277+0.002493 test-rmse:1.274047+0.294557
## [108] train-rmse:0.068915+0.002263 test-rmse:1.272785+0.293389
## [109] train-rmse:0.066911+0.002017 test-rmse:1.272838+0.293408
## [110] train-rmse:0.065527+0.002093 test-rmse:1.272698+0.293336
## [111] train-rmse:0.063936+0.002572 test-rmse:1.272457+0.293559
## [112] train-rmse:0.062466+0.002293 test-rmse:1.272252+0.293402
## [113] train-rmse:0.061637+0.002692 test-rmse:1.272393+0.294006
## [114] train-rmse:0.060102+0.002820 test-rmse:1.272328+0.293931
## [115] train-rmse:0.058974+0.002687 test-rmse:1.272204+0.294235
## [116] train-rmse:0.057959+0.002770 test-rmse:1.272345+0.294136
## [117] train-rmse:0.056279+0.002689 test-rmse:1.272843+0.294136
## [118] train-rmse:0.055117+0.002843 test-rmse:1.273065+0.294676
## [119] train-rmse:0.053488+0.002695 test-rmse:1.272577+0.294821
## [120] train-rmse:0.052052+0.002589 test-rmse:1.272688+0.294743
## [121] train-rmse:0.051091+0.002619 test-rmse:1.272530+0.295051
## Stopping. Best iteration:
## [115] train-rmse:0.058974+0.002687 test-rmse:1.272204+0.294235
##
## 75
## [1] train-rmse:75.538506+0.088543 test-rmse:75.538455+0.512708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:53.112974+0.071800 test-rmse:53.112100+0.555751
## [3] train-rmse:37.457246+0.064447 test-rmse:37.454956+0.599591
## [4] train-rmse:26.556096+0.061192 test-rmse:26.606015+0.614020
## [5] train-rmse:18.931370+0.044260 test-rmse:19.000555+0.544208
## [6] train-rmse:13.588820+0.039821 test-rmse:13.694667+0.521432
## [7] train-rmse:9.834294+0.034362 test-rmse:9.930591+0.522621
## [8] train-rmse:7.205418+0.040331 test-rmse:7.345476+0.553645
## [9] train-rmse:5.363343+0.051821 test-rmse:5.537845+0.474201
## [10] train-rmse:4.060560+0.047637 test-rmse:4.262188+0.456127
## [11] train-rmse:3.168833+0.055964 test-rmse:3.401480+0.389668
## [12] train-rmse:2.526289+0.045494 test-rmse:2.843091+0.390136
## [13] train-rmse:2.080360+0.031278 test-rmse:2.472316+0.348877
## [14] train-rmse:1.769768+0.041331 test-rmse:2.238322+0.333054
## [15] train-rmse:1.540242+0.038547 test-rmse:2.067245+0.288078
## [16] train-rmse:1.368084+0.038001 test-rmse:1.934947+0.280345
## [17] train-rmse:1.225685+0.046497 test-rmse:1.835193+0.301272
## [18] train-rmse:1.107665+0.039238 test-rmse:1.760962+0.303489
## [19] train-rmse:1.017655+0.030117 test-rmse:1.692671+0.306197
## [20] train-rmse:0.932400+0.025889 test-rmse:1.640032+0.315292
## [21] train-rmse:0.863315+0.023553 test-rmse:1.591393+0.291611
## [22] train-rmse:0.798069+0.023278 test-rmse:1.550897+0.300994
## [23] train-rmse:0.748412+0.023158 test-rmse:1.508309+0.293257
## [24] train-rmse:0.700823+0.023643 test-rmse:1.478095+0.293275
## [25] train-rmse:0.655936+0.024011 test-rmse:1.459277+0.291432
## [26] train-rmse:0.618970+0.021772 test-rmse:1.432256+0.303132
## [27] train-rmse:0.585294+0.021334 test-rmse:1.408049+0.284410
## [28] train-rmse:0.553088+0.019349 test-rmse:1.389341+0.291809
## [29] train-rmse:0.525081+0.016730 test-rmse:1.373896+0.298704
## [30] train-rmse:0.497672+0.020536 test-rmse:1.359661+0.298835
## [31] train-rmse:0.472097+0.019706 test-rmse:1.350110+0.302042
## [32] train-rmse:0.449846+0.018595 test-rmse:1.331170+0.291567
## [33] train-rmse:0.423075+0.017173 test-rmse:1.311606+0.288244
## [34] train-rmse:0.404770+0.018165 test-rmse:1.303437+0.289410
## [35] train-rmse:0.384550+0.018008 test-rmse:1.296705+0.290567
## [36] train-rmse:0.367210+0.016095 test-rmse:1.289398+0.291201
## [37] train-rmse:0.349671+0.016461 test-rmse:1.279387+0.286331
## [38] train-rmse:0.336750+0.017201 test-rmse:1.271693+0.286662
## [39] train-rmse:0.323001+0.018294 test-rmse:1.266287+0.285000
## [40] train-rmse:0.309692+0.015331 test-rmse:1.264970+0.285322
## [41] train-rmse:0.296300+0.016425 test-rmse:1.260430+0.289337
## [42] train-rmse:0.283431+0.018557 test-rmse:1.256038+0.287117
## [43] train-rmse:0.268741+0.016935 test-rmse:1.257033+0.286467
## [44] train-rmse:0.255092+0.018567 test-rmse:1.254112+0.290614
## [45] train-rmse:0.245722+0.019742 test-rmse:1.250607+0.292404
## [46] train-rmse:0.231928+0.019435 test-rmse:1.251228+0.291775
## [47] train-rmse:0.223241+0.018380 test-rmse:1.250117+0.290684
## [48] train-rmse:0.213152+0.017573 test-rmse:1.248027+0.290703
## [49] train-rmse:0.205274+0.018677 test-rmse:1.244671+0.289769
## [50] train-rmse:0.194568+0.015635 test-rmse:1.243580+0.289627
## [51] train-rmse:0.187890+0.014385 test-rmse:1.243070+0.290342
## [52] train-rmse:0.181242+0.015550 test-rmse:1.242564+0.290588
## [53] train-rmse:0.174708+0.015817 test-rmse:1.241108+0.291602
## [54] train-rmse:0.169223+0.017064 test-rmse:1.239755+0.292033
## [55] train-rmse:0.164127+0.017116 test-rmse:1.239439+0.292878
## [56] train-rmse:0.158447+0.016236 test-rmse:1.237087+0.289151
## [57] train-rmse:0.152312+0.014918 test-rmse:1.235193+0.288914
## [58] train-rmse:0.144938+0.013746 test-rmse:1.233932+0.289537
## [59] train-rmse:0.140305+0.013300 test-rmse:1.232481+0.289710
## [60] train-rmse:0.134988+0.012535 test-rmse:1.231516+0.289381
## [61] train-rmse:0.131715+0.012169 test-rmse:1.231678+0.290701
## [62] train-rmse:0.126760+0.011924 test-rmse:1.231947+0.290913
## [63] train-rmse:0.121452+0.012195 test-rmse:1.231441+0.291289
## [64] train-rmse:0.117967+0.012309 test-rmse:1.230047+0.292109
## [65] train-rmse:0.111505+0.010816 test-rmse:1.229890+0.292336
## [66] train-rmse:0.108323+0.010555 test-rmse:1.231029+0.294123
## [67] train-rmse:0.104625+0.010868 test-rmse:1.230001+0.294152
## [68] train-rmse:0.100493+0.011064 test-rmse:1.230486+0.295054
## [69] train-rmse:0.097141+0.010405 test-rmse:1.230316+0.295505
## [70] train-rmse:0.094524+0.010124 test-rmse:1.230392+0.296522
## [71] train-rmse:0.091536+0.010113 test-rmse:1.229706+0.295813
## [72] train-rmse:0.087554+0.009785 test-rmse:1.229632+0.296226
## [73] train-rmse:0.084096+0.008873 test-rmse:1.229611+0.298072
## [74] train-rmse:0.081638+0.008801 test-rmse:1.229679+0.298164
## [75] train-rmse:0.078399+0.008265 test-rmse:1.229226+0.297920
## [76] train-rmse:0.076493+0.008353 test-rmse:1.228106+0.297768
## [77] train-rmse:0.074364+0.007928 test-rmse:1.227304+0.297616
## [78] train-rmse:0.072126+0.008090 test-rmse:1.226524+0.298031
## [79] train-rmse:0.069246+0.008627 test-rmse:1.225898+0.297559
## [80] train-rmse:0.067459+0.008143 test-rmse:1.226010+0.297821
## [81] train-rmse:0.064943+0.007804 test-rmse:1.226271+0.297536
## [82] train-rmse:0.062944+0.008236 test-rmse:1.225319+0.297967
## [83] train-rmse:0.060718+0.007783 test-rmse:1.225231+0.297578
## [84] train-rmse:0.058594+0.007615 test-rmse:1.224789+0.298054
## [85] train-rmse:0.056512+0.007788 test-rmse:1.224946+0.298023
## [86] train-rmse:0.054711+0.007819 test-rmse:1.225218+0.297750
## [87] train-rmse:0.053169+0.007367 test-rmse:1.225176+0.297302
## [88] train-rmse:0.051513+0.007199 test-rmse:1.225284+0.297211
## [89] train-rmse:0.049368+0.006650 test-rmse:1.224916+0.298178
## [90] train-rmse:0.048026+0.006472 test-rmse:1.225012+0.298853
## Stopping. Best iteration:
## [84] train-rmse:0.058594+0.007615 test-rmse:1.224789+0.298054
##
## 76
## [1] train-rmse:75.538506+0.088543 test-rmse:75.538455+0.512708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:53.112974+0.071800 test-rmse:53.112100+0.555751
## [3] train-rmse:37.457246+0.064447 test-rmse:37.454956+0.599591
## [4] train-rmse:26.556096+0.061192 test-rmse:26.606015+0.614020
## [5] train-rmse:18.931370+0.044260 test-rmse:19.000555+0.544208
## [6] train-rmse:13.582443+0.039340 test-rmse:13.688913+0.540842
## [7] train-rmse:9.819006+0.034347 test-rmse:9.926690+0.519113
## [8] train-rmse:7.171198+0.042710 test-rmse:7.316840+0.550066
## [9] train-rmse:5.292197+0.046187 test-rmse:5.476231+0.475165
## [10] train-rmse:3.974619+0.040684 test-rmse:4.195248+0.424486
## [11] train-rmse:3.035787+0.041015 test-rmse:3.341221+0.426491
## [12] train-rmse:2.379373+0.029541 test-rmse:2.780383+0.372191
## [13] train-rmse:1.911669+0.023906 test-rmse:2.369950+0.321938
## [14] train-rmse:1.567031+0.029773 test-rmse:2.096041+0.271574
## [15] train-rmse:1.324865+0.035040 test-rmse:1.922096+0.244519
## [16] train-rmse:1.138054+0.042722 test-rmse:1.796194+0.233152
## [17] train-rmse:1.003189+0.043535 test-rmse:1.712646+0.245229
## [18] train-rmse:0.900405+0.037924 test-rmse:1.644754+0.245638
## [19] train-rmse:0.814246+0.035989 test-rmse:1.604693+0.253338
## [20] train-rmse:0.741974+0.030402 test-rmse:1.560413+0.253969
## [21] train-rmse:0.679387+0.032586 test-rmse:1.519914+0.254794
## [22] train-rmse:0.628507+0.029291 test-rmse:1.489445+0.268065
## [23] train-rmse:0.582633+0.025567 test-rmse:1.461330+0.270106
## [24] train-rmse:0.539887+0.014154 test-rmse:1.434125+0.254448
## [25] train-rmse:0.500320+0.019284 test-rmse:1.424404+0.253456
## [26] train-rmse:0.464382+0.020717 test-rmse:1.402686+0.240895
## [27] train-rmse:0.435775+0.021020 test-rmse:1.392924+0.240756
## [28] train-rmse:0.410150+0.018622 test-rmse:1.379030+0.240846
## [29] train-rmse:0.381238+0.014475 test-rmse:1.371813+0.248511
## [30] train-rmse:0.356841+0.014932 test-rmse:1.365738+0.252832
## [31] train-rmse:0.336067+0.016352 test-rmse:1.360031+0.252776
## [32] train-rmse:0.319002+0.014257 test-rmse:1.351037+0.257241
## [33] train-rmse:0.300644+0.014593 test-rmse:1.346254+0.258840
## [34] train-rmse:0.287134+0.013108 test-rmse:1.336504+0.254746
## [35] train-rmse:0.271802+0.012651 test-rmse:1.336554+0.255994
## [36] train-rmse:0.257958+0.008608 test-rmse:1.332513+0.259571
## [37] train-rmse:0.248019+0.007877 test-rmse:1.329137+0.259448
## [38] train-rmse:0.234358+0.008975 test-rmse:1.326658+0.257380
## [39] train-rmse:0.223782+0.007889 test-rmse:1.324077+0.258236
## [40] train-rmse:0.210517+0.010314 test-rmse:1.323580+0.259737
## [41] train-rmse:0.200168+0.008242 test-rmse:1.319317+0.260159
## [42] train-rmse:0.191307+0.010695 test-rmse:1.317934+0.260267
## [43] train-rmse:0.181618+0.009294 test-rmse:1.318691+0.261667
## [44] train-rmse:0.173738+0.011999 test-rmse:1.319156+0.263401
## [45] train-rmse:0.164766+0.010964 test-rmse:1.319002+0.262799
## [46] train-rmse:0.157045+0.010727 test-rmse:1.318106+0.264053
## [47] train-rmse:0.151273+0.010663 test-rmse:1.318369+0.263521
## [48] train-rmse:0.144714+0.008107 test-rmse:1.318473+0.264305
## Stopping. Best iteration:
## [42] train-rmse:0.191307+0.010695 test-rmse:1.317934+0.260267
##
## 77
## [1] train-rmse:73.403534+0.086787 test-rmse:73.403435+0.516279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:50.171526+0.069998 test-rmse:50.170467+0.562705
## [3] train-rmse:34.428066+0.063950 test-rmse:34.425303+0.610826
## [4] train-rmse:23.792074+0.061275 test-rmse:23.847699+0.633078
## [5] train-rmse:16.620777+0.055169 test-rmse:16.658328+0.626730
## [6] train-rmse:11.836149+0.049475 test-rmse:11.913466+0.592000
## [7] train-rmse:8.703803+0.049357 test-rmse:8.830217+0.675554
## [8] train-rmse:6.712750+0.056075 test-rmse:6.828940+0.648417
## [9] train-rmse:5.489811+0.061773 test-rmse:5.609156+0.557697
## [10] train-rmse:4.751560+0.059060 test-rmse:4.900199+0.576393
## [11] train-rmse:4.307482+0.057625 test-rmse:4.462954+0.598807
## [12] train-rmse:4.037358+0.063230 test-rmse:4.218333+0.486382
## [13] train-rmse:3.860417+0.063470 test-rmse:4.069364+0.481722
## [14] train-rmse:3.726569+0.060132 test-rmse:3.887384+0.420717
## [15] train-rmse:3.621830+0.058525 test-rmse:3.791543+0.407890
## [16] train-rmse:3.536263+0.056030 test-rmse:3.746319+0.382640
## [17] train-rmse:3.458066+0.054573 test-rmse:3.717494+0.402805
## [18] train-rmse:3.388013+0.055260 test-rmse:3.605567+0.339402
## [19] train-rmse:3.319080+0.054667 test-rmse:3.556402+0.351679
## [20] train-rmse:3.253530+0.052546 test-rmse:3.525792+0.339991
## [21] train-rmse:3.189984+0.049676 test-rmse:3.464744+0.345190
## [22] train-rmse:3.129663+0.049571 test-rmse:3.384181+0.300473
## [23] train-rmse:3.071007+0.048676 test-rmse:3.318810+0.303857
## [24] train-rmse:3.013785+0.046251 test-rmse:3.247995+0.293276
## [25] train-rmse:2.962233+0.043107 test-rmse:3.213108+0.311287
## [26] train-rmse:2.911362+0.043195 test-rmse:3.128503+0.273759
## [27] train-rmse:2.865014+0.042056 test-rmse:3.091691+0.291100
## [28] train-rmse:2.818808+0.041481 test-rmse:3.042150+0.306096
## [29] train-rmse:2.775220+0.040912 test-rmse:3.005343+0.305974
## [30] train-rmse:2.732430+0.040123 test-rmse:2.977181+0.307880
## [31] train-rmse:2.691015+0.040736 test-rmse:2.937618+0.302817
## [32] train-rmse:2.650866+0.041020 test-rmse:2.926440+0.304927
## [33] train-rmse:2.610665+0.041289 test-rmse:2.897744+0.304336
## [34] train-rmse:2.571337+0.040558 test-rmse:2.865691+0.311278
## [35] train-rmse:2.533016+0.041231 test-rmse:2.813883+0.332863
## [36] train-rmse:2.495212+0.040746 test-rmse:2.779973+0.298038
## [37] train-rmse:2.458365+0.040691 test-rmse:2.748961+0.297180
## [38] train-rmse:2.422759+0.040090 test-rmse:2.705888+0.293039
## [39] train-rmse:2.389170+0.038604 test-rmse:2.679797+0.296430
## [40] train-rmse:2.355671+0.037312 test-rmse:2.645961+0.298306
## [41] train-rmse:2.323006+0.036253 test-rmse:2.607804+0.318122
## [42] train-rmse:2.292618+0.036248 test-rmse:2.593307+0.314571
## [43] train-rmse:2.263777+0.035778 test-rmse:2.564503+0.319153
## [44] train-rmse:2.234630+0.035253 test-rmse:2.521825+0.306688
## [45] train-rmse:2.205649+0.034589 test-rmse:2.495671+0.314007
## [46] train-rmse:2.177311+0.033732 test-rmse:2.435292+0.287228
## [47] train-rmse:2.149285+0.032420 test-rmse:2.412067+0.276700
## [48] train-rmse:2.120796+0.031247 test-rmse:2.391918+0.277483
## [49] train-rmse:2.093293+0.030813 test-rmse:2.368216+0.281232
## [50] train-rmse:2.066220+0.029794 test-rmse:2.346866+0.283942
## [51] train-rmse:2.040168+0.029100 test-rmse:2.319661+0.301598
## [52] train-rmse:2.014360+0.028278 test-rmse:2.290936+0.293508
## [53] train-rmse:1.989314+0.027541 test-rmse:2.262011+0.277480
## [54] train-rmse:1.964853+0.027061 test-rmse:2.227954+0.257066
## [55] train-rmse:1.940943+0.026436 test-rmse:2.208160+0.256930
## [56] train-rmse:1.917771+0.025372 test-rmse:2.179736+0.261453
## [57] train-rmse:1.895157+0.024270 test-rmse:2.163532+0.253225
## [58] train-rmse:1.872818+0.023013 test-rmse:2.152514+0.262796
## [59] train-rmse:1.850758+0.022732 test-rmse:2.131478+0.270052
## [60] train-rmse:1.830043+0.022064 test-rmse:2.114346+0.267391
## [61] train-rmse:1.809361+0.021868 test-rmse:2.096557+0.264009
## [62] train-rmse:1.788645+0.021261 test-rmse:2.067147+0.259449
## [63] train-rmse:1.768337+0.020378 test-rmse:2.067194+0.255539
## [64] train-rmse:1.748685+0.019730 test-rmse:2.047903+0.254454
## [65] train-rmse:1.729455+0.019269 test-rmse:2.035764+0.255672
## [66] train-rmse:1.710187+0.018466 test-rmse:2.017486+0.256419
## [67] train-rmse:1.691380+0.018584 test-rmse:1.998378+0.264212
## [68] train-rmse:1.672892+0.018662 test-rmse:1.964507+0.244037
## [69] train-rmse:1.654915+0.018644 test-rmse:1.938553+0.246412
## [70] train-rmse:1.637453+0.018606 test-rmse:1.919136+0.253207
## [71] train-rmse:1.620333+0.018217 test-rmse:1.920788+0.255272
## [72] train-rmse:1.603499+0.017752 test-rmse:1.888424+0.243514
## [73] train-rmse:1.586954+0.017419 test-rmse:1.875619+0.250673
## [74] train-rmse:1.570908+0.017341 test-rmse:1.869962+0.239369
## [75] train-rmse:1.554658+0.017298 test-rmse:1.860883+0.241089
## [76] train-rmse:1.538998+0.016988 test-rmse:1.848865+0.241339
## [77] train-rmse:1.523231+0.016680 test-rmse:1.829464+0.241356
## [78] train-rmse:1.508076+0.015838 test-rmse:1.813230+0.245072
## [79] train-rmse:1.493250+0.015220 test-rmse:1.797936+0.243422
## [80] train-rmse:1.478473+0.014229 test-rmse:1.784387+0.237267
## [81] train-rmse:1.464347+0.013763 test-rmse:1.769816+0.230411
## [82] train-rmse:1.450545+0.013260 test-rmse:1.757971+0.232268
## [83] train-rmse:1.437030+0.012932 test-rmse:1.746853+0.229801
## [84] train-rmse:1.423524+0.012552 test-rmse:1.740575+0.225160
## [85] train-rmse:1.409912+0.012364 test-rmse:1.731959+0.224765
## [86] train-rmse:1.396466+0.012138 test-rmse:1.701792+0.229809
## [87] train-rmse:1.383608+0.012179 test-rmse:1.690516+0.230612
## [88] train-rmse:1.371138+0.012007 test-rmse:1.677555+0.232424
## [89] train-rmse:1.359252+0.012054 test-rmse:1.669359+0.223997
## [90] train-rmse:1.347377+0.012159 test-rmse:1.654304+0.220273
## [91] train-rmse:1.335423+0.011846 test-rmse:1.654169+0.219714
## [92] train-rmse:1.323855+0.011989 test-rmse:1.636572+0.223543
## [93] train-rmse:1.312699+0.011843 test-rmse:1.626147+0.227516
## [94] train-rmse:1.301843+0.011743 test-rmse:1.601391+0.210567
## [95] train-rmse:1.291242+0.011416 test-rmse:1.595175+0.213907
## [96] train-rmse:1.280826+0.011245 test-rmse:1.590059+0.215606
## [97] train-rmse:1.270633+0.011167 test-rmse:1.582664+0.219788
## [98] train-rmse:1.260638+0.010999 test-rmse:1.572667+0.219680
## [99] train-rmse:1.250743+0.010759 test-rmse:1.564346+0.223482
## [100] train-rmse:1.240908+0.010561 test-rmse:1.559591+0.216780
## [101] train-rmse:1.231391+0.010595 test-rmse:1.555391+0.219350
## [102] train-rmse:1.222148+0.010747 test-rmse:1.542354+0.211426
## [103] train-rmse:1.212965+0.010817 test-rmse:1.535108+0.216392
## [104] train-rmse:1.203841+0.011071 test-rmse:1.521001+0.217935
## [105] train-rmse:1.194806+0.010925 test-rmse:1.511237+0.214759
## [106] train-rmse:1.186031+0.010692 test-rmse:1.503419+0.217020
## [107] train-rmse:1.177322+0.010600 test-rmse:1.498520+0.221511
## [108] train-rmse:1.168583+0.010549 test-rmse:1.490876+0.220549
## [109] train-rmse:1.160109+0.010527 test-rmse:1.481075+0.221293
## [110] train-rmse:1.151773+0.010642 test-rmse:1.474734+0.220053
## [111] train-rmse:1.143792+0.010725 test-rmse:1.469604+0.216433
## [112] train-rmse:1.136019+0.010905 test-rmse:1.461994+0.211500
## [113] train-rmse:1.128449+0.010920 test-rmse:1.451049+0.212283
## [114] train-rmse:1.121077+0.011029 test-rmse:1.432453+0.197653
## [115] train-rmse:1.113889+0.010965 test-rmse:1.425529+0.199043
## [116] train-rmse:1.106733+0.010831 test-rmse:1.412657+0.205034
## [117] train-rmse:1.099462+0.010528 test-rmse:1.414902+0.204947
## [118] train-rmse:1.092567+0.010400 test-rmse:1.405783+0.210591
## [119] train-rmse:1.085667+0.010435 test-rmse:1.402737+0.212762
## [120] train-rmse:1.078670+0.010499 test-rmse:1.395308+0.212910
## [121] train-rmse:1.072180+0.010604 test-rmse:1.391004+0.217336
## [122] train-rmse:1.065848+0.010704 test-rmse:1.384747+0.208873
## [123] train-rmse:1.059620+0.010728 test-rmse:1.379653+0.208571
## [124] train-rmse:1.053560+0.010957 test-rmse:1.373135+0.212515
## [125] train-rmse:1.047671+0.010989 test-rmse:1.370509+0.212936
## [126] train-rmse:1.041719+0.011020 test-rmse:1.367493+0.210133
## [127] train-rmse:1.035932+0.010910 test-rmse:1.361697+0.209280
## [128] train-rmse:1.030423+0.010731 test-rmse:1.356488+0.212214
## [129] train-rmse:1.024842+0.010511 test-rmse:1.349701+0.208514
## [130] train-rmse:1.019281+0.010450 test-rmse:1.337204+0.196134
## [131] train-rmse:1.013810+0.010291 test-rmse:1.331113+0.192763
## [132] train-rmse:1.008442+0.010277 test-rmse:1.326569+0.194266
## [133] train-rmse:1.003247+0.010407 test-rmse:1.324570+0.192438
## [134] train-rmse:0.998112+0.010540 test-rmse:1.318681+0.194981
## [135] train-rmse:0.993148+0.010773 test-rmse:1.316489+0.194377
## [136] train-rmse:0.988345+0.010874 test-rmse:1.309413+0.196384
## [137] train-rmse:0.983650+0.010991 test-rmse:1.308364+0.197234
## [138] train-rmse:0.978927+0.011114 test-rmse:1.304041+0.199280
## [139] train-rmse:0.974289+0.011236 test-rmse:1.294340+0.201018
## [140] train-rmse:0.969926+0.011381 test-rmse:1.293377+0.200674
## [141] train-rmse:0.965608+0.011543 test-rmse:1.289217+0.200158
## [142] train-rmse:0.961466+0.011473 test-rmse:1.284602+0.196767
## [143] train-rmse:0.957375+0.011457 test-rmse:1.274232+0.193969
## [144] train-rmse:0.953392+0.011455 test-rmse:1.271313+0.195993
## [145] train-rmse:0.949380+0.011494 test-rmse:1.267877+0.199046
## [146] train-rmse:0.945455+0.011522 test-rmse:1.267785+0.197226
## [147] train-rmse:0.941682+0.011590 test-rmse:1.264925+0.199428
## [148] train-rmse:0.937835+0.011655 test-rmse:1.264984+0.199994
## [149] train-rmse:0.934025+0.011776 test-rmse:1.260391+0.202019
## [150] train-rmse:0.930353+0.011802 test-rmse:1.258685+0.202133
## [151] train-rmse:0.926594+0.011904 test-rmse:1.257277+0.203650
## [152] train-rmse:0.922800+0.012124 test-rmse:1.248257+0.199947
## [153] train-rmse:0.919279+0.012217 test-rmse:1.246388+0.199110
## [154] train-rmse:0.915848+0.012190 test-rmse:1.244994+0.197567
## [155] train-rmse:0.912588+0.012143 test-rmse:1.244029+0.197173
## [156] train-rmse:0.909380+0.012070 test-rmse:1.241285+0.196494
## [157] train-rmse:0.906178+0.012034 test-rmse:1.237839+0.197633
## [158] train-rmse:0.902977+0.011974 test-rmse:1.237210+0.194175
## [159] train-rmse:0.899906+0.011965 test-rmse:1.234260+0.195646
## [160] train-rmse:0.896925+0.011942 test-rmse:1.229736+0.196970
## [161] train-rmse:0.894016+0.012001 test-rmse:1.227427+0.196671
## [162] train-rmse:0.891064+0.012051 test-rmse:1.227257+0.197319
## [163] train-rmse:0.888184+0.012149 test-rmse:1.217886+0.190265
## [164] train-rmse:0.885394+0.012272 test-rmse:1.216594+0.190517
## [165] train-rmse:0.882674+0.012382 test-rmse:1.215882+0.190088
## [166] train-rmse:0.879967+0.012502 test-rmse:1.206776+0.186509
## [167] train-rmse:0.877341+0.012640 test-rmse:1.204876+0.186583
## [168] train-rmse:0.874705+0.012771 test-rmse:1.203531+0.185972
## [169] train-rmse:0.872164+0.012958 test-rmse:1.198730+0.184708
## [170] train-rmse:0.869599+0.013023 test-rmse:1.197652+0.185395
## [171] train-rmse:0.867087+0.013191 test-rmse:1.194272+0.188380
## [172] train-rmse:0.864726+0.013269 test-rmse:1.191864+0.191053
## [173] train-rmse:0.862386+0.013326 test-rmse:1.193659+0.191212
## [174] train-rmse:0.860087+0.013376 test-rmse:1.192604+0.191982
## [175] train-rmse:0.857848+0.013455 test-rmse:1.192059+0.191317
## [176] train-rmse:0.855588+0.013404 test-rmse:1.191446+0.190701
## [177] train-rmse:0.853418+0.013401 test-rmse:1.191241+0.191614
## [178] train-rmse:0.851284+0.013434 test-rmse:1.188791+0.191909
## [179] train-rmse:0.849212+0.013494 test-rmse:1.184700+0.192068
## [180] train-rmse:0.847111+0.013610 test-rmse:1.183899+0.190667
## [181] train-rmse:0.844973+0.013725 test-rmse:1.182549+0.190305
## [182] train-rmse:0.842937+0.013840 test-rmse:1.181249+0.192292
## [183] train-rmse:0.840996+0.013935 test-rmse:1.180457+0.193248
## [184] train-rmse:0.839109+0.014011 test-rmse:1.177484+0.193715
## [185] train-rmse:0.837280+0.014133 test-rmse:1.175171+0.195499
## [186] train-rmse:0.835428+0.014188 test-rmse:1.173740+0.193823
## [187] train-rmse:0.833618+0.014317 test-rmse:1.173467+0.193887
## [188] train-rmse:0.831881+0.014421 test-rmse:1.171948+0.194304
## [189] train-rmse:0.830177+0.014537 test-rmse:1.170640+0.195446
## [190] train-rmse:0.828497+0.014653 test-rmse:1.169898+0.195899
## [191] train-rmse:0.826777+0.014714 test-rmse:1.168192+0.192138
## [192] train-rmse:0.825076+0.014797 test-rmse:1.166815+0.188373
## [193] train-rmse:0.823310+0.014869 test-rmse:1.160723+0.183195
## [194] train-rmse:0.821649+0.014928 test-rmse:1.157743+0.186955
## [195] train-rmse:0.819988+0.014974 test-rmse:1.159274+0.185392
## [196] train-rmse:0.818350+0.015042 test-rmse:1.156320+0.186615
## [197] train-rmse:0.816768+0.015104 test-rmse:1.155088+0.186780
## [198] train-rmse:0.815221+0.015131 test-rmse:1.153912+0.186892
## [199] train-rmse:0.813696+0.015143 test-rmse:1.155271+0.185442
## [200] train-rmse:0.812199+0.015197 test-rmse:1.153367+0.186196
## [201] train-rmse:0.810714+0.015262 test-rmse:1.152143+0.185998
## [202] train-rmse:0.809211+0.015300 test-rmse:1.150618+0.186727
## [203] train-rmse:0.807703+0.015323 test-rmse:1.150516+0.188218
## [204] train-rmse:0.806247+0.015368 test-rmse:1.148692+0.189979
## [205] train-rmse:0.804853+0.015444 test-rmse:1.148756+0.191307
## [206] train-rmse:0.803460+0.015527 test-rmse:1.147443+0.191733
## [207] train-rmse:0.802108+0.015629 test-rmse:1.147946+0.192053
## [208] train-rmse:0.800763+0.015717 test-rmse:1.146623+0.192228
## [209] train-rmse:0.799420+0.015801 test-rmse:1.144943+0.192393
## [210] train-rmse:0.798142+0.015852 test-rmse:1.143414+0.192189
## [211] train-rmse:0.796893+0.015889 test-rmse:1.143078+0.189006
## [212] train-rmse:0.795591+0.015929 test-rmse:1.139192+0.190303
## [213] train-rmse:0.794379+0.015991 test-rmse:1.137478+0.192262
## [214] train-rmse:0.793152+0.016018 test-rmse:1.137696+0.191165
## [215] train-rmse:0.791980+0.016034 test-rmse:1.138232+0.190694
## [216] train-rmse:0.790693+0.015973 test-rmse:1.137276+0.192482
## [217] train-rmse:0.789469+0.015993 test-rmse:1.135589+0.194024
## [218] train-rmse:0.788289+0.016044 test-rmse:1.134534+0.194551
## [219] train-rmse:0.787152+0.016075 test-rmse:1.134204+0.195016
## [220] train-rmse:0.785923+0.016043 test-rmse:1.131269+0.195062
## [221] train-rmse:0.784758+0.016076 test-rmse:1.131787+0.195830
## [222] train-rmse:0.783635+0.016119 test-rmse:1.129451+0.194690
## [223] train-rmse:0.782538+0.016165 test-rmse:1.129284+0.195063
## [224] train-rmse:0.781440+0.016180 test-rmse:1.127334+0.189342
## [225] train-rmse:0.780316+0.016174 test-rmse:1.123981+0.190333
## [226] train-rmse:0.779255+0.016203 test-rmse:1.122505+0.188530
## [227] train-rmse:0.778205+0.016270 test-rmse:1.121012+0.189707
## [228] train-rmse:0.777140+0.016271 test-rmse:1.119891+0.190014
## [229] train-rmse:0.776122+0.016289 test-rmse:1.119227+0.190449
## [230] train-rmse:0.775130+0.016300 test-rmse:1.118569+0.190659
## [231] train-rmse:0.774121+0.016369 test-rmse:1.117061+0.189190
## [232] train-rmse:0.773111+0.016389 test-rmse:1.116773+0.187865
## [233] train-rmse:0.772133+0.016383 test-rmse:1.114929+0.189909
## [234] train-rmse:0.771176+0.016404 test-rmse:1.114293+0.190056
## [235] train-rmse:0.770185+0.016402 test-rmse:1.112220+0.190616
## [236] train-rmse:0.769234+0.016356 test-rmse:1.111586+0.191045
## [237] train-rmse:0.768307+0.016347 test-rmse:1.111949+0.191106
## [238] train-rmse:0.767416+0.016361 test-rmse:1.110396+0.190763
## [239] train-rmse:0.766473+0.016389 test-rmse:1.110366+0.190052
## [240] train-rmse:0.765561+0.016382 test-rmse:1.108572+0.191584
## [241] train-rmse:0.764615+0.016414 test-rmse:1.109436+0.191574
## [242] train-rmse:0.763703+0.016442 test-rmse:1.109076+0.192068
## [243] train-rmse:0.762813+0.016441 test-rmse:1.109394+0.191866
## [244] train-rmse:0.761963+0.016469 test-rmse:1.107641+0.190556
## [245] train-rmse:0.761138+0.016481 test-rmse:1.106903+0.189947
## [246] train-rmse:0.760258+0.016475 test-rmse:1.107176+0.190511
## [247] train-rmse:0.759434+0.016476 test-rmse:1.106840+0.189981
## [248] train-rmse:0.758604+0.016501 test-rmse:1.105611+0.190114
## [249] train-rmse:0.757785+0.016495 test-rmse:1.104884+0.189133
## [250] train-rmse:0.756952+0.016489 test-rmse:1.102490+0.185131
## [251] train-rmse:0.756080+0.016462 test-rmse:1.101581+0.185274
## [252] train-rmse:0.755271+0.016447 test-rmse:1.101889+0.184801
## [253] train-rmse:0.754462+0.016462 test-rmse:1.101880+0.183426
## [254] train-rmse:0.753679+0.016452 test-rmse:1.102188+0.181894
## [255] train-rmse:0.752865+0.016487 test-rmse:1.099362+0.182912
## [256] train-rmse:0.752096+0.016490 test-rmse:1.098665+0.182839
## [257] train-rmse:0.751360+0.016496 test-rmse:1.098361+0.183197
## [258] train-rmse:0.750600+0.016450 test-rmse:1.096583+0.183720
## [259] train-rmse:0.749864+0.016454 test-rmse:1.097566+0.182933
## [260] train-rmse:0.749118+0.016477 test-rmse:1.096498+0.184161
## [261] train-rmse:0.748400+0.016448 test-rmse:1.094828+0.182021
## [262] train-rmse:0.747620+0.016425 test-rmse:1.092288+0.181913
## [263] train-rmse:0.746889+0.016410 test-rmse:1.090989+0.180437
## [264] train-rmse:0.746139+0.016368 test-rmse:1.089195+0.178845
## [265] train-rmse:0.745398+0.016321 test-rmse:1.089449+0.179550
## [266] train-rmse:0.744701+0.016287 test-rmse:1.087696+0.178782
## [267] train-rmse:0.744001+0.016252 test-rmse:1.085833+0.178239
## [268] train-rmse:0.743289+0.016215 test-rmse:1.085886+0.176460
## [269] train-rmse:0.742557+0.016213 test-rmse:1.084651+0.175823
## [270] train-rmse:0.741879+0.016183 test-rmse:1.083970+0.176025
## [271] train-rmse:0.741198+0.016173 test-rmse:1.082737+0.174326
## [272] train-rmse:0.740528+0.016162 test-rmse:1.081207+0.172004
## [273] train-rmse:0.739880+0.016143 test-rmse:1.080106+0.172928
## [274] train-rmse:0.739242+0.016113 test-rmse:1.078770+0.173700
## [275] train-rmse:0.738558+0.016126 test-rmse:1.076081+0.173234
## [276] train-rmse:0.737908+0.016085 test-rmse:1.075405+0.172868
## [277] train-rmse:0.737267+0.016064 test-rmse:1.075511+0.172222
## [278] train-rmse:0.736646+0.016064 test-rmse:1.074082+0.173000
## [279] train-rmse:0.736003+0.016039 test-rmse:1.075021+0.171929
## [280] train-rmse:0.735369+0.016007 test-rmse:1.075344+0.171622
## [281] train-rmse:0.734740+0.016010 test-rmse:1.075787+0.170823
## [282] train-rmse:0.734121+0.015963 test-rmse:1.075795+0.171573
## [283] train-rmse:0.733497+0.015931 test-rmse:1.075679+0.171289
## [284] train-rmse:0.732892+0.015897 test-rmse:1.075012+0.171837
## Stopping. Best iteration:
## [278] train-rmse:0.736646+0.016064 test-rmse:1.074082+0.173000
##
## 78
## [1] train-rmse:73.403534+0.086787 test-rmse:73.403435+0.516279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:50.171526+0.069998 test-rmse:50.170467+0.562705
## [3] train-rmse:34.428066+0.063950 test-rmse:34.425303+0.610826
## [4] train-rmse:23.772392+0.053545 test-rmse:23.842593+0.630501
## [5] train-rmse:16.553434+0.035975 test-rmse:16.646706+0.556223
## [6] train-rmse:11.695168+0.029757 test-rmse:11.826321+0.567004
## [7] train-rmse:8.472388+0.033193 test-rmse:8.590248+0.586984
## [8] train-rmse:6.376988+0.037572 test-rmse:6.564277+0.602368
## [9] train-rmse:5.043774+0.043762 test-rmse:5.206799+0.521689
## [10] train-rmse:4.217811+0.044845 test-rmse:4.429132+0.499954
## [11] train-rmse:3.701687+0.048991 test-rmse:3.975679+0.478174
## [12] train-rmse:3.375247+0.042830 test-rmse:3.656845+0.420421
## [13] train-rmse:3.146143+0.053457 test-rmse:3.416005+0.371184
## [14] train-rmse:2.982737+0.046914 test-rmse:3.260120+0.359112
## [15] train-rmse:2.854904+0.042977 test-rmse:3.114308+0.333199
## [16] train-rmse:2.732615+0.031307 test-rmse:3.041251+0.350824
## [17] train-rmse:2.642613+0.028180 test-rmse:2.955466+0.338856
## [18] train-rmse:2.554476+0.023025 test-rmse:2.886729+0.313958
## [19] train-rmse:2.474675+0.021201 test-rmse:2.791735+0.305704
## [20] train-rmse:2.394845+0.019428 test-rmse:2.743822+0.317108
## [21] train-rmse:2.321926+0.017110 test-rmse:2.704514+0.317178
## [22] train-rmse:2.253645+0.016014 test-rmse:2.633396+0.336758
## [23] train-rmse:2.188370+0.016298 test-rmse:2.570696+0.303149
## [24] train-rmse:2.127440+0.012510 test-rmse:2.506780+0.298105
## [25] train-rmse:2.068299+0.014165 test-rmse:2.451176+0.302392
## [26] train-rmse:2.008748+0.010190 test-rmse:2.400438+0.280167
## [27] train-rmse:1.955189+0.010698 test-rmse:2.354865+0.289662
## [28] train-rmse:1.901595+0.009867 test-rmse:2.295188+0.257645
## [29] train-rmse:1.853278+0.009739 test-rmse:2.225142+0.258991
## [30] train-rmse:1.805849+0.009500 test-rmse:2.183005+0.279153
## [31] train-rmse:1.760830+0.008717 test-rmse:2.156443+0.279139
## [32] train-rmse:1.715475+0.009626 test-rmse:2.114419+0.273704
## [33] train-rmse:1.673175+0.011730 test-rmse:2.080195+0.278514
## [34] train-rmse:1.634323+0.011675 test-rmse:2.038859+0.272772
## [35] train-rmse:1.594254+0.013407 test-rmse:2.004397+0.283487
## [36] train-rmse:1.555845+0.012593 test-rmse:1.973297+0.270744
## [37] train-rmse:1.519010+0.013265 test-rmse:1.953007+0.275998
## [38] train-rmse:1.484102+0.014133 test-rmse:1.912441+0.268168
## [39] train-rmse:1.449189+0.014901 test-rmse:1.894776+0.273095
## [40] train-rmse:1.413977+0.012155 test-rmse:1.866777+0.275481
## [41] train-rmse:1.383280+0.011738 test-rmse:1.834326+0.278647
## [42] train-rmse:1.352341+0.014146 test-rmse:1.813164+0.274964
## [43] train-rmse:1.317711+0.014504 test-rmse:1.794390+0.268305
## [44] train-rmse:1.289876+0.015264 test-rmse:1.781186+0.267180
## [45] train-rmse:1.261519+0.015698 test-rmse:1.758016+0.263705
## [46] train-rmse:1.235192+0.016450 test-rmse:1.716145+0.244379
## [47] train-rmse:1.210630+0.016274 test-rmse:1.704328+0.244263
## [48] train-rmse:1.186435+0.016172 test-rmse:1.667890+0.237003
## [49] train-rmse:1.162336+0.017989 test-rmse:1.644250+0.246738
## [50] train-rmse:1.139814+0.018827 test-rmse:1.630960+0.253554
## [51] train-rmse:1.117660+0.018533 test-rmse:1.615925+0.254192
## [52] train-rmse:1.097158+0.018181 test-rmse:1.595550+0.252491
## [53] train-rmse:1.075851+0.019127 test-rmse:1.586080+0.251826
## [54] train-rmse:1.056346+0.019216 test-rmse:1.565934+0.248390
## [55] train-rmse:1.037133+0.019074 test-rmse:1.549595+0.254565
## [56] train-rmse:1.015272+0.016101 test-rmse:1.536363+0.249620
## [57] train-rmse:0.997889+0.015973 test-rmse:1.523138+0.247391
## [58] train-rmse:0.980323+0.016743 test-rmse:1.515032+0.248836
## [59] train-rmse:0.964944+0.017228 test-rmse:1.491025+0.251996
## [60] train-rmse:0.949292+0.018426 test-rmse:1.473278+0.249175
## [61] train-rmse:0.933840+0.018021 test-rmse:1.462098+0.253318
## [62] train-rmse:0.918826+0.017889 test-rmse:1.450328+0.252776
## [63] train-rmse:0.904749+0.017977 test-rmse:1.439629+0.254969
## [64] train-rmse:0.891288+0.017908 test-rmse:1.428191+0.251900
## [65] train-rmse:0.877003+0.019974 test-rmse:1.417457+0.249523
## [66] train-rmse:0.861213+0.020391 test-rmse:1.410481+0.246326
## [67] train-rmse:0.848046+0.019457 test-rmse:1.404311+0.240692
## [68] train-rmse:0.835148+0.017861 test-rmse:1.393563+0.239880
## [69] train-rmse:0.822769+0.017385 test-rmse:1.377323+0.239870
## [70] train-rmse:0.811575+0.017484 test-rmse:1.373816+0.243455
## [71] train-rmse:0.800868+0.017371 test-rmse:1.362049+0.244193
## [72] train-rmse:0.787968+0.017972 test-rmse:1.348456+0.244516
## [73] train-rmse:0.777491+0.017476 test-rmse:1.343616+0.242774
## [74] train-rmse:0.766860+0.017907 test-rmse:1.337420+0.242544
## [75] train-rmse:0.756633+0.017174 test-rmse:1.330761+0.238643
## [76] train-rmse:0.746040+0.016870 test-rmse:1.325317+0.241915
## [77] train-rmse:0.736933+0.016151 test-rmse:1.320655+0.243783
## [78] train-rmse:0.727385+0.015892 test-rmse:1.313833+0.243418
## [79] train-rmse:0.718157+0.016961 test-rmse:1.301425+0.244160
## [80] train-rmse:0.709758+0.018244 test-rmse:1.296552+0.244722
## [81] train-rmse:0.701350+0.019791 test-rmse:1.292126+0.245236
## [82] train-rmse:0.690284+0.015997 test-rmse:1.288853+0.244618
## [83] train-rmse:0.681681+0.015690 test-rmse:1.282482+0.234485
## [84] train-rmse:0.675276+0.015545 test-rmse:1.277886+0.235372
## [85] train-rmse:0.668061+0.015579 test-rmse:1.274129+0.239153
## [86] train-rmse:0.660127+0.014903 test-rmse:1.273312+0.239709
## [87] train-rmse:0.653088+0.014686 test-rmse:1.268456+0.242215
## [88] train-rmse:0.646633+0.013830 test-rmse:1.262109+0.240971
## [89] train-rmse:0.640488+0.013832 test-rmse:1.256490+0.239165
## [90] train-rmse:0.634248+0.013549 test-rmse:1.255865+0.241755
## [91] train-rmse:0.626054+0.015244 test-rmse:1.251083+0.240712
## [92] train-rmse:0.620207+0.016143 test-rmse:1.245728+0.237447
## [93] train-rmse:0.614138+0.016345 test-rmse:1.242972+0.237603
## [94] train-rmse:0.608437+0.017044 test-rmse:1.241068+0.238405
## [95] train-rmse:0.601014+0.015938 test-rmse:1.234025+0.231954
## [96] train-rmse:0.595039+0.017498 test-rmse:1.235084+0.233690
## [97] train-rmse:0.588195+0.017697 test-rmse:1.228764+0.234203
## [98] train-rmse:0.583724+0.017820 test-rmse:1.222554+0.236377
## [99] train-rmse:0.578604+0.019086 test-rmse:1.221927+0.236395
## [100] train-rmse:0.571694+0.018937 test-rmse:1.224145+0.236203
## [101] train-rmse:0.564233+0.015132 test-rmse:1.223124+0.239105
## [102] train-rmse:0.559567+0.015791 test-rmse:1.216420+0.240646
## [103] train-rmse:0.554612+0.016225 test-rmse:1.214700+0.241057
## [104] train-rmse:0.548812+0.014806 test-rmse:1.207818+0.245897
## [105] train-rmse:0.544964+0.014547 test-rmse:1.205395+0.244977
## [106] train-rmse:0.540592+0.014443 test-rmse:1.203459+0.244649
## [107] train-rmse:0.536615+0.014109 test-rmse:1.199284+0.242727
## [108] train-rmse:0.532627+0.013413 test-rmse:1.197856+0.242189
## [109] train-rmse:0.528780+0.013660 test-rmse:1.195300+0.242641
## [110] train-rmse:0.524756+0.013649 test-rmse:1.190640+0.246307
## [111] train-rmse:0.520730+0.012305 test-rmse:1.189789+0.246575
## [112] train-rmse:0.516711+0.013004 test-rmse:1.187557+0.249021
## [113] train-rmse:0.513091+0.014152 test-rmse:1.188417+0.251527
## [114] train-rmse:0.509907+0.014558 test-rmse:1.186210+0.252569
## [115] train-rmse:0.504826+0.015467 test-rmse:1.187121+0.253249
## [116] train-rmse:0.501184+0.016243 test-rmse:1.186409+0.256302
## [117] train-rmse:0.497921+0.016382 test-rmse:1.184194+0.258225
## [118] train-rmse:0.493847+0.015252 test-rmse:1.182319+0.258561
## [119] train-rmse:0.489587+0.014265 test-rmse:1.181931+0.257774
## [120] train-rmse:0.485936+0.014403 test-rmse:1.182092+0.258063
## [121] train-rmse:0.483095+0.014007 test-rmse:1.178238+0.257573
## [122] train-rmse:0.479928+0.014068 test-rmse:1.176664+0.256788
## [123] train-rmse:0.476174+0.014081 test-rmse:1.174756+0.258500
## [124] train-rmse:0.471536+0.013605 test-rmse:1.169883+0.261318
## [125] train-rmse:0.467743+0.010902 test-rmse:1.166859+0.259086
## [126] train-rmse:0.463253+0.013712 test-rmse:1.165437+0.259538
## [127] train-rmse:0.460545+0.014276 test-rmse:1.165642+0.259754
## [128] train-rmse:0.458043+0.014540 test-rmse:1.163147+0.258036
## [129] train-rmse:0.454923+0.014526 test-rmse:1.163105+0.258708
## [130] train-rmse:0.453067+0.014553 test-rmse:1.161266+0.257807
## [131] train-rmse:0.450249+0.014058 test-rmse:1.160937+0.260223
## [132] train-rmse:0.446484+0.015244 test-rmse:1.158973+0.261663
## [133] train-rmse:0.442656+0.014998 test-rmse:1.157581+0.264062
## [134] train-rmse:0.439213+0.015212 test-rmse:1.154784+0.266217
## [135] train-rmse:0.436331+0.015687 test-rmse:1.154011+0.265796
## [136] train-rmse:0.433407+0.013925 test-rmse:1.150166+0.266836
## [137] train-rmse:0.431305+0.014189 test-rmse:1.149665+0.267269
## [138] train-rmse:0.427311+0.015267 test-rmse:1.148186+0.267351
## [139] train-rmse:0.424589+0.015554 test-rmse:1.146867+0.267546
## [140] train-rmse:0.421640+0.015269 test-rmse:1.146276+0.268057
## [141] train-rmse:0.418936+0.015566 test-rmse:1.143386+0.267336
## [142] train-rmse:0.415639+0.014362 test-rmse:1.141511+0.268599
## [143] train-rmse:0.413008+0.014776 test-rmse:1.134513+0.261611
## [144] train-rmse:0.410938+0.015553 test-rmse:1.133011+0.261287
## [145] train-rmse:0.409197+0.015394 test-rmse:1.133067+0.261022
## [146] train-rmse:0.406718+0.015193 test-rmse:1.132526+0.260077
## [147] train-rmse:0.405182+0.015252 test-rmse:1.131342+0.260424
## [148] train-rmse:0.403111+0.015828 test-rmse:1.131731+0.261334
## [149] train-rmse:0.401700+0.015923 test-rmse:1.132324+0.260106
## [150] train-rmse:0.399561+0.015643 test-rmse:1.130998+0.261120
## [151] train-rmse:0.398074+0.015621 test-rmse:1.131609+0.261731
## [152] train-rmse:0.395985+0.015739 test-rmse:1.131084+0.263737
## [153] train-rmse:0.394484+0.015666 test-rmse:1.128761+0.262545
## [154] train-rmse:0.391658+0.015219 test-rmse:1.127556+0.261380
## [155] train-rmse:0.389903+0.015029 test-rmse:1.127699+0.261634
## [156] train-rmse:0.387802+0.015145 test-rmse:1.128128+0.261417
## [157] train-rmse:0.384282+0.015395 test-rmse:1.124673+0.262437
## [158] train-rmse:0.381459+0.015072 test-rmse:1.123042+0.265609
## [159] train-rmse:0.378445+0.016973 test-rmse:1.122877+0.265928
## [160] train-rmse:0.375889+0.017336 test-rmse:1.122369+0.265499
## [161] train-rmse:0.373763+0.016436 test-rmse:1.120183+0.266296
## [162] train-rmse:0.372226+0.017143 test-rmse:1.120234+0.266807
## [163] train-rmse:0.369358+0.017928 test-rmse:1.119200+0.267071
## [164] train-rmse:0.367809+0.017783 test-rmse:1.119597+0.266214
## [165] train-rmse:0.366160+0.018041 test-rmse:1.117680+0.266235
## [166] train-rmse:0.363765+0.019309 test-rmse:1.116839+0.265689
## [167] train-rmse:0.361817+0.019865 test-rmse:1.116577+0.266587
## [168] train-rmse:0.360625+0.019912 test-rmse:1.116081+0.266535
## [169] train-rmse:0.359442+0.020339 test-rmse:1.116474+0.266935
## [170] train-rmse:0.357728+0.020193 test-rmse:1.114145+0.265934
## [171] train-rmse:0.356192+0.020509 test-rmse:1.114502+0.265911
## [172] train-rmse:0.354486+0.021019 test-rmse:1.115465+0.265830
## [173] train-rmse:0.352199+0.020976 test-rmse:1.114893+0.265249
## [174] train-rmse:0.349828+0.020402 test-rmse:1.114777+0.265094
## [175] train-rmse:0.347907+0.020663 test-rmse:1.114737+0.264570
## [176] train-rmse:0.346375+0.020546 test-rmse:1.113607+0.264828
## [177] train-rmse:0.344736+0.019980 test-rmse:1.112478+0.265986
## [178] train-rmse:0.342746+0.020555 test-rmse:1.111786+0.266160
## [179] train-rmse:0.339079+0.018680 test-rmse:1.111402+0.267290
## [180] train-rmse:0.336969+0.018408 test-rmse:1.109753+0.267209
## [181] train-rmse:0.335382+0.018493 test-rmse:1.110936+0.267988
## [182] train-rmse:0.334310+0.018552 test-rmse:1.109930+0.266360
## [183] train-rmse:0.332789+0.018715 test-rmse:1.109935+0.265310
## [184] train-rmse:0.330916+0.018689 test-rmse:1.108224+0.266179
## [185] train-rmse:0.329554+0.018864 test-rmse:1.108693+0.266473
## [186] train-rmse:0.328444+0.018605 test-rmse:1.108937+0.265666
## [187] train-rmse:0.326214+0.018269 test-rmse:1.107805+0.265934
## [188] train-rmse:0.324311+0.016622 test-rmse:1.107570+0.265826
## [189] train-rmse:0.322410+0.016450 test-rmse:1.108216+0.266343
## [190] train-rmse:0.321327+0.016618 test-rmse:1.107981+0.266134
## [191] train-rmse:0.319299+0.015819 test-rmse:1.106896+0.265648
## [192] train-rmse:0.317736+0.015852 test-rmse:1.107432+0.266912
## [193] train-rmse:0.316603+0.016252 test-rmse:1.107236+0.266010
## [194] train-rmse:0.314501+0.015061 test-rmse:1.103648+0.267472
## [195] train-rmse:0.312626+0.014172 test-rmse:1.101714+0.267415
## [196] train-rmse:0.311648+0.014401 test-rmse:1.100022+0.267513
## [197] train-rmse:0.310422+0.014580 test-rmse:1.100533+0.267659
## [198] train-rmse:0.309750+0.014638 test-rmse:1.099998+0.268036
## [199] train-rmse:0.308542+0.014213 test-rmse:1.099326+0.268171
## [200] train-rmse:0.307281+0.013941 test-rmse:1.099566+0.267807
## [201] train-rmse:0.306193+0.014072 test-rmse:1.099081+0.268020
## [202] train-rmse:0.304090+0.013727 test-rmse:1.098539+0.270812
## [203] train-rmse:0.302324+0.014640 test-rmse:1.098443+0.270988
## [204] train-rmse:0.301004+0.014577 test-rmse:1.098364+0.270652
## [205] train-rmse:0.299535+0.014833 test-rmse:1.099205+0.271680
## [206] train-rmse:0.298274+0.014943 test-rmse:1.098462+0.271289
## [207] train-rmse:0.296204+0.014596 test-rmse:1.098511+0.270604
## [208] train-rmse:0.294749+0.013978 test-rmse:1.099215+0.269984
## [209] train-rmse:0.293628+0.013476 test-rmse:1.099064+0.270027
## [210] train-rmse:0.291943+0.013639 test-rmse:1.098176+0.270163
## [211] train-rmse:0.291288+0.013658 test-rmse:1.098412+0.271037
## [212] train-rmse:0.290437+0.013582 test-rmse:1.098252+0.271749
## [213] train-rmse:0.289319+0.013752 test-rmse:1.098551+0.271605
## [214] train-rmse:0.288337+0.014104 test-rmse:1.098267+0.271626
## [215] train-rmse:0.286761+0.013453 test-rmse:1.097927+0.270511
## [216] train-rmse:0.285859+0.013366 test-rmse:1.097716+0.270621
## [217] train-rmse:0.284354+0.013190 test-rmse:1.097067+0.271148
## [218] train-rmse:0.283069+0.013770 test-rmse:1.096314+0.271749
## [219] train-rmse:0.281319+0.013820 test-rmse:1.095612+0.271458
## [220] train-rmse:0.280075+0.013267 test-rmse:1.094080+0.270967
## [221] train-rmse:0.278914+0.013265 test-rmse:1.093519+0.271107
## [222] train-rmse:0.277228+0.012847 test-rmse:1.093729+0.270182
## [223] train-rmse:0.276325+0.012915 test-rmse:1.094019+0.270020
## [224] train-rmse:0.275240+0.012699 test-rmse:1.093599+0.270292
## [225] train-rmse:0.274331+0.012663 test-rmse:1.093588+0.270352
## [226] train-rmse:0.273267+0.012209 test-rmse:1.093707+0.270304
## [227] train-rmse:0.272278+0.012101 test-rmse:1.094594+0.269952
## Stopping. Best iteration:
## [221] train-rmse:0.278914+0.013265 test-rmse:1.093519+0.271107
##
## 79
## [1] train-rmse:73.403534+0.086787 test-rmse:73.403435+0.516279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:50.171526+0.069998 test-rmse:50.170467+0.562705
## [3] train-rmse:34.428066+0.063950 test-rmse:34.425303+0.610826
## [4] train-rmse:23.768078+0.049908 test-rmse:23.846854+0.624752
## [5] train-rmse:16.530009+0.033035 test-rmse:16.637555+0.589382
## [6] train-rmse:11.644632+0.031538 test-rmse:11.722669+0.538413
## [7] train-rmse:8.360396+0.025325 test-rmse:8.499510+0.554258
## [8] train-rmse:6.186034+0.030270 test-rmse:6.370310+0.574419
## [9] train-rmse:4.771373+0.032416 test-rmse:4.972753+0.511759
## [10] train-rmse:3.863985+0.036624 test-rmse:4.092527+0.457220
## [11] train-rmse:3.290191+0.034063 test-rmse:3.543850+0.441395
## [12] train-rmse:2.910822+0.039626 test-rmse:3.250616+0.436433
## [13] train-rmse:2.662058+0.039765 test-rmse:3.022930+0.372803
## [14] train-rmse:2.477222+0.045763 test-rmse:2.866218+0.334796
## [15] train-rmse:2.322403+0.044159 test-rmse:2.762100+0.308012
## [16] train-rmse:2.198896+0.040184 test-rmse:2.639916+0.292471
## [17] train-rmse:2.096723+0.039965 test-rmse:2.547447+0.249929
## [18] train-rmse:2.001101+0.038244 test-rmse:2.461435+0.239776
## [19] train-rmse:1.907618+0.032003 test-rmse:2.378592+0.223973
## [20] train-rmse:1.824472+0.035944 test-rmse:2.334564+0.229948
## [21] train-rmse:1.750301+0.036761 test-rmse:2.280698+0.235857
## [22] train-rmse:1.681920+0.036865 test-rmse:2.220036+0.243104
## [23] train-rmse:1.615432+0.040064 test-rmse:2.163916+0.253383
## [24] train-rmse:1.549801+0.041787 test-rmse:2.121722+0.251322
## [25] train-rmse:1.490377+0.037040 test-rmse:2.076406+0.242258
## [26] train-rmse:1.433703+0.035286 test-rmse:2.018778+0.234916
## [27] train-rmse:1.379634+0.034405 test-rmse:1.990330+0.235274
## [28] train-rmse:1.329571+0.035608 test-rmse:1.950391+0.230222
## [29] train-rmse:1.283109+0.034733 test-rmse:1.904930+0.229225
## [30] train-rmse:1.234642+0.030557 test-rmse:1.870617+0.240090
## [31] train-rmse:1.190520+0.028116 test-rmse:1.825616+0.238819
## [32] train-rmse:1.147681+0.030039 test-rmse:1.800953+0.237446
## [33] train-rmse:1.110224+0.031363 test-rmse:1.775167+0.245405
## [34] train-rmse:1.071850+0.032064 test-rmse:1.750668+0.245555
## [35] train-rmse:1.037409+0.030664 test-rmse:1.726258+0.240363
## [36] train-rmse:1.004019+0.032733 test-rmse:1.700350+0.246311
## [37] train-rmse:0.972570+0.031931 test-rmse:1.663565+0.224966
## [38] train-rmse:0.943125+0.032115 test-rmse:1.635206+0.230496
## [39] train-rmse:0.914435+0.030976 test-rmse:1.609882+0.229839
## [40] train-rmse:0.886814+0.030054 test-rmse:1.593636+0.232006
## [41] train-rmse:0.860876+0.028693 test-rmse:1.570896+0.239264
## [42] train-rmse:0.837537+0.028759 test-rmse:1.540904+0.241311
## [43] train-rmse:0.815145+0.030073 test-rmse:1.519420+0.240696
## [44] train-rmse:0.794430+0.030522 test-rmse:1.507573+0.236438
## [45] train-rmse:0.773350+0.025981 test-rmse:1.493259+0.232421
## [46] train-rmse:0.751825+0.028638 test-rmse:1.477050+0.242403
## [47] train-rmse:0.731469+0.029349 test-rmse:1.459915+0.238879
## [48] train-rmse:0.711775+0.030394 test-rmse:1.437003+0.241549
## [49] train-rmse:0.696061+0.030585 test-rmse:1.424807+0.240718
## [50] train-rmse:0.676756+0.027157 test-rmse:1.418012+0.243042
## [51] train-rmse:0.660389+0.026934 test-rmse:1.410539+0.239467
## [52] train-rmse:0.644549+0.027566 test-rmse:1.398255+0.236467
## [53] train-rmse:0.629545+0.028091 test-rmse:1.392439+0.237986
## [54] train-rmse:0.616579+0.025833 test-rmse:1.376582+0.239660
## [55] train-rmse:0.603678+0.025851 test-rmse:1.371462+0.243869
## [56] train-rmse:0.588600+0.023694 test-rmse:1.361417+0.247250
## [57] train-rmse:0.576542+0.023783 test-rmse:1.354667+0.246223
## [58] train-rmse:0.565713+0.024041 test-rmse:1.343983+0.242187
## [59] train-rmse:0.555594+0.023780 test-rmse:1.338767+0.244834
## [60] train-rmse:0.544220+0.024073 test-rmse:1.333074+0.248964
## [61] train-rmse:0.533625+0.024528 test-rmse:1.327083+0.246990
## [62] train-rmse:0.521604+0.025880 test-rmse:1.323272+0.246908
## [63] train-rmse:0.512450+0.024907 test-rmse:1.320725+0.250004
## [64] train-rmse:0.503112+0.024206 test-rmse:1.318011+0.253502
## [65] train-rmse:0.495230+0.024425 test-rmse:1.312229+0.255528
## [66] train-rmse:0.486698+0.024922 test-rmse:1.300467+0.264297
## [67] train-rmse:0.477857+0.026140 test-rmse:1.297748+0.265163
## [68] train-rmse:0.466529+0.023986 test-rmse:1.291765+0.264697
## [69] train-rmse:0.458350+0.021951 test-rmse:1.284296+0.272538
## [70] train-rmse:0.451058+0.021848 test-rmse:1.282031+0.273934
## [71] train-rmse:0.443260+0.023103 test-rmse:1.278859+0.276274
## [72] train-rmse:0.435325+0.024955 test-rmse:1.278198+0.275938
## [73] train-rmse:0.428974+0.025824 test-rmse:1.266867+0.284331
## [74] train-rmse:0.422524+0.025781 test-rmse:1.262572+0.279991
## [75] train-rmse:0.416795+0.024869 test-rmse:1.257219+0.286949
## [76] train-rmse:0.410644+0.024590 test-rmse:1.256444+0.285319
## [77] train-rmse:0.405661+0.024486 test-rmse:1.252066+0.286448
## [78] train-rmse:0.396295+0.023425 test-rmse:1.251081+0.288643
## [79] train-rmse:0.388906+0.022656 test-rmse:1.246463+0.288392
## [80] train-rmse:0.383254+0.023116 test-rmse:1.245305+0.287577
## [81] train-rmse:0.376187+0.024071 test-rmse:1.240876+0.286526
## [82] train-rmse:0.369282+0.025386 test-rmse:1.239821+0.287488
## [83] train-rmse:0.364511+0.023342 test-rmse:1.238159+0.288643
## [84] train-rmse:0.357140+0.023353 test-rmse:1.233444+0.295343
## [85] train-rmse:0.352278+0.022850 test-rmse:1.230428+0.295625
## [86] train-rmse:0.349176+0.022808 test-rmse:1.229272+0.294133
## [87] train-rmse:0.344953+0.023145 test-rmse:1.221930+0.300877
## [88] train-rmse:0.339308+0.023626 test-rmse:1.219576+0.301636
## [89] train-rmse:0.334824+0.023605 test-rmse:1.218888+0.300254
## [90] train-rmse:0.329884+0.024134 test-rmse:1.219661+0.306663
## [91] train-rmse:0.326393+0.024316 test-rmse:1.219096+0.305995
## [92] train-rmse:0.322375+0.023428 test-rmse:1.217669+0.307157
## [93] train-rmse:0.315754+0.023064 test-rmse:1.216241+0.306491
## [94] train-rmse:0.310665+0.023406 test-rmse:1.215903+0.306305
## [95] train-rmse:0.305774+0.022478 test-rmse:1.214224+0.305535
## [96] train-rmse:0.302353+0.022064 test-rmse:1.212479+0.304769
## [97] train-rmse:0.299290+0.022653 test-rmse:1.210788+0.305755
## [98] train-rmse:0.294951+0.021375 test-rmse:1.212709+0.306872
## [99] train-rmse:0.289724+0.019214 test-rmse:1.212268+0.306392
## [100] train-rmse:0.286967+0.019554 test-rmse:1.211121+0.306951
## [101] train-rmse:0.284248+0.018656 test-rmse:1.209748+0.309300
## [102] train-rmse:0.281249+0.017956 test-rmse:1.209495+0.310587
## [103] train-rmse:0.277218+0.016977 test-rmse:1.208401+0.309161
## [104] train-rmse:0.275126+0.016775 test-rmse:1.207546+0.309400
## [105] train-rmse:0.270696+0.015208 test-rmse:1.207194+0.311685
## [106] train-rmse:0.266598+0.016047 test-rmse:1.207554+0.310550
## [107] train-rmse:0.263033+0.014204 test-rmse:1.205873+0.312297
## [108] train-rmse:0.260585+0.015124 test-rmse:1.205254+0.313068
## [109] train-rmse:0.257477+0.015371 test-rmse:1.204994+0.313300
## [110] train-rmse:0.253498+0.015559 test-rmse:1.202277+0.313774
## [111] train-rmse:0.249874+0.015610 test-rmse:1.203190+0.314348
## [112] train-rmse:0.246796+0.015498 test-rmse:1.204029+0.315928
## [113] train-rmse:0.245048+0.015823 test-rmse:1.202620+0.316378
## [114] train-rmse:0.242491+0.015661 test-rmse:1.201841+0.316732
## [115] train-rmse:0.239721+0.015690 test-rmse:1.201628+0.318709
## [116] train-rmse:0.237337+0.016305 test-rmse:1.200412+0.320134
## [117] train-rmse:0.234084+0.016275 test-rmse:1.199873+0.320017
## [118] train-rmse:0.231597+0.015471 test-rmse:1.199101+0.319895
## [119] train-rmse:0.229871+0.015135 test-rmse:1.199676+0.318867
## [120] train-rmse:0.228144+0.014949 test-rmse:1.198467+0.319199
## [121] train-rmse:0.225510+0.015105 test-rmse:1.198493+0.318854
## [122] train-rmse:0.222603+0.014328 test-rmse:1.198461+0.319635
## [123] train-rmse:0.220852+0.014375 test-rmse:1.198809+0.318763
## [124] train-rmse:0.218599+0.013923 test-rmse:1.198415+0.320107
## [125] train-rmse:0.215251+0.013072 test-rmse:1.197158+0.321104
## [126] train-rmse:0.212951+0.013451 test-rmse:1.197213+0.321655
## [127] train-rmse:0.210673+0.013295 test-rmse:1.197237+0.321334
## [128] train-rmse:0.207852+0.013083 test-rmse:1.195228+0.321811
## [129] train-rmse:0.205423+0.013639 test-rmse:1.196056+0.322106
## [130] train-rmse:0.203607+0.013868 test-rmse:1.195391+0.322499
## [131] train-rmse:0.200206+0.013534 test-rmse:1.195193+0.322983
## [132] train-rmse:0.198420+0.013789 test-rmse:1.195012+0.323841
## [133] train-rmse:0.196692+0.014140 test-rmse:1.194362+0.324148
## [134] train-rmse:0.194370+0.014043 test-rmse:1.193878+0.324598
## [135] train-rmse:0.191432+0.012937 test-rmse:1.193827+0.325745
## [136] train-rmse:0.189149+0.012309 test-rmse:1.193816+0.325367
## [137] train-rmse:0.186556+0.012700 test-rmse:1.193317+0.325905
## [138] train-rmse:0.184921+0.013044 test-rmse:1.193370+0.325673
## [139] train-rmse:0.183203+0.012714 test-rmse:1.194455+0.326349
## [140] train-rmse:0.181292+0.012136 test-rmse:1.194487+0.326404
## [141] train-rmse:0.179148+0.012139 test-rmse:1.194544+0.325787
## [142] train-rmse:0.177422+0.012342 test-rmse:1.194189+0.325429
## [143] train-rmse:0.175860+0.012385 test-rmse:1.193903+0.325171
## Stopping. Best iteration:
## [137] train-rmse:0.186556+0.012700 test-rmse:1.193317+0.325905
##
## 80
## [1] train-rmse:73.403534+0.086787 test-rmse:73.403435+0.516279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:50.171526+0.069998 test-rmse:50.170467+0.562705
## [3] train-rmse:34.428066+0.063950 test-rmse:34.425303+0.610826
## [4] train-rmse:23.768078+0.049908 test-rmse:23.846854+0.624752
## [5] train-rmse:16.527301+0.033241 test-rmse:16.614856+0.580859
## [6] train-rmse:11.618130+0.029376 test-rmse:11.680792+0.528546
## [7] train-rmse:8.297184+0.014445 test-rmse:8.395584+0.545975
## [8] train-rmse:6.060465+0.020962 test-rmse:6.253849+0.558111
## [9] train-rmse:4.561811+0.019440 test-rmse:4.758599+0.481624
## [10] train-rmse:3.590906+0.023817 test-rmse:3.847019+0.446367
## [11] train-rmse:2.960964+0.029861 test-rmse:3.317022+0.402283
## [12] train-rmse:2.528626+0.013407 test-rmse:2.951213+0.337432
## [13] train-rmse:2.247867+0.012320 test-rmse:2.695644+0.308759
## [14] train-rmse:2.038023+0.010919 test-rmse:2.521117+0.264189
## [15] train-rmse:1.887374+0.009299 test-rmse:2.410018+0.261757
## [16] train-rmse:1.759557+0.008808 test-rmse:2.319292+0.262059
## [17] train-rmse:1.650165+0.013010 test-rmse:2.222980+0.230731
## [18] train-rmse:1.558660+0.014112 test-rmse:2.149157+0.246960
## [19] train-rmse:1.474292+0.013752 test-rmse:2.081583+0.250269
## [20] train-rmse:1.398734+0.016058 test-rmse:2.002376+0.229642
## [21] train-rmse:1.316938+0.011422 test-rmse:1.950695+0.238831
## [22] train-rmse:1.250503+0.010973 test-rmse:1.913292+0.237090
## [23] train-rmse:1.188734+0.011616 test-rmse:1.864495+0.243845
## [24] train-rmse:1.131706+0.011179 test-rmse:1.821006+0.246813
## [25] train-rmse:1.073662+0.015030 test-rmse:1.782037+0.253725
## [26] train-rmse:1.023319+0.013717 test-rmse:1.739018+0.255655
## [27] train-rmse:0.976089+0.013593 test-rmse:1.714460+0.243454
## [28] train-rmse:0.934397+0.014944 test-rmse:1.676337+0.257510
## [29] train-rmse:0.893571+0.012556 test-rmse:1.650067+0.262656
## [30] train-rmse:0.854987+0.015368 test-rmse:1.620129+0.268415
## [31] train-rmse:0.819907+0.017177 test-rmse:1.595027+0.259259
## [32] train-rmse:0.788106+0.015714 test-rmse:1.570946+0.252130
## [33] train-rmse:0.757255+0.015650 test-rmse:1.552421+0.257209
## [34] train-rmse:0.727019+0.019167 test-rmse:1.545076+0.263187
## [35] train-rmse:0.697345+0.019149 test-rmse:1.526936+0.270452
## [36] train-rmse:0.669352+0.016301 test-rmse:1.511123+0.266098
## [37] train-rmse:0.646245+0.014784 test-rmse:1.496916+0.265620
## [38] train-rmse:0.625245+0.013486 test-rmse:1.480718+0.267091
## [39] train-rmse:0.603958+0.013202 test-rmse:1.459589+0.279734
## [40] train-rmse:0.583044+0.012243 test-rmse:1.451481+0.283117
## [41] train-rmse:0.561987+0.011063 test-rmse:1.443646+0.286466
## [42] train-rmse:0.544112+0.010529 test-rmse:1.431657+0.291012
## [43] train-rmse:0.525781+0.011900 test-rmse:1.429671+0.292095
## [44] train-rmse:0.512816+0.011637 test-rmse:1.415391+0.296008
## [45] train-rmse:0.498512+0.011223 test-rmse:1.406972+0.296374
## [46] train-rmse:0.483300+0.013586 test-rmse:1.404089+0.297664
## [47] train-rmse:0.470179+0.013024 test-rmse:1.390932+0.300587
## [48] train-rmse:0.455880+0.014295 test-rmse:1.387994+0.304625
## [49] train-rmse:0.444312+0.015167 test-rmse:1.382135+0.311016
## [50] train-rmse:0.431215+0.016241 test-rmse:1.377247+0.311996
## [51] train-rmse:0.418443+0.010372 test-rmse:1.375216+0.308123
## [52] train-rmse:0.406254+0.012052 test-rmse:1.370598+0.305812
## [53] train-rmse:0.395630+0.013112 test-rmse:1.364705+0.309951
## [54] train-rmse:0.382659+0.012989 test-rmse:1.363067+0.313745
## [55] train-rmse:0.373545+0.012416 test-rmse:1.359406+0.317885
## [56] train-rmse:0.364112+0.014181 test-rmse:1.353288+0.311186
## [57] train-rmse:0.354834+0.015698 test-rmse:1.348144+0.315557
## [58] train-rmse:0.346365+0.017753 test-rmse:1.344844+0.313105
## [59] train-rmse:0.338476+0.015767 test-rmse:1.346989+0.314374
## [60] train-rmse:0.330390+0.013805 test-rmse:1.338395+0.315716
## [61] train-rmse:0.322802+0.015204 test-rmse:1.337419+0.319204
## [62] train-rmse:0.316110+0.013527 test-rmse:1.337577+0.317671
## [63] train-rmse:0.309259+0.014339 test-rmse:1.337185+0.319823
## [64] train-rmse:0.302718+0.015618 test-rmse:1.334074+0.320366
## [65] train-rmse:0.297526+0.015993 test-rmse:1.328565+0.321712
## [66] train-rmse:0.289412+0.015679 test-rmse:1.328339+0.322544
## [67] train-rmse:0.284895+0.015914 test-rmse:1.326768+0.321880
## [68] train-rmse:0.279583+0.016653 test-rmse:1.326426+0.320413
## [69] train-rmse:0.273234+0.017375 test-rmse:1.324947+0.322101
## [70] train-rmse:0.267790+0.015719 test-rmse:1.324131+0.324005
## [71] train-rmse:0.263115+0.014966 test-rmse:1.323378+0.322832
## [72] train-rmse:0.255818+0.014886 test-rmse:1.317484+0.319652
## [73] train-rmse:0.251658+0.015438 test-rmse:1.316046+0.320473
## [74] train-rmse:0.247774+0.014453 test-rmse:1.316109+0.319457
## [75] train-rmse:0.242305+0.013913 test-rmse:1.315395+0.320547
## [76] train-rmse:0.238158+0.013678 test-rmse:1.314317+0.320973
## [77] train-rmse:0.233252+0.011594 test-rmse:1.314088+0.321356
## [78] train-rmse:0.228854+0.011740 test-rmse:1.313194+0.323749
## [79] train-rmse:0.224222+0.011747 test-rmse:1.313003+0.324322
## [80] train-rmse:0.219644+0.012391 test-rmse:1.311695+0.323308
## [81] train-rmse:0.215224+0.013110 test-rmse:1.311778+0.323580
## [82] train-rmse:0.210017+0.013361 test-rmse:1.308681+0.323707
## [83] train-rmse:0.206530+0.013503 test-rmse:1.306685+0.323847
## [84] train-rmse:0.201701+0.012749 test-rmse:1.306755+0.323374
## [85] train-rmse:0.198381+0.012883 test-rmse:1.304912+0.323470
## [86] train-rmse:0.194625+0.012511 test-rmse:1.305731+0.322247
## [87] train-rmse:0.191301+0.013400 test-rmse:1.305339+0.321810
## [88] train-rmse:0.188644+0.013074 test-rmse:1.304445+0.324280
## [89] train-rmse:0.183149+0.012358 test-rmse:1.302401+0.322893
## [90] train-rmse:0.180938+0.012206 test-rmse:1.301249+0.322815
## [91] train-rmse:0.177383+0.012178 test-rmse:1.301167+0.323298
## [92] train-rmse:0.174815+0.012172 test-rmse:1.301333+0.322313
## [93] train-rmse:0.171834+0.012087 test-rmse:1.301434+0.322118
## [94] train-rmse:0.168562+0.012922 test-rmse:1.303500+0.321433
## [95] train-rmse:0.166127+0.012811 test-rmse:1.302910+0.321518
## [96] train-rmse:0.162954+0.012199 test-rmse:1.301833+0.321393
## [97] train-rmse:0.160065+0.012497 test-rmse:1.301737+0.321760
## Stopping. Best iteration:
## [91] train-rmse:0.177383+0.012178 test-rmse:1.301167+0.323298
##
## 81
## [1] train-rmse:73.403534+0.086787 test-rmse:73.403435+0.516279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:50.171526+0.069998 test-rmse:50.170467+0.562705
## [3] train-rmse:34.428066+0.063950 test-rmse:34.425303+0.610826
## [4] train-rmse:23.768078+0.049908 test-rmse:23.846854+0.624752
## [5] train-rmse:16.527301+0.033241 test-rmse:16.614856+0.580859
## [6] train-rmse:11.604444+0.028975 test-rmse:11.681338+0.520687
## [7] train-rmse:8.256470+0.009775 test-rmse:8.374769+0.567834
## [8] train-rmse:5.984915+0.020043 test-rmse:6.126638+0.502057
## [9] train-rmse:4.440862+0.024047 test-rmse:4.624830+0.446275
## [10] train-rmse:3.413823+0.015083 test-rmse:3.683298+0.420204
## [11] train-rmse:2.731005+0.030628 test-rmse:3.063602+0.361764
## [12] train-rmse:2.270870+0.037634 test-rmse:2.663004+0.291442
## [13] train-rmse:1.953868+0.027245 test-rmse:2.411785+0.266230
## [14] train-rmse:1.729666+0.027673 test-rmse:2.220943+0.233273
## [15] train-rmse:1.562107+0.032079 test-rmse:2.096521+0.197043
## [16] train-rmse:1.427556+0.035765 test-rmse:2.029886+0.195952
## [17] train-rmse:1.313049+0.033603 test-rmse:1.934710+0.221225
## [18] train-rmse:1.215910+0.034194 test-rmse:1.862148+0.217033
## [19] train-rmse:1.123264+0.023111 test-rmse:1.796555+0.206411
## [20] train-rmse:1.052204+0.021540 test-rmse:1.744625+0.218288
## [21] train-rmse:0.986048+0.021867 test-rmse:1.702913+0.217992
## [22] train-rmse:0.926113+0.019127 test-rmse:1.672606+0.221833
## [23] train-rmse:0.869992+0.017093 test-rmse:1.636797+0.220389
## [24] train-rmse:0.819554+0.015162 test-rmse:1.598233+0.215329
## [25] train-rmse:0.775199+0.013897 test-rmse:1.565724+0.218920
## [26] train-rmse:0.732746+0.009443 test-rmse:1.547072+0.221269
## [27] train-rmse:0.696744+0.008165 test-rmse:1.525783+0.224809
## [28] train-rmse:0.663543+0.009751 test-rmse:1.509458+0.225159
## [29] train-rmse:0.626111+0.015146 test-rmse:1.485236+0.228547
## [30] train-rmse:0.590544+0.014698 test-rmse:1.469813+0.236412
## [31] train-rmse:0.560386+0.013015 test-rmse:1.458062+0.237317
## [32] train-rmse:0.537073+0.013288 test-rmse:1.448392+0.234850
## [33] train-rmse:0.512416+0.013982 test-rmse:1.429235+0.238907
## [34] train-rmse:0.491605+0.014824 test-rmse:1.425277+0.240574
## [35] train-rmse:0.470521+0.015076 test-rmse:1.407813+0.247141
## [36] train-rmse:0.452914+0.014980 test-rmse:1.400815+0.248234
## [37] train-rmse:0.435433+0.015027 test-rmse:1.392908+0.251297
## [38] train-rmse:0.418195+0.016193 test-rmse:1.389254+0.253468
## [39] train-rmse:0.404207+0.017940 test-rmse:1.385658+0.253223
## [40] train-rmse:0.390502+0.016252 test-rmse:1.371587+0.250827
## [41] train-rmse:0.374991+0.017169 test-rmse:1.366667+0.247974
## [42] train-rmse:0.364885+0.018178 test-rmse:1.361191+0.247009
## [43] train-rmse:0.353253+0.017440 test-rmse:1.356595+0.251118
## [44] train-rmse:0.341997+0.017666 test-rmse:1.352775+0.256335
## [45] train-rmse:0.330470+0.017643 test-rmse:1.349339+0.257300
## [46] train-rmse:0.321067+0.016362 test-rmse:1.350961+0.267345
## [47] train-rmse:0.311824+0.017015 test-rmse:1.348209+0.267137
## [48] train-rmse:0.298484+0.017234 test-rmse:1.344245+0.266439
## [49] train-rmse:0.289265+0.015983 test-rmse:1.346054+0.270752
## [50] train-rmse:0.279271+0.014788 test-rmse:1.345811+0.274510
## [51] train-rmse:0.270095+0.015021 test-rmse:1.343201+0.275840
## [52] train-rmse:0.261899+0.014016 test-rmse:1.340029+0.274938
## [53] train-rmse:0.254117+0.014808 test-rmse:1.339207+0.275307
## [54] train-rmse:0.242385+0.010615 test-rmse:1.341554+0.277465
## [55] train-rmse:0.236358+0.010879 test-rmse:1.337625+0.280881
## [56] train-rmse:0.230086+0.010391 test-rmse:1.335403+0.282016
## [57] train-rmse:0.222549+0.011286 test-rmse:1.335808+0.283760
## [58] train-rmse:0.217497+0.011427 test-rmse:1.335108+0.283409
## [59] train-rmse:0.211673+0.010859 test-rmse:1.334629+0.284237
## [60] train-rmse:0.206033+0.012280 test-rmse:1.332786+0.284096
## [61] train-rmse:0.200556+0.013237 test-rmse:1.331069+0.284617
## [62] train-rmse:0.193322+0.012929 test-rmse:1.331311+0.283043
## [63] train-rmse:0.185104+0.010543 test-rmse:1.329062+0.284459
## [64] train-rmse:0.179859+0.009385 test-rmse:1.327258+0.284027
## [65] train-rmse:0.176018+0.010010 test-rmse:1.327674+0.283800
## [66] train-rmse:0.170788+0.010223 test-rmse:1.326963+0.287356
## [67] train-rmse:0.165028+0.006995 test-rmse:1.325982+0.287181
## [68] train-rmse:0.160733+0.006127 test-rmse:1.328246+0.287140
## [69] train-rmse:0.155726+0.006738 test-rmse:1.326038+0.285463
## [70] train-rmse:0.152322+0.006921 test-rmse:1.326423+0.285056
## [71] train-rmse:0.147646+0.006144 test-rmse:1.325259+0.285887
## [72] train-rmse:0.144432+0.005430 test-rmse:1.322817+0.287682
## [73] train-rmse:0.142127+0.005146 test-rmse:1.321822+0.287864
## [74] train-rmse:0.136676+0.005358 test-rmse:1.321143+0.288650
## [75] train-rmse:0.133130+0.004509 test-rmse:1.321608+0.289473
## [76] train-rmse:0.129451+0.004170 test-rmse:1.321570+0.289387
## [77] train-rmse:0.126388+0.003921 test-rmse:1.321494+0.289910
## [78] train-rmse:0.122486+0.004242 test-rmse:1.322117+0.290219
## [79] train-rmse:0.118995+0.004851 test-rmse:1.319845+0.288223
## [80] train-rmse:0.114728+0.004011 test-rmse:1.320024+0.287161
## [81] train-rmse:0.111259+0.004202 test-rmse:1.318991+0.287392
## [82] train-rmse:0.108725+0.004205 test-rmse:1.319416+0.287406
## [83] train-rmse:0.105519+0.004771 test-rmse:1.318890+0.287225
## [84] train-rmse:0.102134+0.005331 test-rmse:1.318128+0.286540
## [85] train-rmse:0.100126+0.005167 test-rmse:1.317222+0.285998
## [86] train-rmse:0.097425+0.004789 test-rmse:1.316321+0.286391
## [87] train-rmse:0.094822+0.004602 test-rmse:1.316276+0.286391
## [88] train-rmse:0.093116+0.004566 test-rmse:1.316094+0.286127
## [89] train-rmse:0.090943+0.003901 test-rmse:1.315779+0.286325
## [90] train-rmse:0.089060+0.004549 test-rmse:1.314830+0.286029
## [91] train-rmse:0.086529+0.003758 test-rmse:1.314674+0.285808
## [92] train-rmse:0.084270+0.004037 test-rmse:1.313774+0.285051
## [93] train-rmse:0.082199+0.003653 test-rmse:1.313809+0.285479
## [94] train-rmse:0.080288+0.003945 test-rmse:1.314182+0.285048
## [95] train-rmse:0.078343+0.003422 test-rmse:1.314148+0.284265
## [96] train-rmse:0.076017+0.004363 test-rmse:1.314373+0.284591
## [97] train-rmse:0.074557+0.004607 test-rmse:1.313795+0.284978
## [98] train-rmse:0.072832+0.005071 test-rmse:1.313992+0.285119
## Stopping. Best iteration:
## [92] train-rmse:0.084270+0.004037 test-rmse:1.313774+0.285051
##
## 82
## [1] train-rmse:73.403534+0.086787 test-rmse:73.403435+0.516279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:50.171526+0.069998 test-rmse:50.170467+0.562705
## [3] train-rmse:34.428066+0.063950 test-rmse:34.425303+0.610826
## [4] train-rmse:23.768078+0.049908 test-rmse:23.846854+0.624752
## [5] train-rmse:16.527301+0.033241 test-rmse:16.614856+0.580859
## [6] train-rmse:11.594831+0.029443 test-rmse:11.674758+0.539187
## [7] train-rmse:8.229022+0.015288 test-rmse:8.394382+0.583780
## [8] train-rmse:5.927856+0.025414 test-rmse:6.104014+0.523683
## [9] train-rmse:4.368850+0.039145 test-rmse:4.560961+0.488387
## [10] train-rmse:3.315135+0.049764 test-rmse:3.578472+0.485208
## [11] train-rmse:2.582013+0.046744 test-rmse:2.955050+0.390031
## [12] train-rmse:2.096748+0.050730 test-rmse:2.534167+0.362140
## [13] train-rmse:1.758217+0.047882 test-rmse:2.226690+0.324987
## [14] train-rmse:1.531106+0.049330 test-rmse:2.050935+0.312035
## [15] train-rmse:1.335220+0.028077 test-rmse:1.929301+0.285143
## [16] train-rmse:1.199822+0.022217 test-rmse:1.828215+0.310134
## [17] train-rmse:1.083877+0.013732 test-rmse:1.731624+0.312104
## [18] train-rmse:0.979929+0.023258 test-rmse:1.668301+0.306866
## [19] train-rmse:0.897807+0.016420 test-rmse:1.601575+0.305659
## [20] train-rmse:0.830695+0.019904 test-rmse:1.555415+0.322626
## [21] train-rmse:0.769230+0.015287 test-rmse:1.517512+0.316654
## [22] train-rmse:0.715533+0.012383 test-rmse:1.475168+0.323600
## [23] train-rmse:0.667762+0.012893 test-rmse:1.450388+0.322044
## [24] train-rmse:0.626143+0.013502 test-rmse:1.432196+0.325844
## [25] train-rmse:0.584870+0.016476 test-rmse:1.405851+0.329896
## [26] train-rmse:0.548668+0.016522 test-rmse:1.384688+0.338666
## [27] train-rmse:0.514993+0.013725 test-rmse:1.365544+0.338711
## [28] train-rmse:0.485697+0.011789 test-rmse:1.351939+0.339627
## [29] train-rmse:0.456357+0.018164 test-rmse:1.335367+0.343100
## [30] train-rmse:0.433187+0.016849 test-rmse:1.329328+0.345132
## [31] train-rmse:0.414126+0.016003 test-rmse:1.316987+0.345426
## [32] train-rmse:0.395798+0.016115 test-rmse:1.300478+0.350442
## [33] train-rmse:0.377249+0.017351 test-rmse:1.293207+0.352411
## [34] train-rmse:0.361835+0.017500 test-rmse:1.285480+0.352919
## [35] train-rmse:0.340529+0.013607 test-rmse:1.276349+0.349006
## [36] train-rmse:0.323253+0.015564 test-rmse:1.273191+0.353251
## [37] train-rmse:0.310449+0.014010 test-rmse:1.268750+0.356420
## [38] train-rmse:0.292996+0.011389 test-rmse:1.263309+0.359081
## [39] train-rmse:0.280071+0.011406 test-rmse:1.259759+0.360553
## [40] train-rmse:0.271642+0.011130 test-rmse:1.251893+0.362210
## [41] train-rmse:0.254778+0.011484 test-rmse:1.245713+0.366293
## [42] train-rmse:0.242798+0.009331 test-rmse:1.245325+0.366542
## [43] train-rmse:0.228962+0.004636 test-rmse:1.242600+0.369236
## [44] train-rmse:0.219545+0.004953 test-rmse:1.241022+0.371023
## [45] train-rmse:0.210041+0.006277 test-rmse:1.239430+0.370956
## [46] train-rmse:0.201946+0.007151 test-rmse:1.237544+0.369676
## [47] train-rmse:0.195006+0.006709 test-rmse:1.234163+0.369624
## [48] train-rmse:0.187180+0.006072 test-rmse:1.232661+0.370609
## [49] train-rmse:0.179488+0.006415 test-rmse:1.231561+0.372833
## [50] train-rmse:0.171223+0.005745 test-rmse:1.230346+0.375620
## [51] train-rmse:0.164883+0.005926 test-rmse:1.228234+0.376075
## [52] train-rmse:0.157370+0.006156 test-rmse:1.226590+0.376885
## [53] train-rmse:0.152342+0.005834 test-rmse:1.226214+0.377910
## [54] train-rmse:0.146755+0.006682 test-rmse:1.225921+0.377680
## [55] train-rmse:0.142156+0.007303 test-rmse:1.225234+0.377489
## [56] train-rmse:0.135524+0.007713 test-rmse:1.222613+0.379623
## [57] train-rmse:0.131004+0.006082 test-rmse:1.222986+0.379671
## [58] train-rmse:0.124771+0.004784 test-rmse:1.219255+0.378983
## [59] train-rmse:0.120528+0.005019 test-rmse:1.217647+0.378326
## [60] train-rmse:0.117366+0.004074 test-rmse:1.216069+0.376695
## [61] train-rmse:0.112643+0.004188 test-rmse:1.214938+0.377580
## [62] train-rmse:0.109980+0.004448 test-rmse:1.214219+0.377797
## [63] train-rmse:0.106523+0.003919 test-rmse:1.213140+0.378697
## [64] train-rmse:0.101749+0.003442 test-rmse:1.212193+0.378171
## [65] train-rmse:0.096774+0.001971 test-rmse:1.210936+0.378689
## [66] train-rmse:0.092298+0.003007 test-rmse:1.210692+0.378367
## [67] train-rmse:0.088560+0.002277 test-rmse:1.209911+0.378298
## [68] train-rmse:0.086194+0.002728 test-rmse:1.209649+0.379044
## [69] train-rmse:0.082766+0.002865 test-rmse:1.208970+0.380041
## [70] train-rmse:0.079224+0.002379 test-rmse:1.208673+0.380874
## [71] train-rmse:0.076927+0.002471 test-rmse:1.208182+0.381337
## [72] train-rmse:0.073454+0.002892 test-rmse:1.207058+0.381751
## [73] train-rmse:0.070542+0.001605 test-rmse:1.206921+0.380727
## [74] train-rmse:0.068104+0.002203 test-rmse:1.206762+0.380740
## [75] train-rmse:0.065096+0.002540 test-rmse:1.206421+0.380620
## [76] train-rmse:0.062444+0.002299 test-rmse:1.206088+0.380158
## [77] train-rmse:0.060345+0.002356 test-rmse:1.205418+0.379794
## [78] train-rmse:0.058488+0.002184 test-rmse:1.204944+0.379749
## [79] train-rmse:0.055987+0.002480 test-rmse:1.204964+0.380078
## [80] train-rmse:0.053898+0.002645 test-rmse:1.204368+0.379593
## [81] train-rmse:0.052469+0.002838 test-rmse:1.204541+0.380013
## [82] train-rmse:0.050772+0.003042 test-rmse:1.204412+0.379778
## [83] train-rmse:0.049019+0.002760 test-rmse:1.204544+0.379778
## [84] train-rmse:0.047128+0.002470 test-rmse:1.204676+0.380120
## [85] train-rmse:0.045412+0.002337 test-rmse:1.204534+0.380068
## [86] train-rmse:0.044025+0.002434 test-rmse:1.204610+0.380267
## Stopping. Best iteration:
## [80] train-rmse:0.053898+0.002645 test-rmse:1.204368+0.379593
##
## 83
## [1] train-rmse:73.403534+0.086787 test-rmse:73.403435+0.516279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:50.171526+0.069998 test-rmse:50.170467+0.562705
## [3] train-rmse:34.428066+0.063950 test-rmse:34.425303+0.610826
## [4] train-rmse:23.768078+0.049908 test-rmse:23.846854+0.624752
## [5] train-rmse:16.527301+0.033241 test-rmse:16.614856+0.580859
## [6] train-rmse:11.585745+0.030946 test-rmse:11.673044+0.530818
## [7] train-rmse:8.204847+0.014718 test-rmse:8.349196+0.587266
## [8] train-rmse:5.882313+0.026353 test-rmse:6.069472+0.526283
## [9] train-rmse:4.288162+0.028743 test-rmse:4.512218+0.468776
## [10] train-rmse:3.202592+0.033952 test-rmse:3.505807+0.506332
## [11] train-rmse:2.450884+0.029988 test-rmse:2.821346+0.443735
## [12] train-rmse:1.944614+0.025867 test-rmse:2.378345+0.440068
## [13] train-rmse:1.589521+0.030304 test-rmse:2.087567+0.376915
## [14] train-rmse:1.338829+0.032732 test-rmse:1.906410+0.345096
## [15] train-rmse:1.154348+0.028921 test-rmse:1.794547+0.352635
## [16] train-rmse:1.009118+0.045697 test-rmse:1.711926+0.369311
## [17] train-rmse:0.888579+0.044100 test-rmse:1.641792+0.357136
## [18] train-rmse:0.792402+0.037251 test-rmse:1.575279+0.363185
## [19] train-rmse:0.727065+0.036836 test-rmse:1.511064+0.360271
## [20] train-rmse:0.664941+0.036006 test-rmse:1.474911+0.358263
## [21] train-rmse:0.609177+0.036974 test-rmse:1.440973+0.365477
## [22] train-rmse:0.564153+0.035580 test-rmse:1.422181+0.375601
## [23] train-rmse:0.523334+0.029924 test-rmse:1.408899+0.372634
## [24] train-rmse:0.481565+0.037195 test-rmse:1.395235+0.373699
## [25] train-rmse:0.450489+0.031629 test-rmse:1.382229+0.374127
## [26] train-rmse:0.421836+0.027996 test-rmse:1.367814+0.386171
## [27] train-rmse:0.393308+0.030234 test-rmse:1.354505+0.387260
## [28] train-rmse:0.367128+0.031356 test-rmse:1.346408+0.392485
## [29] train-rmse:0.347218+0.030302 test-rmse:1.340094+0.389048
## [30] train-rmse:0.325606+0.026497 test-rmse:1.335816+0.391459
## [31] train-rmse:0.305943+0.021054 test-rmse:1.329642+0.391194
## [32] train-rmse:0.290989+0.021415 test-rmse:1.319792+0.393191
## [33] train-rmse:0.277992+0.022633 test-rmse:1.314913+0.391494
## [34] train-rmse:0.258110+0.018478 test-rmse:1.308981+0.399227
## [35] train-rmse:0.245121+0.015175 test-rmse:1.306354+0.399130
## [36] train-rmse:0.232528+0.017240 test-rmse:1.299760+0.399394
## [37] train-rmse:0.217782+0.015699 test-rmse:1.295911+0.396777
## [38] train-rmse:0.203887+0.015479 test-rmse:1.291920+0.398177
## [39] train-rmse:0.193837+0.015407 test-rmse:1.290183+0.397832
## [40] train-rmse:0.183529+0.015850 test-rmse:1.288926+0.397619
## [41] train-rmse:0.173437+0.015466 test-rmse:1.286747+0.398735
## [42] train-rmse:0.163978+0.016646 test-rmse:1.284997+0.400531
## [43] train-rmse:0.158768+0.016534 test-rmse:1.283845+0.401001
## [44] train-rmse:0.152289+0.017863 test-rmse:1.282741+0.400561
## [45] train-rmse:0.145424+0.015944 test-rmse:1.278987+0.401190
## [46] train-rmse:0.137874+0.014137 test-rmse:1.280352+0.405159
## [47] train-rmse:0.130639+0.015654 test-rmse:1.278001+0.404852
## [48] train-rmse:0.122156+0.011270 test-rmse:1.276864+0.405034
## [49] train-rmse:0.115674+0.010645 test-rmse:1.277324+0.406232
## [50] train-rmse:0.110071+0.010887 test-rmse:1.275689+0.405233
## [51] train-rmse:0.106245+0.010698 test-rmse:1.274018+0.404320
## [52] train-rmse:0.100298+0.009455 test-rmse:1.274037+0.404088
## [53] train-rmse:0.095954+0.008968 test-rmse:1.274522+0.404769
## [54] train-rmse:0.091457+0.009796 test-rmse:1.273974+0.405100
## [55] train-rmse:0.085720+0.009285 test-rmse:1.273582+0.405944
## [56] train-rmse:0.080871+0.007869 test-rmse:1.273512+0.405811
## [57] train-rmse:0.077259+0.007943 test-rmse:1.274067+0.406365
## [58] train-rmse:0.073877+0.006952 test-rmse:1.273822+0.406805
## [59] train-rmse:0.069914+0.006589 test-rmse:1.274125+0.407561
## [60] train-rmse:0.066556+0.006347 test-rmse:1.274274+0.408474
## [61] train-rmse:0.062491+0.005379 test-rmse:1.273417+0.408303
## [62] train-rmse:0.059794+0.005163 test-rmse:1.273980+0.408536
## [63] train-rmse:0.057582+0.005194 test-rmse:1.273358+0.409206
## [64] train-rmse:0.054553+0.005247 test-rmse:1.273116+0.409011
## [65] train-rmse:0.051999+0.005370 test-rmse:1.273264+0.408890
## [66] train-rmse:0.050078+0.005435 test-rmse:1.273043+0.408609
## [67] train-rmse:0.048271+0.005459 test-rmse:1.272798+0.408779
## [68] train-rmse:0.045998+0.005281 test-rmse:1.272562+0.408950
## [69] train-rmse:0.043500+0.004632 test-rmse:1.272173+0.408957
## [70] train-rmse:0.041472+0.004568 test-rmse:1.272314+0.409059
## [71] train-rmse:0.039490+0.004165 test-rmse:1.271727+0.408949
## [72] train-rmse:0.037001+0.004257 test-rmse:1.271275+0.408812
## [73] train-rmse:0.035767+0.003691 test-rmse:1.271417+0.409133
## [74] train-rmse:0.034322+0.003634 test-rmse:1.271841+0.409511
## [75] train-rmse:0.032959+0.003445 test-rmse:1.271823+0.409570
## [76] train-rmse:0.031413+0.003595 test-rmse:1.271669+0.409413
## [77] train-rmse:0.029837+0.002823 test-rmse:1.271382+0.409323
## [78] train-rmse:0.028635+0.002988 test-rmse:1.271201+0.409253
## [79] train-rmse:0.027364+0.002579 test-rmse:1.271381+0.409418
## [80] train-rmse:0.026250+0.002704 test-rmse:1.271348+0.409436
## [81] train-rmse:0.025269+0.002748 test-rmse:1.271329+0.409595
## [82] train-rmse:0.024292+0.002606 test-rmse:1.271189+0.409674
## [83] train-rmse:0.023551+0.002524 test-rmse:1.271298+0.409701
## [84] train-rmse:0.022439+0.002258 test-rmse:1.271086+0.409574
## [85] train-rmse:0.021422+0.002254 test-rmse:1.271161+0.409791
## [86] train-rmse:0.020143+0.002032 test-rmse:1.271366+0.409707
## [87] train-rmse:0.019480+0.001848 test-rmse:1.271246+0.409759
## [88] train-rmse:0.018317+0.001950 test-rmse:1.271367+0.409881
## [89] train-rmse:0.017455+0.001824 test-rmse:1.271609+0.409942
## [90] train-rmse:0.016682+0.001718 test-rmse:1.271655+0.409866
## Stopping. Best iteration:
## [84] train-rmse:0.022439+0.002258 test-rmse:1.271086+0.409574
##
## 84
## [1] train-rmse:71.268932+0.085068 test-rmse:71.268778+0.519929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:47.317069+0.068405 test-rmse:47.315810+0.569907
## [3] train-rmse:31.577981+0.063944 test-rmse:31.574661+0.622724
## [4] train-rmse:21.270000+0.059695 test-rmse:21.349113+0.640231
## [5] train-rmse:14.545765+0.051633 test-rmse:14.599417+0.639019
## [6] train-rmse:10.236668+0.053984 test-rmse:10.325185+0.593650
## [7] train-rmse:7.537961+0.057525 test-rmse:7.681887+0.677644
## [8] train-rmse:5.915556+0.062329 test-rmse:6.012658+0.617990
## [9] train-rmse:4.971713+0.068833 test-rmse:5.072588+0.522516
## [10] train-rmse:4.423832+0.063148 test-rmse:4.566844+0.547872
## [11] train-rmse:4.098285+0.066679 test-rmse:4.209128+0.473803
## [12] train-rmse:3.898362+0.066919 test-rmse:4.080565+0.444477
## [13] train-rmse:3.755117+0.064991 test-rmse:3.926346+0.425111
## [14] train-rmse:3.640679+0.060984 test-rmse:3.758801+0.366186
## [15] train-rmse:3.546171+0.060252 test-rmse:3.720384+0.363066
## [16] train-rmse:3.464328+0.057100 test-rmse:3.673290+0.348728
## [17] train-rmse:3.386064+0.056027 test-rmse:3.608508+0.364767
## [18] train-rmse:3.316454+0.053991 test-rmse:3.578446+0.368854
## [19] train-rmse:3.247506+0.051231 test-rmse:3.486771+0.329625
## [20] train-rmse:3.179847+0.048165 test-rmse:3.440676+0.321662
## [21] train-rmse:3.114966+0.047246 test-rmse:3.386578+0.322407
## [22] train-rmse:3.053758+0.046478 test-rmse:3.331661+0.306948
## [23] train-rmse:2.994473+0.044479 test-rmse:3.246532+0.276491
## [24] train-rmse:2.937067+0.042919 test-rmse:3.171975+0.265718
## [25] train-rmse:2.884367+0.040684 test-rmse:3.131520+0.281846
## [26] train-rmse:2.834734+0.040109 test-rmse:3.083805+0.294841
## [27] train-rmse:2.787651+0.039679 test-rmse:3.017897+0.318379
## [28] train-rmse:2.741848+0.039797 test-rmse:2.995660+0.331070
## [29] train-rmse:2.696533+0.040866 test-rmse:2.969803+0.323865
## [30] train-rmse:2.652957+0.040671 test-rmse:2.929613+0.324540
## [31] train-rmse:2.609976+0.041556 test-rmse:2.892175+0.319135
## [32] train-rmse:2.569196+0.041576 test-rmse:2.834450+0.274927
## [33] train-rmse:2.529032+0.040984 test-rmse:2.801827+0.285824
## [34] train-rmse:2.490076+0.040846 test-rmse:2.765802+0.301414
## [35] train-rmse:2.452038+0.039452 test-rmse:2.734139+0.308664
## [36] train-rmse:2.414436+0.038031 test-rmse:2.718506+0.292038
## [37] train-rmse:2.377447+0.037049 test-rmse:2.670805+0.300431
## [38] train-rmse:2.341081+0.035300 test-rmse:2.612236+0.282523
## [39] train-rmse:2.306748+0.034033 test-rmse:2.573783+0.271249
## [40] train-rmse:2.274490+0.033163 test-rmse:2.538820+0.284409
## [41] train-rmse:2.243240+0.033057 test-rmse:2.514893+0.296602
## [42] train-rmse:2.212687+0.032284 test-rmse:2.483818+0.308109
## [43] train-rmse:2.182137+0.032075 test-rmse:2.453716+0.286792
## [44] train-rmse:2.152138+0.032004 test-rmse:2.426236+0.291891
## [45] train-rmse:2.123380+0.032437 test-rmse:2.405484+0.291405
## [46] train-rmse:2.094948+0.032569 test-rmse:2.382054+0.282647
## [47] train-rmse:2.066985+0.031650 test-rmse:2.354555+0.287440
## [48] train-rmse:2.038792+0.030931 test-rmse:2.335079+0.289574
## [49] train-rmse:2.010856+0.029408 test-rmse:2.322828+0.290183
## [50] train-rmse:1.983905+0.027229 test-rmse:2.291685+0.280009
## [51] train-rmse:1.957650+0.025785 test-rmse:2.251826+0.279113
## [52] train-rmse:1.932442+0.025235 test-rmse:2.236025+0.274868
## [53] train-rmse:1.907296+0.024977 test-rmse:2.201260+0.266263
## [54] train-rmse:1.882786+0.024458 test-rmse:2.169078+0.280759
## [55] train-rmse:1.858718+0.023668 test-rmse:2.126533+0.256063
## [56] train-rmse:1.835391+0.022372 test-rmse:2.099398+0.256534
## [57] train-rmse:1.812801+0.021098 test-rmse:2.082043+0.256272
## [58] train-rmse:1.790735+0.020356 test-rmse:2.053145+0.250618
## [59] train-rmse:1.769376+0.020436 test-rmse:2.039515+0.253000
## [60] train-rmse:1.748032+0.020071 test-rmse:2.029425+0.251918
## [61] train-rmse:1.727690+0.019349 test-rmse:2.013509+0.243957
## [62] train-rmse:1.707348+0.018499 test-rmse:1.986556+0.226126
## [63] train-rmse:1.688145+0.017778 test-rmse:1.974296+0.229212
## [64] train-rmse:1.668876+0.017268 test-rmse:1.961757+0.232696
## [65] train-rmse:1.649582+0.016933 test-rmse:1.945402+0.228738
## [66] train-rmse:1.630680+0.017152 test-rmse:1.927230+0.240146
## [67] train-rmse:1.612235+0.017448 test-rmse:1.926022+0.239933
## [68] train-rmse:1.593975+0.017772 test-rmse:1.910133+0.237350
## [69] train-rmse:1.576048+0.017936 test-rmse:1.895237+0.234634
## [70] train-rmse:1.559295+0.017706 test-rmse:1.869536+0.228392
## [71] train-rmse:1.542597+0.017630 test-rmse:1.853271+0.238318
## [72] train-rmse:1.526238+0.017234 test-rmse:1.829841+0.217353
## [73] train-rmse:1.510212+0.016646 test-rmse:1.810456+0.220595
## [74] train-rmse:1.494527+0.015922 test-rmse:1.795108+0.227044
## [75] train-rmse:1.479286+0.015867 test-rmse:1.779685+0.221466
## [76] train-rmse:1.464384+0.015941 test-rmse:1.768699+0.220821
## [77] train-rmse:1.449579+0.015814 test-rmse:1.753463+0.222724
## [78] train-rmse:1.435006+0.015819 test-rmse:1.747779+0.223695
## [79] train-rmse:1.420549+0.015625 test-rmse:1.729663+0.220349
## [80] train-rmse:1.406502+0.015014 test-rmse:1.709525+0.222807
## [81] train-rmse:1.392716+0.014579 test-rmse:1.690998+0.213009
## [82] train-rmse:1.379207+0.014332 test-rmse:1.682932+0.215676
## [83] train-rmse:1.365766+0.013875 test-rmse:1.672992+0.216500
## [84] train-rmse:1.352366+0.013570 test-rmse:1.665597+0.212926
## [85] train-rmse:1.339409+0.013270 test-rmse:1.657303+0.208964
## [86] train-rmse:1.326768+0.012988 test-rmse:1.649226+0.208433
## [87] train-rmse:1.315071+0.012632 test-rmse:1.630222+0.209930
## [88] train-rmse:1.303750+0.012489 test-rmse:1.621645+0.213169
## [89] train-rmse:1.292642+0.012383 test-rmse:1.609896+0.216118
## [90] train-rmse:1.281500+0.012260 test-rmse:1.608048+0.215570
## [91] train-rmse:1.270313+0.011789 test-rmse:1.587285+0.220185
## [92] train-rmse:1.259859+0.011713 test-rmse:1.585684+0.220321
## [93] train-rmse:1.249463+0.011848 test-rmse:1.578308+0.221655
## [94] train-rmse:1.239134+0.011746 test-rmse:1.569185+0.227673
## [95] train-rmse:1.229052+0.011878 test-rmse:1.562024+0.231725
## [96] train-rmse:1.219055+0.012048 test-rmse:1.550403+0.232698
## [97] train-rmse:1.209140+0.012332 test-rmse:1.530665+0.213851
## [98] train-rmse:1.199418+0.012585 test-rmse:1.523692+0.215551
## [99] train-rmse:1.189953+0.012368 test-rmse:1.513795+0.206272
## [100] train-rmse:1.180647+0.012214 test-rmse:1.504588+0.200506
## [101] train-rmse:1.171828+0.012141 test-rmse:1.495603+0.199306
## [102] train-rmse:1.162977+0.012106 test-rmse:1.490194+0.202015
## [103] train-rmse:1.154384+0.012113 test-rmse:1.478604+0.203576
## [104] train-rmse:1.146053+0.011997 test-rmse:1.471010+0.201590
## [105] train-rmse:1.137799+0.011869 test-rmse:1.460263+0.199854
## [106] train-rmse:1.129531+0.011735 test-rmse:1.451789+0.197440
## [107] train-rmse:1.121346+0.011766 test-rmse:1.439753+0.198696
## [108] train-rmse:1.113316+0.011700 test-rmse:1.431098+0.203025
## [109] train-rmse:1.105539+0.011758 test-rmse:1.424976+0.202091
## [110] train-rmse:1.097945+0.011749 test-rmse:1.422706+0.203715
## [111] train-rmse:1.090858+0.011573 test-rmse:1.420759+0.201137
## [112] train-rmse:1.083798+0.011490 test-rmse:1.407483+0.198124
## [113] train-rmse:1.076934+0.011505 test-rmse:1.394547+0.206878
## [114] train-rmse:1.070162+0.011481 test-rmse:1.389082+0.208773
## [115] train-rmse:1.063601+0.011468 test-rmse:1.387253+0.209475
## [116] train-rmse:1.057085+0.011414 test-rmse:1.381816+0.209281
## [117] train-rmse:1.050779+0.011286 test-rmse:1.382333+0.208509
## [118] train-rmse:1.044591+0.011223 test-rmse:1.375166+0.214864
## [119] train-rmse:1.038601+0.011112 test-rmse:1.372074+0.214241
## [120] train-rmse:1.032587+0.010852 test-rmse:1.369042+0.213064
## [121] train-rmse:1.026788+0.010843 test-rmse:1.361765+0.212006
## [122] train-rmse:1.021049+0.010800 test-rmse:1.351961+0.209209
## [123] train-rmse:1.015407+0.010636 test-rmse:1.347352+0.208737
## [124] train-rmse:1.009895+0.010537 test-rmse:1.335659+0.197593
## [125] train-rmse:1.004589+0.010557 test-rmse:1.331729+0.192860
## [126] train-rmse:0.999313+0.010627 test-rmse:1.326117+0.191646
## [127] train-rmse:0.994112+0.010689 test-rmse:1.326154+0.190387
## [128] train-rmse:0.988945+0.010771 test-rmse:1.318827+0.184021
## [129] train-rmse:0.984011+0.010721 test-rmse:1.312626+0.187101
## [130] train-rmse:0.979157+0.010784 test-rmse:1.309560+0.186898
## [131] train-rmse:0.974474+0.010951 test-rmse:1.305912+0.190967
## [132] train-rmse:0.969896+0.011087 test-rmse:1.301304+0.193404
## [133] train-rmse:0.965227+0.011209 test-rmse:1.299765+0.193752
## [134] train-rmse:0.960752+0.011419 test-rmse:1.294335+0.192339
## [135] train-rmse:0.956288+0.011455 test-rmse:1.288864+0.192135
## [136] train-rmse:0.951889+0.011469 test-rmse:1.287329+0.194689
## [137] train-rmse:0.947623+0.011485 test-rmse:1.280731+0.199310
## [138] train-rmse:0.943451+0.011532 test-rmse:1.277950+0.199628
## [139] train-rmse:0.939529+0.011587 test-rmse:1.278052+0.201224
## [140] train-rmse:0.935666+0.011636 test-rmse:1.273402+0.200970
## [141] train-rmse:0.931826+0.011746 test-rmse:1.267341+0.202957
## [142] train-rmse:0.927913+0.011826 test-rmse:1.268230+0.200159
## [143] train-rmse:0.924231+0.011860 test-rmse:1.268290+0.198996
## [144] train-rmse:0.920541+0.011895 test-rmse:1.264189+0.201087
## [145] train-rmse:0.917020+0.011922 test-rmse:1.260852+0.201879
## [146] train-rmse:0.913594+0.011913 test-rmse:1.249358+0.196626
## [147] train-rmse:0.910307+0.011932 test-rmse:1.248175+0.193825
## [148] train-rmse:0.907036+0.011937 test-rmse:1.245532+0.195451
## [149] train-rmse:0.903762+0.011936 test-rmse:1.242395+0.193956
## [150] train-rmse:0.900619+0.012016 test-rmse:1.235831+0.190754
## [151] train-rmse:0.897519+0.012132 test-rmse:1.233553+0.192403
## [152] train-rmse:0.894437+0.012214 test-rmse:1.233007+0.194048
## [153] train-rmse:0.891345+0.012277 test-rmse:1.228873+0.190351
## [154] train-rmse:0.888454+0.012320 test-rmse:1.225084+0.191331
## [155] train-rmse:0.885494+0.012348 test-rmse:1.224403+0.193966
## [156] train-rmse:0.882656+0.012431 test-rmse:1.226385+0.192902
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## [158] train-rmse:0.877015+0.012404 test-rmse:1.220343+0.188418
## [159] train-rmse:0.874289+0.012367 test-rmse:1.216955+0.186534
## [160] train-rmse:0.871684+0.012380 test-rmse:1.217341+0.186579
## [161] train-rmse:0.869183+0.012449 test-rmse:1.214682+0.188025
## [162] train-rmse:0.866748+0.012493 test-rmse:1.212307+0.188865
## [163] train-rmse:0.864363+0.012529 test-rmse:1.208714+0.190498
## [164] train-rmse:0.862047+0.012660 test-rmse:1.206990+0.191374
## [165] train-rmse:0.859774+0.012784 test-rmse:1.205328+0.192415
## [166] train-rmse:0.857506+0.012897 test-rmse:1.202624+0.189475
## [167] train-rmse:0.855240+0.013018 test-rmse:1.202509+0.190488
## [168] train-rmse:0.853029+0.013157 test-rmse:1.201907+0.188607
## [169] train-rmse:0.850672+0.013243 test-rmse:1.199197+0.191835
## [170] train-rmse:0.848528+0.013382 test-rmse:1.196373+0.192302
## [171] train-rmse:0.846425+0.013531 test-rmse:1.193821+0.194912
## [172] train-rmse:0.844342+0.013669 test-rmse:1.193608+0.195300
## [173] train-rmse:0.842377+0.013757 test-rmse:1.192429+0.194922
## [174] train-rmse:0.840426+0.013813 test-rmse:1.191531+0.196095
## [175] train-rmse:0.838460+0.013814 test-rmse:1.191641+0.195237
## [176] train-rmse:0.836584+0.013903 test-rmse:1.184984+0.194234
## [177] train-rmse:0.834682+0.014006 test-rmse:1.183492+0.196666
## [178] train-rmse:0.832773+0.014097 test-rmse:1.181754+0.192561
## [179] train-rmse:0.830912+0.014208 test-rmse:1.182413+0.191337
## [180] train-rmse:0.829119+0.014340 test-rmse:1.176692+0.194863
## [181] train-rmse:0.827142+0.014330 test-rmse:1.175809+0.191671
## [182] train-rmse:0.825371+0.014484 test-rmse:1.175187+0.191178
## [183] train-rmse:0.823418+0.014561 test-rmse:1.174140+0.190503
## [184] train-rmse:0.821650+0.014660 test-rmse:1.171542+0.191328
## [185] train-rmse:0.819913+0.014686 test-rmse:1.165129+0.185868
## [186] train-rmse:0.818193+0.014803 test-rmse:1.164194+0.186087
## [187] train-rmse:0.816500+0.014768 test-rmse:1.163638+0.184936
## [188] train-rmse:0.814919+0.014815 test-rmse:1.162719+0.186619
## [189] train-rmse:0.813264+0.014979 test-rmse:1.161255+0.188559
## [190] train-rmse:0.811658+0.015135 test-rmse:1.158490+0.190524
## [191] train-rmse:0.810042+0.015245 test-rmse:1.157766+0.188412
## [192] train-rmse:0.808532+0.015327 test-rmse:1.158002+0.189167
## [193] train-rmse:0.807023+0.015445 test-rmse:1.153885+0.190051
## [194] train-rmse:0.805552+0.015562 test-rmse:1.153461+0.190041
## [195] train-rmse:0.804103+0.015668 test-rmse:1.150021+0.186243
## [196] train-rmse:0.802640+0.015676 test-rmse:1.148947+0.187764
## [197] train-rmse:0.801176+0.015727 test-rmse:1.149558+0.188571
## [198] train-rmse:0.799794+0.015762 test-rmse:1.147284+0.190194
## [199] train-rmse:0.798398+0.015765 test-rmse:1.147200+0.191975
## [200] train-rmse:0.796947+0.015808 test-rmse:1.147685+0.191294
## [201] train-rmse:0.795641+0.015889 test-rmse:1.145863+0.192839
## [202] train-rmse:0.794318+0.015943 test-rmse:1.144811+0.187960
## [203] train-rmse:0.793026+0.016026 test-rmse:1.144243+0.188871
## [204] train-rmse:0.791756+0.016105 test-rmse:1.140005+0.183945
## [205] train-rmse:0.790495+0.016188 test-rmse:1.138168+0.183208
## [206] train-rmse:0.789242+0.016285 test-rmse:1.136170+0.182921
## [207] train-rmse:0.787971+0.016357 test-rmse:1.134999+0.181785
## [208] train-rmse:0.786686+0.016445 test-rmse:1.135062+0.182996
## [209] train-rmse:0.785426+0.016480 test-rmse:1.134164+0.182620
## [210] train-rmse:0.784196+0.016546 test-rmse:1.132604+0.183507
## [211] train-rmse:0.782989+0.016599 test-rmse:1.129433+0.185258
## [212] train-rmse:0.781748+0.016615 test-rmse:1.129622+0.186019
## [213] train-rmse:0.780580+0.016666 test-rmse:1.129466+0.184438
## [214] train-rmse:0.779453+0.016738 test-rmse:1.129921+0.184027
## [215] train-rmse:0.778332+0.016768 test-rmse:1.127026+0.187360
## [216] train-rmse:0.777217+0.016796 test-rmse:1.127420+0.187488
## [217] train-rmse:0.776041+0.016881 test-rmse:1.126391+0.188226
## [218] train-rmse:0.774948+0.016929 test-rmse:1.126477+0.189025
## [219] train-rmse:0.773783+0.017059 test-rmse:1.125064+0.190004
## [220] train-rmse:0.772746+0.017143 test-rmse:1.123999+0.189974
## [221] train-rmse:0.771677+0.017165 test-rmse:1.122673+0.191932
## [222] train-rmse:0.770643+0.017210 test-rmse:1.119143+0.191962
## [223] train-rmse:0.769603+0.017265 test-rmse:1.118762+0.192177
## [224] train-rmse:0.768585+0.017315 test-rmse:1.117872+0.192234
## [225] train-rmse:0.767601+0.017365 test-rmse:1.116811+0.192001
## [226] train-rmse:0.766622+0.017434 test-rmse:1.115642+0.195100
## [227] train-rmse:0.765578+0.017514 test-rmse:1.117489+0.192661
## [228] train-rmse:0.764572+0.017532 test-rmse:1.116110+0.193922
## [229] train-rmse:0.763606+0.017546 test-rmse:1.116287+0.193138
## [230] train-rmse:0.762647+0.017563 test-rmse:1.114445+0.192387
## [231] train-rmse:0.761698+0.017608 test-rmse:1.113662+0.190412
## [232] train-rmse:0.760750+0.017613 test-rmse:1.110624+0.191412
## [233] train-rmse:0.759801+0.017700 test-rmse:1.111389+0.191901
## [234] train-rmse:0.758894+0.017727 test-rmse:1.111268+0.192336
## [235] train-rmse:0.757979+0.017748 test-rmse:1.111914+0.190198
## [236] train-rmse:0.757082+0.017775 test-rmse:1.110538+0.190852
## [237] train-rmse:0.756203+0.017803 test-rmse:1.110323+0.191159
## [238] train-rmse:0.755316+0.017820 test-rmse:1.110597+0.192871
## [239] train-rmse:0.754489+0.017842 test-rmse:1.111122+0.192701
## [240] train-rmse:0.753652+0.017859 test-rmse:1.108165+0.193786
## [241] train-rmse:0.752803+0.017896 test-rmse:1.108153+0.194784
## [242] train-rmse:0.751968+0.017956 test-rmse:1.108994+0.193796
## [243] train-rmse:0.751116+0.017971 test-rmse:1.107873+0.194445
## [244] train-rmse:0.750201+0.018006 test-rmse:1.107873+0.192688
## [245] train-rmse:0.749308+0.017904 test-rmse:1.105779+0.191667
## [246] train-rmse:0.748474+0.017937 test-rmse:1.103843+0.188364
## [247] train-rmse:0.747636+0.017883 test-rmse:1.103490+0.187416
## [248] train-rmse:0.746836+0.017884 test-rmse:1.101717+0.186291
## [249] train-rmse:0.746047+0.017852 test-rmse:1.100879+0.185616
## [250] train-rmse:0.745274+0.017857 test-rmse:1.097713+0.184004
## [251] train-rmse:0.744500+0.017868 test-rmse:1.096610+0.184065
## [252] train-rmse:0.743765+0.017871 test-rmse:1.093774+0.185619
## [253] train-rmse:0.742992+0.017870 test-rmse:1.092200+0.184152
## [254] train-rmse:0.742280+0.017862 test-rmse:1.090327+0.183448
## [255] train-rmse:0.741543+0.017821 test-rmse:1.088107+0.179755
## [256] train-rmse:0.740805+0.017828 test-rmse:1.086807+0.178875
## [257] train-rmse:0.740058+0.017839 test-rmse:1.087495+0.178549
## [258] train-rmse:0.739321+0.017847 test-rmse:1.087177+0.178434
## [259] train-rmse:0.738610+0.017870 test-rmse:1.087763+0.177760
## [260] train-rmse:0.737910+0.017868 test-rmse:1.086893+0.178338
## [261] train-rmse:0.737183+0.017859 test-rmse:1.086768+0.177488
## [262] train-rmse:0.736488+0.017854 test-rmse:1.086071+0.177801
## [263] train-rmse:0.735790+0.017850 test-rmse:1.086258+0.177124
## [264] train-rmse:0.735084+0.017876 test-rmse:1.084528+0.175882
## [265] train-rmse:0.734409+0.017868 test-rmse:1.083803+0.176335
## [266] train-rmse:0.733746+0.017874 test-rmse:1.081464+0.176989
## [267] train-rmse:0.733089+0.017876 test-rmse:1.081690+0.177680
## [268] train-rmse:0.732413+0.017904 test-rmse:1.082167+0.176981
## [269] train-rmse:0.731728+0.017856 test-rmse:1.080641+0.178072
## [270] train-rmse:0.731068+0.017840 test-rmse:1.079878+0.176949
## [271] train-rmse:0.730422+0.017793 test-rmse:1.079135+0.176660
## [272] train-rmse:0.729792+0.017756 test-rmse:1.078101+0.177183
## [273] train-rmse:0.729165+0.017720 test-rmse:1.078387+0.174737
## [274] train-rmse:0.728549+0.017720 test-rmse:1.077772+0.175530
## [275] train-rmse:0.727951+0.017714 test-rmse:1.076672+0.174382
## [276] train-rmse:0.727329+0.017679 test-rmse:1.075583+0.174198
## [277] train-rmse:0.726714+0.017665 test-rmse:1.076696+0.172689
## [278] train-rmse:0.726096+0.017636 test-rmse:1.076443+0.173804
## [279] train-rmse:0.725512+0.017609 test-rmse:1.075575+0.174003
## [280] train-rmse:0.724912+0.017578 test-rmse:1.075623+0.173838
## [281] train-rmse:0.724333+0.017557 test-rmse:1.074001+0.174225
## [282] train-rmse:0.723690+0.017455 test-rmse:1.072599+0.172504
## [283] train-rmse:0.723077+0.017419 test-rmse:1.071640+0.173086
## [284] train-rmse:0.722471+0.017350 test-rmse:1.069498+0.172416
## [285] train-rmse:0.721899+0.017317 test-rmse:1.069042+0.171857
## [286] train-rmse:0.721339+0.017263 test-rmse:1.066660+0.171698
## [287] train-rmse:0.720764+0.017262 test-rmse:1.066133+0.171070
## [288] train-rmse:0.720225+0.017227 test-rmse:1.064904+0.169843
## [289] train-rmse:0.719695+0.017208 test-rmse:1.064690+0.169664
## [290] train-rmse:0.719119+0.017177 test-rmse:1.066359+0.169084
## [291] train-rmse:0.718557+0.017145 test-rmse:1.065391+0.167914
## [292] train-rmse:0.717971+0.017170 test-rmse:1.065230+0.166346
## [293] train-rmse:0.717451+0.017138 test-rmse:1.063724+0.166091
## [294] train-rmse:0.716895+0.017150 test-rmse:1.064107+0.165947
## [295] train-rmse:0.716366+0.017124 test-rmse:1.062431+0.166563
## [296] train-rmse:0.715850+0.017088 test-rmse:1.063207+0.166469
## [297] train-rmse:0.715335+0.017066 test-rmse:1.062238+0.166513
## [298] train-rmse:0.714820+0.017038 test-rmse:1.061758+0.165414
## [299] train-rmse:0.714314+0.017000 test-rmse:1.061236+0.166603
## [300] train-rmse:0.713812+0.016979 test-rmse:1.060760+0.165323
## [301] train-rmse:0.713297+0.016932 test-rmse:1.059568+0.164803
## [302] train-rmse:0.712781+0.016887 test-rmse:1.059604+0.164829
## [303] train-rmse:0.712272+0.016852 test-rmse:1.060230+0.164736
## [304] train-rmse:0.711770+0.016812 test-rmse:1.060587+0.164683
## [305] train-rmse:0.711289+0.016780 test-rmse:1.058998+0.165254
## [306] train-rmse:0.710751+0.016743 test-rmse:1.059889+0.164317
## [307] train-rmse:0.710241+0.016729 test-rmse:1.058870+0.163041
## [308] train-rmse:0.709717+0.016723 test-rmse:1.058525+0.163146
## [309] train-rmse:0.709233+0.016683 test-rmse:1.058029+0.163069
## [310] train-rmse:0.708751+0.016633 test-rmse:1.057698+0.163463
## [311] train-rmse:0.708266+0.016568 test-rmse:1.058222+0.163249
## [312] train-rmse:0.707772+0.016489 test-rmse:1.057263+0.162663
## [313] train-rmse:0.707281+0.016447 test-rmse:1.056575+0.160391
## [314] train-rmse:0.706807+0.016433 test-rmse:1.055776+0.160237
## [315] train-rmse:0.706337+0.016384 test-rmse:1.054122+0.159159
## [316] train-rmse:0.705892+0.016362 test-rmse:1.052677+0.158354
## [317] train-rmse:0.705438+0.016335 test-rmse:1.052533+0.157819
## [318] train-rmse:0.704987+0.016286 test-rmse:1.051072+0.156798
## [319] train-rmse:0.704522+0.016299 test-rmse:1.051437+0.156005
## [320] train-rmse:0.704064+0.016281 test-rmse:1.051220+0.154773
## [321] train-rmse:0.703583+0.016241 test-rmse:1.050782+0.155266
## [322] train-rmse:0.703125+0.016209 test-rmse:1.048450+0.151917
## [323] train-rmse:0.702644+0.016176 test-rmse:1.049449+0.152224
## [324] train-rmse:0.702227+0.016153 test-rmse:1.048916+0.152685
## [325] train-rmse:0.701774+0.016095 test-rmse:1.049141+0.152691
## [326] train-rmse:0.701324+0.016080 test-rmse:1.051051+0.151691
## [327] train-rmse:0.700866+0.016040 test-rmse:1.049379+0.153073
## [328] train-rmse:0.700424+0.015992 test-rmse:1.049098+0.152675
## Stopping. Best iteration:
## [322] train-rmse:0.703125+0.016209 test-rmse:1.048450+0.151917
##
## 85
## [1] train-rmse:71.268932+0.085068 test-rmse:71.268778+0.519929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:47.317069+0.068405 test-rmse:47.315810+0.569907
## [3] train-rmse:31.577981+0.063944 test-rmse:31.574661+0.622724
## [4] train-rmse:21.238430+0.046850 test-rmse:21.330363+0.644487
## [5] train-rmse:14.459752+0.034269 test-rmse:14.562542+0.562943
## [6] train-rmse:10.054961+0.032005 test-rmse:10.145731+0.549670
## [7] train-rmse:7.245538+0.042487 test-rmse:7.393070+0.575505
## [8] train-rmse:5.507863+0.044817 test-rmse:5.715325+0.576698
## [9] train-rmse:4.460585+0.044530 test-rmse:4.635752+0.527530
## [10] train-rmse:3.834418+0.051651 test-rmse:4.047216+0.456605
## [11] train-rmse:3.457668+0.055571 test-rmse:3.739311+0.456212
## [12] train-rmse:3.212997+0.053504 test-rmse:3.516225+0.459463
## [13] train-rmse:3.026777+0.044582 test-rmse:3.335645+0.368295
## [14] train-rmse:2.888335+0.040015 test-rmse:3.147940+0.341751
## [15] train-rmse:2.775835+0.041232 test-rmse:3.070961+0.357058
## [16] train-rmse:2.676099+0.040442 test-rmse:2.992428+0.359191
## [17] train-rmse:2.580750+0.035406 test-rmse:2.918566+0.363846
## [18] train-rmse:2.496617+0.031219 test-rmse:2.824415+0.292460
## [19] train-rmse:2.415958+0.029071 test-rmse:2.766954+0.298639
## [20] train-rmse:2.336967+0.027826 test-rmse:2.701359+0.269806
## [21] train-rmse:2.261780+0.024653 test-rmse:2.599828+0.283922
## [22] train-rmse:2.192283+0.025234 test-rmse:2.532218+0.267796
## [23] train-rmse:2.113940+0.022922 test-rmse:2.486792+0.292603
## [24] train-rmse:2.050176+0.021367 test-rmse:2.424461+0.288228
## [25] train-rmse:1.989550+0.020928 test-rmse:2.384497+0.283669
## [26] train-rmse:1.934715+0.019397 test-rmse:2.316806+0.271411
## [27] train-rmse:1.880816+0.017703 test-rmse:2.257953+0.275969
## [28] train-rmse:1.827776+0.016771 test-rmse:2.192630+0.278597
## [29] train-rmse:1.775789+0.015139 test-rmse:2.136714+0.270980
## [30] train-rmse:1.727340+0.015436 test-rmse:2.096062+0.255934
## [31] train-rmse:1.680835+0.014963 test-rmse:2.076046+0.263494
## [32] train-rmse:1.637827+0.013966 test-rmse:2.038087+0.260090
## [33] train-rmse:1.596620+0.012928 test-rmse:1.984949+0.257608
## [34] train-rmse:1.555637+0.012341 test-rmse:1.961457+0.253911
## [35] train-rmse:1.513199+0.009735 test-rmse:1.941603+0.258910
## [36] train-rmse:1.472319+0.013356 test-rmse:1.905995+0.268878
## [37] train-rmse:1.435809+0.012391 test-rmse:1.872101+0.253164
## [38] train-rmse:1.399333+0.010779 test-rmse:1.842187+0.268076
## [39] train-rmse:1.365034+0.008741 test-rmse:1.809526+0.259529
## [40] train-rmse:1.331726+0.009328 test-rmse:1.785868+0.260499
## [41] train-rmse:1.299052+0.008208 test-rmse:1.754656+0.253146
## [42] train-rmse:1.267194+0.007970 test-rmse:1.736154+0.259806
## [43] train-rmse:1.240932+0.007888 test-rmse:1.718384+0.258753
## [44] train-rmse:1.212828+0.009056 test-rmse:1.695132+0.248344
## [45] train-rmse:1.186477+0.009407 test-rmse:1.679135+0.251953
## [46] train-rmse:1.161057+0.008836 test-rmse:1.664477+0.259013
## [47] train-rmse:1.135885+0.009704 test-rmse:1.635112+0.256057
## [48] train-rmse:1.112110+0.008888 test-rmse:1.614567+0.260360
## [49] train-rmse:1.088398+0.009208 test-rmse:1.590789+0.256235
## [50] train-rmse:1.066700+0.009190 test-rmse:1.569717+0.249400
## [51] train-rmse:1.041126+0.006089 test-rmse:1.558196+0.249509
## [52] train-rmse:1.021452+0.006358 test-rmse:1.541083+0.250676
## [53] train-rmse:1.001600+0.006803 test-rmse:1.523309+0.250056
## [54] train-rmse:0.982477+0.006009 test-rmse:1.510964+0.258997
## [55] train-rmse:0.961150+0.007617 test-rmse:1.500172+0.261909
## [56] train-rmse:0.943684+0.007339 test-rmse:1.488639+0.260141
## [57] train-rmse:0.927335+0.007001 test-rmse:1.476731+0.264680
## [58] train-rmse:0.912233+0.007077 test-rmse:1.460138+0.267530
## [59] train-rmse:0.897663+0.006852 test-rmse:1.436069+0.255357
## [60] train-rmse:0.881135+0.007265 test-rmse:1.412873+0.250119
## [61] train-rmse:0.864696+0.006332 test-rmse:1.395882+0.240476
## [62] train-rmse:0.851235+0.006887 test-rmse:1.387301+0.239019
## [63] train-rmse:0.837415+0.007334 test-rmse:1.380336+0.244675
## [64] train-rmse:0.821884+0.007044 test-rmse:1.378596+0.243612
## [65] train-rmse:0.808157+0.006830 test-rmse:1.374077+0.238003
## [66] train-rmse:0.795557+0.005337 test-rmse:1.363502+0.241833
## [67] train-rmse:0.784397+0.005425 test-rmse:1.351640+0.243489
## [68] train-rmse:0.773210+0.005194 test-rmse:1.344845+0.246641
## [69] train-rmse:0.762649+0.004878 test-rmse:1.333105+0.251108
## [70] train-rmse:0.750887+0.007826 test-rmse:1.322512+0.249497
## [71] train-rmse:0.739733+0.008229 test-rmse:1.307523+0.247479
## [72] train-rmse:0.728619+0.009378 test-rmse:1.304854+0.246584
## [73] train-rmse:0.719261+0.010230 test-rmse:1.299079+0.243401
## [74] train-rmse:0.709727+0.009576 test-rmse:1.295291+0.244342
## [75] train-rmse:0.701401+0.009397 test-rmse:1.290519+0.244986
## [76] train-rmse:0.693245+0.009521 test-rmse:1.289552+0.245680
## [77] train-rmse:0.683477+0.009144 test-rmse:1.285491+0.245626
## [78] train-rmse:0.672502+0.008265 test-rmse:1.282469+0.247506
## [79] train-rmse:0.663671+0.006565 test-rmse:1.280750+0.248930
## [80] train-rmse:0.655810+0.007773 test-rmse:1.274430+0.249352
## [81] train-rmse:0.648887+0.007698 test-rmse:1.269961+0.248667
## [82] train-rmse:0.640399+0.006806 test-rmse:1.268228+0.250763
## [83] train-rmse:0.632039+0.009052 test-rmse:1.260759+0.251467
## [84] train-rmse:0.625529+0.009767 test-rmse:1.251996+0.252238
## [85] train-rmse:0.618498+0.008373 test-rmse:1.247641+0.258307
## [86] train-rmse:0.609239+0.005955 test-rmse:1.245239+0.254558
## [87] train-rmse:0.602856+0.005872 test-rmse:1.244766+0.255798
## [88] train-rmse:0.597278+0.006022 test-rmse:1.243905+0.256516
## [89] train-rmse:0.589752+0.004985 test-rmse:1.241025+0.257173
## [90] train-rmse:0.584087+0.005654 test-rmse:1.231091+0.261500
## [91] train-rmse:0.578791+0.005824 test-rmse:1.227375+0.262346
## [92] train-rmse:0.573218+0.005511 test-rmse:1.225886+0.263966
## [93] train-rmse:0.565925+0.005338 test-rmse:1.223070+0.265124
## [94] train-rmse:0.561037+0.005256 test-rmse:1.218354+0.266301
## [95] train-rmse:0.556136+0.005910 test-rmse:1.214593+0.265081
## [96] train-rmse:0.549612+0.006738 test-rmse:1.214734+0.265155
## [97] train-rmse:0.544178+0.005708 test-rmse:1.213015+0.267338
## [98] train-rmse:0.539187+0.005492 test-rmse:1.212198+0.269417
## [99] train-rmse:0.534361+0.005470 test-rmse:1.208601+0.266808
## [100] train-rmse:0.530409+0.005474 test-rmse:1.207357+0.267052
## [101] train-rmse:0.526617+0.004991 test-rmse:1.204086+0.269956
## [102] train-rmse:0.522095+0.004898 test-rmse:1.196761+0.269000
## [103] train-rmse:0.517956+0.004273 test-rmse:1.195878+0.269551
## [104] train-rmse:0.513570+0.004223 test-rmse:1.194988+0.272081
## [105] train-rmse:0.509313+0.004787 test-rmse:1.192522+0.275486
## [106] train-rmse:0.505202+0.005181 test-rmse:1.186933+0.270645
## [107] train-rmse:0.501984+0.004788 test-rmse:1.186130+0.269635
## [108] train-rmse:0.498033+0.004159 test-rmse:1.183126+0.271160
## [109] train-rmse:0.494460+0.004998 test-rmse:1.180623+0.272034
## [110] train-rmse:0.490384+0.005271 test-rmse:1.179435+0.271834
## [111] train-rmse:0.487423+0.004811 test-rmse:1.178757+0.272201
## [112] train-rmse:0.483271+0.006146 test-rmse:1.177103+0.275043
## [113] train-rmse:0.479063+0.007451 test-rmse:1.176182+0.274878
## [114] train-rmse:0.475070+0.006946 test-rmse:1.173603+0.276383
## [115] train-rmse:0.471801+0.007575 test-rmse:1.170488+0.276835
## [116] train-rmse:0.467520+0.007843 test-rmse:1.169165+0.279888
## [117] train-rmse:0.462792+0.007496 test-rmse:1.159647+0.271408
## [118] train-rmse:0.458707+0.008603 test-rmse:1.156031+0.270923
## [119] train-rmse:0.455949+0.008695 test-rmse:1.155856+0.270272
## [120] train-rmse:0.452558+0.009355 test-rmse:1.154529+0.269760
## [121] train-rmse:0.449862+0.009319 test-rmse:1.153934+0.268837
## [122] train-rmse:0.447517+0.009226 test-rmse:1.154439+0.267970
## [123] train-rmse:0.443202+0.006706 test-rmse:1.151099+0.268158
## [124] train-rmse:0.439828+0.006463 test-rmse:1.150559+0.266964
## [125] train-rmse:0.435388+0.006012 test-rmse:1.148561+0.265141
## [126] train-rmse:0.432741+0.006251 test-rmse:1.147115+0.265200
## [127] train-rmse:0.430486+0.006744 test-rmse:1.145461+0.265188
## [128] train-rmse:0.427938+0.007126 test-rmse:1.145587+0.266320
## [129] train-rmse:0.425085+0.007029 test-rmse:1.137572+0.257472
## [130] train-rmse:0.423212+0.007066 test-rmse:1.135849+0.258220
## [131] train-rmse:0.421406+0.006963 test-rmse:1.134625+0.257892
## [132] train-rmse:0.419374+0.007481 test-rmse:1.133552+0.258115
## [133] train-rmse:0.417285+0.007127 test-rmse:1.132256+0.257688
## [134] train-rmse:0.414638+0.007619 test-rmse:1.130992+0.257505
## [135] train-rmse:0.412598+0.007712 test-rmse:1.129765+0.258496
## [136] train-rmse:0.409919+0.008965 test-rmse:1.130684+0.259663
## [137] train-rmse:0.406997+0.010313 test-rmse:1.130063+0.260703
## [138] train-rmse:0.402197+0.009088 test-rmse:1.127680+0.259798
## [139] train-rmse:0.399938+0.009049 test-rmse:1.125787+0.260542
## [140] train-rmse:0.397810+0.008607 test-rmse:1.124961+0.260427
## [141] train-rmse:0.395059+0.007735 test-rmse:1.125283+0.261718
## [142] train-rmse:0.393359+0.007739 test-rmse:1.125042+0.263043
## [143] train-rmse:0.390941+0.008447 test-rmse:1.123734+0.264050
## [144] train-rmse:0.389208+0.008577 test-rmse:1.121806+0.263900
## [145] train-rmse:0.387336+0.008711 test-rmse:1.120440+0.264972
## [146] train-rmse:0.385673+0.008758 test-rmse:1.121740+0.266101
## [147] train-rmse:0.383440+0.009431 test-rmse:1.122488+0.264825
## [148] train-rmse:0.381184+0.010201 test-rmse:1.119523+0.269310
## [149] train-rmse:0.378073+0.009390 test-rmse:1.119206+0.270342
## [150] train-rmse:0.375842+0.009547 test-rmse:1.119305+0.271026
## [151] train-rmse:0.373763+0.009547 test-rmse:1.118028+0.270224
## [152] train-rmse:0.371749+0.009330 test-rmse:1.118025+0.269256
## [153] train-rmse:0.369073+0.008904 test-rmse:1.118262+0.269527
## [154] train-rmse:0.367881+0.008984 test-rmse:1.116863+0.269773
## [155] train-rmse:0.365857+0.009012 test-rmse:1.116512+0.267669
## [156] train-rmse:0.364176+0.008639 test-rmse:1.115631+0.267907
## [157] train-rmse:0.363206+0.008783 test-rmse:1.114265+0.267737
## [158] train-rmse:0.359837+0.007615 test-rmse:1.113092+0.267792
## [159] train-rmse:0.357370+0.007520 test-rmse:1.111883+0.268330
## [160] train-rmse:0.355170+0.008060 test-rmse:1.111879+0.267788
## [161] train-rmse:0.353401+0.008980 test-rmse:1.111185+0.268237
## [162] train-rmse:0.351599+0.008829 test-rmse:1.110917+0.268978
## [163] train-rmse:0.349746+0.008922 test-rmse:1.110235+0.267677
## [164] train-rmse:0.348593+0.008957 test-rmse:1.109211+0.266926
## [165] train-rmse:0.346187+0.009025 test-rmse:1.110269+0.268773
## [166] train-rmse:0.343428+0.007787 test-rmse:1.106732+0.270394
## [167] train-rmse:0.342028+0.008189 test-rmse:1.106315+0.270904
## [168] train-rmse:0.339942+0.008377 test-rmse:1.107800+0.270511
## [169] train-rmse:0.337978+0.009158 test-rmse:1.108041+0.271445
## [170] train-rmse:0.336620+0.009128 test-rmse:1.108645+0.270788
## [171] train-rmse:0.334140+0.009752 test-rmse:1.107108+0.271097
## [172] train-rmse:0.332370+0.009339 test-rmse:1.105972+0.270908
## [173] train-rmse:0.331016+0.009413 test-rmse:1.105105+0.270214
## [174] train-rmse:0.329355+0.009606 test-rmse:1.102696+0.271662
## [175] train-rmse:0.327425+0.009548 test-rmse:1.102217+0.271449
## [176] train-rmse:0.326171+0.009803 test-rmse:1.104376+0.273329
## [177] train-rmse:0.324124+0.010628 test-rmse:1.103301+0.273473
## [178] train-rmse:0.322620+0.010910 test-rmse:1.104206+0.272995
## [179] train-rmse:0.321730+0.011132 test-rmse:1.102499+0.272852
## [180] train-rmse:0.319346+0.010736 test-rmse:1.101700+0.270905
## [181] train-rmse:0.317776+0.010117 test-rmse:1.100975+0.271727
## [182] train-rmse:0.315256+0.010985 test-rmse:1.100839+0.271425
## [183] train-rmse:0.313238+0.010703 test-rmse:1.100647+0.271901
## [184] train-rmse:0.312170+0.010799 test-rmse:1.099740+0.272294
## [185] train-rmse:0.310517+0.010137 test-rmse:1.099458+0.272889
## [186] train-rmse:0.309016+0.010360 test-rmse:1.099569+0.272855
## [187] train-rmse:0.307073+0.010641 test-rmse:1.099928+0.273774
## [188] train-rmse:0.305297+0.010274 test-rmse:1.099130+0.272711
## [189] train-rmse:0.303668+0.010318 test-rmse:1.098308+0.273405
## [190] train-rmse:0.302401+0.010535 test-rmse:1.099610+0.272174
## [191] train-rmse:0.301460+0.010492 test-rmse:1.098497+0.271869
## [192] train-rmse:0.299402+0.010136 test-rmse:1.098113+0.270210
## [193] train-rmse:0.298392+0.010374 test-rmse:1.098614+0.270040
## [194] train-rmse:0.296707+0.010494 test-rmse:1.098053+0.270492
## [195] train-rmse:0.295250+0.010213 test-rmse:1.096652+0.270001
## [196] train-rmse:0.293270+0.009773 test-rmse:1.096382+0.270048
## [197] train-rmse:0.291380+0.009288 test-rmse:1.096116+0.270300
## [198] train-rmse:0.290289+0.009455 test-rmse:1.095832+0.269751
## [199] train-rmse:0.289065+0.009992 test-rmse:1.093623+0.271870
## [200] train-rmse:0.288000+0.010043 test-rmse:1.093130+0.272083
## [201] train-rmse:0.286963+0.009800 test-rmse:1.092264+0.272231
## [202] train-rmse:0.285880+0.009236 test-rmse:1.092736+0.272058
## [203] train-rmse:0.283138+0.010184 test-rmse:1.093167+0.271678
## [204] train-rmse:0.281779+0.009936 test-rmse:1.093186+0.272339
## [205] train-rmse:0.280083+0.009164 test-rmse:1.093837+0.273025
## [206] train-rmse:0.279010+0.008797 test-rmse:1.092828+0.271532
## [207] train-rmse:0.277421+0.008057 test-rmse:1.091564+0.272999
## [208] train-rmse:0.276496+0.007679 test-rmse:1.091658+0.274268
## [209] train-rmse:0.275144+0.007533 test-rmse:1.092493+0.273901
## [210] train-rmse:0.274026+0.007283 test-rmse:1.092240+0.274353
## [211] train-rmse:0.272838+0.007265 test-rmse:1.091765+0.273811
## [212] train-rmse:0.270841+0.007709 test-rmse:1.090909+0.273612
## [213] train-rmse:0.269835+0.007191 test-rmse:1.090798+0.273350
## [214] train-rmse:0.268890+0.007421 test-rmse:1.091076+0.273003
## [215] train-rmse:0.267542+0.006855 test-rmse:1.089526+0.274254
## [216] train-rmse:0.266560+0.006887 test-rmse:1.089509+0.273857
## [217] train-rmse:0.265233+0.006714 test-rmse:1.088475+0.273348
## [218] train-rmse:0.263023+0.008123 test-rmse:1.088025+0.273237
## [219] train-rmse:0.261772+0.008764 test-rmse:1.087564+0.273105
## [220] train-rmse:0.260705+0.008830 test-rmse:1.087394+0.273071
## [221] train-rmse:0.260051+0.008994 test-rmse:1.087632+0.273115
## [222] train-rmse:0.257843+0.009576 test-rmse:1.087110+0.273443
## [223] train-rmse:0.256742+0.009828 test-rmse:1.088060+0.273590
## [224] train-rmse:0.255916+0.009911 test-rmse:1.087938+0.273608
## [225] train-rmse:0.255134+0.009871 test-rmse:1.087662+0.273334
## [226] train-rmse:0.254056+0.009712 test-rmse:1.087592+0.272670
## [227] train-rmse:0.252455+0.009883 test-rmse:1.087820+0.274300
## [228] train-rmse:0.251520+0.010032 test-rmse:1.088779+0.274118
## Stopping. Best iteration:
## [222] train-rmse:0.257843+0.009576 test-rmse:1.087110+0.273443
##
## 86
## [1] train-rmse:71.268932+0.085068 test-rmse:71.268778+0.519929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:47.317069+0.068405 test-rmse:47.315810+0.569907
## [3] train-rmse:31.577981+0.063944 test-rmse:31.574661+0.622724
## [4] train-rmse:21.229897+0.041979 test-rmse:21.327855+0.649898
## [5] train-rmse:14.416945+0.027682 test-rmse:14.525238+0.609073
## [6] train-rmse:9.962237+0.030899 test-rmse:10.049095+0.554279
## [7] train-rmse:7.084963+0.032216 test-rmse:7.231692+0.559066
## [8] train-rmse:5.250240+0.046396 test-rmse:5.433917+0.501762
## [9] train-rmse:4.114151+0.054115 test-rmse:4.353346+0.479562
## [10] train-rmse:3.395615+0.069459 test-rmse:3.664185+0.440656
## [11] train-rmse:2.965188+0.069629 test-rmse:3.278946+0.387445
## [12] train-rmse:2.679213+0.073835 test-rmse:3.014273+0.351389
## [13] train-rmse:2.485071+0.068163 test-rmse:2.853608+0.341741
## [14] train-rmse:2.329075+0.057307 test-rmse:2.730407+0.311395
## [15] train-rmse:2.193049+0.045167 test-rmse:2.624222+0.330557
## [16] train-rmse:2.086115+0.042121 test-rmse:2.518351+0.310542
## [17] train-rmse:1.986065+0.035368 test-rmse:2.440209+0.300808
## [18] train-rmse:1.894517+0.036939 test-rmse:2.345983+0.286599
## [19] train-rmse:1.801016+0.031196 test-rmse:2.269316+0.287607
## [20] train-rmse:1.721014+0.028569 test-rmse:2.187772+0.269427
## [21] train-rmse:1.648746+0.026972 test-rmse:2.140115+0.273099
## [22] train-rmse:1.571562+0.018332 test-rmse:2.108411+0.284376
## [23] train-rmse:1.505357+0.016077 test-rmse:2.062780+0.296311
## [24] train-rmse:1.445680+0.014143 test-rmse:2.009054+0.273723
## [25] train-rmse:1.385737+0.009148 test-rmse:1.966446+0.266662
## [26] train-rmse:1.326768+0.015183 test-rmse:1.933115+0.260482
## [27] train-rmse:1.276603+0.015760 test-rmse:1.887268+0.266396
## [28] train-rmse:1.228518+0.015970 test-rmse:1.851488+0.265221
## [29] train-rmse:1.183826+0.016459 test-rmse:1.826886+0.276529
## [30] train-rmse:1.136670+0.014676 test-rmse:1.792078+0.266224
## [31] train-rmse:1.092260+0.015192 test-rmse:1.758031+0.252562
## [32] train-rmse:1.053282+0.014340 test-rmse:1.736115+0.248322
## [33] train-rmse:1.018570+0.014815 test-rmse:1.704296+0.252303
## [34] train-rmse:0.985021+0.016629 test-rmse:1.666778+0.251052
## [35] train-rmse:0.953936+0.017179 test-rmse:1.647925+0.246069
## [36] train-rmse:0.922499+0.019510 test-rmse:1.623609+0.250372
## [37] train-rmse:0.891877+0.021140 test-rmse:1.612204+0.258498
## [38] train-rmse:0.864671+0.022025 test-rmse:1.597167+0.259160
## [39] train-rmse:0.835295+0.018618 test-rmse:1.579949+0.253653
## [40] train-rmse:0.810274+0.018516 test-rmse:1.562301+0.253937
## [41] train-rmse:0.783612+0.013853 test-rmse:1.537724+0.263404
## [42] train-rmse:0.759422+0.015426 test-rmse:1.527051+0.252887
## [43] train-rmse:0.737192+0.014310 test-rmse:1.515759+0.253812
## [44] train-rmse:0.716138+0.014645 test-rmse:1.498303+0.256169
## [45] train-rmse:0.695962+0.015291 test-rmse:1.489298+0.260400
## [46] train-rmse:0.679296+0.014676 test-rmse:1.482233+0.259180
## [47] train-rmse:0.663590+0.014375 test-rmse:1.473900+0.259326
## [48] train-rmse:0.648381+0.014732 test-rmse:1.463613+0.261881
## [49] train-rmse:0.630978+0.013378 test-rmse:1.451750+0.249496
## [50] train-rmse:0.614746+0.013797 test-rmse:1.435369+0.250107
## [51] train-rmse:0.599320+0.012510 test-rmse:1.431921+0.250217
## [52] train-rmse:0.586188+0.013291 test-rmse:1.424974+0.255759
## [53] train-rmse:0.573212+0.015609 test-rmse:1.419979+0.261554
## [54] train-rmse:0.561881+0.016478 test-rmse:1.416247+0.262995
## [55] train-rmse:0.548638+0.017829 test-rmse:1.410217+0.263205
## [56] train-rmse:0.535972+0.017886 test-rmse:1.407348+0.265394
## [57] train-rmse:0.521679+0.016684 test-rmse:1.404656+0.265651
## [58] train-rmse:0.509745+0.015371 test-rmse:1.401768+0.261852
## [59] train-rmse:0.500645+0.016470 test-rmse:1.399045+0.262592
## [60] train-rmse:0.492446+0.016682 test-rmse:1.396788+0.263526
## [61] train-rmse:0.483059+0.016401 test-rmse:1.387455+0.264099
## [62] train-rmse:0.474918+0.016145 test-rmse:1.383917+0.265121
## [63] train-rmse:0.466376+0.015782 test-rmse:1.380280+0.265263
## [64] train-rmse:0.457514+0.015943 test-rmse:1.374852+0.266503
## [65] train-rmse:0.448252+0.015952 test-rmse:1.368816+0.266059
## [66] train-rmse:0.440853+0.016253 test-rmse:1.365289+0.265620
## [67] train-rmse:0.434614+0.016327 test-rmse:1.362044+0.265776
## [68] train-rmse:0.425998+0.018019 test-rmse:1.359401+0.263522
## [69] train-rmse:0.418632+0.016795 test-rmse:1.356141+0.266285
## [70] train-rmse:0.411117+0.014627 test-rmse:1.350506+0.268476
## [71] train-rmse:0.404373+0.014653 test-rmse:1.348697+0.268899
## [72] train-rmse:0.398457+0.014605 test-rmse:1.345820+0.270222
## [73] train-rmse:0.389901+0.016188 test-rmse:1.344379+0.274753
## [74] train-rmse:0.383072+0.017502 test-rmse:1.336920+0.263734
## [75] train-rmse:0.378629+0.017404 test-rmse:1.334335+0.265684
## [76] train-rmse:0.372319+0.016309 test-rmse:1.332648+0.267129
## [77] train-rmse:0.367310+0.017730 test-rmse:1.332696+0.268504
## [78] train-rmse:0.361002+0.017508 test-rmse:1.331124+0.265626
## [79] train-rmse:0.355158+0.017484 test-rmse:1.329343+0.267654
## [80] train-rmse:0.348332+0.018976 test-rmse:1.326221+0.267890
## [81] train-rmse:0.343459+0.018864 test-rmse:1.321826+0.269233
## [82] train-rmse:0.340030+0.018517 test-rmse:1.320457+0.269560
## [83] train-rmse:0.336467+0.017979 test-rmse:1.317194+0.268696
## [84] train-rmse:0.331346+0.017735 test-rmse:1.315851+0.269749
## [85] train-rmse:0.324257+0.015515 test-rmse:1.316947+0.269067
## [86] train-rmse:0.319658+0.015634 test-rmse:1.316905+0.270552
## [87] train-rmse:0.315604+0.016053 test-rmse:1.316117+0.270834
## [88] train-rmse:0.309267+0.014933 test-rmse:1.314418+0.273357
## [89] train-rmse:0.303551+0.012338 test-rmse:1.313253+0.273901
## [90] train-rmse:0.299619+0.012078 test-rmse:1.313130+0.272314
## [91] train-rmse:0.295417+0.014067 test-rmse:1.313326+0.272196
## [92] train-rmse:0.291983+0.014019 test-rmse:1.311469+0.273258
## [93] train-rmse:0.288262+0.014395 test-rmse:1.307752+0.274784
## [94] train-rmse:0.285852+0.014092 test-rmse:1.306276+0.275625
## [95] train-rmse:0.281897+0.013840 test-rmse:1.302609+0.276550
## [96] train-rmse:0.275123+0.012038 test-rmse:1.301176+0.277189
## [97] train-rmse:0.270854+0.011464 test-rmse:1.296269+0.273372
## [98] train-rmse:0.266802+0.011891 test-rmse:1.296842+0.273660
## [99] train-rmse:0.262849+0.012617 test-rmse:1.294448+0.272342
## [100] train-rmse:0.258153+0.012474 test-rmse:1.293175+0.270146
## [101] train-rmse:0.254985+0.013420 test-rmse:1.292238+0.271017
## [102] train-rmse:0.252411+0.014555 test-rmse:1.289983+0.270791
## [103] train-rmse:0.249886+0.014041 test-rmse:1.290532+0.270829
## [104] train-rmse:0.245983+0.014043 test-rmse:1.288616+0.268982
## [105] train-rmse:0.242497+0.013391 test-rmse:1.289307+0.269294
## [106] train-rmse:0.239476+0.013172 test-rmse:1.288174+0.267615
## [107] train-rmse:0.236584+0.012944 test-rmse:1.288219+0.268635
## [108] train-rmse:0.233892+0.012861 test-rmse:1.288070+0.268450
## [109] train-rmse:0.230974+0.012201 test-rmse:1.287815+0.269330
## [110] train-rmse:0.228517+0.011695 test-rmse:1.288215+0.269559
## [111] train-rmse:0.226283+0.012280 test-rmse:1.287815+0.270579
## [112] train-rmse:0.223780+0.011694 test-rmse:1.288115+0.271724
## [113] train-rmse:0.221176+0.011569 test-rmse:1.289089+0.272734
## [114] train-rmse:0.219225+0.011284 test-rmse:1.288618+0.272757
## [115] train-rmse:0.216963+0.011966 test-rmse:1.287339+0.274526
## [116] train-rmse:0.213578+0.011531 test-rmse:1.285212+0.274201
## [117] train-rmse:0.209985+0.011076 test-rmse:1.285034+0.274319
## [118] train-rmse:0.207914+0.011430 test-rmse:1.284740+0.275556
## [119] train-rmse:0.206342+0.012133 test-rmse:1.284173+0.276286
## [120] train-rmse:0.203397+0.012374 test-rmse:1.283014+0.276790
## [121] train-rmse:0.201387+0.011882 test-rmse:1.282054+0.276990
## [122] train-rmse:0.198647+0.012238 test-rmse:1.281898+0.277496
## [123] train-rmse:0.196284+0.012056 test-rmse:1.282703+0.277469
## [124] train-rmse:0.193330+0.012609 test-rmse:1.280577+0.276118
## [125] train-rmse:0.191358+0.012294 test-rmse:1.280501+0.275467
## [126] train-rmse:0.189142+0.012784 test-rmse:1.279892+0.274350
## [127] train-rmse:0.186665+0.012936 test-rmse:1.279308+0.274679
## [128] train-rmse:0.184365+0.012041 test-rmse:1.278657+0.274182
## [129] train-rmse:0.181139+0.011573 test-rmse:1.278418+0.273278
## [130] train-rmse:0.179123+0.011841 test-rmse:1.278444+0.273291
## [131] train-rmse:0.177341+0.011279 test-rmse:1.278477+0.272801
## [132] train-rmse:0.174691+0.011827 test-rmse:1.277127+0.272227
## [133] train-rmse:0.172850+0.012391 test-rmse:1.277225+0.272771
## [134] train-rmse:0.171366+0.012314 test-rmse:1.277191+0.272543
## [135] train-rmse:0.170410+0.012153 test-rmse:1.277227+0.272160
## [136] train-rmse:0.167931+0.012192 test-rmse:1.277214+0.271670
## [137] train-rmse:0.166317+0.011375 test-rmse:1.276199+0.270956
## [138] train-rmse:0.164895+0.011143 test-rmse:1.274406+0.271243
## [139] train-rmse:0.162597+0.011606 test-rmse:1.273898+0.271380
## [140] train-rmse:0.160924+0.010915 test-rmse:1.273725+0.271591
## [141] train-rmse:0.159421+0.011101 test-rmse:1.273738+0.271854
## [142] train-rmse:0.157493+0.010533 test-rmse:1.272540+0.273121
## [143] train-rmse:0.155230+0.010323 test-rmse:1.273226+0.272886
## [144] train-rmse:0.153833+0.010415 test-rmse:1.272841+0.272794
## [145] train-rmse:0.152439+0.010375 test-rmse:1.272405+0.271320
## [146] train-rmse:0.151253+0.010898 test-rmse:1.272410+0.270692
## [147] train-rmse:0.150016+0.010711 test-rmse:1.272403+0.270811
## [148] train-rmse:0.148702+0.011086 test-rmse:1.271250+0.269883
## [149] train-rmse:0.147260+0.010549 test-rmse:1.270431+0.269869
## [150] train-rmse:0.145987+0.009756 test-rmse:1.270798+0.269965
## [151] train-rmse:0.143905+0.008922 test-rmse:1.270601+0.269449
## [152] train-rmse:0.142276+0.008474 test-rmse:1.270313+0.269357
## [153] train-rmse:0.141320+0.008451 test-rmse:1.270674+0.269638
## [154] train-rmse:0.139529+0.008326 test-rmse:1.269868+0.271098
## [155] train-rmse:0.137905+0.008275 test-rmse:1.270020+0.271229
## [156] train-rmse:0.136945+0.008204 test-rmse:1.269897+0.271212
## [157] train-rmse:0.135861+0.008251 test-rmse:1.269765+0.271368
## [158] train-rmse:0.134312+0.009037 test-rmse:1.269720+0.271208
## [159] train-rmse:0.132740+0.009377 test-rmse:1.269155+0.270974
## [160] train-rmse:0.131115+0.009715 test-rmse:1.268587+0.271243
## [161] train-rmse:0.129887+0.009965 test-rmse:1.268729+0.271434
## [162] train-rmse:0.128591+0.009980 test-rmse:1.268429+0.271699
## [163] train-rmse:0.127429+0.010141 test-rmse:1.268502+0.271976
## [164] train-rmse:0.125875+0.010296 test-rmse:1.267616+0.271346
## [165] train-rmse:0.124973+0.010304 test-rmse:1.267394+0.271570
## [166] train-rmse:0.123604+0.010362 test-rmse:1.268525+0.273073
## [167] train-rmse:0.122782+0.010386 test-rmse:1.268140+0.273044
## [168] train-rmse:0.121549+0.010144 test-rmse:1.268301+0.273348
## [169] train-rmse:0.120528+0.010017 test-rmse:1.268014+0.273673
## [170] train-rmse:0.119771+0.010214 test-rmse:1.267256+0.274111
## [171] train-rmse:0.118297+0.009469 test-rmse:1.267267+0.274109
## [172] train-rmse:0.117065+0.009298 test-rmse:1.266765+0.274044
## [173] train-rmse:0.116331+0.009680 test-rmse:1.267026+0.274210
## [174] train-rmse:0.115399+0.009415 test-rmse:1.266906+0.273858
## [175] train-rmse:0.113975+0.009073 test-rmse:1.266429+0.274012
## [176] train-rmse:0.113104+0.008941 test-rmse:1.266388+0.273288
## [177] train-rmse:0.112069+0.008784 test-rmse:1.266703+0.273101
## [178] train-rmse:0.111078+0.008602 test-rmse:1.266251+0.273293
## [179] train-rmse:0.110313+0.008637 test-rmse:1.266546+0.272994
## [180] train-rmse:0.109735+0.008723 test-rmse:1.266206+0.273128
## [181] train-rmse:0.109075+0.008917 test-rmse:1.265995+0.274010
## [182] train-rmse:0.107862+0.009304 test-rmse:1.265903+0.273545
## [183] train-rmse:0.106722+0.009809 test-rmse:1.266355+0.273184
## [184] train-rmse:0.105624+0.009642 test-rmse:1.265686+0.273054
## [185] train-rmse:0.104732+0.009633 test-rmse:1.265508+0.273159
## [186] train-rmse:0.103526+0.009635 test-rmse:1.265219+0.272839
## [187] train-rmse:0.102407+0.009658 test-rmse:1.265373+0.272863
## [188] train-rmse:0.101247+0.009312 test-rmse:1.265013+0.272615
## [189] train-rmse:0.100579+0.009481 test-rmse:1.264794+0.272894
## [190] train-rmse:0.099476+0.009022 test-rmse:1.264660+0.273169
## [191] train-rmse:0.098385+0.009107 test-rmse:1.264541+0.273503
## [192] train-rmse:0.097476+0.008662 test-rmse:1.265759+0.273203
## [193] train-rmse:0.096593+0.008786 test-rmse:1.265017+0.272840
## [194] train-rmse:0.095916+0.008859 test-rmse:1.264870+0.273062
## [195] train-rmse:0.095276+0.008799 test-rmse:1.264992+0.273230
## [196] train-rmse:0.094403+0.008409 test-rmse:1.264271+0.272963
## [197] train-rmse:0.093632+0.008311 test-rmse:1.264260+0.272965
## [198] train-rmse:0.092900+0.008513 test-rmse:1.264382+0.273044
## [199] train-rmse:0.091826+0.008714 test-rmse:1.264658+0.273507
## [200] train-rmse:0.091165+0.008751 test-rmse:1.264120+0.273813
## [201] train-rmse:0.090007+0.008099 test-rmse:1.264668+0.273960
## [202] train-rmse:0.088915+0.007805 test-rmse:1.264562+0.273626
## [203] train-rmse:0.088339+0.007923 test-rmse:1.264123+0.273869
## [204] train-rmse:0.087614+0.007964 test-rmse:1.263736+0.273740
## [205] train-rmse:0.086942+0.007710 test-rmse:1.263699+0.273723
## [206] train-rmse:0.086019+0.007551 test-rmse:1.263760+0.273881
## [207] train-rmse:0.084768+0.006756 test-rmse:1.263788+0.273888
## [208] train-rmse:0.084263+0.006694 test-rmse:1.263900+0.274147
## [209] train-rmse:0.083599+0.006739 test-rmse:1.263856+0.274259
## [210] train-rmse:0.082967+0.006764 test-rmse:1.263753+0.274312
## [211] train-rmse:0.082445+0.006860 test-rmse:1.262842+0.273549
## [212] train-rmse:0.081756+0.007267 test-rmse:1.263140+0.273507
## [213] train-rmse:0.081073+0.007225 test-rmse:1.262949+0.273489
## [214] train-rmse:0.080250+0.007126 test-rmse:1.262822+0.273177
## [215] train-rmse:0.079435+0.007089 test-rmse:1.262768+0.273004
## [216] train-rmse:0.078576+0.006818 test-rmse:1.262916+0.273202
## [217] train-rmse:0.077488+0.006474 test-rmse:1.262786+0.273497
## [218] train-rmse:0.076730+0.006373 test-rmse:1.262846+0.273762
## [219] train-rmse:0.075906+0.006485 test-rmse:1.262532+0.273416
## [220] train-rmse:0.075206+0.006350 test-rmse:1.262358+0.273383
## [221] train-rmse:0.074395+0.006650 test-rmse:1.262494+0.273406
## [222] train-rmse:0.073804+0.006752 test-rmse:1.262053+0.273858
## [223] train-rmse:0.073197+0.006745 test-rmse:1.262301+0.274280
## [224] train-rmse:0.072359+0.006745 test-rmse:1.262313+0.274199
## [225] train-rmse:0.071828+0.006726 test-rmse:1.262165+0.274325
## [226] train-rmse:0.071037+0.006823 test-rmse:1.262402+0.274299
## [227] train-rmse:0.070322+0.006783 test-rmse:1.262141+0.274089
## [228] train-rmse:0.069913+0.006862 test-rmse:1.262248+0.273827
## Stopping. Best iteration:
## [222] train-rmse:0.073804+0.006752 test-rmse:1.262053+0.273858
##
## 87
## [1] train-rmse:71.268932+0.085068 test-rmse:71.268778+0.519929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:47.317069+0.068405 test-rmse:47.315810+0.569907
## [3] train-rmse:31.577981+0.063944 test-rmse:31.574661+0.622724
## [4] train-rmse:21.229897+0.041979 test-rmse:21.327855+0.649898
## [5] train-rmse:14.410720+0.028260 test-rmse:14.524585+0.599181
## [6] train-rmse:9.922825+0.028980 test-rmse:10.043284+0.529124
## [7] train-rmse:6.990207+0.025778 test-rmse:7.175532+0.574097
## [8] train-rmse:5.075617+0.028706 test-rmse:5.269135+0.549077
## [9] train-rmse:3.858644+0.028337 test-rmse:4.095585+0.505964
## [10] train-rmse:3.092085+0.050927 test-rmse:3.436769+0.453970
## [11] train-rmse:2.601005+0.044652 test-rmse:2.971732+0.347205
## [12] train-rmse:2.282703+0.051338 test-rmse:2.693996+0.309916
## [13] train-rmse:2.063521+0.047015 test-rmse:2.522844+0.271557
## [14] train-rmse:1.894162+0.042698 test-rmse:2.422603+0.258727
## [15] train-rmse:1.764367+0.038239 test-rmse:2.288702+0.256952
## [16] train-rmse:1.638796+0.042611 test-rmse:2.189340+0.231020
## [17] train-rmse:1.542997+0.038450 test-rmse:2.114522+0.218555
## [18] train-rmse:1.457048+0.038588 test-rmse:2.068015+0.222854
## [19] train-rmse:1.379365+0.036598 test-rmse:1.977771+0.220778
## [20] train-rmse:1.292665+0.038446 test-rmse:1.933241+0.215232
## [21] train-rmse:1.219338+0.039059 test-rmse:1.859668+0.207922
## [22] train-rmse:1.156456+0.037305 test-rmse:1.819728+0.202366
## [23] train-rmse:1.098696+0.035383 test-rmse:1.778773+0.217304
## [24] train-rmse:1.048636+0.035746 test-rmse:1.719562+0.222524
## [25] train-rmse:0.999176+0.033987 test-rmse:1.687747+0.231646
## [26] train-rmse:0.953089+0.032409 test-rmse:1.661905+0.242858
## [27] train-rmse:0.905060+0.036136 test-rmse:1.630898+0.242337
## [28] train-rmse:0.860745+0.035406 test-rmse:1.602673+0.253084
## [29] train-rmse:0.821369+0.032065 test-rmse:1.559237+0.260159
## [30] train-rmse:0.785026+0.030477 test-rmse:1.532911+0.265845
## [31] train-rmse:0.750559+0.031422 test-rmse:1.506785+0.253279
## [32] train-rmse:0.723039+0.029467 test-rmse:1.491341+0.260712
## [33] train-rmse:0.693370+0.027782 test-rmse:1.472061+0.263693
## [34] train-rmse:0.668259+0.026370 test-rmse:1.458097+0.275028
## [35] train-rmse:0.644294+0.024320 test-rmse:1.437119+0.279864
## [36] train-rmse:0.621839+0.026478 test-rmse:1.434565+0.277995
## [37] train-rmse:0.594687+0.030362 test-rmse:1.421297+0.279428
## [38] train-rmse:0.573608+0.031454 test-rmse:1.404967+0.284116
## [39] train-rmse:0.555926+0.031580 test-rmse:1.392616+0.289359
## [40] train-rmse:0.533697+0.027870 test-rmse:1.368380+0.286498
## [41] train-rmse:0.515919+0.027726 test-rmse:1.365482+0.289331
## [42] train-rmse:0.499338+0.026584 test-rmse:1.358160+0.292181
## [43] train-rmse:0.484496+0.027107 test-rmse:1.349766+0.293162
## [44] train-rmse:0.470593+0.027110 test-rmse:1.338446+0.295007
## [45] train-rmse:0.458978+0.026435 test-rmse:1.336514+0.297586
## [46] train-rmse:0.445215+0.026351 test-rmse:1.320159+0.283516
## [47] train-rmse:0.433044+0.024023 test-rmse:1.311049+0.290218
## [48] train-rmse:0.418527+0.025621 test-rmse:1.309345+0.291936
## [49] train-rmse:0.407035+0.026880 test-rmse:1.301574+0.292732
## [50] train-rmse:0.395843+0.022946 test-rmse:1.299868+0.293354
## [51] train-rmse:0.384801+0.022813 test-rmse:1.290961+0.299014
## [52] train-rmse:0.374559+0.023945 test-rmse:1.291588+0.302348
## [53] train-rmse:0.365291+0.021238 test-rmse:1.289589+0.300927
## [54] train-rmse:0.355057+0.023588 test-rmse:1.286546+0.300989
## [55] train-rmse:0.347048+0.024284 test-rmse:1.286281+0.303553
## [56] train-rmse:0.337967+0.023012 test-rmse:1.284338+0.306343
## [57] train-rmse:0.330345+0.020547 test-rmse:1.283464+0.304995
## [58] train-rmse:0.322056+0.021654 test-rmse:1.280016+0.305321
## [59] train-rmse:0.313704+0.022868 test-rmse:1.278675+0.305705
## [60] train-rmse:0.305017+0.022033 test-rmse:1.277096+0.306727
## [61] train-rmse:0.299933+0.021981 test-rmse:1.272901+0.311479
## [62] train-rmse:0.293436+0.021005 test-rmse:1.275312+0.309290
## [63] train-rmse:0.285893+0.018670 test-rmse:1.273566+0.309408
## [64] train-rmse:0.278253+0.018102 test-rmse:1.272946+0.310047
## [65] train-rmse:0.271297+0.018244 test-rmse:1.272917+0.310004
## [66] train-rmse:0.266826+0.017439 test-rmse:1.270173+0.309064
## [67] train-rmse:0.261994+0.015772 test-rmse:1.266900+0.309898
## [68] train-rmse:0.255586+0.015747 test-rmse:1.265072+0.311081
## [69] train-rmse:0.250684+0.017022 test-rmse:1.265841+0.310607
## [70] train-rmse:0.245674+0.015781 test-rmse:1.264749+0.313276
## [71] train-rmse:0.240622+0.014532 test-rmse:1.264889+0.313937
## [72] train-rmse:0.236523+0.015091 test-rmse:1.264871+0.315197
## [73] train-rmse:0.230420+0.015315 test-rmse:1.263101+0.315535
## [74] train-rmse:0.224626+0.014259 test-rmse:1.260797+0.317522
## [75] train-rmse:0.221097+0.014357 test-rmse:1.258219+0.318935
## [76] train-rmse:0.216440+0.013714 test-rmse:1.257439+0.319513
## [77] train-rmse:0.211586+0.011503 test-rmse:1.256653+0.319996
## [78] train-rmse:0.207696+0.011369 test-rmse:1.256975+0.319278
## [79] train-rmse:0.204298+0.012104 test-rmse:1.257079+0.319611
## [80] train-rmse:0.201705+0.011860 test-rmse:1.255834+0.321130
## [81] train-rmse:0.197968+0.011972 test-rmse:1.255437+0.322424
## [82] train-rmse:0.194694+0.011142 test-rmse:1.256587+0.321254
## [83] train-rmse:0.190304+0.010487 test-rmse:1.255926+0.321553
## [84] train-rmse:0.187468+0.009779 test-rmse:1.255282+0.321992
## [85] train-rmse:0.184422+0.010153 test-rmse:1.255216+0.322126
## [86] train-rmse:0.180556+0.009737 test-rmse:1.255917+0.323930
## [87] train-rmse:0.176960+0.009698 test-rmse:1.256384+0.323786
## [88] train-rmse:0.174536+0.010730 test-rmse:1.254890+0.325889
## [89] train-rmse:0.170286+0.010299 test-rmse:1.254736+0.326121
## [90] train-rmse:0.166275+0.011099 test-rmse:1.253204+0.325925
## [91] train-rmse:0.163781+0.011903 test-rmse:1.252711+0.326463
## [92] train-rmse:0.159054+0.010593 test-rmse:1.253051+0.326412
## [93] train-rmse:0.156911+0.010380 test-rmse:1.252224+0.326029
## [94] train-rmse:0.153619+0.009619 test-rmse:1.251850+0.326650
## [95] train-rmse:0.151966+0.009903 test-rmse:1.251639+0.326354
## [96] train-rmse:0.148383+0.008900 test-rmse:1.250780+0.325894
## [97] train-rmse:0.145190+0.008942 test-rmse:1.248987+0.324584
## [98] train-rmse:0.141185+0.007372 test-rmse:1.248123+0.325434
## [99] train-rmse:0.137342+0.006654 test-rmse:1.246759+0.325583
## [100] train-rmse:0.134895+0.006362 test-rmse:1.246534+0.326318
## [101] train-rmse:0.132350+0.005761 test-rmse:1.246804+0.326304
## [102] train-rmse:0.129559+0.005707 test-rmse:1.246841+0.325741
## [103] train-rmse:0.128462+0.005803 test-rmse:1.246315+0.325906
## [104] train-rmse:0.125728+0.005091 test-rmse:1.246282+0.326299
## [105] train-rmse:0.123978+0.005042 test-rmse:1.245850+0.327157
## [106] train-rmse:0.121069+0.004231 test-rmse:1.245986+0.328426
## [107] train-rmse:0.119394+0.004307 test-rmse:1.245923+0.329040
## [108] train-rmse:0.116956+0.004264 test-rmse:1.245383+0.328986
## [109] train-rmse:0.114743+0.003619 test-rmse:1.244769+0.328326
## [110] train-rmse:0.112901+0.003547 test-rmse:1.245175+0.328331
## [111] train-rmse:0.111321+0.003581 test-rmse:1.244661+0.327980
## [112] train-rmse:0.109392+0.004026 test-rmse:1.244143+0.327471
## [113] train-rmse:0.107222+0.003673 test-rmse:1.243692+0.327652
## [114] train-rmse:0.104845+0.004552 test-rmse:1.243986+0.328310
## [115] train-rmse:0.103072+0.004775 test-rmse:1.243575+0.328310
## [116] train-rmse:0.101781+0.004452 test-rmse:1.243543+0.328137
## [117] train-rmse:0.100162+0.004291 test-rmse:1.243499+0.328318
## [118] train-rmse:0.098312+0.004526 test-rmse:1.243091+0.328315
## [119] train-rmse:0.096335+0.004935 test-rmse:1.243075+0.328782
## [120] train-rmse:0.095208+0.004499 test-rmse:1.243043+0.328733
## [121] train-rmse:0.093159+0.003222 test-rmse:1.242909+0.329694
## [122] train-rmse:0.091481+0.003773 test-rmse:1.242281+0.329929
## [123] train-rmse:0.090319+0.003914 test-rmse:1.242684+0.330985
## [124] train-rmse:0.088625+0.003450 test-rmse:1.242222+0.330970
## [125] train-rmse:0.087568+0.003674 test-rmse:1.242248+0.331116
## [126] train-rmse:0.086025+0.003559 test-rmse:1.241587+0.331278
## [127] train-rmse:0.084317+0.003086 test-rmse:1.241706+0.331300
## [128] train-rmse:0.082818+0.003143 test-rmse:1.241447+0.330939
## [129] train-rmse:0.081673+0.003398 test-rmse:1.241986+0.332227
## [130] train-rmse:0.080011+0.003408 test-rmse:1.242223+0.332586
## [131] train-rmse:0.079118+0.003468 test-rmse:1.241976+0.332726
## [132] train-rmse:0.077912+0.003747 test-rmse:1.241790+0.332520
## [133] train-rmse:0.076740+0.003836 test-rmse:1.241777+0.332578
## [134] train-rmse:0.075883+0.003999 test-rmse:1.241095+0.332788
## [135] train-rmse:0.074540+0.003845 test-rmse:1.241030+0.332955
## [136] train-rmse:0.073128+0.004494 test-rmse:1.240929+0.332742
## [137] train-rmse:0.071803+0.004365 test-rmse:1.240866+0.332627
## [138] train-rmse:0.070142+0.004260 test-rmse:1.241018+0.332417
## [139] train-rmse:0.068980+0.004525 test-rmse:1.241188+0.332479
## [140] train-rmse:0.067810+0.004154 test-rmse:1.241068+0.332751
## [141] train-rmse:0.067035+0.004042 test-rmse:1.240674+0.333056
## [142] train-rmse:0.066233+0.004282 test-rmse:1.240689+0.333486
## [143] train-rmse:0.064785+0.003968 test-rmse:1.240694+0.333388
## [144] train-rmse:0.063577+0.003918 test-rmse:1.240823+0.333281
## [145] train-rmse:0.062776+0.004165 test-rmse:1.240301+0.333546
## [146] train-rmse:0.060961+0.004007 test-rmse:1.240468+0.333628
## [147] train-rmse:0.059736+0.003686 test-rmse:1.240247+0.333920
## [148] train-rmse:0.058974+0.003436 test-rmse:1.240415+0.334076
## [149] train-rmse:0.058312+0.003269 test-rmse:1.240182+0.334220
## [150] train-rmse:0.057166+0.002856 test-rmse:1.240306+0.334614
## [151] train-rmse:0.056154+0.002821 test-rmse:1.240552+0.335261
## [152] train-rmse:0.055093+0.002565 test-rmse:1.239986+0.334806
## [153] train-rmse:0.053831+0.002128 test-rmse:1.240015+0.334898
## [154] train-rmse:0.052521+0.002560 test-rmse:1.240137+0.334797
## [155] train-rmse:0.051561+0.002514 test-rmse:1.239880+0.334940
## [156] train-rmse:0.050868+0.002671 test-rmse:1.239941+0.335021
## [157] train-rmse:0.050224+0.002858 test-rmse:1.240052+0.334833
## [158] train-rmse:0.049375+0.002813 test-rmse:1.240087+0.334868
## [159] train-rmse:0.048510+0.002639 test-rmse:1.240277+0.334958
## [160] train-rmse:0.047976+0.002892 test-rmse:1.239747+0.335152
## [161] train-rmse:0.047036+0.002460 test-rmse:1.239693+0.335149
## [162] train-rmse:0.046531+0.002628 test-rmse:1.239872+0.335175
## [163] train-rmse:0.045810+0.002392 test-rmse:1.239734+0.335445
## [164] train-rmse:0.044905+0.002238 test-rmse:1.239833+0.335494
## [165] train-rmse:0.044367+0.002183 test-rmse:1.239448+0.335412
## [166] train-rmse:0.043441+0.002307 test-rmse:1.240238+0.336523
## [167] train-rmse:0.042661+0.002394 test-rmse:1.240306+0.336385
## [168] train-rmse:0.041971+0.002742 test-rmse:1.239831+0.336479
## [169] train-rmse:0.041162+0.002618 test-rmse:1.240068+0.336920
## [170] train-rmse:0.040701+0.002595 test-rmse:1.240094+0.336880
## [171] train-rmse:0.039906+0.002564 test-rmse:1.240168+0.336878
## Stopping. Best iteration:
## [165] train-rmse:0.044367+0.002183 test-rmse:1.239448+0.335412
##
## 88
## [1] train-rmse:71.268932+0.085068 test-rmse:71.268778+0.519929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:47.317069+0.068405 test-rmse:47.315810+0.569907
## [3] train-rmse:31.577981+0.063944 test-rmse:31.574661+0.622724
## [4] train-rmse:21.229897+0.041979 test-rmse:21.327855+0.649898
## [5] train-rmse:14.405247+0.028647 test-rmse:14.519507+0.596673
## [6] train-rmse:9.891010+0.027809 test-rmse:10.011839+0.565689
## [7] train-rmse:6.934785+0.031576 test-rmse:7.122765+0.589758
## [8] train-rmse:4.979566+0.021816 test-rmse:5.164149+0.545112
## [9] train-rmse:3.698043+0.040503 test-rmse:3.930244+0.469190
## [10] train-rmse:2.881188+0.052369 test-rmse:3.228337+0.456684
## [11] train-rmse:2.342153+0.056599 test-rmse:2.766304+0.359351
## [12] train-rmse:1.981199+0.050782 test-rmse:2.466238+0.294825
## [13] train-rmse:1.740433+0.050163 test-rmse:2.266862+0.259742
## [14] train-rmse:1.564689+0.046820 test-rmse:2.156466+0.235712
## [15] train-rmse:1.431192+0.037327 test-rmse:2.070872+0.211400
## [16] train-rmse:1.313342+0.037749 test-rmse:1.985733+0.185724
## [17] train-rmse:1.213891+0.035670 test-rmse:1.905860+0.172892
## [18] train-rmse:1.127707+0.036598 test-rmse:1.838309+0.178704
## [19] train-rmse:1.054238+0.034250 test-rmse:1.788775+0.191788
## [20] train-rmse:0.985989+0.027282 test-rmse:1.738947+0.197225
## [21] train-rmse:0.922936+0.018478 test-rmse:1.694686+0.186443
## [22] train-rmse:0.865462+0.018078 test-rmse:1.642667+0.185864
## [23] train-rmse:0.817172+0.020198 test-rmse:1.612766+0.195242
## [24] train-rmse:0.760701+0.021499 test-rmse:1.575209+0.192977
## [25] train-rmse:0.717703+0.022722 test-rmse:1.557195+0.195849
## [26] train-rmse:0.682328+0.021324 test-rmse:1.530019+0.200241
## [27] train-rmse:0.643774+0.016656 test-rmse:1.503146+0.200317
## [28] train-rmse:0.613009+0.016643 test-rmse:1.489284+0.196206
## [29] train-rmse:0.582444+0.015016 test-rmse:1.473624+0.206341
## [30] train-rmse:0.555786+0.017664 test-rmse:1.458656+0.209510
## [31] train-rmse:0.530013+0.017996 test-rmse:1.444632+0.219114
## [32] train-rmse:0.507252+0.020238 test-rmse:1.427983+0.220586
## [33] train-rmse:0.478699+0.021683 test-rmse:1.420167+0.214103
## [34] train-rmse:0.457930+0.018712 test-rmse:1.394617+0.203965
## [35] train-rmse:0.442655+0.020345 test-rmse:1.383655+0.209702
## [36] train-rmse:0.426594+0.018999 test-rmse:1.377015+0.215014
## [37] train-rmse:0.412931+0.020812 test-rmse:1.375684+0.214989
## [38] train-rmse:0.392924+0.014590 test-rmse:1.373791+0.214180
## [39] train-rmse:0.374692+0.015756 test-rmse:1.372035+0.223248
## [40] train-rmse:0.364005+0.015897 test-rmse:1.370520+0.226450
## [41] train-rmse:0.348781+0.015612 test-rmse:1.363402+0.232297
## [42] train-rmse:0.332987+0.013529 test-rmse:1.358724+0.237089
## [43] train-rmse:0.319812+0.014349 test-rmse:1.355263+0.238622
## [44] train-rmse:0.308692+0.013203 test-rmse:1.352037+0.241396
## [45] train-rmse:0.298474+0.012696 test-rmse:1.349070+0.242332
## [46] train-rmse:0.290202+0.013404 test-rmse:1.340012+0.233055
## [47] train-rmse:0.281092+0.016456 test-rmse:1.339060+0.237416
## [48] train-rmse:0.273563+0.015837 test-rmse:1.338045+0.239029
## [49] train-rmse:0.264652+0.016436 test-rmse:1.337779+0.238030
## [50] train-rmse:0.256232+0.016568 test-rmse:1.331454+0.234765
## [51] train-rmse:0.248512+0.017178 test-rmse:1.330031+0.234609
## [52] train-rmse:0.240027+0.015340 test-rmse:1.328200+0.238550
## [53] train-rmse:0.232580+0.013273 test-rmse:1.326190+0.240350
## [54] train-rmse:0.224879+0.012203 test-rmse:1.325055+0.242561
## [55] train-rmse:0.217292+0.011937 test-rmse:1.323070+0.242412
## [56] train-rmse:0.209575+0.011389 test-rmse:1.320062+0.245934
## [57] train-rmse:0.202401+0.011207 test-rmse:1.318791+0.245817
## [58] train-rmse:0.198131+0.011695 test-rmse:1.318679+0.246421
## [59] train-rmse:0.192263+0.012903 test-rmse:1.316836+0.249249
## [60] train-rmse:0.188155+0.012848 test-rmse:1.315786+0.251332
## [61] train-rmse:0.182365+0.013338 test-rmse:1.313745+0.249702
## [62] train-rmse:0.177963+0.012366 test-rmse:1.311719+0.251322
## [63] train-rmse:0.174281+0.013387 test-rmse:1.310857+0.253027
## [64] train-rmse:0.170255+0.014250 test-rmse:1.310215+0.253417
## [65] train-rmse:0.163492+0.013541 test-rmse:1.308878+0.254327
## [66] train-rmse:0.158137+0.014229 test-rmse:1.306395+0.254123
## [67] train-rmse:0.154123+0.014076 test-rmse:1.306774+0.254342
## [68] train-rmse:0.148735+0.012610 test-rmse:1.306052+0.255399
## [69] train-rmse:0.144711+0.014069 test-rmse:1.305345+0.256769
## [70] train-rmse:0.139963+0.012894 test-rmse:1.304938+0.256663
## [71] train-rmse:0.136393+0.012030 test-rmse:1.304524+0.257099
## [72] train-rmse:0.130364+0.010370 test-rmse:1.304936+0.256893
## [73] train-rmse:0.127405+0.010019 test-rmse:1.304672+0.257245
## [74] train-rmse:0.123367+0.011115 test-rmse:1.304510+0.257641
## [75] train-rmse:0.119689+0.010145 test-rmse:1.304837+0.258108
## [76] train-rmse:0.116054+0.010037 test-rmse:1.303158+0.257481
## [77] train-rmse:0.112126+0.009070 test-rmse:1.302678+0.258107
## [78] train-rmse:0.108803+0.008402 test-rmse:1.303188+0.258130
## [79] train-rmse:0.105532+0.009014 test-rmse:1.302560+0.257826
## [80] train-rmse:0.102200+0.008184 test-rmse:1.302047+0.257999
## [81] train-rmse:0.099495+0.007420 test-rmse:1.301683+0.259159
## [82] train-rmse:0.096096+0.007435 test-rmse:1.301506+0.259987
## [83] train-rmse:0.093602+0.007271 test-rmse:1.302007+0.261511
## [84] train-rmse:0.090826+0.007065 test-rmse:1.300873+0.259084
## [85] train-rmse:0.088489+0.006913 test-rmse:1.300860+0.258975
## [86] train-rmse:0.086864+0.006642 test-rmse:1.300749+0.259608
## [87] train-rmse:0.084447+0.006103 test-rmse:1.300323+0.259634
## [88] train-rmse:0.081362+0.004888 test-rmse:1.300016+0.260081
## [89] train-rmse:0.079493+0.005110 test-rmse:1.300308+0.259828
## [90] train-rmse:0.076981+0.005383 test-rmse:1.300134+0.259489
## [91] train-rmse:0.074967+0.005851 test-rmse:1.300482+0.260026
## [92] train-rmse:0.072821+0.006010 test-rmse:1.301277+0.261664
## [93] train-rmse:0.070983+0.006397 test-rmse:1.300847+0.262062
## [94] train-rmse:0.068620+0.006582 test-rmse:1.299997+0.262263
## [95] train-rmse:0.067331+0.006427 test-rmse:1.300050+0.263042
## [96] train-rmse:0.065687+0.006812 test-rmse:1.299878+0.262979
## [97] train-rmse:0.064255+0.006450 test-rmse:1.299575+0.263160
## [98] train-rmse:0.061990+0.005607 test-rmse:1.299100+0.262760
## [99] train-rmse:0.060521+0.005530 test-rmse:1.298981+0.262573
## [100] train-rmse:0.059242+0.005161 test-rmse:1.298377+0.262949
## [101] train-rmse:0.057996+0.004955 test-rmse:1.298579+0.263342
## [102] train-rmse:0.056051+0.004287 test-rmse:1.298479+0.263513
## [103] train-rmse:0.054484+0.004305 test-rmse:1.297812+0.264215
## [104] train-rmse:0.053243+0.004055 test-rmse:1.297166+0.263303
## [105] train-rmse:0.051151+0.003494 test-rmse:1.297542+0.263520
## [106] train-rmse:0.049749+0.003350 test-rmse:1.297219+0.263381
## [107] train-rmse:0.048871+0.002838 test-rmse:1.296790+0.263764
## [108] train-rmse:0.047721+0.003069 test-rmse:1.296430+0.264381
## [109] train-rmse:0.046609+0.002707 test-rmse:1.296577+0.264548
## [110] train-rmse:0.045374+0.002676 test-rmse:1.296650+0.264339
## [111] train-rmse:0.044568+0.002448 test-rmse:1.295758+0.264863
## [112] train-rmse:0.043729+0.002445 test-rmse:1.295767+0.264776
## [113] train-rmse:0.042507+0.002351 test-rmse:1.295521+0.264718
## [114] train-rmse:0.041184+0.002215 test-rmse:1.295475+0.264808
## [115] train-rmse:0.040509+0.002266 test-rmse:1.295319+0.265032
## [116] train-rmse:0.039519+0.002161 test-rmse:1.295247+0.265317
## [117] train-rmse:0.038402+0.001692 test-rmse:1.295258+0.265476
## [118] train-rmse:0.037112+0.001507 test-rmse:1.294501+0.264837
## [119] train-rmse:0.035986+0.001576 test-rmse:1.294276+0.265081
## [120] train-rmse:0.034766+0.001807 test-rmse:1.294254+0.265158
## [121] train-rmse:0.033892+0.001746 test-rmse:1.294328+0.265244
## [122] train-rmse:0.033023+0.001680 test-rmse:1.294259+0.265470
## [123] train-rmse:0.032263+0.001783 test-rmse:1.294377+0.265271
## [124] train-rmse:0.031591+0.001753 test-rmse:1.294494+0.265174
## [125] train-rmse:0.030722+0.001804 test-rmse:1.294583+0.265244
## [126] train-rmse:0.030120+0.001555 test-rmse:1.294630+0.265215
## Stopping. Best iteration:
## [120] train-rmse:0.034766+0.001807 test-rmse:1.294254+0.265158
##
## 89
## [1] train-rmse:71.268932+0.085068 test-rmse:71.268778+0.519929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:47.317069+0.068405 test-rmse:47.315810+0.569907
## [3] train-rmse:31.577981+0.063944 test-rmse:31.574661+0.622724
## [4] train-rmse:21.229897+0.041979 test-rmse:21.327855+0.649898
## [5] train-rmse:14.399559+0.028339 test-rmse:14.503341+0.589731
## [6] train-rmse:9.867454+0.025916 test-rmse:9.999437+0.572635
## [7] train-rmse:6.886197+0.033014 test-rmse:7.053607+0.585200
## [8] train-rmse:4.883757+0.036473 test-rmse:5.066199+0.532926
## [9] train-rmse:3.563478+0.044102 test-rmse:3.806181+0.460402
## [10] train-rmse:2.695725+0.054199 test-rmse:3.011521+0.414457
## [11] train-rmse:2.131575+0.073801 test-rmse:2.536187+0.401456
## [12] train-rmse:1.743845+0.070664 test-rmse:2.246281+0.370623
## [13] train-rmse:1.480701+0.068327 test-rmse:2.052067+0.316297
## [14] train-rmse:1.291807+0.073092 test-rmse:1.931819+0.274474
## [15] train-rmse:1.142723+0.068231 test-rmse:1.840931+0.255101
## [16] train-rmse:1.031230+0.063338 test-rmse:1.777070+0.222537
## [17] train-rmse:0.943761+0.058113 test-rmse:1.699598+0.186124
## [18] train-rmse:0.864695+0.052131 test-rmse:1.635268+0.194095
## [19] train-rmse:0.798507+0.044603 test-rmse:1.593662+0.196321
## [20] train-rmse:0.731528+0.039711 test-rmse:1.559211+0.183693
## [21] train-rmse:0.682749+0.033585 test-rmse:1.532024+0.193393
## [22] train-rmse:0.637082+0.030895 test-rmse:1.492206+0.196600
## [23] train-rmse:0.588459+0.037776 test-rmse:1.461528+0.200947
## [24] train-rmse:0.548513+0.038056 test-rmse:1.440791+0.190180
## [25] train-rmse:0.517002+0.033673 test-rmse:1.415425+0.193868
## [26] train-rmse:0.483169+0.026561 test-rmse:1.407589+0.194557
## [27] train-rmse:0.455483+0.025673 test-rmse:1.394307+0.201808
## [28] train-rmse:0.425747+0.026760 test-rmse:1.383486+0.203492
## [29] train-rmse:0.403812+0.028751 test-rmse:1.372628+0.207678
## [30] train-rmse:0.379657+0.026128 test-rmse:1.358122+0.193462
## [31] train-rmse:0.361837+0.022934 test-rmse:1.348239+0.201431
## [32] train-rmse:0.344801+0.020691 test-rmse:1.337971+0.188349
## [33] train-rmse:0.329203+0.020106 test-rmse:1.332788+0.190810
## [34] train-rmse:0.315213+0.019987 test-rmse:1.325293+0.191634
## [35] train-rmse:0.301240+0.020049 test-rmse:1.316475+0.195699
## [36] train-rmse:0.283204+0.017828 test-rmse:1.312599+0.199181
## [37] train-rmse:0.270935+0.018626 test-rmse:1.309968+0.198441
## [38] train-rmse:0.260183+0.018213 test-rmse:1.308076+0.201211
## [39] train-rmse:0.248926+0.016214 test-rmse:1.307287+0.199623
## [40] train-rmse:0.237886+0.014574 test-rmse:1.305272+0.200033
## [41] train-rmse:0.227061+0.014770 test-rmse:1.302066+0.202344
## [42] train-rmse:0.216762+0.014131 test-rmse:1.301692+0.201104
## [43] train-rmse:0.208732+0.014022 test-rmse:1.297770+0.201966
## [44] train-rmse:0.198322+0.013797 test-rmse:1.294029+0.201262
## [45] train-rmse:0.191590+0.013214 test-rmse:1.294169+0.203133
## [46] train-rmse:0.181934+0.013197 test-rmse:1.293507+0.203902
## [47] train-rmse:0.173983+0.010502 test-rmse:1.291931+0.204535
## [48] train-rmse:0.166165+0.011689 test-rmse:1.290942+0.204946
## [49] train-rmse:0.159023+0.011406 test-rmse:1.288109+0.205549
## [50] train-rmse:0.151722+0.010915 test-rmse:1.286468+0.204357
## [51] train-rmse:0.144182+0.011588 test-rmse:1.285361+0.204931
## [52] train-rmse:0.137553+0.010579 test-rmse:1.284839+0.206227
## [53] train-rmse:0.133595+0.009798 test-rmse:1.285421+0.206778
## [54] train-rmse:0.127730+0.008474 test-rmse:1.284292+0.207822
## [55] train-rmse:0.123246+0.008954 test-rmse:1.283468+0.208753
## [56] train-rmse:0.118990+0.009075 test-rmse:1.282996+0.208735
## [57] train-rmse:0.115093+0.009001 test-rmse:1.281857+0.208393
## [58] train-rmse:0.111330+0.008928 test-rmse:1.281673+0.208111
## [59] train-rmse:0.106464+0.009373 test-rmse:1.281096+0.208749
## [60] train-rmse:0.101423+0.008897 test-rmse:1.280206+0.209678
## [61] train-rmse:0.097790+0.008025 test-rmse:1.279552+0.209534
## [62] train-rmse:0.094538+0.007796 test-rmse:1.278251+0.210545
## [63] train-rmse:0.091321+0.007772 test-rmse:1.278329+0.210293
## [64] train-rmse:0.087546+0.008097 test-rmse:1.278014+0.211445
## [65] train-rmse:0.084981+0.007390 test-rmse:1.277541+0.210147
## [66] train-rmse:0.081397+0.006644 test-rmse:1.277146+0.210946
## [67] train-rmse:0.077768+0.005339 test-rmse:1.277486+0.211229
## [68] train-rmse:0.076260+0.005299 test-rmse:1.276867+0.211156
## [69] train-rmse:0.073077+0.005193 test-rmse:1.275659+0.211339
## [70] train-rmse:0.070110+0.004555 test-rmse:1.276970+0.212202
## [71] train-rmse:0.067932+0.004617 test-rmse:1.276238+0.212404
## [72] train-rmse:0.065504+0.004824 test-rmse:1.276193+0.213009
## [73] train-rmse:0.063275+0.004900 test-rmse:1.275901+0.213433
## [74] train-rmse:0.059806+0.004859 test-rmse:1.275887+0.214054
## [75] train-rmse:0.057031+0.004427 test-rmse:1.274940+0.213941
## [76] train-rmse:0.054951+0.004327 test-rmse:1.274770+0.213755
## [77] train-rmse:0.052307+0.003900 test-rmse:1.273676+0.213666
## [78] train-rmse:0.050603+0.004316 test-rmse:1.274058+0.213485
## [79] train-rmse:0.049070+0.004161 test-rmse:1.274040+0.213419
## [80] train-rmse:0.047160+0.003381 test-rmse:1.273963+0.213877
## [81] train-rmse:0.044779+0.003063 test-rmse:1.274138+0.213981
## [82] train-rmse:0.043103+0.003032 test-rmse:1.274438+0.214194
## [83] train-rmse:0.041670+0.003125 test-rmse:1.274433+0.214333
## Stopping. Best iteration:
## [77] train-rmse:0.052307+0.003900 test-rmse:1.273676+0.213666
##
## 90
## [1] train-rmse:71.268932+0.085068 test-rmse:71.268778+0.519929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:47.317069+0.068405 test-rmse:47.315810+0.569907
## [3] train-rmse:31.577981+0.063944 test-rmse:31.574661+0.622724
## [4] train-rmse:21.229897+0.041979 test-rmse:21.327855+0.649898
## [5] train-rmse:14.394689+0.028024 test-rmse:14.497131+0.588484
## [6] train-rmse:9.849677+0.021792 test-rmse:9.972395+0.539016
## [7] train-rmse:6.832820+0.016877 test-rmse:6.963446+0.478993
## [8] train-rmse:4.811644+0.015021 test-rmse:4.996803+0.515946
## [9] train-rmse:3.472363+0.032865 test-rmse:3.700079+0.481195
## [10] train-rmse:2.577830+0.037346 test-rmse:2.890858+0.480474
## [11] train-rmse:1.984264+0.045988 test-rmse:2.363186+0.397940
## [12] train-rmse:1.590095+0.057015 test-rmse:2.076177+0.315364
## [13] train-rmse:1.318062+0.073103 test-rmse:1.867789+0.275218
## [14] train-rmse:1.116586+0.072637 test-rmse:1.736272+0.251705
## [15] train-rmse:0.963820+0.070955 test-rmse:1.661453+0.240807
## [16] train-rmse:0.845607+0.071221 test-rmse:1.607666+0.219653
## [17] train-rmse:0.754505+0.058959 test-rmse:1.545322+0.212539
## [18] train-rmse:0.682568+0.062667 test-rmse:1.503297+0.208758
## [19] train-rmse:0.619683+0.061436 test-rmse:1.482174+0.195835
## [20] train-rmse:0.564801+0.049605 test-rmse:1.447552+0.201184
## [21] train-rmse:0.517210+0.043377 test-rmse:1.422250+0.206580
## [22] train-rmse:0.481284+0.042039 test-rmse:1.392634+0.210952
## [23] train-rmse:0.444069+0.038420 test-rmse:1.373499+0.227628
## [24] train-rmse:0.413743+0.034830 test-rmse:1.366951+0.233016
## [25] train-rmse:0.381930+0.024309 test-rmse:1.352652+0.224814
## [26] train-rmse:0.356336+0.021481 test-rmse:1.342572+0.237632
## [27] train-rmse:0.331937+0.024838 test-rmse:1.333448+0.238162
## [28] train-rmse:0.310187+0.022847 test-rmse:1.329101+0.243486
## [29] train-rmse:0.290118+0.021259 test-rmse:1.324009+0.244498
## [30] train-rmse:0.275355+0.020609 test-rmse:1.317264+0.244018
## [31] train-rmse:0.259240+0.018568 test-rmse:1.313547+0.245973
## [32] train-rmse:0.244932+0.015960 test-rmse:1.310253+0.246073
## [33] train-rmse:0.229619+0.016502 test-rmse:1.304703+0.247311
## [34] train-rmse:0.215093+0.013480 test-rmse:1.299902+0.248737
## [35] train-rmse:0.202898+0.014364 test-rmse:1.293660+0.249964
## [36] train-rmse:0.191655+0.012961 test-rmse:1.292220+0.251553
## [37] train-rmse:0.184176+0.013252 test-rmse:1.290817+0.252980
## [38] train-rmse:0.174425+0.009401 test-rmse:1.289877+0.255221
## [39] train-rmse:0.166073+0.010014 test-rmse:1.286699+0.254983
## [40] train-rmse:0.155507+0.011960 test-rmse:1.285548+0.257000
## [41] train-rmse:0.149276+0.010901 test-rmse:1.284493+0.259614
## [42] train-rmse:0.140960+0.012657 test-rmse:1.284230+0.259319
## [43] train-rmse:0.131934+0.012750 test-rmse:1.284784+0.261982
## [44] train-rmse:0.126299+0.012088 test-rmse:1.282700+0.261684
## [45] train-rmse:0.117982+0.011247 test-rmse:1.281700+0.261421
## [46] train-rmse:0.111534+0.010432 test-rmse:1.281518+0.259655
## [47] train-rmse:0.104455+0.007756 test-rmse:1.281067+0.259659
## [48] train-rmse:0.100750+0.008033 test-rmse:1.280405+0.260665
## [49] train-rmse:0.095778+0.008244 test-rmse:1.278113+0.260721
## [50] train-rmse:0.090228+0.006971 test-rmse:1.276708+0.261801
## [51] train-rmse:0.085405+0.006725 test-rmse:1.276495+0.262860
## [52] train-rmse:0.081282+0.006279 test-rmse:1.275772+0.262817
## [53] train-rmse:0.078033+0.005595 test-rmse:1.275381+0.262999
## [54] train-rmse:0.073198+0.006634 test-rmse:1.275000+0.263881
## [55] train-rmse:0.069133+0.006140 test-rmse:1.274384+0.264011
## [56] train-rmse:0.066475+0.006473 test-rmse:1.274858+0.264982
## [57] train-rmse:0.062668+0.006244 test-rmse:1.274889+0.265267
## [58] train-rmse:0.058888+0.005518 test-rmse:1.275091+0.265368
## [59] train-rmse:0.055657+0.005195 test-rmse:1.274922+0.265942
## [60] train-rmse:0.053600+0.005522 test-rmse:1.273984+0.266587
## [61] train-rmse:0.051695+0.005282 test-rmse:1.273699+0.267195
## [62] train-rmse:0.049243+0.005818 test-rmse:1.273808+0.267770
## [63] train-rmse:0.047538+0.005205 test-rmse:1.273486+0.267814
## [64] train-rmse:0.045292+0.005169 test-rmse:1.273547+0.268301
## [65] train-rmse:0.042940+0.004588 test-rmse:1.273793+0.268484
## [66] train-rmse:0.040861+0.003952 test-rmse:1.273389+0.268560
## [67] train-rmse:0.038494+0.003625 test-rmse:1.273383+0.268560
## [68] train-rmse:0.036800+0.003478 test-rmse:1.273154+0.268536
## [69] train-rmse:0.034601+0.003911 test-rmse:1.273250+0.268275
## [70] train-rmse:0.033012+0.003341 test-rmse:1.272762+0.267915
## [71] train-rmse:0.031328+0.003755 test-rmse:1.272633+0.267357
## [72] train-rmse:0.029705+0.003661 test-rmse:1.272578+0.267371
## [73] train-rmse:0.028358+0.003434 test-rmse:1.272883+0.267838
## [74] train-rmse:0.027076+0.003343 test-rmse:1.272928+0.267858
## [75] train-rmse:0.026068+0.003092 test-rmse:1.272497+0.267910
## [76] train-rmse:0.024712+0.002844 test-rmse:1.272393+0.268137
## [77] train-rmse:0.023408+0.003062 test-rmse:1.272601+0.268089
## [78] train-rmse:0.022199+0.002949 test-rmse:1.273141+0.268574
## [79] train-rmse:0.021035+0.002650 test-rmse:1.272895+0.268589
## [80] train-rmse:0.019587+0.002330 test-rmse:1.273021+0.268991
## [81] train-rmse:0.018738+0.002061 test-rmse:1.272558+0.268573
## [82] train-rmse:0.017633+0.001881 test-rmse:1.272616+0.268751
## Stopping. Best iteration:
## [76] train-rmse:0.024712+0.002844 test-rmse:1.272393+0.268137
##
## 91
## [1] train-rmse:69.134740+0.083369 test-rmse:69.134537+0.523672
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:44.549814+0.067020 test-rmse:44.548320+0.577398
## [3] train-rmse:28.902591+0.064471 test-rmse:28.898605+0.635371
## [4] train-rmse:18.983275+0.059510 test-rmse:19.072898+0.659096
## [5] train-rmse:12.735609+0.050548 test-rmse:12.797549+0.657194
## [6] train-rmse:8.900907+0.056620 test-rmse:9.001007+0.595841
## [7] train-rmse:6.617228+0.062627 test-rmse:6.779030+0.676910
## [8] train-rmse:5.320866+0.066017 test-rmse:5.431449+0.599056
## [9] train-rmse:4.606122+0.070667 test-rmse:4.716379+0.492620
## [10] train-rmse:4.196975+0.065699 test-rmse:4.345866+0.510582
## [11] train-rmse:3.954170+0.067734 test-rmse:4.088585+0.423972
## [12] train-rmse:3.794484+0.063583 test-rmse:3.960193+0.441878
## [13] train-rmse:3.667457+0.062516 test-rmse:3.821125+0.425059
## [14] train-rmse:3.563794+0.060441 test-rmse:3.728804+0.320241
## [15] train-rmse:3.474962+0.058170 test-rmse:3.677345+0.327584
## [16] train-rmse:3.395159+0.056209 test-rmse:3.603821+0.324421
## [17] train-rmse:3.315104+0.055827 test-rmse:3.553974+0.329972
## [18] train-rmse:3.243164+0.052617 test-rmse:3.515535+0.326229
## [19] train-rmse:3.172721+0.049533 test-rmse:3.438189+0.338604
## [20] train-rmse:3.104742+0.046914 test-rmse:3.396945+0.357221
## [21] train-rmse:3.039514+0.044252 test-rmse:3.309467+0.301214
## [22] train-rmse:2.977184+0.042670 test-rmse:3.231154+0.308299
## [23] train-rmse:2.917936+0.040870 test-rmse:3.173595+0.301779
## [24] train-rmse:2.861844+0.039069 test-rmse:3.099588+0.276340
## [25] train-rmse:2.807002+0.038362 test-rmse:3.049584+0.272320
## [26] train-rmse:2.757399+0.037490 test-rmse:2.976443+0.279296
## [27] train-rmse:2.710001+0.037781 test-rmse:2.938247+0.288659
## [28] train-rmse:2.663287+0.039642 test-rmse:2.908704+0.297419
## [29] train-rmse:2.617869+0.040411 test-rmse:2.879103+0.303992
## [30] train-rmse:2.573717+0.040886 test-rmse:2.846984+0.303700
## [31] train-rmse:2.529852+0.041955 test-rmse:2.818702+0.316167
## [32] train-rmse:2.488879+0.042099 test-rmse:2.776077+0.327185
## [33] train-rmse:2.448503+0.041503 test-rmse:2.755945+0.324644
## [34] train-rmse:2.409826+0.040438 test-rmse:2.703096+0.325368
## [35] train-rmse:2.370584+0.038439 test-rmse:2.665939+0.320291
## [36] train-rmse:2.332865+0.036696 test-rmse:2.607278+0.273999
## [37] train-rmse:2.296817+0.033529 test-rmse:2.589448+0.282666
## [38] train-rmse:2.260518+0.032344 test-rmse:2.543384+0.292113
## [39] train-rmse:2.226673+0.031117 test-rmse:2.514568+0.274813
## [40] train-rmse:2.194100+0.030553 test-rmse:2.477048+0.292114
## [41] train-rmse:2.161814+0.029958 test-rmse:2.461867+0.296010
## [42] train-rmse:2.130175+0.029349 test-rmse:2.434900+0.293634
## [43] train-rmse:2.100199+0.028534 test-rmse:2.393798+0.271699
## [44] train-rmse:2.071967+0.028365 test-rmse:2.360806+0.281849
## [45] train-rmse:2.043288+0.028443 test-rmse:2.326725+0.276080
## [46] train-rmse:2.014350+0.028707 test-rmse:2.303251+0.275687
## [47] train-rmse:1.986837+0.027318 test-rmse:2.283895+0.261872
## [48] train-rmse:1.959062+0.025750 test-rmse:2.253618+0.266938
## [49] train-rmse:1.931333+0.023961 test-rmse:2.225983+0.261265
## [50] train-rmse:1.904824+0.023070 test-rmse:2.200165+0.260328
## [51] train-rmse:1.878160+0.022026 test-rmse:2.161506+0.284629
## [52] train-rmse:1.852806+0.020747 test-rmse:2.148442+0.283053
## [53] train-rmse:1.827518+0.019828 test-rmse:2.124874+0.268684
## [54] train-rmse:1.802425+0.019220 test-rmse:2.097863+0.264468
## [55] train-rmse:1.778373+0.019181 test-rmse:2.073143+0.264212
## [56] train-rmse:1.754911+0.018154 test-rmse:2.048909+0.253422
## [57] train-rmse:1.732540+0.017575 test-rmse:2.013061+0.263901
## [58] train-rmse:1.710873+0.017488 test-rmse:2.001823+0.261663
## [59] train-rmse:1.689644+0.017105 test-rmse:1.991894+0.262600
## [60] train-rmse:1.668503+0.016911 test-rmse:1.976062+0.254457
## [61] train-rmse:1.648511+0.017268 test-rmse:1.931870+0.228643
## [62] train-rmse:1.629446+0.017034 test-rmse:1.920639+0.232806
## [63] train-rmse:1.610649+0.016542 test-rmse:1.910979+0.236529
## [64] train-rmse:1.592052+0.016090 test-rmse:1.901472+0.236902
## [65] train-rmse:1.573132+0.015333 test-rmse:1.892229+0.242506
## [66] train-rmse:1.554687+0.015506 test-rmse:1.882464+0.238661
## [67] train-rmse:1.537045+0.016058 test-rmse:1.857289+0.230243
## [68] train-rmse:1.519566+0.016259 test-rmse:1.847229+0.226352
## [69] train-rmse:1.502352+0.016304 test-rmse:1.827053+0.232580
## [70] train-rmse:1.486332+0.016173 test-rmse:1.794646+0.224531
## [71] train-rmse:1.470526+0.015719 test-rmse:1.780952+0.226196
## [72] train-rmse:1.454546+0.015364 test-rmse:1.763636+0.234478
## [73] train-rmse:1.438723+0.015112 test-rmse:1.740956+0.243719
## [74] train-rmse:1.423812+0.015167 test-rmse:1.733272+0.239358
## [75] train-rmse:1.408763+0.015360 test-rmse:1.716103+0.234926
## [76] train-rmse:1.393486+0.015515 test-rmse:1.697300+0.240008
## [77] train-rmse:1.378568+0.015910 test-rmse:1.682290+0.236592
## [78] train-rmse:1.364447+0.015421 test-rmse:1.675939+0.226587
## [79] train-rmse:1.350801+0.014678 test-rmse:1.652500+0.203387
## [80] train-rmse:1.337100+0.013881 test-rmse:1.643208+0.200377
## [81] train-rmse:1.323558+0.013559 test-rmse:1.633528+0.204046
## [82] train-rmse:1.310737+0.012989 test-rmse:1.625598+0.213123
## [83] train-rmse:1.298372+0.012467 test-rmse:1.618371+0.211021
## [84] train-rmse:1.286131+0.012339 test-rmse:1.609102+0.213192
## [85] train-rmse:1.274656+0.012362 test-rmse:1.612000+0.208976
## [86] train-rmse:1.263070+0.012597 test-rmse:1.599120+0.213976
## [87] train-rmse:1.251805+0.012758 test-rmse:1.578109+0.209256
## [88] train-rmse:1.241211+0.012695 test-rmse:1.566529+0.215660
## [89] train-rmse:1.230436+0.012262 test-rmse:1.551898+0.208212
## [90] train-rmse:1.219799+0.012346 test-rmse:1.539300+0.198581
## [91] train-rmse:1.209434+0.012667 test-rmse:1.522323+0.199937
## [92] train-rmse:1.199249+0.013024 test-rmse:1.516137+0.202517
## [93] train-rmse:1.189078+0.013467 test-rmse:1.506482+0.204593
## [94] train-rmse:1.179391+0.013243 test-rmse:1.494811+0.209786
## [95] train-rmse:1.169928+0.013114 test-rmse:1.488144+0.213979
## [96] train-rmse:1.160897+0.012633 test-rmse:1.486252+0.212647
## [97] train-rmse:1.151952+0.012383 test-rmse:1.471367+0.195454
## [98] train-rmse:1.142771+0.012149 test-rmse:1.465127+0.195245
## [99] train-rmse:1.134156+0.012119 test-rmse:1.453879+0.205918
## [100] train-rmse:1.125608+0.011963 test-rmse:1.443894+0.204888
## [101] train-rmse:1.117324+0.011821 test-rmse:1.434694+0.202278
## [102] train-rmse:1.109103+0.011664 test-rmse:1.425280+0.200792
## [103] train-rmse:1.101082+0.011698 test-rmse:1.419595+0.202382
## [104] train-rmse:1.093158+0.011772 test-rmse:1.407076+0.199528
## [105] train-rmse:1.085544+0.011923 test-rmse:1.400736+0.203573
## [106] train-rmse:1.077888+0.011946 test-rmse:1.394408+0.205280
## [107] train-rmse:1.070507+0.012012 test-rmse:1.386544+0.207928
## [108] train-rmse:1.063414+0.011972 test-rmse:1.380767+0.211223
## [109] train-rmse:1.056469+0.011988 test-rmse:1.369371+0.203189
## [110] train-rmse:1.049782+0.012039 test-rmse:1.362371+0.205771
## [111] train-rmse:1.043191+0.012075 test-rmse:1.355639+0.210334
## [112] train-rmse:1.036781+0.012227 test-rmse:1.346931+0.206621
## [113] train-rmse:1.030438+0.012317 test-rmse:1.345669+0.205115
## [114] train-rmse:1.024522+0.012367 test-rmse:1.342649+0.203501
## [115] train-rmse:1.018554+0.012166 test-rmse:1.345216+0.200841
## [116] train-rmse:1.012704+0.012139 test-rmse:1.340224+0.202050
## [117] train-rmse:1.006969+0.012261 test-rmse:1.335957+0.204375
## [118] train-rmse:1.001299+0.012432 test-rmse:1.334010+0.203590
## [119] train-rmse:0.995592+0.012523 test-rmse:1.331844+0.203554
## [120] train-rmse:0.990126+0.012370 test-rmse:1.329290+0.204438
## [121] train-rmse:0.984841+0.012164 test-rmse:1.329665+0.204491
## [122] train-rmse:0.979662+0.011975 test-rmse:1.315268+0.196195
## [123] train-rmse:0.974834+0.011968 test-rmse:1.305409+0.193355
## [124] train-rmse:0.970092+0.012141 test-rmse:1.302018+0.190287
## [125] train-rmse:0.965506+0.012165 test-rmse:1.296418+0.184835
## [126] train-rmse:0.960996+0.012113 test-rmse:1.297212+0.180638
## [127] train-rmse:0.956458+0.011969 test-rmse:1.292461+0.182695
## [128] train-rmse:0.951941+0.011867 test-rmse:1.285847+0.183057
## [129] train-rmse:0.947592+0.011889 test-rmse:1.280408+0.183880
## [130] train-rmse:0.943213+0.011939 test-rmse:1.274243+0.183976
## [131] train-rmse:0.939115+0.011917 test-rmse:1.269538+0.189500
## [132] train-rmse:0.935058+0.011851 test-rmse:1.268193+0.191334
## [133] train-rmse:0.931058+0.011789 test-rmse:1.259981+0.188375
## [134] train-rmse:0.927097+0.011671 test-rmse:1.258684+0.187644
## [135] train-rmse:0.923188+0.011632 test-rmse:1.257004+0.187953
## [136] train-rmse:0.919479+0.011556 test-rmse:1.253107+0.185526
## [137] train-rmse:0.915742+0.011540 test-rmse:1.250212+0.186255
## [138] train-rmse:0.912050+0.011563 test-rmse:1.243734+0.190643
## [139] train-rmse:0.908521+0.011596 test-rmse:1.239343+0.193638
## [140] train-rmse:0.905052+0.011694 test-rmse:1.236779+0.195073
## [141] train-rmse:0.901566+0.011889 test-rmse:1.235228+0.195057
## [142] train-rmse:0.898304+0.012015 test-rmse:1.230718+0.195856
## [143] train-rmse:0.895104+0.012250 test-rmse:1.229303+0.195999
## [144] train-rmse:0.892038+0.012362 test-rmse:1.229164+0.198633
## [145] train-rmse:0.888979+0.012464 test-rmse:1.228366+0.199770
## [146] train-rmse:0.886012+0.012613 test-rmse:1.227868+0.199300
## [147] train-rmse:0.883180+0.012717 test-rmse:1.227871+0.196689
## [148] train-rmse:0.880364+0.012836 test-rmse:1.226115+0.196732
## [149] train-rmse:0.877621+0.012863 test-rmse:1.222337+0.199792
## [150] train-rmse:0.874935+0.012892 test-rmse:1.222338+0.199775
## [151] train-rmse:0.872296+0.012839 test-rmse:1.220519+0.195754
## [152] train-rmse:0.869658+0.012838 test-rmse:1.217796+0.196049
## [153] train-rmse:0.866983+0.012709 test-rmse:1.211603+0.189596
## [154] train-rmse:0.864440+0.012750 test-rmse:1.210443+0.191571
## [155] train-rmse:0.861889+0.012804 test-rmse:1.204001+0.190655
## [156] train-rmse:0.859461+0.012918 test-rmse:1.203665+0.189980
## [157] train-rmse:0.856927+0.013112 test-rmse:1.201603+0.186515
## [158] train-rmse:0.854545+0.013188 test-rmse:1.204061+0.185092
## [159] train-rmse:0.852211+0.013249 test-rmse:1.199608+0.186780
## [160] train-rmse:0.849894+0.013280 test-rmse:1.194995+0.188761
## [161] train-rmse:0.847679+0.013284 test-rmse:1.195998+0.187364
## [162] train-rmse:0.845457+0.013380 test-rmse:1.192199+0.190051
## [163] train-rmse:0.843257+0.013480 test-rmse:1.190002+0.191111
## [164] train-rmse:0.841001+0.013623 test-rmse:1.191630+0.191806
## [165] train-rmse:0.838976+0.013695 test-rmse:1.188746+0.191106
## [166] train-rmse:0.836966+0.013860 test-rmse:1.188278+0.190590
## [167] train-rmse:0.834993+0.013992 test-rmse:1.186482+0.189974
## [168] train-rmse:0.833060+0.014110 test-rmse:1.184302+0.189145
## [169] train-rmse:0.831171+0.014200 test-rmse:1.181121+0.191113
## [170] train-rmse:0.829327+0.014365 test-rmse:1.178470+0.192627
## [171] train-rmse:0.827507+0.014462 test-rmse:1.178522+0.191680
## [172] train-rmse:0.825751+0.014567 test-rmse:1.172312+0.187632
## [173] train-rmse:0.824013+0.014668 test-rmse:1.168418+0.186134
## [174] train-rmse:0.822242+0.014753 test-rmse:1.164179+0.188693
## [175] train-rmse:0.820451+0.014852 test-rmse:1.161366+0.191186
## [176] train-rmse:0.818632+0.015037 test-rmse:1.160676+0.189095
## [177] train-rmse:0.816986+0.015135 test-rmse:1.154815+0.188089
## [178] train-rmse:0.815355+0.015242 test-rmse:1.155263+0.189637
## [179] train-rmse:0.813670+0.015265 test-rmse:1.153437+0.190433
## [180] train-rmse:0.812096+0.015385 test-rmse:1.149769+0.192880
## [181] train-rmse:0.810298+0.015455 test-rmse:1.152439+0.192063
## [182] train-rmse:0.808619+0.015489 test-rmse:1.150940+0.190749
## [183] train-rmse:0.806841+0.015553 test-rmse:1.148618+0.194408
## [184] train-rmse:0.805201+0.015678 test-rmse:1.147866+0.193069
## [185] train-rmse:0.803614+0.015829 test-rmse:1.148567+0.190478
## [186] train-rmse:0.802034+0.015920 test-rmse:1.146554+0.191315
## [187] train-rmse:0.800529+0.015967 test-rmse:1.144786+0.190025
## [188] train-rmse:0.799046+0.016008 test-rmse:1.145128+0.188709
## [189] train-rmse:0.797602+0.016055 test-rmse:1.145415+0.187787
## [190] train-rmse:0.796150+0.016126 test-rmse:1.142329+0.189383
## [191] train-rmse:0.794752+0.016145 test-rmse:1.138708+0.190692
## [192] train-rmse:0.793362+0.016199 test-rmse:1.140518+0.188072
## [193] train-rmse:0.792002+0.016256 test-rmse:1.139393+0.189697
## [194] train-rmse:0.790653+0.016331 test-rmse:1.138593+0.189994
## [195] train-rmse:0.789223+0.016410 test-rmse:1.135566+0.189026
## [196] train-rmse:0.787872+0.016488 test-rmse:1.133257+0.188757
## [197] train-rmse:0.786559+0.016459 test-rmse:1.133049+0.190573
## [198] train-rmse:0.785313+0.016465 test-rmse:1.130339+0.192162
## [199] train-rmse:0.784018+0.016508 test-rmse:1.130703+0.191395
## [200] train-rmse:0.782775+0.016544 test-rmse:1.131529+0.190868
## [201] train-rmse:0.781538+0.016566 test-rmse:1.132149+0.190241
## [202] train-rmse:0.780286+0.016605 test-rmse:1.129396+0.191540
## [203] train-rmse:0.779054+0.016758 test-rmse:1.129748+0.192157
## [204] train-rmse:0.777925+0.016810 test-rmse:1.128245+0.191802
## [205] train-rmse:0.776815+0.016885 test-rmse:1.128479+0.191211
## [206] train-rmse:0.775653+0.017002 test-rmse:1.129081+0.192794
## [207] train-rmse:0.774526+0.017091 test-rmse:1.129085+0.192752
## [208] train-rmse:0.773443+0.017134 test-rmse:1.129350+0.192318
## [209] train-rmse:0.772363+0.017155 test-rmse:1.127679+0.191879
## [210] train-rmse:0.771233+0.017170 test-rmse:1.124549+0.188804
## [211] train-rmse:0.770148+0.017185 test-rmse:1.122597+0.188609
## [212] train-rmse:0.769042+0.017238 test-rmse:1.120618+0.190505
## [213] train-rmse:0.767985+0.017271 test-rmse:1.118039+0.188306
## [214] train-rmse:0.766944+0.017322 test-rmse:1.115713+0.188883
## [215] train-rmse:0.765926+0.017371 test-rmse:1.115905+0.189194
## [216] train-rmse:0.764884+0.017316 test-rmse:1.115251+0.188171
## [217] train-rmse:0.763858+0.017333 test-rmse:1.115256+0.189706
## [218] train-rmse:0.762821+0.017326 test-rmse:1.112269+0.185629
## [219] train-rmse:0.761818+0.017353 test-rmse:1.114220+0.184090
## [220] train-rmse:0.760810+0.017376 test-rmse:1.115011+0.183346
## [221] train-rmse:0.759851+0.017394 test-rmse:1.113473+0.182806
## [222] train-rmse:0.758910+0.017379 test-rmse:1.113109+0.184064
## [223] train-rmse:0.757962+0.017369 test-rmse:1.111955+0.182370
## [224] train-rmse:0.757046+0.017386 test-rmse:1.111559+0.183116
## [225] train-rmse:0.756142+0.017411 test-rmse:1.109020+0.181382
## [226] train-rmse:0.755205+0.017396 test-rmse:1.108387+0.182978
## [227] train-rmse:0.754317+0.017383 test-rmse:1.109873+0.183058
## [228] train-rmse:0.753401+0.017413 test-rmse:1.108703+0.181195
## [229] train-rmse:0.752530+0.017402 test-rmse:1.106955+0.181724
## [230] train-rmse:0.751704+0.017407 test-rmse:1.105082+0.180761
## [231] train-rmse:0.750885+0.017419 test-rmse:1.105848+0.180780
## [232] train-rmse:0.750013+0.017427 test-rmse:1.104977+0.179975
## [233] train-rmse:0.749211+0.017436 test-rmse:1.104607+0.178891
## [234] train-rmse:0.748408+0.017446 test-rmse:1.102108+0.178067
## [235] train-rmse:0.747574+0.017465 test-rmse:1.101605+0.178300
## [236] train-rmse:0.746760+0.017466 test-rmse:1.100725+0.178894
## [237] train-rmse:0.745936+0.017407 test-rmse:1.099766+0.179319
## [238] train-rmse:0.745127+0.017397 test-rmse:1.098864+0.178642
## [239] train-rmse:0.744331+0.017367 test-rmse:1.098822+0.178834
## [240] train-rmse:0.743530+0.017304 test-rmse:1.097035+0.178855
## [241] train-rmse:0.742774+0.017278 test-rmse:1.095752+0.178742
## [242] train-rmse:0.742007+0.017254 test-rmse:1.093070+0.179324
## [243] train-rmse:0.741250+0.017230 test-rmse:1.092314+0.176695
## [244] train-rmse:0.740511+0.017221 test-rmse:1.091078+0.177419
## [245] train-rmse:0.739703+0.017203 test-rmse:1.087462+0.174458
## [246] train-rmse:0.738951+0.017225 test-rmse:1.088036+0.174261
## [247] train-rmse:0.738205+0.017243 test-rmse:1.086508+0.173922
## [248] train-rmse:0.737452+0.017245 test-rmse:1.086912+0.174018
## [249] train-rmse:0.736731+0.017238 test-rmse:1.084892+0.174161
## [250] train-rmse:0.736024+0.017250 test-rmse:1.085196+0.174414
## [251] train-rmse:0.735299+0.017249 test-rmse:1.083595+0.172778
## [252] train-rmse:0.734573+0.017222 test-rmse:1.082209+0.170524
## [253] train-rmse:0.733831+0.017213 test-rmse:1.081082+0.171059
## [254] train-rmse:0.733138+0.017217 test-rmse:1.082801+0.170120
## [255] train-rmse:0.732477+0.017209 test-rmse:1.081335+0.171690
## [256] train-rmse:0.731832+0.017179 test-rmse:1.081784+0.170430
## [257] train-rmse:0.731152+0.017139 test-rmse:1.080083+0.171818
## [258] train-rmse:0.730500+0.017096 test-rmse:1.080161+0.172912
## [259] train-rmse:0.729807+0.017077 test-rmse:1.079946+0.173010
## [260] train-rmse:0.729165+0.017029 test-rmse:1.078925+0.171837
## [261] train-rmse:0.728512+0.016978 test-rmse:1.078448+0.171356
## [262] train-rmse:0.727837+0.016916 test-rmse:1.077206+0.169351
## [263] train-rmse:0.727158+0.016879 test-rmse:1.077326+0.169540
## [264] train-rmse:0.726492+0.016855 test-rmse:1.077536+0.168879
## [265] train-rmse:0.725845+0.016788 test-rmse:1.076220+0.167438
## [266] train-rmse:0.725229+0.016758 test-rmse:1.077149+0.166857
## [267] train-rmse:0.724613+0.016723 test-rmse:1.076096+0.166242
## [268] train-rmse:0.723986+0.016722 test-rmse:1.074171+0.163856
## [269] train-rmse:0.723340+0.016719 test-rmse:1.074106+0.164044
## [270] train-rmse:0.722721+0.016695 test-rmse:1.074520+0.164010
## [271] train-rmse:0.722114+0.016654 test-rmse:1.073731+0.163278
## [272] train-rmse:0.721484+0.016632 test-rmse:1.074887+0.162891
## [273] train-rmse:0.720843+0.016604 test-rmse:1.071318+0.162691
## [274] train-rmse:0.720232+0.016516 test-rmse:1.071820+0.162856
## [275] train-rmse:0.719641+0.016484 test-rmse:1.070289+0.162779
## [276] train-rmse:0.719057+0.016461 test-rmse:1.069456+0.161929
## [277] train-rmse:0.718485+0.016455 test-rmse:1.069417+0.161304
## [278] train-rmse:0.717918+0.016443 test-rmse:1.069337+0.161775
## [279] train-rmse:0.717333+0.016442 test-rmse:1.069193+0.163192
## [280] train-rmse:0.716773+0.016449 test-rmse:1.069463+0.162077
## [281] train-rmse:0.716205+0.016429 test-rmse:1.067598+0.157714
## [282] train-rmse:0.715655+0.016401 test-rmse:1.066679+0.156920
## [283] train-rmse:0.715100+0.016387 test-rmse:1.066433+0.157843
## [284] train-rmse:0.714536+0.016355 test-rmse:1.066049+0.157411
## [285] train-rmse:0.713977+0.016304 test-rmse:1.063680+0.157079
## [286] train-rmse:0.713427+0.016257 test-rmse:1.063158+0.156316
## [287] train-rmse:0.712879+0.016196 test-rmse:1.063534+0.156586
## [288] train-rmse:0.712361+0.016174 test-rmse:1.062872+0.157076
## [289] train-rmse:0.711828+0.016117 test-rmse:1.062475+0.157394
## [290] train-rmse:0.711326+0.016090 test-rmse:1.063190+0.156902
## [291] train-rmse:0.710823+0.016055 test-rmse:1.063322+0.156487
## [292] train-rmse:0.710261+0.016062 test-rmse:1.062113+0.155334
## [293] train-rmse:0.709747+0.016063 test-rmse:1.061264+0.155210
## [294] train-rmse:0.709204+0.016040 test-rmse:1.061973+0.154609
## [295] train-rmse:0.708695+0.015982 test-rmse:1.058363+0.153866
## [296] train-rmse:0.708161+0.015971 test-rmse:1.059563+0.153147
## [297] train-rmse:0.707665+0.015967 test-rmse:1.058825+0.152914
## [298] train-rmse:0.707180+0.015927 test-rmse:1.058848+0.151460
## [299] train-rmse:0.706672+0.015940 test-rmse:1.056294+0.150160
## [300] train-rmse:0.706199+0.015911 test-rmse:1.056128+0.148302
## [301] train-rmse:0.705703+0.015874 test-rmse:1.056362+0.148706
## [302] train-rmse:0.705203+0.015843 test-rmse:1.055679+0.147825
## [303] train-rmse:0.704733+0.015819 test-rmse:1.054813+0.148318
## [304] train-rmse:0.704273+0.015796 test-rmse:1.055090+0.148540
## [305] train-rmse:0.703806+0.015783 test-rmse:1.054639+0.148875
## [306] train-rmse:0.703361+0.015766 test-rmse:1.053630+0.148333
## [307] train-rmse:0.702865+0.015769 test-rmse:1.051759+0.148089
## [308] train-rmse:0.702393+0.015775 test-rmse:1.053824+0.147628
## [309] train-rmse:0.701904+0.015731 test-rmse:1.052695+0.148532
## [310] train-rmse:0.701429+0.015672 test-rmse:1.053284+0.147459
## [311] train-rmse:0.700945+0.015644 test-rmse:1.052648+0.147090
## [312] train-rmse:0.700462+0.015622 test-rmse:1.050693+0.146795
## [313] train-rmse:0.700006+0.015596 test-rmse:1.049895+0.146250
## [314] train-rmse:0.699576+0.015568 test-rmse:1.047704+0.144286
## [315] train-rmse:0.699144+0.015553 test-rmse:1.047813+0.144074
## [316] train-rmse:0.698691+0.015549 test-rmse:1.047966+0.143682
## [317] train-rmse:0.698257+0.015517 test-rmse:1.047845+0.142294
## [318] train-rmse:0.697795+0.015493 test-rmse:1.047619+0.143602
## [319] train-rmse:0.697365+0.015469 test-rmse:1.046690+0.143882
## [320] train-rmse:0.696930+0.015438 test-rmse:1.046574+0.144156
## [321] train-rmse:0.696514+0.015422 test-rmse:1.046556+0.142201
## [322] train-rmse:0.696096+0.015410 test-rmse:1.046606+0.142259
## [323] train-rmse:0.695650+0.015358 test-rmse:1.046066+0.142264
## [324] train-rmse:0.695214+0.015325 test-rmse:1.046430+0.142295
## [325] train-rmse:0.694787+0.015299 test-rmse:1.046479+0.141392
## [326] train-rmse:0.694374+0.015269 test-rmse:1.046276+0.141730
## [327] train-rmse:0.693930+0.015262 test-rmse:1.046820+0.141655
## [328] train-rmse:0.693521+0.015241 test-rmse:1.047260+0.141363
## [329] train-rmse:0.693110+0.015231 test-rmse:1.047129+0.140032
## Stopping. Best iteration:
## [323] train-rmse:0.695650+0.015358 test-rmse:1.046066+0.142264
##
## 92
## [1] train-rmse:69.134740+0.083369 test-rmse:69.134537+0.523672
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:44.549814+0.067020 test-rmse:44.548320+0.577398
## [3] train-rmse:28.902591+0.064471 test-rmse:28.898605+0.635371
## [4] train-rmse:18.937317+0.040829 test-rmse:19.041522+0.668695
## [5] train-rmse:12.624304+0.033165 test-rmse:12.736479+0.571299
## [6] train-rmse:8.673716+0.036045 test-rmse:8.763494+0.562853
## [7] train-rmse:6.270634+0.048807 test-rmse:6.406820+0.594784
## [8] train-rmse:4.863757+0.051246 test-rmse:5.058846+0.595578
## [9] train-rmse:4.055699+0.048864 test-rmse:4.238751+0.503527
## [10] train-rmse:3.587957+0.050230 test-rmse:3.803427+0.425174
## [11] train-rmse:3.294281+0.043854 test-rmse:3.585424+0.389489
## [12] train-rmse:3.097735+0.038122 test-rmse:3.432272+0.400962
## [13] train-rmse:2.935408+0.032819 test-rmse:3.229126+0.337074
## [14] train-rmse:2.810993+0.026744 test-rmse:3.096750+0.296959
## [15] train-rmse:2.701264+0.030403 test-rmse:2.967759+0.322367
## [16] train-rmse:2.596609+0.027857 test-rmse:2.883300+0.291007
## [17] train-rmse:2.507288+0.028201 test-rmse:2.792414+0.304198
## [18] train-rmse:2.418762+0.022850 test-rmse:2.739210+0.276209
## [19] train-rmse:2.330991+0.016318 test-rmse:2.687979+0.286119
## [20] train-rmse:2.253156+0.015447 test-rmse:2.617707+0.283422
## [21] train-rmse:2.170379+0.009401 test-rmse:2.545172+0.296363
## [22] train-rmse:2.100335+0.009544 test-rmse:2.468185+0.277322
## [23] train-rmse:2.034264+0.007940 test-rmse:2.403396+0.244917
## [24] train-rmse:1.970669+0.008367 test-rmse:2.346489+0.265363
## [25] train-rmse:1.912368+0.007973 test-rmse:2.295282+0.261029
## [26] train-rmse:1.850331+0.011939 test-rmse:2.234368+0.268614
## [27] train-rmse:1.792949+0.013708 test-rmse:2.205518+0.261134
## [28] train-rmse:1.737561+0.014382 test-rmse:2.161303+0.250784
## [29] train-rmse:1.688289+0.013623 test-rmse:2.101606+0.247366
## [30] train-rmse:1.641708+0.015922 test-rmse:2.067014+0.259966
## [31] train-rmse:1.594494+0.014490 test-rmse:2.022640+0.248199
## [32] train-rmse:1.550680+0.013648 test-rmse:2.009630+0.254451
## [33] train-rmse:1.509382+0.014951 test-rmse:1.978013+0.272678
## [34] train-rmse:1.470902+0.015703 test-rmse:1.941674+0.270897
## [35] train-rmse:1.431335+0.012058 test-rmse:1.910181+0.275221
## [36] train-rmse:1.393137+0.010773 test-rmse:1.881973+0.278119
## [37] train-rmse:1.355940+0.008582 test-rmse:1.843925+0.273019
## [38] train-rmse:1.322596+0.009009 test-rmse:1.811725+0.276164
## [39] train-rmse:1.287488+0.012293 test-rmse:1.787050+0.278196
## [40] train-rmse:1.255293+0.012584 test-rmse:1.760997+0.281045
## [41] train-rmse:1.223032+0.010015 test-rmse:1.719131+0.260138
## [42] train-rmse:1.194922+0.010311 test-rmse:1.676403+0.255930
## [43] train-rmse:1.168007+0.011974 test-rmse:1.664043+0.256879
## [44] train-rmse:1.141320+0.013484 test-rmse:1.654440+0.250708
## [45] train-rmse:1.115394+0.012797 test-rmse:1.625270+0.252722
## [46] train-rmse:1.088419+0.013458 test-rmse:1.597814+0.254859
## [47] train-rmse:1.065220+0.014586 test-rmse:1.579472+0.265118
## [48] train-rmse:1.041419+0.014602 test-rmse:1.568747+0.270756
## [49] train-rmse:1.020662+0.015259 test-rmse:1.555512+0.281298
## [50] train-rmse:0.997517+0.019313 test-rmse:1.532153+0.266891
## [51] train-rmse:0.972931+0.022081 test-rmse:1.516452+0.268307
## [52] train-rmse:0.953730+0.022712 test-rmse:1.498963+0.269164
## [53] train-rmse:0.935286+0.023168 test-rmse:1.480190+0.270910
## [54] train-rmse:0.918819+0.023746 test-rmse:1.460663+0.270270
## [55] train-rmse:0.899607+0.020685 test-rmse:1.450646+0.267449
## [56] train-rmse:0.881563+0.020200 test-rmse:1.448842+0.269562
## [57] train-rmse:0.866242+0.019788 test-rmse:1.438902+0.273095
## [58] train-rmse:0.851465+0.020251 test-rmse:1.428592+0.272172
## [59] train-rmse:0.836289+0.019052 test-rmse:1.417027+0.269621
## [60] train-rmse:0.822781+0.019479 test-rmse:1.405407+0.271283
## [61] train-rmse:0.809473+0.020779 test-rmse:1.398249+0.273861
## [62] train-rmse:0.795298+0.022326 test-rmse:1.385974+0.280832
## [63] train-rmse:0.783687+0.023117 test-rmse:1.378321+0.283496
## [64] train-rmse:0.767365+0.022245 test-rmse:1.365960+0.276171
## [65] train-rmse:0.756036+0.021888 test-rmse:1.354720+0.270991
## [66] train-rmse:0.744760+0.020967 test-rmse:1.348214+0.274716
## [67] train-rmse:0.733430+0.020048 test-rmse:1.333571+0.280006
## [68] train-rmse:0.721351+0.018564 test-rmse:1.331043+0.283870
## [69] train-rmse:0.711376+0.017777 test-rmse:1.323224+0.285058
## [70] train-rmse:0.702087+0.017324 test-rmse:1.318881+0.285722
## [71] train-rmse:0.693475+0.017013 test-rmse:1.310804+0.287836
## [72] train-rmse:0.683178+0.016464 test-rmse:1.304539+0.284101
## [73] train-rmse:0.675362+0.016659 test-rmse:1.300921+0.284734
## [74] train-rmse:0.667525+0.015391 test-rmse:1.296447+0.288363
## [75] train-rmse:0.657893+0.015857 test-rmse:1.293708+0.285886
## [76] train-rmse:0.650962+0.016070 test-rmse:1.290288+0.284448
## [77] train-rmse:0.644840+0.015868 test-rmse:1.280046+0.289727
## [78] train-rmse:0.637616+0.016283 test-rmse:1.273324+0.282807
## [79] train-rmse:0.630732+0.016737 test-rmse:1.271829+0.286136
## [80] train-rmse:0.622826+0.015289 test-rmse:1.268618+0.282570
## [81] train-rmse:0.615781+0.015803 test-rmse:1.263423+0.286551
## [82] train-rmse:0.608858+0.015187 test-rmse:1.259820+0.291114
## [83] train-rmse:0.602508+0.015940 test-rmse:1.255429+0.291171
## [84] train-rmse:0.594665+0.014632 test-rmse:1.252911+0.293343
## [85] train-rmse:0.588059+0.016066 test-rmse:1.249782+0.297211
## [86] train-rmse:0.581309+0.015834 test-rmse:1.247479+0.299104
## [87] train-rmse:0.574991+0.015733 test-rmse:1.237613+0.293684
## [88] train-rmse:0.569141+0.015602 test-rmse:1.235786+0.293366
## [89] train-rmse:0.563234+0.017859 test-rmse:1.230778+0.296145
## [90] train-rmse:0.558634+0.018887 test-rmse:1.225521+0.295067
## [91] train-rmse:0.552735+0.019160 test-rmse:1.222601+0.291193
## [92] train-rmse:0.548469+0.019526 test-rmse:1.214707+0.294993
## [93] train-rmse:0.544278+0.019740 test-rmse:1.212055+0.295104
## [94] train-rmse:0.539755+0.020614 test-rmse:1.209817+0.293479
## [95] train-rmse:0.534788+0.020372 test-rmse:1.208192+0.295844
## [96] train-rmse:0.528290+0.019732 test-rmse:1.202988+0.296598
## [97] train-rmse:0.524265+0.019733 test-rmse:1.202568+0.295652
## [98] train-rmse:0.520274+0.019889 test-rmse:1.200507+0.290323
## [99] train-rmse:0.513738+0.019182 test-rmse:1.199724+0.289384
## [100] train-rmse:0.508246+0.019485 test-rmse:1.200681+0.289228
## [101] train-rmse:0.505279+0.019796 test-rmse:1.199610+0.291449
## [102] train-rmse:0.501345+0.019411 test-rmse:1.198865+0.291322
## [103] train-rmse:0.497442+0.019925 test-rmse:1.196473+0.294205
## [104] train-rmse:0.494160+0.020031 test-rmse:1.192682+0.291305
## [105] train-rmse:0.491365+0.020227 test-rmse:1.190684+0.291330
## [106] train-rmse:0.485671+0.020387 test-rmse:1.188305+0.292415
## [107] train-rmse:0.478405+0.014090 test-rmse:1.187836+0.292490
## [108] train-rmse:0.472894+0.010308 test-rmse:1.180125+0.297957
## [109] train-rmse:0.469399+0.009248 test-rmse:1.179193+0.298720
## [110] train-rmse:0.465370+0.009429 test-rmse:1.172359+0.294118
## [111] train-rmse:0.462106+0.009697 test-rmse:1.172649+0.296084
## [112] train-rmse:0.458621+0.010626 test-rmse:1.171783+0.298155
## [113] train-rmse:0.455931+0.010489 test-rmse:1.165401+0.300763
## [114] train-rmse:0.453327+0.010883 test-rmse:1.162683+0.299952
## [115] train-rmse:0.450889+0.010401 test-rmse:1.161422+0.300832
## [116] train-rmse:0.447945+0.011139 test-rmse:1.160609+0.301583
## [117] train-rmse:0.445582+0.010493 test-rmse:1.159721+0.301524
## [118] train-rmse:0.440944+0.011732 test-rmse:1.155353+0.303151
## [119] train-rmse:0.437604+0.011601 test-rmse:1.154218+0.303750
## [120] train-rmse:0.434446+0.010910 test-rmse:1.152105+0.305638
## [121] train-rmse:0.431298+0.010718 test-rmse:1.151782+0.307340
## [122] train-rmse:0.429190+0.010673 test-rmse:1.151845+0.306310
## [123] train-rmse:0.425327+0.009877 test-rmse:1.148868+0.306703
## [124] train-rmse:0.423315+0.010054 test-rmse:1.147919+0.306289
## [125] train-rmse:0.419490+0.008967 test-rmse:1.147872+0.306578
## [126] train-rmse:0.417157+0.009465 test-rmse:1.147583+0.305535
## [127] train-rmse:0.415050+0.009759 test-rmse:1.146105+0.307238
## [128] train-rmse:0.412716+0.010104 test-rmse:1.145947+0.306896
## [129] train-rmse:0.408627+0.012441 test-rmse:1.144977+0.307934
## [130] train-rmse:0.406485+0.012287 test-rmse:1.143327+0.309606
## [131] train-rmse:0.404702+0.012571 test-rmse:1.141939+0.310702
## [132] train-rmse:0.399410+0.011412 test-rmse:1.139427+0.313287
## [133] train-rmse:0.396529+0.011003 test-rmse:1.139684+0.314291
## [134] train-rmse:0.395156+0.010817 test-rmse:1.138277+0.313316
## [135] train-rmse:0.392240+0.010689 test-rmse:1.139766+0.311721
## [136] train-rmse:0.388287+0.011628 test-rmse:1.139339+0.311528
## [137] train-rmse:0.385536+0.011742 test-rmse:1.139129+0.310712
## [138] train-rmse:0.381679+0.011353 test-rmse:1.136887+0.312276
## [139] train-rmse:0.379215+0.012107 test-rmse:1.136967+0.311708
## [140] train-rmse:0.376865+0.012148 test-rmse:1.135797+0.310038
## [141] train-rmse:0.374552+0.011847 test-rmse:1.140300+0.318137
## [142] train-rmse:0.372503+0.010988 test-rmse:1.140508+0.317559
## [143] train-rmse:0.370724+0.011325 test-rmse:1.139326+0.317979
## [144] train-rmse:0.369066+0.011260 test-rmse:1.139754+0.319148
## [145] train-rmse:0.366944+0.010658 test-rmse:1.140137+0.319246
## [146] train-rmse:0.365220+0.010437 test-rmse:1.138883+0.319822
## Stopping. Best iteration:
## [140] train-rmse:0.376865+0.012148 test-rmse:1.135797+0.310038
##
## 93
## [1] train-rmse:69.134740+0.083369 test-rmse:69.134537+0.523672
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:44.549814+0.067020 test-rmse:44.548320+0.577398
## [3] train-rmse:28.902591+0.064471 test-rmse:28.898605+0.635371
## [4] train-rmse:18.923118+0.033501 test-rmse:19.032755+0.676351
## [5] train-rmse:12.559415+0.024442 test-rmse:12.673872+0.614188
## [6] train-rmse:8.549528+0.030336 test-rmse:8.665854+0.552284
## [7] train-rmse:6.064293+0.037220 test-rmse:6.222717+0.598755
## [8] train-rmse:4.553170+0.042025 test-rmse:4.750795+0.519411
## [9] train-rmse:3.649543+0.048170 test-rmse:3.939266+0.428767
## [10] train-rmse:3.122135+0.046315 test-rmse:3.402935+0.396074
## [11] train-rmse:2.790589+0.044874 test-rmse:3.128419+0.354059
## [12] train-rmse:2.555324+0.040374 test-rmse:2.935200+0.293571
## [13] train-rmse:2.382682+0.039892 test-rmse:2.783947+0.288727
## [14] train-rmse:2.246209+0.034826 test-rmse:2.637756+0.252306
## [15] train-rmse:2.121490+0.045989 test-rmse:2.547710+0.229859
## [16] train-rmse:2.016080+0.046750 test-rmse:2.432627+0.203935
## [17] train-rmse:1.910862+0.047047 test-rmse:2.368254+0.218453
## [18] train-rmse:1.817777+0.043802 test-rmse:2.299274+0.210718
## [19] train-rmse:1.729051+0.038786 test-rmse:2.225461+0.233853
## [20] train-rmse:1.644464+0.037433 test-rmse:2.159614+0.229096
## [21] train-rmse:1.563729+0.030569 test-rmse:2.113230+0.208876
## [22] train-rmse:1.497851+0.030179 test-rmse:2.063081+0.191919
## [23] train-rmse:1.433623+0.025869 test-rmse:2.022581+0.209081
## [24] train-rmse:1.371270+0.025617 test-rmse:1.980135+0.199727
## [25] train-rmse:1.314366+0.023056 test-rmse:1.936357+0.207057
## [26] train-rmse:1.257272+0.020926 test-rmse:1.887654+0.210680
## [27] train-rmse:1.206101+0.020552 test-rmse:1.844406+0.222804
## [28] train-rmse:1.157008+0.020202 test-rmse:1.808456+0.223834
## [29] train-rmse:1.105920+0.028168 test-rmse:1.768132+0.224294
## [30] train-rmse:1.065080+0.027910 test-rmse:1.732670+0.219937
## [31] train-rmse:1.024539+0.027132 test-rmse:1.689084+0.214296
## [32] train-rmse:0.986040+0.024069 test-rmse:1.663283+0.225713
## [33] train-rmse:0.952015+0.023194 test-rmse:1.645632+0.230873
## [34] train-rmse:0.916058+0.024364 test-rmse:1.625707+0.225546
## [35] train-rmse:0.884700+0.025159 test-rmse:1.603360+0.225551
## [36] train-rmse:0.854778+0.024252 test-rmse:1.582846+0.227125
## [37] train-rmse:0.827718+0.024895 test-rmse:1.568422+0.237026
## [38] train-rmse:0.802047+0.023250 test-rmse:1.550756+0.240622
## [39] train-rmse:0.778479+0.022792 test-rmse:1.542420+0.248117
## [40] train-rmse:0.755226+0.022486 test-rmse:1.525556+0.249199
## [41] train-rmse:0.734112+0.023845 test-rmse:1.513037+0.248645
## [42] train-rmse:0.712097+0.024671 test-rmse:1.503155+0.262208
## [43] train-rmse:0.690552+0.020916 test-rmse:1.482963+0.258211
## [44] train-rmse:0.670817+0.022537 test-rmse:1.470575+0.259674
## [45] train-rmse:0.653160+0.022095 test-rmse:1.463334+0.262929
## [46] train-rmse:0.636922+0.021552 test-rmse:1.455020+0.267268
## [47] train-rmse:0.619992+0.022875 test-rmse:1.442621+0.260496
## [48] train-rmse:0.605538+0.022773 test-rmse:1.433154+0.261907
## [49] train-rmse:0.590674+0.022723 test-rmse:1.412780+0.248237
## [50] train-rmse:0.574619+0.021247 test-rmse:1.408734+0.250509
## [51] train-rmse:0.561932+0.022070 test-rmse:1.402352+0.253831
## [52] train-rmse:0.549235+0.023946 test-rmse:1.389011+0.264658
## [53] train-rmse:0.535981+0.023817 test-rmse:1.381825+0.262757
## [54] train-rmse:0.521903+0.022446 test-rmse:1.379532+0.265372
## [55] train-rmse:0.510977+0.021196 test-rmse:1.372456+0.267143
## [56] train-rmse:0.500286+0.020466 test-rmse:1.372446+0.265274
## [57] train-rmse:0.490925+0.019858 test-rmse:1.366626+0.268556
## [58] train-rmse:0.479741+0.018740 test-rmse:1.361846+0.271653
## [59] train-rmse:0.468834+0.016985 test-rmse:1.360017+0.275178
## [60] train-rmse:0.459279+0.016950 test-rmse:1.359185+0.277617
## [61] train-rmse:0.452000+0.017641 test-rmse:1.355671+0.278974
## [62] train-rmse:0.441993+0.015636 test-rmse:1.353496+0.276536
## [63] train-rmse:0.434347+0.014979 test-rmse:1.352465+0.279928
## [64] train-rmse:0.426469+0.014618 test-rmse:1.346193+0.279020
## [65] train-rmse:0.414693+0.013557 test-rmse:1.338286+0.280409
## [66] train-rmse:0.407287+0.013873 test-rmse:1.337208+0.281586
## [67] train-rmse:0.401002+0.013355 test-rmse:1.335774+0.281172
## [68] train-rmse:0.394228+0.014787 test-rmse:1.333639+0.281989
## [69] train-rmse:0.389290+0.014752 test-rmse:1.331553+0.281469
## [70] train-rmse:0.384320+0.014683 test-rmse:1.326998+0.281335
## [71] train-rmse:0.378199+0.015711 test-rmse:1.324516+0.282925
## [72] train-rmse:0.371271+0.015234 test-rmse:1.321931+0.288871
## [73] train-rmse:0.365262+0.015277 test-rmse:1.320175+0.291143
## [74] train-rmse:0.358722+0.015293 test-rmse:1.319655+0.291123
## [75] train-rmse:0.351409+0.016602 test-rmse:1.314409+0.290154
## [76] train-rmse:0.346318+0.016908 test-rmse:1.314700+0.289821
## [77] train-rmse:0.342218+0.017424 test-rmse:1.311333+0.292174
## [78] train-rmse:0.336347+0.016082 test-rmse:1.307002+0.295399
## [79] train-rmse:0.331165+0.016438 test-rmse:1.300537+0.298052
## [80] train-rmse:0.328164+0.016279 test-rmse:1.297165+0.297919
## [81] train-rmse:0.322044+0.015258 test-rmse:1.298494+0.298596
## [82] train-rmse:0.318423+0.015282 test-rmse:1.297787+0.302072
## [83] train-rmse:0.314356+0.015106 test-rmse:1.296727+0.301133
## [84] train-rmse:0.308725+0.015357 test-rmse:1.294535+0.302944
## [85] train-rmse:0.305711+0.015099 test-rmse:1.293002+0.301071
## [86] train-rmse:0.302813+0.015495 test-rmse:1.290080+0.300113
## [87] train-rmse:0.299060+0.015758 test-rmse:1.289378+0.300632
## [88] train-rmse:0.293634+0.012992 test-rmse:1.290309+0.303390
## [89] train-rmse:0.289440+0.013093 test-rmse:1.286917+0.300981
## [90] train-rmse:0.285310+0.013125 test-rmse:1.284467+0.301845
## [91] train-rmse:0.279771+0.010719 test-rmse:1.284975+0.303335
## [92] train-rmse:0.274665+0.011418 test-rmse:1.282987+0.304194
## [93] train-rmse:0.270413+0.010758 test-rmse:1.283317+0.304177
## [94] train-rmse:0.267680+0.010571 test-rmse:1.279628+0.305998
## [95] train-rmse:0.264384+0.010077 test-rmse:1.277983+0.305283
## [96] train-rmse:0.260947+0.012455 test-rmse:1.278888+0.304549
## [97] train-rmse:0.257043+0.012991 test-rmse:1.277572+0.306288
## [98] train-rmse:0.253788+0.013220 test-rmse:1.277897+0.307027
## [99] train-rmse:0.250466+0.013552 test-rmse:1.278433+0.307370
## [100] train-rmse:0.246714+0.013058 test-rmse:1.278300+0.306221
## [101] train-rmse:0.244171+0.013134 test-rmse:1.276524+0.307303
## [102] train-rmse:0.242033+0.012779 test-rmse:1.275388+0.306659
## [103] train-rmse:0.237349+0.011850 test-rmse:1.274462+0.305429
## [104] train-rmse:0.235223+0.012188 test-rmse:1.274406+0.305293
## [105] train-rmse:0.230996+0.011011 test-rmse:1.274260+0.305033
## [106] train-rmse:0.228286+0.011361 test-rmse:1.273228+0.304296
## [107] train-rmse:0.224978+0.011370 test-rmse:1.272375+0.307106
## [108] train-rmse:0.222669+0.012088 test-rmse:1.271774+0.308518
## [109] train-rmse:0.219747+0.011202 test-rmse:1.271525+0.309110
## [110] train-rmse:0.217248+0.011599 test-rmse:1.270361+0.309465
## [111] train-rmse:0.215012+0.012029 test-rmse:1.269581+0.309370
## [112] train-rmse:0.212631+0.011939 test-rmse:1.269105+0.309162
## [113] train-rmse:0.210001+0.011298 test-rmse:1.268899+0.309947
## [114] train-rmse:0.206950+0.011363 test-rmse:1.264654+0.311209
## [115] train-rmse:0.204071+0.010682 test-rmse:1.264811+0.312377
## [116] train-rmse:0.201297+0.009971 test-rmse:1.265376+0.311432
## [117] train-rmse:0.198725+0.009585 test-rmse:1.265056+0.314088
## [118] train-rmse:0.196522+0.008942 test-rmse:1.263694+0.314974
## [119] train-rmse:0.193997+0.008691 test-rmse:1.262092+0.316744
## [120] train-rmse:0.191794+0.009140 test-rmse:1.262299+0.318317
## [121] train-rmse:0.189953+0.009155 test-rmse:1.262007+0.318234
## [122] train-rmse:0.187132+0.008897 test-rmse:1.261744+0.317467
## [123] train-rmse:0.184655+0.008551 test-rmse:1.262451+0.319092
## [124] train-rmse:0.182379+0.008431 test-rmse:1.262290+0.319854
## [125] train-rmse:0.179221+0.008332 test-rmse:1.262034+0.320014
## [126] train-rmse:0.176766+0.007495 test-rmse:1.261627+0.318692
## [127] train-rmse:0.174625+0.007339 test-rmse:1.260765+0.318490
## [128] train-rmse:0.172598+0.007454 test-rmse:1.260818+0.318137
## [129] train-rmse:0.170990+0.007903 test-rmse:1.261058+0.318080
## [130] train-rmse:0.169479+0.008039 test-rmse:1.260197+0.319075
## [131] train-rmse:0.167119+0.008587 test-rmse:1.261217+0.318918
## [132] train-rmse:0.164805+0.008603 test-rmse:1.261659+0.319089
## [133] train-rmse:0.163201+0.008339 test-rmse:1.260935+0.317268
## [134] train-rmse:0.161787+0.008387 test-rmse:1.259506+0.317822
## [135] train-rmse:0.160048+0.008452 test-rmse:1.258596+0.319552
## [136] train-rmse:0.157349+0.008349 test-rmse:1.258457+0.319831
## [137] train-rmse:0.155868+0.007922 test-rmse:1.257577+0.320087
## [138] train-rmse:0.153269+0.007309 test-rmse:1.258610+0.320133
## [139] train-rmse:0.151983+0.007384 test-rmse:1.257704+0.319889
## [140] train-rmse:0.149775+0.006768 test-rmse:1.257404+0.320175
## [141] train-rmse:0.147770+0.006531 test-rmse:1.256841+0.319827
## [142] train-rmse:0.145727+0.006750 test-rmse:1.256875+0.320275
## [143] train-rmse:0.143764+0.007351 test-rmse:1.257190+0.320801
## [144] train-rmse:0.142472+0.007562 test-rmse:1.257222+0.320659
## [145] train-rmse:0.140992+0.007701 test-rmse:1.257078+0.320420
## [146] train-rmse:0.139491+0.007425 test-rmse:1.257253+0.320322
## [147] train-rmse:0.137827+0.008037 test-rmse:1.257507+0.319997
## Stopping. Best iteration:
## [141] train-rmse:0.147770+0.006531 test-rmse:1.256841+0.319827
##
## 94
## [1] train-rmse:69.134740+0.083369 test-rmse:69.134537+0.523672
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:44.549814+0.067020 test-rmse:44.548320+0.577398
## [3] train-rmse:28.902591+0.064471 test-rmse:28.898605+0.635371
## [4] train-rmse:18.923118+0.033501 test-rmse:19.032755+0.676351
## [5] train-rmse:12.545487+0.023366 test-rmse:12.648836+0.594647
## [6] train-rmse:8.492777+0.026415 test-rmse:8.580346+0.555171
## [7] train-rmse:5.947229+0.039065 test-rmse:6.079931+0.598170
## [8] train-rmse:4.349956+0.055832 test-rmse:4.543943+0.539797
## [9] train-rmse:3.380062+0.066649 test-rmse:3.603186+0.437574
## [10] train-rmse:2.775626+0.068544 test-rmse:3.053749+0.344326
## [11] train-rmse:2.393293+0.050410 test-rmse:2.719750+0.322115
## [12] train-rmse:2.147440+0.047721 test-rmse:2.527226+0.307668
## [13] train-rmse:1.960111+0.048816 test-rmse:2.344884+0.285466
## [14] train-rmse:1.806622+0.045826 test-rmse:2.207646+0.282164
## [15] train-rmse:1.671230+0.028255 test-rmse:2.129036+0.297638
## [16] train-rmse:1.562934+0.034022 test-rmse:2.053179+0.290775
## [17] train-rmse:1.458192+0.049234 test-rmse:1.980438+0.290003
## [18] train-rmse:1.361542+0.038471 test-rmse:1.904291+0.277299
## [19] train-rmse:1.281151+0.033237 test-rmse:1.838897+0.274774
## [20] train-rmse:1.203633+0.023437 test-rmse:1.768812+0.274257
## [21] train-rmse:1.134571+0.024480 test-rmse:1.723690+0.288810
## [22] train-rmse:1.073303+0.025177 test-rmse:1.676058+0.283638
## [23] train-rmse:1.019320+0.023950 test-rmse:1.626115+0.293710
## [24] train-rmse:0.965598+0.022722 test-rmse:1.578781+0.285134
## [25] train-rmse:0.912052+0.012923 test-rmse:1.538458+0.279274
## [26] train-rmse:0.868582+0.012709 test-rmse:1.510892+0.280687
## [27] train-rmse:0.820032+0.015237 test-rmse:1.476524+0.279231
## [28] train-rmse:0.777032+0.019433 test-rmse:1.454981+0.284067
## [29] train-rmse:0.744815+0.019336 test-rmse:1.420428+0.295782
## [30] train-rmse:0.713771+0.021556 test-rmse:1.400712+0.300228
## [31] train-rmse:0.678839+0.030110 test-rmse:1.385802+0.303075
## [32] train-rmse:0.648667+0.028192 test-rmse:1.369190+0.300672
## [33] train-rmse:0.623748+0.025386 test-rmse:1.350387+0.303988
## [34] train-rmse:0.598244+0.021112 test-rmse:1.335609+0.301032
## [35] train-rmse:0.576715+0.020232 test-rmse:1.324136+0.303245
## [36] train-rmse:0.557139+0.020950 test-rmse:1.310575+0.300428
## [37] train-rmse:0.537990+0.019612 test-rmse:1.295959+0.299987
## [38] train-rmse:0.520922+0.018730 test-rmse:1.290343+0.305259
## [39] train-rmse:0.505941+0.018881 test-rmse:1.278586+0.302544
## [40] train-rmse:0.486471+0.020839 test-rmse:1.260925+0.299988
## [41] train-rmse:0.473238+0.020993 test-rmse:1.240886+0.301117
## [42] train-rmse:0.455967+0.022069 test-rmse:1.238308+0.303337
## [43] train-rmse:0.438188+0.021770 test-rmse:1.237302+0.303161
## [44] train-rmse:0.425760+0.022396 test-rmse:1.234076+0.304752
## [45] train-rmse:0.413312+0.021597 test-rmse:1.234010+0.306016
## [46] train-rmse:0.399018+0.018713 test-rmse:1.221567+0.300959
## [47] train-rmse:0.385837+0.019874 test-rmse:1.214481+0.303810
## [48] train-rmse:0.373841+0.021937 test-rmse:1.203446+0.304006
## [49] train-rmse:0.363786+0.018331 test-rmse:1.199267+0.303780
## [50] train-rmse:0.355480+0.017216 test-rmse:1.196792+0.303153
## [51] train-rmse:0.344073+0.016433 test-rmse:1.194097+0.305511
## [52] train-rmse:0.333826+0.013649 test-rmse:1.188478+0.304887
## [53] train-rmse:0.327107+0.013883 test-rmse:1.187785+0.303407
## [54] train-rmse:0.318474+0.015072 test-rmse:1.187325+0.299043
## [55] train-rmse:0.309046+0.011966 test-rmse:1.187242+0.299858
## [56] train-rmse:0.301003+0.010818 test-rmse:1.185031+0.297785
## [57] train-rmse:0.293655+0.010454 test-rmse:1.179658+0.301565
## [58] train-rmse:0.284507+0.013290 test-rmse:1.178842+0.301696
## [59] train-rmse:0.277257+0.013068 test-rmse:1.179684+0.299973
## [60] train-rmse:0.270407+0.016394 test-rmse:1.178660+0.299770
## [61] train-rmse:0.261478+0.013244 test-rmse:1.177954+0.301069
## [62] train-rmse:0.256813+0.013960 test-rmse:1.178087+0.302833
## [63] train-rmse:0.250712+0.015627 test-rmse:1.176588+0.300384
## [64] train-rmse:0.245600+0.015516 test-rmse:1.176174+0.302665
## [65] train-rmse:0.240813+0.016390 test-rmse:1.174652+0.301718
## [66] train-rmse:0.235981+0.016070 test-rmse:1.173507+0.301454
## [67] train-rmse:0.231050+0.015283 test-rmse:1.174195+0.301995
## [68] train-rmse:0.225597+0.014549 test-rmse:1.174748+0.302023
## [69] train-rmse:0.220999+0.014131 test-rmse:1.174304+0.303929
## [70] train-rmse:0.216510+0.015263 test-rmse:1.173931+0.304258
## [71] train-rmse:0.211721+0.015282 test-rmse:1.173287+0.304492
## [72] train-rmse:0.207000+0.014499 test-rmse:1.173525+0.306217
## [73] train-rmse:0.201696+0.016141 test-rmse:1.172036+0.305768
## [74] train-rmse:0.195596+0.016472 test-rmse:1.172731+0.306543
## [75] train-rmse:0.191171+0.016105 test-rmse:1.171581+0.306032
## [76] train-rmse:0.187959+0.015998 test-rmse:1.168811+0.306962
## [77] train-rmse:0.184536+0.015347 test-rmse:1.169139+0.308273
## [78] train-rmse:0.180744+0.016329 test-rmse:1.169419+0.307824
## [79] train-rmse:0.177178+0.015522 test-rmse:1.167452+0.308555
## [80] train-rmse:0.174057+0.014666 test-rmse:1.166219+0.309519
## [81] train-rmse:0.171414+0.014518 test-rmse:1.165866+0.308218
## [82] train-rmse:0.168633+0.015171 test-rmse:1.165031+0.308317
## [83] train-rmse:0.164742+0.015895 test-rmse:1.162858+0.308436
## [84] train-rmse:0.160497+0.014520 test-rmse:1.163913+0.308481
## [85] train-rmse:0.157628+0.015406 test-rmse:1.162297+0.306887
## [86] train-rmse:0.154952+0.015180 test-rmse:1.161419+0.307490
## [87] train-rmse:0.151636+0.015620 test-rmse:1.159988+0.308044
## [88] train-rmse:0.149354+0.015919 test-rmse:1.160001+0.307236
## [89] train-rmse:0.146593+0.015741 test-rmse:1.160293+0.307762
## [90] train-rmse:0.144601+0.015422 test-rmse:1.159924+0.307175
## [91] train-rmse:0.141480+0.014812 test-rmse:1.160576+0.307069
## [92] train-rmse:0.138455+0.014173 test-rmse:1.160615+0.306731
## [93] train-rmse:0.134845+0.013651 test-rmse:1.159940+0.307589
## [94] train-rmse:0.131139+0.012737 test-rmse:1.159124+0.306909
## [95] train-rmse:0.128792+0.011630 test-rmse:1.159086+0.308424
## [96] train-rmse:0.126364+0.011335 test-rmse:1.158868+0.307254
## [97] train-rmse:0.123549+0.010772 test-rmse:1.159170+0.307580
## [98] train-rmse:0.121190+0.010805 test-rmse:1.159301+0.308605
## [99] train-rmse:0.118584+0.011000 test-rmse:1.159307+0.308561
## [100] train-rmse:0.116529+0.010943 test-rmse:1.157879+0.308934
## [101] train-rmse:0.113759+0.011165 test-rmse:1.156257+0.307864
## [102] train-rmse:0.111240+0.010792 test-rmse:1.155928+0.308305
## [103] train-rmse:0.108652+0.010479 test-rmse:1.155018+0.309203
## [104] train-rmse:0.107510+0.010616 test-rmse:1.154930+0.309264
## [105] train-rmse:0.105806+0.010350 test-rmse:1.154015+0.308986
## [106] train-rmse:0.103920+0.010217 test-rmse:1.153835+0.308624
## [107] train-rmse:0.102058+0.010116 test-rmse:1.153425+0.308920
## [108] train-rmse:0.100518+0.009863 test-rmse:1.153687+0.309131
## [109] train-rmse:0.098234+0.009865 test-rmse:1.152576+0.310729
## [110] train-rmse:0.096457+0.009957 test-rmse:1.153100+0.311092
## [111] train-rmse:0.093424+0.009034 test-rmse:1.152327+0.310613
## [112] train-rmse:0.091693+0.008500 test-rmse:1.152374+0.310230
## [113] train-rmse:0.089439+0.008580 test-rmse:1.151052+0.309227
## [114] train-rmse:0.087737+0.008163 test-rmse:1.150549+0.309398
## [115] train-rmse:0.086567+0.008121 test-rmse:1.150939+0.309756
## [116] train-rmse:0.084997+0.007456 test-rmse:1.150579+0.309094
## [117] train-rmse:0.083580+0.007165 test-rmse:1.149802+0.308185
## [118] train-rmse:0.082266+0.006893 test-rmse:1.150672+0.308735
## [119] train-rmse:0.079174+0.006073 test-rmse:1.150187+0.308092
## [120] train-rmse:0.077532+0.006152 test-rmse:1.149683+0.308498
## [121] train-rmse:0.076724+0.006095 test-rmse:1.149373+0.308384
## [122] train-rmse:0.075496+0.006216 test-rmse:1.149116+0.308519
## [123] train-rmse:0.074538+0.006215 test-rmse:1.149310+0.308249
## [124] train-rmse:0.072981+0.005734 test-rmse:1.149074+0.308278
## [125] train-rmse:0.071607+0.005413 test-rmse:1.147504+0.307392
## [126] train-rmse:0.070276+0.005361 test-rmse:1.147874+0.307220
## [127] train-rmse:0.068182+0.005678 test-rmse:1.146595+0.306943
## [128] train-rmse:0.066455+0.006124 test-rmse:1.146182+0.306602
## [129] train-rmse:0.065068+0.006120 test-rmse:1.146484+0.305723
## [130] train-rmse:0.063766+0.005978 test-rmse:1.146851+0.306440
## [131] train-rmse:0.062870+0.006187 test-rmse:1.146991+0.306672
## [132] train-rmse:0.061874+0.006345 test-rmse:1.147182+0.306371
## [133] train-rmse:0.060392+0.006618 test-rmse:1.147250+0.306301
## [134] train-rmse:0.059669+0.006801 test-rmse:1.147293+0.306315
## Stopping. Best iteration:
## [128] train-rmse:0.066455+0.006124 test-rmse:1.146182+0.306602
##
## 95
## [1] train-rmse:69.134740+0.083369 test-rmse:69.134537+0.523672
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:44.549814+0.067020 test-rmse:44.548320+0.577398
## [3] train-rmse:28.902591+0.064471 test-rmse:28.898605+0.635371
## [4] train-rmse:18.923118+0.033501 test-rmse:19.032755+0.676351
## [5] train-rmse:12.535960+0.024062 test-rmse:12.648281+0.592023
## [6] train-rmse:8.452019+0.024821 test-rmse:8.584236+0.564181
## [7] train-rmse:5.857146+0.030253 test-rmse:6.049861+0.579489
## [8] train-rmse:4.208157+0.036637 test-rmse:4.384103+0.498485
## [9] train-rmse:3.135731+0.041780 test-rmse:3.387290+0.400844
## [10] train-rmse:2.477035+0.060931 test-rmse:2.865436+0.349866
## [11] train-rmse:2.065073+0.065327 test-rmse:2.521203+0.321769
## [12] train-rmse:1.791927+0.055055 test-rmse:2.295269+0.295541
## [13] train-rmse:1.597107+0.054779 test-rmse:2.136616+0.301300
## [14] train-rmse:1.450605+0.056013 test-rmse:2.025072+0.294781
## [15] train-rmse:1.314010+0.054537 test-rmse:1.895928+0.299272
## [16] train-rmse:1.207437+0.047827 test-rmse:1.825172+0.308126
## [17] train-rmse:1.116110+0.047315 test-rmse:1.764128+0.279568
## [18] train-rmse:1.036593+0.043890 test-rmse:1.698801+0.280803
## [19] train-rmse:0.968290+0.046109 test-rmse:1.660366+0.288885
## [20] train-rmse:0.898414+0.037053 test-rmse:1.596312+0.285397
## [21] train-rmse:0.835789+0.040718 test-rmse:1.559850+0.277453
## [22] train-rmse:0.776031+0.037103 test-rmse:1.525172+0.279836
## [23] train-rmse:0.728589+0.036541 test-rmse:1.499882+0.288555
## [24] train-rmse:0.687031+0.035648 test-rmse:1.472596+0.287266
## [25] train-rmse:0.646272+0.031514 test-rmse:1.440575+0.289865
## [26] train-rmse:0.611501+0.030001 test-rmse:1.422982+0.291259
## [27] train-rmse:0.578670+0.033234 test-rmse:1.408166+0.287494
## [28] train-rmse:0.551072+0.030931 test-rmse:1.397423+0.280949
## [29] train-rmse:0.525228+0.030110 test-rmse:1.384570+0.287157
## [30] train-rmse:0.502875+0.031167 test-rmse:1.372117+0.288837
## [31] train-rmse:0.477822+0.033956 test-rmse:1.352862+0.289773
## [32] train-rmse:0.455028+0.028990 test-rmse:1.357914+0.300099
## [33] train-rmse:0.437499+0.026574 test-rmse:1.349281+0.301526
## [34] train-rmse:0.418822+0.028234 test-rmse:1.340664+0.307862
## [35] train-rmse:0.401141+0.027274 test-rmse:1.339026+0.319288
## [36] train-rmse:0.386147+0.029977 test-rmse:1.324493+0.319475
## [37] train-rmse:0.372208+0.031460 test-rmse:1.316202+0.322179
## [38] train-rmse:0.352112+0.024662 test-rmse:1.308703+0.319461
## [39] train-rmse:0.339196+0.024017 test-rmse:1.309001+0.323741
## [40] train-rmse:0.327821+0.023411 test-rmse:1.306833+0.328980
## [41] train-rmse:0.317368+0.021497 test-rmse:1.305196+0.329789
## [42] train-rmse:0.304160+0.017697 test-rmse:1.302987+0.330847
## [43] train-rmse:0.292448+0.015850 test-rmse:1.304945+0.331382
## [44] train-rmse:0.283530+0.017983 test-rmse:1.302588+0.329336
## [45] train-rmse:0.272785+0.017414 test-rmse:1.297284+0.331259
## [46] train-rmse:0.264034+0.016730 test-rmse:1.297893+0.330551
## [47] train-rmse:0.254398+0.014164 test-rmse:1.295117+0.328797
## [48] train-rmse:0.246085+0.014006 test-rmse:1.292249+0.328097
## [49] train-rmse:0.238822+0.011786 test-rmse:1.289093+0.329855
## [50] train-rmse:0.232991+0.012267 test-rmse:1.289107+0.330326
## [51] train-rmse:0.224509+0.012084 test-rmse:1.288305+0.328764
## [52] train-rmse:0.217154+0.015515 test-rmse:1.287008+0.331086
## [53] train-rmse:0.211302+0.015676 test-rmse:1.282621+0.332792
## [54] train-rmse:0.202933+0.017867 test-rmse:1.282777+0.333934
## [55] train-rmse:0.195921+0.016833 test-rmse:1.282708+0.333409
## [56] train-rmse:0.190977+0.017994 test-rmse:1.281763+0.334418
## [57] train-rmse:0.185514+0.018257 test-rmse:1.280893+0.334950
## [58] train-rmse:0.177246+0.015065 test-rmse:1.278950+0.333286
## [59] train-rmse:0.171627+0.014891 test-rmse:1.281293+0.333070
## [60] train-rmse:0.167672+0.015906 test-rmse:1.280401+0.332190
## [61] train-rmse:0.162803+0.015814 test-rmse:1.279677+0.332346
## [62] train-rmse:0.156702+0.014616 test-rmse:1.278984+0.332676
## [63] train-rmse:0.153911+0.015218 test-rmse:1.277720+0.332839
## [64] train-rmse:0.148495+0.015934 test-rmse:1.275384+0.332050
## [65] train-rmse:0.142369+0.015210 test-rmse:1.274983+0.331521
## [66] train-rmse:0.138270+0.014412 test-rmse:1.274647+0.331548
## [67] train-rmse:0.132168+0.012408 test-rmse:1.274397+0.334594
## [68] train-rmse:0.127575+0.010962 test-rmse:1.276956+0.341088
## [69] train-rmse:0.123334+0.009855 test-rmse:1.277097+0.341227
## [70] train-rmse:0.118761+0.010888 test-rmse:1.275980+0.340461
## [71] train-rmse:0.115507+0.011563 test-rmse:1.275352+0.340774
## [72] train-rmse:0.111613+0.011457 test-rmse:1.274043+0.341897
## [73] train-rmse:0.108285+0.010618 test-rmse:1.273697+0.341900
## [74] train-rmse:0.105364+0.010789 test-rmse:1.272979+0.341852
## [75] train-rmse:0.102421+0.010609 test-rmse:1.272445+0.341768
## [76] train-rmse:0.099929+0.009937 test-rmse:1.274606+0.346002
## [77] train-rmse:0.096524+0.008787 test-rmse:1.275252+0.346904
## [78] train-rmse:0.093337+0.009123 test-rmse:1.274654+0.347246
## [79] train-rmse:0.090920+0.009240 test-rmse:1.276390+0.350312
## [80] train-rmse:0.086952+0.008815 test-rmse:1.275680+0.348973
## [81] train-rmse:0.084801+0.008200 test-rmse:1.275714+0.349682
## Stopping. Best iteration:
## [75] train-rmse:0.102421+0.010609 test-rmse:1.272445+0.341768
##
## 96
## [1] train-rmse:69.134740+0.083369 test-rmse:69.134537+0.523672
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:44.549814+0.067020 test-rmse:44.548320+0.577398
## [3] train-rmse:28.902591+0.064471 test-rmse:28.898605+0.635371
## [4] train-rmse:18.923118+0.033501 test-rmse:19.032755+0.676351
## [5] train-rmse:12.527486+0.024392 test-rmse:12.639151+0.589052
## [6] train-rmse:8.418325+0.024096 test-rmse:8.537720+0.550442
## [7] train-rmse:5.796539+0.024337 test-rmse:6.003633+0.556752
## [8] train-rmse:4.092730+0.015196 test-rmse:4.306307+0.494641
## [9] train-rmse:3.019057+0.040491 test-rmse:3.276305+0.431040
## [10] train-rmse:2.315040+0.054978 test-rmse:2.643738+0.358174
## [11] train-rmse:1.874558+0.067482 test-rmse:2.280548+0.311735
## [12] train-rmse:1.559716+0.073760 test-rmse:2.074320+0.286680
## [13] train-rmse:1.359216+0.073186 test-rmse:1.923553+0.231483
## [14] train-rmse:1.199525+0.080975 test-rmse:1.838720+0.242677
## [15] train-rmse:1.081772+0.069735 test-rmse:1.747337+0.217548
## [16] train-rmse:0.982691+0.065434 test-rmse:1.646430+0.239921
## [17] train-rmse:0.891596+0.056365 test-rmse:1.573250+0.244648
## [18] train-rmse:0.816582+0.052417 test-rmse:1.533891+0.245854
## [19] train-rmse:0.752073+0.044113 test-rmse:1.488760+0.247853
## [20] train-rmse:0.695311+0.036053 test-rmse:1.445380+0.257459
## [21] train-rmse:0.643902+0.030869 test-rmse:1.410163+0.248712
## [22] train-rmse:0.593930+0.034377 test-rmse:1.395011+0.258931
## [23] train-rmse:0.551967+0.036007 test-rmse:1.380221+0.255215
## [24] train-rmse:0.511167+0.027685 test-rmse:1.350707+0.257862
## [25] train-rmse:0.482269+0.025809 test-rmse:1.331195+0.257241
## [26] train-rmse:0.450605+0.021555 test-rmse:1.324149+0.255931
## [27] train-rmse:0.423430+0.023601 test-rmse:1.310108+0.266910
## [28] train-rmse:0.400557+0.024573 test-rmse:1.301111+0.268093
## [29] train-rmse:0.380061+0.021771 test-rmse:1.286992+0.281101
## [30] train-rmse:0.359748+0.022056 test-rmse:1.284003+0.288520
## [31] train-rmse:0.342589+0.022218 test-rmse:1.275947+0.298559
## [32] train-rmse:0.327148+0.022182 test-rmse:1.272524+0.297297
## [33] train-rmse:0.307695+0.016145 test-rmse:1.266697+0.303357
## [34] train-rmse:0.293015+0.013402 test-rmse:1.263092+0.302052
## [35] train-rmse:0.278328+0.014507 test-rmse:1.256521+0.306435
## [36] train-rmse:0.260064+0.020043 test-rmse:1.263687+0.306125
## [37] train-rmse:0.244173+0.016781 test-rmse:1.261365+0.305295
## [38] train-rmse:0.234481+0.013599 test-rmse:1.253675+0.298202
## [39] train-rmse:0.223686+0.011719 test-rmse:1.251064+0.296868
## [40] train-rmse:0.214664+0.012101 test-rmse:1.248344+0.295769
## [41] train-rmse:0.204731+0.013468 test-rmse:1.243926+0.297678
## [42] train-rmse:0.194741+0.011173 test-rmse:1.243547+0.298993
## [43] train-rmse:0.188253+0.010413 test-rmse:1.241413+0.301897
## [44] train-rmse:0.178287+0.010934 test-rmse:1.239548+0.304386
## [45] train-rmse:0.172340+0.012032 test-rmse:1.236661+0.306161
## [46] train-rmse:0.163065+0.014288 test-rmse:1.237244+0.306623
## [47] train-rmse:0.157261+0.014206 test-rmse:1.237020+0.308204
## [48] train-rmse:0.150072+0.011436 test-rmse:1.234796+0.306859
## [49] train-rmse:0.141392+0.013138 test-rmse:1.232457+0.307113
## [50] train-rmse:0.132844+0.011453 test-rmse:1.230544+0.307569
## [51] train-rmse:0.125171+0.008653 test-rmse:1.229517+0.308910
## [52] train-rmse:0.119669+0.008632 test-rmse:1.229876+0.307286
## [53] train-rmse:0.113524+0.008720 test-rmse:1.230393+0.308528
## [54] train-rmse:0.109129+0.009681 test-rmse:1.231145+0.308036
## [55] train-rmse:0.106087+0.009214 test-rmse:1.229507+0.306685
## [56] train-rmse:0.100769+0.009166 test-rmse:1.228848+0.307454
## [57] train-rmse:0.096935+0.008662 test-rmse:1.228004+0.307391
## [58] train-rmse:0.092684+0.008103 test-rmse:1.227504+0.308375
## [59] train-rmse:0.089501+0.007920 test-rmse:1.228181+0.308652
## [60] train-rmse:0.085206+0.008970 test-rmse:1.227478+0.309019
## [61] train-rmse:0.082373+0.009204 test-rmse:1.227076+0.308760
## [62] train-rmse:0.078948+0.009120 test-rmse:1.227121+0.309547
## [63] train-rmse:0.075390+0.009288 test-rmse:1.227232+0.309840
## [64] train-rmse:0.072222+0.008596 test-rmse:1.227069+0.309966
## [65] train-rmse:0.068594+0.008247 test-rmse:1.226492+0.310215
## [66] train-rmse:0.065968+0.006932 test-rmse:1.225500+0.309925
## [67] train-rmse:0.063630+0.006774 test-rmse:1.225259+0.309249
## [68] train-rmse:0.061171+0.006452 test-rmse:1.224871+0.308885
## [69] train-rmse:0.059253+0.005989 test-rmse:1.224529+0.309190
## [70] train-rmse:0.056998+0.005651 test-rmse:1.223929+0.308493
## [71] train-rmse:0.054681+0.005448 test-rmse:1.224447+0.308235
## [72] train-rmse:0.052701+0.004634 test-rmse:1.224661+0.308326
## [73] train-rmse:0.050297+0.004761 test-rmse:1.224336+0.308846
## [74] train-rmse:0.048058+0.004572 test-rmse:1.224222+0.308714
## [75] train-rmse:0.046382+0.004432 test-rmse:1.224229+0.308547
## [76] train-rmse:0.044054+0.003685 test-rmse:1.223962+0.308059
## Stopping. Best iteration:
## [70] train-rmse:0.056998+0.005651 test-rmse:1.223929+0.308493
##
## 97
## [1] train-rmse:69.134740+0.083369 test-rmse:69.134537+0.523672
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:44.549814+0.067020 test-rmse:44.548320+0.577398
## [3] train-rmse:28.902591+0.064471 test-rmse:28.898605+0.635371
## [4] train-rmse:18.923118+0.033501 test-rmse:19.032755+0.676351
## [5] train-rmse:12.519794+0.024401 test-rmse:12.621871+0.582864
## [6] train-rmse:8.391540+0.019520 test-rmse:8.515625+0.562003
## [7] train-rmse:5.744750+0.024611 test-rmse:5.936331+0.555582
## [8] train-rmse:4.035052+0.024818 test-rmse:4.285416+0.487156
## [9] train-rmse:2.924395+0.029759 test-rmse:3.232171+0.429452
## [10] train-rmse:2.175937+0.036411 test-rmse:2.573063+0.359926
## [11] train-rmse:1.691516+0.038655 test-rmse:2.185756+0.305108
## [12] train-rmse:1.353478+0.036296 test-rmse:1.967118+0.262638
## [13] train-rmse:1.132788+0.047141 test-rmse:1.838597+0.233814
## [14] train-rmse:0.975085+0.041747 test-rmse:1.732894+0.215279
## [15] train-rmse:0.855319+0.035571 test-rmse:1.664623+0.224502
## [16] train-rmse:0.765547+0.030155 test-rmse:1.616871+0.217641
## [17] train-rmse:0.679300+0.025900 test-rmse:1.563376+0.206501
## [18] train-rmse:0.613094+0.019498 test-rmse:1.516962+0.208726
## [19] train-rmse:0.562431+0.021049 test-rmse:1.492137+0.213534
## [20] train-rmse:0.517943+0.020413 test-rmse:1.472604+0.223853
## [21] train-rmse:0.477990+0.012844 test-rmse:1.461780+0.220272
## [22] train-rmse:0.442589+0.007714 test-rmse:1.435159+0.234034
## [23] train-rmse:0.407318+0.006416 test-rmse:1.423968+0.234652
## [24] train-rmse:0.379303+0.011227 test-rmse:1.407923+0.241891
## [25] train-rmse:0.355870+0.009236 test-rmse:1.404130+0.237288
## [26] train-rmse:0.329511+0.006440 test-rmse:1.396779+0.239394
## [27] train-rmse:0.307761+0.006856 test-rmse:1.386270+0.245768
## [28] train-rmse:0.291743+0.009190 test-rmse:1.380943+0.246104
## [29] train-rmse:0.274067+0.008785 test-rmse:1.370533+0.251612
## [30] train-rmse:0.254403+0.010543 test-rmse:1.371962+0.254469
## [31] train-rmse:0.239463+0.011408 test-rmse:1.369900+0.256698
## [32] train-rmse:0.220431+0.014227 test-rmse:1.365969+0.258350
## [33] train-rmse:0.206110+0.011258 test-rmse:1.362183+0.261431
## [34] train-rmse:0.195078+0.014074 test-rmse:1.362967+0.258631
## [35] train-rmse:0.181951+0.015849 test-rmse:1.361770+0.257396
## [36] train-rmse:0.170889+0.015940 test-rmse:1.360983+0.258125
## [37] train-rmse:0.162881+0.015961 test-rmse:1.358776+0.258898
## [38] train-rmse:0.153892+0.016093 test-rmse:1.357076+0.260236
## [39] train-rmse:0.144660+0.014941 test-rmse:1.355262+0.261512
## [40] train-rmse:0.136248+0.013665 test-rmse:1.355810+0.260724
## [41] train-rmse:0.128708+0.015202 test-rmse:1.352867+0.263562
## [42] train-rmse:0.121891+0.015662 test-rmse:1.352083+0.263470
## [43] train-rmse:0.113765+0.014200 test-rmse:1.351504+0.263428
## [44] train-rmse:0.109619+0.013874 test-rmse:1.351278+0.263459
## [45] train-rmse:0.105128+0.013688 test-rmse:1.350975+0.264023
## [46] train-rmse:0.098131+0.010196 test-rmse:1.350026+0.264369
## [47] train-rmse:0.092027+0.008845 test-rmse:1.349981+0.263892
## [48] train-rmse:0.086202+0.007306 test-rmse:1.348715+0.264831
## [49] train-rmse:0.081683+0.007208 test-rmse:1.349006+0.264921
## [50] train-rmse:0.076159+0.007540 test-rmse:1.346801+0.267408
## [51] train-rmse:0.071767+0.008591 test-rmse:1.346594+0.267342
## [52] train-rmse:0.069017+0.008142 test-rmse:1.346093+0.267781
## [53] train-rmse:0.065182+0.008425 test-rmse:1.345300+0.267616
## [54] train-rmse:0.061728+0.007175 test-rmse:1.345799+0.268845
## [55] train-rmse:0.058394+0.006195 test-rmse:1.345540+0.269524
## [56] train-rmse:0.056063+0.006533 test-rmse:1.345490+0.269559
## [57] train-rmse:0.051739+0.004699 test-rmse:1.344712+0.270118
## [58] train-rmse:0.048671+0.004451 test-rmse:1.345031+0.270672
## [59] train-rmse:0.046485+0.004823 test-rmse:1.345313+0.271044
## [60] train-rmse:0.044401+0.004691 test-rmse:1.344867+0.271207
## [61] train-rmse:0.042668+0.004672 test-rmse:1.344358+0.271641
## [62] train-rmse:0.040385+0.003892 test-rmse:1.344680+0.272496
## [63] train-rmse:0.038066+0.003600 test-rmse:1.344474+0.272453
## [64] train-rmse:0.036321+0.003210 test-rmse:1.344605+0.272152
## [65] train-rmse:0.034374+0.002189 test-rmse:1.344562+0.272060
## [66] train-rmse:0.032806+0.002302 test-rmse:1.344494+0.271901
## [67] train-rmse:0.031344+0.002278 test-rmse:1.344506+0.271972
## Stopping. Best iteration:
## [61] train-rmse:0.042668+0.004672 test-rmse:1.344358+0.271641
##
## 98
## [1] train-rmse:67.001016+0.081705 test-rmse:67.000742+0.527509
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:41.869994+0.065854 test-rmse:41.868242+0.585212
## [3] train-rmse:26.397598+0.065573 test-rmse:26.392831+0.648853
## [4] train-rmse:16.918549+0.059492 test-rmse:17.019662+0.679812
## [5] train-rmse:11.167546+0.049339 test-rmse:11.238692+0.676380
## [6] train-rmse:7.803864+0.059623 test-rmse:7.916185+0.594368
## [7] train-rmse:5.908594+0.068001 test-rmse:6.088209+0.668286
## [8] train-rmse:4.891551+0.069415 test-rmse:5.014477+0.572964
## [9] train-rmse:4.350585+0.067932 test-rmse:4.434276+0.506385
## [10] train-rmse:4.040012+0.067252 test-rmse:4.193295+0.474123
## [11] train-rmse:3.849486+0.067030 test-rmse:4.043675+0.475350
## [12] train-rmse:3.709875+0.061519 test-rmse:3.900535+0.458681
## [13] train-rmse:3.592393+0.058562 test-rmse:3.720923+0.378250
## [14] train-rmse:3.493602+0.059859 test-rmse:3.707805+0.359996
## [15] train-rmse:3.407580+0.055422 test-rmse:3.601998+0.297234
## [16] train-rmse:3.329015+0.054983 test-rmse:3.526592+0.318988
## [17] train-rmse:3.249132+0.054848 test-rmse:3.487364+0.306356
## [18] train-rmse:3.175370+0.051507 test-rmse:3.459878+0.319190
## [19] train-rmse:3.102701+0.047468 test-rmse:3.406591+0.316657
## [20] train-rmse:3.034497+0.044514 test-rmse:3.345017+0.289216
## [21] train-rmse:2.967177+0.042318 test-rmse:3.263714+0.322595
## [22] train-rmse:2.904177+0.041072 test-rmse:3.201074+0.320536
## [23] train-rmse:2.843676+0.038204 test-rmse:3.086545+0.279396
## [24] train-rmse:2.788195+0.036771 test-rmse:3.023549+0.285132
## [25] train-rmse:2.735571+0.035323 test-rmse:2.991851+0.299215
## [26] train-rmse:2.685073+0.036447 test-rmse:2.932270+0.308911
## [27] train-rmse:2.636631+0.037126 test-rmse:2.909464+0.311513
## [28] train-rmse:2.587968+0.039859 test-rmse:2.888980+0.318854
## [29] train-rmse:2.542670+0.040608 test-rmse:2.828184+0.337699
## [30] train-rmse:2.497485+0.042194 test-rmse:2.778581+0.297837
## [31] train-rmse:2.452902+0.042054 test-rmse:2.737354+0.300206
## [32] train-rmse:2.410919+0.041637 test-rmse:2.696341+0.310481
## [33] train-rmse:2.371146+0.039198 test-rmse:2.669482+0.309359
## [34] train-rmse:2.331370+0.037046 test-rmse:2.608873+0.320940
## [35] train-rmse:2.292350+0.035529 test-rmse:2.576996+0.303318
## [36] train-rmse:2.255000+0.032988 test-rmse:2.518138+0.285835
## [37] train-rmse:2.217970+0.031677 test-rmse:2.493668+0.300039
## [38] train-rmse:2.182866+0.032199 test-rmse:2.466399+0.311504
## [39] train-rmse:2.148589+0.031717 test-rmse:2.456399+0.303281
## [40] train-rmse:2.115258+0.031021 test-rmse:2.416985+0.301541
## [41] train-rmse:2.082452+0.030393 test-rmse:2.394270+0.300364
## [42] train-rmse:2.050967+0.028770 test-rmse:2.346311+0.270105
## [43] train-rmse:2.020985+0.028308 test-rmse:2.326045+0.269360
## [44] train-rmse:1.991993+0.027519 test-rmse:2.293489+0.274155
## [45] train-rmse:1.963250+0.026713 test-rmse:2.263088+0.283440
## [46] train-rmse:1.935647+0.025356 test-rmse:2.230392+0.281742
## [47] train-rmse:1.907945+0.023811 test-rmse:2.198686+0.255505
## [48] train-rmse:1.880497+0.022765 test-rmse:2.149125+0.257511
## [49] train-rmse:1.853301+0.021320 test-rmse:2.124916+0.260522
## [50] train-rmse:1.826639+0.019389 test-rmse:2.098332+0.253236
## [51] train-rmse:1.800135+0.017497 test-rmse:2.081991+0.253936
## [52] train-rmse:1.774990+0.016364 test-rmse:2.051127+0.260068
## [53] train-rmse:1.749860+0.015562 test-rmse:2.032676+0.269874
## [54] train-rmse:1.725539+0.015047 test-rmse:2.010896+0.258336
## [55] train-rmse:1.702012+0.015461 test-rmse:2.000704+0.256546
## [56] train-rmse:1.679471+0.015693 test-rmse:1.975094+0.244034
## [57] train-rmse:1.657528+0.015778 test-rmse:1.961130+0.249073
## [58] train-rmse:1.635699+0.015749 test-rmse:1.942331+0.259028
## [59] train-rmse:1.614556+0.015750 test-rmse:1.922173+0.266104
## [60] train-rmse:1.594356+0.015552 test-rmse:1.901578+0.274940
## [61] train-rmse:1.574851+0.016003 test-rmse:1.897051+0.280622
## [62] train-rmse:1.555407+0.016587 test-rmse:1.871623+0.252083
## [63] train-rmse:1.536769+0.016279 test-rmse:1.861485+0.247830
## [64] train-rmse:1.518832+0.015634 test-rmse:1.845862+0.241985
## [65] train-rmse:1.501400+0.015244 test-rmse:1.833992+0.241912
## [66] train-rmse:1.483814+0.015367 test-rmse:1.793390+0.241530
## [67] train-rmse:1.466263+0.015312 test-rmse:1.788594+0.236826
## [68] train-rmse:1.449130+0.015197 test-rmse:1.773095+0.237841
## [69] train-rmse:1.432998+0.014552 test-rmse:1.744114+0.232587
## [70] train-rmse:1.417262+0.014248 test-rmse:1.733630+0.229652
## [71] train-rmse:1.401429+0.013959 test-rmse:1.716903+0.225176
## [72] train-rmse:1.386432+0.014028 test-rmse:1.698222+0.223583
## [73] train-rmse:1.371574+0.014248 test-rmse:1.681534+0.226230
## [74] train-rmse:1.357233+0.014220 test-rmse:1.665999+0.226605
## [75] train-rmse:1.342779+0.014204 test-rmse:1.650357+0.230810
## [76] train-rmse:1.328356+0.014121 test-rmse:1.645136+0.229882
## [77] train-rmse:1.314044+0.013801 test-rmse:1.630716+0.236811
## [78] train-rmse:1.300512+0.013560 test-rmse:1.616213+0.232711
## [79] train-rmse:1.287697+0.013301 test-rmse:1.609444+0.228034
## [80] train-rmse:1.275207+0.012829 test-rmse:1.597030+0.229187
## [81] train-rmse:1.262856+0.012338 test-rmse:1.585956+0.235457
## [82] train-rmse:1.251029+0.011946 test-rmse:1.576387+0.240079
## [83] train-rmse:1.239477+0.011790 test-rmse:1.568994+0.239841
## [84] train-rmse:1.228141+0.011726 test-rmse:1.554655+0.245748
## [85] train-rmse:1.217130+0.011723 test-rmse:1.545922+0.240614
## [86] train-rmse:1.206230+0.011509 test-rmse:1.541436+0.242129
## [87] train-rmse:1.195358+0.011299 test-rmse:1.527191+0.239690
## [88] train-rmse:1.185151+0.011501 test-rmse:1.521577+0.242165
## [89] train-rmse:1.174897+0.011825 test-rmse:1.489154+0.224516
## [90] train-rmse:1.165047+0.012096 test-rmse:1.482233+0.224885
## [91] train-rmse:1.155527+0.012375 test-rmse:1.475622+0.222146
## [92] train-rmse:1.146233+0.012356 test-rmse:1.462321+0.230032
## [93] train-rmse:1.137246+0.012609 test-rmse:1.461705+0.234231
## [94] train-rmse:1.127991+0.012796 test-rmse:1.446433+0.224868
## [95] train-rmse:1.119168+0.012750 test-rmse:1.432674+0.217231
## [96] train-rmse:1.110528+0.012546 test-rmse:1.427951+0.214538
## [97] train-rmse:1.101987+0.012421 test-rmse:1.419778+0.215229
## [98] train-rmse:1.093445+0.012515 test-rmse:1.420805+0.214526
## [99] train-rmse:1.085309+0.012266 test-rmse:1.413127+0.215172
## [100] train-rmse:1.077510+0.012114 test-rmse:1.405359+0.219310
## [101] train-rmse:1.069774+0.012042 test-rmse:1.397662+0.220267
## [102] train-rmse:1.062428+0.011934 test-rmse:1.388582+0.224417
## [103] train-rmse:1.055344+0.011989 test-rmse:1.379830+0.230044
## [104] train-rmse:1.048651+0.012087 test-rmse:1.368038+0.234943
## [105] train-rmse:1.041963+0.012131 test-rmse:1.364133+0.239016
## [106] train-rmse:1.035371+0.012212 test-rmse:1.357911+0.236752
## [107] train-rmse:1.028729+0.012161 test-rmse:1.350063+0.218402
## [108] train-rmse:1.022352+0.012320 test-rmse:1.344323+0.221578
## [109] train-rmse:1.016239+0.012328 test-rmse:1.340606+0.217536
## [110] train-rmse:1.010394+0.012339 test-rmse:1.335374+0.210891
## [111] train-rmse:1.004631+0.012368 test-rmse:1.326292+0.207766
## [112] train-rmse:0.998844+0.012406 test-rmse:1.321729+0.208232
## [113] train-rmse:0.993173+0.012497 test-rmse:1.316900+0.210754
## [114] train-rmse:0.987364+0.012425 test-rmse:1.317306+0.211050
## [115] train-rmse:0.981819+0.012513 test-rmse:1.316405+0.209550
## [116] train-rmse:0.976232+0.012355 test-rmse:1.314614+0.212549
## [117] train-rmse:0.971173+0.012204 test-rmse:1.311526+0.213568
## [118] train-rmse:0.966352+0.012251 test-rmse:1.303719+0.217335
## [119] train-rmse:0.961590+0.012432 test-rmse:1.297789+0.215428
## [120] train-rmse:0.956749+0.012542 test-rmse:1.290777+0.209777
## [121] train-rmse:0.952264+0.012483 test-rmse:1.284482+0.209437
## [122] train-rmse:0.947904+0.012488 test-rmse:1.281621+0.208145
## [123] train-rmse:0.943612+0.012524 test-rmse:1.276907+0.205370
## [124] train-rmse:0.939483+0.012543 test-rmse:1.275550+0.206926
## [125] train-rmse:0.935365+0.012589 test-rmse:1.274645+0.207236
## [126] train-rmse:0.931310+0.012724 test-rmse:1.273836+0.207924
## [127] train-rmse:0.927412+0.012752 test-rmse:1.269741+0.209503
## [128] train-rmse:0.923510+0.012746 test-rmse:1.264145+0.206823
## [129] train-rmse:0.919631+0.012830 test-rmse:1.260627+0.204352
## [130] train-rmse:0.915777+0.012832 test-rmse:1.258660+0.202039
## [131] train-rmse:0.912140+0.012862 test-rmse:1.256815+0.202376
## [132] train-rmse:0.908585+0.012850 test-rmse:1.249004+0.195127
## [133] train-rmse:0.905018+0.012771 test-rmse:1.246219+0.196941
## [134] train-rmse:0.901575+0.012648 test-rmse:1.242725+0.199274
## [135] train-rmse:0.898306+0.012637 test-rmse:1.241725+0.201018
## [136] train-rmse:0.895150+0.012698 test-rmse:1.237171+0.203606
## [137] train-rmse:0.891978+0.012781 test-rmse:1.235670+0.203603
## [138] train-rmse:0.888913+0.012763 test-rmse:1.231881+0.203172
## [139] train-rmse:0.885879+0.012802 test-rmse:1.231425+0.203037
## [140] train-rmse:0.882844+0.012827 test-rmse:1.226457+0.202998
## [141] train-rmse:0.879829+0.012711 test-rmse:1.223903+0.204619
## [142] train-rmse:0.876747+0.012832 test-rmse:1.224749+0.202818
## [143] train-rmse:0.873836+0.012943 test-rmse:1.220549+0.201559
## [144] train-rmse:0.871095+0.013047 test-rmse:1.216675+0.201931
## [145] train-rmse:0.868231+0.013220 test-rmse:1.214116+0.204165
## [146] train-rmse:0.865544+0.013292 test-rmse:1.213543+0.198972
## [147] train-rmse:0.862605+0.013508 test-rmse:1.209547+0.201832
## [148] train-rmse:0.860082+0.013734 test-rmse:1.208514+0.202574
## [149] train-rmse:0.857625+0.013863 test-rmse:1.207252+0.204784
## [150] train-rmse:0.855223+0.013981 test-rmse:1.206631+0.203979
## [151] train-rmse:0.852734+0.014163 test-rmse:1.197718+0.209877
## [152] train-rmse:0.850407+0.014125 test-rmse:1.199315+0.208963
## [153] train-rmse:0.848080+0.014255 test-rmse:1.198308+0.207795
## [154] train-rmse:0.845818+0.014331 test-rmse:1.199040+0.206795
## [155] train-rmse:0.843584+0.014348 test-rmse:1.200016+0.206582
## [156] train-rmse:0.841362+0.014447 test-rmse:1.196607+0.209593
## [157] train-rmse:0.839146+0.014510 test-rmse:1.196285+0.210273
## [158] train-rmse:0.837022+0.014612 test-rmse:1.197714+0.211497
## [159] train-rmse:0.834904+0.014674 test-rmse:1.198370+0.212464
## [160] train-rmse:0.832905+0.014731 test-rmse:1.192983+0.211302
## [161] train-rmse:0.830922+0.014784 test-rmse:1.187400+0.205136
## [162] train-rmse:0.828965+0.014857 test-rmse:1.187147+0.201866
## [163] train-rmse:0.826973+0.014901 test-rmse:1.179523+0.202542
## [164] train-rmse:0.825029+0.014991 test-rmse:1.177440+0.204726
## [165] train-rmse:0.823049+0.015142 test-rmse:1.175806+0.204871
## [166] train-rmse:0.821169+0.015281 test-rmse:1.171912+0.203444
## [167] train-rmse:0.819347+0.015403 test-rmse:1.172701+0.202785
## [168] train-rmse:0.817559+0.015515 test-rmse:1.173054+0.202388
## [169] train-rmse:0.815791+0.015594 test-rmse:1.172344+0.202178
## [170] train-rmse:0.814087+0.015659 test-rmse:1.169808+0.202556
## [171] train-rmse:0.812408+0.015745 test-rmse:1.171429+0.204551
## [172] train-rmse:0.810629+0.015932 test-rmse:1.169844+0.202955
## [173] train-rmse:0.808982+0.015976 test-rmse:1.167226+0.205542
## [174] train-rmse:0.807364+0.016066 test-rmse:1.166998+0.204764
## [175] train-rmse:0.805736+0.016125 test-rmse:1.167242+0.203848
## [176] train-rmse:0.804109+0.016108 test-rmse:1.165622+0.204269
## [177] train-rmse:0.802517+0.016200 test-rmse:1.163881+0.206067
## [178] train-rmse:0.800950+0.016323 test-rmse:1.162203+0.206934
## [179] train-rmse:0.799407+0.016449 test-rmse:1.158001+0.206625
## [180] train-rmse:0.797827+0.016577 test-rmse:1.159439+0.205817
## [181] train-rmse:0.796327+0.016682 test-rmse:1.156784+0.207270
## [182] train-rmse:0.794895+0.016791 test-rmse:1.154927+0.206722
## [183] train-rmse:0.793450+0.016864 test-rmse:1.153378+0.207956
## [184] train-rmse:0.792090+0.016947 test-rmse:1.152960+0.209076
## [185] train-rmse:0.790682+0.017044 test-rmse:1.151324+0.205185
## [186] train-rmse:0.789295+0.017082 test-rmse:1.148550+0.204383
## [187] train-rmse:0.787951+0.017124 test-rmse:1.149758+0.204029
## [188] train-rmse:0.786591+0.017134 test-rmse:1.146986+0.204189
## [189] train-rmse:0.785276+0.017150 test-rmse:1.147560+0.201896
## [190] train-rmse:0.783933+0.017182 test-rmse:1.146624+0.201950
## [191] train-rmse:0.782657+0.017261 test-rmse:1.147252+0.200532
## [192] train-rmse:0.781345+0.017287 test-rmse:1.146614+0.200307
## [193] train-rmse:0.780080+0.017283 test-rmse:1.146633+0.202090
## [194] train-rmse:0.778850+0.017266 test-rmse:1.145711+0.203871
## [195] train-rmse:0.777634+0.017324 test-rmse:1.144167+0.204267
## [196] train-rmse:0.776456+0.017376 test-rmse:1.139118+0.200646
## [197] train-rmse:0.775273+0.017427 test-rmse:1.137281+0.197281
## [198] train-rmse:0.774048+0.017380 test-rmse:1.134248+0.198110
## [199] train-rmse:0.772912+0.017419 test-rmse:1.130833+0.196348
## [200] train-rmse:0.771793+0.017424 test-rmse:1.129592+0.193886
## [201] train-rmse:0.770539+0.017422 test-rmse:1.125353+0.194295
## [202] train-rmse:0.769388+0.017471 test-rmse:1.124215+0.193702
## [203] train-rmse:0.768270+0.017539 test-rmse:1.123656+0.194967
## [204] train-rmse:0.767114+0.017678 test-rmse:1.125792+0.194462
## [205] train-rmse:0.765952+0.017678 test-rmse:1.123130+0.195385
## [206] train-rmse:0.764899+0.017720 test-rmse:1.121704+0.196385
## [207] train-rmse:0.763871+0.017771 test-rmse:1.121170+0.197294
## [208] train-rmse:0.762809+0.017775 test-rmse:1.120720+0.197869
## [209] train-rmse:0.761788+0.017797 test-rmse:1.119815+0.197392
## [210] train-rmse:0.760751+0.017763 test-rmse:1.117707+0.195075
## [211] train-rmse:0.759769+0.017766 test-rmse:1.118218+0.197152
## [212] train-rmse:0.758753+0.017754 test-rmse:1.117471+0.196749
## [213] train-rmse:0.757742+0.017746 test-rmse:1.112868+0.195066
## [214] train-rmse:0.756755+0.017764 test-rmse:1.108952+0.194136
## [215] train-rmse:0.755762+0.017770 test-rmse:1.108795+0.191154
## [216] train-rmse:0.754779+0.017762 test-rmse:1.108016+0.190592
## [217] train-rmse:0.753790+0.017713 test-rmse:1.106284+0.190344
## [218] train-rmse:0.752821+0.017634 test-rmse:1.106226+0.191091
## [219] train-rmse:0.751917+0.017590 test-rmse:1.106432+0.190488
## [220] train-rmse:0.750999+0.017540 test-rmse:1.106280+0.189740
## [221] train-rmse:0.750032+0.017546 test-rmse:1.106372+0.190309
## [222] train-rmse:0.749156+0.017524 test-rmse:1.106850+0.190393
## [223] train-rmse:0.748289+0.017480 test-rmse:1.107318+0.189995
## [224] train-rmse:0.747411+0.017442 test-rmse:1.106194+0.189471
## [225] train-rmse:0.746582+0.017429 test-rmse:1.104263+0.189497
## [226] train-rmse:0.745675+0.017466 test-rmse:1.104372+0.190878
## [227] train-rmse:0.744827+0.017438 test-rmse:1.102342+0.185005
## [228] train-rmse:0.744008+0.017429 test-rmse:1.101302+0.186432
## [229] train-rmse:0.743217+0.017421 test-rmse:1.099767+0.186571
## [230] train-rmse:0.742432+0.017406 test-rmse:1.097452+0.185989
## [231] train-rmse:0.741639+0.017353 test-rmse:1.096144+0.182569
## [232] train-rmse:0.740841+0.017295 test-rmse:1.095325+0.182331
## [233] train-rmse:0.740064+0.017251 test-rmse:1.092809+0.181107
## [234] train-rmse:0.739251+0.017208 test-rmse:1.094100+0.179754
## [235] train-rmse:0.738484+0.017180 test-rmse:1.094324+0.180877
## [236] train-rmse:0.737694+0.017178 test-rmse:1.092500+0.179934
## [237] train-rmse:0.736938+0.017155 test-rmse:1.089761+0.180804
## [238] train-rmse:0.736179+0.017129 test-rmse:1.088789+0.181200
## [239] train-rmse:0.735442+0.017098 test-rmse:1.087065+0.182341
## [240] train-rmse:0.734666+0.017091 test-rmse:1.083819+0.179193
## [241] train-rmse:0.733907+0.017064 test-rmse:1.085877+0.178906
## [242] train-rmse:0.733165+0.017048 test-rmse:1.084055+0.177614
## [243] train-rmse:0.732390+0.017036 test-rmse:1.084677+0.177559
## [244] train-rmse:0.731680+0.016967 test-rmse:1.085218+0.177373
## [245] train-rmse:0.730983+0.016964 test-rmse:1.084383+0.177382
## [246] train-rmse:0.730286+0.016936 test-rmse:1.082603+0.178024
## [247] train-rmse:0.729592+0.016923 test-rmse:1.082359+0.178012
## [248] train-rmse:0.728924+0.016910 test-rmse:1.082236+0.178172
## [249] train-rmse:0.728257+0.016874 test-rmse:1.081352+0.179541
## [250] train-rmse:0.727600+0.016836 test-rmse:1.079031+0.177955
## [251] train-rmse:0.726934+0.016801 test-rmse:1.077983+0.175211
## [252] train-rmse:0.726283+0.016753 test-rmse:1.078300+0.174760
## [253] train-rmse:0.725591+0.016736 test-rmse:1.078369+0.173368
## [254] train-rmse:0.724928+0.016672 test-rmse:1.079497+0.174971
## [255] train-rmse:0.724250+0.016624 test-rmse:1.078980+0.175591
## [256] train-rmse:0.723602+0.016588 test-rmse:1.080012+0.175290
## [257] train-rmse:0.722972+0.016550 test-rmse:1.079099+0.175829
## Stopping. Best iteration:
## [251] train-rmse:0.726934+0.016801 test-rmse:1.077983+0.175211
##
## 99
## [1] train-rmse:67.001016+0.081705 test-rmse:67.000742+0.527509
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:41.869994+0.065854 test-rmse:41.868242+0.585212
## [3] train-rmse:26.397598+0.065573 test-rmse:26.392831+0.648853
## [4] train-rmse:16.849706+0.043896 test-rmse:16.919849+0.608783
## [5] train-rmse:11.020723+0.041417 test-rmse:11.187750+0.632870
## [6] train-rmse:7.516885+0.046723 test-rmse:7.584999+0.509094
## [7] train-rmse:5.493946+0.057112 test-rmse:5.630846+0.561116
## [8] train-rmse:4.373002+0.057797 test-rmse:4.517130+0.486306
## [9] train-rmse:3.749964+0.053962 test-rmse:4.011870+0.436355
## [10] train-rmse:3.390304+0.055889 test-rmse:3.656598+0.369556
## [11] train-rmse:3.157221+0.053349 test-rmse:3.467630+0.350106
## [12] train-rmse:2.980780+0.049773 test-rmse:3.281611+0.314166
## [13] train-rmse:2.834450+0.044765 test-rmse:3.187239+0.320357
## [14] train-rmse:2.713964+0.041020 test-rmse:3.036565+0.343886
## [15] train-rmse:2.605242+0.044431 test-rmse:2.920153+0.313992
## [16] train-rmse:2.507691+0.042602 test-rmse:2.864950+0.311279
## [17] train-rmse:2.416475+0.040713 test-rmse:2.775364+0.331204
## [18] train-rmse:2.324551+0.030183 test-rmse:2.744503+0.312537
## [19] train-rmse:2.242082+0.026940 test-rmse:2.631129+0.295917
## [20] train-rmse:2.163429+0.024093 test-rmse:2.514097+0.275297
## [21] train-rmse:2.085873+0.019941 test-rmse:2.487779+0.265422
## [22] train-rmse:2.018807+0.022199 test-rmse:2.442604+0.260823
## [23] train-rmse:1.951669+0.024260 test-rmse:2.384971+0.279823
## [24] train-rmse:1.884381+0.024423 test-rmse:2.331499+0.272029
## [25] train-rmse:1.823581+0.025458 test-rmse:2.247275+0.268847
## [26] train-rmse:1.767727+0.021271 test-rmse:2.203858+0.261490
## [27] train-rmse:1.714781+0.020076 test-rmse:2.153945+0.258085
## [28] train-rmse:1.662185+0.019912 test-rmse:2.113236+0.261480
## [29] train-rmse:1.609228+0.021762 test-rmse:2.068826+0.266744
## [30] train-rmse:1.556372+0.020433 test-rmse:2.065480+0.260244
## [31] train-rmse:1.507885+0.013710 test-rmse:2.029751+0.279818
## [32] train-rmse:1.466197+0.012456 test-rmse:1.990828+0.271340
## [33] train-rmse:1.426479+0.012415 test-rmse:1.946525+0.273321
## [34] train-rmse:1.385370+0.013898 test-rmse:1.904573+0.258837
## [35] train-rmse:1.346325+0.014678 test-rmse:1.870880+0.263241
## [36] train-rmse:1.311292+0.015214 test-rmse:1.852527+0.270393
## [37] train-rmse:1.276991+0.015520 test-rmse:1.829281+0.268181
## [38] train-rmse:1.244741+0.014252 test-rmse:1.798709+0.260128
## [39] train-rmse:1.214771+0.014816 test-rmse:1.752242+0.240033
## [40] train-rmse:1.185734+0.015115 test-rmse:1.724654+0.237937
## [41] train-rmse:1.156514+0.016807 test-rmse:1.692284+0.230429
## [42] train-rmse:1.126486+0.017059 test-rmse:1.669011+0.228200
## [43] train-rmse:1.100275+0.017135 test-rmse:1.641648+0.234642
## [44] train-rmse:1.075109+0.018035 test-rmse:1.620083+0.227639
## [45] train-rmse:1.048980+0.017922 test-rmse:1.603076+0.229091
## [46] train-rmse:1.025221+0.018784 test-rmse:1.579568+0.248855
## [47] train-rmse:1.002532+0.021375 test-rmse:1.562916+0.241369
## [48] train-rmse:0.981534+0.023654 test-rmse:1.542927+0.234464
## [49] train-rmse:0.959698+0.026504 test-rmse:1.536393+0.237946
## [50] train-rmse:0.937837+0.027270 test-rmse:1.525040+0.238781
## [51] train-rmse:0.919587+0.028914 test-rmse:1.509726+0.242039
## [52] train-rmse:0.901126+0.029433 test-rmse:1.493786+0.243082
## [53] train-rmse:0.884622+0.029753 test-rmse:1.480951+0.237553
## [54] train-rmse:0.867725+0.029530 test-rmse:1.464021+0.237948
## [55] train-rmse:0.851918+0.029147 test-rmse:1.456339+0.239906
## [56] train-rmse:0.835879+0.028835 test-rmse:1.435301+0.246711
## [57] train-rmse:0.819594+0.027579 test-rmse:1.425853+0.244534
## [58] train-rmse:0.805432+0.027878 test-rmse:1.419070+0.249051
## [59] train-rmse:0.791206+0.027185 test-rmse:1.402816+0.249861
## [60] train-rmse:0.777812+0.028026 test-rmse:1.392445+0.251417
## [61] train-rmse:0.765342+0.027816 test-rmse:1.379583+0.257589
## [62] train-rmse:0.751556+0.025738 test-rmse:1.378985+0.251083
## [63] train-rmse:0.739712+0.024502 test-rmse:1.372918+0.254937
## [64] train-rmse:0.725965+0.023537 test-rmse:1.362402+0.255647
## [65] train-rmse:0.714690+0.023164 test-rmse:1.359342+0.258173
## [66] train-rmse:0.702544+0.025066 test-rmse:1.347274+0.243820
## [67] train-rmse:0.692286+0.026173 test-rmse:1.343015+0.246190
## [68] train-rmse:0.683813+0.026243 test-rmse:1.340551+0.247410
## [69] train-rmse:0.669923+0.020458 test-rmse:1.331456+0.253028
## [70] train-rmse:0.659224+0.019284 test-rmse:1.324327+0.252663
## [71] train-rmse:0.649622+0.016788 test-rmse:1.317043+0.257644
## [72] train-rmse:0.640950+0.017361 test-rmse:1.312416+0.258879
## [73] train-rmse:0.633222+0.017890 test-rmse:1.307836+0.258552
## [74] train-rmse:0.626684+0.017697 test-rmse:1.302823+0.262835
## [75] train-rmse:0.617731+0.017516 test-rmse:1.296774+0.260400
## [76] train-rmse:0.608863+0.019440 test-rmse:1.298449+0.263645
## [77] train-rmse:0.602068+0.020075 test-rmse:1.296040+0.260322
## [78] train-rmse:0.593542+0.019194 test-rmse:1.287981+0.262199
## [79] train-rmse:0.585080+0.017427 test-rmse:1.278396+0.265252
## [80] train-rmse:0.577215+0.018526 test-rmse:1.276571+0.263721
## [81] train-rmse:0.570523+0.019359 test-rmse:1.272399+0.266407
## [82] train-rmse:0.564610+0.018355 test-rmse:1.265490+0.264885
## [83] train-rmse:0.558564+0.018889 test-rmse:1.255771+0.252551
## [84] train-rmse:0.553334+0.018390 test-rmse:1.252260+0.253733
## [85] train-rmse:0.548084+0.018520 test-rmse:1.250960+0.257455
## [86] train-rmse:0.542201+0.019058 test-rmse:1.243691+0.259774
## [87] train-rmse:0.536163+0.018087 test-rmse:1.241203+0.261921
## [88] train-rmse:0.530517+0.018565 test-rmse:1.238769+0.263819
## [89] train-rmse:0.525822+0.018239 test-rmse:1.238872+0.262074
## [90] train-rmse:0.520549+0.018831 test-rmse:1.236964+0.261078
## [91] train-rmse:0.515481+0.019006 test-rmse:1.237407+0.260938
## [92] train-rmse:0.509278+0.018279 test-rmse:1.232374+0.260443
## [93] train-rmse:0.504754+0.017432 test-rmse:1.230075+0.262818
## [94] train-rmse:0.500420+0.018126 test-rmse:1.223796+0.264467
## [95] train-rmse:0.492886+0.016162 test-rmse:1.220026+0.267714
## [96] train-rmse:0.488784+0.016207 test-rmse:1.216396+0.268936
## [97] train-rmse:0.483457+0.015932 test-rmse:1.214827+0.272017
## [98] train-rmse:0.479946+0.016180 test-rmse:1.211985+0.272257
## [99] train-rmse:0.476782+0.016205 test-rmse:1.209598+0.272422
## [100] train-rmse:0.473654+0.015973 test-rmse:1.208614+0.275679
## [101] train-rmse:0.470183+0.016535 test-rmse:1.204805+0.274840
## [102] train-rmse:0.466871+0.015517 test-rmse:1.201173+0.272580
## [103] train-rmse:0.462732+0.014712 test-rmse:1.198088+0.276481
## [104] train-rmse:0.459085+0.015110 test-rmse:1.194216+0.278380
## [105] train-rmse:0.454791+0.014688 test-rmse:1.193642+0.278477
## [106] train-rmse:0.451102+0.013480 test-rmse:1.191463+0.279882
## [107] train-rmse:0.448838+0.013429 test-rmse:1.189853+0.279001
## [108] train-rmse:0.445746+0.013150 test-rmse:1.189193+0.279186
## [109] train-rmse:0.441948+0.014454 test-rmse:1.190528+0.280978
## [110] train-rmse:0.437838+0.015592 test-rmse:1.184783+0.278668
## [111] train-rmse:0.434118+0.016506 test-rmse:1.179957+0.278262
## [112] train-rmse:0.428760+0.017109 test-rmse:1.181239+0.278848
## [113] train-rmse:0.424997+0.016505 test-rmse:1.177705+0.278921
## [114] train-rmse:0.422198+0.016556 test-rmse:1.174884+0.278153
## [115] train-rmse:0.418042+0.015913 test-rmse:1.173924+0.279695
## [116] train-rmse:0.413891+0.013850 test-rmse:1.174480+0.279681
## [117] train-rmse:0.410111+0.013529 test-rmse:1.173838+0.279449
## [118] train-rmse:0.407374+0.013986 test-rmse:1.167395+0.281749
## [119] train-rmse:0.404138+0.014472 test-rmse:1.167371+0.280982
## [120] train-rmse:0.400493+0.015461 test-rmse:1.164866+0.282897
## [121] train-rmse:0.397678+0.016361 test-rmse:1.162627+0.282284
## [122] train-rmse:0.394985+0.017028 test-rmse:1.161297+0.282863
## [123] train-rmse:0.392750+0.016745 test-rmse:1.159711+0.282251
## [124] train-rmse:0.391208+0.016764 test-rmse:1.158767+0.283046
## [125] train-rmse:0.388239+0.017128 test-rmse:1.156669+0.282216
## [126] train-rmse:0.385546+0.016122 test-rmse:1.153596+0.283665
## [127] train-rmse:0.383374+0.016066 test-rmse:1.152695+0.283742
## [128] train-rmse:0.380585+0.016531 test-rmse:1.150870+0.283188
## [129] train-rmse:0.378305+0.016686 test-rmse:1.149062+0.283925
## [130] train-rmse:0.376186+0.016263 test-rmse:1.148834+0.284249
## [131] train-rmse:0.373061+0.016035 test-rmse:1.146277+0.283248
## [132] train-rmse:0.369298+0.015672 test-rmse:1.146884+0.284412
## [133] train-rmse:0.366801+0.015621 test-rmse:1.147464+0.285461
## [134] train-rmse:0.362678+0.014270 test-rmse:1.145130+0.286297
## [135] train-rmse:0.359505+0.013589 test-rmse:1.146191+0.286439
## [136] train-rmse:0.357434+0.013879 test-rmse:1.145286+0.286467
## [137] train-rmse:0.354688+0.014404 test-rmse:1.144705+0.285623
## [138] train-rmse:0.352193+0.013863 test-rmse:1.145429+0.287487
## [139] train-rmse:0.350437+0.014306 test-rmse:1.143948+0.287465
## [140] train-rmse:0.347264+0.015037 test-rmse:1.144402+0.288765
## [141] train-rmse:0.344483+0.015012 test-rmse:1.141790+0.286615
## [142] train-rmse:0.341474+0.015363 test-rmse:1.138343+0.289867
## [143] train-rmse:0.339802+0.015561 test-rmse:1.137978+0.289596
## [144] train-rmse:0.338027+0.015235 test-rmse:1.138208+0.290143
## [145] train-rmse:0.336007+0.015196 test-rmse:1.137275+0.287985
## [146] train-rmse:0.335039+0.015183 test-rmse:1.135873+0.287793
## [147] train-rmse:0.333578+0.015304 test-rmse:1.134294+0.288879
## [148] train-rmse:0.331445+0.015650 test-rmse:1.134113+0.289484
## [149] train-rmse:0.328642+0.014076 test-rmse:1.134597+0.288844
## [150] train-rmse:0.324963+0.013235 test-rmse:1.130888+0.293001
## [151] train-rmse:0.322524+0.012276 test-rmse:1.130812+0.292960
## [152] train-rmse:0.320151+0.012788 test-rmse:1.129725+0.291926
## [153] train-rmse:0.317915+0.011620 test-rmse:1.128083+0.293831
## [154] train-rmse:0.315972+0.011690 test-rmse:1.127919+0.293855
## [155] train-rmse:0.313357+0.011513 test-rmse:1.125541+0.292736
## [156] train-rmse:0.311430+0.011866 test-rmse:1.125050+0.292385
## [157] train-rmse:0.309801+0.011997 test-rmse:1.120907+0.286942
## [158] train-rmse:0.308293+0.012034 test-rmse:1.118665+0.288477
## [159] train-rmse:0.305944+0.011611 test-rmse:1.118453+0.286779
## [160] train-rmse:0.304774+0.011262 test-rmse:1.117687+0.286922
## [161] train-rmse:0.302941+0.011971 test-rmse:1.117195+0.286631
## [162] train-rmse:0.301200+0.011821 test-rmse:1.115603+0.285819
## [163] train-rmse:0.298953+0.011647 test-rmse:1.114680+0.285612
## [164] train-rmse:0.297762+0.011757 test-rmse:1.114122+0.285911
## [165] train-rmse:0.295949+0.012134 test-rmse:1.113833+0.285546
## [166] train-rmse:0.293887+0.012001 test-rmse:1.112902+0.286483
## [167] train-rmse:0.292160+0.012068 test-rmse:1.113244+0.286499
## [168] train-rmse:0.290585+0.012757 test-rmse:1.112280+0.287737
## [169] train-rmse:0.288932+0.012691 test-rmse:1.112236+0.287810
## [170] train-rmse:0.287313+0.012724 test-rmse:1.112467+0.287386
## [171] train-rmse:0.285872+0.012496 test-rmse:1.112140+0.287560
## [172] train-rmse:0.283900+0.012900 test-rmse:1.112303+0.288592
## [173] train-rmse:0.282484+0.013360 test-rmse:1.112067+0.288342
## [174] train-rmse:0.281081+0.013722 test-rmse:1.112626+0.289170
## [175] train-rmse:0.278715+0.013134 test-rmse:1.112628+0.288183
## [176] train-rmse:0.277166+0.012822 test-rmse:1.111749+0.288619
## [177] train-rmse:0.275876+0.012843 test-rmse:1.111301+0.289244
## [178] train-rmse:0.274805+0.012924 test-rmse:1.110711+0.288577
## [179] train-rmse:0.274121+0.012739 test-rmse:1.109345+0.287625
## [180] train-rmse:0.272902+0.012764 test-rmse:1.109582+0.288311
## [181] train-rmse:0.272031+0.013103 test-rmse:1.109669+0.288029
## [182] train-rmse:0.270174+0.012318 test-rmse:1.109356+0.287855
## [183] train-rmse:0.268159+0.011938 test-rmse:1.108445+0.288095
## [184] train-rmse:0.266507+0.012004 test-rmse:1.108909+0.287943
## [185] train-rmse:0.264839+0.011886 test-rmse:1.109008+0.289398
## [186] train-rmse:0.264223+0.011980 test-rmse:1.108447+0.289415
## [187] train-rmse:0.263001+0.011305 test-rmse:1.107659+0.288862
## [188] train-rmse:0.262021+0.011325 test-rmse:1.107513+0.288576
## [189] train-rmse:0.260316+0.011728 test-rmse:1.108027+0.287409
## [190] train-rmse:0.258682+0.011176 test-rmse:1.108205+0.286764
## [191] train-rmse:0.256447+0.009935 test-rmse:1.110099+0.286299
## [192] train-rmse:0.255271+0.009861 test-rmse:1.110110+0.286188
## [193] train-rmse:0.253947+0.010583 test-rmse:1.110191+0.285716
## [194] train-rmse:0.252701+0.009953 test-rmse:1.109552+0.285410
## Stopping. Best iteration:
## [188] train-rmse:0.262021+0.011325 test-rmse:1.107513+0.288576
##
## 100
## [1] train-rmse:67.001016+0.081705 test-rmse:67.000742+0.527509
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:41.869994+0.065854 test-rmse:41.868242+0.585212
## [3] train-rmse:26.397598+0.065573 test-rmse:26.392831+0.648853
## [4] train-rmse:16.827689+0.035908 test-rmse:16.891083+0.599172
## [5] train-rmse:10.925817+0.032545 test-rmse:10.994655+0.549852
## [6] train-rmse:7.356710+0.033749 test-rmse:7.474104+0.549826
## [7] train-rmse:5.241271+0.045483 test-rmse:5.450910+0.579151
## [8] train-rmse:3.994278+0.054698 test-rmse:4.264142+0.466754
## [9] train-rmse:3.288628+0.051081 test-rmse:3.564041+0.345138
## [10] train-rmse:2.884923+0.046709 test-rmse:3.203227+0.323083
## [11] train-rmse:2.602158+0.062080 test-rmse:2.972800+0.283341
## [12] train-rmse:2.419654+0.062180 test-rmse:2.790806+0.287877
## [13] train-rmse:2.274061+0.055380 test-rmse:2.678650+0.293081
## [14] train-rmse:2.135140+0.042315 test-rmse:2.541365+0.244255
## [15] train-rmse:2.012158+0.040922 test-rmse:2.422373+0.211747
## [16] train-rmse:1.906816+0.036734 test-rmse:2.335734+0.205593
## [17] train-rmse:1.809509+0.035129 test-rmse:2.263392+0.233277
## [18] train-rmse:1.718419+0.030268 test-rmse:2.193558+0.224700
## [19] train-rmse:1.632474+0.026525 test-rmse:2.103529+0.227369
## [20] train-rmse:1.555065+0.023473 test-rmse:2.041701+0.221324
## [21] train-rmse:1.481001+0.024018 test-rmse:1.995368+0.201584
## [22] train-rmse:1.416564+0.021244 test-rmse:1.939761+0.218027
## [23] train-rmse:1.348121+0.024344 test-rmse:1.895506+0.207561
## [24] train-rmse:1.291127+0.025207 test-rmse:1.850615+0.224543
## [25] train-rmse:1.236509+0.022702 test-rmse:1.777258+0.230902
## [26] train-rmse:1.186447+0.019230 test-rmse:1.744889+0.248494
## [27] train-rmse:1.138376+0.017677 test-rmse:1.711923+0.249285
## [28] train-rmse:1.089612+0.016020 test-rmse:1.679084+0.240956
## [29] train-rmse:1.044259+0.020228 test-rmse:1.645259+0.242347
## [30] train-rmse:1.005104+0.021851 test-rmse:1.601131+0.247485
## [31] train-rmse:0.967944+0.021221 test-rmse:1.569425+0.231247
## [32] train-rmse:0.935157+0.020083 test-rmse:1.547586+0.227819
## [33] train-rmse:0.902750+0.019063 test-rmse:1.522382+0.228129
## [34] train-rmse:0.866084+0.020839 test-rmse:1.504479+0.229037
## [35] train-rmse:0.836590+0.020288 test-rmse:1.484552+0.232268
## [36] train-rmse:0.810674+0.019895 test-rmse:1.466297+0.240221
## [37] train-rmse:0.785562+0.019106 test-rmse:1.450980+0.248561
## [38] train-rmse:0.755559+0.020180 test-rmse:1.430375+0.238320
## [39] train-rmse:0.730869+0.020386 test-rmse:1.417934+0.241468
## [40] train-rmse:0.709077+0.016652 test-rmse:1.402186+0.243071
## [41] train-rmse:0.689809+0.017041 test-rmse:1.378223+0.247331
## [42] train-rmse:0.668626+0.019242 test-rmse:1.363403+0.245583
## [43] train-rmse:0.652185+0.019073 test-rmse:1.360463+0.244822
## [44] train-rmse:0.630975+0.017187 test-rmse:1.347428+0.253069
## [45] train-rmse:0.609791+0.015445 test-rmse:1.331206+0.263881
## [46] train-rmse:0.593888+0.014366 test-rmse:1.316094+0.268658
## [47] train-rmse:0.577809+0.015342 test-rmse:1.305756+0.269156
## [48] train-rmse:0.563290+0.014447 test-rmse:1.298157+0.269710
## [49] train-rmse:0.548597+0.010661 test-rmse:1.292733+0.274978
## [50] train-rmse:0.534922+0.010641 test-rmse:1.286908+0.281198
## [51] train-rmse:0.522006+0.010646 test-rmse:1.278976+0.283992
## [52] train-rmse:0.511178+0.009379 test-rmse:1.276212+0.284375
## [53] train-rmse:0.499312+0.009883 test-rmse:1.272342+0.280155
## [54] train-rmse:0.487421+0.011386 test-rmse:1.264606+0.285443
## [55] train-rmse:0.478782+0.013342 test-rmse:1.261392+0.287089
## [56] train-rmse:0.468281+0.012537 test-rmse:1.261252+0.284335
## [57] train-rmse:0.458755+0.012767 test-rmse:1.251178+0.290978
## [58] train-rmse:0.449486+0.012842 test-rmse:1.247041+0.290139
## [59] train-rmse:0.440488+0.011256 test-rmse:1.243503+0.292830
## [60] train-rmse:0.431251+0.010605 test-rmse:1.240640+0.292235
## [61] train-rmse:0.422118+0.009212 test-rmse:1.235653+0.294935
## [62] train-rmse:0.414368+0.007832 test-rmse:1.234698+0.294798
## [63] train-rmse:0.408023+0.008987 test-rmse:1.228391+0.290597
## [64] train-rmse:0.400652+0.010917 test-rmse:1.230516+0.290950
## [65] train-rmse:0.394453+0.011884 test-rmse:1.226760+0.289712
## [66] train-rmse:0.387578+0.012472 test-rmse:1.218225+0.294402
## [67] train-rmse:0.382708+0.012974 test-rmse:1.217171+0.295111
## [68] train-rmse:0.376278+0.011742 test-rmse:1.213487+0.293786
## [69] train-rmse:0.370234+0.012848 test-rmse:1.214028+0.294376
## [70] train-rmse:0.364483+0.013417 test-rmse:1.213866+0.293993
## [71] train-rmse:0.357627+0.012503 test-rmse:1.210675+0.295001
## [72] train-rmse:0.353127+0.013850 test-rmse:1.206147+0.295059
## [73] train-rmse:0.348803+0.013554 test-rmse:1.204411+0.296757
## [74] train-rmse:0.342159+0.013603 test-rmse:1.200491+0.298996
## [75] train-rmse:0.334545+0.014052 test-rmse:1.196140+0.299010
## [76] train-rmse:0.330412+0.013950 test-rmse:1.195217+0.300630
## [77] train-rmse:0.325608+0.014144 test-rmse:1.192635+0.296844
## [78] train-rmse:0.319851+0.013775 test-rmse:1.190814+0.297956
## [79] train-rmse:0.314611+0.011593 test-rmse:1.188340+0.297999
## [80] train-rmse:0.309744+0.011895 test-rmse:1.187429+0.297754
## [81] train-rmse:0.304797+0.012883 test-rmse:1.183160+0.295552
## [82] train-rmse:0.300169+0.012354 test-rmse:1.183136+0.298478
## [83] train-rmse:0.296392+0.013040 test-rmse:1.181944+0.297606
## [84] train-rmse:0.291722+0.012986 test-rmse:1.180419+0.300321
## [85] train-rmse:0.288295+0.013486 test-rmse:1.179833+0.301201
## [86] train-rmse:0.283073+0.011692 test-rmse:1.177958+0.298793
## [87] train-rmse:0.280194+0.012053 test-rmse:1.178396+0.298758
## [88] train-rmse:0.273958+0.013239 test-rmse:1.177568+0.300302
## [89] train-rmse:0.270547+0.012956 test-rmse:1.171553+0.291390
## [90] train-rmse:0.266980+0.012753 test-rmse:1.172184+0.291645
## [91] train-rmse:0.263979+0.012829 test-rmse:1.171037+0.292998
## [92] train-rmse:0.260271+0.012959 test-rmse:1.169205+0.293399
## [93] train-rmse:0.256554+0.013087 test-rmse:1.168146+0.295811
## [94] train-rmse:0.254023+0.013573 test-rmse:1.167887+0.296111
## [95] train-rmse:0.249820+0.013578 test-rmse:1.167167+0.298512
## [96] train-rmse:0.246212+0.011548 test-rmse:1.168915+0.298500
## [97] train-rmse:0.242351+0.011162 test-rmse:1.168978+0.298966
## [98] train-rmse:0.238440+0.011216 test-rmse:1.168424+0.299734
## [99] train-rmse:0.234671+0.011107 test-rmse:1.168683+0.300016
## [100] train-rmse:0.231718+0.010329 test-rmse:1.166238+0.301609
## [101] train-rmse:0.228262+0.011581 test-rmse:1.165745+0.301295
## [102] train-rmse:0.224883+0.011978 test-rmse:1.167283+0.301199
## [103] train-rmse:0.222355+0.011925 test-rmse:1.167305+0.300962
## [104] train-rmse:0.219570+0.012174 test-rmse:1.167372+0.301125
## [105] train-rmse:0.217369+0.011746 test-rmse:1.166379+0.302418
## [106] train-rmse:0.214432+0.011998 test-rmse:1.166205+0.304291
## [107] train-rmse:0.212523+0.012010 test-rmse:1.166262+0.304619
## Stopping. Best iteration:
## [101] train-rmse:0.228262+0.011581 test-rmse:1.165745+0.301295
##
## 101
## [1] train-rmse:67.001016+0.081705 test-rmse:67.000742+0.527509
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:41.869994+0.065854 test-rmse:41.868242+0.585212
## [3] train-rmse:26.397598+0.065573 test-rmse:26.392831+0.648853
## [4] train-rmse:16.826265+0.038279 test-rmse:16.885249+0.590232
## [5] train-rmse:10.901592+0.038279 test-rmse:10.974032+0.526604
## [6] train-rmse:7.276242+0.039943 test-rmse:7.367636+0.529662
## [7] train-rmse:5.081668+0.057807 test-rmse:5.240058+0.540646
## [8] train-rmse:3.780630+0.068267 test-rmse:3.954017+0.395325
## [9] train-rmse:3.011568+0.069916 test-rmse:3.288256+0.361397
## [10] train-rmse:2.537753+0.103501 test-rmse:2.864497+0.291233
## [11] train-rmse:2.224356+0.071194 test-rmse:2.616511+0.294781
## [12] train-rmse:2.015487+0.055361 test-rmse:2.445970+0.271250
## [13] train-rmse:1.849068+0.053824 test-rmse:2.314988+0.270283
## [14] train-rmse:1.718854+0.052149 test-rmse:2.208973+0.264760
## [15] train-rmse:1.596103+0.041129 test-rmse:2.120024+0.238690
## [16] train-rmse:1.489663+0.041572 test-rmse:2.046836+0.245814
## [17] train-rmse:1.395490+0.041312 test-rmse:1.942129+0.232081
## [18] train-rmse:1.310280+0.038115 test-rmse:1.880652+0.226750
## [19] train-rmse:1.231906+0.035966 test-rmse:1.813729+0.218814
## [20] train-rmse:1.161305+0.031034 test-rmse:1.751127+0.209702
## [21] train-rmse:1.101220+0.034726 test-rmse:1.703369+0.211517
## [22] train-rmse:1.042765+0.032083 test-rmse:1.635879+0.221690
## [23] train-rmse:0.983031+0.027896 test-rmse:1.595221+0.222542
## [24] train-rmse:0.927879+0.024204 test-rmse:1.566251+0.235985
## [25] train-rmse:0.882625+0.024835 test-rmse:1.527696+0.222837
## [26] train-rmse:0.835088+0.015590 test-rmse:1.502626+0.214504
## [27] train-rmse:0.794355+0.019809 test-rmse:1.477262+0.230994
## [28] train-rmse:0.754203+0.022890 test-rmse:1.454125+0.224552
## [29] train-rmse:0.718618+0.020570 test-rmse:1.429779+0.235545
## [30] train-rmse:0.687373+0.019960 test-rmse:1.407395+0.223018
## [31] train-rmse:0.659520+0.021598 test-rmse:1.386730+0.233382
## [32] train-rmse:0.628978+0.014675 test-rmse:1.368181+0.234357
## [33] train-rmse:0.600491+0.011620 test-rmse:1.349790+0.234411
## [34] train-rmse:0.575677+0.009606 test-rmse:1.332488+0.229147
## [35] train-rmse:0.554784+0.011930 test-rmse:1.314358+0.231048
## [36] train-rmse:0.536255+0.011570 test-rmse:1.300428+0.228685
## [37] train-rmse:0.518662+0.014495 test-rmse:1.286574+0.230166
## [38] train-rmse:0.499979+0.015814 test-rmse:1.285293+0.233207
## [39] train-rmse:0.481553+0.014770 test-rmse:1.272444+0.230163
## [40] train-rmse:0.467120+0.016590 test-rmse:1.264566+0.232351
## [41] train-rmse:0.449453+0.016658 test-rmse:1.257566+0.235877
## [42] train-rmse:0.433590+0.014234 test-rmse:1.245651+0.223827
## [43] train-rmse:0.422522+0.014885 test-rmse:1.241527+0.226502
## [44] train-rmse:0.408089+0.013885 test-rmse:1.239378+0.229760
## [45] train-rmse:0.389762+0.012846 test-rmse:1.232007+0.230313
## [46] train-rmse:0.376886+0.011672 test-rmse:1.225404+0.231442
## [47] train-rmse:0.367425+0.013334 test-rmse:1.223124+0.235566
## [48] train-rmse:0.356564+0.012501 test-rmse:1.222745+0.237120
## [49] train-rmse:0.346429+0.010599 test-rmse:1.218947+0.238454
## [50] train-rmse:0.334900+0.010095 test-rmse:1.218426+0.240092
## [51] train-rmse:0.326229+0.008914 test-rmse:1.213015+0.243279
## [52] train-rmse:0.316932+0.009396 test-rmse:1.208569+0.243538
## [53] train-rmse:0.307683+0.008785 test-rmse:1.206971+0.243642
## [54] train-rmse:0.298506+0.008946 test-rmse:1.204302+0.242037
## [55] train-rmse:0.293106+0.009292 test-rmse:1.200682+0.244332
## [56] train-rmse:0.287622+0.010752 test-rmse:1.197670+0.242929
## [57] train-rmse:0.281238+0.010566 test-rmse:1.195627+0.244867
## [58] train-rmse:0.273110+0.009318 test-rmse:1.193984+0.247229
## [59] train-rmse:0.268172+0.010017 test-rmse:1.191763+0.247244
## [60] train-rmse:0.260594+0.007894 test-rmse:1.189344+0.247990
## [61] train-rmse:0.252185+0.009016 test-rmse:1.188750+0.249205
## [62] train-rmse:0.245735+0.009568 test-rmse:1.189994+0.251461
## [63] train-rmse:0.240903+0.009086 test-rmse:1.189432+0.250934
## [64] train-rmse:0.232340+0.007296 test-rmse:1.187299+0.249812
## [65] train-rmse:0.227268+0.007873 test-rmse:1.187112+0.251402
## [66] train-rmse:0.221283+0.008929 test-rmse:1.184732+0.251185
## [67] train-rmse:0.216315+0.009229 test-rmse:1.183419+0.250205
## [68] train-rmse:0.212546+0.009579 test-rmse:1.182491+0.250508
## [69] train-rmse:0.205766+0.007169 test-rmse:1.181437+0.249981
## [70] train-rmse:0.200935+0.006911 test-rmse:1.179535+0.246962
## [71] train-rmse:0.197180+0.007603 test-rmse:1.178139+0.247501
## [72] train-rmse:0.192997+0.008897 test-rmse:1.174603+0.248573
## [73] train-rmse:0.188557+0.008765 test-rmse:1.173513+0.247952
## [74] train-rmse:0.181527+0.004757 test-rmse:1.171565+0.249045
## [75] train-rmse:0.177443+0.005196 test-rmse:1.171214+0.248307
## [76] train-rmse:0.174497+0.006177 test-rmse:1.170177+0.249600
## [77] train-rmse:0.171901+0.005489 test-rmse:1.170386+0.249597
## [78] train-rmse:0.168899+0.005575 test-rmse:1.170573+0.250281
## [79] train-rmse:0.166155+0.005879 test-rmse:1.170104+0.249503
## [80] train-rmse:0.161996+0.007070 test-rmse:1.168354+0.250772
## [81] train-rmse:0.158399+0.007987 test-rmse:1.168679+0.250672
## [82] train-rmse:0.154458+0.007555 test-rmse:1.169110+0.250139
## [83] train-rmse:0.150820+0.006093 test-rmse:1.170039+0.250036
## [84] train-rmse:0.147396+0.006280 test-rmse:1.169699+0.251313
## [85] train-rmse:0.144781+0.006031 test-rmse:1.167998+0.249815
## [86] train-rmse:0.141930+0.007222 test-rmse:1.166956+0.252473
## [87] train-rmse:0.138406+0.007685 test-rmse:1.166694+0.252497
## [88] train-rmse:0.134970+0.006821 test-rmse:1.167254+0.252205
## [89] train-rmse:0.132211+0.007187 test-rmse:1.166854+0.252412
## [90] train-rmse:0.129586+0.007770 test-rmse:1.166177+0.253321
## [91] train-rmse:0.126025+0.007345 test-rmse:1.166803+0.252795
## [92] train-rmse:0.122854+0.007105 test-rmse:1.167100+0.252964
## [93] train-rmse:0.119514+0.007729 test-rmse:1.166425+0.252541
## [94] train-rmse:0.117692+0.007551 test-rmse:1.166159+0.252223
## [95] train-rmse:0.114825+0.007524 test-rmse:1.165798+0.252705
## [96] train-rmse:0.112259+0.006606 test-rmse:1.165522+0.253067
## [97] train-rmse:0.109921+0.006412 test-rmse:1.165665+0.253220
## [98] train-rmse:0.107048+0.005892 test-rmse:1.165027+0.253689
## [99] train-rmse:0.104367+0.005236 test-rmse:1.165256+0.253697
## [100] train-rmse:0.101730+0.006077 test-rmse:1.165695+0.252904
## [101] train-rmse:0.099361+0.007039 test-rmse:1.165780+0.252881
## [102] train-rmse:0.097709+0.006716 test-rmse:1.164755+0.252990
## [103] train-rmse:0.095590+0.006102 test-rmse:1.164290+0.252460
## [104] train-rmse:0.092830+0.005408 test-rmse:1.164107+0.252678
## [105] train-rmse:0.091426+0.005141 test-rmse:1.163638+0.253195
## [106] train-rmse:0.088992+0.005803 test-rmse:1.163365+0.252579
## [107] train-rmse:0.087634+0.005940 test-rmse:1.162915+0.252081
## [108] train-rmse:0.084970+0.006218 test-rmse:1.161885+0.252505
## [109] train-rmse:0.083362+0.005838 test-rmse:1.161686+0.252642
## [110] train-rmse:0.082209+0.005701 test-rmse:1.161619+0.252576
## [111] train-rmse:0.080161+0.005715 test-rmse:1.162039+0.252131
## [112] train-rmse:0.078953+0.005481 test-rmse:1.161602+0.251622
## [113] train-rmse:0.077814+0.005462 test-rmse:1.162238+0.251378
## [114] train-rmse:0.076443+0.005757 test-rmse:1.162373+0.251269
## [115] train-rmse:0.075435+0.005587 test-rmse:1.162682+0.251110
## [116] train-rmse:0.074092+0.005141 test-rmse:1.161974+0.251102
## [117] train-rmse:0.072302+0.004607 test-rmse:1.161180+0.250744
## [118] train-rmse:0.070589+0.005057 test-rmse:1.160804+0.250425
## [119] train-rmse:0.068919+0.005118 test-rmse:1.161121+0.250714
## [120] train-rmse:0.067760+0.005347 test-rmse:1.161096+0.249844
## [121] train-rmse:0.066641+0.005321 test-rmse:1.160814+0.250046
## [122] train-rmse:0.065819+0.005145 test-rmse:1.160405+0.250265
## [123] train-rmse:0.064096+0.004859 test-rmse:1.160295+0.250541
## [124] train-rmse:0.062553+0.004768 test-rmse:1.159799+0.250635
## [125] train-rmse:0.061784+0.004835 test-rmse:1.160176+0.250773
## [126] train-rmse:0.060616+0.004733 test-rmse:1.159946+0.250776
## [127] train-rmse:0.059292+0.004882 test-rmse:1.159521+0.250475
## [128] train-rmse:0.058170+0.005049 test-rmse:1.160234+0.250648
## [129] train-rmse:0.057284+0.004999 test-rmse:1.160224+0.250671
## [130] train-rmse:0.056240+0.004943 test-rmse:1.160244+0.250671
## [131] train-rmse:0.055311+0.004730 test-rmse:1.159973+0.250445
## [132] train-rmse:0.054091+0.004586 test-rmse:1.160007+0.250144
## [133] train-rmse:0.053082+0.004610 test-rmse:1.159446+0.249282
## [134] train-rmse:0.052009+0.004910 test-rmse:1.159208+0.248756
## [135] train-rmse:0.050767+0.004980 test-rmse:1.159217+0.248871
## [136] train-rmse:0.050130+0.005147 test-rmse:1.159217+0.248878
## [137] train-rmse:0.048954+0.004786 test-rmse:1.158711+0.248044
## [138] train-rmse:0.048004+0.005336 test-rmse:1.158646+0.247984
## [139] train-rmse:0.047122+0.005150 test-rmse:1.158403+0.248163
## [140] train-rmse:0.046267+0.005180 test-rmse:1.158414+0.248202
## [141] train-rmse:0.045528+0.004987 test-rmse:1.158492+0.247861
## [142] train-rmse:0.044765+0.005164 test-rmse:1.158111+0.247883
## [143] train-rmse:0.044097+0.005274 test-rmse:1.158402+0.247968
## [144] train-rmse:0.043433+0.005380 test-rmse:1.157947+0.248043
## [145] train-rmse:0.042508+0.005525 test-rmse:1.157845+0.248003
## [146] train-rmse:0.041813+0.005294 test-rmse:1.157416+0.247976
## [147] train-rmse:0.041041+0.005253 test-rmse:1.157221+0.247408
## [148] train-rmse:0.040367+0.005090 test-rmse:1.157007+0.247258
## [149] train-rmse:0.039347+0.004670 test-rmse:1.157047+0.247353
## [150] train-rmse:0.038727+0.004749 test-rmse:1.156681+0.247467
## [151] train-rmse:0.038034+0.004471 test-rmse:1.156633+0.247411
## [152] train-rmse:0.037003+0.004439 test-rmse:1.156643+0.247173
## [153] train-rmse:0.036542+0.004355 test-rmse:1.156195+0.246957
## [154] train-rmse:0.036053+0.004266 test-rmse:1.155973+0.246977
## [155] train-rmse:0.035583+0.004174 test-rmse:1.155851+0.246988
## [156] train-rmse:0.035000+0.004383 test-rmse:1.155841+0.246858
## [157] train-rmse:0.034113+0.004409 test-rmse:1.155802+0.246643
## [158] train-rmse:0.033563+0.004328 test-rmse:1.155897+0.246538
## [159] train-rmse:0.033075+0.004485 test-rmse:1.156087+0.246556
## [160] train-rmse:0.032476+0.004341 test-rmse:1.155925+0.246579
## [161] train-rmse:0.031887+0.004312 test-rmse:1.155792+0.246616
## [162] train-rmse:0.031065+0.004056 test-rmse:1.155555+0.246592
## [163] train-rmse:0.030311+0.003979 test-rmse:1.155363+0.246692
## [164] train-rmse:0.029902+0.003955 test-rmse:1.155720+0.246459
## [165] train-rmse:0.029281+0.003926 test-rmse:1.155952+0.246713
## [166] train-rmse:0.028804+0.003903 test-rmse:1.155775+0.246462
## [167] train-rmse:0.028285+0.003828 test-rmse:1.155876+0.246377
## [168] train-rmse:0.027723+0.003873 test-rmse:1.155937+0.246306
## [169] train-rmse:0.027250+0.003971 test-rmse:1.155881+0.246327
## Stopping. Best iteration:
## [163] train-rmse:0.030311+0.003979 test-rmse:1.155363+0.246692
##
## 102
## [1] train-rmse:67.001016+0.081705 test-rmse:67.000742+0.527509
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:41.869994+0.065854 test-rmse:41.868242+0.585212
## [3] train-rmse:26.397598+0.065573 test-rmse:26.392831+0.648853
## [4] train-rmse:16.826265+0.038279 test-rmse:16.885249+0.590232
## [5] train-rmse:10.888288+0.037416 test-rmse:10.965916+0.529293
## [6] train-rmse:7.214672+0.039929 test-rmse:7.328673+0.520719
## [7] train-rmse:4.957009+0.056387 test-rmse:5.102424+0.519320
## [8] train-rmse:3.559901+0.061246 test-rmse:3.785497+0.410304
## [9] train-rmse:2.701010+0.042329 test-rmse:3.022018+0.321690
## [10] train-rmse:2.179537+0.033808 test-rmse:2.607268+0.249775
## [11] train-rmse:1.857893+0.040200 test-rmse:2.330437+0.238146
## [12] train-rmse:1.632199+0.065198 test-rmse:2.173711+0.221518
## [13] train-rmse:1.463040+0.060431 test-rmse:2.062975+0.182088
## [14] train-rmse:1.320004+0.045534 test-rmse:1.944567+0.156771
## [15] train-rmse:1.209897+0.042759 test-rmse:1.887311+0.181740
## [16] train-rmse:1.115239+0.037062 test-rmse:1.790684+0.181576
## [17] train-rmse:1.031048+0.040516 test-rmse:1.720485+0.179981
## [18] train-rmse:0.945406+0.045808 test-rmse:1.669789+0.194778
## [19] train-rmse:0.880102+0.044612 test-rmse:1.623175+0.199505
## [20] train-rmse:0.821943+0.039078 test-rmse:1.587302+0.192403
## [21] train-rmse:0.768146+0.039825 test-rmse:1.540992+0.203086
## [22] train-rmse:0.721084+0.035383 test-rmse:1.507801+0.181731
## [23] train-rmse:0.675734+0.031726 test-rmse:1.467025+0.175051
## [24] train-rmse:0.633884+0.033911 test-rmse:1.446667+0.175963
## [25] train-rmse:0.597400+0.030406 test-rmse:1.424057+0.189773
## [26] train-rmse:0.559035+0.025270 test-rmse:1.403303+0.188664
## [27] train-rmse:0.530012+0.024576 test-rmse:1.381116+0.197032
## [28] train-rmse:0.502035+0.018741 test-rmse:1.369383+0.200887
## [29] train-rmse:0.476761+0.021600 test-rmse:1.342161+0.212269
## [30] train-rmse:0.457521+0.021634 test-rmse:1.337862+0.212766
## [31] train-rmse:0.433499+0.015850 test-rmse:1.330187+0.220799
## [32] train-rmse:0.416291+0.015387 test-rmse:1.320254+0.222315
## [33] train-rmse:0.398679+0.012088 test-rmse:1.311349+0.224971
## [34] train-rmse:0.381791+0.013056 test-rmse:1.303238+0.235797
## [35] train-rmse:0.364250+0.014669 test-rmse:1.295699+0.242435
## [36] train-rmse:0.351066+0.013450 test-rmse:1.287754+0.247066
## [37] train-rmse:0.334682+0.011114 test-rmse:1.287057+0.247944
## [38] train-rmse:0.322039+0.008966 test-rmse:1.279388+0.259533
## [39] train-rmse:0.308283+0.008592 test-rmse:1.273235+0.259053
## [40] train-rmse:0.294563+0.005584 test-rmse:1.266403+0.259888
## [41] train-rmse:0.283824+0.007045 test-rmse:1.265271+0.260826
## [42] train-rmse:0.269375+0.005033 test-rmse:1.256547+0.259332
## [43] train-rmse:0.258037+0.004161 test-rmse:1.253865+0.260516
## [44] train-rmse:0.249418+0.004987 test-rmse:1.253121+0.258894
## [45] train-rmse:0.240628+0.005874 test-rmse:1.251777+0.260568
## [46] train-rmse:0.233331+0.004533 test-rmse:1.249021+0.261107
## [47] train-rmse:0.223485+0.005133 test-rmse:1.249401+0.260384
## [48] train-rmse:0.214843+0.003227 test-rmse:1.247626+0.263099
## [49] train-rmse:0.206430+0.003466 test-rmse:1.247139+0.260754
## [50] train-rmse:0.198217+0.002564 test-rmse:1.242537+0.253320
## [51] train-rmse:0.190308+0.002125 test-rmse:1.243966+0.253116
## [52] train-rmse:0.183664+0.004320 test-rmse:1.243277+0.254722
## [53] train-rmse:0.176982+0.003462 test-rmse:1.242324+0.254790
## [54] train-rmse:0.171666+0.004296 test-rmse:1.241531+0.255491
## [55] train-rmse:0.167124+0.003334 test-rmse:1.239804+0.257400
## [56] train-rmse:0.161085+0.002618 test-rmse:1.239117+0.257561
## [57] train-rmse:0.154185+0.002177 test-rmse:1.239000+0.256772
## [58] train-rmse:0.148647+0.003131 test-rmse:1.238413+0.256472
## [59] train-rmse:0.142831+0.003312 test-rmse:1.236772+0.257216
## [60] train-rmse:0.137737+0.003125 test-rmse:1.236491+0.256668
## [61] train-rmse:0.132141+0.003068 test-rmse:1.235078+0.256202
## [62] train-rmse:0.128986+0.003266 test-rmse:1.234977+0.255848
## [63] train-rmse:0.123209+0.004481 test-rmse:1.234660+0.256054
## [64] train-rmse:0.119669+0.004627 test-rmse:1.234260+0.257890
## [65] train-rmse:0.114966+0.004627 test-rmse:1.233650+0.258110
## [66] train-rmse:0.112625+0.004298 test-rmse:1.233696+0.258326
## [67] train-rmse:0.108528+0.004609 test-rmse:1.233068+0.258836
## [68] train-rmse:0.104618+0.004319 test-rmse:1.232372+0.259542
## [69] train-rmse:0.101097+0.003339 test-rmse:1.231764+0.259319
## [70] train-rmse:0.097753+0.004104 test-rmse:1.231391+0.259939
## [71] train-rmse:0.094523+0.004305 test-rmse:1.229763+0.261054
## [72] train-rmse:0.091924+0.004124 test-rmse:1.229008+0.261273
## [73] train-rmse:0.087568+0.002596 test-rmse:1.227523+0.261619
## [74] train-rmse:0.084732+0.002585 test-rmse:1.227655+0.261740
## [75] train-rmse:0.081245+0.002314 test-rmse:1.227588+0.261823
## [76] train-rmse:0.078336+0.002038 test-rmse:1.227871+0.261738
## [77] train-rmse:0.075475+0.002312 test-rmse:1.226716+0.261723
## [78] train-rmse:0.073090+0.003239 test-rmse:1.226072+0.261431
## [79] train-rmse:0.070411+0.003310 test-rmse:1.226505+0.261129
## [80] train-rmse:0.068024+0.003376 test-rmse:1.226155+0.261570
## [81] train-rmse:0.065079+0.003731 test-rmse:1.225313+0.260619
## [82] train-rmse:0.063199+0.003565 test-rmse:1.225323+0.260435
## [83] train-rmse:0.061395+0.003522 test-rmse:1.225397+0.259947
## [84] train-rmse:0.059402+0.003675 test-rmse:1.225040+0.260382
## [85] train-rmse:0.057479+0.003696 test-rmse:1.224841+0.260133
## [86] train-rmse:0.055971+0.003417 test-rmse:1.225037+0.260268
## [87] train-rmse:0.054566+0.003672 test-rmse:1.224763+0.260174
## [88] train-rmse:0.052722+0.003596 test-rmse:1.224041+0.260383
## [89] train-rmse:0.051038+0.003391 test-rmse:1.223829+0.260286
## [90] train-rmse:0.049248+0.003299 test-rmse:1.223695+0.260590
## [91] train-rmse:0.048174+0.003115 test-rmse:1.223465+0.260318
## [92] train-rmse:0.046502+0.002849 test-rmse:1.223315+0.260097
## [93] train-rmse:0.045205+0.002992 test-rmse:1.223641+0.260728
## [94] train-rmse:0.043627+0.002980 test-rmse:1.222997+0.261136
## [95] train-rmse:0.042561+0.002986 test-rmse:1.222924+0.261143
## [96] train-rmse:0.041396+0.002978 test-rmse:1.223041+0.261549
## [97] train-rmse:0.040144+0.002992 test-rmse:1.222961+0.261557
## [98] train-rmse:0.039033+0.002661 test-rmse:1.223189+0.262151
## [99] train-rmse:0.037983+0.002829 test-rmse:1.222786+0.262480
## [100] train-rmse:0.037192+0.002910 test-rmse:1.222786+0.262404
## [101] train-rmse:0.036262+0.002835 test-rmse:1.222097+0.262491
## [102] train-rmse:0.035310+0.002490 test-rmse:1.221680+0.262027
## [103] train-rmse:0.034287+0.002517 test-rmse:1.221497+0.262567
## [104] train-rmse:0.033223+0.002876 test-rmse:1.221532+0.262373
## [105] train-rmse:0.032207+0.002719 test-rmse:1.221559+0.262268
## [106] train-rmse:0.030939+0.002197 test-rmse:1.221770+0.262048
## [107] train-rmse:0.029694+0.001760 test-rmse:1.221266+0.261603
## [108] train-rmse:0.028796+0.001885 test-rmse:1.221275+0.261787
## [109] train-rmse:0.027717+0.001620 test-rmse:1.221217+0.261869
## [110] train-rmse:0.026968+0.001637 test-rmse:1.221090+0.262031
## [111] train-rmse:0.026151+0.001509 test-rmse:1.220783+0.262014
## [112] train-rmse:0.025552+0.001415 test-rmse:1.220742+0.262182
## [113] train-rmse:0.024664+0.001274 test-rmse:1.220714+0.262119
## [114] train-rmse:0.023967+0.001337 test-rmse:1.220691+0.262212
## [115] train-rmse:0.023160+0.001164 test-rmse:1.220586+0.261779
## [116] train-rmse:0.022580+0.001217 test-rmse:1.220603+0.261664
## [117] train-rmse:0.021841+0.001378 test-rmse:1.220537+0.261527
## [118] train-rmse:0.021342+0.001229 test-rmse:1.220558+0.262021
## [119] train-rmse:0.020511+0.001142 test-rmse:1.220415+0.262054
## [120] train-rmse:0.019908+0.001028 test-rmse:1.220420+0.262073
## [121] train-rmse:0.019289+0.001177 test-rmse:1.220525+0.261974
## [122] train-rmse:0.018767+0.001072 test-rmse:1.220584+0.261988
## [123] train-rmse:0.018326+0.001076 test-rmse:1.220501+0.261964
## [124] train-rmse:0.017672+0.001040 test-rmse:1.220314+0.262055
## [125] train-rmse:0.017297+0.001085 test-rmse:1.220315+0.262069
## [126] train-rmse:0.016759+0.001270 test-rmse:1.220230+0.262086
## [127] train-rmse:0.016397+0.001328 test-rmse:1.220256+0.262149
## [128] train-rmse:0.015891+0.001438 test-rmse:1.220418+0.262133
## [129] train-rmse:0.015556+0.001484 test-rmse:1.220539+0.262167
## [130] train-rmse:0.014952+0.001286 test-rmse:1.220455+0.262174
## [131] train-rmse:0.014443+0.001247 test-rmse:1.220467+0.262163
## [132] train-rmse:0.014125+0.001170 test-rmse:1.220329+0.262160
## Stopping. Best iteration:
## [126] train-rmse:0.016759+0.001270 test-rmse:1.220230+0.262086
##
## 103
## [1] train-rmse:67.001016+0.081705 test-rmse:67.000742+0.527509
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:41.869994+0.065854 test-rmse:41.868242+0.585212
## [3] train-rmse:26.397598+0.065573 test-rmse:26.392831+0.648853
## [4] train-rmse:16.826265+0.038279 test-rmse:16.885249+0.590232
## [5] train-rmse:10.876512+0.035714 test-rmse:10.964750+0.541435
## [6] train-rmse:7.177508+0.036860 test-rmse:7.283366+0.522607
## [7] train-rmse:4.893392+0.044917 test-rmse:5.044544+0.508051
## [8] train-rmse:3.465018+0.037064 test-rmse:3.690221+0.454791
## [9] train-rmse:2.558364+0.027245 test-rmse:2.894832+0.350784
## [10] train-rmse:1.982153+0.023873 test-rmse:2.424914+0.276184
## [11] train-rmse:1.629514+0.025912 test-rmse:2.171888+0.284067
## [12] train-rmse:1.386975+0.024303 test-rmse:1.987161+0.248423
## [13] train-rmse:1.213031+0.043336 test-rmse:1.882333+0.247782
## [14] train-rmse:1.082436+0.038586 test-rmse:1.782761+0.263426
## [15] train-rmse:0.958046+0.029729 test-rmse:1.689773+0.265798
## [16] train-rmse:0.874647+0.031479 test-rmse:1.633993+0.276408
## [17] train-rmse:0.791319+0.028734 test-rmse:1.562253+0.245342
## [18] train-rmse:0.719963+0.023937 test-rmse:1.495540+0.256068
## [19] train-rmse:0.658033+0.017709 test-rmse:1.469701+0.245363
## [20] train-rmse:0.614389+0.018695 test-rmse:1.426721+0.264280
## [21] train-rmse:0.562727+0.021778 test-rmse:1.402749+0.265325
## [22] train-rmse:0.524817+0.020809 test-rmse:1.383158+0.270709
## [23] train-rmse:0.494418+0.018825 test-rmse:1.370497+0.267721
## [24] train-rmse:0.459530+0.014673 test-rmse:1.341374+0.252939
## [25] train-rmse:0.430449+0.017219 test-rmse:1.324825+0.251650
## [26] train-rmse:0.407325+0.018310 test-rmse:1.305671+0.238328
## [27] train-rmse:0.383119+0.016896 test-rmse:1.303031+0.241497
## [28] train-rmse:0.362285+0.016144 test-rmse:1.297617+0.241425
## [29] train-rmse:0.338055+0.010947 test-rmse:1.287615+0.245220
## [30] train-rmse:0.319662+0.010832 test-rmse:1.284332+0.242708
## [31] train-rmse:0.303085+0.009759 test-rmse:1.280810+0.246666
## [32] train-rmse:0.288139+0.010269 test-rmse:1.276311+0.244464
## [33] train-rmse:0.273912+0.008678 test-rmse:1.274121+0.246533
## [34] train-rmse:0.258547+0.011371 test-rmse:1.270183+0.248908
## [35] train-rmse:0.242260+0.011470 test-rmse:1.265694+0.253982
## [36] train-rmse:0.231335+0.012278 test-rmse:1.264116+0.251328
## [37] train-rmse:0.219076+0.012048 test-rmse:1.259649+0.250381
## [38] train-rmse:0.206665+0.012871 test-rmse:1.257949+0.252365
## [39] train-rmse:0.197309+0.010044 test-rmse:1.256614+0.252700
## [40] train-rmse:0.189743+0.009598 test-rmse:1.253217+0.253833
## [41] train-rmse:0.180275+0.009309 test-rmse:1.252289+0.255814
## [42] train-rmse:0.170597+0.009888 test-rmse:1.250551+0.255224
## [43] train-rmse:0.163181+0.007927 test-rmse:1.248174+0.254225
## [44] train-rmse:0.156624+0.007702 test-rmse:1.247701+0.252458
## [45] train-rmse:0.150776+0.008796 test-rmse:1.244187+0.254668
## [46] train-rmse:0.145311+0.007606 test-rmse:1.243112+0.254351
## [47] train-rmse:0.137794+0.007689 test-rmse:1.241510+0.250134
## [48] train-rmse:0.130670+0.008765 test-rmse:1.238193+0.250880
## [49] train-rmse:0.126471+0.008286 test-rmse:1.237017+0.251426
## [50] train-rmse:0.119382+0.008895 test-rmse:1.235561+0.251168
## [51] train-rmse:0.113660+0.008319 test-rmse:1.234626+0.251336
## [52] train-rmse:0.110197+0.008071 test-rmse:1.232997+0.252273
## [53] train-rmse:0.105856+0.007281 test-rmse:1.231151+0.252990
## [54] train-rmse:0.101182+0.007787 test-rmse:1.230404+0.253033
## [55] train-rmse:0.095683+0.007974 test-rmse:1.231522+0.252886
## [56] train-rmse:0.091694+0.008345 test-rmse:1.231795+0.252820
## [57] train-rmse:0.088282+0.008400 test-rmse:1.231519+0.254018
## [58] train-rmse:0.084643+0.008589 test-rmse:1.230657+0.254283
## [59] train-rmse:0.081532+0.008580 test-rmse:1.231040+0.254400
## [60] train-rmse:0.078151+0.009482 test-rmse:1.230957+0.254182
## Stopping. Best iteration:
## [54] train-rmse:0.101182+0.007787 test-rmse:1.230404+0.253033
##
## 104
## [1] train-rmse:67.001016+0.081705 test-rmse:67.000742+0.527509
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:41.869994+0.065854 test-rmse:41.868242+0.585212
## [3] train-rmse:26.397598+0.065573 test-rmse:26.392831+0.648853
## [4] train-rmse:16.826265+0.038279 test-rmse:16.885249+0.590232
## [5] train-rmse:10.865322+0.034209 test-rmse:10.951186+0.546346
## [6] train-rmse:7.141871+0.034426 test-rmse:7.236395+0.508049
## [7] train-rmse:4.827182+0.029738 test-rmse:4.997802+0.513439
## [8] train-rmse:3.357850+0.016458 test-rmse:3.614097+0.419643
## [9] train-rmse:2.422438+0.043913 test-rmse:2.757449+0.347543
## [10] train-rmse:1.837823+0.038025 test-rmse:2.238836+0.288752
## [11] train-rmse:1.455648+0.050702 test-rmse:1.968075+0.246246
## [12] train-rmse:1.186732+0.053376 test-rmse:1.804255+0.231797
## [13] train-rmse:1.011280+0.050791 test-rmse:1.693794+0.235573
## [14] train-rmse:0.866869+0.038892 test-rmse:1.623087+0.234339
## [15] train-rmse:0.755933+0.038383 test-rmse:1.546099+0.216580
## [16] train-rmse:0.675363+0.034258 test-rmse:1.501589+0.205701
## [17] train-rmse:0.608222+0.031554 test-rmse:1.461350+0.207755
## [18] train-rmse:0.551564+0.030204 test-rmse:1.440715+0.218004
## [19] train-rmse:0.502560+0.030086 test-rmse:1.410581+0.222567
## [20] train-rmse:0.462519+0.021242 test-rmse:1.386655+0.222527
## [21] train-rmse:0.425328+0.012307 test-rmse:1.369060+0.219409
## [22] train-rmse:0.391394+0.010005 test-rmse:1.349071+0.219698
## [23] train-rmse:0.365451+0.007432 test-rmse:1.343222+0.219806
## [24] train-rmse:0.341342+0.009772 test-rmse:1.336288+0.220190
## [25] train-rmse:0.319034+0.007812 test-rmse:1.327273+0.225083
## [26] train-rmse:0.296656+0.010790 test-rmse:1.318720+0.230306
## [27] train-rmse:0.273069+0.014326 test-rmse:1.315496+0.231174
## [28] train-rmse:0.255535+0.011629 test-rmse:1.311210+0.232975
## [29] train-rmse:0.239502+0.014687 test-rmse:1.309421+0.235124
## [30] train-rmse:0.225959+0.014139 test-rmse:1.305484+0.234070
## [31] train-rmse:0.210697+0.011698 test-rmse:1.302035+0.227547
## [32] train-rmse:0.199637+0.011967 test-rmse:1.298364+0.227888
## [33] train-rmse:0.185535+0.011676 test-rmse:1.297556+0.226823
## [34] train-rmse:0.176857+0.010275 test-rmse:1.295400+0.226962
## [35] train-rmse:0.165880+0.009181 test-rmse:1.292504+0.230482
## [36] train-rmse:0.155521+0.010422 test-rmse:1.288281+0.233966
## [37] train-rmse:0.145993+0.009592 test-rmse:1.285774+0.235338
## [38] train-rmse:0.138746+0.010154 test-rmse:1.284732+0.235344
## [39] train-rmse:0.131604+0.010637 test-rmse:1.284713+0.235178
## [40] train-rmse:0.122607+0.009445 test-rmse:1.282191+0.234555
## [41] train-rmse:0.115731+0.008135 test-rmse:1.280907+0.234795
## [42] train-rmse:0.108962+0.008999 test-rmse:1.280786+0.235398
## [43] train-rmse:0.102336+0.009665 test-rmse:1.281563+0.236300
## [44] train-rmse:0.097162+0.008666 test-rmse:1.280100+0.235926
## [45] train-rmse:0.091255+0.006955 test-rmse:1.279449+0.236388
## [46] train-rmse:0.087406+0.006785 test-rmse:1.279823+0.237062
## [47] train-rmse:0.083050+0.007175 test-rmse:1.278185+0.237808
## [48] train-rmse:0.078094+0.007486 test-rmse:1.277716+0.239489
## [49] train-rmse:0.073537+0.006896 test-rmse:1.276756+0.240190
## [50] train-rmse:0.070267+0.006389 test-rmse:1.276734+0.240019
## [51] train-rmse:0.064506+0.005457 test-rmse:1.277045+0.240030
## [52] train-rmse:0.060559+0.003948 test-rmse:1.277196+0.241418
## [53] train-rmse:0.057451+0.004271 test-rmse:1.277089+0.241612
## [54] train-rmse:0.054230+0.003797 test-rmse:1.276790+0.241545
## [55] train-rmse:0.051299+0.003946 test-rmse:1.277106+0.241425
## [56] train-rmse:0.048884+0.004093 test-rmse:1.276631+0.242283
## [57] train-rmse:0.046063+0.004741 test-rmse:1.276841+0.242236
## [58] train-rmse:0.043037+0.003390 test-rmse:1.276299+0.242094
## [59] train-rmse:0.040142+0.003166 test-rmse:1.276555+0.242147
## [60] train-rmse:0.038712+0.003059 test-rmse:1.276171+0.242127
## [61] train-rmse:0.036629+0.002570 test-rmse:1.276013+0.241905
## [62] train-rmse:0.034440+0.002099 test-rmse:1.275995+0.242357
## [63] train-rmse:0.032611+0.002278 test-rmse:1.275647+0.242089
## [64] train-rmse:0.031439+0.002417 test-rmse:1.275797+0.242090
## [65] train-rmse:0.030042+0.002680 test-rmse:1.275730+0.242189
## [66] train-rmse:0.028260+0.002504 test-rmse:1.275544+0.242585
## [67] train-rmse:0.026133+0.002218 test-rmse:1.275393+0.242748
## [68] train-rmse:0.024832+0.002130 test-rmse:1.275183+0.242935
## [69] train-rmse:0.023606+0.001974 test-rmse:1.275348+0.242812
## [70] train-rmse:0.022759+0.001904 test-rmse:1.275086+0.242445
## [71] train-rmse:0.021249+0.001930 test-rmse:1.274930+0.242185
## [72] train-rmse:0.019900+0.001452 test-rmse:1.274984+0.242455
## [73] train-rmse:0.018954+0.001603 test-rmse:1.274941+0.242366
## [74] train-rmse:0.017881+0.001976 test-rmse:1.274985+0.242093
## [75] train-rmse:0.017094+0.002222 test-rmse:1.274862+0.242138
## [76] train-rmse:0.016007+0.002210 test-rmse:1.274516+0.241777
## [77] train-rmse:0.015158+0.002054 test-rmse:1.274455+0.241984
## [78] train-rmse:0.014366+0.001822 test-rmse:1.274358+0.241857
## [79] train-rmse:0.013575+0.001713 test-rmse:1.274349+0.241898
## [80] train-rmse:0.012780+0.001780 test-rmse:1.274392+0.242092
## [81] train-rmse:0.012114+0.001612 test-rmse:1.274305+0.242042
## [82] train-rmse:0.011596+0.001632 test-rmse:1.274055+0.241730
## [83] train-rmse:0.010974+0.001596 test-rmse:1.273966+0.241513
## [84] train-rmse:0.010417+0.001599 test-rmse:1.273900+0.241460
## [85] train-rmse:0.009824+0.001603 test-rmse:1.273794+0.241591
## [86] train-rmse:0.009431+0.001586 test-rmse:1.273676+0.241700
## [87] train-rmse:0.008833+0.001548 test-rmse:1.273651+0.241669
## [88] train-rmse:0.008495+0.001454 test-rmse:1.273657+0.241719
## [89] train-rmse:0.008087+0.001387 test-rmse:1.273693+0.241721
## [90] train-rmse:0.007730+0.001363 test-rmse:1.273644+0.241675
## [91] train-rmse:0.007227+0.001290 test-rmse:1.273677+0.241716
## [92] train-rmse:0.006851+0.001174 test-rmse:1.273592+0.241665
## [93] train-rmse:0.006464+0.001046 test-rmse:1.273560+0.241701
## [94] train-rmse:0.006068+0.000988 test-rmse:1.273615+0.241718
## [95] train-rmse:0.005788+0.001018 test-rmse:1.273632+0.241697
## [96] train-rmse:0.005454+0.000928 test-rmse:1.273683+0.241729
## [97] train-rmse:0.005168+0.000746 test-rmse:1.273664+0.241702
## [98] train-rmse:0.004830+0.000607 test-rmse:1.273566+0.241747
## [99] train-rmse:0.004661+0.000654 test-rmse:1.273503+0.241764
## [100] train-rmse:0.004385+0.000556 test-rmse:1.273533+0.241867
## [101] train-rmse:0.004182+0.000519 test-rmse:1.273526+0.241839
## [102] train-rmse:0.004002+0.000512 test-rmse:1.273536+0.241892
## [103] train-rmse:0.003786+0.000576 test-rmse:1.273564+0.241825
## [104] train-rmse:0.003622+0.000490 test-rmse:1.273577+0.241817
## [105] train-rmse:0.003468+0.000485 test-rmse:1.273561+0.241828
## Stopping. Best iteration:
## [99] train-rmse:0.004661+0.000654 test-rmse:1.273503+0.241764
##
## 105
## [1] train-rmse:64.867792+0.080067 test-rmse:64.867433+0.531460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:39.277875+0.064950 test-rmse:39.275826+0.593398
## [3] train-rmse:24.039232+0.061294 test-rmse:24.078998+0.616566
## [4] train-rmse:15.007295+0.063040 test-rmse:15.082740+0.641553
## [5] train-rmse:9.743448+0.062204 test-rmse:9.814983+0.574306
## [6] train-rmse:6.818003+0.082593 test-rmse:7.006423+0.672639
## [7] train-rmse:5.262240+0.088214 test-rmse:5.390812+0.593950
## [8] train-rmse:4.478714+0.090422 test-rmse:4.578579+0.532778
## [9] train-rmse:4.066379+0.086221 test-rmse:4.200116+0.494194
## [10] train-rmse:3.843109+0.088901 test-rmse:4.049438+0.484520
## [11] train-rmse:3.688207+0.081215 test-rmse:3.880304+0.464111
## [12] train-rmse:3.557695+0.073338 test-rmse:3.689993+0.379638
## [13] train-rmse:3.454053+0.074399 test-rmse:3.664340+0.373226
## [14] train-rmse:3.365007+0.068136 test-rmse:3.586174+0.353387
## [15] train-rmse:3.279407+0.064641 test-rmse:3.542011+0.369460
## [16] train-rmse:3.198428+0.066570 test-rmse:3.435663+0.306595
## [17] train-rmse:3.121284+0.065551 test-rmse:3.360941+0.307003
## [18] train-rmse:3.051216+0.064840 test-rmse:3.277108+0.290249
## [19] train-rmse:2.980813+0.062330 test-rmse:3.240165+0.306732
## [20] train-rmse:2.915529+0.059151 test-rmse:3.167373+0.319074
## [21] train-rmse:2.851848+0.057891 test-rmse:3.047358+0.274213
## [22] train-rmse:2.793571+0.053203 test-rmse:2.987341+0.301077
## [23] train-rmse:2.735467+0.048913 test-rmse:2.944828+0.317324
## [24] train-rmse:2.677798+0.043875 test-rmse:2.895954+0.309338
## [25] train-rmse:2.623917+0.043047 test-rmse:2.851273+0.308626
## [26] train-rmse:2.574408+0.044535 test-rmse:2.827182+0.305839
## [27] train-rmse:2.524875+0.047219 test-rmse:2.807128+0.320298
## [28] train-rmse:2.478021+0.047599 test-rmse:2.766359+0.340141
## [29] train-rmse:2.431137+0.048203 test-rmse:2.703248+0.300736
## [30] train-rmse:2.386926+0.049568 test-rmse:2.673859+0.292170
## [31] train-rmse:2.344955+0.048117 test-rmse:2.631586+0.303121
## [32] train-rmse:2.304311+0.046621 test-rmse:2.577089+0.314150
## [33] train-rmse:2.263940+0.043306 test-rmse:2.544424+0.296319
## [34] train-rmse:2.225770+0.041836 test-rmse:2.494038+0.274757
## [35] train-rmse:2.187000+0.039685 test-rmse:2.460237+0.289606
## [36] train-rmse:2.150448+0.039012 test-rmse:2.441440+0.305047
## [37] train-rmse:2.116033+0.038786 test-rmse:2.406931+0.315056
## [38] train-rmse:2.081123+0.038318 test-rmse:2.370072+0.305404
## [39] train-rmse:2.047269+0.038118 test-rmse:2.335846+0.300274
## [40] train-rmse:2.014243+0.037492 test-rmse:2.290220+0.314701
## [41] train-rmse:1.983263+0.035841 test-rmse:2.268463+0.317567
## [42] train-rmse:1.953227+0.033597 test-rmse:2.218054+0.279344
## [43] train-rmse:1.923435+0.030685 test-rmse:2.197613+0.280037
## [44] train-rmse:1.894413+0.028751 test-rmse:2.169942+0.262894
## [45] train-rmse:1.865635+0.027654 test-rmse:2.116514+0.272412
## [46] train-rmse:1.837221+0.026401 test-rmse:2.092066+0.262107
## [47] train-rmse:1.809902+0.025292 test-rmse:2.071455+0.270086
## [48] train-rmse:1.782897+0.023935 test-rmse:2.022949+0.254177
## [49] train-rmse:1.756524+0.021823 test-rmse:1.998949+0.247804
## [50] train-rmse:1.730626+0.020558 test-rmse:1.972211+0.256277
## [51] train-rmse:1.705621+0.019473 test-rmse:1.947928+0.244113
## [52] train-rmse:1.680983+0.017647 test-rmse:1.929803+0.250955
## [53] train-rmse:1.657198+0.017000 test-rmse:1.919253+0.253519
## [54] train-rmse:1.634050+0.016650 test-rmse:1.908654+0.253141
## [55] train-rmse:1.611452+0.015951 test-rmse:1.888936+0.258751
## [56] train-rmse:1.589115+0.016028 test-rmse:1.868933+0.262421
## [57] train-rmse:1.567604+0.015888 test-rmse:1.847208+0.253882
## [58] train-rmse:1.547425+0.016117 test-rmse:1.827383+0.267941
## [59] train-rmse:1.527561+0.016015 test-rmse:1.810094+0.269711
## [60] train-rmse:1.508342+0.016064 test-rmse:1.779825+0.241248
## [61] train-rmse:1.490154+0.015650 test-rmse:1.773975+0.239912
## [62] train-rmse:1.472257+0.015366 test-rmse:1.759056+0.236809
## [63] train-rmse:1.454546+0.015035 test-rmse:1.736991+0.239194
## [64] train-rmse:1.436818+0.014694 test-rmse:1.718503+0.242793
## [65] train-rmse:1.419447+0.014499 test-rmse:1.697629+0.244981
## [66] train-rmse:1.402558+0.014320 test-rmse:1.695244+0.237893
## [67] train-rmse:1.386518+0.013821 test-rmse:1.674029+0.229943
## [68] train-rmse:1.370280+0.012919 test-rmse:1.658343+0.227727
## [69] train-rmse:1.354973+0.012673 test-rmse:1.642834+0.223514
## [70] train-rmse:1.340035+0.012682 test-rmse:1.621554+0.226284
## [71] train-rmse:1.325216+0.012465 test-rmse:1.608442+0.211616
## [72] train-rmse:1.310594+0.012312 test-rmse:1.584753+0.213895
## [73] train-rmse:1.297065+0.011726 test-rmse:1.573230+0.218431
## [74] train-rmse:1.283634+0.011307 test-rmse:1.563275+0.222147
## [75] train-rmse:1.270243+0.010944 test-rmse:1.553422+0.227261
## [76] train-rmse:1.257126+0.010298 test-rmse:1.542985+0.223212
## [77] train-rmse:1.244561+0.009954 test-rmse:1.529139+0.229620
## [78] train-rmse:1.232355+0.009393 test-rmse:1.519275+0.231918
## [79] train-rmse:1.220378+0.009259 test-rmse:1.506297+0.232812
## [80] train-rmse:1.208447+0.009293 test-rmse:1.496791+0.234421
## [81] train-rmse:1.196315+0.009848 test-rmse:1.486605+0.238619
## [82] train-rmse:1.184683+0.010046 test-rmse:1.478088+0.234144
## [83] train-rmse:1.173764+0.010161 test-rmse:1.471920+0.234880
## [84] train-rmse:1.163025+0.010406 test-rmse:1.447198+0.216227
## [85] train-rmse:1.152644+0.010844 test-rmse:1.435668+0.217536
## [86] train-rmse:1.142699+0.010914 test-rmse:1.428987+0.218421
## [87] train-rmse:1.133243+0.011008 test-rmse:1.424707+0.214572
## [88] train-rmse:1.123894+0.011115 test-rmse:1.406619+0.217960
## [89] train-rmse:1.114784+0.011408 test-rmse:1.395620+0.219489
## [90] train-rmse:1.106113+0.011269 test-rmse:1.391733+0.223978
## [91] train-rmse:1.097624+0.011235 test-rmse:1.382275+0.215864
## [92] train-rmse:1.089326+0.011094 test-rmse:1.370628+0.210104
## [93] train-rmse:1.081052+0.011098 test-rmse:1.366639+0.214347
## [94] train-rmse:1.072819+0.011177 test-rmse:1.356753+0.215670
## [95] train-rmse:1.064722+0.011290 test-rmse:1.340137+0.220326
## [96] train-rmse:1.056983+0.011207 test-rmse:1.333405+0.218064
## [97] train-rmse:1.049621+0.011348 test-rmse:1.328287+0.214399
## [98] train-rmse:1.042157+0.011476 test-rmse:1.320723+0.213955
## [99] train-rmse:1.034872+0.011522 test-rmse:1.320821+0.208344
## [100] train-rmse:1.028004+0.011446 test-rmse:1.310220+0.210961
## [101] train-rmse:1.021272+0.011384 test-rmse:1.304880+0.212367
## [102] train-rmse:1.014788+0.011305 test-rmse:1.295470+0.216287
## [103] train-rmse:1.008424+0.011539 test-rmse:1.294552+0.211432
## [104] train-rmse:1.002288+0.011705 test-rmse:1.289532+0.214103
## [105] train-rmse:0.996338+0.011948 test-rmse:1.287585+0.212110
## [106] train-rmse:0.990578+0.011705 test-rmse:1.285209+0.206122
## [107] train-rmse:0.984819+0.011718 test-rmse:1.281218+0.207541
## [108] train-rmse:0.979143+0.011840 test-rmse:1.277555+0.208477
## [109] train-rmse:0.973663+0.011749 test-rmse:1.267446+0.210693
## [110] train-rmse:0.968384+0.011890 test-rmse:1.266061+0.211446
## [111] train-rmse:0.963246+0.011972 test-rmse:1.262590+0.212404
## [112] train-rmse:0.958101+0.012016 test-rmse:1.250980+0.196350
## [113] train-rmse:0.953099+0.011853 test-rmse:1.245651+0.198910
## [114] train-rmse:0.948298+0.011687 test-rmse:1.238392+0.193183
## [115] train-rmse:0.943351+0.011805 test-rmse:1.234994+0.193556
## [116] train-rmse:0.938834+0.011707 test-rmse:1.226627+0.189707
## [117] train-rmse:0.934392+0.011727 test-rmse:1.227390+0.187904
## [118] train-rmse:0.930013+0.011780 test-rmse:1.226182+0.187397
## [119] train-rmse:0.925827+0.011798 test-rmse:1.220775+0.189490
## [120] train-rmse:0.921701+0.011862 test-rmse:1.217508+0.194110
## [121] train-rmse:0.917498+0.012136 test-rmse:1.210716+0.191202
## [122] train-rmse:0.913564+0.012289 test-rmse:1.208273+0.189393
## [123] train-rmse:0.909851+0.012374 test-rmse:1.206267+0.191063
## [124] train-rmse:0.906078+0.012462 test-rmse:1.200636+0.189539
## [125] train-rmse:0.902512+0.012517 test-rmse:1.197262+0.192320
## [126] train-rmse:0.898918+0.012473 test-rmse:1.197948+0.185035
## [127] train-rmse:0.895205+0.012345 test-rmse:1.197931+0.180905
## [128] train-rmse:0.891788+0.012383 test-rmse:1.196142+0.183336
## [129] train-rmse:0.888545+0.012421 test-rmse:1.192737+0.184937
## [130] train-rmse:0.885250+0.012441 test-rmse:1.191538+0.184439
## [131] train-rmse:0.881770+0.012540 test-rmse:1.185589+0.187394
## [132] train-rmse:0.878588+0.012546 test-rmse:1.187436+0.187695
## [133] train-rmse:0.875476+0.012580 test-rmse:1.184691+0.187445
## [134] train-rmse:0.872395+0.012624 test-rmse:1.180189+0.188592
## [135] train-rmse:0.869397+0.012719 test-rmse:1.181385+0.189763
## [136] train-rmse:0.866462+0.012799 test-rmse:1.178992+0.189639
## [137] train-rmse:0.863621+0.012932 test-rmse:1.177314+0.190084
## [138] train-rmse:0.860851+0.012867 test-rmse:1.176124+0.189723
## [139] train-rmse:0.858211+0.012842 test-rmse:1.175361+0.190639
## [140] train-rmse:0.855636+0.012818 test-rmse:1.168713+0.179826
## [141] train-rmse:0.853043+0.012833 test-rmse:1.164147+0.180607
## [142] train-rmse:0.850450+0.012720 test-rmse:1.157440+0.185093
## [143] train-rmse:0.847577+0.012268 test-rmse:1.160921+0.183006
## [144] train-rmse:0.845136+0.012168 test-rmse:1.155899+0.179692
## [145] train-rmse:0.842695+0.011909 test-rmse:1.149853+0.184206
## [146] train-rmse:0.840233+0.011681 test-rmse:1.147562+0.180551
## [147] train-rmse:0.837889+0.011589 test-rmse:1.142819+0.182712
## [148] train-rmse:0.835595+0.011571 test-rmse:1.142911+0.183191
## [149] train-rmse:0.833164+0.011659 test-rmse:1.142452+0.181491
## [150] train-rmse:0.830968+0.011635 test-rmse:1.142278+0.180460
## [151] train-rmse:0.828803+0.011593 test-rmse:1.137568+0.180141
## [152] train-rmse:0.826684+0.011546 test-rmse:1.135564+0.178804
## [153] train-rmse:0.824573+0.011545 test-rmse:1.135311+0.181541
## [154] train-rmse:0.822558+0.011441 test-rmse:1.137332+0.180898
## [155] train-rmse:0.820483+0.011396 test-rmse:1.137171+0.180506
## [156] train-rmse:0.818339+0.011299 test-rmse:1.135781+0.182168
## [157] train-rmse:0.816346+0.011376 test-rmse:1.132135+0.186399
## [158] train-rmse:0.814430+0.011274 test-rmse:1.130876+0.184471
## [159] train-rmse:0.812477+0.011181 test-rmse:1.130503+0.185352
## [160] train-rmse:0.810483+0.011292 test-rmse:1.128620+0.186973
## [161] train-rmse:0.808719+0.011317 test-rmse:1.128172+0.186083
## [162] train-rmse:0.806958+0.011380 test-rmse:1.128814+0.187645
## [163] train-rmse:0.805212+0.011468 test-rmse:1.125503+0.188211
## [164] train-rmse:0.803501+0.011494 test-rmse:1.125767+0.187372
## [165] train-rmse:0.801816+0.011462 test-rmse:1.122947+0.185332
## [166] train-rmse:0.799961+0.011507 test-rmse:1.122132+0.185544
## [167] train-rmse:0.798251+0.011500 test-rmse:1.122480+0.185384
## [168] train-rmse:0.796552+0.011525 test-rmse:1.120630+0.183851
## [169] train-rmse:0.794967+0.011584 test-rmse:1.119936+0.184020
## [170] train-rmse:0.793401+0.011602 test-rmse:1.118017+0.185645
## [171] train-rmse:0.791776+0.011621 test-rmse:1.116236+0.186851
## [172] train-rmse:0.790210+0.011691 test-rmse:1.113816+0.182638
## [173] train-rmse:0.788737+0.011666 test-rmse:1.113063+0.183830
## [174] train-rmse:0.787332+0.011641 test-rmse:1.107545+0.176602
## [175] train-rmse:0.785909+0.011656 test-rmse:1.107513+0.173942
## [176] train-rmse:0.784272+0.011526 test-rmse:1.104903+0.175882
## [177] train-rmse:0.782783+0.011593 test-rmse:1.108038+0.174660
## [178] train-rmse:0.781273+0.011637 test-rmse:1.104076+0.173022
## [179] train-rmse:0.779875+0.011588 test-rmse:1.101715+0.172906
## [180] train-rmse:0.778482+0.011605 test-rmse:1.102733+0.171938
## [181] train-rmse:0.777093+0.011637 test-rmse:1.098718+0.173435
## [182] train-rmse:0.775686+0.011537 test-rmse:1.099665+0.172197
## [183] train-rmse:0.774411+0.011541 test-rmse:1.098801+0.171918
## [184] train-rmse:0.773126+0.011539 test-rmse:1.096543+0.173060
## [185] train-rmse:0.771835+0.011520 test-rmse:1.093629+0.175348
## [186] train-rmse:0.770567+0.011505 test-rmse:1.093231+0.175193
## [187] train-rmse:0.769264+0.011495 test-rmse:1.093058+0.174774
## [188] train-rmse:0.768016+0.011532 test-rmse:1.090479+0.174802
## [189] train-rmse:0.766852+0.011607 test-rmse:1.090320+0.175644
## [190] train-rmse:0.765579+0.011687 test-rmse:1.090178+0.176180
## [191] train-rmse:0.764417+0.011707 test-rmse:1.089526+0.175975
## [192] train-rmse:0.763282+0.011734 test-rmse:1.088104+0.176970
## [193] train-rmse:0.762137+0.011755 test-rmse:1.088712+0.177599
## [194] train-rmse:0.761037+0.011784 test-rmse:1.088649+0.178178
## [195] train-rmse:0.759954+0.011811 test-rmse:1.084232+0.177669
## [196] train-rmse:0.758858+0.011781 test-rmse:1.083531+0.175533
## [197] train-rmse:0.757697+0.011771 test-rmse:1.082967+0.176075
## [198] train-rmse:0.756596+0.011733 test-rmse:1.082614+0.177271
## [199] train-rmse:0.755521+0.011772 test-rmse:1.081526+0.178610
## [200] train-rmse:0.754457+0.011771 test-rmse:1.082274+0.178124
## [201] train-rmse:0.753418+0.011763 test-rmse:1.082674+0.177561
## [202] train-rmse:0.752402+0.011771 test-rmse:1.082133+0.176762
## [203] train-rmse:0.751395+0.011754 test-rmse:1.082200+0.175727
## [204] train-rmse:0.750316+0.011694 test-rmse:1.079962+0.175587
## [205] train-rmse:0.749308+0.011672 test-rmse:1.079591+0.175840
## [206] train-rmse:0.748327+0.011654 test-rmse:1.077839+0.174489
## [207] train-rmse:0.747324+0.011698 test-rmse:1.076444+0.174272
## [208] train-rmse:0.746344+0.011681 test-rmse:1.076401+0.175469
## [209] train-rmse:0.745422+0.011697 test-rmse:1.075714+0.173271
## [210] train-rmse:0.744472+0.011689 test-rmse:1.075771+0.172946
## [211] train-rmse:0.743586+0.011666 test-rmse:1.075468+0.172994
## [212] train-rmse:0.742718+0.011636 test-rmse:1.073138+0.167802
## [213] train-rmse:0.741853+0.011613 test-rmse:1.070832+0.169926
## [214] train-rmse:0.740964+0.011589 test-rmse:1.071107+0.168440
## [215] train-rmse:0.740087+0.011561 test-rmse:1.066254+0.168068
## [216] train-rmse:0.739245+0.011585 test-rmse:1.066153+0.168642
## [217] train-rmse:0.738363+0.011574 test-rmse:1.065504+0.168713
## [218] train-rmse:0.737410+0.011623 test-rmse:1.066124+0.167648
## [219] train-rmse:0.736589+0.011612 test-rmse:1.063836+0.167571
## [220] train-rmse:0.735774+0.011584 test-rmse:1.064491+0.167594
## [221] train-rmse:0.734950+0.011578 test-rmse:1.064254+0.168512
## [222] train-rmse:0.734110+0.011575 test-rmse:1.064151+0.165591
## [223] train-rmse:0.733245+0.011515 test-rmse:1.063505+0.166278
## [224] train-rmse:0.732462+0.011502 test-rmse:1.062443+0.167158
## [225] train-rmse:0.731712+0.011476 test-rmse:1.061192+0.167858
## [226] train-rmse:0.730946+0.011452 test-rmse:1.059168+0.167991
## [227] train-rmse:0.730120+0.011352 test-rmse:1.059785+0.166275
## [228] train-rmse:0.729337+0.011310 test-rmse:1.060015+0.166253
## [229] train-rmse:0.728607+0.011284 test-rmse:1.058685+0.167422
## [230] train-rmse:0.727841+0.011242 test-rmse:1.059085+0.165785
## [231] train-rmse:0.726998+0.011264 test-rmse:1.058190+0.166179
## [232] train-rmse:0.726235+0.011217 test-rmse:1.057148+0.166554
## [233] train-rmse:0.725518+0.011212 test-rmse:1.055934+0.165824
## [234] train-rmse:0.724748+0.011262 test-rmse:1.056101+0.166944
## [235] train-rmse:0.724036+0.011257 test-rmse:1.055883+0.167002
## [236] train-rmse:0.723348+0.011267 test-rmse:1.055991+0.166991
## [237] train-rmse:0.722640+0.011280 test-rmse:1.056223+0.166442
## [238] train-rmse:0.721955+0.011241 test-rmse:1.055011+0.165451
## [239] train-rmse:0.721243+0.011235 test-rmse:1.053441+0.165054
## [240] train-rmse:0.720563+0.011230 test-rmse:1.052061+0.163165
## [241] train-rmse:0.719878+0.011239 test-rmse:1.050427+0.163997
## [242] train-rmse:0.719180+0.011199 test-rmse:1.050753+0.163390
## [243] train-rmse:0.718520+0.011158 test-rmse:1.050981+0.165405
## [244] train-rmse:0.717879+0.011128 test-rmse:1.050696+0.166124
## [245] train-rmse:0.717244+0.011093 test-rmse:1.050700+0.164394
## [246] train-rmse:0.716564+0.011069 test-rmse:1.049858+0.164237
## [247] train-rmse:0.715927+0.011043 test-rmse:1.047959+0.164298
## [248] train-rmse:0.715253+0.011082 test-rmse:1.048423+0.164443
## [249] train-rmse:0.714630+0.011044 test-rmse:1.047477+0.164919
## [250] train-rmse:0.713977+0.011013 test-rmse:1.046959+0.164636
## [251] train-rmse:0.713349+0.010992 test-rmse:1.045788+0.163238
## [252] train-rmse:0.712684+0.010963 test-rmse:1.046365+0.161482
## [253] train-rmse:0.712072+0.010952 test-rmse:1.044928+0.162352
## [254] train-rmse:0.711461+0.010944 test-rmse:1.043990+0.162277
## [255] train-rmse:0.710883+0.010951 test-rmse:1.042189+0.162390
## [256] train-rmse:0.710276+0.010928 test-rmse:1.042720+0.161516
## [257] train-rmse:0.709709+0.010914 test-rmse:1.043322+0.161461
## [258] train-rmse:0.709128+0.010912 test-rmse:1.043028+0.162487
## [259] train-rmse:0.708525+0.010898 test-rmse:1.043127+0.159868
## [260] train-rmse:0.707912+0.010852 test-rmse:1.042009+0.158828
## [261] train-rmse:0.707333+0.010791 test-rmse:1.041032+0.156669
## [262] train-rmse:0.706781+0.010754 test-rmse:1.040821+0.156002
## [263] train-rmse:0.706180+0.010741 test-rmse:1.041147+0.155627
## [264] train-rmse:0.705584+0.010756 test-rmse:1.040833+0.155433
## [265] train-rmse:0.705039+0.010733 test-rmse:1.041185+0.154876
## [266] train-rmse:0.704462+0.010679 test-rmse:1.041048+0.154340
## [267] train-rmse:0.703895+0.010646 test-rmse:1.040190+0.154516
## [268] train-rmse:0.703334+0.010647 test-rmse:1.040029+0.154947
## [269] train-rmse:0.702762+0.010603 test-rmse:1.040185+0.154241
## [270] train-rmse:0.702250+0.010583 test-rmse:1.039287+0.154548
## [271] train-rmse:0.701744+0.010567 test-rmse:1.038523+0.154116
## [272] train-rmse:0.701198+0.010499 test-rmse:1.038981+0.153030
## [273] train-rmse:0.700674+0.010465 test-rmse:1.038150+0.154072
## [274] train-rmse:0.700130+0.010386 test-rmse:1.038947+0.153586
## [275] train-rmse:0.699608+0.010354 test-rmse:1.038015+0.154449
## [276] train-rmse:0.699099+0.010311 test-rmse:1.037332+0.155003
## [277] train-rmse:0.698606+0.010261 test-rmse:1.035561+0.150731
## [278] train-rmse:0.698098+0.010213 test-rmse:1.035117+0.151818
## [279] train-rmse:0.697583+0.010224 test-rmse:1.035096+0.152612
## [280] train-rmse:0.697077+0.010189 test-rmse:1.033184+0.151596
## [281] train-rmse:0.696559+0.010160 test-rmse:1.034085+0.150114
## [282] train-rmse:0.696058+0.010132 test-rmse:1.033615+0.150886
## [283] train-rmse:0.695566+0.010093 test-rmse:1.033447+0.150733
## [284] train-rmse:0.695033+0.010120 test-rmse:1.033036+0.149753
## [285] train-rmse:0.694539+0.010074 test-rmse:1.031699+0.149982
## [286] train-rmse:0.694056+0.010012 test-rmse:1.031450+0.148159
## [287] train-rmse:0.693576+0.009971 test-rmse:1.031345+0.148225
## [288] train-rmse:0.693091+0.009967 test-rmse:1.029539+0.148432
## [289] train-rmse:0.692630+0.009939 test-rmse:1.030833+0.149359
## [290] train-rmse:0.692171+0.009926 test-rmse:1.029869+0.148320
## [291] train-rmse:0.691707+0.009928 test-rmse:1.029741+0.146909
## [292] train-rmse:0.691265+0.009940 test-rmse:1.029492+0.148318
## [293] train-rmse:0.690779+0.009896 test-rmse:1.029469+0.147015
## [294] train-rmse:0.690333+0.009899 test-rmse:1.027579+0.146157
## [295] train-rmse:0.689816+0.009854 test-rmse:1.027111+0.144778
## [296] train-rmse:0.689334+0.009873 test-rmse:1.027133+0.141714
## [297] train-rmse:0.688841+0.009868 test-rmse:1.026723+0.142392
## [298] train-rmse:0.688391+0.009839 test-rmse:1.027069+0.142354
## [299] train-rmse:0.687949+0.009814 test-rmse:1.026053+0.141874
## [300] train-rmse:0.687488+0.009787 test-rmse:1.026514+0.141508
## [301] train-rmse:0.687051+0.009760 test-rmse:1.025718+0.140893
## [302] train-rmse:0.686608+0.009723 test-rmse:1.025116+0.141248
## [303] train-rmse:0.686203+0.009701 test-rmse:1.027179+0.141452
## [304] train-rmse:0.685749+0.009668 test-rmse:1.026730+0.142438
## [305] train-rmse:0.685315+0.009603 test-rmse:1.027731+0.142353
## [306] train-rmse:0.684877+0.009606 test-rmse:1.025829+0.144448
## [307] train-rmse:0.684449+0.009555 test-rmse:1.025981+0.142126
## [308] train-rmse:0.684023+0.009551 test-rmse:1.027164+0.141699
## Stopping. Best iteration:
## [302] train-rmse:0.686608+0.009723 test-rmse:1.025116+0.141248
##
## 106
## [1] train-rmse:64.867792+0.080067 test-rmse:64.867433+0.531460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:39.277875+0.064950 test-rmse:39.275826+0.593398
## [3] train-rmse:24.039232+0.061294 test-rmse:24.078998+0.616566
## [4] train-rmse:14.940199+0.046990 test-rmse:14.974273+0.552000
## [5] train-rmse:9.585281+0.053434 test-rmse:9.700390+0.570676
## [6] train-rmse:6.518026+0.049196 test-rmse:6.608745+0.536180
## [7] train-rmse:4.852392+0.059848 test-rmse:5.020800+0.515698
## [8] train-rmse:3.963605+0.062512 test-rmse:4.201099+0.550865
## [9] train-rmse:3.481845+0.063508 test-rmse:3.748229+0.430303
## [10] train-rmse:3.189518+0.064596 test-rmse:3.462655+0.350944
## [11] train-rmse:2.998902+0.058792 test-rmse:3.304467+0.356085
## [12] train-rmse:2.849492+0.054193 test-rmse:3.129424+0.312733
## [13] train-rmse:2.716292+0.035369 test-rmse:2.997260+0.288930
## [14] train-rmse:2.598070+0.037450 test-rmse:2.893538+0.310339
## [15] train-rmse:2.486668+0.037024 test-rmse:2.815545+0.304733
## [16] train-rmse:2.393450+0.034839 test-rmse:2.743603+0.302085
## [17] train-rmse:2.304607+0.034043 test-rmse:2.662338+0.320142
## [18] train-rmse:2.220269+0.029454 test-rmse:2.571134+0.278355
## [19] train-rmse:2.139231+0.026696 test-rmse:2.508175+0.288218
## [20] train-rmse:2.049134+0.021026 test-rmse:2.428852+0.279661
## [21] train-rmse:1.979563+0.023564 test-rmse:2.363170+0.257271
## [22] train-rmse:1.912900+0.022598 test-rmse:2.298464+0.236416
## [23] train-rmse:1.845518+0.024437 test-rmse:2.232612+0.256173
## [24] train-rmse:1.784493+0.026761 test-rmse:2.162470+0.260747
## [25] train-rmse:1.726320+0.027351 test-rmse:2.094732+0.244323
## [26] train-rmse:1.671957+0.026904 test-rmse:2.061921+0.246137
## [27] train-rmse:1.615305+0.021500 test-rmse:2.022717+0.255955
## [28] train-rmse:1.563454+0.020352 test-rmse:1.973122+0.245889
## [29] train-rmse:1.514731+0.023258 test-rmse:1.952453+0.246900
## [30] train-rmse:1.468961+0.022393 test-rmse:1.924207+0.257768
## [31] train-rmse:1.424475+0.019609 test-rmse:1.878491+0.242460
## [32] train-rmse:1.384636+0.019717 test-rmse:1.828451+0.248430
## [33] train-rmse:1.346729+0.019616 test-rmse:1.791499+0.243094
## [34] train-rmse:1.307419+0.016469 test-rmse:1.746705+0.235497
## [35] train-rmse:1.271237+0.015808 test-rmse:1.724017+0.247292
## [36] train-rmse:1.235691+0.017543 test-rmse:1.698159+0.246115
## [37] train-rmse:1.200589+0.016822 test-rmse:1.671004+0.243285
## [38] train-rmse:1.171414+0.016615 test-rmse:1.640928+0.253615
## [39] train-rmse:1.141136+0.015652 test-rmse:1.622909+0.258095
## [40] train-rmse:1.113667+0.015621 test-rmse:1.605193+0.260400
## [41] train-rmse:1.085740+0.015218 test-rmse:1.580520+0.246648
## [42] train-rmse:1.057267+0.019118 test-rmse:1.567442+0.249673
## [43] train-rmse:1.032158+0.017849 test-rmse:1.544739+0.246327
## [44] train-rmse:1.009049+0.016443 test-rmse:1.513807+0.243017
## [45] train-rmse:0.986758+0.017524 test-rmse:1.489545+0.244770
## [46] train-rmse:0.966374+0.016432 test-rmse:1.472166+0.245726
## [47] train-rmse:0.945181+0.018245 test-rmse:1.456078+0.245024
## [48] train-rmse:0.926231+0.017514 test-rmse:1.440219+0.244402
## [49] train-rmse:0.908266+0.019041 test-rmse:1.429783+0.247529
## [50] train-rmse:0.890834+0.018833 test-rmse:1.417491+0.251801
## [51] train-rmse:0.872738+0.020179 test-rmse:1.413095+0.254197
## [52] train-rmse:0.852986+0.022012 test-rmse:1.395333+0.255737
## [53] train-rmse:0.838485+0.022075 test-rmse:1.384499+0.256823
## [54] train-rmse:0.824448+0.022169 test-rmse:1.370827+0.248262
## [55] train-rmse:0.810584+0.022361 test-rmse:1.357847+0.232917
## [56] train-rmse:0.798085+0.021663 test-rmse:1.344506+0.240934
## [57] train-rmse:0.780886+0.021733 test-rmse:1.327375+0.242806
## [58] train-rmse:0.768282+0.021209 test-rmse:1.316500+0.245032
## [59] train-rmse:0.754350+0.018462 test-rmse:1.311005+0.245557
## [60] train-rmse:0.743090+0.017898 test-rmse:1.301243+0.246717
## [61] train-rmse:0.730031+0.015860 test-rmse:1.294025+0.243043
## [62] train-rmse:0.716784+0.019154 test-rmse:1.285828+0.247984
## [63] train-rmse:0.705942+0.017672 test-rmse:1.270787+0.250090
## [64] train-rmse:0.695643+0.016678 test-rmse:1.264621+0.253794
## [65] train-rmse:0.686671+0.016967 test-rmse:1.260152+0.254949
## [66] train-rmse:0.676978+0.014962 test-rmse:1.251082+0.254507
## [67] train-rmse:0.664734+0.016647 test-rmse:1.243637+0.253283
## [68] train-rmse:0.653808+0.017514 test-rmse:1.237225+0.248664
## [69] train-rmse:0.645685+0.016179 test-rmse:1.230497+0.250119
## [70] train-rmse:0.636392+0.016801 test-rmse:1.226338+0.249460
## [71] train-rmse:0.629240+0.016712 test-rmse:1.221365+0.252915
## [72] train-rmse:0.618309+0.015986 test-rmse:1.223580+0.256851
## [73] train-rmse:0.611418+0.016806 test-rmse:1.222977+0.258766
## [74] train-rmse:0.603425+0.017110 test-rmse:1.220692+0.257342
## [75] train-rmse:0.596941+0.015902 test-rmse:1.217624+0.259788
## [76] train-rmse:0.590783+0.015302 test-rmse:1.203347+0.263832
## [77] train-rmse:0.585553+0.015129 test-rmse:1.197653+0.263038
## [78] train-rmse:0.579745+0.013803 test-rmse:1.194366+0.263848
## [79] train-rmse:0.574691+0.013840 test-rmse:1.191844+0.263181
## [80] train-rmse:0.568143+0.014791 test-rmse:1.185838+0.266110
## [81] train-rmse:0.559537+0.014186 test-rmse:1.181399+0.267267
## [82] train-rmse:0.554059+0.013818 test-rmse:1.180194+0.267984
## [83] train-rmse:0.546419+0.010149 test-rmse:1.173597+0.268529
## [84] train-rmse:0.539987+0.012696 test-rmse:1.171476+0.268226
## [85] train-rmse:0.533965+0.011210 test-rmse:1.165224+0.271542
## [86] train-rmse:0.528512+0.011633 test-rmse:1.161069+0.268437
## [87] train-rmse:0.522711+0.010132 test-rmse:1.157001+0.259742
## [88] train-rmse:0.518283+0.009716 test-rmse:1.154085+0.261866
## [89] train-rmse:0.513010+0.012549 test-rmse:1.148808+0.246259
## [90] train-rmse:0.509306+0.013249 test-rmse:1.146348+0.247340
## [91] train-rmse:0.505089+0.014092 test-rmse:1.145180+0.246896
## [92] train-rmse:0.500262+0.015991 test-rmse:1.140458+0.245442
## [93] train-rmse:0.495835+0.016737 test-rmse:1.138630+0.247414
## [94] train-rmse:0.491737+0.016362 test-rmse:1.135183+0.249575
## [95] train-rmse:0.486817+0.015009 test-rmse:1.130901+0.248954
## [96] train-rmse:0.480437+0.013360 test-rmse:1.124431+0.254615
## [97] train-rmse:0.475586+0.013940 test-rmse:1.123760+0.257042
## [98] train-rmse:0.470731+0.015726 test-rmse:1.122526+0.257324
## [99] train-rmse:0.465309+0.015629 test-rmse:1.117846+0.256637
## [100] train-rmse:0.461805+0.015332 test-rmse:1.115308+0.260150
## [101] train-rmse:0.456681+0.015388 test-rmse:1.111413+0.263308
## [102] train-rmse:0.451790+0.013451 test-rmse:1.112219+0.262921
## [103] train-rmse:0.447365+0.013286 test-rmse:1.108119+0.264510
## [104] train-rmse:0.443979+0.013181 test-rmse:1.108597+0.265666
## [105] train-rmse:0.440699+0.014079 test-rmse:1.107122+0.266893
## [106] train-rmse:0.438107+0.014436 test-rmse:1.106895+0.265373
## [107] train-rmse:0.433557+0.014135 test-rmse:1.105553+0.264630
## [108] train-rmse:0.430680+0.013886 test-rmse:1.105091+0.264652
## [109] train-rmse:0.424772+0.011234 test-rmse:1.104105+0.264087
## [110] train-rmse:0.421962+0.011625 test-rmse:1.103299+0.263222
## [111] train-rmse:0.419352+0.011486 test-rmse:1.103067+0.260735
## [112] train-rmse:0.416387+0.012066 test-rmse:1.101594+0.261321
## [113] train-rmse:0.413552+0.012575 test-rmse:1.099465+0.262310
## [114] train-rmse:0.411560+0.012966 test-rmse:1.097232+0.263027
## [115] train-rmse:0.408162+0.013011 test-rmse:1.096673+0.263906
## [116] train-rmse:0.406136+0.013389 test-rmse:1.097169+0.268877
## [117] train-rmse:0.403048+0.014429 test-rmse:1.099820+0.268994
## [118] train-rmse:0.400348+0.014227 test-rmse:1.096535+0.268257
## [119] train-rmse:0.395935+0.013134 test-rmse:1.096255+0.266837
## [120] train-rmse:0.392753+0.012214 test-rmse:1.096906+0.267405
## [121] train-rmse:0.390808+0.012394 test-rmse:1.096492+0.267464
## [122] train-rmse:0.387082+0.012284 test-rmse:1.091299+0.268895
## [123] train-rmse:0.383881+0.012887 test-rmse:1.092247+0.269473
## [124] train-rmse:0.381233+0.012183 test-rmse:1.089531+0.269937
## [125] train-rmse:0.378174+0.011420 test-rmse:1.089542+0.269629
## [126] train-rmse:0.376345+0.011883 test-rmse:1.087966+0.268157
## [127] train-rmse:0.373936+0.012533 test-rmse:1.084395+0.268900
## [128] train-rmse:0.372239+0.012740 test-rmse:1.083591+0.269046
## [129] train-rmse:0.368970+0.012061 test-rmse:1.081138+0.270212
## [130] train-rmse:0.366751+0.011410 test-rmse:1.080402+0.270872
## [131] train-rmse:0.364607+0.010859 test-rmse:1.079563+0.271099
## [132] train-rmse:0.362998+0.010584 test-rmse:1.078169+0.271222
## [133] train-rmse:0.361121+0.010785 test-rmse:1.076964+0.271831
## [134] train-rmse:0.357207+0.011286 test-rmse:1.073106+0.270285
## [135] train-rmse:0.355523+0.011553 test-rmse:1.071365+0.269541
## [136] train-rmse:0.353940+0.011805 test-rmse:1.072485+0.269028
## [137] train-rmse:0.351998+0.011489 test-rmse:1.071841+0.268722
## [138] train-rmse:0.350031+0.011301 test-rmse:1.071314+0.268846
## [139] train-rmse:0.348002+0.012048 test-rmse:1.072811+0.268438
## [140] train-rmse:0.344329+0.012375 test-rmse:1.071306+0.267944
## [141] train-rmse:0.342204+0.012256 test-rmse:1.070726+0.267580
## [142] train-rmse:0.340321+0.012746 test-rmse:1.071338+0.267767
## [143] train-rmse:0.338106+0.012888 test-rmse:1.071174+0.267835
## [144] train-rmse:0.335755+0.012846 test-rmse:1.072043+0.268477
## [145] train-rmse:0.333908+0.012287 test-rmse:1.070259+0.268097
## [146] train-rmse:0.331699+0.011396 test-rmse:1.071108+0.267775
## [147] train-rmse:0.329929+0.010364 test-rmse:1.068888+0.269297
## [148] train-rmse:0.328012+0.009904 test-rmse:1.068122+0.268291
## [149] train-rmse:0.325732+0.009870 test-rmse:1.068593+0.269560
## [150] train-rmse:0.324649+0.009690 test-rmse:1.068970+0.269326
## [151] train-rmse:0.322769+0.009257 test-rmse:1.067431+0.270082
## [152] train-rmse:0.321299+0.009486 test-rmse:1.066250+0.269844
## [153] train-rmse:0.318850+0.009062 test-rmse:1.065959+0.270371
## [154] train-rmse:0.316480+0.009008 test-rmse:1.065632+0.270488
## [155] train-rmse:0.313886+0.009224 test-rmse:1.065388+0.268370
## [156] train-rmse:0.312430+0.008817 test-rmse:1.064245+0.267891
## [157] train-rmse:0.310324+0.009202 test-rmse:1.064251+0.267685
## [158] train-rmse:0.308687+0.009110 test-rmse:1.063350+0.268486
## [159] train-rmse:0.306927+0.008992 test-rmse:1.063220+0.269233
## [160] train-rmse:0.305271+0.008745 test-rmse:1.062284+0.269599
## [161] train-rmse:0.302661+0.007590 test-rmse:1.059787+0.270846
## [162] train-rmse:0.300666+0.006658 test-rmse:1.061121+0.271542
## [163] train-rmse:0.298810+0.006193 test-rmse:1.059093+0.273102
## [164] train-rmse:0.297145+0.007078 test-rmse:1.058768+0.273158
## [165] train-rmse:0.295324+0.007842 test-rmse:1.058817+0.272707
## [166] train-rmse:0.294381+0.007950 test-rmse:1.058262+0.273142
## [167] train-rmse:0.292457+0.008097 test-rmse:1.058150+0.273252
## [168] train-rmse:0.290744+0.007815 test-rmse:1.057769+0.273369
## [169] train-rmse:0.289242+0.007858 test-rmse:1.057247+0.273758
## [170] train-rmse:0.287982+0.008296 test-rmse:1.057243+0.274065
## [171] train-rmse:0.286274+0.008148 test-rmse:1.055910+0.273975
## [172] train-rmse:0.284567+0.008991 test-rmse:1.055111+0.274953
## [173] train-rmse:0.282776+0.008713 test-rmse:1.054238+0.274125
## [174] train-rmse:0.280536+0.009546 test-rmse:1.054593+0.275605
## [175] train-rmse:0.279143+0.009371 test-rmse:1.054400+0.275912
## [176] train-rmse:0.277397+0.009183 test-rmse:1.054819+0.275938
## [177] train-rmse:0.276002+0.009528 test-rmse:1.055964+0.277329
## [178] train-rmse:0.274435+0.009800 test-rmse:1.054983+0.275529
## [179] train-rmse:0.272830+0.010208 test-rmse:1.054776+0.275245
## Stopping. Best iteration:
## [173] train-rmse:0.282776+0.008713 test-rmse:1.054238+0.274125
##
## 107
## [1] train-rmse:64.867792+0.080067 test-rmse:64.867433+0.531460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:39.277875+0.064950 test-rmse:39.275826+0.593398
## [3] train-rmse:24.039232+0.061294 test-rmse:24.078998+0.616566
## [4] train-rmse:14.913497+0.045319 test-rmse:15.010122+0.558959
## [5] train-rmse:9.495933+0.040103 test-rmse:9.604309+0.555339
## [6] train-rmse:6.359953+0.050142 test-rmse:6.493432+0.588806
## [7] train-rmse:4.560693+0.057036 test-rmse:4.788800+0.550935
## [8] train-rmse:3.559187+0.072099 test-rmse:3.786533+0.377381
## [9] train-rmse:3.010500+0.048581 test-rmse:3.261072+0.315330
## [10] train-rmse:2.687685+0.046907 test-rmse:2.982347+0.296704
## [11] train-rmse:2.431635+0.055650 test-rmse:2.797974+0.250835
## [12] train-rmse:2.267550+0.048824 test-rmse:2.665361+0.285845
## [13] train-rmse:2.132495+0.047260 test-rmse:2.546679+0.294611
## [14] train-rmse:2.006589+0.043217 test-rmse:2.417532+0.259323
## [15] train-rmse:1.883681+0.039198 test-rmse:2.347059+0.255381
## [16] train-rmse:1.786780+0.035558 test-rmse:2.262379+0.250112
## [17] train-rmse:1.692538+0.034884 test-rmse:2.162034+0.223140
## [18] train-rmse:1.605955+0.029435 test-rmse:2.087282+0.256595
## [19] train-rmse:1.523440+0.031390 test-rmse:2.020180+0.228164
## [20] train-rmse:1.451125+0.030971 test-rmse:1.947218+0.236609
## [21] train-rmse:1.384015+0.027703 test-rmse:1.909020+0.226334
## [22] train-rmse:1.322534+0.026233 test-rmse:1.861525+0.231371
## [23] train-rmse:1.262500+0.023485 test-rmse:1.817961+0.227073
## [24] train-rmse:1.205981+0.022579 test-rmse:1.769406+0.240294
## [25] train-rmse:1.154739+0.022839 test-rmse:1.717176+0.233734
## [26] train-rmse:1.103316+0.026274 test-rmse:1.669878+0.224679
## [27] train-rmse:1.057858+0.025193 test-rmse:1.632339+0.216763
## [28] train-rmse:1.013523+0.026276 test-rmse:1.619996+0.207156
## [29] train-rmse:0.974896+0.029568 test-rmse:1.595090+0.213549
## [30] train-rmse:0.938303+0.028197 test-rmse:1.555008+0.227777
## [31] train-rmse:0.897559+0.025233 test-rmse:1.522099+0.235724
## [32] train-rmse:0.864147+0.027961 test-rmse:1.503079+0.245483
## [33] train-rmse:0.832648+0.027034 test-rmse:1.490651+0.243299
## [34] train-rmse:0.805749+0.028144 test-rmse:1.457346+0.244792
## [35] train-rmse:0.781278+0.027815 test-rmse:1.448038+0.243189
## [36] train-rmse:0.755580+0.027030 test-rmse:1.435992+0.241506
## [37] train-rmse:0.727049+0.029261 test-rmse:1.413419+0.235718
## [38] train-rmse:0.705689+0.029240 test-rmse:1.395492+0.235412
## [39] train-rmse:0.679745+0.019973 test-rmse:1.375566+0.235883
## [40] train-rmse:0.660985+0.018660 test-rmse:1.351490+0.240557
## [41] train-rmse:0.642632+0.017973 test-rmse:1.341149+0.244945
## [42] train-rmse:0.625588+0.018792 test-rmse:1.339422+0.249789
## [43] train-rmse:0.608806+0.019298 test-rmse:1.330757+0.256954
## [44] train-rmse:0.592594+0.021679 test-rmse:1.321009+0.254992
## [45] train-rmse:0.575680+0.019726 test-rmse:1.305861+0.260580
## [46] train-rmse:0.561747+0.018374 test-rmse:1.296615+0.264758
## [47] train-rmse:0.548160+0.017727 test-rmse:1.295627+0.258809
## [48] train-rmse:0.536470+0.017875 test-rmse:1.292531+0.254050
## [49] train-rmse:0.524465+0.017926 test-rmse:1.279551+0.261605
## [50] train-rmse:0.513088+0.017731 test-rmse:1.266856+0.266130
## [51] train-rmse:0.500582+0.016758 test-rmse:1.262313+0.268707
## [52] train-rmse:0.490094+0.017300 test-rmse:1.258686+0.272900
## [53] train-rmse:0.479184+0.018544 test-rmse:1.254360+0.272557
## [54] train-rmse:0.468545+0.019495 test-rmse:1.248724+0.277553
## [55] train-rmse:0.460051+0.021221 test-rmse:1.246481+0.279792
## [56] train-rmse:0.449316+0.023758 test-rmse:1.244524+0.282910
## [57] train-rmse:0.439577+0.025061 test-rmse:1.241083+0.286946
## [58] train-rmse:0.431248+0.023856 test-rmse:1.234458+0.277387
## [59] train-rmse:0.423655+0.024518 test-rmse:1.229456+0.283643
## [60] train-rmse:0.416023+0.026814 test-rmse:1.228388+0.284606
## [61] train-rmse:0.402854+0.017042 test-rmse:1.225778+0.284512
## [62] train-rmse:0.396450+0.015925 test-rmse:1.225030+0.283175
## [63] train-rmse:0.390091+0.017233 test-rmse:1.219811+0.280096
## [64] train-rmse:0.381489+0.017690 test-rmse:1.219295+0.280799
## [65] train-rmse:0.372848+0.016748 test-rmse:1.212614+0.284124
## [66] train-rmse:0.366357+0.018920 test-rmse:1.202083+0.276133
## [67] train-rmse:0.359441+0.019328 test-rmse:1.196546+0.274278
## [68] train-rmse:0.353630+0.018637 test-rmse:1.197537+0.277488
## [69] train-rmse:0.347024+0.016047 test-rmse:1.195464+0.278211
## [70] train-rmse:0.342791+0.016801 test-rmse:1.190199+0.281450
## [71] train-rmse:0.336148+0.016557 test-rmse:1.188549+0.283014
## [72] train-rmse:0.331372+0.015501 test-rmse:1.188695+0.284182
## [73] train-rmse:0.326479+0.015139 test-rmse:1.187519+0.283325
## [74] train-rmse:0.321152+0.014636 test-rmse:1.187635+0.286420
## [75] train-rmse:0.315141+0.015859 test-rmse:1.185705+0.285941
## [76] train-rmse:0.310190+0.015292 test-rmse:1.183940+0.286749
## [77] train-rmse:0.304101+0.015938 test-rmse:1.179864+0.286216
## [78] train-rmse:0.298466+0.015644 test-rmse:1.181907+0.286228
## [79] train-rmse:0.292465+0.012704 test-rmse:1.177162+0.283529
## [80] train-rmse:0.287751+0.012686 test-rmse:1.176391+0.282954
## [81] train-rmse:0.283952+0.013217 test-rmse:1.175928+0.283044
## [82] train-rmse:0.280269+0.014138 test-rmse:1.174246+0.282661
## [83] train-rmse:0.274362+0.013800 test-rmse:1.172696+0.282859
## [84] train-rmse:0.269209+0.015225 test-rmse:1.172231+0.283629
## [85] train-rmse:0.265352+0.014751 test-rmse:1.167542+0.284118
## [86] train-rmse:0.260274+0.015062 test-rmse:1.165223+0.284361
## [87] train-rmse:0.256458+0.012771 test-rmse:1.162582+0.285049
## [88] train-rmse:0.253566+0.013604 test-rmse:1.159876+0.283863
## [89] train-rmse:0.248291+0.011848 test-rmse:1.158394+0.284556
## [90] train-rmse:0.243687+0.010529 test-rmse:1.156203+0.285266
## [91] train-rmse:0.239174+0.008856 test-rmse:1.151365+0.282398
## [92] train-rmse:0.235901+0.008752 test-rmse:1.151373+0.283430
## [93] train-rmse:0.233192+0.007936 test-rmse:1.152250+0.283454
## [94] train-rmse:0.230908+0.007715 test-rmse:1.151777+0.283710
## [95] train-rmse:0.228718+0.007924 test-rmse:1.151936+0.283652
## [96] train-rmse:0.225510+0.008583 test-rmse:1.151765+0.283461
## [97] train-rmse:0.220954+0.009218 test-rmse:1.150460+0.283221
## [98] train-rmse:0.218094+0.009350 test-rmse:1.151759+0.282082
## [99] train-rmse:0.214399+0.009357 test-rmse:1.151847+0.281531
## [100] train-rmse:0.212430+0.010295 test-rmse:1.150991+0.282394
## [101] train-rmse:0.209343+0.011859 test-rmse:1.150942+0.284448
## [102] train-rmse:0.206253+0.012349 test-rmse:1.150518+0.282853
## [103] train-rmse:0.203013+0.012129 test-rmse:1.151352+0.282262
## Stopping. Best iteration:
## [97] train-rmse:0.220954+0.009218 test-rmse:1.150460+0.283221
##
## 108
## [1] train-rmse:64.867792+0.080067 test-rmse:64.867433+0.531460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:39.277875+0.064950 test-rmse:39.275826+0.593398
## [3] train-rmse:24.039232+0.061294 test-rmse:24.078998+0.616566
## [4] train-rmse:14.908946+0.045214 test-rmse:14.982769+0.547179
## [5] train-rmse:9.449514+0.037624 test-rmse:9.518927+0.487140
## [6] train-rmse:6.238915+0.035866 test-rmse:6.388581+0.566298
## [7] train-rmse:4.367854+0.048289 test-rmse:4.522701+0.472314
## [8] train-rmse:3.265791+0.038316 test-rmse:3.592969+0.399628
## [9] train-rmse:2.637027+0.061847 test-rmse:2.980752+0.311731
## [10] train-rmse:2.265219+0.057912 test-rmse:2.680453+0.242036
## [11] train-rmse:2.020091+0.047759 test-rmse:2.458579+0.225992
## [12] train-rmse:1.847392+0.040731 test-rmse:2.279940+0.199903
## [13] train-rmse:1.698562+0.029493 test-rmse:2.167111+0.222190
## [14] train-rmse:1.574502+0.024196 test-rmse:2.074591+0.214054
## [15] train-rmse:1.469550+0.026453 test-rmse:1.979424+0.195398
## [16] train-rmse:1.359503+0.026975 test-rmse:1.917652+0.191612
## [17] train-rmse:1.268450+0.028423 test-rmse:1.840535+0.180490
## [18] train-rmse:1.191121+0.023954 test-rmse:1.770778+0.186521
## [19] train-rmse:1.113045+0.017981 test-rmse:1.690347+0.173707
## [20] train-rmse:1.051879+0.020935 test-rmse:1.639548+0.158963
## [21] train-rmse:0.993436+0.023816 test-rmse:1.592912+0.161793
## [22] train-rmse:0.933015+0.026039 test-rmse:1.554765+0.156064
## [23] train-rmse:0.883243+0.024899 test-rmse:1.522729+0.157295
## [24] train-rmse:0.842883+0.023055 test-rmse:1.483799+0.166012
## [25] train-rmse:0.799626+0.021840 test-rmse:1.461472+0.171365
## [26] train-rmse:0.761565+0.022107 test-rmse:1.435630+0.177623
## [27] train-rmse:0.723478+0.021100 test-rmse:1.420647+0.170291
## [28] train-rmse:0.689298+0.015654 test-rmse:1.407624+0.177113
## [29] train-rmse:0.660449+0.013013 test-rmse:1.392026+0.177760
## [30] train-rmse:0.629639+0.013063 test-rmse:1.367559+0.180392
## [31] train-rmse:0.603317+0.011836 test-rmse:1.360342+0.179910
## [32] train-rmse:0.579031+0.012859 test-rmse:1.357685+0.174702
## [33] train-rmse:0.555423+0.011239 test-rmse:1.342913+0.188469
## [34] train-rmse:0.534486+0.010203 test-rmse:1.334490+0.193518
## [35] train-rmse:0.515636+0.007394 test-rmse:1.318100+0.195902
## [36] train-rmse:0.498109+0.009226 test-rmse:1.311872+0.194629
## [37] train-rmse:0.478753+0.010785 test-rmse:1.307309+0.197705
## [38] train-rmse:0.461470+0.008277 test-rmse:1.301409+0.201479
## [39] train-rmse:0.443926+0.009274 test-rmse:1.296710+0.203072
## [40] train-rmse:0.430900+0.010689 test-rmse:1.281336+0.199474
## [41] train-rmse:0.416815+0.013353 test-rmse:1.266887+0.208012
## [42] train-rmse:0.404988+0.012375 test-rmse:1.266511+0.208288
## [43] train-rmse:0.390108+0.009633 test-rmse:1.258867+0.208800
## [44] train-rmse:0.380111+0.009622 test-rmse:1.254457+0.207692
## [45] train-rmse:0.365582+0.011558 test-rmse:1.250089+0.201497
## [46] train-rmse:0.355087+0.014195 test-rmse:1.248140+0.202297
## [47] train-rmse:0.343651+0.012413 test-rmse:1.243499+0.204969
## [48] train-rmse:0.335113+0.012615 test-rmse:1.241305+0.205510
## [49] train-rmse:0.324416+0.010865 test-rmse:1.238270+0.207187
## [50] train-rmse:0.314763+0.011046 test-rmse:1.238648+0.210696
## [51] train-rmse:0.306567+0.011828 test-rmse:1.231426+0.215278
## [52] train-rmse:0.299660+0.011033 test-rmse:1.225623+0.220279
## [53] train-rmse:0.293422+0.011585 test-rmse:1.218792+0.212586
## [54] train-rmse:0.282483+0.011155 test-rmse:1.218371+0.212736
## [55] train-rmse:0.275887+0.010364 test-rmse:1.216299+0.212196
## [56] train-rmse:0.269930+0.008979 test-rmse:1.214348+0.213111
## [57] train-rmse:0.262551+0.009772 test-rmse:1.211260+0.217556
## [58] train-rmse:0.254061+0.009020 test-rmse:1.208850+0.218204
## [59] train-rmse:0.248358+0.009042 test-rmse:1.208160+0.217758
## [60] train-rmse:0.243598+0.009585 test-rmse:1.206321+0.218846
## [61] train-rmse:0.236477+0.007630 test-rmse:1.203627+0.217546
## [62] train-rmse:0.232277+0.007237 test-rmse:1.200159+0.219381
## [63] train-rmse:0.224691+0.010392 test-rmse:1.199034+0.223229
## [64] train-rmse:0.219508+0.010708 test-rmse:1.198946+0.226734
## [65] train-rmse:0.212303+0.009132 test-rmse:1.194985+0.227478
## [66] train-rmse:0.206391+0.007398 test-rmse:1.196966+0.226180
## [67] train-rmse:0.201528+0.006349 test-rmse:1.195567+0.227498
## [68] train-rmse:0.196135+0.005625 test-rmse:1.196115+0.227624
## [69] train-rmse:0.189211+0.007796 test-rmse:1.194783+0.226418
## [70] train-rmse:0.183402+0.008186 test-rmse:1.191767+0.226310
## [71] train-rmse:0.179032+0.008907 test-rmse:1.191538+0.226682
## [72] train-rmse:0.173641+0.008352 test-rmse:1.190884+0.221777
## [73] train-rmse:0.170202+0.008387 test-rmse:1.191216+0.222525
## [74] train-rmse:0.166479+0.009774 test-rmse:1.191383+0.222768
## [75] train-rmse:0.161961+0.009729 test-rmse:1.189665+0.220801
## [76] train-rmse:0.158525+0.009959 test-rmse:1.189222+0.220166
## [77] train-rmse:0.154085+0.008623 test-rmse:1.190417+0.221989
## [78] train-rmse:0.150544+0.008649 test-rmse:1.190999+0.223130
## [79] train-rmse:0.147228+0.008227 test-rmse:1.190817+0.223042
## [80] train-rmse:0.143948+0.008489 test-rmse:1.190614+0.223993
## [81] train-rmse:0.140400+0.007965 test-rmse:1.190421+0.224416
## [82] train-rmse:0.138691+0.007966 test-rmse:1.189646+0.224381
## Stopping. Best iteration:
## [76] train-rmse:0.158525+0.009959 test-rmse:1.189222+0.220166
##
## 109
## [1] train-rmse:64.867792+0.080067 test-rmse:64.867433+0.531460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:39.277875+0.064950 test-rmse:39.275826+0.593398
## [3] train-rmse:24.039232+0.061294 test-rmse:24.078998+0.616566
## [4] train-rmse:14.907449+0.044271 test-rmse:14.976273+0.555419
## [5] train-rmse:9.427856+0.036582 test-rmse:9.514035+0.504380
## [6] train-rmse:6.165895+0.024853 test-rmse:6.328500+0.569241
## [7] train-rmse:4.218326+0.044181 test-rmse:4.401909+0.468903
## [8] train-rmse:3.086144+0.046185 test-rmse:3.319613+0.370007
## [9] train-rmse:2.417799+0.041790 test-rmse:2.785866+0.321700
## [10] train-rmse:1.996536+0.028997 test-rmse:2.455169+0.293249
## [11] train-rmse:1.728731+0.017316 test-rmse:2.198729+0.250665
## [12] train-rmse:1.530977+0.008217 test-rmse:2.054464+0.250329
## [13] train-rmse:1.369114+0.024087 test-rmse:1.966980+0.228414
## [14] train-rmse:1.245966+0.021575 test-rmse:1.840631+0.206659
## [15] train-rmse:1.138843+0.017737 test-rmse:1.761086+0.204768
## [16] train-rmse:1.043501+0.013460 test-rmse:1.696021+0.198423
## [17] train-rmse:0.957245+0.016607 test-rmse:1.651193+0.202627
## [18] train-rmse:0.881695+0.009672 test-rmse:1.584338+0.214810
## [19] train-rmse:0.823435+0.008531 test-rmse:1.535244+0.217390
## [20] train-rmse:0.770902+0.009064 test-rmse:1.499250+0.212065
## [21] train-rmse:0.715793+0.014084 test-rmse:1.460832+0.212046
## [22] train-rmse:0.669213+0.010805 test-rmse:1.421293+0.217448
## [23] train-rmse:0.631119+0.014043 test-rmse:1.399869+0.232648
## [24] train-rmse:0.587112+0.023572 test-rmse:1.378831+0.237432
## [25] train-rmse:0.555966+0.021455 test-rmse:1.355696+0.225467
## [26] train-rmse:0.522550+0.025180 test-rmse:1.341621+0.230947
## [27] train-rmse:0.497361+0.020359 test-rmse:1.320688+0.234057
## [28] train-rmse:0.472380+0.016199 test-rmse:1.312676+0.244027
## [29] train-rmse:0.446639+0.013773 test-rmse:1.299101+0.247992
## [30] train-rmse:0.430739+0.015796 test-rmse:1.286989+0.257930
## [31] train-rmse:0.407351+0.017404 test-rmse:1.277502+0.266302
## [32] train-rmse:0.387608+0.016066 test-rmse:1.272602+0.264209
## [33] train-rmse:0.371995+0.015967 test-rmse:1.268158+0.265435
## [34] train-rmse:0.357041+0.016580 test-rmse:1.259743+0.267867
## [35] train-rmse:0.345178+0.015409 test-rmse:1.251187+0.263101
## [36] train-rmse:0.330845+0.012763 test-rmse:1.250716+0.263324
## [37] train-rmse:0.317216+0.011999 test-rmse:1.246082+0.263514
## [38] train-rmse:0.303046+0.010186 test-rmse:1.242399+0.267486
## [39] train-rmse:0.291176+0.006928 test-rmse:1.239294+0.270533
## [40] train-rmse:0.279526+0.008481 test-rmse:1.236631+0.271544
## [41] train-rmse:0.269121+0.007392 test-rmse:1.236374+0.270232
## [42] train-rmse:0.259376+0.009205 test-rmse:1.236796+0.272350
## [43] train-rmse:0.250440+0.008672 test-rmse:1.236019+0.274405
## [44] train-rmse:0.237333+0.008284 test-rmse:1.232240+0.275875
## [45] train-rmse:0.228673+0.007733 test-rmse:1.231043+0.279882
## [46] train-rmse:0.220845+0.007282 test-rmse:1.230085+0.281133
## [47] train-rmse:0.210746+0.009125 test-rmse:1.225581+0.278873
## [48] train-rmse:0.201909+0.007433 test-rmse:1.222636+0.279743
## [49] train-rmse:0.192844+0.008308 test-rmse:1.223108+0.281073
## [50] train-rmse:0.184911+0.008665 test-rmse:1.222622+0.281313
## [51] train-rmse:0.177116+0.005337 test-rmse:1.217139+0.276040
## [52] train-rmse:0.169210+0.007172 test-rmse:1.214916+0.277735
## [53] train-rmse:0.161968+0.004698 test-rmse:1.214142+0.277209
## [54] train-rmse:0.154441+0.004846 test-rmse:1.211828+0.276164
## [55] train-rmse:0.149269+0.004385 test-rmse:1.212236+0.278168
## [56] train-rmse:0.144868+0.005345 test-rmse:1.212440+0.278313
## [57] train-rmse:0.140019+0.003624 test-rmse:1.211774+0.279717
## [58] train-rmse:0.136889+0.003578 test-rmse:1.211423+0.280413
## [59] train-rmse:0.130981+0.004346 test-rmse:1.210524+0.280586
## [60] train-rmse:0.127967+0.003969 test-rmse:1.210592+0.280786
## [61] train-rmse:0.122937+0.004001 test-rmse:1.210403+0.282225
## [62] train-rmse:0.119308+0.004487 test-rmse:1.209251+0.284157
## [63] train-rmse:0.114988+0.003676 test-rmse:1.207696+0.285813
## [64] train-rmse:0.108687+0.003801 test-rmse:1.206719+0.286039
## [65] train-rmse:0.105557+0.003794 test-rmse:1.204639+0.286839
## [66] train-rmse:0.100887+0.003376 test-rmse:1.204373+0.287518
## [67] train-rmse:0.098040+0.002970 test-rmse:1.204623+0.288178
## [68] train-rmse:0.094810+0.002770 test-rmse:1.204315+0.288833
## [69] train-rmse:0.091120+0.002507 test-rmse:1.202912+0.290466
## [70] train-rmse:0.087913+0.002859 test-rmse:1.201811+0.291413
## [71] train-rmse:0.083944+0.002592 test-rmse:1.201384+0.291418
## [72] train-rmse:0.081299+0.001777 test-rmse:1.201760+0.291239
## [73] train-rmse:0.078261+0.001816 test-rmse:1.200786+0.291308
## [74] train-rmse:0.076111+0.001887 test-rmse:1.201107+0.291895
## [75] train-rmse:0.074090+0.001665 test-rmse:1.201224+0.292527
## [76] train-rmse:0.071619+0.001766 test-rmse:1.200457+0.292954
## [77] train-rmse:0.068765+0.002742 test-rmse:1.199530+0.291609
## [78] train-rmse:0.066801+0.002219 test-rmse:1.199309+0.292260
## [79] train-rmse:0.064628+0.002613 test-rmse:1.199070+0.291669
## [80] train-rmse:0.063381+0.002814 test-rmse:1.198980+0.291840
## [81] train-rmse:0.061650+0.003009 test-rmse:1.198044+0.291472
## [82] train-rmse:0.059736+0.003080 test-rmse:1.197776+0.292030
## [83] train-rmse:0.057668+0.003505 test-rmse:1.197602+0.292721
## [84] train-rmse:0.055349+0.003316 test-rmse:1.197324+0.292603
## [85] train-rmse:0.053729+0.003081 test-rmse:1.196609+0.293026
## [86] train-rmse:0.051757+0.002888 test-rmse:1.195900+0.293886
## [87] train-rmse:0.050013+0.002264 test-rmse:1.196177+0.294180
## [88] train-rmse:0.048438+0.001864 test-rmse:1.195801+0.293793
## [89] train-rmse:0.047127+0.001545 test-rmse:1.195283+0.294293
## [90] train-rmse:0.045803+0.001850 test-rmse:1.194782+0.293657
## [91] train-rmse:0.044542+0.001684 test-rmse:1.194511+0.293757
## [92] train-rmse:0.043284+0.001798 test-rmse:1.195038+0.294190
## [93] train-rmse:0.041754+0.001827 test-rmse:1.194765+0.294366
## [94] train-rmse:0.040481+0.002196 test-rmse:1.194929+0.295001
## [95] train-rmse:0.039067+0.001919 test-rmse:1.194975+0.294901
## [96] train-rmse:0.037794+0.001662 test-rmse:1.194932+0.295046
## [97] train-rmse:0.037257+0.001768 test-rmse:1.194504+0.295168
## [98] train-rmse:0.036101+0.001745 test-rmse:1.194326+0.294709
## [99] train-rmse:0.035195+0.001790 test-rmse:1.194730+0.294406
## [100] train-rmse:0.034098+0.002305 test-rmse:1.194553+0.294455
## [101] train-rmse:0.033478+0.002346 test-rmse:1.194526+0.294675
## [102] train-rmse:0.032388+0.002118 test-rmse:1.194214+0.294858
## [103] train-rmse:0.031116+0.002043 test-rmse:1.193937+0.295179
## [104] train-rmse:0.030182+0.002100 test-rmse:1.193694+0.295171
## [105] train-rmse:0.029265+0.002164 test-rmse:1.193883+0.295403
## [106] train-rmse:0.028543+0.002412 test-rmse:1.193754+0.295065
## [107] train-rmse:0.027653+0.002382 test-rmse:1.193381+0.295083
## [108] train-rmse:0.026999+0.002347 test-rmse:1.193263+0.295138
## [109] train-rmse:0.026370+0.002520 test-rmse:1.192992+0.295235
## [110] train-rmse:0.025278+0.002512 test-rmse:1.192866+0.294960
## [111] train-rmse:0.024683+0.002577 test-rmse:1.192803+0.295049
## [112] train-rmse:0.023962+0.002591 test-rmse:1.192468+0.295257
## [113] train-rmse:0.023392+0.002452 test-rmse:1.192356+0.295006
## [114] train-rmse:0.022649+0.002536 test-rmse:1.192241+0.294989
## [115] train-rmse:0.022034+0.002570 test-rmse:1.192144+0.295063
## [116] train-rmse:0.021688+0.002592 test-rmse:1.192053+0.295117
## [117] train-rmse:0.021085+0.002382 test-rmse:1.192036+0.295031
## [118] train-rmse:0.020638+0.002387 test-rmse:1.191930+0.294967
## [119] train-rmse:0.019975+0.002222 test-rmse:1.191881+0.294965
## [120] train-rmse:0.019189+0.002044 test-rmse:1.191919+0.295049
## [121] train-rmse:0.018495+0.001839 test-rmse:1.192019+0.295220
## [122] train-rmse:0.017958+0.001651 test-rmse:1.191993+0.295089
## [123] train-rmse:0.017505+0.001682 test-rmse:1.191845+0.295102
## [124] train-rmse:0.017058+0.001545 test-rmse:1.191727+0.295192
## [125] train-rmse:0.016562+0.001601 test-rmse:1.191545+0.295048
## [126] train-rmse:0.016121+0.001643 test-rmse:1.191481+0.295059
## [127] train-rmse:0.015502+0.001351 test-rmse:1.191499+0.295154
## [128] train-rmse:0.015171+0.001343 test-rmse:1.191459+0.295193
## [129] train-rmse:0.014829+0.001366 test-rmse:1.191541+0.295201
## [130] train-rmse:0.014328+0.001252 test-rmse:1.191587+0.295103
## [131] train-rmse:0.013926+0.001310 test-rmse:1.191333+0.295225
## [132] train-rmse:0.013608+0.001258 test-rmse:1.191292+0.295175
## [133] train-rmse:0.013304+0.001233 test-rmse:1.191195+0.295293
## [134] train-rmse:0.012907+0.001258 test-rmse:1.191240+0.295311
## [135] train-rmse:0.012626+0.001365 test-rmse:1.191268+0.295320
## [136] train-rmse:0.012369+0.001309 test-rmse:1.191224+0.295343
## [137] train-rmse:0.011993+0.001175 test-rmse:1.191178+0.295457
## [138] train-rmse:0.011530+0.001187 test-rmse:1.191095+0.295398
## [139] train-rmse:0.011189+0.001198 test-rmse:1.191048+0.295387
## [140] train-rmse:0.010945+0.001164 test-rmse:1.191063+0.295362
## [141] train-rmse:0.010662+0.001209 test-rmse:1.191092+0.295375
## [142] train-rmse:0.010347+0.001169 test-rmse:1.191083+0.295306
## [143] train-rmse:0.010013+0.001139 test-rmse:1.191053+0.295268
## [144] train-rmse:0.009868+0.001141 test-rmse:1.191070+0.295331
## [145] train-rmse:0.009501+0.001092 test-rmse:1.190953+0.295247
## [146] train-rmse:0.009163+0.001118 test-rmse:1.190895+0.295264
## [147] train-rmse:0.008913+0.001004 test-rmse:1.190819+0.295272
## [148] train-rmse:0.008683+0.000998 test-rmse:1.190817+0.295237
## [149] train-rmse:0.008488+0.001012 test-rmse:1.190733+0.295257
## [150] train-rmse:0.008222+0.000924 test-rmse:1.190700+0.295169
## [151] train-rmse:0.008032+0.000909 test-rmse:1.190698+0.295159
## [152] train-rmse:0.007841+0.000878 test-rmse:1.190635+0.295160
## [153] train-rmse:0.007594+0.000831 test-rmse:1.190573+0.295166
## [154] train-rmse:0.007322+0.000756 test-rmse:1.190504+0.295171
## [155] train-rmse:0.007112+0.000694 test-rmse:1.190443+0.295184
## [156] train-rmse:0.006904+0.000669 test-rmse:1.190407+0.295210
## [157] train-rmse:0.006734+0.000637 test-rmse:1.190370+0.295230
## [158] train-rmse:0.006545+0.000610 test-rmse:1.190343+0.295249
## [159] train-rmse:0.006376+0.000552 test-rmse:1.190342+0.295274
## [160] train-rmse:0.006196+0.000500 test-rmse:1.190368+0.295265
## [161] train-rmse:0.005963+0.000443 test-rmse:1.190325+0.295265
## [162] train-rmse:0.005774+0.000395 test-rmse:1.190275+0.295265
## [163] train-rmse:0.005672+0.000385 test-rmse:1.190257+0.295254
## [164] train-rmse:0.005559+0.000392 test-rmse:1.190228+0.295251
## [165] train-rmse:0.005422+0.000373 test-rmse:1.190240+0.295307
## [166] train-rmse:0.005311+0.000340 test-rmse:1.190196+0.295269
## [167] train-rmse:0.005142+0.000373 test-rmse:1.190184+0.295258
## [168] train-rmse:0.005001+0.000318 test-rmse:1.190202+0.295297
## [169] train-rmse:0.004842+0.000298 test-rmse:1.190189+0.295275
## [170] train-rmse:0.004714+0.000283 test-rmse:1.190189+0.295257
## [171] train-rmse:0.004576+0.000287 test-rmse:1.190121+0.295242
## [172] train-rmse:0.004441+0.000261 test-rmse:1.190163+0.295253
## [173] train-rmse:0.004274+0.000252 test-rmse:1.190148+0.295251
## [174] train-rmse:0.004155+0.000222 test-rmse:1.190135+0.295244
## [175] train-rmse:0.004047+0.000195 test-rmse:1.190116+0.295238
## [176] train-rmse:0.003927+0.000167 test-rmse:1.190084+0.295269
## [177] train-rmse:0.003838+0.000189 test-rmse:1.190071+0.295289
## [178] train-rmse:0.003753+0.000204 test-rmse:1.190040+0.295299
## [179] train-rmse:0.003652+0.000171 test-rmse:1.189996+0.295278
## [180] train-rmse:0.003545+0.000154 test-rmse:1.189974+0.295338
## [181] train-rmse:0.003428+0.000151 test-rmse:1.189992+0.295342
## [182] train-rmse:0.003314+0.000157 test-rmse:1.190007+0.295362
## [183] train-rmse:0.003195+0.000186 test-rmse:1.189964+0.295395
## [184] train-rmse:0.003122+0.000184 test-rmse:1.189938+0.295411
## [185] train-rmse:0.003046+0.000197 test-rmse:1.189964+0.295460
## [186] train-rmse:0.002959+0.000212 test-rmse:1.189963+0.295452
## [187] train-rmse:0.002878+0.000222 test-rmse:1.189955+0.295451
## [188] train-rmse:0.002796+0.000241 test-rmse:1.189976+0.295470
## [189] train-rmse:0.002728+0.000230 test-rmse:1.189968+0.295486
## [190] train-rmse:0.002668+0.000224 test-rmse:1.189933+0.295507
## [191] train-rmse:0.002579+0.000218 test-rmse:1.189926+0.295481
## [192] train-rmse:0.002492+0.000199 test-rmse:1.189924+0.295490
## [193] train-rmse:0.002427+0.000197 test-rmse:1.189898+0.295494
## [194] train-rmse:0.002330+0.000185 test-rmse:1.189896+0.295496
## [195] train-rmse:0.002274+0.000170 test-rmse:1.189889+0.295492
## [196] train-rmse:0.002211+0.000168 test-rmse:1.189892+0.295493
## [197] train-rmse:0.002159+0.000182 test-rmse:1.189879+0.295486
## [198] train-rmse:0.002104+0.000183 test-rmse:1.189866+0.295498
## [199] train-rmse:0.002033+0.000168 test-rmse:1.189878+0.295515
## [200] train-rmse:0.001977+0.000166 test-rmse:1.189868+0.295510
## [201] train-rmse:0.001901+0.000163 test-rmse:1.189851+0.295491
## [202] train-rmse:0.001867+0.000142 test-rmse:1.189846+0.295486
## [203] train-rmse:0.001816+0.000138 test-rmse:1.189843+0.295493
## [204] train-rmse:0.001774+0.000122 test-rmse:1.189830+0.295486
## [205] train-rmse:0.001736+0.000111 test-rmse:1.189813+0.295494
## [206] train-rmse:0.001701+0.000103 test-rmse:1.189805+0.295489
## [207] train-rmse:0.001671+0.000108 test-rmse:1.189791+0.295488
## [208] train-rmse:0.001651+0.000122 test-rmse:1.189791+0.295479
## [209] train-rmse:0.001627+0.000122 test-rmse:1.189785+0.295478
## [210] train-rmse:0.001626+0.000121 test-rmse:1.189786+0.295478
## [211] train-rmse:0.001626+0.000121 test-rmse:1.189786+0.295478
## [212] train-rmse:0.001626+0.000121 test-rmse:1.189786+0.295478
## [213] train-rmse:0.001626+0.000121 test-rmse:1.189786+0.295478
## [214] train-rmse:0.001626+0.000121 test-rmse:1.189786+0.295478
## [215] train-rmse:0.001626+0.000121 test-rmse:1.189786+0.295478
## Stopping. Best iteration:
## [209] train-rmse:0.001627+0.000122 test-rmse:1.189785+0.295478
##
## 110
## [1] train-rmse:64.867792+0.080067 test-rmse:64.867433+0.531460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:39.277875+0.064950 test-rmse:39.275826+0.593398
## [3] train-rmse:24.039232+0.061294 test-rmse:24.078998+0.616566
## [4] train-rmse:14.906316+0.043679 test-rmse:14.968624+0.565349
## [5] train-rmse:9.408744+0.037946 test-rmse:9.503911+0.497594
## [6] train-rmse:6.114302+0.041175 test-rmse:6.313366+0.523175
## [7] train-rmse:4.120060+0.068756 test-rmse:4.297438+0.466152
## [8] train-rmse:2.916072+0.067775 test-rmse:3.175216+0.401677
## [9] train-rmse:2.163388+0.048109 test-rmse:2.555630+0.365690
## [10] train-rmse:1.720976+0.061494 test-rmse:2.239529+0.345444
## [11] train-rmse:1.434498+0.042978 test-rmse:2.046856+0.314088
## [12] train-rmse:1.247738+0.051215 test-rmse:1.916630+0.323979
## [13] train-rmse:1.107106+0.045942 test-rmse:1.779170+0.328181
## [14] train-rmse:0.996145+0.041306 test-rmse:1.691951+0.331178
## [15] train-rmse:0.893961+0.043701 test-rmse:1.642852+0.320894
## [16] train-rmse:0.795788+0.029210 test-rmse:1.583221+0.337898
## [17] train-rmse:0.728358+0.024699 test-rmse:1.537897+0.351975
## [18] train-rmse:0.671679+0.026513 test-rmse:1.495804+0.358785
## [19] train-rmse:0.617855+0.025435 test-rmse:1.445197+0.355287
## [20] train-rmse:0.568104+0.030048 test-rmse:1.414507+0.343285
## [21] train-rmse:0.531293+0.031500 test-rmse:1.403272+0.337897
## [22] train-rmse:0.496716+0.031780 test-rmse:1.382611+0.339950
## [23] train-rmse:0.462556+0.025947 test-rmse:1.358410+0.340186
## [24] train-rmse:0.433945+0.027059 test-rmse:1.352237+0.349172
## [25] train-rmse:0.407775+0.025506 test-rmse:1.343770+0.346908
## [26] train-rmse:0.381190+0.024125 test-rmse:1.339908+0.350400
## [27] train-rmse:0.355796+0.021432 test-rmse:1.329302+0.351114
## [28] train-rmse:0.335426+0.021789 test-rmse:1.319295+0.356053
## [29] train-rmse:0.318631+0.022186 test-rmse:1.318217+0.357312
## [30] train-rmse:0.301595+0.026768 test-rmse:1.313208+0.360747
## [31] train-rmse:0.285494+0.022709 test-rmse:1.308027+0.362710
## [32] train-rmse:0.269113+0.026491 test-rmse:1.303068+0.362201
## [33] train-rmse:0.256049+0.024763 test-rmse:1.297473+0.361499
## [34] train-rmse:0.241222+0.023822 test-rmse:1.294650+0.361895
## [35] train-rmse:0.224931+0.025223 test-rmse:1.290590+0.361486
## [36] train-rmse:0.215628+0.025853 test-rmse:1.289315+0.361389
## [37] train-rmse:0.205925+0.026325 test-rmse:1.284954+0.357861
## [38] train-rmse:0.198239+0.025616 test-rmse:1.283344+0.355536
## [39] train-rmse:0.187723+0.024424 test-rmse:1.278948+0.357072
## [40] train-rmse:0.178280+0.023482 test-rmse:1.278416+0.358539
## [41] train-rmse:0.167963+0.020466 test-rmse:1.276572+0.358901
## [42] train-rmse:0.158439+0.020827 test-rmse:1.279299+0.358290
## [43] train-rmse:0.151789+0.018821 test-rmse:1.278816+0.358987
## [44] train-rmse:0.142304+0.015956 test-rmse:1.277201+0.360760
## [45] train-rmse:0.137428+0.014049 test-rmse:1.277107+0.360313
## [46] train-rmse:0.133437+0.014328 test-rmse:1.276462+0.361107
## [47] train-rmse:0.125656+0.014470 test-rmse:1.275085+0.360651
## [48] train-rmse:0.120178+0.013508 test-rmse:1.274460+0.360157
## [49] train-rmse:0.115330+0.014130 test-rmse:1.274326+0.361265
## [50] train-rmse:0.109771+0.012429 test-rmse:1.274498+0.363391
## [51] train-rmse:0.103358+0.010428 test-rmse:1.273623+0.362359
## [52] train-rmse:0.099473+0.010416 test-rmse:1.273706+0.363140
## [53] train-rmse:0.094620+0.009602 test-rmse:1.273833+0.362128
## [54] train-rmse:0.090404+0.009942 test-rmse:1.272308+0.362783
## [55] train-rmse:0.085848+0.007001 test-rmse:1.271829+0.362459
## [56] train-rmse:0.081805+0.007114 test-rmse:1.271006+0.362440
## [57] train-rmse:0.078506+0.006183 test-rmse:1.270602+0.361838
## [58] train-rmse:0.072924+0.004751 test-rmse:1.268895+0.361652
## [59] train-rmse:0.069950+0.005531 test-rmse:1.268629+0.361406
## [60] train-rmse:0.067669+0.005881 test-rmse:1.268266+0.362112
## [61] train-rmse:0.064861+0.005757 test-rmse:1.267951+0.362691
## [62] train-rmse:0.062364+0.005999 test-rmse:1.266485+0.361749
## [63] train-rmse:0.059333+0.004719 test-rmse:1.265694+0.362339
## [64] train-rmse:0.056856+0.003990 test-rmse:1.265104+0.362523
## [65] train-rmse:0.055154+0.003750 test-rmse:1.265321+0.362335
## [66] train-rmse:0.052685+0.004034 test-rmse:1.265001+0.362810
## [67] train-rmse:0.049099+0.004127 test-rmse:1.264988+0.361937
## [68] train-rmse:0.047753+0.004442 test-rmse:1.265071+0.361741
## [69] train-rmse:0.045969+0.003952 test-rmse:1.264366+0.361498
## [70] train-rmse:0.043320+0.003718 test-rmse:1.264087+0.361596
## [71] train-rmse:0.041149+0.003671 test-rmse:1.264359+0.362045
## [72] train-rmse:0.038625+0.003303 test-rmse:1.264694+0.361894
## [73] train-rmse:0.037146+0.002762 test-rmse:1.264331+0.361929
## [74] train-rmse:0.035469+0.002726 test-rmse:1.263847+0.362182
## [75] train-rmse:0.034111+0.002831 test-rmse:1.263644+0.362003
## [76] train-rmse:0.032223+0.002666 test-rmse:1.263469+0.362261
## [77] train-rmse:0.031337+0.002558 test-rmse:1.263280+0.362175
## [78] train-rmse:0.030247+0.002436 test-rmse:1.263294+0.362198
## [79] train-rmse:0.028894+0.001909 test-rmse:1.263242+0.362133
## [80] train-rmse:0.027545+0.001810 test-rmse:1.263304+0.362151
## [81] train-rmse:0.026642+0.001778 test-rmse:1.263350+0.362040
## [82] train-rmse:0.025449+0.001709 test-rmse:1.263631+0.361975
## [83] train-rmse:0.024161+0.001652 test-rmse:1.263388+0.361969
## [84] train-rmse:0.023141+0.001569 test-rmse:1.263263+0.362086
## [85] train-rmse:0.022427+0.001693 test-rmse:1.263198+0.361975
## [86] train-rmse:0.021447+0.001552 test-rmse:1.263140+0.361926
## [87] train-rmse:0.020682+0.001500 test-rmse:1.263239+0.361729
## [88] train-rmse:0.020180+0.001430 test-rmse:1.263133+0.361572
## [89] train-rmse:0.019531+0.001459 test-rmse:1.263067+0.361688
## [90] train-rmse:0.018437+0.001069 test-rmse:1.262939+0.361606
## [91] train-rmse:0.017826+0.001122 test-rmse:1.262715+0.361632
## [92] train-rmse:0.017319+0.001089 test-rmse:1.262604+0.361469
## [93] train-rmse:0.016785+0.000996 test-rmse:1.262788+0.361380
## [94] train-rmse:0.016174+0.001010 test-rmse:1.262896+0.361420
## [95] train-rmse:0.015624+0.001084 test-rmse:1.262808+0.361456
## [96] train-rmse:0.014997+0.000993 test-rmse:1.262765+0.361433
## [97] train-rmse:0.014571+0.001189 test-rmse:1.262603+0.361493
## [98] train-rmse:0.013935+0.001193 test-rmse:1.262540+0.361566
## [99] train-rmse:0.013403+0.001226 test-rmse:1.262627+0.361592
## [100] train-rmse:0.012730+0.001132 test-rmse:1.262731+0.361883
## [101] train-rmse:0.012311+0.001158 test-rmse:1.262735+0.361814
## [102] train-rmse:0.011872+0.001149 test-rmse:1.262713+0.361848
## [103] train-rmse:0.011338+0.000968 test-rmse:1.262758+0.361726
## [104] train-rmse:0.010755+0.000794 test-rmse:1.262560+0.361517
## Stopping. Best iteration:
## [98] train-rmse:0.013935+0.001193 test-rmse:1.262540+0.361566
##
## 111
## [1] train-rmse:64.867792+0.080067 test-rmse:64.867433+0.531460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:39.277875+0.064950 test-rmse:39.275826+0.593398
## [3] train-rmse:24.039232+0.061294 test-rmse:24.078998+0.616566
## [4] train-rmse:14.906316+0.043679 test-rmse:14.968624+0.565349
## [5] train-rmse:9.393760+0.037744 test-rmse:9.489749+0.503628
## [6] train-rmse:6.065604+0.028653 test-rmse:6.233963+0.574930
## [7] train-rmse:4.041850+0.045877 test-rmse:4.292720+0.485391
## [8] train-rmse:2.800419+0.047786 test-rmse:3.096050+0.416723
## [9] train-rmse:2.053078+0.053332 test-rmse:2.451604+0.366310
## [10] train-rmse:1.568463+0.055516 test-rmse:2.070640+0.308184
## [11] train-rmse:1.269586+0.047115 test-rmse:1.855932+0.224267
## [12] train-rmse:1.066738+0.048317 test-rmse:1.732474+0.229073
## [13] train-rmse:0.921422+0.047114 test-rmse:1.622772+0.233407
## [14] train-rmse:0.806851+0.036733 test-rmse:1.523892+0.243518
## [15] train-rmse:0.717689+0.029433 test-rmse:1.466992+0.264744
## [16] train-rmse:0.642036+0.021275 test-rmse:1.434823+0.252373
## [17] train-rmse:0.576334+0.016490 test-rmse:1.398112+0.254319
## [18] train-rmse:0.522952+0.020219 test-rmse:1.363018+0.250961
## [19] train-rmse:0.476009+0.012514 test-rmse:1.348078+0.259395
## [20] train-rmse:0.437169+0.014127 test-rmse:1.324702+0.268146
## [21] train-rmse:0.398147+0.015104 test-rmse:1.305851+0.272748
## [22] train-rmse:0.363838+0.016768 test-rmse:1.292033+0.277472
## [23] train-rmse:0.331926+0.017553 test-rmse:1.290529+0.279322
## [24] train-rmse:0.309262+0.013717 test-rmse:1.277441+0.279079
## [25] train-rmse:0.285723+0.013000 test-rmse:1.270923+0.283359
## [26] train-rmse:0.264567+0.011334 test-rmse:1.262487+0.279957
## [27] train-rmse:0.247293+0.012219 test-rmse:1.256551+0.284027
## [28] train-rmse:0.228135+0.011204 test-rmse:1.252254+0.284660
## [29] train-rmse:0.211450+0.014990 test-rmse:1.252825+0.288152
## [30] train-rmse:0.198801+0.012843 test-rmse:1.252804+0.290289
## [31] train-rmse:0.185150+0.012068 test-rmse:1.252841+0.294244
## [32] train-rmse:0.173820+0.013963 test-rmse:1.250300+0.295648
## [33] train-rmse:0.163712+0.014710 test-rmse:1.250261+0.298091
## [34] train-rmse:0.154418+0.013626 test-rmse:1.249280+0.295069
## [35] train-rmse:0.144651+0.011880 test-rmse:1.246140+0.296258
## [36] train-rmse:0.136215+0.009682 test-rmse:1.246345+0.297698
## [37] train-rmse:0.126212+0.009770 test-rmse:1.245204+0.299121
## [38] train-rmse:0.120765+0.008751 test-rmse:1.244782+0.299102
## [39] train-rmse:0.111574+0.009569 test-rmse:1.245854+0.299068
## [40] train-rmse:0.104586+0.010025 test-rmse:1.246344+0.297289
## [41] train-rmse:0.098190+0.007917 test-rmse:1.242769+0.294729
## [42] train-rmse:0.093203+0.008632 test-rmse:1.242001+0.294841
## [43] train-rmse:0.086070+0.006501 test-rmse:1.240597+0.294813
## [44] train-rmse:0.080583+0.007145 test-rmse:1.239636+0.295745
## [45] train-rmse:0.076682+0.006792 test-rmse:1.240164+0.295794
## [46] train-rmse:0.071416+0.005420 test-rmse:1.239958+0.295703
## [47] train-rmse:0.065990+0.004547 test-rmse:1.239986+0.295278
## [48] train-rmse:0.062526+0.005102 test-rmse:1.239600+0.295873
## [49] train-rmse:0.059481+0.005030 test-rmse:1.239704+0.295860
## [50] train-rmse:0.055603+0.004290 test-rmse:1.238970+0.296317
## [51] train-rmse:0.052226+0.004860 test-rmse:1.238487+0.296450
## [52] train-rmse:0.049835+0.005027 test-rmse:1.238857+0.296105
## [53] train-rmse:0.046291+0.004056 test-rmse:1.238461+0.296084
## [54] train-rmse:0.044051+0.003910 test-rmse:1.237281+0.295283
## [55] train-rmse:0.041464+0.003142 test-rmse:1.237031+0.295199
## [56] train-rmse:0.038910+0.003027 test-rmse:1.236333+0.294410
## [57] train-rmse:0.036466+0.002500 test-rmse:1.236184+0.294313
## [58] train-rmse:0.034382+0.002354 test-rmse:1.235974+0.294831
## [59] train-rmse:0.032243+0.002827 test-rmse:1.235994+0.294803
## [60] train-rmse:0.030408+0.002457 test-rmse:1.236125+0.295025
## [61] train-rmse:0.028746+0.002181 test-rmse:1.235873+0.294863
## [62] train-rmse:0.027441+0.002310 test-rmse:1.235799+0.294992
## [63] train-rmse:0.025867+0.001965 test-rmse:1.235970+0.294971
## [64] train-rmse:0.024563+0.001598 test-rmse:1.235951+0.295021
## [65] train-rmse:0.023535+0.001540 test-rmse:1.235269+0.294743
## [66] train-rmse:0.021795+0.001636 test-rmse:1.235056+0.294873
## [67] train-rmse:0.020539+0.001494 test-rmse:1.234896+0.295020
## [68] train-rmse:0.019561+0.001706 test-rmse:1.234769+0.294916
## [69] train-rmse:0.018484+0.001349 test-rmse:1.234693+0.294964
## [70] train-rmse:0.017554+0.001222 test-rmse:1.234802+0.294888
## [71] train-rmse:0.016601+0.001070 test-rmse:1.234763+0.294890
## [72] train-rmse:0.015538+0.000705 test-rmse:1.234649+0.294873
## [73] train-rmse:0.014510+0.000676 test-rmse:1.234578+0.295038
## [74] train-rmse:0.013756+0.000903 test-rmse:1.234583+0.295094
## [75] train-rmse:0.013086+0.000961 test-rmse:1.234709+0.294971
## [76] train-rmse:0.012326+0.000846 test-rmse:1.234714+0.294866
## [77] train-rmse:0.011756+0.000812 test-rmse:1.234826+0.294941
## [78] train-rmse:0.011128+0.000923 test-rmse:1.234645+0.295058
## [79] train-rmse:0.010439+0.000920 test-rmse:1.234521+0.295092
## [80] train-rmse:0.009951+0.000891 test-rmse:1.234320+0.294983
## [81] train-rmse:0.009429+0.000868 test-rmse:1.234322+0.294769
## [82] train-rmse:0.008980+0.000805 test-rmse:1.234334+0.294731
## [83] train-rmse:0.008511+0.000813 test-rmse:1.234219+0.294592
## [84] train-rmse:0.007994+0.000734 test-rmse:1.234121+0.294470
## [85] train-rmse:0.007413+0.000646 test-rmse:1.234082+0.294439
## [86] train-rmse:0.007017+0.000634 test-rmse:1.233969+0.294446
## [87] train-rmse:0.006614+0.000622 test-rmse:1.233990+0.294354
## [88] train-rmse:0.006243+0.000645 test-rmse:1.234021+0.294329
## [89] train-rmse:0.005936+0.000763 test-rmse:1.234022+0.294319
## [90] train-rmse:0.005581+0.000750 test-rmse:1.233971+0.294215
## [91] train-rmse:0.005319+0.000671 test-rmse:1.233974+0.294241
## [92] train-rmse:0.005092+0.000686 test-rmse:1.233930+0.294296
## [93] train-rmse:0.004796+0.000649 test-rmse:1.233872+0.294298
## [94] train-rmse:0.004522+0.000638 test-rmse:1.233875+0.294326
## [95] train-rmse:0.004318+0.000661 test-rmse:1.233927+0.294412
## [96] train-rmse:0.004114+0.000638 test-rmse:1.233949+0.294455
## [97] train-rmse:0.003873+0.000577 test-rmse:1.233965+0.294448
## [98] train-rmse:0.003677+0.000582 test-rmse:1.233956+0.294458
## [99] train-rmse:0.003508+0.000582 test-rmse:1.233990+0.294427
## Stopping. Best iteration:
## [93] train-rmse:0.004796+0.000649 test-rmse:1.233872+0.294298
##
## 112## max_depth eta
## 106 1 0.4## [1] train-rmse:7.594751+0.162507 test-rmse:7.574602+0.679937
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.074706+0.151635 test-rmse:7.068813+0.694239
## [3] train-rmse:6.616377+0.141892 test-rmse:6.625057+0.684628
## [4] train-rmse:6.215470+0.134075 test-rmse:6.215961+0.676572
## [5] train-rmse:5.864415+0.127690 test-rmse:5.899676+0.662675
## [6] train-rmse:5.558531+0.123021 test-rmse:5.588962+0.662945
## [7] train-rmse:5.291803+0.118678 test-rmse:5.325339+0.641245
## [8] train-rmse:5.060391+0.116615 test-rmse:5.099139+0.624768
## [9] train-rmse:4.858398+0.113990 test-rmse:4.923686+0.596696
## [10] train-rmse:4.683789+0.112245 test-rmse:4.744346+0.593690
## [11] train-rmse:4.532123+0.112080 test-rmse:4.604116+0.569895
## [12] train-rmse:4.398900+0.111991 test-rmse:4.500157+0.560603
## [13] train-rmse:4.283302+0.111798 test-rmse:4.379400+0.541457
## [14] train-rmse:4.182020+0.111808 test-rmse:4.272471+0.537265
## [15] train-rmse:4.092944+0.112648 test-rmse:4.193918+0.514793
## [16] train-rmse:4.013913+0.113025 test-rmse:4.118055+0.514139
## [17] train-rmse:3.943344+0.113449 test-rmse:4.056530+0.501083
## [18] train-rmse:3.880692+0.113651 test-rmse:3.988210+0.486912
## [19] train-rmse:3.824077+0.114399 test-rmse:3.937553+0.475025
## [20] train-rmse:3.772590+0.115445 test-rmse:3.880207+0.468262
## [21] train-rmse:3.725399+0.116032 test-rmse:3.851509+0.458680
## [22] train-rmse:3.682291+0.116767 test-rmse:3.807391+0.453795
## [23] train-rmse:3.642492+0.117248 test-rmse:3.758773+0.445939
## [24] train-rmse:3.605473+0.117993 test-rmse:3.728706+0.440683
## [25] train-rmse:3.570877+0.118670 test-rmse:3.690555+0.438305
## [26] train-rmse:3.538349+0.119030 test-rmse:3.677488+0.439029
## [27] train-rmse:3.507910+0.119397 test-rmse:3.634896+0.432734
## [28] train-rmse:3.478659+0.119884 test-rmse:3.599907+0.425378
## [29] train-rmse:3.451022+0.120611 test-rmse:3.580462+0.423059
## [30] train-rmse:3.424621+0.121167 test-rmse:3.556834+0.421725
## [31] train-rmse:3.399217+0.121547 test-rmse:3.546986+0.424192
## [32] train-rmse:3.375033+0.121717 test-rmse:3.510791+0.423404
## [33] train-rmse:3.351747+0.122022 test-rmse:3.478159+0.414596
## [34] train-rmse:3.329157+0.122362 test-rmse:3.463438+0.416094
## [35] train-rmse:3.307729+0.122797 test-rmse:3.446472+0.419127
## [36] train-rmse:3.286836+0.122957 test-rmse:3.433917+0.428446
## [37] train-rmse:3.266662+0.123230 test-rmse:3.411063+0.427595
## [38] train-rmse:3.246942+0.123471 test-rmse:3.387420+0.419405
## [39] train-rmse:3.227600+0.123901 test-rmse:3.367834+0.415701
## [40] train-rmse:3.209005+0.124801 test-rmse:3.345942+0.419877
## [41] train-rmse:3.191214+0.124983 test-rmse:3.334720+0.427125
## [42] train-rmse:3.173946+0.125201 test-rmse:3.319415+0.420448
## [43] train-rmse:3.157052+0.125401 test-rmse:3.304661+0.423112
## [44] train-rmse:3.140385+0.125754 test-rmse:3.286286+0.423359
## [45] train-rmse:3.124363+0.126519 test-rmse:3.269998+0.420903
## [46] train-rmse:3.108549+0.126898 test-rmse:3.254617+0.422672
## [47] train-rmse:3.093632+0.127160 test-rmse:3.234275+0.420079
## [48] train-rmse:3.079001+0.127415 test-rmse:3.219310+0.422442
## [49] train-rmse:3.064609+0.127596 test-rmse:3.210997+0.417011
## [50] train-rmse:3.050378+0.127893 test-rmse:3.206451+0.420323
## [51] train-rmse:3.036517+0.128411 test-rmse:3.184963+0.423739
## [52] train-rmse:3.022969+0.128702 test-rmse:3.178289+0.425482
## [53] train-rmse:3.010039+0.128806 test-rmse:3.160773+0.424899
## [54] train-rmse:2.997239+0.129031 test-rmse:3.147546+0.424599
## [55] train-rmse:2.984762+0.129276 test-rmse:3.139988+0.422296
## [56] train-rmse:2.972277+0.129553 test-rmse:3.134301+0.429365
## [57] train-rmse:2.960189+0.129965 test-rmse:3.123382+0.436935
## [58] train-rmse:2.948281+0.130414 test-rmse:3.113276+0.431771
## [59] train-rmse:2.936643+0.130790 test-rmse:3.104437+0.436939
## [60] train-rmse:2.925199+0.131046 test-rmse:3.090205+0.441038
## [61] train-rmse:2.914013+0.131324 test-rmse:3.082427+0.437950
## [62] train-rmse:2.902969+0.131635 test-rmse:3.074349+0.437583
## [63] train-rmse:2.892293+0.131899 test-rmse:3.065332+0.434036
## [64] train-rmse:2.881744+0.132182 test-rmse:3.060968+0.438481
## [65] train-rmse:2.871358+0.132468 test-rmse:3.053937+0.445188
## [66] train-rmse:2.861208+0.132914 test-rmse:3.042718+0.447851
## [67] train-rmse:2.851150+0.133263 test-rmse:3.029267+0.442005
## [68] train-rmse:2.841302+0.133594 test-rmse:3.021756+0.437342
## [69] train-rmse:2.831673+0.133845 test-rmse:3.013266+0.445319
## [70] train-rmse:2.822100+0.134085 test-rmse:3.008904+0.444193
## [71] train-rmse:2.812539+0.134335 test-rmse:2.995187+0.438204
## [72] train-rmse:2.803207+0.134746 test-rmse:2.993830+0.442354
## [73] train-rmse:2.793994+0.135150 test-rmse:2.980909+0.447019
## [74] train-rmse:2.784887+0.135511 test-rmse:2.978247+0.446850
## [75] train-rmse:2.775938+0.135869 test-rmse:2.970316+0.442954
## [76] train-rmse:2.767030+0.136128 test-rmse:2.969193+0.447959
## [77] train-rmse:2.758276+0.136452 test-rmse:2.958223+0.450009
## [78] train-rmse:2.749790+0.136536 test-rmse:2.946385+0.446117
## [79] train-rmse:2.741407+0.136661 test-rmse:2.943064+0.448836
## [80] train-rmse:2.733153+0.136904 test-rmse:2.933253+0.448567
## [81] train-rmse:2.724937+0.137046 test-rmse:2.921992+0.453082
## [82] train-rmse:2.716838+0.137280 test-rmse:2.917026+0.457132
## [83] train-rmse:2.708917+0.137534 test-rmse:2.917037+0.458851
## [84] train-rmse:2.701075+0.137713 test-rmse:2.908359+0.451897
## [85] train-rmse:2.693266+0.137853 test-rmse:2.902490+0.453970
## [86] train-rmse:2.685542+0.138091 test-rmse:2.889665+0.456332
## [87] train-rmse:2.677901+0.138297 test-rmse:2.881108+0.455436
## [88] train-rmse:2.670323+0.138500 test-rmse:2.882232+0.458891
## [89] train-rmse:2.662854+0.138646 test-rmse:2.878442+0.458937
## [90] train-rmse:2.655520+0.138732 test-rmse:2.873934+0.464930
## [91] train-rmse:2.648361+0.138743 test-rmse:2.870589+0.463750
## [92] train-rmse:2.641286+0.138752 test-rmse:2.862602+0.460981
## [93] train-rmse:2.634286+0.138817 test-rmse:2.854485+0.465540
## [94] train-rmse:2.627318+0.138916 test-rmse:2.849699+0.466119
## [95] train-rmse:2.620501+0.138959 test-rmse:2.845208+0.468145
## [96] train-rmse:2.613681+0.139054 test-rmse:2.837181+0.466116
## [97] train-rmse:2.607054+0.139133 test-rmse:2.830795+0.464956
## [98] train-rmse:2.600486+0.139191 test-rmse:2.827650+0.470673
## [99] train-rmse:2.593921+0.139259 test-rmse:2.826691+0.468871
## [100] train-rmse:2.587478+0.139393 test-rmse:2.820517+0.469842
## [101] train-rmse:2.581079+0.139469 test-rmse:2.810568+0.467906
## [102] train-rmse:2.574720+0.139569 test-rmse:2.806098+0.475399
## [103] train-rmse:2.568452+0.139621 test-rmse:2.805165+0.478250
## [104] train-rmse:2.562394+0.139665 test-rmse:2.800444+0.475620
## [105] train-rmse:2.556347+0.139660 test-rmse:2.796354+0.473366
## [106] train-rmse:2.550396+0.139669 test-rmse:2.789633+0.477287
## [107] train-rmse:2.544443+0.139651 test-rmse:2.782535+0.475373
## [108] train-rmse:2.538539+0.139679 test-rmse:2.773237+0.477368
## [109] train-rmse:2.532713+0.139713 test-rmse:2.767195+0.478153
## [110] train-rmse:2.526985+0.139733 test-rmse:2.761424+0.474869
## [111] train-rmse:2.521350+0.139819 test-rmse:2.760776+0.472684
## [112] train-rmse:2.515786+0.139883 test-rmse:2.758392+0.471543
## [113] train-rmse:2.510268+0.139952 test-rmse:2.756505+0.474136
## [114] train-rmse:2.504753+0.140037 test-rmse:2.753209+0.476723
## [115] train-rmse:2.499332+0.140154 test-rmse:2.749646+0.482538
## [116] train-rmse:2.493987+0.140205 test-rmse:2.743250+0.481584
## [117] train-rmse:2.488742+0.140191 test-rmse:2.739678+0.481409
## [118] train-rmse:2.483528+0.140221 test-rmse:2.733694+0.484746
## [119] train-rmse:2.478340+0.140285 test-rmse:2.728786+0.484421
## [120] train-rmse:2.473209+0.140338 test-rmse:2.721911+0.485187
## [121] train-rmse:2.468149+0.140385 test-rmse:2.719277+0.484457
## [122] train-rmse:2.463113+0.140457 test-rmse:2.710445+0.484098
## [123] train-rmse:2.458175+0.140482 test-rmse:2.706054+0.485678
## [124] train-rmse:2.453310+0.140577 test-rmse:2.702177+0.484587
## [125] train-rmse:2.448472+0.140629 test-rmse:2.698385+0.482874
## [126] train-rmse:2.443749+0.140666 test-rmse:2.697464+0.486101
## [127] train-rmse:2.439089+0.140699 test-rmse:2.696202+0.484027
## [128] train-rmse:2.434441+0.140763 test-rmse:2.693632+0.484227
## [129] train-rmse:2.429838+0.140792 test-rmse:2.686847+0.484311
## [130] train-rmse:2.425304+0.140857 test-rmse:2.681673+0.489589
## [131] train-rmse:2.420835+0.140879 test-rmse:2.677394+0.488036
## [132] train-rmse:2.416408+0.140910 test-rmse:2.669263+0.488462
## [133] train-rmse:2.412011+0.140975 test-rmse:2.663864+0.491607
## [134] train-rmse:2.407605+0.141050 test-rmse:2.662138+0.492165
## [135] train-rmse:2.403259+0.141170 test-rmse:2.657815+0.491460
## [136] train-rmse:2.398959+0.141277 test-rmse:2.656135+0.488917
## [137] train-rmse:2.394753+0.141392 test-rmse:2.651762+0.491378
## [138] train-rmse:2.390570+0.141450 test-rmse:2.649093+0.491725
## [139] train-rmse:2.386465+0.141533 test-rmse:2.646734+0.492137
## [140] train-rmse:2.382384+0.141578 test-rmse:2.640636+0.489559
## [141] train-rmse:2.378348+0.141625 test-rmse:2.639667+0.494177
## [142] train-rmse:2.374327+0.141673 test-rmse:2.635645+0.491534
## [143] train-rmse:2.370361+0.141695 test-rmse:2.632157+0.492591
## [144] train-rmse:2.366419+0.141703 test-rmse:2.628917+0.493543
## [145] train-rmse:2.362541+0.141767 test-rmse:2.622492+0.496465
## [146] train-rmse:2.358731+0.141845 test-rmse:2.618046+0.498112
## [147] train-rmse:2.354926+0.141932 test-rmse:2.618819+0.498301
## [148] train-rmse:2.351197+0.142044 test-rmse:2.613501+0.494368
## [149] train-rmse:2.347486+0.142151 test-rmse:2.611155+0.496345
## [150] train-rmse:2.343819+0.142237 test-rmse:2.608285+0.497346
## [151] train-rmse:2.340186+0.142352 test-rmse:2.605174+0.497013
## [152] train-rmse:2.336567+0.142457 test-rmse:2.601656+0.496149
## [153] train-rmse:2.332968+0.142572 test-rmse:2.601531+0.498429
## [154] train-rmse:2.329420+0.142671 test-rmse:2.597594+0.500669
## [155] train-rmse:2.325904+0.142759 test-rmse:2.591666+0.503392
## [156] train-rmse:2.322416+0.142842 test-rmse:2.590145+0.501000
## [157] train-rmse:2.318961+0.142910 test-rmse:2.587597+0.499546
## [158] train-rmse:2.315519+0.143009 test-rmse:2.587105+0.503801
## [159] train-rmse:2.312133+0.143094 test-rmse:2.583552+0.501095
## [160] train-rmse:2.308790+0.143190 test-rmse:2.582120+0.503082
## [161] train-rmse:2.305478+0.143333 test-rmse:2.577135+0.501730
## [162] train-rmse:2.302217+0.143460 test-rmse:2.571086+0.500407
## [163] train-rmse:2.299002+0.143584 test-rmse:2.570569+0.499831
## [164] train-rmse:2.295811+0.143688 test-rmse:2.569273+0.501763
## [165] train-rmse:2.292648+0.143794 test-rmse:2.565981+0.502637
## [166] train-rmse:2.289509+0.143905 test-rmse:2.562455+0.502151
## [167] train-rmse:2.286377+0.144021 test-rmse:2.561214+0.503462
## [168] train-rmse:2.283320+0.144106 test-rmse:2.557167+0.505384
## [169] train-rmse:2.280265+0.144208 test-rmse:2.555622+0.503650
## [170] train-rmse:2.277223+0.144309 test-rmse:2.553538+0.507236
## [171] train-rmse:2.274216+0.144385 test-rmse:2.550854+0.508089
## [172] train-rmse:2.271247+0.144455 test-rmse:2.547101+0.508504
## [173] train-rmse:2.268289+0.144541 test-rmse:2.544752+0.509417
## [174] train-rmse:2.265381+0.144656 test-rmse:2.545172+0.512660
## [175] train-rmse:2.262503+0.144750 test-rmse:2.541393+0.509931
## [176] train-rmse:2.259661+0.144852 test-rmse:2.539211+0.510107
## [177] train-rmse:2.256847+0.144936 test-rmse:2.538968+0.507789
## [178] train-rmse:2.254028+0.145027 test-rmse:2.534858+0.508344
## [179] train-rmse:2.251252+0.145117 test-rmse:2.532920+0.509023
## [180] train-rmse:2.248493+0.145221 test-rmse:2.527912+0.510286
## [181] train-rmse:2.245767+0.145321 test-rmse:2.525593+0.509833
## [182] train-rmse:2.243065+0.145431 test-rmse:2.526789+0.510148
## [183] train-rmse:2.240351+0.145534 test-rmse:2.520490+0.511490
## [184] train-rmse:2.237704+0.145636 test-rmse:2.518287+0.509801
## [185] train-rmse:2.235090+0.145731 test-rmse:2.515233+0.509220
## [186] train-rmse:2.232490+0.145819 test-rmse:2.514003+0.507870
## [187] train-rmse:2.229893+0.145882 test-rmse:2.512195+0.507556
## [188] train-rmse:2.227323+0.145971 test-rmse:2.509925+0.509133
## [189] train-rmse:2.224799+0.146056 test-rmse:2.510841+0.510887
## [190] train-rmse:2.222297+0.146153 test-rmse:2.508174+0.509253
## [191] train-rmse:2.219817+0.146250 test-rmse:2.504363+0.510354
## [192] train-rmse:2.217380+0.146344 test-rmse:2.503634+0.509596
## [193] train-rmse:2.214948+0.146439 test-rmse:2.504104+0.513923
## [194] train-rmse:2.212523+0.146524 test-rmse:2.503955+0.514488
## [195] train-rmse:2.210116+0.146622 test-rmse:2.503234+0.515521
## [196] train-rmse:2.207742+0.146723 test-rmse:2.498880+0.515496
## [197] train-rmse:2.205392+0.146800 test-rmse:2.496332+0.515791
## [198] train-rmse:2.203067+0.146874 test-rmse:2.497639+0.512529
## [199] train-rmse:2.200762+0.146943 test-rmse:2.493252+0.514322
## [200] train-rmse:2.198465+0.147019 test-rmse:2.491245+0.514439
## [201] train-rmse:2.196172+0.147105 test-rmse:2.491814+0.514814
## [202] train-rmse:2.193897+0.147187 test-rmse:2.489485+0.514925
## [203] train-rmse:2.191642+0.147269 test-rmse:2.486614+0.515073
## [204] train-rmse:2.189394+0.147350 test-rmse:2.481524+0.516876
## [205] train-rmse:2.187197+0.147438 test-rmse:2.480834+0.516373
## [206] train-rmse:2.185014+0.147523 test-rmse:2.477781+0.514872
## [207] train-rmse:2.182833+0.147593 test-rmse:2.474582+0.516981
## [208] train-rmse:2.180672+0.147666 test-rmse:2.472332+0.516554
## [209] train-rmse:2.178559+0.147762 test-rmse:2.472734+0.516290
## [210] train-rmse:2.176474+0.147847 test-rmse:2.472075+0.515790
## [211] train-rmse:2.174390+0.147919 test-rmse:2.471687+0.517710
## [212] train-rmse:2.172313+0.147998 test-rmse:2.469557+0.517401
## [213] train-rmse:2.170260+0.148086 test-rmse:2.465971+0.517558
## [214] train-rmse:2.168211+0.148183 test-rmse:2.466842+0.521510
## [215] train-rmse:2.166165+0.148271 test-rmse:2.465207+0.521426
## [216] train-rmse:2.164160+0.148331 test-rmse:2.464284+0.521531
## [217] train-rmse:2.162172+0.148397 test-rmse:2.463875+0.521384
## [218] train-rmse:2.160188+0.148466 test-rmse:2.463311+0.520685
## [219] train-rmse:2.158210+0.148522 test-rmse:2.460584+0.520530
## [220] train-rmse:2.156258+0.148577 test-rmse:2.458249+0.520604
## [221] train-rmse:2.154322+0.148644 test-rmse:2.456522+0.521154
## [222] train-rmse:2.152394+0.148726 test-rmse:2.453418+0.521350
## [223] train-rmse:2.150493+0.148828 test-rmse:2.453878+0.520339
## [224] train-rmse:2.148590+0.148933 test-rmse:2.452769+0.519998
## [225] train-rmse:2.146733+0.149030 test-rmse:2.449013+0.520759
## [226] train-rmse:2.144902+0.149117 test-rmse:2.446910+0.520529
## [227] train-rmse:2.143082+0.149212 test-rmse:2.447450+0.522024
## [228] train-rmse:2.141272+0.149298 test-rmse:2.446400+0.522096
## [229] train-rmse:2.139478+0.149382 test-rmse:2.443833+0.523373
## [230] train-rmse:2.137688+0.149455 test-rmse:2.443387+0.522901
## [231] train-rmse:2.135894+0.149539 test-rmse:2.443305+0.522766
## [232] train-rmse:2.134121+0.149622 test-rmse:2.441254+0.524395
## [233] train-rmse:2.132367+0.149715 test-rmse:2.440599+0.523361
## [234] train-rmse:2.130619+0.149789 test-rmse:2.439449+0.522366
## [235] train-rmse:2.128889+0.149859 test-rmse:2.439071+0.525082
## [236] train-rmse:2.127181+0.149932 test-rmse:2.438245+0.525790
## [237] train-rmse:2.125463+0.149996 test-rmse:2.436919+0.524541
## [238] train-rmse:2.123770+0.150076 test-rmse:2.437096+0.528337
## [239] train-rmse:2.122090+0.150157 test-rmse:2.434605+0.527688
## [240] train-rmse:2.120420+0.150236 test-rmse:2.431942+0.527756
## [241] train-rmse:2.118761+0.150298 test-rmse:2.430291+0.525351
## [242] train-rmse:2.117120+0.150352 test-rmse:2.429056+0.523924
## [243] train-rmse:2.115486+0.150431 test-rmse:2.428033+0.523674
## [244] train-rmse:2.113841+0.150502 test-rmse:2.428157+0.524530
## [245] train-rmse:2.112230+0.150583 test-rmse:2.427617+0.524018
## [246] train-rmse:2.110640+0.150662 test-rmse:2.425325+0.523970
## [247] train-rmse:2.109051+0.150749 test-rmse:2.423412+0.524148
## [248] train-rmse:2.107484+0.150834 test-rmse:2.421620+0.523511
## [249] train-rmse:2.105929+0.150929 test-rmse:2.421536+0.522507
## [250] train-rmse:2.104372+0.151027 test-rmse:2.417183+0.523789
## [251] train-rmse:2.102813+0.151129 test-rmse:2.415876+0.522897
## [252] train-rmse:2.101263+0.151209 test-rmse:2.414979+0.524563
## [253] train-rmse:2.099737+0.151293 test-rmse:2.414812+0.524216
## [254] train-rmse:2.098233+0.151381 test-rmse:2.414127+0.524363
## [255] train-rmse:2.096740+0.151445 test-rmse:2.413178+0.524042
## [256] train-rmse:2.095257+0.151525 test-rmse:2.412863+0.526959
## [257] train-rmse:2.093783+0.151610 test-rmse:2.413933+0.527386
## [258] train-rmse:2.092317+0.151694 test-rmse:2.413355+0.527732
## [259] train-rmse:2.090847+0.151759 test-rmse:2.414871+0.526098
## [260] train-rmse:2.089415+0.151843 test-rmse:2.413610+0.525535
## [261] train-rmse:2.087988+0.151925 test-rmse:2.412991+0.527058
## [262] train-rmse:2.086563+0.152008 test-rmse:2.413509+0.530232
## Stopping. Best iteration:
## [256] train-rmse:2.095257+0.151525 test-rmse:2.412863+0.526959
##
## 1
## [1] train-rmse:7.552356+0.159201 test-rmse:7.544257+0.665685
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.990795+0.146166 test-rmse:6.988471+0.637084
## [3] train-rmse:6.491905+0.138495 test-rmse:6.522744+0.623290
## [4] train-rmse:6.048545+0.131937 test-rmse:6.105588+0.618054
## [5] train-rmse:5.658707+0.121719 test-rmse:5.735159+0.574983
## [6] train-rmse:5.311330+0.115922 test-rmse:5.409366+0.547563
## [7] train-rmse:5.001410+0.109604 test-rmse:5.110005+0.557716
## [8] train-rmse:4.733937+0.103101 test-rmse:4.865190+0.515065
## [9] train-rmse:4.492127+0.101394 test-rmse:4.637609+0.505032
## [10] train-rmse:4.275476+0.095853 test-rmse:4.415768+0.499932
## [11] train-rmse:4.087049+0.094763 test-rmse:4.245308+0.467333
## [12] train-rmse:3.919655+0.088486 test-rmse:4.094864+0.470396
## [13] train-rmse:3.759496+0.086741 test-rmse:3.942435+0.475507
## [14] train-rmse:3.625916+0.087600 test-rmse:3.813630+0.460318
## [15] train-rmse:3.508055+0.088847 test-rmse:3.711606+0.427745
## [16] train-rmse:3.398520+0.087157 test-rmse:3.612967+0.439351
## [17] train-rmse:3.294607+0.086040 test-rmse:3.508233+0.440475
## [18] train-rmse:3.209198+0.087298 test-rmse:3.441939+0.419453
## [19] train-rmse:3.132264+0.083799 test-rmse:3.386426+0.433250
## [20] train-rmse:3.054394+0.083507 test-rmse:3.301097+0.436309
## [21] train-rmse:2.988382+0.083969 test-rmse:3.249074+0.434836
## [22] train-rmse:2.930294+0.086028 test-rmse:3.195242+0.450097
## [23] train-rmse:2.877055+0.088851 test-rmse:3.155353+0.440852
## [24] train-rmse:2.830423+0.086593 test-rmse:3.123364+0.448814
## [25] train-rmse:2.783620+0.087342 test-rmse:3.088300+0.451688
## [26] train-rmse:2.741758+0.090666 test-rmse:3.051897+0.444056
## [27] train-rmse:2.706682+0.090964 test-rmse:3.034555+0.446403
## [28] train-rmse:2.671426+0.091882 test-rmse:3.008900+0.447955
## [29] train-rmse:2.635601+0.089883 test-rmse:2.982479+0.457718
## [30] train-rmse:2.599731+0.081814 test-rmse:2.966607+0.464363
## [31] train-rmse:2.569215+0.083391 test-rmse:2.944636+0.462505
## [32] train-rmse:2.540765+0.081510 test-rmse:2.925351+0.468108
## [33] train-rmse:2.514354+0.082425 test-rmse:2.904608+0.466929
## [34] train-rmse:2.489204+0.082917 test-rmse:2.885291+0.475578
## [35] train-rmse:2.459054+0.087218 test-rmse:2.860277+0.465431
## [36] train-rmse:2.435400+0.088859 test-rmse:2.846735+0.457391
## [37] train-rmse:2.411095+0.082031 test-rmse:2.830851+0.467564
## [38] train-rmse:2.388575+0.080362 test-rmse:2.821075+0.461065
## [39] train-rmse:2.367863+0.081688 test-rmse:2.804790+0.465557
## [40] train-rmse:2.348684+0.082168 test-rmse:2.785207+0.461952
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## [56] train-rmse:2.072616+0.078649 test-rmse:2.602747+0.484856
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## [113] train-rmse:1.561483+0.068117 test-rmse:2.300114+0.511361
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## [125] train-rmse:1.497859+0.068882 test-rmse:2.272197+0.517406
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## [127] train-rmse:1.486532+0.068515 test-rmse:2.269380+0.517200
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## [133] train-rmse:1.461136+0.067651 test-rmse:2.259260+0.518035
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## [167] train-rmse:1.318417+0.067171 test-rmse:2.212858+0.525152
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## [192] train-rmse:1.237803+0.065309 test-rmse:2.182067+0.538363
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## [201] train-rmse:1.210654+0.059301 test-rmse:2.174625+0.539234
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## [206] train-rmse:1.199226+0.058050 test-rmse:2.172016+0.538934
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## [209] train-rmse:1.191449+0.056629 test-rmse:2.167063+0.539050
## [210] train-rmse:1.189101+0.057044 test-rmse:2.166061+0.539199
## [211] train-rmse:1.186894+0.056549 test-rmse:2.163510+0.541361
## [212] train-rmse:1.184048+0.056451 test-rmse:2.164125+0.541131
## [213] train-rmse:1.180884+0.057038 test-rmse:2.164320+0.543055
## [214] train-rmse:1.176781+0.057524 test-rmse:2.162627+0.543880
## [215] train-rmse:1.174154+0.058026 test-rmse:2.160222+0.543309
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## [223] train-rmse:1.153794+0.059864 test-rmse:2.157434+0.548649
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## [228] train-rmse:1.140803+0.057065 test-rmse:2.153386+0.550856
## [229] train-rmse:1.138255+0.057578 test-rmse:2.152761+0.549986
## [230] train-rmse:1.134408+0.059590 test-rmse:2.150169+0.548320
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## [238] train-rmse:1.117348+0.061231 test-rmse:2.144085+0.552295
## [239] train-rmse:1.112992+0.060655 test-rmse:2.146478+0.551796
## [240] train-rmse:1.111136+0.060654 test-rmse:2.145491+0.552109
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## [242] train-rmse:1.107913+0.061190 test-rmse:2.142746+0.552158
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## [244] train-rmse:1.103108+0.062610 test-rmse:2.141082+0.550313
## [245] train-rmse:1.101521+0.062846 test-rmse:2.141244+0.549811
## [246] train-rmse:1.098999+0.062196 test-rmse:2.139157+0.550435
## [247] train-rmse:1.096725+0.062823 test-rmse:2.138625+0.549630
## [248] train-rmse:1.093581+0.063455 test-rmse:2.137136+0.550967
## [249] train-rmse:1.092153+0.063598 test-rmse:2.137781+0.551227
## [250] train-rmse:1.090505+0.063804 test-rmse:2.136784+0.549890
## [251] train-rmse:1.087548+0.064063 test-rmse:2.138040+0.550735
## [252] train-rmse:1.083479+0.063981 test-rmse:2.138149+0.552073
## [253] train-rmse:1.081191+0.063776 test-rmse:2.136203+0.553596
## [254] train-rmse:1.079238+0.064136 test-rmse:2.136826+0.553731
## [255] train-rmse:1.076434+0.065498 test-rmse:2.135817+0.552810
## [256] train-rmse:1.073786+0.066343 test-rmse:2.135838+0.551271
## [257] train-rmse:1.071712+0.066106 test-rmse:2.134781+0.550429
## [258] train-rmse:1.070387+0.065975 test-rmse:2.135042+0.552341
## [259] train-rmse:1.068538+0.065747 test-rmse:2.133451+0.553491
## [260] train-rmse:1.066999+0.065999 test-rmse:2.132360+0.553899
## [261] train-rmse:1.064751+0.066150 test-rmse:2.132685+0.554679
## [262] train-rmse:1.062648+0.066334 test-rmse:2.132580+0.555075
## [263] train-rmse:1.061277+0.066036 test-rmse:2.134022+0.555607
## [264] train-rmse:1.058367+0.066130 test-rmse:2.135249+0.555382
## [265] train-rmse:1.056136+0.066738 test-rmse:2.135483+0.556704
## [266] train-rmse:1.054783+0.066889 test-rmse:2.136579+0.554902
## Stopping. Best iteration:
## [260] train-rmse:1.066999+0.065999 test-rmse:2.132360+0.553899
##
## 2
## [1] train-rmse:7.524102+0.157920 test-rmse:7.532223+0.659216
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.932589+0.143348 test-rmse:6.960580+0.626269
## [3] train-rmse:6.407597+0.130462 test-rmse:6.465128+0.586887
## [4] train-rmse:5.938180+0.121474 test-rmse:6.018113+0.573020
## [5] train-rmse:5.515462+0.117045 test-rmse:5.609197+0.555504
## [6] train-rmse:5.140082+0.111442 test-rmse:5.251896+0.530416
## [7] train-rmse:4.806548+0.101371 test-rmse:4.944000+0.519963
## [8] train-rmse:4.503888+0.100409 test-rmse:4.658059+0.484051
## [9] train-rmse:4.238461+0.099917 test-rmse:4.413063+0.464314
## [10] train-rmse:3.997083+0.092060 test-rmse:4.190250+0.445539
## [11] train-rmse:3.784576+0.092324 test-rmse:3.991503+0.436844
## [12] train-rmse:3.586110+0.084437 test-rmse:3.810489+0.430021
## [13] train-rmse:3.415554+0.083086 test-rmse:3.669860+0.422960
## [14] train-rmse:3.265791+0.079232 test-rmse:3.553022+0.419545
## [15] train-rmse:3.132042+0.075708 test-rmse:3.436911+0.420695
## [16] train-rmse:3.011538+0.073548 test-rmse:3.335278+0.425349
## [17] train-rmse:2.905503+0.072169 test-rmse:3.256610+0.429906
## [18] train-rmse:2.807567+0.072728 test-rmse:3.171009+0.435455
## [19] train-rmse:2.720127+0.070850 test-rmse:3.109081+0.434179
## [20] train-rmse:2.641578+0.067773 test-rmse:3.040942+0.444203
## [21] train-rmse:2.567363+0.061168 test-rmse:2.992795+0.441517
## [22] train-rmse:2.508925+0.062494 test-rmse:2.945692+0.442372
## [23] train-rmse:2.449016+0.061791 test-rmse:2.912380+0.442250
## [24] train-rmse:2.388438+0.055869 test-rmse:2.856729+0.447209
## [25] train-rmse:2.341329+0.055770 test-rmse:2.833399+0.437475
## [26] train-rmse:2.300087+0.055980 test-rmse:2.811515+0.443028
## [27] train-rmse:2.255640+0.054393 test-rmse:2.784337+0.459805
## [28] train-rmse:2.211078+0.054654 test-rmse:2.759884+0.465811
## [29] train-rmse:2.172043+0.057638 test-rmse:2.735603+0.469137
## [30] train-rmse:2.139896+0.054268 test-rmse:2.716693+0.468799
## [31] train-rmse:2.103161+0.056836 test-rmse:2.690058+0.467290
## [32] train-rmse:2.068411+0.053845 test-rmse:2.669434+0.472002
## [33] train-rmse:2.042444+0.053454 test-rmse:2.650436+0.472404
## [34] train-rmse:2.009681+0.053580 test-rmse:2.628589+0.461211
## [35] train-rmse:1.981083+0.052845 test-rmse:2.613670+0.465150
## [36] train-rmse:1.956780+0.052568 test-rmse:2.594387+0.470727
## [37] train-rmse:1.931079+0.051560 test-rmse:2.589937+0.479097
## [38] train-rmse:1.905284+0.049529 test-rmse:2.575817+0.473284
## [39] train-rmse:1.880549+0.054078 test-rmse:2.551965+0.463944
## [40] train-rmse:1.860063+0.052114 test-rmse:2.533559+0.469199
## [41] train-rmse:1.840629+0.052274 test-rmse:2.529886+0.469376
## [42] train-rmse:1.819556+0.054242 test-rmse:2.511848+0.463552
## [43] train-rmse:1.798479+0.055043 test-rmse:2.510893+0.462950
## [44] train-rmse:1.776121+0.053252 test-rmse:2.497588+0.465937
## [45] train-rmse:1.759306+0.055017 test-rmse:2.487836+0.465638
## [46] train-rmse:1.739587+0.052090 test-rmse:2.477012+0.468450
## [47] train-rmse:1.720525+0.053216 test-rmse:2.462818+0.463686
## [48] train-rmse:1.703768+0.054514 test-rmse:2.455941+0.461946
## [49] train-rmse:1.689720+0.054552 test-rmse:2.447556+0.463209
## [50] train-rmse:1.673476+0.054259 test-rmse:2.434719+0.468324
## [51] train-rmse:1.660203+0.054615 test-rmse:2.425691+0.464455
## [52] train-rmse:1.642619+0.052801 test-rmse:2.412933+0.468040
## [53] train-rmse:1.626017+0.051665 test-rmse:2.405832+0.467793
## [54] train-rmse:1.611364+0.052948 test-rmse:2.396749+0.472229
## [55] train-rmse:1.595410+0.051036 test-rmse:2.384248+0.474402
## [56] train-rmse:1.581450+0.049988 test-rmse:2.383662+0.472613
## [57] train-rmse:1.567408+0.049024 test-rmse:2.382230+0.470299
## [58] train-rmse:1.553978+0.047950 test-rmse:2.375275+0.470571
## [59] train-rmse:1.540572+0.046235 test-rmse:2.371976+0.469721
## [60] train-rmse:1.527630+0.045754 test-rmse:2.360191+0.471270
## [61] train-rmse:1.514541+0.046579 test-rmse:2.353254+0.474193
## [62] train-rmse:1.502882+0.046086 test-rmse:2.350247+0.475067
## [63] train-rmse:1.491591+0.047879 test-rmse:2.342802+0.467929
## [64] train-rmse:1.474968+0.044524 test-rmse:2.338742+0.473880
## [65] train-rmse:1.462255+0.044490 test-rmse:2.333019+0.474514
## [66] train-rmse:1.452961+0.044458 test-rmse:2.329612+0.476468
## [67] train-rmse:1.437713+0.043102 test-rmse:2.318162+0.476901
## [68] train-rmse:1.423412+0.044024 test-rmse:2.309598+0.471556
## [69] train-rmse:1.411328+0.042939 test-rmse:2.308227+0.472581
## [70] train-rmse:1.399922+0.042733 test-rmse:2.304807+0.470461
## [71] train-rmse:1.391060+0.042959 test-rmse:2.300697+0.472995
## [72] train-rmse:1.380650+0.042457 test-rmse:2.297181+0.474085
## [73] train-rmse:1.370949+0.041892 test-rmse:2.291734+0.474943
## [74] train-rmse:1.362877+0.043138 test-rmse:2.288110+0.474180
## [75] train-rmse:1.353429+0.044195 test-rmse:2.288345+0.473761
## [76] train-rmse:1.343522+0.043905 test-rmse:2.285601+0.473092
## [77] train-rmse:1.330361+0.042842 test-rmse:2.279917+0.471283
## [78] train-rmse:1.321889+0.043265 test-rmse:2.272878+0.468367
## [79] train-rmse:1.311863+0.042608 test-rmse:2.275872+0.471147
## [80] train-rmse:1.303262+0.043676 test-rmse:2.270248+0.473844
## [81] train-rmse:1.293095+0.042353 test-rmse:2.265061+0.475382
## [82] train-rmse:1.285114+0.041880 test-rmse:2.261941+0.474505
## [83] train-rmse:1.276787+0.041572 test-rmse:2.259592+0.476949
## [84] train-rmse:1.267606+0.039966 test-rmse:2.255171+0.476221
## [85] train-rmse:1.260337+0.041975 test-rmse:2.252725+0.474067
## [86] train-rmse:1.249199+0.039872 test-rmse:2.251816+0.475749
## [87] train-rmse:1.242778+0.040232 test-rmse:2.246500+0.475945
## [88] train-rmse:1.233942+0.039261 test-rmse:2.240623+0.476057
## [89] train-rmse:1.224956+0.037300 test-rmse:2.242102+0.477452
## [90] train-rmse:1.215462+0.039954 test-rmse:2.233784+0.474982
## [91] train-rmse:1.206820+0.038732 test-rmse:2.230882+0.476336
## [92] train-rmse:1.199029+0.038704 test-rmse:2.227838+0.476782
## [93] train-rmse:1.192154+0.038346 test-rmse:2.223133+0.475914
## [94] train-rmse:1.185049+0.038010 test-rmse:2.224080+0.478907
## [95] train-rmse:1.176890+0.036874 test-rmse:2.223164+0.481696
## [96] train-rmse:1.169460+0.036194 test-rmse:2.221388+0.480909
## [97] train-rmse:1.162986+0.035151 test-rmse:2.218973+0.480149
## [98] train-rmse:1.156215+0.035491 test-rmse:2.219587+0.483766
## [99] train-rmse:1.151224+0.035562 test-rmse:2.216948+0.484939
## [100] train-rmse:1.145720+0.035536 test-rmse:2.214392+0.485831
## [101] train-rmse:1.139356+0.036614 test-rmse:2.211000+0.486820
## [102] train-rmse:1.133980+0.036551 test-rmse:2.208409+0.487713
## [103] train-rmse:1.127593+0.036498 test-rmse:2.206472+0.487345
## [104] train-rmse:1.120200+0.034617 test-rmse:2.204244+0.487605
## [105] train-rmse:1.113234+0.035944 test-rmse:2.201520+0.482654
## [106] train-rmse:1.105817+0.035976 test-rmse:2.201844+0.481564
## [107] train-rmse:1.098089+0.034549 test-rmse:2.200190+0.482724
## [108] train-rmse:1.092411+0.034669 test-rmse:2.199914+0.482679
## [109] train-rmse:1.086526+0.036468 test-rmse:2.197209+0.482667
## [110] train-rmse:1.080607+0.036055 test-rmse:2.193977+0.486606
## [111] train-rmse:1.075023+0.035319 test-rmse:2.194387+0.487811
## [112] train-rmse:1.070373+0.035041 test-rmse:2.193060+0.486998
## [113] train-rmse:1.063727+0.033977 test-rmse:2.191029+0.486675
## [114] train-rmse:1.059121+0.035011 test-rmse:2.188747+0.487807
## [115] train-rmse:1.053473+0.035757 test-rmse:2.186747+0.490032
## [116] train-rmse:1.044634+0.036049 test-rmse:2.185249+0.487381
## [117] train-rmse:1.034613+0.035752 test-rmse:2.183101+0.489204
## [118] train-rmse:1.028578+0.034218 test-rmse:2.181417+0.489392
## [119] train-rmse:1.021787+0.032406 test-rmse:2.177361+0.487965
## [120] train-rmse:1.016487+0.034047 test-rmse:2.172629+0.484652
## [121] train-rmse:1.012273+0.034796 test-rmse:2.171115+0.485152
## [122] train-rmse:1.007050+0.035659 test-rmse:2.170289+0.485571
## [123] train-rmse:1.002872+0.035216 test-rmse:2.169465+0.486310
## [124] train-rmse:0.998070+0.036095 test-rmse:2.168595+0.488551
## [125] train-rmse:0.993410+0.035795 test-rmse:2.164938+0.489637
## [126] train-rmse:0.987925+0.035386 test-rmse:2.164177+0.489993
## [127] train-rmse:0.983802+0.035264 test-rmse:2.162546+0.489764
## [128] train-rmse:0.979220+0.037195 test-rmse:2.160264+0.486417
## [129] train-rmse:0.972798+0.039150 test-rmse:2.158697+0.486126
## [130] train-rmse:0.965406+0.037797 test-rmse:2.155305+0.485507
## [131] train-rmse:0.960551+0.036643 test-rmse:2.152288+0.487661
## [132] train-rmse:0.955457+0.036585 test-rmse:2.148843+0.489504
## [133] train-rmse:0.950250+0.035311 test-rmse:2.145507+0.490541
## [134] train-rmse:0.946432+0.037100 test-rmse:2.143646+0.491321
## [135] train-rmse:0.942087+0.037455 test-rmse:2.143999+0.491489
## [136] train-rmse:0.936886+0.039651 test-rmse:2.142642+0.489940
## [137] train-rmse:0.931293+0.038972 test-rmse:2.141889+0.490564
## [138] train-rmse:0.925841+0.039459 test-rmse:2.136787+0.490266
## [139] train-rmse:0.920954+0.038993 test-rmse:2.138727+0.488804
## [140] train-rmse:0.917557+0.039039 test-rmse:2.137000+0.489513
## [141] train-rmse:0.909939+0.036432 test-rmse:2.134641+0.490013
## [142] train-rmse:0.903412+0.033847 test-rmse:2.132151+0.491320
## [143] train-rmse:0.898075+0.034232 test-rmse:2.128606+0.493266
## [144] train-rmse:0.894884+0.034652 test-rmse:2.128232+0.491952
## [145] train-rmse:0.889821+0.034466 test-rmse:2.127763+0.492273
## [146] train-rmse:0.886183+0.034569 test-rmse:2.126134+0.493181
## [147] train-rmse:0.883212+0.035157 test-rmse:2.123888+0.492299
## [148] train-rmse:0.878343+0.035875 test-rmse:2.121987+0.493098
## [149] train-rmse:0.872497+0.033939 test-rmse:2.118875+0.493600
## [150] train-rmse:0.867397+0.035354 test-rmse:2.118800+0.492018
## [151] train-rmse:0.863219+0.036485 test-rmse:2.118745+0.492973
## [152] train-rmse:0.859350+0.037229 test-rmse:2.116466+0.491792
## [153] train-rmse:0.855946+0.038210 test-rmse:2.113091+0.491535
## [154] train-rmse:0.852032+0.038819 test-rmse:2.113502+0.492900
## [155] train-rmse:0.849433+0.038987 test-rmse:2.113014+0.493169
## [156] train-rmse:0.843671+0.037993 test-rmse:2.111728+0.493642
## [157] train-rmse:0.839804+0.038429 test-rmse:2.111122+0.493663
## [158] train-rmse:0.835931+0.037805 test-rmse:2.109382+0.493743
## [159] train-rmse:0.831122+0.036716 test-rmse:2.106409+0.494298
## [160] train-rmse:0.827368+0.036301 test-rmse:2.107387+0.493611
## [161] train-rmse:0.825061+0.036345 test-rmse:2.106520+0.493713
## [162] train-rmse:0.821107+0.035730 test-rmse:2.105461+0.493207
## [163] train-rmse:0.814922+0.035342 test-rmse:2.102543+0.494209
## [164] train-rmse:0.810605+0.036337 test-rmse:2.101730+0.497159
## [165] train-rmse:0.805986+0.039719 test-rmse:2.100617+0.496411
## [166] train-rmse:0.802732+0.039903 test-rmse:2.099784+0.496848
## [167] train-rmse:0.799831+0.039531 test-rmse:2.098924+0.496802
## [168] train-rmse:0.797352+0.039527 test-rmse:2.098942+0.496119
## [169] train-rmse:0.792017+0.039072 test-rmse:2.098389+0.496131
## [170] train-rmse:0.788153+0.038472 test-rmse:2.096467+0.495781
## [171] train-rmse:0.784318+0.038229 test-rmse:2.095843+0.496558
## [172] train-rmse:0.779116+0.039655 test-rmse:2.097300+0.497115
## [173] train-rmse:0.775229+0.038255 test-rmse:2.095300+0.496988
## [174] train-rmse:0.771416+0.037503 test-rmse:2.092607+0.499009
## [175] train-rmse:0.767218+0.037969 test-rmse:2.090674+0.498760
## [176] train-rmse:0.763350+0.038164 test-rmse:2.090373+0.499708
## [177] train-rmse:0.760307+0.039255 test-rmse:2.090433+0.499463
## [178] train-rmse:0.755396+0.040304 test-rmse:2.089591+0.498113
## [179] train-rmse:0.751907+0.039982 test-rmse:2.088513+0.498419
## [180] train-rmse:0.748607+0.039006 test-rmse:2.088143+0.498720
## [181] train-rmse:0.746789+0.038850 test-rmse:2.087289+0.498584
## [182] train-rmse:0.743632+0.038612 test-rmse:2.086469+0.499507
## [183] train-rmse:0.740231+0.037879 test-rmse:2.085923+0.499516
## [184] train-rmse:0.736525+0.037980 test-rmse:2.086724+0.500874
## [185] train-rmse:0.731398+0.037124 test-rmse:2.082868+0.502256
## [186] train-rmse:0.727720+0.037038 test-rmse:2.081920+0.502713
## [187] train-rmse:0.724233+0.038339 test-rmse:2.081965+0.502496
## [188] train-rmse:0.721457+0.038525 test-rmse:2.081933+0.502075
## [189] train-rmse:0.718144+0.037710 test-rmse:2.081325+0.502577
## [190] train-rmse:0.715768+0.037173 test-rmse:2.081039+0.502801
## [191] train-rmse:0.713879+0.037518 test-rmse:2.080018+0.502913
## [192] train-rmse:0.708888+0.036546 test-rmse:2.080938+0.503727
## [193] train-rmse:0.706322+0.035689 test-rmse:2.080453+0.503589
## [194] train-rmse:0.703332+0.036435 test-rmse:2.079653+0.503170
## [195] train-rmse:0.699672+0.037902 test-rmse:2.078650+0.502354
## [196] train-rmse:0.696915+0.038330 test-rmse:2.078013+0.502813
## [197] train-rmse:0.695053+0.038113 test-rmse:2.079315+0.504177
## [198] train-rmse:0.691975+0.039162 test-rmse:2.078626+0.503943
## [199] train-rmse:0.687673+0.037575 test-rmse:2.076408+0.505084
## [200] train-rmse:0.684409+0.037688 test-rmse:2.075736+0.505057
## [201] train-rmse:0.681027+0.037118 test-rmse:2.074235+0.504858
## [202] train-rmse:0.677327+0.036874 test-rmse:2.074594+0.505606
## [203] train-rmse:0.674259+0.037836 test-rmse:2.074927+0.505739
## [204] train-rmse:0.672154+0.038169 test-rmse:2.075099+0.504529
## [205] train-rmse:0.669806+0.037972 test-rmse:2.074209+0.504637
## [206] train-rmse:0.666928+0.037482 test-rmse:2.073905+0.505566
## [207] train-rmse:0.664264+0.036436 test-rmse:2.073148+0.505863
## [208] train-rmse:0.660976+0.035825 test-rmse:2.072567+0.506081
## [209] train-rmse:0.658504+0.035014 test-rmse:2.072720+0.506201
## [210] train-rmse:0.654291+0.034382 test-rmse:2.071016+0.505905
## [211] train-rmse:0.651358+0.033550 test-rmse:2.070539+0.506114
## [212] train-rmse:0.648326+0.032485 test-rmse:2.070985+0.506255
## [213] train-rmse:0.645892+0.033907 test-rmse:2.071088+0.506116
## [214] train-rmse:0.643622+0.033988 test-rmse:2.069667+0.505997
## [215] train-rmse:0.641672+0.033468 test-rmse:2.069354+0.506262
## [216] train-rmse:0.638820+0.032699 test-rmse:2.068984+0.505989
## [217] train-rmse:0.636697+0.032908 test-rmse:2.068101+0.506481
## [218] train-rmse:0.634508+0.032585 test-rmse:2.068117+0.505938
## [219] train-rmse:0.631763+0.031800 test-rmse:2.067518+0.505946
## [220] train-rmse:0.629921+0.031560 test-rmse:2.067497+0.506079
## [221] train-rmse:0.627976+0.031245 test-rmse:2.067387+0.505476
## [222] train-rmse:0.625550+0.030743 test-rmse:2.067187+0.505910
## [223] train-rmse:0.622324+0.031144 test-rmse:2.067444+0.505954
## [224] train-rmse:0.618913+0.031653 test-rmse:2.067422+0.505883
## [225] train-rmse:0.616740+0.031262 test-rmse:2.068340+0.506146
## [226] train-rmse:0.613317+0.030796 test-rmse:2.069120+0.507111
## [227] train-rmse:0.610767+0.031286 test-rmse:2.069532+0.507425
## [228] train-rmse:0.608487+0.031009 test-rmse:2.069156+0.507616
## Stopping. Best iteration:
## [222] train-rmse:0.625550+0.030743 test-rmse:2.067187+0.505910
##
## 3
## [1] train-rmse:7.498217+0.157191 test-rmse:7.495779+0.653569
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.883003+0.141198 test-rmse:6.909630+0.621051
## [3] train-rmse:6.327433+0.127156 test-rmse:6.381353+0.579564
## [4] train-rmse:5.832149+0.115349 test-rmse:5.911930+0.544250
## [5] train-rmse:5.388517+0.101483 test-rmse:5.485037+0.517168
## [6] train-rmse:4.989124+0.092915 test-rmse:5.109017+0.492027
## [7] train-rmse:4.634451+0.082004 test-rmse:4.779451+0.461339
## [8] train-rmse:4.317313+0.080167 test-rmse:4.487915+0.437975
## [9] train-rmse:4.037668+0.075008 test-rmse:4.247172+0.427507
## [10] train-rmse:3.788615+0.074750 test-rmse:4.029248+0.418384
## [11] train-rmse:3.562745+0.067064 test-rmse:3.832624+0.419365
## [12] train-rmse:3.362907+0.063211 test-rmse:3.661829+0.407234
## [13] train-rmse:3.181508+0.057212 test-rmse:3.496466+0.416126
## [14] train-rmse:3.022375+0.056136 test-rmse:3.369013+0.411736
## [15] train-rmse:2.885960+0.055150 test-rmse:3.260767+0.413733
## [16] train-rmse:2.756668+0.055962 test-rmse:3.150111+0.417917
## [17] train-rmse:2.644979+0.054831 test-rmse:3.075960+0.413619
## [18] train-rmse:2.545051+0.053666 test-rmse:3.001214+0.418489
## [19] train-rmse:2.455911+0.052219 test-rmse:2.940089+0.427726
## [20] train-rmse:2.370844+0.046361 test-rmse:2.878190+0.427423
## [21] train-rmse:2.292379+0.045091 test-rmse:2.821928+0.436555
## [22] train-rmse:2.222442+0.050594 test-rmse:2.780650+0.429116
## [23] train-rmse:2.159664+0.050532 test-rmse:2.744101+0.426584
## [24] train-rmse:2.100406+0.046915 test-rmse:2.700857+0.440346
## [25] train-rmse:2.049588+0.048975 test-rmse:2.659180+0.430757
## [26] train-rmse:2.000465+0.049786 test-rmse:2.645671+0.434558
## [27] train-rmse:1.953935+0.043221 test-rmse:2.616617+0.437576
## [28] train-rmse:1.912850+0.044903 test-rmse:2.589242+0.438416
## [29] train-rmse:1.875480+0.045684 test-rmse:2.569490+0.436473
## [30] train-rmse:1.840923+0.044616 test-rmse:2.549971+0.424780
## [31] train-rmse:1.806917+0.042973 test-rmse:2.530911+0.424135
## [32] train-rmse:1.772340+0.042372 test-rmse:2.518335+0.429409
## [33] train-rmse:1.744447+0.040535 test-rmse:2.503683+0.427037
## [34] train-rmse:1.713594+0.043710 test-rmse:2.487451+0.427848
## [35] train-rmse:1.689949+0.044431 test-rmse:2.475254+0.429357
## [36] train-rmse:1.663119+0.044341 test-rmse:2.462092+0.431572
## [37] train-rmse:1.634131+0.043307 test-rmse:2.441182+0.431565
## [38] train-rmse:1.608164+0.043005 test-rmse:2.422491+0.422448
## [39] train-rmse:1.588240+0.043165 test-rmse:2.414563+0.423663
## [40] train-rmse:1.564441+0.041930 test-rmse:2.406243+0.428831
## [41] train-rmse:1.542676+0.040358 test-rmse:2.388381+0.428039
## [42] train-rmse:1.523894+0.037700 test-rmse:2.378162+0.430492
## [43] train-rmse:1.501477+0.037181 test-rmse:2.368240+0.426316
## [44] train-rmse:1.481032+0.034992 test-rmse:2.361838+0.429690
## [45] train-rmse:1.462963+0.036947 test-rmse:2.356836+0.431396
## [46] train-rmse:1.443363+0.033761 test-rmse:2.341781+0.431307
## [47] train-rmse:1.426876+0.036136 test-rmse:2.332059+0.430911
## [48] train-rmse:1.408013+0.034559 test-rmse:2.326448+0.427252
## [49] train-rmse:1.393546+0.035815 test-rmse:2.323687+0.433296
## [50] train-rmse:1.375748+0.035844 test-rmse:2.318044+0.437488
## [51] train-rmse:1.354838+0.027914 test-rmse:2.309523+0.436995
## [52] train-rmse:1.340208+0.029697 test-rmse:2.302074+0.434830
## [53] train-rmse:1.326336+0.029101 test-rmse:2.300239+0.438407
## [54] train-rmse:1.312435+0.030222 test-rmse:2.293017+0.438466
## [55] train-rmse:1.297570+0.026534 test-rmse:2.284640+0.440025
## [56] train-rmse:1.283003+0.028150 test-rmse:2.274869+0.435114
## [57] train-rmse:1.269444+0.027615 test-rmse:2.266013+0.436920
## [58] train-rmse:1.255390+0.023441 test-rmse:2.260389+0.440252
## [59] train-rmse:1.243329+0.024372 test-rmse:2.258696+0.439701
## [60] train-rmse:1.231766+0.022938 test-rmse:2.253782+0.440939
## [61] train-rmse:1.220307+0.024690 test-rmse:2.245989+0.437828
## [62] train-rmse:1.209295+0.025135 test-rmse:2.240922+0.438477
## [63] train-rmse:1.192644+0.027986 test-rmse:2.238855+0.439002
## [64] train-rmse:1.179843+0.029125 test-rmse:2.233700+0.441811
## [65] train-rmse:1.169650+0.030763 test-rmse:2.229763+0.440170
## [66] train-rmse:1.159547+0.031600 test-rmse:2.228543+0.444589
## [67] train-rmse:1.146697+0.030850 test-rmse:2.228916+0.443331
## [68] train-rmse:1.138399+0.030886 test-rmse:2.225161+0.445983
## [69] train-rmse:1.122883+0.027840 test-rmse:2.222568+0.447112
## [70] train-rmse:1.114924+0.028160 test-rmse:2.219547+0.444101
## [71] train-rmse:1.103856+0.028799 test-rmse:2.216161+0.446634
## [72] train-rmse:1.095724+0.029347 test-rmse:2.211796+0.446107
## [73] train-rmse:1.084860+0.030254 test-rmse:2.205114+0.440764
## [74] train-rmse:1.077053+0.030507 test-rmse:2.200812+0.442040
## [75] train-rmse:1.068047+0.030010 test-rmse:2.194780+0.441149
## [76] train-rmse:1.055373+0.028222 test-rmse:2.196547+0.445628
## [77] train-rmse:1.046164+0.026794 test-rmse:2.196523+0.446184
## [78] train-rmse:1.037394+0.026727 test-rmse:2.196098+0.449025
## [79] train-rmse:1.029707+0.026760 test-rmse:2.196296+0.448207
## [80] train-rmse:1.018486+0.023916 test-rmse:2.191916+0.448386
## [81] train-rmse:1.006702+0.026441 test-rmse:2.188602+0.447502
## [82] train-rmse:0.998400+0.027641 test-rmse:2.185982+0.446616
## [83] train-rmse:0.992174+0.027945 test-rmse:2.184522+0.447491
## [84] train-rmse:0.983855+0.030403 test-rmse:2.180552+0.446638
## [85] train-rmse:0.973690+0.030715 test-rmse:2.176920+0.449527
## [86] train-rmse:0.964126+0.032944 test-rmse:2.171015+0.452647
## [87] train-rmse:0.955357+0.032025 test-rmse:2.166102+0.451078
## [88] train-rmse:0.945259+0.027724 test-rmse:2.165168+0.452173
## [89] train-rmse:0.936548+0.028867 test-rmse:2.163889+0.452799
## [90] train-rmse:0.929197+0.026544 test-rmse:2.162411+0.451850
## [91] train-rmse:0.920917+0.028434 test-rmse:2.162723+0.450773
## [92] train-rmse:0.914789+0.027558 test-rmse:2.160951+0.452220
## [93] train-rmse:0.901877+0.023841 test-rmse:2.154074+0.452232
## [94] train-rmse:0.893733+0.027057 test-rmse:2.151476+0.450826
## [95] train-rmse:0.886434+0.028485 test-rmse:2.150460+0.450165
## [96] train-rmse:0.878950+0.028469 test-rmse:2.152474+0.448631
## [97] train-rmse:0.870852+0.028586 test-rmse:2.151614+0.448579
## [98] train-rmse:0.862297+0.026384 test-rmse:2.156256+0.448706
## [99] train-rmse:0.855642+0.027796 test-rmse:2.154785+0.449301
## [100] train-rmse:0.848837+0.029340 test-rmse:2.150681+0.450985
## [101] train-rmse:0.841355+0.029557 test-rmse:2.147807+0.452329
## [102] train-rmse:0.834843+0.028892 test-rmse:2.147606+0.450215
## [103] train-rmse:0.829103+0.029348 test-rmse:2.144958+0.451479
## [104] train-rmse:0.822654+0.027523 test-rmse:2.143248+0.451670
## [105] train-rmse:0.815193+0.024571 test-rmse:2.139587+0.453384
## [106] train-rmse:0.809601+0.023258 test-rmse:2.138649+0.451732
## [107] train-rmse:0.802541+0.023874 test-rmse:2.135656+0.452220
## [108] train-rmse:0.795573+0.023134 test-rmse:2.136413+0.452111
## [109] train-rmse:0.789012+0.025954 test-rmse:2.135026+0.450619
## [110] train-rmse:0.781591+0.027489 test-rmse:2.133648+0.450964
## [111] train-rmse:0.777686+0.027929 test-rmse:2.130215+0.451892
## [112] train-rmse:0.770862+0.025944 test-rmse:2.127435+0.453585
## [113] train-rmse:0.764547+0.025148 test-rmse:2.126238+0.453270
## [114] train-rmse:0.759455+0.026864 test-rmse:2.125955+0.453502
## [115] train-rmse:0.750557+0.025845 test-rmse:2.125491+0.452690
## [116] train-rmse:0.746509+0.026098 test-rmse:2.122786+0.453804
## [117] train-rmse:0.741216+0.024698 test-rmse:2.124232+0.453315
## [118] train-rmse:0.735728+0.025070 test-rmse:2.123648+0.453566
## [119] train-rmse:0.728700+0.023642 test-rmse:2.122698+0.454190
## [120] train-rmse:0.723512+0.023209 test-rmse:2.122465+0.454096
## [121] train-rmse:0.718362+0.023461 test-rmse:2.120378+0.453911
## [122] train-rmse:0.713021+0.023199 test-rmse:2.119616+0.453460
## [123] train-rmse:0.707020+0.022241 test-rmse:2.118218+0.453217
## [124] train-rmse:0.701313+0.023609 test-rmse:2.115100+0.452750
## [125] train-rmse:0.696024+0.022810 test-rmse:2.114039+0.451419
## [126] train-rmse:0.692124+0.022324 test-rmse:2.113064+0.453412
## [127] train-rmse:0.687347+0.023332 test-rmse:2.111504+0.454178
## [128] train-rmse:0.680731+0.024018 test-rmse:2.110424+0.454742
## [129] train-rmse:0.671900+0.021485 test-rmse:2.112807+0.454329
## [130] train-rmse:0.668305+0.020671 test-rmse:2.113141+0.454710
## [131] train-rmse:0.663353+0.020748 test-rmse:2.112218+0.454792
## [132] train-rmse:0.657678+0.020731 test-rmse:2.109784+0.454802
## [133] train-rmse:0.654406+0.021642 test-rmse:2.106591+0.455722
## [134] train-rmse:0.647096+0.022371 test-rmse:2.107029+0.455873
## [135] train-rmse:0.642074+0.021670 test-rmse:2.106463+0.455388
## [136] train-rmse:0.638479+0.021568 test-rmse:2.105387+0.455926
## [137] train-rmse:0.632755+0.023843 test-rmse:2.103074+0.455721
## [138] train-rmse:0.628498+0.022827 test-rmse:2.103596+0.455228
## [139] train-rmse:0.625097+0.024677 test-rmse:2.102142+0.455771
## [140] train-rmse:0.622228+0.024415 test-rmse:2.101760+0.455647
## [141] train-rmse:0.618672+0.024383 test-rmse:2.101744+0.456472
## [142] train-rmse:0.613483+0.023882 test-rmse:2.100744+0.457307
## [143] train-rmse:0.609926+0.024140 test-rmse:2.100774+0.458514
## [144] train-rmse:0.606104+0.024088 test-rmse:2.100161+0.458647
## [145] train-rmse:0.601573+0.023381 test-rmse:2.099133+0.458935
## [146] train-rmse:0.596345+0.024787 test-rmse:2.098526+0.457744
## [147] train-rmse:0.592969+0.023984 test-rmse:2.097621+0.458558
## [148] train-rmse:0.590338+0.023547 test-rmse:2.098276+0.458212
## [149] train-rmse:0.585755+0.021771 test-rmse:2.098931+0.458042
## [150] train-rmse:0.582180+0.020519 test-rmse:2.098065+0.457015
## [151] train-rmse:0.576617+0.019113 test-rmse:2.097228+0.457909
## [152] train-rmse:0.572689+0.020506 test-rmse:2.096326+0.457953
## [153] train-rmse:0.568503+0.022598 test-rmse:2.096496+0.458530
## [154] train-rmse:0.563775+0.023331 test-rmse:2.094212+0.458266
## [155] train-rmse:0.560491+0.022611 test-rmse:2.093320+0.458490
## [156] train-rmse:0.556535+0.021929 test-rmse:2.093214+0.458952
## [157] train-rmse:0.554063+0.021841 test-rmse:2.093140+0.458296
## [158] train-rmse:0.549940+0.023524 test-rmse:2.093278+0.460130
## [159] train-rmse:0.546836+0.023197 test-rmse:2.092636+0.460789
## [160] train-rmse:0.542687+0.021775 test-rmse:2.092543+0.461167
## [161] train-rmse:0.539970+0.021958 test-rmse:2.092321+0.462329
## [162] train-rmse:0.536385+0.022853 test-rmse:2.092319+0.461930
## [163] train-rmse:0.531549+0.022817 test-rmse:2.091958+0.463097
## [164] train-rmse:0.527604+0.021833 test-rmse:2.089483+0.463665
## [165] train-rmse:0.524605+0.022288 test-rmse:2.089384+0.463510
## [166] train-rmse:0.520910+0.021910 test-rmse:2.088951+0.463885
## [167] train-rmse:0.517437+0.021897 test-rmse:2.088077+0.464007
## [168] train-rmse:0.515042+0.021915 test-rmse:2.088074+0.464084
## [169] train-rmse:0.512790+0.021625 test-rmse:2.088227+0.464057
## [170] train-rmse:0.507137+0.023042 test-rmse:2.085860+0.463255
## [171] train-rmse:0.503909+0.022493 test-rmse:2.087609+0.462918
## [172] train-rmse:0.500858+0.022183 test-rmse:2.087669+0.463322
## [173] train-rmse:0.496683+0.023576 test-rmse:2.087278+0.463487
## [174] train-rmse:0.494378+0.023227 test-rmse:2.086670+0.463153
## [175] train-rmse:0.492078+0.022678 test-rmse:2.086591+0.463095
## [176] train-rmse:0.490119+0.022418 test-rmse:2.086096+0.463730
## Stopping. Best iteration:
## [170] train-rmse:0.507137+0.023042 test-rmse:2.085860+0.463255
##
## 4
## [1] train-rmse:7.480292+0.156033 test-rmse:7.464837+0.645716
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.844487+0.139664 test-rmse:6.849760+0.604625
## [3] train-rmse:6.274671+0.125253 test-rmse:6.301242+0.566609
## [4] train-rmse:5.761710+0.116776 test-rmse:5.814442+0.531514
## [5] train-rmse:5.303412+0.109975 test-rmse:5.383958+0.499849
## [6] train-rmse:4.893840+0.098698 test-rmse:5.014735+0.480387
## [7] train-rmse:4.530695+0.092866 test-rmse:4.689189+0.459591
## [8] train-rmse:4.203730+0.086466 test-rmse:4.400835+0.443429
## [9] train-rmse:3.916126+0.077837 test-rmse:4.162085+0.436074
## [10] train-rmse:3.657283+0.071017 test-rmse:3.944800+0.437215
## [11] train-rmse:3.429524+0.065104 test-rmse:3.767076+0.439956
## [12] train-rmse:3.227345+0.060892 test-rmse:3.607233+0.449097
## [13] train-rmse:3.042745+0.057772 test-rmse:3.464093+0.447624
## [14] train-rmse:2.880842+0.055065 test-rmse:3.349097+0.451752
## [15] train-rmse:2.740806+0.054652 test-rmse:3.254288+0.454701
## [16] train-rmse:2.612182+0.053046 test-rmse:3.165194+0.469132
## [17] train-rmse:2.491420+0.052421 test-rmse:3.082767+0.472423
## [18] train-rmse:2.382060+0.048972 test-rmse:3.016578+0.489595
## [19] train-rmse:2.284871+0.046173 test-rmse:2.947069+0.489066
## [20] train-rmse:2.200305+0.045754 test-rmse:2.890052+0.494849
## [21] train-rmse:2.117868+0.042690 test-rmse:2.854437+0.505753
## [22] train-rmse:2.042597+0.037797 test-rmse:2.809266+0.510134
## [23] train-rmse:1.974708+0.033274 test-rmse:2.764762+0.522380
## [24] train-rmse:1.916822+0.036195 test-rmse:2.732259+0.519070
## [25] train-rmse:1.859241+0.031595 test-rmse:2.697155+0.516632
## [26] train-rmse:1.809393+0.032640 test-rmse:2.674062+0.516840
## [27] train-rmse:1.756151+0.042361 test-rmse:2.650763+0.522425
## [28] train-rmse:1.715864+0.041031 test-rmse:2.627873+0.518004
## [29] train-rmse:1.673853+0.035042 test-rmse:2.606588+0.524521
## [30] train-rmse:1.630690+0.036384 test-rmse:2.590344+0.524643
## [31] train-rmse:1.595689+0.040603 test-rmse:2.575145+0.526330
## [32] train-rmse:1.564617+0.040253 test-rmse:2.559037+0.524473
## [33] train-rmse:1.526183+0.038412 test-rmse:2.540144+0.520344
## [34] train-rmse:1.497952+0.040356 test-rmse:2.524702+0.523225
## [35] train-rmse:1.470977+0.040261 test-rmse:2.514950+0.533792
## [36] train-rmse:1.442876+0.042327 test-rmse:2.508174+0.531393
## [37] train-rmse:1.417597+0.042056 test-rmse:2.492494+0.533082
## [38] train-rmse:1.392887+0.039505 test-rmse:2.485184+0.535057
## [39] train-rmse:1.363392+0.036725 test-rmse:2.473791+0.531393
## [40] train-rmse:1.341339+0.036320 test-rmse:2.466081+0.532800
## [41] train-rmse:1.322320+0.035956 test-rmse:2.461570+0.531085
## [42] train-rmse:1.304057+0.036313 test-rmse:2.452808+0.534045
## [43] train-rmse:1.279862+0.030934 test-rmse:2.446536+0.537680
## [44] train-rmse:1.258688+0.032704 test-rmse:2.436903+0.532622
## [45] train-rmse:1.241143+0.035817 test-rmse:2.432173+0.528360
## [46] train-rmse:1.227153+0.036059 test-rmse:2.423906+0.531320
## [47] train-rmse:1.211953+0.035632 test-rmse:2.422756+0.531063
## [48] train-rmse:1.196599+0.035788 test-rmse:2.416683+0.533200
## [49] train-rmse:1.177901+0.037656 test-rmse:2.404451+0.536330
## [50] train-rmse:1.164223+0.037298 test-rmse:2.396622+0.538316
## [51] train-rmse:1.151731+0.036371 test-rmse:2.392977+0.538226
## [52] train-rmse:1.136389+0.036637 test-rmse:2.395074+0.541269
## [53] train-rmse:1.121267+0.039410 test-rmse:2.390081+0.536776
## [54] train-rmse:1.106636+0.041662 test-rmse:2.386274+0.536417
## [55] train-rmse:1.092075+0.044216 test-rmse:2.384088+0.539099
## [56] train-rmse:1.078185+0.044234 test-rmse:2.382402+0.538073
## [57] train-rmse:1.065694+0.043958 test-rmse:2.378175+0.539175
## [58] train-rmse:1.056291+0.044745 test-rmse:2.374906+0.540648
## [59] train-rmse:1.040146+0.044412 test-rmse:2.364231+0.541434
## [60] train-rmse:1.028467+0.042796 test-rmse:2.362080+0.541146
## [61] train-rmse:1.013945+0.039867 test-rmse:2.358395+0.542219
## [62] train-rmse:1.000586+0.040634 test-rmse:2.358393+0.543791
## [63] train-rmse:0.985261+0.037205 test-rmse:2.351093+0.542282
## [64] train-rmse:0.973777+0.036628 test-rmse:2.350489+0.543750
## [65] train-rmse:0.962972+0.035800 test-rmse:2.348750+0.546892
## [66] train-rmse:0.949475+0.032581 test-rmse:2.343201+0.549517
## [67] train-rmse:0.938337+0.033853 test-rmse:2.340671+0.547192
## [68] train-rmse:0.930224+0.034290 test-rmse:2.337072+0.547572
## [69] train-rmse:0.920716+0.035533 test-rmse:2.333196+0.545243
## [70] train-rmse:0.905205+0.033498 test-rmse:2.330846+0.544525
## [71] train-rmse:0.895937+0.032029 test-rmse:2.328791+0.546666
## [72] train-rmse:0.885311+0.031408 test-rmse:2.326522+0.547367
## [73] train-rmse:0.875539+0.030983 test-rmse:2.323252+0.548800
## [74] train-rmse:0.864368+0.029759 test-rmse:2.323875+0.546574
## [75] train-rmse:0.853227+0.028065 test-rmse:2.322205+0.549107
## [76] train-rmse:0.844164+0.030163 test-rmse:2.321546+0.548481
## [77] train-rmse:0.832304+0.029185 test-rmse:2.319321+0.548882
## [78] train-rmse:0.821510+0.026066 test-rmse:2.315496+0.552631
## [79] train-rmse:0.814402+0.026893 test-rmse:2.314253+0.553462
## [80] train-rmse:0.807003+0.027774 test-rmse:2.311832+0.550803
## [81] train-rmse:0.801194+0.026998 test-rmse:2.313087+0.551625
## [82] train-rmse:0.793810+0.026202 test-rmse:2.311818+0.551449
## [83] train-rmse:0.783903+0.028169 test-rmse:2.310301+0.550577
## [84] train-rmse:0.777143+0.028991 test-rmse:2.306450+0.551786
## [85] train-rmse:0.768713+0.026031 test-rmse:2.306525+0.554715
## [86] train-rmse:0.759919+0.023728 test-rmse:2.303248+0.556167
## [87] train-rmse:0.751726+0.025280 test-rmse:2.301056+0.558613
## [88] train-rmse:0.741327+0.024670 test-rmse:2.299521+0.559384
## [89] train-rmse:0.733472+0.025166 test-rmse:2.299115+0.560077
## [90] train-rmse:0.726036+0.027007 test-rmse:2.296911+0.562813
## [91] train-rmse:0.717403+0.026359 test-rmse:2.296060+0.562629
## [92] train-rmse:0.708831+0.024535 test-rmse:2.292639+0.560550
## [93] train-rmse:0.701375+0.023478 test-rmse:2.291585+0.560905
## [94] train-rmse:0.690558+0.023203 test-rmse:2.290490+0.561286
## [95] train-rmse:0.684160+0.022442 test-rmse:2.288432+0.561841
## [96] train-rmse:0.676603+0.023972 test-rmse:2.286363+0.562456
## [97] train-rmse:0.670974+0.023627 test-rmse:2.285222+0.563292
## [98] train-rmse:0.665693+0.024278 test-rmse:2.284296+0.562862
## [99] train-rmse:0.656798+0.022046 test-rmse:2.281568+0.564748
## [100] train-rmse:0.648955+0.019678 test-rmse:2.284135+0.563501
## [101] train-rmse:0.641192+0.019054 test-rmse:2.283786+0.565304
## [102] train-rmse:0.636413+0.019293 test-rmse:2.282872+0.565285
## [103] train-rmse:0.629274+0.019289 test-rmse:2.280054+0.565951
## [104] train-rmse:0.622407+0.018371 test-rmse:2.279192+0.565634
## [105] train-rmse:0.616287+0.020740 test-rmse:2.279257+0.564765
## [106] train-rmse:0.609461+0.019620 test-rmse:2.278153+0.564242
## [107] train-rmse:0.603718+0.019086 test-rmse:2.277307+0.564741
## [108] train-rmse:0.597089+0.018303 test-rmse:2.278692+0.564560
## [109] train-rmse:0.590883+0.017983 test-rmse:2.276805+0.566399
## [110] train-rmse:0.586128+0.019267 test-rmse:2.276439+0.568179
## [111] train-rmse:0.581688+0.019511 test-rmse:2.276078+0.568155
## [112] train-rmse:0.574756+0.021649 test-rmse:2.275162+0.566947
## [113] train-rmse:0.570080+0.020621 test-rmse:2.273706+0.568396
## [114] train-rmse:0.563493+0.019204 test-rmse:2.273005+0.568125
## [115] train-rmse:0.559593+0.018228 test-rmse:2.272827+0.568765
## [116] train-rmse:0.553422+0.016017 test-rmse:2.270945+0.569626
## [117] train-rmse:0.547391+0.019584 test-rmse:2.269743+0.569879
## [118] train-rmse:0.543970+0.019486 test-rmse:2.268869+0.569240
## [119] train-rmse:0.539599+0.018941 test-rmse:2.268395+0.569951
## [120] train-rmse:0.533499+0.018028 test-rmse:2.266263+0.570999
## [121] train-rmse:0.528992+0.018541 test-rmse:2.265371+0.571946
## [122] train-rmse:0.524785+0.018025 test-rmse:2.265092+0.572430
## [123] train-rmse:0.521164+0.017680 test-rmse:2.264780+0.574316
## [124] train-rmse:0.515270+0.017586 test-rmse:2.262945+0.574427
## [125] train-rmse:0.512450+0.017350 test-rmse:2.262632+0.574658
## [126] train-rmse:0.508330+0.017817 test-rmse:2.261654+0.575845
## [127] train-rmse:0.503813+0.017700 test-rmse:2.261687+0.576227
## [128] train-rmse:0.499718+0.016352 test-rmse:2.262949+0.576762
## [129] train-rmse:0.493746+0.015676 test-rmse:2.261085+0.575925
## [130] train-rmse:0.486609+0.015534 test-rmse:2.260286+0.574403
## [131] train-rmse:0.483832+0.015962 test-rmse:2.259854+0.574862
## [132] train-rmse:0.479209+0.015866 test-rmse:2.258888+0.575739
## [133] train-rmse:0.474748+0.018039 test-rmse:2.259820+0.575873
## [134] train-rmse:0.470021+0.017525 test-rmse:2.258780+0.575972
## [135] train-rmse:0.466868+0.017463 test-rmse:2.258167+0.576438
## [136] train-rmse:0.461933+0.017055 test-rmse:2.257776+0.576966
## [137] train-rmse:0.457930+0.017949 test-rmse:2.257297+0.578037
## [138] train-rmse:0.452861+0.018720 test-rmse:2.256393+0.577949
## [139] train-rmse:0.449273+0.017390 test-rmse:2.256213+0.578623
## [140] train-rmse:0.445264+0.019309 test-rmse:2.255647+0.578069
## [141] train-rmse:0.440084+0.020273 test-rmse:2.255737+0.578807
## [142] train-rmse:0.435769+0.020532 test-rmse:2.256062+0.578621
## [143] train-rmse:0.433354+0.020765 test-rmse:2.255735+0.578849
## [144] train-rmse:0.429348+0.020257 test-rmse:2.255309+0.579561
## [145] train-rmse:0.425646+0.020393 test-rmse:2.254049+0.579878
## [146] train-rmse:0.420538+0.018834 test-rmse:2.254489+0.580736
## [147] train-rmse:0.417038+0.018872 test-rmse:2.254160+0.580827
## [148] train-rmse:0.412509+0.019848 test-rmse:2.254223+0.580654
## [149] train-rmse:0.409755+0.019968 test-rmse:2.255179+0.581494
## [150] train-rmse:0.405035+0.019280 test-rmse:2.255446+0.581796
## [151] train-rmse:0.401237+0.019664 test-rmse:2.254697+0.582867
## Stopping. Best iteration:
## [145] train-rmse:0.425646+0.020393 test-rmse:2.254049+0.579878
##
## 5
## [1] train-rmse:7.468952+0.154456 test-rmse:7.456881+0.656040
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.821931+0.136667 test-rmse:6.833865+0.624351
## [3] train-rmse:6.240418+0.121177 test-rmse:6.282598+0.594276
## [4] train-rmse:5.716149+0.108191 test-rmse:5.797644+0.562780
## [5] train-rmse:5.246960+0.095087 test-rmse:5.364626+0.540526
## [6] train-rmse:4.826605+0.085235 test-rmse:4.990985+0.513716
## [7] train-rmse:4.450981+0.075362 test-rmse:4.661745+0.496072
## [8] train-rmse:4.116637+0.069040 test-rmse:4.375711+0.481237
## [9] train-rmse:3.817062+0.061290 test-rmse:4.126312+0.470429
## [10] train-rmse:3.550221+0.052190 test-rmse:3.910394+0.461040
## [11] train-rmse:3.310531+0.049984 test-rmse:3.720819+0.447930
## [12] train-rmse:3.097968+0.041373 test-rmse:3.560931+0.450800
## [13] train-rmse:2.906702+0.037569 test-rmse:3.424813+0.454944
## [14] train-rmse:2.737042+0.033307 test-rmse:3.307117+0.459640
## [15] train-rmse:2.587449+0.029810 test-rmse:3.203532+0.474006
## [16] train-rmse:2.450597+0.030481 test-rmse:3.114630+0.486122
## [17] train-rmse:2.324045+0.027913 test-rmse:3.031533+0.480483
## [18] train-rmse:2.212094+0.027256 test-rmse:2.962952+0.498737
## [19] train-rmse:2.102923+0.033437 test-rmse:2.888177+0.513664
## [20] train-rmse:2.006187+0.037771 test-rmse:2.841489+0.526960
## [21] train-rmse:1.922015+0.036272 test-rmse:2.789127+0.522708
## [22] train-rmse:1.842035+0.039243 test-rmse:2.748221+0.527798
## [23] train-rmse:1.767024+0.041153 test-rmse:2.709310+0.528186
## [24] train-rmse:1.701595+0.043780 test-rmse:2.677479+0.533546
## [25] train-rmse:1.643397+0.042490 test-rmse:2.645584+0.530853
## [26] train-rmse:1.589695+0.048428 test-rmse:2.616395+0.539324
## [27] train-rmse:1.540678+0.044733 test-rmse:2.601405+0.541963
## [28] train-rmse:1.498456+0.042882 test-rmse:2.576376+0.542691
## [29] train-rmse:1.452266+0.046584 test-rmse:2.563289+0.546017
## [30] train-rmse:1.411866+0.051963 test-rmse:2.546829+0.549473
## [31] train-rmse:1.374768+0.054920 test-rmse:2.538838+0.553107
## [32] train-rmse:1.338841+0.051177 test-rmse:2.521367+0.554148
## [33] train-rmse:1.308857+0.051395 test-rmse:2.511480+0.558907
## [34] train-rmse:1.275841+0.047770 test-rmse:2.502246+0.555606
## [35] train-rmse:1.239958+0.049341 test-rmse:2.491111+0.558596
## [36] train-rmse:1.214661+0.047732 test-rmse:2.479850+0.567844
## [37] train-rmse:1.190574+0.050161 test-rmse:2.470629+0.568185
## [38] train-rmse:1.163705+0.049948 test-rmse:2.459985+0.572312
## [39] train-rmse:1.140981+0.051229 test-rmse:2.451555+0.572510
## [40] train-rmse:1.112214+0.047969 test-rmse:2.444252+0.571983
## [41] train-rmse:1.089138+0.051549 test-rmse:2.436371+0.570084
## [42] train-rmse:1.071461+0.051299 test-rmse:2.427559+0.575585
## [43] train-rmse:1.053956+0.051053 test-rmse:2.423630+0.579807
## [44] train-rmse:1.031514+0.054942 test-rmse:2.419026+0.582601
## [45] train-rmse:1.011419+0.053716 test-rmse:2.411420+0.583456
## [46] train-rmse:0.993842+0.052368 test-rmse:2.408095+0.583830
## [47] train-rmse:0.973596+0.050036 test-rmse:2.402768+0.582157
## [48] train-rmse:0.956649+0.049409 test-rmse:2.401019+0.583924
## [49] train-rmse:0.938305+0.044505 test-rmse:2.392940+0.583899
## [50] train-rmse:0.924537+0.044468 test-rmse:2.390509+0.588515
## [51] train-rmse:0.906551+0.038386 test-rmse:2.384503+0.590074
## [52] train-rmse:0.889317+0.037117 test-rmse:2.381595+0.590932
## [53] train-rmse:0.872798+0.038415 test-rmse:2.378564+0.592438
## [54] train-rmse:0.861497+0.037130 test-rmse:2.378070+0.593361
## [55] train-rmse:0.846815+0.038327 test-rmse:2.371378+0.596385
## [56] train-rmse:0.832304+0.040034 test-rmse:2.367208+0.597311
## [57] train-rmse:0.821589+0.039156 test-rmse:2.363416+0.600140
## [58] train-rmse:0.807063+0.041962 test-rmse:2.359881+0.601338
## [59] train-rmse:0.794296+0.041029 test-rmse:2.355820+0.602574
## [60] train-rmse:0.779016+0.037801 test-rmse:2.351698+0.603171
## [61] train-rmse:0.768674+0.037857 test-rmse:2.349282+0.602668
## [62] train-rmse:0.757465+0.038175 test-rmse:2.348336+0.603224
## [63] train-rmse:0.745019+0.040595 test-rmse:2.344842+0.602998
## [64] train-rmse:0.731138+0.037694 test-rmse:2.345691+0.604734
## [65] train-rmse:0.720089+0.036485 test-rmse:2.342775+0.605725
## [66] train-rmse:0.711578+0.037572 test-rmse:2.341943+0.606526
## [67] train-rmse:0.700906+0.033228 test-rmse:2.340618+0.606662
## [68] train-rmse:0.688072+0.031320 test-rmse:2.338311+0.607308
## [69] train-rmse:0.677863+0.031067 test-rmse:2.336740+0.608309
## [70] train-rmse:0.669201+0.029835 test-rmse:2.333850+0.610407
## [71] train-rmse:0.655988+0.027783 test-rmse:2.332459+0.611420
## [72] train-rmse:0.647766+0.027029 test-rmse:2.330523+0.611320
## [73] train-rmse:0.638876+0.028286 test-rmse:2.330574+0.611595
## [74] train-rmse:0.627165+0.030046 test-rmse:2.330130+0.613096
## [75] train-rmse:0.620212+0.030827 test-rmse:2.329497+0.614036
## [76] train-rmse:0.610391+0.031518 test-rmse:2.328843+0.614340
## [77] train-rmse:0.601108+0.031925 test-rmse:2.327445+0.614106
## [78] train-rmse:0.594128+0.033090 test-rmse:2.326649+0.615286
## [79] train-rmse:0.585625+0.031833 test-rmse:2.323692+0.615289
## [80] train-rmse:0.577910+0.032541 test-rmse:2.323147+0.614847
## [81] train-rmse:0.567337+0.031315 test-rmse:2.323299+0.615661
## [82] train-rmse:0.559436+0.031098 test-rmse:2.323359+0.615882
## [83] train-rmse:0.552497+0.029749 test-rmse:2.323941+0.616725
## [84] train-rmse:0.543879+0.027418 test-rmse:2.322028+0.617909
## [85] train-rmse:0.536322+0.029016 test-rmse:2.322721+0.617929
## [86] train-rmse:0.528450+0.031324 test-rmse:2.320474+0.619057
## [87] train-rmse:0.522558+0.031305 test-rmse:2.318780+0.617930
## [88] train-rmse:0.513864+0.029283 test-rmse:2.317505+0.619956
## [89] train-rmse:0.507313+0.030231 test-rmse:2.316622+0.620235
## [90] train-rmse:0.500668+0.026707 test-rmse:2.315424+0.620232
## [91] train-rmse:0.491448+0.024416 test-rmse:2.314995+0.619995
## [92] train-rmse:0.484400+0.024925 test-rmse:2.315194+0.621713
## [93] train-rmse:0.477705+0.026343 test-rmse:2.315215+0.621870
## [94] train-rmse:0.471902+0.026181 test-rmse:2.314133+0.622900
## [95] train-rmse:0.465541+0.025205 test-rmse:2.314738+0.623810
## [96] train-rmse:0.460041+0.024394 test-rmse:2.314028+0.625023
## [97] train-rmse:0.455235+0.024570 test-rmse:2.313141+0.625158
## [98] train-rmse:0.448738+0.023166 test-rmse:2.313021+0.624631
## [99] train-rmse:0.443408+0.022045 test-rmse:2.312370+0.624635
## [100] train-rmse:0.437633+0.022773 test-rmse:2.310891+0.624631
## [101] train-rmse:0.432745+0.023767 test-rmse:2.311669+0.624141
## [102] train-rmse:0.427513+0.023942 test-rmse:2.310818+0.624684
## [103] train-rmse:0.423306+0.023741 test-rmse:2.311002+0.624874
## [104] train-rmse:0.416616+0.021286 test-rmse:2.310431+0.625142
## [105] train-rmse:0.412569+0.022130 test-rmse:2.310165+0.625569
## [106] train-rmse:0.406216+0.023274 test-rmse:2.308768+0.626259
## [107] train-rmse:0.402140+0.022428 test-rmse:2.308579+0.626437
## [108] train-rmse:0.397096+0.022531 test-rmse:2.309337+0.626681
## [109] train-rmse:0.390235+0.023711 test-rmse:2.309550+0.627039
## [110] train-rmse:0.383792+0.024516 test-rmse:2.309177+0.628143
## [111] train-rmse:0.378588+0.023592 test-rmse:2.308808+0.628750
## [112] train-rmse:0.374902+0.023953 test-rmse:2.308124+0.629309
## [113] train-rmse:0.368982+0.024931 test-rmse:2.308813+0.629563
## [114] train-rmse:0.364576+0.025955 test-rmse:2.308752+0.630223
## [115] train-rmse:0.361336+0.024858 test-rmse:2.309414+0.630784
## [116] train-rmse:0.357342+0.023261 test-rmse:2.307996+0.630584
## [117] train-rmse:0.353610+0.022935 test-rmse:2.307512+0.630847
## [118] train-rmse:0.349372+0.022957 test-rmse:2.306753+0.630839
## [119] train-rmse:0.345616+0.023132 test-rmse:2.306564+0.631347
## [120] train-rmse:0.342319+0.023248 test-rmse:2.306987+0.630562
## [121] train-rmse:0.337273+0.023670 test-rmse:2.306819+0.632430
## [122] train-rmse:0.333848+0.022693 test-rmse:2.306930+0.632651
## [123] train-rmse:0.330720+0.023254 test-rmse:2.306865+0.632852
## [124] train-rmse:0.326529+0.022043 test-rmse:2.307547+0.632771
## [125] train-rmse:0.321020+0.019966 test-rmse:2.306238+0.631480
## [126] train-rmse:0.317880+0.019598 test-rmse:2.304955+0.631824
## [127] train-rmse:0.314022+0.019375 test-rmse:2.305078+0.631691
## [128] train-rmse:0.310104+0.020566 test-rmse:2.305162+0.632756
## [129] train-rmse:0.305801+0.019636 test-rmse:2.305007+0.632647
## [130] train-rmse:0.301769+0.017077 test-rmse:2.304906+0.632129
## [131] train-rmse:0.297906+0.016883 test-rmse:2.305012+0.632416
## [132] train-rmse:0.294585+0.017306 test-rmse:2.304501+0.632724
## [133] train-rmse:0.290687+0.016101 test-rmse:2.304131+0.632860
## [134] train-rmse:0.286880+0.015086 test-rmse:2.304531+0.633277
## [135] train-rmse:0.284247+0.014795 test-rmse:2.304793+0.632974
## [136] train-rmse:0.280754+0.015363 test-rmse:2.304690+0.632828
## [137] train-rmse:0.277504+0.015632 test-rmse:2.305330+0.632518
## [138] train-rmse:0.273065+0.016244 test-rmse:2.305682+0.633476
## [139] train-rmse:0.269869+0.016404 test-rmse:2.305110+0.633476
## Stopping. Best iteration:
## [133] train-rmse:0.290687+0.016101 test-rmse:2.304131+0.632860
##
## 6
## [1] train-rmse:7.464939+0.154128 test-rmse:7.454880+0.655764
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.813501+0.135919 test-rmse:6.828655+0.625962
## [3] train-rmse:6.226078+0.118472 test-rmse:6.279075+0.589763
## [4] train-rmse:5.696568+0.105342 test-rmse:5.781920+0.567596
## [5] train-rmse:5.220667+0.090739 test-rmse:5.348864+0.536774
## [6] train-rmse:4.792652+0.080122 test-rmse:4.969502+0.515068
## [7] train-rmse:4.407609+0.069823 test-rmse:4.631383+0.501701
## [8] train-rmse:4.064901+0.061969 test-rmse:4.343550+0.486087
## [9] train-rmse:3.757004+0.056705 test-rmse:4.087015+0.470485
## [10] train-rmse:3.481640+0.052780 test-rmse:3.856126+0.463182
## [11] train-rmse:3.235377+0.049433 test-rmse:3.668723+0.459879
## [12] train-rmse:3.014628+0.042473 test-rmse:3.503861+0.461241
## [13] train-rmse:2.818804+0.038484 test-rmse:3.372848+0.456257
## [14] train-rmse:2.637717+0.033909 test-rmse:3.255240+0.456455
## [15] train-rmse:2.478764+0.036240 test-rmse:3.154407+0.465507
## [16] train-rmse:2.334011+0.030139 test-rmse:3.060710+0.475412
## [17] train-rmse:2.205789+0.032772 test-rmse:2.985379+0.481613
## [18] train-rmse:2.087452+0.030448 test-rmse:2.912924+0.480416
## [19] train-rmse:1.981541+0.033786 test-rmse:2.857818+0.484194
## [20] train-rmse:1.886820+0.032665 test-rmse:2.799645+0.493316
## [21] train-rmse:1.798997+0.028604 test-rmse:2.747077+0.495739
## [22] train-rmse:1.713617+0.028276 test-rmse:2.706072+0.508385
## [23] train-rmse:1.640384+0.032288 test-rmse:2.664856+0.518911
## [24] train-rmse:1.572202+0.035709 test-rmse:2.632355+0.525278
## [25] train-rmse:1.514571+0.034974 test-rmse:2.606987+0.536802
## [26] train-rmse:1.451490+0.041719 test-rmse:2.581201+0.542687
## [27] train-rmse:1.402729+0.040968 test-rmse:2.565359+0.539379
## [28] train-rmse:1.353854+0.033543 test-rmse:2.550450+0.545254
## [29] train-rmse:1.308557+0.041203 test-rmse:2.538123+0.544258
## [30] train-rmse:1.265621+0.048358 test-rmse:2.521549+0.543930
## [31] train-rmse:1.223865+0.044988 test-rmse:2.507606+0.544401
## [32] train-rmse:1.187154+0.043153 test-rmse:2.496617+0.546662
## [33] train-rmse:1.147926+0.044744 test-rmse:2.486630+0.546951
## [34] train-rmse:1.117098+0.041613 test-rmse:2.475291+0.547763
## [35] train-rmse:1.083271+0.043149 test-rmse:2.469600+0.552228
## [36] train-rmse:1.053072+0.042568 test-rmse:2.463179+0.558013
## [37] train-rmse:1.027774+0.039952 test-rmse:2.452272+0.556816
## [38] train-rmse:1.001175+0.037724 test-rmse:2.444160+0.559883
## [39] train-rmse:0.976970+0.042639 test-rmse:2.437778+0.563489
## [40] train-rmse:0.952905+0.037162 test-rmse:2.433105+0.565676
## [41] train-rmse:0.929520+0.037949 test-rmse:2.428547+0.568753
## [42] train-rmse:0.905389+0.041659 test-rmse:2.421295+0.572069
## [43] train-rmse:0.884163+0.040849 test-rmse:2.412573+0.570194
## [44] train-rmse:0.867213+0.041414 test-rmse:2.406669+0.569642
## [45] train-rmse:0.845978+0.037894 test-rmse:2.405604+0.567934
## [46] train-rmse:0.832006+0.038022 test-rmse:2.401545+0.567763
## [47] train-rmse:0.813365+0.036942 test-rmse:2.397189+0.571664
## [48] train-rmse:0.795373+0.034566 test-rmse:2.392297+0.571597
## [49] train-rmse:0.775675+0.033963 test-rmse:2.386654+0.574293
## [50] train-rmse:0.756724+0.030655 test-rmse:2.385267+0.573730
## [51] train-rmse:0.743534+0.030839 test-rmse:2.384224+0.576110
## [52] train-rmse:0.725834+0.032819 test-rmse:2.382266+0.577013
## [53] train-rmse:0.710179+0.035275 test-rmse:2.380373+0.578209
## [54] train-rmse:0.698572+0.033189 test-rmse:2.377363+0.579300
## [55] train-rmse:0.683657+0.031454 test-rmse:2.374171+0.579339
## [56] train-rmse:0.667057+0.031841 test-rmse:2.373921+0.580932
## [57] train-rmse:0.654143+0.029229 test-rmse:2.369642+0.581905
## [58] train-rmse:0.640304+0.033369 test-rmse:2.366945+0.584632
## [59] train-rmse:0.624592+0.031980 test-rmse:2.362437+0.587244
## [60] train-rmse:0.612147+0.026457 test-rmse:2.360430+0.589989
## [61] train-rmse:0.601835+0.026117 test-rmse:2.358036+0.590568
## [62] train-rmse:0.589345+0.024975 test-rmse:2.356566+0.591237
## [63] train-rmse:0.578243+0.021519 test-rmse:2.355084+0.592590
## [64] train-rmse:0.565933+0.019696 test-rmse:2.353227+0.590976
## [65] train-rmse:0.555898+0.018825 test-rmse:2.353299+0.590169
## [66] train-rmse:0.545859+0.019537 test-rmse:2.352555+0.592225
## [67] train-rmse:0.536138+0.018366 test-rmse:2.350239+0.592692
## [68] train-rmse:0.529437+0.019265 test-rmse:2.349273+0.592992
## [69] train-rmse:0.521683+0.020204 test-rmse:2.348443+0.592874
## [70] train-rmse:0.511375+0.019856 test-rmse:2.346937+0.593630
## [71] train-rmse:0.502038+0.021389 test-rmse:2.347674+0.593732
## [72] train-rmse:0.492137+0.019545 test-rmse:2.346937+0.593596
## [73] train-rmse:0.483781+0.018361 test-rmse:2.344928+0.592360
## [74] train-rmse:0.475243+0.017918 test-rmse:2.344121+0.593182
## [75] train-rmse:0.465239+0.014934 test-rmse:2.342623+0.593086
## [76] train-rmse:0.456187+0.015018 test-rmse:2.343802+0.593618
## [77] train-rmse:0.450595+0.015508 test-rmse:2.343149+0.593880
## [78] train-rmse:0.441467+0.014809 test-rmse:2.343096+0.593626
## [79] train-rmse:0.432414+0.012608 test-rmse:2.344765+0.593493
## [80] train-rmse:0.425966+0.013531 test-rmse:2.344762+0.594520
## [81] train-rmse:0.419239+0.012343 test-rmse:2.343085+0.595450
## Stopping. Best iteration:
## [75] train-rmse:0.465239+0.014934 test-rmse:2.342623+0.593086
##
## 7
## [1] train-rmse:7.478345+0.160179 test-rmse:7.460255+0.677495
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.872826+0.146820 test-rmse:6.876525+0.701060
## [3] train-rmse:6.354528+0.136507 test-rmse:6.371928+0.681180
## [4] train-rmse:5.913547+0.128788 test-rmse:5.921723+0.668404
## [5] train-rmse:5.540435+0.122759 test-rmse:5.577674+0.661946
## [6] train-rmse:5.224933+0.118412 test-rmse:5.271653+0.646745
## [7] train-rmse:4.958007+0.115444 test-rmse:5.007546+0.619711
## [8] train-rmse:4.733526+0.112816 test-rmse:4.806486+0.614025
## [9] train-rmse:4.543762+0.112380 test-rmse:4.624998+0.585355
## [10] train-rmse:4.383765+0.111584 test-rmse:4.459835+0.557254
## [11] train-rmse:4.248086+0.111688 test-rmse:4.338996+0.542674
## [12] train-rmse:4.132388+0.111843 test-rmse:4.227125+0.526424
## [13] train-rmse:4.032131+0.112667 test-rmse:4.145259+0.526423
## [14] train-rmse:3.945786+0.113064 test-rmse:4.059895+0.507849
## [15] train-rmse:3.870111+0.114119 test-rmse:3.966962+0.486721
## [16] train-rmse:3.803383+0.114404 test-rmse:3.912675+0.474823
## [17] train-rmse:3.744471+0.115644 test-rmse:3.875113+0.464177
## [18] train-rmse:3.690828+0.116544 test-rmse:3.817212+0.465619
## [19] train-rmse:3.641929+0.117022 test-rmse:3.766722+0.455265
## [20] train-rmse:3.597536+0.118565 test-rmse:3.709681+0.436913
## [21] train-rmse:3.556757+0.118900 test-rmse:3.675325+0.435067
## [22] train-rmse:3.518652+0.119378 test-rmse:3.644813+0.448572
## [23] train-rmse:3.482671+0.119892 test-rmse:3.620644+0.439520
## [24] train-rmse:3.448836+0.120441 test-rmse:3.579608+0.430217
## [25] train-rmse:3.417454+0.120981 test-rmse:3.531649+0.421136
## [26] train-rmse:3.387407+0.121277 test-rmse:3.515326+0.419005
## [27] train-rmse:3.359116+0.121834 test-rmse:3.499635+0.424073
## [28] train-rmse:3.331462+0.122213 test-rmse:3.468021+0.422585
## [29] train-rmse:3.304833+0.122749 test-rmse:3.445226+0.420925
## [30] train-rmse:3.280109+0.122934 test-rmse:3.412381+0.421528
## [31] train-rmse:3.255836+0.123386 test-rmse:3.387401+0.412284
## [32] train-rmse:3.232294+0.123753 test-rmse:3.361496+0.420438
## [33] train-rmse:3.209719+0.124323 test-rmse:3.354503+0.426668
## [34] train-rmse:3.187912+0.124634 test-rmse:3.331940+0.423067
## [35] train-rmse:3.167082+0.125019 test-rmse:3.308345+0.429012
## [36] train-rmse:3.147028+0.125750 test-rmse:3.282453+0.416310
## [37] train-rmse:3.127319+0.126330 test-rmse:3.267201+0.417929
## [38] train-rmse:3.108613+0.126627 test-rmse:3.255452+0.428322
## [39] train-rmse:3.090301+0.127065 test-rmse:3.243815+0.428354
## [40] train-rmse:3.072534+0.127401 test-rmse:3.225979+0.429467
## [41] train-rmse:3.055316+0.127795 test-rmse:3.200159+0.417813
## [42] train-rmse:3.038583+0.128158 test-rmse:3.193431+0.418828
## [43] train-rmse:3.022292+0.128636 test-rmse:3.169783+0.422736
## [44] train-rmse:3.006431+0.129003 test-rmse:3.159638+0.425066
## [45] train-rmse:2.990899+0.129247 test-rmse:3.152933+0.440146
## [46] train-rmse:2.975833+0.129556 test-rmse:3.136677+0.432615
## [47] train-rmse:2.961319+0.129856 test-rmse:3.131416+0.433427
## [48] train-rmse:2.946892+0.130185 test-rmse:3.109636+0.424667
## [49] train-rmse:2.932952+0.130811 test-rmse:3.103715+0.429640
## [50] train-rmse:2.919424+0.131283 test-rmse:3.094992+0.427089
## [51] train-rmse:2.906126+0.131690 test-rmse:3.080061+0.432888
## [52] train-rmse:2.893126+0.131984 test-rmse:3.068176+0.424068
## [53] train-rmse:2.880226+0.132409 test-rmse:3.051934+0.430650
## [54] train-rmse:2.867570+0.132583 test-rmse:3.048436+0.442570
## [55] train-rmse:2.855172+0.132878 test-rmse:3.039868+0.445662
## [56] train-rmse:2.843142+0.133364 test-rmse:3.032973+0.447550
## [57] train-rmse:2.831353+0.133870 test-rmse:3.021227+0.449092
## [58] train-rmse:2.819616+0.134456 test-rmse:3.004798+0.450538
## [59] train-rmse:2.808291+0.134896 test-rmse:2.983600+0.450955
## [60] train-rmse:2.797130+0.135250 test-rmse:2.975973+0.451382
## [61] train-rmse:2.786021+0.135636 test-rmse:2.974723+0.449752
## [62] train-rmse:2.775358+0.135985 test-rmse:2.962578+0.445993
## [63] train-rmse:2.764766+0.136197 test-rmse:2.957186+0.447641
## [64] train-rmse:2.754410+0.136403 test-rmse:2.946898+0.440912
## [65] train-rmse:2.744253+0.136562 test-rmse:2.937576+0.444616
## [66] train-rmse:2.734179+0.136761 test-rmse:2.932759+0.453333
## [67] train-rmse:2.724227+0.137073 test-rmse:2.926606+0.447597
## [68] train-rmse:2.714295+0.137429 test-rmse:2.919149+0.453250
## [69] train-rmse:2.704556+0.137737 test-rmse:2.907167+0.460203
## [70] train-rmse:2.694983+0.137921 test-rmse:2.899435+0.463618
## [71] train-rmse:2.685683+0.138065 test-rmse:2.897539+0.469444
## [72] train-rmse:2.676470+0.138314 test-rmse:2.890391+0.463871
## [73] train-rmse:2.667470+0.138563 test-rmse:2.879812+0.457171
## [74] train-rmse:2.658683+0.138726 test-rmse:2.872520+0.456910
## [75] train-rmse:2.649878+0.138839 test-rmse:2.871588+0.461496
## [76] train-rmse:2.641198+0.138847 test-rmse:2.863470+0.466930
## [77] train-rmse:2.632730+0.138893 test-rmse:2.858466+0.472358
## [78] train-rmse:2.624391+0.139048 test-rmse:2.850202+0.469205
## [79] train-rmse:2.616139+0.139240 test-rmse:2.843569+0.464797
## [80] train-rmse:2.607961+0.139428 test-rmse:2.835465+0.462680
## [81] train-rmse:2.599834+0.139479 test-rmse:2.827623+0.465033
## [82] train-rmse:2.592019+0.139609 test-rmse:2.820021+0.467112
## [83] train-rmse:2.584312+0.139641 test-rmse:2.818413+0.465597
## [84] train-rmse:2.576678+0.139661 test-rmse:2.809701+0.467882
## [85] train-rmse:2.569117+0.139621 test-rmse:2.802710+0.476894
## [86] train-rmse:2.561660+0.139620 test-rmse:2.799377+0.476795
## [87] train-rmse:2.554336+0.139690 test-rmse:2.790881+0.476097
## [88] train-rmse:2.547099+0.139668 test-rmse:2.784535+0.473823
## [89] train-rmse:2.540008+0.139631 test-rmse:2.784065+0.476234
## [90] train-rmse:2.533012+0.139731 test-rmse:2.775429+0.473644
## [91] train-rmse:2.526082+0.139850 test-rmse:2.768006+0.476271
## [92] train-rmse:2.519266+0.139941 test-rmse:2.765404+0.480502
## [93] train-rmse:2.512498+0.140023 test-rmse:2.760912+0.478884
## [94] train-rmse:2.505792+0.140084 test-rmse:2.750156+0.480546
## [95] train-rmse:2.499305+0.140184 test-rmse:2.743096+0.484737
## [96] train-rmse:2.492835+0.140270 test-rmse:2.738383+0.480087
## [97] train-rmse:2.486503+0.140346 test-rmse:2.738108+0.484963
## [98] train-rmse:2.480189+0.140381 test-rmse:2.729117+0.490633
## [99] train-rmse:2.473964+0.140453 test-rmse:2.722582+0.488872
## [100] train-rmse:2.467806+0.140480 test-rmse:2.713385+0.486819
## [101] train-rmse:2.461798+0.140627 test-rmse:2.709639+0.487092
## [102] train-rmse:2.455898+0.140720 test-rmse:2.708344+0.484285
## [103] train-rmse:2.450071+0.140773 test-rmse:2.700690+0.483186
## [104] train-rmse:2.444298+0.140829 test-rmse:2.696770+0.477442
## [105] train-rmse:2.438634+0.140908 test-rmse:2.693025+0.478378
## [106] train-rmse:2.432960+0.140990 test-rmse:2.690775+0.480562
## [107] train-rmse:2.427364+0.140960 test-rmse:2.687970+0.481738
## [108] train-rmse:2.421905+0.140993 test-rmse:2.681286+0.488542
## [109] train-rmse:2.416519+0.140987 test-rmse:2.675504+0.485902
## [110] train-rmse:2.411264+0.141061 test-rmse:2.669828+0.487372
## [111] train-rmse:2.406071+0.141134 test-rmse:2.663510+0.490482
## [112] train-rmse:2.400935+0.141205 test-rmse:2.661840+0.488583
## [113] train-rmse:2.395849+0.141293 test-rmse:2.658227+0.489192
## [114] train-rmse:2.390806+0.141417 test-rmse:2.654420+0.498339
## [115] train-rmse:2.385812+0.141524 test-rmse:2.649537+0.498325
## [116] train-rmse:2.380823+0.141640 test-rmse:2.645138+0.500302
## [117] train-rmse:2.375905+0.141778 test-rmse:2.641089+0.497689
## [118] train-rmse:2.371058+0.141884 test-rmse:2.640151+0.496240
## [119] train-rmse:2.366282+0.141978 test-rmse:2.636613+0.493897
## [120] train-rmse:2.361592+0.142139 test-rmse:2.630706+0.494827
## [121] train-rmse:2.356950+0.142240 test-rmse:2.620724+0.492129
## [122] train-rmse:2.352346+0.142380 test-rmse:2.617553+0.490971
## [123] train-rmse:2.347832+0.142389 test-rmse:2.610891+0.487459
## [124] train-rmse:2.343419+0.142447 test-rmse:2.605060+0.487962
## [125] train-rmse:2.339028+0.142483 test-rmse:2.601566+0.489841
## [126] train-rmse:2.334725+0.142572 test-rmse:2.596990+0.490178
## [127] train-rmse:2.330455+0.142689 test-rmse:2.597175+0.491126
## [128] train-rmse:2.326243+0.142864 test-rmse:2.592640+0.490233
## [129] train-rmse:2.322069+0.143013 test-rmse:2.589335+0.490078
## [130] train-rmse:2.317964+0.143148 test-rmse:2.587352+0.498221
## [131] train-rmse:2.313903+0.143298 test-rmse:2.587948+0.502612
## [132] train-rmse:2.309818+0.143437 test-rmse:2.584981+0.501296
## [133] train-rmse:2.305787+0.143608 test-rmse:2.582077+0.501054
## [134] train-rmse:2.301747+0.143773 test-rmse:2.576900+0.505575
## [135] train-rmse:2.297790+0.143853 test-rmse:2.574579+0.504127
## [136] train-rmse:2.293901+0.143963 test-rmse:2.570280+0.504312
## [137] train-rmse:2.290075+0.144050 test-rmse:2.566543+0.502898
## [138] train-rmse:2.286284+0.144162 test-rmse:2.561237+0.498654
## [139] train-rmse:2.282542+0.144257 test-rmse:2.557189+0.498344
## [140] train-rmse:2.278867+0.144367 test-rmse:2.553383+0.499707
## [141] train-rmse:2.275254+0.144479 test-rmse:2.549011+0.499134
## [142] train-rmse:2.271656+0.144580 test-rmse:2.545845+0.499024
## [143] train-rmse:2.268094+0.144657 test-rmse:2.542857+0.499534
## [144] train-rmse:2.264630+0.144768 test-rmse:2.538835+0.501088
## [145] train-rmse:2.261179+0.144896 test-rmse:2.538183+0.505550
## [146] train-rmse:2.257771+0.145016 test-rmse:2.537618+0.508481
## [147] train-rmse:2.254425+0.145157 test-rmse:2.534502+0.512338
## [148] train-rmse:2.251074+0.145289 test-rmse:2.532237+0.513344
## [149] train-rmse:2.247752+0.145436 test-rmse:2.532446+0.513891
## [150] train-rmse:2.244445+0.145520 test-rmse:2.528995+0.510777
## [151] train-rmse:2.241203+0.145664 test-rmse:2.524581+0.512917
## [152] train-rmse:2.237955+0.145799 test-rmse:2.517947+0.514095
## [153] train-rmse:2.234734+0.145926 test-rmse:2.517415+0.513824
## [154] train-rmse:2.231546+0.146035 test-rmse:2.516936+0.512645
## [155] train-rmse:2.228425+0.146179 test-rmse:2.515092+0.513268
## [156] train-rmse:2.225322+0.146290 test-rmse:2.510743+0.510139
## [157] train-rmse:2.222278+0.146364 test-rmse:2.507002+0.510117
## [158] train-rmse:2.219284+0.146463 test-rmse:2.507916+0.507660
## [159] train-rmse:2.216315+0.146537 test-rmse:2.505236+0.506406
## [160] train-rmse:2.213380+0.146648 test-rmse:2.505818+0.505111
## [161] train-rmse:2.210461+0.146735 test-rmse:2.504835+0.505632
## [162] train-rmse:2.207565+0.146826 test-rmse:2.502838+0.506870
## [163] train-rmse:2.204731+0.146933 test-rmse:2.498596+0.507235
## [164] train-rmse:2.201919+0.147043 test-rmse:2.494351+0.508503
## [165] train-rmse:2.199131+0.147139 test-rmse:2.487563+0.510302
## [166] train-rmse:2.196368+0.147258 test-rmse:2.487301+0.510258
## [167] train-rmse:2.193643+0.147373 test-rmse:2.489623+0.516178
## [168] train-rmse:2.190938+0.147458 test-rmse:2.487082+0.520167
## [169] train-rmse:2.188276+0.147550 test-rmse:2.484474+0.519905
## [170] train-rmse:2.185611+0.147649 test-rmse:2.480183+0.523848
## [171] train-rmse:2.182981+0.147749 test-rmse:2.475757+0.522138
## [172] train-rmse:2.180408+0.147835 test-rmse:2.473919+0.520927
## [173] train-rmse:2.177866+0.147907 test-rmse:2.471988+0.520109
## [174] train-rmse:2.175326+0.147994 test-rmse:2.469408+0.519391
## [175] train-rmse:2.172800+0.148089 test-rmse:2.466712+0.519621
## [176] train-rmse:2.170309+0.148205 test-rmse:2.466553+0.518652
## [177] train-rmse:2.167835+0.148337 test-rmse:2.466679+0.518914
## [178] train-rmse:2.165397+0.148445 test-rmse:2.464019+0.515229
## [179] train-rmse:2.162959+0.148491 test-rmse:2.461383+0.513821
## [180] train-rmse:2.160558+0.148568 test-rmse:2.459454+0.514427
## [181] train-rmse:2.158214+0.148636 test-rmse:2.455588+0.515655
## [182] train-rmse:2.155908+0.148723 test-rmse:2.451192+0.514101
## [183] train-rmse:2.153585+0.148821 test-rmse:2.453913+0.516394
## [184] train-rmse:2.151280+0.148925 test-rmse:2.451702+0.517277
## [185] train-rmse:2.149012+0.149022 test-rmse:2.449288+0.517897
## [186] train-rmse:2.146771+0.149094 test-rmse:2.449732+0.519542
## [187] train-rmse:2.144502+0.149198 test-rmse:2.450320+0.518790
## [188] train-rmse:2.142285+0.149295 test-rmse:2.447209+0.519347
## [189] train-rmse:2.140072+0.149403 test-rmse:2.447679+0.520626
## [190] train-rmse:2.137877+0.149523 test-rmse:2.444956+0.521063
## [191] train-rmse:2.135715+0.149638 test-rmse:2.444974+0.521418
## [192] train-rmse:2.133548+0.149748 test-rmse:2.440515+0.524086
## [193] train-rmse:2.131423+0.149850 test-rmse:2.438002+0.522951
## [194] train-rmse:2.129314+0.149940 test-rmse:2.438577+0.525529
## [195] train-rmse:2.127232+0.150004 test-rmse:2.436960+0.525595
## [196] train-rmse:2.125162+0.150076 test-rmse:2.435832+0.523523
## [197] train-rmse:2.123078+0.150147 test-rmse:2.436257+0.525344
## [198] train-rmse:2.121059+0.150251 test-rmse:2.437262+0.523833
## [199] train-rmse:2.119058+0.150361 test-rmse:2.434354+0.524004
## [200] train-rmse:2.117080+0.150471 test-rmse:2.434352+0.522071
## [201] train-rmse:2.115109+0.150566 test-rmse:2.432094+0.521387
## [202] train-rmse:2.113155+0.150658 test-rmse:2.429164+0.520305
## [203] train-rmse:2.111225+0.150777 test-rmse:2.428375+0.521960
## [204] train-rmse:2.109305+0.150882 test-rmse:2.425871+0.522329
## [205] train-rmse:2.107411+0.150984 test-rmse:2.422355+0.522509
## [206] train-rmse:2.105523+0.151086 test-rmse:2.418497+0.523618
## [207] train-rmse:2.103660+0.151184 test-rmse:2.417651+0.523570
## [208] train-rmse:2.101799+0.151286 test-rmse:2.415910+0.523101
## [209] train-rmse:2.099954+0.151379 test-rmse:2.419401+0.525378
## [210] train-rmse:2.098146+0.151458 test-rmse:2.417193+0.524223
## [211] train-rmse:2.096344+0.151543 test-rmse:2.417635+0.524936
## [212] train-rmse:2.094539+0.151632 test-rmse:2.414373+0.525016
## [213] train-rmse:2.092755+0.151724 test-rmse:2.412431+0.527064
## [214] train-rmse:2.090981+0.151825 test-rmse:2.410511+0.527712
## [215] train-rmse:2.089238+0.151916 test-rmse:2.409062+0.528636
## [216] train-rmse:2.087497+0.152011 test-rmse:2.408172+0.529020
## [217] train-rmse:2.085788+0.152104 test-rmse:2.410096+0.528934
## [218] train-rmse:2.084090+0.152211 test-rmse:2.408978+0.527580
## [219] train-rmse:2.082399+0.152298 test-rmse:2.409486+0.526511
## [220] train-rmse:2.080722+0.152382 test-rmse:2.409069+0.525810
## [221] train-rmse:2.079050+0.152449 test-rmse:2.407983+0.528220
## [222] train-rmse:2.077386+0.152524 test-rmse:2.406045+0.526808
## [223] train-rmse:2.075723+0.152589 test-rmse:2.404584+0.527557
## [224] train-rmse:2.074083+0.152667 test-rmse:2.403456+0.528954
## [225] train-rmse:2.072491+0.152763 test-rmse:2.400840+0.529662
## [226] train-rmse:2.070916+0.152863 test-rmse:2.399011+0.528788
## [227] train-rmse:2.069344+0.152950 test-rmse:2.397570+0.528846
## [228] train-rmse:2.067785+0.153041 test-rmse:2.395356+0.526625
## [229] train-rmse:2.066233+0.153112 test-rmse:2.395175+0.525624
## [230] train-rmse:2.064700+0.153189 test-rmse:2.392484+0.526323
## [231] train-rmse:2.063168+0.153270 test-rmse:2.392225+0.526420
## [232] train-rmse:2.061652+0.153345 test-rmse:2.392807+0.526155
## [233] train-rmse:2.060133+0.153433 test-rmse:2.391063+0.526369
## [234] train-rmse:2.058585+0.153464 test-rmse:2.392507+0.529455
## [235] train-rmse:2.057088+0.153526 test-rmse:2.390442+0.530706
## [236] train-rmse:2.055594+0.153589 test-rmse:2.390148+0.531444
## [237] train-rmse:2.054117+0.153647 test-rmse:2.388217+0.529875
## [238] train-rmse:2.052673+0.153711 test-rmse:2.388533+0.531045
## [239] train-rmse:2.051203+0.153776 test-rmse:2.389368+0.530420
## [240] train-rmse:2.049761+0.153830 test-rmse:2.389535+0.529702
## [241] train-rmse:2.048326+0.153874 test-rmse:2.389660+0.529684
## [242] train-rmse:2.046899+0.153931 test-rmse:2.388272+0.529704
## [243] train-rmse:2.045471+0.153986 test-rmse:2.385868+0.529381
## [244] train-rmse:2.044043+0.154039 test-rmse:2.384202+0.529498
## [245] train-rmse:2.042641+0.154097 test-rmse:2.384944+0.529797
## [246] train-rmse:2.041247+0.154164 test-rmse:2.383637+0.530369
## [247] train-rmse:2.039864+0.154228 test-rmse:2.382276+0.530372
## [248] train-rmse:2.038498+0.154281 test-rmse:2.381853+0.531155
## [249] train-rmse:2.037097+0.154334 test-rmse:2.380878+0.529842
## [250] train-rmse:2.035743+0.154395 test-rmse:2.377800+0.527603
## [251] train-rmse:2.034378+0.154436 test-rmse:2.376980+0.525780
## [252] train-rmse:2.033029+0.154477 test-rmse:2.374741+0.526668
## [253] train-rmse:2.031700+0.154532 test-rmse:2.373708+0.525934
## [254] train-rmse:2.030378+0.154578 test-rmse:2.373799+0.527838
## [255] train-rmse:2.029077+0.154632 test-rmse:2.373583+0.527181
## [256] train-rmse:2.027778+0.154711 test-rmse:2.372491+0.528099
## [257] train-rmse:2.026496+0.154783 test-rmse:2.373328+0.527852
## [258] train-rmse:2.025206+0.154838 test-rmse:2.372446+0.527401
## [259] train-rmse:2.023930+0.154892 test-rmse:2.371891+0.529080
## [260] train-rmse:2.022660+0.154957 test-rmse:2.370992+0.530871
## [261] train-rmse:2.021392+0.155004 test-rmse:2.373222+0.535140
## [262] train-rmse:2.020128+0.155059 test-rmse:2.371279+0.534583
## [263] train-rmse:2.018889+0.155111 test-rmse:2.370660+0.532332
## [264] train-rmse:2.017614+0.155220 test-rmse:2.370790+0.531808
## [265] train-rmse:2.016355+0.155245 test-rmse:2.369933+0.531439
## [266] train-rmse:2.015140+0.155296 test-rmse:2.369333+0.531954
## [267] train-rmse:2.013929+0.155347 test-rmse:2.368398+0.531222
## [268] train-rmse:2.012714+0.155432 test-rmse:2.369025+0.531295
## [269] train-rmse:2.011515+0.155485 test-rmse:2.369971+0.531331
## [270] train-rmse:2.010307+0.155535 test-rmse:2.368885+0.529484
## [271] train-rmse:2.009119+0.155580 test-rmse:2.367326+0.529984
## [272] train-rmse:2.007928+0.155634 test-rmse:2.367779+0.528700
## [273] train-rmse:2.006744+0.155719 test-rmse:2.367976+0.529678
## [274] train-rmse:2.005554+0.155776 test-rmse:2.368881+0.528807
## [275] train-rmse:2.004391+0.155843 test-rmse:2.367405+0.529646
## [276] train-rmse:2.003237+0.155905 test-rmse:2.364171+0.531476
## [277] train-rmse:2.002093+0.155951 test-rmse:2.362066+0.530649
## [278] train-rmse:2.000937+0.156045 test-rmse:2.360256+0.531075
## [279] train-rmse:1.999808+0.156102 test-rmse:2.358697+0.531267
## [280] train-rmse:1.998666+0.156181 test-rmse:2.357441+0.532061
## [281] train-rmse:1.997543+0.156243 test-rmse:2.356532+0.530880
## [282] train-rmse:1.996432+0.156303 test-rmse:2.354716+0.530604
## [283] train-rmse:1.995322+0.156367 test-rmse:2.355088+0.530614
## [284] train-rmse:1.994210+0.156425 test-rmse:2.355788+0.530763
## [285] train-rmse:1.993096+0.156476 test-rmse:2.355954+0.530433
## [286] train-rmse:1.991974+0.156508 test-rmse:2.355601+0.531511
## [287] train-rmse:1.990895+0.156562 test-rmse:2.358919+0.530237
## [288] train-rmse:1.989814+0.156622 test-rmse:2.359106+0.530861
## Stopping. Best iteration:
## [282] train-rmse:1.996432+0.156303 test-rmse:2.354716+0.530604
##
## 8
## [1] train-rmse:7.427045+0.156214 test-rmse:7.423641+0.660314
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.772314+0.141401 test-rmse:6.788771+0.646805
## [3] train-rmse:6.206743+0.132520 test-rmse:6.263520+0.604745
## [4] train-rmse:5.714442+0.124377 test-rmse:5.806287+0.598579
## [5] train-rmse:5.298285+0.116353 test-rmse:5.395127+0.557533
## [6] train-rmse:4.928735+0.112765 test-rmse:5.032192+0.541685
## [7] train-rmse:4.615040+0.107796 test-rmse:4.746926+0.529485
## [8] train-rmse:4.340093+0.103797 test-rmse:4.496761+0.510019
## [9] train-rmse:4.105665+0.102910 test-rmse:4.257504+0.492046
## [10] train-rmse:3.902590+0.101233 test-rmse:4.081438+0.481315
## [11] train-rmse:3.723381+0.097824 test-rmse:3.897165+0.478054
## [12] train-rmse:3.571643+0.098041 test-rmse:3.757026+0.453339
## [13] train-rmse:3.431228+0.097850 test-rmse:3.630710+0.426888
## [14] train-rmse:3.308590+0.084982 test-rmse:3.536652+0.437280
## [15] train-rmse:3.193402+0.082876 test-rmse:3.423055+0.446760
## [16] train-rmse:3.103225+0.085369 test-rmse:3.363751+0.444460
## [17] train-rmse:3.020074+0.094534 test-rmse:3.290809+0.429230
## [18] train-rmse:2.940943+0.091381 test-rmse:3.215132+0.447513
## [19] train-rmse:2.871714+0.089476 test-rmse:3.168110+0.432315
## [20] train-rmse:2.807974+0.076732 test-rmse:3.115852+0.443238
## [21] train-rmse:2.749584+0.074795 test-rmse:3.070233+0.455437
## [22] train-rmse:2.704871+0.076950 test-rmse:3.034726+0.460336
## [23] train-rmse:2.660419+0.077945 test-rmse:3.002650+0.461770
## [24] train-rmse:2.616802+0.067510 test-rmse:2.976789+0.461780
## [25] train-rmse:2.574489+0.066025 test-rmse:2.942196+0.464044
## [26] train-rmse:2.535834+0.067000 test-rmse:2.907997+0.465059
## [27] train-rmse:2.499927+0.069028 test-rmse:2.894271+0.463745
## [28] train-rmse:2.470732+0.069616 test-rmse:2.869609+0.473340
## [29] train-rmse:2.439509+0.067003 test-rmse:2.846201+0.477169
## [30] train-rmse:2.412285+0.067499 test-rmse:2.832413+0.479430
## [31] train-rmse:2.385142+0.069744 test-rmse:2.816159+0.475276
## [32] train-rmse:2.358003+0.070623 test-rmse:2.803666+0.472239
## [33] train-rmse:2.329074+0.062163 test-rmse:2.781905+0.479645
## [34] train-rmse:2.305891+0.061297 test-rmse:2.776446+0.481452
## [35] train-rmse:2.281668+0.062197 test-rmse:2.757631+0.495280
## [36] train-rmse:2.254772+0.063502 test-rmse:2.735220+0.485681
## [37] train-rmse:2.234711+0.063319 test-rmse:2.719576+0.485283
## [38] train-rmse:2.213991+0.063632 test-rmse:2.705489+0.487183
## [39] train-rmse:2.190205+0.057100 test-rmse:2.701150+0.497828
## [40] train-rmse:2.172129+0.056364 test-rmse:2.697120+0.498719
## [41] train-rmse:2.148932+0.060866 test-rmse:2.674370+0.491128
## [42] train-rmse:2.129138+0.056753 test-rmse:2.662124+0.496631
## [43] train-rmse:2.111879+0.056246 test-rmse:2.649640+0.499513
## [44] train-rmse:2.095599+0.056491 test-rmse:2.637794+0.494018
## [45] train-rmse:2.077621+0.057461 test-rmse:2.635386+0.494057
## [46] train-rmse:2.059647+0.057366 test-rmse:2.624118+0.487771
## [47] train-rmse:2.038547+0.054981 test-rmse:2.607670+0.480526
## [48] train-rmse:2.024177+0.055009 test-rmse:2.600079+0.486059
## [49] train-rmse:2.007306+0.054895 test-rmse:2.590743+0.487685
## [50] train-rmse:1.992473+0.054095 test-rmse:2.581682+0.490905
## [51] train-rmse:1.977927+0.054521 test-rmse:2.570275+0.489398
## [52] train-rmse:1.964646+0.054039 test-rmse:2.560058+0.489471
## [53] train-rmse:1.949443+0.051629 test-rmse:2.554637+0.499372
## [54] train-rmse:1.933754+0.053345 test-rmse:2.535538+0.501026
## [55] train-rmse:1.919730+0.055505 test-rmse:2.528277+0.486561
## [56] train-rmse:1.905202+0.054797 test-rmse:2.517030+0.489863
## [57] train-rmse:1.891833+0.057638 test-rmse:2.508276+0.491951
## [58] train-rmse:1.880317+0.057533 test-rmse:2.497677+0.495068
## [59] train-rmse:1.868008+0.058077 test-rmse:2.489124+0.497341
## [60] train-rmse:1.855431+0.055800 test-rmse:2.479059+0.500997
## [61] train-rmse:1.842641+0.053164 test-rmse:2.471514+0.502085
## [62] train-rmse:1.830176+0.052522 test-rmse:2.461755+0.499477
## [63] train-rmse:1.819558+0.052604 test-rmse:2.454291+0.499361
## [64] train-rmse:1.806405+0.054922 test-rmse:2.448383+0.489670
## [65] train-rmse:1.792265+0.053847 test-rmse:2.438409+0.492940
## [66] train-rmse:1.780590+0.052789 test-rmse:2.434315+0.492824
## [67] train-rmse:1.771196+0.052582 test-rmse:2.432323+0.493036
## [68] train-rmse:1.760353+0.053262 test-rmse:2.421732+0.498156
## [69] train-rmse:1.751264+0.053307 test-rmse:2.415313+0.496936
## [70] train-rmse:1.742566+0.053720 test-rmse:2.408688+0.493550
## [71] train-rmse:1.732579+0.052234 test-rmse:2.403142+0.496674
## [72] train-rmse:1.721109+0.049213 test-rmse:2.395632+0.492710
## [73] train-rmse:1.712579+0.048577 test-rmse:2.391115+0.492422
## [74] train-rmse:1.704618+0.048481 test-rmse:2.387501+0.496031
## [75] train-rmse:1.695802+0.047888 test-rmse:2.386642+0.498595
## [76] train-rmse:1.687422+0.047526 test-rmse:2.384771+0.499225
## [77] train-rmse:1.678763+0.046526 test-rmse:2.381998+0.498285
## [78] train-rmse:1.669246+0.044284 test-rmse:2.375501+0.498746
## [79] train-rmse:1.661885+0.043967 test-rmse:2.362943+0.504140
## [80] train-rmse:1.653276+0.041699 test-rmse:2.355946+0.503355
## [81] train-rmse:1.644346+0.041883 test-rmse:2.349480+0.503782
## [82] train-rmse:1.636051+0.042922 test-rmse:2.345468+0.503683
## [83] train-rmse:1.629384+0.042832 test-rmse:2.342735+0.503710
## [84] train-rmse:1.621229+0.042509 test-rmse:2.337128+0.499031
## [85] train-rmse:1.613751+0.041619 test-rmse:2.331702+0.500480
## [86] train-rmse:1.605209+0.042103 test-rmse:2.326706+0.501432
## [87] train-rmse:1.598185+0.042201 test-rmse:2.322934+0.498675
## [88] train-rmse:1.588790+0.040869 test-rmse:2.318523+0.499274
## [89] train-rmse:1.581926+0.041290 test-rmse:2.313917+0.500061
## [90] train-rmse:1.575700+0.041336 test-rmse:2.314906+0.507629
## [91] train-rmse:1.568313+0.040821 test-rmse:2.310570+0.507360
## [92] train-rmse:1.561265+0.040947 test-rmse:2.305396+0.508851
## [93] train-rmse:1.553981+0.042774 test-rmse:2.301546+0.506029
## [94] train-rmse:1.544729+0.041752 test-rmse:2.297218+0.505882
## [95] train-rmse:1.534494+0.041275 test-rmse:2.293852+0.505764
## [96] train-rmse:1.527100+0.043621 test-rmse:2.295236+0.506316
## [97] train-rmse:1.520005+0.042193 test-rmse:2.294094+0.507507
## [98] train-rmse:1.513961+0.042744 test-rmse:2.290624+0.503662
## [99] train-rmse:1.508823+0.042543 test-rmse:2.288371+0.504474
## [100] train-rmse:1.503643+0.042715 test-rmse:2.285368+0.504529
## [101] train-rmse:1.495010+0.039869 test-rmse:2.281470+0.505447
## [102] train-rmse:1.489665+0.039555 test-rmse:2.279539+0.505084
## [103] train-rmse:1.484068+0.039642 test-rmse:2.278364+0.505295
## [104] train-rmse:1.477378+0.037921 test-rmse:2.275816+0.506636
## [105] train-rmse:1.472379+0.037772 test-rmse:2.274053+0.506021
## [106] train-rmse:1.465860+0.037357 test-rmse:2.266326+0.509075
## [107] train-rmse:1.459583+0.036447 test-rmse:2.261753+0.509716
## [108] train-rmse:1.453812+0.036266 test-rmse:2.264715+0.508909
## [109] train-rmse:1.446870+0.040841 test-rmse:2.260925+0.511486
## [110] train-rmse:1.442515+0.040980 test-rmse:2.258442+0.510516
## [111] train-rmse:1.437022+0.043334 test-rmse:2.253930+0.507320
## [112] train-rmse:1.431771+0.042502 test-rmse:2.250093+0.508906
## [113] train-rmse:1.426139+0.041957 test-rmse:2.249319+0.508663
## [114] train-rmse:1.420798+0.042623 test-rmse:2.245575+0.508314
## [115] train-rmse:1.416138+0.042492 test-rmse:2.244773+0.507648
## [116] train-rmse:1.408825+0.039645 test-rmse:2.242480+0.511075
## [117] train-rmse:1.403216+0.039421 test-rmse:2.237474+0.513220
## [118] train-rmse:1.398623+0.039723 test-rmse:2.237114+0.517342
## [119] train-rmse:1.393080+0.039852 test-rmse:2.235000+0.517554
## [120] train-rmse:1.387471+0.039145 test-rmse:2.234578+0.516008
## [121] train-rmse:1.383393+0.039321 test-rmse:2.229969+0.517656
## [122] train-rmse:1.377329+0.038200 test-rmse:2.229008+0.517038
## [123] train-rmse:1.372398+0.037682 test-rmse:2.226425+0.520353
## [124] train-rmse:1.368873+0.037633 test-rmse:2.224191+0.521477
## [125] train-rmse:1.363553+0.036143 test-rmse:2.223504+0.522948
## [126] train-rmse:1.357503+0.040393 test-rmse:2.220844+0.520429
## [127] train-rmse:1.353391+0.040628 test-rmse:2.221545+0.523459
## [128] train-rmse:1.348338+0.039704 test-rmse:2.220925+0.522310
## [129] train-rmse:1.343931+0.039048 test-rmse:2.220073+0.522948
## [130] train-rmse:1.339287+0.038348 test-rmse:2.216624+0.523534
## [131] train-rmse:1.332865+0.037009 test-rmse:2.212315+0.525534
## [132] train-rmse:1.328376+0.036079 test-rmse:2.211146+0.525745
## [133] train-rmse:1.323956+0.035912 test-rmse:2.208763+0.525529
## [134] train-rmse:1.319615+0.035618 test-rmse:2.206226+0.526466
## [135] train-rmse:1.312809+0.037213 test-rmse:2.205192+0.526879
## [136] train-rmse:1.308760+0.037262 test-rmse:2.204272+0.528516
## [137] train-rmse:1.304189+0.036974 test-rmse:2.203351+0.526227
## [138] train-rmse:1.300494+0.036877 test-rmse:2.204594+0.526860
## [139] train-rmse:1.296626+0.036400 test-rmse:2.202747+0.529033
## [140] train-rmse:1.289696+0.035561 test-rmse:2.196129+0.530852
## [141] train-rmse:1.286568+0.035648 test-rmse:2.196289+0.532503
## [142] train-rmse:1.283533+0.035966 test-rmse:2.195031+0.532532
## [143] train-rmse:1.278604+0.040049 test-rmse:2.193005+0.531914
## [144] train-rmse:1.275448+0.039897 test-rmse:2.192885+0.531589
## [145] train-rmse:1.272059+0.040440 test-rmse:2.191082+0.530252
## [146] train-rmse:1.268275+0.040550 test-rmse:2.192382+0.530516
## [147] train-rmse:1.262091+0.043877 test-rmse:2.189240+0.534208
## [148] train-rmse:1.258391+0.043896 test-rmse:2.189343+0.535447
## [149] train-rmse:1.254166+0.043478 test-rmse:2.187176+0.537546
## [150] train-rmse:1.250648+0.043087 test-rmse:2.186702+0.537311
## [151] train-rmse:1.247008+0.043281 test-rmse:2.185528+0.535473
## [152] train-rmse:1.242004+0.043427 test-rmse:2.184783+0.535737
## [153] train-rmse:1.238505+0.043271 test-rmse:2.182044+0.535507
## [154] train-rmse:1.233336+0.045505 test-rmse:2.182426+0.536477
## [155] train-rmse:1.227447+0.047564 test-rmse:2.181067+0.534722
## [156] train-rmse:1.223470+0.047675 test-rmse:2.181116+0.535822
## [157] train-rmse:1.220539+0.047656 test-rmse:2.180336+0.536959
## [158] train-rmse:1.216820+0.047427 test-rmse:2.178433+0.539728
## [159] train-rmse:1.212711+0.046881 test-rmse:2.178093+0.538893
## [160] train-rmse:1.208017+0.049289 test-rmse:2.183868+0.538413
## [161] train-rmse:1.205369+0.049212 test-rmse:2.183518+0.539675
## [162] train-rmse:1.202294+0.048850 test-rmse:2.181199+0.540700
## [163] train-rmse:1.199253+0.048431 test-rmse:2.180309+0.541489
## [164] train-rmse:1.196669+0.048295 test-rmse:2.178870+0.540471
## [165] train-rmse:1.193731+0.047569 test-rmse:2.177273+0.540756
## [166] train-rmse:1.190726+0.047025 test-rmse:2.175762+0.540550
## [167] train-rmse:1.187501+0.046590 test-rmse:2.173040+0.541692
## [168] train-rmse:1.182027+0.046223 test-rmse:2.172743+0.541515
## [169] train-rmse:1.178051+0.045119 test-rmse:2.173888+0.541442
## [170] train-rmse:1.175472+0.045141 test-rmse:2.174186+0.541499
## [171] train-rmse:1.172369+0.045579 test-rmse:2.170942+0.542551
## [172] train-rmse:1.169411+0.045303 test-rmse:2.168916+0.543058
## [173] train-rmse:1.167044+0.045458 test-rmse:2.168355+0.542942
## [174] train-rmse:1.161818+0.047053 test-rmse:2.167859+0.545764
## [175] train-rmse:1.158292+0.049424 test-rmse:2.170571+0.545244
## [176] train-rmse:1.155591+0.049889 test-rmse:2.169085+0.545519
## [177] train-rmse:1.151930+0.049159 test-rmse:2.168225+0.546309
## [178] train-rmse:1.148447+0.048530 test-rmse:2.168440+0.546802
## [179] train-rmse:1.145649+0.048119 test-rmse:2.165041+0.548479
## [180] train-rmse:1.142691+0.048302 test-rmse:2.166055+0.548076
## [181] train-rmse:1.138805+0.048595 test-rmse:2.165619+0.549081
## [182] train-rmse:1.134874+0.048035 test-rmse:2.164499+0.548713
## [183] train-rmse:1.132668+0.048183 test-rmse:2.163502+0.548642
## [184] train-rmse:1.130461+0.047927 test-rmse:2.162675+0.548771
## [185] train-rmse:1.127975+0.047682 test-rmse:2.161475+0.548673
## [186] train-rmse:1.125235+0.047476 test-rmse:2.163308+0.548990
## [187] train-rmse:1.119503+0.050272 test-rmse:2.164695+0.549514
## [188] train-rmse:1.116070+0.051373 test-rmse:2.160784+0.548760
## [189] train-rmse:1.112377+0.050677 test-rmse:2.161454+0.548431
## [190] train-rmse:1.108597+0.049309 test-rmse:2.161395+0.548423
## [191] train-rmse:1.105229+0.049557 test-rmse:2.162212+0.548772
## [192] train-rmse:1.102460+0.050086 test-rmse:2.160379+0.548179
## [193] train-rmse:1.100763+0.050177 test-rmse:2.160799+0.548848
## [194] train-rmse:1.097064+0.051699 test-rmse:2.159937+0.548183
## [195] train-rmse:1.094150+0.051679 test-rmse:2.159728+0.549418
## [196] train-rmse:1.091943+0.051748 test-rmse:2.161153+0.553234
## [197] train-rmse:1.088841+0.050927 test-rmse:2.158582+0.554293
## [198] train-rmse:1.085550+0.049993 test-rmse:2.156978+0.553601
## [199] train-rmse:1.082159+0.050211 test-rmse:2.155250+0.553550
## [200] train-rmse:1.079326+0.049801 test-rmse:2.154017+0.553805
## [201] train-rmse:1.077134+0.049906 test-rmse:2.152243+0.553496
## [202] train-rmse:1.074898+0.050137 test-rmse:2.150091+0.554418
## [203] train-rmse:1.073016+0.050196 test-rmse:2.149641+0.556752
## [204] train-rmse:1.069955+0.049294 test-rmse:2.150147+0.556042
## [205] train-rmse:1.067981+0.049300 test-rmse:2.149897+0.555706
## [206] train-rmse:1.065776+0.049306 test-rmse:2.146833+0.556351
## [207] train-rmse:1.061825+0.051338 test-rmse:2.145428+0.556692
## [208] train-rmse:1.059888+0.051627 test-rmse:2.145998+0.557279
## [209] train-rmse:1.057089+0.051929 test-rmse:2.145089+0.556372
## [210] train-rmse:1.055522+0.051870 test-rmse:2.145401+0.557661
## [211] train-rmse:1.052622+0.053879 test-rmse:2.143859+0.556745
## [212] train-rmse:1.050430+0.054246 test-rmse:2.144864+0.554098
## [213] train-rmse:1.048469+0.054459 test-rmse:2.143760+0.553602
## [214] train-rmse:1.045926+0.053807 test-rmse:2.143674+0.553565
## [215] train-rmse:1.042908+0.052996 test-rmse:2.143199+0.553030
## [216] train-rmse:1.040787+0.052447 test-rmse:2.143824+0.554719
## [217] train-rmse:1.038107+0.051797 test-rmse:2.144294+0.554330
## [218] train-rmse:1.035050+0.051306 test-rmse:2.144204+0.554111
## [219] train-rmse:1.031731+0.050797 test-rmse:2.143855+0.556146
## [220] train-rmse:1.029251+0.050845 test-rmse:2.143934+0.556615
## [221] train-rmse:1.027634+0.051010 test-rmse:2.143588+0.556306
## Stopping. Best iteration:
## [215] train-rmse:1.042908+0.052996 test-rmse:2.143199+0.553030
##
## 9
## [1] train-rmse:7.392836+0.154580 test-rmse:7.409093+0.652744
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.701523+0.137715 test-rmse:6.742279+0.613775
## [3] train-rmse:6.101857+0.124332 test-rmse:6.171889+0.575258
## [4] train-rmse:5.578793+0.114880 test-rmse:5.670776+0.547556
## [5] train-rmse:5.121657+0.111845 test-rmse:5.243130+0.528957
## [6] train-rmse:4.724661+0.106096 test-rmse:4.871040+0.494621
## [7] train-rmse:4.379694+0.105752 test-rmse:4.548977+0.482958
## [8] train-rmse:4.074650+0.101873 test-rmse:4.255356+0.448630
## [9] train-rmse:3.804958+0.096251 test-rmse:3.985148+0.443789
## [10] train-rmse:3.576328+0.095556 test-rmse:3.807137+0.423178
## [11] train-rmse:3.367156+0.083152 test-rmse:3.613244+0.434551
## [12] train-rmse:3.198077+0.081877 test-rmse:3.481733+0.428475
## [13] train-rmse:3.039263+0.074926 test-rmse:3.334951+0.438521
## [14] train-rmse:2.908970+0.073717 test-rmse:3.223505+0.435835
## [15] train-rmse:2.797976+0.077319 test-rmse:3.148518+0.433438
## [16] train-rmse:2.701945+0.077700 test-rmse:3.066349+0.441305
## [17] train-rmse:2.616367+0.077486 test-rmse:3.001474+0.428525
## [18] train-rmse:2.531938+0.072251 test-rmse:2.943923+0.438390
## [19] train-rmse:2.463021+0.074600 test-rmse:2.889565+0.438313
## [20] train-rmse:2.401240+0.075340 test-rmse:2.854270+0.440349
## [21] train-rmse:2.345195+0.071910 test-rmse:2.820705+0.437669
## [22] train-rmse:2.290781+0.068334 test-rmse:2.785360+0.431720
## [23] train-rmse:2.235972+0.067425 test-rmse:2.738827+0.438086
## [24] train-rmse:2.188346+0.065807 test-rmse:2.716951+0.438510
## [25] train-rmse:2.143315+0.064962 test-rmse:2.688817+0.447199
## [26] train-rmse:2.105943+0.067224 test-rmse:2.657953+0.452364
## [27] train-rmse:2.068556+0.062453 test-rmse:2.637814+0.457116
## [28] train-rmse:2.031863+0.062276 test-rmse:2.614596+0.450858
## [29] train-rmse:2.001374+0.060866 test-rmse:2.599516+0.441493
## [30] train-rmse:1.968356+0.059609 test-rmse:2.572925+0.450063
## [31] train-rmse:1.938791+0.057478 test-rmse:2.559626+0.453563
## [32] train-rmse:1.912204+0.056712 test-rmse:2.536383+0.454875
## [33] train-rmse:1.882588+0.059230 test-rmse:2.508588+0.443636
## [34] train-rmse:1.859845+0.059293 test-rmse:2.496597+0.445685
## [35] train-rmse:1.834103+0.060802 test-rmse:2.481007+0.447663
## [36] train-rmse:1.808438+0.056934 test-rmse:2.468791+0.449772
## [37] train-rmse:1.784332+0.056975 test-rmse:2.460800+0.456059
## [38] train-rmse:1.762177+0.059466 test-rmse:2.445884+0.445026
## [39] train-rmse:1.741850+0.060006 test-rmse:2.437933+0.440136
## [40] train-rmse:1.722997+0.060450 test-rmse:2.429181+0.444067
## [41] train-rmse:1.697396+0.052148 test-rmse:2.422264+0.453191
## [42] train-rmse:1.675778+0.048400 test-rmse:2.409217+0.457731
## [43] train-rmse:1.652495+0.052138 test-rmse:2.394030+0.448329
## [44] train-rmse:1.635930+0.051399 test-rmse:2.385074+0.446150
## [45] train-rmse:1.617024+0.048148 test-rmse:2.379336+0.446889
## [46] train-rmse:1.597332+0.049525 test-rmse:2.367399+0.448671
## [47] train-rmse:1.582163+0.050680 test-rmse:2.363134+0.454708
## [48] train-rmse:1.564923+0.051769 test-rmse:2.352756+0.444598
## [49] train-rmse:1.551797+0.051834 test-rmse:2.343672+0.443341
## [50] train-rmse:1.535690+0.050505 test-rmse:2.335053+0.446504
## [51] train-rmse:1.520572+0.050354 test-rmse:2.323388+0.446209
## [52] train-rmse:1.503499+0.047447 test-rmse:2.317905+0.453908
## [53] train-rmse:1.489418+0.046041 test-rmse:2.307342+0.457834
## [54] train-rmse:1.476549+0.048414 test-rmse:2.301304+0.456971
## [55] train-rmse:1.462304+0.048561 test-rmse:2.297908+0.462822
## [56] train-rmse:1.449858+0.048347 test-rmse:2.288964+0.463403
## [57] train-rmse:1.436032+0.046903 test-rmse:2.281693+0.458488
## [58] train-rmse:1.422980+0.045855 test-rmse:2.274828+0.458485
## [59] train-rmse:1.408527+0.043745 test-rmse:2.271884+0.456406
## [60] train-rmse:1.397356+0.045486 test-rmse:2.265963+0.456042
## [61] train-rmse:1.386696+0.045610 test-rmse:2.260580+0.455330
## [62] train-rmse:1.373443+0.045800 test-rmse:2.254096+0.458380
## [63] train-rmse:1.362363+0.048059 test-rmse:2.250553+0.459513
## [64] train-rmse:1.351225+0.046430 test-rmse:2.244711+0.457064
## [65] train-rmse:1.338179+0.045841 test-rmse:2.235682+0.455888
## [66] train-rmse:1.326020+0.043752 test-rmse:2.233204+0.455142
## [67] train-rmse:1.317852+0.043977 test-rmse:2.230172+0.457576
## [68] train-rmse:1.309422+0.044844 test-rmse:2.226970+0.456827
## [69] train-rmse:1.299033+0.043289 test-rmse:2.222850+0.457409
## [70] train-rmse:1.284245+0.043721 test-rmse:2.218247+0.456786
## [71] train-rmse:1.274151+0.043449 test-rmse:2.217178+0.458636
## [72] train-rmse:1.264074+0.044922 test-rmse:2.213335+0.460751
## [73] train-rmse:1.253594+0.043034 test-rmse:2.205631+0.461411
## [74] train-rmse:1.238588+0.042571 test-rmse:2.201641+0.461389
## [75] train-rmse:1.230086+0.042314 test-rmse:2.197418+0.464065
## [76] train-rmse:1.220239+0.041001 test-rmse:2.191694+0.463063
## [77] train-rmse:1.210011+0.040134 test-rmse:2.187724+0.463091
## [78] train-rmse:1.201691+0.043093 test-rmse:2.185038+0.460425
## [79] train-rmse:1.194741+0.042780 test-rmse:2.185048+0.466533
## [80] train-rmse:1.184318+0.043419 test-rmse:2.181700+0.465640
## [81] train-rmse:1.175634+0.044114 test-rmse:2.177399+0.467647
## [82] train-rmse:1.165886+0.043670 test-rmse:2.175017+0.464821
## [83] train-rmse:1.157558+0.043122 test-rmse:2.170915+0.466189
## [84] train-rmse:1.151268+0.043537 test-rmse:2.171635+0.468040
## [85] train-rmse:1.143712+0.042264 test-rmse:2.164808+0.469923
## [86] train-rmse:1.135273+0.041171 test-rmse:2.163840+0.471400
## [87] train-rmse:1.128436+0.040811 test-rmse:2.160087+0.472835
## [88] train-rmse:1.121253+0.040778 test-rmse:2.155889+0.471875
## [89] train-rmse:1.110803+0.042648 test-rmse:2.155923+0.473650
## [90] train-rmse:1.103539+0.044033 test-rmse:2.152825+0.471741
## [91] train-rmse:1.094988+0.040954 test-rmse:2.146692+0.476508
## [92] train-rmse:1.085346+0.040868 test-rmse:2.143127+0.476677
## [93] train-rmse:1.079504+0.040546 test-rmse:2.140430+0.476616
## [94] train-rmse:1.072385+0.042591 test-rmse:2.142170+0.476063
## [95] train-rmse:1.062988+0.043526 test-rmse:2.140597+0.473752
## [96] train-rmse:1.057350+0.043636 test-rmse:2.138554+0.477368
## [97] train-rmse:1.051050+0.043631 test-rmse:2.135527+0.477220
## [98] train-rmse:1.044464+0.043853 test-rmse:2.133684+0.476751
## [99] train-rmse:1.035329+0.042142 test-rmse:2.131806+0.478248
## [100] train-rmse:1.027727+0.041642 test-rmse:2.129155+0.477948
## [101] train-rmse:1.019616+0.042828 test-rmse:2.128936+0.476320
## [102] train-rmse:1.011915+0.044468 test-rmse:2.126478+0.472661
## [103] train-rmse:1.004648+0.045732 test-rmse:2.124425+0.474961
## [104] train-rmse:0.998761+0.046242 test-rmse:2.123751+0.477999
## [105] train-rmse:0.992141+0.045022 test-rmse:2.121221+0.479955
## [106] train-rmse:0.983299+0.044014 test-rmse:2.117781+0.479373
## [107] train-rmse:0.975478+0.043657 test-rmse:2.115263+0.478037
## [108] train-rmse:0.968653+0.043330 test-rmse:2.114917+0.477257
## [109] train-rmse:0.964123+0.044647 test-rmse:2.113769+0.477830
## [110] train-rmse:0.955622+0.043578 test-rmse:2.109187+0.480167
## [111] train-rmse:0.950239+0.043390 test-rmse:2.107739+0.481975
## [112] train-rmse:0.942563+0.041509 test-rmse:2.103502+0.482949
## [113] train-rmse:0.936163+0.040087 test-rmse:2.104075+0.483254
## [114] train-rmse:0.932187+0.039791 test-rmse:2.104071+0.484177
## [115] train-rmse:0.927461+0.039181 test-rmse:2.102521+0.483469
## [116] train-rmse:0.923492+0.040451 test-rmse:2.101256+0.483634
## [117] train-rmse:0.919477+0.040107 test-rmse:2.100105+0.483313
## [118] train-rmse:0.913490+0.039703 test-rmse:2.099475+0.484389
## [119] train-rmse:0.907798+0.041311 test-rmse:2.095177+0.485551
## [120] train-rmse:0.901756+0.041095 test-rmse:2.093547+0.484859
## [121] train-rmse:0.896354+0.040421 test-rmse:2.090574+0.485210
## [122] train-rmse:0.891997+0.039739 test-rmse:2.089327+0.485900
## [123] train-rmse:0.887185+0.039458 test-rmse:2.086246+0.487135
## [124] train-rmse:0.881368+0.040903 test-rmse:2.083158+0.485466
## [125] train-rmse:0.875607+0.042852 test-rmse:2.083507+0.487025
## [126] train-rmse:0.871064+0.042573 test-rmse:2.083300+0.488096
## [127] train-rmse:0.867230+0.042918 test-rmse:2.081391+0.488085
## [128] train-rmse:0.862375+0.040744 test-rmse:2.081762+0.488100
## [129] train-rmse:0.857828+0.039818 test-rmse:2.080251+0.487164
## [130] train-rmse:0.853092+0.040038 test-rmse:2.079149+0.487102
## [131] train-rmse:0.847683+0.039860 test-rmse:2.077831+0.488894
## [132] train-rmse:0.842948+0.039738 test-rmse:2.077628+0.488379
## [133] train-rmse:0.836156+0.040843 test-rmse:2.076413+0.490651
## [134] train-rmse:0.829999+0.042134 test-rmse:2.074116+0.487253
## [135] train-rmse:0.825977+0.040379 test-rmse:2.072973+0.486975
## [136] train-rmse:0.821806+0.041087 test-rmse:2.072557+0.486438
## [137] train-rmse:0.817842+0.042839 test-rmse:2.072211+0.485380
## [138] train-rmse:0.814466+0.043720 test-rmse:2.072074+0.485849
## [139] train-rmse:0.810837+0.044328 test-rmse:2.072477+0.486333
## [140] train-rmse:0.805536+0.043613 test-rmse:2.071329+0.486251
## [141] train-rmse:0.801240+0.043116 test-rmse:2.069593+0.486029
## [142] train-rmse:0.794492+0.043566 test-rmse:2.070861+0.487545
## [143] train-rmse:0.788204+0.042469 test-rmse:2.067480+0.488820
## [144] train-rmse:0.784028+0.043654 test-rmse:2.067977+0.488568
## [145] train-rmse:0.780524+0.044266 test-rmse:2.068141+0.488943
## [146] train-rmse:0.777591+0.045585 test-rmse:2.068286+0.489564
## [147] train-rmse:0.774937+0.045957 test-rmse:2.067044+0.488953
## [148] train-rmse:0.771565+0.044891 test-rmse:2.067267+0.488634
## [149] train-rmse:0.767895+0.046348 test-rmse:2.066870+0.488194
## [150] train-rmse:0.764128+0.046223 test-rmse:2.066026+0.489061
## [151] train-rmse:0.759876+0.047371 test-rmse:2.064422+0.489699
## [152] train-rmse:0.754297+0.045925 test-rmse:2.065301+0.488760
## [153] train-rmse:0.750657+0.045943 test-rmse:2.062519+0.489231
## [154] train-rmse:0.746110+0.045546 test-rmse:2.061304+0.488642
## [155] train-rmse:0.742143+0.044668 test-rmse:2.058695+0.489010
## [156] train-rmse:0.739913+0.044960 test-rmse:2.057751+0.489272
## [157] train-rmse:0.736963+0.046308 test-rmse:2.058079+0.488569
## [158] train-rmse:0.732563+0.047956 test-rmse:2.055803+0.488279
## [159] train-rmse:0.726205+0.048701 test-rmse:2.056568+0.489590
## [160] train-rmse:0.722494+0.048683 test-rmse:2.056441+0.490858
## [161] train-rmse:0.719852+0.048342 test-rmse:2.055057+0.491445
## [162] train-rmse:0.714620+0.046437 test-rmse:2.053017+0.491822
## [163] train-rmse:0.709362+0.047407 test-rmse:2.051901+0.490798
## [164] train-rmse:0.705502+0.047811 test-rmse:2.051192+0.492218
## [165] train-rmse:0.702700+0.047387 test-rmse:2.049647+0.492119
## [166] train-rmse:0.699574+0.046813 test-rmse:2.049704+0.491802
## [167] train-rmse:0.697025+0.045804 test-rmse:2.049002+0.491690
## [168] train-rmse:0.694812+0.046238 test-rmse:2.049406+0.491728
## [169] train-rmse:0.691919+0.044905 test-rmse:2.046536+0.492530
## [170] train-rmse:0.688089+0.044412 test-rmse:2.044219+0.492057
## [171] train-rmse:0.684915+0.046328 test-rmse:2.044858+0.494392
## [172] train-rmse:0.682209+0.046372 test-rmse:2.045827+0.494676
## [173] train-rmse:0.679814+0.046445 test-rmse:2.045067+0.496332
## [174] train-rmse:0.676762+0.046456 test-rmse:2.043853+0.496547
## [175] train-rmse:0.673102+0.047659 test-rmse:2.043028+0.496733
## [176] train-rmse:0.669168+0.047510 test-rmse:2.043034+0.494755
## [177] train-rmse:0.664683+0.047146 test-rmse:2.042390+0.495690
## [178] train-rmse:0.661968+0.046879 test-rmse:2.043327+0.496150
## [179] train-rmse:0.659710+0.047559 test-rmse:2.042772+0.496364
## [180] train-rmse:0.657205+0.046348 test-rmse:2.041575+0.497306
## [181] train-rmse:0.654240+0.046541 test-rmse:2.043525+0.495813
## [182] train-rmse:0.651664+0.046187 test-rmse:2.042976+0.495987
## [183] train-rmse:0.648348+0.046111 test-rmse:2.041764+0.495559
## [184] train-rmse:0.645657+0.045572 test-rmse:2.041425+0.495657
## [185] train-rmse:0.643418+0.044530 test-rmse:2.042258+0.495255
## [186] train-rmse:0.641975+0.044579 test-rmse:2.043007+0.494614
## [187] train-rmse:0.638365+0.045098 test-rmse:2.042223+0.494823
## [188] train-rmse:0.634055+0.045854 test-rmse:2.040030+0.493717
## [189] train-rmse:0.629833+0.044336 test-rmse:2.038797+0.493345
## [190] train-rmse:0.627830+0.044282 test-rmse:2.038337+0.493455
## [191] train-rmse:0.624455+0.043392 test-rmse:2.040098+0.492193
## [192] train-rmse:0.620774+0.043786 test-rmse:2.038864+0.492743
## [193] train-rmse:0.617506+0.044519 test-rmse:2.037969+0.492177
## [194] train-rmse:0.614896+0.044102 test-rmse:2.037820+0.492120
## [195] train-rmse:0.612248+0.044198 test-rmse:2.037757+0.492881
## [196] train-rmse:0.608079+0.044218 test-rmse:2.038178+0.494753
## [197] train-rmse:0.606573+0.044035 test-rmse:2.037839+0.495207
## [198] train-rmse:0.604076+0.044448 test-rmse:2.037607+0.495747
## [199] train-rmse:0.601818+0.045271 test-rmse:2.036550+0.495087
## [200] train-rmse:0.599184+0.045119 test-rmse:2.035242+0.495269
## [201] train-rmse:0.596586+0.043927 test-rmse:2.035715+0.495185
## [202] train-rmse:0.593952+0.043372 test-rmse:2.035640+0.495917
## [203] train-rmse:0.590412+0.042376 test-rmse:2.034659+0.496696
## [204] train-rmse:0.588701+0.042625 test-rmse:2.034029+0.496709
## [205] train-rmse:0.585974+0.041845 test-rmse:2.034973+0.497226
## [206] train-rmse:0.582503+0.040576 test-rmse:2.036797+0.495513
## [207] train-rmse:0.579407+0.041144 test-rmse:2.037279+0.495415
## [208] train-rmse:0.576875+0.040660 test-rmse:2.037145+0.495899
## [209] train-rmse:0.574617+0.040428 test-rmse:2.037402+0.495284
## [210] train-rmse:0.572608+0.040311 test-rmse:2.037478+0.495462
## Stopping. Best iteration:
## [204] train-rmse:0.588701+0.042625 test-rmse:2.034029+0.496709
##
## 10
## [1] train-rmse:7.361423+0.153731 test-rmse:7.365079+0.646034
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.641423+0.134963 test-rmse:6.679375+0.607241
## [3] train-rmse:6.003997+0.120160 test-rmse:6.077969+0.559640
## [4] train-rmse:5.452109+0.109450 test-rmse:5.561131+0.521915
## [5] train-rmse:4.965943+0.095549 test-rmse:5.098194+0.497686
## [6] train-rmse:4.545136+0.090973 test-rmse:4.709590+0.468402
## [7] train-rmse:4.177563+0.076825 test-rmse:4.366099+0.450636
## [8] train-rmse:3.857969+0.073767 test-rmse:4.084722+0.438361
## [9] train-rmse:3.581869+0.074859 test-rmse:3.857565+0.429685
## [10] train-rmse:3.338801+0.064475 test-rmse:3.635352+0.439034
## [11] train-rmse:3.130148+0.058423 test-rmse:3.470698+0.443785
## [12] train-rmse:2.947657+0.058831 test-rmse:3.322232+0.437180
## [13] train-rmse:2.794229+0.058997 test-rmse:3.209178+0.442887
## [14] train-rmse:2.648940+0.046231 test-rmse:3.101333+0.463520
## [15] train-rmse:2.530002+0.047416 test-rmse:3.032015+0.451825
## [16] train-rmse:2.421667+0.042368 test-rmse:2.946703+0.458108
## [17] train-rmse:2.317062+0.040171 test-rmse:2.862917+0.455209
## [18] train-rmse:2.233450+0.041436 test-rmse:2.819844+0.455000
## [19] train-rmse:2.157676+0.037779 test-rmse:2.769850+0.479679
## [20] train-rmse:2.088928+0.038956 test-rmse:2.721934+0.473552
## [21] train-rmse:2.028779+0.041632 test-rmse:2.681447+0.461868
## [22] train-rmse:1.972203+0.040092 test-rmse:2.645804+0.459405
## [23] train-rmse:1.921470+0.043256 test-rmse:2.620406+0.459988
## [24] train-rmse:1.874584+0.037161 test-rmse:2.588653+0.461874
## [25] train-rmse:1.827716+0.037161 test-rmse:2.569981+0.464147
## [26] train-rmse:1.788192+0.036420 test-rmse:2.546411+0.459138
## [27] train-rmse:1.751996+0.036413 test-rmse:2.535236+0.464118
## [28] train-rmse:1.721807+0.036714 test-rmse:2.516055+0.461628
## [29] train-rmse:1.690242+0.036081 test-rmse:2.493521+0.457861
## [30] train-rmse:1.657985+0.037298 test-rmse:2.473666+0.457488
## [31] train-rmse:1.630935+0.035833 test-rmse:2.459377+0.458687
## [32] train-rmse:1.602116+0.036240 test-rmse:2.442686+0.469310
## [33] train-rmse:1.577381+0.034864 test-rmse:2.431361+0.473723
## [34] train-rmse:1.554051+0.033947 test-rmse:2.426125+0.468853
## [35] train-rmse:1.522685+0.033697 test-rmse:2.406291+0.456479
## [36] train-rmse:1.502157+0.033532 test-rmse:2.395711+0.460812
## [37] train-rmse:1.479693+0.027257 test-rmse:2.382860+0.459693
## [38] train-rmse:1.457640+0.028735 test-rmse:2.372442+0.458330
## [39] train-rmse:1.426822+0.021384 test-rmse:2.359087+0.461355
## [40] train-rmse:1.405767+0.021407 test-rmse:2.349819+0.462339
## [41] train-rmse:1.385949+0.023093 test-rmse:2.344883+0.461093
## [42] train-rmse:1.366454+0.021029 test-rmse:2.346292+0.466414
## [43] train-rmse:1.346425+0.020312 test-rmse:2.332659+0.471621
## [44] train-rmse:1.326078+0.023841 test-rmse:2.324027+0.472344
## [45] train-rmse:1.308942+0.022756 test-rmse:2.314160+0.469328
## [46] train-rmse:1.295748+0.023111 test-rmse:2.308946+0.466666
## [47] train-rmse:1.278218+0.024892 test-rmse:2.299861+0.469930
## [48] train-rmse:1.260720+0.027730 test-rmse:2.295257+0.464086
## [49] train-rmse:1.246136+0.026976 test-rmse:2.292665+0.464997
## [50] train-rmse:1.233459+0.028591 test-rmse:2.286082+0.466555
## [51] train-rmse:1.217749+0.030740 test-rmse:2.281602+0.462469
## [52] train-rmse:1.205361+0.032006 test-rmse:2.278462+0.464920
## [53] train-rmse:1.191261+0.032174 test-rmse:2.272844+0.470275
## [54] train-rmse:1.175080+0.035464 test-rmse:2.266068+0.467094
## [55] train-rmse:1.153701+0.036788 test-rmse:2.264381+0.468116
## [56] train-rmse:1.141028+0.037010 test-rmse:2.260174+0.470652
## [57] train-rmse:1.126210+0.038285 test-rmse:2.259559+0.466545
## [58] train-rmse:1.113789+0.034087 test-rmse:2.262094+0.471737
## [59] train-rmse:1.102612+0.034024 test-rmse:2.260632+0.472577
## [60] train-rmse:1.093721+0.033107 test-rmse:2.255640+0.476798
## [61] train-rmse:1.076890+0.024020 test-rmse:2.252403+0.479958
## [62] train-rmse:1.062825+0.023801 test-rmse:2.251627+0.479519
## [63] train-rmse:1.052933+0.025102 test-rmse:2.247589+0.481424
## [64] train-rmse:1.044240+0.025657 test-rmse:2.245683+0.481452
## [65] train-rmse:1.034163+0.024562 test-rmse:2.244883+0.479492
## [66] train-rmse:1.021304+0.025015 test-rmse:2.242899+0.483030
## [67] train-rmse:1.004491+0.019737 test-rmse:2.236178+0.479005
## [68] train-rmse:0.995370+0.021526 test-rmse:2.234345+0.478122
## [69] train-rmse:0.982842+0.022001 test-rmse:2.231478+0.476820
## [70] train-rmse:0.972367+0.022357 test-rmse:2.225675+0.480503
## [71] train-rmse:0.963123+0.022094 test-rmse:2.223928+0.479840
## [72] train-rmse:0.952724+0.021455 test-rmse:2.220156+0.481021
## [73] train-rmse:0.943122+0.021253 test-rmse:2.216374+0.482361
## [74] train-rmse:0.935058+0.023777 test-rmse:2.211364+0.480967
## [75] train-rmse:0.925952+0.022935 test-rmse:2.209786+0.481230
## [76] train-rmse:0.912556+0.022171 test-rmse:2.202855+0.483901
## [77] train-rmse:0.905360+0.022369 test-rmse:2.201487+0.485139
## [78] train-rmse:0.898228+0.023994 test-rmse:2.200848+0.484946
## [79] train-rmse:0.887527+0.022430 test-rmse:2.199255+0.485846
## [80] train-rmse:0.879620+0.023492 test-rmse:2.196955+0.485373
## [81] train-rmse:0.871466+0.024331 test-rmse:2.189723+0.488553
## [82] train-rmse:0.863356+0.021207 test-rmse:2.188727+0.488603
## [83] train-rmse:0.854246+0.021711 test-rmse:2.184088+0.488419
## [84] train-rmse:0.847362+0.021543 test-rmse:2.183063+0.490530
## [85] train-rmse:0.839212+0.021715 test-rmse:2.183032+0.491124
## [86] train-rmse:0.832292+0.022308 test-rmse:2.181712+0.493132
## [87] train-rmse:0.825334+0.021103 test-rmse:2.180692+0.492562
## [88] train-rmse:0.818964+0.021950 test-rmse:2.179792+0.491782
## [89] train-rmse:0.808997+0.019615 test-rmse:2.177982+0.495403
## [90] train-rmse:0.800081+0.021810 test-rmse:2.176068+0.494190
## [91] train-rmse:0.791867+0.025013 test-rmse:2.172848+0.494678
## [92] train-rmse:0.783995+0.026161 test-rmse:2.169942+0.494149
## [93] train-rmse:0.775890+0.028983 test-rmse:2.166791+0.494151
## [94] train-rmse:0.767920+0.026982 test-rmse:2.166636+0.495407
## [95] train-rmse:0.760290+0.026311 test-rmse:2.164801+0.496686
## [96] train-rmse:0.752785+0.025982 test-rmse:2.161593+0.497372
## [97] train-rmse:0.746725+0.027429 test-rmse:2.159300+0.495700
## [98] train-rmse:0.739108+0.024619 test-rmse:2.158432+0.495227
## [99] train-rmse:0.731761+0.023323 test-rmse:2.158156+0.493988
## [100] train-rmse:0.726523+0.024166 test-rmse:2.155750+0.494877
## [101] train-rmse:0.717716+0.022347 test-rmse:2.155767+0.494324
## [102] train-rmse:0.712117+0.020841 test-rmse:2.154530+0.494847
## [103] train-rmse:0.703267+0.022871 test-rmse:2.153708+0.494553
## [104] train-rmse:0.699551+0.023361 test-rmse:2.153217+0.494259
## [105] train-rmse:0.694406+0.024133 test-rmse:2.153039+0.495792
## [106] train-rmse:0.686800+0.025290 test-rmse:2.151728+0.495009
## [107] train-rmse:0.680548+0.025890 test-rmse:2.148948+0.494380
## [108] train-rmse:0.673576+0.025830 test-rmse:2.149221+0.494228
## [109] train-rmse:0.666092+0.025589 test-rmse:2.150382+0.494843
## [110] train-rmse:0.661122+0.026110 test-rmse:2.150102+0.494614
## [111] train-rmse:0.656198+0.025121 test-rmse:2.149292+0.493832
## [112] train-rmse:0.651330+0.024303 test-rmse:2.149489+0.493833
## [113] train-rmse:0.645483+0.022983 test-rmse:2.148725+0.493925
## [114] train-rmse:0.638825+0.021490 test-rmse:2.147061+0.493160
## [115] train-rmse:0.634186+0.021107 test-rmse:2.146321+0.493296
## [116] train-rmse:0.630043+0.021216 test-rmse:2.148485+0.494656
## [117] train-rmse:0.622640+0.021866 test-rmse:2.147053+0.493274
## [118] train-rmse:0.615227+0.022217 test-rmse:2.146464+0.493365
## [119] train-rmse:0.612019+0.021777 test-rmse:2.146533+0.492338
## [120] train-rmse:0.607443+0.021120 test-rmse:2.144663+0.492244
## [121] train-rmse:0.603286+0.022702 test-rmse:2.143887+0.492252
## [122] train-rmse:0.598372+0.023545 test-rmse:2.142563+0.492155
## [123] train-rmse:0.594139+0.024324 test-rmse:2.141353+0.492111
## [124] train-rmse:0.588616+0.024313 test-rmse:2.141427+0.492829
## [125] train-rmse:0.584664+0.023633 test-rmse:2.141795+0.492896
## [126] train-rmse:0.579950+0.023444 test-rmse:2.141318+0.493243
## [127] train-rmse:0.573917+0.023575 test-rmse:2.140691+0.493180
## [128] train-rmse:0.568667+0.024768 test-rmse:2.142651+0.495214
## [129] train-rmse:0.561341+0.024083 test-rmse:2.139976+0.495877
## [130] train-rmse:0.558673+0.024147 test-rmse:2.139851+0.495746
## [131] train-rmse:0.555496+0.023973 test-rmse:2.139951+0.495588
## [132] train-rmse:0.552463+0.023543 test-rmse:2.139907+0.496066
## [133] train-rmse:0.548704+0.023732 test-rmse:2.140653+0.495235
## [134] train-rmse:0.541321+0.023990 test-rmse:2.139623+0.493970
## [135] train-rmse:0.537022+0.023286 test-rmse:2.138277+0.494997
## [136] train-rmse:0.531266+0.023313 test-rmse:2.137978+0.495378
## [137] train-rmse:0.526957+0.023525 test-rmse:2.137202+0.494619
## [138] train-rmse:0.522671+0.026259 test-rmse:2.137630+0.494429
## [139] train-rmse:0.519226+0.026203 test-rmse:2.137639+0.494880
## [140] train-rmse:0.515285+0.026270 test-rmse:2.137966+0.494642
## [141] train-rmse:0.510722+0.026566 test-rmse:2.137822+0.495012
## [142] train-rmse:0.506169+0.025077 test-rmse:2.138115+0.494376
## [143] train-rmse:0.501798+0.025024 test-rmse:2.136886+0.494637
## [144] train-rmse:0.497869+0.024327 test-rmse:2.137804+0.494573
## [145] train-rmse:0.493086+0.023082 test-rmse:2.137469+0.495307
## [146] train-rmse:0.489951+0.024325 test-rmse:2.137464+0.495317
## [147] train-rmse:0.486903+0.024366 test-rmse:2.137009+0.494698
## [148] train-rmse:0.484126+0.024270 test-rmse:2.136295+0.494165
## [149] train-rmse:0.481614+0.024694 test-rmse:2.136008+0.494174
## [150] train-rmse:0.477455+0.023639 test-rmse:2.136812+0.495163
## [151] train-rmse:0.474121+0.024285 test-rmse:2.136738+0.495699
## [152] train-rmse:0.470489+0.023823 test-rmse:2.136282+0.494970
## [153] train-rmse:0.466792+0.022958 test-rmse:2.137001+0.494763
## [154] train-rmse:0.462911+0.022686 test-rmse:2.135390+0.495861
## [155] train-rmse:0.461242+0.022505 test-rmse:2.136321+0.495859
## [156] train-rmse:0.457899+0.022562 test-rmse:2.135349+0.496207
## [157] train-rmse:0.454725+0.021967 test-rmse:2.134903+0.496521
## [158] train-rmse:0.451008+0.021457 test-rmse:2.134328+0.495786
## [159] train-rmse:0.448279+0.021022 test-rmse:2.133996+0.496333
## [160] train-rmse:0.445701+0.020793 test-rmse:2.134923+0.496270
## [161] train-rmse:0.443361+0.021637 test-rmse:2.134918+0.495945
## [162] train-rmse:0.440170+0.021428 test-rmse:2.134341+0.496208
## [163] train-rmse:0.436395+0.021412 test-rmse:2.133785+0.496175
## [164] train-rmse:0.433700+0.020931 test-rmse:2.133660+0.496304
## [165] train-rmse:0.429931+0.019657 test-rmse:2.133076+0.495604
## [166] train-rmse:0.426990+0.019515 test-rmse:2.132984+0.494969
## [167] train-rmse:0.424551+0.018946 test-rmse:2.132662+0.494999
## [168] train-rmse:0.421184+0.019180 test-rmse:2.132238+0.494823
## [169] train-rmse:0.417793+0.018856 test-rmse:2.132146+0.495311
## [170] train-rmse:0.415877+0.019040 test-rmse:2.131911+0.495686
## [171] train-rmse:0.412138+0.020408 test-rmse:2.132691+0.496995
## [172] train-rmse:0.407492+0.019666 test-rmse:2.131597+0.496658
## [173] train-rmse:0.403685+0.020827 test-rmse:2.132952+0.497914
## [174] train-rmse:0.401455+0.020766 test-rmse:2.132084+0.498104
## [175] train-rmse:0.398462+0.020764 test-rmse:2.132228+0.498399
## [176] train-rmse:0.395318+0.020190 test-rmse:2.133185+0.498237
## [177] train-rmse:0.392658+0.020415 test-rmse:2.132925+0.498380
## [178] train-rmse:0.390373+0.019985 test-rmse:2.131972+0.499093
## Stopping. Best iteration:
## [172] train-rmse:0.407492+0.019666 test-rmse:2.131597+0.496658
##
## 11
## [1] train-rmse:7.339678+0.152357 test-rmse:7.327687+0.636508
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.594806+0.133246 test-rmse:6.608806+0.588349
## [3] train-rmse:5.943220+0.117104 test-rmse:5.984673+0.544810
## [4] train-rmse:5.370744+0.108462 test-rmse:5.445995+0.506106
## [5] train-rmse:4.873762+0.100796 test-rmse:4.990864+0.467336
## [6] train-rmse:4.439894+0.088476 test-rmse:4.606496+0.456683
## [7] train-rmse:4.058548+0.080058 test-rmse:4.272330+0.435994
## [8] train-rmse:3.732495+0.073267 test-rmse:3.997218+0.430183
## [9] train-rmse:3.447341+0.065313 test-rmse:3.770764+0.436544
## [10] train-rmse:3.202197+0.062077 test-rmse:3.582563+0.431457
## [11] train-rmse:2.983259+0.054993 test-rmse:3.408553+0.444747
## [12] train-rmse:2.799780+0.050719 test-rmse:3.278470+0.463325
## [13] train-rmse:2.633914+0.048831 test-rmse:3.170205+0.468494
## [14] train-rmse:2.493712+0.053075 test-rmse:3.079114+0.488616
## [15] train-rmse:2.357540+0.052732 test-rmse:2.992547+0.477567
## [16] train-rmse:2.234249+0.054773 test-rmse:2.902826+0.496721
## [17] train-rmse:2.135910+0.053475 test-rmse:2.836330+0.497117
## [18] train-rmse:2.043249+0.049603 test-rmse:2.775573+0.496238
## [19] train-rmse:1.965390+0.047678 test-rmse:2.726732+0.496205
## [20] train-rmse:1.891065+0.044222 test-rmse:2.685398+0.500572
## [21] train-rmse:1.823526+0.043483 test-rmse:2.638426+0.510480
## [22] train-rmse:1.761121+0.038306 test-rmse:2.604071+0.517371
## [23] train-rmse:1.710222+0.044833 test-rmse:2.578270+0.522067
## [24] train-rmse:1.654790+0.043316 test-rmse:2.547407+0.520959
## [25] train-rmse:1.609332+0.035572 test-rmse:2.529683+0.517195
## [26] train-rmse:1.571743+0.037114 test-rmse:2.523492+0.524226
## [27] train-rmse:1.534047+0.032413 test-rmse:2.512104+0.521076
## [28] train-rmse:1.491960+0.034684 test-rmse:2.496496+0.522122
## [29] train-rmse:1.458667+0.031265 test-rmse:2.487667+0.528341
## [30] train-rmse:1.429285+0.036109 test-rmse:2.469989+0.533412
## [31] train-rmse:1.399532+0.033554 test-rmse:2.468320+0.537409
## [32] train-rmse:1.370867+0.032582 test-rmse:2.462784+0.539686
## [33] train-rmse:1.343854+0.032216 test-rmse:2.451365+0.535429
## [34] train-rmse:1.310645+0.033037 test-rmse:2.438640+0.542971
## [35] train-rmse:1.288473+0.034048 test-rmse:2.427711+0.545959
## [36] train-rmse:1.264700+0.037470 test-rmse:2.414200+0.541544
## [37] train-rmse:1.243921+0.037074 test-rmse:2.408537+0.542099
## [38] train-rmse:1.218329+0.032003 test-rmse:2.403028+0.538612
## [39] train-rmse:1.199239+0.030223 test-rmse:2.394738+0.532689
## [40] train-rmse:1.177250+0.030802 test-rmse:2.386091+0.537483
## [41] train-rmse:1.156112+0.028224 test-rmse:2.386064+0.535027
## [42] train-rmse:1.137401+0.029217 test-rmse:2.377632+0.536225
## [43] train-rmse:1.120324+0.032539 test-rmse:2.370717+0.536439
## [44] train-rmse:1.097749+0.028851 test-rmse:2.364540+0.538688
## [45] train-rmse:1.079500+0.026479 test-rmse:2.361953+0.541265
## [46] train-rmse:1.061690+0.025831 test-rmse:2.359483+0.542678
## [47] train-rmse:1.046077+0.025807 test-rmse:2.351864+0.538247
## [48] train-rmse:1.031559+0.024838 test-rmse:2.348439+0.538572
## [49] train-rmse:1.012450+0.027562 test-rmse:2.344632+0.534920
## [50] train-rmse:0.998399+0.030638 test-rmse:2.339563+0.534135
## [51] train-rmse:0.983435+0.029731 test-rmse:2.334335+0.540209
## [52] train-rmse:0.966952+0.031015 test-rmse:2.334877+0.540185
## [53] train-rmse:0.949348+0.034098 test-rmse:2.330178+0.543054
## [54] train-rmse:0.938123+0.034244 test-rmse:2.325820+0.540722
## [55] train-rmse:0.921680+0.027707 test-rmse:2.323284+0.541902
## [56] train-rmse:0.906082+0.030100 test-rmse:2.315699+0.542766
## [57] train-rmse:0.890357+0.031150 test-rmse:2.315756+0.546877
## [58] train-rmse:0.878511+0.031821 test-rmse:2.311657+0.548233
## [59] train-rmse:0.865573+0.027666 test-rmse:2.311010+0.549824
## [60] train-rmse:0.852064+0.029979 test-rmse:2.308300+0.549936
## [61] train-rmse:0.839349+0.031049 test-rmse:2.308670+0.551812
## [62] train-rmse:0.828793+0.033628 test-rmse:2.302291+0.549396
## [63] train-rmse:0.820518+0.031370 test-rmse:2.300141+0.550372
## [64] train-rmse:0.810518+0.032266 test-rmse:2.297912+0.551179
## [65] train-rmse:0.798904+0.031304 test-rmse:2.293756+0.553060
## [66] train-rmse:0.787475+0.031510 test-rmse:2.291402+0.554221
## [67] train-rmse:0.773392+0.028139 test-rmse:2.288530+0.555154
## [68] train-rmse:0.765240+0.027497 test-rmse:2.284660+0.556135
## [69] train-rmse:0.753688+0.030315 test-rmse:2.284506+0.557695
## [70] train-rmse:0.740862+0.029629 test-rmse:2.281166+0.559190
## [71] train-rmse:0.730447+0.029854 test-rmse:2.281289+0.558519
## [72] train-rmse:0.720512+0.027321 test-rmse:2.281199+0.559372
## [73] train-rmse:0.710795+0.028717 test-rmse:2.281045+0.559418
## [74] train-rmse:0.703480+0.030259 test-rmse:2.278664+0.561074
## [75] train-rmse:0.695528+0.028434 test-rmse:2.277861+0.559531
## [76] train-rmse:0.683150+0.026288 test-rmse:2.276440+0.559309
## [77] train-rmse:0.673824+0.026068 test-rmse:2.274843+0.562409
## [78] train-rmse:0.666171+0.026777 test-rmse:2.274708+0.563954
## [79] train-rmse:0.656480+0.026890 test-rmse:2.273315+0.565751
## [80] train-rmse:0.646609+0.024575 test-rmse:2.269866+0.566164
## [81] train-rmse:0.638620+0.026045 test-rmse:2.269026+0.568067
## [82] train-rmse:0.630960+0.026689 test-rmse:2.268385+0.567758
## [83] train-rmse:0.622775+0.026221 test-rmse:2.268211+0.568501
## [84] train-rmse:0.616866+0.027631 test-rmse:2.267145+0.567649
## [85] train-rmse:0.608992+0.026814 test-rmse:2.267155+0.567209
## [86] train-rmse:0.602047+0.026510 test-rmse:2.265488+0.568162
## [87] train-rmse:0.596320+0.027135 test-rmse:2.263928+0.568538
## [88] train-rmse:0.587528+0.027615 test-rmse:2.263276+0.567488
## [89] train-rmse:0.580739+0.026883 test-rmse:2.263990+0.567745
## [90] train-rmse:0.574054+0.028210 test-rmse:2.264023+0.568662
## [91] train-rmse:0.565339+0.026591 test-rmse:2.261071+0.570485
## [92] train-rmse:0.560958+0.026659 test-rmse:2.259466+0.570751
## [93] train-rmse:0.555760+0.026479 test-rmse:2.259138+0.570607
## [94] train-rmse:0.549906+0.027363 test-rmse:2.259139+0.571537
## [95] train-rmse:0.543848+0.026729 test-rmse:2.258787+0.571579
## [96] train-rmse:0.536615+0.026641 test-rmse:2.258063+0.571880
## [97] train-rmse:0.528952+0.028177 test-rmse:2.256875+0.572214
## [98] train-rmse:0.522860+0.025861 test-rmse:2.255370+0.571827
## [99] train-rmse:0.515251+0.024244 test-rmse:2.254549+0.572651
## [100] train-rmse:0.508353+0.025113 test-rmse:2.254487+0.572033
## [101] train-rmse:0.501546+0.023698 test-rmse:2.254971+0.572356
## [102] train-rmse:0.494243+0.022713 test-rmse:2.255139+0.573381
## [103] train-rmse:0.487982+0.021556 test-rmse:2.254903+0.573337
## [104] train-rmse:0.482302+0.019957 test-rmse:2.255230+0.573930
## [105] train-rmse:0.476111+0.016917 test-rmse:2.253939+0.573801
## [106] train-rmse:0.470479+0.016997 test-rmse:2.254016+0.573582
## [107] train-rmse:0.463883+0.018527 test-rmse:2.254771+0.573391
## [108] train-rmse:0.460242+0.019267 test-rmse:2.254213+0.574211
## [109] train-rmse:0.455093+0.019019 test-rmse:2.253809+0.575319
## [110] train-rmse:0.449900+0.018084 test-rmse:2.255155+0.575160
## [111] train-rmse:0.445272+0.016832 test-rmse:2.255644+0.575613
## [112] train-rmse:0.442108+0.016881 test-rmse:2.255032+0.576258
## [113] train-rmse:0.437526+0.018052 test-rmse:2.254154+0.576368
## [114] train-rmse:0.434719+0.018406 test-rmse:2.253930+0.576299
## [115] train-rmse:0.430191+0.019067 test-rmse:2.253304+0.578340
## [116] train-rmse:0.425376+0.019135 test-rmse:2.254058+0.578179
## [117] train-rmse:0.421631+0.019723 test-rmse:2.253313+0.578216
## [118] train-rmse:0.416725+0.017544 test-rmse:2.254670+0.577790
## [119] train-rmse:0.413943+0.017154 test-rmse:2.254475+0.577723
## [120] train-rmse:0.409228+0.016919 test-rmse:2.254710+0.578765
## [121] train-rmse:0.405440+0.018606 test-rmse:2.255110+0.580111
## Stopping. Best iteration:
## [115] train-rmse:0.430191+0.019067 test-rmse:2.253304+0.578340
##
## 12
## [1] train-rmse:7.325933+0.150419 test-rmse:7.318150+0.649059
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.567494+0.129616 test-rmse:6.590112+0.612137
## [3] train-rmse:5.900850+0.111510 test-rmse:5.966409+0.573609
## [4] train-rmse:5.316454+0.097950 test-rmse:5.427718+0.538418
## [5] train-rmse:4.805128+0.086822 test-rmse:4.964424+0.514004
## [6] train-rmse:4.359337+0.076060 test-rmse:4.577300+0.491854
## [7] train-rmse:3.969306+0.068382 test-rmse:4.251144+0.468226
## [8] train-rmse:3.633462+0.061661 test-rmse:3.968558+0.463627
## [9] train-rmse:3.335277+0.052790 test-rmse:3.734351+0.449334
## [10] train-rmse:3.078657+0.049377 test-rmse:3.539811+0.431375
## [11] train-rmse:2.852289+0.038924 test-rmse:3.368227+0.449062
## [12] train-rmse:2.659658+0.035858 test-rmse:3.248479+0.451920
## [13] train-rmse:2.487021+0.032910 test-rmse:3.125633+0.452147
## [14] train-rmse:2.336598+0.025241 test-rmse:3.020079+0.464726
## [15] train-rmse:2.206782+0.020020 test-rmse:2.937773+0.483299
## [16] train-rmse:2.077833+0.024935 test-rmse:2.864792+0.485220
## [17] train-rmse:1.971005+0.022372 test-rmse:2.800350+0.497784
## [18] train-rmse:1.875602+0.025179 test-rmse:2.759533+0.501661
## [19] train-rmse:1.788757+0.032972 test-rmse:2.713660+0.504364
## [20] train-rmse:1.714240+0.033318 test-rmse:2.672262+0.505261
## [21] train-rmse:1.643274+0.036187 test-rmse:2.639912+0.513883
## [22] train-rmse:1.579828+0.038166 test-rmse:2.611769+0.519718
## [23] train-rmse:1.520552+0.038179 test-rmse:2.578265+0.521689
## [24] train-rmse:1.469237+0.038777 test-rmse:2.559452+0.528644
## [25] train-rmse:1.413125+0.033350 test-rmse:2.536308+0.524118
## [26] train-rmse:1.367903+0.030899 test-rmse:2.513155+0.526777
## [27] train-rmse:1.324194+0.033849 test-rmse:2.489354+0.533185
## [28] train-rmse:1.281447+0.025289 test-rmse:2.476357+0.534219
## [29] train-rmse:1.245789+0.023209 test-rmse:2.463965+0.534854
## [30] train-rmse:1.213017+0.026584 test-rmse:2.455936+0.535362
## [31] train-rmse:1.176208+0.024117 test-rmse:2.448795+0.533841
## [32] train-rmse:1.150791+0.024179 test-rmse:2.436380+0.539451
## [33] train-rmse:1.125001+0.023873 test-rmse:2.427468+0.541755
## [34] train-rmse:1.096844+0.028013 test-rmse:2.419386+0.543307
## [35] train-rmse:1.068598+0.026906 test-rmse:2.409155+0.547374
## [36] train-rmse:1.046811+0.026042 test-rmse:2.408266+0.548826
## [37] train-rmse:1.022042+0.022417 test-rmse:2.404007+0.549333
## [38] train-rmse:0.994657+0.023081 test-rmse:2.401658+0.554448
## [39] train-rmse:0.974523+0.025630 test-rmse:2.399241+0.554249
## [40] train-rmse:0.952567+0.024494 test-rmse:2.399230+0.555693
## [41] train-rmse:0.936673+0.021589 test-rmse:2.395694+0.557374
## [42] train-rmse:0.919990+0.021313 test-rmse:2.391207+0.560258
## [43] train-rmse:0.899683+0.020080 test-rmse:2.390867+0.558453
## [44] train-rmse:0.877568+0.019956 test-rmse:2.381507+0.559868
## [45] train-rmse:0.858949+0.021423 test-rmse:2.379868+0.559505
## [46] train-rmse:0.845682+0.021661 test-rmse:2.377516+0.561145
## [47] train-rmse:0.832858+0.020599 test-rmse:2.375008+0.559425
## [48] train-rmse:0.814283+0.022696 test-rmse:2.369996+0.564309
## [49] train-rmse:0.792940+0.020938 test-rmse:2.368441+0.561542
## [50] train-rmse:0.779677+0.019175 test-rmse:2.366366+0.558044
## [51] train-rmse:0.769183+0.019347 test-rmse:2.360958+0.561526
## [52] train-rmse:0.751296+0.022436 test-rmse:2.355761+0.565831
## [53] train-rmse:0.736578+0.022261 test-rmse:2.353566+0.566334
## [54] train-rmse:0.719438+0.024709 test-rmse:2.349893+0.567467
## [55] train-rmse:0.703823+0.022951 test-rmse:2.346047+0.567312
## [56] train-rmse:0.689581+0.025690 test-rmse:2.344269+0.566901
## [57] train-rmse:0.677575+0.028227 test-rmse:2.341389+0.568512
## [58] train-rmse:0.665830+0.029002 test-rmse:2.338711+0.566596
## [59] train-rmse:0.652588+0.031198 test-rmse:2.334695+0.567783
## [60] train-rmse:0.639766+0.028632 test-rmse:2.332474+0.565913
## [61] train-rmse:0.628770+0.029032 test-rmse:2.332357+0.566439
## [62] train-rmse:0.618114+0.026778 test-rmse:2.332851+0.565782
## [63] train-rmse:0.608499+0.024109 test-rmse:2.330153+0.566525
## [64] train-rmse:0.594923+0.025676 test-rmse:2.327730+0.568506
## [65] train-rmse:0.585878+0.024967 test-rmse:2.326990+0.568240
## [66] train-rmse:0.577049+0.024644 test-rmse:2.327689+0.566801
## [67] train-rmse:0.566017+0.021415 test-rmse:2.322555+0.569249
## [68] train-rmse:0.557401+0.022889 test-rmse:2.321446+0.570048
## [69] train-rmse:0.548870+0.022954 test-rmse:2.320805+0.572559
## [70] train-rmse:0.538157+0.025680 test-rmse:2.319937+0.570801
## [71] train-rmse:0.529061+0.021928 test-rmse:2.317616+0.572121
## [72] train-rmse:0.518333+0.022154 test-rmse:2.317539+0.572294
## [73] train-rmse:0.509713+0.023976 test-rmse:2.317685+0.570742
## [74] train-rmse:0.499536+0.022654 test-rmse:2.317214+0.571425
## [75] train-rmse:0.493341+0.020721 test-rmse:2.317759+0.572361
## [76] train-rmse:0.483916+0.023048 test-rmse:2.316367+0.572988
## [77] train-rmse:0.477582+0.024615 test-rmse:2.316308+0.575120
## [78] train-rmse:0.469268+0.022296 test-rmse:2.316687+0.574025
## [79] train-rmse:0.460991+0.020723 test-rmse:2.315126+0.574409
## [80] train-rmse:0.454973+0.019164 test-rmse:2.313739+0.575431
## [81] train-rmse:0.448280+0.018990 test-rmse:2.314930+0.573641
## [82] train-rmse:0.439789+0.022127 test-rmse:2.314630+0.573845
## [83] train-rmse:0.432988+0.023238 test-rmse:2.311903+0.575255
## [84] train-rmse:0.426430+0.021876 test-rmse:2.311359+0.575963
## [85] train-rmse:0.421587+0.021769 test-rmse:2.311884+0.574824
## [86] train-rmse:0.414557+0.021636 test-rmse:2.311014+0.575649
## [87] train-rmse:0.408982+0.021645 test-rmse:2.310328+0.576376
## [88] train-rmse:0.403278+0.020185 test-rmse:2.310689+0.576245
## [89] train-rmse:0.398257+0.020519 test-rmse:2.311542+0.575542
## [90] train-rmse:0.391962+0.019800 test-rmse:2.311897+0.576079
## [91] train-rmse:0.383340+0.018295 test-rmse:2.310776+0.577341
## [92] train-rmse:0.379689+0.018557 test-rmse:2.310731+0.577669
## [93] train-rmse:0.374589+0.017840 test-rmse:2.310554+0.579164
## Stopping. Best iteration:
## [87] train-rmse:0.408982+0.021645 test-rmse:2.310328+0.576376
##
## 13
## [1] train-rmse:7.321069+0.150020 test-rmse:7.315670+0.648743
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.557470+0.129025 test-rmse:6.590933+0.607365
## [3] train-rmse:5.883455+0.108388 test-rmse:5.960360+0.572809
## [4] train-rmse:5.289537+0.093907 test-rmse:5.413071+0.542775
## [5] train-rmse:4.767195+0.079961 test-rmse:4.946051+0.517304
## [6] train-rmse:4.312666+0.067633 test-rmse:4.546784+0.498988
## [7] train-rmse:3.912351+0.059488 test-rmse:4.211136+0.477891
## [8] train-rmse:3.563159+0.053792 test-rmse:3.920117+0.464382
## [9] train-rmse:3.258227+0.049100 test-rmse:3.685397+0.453042
## [10] train-rmse:2.991211+0.045166 test-rmse:3.489380+0.455090
## [11] train-rmse:2.758803+0.041985 test-rmse:3.332664+0.450574
## [12] train-rmse:2.549476+0.035655 test-rmse:3.191431+0.467151
## [13] train-rmse:2.369422+0.033239 test-rmse:3.079823+0.470701
## [14] train-rmse:2.211440+0.027863 test-rmse:2.993374+0.472217
## [15] train-rmse:2.074503+0.027516 test-rmse:2.913227+0.494572
## [16] train-rmse:1.952356+0.025785 test-rmse:2.841531+0.500140
## [17] train-rmse:1.839607+0.023865 test-rmse:2.781730+0.510665
## [18] train-rmse:1.741432+0.024169 test-rmse:2.721482+0.521261
## [19] train-rmse:1.649612+0.025008 test-rmse:2.676397+0.524480
## [20] train-rmse:1.571254+0.028640 test-rmse:2.642206+0.533386
## [21] train-rmse:1.496100+0.029595 test-rmse:2.605817+0.542402
## [22] train-rmse:1.431559+0.028151 test-rmse:2.587565+0.544703
## [23] train-rmse:1.373882+0.029665 test-rmse:2.565904+0.549654
## [24] train-rmse:1.316726+0.032533 test-rmse:2.552291+0.548876
## [25] train-rmse:1.270111+0.032818 test-rmse:2.537971+0.552240
## [26] train-rmse:1.223147+0.028463 test-rmse:2.520548+0.557200
## [27] train-rmse:1.184158+0.027800 test-rmse:2.508513+0.563341
## [28] train-rmse:1.141218+0.032147 test-rmse:2.496563+0.569138
## [29] train-rmse:1.104860+0.035358 test-rmse:2.482987+0.569844
## [30] train-rmse:1.068138+0.027377 test-rmse:2.471464+0.569196
## [31] train-rmse:1.030065+0.032660 test-rmse:2.464367+0.572704
## [32] train-rmse:0.994525+0.033184 test-rmse:2.457964+0.566676
## [33] train-rmse:0.970808+0.035273 test-rmse:2.452124+0.566119
## [34] train-rmse:0.942143+0.037480 test-rmse:2.449797+0.573090
## [35] train-rmse:0.914455+0.036365 test-rmse:2.438845+0.571766
## [36] train-rmse:0.887530+0.037643 test-rmse:2.431922+0.575127
## [37] train-rmse:0.860533+0.033450 test-rmse:2.426387+0.580559
## [38] train-rmse:0.839069+0.034546 test-rmse:2.422916+0.578552
## [39] train-rmse:0.819521+0.031579 test-rmse:2.416419+0.576672
## [40] train-rmse:0.801602+0.029065 test-rmse:2.412674+0.580770
## [41] train-rmse:0.777996+0.031926 test-rmse:2.410336+0.585238
## [42] train-rmse:0.761755+0.032345 test-rmse:2.405714+0.591274
## [43] train-rmse:0.741685+0.028909 test-rmse:2.403249+0.595115
## [44] train-rmse:0.726677+0.030180 test-rmse:2.399754+0.595082
## [45] train-rmse:0.713404+0.031005 test-rmse:2.394352+0.596262
## [46] train-rmse:0.694746+0.030423 test-rmse:2.390836+0.597979
## [47] train-rmse:0.681159+0.029737 test-rmse:2.387235+0.600527
## [48] train-rmse:0.662402+0.029677 test-rmse:2.386296+0.597690
## [49] train-rmse:0.647916+0.025376 test-rmse:2.382497+0.597711
## [50] train-rmse:0.626932+0.026247 test-rmse:2.381198+0.596879
## [51] train-rmse:0.609364+0.030538 test-rmse:2.378602+0.595396
## [52] train-rmse:0.599170+0.029841 test-rmse:2.375945+0.596227
## [53] train-rmse:0.585973+0.026853 test-rmse:2.375556+0.598922
## [54] train-rmse:0.574039+0.026469 test-rmse:2.374547+0.597743
## [55] train-rmse:0.555226+0.027697 test-rmse:2.373077+0.597779
## [56] train-rmse:0.543291+0.029166 test-rmse:2.369163+0.600439
## [57] train-rmse:0.533164+0.029554 test-rmse:2.367123+0.602852
## [58] train-rmse:0.519027+0.027687 test-rmse:2.365670+0.603806
## [59] train-rmse:0.507575+0.024746 test-rmse:2.368307+0.607192
## [60] train-rmse:0.499779+0.025435 test-rmse:2.366473+0.607234
## [61] train-rmse:0.488950+0.027173 test-rmse:2.366688+0.607105
## [62] train-rmse:0.477193+0.024501 test-rmse:2.364502+0.608678
## [63] train-rmse:0.467944+0.025523 test-rmse:2.363181+0.609009
## [64] train-rmse:0.458078+0.025127 test-rmse:2.362847+0.611116
## [65] train-rmse:0.447676+0.027522 test-rmse:2.361784+0.609267
## [66] train-rmse:0.438310+0.027722 test-rmse:2.360894+0.609357
## [67] train-rmse:0.427692+0.026984 test-rmse:2.359467+0.608341
## [68] train-rmse:0.419773+0.028266 test-rmse:2.359247+0.608338
## [69] train-rmse:0.412927+0.027750 test-rmse:2.358500+0.607690
## [70] train-rmse:0.402711+0.025380 test-rmse:2.357229+0.607072
## [71] train-rmse:0.392988+0.026793 test-rmse:2.355788+0.607856
## [72] train-rmse:0.384667+0.028403 test-rmse:2.354140+0.609068
## [73] train-rmse:0.380189+0.027875 test-rmse:2.353298+0.608829
## [74] train-rmse:0.371570+0.025487 test-rmse:2.352394+0.608690
## [75] train-rmse:0.363249+0.027439 test-rmse:2.352426+0.608356
## [76] train-rmse:0.357063+0.025699 test-rmse:2.351855+0.609148
## [77] train-rmse:0.349988+0.023600 test-rmse:2.351461+0.609669
## [78] train-rmse:0.343143+0.022830 test-rmse:2.350813+0.608585
## [79] train-rmse:0.337884+0.023049 test-rmse:2.350624+0.609314
## [80] train-rmse:0.332151+0.021278 test-rmse:2.350101+0.609880
## [81] train-rmse:0.324904+0.020738 test-rmse:2.349027+0.611618
## [82] train-rmse:0.318988+0.020881 test-rmse:2.348477+0.610989
## [83] train-rmse:0.313278+0.019417 test-rmse:2.347788+0.611055
## [84] train-rmse:0.305711+0.020541 test-rmse:2.348680+0.611407
## [85] train-rmse:0.299894+0.019887 test-rmse:2.348601+0.611371
## [86] train-rmse:0.293524+0.021377 test-rmse:2.348293+0.611549
## [87] train-rmse:0.288466+0.021627 test-rmse:2.347806+0.611885
## [88] train-rmse:0.284517+0.021822 test-rmse:2.349062+0.613243
## [89] train-rmse:0.279683+0.021990 test-rmse:2.348432+0.613677
## Stopping. Best iteration:
## [83] train-rmse:0.313278+0.019417 test-rmse:2.347788+0.611055
##
## 14
## [1] train-rmse:7.362714+0.157883 test-rmse:7.346713+0.675021
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.677497+0.142627 test-rmse:6.687488+0.701404
## [3] train-rmse:6.109435+0.131477 test-rmse:6.148116+0.679669
## [4] train-rmse:5.639260+0.124456 test-rmse:5.655526+0.658513
## [5] train-rmse:5.255033+0.118438 test-rmse:5.305093+0.649560
## [6] train-rmse:4.941120+0.115207 test-rmse:5.004346+0.628903
## [7] train-rmse:4.682516+0.113511 test-rmse:4.761688+0.581907
## [8] train-rmse:4.471146+0.110895 test-rmse:4.562138+0.585569
## [9] train-rmse:4.299058+0.111890 test-rmse:4.403525+0.554862
## [10] train-rmse:4.155777+0.112308 test-rmse:4.260425+0.517351
## [11] train-rmse:4.035693+0.112761 test-rmse:4.148392+0.507262
## [12] train-rmse:3.934361+0.112676 test-rmse:4.048001+0.512759
## [13] train-rmse:3.848814+0.113749 test-rmse:3.962275+0.501561
## [14] train-rmse:3.774041+0.115536 test-rmse:3.897261+0.474586
## [15] train-rmse:3.708136+0.116721 test-rmse:3.832287+0.440698
## [16] train-rmse:3.649073+0.116663 test-rmse:3.778973+0.443644
## [17] train-rmse:3.596321+0.117529 test-rmse:3.722310+0.451778
## [18] train-rmse:3.549188+0.118334 test-rmse:3.669631+0.432498
## [19] train-rmse:3.505167+0.120061 test-rmse:3.636722+0.434882
## [20] train-rmse:3.463825+0.121254 test-rmse:3.590446+0.413705
## [21] train-rmse:3.425860+0.120767 test-rmse:3.560267+0.420916
## [22] train-rmse:3.390116+0.120708 test-rmse:3.530015+0.430903
## [23] train-rmse:3.356777+0.121289 test-rmse:3.501512+0.446144
## [24] train-rmse:3.325180+0.122307 test-rmse:3.470324+0.423223
## [25] train-rmse:3.294136+0.123441 test-rmse:3.430746+0.415160
## [26] train-rmse:3.264677+0.123203 test-rmse:3.412263+0.418589
## [27] train-rmse:3.237047+0.123078 test-rmse:3.385323+0.430745
## [28] train-rmse:3.210948+0.123607 test-rmse:3.339290+0.419835
## [29] train-rmse:3.185751+0.124597 test-rmse:3.314850+0.416407
## [30] train-rmse:3.161094+0.125666 test-rmse:3.303617+0.419397
## [31] train-rmse:3.136747+0.126421 test-rmse:3.278073+0.420619
## [32] train-rmse:3.114252+0.126164 test-rmse:3.260615+0.429923
## [33] train-rmse:3.092968+0.126404 test-rmse:3.243760+0.425304
## [34] train-rmse:3.072261+0.126956 test-rmse:3.211661+0.413731
## [35] train-rmse:3.052265+0.127738 test-rmse:3.203530+0.426401
## [36] train-rmse:3.032485+0.128681 test-rmse:3.182882+0.415142
## [37] train-rmse:3.013733+0.129075 test-rmse:3.165471+0.429605
## [38] train-rmse:2.995386+0.129274 test-rmse:3.161474+0.427745
## [39] train-rmse:2.977521+0.129478 test-rmse:3.148714+0.434765
## [40] train-rmse:2.959888+0.129669 test-rmse:3.128348+0.429525
## [41] train-rmse:2.943017+0.130231 test-rmse:3.106513+0.427797
## [42] train-rmse:2.926729+0.130604 test-rmse:3.092499+0.441190
## [43] train-rmse:2.911032+0.131177 test-rmse:3.084843+0.438300
## [44] train-rmse:2.895466+0.131801 test-rmse:3.068502+0.440235
## [45] train-rmse:2.880234+0.132412 test-rmse:3.052090+0.443668
## [46] train-rmse:2.865592+0.132761 test-rmse:3.041906+0.433129
## [47] train-rmse:2.851261+0.133145 test-rmse:3.037169+0.440262
## [48] train-rmse:2.837243+0.133337 test-rmse:3.023626+0.440770
## [49] train-rmse:2.823648+0.133882 test-rmse:3.005650+0.449554
## [50] train-rmse:2.810310+0.134402 test-rmse:2.997442+0.457615
## [51] train-rmse:2.797054+0.134972 test-rmse:2.973679+0.452051
## [52] train-rmse:2.783945+0.135334 test-rmse:2.959893+0.448064
## [53] train-rmse:2.771121+0.135752 test-rmse:2.961986+0.456042
## [54] train-rmse:2.758761+0.136237 test-rmse:2.952058+0.454535
## [55] train-rmse:2.746596+0.136682 test-rmse:2.939800+0.450784
## [56] train-rmse:2.734687+0.137067 test-rmse:2.934541+0.447587
## [57] train-rmse:2.722856+0.137542 test-rmse:2.924814+0.460647
## [58] train-rmse:2.711596+0.137654 test-rmse:2.922338+0.462313
## [59] train-rmse:2.700418+0.137964 test-rmse:2.908041+0.453749
## [60] train-rmse:2.689460+0.138115 test-rmse:2.899799+0.450007
## [61] train-rmse:2.678649+0.138306 test-rmse:2.890296+0.456731
## [62] train-rmse:2.668005+0.138338 test-rmse:2.884646+0.459320
## [63] train-rmse:2.657452+0.138360 test-rmse:2.875083+0.470182
## [64] train-rmse:2.647139+0.138542 test-rmse:2.860815+0.468326
## [65] train-rmse:2.637111+0.138666 test-rmse:2.851550+0.463351
## [66] train-rmse:2.627115+0.138934 test-rmse:2.846071+0.471011
## [67] train-rmse:2.617231+0.139195 test-rmse:2.838383+0.470843
## [68] train-rmse:2.607601+0.139433 test-rmse:2.833954+0.472235
## [69] train-rmse:2.598083+0.139651 test-rmse:2.829111+0.472077
## [70] train-rmse:2.588928+0.139775 test-rmse:2.822820+0.469163
## [71] train-rmse:2.579912+0.139967 test-rmse:2.810491+0.470439
## [72] train-rmse:2.571057+0.140043 test-rmse:2.799865+0.472973
## [73] train-rmse:2.562367+0.139923 test-rmse:2.789803+0.468072
## [74] train-rmse:2.553832+0.139892 test-rmse:2.792343+0.462720
## [75] train-rmse:2.545387+0.139863 test-rmse:2.782148+0.467018
## [76] train-rmse:2.537071+0.139873 test-rmse:2.780306+0.471179
## [77] train-rmse:2.528765+0.139856 test-rmse:2.770276+0.481866
## [78] train-rmse:2.520623+0.139843 test-rmse:2.759613+0.481699
## [79] train-rmse:2.512694+0.139999 test-rmse:2.757639+0.486012
## [80] train-rmse:2.504865+0.140152 test-rmse:2.748462+0.484631
## [81] train-rmse:2.497238+0.140271 test-rmse:2.736612+0.482301
## [82] train-rmse:2.489633+0.140398 test-rmse:2.733504+0.483880
## [83] train-rmse:2.482212+0.140534 test-rmse:2.726859+0.482735
## [84] train-rmse:2.474805+0.140629 test-rmse:2.724635+0.483002
## [85] train-rmse:2.467536+0.140750 test-rmse:2.714021+0.486892
## [86] train-rmse:2.460374+0.140827 test-rmse:2.707092+0.483945
## [87] train-rmse:2.453425+0.140988 test-rmse:2.700956+0.483917
## [88] train-rmse:2.446582+0.141040 test-rmse:2.689069+0.485446
## [89] train-rmse:2.439777+0.141071 test-rmse:2.685147+0.485175
## [90] train-rmse:2.433198+0.141101 test-rmse:2.677326+0.489356
## [91] train-rmse:2.426735+0.141131 test-rmse:2.679081+0.488562
## [92] train-rmse:2.420279+0.141201 test-rmse:2.678098+0.491138
## [93] train-rmse:2.414007+0.141294 test-rmse:2.671925+0.487665
## [94] train-rmse:2.407868+0.141434 test-rmse:2.668963+0.486100
## [95] train-rmse:2.401733+0.141551 test-rmse:2.661520+0.489115
## [96] train-rmse:2.395736+0.141601 test-rmse:2.657614+0.491123
## [97] train-rmse:2.389814+0.141701 test-rmse:2.654878+0.495874
## [98] train-rmse:2.383934+0.141785 test-rmse:2.642542+0.491489
## [99] train-rmse:2.378119+0.141812 test-rmse:2.637786+0.492289
## [100] train-rmse:2.372384+0.141844 test-rmse:2.631128+0.495046
## [101] train-rmse:2.366751+0.141933 test-rmse:2.625142+0.495241
## [102] train-rmse:2.361150+0.142071 test-rmse:2.620170+0.493715
## [103] train-rmse:2.355733+0.142228 test-rmse:2.613349+0.491961
## [104] train-rmse:2.350346+0.142418 test-rmse:2.605943+0.490744
## [105] train-rmse:2.345108+0.142555 test-rmse:2.598018+0.493773
## [106] train-rmse:2.339950+0.142705 test-rmse:2.596120+0.499781
## [107] train-rmse:2.334886+0.142843 test-rmse:2.593792+0.501320
## [108] train-rmse:2.329864+0.142984 test-rmse:2.593921+0.503369
## [109] train-rmse:2.324840+0.143182 test-rmse:2.590216+0.498517
## [110] train-rmse:2.319852+0.143332 test-rmse:2.587842+0.497265
## [111] train-rmse:2.314947+0.143537 test-rmse:2.584385+0.496400
## [112] train-rmse:2.310129+0.143693 test-rmse:2.580134+0.497739
## [113] train-rmse:2.305416+0.143828 test-rmse:2.573878+0.501031
## [114] train-rmse:2.300735+0.143902 test-rmse:2.570357+0.509487
## [115] train-rmse:2.296151+0.144010 test-rmse:2.569302+0.511683
## [116] train-rmse:2.291630+0.144095 test-rmse:2.560597+0.507497
## [117] train-rmse:2.287166+0.144222 test-rmse:2.561373+0.506281
## [118] train-rmse:2.282815+0.144416 test-rmse:2.557107+0.506150
## [119] train-rmse:2.278542+0.144588 test-rmse:2.553142+0.505589
## [120] train-rmse:2.274253+0.144769 test-rmse:2.550447+0.505950
## [121] train-rmse:2.270059+0.144937 test-rmse:2.548159+0.501138
## [122] train-rmse:2.265914+0.145083 test-rmse:2.541009+0.500198
## [123] train-rmse:2.261801+0.145277 test-rmse:2.540188+0.505706
## [124] train-rmse:2.257750+0.145435 test-rmse:2.534369+0.505757
## [125] train-rmse:2.253713+0.145599 test-rmse:2.529944+0.504148
## [126] train-rmse:2.249760+0.145739 test-rmse:2.530310+0.504766
## [127] train-rmse:2.245843+0.145865 test-rmse:2.524136+0.508069
## [128] train-rmse:2.241968+0.146020 test-rmse:2.519892+0.508423
## [129] train-rmse:2.238138+0.146208 test-rmse:2.516313+0.509698
## [130] train-rmse:2.234360+0.146375 test-rmse:2.514190+0.510411
## [131] train-rmse:2.230653+0.146435 test-rmse:2.512911+0.508481
## [132] train-rmse:2.226982+0.146586 test-rmse:2.507428+0.508943
## [133] train-rmse:2.223392+0.146706 test-rmse:2.503156+0.510676
## [134] train-rmse:2.219831+0.146796 test-rmse:2.501275+0.508848
## [135] train-rmse:2.216301+0.146852 test-rmse:2.495789+0.512907
## [136] train-rmse:2.212857+0.146899 test-rmse:2.492258+0.513597
## [137] train-rmse:2.209439+0.146996 test-rmse:2.490875+0.513410
## [138] train-rmse:2.206107+0.147098 test-rmse:2.492827+0.514713
## [139] train-rmse:2.202796+0.147176 test-rmse:2.492287+0.515190
## [140] train-rmse:2.199526+0.147273 test-rmse:2.487580+0.512416
## [141] train-rmse:2.196314+0.147384 test-rmse:2.486686+0.512434
## [142] train-rmse:2.193135+0.147492 test-rmse:2.486481+0.518963
## [143] train-rmse:2.190013+0.147612 test-rmse:2.483843+0.517528
## [144] train-rmse:2.186917+0.147732 test-rmse:2.482465+0.515760
## [145] train-rmse:2.183820+0.147826 test-rmse:2.475094+0.519877
## [146] train-rmse:2.180736+0.147929 test-rmse:2.474277+0.520684
## [147] train-rmse:2.177693+0.148038 test-rmse:2.473059+0.520143
## [148] train-rmse:2.174678+0.148170 test-rmse:2.467713+0.521336
## [149] train-rmse:2.171684+0.148314 test-rmse:2.461691+0.517851
## [150] train-rmse:2.168754+0.148468 test-rmse:2.460521+0.518298
## [151] train-rmse:2.165853+0.148604 test-rmse:2.457714+0.518665
## [152] train-rmse:2.162965+0.148765 test-rmse:2.457842+0.519027
## [153] train-rmse:2.160139+0.148868 test-rmse:2.455817+0.518504
## [154] train-rmse:2.157305+0.148972 test-rmse:2.454807+0.516722
## [155] train-rmse:2.154536+0.149091 test-rmse:2.450391+0.516555
## [156] train-rmse:2.151820+0.149144 test-rmse:2.451625+0.520144
## [157] train-rmse:2.149114+0.149183 test-rmse:2.449075+0.521939
## [158] train-rmse:2.146456+0.149244 test-rmse:2.450372+0.520331
## [159] train-rmse:2.143852+0.149330 test-rmse:2.447356+0.518632
## [160] train-rmse:2.141253+0.149440 test-rmse:2.442312+0.518059
## [161] train-rmse:2.138709+0.149567 test-rmse:2.439347+0.523931
## [162] train-rmse:2.136209+0.149659 test-rmse:2.437744+0.524667
## [163] train-rmse:2.133717+0.149751 test-rmse:2.435416+0.524885
## [164] train-rmse:2.131245+0.149884 test-rmse:2.435064+0.524686
## [165] train-rmse:2.128814+0.149997 test-rmse:2.432976+0.523716
## [166] train-rmse:2.126403+0.150115 test-rmse:2.434076+0.522749
## [167] train-rmse:2.123947+0.150211 test-rmse:2.430960+0.523846
## [168] train-rmse:2.121556+0.150307 test-rmse:2.426878+0.526465
## [169] train-rmse:2.119163+0.150392 test-rmse:2.424605+0.526256
## [170] train-rmse:2.116796+0.150500 test-rmse:2.425965+0.527422
## [171] train-rmse:2.114470+0.150622 test-rmse:2.424055+0.525232
## [172] train-rmse:2.112162+0.150754 test-rmse:2.422708+0.525389
## [173] train-rmse:2.109871+0.150890 test-rmse:2.421701+0.527940
## [174] train-rmse:2.107596+0.151057 test-rmse:2.419569+0.526123
## [175] train-rmse:2.105317+0.151210 test-rmse:2.419278+0.524711
## [176] train-rmse:2.103085+0.151384 test-rmse:2.416886+0.523452
## [177] train-rmse:2.100857+0.151537 test-rmse:2.415800+0.524362
## [178] train-rmse:2.098664+0.151637 test-rmse:2.412683+0.524259
## [179] train-rmse:2.096531+0.151745 test-rmse:2.410701+0.524964
## [180] train-rmse:2.094459+0.151818 test-rmse:2.413515+0.525441
## [181] train-rmse:2.092408+0.151905 test-rmse:2.414615+0.527160
## [182] train-rmse:2.090360+0.151979 test-rmse:2.410912+0.529567
## [183] train-rmse:2.088318+0.152068 test-rmse:2.408517+0.526726
## [184] train-rmse:2.086275+0.152163 test-rmse:2.404716+0.527010
## [185] train-rmse:2.084269+0.152271 test-rmse:2.407343+0.529618
## [186] train-rmse:2.082274+0.152364 test-rmse:2.404947+0.530093
## [187] train-rmse:2.080280+0.152488 test-rmse:2.402306+0.529831
## [188] train-rmse:2.078341+0.152594 test-rmse:2.401450+0.530116
## [189] train-rmse:2.076425+0.152670 test-rmse:2.398813+0.529710
## [190] train-rmse:2.074541+0.152783 test-rmse:2.398214+0.529699
## [191] train-rmse:2.072657+0.152858 test-rmse:2.398990+0.528691
## [192] train-rmse:2.070785+0.152944 test-rmse:2.396597+0.527832
## [193] train-rmse:2.068915+0.153011 test-rmse:2.394503+0.527456
## [194] train-rmse:2.067063+0.153112 test-rmse:2.392127+0.528057
## [195] train-rmse:2.065209+0.153216 test-rmse:2.389945+0.527753
## [196] train-rmse:2.063370+0.153333 test-rmse:2.391145+0.529472
## [197] train-rmse:2.061531+0.153448 test-rmse:2.388114+0.530285
## [198] train-rmse:2.059712+0.153528 test-rmse:2.387381+0.532520
## [199] train-rmse:2.057934+0.153593 test-rmse:2.386608+0.532362
## [200] train-rmse:2.056173+0.153666 test-rmse:2.385842+0.529364
## [201] train-rmse:2.054388+0.153790 test-rmse:2.383080+0.530953
## [202] train-rmse:2.052643+0.153850 test-rmse:2.382249+0.529784
## [203] train-rmse:2.050899+0.153924 test-rmse:2.381628+0.530919
## [204] train-rmse:2.049194+0.153986 test-rmse:2.379667+0.530545
## [205] train-rmse:2.047495+0.154012 test-rmse:2.380056+0.531941
## [206] train-rmse:2.045810+0.154049 test-rmse:2.379730+0.530651
## [207] train-rmse:2.044136+0.154136 test-rmse:2.378553+0.531082
## [208] train-rmse:2.042488+0.154206 test-rmse:2.377490+0.533252
## [209] train-rmse:2.040831+0.154255 test-rmse:2.377580+0.529718
## [210] train-rmse:2.039205+0.154289 test-rmse:2.376237+0.528738
## [211] train-rmse:2.037567+0.154342 test-rmse:2.378003+0.526982
## [212] train-rmse:2.035977+0.154414 test-rmse:2.375974+0.525372
## [213] train-rmse:2.034380+0.154518 test-rmse:2.373421+0.528745
## [214] train-rmse:2.032825+0.154597 test-rmse:2.373177+0.528797
## [215] train-rmse:2.031272+0.154664 test-rmse:2.372406+0.528592
## [216] train-rmse:2.029715+0.154722 test-rmse:2.370781+0.527992
## [217] train-rmse:2.028173+0.154775 test-rmse:2.369406+0.526904
## [218] train-rmse:2.026652+0.154842 test-rmse:2.368644+0.527702
## [219] train-rmse:2.025140+0.154910 test-rmse:2.369526+0.526655
## [220] train-rmse:2.023617+0.154971 test-rmse:2.369968+0.525694
## [221] train-rmse:2.022111+0.155039 test-rmse:2.370661+0.530137
## [222] train-rmse:2.020625+0.155107 test-rmse:2.368034+0.529808
## [223] train-rmse:2.019153+0.155159 test-rmse:2.366246+0.530488
## [224] train-rmse:2.017653+0.155255 test-rmse:2.368966+0.529497
## [225] train-rmse:2.016186+0.155320 test-rmse:2.368170+0.529685
## [226] train-rmse:2.014735+0.155416 test-rmse:2.370775+0.531703
## [227] train-rmse:2.013319+0.155488 test-rmse:2.368263+0.530533
## [228] train-rmse:2.011910+0.155599 test-rmse:2.368567+0.530915
## [229] train-rmse:2.010453+0.155641 test-rmse:2.367336+0.531971
## Stopping. Best iteration:
## [223] train-rmse:2.019153+0.155159 test-rmse:2.366246+0.530488
##
## 15
## [1] train-rmse:7.302359+0.153266 test-rmse:7.303759+0.654899
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.557549+0.138125 test-rmse:6.582337+0.638434
## [3] train-rmse:5.933481+0.125342 test-rmse:5.996466+0.619396
## [4] train-rmse:5.407163+0.116097 test-rmse:5.528855+0.584077
## [5] train-rmse:4.965383+0.108223 test-rmse:5.111464+0.538515
## [6] train-rmse:4.588944+0.100477 test-rmse:4.734727+0.533114
## [7] train-rmse:4.281612+0.094538 test-rmse:4.456604+0.514180
## [8] train-rmse:4.020448+0.089985 test-rmse:4.206346+0.491365
## [9] train-rmse:3.788032+0.083754 test-rmse:3.972400+0.493440
## [10] train-rmse:3.598253+0.081286 test-rmse:3.796995+0.484133
## [11] train-rmse:3.432665+0.081471 test-rmse:3.652839+0.462501
## [12] train-rmse:3.294856+0.084099 test-rmse:3.552069+0.440319
## [13] train-rmse:3.167819+0.081117 test-rmse:3.430531+0.445420
## [14] train-rmse:3.062289+0.075596 test-rmse:3.340590+0.455699
## [15] train-rmse:2.968197+0.079468 test-rmse:3.262021+0.463136
## [16] train-rmse:2.886077+0.087393 test-rmse:3.196438+0.446579
## [17] train-rmse:2.813718+0.085326 test-rmse:3.125683+0.463556
## [18] train-rmse:2.753416+0.083002 test-rmse:3.094822+0.461665
## [19] train-rmse:2.702059+0.082058 test-rmse:3.072621+0.475106
## [20] train-rmse:2.643433+0.079092 test-rmse:3.013243+0.478327
## [21] train-rmse:2.593968+0.081901 test-rmse:2.971800+0.482570
## [22] train-rmse:2.553657+0.083637 test-rmse:2.955168+0.486444
## [23] train-rmse:2.516638+0.084365 test-rmse:2.932496+0.485611
## [24] train-rmse:2.481500+0.085978 test-rmse:2.915594+0.490680
## [25] train-rmse:2.444682+0.088957 test-rmse:2.886632+0.501891
## [26] train-rmse:2.407398+0.091981 test-rmse:2.865747+0.498211
## [27] train-rmse:2.372674+0.091969 test-rmse:2.845484+0.504796
## [28] train-rmse:2.336437+0.085160 test-rmse:2.826106+0.509361
## [29] train-rmse:2.309915+0.085685 test-rmse:2.813142+0.509601
## [30] train-rmse:2.279548+0.089932 test-rmse:2.793363+0.486914
## [31] train-rmse:2.253291+0.091295 test-rmse:2.778608+0.500372
## [32] train-rmse:2.228033+0.092074 test-rmse:2.754188+0.501851
## [33] train-rmse:2.206376+0.091368 test-rmse:2.741340+0.503867
## [34] train-rmse:2.179349+0.088977 test-rmse:2.726383+0.503503
## [35] train-rmse:2.157682+0.089979 test-rmse:2.718987+0.499930
## [36] train-rmse:2.129507+0.082319 test-rmse:2.708351+0.512412
## [37] train-rmse:2.108840+0.079953 test-rmse:2.689215+0.504369
## [38] train-rmse:2.088265+0.079681 test-rmse:2.686927+0.502314
## [39] train-rmse:2.070461+0.079249 test-rmse:2.671927+0.508407
## [40] train-rmse:2.050359+0.079782 test-rmse:2.657103+0.516653
## [41] train-rmse:2.032576+0.080068 test-rmse:2.648011+0.512585
## [42] train-rmse:2.012234+0.072611 test-rmse:2.640019+0.514874
## [43] train-rmse:1.995633+0.073487 test-rmse:2.624902+0.518423
## [44] train-rmse:1.977997+0.073221 test-rmse:2.610560+0.511100
## [45] train-rmse:1.959162+0.077028 test-rmse:2.596229+0.500523
## [46] train-rmse:1.944662+0.077025 test-rmse:2.597572+0.513824
## [47] train-rmse:1.929475+0.075330 test-rmse:2.585032+0.520545
## [48] train-rmse:1.908862+0.077558 test-rmse:2.570755+0.520848
## [49] train-rmse:1.895067+0.078222 test-rmse:2.567654+0.518711
## [50] train-rmse:1.881330+0.079244 test-rmse:2.555994+0.517967
## [51] train-rmse:1.867619+0.080084 test-rmse:2.550637+0.515789
## [52] train-rmse:1.854343+0.080185 test-rmse:2.539199+0.513743
## [53] train-rmse:1.838140+0.084169 test-rmse:2.521977+0.505408
## [54] train-rmse:1.825940+0.082833 test-rmse:2.515644+0.507827
## [55] train-rmse:1.814010+0.082157 test-rmse:2.509217+0.506453
## [56] train-rmse:1.793816+0.074208 test-rmse:2.497233+0.514136
## [57] train-rmse:1.780745+0.074258 test-rmse:2.491409+0.516904
## [58] train-rmse:1.767548+0.075036 test-rmse:2.485533+0.513934
## [59] train-rmse:1.754708+0.074797 test-rmse:2.473691+0.515182
## [60] train-rmse:1.744028+0.073450 test-rmse:2.466167+0.512461
## [61] train-rmse:1.734042+0.073279 test-rmse:2.458392+0.514387
## [62] train-rmse:1.724366+0.073189 test-rmse:2.456384+0.515323
## [63] train-rmse:1.714512+0.072793 test-rmse:2.451085+0.515564
## [64] train-rmse:1.700526+0.073450 test-rmse:2.451319+0.519448
## [65] train-rmse:1.689523+0.074040 test-rmse:2.440164+0.523665
## [66] train-rmse:1.678047+0.075833 test-rmse:2.425775+0.525594
## [67] train-rmse:1.669697+0.075382 test-rmse:2.420081+0.524298
## [68] train-rmse:1.659261+0.075533 test-rmse:2.416232+0.525216
## [69] train-rmse:1.647040+0.067821 test-rmse:2.407626+0.530022
## [70] train-rmse:1.638450+0.067995 test-rmse:2.406648+0.525504
## [71] train-rmse:1.629682+0.068671 test-rmse:2.400450+0.526448
## [72] train-rmse:1.619600+0.067582 test-rmse:2.398666+0.521783
## [73] train-rmse:1.605961+0.067263 test-rmse:2.393300+0.524204
## [74] train-rmse:1.597903+0.067397 test-rmse:2.392859+0.529466
## [75] train-rmse:1.589588+0.068324 test-rmse:2.383933+0.534829
## [76] train-rmse:1.582277+0.067889 test-rmse:2.377314+0.535457
## [77] train-rmse:1.573814+0.068769 test-rmse:2.371077+0.537657
## [78] train-rmse:1.567157+0.068730 test-rmse:2.367269+0.536700
## [79] train-rmse:1.560416+0.068636 test-rmse:2.362807+0.537365
## [80] train-rmse:1.553003+0.067970 test-rmse:2.362814+0.536264
## [81] train-rmse:1.541246+0.063066 test-rmse:2.351503+0.534507
## [82] train-rmse:1.534254+0.063016 test-rmse:2.352449+0.535458
## [83] train-rmse:1.525698+0.063022 test-rmse:2.346259+0.533771
## [84] train-rmse:1.518548+0.061661 test-rmse:2.346522+0.536052
## [85] train-rmse:1.510679+0.062079 test-rmse:2.335757+0.533912
## [86] train-rmse:1.503000+0.063425 test-rmse:2.333906+0.534968
## [87] train-rmse:1.494646+0.067064 test-rmse:2.328666+0.533910
## [88] train-rmse:1.487835+0.068184 test-rmse:2.326290+0.531737
## [89] train-rmse:1.482347+0.068518 test-rmse:2.320169+0.534949
## [90] train-rmse:1.476264+0.067912 test-rmse:2.319703+0.537709
## [91] train-rmse:1.470007+0.068402 test-rmse:2.315964+0.536200
## [92] train-rmse:1.463901+0.067772 test-rmse:2.315818+0.533503
## [93] train-rmse:1.457786+0.067026 test-rmse:2.318081+0.535880
## [94] train-rmse:1.447301+0.062052 test-rmse:2.316936+0.540597
## [95] train-rmse:1.436744+0.059120 test-rmse:2.311204+0.541006
## [96] train-rmse:1.428865+0.060977 test-rmse:2.309000+0.543800
## [97] train-rmse:1.423059+0.062248 test-rmse:2.305272+0.543006
## [98] train-rmse:1.416744+0.061740 test-rmse:2.297206+0.543788
## [99] train-rmse:1.411816+0.062154 test-rmse:2.293357+0.540505
## [100] train-rmse:1.406515+0.062346 test-rmse:2.291294+0.541166
## [101] train-rmse:1.398999+0.066239 test-rmse:2.286675+0.539236
## [102] train-rmse:1.393204+0.064504 test-rmse:2.284836+0.538567
## [103] train-rmse:1.387665+0.065224 test-rmse:2.279805+0.537567
## [104] train-rmse:1.383033+0.065661 test-rmse:2.277371+0.538093
## [105] train-rmse:1.375736+0.068221 test-rmse:2.272355+0.540140
## [106] train-rmse:1.370913+0.068708 test-rmse:2.270065+0.541131
## [107] train-rmse:1.365838+0.068295 test-rmse:2.268623+0.542836
## [108] train-rmse:1.360031+0.068809 test-rmse:2.267082+0.540462
## [109] train-rmse:1.352621+0.066558 test-rmse:2.265617+0.540996
## [110] train-rmse:1.347740+0.066326 test-rmse:2.263778+0.539619
## [111] train-rmse:1.343886+0.066135 test-rmse:2.262524+0.539379
## [112] train-rmse:1.338705+0.065454 test-rmse:2.256127+0.542795
## [113] train-rmse:1.333193+0.064328 test-rmse:2.255629+0.542595
## [114] train-rmse:1.327414+0.064847 test-rmse:2.251749+0.541999
## [115] train-rmse:1.320860+0.064584 test-rmse:2.251098+0.544072
## [116] train-rmse:1.317407+0.064666 test-rmse:2.249967+0.544533
## [117] train-rmse:1.313703+0.064943 test-rmse:2.246854+0.545751
## [118] train-rmse:1.308349+0.065393 test-rmse:2.241977+0.548600
## [119] train-rmse:1.304451+0.065769 test-rmse:2.238662+0.548384
## [120] train-rmse:1.298636+0.065402 test-rmse:2.234582+0.549272
## [121] train-rmse:1.290301+0.066731 test-rmse:2.232070+0.549562
## [122] train-rmse:1.285126+0.068530 test-rmse:2.232435+0.549484
## [123] train-rmse:1.280345+0.069298 test-rmse:2.230083+0.549314
## [124] train-rmse:1.275113+0.069281 test-rmse:2.230865+0.547092
## [125] train-rmse:1.269685+0.069307 test-rmse:2.225961+0.547568
## [126] train-rmse:1.266119+0.069234 test-rmse:2.227125+0.550341
## [127] train-rmse:1.261638+0.068319 test-rmse:2.225725+0.549872
## [128] train-rmse:1.256425+0.066983 test-rmse:2.224842+0.550432
## [129] train-rmse:1.251755+0.066758 test-rmse:2.224125+0.554767
## [130] train-rmse:1.248228+0.065876 test-rmse:2.224239+0.553659
## [131] train-rmse:1.242084+0.068968 test-rmse:2.223897+0.551636
## [132] train-rmse:1.238659+0.068812 test-rmse:2.224395+0.553193
## [133] train-rmse:1.235313+0.068098 test-rmse:2.221360+0.553776
## [134] train-rmse:1.230822+0.070028 test-rmse:2.220049+0.555259
## [135] train-rmse:1.223344+0.065484 test-rmse:2.215745+0.556856
## [136] train-rmse:1.219892+0.065731 test-rmse:2.217041+0.554700
## [137] train-rmse:1.216818+0.065280 test-rmse:2.215628+0.555484
## [138] train-rmse:1.213931+0.065375 test-rmse:2.214039+0.554785
## [139] train-rmse:1.210617+0.064717 test-rmse:2.212797+0.554613
## [140] train-rmse:1.207551+0.064595 test-rmse:2.211521+0.555841
## [141] train-rmse:1.203539+0.063573 test-rmse:2.210110+0.556169
## [142] train-rmse:1.199243+0.061967 test-rmse:2.205746+0.557098
## [143] train-rmse:1.195078+0.061391 test-rmse:2.205530+0.557928
## [144] train-rmse:1.191365+0.062339 test-rmse:2.203947+0.558449
## [145] train-rmse:1.187423+0.063350 test-rmse:2.202578+0.556624
## [146] train-rmse:1.184031+0.064137 test-rmse:2.200607+0.556086
## [147] train-rmse:1.179505+0.064379 test-rmse:2.202645+0.556706
## [148] train-rmse:1.175488+0.064476 test-rmse:2.200207+0.558110
## [149] train-rmse:1.173013+0.064472 test-rmse:2.200036+0.558847
## [150] train-rmse:1.168731+0.063855 test-rmse:2.199518+0.558411
## [151] train-rmse:1.165080+0.065255 test-rmse:2.196520+0.555208
## [152] train-rmse:1.162623+0.065346 test-rmse:2.196738+0.555071
## [153] train-rmse:1.158989+0.065433 test-rmse:2.195475+0.556393
## [154] train-rmse:1.155290+0.064950 test-rmse:2.191533+0.560425
## [155] train-rmse:1.149849+0.064857 test-rmse:2.190980+0.561942
## [156] train-rmse:1.144462+0.066173 test-rmse:2.188970+0.561944
## [157] train-rmse:1.140974+0.065182 test-rmse:2.187497+0.561749
## [158] train-rmse:1.137291+0.065067 test-rmse:2.187210+0.561467
## [159] train-rmse:1.132244+0.064328 test-rmse:2.189346+0.559868
## [160] train-rmse:1.129102+0.063772 test-rmse:2.188658+0.559719
## [161] train-rmse:1.126530+0.063801 test-rmse:2.186883+0.560572
## [162] train-rmse:1.123754+0.063677 test-rmse:2.186048+0.559996
## [163] train-rmse:1.119772+0.062969 test-rmse:2.183836+0.562684
## [164] train-rmse:1.116248+0.063025 test-rmse:2.182244+0.565148
## [165] train-rmse:1.114179+0.063043 test-rmse:2.181856+0.564985
## [166] train-rmse:1.110878+0.063549 test-rmse:2.180102+0.565814
## [167] train-rmse:1.108314+0.064057 test-rmse:2.178536+0.566405
## [168] train-rmse:1.104516+0.064366 test-rmse:2.175633+0.565664
## [169] train-rmse:1.098863+0.067247 test-rmse:2.174564+0.564016
## [170] train-rmse:1.096256+0.067311 test-rmse:2.173243+0.565226
## [171] train-rmse:1.093749+0.067513 test-rmse:2.174667+0.564912
## [172] train-rmse:1.090708+0.067786 test-rmse:2.175534+0.566751
## [173] train-rmse:1.088483+0.067551 test-rmse:2.174958+0.568118
## [174] train-rmse:1.085956+0.067730 test-rmse:2.175491+0.570342
## [175] train-rmse:1.084134+0.067594 test-rmse:2.173014+0.571938
## [176] train-rmse:1.081246+0.067921 test-rmse:2.171269+0.573729
## [177] train-rmse:1.078751+0.067499 test-rmse:2.168544+0.574329
## [178] train-rmse:1.076564+0.066879 test-rmse:2.169237+0.574477
## [179] train-rmse:1.074934+0.066836 test-rmse:2.168964+0.574441
## [180] train-rmse:1.072859+0.066835 test-rmse:2.170134+0.572561
## [181] train-rmse:1.069902+0.067381 test-rmse:2.172077+0.572176
## [182] train-rmse:1.068044+0.067498 test-rmse:2.171352+0.573903
## [183] train-rmse:1.064514+0.070077 test-rmse:2.169526+0.573043
## Stopping. Best iteration:
## [177] train-rmse:1.078751+0.067499 test-rmse:2.168544+0.574329
##
## 16
## [1] train-rmse:7.262084+0.151238 test-rmse:7.286660+0.646293
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.477224+0.132304 test-rmse:6.531172+0.601479
## [3] train-rmse:5.814509+0.117745 test-rmse:5.895394+0.568621
## [4] train-rmse:5.249486+0.108750 test-rmse:5.352182+0.526497
## [5] train-rmse:4.765412+0.106716 test-rmse:4.896590+0.471986
## [6] train-rmse:4.357643+0.099567 test-rmse:4.517075+0.455093
## [7] train-rmse:4.009706+0.099604 test-rmse:4.202342+0.436380
## [8] train-rmse:3.715881+0.089868 test-rmse:3.928009+0.416798
## [9] train-rmse:3.455244+0.086505 test-rmse:3.671752+0.429488
## [10] train-rmse:3.235614+0.081231 test-rmse:3.485752+0.420856
## [11] train-rmse:3.054792+0.073223 test-rmse:3.335632+0.432720
## [12] train-rmse:2.901011+0.068998 test-rmse:3.216229+0.416783
## [13] train-rmse:2.767844+0.067066 test-rmse:3.104452+0.433788
## [14] train-rmse:2.659617+0.067844 test-rmse:3.025322+0.440972
## [15] train-rmse:2.566854+0.066967 test-rmse:2.959828+0.433670
## [16] train-rmse:2.479332+0.070348 test-rmse:2.876791+0.430078
## [17] train-rmse:2.402343+0.067523 test-rmse:2.823459+0.426173
## [18] train-rmse:2.332736+0.061792 test-rmse:2.778785+0.437829
## [19] train-rmse:2.271152+0.054195 test-rmse:2.751842+0.458211
## [20] train-rmse:2.211196+0.057815 test-rmse:2.719187+0.454529
## [21] train-rmse:2.160604+0.062622 test-rmse:2.676530+0.462802
## [22] train-rmse:2.116812+0.058721 test-rmse:2.657584+0.464422
## [23] train-rmse:2.067696+0.050730 test-rmse:2.631946+0.464630
## [24] train-rmse:2.030995+0.048625 test-rmse:2.606429+0.455586
## [25] train-rmse:1.994131+0.042098 test-rmse:2.594495+0.456625
## [26] train-rmse:1.956934+0.042245 test-rmse:2.559564+0.459699
## [27] train-rmse:1.922932+0.044731 test-rmse:2.548995+0.466432
## [28] train-rmse:1.894492+0.042786 test-rmse:2.535192+0.458950
## [29] train-rmse:1.857300+0.042774 test-rmse:2.506109+0.452780
## [30] train-rmse:1.830498+0.043168 test-rmse:2.494064+0.455503
## [31] train-rmse:1.803359+0.039482 test-rmse:2.473396+0.457133
## [32] train-rmse:1.776477+0.036792 test-rmse:2.466882+0.466621
## [33] train-rmse:1.752306+0.036208 test-rmse:2.453844+0.467906
## [34] train-rmse:1.730719+0.035545 test-rmse:2.445797+0.457154
## [35] train-rmse:1.701662+0.037924 test-rmse:2.427485+0.450908
## [36] train-rmse:1.677657+0.037931 test-rmse:2.411111+0.455862
## [37] train-rmse:1.657501+0.041847 test-rmse:2.399434+0.461575
## [38] train-rmse:1.637841+0.040672 test-rmse:2.391702+0.459896
## [39] train-rmse:1.618319+0.038534 test-rmse:2.387620+0.460689
## [40] train-rmse:1.593294+0.033613 test-rmse:2.369021+0.462559
## [41] train-rmse:1.574793+0.032908 test-rmse:2.360825+0.463052
## [42] train-rmse:1.554389+0.034431 test-rmse:2.349039+0.467031
## [43] train-rmse:1.537018+0.037043 test-rmse:2.342831+0.470940
## [44] train-rmse:1.516805+0.041097 test-rmse:2.327129+0.463404
## [45] train-rmse:1.496852+0.037695 test-rmse:2.321327+0.459417
## [46] train-rmse:1.478710+0.035813 test-rmse:2.312231+0.459481
## [47] train-rmse:1.466169+0.035781 test-rmse:2.302401+0.463535
## [48] train-rmse:1.448072+0.034433 test-rmse:2.300707+0.465300
## [49] train-rmse:1.429896+0.034335 test-rmse:2.288693+0.466099
## [50] train-rmse:1.415473+0.039631 test-rmse:2.279745+0.456885
## [51] train-rmse:1.401603+0.040456 test-rmse:2.276037+0.459522
## [52] train-rmse:1.389531+0.041691 test-rmse:2.265170+0.462951
## [53] train-rmse:1.376607+0.040753 test-rmse:2.259422+0.465238
## [54] train-rmse:1.364873+0.040118 test-rmse:2.253156+0.465059
## [55] train-rmse:1.349500+0.038342 test-rmse:2.241007+0.468092
## [56] train-rmse:1.335113+0.037837 test-rmse:2.235733+0.466786
## [57] train-rmse:1.318531+0.039908 test-rmse:2.233130+0.462917
## [58] train-rmse:1.307800+0.042409 test-rmse:2.229589+0.464851
## [59] train-rmse:1.296130+0.041954 test-rmse:2.227055+0.464935
## [60] train-rmse:1.285166+0.040693 test-rmse:2.223006+0.466042
## [61] train-rmse:1.271686+0.040870 test-rmse:2.217115+0.466254
## [62] train-rmse:1.260802+0.039333 test-rmse:2.216148+0.463203
## [63] train-rmse:1.250682+0.038315 test-rmse:2.212604+0.464602
## [64] train-rmse:1.241644+0.037919 test-rmse:2.211387+0.467260
## [65] train-rmse:1.232392+0.038166 test-rmse:2.207739+0.468131
## [66] train-rmse:1.220189+0.037168 test-rmse:2.210431+0.468759
## [67] train-rmse:1.207440+0.039621 test-rmse:2.205063+0.462271
## [68] train-rmse:1.198299+0.041782 test-rmse:2.202657+0.467140
## [69] train-rmse:1.189663+0.043623 test-rmse:2.201185+0.469538
## [70] train-rmse:1.180754+0.044516 test-rmse:2.198583+0.471806
## [71] train-rmse:1.172978+0.044671 test-rmse:2.194318+0.469821
## [72] train-rmse:1.161553+0.043369 test-rmse:2.188897+0.466859
## [73] train-rmse:1.151180+0.042598 test-rmse:2.186086+0.471185
## [74] train-rmse:1.144008+0.042237 test-rmse:2.184564+0.472363
## [75] train-rmse:1.136667+0.041502 test-rmse:2.181281+0.471549
## [76] train-rmse:1.121681+0.039727 test-rmse:2.181019+0.473032
## [77] train-rmse:1.114418+0.042110 test-rmse:2.177011+0.472714
## [78] train-rmse:1.103871+0.039698 test-rmse:2.174895+0.474571
## [79] train-rmse:1.092637+0.042201 test-rmse:2.170019+0.468650
## [80] train-rmse:1.083715+0.039353 test-rmse:2.162945+0.469785
## [81] train-rmse:1.076436+0.040439 test-rmse:2.161756+0.469611
## [82] train-rmse:1.068291+0.039674 test-rmse:2.159839+0.468438
## [83] train-rmse:1.060850+0.040393 test-rmse:2.158722+0.473272
## [84] train-rmse:1.053493+0.040347 test-rmse:2.157559+0.474773
## [85] train-rmse:1.044652+0.039545 test-rmse:2.156209+0.476212
## [86] train-rmse:1.035602+0.039138 test-rmse:2.157158+0.476631
## [87] train-rmse:1.026876+0.036236 test-rmse:2.152222+0.476751
## [88] train-rmse:1.016962+0.036682 test-rmse:2.151143+0.473670
## [89] train-rmse:1.009993+0.038252 test-rmse:2.146109+0.473113
## [90] train-rmse:1.003641+0.038918 test-rmse:2.143913+0.473852
## [91] train-rmse:0.998625+0.039675 test-rmse:2.142360+0.474476
## [92] train-rmse:0.991794+0.038879 test-rmse:2.136885+0.476787
## [93] train-rmse:0.986147+0.038113 test-rmse:2.133505+0.482169
## [94] train-rmse:0.977115+0.037060 test-rmse:2.132197+0.480982
## [95] train-rmse:0.971546+0.036084 test-rmse:2.131130+0.481986
## [96] train-rmse:0.963491+0.036452 test-rmse:2.131259+0.478392
## [97] train-rmse:0.957445+0.036838 test-rmse:2.130597+0.479418
## [98] train-rmse:0.951263+0.036063 test-rmse:2.125045+0.481270
## [99] train-rmse:0.945165+0.038525 test-rmse:2.124442+0.481694
## [100] train-rmse:0.934288+0.041908 test-rmse:2.122512+0.479863
## [101] train-rmse:0.929291+0.041504 test-rmse:2.120080+0.480422
## [102] train-rmse:0.920895+0.042586 test-rmse:2.119149+0.479878
## [103] train-rmse:0.914774+0.042199 test-rmse:2.120392+0.478930
## [104] train-rmse:0.907121+0.040159 test-rmse:2.117430+0.480859
## [105] train-rmse:0.902546+0.040839 test-rmse:2.115738+0.480960
## [106] train-rmse:0.896141+0.040721 test-rmse:2.114569+0.482563
## [107] train-rmse:0.887262+0.042302 test-rmse:2.111525+0.481813
## [108] train-rmse:0.881270+0.040350 test-rmse:2.108712+0.481431
## [109] train-rmse:0.874108+0.038544 test-rmse:2.107334+0.482138
## [110] train-rmse:0.870086+0.039187 test-rmse:2.106349+0.484362
## [111] train-rmse:0.861951+0.043007 test-rmse:2.105286+0.483711
## [112] train-rmse:0.856066+0.042323 test-rmse:2.104847+0.485180
## [113] train-rmse:0.847691+0.044180 test-rmse:2.102942+0.483461
## [114] train-rmse:0.841143+0.043927 test-rmse:2.101123+0.484656
## [115] train-rmse:0.836660+0.045258 test-rmse:2.098999+0.484678
## [116] train-rmse:0.830473+0.045423 test-rmse:2.097649+0.485274
## [117] train-rmse:0.826079+0.044105 test-rmse:2.097719+0.484848
## [118] train-rmse:0.818713+0.044685 test-rmse:2.097869+0.486626
## [119] train-rmse:0.811243+0.042146 test-rmse:2.097098+0.486530
## [120] train-rmse:0.807654+0.043156 test-rmse:2.095150+0.488535
## [121] train-rmse:0.799830+0.041967 test-rmse:2.092021+0.488100
## [122] train-rmse:0.794594+0.041206 test-rmse:2.090173+0.488693
## [123] train-rmse:0.788644+0.039978 test-rmse:2.088584+0.487745
## [124] train-rmse:0.781798+0.042165 test-rmse:2.086306+0.486911
## [125] train-rmse:0.774925+0.040717 test-rmse:2.085634+0.488435
## [126] train-rmse:0.769592+0.039842 test-rmse:2.085122+0.490483
## [127] train-rmse:0.765087+0.040572 test-rmse:2.084527+0.490718
## [128] train-rmse:0.759300+0.039593 test-rmse:2.085441+0.489420
## [129] train-rmse:0.755254+0.038078 test-rmse:2.085443+0.491485
## [130] train-rmse:0.750116+0.037097 test-rmse:2.085432+0.491982
## [131] train-rmse:0.746096+0.036857 test-rmse:2.084862+0.490991
## [132] train-rmse:0.742066+0.037827 test-rmse:2.083729+0.492434
## [133] train-rmse:0.737024+0.037243 test-rmse:2.080852+0.493378
## [134] train-rmse:0.733494+0.037561 test-rmse:2.080840+0.493177
## [135] train-rmse:0.726297+0.037318 test-rmse:2.078495+0.492942
## [136] train-rmse:0.722290+0.037669 test-rmse:2.077708+0.492497
## [137] train-rmse:0.717637+0.036497 test-rmse:2.077233+0.493116
## [138] train-rmse:0.712322+0.037024 test-rmse:2.076393+0.492830
## [139] train-rmse:0.708370+0.036826 test-rmse:2.077136+0.494454
## [140] train-rmse:0.704139+0.036604 test-rmse:2.077178+0.494431
## [141] train-rmse:0.701065+0.037689 test-rmse:2.076706+0.494211
## [142] train-rmse:0.697659+0.036211 test-rmse:2.075269+0.495731
## [143] train-rmse:0.693021+0.035160 test-rmse:2.073900+0.495433
## [144] train-rmse:0.688757+0.034348 test-rmse:2.074040+0.492938
## [145] train-rmse:0.683505+0.033564 test-rmse:2.072679+0.493285
## [146] train-rmse:0.679384+0.033816 test-rmse:2.073622+0.493531
## [147] train-rmse:0.675415+0.033800 test-rmse:2.074913+0.491850
## [148] train-rmse:0.671368+0.033751 test-rmse:2.072759+0.493155
## [149] train-rmse:0.667740+0.032635 test-rmse:2.072195+0.492447
## [150] train-rmse:0.663023+0.034889 test-rmse:2.071440+0.491729
## [151] train-rmse:0.659714+0.034749 test-rmse:2.070541+0.492182
## [152] train-rmse:0.655625+0.034106 test-rmse:2.070759+0.492260
## [153] train-rmse:0.652235+0.034025 test-rmse:2.069826+0.492867
## [154] train-rmse:0.648100+0.033970 test-rmse:2.069156+0.493669
## [155] train-rmse:0.643066+0.036532 test-rmse:2.067532+0.491717
## [156] train-rmse:0.639685+0.036060 test-rmse:2.066788+0.492733
## [157] train-rmse:0.637946+0.036126 test-rmse:2.065714+0.492653
## [158] train-rmse:0.635187+0.035982 test-rmse:2.064122+0.492520
## [159] train-rmse:0.631710+0.035307 test-rmse:2.064509+0.493798
## [160] train-rmse:0.626651+0.035703 test-rmse:2.064499+0.493559
## [161] train-rmse:0.624942+0.035304 test-rmse:2.064914+0.493587
## [162] train-rmse:0.621111+0.036431 test-rmse:2.065128+0.493933
## [163] train-rmse:0.616983+0.036143 test-rmse:2.065561+0.493978
## [164] train-rmse:0.614271+0.034804 test-rmse:2.064515+0.494167
## Stopping. Best iteration:
## [158] train-rmse:0.635187+0.035982 test-rmse:2.064122+0.492520
##
## 17
## [1] train-rmse:7.225012+0.150279 test-rmse:7.234975+0.638548
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.403515+0.124922 test-rmse:6.442823+0.571983
## [3] train-rmse:5.700224+0.108045 test-rmse:5.785383+0.536862
## [4] train-rmse:5.102880+0.097591 test-rmse:5.222339+0.505123
## [5] train-rmse:4.586350+0.077869 test-rmse:4.736256+0.475903
## [6] train-rmse:4.151434+0.075703 test-rmse:4.355784+0.448784
## [7] train-rmse:3.785353+0.075337 test-rmse:4.038899+0.431733
## [8] train-rmse:3.477598+0.063084 test-rmse:3.764715+0.427504
## [9] train-rmse:3.209392+0.062219 test-rmse:3.545549+0.436035
## [10] train-rmse:2.988145+0.056388 test-rmse:3.338867+0.447415
## [11] train-rmse:2.794297+0.058036 test-rmse:3.190156+0.437924
## [12] train-rmse:2.637375+0.056925 test-rmse:3.076134+0.447165
## [13] train-rmse:2.508053+0.054535 test-rmse:2.988712+0.449752
## [14] train-rmse:2.385086+0.049980 test-rmse:2.919593+0.456885
## [15] train-rmse:2.270820+0.042431 test-rmse:2.827917+0.456132
## [16] train-rmse:2.175022+0.042037 test-rmse:2.782868+0.465571
## [17] train-rmse:2.103080+0.040871 test-rmse:2.728091+0.473782
## [18] train-rmse:2.022641+0.033529 test-rmse:2.676390+0.465310
## [19] train-rmse:1.958561+0.033065 test-rmse:2.637832+0.447559
## [20] train-rmse:1.907395+0.032712 test-rmse:2.606420+0.461886
## [21] train-rmse:1.854007+0.027770 test-rmse:2.580061+0.468193
## [22] train-rmse:1.806637+0.030169 test-rmse:2.554036+0.466638
## [23] train-rmse:1.759858+0.027096 test-rmse:2.531495+0.472361
## [24] train-rmse:1.721945+0.028484 test-rmse:2.510318+0.466869
## [25] train-rmse:1.685534+0.028844 test-rmse:2.496599+0.466550
## [26] train-rmse:1.649396+0.023239 test-rmse:2.487583+0.471109
## [27] train-rmse:1.616681+0.027133 test-rmse:2.467461+0.484400
## [28] train-rmse:1.587405+0.029706 test-rmse:2.448909+0.484403
## [29] train-rmse:1.554720+0.029992 test-rmse:2.429633+0.481118
## [30] train-rmse:1.519571+0.029289 test-rmse:2.417249+0.483241
## [31] train-rmse:1.492314+0.025018 test-rmse:2.406047+0.486832
## [32] train-rmse:1.462245+0.025998 test-rmse:2.389994+0.487205
## [33] train-rmse:1.435558+0.026643 test-rmse:2.372311+0.476636
## [34] train-rmse:1.407661+0.026477 test-rmse:2.365513+0.479540
## [35] train-rmse:1.386628+0.023798 test-rmse:2.354865+0.481798
## [36] train-rmse:1.362355+0.019518 test-rmse:2.350906+0.481232
## [37] train-rmse:1.342361+0.018534 test-rmse:2.342147+0.481790
## [38] train-rmse:1.319741+0.024370 test-rmse:2.342892+0.480547
## [39] train-rmse:1.302682+0.025234 test-rmse:2.337312+0.492666
## [40] train-rmse:1.285380+0.024730 test-rmse:2.327850+0.482766
## [41] train-rmse:1.265959+0.026081 test-rmse:2.319108+0.481538
## [42] train-rmse:1.244862+0.027261 test-rmse:2.310201+0.481399
## [43] train-rmse:1.224978+0.026393 test-rmse:2.299558+0.475948
## [44] train-rmse:1.209930+0.024693 test-rmse:2.294964+0.475878
## [45] train-rmse:1.195412+0.025982 test-rmse:2.286568+0.480359
## [46] train-rmse:1.180576+0.028782 test-rmse:2.285241+0.482260
## [47] train-rmse:1.161955+0.029639 test-rmse:2.282792+0.482815
## [48] train-rmse:1.143613+0.035174 test-rmse:2.274977+0.478528
## [49] train-rmse:1.128773+0.030554 test-rmse:2.270163+0.479660
## [50] train-rmse:1.114198+0.029885 test-rmse:2.262039+0.487799
## [51] train-rmse:1.095600+0.027275 test-rmse:2.260268+0.483644
## [52] train-rmse:1.084465+0.026499 test-rmse:2.255625+0.485046
## [53] train-rmse:1.073594+0.025419 test-rmse:2.252017+0.486498
## [54] train-rmse:1.062495+0.026040 test-rmse:2.250458+0.488134
## [55] train-rmse:1.050204+0.025298 test-rmse:2.238269+0.490885
## [56] train-rmse:1.038060+0.024181 test-rmse:2.236330+0.493762
## [57] train-rmse:1.026725+0.021698 test-rmse:2.232824+0.492309
## [58] train-rmse:1.014123+0.021253 test-rmse:2.225533+0.490811
## [59] train-rmse:1.001761+0.018111 test-rmse:2.221550+0.491473
## [60] train-rmse:0.991191+0.019062 test-rmse:2.222635+0.492611
## [61] train-rmse:0.976829+0.022112 test-rmse:2.222485+0.496404
## [62] train-rmse:0.967318+0.023130 test-rmse:2.217894+0.493292
## [63] train-rmse:0.957187+0.021682 test-rmse:2.212330+0.493505
## [64] train-rmse:0.944807+0.027727 test-rmse:2.214760+0.492793
## [65] train-rmse:0.934578+0.028541 test-rmse:2.210769+0.493851
## [66] train-rmse:0.923331+0.028309 test-rmse:2.206462+0.497465
## [67] train-rmse:0.914787+0.029365 test-rmse:2.203420+0.497100
## [68] train-rmse:0.903126+0.023816 test-rmse:2.200411+0.498721
## [69] train-rmse:0.893784+0.023720 test-rmse:2.193814+0.498323
## [70] train-rmse:0.884248+0.026604 test-rmse:2.191443+0.499608
## [71] train-rmse:0.872531+0.027772 test-rmse:2.192584+0.497890
## [72] train-rmse:0.856688+0.027420 test-rmse:2.190057+0.499518
## [73] train-rmse:0.845268+0.024369 test-rmse:2.189802+0.499577
## [74] train-rmse:0.837231+0.025076 test-rmse:2.187134+0.500522
## [75] train-rmse:0.825962+0.027079 test-rmse:2.185113+0.498223
## [76] train-rmse:0.816705+0.025856 test-rmse:2.182926+0.497908
## [77] train-rmse:0.807374+0.026761 test-rmse:2.180892+0.499093
## [78] train-rmse:0.802021+0.026047 test-rmse:2.179804+0.499273
## [79] train-rmse:0.792433+0.023449 test-rmse:2.176646+0.499865
## [80] train-rmse:0.782849+0.022471 test-rmse:2.173787+0.500619
## [81] train-rmse:0.773337+0.022292 test-rmse:2.175012+0.500810
## [82] train-rmse:0.765247+0.022933 test-rmse:2.174259+0.500128
## [83] train-rmse:0.758153+0.025510 test-rmse:2.172978+0.499070
## [84] train-rmse:0.747206+0.023976 test-rmse:2.170627+0.500304
## [85] train-rmse:0.735547+0.023278 test-rmse:2.169346+0.498515
## [86] train-rmse:0.725034+0.019945 test-rmse:2.165886+0.501531
## [87] train-rmse:0.715608+0.018329 test-rmse:2.165460+0.501278
## [88] train-rmse:0.709689+0.019804 test-rmse:2.164254+0.501489
## [89] train-rmse:0.701398+0.019129 test-rmse:2.162329+0.499647
## [90] train-rmse:0.695308+0.017938 test-rmse:2.160661+0.498971
## [91] train-rmse:0.685787+0.014957 test-rmse:2.161085+0.500417
## [92] train-rmse:0.679391+0.015303 test-rmse:2.160226+0.500691
## [93] train-rmse:0.673847+0.015779 test-rmse:2.159069+0.500209
## [94] train-rmse:0.666989+0.014798 test-rmse:2.158675+0.500599
## [95] train-rmse:0.662817+0.015292 test-rmse:2.157270+0.500826
## [96] train-rmse:0.658186+0.015684 test-rmse:2.156935+0.500611
## [97] train-rmse:0.648084+0.014106 test-rmse:2.154663+0.499806
## [98] train-rmse:0.641141+0.017018 test-rmse:2.154855+0.501049
## [99] train-rmse:0.636011+0.016473 test-rmse:2.154359+0.499990
## [100] train-rmse:0.629926+0.015238 test-rmse:2.152636+0.500595
## [101] train-rmse:0.623555+0.016316 test-rmse:2.152695+0.499812
## [102] train-rmse:0.613828+0.014429 test-rmse:2.150317+0.499252
## [103] train-rmse:0.605960+0.014426 test-rmse:2.149056+0.499867
## [104] train-rmse:0.600689+0.014157 test-rmse:2.148713+0.500468
## [105] train-rmse:0.596560+0.015513 test-rmse:2.146858+0.499448
## [106] train-rmse:0.592488+0.016665 test-rmse:2.146226+0.499310
## [107] train-rmse:0.588457+0.015943 test-rmse:2.145805+0.498796
## [108] train-rmse:0.583202+0.015174 test-rmse:2.146316+0.499499
## [109] train-rmse:0.577080+0.013760 test-rmse:2.148895+0.498816
## [110] train-rmse:0.571028+0.012710 test-rmse:2.148456+0.499238
## [111] train-rmse:0.566214+0.012832 test-rmse:2.147648+0.498660
## [112] train-rmse:0.561431+0.012184 test-rmse:2.148825+0.498536
## [113] train-rmse:0.551829+0.012311 test-rmse:2.148682+0.498035
## Stopping. Best iteration:
## [107] train-rmse:0.588457+0.015943 test-rmse:2.145805+0.498796
##
## 18
## [1] train-rmse:7.199357+0.148698 test-rmse:7.191038+0.627313
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.351764+0.126697 test-rmse:6.374904+0.572541
## [3] train-rmse:5.624545+0.114901 test-rmse:5.683823+0.521093
## [4] train-rmse:5.001475+0.101428 test-rmse:5.103561+0.486326
## [5] train-rmse:4.479184+0.091436 test-rmse:4.646187+0.455898
## [6] train-rmse:4.031603+0.082680 test-rmse:4.257207+0.435467
## [7] train-rmse:3.657011+0.074932 test-rmse:3.948711+0.432949
## [8] train-rmse:3.336265+0.068664 test-rmse:3.686642+0.429647
## [9] train-rmse:3.065662+0.057245 test-rmse:3.468182+0.440982
## [10] train-rmse:2.833910+0.054108 test-rmse:3.308921+0.450030
## [11] train-rmse:2.640864+0.050470 test-rmse:3.161948+0.466208
## [12] train-rmse:2.475109+0.052973 test-rmse:3.056496+0.479356
## [13] train-rmse:2.327554+0.062036 test-rmse:2.962739+0.489894
## [14] train-rmse:2.198984+0.060613 test-rmse:2.873803+0.502032
## [15] train-rmse:2.083933+0.066548 test-rmse:2.799072+0.509777
## [16] train-rmse:1.989560+0.072864 test-rmse:2.749934+0.512415
## [17] train-rmse:1.907080+0.070988 test-rmse:2.707853+0.506420
## [18] train-rmse:1.827077+0.063838 test-rmse:2.680065+0.523835
## [19] train-rmse:1.755624+0.057601 test-rmse:2.637410+0.524971
## [20] train-rmse:1.688732+0.055613 test-rmse:2.604841+0.529340
## [21] train-rmse:1.634388+0.049429 test-rmse:2.587606+0.527518
## [22] train-rmse:1.583802+0.049980 test-rmse:2.562896+0.523117
## [23] train-rmse:1.537064+0.053017 test-rmse:2.538048+0.527931
## [24] train-rmse:1.494621+0.051590 test-rmse:2.521279+0.537550
## [25] train-rmse:1.456070+0.051982 test-rmse:2.497542+0.529307
## [26] train-rmse:1.422521+0.053591 test-rmse:2.477164+0.537484
## [27] train-rmse:1.383778+0.059782 test-rmse:2.463522+0.529813
## [28] train-rmse:1.348355+0.051295 test-rmse:2.450899+0.525546
## [29] train-rmse:1.318399+0.051494 test-rmse:2.438937+0.528656
## [30] train-rmse:1.287668+0.056850 test-rmse:2.430809+0.524470
## [31] train-rmse:1.259654+0.052443 test-rmse:2.430641+0.533490
## [32] train-rmse:1.234624+0.053316 test-rmse:2.418619+0.532925
## [33] train-rmse:1.212420+0.055231 test-rmse:2.408362+0.535078
## [34] train-rmse:1.185104+0.056195 test-rmse:2.406617+0.530530
## [35] train-rmse:1.164996+0.053706 test-rmse:2.398901+0.530480
## [36] train-rmse:1.142149+0.047479 test-rmse:2.391335+0.533316
## [37] train-rmse:1.111467+0.054078 test-rmse:2.376881+0.533495
## [38] train-rmse:1.090665+0.052036 test-rmse:2.372864+0.534980
## [39] train-rmse:1.067671+0.047014 test-rmse:2.372932+0.538572
## [40] train-rmse:1.046942+0.049000 test-rmse:2.372200+0.542342
## [41] train-rmse:1.030702+0.049746 test-rmse:2.367213+0.541792
## [42] train-rmse:1.010735+0.050440 test-rmse:2.361619+0.542711
## [43] train-rmse:0.991689+0.043705 test-rmse:2.356052+0.541600
## [44] train-rmse:0.973950+0.047549 test-rmse:2.347760+0.541864
## [45] train-rmse:0.956280+0.050419 test-rmse:2.347252+0.539026
## [46] train-rmse:0.944520+0.051405 test-rmse:2.342651+0.540862
## [47] train-rmse:0.924240+0.046234 test-rmse:2.341038+0.542195
## [48] train-rmse:0.901840+0.046467 test-rmse:2.334222+0.538393
## [49] train-rmse:0.886780+0.043798 test-rmse:2.334643+0.541301
## [50] train-rmse:0.872243+0.044610 test-rmse:2.330578+0.546762
## [51] train-rmse:0.859726+0.044786 test-rmse:2.326754+0.547699
## [52] train-rmse:0.846015+0.043835 test-rmse:2.328283+0.547374
## [53] train-rmse:0.831805+0.042877 test-rmse:2.323684+0.551726
## [54] train-rmse:0.815064+0.041543 test-rmse:2.319890+0.550655
## [55] train-rmse:0.800894+0.038933 test-rmse:2.316987+0.554724
## [56] train-rmse:0.786587+0.040057 test-rmse:2.317999+0.556384
## [57] train-rmse:0.771711+0.037974 test-rmse:2.314765+0.557758
## [58] train-rmse:0.761979+0.039683 test-rmse:2.313523+0.557539
## [59] train-rmse:0.747796+0.037933 test-rmse:2.310305+0.558012
## [60] train-rmse:0.735184+0.037576 test-rmse:2.305300+0.560277
## [61] train-rmse:0.723552+0.038499 test-rmse:2.309207+0.563118
## [62] train-rmse:0.712514+0.040379 test-rmse:2.307673+0.562401
## [63] train-rmse:0.705547+0.040756 test-rmse:2.303902+0.565172
## [64] train-rmse:0.692907+0.040915 test-rmse:2.301684+0.567082
## [65] train-rmse:0.679850+0.041345 test-rmse:2.301764+0.568135
## [66] train-rmse:0.670651+0.044118 test-rmse:2.299856+0.567867
## [67] train-rmse:0.661344+0.041810 test-rmse:2.298700+0.567977
## [68] train-rmse:0.645849+0.034764 test-rmse:2.297224+0.570509
## [69] train-rmse:0.636741+0.032498 test-rmse:2.295330+0.572088
## [70] train-rmse:0.624205+0.032123 test-rmse:2.293888+0.571558
## [71] train-rmse:0.616145+0.033590 test-rmse:2.292649+0.572325
## [72] train-rmse:0.605452+0.036084 test-rmse:2.292533+0.572530
## [73] train-rmse:0.597193+0.036926 test-rmse:2.290907+0.572486
## [74] train-rmse:0.590004+0.035250 test-rmse:2.291295+0.572247
## [75] train-rmse:0.579953+0.032738 test-rmse:2.290212+0.571561
## [76] train-rmse:0.572740+0.032831 test-rmse:2.289344+0.573035
## [77] train-rmse:0.560709+0.035316 test-rmse:2.286873+0.573611
## [78] train-rmse:0.550467+0.031842 test-rmse:2.287968+0.573122
## [79] train-rmse:0.541819+0.033872 test-rmse:2.287986+0.572964
## [80] train-rmse:0.535458+0.032354 test-rmse:2.286824+0.574090
## [81] train-rmse:0.524656+0.033466 test-rmse:2.285950+0.576678
## [82] train-rmse:0.515762+0.031344 test-rmse:2.287123+0.576015
## [83] train-rmse:0.509884+0.029961 test-rmse:2.286722+0.576491
## [84] train-rmse:0.502101+0.028994 test-rmse:2.288172+0.577714
## [85] train-rmse:0.496884+0.029878 test-rmse:2.287266+0.578582
## [86] train-rmse:0.488681+0.032929 test-rmse:2.287884+0.578977
## [87] train-rmse:0.481732+0.033232 test-rmse:2.286854+0.578865
## Stopping. Best iteration:
## [81] train-rmse:0.524656+0.033466 test-rmse:2.285950+0.576678
##
## 19
## [1] train-rmse:7.183159+0.146381 test-rmse:7.179923+0.642150
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.319248+0.122719 test-rmse:6.356838+0.597715
## [3] train-rmse:5.576071+0.104891 test-rmse:5.667550+0.555917
## [4] train-rmse:4.942387+0.089122 test-rmse:5.093068+0.522494
## [5] train-rmse:4.402224+0.077357 test-rmse:4.617486+0.494779
## [6] train-rmse:3.941854+0.066093 test-rmse:4.227172+0.481053
## [7] train-rmse:3.553105+0.059418 test-rmse:3.905723+0.464738
## [8] train-rmse:3.218095+0.048693 test-rmse:3.651784+0.458349
## [9] train-rmse:2.933121+0.038463 test-rmse:3.444711+0.465582
## [10] train-rmse:2.697828+0.035052 test-rmse:3.275127+0.477373
## [11] train-rmse:2.495183+0.030474 test-rmse:3.135219+0.494510
## [12] train-rmse:2.314490+0.027877 test-rmse:3.005481+0.493147
## [13] train-rmse:2.165099+0.027129 test-rmse:2.900567+0.506106
## [14] train-rmse:2.029410+0.025843 test-rmse:2.827872+0.512832
## [15] train-rmse:1.912052+0.026458 test-rmse:2.766182+0.506298
## [16] train-rmse:1.810372+0.024195 test-rmse:2.707215+0.518656
## [17] train-rmse:1.713444+0.015967 test-rmse:2.651602+0.531628
## [18] train-rmse:1.622846+0.014184 test-rmse:2.611447+0.529042
## [19] train-rmse:1.551210+0.019990 test-rmse:2.578625+0.531407
## [20] train-rmse:1.490772+0.020439 test-rmse:2.556806+0.537061
## [21] train-rmse:1.433835+0.022187 test-rmse:2.530422+0.540834
## [22] train-rmse:1.379867+0.019060 test-rmse:2.513972+0.552386
## [23] train-rmse:1.334974+0.018349 test-rmse:2.494047+0.545211
## [24] train-rmse:1.288483+0.021884 test-rmse:2.476113+0.550023
## [25] train-rmse:1.249764+0.019679 test-rmse:2.462680+0.549526
## [26] train-rmse:1.200567+0.008338 test-rmse:2.446245+0.548953
## [27] train-rmse:1.158761+0.014454 test-rmse:2.443108+0.552058
## [28] train-rmse:1.122397+0.022000 test-rmse:2.431707+0.549871
## [29] train-rmse:1.094908+0.022620 test-rmse:2.422180+0.553617
## [30] train-rmse:1.064064+0.019040 test-rmse:2.420759+0.556401
## [31] train-rmse:1.035612+0.023169 test-rmse:2.410230+0.562126
## [32] train-rmse:1.011413+0.020465 test-rmse:2.405487+0.564978
## [33] train-rmse:0.983640+0.022833 test-rmse:2.402731+0.562588
## [34] train-rmse:0.953754+0.018164 test-rmse:2.400052+0.566890
## [35] train-rmse:0.932104+0.020789 test-rmse:2.389747+0.570768
## [36] train-rmse:0.908808+0.021721 test-rmse:2.383838+0.569037
## [37] train-rmse:0.893255+0.020110 test-rmse:2.379547+0.567850
## [38] train-rmse:0.877116+0.019271 test-rmse:2.372879+0.571558
## [39] train-rmse:0.855197+0.020962 test-rmse:2.367550+0.574695
## [40] train-rmse:0.832672+0.022736 test-rmse:2.359799+0.575598
## [41] train-rmse:0.811879+0.017633 test-rmse:2.356579+0.575267
## [42] train-rmse:0.793018+0.017380 test-rmse:2.352940+0.572362
## [43] train-rmse:0.776735+0.017503 test-rmse:2.348123+0.574382
## [44] train-rmse:0.754810+0.013234 test-rmse:2.342665+0.578545
## [45] train-rmse:0.739236+0.009740 test-rmse:2.342702+0.576169
## [46] train-rmse:0.724208+0.009560 test-rmse:2.339748+0.574489
## [47] train-rmse:0.710974+0.010266 test-rmse:2.335367+0.576929
## [48] train-rmse:0.694578+0.012261 test-rmse:2.333818+0.577046
## [49] train-rmse:0.680703+0.009186 test-rmse:2.330947+0.579386
## [50] train-rmse:0.661494+0.008205 test-rmse:2.328356+0.581129
## [51] train-rmse:0.646651+0.005058 test-rmse:2.326553+0.581308
## [52] train-rmse:0.631982+0.008422 test-rmse:2.322791+0.581304
## [53] train-rmse:0.619683+0.007670 test-rmse:2.323474+0.582026
## [54] train-rmse:0.605911+0.006271 test-rmse:2.319943+0.585503
## [55] train-rmse:0.595386+0.007977 test-rmse:2.321532+0.588284
## [56] train-rmse:0.584093+0.009480 test-rmse:2.319975+0.586919
## [57] train-rmse:0.568968+0.010991 test-rmse:2.320350+0.587625
## [58] train-rmse:0.553285+0.014021 test-rmse:2.318597+0.587502
## [59] train-rmse:0.540105+0.013736 test-rmse:2.317932+0.586143
## [60] train-rmse:0.527467+0.014954 test-rmse:2.315209+0.587577
## [61] train-rmse:0.517147+0.014318 test-rmse:2.316287+0.586947
## [62] train-rmse:0.508671+0.012616 test-rmse:2.314016+0.587571
## [63] train-rmse:0.498039+0.016984 test-rmse:2.312804+0.585749
## [64] train-rmse:0.486315+0.018161 test-rmse:2.314469+0.583767
## [65] train-rmse:0.477347+0.016345 test-rmse:2.314165+0.582801
## [66] train-rmse:0.471236+0.016632 test-rmse:2.313786+0.583236
## [67] train-rmse:0.460207+0.016776 test-rmse:2.311551+0.583729
## [68] train-rmse:0.451829+0.016862 test-rmse:2.311265+0.583382
## [69] train-rmse:0.446281+0.017766 test-rmse:2.310863+0.583173
## [70] train-rmse:0.438535+0.020417 test-rmse:2.308679+0.580995
## [71] train-rmse:0.430971+0.020083 test-rmse:2.308487+0.580374
## [72] train-rmse:0.421955+0.018152 test-rmse:2.308003+0.581466
## [73] train-rmse:0.412374+0.016680 test-rmse:2.307292+0.580682
## [74] train-rmse:0.403759+0.017559 test-rmse:2.306670+0.582003
## [75] train-rmse:0.395106+0.016875 test-rmse:2.305853+0.583788
## [76] train-rmse:0.387984+0.016157 test-rmse:2.305147+0.583257
## [77] train-rmse:0.382285+0.014835 test-rmse:2.304723+0.583374
## [78] train-rmse:0.375359+0.014934 test-rmse:2.304733+0.582833
## [79] train-rmse:0.367880+0.014674 test-rmse:2.304647+0.582516
## [80] train-rmse:0.360854+0.014019 test-rmse:2.303734+0.584348
## [81] train-rmse:0.353576+0.016281 test-rmse:2.304292+0.586609
## [82] train-rmse:0.348562+0.017187 test-rmse:2.302586+0.588051
## [83] train-rmse:0.344103+0.017537 test-rmse:2.302265+0.587935
## [84] train-rmse:0.338313+0.017552 test-rmse:2.302112+0.588012
## [85] train-rmse:0.328514+0.017149 test-rmse:2.301236+0.587608
## [86] train-rmse:0.322195+0.016638 test-rmse:2.301197+0.589534
## [87] train-rmse:0.316190+0.015909 test-rmse:2.300968+0.589108
## [88] train-rmse:0.309536+0.014786 test-rmse:2.301809+0.589380
## [89] train-rmse:0.305519+0.014347 test-rmse:2.301432+0.589265
## [90] train-rmse:0.299217+0.013687 test-rmse:2.301680+0.589546
## [91] train-rmse:0.294887+0.013604 test-rmse:2.300618+0.589433
## [92] train-rmse:0.290179+0.012429 test-rmse:2.300257+0.589572
## [93] train-rmse:0.284838+0.014723 test-rmse:2.299443+0.588090
## [94] train-rmse:0.280524+0.015220 test-rmse:2.298968+0.588076
## [95] train-rmse:0.275095+0.013678 test-rmse:2.299285+0.588563
## [96] train-rmse:0.270022+0.011594 test-rmse:2.298011+0.588938
## [97] train-rmse:0.266075+0.013076 test-rmse:2.297792+0.588907
## [98] train-rmse:0.262806+0.012786 test-rmse:2.297436+0.588882
## [99] train-rmse:0.258974+0.013276 test-rmse:2.298142+0.590632
## [100] train-rmse:0.255136+0.012674 test-rmse:2.298593+0.591681
## [101] train-rmse:0.250224+0.013666 test-rmse:2.297446+0.591875
## [102] train-rmse:0.245814+0.012437 test-rmse:2.296864+0.591644
## [103] train-rmse:0.241700+0.013159 test-rmse:2.297355+0.592079
## [104] train-rmse:0.236673+0.012262 test-rmse:2.296197+0.592164
## [105] train-rmse:0.233775+0.012921 test-rmse:2.295231+0.591339
## [106] train-rmse:0.229186+0.012387 test-rmse:2.296011+0.591628
## [107] train-rmse:0.224521+0.011390 test-rmse:2.296094+0.591661
## [108] train-rmse:0.221489+0.011262 test-rmse:2.296176+0.591486
## [109] train-rmse:0.218112+0.011061 test-rmse:2.295541+0.591493
## [110] train-rmse:0.214548+0.011126 test-rmse:2.296054+0.592324
## [111] train-rmse:0.209023+0.009868 test-rmse:2.296004+0.592110
## Stopping. Best iteration:
## [105] train-rmse:0.233775+0.012921 test-rmse:2.295231+0.591339
##
## 20
## [1] train-rmse:7.177423+0.145908 test-rmse:7.176935+0.641800
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.306112+0.120606 test-rmse:6.359445+0.588621
## [3] train-rmse:5.554681+0.101197 test-rmse:5.654394+0.560183
## [4] train-rmse:4.910183+0.082959 test-rmse:5.072406+0.523030
## [5] train-rmse:4.356136+0.068813 test-rmse:4.584057+0.497601
## [6] train-rmse:3.887063+0.059781 test-rmse:4.189745+0.483410
## [7] train-rmse:3.481601+0.051564 test-rmse:3.862261+0.462391
## [8] train-rmse:3.138203+0.045392 test-rmse:3.598128+0.455426
## [9] train-rmse:2.844624+0.037423 test-rmse:3.395686+0.453448
## [10] train-rmse:2.593727+0.031964 test-rmse:3.221624+0.460787
## [11] train-rmse:2.374748+0.026468 test-rmse:3.098856+0.466440
## [12] train-rmse:2.191721+0.023808 test-rmse:2.977415+0.486496
## [13] train-rmse:2.032642+0.021461 test-rmse:2.885154+0.497091
## [14] train-rmse:1.898181+0.021124 test-rmse:2.811304+0.509902
## [15] train-rmse:1.769129+0.029212 test-rmse:2.746924+0.514778
## [16] train-rmse:1.666593+0.029146 test-rmse:2.696358+0.522856
## [17] train-rmse:1.567670+0.038577 test-rmse:2.653109+0.540185
## [18] train-rmse:1.483099+0.046880 test-rmse:2.614415+0.547741
## [19] train-rmse:1.403061+0.046970 test-rmse:2.578932+0.552165
## [20] train-rmse:1.336959+0.049485 test-rmse:2.557865+0.555127
## [21] train-rmse:1.272560+0.044952 test-rmse:2.538870+0.564082
## [22] train-rmse:1.218550+0.042105 test-rmse:2.521086+0.572326
## [23] train-rmse:1.167884+0.036136 test-rmse:2.507702+0.565325
## [24] train-rmse:1.121030+0.035487 test-rmse:2.487960+0.564008
## [25] train-rmse:1.082532+0.038029 test-rmse:2.474240+0.569898
## [26] train-rmse:1.033738+0.039754 test-rmse:2.464826+0.575793
## [27] train-rmse:0.999795+0.033943 test-rmse:2.463672+0.579097
## [28] train-rmse:0.966832+0.032755 test-rmse:2.453081+0.576688
## [29] train-rmse:0.929552+0.032875 test-rmse:2.449901+0.580602
## [30] train-rmse:0.904642+0.034077 test-rmse:2.440454+0.584852
## [31] train-rmse:0.879401+0.034235 test-rmse:2.431626+0.584571
## [32] train-rmse:0.858813+0.034681 test-rmse:2.426321+0.586202
## [33] train-rmse:0.832500+0.033005 test-rmse:2.420266+0.589114
## [34] train-rmse:0.803142+0.033563 test-rmse:2.415758+0.590308
## [35] train-rmse:0.777775+0.034640 test-rmse:2.412758+0.593797
## [36] train-rmse:0.756928+0.033544 test-rmse:2.406990+0.597410
## [37] train-rmse:0.727429+0.037981 test-rmse:2.401726+0.598743
## [38] train-rmse:0.706753+0.032999 test-rmse:2.397172+0.599077
## [39] train-rmse:0.686150+0.031333 test-rmse:2.392145+0.601849
## [40] train-rmse:0.669503+0.027445 test-rmse:2.390074+0.603513
## [41] train-rmse:0.648806+0.028395 test-rmse:2.385461+0.601360
## [42] train-rmse:0.635564+0.027908 test-rmse:2.384236+0.603067
## [43] train-rmse:0.618052+0.032837 test-rmse:2.381282+0.604924
## [44] train-rmse:0.600561+0.030752 test-rmse:2.377912+0.605655
## [45] train-rmse:0.585607+0.031961 test-rmse:2.374815+0.606105
## [46] train-rmse:0.570626+0.036155 test-rmse:2.375097+0.605904
## [47] train-rmse:0.553827+0.034294 test-rmse:2.372989+0.607171
## [48] train-rmse:0.540919+0.036019 test-rmse:2.371945+0.609043
## [49] train-rmse:0.525402+0.034481 test-rmse:2.368529+0.609655
## [50] train-rmse:0.511721+0.036296 test-rmse:2.367436+0.610687
## [51] train-rmse:0.498339+0.038150 test-rmse:2.367044+0.609845
## [52] train-rmse:0.483148+0.035932 test-rmse:2.364143+0.610417
## [53] train-rmse:0.469772+0.039171 test-rmse:2.364377+0.608633
## [54] train-rmse:0.457786+0.035709 test-rmse:2.363463+0.607861
## [55] train-rmse:0.448146+0.034193 test-rmse:2.361727+0.608719
## [56] train-rmse:0.439061+0.034416 test-rmse:2.359966+0.609072
## [57] train-rmse:0.423625+0.034669 test-rmse:2.358948+0.607520
## [58] train-rmse:0.412526+0.031831 test-rmse:2.358973+0.607670
## [59] train-rmse:0.402940+0.030596 test-rmse:2.359938+0.606734
## [60] train-rmse:0.394974+0.029133 test-rmse:2.357537+0.606807
## [61] train-rmse:0.386732+0.029391 test-rmse:2.355366+0.608203
## [62] train-rmse:0.376284+0.027127 test-rmse:2.353007+0.607481
## [63] train-rmse:0.369933+0.024240 test-rmse:2.352110+0.607269
## [64] train-rmse:0.361481+0.023100 test-rmse:2.351531+0.608313
## [65] train-rmse:0.350742+0.022264 test-rmse:2.351427+0.608219
## [66] train-rmse:0.345210+0.021728 test-rmse:2.351135+0.608743
## [67] train-rmse:0.337284+0.023201 test-rmse:2.350485+0.607902
## [68] train-rmse:0.328247+0.023211 test-rmse:2.351496+0.608252
## [69] train-rmse:0.319603+0.024727 test-rmse:2.350487+0.607842
## [70] train-rmse:0.311080+0.023466 test-rmse:2.350292+0.608087
## [71] train-rmse:0.303247+0.024626 test-rmse:2.348090+0.607078
## [72] train-rmse:0.296906+0.023394 test-rmse:2.347990+0.607333
## [73] train-rmse:0.289526+0.022763 test-rmse:2.349413+0.606245
## [74] train-rmse:0.283703+0.022711 test-rmse:2.349766+0.606493
## [75] train-rmse:0.275615+0.023509 test-rmse:2.347474+0.606929
## [76] train-rmse:0.268512+0.022784 test-rmse:2.346218+0.605089
## [77] train-rmse:0.262459+0.022107 test-rmse:2.345475+0.605160
## [78] train-rmse:0.258149+0.022025 test-rmse:2.345317+0.605204
## [79] train-rmse:0.252621+0.019782 test-rmse:2.345424+0.605762
## [80] train-rmse:0.247374+0.020244 test-rmse:2.344298+0.605860
## [81] train-rmse:0.241223+0.018594 test-rmse:2.343123+0.606153
## [82] train-rmse:0.236263+0.019593 test-rmse:2.343555+0.605700
## [83] train-rmse:0.230584+0.019031 test-rmse:2.343978+0.605353
## [84] train-rmse:0.225482+0.020077 test-rmse:2.344638+0.605483
## [85] train-rmse:0.220981+0.019734 test-rmse:2.344413+0.605400
## [86] train-rmse:0.216246+0.019263 test-rmse:2.343467+0.605773
## [87] train-rmse:0.212330+0.019646 test-rmse:2.343115+0.606286
## [88] train-rmse:0.206943+0.019025 test-rmse:2.342729+0.606119
## [89] train-rmse:0.202391+0.017166 test-rmse:2.342746+0.606244
## [90] train-rmse:0.197811+0.018016 test-rmse:2.342584+0.605658
## [91] train-rmse:0.194192+0.016644 test-rmse:2.342366+0.605859
## [92] train-rmse:0.189891+0.017882 test-rmse:2.341997+0.605828
## [93] train-rmse:0.187119+0.018157 test-rmse:2.342076+0.606036
## [94] train-rmse:0.183616+0.016863 test-rmse:2.341836+0.606133
## [95] train-rmse:0.180171+0.017405 test-rmse:2.341440+0.605580
## [96] train-rmse:0.175291+0.016540 test-rmse:2.341001+0.606066
## [97] train-rmse:0.171219+0.015818 test-rmse:2.340876+0.606288
## [98] train-rmse:0.167709+0.015310 test-rmse:2.340956+0.606305
## [99] train-rmse:0.164513+0.014753 test-rmse:2.340681+0.606304
## [100] train-rmse:0.161006+0.015285 test-rmse:2.340651+0.606216
## [101] train-rmse:0.157820+0.015624 test-rmse:2.340873+0.606133
## [102] train-rmse:0.154411+0.015987 test-rmse:2.340958+0.606098
## [103] train-rmse:0.151801+0.015662 test-rmse:2.340032+0.605930
## [104] train-rmse:0.149119+0.015487 test-rmse:2.340020+0.606222
## [105] train-rmse:0.146710+0.015482 test-rmse:2.339487+0.606690
## [106] train-rmse:0.144001+0.014873 test-rmse:2.338812+0.606399
## [107] train-rmse:0.141797+0.015225 test-rmse:2.338732+0.606216
## [108] train-rmse:0.138432+0.015266 test-rmse:2.338541+0.606458
## [109] train-rmse:0.136089+0.015325 test-rmse:2.338761+0.606220
## [110] train-rmse:0.133056+0.014720 test-rmse:2.338693+0.606538
## [111] train-rmse:0.130033+0.013587 test-rmse:2.338920+0.606463
## [112] train-rmse:0.127237+0.013776 test-rmse:2.338445+0.606966
## [113] train-rmse:0.125387+0.013485 test-rmse:2.338418+0.606981
## [114] train-rmse:0.122734+0.012808 test-rmse:2.338071+0.607272
## [115] train-rmse:0.120267+0.012622 test-rmse:2.337731+0.607425
## [116] train-rmse:0.117261+0.011996 test-rmse:2.337428+0.607565
## [117] train-rmse:0.115150+0.012284 test-rmse:2.337410+0.607574
## [118] train-rmse:0.112913+0.011971 test-rmse:2.337362+0.607635
## [119] train-rmse:0.110600+0.011917 test-rmse:2.337160+0.607451
## [120] train-rmse:0.108536+0.012823 test-rmse:2.336981+0.607170
## [121] train-rmse:0.106724+0.013005 test-rmse:2.337206+0.608206
## [122] train-rmse:0.105174+0.012695 test-rmse:2.337139+0.608387
## [123] train-rmse:0.102394+0.012161 test-rmse:2.337035+0.608346
## [124] train-rmse:0.100930+0.012011 test-rmse:2.336763+0.608236
## [125] train-rmse:0.099112+0.011655 test-rmse:2.336968+0.608330
## [126] train-rmse:0.097428+0.011621 test-rmse:2.336954+0.608188
## [127] train-rmse:0.095036+0.010823 test-rmse:2.336978+0.608132
## [128] train-rmse:0.093283+0.011220 test-rmse:2.336968+0.608091
## [129] train-rmse:0.091415+0.010962 test-rmse:2.336876+0.607864
## [130] train-rmse:0.089572+0.010723 test-rmse:2.336873+0.607920
## Stopping. Best iteration:
## [124] train-rmse:0.100930+0.012011 test-rmse:2.336763+0.608236
##
## 21
## [1] train-rmse:7.247898+0.155625 test-rmse:7.234017+0.672503
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.489066+0.138569 test-rmse:6.505553+0.701370
## [3] train-rmse:5.879890+0.127137 test-rmse:5.928813+0.674843
## [4] train-rmse:5.391158+0.121112 test-rmse:5.416146+0.647033
## [5] train-rmse:5.005461+0.115215 test-rmse:5.068700+0.635681
## [6] train-rmse:4.700549+0.112254 test-rmse:4.783698+0.613222
## [7] train-rmse:4.455932+0.112007 test-rmse:4.538013+0.572926
## [8] train-rmse:4.261161+0.111033 test-rmse:4.351160+0.549164
## [9] train-rmse:4.106283+0.111661 test-rmse:4.225950+0.530984
## [10] train-rmse:3.979821+0.112596 test-rmse:4.085371+0.506015
## [11] train-rmse:3.874582+0.113924 test-rmse:3.998060+0.492468
## [12] train-rmse:3.784503+0.115677 test-rmse:3.920281+0.484078
## [13] train-rmse:3.707828+0.115884 test-rmse:3.837151+0.474387
## [14] train-rmse:3.642064+0.116734 test-rmse:3.777057+0.455412
## [15] train-rmse:3.582779+0.118110 test-rmse:3.704208+0.437703
## [16] train-rmse:3.528668+0.119520 test-rmse:3.667276+0.442181
## [17] train-rmse:3.479379+0.120559 test-rmse:3.625312+0.433044
## [18] train-rmse:3.434281+0.120156 test-rmse:3.570856+0.438001
## [19] train-rmse:3.393204+0.120809 test-rmse:3.534429+0.429069
## [20] train-rmse:3.353932+0.122134 test-rmse:3.482670+0.416796
## [21] train-rmse:3.317506+0.123145 test-rmse:3.461728+0.423285
## [22] train-rmse:3.283459+0.123087 test-rmse:3.414147+0.400310
## [23] train-rmse:3.250377+0.122519 test-rmse:3.392014+0.419689
## [24] train-rmse:3.219721+0.123299 test-rmse:3.370204+0.429740
## [25] train-rmse:3.189508+0.124562 test-rmse:3.331948+0.430405
## [26] train-rmse:3.160559+0.125622 test-rmse:3.313249+0.418899
## [27] train-rmse:3.133359+0.125962 test-rmse:3.287508+0.423187
## [28] train-rmse:3.107159+0.126438 test-rmse:3.245080+0.422494
## [29] train-rmse:3.083085+0.126948 test-rmse:3.230988+0.424317
## [30] train-rmse:3.059494+0.127556 test-rmse:3.213540+0.437843
## [31] train-rmse:3.036399+0.128588 test-rmse:3.184060+0.429734
## [32] train-rmse:3.014963+0.129130 test-rmse:3.168381+0.417048
## [33] train-rmse:2.993684+0.129574 test-rmse:3.158980+0.430617
## [34] train-rmse:2.972983+0.129737 test-rmse:3.141020+0.419687
## [35] train-rmse:2.953390+0.130171 test-rmse:3.125279+0.428017
## [36] train-rmse:2.934441+0.130585 test-rmse:3.103769+0.430245
## [37] train-rmse:2.915597+0.131314 test-rmse:3.084217+0.435822
## [38] train-rmse:2.897729+0.132145 test-rmse:3.076797+0.450977
## [39] train-rmse:2.880254+0.132612 test-rmse:3.060939+0.458297
## [40] train-rmse:2.863182+0.133114 test-rmse:3.044274+0.452643
## [41] train-rmse:2.846555+0.133738 test-rmse:3.029000+0.444245
## [42] train-rmse:2.830560+0.133686 test-rmse:3.012038+0.447293
## [43] train-rmse:2.814934+0.133963 test-rmse:2.999973+0.439095
## [44] train-rmse:2.799500+0.134400 test-rmse:2.987809+0.444192
## [45] train-rmse:2.784600+0.135146 test-rmse:2.971798+0.444628
## [46] train-rmse:2.769971+0.135926 test-rmse:2.959166+0.439782
## [47] train-rmse:2.755507+0.136648 test-rmse:2.949081+0.448135
## [48] train-rmse:2.741393+0.136955 test-rmse:2.939809+0.444109
## [49] train-rmse:2.727651+0.137152 test-rmse:2.925366+0.453621
## [50] train-rmse:2.714165+0.137505 test-rmse:2.919052+0.463716
## [51] train-rmse:2.701182+0.138011 test-rmse:2.909260+0.460922
## [52] train-rmse:2.688600+0.138345 test-rmse:2.906073+0.464794
## [53] train-rmse:2.676337+0.138474 test-rmse:2.893458+0.471420
## [54] train-rmse:2.664196+0.138580 test-rmse:2.876725+0.462783
## [55] train-rmse:2.652347+0.138742 test-rmse:2.864903+0.469903
## [56] train-rmse:2.640402+0.138986 test-rmse:2.862961+0.468942
## [57] train-rmse:2.628807+0.139040 test-rmse:2.851211+0.473771
## [58] train-rmse:2.617466+0.139152 test-rmse:2.841174+0.465332
## [59] train-rmse:2.606338+0.139182 test-rmse:2.831289+0.463201
## [60] train-rmse:2.595405+0.139303 test-rmse:2.820547+0.466962
## [61] train-rmse:2.584693+0.139309 test-rmse:2.813801+0.470747
## [62] train-rmse:2.574194+0.139427 test-rmse:2.810424+0.475466
## [63] train-rmse:2.563877+0.139670 test-rmse:2.801505+0.473756
## [64] train-rmse:2.553921+0.140044 test-rmse:2.800764+0.474308
## [65] train-rmse:2.543997+0.140228 test-rmse:2.793628+0.488458
## [66] train-rmse:2.534375+0.140171 test-rmse:2.783914+0.489377
## [67] train-rmse:2.524922+0.140029 test-rmse:2.773823+0.486080
## [68] train-rmse:2.515711+0.139985 test-rmse:2.765218+0.480067
## [69] train-rmse:2.506714+0.140013 test-rmse:2.755015+0.484170
## [70] train-rmse:2.497937+0.140076 test-rmse:2.745893+0.485221
## [71] train-rmse:2.489317+0.140097 test-rmse:2.742028+0.484833
## [72] train-rmse:2.480811+0.140289 test-rmse:2.726925+0.486595
## [73] train-rmse:2.472448+0.140401 test-rmse:2.721911+0.483537
## [74] train-rmse:2.464121+0.140530 test-rmse:2.713620+0.485355
## [75] train-rmse:2.455989+0.140595 test-rmse:2.702143+0.482771
## [76] train-rmse:2.447907+0.140643 test-rmse:2.696724+0.477970
## [77] train-rmse:2.439996+0.140687 test-rmse:2.692458+0.482386
## [78] train-rmse:2.432355+0.140854 test-rmse:2.694648+0.493430
## [79] train-rmse:2.424752+0.141053 test-rmse:2.691489+0.494792
## [80] train-rmse:2.417333+0.141364 test-rmse:2.683043+0.493042
## [81] train-rmse:2.410004+0.141483 test-rmse:2.672878+0.495794
## [82] train-rmse:2.402745+0.141645 test-rmse:2.665693+0.497693
## [83] train-rmse:2.395835+0.141742 test-rmse:2.660579+0.498133
## [84] train-rmse:2.389147+0.141806 test-rmse:2.655969+0.499619
## [85] train-rmse:2.382559+0.141901 test-rmse:2.656481+0.500800
## [86] train-rmse:2.376069+0.141984 test-rmse:2.653741+0.501938
## [87] train-rmse:2.369659+0.142079 test-rmse:2.646665+0.499989
## [88] train-rmse:2.363297+0.142233 test-rmse:2.638178+0.498619
## [89] train-rmse:2.357008+0.142300 test-rmse:2.624685+0.499083
## [90] train-rmse:2.350764+0.142338 test-rmse:2.618386+0.496531
## [91] train-rmse:2.344571+0.142404 test-rmse:2.607830+0.492633
## [92] train-rmse:2.338526+0.142481 test-rmse:2.604122+0.489094
## [93] train-rmse:2.332487+0.142583 test-rmse:2.599154+0.492196
## [94] train-rmse:2.326614+0.142663 test-rmse:2.591843+0.495923
## [95] train-rmse:2.320793+0.142814 test-rmse:2.589549+0.504379
## [96] train-rmse:2.315104+0.143000 test-rmse:2.588501+0.503242
## [97] train-rmse:2.309473+0.143145 test-rmse:2.582268+0.510392
## [98] train-rmse:2.304046+0.143457 test-rmse:2.580261+0.509612
## [99] train-rmse:2.298637+0.143757 test-rmse:2.578613+0.508060
## [100] train-rmse:2.293440+0.143955 test-rmse:2.578336+0.505334
## [101] train-rmse:2.288349+0.144054 test-rmse:2.573125+0.506358
## [102] train-rmse:2.283323+0.144234 test-rmse:2.568627+0.507729
## [103] train-rmse:2.278347+0.144360 test-rmse:2.562463+0.510341
## [104] train-rmse:2.273454+0.144527 test-rmse:2.559299+0.511201
## [105] train-rmse:2.268653+0.144697 test-rmse:2.553100+0.512074
## [106] train-rmse:2.263907+0.144857 test-rmse:2.546409+0.508141
## [107] train-rmse:2.259227+0.145023 test-rmse:2.540691+0.510778
## [108] train-rmse:2.254607+0.145193 test-rmse:2.534675+0.510640
## [109] train-rmse:2.250045+0.145402 test-rmse:2.528033+0.510287
## [110] train-rmse:2.245509+0.145627 test-rmse:2.522713+0.510767
## [111] train-rmse:2.241068+0.145812 test-rmse:2.519166+0.511449
## [112] train-rmse:2.236659+0.145988 test-rmse:2.516315+0.510296
## [113] train-rmse:2.232301+0.146171 test-rmse:2.514793+0.515146
## [114] train-rmse:2.227976+0.146313 test-rmse:2.515935+0.514802
## [115] train-rmse:2.223684+0.146465 test-rmse:2.517711+0.511883
## [116] train-rmse:2.219548+0.146629 test-rmse:2.515492+0.513871
## [117] train-rmse:2.215493+0.146777 test-rmse:2.506546+0.514637
## [118] train-rmse:2.211514+0.146866 test-rmse:2.505661+0.515862
## [119] train-rmse:2.207650+0.146989 test-rmse:2.502421+0.518836
## [120] train-rmse:2.203811+0.147139 test-rmse:2.501413+0.517725
## [121] train-rmse:2.199995+0.147289 test-rmse:2.499459+0.519214
## [122] train-rmse:2.196233+0.147404 test-rmse:2.495717+0.520110
## [123] train-rmse:2.192591+0.147535 test-rmse:2.489575+0.522003
## [124] train-rmse:2.188963+0.147681 test-rmse:2.486507+0.522618
## [125] train-rmse:2.185383+0.147833 test-rmse:2.484494+0.521490
## [126] train-rmse:2.181825+0.147938 test-rmse:2.482282+0.516431
## [127] train-rmse:2.178282+0.148058 test-rmse:2.480746+0.516114
## [128] train-rmse:2.174783+0.148189 test-rmse:2.477462+0.514576
## [129] train-rmse:2.171331+0.148341 test-rmse:2.476001+0.513853
## [130] train-rmse:2.167855+0.148487 test-rmse:2.475815+0.519998
## [131] train-rmse:2.164481+0.148640 test-rmse:2.472860+0.524740
## [132] train-rmse:2.161188+0.148811 test-rmse:2.466629+0.525907
## [133] train-rmse:2.157902+0.148988 test-rmse:2.461050+0.526393
## [134] train-rmse:2.154697+0.149124 test-rmse:2.456250+0.529552
## [135] train-rmse:2.151551+0.149300 test-rmse:2.456522+0.529230
## [136] train-rmse:2.148420+0.149465 test-rmse:2.455826+0.523744
## [137] train-rmse:2.145399+0.149594 test-rmse:2.451257+0.525065
## [138] train-rmse:2.142375+0.149727 test-rmse:2.448918+0.526203
## [139] train-rmse:2.139383+0.149870 test-rmse:2.450086+0.523304
## [140] train-rmse:2.136470+0.150001 test-rmse:2.445753+0.525060
## [141] train-rmse:2.133560+0.150143 test-rmse:2.446107+0.524380
## [142] train-rmse:2.130685+0.150260 test-rmse:2.446810+0.526758
## [143] train-rmse:2.127833+0.150394 test-rmse:2.444063+0.527875
## [144] train-rmse:2.125005+0.150536 test-rmse:2.442068+0.528068
## [145] train-rmse:2.122209+0.150669 test-rmse:2.439566+0.528425
## [146] train-rmse:2.119446+0.150813 test-rmse:2.436383+0.528061
## [147] train-rmse:2.116736+0.150945 test-rmse:2.432160+0.529196
## [148] train-rmse:2.114034+0.151093 test-rmse:2.432304+0.529397
## [149] train-rmse:2.111354+0.151232 test-rmse:2.430698+0.528637
## [150] train-rmse:2.108726+0.151380 test-rmse:2.430293+0.527039
## [151] train-rmse:2.106082+0.151454 test-rmse:2.425014+0.527308
## [152] train-rmse:2.103528+0.151577 test-rmse:2.424618+0.524837
## [153] train-rmse:2.101016+0.151706 test-rmse:2.422567+0.526147
## [154] train-rmse:2.098522+0.151867 test-rmse:2.420695+0.523888
## [155] train-rmse:2.096096+0.152023 test-rmse:2.418501+0.523087
## [156] train-rmse:2.093643+0.152171 test-rmse:2.417006+0.522386
## [157] train-rmse:2.091149+0.152344 test-rmse:2.411961+0.525026
## [158] train-rmse:2.088757+0.152523 test-rmse:2.409165+0.522854
## [159] train-rmse:2.086385+0.152679 test-rmse:2.410875+0.528639
## [160] train-rmse:2.084075+0.152806 test-rmse:2.404807+0.529265
## [161] train-rmse:2.081782+0.152939 test-rmse:2.403611+0.530766
## [162] train-rmse:2.079487+0.153011 test-rmse:2.407229+0.534377
## [163] train-rmse:2.077259+0.153121 test-rmse:2.407287+0.536607
## [164] train-rmse:2.074970+0.153208 test-rmse:2.404821+0.537342
## [165] train-rmse:2.072759+0.153294 test-rmse:2.402378+0.536206
## [166] train-rmse:2.070559+0.153389 test-rmse:2.400768+0.533558
## [167] train-rmse:2.068382+0.153499 test-rmse:2.402267+0.535987
## [168] train-rmse:2.066228+0.153602 test-rmse:2.403427+0.534063
## [169] train-rmse:2.064108+0.153693 test-rmse:2.402850+0.532436
## [170] train-rmse:2.062008+0.153785 test-rmse:2.400805+0.530890
## [171] train-rmse:2.059906+0.153841 test-rmse:2.399236+0.530578
## [172] train-rmse:2.057855+0.153909 test-rmse:2.400665+0.528660
## [173] train-rmse:2.055764+0.154019 test-rmse:2.398138+0.528475
## [174] train-rmse:2.053757+0.154135 test-rmse:2.394872+0.528667
## [175] train-rmse:2.051768+0.154240 test-rmse:2.391706+0.528676
## [176] train-rmse:2.049822+0.154311 test-rmse:2.392523+0.527620
## [177] train-rmse:2.047906+0.154388 test-rmse:2.388551+0.528498
## [178] train-rmse:2.045970+0.154483 test-rmse:2.388785+0.528276
## [179] train-rmse:2.044049+0.154577 test-rmse:2.386789+0.525252
## [180] train-rmse:2.042159+0.154683 test-rmse:2.382654+0.526911
## [181] train-rmse:2.040257+0.154779 test-rmse:2.381282+0.527313
## [182] train-rmse:2.038358+0.154849 test-rmse:2.381257+0.527078
## [183] train-rmse:2.036501+0.154949 test-rmse:2.385777+0.531268
## [184] train-rmse:2.034597+0.154995 test-rmse:2.385481+0.531041
## [185] train-rmse:2.032773+0.155055 test-rmse:2.387096+0.534175
## [186] train-rmse:2.030914+0.155151 test-rmse:2.385719+0.534742
## [187] train-rmse:2.029064+0.155322 test-rmse:2.383506+0.532211
## [188] train-rmse:2.027296+0.155408 test-rmse:2.382096+0.533759
## Stopping. Best iteration:
## [182] train-rmse:2.038358+0.154849 test-rmse:2.381257+0.527078
##
## 22
## [1] train-rmse:7.178327+0.150359 test-rmse:7.184649+0.649439
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.350962+0.136618 test-rmse:6.381575+0.622884
## [3] train-rmse:5.677065+0.125382 test-rmse:5.763054+0.618154
## [4] train-rmse:5.124782+0.111402 test-rmse:5.253037+0.562582
## [5] train-rmse:4.669347+0.106677 test-rmse:4.807962+0.528011
## [6] train-rmse:4.303987+0.094587 test-rmse:4.454397+0.491240
## [7] train-rmse:3.995436+0.091817 test-rmse:4.159776+0.476454
## [8] train-rmse:3.751162+0.094873 test-rmse:3.948332+0.437497
## [9] train-rmse:3.542886+0.087636 test-rmse:3.759655+0.430970
## [10] train-rmse:3.369486+0.080523 test-rmse:3.594040+0.446601
## [11] train-rmse:3.214775+0.093844 test-rmse:3.460094+0.419039
## [12] train-rmse:3.086609+0.090108 test-rmse:3.342002+0.404390
## [13] train-rmse:2.976575+0.071461 test-rmse:3.253906+0.432518
## [14] train-rmse:2.883868+0.080525 test-rmse:3.171719+0.419288
## [15] train-rmse:2.809082+0.076755 test-rmse:3.117480+0.420906
## [16] train-rmse:2.741676+0.072951 test-rmse:3.080606+0.444922
## [17] train-rmse:2.672010+0.074902 test-rmse:3.014434+0.436748
## [18] train-rmse:2.620696+0.078610 test-rmse:2.984416+0.437905
## [19] train-rmse:2.571754+0.078833 test-rmse:2.960315+0.448699
## [20] train-rmse:2.525193+0.067819 test-rmse:2.924065+0.469510
## [21] train-rmse:2.484666+0.064433 test-rmse:2.895987+0.464486
## [22] train-rmse:2.442390+0.066374 test-rmse:2.864945+0.472606
## [23] train-rmse:2.400102+0.062385 test-rmse:2.834076+0.468938
## [24] train-rmse:2.358170+0.058629 test-rmse:2.808448+0.482987
## [25] train-rmse:2.323627+0.057871 test-rmse:2.792068+0.485463
## [26] train-rmse:2.286598+0.057886 test-rmse:2.774521+0.473668
## [27] train-rmse:2.259243+0.058096 test-rmse:2.746186+0.471715
## [28] train-rmse:2.224558+0.060833 test-rmse:2.730309+0.468656
## [29] train-rmse:2.196800+0.063721 test-rmse:2.710824+0.478330
## [30] train-rmse:2.169492+0.058896 test-rmse:2.689983+0.483328
## [31] train-rmse:2.141861+0.051256 test-rmse:2.674082+0.490783
## [32] train-rmse:2.117548+0.049626 test-rmse:2.650650+0.493432
## [33] train-rmse:2.093324+0.051066 test-rmse:2.628672+0.494544
## [34] train-rmse:2.071678+0.053063 test-rmse:2.616355+0.484723
## [35] train-rmse:2.049623+0.054298 test-rmse:2.599876+0.480446
## [36] train-rmse:2.027037+0.053928 test-rmse:2.580818+0.496114
## [37] train-rmse:2.005468+0.056597 test-rmse:2.575274+0.489389
## [38] train-rmse:1.985053+0.053027 test-rmse:2.556349+0.490896
## [39] train-rmse:1.963566+0.045816 test-rmse:2.547079+0.501045
## [40] train-rmse:1.945183+0.046633 test-rmse:2.530685+0.494916
## [41] train-rmse:1.928740+0.046854 test-rmse:2.515116+0.496400
## [42] train-rmse:1.905353+0.048835 test-rmse:2.505647+0.494268
## [43] train-rmse:1.887845+0.049673 test-rmse:2.497586+0.493822
## [44] train-rmse:1.872365+0.049817 test-rmse:2.487340+0.494158
## [45] train-rmse:1.858277+0.049925 test-rmse:2.479603+0.497595
## [46] train-rmse:1.843418+0.048446 test-rmse:2.474849+0.491716
## [47] train-rmse:1.825815+0.048600 test-rmse:2.463598+0.490704
## [48] train-rmse:1.811867+0.048635 test-rmse:2.454426+0.497482
## [49] train-rmse:1.797299+0.048481 test-rmse:2.445587+0.502994
## [50] train-rmse:1.778726+0.045059 test-rmse:2.423116+0.506732
## [51] train-rmse:1.765125+0.046160 test-rmse:2.420460+0.505222
## [52] train-rmse:1.752698+0.044620 test-rmse:2.419224+0.504717
## [53] train-rmse:1.741468+0.044404 test-rmse:2.410592+0.508153
## [54] train-rmse:1.729412+0.042941 test-rmse:2.403393+0.504666
## [55] train-rmse:1.715880+0.043062 test-rmse:2.394872+0.508202
## [56] train-rmse:1.701268+0.039126 test-rmse:2.392701+0.518366
## [57] train-rmse:1.689794+0.038937 test-rmse:2.388741+0.513243
## [58] train-rmse:1.679012+0.039465 test-rmse:2.380510+0.515702
## [59] train-rmse:1.664398+0.039660 test-rmse:2.370755+0.515533
## [60] train-rmse:1.652856+0.037601 test-rmse:2.364671+0.511411
## [61] train-rmse:1.640339+0.032628 test-rmse:2.359774+0.520604
## [62] train-rmse:1.629442+0.033432 test-rmse:2.351852+0.517690
## [63] train-rmse:1.618950+0.033410 test-rmse:2.341127+0.512746
## [64] train-rmse:1.608606+0.034949 test-rmse:2.340298+0.511055
## [65] train-rmse:1.599885+0.035343 test-rmse:2.330191+0.519082
## [66] train-rmse:1.591869+0.035435 test-rmse:2.329086+0.518999
## [67] train-rmse:1.581023+0.033988 test-rmse:2.324331+0.522255
## [68] train-rmse:1.572497+0.034171 test-rmse:2.322081+0.527384
## [69] train-rmse:1.559017+0.033990 test-rmse:2.316837+0.527510
## [70] train-rmse:1.548211+0.037234 test-rmse:2.318845+0.528637
## [71] train-rmse:1.540695+0.037332 test-rmse:2.315416+0.531726
## [72] train-rmse:1.527207+0.036069 test-rmse:2.309552+0.531130
## [73] train-rmse:1.517420+0.036034 test-rmse:2.301097+0.526535
## [74] train-rmse:1.509516+0.034748 test-rmse:2.297063+0.525385
## [75] train-rmse:1.498937+0.032253 test-rmse:2.293553+0.527560
## [76] train-rmse:1.492136+0.032754 test-rmse:2.291191+0.533556
## [77] train-rmse:1.481235+0.035192 test-rmse:2.282629+0.530782
## [78] train-rmse:1.474701+0.034990 test-rmse:2.279150+0.530028
## [79] train-rmse:1.468031+0.035955 test-rmse:2.274401+0.528134
## [80] train-rmse:1.458269+0.033609 test-rmse:2.273526+0.528933
## [81] train-rmse:1.451418+0.032889 test-rmse:2.272123+0.528968
## [82] train-rmse:1.444254+0.031885 test-rmse:2.269641+0.530563
## [83] train-rmse:1.436248+0.030462 test-rmse:2.271375+0.527188
## [84] train-rmse:1.428243+0.028747 test-rmse:2.270637+0.537896
## [85] train-rmse:1.420033+0.027546 test-rmse:2.267688+0.538479
## [86] train-rmse:1.413707+0.028350 test-rmse:2.264280+0.535092
## [87] train-rmse:1.405502+0.027455 test-rmse:2.259284+0.534005
## [88] train-rmse:1.399894+0.028130 test-rmse:2.254683+0.535702
## [89] train-rmse:1.394111+0.028928 test-rmse:2.250254+0.533591
## [90] train-rmse:1.388909+0.028774 test-rmse:2.247471+0.534448
## [91] train-rmse:1.383766+0.028473 test-rmse:2.245219+0.537073
## [92] train-rmse:1.375369+0.027435 test-rmse:2.241216+0.536949
## [93] train-rmse:1.369332+0.026985 test-rmse:2.236873+0.537307
## [94] train-rmse:1.362228+0.026579 test-rmse:2.232161+0.535283
## [95] train-rmse:1.350957+0.028457 test-rmse:2.232951+0.535621
## [96] train-rmse:1.342644+0.032193 test-rmse:2.234157+0.533037
## [97] train-rmse:1.334575+0.031732 test-rmse:2.232999+0.533153
## [98] train-rmse:1.326470+0.028965 test-rmse:2.226148+0.535254
## [99] train-rmse:1.318645+0.028628 test-rmse:2.224105+0.533370
## [100] train-rmse:1.313923+0.029259 test-rmse:2.221951+0.532093
## [101] train-rmse:1.309288+0.029602 test-rmse:2.220910+0.531290
## [102] train-rmse:1.303468+0.029465 test-rmse:2.214125+0.531075
## [103] train-rmse:1.299217+0.029308 test-rmse:2.215410+0.532486
## [104] train-rmse:1.292145+0.030310 test-rmse:2.210740+0.532965
## [105] train-rmse:1.286597+0.029982 test-rmse:2.210305+0.533100
## [106] train-rmse:1.280337+0.030220 test-rmse:2.209525+0.534015
## [107] train-rmse:1.276276+0.030078 test-rmse:2.209171+0.535526
## [108] train-rmse:1.267924+0.026657 test-rmse:2.204884+0.539029
## [109] train-rmse:1.261967+0.025330 test-rmse:2.204953+0.538284
## [110] train-rmse:1.256763+0.025043 test-rmse:2.200864+0.538988
## [111] train-rmse:1.251670+0.025395 test-rmse:2.199733+0.543289
## [112] train-rmse:1.245906+0.027176 test-rmse:2.196066+0.541047
## [113] train-rmse:1.240025+0.028635 test-rmse:2.195925+0.542586
## [114] train-rmse:1.236077+0.029006 test-rmse:2.193620+0.542551
## [115] train-rmse:1.230052+0.029588 test-rmse:2.186256+0.542987
## [116] train-rmse:1.223668+0.033129 test-rmse:2.180926+0.542685
## [117] train-rmse:1.216897+0.032756 test-rmse:2.183531+0.539440
## [118] train-rmse:1.211572+0.033735 test-rmse:2.179800+0.537051
## [119] train-rmse:1.206284+0.033968 test-rmse:2.180529+0.538398
## [120] train-rmse:1.201282+0.032975 test-rmse:2.178946+0.537602
## [121] train-rmse:1.197037+0.033333 test-rmse:2.176663+0.540317
## [122] train-rmse:1.193934+0.033276 test-rmse:2.174178+0.541566
## [123] train-rmse:1.189577+0.033980 test-rmse:2.173165+0.543619
## [124] train-rmse:1.186246+0.034393 test-rmse:2.172442+0.543583
## [125] train-rmse:1.182465+0.034944 test-rmse:2.173980+0.543278
## [126] train-rmse:1.178269+0.035520 test-rmse:2.170664+0.543766
## [127] train-rmse:1.175172+0.035169 test-rmse:2.169354+0.548007
## [128] train-rmse:1.170754+0.035017 test-rmse:2.166537+0.547764
## [129] train-rmse:1.165917+0.037958 test-rmse:2.163242+0.546594
## [130] train-rmse:1.160961+0.036768 test-rmse:2.161295+0.545611
## [131] train-rmse:1.158244+0.036622 test-rmse:2.162047+0.549552
## [132] train-rmse:1.154107+0.035603 test-rmse:2.160251+0.551316
## [133] train-rmse:1.149590+0.035139 test-rmse:2.160404+0.550823
## [134] train-rmse:1.143906+0.037574 test-rmse:2.158402+0.552508
## [135] train-rmse:1.139334+0.038323 test-rmse:2.159229+0.553401
## [136] train-rmse:1.132462+0.034214 test-rmse:2.164001+0.553797
## [137] train-rmse:1.127918+0.034299 test-rmse:2.164132+0.553164
## [138] train-rmse:1.123477+0.036663 test-rmse:2.163435+0.556956
## [139] train-rmse:1.118243+0.039018 test-rmse:2.161484+0.558794
## [140] train-rmse:1.112226+0.039158 test-rmse:2.164391+0.558826
## Stopping. Best iteration:
## [134] train-rmse:1.143906+0.037574 test-rmse:2.158402+0.552508
##
## 23
## [1] train-rmse:7.131872+0.147895 test-rmse:7.164958+0.639863
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.259756+0.127128 test-rmse:6.327340+0.589379
## [3] train-rmse:5.531664+0.122050 test-rmse:5.614142+0.546927
## [4] train-rmse:4.934974+0.116936 test-rmse:5.074223+0.497883
## [5] train-rmse:4.434169+0.103635 test-rmse:4.609648+0.480716
## [6] train-rmse:4.028579+0.100514 test-rmse:4.248003+0.460212
## [7] train-rmse:3.675831+0.091855 test-rmse:3.904237+0.449057
## [8] train-rmse:3.394752+0.088095 test-rmse:3.671323+0.432327
## [9] train-rmse:3.156632+0.079098 test-rmse:3.452481+0.462385
## [10] train-rmse:2.962154+0.075193 test-rmse:3.282481+0.448333
## [11] train-rmse:2.803380+0.078719 test-rmse:3.153545+0.443865
## [12] train-rmse:2.675108+0.072697 test-rmse:3.077821+0.443579
## [13] train-rmse:2.549250+0.060445 test-rmse:2.986044+0.444082
## [14] train-rmse:2.462150+0.060994 test-rmse:2.915003+0.450929
## [15] train-rmse:2.372241+0.059862 test-rmse:2.859645+0.446498
## [16] train-rmse:2.290126+0.064727 test-rmse:2.799401+0.444087
## [17] train-rmse:2.218720+0.056452 test-rmse:2.765039+0.445455
## [18] train-rmse:2.154161+0.052591 test-rmse:2.727759+0.454264
## [19] train-rmse:2.101025+0.047694 test-rmse:2.700944+0.466702
## [20] train-rmse:2.054537+0.048174 test-rmse:2.675825+0.469149
## [21] train-rmse:2.007381+0.054551 test-rmse:2.637640+0.448662
## [22] train-rmse:1.966712+0.054611 test-rmse:2.616312+0.440646
## [23] train-rmse:1.927649+0.052558 test-rmse:2.601814+0.431846
## [24] train-rmse:1.888746+0.052224 test-rmse:2.574481+0.424298
## [25] train-rmse:1.855228+0.055748 test-rmse:2.564074+0.438246
## [26] train-rmse:1.817097+0.046635 test-rmse:2.531188+0.453429
## [27] train-rmse:1.783738+0.044048 test-rmse:2.508177+0.450110
## [28] train-rmse:1.758194+0.044405 test-rmse:2.495235+0.452641
## [29] train-rmse:1.728762+0.045930 test-rmse:2.487200+0.453002
## [30] train-rmse:1.701241+0.045612 test-rmse:2.466759+0.454699
## [31] train-rmse:1.672736+0.048305 test-rmse:2.448188+0.464133
## [32] train-rmse:1.647360+0.050539 test-rmse:2.427923+0.456459
## [33] train-rmse:1.621282+0.052583 test-rmse:2.413661+0.452131
## [34] train-rmse:1.596563+0.047715 test-rmse:2.402837+0.460492
## [35] train-rmse:1.569704+0.043030 test-rmse:2.393662+0.461346
## [36] train-rmse:1.548735+0.042364 test-rmse:2.380911+0.465542
## [37] train-rmse:1.529542+0.043816 test-rmse:2.374117+0.458827
## [38] train-rmse:1.512434+0.042851 test-rmse:2.364031+0.453949
## [39] train-rmse:1.496274+0.042977 test-rmse:2.351819+0.457126
## [40] train-rmse:1.476517+0.041762 test-rmse:2.347585+0.463647
## [41] train-rmse:1.454966+0.038664 test-rmse:2.341702+0.473359
## [42] train-rmse:1.436867+0.038891 test-rmse:2.335613+0.471111
## [43] train-rmse:1.415281+0.038215 test-rmse:2.334656+0.468746
## [44] train-rmse:1.397972+0.035423 test-rmse:2.330707+0.472809
## [45] train-rmse:1.382362+0.041464 test-rmse:2.322230+0.470739
## [46] train-rmse:1.366350+0.040397 test-rmse:2.312508+0.467958
## [47] train-rmse:1.350787+0.038603 test-rmse:2.303284+0.469313
## [48] train-rmse:1.337081+0.039618 test-rmse:2.300506+0.473885
## [49] train-rmse:1.322728+0.041351 test-rmse:2.297097+0.478511
## [50] train-rmse:1.307905+0.044161 test-rmse:2.294451+0.481049
## [51] train-rmse:1.290090+0.047180 test-rmse:2.286050+0.478416
## [52] train-rmse:1.276727+0.046607 test-rmse:2.279597+0.478257
## [53] train-rmse:1.262306+0.045150 test-rmse:2.269754+0.479711
## [54] train-rmse:1.247175+0.044503 test-rmse:2.264857+0.481212
## [55] train-rmse:1.231971+0.047572 test-rmse:2.253674+0.474595
## [56] train-rmse:1.218811+0.044032 test-rmse:2.252214+0.480426
## [57] train-rmse:1.206457+0.045096 test-rmse:2.244898+0.478665
## [58] train-rmse:1.193221+0.045777 test-rmse:2.242510+0.478714
## [59] train-rmse:1.184017+0.046021 test-rmse:2.241229+0.478584
## [60] train-rmse:1.171228+0.046331 test-rmse:2.237170+0.479797
## [61] train-rmse:1.158134+0.047111 test-rmse:2.233596+0.480300
## [62] train-rmse:1.146636+0.046413 test-rmse:2.231100+0.478284
## [63] train-rmse:1.135812+0.047520 test-rmse:2.229247+0.482513
## [64] train-rmse:1.124288+0.046450 test-rmse:2.225704+0.483593
## [65] train-rmse:1.114281+0.049789 test-rmse:2.221363+0.477499
## [66] train-rmse:1.104559+0.050541 test-rmse:2.218361+0.480475
## [67] train-rmse:1.097186+0.050348 test-rmse:2.217031+0.480201
## [68] train-rmse:1.084794+0.051311 test-rmse:2.212355+0.480775
## [69] train-rmse:1.074652+0.049129 test-rmse:2.210083+0.480800
## [70] train-rmse:1.065128+0.049452 test-rmse:2.210285+0.480876
## [71] train-rmse:1.053990+0.049764 test-rmse:2.205725+0.484435
## [72] train-rmse:1.046715+0.049080 test-rmse:2.200927+0.486518
## [73] train-rmse:1.037464+0.048969 test-rmse:2.198892+0.486067
## [74] train-rmse:1.029519+0.049324 test-rmse:2.197743+0.486573
## [75] train-rmse:1.017829+0.046401 test-rmse:2.194999+0.487929
## [76] train-rmse:1.010880+0.047211 test-rmse:2.192158+0.489187
## [77] train-rmse:1.000927+0.044444 test-rmse:2.185569+0.484813
## [78] train-rmse:0.988929+0.043254 test-rmse:2.187651+0.485453
## [79] train-rmse:0.980934+0.043943 test-rmse:2.187176+0.487140
## [80] train-rmse:0.971042+0.044448 test-rmse:2.186623+0.492985
## [81] train-rmse:0.961435+0.041607 test-rmse:2.185108+0.489523
## [82] train-rmse:0.950827+0.040415 test-rmse:2.184179+0.489555
## [83] train-rmse:0.945787+0.040853 test-rmse:2.182272+0.487580
## [84] train-rmse:0.937877+0.040915 test-rmse:2.180315+0.486121
## [85] train-rmse:0.932222+0.040817 test-rmse:2.177408+0.492693
## [86] train-rmse:0.926525+0.041415 test-rmse:2.175662+0.493047
## [87] train-rmse:0.919180+0.040566 test-rmse:2.173436+0.492853
## [88] train-rmse:0.910830+0.039562 test-rmse:2.171725+0.495138
## [89] train-rmse:0.903199+0.041453 test-rmse:2.166786+0.493191
## [90] train-rmse:0.893553+0.042169 test-rmse:2.161294+0.491264
## [91] train-rmse:0.884912+0.041843 test-rmse:2.161715+0.488877
## [92] train-rmse:0.877500+0.040869 test-rmse:2.160090+0.486234
## [93] train-rmse:0.869969+0.039592 test-rmse:2.156786+0.489642
## [94] train-rmse:0.862856+0.040084 test-rmse:2.156457+0.490272
## [95] train-rmse:0.855245+0.041681 test-rmse:2.150914+0.490888
## [96] train-rmse:0.849088+0.040321 test-rmse:2.146115+0.491236
## [97] train-rmse:0.842109+0.043910 test-rmse:2.144883+0.489291
## [98] train-rmse:0.837241+0.044315 test-rmse:2.144248+0.488815
## [99] train-rmse:0.831158+0.044303 test-rmse:2.141669+0.491778
## [100] train-rmse:0.824705+0.044095 test-rmse:2.140581+0.490173
## [101] train-rmse:0.820188+0.043673 test-rmse:2.138748+0.490285
## [102] train-rmse:0.815289+0.045626 test-rmse:2.138499+0.492070
## [103] train-rmse:0.808198+0.043275 test-rmse:2.138020+0.493387
## [104] train-rmse:0.800768+0.041355 test-rmse:2.135970+0.494407
## [105] train-rmse:0.793703+0.038278 test-rmse:2.135820+0.496380
## [106] train-rmse:0.784233+0.039068 test-rmse:2.134666+0.491915
## [107] train-rmse:0.775818+0.036047 test-rmse:2.132801+0.491926
## [108] train-rmse:0.771066+0.034898 test-rmse:2.132480+0.491924
## [109] train-rmse:0.767196+0.034358 test-rmse:2.132354+0.492359
## [110] train-rmse:0.762515+0.038174 test-rmse:2.130727+0.490499
## [111] train-rmse:0.756983+0.039950 test-rmse:2.129362+0.492686
## [112] train-rmse:0.752160+0.042419 test-rmse:2.127873+0.492175
## [113] train-rmse:0.745875+0.043982 test-rmse:2.126905+0.491189
## [114] train-rmse:0.742129+0.043614 test-rmse:2.125721+0.491699
## [115] train-rmse:0.737567+0.045085 test-rmse:2.122805+0.490034
## [116] train-rmse:0.733894+0.044183 test-rmse:2.121371+0.491414
## [117] train-rmse:0.729488+0.043381 test-rmse:2.120233+0.492631
## [118] train-rmse:0.722960+0.043910 test-rmse:2.118328+0.492980
## [119] train-rmse:0.716468+0.045091 test-rmse:2.116773+0.493061
## [120] train-rmse:0.709605+0.043487 test-rmse:2.112703+0.493224
## [121] train-rmse:0.703205+0.043947 test-rmse:2.110610+0.495999
## [122] train-rmse:0.696606+0.043384 test-rmse:2.109784+0.497163
## [123] train-rmse:0.690277+0.044081 test-rmse:2.110282+0.496786
## [124] train-rmse:0.684703+0.044885 test-rmse:2.108681+0.494895
## [125] train-rmse:0.679992+0.044940 test-rmse:2.107093+0.495017
## [126] train-rmse:0.677132+0.045304 test-rmse:2.105686+0.493642
## [127] train-rmse:0.674173+0.046554 test-rmse:2.104273+0.494290
## [128] train-rmse:0.668757+0.045563 test-rmse:2.104464+0.494995
## [129] train-rmse:0.661670+0.043251 test-rmse:2.102522+0.495408
## [130] train-rmse:0.659087+0.043441 test-rmse:2.101979+0.494919
## [131] train-rmse:0.654679+0.043594 test-rmse:2.102850+0.496663
## [132] train-rmse:0.649380+0.043467 test-rmse:2.104443+0.496542
## [133] train-rmse:0.645156+0.042486 test-rmse:2.103821+0.497339
## [134] train-rmse:0.641732+0.043019 test-rmse:2.103106+0.496699
## [135] train-rmse:0.637016+0.041331 test-rmse:2.103211+0.496467
## [136] train-rmse:0.632889+0.042398 test-rmse:2.104540+0.495016
## Stopping. Best iteration:
## [130] train-rmse:0.659087+0.043441 test-rmse:2.101979+0.494919
##
## 24
## [1] train-rmse:7.089002+0.146837 test-rmse:7.105498+0.631113
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.173838+0.118719 test-rmse:6.225084+0.556294
## [3] train-rmse:5.410551+0.100999 test-rmse:5.515960+0.518813
## [4] train-rmse:4.771150+0.088723 test-rmse:4.916378+0.487324
## [5] train-rmse:4.246106+0.069349 test-rmse:4.439460+0.467862
## [6] train-rmse:3.806750+0.068697 test-rmse:4.058476+0.435204
## [7] train-rmse:3.453122+0.068715 test-rmse:3.750990+0.433773
## [8] train-rmse:3.157423+0.067788 test-rmse:3.489111+0.429642
## [9] train-rmse:2.909985+0.060416 test-rmse:3.285996+0.445077
## [10] train-rmse:2.701908+0.063936 test-rmse:3.133449+0.437253
## [11] train-rmse:2.533387+0.056389 test-rmse:3.011715+0.464599
## [12] train-rmse:2.382947+0.054370 test-rmse:2.910438+0.457067
## [13] train-rmse:2.256686+0.062185 test-rmse:2.822444+0.443489
## [14] train-rmse:2.152103+0.052746 test-rmse:2.749071+0.464814
## [15] train-rmse:2.063645+0.052445 test-rmse:2.709210+0.475192
## [16] train-rmse:1.986041+0.057499 test-rmse:2.659983+0.452053
## [17] train-rmse:1.919559+0.057711 test-rmse:2.623794+0.436570
## [18] train-rmse:1.853252+0.050634 test-rmse:2.587695+0.442690
## [19] train-rmse:1.799199+0.050989 test-rmse:2.545064+0.450662
## [20] train-rmse:1.750576+0.049118 test-rmse:2.512078+0.453123
## [21] train-rmse:1.705400+0.046090 test-rmse:2.491355+0.454075
## [22] train-rmse:1.664716+0.049022 test-rmse:2.465903+0.453287
## [23] train-rmse:1.622204+0.037805 test-rmse:2.441742+0.456311
## [24] train-rmse:1.577036+0.041457 test-rmse:2.411260+0.461269
## [25] train-rmse:1.546507+0.038852 test-rmse:2.405212+0.459780
## [26] train-rmse:1.507086+0.037919 test-rmse:2.392220+0.465877
## [27] train-rmse:1.475451+0.039631 test-rmse:2.388795+0.469563
## [28] train-rmse:1.442408+0.038070 test-rmse:2.375277+0.471101
## [29] train-rmse:1.414662+0.033454 test-rmse:2.375863+0.475194
## [30] train-rmse:1.382284+0.032613 test-rmse:2.367506+0.475884
## [31] train-rmse:1.360407+0.030756 test-rmse:2.362532+0.478617
## [32] train-rmse:1.333750+0.026565 test-rmse:2.347744+0.476299
## [33] train-rmse:1.313371+0.025767 test-rmse:2.337964+0.477749
## [34] train-rmse:1.284025+0.035293 test-rmse:2.328111+0.465205
## [35] train-rmse:1.264494+0.034728 test-rmse:2.321016+0.469752
## [36] train-rmse:1.244479+0.036634 test-rmse:2.313344+0.470451
## [37] train-rmse:1.221811+0.031767 test-rmse:2.309500+0.476821
## [38] train-rmse:1.202202+0.030562 test-rmse:2.307628+0.480537
## [39] train-rmse:1.185522+0.028435 test-rmse:2.303094+0.480172
## [40] train-rmse:1.166390+0.031896 test-rmse:2.294711+0.473039
## [41] train-rmse:1.147187+0.030190 test-rmse:2.288497+0.471998
## [42] train-rmse:1.130850+0.032121 test-rmse:2.282595+0.472548
## [43] train-rmse:1.114643+0.034005 test-rmse:2.274894+0.477356
## [44] train-rmse:1.089889+0.028332 test-rmse:2.270650+0.474377
## [45] train-rmse:1.073340+0.030019 test-rmse:2.265876+0.477023
## [46] train-rmse:1.057816+0.024918 test-rmse:2.262267+0.476651
## [47] train-rmse:1.041239+0.030699 test-rmse:2.260473+0.476503
## [48] train-rmse:1.024778+0.037067 test-rmse:2.254005+0.477310
## [49] train-rmse:1.009633+0.041158 test-rmse:2.245503+0.474739
## [50] train-rmse:0.996254+0.040707 test-rmse:2.243090+0.471504
## [51] train-rmse:0.982219+0.041824 test-rmse:2.242623+0.468818
## [52] train-rmse:0.963371+0.042622 test-rmse:2.242664+0.470472
## [53] train-rmse:0.942414+0.030419 test-rmse:2.241934+0.470406
## [54] train-rmse:0.928075+0.031616 test-rmse:2.237898+0.470981
## [55] train-rmse:0.913460+0.030991 test-rmse:2.234928+0.473518
## [56] train-rmse:0.901596+0.034801 test-rmse:2.232717+0.472012
## [57] train-rmse:0.892282+0.032970 test-rmse:2.228542+0.470787
## [58] train-rmse:0.880270+0.029596 test-rmse:2.230415+0.471902
## [59] train-rmse:0.862870+0.033944 test-rmse:2.220439+0.474676
## [60] train-rmse:0.852256+0.036724 test-rmse:2.217367+0.475136
## [61] train-rmse:0.844346+0.036818 test-rmse:2.214590+0.477304
## [62] train-rmse:0.829288+0.030838 test-rmse:2.214804+0.475532
## [63] train-rmse:0.816735+0.033223 test-rmse:2.211549+0.475462
## [64] train-rmse:0.808834+0.033064 test-rmse:2.207400+0.477220
## [65] train-rmse:0.798626+0.030883 test-rmse:2.205963+0.477538
## [66] train-rmse:0.781865+0.026654 test-rmse:2.202213+0.478456
## [67] train-rmse:0.770186+0.026025 test-rmse:2.201195+0.479302
## [68] train-rmse:0.763053+0.027686 test-rmse:2.201866+0.481830
## [69] train-rmse:0.753615+0.026030 test-rmse:2.202879+0.484484
## [70] train-rmse:0.743907+0.029202 test-rmse:2.199241+0.486731
## [71] train-rmse:0.732060+0.030907 test-rmse:2.195426+0.487075
## [72] train-rmse:0.719840+0.026716 test-rmse:2.194308+0.486493
## [73] train-rmse:0.713587+0.026873 test-rmse:2.194869+0.486467
## [74] train-rmse:0.705450+0.027186 test-rmse:2.189120+0.485069
## [75] train-rmse:0.696916+0.032256 test-rmse:2.184885+0.483347
## [76] train-rmse:0.691947+0.032402 test-rmse:2.186216+0.483334
## [77] train-rmse:0.680661+0.031302 test-rmse:2.185276+0.483813
## [78] train-rmse:0.671041+0.032081 test-rmse:2.183984+0.482717
## [79] train-rmse:0.663853+0.030875 test-rmse:2.182886+0.483463
## [80] train-rmse:0.655415+0.030841 test-rmse:2.177338+0.485595
## [81] train-rmse:0.646608+0.033916 test-rmse:2.175810+0.486002
## [82] train-rmse:0.639307+0.033613 test-rmse:2.172030+0.486774
## [83] train-rmse:0.632279+0.032869 test-rmse:2.169331+0.487516
## [84] train-rmse:0.625345+0.032680 test-rmse:2.171456+0.488969
## [85] train-rmse:0.620088+0.031655 test-rmse:2.170697+0.489433
## [86] train-rmse:0.614683+0.031405 test-rmse:2.168043+0.489518
## [87] train-rmse:0.606900+0.031944 test-rmse:2.167624+0.491654
## [88] train-rmse:0.601424+0.032247 test-rmse:2.167431+0.490529
## [89] train-rmse:0.595394+0.032225 test-rmse:2.166034+0.490268
## [90] train-rmse:0.588980+0.031319 test-rmse:2.166953+0.490013
## [91] train-rmse:0.582788+0.030243 test-rmse:2.168453+0.491589
## [92] train-rmse:0.576695+0.033070 test-rmse:2.167338+0.490572
## [93] train-rmse:0.571098+0.033342 test-rmse:2.166923+0.489922
## [94] train-rmse:0.565687+0.032655 test-rmse:2.166164+0.490464
## [95] train-rmse:0.560729+0.032580 test-rmse:2.164401+0.491045
## [96] train-rmse:0.552145+0.032064 test-rmse:2.164830+0.491743
## [97] train-rmse:0.544123+0.032644 test-rmse:2.165875+0.492141
## [98] train-rmse:0.537708+0.032115 test-rmse:2.164817+0.491965
## [99] train-rmse:0.533020+0.031439 test-rmse:2.163919+0.491504
## [100] train-rmse:0.529604+0.031825 test-rmse:2.164404+0.493151
## [101] train-rmse:0.523231+0.032852 test-rmse:2.163076+0.493701
## [102] train-rmse:0.517476+0.033064 test-rmse:2.162099+0.493553
## [103] train-rmse:0.514212+0.032901 test-rmse:2.161299+0.494113
## [104] train-rmse:0.508815+0.033492 test-rmse:2.160475+0.494421
## [105] train-rmse:0.503423+0.034994 test-rmse:2.159410+0.494517
## [106] train-rmse:0.496334+0.034899 test-rmse:2.159464+0.494715
## [107] train-rmse:0.491865+0.036959 test-rmse:2.158839+0.497826
## [108] train-rmse:0.486773+0.036074 test-rmse:2.155934+0.499524
## [109] train-rmse:0.483289+0.036021 test-rmse:2.153663+0.500780
## [110] train-rmse:0.477941+0.035220 test-rmse:2.154127+0.501497
## [111] train-rmse:0.472631+0.036465 test-rmse:2.153203+0.501208
## [112] train-rmse:0.468494+0.035917 test-rmse:2.152910+0.501024
## [113] train-rmse:0.464008+0.035279 test-rmse:2.153013+0.500796
## [114] train-rmse:0.459394+0.036617 test-rmse:2.151562+0.500609
## [115] train-rmse:0.455079+0.035577 test-rmse:2.151381+0.500646
## [116] train-rmse:0.450731+0.034925 test-rmse:2.150830+0.501094
## [117] train-rmse:0.446466+0.034179 test-rmse:2.149858+0.501989
## [118] train-rmse:0.442363+0.033922 test-rmse:2.150558+0.501038
## [119] train-rmse:0.437560+0.033185 test-rmse:2.149293+0.501475
## [120] train-rmse:0.432807+0.032004 test-rmse:2.149279+0.501476
## [121] train-rmse:0.428015+0.032781 test-rmse:2.149833+0.500863
## [122] train-rmse:0.423667+0.033565 test-rmse:2.149080+0.500603
## [123] train-rmse:0.419865+0.034866 test-rmse:2.150282+0.500770
## [124] train-rmse:0.415470+0.033603 test-rmse:2.149166+0.500980
## [125] train-rmse:0.411395+0.034055 test-rmse:2.150294+0.502541
## [126] train-rmse:0.407168+0.034623 test-rmse:2.149127+0.502815
## [127] train-rmse:0.404022+0.034456 test-rmse:2.149199+0.502582
## [128] train-rmse:0.399402+0.033624 test-rmse:2.149544+0.501987
## Stopping. Best iteration:
## [122] train-rmse:0.423667+0.033565 test-rmse:2.149080+0.500603
##
## 25
## [1] train-rmse:7.059347+0.145057 test-rmse:7.054918+0.618135
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.114990+0.120688 test-rmse:6.148119+0.557187
## [3] train-rmse:5.325287+0.108786 test-rmse:5.402932+0.500971
## [4] train-rmse:4.672923+0.098485 test-rmse:4.810322+0.453120
## [5] train-rmse:4.130717+0.081002 test-rmse:4.338176+0.442502
## [6] train-rmse:3.682514+0.072992 test-rmse:3.949441+0.426776
## [7] train-rmse:3.314366+0.062961 test-rmse:3.659630+0.413788
## [8] train-rmse:3.003798+0.056192 test-rmse:3.406990+0.428267
## [9] train-rmse:2.744439+0.049857 test-rmse:3.217855+0.427506
## [10] train-rmse:2.531948+0.054645 test-rmse:3.064752+0.437872
## [11] train-rmse:2.357983+0.055417 test-rmse:2.954838+0.453092
## [12] train-rmse:2.201387+0.047861 test-rmse:2.861868+0.487634
## [13] train-rmse:2.073245+0.046044 test-rmse:2.776578+0.470869
## [14] train-rmse:1.969590+0.045017 test-rmse:2.714603+0.481009
## [15] train-rmse:1.870860+0.037887 test-rmse:2.651045+0.491484
## [16] train-rmse:1.781225+0.041446 test-rmse:2.612877+0.496704
## [17] train-rmse:1.709390+0.041490 test-rmse:2.567072+0.487957
## [18] train-rmse:1.648245+0.037322 test-rmse:2.532318+0.489103
## [19] train-rmse:1.590057+0.036774 test-rmse:2.508009+0.492975
## [20] train-rmse:1.531638+0.029260 test-rmse:2.488174+0.495477
## [21] train-rmse:1.492667+0.029649 test-rmse:2.464091+0.492192
## [22] train-rmse:1.447372+0.029072 test-rmse:2.441480+0.494002
## [23] train-rmse:1.398586+0.032874 test-rmse:2.417893+0.495596
## [24] train-rmse:1.359334+0.028718 test-rmse:2.392447+0.505401
## [25] train-rmse:1.321567+0.037696 test-rmse:2.375978+0.512463
## [26] train-rmse:1.288577+0.035761 test-rmse:2.361232+0.504680
## [27] train-rmse:1.256424+0.040427 test-rmse:2.351111+0.508504
## [28] train-rmse:1.226432+0.045349 test-rmse:2.342037+0.510647
## [29] train-rmse:1.202290+0.044678 test-rmse:2.340706+0.510702
## [30] train-rmse:1.173647+0.044053 test-rmse:2.331392+0.506274
## [31] train-rmse:1.147203+0.041701 test-rmse:2.323261+0.508011
## [32] train-rmse:1.112202+0.038286 test-rmse:2.319703+0.513414
## [33] train-rmse:1.089530+0.037146 test-rmse:2.313408+0.519787
## [34] train-rmse:1.066978+0.034450 test-rmse:2.307134+0.517949
## [35] train-rmse:1.049715+0.034583 test-rmse:2.300884+0.519395
## [36] train-rmse:1.021683+0.034224 test-rmse:2.298552+0.512872
## [37] train-rmse:1.000656+0.031938 test-rmse:2.284740+0.515578
## [38] train-rmse:0.986741+0.031409 test-rmse:2.283307+0.521284
## [39] train-rmse:0.967066+0.036926 test-rmse:2.282071+0.522986
## [40] train-rmse:0.945633+0.039439 test-rmse:2.279374+0.526984
## [41] train-rmse:0.927047+0.039331 test-rmse:2.277643+0.528609
## [42] train-rmse:0.909980+0.037252 test-rmse:2.268231+0.530595
## [43] train-rmse:0.894632+0.040722 test-rmse:2.271933+0.532955
## [44] train-rmse:0.877602+0.033445 test-rmse:2.266560+0.535044
## [45] train-rmse:0.861447+0.030402 test-rmse:2.258506+0.532661
## [46] train-rmse:0.845218+0.029450 test-rmse:2.253544+0.533742
## [47] train-rmse:0.828719+0.030954 test-rmse:2.257374+0.530802
## [48] train-rmse:0.807978+0.034467 test-rmse:2.252831+0.533468
## [49] train-rmse:0.792556+0.035136 test-rmse:2.247318+0.531470
## [50] train-rmse:0.777134+0.033233 test-rmse:2.244986+0.535454
## [51] train-rmse:0.761533+0.031977 test-rmse:2.243766+0.535282
## [52] train-rmse:0.748446+0.032989 test-rmse:2.241130+0.537618
## [53] train-rmse:0.737388+0.028885 test-rmse:2.238672+0.538846
## [54] train-rmse:0.721276+0.030967 test-rmse:2.235796+0.536813
## [55] train-rmse:0.708911+0.028408 test-rmse:2.238543+0.537626
## [56] train-rmse:0.694745+0.030040 test-rmse:2.232717+0.535470
## [57] train-rmse:0.682121+0.029606 test-rmse:2.233839+0.533361
## [58] train-rmse:0.668711+0.026852 test-rmse:2.231877+0.530770
## [59] train-rmse:0.660019+0.028325 test-rmse:2.228979+0.532403
## [60] train-rmse:0.646941+0.026893 test-rmse:2.227702+0.532747
## [61] train-rmse:0.632330+0.022990 test-rmse:2.228777+0.533549
## [62] train-rmse:0.618648+0.028882 test-rmse:2.228163+0.533398
## [63] train-rmse:0.610956+0.028911 test-rmse:2.226019+0.534168
## [64] train-rmse:0.601360+0.028355 test-rmse:2.222842+0.532420
## [65] train-rmse:0.592833+0.028333 test-rmse:2.221071+0.532042
## [66] train-rmse:0.582511+0.028235 test-rmse:2.221284+0.533225
## [67] train-rmse:0.573928+0.028264 test-rmse:2.220677+0.534669
## [68] train-rmse:0.562950+0.029945 test-rmse:2.217144+0.533921
## [69] train-rmse:0.554343+0.028073 test-rmse:2.216134+0.531767
## [70] train-rmse:0.543704+0.027021 test-rmse:2.216842+0.531362
## [71] train-rmse:0.536022+0.027268 test-rmse:2.215535+0.531364
## [72] train-rmse:0.526484+0.025055 test-rmse:2.211727+0.531298
## [73] train-rmse:0.518849+0.025889 test-rmse:2.211979+0.531243
## [74] train-rmse:0.511291+0.027442 test-rmse:2.210370+0.532914
## [75] train-rmse:0.503629+0.027659 test-rmse:2.208995+0.533420
## [76] train-rmse:0.496922+0.028363 test-rmse:2.209643+0.534979
## [77] train-rmse:0.491519+0.028353 test-rmse:2.208577+0.534950
## [78] train-rmse:0.483699+0.027580 test-rmse:2.208151+0.535618
## [79] train-rmse:0.476004+0.028720 test-rmse:2.208793+0.534512
## [80] train-rmse:0.469453+0.029245 test-rmse:2.208124+0.535628
## [81] train-rmse:0.462471+0.030913 test-rmse:2.206755+0.535983
## [82] train-rmse:0.456504+0.029791 test-rmse:2.204896+0.535998
## [83] train-rmse:0.448112+0.025315 test-rmse:2.201049+0.537526
## [84] train-rmse:0.438082+0.026286 test-rmse:2.198508+0.537617
## [85] train-rmse:0.432727+0.025863 test-rmse:2.198882+0.537623
## [86] train-rmse:0.425831+0.023598 test-rmse:2.197991+0.538017
## [87] train-rmse:0.418214+0.021996 test-rmse:2.195328+0.538157
## [88] train-rmse:0.412772+0.020367 test-rmse:2.195507+0.539595
## [89] train-rmse:0.406038+0.019124 test-rmse:2.194933+0.538540
## [90] train-rmse:0.400416+0.016476 test-rmse:2.193846+0.539332
## [91] train-rmse:0.393907+0.017899 test-rmse:2.192666+0.538918
## [92] train-rmse:0.390181+0.017309 test-rmse:2.192603+0.539185
## [93] train-rmse:0.384119+0.018411 test-rmse:2.192899+0.540040
## [94] train-rmse:0.378760+0.018549 test-rmse:2.194155+0.541043
## [95] train-rmse:0.373638+0.019406 test-rmse:2.194115+0.542306
## [96] train-rmse:0.368116+0.020898 test-rmse:2.195291+0.543293
## [97] train-rmse:0.364355+0.021430 test-rmse:2.194201+0.543765
## [98] train-rmse:0.359832+0.019916 test-rmse:2.194178+0.542867
## Stopping. Best iteration:
## [92] train-rmse:0.390181+0.017309 test-rmse:2.192603+0.539185
##
## 26
## [1] train-rmse:7.040642+0.142340 test-rmse:7.042231+0.635318
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.077864+0.116610 test-rmse:6.128688+0.585199
## [3] train-rmse:5.268170+0.096771 test-rmse:5.387365+0.538158
## [4] train-rmse:4.598136+0.081755 test-rmse:4.791172+0.497027
## [5] train-rmse:4.039333+0.065811 test-rmse:4.307015+0.488508
## [6] train-rmse:3.576997+0.056929 test-rmse:3.928501+0.471612
## [7] train-rmse:3.192923+0.047491 test-rmse:3.624270+0.467888
## [8] train-rmse:2.869447+0.037009 test-rmse:3.378209+0.463700
## [9] train-rmse:2.611312+0.034338 test-rmse:3.205346+0.466625
## [10] train-rmse:2.391351+0.035910 test-rmse:3.057973+0.485098
## [11] train-rmse:2.203568+0.034288 test-rmse:2.933431+0.501677
## [12] train-rmse:2.039663+0.036319 test-rmse:2.837911+0.521864
## [13] train-rmse:1.905090+0.032585 test-rmse:2.771692+0.534376
## [14] train-rmse:1.795451+0.030084 test-rmse:2.710914+0.542965
## [15] train-rmse:1.689366+0.046389 test-rmse:2.647371+0.555050
## [16] train-rmse:1.591682+0.057569 test-rmse:2.604953+0.559025
## [17] train-rmse:1.509804+0.050556 test-rmse:2.571176+0.554499
## [18] train-rmse:1.438391+0.063004 test-rmse:2.540808+0.565584
## [19] train-rmse:1.372464+0.064279 test-rmse:2.519499+0.554695
## [20] train-rmse:1.322882+0.064973 test-rmse:2.497331+0.554561
## [21] train-rmse:1.274589+0.064601 test-rmse:2.478388+0.556313
## [22] train-rmse:1.224083+0.059379 test-rmse:2.469807+0.564952
## [23] train-rmse:1.187460+0.060765 test-rmse:2.460762+0.564905
## [24] train-rmse:1.151977+0.059528 test-rmse:2.451336+0.561391
## [25] train-rmse:1.117656+0.056791 test-rmse:2.437388+0.569852
## [26] train-rmse:1.074476+0.049425 test-rmse:2.428502+0.571457
## [27] train-rmse:1.038917+0.046099 test-rmse:2.414759+0.573933
## [28] train-rmse:1.010463+0.048338 test-rmse:2.413057+0.577741
## [29] train-rmse:0.984742+0.044581 test-rmse:2.404601+0.585820
## [30] train-rmse:0.960179+0.044392 test-rmse:2.397739+0.586070
## [31] train-rmse:0.936899+0.046955 test-rmse:2.389236+0.591785
## [32] train-rmse:0.903637+0.043451 test-rmse:2.383729+0.590945
## [33] train-rmse:0.884443+0.046590 test-rmse:2.383463+0.588522
## [34] train-rmse:0.857609+0.044234 test-rmse:2.386977+0.590607
## [35] train-rmse:0.830196+0.036023 test-rmse:2.376389+0.583459
## [36] train-rmse:0.811569+0.035902 test-rmse:2.368859+0.587627
## [37] train-rmse:0.792761+0.038485 test-rmse:2.364617+0.589537
## [38] train-rmse:0.767801+0.031520 test-rmse:2.366526+0.585125
## [39] train-rmse:0.748662+0.026343 test-rmse:2.363834+0.584478
## [40] train-rmse:0.727486+0.025314 test-rmse:2.356460+0.587122
## [41] train-rmse:0.707602+0.024199 test-rmse:2.354718+0.585500
## [42] train-rmse:0.688627+0.025254 test-rmse:2.349927+0.587888
## [43] train-rmse:0.672556+0.023527 test-rmse:2.343360+0.588991
## [44] train-rmse:0.660478+0.021856 test-rmse:2.340069+0.589258
## [45] train-rmse:0.646298+0.021445 test-rmse:2.339565+0.589132
## [46] train-rmse:0.631466+0.019597 test-rmse:2.337300+0.586921
## [47] train-rmse:0.617948+0.021752 test-rmse:2.335148+0.588107
## [48] train-rmse:0.601617+0.021688 test-rmse:2.335311+0.589435
## [49] train-rmse:0.581548+0.021875 test-rmse:2.333887+0.587242
## [50] train-rmse:0.570168+0.023766 test-rmse:2.331442+0.586539
## [51] train-rmse:0.554262+0.019398 test-rmse:2.327827+0.585894
## [52] train-rmse:0.539857+0.024619 test-rmse:2.323463+0.588056
## [53] train-rmse:0.527888+0.023675 test-rmse:2.320956+0.586176
## [54] train-rmse:0.515092+0.018452 test-rmse:2.319438+0.587970
## [55] train-rmse:0.503298+0.019451 test-rmse:2.319938+0.587406
## [56] train-rmse:0.488081+0.017400 test-rmse:2.318777+0.587981
## [57] train-rmse:0.477699+0.019355 test-rmse:2.320321+0.591111
## [58] train-rmse:0.466345+0.020273 test-rmse:2.318949+0.590576
## [59] train-rmse:0.457723+0.020996 test-rmse:2.316684+0.591695
## [60] train-rmse:0.448673+0.018691 test-rmse:2.316078+0.591280
## [61] train-rmse:0.438580+0.021626 test-rmse:2.314192+0.592470
## [62] train-rmse:0.429902+0.020687 test-rmse:2.313423+0.592823
## [63] train-rmse:0.421040+0.020853 test-rmse:2.312360+0.593585
## [64] train-rmse:0.413102+0.022035 test-rmse:2.314360+0.593923
## [65] train-rmse:0.403405+0.022554 test-rmse:2.314433+0.594212
## [66] train-rmse:0.396521+0.022931 test-rmse:2.312433+0.594115
## [67] train-rmse:0.386793+0.023768 test-rmse:2.313141+0.593306
## [68] train-rmse:0.381659+0.023061 test-rmse:2.311674+0.594349
## [69] train-rmse:0.374844+0.022942 test-rmse:2.310813+0.594196
## [70] train-rmse:0.368388+0.021006 test-rmse:2.309001+0.594347
## [71] train-rmse:0.361032+0.020745 test-rmse:2.308238+0.595089
## [72] train-rmse:0.352864+0.021827 test-rmse:2.308578+0.592078
## [73] train-rmse:0.346953+0.023384 test-rmse:2.307696+0.591868
## [74] train-rmse:0.340281+0.025061 test-rmse:2.307204+0.591321
## [75] train-rmse:0.334660+0.025441 test-rmse:2.307711+0.591203
## [76] train-rmse:0.328279+0.022761 test-rmse:2.307683+0.591512
## [77] train-rmse:0.321064+0.023820 test-rmse:2.306589+0.589069
## [78] train-rmse:0.315331+0.023570 test-rmse:2.305630+0.589360
## [79] train-rmse:0.308059+0.021657 test-rmse:2.305822+0.589453
## [80] train-rmse:0.302647+0.019397 test-rmse:2.304392+0.591081
## [81] train-rmse:0.296384+0.020235 test-rmse:2.303783+0.591243
## [82] train-rmse:0.289326+0.019681 test-rmse:2.302324+0.590233
## [83] train-rmse:0.284290+0.019782 test-rmse:2.301957+0.589992
## [84] train-rmse:0.279915+0.019764 test-rmse:2.303166+0.592131
## [85] train-rmse:0.274215+0.019625 test-rmse:2.303180+0.591410
## [86] train-rmse:0.270472+0.018278 test-rmse:2.303954+0.591683
## [87] train-rmse:0.265642+0.019098 test-rmse:2.304032+0.591993
## [88] train-rmse:0.261214+0.020303 test-rmse:2.303484+0.592156
## [89] train-rmse:0.256662+0.020165 test-rmse:2.303264+0.593345
## Stopping. Best iteration:
## [83] train-rmse:0.284290+0.019782 test-rmse:2.301957+0.589992
##
## 27
## [1] train-rmse:7.034015+0.141791 test-rmse:7.038703+0.634939
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.061716+0.113241 test-rmse:6.130397+0.576066
## [3] train-rmse:5.242670+0.092471 test-rmse:5.366739+0.545563
## [4] train-rmse:4.557776+0.072914 test-rmse:4.758155+0.503809
## [5] train-rmse:3.985135+0.059884 test-rmse:4.275058+0.477956
## [6] train-rmse:3.511777+0.052689 test-rmse:3.886045+0.470013
## [7] train-rmse:3.116612+0.047024 test-rmse:3.591654+0.461325
## [8] train-rmse:2.786628+0.039361 test-rmse:3.346741+0.462125
## [9] train-rmse:2.505884+0.034252 test-rmse:3.173404+0.462703
## [10] train-rmse:2.278792+0.028393 test-rmse:3.037062+0.468362
## [11] train-rmse:2.087524+0.025347 test-rmse:2.925869+0.492161
## [12] train-rmse:1.918984+0.027908 test-rmse:2.809087+0.505899
## [13] train-rmse:1.778623+0.026528 test-rmse:2.728832+0.508324
## [14] train-rmse:1.660383+0.026674 test-rmse:2.652740+0.521430
## [15] train-rmse:1.554598+0.020770 test-rmse:2.601143+0.532048
## [16] train-rmse:1.456308+0.024156 test-rmse:2.553336+0.528249
## [17] train-rmse:1.366589+0.015877 test-rmse:2.523990+0.530041
## [18] train-rmse:1.290824+0.020052 test-rmse:2.500889+0.547622
## [19] train-rmse:1.237438+0.018798 test-rmse:2.479496+0.554532
## [20] train-rmse:1.173223+0.027131 test-rmse:2.457420+0.557167
## [21] train-rmse:1.121646+0.025836 test-rmse:2.436218+0.558053
## [22] train-rmse:1.076045+0.031612 test-rmse:2.424925+0.565687
## [23] train-rmse:1.031057+0.037671 test-rmse:2.412832+0.560910
## [24] train-rmse:0.985872+0.021662 test-rmse:2.408791+0.569599
## [25] train-rmse:0.938449+0.026104 test-rmse:2.399609+0.569636
## [26] train-rmse:0.901290+0.023716 test-rmse:2.389415+0.572273
## [27] train-rmse:0.875605+0.025047 test-rmse:2.379249+0.568156
## [28] train-rmse:0.847380+0.024728 test-rmse:2.371645+0.573602
## [29] train-rmse:0.819045+0.027581 test-rmse:2.362541+0.570290
## [30] train-rmse:0.793295+0.027569 test-rmse:2.354113+0.573339
## [31] train-rmse:0.760135+0.022500 test-rmse:2.352129+0.578994
## [32] train-rmse:0.732240+0.026720 test-rmse:2.350768+0.582007
## [33] train-rmse:0.706988+0.031573 test-rmse:2.343329+0.581759
## [34] train-rmse:0.682276+0.024115 test-rmse:2.340110+0.583324
## [35] train-rmse:0.663674+0.023400 test-rmse:2.336777+0.585364
## [36] train-rmse:0.642104+0.022691 test-rmse:2.330974+0.587188
## [37] train-rmse:0.619581+0.013733 test-rmse:2.329552+0.587746
## [38] train-rmse:0.599173+0.013260 test-rmse:2.330029+0.589930
## [39] train-rmse:0.579921+0.011441 test-rmse:2.325508+0.590494
## [40] train-rmse:0.561177+0.011039 test-rmse:2.320260+0.591660
## [41] train-rmse:0.544026+0.015182 test-rmse:2.319368+0.593653
## [42] train-rmse:0.528332+0.014287 test-rmse:2.316788+0.592180
## [43] train-rmse:0.510220+0.014629 test-rmse:2.317619+0.591377
## [44] train-rmse:0.496397+0.015722 test-rmse:2.313248+0.593227
## [45] train-rmse:0.482243+0.015208 test-rmse:2.310199+0.592470
## [46] train-rmse:0.466195+0.018448 test-rmse:2.310461+0.589580
## [47] train-rmse:0.450046+0.017446 test-rmse:2.306969+0.589198
## [48] train-rmse:0.435342+0.015837 test-rmse:2.307999+0.590212
## [49] train-rmse:0.420938+0.018074 test-rmse:2.307694+0.591182
## [50] train-rmse:0.408179+0.016576 test-rmse:2.307452+0.591161
## [51] train-rmse:0.397102+0.013631 test-rmse:2.307012+0.591674
## [52] train-rmse:0.385339+0.012031 test-rmse:2.307087+0.590471
## [53] train-rmse:0.374387+0.013715 test-rmse:2.305509+0.590756
## [54] train-rmse:0.364492+0.015838 test-rmse:2.303975+0.590235
## [55] train-rmse:0.351291+0.014280 test-rmse:2.300861+0.590493
## [56] train-rmse:0.342574+0.015457 test-rmse:2.300090+0.591239
## [57] train-rmse:0.334068+0.013729 test-rmse:2.300013+0.590234
## [58] train-rmse:0.324441+0.013346 test-rmse:2.300079+0.590506
## [59] train-rmse:0.316527+0.013573 test-rmse:2.298868+0.591116
## [60] train-rmse:0.308843+0.013144 test-rmse:2.297892+0.591519
## [61] train-rmse:0.299111+0.011235 test-rmse:2.298674+0.592205
## [62] train-rmse:0.292144+0.011475 test-rmse:2.297576+0.591806
## [63] train-rmse:0.284145+0.011025 test-rmse:2.298261+0.592051
## [64] train-rmse:0.274745+0.009330 test-rmse:2.297220+0.592347
## [65] train-rmse:0.269390+0.007481 test-rmse:2.296531+0.592569
## [66] train-rmse:0.261655+0.009420 test-rmse:2.294990+0.594207
## [67] train-rmse:0.254691+0.009465 test-rmse:2.294532+0.593459
## [68] train-rmse:0.248241+0.008925 test-rmse:2.294263+0.592846
## [69] train-rmse:0.242084+0.008806 test-rmse:2.294359+0.593568
## [70] train-rmse:0.236361+0.009182 test-rmse:2.294308+0.592688
## [71] train-rmse:0.230315+0.009015 test-rmse:2.293860+0.593070
## [72] train-rmse:0.222209+0.006741 test-rmse:2.293165+0.592884
## [73] train-rmse:0.216726+0.006957 test-rmse:2.292747+0.592822
## [74] train-rmse:0.209800+0.004950 test-rmse:2.293803+0.592927
## [75] train-rmse:0.204324+0.003989 test-rmse:2.294860+0.593266
## [76] train-rmse:0.198245+0.004100 test-rmse:2.295353+0.593918
## [77] train-rmse:0.194106+0.003875 test-rmse:2.294920+0.593554
## [78] train-rmse:0.189834+0.005473 test-rmse:2.294129+0.593545
## [79] train-rmse:0.185958+0.004932 test-rmse:2.294051+0.594435
## Stopping. Best iteration:
## [73] train-rmse:0.216726+0.006957 test-rmse:2.292747+0.592822
##
## 28
## [1] train-rmse:7.133930+0.153405 test-rmse:7.122205+0.669932
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.307565+0.134648 test-rmse:6.330760+0.700947
## [3] train-rmse:5.666044+0.123332 test-rmse:5.725269+0.669224
## [4] train-rmse:5.167706+0.118752 test-rmse:5.201904+0.634073
## [5] train-rmse:4.788475+0.112875 test-rmse:4.869480+0.627714
## [6] train-rmse:4.497036+0.109900 test-rmse:4.585152+0.605757
## [7] train-rmse:4.270124+0.110954 test-rmse:4.370555+0.547813
## [8] train-rmse:4.092416+0.112631 test-rmse:4.194915+0.520971
## [9] train-rmse:3.953541+0.111815 test-rmse:4.091990+0.505242
## [10] train-rmse:3.840487+0.112515 test-rmse:3.952154+0.493788
## [11] train-rmse:3.746542+0.115042 test-rmse:3.858419+0.445776
## [12] train-rmse:3.665046+0.118479 test-rmse:3.784529+0.427064
## [13] train-rmse:3.594833+0.117868 test-rmse:3.740703+0.450759
## [14] train-rmse:3.534673+0.118002 test-rmse:3.686961+0.441000
## [15] train-rmse:3.479740+0.118635 test-rmse:3.603612+0.427360
## [16] train-rmse:3.429188+0.120476 test-rmse:3.561683+0.417778
## [17] train-rmse:3.381415+0.122482 test-rmse:3.512160+0.407188
## [18] train-rmse:3.337651+0.121702 test-rmse:3.486370+0.432013
## [19] train-rmse:3.298400+0.121455 test-rmse:3.449130+0.420608
## [20] train-rmse:3.260639+0.122295 test-rmse:3.394738+0.423982
## [21] train-rmse:3.224529+0.123685 test-rmse:3.352954+0.421974
## [22] train-rmse:3.190110+0.125218 test-rmse:3.323837+0.402382
## [23] train-rmse:3.156781+0.125707 test-rmse:3.298005+0.418976
## [24] train-rmse:3.126803+0.125573 test-rmse:3.283151+0.435361
## [25] train-rmse:3.098305+0.125946 test-rmse:3.256855+0.439652
## [26] train-rmse:3.070759+0.126810 test-rmse:3.225475+0.429508
## [27] train-rmse:3.044850+0.128029 test-rmse:3.186185+0.417122
## [28] train-rmse:3.019376+0.129434 test-rmse:3.171639+0.414741
## [29] train-rmse:2.995484+0.129375 test-rmse:3.153551+0.426623
## [30] train-rmse:2.972464+0.129236 test-rmse:3.142510+0.427409
## [31] train-rmse:2.950310+0.129215 test-rmse:3.123822+0.431412
## [32] train-rmse:2.928777+0.129808 test-rmse:3.095307+0.424942
## [33] train-rmse:2.907859+0.130616 test-rmse:3.082637+0.424388
## [34] train-rmse:2.887255+0.131517 test-rmse:3.064413+0.442844
## [35] train-rmse:2.867793+0.132725 test-rmse:3.047655+0.441326
## [36] train-rmse:2.848872+0.133559 test-rmse:3.042787+0.443252
## [37] train-rmse:2.830568+0.134021 test-rmse:3.027384+0.440017
## [38] train-rmse:2.812888+0.134218 test-rmse:2.996967+0.437954
## [39] train-rmse:2.795862+0.134627 test-rmse:2.986461+0.445028
## [40] train-rmse:2.778974+0.134885 test-rmse:2.969696+0.452250
## [41] train-rmse:2.762470+0.135604 test-rmse:2.960391+0.454599
## [42] train-rmse:2.746313+0.136381 test-rmse:2.951071+0.457579
## [43] train-rmse:2.730659+0.136825 test-rmse:2.943817+0.460632
## [44] train-rmse:2.715398+0.137320 test-rmse:2.932593+0.453475
## [45] train-rmse:2.700382+0.137749 test-rmse:2.904043+0.450021
## [46] train-rmse:2.685887+0.138068 test-rmse:2.889312+0.451339
## [47] train-rmse:2.671811+0.138410 test-rmse:2.881938+0.453108
## [48] train-rmse:2.658202+0.138565 test-rmse:2.875879+0.457849
## [49] train-rmse:2.645037+0.138660 test-rmse:2.879412+0.472088
## [50] train-rmse:2.632147+0.138665 test-rmse:2.865973+0.468852
## [51] train-rmse:2.619458+0.138526 test-rmse:2.838947+0.468731
## [52] train-rmse:2.606772+0.138611 test-rmse:2.825546+0.476843
## [53] train-rmse:2.594260+0.138667 test-rmse:2.818010+0.475968
## [54] train-rmse:2.582018+0.138868 test-rmse:2.814758+0.473158
## [55] train-rmse:2.569993+0.139060 test-rmse:2.807458+0.474026
## [56] train-rmse:2.558412+0.139190 test-rmse:2.801151+0.478703
## [57] train-rmse:2.547217+0.139406 test-rmse:2.788598+0.479707
## [58] train-rmse:2.536405+0.139589 test-rmse:2.779332+0.490520
## [59] train-rmse:2.525821+0.139688 test-rmse:2.772556+0.492751
## [60] train-rmse:2.515383+0.139653 test-rmse:2.761183+0.479646
## [61] train-rmse:2.505022+0.139734 test-rmse:2.747084+0.476707
## [62] train-rmse:2.494919+0.139807 test-rmse:2.746366+0.482679
## [63] train-rmse:2.485167+0.139812 test-rmse:2.739013+0.482184
## [64] train-rmse:2.475657+0.139750 test-rmse:2.732098+0.486115
## [65] train-rmse:2.466414+0.139772 test-rmse:2.722988+0.489579
## [66] train-rmse:2.457234+0.139864 test-rmse:2.713598+0.488874
## [67] train-rmse:2.448176+0.139972 test-rmse:2.698643+0.495391
## [68] train-rmse:2.439137+0.140176 test-rmse:2.689882+0.493609
## [69] train-rmse:2.430389+0.140150 test-rmse:2.679300+0.484794
## [70] train-rmse:2.421810+0.140175 test-rmse:2.676982+0.485474
## [71] train-rmse:2.413366+0.140246 test-rmse:2.667163+0.483032
## [72] train-rmse:2.405282+0.140460 test-rmse:2.665348+0.482771
## [73] train-rmse:2.397350+0.140707 test-rmse:2.655114+0.482021
## [74] train-rmse:2.389550+0.140887 test-rmse:2.646922+0.493777
## [75] train-rmse:2.381985+0.140928 test-rmse:2.642512+0.494120
## [76] train-rmse:2.374394+0.140975 test-rmse:2.650725+0.500460
## [77] train-rmse:2.367162+0.140988 test-rmse:2.645641+0.506701
## [78] train-rmse:2.360121+0.141160 test-rmse:2.639917+0.504078
## [79] train-rmse:2.353141+0.141275 test-rmse:2.638216+0.502630
## [80] train-rmse:2.346331+0.141465 test-rmse:2.626656+0.496806
## [81] train-rmse:2.339589+0.141664 test-rmse:2.615763+0.496587
## [82] train-rmse:2.332899+0.141870 test-rmse:2.605770+0.498551
## [83] train-rmse:2.326328+0.142132 test-rmse:2.597497+0.500188
## [84] train-rmse:2.319808+0.142382 test-rmse:2.592340+0.500119
## [85] train-rmse:2.313480+0.142503 test-rmse:2.586707+0.495801
## [86] train-rmse:2.307220+0.142723 test-rmse:2.580521+0.498993
## [87] train-rmse:2.301054+0.142813 test-rmse:2.578566+0.498350
## [88] train-rmse:2.294998+0.142939 test-rmse:2.571757+0.496948
## [89] train-rmse:2.289056+0.143148 test-rmse:2.567042+0.493072
## [90] train-rmse:2.283175+0.143415 test-rmse:2.558376+0.495234
## [91] train-rmse:2.277472+0.143598 test-rmse:2.556618+0.502001
## [92] train-rmse:2.271891+0.143753 test-rmse:2.553474+0.501822
## [93] train-rmse:2.266393+0.143815 test-rmse:2.553906+0.515438
## [94] train-rmse:2.260970+0.143922 test-rmse:2.549712+0.508285
## [95] train-rmse:2.255665+0.144180 test-rmse:2.545852+0.507472
## [96] train-rmse:2.250586+0.144330 test-rmse:2.541020+0.509952
## [97] train-rmse:2.245583+0.144417 test-rmse:2.536007+0.510834
## [98] train-rmse:2.240749+0.144567 test-rmse:2.529027+0.511265
## [99] train-rmse:2.235890+0.144735 test-rmse:2.521479+0.514278
## [100] train-rmse:2.231050+0.144924 test-rmse:2.519717+0.511382
## [101] train-rmse:2.226143+0.145006 test-rmse:2.520811+0.513714
## [102] train-rmse:2.221488+0.145218 test-rmse:2.511847+0.515614
## [103] train-rmse:2.216903+0.145391 test-rmse:2.511108+0.516416
## [104] train-rmse:2.212405+0.145533 test-rmse:2.502366+0.512167
## [105] train-rmse:2.208004+0.145730 test-rmse:2.497382+0.514389
## [106] train-rmse:2.203664+0.145923 test-rmse:2.494012+0.512813
## [107] train-rmse:2.199336+0.146037 test-rmse:2.489012+0.514080
## [108] train-rmse:2.195075+0.146195 test-rmse:2.486050+0.517840
## [109] train-rmse:2.190947+0.146310 test-rmse:2.485084+0.521196
## [110] train-rmse:2.186875+0.146439 test-rmse:2.483984+0.525317
## [111] train-rmse:2.182824+0.146548 test-rmse:2.477796+0.521998
## [112] train-rmse:2.178815+0.146744 test-rmse:2.471385+0.525795
## [113] train-rmse:2.174869+0.146937 test-rmse:2.468573+0.521683
## [114] train-rmse:2.170991+0.147115 test-rmse:2.468454+0.528630
## [115] train-rmse:2.167177+0.147282 test-rmse:2.468231+0.526665
## [116] train-rmse:2.163353+0.147380 test-rmse:2.471162+0.525841
## [117] train-rmse:2.159593+0.147467 test-rmse:2.473244+0.522228
## [118] train-rmse:2.155893+0.147576 test-rmse:2.469017+0.523810
## [119] train-rmse:2.152284+0.147663 test-rmse:2.468745+0.524821
## [120] train-rmse:2.148772+0.147806 test-rmse:2.465204+0.524445
## [121] train-rmse:2.145299+0.147942 test-rmse:2.458045+0.525767
## [122] train-rmse:2.141933+0.148062 test-rmse:2.456611+0.524789
## [123] train-rmse:2.138581+0.148183 test-rmse:2.455735+0.526400
## [124] train-rmse:2.135280+0.148278 test-rmse:2.444937+0.524081
## [125] train-rmse:2.131990+0.148368 test-rmse:2.441349+0.527578
## [126] train-rmse:2.128792+0.148556 test-rmse:2.440240+0.526874
## [127] train-rmse:2.125631+0.148722 test-rmse:2.437098+0.523927
## [128] train-rmse:2.122483+0.148887 test-rmse:2.437387+0.526811
## [129] train-rmse:2.119396+0.149084 test-rmse:2.433453+0.525432
## [130] train-rmse:2.116395+0.149220 test-rmse:2.430429+0.528636
## [131] train-rmse:2.113390+0.149386 test-rmse:2.428461+0.527890
## [132] train-rmse:2.110438+0.149553 test-rmse:2.427594+0.527095
## [133] train-rmse:2.107372+0.149728 test-rmse:2.423021+0.524856
## [134] train-rmse:2.104504+0.149887 test-rmse:2.421757+0.523571
## [135] train-rmse:2.101679+0.150032 test-rmse:2.420652+0.523539
## [136] train-rmse:2.098897+0.150197 test-rmse:2.419336+0.524271
## [137] train-rmse:2.096080+0.150364 test-rmse:2.420270+0.522980
## [138] train-rmse:2.093346+0.150495 test-rmse:2.418289+0.522232
## [139] train-rmse:2.090642+0.150626 test-rmse:2.419074+0.523441
## [140] train-rmse:2.087935+0.150760 test-rmse:2.417612+0.522782
## [141] train-rmse:2.085284+0.150857 test-rmse:2.413014+0.522847
## [142] train-rmse:2.082617+0.150968 test-rmse:2.414321+0.528774
## [143] train-rmse:2.080032+0.151090 test-rmse:2.409479+0.528871
## [144] train-rmse:2.077467+0.151219 test-rmse:2.407400+0.533245
## [145] train-rmse:2.074965+0.151380 test-rmse:2.407065+0.530620
## [146] train-rmse:2.072496+0.151508 test-rmse:2.405757+0.532073
## [147] train-rmse:2.070034+0.151635 test-rmse:2.407050+0.529360
## [148] train-rmse:2.067600+0.151771 test-rmse:2.404096+0.530708
## [149] train-rmse:2.065177+0.151895 test-rmse:2.401248+0.528864
## [150] train-rmse:2.062816+0.152025 test-rmse:2.399349+0.526956
## [151] train-rmse:2.060483+0.152091 test-rmse:2.398262+0.524101
## [152] train-rmse:2.058148+0.152172 test-rmse:2.393151+0.525707
## [153] train-rmse:2.055833+0.152313 test-rmse:2.390090+0.524095
## [154] train-rmse:2.053517+0.152384 test-rmse:2.389617+0.523073
## [155] train-rmse:2.051304+0.152497 test-rmse:2.390136+0.525321
## [156] train-rmse:2.049069+0.152630 test-rmse:2.387902+0.525666
## [157] train-rmse:2.046844+0.152755 test-rmse:2.385483+0.525949
## [158] train-rmse:2.044612+0.152889 test-rmse:2.384957+0.523408
## [159] train-rmse:2.042452+0.153004 test-rmse:2.383058+0.520714
## [160] train-rmse:2.040307+0.153080 test-rmse:2.385045+0.521998
## [161] train-rmse:2.038175+0.153156 test-rmse:2.381733+0.522931
## [162] train-rmse:2.036105+0.153234 test-rmse:2.382134+0.525800
## [163] train-rmse:2.034043+0.153323 test-rmse:2.380760+0.526743
## [164] train-rmse:2.032005+0.153406 test-rmse:2.381357+0.526480
## [165] train-rmse:2.029976+0.153509 test-rmse:2.383380+0.531916
## [166] train-rmse:2.027944+0.153603 test-rmse:2.385565+0.532304
## [167] train-rmse:2.025945+0.153677 test-rmse:2.383592+0.532752
## [168] train-rmse:2.023945+0.153744 test-rmse:2.379103+0.534096
## [169] train-rmse:2.021978+0.153822 test-rmse:2.378687+0.531469
## [170] train-rmse:2.020017+0.153888 test-rmse:2.378087+0.530279
## [171] train-rmse:2.018093+0.154032 test-rmse:2.376758+0.530474
## [172] train-rmse:2.016160+0.154158 test-rmse:2.373004+0.529311
## [173] train-rmse:2.014221+0.154249 test-rmse:2.371262+0.530037
## [174] train-rmse:2.012342+0.154347 test-rmse:2.371735+0.528084
## [175] train-rmse:2.010497+0.154449 test-rmse:2.370382+0.527482
## [176] train-rmse:2.008643+0.154547 test-rmse:2.369624+0.526917
## [177] train-rmse:2.006844+0.154628 test-rmse:2.370076+0.527143
## [178] train-rmse:2.004997+0.154657 test-rmse:2.370820+0.525886
## [179] train-rmse:2.003221+0.154736 test-rmse:2.368941+0.527500
## [180] train-rmse:2.001450+0.154801 test-rmse:2.366986+0.527938
## [181] train-rmse:1.999686+0.154840 test-rmse:2.365940+0.530054
## [182] train-rmse:1.997946+0.154885 test-rmse:2.363460+0.532203
## [183] train-rmse:1.996235+0.154985 test-rmse:2.359923+0.531827
## [184] train-rmse:1.994542+0.155063 test-rmse:2.359089+0.531248
## [185] train-rmse:1.992863+0.155154 test-rmse:2.355420+0.532752
## [186] train-rmse:1.991205+0.155248 test-rmse:2.357090+0.532648
## [187] train-rmse:1.989564+0.155342 test-rmse:2.354423+0.531462
## [188] train-rmse:1.987870+0.155537 test-rmse:2.354242+0.532717
## [189] train-rmse:1.986206+0.155662 test-rmse:2.354100+0.531401
## [190] train-rmse:1.984538+0.155739 test-rmse:2.354507+0.532542
## [191] train-rmse:1.982907+0.155881 test-rmse:2.354038+0.531698
## [192] train-rmse:1.981304+0.155979 test-rmse:2.354300+0.530835
## [193] train-rmse:1.979686+0.156082 test-rmse:2.354886+0.530328
## [194] train-rmse:1.978101+0.156189 test-rmse:2.353718+0.530652
## [195] train-rmse:1.976530+0.156283 test-rmse:2.351158+0.532352
## [196] train-rmse:1.974992+0.156351 test-rmse:2.349230+0.531587
## [197] train-rmse:1.973449+0.156438 test-rmse:2.350651+0.531386
## [198] train-rmse:1.971909+0.156537 test-rmse:2.348514+0.531683
## [199] train-rmse:1.970343+0.156635 test-rmse:2.346917+0.534129
## [200] train-rmse:1.968829+0.156725 test-rmse:2.343846+0.533198
## [201] train-rmse:1.967298+0.156825 test-rmse:2.343096+0.534493
## [202] train-rmse:1.965787+0.156955 test-rmse:2.342374+0.534613
## [203] train-rmse:1.964279+0.157044 test-rmse:2.339756+0.534850
## [204] train-rmse:1.962802+0.157151 test-rmse:2.340096+0.532569
## [205] train-rmse:1.961330+0.157207 test-rmse:2.338490+0.534918
## [206] train-rmse:1.959825+0.157265 test-rmse:2.340562+0.539086
## [207] train-rmse:1.958366+0.157316 test-rmse:2.344285+0.539525
## [208] train-rmse:1.956932+0.157387 test-rmse:2.344399+0.538720
## [209] train-rmse:1.955496+0.157460 test-rmse:2.342357+0.537808
## [210] train-rmse:1.954054+0.157561 test-rmse:2.341481+0.537243
## [211] train-rmse:1.952648+0.157643 test-rmse:2.337873+0.535894
## [212] train-rmse:1.951215+0.157816 test-rmse:2.341618+0.536435
## [213] train-rmse:1.949823+0.157908 test-rmse:2.343074+0.535164
## [214] train-rmse:1.948458+0.158001 test-rmse:2.339283+0.536382
## [215] train-rmse:1.947104+0.158098 test-rmse:2.336724+0.537257
## [216] train-rmse:1.945769+0.158185 test-rmse:2.336215+0.538943
## [217] train-rmse:1.944388+0.158309 test-rmse:2.334559+0.536793
## [218] train-rmse:1.942986+0.158348 test-rmse:2.336147+0.539861
## [219] train-rmse:1.941659+0.158406 test-rmse:2.335108+0.537768
## [220] train-rmse:1.940326+0.158470 test-rmse:2.333186+0.536714
## [221] train-rmse:1.938963+0.158580 test-rmse:2.333496+0.536982
## [222] train-rmse:1.937697+0.158650 test-rmse:2.332213+0.536421
## [223] train-rmse:1.936343+0.158675 test-rmse:2.329267+0.538664
## [224] train-rmse:1.935084+0.158748 test-rmse:2.328698+0.537848
## [225] train-rmse:1.933785+0.158822 test-rmse:2.329075+0.536963
## [226] train-rmse:1.932532+0.158899 test-rmse:2.327509+0.537200
## [227] train-rmse:1.931292+0.158991 test-rmse:2.325932+0.538889
## [228] train-rmse:1.930050+0.159074 test-rmse:2.323339+0.537815
## [229] train-rmse:1.928770+0.159145 test-rmse:2.323783+0.537248
## [230] train-rmse:1.927534+0.159242 test-rmse:2.324479+0.536810
## [231] train-rmse:1.926309+0.159337 test-rmse:2.324159+0.535975
## [232] train-rmse:1.925086+0.159421 test-rmse:2.321759+0.536479
## [233] train-rmse:1.923830+0.159475 test-rmse:2.320959+0.536968
## [234] train-rmse:1.922627+0.159553 test-rmse:2.321020+0.535223
## [235] train-rmse:1.921412+0.159645 test-rmse:2.323192+0.535380
## [236] train-rmse:1.920221+0.159732 test-rmse:2.320985+0.533813
## [237] train-rmse:1.918989+0.159853 test-rmse:2.317402+0.536645
## [238] train-rmse:1.917784+0.159944 test-rmse:2.317270+0.534379
## [239] train-rmse:1.916591+0.159998 test-rmse:2.321314+0.537461
## [240] train-rmse:1.915391+0.160039 test-rmse:2.320560+0.535391
## [241] train-rmse:1.914245+0.160107 test-rmse:2.321958+0.539146
## [242] train-rmse:1.913059+0.160163 test-rmse:2.319559+0.540857
## [243] train-rmse:1.911879+0.160218 test-rmse:2.320556+0.541294
## [244] train-rmse:1.910734+0.160275 test-rmse:2.320558+0.541065
## Stopping. Best iteration:
## [238] train-rmse:1.917784+0.159944 test-rmse:2.317270+0.534379
##
## 29
## [1] train-rmse:7.054984+0.147494 test-rmse:7.066349+0.643934
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.152403+0.134059 test-rmse:6.191756+0.613155
## [3] train-rmse:5.439161+0.122675 test-rmse:5.540505+0.607670
## [4] train-rmse:4.869488+0.108595 test-rmse:5.019526+0.545521
## [5] train-rmse:4.411689+0.099955 test-rmse:4.539756+0.494193
## [6] train-rmse:4.043589+0.099714 test-rmse:4.194044+0.474193
## [7] train-rmse:3.752366+0.100757 test-rmse:3.944945+0.442297
## [8] train-rmse:3.509086+0.093285 test-rmse:3.719139+0.442265
## [9] train-rmse:3.312676+0.093989 test-rmse:3.531100+0.418105
## [10] train-rmse:3.155002+0.092467 test-rmse:3.409589+0.432059
## [11] train-rmse:3.019426+0.094902 test-rmse:3.270727+0.445003
## [12] train-rmse:2.895757+0.086306 test-rmse:3.173362+0.441083
## [13] train-rmse:2.815626+0.087734 test-rmse:3.145801+0.448676
## [14] train-rmse:2.739443+0.087778 test-rmse:3.078044+0.461969
## [15] train-rmse:2.671244+0.090193 test-rmse:3.028112+0.457379
## [16] train-rmse:2.595706+0.082681 test-rmse:2.976490+0.466522
## [17] train-rmse:2.542061+0.081249 test-rmse:2.946379+0.480789
## [18] train-rmse:2.491883+0.082722 test-rmse:2.916326+0.472912
## [19] train-rmse:2.448608+0.082612 test-rmse:2.895544+0.471869
## [20] train-rmse:2.401574+0.090036 test-rmse:2.863302+0.488851
## [21] train-rmse:2.361256+0.091552 test-rmse:2.848406+0.487721
## [22] train-rmse:2.324018+0.093487 test-rmse:2.822494+0.490745
## [23] train-rmse:2.284393+0.091640 test-rmse:2.795218+0.492209
## [24] train-rmse:2.248290+0.081036 test-rmse:2.777591+0.503124
## [25] train-rmse:2.212695+0.084396 test-rmse:2.758152+0.511959
## [26] train-rmse:2.180847+0.086656 test-rmse:2.738927+0.513121
## [27] train-rmse:2.146849+0.091412 test-rmse:2.698029+0.504723
## [28] train-rmse:2.120642+0.091201 test-rmse:2.678693+0.503070
## [29] train-rmse:2.089028+0.090437 test-rmse:2.669030+0.498966
## [30] train-rmse:2.059700+0.088977 test-rmse:2.644305+0.501124
## [31] train-rmse:2.032792+0.081180 test-rmse:2.626072+0.509160
## [32] train-rmse:2.009254+0.082546 test-rmse:2.615460+0.504615
## [33] train-rmse:1.979508+0.081016 test-rmse:2.603192+0.505379
## [34] train-rmse:1.957930+0.080240 test-rmse:2.597071+0.519656
## [35] train-rmse:1.935168+0.074708 test-rmse:2.577942+0.524926
## [36] train-rmse:1.914210+0.074317 test-rmse:2.555652+0.511130
## [37] train-rmse:1.887615+0.071132 test-rmse:2.528548+0.519809
## [38] train-rmse:1.866632+0.072896 test-rmse:2.522914+0.521060
## [39] train-rmse:1.850233+0.072951 test-rmse:2.512903+0.518176
## [40] train-rmse:1.832887+0.073691 test-rmse:2.504083+0.529130
## [41] train-rmse:1.814091+0.074037 test-rmse:2.491862+0.526791
## [42] train-rmse:1.794716+0.075290 test-rmse:2.484539+0.528708
## [43] train-rmse:1.771985+0.076552 test-rmse:2.470285+0.519287
## [44] train-rmse:1.756963+0.077490 test-rmse:2.455633+0.519965
## [45] train-rmse:1.742249+0.078328 test-rmse:2.443613+0.520839
## [46] train-rmse:1.724994+0.079459 test-rmse:2.434475+0.522812
## [47] train-rmse:1.711856+0.080533 test-rmse:2.422038+0.522148
## [48] train-rmse:1.697303+0.079850 test-rmse:2.420602+0.524146
## [49] train-rmse:1.684400+0.080316 test-rmse:2.415046+0.523583
## [50] train-rmse:1.666625+0.070704 test-rmse:2.397970+0.524152
## [51] train-rmse:1.654235+0.069708 test-rmse:2.394338+0.523018
## [52] train-rmse:1.640883+0.070019 test-rmse:2.386967+0.520299
## [53] train-rmse:1.624695+0.064117 test-rmse:2.377742+0.531555
## [54] train-rmse:1.611876+0.061475 test-rmse:2.377495+0.529240
## [55] train-rmse:1.602208+0.061376 test-rmse:2.372907+0.527997
## [56] train-rmse:1.587555+0.061266 test-rmse:2.359332+0.534664
## [57] train-rmse:1.574382+0.060820 test-rmse:2.348253+0.527545
## [58] train-rmse:1.562514+0.060419 test-rmse:2.343529+0.526713
## [59] train-rmse:1.551520+0.058976 test-rmse:2.340126+0.524744
## [60] train-rmse:1.541796+0.058288 test-rmse:2.335086+0.529302
## [61] train-rmse:1.529789+0.059242 test-rmse:2.322820+0.534597
## [62] train-rmse:1.520934+0.058949 test-rmse:2.318604+0.536864
## [63] train-rmse:1.512634+0.059306 test-rmse:2.314635+0.535446
## [64] train-rmse:1.504181+0.058990 test-rmse:2.312965+0.539408
## [65] train-rmse:1.495708+0.060279 test-rmse:2.313300+0.537428
## [66] train-rmse:1.486374+0.059336 test-rmse:2.303395+0.537986
## [67] train-rmse:1.473878+0.056986 test-rmse:2.295360+0.538069
## [68] train-rmse:1.464534+0.058128 test-rmse:2.288538+0.537339
## [69] train-rmse:1.456862+0.058073 test-rmse:2.286406+0.534053
## [70] train-rmse:1.445278+0.056011 test-rmse:2.282973+0.533760
## [71] train-rmse:1.437505+0.054634 test-rmse:2.277995+0.532528
## [72] train-rmse:1.430261+0.054707 test-rmse:2.275341+0.531027
## [73] train-rmse:1.422484+0.054924 test-rmse:2.269355+0.528697
## [74] train-rmse:1.411128+0.054822 test-rmse:2.270959+0.536509
## [75] train-rmse:1.404454+0.054627 test-rmse:2.267833+0.539633
## [76] train-rmse:1.396513+0.058035 test-rmse:2.261975+0.534220
## [77] train-rmse:1.383224+0.051165 test-rmse:2.257643+0.534881
## [78] train-rmse:1.376927+0.050812 test-rmse:2.253853+0.535496
## [79] train-rmse:1.370905+0.050705 test-rmse:2.253234+0.536897
## [80] train-rmse:1.363676+0.049483 test-rmse:2.249855+0.537631
## [81] train-rmse:1.354601+0.049608 test-rmse:2.247148+0.537679
## [82] train-rmse:1.347244+0.048653 test-rmse:2.240336+0.539654
## [83] train-rmse:1.336698+0.047077 test-rmse:2.236745+0.538218
## [84] train-rmse:1.331013+0.046619 test-rmse:2.230321+0.541379
## [85] train-rmse:1.323220+0.048069 test-rmse:2.231682+0.540829
## [86] train-rmse:1.317831+0.048446 test-rmse:2.223790+0.537896
## [87] train-rmse:1.312603+0.049071 test-rmse:2.222060+0.535069
## [88] train-rmse:1.307389+0.049455 test-rmse:2.220350+0.537473
## [89] train-rmse:1.302072+0.049049 test-rmse:2.220722+0.540073
## [90] train-rmse:1.292485+0.048321 test-rmse:2.217400+0.539940
## [91] train-rmse:1.284059+0.048407 test-rmse:2.211922+0.543486
## [92] train-rmse:1.279650+0.048516 test-rmse:2.209047+0.545410
## [93] train-rmse:1.273030+0.048713 test-rmse:2.206583+0.542978
## [94] train-rmse:1.268651+0.048737 test-rmse:2.207301+0.542057
## [95] train-rmse:1.262918+0.049406 test-rmse:2.205550+0.542147
## [96] train-rmse:1.256301+0.048118 test-rmse:2.198641+0.543472
## [97] train-rmse:1.251374+0.047154 test-rmse:2.198402+0.545307
## [98] train-rmse:1.243102+0.050697 test-rmse:2.198519+0.546042
## [99] train-rmse:1.238391+0.050246 test-rmse:2.194647+0.542279
## [100] train-rmse:1.234301+0.050024 test-rmse:2.193870+0.547075
## [101] train-rmse:1.229861+0.049894 test-rmse:2.192944+0.549063
## [102] train-rmse:1.222643+0.050509 test-rmse:2.188740+0.547069
## [103] train-rmse:1.217584+0.050811 test-rmse:2.186171+0.547187
## [104] train-rmse:1.212044+0.052952 test-rmse:2.184312+0.546873
## [105] train-rmse:1.205809+0.053767 test-rmse:2.181828+0.547922
## [106] train-rmse:1.202081+0.054083 test-rmse:2.181177+0.546624
## [107] train-rmse:1.192615+0.052059 test-rmse:2.175357+0.549305
## [108] train-rmse:1.185794+0.054076 test-rmse:2.174021+0.552783
## [109] train-rmse:1.181790+0.054226 test-rmse:2.172762+0.552434
## [110] train-rmse:1.171876+0.051411 test-rmse:2.171906+0.556629
## [111] train-rmse:1.165874+0.050597 test-rmse:2.167673+0.555427
## [112] train-rmse:1.161475+0.050130 test-rmse:2.164667+0.556894
## [113] train-rmse:1.156208+0.049736 test-rmse:2.165143+0.557036
## [114] train-rmse:1.152478+0.049134 test-rmse:2.162741+0.555621
## [115] train-rmse:1.146152+0.048764 test-rmse:2.163329+0.555566
## [116] train-rmse:1.142777+0.048663 test-rmse:2.164466+0.556180
## [117] train-rmse:1.138953+0.049508 test-rmse:2.162923+0.554725
## [118] train-rmse:1.133310+0.052020 test-rmse:2.161943+0.556719
## [119] train-rmse:1.128994+0.051930 test-rmse:2.160838+0.559711
## [120] train-rmse:1.122503+0.054107 test-rmse:2.160087+0.561999
## [121] train-rmse:1.119370+0.054355 test-rmse:2.159077+0.562504
## [122] train-rmse:1.116455+0.054114 test-rmse:2.156816+0.564061
## [123] train-rmse:1.112721+0.055363 test-rmse:2.154004+0.562552
## [124] train-rmse:1.110136+0.055298 test-rmse:2.152738+0.562406
## [125] train-rmse:1.107281+0.055687 test-rmse:2.152877+0.562391
## [126] train-rmse:1.103949+0.056080 test-rmse:2.152368+0.563351
## [127] train-rmse:1.100449+0.056150 test-rmse:2.150930+0.561146
## [128] train-rmse:1.094566+0.055800 test-rmse:2.150901+0.563678
## [129] train-rmse:1.090283+0.054093 test-rmse:2.150158+0.563735
## [130] train-rmse:1.087235+0.054522 test-rmse:2.150140+0.561337
## [131] train-rmse:1.079413+0.052141 test-rmse:2.146544+0.562482
## [132] train-rmse:1.073388+0.055968 test-rmse:2.143188+0.561091
## [133] train-rmse:1.068594+0.055087 test-rmse:2.143063+0.564486
## [134] train-rmse:1.064363+0.054069 test-rmse:2.138887+0.565638
## [135] train-rmse:1.059532+0.057670 test-rmse:2.139725+0.571168
## [136] train-rmse:1.054740+0.054958 test-rmse:2.140091+0.570795
## [137] train-rmse:1.052224+0.054937 test-rmse:2.141102+0.568260
## [138] train-rmse:1.049581+0.054512 test-rmse:2.138659+0.569039
## [139] train-rmse:1.042884+0.053664 test-rmse:2.134734+0.565816
## [140] train-rmse:1.040417+0.053601 test-rmse:2.135491+0.563780
## [141] train-rmse:1.037714+0.053062 test-rmse:2.133403+0.565109
## [142] train-rmse:1.033095+0.055190 test-rmse:2.135788+0.571448
## [143] train-rmse:1.028572+0.053468 test-rmse:2.132803+0.573528
## [144] train-rmse:1.026294+0.053573 test-rmse:2.132395+0.573721
## [145] train-rmse:1.022299+0.052977 test-rmse:2.130052+0.575935
## [146] train-rmse:1.018994+0.054989 test-rmse:2.130867+0.577158
## [147] train-rmse:1.013330+0.054888 test-rmse:2.129205+0.579462
## [148] train-rmse:1.007271+0.054905 test-rmse:2.130963+0.579699
## [149] train-rmse:1.002800+0.055142 test-rmse:2.129942+0.579109
## [150] train-rmse:0.998551+0.056403 test-rmse:2.130281+0.579014
## [151] train-rmse:0.996041+0.056457 test-rmse:2.130219+0.579593
## [152] train-rmse:0.993386+0.056313 test-rmse:2.128443+0.579253
## [153] train-rmse:0.990391+0.056486 test-rmse:2.128685+0.578459
## [154] train-rmse:0.987205+0.056764 test-rmse:2.130068+0.579354
## [155] train-rmse:0.983078+0.056494 test-rmse:2.128347+0.580410
## [156] train-rmse:0.979438+0.057852 test-rmse:2.130401+0.581204
## [157] train-rmse:0.976601+0.057254 test-rmse:2.129366+0.582610
## [158] train-rmse:0.974077+0.058253 test-rmse:2.127330+0.582493
## [159] train-rmse:0.971275+0.058662 test-rmse:2.127880+0.581059
## [160] train-rmse:0.964151+0.056545 test-rmse:2.130746+0.579792
## [161] train-rmse:0.960729+0.055805 test-rmse:2.129928+0.580230
## [162] train-rmse:0.957357+0.054311 test-rmse:2.129386+0.579964
## [163] train-rmse:0.953887+0.054818 test-rmse:2.129003+0.580061
## [164] train-rmse:0.951743+0.054423 test-rmse:2.128200+0.582035
## Stopping. Best iteration:
## [158] train-rmse:0.974077+0.058253 test-rmse:2.127330+0.582493
##
## 30
## [1] train-rmse:7.002229+0.144550 test-rmse:7.044027+0.633452
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.049186+0.122199 test-rmse:6.130863+0.577481
## [3] train-rmse:5.272001+0.116752 test-rmse:5.371749+0.530281
## [4] train-rmse:4.653323+0.111070 test-rmse:4.818312+0.481641
## [5] train-rmse:4.145921+0.111094 test-rmse:4.307833+0.439735
## [6] train-rmse:3.742079+0.093507 test-rmse:3.937917+0.449572
## [7] train-rmse:3.407126+0.082939 test-rmse:3.663092+0.427351
## [8] train-rmse:3.139602+0.074709 test-rmse:3.414347+0.429215
## [9] train-rmse:2.928445+0.066804 test-rmse:3.242121+0.431775
## [10] train-rmse:2.752708+0.066415 test-rmse:3.106670+0.451938
## [11] train-rmse:2.613788+0.061662 test-rmse:3.027551+0.467243
## [12] train-rmse:2.497924+0.059466 test-rmse:2.936171+0.473822
## [13] train-rmse:2.404912+0.059787 test-rmse:2.880996+0.467149
## [14] train-rmse:2.310574+0.060716 test-rmse:2.821321+0.471765
## [15] train-rmse:2.230004+0.063817 test-rmse:2.756484+0.483467
## [16] train-rmse:2.164662+0.062170 test-rmse:2.731955+0.500372
## [17] train-rmse:2.099832+0.068330 test-rmse:2.681717+0.483974
## [18] train-rmse:2.040971+0.065457 test-rmse:2.646652+0.503794
## [19] train-rmse:1.991086+0.064780 test-rmse:2.621964+0.495047
## [20] train-rmse:1.942462+0.060295 test-rmse:2.576507+0.492478
## [21] train-rmse:1.898624+0.056835 test-rmse:2.562660+0.487464
## [22] train-rmse:1.857845+0.058106 test-rmse:2.540430+0.491667
## [23] train-rmse:1.820369+0.057719 test-rmse:2.521143+0.492317
## [24] train-rmse:1.775103+0.060272 test-rmse:2.490858+0.474748
## [25] train-rmse:1.738907+0.063559 test-rmse:2.467938+0.475041
## [26] train-rmse:1.706055+0.060157 test-rmse:2.452962+0.483647
## [27] train-rmse:1.679268+0.060607 test-rmse:2.432553+0.485683
## [28] train-rmse:1.650114+0.060688 test-rmse:2.417225+0.486646
## [29] train-rmse:1.618623+0.064143 test-rmse:2.395916+0.489170
## [30] train-rmse:1.588234+0.061671 test-rmse:2.389565+0.493178
## [31] train-rmse:1.560532+0.055219 test-rmse:2.372888+0.494249
## [32] train-rmse:1.534994+0.051906 test-rmse:2.365126+0.495819
## [33] train-rmse:1.508595+0.057504 test-rmse:2.348552+0.483026
## [34] train-rmse:1.490337+0.057755 test-rmse:2.334873+0.483379
## [35] train-rmse:1.471243+0.057816 test-rmse:2.320665+0.476910
## [36] train-rmse:1.446422+0.056519 test-rmse:2.311313+0.489683
## [37] train-rmse:1.423558+0.053704 test-rmse:2.302718+0.492845
## [38] train-rmse:1.408075+0.053226 test-rmse:2.298584+0.488422
## [39] train-rmse:1.387863+0.054318 test-rmse:2.293909+0.488642
## [40] train-rmse:1.362270+0.050819 test-rmse:2.287772+0.503969
## [41] train-rmse:1.346694+0.051802 test-rmse:2.281343+0.500710
## [42] train-rmse:1.332173+0.052261 test-rmse:2.275369+0.497714
## [43] train-rmse:1.312000+0.045427 test-rmse:2.266490+0.501257
## [44] train-rmse:1.296349+0.048011 test-rmse:2.260200+0.503572
## [45] train-rmse:1.281223+0.046984 test-rmse:2.261082+0.500935
## [46] train-rmse:1.268387+0.046304 test-rmse:2.254350+0.505124
## [47] train-rmse:1.252784+0.044604 test-rmse:2.249286+0.506353
## [48] train-rmse:1.242162+0.044478 test-rmse:2.244079+0.505531
## [49] train-rmse:1.224314+0.045058 test-rmse:2.239097+0.507066
## [50] train-rmse:1.213402+0.044638 test-rmse:2.233794+0.511089
## [51] train-rmse:1.199811+0.048400 test-rmse:2.224751+0.505874
## [52] train-rmse:1.186708+0.050297 test-rmse:2.214237+0.503058
## [53] train-rmse:1.169921+0.053603 test-rmse:2.213502+0.506291
## [54] train-rmse:1.153113+0.054279 test-rmse:2.210600+0.514210
## [55] train-rmse:1.140463+0.054072 test-rmse:2.210632+0.518123
## [56] train-rmse:1.127533+0.054073 test-rmse:2.205775+0.520525
## [57] train-rmse:1.118367+0.054063 test-rmse:2.202380+0.520614
## [58] train-rmse:1.106644+0.053717 test-rmse:2.199797+0.522544
## [59] train-rmse:1.096033+0.052661 test-rmse:2.197332+0.519993
## [60] train-rmse:1.077797+0.055379 test-rmse:2.193332+0.515776
## [61] train-rmse:1.067470+0.056427 test-rmse:2.188467+0.514444
## [62] train-rmse:1.055775+0.057537 test-rmse:2.187815+0.515652
## [63] train-rmse:1.044350+0.054967 test-rmse:2.183051+0.512993
## [64] train-rmse:1.034785+0.054128 test-rmse:2.178573+0.514452
## [65] train-rmse:1.025279+0.056030 test-rmse:2.171057+0.513008
## [66] train-rmse:1.017809+0.056332 test-rmse:2.168047+0.512400
## [67] train-rmse:1.008663+0.058194 test-rmse:2.165388+0.512495
## [68] train-rmse:1.001462+0.058632 test-rmse:2.165910+0.517581
## [69] train-rmse:0.992266+0.058796 test-rmse:2.159998+0.519730
## [70] train-rmse:0.984539+0.059765 test-rmse:2.158624+0.519582
## [71] train-rmse:0.976052+0.056908 test-rmse:2.157389+0.520953
## [72] train-rmse:0.967541+0.056510 test-rmse:2.152806+0.522182
## [73] train-rmse:0.958985+0.058309 test-rmse:2.150956+0.525424
## [74] train-rmse:0.951573+0.060476 test-rmse:2.151749+0.526522
## [75] train-rmse:0.941916+0.062805 test-rmse:2.152069+0.529387
## [76] train-rmse:0.934000+0.065006 test-rmse:2.146605+0.523501
## [77] train-rmse:0.924283+0.063214 test-rmse:2.143908+0.523360
## [78] train-rmse:0.919551+0.063841 test-rmse:2.141100+0.524256
## [79] train-rmse:0.909693+0.064143 test-rmse:2.136974+0.524860
## [80] train-rmse:0.902966+0.065722 test-rmse:2.135484+0.526424
## [81] train-rmse:0.890503+0.064729 test-rmse:2.131867+0.527847
## [82] train-rmse:0.884635+0.064282 test-rmse:2.132310+0.529522
## [83] train-rmse:0.879102+0.063630 test-rmse:2.132438+0.533297
## [84] train-rmse:0.873365+0.064529 test-rmse:2.133623+0.532861
## [85] train-rmse:0.865156+0.066764 test-rmse:2.127802+0.526541
## [86] train-rmse:0.853000+0.062126 test-rmse:2.125518+0.526053
## [87] train-rmse:0.843356+0.062545 test-rmse:2.124617+0.525809
## [88] train-rmse:0.835684+0.062500 test-rmse:2.120499+0.525537
## [89] train-rmse:0.827961+0.061972 test-rmse:2.117199+0.527906
## [90] train-rmse:0.819121+0.058051 test-rmse:2.115623+0.529802
## [91] train-rmse:0.813229+0.059237 test-rmse:2.115185+0.527658
## [92] train-rmse:0.807087+0.060339 test-rmse:2.114667+0.528356
## [93] train-rmse:0.801291+0.061786 test-rmse:2.112056+0.527573
## [94] train-rmse:0.796299+0.061273 test-rmse:2.112511+0.527005
## [95] train-rmse:0.789575+0.060818 test-rmse:2.113712+0.528738
## [96] train-rmse:0.783559+0.058525 test-rmse:2.113029+0.530110
## [97] train-rmse:0.773564+0.059240 test-rmse:2.111143+0.529842
## [98] train-rmse:0.767222+0.061289 test-rmse:2.110846+0.530332
## [99] train-rmse:0.760634+0.063342 test-rmse:2.108269+0.530545
## [100] train-rmse:0.751803+0.063974 test-rmse:2.106414+0.528647
## [101] train-rmse:0.745800+0.063413 test-rmse:2.106493+0.528711
## [102] train-rmse:0.740362+0.061956 test-rmse:2.106688+0.526798
## [103] train-rmse:0.734630+0.061456 test-rmse:2.104217+0.528126
## [104] train-rmse:0.731621+0.061730 test-rmse:2.103402+0.528578
## [105] train-rmse:0.725518+0.063588 test-rmse:2.099999+0.528616
## [106] train-rmse:0.720565+0.061718 test-rmse:2.100420+0.528295
## [107] train-rmse:0.714721+0.063835 test-rmse:2.099952+0.528017
## [108] train-rmse:0.709304+0.062495 test-rmse:2.098789+0.527489
## [109] train-rmse:0.702215+0.061995 test-rmse:2.098601+0.528197
## [110] train-rmse:0.695661+0.059843 test-rmse:2.098825+0.530629
## [111] train-rmse:0.692151+0.059745 test-rmse:2.099466+0.531294
## [112] train-rmse:0.687519+0.061432 test-rmse:2.098899+0.530445
## [113] train-rmse:0.681480+0.059466 test-rmse:2.097750+0.528624
## [114] train-rmse:0.677289+0.057692 test-rmse:2.096878+0.529316
## [115] train-rmse:0.673669+0.058688 test-rmse:2.099060+0.530558
## [116] train-rmse:0.668765+0.060849 test-rmse:2.096082+0.529108
## [117] train-rmse:0.664071+0.060786 test-rmse:2.098386+0.527339
## [118] train-rmse:0.657262+0.060735 test-rmse:2.096624+0.527522
## [119] train-rmse:0.653962+0.059562 test-rmse:2.096086+0.527898
## [120] train-rmse:0.647835+0.059763 test-rmse:2.098929+0.526496
## [121] train-rmse:0.642720+0.059667 test-rmse:2.096169+0.528444
## [122] train-rmse:0.637400+0.057714 test-rmse:2.096014+0.528617
## [123] train-rmse:0.632102+0.060597 test-rmse:2.097849+0.530932
## [124] train-rmse:0.628242+0.060103 test-rmse:2.095773+0.531182
## [125] train-rmse:0.624323+0.058656 test-rmse:2.093555+0.532477
## [126] train-rmse:0.617026+0.056468 test-rmse:2.096193+0.536142
## [127] train-rmse:0.613499+0.057345 test-rmse:2.096536+0.535742
## [128] train-rmse:0.610044+0.057001 test-rmse:2.096835+0.537038
## [129] train-rmse:0.605660+0.057643 test-rmse:2.098614+0.538053
## [130] train-rmse:0.600679+0.057323 test-rmse:2.099097+0.537004
## [131] train-rmse:0.598027+0.056619 test-rmse:2.097893+0.535424
## Stopping. Best iteration:
## [125] train-rmse:0.624323+0.058656 test-rmse:2.093555+0.532477
##
## 31
## [1] train-rmse:6.953420+0.143406 test-rmse:6.976686+0.623730
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.947071+0.118375 test-rmse:6.020649+0.550811
## [3] train-rmse:5.132520+0.100589 test-rmse:5.257560+0.493455
## [4] train-rmse:4.471991+0.083341 test-rmse:4.630360+0.454070
## [5] train-rmse:3.940485+0.083899 test-rmse:4.158635+0.428202
## [6] train-rmse:3.505586+0.071107 test-rmse:3.798733+0.426947
## [7] train-rmse:3.168228+0.059062 test-rmse:3.494644+0.430373
## [8] train-rmse:2.892689+0.059381 test-rmse:3.274583+0.437298
## [9] train-rmse:2.662402+0.058940 test-rmse:3.091678+0.444964
## [10] train-rmse:2.477384+0.064022 test-rmse:2.956497+0.445585
## [11] train-rmse:2.328453+0.058677 test-rmse:2.862206+0.465633
## [12] train-rmse:2.209503+0.067080 test-rmse:2.782383+0.472118
## [13] train-rmse:2.099627+0.071770 test-rmse:2.692639+0.447910
## [14] train-rmse:2.005290+0.061265 test-rmse:2.642816+0.463544
## [15] train-rmse:1.916357+0.061125 test-rmse:2.572941+0.447453
## [16] train-rmse:1.846034+0.046955 test-rmse:2.522694+0.460052
## [17] train-rmse:1.782634+0.052619 test-rmse:2.504978+0.455025
## [18] train-rmse:1.732600+0.050063 test-rmse:2.478490+0.451854
## [19] train-rmse:1.679398+0.054707 test-rmse:2.460287+0.462151
## [20] train-rmse:1.624162+0.048107 test-rmse:2.429902+0.468840
## [21] train-rmse:1.583706+0.047517 test-rmse:2.411277+0.467635
## [22] train-rmse:1.546613+0.040457 test-rmse:2.390274+0.465691
## [23] train-rmse:1.510989+0.045746 test-rmse:2.369404+0.463249
## [24] train-rmse:1.467357+0.048284 test-rmse:2.348269+0.446433
## [25] train-rmse:1.436717+0.045793 test-rmse:2.339318+0.449406
## [26] train-rmse:1.402903+0.041093 test-rmse:2.326079+0.452910
## [27] train-rmse:1.369948+0.033546 test-rmse:2.308805+0.458324
## [28] train-rmse:1.344985+0.036234 test-rmse:2.299720+0.458883
## [29] train-rmse:1.322072+0.034291 test-rmse:2.292143+0.449302
## [30] train-rmse:1.291038+0.031406 test-rmse:2.279289+0.460308
## [31] train-rmse:1.261974+0.022191 test-rmse:2.271308+0.462985
## [32] train-rmse:1.238948+0.027119 test-rmse:2.256490+0.458646
## [33] train-rmse:1.216378+0.030920 test-rmse:2.247782+0.461723
## [34] train-rmse:1.195259+0.030339 test-rmse:2.241321+0.461273
## [35] train-rmse:1.169872+0.029658 test-rmse:2.238771+0.463516
## [36] train-rmse:1.148076+0.032566 test-rmse:2.237114+0.464525
## [37] train-rmse:1.130161+0.030839 test-rmse:2.228009+0.464182
## [38] train-rmse:1.108258+0.028627 test-rmse:2.222696+0.464714
## [39] train-rmse:1.088386+0.032496 test-rmse:2.213847+0.457586
## [40] train-rmse:1.071252+0.031882 test-rmse:2.204584+0.459086
## [41] train-rmse:1.056104+0.031983 test-rmse:2.199152+0.467102
## [42] train-rmse:1.029976+0.032638 test-rmse:2.198695+0.467951
## [43] train-rmse:1.011893+0.027074 test-rmse:2.194808+0.469329
## [44] train-rmse:0.993455+0.028940 test-rmse:2.188101+0.469464
## [45] train-rmse:0.978221+0.026568 test-rmse:2.182231+0.473627
## [46] train-rmse:0.966196+0.025530 test-rmse:2.181285+0.478264
## [47] train-rmse:0.949114+0.025467 test-rmse:2.179564+0.479595
## [48] train-rmse:0.938882+0.026331 test-rmse:2.174763+0.475687
## [49] train-rmse:0.925595+0.026511 test-rmse:2.169915+0.474418
## [50] train-rmse:0.906846+0.024467 test-rmse:2.166453+0.472640
## [51] train-rmse:0.893308+0.022358 test-rmse:2.167592+0.474078
## [52] train-rmse:0.880297+0.018915 test-rmse:2.161378+0.476912
## [53] train-rmse:0.867960+0.019841 test-rmse:2.156018+0.479147
## [54] train-rmse:0.849863+0.016925 test-rmse:2.155898+0.476083
## [55] train-rmse:0.841379+0.018500 test-rmse:2.155245+0.474534
## [56] train-rmse:0.827944+0.019857 test-rmse:2.155063+0.475545
## [57] train-rmse:0.816523+0.015538 test-rmse:2.151975+0.476843
## [58] train-rmse:0.802367+0.013729 test-rmse:2.143914+0.479522
## [59] train-rmse:0.787710+0.017338 test-rmse:2.139513+0.478838
## [60] train-rmse:0.774302+0.015623 test-rmse:2.140445+0.479510
## [61] train-rmse:0.762092+0.012781 test-rmse:2.139053+0.480024
## [62] train-rmse:0.747424+0.015607 test-rmse:2.136419+0.482479
## [63] train-rmse:0.737490+0.016415 test-rmse:2.133245+0.482442
## [64] train-rmse:0.728720+0.013397 test-rmse:2.131966+0.481960
## [65] train-rmse:0.714639+0.015216 test-rmse:2.130074+0.481677
## [66] train-rmse:0.706693+0.015783 test-rmse:2.130250+0.482400
## [67] train-rmse:0.698572+0.013143 test-rmse:2.125244+0.484131
## [68] train-rmse:0.688883+0.015816 test-rmse:2.124738+0.485357
## [69] train-rmse:0.679914+0.013448 test-rmse:2.123782+0.485078
## [70] train-rmse:0.671035+0.014358 test-rmse:2.120456+0.485590
## [71] train-rmse:0.662736+0.013434 test-rmse:2.118840+0.484443
## [72] train-rmse:0.653808+0.010916 test-rmse:2.118733+0.483157
## [73] train-rmse:0.644588+0.011621 test-rmse:2.117653+0.484269
## [74] train-rmse:0.637940+0.012107 test-rmse:2.117530+0.487463
## [75] train-rmse:0.628953+0.011773 test-rmse:2.113426+0.488360
## [76] train-rmse:0.622004+0.010996 test-rmse:2.113288+0.485683
## [77] train-rmse:0.612725+0.012944 test-rmse:2.112875+0.485736
## [78] train-rmse:0.603918+0.013794 test-rmse:2.110676+0.486125
## [79] train-rmse:0.595094+0.011946 test-rmse:2.112139+0.485688
## [80] train-rmse:0.587797+0.011996 test-rmse:2.113115+0.483331
## [81] train-rmse:0.580445+0.012278 test-rmse:2.114381+0.482883
## [82] train-rmse:0.575546+0.012552 test-rmse:2.112445+0.481917
## [83] train-rmse:0.571602+0.013124 test-rmse:2.112167+0.482722
## [84] train-rmse:0.560706+0.010725 test-rmse:2.112235+0.482940
## Stopping. Best iteration:
## [78] train-rmse:0.603918+0.013794 test-rmse:2.110676+0.486125
##
## 32
## [1] train-rmse:6.919667+0.141436 test-rmse:6.919358+0.608975
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.884742+0.114933 test-rmse:5.928558+0.542345
## [3] train-rmse:5.042087+0.103278 test-rmse:5.137363+0.484859
## [4] train-rmse:4.365350+0.093480 test-rmse:4.533268+0.438321
## [5] train-rmse:3.816176+0.076011 test-rmse:4.064793+0.426976
## [6] train-rmse:3.377665+0.070688 test-rmse:3.715258+0.411704
## [7] train-rmse:3.025002+0.059898 test-rmse:3.429907+0.409614
## [8] train-rmse:2.731257+0.049476 test-rmse:3.209269+0.433815
## [9] train-rmse:2.488367+0.066396 test-rmse:3.037619+0.445772
## [10] train-rmse:2.289638+0.069382 test-rmse:2.911497+0.474995
## [11] train-rmse:2.112738+0.059479 test-rmse:2.799055+0.495940
## [12] train-rmse:1.984640+0.057339 test-rmse:2.718941+0.496715
## [13] train-rmse:1.876640+0.060279 test-rmse:2.651459+0.484267
## [14] train-rmse:1.783111+0.058176 test-rmse:2.598274+0.474604
## [15] train-rmse:1.693796+0.057016 test-rmse:2.561226+0.479914
## [16] train-rmse:1.630726+0.055417 test-rmse:2.527513+0.487881
## [17] train-rmse:1.563642+0.060689 test-rmse:2.502839+0.496864
## [18] train-rmse:1.504881+0.062251 test-rmse:2.489784+0.498439
## [19] train-rmse:1.448410+0.068613 test-rmse:2.467069+0.494224
## [20] train-rmse:1.393221+0.065685 test-rmse:2.445006+0.495598
## [21] train-rmse:1.343874+0.053469 test-rmse:2.421174+0.503990
## [22] train-rmse:1.307779+0.050121 test-rmse:2.417783+0.498612
## [23] train-rmse:1.274521+0.053912 test-rmse:2.416256+0.511243
## [24] train-rmse:1.231706+0.058542 test-rmse:2.410120+0.506891
## [25] train-rmse:1.194473+0.053969 test-rmse:2.400492+0.516276
## [26] train-rmse:1.170414+0.054064 test-rmse:2.391773+0.518307
## [27] train-rmse:1.133802+0.058250 test-rmse:2.378408+0.514916
## [28] train-rmse:1.100087+0.059531 test-rmse:2.372772+0.517352
## [29] train-rmse:1.076202+0.053830 test-rmse:2.365284+0.517854
## [30] train-rmse:1.046741+0.049268 test-rmse:2.367314+0.523506
## [31] train-rmse:1.023014+0.055432 test-rmse:2.362105+0.523791
## [32] train-rmse:0.995333+0.058652 test-rmse:2.366730+0.530045
## [33] train-rmse:0.969486+0.055044 test-rmse:2.362706+0.531725
## [34] train-rmse:0.944265+0.056334 test-rmse:2.358366+0.535542
## [35] train-rmse:0.917869+0.046674 test-rmse:2.347514+0.533744
## [36] train-rmse:0.897927+0.046072 test-rmse:2.340683+0.539682
## [37] train-rmse:0.871840+0.044637 test-rmse:2.338063+0.535747
## [38] train-rmse:0.854569+0.047757 test-rmse:2.335218+0.527365
## [39] train-rmse:0.840208+0.044865 test-rmse:2.330830+0.529905
## [40] train-rmse:0.823028+0.045207 test-rmse:2.324853+0.531687
## [41] train-rmse:0.801307+0.043327 test-rmse:2.318257+0.536147
## [42] train-rmse:0.788880+0.042801 test-rmse:2.314189+0.535337
## [43] train-rmse:0.773031+0.040953 test-rmse:2.317660+0.536914
## [44] train-rmse:0.755281+0.039349 test-rmse:2.312350+0.534067
## [45] train-rmse:0.743730+0.039759 test-rmse:2.311084+0.534624
## [46] train-rmse:0.727259+0.046644 test-rmse:2.308754+0.535854
## [47] train-rmse:0.715035+0.048766 test-rmse:2.306452+0.535154
## [48] train-rmse:0.699171+0.046883 test-rmse:2.301883+0.533287
## [49] train-rmse:0.684164+0.042004 test-rmse:2.301440+0.536467
## [50] train-rmse:0.668635+0.043731 test-rmse:2.296522+0.536004
## [51] train-rmse:0.651416+0.045827 test-rmse:2.292039+0.537022
## [52] train-rmse:0.639363+0.046740 test-rmse:2.292288+0.536138
## [53] train-rmse:0.626063+0.045294 test-rmse:2.291925+0.537718
## [54] train-rmse:0.609482+0.044034 test-rmse:2.284863+0.541092
## [55] train-rmse:0.598191+0.045851 test-rmse:2.285780+0.538692
## [56] train-rmse:0.590277+0.044746 test-rmse:2.286031+0.540088
## [57] train-rmse:0.579911+0.041157 test-rmse:2.282092+0.540425
## [58] train-rmse:0.567113+0.043918 test-rmse:2.279918+0.540479
## [59] train-rmse:0.553841+0.042716 test-rmse:2.278650+0.542272
## [60] train-rmse:0.545199+0.043457 test-rmse:2.278697+0.542577
## [61] train-rmse:0.537940+0.045271 test-rmse:2.277353+0.545563
## [62] train-rmse:0.527902+0.044220 test-rmse:2.276691+0.544910
## [63] train-rmse:0.515758+0.046923 test-rmse:2.277014+0.546697
## [64] train-rmse:0.508751+0.044741 test-rmse:2.276816+0.544652
## [65] train-rmse:0.500605+0.043250 test-rmse:2.274198+0.545653
## [66] train-rmse:0.492587+0.043059 test-rmse:2.273026+0.546009
## [67] train-rmse:0.480851+0.041268 test-rmse:2.269261+0.548727
## [68] train-rmse:0.474546+0.041485 test-rmse:2.268569+0.549631
## [69] train-rmse:0.466562+0.042543 test-rmse:2.270345+0.549652
## [70] train-rmse:0.456500+0.042281 test-rmse:2.269945+0.548605
## [71] train-rmse:0.447734+0.039642 test-rmse:2.269005+0.550082
## [72] train-rmse:0.441905+0.040342 test-rmse:2.268970+0.550557
## [73] train-rmse:0.433397+0.040104 test-rmse:2.270945+0.550011
## [74] train-rmse:0.425160+0.037254 test-rmse:2.270872+0.550653
## Stopping. Best iteration:
## [68] train-rmse:0.474546+0.041485 test-rmse:2.268569+0.549631
##
## 33
## [1] train-rmse:6.898398+0.138297 test-rmse:6.905104+0.628567
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.842233+0.110125 test-rmse:5.906915+0.574035
## [3] train-rmse:4.979517+0.090046 test-rmse:5.121473+0.518143
## [4] train-rmse:4.279084+0.074094 test-rmse:4.511843+0.480155
## [5] train-rmse:3.714147+0.060267 test-rmse:4.046897+0.458245
## [6] train-rmse:3.262461+0.051210 test-rmse:3.683436+0.451712
## [7] train-rmse:2.885386+0.040820 test-rmse:3.370228+0.444094
## [8] train-rmse:2.595014+0.037641 test-rmse:3.178712+0.446765
## [9] train-rmse:2.345298+0.031876 test-rmse:3.012453+0.468687
## [10] train-rmse:2.147126+0.023961 test-rmse:2.887478+0.497883
## [11] train-rmse:1.975171+0.036027 test-rmse:2.789038+0.499738
## [12] train-rmse:1.823165+0.038399 test-rmse:2.709485+0.490431
## [13] train-rmse:1.700524+0.040938 test-rmse:2.639163+0.487199
## [14] train-rmse:1.600758+0.041608 test-rmse:2.593891+0.495866
## [15] train-rmse:1.517368+0.044998 test-rmse:2.557410+0.516628
## [16] train-rmse:1.424314+0.038762 test-rmse:2.523306+0.516917
## [17] train-rmse:1.359312+0.033901 test-rmse:2.490494+0.529626
## [18] train-rmse:1.292921+0.039817 test-rmse:2.472515+0.528781
## [19] train-rmse:1.233217+0.037899 test-rmse:2.451969+0.530240
## [20] train-rmse:1.176088+0.031175 test-rmse:2.438369+0.524570
## [21] train-rmse:1.133577+0.032274 test-rmse:2.421754+0.527532
## [22] train-rmse:1.092140+0.034314 test-rmse:2.404993+0.528608
## [23] train-rmse:1.056256+0.037470 test-rmse:2.391331+0.533862
## [24] train-rmse:1.020356+0.027378 test-rmse:2.384658+0.536657
## [25] train-rmse:0.982225+0.033310 test-rmse:2.384100+0.542501
## [26] train-rmse:0.952675+0.028376 test-rmse:2.384548+0.540942
## [27] train-rmse:0.919607+0.032975 test-rmse:2.373684+0.541849
## [28] train-rmse:0.890084+0.029203 test-rmse:2.365817+0.547521
## [29] train-rmse:0.862534+0.031840 test-rmse:2.357673+0.546738
## [30] train-rmse:0.830877+0.038490 test-rmse:2.352921+0.546282
## [31] train-rmse:0.804991+0.039085 test-rmse:2.345246+0.543051
## [32] train-rmse:0.789495+0.038194 test-rmse:2.339780+0.540393
## [33] train-rmse:0.762443+0.037993 test-rmse:2.338375+0.542599
## [34] train-rmse:0.737397+0.035688 test-rmse:2.336586+0.549911
## [35] train-rmse:0.717153+0.033117 test-rmse:2.332809+0.551335
## [36] train-rmse:0.694993+0.035484 test-rmse:2.329182+0.554074
## [37] train-rmse:0.680915+0.035612 test-rmse:2.325232+0.549371
## [38] train-rmse:0.666105+0.033734 test-rmse:2.320678+0.553587
## [39] train-rmse:0.648697+0.035482 test-rmse:2.317150+0.553106
## [40] train-rmse:0.628187+0.034381 test-rmse:2.313574+0.552503
## [41] train-rmse:0.610406+0.032396 test-rmse:2.308486+0.553849
## [42] train-rmse:0.590647+0.033282 test-rmse:2.303964+0.554645
## [43] train-rmse:0.578010+0.033492 test-rmse:2.302667+0.553676
## [44] train-rmse:0.562314+0.030535 test-rmse:2.298789+0.550465
## [45] train-rmse:0.546941+0.027925 test-rmse:2.296493+0.552156
## [46] train-rmse:0.531391+0.029525 test-rmse:2.296010+0.552461
## [47] train-rmse:0.519989+0.032942 test-rmse:2.295901+0.554579
## [48] train-rmse:0.509434+0.033401 test-rmse:2.295626+0.554404
## [49] train-rmse:0.494412+0.035743 test-rmse:2.292333+0.556383
## [50] train-rmse:0.479452+0.032855 test-rmse:2.292864+0.558148
## [51] train-rmse:0.470390+0.032340 test-rmse:2.291728+0.556946
## [52] train-rmse:0.461184+0.029611 test-rmse:2.287774+0.558623
## [53] train-rmse:0.446668+0.032662 test-rmse:2.285989+0.559186
## [54] train-rmse:0.436267+0.029511 test-rmse:2.286393+0.557772
## [55] train-rmse:0.427939+0.030745 test-rmse:2.285778+0.559003
## [56] train-rmse:0.419310+0.031184 test-rmse:2.286687+0.558382
## [57] train-rmse:0.411737+0.028552 test-rmse:2.286953+0.558299
## [58] train-rmse:0.402561+0.026196 test-rmse:2.285491+0.559382
## [59] train-rmse:0.390554+0.021272 test-rmse:2.284371+0.558507
## [60] train-rmse:0.382216+0.021253 test-rmse:2.281567+0.558697
## [61] train-rmse:0.373697+0.021203 test-rmse:2.279669+0.562315
## [62] train-rmse:0.365695+0.021851 test-rmse:2.279656+0.563019
## [63] train-rmse:0.357983+0.019793 test-rmse:2.279349+0.563607
## [64] train-rmse:0.347820+0.017650 test-rmse:2.277701+0.562921
## [65] train-rmse:0.337654+0.016780 test-rmse:2.278496+0.564704
## [66] train-rmse:0.329578+0.013913 test-rmse:2.277038+0.564058
## [67] train-rmse:0.322643+0.013178 test-rmse:2.277624+0.563964
## [68] train-rmse:0.316849+0.014237 test-rmse:2.275825+0.564241
## [69] train-rmse:0.310287+0.015407 test-rmse:2.275808+0.564619
## [70] train-rmse:0.302731+0.015966 test-rmse:2.273595+0.565221
## [71] train-rmse:0.295768+0.015196 test-rmse:2.273914+0.565542
## [72] train-rmse:0.289834+0.014760 test-rmse:2.274295+0.566020
## [73] train-rmse:0.283446+0.015293 test-rmse:2.273037+0.564947
## [74] train-rmse:0.275490+0.016713 test-rmse:2.272072+0.565414
## [75] train-rmse:0.269378+0.016528 test-rmse:2.271357+0.565672
## [76] train-rmse:0.262570+0.014830 test-rmse:2.270863+0.565242
## [77] train-rmse:0.257766+0.014246 test-rmse:2.270138+0.565391
## [78] train-rmse:0.253218+0.013127 test-rmse:2.269928+0.564884
## [79] train-rmse:0.248759+0.013469 test-rmse:2.269144+0.564604
## [80] train-rmse:0.244377+0.013786 test-rmse:2.268779+0.564708
## [81] train-rmse:0.239183+0.011351 test-rmse:2.268783+0.563616
## [82] train-rmse:0.235109+0.011649 test-rmse:2.268845+0.563694
## [83] train-rmse:0.231489+0.010965 test-rmse:2.269409+0.564814
## [84] train-rmse:0.226825+0.009971 test-rmse:2.270666+0.564547
## [85] train-rmse:0.221043+0.011921 test-rmse:2.270165+0.564132
## [86] train-rmse:0.216651+0.011925 test-rmse:2.269663+0.564255
## Stopping. Best iteration:
## [80] train-rmse:0.244377+0.013786 test-rmse:2.268779+0.564708
##
## 34
## [1] train-rmse:6.890859+0.137669 test-rmse:6.901004+0.628164
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.823396+0.105995 test-rmse:5.907981+0.563901
## [3] train-rmse:4.946100+0.083952 test-rmse:5.098408+0.533450
## [4] train-rmse:4.230242+0.066896 test-rmse:4.478139+0.491980
## [5] train-rmse:3.654618+0.053095 test-rmse:3.998118+0.472720
## [6] train-rmse:3.180511+0.046763 test-rmse:3.616437+0.456734
## [7] train-rmse:2.797419+0.043084 test-rmse:3.342851+0.464842
## [8] train-rmse:2.489957+0.036070 test-rmse:3.132930+0.477684
## [9] train-rmse:2.230510+0.029504 test-rmse:2.983506+0.481250
## [10] train-rmse:2.017286+0.023504 test-rmse:2.851487+0.497475
## [11] train-rmse:1.844934+0.016807 test-rmse:2.741228+0.519595
## [12] train-rmse:1.698699+0.019631 test-rmse:2.663423+0.528696
## [13] train-rmse:1.573257+0.020835 test-rmse:2.611666+0.526529
## [14] train-rmse:1.461724+0.017472 test-rmse:2.550229+0.530877
## [15] train-rmse:1.375204+0.020668 test-rmse:2.509807+0.538617
## [16] train-rmse:1.283393+0.020719 test-rmse:2.474870+0.535389
## [17] train-rmse:1.206757+0.032129 test-rmse:2.443543+0.539779
## [18] train-rmse:1.144042+0.028416 test-rmse:2.420079+0.540182
## [19] train-rmse:1.083838+0.030404 test-rmse:2.397732+0.551190
## [20] train-rmse:1.030021+0.030431 test-rmse:2.388909+0.550506
## [21] train-rmse:0.988949+0.027939 test-rmse:2.376829+0.552105
## [22] train-rmse:0.949220+0.029677 test-rmse:2.370330+0.558204
## [23] train-rmse:0.911285+0.021844 test-rmse:2.361523+0.563000
## [24] train-rmse:0.869744+0.024094 test-rmse:2.352738+0.565814
## [25] train-rmse:0.837034+0.030145 test-rmse:2.346367+0.569692
## [26] train-rmse:0.797286+0.026431 test-rmse:2.339040+0.571216
## [27] train-rmse:0.770638+0.027377 test-rmse:2.329374+0.576354
## [28] train-rmse:0.738796+0.024132 test-rmse:2.323403+0.575871
## [29] train-rmse:0.709628+0.026032 test-rmse:2.315322+0.579632
## [30] train-rmse:0.680565+0.024231 test-rmse:2.310521+0.580850
## [31] train-rmse:0.658490+0.022286 test-rmse:2.304516+0.582465
## [32] train-rmse:0.633886+0.020901 test-rmse:2.302007+0.584143
## [33] train-rmse:0.610632+0.026262 test-rmse:2.298664+0.585706
## [34] train-rmse:0.590134+0.024130 test-rmse:2.297330+0.586406
## [35] train-rmse:0.568092+0.021564 test-rmse:2.299866+0.592312
## [36] train-rmse:0.551777+0.016942 test-rmse:2.293014+0.598944
## [37] train-rmse:0.530504+0.017580 test-rmse:2.288709+0.598150
## [38] train-rmse:0.511914+0.017919 test-rmse:2.289418+0.598830
## [39] train-rmse:0.496306+0.016061 test-rmse:2.285254+0.596129
## [40] train-rmse:0.481013+0.012854 test-rmse:2.282711+0.597430
## [41] train-rmse:0.472358+0.012334 test-rmse:2.280205+0.599617
## [42] train-rmse:0.454313+0.014486 test-rmse:2.281242+0.600452
## [43] train-rmse:0.441082+0.016613 test-rmse:2.281390+0.597359
## [44] train-rmse:0.429442+0.016958 test-rmse:2.279221+0.596547
## [45] train-rmse:0.414594+0.012358 test-rmse:2.276870+0.595452
## [46] train-rmse:0.401926+0.015397 test-rmse:2.276171+0.595933
## [47] train-rmse:0.380817+0.014512 test-rmse:2.275724+0.596565
## [48] train-rmse:0.373228+0.014477 test-rmse:2.273860+0.597165
## [49] train-rmse:0.361346+0.017038 test-rmse:2.272384+0.597145
## [50] train-rmse:0.351193+0.015669 test-rmse:2.273079+0.599489
## [51] train-rmse:0.336768+0.016728 test-rmse:2.273655+0.599224
## [52] train-rmse:0.327635+0.016501 test-rmse:2.271657+0.599682
## [53] train-rmse:0.318856+0.014697 test-rmse:2.271609+0.599998
## [54] train-rmse:0.309639+0.014436 test-rmse:2.269903+0.600689
## [55] train-rmse:0.299305+0.016661 test-rmse:2.270211+0.598770
## [56] train-rmse:0.292018+0.017184 test-rmse:2.269445+0.598727
## [57] train-rmse:0.284564+0.016202 test-rmse:2.268865+0.598772
## [58] train-rmse:0.275810+0.019002 test-rmse:2.265824+0.600044
## [59] train-rmse:0.266206+0.016400 test-rmse:2.266213+0.600306
## [60] train-rmse:0.257517+0.015525 test-rmse:2.265827+0.600438
## [61] train-rmse:0.250050+0.016123 test-rmse:2.264936+0.600994
## [62] train-rmse:0.244015+0.016553 test-rmse:2.264038+0.601130
## [63] train-rmse:0.236053+0.016564 test-rmse:2.262783+0.600728
## [64] train-rmse:0.228512+0.016784 test-rmse:2.263761+0.600177
## [65] train-rmse:0.222223+0.015067 test-rmse:2.262838+0.599660
## [66] train-rmse:0.218449+0.014239 test-rmse:2.262940+0.600719
## [67] train-rmse:0.213580+0.014190 test-rmse:2.262991+0.600848
## [68] train-rmse:0.205801+0.012598 test-rmse:2.262689+0.602990
## [69] train-rmse:0.201377+0.011728 test-rmse:2.262464+0.602222
## [70] train-rmse:0.195671+0.012178 test-rmse:2.262301+0.602651
## [71] train-rmse:0.190205+0.012143 test-rmse:2.261875+0.602867
## [72] train-rmse:0.184337+0.012298 test-rmse:2.261770+0.602162
## [73] train-rmse:0.178947+0.011651 test-rmse:2.261591+0.602758
## [74] train-rmse:0.173452+0.013469 test-rmse:2.261176+0.603535
## [75] train-rmse:0.169929+0.013045 test-rmse:2.260166+0.604855
## [76] train-rmse:0.164855+0.012343 test-rmse:2.259632+0.605429
## [77] train-rmse:0.160592+0.011599 test-rmse:2.259375+0.605763
## [78] train-rmse:0.155313+0.012982 test-rmse:2.259131+0.605620
## [79] train-rmse:0.151690+0.013464 test-rmse:2.258728+0.605211
## [80] train-rmse:0.146272+0.011636 test-rmse:2.257770+0.605150
## [81] train-rmse:0.141824+0.010811 test-rmse:2.258127+0.605736
## [82] train-rmse:0.139595+0.010905 test-rmse:2.258331+0.605457
## [83] train-rmse:0.136702+0.010528 test-rmse:2.258432+0.605070
## [84] train-rmse:0.133147+0.011314 test-rmse:2.259114+0.604804
## [85] train-rmse:0.129329+0.008940 test-rmse:2.258296+0.605028
## [86] train-rmse:0.126932+0.009319 test-rmse:2.258061+0.604942
## Stopping. Best iteration:
## [80] train-rmse:0.146272+0.011636 test-rmse:2.257770+0.605150
##
## 35
## [1] train-rmse:7.020857+0.151224 test-rmse:7.011322+0.667298
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.133026+0.130870 test-rmse:6.163142+0.700124
## [3] train-rmse:5.467186+0.120456 test-rmse:5.520561+0.636339
## [4] train-rmse:4.967329+0.117357 test-rmse:5.011418+0.620207
## [5] train-rmse:4.599764+0.112494 test-rmse:4.687296+0.600426
## [6] train-rmse:4.324923+0.108772 test-rmse:4.408095+0.556098
## [7] train-rmse:4.117441+0.111234 test-rmse:4.228306+0.511353
## [8] train-rmse:3.955229+0.113652 test-rmse:4.067093+0.503782
## [9] train-rmse:3.829867+0.113487 test-rmse:3.924159+0.472808
## [10] train-rmse:3.726972+0.113553 test-rmse:3.856724+0.477603
## [11] train-rmse:3.641656+0.116722 test-rmse:3.763231+0.440414
## [12] train-rmse:3.566801+0.119760 test-rmse:3.696251+0.415112
## [13] train-rmse:3.500866+0.120120 test-rmse:3.627931+0.439138
## [14] train-rmse:3.443704+0.120283 test-rmse:3.573330+0.435819
## [15] train-rmse:3.391068+0.119896 test-rmse:3.531261+0.426981
## [16] train-rmse:3.341029+0.120828 test-rmse:3.457412+0.420710
## [17] train-rmse:3.295408+0.123079 test-rmse:3.445521+0.415164
## [18] train-rmse:3.252170+0.123234 test-rmse:3.402098+0.429884
## [19] train-rmse:3.214103+0.123009 test-rmse:3.336580+0.408590
## [20] train-rmse:3.176821+0.122960 test-rmse:3.328264+0.431891
## [21] train-rmse:3.141424+0.124013 test-rmse:3.286595+0.428893
## [22] train-rmse:3.108438+0.126302 test-rmse:3.246028+0.425259
## [23] train-rmse:3.075532+0.128596 test-rmse:3.225919+0.427892
## [24] train-rmse:3.046779+0.127847 test-rmse:3.187237+0.429496
## [25] train-rmse:3.019111+0.127198 test-rmse:3.179711+0.425775
## [26] train-rmse:2.992891+0.127336 test-rmse:3.157303+0.426206
## [27] train-rmse:2.967438+0.128049 test-rmse:3.128395+0.418899
## [28] train-rmse:2.942233+0.129276 test-rmse:3.108854+0.421341
## [29] train-rmse:2.918042+0.130827 test-rmse:3.079860+0.437477
## [30] train-rmse:2.895120+0.131830 test-rmse:3.071542+0.442452
## [31] train-rmse:2.873144+0.132617 test-rmse:3.043920+0.439687
## [32] train-rmse:2.851580+0.132971 test-rmse:3.039176+0.442009
## [33] train-rmse:2.830810+0.133677 test-rmse:3.020748+0.442108
## [34] train-rmse:2.811542+0.134019 test-rmse:3.005151+0.447258
## [35] train-rmse:2.792372+0.134459 test-rmse:2.998262+0.465856
## [36] train-rmse:2.773955+0.134937 test-rmse:2.977794+0.458431
## [37] train-rmse:2.755834+0.135547 test-rmse:2.971237+0.455461
## [38] train-rmse:2.737921+0.136088 test-rmse:2.950778+0.445793
## [39] train-rmse:2.720219+0.136974 test-rmse:2.926555+0.457414
## [40] train-rmse:2.703339+0.137710 test-rmse:2.921053+0.454964
## [41] train-rmse:2.686917+0.138298 test-rmse:2.900851+0.464015
## [42] train-rmse:2.670882+0.138306 test-rmse:2.886082+0.460676
## [43] train-rmse:2.655725+0.138240 test-rmse:2.871013+0.461374
## [44] train-rmse:2.641074+0.138371 test-rmse:2.856911+0.456716
## [45] train-rmse:2.626585+0.138431 test-rmse:2.854210+0.464772
## [46] train-rmse:2.612589+0.138428 test-rmse:2.837901+0.465008
## [47] train-rmse:2.598750+0.138567 test-rmse:2.828332+0.476802
## [48] train-rmse:2.584879+0.138818 test-rmse:2.822529+0.479215
## [49] train-rmse:2.571653+0.138899 test-rmse:2.807358+0.488307
## [50] train-rmse:2.558579+0.139087 test-rmse:2.795778+0.486037
## [51] train-rmse:2.545852+0.139414 test-rmse:2.793900+0.488247
## [52] train-rmse:2.533192+0.139625 test-rmse:2.776682+0.489273
## [53] train-rmse:2.520941+0.139740 test-rmse:2.765112+0.489573
## [54] train-rmse:2.509227+0.139599 test-rmse:2.753281+0.478707
## [55] train-rmse:2.498013+0.139849 test-rmse:2.736242+0.474753
## [56] train-rmse:2.486879+0.140092 test-rmse:2.724830+0.480989
## [57] train-rmse:2.476183+0.140224 test-rmse:2.726432+0.485590
## [58] train-rmse:2.465751+0.140047 test-rmse:2.719341+0.482487
## [59] train-rmse:2.455595+0.139811 test-rmse:2.706976+0.475706
## [60] train-rmse:2.445627+0.139928 test-rmse:2.700701+0.482776
## [61] train-rmse:2.435818+0.140075 test-rmse:2.688710+0.486889
## [62] train-rmse:2.426269+0.140391 test-rmse:2.679790+0.489806
## [63] train-rmse:2.416881+0.140663 test-rmse:2.669032+0.486453
## [64] train-rmse:2.407578+0.140899 test-rmse:2.665265+0.493216
## [65] train-rmse:2.398672+0.141013 test-rmse:2.659924+0.499009
## [66] train-rmse:2.389871+0.141090 test-rmse:2.652664+0.497525
## [67] train-rmse:2.381193+0.141253 test-rmse:2.655652+0.499701
## [68] train-rmse:2.372663+0.141481 test-rmse:2.638973+0.494004
## [69] train-rmse:2.364408+0.141629 test-rmse:2.639662+0.494724
## [70] train-rmse:2.356413+0.141762 test-rmse:2.628252+0.498124
## [71] train-rmse:2.348736+0.141947 test-rmse:2.618702+0.496638
## [72] train-rmse:2.341204+0.142188 test-rmse:2.606651+0.493912
## [73] train-rmse:2.333702+0.142387 test-rmse:2.608331+0.497456
## [74] train-rmse:2.326236+0.142610 test-rmse:2.596271+0.494147
## [75] train-rmse:2.319072+0.142833 test-rmse:2.592472+0.491653
## [76] train-rmse:2.312037+0.143005 test-rmse:2.583052+0.491960
## [77] train-rmse:2.305074+0.143280 test-rmse:2.576193+0.494089
## [78] train-rmse:2.298232+0.143522 test-rmse:2.571313+0.494308
## [79] train-rmse:2.291604+0.143787 test-rmse:2.565256+0.491835
## [80] train-rmse:2.285024+0.144022 test-rmse:2.560518+0.492742
## [81] train-rmse:2.278625+0.144250 test-rmse:2.559965+0.497697
## [82] train-rmse:2.272325+0.144508 test-rmse:2.559037+0.500545
## [83] train-rmse:2.266099+0.144747 test-rmse:2.555572+0.496778
## [84] train-rmse:2.259916+0.144988 test-rmse:2.547512+0.498361
## [85] train-rmse:2.254022+0.145165 test-rmse:2.542008+0.501968
## [86] train-rmse:2.248212+0.145353 test-rmse:2.525369+0.501927
## [87] train-rmse:2.242599+0.145573 test-rmse:2.521887+0.508750
## [88] train-rmse:2.237136+0.145819 test-rmse:2.521329+0.521495
## [89] train-rmse:2.231702+0.146054 test-rmse:2.515556+0.520537
## [90] train-rmse:2.226326+0.146290 test-rmse:2.508148+0.516096
## [91] train-rmse:2.221096+0.146434 test-rmse:2.506590+0.515685
## [92] train-rmse:2.215969+0.146474 test-rmse:2.505071+0.510840
## [93] train-rmse:2.210974+0.146572 test-rmse:2.501949+0.511216
## [94] train-rmse:2.205977+0.146715 test-rmse:2.499386+0.511441
## [95] train-rmse:2.201103+0.146927 test-rmse:2.496905+0.511818
## [96] train-rmse:2.196367+0.147089 test-rmse:2.491203+0.513648
## [97] train-rmse:2.191674+0.147251 test-rmse:2.485256+0.511291
## [98] train-rmse:2.187059+0.147429 test-rmse:2.483830+0.509756
## [99] train-rmse:2.182575+0.147647 test-rmse:2.483913+0.511272
## [100] train-rmse:2.178131+0.147909 test-rmse:2.479997+0.509573
## [101] train-rmse:2.173785+0.148128 test-rmse:2.470002+0.507448
## [102] train-rmse:2.169450+0.148273 test-rmse:2.466489+0.504768
## [103] train-rmse:2.165192+0.148463 test-rmse:2.463834+0.504916
## [104] train-rmse:2.160818+0.148613 test-rmse:2.467371+0.505415
## [105] train-rmse:2.156725+0.148757 test-rmse:2.463289+0.508423
## [106] train-rmse:2.152656+0.148891 test-rmse:2.458879+0.511684
## [107] train-rmse:2.148739+0.149019 test-rmse:2.458795+0.514330
## [108] train-rmse:2.144874+0.149154 test-rmse:2.453149+0.520267
## [109] train-rmse:2.140995+0.149327 test-rmse:2.447521+0.522846
## [110] train-rmse:2.137156+0.149452 test-rmse:2.449210+0.532022
## [111] train-rmse:2.133472+0.149541 test-rmse:2.444691+0.525474
## [112] train-rmse:2.129853+0.149669 test-rmse:2.442249+0.524142
## [113] train-rmse:2.126246+0.149766 test-rmse:2.434719+0.524330
## [114] train-rmse:2.122697+0.149917 test-rmse:2.430685+0.522764
## [115] train-rmse:2.119210+0.150081 test-rmse:2.427342+0.521615
## [116] train-rmse:2.115781+0.150216 test-rmse:2.424525+0.520490
## [117] train-rmse:2.112380+0.150325 test-rmse:2.423313+0.521273
## [118] train-rmse:2.108953+0.150476 test-rmse:2.422517+0.516549
## [119] train-rmse:2.105677+0.150671 test-rmse:2.420423+0.515395
## [120] train-rmse:2.102398+0.150870 test-rmse:2.416531+0.514400
## [121] train-rmse:2.099180+0.151084 test-rmse:2.414416+0.516497
## [122] train-rmse:2.095931+0.151305 test-rmse:2.413859+0.515465
## [123] train-rmse:2.092699+0.151551 test-rmse:2.415142+0.518183
## [124] train-rmse:2.089620+0.151842 test-rmse:2.417587+0.518068
## [125] train-rmse:2.086565+0.151936 test-rmse:2.412923+0.517969
## [126] train-rmse:2.083606+0.152129 test-rmse:2.415516+0.516895
## [127] train-rmse:2.080646+0.152285 test-rmse:2.414026+0.516319
## [128] train-rmse:2.077733+0.152407 test-rmse:2.406086+0.519584
## [129] train-rmse:2.074980+0.152500 test-rmse:2.406851+0.521420
## [130] train-rmse:2.072238+0.152639 test-rmse:2.403601+0.523102
## [131] train-rmse:2.069519+0.152775 test-rmse:2.402458+0.524990
## [132] train-rmse:2.066787+0.152869 test-rmse:2.403949+0.522883
## [133] train-rmse:2.064115+0.152952 test-rmse:2.401448+0.533114
## [134] train-rmse:2.061528+0.153016 test-rmse:2.402942+0.529777
## [135] train-rmse:2.058834+0.153060 test-rmse:2.398523+0.528256
## [136] train-rmse:2.056233+0.153109 test-rmse:2.398027+0.527354
## [137] train-rmse:2.053633+0.153204 test-rmse:2.393847+0.526627
## [138] train-rmse:2.051063+0.153345 test-rmse:2.388441+0.526304
## [139] train-rmse:2.048539+0.153456 test-rmse:2.387279+0.526589
## [140] train-rmse:2.046014+0.153694 test-rmse:2.388353+0.527490
## [141] train-rmse:2.043577+0.153856 test-rmse:2.383595+0.527727
## [142] train-rmse:2.041164+0.154017 test-rmse:2.381066+0.528620
## [143] train-rmse:2.038784+0.154151 test-rmse:2.380500+0.527006
## [144] train-rmse:2.036412+0.154258 test-rmse:2.380707+0.524190
## [145] train-rmse:2.034086+0.154383 test-rmse:2.382855+0.525310
## [146] train-rmse:2.031708+0.154510 test-rmse:2.381313+0.523398
## [147] train-rmse:2.029437+0.154596 test-rmse:2.377989+0.521358
## [148] train-rmse:2.027164+0.154667 test-rmse:2.377713+0.523390
## [149] train-rmse:2.024944+0.154760 test-rmse:2.380080+0.526565
## [150] train-rmse:2.022743+0.154846 test-rmse:2.378791+0.524722
## [151] train-rmse:2.020549+0.154917 test-rmse:2.373761+0.525836
## [152] train-rmse:2.018370+0.155006 test-rmse:2.371462+0.527314
## [153] train-rmse:2.016199+0.155087 test-rmse:2.370762+0.525444
## [154] train-rmse:2.014107+0.155174 test-rmse:2.370955+0.524255
## [155] train-rmse:2.012066+0.155269 test-rmse:2.366714+0.525832
## [156] train-rmse:2.010023+0.155362 test-rmse:2.366091+0.526830
## [157] train-rmse:2.007958+0.155413 test-rmse:2.367146+0.525370
## [158] train-rmse:2.005879+0.155480 test-rmse:2.364436+0.526104
## [159] train-rmse:2.003824+0.155545 test-rmse:2.361291+0.524049
## [160] train-rmse:2.001776+0.155635 test-rmse:2.362336+0.528869
## [161] train-rmse:1.999808+0.155720 test-rmse:2.361522+0.528155
## [162] train-rmse:1.997834+0.155888 test-rmse:2.360490+0.528503
## [163] train-rmse:1.995891+0.155981 test-rmse:2.360227+0.527082
## [164] train-rmse:1.993944+0.156203 test-rmse:2.359318+0.529094
## [165] train-rmse:1.992013+0.156315 test-rmse:2.355058+0.528883
## [166] train-rmse:1.990086+0.156395 test-rmse:2.355479+0.531203
## [167] train-rmse:1.988190+0.156496 test-rmse:2.355700+0.531603
## [168] train-rmse:1.986364+0.156577 test-rmse:2.354811+0.534733
## [169] train-rmse:1.984567+0.156696 test-rmse:2.353075+0.534619
## [170] train-rmse:1.982740+0.156802 test-rmse:2.349891+0.536204
## [171] train-rmse:1.980942+0.156927 test-rmse:2.348468+0.535927
## [172] train-rmse:1.979123+0.157019 test-rmse:2.345923+0.532890
## [173] train-rmse:1.977309+0.157149 test-rmse:2.347406+0.529356
## [174] train-rmse:1.975521+0.157252 test-rmse:2.347165+0.528543
## [175] train-rmse:1.973758+0.157376 test-rmse:2.348810+0.534322
## [176] train-rmse:1.972048+0.157502 test-rmse:2.347994+0.535805
## [177] train-rmse:1.970275+0.157571 test-rmse:2.345786+0.537026
## [178] train-rmse:1.968585+0.157688 test-rmse:2.340637+0.538317
## [179] train-rmse:1.966871+0.157781 test-rmse:2.339436+0.535071
## [180] train-rmse:1.965097+0.157910 test-rmse:2.339688+0.535998
## [181] train-rmse:1.963429+0.158043 test-rmse:2.338828+0.534407
## [182] train-rmse:1.961795+0.158157 test-rmse:2.336076+0.533151
## [183] train-rmse:1.960176+0.158236 test-rmse:2.338860+0.533141
## [184] train-rmse:1.958556+0.158299 test-rmse:2.340356+0.531334
## [185] train-rmse:1.956942+0.158377 test-rmse:2.338940+0.530650
## [186] train-rmse:1.955335+0.158452 test-rmse:2.336943+0.533177
## [187] train-rmse:1.953766+0.158532 test-rmse:2.335884+0.536629
## [188] train-rmse:1.952214+0.158609 test-rmse:2.333962+0.536070
## [189] train-rmse:1.950630+0.158675 test-rmse:2.334178+0.535876
## [190] train-rmse:1.949058+0.158731 test-rmse:2.335226+0.536310
## [191] train-rmse:1.947415+0.158978 test-rmse:2.333988+0.536476
## [192] train-rmse:1.945876+0.159055 test-rmse:2.330258+0.537619
## [193] train-rmse:1.944293+0.159082 test-rmse:2.332427+0.537416
## [194] train-rmse:1.942784+0.159173 test-rmse:2.332998+0.536321
## [195] train-rmse:1.941312+0.159270 test-rmse:2.332104+0.534513
## [196] train-rmse:1.939859+0.159354 test-rmse:2.332857+0.536478
## [197] train-rmse:1.938401+0.159436 test-rmse:2.331459+0.536515
## [198] train-rmse:1.936919+0.159557 test-rmse:2.332298+0.535629
## Stopping. Best iteration:
## [192] train-rmse:1.945876+0.159055 test-rmse:2.330258+0.537619
##
## 36
## [1] train-rmse:6.932368+0.144678 test-rmse:6.948900+0.638384
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.960930+0.132064 test-rmse:6.009314+0.603108
## [3] train-rmse:5.217190+0.120717 test-rmse:5.333999+0.597041
## [4] train-rmse:4.632193+0.107249 test-rmse:4.761541+0.523701
## [5] train-rmse:4.172349+0.102847 test-rmse:4.350363+0.510949
## [6] train-rmse:3.820777+0.100616 test-rmse:3.987368+0.472047
## [7] train-rmse:3.541376+0.104645 test-rmse:3.761096+0.438442
## [8] train-rmse:3.315102+0.093705 test-rmse:3.544378+0.437818
## [9] train-rmse:3.134909+0.094960 test-rmse:3.358410+0.426933
## [10] train-rmse:2.994146+0.093114 test-rmse:3.259494+0.432694
## [11] train-rmse:2.873252+0.105784 test-rmse:3.165052+0.412729
## [12] train-rmse:2.772546+0.110619 test-rmse:3.083968+0.417230
## [13] train-rmse:2.688733+0.102054 test-rmse:3.019434+0.439719
## [14] train-rmse:2.614284+0.101320 test-rmse:2.960815+0.440843
## [15] train-rmse:2.557544+0.103587 test-rmse:2.931617+0.434901
## [16] train-rmse:2.501668+0.104218 test-rmse:2.905082+0.422913
## [17] train-rmse:2.443318+0.096359 test-rmse:2.858084+0.450914
## [18] train-rmse:2.399498+0.097629 test-rmse:2.822579+0.445394
## [19] train-rmse:2.350989+0.099720 test-rmse:2.782550+0.455621
## [20] train-rmse:2.300617+0.096117 test-rmse:2.765617+0.471741
## [21] train-rmse:2.257967+0.080737 test-rmse:2.724085+0.487521
## [22] train-rmse:2.221718+0.080336 test-rmse:2.718665+0.483453
## [23] train-rmse:2.183713+0.084979 test-rmse:2.692612+0.484784
## [24] train-rmse:2.146676+0.086994 test-rmse:2.679839+0.478677
## [25] train-rmse:2.112171+0.088351 test-rmse:2.658507+0.477091
## [26] train-rmse:2.079940+0.088733 test-rmse:2.618220+0.474514
## [27] train-rmse:2.049980+0.084308 test-rmse:2.599014+0.481244
## [28] train-rmse:2.024194+0.084475 test-rmse:2.575188+0.484927
## [29] train-rmse:1.993286+0.090416 test-rmse:2.554229+0.459441
## [30] train-rmse:1.969235+0.092466 test-rmse:2.539198+0.452194
## [31] train-rmse:1.943258+0.092581 test-rmse:2.529917+0.460876
## [32] train-rmse:1.918618+0.086182 test-rmse:2.508788+0.475919
## [33] train-rmse:1.897261+0.085511 test-rmse:2.495852+0.475380
## [34] train-rmse:1.876872+0.086135 test-rmse:2.477607+0.474169
## [35] train-rmse:1.852989+0.086534 test-rmse:2.465972+0.474735
## [36] train-rmse:1.832367+0.087249 test-rmse:2.452559+0.478302
## [37] train-rmse:1.814393+0.087592 test-rmse:2.439940+0.480655
## [38] train-rmse:1.796205+0.084936 test-rmse:2.423716+0.474868
## [39] train-rmse:1.774511+0.084898 test-rmse:2.416745+0.471650
## [40] train-rmse:1.753984+0.085647 test-rmse:2.404946+0.478217
## [41] train-rmse:1.739172+0.085534 test-rmse:2.393415+0.474226
## [42] train-rmse:1.719766+0.076087 test-rmse:2.382278+0.482949
## [43] train-rmse:1.703880+0.075767 test-rmse:2.369026+0.490223
## [44] train-rmse:1.688352+0.075823 test-rmse:2.371133+0.490340
## [45] train-rmse:1.674993+0.075124 test-rmse:2.364192+0.487659
## [46] train-rmse:1.662735+0.075391 test-rmse:2.358972+0.491236
## [47] train-rmse:1.646929+0.070680 test-rmse:2.354664+0.487409
## [48] train-rmse:1.631974+0.071570 test-rmse:2.343816+0.483900
## [49] train-rmse:1.618673+0.073604 test-rmse:2.329918+0.477203
## [50] train-rmse:1.604996+0.076603 test-rmse:2.322255+0.474303
## [51] train-rmse:1.594216+0.076819 test-rmse:2.311492+0.476949
## [52] train-rmse:1.583071+0.076832 test-rmse:2.306387+0.474344
## [53] train-rmse:1.572596+0.077661 test-rmse:2.297399+0.482079
## [54] train-rmse:1.558507+0.075413 test-rmse:2.291613+0.488037
## [55] train-rmse:1.546594+0.077897 test-rmse:2.286965+0.487815
## [56] train-rmse:1.533224+0.077140 test-rmse:2.283836+0.486460
## [57] train-rmse:1.523463+0.078654 test-rmse:2.279273+0.487383
## [58] train-rmse:1.514777+0.078985 test-rmse:2.275328+0.490189
## [59] train-rmse:1.504078+0.079716 test-rmse:2.267245+0.494605
## [60] train-rmse:1.495642+0.080045 test-rmse:2.262791+0.493932
## [61] train-rmse:1.485486+0.077693 test-rmse:2.261814+0.490007
## [62] train-rmse:1.475526+0.078345 test-rmse:2.255104+0.492607
## [63] train-rmse:1.465624+0.080095 test-rmse:2.249943+0.491045
## [64] train-rmse:1.456883+0.081637 test-rmse:2.244923+0.491300
## [65] train-rmse:1.447754+0.081002 test-rmse:2.235853+0.486071
## [66] train-rmse:1.438205+0.084597 test-rmse:2.228555+0.484824
## [67] train-rmse:1.426062+0.085165 test-rmse:2.230783+0.487990
## [68] train-rmse:1.417445+0.085621 test-rmse:2.224017+0.489033
## [69] train-rmse:1.409484+0.087160 test-rmse:2.221438+0.486635
## [70] train-rmse:1.401369+0.086157 test-rmse:2.217621+0.488227
## [71] train-rmse:1.394841+0.085994 test-rmse:2.211330+0.491837
## [72] train-rmse:1.388105+0.085991 test-rmse:2.211572+0.492345
## [73] train-rmse:1.379040+0.086613 test-rmse:2.204750+0.495232
## [74] train-rmse:1.369356+0.084658 test-rmse:2.209238+0.498602
## [75] train-rmse:1.362826+0.083562 test-rmse:2.204709+0.496283
## [76] train-rmse:1.353906+0.084797 test-rmse:2.203435+0.499806
## [77] train-rmse:1.345995+0.088389 test-rmse:2.203698+0.504078
## [78] train-rmse:1.340085+0.089436 test-rmse:2.198607+0.504425
## [79] train-rmse:1.333927+0.090153 test-rmse:2.194902+0.502295
## [80] train-rmse:1.325417+0.089340 test-rmse:2.193418+0.500386
## [81] train-rmse:1.318534+0.089209 test-rmse:2.195257+0.507729
## [82] train-rmse:1.304860+0.081703 test-rmse:2.188145+0.507938
## [83] train-rmse:1.296955+0.082084 test-rmse:2.185450+0.506828
## [84] train-rmse:1.291012+0.082306 test-rmse:2.189875+0.509275
## [85] train-rmse:1.285424+0.082810 test-rmse:2.184789+0.509571
## [86] train-rmse:1.274281+0.084543 test-rmse:2.181022+0.512912
## [87] train-rmse:1.267525+0.084919 test-rmse:2.175475+0.515342
## [88] train-rmse:1.261861+0.085175 test-rmse:2.177839+0.518486
## [89] train-rmse:1.256871+0.085227 test-rmse:2.176884+0.521333
## [90] train-rmse:1.241579+0.076628 test-rmse:2.174904+0.523918
## [91] train-rmse:1.235705+0.076812 test-rmse:2.174457+0.523676
## [92] train-rmse:1.229613+0.076142 test-rmse:2.169570+0.529301
## [93] train-rmse:1.223546+0.078652 test-rmse:2.166143+0.526765
## [94] train-rmse:1.219323+0.078717 test-rmse:2.170284+0.531505
## [95] train-rmse:1.214490+0.079405 test-rmse:2.165263+0.531329
## [96] train-rmse:1.210113+0.079244 test-rmse:2.166867+0.532194
## [97] train-rmse:1.202248+0.082209 test-rmse:2.165016+0.531605
## [98] train-rmse:1.194936+0.082938 test-rmse:2.156735+0.532155
## [99] train-rmse:1.186828+0.084103 test-rmse:2.153170+0.530948
## [100] train-rmse:1.180448+0.084422 test-rmse:2.159408+0.531872
## [101] train-rmse:1.175184+0.083963 test-rmse:2.157500+0.533123
## [102] train-rmse:1.168124+0.085059 test-rmse:2.154980+0.534581
## [103] train-rmse:1.163901+0.085376 test-rmse:2.152431+0.535148
## [104] train-rmse:1.158269+0.084566 test-rmse:2.150454+0.535971
## [105] train-rmse:1.153766+0.083609 test-rmse:2.150946+0.538055
## [106] train-rmse:1.148857+0.084824 test-rmse:2.148765+0.537785
## [107] train-rmse:1.145456+0.084860 test-rmse:2.146697+0.535753
## [108] train-rmse:1.142130+0.084506 test-rmse:2.145912+0.538151
## [109] train-rmse:1.138447+0.085911 test-rmse:2.145402+0.537239
## [110] train-rmse:1.130063+0.088307 test-rmse:2.141796+0.536584
## [111] train-rmse:1.125370+0.088914 test-rmse:2.138590+0.540375
## [112] train-rmse:1.121218+0.089297 test-rmse:2.135880+0.541098
## [113] train-rmse:1.116837+0.089407 test-rmse:2.135064+0.540099
## [114] train-rmse:1.112619+0.088739 test-rmse:2.134390+0.538982
## [115] train-rmse:1.109150+0.087733 test-rmse:2.135616+0.536948
## [116] train-rmse:1.104572+0.088248 test-rmse:2.134600+0.535167
## [117] train-rmse:1.101718+0.088456 test-rmse:2.134942+0.532726
## [118] train-rmse:1.097428+0.089356 test-rmse:2.130199+0.534286
## [119] train-rmse:1.086895+0.088802 test-rmse:2.128277+0.533148
## [120] train-rmse:1.083223+0.089311 test-rmse:2.125704+0.532608
## [121] train-rmse:1.079126+0.088750 test-rmse:2.125272+0.532860
## [122] train-rmse:1.076342+0.089497 test-rmse:2.123581+0.534442
## [123] train-rmse:1.073005+0.089505 test-rmse:2.123237+0.534435
## [124] train-rmse:1.067983+0.090072 test-rmse:2.117647+0.535960
## [125] train-rmse:1.061596+0.089883 test-rmse:2.118196+0.536020
## [126] train-rmse:1.056086+0.090150 test-rmse:2.116709+0.540291
## [127] train-rmse:1.051812+0.090887 test-rmse:2.117630+0.540437
## [128] train-rmse:1.047762+0.091957 test-rmse:2.116967+0.541242
## [129] train-rmse:1.043289+0.093020 test-rmse:2.120046+0.542576
## [130] train-rmse:1.037626+0.096904 test-rmse:2.128147+0.549438
## [131] train-rmse:1.031101+0.089673 test-rmse:2.127820+0.548931
## [132] train-rmse:1.026427+0.089725 test-rmse:2.126916+0.549573
## Stopping. Best iteration:
## [126] train-rmse:1.056086+0.090150 test-rmse:2.116709+0.540291
##
## 37
## [1] train-rmse:6.873187+0.141205 test-rmse:6.923907+0.627060
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.845586+0.117532 test-rmse:5.941820+0.565792
## [3] train-rmse:5.031875+0.110097 test-rmse:5.158349+0.530847
## [4] train-rmse:4.401799+0.099738 test-rmse:4.582974+0.489461
## [5] train-rmse:3.898768+0.087936 test-rmse:4.104075+0.468481
## [6] train-rmse:3.505076+0.075707 test-rmse:3.725996+0.453056
## [7] train-rmse:3.185096+0.064339 test-rmse:3.470070+0.435278
## [8] train-rmse:2.944323+0.071192 test-rmse:3.263966+0.445111
## [9] train-rmse:2.761156+0.075885 test-rmse:3.125208+0.434905
## [10] train-rmse:2.598909+0.072264 test-rmse:3.017163+0.441081
## [11] train-rmse:2.472128+0.059423 test-rmse:2.938055+0.441379
## [12] train-rmse:2.357320+0.056177 test-rmse:2.838296+0.435737
## [13] train-rmse:2.271224+0.058741 test-rmse:2.771466+0.452940
## [14] train-rmse:2.186756+0.063000 test-rmse:2.738349+0.456068
## [15] train-rmse:2.119589+0.065531 test-rmse:2.707483+0.457565
## [16] train-rmse:2.059498+0.066696 test-rmse:2.654932+0.464343
## [17] train-rmse:2.003859+0.073246 test-rmse:2.630036+0.467551
## [18] train-rmse:1.944147+0.065650 test-rmse:2.594395+0.469575
## [19] train-rmse:1.901348+0.067719 test-rmse:2.564277+0.471075
## [20] train-rmse:1.854998+0.063200 test-rmse:2.545658+0.466922
## [21] train-rmse:1.808558+0.053948 test-rmse:2.525368+0.475366
## [22] train-rmse:1.774673+0.054461 test-rmse:2.506615+0.481601
## [23] train-rmse:1.735492+0.056159 test-rmse:2.478889+0.480174
## [24] train-rmse:1.699336+0.060669 test-rmse:2.453451+0.467978
## [25] train-rmse:1.664512+0.057033 test-rmse:2.433634+0.480090
## [26] train-rmse:1.630121+0.051936 test-rmse:2.414527+0.475167
## [27] train-rmse:1.602252+0.051574 test-rmse:2.396770+0.477524
## [28] train-rmse:1.574712+0.051630 test-rmse:2.387147+0.466854
## [29] train-rmse:1.546198+0.052681 test-rmse:2.371674+0.485452
## [30] train-rmse:1.520611+0.051739 test-rmse:2.363258+0.490211
## [31] train-rmse:1.491543+0.051100 test-rmse:2.338805+0.487482
## [32] train-rmse:1.471469+0.052483 test-rmse:2.328170+0.495257
## [33] train-rmse:1.449007+0.050316 test-rmse:2.323195+0.494275
## [34] train-rmse:1.426246+0.047895 test-rmse:2.309392+0.488138
## [35] train-rmse:1.406436+0.049499 test-rmse:2.304607+0.488640
## [36] train-rmse:1.385906+0.054298 test-rmse:2.301725+0.487918
## [37] train-rmse:1.364993+0.054892 test-rmse:2.296562+0.493620
## [38] train-rmse:1.348528+0.056248 test-rmse:2.288466+0.497076
## [39] train-rmse:1.331639+0.062710 test-rmse:2.274176+0.485923
## [40] train-rmse:1.308744+0.059046 test-rmse:2.267297+0.486002
## [41] train-rmse:1.276985+0.048886 test-rmse:2.262756+0.484196
## [42] train-rmse:1.262250+0.048238 test-rmse:2.252942+0.489250
## [43] train-rmse:1.248140+0.048942 test-rmse:2.244313+0.488795
## [44] train-rmse:1.234040+0.049178 test-rmse:2.236555+0.488280
## [45] train-rmse:1.219457+0.047263 test-rmse:2.233880+0.493056
## [46] train-rmse:1.203548+0.045039 test-rmse:2.229750+0.489297
## [47] train-rmse:1.190031+0.044930 test-rmse:2.226502+0.494911
## [48] train-rmse:1.179203+0.045694 test-rmse:2.219859+0.490330
## [49] train-rmse:1.166827+0.043728 test-rmse:2.211033+0.491696
## [50] train-rmse:1.154739+0.044255 test-rmse:2.208421+0.492177
## [51] train-rmse:1.140122+0.044010 test-rmse:2.202983+0.499920
## [52] train-rmse:1.124023+0.044757 test-rmse:2.195426+0.501164
## [53] train-rmse:1.110570+0.040947 test-rmse:2.191910+0.499832
## [54] train-rmse:1.099215+0.042421 test-rmse:2.186980+0.501258
## [55] train-rmse:1.089441+0.043119 test-rmse:2.184896+0.496821
## [56] train-rmse:1.077291+0.038206 test-rmse:2.180157+0.496944
## [57] train-rmse:1.069623+0.038635 test-rmse:2.177773+0.498501
## [58] train-rmse:1.055187+0.040544 test-rmse:2.173264+0.498169
## [59] train-rmse:1.041799+0.043715 test-rmse:2.173010+0.502659
## [60] train-rmse:1.018570+0.044418 test-rmse:2.165041+0.504028
## [61] train-rmse:1.008859+0.045922 test-rmse:2.168476+0.510516
## [62] train-rmse:0.995506+0.044828 test-rmse:2.163911+0.509976
## [63] train-rmse:0.984158+0.045535 test-rmse:2.154667+0.513364
## [64] train-rmse:0.976505+0.046865 test-rmse:2.150597+0.508906
## [65] train-rmse:0.966147+0.048427 test-rmse:2.150687+0.509612
## [66] train-rmse:0.954143+0.051473 test-rmse:2.145442+0.506913
## [67] train-rmse:0.945818+0.053558 test-rmse:2.144345+0.510661
## [68] train-rmse:0.933262+0.053279 test-rmse:2.144373+0.508863
## [69] train-rmse:0.924863+0.053299 test-rmse:2.141399+0.507863
## [70] train-rmse:0.915497+0.051065 test-rmse:2.135640+0.511315
## [71] train-rmse:0.909486+0.050993 test-rmse:2.134112+0.512339
## [72] train-rmse:0.895644+0.046599 test-rmse:2.136675+0.511065
## [73] train-rmse:0.886791+0.046809 test-rmse:2.137395+0.508248
## [74] train-rmse:0.879058+0.048422 test-rmse:2.131334+0.508591
## [75] train-rmse:0.870995+0.048556 test-rmse:2.130394+0.510016
## [76] train-rmse:0.863197+0.048330 test-rmse:2.131336+0.511868
## [77] train-rmse:0.855828+0.046613 test-rmse:2.126343+0.515031
## [78] train-rmse:0.847089+0.045353 test-rmse:2.126329+0.514536
## [79] train-rmse:0.839421+0.046314 test-rmse:2.122782+0.515846
## [80] train-rmse:0.833676+0.049094 test-rmse:2.117987+0.509981
## [81] train-rmse:0.821740+0.049607 test-rmse:2.117735+0.507077
## [82] train-rmse:0.812126+0.049264 test-rmse:2.118570+0.507869
## [83] train-rmse:0.805735+0.050776 test-rmse:2.120008+0.506258
## [84] train-rmse:0.800079+0.050958 test-rmse:2.119268+0.506288
## [85] train-rmse:0.790699+0.049955 test-rmse:2.115227+0.507599
## [86] train-rmse:0.782835+0.049242 test-rmse:2.115715+0.507327
## [87] train-rmse:0.776087+0.053456 test-rmse:2.114062+0.507384
## [88] train-rmse:0.769401+0.052578 test-rmse:2.112144+0.509899
## [89] train-rmse:0.762185+0.051825 test-rmse:2.112157+0.510341
## [90] train-rmse:0.757888+0.051323 test-rmse:2.109726+0.510211
## [91] train-rmse:0.749627+0.049339 test-rmse:2.109440+0.511698
## [92] train-rmse:0.743573+0.049094 test-rmse:2.106570+0.511151
## [93] train-rmse:0.737455+0.049214 test-rmse:2.109972+0.510767
## [94] train-rmse:0.728534+0.049548 test-rmse:2.107169+0.510088
## [95] train-rmse:0.720916+0.051815 test-rmse:2.104894+0.506721
## [96] train-rmse:0.714940+0.055090 test-rmse:2.102786+0.507445
## [97] train-rmse:0.705318+0.050388 test-rmse:2.100948+0.508343
## [98] train-rmse:0.699455+0.049596 test-rmse:2.100064+0.507118
## [99] train-rmse:0.693449+0.049519 test-rmse:2.097147+0.509534
## [100] train-rmse:0.687004+0.050279 test-rmse:2.096730+0.506136
## [101] train-rmse:0.682272+0.051256 test-rmse:2.096909+0.506372
## [102] train-rmse:0.675366+0.054042 test-rmse:2.096011+0.507547
## [103] train-rmse:0.668620+0.049474 test-rmse:2.099646+0.509631
## [104] train-rmse:0.661565+0.044621 test-rmse:2.100384+0.507870
## [105] train-rmse:0.657063+0.043601 test-rmse:2.098581+0.507862
## [106] train-rmse:0.649524+0.041159 test-rmse:2.100128+0.506328
## [107] train-rmse:0.645075+0.040574 test-rmse:2.097832+0.505509
## [108] train-rmse:0.639654+0.040190 test-rmse:2.098090+0.505159
## Stopping. Best iteration:
## [102] train-rmse:0.675366+0.054042 test-rmse:2.096011+0.507547
##
## 38
## [1] train-rmse:6.818291+0.139987 test-rmse:6.848573+0.616402
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.729816+0.113479 test-rmse:5.817880+0.537260
## [3] train-rmse:4.877239+0.093316 test-rmse:5.017484+0.475151
## [4] train-rmse:4.202226+0.076251 test-rmse:4.382041+0.436443
## [5] train-rmse:3.677508+0.077628 test-rmse:3.917839+0.414104
## [6] train-rmse:3.258510+0.063765 test-rmse:3.546266+0.437854
## [7] train-rmse:2.940209+0.059571 test-rmse:3.289734+0.414108
## [8] train-rmse:2.684328+0.043655 test-rmse:3.088219+0.449895
## [9] train-rmse:2.484211+0.045756 test-rmse:2.932888+0.435031
## [10] train-rmse:2.302582+0.043822 test-rmse:2.821557+0.441524
## [11] train-rmse:2.168963+0.048011 test-rmse:2.756432+0.440256
## [12] train-rmse:2.061138+0.052897 test-rmse:2.686088+0.459346
## [13] train-rmse:1.955771+0.044581 test-rmse:2.616788+0.440721
## [14] train-rmse:1.875167+0.047922 test-rmse:2.568852+0.443375
## [15] train-rmse:1.799660+0.042055 test-rmse:2.540031+0.445754
## [16] train-rmse:1.741960+0.048189 test-rmse:2.503362+0.448098
## [17] train-rmse:1.683005+0.049674 test-rmse:2.474824+0.453347
## [18] train-rmse:1.628794+0.044644 test-rmse:2.446407+0.457903
## [19] train-rmse:1.570498+0.037601 test-rmse:2.426248+0.467053
## [20] train-rmse:1.529634+0.035234 test-rmse:2.412393+0.470095
## [21] train-rmse:1.488275+0.033434 test-rmse:2.376922+0.469004
## [22] train-rmse:1.451013+0.038208 test-rmse:2.365222+0.463112
## [23] train-rmse:1.414726+0.038503 test-rmse:2.347251+0.471591
## [24] train-rmse:1.377377+0.039466 test-rmse:2.331198+0.465156
## [25] train-rmse:1.341886+0.030049 test-rmse:2.316671+0.469484
## [26] train-rmse:1.312324+0.027322 test-rmse:2.323159+0.464882
## [27] train-rmse:1.281233+0.031365 test-rmse:2.304777+0.473550
## [28] train-rmse:1.252088+0.030666 test-rmse:2.297430+0.468241
## [29] train-rmse:1.225216+0.029710 test-rmse:2.288479+0.469705
## [30] train-rmse:1.206175+0.028565 test-rmse:2.278923+0.469857
## [31] train-rmse:1.175408+0.029658 test-rmse:2.270532+0.476171
## [32] train-rmse:1.145561+0.035161 test-rmse:2.265915+0.477262
## [33] train-rmse:1.127005+0.036172 test-rmse:2.256977+0.479503
## [34] train-rmse:1.099781+0.033098 test-rmse:2.246680+0.478149
## [35] train-rmse:1.076759+0.026408 test-rmse:2.248043+0.478376
## [36] train-rmse:1.060565+0.027431 test-rmse:2.238927+0.482337
## [37] train-rmse:1.040400+0.027433 test-rmse:2.226920+0.474665
## [38] train-rmse:1.017591+0.031103 test-rmse:2.223311+0.475793
## [39] train-rmse:1.000382+0.029108 test-rmse:2.216984+0.477006
## [40] train-rmse:0.982854+0.021933 test-rmse:2.213223+0.479995
## [41] train-rmse:0.965134+0.025970 test-rmse:2.211004+0.473960
## [42] train-rmse:0.950016+0.024949 test-rmse:2.212455+0.475329
## [43] train-rmse:0.929038+0.015778 test-rmse:2.208684+0.478534
## [44] train-rmse:0.915627+0.013736 test-rmse:2.200528+0.480997
## [45] train-rmse:0.903240+0.014388 test-rmse:2.197140+0.484175
## [46] train-rmse:0.889532+0.013729 test-rmse:2.192518+0.487195
## [47] train-rmse:0.872580+0.010595 test-rmse:2.190087+0.485694
## [48] train-rmse:0.859698+0.012990 test-rmse:2.185426+0.483900
## [49] train-rmse:0.846699+0.013909 test-rmse:2.184881+0.483214
## [50] train-rmse:0.829777+0.017376 test-rmse:2.180442+0.482671
## [51] train-rmse:0.812179+0.018072 test-rmse:2.169228+0.486528
## [52] train-rmse:0.800903+0.019493 test-rmse:2.163404+0.489601
## [53] train-rmse:0.789898+0.022027 test-rmse:2.161865+0.489653
## [54] train-rmse:0.778330+0.024524 test-rmse:2.159622+0.491847
## [55] train-rmse:0.759866+0.023662 test-rmse:2.152578+0.491707
## [56] train-rmse:0.750555+0.024915 test-rmse:2.152184+0.490077
## [57] train-rmse:0.737573+0.024240 test-rmse:2.151024+0.491605
## [58] train-rmse:0.724152+0.028729 test-rmse:2.152935+0.489500
## [59] train-rmse:0.712928+0.025400 test-rmse:2.150774+0.494783
## [60] train-rmse:0.703301+0.026650 test-rmse:2.149374+0.493431
## [61] train-rmse:0.694232+0.029782 test-rmse:2.146377+0.494680
## [62] train-rmse:0.684345+0.029594 test-rmse:2.147085+0.495865
## [63] train-rmse:0.673308+0.026119 test-rmse:2.148166+0.495122
## [64] train-rmse:0.667158+0.025160 test-rmse:2.144898+0.495195
## [65] train-rmse:0.658794+0.027724 test-rmse:2.144708+0.492884
## [66] train-rmse:0.648964+0.027866 test-rmse:2.143985+0.492366
## [67] train-rmse:0.633700+0.025188 test-rmse:2.142272+0.489477
## [68] train-rmse:0.625981+0.023937 test-rmse:2.140752+0.491203
## [69] train-rmse:0.615607+0.022111 test-rmse:2.139711+0.489457
## [70] train-rmse:0.608125+0.020248 test-rmse:2.140158+0.489859
## [71] train-rmse:0.602314+0.019957 test-rmse:2.140797+0.490697
## [72] train-rmse:0.595332+0.019405 test-rmse:2.143353+0.492286
## [73] train-rmse:0.588811+0.023262 test-rmse:2.142503+0.492850
## [74] train-rmse:0.581426+0.022385 test-rmse:2.139373+0.493536
## [75] train-rmse:0.574912+0.023078 test-rmse:2.138023+0.494226
## [76] train-rmse:0.566854+0.022195 test-rmse:2.140606+0.493291
## [77] train-rmse:0.560321+0.023985 test-rmse:2.140086+0.492973
## [78] train-rmse:0.552861+0.023075 test-rmse:2.139384+0.492226
## [79] train-rmse:0.546331+0.021181 test-rmse:2.137108+0.492459
## [80] train-rmse:0.535089+0.023973 test-rmse:2.135313+0.495995
## [81] train-rmse:0.529006+0.022369 test-rmse:2.137391+0.495003
## [82] train-rmse:0.520025+0.023344 test-rmse:2.136322+0.494871
## [83] train-rmse:0.512886+0.023265 test-rmse:2.135624+0.495889
## [84] train-rmse:0.504468+0.024202 test-rmse:2.133344+0.493471
## [85] train-rmse:0.500768+0.025658 test-rmse:2.133029+0.495410
## [86] train-rmse:0.496111+0.024650 test-rmse:2.131913+0.495327
## [87] train-rmse:0.486774+0.023699 test-rmse:2.128515+0.496928
## [88] train-rmse:0.481350+0.021287 test-rmse:2.129712+0.494925
## [89] train-rmse:0.474543+0.019645 test-rmse:2.127846+0.495329
## [90] train-rmse:0.469142+0.022820 test-rmse:2.128322+0.497536
## [91] train-rmse:0.463828+0.021982 test-rmse:2.128594+0.496815
## [92] train-rmse:0.457655+0.020965 test-rmse:2.126172+0.497676
## [93] train-rmse:0.450990+0.021148 test-rmse:2.124425+0.497641
## [94] train-rmse:0.444833+0.020994 test-rmse:2.122745+0.499101
## [95] train-rmse:0.439162+0.023033 test-rmse:2.122524+0.498537
## [96] train-rmse:0.434103+0.023879 test-rmse:2.122169+0.497419
## [97] train-rmse:0.427740+0.023398 test-rmse:2.120672+0.498839
## [98] train-rmse:0.422041+0.022634 test-rmse:2.121713+0.498822
## [99] train-rmse:0.418728+0.022858 test-rmse:2.121503+0.500449
## [100] train-rmse:0.414865+0.023130 test-rmse:2.120386+0.501765
## [101] train-rmse:0.408878+0.022183 test-rmse:2.119202+0.501808
## [102] train-rmse:0.403069+0.021875 test-rmse:2.117561+0.502610
## [103] train-rmse:0.398000+0.020643 test-rmse:2.119450+0.501822
## [104] train-rmse:0.392589+0.020716 test-rmse:2.118662+0.502037
## [105] train-rmse:0.386722+0.022379 test-rmse:2.117412+0.503348
## [106] train-rmse:0.381221+0.024883 test-rmse:2.117341+0.503008
## [107] train-rmse:0.376961+0.024230 test-rmse:2.119109+0.503235
## [108] train-rmse:0.373681+0.024532 test-rmse:2.119224+0.502859
## [109] train-rmse:0.370277+0.024218 test-rmse:2.118944+0.503039
## [110] train-rmse:0.365079+0.023452 test-rmse:2.118218+0.505007
## [111] train-rmse:0.361541+0.023215 test-rmse:2.118487+0.504544
## [112] train-rmse:0.357337+0.022360 test-rmse:2.117654+0.505242
## Stopping. Best iteration:
## [106] train-rmse:0.381221+0.024883 test-rmse:2.117341+0.503008
##
## 39
## [1] train-rmse:6.780337+0.137838 test-rmse:6.784392+0.599835
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.661086+0.109457 test-rmse:5.716318+0.528060
## [3] train-rmse:4.774709+0.098440 test-rmse:4.890532+0.468780
## [4] train-rmse:4.083732+0.090183 test-rmse:4.282429+0.427162
## [5] train-rmse:3.541519+0.075597 test-rmse:3.833585+0.415869
## [6] train-rmse:3.114368+0.065117 test-rmse:3.474975+0.410436
## [7] train-rmse:2.788242+0.068632 test-rmse:3.243584+0.434338
## [8] train-rmse:2.515182+0.077688 test-rmse:3.056926+0.451066
## [9] train-rmse:2.287994+0.064712 test-rmse:2.919292+0.487915
## [10] train-rmse:2.109889+0.062051 test-rmse:2.803378+0.500704
## [11] train-rmse:1.972600+0.061231 test-rmse:2.712741+0.497798
## [12] train-rmse:1.852090+0.051872 test-rmse:2.638607+0.482408
## [13] train-rmse:1.753749+0.053174 test-rmse:2.582517+0.501580
## [14] train-rmse:1.671817+0.048291 test-rmse:2.535929+0.503943
## [15] train-rmse:1.604641+0.049177 test-rmse:2.495408+0.514371
## [16] train-rmse:1.532636+0.055384 test-rmse:2.471850+0.517670
## [17] train-rmse:1.479706+0.053326 test-rmse:2.444597+0.525051
## [18] train-rmse:1.418067+0.043326 test-rmse:2.427980+0.536852
## [19] train-rmse:1.363446+0.029619 test-rmse:2.411202+0.539784
## [20] train-rmse:1.316902+0.020542 test-rmse:2.385063+0.527264
## [21] train-rmse:1.274847+0.022361 test-rmse:2.374361+0.518882
## [22] train-rmse:1.234302+0.017898 test-rmse:2.359947+0.528761
## [23] train-rmse:1.195559+0.022440 test-rmse:2.334444+0.534786
## [24] train-rmse:1.158915+0.023297 test-rmse:2.325141+0.535015
## [25] train-rmse:1.125916+0.026291 test-rmse:2.319434+0.531681
## [26] train-rmse:1.094708+0.017843 test-rmse:2.311426+0.523389
## [27] train-rmse:1.065840+0.018238 test-rmse:2.306788+0.528150
## [28] train-rmse:1.036756+0.024270 test-rmse:2.297735+0.534956
## [29] train-rmse:1.014469+0.021723 test-rmse:2.290323+0.538706
## [30] train-rmse:0.993744+0.020031 test-rmse:2.287868+0.540313
## [31] train-rmse:0.958637+0.018862 test-rmse:2.282139+0.546384
## [32] train-rmse:0.930256+0.011530 test-rmse:2.280092+0.546228
## [33] train-rmse:0.904624+0.007150 test-rmse:2.277252+0.547492
## [34] train-rmse:0.884638+0.005394 test-rmse:2.273134+0.547959
## [35] train-rmse:0.857258+0.006283 test-rmse:2.259632+0.544751
## [36] train-rmse:0.840186+0.007595 test-rmse:2.255930+0.550456
## [37] train-rmse:0.817710+0.011092 test-rmse:2.248706+0.550283
## [38] train-rmse:0.796015+0.009160 test-rmse:2.246922+0.554666
## [39] train-rmse:0.778187+0.011813 test-rmse:2.240307+0.556113
## [40] train-rmse:0.760478+0.008995 test-rmse:2.241584+0.557228
## [41] train-rmse:0.742971+0.012696 test-rmse:2.240581+0.560723
## [42] train-rmse:0.730238+0.011649 test-rmse:2.240506+0.558687
## [43] train-rmse:0.709898+0.009922 test-rmse:2.234733+0.560032
## [44] train-rmse:0.692029+0.015070 test-rmse:2.235278+0.567145
## [45] train-rmse:0.676599+0.016277 test-rmse:2.227688+0.573922
## [46] train-rmse:0.658572+0.022901 test-rmse:2.225621+0.576442
## [47] train-rmse:0.637221+0.019173 test-rmse:2.223000+0.578231
## [48] train-rmse:0.626890+0.020072 test-rmse:2.221635+0.577736
## [49] train-rmse:0.613461+0.019820 test-rmse:2.220987+0.578013
## [50] train-rmse:0.597232+0.016996 test-rmse:2.222581+0.579022
## [51] train-rmse:0.582072+0.016578 test-rmse:2.219060+0.579843
## [52] train-rmse:0.569477+0.017075 test-rmse:2.220369+0.582098
## [53] train-rmse:0.557682+0.018159 test-rmse:2.220091+0.584836
## [54] train-rmse:0.549364+0.019177 test-rmse:2.220946+0.584229
## [55] train-rmse:0.539950+0.019759 test-rmse:2.222214+0.582857
## [56] train-rmse:0.527663+0.019607 test-rmse:2.223316+0.581543
## [57] train-rmse:0.517409+0.020474 test-rmse:2.219941+0.584412
## Stopping. Best iteration:
## [51] train-rmse:0.582072+0.016578 test-rmse:2.219060+0.579843
##
## 40
## [1] train-rmse:6.756445+0.134251 test-rmse:6.768577+0.621900
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.612353+0.103080 test-rmse:5.698829+0.555902
## [3] train-rmse:4.702491+0.082601 test-rmse:4.874663+0.503077
## [4] train-rmse:3.988679+0.069556 test-rmse:4.267409+0.469333
## [5] train-rmse:3.421554+0.053490 test-rmse:3.806016+0.462387
## [6] train-rmse:2.977623+0.040967 test-rmse:3.473957+0.465101
## [7] train-rmse:2.626471+0.034889 test-rmse:3.210907+0.443614
## [8] train-rmse:2.356031+0.033120 test-rmse:3.043130+0.471956
## [9] train-rmse:2.127491+0.027808 test-rmse:2.892477+0.490815
## [10] train-rmse:1.948554+0.034279 test-rmse:2.772286+0.500296
## [11] train-rmse:1.785377+0.032742 test-rmse:2.687467+0.513964
## [12] train-rmse:1.661071+0.023393 test-rmse:2.623265+0.528898
## [13] train-rmse:1.555597+0.030277 test-rmse:2.574465+0.522365
## [14] train-rmse:1.467641+0.030820 test-rmse:2.520164+0.526235
## [15] train-rmse:1.378061+0.037402 test-rmse:2.505105+0.528722
## [16] train-rmse:1.309230+0.030709 test-rmse:2.485888+0.519314
## [17] train-rmse:1.252221+0.028084 test-rmse:2.464866+0.524292
## [18] train-rmse:1.200518+0.026588 test-rmse:2.443092+0.528930
## [19] train-rmse:1.145571+0.026786 test-rmse:2.425013+0.521019
## [20] train-rmse:1.096860+0.030715 test-rmse:2.414079+0.524261
## [21] train-rmse:1.057321+0.034762 test-rmse:2.406415+0.528087
## [22] train-rmse:1.019963+0.031685 test-rmse:2.390804+0.521982
## [23] train-rmse:0.981374+0.026931 test-rmse:2.389253+0.526327
## [24] train-rmse:0.944972+0.026383 test-rmse:2.384134+0.529623
## [25] train-rmse:0.905601+0.026368 test-rmse:2.372369+0.531248
## [26] train-rmse:0.876434+0.030857 test-rmse:2.367186+0.536044
## [27] train-rmse:0.839216+0.031781 test-rmse:2.355919+0.536576
## [28] train-rmse:0.819925+0.029881 test-rmse:2.351581+0.537209
## [29] train-rmse:0.792510+0.020923 test-rmse:2.348693+0.537326
## [30] train-rmse:0.761068+0.020401 test-rmse:2.340715+0.534141
## [31] train-rmse:0.740091+0.021111 test-rmse:2.337008+0.532693
## [32] train-rmse:0.710837+0.018048 test-rmse:2.331936+0.534073
## [33] train-rmse:0.687657+0.021798 test-rmse:2.319823+0.536956
## [34] train-rmse:0.670694+0.018504 test-rmse:2.314150+0.541572
## [35] train-rmse:0.649471+0.020174 test-rmse:2.312637+0.541473
## [36] train-rmse:0.627752+0.022001 test-rmse:2.309710+0.542434
## [37] train-rmse:0.608201+0.025330 test-rmse:2.306280+0.541792
## [38] train-rmse:0.590621+0.025579 test-rmse:2.304785+0.541983
## [39] train-rmse:0.573040+0.028715 test-rmse:2.302733+0.545559
## [40] train-rmse:0.556793+0.033409 test-rmse:2.300409+0.545940
## [41] train-rmse:0.540174+0.028909 test-rmse:2.299220+0.545501
## [42] train-rmse:0.523969+0.027439 test-rmse:2.297997+0.544583
## [43] train-rmse:0.508096+0.028186 test-rmse:2.297893+0.543164
## [44] train-rmse:0.492941+0.026129 test-rmse:2.296778+0.544507
## [45] train-rmse:0.479698+0.026942 test-rmse:2.296822+0.543670
## [46] train-rmse:0.466194+0.029799 test-rmse:2.299225+0.543740
## [47] train-rmse:0.453118+0.027389 test-rmse:2.300025+0.545047
## [48] train-rmse:0.440115+0.023685 test-rmse:2.297383+0.544861
## [49] train-rmse:0.427576+0.025274 test-rmse:2.297657+0.545695
## [50] train-rmse:0.417506+0.024334 test-rmse:2.297998+0.546437
## Stopping. Best iteration:
## [44] train-rmse:0.492941+0.026129 test-rmse:2.296778+0.544507
##
## 41
## [1] train-rmse:6.747972+0.133543 test-rmse:6.763870+0.621481
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.590975+0.099311 test-rmse:5.693064+0.552228
## [3] train-rmse:4.664117+0.076531 test-rmse:4.844877+0.521489
## [4] train-rmse:3.928588+0.061056 test-rmse:4.226565+0.481514
## [5] train-rmse:3.350335+0.053686 test-rmse:3.739994+0.468990
## [6] train-rmse:2.889823+0.043367 test-rmse:3.417381+0.462848
## [7] train-rmse:2.522680+0.036905 test-rmse:3.174228+0.472837
## [8] train-rmse:2.233585+0.031215 test-rmse:3.004834+0.483231
## [9] train-rmse:1.992898+0.028167 test-rmse:2.855119+0.483278
## [10] train-rmse:1.797334+0.022591 test-rmse:2.743056+0.506061
## [11] train-rmse:1.644811+0.023120 test-rmse:2.670551+0.519490
## [12] train-rmse:1.516462+0.023867 test-rmse:2.606308+0.529038
## [13] train-rmse:1.417752+0.022253 test-rmse:2.564559+0.539769
## [14] train-rmse:1.326908+0.019367 test-rmse:2.523724+0.533249
## [15] train-rmse:1.245360+0.026287 test-rmse:2.504667+0.543397
## [16] train-rmse:1.176188+0.026551 test-rmse:2.481616+0.553351
## [17] train-rmse:1.097133+0.025236 test-rmse:2.462222+0.542592
## [18] train-rmse:1.053035+0.023564 test-rmse:2.442971+0.544165
## [19] train-rmse:0.999522+0.028496 test-rmse:2.432810+0.548792
## [20] train-rmse:0.956169+0.034265 test-rmse:2.420077+0.555685
## [21] train-rmse:0.905908+0.033700 test-rmse:2.420908+0.560251
## [22] train-rmse:0.864743+0.027617 test-rmse:2.415364+0.565524
## [23] train-rmse:0.827957+0.029450 test-rmse:2.412784+0.566509
## [24] train-rmse:0.795298+0.026717 test-rmse:2.401788+0.564012
## [25] train-rmse:0.760853+0.033658 test-rmse:2.401373+0.564481
## [26] train-rmse:0.731774+0.026412 test-rmse:2.396474+0.565019
## [27] train-rmse:0.694407+0.033259 test-rmse:2.392898+0.564930
## [28] train-rmse:0.662400+0.026076 test-rmse:2.393634+0.568260
## [29] train-rmse:0.638631+0.020441 test-rmse:2.388057+0.566323
## [30] train-rmse:0.614545+0.017540 test-rmse:2.384220+0.569191
## [31] train-rmse:0.589853+0.013719 test-rmse:2.383722+0.573083
## [32] train-rmse:0.565897+0.011976 test-rmse:2.384209+0.574166
## [33] train-rmse:0.544722+0.010073 test-rmse:2.381302+0.569416
## [34] train-rmse:0.528457+0.014675 test-rmse:2.377292+0.570099
## [35] train-rmse:0.504569+0.010048 test-rmse:2.374371+0.574967
## [36] train-rmse:0.485136+0.011944 test-rmse:2.372597+0.575994
## [37] train-rmse:0.466653+0.015778 test-rmse:2.368947+0.575908
## [38] train-rmse:0.445252+0.014217 test-rmse:2.362491+0.575197
## [39] train-rmse:0.431877+0.011885 test-rmse:2.361413+0.576462
## [40] train-rmse:0.417132+0.015215 test-rmse:2.360019+0.574812
## [41] train-rmse:0.405736+0.010930 test-rmse:2.359980+0.575831
## [42] train-rmse:0.389867+0.010403 test-rmse:2.361032+0.577004
## [43] train-rmse:0.377229+0.007720 test-rmse:2.358099+0.577310
## [44] train-rmse:0.365270+0.004599 test-rmse:2.359392+0.578720
## [45] train-rmse:0.353710+0.004939 test-rmse:2.358807+0.577921
## [46] train-rmse:0.340119+0.006852 test-rmse:2.357289+0.578104
## [47] train-rmse:0.330308+0.005266 test-rmse:2.357376+0.578894
## [48] train-rmse:0.316989+0.004143 test-rmse:2.357652+0.579996
## [49] train-rmse:0.307377+0.007437 test-rmse:2.357570+0.580574
## [50] train-rmse:0.298501+0.008761 test-rmse:2.356966+0.580209
## [51] train-rmse:0.287618+0.010188 test-rmse:2.357844+0.581245
## [52] train-rmse:0.279091+0.010662 test-rmse:2.357271+0.581498
## [53] train-rmse:0.268451+0.011270 test-rmse:2.357632+0.581204
## [54] train-rmse:0.262004+0.013016 test-rmse:2.357824+0.582110
## [55] train-rmse:0.254611+0.012529 test-rmse:2.356263+0.582988
## [56] train-rmse:0.245557+0.012788 test-rmse:2.355625+0.582609
## [57] train-rmse:0.238705+0.011153 test-rmse:2.354251+0.582712
## [58] train-rmse:0.231494+0.011397 test-rmse:2.353588+0.583100
## [59] train-rmse:0.225365+0.012418 test-rmse:2.353585+0.582627
## [60] train-rmse:0.217737+0.011222 test-rmse:2.352142+0.583531
## [61] train-rmse:0.211034+0.010016 test-rmse:2.351284+0.584011
## [62] train-rmse:0.202218+0.011289 test-rmse:2.351597+0.585201
## [63] train-rmse:0.197957+0.011474 test-rmse:2.350708+0.584996
## [64] train-rmse:0.190347+0.010816 test-rmse:2.349619+0.584459
## [65] train-rmse:0.185183+0.010997 test-rmse:2.348528+0.584085
## [66] train-rmse:0.180039+0.010566 test-rmse:2.348755+0.585692
## [67] train-rmse:0.174300+0.008962 test-rmse:2.347121+0.585426
## [68] train-rmse:0.169499+0.009485 test-rmse:2.346664+0.586198
## [69] train-rmse:0.163630+0.009072 test-rmse:2.347091+0.587195
## [70] train-rmse:0.158688+0.008606 test-rmse:2.346707+0.587351
## [71] train-rmse:0.155678+0.008398 test-rmse:2.346585+0.587668
## [72] train-rmse:0.151413+0.009826 test-rmse:2.345290+0.587209
## [73] train-rmse:0.145506+0.009732 test-rmse:2.344495+0.588106
## [74] train-rmse:0.140878+0.010022 test-rmse:2.344149+0.588020
## [75] train-rmse:0.135675+0.009634 test-rmse:2.343529+0.587561
## [76] train-rmse:0.132032+0.009490 test-rmse:2.343431+0.587613
## [77] train-rmse:0.128576+0.008517 test-rmse:2.343840+0.588218
## [78] train-rmse:0.125560+0.008305 test-rmse:2.344078+0.588025
## [79] train-rmse:0.122221+0.008518 test-rmse:2.344134+0.588327
## [80] train-rmse:0.118135+0.008960 test-rmse:2.343597+0.587940
## [81] train-rmse:0.114477+0.008634 test-rmse:2.343195+0.587513
## [82] train-rmse:0.111417+0.009450 test-rmse:2.342464+0.586401
## [83] train-rmse:0.108453+0.009869 test-rmse:2.342460+0.586222
## [84] train-rmse:0.105583+0.009853 test-rmse:2.341583+0.586483
## [85] train-rmse:0.102631+0.008929 test-rmse:2.341560+0.586405
## [86] train-rmse:0.100306+0.008639 test-rmse:2.341454+0.586380
## [87] train-rmse:0.097715+0.008666 test-rmse:2.341613+0.586288
## [88] train-rmse:0.094100+0.007858 test-rmse:2.340624+0.585942
## [89] train-rmse:0.091638+0.007165 test-rmse:2.340073+0.585997
## [90] train-rmse:0.088392+0.007236 test-rmse:2.340035+0.586321
## [91] train-rmse:0.086722+0.007288 test-rmse:2.340121+0.586420
## [92] train-rmse:0.084506+0.006952 test-rmse:2.339802+0.586479
## [93] train-rmse:0.082135+0.006455 test-rmse:2.339449+0.586712
## [94] train-rmse:0.079143+0.006978 test-rmse:2.339019+0.586006
## [95] train-rmse:0.076714+0.006408 test-rmse:2.338926+0.585932
## [96] train-rmse:0.074358+0.006468 test-rmse:2.338420+0.585833
## [97] train-rmse:0.071995+0.006667 test-rmse:2.338677+0.585501
## [98] train-rmse:0.069956+0.006938 test-rmse:2.338258+0.584702
## [99] train-rmse:0.067806+0.006193 test-rmse:2.337951+0.584905
## [100] train-rmse:0.065765+0.006249 test-rmse:2.337590+0.584945
## [101] train-rmse:0.064132+0.006153 test-rmse:2.337566+0.584778
## [102] train-rmse:0.062063+0.006146 test-rmse:2.337370+0.584806
## [103] train-rmse:0.060550+0.006154 test-rmse:2.337267+0.584803
## [104] train-rmse:0.059246+0.006338 test-rmse:2.337250+0.585407
## [105] train-rmse:0.057625+0.005993 test-rmse:2.337356+0.585386
## [106] train-rmse:0.056456+0.005734 test-rmse:2.337451+0.585221
## [107] train-rmse:0.055029+0.005754 test-rmse:2.337123+0.585407
## [108] train-rmse:0.053712+0.005266 test-rmse:2.337051+0.585337
## [109] train-rmse:0.052223+0.005319 test-rmse:2.336700+0.585144
## [110] train-rmse:0.051002+0.005132 test-rmse:2.336587+0.585287
## [111] train-rmse:0.049901+0.005150 test-rmse:2.336569+0.585252
## [112] train-rmse:0.048849+0.004974 test-rmse:2.336594+0.585189
## [113] train-rmse:0.047831+0.004728 test-rmse:2.336559+0.585192
## [114] train-rmse:0.046560+0.004725 test-rmse:2.336431+0.585213
## [115] train-rmse:0.045244+0.004300 test-rmse:2.336365+0.585165
## [116] train-rmse:0.043899+0.004092 test-rmse:2.336194+0.585207
## [117] train-rmse:0.043078+0.004006 test-rmse:2.336138+0.585220
## [118] train-rmse:0.042009+0.003816 test-rmse:2.336069+0.585290
## [119] train-rmse:0.040985+0.003726 test-rmse:2.336091+0.585338
## [120] train-rmse:0.039728+0.003737 test-rmse:2.335917+0.585379
## [121] train-rmse:0.038900+0.003460 test-rmse:2.335968+0.585339
## [122] train-rmse:0.038030+0.003495 test-rmse:2.335852+0.585480
## [123] train-rmse:0.037214+0.003328 test-rmse:2.335636+0.585543
## [124] train-rmse:0.036382+0.003116 test-rmse:2.335878+0.585700
## [125] train-rmse:0.035157+0.003016 test-rmse:2.335613+0.585594
## [126] train-rmse:0.034535+0.002969 test-rmse:2.335682+0.585919
## [127] train-rmse:0.033657+0.002957 test-rmse:2.335764+0.586133
## [128] train-rmse:0.032853+0.002936 test-rmse:2.335687+0.586163
## [129] train-rmse:0.032129+0.002892 test-rmse:2.335755+0.586335
## [130] train-rmse:0.031296+0.003034 test-rmse:2.335782+0.586382
## [131] train-rmse:0.030645+0.003083 test-rmse:2.335759+0.586391
## Stopping. Best iteration:
## [125] train-rmse:0.035157+0.003016 test-rmse:2.335613+0.585594
##
## 42
## [1] train-rmse:6.908719+0.149088 test-rmse:6.901413+0.664591
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.965472+0.127239 test-rmse:6.002722+0.698894
## [3] train-rmse:5.282677+0.118691 test-rmse:5.344873+0.625739
## [4] train-rmse:4.788383+0.116810 test-rmse:4.846487+0.600713
## [5] train-rmse:4.436466+0.113082 test-rmse:4.515644+0.606276
## [6] train-rmse:4.179301+0.108282 test-rmse:4.269683+0.534041
## [7] train-rmse:3.990712+0.111347 test-rmse:4.107927+0.502917
## [8] train-rmse:3.842770+0.114844 test-rmse:3.971128+0.481905
## [9] train-rmse:3.726462+0.115833 test-rmse:3.833982+0.449473
## [10] train-rmse:3.631225+0.115092 test-rmse:3.768724+0.461066
## [11] train-rmse:3.552916+0.118315 test-rmse:3.674067+0.432608
## [12] train-rmse:3.482914+0.121572 test-rmse:3.633062+0.415648
## [13] train-rmse:3.419154+0.122810 test-rmse:3.558275+0.431777
## [14] train-rmse:3.362985+0.122265 test-rmse:3.475954+0.409804
## [15] train-rmse:3.311708+0.121941 test-rmse:3.457299+0.421083
## [16] train-rmse:3.262177+0.121528 test-rmse:3.396585+0.418858
## [17] train-rmse:3.218208+0.123568 test-rmse:3.374098+0.420156
## [18] train-rmse:3.175853+0.124920 test-rmse:3.318282+0.443889
## [19] train-rmse:3.137568+0.125216 test-rmse:3.272449+0.415844
## [20] train-rmse:3.101304+0.125569 test-rmse:3.246260+0.424484
## [21] train-rmse:3.066689+0.126035 test-rmse:3.217016+0.433003
## [22] train-rmse:3.034979+0.127860 test-rmse:3.190269+0.421662
## [23] train-rmse:3.003725+0.129834 test-rmse:3.156344+0.432779
## [24] train-rmse:2.975726+0.130367 test-rmse:3.136135+0.443302
## [25] train-rmse:2.948482+0.130314 test-rmse:3.127800+0.444357
## [26] train-rmse:2.921801+0.130338 test-rmse:3.096985+0.421027
## [27] train-rmse:2.896803+0.130635 test-rmse:3.079999+0.426692
## [28] train-rmse:2.872415+0.131201 test-rmse:3.047384+0.422012
## [29] train-rmse:2.849053+0.131734 test-rmse:3.046067+0.434252
## [30] train-rmse:2.826216+0.132674 test-rmse:3.021975+0.440493
## [31] train-rmse:2.804000+0.133427 test-rmse:3.020096+0.453691
## [32] train-rmse:2.782039+0.134231 test-rmse:2.977851+0.456493
## [33] train-rmse:2.761093+0.135469 test-rmse:2.954339+0.467783
## [34] train-rmse:2.741103+0.136279 test-rmse:2.943865+0.465023
## [35] train-rmse:2.721960+0.136751 test-rmse:2.934760+0.465086
## [36] train-rmse:2.703653+0.137094 test-rmse:2.926833+0.458133
## [37] train-rmse:2.685554+0.137436 test-rmse:2.912040+0.445821
## [38] train-rmse:2.668215+0.137471 test-rmse:2.890873+0.444708
## [39] train-rmse:2.651348+0.137612 test-rmse:2.881548+0.448962
## [40] train-rmse:2.635175+0.137430 test-rmse:2.869156+0.453790
## [41] train-rmse:2.619516+0.137754 test-rmse:2.852632+0.468605
## [42] train-rmse:2.603922+0.138267 test-rmse:2.831447+0.473120
## [43] train-rmse:2.588633+0.138634 test-rmse:2.821449+0.468616
## [44] train-rmse:2.573908+0.138511 test-rmse:2.807237+0.479505
## [45] train-rmse:2.559288+0.138471 test-rmse:2.789782+0.469221
## [46] train-rmse:2.544937+0.138487 test-rmse:2.766766+0.476053
## [47] train-rmse:2.530980+0.138567 test-rmse:2.763872+0.479415
## [48] train-rmse:2.517721+0.138764 test-rmse:2.757067+0.483048
## [49] train-rmse:2.504829+0.138711 test-rmse:2.755577+0.490601
## [50] train-rmse:2.492487+0.138837 test-rmse:2.741045+0.484700
## [51] train-rmse:2.480558+0.138862 test-rmse:2.729719+0.491185
## [52] train-rmse:2.468978+0.139005 test-rmse:2.713415+0.480831
## [53] train-rmse:2.457595+0.139266 test-rmse:2.702217+0.481203
## [54] train-rmse:2.446429+0.139513 test-rmse:2.693179+0.471759
## [55] train-rmse:2.435584+0.139725 test-rmse:2.688377+0.471359
## [56] train-rmse:2.424883+0.139814 test-rmse:2.684443+0.479447
## [57] train-rmse:2.414276+0.139916 test-rmse:2.674758+0.481687
## [58] train-rmse:2.403994+0.140015 test-rmse:2.661706+0.486863
## [59] train-rmse:2.394073+0.140031 test-rmse:2.650662+0.490889
## [60] train-rmse:2.384413+0.140244 test-rmse:2.646223+0.490152
## [61] train-rmse:2.374932+0.140368 test-rmse:2.638114+0.494091
## [62] train-rmse:2.365625+0.140393 test-rmse:2.625071+0.494775
## [63] train-rmse:2.356629+0.140558 test-rmse:2.622859+0.495815
## [64] train-rmse:2.347968+0.140765 test-rmse:2.615182+0.497192
## [65] train-rmse:2.339702+0.141027 test-rmse:2.609439+0.497264
## [66] train-rmse:2.331491+0.141206 test-rmse:2.606929+0.506180
## [67] train-rmse:2.323532+0.141609 test-rmse:2.597382+0.499550
## [68] train-rmse:2.315797+0.141857 test-rmse:2.593126+0.501696
## [69] train-rmse:2.308208+0.142186 test-rmse:2.584593+0.507575
## [70] train-rmse:2.300786+0.142500 test-rmse:2.579267+0.506149
## [71] train-rmse:2.293302+0.142683 test-rmse:2.575230+0.500161
## [72] train-rmse:2.286040+0.143043 test-rmse:2.570597+0.500648
## [73] train-rmse:2.278839+0.143338 test-rmse:2.567689+0.499826
## [74] train-rmse:2.271827+0.143564 test-rmse:2.563260+0.507362
## [75] train-rmse:2.264813+0.143764 test-rmse:2.556522+0.507933
## [76] train-rmse:2.258086+0.143871 test-rmse:2.551531+0.503624
## [77] train-rmse:2.251731+0.144001 test-rmse:2.540690+0.499261
## [78] train-rmse:2.245404+0.144262 test-rmse:2.535035+0.493918
## [79] train-rmse:2.239069+0.144606 test-rmse:2.520905+0.497447
## [80] train-rmse:2.232789+0.144942 test-rmse:2.516360+0.499411
## [81] train-rmse:2.226695+0.145224 test-rmse:2.510312+0.500327
## [82] train-rmse:2.220636+0.145445 test-rmse:2.516719+0.511288
## [83] train-rmse:2.214888+0.145645 test-rmse:2.503745+0.512965
## [84] train-rmse:2.209386+0.145693 test-rmse:2.500325+0.512715
## [85] train-rmse:2.203925+0.145869 test-rmse:2.504472+0.512001
## [86] train-rmse:2.198616+0.146075 test-rmse:2.502052+0.513105
## [87] train-rmse:2.193471+0.146255 test-rmse:2.496581+0.517482
## [88] train-rmse:2.188412+0.146457 test-rmse:2.492335+0.516525
## [89] train-rmse:2.183387+0.146700 test-rmse:2.487133+0.519108
## [90] train-rmse:2.178511+0.146874 test-rmse:2.484014+0.517281
## [91] train-rmse:2.173647+0.147082 test-rmse:2.481335+0.517604
## [92] train-rmse:2.168854+0.147236 test-rmse:2.474468+0.516432
## [93] train-rmse:2.164178+0.147510 test-rmse:2.474135+0.516577
## [94] train-rmse:2.159642+0.147703 test-rmse:2.467194+0.514733
## [95] train-rmse:2.155126+0.147887 test-rmse:2.465354+0.507642
## [96] train-rmse:2.150620+0.148086 test-rmse:2.456686+0.511769
## [97] train-rmse:2.146204+0.148312 test-rmse:2.454821+0.515759
## [98] train-rmse:2.141855+0.148514 test-rmse:2.456630+0.513958
## [99] train-rmse:2.137555+0.148717 test-rmse:2.453513+0.521843
## [100] train-rmse:2.133346+0.148944 test-rmse:2.450303+0.520082
## [101] train-rmse:2.129322+0.149097 test-rmse:2.443763+0.524859
## [102] train-rmse:2.125335+0.149198 test-rmse:2.443368+0.522174
## [103] train-rmse:2.121387+0.149293 test-rmse:2.442611+0.518373
## [104] train-rmse:2.117561+0.149400 test-rmse:2.437124+0.523096
## [105] train-rmse:2.113762+0.149490 test-rmse:2.435645+0.520331
## [106] train-rmse:2.110088+0.149642 test-rmse:2.429916+0.519273
## [107] train-rmse:2.106571+0.149844 test-rmse:2.430776+0.526826
## [108] train-rmse:2.103062+0.150042 test-rmse:2.430055+0.528349
## [109] train-rmse:2.099444+0.150234 test-rmse:2.423650+0.524656
## [110] train-rmse:2.096027+0.150455 test-rmse:2.419041+0.521503
## [111] train-rmse:2.092610+0.150704 test-rmse:2.414201+0.521575
## [112] train-rmse:2.089211+0.150960 test-rmse:2.415295+0.522977
## [113] train-rmse:2.085952+0.151126 test-rmse:2.411531+0.521317
## [114] train-rmse:2.082687+0.151296 test-rmse:2.411274+0.524354
## [115] train-rmse:2.079445+0.151487 test-rmse:2.408327+0.522867
## [116] train-rmse:2.076256+0.151713 test-rmse:2.408660+0.521048
## [117] train-rmse:2.073100+0.151894 test-rmse:2.405482+0.519193
## [118] train-rmse:2.070030+0.152093 test-rmse:2.404561+0.518108
## [119] train-rmse:2.066910+0.152239 test-rmse:2.398429+0.520975
## [120] train-rmse:2.063932+0.152386 test-rmse:2.395757+0.522377
## [121] train-rmse:2.060954+0.152512 test-rmse:2.394735+0.524006
## [122] train-rmse:2.058016+0.152680 test-rmse:2.395157+0.522805
## [123] train-rmse:2.055104+0.152777 test-rmse:2.394829+0.519353
## [124] train-rmse:2.052232+0.152844 test-rmse:2.393576+0.519203
## [125] train-rmse:2.049430+0.152968 test-rmse:2.392444+0.522608
## [126] train-rmse:2.046720+0.153047 test-rmse:2.390369+0.522898
## [127] train-rmse:2.044038+0.153132 test-rmse:2.399053+0.517842
## [128] train-rmse:2.041333+0.153199 test-rmse:2.391914+0.522245
## [129] train-rmse:2.038669+0.153283 test-rmse:2.393674+0.529058
## [130] train-rmse:2.036006+0.153356 test-rmse:2.389041+0.526394
## [131] train-rmse:2.033313+0.153443 test-rmse:2.389230+0.528803
## [132] train-rmse:2.030761+0.153558 test-rmse:2.386643+0.527871
## [133] train-rmse:2.028252+0.153680 test-rmse:2.385422+0.529437
## [134] train-rmse:2.025714+0.153770 test-rmse:2.382152+0.525881
## [135] train-rmse:2.023264+0.153900 test-rmse:2.380258+0.524462
## [136] train-rmse:2.020843+0.154017 test-rmse:2.378088+0.525221
## [137] train-rmse:2.018457+0.154148 test-rmse:2.377597+0.528194
## [138] train-rmse:2.016061+0.154363 test-rmse:2.380545+0.529079
## [139] train-rmse:2.013689+0.154523 test-rmse:2.378190+0.528617
## [140] train-rmse:2.011395+0.154627 test-rmse:2.381968+0.531018
## [141] train-rmse:2.009156+0.154752 test-rmse:2.378235+0.532273
## [142] train-rmse:2.006912+0.154823 test-rmse:2.378604+0.530278
## [143] train-rmse:2.004711+0.154906 test-rmse:2.376005+0.527087
## [144] train-rmse:2.002424+0.155224 test-rmse:2.374776+0.528094
## [145] train-rmse:2.000166+0.155343 test-rmse:2.370788+0.529172
## [146] train-rmse:1.997992+0.155465 test-rmse:2.367130+0.530193
## [147] train-rmse:1.995798+0.155567 test-rmse:2.370577+0.527010
## [148] train-rmse:1.993684+0.155704 test-rmse:2.370827+0.528924
## [149] train-rmse:1.991528+0.155843 test-rmse:2.366462+0.529236
## [150] train-rmse:1.989486+0.155915 test-rmse:2.362371+0.532480
## [151] train-rmse:1.987411+0.156035 test-rmse:2.363595+0.530652
## [152] train-rmse:1.985396+0.156162 test-rmse:2.362878+0.528917
## [153] train-rmse:1.983335+0.156304 test-rmse:2.360987+0.532416
## [154] train-rmse:1.981345+0.156439 test-rmse:2.356589+0.533758
## [155] train-rmse:1.979345+0.156600 test-rmse:2.354282+0.533276
## [156] train-rmse:1.977347+0.156749 test-rmse:2.350558+0.531356
## [157] train-rmse:1.975347+0.156986 test-rmse:2.350945+0.533825
## [158] train-rmse:1.973400+0.157095 test-rmse:2.350966+0.535294
## [159] train-rmse:1.971469+0.157162 test-rmse:2.346752+0.533842
## [160] train-rmse:1.969584+0.157270 test-rmse:2.347229+0.531650
## [161] train-rmse:1.967736+0.157393 test-rmse:2.343579+0.529631
## [162] train-rmse:1.965906+0.157514 test-rmse:2.344747+0.531235
## [163] train-rmse:1.964060+0.157610 test-rmse:2.345044+0.529788
## [164] train-rmse:1.962155+0.157729 test-rmse:2.342615+0.528766
## [165] train-rmse:1.960344+0.157860 test-rmse:2.345073+0.531474
## [166] train-rmse:1.958464+0.157901 test-rmse:2.343650+0.531726
## [167] train-rmse:1.956679+0.158019 test-rmse:2.342652+0.531675
## [168] train-rmse:1.954906+0.158112 test-rmse:2.340009+0.531834
## [169] train-rmse:1.953153+0.158206 test-rmse:2.338331+0.534062
## [170] train-rmse:1.951391+0.158299 test-rmse:2.334932+0.538790
## [171] train-rmse:1.949687+0.158405 test-rmse:2.333953+0.541691
## [172] train-rmse:1.947951+0.158476 test-rmse:2.333297+0.541659
## [173] train-rmse:1.946227+0.158570 test-rmse:2.336728+0.537682
## [174] train-rmse:1.944539+0.158656 test-rmse:2.334532+0.539022
## [175] train-rmse:1.942854+0.158735 test-rmse:2.334502+0.540709
## [176] train-rmse:1.941078+0.158948 test-rmse:2.333080+0.538141
## [177] train-rmse:1.939362+0.159036 test-rmse:2.331795+0.538367
## [178] train-rmse:1.937711+0.159136 test-rmse:2.330097+0.538512
## [179] train-rmse:1.936081+0.159299 test-rmse:2.326369+0.539432
## [180] train-rmse:1.934416+0.159376 test-rmse:2.327100+0.539376
## [181] train-rmse:1.932831+0.159469 test-rmse:2.324345+0.538817
## [182] train-rmse:1.931273+0.159547 test-rmse:2.321745+0.536430
## [183] train-rmse:1.929700+0.159650 test-rmse:2.322228+0.536751
## [184] train-rmse:1.928118+0.159749 test-rmse:2.321462+0.535864
## [185] train-rmse:1.926441+0.159732 test-rmse:2.319511+0.533427
## [186] train-rmse:1.924901+0.159871 test-rmse:2.318885+0.537086
## [187] train-rmse:1.923391+0.160018 test-rmse:2.316943+0.536917
## [188] train-rmse:1.921806+0.160130 test-rmse:2.319160+0.537313
## [189] train-rmse:1.920289+0.160222 test-rmse:2.316727+0.534764
## [190] train-rmse:1.918829+0.160323 test-rmse:2.315254+0.534723
## [191] train-rmse:1.917376+0.160409 test-rmse:2.316302+0.536123
## [192] train-rmse:1.915917+0.160517 test-rmse:2.315883+0.533944
## [193] train-rmse:1.914508+0.160585 test-rmse:2.313055+0.535175
## [194] train-rmse:1.913054+0.160714 test-rmse:2.313206+0.534993
## [195] train-rmse:1.911575+0.160756 test-rmse:2.312737+0.534276
## [196] train-rmse:1.910160+0.160862 test-rmse:2.316426+0.536475
## [197] train-rmse:1.908685+0.160924 test-rmse:2.314977+0.539043
## [198] train-rmse:1.907256+0.161115 test-rmse:2.312596+0.539010
## [199] train-rmse:1.905832+0.161180 test-rmse:2.311310+0.536795
## [200] train-rmse:1.904411+0.161305 test-rmse:2.313492+0.535280
## [201] train-rmse:1.903047+0.161402 test-rmse:2.313461+0.535869
## [202] train-rmse:1.901692+0.161457 test-rmse:2.314810+0.540783
## [203] train-rmse:1.900348+0.161516 test-rmse:2.315808+0.537130
## [204] train-rmse:1.898981+0.161570 test-rmse:2.314528+0.538839
## [205] train-rmse:1.897686+0.161654 test-rmse:2.312619+0.538623
## Stopping. Best iteration:
## [199] train-rmse:1.905832+0.161180 test-rmse:2.311310+0.536795
##
## 43
## [1] train-rmse:6.810517+0.141913 test-rmse:6.832346+0.632789
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.772934+0.135773 test-rmse:5.848137+0.566532
## [3] train-rmse:5.005476+0.115587 test-rmse:5.126354+0.529097
## [4] train-rmse:4.423219+0.110446 test-rmse:4.519824+0.494154
## [5] train-rmse:3.977086+0.109633 test-rmse:4.141602+0.456627
## [6] train-rmse:3.636140+0.100792 test-rmse:3.823061+0.425142
## [7] train-rmse:3.370854+0.102619 test-rmse:3.592913+0.419405
## [8] train-rmse:3.156614+0.108868 test-rmse:3.383404+0.403184
## [9] train-rmse:2.992849+0.098692 test-rmse:3.235494+0.401273
## [10] train-rmse:2.863775+0.088305 test-rmse:3.125469+0.417734
## [11] train-rmse:2.751871+0.091879 test-rmse:3.031169+0.430809
## [12] train-rmse:2.673811+0.092021 test-rmse:2.986589+0.447652
## [13] train-rmse:2.593827+0.094909 test-rmse:2.922839+0.435495
## [14] train-rmse:2.526879+0.088386 test-rmse:2.864958+0.456627
## [15] train-rmse:2.465951+0.088998 test-rmse:2.843596+0.460546
## [16] train-rmse:2.414024+0.092681 test-rmse:2.815279+0.443483
## [17] train-rmse:2.369115+0.092678 test-rmse:2.783286+0.461963
## [18] train-rmse:2.316573+0.097228 test-rmse:2.757174+0.454717
## [19] train-rmse:2.273671+0.098687 test-rmse:2.726763+0.446616
## [20] train-rmse:2.233844+0.097897 test-rmse:2.708490+0.449874
## [21] train-rmse:2.197137+0.092484 test-rmse:2.696875+0.454809
## [22] train-rmse:2.151950+0.101034 test-rmse:2.661716+0.432973
## [23] train-rmse:2.114120+0.093408 test-rmse:2.633532+0.449237
## [24] train-rmse:2.073694+0.094883 test-rmse:2.587821+0.458766
## [25] train-rmse:2.043055+0.093251 test-rmse:2.572809+0.449915
## [26] train-rmse:2.014295+0.094072 test-rmse:2.549510+0.446930
## [27] train-rmse:1.987279+0.095541 test-rmse:2.530083+0.459528
## [28] train-rmse:1.956190+0.086998 test-rmse:2.507591+0.472118
## [29] train-rmse:1.932295+0.087281 test-rmse:2.489789+0.475452
## [30] train-rmse:1.906966+0.091040 test-rmse:2.466843+0.454798
## [31] train-rmse:1.883928+0.087389 test-rmse:2.461247+0.465019
## [32] train-rmse:1.854934+0.079731 test-rmse:2.445575+0.470490
## [33] train-rmse:1.833351+0.079604 test-rmse:2.428985+0.468383
## [34] train-rmse:1.813843+0.080211 test-rmse:2.418364+0.459417
## [35] train-rmse:1.791352+0.078342 test-rmse:2.399587+0.461805
## [36] train-rmse:1.772183+0.078059 test-rmse:2.379294+0.460611
## [37] train-rmse:1.753082+0.080955 test-rmse:2.363128+0.450222
## [38] train-rmse:1.737090+0.080979 test-rmse:2.357162+0.459593
## [39] train-rmse:1.719718+0.082720 test-rmse:2.345619+0.452201
## [40] train-rmse:1.704057+0.082296 test-rmse:2.339482+0.463239
## [41] train-rmse:1.685310+0.083177 test-rmse:2.326920+0.459350
## [42] train-rmse:1.670726+0.081471 test-rmse:2.314570+0.464685
## [43] train-rmse:1.654189+0.081686 test-rmse:2.311431+0.468821
## [44] train-rmse:1.636944+0.075076 test-rmse:2.307066+0.465067
## [45] train-rmse:1.620465+0.076536 test-rmse:2.298356+0.458100
## [46] train-rmse:1.604539+0.078383 test-rmse:2.285292+0.461961
## [47] train-rmse:1.589100+0.080721 test-rmse:2.273719+0.453202
## [48] train-rmse:1.577195+0.081045 test-rmse:2.268611+0.456785
## [49] train-rmse:1.563531+0.078315 test-rmse:2.267577+0.453004
## [50] train-rmse:1.552108+0.079173 test-rmse:2.263552+0.455172
## [51] train-rmse:1.541576+0.079248 test-rmse:2.258781+0.459222
## [52] train-rmse:1.532196+0.079552 test-rmse:2.257817+0.468592
## [53] train-rmse:1.515521+0.073948 test-rmse:2.250106+0.472442
## [54] train-rmse:1.501226+0.075142 test-rmse:2.241901+0.474087
## [55] train-rmse:1.491070+0.074989 test-rmse:2.245902+0.473858
## [56] train-rmse:1.480573+0.075629 test-rmse:2.239545+0.478951
## [57] train-rmse:1.469446+0.076819 test-rmse:2.234909+0.479022
## [58] train-rmse:1.458511+0.078394 test-rmse:2.229630+0.476180
## [59] train-rmse:1.442294+0.082511 test-rmse:2.216792+0.470327
## [60] train-rmse:1.434757+0.082820 test-rmse:2.217281+0.468678
## [61] train-rmse:1.422338+0.081899 test-rmse:2.212578+0.468141
## [62] train-rmse:1.403078+0.066173 test-rmse:2.200706+0.473833
## [63] train-rmse:1.394011+0.064255 test-rmse:2.197171+0.472106
## [64] train-rmse:1.386423+0.064723 test-rmse:2.193260+0.471477
## [65] train-rmse:1.376883+0.062591 test-rmse:2.194133+0.480099
## [66] train-rmse:1.365939+0.067182 test-rmse:2.193342+0.477225
## [67] train-rmse:1.356845+0.068458 test-rmse:2.188830+0.482212
## [68] train-rmse:1.349331+0.069818 test-rmse:2.188663+0.482217
## [69] train-rmse:1.341048+0.069576 test-rmse:2.185349+0.484458
## [70] train-rmse:1.331552+0.066878 test-rmse:2.181932+0.486828
## [71] train-rmse:1.322194+0.064989 test-rmse:2.179049+0.488374
## [72] train-rmse:1.313153+0.068416 test-rmse:2.173975+0.486806
## [73] train-rmse:1.305040+0.071839 test-rmse:2.168388+0.480843
## [74] train-rmse:1.299546+0.072029 test-rmse:2.164674+0.480137
## [75] train-rmse:1.292719+0.072357 test-rmse:2.162548+0.478286
## [76] train-rmse:1.284930+0.072606 test-rmse:2.156807+0.478976
## [77] train-rmse:1.272128+0.074402 test-rmse:2.154618+0.483004
## [78] train-rmse:1.260612+0.075616 test-rmse:2.147959+0.492678
## [79] train-rmse:1.250206+0.075793 test-rmse:2.150073+0.491947
## [80] train-rmse:1.238648+0.071016 test-rmse:2.147923+0.491372
## [81] train-rmse:1.234081+0.071281 test-rmse:2.144162+0.492191
## [82] train-rmse:1.228724+0.071401 test-rmse:2.142399+0.494044
## [83] train-rmse:1.223268+0.069337 test-rmse:2.143518+0.492507
## [84] train-rmse:1.215938+0.067850 test-rmse:2.144510+0.498230
## [85] train-rmse:1.209775+0.068058 test-rmse:2.141048+0.498235
## [86] train-rmse:1.202918+0.067592 test-rmse:2.131775+0.503526
## [87] train-rmse:1.193040+0.072307 test-rmse:2.135863+0.506336
## [88] train-rmse:1.186113+0.076097 test-rmse:2.129004+0.504564
## [89] train-rmse:1.179839+0.077268 test-rmse:2.127058+0.502917
## [90] train-rmse:1.171699+0.078326 test-rmse:2.128330+0.506756
## [91] train-rmse:1.167676+0.078063 test-rmse:2.125607+0.508020
## [92] train-rmse:1.163811+0.077858 test-rmse:2.124400+0.507104
## [93] train-rmse:1.158627+0.078099 test-rmse:2.121644+0.507464
## [94] train-rmse:1.152976+0.077601 test-rmse:2.118154+0.508771
## [95] train-rmse:1.147366+0.078183 test-rmse:2.118413+0.509396
## [96] train-rmse:1.141444+0.079207 test-rmse:2.119326+0.510062
## [97] train-rmse:1.135632+0.077829 test-rmse:2.118040+0.510539
## [98] train-rmse:1.130524+0.079187 test-rmse:2.115821+0.508190
## [99] train-rmse:1.124867+0.076317 test-rmse:2.114849+0.510506
## [100] train-rmse:1.119347+0.077710 test-rmse:2.110795+0.510074
## [101] train-rmse:1.112013+0.077344 test-rmse:2.114623+0.508065
## [102] train-rmse:1.105325+0.077910 test-rmse:2.109653+0.508366
## [103] train-rmse:1.096422+0.075149 test-rmse:2.109411+0.508027
## [104] train-rmse:1.092888+0.075763 test-rmse:2.107774+0.508716
## [105] train-rmse:1.088616+0.076048 test-rmse:2.103794+0.508235
## [106] train-rmse:1.083098+0.075716 test-rmse:2.102087+0.510914
## [107] train-rmse:1.078718+0.074438 test-rmse:2.104238+0.514030
## [108] train-rmse:1.072421+0.075412 test-rmse:2.108556+0.511274
## [109] train-rmse:1.066507+0.075874 test-rmse:2.107165+0.514991
## [110] train-rmse:1.060901+0.076807 test-rmse:2.105214+0.512684
## [111] train-rmse:1.050442+0.074972 test-rmse:2.109606+0.520405
## [112] train-rmse:1.047272+0.075692 test-rmse:2.107698+0.519398
## Stopping. Best iteration:
## [106] train-rmse:1.083098+0.075716 test-rmse:2.102087+0.510914
##
## 44
## [1] train-rmse:6.744784+0.137858 test-rmse:6.804640+0.620685
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.649021+0.113145 test-rmse:5.760286+0.554329
## [3] train-rmse:4.794825+0.098419 test-rmse:4.934679+0.530009
## [4] train-rmse:4.148064+0.089692 test-rmse:4.332180+0.491567
## [5] train-rmse:3.661203+0.078324 test-rmse:3.886157+0.466245
## [6] train-rmse:3.277400+0.059029 test-rmse:3.538540+0.452029
## [7] train-rmse:2.981581+0.055028 test-rmse:3.276110+0.458588
## [8] train-rmse:2.763237+0.069405 test-rmse:3.114076+0.440381
## [9] train-rmse:2.591715+0.072049 test-rmse:2.977888+0.463596
## [10] train-rmse:2.452875+0.061097 test-rmse:2.901688+0.442512
## [11] train-rmse:2.333101+0.067325 test-rmse:2.822950+0.458210
## [12] train-rmse:2.244742+0.064587 test-rmse:2.756746+0.478941
## [13] train-rmse:2.156345+0.054382 test-rmse:2.719124+0.485340
## [14] train-rmse:2.088062+0.060833 test-rmse:2.680196+0.496875
## [15] train-rmse:2.018423+0.056908 test-rmse:2.637144+0.519894
## [16] train-rmse:1.958458+0.059750 test-rmse:2.592751+0.518004
## [17] train-rmse:1.900508+0.055821 test-rmse:2.549753+0.501191
## [18] train-rmse:1.846878+0.055663 test-rmse:2.538770+0.507555
## [19] train-rmse:1.799795+0.051929 test-rmse:2.514351+0.506098
## [20] train-rmse:1.763824+0.051442 test-rmse:2.495058+0.517671
## [21] train-rmse:1.723950+0.057495 test-rmse:2.454052+0.495869
## [22] train-rmse:1.688694+0.051580 test-rmse:2.435754+0.503352
## [23] train-rmse:1.655578+0.053738 test-rmse:2.430171+0.497367
## [24] train-rmse:1.620413+0.047649 test-rmse:2.418571+0.500048
## [25] train-rmse:1.595421+0.047420 test-rmse:2.399097+0.502696
## [26] train-rmse:1.560621+0.052555 test-rmse:2.374060+0.498248
## [27] train-rmse:1.529180+0.049695 test-rmse:2.352743+0.510664
## [28] train-rmse:1.501717+0.049585 test-rmse:2.338647+0.518503
## [29] train-rmse:1.475817+0.051197 test-rmse:2.333374+0.518940
## [30] train-rmse:1.439272+0.051768 test-rmse:2.329671+0.523948
## [31] train-rmse:1.413981+0.051970 test-rmse:2.317977+0.512422
## [32] train-rmse:1.391035+0.052610 test-rmse:2.305984+0.510224
## [33] train-rmse:1.371602+0.053405 test-rmse:2.301203+0.507311
## [34] train-rmse:1.351565+0.055492 test-rmse:2.289278+0.516645
## [35] train-rmse:1.331067+0.059458 test-rmse:2.284451+0.509300
## [36] train-rmse:1.309049+0.055736 test-rmse:2.270677+0.515304
## [37] train-rmse:1.291486+0.051561 test-rmse:2.271337+0.516990
## [38] train-rmse:1.269400+0.053066 test-rmse:2.272014+0.517819
## [39] train-rmse:1.254281+0.055329 test-rmse:2.259634+0.516988
## [40] train-rmse:1.239791+0.052208 test-rmse:2.252911+0.524199
## [41] train-rmse:1.224280+0.055410 test-rmse:2.241416+0.527844
## [42] train-rmse:1.205017+0.057108 test-rmse:2.233540+0.530466
## [43] train-rmse:1.190790+0.054034 test-rmse:2.242780+0.540485
## [44] train-rmse:1.170749+0.043259 test-rmse:2.241600+0.537888
## [45] train-rmse:1.156960+0.042948 test-rmse:2.240303+0.538553
## [46] train-rmse:1.141671+0.041981 test-rmse:2.229953+0.538300
## [47] train-rmse:1.126148+0.048745 test-rmse:2.222478+0.539220
## [48] train-rmse:1.111367+0.042335 test-rmse:2.218412+0.537736
## [49] train-rmse:1.095959+0.036631 test-rmse:2.216949+0.539000
## [50] train-rmse:1.081632+0.035878 test-rmse:2.211396+0.539912
## [51] train-rmse:1.067939+0.038491 test-rmse:2.207625+0.535026
## [52] train-rmse:1.053885+0.039085 test-rmse:2.200525+0.536676
## [53] train-rmse:1.036673+0.041691 test-rmse:2.197764+0.543411
## [54] train-rmse:1.026546+0.046012 test-rmse:2.197707+0.548752
## [55] train-rmse:1.015403+0.042054 test-rmse:2.194278+0.548749
## [56] train-rmse:1.001880+0.042940 test-rmse:2.187863+0.551627
## [57] train-rmse:0.993710+0.042465 test-rmse:2.182702+0.547549
## [58] train-rmse:0.973757+0.034113 test-rmse:2.183964+0.557300
## [59] train-rmse:0.963422+0.034002 test-rmse:2.181132+0.557064
## [60] train-rmse:0.952851+0.032794 test-rmse:2.177557+0.559363
## [61] train-rmse:0.942804+0.034095 test-rmse:2.173784+0.557621
## [62] train-rmse:0.935987+0.034582 test-rmse:2.173461+0.557229
## [63] train-rmse:0.923528+0.039055 test-rmse:2.170113+0.557499
## [64] train-rmse:0.910127+0.040595 test-rmse:2.171084+0.556972
## [65] train-rmse:0.895842+0.037498 test-rmse:2.165527+0.560513
## [66] train-rmse:0.884720+0.040710 test-rmse:2.157379+0.557725
## [67] train-rmse:0.874640+0.038597 test-rmse:2.153948+0.559227
## [68] train-rmse:0.864502+0.039939 test-rmse:2.146875+0.554151
## [69] train-rmse:0.856088+0.040391 test-rmse:2.144336+0.553776
## [70] train-rmse:0.845118+0.038165 test-rmse:2.146250+0.549614
## [71] train-rmse:0.837696+0.036437 test-rmse:2.144862+0.553785
## [72] train-rmse:0.831845+0.035465 test-rmse:2.142793+0.553597
## [73] train-rmse:0.821262+0.033580 test-rmse:2.142878+0.553615
## [74] train-rmse:0.809965+0.032594 test-rmse:2.136958+0.549881
## [75] train-rmse:0.801386+0.035874 test-rmse:2.134129+0.548134
## [76] train-rmse:0.791633+0.033240 test-rmse:2.136528+0.547433
## [77] train-rmse:0.783243+0.031441 test-rmse:2.132673+0.545820
## [78] train-rmse:0.777335+0.031490 test-rmse:2.131511+0.545886
## [79] train-rmse:0.764784+0.032367 test-rmse:2.127333+0.544754
## [80] train-rmse:0.757176+0.032570 test-rmse:2.124845+0.543381
## [81] train-rmse:0.751201+0.033819 test-rmse:2.124823+0.547581
## [82] train-rmse:0.744966+0.035734 test-rmse:2.129434+0.549819
## [83] train-rmse:0.739385+0.037892 test-rmse:2.128703+0.550243
## [84] train-rmse:0.731968+0.035777 test-rmse:2.127130+0.550059
## [85] train-rmse:0.721969+0.035786 test-rmse:2.129617+0.555196
## [86] train-rmse:0.718039+0.035299 test-rmse:2.129590+0.557186
## [87] train-rmse:0.713116+0.035288 test-rmse:2.126345+0.557080
## Stopping. Best iteration:
## [81] train-rmse:0.751201+0.033819 test-rmse:2.124823+0.547581
##
## 45
## [1] train-rmse:6.683643+0.136581 test-rmse:6.721201+0.609132
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.518563+0.109197 test-rmse:5.621737+0.524024
## [3] train-rmse:4.623669+0.081647 test-rmse:4.777244+0.482100
## [4] train-rmse:3.941335+0.061203 test-rmse:4.149943+0.444138
## [5] train-rmse:3.430624+0.057166 test-rmse:3.722842+0.428355
## [6] train-rmse:3.034445+0.047415 test-rmse:3.394865+0.454033
## [7] train-rmse:2.726078+0.044546 test-rmse:3.164268+0.463492
## [8] train-rmse:2.499990+0.044044 test-rmse:2.998935+0.464642
## [9] train-rmse:2.312436+0.051265 test-rmse:2.889999+0.471037
## [10] train-rmse:2.144733+0.052560 test-rmse:2.780357+0.453449
## [11] train-rmse:2.025882+0.039982 test-rmse:2.703027+0.487540
## [12] train-rmse:1.922372+0.049736 test-rmse:2.643686+0.470178
## [13] train-rmse:1.838030+0.047431 test-rmse:2.598091+0.437577
## [14] train-rmse:1.762000+0.056076 test-rmse:2.546540+0.445706
## [15] train-rmse:1.696742+0.053730 test-rmse:2.505031+0.439705
## [16] train-rmse:1.636801+0.050744 test-rmse:2.471228+0.442590
## [17] train-rmse:1.581444+0.047065 test-rmse:2.444299+0.445927
## [18] train-rmse:1.536260+0.051005 test-rmse:2.432674+0.455996
## [19] train-rmse:1.492087+0.051404 test-rmse:2.410477+0.462738
## [20] train-rmse:1.452448+0.047454 test-rmse:2.406577+0.459735
## [21] train-rmse:1.406776+0.038951 test-rmse:2.402673+0.466443
## [22] train-rmse:1.366927+0.036887 test-rmse:2.390186+0.471436
## [23] train-rmse:1.327462+0.031446 test-rmse:2.367743+0.463245
## [24] train-rmse:1.298062+0.033492 test-rmse:2.356625+0.462757
## [25] train-rmse:1.261048+0.032706 test-rmse:2.350414+0.466523
## [26] train-rmse:1.235598+0.030662 test-rmse:2.340715+0.473346
## [27] train-rmse:1.198569+0.028821 test-rmse:2.325557+0.465344
## [28] train-rmse:1.170279+0.032200 test-rmse:2.316229+0.466488
## [29] train-rmse:1.141764+0.027441 test-rmse:2.305286+0.464682
## [30] train-rmse:1.117138+0.031708 test-rmse:2.292193+0.468448
## [31] train-rmse:1.089780+0.036129 test-rmse:2.282472+0.472898
## [32] train-rmse:1.072270+0.036558 test-rmse:2.276698+0.470636
## [33] train-rmse:1.052084+0.032801 test-rmse:2.274452+0.472090
## [34] train-rmse:1.027577+0.030661 test-rmse:2.268305+0.469141
## [35] train-rmse:1.007648+0.031442 test-rmse:2.268126+0.464076
## [36] train-rmse:0.990038+0.032880 test-rmse:2.259850+0.465152
## [37] train-rmse:0.972745+0.022833 test-rmse:2.257921+0.473680
## [38] train-rmse:0.947435+0.022174 test-rmse:2.249530+0.476623
## [39] train-rmse:0.923016+0.018844 test-rmse:2.237364+0.476029
## [40] train-rmse:0.901740+0.021303 test-rmse:2.234069+0.481801
## [41] train-rmse:0.887390+0.020027 test-rmse:2.232760+0.478807
## [42] train-rmse:0.873808+0.019434 test-rmse:2.232359+0.481154
## [43] train-rmse:0.858312+0.022892 test-rmse:2.224244+0.484324
## [44] train-rmse:0.839999+0.021906 test-rmse:2.215191+0.486921
## [45] train-rmse:0.820936+0.023792 test-rmse:2.211810+0.491555
## [46] train-rmse:0.806179+0.023496 test-rmse:2.207988+0.488627
## [47] train-rmse:0.795281+0.019241 test-rmse:2.203649+0.489722
## [48] train-rmse:0.776470+0.015164 test-rmse:2.196485+0.490453
## [49] train-rmse:0.762847+0.013672 test-rmse:2.191053+0.489993
## [50] train-rmse:0.749929+0.017368 test-rmse:2.189785+0.491056
## [51] train-rmse:0.739408+0.017238 test-rmse:2.184820+0.491853
## [52] train-rmse:0.723468+0.024900 test-rmse:2.186695+0.493231
## [53] train-rmse:0.712974+0.026949 test-rmse:2.181685+0.495320
## [54] train-rmse:0.693535+0.027791 test-rmse:2.174585+0.494504
## [55] train-rmse:0.684574+0.030525 test-rmse:2.177654+0.497811
## [56] train-rmse:0.670133+0.028686 test-rmse:2.177407+0.496578
## [57] train-rmse:0.655891+0.027580 test-rmse:2.175435+0.501084
## [58] train-rmse:0.640061+0.026589 test-rmse:2.174916+0.500789
## [59] train-rmse:0.631961+0.027627 test-rmse:2.173356+0.500273
## [60] train-rmse:0.625416+0.027445 test-rmse:2.171122+0.501777
## [61] train-rmse:0.615963+0.028932 test-rmse:2.169605+0.503200
## [62] train-rmse:0.602883+0.028298 test-rmse:2.169670+0.503348
## [63] train-rmse:0.594192+0.029490 test-rmse:2.169883+0.501761
## [64] train-rmse:0.585018+0.028752 test-rmse:2.168065+0.503004
## [65] train-rmse:0.575770+0.027695 test-rmse:2.173040+0.505186
## [66] train-rmse:0.566364+0.030075 test-rmse:2.172513+0.504998
## [67] train-rmse:0.560012+0.031486 test-rmse:2.169847+0.504489
## [68] train-rmse:0.551657+0.033052 test-rmse:2.170812+0.505633
## [69] train-rmse:0.543084+0.034397 test-rmse:2.169389+0.506742
## [70] train-rmse:0.535399+0.034874 test-rmse:2.173129+0.509359
## Stopping. Best iteration:
## [64] train-rmse:0.585018+0.028752 test-rmse:2.168065+0.503004
##
## 46
## [1] train-rmse:6.641380+0.134266 test-rmse:6.650056+0.590720
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.444091+0.104284 test-rmse:5.511503+0.514386
## [3] train-rmse:4.522743+0.093897 test-rmse:4.672128+0.441376
## [4] train-rmse:3.814346+0.070253 test-rmse:4.058290+0.430485
## [5] train-rmse:3.286395+0.067599 test-rmse:3.622711+0.422477
## [6] train-rmse:2.882163+0.060745 test-rmse:3.330696+0.404020
## [7] train-rmse:2.557907+0.056568 test-rmse:3.106325+0.453606
## [8] train-rmse:2.326375+0.054603 test-rmse:2.957393+0.475934
## [9] train-rmse:2.119225+0.058647 test-rmse:2.833944+0.495787
## [10] train-rmse:1.973698+0.071827 test-rmse:2.765160+0.496221
## [11] train-rmse:1.853267+0.067632 test-rmse:2.700551+0.497501
## [12] train-rmse:1.749073+0.058295 test-rmse:2.651167+0.523994
## [13] train-rmse:1.661700+0.060516 test-rmse:2.592275+0.525595
## [14] train-rmse:1.588407+0.064401 test-rmse:2.555380+0.538268
## [15] train-rmse:1.513711+0.058191 test-rmse:2.518840+0.525968
## [16] train-rmse:1.435134+0.055187 test-rmse:2.486310+0.545864
## [17] train-rmse:1.381707+0.060916 test-rmse:2.465295+0.546463
## [18] train-rmse:1.335005+0.055581 test-rmse:2.441957+0.538801
## [19] train-rmse:1.279967+0.049765 test-rmse:2.429186+0.534316
## [20] train-rmse:1.236176+0.059110 test-rmse:2.412465+0.532835
## [21] train-rmse:1.191826+0.054845 test-rmse:2.396571+0.528162
## [22] train-rmse:1.155466+0.059817 test-rmse:2.382991+0.539328
## [23] train-rmse:1.116322+0.057935 test-rmse:2.374851+0.539259
## [24] train-rmse:1.078304+0.052621 test-rmse:2.362381+0.539958
## [25] train-rmse:1.042954+0.057452 test-rmse:2.365659+0.535846
## [26] train-rmse:1.019326+0.055545 test-rmse:2.357747+0.540869
## [27] train-rmse:0.996315+0.056232 test-rmse:2.354156+0.540125
## [28] train-rmse:0.972162+0.053159 test-rmse:2.345021+0.541483
## [29] train-rmse:0.950676+0.052088 test-rmse:2.343177+0.543674
## [30] train-rmse:0.920266+0.056139 test-rmse:2.330673+0.553232
## [31] train-rmse:0.888845+0.046670 test-rmse:2.325767+0.558084
## [32] train-rmse:0.863470+0.041442 test-rmse:2.322845+0.557003
## [33] train-rmse:0.839418+0.040756 test-rmse:2.315331+0.564575
## [34] train-rmse:0.819955+0.034807 test-rmse:2.314103+0.568153
## [35] train-rmse:0.790224+0.030750 test-rmse:2.309365+0.568084
## [36] train-rmse:0.768339+0.029697 test-rmse:2.308584+0.570450
## [37] train-rmse:0.750547+0.028769 test-rmse:2.299953+0.579644
## [38] train-rmse:0.724774+0.022940 test-rmse:2.295719+0.580318
## [39] train-rmse:0.705425+0.019857 test-rmse:2.299430+0.584049
## [40] train-rmse:0.692146+0.021319 test-rmse:2.296158+0.583194
## [41] train-rmse:0.673408+0.018788 test-rmse:2.295071+0.584458
## [42] train-rmse:0.654230+0.016814 test-rmse:2.291928+0.587705
## [43] train-rmse:0.638048+0.019221 test-rmse:2.288096+0.589356
## [44] train-rmse:0.624515+0.016300 test-rmse:2.285444+0.589668
## [45] train-rmse:0.608471+0.013599 test-rmse:2.283407+0.592442
## [46] train-rmse:0.596440+0.012240 test-rmse:2.280540+0.593010
## [47] train-rmse:0.583197+0.011051 test-rmse:2.282100+0.592559
## [48] train-rmse:0.570866+0.008726 test-rmse:2.282571+0.592412
## [49] train-rmse:0.556387+0.007481 test-rmse:2.281207+0.594699
## [50] train-rmse:0.546929+0.009350 test-rmse:2.276588+0.596066
## [51] train-rmse:0.533475+0.007563 test-rmse:2.271703+0.591416
## [52] train-rmse:0.521074+0.007159 test-rmse:2.272534+0.592201
## [53] train-rmse:0.509797+0.011937 test-rmse:2.271277+0.591133
## [54] train-rmse:0.498137+0.008589 test-rmse:2.271205+0.593594
## [55] train-rmse:0.486305+0.007019 test-rmse:2.272455+0.592185
## [56] train-rmse:0.475427+0.006145 test-rmse:2.272593+0.593753
## [57] train-rmse:0.466332+0.006354 test-rmse:2.270348+0.595172
## [58] train-rmse:0.457629+0.007173 test-rmse:2.268313+0.597452
## [59] train-rmse:0.446935+0.007366 test-rmse:2.265756+0.597747
## [60] train-rmse:0.436528+0.007786 test-rmse:2.265626+0.596846
## [61] train-rmse:0.424004+0.008001 test-rmse:2.263975+0.596658
## [62] train-rmse:0.415138+0.008545 test-rmse:2.261388+0.602012
## [63] train-rmse:0.403846+0.011832 test-rmse:2.257819+0.600820
## [64] train-rmse:0.397426+0.011204 test-rmse:2.255674+0.602022
## [65] train-rmse:0.392335+0.011466 test-rmse:2.254748+0.601035
## [66] train-rmse:0.387764+0.010404 test-rmse:2.254481+0.600758
## [67] train-rmse:0.380839+0.009721 test-rmse:2.254554+0.600514
## [68] train-rmse:0.371630+0.007542 test-rmse:2.252613+0.600627
## [69] train-rmse:0.363615+0.007711 test-rmse:2.251848+0.597333
## [70] train-rmse:0.354001+0.004952 test-rmse:2.250078+0.597582
## [71] train-rmse:0.343964+0.007337 test-rmse:2.249327+0.596290
## [72] train-rmse:0.336815+0.009021 test-rmse:2.247708+0.598451
## [73] train-rmse:0.329454+0.010353 test-rmse:2.247776+0.599186
## [74] train-rmse:0.322448+0.009377 test-rmse:2.246655+0.600288
## [75] train-rmse:0.315843+0.008995 test-rmse:2.244706+0.600024
## [76] train-rmse:0.306648+0.006608 test-rmse:2.243835+0.600301
## [77] train-rmse:0.300736+0.004582 test-rmse:2.245195+0.601267
## [78] train-rmse:0.295635+0.005695 test-rmse:2.244071+0.600053
## [79] train-rmse:0.289562+0.004348 test-rmse:2.244107+0.599476
## [80] train-rmse:0.283821+0.006103 test-rmse:2.243393+0.597988
## [81] train-rmse:0.279179+0.005782 test-rmse:2.244650+0.599999
## [82] train-rmse:0.274124+0.005673 test-rmse:2.244506+0.600414
## [83] train-rmse:0.266409+0.005354 test-rmse:2.243368+0.599687
## [84] train-rmse:0.260717+0.005148 test-rmse:2.243487+0.600105
## [85] train-rmse:0.255997+0.007317 test-rmse:2.242917+0.601950
## [86] train-rmse:0.252177+0.006716 test-rmse:2.243559+0.601952
## [87] train-rmse:0.246267+0.005784 test-rmse:2.243252+0.602966
## [88] train-rmse:0.241238+0.007325 test-rmse:2.242923+0.602306
## [89] train-rmse:0.235733+0.007748 test-rmse:2.244025+0.602128
## [90] train-rmse:0.231947+0.007853 test-rmse:2.244086+0.601911
## [91] train-rmse:0.228304+0.009424 test-rmse:2.244384+0.602646
## Stopping. Best iteration:
## [85] train-rmse:0.255997+0.007317 test-rmse:2.242917+0.601950
##
## 47
## [1] train-rmse:6.614803+0.130204 test-rmse:6.632687+0.615322
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.389400+0.096789 test-rmse:5.492629+0.545107
## [3] train-rmse:4.442451+0.075529 test-rmse:4.648604+0.493068
## [4] train-rmse:3.722341+0.062329 test-rmse:4.052704+0.468397
## [5] train-rmse:3.163644+0.046145 test-rmse:3.599831+0.454502
## [6] train-rmse:2.730568+0.040521 test-rmse:3.284674+0.467864
## [7] train-rmse:2.399318+0.040449 test-rmse:3.087921+0.496319
## [8] train-rmse:2.146514+0.038568 test-rmse:2.909270+0.515880
## [9] train-rmse:1.935152+0.036383 test-rmse:2.780548+0.545440
## [10] train-rmse:1.770054+0.030803 test-rmse:2.710815+0.532045
## [11] train-rmse:1.646145+0.029538 test-rmse:2.651384+0.539589
## [12] train-rmse:1.523235+0.040308 test-rmse:2.609097+0.563022
## [13] train-rmse:1.416974+0.035626 test-rmse:2.569320+0.555049
## [14] train-rmse:1.335395+0.035582 test-rmse:2.528176+0.562654
## [15] train-rmse:1.266817+0.027491 test-rmse:2.498198+0.564679
## [16] train-rmse:1.210330+0.032523 test-rmse:2.482908+0.564521
## [17] train-rmse:1.150708+0.030190 test-rmse:2.464861+0.561081
## [18] train-rmse:1.100778+0.027695 test-rmse:2.446822+0.564086
## [19] train-rmse:1.053424+0.024764 test-rmse:2.433931+0.577426
## [20] train-rmse:1.009976+0.015829 test-rmse:2.431004+0.574866
## [21] train-rmse:0.972946+0.014739 test-rmse:2.422936+0.568459
## [22] train-rmse:0.940603+0.021006 test-rmse:2.411835+0.569695
## [23] train-rmse:0.904130+0.020515 test-rmse:2.399890+0.574075
## [24] train-rmse:0.857563+0.018144 test-rmse:2.389750+0.559200
## [25] train-rmse:0.822807+0.015490 test-rmse:2.383304+0.566928
## [26] train-rmse:0.793514+0.016040 test-rmse:2.378609+0.570624
## [27] train-rmse:0.766210+0.016379 test-rmse:2.363851+0.574110
## [28] train-rmse:0.732495+0.021398 test-rmse:2.361413+0.572215
## [29] train-rmse:0.704888+0.023471 test-rmse:2.357560+0.573128
## [30] train-rmse:0.682036+0.017309 test-rmse:2.354049+0.576975
## [31] train-rmse:0.656685+0.019640 test-rmse:2.344452+0.580741
## [32] train-rmse:0.628811+0.026023 test-rmse:2.343135+0.579108
## [33] train-rmse:0.611742+0.022322 test-rmse:2.336427+0.584337
## [34] train-rmse:0.593254+0.021748 test-rmse:2.335343+0.582668
## [35] train-rmse:0.571288+0.018849 test-rmse:2.338260+0.581080
## [36] train-rmse:0.557669+0.020182 test-rmse:2.337204+0.582680
## [37] train-rmse:0.540887+0.024717 test-rmse:2.333565+0.584003
## [38] train-rmse:0.525241+0.022439 test-rmse:2.333540+0.581699
## [39] train-rmse:0.508796+0.016109 test-rmse:2.330802+0.584322
## [40] train-rmse:0.489656+0.018878 test-rmse:2.329139+0.585089
## [41] train-rmse:0.474599+0.019281 test-rmse:2.325102+0.585730
## [42] train-rmse:0.457299+0.018419 test-rmse:2.321956+0.586026
## [43] train-rmse:0.443600+0.023577 test-rmse:2.319653+0.587096
## [44] train-rmse:0.429277+0.020679 test-rmse:2.319062+0.584658
## [45] train-rmse:0.418636+0.020502 test-rmse:2.318075+0.586130
## [46] train-rmse:0.405539+0.022222 test-rmse:2.317001+0.586315
## [47] train-rmse:0.394769+0.025132 test-rmse:2.316383+0.586578
## [48] train-rmse:0.383152+0.021693 test-rmse:2.314561+0.586423
## [49] train-rmse:0.371603+0.019969 test-rmse:2.315388+0.585128
## [50] train-rmse:0.361926+0.019233 test-rmse:2.315120+0.584737
## [51] train-rmse:0.350090+0.012059 test-rmse:2.315100+0.585197
## [52] train-rmse:0.340128+0.013052 test-rmse:2.310905+0.585716
## [53] train-rmse:0.327318+0.013701 test-rmse:2.309630+0.588654
## [54] train-rmse:0.320514+0.013087 test-rmse:2.308830+0.589384
## [55] train-rmse:0.312382+0.013151 test-rmse:2.305285+0.591858
## [56] train-rmse:0.301550+0.016448 test-rmse:2.303720+0.592720
## [57] train-rmse:0.293806+0.017956 test-rmse:2.302357+0.592141
## [58] train-rmse:0.287205+0.020551 test-rmse:2.300755+0.592651
## [59] train-rmse:0.280395+0.019014 test-rmse:2.302295+0.593037
## [60] train-rmse:0.273394+0.018583 test-rmse:2.304933+0.593338
## [61] train-rmse:0.266001+0.020117 test-rmse:2.303989+0.593311
## [62] train-rmse:0.259066+0.020055 test-rmse:2.303666+0.593710
## [63] train-rmse:0.254543+0.021735 test-rmse:2.304094+0.593850
## [64] train-rmse:0.247608+0.022981 test-rmse:2.304607+0.590792
## Stopping. Best iteration:
## [58] train-rmse:0.287205+0.020551 test-rmse:2.300755+0.592651
##
## 48
## [1] train-rmse:6.605372+0.129411 test-rmse:6.627336+0.614893
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.364980+0.092460 test-rmse:5.486353+0.542474
## [3] train-rmse:4.397326+0.069133 test-rmse:4.610540+0.511803
## [4] train-rmse:3.651502+0.054260 test-rmse:3.984206+0.470766
## [5] train-rmse:3.079821+0.046775 test-rmse:3.529958+0.462593
## [6] train-rmse:2.630044+0.032222 test-rmse:3.241158+0.461753
## [7] train-rmse:2.283090+0.030394 test-rmse:3.049533+0.471323
## [8] train-rmse:2.014165+0.027417 test-rmse:2.871664+0.502362
## [9] train-rmse:1.800260+0.025173 test-rmse:2.759805+0.519740
## [10] train-rmse:1.630590+0.029723 test-rmse:2.661061+0.528888
## [11] train-rmse:1.487380+0.041293 test-rmse:2.620993+0.536551
## [12] train-rmse:1.363548+0.039605 test-rmse:2.561693+0.544782
## [13] train-rmse:1.268792+0.034770 test-rmse:2.526781+0.552982
## [14] train-rmse:1.173755+0.029101 test-rmse:2.489465+0.572441
## [15] train-rmse:1.107555+0.028543 test-rmse:2.461112+0.571959
## [16] train-rmse:1.038215+0.030318 test-rmse:2.442757+0.573554
## [17] train-rmse:0.974435+0.038784 test-rmse:2.419342+0.556779
## [18] train-rmse:0.922678+0.025895 test-rmse:2.411258+0.563069
## [19] train-rmse:0.879462+0.016052 test-rmse:2.396847+0.564644
## [20] train-rmse:0.833796+0.023525 test-rmse:2.387017+0.567234
## [21] train-rmse:0.791648+0.014342 test-rmse:2.389714+0.565536
## [22] train-rmse:0.751691+0.018222 test-rmse:2.383010+0.572251
## [23] train-rmse:0.704901+0.022684 test-rmse:2.377352+0.574710
## [24] train-rmse:0.674993+0.018589 test-rmse:2.371460+0.578374
## [25] train-rmse:0.650770+0.024350 test-rmse:2.366241+0.579671
## [26] train-rmse:0.621180+0.019206 test-rmse:2.361067+0.583664
## [27] train-rmse:0.583899+0.028622 test-rmse:2.360142+0.582813
## [28] train-rmse:0.555405+0.033957 test-rmse:2.356271+0.585533
## [29] train-rmse:0.531736+0.041485 test-rmse:2.351019+0.586187
## [30] train-rmse:0.510038+0.035899 test-rmse:2.351914+0.590623
## [31] train-rmse:0.477996+0.027060 test-rmse:2.348296+0.594368
## [32] train-rmse:0.461944+0.024181 test-rmse:2.348917+0.595227
## [33] train-rmse:0.447008+0.028196 test-rmse:2.347519+0.598289
## [34] train-rmse:0.429877+0.031546 test-rmse:2.343853+0.597622
## [35] train-rmse:0.409089+0.030790 test-rmse:2.340566+0.596182
## [36] train-rmse:0.389022+0.025969 test-rmse:2.341019+0.594567
## [37] train-rmse:0.374087+0.026316 test-rmse:2.342766+0.592784
## [38] train-rmse:0.358553+0.022814 test-rmse:2.342586+0.592611
## [39] train-rmse:0.345944+0.020831 test-rmse:2.342642+0.593248
## [40] train-rmse:0.331422+0.016399 test-rmse:2.341306+0.593934
## [41] train-rmse:0.321593+0.016729 test-rmse:2.340978+0.593992
## Stopping. Best iteration:
## [35] train-rmse:0.409089+0.030790 test-rmse:2.340566+0.596182
##
## 49
## [1] train-rmse:6.797565+0.146997 test-rmse:6.792528+0.661795
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.804918+0.123759 test-rmse:5.849516+0.697252
## [3] train-rmse:5.112160+0.117511 test-rmse:5.161033+0.637943
## [4] train-rmse:4.628672+0.116246 test-rmse:4.698430+0.583709
## [5] train-rmse:4.294678+0.113334 test-rmse:4.387655+0.589833
## [6] train-rmse:4.055825+0.108021 test-rmse:4.159332+0.514392
## [7] train-rmse:3.883506+0.112369 test-rmse:4.014231+0.482297
## [8] train-rmse:3.748805+0.115313 test-rmse:3.865371+0.487509
## [9] train-rmse:3.639320+0.117630 test-rmse:3.745139+0.450909
## [10] train-rmse:3.549929+0.116633 test-rmse:3.699899+0.449062
## [11] train-rmse:3.474950+0.118688 test-rmse:3.625488+0.442485
## [12] train-rmse:3.408084+0.122023 test-rmse:3.562385+0.421732
## [13] train-rmse:3.345903+0.123683 test-rmse:3.484106+0.435353
## [14] train-rmse:3.290726+0.123757 test-rmse:3.407789+0.414711
## [15] train-rmse:3.239678+0.122270 test-rmse:3.356226+0.411235
## [16] train-rmse:3.191476+0.121811 test-rmse:3.322598+0.420363
## [17] train-rmse:3.148930+0.124120 test-rmse:3.314904+0.428107
## [18] train-rmse:3.107623+0.126397 test-rmse:3.267151+0.440829
## [19] train-rmse:3.069278+0.127796 test-rmse:3.214608+0.420616
## [20] train-rmse:3.034254+0.128387 test-rmse:3.189172+0.430881
## [21] train-rmse:3.001120+0.128425 test-rmse:3.160634+0.440733
## [22] train-rmse:2.969780+0.129156 test-rmse:3.135608+0.430057
## [23] train-rmse:2.939696+0.130177 test-rmse:3.128014+0.429595
## [24] train-rmse:2.910928+0.131140 test-rmse:3.093432+0.436047
## [25] train-rmse:2.883839+0.132391 test-rmse:3.065620+0.453666
## [26] train-rmse:2.857836+0.133129 test-rmse:3.048687+0.442235
## [27] train-rmse:2.832098+0.133913 test-rmse:3.033541+0.448355
## [28] train-rmse:2.807549+0.134304 test-rmse:3.005326+0.433086
## [29] train-rmse:2.785201+0.134688 test-rmse:2.973215+0.420609
## [30] train-rmse:2.762974+0.135253 test-rmse:2.946103+0.435128
## [31] train-rmse:2.741353+0.135710 test-rmse:2.927934+0.432321
## [32] train-rmse:2.720305+0.136364 test-rmse:2.920089+0.447381
## [33] train-rmse:2.698880+0.137207 test-rmse:2.913423+0.463535
## [34] train-rmse:2.678070+0.138345 test-rmse:2.896316+0.454438
## [35] train-rmse:2.658380+0.138910 test-rmse:2.883433+0.471390
## [36] train-rmse:2.639754+0.139138 test-rmse:2.868267+0.473848
## [37] train-rmse:2.621975+0.138965 test-rmse:2.858968+0.469431
## [38] train-rmse:2.605200+0.139314 test-rmse:2.842302+0.474841
## [39] train-rmse:2.589047+0.138980 test-rmse:2.820550+0.460450
## [40] train-rmse:2.573139+0.138918 test-rmse:2.798365+0.463936
## [41] train-rmse:2.557534+0.138391 test-rmse:2.798564+0.473482
## [42] train-rmse:2.542415+0.138005 test-rmse:2.784300+0.473379
## [43] train-rmse:2.527977+0.137796 test-rmse:2.770441+0.468079
## [44] train-rmse:2.513791+0.138126 test-rmse:2.764919+0.471908
## [45] train-rmse:2.499977+0.138509 test-rmse:2.751390+0.474355
## [46] train-rmse:2.486215+0.138934 test-rmse:2.731929+0.470177
## [47] train-rmse:2.472558+0.139354 test-rmse:2.728962+0.482264
## [48] train-rmse:2.459600+0.139821 test-rmse:2.714584+0.483600
## [49] train-rmse:2.446797+0.140413 test-rmse:2.701513+0.490711
## [50] train-rmse:2.434453+0.140718 test-rmse:2.688879+0.490079
## [51] train-rmse:2.422696+0.140950 test-rmse:2.678971+0.487051
## [52] train-rmse:2.411183+0.141231 test-rmse:2.668731+0.485863
## [53] train-rmse:2.400226+0.141186 test-rmse:2.663890+0.482866
## [54] train-rmse:2.389718+0.141023 test-rmse:2.659978+0.494658
## [55] train-rmse:2.379430+0.140736 test-rmse:2.654333+0.496317
## [56] train-rmse:2.369363+0.140596 test-rmse:2.638330+0.497166
## [57] train-rmse:2.359547+0.140561 test-rmse:2.625087+0.489790
## [58] train-rmse:2.349957+0.140486 test-rmse:2.618266+0.497360
## [59] train-rmse:2.340761+0.140635 test-rmse:2.606821+0.495012
## [60] train-rmse:2.331430+0.140839 test-rmse:2.601597+0.496784
## [61] train-rmse:2.322588+0.141269 test-rmse:2.603287+0.500104
## [62] train-rmse:2.314052+0.141477 test-rmse:2.590888+0.500954
## [63] train-rmse:2.305603+0.141756 test-rmse:2.583703+0.501746
## [64] train-rmse:2.297351+0.142198 test-rmse:2.571129+0.501215
## [65] train-rmse:2.289426+0.142518 test-rmse:2.558872+0.502847
## [66] train-rmse:2.281677+0.142921 test-rmse:2.549023+0.498453
## [67] train-rmse:2.274024+0.143393 test-rmse:2.547472+0.505763
## [68] train-rmse:2.266292+0.143770 test-rmse:2.543130+0.512340
## [69] train-rmse:2.258832+0.144066 test-rmse:2.536907+0.512286
## [70] train-rmse:2.251432+0.144293 test-rmse:2.533647+0.508338
## [71] train-rmse:2.244315+0.144639 test-rmse:2.525681+0.509806
## [72] train-rmse:2.237486+0.145078 test-rmse:2.516885+0.509978
## [73] train-rmse:2.230853+0.145314 test-rmse:2.521072+0.516480
## [74] train-rmse:2.224310+0.145567 test-rmse:2.517769+0.512502
## [75] train-rmse:2.218081+0.145622 test-rmse:2.507802+0.518409
## [76] train-rmse:2.211958+0.145732 test-rmse:2.498766+0.517412
## [77] train-rmse:2.206010+0.145877 test-rmse:2.497425+0.515294
## [78] train-rmse:2.200083+0.145918 test-rmse:2.493602+0.511456
## [79] train-rmse:2.194363+0.145950 test-rmse:2.491481+0.510203
## [80] train-rmse:2.188685+0.145974 test-rmse:2.484711+0.511428
## [81] train-rmse:2.183123+0.146109 test-rmse:2.486162+0.510961
## [82] train-rmse:2.177710+0.146219 test-rmse:2.481888+0.510748
## [83] train-rmse:2.172502+0.146501 test-rmse:2.477724+0.513214
## [84] train-rmse:2.167410+0.146727 test-rmse:2.470564+0.506041
## [85] train-rmse:2.162369+0.146943 test-rmse:2.466479+0.505007
## [86] train-rmse:2.157343+0.147176 test-rmse:2.458594+0.505831
## [87] train-rmse:2.152379+0.147419 test-rmse:2.456852+0.512293
## [88] train-rmse:2.147494+0.147649 test-rmse:2.447676+0.513357
## [89] train-rmse:2.142738+0.147941 test-rmse:2.450536+0.517523
## [90] train-rmse:2.137943+0.148187 test-rmse:2.449699+0.522463
## [91] train-rmse:2.133337+0.148466 test-rmse:2.448716+0.522007
## [92] train-rmse:2.128880+0.148650 test-rmse:2.442707+0.519943
## [93] train-rmse:2.124496+0.148871 test-rmse:2.440390+0.520786
## [94] train-rmse:2.120168+0.149041 test-rmse:2.440119+0.522057
## [95] train-rmse:2.115969+0.149163 test-rmse:2.434855+0.524760
## [96] train-rmse:2.111892+0.149313 test-rmse:2.425362+0.529392
## [97] train-rmse:2.107854+0.149486 test-rmse:2.426831+0.526686
## [98] train-rmse:2.103917+0.149596 test-rmse:2.423046+0.527507
## [99] train-rmse:2.099996+0.149786 test-rmse:2.418200+0.524623
## [100] train-rmse:2.096225+0.149925 test-rmse:2.415402+0.520724
## [101] train-rmse:2.092534+0.150119 test-rmse:2.411949+0.520119
## [102] train-rmse:2.088904+0.150382 test-rmse:2.417156+0.524928
## [103] train-rmse:2.085333+0.150573 test-rmse:2.411870+0.523298
## [104] train-rmse:2.081806+0.150732 test-rmse:2.409814+0.521718
## [105] train-rmse:2.078333+0.150901 test-rmse:2.409615+0.524584
## [106] train-rmse:2.074936+0.151031 test-rmse:2.408561+0.525805
## [107] train-rmse:2.071550+0.151178 test-rmse:2.408661+0.523253
## [108] train-rmse:2.068217+0.151335 test-rmse:2.402614+0.523003
## [109] train-rmse:2.064914+0.151511 test-rmse:2.397947+0.525072
## [110] train-rmse:2.061646+0.151606 test-rmse:2.396026+0.526239
## [111] train-rmse:2.058481+0.151727 test-rmse:2.392946+0.523503
## [112] train-rmse:2.055357+0.151831 test-rmse:2.388553+0.523008
## [113] train-rmse:2.052303+0.151948 test-rmse:2.388762+0.525132
## [114] train-rmse:2.049260+0.152029 test-rmse:2.386048+0.525412
## [115] train-rmse:2.046272+0.152183 test-rmse:2.389537+0.526957
## [116] train-rmse:2.043144+0.152227 test-rmse:2.390012+0.526896
## [117] train-rmse:2.040061+0.152589 test-rmse:2.387494+0.522591
## [118] train-rmse:2.037106+0.152725 test-rmse:2.385785+0.521053
## [119] train-rmse:2.034159+0.152855 test-rmse:2.380226+0.524636
## [120] train-rmse:2.031340+0.152999 test-rmse:2.377579+0.523334
## [121] train-rmse:2.028538+0.153154 test-rmse:2.376434+0.523858
## [122] train-rmse:2.025736+0.153221 test-rmse:2.376999+0.523493
## [123] train-rmse:2.023049+0.153350 test-rmse:2.377370+0.523485
## [124] train-rmse:2.020387+0.153414 test-rmse:2.375625+0.523716
## [125] train-rmse:2.017782+0.153459 test-rmse:2.373544+0.524039
## [126] train-rmse:2.015214+0.153515 test-rmse:2.371341+0.524759
## [127] train-rmse:2.012682+0.153593 test-rmse:2.370155+0.522923
## [128] train-rmse:2.010124+0.153684 test-rmse:2.367256+0.523989
## [129] train-rmse:2.007606+0.153812 test-rmse:2.366250+0.525162
## [130] train-rmse:2.005091+0.153963 test-rmse:2.368386+0.531250
## [131] train-rmse:2.002644+0.154125 test-rmse:2.366219+0.530538
## [132] train-rmse:2.000236+0.154310 test-rmse:2.362667+0.534918
## [133] train-rmse:1.997773+0.154463 test-rmse:2.362844+0.533110
## [134] train-rmse:1.995168+0.154859 test-rmse:2.363307+0.530867
## [135] train-rmse:1.992812+0.154929 test-rmse:2.365345+0.531473
## [136] train-rmse:1.990511+0.155097 test-rmse:2.363292+0.532840
## [137] train-rmse:1.988252+0.155191 test-rmse:2.359766+0.532416
## [138] train-rmse:1.986031+0.155297 test-rmse:2.354227+0.536610
## [139] train-rmse:1.983708+0.155431 test-rmse:2.350290+0.537598
## [140] train-rmse:1.981536+0.155563 test-rmse:2.349218+0.536011
## [141] train-rmse:1.979385+0.155706 test-rmse:2.348971+0.535509
## [142] train-rmse:1.977218+0.155802 test-rmse:2.349460+0.536846
## [143] train-rmse:1.975058+0.155898 test-rmse:2.347230+0.536321
## [144] train-rmse:1.972942+0.156010 test-rmse:2.345208+0.535377
## [145] train-rmse:1.970842+0.156134 test-rmse:2.343029+0.533466
## [146] train-rmse:1.968700+0.156341 test-rmse:2.346489+0.533554
## [147] train-rmse:1.966647+0.156562 test-rmse:2.347159+0.532229
## [148] train-rmse:1.964551+0.156625 test-rmse:2.344174+0.531946
## [149] train-rmse:1.962546+0.156739 test-rmse:2.342246+0.531640
## [150] train-rmse:1.960514+0.156927 test-rmse:2.343568+0.533408
## [151] train-rmse:1.958475+0.157027 test-rmse:2.345629+0.532574
## [152] train-rmse:1.956484+0.157135 test-rmse:2.342456+0.531678
## [153] train-rmse:1.954550+0.157256 test-rmse:2.340096+0.529605
## [154] train-rmse:1.952648+0.157392 test-rmse:2.336673+0.529686
## [155] train-rmse:1.950774+0.157492 test-rmse:2.335910+0.527220
## [156] train-rmse:1.948904+0.157688 test-rmse:2.337516+0.530835
## [157] train-rmse:1.947025+0.157860 test-rmse:2.333898+0.533865
## [158] train-rmse:1.945108+0.157930 test-rmse:2.333350+0.533957
## [159] train-rmse:1.943280+0.158046 test-rmse:2.332085+0.534053
## [160] train-rmse:1.941497+0.158165 test-rmse:2.333609+0.536069
## [161] train-rmse:1.939630+0.158264 test-rmse:2.335172+0.533916
## [162] train-rmse:1.937834+0.158365 test-rmse:2.334845+0.532556
## [163] train-rmse:1.936073+0.158507 test-rmse:2.330572+0.531952
## [164] train-rmse:1.934324+0.158634 test-rmse:2.327987+0.529828
## [165] train-rmse:1.932523+0.158802 test-rmse:2.327959+0.533101
## [166] train-rmse:1.930690+0.158936 test-rmse:2.325900+0.530602
## [167] train-rmse:1.928972+0.159022 test-rmse:2.326464+0.529902
## [168] train-rmse:1.927269+0.159112 test-rmse:2.326849+0.531414
## [169] train-rmse:1.925571+0.159230 test-rmse:2.324801+0.531341
## [170] train-rmse:1.923884+0.159243 test-rmse:2.323442+0.530630
## [171] train-rmse:1.922255+0.159313 test-rmse:2.321030+0.530837
## [172] train-rmse:1.920560+0.159371 test-rmse:2.316268+0.527989
## [173] train-rmse:1.918964+0.159481 test-rmse:2.314126+0.529735
## [174] train-rmse:1.917361+0.159573 test-rmse:2.315521+0.530180
## [175] train-rmse:1.915704+0.159590 test-rmse:2.315371+0.528615
## [176] train-rmse:1.914143+0.159659 test-rmse:2.317093+0.527480
## [177] train-rmse:1.912606+0.159704 test-rmse:2.318911+0.527070
## [178] train-rmse:1.910999+0.159741 test-rmse:2.317667+0.527147
## [179] train-rmse:1.909464+0.159795 test-rmse:2.315478+0.530953
## Stopping. Best iteration:
## [173] train-rmse:1.918964+0.159481 test-rmse:2.314126+0.529735
##
## 50
## [1] train-rmse:6.689475+0.139207 test-rmse:6.716734+0.627150
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.590473+0.128846 test-rmse:5.634431+0.601013
## [3] train-rmse:4.803106+0.108284 test-rmse:4.926579+0.532729
## [4] train-rmse:4.224839+0.110653 test-rmse:4.362254+0.519654
## [5] train-rmse:3.788401+0.111728 test-rmse:3.999339+0.470621
## [6] train-rmse:3.467759+0.107259 test-rmse:3.658930+0.455451
## [7] train-rmse:3.233454+0.117652 test-rmse:3.494705+0.450240
## [8] train-rmse:3.038215+0.124964 test-rmse:3.314933+0.393244
## [9] train-rmse:2.882830+0.104265 test-rmse:3.163223+0.403421
## [10] train-rmse:2.772856+0.111430 test-rmse:3.099616+0.413305
## [11] train-rmse:2.685303+0.108169 test-rmse:3.041307+0.419231
## [12] train-rmse:2.606729+0.117731 test-rmse:2.983619+0.417322
## [13] train-rmse:2.528770+0.119135 test-rmse:2.911773+0.423789
## [14] train-rmse:2.469874+0.120896 test-rmse:2.887453+0.430204
## [15] train-rmse:2.400465+0.125026 test-rmse:2.799956+0.435402
## [16] train-rmse:2.344074+0.127659 test-rmse:2.786002+0.450936
## [17] train-rmse:2.296671+0.124101 test-rmse:2.765279+0.440962
## [18] train-rmse:2.236868+0.114435 test-rmse:2.719961+0.460420
## [19] train-rmse:2.197760+0.111430 test-rmse:2.694153+0.453220
## [20] train-rmse:2.157427+0.110662 test-rmse:2.674749+0.457945
## [21] train-rmse:2.118183+0.111606 test-rmse:2.652080+0.454551
## [22] train-rmse:2.083999+0.111991 test-rmse:2.623515+0.452642
## [23] train-rmse:2.041208+0.103465 test-rmse:2.603470+0.468553
## [24] train-rmse:2.004697+0.107941 test-rmse:2.587856+0.466723
## [25] train-rmse:1.971680+0.105966 test-rmse:2.574333+0.457160
## [26] train-rmse:1.937388+0.094705 test-rmse:2.543196+0.464513
## [27] train-rmse:1.906125+0.093189 test-rmse:2.509196+0.472877
## [28] train-rmse:1.882244+0.092943 test-rmse:2.501231+0.475166
## [29] train-rmse:1.859711+0.092110 test-rmse:2.496156+0.473564
## [30] train-rmse:1.835349+0.093741 test-rmse:2.476249+0.461286
## [31] train-rmse:1.814470+0.093734 test-rmse:2.465881+0.463234
## [32] train-rmse:1.794139+0.094004 test-rmse:2.454857+0.468346
## [33] train-rmse:1.774515+0.094066 test-rmse:2.437027+0.473906
## [34] train-rmse:1.755817+0.093506 test-rmse:2.423069+0.475959
## [35] train-rmse:1.732086+0.095672 test-rmse:2.408271+0.478080
## [36] train-rmse:1.714421+0.095579 test-rmse:2.399240+0.474792
## [37] train-rmse:1.693734+0.085993 test-rmse:2.387233+0.479073
## [38] train-rmse:1.672197+0.088847 test-rmse:2.371112+0.483182
## [39] train-rmse:1.655397+0.089200 test-rmse:2.379624+0.495021
## [40] train-rmse:1.637579+0.088863 test-rmse:2.361189+0.498684
## [41] train-rmse:1.624045+0.088266 test-rmse:2.347043+0.489605
## [42] train-rmse:1.609879+0.087114 test-rmse:2.345775+0.493890
## [43] train-rmse:1.594066+0.088236 test-rmse:2.350774+0.494637
## [44] train-rmse:1.576239+0.092106 test-rmse:2.335125+0.488664
## [45] train-rmse:1.559029+0.091276 test-rmse:2.315104+0.485915
## [46] train-rmse:1.544045+0.091850 test-rmse:2.307891+0.486150
## [47] train-rmse:1.532750+0.091464 test-rmse:2.301326+0.486747
## [48] train-rmse:1.522390+0.091837 test-rmse:2.299081+0.484277
## [49] train-rmse:1.510753+0.088805 test-rmse:2.296475+0.484471
## [50] train-rmse:1.494066+0.089549 test-rmse:2.294290+0.490794
## [51] train-rmse:1.473069+0.082255 test-rmse:2.280627+0.494050
## [52] train-rmse:1.461385+0.083962 test-rmse:2.279352+0.489685
## [53] train-rmse:1.450352+0.085229 test-rmse:2.277097+0.494185
## [54] train-rmse:1.439912+0.086834 test-rmse:2.280928+0.496301
## [55] train-rmse:1.429722+0.088380 test-rmse:2.271699+0.492820
## [56] train-rmse:1.420238+0.088588 test-rmse:2.265536+0.499206
## [57] train-rmse:1.407779+0.092975 test-rmse:2.262011+0.496428
## [58] train-rmse:1.393267+0.091148 test-rmse:2.254039+0.491716
## [59] train-rmse:1.384470+0.090697 test-rmse:2.251817+0.490744
## [60] train-rmse:1.369601+0.089383 test-rmse:2.243726+0.492041
## [61] train-rmse:1.361723+0.090084 test-rmse:2.239981+0.489517
## [62] train-rmse:1.349935+0.089917 test-rmse:2.238565+0.493835
## [63] train-rmse:1.341442+0.090525 test-rmse:2.232540+0.497980
## [64] train-rmse:1.325731+0.084395 test-rmse:2.225120+0.498896
## [65] train-rmse:1.318249+0.082252 test-rmse:2.222736+0.498849
## [66] train-rmse:1.309662+0.079851 test-rmse:2.222285+0.500251
## [67] train-rmse:1.301969+0.081150 test-rmse:2.217448+0.501632
## [68] train-rmse:1.293960+0.082065 test-rmse:2.212837+0.502540
## [69] train-rmse:1.284275+0.084748 test-rmse:2.213661+0.505846
## [70] train-rmse:1.274597+0.088423 test-rmse:2.206515+0.498085
## [71] train-rmse:1.264873+0.089003 test-rmse:2.199637+0.497844
## [72] train-rmse:1.258088+0.089882 test-rmse:2.199420+0.499436
## [73] train-rmse:1.250535+0.090094 test-rmse:2.196027+0.500117
## [74] train-rmse:1.242896+0.090370 test-rmse:2.187660+0.506219
## [75] train-rmse:1.236583+0.090986 test-rmse:2.185426+0.506178
## [76] train-rmse:1.227106+0.093824 test-rmse:2.184339+0.502529
## [77] train-rmse:1.219768+0.093242 test-rmse:2.179949+0.503542
## [78] train-rmse:1.213860+0.093965 test-rmse:2.175750+0.503009
## [79] train-rmse:1.208964+0.094608 test-rmse:2.173366+0.506429
## [80] train-rmse:1.203951+0.094456 test-rmse:2.168072+0.508892
## [81] train-rmse:1.198929+0.094314 test-rmse:2.164428+0.509974
## [82] train-rmse:1.193956+0.095210 test-rmse:2.164598+0.511006
## [83] train-rmse:1.187168+0.095004 test-rmse:2.168325+0.510837
## [84] train-rmse:1.181102+0.097673 test-rmse:2.167054+0.512437
## [85] train-rmse:1.172974+0.101565 test-rmse:2.165287+0.514579
## [86] train-rmse:1.164401+0.103961 test-rmse:2.164621+0.513972
## [87] train-rmse:1.159461+0.103349 test-rmse:2.162443+0.515614
## [88] train-rmse:1.153793+0.103803 test-rmse:2.159179+0.516420
## [89] train-rmse:1.146468+0.103235 test-rmse:2.158903+0.515621
## [90] train-rmse:1.139728+0.104515 test-rmse:2.153148+0.516217
## [91] train-rmse:1.134545+0.104733 test-rmse:2.151521+0.514931
## [92] train-rmse:1.129407+0.106770 test-rmse:2.149894+0.517986
## [93] train-rmse:1.125169+0.107608 test-rmse:2.150154+0.518714
## [94] train-rmse:1.119937+0.108061 test-rmse:2.146372+0.522361
## [95] train-rmse:1.112525+0.108795 test-rmse:2.142283+0.522188
## [96] train-rmse:1.108403+0.108007 test-rmse:2.139490+0.521148
## [97] train-rmse:1.101659+0.107898 test-rmse:2.138463+0.519502
## [98] train-rmse:1.097024+0.107930 test-rmse:2.137115+0.517197
## [99] train-rmse:1.089382+0.106858 test-rmse:2.135837+0.516573
## [100] train-rmse:1.083935+0.104009 test-rmse:2.132891+0.515811
## [101] train-rmse:1.078942+0.104449 test-rmse:2.134547+0.515430
## [102] train-rmse:1.075251+0.103017 test-rmse:2.132850+0.516285
## [103] train-rmse:1.071988+0.103027 test-rmse:2.132478+0.519467
## [104] train-rmse:1.065077+0.102152 test-rmse:2.136031+0.521433
## [105] train-rmse:1.057167+0.103071 test-rmse:2.134787+0.524991
## [106] train-rmse:1.048862+0.102271 test-rmse:2.137549+0.523718
## [107] train-rmse:1.042574+0.104008 test-rmse:2.138427+0.522454
## [108] train-rmse:1.035296+0.104264 test-rmse:2.139008+0.519264
## [109] train-rmse:1.028234+0.104420 test-rmse:2.138163+0.518278
## Stopping. Best iteration:
## [103] train-rmse:1.071988+0.103027 test-rmse:2.132478+0.519467
##
## 51
## [1] train-rmse:6.617054+0.134512 test-rmse:6.686273+0.614327
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.460306+0.107898 test-rmse:5.574853+0.566844
## [3] train-rmse:4.585308+0.109282 test-rmse:4.758756+0.520692
## [4] train-rmse:3.929438+0.078423 test-rmse:4.132677+0.509172
## [5] train-rmse:3.457237+0.068121 test-rmse:3.713109+0.468441
## [6] train-rmse:3.098534+0.069861 test-rmse:3.424975+0.488981
## [7] train-rmse:2.821229+0.066699 test-rmse:3.195712+0.497946
## [8] train-rmse:2.612606+0.059041 test-rmse:3.033104+0.507174
## [9] train-rmse:2.446168+0.052783 test-rmse:2.928528+0.494007
## [10] train-rmse:2.327197+0.048128 test-rmse:2.842233+0.491089
## [11] train-rmse:2.221184+0.044671 test-rmse:2.788062+0.482876
## [12] train-rmse:2.124936+0.037367 test-rmse:2.741610+0.495191
## [13] train-rmse:2.050900+0.042815 test-rmse:2.693444+0.495741
## [14] train-rmse:1.975667+0.042135 test-rmse:2.655026+0.488199
## [15] train-rmse:1.918242+0.042152 test-rmse:2.624159+0.486789
## [16] train-rmse:1.858586+0.046358 test-rmse:2.589943+0.486843
## [17] train-rmse:1.809563+0.045048 test-rmse:2.559269+0.496985
## [18] train-rmse:1.765813+0.048165 test-rmse:2.522992+0.503164
## [19] train-rmse:1.721549+0.044929 test-rmse:2.494716+0.493917
## [20] train-rmse:1.674351+0.051838 test-rmse:2.485241+0.505222
## [21] train-rmse:1.634438+0.048023 test-rmse:2.462289+0.494877
## [22] train-rmse:1.596398+0.044394 test-rmse:2.439482+0.492524
## [23] train-rmse:1.568727+0.043637 test-rmse:2.417704+0.493461
## [24] train-rmse:1.537904+0.042643 test-rmse:2.406454+0.503869
## [25] train-rmse:1.500207+0.038053 test-rmse:2.381573+0.506682
## [26] train-rmse:1.471202+0.039679 test-rmse:2.362515+0.508134
## [27] train-rmse:1.445138+0.042634 test-rmse:2.348961+0.514698
## [28] train-rmse:1.417746+0.045933 test-rmse:2.346937+0.507978
## [29] train-rmse:1.391187+0.051149 test-rmse:2.342861+0.505968
## [30] train-rmse:1.371028+0.052916 test-rmse:2.332137+0.506677
## [31] train-rmse:1.349399+0.052823 test-rmse:2.327241+0.508145
## [32] train-rmse:1.330089+0.052471 test-rmse:2.321048+0.509680
## [33] train-rmse:1.300165+0.050348 test-rmse:2.313544+0.514140
## [34] train-rmse:1.282855+0.049456 test-rmse:2.306501+0.512514
## [35] train-rmse:1.257868+0.056269 test-rmse:2.294313+0.513310
## [36] train-rmse:1.236435+0.058150 test-rmse:2.290768+0.521327
## [37] train-rmse:1.217525+0.050843 test-rmse:2.299494+0.530032
## [38] train-rmse:1.193346+0.040484 test-rmse:2.294212+0.528719
## [39] train-rmse:1.172576+0.043152 test-rmse:2.289748+0.531805
## [40] train-rmse:1.150481+0.043837 test-rmse:2.277979+0.526246
## [41] train-rmse:1.135348+0.043936 test-rmse:2.269025+0.534712
## [42] train-rmse:1.115944+0.047847 test-rmse:2.265828+0.539165
## [43] train-rmse:1.103533+0.046337 test-rmse:2.256069+0.535246
## [44] train-rmse:1.088083+0.049070 test-rmse:2.248888+0.536237
## [45] train-rmse:1.074994+0.049398 test-rmse:2.241965+0.533424
## [46] train-rmse:1.059093+0.042551 test-rmse:2.233110+0.533112
## [47] train-rmse:1.041250+0.036467 test-rmse:2.228269+0.535176
## [48] train-rmse:1.024706+0.037290 test-rmse:2.223099+0.529593
## [49] train-rmse:1.010440+0.037688 test-rmse:2.225950+0.535682
## [50] train-rmse:0.997525+0.038606 test-rmse:2.223589+0.535520
## [51] train-rmse:0.978825+0.033800 test-rmse:2.216089+0.540541
## [52] train-rmse:0.964866+0.035027 test-rmse:2.212254+0.540278
## [53] train-rmse:0.953810+0.030874 test-rmse:2.209272+0.542072
## [54] train-rmse:0.938608+0.026626 test-rmse:2.203859+0.543255
## [55] train-rmse:0.924986+0.030173 test-rmse:2.198202+0.553023
## [56] train-rmse:0.913623+0.026278 test-rmse:2.199033+0.555014
## [57] train-rmse:0.900337+0.031778 test-rmse:2.195770+0.557501
## [58] train-rmse:0.889828+0.030965 test-rmse:2.193232+0.560783
## [59] train-rmse:0.881434+0.029691 test-rmse:2.194222+0.558592
## [60] train-rmse:0.868730+0.027628 test-rmse:2.186002+0.559442
## [61] train-rmse:0.861802+0.026795 test-rmse:2.179947+0.563192
## [62] train-rmse:0.849697+0.026175 test-rmse:2.178624+0.564860
## [63] train-rmse:0.839954+0.026543 test-rmse:2.175239+0.559361
## [64] train-rmse:0.827921+0.026291 test-rmse:2.173952+0.558535
## [65] train-rmse:0.820116+0.025023 test-rmse:2.171293+0.559321
## [66] train-rmse:0.813877+0.024445 test-rmse:2.170464+0.557442
## [67] train-rmse:0.803112+0.023219 test-rmse:2.165728+0.557805
## [68] train-rmse:0.791048+0.024727 test-rmse:2.165455+0.558364
## [69] train-rmse:0.780157+0.026132 test-rmse:2.165512+0.558936
## [70] train-rmse:0.765423+0.020008 test-rmse:2.161315+0.555663
## [71] train-rmse:0.752998+0.023202 test-rmse:2.163819+0.561077
## [72] train-rmse:0.746493+0.023256 test-rmse:2.166018+0.560455
## [73] train-rmse:0.737110+0.024739 test-rmse:2.162163+0.561215
## [74] train-rmse:0.730652+0.025065 test-rmse:2.161715+0.562378
## [75] train-rmse:0.722220+0.023455 test-rmse:2.162942+0.563490
## [76] train-rmse:0.713274+0.026218 test-rmse:2.160830+0.564137
## [77] train-rmse:0.704669+0.023586 test-rmse:2.159314+0.565980
## [78] train-rmse:0.693986+0.027483 test-rmse:2.161928+0.563351
## [79] train-rmse:0.688168+0.028817 test-rmse:2.161799+0.564153
## [80] train-rmse:0.683385+0.029194 test-rmse:2.160139+0.563034
## [81] train-rmse:0.674992+0.025700 test-rmse:2.159733+0.563756
## [82] train-rmse:0.665954+0.026373 test-rmse:2.159319+0.562409
## [83] train-rmse:0.659932+0.027304 test-rmse:2.155611+0.563366
## [84] train-rmse:0.652231+0.027233 test-rmse:2.153515+0.564389
## [85] train-rmse:0.644491+0.031550 test-rmse:2.157056+0.568855
## [86] train-rmse:0.637605+0.030948 test-rmse:2.155445+0.568727
## [87] train-rmse:0.628184+0.031141 test-rmse:2.155391+0.572142
## [88] train-rmse:0.622859+0.030003 test-rmse:2.155769+0.574643
## [89] train-rmse:0.617484+0.027539 test-rmse:2.160235+0.575012
## [90] train-rmse:0.612337+0.026794 test-rmse:2.160945+0.575028
## Stopping. Best iteration:
## [84] train-rmse:0.652231+0.027233 test-rmse:2.153515+0.564389
##
## 52
## [1] train-rmse:6.549503+0.133190 test-rmse:6.594613+0.601921
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.314227+0.105095 test-rmse:5.433249+0.511508
## [3] train-rmse:4.404696+0.085791 test-rmse:4.582514+0.469573
## [4] train-rmse:3.716019+0.080232 test-rmse:3.984650+0.451045
## [5] train-rmse:3.226349+0.081956 test-rmse:3.527210+0.415101
## [6] train-rmse:2.844361+0.065870 test-rmse:3.204261+0.455998
## [7] train-rmse:2.569962+0.058877 test-rmse:3.027993+0.446830
## [8] train-rmse:2.357007+0.060132 test-rmse:2.865904+0.421634
## [9] train-rmse:2.184888+0.050953 test-rmse:2.739486+0.448669
## [10] train-rmse:2.035429+0.044928 test-rmse:2.619229+0.415981
## [11] train-rmse:1.933266+0.040609 test-rmse:2.557826+0.422390
## [12] train-rmse:1.843945+0.033315 test-rmse:2.512777+0.405222
## [13] train-rmse:1.765249+0.035251 test-rmse:2.476412+0.420943
## [14] train-rmse:1.706219+0.034692 test-rmse:2.445998+0.415779
## [15] train-rmse:1.635217+0.039541 test-rmse:2.390692+0.406094
## [16] train-rmse:1.580831+0.040208 test-rmse:2.388860+0.424771
## [17] train-rmse:1.527736+0.021788 test-rmse:2.373096+0.420557
## [18] train-rmse:1.481749+0.025596 test-rmse:2.339173+0.425066
## [19] train-rmse:1.432786+0.031095 test-rmse:2.313526+0.427812
## [20] train-rmse:1.392352+0.032614 test-rmse:2.298320+0.430314
## [21] train-rmse:1.359280+0.030866 test-rmse:2.289174+0.428126
## [22] train-rmse:1.317643+0.030848 test-rmse:2.271330+0.434847
## [23] train-rmse:1.274861+0.030453 test-rmse:2.262729+0.432628
## [24] train-rmse:1.241593+0.027963 test-rmse:2.246246+0.436025
## [25] train-rmse:1.214883+0.027906 test-rmse:2.249670+0.438580
## [26] train-rmse:1.179138+0.021418 test-rmse:2.238681+0.441372
## [27] train-rmse:1.154393+0.022990 test-rmse:2.234164+0.444524
## [28] train-rmse:1.134424+0.022709 test-rmse:2.220583+0.451524
## [29] train-rmse:1.106923+0.019892 test-rmse:2.210465+0.457100
## [30] train-rmse:1.075545+0.026341 test-rmse:2.208380+0.453255
## [31] train-rmse:1.045616+0.034797 test-rmse:2.207362+0.447976
## [32] train-rmse:1.023918+0.029665 test-rmse:2.197037+0.449876
## [33] train-rmse:1.006354+0.030386 test-rmse:2.195711+0.452245
## [34] train-rmse:0.982256+0.024383 test-rmse:2.189713+0.453926
## [35] train-rmse:0.957716+0.024954 test-rmse:2.195176+0.457747
## [36] train-rmse:0.928930+0.025731 test-rmse:2.186749+0.459780
## [37] train-rmse:0.914075+0.026033 test-rmse:2.185367+0.458480
## [38] train-rmse:0.888713+0.024820 test-rmse:2.172571+0.466683
## [39] train-rmse:0.871997+0.017598 test-rmse:2.159817+0.458705
## [40] train-rmse:0.855571+0.015121 test-rmse:2.153716+0.459046
## [41] train-rmse:0.832891+0.019828 test-rmse:2.151688+0.458172
## [42] train-rmse:0.817943+0.025392 test-rmse:2.149048+0.460439
## [43] train-rmse:0.800366+0.026095 test-rmse:2.143509+0.459516
## [44] train-rmse:0.782224+0.019432 test-rmse:2.137882+0.453590
## [45] train-rmse:0.763596+0.017217 test-rmse:2.134408+0.461722
## [46] train-rmse:0.749372+0.019725 test-rmse:2.131914+0.465661
## [47] train-rmse:0.728116+0.019260 test-rmse:2.131257+0.465037
## [48] train-rmse:0.710867+0.018289 test-rmse:2.125448+0.461341
## [49] train-rmse:0.700024+0.016308 test-rmse:2.123043+0.464662
## [50] train-rmse:0.690383+0.012838 test-rmse:2.122321+0.463448
## [51] train-rmse:0.676678+0.014331 test-rmse:2.122553+0.467104
## [52] train-rmse:0.663167+0.009508 test-rmse:2.122552+0.464539
## [53] train-rmse:0.648830+0.012130 test-rmse:2.125268+0.464719
## [54] train-rmse:0.638828+0.013171 test-rmse:2.126429+0.463075
## [55] train-rmse:0.628868+0.013186 test-rmse:2.125609+0.466042
## [56] train-rmse:0.616734+0.010303 test-rmse:2.121268+0.463229
## [57] train-rmse:0.606656+0.009327 test-rmse:2.118889+0.462912
## [58] train-rmse:0.598388+0.008859 test-rmse:2.118325+0.463795
## [59] train-rmse:0.590555+0.010284 test-rmse:2.114590+0.466151
## [60] train-rmse:0.581572+0.008153 test-rmse:2.114574+0.467616
## [61] train-rmse:0.572353+0.008872 test-rmse:2.112450+0.470261
## [62] train-rmse:0.560355+0.007353 test-rmse:2.112436+0.471398
## [63] train-rmse:0.550906+0.014345 test-rmse:2.111616+0.474113
## [64] train-rmse:0.543669+0.013253 test-rmse:2.112400+0.472751
## [65] train-rmse:0.535826+0.012903 test-rmse:2.110265+0.470902
## [66] train-rmse:0.525490+0.015758 test-rmse:2.107811+0.471942
## [67] train-rmse:0.517800+0.016185 test-rmse:2.107580+0.469713
## [68] train-rmse:0.507047+0.017879 test-rmse:2.110889+0.468397
## [69] train-rmse:0.499343+0.017891 test-rmse:2.109493+0.470119
## [70] train-rmse:0.490346+0.015839 test-rmse:2.109671+0.467511
## [71] train-rmse:0.485917+0.017292 test-rmse:2.109009+0.467808
## [72] train-rmse:0.476880+0.016502 test-rmse:2.107509+0.466746
## [73] train-rmse:0.467633+0.016454 test-rmse:2.104607+0.467135
## [74] train-rmse:0.460939+0.013937 test-rmse:2.105619+0.467118
## [75] train-rmse:0.451862+0.012793 test-rmse:2.105332+0.467873
## [76] train-rmse:0.443812+0.014480 test-rmse:2.105346+0.467392
## [77] train-rmse:0.438570+0.015368 test-rmse:2.102587+0.465704
## [78] train-rmse:0.430485+0.016098 test-rmse:2.102102+0.467494
## [79] train-rmse:0.426030+0.016982 test-rmse:2.101515+0.467174
## [80] train-rmse:0.420398+0.020338 test-rmse:2.102722+0.466785
## [81] train-rmse:0.413091+0.022128 test-rmse:2.101356+0.467746
## [82] train-rmse:0.407679+0.022164 test-rmse:2.102299+0.467584
## [83] train-rmse:0.403899+0.021025 test-rmse:2.101158+0.468076
## [84] train-rmse:0.398388+0.022867 test-rmse:2.099484+0.469053
## [85] train-rmse:0.392486+0.021309 test-rmse:2.098554+0.468447
## [86] train-rmse:0.387756+0.021898 test-rmse:2.099975+0.469657
## [87] train-rmse:0.383251+0.024626 test-rmse:2.100244+0.471076
## [88] train-rmse:0.375682+0.024594 test-rmse:2.097847+0.471038
## [89] train-rmse:0.369626+0.023261 test-rmse:2.096922+0.471307
## [90] train-rmse:0.363908+0.022459 test-rmse:2.098327+0.469459
## [91] train-rmse:0.356194+0.020934 test-rmse:2.099137+0.473697
## [92] train-rmse:0.348973+0.020074 test-rmse:2.099759+0.473210
## [93] train-rmse:0.342315+0.017777 test-rmse:2.098857+0.472698
## [94] train-rmse:0.337793+0.015881 test-rmse:2.097951+0.472585
## [95] train-rmse:0.331975+0.017846 test-rmse:2.095437+0.470359
## [96] train-rmse:0.325156+0.019993 test-rmse:2.095754+0.470273
## [97] train-rmse:0.322018+0.019922 test-rmse:2.096300+0.469524
## [98] train-rmse:0.318607+0.018262 test-rmse:2.096821+0.470104
## [99] train-rmse:0.314147+0.017800 test-rmse:2.097951+0.470709
## [100] train-rmse:0.309609+0.019020 test-rmse:2.098070+0.471338
## [101] train-rmse:0.302922+0.019699 test-rmse:2.096942+0.472892
## Stopping. Best iteration:
## [95] train-rmse:0.331975+0.017846 test-rmse:2.095437+0.470359
##
## 53
## [1] train-rmse:6.502818+0.130723 test-rmse:6.516388+0.581632
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.226718+0.108989 test-rmse:5.310894+0.496629
## [3] train-rmse:4.285477+0.091279 test-rmse:4.476299+0.446932
## [4] train-rmse:3.590032+0.061733 test-rmse:3.902146+0.446118
## [5] train-rmse:3.074015+0.050391 test-rmse:3.506593+0.433664
## [6] train-rmse:2.677677+0.055352 test-rmse:3.195923+0.464987
## [7] train-rmse:2.379216+0.053755 test-rmse:3.012836+0.470247
## [8] train-rmse:2.147546+0.050569 test-rmse:2.874964+0.526493
## [9] train-rmse:1.966707+0.054747 test-rmse:2.768101+0.546506
## [10] train-rmse:1.836436+0.044615 test-rmse:2.702850+0.541894
## [11] train-rmse:1.707095+0.053322 test-rmse:2.618446+0.552838
## [12] train-rmse:1.598992+0.049426 test-rmse:2.591382+0.540309
## [13] train-rmse:1.520837+0.048569 test-rmse:2.556404+0.538381
## [14] train-rmse:1.452733+0.044208 test-rmse:2.533797+0.528329
## [15] train-rmse:1.383956+0.031425 test-rmse:2.509149+0.542290
## [16] train-rmse:1.332587+0.033165 test-rmse:2.493190+0.548461
## [17] train-rmse:1.282997+0.038196 test-rmse:2.469246+0.552945
## [18] train-rmse:1.233460+0.045589 test-rmse:2.438169+0.542321
## [19] train-rmse:1.188195+0.041014 test-rmse:2.422154+0.547661
## [20] train-rmse:1.152779+0.045976 test-rmse:2.411646+0.546949
## [21] train-rmse:1.112435+0.048753 test-rmse:2.403509+0.551056
## [22] train-rmse:1.073539+0.039622 test-rmse:2.393201+0.552164
## [23] train-rmse:1.037268+0.030612 test-rmse:2.381715+0.557265
## [24] train-rmse:1.000275+0.032379 test-rmse:2.372376+0.564871
## [25] train-rmse:0.960516+0.023248 test-rmse:2.357646+0.565662
## [26] train-rmse:0.940222+0.022694 test-rmse:2.354078+0.562004
## [27] train-rmse:0.911935+0.020600 test-rmse:2.352121+0.565226
## [28] train-rmse:0.877110+0.021023 test-rmse:2.343398+0.566286
## [29] train-rmse:0.855177+0.014252 test-rmse:2.335873+0.567432
## [30] train-rmse:0.834834+0.018498 test-rmse:2.330462+0.571404
## [31] train-rmse:0.809876+0.016017 test-rmse:2.320037+0.577311
## [32] train-rmse:0.791948+0.014586 test-rmse:2.316145+0.577616
## [33] train-rmse:0.765409+0.019662 test-rmse:2.314602+0.572406
## [34] train-rmse:0.742560+0.026684 test-rmse:2.310658+0.573624
## [35] train-rmse:0.722273+0.021189 test-rmse:2.308645+0.576650
## [36] train-rmse:0.698564+0.021515 test-rmse:2.303994+0.577046
## [37] train-rmse:0.677425+0.020283 test-rmse:2.299412+0.577835
## [38] train-rmse:0.662471+0.023057 test-rmse:2.299899+0.583007
## [39] train-rmse:0.644722+0.020302 test-rmse:2.294477+0.583529
## [40] train-rmse:0.622495+0.020044 test-rmse:2.291801+0.584847
## [41] train-rmse:0.603888+0.021158 test-rmse:2.284066+0.584083
## [42] train-rmse:0.586027+0.019843 test-rmse:2.284294+0.585807
## [43] train-rmse:0.573166+0.017830 test-rmse:2.281689+0.588744
## [44] train-rmse:0.559906+0.018704 test-rmse:2.278475+0.588329
## [45] train-rmse:0.543578+0.017001 test-rmse:2.270464+0.590863
## [46] train-rmse:0.534227+0.017370 test-rmse:2.266202+0.591798
## [47] train-rmse:0.522240+0.017385 test-rmse:2.266348+0.594159
## [48] train-rmse:0.505841+0.018471 test-rmse:2.264729+0.595333
## [49] train-rmse:0.493237+0.014237 test-rmse:2.265002+0.595849
## [50] train-rmse:0.478155+0.020929 test-rmse:2.263966+0.591672
## [51] train-rmse:0.470641+0.022412 test-rmse:2.264226+0.592737
## [52] train-rmse:0.455939+0.019951 test-rmse:2.259293+0.593598
## [53] train-rmse:0.445615+0.021021 test-rmse:2.257915+0.593921
## [54] train-rmse:0.432224+0.018026 test-rmse:2.257241+0.592841
## [55] train-rmse:0.422045+0.017983 test-rmse:2.256988+0.591778
## [56] train-rmse:0.413498+0.016685 test-rmse:2.257658+0.592853
## [57] train-rmse:0.403640+0.014359 test-rmse:2.256916+0.593219
## [58] train-rmse:0.392339+0.016307 test-rmse:2.257537+0.596618
## [59] train-rmse:0.384258+0.017468 test-rmse:2.261391+0.597784
## [60] train-rmse:0.374576+0.019694 test-rmse:2.262118+0.597938
## [61] train-rmse:0.365863+0.019971 test-rmse:2.260384+0.597456
## [62] train-rmse:0.356370+0.017732 test-rmse:2.256608+0.596097
## [63] train-rmse:0.346931+0.016966 test-rmse:2.258342+0.597014
## [64] train-rmse:0.338793+0.013271 test-rmse:2.258321+0.596709
## [65] train-rmse:0.332294+0.016062 test-rmse:2.256621+0.594140
## [66] train-rmse:0.324531+0.017553 test-rmse:2.256929+0.593798
## [67] train-rmse:0.319519+0.017675 test-rmse:2.256433+0.593178
## [68] train-rmse:0.313806+0.017174 test-rmse:2.255412+0.593129
## [69] train-rmse:0.305497+0.015544 test-rmse:2.257565+0.592916
## [70] train-rmse:0.300962+0.016159 test-rmse:2.258173+0.593855
## [71] train-rmse:0.293381+0.016233 test-rmse:2.256624+0.594545
## [72] train-rmse:0.287580+0.016532 test-rmse:2.256460+0.595298
## [73] train-rmse:0.280901+0.016413 test-rmse:2.254865+0.594974
## [74] train-rmse:0.275850+0.016993 test-rmse:2.254885+0.594982
## [75] train-rmse:0.272213+0.017206 test-rmse:2.255171+0.594662
## [76] train-rmse:0.265249+0.018763 test-rmse:2.253556+0.595195
## [77] train-rmse:0.259360+0.021048 test-rmse:2.253845+0.597924
## [78] train-rmse:0.255312+0.019970 test-rmse:2.253970+0.597628
## [79] train-rmse:0.250613+0.021730 test-rmse:2.254027+0.596813
## [80] train-rmse:0.243240+0.017095 test-rmse:2.254247+0.596547
## [81] train-rmse:0.238654+0.017716 test-rmse:2.252988+0.595749
## [82] train-rmse:0.234195+0.018293 test-rmse:2.253077+0.594964
## [83] train-rmse:0.229968+0.016960 test-rmse:2.252602+0.595881
## [84] train-rmse:0.225466+0.018086 test-rmse:2.252416+0.597969
## [85] train-rmse:0.220020+0.018326 test-rmse:2.251638+0.598212
## [86] train-rmse:0.216553+0.018996 test-rmse:2.252011+0.597708
## [87] train-rmse:0.209872+0.017419 test-rmse:2.252073+0.598070
## [88] train-rmse:0.204291+0.016858 test-rmse:2.252710+0.598266
## [89] train-rmse:0.198676+0.015111 test-rmse:2.252109+0.598909
## [90] train-rmse:0.195204+0.014447 test-rmse:2.252292+0.598535
## [91] train-rmse:0.191496+0.012999 test-rmse:2.252061+0.599119
## Stopping. Best iteration:
## [85] train-rmse:0.220020+0.018326 test-rmse:2.251638+0.598212
##
## 54
## [1] train-rmse:6.473490+0.126154 test-rmse:6.497475+0.608838
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.172935+0.090673 test-rmse:5.294049+0.534920
## [3] train-rmse:4.197416+0.071022 test-rmse:4.438633+0.478238
## [4] train-rmse:3.469591+0.053089 test-rmse:3.852354+0.454741
## [5] train-rmse:2.936457+0.045322 test-rmse:3.449489+0.459231
## [6] train-rmse:2.527151+0.040619 test-rmse:3.150183+0.484144
## [7] train-rmse:2.219824+0.031130 test-rmse:2.938074+0.519406
## [8] train-rmse:1.971829+0.044888 test-rmse:2.800262+0.541210
## [9] train-rmse:1.773216+0.045399 test-rmse:2.691808+0.533051
## [10] train-rmse:1.628520+0.038173 test-rmse:2.614374+0.538455
## [11] train-rmse:1.489073+0.023202 test-rmse:2.577403+0.537130
## [12] train-rmse:1.393331+0.016332 test-rmse:2.537852+0.535981
## [13] train-rmse:1.300821+0.024501 test-rmse:2.505769+0.546149
## [14] train-rmse:1.233193+0.026379 test-rmse:2.482313+0.549815
## [15] train-rmse:1.159117+0.014986 test-rmse:2.462136+0.553701
## [16] train-rmse:1.111014+0.015325 test-rmse:2.447215+0.545731
## [17] train-rmse:1.056616+0.007220 test-rmse:2.428658+0.543255
## [18] train-rmse:1.011616+0.020168 test-rmse:2.415240+0.551649
## [19] train-rmse:0.967648+0.017573 test-rmse:2.402582+0.550503
## [20] train-rmse:0.923404+0.022960 test-rmse:2.395638+0.550245
## [21] train-rmse:0.873413+0.025865 test-rmse:2.395233+0.549739
## [22] train-rmse:0.839054+0.027323 test-rmse:2.391203+0.545033
## [23] train-rmse:0.810377+0.023921 test-rmse:2.381577+0.546538
## [24] train-rmse:0.775808+0.025590 test-rmse:2.373726+0.545636
## [25] train-rmse:0.741719+0.029196 test-rmse:2.370240+0.548466
## [26] train-rmse:0.708484+0.021557 test-rmse:2.368248+0.546552
## [27] train-rmse:0.683334+0.021621 test-rmse:2.357485+0.549711
## [28] train-rmse:0.656345+0.019691 test-rmse:2.354541+0.555283
## [29] train-rmse:0.633299+0.026707 test-rmse:2.351763+0.553356
## [30] train-rmse:0.608821+0.029222 test-rmse:2.349080+0.550326
## [31] train-rmse:0.587876+0.030890 test-rmse:2.346158+0.553089
## [32] train-rmse:0.564677+0.027097 test-rmse:2.343778+0.552708
## [33] train-rmse:0.547759+0.021133 test-rmse:2.343734+0.552656
## [34] train-rmse:0.531650+0.024365 test-rmse:2.343445+0.549279
## [35] train-rmse:0.508628+0.026188 test-rmse:2.337918+0.551673
## [36] train-rmse:0.494830+0.029709 test-rmse:2.335137+0.553095
## [37] train-rmse:0.482184+0.023922 test-rmse:2.332325+0.552349
## [38] train-rmse:0.466968+0.017250 test-rmse:2.329084+0.552698
## [39] train-rmse:0.448769+0.021650 test-rmse:2.329811+0.550400
## [40] train-rmse:0.436857+0.021842 test-rmse:2.323235+0.552535
## [41] train-rmse:0.419591+0.023066 test-rmse:2.316468+0.553230
## [42] train-rmse:0.407117+0.021266 test-rmse:2.315532+0.553843
## [43] train-rmse:0.393698+0.022054 test-rmse:2.315656+0.553937
## [44] train-rmse:0.383869+0.023905 test-rmse:2.314046+0.551758
## [45] train-rmse:0.369552+0.021478 test-rmse:2.312583+0.552359
## [46] train-rmse:0.360392+0.023804 test-rmse:2.314699+0.555564
## [47] train-rmse:0.346585+0.024402 test-rmse:2.315171+0.555355
## [48] train-rmse:0.336768+0.021314 test-rmse:2.314095+0.555262
## [49] train-rmse:0.325822+0.019482 test-rmse:2.313385+0.555608
## [50] train-rmse:0.310318+0.020409 test-rmse:2.313877+0.554281
## [51] train-rmse:0.298951+0.019480 test-rmse:2.313618+0.556368
## Stopping. Best iteration:
## [45] train-rmse:0.369552+0.021478 test-rmse:2.312583+0.552359
##
## 55
## [1] train-rmse:6.463075+0.125274 test-rmse:6.491441+0.608406
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.145252+0.085780 test-rmse:5.286403+0.532263
## [3] train-rmse:4.145247+0.062125 test-rmse:4.393345+0.503767
## [4] train-rmse:3.394963+0.047503 test-rmse:3.784525+0.458850
## [5] train-rmse:2.837559+0.041056 test-rmse:3.366648+0.460859
## [6] train-rmse:2.409586+0.032826 test-rmse:3.099088+0.481660
## [7] train-rmse:2.093663+0.030944 test-rmse:2.919930+0.497713
## [8] train-rmse:1.829140+0.033228 test-rmse:2.751609+0.522339
## [9] train-rmse:1.632322+0.045349 test-rmse:2.674564+0.526898
## [10] train-rmse:1.471179+0.031620 test-rmse:2.586650+0.551979
## [11] train-rmse:1.347532+0.053679 test-rmse:2.536142+0.552471
## [12] train-rmse:1.233615+0.049121 test-rmse:2.507848+0.551654
## [13] train-rmse:1.125676+0.049198 test-rmse:2.482377+0.569033
## [14] train-rmse:1.042259+0.042901 test-rmse:2.454395+0.565681
## [15] train-rmse:0.986977+0.049542 test-rmse:2.436897+0.567609
## [16] train-rmse:0.928236+0.052395 test-rmse:2.430817+0.564355
## [17] train-rmse:0.876126+0.046740 test-rmse:2.425270+0.573918
## [18] train-rmse:0.830474+0.043364 test-rmse:2.416245+0.576183
## [19] train-rmse:0.783159+0.048419 test-rmse:2.410698+0.581692
## [20] train-rmse:0.739399+0.048867 test-rmse:2.406429+0.580485
## [21] train-rmse:0.696750+0.040528 test-rmse:2.402143+0.582488
## [22] train-rmse:0.662282+0.037660 test-rmse:2.398502+0.580067
## [23] train-rmse:0.632867+0.037540 test-rmse:2.396595+0.583297
## [24] train-rmse:0.605903+0.034553 test-rmse:2.391914+0.590896
## [25] train-rmse:0.575972+0.032562 test-rmse:2.389173+0.589285
## [26] train-rmse:0.550218+0.025991 test-rmse:2.385902+0.593324
## [27] train-rmse:0.526576+0.029160 test-rmse:2.382419+0.593834
## [28] train-rmse:0.501106+0.031986 test-rmse:2.380727+0.590689
## [29] train-rmse:0.480628+0.030907 test-rmse:2.384925+0.590307
## [30] train-rmse:0.460336+0.033215 test-rmse:2.380151+0.591458
## [31] train-rmse:0.439793+0.035180 test-rmse:2.379593+0.591000
## [32] train-rmse:0.425356+0.033422 test-rmse:2.376716+0.592732
## [33] train-rmse:0.405078+0.029739 test-rmse:2.375066+0.594080
## [34] train-rmse:0.387650+0.031830 test-rmse:2.377186+0.597051
## [35] train-rmse:0.369811+0.028983 test-rmse:2.372789+0.598511
## [36] train-rmse:0.356428+0.028127 test-rmse:2.373317+0.596454
## [37] train-rmse:0.338393+0.028396 test-rmse:2.372047+0.598409
## [38] train-rmse:0.322830+0.025935 test-rmse:2.370473+0.600279
## [39] train-rmse:0.310485+0.024463 test-rmse:2.369020+0.602569
## [40] train-rmse:0.298767+0.019707 test-rmse:2.368119+0.602487
## [41] train-rmse:0.285367+0.017286 test-rmse:2.365700+0.600283
## [42] train-rmse:0.272607+0.013808 test-rmse:2.365552+0.600993
## [43] train-rmse:0.260419+0.016304 test-rmse:2.362324+0.600617
## [44] train-rmse:0.250776+0.014084 test-rmse:2.360859+0.600844
## [45] train-rmse:0.239445+0.013183 test-rmse:2.359454+0.602899
## [46] train-rmse:0.230776+0.012135 test-rmse:2.357453+0.605070
## [47] train-rmse:0.223005+0.013081 test-rmse:2.356182+0.603695
## [48] train-rmse:0.215877+0.013170 test-rmse:2.354976+0.604421
## [49] train-rmse:0.207541+0.011158 test-rmse:2.353911+0.603968
## [50] train-rmse:0.201255+0.010374 test-rmse:2.354999+0.604894
## [51] train-rmse:0.194329+0.010989 test-rmse:2.354316+0.605352
## [52] train-rmse:0.188180+0.010502 test-rmse:2.354766+0.605377
## [53] train-rmse:0.181982+0.008297 test-rmse:2.354908+0.605598
## [54] train-rmse:0.175998+0.009289 test-rmse:2.353327+0.605909
## [55] train-rmse:0.170004+0.009398 test-rmse:2.352083+0.606255
## [56] train-rmse:0.163067+0.008595 test-rmse:2.351012+0.606252
## [57] train-rmse:0.159040+0.008104 test-rmse:2.349780+0.606931
## [58] train-rmse:0.154038+0.008625 test-rmse:2.348672+0.607705
## [59] train-rmse:0.148737+0.007470 test-rmse:2.347978+0.609501
## [60] train-rmse:0.142594+0.006252 test-rmse:2.347299+0.610887
## [61] train-rmse:0.138226+0.006107 test-rmse:2.347197+0.611241
## [62] train-rmse:0.132725+0.005868 test-rmse:2.346836+0.611063
## [63] train-rmse:0.129052+0.004938 test-rmse:2.346504+0.611366
## [64] train-rmse:0.125556+0.003894 test-rmse:2.345945+0.610480
## [65] train-rmse:0.121244+0.003811 test-rmse:2.345070+0.610601
## [66] train-rmse:0.117741+0.003936 test-rmse:2.345433+0.611800
## [67] train-rmse:0.114215+0.003638 test-rmse:2.344905+0.611294
## [68] train-rmse:0.110987+0.003634 test-rmse:2.344574+0.611550
## [69] train-rmse:0.105288+0.003275 test-rmse:2.344327+0.612635
## [70] train-rmse:0.101910+0.003559 test-rmse:2.344013+0.612352
## [71] train-rmse:0.097031+0.002913 test-rmse:2.343332+0.612774
## [72] train-rmse:0.094820+0.003269 test-rmse:2.342679+0.612965
## [73] train-rmse:0.091636+0.003514 test-rmse:2.342502+0.612813
## [74] train-rmse:0.088514+0.004396 test-rmse:2.341705+0.612433
## [75] train-rmse:0.084903+0.004702 test-rmse:2.341385+0.612308
## [76] train-rmse:0.082858+0.004711 test-rmse:2.341124+0.612533
## [77] train-rmse:0.079909+0.004868 test-rmse:2.340952+0.612698
## [78] train-rmse:0.077019+0.004606 test-rmse:2.341009+0.612429
## [79] train-rmse:0.073974+0.005312 test-rmse:2.340432+0.612427
## [80] train-rmse:0.071690+0.004928 test-rmse:2.340238+0.612017
## [81] train-rmse:0.069106+0.004559 test-rmse:2.340073+0.612678
## [82] train-rmse:0.066894+0.005322 test-rmse:2.339934+0.613227
## [83] train-rmse:0.065002+0.005421 test-rmse:2.339769+0.613201
## [84] train-rmse:0.063036+0.005665 test-rmse:2.339596+0.613336
## [85] train-rmse:0.060696+0.005023 test-rmse:2.339133+0.613132
## [86] train-rmse:0.058833+0.005257 test-rmse:2.338776+0.613461
## [87] train-rmse:0.057391+0.004974 test-rmse:2.338515+0.613545
## [88] train-rmse:0.055337+0.004343 test-rmse:2.338308+0.613650
## [89] train-rmse:0.053370+0.003837 test-rmse:2.338244+0.613710
## [90] train-rmse:0.051607+0.003629 test-rmse:2.337776+0.613953
## [91] train-rmse:0.050113+0.002992 test-rmse:2.337905+0.614092
## [92] train-rmse:0.047897+0.003089 test-rmse:2.337285+0.613580
## [93] train-rmse:0.046124+0.002772 test-rmse:2.336917+0.613532
## [94] train-rmse:0.044407+0.002712 test-rmse:2.336956+0.613329
## [95] train-rmse:0.043206+0.002767 test-rmse:2.336720+0.613550
## [96] train-rmse:0.041893+0.002955 test-rmse:2.336618+0.613633
## [97] train-rmse:0.040270+0.003385 test-rmse:2.336618+0.613855
## [98] train-rmse:0.038768+0.003144 test-rmse:2.336443+0.613999
## [99] train-rmse:0.037232+0.002885 test-rmse:2.336567+0.614222
## [100] train-rmse:0.036345+0.003117 test-rmse:2.336764+0.614090
## [101] train-rmse:0.035238+0.002632 test-rmse:2.336783+0.614093
## [102] train-rmse:0.034058+0.002506 test-rmse:2.336949+0.614076
## [103] train-rmse:0.032884+0.002033 test-rmse:2.336870+0.614006
## [104] train-rmse:0.031787+0.002250 test-rmse:2.336909+0.613966
## Stopping. Best iteration:
## [98] train-rmse:0.038768+0.003144 test-rmse:2.336443+0.613999
##
## 56
## [1] train-rmse:6.687441+0.144953 test-rmse:6.684716+0.658902
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.651374+0.120436 test-rmse:5.703527+0.695198
## [3] train-rmse:4.954580+0.116228 test-rmse:5.011616+0.627811
## [4] train-rmse:4.487085+0.116316 test-rmse:4.568521+0.566369
## [5] train-rmse:4.172085+0.113421 test-rmse:4.303594+0.544574
## [6] train-rmse:3.951523+0.108237 test-rmse:4.067139+0.497110
## [7] train-rmse:3.792603+0.112679 test-rmse:3.928840+0.490421
## [8] train-rmse:3.667134+0.115491 test-rmse:3.803994+0.480972
## [9] train-rmse:3.563280+0.119497 test-rmse:3.693030+0.410171
## [10] train-rmse:3.479325+0.118552 test-rmse:3.639991+0.441515
## [11] train-rmse:3.406085+0.119472 test-rmse:3.580736+0.437309
## [12] train-rmse:3.339965+0.121815 test-rmse:3.491888+0.434963
## [13] train-rmse:3.279554+0.123646 test-rmse:3.428056+0.436397
## [14] train-rmse:3.224747+0.124899 test-rmse:3.359668+0.394268
## [15] train-rmse:3.174402+0.123523 test-rmse:3.313703+0.430942
## [16] train-rmse:3.127658+0.122284 test-rmse:3.279151+0.446991
## [17] train-rmse:3.085397+0.125157 test-rmse:3.261314+0.433891
## [18] train-rmse:3.045678+0.127123 test-rmse:3.193144+0.426175
## [19] train-rmse:3.008471+0.129802 test-rmse:3.168328+0.421476
## [20] train-rmse:2.974149+0.131043 test-rmse:3.128969+0.431567
## [21] train-rmse:2.941448+0.130852 test-rmse:3.110148+0.433721
## [22] train-rmse:2.909367+0.130601 test-rmse:3.090725+0.445358
## [23] train-rmse:2.879408+0.130548 test-rmse:3.065244+0.456909
## [24] train-rmse:2.851038+0.131428 test-rmse:3.036976+0.460805
## [25] train-rmse:2.823917+0.132525 test-rmse:3.028390+0.463935
## [26] train-rmse:2.797695+0.133383 test-rmse:3.002879+0.444749
## [27] train-rmse:2.772168+0.134982 test-rmse:2.975122+0.463791
## [28] train-rmse:2.748423+0.135559 test-rmse:2.956614+0.476480
## [29] train-rmse:2.725703+0.136338 test-rmse:2.932557+0.459146
## [30] train-rmse:2.703093+0.137371 test-rmse:2.906531+0.454973
## [31] train-rmse:2.681454+0.137705 test-rmse:2.896120+0.455839
## [32] train-rmse:2.660273+0.138301 test-rmse:2.873208+0.453899
## [33] train-rmse:2.639815+0.138436 test-rmse:2.866140+0.458229
## [34] train-rmse:2.620253+0.138200 test-rmse:2.854439+0.468442
## [35] train-rmse:2.601556+0.138213 test-rmse:2.859698+0.477373
## [36] train-rmse:2.583187+0.138619 test-rmse:2.835901+0.470955
## [37] train-rmse:2.565640+0.138990 test-rmse:2.809573+0.486031
## [38] train-rmse:2.548446+0.139421 test-rmse:2.802652+0.485988
## [39] train-rmse:2.531753+0.139436 test-rmse:2.783484+0.483130
## [40] train-rmse:2.515848+0.139016 test-rmse:2.759015+0.483219
## [41] train-rmse:2.500385+0.138830 test-rmse:2.751799+0.485690
## [42] train-rmse:2.485472+0.138829 test-rmse:2.734449+0.488593
## [43] train-rmse:2.471517+0.138919 test-rmse:2.723699+0.494974
## [44] train-rmse:2.457707+0.138734 test-rmse:2.702998+0.483518
## [45] train-rmse:2.444286+0.139166 test-rmse:2.688344+0.479596
## [46] train-rmse:2.431647+0.139535 test-rmse:2.681991+0.472098
## [47] train-rmse:2.419252+0.140005 test-rmse:2.670112+0.478168
## [48] train-rmse:2.407190+0.140288 test-rmse:2.649227+0.485399
## [49] train-rmse:2.395054+0.140483 test-rmse:2.646863+0.499571
## [50] train-rmse:2.383318+0.140799 test-rmse:2.637552+0.494267
## [51] train-rmse:2.372183+0.141222 test-rmse:2.632073+0.489063
## [52] train-rmse:2.361449+0.141501 test-rmse:2.625728+0.499476
## [53] train-rmse:2.350762+0.141817 test-rmse:2.626207+0.491377
## [54] train-rmse:2.340305+0.141760 test-rmse:2.615666+0.495401
## [55] train-rmse:2.330192+0.141819 test-rmse:2.607278+0.493600
## [56] train-rmse:2.320108+0.141766 test-rmse:2.594682+0.502830
## [57] train-rmse:2.310576+0.141791 test-rmse:2.577929+0.500641
## [58] train-rmse:2.301408+0.141842 test-rmse:2.572372+0.501349
## [59] train-rmse:2.292375+0.141868 test-rmse:2.564086+0.500802
## [60] train-rmse:2.283612+0.142061 test-rmse:2.561005+0.500664
## [61] train-rmse:2.275287+0.142472 test-rmse:2.557414+0.504742
## [62] train-rmse:2.267065+0.142650 test-rmse:2.552579+0.505919
## [63] train-rmse:2.259077+0.143000 test-rmse:2.539892+0.500596
## [64] train-rmse:2.251430+0.143437 test-rmse:2.540780+0.511115
## [65] train-rmse:2.244036+0.143894 test-rmse:2.528655+0.509801
## [66] train-rmse:2.236546+0.144289 test-rmse:2.530498+0.513050
## [67] train-rmse:2.229385+0.144630 test-rmse:2.521732+0.515305
## [68] train-rmse:2.222256+0.144936 test-rmse:2.522232+0.518232
## [69] train-rmse:2.215328+0.145295 test-rmse:2.509959+0.521759
## [70] train-rmse:2.208632+0.145614 test-rmse:2.496686+0.519975
## [71] train-rmse:2.202038+0.145763 test-rmse:2.493055+0.516492
## [72] train-rmse:2.195671+0.146035 test-rmse:2.487343+0.509832
## [73] train-rmse:2.189380+0.146259 test-rmse:2.485777+0.515299
## [74] train-rmse:2.183158+0.146285 test-rmse:2.477195+0.511947
## [75] train-rmse:2.177172+0.146354 test-rmse:2.472471+0.510849
## [76] train-rmse:2.171336+0.146446 test-rmse:2.474210+0.507006
## [77] train-rmse:2.165549+0.146593 test-rmse:2.470332+0.513882
## [78] train-rmse:2.159920+0.146865 test-rmse:2.466274+0.514437
## [79] train-rmse:2.154353+0.147092 test-rmse:2.467333+0.511702
## [80] train-rmse:2.148995+0.147269 test-rmse:2.461612+0.510836
## [81] train-rmse:2.143897+0.147497 test-rmse:2.456824+0.510466
## [82] train-rmse:2.138889+0.147764 test-rmse:2.455045+0.510145
## [83] train-rmse:2.133999+0.147908 test-rmse:2.449848+0.510146
## [84] train-rmse:2.129191+0.148074 test-rmse:2.442736+0.510121
## [85] train-rmse:2.124496+0.148242 test-rmse:2.438793+0.511390
## [86] train-rmse:2.119886+0.148504 test-rmse:2.431393+0.511500
## [87] train-rmse:2.115265+0.148791 test-rmse:2.425247+0.514857
## [88] train-rmse:2.110842+0.148852 test-rmse:2.420504+0.517199
## [89] train-rmse:2.106546+0.149055 test-rmse:2.424465+0.529433
## [90] train-rmse:2.102291+0.149255 test-rmse:2.421746+0.531114
## [91] train-rmse:2.097992+0.149427 test-rmse:2.421230+0.533425
## [92] train-rmse:2.093693+0.149577 test-rmse:2.415366+0.530842
## [93] train-rmse:2.089548+0.149787 test-rmse:2.404010+0.529895
## [94] train-rmse:2.085433+0.150011 test-rmse:2.407483+0.531939
## [95] train-rmse:2.081233+0.150331 test-rmse:2.402401+0.528267
## [96] train-rmse:2.077352+0.150444 test-rmse:2.407380+0.532196
## [97] train-rmse:2.073615+0.150568 test-rmse:2.405666+0.530358
## [98] train-rmse:2.069929+0.150759 test-rmse:2.402504+0.526933
## [99] train-rmse:2.066188+0.150927 test-rmse:2.399685+0.527912
## [100] train-rmse:2.062657+0.151071 test-rmse:2.396482+0.528051
## [101] train-rmse:2.059186+0.151240 test-rmse:2.395426+0.527472
## [102] train-rmse:2.055765+0.151397 test-rmse:2.395755+0.529630
## [103] train-rmse:2.052355+0.151562 test-rmse:2.396590+0.530529
## [104] train-rmse:2.049032+0.151742 test-rmse:2.391539+0.529115
## [105] train-rmse:2.045689+0.151882 test-rmse:2.392396+0.521897
## [106] train-rmse:2.042512+0.152013 test-rmse:2.388430+0.523734
## [107] train-rmse:2.039216+0.152089 test-rmse:2.387545+0.523075
## [108] train-rmse:2.036095+0.152225 test-rmse:2.385367+0.520385
## [109] train-rmse:2.033021+0.152382 test-rmse:2.381868+0.525237
## [110] train-rmse:2.029961+0.152470 test-rmse:2.376745+0.522837
## [111] train-rmse:2.027003+0.152600 test-rmse:2.372880+0.523518
## [112] train-rmse:2.023985+0.152741 test-rmse:2.372171+0.526331
## [113] train-rmse:2.021029+0.152847 test-rmse:2.366423+0.531233
## [114] train-rmse:2.018114+0.152981 test-rmse:2.366521+0.524872
## [115] train-rmse:2.015213+0.153183 test-rmse:2.365641+0.525541
## [116] train-rmse:2.012356+0.153302 test-rmse:2.367411+0.531019
## [117] train-rmse:2.009573+0.153432 test-rmse:2.367646+0.531705
## [118] train-rmse:2.006828+0.153601 test-rmse:2.364771+0.532538
## [119] train-rmse:2.004081+0.153819 test-rmse:2.360146+0.532683
## [120] train-rmse:2.001391+0.153942 test-rmse:2.360235+0.529769
## [121] train-rmse:1.998687+0.154022 test-rmse:2.358118+0.530250
## [122] train-rmse:1.996005+0.154187 test-rmse:2.360783+0.528886
## [123] train-rmse:1.993358+0.154317 test-rmse:2.362955+0.530777
## [124] train-rmse:1.990826+0.154426 test-rmse:2.364316+0.533891
## [125] train-rmse:1.988352+0.154560 test-rmse:2.364896+0.538814
## [126] train-rmse:1.985904+0.154689 test-rmse:2.363998+0.540368
## [127] train-rmse:1.983517+0.154799 test-rmse:2.363529+0.538514
## Stopping. Best iteration:
## [121] train-rmse:1.998687+0.154022 test-rmse:2.358118+0.530250
##
## 57
## [1] train-rmse:6.569283+0.136561 test-rmse:6.602112+0.621469
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.418914+0.127330 test-rmse:5.471840+0.592572
## [3] train-rmse:4.614884+0.100071 test-rmse:4.757809+0.519990
## [4] train-rmse:4.036789+0.091227 test-rmse:4.210301+0.518890
## [5] train-rmse:3.630496+0.091440 test-rmse:3.876645+0.475578
## [6] train-rmse:3.317602+0.087628 test-rmse:3.560825+0.471853
## [7] train-rmse:3.089982+0.078861 test-rmse:3.353337+0.486838
## [8] train-rmse:2.920944+0.085080 test-rmse:3.229155+0.465328
## [9] train-rmse:2.780596+0.100363 test-rmse:3.125924+0.477684
## [10] train-rmse:2.679150+0.098009 test-rmse:3.057447+0.481284
## [11] train-rmse:2.593781+0.097240 test-rmse:3.001948+0.473549
## [12] train-rmse:2.523815+0.095540 test-rmse:2.947980+0.484010
## [13] train-rmse:2.451646+0.093337 test-rmse:2.905375+0.500070
## [14] train-rmse:2.395973+0.095086 test-rmse:2.853245+0.513290
## [15] train-rmse:2.341035+0.095594 test-rmse:2.821842+0.487558
## [16] train-rmse:2.286753+0.089968 test-rmse:2.804646+0.499487
## [17] train-rmse:2.235662+0.093708 test-rmse:2.793034+0.492211
## [18] train-rmse:2.193740+0.093156 test-rmse:2.763967+0.496015
## [19] train-rmse:2.156856+0.091171 test-rmse:2.740874+0.503161
## [20] train-rmse:2.113839+0.094249 test-rmse:2.703444+0.509475
## [21] train-rmse:2.060216+0.084415 test-rmse:2.646210+0.529689
## [22] train-rmse:2.025225+0.085676 test-rmse:2.632587+0.520719
## [23] train-rmse:1.986144+0.085120 test-rmse:2.607646+0.512552
## [24] train-rmse:1.954433+0.087749 test-rmse:2.596597+0.505878
## [25] train-rmse:1.923618+0.088076 test-rmse:2.584597+0.520272
## [26] train-rmse:1.894577+0.087636 test-rmse:2.570513+0.512003
## [27] train-rmse:1.866077+0.086124 test-rmse:2.553931+0.516333
## [28] train-rmse:1.842019+0.086756 test-rmse:2.533641+0.518716
## [29] train-rmse:1.818080+0.085845 test-rmse:2.521169+0.527081
## [30] train-rmse:1.790087+0.075708 test-rmse:2.500487+0.539839
## [31] train-rmse:1.769441+0.075640 test-rmse:2.484560+0.539953
## [32] train-rmse:1.749624+0.075480 test-rmse:2.477153+0.538475
## [33] train-rmse:1.731606+0.075302 test-rmse:2.485060+0.551379
## [34] train-rmse:1.712436+0.073130 test-rmse:2.479495+0.542765
## [35] train-rmse:1.694604+0.074124 test-rmse:2.464560+0.533870
## [36] train-rmse:1.671126+0.078425 test-rmse:2.439212+0.529492
## [37] train-rmse:1.652281+0.075770 test-rmse:2.437324+0.537785
## [38] train-rmse:1.627513+0.056424 test-rmse:2.423673+0.543183
## [39] train-rmse:1.610166+0.057532 test-rmse:2.420674+0.549974
## [40] train-rmse:1.592846+0.056494 test-rmse:2.406872+0.548000
## [41] train-rmse:1.578232+0.055691 test-rmse:2.402359+0.548730
## [42] train-rmse:1.565033+0.056128 test-rmse:2.391843+0.545766
## [43] train-rmse:1.545964+0.051297 test-rmse:2.377792+0.552986
## [44] train-rmse:1.532115+0.050435 test-rmse:2.375253+0.555535
## [45] train-rmse:1.518971+0.052747 test-rmse:2.382170+0.556145
## [46] train-rmse:1.502472+0.055794 test-rmse:2.378109+0.561851
## [47] train-rmse:1.488659+0.055961 test-rmse:2.363803+0.562660
## [48] train-rmse:1.476712+0.053463 test-rmse:2.363042+0.564822
## [49] train-rmse:1.463465+0.054226 test-rmse:2.350416+0.566245
## [50] train-rmse:1.452992+0.054251 test-rmse:2.347762+0.564368
## [51] train-rmse:1.439036+0.057411 test-rmse:2.344321+0.558541
## [52] train-rmse:1.428835+0.056647 test-rmse:2.339987+0.555882
## [53] train-rmse:1.420044+0.056346 test-rmse:2.332507+0.559147
## [54] train-rmse:1.410957+0.057027 test-rmse:2.336130+0.568105
## [55] train-rmse:1.403011+0.057119 test-rmse:2.336031+0.575686
## [56] train-rmse:1.393438+0.054689 test-rmse:2.334934+0.576944
## [57] train-rmse:1.377052+0.050322 test-rmse:2.322867+0.579558
## [58] train-rmse:1.366301+0.051868 test-rmse:2.318404+0.580500
## [59] train-rmse:1.351294+0.053627 test-rmse:2.313660+0.579101
## [60] train-rmse:1.340760+0.055970 test-rmse:2.304524+0.573349
## [61] train-rmse:1.331194+0.056821 test-rmse:2.299685+0.567147
## [62] train-rmse:1.323759+0.057788 test-rmse:2.291814+0.568922
## [63] train-rmse:1.316191+0.058078 test-rmse:2.292989+0.565144
## [64] train-rmse:1.309658+0.057687 test-rmse:2.288048+0.569083
## [65] train-rmse:1.298758+0.053768 test-rmse:2.287351+0.572752
## [66] train-rmse:1.291656+0.054483 test-rmse:2.281338+0.573327
## [67] train-rmse:1.275105+0.054998 test-rmse:2.275575+0.574960
## [68] train-rmse:1.267576+0.054035 test-rmse:2.276251+0.576004
## [69] train-rmse:1.255104+0.053885 test-rmse:2.272600+0.581126
## [70] train-rmse:1.249123+0.053897 test-rmse:2.273879+0.578682
## [71] train-rmse:1.242631+0.054243 test-rmse:2.271574+0.576976
## [72] train-rmse:1.236420+0.052698 test-rmse:2.268315+0.576866
## [73] train-rmse:1.229478+0.053375 test-rmse:2.267042+0.576507
## [74] train-rmse:1.222317+0.054619 test-rmse:2.271311+0.580992
## [75] train-rmse:1.211972+0.057599 test-rmse:2.270444+0.579758
## [76] train-rmse:1.203678+0.058473 test-rmse:2.268498+0.578099
## [77] train-rmse:1.190037+0.062537 test-rmse:2.267962+0.578895
## [78] train-rmse:1.182642+0.063482 test-rmse:2.273190+0.584531
## [79] train-rmse:1.175233+0.062788 test-rmse:2.270400+0.589672
## Stopping. Best iteration:
## [73] train-rmse:1.229478+0.053375 test-rmse:2.267042+0.576507
##
## 58
## [1] train-rmse:6.490036+0.131167 test-rmse:6.568854+0.607984
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.277575+0.103559 test-rmse:5.407343+0.557951
## [3] train-rmse:4.384502+0.108251 test-rmse:4.559982+0.531291
## [4] train-rmse:3.735139+0.082818 test-rmse:3.932710+0.483911
## [5] train-rmse:3.287611+0.081290 test-rmse:3.542297+0.442411
## [6] train-rmse:2.938094+0.059767 test-rmse:3.282502+0.440409
## [7] train-rmse:2.694420+0.064585 test-rmse:3.091641+0.471090
## [8] train-rmse:2.504700+0.064096 test-rmse:2.951463+0.477353
## [9] train-rmse:2.363321+0.074334 test-rmse:2.867442+0.475121
## [10] train-rmse:2.250638+0.074579 test-rmse:2.789310+0.471542
## [11] train-rmse:2.152273+0.067108 test-rmse:2.722029+0.473426
## [12] train-rmse:2.060046+0.067883 test-rmse:2.691512+0.482068
## [13] train-rmse:1.989493+0.050595 test-rmse:2.649402+0.485018
## [14] train-rmse:1.918831+0.059524 test-rmse:2.597833+0.485715
## [15] train-rmse:1.862578+0.065154 test-rmse:2.562136+0.476807
## [16] train-rmse:1.806886+0.057825 test-rmse:2.526450+0.477973
## [17] train-rmse:1.751338+0.060182 test-rmse:2.504270+0.471667
## [18] train-rmse:1.713280+0.058568 test-rmse:2.480280+0.481145
## [19] train-rmse:1.668713+0.062049 test-rmse:2.455102+0.479647
## [20] train-rmse:1.635107+0.061711 test-rmse:2.435398+0.485961
## [21] train-rmse:1.591315+0.058052 test-rmse:2.416426+0.486551
## [22] train-rmse:1.551108+0.049067 test-rmse:2.397274+0.488091
## [23] train-rmse:1.518965+0.046611 test-rmse:2.380391+0.466304
## [24] train-rmse:1.484116+0.045850 test-rmse:2.369384+0.480854
## [25] train-rmse:1.452750+0.053162 test-rmse:2.345666+0.483720
## [26] train-rmse:1.421069+0.054139 test-rmse:2.339256+0.491752
## [27] train-rmse:1.387850+0.046689 test-rmse:2.337868+0.501480
## [28] train-rmse:1.364724+0.049500 test-rmse:2.328127+0.502444
## [29] train-rmse:1.342066+0.051809 test-rmse:2.319010+0.504630
## [30] train-rmse:1.321681+0.051015 test-rmse:2.307372+0.505084
## [31] train-rmse:1.293521+0.043879 test-rmse:2.296565+0.504102
## [32] train-rmse:1.274683+0.042107 test-rmse:2.283560+0.500418
## [33] train-rmse:1.249992+0.036779 test-rmse:2.266904+0.501241
## [34] train-rmse:1.229119+0.029171 test-rmse:2.260707+0.507346
## [35] train-rmse:1.212313+0.028672 test-rmse:2.252736+0.504642
## [36] train-rmse:1.181812+0.032795 test-rmse:2.248318+0.507332
## [37] train-rmse:1.162648+0.030172 test-rmse:2.239616+0.513845
## [38] train-rmse:1.150279+0.030903 test-rmse:2.238862+0.513174
## [39] train-rmse:1.132061+0.025975 test-rmse:2.240024+0.511607
## [40] train-rmse:1.111476+0.022366 test-rmse:2.232806+0.520642
## [41] train-rmse:1.095492+0.019889 test-rmse:2.235214+0.523142
## [42] train-rmse:1.082110+0.023735 test-rmse:2.227631+0.528153
## [43] train-rmse:1.062539+0.031029 test-rmse:2.220107+0.516876
## [44] train-rmse:1.049602+0.032048 test-rmse:2.214478+0.513413
## [45] train-rmse:1.034538+0.036441 test-rmse:2.208136+0.517364
## [46] train-rmse:1.017165+0.036667 test-rmse:2.201422+0.519048
## [47] train-rmse:1.002672+0.034179 test-rmse:2.194613+0.518107
## [48] train-rmse:0.987314+0.036361 test-rmse:2.190395+0.516510
## [49] train-rmse:0.969795+0.035181 test-rmse:2.186791+0.518746
## [50] train-rmse:0.959349+0.035529 test-rmse:2.185279+0.516125
## [51] train-rmse:0.946164+0.031401 test-rmse:2.179830+0.520093
## [52] train-rmse:0.933721+0.031774 test-rmse:2.176420+0.521131
## [53] train-rmse:0.923120+0.028271 test-rmse:2.173125+0.524564
## [54] train-rmse:0.907578+0.028365 test-rmse:2.165221+0.519784
## [55] train-rmse:0.897103+0.030128 test-rmse:2.161461+0.519073
## [56] train-rmse:0.887085+0.032930 test-rmse:2.159088+0.518842
## [57] train-rmse:0.872356+0.029629 test-rmse:2.156303+0.520309
## [58] train-rmse:0.862144+0.030971 test-rmse:2.156246+0.517255
## [59] train-rmse:0.851704+0.030413 test-rmse:2.157742+0.515515
## [60] train-rmse:0.840061+0.030503 test-rmse:2.150426+0.513371
## [61] train-rmse:0.830194+0.030889 test-rmse:2.157279+0.512946
## [62] train-rmse:0.822088+0.034087 test-rmse:2.159880+0.516564
## [63] train-rmse:0.813924+0.038890 test-rmse:2.156318+0.515185
## [64] train-rmse:0.806144+0.039147 test-rmse:2.155648+0.512881
## [65] train-rmse:0.794199+0.037511 test-rmse:2.155920+0.513112
## [66] train-rmse:0.781817+0.036553 test-rmse:2.163344+0.515141
## Stopping. Best iteration:
## [60] train-rmse:0.840061+0.030503 test-rmse:2.150426+0.513371
##
## 59
## [1] train-rmse:6.415906+0.129817 test-rmse:6.468854+0.594774
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.109404+0.093325 test-rmse:5.245815+0.506427
## [3] train-rmse:4.171723+0.065037 test-rmse:4.385567+0.449039
## [4] train-rmse:3.491542+0.059242 test-rmse:3.770254+0.458426
## [5] train-rmse:3.016733+0.037984 test-rmse:3.373566+0.478591
## [6] train-rmse:2.652694+0.035478 test-rmse:3.084696+0.489667
## [7] train-rmse:2.409218+0.034596 test-rmse:2.921998+0.502008
## [8] train-rmse:2.212742+0.034616 test-rmse:2.810904+0.511992
## [9] train-rmse:2.052031+0.043198 test-rmse:2.737361+0.534425
## [10] train-rmse:1.928580+0.042228 test-rmse:2.674702+0.543563
## [11] train-rmse:1.825779+0.041365 test-rmse:2.596653+0.531023
## [12] train-rmse:1.729761+0.042395 test-rmse:2.544652+0.543692
## [13] train-rmse:1.664767+0.041226 test-rmse:2.508432+0.537189
## [14] train-rmse:1.599303+0.039730 test-rmse:2.459452+0.544662
## [15] train-rmse:1.541299+0.042204 test-rmse:2.432270+0.547368
## [16] train-rmse:1.483559+0.043751 test-rmse:2.403875+0.558720
## [17] train-rmse:1.424712+0.040589 test-rmse:2.386203+0.572452
## [18] train-rmse:1.380408+0.039509 test-rmse:2.376931+0.571853
## [19] train-rmse:1.341235+0.043691 test-rmse:2.346149+0.563112
## [20] train-rmse:1.292445+0.048890 test-rmse:2.334376+0.556232
## [21] train-rmse:1.240344+0.036961 test-rmse:2.323032+0.558364
## [22] train-rmse:1.209578+0.038345 test-rmse:2.309444+0.561610
## [23] train-rmse:1.173583+0.031797 test-rmse:2.293999+0.558406
## [24] train-rmse:1.140197+0.033819 test-rmse:2.293798+0.565059
## [25] train-rmse:1.108028+0.026566 test-rmse:2.275092+0.564652
## [26] train-rmse:1.086096+0.024107 test-rmse:2.268452+0.564759
## [27] train-rmse:1.061610+0.028043 test-rmse:2.261365+0.565663
## [28] train-rmse:1.029334+0.035015 test-rmse:2.250759+0.574210
## [29] train-rmse:1.003893+0.041229 test-rmse:2.247487+0.569689
## [30] train-rmse:0.978554+0.040065 test-rmse:2.226844+0.573806
## [31] train-rmse:0.960048+0.038744 test-rmse:2.218368+0.576445
## [32] train-rmse:0.940245+0.039276 test-rmse:2.210325+0.579277
## [33] train-rmse:0.918224+0.029162 test-rmse:2.202846+0.579415
## [34] train-rmse:0.891404+0.032979 test-rmse:2.200032+0.579098
## [35] train-rmse:0.872880+0.032120 test-rmse:2.197188+0.582286
## [36] train-rmse:0.853710+0.030521 test-rmse:2.198193+0.578600
## [37] train-rmse:0.835197+0.030253 test-rmse:2.202412+0.585134
## [38] train-rmse:0.813440+0.028045 test-rmse:2.196986+0.582736
## [39] train-rmse:0.796153+0.030624 test-rmse:2.194644+0.587851
## [40] train-rmse:0.777048+0.022199 test-rmse:2.194115+0.587569
## [41] train-rmse:0.760501+0.024063 test-rmse:2.185054+0.593302
## [42] train-rmse:0.740240+0.023218 test-rmse:2.180926+0.596098
## [43] train-rmse:0.721704+0.023348 test-rmse:2.176798+0.593716
## [44] train-rmse:0.706366+0.026792 test-rmse:2.173132+0.589601
## [45] train-rmse:0.690977+0.020979 test-rmse:2.166695+0.592860
## [46] train-rmse:0.677104+0.026715 test-rmse:2.169058+0.591397
## [47] train-rmse:0.665751+0.027031 test-rmse:2.166523+0.591360
## [48] train-rmse:0.653181+0.023656 test-rmse:2.163472+0.592441
## [49] train-rmse:0.641208+0.022453 test-rmse:2.161879+0.593386
## [50] train-rmse:0.623940+0.021471 test-rmse:2.159436+0.597276
## [51] train-rmse:0.612172+0.024629 test-rmse:2.156746+0.596634
## [52] train-rmse:0.601380+0.022196 test-rmse:2.157939+0.599237
## [53] train-rmse:0.589197+0.022706 test-rmse:2.153245+0.602802
## [54] train-rmse:0.581224+0.022441 test-rmse:2.152216+0.601026
## [55] train-rmse:0.566349+0.024307 test-rmse:2.151649+0.599267
## [56] train-rmse:0.549941+0.019211 test-rmse:2.149589+0.600847
## [57] train-rmse:0.541748+0.018698 test-rmse:2.151506+0.601124
## [58] train-rmse:0.532492+0.019274 test-rmse:2.150064+0.600603
## [59] train-rmse:0.524991+0.016918 test-rmse:2.151569+0.598315
## [60] train-rmse:0.516260+0.016150 test-rmse:2.148451+0.600239
## [61] train-rmse:0.501408+0.017124 test-rmse:2.145110+0.597374
## [62] train-rmse:0.493709+0.018922 test-rmse:2.143934+0.597754
## [63] train-rmse:0.487161+0.019028 test-rmse:2.143389+0.597303
## [64] train-rmse:0.478164+0.017460 test-rmse:2.138791+0.598769
## [65] train-rmse:0.470344+0.019390 test-rmse:2.138927+0.599143
## [66] train-rmse:0.461260+0.017854 test-rmse:2.135550+0.599473
## [67] train-rmse:0.454421+0.017931 test-rmse:2.134179+0.598696
## [68] train-rmse:0.446422+0.018062 test-rmse:2.134466+0.598429
## [69] train-rmse:0.439319+0.018046 test-rmse:2.132990+0.600908
## [70] train-rmse:0.432158+0.018359 test-rmse:2.135215+0.603628
## [71] train-rmse:0.421421+0.014028 test-rmse:2.132419+0.605803
## [72] train-rmse:0.414575+0.014354 test-rmse:2.131565+0.605572
## [73] train-rmse:0.408743+0.012898 test-rmse:2.133279+0.605197
## [74] train-rmse:0.400075+0.013621 test-rmse:2.132515+0.607225
## [75] train-rmse:0.394966+0.013309 test-rmse:2.133445+0.606740
## [76] train-rmse:0.389364+0.012661 test-rmse:2.131696+0.608725
## [77] train-rmse:0.382830+0.011872 test-rmse:2.132609+0.608652
## [78] train-rmse:0.373061+0.013590 test-rmse:2.130309+0.610845
## [79] train-rmse:0.368290+0.015485 test-rmse:2.130319+0.610926
## [80] train-rmse:0.362422+0.013991 test-rmse:2.128796+0.611839
## [81] train-rmse:0.355942+0.012858 test-rmse:2.124516+0.614363
## [82] train-rmse:0.350123+0.012400 test-rmse:2.125173+0.614982
## [83] train-rmse:0.344838+0.011021 test-rmse:2.124178+0.614635
## [84] train-rmse:0.339952+0.011093 test-rmse:2.124060+0.613942
## [85] train-rmse:0.334667+0.011628 test-rmse:2.123657+0.612924
## [86] train-rmse:0.327835+0.010859 test-rmse:2.123521+0.613748
## [87] train-rmse:0.324476+0.011780 test-rmse:2.124425+0.613721
## [88] train-rmse:0.316552+0.011959 test-rmse:2.124862+0.614420
## [89] train-rmse:0.313162+0.013357 test-rmse:2.125096+0.614457
## [90] train-rmse:0.307796+0.013649 test-rmse:2.124715+0.614026
## [91] train-rmse:0.302727+0.013849 test-rmse:2.123602+0.614103
## [92] train-rmse:0.297690+0.011552 test-rmse:2.122577+0.613923
## [93] train-rmse:0.290095+0.011122 test-rmse:2.123088+0.613727
## [94] train-rmse:0.285060+0.011349 test-rmse:2.122764+0.614531
## [95] train-rmse:0.280588+0.012488 test-rmse:2.123350+0.615054
## [96] train-rmse:0.276506+0.012621 test-rmse:2.123425+0.615756
## [97] train-rmse:0.271780+0.012990 test-rmse:2.123867+0.616619
## [98] train-rmse:0.267850+0.011985 test-rmse:2.122750+0.617056
## Stopping. Best iteration:
## [92] train-rmse:0.297690+0.011552 test-rmse:2.122577+0.613923
##
## 60
## [1] train-rmse:6.364678+0.127213 test-rmse:6.383430+0.572577
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.015577+0.097944 test-rmse:5.113946+0.492625
## [3] train-rmse:4.054524+0.080137 test-rmse:4.278192+0.446993
## [4] train-rmse:3.366107+0.064885 test-rmse:3.708720+0.439486
## [5] train-rmse:2.871056+0.059961 test-rmse:3.336974+0.418063
## [6] train-rmse:2.485830+0.049804 test-rmse:3.057499+0.451011
## [7] train-rmse:2.225924+0.056034 test-rmse:2.906835+0.461847
## [8] train-rmse:2.009428+0.044329 test-rmse:2.751725+0.483337
## [9] train-rmse:1.820510+0.049691 test-rmse:2.664617+0.495325
## [10] train-rmse:1.672718+0.058551 test-rmse:2.596202+0.498325
## [11] train-rmse:1.572912+0.062561 test-rmse:2.531600+0.506131
## [12] train-rmse:1.488711+0.057793 test-rmse:2.498226+0.515962
## [13] train-rmse:1.421035+0.059758 test-rmse:2.463634+0.504029
## [14] train-rmse:1.349644+0.063463 test-rmse:2.433833+0.527622
## [15] train-rmse:1.287927+0.057416 test-rmse:2.430652+0.530665
## [16] train-rmse:1.233671+0.058713 test-rmse:2.405848+0.532375
## [17] train-rmse:1.177385+0.052010 test-rmse:2.390611+0.532403
## [18] train-rmse:1.122913+0.048638 test-rmse:2.364671+0.515169
## [19] train-rmse:1.080304+0.046446 test-rmse:2.361183+0.508080
## [20] train-rmse:1.047602+0.049329 test-rmse:2.339690+0.516211
## [21] train-rmse:1.001439+0.061397 test-rmse:2.332760+0.520386
## [22] train-rmse:0.968707+0.050595 test-rmse:2.327821+0.520644
## [23] train-rmse:0.932984+0.039884 test-rmse:2.308860+0.511026
## [24] train-rmse:0.904682+0.039016 test-rmse:2.298212+0.508762
## [25] train-rmse:0.870798+0.049520 test-rmse:2.292985+0.511188
## [26] train-rmse:0.839069+0.048899 test-rmse:2.276169+0.516236
## [27] train-rmse:0.818433+0.046034 test-rmse:2.265877+0.521821
## [28] train-rmse:0.792320+0.044302 test-rmse:2.260826+0.519679
## [29] train-rmse:0.774729+0.041355 test-rmse:2.254851+0.517772
## [30] train-rmse:0.751103+0.047529 test-rmse:2.248174+0.522325
## [31] train-rmse:0.730111+0.046445 test-rmse:2.245618+0.520525
## [32] train-rmse:0.705003+0.037207 test-rmse:2.252407+0.531601
## [33] train-rmse:0.686795+0.034706 test-rmse:2.245609+0.533991
## [34] train-rmse:0.669149+0.034855 test-rmse:2.242685+0.526967
## [35] train-rmse:0.646460+0.038002 test-rmse:2.238498+0.530029
## [36] train-rmse:0.626944+0.031489 test-rmse:2.234294+0.536776
## [37] train-rmse:0.607998+0.032533 test-rmse:2.232672+0.536232
## [38] train-rmse:0.591196+0.031240 test-rmse:2.233458+0.538269
## [39] train-rmse:0.571780+0.028622 test-rmse:2.234405+0.539240
## [40] train-rmse:0.557079+0.028269 test-rmse:2.230794+0.541297
## [41] train-rmse:0.545986+0.025657 test-rmse:2.227531+0.541554
## [42] train-rmse:0.529752+0.026444 test-rmse:2.229166+0.542843
## [43] train-rmse:0.514055+0.024688 test-rmse:2.227807+0.540644
## [44] train-rmse:0.501021+0.026836 test-rmse:2.229592+0.539941
## [45] train-rmse:0.490435+0.026851 test-rmse:2.229235+0.540540
## [46] train-rmse:0.478460+0.023848 test-rmse:2.230583+0.541223
## [47] train-rmse:0.468215+0.025374 test-rmse:2.227267+0.541152
## [48] train-rmse:0.458823+0.024361 test-rmse:2.225527+0.542401
## [49] train-rmse:0.444220+0.025787 test-rmse:2.226472+0.541556
## [50] train-rmse:0.435834+0.029207 test-rmse:2.225300+0.538296
## [51] train-rmse:0.427534+0.027702 test-rmse:2.224795+0.538509
## [52] train-rmse:0.420984+0.029709 test-rmse:2.223731+0.537733
## [53] train-rmse:0.406984+0.030042 test-rmse:2.222307+0.537100
## [54] train-rmse:0.391860+0.028614 test-rmse:2.224221+0.538217
## [55] train-rmse:0.378559+0.028861 test-rmse:2.221722+0.539345
## [56] train-rmse:0.370191+0.029313 test-rmse:2.221375+0.539035
## [57] train-rmse:0.362920+0.028916 test-rmse:2.221485+0.539369
## [58] train-rmse:0.352468+0.033564 test-rmse:2.220723+0.540190
## [59] train-rmse:0.342797+0.035382 test-rmse:2.216121+0.539791
## [60] train-rmse:0.334297+0.035921 test-rmse:2.216073+0.539592
## [61] train-rmse:0.327236+0.035584 test-rmse:2.215492+0.538805
## [62] train-rmse:0.320365+0.036283 test-rmse:2.215238+0.539413
## [63] train-rmse:0.311783+0.032946 test-rmse:2.213996+0.539534
## [64] train-rmse:0.304829+0.033611 test-rmse:2.213101+0.539809
## [65] train-rmse:0.295073+0.027932 test-rmse:2.212256+0.539594
## [66] train-rmse:0.287176+0.029169 test-rmse:2.213487+0.540785
## [67] train-rmse:0.280646+0.026002 test-rmse:2.211899+0.540596
## [68] train-rmse:0.275166+0.027852 test-rmse:2.211557+0.541052
## [69] train-rmse:0.269039+0.030317 test-rmse:2.212287+0.539982
## [70] train-rmse:0.265251+0.028714 test-rmse:2.210472+0.538906
## [71] train-rmse:0.260110+0.028882 test-rmse:2.211441+0.538726
## [72] train-rmse:0.254032+0.030941 test-rmse:2.210991+0.540063
## [73] train-rmse:0.248272+0.030491 test-rmse:2.211560+0.540598
## [74] train-rmse:0.240972+0.027805 test-rmse:2.210750+0.541585
## [75] train-rmse:0.234841+0.028454 test-rmse:2.209737+0.541581
## [76] train-rmse:0.228071+0.028240 test-rmse:2.208337+0.541018
## [77] train-rmse:0.223306+0.025732 test-rmse:2.207866+0.540341
## [78] train-rmse:0.218653+0.024090 test-rmse:2.207068+0.540747
## [79] train-rmse:0.214380+0.024264 test-rmse:2.207085+0.541006
## [80] train-rmse:0.207363+0.022611 test-rmse:2.207880+0.540388
## [81] train-rmse:0.202743+0.021764 test-rmse:2.206948+0.540494
## [82] train-rmse:0.198121+0.019747 test-rmse:2.206299+0.539998
## [83] train-rmse:0.192581+0.018618 test-rmse:2.205859+0.539689
## [84] train-rmse:0.187586+0.018837 test-rmse:2.206788+0.539134
## [85] train-rmse:0.183219+0.018046 test-rmse:2.206518+0.539592
## [86] train-rmse:0.179644+0.016770 test-rmse:2.206385+0.539509
## [87] train-rmse:0.176084+0.016973 test-rmse:2.207168+0.539236
## [88] train-rmse:0.173103+0.017258 test-rmse:2.206164+0.539130
## [89] train-rmse:0.170177+0.017687 test-rmse:2.205736+0.538774
## [90] train-rmse:0.166241+0.017744 test-rmse:2.206434+0.538970
## [91] train-rmse:0.162668+0.018545 test-rmse:2.206008+0.537119
## [92] train-rmse:0.160371+0.019036 test-rmse:2.206084+0.537079
## [93] train-rmse:0.156824+0.019723 test-rmse:2.206314+0.536884
## [94] train-rmse:0.154409+0.019976 test-rmse:2.207104+0.538335
## [95] train-rmse:0.152123+0.020359 test-rmse:2.207292+0.537946
## Stopping. Best iteration:
## [89] train-rmse:0.170177+0.017687 test-rmse:2.205736+0.538774
##
## 61
## [1] train-rmse:6.332529+0.122103 test-rmse:6.362981+0.602454
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.959661+0.089450 test-rmse:5.108104+0.528266
## [3] train-rmse:3.963252+0.066947 test-rmse:4.245030+0.476484
## [4] train-rmse:3.243744+0.046971 test-rmse:3.674488+0.454703
## [5] train-rmse:2.712789+0.036895 test-rmse:3.286455+0.473490
## [6] train-rmse:2.337488+0.038450 test-rmse:3.035434+0.483029
## [7] train-rmse:2.041846+0.030997 test-rmse:2.848928+0.490511
## [8] train-rmse:1.826872+0.055223 test-rmse:2.737139+0.507125
## [9] train-rmse:1.667513+0.048895 test-rmse:2.667324+0.542676
## [10] train-rmse:1.510694+0.050910 test-rmse:2.616539+0.555163
## [11] train-rmse:1.398603+0.048805 test-rmse:2.557766+0.558208
## [12] train-rmse:1.318525+0.043215 test-rmse:2.528902+0.561474
## [13] train-rmse:1.250050+0.039769 test-rmse:2.510139+0.541565
## [14] train-rmse:1.171601+0.039848 test-rmse:2.491682+0.531755
## [15] train-rmse:1.110135+0.036891 test-rmse:2.481105+0.535398
## [16] train-rmse:1.049751+0.038441 test-rmse:2.458549+0.537800
## [17] train-rmse:0.993175+0.030646 test-rmse:2.450213+0.534380
## [18] train-rmse:0.940014+0.035678 test-rmse:2.436340+0.536498
## [19] train-rmse:0.893037+0.030698 test-rmse:2.429416+0.547499
## [20] train-rmse:0.864134+0.030054 test-rmse:2.419054+0.550231
## [21] train-rmse:0.839956+0.028630 test-rmse:2.413444+0.552128
## [22] train-rmse:0.798887+0.029535 test-rmse:2.401724+0.555934
## [23] train-rmse:0.755692+0.036059 test-rmse:2.395452+0.553406
## [24] train-rmse:0.720298+0.027626 test-rmse:2.392344+0.553469
## [25] train-rmse:0.691822+0.020370 test-rmse:2.389862+0.560164
## [26] train-rmse:0.659309+0.027635 test-rmse:2.382539+0.562121
## [27] train-rmse:0.638041+0.031728 test-rmse:2.378550+0.563314
## [28] train-rmse:0.614709+0.032055 test-rmse:2.378835+0.563624
## [29] train-rmse:0.587039+0.028389 test-rmse:2.375377+0.562312
## [30] train-rmse:0.559316+0.034469 test-rmse:2.365878+0.567182
## [31] train-rmse:0.537865+0.033453 test-rmse:2.357744+0.572002
## [32] train-rmse:0.515087+0.034615 test-rmse:2.354981+0.573017
## [33] train-rmse:0.493408+0.033416 test-rmse:2.351713+0.572479
## [34] train-rmse:0.477848+0.032688 test-rmse:2.348894+0.574930
## [35] train-rmse:0.465615+0.031802 test-rmse:2.346889+0.573077
## [36] train-rmse:0.443077+0.033905 test-rmse:2.342088+0.579196
## [37] train-rmse:0.425439+0.033103 test-rmse:2.336490+0.580859
## [38] train-rmse:0.410083+0.029544 test-rmse:2.338894+0.582191
## [39] train-rmse:0.394207+0.035553 test-rmse:2.337939+0.584072
## [40] train-rmse:0.380199+0.037670 test-rmse:2.336190+0.584364
## [41] train-rmse:0.361659+0.033548 test-rmse:2.330536+0.583231
## [42] train-rmse:0.350075+0.030712 test-rmse:2.328750+0.581874
## [43] train-rmse:0.340250+0.030480 test-rmse:2.328709+0.580971
## [44] train-rmse:0.329976+0.028628 test-rmse:2.326516+0.582110
## [45] train-rmse:0.319981+0.025880 test-rmse:2.322469+0.582515
## [46] train-rmse:0.305875+0.024151 test-rmse:2.318963+0.584553
## [47] train-rmse:0.297637+0.024262 test-rmse:2.317329+0.584541
## [48] train-rmse:0.286284+0.024877 test-rmse:2.315839+0.585779
## [49] train-rmse:0.276991+0.023644 test-rmse:2.311503+0.586432
## [50] train-rmse:0.268415+0.027489 test-rmse:2.311478+0.587276
## [51] train-rmse:0.261278+0.028177 test-rmse:2.310386+0.586969
## [52] train-rmse:0.251492+0.026818 test-rmse:2.307638+0.585735
## [53] train-rmse:0.240437+0.027306 test-rmse:2.307890+0.585979
## [54] train-rmse:0.231711+0.026814 test-rmse:2.307708+0.588132
## [55] train-rmse:0.222720+0.025965 test-rmse:2.306928+0.588511
## [56] train-rmse:0.213948+0.025644 test-rmse:2.307120+0.588043
## [57] train-rmse:0.206733+0.024259 test-rmse:2.304854+0.587937
## [58] train-rmse:0.199919+0.022856 test-rmse:2.305928+0.589272
## [59] train-rmse:0.194770+0.024057 test-rmse:2.305487+0.589467
## [60] train-rmse:0.189689+0.023066 test-rmse:2.305703+0.590051
## [61] train-rmse:0.183688+0.021641 test-rmse:2.305581+0.591451
## [62] train-rmse:0.178300+0.020675 test-rmse:2.305543+0.590842
## [63] train-rmse:0.173900+0.020155 test-rmse:2.304350+0.590313
## [64] train-rmse:0.166580+0.020543 test-rmse:2.303127+0.590547
## [65] train-rmse:0.160522+0.018316 test-rmse:2.302797+0.589831
## [66] train-rmse:0.156295+0.018617 test-rmse:2.302349+0.589216
## [67] train-rmse:0.151982+0.019682 test-rmse:2.301654+0.589502
## [68] train-rmse:0.147902+0.018441 test-rmse:2.302753+0.590213
## [69] train-rmse:0.144070+0.017564 test-rmse:2.303199+0.589828
## [70] train-rmse:0.139933+0.017410 test-rmse:2.303808+0.589814
## [71] train-rmse:0.135672+0.016130 test-rmse:2.302622+0.590560
## [72] train-rmse:0.130329+0.015265 test-rmse:2.301479+0.589595
## [73] train-rmse:0.126309+0.014187 test-rmse:2.301442+0.589964
## [74] train-rmse:0.123286+0.013844 test-rmse:2.301203+0.590109
## [75] train-rmse:0.119676+0.013482 test-rmse:2.301222+0.590721
## [76] train-rmse:0.115584+0.013563 test-rmse:2.301006+0.590717
## [77] train-rmse:0.111528+0.012923 test-rmse:2.300378+0.590993
## [78] train-rmse:0.107678+0.012485 test-rmse:2.299609+0.590439
## [79] train-rmse:0.103218+0.011143 test-rmse:2.298705+0.591256
## [80] train-rmse:0.100799+0.010848 test-rmse:2.298244+0.591611
## [81] train-rmse:0.098795+0.010746 test-rmse:2.298740+0.591318
## [82] train-rmse:0.096420+0.011045 test-rmse:2.298765+0.591169
## [83] train-rmse:0.093279+0.010491 test-rmse:2.298708+0.591379
## [84] train-rmse:0.090629+0.010134 test-rmse:2.298872+0.591229
## [85] train-rmse:0.087590+0.009083 test-rmse:2.299583+0.591930
## [86] train-rmse:0.084505+0.009185 test-rmse:2.299568+0.591849
## Stopping. Best iteration:
## [80] train-rmse:0.100799+0.010848 test-rmse:2.298244+0.591611
##
## 62
## [1] train-rmse:6.321105+0.121135 test-rmse:6.356225+0.602026
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.928922+0.083572 test-rmse:5.093405+0.522361
## [3] train-rmse:3.908484+0.054822 test-rmse:4.195993+0.497491
## [4] train-rmse:3.165500+0.037355 test-rmse:3.607269+0.459955
## [5] train-rmse:2.627419+0.031569 test-rmse:3.232138+0.473721
## [6] train-rmse:2.218879+0.025751 test-rmse:2.990703+0.497128
## [7] train-rmse:1.927006+0.028433 test-rmse:2.794064+0.527698
## [8] train-rmse:1.673460+0.029521 test-rmse:2.678747+0.508064
## [9] train-rmse:1.483222+0.041500 test-rmse:2.603113+0.499435
## [10] train-rmse:1.342000+0.050011 test-rmse:2.541802+0.522661
## [11] train-rmse:1.222198+0.053306 test-rmse:2.509289+0.531037
## [12] train-rmse:1.115003+0.057430 test-rmse:2.501442+0.545277
## [13] train-rmse:1.036385+0.052882 test-rmse:2.478581+0.541199
## [14] train-rmse:0.963379+0.058841 test-rmse:2.466426+0.555770
## [15] train-rmse:0.911822+0.054435 test-rmse:2.455007+0.553386
## [16] train-rmse:0.858084+0.049536 test-rmse:2.452626+0.559931
## [17] train-rmse:0.793837+0.054615 test-rmse:2.446271+0.557346
## [18] train-rmse:0.749732+0.056371 test-rmse:2.439644+0.564007
## [19] train-rmse:0.701899+0.054787 test-rmse:2.422581+0.571338
## [20] train-rmse:0.660098+0.045173 test-rmse:2.411732+0.581492
## [21] train-rmse:0.627674+0.051141 test-rmse:2.408698+0.578076
## [22] train-rmse:0.592869+0.045311 test-rmse:2.404771+0.582936
## [23] train-rmse:0.568192+0.044144 test-rmse:2.395843+0.584366
## [24] train-rmse:0.543004+0.041687 test-rmse:2.393324+0.588563
## [25] train-rmse:0.505972+0.041701 test-rmse:2.388081+0.588185
## [26] train-rmse:0.486924+0.041662 test-rmse:2.383647+0.586471
## [27] train-rmse:0.465508+0.041448 test-rmse:2.380078+0.586943
## [28] train-rmse:0.442245+0.037908 test-rmse:2.372760+0.591021
## [29] train-rmse:0.419591+0.038072 test-rmse:2.366993+0.591425
## [30] train-rmse:0.403429+0.039760 test-rmse:2.365042+0.593195
## [31] train-rmse:0.385265+0.037399 test-rmse:2.360633+0.592989
## [32] train-rmse:0.363704+0.037870 test-rmse:2.359760+0.594404
## [33] train-rmse:0.347369+0.038752 test-rmse:2.358375+0.594365
## [34] train-rmse:0.330438+0.041161 test-rmse:2.358051+0.592947
## [35] train-rmse:0.315453+0.040922 test-rmse:2.355728+0.595367
## [36] train-rmse:0.302258+0.036601 test-rmse:2.355182+0.594164
## [37] train-rmse:0.290086+0.036146 test-rmse:2.352490+0.593777
## [38] train-rmse:0.280327+0.033266 test-rmse:2.353062+0.595334
## [39] train-rmse:0.270158+0.033229 test-rmse:2.352397+0.596400
## [40] train-rmse:0.258165+0.032189 test-rmse:2.355340+0.599243
## [41] train-rmse:0.247334+0.029735 test-rmse:2.355639+0.601106
## [42] train-rmse:0.237402+0.030859 test-rmse:2.355081+0.602435
## [43] train-rmse:0.225433+0.026290 test-rmse:2.354241+0.602189
## [44] train-rmse:0.217181+0.024206 test-rmse:2.353488+0.601271
## [45] train-rmse:0.209601+0.025242 test-rmse:2.353197+0.601214
## Stopping. Best iteration:
## [39] train-rmse:0.270158+0.033229 test-rmse:2.352397+0.596400
##
## 63
## [1] train-rmse:6.578402+0.142961 test-rmse:6.578032+0.655895
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.504833+0.117276 test-rmse:5.564744+0.692737
## [3] train-rmse:4.809794+0.115411 test-rmse:4.875141+0.617034
## [4] train-rmse:4.361862+0.116750 test-rmse:4.452130+0.552455
## [5] train-rmse:4.066091+0.113320 test-rmse:4.212805+0.527158
## [6] train-rmse:3.861677+0.107634 test-rmse:3.981688+0.492928
## [7] train-rmse:3.714085+0.112329 test-rmse:3.823922+0.511313
## [8] train-rmse:3.595106+0.116980 test-rmse:3.716457+0.441245
## [9] train-rmse:3.495933+0.121137 test-rmse:3.636803+0.399047
## [10] train-rmse:3.415747+0.120305 test-rmse:3.581670+0.440613
## [11] train-rmse:3.343779+0.119957 test-rmse:3.513129+0.445376
## [12] train-rmse:3.277144+0.121962 test-rmse:3.414070+0.451685
## [13] train-rmse:3.218236+0.123686 test-rmse:3.360408+0.427657
## [14] train-rmse:3.163696+0.126154 test-rmse:3.285381+0.399197
## [15] train-rmse:3.114323+0.126526 test-rmse:3.265557+0.414274
## [16] train-rmse:3.068605+0.124831 test-rmse:3.218963+0.446551
## [17] train-rmse:3.027156+0.126298 test-rmse:3.205160+0.423711
## [18] train-rmse:2.988046+0.128593 test-rmse:3.143202+0.402857
## [19] train-rmse:2.952534+0.131152 test-rmse:3.125360+0.411619
## [20] train-rmse:2.918062+0.133008 test-rmse:3.085990+0.419781
## [21] train-rmse:2.885256+0.133971 test-rmse:3.051863+0.435625
## [22] train-rmse:2.854002+0.135301 test-rmse:3.045877+0.439416
## [23] train-rmse:2.824364+0.135902 test-rmse:3.025166+0.449196
## [24] train-rmse:2.796404+0.135721 test-rmse:3.015478+0.469798
## [25] train-rmse:2.769398+0.135915 test-rmse:2.982155+0.459011
## [26] train-rmse:2.742339+0.135925 test-rmse:2.942097+0.445176
## [27] train-rmse:2.717194+0.136474 test-rmse:2.920735+0.454949
## [28] train-rmse:2.693053+0.137078 test-rmse:2.915805+0.465510
## [29] train-rmse:2.669466+0.138656 test-rmse:2.910078+0.468177
## [30] train-rmse:2.647195+0.139840 test-rmse:2.876189+0.467322
## [31] train-rmse:2.625307+0.140487 test-rmse:2.857108+0.463777
## [32] train-rmse:2.604997+0.140721 test-rmse:2.848373+0.465374
## [33] train-rmse:2.585547+0.140949 test-rmse:2.828324+0.475953
## [34] train-rmse:2.566607+0.140910 test-rmse:2.820837+0.473964
## [35] train-rmse:2.548245+0.140951 test-rmse:2.804510+0.472252
## [36] train-rmse:2.530680+0.141072 test-rmse:2.785936+0.464491
## [37] train-rmse:2.513539+0.140450 test-rmse:2.766319+0.482872
## [38] train-rmse:2.496926+0.140126 test-rmse:2.744971+0.480073
## [39] train-rmse:2.480265+0.139927 test-rmse:2.745477+0.488532
## [40] train-rmse:2.464529+0.139997 test-rmse:2.728881+0.499997
## [41] train-rmse:2.449573+0.140258 test-rmse:2.712029+0.494750
## [42] train-rmse:2.435180+0.140673 test-rmse:2.695945+0.485030
## [43] train-rmse:2.420649+0.141080 test-rmse:2.688447+0.489726
## [44] train-rmse:2.407379+0.141423 test-rmse:2.670026+0.496811
## [45] train-rmse:2.394385+0.141956 test-rmse:2.666342+0.501258
## [46] train-rmse:2.381978+0.142366 test-rmse:2.660351+0.505730
## [47] train-rmse:2.370082+0.142595 test-rmse:2.647471+0.491497
## [48] train-rmse:2.358709+0.142811 test-rmse:2.639923+0.496139
## [49] train-rmse:2.347420+0.143063 test-rmse:2.628246+0.502275
## [50] train-rmse:2.336401+0.143339 test-rmse:2.617249+0.508089
## [51] train-rmse:2.325575+0.143601 test-rmse:2.604600+0.506730
## [52] train-rmse:2.315415+0.143748 test-rmse:2.596079+0.505999
## [53] train-rmse:2.305636+0.144025 test-rmse:2.587030+0.497995
## [54] train-rmse:2.296067+0.144267 test-rmse:2.586340+0.508116
## [55] train-rmse:2.286512+0.144521 test-rmse:2.574498+0.503229
## [56] train-rmse:2.277001+0.144800 test-rmse:2.564966+0.503782
## [57] train-rmse:2.267857+0.145012 test-rmse:2.556225+0.502396
## [58] train-rmse:2.258908+0.145304 test-rmse:2.551810+0.505225
## [59] train-rmse:2.250256+0.145779 test-rmse:2.541201+0.508618
## [60] train-rmse:2.241810+0.146362 test-rmse:2.538586+0.516811
## [61] train-rmse:2.233749+0.146643 test-rmse:2.530116+0.521862
## [62] train-rmse:2.225849+0.146980 test-rmse:2.517433+0.522166
## [63] train-rmse:2.218135+0.147271 test-rmse:2.510073+0.529671
## [64] train-rmse:2.210578+0.147419 test-rmse:2.510728+0.525813
## [65] train-rmse:2.203431+0.147533 test-rmse:2.506061+0.524706
## [66] train-rmse:2.196506+0.147760 test-rmse:2.500068+0.518999
## [67] train-rmse:2.189778+0.147913 test-rmse:2.495010+0.521179
## [68] train-rmse:2.183168+0.148191 test-rmse:2.485568+0.519682
## [69] train-rmse:2.176546+0.148419 test-rmse:2.474841+0.518736
## [70] train-rmse:2.170212+0.148585 test-rmse:2.468775+0.522377
## [71] train-rmse:2.164041+0.148740 test-rmse:2.463160+0.521960
## [72] train-rmse:2.158200+0.148908 test-rmse:2.465192+0.532395
## [73] train-rmse:2.152439+0.149099 test-rmse:2.465734+0.527552
## [74] train-rmse:2.146889+0.149383 test-rmse:2.456181+0.532483
## [75] train-rmse:2.141412+0.149711 test-rmse:2.447068+0.528939
## [76] train-rmse:2.136039+0.150105 test-rmse:2.449704+0.525182
## [77] train-rmse:2.130647+0.150555 test-rmse:2.441549+0.524675
## [78] train-rmse:2.125412+0.150885 test-rmse:2.438143+0.518972
## [79] train-rmse:2.120278+0.151111 test-rmse:2.445763+0.520824
## [80] train-rmse:2.115217+0.151354 test-rmse:2.439588+0.521449
## [81] train-rmse:2.110139+0.151624 test-rmse:2.432452+0.521502
## [82] train-rmse:2.105181+0.151829 test-rmse:2.428736+0.521053
## [83] train-rmse:2.100390+0.152020 test-rmse:2.424470+0.525771
## [84] train-rmse:2.095758+0.152216 test-rmse:2.418315+0.533343
## [85] train-rmse:2.091285+0.152507 test-rmse:2.417183+0.535672
## [86] train-rmse:2.086843+0.152779 test-rmse:2.421566+0.532742
## [87] train-rmse:2.082466+0.153046 test-rmse:2.416256+0.534949
## [88] train-rmse:2.078139+0.153206 test-rmse:2.413885+0.539953
## [89] train-rmse:2.073962+0.153366 test-rmse:2.410898+0.538152
## [90] train-rmse:2.070011+0.153514 test-rmse:2.408785+0.534602
## [91] train-rmse:2.066125+0.153604 test-rmse:2.405892+0.536352
## [92] train-rmse:2.062242+0.153605 test-rmse:2.402207+0.534286
## [93] train-rmse:2.058440+0.153682 test-rmse:2.404867+0.543051
## [94] train-rmse:2.054849+0.153782 test-rmse:2.398498+0.541383
## [95] train-rmse:2.050963+0.153736 test-rmse:2.393910+0.539104
## [96] train-rmse:2.047418+0.153861 test-rmse:2.388939+0.535287
## [97] train-rmse:2.043806+0.154113 test-rmse:2.394158+0.536907
## [98] train-rmse:2.040361+0.154276 test-rmse:2.388798+0.536622
## [99] train-rmse:2.036891+0.154477 test-rmse:2.384439+0.533187
## [100] train-rmse:2.033456+0.154704 test-rmse:2.382088+0.530798
## [101] train-rmse:2.030076+0.154849 test-rmse:2.381993+0.528728
## [102] train-rmse:2.026785+0.155093 test-rmse:2.378779+0.530399
## [103] train-rmse:2.023459+0.155266 test-rmse:2.376184+0.533358
## [104] train-rmse:2.020176+0.155398 test-rmse:2.378629+0.535592
## [105] train-rmse:2.016962+0.155572 test-rmse:2.378829+0.529445
## [106] train-rmse:2.013773+0.155701 test-rmse:2.379004+0.528960
## [107] train-rmse:2.010733+0.155843 test-rmse:2.371327+0.532160
## [108] train-rmse:2.007794+0.155977 test-rmse:2.370556+0.532141
## [109] train-rmse:2.004841+0.156103 test-rmse:2.367822+0.531466
## [110] train-rmse:2.001973+0.156174 test-rmse:2.363575+0.532603
## [111] train-rmse:1.999179+0.156213 test-rmse:2.364610+0.531964
## [112] train-rmse:1.996372+0.156184 test-rmse:2.361956+0.535265
## [113] train-rmse:1.993545+0.156401 test-rmse:2.361391+0.539091
## [114] train-rmse:1.990738+0.156557 test-rmse:2.362532+0.540810
## [115] train-rmse:1.988041+0.156616 test-rmse:2.357765+0.537155
## [116] train-rmse:1.985354+0.156647 test-rmse:2.362309+0.538836
## [117] train-rmse:1.982769+0.156701 test-rmse:2.356345+0.536874
## [118] train-rmse:1.980179+0.156974 test-rmse:2.356634+0.537689
## [119] train-rmse:1.977716+0.157120 test-rmse:2.353986+0.539470
## [120] train-rmse:1.975140+0.157153 test-rmse:2.356545+0.537161
## [121] train-rmse:1.972713+0.157266 test-rmse:2.352705+0.538134
## [122] train-rmse:1.970161+0.157397 test-rmse:2.349735+0.539132
## [123] train-rmse:1.967723+0.157582 test-rmse:2.350158+0.540533
## [124] train-rmse:1.965239+0.157651 test-rmse:2.350005+0.537507
## [125] train-rmse:1.962705+0.157805 test-rmse:2.347755+0.535537
## [126] train-rmse:1.960339+0.157929 test-rmse:2.350875+0.536675
## [127] train-rmse:1.957977+0.158076 test-rmse:2.357252+0.538924
## [128] train-rmse:1.955670+0.158171 test-rmse:2.355211+0.540292
## [129] train-rmse:1.953407+0.158305 test-rmse:2.353330+0.540851
## [130] train-rmse:1.951171+0.158443 test-rmse:2.350637+0.542111
## [131] train-rmse:1.948818+0.158513 test-rmse:2.348995+0.543486
## Stopping. Best iteration:
## [125] train-rmse:1.962705+0.157805 test-rmse:2.347755+0.535537
##
## 64
## [1] train-rmse:6.449990+0.133982 test-rmse:6.488533+0.615748
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.254494+0.126157 test-rmse:5.316845+0.584046
## [3] train-rmse:4.444581+0.098716 test-rmse:4.603486+0.507011
## [4] train-rmse:3.871451+0.091185 test-rmse:4.045843+0.484052
## [5] train-rmse:3.480923+0.098573 test-rmse:3.722003+0.451836
## [6] train-rmse:3.197647+0.089165 test-rmse:3.438326+0.451626
## [7] train-rmse:2.985875+0.080154 test-rmse:3.242007+0.472346
## [8] train-rmse:2.829852+0.089521 test-rmse:3.128689+0.456697
## [9] train-rmse:2.706836+0.102323 test-rmse:3.026155+0.449087
## [10] train-rmse:2.612015+0.109977 test-rmse:2.964633+0.453865
## [11] train-rmse:2.525919+0.096064 test-rmse:2.894507+0.467840
## [12] train-rmse:2.456150+0.093194 test-rmse:2.867286+0.464824
## [13] train-rmse:2.383468+0.092832 test-rmse:2.817861+0.462796
## [14] train-rmse:2.323014+0.094522 test-rmse:2.776569+0.464677
## [15] train-rmse:2.260150+0.091463 test-rmse:2.731972+0.466886
## [16] train-rmse:2.208817+0.096584 test-rmse:2.713778+0.467727
## [17] train-rmse:2.156195+0.086228 test-rmse:2.667863+0.480304
## [18] train-rmse:2.116086+0.085050 test-rmse:2.632814+0.501013
## [19] train-rmse:2.066475+0.087348 test-rmse:2.630594+0.490112
## [20] train-rmse:2.027804+0.086818 test-rmse:2.591422+0.499452
## [21] train-rmse:1.983850+0.091629 test-rmse:2.566408+0.463170
## [22] train-rmse:1.949239+0.092914 test-rmse:2.544341+0.460468
## [23] train-rmse:1.913260+0.091165 test-rmse:2.537191+0.468231
## [24] train-rmse:1.885727+0.091039 test-rmse:2.520577+0.466790
## [25] train-rmse:1.855902+0.092522 test-rmse:2.515318+0.469981
## [26] train-rmse:1.831259+0.092197 test-rmse:2.496844+0.472123
## [27] train-rmse:1.805588+0.092815 test-rmse:2.477255+0.485275
## [28] train-rmse:1.777776+0.082781 test-rmse:2.455806+0.491529
## [29] train-rmse:1.749426+0.086141 test-rmse:2.432361+0.505969
## [30] train-rmse:1.723338+0.084766 test-rmse:2.419355+0.499126
## [31] train-rmse:1.698180+0.082652 test-rmse:2.406572+0.500112
## [32] train-rmse:1.670870+0.081892 test-rmse:2.388192+0.495407
## [33] train-rmse:1.644053+0.086343 test-rmse:2.369121+0.473341
## [34] train-rmse:1.620587+0.088224 test-rmse:2.360990+0.475481
## [35] train-rmse:1.601161+0.089240 test-rmse:2.361281+0.475822
## [36] train-rmse:1.585347+0.089928 test-rmse:2.349746+0.482599
## [37] train-rmse:1.571008+0.089656 test-rmse:2.344236+0.482007
## [38] train-rmse:1.553782+0.090122 test-rmse:2.333140+0.482844
## [39] train-rmse:1.538744+0.089365 test-rmse:2.326235+0.487493
## [40] train-rmse:1.526451+0.089317 test-rmse:2.321253+0.488830
## [41] train-rmse:1.510941+0.090325 test-rmse:2.313813+0.488728
## [42] train-rmse:1.492716+0.088097 test-rmse:2.294533+0.489229
## [43] train-rmse:1.470211+0.089317 test-rmse:2.273909+0.490408
## [44] train-rmse:1.452733+0.078770 test-rmse:2.273350+0.496221
## [45] train-rmse:1.432618+0.062439 test-rmse:2.262751+0.495482
## [46] train-rmse:1.420794+0.062875 test-rmse:2.261201+0.500408
## [47] train-rmse:1.410515+0.062592 test-rmse:2.257697+0.501803
## [48] train-rmse:1.395552+0.059945 test-rmse:2.257069+0.505333
## [49] train-rmse:1.386189+0.059549 test-rmse:2.253481+0.504896
## [50] train-rmse:1.375405+0.060565 test-rmse:2.244142+0.494256
## [51] train-rmse:1.364945+0.061554 test-rmse:2.239009+0.502331
## [52] train-rmse:1.357082+0.061418 test-rmse:2.234187+0.500681
## [53] train-rmse:1.343687+0.065274 test-rmse:2.233022+0.502133
## [54] train-rmse:1.333446+0.065383 test-rmse:2.226800+0.506267
## [55] train-rmse:1.322435+0.062664 test-rmse:2.223532+0.507393
## [56] train-rmse:1.311444+0.063861 test-rmse:2.220789+0.500145
## [57] train-rmse:1.292006+0.059662 test-rmse:2.216596+0.499178
## [58] train-rmse:1.281099+0.052725 test-rmse:2.214095+0.509450
## [59] train-rmse:1.269535+0.053575 test-rmse:2.211424+0.521356
## [60] train-rmse:1.259189+0.050157 test-rmse:2.203515+0.516507
## [61] train-rmse:1.249702+0.052080 test-rmse:2.196758+0.514326
## [62] train-rmse:1.242252+0.052469 test-rmse:2.195368+0.514379
## [63] train-rmse:1.236048+0.052752 test-rmse:2.192755+0.513327
## [64] train-rmse:1.230522+0.052363 test-rmse:2.189606+0.514678
## [65] train-rmse:1.223679+0.051233 test-rmse:2.184189+0.518080
## [66] train-rmse:1.214007+0.054192 test-rmse:2.178388+0.525279
## [67] train-rmse:1.206514+0.050760 test-rmse:2.172060+0.530719
## [68] train-rmse:1.200795+0.051038 test-rmse:2.163328+0.534493
## [69] train-rmse:1.187718+0.050972 test-rmse:2.156660+0.536565
## [70] train-rmse:1.180717+0.051509 test-rmse:2.156941+0.538399
## [71] train-rmse:1.175854+0.052012 test-rmse:2.156058+0.538484
## [72] train-rmse:1.166157+0.055138 test-rmse:2.146458+0.534348
## [73] train-rmse:1.155009+0.056448 test-rmse:2.145495+0.536055
## [74] train-rmse:1.147554+0.057710 test-rmse:2.147594+0.539059
## [75] train-rmse:1.139114+0.058412 test-rmse:2.145904+0.537367
## [76] train-rmse:1.130634+0.054378 test-rmse:2.140778+0.532208
## [77] train-rmse:1.124093+0.054875 test-rmse:2.138462+0.537528
## [78] train-rmse:1.114218+0.059196 test-rmse:2.137736+0.528911
## [79] train-rmse:1.107843+0.058334 test-rmse:2.140399+0.525233
## [80] train-rmse:1.096376+0.059537 test-rmse:2.144154+0.522433
## [81] train-rmse:1.089514+0.060599 test-rmse:2.141099+0.529690
## [82] train-rmse:1.084350+0.059666 test-rmse:2.135708+0.533767
## [83] train-rmse:1.080535+0.060153 test-rmse:2.135717+0.535157
## [84] train-rmse:1.072346+0.063300 test-rmse:2.138167+0.543236
## [85] train-rmse:1.066091+0.065614 test-rmse:2.138097+0.543681
## [86] train-rmse:1.058662+0.066215 test-rmse:2.136473+0.543032
## [87] train-rmse:1.053217+0.065660 test-rmse:2.132578+0.536873
## [88] train-rmse:1.046521+0.065896 test-rmse:2.130823+0.538281
## [89] train-rmse:1.041535+0.066253 test-rmse:2.126512+0.540377
## [90] train-rmse:1.032699+0.069982 test-rmse:2.124670+0.539246
## [91] train-rmse:1.024266+0.068131 test-rmse:2.120668+0.531530
## [92] train-rmse:1.009610+0.071373 test-rmse:2.128789+0.528206
## [93] train-rmse:0.998933+0.069199 test-rmse:2.132800+0.531236
## [94] train-rmse:0.994436+0.068751 test-rmse:2.129216+0.530865
## [95] train-rmse:0.991196+0.068780 test-rmse:2.132870+0.526992
## [96] train-rmse:0.987898+0.068493 test-rmse:2.130096+0.526071
## [97] train-rmse:0.983337+0.069001 test-rmse:2.128317+0.529823
## Stopping. Best iteration:
## [91] train-rmse:1.024266+0.068131 test-rmse:2.120668+0.531530
##
## 65
## [1] train-rmse:6.363775+0.127822 test-rmse:6.452436+0.601658
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.101991+0.099441 test-rmse:5.247493+0.549418
## [3] train-rmse:4.186012+0.093999 test-rmse:4.412160+0.470104
## [4] train-rmse:3.551254+0.070236 test-rmse:3.768714+0.480013
## [5] train-rmse:3.115832+0.075342 test-rmse:3.412238+0.441406
## [6] train-rmse:2.799575+0.074800 test-rmse:3.204582+0.439272
## [7] train-rmse:2.568228+0.063707 test-rmse:3.018973+0.488273
## [8] train-rmse:2.383946+0.053990 test-rmse:2.891512+0.471840
## [9] train-rmse:2.241081+0.046402 test-rmse:2.805768+0.463495
## [10] train-rmse:2.143572+0.048369 test-rmse:2.747188+0.478277
## [11] train-rmse:2.031208+0.042367 test-rmse:2.663463+0.490091
## [12] train-rmse:1.956696+0.042262 test-rmse:2.615062+0.503511
## [13] train-rmse:1.886310+0.045250 test-rmse:2.593117+0.483193
## [14] train-rmse:1.820791+0.048072 test-rmse:2.551752+0.497947
## [15] train-rmse:1.755242+0.049615 test-rmse:2.533369+0.511646
## [16] train-rmse:1.705121+0.052466 test-rmse:2.511945+0.512943
## [17] train-rmse:1.658831+0.046036 test-rmse:2.481574+0.511772
## [18] train-rmse:1.611818+0.047480 test-rmse:2.444999+0.507069
## [19] train-rmse:1.568577+0.050068 test-rmse:2.420378+0.498690
## [20] train-rmse:1.530784+0.046992 test-rmse:2.400402+0.498874
## [21] train-rmse:1.493944+0.042997 test-rmse:2.396597+0.501953
## [22] train-rmse:1.452037+0.037004 test-rmse:2.375792+0.502679
## [23] train-rmse:1.416115+0.032977 test-rmse:2.349741+0.497059
## [24] train-rmse:1.384119+0.036871 test-rmse:2.338190+0.492467
## [25] train-rmse:1.350757+0.035083 test-rmse:2.321613+0.495839
## [26] train-rmse:1.322640+0.027314 test-rmse:2.308138+0.495990
## [27] train-rmse:1.300682+0.027592 test-rmse:2.304559+0.512812
## [28] train-rmse:1.271177+0.027293 test-rmse:2.300205+0.512616
## [29] train-rmse:1.246399+0.028016 test-rmse:2.285290+0.511452
## [30] train-rmse:1.224362+0.027402 test-rmse:2.273742+0.510886
## [31] train-rmse:1.199812+0.032721 test-rmse:2.267422+0.512909
## [32] train-rmse:1.180752+0.038301 test-rmse:2.261754+0.505935
## [33] train-rmse:1.163273+0.036228 test-rmse:2.251932+0.509935
## [34] train-rmse:1.144897+0.035363 test-rmse:2.250231+0.506783
## [35] train-rmse:1.129365+0.037883 test-rmse:2.245521+0.515735
## [36] train-rmse:1.111547+0.037155 test-rmse:2.233221+0.516201
## [37] train-rmse:1.092422+0.044377 test-rmse:2.227624+0.510775
## [38] train-rmse:1.077464+0.041863 test-rmse:2.225480+0.520498
## [39] train-rmse:1.051696+0.042656 test-rmse:2.222903+0.517625
## [40] train-rmse:1.036444+0.040387 test-rmse:2.220440+0.515880
## [41] train-rmse:1.013421+0.036275 test-rmse:2.219890+0.515423
## [42] train-rmse:1.000404+0.039057 test-rmse:2.216527+0.518330
## [43] train-rmse:0.988820+0.040964 test-rmse:2.210837+0.522040
## [44] train-rmse:0.974836+0.042319 test-rmse:2.206261+0.522796
## [45] train-rmse:0.962495+0.040816 test-rmse:2.203901+0.524498
## [46] train-rmse:0.948251+0.038804 test-rmse:2.194861+0.515069
## [47] train-rmse:0.935283+0.039178 test-rmse:2.191276+0.519762
## [48] train-rmse:0.922209+0.042309 test-rmse:2.187446+0.520402
## [49] train-rmse:0.899767+0.042756 test-rmse:2.177837+0.520173
## [50] train-rmse:0.884705+0.042988 test-rmse:2.172895+0.522594
## [51] train-rmse:0.875806+0.043842 test-rmse:2.170500+0.525530
## [52] train-rmse:0.866529+0.043515 test-rmse:2.170237+0.524752
## [53] train-rmse:0.849122+0.047307 test-rmse:2.164931+0.524843
## [54] train-rmse:0.834777+0.050358 test-rmse:2.159087+0.522382
## [55] train-rmse:0.819590+0.052037 test-rmse:2.157457+0.526517
## [56] train-rmse:0.805956+0.049276 test-rmse:2.155911+0.525815
## [57] train-rmse:0.794558+0.051700 test-rmse:2.154171+0.531824
## [58] train-rmse:0.785655+0.052494 test-rmse:2.154622+0.532424
## [59] train-rmse:0.772173+0.053917 test-rmse:2.160075+0.525553
## [60] train-rmse:0.760841+0.050624 test-rmse:2.152812+0.526217
## [61] train-rmse:0.754661+0.051077 test-rmse:2.151780+0.525913
## [62] train-rmse:0.748546+0.050395 test-rmse:2.149112+0.530366
## [63] train-rmse:0.733301+0.047212 test-rmse:2.147319+0.531031
## [64] train-rmse:0.722769+0.050601 test-rmse:2.144341+0.530458
## [65] train-rmse:0.714691+0.051579 test-rmse:2.144244+0.530626
## [66] train-rmse:0.704240+0.051754 test-rmse:2.140947+0.524099
## [67] train-rmse:0.687672+0.042262 test-rmse:2.139989+0.522219
## [68] train-rmse:0.680386+0.043817 test-rmse:2.141469+0.523793
## [69] train-rmse:0.671099+0.039450 test-rmse:2.139991+0.524624
## [70] train-rmse:0.662863+0.040478 test-rmse:2.145015+0.526335
## [71] train-rmse:0.656059+0.040446 test-rmse:2.142003+0.528430
## [72] train-rmse:0.647043+0.042821 test-rmse:2.139984+0.526004
## [73] train-rmse:0.638689+0.042750 test-rmse:2.139317+0.530028
## [74] train-rmse:0.627345+0.041492 test-rmse:2.135507+0.522442
## [75] train-rmse:0.618574+0.045878 test-rmse:2.134642+0.520701
## [76] train-rmse:0.609755+0.042832 test-rmse:2.135188+0.521657
## [77] train-rmse:0.604856+0.042973 test-rmse:2.136614+0.522478
## [78] train-rmse:0.596817+0.040708 test-rmse:2.140376+0.525239
## [79] train-rmse:0.587988+0.037871 test-rmse:2.139605+0.524723
## [80] train-rmse:0.578214+0.036711 test-rmse:2.138045+0.523947
## [81] train-rmse:0.569368+0.039329 test-rmse:2.133766+0.523399
## [82] train-rmse:0.560556+0.038588 test-rmse:2.128909+0.527548
## [83] train-rmse:0.555672+0.040068 test-rmse:2.129049+0.524507
## [84] train-rmse:0.550048+0.038775 test-rmse:2.129520+0.523441
## [85] train-rmse:0.541052+0.037494 test-rmse:2.126271+0.521448
## [86] train-rmse:0.534488+0.038092 test-rmse:2.127908+0.520921
## [87] train-rmse:0.529309+0.039380 test-rmse:2.127565+0.521588
## [88] train-rmse:0.524100+0.038107 test-rmse:2.127969+0.521010
## [89] train-rmse:0.518154+0.036003 test-rmse:2.127983+0.520581
## [90] train-rmse:0.514292+0.035418 test-rmse:2.128210+0.520878
## [91] train-rmse:0.511049+0.036215 test-rmse:2.126377+0.521989
## Stopping. Best iteration:
## [85] train-rmse:0.541052+0.037494 test-rmse:2.126271+0.521448
##
## 66
## [1] train-rmse:6.282884+0.126464 test-rmse:6.343974+0.587695
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.917502+0.088835 test-rmse:5.071497+0.496095
## [3] train-rmse:3.968587+0.060445 test-rmse:4.198036+0.434413
## [4] train-rmse:3.313811+0.055478 test-rmse:3.630963+0.430879
## [5] train-rmse:2.848794+0.055579 test-rmse:3.230414+0.434091
## [6] train-rmse:2.517420+0.044870 test-rmse:2.987600+0.462611
## [7] train-rmse:2.284541+0.050839 test-rmse:2.836231+0.440749
## [8] train-rmse:2.108184+0.061196 test-rmse:2.714707+0.441275
## [9] train-rmse:1.965287+0.064374 test-rmse:2.653011+0.433986
## [10] train-rmse:1.850825+0.057181 test-rmse:2.572442+0.450364
## [11] train-rmse:1.726183+0.075905 test-rmse:2.509186+0.474996
## [12] train-rmse:1.646969+0.066615 test-rmse:2.481292+0.478824
## [13] train-rmse:1.581582+0.069637 test-rmse:2.446142+0.480930
## [14] train-rmse:1.520227+0.068227 test-rmse:2.414615+0.471490
## [15] train-rmse:1.461972+0.061117 test-rmse:2.411181+0.474498
## [16] train-rmse:1.406208+0.044678 test-rmse:2.394384+0.481909
## [17] train-rmse:1.351505+0.039579 test-rmse:2.365854+0.493226
## [18] train-rmse:1.302410+0.032079 test-rmse:2.364618+0.497527
## [19] train-rmse:1.262011+0.031735 test-rmse:2.350311+0.497626
## [20] train-rmse:1.220131+0.042351 test-rmse:2.321336+0.482426
## [21] train-rmse:1.177965+0.044921 test-rmse:2.310353+0.490682
## [22] train-rmse:1.137196+0.036947 test-rmse:2.285665+0.481152
## [23] train-rmse:1.101909+0.040274 test-rmse:2.257870+0.477021
## [24] train-rmse:1.070593+0.042611 test-rmse:2.245885+0.475334
## [25] train-rmse:1.036128+0.043423 test-rmse:2.245069+0.477688
## [26] train-rmse:1.015095+0.043620 test-rmse:2.235854+0.469066
## [27] train-rmse:0.994128+0.042303 test-rmse:2.233045+0.475685
## [28] train-rmse:0.966792+0.041621 test-rmse:2.221176+0.478182
## [29] train-rmse:0.943505+0.041785 test-rmse:2.205563+0.481523
## [30] train-rmse:0.914190+0.044585 test-rmse:2.202106+0.478508
## [31] train-rmse:0.891594+0.046910 test-rmse:2.200131+0.476879
## [32] train-rmse:0.872861+0.043596 test-rmse:2.191236+0.480109
## [33] train-rmse:0.853378+0.041325 test-rmse:2.193236+0.491280
## [34] train-rmse:0.825315+0.042164 test-rmse:2.187595+0.486584
## [35] train-rmse:0.808488+0.041639 test-rmse:2.185074+0.487121
## [36] train-rmse:0.785849+0.036547 test-rmse:2.185802+0.489168
## [37] train-rmse:0.764639+0.029948 test-rmse:2.183257+0.488354
## [38] train-rmse:0.750441+0.032824 test-rmse:2.183902+0.489024
## [39] train-rmse:0.724989+0.035803 test-rmse:2.175851+0.491426
## [40] train-rmse:0.707799+0.037337 test-rmse:2.175373+0.493223
## [41] train-rmse:0.688838+0.034070 test-rmse:2.175666+0.498214
## [42] train-rmse:0.672448+0.027475 test-rmse:2.171892+0.493481
## [43] train-rmse:0.659847+0.028063 test-rmse:2.166747+0.495915
## [44] train-rmse:0.642814+0.027458 test-rmse:2.166765+0.502675
## [45] train-rmse:0.630438+0.032111 test-rmse:2.163900+0.502663
## [46] train-rmse:0.617419+0.031938 test-rmse:2.165048+0.502888
## [47] train-rmse:0.601654+0.026217 test-rmse:2.156879+0.504942
## [48] train-rmse:0.591632+0.026109 test-rmse:2.151390+0.508164
## [49] train-rmse:0.580321+0.026865 test-rmse:2.148745+0.509437
## [50] train-rmse:0.572306+0.023846 test-rmse:2.148278+0.510855
## [51] train-rmse:0.560648+0.025664 test-rmse:2.149526+0.508096
## [52] train-rmse:0.548558+0.025198 test-rmse:2.147412+0.509711
## [53] train-rmse:0.535193+0.023051 test-rmse:2.144214+0.511140
## [54] train-rmse:0.523447+0.026408 test-rmse:2.146395+0.510259
## [55] train-rmse:0.508958+0.026480 test-rmse:2.147661+0.513543
## [56] train-rmse:0.498577+0.028337 test-rmse:2.146460+0.513745
## [57] train-rmse:0.488757+0.030097 test-rmse:2.144975+0.511416
## [58] train-rmse:0.478794+0.028695 test-rmse:2.142615+0.514212
## [59] train-rmse:0.469592+0.031535 test-rmse:2.141164+0.514617
## [60] train-rmse:0.463188+0.030444 test-rmse:2.139950+0.515911
## [61] train-rmse:0.453335+0.030166 test-rmse:2.139394+0.515214
## [62] train-rmse:0.445262+0.028544 test-rmse:2.139921+0.515905
## [63] train-rmse:0.434367+0.028500 test-rmse:2.138784+0.517481
## [64] train-rmse:0.425564+0.030187 test-rmse:2.139210+0.518820
## [65] train-rmse:0.420221+0.031123 test-rmse:2.138110+0.519474
## [66] train-rmse:0.409387+0.033346 test-rmse:2.138745+0.519689
## [67] train-rmse:0.398077+0.029787 test-rmse:2.139245+0.519388
## [68] train-rmse:0.389736+0.030250 test-rmse:2.139346+0.518889
## [69] train-rmse:0.383254+0.029557 test-rmse:2.140647+0.519737
## [70] train-rmse:0.375742+0.028752 test-rmse:2.137390+0.521530
## [71] train-rmse:0.367618+0.029250 test-rmse:2.136437+0.522861
## [72] train-rmse:0.360858+0.028062 test-rmse:2.138322+0.524477
## [73] train-rmse:0.355509+0.027579 test-rmse:2.137677+0.525126
## [74] train-rmse:0.346757+0.027590 test-rmse:2.135525+0.524127
## [75] train-rmse:0.341681+0.027406 test-rmse:2.135042+0.521265
## [76] train-rmse:0.333994+0.028119 test-rmse:2.133278+0.522013
## [77] train-rmse:0.329536+0.029900 test-rmse:2.133548+0.521707
## [78] train-rmse:0.320287+0.026685 test-rmse:2.136158+0.523207
## [79] train-rmse:0.314876+0.026279 test-rmse:2.134581+0.524125
## [80] train-rmse:0.309211+0.024992 test-rmse:2.134896+0.524097
## [81] train-rmse:0.302081+0.022445 test-rmse:2.134423+0.524760
## [82] train-rmse:0.296779+0.021504 test-rmse:2.136342+0.526287
## Stopping. Best iteration:
## [76] train-rmse:0.333994+0.028119 test-rmse:2.133278+0.522013
##
## 67
## [1] train-rmse:6.226988+0.123739 test-rmse:6.251227+0.563562
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.817585+0.093929 test-rmse:4.931225+0.481509
## [3] train-rmse:3.845969+0.078012 test-rmse:4.099317+0.431754
## [4] train-rmse:3.156992+0.071543 test-rmse:3.541626+0.390891
## [5] train-rmse:2.677789+0.053535 test-rmse:3.154217+0.424681
## [6] train-rmse:2.330258+0.052710 test-rmse:2.931498+0.466891
## [7] train-rmse:2.085723+0.075665 test-rmse:2.765879+0.482449
## [8] train-rmse:1.882495+0.070337 test-rmse:2.641236+0.492397
## [9] train-rmse:1.741179+0.067734 test-rmse:2.562060+0.513407
## [10] train-rmse:1.610251+0.074155 test-rmse:2.512194+0.529090
## [11] train-rmse:1.499878+0.071303 test-rmse:2.462095+0.516807
## [12] train-rmse:1.410170+0.061715 test-rmse:2.419460+0.516822
## [13] train-rmse:1.331244+0.044324 test-rmse:2.394085+0.526637
## [14] train-rmse:1.269192+0.047905 test-rmse:2.368299+0.524497
## [15] train-rmse:1.201180+0.045935 test-rmse:2.364469+0.516783
## [16] train-rmse:1.152317+0.051496 test-rmse:2.348048+0.515478
## [17] train-rmse:1.099768+0.041505 test-rmse:2.333376+0.522075
## [18] train-rmse:1.058206+0.040186 test-rmse:2.313811+0.534197
## [19] train-rmse:1.014006+0.037028 test-rmse:2.286773+0.552379
## [20] train-rmse:0.975658+0.029858 test-rmse:2.283396+0.563085
## [21] train-rmse:0.936894+0.031726 test-rmse:2.285104+0.567666
## [22] train-rmse:0.907658+0.032338 test-rmse:2.272044+0.561344
## [23] train-rmse:0.872874+0.031840 test-rmse:2.261880+0.560876
## [24] train-rmse:0.845350+0.030347 test-rmse:2.254179+0.554100
## [25] train-rmse:0.814405+0.038774 test-rmse:2.249046+0.555510
## [26] train-rmse:0.787003+0.026938 test-rmse:2.240185+0.559467
## [27] train-rmse:0.749485+0.034475 test-rmse:2.236178+0.560301
## [28] train-rmse:0.726943+0.032084 test-rmse:2.226541+0.558098
## [29] train-rmse:0.700578+0.031390 test-rmse:2.223427+0.564088
## [30] train-rmse:0.682566+0.035411 test-rmse:2.219539+0.567068
## [31] train-rmse:0.664902+0.041452 test-rmse:2.219273+0.565245
## [32] train-rmse:0.646202+0.039277 test-rmse:2.218133+0.561814
## [33] train-rmse:0.628318+0.036999 test-rmse:2.219057+0.569095
## [34] train-rmse:0.603620+0.034229 test-rmse:2.214868+0.568859
## [35] train-rmse:0.588637+0.031051 test-rmse:2.212351+0.570106
## [36] train-rmse:0.570759+0.031637 test-rmse:2.214908+0.570340
## [37] train-rmse:0.557946+0.032353 test-rmse:2.208813+0.574220
## [38] train-rmse:0.539783+0.032920 test-rmse:2.204598+0.574298
## [39] train-rmse:0.517133+0.031453 test-rmse:2.211970+0.574180
## [40] train-rmse:0.502387+0.029878 test-rmse:2.211413+0.573891
## [41] train-rmse:0.486870+0.031280 test-rmse:2.212478+0.579586
## [42] train-rmse:0.473867+0.031790 test-rmse:2.210179+0.581805
## [43] train-rmse:0.461694+0.033225 test-rmse:2.209088+0.581772
## [44] train-rmse:0.447109+0.031955 test-rmse:2.209426+0.585808
## Stopping. Best iteration:
## [38] train-rmse:0.539783+0.032920 test-rmse:2.204598+0.574298
##
## 68
## [1] train-rmse:6.191944+0.118052 test-rmse:6.229254+0.596176
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.759855+0.078544 test-rmse:4.924746+0.518467
## [3] train-rmse:3.746651+0.058419 test-rmse:4.070847+0.462192
## [4] train-rmse:3.031844+0.034192 test-rmse:3.506471+0.467142
## [5] train-rmse:2.539297+0.033876 test-rmse:3.149099+0.497508
## [6] train-rmse:2.182567+0.023958 test-rmse:2.921154+0.500446
## [7] train-rmse:1.903779+0.043782 test-rmse:2.773727+0.522067
## [8] train-rmse:1.710549+0.035902 test-rmse:2.693127+0.539383
## [9] train-rmse:1.558019+0.036100 test-rmse:2.595704+0.545674
## [10] train-rmse:1.429581+0.039997 test-rmse:2.534688+0.520049
## [11] train-rmse:1.322778+0.041059 test-rmse:2.510901+0.537844
## [12] train-rmse:1.252042+0.042421 test-rmse:2.482020+0.538354
## [13] train-rmse:1.175829+0.038403 test-rmse:2.445163+0.538820
## [14] train-rmse:1.115761+0.047414 test-rmse:2.414594+0.542080
## [15] train-rmse:1.036951+0.050482 test-rmse:2.407566+0.557376
## [16] train-rmse:0.984050+0.059027 test-rmse:2.389147+0.557764
## [17] train-rmse:0.935399+0.053796 test-rmse:2.377426+0.560224
## [18] train-rmse:0.891694+0.058054 test-rmse:2.378678+0.569523
## [19] train-rmse:0.846516+0.053727 test-rmse:2.363693+0.568003
## [20] train-rmse:0.814283+0.054941 test-rmse:2.359312+0.567964
## [21] train-rmse:0.779428+0.059995 test-rmse:2.358024+0.569758
## [22] train-rmse:0.742920+0.056319 test-rmse:2.350513+0.577285
## [23] train-rmse:0.706500+0.039209 test-rmse:2.349320+0.578565
## [24] train-rmse:0.663957+0.033405 test-rmse:2.349586+0.588239
## [25] train-rmse:0.642339+0.034991 test-rmse:2.339734+0.589919
## [26] train-rmse:0.621168+0.039065 test-rmse:2.331825+0.590190
## [27] train-rmse:0.593157+0.042613 test-rmse:2.325842+0.595882
## [28] train-rmse:0.561646+0.039142 test-rmse:2.328542+0.595202
## [29] train-rmse:0.533997+0.033701 test-rmse:2.330606+0.590297
## [30] train-rmse:0.502107+0.025371 test-rmse:2.326042+0.590843
## [31] train-rmse:0.486888+0.028677 test-rmse:2.324620+0.593644
## [32] train-rmse:0.467533+0.024498 test-rmse:2.322144+0.592471
## [33] train-rmse:0.451785+0.025796 test-rmse:2.321088+0.593664
## [34] train-rmse:0.432130+0.026432 test-rmse:2.320402+0.594666
## [35] train-rmse:0.417682+0.025760 test-rmse:2.318753+0.594324
## [36] train-rmse:0.399340+0.027440 test-rmse:2.319237+0.595938
## [37] train-rmse:0.387998+0.029986 test-rmse:2.317447+0.595330
## [38] train-rmse:0.369681+0.031780 test-rmse:2.316185+0.600498
## [39] train-rmse:0.357567+0.028481 test-rmse:2.316157+0.599379
## [40] train-rmse:0.339485+0.025583 test-rmse:2.316693+0.597947
## [41] train-rmse:0.326793+0.025830 test-rmse:2.315730+0.599978
## [42] train-rmse:0.315419+0.025784 test-rmse:2.316102+0.601864
## [43] train-rmse:0.303480+0.021904 test-rmse:2.316583+0.600339
## [44] train-rmse:0.295475+0.022441 test-rmse:2.316368+0.602139
## [45] train-rmse:0.287002+0.020960 test-rmse:2.318327+0.601952
## [46] train-rmse:0.271936+0.018270 test-rmse:2.317350+0.601439
## [47] train-rmse:0.263421+0.019878 test-rmse:2.318074+0.600451
## Stopping. Best iteration:
## [41] train-rmse:0.326793+0.025830 test-rmse:2.315730+0.599978
##
## 69
## [1] train-rmse:6.179482+0.116989 test-rmse:6.221731+0.595762
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.721513+0.077695 test-rmse:4.908799+0.513744
## [3] train-rmse:3.685379+0.049483 test-rmse:4.011809+0.492989
## [4] train-rmse:2.951940+0.033669 test-rmse:3.456626+0.470173
## [5] train-rmse:2.432226+0.031156 test-rmse:3.108776+0.485577
## [6] train-rmse:2.055290+0.025124 test-rmse:2.881255+0.540135
## [7] train-rmse:1.764409+0.036606 test-rmse:2.741384+0.541238
## [8] train-rmse:1.555568+0.043720 test-rmse:2.633843+0.556852
## [9] train-rmse:1.395560+0.036800 test-rmse:2.565288+0.549285
## [10] train-rmse:1.256542+0.038764 test-rmse:2.500382+0.549921
## [11] train-rmse:1.160316+0.043725 test-rmse:2.466345+0.551706
## [12] train-rmse:1.085642+0.038960 test-rmse:2.443390+0.551002
## [13] train-rmse:1.001023+0.023363 test-rmse:2.437156+0.578294
## [14] train-rmse:0.941527+0.023949 test-rmse:2.427006+0.578136
## [15] train-rmse:0.892451+0.026392 test-rmse:2.418261+0.577117
## [16] train-rmse:0.845703+0.025770 test-rmse:2.413343+0.580086
## [17] train-rmse:0.793098+0.037139 test-rmse:2.396167+0.591096
## [18] train-rmse:0.741148+0.037577 test-rmse:2.389840+0.598046
## [19] train-rmse:0.693721+0.024023 test-rmse:2.376130+0.594941
## [20] train-rmse:0.647180+0.021870 test-rmse:2.364763+0.601816
## [21] train-rmse:0.613777+0.021797 test-rmse:2.360481+0.608575
## [22] train-rmse:0.579298+0.017670 test-rmse:2.354993+0.605296
## [23] train-rmse:0.543742+0.016550 test-rmse:2.357971+0.617800
## [24] train-rmse:0.513425+0.014026 test-rmse:2.353714+0.618689
## [25] train-rmse:0.483716+0.012507 test-rmse:2.350869+0.616394
## [26] train-rmse:0.458617+0.013528 test-rmse:2.350872+0.618864
## [27] train-rmse:0.433339+0.014888 test-rmse:2.348239+0.618467
## [28] train-rmse:0.408669+0.016479 test-rmse:2.348992+0.622145
## [29] train-rmse:0.387698+0.011929 test-rmse:2.348505+0.621996
## [30] train-rmse:0.370080+0.010663 test-rmse:2.342527+0.618384
## [31] train-rmse:0.351544+0.012799 test-rmse:2.340691+0.620219
## [32] train-rmse:0.336098+0.017699 test-rmse:2.340191+0.622158
## [33] train-rmse:0.321369+0.020682 test-rmse:2.340080+0.621220
## [34] train-rmse:0.307832+0.019438 test-rmse:2.339627+0.623345
## [35] train-rmse:0.288962+0.018672 test-rmse:2.335773+0.623716
## [36] train-rmse:0.278143+0.017966 test-rmse:2.332723+0.622567
## [37] train-rmse:0.263744+0.015533 test-rmse:2.330707+0.619360
## [38] train-rmse:0.254341+0.017504 test-rmse:2.329101+0.619970
## [39] train-rmse:0.243014+0.019379 test-rmse:2.328903+0.619706
## [40] train-rmse:0.228984+0.019807 test-rmse:2.330986+0.622556
## [41] train-rmse:0.221268+0.019066 test-rmse:2.329102+0.622296
## [42] train-rmse:0.212489+0.017563 test-rmse:2.330183+0.622874
## [43] train-rmse:0.203740+0.019303 test-rmse:2.329751+0.623697
## [44] train-rmse:0.192349+0.016010 test-rmse:2.331107+0.624842
## [45] train-rmse:0.185815+0.017911 test-rmse:2.330837+0.625895
## Stopping. Best iteration:
## [39] train-rmse:0.243014+0.019379 test-rmse:2.328903+0.619706
##
## 70
## [1] train-rmse:6.470501+0.141022 test-rmse:6.472533+0.652763
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.365281+0.114281 test-rmse:5.433145+0.689876
## [3] train-rmse:4.677165+0.115025 test-rmse:4.750931+0.605718
## [4] train-rmse:4.250548+0.116217 test-rmse:4.353208+0.535730
## [5] train-rmse:3.974725+0.113072 test-rmse:4.095233+0.485979
## [6] train-rmse:3.783335+0.107477 test-rmse:3.914839+0.478034
## [7] train-rmse:3.644152+0.110771 test-rmse:3.764581+0.501077
## [8] train-rmse:3.529926+0.118490 test-rmse:3.651138+0.428241
## [9] train-rmse:3.435646+0.122795 test-rmse:3.585578+0.391064
## [10] train-rmse:3.358102+0.122108 test-rmse:3.533948+0.437469
## [11] train-rmse:3.286100+0.120284 test-rmse:3.465546+0.443876
## [12] train-rmse:3.218793+0.121984 test-rmse:3.364489+0.452234
## [13] train-rmse:3.161000+0.124141 test-rmse:3.276457+0.409100
## [14] train-rmse:3.106969+0.126771 test-rmse:3.228908+0.412998
## [15] train-rmse:3.058629+0.128772 test-rmse:3.203318+0.441751
## [16] train-rmse:3.014562+0.127339 test-rmse:3.185093+0.461357
## [17] train-rmse:2.973921+0.127499 test-rmse:3.143873+0.439563
## [18] train-rmse:2.935331+0.129383 test-rmse:3.123402+0.419573
## [19] train-rmse:2.900616+0.131283 test-rmse:3.083716+0.413350
## [20] train-rmse:2.866909+0.133145 test-rmse:3.061531+0.426430
## [21] train-rmse:2.834028+0.134650 test-rmse:3.039103+0.439728
## [22] train-rmse:2.802805+0.136680 test-rmse:2.998017+0.457600
## [23] train-rmse:2.773506+0.138661 test-rmse:2.979802+0.454104
## [24] train-rmse:2.745783+0.138796 test-rmse:2.963797+0.452995
## [25] train-rmse:2.718668+0.138488 test-rmse:2.935990+0.437139
## [26] train-rmse:2.692566+0.137983 test-rmse:2.902814+0.446688
## [27] train-rmse:2.666843+0.138014 test-rmse:2.887079+0.478852
## [28] train-rmse:2.642200+0.137731 test-rmse:2.861612+0.467804
## [29] train-rmse:2.618545+0.137818 test-rmse:2.860294+0.487553
## [30] train-rmse:2.596537+0.139094 test-rmse:2.845934+0.468981
## [31] train-rmse:2.575686+0.140327 test-rmse:2.816337+0.472892
## [32] train-rmse:2.555797+0.140465 test-rmse:2.806379+0.472660
## [33] train-rmse:2.536442+0.141101 test-rmse:2.785884+0.485008
## [34] train-rmse:2.517624+0.141287 test-rmse:2.776045+0.477378
## [35] train-rmse:2.499376+0.141895 test-rmse:2.771741+0.480093
## [36] train-rmse:2.481679+0.141615 test-rmse:2.751845+0.470165
## [37] train-rmse:2.464730+0.141292 test-rmse:2.729331+0.480963
## [38] train-rmse:2.448693+0.141126 test-rmse:2.705486+0.482852
## [39] train-rmse:2.433502+0.141198 test-rmse:2.692550+0.488808
## [40] train-rmse:2.418575+0.141289 test-rmse:2.669684+0.487026
## [41] train-rmse:2.404319+0.141142 test-rmse:2.659710+0.474994
## [42] train-rmse:2.390083+0.140900 test-rmse:2.637725+0.478002
## [43] train-rmse:2.376360+0.140885 test-rmse:2.644983+0.495215
## [44] train-rmse:2.363373+0.141227 test-rmse:2.628308+0.497821
## [45] train-rmse:2.351090+0.141892 test-rmse:2.629646+0.507638
## [46] train-rmse:2.339171+0.142390 test-rmse:2.630291+0.507167
## [47] train-rmse:2.327778+0.142765 test-rmse:2.622107+0.505389
## [48] train-rmse:2.316631+0.143276 test-rmse:2.613891+0.509715
## [49] train-rmse:2.305685+0.143855 test-rmse:2.599720+0.515960
## [50] train-rmse:2.295235+0.144319 test-rmse:2.590509+0.510227
## [51] train-rmse:2.284908+0.144753 test-rmse:2.573754+0.507631
## [52] train-rmse:2.274771+0.145080 test-rmse:2.560706+0.511332
## [53] train-rmse:2.264975+0.145540 test-rmse:2.545972+0.510386
## [54] train-rmse:2.255734+0.145729 test-rmse:2.544904+0.503993
## [55] train-rmse:2.246731+0.145909 test-rmse:2.533655+0.500163
## [56] train-rmse:2.237839+0.146319 test-rmse:2.531533+0.505302
## [57] train-rmse:2.229373+0.146639 test-rmse:2.515600+0.509018
## [58] train-rmse:2.221046+0.146904 test-rmse:2.515438+0.512109
## [59] train-rmse:2.212840+0.147189 test-rmse:2.513446+0.526645
## [60] train-rmse:2.205002+0.147458 test-rmse:2.500516+0.527411
## [61] train-rmse:2.197454+0.147747 test-rmse:2.496545+0.527678
## [62] train-rmse:2.190189+0.147894 test-rmse:2.488810+0.525438
## [63] train-rmse:2.183052+0.148183 test-rmse:2.486322+0.528950
## [64] train-rmse:2.176014+0.148310 test-rmse:2.479018+0.527392
## [65] train-rmse:2.169168+0.148525 test-rmse:2.478795+0.526464
## [66] train-rmse:2.162423+0.148794 test-rmse:2.483392+0.526960
## [67] train-rmse:2.156003+0.149162 test-rmse:2.482856+0.526524
## [68] train-rmse:2.149672+0.149568 test-rmse:2.472699+0.530554
## [69] train-rmse:2.143537+0.149824 test-rmse:2.463180+0.538618
## [70] train-rmse:2.137519+0.150245 test-rmse:2.456220+0.532349
## [71] train-rmse:2.131651+0.150537 test-rmse:2.448735+0.537217
## [72] train-rmse:2.125887+0.150820 test-rmse:2.434405+0.534520
## [73] train-rmse:2.120230+0.151100 test-rmse:2.431671+0.535272
## [74] train-rmse:2.114718+0.151461 test-rmse:2.432306+0.534863
## [75] train-rmse:2.109426+0.151587 test-rmse:2.433970+0.531204
## [76] train-rmse:2.104309+0.151780 test-rmse:2.425605+0.533264
## [77] train-rmse:2.099347+0.152009 test-rmse:2.422092+0.531816
## [78] train-rmse:2.094389+0.152040 test-rmse:2.414399+0.530344
## [79] train-rmse:2.089659+0.152193 test-rmse:2.411142+0.529300
## [80] train-rmse:2.085078+0.152430 test-rmse:2.408537+0.524507
## [81] train-rmse:2.080431+0.152709 test-rmse:2.402038+0.527468
## [82] train-rmse:2.075933+0.152903 test-rmse:2.397269+0.529087
## [83] train-rmse:2.071427+0.152985 test-rmse:2.398281+0.530982
## [84] train-rmse:2.066869+0.153119 test-rmse:2.396316+0.542081
## [85] train-rmse:2.062599+0.153414 test-rmse:2.390344+0.539734
## [86] train-rmse:2.058319+0.153749 test-rmse:2.398371+0.536547
## [87] train-rmse:2.054181+0.153857 test-rmse:2.397028+0.535440
## [88] train-rmse:2.050269+0.153944 test-rmse:2.395175+0.532742
## [89] train-rmse:2.046202+0.153930 test-rmse:2.392380+0.533361
## [90] train-rmse:2.042403+0.154049 test-rmse:2.395157+0.535865
## [91] train-rmse:2.038740+0.154183 test-rmse:2.390569+0.539764
## Stopping. Best iteration:
## [85] train-rmse:2.062599+0.153414 test-rmse:2.390344+0.539734
##
## 71
## [1] train-rmse:6.331649+0.131476 test-rmse:6.376052+0.609992
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.097252+0.125319 test-rmse:5.169473+0.575475
## [3] train-rmse:4.286609+0.097273 test-rmse:4.450465+0.505373
## [4] train-rmse:3.724956+0.092913 test-rmse:3.906456+0.475079
## [5] train-rmse:3.368766+0.093410 test-rmse:3.626746+0.451694
## [6] train-rmse:3.103441+0.090107 test-rmse:3.341920+0.448946
## [7] train-rmse:2.900352+0.076964 test-rmse:3.169079+0.477636
## [8] train-rmse:2.757636+0.085514 test-rmse:3.083382+0.463128
## [9] train-rmse:2.656853+0.089230 test-rmse:2.994827+0.449432
## [10] train-rmse:2.561735+0.096386 test-rmse:2.945867+0.442048
## [11] train-rmse:2.486776+0.089983 test-rmse:2.882566+0.471371
## [12] train-rmse:2.410134+0.090819 test-rmse:2.832444+0.475749
## [13] train-rmse:2.338298+0.080097 test-rmse:2.801241+0.458697
## [14] train-rmse:2.266999+0.087014 test-rmse:2.725161+0.483466
## [15] train-rmse:2.217943+0.088302 test-rmse:2.686263+0.481449
## [16] train-rmse:2.172890+0.088795 test-rmse:2.659925+0.488965
## [17] train-rmse:2.129777+0.083847 test-rmse:2.642202+0.484099
## [18] train-rmse:2.078382+0.064194 test-rmse:2.599992+0.495627
## [19] train-rmse:2.037219+0.065154 test-rmse:2.575085+0.495494
## [20] train-rmse:2.000460+0.065017 test-rmse:2.537849+0.486660
## [21] train-rmse:1.957609+0.066849 test-rmse:2.533420+0.488702
## [22] train-rmse:1.925233+0.065416 test-rmse:2.525745+0.497221
## [23] train-rmse:1.893830+0.066895 test-rmse:2.511351+0.505767
## [24] train-rmse:1.866022+0.067500 test-rmse:2.494554+0.509841
## [25] train-rmse:1.827043+0.074686 test-rmse:2.474950+0.501552
## [26] train-rmse:1.799937+0.076912 test-rmse:2.440224+0.492071
## [27] train-rmse:1.773675+0.076711 test-rmse:2.419963+0.489228
## [28] train-rmse:1.743857+0.075362 test-rmse:2.410620+0.494210
## [29] train-rmse:1.720569+0.074480 test-rmse:2.407935+0.490946
## [30] train-rmse:1.695446+0.076339 test-rmse:2.403164+0.499605
## [31] train-rmse:1.677873+0.075902 test-rmse:2.397053+0.498953
## [32] train-rmse:1.659571+0.074720 test-rmse:2.385273+0.491368
## [33] train-rmse:1.642775+0.074520 test-rmse:2.381204+0.494926
## [34] train-rmse:1.624150+0.076596 test-rmse:2.371448+0.507533
## [35] train-rmse:1.604742+0.074544 test-rmse:2.358295+0.508874
## [36] train-rmse:1.574302+0.062258 test-rmse:2.329429+0.516153
## [37] train-rmse:1.558734+0.063867 test-rmse:2.313625+0.512896
## [38] train-rmse:1.542185+0.064931 test-rmse:2.309121+0.512343
## [39] train-rmse:1.525361+0.056840 test-rmse:2.299665+0.520038
## [40] train-rmse:1.503454+0.059846 test-rmse:2.296523+0.522448
## [41] train-rmse:1.488077+0.060862 test-rmse:2.296274+0.523686
## [42] train-rmse:1.472889+0.063355 test-rmse:2.297148+0.515060
## [43] train-rmse:1.451726+0.071394 test-rmse:2.291997+0.507891
## [44] train-rmse:1.439838+0.071885 test-rmse:2.291049+0.508450
## [45] train-rmse:1.428553+0.072294 test-rmse:2.295022+0.514735
## [46] train-rmse:1.413197+0.069837 test-rmse:2.278671+0.513371
## [47] train-rmse:1.393160+0.070295 test-rmse:2.271006+0.507551
## [48] train-rmse:1.379014+0.071292 test-rmse:2.263196+0.510120
## [49] train-rmse:1.368986+0.068824 test-rmse:2.259698+0.509880
## [50] train-rmse:1.357302+0.072331 test-rmse:2.260746+0.521537
## [51] train-rmse:1.349262+0.072630 test-rmse:2.256299+0.520980
## [52] train-rmse:1.336885+0.075434 test-rmse:2.260199+0.528851
## [53] train-rmse:1.324098+0.074856 test-rmse:2.261531+0.539033
## [54] train-rmse:1.310027+0.071772 test-rmse:2.263858+0.536305
## [55] train-rmse:1.300618+0.072465 test-rmse:2.261184+0.533121
## [56] train-rmse:1.292968+0.072195 test-rmse:2.259086+0.533464
## [57] train-rmse:1.281919+0.071209 test-rmse:2.251858+0.535762
## [58] train-rmse:1.273492+0.071598 test-rmse:2.252448+0.541885
## [59] train-rmse:1.260651+0.064437 test-rmse:2.244774+0.544293
## [60] train-rmse:1.248905+0.062783 test-rmse:2.244449+0.540326
## [61] train-rmse:1.240531+0.062756 test-rmse:2.244803+0.539661
## [62] train-rmse:1.227870+0.064124 test-rmse:2.244912+0.543480
## [63] train-rmse:1.219425+0.067161 test-rmse:2.234122+0.536934
## [64] train-rmse:1.206362+0.073533 test-rmse:2.228148+0.529175
## [65] train-rmse:1.198973+0.076540 test-rmse:2.227006+0.527203
## [66] train-rmse:1.189497+0.080718 test-rmse:2.221010+0.528266
## [67] train-rmse:1.181652+0.079716 test-rmse:2.213721+0.534712
## [68] train-rmse:1.174229+0.081953 test-rmse:2.210671+0.528691
## [69] train-rmse:1.163576+0.079321 test-rmse:2.210224+0.529956
## [70] train-rmse:1.140941+0.063360 test-rmse:2.206547+0.531790
## [71] train-rmse:1.133270+0.061005 test-rmse:2.200072+0.534412
## [72] train-rmse:1.124425+0.059291 test-rmse:2.197727+0.533007
## [73] train-rmse:1.118245+0.060201 test-rmse:2.191395+0.535344
## [74] train-rmse:1.111304+0.058057 test-rmse:2.193192+0.532260
## [75] train-rmse:1.101931+0.062504 test-rmse:2.188374+0.529785
## [76] train-rmse:1.095222+0.062423 test-rmse:2.189866+0.532589
## [77] train-rmse:1.086669+0.060187 test-rmse:2.190727+0.531711
## [78] train-rmse:1.076240+0.057583 test-rmse:2.190676+0.534360
## [79] train-rmse:1.066514+0.057019 test-rmse:2.195794+0.542083
## [80] train-rmse:1.060688+0.057797 test-rmse:2.193117+0.541523
## [81] train-rmse:1.050020+0.058220 test-rmse:2.187467+0.544162
## [82] train-rmse:1.040236+0.062567 test-rmse:2.176585+0.550333
## [83] train-rmse:1.035349+0.063055 test-rmse:2.174954+0.548049
## [84] train-rmse:1.027780+0.059947 test-rmse:2.174755+0.550494
## [85] train-rmse:1.019833+0.061367 test-rmse:2.176118+0.547831
## [86] train-rmse:1.012090+0.062133 test-rmse:2.178117+0.547119
## [87] train-rmse:1.006675+0.062405 test-rmse:2.175217+0.546269
## [88] train-rmse:0.999090+0.064218 test-rmse:2.173574+0.547426
## [89] train-rmse:0.993248+0.063591 test-rmse:2.173612+0.549533
## [90] train-rmse:0.990093+0.063745 test-rmse:2.169633+0.552487
## [91] train-rmse:0.982681+0.067333 test-rmse:2.165817+0.552136
## [92] train-rmse:0.978637+0.068613 test-rmse:2.165967+0.553749
## [93] train-rmse:0.974169+0.068908 test-rmse:2.164111+0.554810
## [94] train-rmse:0.970047+0.066199 test-rmse:2.163088+0.555490
## [95] train-rmse:0.962599+0.064547 test-rmse:2.159502+0.554752
## [96] train-rmse:0.958599+0.064484 test-rmse:2.160988+0.555481
## [97] train-rmse:0.946441+0.065968 test-rmse:2.168855+0.554691
## [98] train-rmse:0.939841+0.068823 test-rmse:2.169357+0.556118
## [99] train-rmse:0.936062+0.068456 test-rmse:2.166268+0.558924
## [100] train-rmse:0.930709+0.068770 test-rmse:2.171364+0.555251
## [101] train-rmse:0.927388+0.069498 test-rmse:2.172070+0.556180
## Stopping. Best iteration:
## [95] train-rmse:0.962599+0.064547 test-rmse:2.159502+0.554752
##
## 72
## [1] train-rmse:6.238318+0.124478 test-rmse:6.337073+0.595347
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.920812+0.095565 test-rmse:5.043284+0.546065
## [3] train-rmse:3.995546+0.095326 test-rmse:4.225340+0.451571
## [4] train-rmse:3.365972+0.053137 test-rmse:3.604617+0.473353
## [5] train-rmse:2.965920+0.051241 test-rmse:3.285966+0.447124
## [6] train-rmse:2.690571+0.053284 test-rmse:3.054163+0.434947
## [7] train-rmse:2.471338+0.042269 test-rmse:2.922731+0.461803
## [8] train-rmse:2.307275+0.054338 test-rmse:2.829853+0.431199
## [9] train-rmse:2.186638+0.055885 test-rmse:2.735474+0.449473
## [10] train-rmse:2.076936+0.063556 test-rmse:2.669237+0.435688
## [11] train-rmse:1.985975+0.070251 test-rmse:2.587283+0.430443
## [12] train-rmse:1.901575+0.068138 test-rmse:2.535998+0.444801
## [13] train-rmse:1.830597+0.059694 test-rmse:2.488765+0.440902
## [14] train-rmse:1.758037+0.059928 test-rmse:2.469396+0.427452
## [15] train-rmse:1.710441+0.061087 test-rmse:2.438688+0.425128
## [16] train-rmse:1.647379+0.066545 test-rmse:2.407068+0.444454
## [17] train-rmse:1.605871+0.066231 test-rmse:2.386837+0.442288
## [18] train-rmse:1.567384+0.065178 test-rmse:2.365639+0.451319
## [19] train-rmse:1.525885+0.060958 test-rmse:2.344302+0.434323
## [20] train-rmse:1.484502+0.053690 test-rmse:2.323093+0.435500
## [21] train-rmse:1.444422+0.053058 test-rmse:2.302674+0.435932
## [22] train-rmse:1.410299+0.052961 test-rmse:2.271262+0.423651
## [23] train-rmse:1.373004+0.057257 test-rmse:2.261867+0.421466
## [24] train-rmse:1.343870+0.061874 test-rmse:2.241710+0.430020
## [25] train-rmse:1.318135+0.065647 test-rmse:2.232946+0.436625
## [26] train-rmse:1.290760+0.063396 test-rmse:2.216691+0.445826
## [27] train-rmse:1.261445+0.057021 test-rmse:2.210295+0.452901
## [28] train-rmse:1.226592+0.049889 test-rmse:2.211040+0.459977
## [29] train-rmse:1.199399+0.046495 test-rmse:2.200042+0.451709
## [30] train-rmse:1.182085+0.047818 test-rmse:2.200243+0.454483
## [31] train-rmse:1.159774+0.047277 test-rmse:2.193343+0.457307
## [32] train-rmse:1.138667+0.045267 test-rmse:2.183088+0.460974
## [33] train-rmse:1.118191+0.052659 test-rmse:2.162072+0.461560
## [34] train-rmse:1.096394+0.054885 test-rmse:2.159061+0.460459
## [35] train-rmse:1.080215+0.059148 test-rmse:2.151528+0.456267
## [36] train-rmse:1.058930+0.055918 test-rmse:2.143743+0.456218
## [37] train-rmse:1.039006+0.061747 test-rmse:2.140951+0.454638
## [38] train-rmse:1.016216+0.056755 test-rmse:2.120788+0.450965
## [39] train-rmse:0.992704+0.051527 test-rmse:2.116036+0.453874
## [40] train-rmse:0.975746+0.053251 test-rmse:2.109775+0.457322
## [41] train-rmse:0.964905+0.053204 test-rmse:2.106447+0.453031
## [42] train-rmse:0.942375+0.052838 test-rmse:2.100605+0.452509
## [43] train-rmse:0.928081+0.051605 test-rmse:2.100732+0.457948
## [44] train-rmse:0.917477+0.048749 test-rmse:2.093238+0.460854
## [45] train-rmse:0.905787+0.046800 test-rmse:2.091989+0.460226
## [46] train-rmse:0.891721+0.049455 test-rmse:2.091350+0.458433
## [47] train-rmse:0.877254+0.046421 test-rmse:2.087043+0.457446
## [48] train-rmse:0.863296+0.049271 test-rmse:2.080679+0.462751
## [49] train-rmse:0.849715+0.053839 test-rmse:2.082111+0.460249
## [50] train-rmse:0.836757+0.056681 test-rmse:2.080925+0.462555
## [51] train-rmse:0.825916+0.056194 test-rmse:2.078135+0.466233
## [52] train-rmse:0.817776+0.055841 test-rmse:2.078105+0.468578
## [53] train-rmse:0.804276+0.055360 test-rmse:2.073974+0.471935
## [54] train-rmse:0.789257+0.054512 test-rmse:2.069468+0.474564
## [55] train-rmse:0.779753+0.055029 test-rmse:2.068242+0.474840
## [56] train-rmse:0.770221+0.055003 test-rmse:2.066719+0.472385
## [57] train-rmse:0.759991+0.051887 test-rmse:2.062198+0.475128
## [58] train-rmse:0.744155+0.049778 test-rmse:2.060002+0.474444
## [59] train-rmse:0.734052+0.050377 test-rmse:2.062992+0.476124
## [60] train-rmse:0.722865+0.053142 test-rmse:2.058853+0.474727
## [61] train-rmse:0.714174+0.053942 test-rmse:2.059557+0.475110
## [62] train-rmse:0.704971+0.054408 test-rmse:2.060008+0.474217
## [63] train-rmse:0.696876+0.056737 test-rmse:2.063268+0.472744
## [64] train-rmse:0.687161+0.060609 test-rmse:2.059260+0.470927
## [65] train-rmse:0.679234+0.059942 test-rmse:2.054967+0.475533
## [66] train-rmse:0.671869+0.056164 test-rmse:2.051285+0.477436
## [67] train-rmse:0.658273+0.055788 test-rmse:2.053017+0.477347
## [68] train-rmse:0.646488+0.057265 test-rmse:2.053423+0.479040
## [69] train-rmse:0.638454+0.059411 test-rmse:2.056338+0.484220
## [70] train-rmse:0.626434+0.062161 test-rmse:2.057126+0.490629
## [71] train-rmse:0.615305+0.058848 test-rmse:2.056148+0.493171
## [72] train-rmse:0.610216+0.058043 test-rmse:2.055165+0.493279
## Stopping. Best iteration:
## [66] train-rmse:0.671869+0.056164 test-rmse:2.051285+0.477436
##
## 73
## [1] train-rmse:6.150475+0.123131 test-rmse:6.220024+0.580687
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.732291+0.084681 test-rmse:4.904651+0.486618
## [3] train-rmse:3.780101+0.056657 test-rmse:4.035358+0.426462
## [4] train-rmse:3.140692+0.057446 test-rmse:3.483480+0.418962
## [5] train-rmse:2.711016+0.047660 test-rmse:3.106470+0.457232
## [6] train-rmse:2.419819+0.048861 test-rmse:2.909540+0.438220
## [7] train-rmse:2.183098+0.043177 test-rmse:2.776891+0.478332
## [8] train-rmse:2.016147+0.047149 test-rmse:2.685580+0.472298
## [9] train-rmse:1.882975+0.044296 test-rmse:2.597573+0.463885
## [10] train-rmse:1.779264+0.039236 test-rmse:2.522911+0.475561
## [11] train-rmse:1.671183+0.039358 test-rmse:2.453639+0.455788
## [12] train-rmse:1.601580+0.042894 test-rmse:2.417463+0.462821
## [13] train-rmse:1.529978+0.047298 test-rmse:2.359642+0.459251
## [14] train-rmse:1.471285+0.037761 test-rmse:2.335634+0.470924
## [15] train-rmse:1.412907+0.032853 test-rmse:2.308711+0.474931
## [16] train-rmse:1.357557+0.034699 test-rmse:2.277386+0.443014
## [17] train-rmse:1.302130+0.034016 test-rmse:2.268024+0.454087
## [18] train-rmse:1.258709+0.031424 test-rmse:2.252548+0.455470
## [19] train-rmse:1.222990+0.028070 test-rmse:2.238228+0.452563
## [20] train-rmse:1.181424+0.032958 test-rmse:2.226833+0.460844
## [21] train-rmse:1.150967+0.036022 test-rmse:2.214339+0.470967
## [22] train-rmse:1.112221+0.039904 test-rmse:2.210187+0.452073
## [23] train-rmse:1.069129+0.050121 test-rmse:2.186436+0.443611
## [24] train-rmse:1.043867+0.046026 test-rmse:2.183169+0.447580
## [25] train-rmse:1.005791+0.035488 test-rmse:2.179498+0.450341
## [26] train-rmse:0.984163+0.036266 test-rmse:2.177091+0.450857
## [27] train-rmse:0.959109+0.036667 test-rmse:2.158981+0.457067
## [28] train-rmse:0.934109+0.031058 test-rmse:2.150911+0.452526
## [29] train-rmse:0.904973+0.023984 test-rmse:2.144266+0.452201
## [30] train-rmse:0.881709+0.030958 test-rmse:2.139176+0.456421
## [31] train-rmse:0.860371+0.032742 test-rmse:2.137174+0.447591
## [32] train-rmse:0.840658+0.032709 test-rmse:2.135624+0.453118
## [33] train-rmse:0.813339+0.032608 test-rmse:2.126959+0.456952
## [34] train-rmse:0.798025+0.031771 test-rmse:2.123233+0.455069
## [35] train-rmse:0.779044+0.035179 test-rmse:2.117826+0.452197
## [36] train-rmse:0.765437+0.033323 test-rmse:2.116804+0.453048
## [37] train-rmse:0.740549+0.028150 test-rmse:2.110605+0.448947
## [38] train-rmse:0.727211+0.030730 test-rmse:2.106372+0.447166
## [39] train-rmse:0.711216+0.028718 test-rmse:2.103537+0.446421
## [40] train-rmse:0.687766+0.034202 test-rmse:2.100930+0.446646
## [41] train-rmse:0.667508+0.037205 test-rmse:2.101393+0.442896
## [42] train-rmse:0.647442+0.036550 test-rmse:2.102979+0.450645
## [43] train-rmse:0.635508+0.035277 test-rmse:2.100229+0.447410
## [44] train-rmse:0.619927+0.035732 test-rmse:2.109942+0.451442
## [45] train-rmse:0.600401+0.039658 test-rmse:2.113847+0.452654
## [46] train-rmse:0.587450+0.037159 test-rmse:2.110429+0.450111
## [47] train-rmse:0.578668+0.035731 test-rmse:2.110958+0.449742
## [48] train-rmse:0.562060+0.033763 test-rmse:2.106721+0.449543
## [49] train-rmse:0.549247+0.034995 test-rmse:2.102713+0.454353
## Stopping. Best iteration:
## [43] train-rmse:0.635508+0.035277 test-rmse:2.100229+0.447410
##
## 74
## [1] train-rmse:6.089779+0.120308 test-rmse:6.119827+0.554591
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.626522+0.090243 test-rmse:4.756353+0.471317
## [3] train-rmse:3.650652+0.076005 test-rmse:3.935621+0.426962
## [4] train-rmse:2.983212+0.076861 test-rmse:3.402458+0.406868
## [5] train-rmse:2.515084+0.061969 test-rmse:3.046408+0.463181
## [6] train-rmse:2.203351+0.072193 test-rmse:2.864374+0.486371
## [7] train-rmse:1.976028+0.057388 test-rmse:2.716593+0.490040
## [8] train-rmse:1.815772+0.059805 test-rmse:2.647659+0.474452
## [9] train-rmse:1.664704+0.062563 test-rmse:2.575376+0.505888
## [10] train-rmse:1.566754+0.075823 test-rmse:2.525069+0.515376
## [11] train-rmse:1.468273+0.057067 test-rmse:2.480516+0.528460
## [12] train-rmse:1.381059+0.076073 test-rmse:2.450964+0.531720
## [13] train-rmse:1.306794+0.079513 test-rmse:2.425084+0.537690
## [14] train-rmse:1.236700+0.064375 test-rmse:2.400619+0.546503
## [15] train-rmse:1.178912+0.075507 test-rmse:2.379058+0.551973
## [16] train-rmse:1.140541+0.078657 test-rmse:2.366544+0.559427
## [17] train-rmse:1.087615+0.072191 test-rmse:2.356419+0.565737
## [18] train-rmse:1.038739+0.063896 test-rmse:2.339721+0.561465
## [19] train-rmse:0.993822+0.063246 test-rmse:2.334214+0.568720
## [20] train-rmse:0.953609+0.042563 test-rmse:2.327115+0.572257
## [21] train-rmse:0.925481+0.047913 test-rmse:2.318957+0.568073
## [22] train-rmse:0.876801+0.038846 test-rmse:2.308136+0.565879
## [23] train-rmse:0.843917+0.036377 test-rmse:2.296713+0.565782
## [24] train-rmse:0.800278+0.046718 test-rmse:2.287932+0.575783
## [25] train-rmse:0.769363+0.048163 test-rmse:2.279876+0.582320
## [26] train-rmse:0.751662+0.049113 test-rmse:2.273616+0.582574
## [27] train-rmse:0.724257+0.040897 test-rmse:2.264870+0.591516
## [28] train-rmse:0.693393+0.030777 test-rmse:2.258801+0.586130
## [29] train-rmse:0.667789+0.028233 test-rmse:2.258191+0.597302
## [30] train-rmse:0.648556+0.032674 test-rmse:2.256661+0.594890
## [31] train-rmse:0.631612+0.035396 test-rmse:2.250377+0.590034
## [32] train-rmse:0.610583+0.030604 test-rmse:2.245318+0.594148
## [33] train-rmse:0.590124+0.032964 test-rmse:2.241029+0.591521
## [34] train-rmse:0.574462+0.027378 test-rmse:2.238520+0.594565
## [35] train-rmse:0.556344+0.025865 test-rmse:2.234208+0.595934
## [36] train-rmse:0.537940+0.033313 test-rmse:2.231558+0.595896
## [37] train-rmse:0.524997+0.033603 test-rmse:2.233500+0.597776
## [38] train-rmse:0.509181+0.033476 test-rmse:2.229159+0.592461
## [39] train-rmse:0.494123+0.031811 test-rmse:2.229700+0.592897
## [40] train-rmse:0.484495+0.031592 test-rmse:2.227224+0.592632
## [41] train-rmse:0.468773+0.029763 test-rmse:2.228782+0.588515
## [42] train-rmse:0.449297+0.027559 test-rmse:2.230645+0.591695
## [43] train-rmse:0.433893+0.026986 test-rmse:2.232519+0.589549
## [44] train-rmse:0.422185+0.029554 test-rmse:2.236282+0.593865
## [45] train-rmse:0.406422+0.029782 test-rmse:2.235334+0.595049
## [46] train-rmse:0.398834+0.028569 test-rmse:2.233153+0.595737
## Stopping. Best iteration:
## [40] train-rmse:0.484495+0.031592 test-rmse:2.227224+0.592632
##
## 75
## [1] train-rmse:6.051761+0.114000 test-rmse:6.096343+0.590011
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.563180+0.073078 test-rmse:4.750107+0.510557
## [3] train-rmse:3.542101+0.053168 test-rmse:3.905836+0.457469
## [4] train-rmse:2.845972+0.030370 test-rmse:3.374253+0.472945
## [5] train-rmse:2.377848+0.037986 test-rmse:3.043595+0.508443
## [6] train-rmse:2.035738+0.031255 test-rmse:2.834654+0.510281
## [7] train-rmse:1.782895+0.034437 test-rmse:2.688540+0.529856
## [8] train-rmse:1.603770+0.036404 test-rmse:2.610327+0.547821
## [9] train-rmse:1.466574+0.043898 test-rmse:2.527113+0.556742
## [10] train-rmse:1.330890+0.052245 test-rmse:2.469725+0.575027
## [11] train-rmse:1.226033+0.051776 test-rmse:2.460969+0.589931
## [12] train-rmse:1.146959+0.060052 test-rmse:2.437531+0.598242
## [13] train-rmse:1.087356+0.058495 test-rmse:2.415933+0.588786
## [14] train-rmse:1.031822+0.055421 test-rmse:2.399070+0.589524
## [15] train-rmse:0.971734+0.052129 test-rmse:2.385093+0.592730
## [16] train-rmse:0.916849+0.050799 test-rmse:2.379448+0.591372
## [17] train-rmse:0.877210+0.054572 test-rmse:2.365998+0.590355
## [18] train-rmse:0.837808+0.050017 test-rmse:2.355029+0.595072
## [19] train-rmse:0.792057+0.031314 test-rmse:2.355180+0.596325
## [20] train-rmse:0.741488+0.035572 test-rmse:2.344169+0.601248
## [21] train-rmse:0.698714+0.030983 test-rmse:2.337877+0.595971
## [22] train-rmse:0.673379+0.027661 test-rmse:2.332336+0.603270
## [23] train-rmse:0.644322+0.021411 test-rmse:2.333019+0.601770
## [24] train-rmse:0.613514+0.021886 test-rmse:2.327259+0.600735
## [25] train-rmse:0.593460+0.020521 test-rmse:2.322639+0.606884
## [26] train-rmse:0.568846+0.023527 test-rmse:2.316378+0.604457
## [27] train-rmse:0.541659+0.018714 test-rmse:2.313857+0.605225
## [28] train-rmse:0.516895+0.023401 test-rmse:2.307541+0.605098
## [29] train-rmse:0.490269+0.030320 test-rmse:2.309131+0.607495
## [30] train-rmse:0.471523+0.030764 test-rmse:2.306097+0.601750
## [31] train-rmse:0.447543+0.026246 test-rmse:2.310241+0.602299
## [32] train-rmse:0.429840+0.027509 test-rmse:2.308829+0.604056
## [33] train-rmse:0.408913+0.026096 test-rmse:2.307212+0.603398
## [34] train-rmse:0.396426+0.019966 test-rmse:2.305927+0.603071
## [35] train-rmse:0.380823+0.019722 test-rmse:2.299493+0.606616
## [36] train-rmse:0.364641+0.019524 test-rmse:2.298794+0.606977
## [37] train-rmse:0.351708+0.020729 test-rmse:2.299920+0.607354
## [38] train-rmse:0.335527+0.025370 test-rmse:2.298594+0.608580
## [39] train-rmse:0.322481+0.025027 test-rmse:2.297763+0.608953
## [40] train-rmse:0.310191+0.025789 test-rmse:2.298117+0.609967
## [41] train-rmse:0.298306+0.023777 test-rmse:2.297060+0.609694
## [42] train-rmse:0.285524+0.020983 test-rmse:2.295131+0.607888
## [43] train-rmse:0.277636+0.020436 test-rmse:2.296051+0.606291
## [44] train-rmse:0.268755+0.020242 test-rmse:2.295320+0.606309
## [45] train-rmse:0.254990+0.016059 test-rmse:2.290636+0.610549
## [46] train-rmse:0.246236+0.017105 test-rmse:2.290601+0.611577
## [47] train-rmse:0.237280+0.016210 test-rmse:2.290176+0.611518
## [48] train-rmse:0.228500+0.017510 test-rmse:2.288236+0.610075
## [49] train-rmse:0.221499+0.018375 test-rmse:2.288690+0.609619
## [50] train-rmse:0.212129+0.016991 test-rmse:2.288212+0.610677
## [51] train-rmse:0.203113+0.018956 test-rmse:2.289780+0.609671
## [52] train-rmse:0.197168+0.017772 test-rmse:2.288468+0.608970
## [53] train-rmse:0.187903+0.015630 test-rmse:2.287683+0.608400
## [54] train-rmse:0.179296+0.014689 test-rmse:2.285547+0.608266
## [55] train-rmse:0.173188+0.013756 test-rmse:2.285432+0.608192
## [56] train-rmse:0.167003+0.011394 test-rmse:2.285606+0.608614
## [57] train-rmse:0.159729+0.012795 test-rmse:2.282935+0.608357
## [58] train-rmse:0.151956+0.012019 test-rmse:2.282848+0.606605
## [59] train-rmse:0.147681+0.011112 test-rmse:2.282707+0.606669
## [60] train-rmse:0.142078+0.010032 test-rmse:2.281901+0.607506
## [61] train-rmse:0.137519+0.009789 test-rmse:2.281986+0.605617
## [62] train-rmse:0.131991+0.009696 test-rmse:2.282065+0.605860
## [63] train-rmse:0.127134+0.010365 test-rmse:2.281567+0.604961
## [64] train-rmse:0.124184+0.009474 test-rmse:2.281605+0.604758
## [65] train-rmse:0.118747+0.007662 test-rmse:2.281816+0.604311
## [66] train-rmse:0.114572+0.007886 test-rmse:2.281472+0.604243
## [67] train-rmse:0.110772+0.007772 test-rmse:2.281156+0.604488
## [68] train-rmse:0.106359+0.007957 test-rmse:2.280935+0.604490
## [69] train-rmse:0.102975+0.008095 test-rmse:2.280708+0.604804
## [70] train-rmse:0.099034+0.009271 test-rmse:2.280501+0.604914
## [71] train-rmse:0.095305+0.009943 test-rmse:2.280635+0.604760
## [72] train-rmse:0.092913+0.010447 test-rmse:2.280684+0.604757
## [73] train-rmse:0.089737+0.010970 test-rmse:2.281019+0.604584
## [74] train-rmse:0.086638+0.010797 test-rmse:2.280662+0.605030
## [75] train-rmse:0.082772+0.010598 test-rmse:2.280118+0.604770
## [76] train-rmse:0.078772+0.009489 test-rmse:2.280353+0.604975
## [77] train-rmse:0.076174+0.008880 test-rmse:2.280491+0.605162
## [78] train-rmse:0.073944+0.009260 test-rmse:2.280596+0.605172
## [79] train-rmse:0.070939+0.008176 test-rmse:2.280036+0.605127
## [80] train-rmse:0.068016+0.007712 test-rmse:2.280500+0.604769
## [81] train-rmse:0.066239+0.008173 test-rmse:2.280112+0.604497
## [82] train-rmse:0.064528+0.008353 test-rmse:2.280069+0.604630
## [83] train-rmse:0.062605+0.008012 test-rmse:2.279425+0.604571
## [84] train-rmse:0.060419+0.007348 test-rmse:2.279075+0.604684
## [85] train-rmse:0.058635+0.007051 test-rmse:2.279189+0.604594
## [86] train-rmse:0.056658+0.007270 test-rmse:2.279044+0.604619
## [87] train-rmse:0.054669+0.007043 test-rmse:2.279404+0.604680
## [88] train-rmse:0.053174+0.006443 test-rmse:2.279206+0.604575
## [89] train-rmse:0.051708+0.006326 test-rmse:2.278624+0.605109
## [90] train-rmse:0.050468+0.006262 test-rmse:2.278619+0.604662
## [91] train-rmse:0.048516+0.005666 test-rmse:2.278645+0.605285
## [92] train-rmse:0.046859+0.005312 test-rmse:2.278952+0.605296
## [93] train-rmse:0.045494+0.005295 test-rmse:2.278886+0.605297
## [94] train-rmse:0.044358+0.005442 test-rmse:2.278700+0.605756
## [95] train-rmse:0.042691+0.005615 test-rmse:2.278575+0.605891
## [96] train-rmse:0.041570+0.005152 test-rmse:2.278614+0.605873
## [97] train-rmse:0.040682+0.005128 test-rmse:2.278019+0.605352
## [98] train-rmse:0.039789+0.005173 test-rmse:2.277846+0.605313
## [99] train-rmse:0.038555+0.004995 test-rmse:2.277903+0.605301
## [100] train-rmse:0.037556+0.004916 test-rmse:2.277891+0.605205
## [101] train-rmse:0.036410+0.004511 test-rmse:2.277778+0.605242
## [102] train-rmse:0.035573+0.004350 test-rmse:2.277849+0.605189
## [103] train-rmse:0.034338+0.004522 test-rmse:2.277784+0.605302
## [104] train-rmse:0.033217+0.004517 test-rmse:2.277513+0.605139
## [105] train-rmse:0.032641+0.004748 test-rmse:2.277751+0.605025
## [106] train-rmse:0.031674+0.004790 test-rmse:2.277782+0.604951
## [107] train-rmse:0.030497+0.004294 test-rmse:2.277500+0.605017
## [108] train-rmse:0.029405+0.004027 test-rmse:2.277490+0.604884
## [109] train-rmse:0.028267+0.003573 test-rmse:2.277266+0.605098
## [110] train-rmse:0.027726+0.003489 test-rmse:2.277170+0.605160
## [111] train-rmse:0.026670+0.003476 test-rmse:2.277256+0.605150
## [112] train-rmse:0.025700+0.003509 test-rmse:2.277283+0.605014
## [113] train-rmse:0.025176+0.003618 test-rmse:2.277304+0.604965
## [114] train-rmse:0.024466+0.003502 test-rmse:2.277277+0.605066
## [115] train-rmse:0.023879+0.003549 test-rmse:2.277194+0.605092
## [116] train-rmse:0.023000+0.003086 test-rmse:2.277271+0.605077
## Stopping. Best iteration:
## [110] train-rmse:0.027726+0.003489 test-rmse:2.277170+0.605160
##
## 76
## [1] train-rmse:6.038232+0.112841 test-rmse:6.088006+0.589619
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.520708+0.071716 test-rmse:4.732437+0.506595
## [3] train-rmse:3.477441+0.049009 test-rmse:3.838164+0.479654
## [4] train-rmse:2.765773+0.032800 test-rmse:3.330830+0.467369
## [5] train-rmse:2.262445+0.031523 test-rmse:3.035095+0.514645
## [6] train-rmse:1.916161+0.034448 test-rmse:2.833195+0.494681
## [7] train-rmse:1.671654+0.020353 test-rmse:2.713328+0.534666
## [8] train-rmse:1.479945+0.022461 test-rmse:2.604444+0.548247
## [9] train-rmse:1.336489+0.027099 test-rmse:2.543261+0.554841
## [10] train-rmse:1.230381+0.022103 test-rmse:2.508360+0.564687
## [11] train-rmse:1.136846+0.026466 test-rmse:2.492106+0.576256
## [12] train-rmse:1.054494+0.032359 test-rmse:2.470515+0.586533
## [13] train-rmse:0.965244+0.037138 test-rmse:2.455398+0.597539
## [14] train-rmse:0.918239+0.037237 test-rmse:2.438933+0.597554
## [15] train-rmse:0.849192+0.038625 test-rmse:2.418293+0.603289
## [16] train-rmse:0.802941+0.029952 test-rmse:2.402499+0.616044
## [17] train-rmse:0.746494+0.043128 test-rmse:2.402481+0.614934
## [18] train-rmse:0.708438+0.040434 test-rmse:2.391358+0.617838
## [19] train-rmse:0.667226+0.031399 test-rmse:2.384334+0.622289
## [20] train-rmse:0.622442+0.023730 test-rmse:2.384533+0.620261
## [21] train-rmse:0.582980+0.020363 test-rmse:2.382266+0.616522
## [22] train-rmse:0.539595+0.022513 test-rmse:2.376322+0.618318
## [23] train-rmse:0.511605+0.022437 test-rmse:2.377502+0.622480
## [24] train-rmse:0.486115+0.021387 test-rmse:2.368706+0.621276
## [25] train-rmse:0.459581+0.022250 test-rmse:2.365899+0.619411
## [26] train-rmse:0.433498+0.026157 test-rmse:2.370087+0.624036
## [27] train-rmse:0.406445+0.022302 test-rmse:2.362976+0.616754
## [28] train-rmse:0.386488+0.018043 test-rmse:2.362241+0.616976
## [29] train-rmse:0.358652+0.015028 test-rmse:2.357201+0.613551
## [30] train-rmse:0.337892+0.016003 test-rmse:2.356389+0.611776
## [31] train-rmse:0.322440+0.014260 test-rmse:2.353920+0.610013
## [32] train-rmse:0.304542+0.013675 test-rmse:2.349484+0.611524
## [33] train-rmse:0.292763+0.010681 test-rmse:2.347794+0.611776
## [34] train-rmse:0.279979+0.013512 test-rmse:2.348545+0.611573
## [35] train-rmse:0.269061+0.014931 test-rmse:2.350265+0.608398
## [36] train-rmse:0.257127+0.011579 test-rmse:2.349781+0.608189
## [37] train-rmse:0.242903+0.009893 test-rmse:2.347662+0.608398
## [38] train-rmse:0.232269+0.009230 test-rmse:2.347359+0.608697
## [39] train-rmse:0.220409+0.010072 test-rmse:2.347235+0.609880
## [40] train-rmse:0.211350+0.011746 test-rmse:2.346089+0.608967
## [41] train-rmse:0.204427+0.012129 test-rmse:2.346228+0.609301
## [42] train-rmse:0.198010+0.010769 test-rmse:2.346542+0.609337
## [43] train-rmse:0.184835+0.010733 test-rmse:2.346405+0.610753
## [44] train-rmse:0.177664+0.010972 test-rmse:2.347255+0.610607
## [45] train-rmse:0.165971+0.006732 test-rmse:2.344773+0.609829
## [46] train-rmse:0.159428+0.004969 test-rmse:2.344293+0.610427
## [47] train-rmse:0.153716+0.006734 test-rmse:2.346536+0.611447
## [48] train-rmse:0.145686+0.006370 test-rmse:2.345948+0.611365
## [49] train-rmse:0.137777+0.006497 test-rmse:2.346177+0.610795
## [50] train-rmse:0.134107+0.007200 test-rmse:2.345213+0.611068
## [51] train-rmse:0.128346+0.006003 test-rmse:2.345647+0.611135
## [52] train-rmse:0.122569+0.005762 test-rmse:2.345201+0.611115
## Stopping. Best iteration:
## [46] train-rmse:0.159428+0.004969 test-rmse:2.344293+0.610427
##
## 77
## [1] train-rmse:6.363796+0.139139 test-rmse:6.368280+0.649489
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.232691+0.111457 test-rmse:5.308688+0.686633
## [3] train-rmse:4.556026+0.115034 test-rmse:4.638275+0.593986
## [4] train-rmse:4.152690+0.116060 test-rmse:4.245878+0.540345
## [5] train-rmse:3.893952+0.113966 test-rmse:4.018195+0.466463
## [6] train-rmse:3.714861+0.107471 test-rmse:3.845173+0.450971
## [7] train-rmse:3.579942+0.109627 test-rmse:3.646706+0.439389
## [8] train-rmse:3.468678+0.119718 test-rmse:3.591259+0.416433
## [9] train-rmse:3.380414+0.123869 test-rmse:3.521102+0.399376
## [10] train-rmse:3.303574+0.122204 test-rmse:3.486803+0.418643
## [11] train-rmse:3.231460+0.119522 test-rmse:3.400830+0.445330
## [12] train-rmse:3.165281+0.121207 test-rmse:3.317344+0.455046
## [13] train-rmse:3.107017+0.124765 test-rmse:3.225932+0.410972
## [14] train-rmse:3.054910+0.126617 test-rmse:3.180892+0.418478
## [15] train-rmse:3.007293+0.130474 test-rmse:3.169548+0.447105
## [16] train-rmse:2.964778+0.129447 test-rmse:3.152780+0.465833
## [17] train-rmse:2.924032+0.128988 test-rmse:3.118863+0.463812
## [18] train-rmse:2.886690+0.129126 test-rmse:3.079654+0.439654
## [19] train-rmse:2.851238+0.131693 test-rmse:3.054462+0.428764
## [20] train-rmse:2.817082+0.134587 test-rmse:3.015174+0.425617
## [21] train-rmse:2.784612+0.136331 test-rmse:2.999552+0.430667
## [22] train-rmse:2.754573+0.138149 test-rmse:2.961155+0.437266
## [23] train-rmse:2.724766+0.140131 test-rmse:2.944061+0.461658
## [24] train-rmse:2.696586+0.141116 test-rmse:2.926374+0.468923
## [25] train-rmse:2.669086+0.141307 test-rmse:2.880884+0.466967
## [26] train-rmse:2.643852+0.140352 test-rmse:2.868501+0.476619
## [27] train-rmse:2.618449+0.138784 test-rmse:2.837310+0.466176
## [28] train-rmse:2.594398+0.138580 test-rmse:2.815770+0.464189
## [29] train-rmse:2.571835+0.137682 test-rmse:2.795526+0.474869
## [30] train-rmse:2.550843+0.138156 test-rmse:2.793116+0.467078
## [31] train-rmse:2.530390+0.138625 test-rmse:2.784730+0.490436
## [32] train-rmse:2.510389+0.138637 test-rmse:2.785744+0.484454
## [33] train-rmse:2.490978+0.139103 test-rmse:2.761502+0.480147
## [34] train-rmse:2.472293+0.140039 test-rmse:2.751039+0.476899
## [35] train-rmse:2.454228+0.140945 test-rmse:2.730683+0.489235
## [36] train-rmse:2.437045+0.141675 test-rmse:2.706900+0.483855
## [37] train-rmse:2.420686+0.141792 test-rmse:2.687568+0.492794
## [38] train-rmse:2.405143+0.142322 test-rmse:2.676502+0.486163
## [39] train-rmse:2.389782+0.142445 test-rmse:2.658956+0.496292
## [40] train-rmse:2.375328+0.142584 test-rmse:2.645517+0.493801
## [41] train-rmse:2.361565+0.142421 test-rmse:2.619676+0.492386
## [42] train-rmse:2.348649+0.142556 test-rmse:2.605560+0.493591
## [43] train-rmse:2.336152+0.142592 test-rmse:2.599720+0.495208
## [44] train-rmse:2.323878+0.142682 test-rmse:2.597922+0.498442
## [45] train-rmse:2.311955+0.143380 test-rmse:2.585405+0.494375
## [46] train-rmse:2.300449+0.143674 test-rmse:2.585429+0.497934
## [47] train-rmse:2.289114+0.144026 test-rmse:2.580394+0.495732
## [48] train-rmse:2.278380+0.144052 test-rmse:2.566397+0.503045
## [49] train-rmse:2.268026+0.144261 test-rmse:2.553466+0.496761
## [50] train-rmse:2.257754+0.144576 test-rmse:2.541720+0.495998
## [51] train-rmse:2.247630+0.145188 test-rmse:2.543272+0.507740
## [52] train-rmse:2.238062+0.145877 test-rmse:2.538274+0.515891
## [53] train-rmse:2.228416+0.146581 test-rmse:2.530390+0.516180
## [54] train-rmse:2.219547+0.146869 test-rmse:2.516612+0.511478
## [55] train-rmse:2.210842+0.147215 test-rmse:2.502443+0.517923
## [56] train-rmse:2.202328+0.147525 test-rmse:2.508586+0.522063
## [57] train-rmse:2.194186+0.147925 test-rmse:2.503170+0.520522
## [58] train-rmse:2.186488+0.148155 test-rmse:2.496002+0.526667
## [59] train-rmse:2.178959+0.148613 test-rmse:2.490682+0.524854
## [60] train-rmse:2.171586+0.149007 test-rmse:2.490940+0.529451
## [61] train-rmse:2.164569+0.149122 test-rmse:2.486921+0.526153
## [62] train-rmse:2.157640+0.149375 test-rmse:2.483440+0.521511
## [63] train-rmse:2.150980+0.149598 test-rmse:2.473948+0.522430
## [64] train-rmse:2.144347+0.149857 test-rmse:2.462521+0.526370
## [65] train-rmse:2.137967+0.150027 test-rmse:2.457130+0.526874
## [66] train-rmse:2.131670+0.150297 test-rmse:2.455206+0.521557
## [67] train-rmse:2.125430+0.150668 test-rmse:2.441629+0.517828
## [68] train-rmse:2.119440+0.151059 test-rmse:2.443516+0.530187
## [69] train-rmse:2.113514+0.151495 test-rmse:2.443093+0.535225
## [70] train-rmse:2.107738+0.151691 test-rmse:2.438845+0.535959
## [71] train-rmse:2.102001+0.151887 test-rmse:2.429845+0.539288
## [72] train-rmse:2.096473+0.152240 test-rmse:2.426480+0.538878
## [73] train-rmse:2.091176+0.152353 test-rmse:2.422005+0.544350
## [74] train-rmse:2.085962+0.152517 test-rmse:2.425595+0.547829
## [75] train-rmse:2.080612+0.152639 test-rmse:2.417830+0.542247
## [76] train-rmse:2.075601+0.152861 test-rmse:2.412268+0.542439
## [77] train-rmse:2.070793+0.153302 test-rmse:2.406108+0.538898
## [78] train-rmse:2.066177+0.153545 test-rmse:2.402348+0.540118
## [79] train-rmse:2.061611+0.153696 test-rmse:2.405686+0.536646
## [80] train-rmse:2.057230+0.153946 test-rmse:2.406958+0.535036
## [81] train-rmse:2.052768+0.154009 test-rmse:2.399909+0.539173
## [82] train-rmse:2.048492+0.154009 test-rmse:2.399663+0.537343
## [83] train-rmse:2.044324+0.154135 test-rmse:2.396338+0.539004
## [84] train-rmse:2.040232+0.154135 test-rmse:2.392330+0.531976
## [85] train-rmse:2.035997+0.154340 test-rmse:2.393995+0.539955
## [86] train-rmse:2.032060+0.154528 test-rmse:2.390246+0.537410
## [87] train-rmse:2.028156+0.154676 test-rmse:2.384134+0.539094
## [88] train-rmse:2.024318+0.154809 test-rmse:2.379773+0.534890
## [89] train-rmse:2.020596+0.154911 test-rmse:2.377911+0.533438
## [90] train-rmse:2.016932+0.155095 test-rmse:2.372961+0.533457
## [91] train-rmse:2.013246+0.155112 test-rmse:2.375144+0.530826
## [92] train-rmse:2.009627+0.155230 test-rmse:2.386769+0.539681
## [93] train-rmse:2.006008+0.155314 test-rmse:2.378426+0.539490
## [94] train-rmse:2.002483+0.155494 test-rmse:2.376906+0.538544
## [95] train-rmse:1.999074+0.155704 test-rmse:2.375023+0.539084
## [96] train-rmse:1.995753+0.155878 test-rmse:2.368074+0.538053
## [97] train-rmse:1.992509+0.156084 test-rmse:2.365259+0.543618
## [98] train-rmse:1.989243+0.156277 test-rmse:2.365872+0.543790
## [99] train-rmse:1.986110+0.156355 test-rmse:2.359234+0.544096
## [100] train-rmse:1.982878+0.156596 test-rmse:2.356578+0.545301
## [101] train-rmse:1.979872+0.156777 test-rmse:2.358516+0.549864
## [102] train-rmse:1.976818+0.156959 test-rmse:2.356254+0.549551
## [103] train-rmse:1.973807+0.157220 test-rmse:2.353430+0.549583
## [104] train-rmse:1.970899+0.157398 test-rmse:2.348757+0.547292
## [105] train-rmse:1.967965+0.157745 test-rmse:2.348129+0.551794
## [106] train-rmse:1.965177+0.157902 test-rmse:2.352408+0.547791
## [107] train-rmse:1.962297+0.158016 test-rmse:2.350336+0.551766
## [108] train-rmse:1.959542+0.158152 test-rmse:2.347268+0.553417
## [109] train-rmse:1.956665+0.158239 test-rmse:2.346453+0.553098
## [110] train-rmse:1.953981+0.158334 test-rmse:2.341311+0.555740
## [111] train-rmse:1.951290+0.158540 test-rmse:2.340085+0.559278
## [112] train-rmse:1.948722+0.158600 test-rmse:2.337790+0.556157
## [113] train-rmse:1.945956+0.158691 test-rmse:2.333192+0.554680
## [114] train-rmse:1.943174+0.158714 test-rmse:2.334157+0.554780
## [115] train-rmse:1.940679+0.158887 test-rmse:2.336475+0.557175
## [116] train-rmse:1.938219+0.158983 test-rmse:2.330136+0.557267
## [117] train-rmse:1.935651+0.158937 test-rmse:2.329304+0.559434
## [118] train-rmse:1.933266+0.159067 test-rmse:2.329380+0.555868
## [119] train-rmse:1.930802+0.159056 test-rmse:2.325597+0.555018
## [120] train-rmse:1.928501+0.159137 test-rmse:2.326531+0.555112
## [121] train-rmse:1.926116+0.159429 test-rmse:2.323204+0.552988
## [122] train-rmse:1.923793+0.159561 test-rmse:2.324695+0.549967
## [123] train-rmse:1.921389+0.159535 test-rmse:2.323617+0.549106
## [124] train-rmse:1.919192+0.159637 test-rmse:2.323279+0.547627
## [125] train-rmse:1.917017+0.159784 test-rmse:2.326062+0.544772
## [126] train-rmse:1.914834+0.159937 test-rmse:2.324001+0.545052
## [127] train-rmse:1.912673+0.160050 test-rmse:2.328109+0.547426
## Stopping. Best iteration:
## [121] train-rmse:1.926116+0.159429 test-rmse:2.323204+0.552988
##
## 78
## [1] train-rmse:6.214311+0.129047 test-rmse:6.264728+0.604203
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.949299+0.121857 test-rmse:5.031736+0.570197
## [3] train-rmse:4.132590+0.097088 test-rmse:4.250730+0.474621
## [4] train-rmse:3.592045+0.090063 test-rmse:3.764424+0.462262
## [5] train-rmse:3.251185+0.097737 test-rmse:3.496949+0.438217
## [6] train-rmse:3.008328+0.092729 test-rmse:3.241106+0.449737
## [7] train-rmse:2.822797+0.074921 test-rmse:3.084713+0.482502
## [8] train-rmse:2.690180+0.081513 test-rmse:2.997680+0.472678
## [9] train-rmse:2.593382+0.083635 test-rmse:2.920205+0.463957
## [10] train-rmse:2.499453+0.090814 test-rmse:2.870366+0.438153
## [11] train-rmse:2.410600+0.090387 test-rmse:2.803848+0.471672
## [12] train-rmse:2.328520+0.099969 test-rmse:2.746487+0.476789
## [13] train-rmse:2.265303+0.100277 test-rmse:2.705709+0.478050
## [14] train-rmse:2.197751+0.078198 test-rmse:2.657618+0.512842
## [15] train-rmse:2.150642+0.078414 test-rmse:2.652172+0.540604
## [16] train-rmse:2.106671+0.078118 test-rmse:2.618756+0.549762
## [17] train-rmse:2.061760+0.082482 test-rmse:2.583214+0.545735
## [18] train-rmse:2.013977+0.069612 test-rmse:2.547829+0.554302
## [19] train-rmse:1.968135+0.076793 test-rmse:2.503020+0.555228
## [20] train-rmse:1.925756+0.075806 test-rmse:2.500766+0.550159
## [21] train-rmse:1.891022+0.074991 test-rmse:2.491685+0.547798
## [22] train-rmse:1.849241+0.065595 test-rmse:2.486882+0.550184
## [23] train-rmse:1.814106+0.063998 test-rmse:2.468451+0.551453
## [24] train-rmse:1.780579+0.066338 test-rmse:2.449799+0.550274
## [25] train-rmse:1.756030+0.065613 test-rmse:2.430905+0.567578
## [26] train-rmse:1.729523+0.068291 test-rmse:2.406331+0.551090
## [27] train-rmse:1.706005+0.068605 test-rmse:2.377693+0.558867
## [28] train-rmse:1.680247+0.070654 test-rmse:2.362886+0.549727
## [29] train-rmse:1.657058+0.072657 test-rmse:2.349591+0.538611
## [30] train-rmse:1.621656+0.058158 test-rmse:2.330506+0.534672
## [31] train-rmse:1.591569+0.054230 test-rmse:2.313523+0.548537
## [32] train-rmse:1.570608+0.054909 test-rmse:2.307226+0.560863
## [33] train-rmse:1.553023+0.058348 test-rmse:2.291009+0.549433
## [34] train-rmse:1.535904+0.058854 test-rmse:2.281809+0.558697
## [35] train-rmse:1.519673+0.059036 test-rmse:2.276730+0.557531
## [36] train-rmse:1.501798+0.060999 test-rmse:2.275917+0.554893
## [37] train-rmse:1.482137+0.057386 test-rmse:2.266114+0.563699
## [38] train-rmse:1.465140+0.060182 test-rmse:2.270949+0.556345
## [39] train-rmse:1.447181+0.050783 test-rmse:2.263654+0.552913
## [40] train-rmse:1.435140+0.051291 test-rmse:2.261718+0.545389
## [41] train-rmse:1.418528+0.052460 test-rmse:2.255275+0.541307
## [42] train-rmse:1.404623+0.053902 test-rmse:2.245992+0.535027
## [43] train-rmse:1.384935+0.050678 test-rmse:2.243616+0.535743
## [44] train-rmse:1.372174+0.049679 test-rmse:2.237259+0.538767
## [45] train-rmse:1.360991+0.051114 test-rmse:2.244612+0.544397
## [46] train-rmse:1.351510+0.052046 test-rmse:2.241158+0.545231
## [47] train-rmse:1.338348+0.055447 test-rmse:2.233186+0.541768
## [48] train-rmse:1.327040+0.054527 test-rmse:2.227774+0.536135
## [49] train-rmse:1.317331+0.056767 test-rmse:2.223798+0.544581
## [50] train-rmse:1.308567+0.057014 test-rmse:2.217367+0.549011
## [51] train-rmse:1.300782+0.056802 test-rmse:2.210843+0.548318
## [52] train-rmse:1.288772+0.055592 test-rmse:2.203588+0.555185
## [53] train-rmse:1.280085+0.055467 test-rmse:2.200173+0.551738
## [54] train-rmse:1.271149+0.056495 test-rmse:2.196312+0.555199
## [55] train-rmse:1.259780+0.061106 test-rmse:2.193486+0.554314
## [56] train-rmse:1.245617+0.057931 test-rmse:2.195229+0.562121
## [57] train-rmse:1.239303+0.058662 test-rmse:2.189092+0.560281
## [58] train-rmse:1.226057+0.058041 test-rmse:2.192111+0.562389
## [59] train-rmse:1.212991+0.055403 test-rmse:2.185325+0.566218
## [60] train-rmse:1.202999+0.059041 test-rmse:2.189433+0.569403
## [61] train-rmse:1.193238+0.059563 test-rmse:2.188106+0.569111
## [62] train-rmse:1.183726+0.061245 test-rmse:2.193549+0.565047
## [63] train-rmse:1.173815+0.064819 test-rmse:2.190062+0.571837
## [64] train-rmse:1.167086+0.066229 test-rmse:2.189985+0.570176
## [65] train-rmse:1.160728+0.065836 test-rmse:2.188575+0.570680
## Stopping. Best iteration:
## [59] train-rmse:1.212991+0.055403 test-rmse:2.185325+0.566218
##
## 79
## [1] train-rmse:6.113711+0.121138 test-rmse:6.222824+0.589053
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.758180+0.091976 test-rmse:4.894820+0.539192
## [3] train-rmse:3.829946+0.095494 test-rmse:4.080573+0.438391
## [4] train-rmse:3.213847+0.048186 test-rmse:3.476400+0.469847
## [5] train-rmse:2.834305+0.066084 test-rmse:3.183145+0.425033
## [6] train-rmse:2.567432+0.052731 test-rmse:2.952621+0.429645
## [7] train-rmse:2.374622+0.054209 test-rmse:2.831875+0.476524
## [8] train-rmse:2.216490+0.045175 test-rmse:2.723317+0.459392
## [9] train-rmse:2.110269+0.046746 test-rmse:2.679055+0.467296
## [10] train-rmse:2.010670+0.048323 test-rmse:2.609863+0.462904
## [11] train-rmse:1.905226+0.052218 test-rmse:2.560287+0.433967
## [12] train-rmse:1.835114+0.044627 test-rmse:2.518494+0.415540
## [13] train-rmse:1.755589+0.042732 test-rmse:2.478891+0.417917
## [14] train-rmse:1.685781+0.042180 test-rmse:2.443544+0.420526
## [15] train-rmse:1.634933+0.045276 test-rmse:2.422312+0.434389
## [16] train-rmse:1.592521+0.044669 test-rmse:2.389067+0.438819
## [17] train-rmse:1.544321+0.049283 test-rmse:2.362923+0.448462
## [18] train-rmse:1.499284+0.031890 test-rmse:2.337471+0.444448
## [19] train-rmse:1.446938+0.026828 test-rmse:2.329117+0.434297
## [20] train-rmse:1.410082+0.030058 test-rmse:2.310936+0.437049
## [21] train-rmse:1.373110+0.033761 test-rmse:2.283550+0.437982
## [22] train-rmse:1.337386+0.037373 test-rmse:2.277681+0.450511
## [23] train-rmse:1.308359+0.029653 test-rmse:2.265094+0.443505
## [24] train-rmse:1.279910+0.033459 test-rmse:2.260662+0.438735
## [25] train-rmse:1.245147+0.029252 test-rmse:2.256720+0.438471
## [26] train-rmse:1.219957+0.031334 test-rmse:2.246801+0.436280
## [27] train-rmse:1.194868+0.029101 test-rmse:2.245169+0.442170
## [28] train-rmse:1.164481+0.032449 test-rmse:2.226410+0.449048
## [29] train-rmse:1.143772+0.034048 test-rmse:2.213237+0.450368
## [30] train-rmse:1.118195+0.034381 test-rmse:2.213129+0.454887
## [31] train-rmse:1.093344+0.031109 test-rmse:2.209571+0.461275
## [32] train-rmse:1.074814+0.033387 test-rmse:2.207329+0.457320
## [33] train-rmse:1.051021+0.023062 test-rmse:2.199139+0.458049
## [34] train-rmse:1.032607+0.023196 test-rmse:2.193118+0.459692
## [35] train-rmse:1.017547+0.017873 test-rmse:2.183333+0.464073
## [36] train-rmse:0.996639+0.022940 test-rmse:2.165859+0.450915
## [37] train-rmse:0.979446+0.021048 test-rmse:2.155964+0.453154
## [38] train-rmse:0.961931+0.022074 test-rmse:2.153117+0.461979
## [39] train-rmse:0.943167+0.026558 test-rmse:2.153587+0.470262
## [40] train-rmse:0.930463+0.026089 test-rmse:2.153702+0.471615
## [41] train-rmse:0.911466+0.027256 test-rmse:2.143324+0.470379
## [42] train-rmse:0.898205+0.025508 test-rmse:2.143989+0.469630
## [43] train-rmse:0.884227+0.027729 test-rmse:2.136888+0.466015
## [44] train-rmse:0.870421+0.027090 test-rmse:2.138632+0.466858
## [45] train-rmse:0.847654+0.032443 test-rmse:2.132236+0.469499
## [46] train-rmse:0.835303+0.032551 test-rmse:2.128367+0.475923
## [47] train-rmse:0.817510+0.034296 test-rmse:2.124024+0.474812
## [48] train-rmse:0.803177+0.036155 test-rmse:2.113151+0.479569
## [49] train-rmse:0.793260+0.034706 test-rmse:2.113179+0.484113
## [50] train-rmse:0.783170+0.035400 test-rmse:2.109418+0.483992
## [51] train-rmse:0.772315+0.036291 test-rmse:2.106836+0.481526
## [52] train-rmse:0.761697+0.038903 test-rmse:2.105360+0.481933
## [53] train-rmse:0.748254+0.039995 test-rmse:2.102434+0.481738
## [54] train-rmse:0.737581+0.031530 test-rmse:2.104588+0.481088
## [55] train-rmse:0.725869+0.032460 test-rmse:2.104572+0.485093
## [56] train-rmse:0.714201+0.032860 test-rmse:2.102129+0.485288
## [57] train-rmse:0.705086+0.034279 test-rmse:2.101914+0.485447
## [58] train-rmse:0.697725+0.034271 test-rmse:2.101007+0.487440
## [59] train-rmse:0.689252+0.034768 test-rmse:2.100147+0.484896
## [60] train-rmse:0.679079+0.033292 test-rmse:2.098705+0.483981
## [61] train-rmse:0.667823+0.032123 test-rmse:2.094395+0.487032
## [62] train-rmse:0.656062+0.028306 test-rmse:2.091112+0.485718
## [63] train-rmse:0.646499+0.022355 test-rmse:2.088104+0.484418
## [64] train-rmse:0.635422+0.020160 test-rmse:2.082800+0.481667
## [65] train-rmse:0.623609+0.024207 test-rmse:2.076391+0.479835
## [66] train-rmse:0.615766+0.024114 test-rmse:2.076710+0.481380
## [67] train-rmse:0.606071+0.020974 test-rmse:2.076894+0.484543
## [68] train-rmse:0.597238+0.021599 test-rmse:2.077898+0.484001
## [69] train-rmse:0.590959+0.018188 test-rmse:2.082154+0.485034
## [70] train-rmse:0.587650+0.017829 test-rmse:2.081775+0.485955
## [71] train-rmse:0.578212+0.017163 test-rmse:2.079719+0.484150
## Stopping. Best iteration:
## [65] train-rmse:0.623609+0.024207 test-rmse:2.076391+0.479835
##
## 80
## [1] train-rmse:6.018722+0.119823 test-rmse:6.097063+0.573756
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.563952+0.093388 test-rmse:4.750587+0.471730
## [3] train-rmse:3.608392+0.058406 test-rmse:3.894321+0.453316
## [4] train-rmse:2.993446+0.066228 test-rmse:3.384016+0.431604
## [5] train-rmse:2.578013+0.050995 test-rmse:3.055002+0.472135
## [6] train-rmse:2.294621+0.049077 test-rmse:2.884130+0.457388
## [7] train-rmse:2.085643+0.043693 test-rmse:2.721847+0.437522
## [8] train-rmse:1.918341+0.043871 test-rmse:2.642145+0.442970
## [9] train-rmse:1.807880+0.043041 test-rmse:2.557215+0.463154
## [10] train-rmse:1.708469+0.042463 test-rmse:2.521852+0.448165
## [11] train-rmse:1.606400+0.030919 test-rmse:2.495378+0.464680
## [12] train-rmse:1.532528+0.031739 test-rmse:2.475445+0.466473
## [13] train-rmse:1.468978+0.026244 test-rmse:2.434070+0.472302
## [14] train-rmse:1.412642+0.025377 test-rmse:2.423899+0.463823
## [15] train-rmse:1.361185+0.034813 test-rmse:2.397147+0.471033
## [16] train-rmse:1.316013+0.027383 test-rmse:2.379050+0.465259
## [17] train-rmse:1.262724+0.025133 test-rmse:2.349034+0.460876
## [18] train-rmse:1.226023+0.029057 test-rmse:2.332884+0.467739
## [19] train-rmse:1.191468+0.036256 test-rmse:2.328214+0.476389
## [20] train-rmse:1.147312+0.031454 test-rmse:2.317561+0.487776
## [21] train-rmse:1.104847+0.026424 test-rmse:2.310551+0.490497
## [22] train-rmse:1.065705+0.036054 test-rmse:2.302346+0.485999
## [23] train-rmse:1.037611+0.041209 test-rmse:2.290548+0.492160
## [24] train-rmse:1.002810+0.041899 test-rmse:2.291192+0.502092
## [25] train-rmse:0.960594+0.046836 test-rmse:2.284995+0.504577
## [26] train-rmse:0.932639+0.044986 test-rmse:2.270268+0.497596
## [27] train-rmse:0.902760+0.036416 test-rmse:2.246803+0.499636
## [28] train-rmse:0.878839+0.039608 test-rmse:2.240829+0.507940
## [29] train-rmse:0.862097+0.039018 test-rmse:2.240822+0.512894
## [30] train-rmse:0.842554+0.041863 test-rmse:2.238433+0.508742
## [31] train-rmse:0.821839+0.040920 test-rmse:2.229001+0.505582
## [32] train-rmse:0.803211+0.039167 test-rmse:2.226164+0.506711
## [33] train-rmse:0.779364+0.030557 test-rmse:2.218274+0.506769
## [34] train-rmse:0.750901+0.021408 test-rmse:2.221471+0.506321
## [35] train-rmse:0.728443+0.032100 test-rmse:2.212892+0.508330
## [36] train-rmse:0.711750+0.037585 test-rmse:2.205437+0.509361
## [37] train-rmse:0.694821+0.031709 test-rmse:2.203038+0.509087
## [38] train-rmse:0.678084+0.031089 test-rmse:2.199778+0.515230
## [39] train-rmse:0.663463+0.032263 test-rmse:2.196745+0.516296
## [40] train-rmse:0.640108+0.028503 test-rmse:2.193907+0.516018
## [41] train-rmse:0.624243+0.028742 test-rmse:2.193025+0.516784
## [42] train-rmse:0.612502+0.026654 test-rmse:2.194466+0.516250
## [43] train-rmse:0.596971+0.025731 test-rmse:2.198674+0.525016
## [44] train-rmse:0.582539+0.028686 test-rmse:2.195062+0.524265
## [45] train-rmse:0.561022+0.023363 test-rmse:2.186488+0.526596
## [46] train-rmse:0.550819+0.024782 test-rmse:2.184830+0.525208
## [47] train-rmse:0.537289+0.027010 test-rmse:2.179161+0.523865
## [48] train-rmse:0.523694+0.021653 test-rmse:2.178621+0.525183
## [49] train-rmse:0.508402+0.016994 test-rmse:2.178252+0.524941
## [50] train-rmse:0.496348+0.022914 test-rmse:2.175169+0.523890
## [51] train-rmse:0.489214+0.021768 test-rmse:2.178238+0.522363
## [52] train-rmse:0.479075+0.021527 test-rmse:2.178347+0.523344
## [53] train-rmse:0.466226+0.019161 test-rmse:2.178242+0.521783
## [54] train-rmse:0.457318+0.021219 test-rmse:2.178146+0.520819
## [55] train-rmse:0.450296+0.021498 test-rmse:2.176310+0.520686
## [56] train-rmse:0.438395+0.018453 test-rmse:2.179174+0.522228
## Stopping. Best iteration:
## [50] train-rmse:0.496348+0.022914 test-rmse:2.175169+0.523890
##
## 81
## [1] train-rmse:5.953084+0.116924 test-rmse:5.989284+0.545674
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.442407+0.086882 test-rmse:4.589337+0.462092
## [3] train-rmse:3.466519+0.068847 test-rmse:3.777814+0.443144
## [4] train-rmse:2.819646+0.059843 test-rmse:3.290062+0.427409
## [5] train-rmse:2.385736+0.052110 test-rmse:3.001219+0.503987
## [6] train-rmse:2.083462+0.065209 test-rmse:2.840872+0.491270
## [7] train-rmse:1.853174+0.056359 test-rmse:2.696515+0.506714
## [8] train-rmse:1.706597+0.057758 test-rmse:2.618051+0.503100
## [9] train-rmse:1.560463+0.077449 test-rmse:2.586950+0.508754
## [10] train-rmse:1.473147+0.075164 test-rmse:2.549518+0.527799
## [11] train-rmse:1.382980+0.069231 test-rmse:2.502665+0.519738
## [12] train-rmse:1.290967+0.049930 test-rmse:2.483564+0.532124
## [13] train-rmse:1.229340+0.049483 test-rmse:2.473062+0.542216
## [14] train-rmse:1.164904+0.060702 test-rmse:2.441055+0.540423
## [15] train-rmse:1.122622+0.063224 test-rmse:2.430268+0.539828
## [16] train-rmse:1.063622+0.055711 test-rmse:2.419163+0.561920
## [17] train-rmse:1.019729+0.057001 test-rmse:2.418869+0.566679
## [18] train-rmse:0.957455+0.042460 test-rmse:2.416889+0.571174
## [19] train-rmse:0.919058+0.041843 test-rmse:2.409096+0.577196
## [20] train-rmse:0.887362+0.046039 test-rmse:2.399654+0.573899
## [21] train-rmse:0.854605+0.044307 test-rmse:2.391789+0.565146
## [22] train-rmse:0.820700+0.038303 test-rmse:2.385872+0.574623
## [23] train-rmse:0.783529+0.037221 test-rmse:2.377561+0.581329
## [24] train-rmse:0.759142+0.038258 test-rmse:2.370058+0.586308
## [25] train-rmse:0.729926+0.048549 test-rmse:2.354491+0.581150
## [26] train-rmse:0.702246+0.046105 test-rmse:2.353566+0.585412
## [27] train-rmse:0.677489+0.050275 test-rmse:2.354998+0.582078
## [28] train-rmse:0.645155+0.042573 test-rmse:2.348162+0.583936
## [29] train-rmse:0.627052+0.045783 test-rmse:2.340344+0.581364
## [30] train-rmse:0.605442+0.035890 test-rmse:2.343547+0.586086
## [31] train-rmse:0.591949+0.035832 test-rmse:2.340543+0.588080
## [32] train-rmse:0.575655+0.033360 test-rmse:2.335633+0.589955
## [33] train-rmse:0.550186+0.040147 test-rmse:2.336748+0.592881
## [34] train-rmse:0.526316+0.036786 test-rmse:2.331213+0.594956
## [35] train-rmse:0.508228+0.039131 test-rmse:2.328169+0.598954
## [36] train-rmse:0.495843+0.036344 test-rmse:2.326967+0.597823
## [37] train-rmse:0.483760+0.040021 test-rmse:2.327715+0.598866
## [38] train-rmse:0.468003+0.038031 test-rmse:2.319894+0.599852
## [39] train-rmse:0.455220+0.039297 test-rmse:2.314333+0.601286
## [40] train-rmse:0.443894+0.039236 test-rmse:2.311902+0.602516
## [41] train-rmse:0.427765+0.036791 test-rmse:2.313415+0.605507
## [42] train-rmse:0.412121+0.036782 test-rmse:2.311736+0.605983
## [43] train-rmse:0.400382+0.039201 test-rmse:2.309820+0.609241
## [44] train-rmse:0.385544+0.043840 test-rmse:2.304464+0.613310
## [45] train-rmse:0.375710+0.043534 test-rmse:2.305431+0.617116
## [46] train-rmse:0.362991+0.039192 test-rmse:2.302774+0.618408
## [47] train-rmse:0.354623+0.040179 test-rmse:2.302853+0.616447
## [48] train-rmse:0.342368+0.042534 test-rmse:2.302695+0.618615
## [49] train-rmse:0.335982+0.043575 test-rmse:2.302994+0.619333
## [50] train-rmse:0.328150+0.045793 test-rmse:2.300477+0.619946
## [51] train-rmse:0.319217+0.046728 test-rmse:2.299721+0.621467
## [52] train-rmse:0.307405+0.044763 test-rmse:2.296325+0.621676
## [53] train-rmse:0.299177+0.044169 test-rmse:2.294380+0.622928
## [54] train-rmse:0.292529+0.044881 test-rmse:2.293435+0.624756
## [55] train-rmse:0.283332+0.044691 test-rmse:2.291749+0.624434
## [56] train-rmse:0.272885+0.042542 test-rmse:2.292145+0.623691
## [57] train-rmse:0.265519+0.040972 test-rmse:2.291667+0.624402
## [58] train-rmse:0.258032+0.039928 test-rmse:2.292738+0.626058
## [59] train-rmse:0.251012+0.037387 test-rmse:2.291685+0.626235
## [60] train-rmse:0.243896+0.038410 test-rmse:2.291240+0.627602
## [61] train-rmse:0.237675+0.035170 test-rmse:2.292183+0.627021
## [62] train-rmse:0.231820+0.035400 test-rmse:2.291652+0.626599
## [63] train-rmse:0.228243+0.035927 test-rmse:2.291434+0.627032
## [64] train-rmse:0.221267+0.033463 test-rmse:2.289291+0.627470
## [65] train-rmse:0.215040+0.031971 test-rmse:2.288277+0.628121
## [66] train-rmse:0.210309+0.033278 test-rmse:2.287827+0.628845
## [67] train-rmse:0.204241+0.033705 test-rmse:2.289534+0.629424
## [68] train-rmse:0.199605+0.032138 test-rmse:2.289412+0.629522
## [69] train-rmse:0.194549+0.033146 test-rmse:2.288280+0.630063
## [70] train-rmse:0.187531+0.030892 test-rmse:2.286556+0.630677
## [71] train-rmse:0.183146+0.030452 test-rmse:2.285495+0.630675
## [72] train-rmse:0.176922+0.030230 test-rmse:2.285539+0.629841
## [73] train-rmse:0.169921+0.030385 test-rmse:2.285675+0.630497
## [74] train-rmse:0.165454+0.028400 test-rmse:2.285741+0.630425
## [75] train-rmse:0.162263+0.028537 test-rmse:2.285999+0.631293
## [76] train-rmse:0.157770+0.028325 test-rmse:2.285829+0.631354
## [77] train-rmse:0.153460+0.029469 test-rmse:2.285306+0.630960
## [78] train-rmse:0.148910+0.028719 test-rmse:2.285452+0.630827
## [79] train-rmse:0.144453+0.028018 test-rmse:2.286238+0.630800
## [80] train-rmse:0.141427+0.027537 test-rmse:2.285342+0.630588
## [81] train-rmse:0.137320+0.025018 test-rmse:2.286438+0.630825
## [82] train-rmse:0.135339+0.025021 test-rmse:2.286672+0.631140
## [83] train-rmse:0.131411+0.022542 test-rmse:2.286887+0.630680
## Stopping. Best iteration:
## [77] train-rmse:0.153460+0.029469 test-rmse:2.285306+0.630960
##
## 82
## [1] train-rmse:5.912009+0.109948 test-rmse:5.964302+0.583969
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.373770+0.075732 test-rmse:4.596368+0.480254
## [3] train-rmse:3.348215+0.041786 test-rmse:3.740059+0.461112
## [4] train-rmse:2.689438+0.028100 test-rmse:3.257480+0.488619
## [5] train-rmse:2.258582+0.028491 test-rmse:2.975851+0.512920
## [6] train-rmse:1.897324+0.045276 test-rmse:2.757454+0.525526
## [7] train-rmse:1.675749+0.052034 test-rmse:2.684120+0.520464
## [8] train-rmse:1.513901+0.053951 test-rmse:2.610266+0.545766
## [9] train-rmse:1.387497+0.047458 test-rmse:2.532152+0.541832
## [10] train-rmse:1.282042+0.047654 test-rmse:2.494235+0.555510
## [11] train-rmse:1.192874+0.048413 test-rmse:2.477591+0.568430
## [12] train-rmse:1.123464+0.050719 test-rmse:2.465118+0.576300
## [13] train-rmse:1.058898+0.064667 test-rmse:2.446116+0.585173
## [14] train-rmse:0.983316+0.064090 test-rmse:2.433843+0.576303
## [15] train-rmse:0.939366+0.059230 test-rmse:2.422073+0.577934
## [16] train-rmse:0.899684+0.057729 test-rmse:2.409625+0.585583
## [17] train-rmse:0.848518+0.054662 test-rmse:2.404123+0.589196
## [18] train-rmse:0.811145+0.049385 test-rmse:2.384518+0.591811
## [19] train-rmse:0.764362+0.050054 test-rmse:2.385589+0.587915
## [20] train-rmse:0.723633+0.050468 test-rmse:2.374997+0.593466
## [21] train-rmse:0.679844+0.025452 test-rmse:2.370308+0.597670
## [22] train-rmse:0.640197+0.026566 test-rmse:2.370190+0.590135
## [23] train-rmse:0.613079+0.022260 test-rmse:2.359238+0.594294
## [24] train-rmse:0.583221+0.017805 test-rmse:2.346409+0.598824
## [25] train-rmse:0.551019+0.015462 test-rmse:2.345525+0.599633
## [26] train-rmse:0.525165+0.011906 test-rmse:2.349516+0.603355
## [27] train-rmse:0.499046+0.009135 test-rmse:2.344403+0.606041
## [28] train-rmse:0.471159+0.011845 test-rmse:2.341939+0.606012
## [29] train-rmse:0.450140+0.012509 test-rmse:2.336376+0.609105
## [30] train-rmse:0.431030+0.016602 test-rmse:2.333189+0.605110
## [31] train-rmse:0.417918+0.018311 test-rmse:2.332856+0.605576
## [32] train-rmse:0.394807+0.013652 test-rmse:2.333413+0.610408
## [33] train-rmse:0.381503+0.016028 test-rmse:2.330612+0.609704
## [34] train-rmse:0.364487+0.018291 test-rmse:2.329016+0.610876
## [35] train-rmse:0.346722+0.020780 test-rmse:2.327329+0.606871
## [36] train-rmse:0.331190+0.024950 test-rmse:2.325011+0.608749
## [37] train-rmse:0.316983+0.022856 test-rmse:2.323286+0.607479
## [38] train-rmse:0.305452+0.020311 test-rmse:2.323501+0.606340
## [39] train-rmse:0.293064+0.013899 test-rmse:2.323253+0.606156
## [40] train-rmse:0.273302+0.012006 test-rmse:2.322457+0.607200
## [41] train-rmse:0.260826+0.011055 test-rmse:2.320089+0.607455
## [42] train-rmse:0.250402+0.010431 test-rmse:2.321936+0.608312
## [43] train-rmse:0.240410+0.010123 test-rmse:2.322839+0.607680
## [44] train-rmse:0.228863+0.009113 test-rmse:2.321855+0.608634
## [45] train-rmse:0.221440+0.006597 test-rmse:2.320500+0.610644
## [46] train-rmse:0.212789+0.009171 test-rmse:2.320329+0.611132
## [47] train-rmse:0.201683+0.007082 test-rmse:2.318959+0.611610
## [48] train-rmse:0.195035+0.006078 test-rmse:2.318029+0.612353
## [49] train-rmse:0.188715+0.006621 test-rmse:2.316102+0.611053
## [50] train-rmse:0.180588+0.006537 test-rmse:2.314795+0.611300
## [51] train-rmse:0.174998+0.006287 test-rmse:2.314081+0.611655
## [52] train-rmse:0.165493+0.008300 test-rmse:2.316286+0.612406
## [53] train-rmse:0.155510+0.008493 test-rmse:2.314239+0.612913
## [54] train-rmse:0.150545+0.009629 test-rmse:2.313855+0.613357
## [55] train-rmse:0.146148+0.009746 test-rmse:2.314602+0.614734
## [56] train-rmse:0.140890+0.010114 test-rmse:2.314618+0.615373
## [57] train-rmse:0.134235+0.010217 test-rmse:2.313581+0.614759
## [58] train-rmse:0.130852+0.010017 test-rmse:2.313505+0.615485
## [59] train-rmse:0.125559+0.010234 test-rmse:2.312850+0.615427
## [60] train-rmse:0.120377+0.011167 test-rmse:2.312166+0.615708
## [61] train-rmse:0.115579+0.012217 test-rmse:2.311813+0.617019
## [62] train-rmse:0.111577+0.011315 test-rmse:2.311629+0.616818
## [63] train-rmse:0.108473+0.011406 test-rmse:2.311637+0.616455
## [64] train-rmse:0.105182+0.011081 test-rmse:2.311028+0.616252
## [65] train-rmse:0.100336+0.011784 test-rmse:2.310738+0.617258
## [66] train-rmse:0.097447+0.011338 test-rmse:2.310864+0.617417
## [67] train-rmse:0.094062+0.010312 test-rmse:2.310742+0.617448
## [68] train-rmse:0.090745+0.009364 test-rmse:2.310154+0.617200
## [69] train-rmse:0.087911+0.008837 test-rmse:2.309852+0.618496
## [70] train-rmse:0.084593+0.008268 test-rmse:2.309330+0.619201
## [71] train-rmse:0.081220+0.007563 test-rmse:2.309522+0.618971
## [72] train-rmse:0.079293+0.007611 test-rmse:2.309484+0.619059
## [73] train-rmse:0.077049+0.007127 test-rmse:2.309594+0.619631
## [74] train-rmse:0.074387+0.006810 test-rmse:2.310111+0.619391
## [75] train-rmse:0.071509+0.006440 test-rmse:2.309440+0.619202
## [76] train-rmse:0.068246+0.007016 test-rmse:2.309160+0.619003
## [77] train-rmse:0.066269+0.006886 test-rmse:2.309573+0.618914
## [78] train-rmse:0.064077+0.006257 test-rmse:2.309225+0.619013
## [79] train-rmse:0.062074+0.006548 test-rmse:2.309238+0.618980
## [80] train-rmse:0.060559+0.006872 test-rmse:2.309997+0.619373
## [81] train-rmse:0.058975+0.006715 test-rmse:2.310023+0.619448
## [82] train-rmse:0.056229+0.006940 test-rmse:2.310330+0.619455
## Stopping. Best iteration:
## [76] train-rmse:0.068246+0.007016 test-rmse:2.309160+0.619003
##
## 83
## [1] train-rmse:5.897381+0.108690 test-rmse:5.955104+0.583608
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.326158+0.066152 test-rmse:4.563501+0.499676
## [3] train-rmse:3.282441+0.042109 test-rmse:3.685376+0.481236
## [4] train-rmse:2.580196+0.035548 test-rmse:3.203017+0.470335
## [5] train-rmse:2.106816+0.034433 test-rmse:2.913691+0.507415
## [6] train-rmse:1.782385+0.031384 test-rmse:2.711689+0.524151
## [7] train-rmse:1.531420+0.040872 test-rmse:2.631895+0.516670
## [8] train-rmse:1.375508+0.041668 test-rmse:2.570713+0.537026
## [9] train-rmse:1.222311+0.037190 test-rmse:2.508234+0.541518
## [10] train-rmse:1.123082+0.027388 test-rmse:2.466122+0.557043
## [11] train-rmse:1.034499+0.034550 test-rmse:2.440597+0.576658
## [12] train-rmse:0.953125+0.035270 test-rmse:2.423875+0.573499
## [13] train-rmse:0.889292+0.041633 test-rmse:2.400730+0.584354
## [14] train-rmse:0.826033+0.045702 test-rmse:2.389435+0.585004
## [15] train-rmse:0.757872+0.031405 test-rmse:2.375741+0.600722
## [16] train-rmse:0.704846+0.022828 test-rmse:2.371727+0.606807
## [17] train-rmse:0.662102+0.032971 test-rmse:2.368928+0.610679
## [18] train-rmse:0.617141+0.039242 test-rmse:2.365440+0.612716
## [19] train-rmse:0.577241+0.039233 test-rmse:2.357184+0.618029
## [20] train-rmse:0.543789+0.023191 test-rmse:2.355567+0.618640
## [21] train-rmse:0.509670+0.024385 test-rmse:2.343790+0.617675
## [22] train-rmse:0.484802+0.029237 test-rmse:2.339497+0.616706
## [23] train-rmse:0.456159+0.028517 test-rmse:2.335965+0.620202
## [24] train-rmse:0.418740+0.030723 test-rmse:2.335977+0.624133
## [25] train-rmse:0.395661+0.032630 test-rmse:2.332054+0.623684
## [26] train-rmse:0.371378+0.031888 test-rmse:2.329022+0.626209
## [27] train-rmse:0.351852+0.028132 test-rmse:2.324672+0.629050
## [28] train-rmse:0.336238+0.031080 test-rmse:2.322546+0.629197
## [29] train-rmse:0.314712+0.031197 test-rmse:2.319444+0.630224
## [30] train-rmse:0.300316+0.029156 test-rmse:2.319946+0.631922
## [31] train-rmse:0.286312+0.027654 test-rmse:2.319964+0.631864
## [32] train-rmse:0.271554+0.027091 test-rmse:2.319744+0.631130
## [33] train-rmse:0.255333+0.027834 test-rmse:2.317343+0.630234
## [34] train-rmse:0.240635+0.024267 test-rmse:2.315364+0.631141
## [35] train-rmse:0.230136+0.023245 test-rmse:2.315584+0.630209
## [36] train-rmse:0.219940+0.023554 test-rmse:2.312649+0.631878
## [37] train-rmse:0.207271+0.022518 test-rmse:2.309882+0.630729
## [38] train-rmse:0.197226+0.024084 test-rmse:2.308786+0.630915
## [39] train-rmse:0.189544+0.023944 test-rmse:2.310096+0.631336
## [40] train-rmse:0.181722+0.023411 test-rmse:2.310486+0.629629
## [41] train-rmse:0.169179+0.022488 test-rmse:2.309259+0.628422
## [42] train-rmse:0.162588+0.020542 test-rmse:2.307658+0.629016
## [43] train-rmse:0.154161+0.020308 test-rmse:2.309190+0.629249
## [44] train-rmse:0.149082+0.021399 test-rmse:2.307548+0.629511
## [45] train-rmse:0.139583+0.018247 test-rmse:2.306329+0.633606
## [46] train-rmse:0.132611+0.016156 test-rmse:2.304972+0.634391
## [47] train-rmse:0.127134+0.016064 test-rmse:2.305265+0.634748
## [48] train-rmse:0.122271+0.014193 test-rmse:2.305682+0.633925
## [49] train-rmse:0.115225+0.013621 test-rmse:2.305307+0.635300
## [50] train-rmse:0.110588+0.013394 test-rmse:2.305869+0.635464
## [51] train-rmse:0.107019+0.013453 test-rmse:2.306076+0.635546
## [52] train-rmse:0.102201+0.013723 test-rmse:2.307182+0.636163
## Stopping. Best iteration:
## [46] train-rmse:0.132611+0.016156 test-rmse:2.304972+0.634391
##
## 84
## [1] train-rmse:6.258348+0.137319 test-rmse:6.265335+0.646059
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.107018+0.108807 test-rmse:5.191316+0.683026
## [3] train-rmse:4.445692+0.115397 test-rmse:4.536440+0.581968
## [4] train-rmse:4.065281+0.115025 test-rmse:4.169625+0.526251
## [5] train-rmse:3.823033+0.115091 test-rmse:3.958753+0.449597
## [6] train-rmse:3.654032+0.108255 test-rmse:3.793918+0.438675
## [7] train-rmse:3.521724+0.109398 test-rmse:3.610125+0.449892
## [8] train-rmse:3.413172+0.121018 test-rmse:3.541989+0.409317
## [9] train-rmse:3.328456+0.123957 test-rmse:3.477147+0.397829
## [10] train-rmse:3.253016+0.122198 test-rmse:3.430699+0.428206
## [11] train-rmse:3.181662+0.120097 test-rmse:3.378793+0.474113
## [12] train-rmse:3.117480+0.118547 test-rmse:3.279072+0.464019
## [13] train-rmse:3.059257+0.122987 test-rmse:3.194845+0.433072
## [14] train-rmse:3.006950+0.127629 test-rmse:3.138755+0.425193
## [15] train-rmse:2.960850+0.131956 test-rmse:3.141335+0.435132
## [16] train-rmse:2.919943+0.132134 test-rmse:3.109013+0.454885
## [17] train-rmse:2.879010+0.132256 test-rmse:3.085585+0.456963
## [18] train-rmse:2.840310+0.131932 test-rmse:3.024772+0.436953
## [19] train-rmse:2.805456+0.133998 test-rmse:3.006502+0.450778
## [20] train-rmse:2.771163+0.135162 test-rmse:3.002704+0.469816
## [21] train-rmse:2.739160+0.137084 test-rmse:2.933256+0.471067
## [22] train-rmse:2.708613+0.138803 test-rmse:2.904252+0.469003
## [23] train-rmse:2.678978+0.140442 test-rmse:2.898513+0.475937
## [24] train-rmse:2.650575+0.141534 test-rmse:2.868100+0.463156
## [25] train-rmse:2.622214+0.143366 test-rmse:2.861271+0.462198
## [26] train-rmse:2.596828+0.143112 test-rmse:2.840869+0.460915
## [27] train-rmse:2.572379+0.141442 test-rmse:2.811849+0.480914
## [28] train-rmse:2.548990+0.140591 test-rmse:2.791906+0.486374
## [29] train-rmse:2.527041+0.141038 test-rmse:2.772669+0.479916
## [30] train-rmse:2.506267+0.140887 test-rmse:2.767084+0.477516
## [31] train-rmse:2.486393+0.140819 test-rmse:2.743061+0.499542
## [32] train-rmse:2.467146+0.141185 test-rmse:2.725522+0.490499
## [33] train-rmse:2.448319+0.141378 test-rmse:2.722014+0.497485
## [34] train-rmse:2.430014+0.142025 test-rmse:2.715744+0.481956
## [35] train-rmse:2.412208+0.142618 test-rmse:2.680480+0.474790
## [36] train-rmse:2.395074+0.142788 test-rmse:2.665949+0.498912
## [37] train-rmse:2.378843+0.143128 test-rmse:2.656935+0.496824
## [38] train-rmse:2.363723+0.144323 test-rmse:2.646014+0.495460
## [39] train-rmse:2.348623+0.144883 test-rmse:2.628523+0.492493
## [40] train-rmse:2.334837+0.144887 test-rmse:2.613590+0.502069
## [41] train-rmse:2.321760+0.145201 test-rmse:2.597450+0.509954
## [42] train-rmse:2.309489+0.145556 test-rmse:2.590496+0.511890
## [43] train-rmse:2.297633+0.146047 test-rmse:2.579734+0.499603
## [44] train-rmse:2.286226+0.146410 test-rmse:2.571801+0.498004
## [45] train-rmse:2.274946+0.146642 test-rmse:2.559359+0.501929
## [46] train-rmse:2.263842+0.146974 test-rmse:2.555782+0.504131
## [47] train-rmse:2.253020+0.147308 test-rmse:2.536848+0.501961
## [48] train-rmse:2.242375+0.147899 test-rmse:2.522934+0.501159
## [49] train-rmse:2.232096+0.148200 test-rmse:2.513132+0.508464
## [50] train-rmse:2.222471+0.148538 test-rmse:2.513325+0.512426
## [51] train-rmse:2.213109+0.149184 test-rmse:2.495395+0.508822
## [52] train-rmse:2.204086+0.149583 test-rmse:2.492616+0.524155
## [53] train-rmse:2.195377+0.150098 test-rmse:2.497844+0.533826
## [54] train-rmse:2.186720+0.150442 test-rmse:2.480452+0.530161
## [55] train-rmse:2.178408+0.150700 test-rmse:2.478961+0.528257
## [56] train-rmse:2.170334+0.151050 test-rmse:2.473680+0.525844
## [57] train-rmse:2.162327+0.151187 test-rmse:2.465949+0.522652
## [58] train-rmse:2.155024+0.151614 test-rmse:2.468296+0.518284
## [59] train-rmse:2.147896+0.152064 test-rmse:2.456452+0.523278
## [60] train-rmse:2.141072+0.152430 test-rmse:2.451838+0.523817
## [61] train-rmse:2.134180+0.152783 test-rmse:2.457161+0.528311
## [62] train-rmse:2.127709+0.153034 test-rmse:2.452097+0.535088
## [63] train-rmse:2.121320+0.153397 test-rmse:2.446900+0.526758
## [64] train-rmse:2.114900+0.153709 test-rmse:2.442429+0.519429
## [65] train-rmse:2.108757+0.154032 test-rmse:2.436136+0.519186
## [66] train-rmse:2.102870+0.154253 test-rmse:2.432714+0.522910
## [67] train-rmse:2.097021+0.154414 test-rmse:2.430584+0.524967
## [68] train-rmse:2.091362+0.154628 test-rmse:2.430951+0.527621
## [69] train-rmse:2.085831+0.154798 test-rmse:2.426568+0.523956
## [70] train-rmse:2.080199+0.154943 test-rmse:2.418467+0.524784
## [71] train-rmse:2.074701+0.155337 test-rmse:2.411808+0.522341
## [72] train-rmse:2.069476+0.155489 test-rmse:2.404583+0.528196
## [73] train-rmse:2.064441+0.155782 test-rmse:2.399679+0.530235
## [74] train-rmse:2.059392+0.155945 test-rmse:2.395619+0.530838
## [75] train-rmse:2.054739+0.156166 test-rmse:2.393531+0.532165
## [76] train-rmse:2.050290+0.156511 test-rmse:2.395481+0.543763
## [77] train-rmse:2.045875+0.156843 test-rmse:2.396326+0.549438
## [78] train-rmse:2.041654+0.156964 test-rmse:2.391427+0.549853
## [79] train-rmse:2.037486+0.157155 test-rmse:2.394164+0.547807
## [80] train-rmse:2.033410+0.157256 test-rmse:2.387442+0.545659
## [81] train-rmse:2.029281+0.157424 test-rmse:2.388309+0.550894
## [82] train-rmse:2.025105+0.157482 test-rmse:2.388633+0.544955
## [83] train-rmse:2.021074+0.157618 test-rmse:2.388501+0.544321
## [84] train-rmse:2.017095+0.157770 test-rmse:2.382600+0.544864
## [85] train-rmse:2.013259+0.157846 test-rmse:2.377701+0.544815
## [86] train-rmse:2.009575+0.157865 test-rmse:2.377826+0.543533
## [87] train-rmse:2.005783+0.158016 test-rmse:2.376376+0.535560
## [88] train-rmse:2.002180+0.158007 test-rmse:2.368255+0.540453
## [89] train-rmse:1.998809+0.158063 test-rmse:2.368332+0.539629
## [90] train-rmse:1.995241+0.158019 test-rmse:2.371422+0.544187
## [91] train-rmse:1.991604+0.158201 test-rmse:2.366389+0.543849
## [92] train-rmse:1.988113+0.158243 test-rmse:2.360391+0.540686
## [93] train-rmse:1.984792+0.158368 test-rmse:2.361631+0.540180
## [94] train-rmse:1.981365+0.158589 test-rmse:2.365420+0.543050
## [95] train-rmse:1.978072+0.158857 test-rmse:2.359799+0.544659
## [96] train-rmse:1.974727+0.159118 test-rmse:2.356663+0.543088
## [97] train-rmse:1.971484+0.159281 test-rmse:2.356644+0.545054
## [98] train-rmse:1.968279+0.159500 test-rmse:2.352332+0.544667
## [99] train-rmse:1.965183+0.159692 test-rmse:2.350227+0.541494
## [100] train-rmse:1.962088+0.159837 test-rmse:2.348399+0.542492
## [101] train-rmse:1.958914+0.160293 test-rmse:2.343142+0.536956
## [102] train-rmse:1.955992+0.160444 test-rmse:2.340536+0.537086
## [103] train-rmse:1.953140+0.160610 test-rmse:2.337815+0.535845
## [104] train-rmse:1.950128+0.160711 test-rmse:2.336531+0.540399
## [105] train-rmse:1.947262+0.161118 test-rmse:2.335328+0.543872
## [106] train-rmse:1.944431+0.161303 test-rmse:2.331884+0.543481
## [107] train-rmse:1.941643+0.161395 test-rmse:2.331738+0.541868
## [108] train-rmse:1.938826+0.161516 test-rmse:2.330568+0.545597
## [109] train-rmse:1.935915+0.161757 test-rmse:2.333501+0.540329
## [110] train-rmse:1.933373+0.161826 test-rmse:2.333565+0.533911
## [111] train-rmse:1.930879+0.161903 test-rmse:2.330359+0.533802
## [112] train-rmse:1.928391+0.162120 test-rmse:2.327794+0.534951
## [113] train-rmse:1.925884+0.162261 test-rmse:2.331761+0.535964
## [114] train-rmse:1.923450+0.162444 test-rmse:2.331960+0.536398
## [115] train-rmse:1.921062+0.162561 test-rmse:2.332668+0.538587
## [116] train-rmse:1.918607+0.162618 test-rmse:2.332241+0.542422
## [117] train-rmse:1.916152+0.162789 test-rmse:2.333894+0.551395
## [118] train-rmse:1.913539+0.162719 test-rmse:2.327696+0.551890
## [119] train-rmse:1.911261+0.162728 test-rmse:2.324768+0.552713
## [120] train-rmse:1.908965+0.162747 test-rmse:2.323758+0.548149
## [121] train-rmse:1.906728+0.162780 test-rmse:2.320685+0.548921
## [122] train-rmse:1.904459+0.162900 test-rmse:2.317743+0.549075
## [123] train-rmse:1.901999+0.162880 test-rmse:2.317901+0.549324
## [124] train-rmse:1.899872+0.162939 test-rmse:2.314546+0.548078
## [125] train-rmse:1.897745+0.162996 test-rmse:2.312169+0.547630
## [126] train-rmse:1.895545+0.163022 test-rmse:2.311424+0.547827
## [127] train-rmse:1.893210+0.163357 test-rmse:2.309380+0.554353
## [128] train-rmse:1.891095+0.163448 test-rmse:2.309217+0.551184
## [129] train-rmse:1.889095+0.163526 test-rmse:2.306977+0.552391
## [130] train-rmse:1.887053+0.163547 test-rmse:2.307193+0.553880
## [131] train-rmse:1.885017+0.163617 test-rmse:2.312939+0.548161
## [132] train-rmse:1.883006+0.163725 test-rmse:2.311848+0.546188
## [133] train-rmse:1.880970+0.163836 test-rmse:2.311475+0.545237
## [134] train-rmse:1.879040+0.163980 test-rmse:2.311222+0.542417
## [135] train-rmse:1.877092+0.164048 test-rmse:2.310688+0.540527
## Stopping. Best iteration:
## [129] train-rmse:1.889095+0.163526 test-rmse:2.306977+0.552391
##
## 85
## [1] train-rmse:6.098036+0.126702 test-rmse:6.154623+0.598390
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.806645+0.121511 test-rmse:4.899701+0.561781
## [3] train-rmse:3.990774+0.091815 test-rmse:4.112025+0.474889
## [4] train-rmse:3.466699+0.086599 test-rmse:3.672438+0.458029
## [5] train-rmse:3.145123+0.099689 test-rmse:3.425164+0.429134
## [6] train-rmse:2.899085+0.102578 test-rmse:3.173734+0.445520
## [7] train-rmse:2.727567+0.085864 test-rmse:3.053257+0.510412
## [8] train-rmse:2.604248+0.099051 test-rmse:2.952427+0.485778
## [9] train-rmse:2.504931+0.098657 test-rmse:2.903762+0.486023
## [10] train-rmse:2.414698+0.103975 test-rmse:2.836096+0.488037
## [11] train-rmse:2.327426+0.108951 test-rmse:2.779535+0.487322
## [12] train-rmse:2.262892+0.109316 test-rmse:2.718219+0.487299
## [13] train-rmse:2.191435+0.086714 test-rmse:2.667157+0.498384
## [14] train-rmse:2.140211+0.084510 test-rmse:2.637691+0.496459
## [15] train-rmse:2.090775+0.085503 test-rmse:2.611762+0.502532
## [16] train-rmse:2.045106+0.085850 test-rmse:2.574885+0.488754
## [17] train-rmse:1.999794+0.088025 test-rmse:2.546966+0.501427
## [18] train-rmse:1.962251+0.085993 test-rmse:2.527004+0.525151
## [19] train-rmse:1.914748+0.088561 test-rmse:2.515982+0.524830
## [20] train-rmse:1.877361+0.091274 test-rmse:2.492333+0.520551
## [21] train-rmse:1.833990+0.085131 test-rmse:2.465641+0.515073
## [22] train-rmse:1.804952+0.084359 test-rmse:2.457167+0.520335
## [23] train-rmse:1.774456+0.087040 test-rmse:2.442096+0.521919
## [24] train-rmse:1.748195+0.087691 test-rmse:2.414681+0.525332
## [25] train-rmse:1.718959+0.089073 test-rmse:2.412735+0.510279
## [26] train-rmse:1.695175+0.086907 test-rmse:2.395326+0.518186
## [27] train-rmse:1.653586+0.063228 test-rmse:2.366529+0.533003
## [28] train-rmse:1.631063+0.057290 test-rmse:2.379222+0.557534
## [29] train-rmse:1.606218+0.061817 test-rmse:2.369058+0.548998
## [30] train-rmse:1.579390+0.061410 test-rmse:2.356804+0.547557
## [31] train-rmse:1.561225+0.061804 test-rmse:2.344024+0.550546
## [32] train-rmse:1.541602+0.055920 test-rmse:2.326979+0.559401
## [33] train-rmse:1.522865+0.055906 test-rmse:2.321816+0.555797
## [34] train-rmse:1.509279+0.056096 test-rmse:2.318718+0.561489
## [35] train-rmse:1.489043+0.059849 test-rmse:2.310945+0.571135
## [36] train-rmse:1.475670+0.058874 test-rmse:2.307311+0.573430
## [37] train-rmse:1.462748+0.058451 test-rmse:2.294140+0.575133
## [38] train-rmse:1.443595+0.059218 test-rmse:2.296521+0.569417
## [39] train-rmse:1.431284+0.059803 test-rmse:2.293180+0.572885
## [40] train-rmse:1.408263+0.061072 test-rmse:2.275618+0.552771
## [41] train-rmse:1.392608+0.061149 test-rmse:2.261070+0.563175
## [42] train-rmse:1.372320+0.043183 test-rmse:2.243086+0.567283
## [43] train-rmse:1.361697+0.043822 test-rmse:2.245014+0.560146
## [44] train-rmse:1.347714+0.049341 test-rmse:2.233696+0.556986
## [45] train-rmse:1.328260+0.058221 test-rmse:2.231634+0.564329
## [46] train-rmse:1.313009+0.057202 test-rmse:2.232553+0.567816
## [47] train-rmse:1.303021+0.057592 test-rmse:2.222073+0.574018
## [48] train-rmse:1.294612+0.057508 test-rmse:2.220869+0.577880
## [49] train-rmse:1.279075+0.056071 test-rmse:2.217192+0.578462
## [50] train-rmse:1.266380+0.055820 test-rmse:2.220616+0.578649
## [51] train-rmse:1.255089+0.052913 test-rmse:2.219655+0.580410
## [52] train-rmse:1.239397+0.050687 test-rmse:2.214182+0.591833
## [53] train-rmse:1.228716+0.051388 test-rmse:2.207489+0.589982
## [54] train-rmse:1.214260+0.051190 test-rmse:2.209977+0.599269
## [55] train-rmse:1.204854+0.050221 test-rmse:2.208136+0.597479
## [56] train-rmse:1.194402+0.049378 test-rmse:2.205866+0.594473
## [57] train-rmse:1.186913+0.050227 test-rmse:2.207898+0.599299
## [58] train-rmse:1.178734+0.050058 test-rmse:2.205372+0.604740
## [59] train-rmse:1.166107+0.053463 test-rmse:2.203683+0.602686
## [60] train-rmse:1.157729+0.054752 test-rmse:2.203763+0.599625
## [61] train-rmse:1.141480+0.056987 test-rmse:2.209031+0.594728
## [62] train-rmse:1.129187+0.057888 test-rmse:2.213208+0.601514
## [63] train-rmse:1.121632+0.057387 test-rmse:2.202499+0.607576
## [64] train-rmse:1.109034+0.065277 test-rmse:2.208063+0.604101
## [65] train-rmse:1.103023+0.067173 test-rmse:2.205637+0.599115
## [66] train-rmse:1.097480+0.067518 test-rmse:2.204241+0.598090
## [67] train-rmse:1.091204+0.068465 test-rmse:2.197182+0.599667
## [68] train-rmse:1.086363+0.068531 test-rmse:2.197288+0.605334
## [69] train-rmse:1.078632+0.066207 test-rmse:2.193111+0.606439
## [70] train-rmse:1.072736+0.068972 test-rmse:2.188485+0.606759
## [71] train-rmse:1.065853+0.067465 test-rmse:2.190872+0.610852
## [72] train-rmse:1.053620+0.064405 test-rmse:2.185408+0.609616
## [73] train-rmse:1.044070+0.065239 test-rmse:2.186489+0.609974
## [74] train-rmse:1.039475+0.063617 test-rmse:2.186174+0.611749
## [75] train-rmse:1.033749+0.064395 test-rmse:2.190943+0.610747
## [76] train-rmse:1.028623+0.064145 test-rmse:2.188169+0.610021
## [77] train-rmse:1.015266+0.069043 test-rmse:2.185514+0.612565
## [78] train-rmse:1.003046+0.072006 test-rmse:2.179145+0.615548
## [79] train-rmse:0.991469+0.061817 test-rmse:2.171155+0.618384
## [80] train-rmse:0.986217+0.062192 test-rmse:2.163633+0.624572
## [81] train-rmse:0.980997+0.064450 test-rmse:2.164278+0.627206
## [82] train-rmse:0.976704+0.064566 test-rmse:2.164879+0.622812
## [83] train-rmse:0.967524+0.068266 test-rmse:2.166514+0.617289
## [84] train-rmse:0.961529+0.070417 test-rmse:2.164642+0.621259
## [85] train-rmse:0.954770+0.073880 test-rmse:2.168101+0.627272
## [86] train-rmse:0.950099+0.072032 test-rmse:2.164268+0.627197
## Stopping. Best iteration:
## [80] train-rmse:0.986217+0.062192 test-rmse:2.163633+0.624572
##
## 86
## [1] train-rmse:5.990009+0.117801 test-rmse:6.109752+0.582776
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.602781+0.088683 test-rmse:4.754133+0.532722
## [3] train-rmse:3.675899+0.096563 test-rmse:3.930270+0.401487
## [4] train-rmse:3.067499+0.056806 test-rmse:3.334836+0.438249
## [5] train-rmse:2.727643+0.082506 test-rmse:3.080883+0.407764
## [6] train-rmse:2.464599+0.073891 test-rmse:2.877985+0.425796
## [7] train-rmse:2.292133+0.066086 test-rmse:2.770847+0.438302
## [8] train-rmse:2.153923+0.066629 test-rmse:2.673402+0.434985
## [9] train-rmse:2.047428+0.069403 test-rmse:2.617572+0.428771
## [10] train-rmse:1.959158+0.074863 test-rmse:2.569652+0.435032
## [11] train-rmse:1.851630+0.066721 test-rmse:2.510427+0.431225
## [12] train-rmse:1.777273+0.060234 test-rmse:2.472373+0.450744
## [13] train-rmse:1.712530+0.070448 test-rmse:2.430446+0.430426
## [14] train-rmse:1.651712+0.062893 test-rmse:2.392013+0.439721
## [15] train-rmse:1.596837+0.069283 test-rmse:2.368427+0.450279
## [16] train-rmse:1.548852+0.066137 test-rmse:2.339065+0.440379
## [17] train-rmse:1.499998+0.064242 test-rmse:2.320038+0.447180
## [18] train-rmse:1.461627+0.060765 test-rmse:2.303864+0.445994
## [19] train-rmse:1.423532+0.065458 test-rmse:2.277214+0.459963
## [20] train-rmse:1.390673+0.070496 test-rmse:2.270502+0.457516
## [21] train-rmse:1.342865+0.056164 test-rmse:2.267194+0.456350
## [22] train-rmse:1.315428+0.062214 test-rmse:2.240329+0.438041
## [23] train-rmse:1.281959+0.062315 test-rmse:2.221110+0.441540
## [24] train-rmse:1.239542+0.067800 test-rmse:2.204387+0.450247
## [25] train-rmse:1.204175+0.062946 test-rmse:2.191898+0.455735
## [26] train-rmse:1.181406+0.063826 test-rmse:2.187795+0.456185
## [27] train-rmse:1.154326+0.058729 test-rmse:2.188280+0.474776
## [28] train-rmse:1.123764+0.053675 test-rmse:2.194437+0.458194
## [29] train-rmse:1.100637+0.052169 test-rmse:2.183191+0.456027
## [30] train-rmse:1.068409+0.054328 test-rmse:2.177448+0.459755
## [31] train-rmse:1.049158+0.054486 test-rmse:2.162583+0.467131
## [32] train-rmse:1.027295+0.047862 test-rmse:2.151082+0.472584
## [33] train-rmse:1.008823+0.053590 test-rmse:2.149008+0.470246
## [34] train-rmse:0.993934+0.056856 test-rmse:2.137604+0.467982
## [35] train-rmse:0.974832+0.059321 test-rmse:2.128767+0.471875
## [36] train-rmse:0.954469+0.056781 test-rmse:2.118576+0.473971
## [37] train-rmse:0.928660+0.045560 test-rmse:2.102384+0.474550
## [38] train-rmse:0.915074+0.043335 test-rmse:2.106291+0.470198
## [39] train-rmse:0.892091+0.049039 test-rmse:2.101934+0.478830
## [40] train-rmse:0.876836+0.045587 test-rmse:2.099251+0.476497
## [41] train-rmse:0.865331+0.045153 test-rmse:2.094253+0.479854
## [42] train-rmse:0.845864+0.041715 test-rmse:2.090414+0.479857
## [43] train-rmse:0.834466+0.044689 test-rmse:2.091519+0.478377
## [44] train-rmse:0.822056+0.040727 test-rmse:2.088962+0.477487
## [45] train-rmse:0.809733+0.041454 test-rmse:2.084195+0.476728
## [46] train-rmse:0.791515+0.039784 test-rmse:2.079740+0.483981
## [47] train-rmse:0.776410+0.036690 test-rmse:2.082077+0.480159
## [48] train-rmse:0.759944+0.030450 test-rmse:2.077942+0.480600
## [49] train-rmse:0.750151+0.030530 test-rmse:2.073589+0.483758
## [50] train-rmse:0.735267+0.037900 test-rmse:2.067159+0.485388
## [51] train-rmse:0.719669+0.043119 test-rmse:2.063252+0.483462
## [52] train-rmse:0.712810+0.043284 test-rmse:2.061272+0.482044
## [53] train-rmse:0.701105+0.042302 test-rmse:2.065817+0.485324
## [54] train-rmse:0.689349+0.039663 test-rmse:2.060814+0.483065
## [55] train-rmse:0.679605+0.042120 test-rmse:2.062053+0.485366
## [56] train-rmse:0.662825+0.037412 test-rmse:2.067409+0.485525
## [57] train-rmse:0.655581+0.036408 test-rmse:2.064497+0.485189
## [58] train-rmse:0.648187+0.033960 test-rmse:2.063665+0.488053
## [59] train-rmse:0.640831+0.033380 test-rmse:2.059281+0.490600
## [60] train-rmse:0.634720+0.034177 test-rmse:2.059407+0.490601
## [61] train-rmse:0.625348+0.035705 test-rmse:2.056188+0.490425
## [62] train-rmse:0.613528+0.031427 test-rmse:2.056930+0.488082
## [63] train-rmse:0.603388+0.031232 test-rmse:2.057595+0.486853
## [64] train-rmse:0.587576+0.029384 test-rmse:2.063854+0.490377
## [65] train-rmse:0.578883+0.029403 test-rmse:2.061120+0.488724
## [66] train-rmse:0.572214+0.028120 test-rmse:2.062262+0.485428
## [67] train-rmse:0.568982+0.028062 test-rmse:2.060856+0.486585
## Stopping. Best iteration:
## [61] train-rmse:0.625348+0.035705 test-rmse:2.056188+0.490425
##
## 87
## [1] train-rmse:5.887665+0.116545 test-rmse:5.975150+0.566910
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.393037+0.091246 test-rmse:4.598958+0.463657
## [3] train-rmse:3.445792+0.055726 test-rmse:3.758691+0.452329
## [4] train-rmse:2.847512+0.054150 test-rmse:3.256411+0.474657
## [5] train-rmse:2.450876+0.048054 test-rmse:2.951318+0.485297
## [6] train-rmse:2.192755+0.038559 test-rmse:2.789348+0.490540
## [7] train-rmse:1.998170+0.055314 test-rmse:2.673023+0.493047
## [8] train-rmse:1.853884+0.056093 test-rmse:2.572005+0.498302
## [9] train-rmse:1.740677+0.047892 test-rmse:2.507404+0.494013
## [10] train-rmse:1.640952+0.058209 test-rmse:2.486838+0.508594
## [11] train-rmse:1.539180+0.039545 test-rmse:2.473099+0.508135
## [12] train-rmse:1.470966+0.043954 test-rmse:2.449984+0.492919
## [13] train-rmse:1.407752+0.035285 test-rmse:2.417008+0.487883
## [14] train-rmse:1.342082+0.036068 test-rmse:2.373677+0.477715
## [15] train-rmse:1.291831+0.041334 test-rmse:2.360129+0.480440
## [16] train-rmse:1.239936+0.034208 test-rmse:2.345861+0.490462
## [17] train-rmse:1.198618+0.031524 test-rmse:2.329869+0.490056
## [18] train-rmse:1.157828+0.029691 test-rmse:2.311293+0.501614
## [19] train-rmse:1.110141+0.034133 test-rmse:2.294910+0.500009
## [20] train-rmse:1.077740+0.033970 test-rmse:2.283375+0.498149
## [21] train-rmse:1.037698+0.025202 test-rmse:2.277974+0.498982
## [22] train-rmse:1.005109+0.029493 test-rmse:2.266461+0.498869
## [23] train-rmse:0.973122+0.032783 test-rmse:2.261725+0.506073
## [24] train-rmse:0.945736+0.024887 test-rmse:2.250604+0.510470
## [25] train-rmse:0.917696+0.034139 test-rmse:2.242748+0.518337
## [26] train-rmse:0.883474+0.030854 test-rmse:2.219926+0.507335
## [27] train-rmse:0.856758+0.028559 test-rmse:2.214802+0.509250
## [28] train-rmse:0.828315+0.029657 test-rmse:2.202182+0.514474
## [29] train-rmse:0.805286+0.026199 test-rmse:2.207621+0.519459
## [30] train-rmse:0.783152+0.026155 test-rmse:2.202543+0.528455
## [31] train-rmse:0.754572+0.022893 test-rmse:2.198151+0.529396
## [32] train-rmse:0.736974+0.022647 test-rmse:2.198290+0.522981
## [33] train-rmse:0.720835+0.019501 test-rmse:2.195221+0.528097
## [34] train-rmse:0.699297+0.020436 test-rmse:2.195737+0.525634
## [35] train-rmse:0.680143+0.020493 test-rmse:2.194232+0.525526
## [36] train-rmse:0.663306+0.025325 test-rmse:2.183522+0.525968
## [37] train-rmse:0.649989+0.028261 test-rmse:2.179803+0.527418
## [38] train-rmse:0.631401+0.021155 test-rmse:2.180350+0.525173
## [39] train-rmse:0.618007+0.019730 test-rmse:2.179538+0.529095
## [40] train-rmse:0.595307+0.020338 test-rmse:2.171910+0.524340
## [41] train-rmse:0.583251+0.019446 test-rmse:2.172390+0.520090
## [42] train-rmse:0.571489+0.016096 test-rmse:2.171140+0.521343
## [43] train-rmse:0.553543+0.010494 test-rmse:2.170908+0.524469
## [44] train-rmse:0.543487+0.013867 test-rmse:2.169234+0.525408
## [45] train-rmse:0.529696+0.015085 test-rmse:2.173559+0.530587
## [46] train-rmse:0.514777+0.019024 test-rmse:2.170751+0.525704
## [47] train-rmse:0.501182+0.014950 test-rmse:2.169479+0.525669
## [48] train-rmse:0.488212+0.016670 test-rmse:2.159599+0.527137
## [49] train-rmse:0.476072+0.013222 test-rmse:2.159075+0.526631
## [50] train-rmse:0.466077+0.018837 test-rmse:2.163556+0.529054
## [51] train-rmse:0.455501+0.018258 test-rmse:2.160935+0.530805
## [52] train-rmse:0.439553+0.011391 test-rmse:2.160446+0.531188
## [53] train-rmse:0.429925+0.018204 test-rmse:2.155446+0.531581
## [54] train-rmse:0.416271+0.015415 test-rmse:2.154266+0.533595
## [55] train-rmse:0.406948+0.016016 test-rmse:2.153434+0.534567
## [56] train-rmse:0.396112+0.016516 test-rmse:2.154846+0.532699
## [57] train-rmse:0.389629+0.017416 test-rmse:2.152086+0.533477
## [58] train-rmse:0.382254+0.015574 test-rmse:2.151764+0.531628
## [59] train-rmse:0.375659+0.015306 test-rmse:2.151856+0.531605
## [60] train-rmse:0.364852+0.016512 test-rmse:2.153236+0.531620
## [61] train-rmse:0.359904+0.016450 test-rmse:2.152411+0.532172
## [62] train-rmse:0.350377+0.017900 test-rmse:2.152694+0.532008
## [63] train-rmse:0.342530+0.017086 test-rmse:2.150886+0.530333
## [64] train-rmse:0.335675+0.014614 test-rmse:2.150967+0.530117
## [65] train-rmse:0.330207+0.015116 test-rmse:2.150157+0.530262
## [66] train-rmse:0.324350+0.014494 test-rmse:2.151743+0.530296
## [67] train-rmse:0.317832+0.014777 test-rmse:2.152017+0.530958
## [68] train-rmse:0.310074+0.013099 test-rmse:2.151338+0.532175
## [69] train-rmse:0.304398+0.015326 test-rmse:2.151271+0.532747
## [70] train-rmse:0.300374+0.014665 test-rmse:2.149440+0.532998
## [71] train-rmse:0.293427+0.017625 test-rmse:2.149704+0.532370
## [72] train-rmse:0.286991+0.015706 test-rmse:2.149399+0.534304
## [73] train-rmse:0.280589+0.015205 test-rmse:2.147597+0.536112
## [74] train-rmse:0.275479+0.016563 test-rmse:2.147085+0.536135
## [75] train-rmse:0.271077+0.015302 test-rmse:2.147123+0.537072
## [76] train-rmse:0.263019+0.014692 test-rmse:2.148936+0.539551
## [77] train-rmse:0.256397+0.011357 test-rmse:2.144489+0.537846
## [78] train-rmse:0.250927+0.010846 test-rmse:2.143272+0.538126
## [79] train-rmse:0.245969+0.010249 test-rmse:2.143107+0.538730
## [80] train-rmse:0.241286+0.010593 test-rmse:2.138988+0.534225
## [81] train-rmse:0.236799+0.010958 test-rmse:2.137650+0.534351
## [82] train-rmse:0.230774+0.009444 test-rmse:2.135370+0.533927
## [83] train-rmse:0.227771+0.008635 test-rmse:2.136243+0.534854
## [84] train-rmse:0.223460+0.007950 test-rmse:2.134678+0.535049
## [85] train-rmse:0.218353+0.009278 test-rmse:2.136525+0.536429
## [86] train-rmse:0.214614+0.009442 test-rmse:2.137729+0.535922
## [87] train-rmse:0.211193+0.009386 test-rmse:2.136832+0.536679
## [88] train-rmse:0.208523+0.009336 test-rmse:2.136821+0.536348
## [89] train-rmse:0.202531+0.008075 test-rmse:2.137454+0.536137
## [90] train-rmse:0.197626+0.008537 test-rmse:2.137632+0.535066
## Stopping. Best iteration:
## [84] train-rmse:0.223460+0.007950 test-rmse:2.134678+0.535049
##
## 88
## [1] train-rmse:5.816938+0.113593 test-rmse:5.859651+0.536821
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.265359+0.083927 test-rmse:4.430262+0.453872
## [3] train-rmse:3.303932+0.062074 test-rmse:3.647927+0.448092
## [4] train-rmse:2.686322+0.064787 test-rmse:3.175680+0.463684
## [5] train-rmse:2.280472+0.047571 test-rmse:2.944715+0.529810
## [6] train-rmse:1.985083+0.059112 test-rmse:2.751999+0.534026
## [7] train-rmse:1.767921+0.054373 test-rmse:2.629759+0.560343
## [8] train-rmse:1.628301+0.067389 test-rmse:2.574780+0.552119
## [9] train-rmse:1.502199+0.048411 test-rmse:2.515469+0.564981
## [10] train-rmse:1.403624+0.042795 test-rmse:2.482563+0.557101
## [11] train-rmse:1.319831+0.035821 test-rmse:2.452568+0.566814
## [12] train-rmse:1.247433+0.027219 test-rmse:2.447064+0.564061
## [13] train-rmse:1.173204+0.037178 test-rmse:2.422561+0.542447
## [14] train-rmse:1.119880+0.046000 test-rmse:2.394241+0.551909
## [15] train-rmse:1.056815+0.048427 test-rmse:2.385766+0.560826
## [16] train-rmse:0.994032+0.035579 test-rmse:2.369099+0.564731
## [17] train-rmse:0.945325+0.042410 test-rmse:2.350500+0.555132
## [18] train-rmse:0.899367+0.038473 test-rmse:2.337051+0.564033
## [19] train-rmse:0.870265+0.038380 test-rmse:2.330822+0.563447
## [20] train-rmse:0.828740+0.027276 test-rmse:2.324638+0.567235
## [21] train-rmse:0.795777+0.027081 test-rmse:2.318999+0.573709
## [22] train-rmse:0.754560+0.023926 test-rmse:2.311142+0.578522
## [23] train-rmse:0.731509+0.028433 test-rmse:2.300848+0.580476
## [24] train-rmse:0.706153+0.032174 test-rmse:2.298767+0.575682
## [25] train-rmse:0.663499+0.020028 test-rmse:2.293705+0.578958
## [26] train-rmse:0.637430+0.017948 test-rmse:2.293889+0.583135
## [27] train-rmse:0.617832+0.017540 test-rmse:2.295367+0.582698
## [28] train-rmse:0.594172+0.020996 test-rmse:2.299398+0.586819
## [29] train-rmse:0.563825+0.025604 test-rmse:2.293892+0.580192
## [30] train-rmse:0.543975+0.024062 test-rmse:2.292861+0.581856
## [31] train-rmse:0.528137+0.024850 test-rmse:2.288944+0.581547
## [32] train-rmse:0.511506+0.032231 test-rmse:2.283510+0.576218
## [33] train-rmse:0.485078+0.035282 test-rmse:2.283550+0.579326
## [34] train-rmse:0.466132+0.029809 test-rmse:2.283697+0.578978
## [35] train-rmse:0.450942+0.032815 test-rmse:2.282252+0.581784
## [36] train-rmse:0.433283+0.030260 test-rmse:2.275856+0.583959
## [37] train-rmse:0.418767+0.032489 test-rmse:2.275171+0.581571
## [38] train-rmse:0.405962+0.030886 test-rmse:2.273861+0.581814
## [39] train-rmse:0.394269+0.030119 test-rmse:2.277324+0.581149
## [40] train-rmse:0.384107+0.027626 test-rmse:2.276304+0.581431
## [41] train-rmse:0.369908+0.022031 test-rmse:2.279484+0.583595
## [42] train-rmse:0.352264+0.023185 test-rmse:2.279223+0.583456
## [43] train-rmse:0.336260+0.024780 test-rmse:2.279792+0.584600
## [44] train-rmse:0.328671+0.022981 test-rmse:2.278602+0.583224
## Stopping. Best iteration:
## [38] train-rmse:0.405962+0.030886 test-rmse:2.273861+0.581814
##
## 89
## [1] train-rmse:5.772721+0.105898 test-rmse:5.833190+0.578057
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.190005+0.070889 test-rmse:4.438178+0.472560
## [3] train-rmse:3.173839+0.032762 test-rmse:3.604450+0.462355
## [4] train-rmse:2.527110+0.025218 test-rmse:3.167487+0.508070
## [5] train-rmse:2.104102+0.026616 test-rmse:2.897756+0.547119
## [6] train-rmse:1.798567+0.030294 test-rmse:2.766129+0.547237
## [7] train-rmse:1.602864+0.031185 test-rmse:2.651332+0.560323
## [8] train-rmse:1.456670+0.046904 test-rmse:2.582459+0.558598
## [9] train-rmse:1.334104+0.035049 test-rmse:2.549698+0.585999
## [10] train-rmse:1.202837+0.024005 test-rmse:2.516260+0.606063
## [11] train-rmse:1.132005+0.027236 test-rmse:2.484821+0.615412
## [12] train-rmse:1.060064+0.029096 test-rmse:2.465315+0.621444
## [13] train-rmse:1.004761+0.033673 test-rmse:2.459884+0.623321
## [14] train-rmse:0.939716+0.035419 test-rmse:2.444532+0.629493
## [15] train-rmse:0.879545+0.030350 test-rmse:2.424074+0.641318
## [16] train-rmse:0.829858+0.040261 test-rmse:2.402357+0.631019
## [17] train-rmse:0.779663+0.032714 test-rmse:2.402006+0.624330
## [18] train-rmse:0.733898+0.025705 test-rmse:2.390623+0.639498
## [19] train-rmse:0.696377+0.021498 test-rmse:2.389039+0.633230
## [20] train-rmse:0.660065+0.028922 test-rmse:2.385623+0.642815
## [21] train-rmse:0.625600+0.027242 test-rmse:2.376541+0.649890
## [22] train-rmse:0.588667+0.021643 test-rmse:2.373869+0.657701
## [23] train-rmse:0.558714+0.022908 test-rmse:2.364530+0.660189
## [24] train-rmse:0.529148+0.021129 test-rmse:2.362601+0.659207
## [25] train-rmse:0.504759+0.019815 test-rmse:2.355407+0.664778
## [26] train-rmse:0.483007+0.022379 test-rmse:2.351856+0.663027
## [27] train-rmse:0.463428+0.021872 test-rmse:2.352284+0.665065
## [28] train-rmse:0.432365+0.015219 test-rmse:2.355488+0.672727
## [29] train-rmse:0.415140+0.014944 test-rmse:2.352939+0.673762
## [30] train-rmse:0.397392+0.015878 test-rmse:2.353609+0.674810
## [31] train-rmse:0.372088+0.023283 test-rmse:2.353654+0.676042
## [32] train-rmse:0.357763+0.019980 test-rmse:2.355278+0.678412
## Stopping. Best iteration:
## [26] train-rmse:0.483007+0.022379 test-rmse:2.351856+0.663027
##
## 90
## [1] train-rmse:5.756959+0.104536 test-rmse:5.823078+0.577737
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.141547+0.065599 test-rmse:4.406630+0.488690
## [3] train-rmse:3.101539+0.043212 test-rmse:3.557647+0.489337
## [4] train-rmse:2.422264+0.039706 test-rmse:3.099901+0.500869
## [5] train-rmse:1.977510+0.035495 test-rmse:2.858321+0.504474
## [6] train-rmse:1.675442+0.024605 test-rmse:2.671049+0.542849
## [7] train-rmse:1.435245+0.042357 test-rmse:2.589790+0.512889
## [8] train-rmse:1.283277+0.039057 test-rmse:2.549258+0.533712
## [9] train-rmse:1.131869+0.036355 test-rmse:2.508589+0.551264
## [10] train-rmse:1.029947+0.045042 test-rmse:2.462805+0.544704
## [11] train-rmse:0.943579+0.048278 test-rmse:2.452017+0.538217
## [12] train-rmse:0.861446+0.051689 test-rmse:2.425530+0.556906
## [13] train-rmse:0.801353+0.047300 test-rmse:2.406223+0.575619
## [14] train-rmse:0.737982+0.027522 test-rmse:2.403857+0.561439
## [15] train-rmse:0.688864+0.032130 test-rmse:2.396574+0.571378
## [16] train-rmse:0.646079+0.038545 test-rmse:2.394646+0.569342
## [17] train-rmse:0.589952+0.037103 test-rmse:2.390508+0.570143
## [18] train-rmse:0.562991+0.031679 test-rmse:2.383490+0.577035
## [19] train-rmse:0.518941+0.036625 test-rmse:2.377424+0.574340
## [20] train-rmse:0.496262+0.032337 test-rmse:2.376111+0.571484
## [21] train-rmse:0.458209+0.033867 test-rmse:2.374749+0.575619
## [22] train-rmse:0.426567+0.035911 test-rmse:2.371193+0.572927
## [23] train-rmse:0.403670+0.035036 test-rmse:2.367275+0.574673
## [24] train-rmse:0.376773+0.031115 test-rmse:2.370433+0.578645
## [25] train-rmse:0.355803+0.031073 test-rmse:2.369350+0.577185
## [26] train-rmse:0.333438+0.034241 test-rmse:2.367772+0.574296
## [27] train-rmse:0.316680+0.025600 test-rmse:2.366412+0.577176
## [28] train-rmse:0.298956+0.023895 test-rmse:2.362585+0.578853
## [29] train-rmse:0.287336+0.020843 test-rmse:2.361743+0.580313
## [30] train-rmse:0.269314+0.023674 test-rmse:2.362191+0.585522
## [31] train-rmse:0.255110+0.022142 test-rmse:2.362807+0.583736
## [32] train-rmse:0.242992+0.022799 test-rmse:2.362561+0.585660
## [33] train-rmse:0.226657+0.022775 test-rmse:2.363298+0.587588
## [34] train-rmse:0.208770+0.020978 test-rmse:2.363920+0.592620
## [35] train-rmse:0.199445+0.020508 test-rmse:2.365099+0.590355
## Stopping. Best iteration:
## [29] train-rmse:0.287336+0.020843 test-rmse:2.361743+0.580313
##
## 91
## [1] train-rmse:6.154224+0.135559 test-rmse:6.163765+0.642457
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.988204+0.106334 test-rmse:5.080952+0.679084
## [3] train-rmse:4.345461+0.116078 test-rmse:4.444678+0.569806
## [4] train-rmse:3.987750+0.114263 test-rmse:4.105137+0.515501
## [5] train-rmse:3.760637+0.116352 test-rmse:3.907244+0.434196
## [6] train-rmse:3.598736+0.110743 test-rmse:3.717654+0.397094
## [7] train-rmse:3.468133+0.109739 test-rmse:3.562675+0.444969
## [8] train-rmse:3.361815+0.122303 test-rmse:3.496777+0.404849
## [9] train-rmse:3.278803+0.124294 test-rmse:3.426076+0.427208
## [10] train-rmse:3.204829+0.123457 test-rmse:3.380149+0.419018
## [11] train-rmse:3.135242+0.121177 test-rmse:3.339485+0.479496
## [12] train-rmse:3.070277+0.120221 test-rmse:3.229276+0.460619
## [13] train-rmse:3.012521+0.124104 test-rmse:3.147421+0.455811
## [14] train-rmse:2.961307+0.129392 test-rmse:3.120647+0.437229
## [15] train-rmse:2.916419+0.132454 test-rmse:3.102023+0.450623
## [16] train-rmse:2.875215+0.133415 test-rmse:3.078352+0.446069
## [17] train-rmse:2.834499+0.134765 test-rmse:3.034477+0.474011
## [18] train-rmse:2.797619+0.135775 test-rmse:2.984980+0.465475
## [19] train-rmse:2.761832+0.138132 test-rmse:2.979965+0.468332
## [20] train-rmse:2.728053+0.137839 test-rmse:2.961896+0.449577
## [21] train-rmse:2.696908+0.138523 test-rmse:2.933828+0.455738
## [22] train-rmse:2.666237+0.139076 test-rmse:2.890304+0.463610
## [23] train-rmse:2.635637+0.140095 test-rmse:2.863298+0.449835
## [24] train-rmse:2.607191+0.141009 test-rmse:2.837030+0.437586
## [25] train-rmse:2.578545+0.142720 test-rmse:2.826193+0.468436
## [26] train-rmse:2.553162+0.142774 test-rmse:2.810814+0.470804
## [27] train-rmse:2.529423+0.142339 test-rmse:2.788442+0.490464
## [28] train-rmse:2.506858+0.142361 test-rmse:2.765849+0.487398
## [29] train-rmse:2.485543+0.142587 test-rmse:2.745946+0.499959
## [30] train-rmse:2.464734+0.143154 test-rmse:2.727509+0.503926
## [31] train-rmse:2.445321+0.143502 test-rmse:2.712368+0.485821
## [32] train-rmse:2.426451+0.144185 test-rmse:2.694791+0.490280
## [33] train-rmse:2.407977+0.144184 test-rmse:2.676000+0.485217
## [34] train-rmse:2.390752+0.143695 test-rmse:2.657803+0.473319
## [35] train-rmse:2.373584+0.143732 test-rmse:2.634180+0.483008
## [36] train-rmse:2.357511+0.143734 test-rmse:2.615522+0.482044
## [37] train-rmse:2.342236+0.144049 test-rmse:2.621794+0.490744
## [38] train-rmse:2.327866+0.143950 test-rmse:2.605968+0.490211
## [39] train-rmse:2.313881+0.144175 test-rmse:2.596102+0.503974
## [40] train-rmse:2.300948+0.145035 test-rmse:2.576710+0.490791
## [41] train-rmse:2.288414+0.145976 test-rmse:2.571941+0.500702
## [42] train-rmse:2.276146+0.146775 test-rmse:2.568964+0.500281
## [43] train-rmse:2.264238+0.147273 test-rmse:2.562058+0.502872
## [44] train-rmse:2.252902+0.147681 test-rmse:2.560333+0.500657
## [45] train-rmse:2.242049+0.148520 test-rmse:2.541600+0.507707
## [46] train-rmse:2.231585+0.149273 test-rmse:2.533717+0.513318
## [47] train-rmse:2.221441+0.149693 test-rmse:2.512916+0.516780
## [48] train-rmse:2.211267+0.150083 test-rmse:2.509740+0.522371
## [49] train-rmse:2.201603+0.150585 test-rmse:2.496993+0.516735
## [50] train-rmse:2.192358+0.151002 test-rmse:2.495925+0.516872
## [51] train-rmse:2.183563+0.151109 test-rmse:2.481628+0.516051
## [52] train-rmse:2.175228+0.151155 test-rmse:2.474938+0.517461
## [53] train-rmse:2.167086+0.151313 test-rmse:2.464341+0.518596
## [54] train-rmse:2.158566+0.151420 test-rmse:2.468461+0.517039
## [55] train-rmse:2.150786+0.151450 test-rmse:2.462561+0.516413
## [56] train-rmse:2.143138+0.151486 test-rmse:2.452169+0.514128
## [57] train-rmse:2.135883+0.151990 test-rmse:2.439350+0.508280
## [58] train-rmse:2.128779+0.152314 test-rmse:2.444256+0.514766
## [59] train-rmse:2.121797+0.152706 test-rmse:2.438848+0.533340
## [60] train-rmse:2.115133+0.153131 test-rmse:2.437541+0.544025
## [61] train-rmse:2.108702+0.153434 test-rmse:2.436380+0.535458
## [62] train-rmse:2.102484+0.153834 test-rmse:2.429943+0.521838
## [63] train-rmse:2.096227+0.154155 test-rmse:2.426411+0.521284
## [64] train-rmse:2.090253+0.154434 test-rmse:2.420892+0.518051
## [65] train-rmse:2.084433+0.154740 test-rmse:2.420844+0.521328
## [66] train-rmse:2.078754+0.155092 test-rmse:2.417206+0.522025
## [67] train-rmse:2.073138+0.155426 test-rmse:2.408272+0.520401
## [68] train-rmse:2.067498+0.155693 test-rmse:2.410486+0.525195
## [69] train-rmse:2.061966+0.156039 test-rmse:2.411554+0.532000
## [70] train-rmse:2.056611+0.156326 test-rmse:2.404705+0.533215
## [71] train-rmse:2.051500+0.156559 test-rmse:2.398632+0.536523
## [72] train-rmse:2.046707+0.156812 test-rmse:2.402089+0.536572
## [73] train-rmse:2.041928+0.156985 test-rmse:2.398055+0.533693
## [74] train-rmse:2.037556+0.157112 test-rmse:2.394478+0.532834
## [75] train-rmse:2.033104+0.157007 test-rmse:2.391172+0.529774
## [76] train-rmse:2.028793+0.157054 test-rmse:2.391080+0.530890
## [77] train-rmse:2.024321+0.157463 test-rmse:2.389300+0.528922
## [78] train-rmse:2.020014+0.157641 test-rmse:2.382661+0.527741
## [79] train-rmse:2.016048+0.157825 test-rmse:2.382021+0.531203
## [80] train-rmse:2.011995+0.157883 test-rmse:2.377183+0.529842
## [81] train-rmse:2.008056+0.158009 test-rmse:2.372551+0.530485
## [82] train-rmse:2.004212+0.158119 test-rmse:2.369464+0.535157
## [83] train-rmse:2.000355+0.158255 test-rmse:2.369499+0.539020
## [84] train-rmse:1.996552+0.158437 test-rmse:2.372151+0.535726
## [85] train-rmse:1.992753+0.158532 test-rmse:2.367538+0.534765
## [86] train-rmse:1.989079+0.158654 test-rmse:2.374914+0.544122
## [87] train-rmse:1.985433+0.158824 test-rmse:2.366063+0.545459
## [88] train-rmse:1.981823+0.159016 test-rmse:2.357139+0.543632
## [89] train-rmse:1.978153+0.159094 test-rmse:2.360868+0.544650
## [90] train-rmse:1.974723+0.159237 test-rmse:2.356544+0.547644
## [91] train-rmse:1.971383+0.159472 test-rmse:2.353402+0.543684
## [92] train-rmse:1.968135+0.159664 test-rmse:2.351700+0.545871
## [93] train-rmse:1.964740+0.159721 test-rmse:2.352686+0.545100
## [94] train-rmse:1.961424+0.160115 test-rmse:2.351809+0.545841
## [95] train-rmse:1.958277+0.160276 test-rmse:2.346343+0.545828
## [96] train-rmse:1.954942+0.160336 test-rmse:2.352509+0.543320
## [97] train-rmse:1.951966+0.160518 test-rmse:2.352692+0.544136
## [98] train-rmse:1.948875+0.160583 test-rmse:2.351888+0.544032
## [99] train-rmse:1.945941+0.160712 test-rmse:2.350996+0.547321
## [100] train-rmse:1.943042+0.160893 test-rmse:2.347088+0.544180
## [101] train-rmse:1.940186+0.161063 test-rmse:2.346266+0.540938
## [102] train-rmse:1.937266+0.161062 test-rmse:2.340768+0.545719
## [103] train-rmse:1.934487+0.161153 test-rmse:2.338222+0.545398
## [104] train-rmse:1.931832+0.161214 test-rmse:2.338764+0.545383
## [105] train-rmse:1.929076+0.161302 test-rmse:2.338054+0.542881
## [106] train-rmse:1.926312+0.161450 test-rmse:2.337387+0.542840
## [107] train-rmse:1.923667+0.161555 test-rmse:2.333591+0.545794
## [108] train-rmse:1.920907+0.161634 test-rmse:2.333940+0.546672
## [109] train-rmse:1.918122+0.161677 test-rmse:2.331527+0.548478
## [110] train-rmse:1.915602+0.161753 test-rmse:2.331534+0.540742
## [111] train-rmse:1.912806+0.162346 test-rmse:2.330727+0.543518
## [112] train-rmse:1.910437+0.162406 test-rmse:2.328915+0.541696
## [113] train-rmse:1.908052+0.162439 test-rmse:2.333329+0.540582
## [114] train-rmse:1.905529+0.162601 test-rmse:2.333783+0.542238
## [115] train-rmse:1.903136+0.162700 test-rmse:2.329449+0.543766
## [116] train-rmse:1.900736+0.162712 test-rmse:2.325684+0.545004
## [117] train-rmse:1.898425+0.162765 test-rmse:2.325707+0.544129
## [118] train-rmse:1.896016+0.162828 test-rmse:2.329527+0.546920
## [119] train-rmse:1.893650+0.163101 test-rmse:2.323801+0.549463
## [120] train-rmse:1.891478+0.163224 test-rmse:2.320452+0.548075
## [121] train-rmse:1.889213+0.163419 test-rmse:2.321563+0.547387
## [122] train-rmse:1.887113+0.163537 test-rmse:2.320729+0.547901
## [123] train-rmse:1.884951+0.163644 test-rmse:2.321391+0.553745
## [124] train-rmse:1.882866+0.163770 test-rmse:2.313121+0.551534
## [125] train-rmse:1.880827+0.163835 test-rmse:2.312320+0.551132
## [126] train-rmse:1.878702+0.163845 test-rmse:2.309490+0.549172
## [127] train-rmse:1.876708+0.163956 test-rmse:2.304447+0.548615
## [128] train-rmse:1.874626+0.164036 test-rmse:2.307032+0.548409
## [129] train-rmse:1.872576+0.164205 test-rmse:2.305365+0.545688
## [130] train-rmse:1.870594+0.164230 test-rmse:2.304025+0.544146
## [131] train-rmse:1.868692+0.164306 test-rmse:2.299714+0.542721
## [132] train-rmse:1.866685+0.164317 test-rmse:2.301419+0.543473
## [133] train-rmse:1.864796+0.164357 test-rmse:2.301917+0.540398
## [134] train-rmse:1.862873+0.164376 test-rmse:2.296541+0.542797
## [135] train-rmse:1.860886+0.164449 test-rmse:2.296827+0.544270
## [136] train-rmse:1.859039+0.164551 test-rmse:2.291943+0.543187
## [137] train-rmse:1.857150+0.164649 test-rmse:2.296032+0.539044
## [138] train-rmse:1.855269+0.164736 test-rmse:2.298840+0.538512
## [139] train-rmse:1.853387+0.164764 test-rmse:2.297807+0.541138
## [140] train-rmse:1.851585+0.164881 test-rmse:2.296383+0.540660
## [141] train-rmse:1.849868+0.164932 test-rmse:2.292887+0.543581
## [142] train-rmse:1.847944+0.165191 test-rmse:2.289106+0.541094
## [143] train-rmse:1.846042+0.165200 test-rmse:2.289921+0.540913
## [144] train-rmse:1.844289+0.165304 test-rmse:2.287491+0.542896
## [145] train-rmse:1.842486+0.165298 test-rmse:2.289667+0.541773
## [146] train-rmse:1.840818+0.165350 test-rmse:2.287819+0.543128
## [147] train-rmse:1.839061+0.165282 test-rmse:2.291595+0.542009
## [148] train-rmse:1.837435+0.165289 test-rmse:2.291021+0.542420
## [149] train-rmse:1.835746+0.165308 test-rmse:2.290948+0.540248
## [150] train-rmse:1.834128+0.165354 test-rmse:2.289925+0.542365
## Stopping. Best iteration:
## [144] train-rmse:1.844289+0.165304 test-rmse:2.287491+0.542896
##
## 92
## [1] train-rmse:5.982886+0.124450 test-rmse:6.045802+0.592558
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.671218+0.121490 test-rmse:4.775261+0.553464
## [3] train-rmse:3.863615+0.091562 test-rmse:4.002803+0.443587
## [4] train-rmse:3.364456+0.081311 test-rmse:3.559374+0.425868
## [5] train-rmse:3.061184+0.097876 test-rmse:3.315610+0.390400
## [6] train-rmse:2.836654+0.102064 test-rmse:3.080077+0.440411
## [7] train-rmse:2.699231+0.107793 test-rmse:2.996107+0.432250
## [8] train-rmse:2.584055+0.102431 test-rmse:2.944647+0.449546
## [9] train-rmse:2.477249+0.079988 test-rmse:2.859602+0.488745
## [10] train-rmse:2.396291+0.081828 test-rmse:2.827479+0.456702
## [11] train-rmse:2.303992+0.095482 test-rmse:2.748844+0.452439
## [12] train-rmse:2.223753+0.075238 test-rmse:2.722339+0.511500
## [13] train-rmse:2.153038+0.080599 test-rmse:2.681872+0.516152
## [14] train-rmse:2.089519+0.072700 test-rmse:2.598127+0.539542
## [15] train-rmse:2.038350+0.070129 test-rmse:2.558207+0.520785
## [16] train-rmse:1.993187+0.069812 test-rmse:2.531576+0.523816
## [17] train-rmse:1.943812+0.070397 test-rmse:2.507423+0.522410
## [18] train-rmse:1.897446+0.070977 test-rmse:2.496343+0.518335
## [19] train-rmse:1.861666+0.070162 test-rmse:2.469317+0.504411
## [20] train-rmse:1.820628+0.055407 test-rmse:2.457993+0.552117
## [21] train-rmse:1.786098+0.050036 test-rmse:2.447631+0.553191
## [22] train-rmse:1.757223+0.044378 test-rmse:2.432908+0.550910
## [23] train-rmse:1.730836+0.041671 test-rmse:2.416251+0.543953
## [24] train-rmse:1.705133+0.040704 test-rmse:2.398971+0.551707
## [25] train-rmse:1.682243+0.039448 test-rmse:2.387920+0.555257
## [26] train-rmse:1.655499+0.037453 test-rmse:2.357716+0.568315
## [27] train-rmse:1.632925+0.035995 test-rmse:2.347596+0.566063
## [28] train-rmse:1.602997+0.039468 test-rmse:2.349879+0.572614
## [29] train-rmse:1.574267+0.040744 test-rmse:2.334684+0.562242
## [30] train-rmse:1.552498+0.042503 test-rmse:2.325295+0.548513
## [31] train-rmse:1.531122+0.044589 test-rmse:2.325699+0.567482
## [32] train-rmse:1.512451+0.036833 test-rmse:2.316932+0.574971
## [33] train-rmse:1.492951+0.029616 test-rmse:2.304436+0.577241
## [34] train-rmse:1.474720+0.025749 test-rmse:2.287822+0.579360
## [35] train-rmse:1.457619+0.021857 test-rmse:2.281165+0.581123
## [36] train-rmse:1.439525+0.025096 test-rmse:2.278747+0.581277
## [37] train-rmse:1.415715+0.034397 test-rmse:2.271003+0.577328
## [38] train-rmse:1.400430+0.037002 test-rmse:2.268105+0.578737
## [39] train-rmse:1.388443+0.037847 test-rmse:2.261818+0.577160
## [40] train-rmse:1.366037+0.043388 test-rmse:2.245429+0.563141
## [41] train-rmse:1.353124+0.046581 test-rmse:2.242851+0.567350
## [42] train-rmse:1.339797+0.048157 test-rmse:2.236828+0.565031
## [43] train-rmse:1.327113+0.048434 test-rmse:2.235243+0.575686
## [44] train-rmse:1.306471+0.048410 test-rmse:2.236762+0.574675
## [45] train-rmse:1.294620+0.052012 test-rmse:2.237972+0.573358
## [46] train-rmse:1.284210+0.053641 test-rmse:2.228328+0.562000
## [47] train-rmse:1.270693+0.051534 test-rmse:2.234026+0.557751
## [48] train-rmse:1.258551+0.051822 test-rmse:2.233735+0.560482
## [49] train-rmse:1.244230+0.050110 test-rmse:2.233111+0.558194
## [50] train-rmse:1.231256+0.050555 test-rmse:2.215908+0.570845
## [51] train-rmse:1.221006+0.047820 test-rmse:2.209171+0.570984
## [52] train-rmse:1.208839+0.044714 test-rmse:2.208618+0.574688
## [53] train-rmse:1.200752+0.045314 test-rmse:2.204702+0.579032
## [54] train-rmse:1.192041+0.042864 test-rmse:2.207523+0.577445
## [55] train-rmse:1.177847+0.042549 test-rmse:2.200397+0.580622
## [56] train-rmse:1.163061+0.047409 test-rmse:2.198776+0.563769
## [57] train-rmse:1.151746+0.049586 test-rmse:2.192797+0.566871
## [58] train-rmse:1.144287+0.048337 test-rmse:2.191072+0.565619
## [59] train-rmse:1.135278+0.051467 test-rmse:2.188567+0.565547
## [60] train-rmse:1.125720+0.048746 test-rmse:2.179899+0.569469
## [61] train-rmse:1.117396+0.047193 test-rmse:2.176664+0.572134
## [62] train-rmse:1.111991+0.047335 test-rmse:2.175672+0.570713
## [63] train-rmse:1.100319+0.045350 test-rmse:2.179050+0.567416
## [64] train-rmse:1.094555+0.044238 test-rmse:2.181537+0.568243
## [65] train-rmse:1.088671+0.045049 test-rmse:2.181016+0.570052
## [66] train-rmse:1.083312+0.046186 test-rmse:2.182471+0.564665
## [67] train-rmse:1.074227+0.043431 test-rmse:2.180832+0.569292
## [68] train-rmse:1.058966+0.045580 test-rmse:2.181809+0.565625
## Stopping. Best iteration:
## [62] train-rmse:1.111991+0.047335 test-rmse:2.175672+0.570713
##
## 93
## [1] train-rmse:5.867268+0.114469 test-rmse:5.997922+0.576519
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.454293+0.085837 test-rmse:4.601266+0.513754
## [3] train-rmse:3.524120+0.071849 test-rmse:3.754776+0.486958
## [4] train-rmse:2.967254+0.050992 test-rmse:3.265786+0.458348
## [5] train-rmse:2.645502+0.083268 test-rmse:3.032049+0.471942
## [6] train-rmse:2.420327+0.073922 test-rmse:2.851969+0.459181
## [7] train-rmse:2.252709+0.065601 test-rmse:2.798602+0.442637
## [8] train-rmse:2.121442+0.071718 test-rmse:2.710172+0.476761
## [9] train-rmse:1.997641+0.050427 test-rmse:2.621106+0.461547
## [10] train-rmse:1.913521+0.049571 test-rmse:2.579103+0.462926
## [11] train-rmse:1.835627+0.041145 test-rmse:2.524997+0.451594
## [12] train-rmse:1.759317+0.039972 test-rmse:2.467214+0.456442
## [13] train-rmse:1.687825+0.038650 test-rmse:2.449212+0.479507
## [14] train-rmse:1.631731+0.035267 test-rmse:2.415070+0.482958
## [15] train-rmse:1.571155+0.040662 test-rmse:2.386455+0.488834
## [16] train-rmse:1.513114+0.052848 test-rmse:2.352756+0.473267
## [17] train-rmse:1.466990+0.055879 test-rmse:2.347008+0.473245
## [18] train-rmse:1.430456+0.057806 test-rmse:2.339526+0.473603
## [19] train-rmse:1.395349+0.058131 test-rmse:2.316894+0.475964
## [20] train-rmse:1.352813+0.047090 test-rmse:2.313148+0.478593
## [21] train-rmse:1.318206+0.058490 test-rmse:2.293068+0.477208
## [22] train-rmse:1.287958+0.060824 test-rmse:2.276181+0.481005
## [23] train-rmse:1.257248+0.056917 test-rmse:2.270124+0.473316
## [24] train-rmse:1.229324+0.055703 test-rmse:2.266403+0.465649
## [25] train-rmse:1.191571+0.062795 test-rmse:2.246706+0.467933
## [26] train-rmse:1.160057+0.057515 test-rmse:2.232078+0.469581
## [27] train-rmse:1.138574+0.062564 test-rmse:2.224186+0.469898
## [28] train-rmse:1.113212+0.058321 test-rmse:2.211674+0.477211
## [29] train-rmse:1.088215+0.055731 test-rmse:2.203264+0.477419
## [30] train-rmse:1.070266+0.056351 test-rmse:2.193715+0.485617
## [31] train-rmse:1.053299+0.054981 test-rmse:2.189120+0.497501
## [32] train-rmse:1.028376+0.048908 test-rmse:2.185544+0.494604
## [33] train-rmse:1.013798+0.047545 test-rmse:2.177661+0.495261
## [34] train-rmse:0.986846+0.053043 test-rmse:2.174077+0.493060
## [35] train-rmse:0.967624+0.059548 test-rmse:2.172809+0.493800
## [36] train-rmse:0.949167+0.063842 test-rmse:2.166493+0.481998
## [37] train-rmse:0.920932+0.051564 test-rmse:2.165782+0.478872
## [38] train-rmse:0.904563+0.054366 test-rmse:2.158550+0.481763
## [39] train-rmse:0.890057+0.051835 test-rmse:2.157156+0.482285
## [40] train-rmse:0.871871+0.049326 test-rmse:2.148227+0.490872
## [41] train-rmse:0.862045+0.050887 test-rmse:2.142189+0.488419
## [42] train-rmse:0.852655+0.051372 test-rmse:2.138220+0.486384
## [43] train-rmse:0.835473+0.052528 test-rmse:2.129464+0.485535
## [44] train-rmse:0.820503+0.054150 test-rmse:2.120759+0.471712
## [45] train-rmse:0.806792+0.055317 test-rmse:2.120615+0.475712
## [46] train-rmse:0.789391+0.058871 test-rmse:2.118446+0.479050
## [47] train-rmse:0.771166+0.051931 test-rmse:2.124700+0.482262
## [48] train-rmse:0.757365+0.057976 test-rmse:2.120390+0.491942
## [49] train-rmse:0.748322+0.059740 test-rmse:2.114858+0.495380
## [50] train-rmse:0.736394+0.061964 test-rmse:2.130017+0.498595
## [51] train-rmse:0.723478+0.059899 test-rmse:2.128477+0.499825
## [52] train-rmse:0.709955+0.055261 test-rmse:2.128380+0.500238
## [53] train-rmse:0.697221+0.050860 test-rmse:2.127990+0.500792
## [54] train-rmse:0.682228+0.052610 test-rmse:2.122437+0.500990
## [55] train-rmse:0.669312+0.047975 test-rmse:2.120629+0.499695
## Stopping. Best iteration:
## [49] train-rmse:0.748322+0.059740 test-rmse:2.114858+0.495380
##
## 94
## [1] train-rmse:5.757354+0.113297 test-rmse:5.854349+0.560155
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.218347+0.089434 test-rmse:4.432214+0.446689
## [3] train-rmse:3.282075+0.057017 test-rmse:3.622836+0.442871
## [4] train-rmse:2.715809+0.056456 test-rmse:3.163267+0.476956
## [5] train-rmse:2.351926+0.039526 test-rmse:2.877200+0.499205
## [6] train-rmse:2.100558+0.040043 test-rmse:2.768968+0.508926
## [7] train-rmse:1.932421+0.045440 test-rmse:2.679064+0.509007
## [8] train-rmse:1.811407+0.047449 test-rmse:2.617381+0.513235
## [9] train-rmse:1.678415+0.053864 test-rmse:2.580146+0.506148
## [10] train-rmse:1.593321+0.052575 test-rmse:2.530973+0.501973
## [11] train-rmse:1.503181+0.042390 test-rmse:2.477239+0.516780
## [12] train-rmse:1.438104+0.047521 test-rmse:2.446609+0.521657
## [13] train-rmse:1.371760+0.054106 test-rmse:2.431021+0.514286
## [14] train-rmse:1.318595+0.058230 test-rmse:2.411541+0.540078
## [15] train-rmse:1.265991+0.048332 test-rmse:2.385635+0.536391
## [16] train-rmse:1.225547+0.046927 test-rmse:2.369980+0.548939
## [17] train-rmse:1.178231+0.033990 test-rmse:2.362673+0.549684
## [18] train-rmse:1.135725+0.047225 test-rmse:2.339833+0.537342
## [19] train-rmse:1.081991+0.037890 test-rmse:2.333106+0.540948
## [20] train-rmse:1.044644+0.041489 test-rmse:2.319683+0.530994
## [21] train-rmse:1.019146+0.040042 test-rmse:2.315793+0.529205
## [22] train-rmse:0.985677+0.042878 test-rmse:2.292050+0.548122
## [23] train-rmse:0.954878+0.028559 test-rmse:2.280713+0.549691
## [24] train-rmse:0.913753+0.031251 test-rmse:2.270481+0.550304
## [25] train-rmse:0.879526+0.039348 test-rmse:2.256571+0.535523
## [26] train-rmse:0.853664+0.037821 test-rmse:2.249059+0.532803
## [27] train-rmse:0.837257+0.038421 test-rmse:2.248492+0.539146
## [28] train-rmse:0.802841+0.032894 test-rmse:2.241002+0.544545
## [29] train-rmse:0.786793+0.036511 test-rmse:2.237517+0.548618
## [30] train-rmse:0.763190+0.041570 test-rmse:2.230361+0.550022
## [31] train-rmse:0.737505+0.037047 test-rmse:2.222876+0.549614
## [32] train-rmse:0.720629+0.038814 test-rmse:2.223530+0.549417
## [33] train-rmse:0.697102+0.038291 test-rmse:2.220208+0.553095
## [34] train-rmse:0.674646+0.037574 test-rmse:2.218671+0.550465
## [35] train-rmse:0.648256+0.037078 test-rmse:2.218693+0.550460
## [36] train-rmse:0.628064+0.037481 test-rmse:2.214539+0.549654
## [37] train-rmse:0.608485+0.031724 test-rmse:2.217647+0.550041
## [38] train-rmse:0.596039+0.029855 test-rmse:2.215056+0.548147
## [39] train-rmse:0.581593+0.027398 test-rmse:2.214333+0.546124
## [40] train-rmse:0.562652+0.027606 test-rmse:2.214397+0.546289
## [41] train-rmse:0.548371+0.027327 test-rmse:2.209350+0.549097
## [42] train-rmse:0.536242+0.025553 test-rmse:2.209862+0.546182
## [43] train-rmse:0.527136+0.025249 test-rmse:2.207615+0.545361
## [44] train-rmse:0.514911+0.027341 test-rmse:2.206504+0.545356
## [45] train-rmse:0.499679+0.023742 test-rmse:2.204484+0.550024
## [46] train-rmse:0.484748+0.021870 test-rmse:2.199197+0.549912
## [47] train-rmse:0.474624+0.019999 test-rmse:2.197184+0.551699
## [48] train-rmse:0.466745+0.020959 test-rmse:2.198343+0.546501
## [49] train-rmse:0.454869+0.018292 test-rmse:2.199549+0.547214
## [50] train-rmse:0.443982+0.023117 test-rmse:2.195423+0.546217
## [51] train-rmse:0.432350+0.027080 test-rmse:2.193499+0.547264
## [52] train-rmse:0.423506+0.029417 test-rmse:2.193600+0.547162
## [53] train-rmse:0.406417+0.027671 test-rmse:2.184364+0.538181
## [54] train-rmse:0.394833+0.021830 test-rmse:2.180515+0.539582
## [55] train-rmse:0.382999+0.020131 test-rmse:2.179983+0.543077
## [56] train-rmse:0.372504+0.019040 test-rmse:2.179502+0.542403
## [57] train-rmse:0.363209+0.019827 test-rmse:2.181725+0.541730
## [58] train-rmse:0.357737+0.020867 test-rmse:2.178596+0.537029
## [59] train-rmse:0.351174+0.022722 test-rmse:2.178749+0.537710
## [60] train-rmse:0.346347+0.021996 test-rmse:2.179565+0.537196
## [61] train-rmse:0.335228+0.018578 test-rmse:2.178043+0.535876
## [62] train-rmse:0.324791+0.018510 test-rmse:2.174011+0.529934
## [63] train-rmse:0.317614+0.019033 test-rmse:2.171846+0.531588
## [64] train-rmse:0.308337+0.015347 test-rmse:2.168685+0.532422
## [65] train-rmse:0.302964+0.014492 test-rmse:2.169389+0.533511
## [66] train-rmse:0.297740+0.014399 test-rmse:2.169611+0.536103
## [67] train-rmse:0.292777+0.015378 test-rmse:2.169341+0.536175
## [68] train-rmse:0.288316+0.014779 test-rmse:2.169233+0.535110
## [69] train-rmse:0.281208+0.014367 test-rmse:2.170087+0.535369
## [70] train-rmse:0.276297+0.013036 test-rmse:2.170352+0.535084
## Stopping. Best iteration:
## [64] train-rmse:0.308337+0.015347 test-rmse:2.168685+0.532422
##
## 95
## [1] train-rmse:5.681381+0.110323 test-rmse:5.730993+0.528044
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.095520+0.081347 test-rmse:4.279301+0.446865
## [3] train-rmse:3.152176+0.060855 test-rmse:3.533389+0.454069
## [4] train-rmse:2.570234+0.064375 test-rmse:3.127618+0.450363
## [5] train-rmse:2.167044+0.060248 test-rmse:2.897721+0.502461
## [6] train-rmse:1.918014+0.055513 test-rmse:2.765406+0.505018
## [7] train-rmse:1.736023+0.056621 test-rmse:2.632380+0.489788
## [8] train-rmse:1.594819+0.082711 test-rmse:2.579448+0.491181
## [9] train-rmse:1.479169+0.083571 test-rmse:2.530867+0.508138
## [10] train-rmse:1.386896+0.082832 test-rmse:2.500874+0.521620
## [11] train-rmse:1.277954+0.053090 test-rmse:2.461408+0.520539
## [12] train-rmse:1.211643+0.059819 test-rmse:2.463328+0.518526
## [13] train-rmse:1.159610+0.068090 test-rmse:2.455721+0.530186
## [14] train-rmse:1.067303+0.055013 test-rmse:2.418073+0.524245
## [15] train-rmse:1.006118+0.040924 test-rmse:2.412809+0.524533
## [16] train-rmse:0.974563+0.041366 test-rmse:2.392297+0.534263
## [17] train-rmse:0.915514+0.042328 test-rmse:2.378769+0.528144
## [18] train-rmse:0.882580+0.043194 test-rmse:2.366937+0.536956
## [19] train-rmse:0.842090+0.039112 test-rmse:2.356176+0.532732
## [20] train-rmse:0.810613+0.042632 test-rmse:2.348166+0.536295
## [21] train-rmse:0.765907+0.034530 test-rmse:2.339917+0.538313
## [22] train-rmse:0.736070+0.027890 test-rmse:2.328843+0.535536
## [23] train-rmse:0.704888+0.027322 test-rmse:2.321558+0.537821
## [24] train-rmse:0.679959+0.027316 test-rmse:2.321978+0.532837
## [25] train-rmse:0.648078+0.026330 test-rmse:2.323865+0.530684
## [26] train-rmse:0.615856+0.023295 test-rmse:2.318669+0.533976
## [27] train-rmse:0.594153+0.021593 test-rmse:2.315663+0.534549
## [28] train-rmse:0.571236+0.021444 test-rmse:2.311797+0.539071
## [29] train-rmse:0.548131+0.015589 test-rmse:2.311794+0.535834
## [30] train-rmse:0.526139+0.021048 test-rmse:2.311201+0.539048
## [31] train-rmse:0.504582+0.023739 test-rmse:2.313505+0.547703
## [32] train-rmse:0.478689+0.020629 test-rmse:2.307723+0.548648
## [33] train-rmse:0.458016+0.021696 test-rmse:2.316015+0.560758
## [34] train-rmse:0.442484+0.023283 test-rmse:2.313016+0.558295
## [35] train-rmse:0.429465+0.019945 test-rmse:2.314998+0.561736
## [36] train-rmse:0.418039+0.022202 test-rmse:2.312440+0.560977
## [37] train-rmse:0.403415+0.019781 test-rmse:2.307736+0.562233
## [38] train-rmse:0.388327+0.010609 test-rmse:2.308506+0.563384
## Stopping. Best iteration:
## [32] train-rmse:0.478689+0.020629 test-rmse:2.307723+0.548648
##
## 96
## [1] train-rmse:5.633926+0.101851 test-rmse:5.703070+0.572286
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.013067+0.066211 test-rmse:4.288174+0.465830
## [3] train-rmse:3.015585+0.034300 test-rmse:3.492429+0.465507
## [4] train-rmse:2.404612+0.030049 test-rmse:3.076868+0.526766
## [5] train-rmse:1.995673+0.045912 test-rmse:2.808664+0.586047
## [6] train-rmse:1.734649+0.050363 test-rmse:2.653564+0.588303
## [7] train-rmse:1.515776+0.046566 test-rmse:2.579832+0.568648
## [8] train-rmse:1.380143+0.041624 test-rmse:2.528047+0.575792
## [9] train-rmse:1.260775+0.059264 test-rmse:2.503334+0.587651
## [10] train-rmse:1.162878+0.044316 test-rmse:2.479199+0.591426
## [11] train-rmse:1.080418+0.039055 test-rmse:2.460945+0.609240
## [12] train-rmse:1.016082+0.042202 test-rmse:2.441035+0.610754
## [13] train-rmse:0.950221+0.052508 test-rmse:2.429851+0.618926
## [14] train-rmse:0.881152+0.052632 test-rmse:2.417913+0.611373
## [15] train-rmse:0.811434+0.029477 test-rmse:2.394011+0.617752
## [16] train-rmse:0.752898+0.034244 test-rmse:2.386608+0.633725
## [17] train-rmse:0.712439+0.031910 test-rmse:2.380855+0.639946
## [18] train-rmse:0.670500+0.039877 test-rmse:2.370345+0.638562
## [19] train-rmse:0.637536+0.039299 test-rmse:2.371624+0.642060
## [20] train-rmse:0.601373+0.032160 test-rmse:2.363982+0.641400
## [21] train-rmse:0.566271+0.031261 test-rmse:2.356497+0.644896
## [22] train-rmse:0.538024+0.024655 test-rmse:2.358894+0.642265
## [23] train-rmse:0.507777+0.030106 test-rmse:2.350307+0.639105
## [24] train-rmse:0.486140+0.030268 test-rmse:2.347003+0.642706
## [25] train-rmse:0.457264+0.040612 test-rmse:2.348775+0.645551
## [26] train-rmse:0.438560+0.034627 test-rmse:2.347940+0.645932
## [27] train-rmse:0.415217+0.029428 test-rmse:2.342550+0.648034
## [28] train-rmse:0.392859+0.030243 test-rmse:2.344910+0.646004
## [29] train-rmse:0.366414+0.029958 test-rmse:2.344562+0.645727
## [30] train-rmse:0.350293+0.030121 test-rmse:2.344259+0.647669
## [31] train-rmse:0.334432+0.027296 test-rmse:2.342975+0.645056
## [32] train-rmse:0.311444+0.026678 test-rmse:2.338050+0.646711
## [33] train-rmse:0.293815+0.029137 test-rmse:2.341518+0.652570
## [34] train-rmse:0.283134+0.025617 test-rmse:2.340746+0.652033
## [35] train-rmse:0.267283+0.024131 test-rmse:2.342502+0.648081
## [36] train-rmse:0.255499+0.020677 test-rmse:2.343362+0.648156
## [37] train-rmse:0.243018+0.018171 test-rmse:2.340793+0.648889
## [38] train-rmse:0.230854+0.017916 test-rmse:2.337975+0.647504
## [39] train-rmse:0.221158+0.017236 test-rmse:2.338123+0.646011
## [40] train-rmse:0.211634+0.015215 test-rmse:2.337428+0.646951
## [41] train-rmse:0.199551+0.013413 test-rmse:2.334213+0.643796
## [42] train-rmse:0.191172+0.012848 test-rmse:2.333176+0.644028
## [43] train-rmse:0.184988+0.012292 test-rmse:2.333061+0.644178
## [44] train-rmse:0.177862+0.012734 test-rmse:2.334687+0.643121
## [45] train-rmse:0.165610+0.009184 test-rmse:2.331444+0.642561
## [46] train-rmse:0.159131+0.009907 test-rmse:2.331358+0.642833
## [47] train-rmse:0.152854+0.009964 test-rmse:2.331262+0.642852
## [48] train-rmse:0.146346+0.010424 test-rmse:2.331416+0.642754
## [49] train-rmse:0.139624+0.007745 test-rmse:2.330596+0.642516
## [50] train-rmse:0.133060+0.006640 test-rmse:2.330119+0.643498
## [51] train-rmse:0.126229+0.008075 test-rmse:2.329793+0.642995
## [52] train-rmse:0.119718+0.007806 test-rmse:2.329662+0.643239
## [53] train-rmse:0.114996+0.007239 test-rmse:2.327989+0.643733
## [54] train-rmse:0.111435+0.006437 test-rmse:2.327418+0.643870
## [55] train-rmse:0.107748+0.005599 test-rmse:2.327202+0.644137
## [56] train-rmse:0.103578+0.006115 test-rmse:2.326895+0.644888
## [57] train-rmse:0.099173+0.007444 test-rmse:2.326904+0.643792
## [58] train-rmse:0.095439+0.007365 test-rmse:2.326373+0.644174
## [59] train-rmse:0.091989+0.006505 test-rmse:2.325656+0.644796
## [60] train-rmse:0.088237+0.006261 test-rmse:2.325888+0.644854
## [61] train-rmse:0.084134+0.007008 test-rmse:2.325528+0.644964
## [62] train-rmse:0.081244+0.006735 test-rmse:2.325108+0.645293
## [63] train-rmse:0.077573+0.006117 test-rmse:2.325777+0.645142
## [64] train-rmse:0.073671+0.005795 test-rmse:2.325442+0.646076
## [65] train-rmse:0.069731+0.004185 test-rmse:2.325696+0.645647
## [66] train-rmse:0.066599+0.002937 test-rmse:2.326056+0.645320
## [67] train-rmse:0.063882+0.003502 test-rmse:2.325979+0.645490
## [68] train-rmse:0.061760+0.003346 test-rmse:2.326189+0.645722
## Stopping. Best iteration:
## [62] train-rmse:0.081244+0.006735 test-rmse:2.325108+0.645293
##
## 97
## [1] train-rmse:5.616997+0.100380 test-rmse:5.691990+0.572019
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.960246+0.060911 test-rmse:4.253966+0.483147
## [3] train-rmse:2.936114+0.038264 test-rmse:3.436436+0.495715
## [4] train-rmse:2.282579+0.040483 test-rmse:3.024076+0.519414
## [5] train-rmse:1.852717+0.051435 test-rmse:2.760657+0.544639
## [6] train-rmse:1.546085+0.039717 test-rmse:2.657452+0.549452
## [7] train-rmse:1.345678+0.049982 test-rmse:2.564750+0.550903
## [8] train-rmse:1.187476+0.035566 test-rmse:2.516628+0.527335
## [9] train-rmse:1.055303+0.029655 test-rmse:2.502455+0.540834
## [10] train-rmse:0.945141+0.033348 test-rmse:2.466197+0.532294
## [11] train-rmse:0.869212+0.031745 test-rmse:2.441266+0.543724
## [12] train-rmse:0.815275+0.028991 test-rmse:2.433883+0.544141
## [13] train-rmse:0.745860+0.030240 test-rmse:2.407464+0.549943
## [14] train-rmse:0.690820+0.033711 test-rmse:2.413176+0.559499
## [15] train-rmse:0.638616+0.033350 test-rmse:2.411526+0.568711
## [16] train-rmse:0.584178+0.020746 test-rmse:2.398402+0.571290
## [17] train-rmse:0.534858+0.028055 test-rmse:2.390161+0.571208
## [18] train-rmse:0.501277+0.024968 test-rmse:2.386829+0.574985
## [19] train-rmse:0.471900+0.028345 test-rmse:2.384313+0.576550
## [20] train-rmse:0.435164+0.018757 test-rmse:2.374755+0.578305
## [21] train-rmse:0.408151+0.021743 test-rmse:2.374082+0.579651
## [22] train-rmse:0.383653+0.015203 test-rmse:2.367198+0.578075
## [23] train-rmse:0.360654+0.017613 test-rmse:2.367341+0.577356
## [24] train-rmse:0.342292+0.013592 test-rmse:2.366069+0.580954
## [25] train-rmse:0.318981+0.019039 test-rmse:2.359071+0.581328
## [26] train-rmse:0.300298+0.018674 test-rmse:2.360336+0.583714
## [27] train-rmse:0.280866+0.017477 test-rmse:2.357206+0.584263
## [28] train-rmse:0.260587+0.014129 test-rmse:2.353783+0.584676
## [29] train-rmse:0.244830+0.014273 test-rmse:2.349661+0.587606
## [30] train-rmse:0.230451+0.011271 test-rmse:2.348061+0.587546
## [31] train-rmse:0.215340+0.014520 test-rmse:2.344227+0.589134
## [32] train-rmse:0.197728+0.020929 test-rmse:2.346714+0.591115
## [33] train-rmse:0.189499+0.019545 test-rmse:2.346831+0.590656
## [34] train-rmse:0.178803+0.021059 test-rmse:2.346422+0.590326
## [35] train-rmse:0.168418+0.019488 test-rmse:2.346302+0.591140
## [36] train-rmse:0.157362+0.014936 test-rmse:2.348022+0.594039
## [37] train-rmse:0.148278+0.015785 test-rmse:2.347683+0.593549
## Stopping. Best iteration:
## [31] train-rmse:0.215340+0.014520 test-rmse:2.344227+0.589134
##
## 98
## [1] train-rmse:6.051489+0.133867 test-rmse:6.063642+0.638666
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.876173+0.104040 test-rmse:4.977498+0.674841
## [3] train-rmse:4.254621+0.117041 test-rmse:4.362234+0.557640
## [4] train-rmse:3.918233+0.114223 test-rmse:4.047886+0.503617
## [5] train-rmse:3.705315+0.117784 test-rmse:3.862625+0.420934
## [6] train-rmse:3.548559+0.113787 test-rmse:3.675694+0.385543
## [7] train-rmse:3.418487+0.110226 test-rmse:3.518610+0.441510
## [8] train-rmse:3.313822+0.123658 test-rmse:3.454294+0.401862
## [9] train-rmse:3.232002+0.124133 test-rmse:3.387119+0.429739
## [10] train-rmse:3.158780+0.124546 test-rmse:3.331282+0.429490
## [11] train-rmse:3.091429+0.123102 test-rmse:3.230457+0.450111
## [12] train-rmse:3.026294+0.122336 test-rmse:3.191912+0.464351
## [13] train-rmse:2.968807+0.126039 test-rmse:3.109700+0.460335
## [14] train-rmse:2.918828+0.130826 test-rmse:3.075041+0.434942
## [15] train-rmse:2.874675+0.132932 test-rmse:3.069446+0.457255
## [16] train-rmse:2.833154+0.133676 test-rmse:3.042020+0.464491
## [17] train-rmse:2.792685+0.136370 test-rmse:2.995707+0.482984
## [18] train-rmse:2.755482+0.138320 test-rmse:2.978718+0.494770
## [19] train-rmse:2.719819+0.141012 test-rmse:2.950746+0.476432
## [20] train-rmse:2.685846+0.141304 test-rmse:2.932068+0.455901
## [21] train-rmse:2.655062+0.140612 test-rmse:2.899131+0.463161
## [22] train-rmse:2.624341+0.140240 test-rmse:2.885838+0.477562
## [23] train-rmse:2.594447+0.139937 test-rmse:2.853609+0.467551
## [24] train-rmse:2.565703+0.140040 test-rmse:2.825803+0.456586
## [25] train-rmse:2.538492+0.140733 test-rmse:2.776797+0.463781
## [26] train-rmse:2.513889+0.141464 test-rmse:2.782348+0.481857
## [27] train-rmse:2.490778+0.142151 test-rmse:2.765653+0.494879
## [28] train-rmse:2.468342+0.143008 test-rmse:2.740476+0.494702
## [29] train-rmse:2.447741+0.143682 test-rmse:2.719947+0.509326
## [30] train-rmse:2.427546+0.144512 test-rmse:2.708673+0.499667
## [31] train-rmse:2.408096+0.145386 test-rmse:2.673534+0.495309
## [32] train-rmse:2.389673+0.146423 test-rmse:2.666502+0.495234
## [33] train-rmse:2.371912+0.146885 test-rmse:2.657872+0.485405
## [34] train-rmse:2.354630+0.146360 test-rmse:2.633208+0.498666
## [35] train-rmse:2.337787+0.145715 test-rmse:2.607217+0.492064
## [36] train-rmse:2.322328+0.145179 test-rmse:2.596577+0.491267
## [37] train-rmse:2.308030+0.144637 test-rmse:2.578237+0.496252
## [38] train-rmse:2.294075+0.144446 test-rmse:2.573982+0.497809
## [39] train-rmse:2.281087+0.144628 test-rmse:2.568958+0.500320
## [40] train-rmse:2.268668+0.145006 test-rmse:2.559299+0.490978
## [41] train-rmse:2.256646+0.145650 test-rmse:2.546117+0.491434
## [42] train-rmse:2.244724+0.146448 test-rmse:2.535807+0.496216
## [43] train-rmse:2.233041+0.147416 test-rmse:2.522052+0.492311
## [44] train-rmse:2.221817+0.148606 test-rmse:2.531091+0.506360
## [45] train-rmse:2.210794+0.149370 test-rmse:2.523975+0.514615
## [46] train-rmse:2.200579+0.150035 test-rmse:2.512348+0.514678
## [47] train-rmse:2.190885+0.150777 test-rmse:2.502736+0.520520
## [48] train-rmse:2.181521+0.151472 test-rmse:2.493180+0.523120
## [49] train-rmse:2.172420+0.151732 test-rmse:2.475288+0.529906
## [50] train-rmse:2.163566+0.152134 test-rmse:2.471093+0.528748
## [51] train-rmse:2.155057+0.152274 test-rmse:2.471668+0.531215
## [52] train-rmse:2.146722+0.152395 test-rmse:2.466795+0.525732
## [53] train-rmse:2.138429+0.152509 test-rmse:2.458746+0.525916
## [54] train-rmse:2.130960+0.152698 test-rmse:2.445294+0.533634
## [55] train-rmse:2.123789+0.153132 test-rmse:2.440688+0.532502
## [56] train-rmse:2.116845+0.153467 test-rmse:2.428947+0.528963
## [57] train-rmse:2.109850+0.153856 test-rmse:2.420067+0.534705
## [58] train-rmse:2.103191+0.154157 test-rmse:2.416252+0.532527
## [59] train-rmse:2.096638+0.154508 test-rmse:2.418817+0.532113
## [60] train-rmse:2.090025+0.154568 test-rmse:2.421745+0.524364
## [61] train-rmse:2.083577+0.154729 test-rmse:2.417320+0.524374
## [62] train-rmse:2.077349+0.154975 test-rmse:2.405534+0.528032
## [63] train-rmse:2.071531+0.155467 test-rmse:2.401676+0.523719
## [64] train-rmse:2.065831+0.155821 test-rmse:2.396119+0.520271
## [65] train-rmse:2.060317+0.156069 test-rmse:2.394636+0.517952
## [66] train-rmse:2.054856+0.156355 test-rmse:2.394446+0.516378
## [67] train-rmse:2.049545+0.156607 test-rmse:2.394120+0.523462
## [68] train-rmse:2.044371+0.157008 test-rmse:2.398929+0.536313
## [69] train-rmse:2.039333+0.157356 test-rmse:2.400660+0.530201
## [70] train-rmse:2.034418+0.157546 test-rmse:2.390050+0.538042
## [71] train-rmse:2.029528+0.157752 test-rmse:2.387445+0.536782
## [72] train-rmse:2.024818+0.157837 test-rmse:2.385320+0.533043
## [73] train-rmse:2.020526+0.158000 test-rmse:2.378716+0.535998
## [74] train-rmse:2.016373+0.158157 test-rmse:2.383158+0.536240
## [75] train-rmse:2.011849+0.158602 test-rmse:2.378048+0.533334
## [76] train-rmse:2.007701+0.158774 test-rmse:2.383343+0.534094
## [77] train-rmse:2.003574+0.158782 test-rmse:2.383864+0.529649
## [78] train-rmse:1.999371+0.158870 test-rmse:2.384962+0.532945
## [79] train-rmse:1.995076+0.159028 test-rmse:2.383971+0.536963
## [80] train-rmse:1.991155+0.159249 test-rmse:2.368862+0.546281
## [81] train-rmse:1.987313+0.159241 test-rmse:2.367956+0.544445
## [82] train-rmse:1.983510+0.159298 test-rmse:2.359374+0.540902
## [83] train-rmse:1.979710+0.159348 test-rmse:2.358672+0.540042
## [84] train-rmse:1.975993+0.159459 test-rmse:2.356598+0.543228
## [85] train-rmse:1.972367+0.159680 test-rmse:2.358733+0.535091
## [86] train-rmse:1.968878+0.159838 test-rmse:2.359022+0.542352
## [87] train-rmse:1.965369+0.160023 test-rmse:2.357745+0.539971
## [88] train-rmse:1.962020+0.160292 test-rmse:2.357810+0.540440
## [89] train-rmse:1.958589+0.160356 test-rmse:2.358505+0.535734
## [90] train-rmse:1.955196+0.160627 test-rmse:2.358304+0.535096
## Stopping. Best iteration:
## [84] train-rmse:1.975993+0.159459 test-rmse:2.356598+0.543228
##
## 99
## [1] train-rmse:5.868926+0.122296 test-rmse:5.938335+0.586717
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.541394+0.119339 test-rmse:4.692044+0.500737
## [3] train-rmse:3.761516+0.120444 test-rmse:3.909705+0.414142
## [4] train-rmse:3.281873+0.110026 test-rmse:3.489448+0.402600
## [5] train-rmse:2.978171+0.091227 test-rmse:3.255584+0.391469
## [6] train-rmse:2.765356+0.093205 test-rmse:3.034054+0.442525
## [7] train-rmse:2.625377+0.084316 test-rmse:2.932925+0.437814
## [8] train-rmse:2.518301+0.089847 test-rmse:2.907190+0.426425
## [9] train-rmse:2.414858+0.058214 test-rmse:2.809354+0.452135
## [10] train-rmse:2.314491+0.070882 test-rmse:2.735665+0.386488
## [11] train-rmse:2.234737+0.065702 test-rmse:2.674605+0.415638
## [12] train-rmse:2.161401+0.070077 test-rmse:2.639536+0.438214
## [13] train-rmse:2.094406+0.076762 test-rmse:2.599441+0.457259
## [14] train-rmse:2.042467+0.072443 test-rmse:2.577319+0.454161
## [15] train-rmse:1.993435+0.072084 test-rmse:2.528533+0.429797
## [16] train-rmse:1.944754+0.076118 test-rmse:2.463743+0.461549
## [17] train-rmse:1.900469+0.074039 test-rmse:2.439099+0.453212
## [18] train-rmse:1.862945+0.075554 test-rmse:2.444026+0.431906
## [19] train-rmse:1.809544+0.057885 test-rmse:2.437878+0.457796
## [20] train-rmse:1.773354+0.059714 test-rmse:2.417624+0.480621
## [21] train-rmse:1.743278+0.056453 test-rmse:2.403968+0.483802
## [22] train-rmse:1.703797+0.052656 test-rmse:2.377791+0.493701
## [23] train-rmse:1.678724+0.049803 test-rmse:2.352689+0.490086
## [24] train-rmse:1.651475+0.044180 test-rmse:2.333172+0.495431
## [25] train-rmse:1.628289+0.043127 test-rmse:2.318281+0.499101
## [26] train-rmse:1.606633+0.041159 test-rmse:2.312603+0.499196
## [27] train-rmse:1.583534+0.042577 test-rmse:2.300730+0.498295
## [28] train-rmse:1.563498+0.043300 test-rmse:2.275031+0.501575
## [29] train-rmse:1.524475+0.053625 test-rmse:2.256047+0.501678
## [30] train-rmse:1.492207+0.033859 test-rmse:2.247596+0.517693
## [31] train-rmse:1.472551+0.030456 test-rmse:2.241368+0.535505
## [32] train-rmse:1.445040+0.035185 test-rmse:2.237240+0.520794
## [33] train-rmse:1.427854+0.033701 test-rmse:2.241741+0.525197
## [34] train-rmse:1.412104+0.029941 test-rmse:2.235419+0.526134
## [35] train-rmse:1.393281+0.033636 test-rmse:2.220469+0.514344
## [36] train-rmse:1.372800+0.033493 test-rmse:2.221927+0.513410
## [37] train-rmse:1.358630+0.033966 test-rmse:2.215599+0.517742
## [38] train-rmse:1.344271+0.034026 test-rmse:2.211350+0.514278
## [39] train-rmse:1.331372+0.034756 test-rmse:2.206775+0.514326
## [40] train-rmse:1.319523+0.033866 test-rmse:2.200651+0.514949
## [41] train-rmse:1.306725+0.033727 test-rmse:2.199495+0.520257
## [42] train-rmse:1.294158+0.030763 test-rmse:2.187964+0.523649
## [43] train-rmse:1.285194+0.031163 test-rmse:2.180974+0.528604
## [44] train-rmse:1.267803+0.036411 test-rmse:2.167986+0.536960
## [45] train-rmse:1.252801+0.040594 test-rmse:2.156643+0.533200
## [46] train-rmse:1.241309+0.043394 test-rmse:2.156974+0.537692
## [47] train-rmse:1.232056+0.045981 test-rmse:2.160270+0.544435
## [48] train-rmse:1.219738+0.049277 test-rmse:2.147983+0.540738
## [49] train-rmse:1.201128+0.057945 test-rmse:2.139344+0.531690
## [50] train-rmse:1.188173+0.058041 test-rmse:2.136945+0.533891
## [51] train-rmse:1.180677+0.057911 test-rmse:2.135491+0.533749
## [52] train-rmse:1.171360+0.058195 test-rmse:2.136210+0.531753
## [53] train-rmse:1.160950+0.055209 test-rmse:2.133135+0.527105
## [54] train-rmse:1.154238+0.055440 test-rmse:2.129627+0.527983
## [55] train-rmse:1.147379+0.056717 test-rmse:2.121976+0.531634
## [56] train-rmse:1.135024+0.055451 test-rmse:2.116950+0.532207
## [57] train-rmse:1.114881+0.063967 test-rmse:2.108563+0.540799
## [58] train-rmse:1.102541+0.065196 test-rmse:2.115068+0.530741
## [59] train-rmse:1.092796+0.067890 test-rmse:2.113836+0.527794
## [60] train-rmse:1.085005+0.069280 test-rmse:2.111151+0.524510
## [61] train-rmse:1.072602+0.068755 test-rmse:2.099815+0.528435
## [62] train-rmse:1.060521+0.064977 test-rmse:2.103149+0.545332
## [63] train-rmse:1.042446+0.049554 test-rmse:2.097232+0.550112
## [64] train-rmse:1.035977+0.048951 test-rmse:2.096880+0.550359
## [65] train-rmse:1.026474+0.047501 test-rmse:2.093659+0.551707
## [66] train-rmse:1.012549+0.043931 test-rmse:2.086478+0.538735
## [67] train-rmse:1.002412+0.044319 test-rmse:2.073956+0.544217
## [68] train-rmse:0.995459+0.043709 test-rmse:2.076718+0.544564
## [69] train-rmse:0.977946+0.040382 test-rmse:2.082012+0.543126
## [70] train-rmse:0.970080+0.042133 test-rmse:2.080563+0.545035
## [71] train-rmse:0.959813+0.041105 test-rmse:2.085438+0.540672
## [72] train-rmse:0.954082+0.041619 test-rmse:2.078424+0.534242
## [73] train-rmse:0.949307+0.041962 test-rmse:2.075599+0.539849
## Stopping. Best iteration:
## [67] train-rmse:1.002412+0.044319 test-rmse:2.073956+0.544217
##
## 100
## [1] train-rmse:5.745553+0.111143 test-rmse:5.887407+0.570284
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.311638+0.084252 test-rmse:4.454818+0.476018
## [3] train-rmse:3.391970+0.068739 test-rmse:3.606303+0.442296
## [4] train-rmse:2.861174+0.049448 test-rmse:3.180983+0.460256
## [5] train-rmse:2.550873+0.076516 test-rmse:2.924376+0.460459
## [6] train-rmse:2.327835+0.071863 test-rmse:2.822275+0.465678
## [7] train-rmse:2.173747+0.055498 test-rmse:2.724882+0.470068
## [8] train-rmse:2.035308+0.060728 test-rmse:2.668566+0.492396
## [9] train-rmse:1.938757+0.069501 test-rmse:2.607254+0.496103
## [10] train-rmse:1.835729+0.062596 test-rmse:2.564521+0.474799
## [11] train-rmse:1.746425+0.061082 test-rmse:2.514831+0.453534
## [12] train-rmse:1.686619+0.058752 test-rmse:2.463747+0.462837
## [13] train-rmse:1.608085+0.057011 test-rmse:2.418540+0.459550
## [14] train-rmse:1.548275+0.059286 test-rmse:2.405773+0.465578
## [15] train-rmse:1.496141+0.063786 test-rmse:2.371892+0.456470
## [16] train-rmse:1.454323+0.064401 test-rmse:2.355466+0.456557
## [17] train-rmse:1.416120+0.066782 test-rmse:2.335753+0.460818
## [18] train-rmse:1.378801+0.068043 test-rmse:2.316625+0.463688
## [19] train-rmse:1.341817+0.067168 test-rmse:2.298643+0.469942
## [20] train-rmse:1.310550+0.073844 test-rmse:2.281129+0.475690
## [21] train-rmse:1.262267+0.062139 test-rmse:2.253750+0.478691
## [22] train-rmse:1.231168+0.069639 test-rmse:2.253104+0.470409
## [23] train-rmse:1.198401+0.060662 test-rmse:2.241069+0.473964
## [24] train-rmse:1.176112+0.060334 test-rmse:2.235231+0.474707
## [25] train-rmse:1.152396+0.063620 test-rmse:2.230042+0.469173
## [26] train-rmse:1.123827+0.065954 test-rmse:2.222728+0.468230
## [27] train-rmse:1.096210+0.070911 test-rmse:2.225947+0.475045
## [28] train-rmse:1.068612+0.070799 test-rmse:2.220011+0.474350
## [29] train-rmse:1.040789+0.063636 test-rmse:2.210990+0.470352
## [30] train-rmse:1.022797+0.065035 test-rmse:2.209683+0.474908
## [31] train-rmse:1.008989+0.066076 test-rmse:2.205946+0.471502
## [32] train-rmse:0.976923+0.083669 test-rmse:2.194152+0.468107
## [33] train-rmse:0.946454+0.080402 test-rmse:2.186083+0.465857
## [34] train-rmse:0.926891+0.076895 test-rmse:2.178285+0.463935
## [35] train-rmse:0.911264+0.080198 test-rmse:2.171706+0.465945
## [36] train-rmse:0.890313+0.077774 test-rmse:2.165474+0.464945
## [37] train-rmse:0.868400+0.070930 test-rmse:2.168247+0.466327
## [38] train-rmse:0.855324+0.068268 test-rmse:2.171801+0.482191
## [39] train-rmse:0.832742+0.074110 test-rmse:2.174458+0.476816
## [40] train-rmse:0.823408+0.072757 test-rmse:2.173485+0.477103
## [41] train-rmse:0.803338+0.073501 test-rmse:2.169567+0.481110
## [42] train-rmse:0.785479+0.075611 test-rmse:2.168300+0.479434
## Stopping. Best iteration:
## [36] train-rmse:0.890313+0.077774 test-rmse:2.165474+0.464945
##
## 101
## [1] train-rmse:5.627841+0.110084 test-rmse:5.734733+0.553501
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.061201+0.086269 test-rmse:4.278806+0.434229
## [3] train-rmse:3.143977+0.066681 test-rmse:3.538810+0.404271
## [4] train-rmse:2.609172+0.058490 test-rmse:3.085606+0.475817
## [5] train-rmse:2.278053+0.065457 test-rmse:2.848483+0.446006
## [6] train-rmse:2.032900+0.063915 test-rmse:2.694802+0.457644
## [7] train-rmse:1.869186+0.077546 test-rmse:2.635403+0.457613
## [8] train-rmse:1.737703+0.044240 test-rmse:2.550153+0.451260
## [9] train-rmse:1.624805+0.030917 test-rmse:2.497636+0.460864
## [10] train-rmse:1.518675+0.048109 test-rmse:2.465554+0.465765
## [11] train-rmse:1.428945+0.057386 test-rmse:2.388344+0.438854
## [12] train-rmse:1.360554+0.050611 test-rmse:2.351289+0.443769
## [13] train-rmse:1.295789+0.047197 test-rmse:2.335701+0.453021
## [14] train-rmse:1.245678+0.054747 test-rmse:2.329020+0.449329
## [15] train-rmse:1.194398+0.053243 test-rmse:2.303803+0.457424
## [16] train-rmse:1.148056+0.051493 test-rmse:2.286928+0.457860
## [17] train-rmse:1.095945+0.035567 test-rmse:2.281417+0.480195
## [18] train-rmse:1.046216+0.046941 test-rmse:2.258382+0.481964
## [19] train-rmse:1.017438+0.049519 test-rmse:2.244058+0.482409
## [20] train-rmse:0.972524+0.040995 test-rmse:2.241855+0.493952
## [21] train-rmse:0.929620+0.043474 test-rmse:2.244479+0.500768
## [22] train-rmse:0.899656+0.041866 test-rmse:2.239555+0.494958
## [23] train-rmse:0.876691+0.039407 test-rmse:2.229335+0.497590
## [24] train-rmse:0.836886+0.043751 test-rmse:2.237709+0.515667
## [25] train-rmse:0.809307+0.043869 test-rmse:2.227068+0.519475
## [26] train-rmse:0.779286+0.040253 test-rmse:2.211027+0.525749
## [27] train-rmse:0.756590+0.039964 test-rmse:2.209432+0.524754
## [28] train-rmse:0.732963+0.050214 test-rmse:2.210933+0.525900
## [29] train-rmse:0.713450+0.048584 test-rmse:2.217013+0.536806
## [30] train-rmse:0.694350+0.050635 test-rmse:2.217754+0.535952
## [31] train-rmse:0.666517+0.049263 test-rmse:2.207797+0.529202
## [32] train-rmse:0.648255+0.048008 test-rmse:2.204933+0.533582
## [33] train-rmse:0.634281+0.050191 test-rmse:2.201310+0.531591
## [34] train-rmse:0.611609+0.038887 test-rmse:2.200086+0.534561
## [35] train-rmse:0.598136+0.038407 test-rmse:2.197821+0.536198
## [36] train-rmse:0.576810+0.031829 test-rmse:2.189857+0.534058
## [37] train-rmse:0.561684+0.035447 test-rmse:2.188662+0.527218
## [38] train-rmse:0.540565+0.036675 test-rmse:2.177980+0.531892
## [39] train-rmse:0.522468+0.032795 test-rmse:2.177184+0.535110
## [40] train-rmse:0.509545+0.031174 test-rmse:2.175287+0.535340
## [41] train-rmse:0.492603+0.028730 test-rmse:2.182651+0.541689
## [42] train-rmse:0.480430+0.028272 test-rmse:2.173194+0.550682
## [43] train-rmse:0.468664+0.029518 test-rmse:2.168562+0.549398
## [44] train-rmse:0.451507+0.034390 test-rmse:2.164846+0.545155
## [45] train-rmse:0.441506+0.034028 test-rmse:2.162918+0.545899
## [46] train-rmse:0.428986+0.029840 test-rmse:2.159214+0.547098
## [47] train-rmse:0.417876+0.026641 test-rmse:2.160428+0.550411
## [48] train-rmse:0.410818+0.027856 test-rmse:2.163246+0.549301
## [49] train-rmse:0.399464+0.027104 test-rmse:2.165315+0.553254
## [50] train-rmse:0.390958+0.028006 test-rmse:2.164053+0.553576
## [51] train-rmse:0.382929+0.028479 test-rmse:2.164248+0.553618
## [52] train-rmse:0.372383+0.027762 test-rmse:2.169539+0.559774
## Stopping. Best iteration:
## [46] train-rmse:0.428986+0.029840 test-rmse:2.159214+0.547098
##
## 102
## [1] train-rmse:5.546457+0.107123 test-rmse:5.603375+0.519358
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.931178+0.076405 test-rmse:4.137944+0.439356
## [3] train-rmse:3.018279+0.067661 test-rmse:3.419430+0.450491
## [4] train-rmse:2.433757+0.065985 test-rmse:3.000361+0.462758
## [5] train-rmse:2.078055+0.071136 test-rmse:2.841404+0.500968
## [6] train-rmse:1.821529+0.080725 test-rmse:2.714874+0.508790
## [7] train-rmse:1.627516+0.089134 test-rmse:2.611620+0.545631
## [8] train-rmse:1.500621+0.073725 test-rmse:2.557860+0.522565
## [9] train-rmse:1.401847+0.080749 test-rmse:2.524923+0.535711
## [10] train-rmse:1.317482+0.076564 test-rmse:2.507455+0.528598
## [11] train-rmse:1.234413+0.073480 test-rmse:2.489337+0.536009
## [12] train-rmse:1.142572+0.062433 test-rmse:2.436302+0.517520
## [13] train-rmse:1.089632+0.068817 test-rmse:2.426070+0.527767
## [14] train-rmse:1.038595+0.060281 test-rmse:2.408024+0.527458
## [15] train-rmse:0.990457+0.055974 test-rmse:2.380882+0.521212
## [16] train-rmse:0.942534+0.047991 test-rmse:2.369744+0.517446
## [17] train-rmse:0.896058+0.045130 test-rmse:2.360707+0.519325
## [18] train-rmse:0.847427+0.041705 test-rmse:2.350304+0.524008
## [19] train-rmse:0.806489+0.029201 test-rmse:2.338295+0.532523
## [20] train-rmse:0.778802+0.032704 test-rmse:2.326951+0.535408
## [21] train-rmse:0.739995+0.034489 test-rmse:2.323923+0.527317
## [22] train-rmse:0.708608+0.033739 test-rmse:2.307675+0.547314
## [23] train-rmse:0.677743+0.030541 test-rmse:2.303571+0.552354
## [24] train-rmse:0.634028+0.034826 test-rmse:2.289771+0.557835
## [25] train-rmse:0.609689+0.026022 test-rmse:2.284662+0.557274
## [26] train-rmse:0.587693+0.022791 test-rmse:2.284708+0.557415
## [27] train-rmse:0.560706+0.019040 test-rmse:2.285907+0.560566
## [28] train-rmse:0.533476+0.017025 test-rmse:2.288865+0.563774
## [29] train-rmse:0.508282+0.024493 test-rmse:2.278956+0.569727
## [30] train-rmse:0.491892+0.021856 test-rmse:2.278558+0.574298
## [31] train-rmse:0.475039+0.013512 test-rmse:2.276183+0.574430
## [32] train-rmse:0.458295+0.013890 test-rmse:2.274001+0.577382
## [33] train-rmse:0.436080+0.021049 test-rmse:2.275299+0.576183
## [34] train-rmse:0.417825+0.021249 test-rmse:2.272581+0.579535
## [35] train-rmse:0.395867+0.021429 test-rmse:2.271345+0.580223
## [36] train-rmse:0.383176+0.023245 test-rmse:2.271473+0.579028
## [37] train-rmse:0.370401+0.019386 test-rmse:2.275924+0.575226
## [38] train-rmse:0.356385+0.019738 test-rmse:2.273655+0.576463
## [39] train-rmse:0.345697+0.019964 test-rmse:2.274266+0.577339
## [40] train-rmse:0.334019+0.017198 test-rmse:2.273444+0.576185
## [41] train-rmse:0.314368+0.012704 test-rmse:2.274854+0.577052
## Stopping. Best iteration:
## [35] train-rmse:0.395867+0.021429 test-rmse:2.271345+0.580223
##
## 103
## [1] train-rmse:5.495667+0.097807 test-rmse:5.574011+0.566667
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.855312+0.067994 test-rmse:4.142708+0.471284
## [3] train-rmse:2.872532+0.036365 test-rmse:3.377454+0.467900
## [4] train-rmse:2.276823+0.029986 test-rmse:2.961833+0.506308
## [5] train-rmse:1.887726+0.050679 test-rmse:2.767639+0.525706
## [6] train-rmse:1.610820+0.082522 test-rmse:2.623046+0.582208
## [7] train-rmse:1.427987+0.087886 test-rmse:2.561907+0.582826
## [8] train-rmse:1.271683+0.082583 test-rmse:2.500888+0.577081
## [9] train-rmse:1.152218+0.063280 test-rmse:2.464399+0.582137
## [10] train-rmse:1.060739+0.068035 test-rmse:2.446951+0.577275
## [11] train-rmse:0.987430+0.065940 test-rmse:2.440691+0.593647
## [12] train-rmse:0.908631+0.078496 test-rmse:2.422815+0.599468
## [13] train-rmse:0.850646+0.089924 test-rmse:2.401504+0.609757
## [14] train-rmse:0.794920+0.075198 test-rmse:2.376522+0.597250
## [15] train-rmse:0.755997+0.073870 test-rmse:2.371630+0.598312
## [16] train-rmse:0.713234+0.076451 test-rmse:2.354527+0.597786
## [17] train-rmse:0.661529+0.049912 test-rmse:2.347448+0.597555
## [18] train-rmse:0.623793+0.052485 test-rmse:2.339957+0.596056
## [19] train-rmse:0.579275+0.043722 test-rmse:2.331341+0.600423
## [20] train-rmse:0.543085+0.038651 test-rmse:2.336004+0.605720
## [21] train-rmse:0.510783+0.046026 test-rmse:2.331341+0.603926
## [22] train-rmse:0.488908+0.043736 test-rmse:2.326031+0.606076
## [23] train-rmse:0.466008+0.041028 test-rmse:2.326005+0.605543
## [24] train-rmse:0.438282+0.036597 test-rmse:2.324754+0.603910
## [25] train-rmse:0.414894+0.035492 test-rmse:2.324200+0.600428
## [26] train-rmse:0.389733+0.029435 test-rmse:2.319665+0.602567
## [27] train-rmse:0.372991+0.028812 test-rmse:2.314244+0.604480
## [28] train-rmse:0.349802+0.023700 test-rmse:2.312024+0.604036
## [29] train-rmse:0.332451+0.016790 test-rmse:2.313333+0.608293
## [30] train-rmse:0.312053+0.016061 test-rmse:2.314258+0.610447
## [31] train-rmse:0.295793+0.014581 test-rmse:2.309718+0.612888
## [32] train-rmse:0.282180+0.013466 test-rmse:2.310180+0.611639
## [33] train-rmse:0.270386+0.014829 test-rmse:2.309347+0.610712
## [34] train-rmse:0.253836+0.011792 test-rmse:2.308320+0.611390
## [35] train-rmse:0.238887+0.010623 test-rmse:2.308977+0.611821
## [36] train-rmse:0.228270+0.011652 test-rmse:2.306685+0.613634
## [37] train-rmse:0.212026+0.011168 test-rmse:2.308492+0.613339
## [38] train-rmse:0.201850+0.009736 test-rmse:2.307705+0.611692
## [39] train-rmse:0.190126+0.012367 test-rmse:2.306336+0.609399
## [40] train-rmse:0.179808+0.009806 test-rmse:2.304865+0.610721
## [41] train-rmse:0.171372+0.009640 test-rmse:2.304651+0.610439
## [42] train-rmse:0.165722+0.010427 test-rmse:2.305540+0.610382
## [43] train-rmse:0.157447+0.011776 test-rmse:2.304607+0.609572
## [44] train-rmse:0.149739+0.008053 test-rmse:2.304174+0.608737
## [45] train-rmse:0.141017+0.006603 test-rmse:2.303770+0.609496
## [46] train-rmse:0.135778+0.008863 test-rmse:2.302697+0.609779
## [47] train-rmse:0.130199+0.008833 test-rmse:2.301122+0.609016
## [48] train-rmse:0.124113+0.010001 test-rmse:2.299999+0.609301
## [49] train-rmse:0.118942+0.009513 test-rmse:2.300282+0.608660
## [50] train-rmse:0.114204+0.007991 test-rmse:2.300696+0.609309
## [51] train-rmse:0.109656+0.007249 test-rmse:2.299866+0.607838
## [52] train-rmse:0.105453+0.006919 test-rmse:2.299572+0.608050
## [53] train-rmse:0.099706+0.004869 test-rmse:2.299949+0.608564
## [54] train-rmse:0.095295+0.004628 test-rmse:2.299351+0.607838
## [55] train-rmse:0.091953+0.004889 test-rmse:2.299895+0.607458
## [56] train-rmse:0.088008+0.005663 test-rmse:2.299714+0.606772
## [57] train-rmse:0.084405+0.005969 test-rmse:2.299012+0.606689
## [58] train-rmse:0.081584+0.006084 test-rmse:2.298107+0.606211
## [59] train-rmse:0.077758+0.005882 test-rmse:2.298179+0.605828
## [60] train-rmse:0.074231+0.005368 test-rmse:2.298710+0.605612
## [61] train-rmse:0.070749+0.006707 test-rmse:2.298090+0.605782
## [62] train-rmse:0.066527+0.006712 test-rmse:2.298037+0.604964
## [63] train-rmse:0.064054+0.006527 test-rmse:2.297840+0.604997
## [64] train-rmse:0.060736+0.006225 test-rmse:2.297641+0.604299
## [65] train-rmse:0.058590+0.005855 test-rmse:2.297017+0.603760
## [66] train-rmse:0.056854+0.005613 test-rmse:2.296770+0.603746
## [67] train-rmse:0.055071+0.005529 test-rmse:2.297197+0.603406
## [68] train-rmse:0.052136+0.006070 test-rmse:2.296059+0.604326
## [69] train-rmse:0.050123+0.005568 test-rmse:2.296017+0.604376
## [70] train-rmse:0.046820+0.005428 test-rmse:2.295781+0.604148
## [71] train-rmse:0.044770+0.005124 test-rmse:2.295787+0.604322
## [72] train-rmse:0.043011+0.005304 test-rmse:2.295498+0.604130
## [73] train-rmse:0.041460+0.004808 test-rmse:2.295413+0.604405
## [74] train-rmse:0.040154+0.004758 test-rmse:2.295431+0.604440
## [75] train-rmse:0.038533+0.004556 test-rmse:2.295688+0.604085
## [76] train-rmse:0.037127+0.004194 test-rmse:2.295244+0.604444
## [77] train-rmse:0.035794+0.003994 test-rmse:2.295041+0.604178
## [78] train-rmse:0.034289+0.003904 test-rmse:2.295288+0.604269
## [79] train-rmse:0.032840+0.003737 test-rmse:2.295045+0.604501
## [80] train-rmse:0.031490+0.003864 test-rmse:2.295006+0.604554
## [81] train-rmse:0.030503+0.003724 test-rmse:2.295064+0.604635
## [82] train-rmse:0.029620+0.003698 test-rmse:2.294990+0.604678
## [83] train-rmse:0.028244+0.003535 test-rmse:2.295117+0.604792
## [84] train-rmse:0.027159+0.003486 test-rmse:2.295133+0.604888
## [85] train-rmse:0.026042+0.003086 test-rmse:2.295139+0.604823
## [86] train-rmse:0.025349+0.002976 test-rmse:2.294945+0.604915
## [87] train-rmse:0.024307+0.002637 test-rmse:2.294709+0.605063
## [88] train-rmse:0.023318+0.002705 test-rmse:2.294579+0.605096
## [89] train-rmse:0.022735+0.002742 test-rmse:2.294538+0.605242
## [90] train-rmse:0.022056+0.002600 test-rmse:2.294604+0.605088
## [91] train-rmse:0.020874+0.002394 test-rmse:2.294595+0.605372
## [92] train-rmse:0.020125+0.002170 test-rmse:2.294731+0.605705
## [93] train-rmse:0.019422+0.002103 test-rmse:2.294656+0.605607
## [94] train-rmse:0.018729+0.002055 test-rmse:2.294774+0.605694
## [95] train-rmse:0.018248+0.001953 test-rmse:2.294893+0.605604
## Stopping. Best iteration:
## [89] train-rmse:0.022735+0.002742 test-rmse:2.294538+0.605242
##
## 104
## [1] train-rmse:5.477530+0.096224 test-rmse:5.561905+0.566466
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.785644+0.056499 test-rmse:4.109492+0.478658
## [3] train-rmse:2.779084+0.034952 test-rmse:3.321672+0.503710
## [4] train-rmse:2.151117+0.035089 test-rmse:2.958275+0.502166
## [5] train-rmse:1.742654+0.056462 test-rmse:2.718995+0.519232
## [6] train-rmse:1.459789+0.038238 test-rmse:2.591811+0.506458
## [7] train-rmse:1.270320+0.031011 test-rmse:2.507335+0.534815
## [8] train-rmse:1.157586+0.040200 test-rmse:2.462801+0.532907
## [9] train-rmse:1.034391+0.025013 test-rmse:2.437770+0.538020
## [10] train-rmse:0.944694+0.033946 test-rmse:2.408891+0.546647
## [11] train-rmse:0.848146+0.031936 test-rmse:2.398292+0.549311
## [12] train-rmse:0.775395+0.020191 test-rmse:2.388900+0.549584
## [13] train-rmse:0.710711+0.019869 test-rmse:2.387252+0.537349
## [14] train-rmse:0.655035+0.018135 test-rmse:2.363463+0.549294
## [15] train-rmse:0.608394+0.016371 test-rmse:2.371278+0.562137
## [16] train-rmse:0.554495+0.008874 test-rmse:2.374350+0.571080
## [17] train-rmse:0.523698+0.007005 test-rmse:2.374695+0.571978
## [18] train-rmse:0.490444+0.007167 test-rmse:2.370578+0.569045
## [19] train-rmse:0.460148+0.011110 test-rmse:2.363446+0.566438
## [20] train-rmse:0.415625+0.012572 test-rmse:2.361071+0.560188
## [21] train-rmse:0.395690+0.008574 test-rmse:2.352067+0.567107
## [22] train-rmse:0.370618+0.011381 test-rmse:2.352436+0.571824
## [23] train-rmse:0.344038+0.004488 test-rmse:2.346950+0.574126
## [24] train-rmse:0.322904+0.008066 test-rmse:2.348424+0.572598
## [25] train-rmse:0.302459+0.011528 test-rmse:2.349909+0.573074
## [26] train-rmse:0.283321+0.009452 test-rmse:2.342912+0.572380
## [27] train-rmse:0.263987+0.010889 test-rmse:2.341898+0.572351
## [28] train-rmse:0.246119+0.012587 test-rmse:2.342234+0.572251
## [29] train-rmse:0.225199+0.011941 test-rmse:2.340988+0.574641
## [30] train-rmse:0.212591+0.012507 test-rmse:2.339524+0.575396
## [31] train-rmse:0.200077+0.013317 test-rmse:2.337900+0.575010
## [32] train-rmse:0.189185+0.011543 test-rmse:2.338155+0.575381
## [33] train-rmse:0.178193+0.009837 test-rmse:2.336880+0.575383
## [34] train-rmse:0.169007+0.010499 test-rmse:2.336346+0.575975
## [35] train-rmse:0.158816+0.009145 test-rmse:2.336795+0.575992
## [36] train-rmse:0.144650+0.006542 test-rmse:2.334339+0.575761
## [37] train-rmse:0.135317+0.006489 test-rmse:2.336446+0.578080
## [38] train-rmse:0.125910+0.005000 test-rmse:2.332960+0.578485
## [39] train-rmse:0.119125+0.002726 test-rmse:2.333591+0.578781
## [40] train-rmse:0.111499+0.004421 test-rmse:2.331395+0.578071
## [41] train-rmse:0.106879+0.004499 test-rmse:2.331526+0.578995
## [42] train-rmse:0.101120+0.004437 test-rmse:2.331162+0.579227
## [43] train-rmse:0.096739+0.003635 test-rmse:2.332715+0.579877
## [44] train-rmse:0.092991+0.004205 test-rmse:2.332160+0.579261
## [45] train-rmse:0.088008+0.004089 test-rmse:2.332550+0.579142
## [46] train-rmse:0.083155+0.004258 test-rmse:2.332422+0.580115
## [47] train-rmse:0.078859+0.004796 test-rmse:2.332041+0.580129
## [48] train-rmse:0.073821+0.003928 test-rmse:2.331441+0.578956
## Stopping. Best iteration:
## [42] train-rmse:0.101120+0.004437 test-rmse:2.331162+0.579227
##
## 105
## [1] train-rmse:5.950215+0.132245 test-rmse:5.965040+0.634669
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.770830+0.101927 test-rmse:4.880836+0.670336
## [3] train-rmse:4.173035+0.118733 test-rmse:4.252701+0.579564
## [4] train-rmse:3.857214+0.112667 test-rmse:3.995632+0.491239
## [5] train-rmse:3.653895+0.118027 test-rmse:3.809205+0.420071
## [6] train-rmse:3.502018+0.117137 test-rmse:3.637375+0.375184
## [7] train-rmse:3.371395+0.110901 test-rmse:3.477143+0.438689
## [8] train-rmse:3.268243+0.125319 test-rmse:3.413991+0.399551
## [9] train-rmse:3.187359+0.124381 test-rmse:3.343530+0.432766
## [10] train-rmse:3.114704+0.126033 test-rmse:3.294261+0.433663
## [11] train-rmse:3.048112+0.125960 test-rmse:3.204229+0.446591
## [12] train-rmse:2.984730+0.125005 test-rmse:3.150037+0.464188
## [13] train-rmse:2.928363+0.128285 test-rmse:3.067451+0.460962
## [14] train-rmse:2.878254+0.132225 test-rmse:3.030333+0.437330
## [15] train-rmse:2.834775+0.133248 test-rmse:3.016482+0.437555
## [16] train-rmse:2.793450+0.135400 test-rmse:2.991880+0.450450
## [17] train-rmse:2.754563+0.138062 test-rmse:2.981030+0.464207
## [18] train-rmse:2.717321+0.141026 test-rmse:2.938888+0.500154
## [19] train-rmse:2.681402+0.143266 test-rmse:2.891994+0.492877
## [20] train-rmse:2.646758+0.144683 test-rmse:2.861007+0.475728
## [21] train-rmse:2.614611+0.142801 test-rmse:2.859632+0.482353
## [22] train-rmse:2.584359+0.141044 test-rmse:2.853734+0.509411
## [23] train-rmse:2.555432+0.139863 test-rmse:2.825510+0.498728
## [24] train-rmse:2.528059+0.139635 test-rmse:2.797985+0.476418
## [25] train-rmse:2.502087+0.139947 test-rmse:2.746024+0.473830
## [26] train-rmse:2.478094+0.140342 test-rmse:2.735336+0.468472
## [27] train-rmse:2.455104+0.141475 test-rmse:2.722377+0.479039
## [28] train-rmse:2.432861+0.142795 test-rmse:2.708557+0.505716
## [29] train-rmse:2.412305+0.143298 test-rmse:2.700183+0.499672
## [30] train-rmse:2.392686+0.144564 test-rmse:2.673186+0.503993
## [31] train-rmse:2.373835+0.146372 test-rmse:2.660185+0.498520
## [32] train-rmse:2.355568+0.147224 test-rmse:2.650610+0.496932
## [33] train-rmse:2.338342+0.148397 test-rmse:2.618812+0.504278
## [34] train-rmse:2.322028+0.148201 test-rmse:2.602308+0.508990
## [35] train-rmse:2.306077+0.147960 test-rmse:2.590162+0.512696
## [36] train-rmse:2.290513+0.147144 test-rmse:2.558140+0.506374
## [37] train-rmse:2.276031+0.146096 test-rmse:2.544167+0.496614
## [38] train-rmse:2.262722+0.146434 test-rmse:2.544327+0.502881
## [39] train-rmse:2.250135+0.146945 test-rmse:2.533359+0.505865
## [40] train-rmse:2.238153+0.147368 test-rmse:2.525372+0.507765
## [41] train-rmse:2.226233+0.147857 test-rmse:2.505368+0.500441
## [42] train-rmse:2.214906+0.148127 test-rmse:2.492642+0.492801
## [43] train-rmse:2.204012+0.148690 test-rmse:2.469282+0.503299
## [44] train-rmse:2.193807+0.149299 test-rmse:2.476817+0.497654
## [45] train-rmse:2.183548+0.149965 test-rmse:2.486869+0.502340
## [46] train-rmse:2.173225+0.150483 test-rmse:2.493751+0.519056
## [47] train-rmse:2.163905+0.151493 test-rmse:2.493593+0.519993
## [48] train-rmse:2.154936+0.152072 test-rmse:2.490054+0.526012
## [49] train-rmse:2.146216+0.152357 test-rmse:2.476314+0.526642
## Stopping. Best iteration:
## [43] train-rmse:2.204012+0.148690 test-rmse:2.469282+0.503299
##
## 106
## [1] train-rmse:5.756227+0.120247 test-rmse:5.832297+0.580880
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.418148+0.117279 test-rmse:4.576134+0.495612
## [3] train-rmse:3.667526+0.128529 test-rmse:3.874231+0.442382
## [4] train-rmse:3.172498+0.124794 test-rmse:3.364661+0.429020
## [5] train-rmse:2.877486+0.094651 test-rmse:3.112249+0.426054
## [6] train-rmse:2.678909+0.107582 test-rmse:2.953518+0.454939
## [7] train-rmse:2.563237+0.108750 test-rmse:2.904313+0.468179
## [8] train-rmse:2.439136+0.113796 test-rmse:2.811877+0.464603
## [9] train-rmse:2.339335+0.118757 test-rmse:2.767737+0.474046
## [10] train-rmse:2.249976+0.117040 test-rmse:2.698185+0.482096
## [11] train-rmse:2.170492+0.115540 test-rmse:2.648386+0.517592
## [12] train-rmse:2.093002+0.117368 test-rmse:2.581785+0.524381
## [13] train-rmse:2.032165+0.118287 test-rmse:2.557719+0.515416
## [14] train-rmse:1.971926+0.126439 test-rmse:2.492804+0.481732
## [15] train-rmse:1.921134+0.127336 test-rmse:2.457509+0.474682
## [16] train-rmse:1.877951+0.125282 test-rmse:2.428111+0.480080
## [17] train-rmse:1.836756+0.127462 test-rmse:2.426125+0.514730
## [18] train-rmse:1.787710+0.107934 test-rmse:2.359453+0.525014
## [19] train-rmse:1.754547+0.109308 test-rmse:2.338260+0.523929
## [20] train-rmse:1.721407+0.109986 test-rmse:2.326569+0.535123
## [21] train-rmse:1.680053+0.111799 test-rmse:2.316827+0.536121
## [22] train-rmse:1.655018+0.111317 test-rmse:2.298013+0.537333
## [23] train-rmse:1.625699+0.110107 test-rmse:2.275926+0.564074
## [24] train-rmse:1.598773+0.112157 test-rmse:2.246726+0.558918
## [25] train-rmse:1.565996+0.118058 test-rmse:2.233358+0.539444
## [26] train-rmse:1.543941+0.116988 test-rmse:2.225509+0.556166
## [27] train-rmse:1.523264+0.113879 test-rmse:2.210894+0.560926
## [28] train-rmse:1.499288+0.119766 test-rmse:2.195654+0.559940
## [29] train-rmse:1.477375+0.112950 test-rmse:2.201311+0.561464
## [30] train-rmse:1.458467+0.115639 test-rmse:2.187932+0.562840
## [31] train-rmse:1.439196+0.117640 test-rmse:2.200000+0.568090
## [32] train-rmse:1.423447+0.118919 test-rmse:2.193932+0.572028
## [33] train-rmse:1.403689+0.121471 test-rmse:2.181053+0.573403
## [34] train-rmse:1.389006+0.123192 test-rmse:2.186365+0.573104
## [35] train-rmse:1.365297+0.128974 test-rmse:2.186225+0.579268
## [36] train-rmse:1.344969+0.134092 test-rmse:2.170755+0.579733
## [37] train-rmse:1.332793+0.134939 test-rmse:2.160129+0.579760
## [38] train-rmse:1.316572+0.133318 test-rmse:2.147024+0.585573
## [39] train-rmse:1.304933+0.133515 test-rmse:2.138181+0.589497
## [40] train-rmse:1.266658+0.098631 test-rmse:2.130912+0.597122
## [41] train-rmse:1.242002+0.089817 test-rmse:2.117647+0.590963
## [42] train-rmse:1.230681+0.090896 test-rmse:2.106177+0.587173
## [43] train-rmse:1.221249+0.090920 test-rmse:2.105861+0.590409
## [44] train-rmse:1.207736+0.093869 test-rmse:2.102030+0.594978
## [45] train-rmse:1.197020+0.092741 test-rmse:2.096887+0.592977
## [46] train-rmse:1.184122+0.097134 test-rmse:2.095061+0.580369
## [47] train-rmse:1.168646+0.098467 test-rmse:2.084044+0.588334
## [48] train-rmse:1.154068+0.099244 test-rmse:2.074051+0.592796
## [49] train-rmse:1.143860+0.099980 test-rmse:2.070410+0.595699
## [50] train-rmse:1.133414+0.099770 test-rmse:2.071300+0.589747
## [51] train-rmse:1.109699+0.079653 test-rmse:2.072290+0.597625
## [52] train-rmse:1.097787+0.081281 test-rmse:2.068661+0.603694
## [53] train-rmse:1.088060+0.077980 test-rmse:2.067566+0.604318
## [54] train-rmse:1.078391+0.076323 test-rmse:2.062152+0.608895
## [55] train-rmse:1.068041+0.075309 test-rmse:2.049333+0.613430
## [56] train-rmse:1.058845+0.078524 test-rmse:2.049115+0.610438
## [57] train-rmse:1.052353+0.079675 test-rmse:2.048236+0.615649
## [58] train-rmse:1.044878+0.081211 test-rmse:2.044435+0.616042
## [59] train-rmse:1.029598+0.082710 test-rmse:2.037672+0.617212
## [60] train-rmse:1.018609+0.086052 test-rmse:2.043851+0.620819
## [61] train-rmse:1.002553+0.088596 test-rmse:2.053068+0.626623
## [62] train-rmse:0.990427+0.081186 test-rmse:2.054344+0.621346
## [63] train-rmse:0.985201+0.079925 test-rmse:2.048678+0.626811
## [64] train-rmse:0.977717+0.078553 test-rmse:2.044954+0.625174
## [65] train-rmse:0.968334+0.079604 test-rmse:2.041024+0.630893
## Stopping. Best iteration:
## [59] train-rmse:1.029598+0.082710 test-rmse:2.037672+0.617212
##
## 107
## [1] train-rmse:5.624925+0.107826 test-rmse:5.778279+0.564075
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.154124+0.074725 test-rmse:4.272764+0.513544
## [3] train-rmse:3.279401+0.066854 test-rmse:3.484895+0.412927
## [4] train-rmse:2.763757+0.051288 test-rmse:3.081835+0.439658
## [5] train-rmse:2.465066+0.054353 test-rmse:2.824640+0.466026
## [6] train-rmse:2.260589+0.050966 test-rmse:2.730530+0.466485
## [7] train-rmse:2.088660+0.062918 test-rmse:2.612652+0.451348
## [8] train-rmse:1.965654+0.064735 test-rmse:2.564613+0.473524
## [9] train-rmse:1.860514+0.060322 test-rmse:2.498157+0.465330
## [10] train-rmse:1.782118+0.061455 test-rmse:2.454904+0.459639
## [11] train-rmse:1.692172+0.052466 test-rmse:2.427568+0.463964
## [12] train-rmse:1.631112+0.055412 test-rmse:2.403484+0.461147
## [13] train-rmse:1.568551+0.055630 test-rmse:2.369049+0.462468
## [14] train-rmse:1.504121+0.048825 test-rmse:2.347692+0.469937
## [15] train-rmse:1.452471+0.050979 test-rmse:2.298646+0.471436
## [16] train-rmse:1.389233+0.053904 test-rmse:2.259063+0.466887
## [17] train-rmse:1.348877+0.059746 test-rmse:2.264019+0.473074
## [18] train-rmse:1.312721+0.066875 test-rmse:2.241068+0.460111
## [19] train-rmse:1.274864+0.067422 test-rmse:2.230837+0.454721
## [20] train-rmse:1.229738+0.053133 test-rmse:2.209618+0.462154
## [21] train-rmse:1.198168+0.053693 test-rmse:2.184226+0.451701
## [22] train-rmse:1.171333+0.053691 test-rmse:2.170246+0.456853
## [23] train-rmse:1.141432+0.054696 test-rmse:2.164489+0.456695
## [24] train-rmse:1.111025+0.056964 test-rmse:2.156797+0.453233
## [25] train-rmse:1.079323+0.061309 test-rmse:2.152459+0.453185
## [26] train-rmse:1.054337+0.052256 test-rmse:2.158758+0.464206
## [27] train-rmse:1.021050+0.039095 test-rmse:2.177312+0.474521
## [28] train-rmse:1.003189+0.038756 test-rmse:2.175994+0.477839
## [29] train-rmse:0.980969+0.033885 test-rmse:2.162906+0.474362
## [30] train-rmse:0.953015+0.024610 test-rmse:2.139179+0.468363
## [31] train-rmse:0.937519+0.027018 test-rmse:2.129197+0.465733
## [32] train-rmse:0.921451+0.031516 test-rmse:2.121015+0.472809
## [33] train-rmse:0.894781+0.027884 test-rmse:2.109572+0.491205
## [34] train-rmse:0.869854+0.026265 test-rmse:2.106907+0.487532
## [35] train-rmse:0.850398+0.024816 test-rmse:2.104545+0.485688
## [36] train-rmse:0.835824+0.023182 test-rmse:2.097698+0.492675
## [37] train-rmse:0.813285+0.022340 test-rmse:2.087675+0.495994
## [38] train-rmse:0.803685+0.021366 test-rmse:2.083032+0.497940
## [39] train-rmse:0.786804+0.022493 test-rmse:2.079311+0.499451
## [40] train-rmse:0.768003+0.022100 test-rmse:2.074053+0.501740
## [41] train-rmse:0.752480+0.014255 test-rmse:2.077531+0.503890
## [42] train-rmse:0.736190+0.012409 test-rmse:2.073054+0.504151
## [43] train-rmse:0.725926+0.012933 test-rmse:2.071832+0.504644
## [44] train-rmse:0.714605+0.013466 test-rmse:2.069865+0.501615
## [45] train-rmse:0.700391+0.010514 test-rmse:2.072999+0.504468
## [46] train-rmse:0.688182+0.015005 test-rmse:2.068311+0.506181
## [47] train-rmse:0.676323+0.010032 test-rmse:2.069618+0.506263
## [48] train-rmse:0.664651+0.007929 test-rmse:2.067710+0.505867
## [49] train-rmse:0.648595+0.009784 test-rmse:2.063477+0.507335
## [50] train-rmse:0.635057+0.010546 test-rmse:2.062764+0.510235
## [51] train-rmse:0.624882+0.010263 test-rmse:2.061150+0.509933
## [52] train-rmse:0.605719+0.012703 test-rmse:2.060764+0.505017
## [53] train-rmse:0.597697+0.014920 test-rmse:2.062712+0.504915
## [54] train-rmse:0.589102+0.013022 test-rmse:2.067249+0.512483
## [55] train-rmse:0.579426+0.014206 test-rmse:2.072927+0.514731
## [56] train-rmse:0.569967+0.013196 test-rmse:2.069985+0.515801
## [57] train-rmse:0.561766+0.011692 test-rmse:2.067025+0.516076
## [58] train-rmse:0.553689+0.012677 test-rmse:2.067276+0.516915
## Stopping. Best iteration:
## [52] train-rmse:0.605719+0.012703 test-rmse:2.060764+0.505017
##
## 108
## [1] train-rmse:5.499180+0.106910 test-rmse:5.616375+0.546957
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.910424+0.085138 test-rmse:4.147719+0.426867
## [3] train-rmse:3.006484+0.063702 test-rmse:3.378792+0.376636
## [4] train-rmse:2.494177+0.051336 test-rmse:2.991538+0.421633
## [5] train-rmse:2.182996+0.058691 test-rmse:2.808057+0.408081
## [6] train-rmse:1.962717+0.039150 test-rmse:2.714177+0.450594
## [7] train-rmse:1.790056+0.059781 test-rmse:2.631938+0.427292
## [8] train-rmse:1.678616+0.055264 test-rmse:2.557894+0.438257
## [9] train-rmse:1.563124+0.067950 test-rmse:2.511798+0.427101
## [10] train-rmse:1.482189+0.059944 test-rmse:2.466711+0.450437
## [11] train-rmse:1.410227+0.061381 test-rmse:2.432293+0.448289
## [12] train-rmse:1.335730+0.071050 test-rmse:2.393907+0.448295
## [13] train-rmse:1.274217+0.061143 test-rmse:2.381606+0.436138
## [14] train-rmse:1.201407+0.053481 test-rmse:2.342641+0.429591
## [15] train-rmse:1.154007+0.054580 test-rmse:2.328724+0.424625
## [16] train-rmse:1.111657+0.063915 test-rmse:2.322916+0.441344
## [17] train-rmse:1.051448+0.044570 test-rmse:2.303047+0.429601
## [18] train-rmse:0.991970+0.030396 test-rmse:2.287728+0.427304
## [19] train-rmse:0.954970+0.035191 test-rmse:2.283872+0.431691
## [20] train-rmse:0.924627+0.031045 test-rmse:2.275116+0.440614
## [21] train-rmse:0.883842+0.041493 test-rmse:2.262263+0.444539
## [22] train-rmse:0.850877+0.041570 test-rmse:2.256889+0.439743
## [23] train-rmse:0.819986+0.037854 test-rmse:2.251990+0.440625
## [24] train-rmse:0.789792+0.033698 test-rmse:2.263087+0.453944
## [25] train-rmse:0.762564+0.030431 test-rmse:2.260091+0.455282
## [26] train-rmse:0.738363+0.022127 test-rmse:2.251488+0.457199
## [27] train-rmse:0.707849+0.018612 test-rmse:2.246388+0.461692
## [28] train-rmse:0.682552+0.016968 test-rmse:2.239140+0.466210
## [29] train-rmse:0.666452+0.018980 test-rmse:2.234024+0.468582
## [30] train-rmse:0.647652+0.019754 test-rmse:2.231646+0.468227
## [31] train-rmse:0.630509+0.021071 test-rmse:2.244860+0.475692
## [32] train-rmse:0.607043+0.026924 test-rmse:2.237346+0.477236
## [33] train-rmse:0.589302+0.021926 test-rmse:2.230190+0.480417
## [34] train-rmse:0.567979+0.027188 test-rmse:2.226762+0.493297
## [35] train-rmse:0.544229+0.028877 test-rmse:2.218618+0.495034
## [36] train-rmse:0.521891+0.023992 test-rmse:2.218100+0.499653
## [37] train-rmse:0.504415+0.023502 test-rmse:2.212546+0.501629
## [38] train-rmse:0.490148+0.020573 test-rmse:2.209230+0.498148
## [39] train-rmse:0.473464+0.023282 test-rmse:2.207346+0.499161
## [40] train-rmse:0.457557+0.024444 test-rmse:2.204563+0.496060
## [41] train-rmse:0.443914+0.022325 test-rmse:2.207409+0.500850
## [42] train-rmse:0.431599+0.019268 test-rmse:2.204959+0.496047
## [43] train-rmse:0.421254+0.021267 test-rmse:2.204630+0.497534
## [44] train-rmse:0.406047+0.022293 test-rmse:2.205277+0.497015
## [45] train-rmse:0.392973+0.023453 test-rmse:2.202161+0.495425
## [46] train-rmse:0.384607+0.023727 test-rmse:2.199922+0.496039
## [47] train-rmse:0.376193+0.023443 test-rmse:2.199617+0.493206
## [48] train-rmse:0.367940+0.020542 test-rmse:2.197215+0.492399
## [49] train-rmse:0.358226+0.018549 test-rmse:2.196413+0.493351
## [50] train-rmse:0.347004+0.016962 test-rmse:2.193938+0.493300
## [51] train-rmse:0.329673+0.017029 test-rmse:2.191785+0.491620
## [52] train-rmse:0.321667+0.017108 test-rmse:2.190773+0.492870
## [53] train-rmse:0.313469+0.015285 test-rmse:2.188029+0.493468
## [54] train-rmse:0.303095+0.016709 test-rmse:2.186689+0.495822
## [55] train-rmse:0.295585+0.017422 test-rmse:2.187626+0.495555
## [56] train-rmse:0.287059+0.017399 test-rmse:2.187487+0.494632
## [57] train-rmse:0.278892+0.016386 test-rmse:2.187835+0.495032
## [58] train-rmse:0.272266+0.014845 test-rmse:2.187838+0.495272
## [59] train-rmse:0.265064+0.013630 test-rmse:2.187828+0.495051
## [60] train-rmse:0.257095+0.010758 test-rmse:2.186289+0.496186
## [61] train-rmse:0.250642+0.010810 test-rmse:2.183063+0.498368
## [62] train-rmse:0.244173+0.014529 test-rmse:2.181862+0.498013
## [63] train-rmse:0.239403+0.014246 test-rmse:2.182669+0.497098
## [64] train-rmse:0.234494+0.013045 test-rmse:2.183999+0.496792
## [65] train-rmse:0.228426+0.012940 test-rmse:2.184431+0.496935
## [66] train-rmse:0.223341+0.011408 test-rmse:2.186359+0.497799
## [67] train-rmse:0.218784+0.010190 test-rmse:2.185965+0.496615
## [68] train-rmse:0.214231+0.009793 test-rmse:2.185166+0.495491
## Stopping. Best iteration:
## [62] train-rmse:0.244173+0.014529 test-rmse:2.181862+0.498013
##
## 109
## [1] train-rmse:5.412213+0.104001 test-rmse:5.476866+0.510779
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.775840+0.074201 test-rmse:4.003196+0.435319
## [3] train-rmse:2.883938+0.063261 test-rmse:3.304095+0.484817
## [4] train-rmse:2.300578+0.063836 test-rmse:2.877624+0.514337
## [5] train-rmse:1.975414+0.066890 test-rmse:2.737961+0.570176
## [6] train-rmse:1.748899+0.097613 test-rmse:2.632839+0.595618
## [7] train-rmse:1.585824+0.096707 test-rmse:2.566244+0.591605
## [8] train-rmse:1.445456+0.080853 test-rmse:2.508075+0.588816
## [9] train-rmse:1.339979+0.066911 test-rmse:2.481131+0.577231
## [10] train-rmse:1.258379+0.070095 test-rmse:2.441235+0.579090
## [11] train-rmse:1.171211+0.070300 test-rmse:2.412688+0.578014
## [12] train-rmse:1.101837+0.074320 test-rmse:2.417761+0.582499
## [13] train-rmse:1.045796+0.062804 test-rmse:2.388298+0.586291
## [14] train-rmse:0.984861+0.051668 test-rmse:2.371970+0.577347
## [15] train-rmse:0.934007+0.056311 test-rmse:2.364803+0.585448
## [16] train-rmse:0.890397+0.060332 test-rmse:2.352457+0.575010
## [17] train-rmse:0.849951+0.056510 test-rmse:2.350950+0.573759
## [18] train-rmse:0.815510+0.056480 test-rmse:2.345644+0.581068
## [19] train-rmse:0.771208+0.050448 test-rmse:2.343376+0.575182
## [20] train-rmse:0.731636+0.043871 test-rmse:2.333891+0.580893
## [21] train-rmse:0.699698+0.033069 test-rmse:2.323078+0.590566
## [22] train-rmse:0.664068+0.020282 test-rmse:2.326452+0.598831
## [23] train-rmse:0.630131+0.024845 test-rmse:2.322783+0.599037
## [24] train-rmse:0.603055+0.024114 test-rmse:2.331119+0.609339
## [25] train-rmse:0.576780+0.021674 test-rmse:2.321402+0.609711
## [26] train-rmse:0.552763+0.018716 test-rmse:2.308822+0.612100
## [27] train-rmse:0.519475+0.016838 test-rmse:2.302717+0.611270
## [28] train-rmse:0.489348+0.018050 test-rmse:2.314587+0.614924
## [29] train-rmse:0.475583+0.018563 test-rmse:2.314655+0.615233
## [30] train-rmse:0.458497+0.025215 test-rmse:2.304903+0.607849
## [31] train-rmse:0.442379+0.027345 test-rmse:2.305069+0.610161
## [32] train-rmse:0.431105+0.025774 test-rmse:2.300139+0.615656
## [33] train-rmse:0.413349+0.024206 test-rmse:2.297259+0.616791
## [34] train-rmse:0.390815+0.023697 test-rmse:2.291348+0.616171
## [35] train-rmse:0.380126+0.021360 test-rmse:2.292225+0.618794
## [36] train-rmse:0.361507+0.018148 test-rmse:2.287944+0.618390
## [37] train-rmse:0.351075+0.017069 test-rmse:2.286452+0.620779
## [38] train-rmse:0.335053+0.024159 test-rmse:2.287990+0.623923
## [39] train-rmse:0.320999+0.025588 test-rmse:2.287045+0.625535
## [40] train-rmse:0.309670+0.029209 test-rmse:2.286944+0.622350
## [41] train-rmse:0.296414+0.024386 test-rmse:2.283197+0.623778
## [42] train-rmse:0.283465+0.025902 test-rmse:2.278986+0.626346
## [43] train-rmse:0.272912+0.026062 test-rmse:2.278770+0.627011
## [44] train-rmse:0.263864+0.026547 test-rmse:2.280161+0.628065
## [45] train-rmse:0.250645+0.025622 test-rmse:2.278847+0.630128
## [46] train-rmse:0.241776+0.023977 test-rmse:2.282533+0.626273
## [47] train-rmse:0.234692+0.023613 test-rmse:2.281614+0.628411
## [48] train-rmse:0.226885+0.023480 test-rmse:2.281091+0.630154
## [49] train-rmse:0.218065+0.025020 test-rmse:2.278905+0.630666
## Stopping. Best iteration:
## [43] train-rmse:0.272912+0.026062 test-rmse:2.278770+0.627011
##
## 110
## [1] train-rmse:5.357983+0.093768 test-rmse:5.446087+0.561213
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.693181+0.063796 test-rmse:4.009262+0.467678
## [3] train-rmse:2.739141+0.033189 test-rmse:3.285400+0.489625
## [4] train-rmse:2.165486+0.024889 test-rmse:2.875536+0.556486
## [5] train-rmse:1.785500+0.048594 test-rmse:2.681458+0.578015
## [6] train-rmse:1.550391+0.022686 test-rmse:2.575895+0.557276
## [7] train-rmse:1.362336+0.054788 test-rmse:2.488441+0.585022
## [8] train-rmse:1.230784+0.046756 test-rmse:2.424439+0.564774
## [9] train-rmse:1.139054+0.055443 test-rmse:2.394543+0.558852
## [10] train-rmse:1.021930+0.052595 test-rmse:2.358169+0.548121
## [11] train-rmse:0.948505+0.050197 test-rmse:2.347541+0.545104
## [12] train-rmse:0.865019+0.045855 test-rmse:2.331010+0.558165
## [13] train-rmse:0.804959+0.050300 test-rmse:2.314861+0.561951
## [14] train-rmse:0.761804+0.055557 test-rmse:2.302363+0.555430
## [15] train-rmse:0.707470+0.043983 test-rmse:2.285064+0.551810
## [16] train-rmse:0.662452+0.038748 test-rmse:2.278979+0.556831
## [17] train-rmse:0.606503+0.024815 test-rmse:2.283153+0.561574
## [18] train-rmse:0.578203+0.021419 test-rmse:2.284731+0.564556
## [19] train-rmse:0.537969+0.008135 test-rmse:2.283907+0.569258
## [20] train-rmse:0.514124+0.010223 test-rmse:2.278262+0.571543
## [21] train-rmse:0.472307+0.016839 test-rmse:2.262772+0.562136
## [22] train-rmse:0.449277+0.013409 test-rmse:2.260295+0.561611
## [23] train-rmse:0.421006+0.018365 test-rmse:2.255417+0.563122
## [24] train-rmse:0.395492+0.014460 test-rmse:2.245477+0.562508
## [25] train-rmse:0.379262+0.016374 test-rmse:2.244457+0.561395
## [26] train-rmse:0.360525+0.023295 test-rmse:2.241255+0.560790
## [27] train-rmse:0.343274+0.019845 test-rmse:2.242403+0.561255
## [28] train-rmse:0.324063+0.020310 test-rmse:2.240569+0.561601
## [29] train-rmse:0.307052+0.018329 test-rmse:2.239626+0.560379
## [30] train-rmse:0.288863+0.013277 test-rmse:2.237503+0.562153
## [31] train-rmse:0.278281+0.014004 test-rmse:2.238239+0.563053
## [32] train-rmse:0.264657+0.012756 test-rmse:2.238701+0.562275
## [33] train-rmse:0.249207+0.009974 test-rmse:2.237683+0.562661
## [34] train-rmse:0.233979+0.011900 test-rmse:2.237460+0.561505
## [35] train-rmse:0.222679+0.008353 test-rmse:2.237673+0.557982
## [36] train-rmse:0.210783+0.008475 test-rmse:2.236304+0.556908
## [37] train-rmse:0.197570+0.009022 test-rmse:2.235087+0.557486
## [38] train-rmse:0.189176+0.008642 test-rmse:2.233170+0.555370
## [39] train-rmse:0.178715+0.007630 test-rmse:2.234017+0.555625
## [40] train-rmse:0.170670+0.009715 test-rmse:2.234437+0.554669
## [41] train-rmse:0.163062+0.010100 test-rmse:2.235010+0.553836
## [42] train-rmse:0.158690+0.010330 test-rmse:2.235420+0.552906
## [43] train-rmse:0.150728+0.008570 test-rmse:2.234705+0.552087
## [44] train-rmse:0.143876+0.009468 test-rmse:2.235291+0.551158
## Stopping. Best iteration:
## [38] train-rmse:0.189176+0.008642 test-rmse:2.233170+0.555370
##
## 111
## [1] train-rmse:5.338599+0.092070 test-rmse:5.432893+0.561093
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.618019+0.052290 test-rmse:3.973736+0.475476
## [3] train-rmse:2.632378+0.033575 test-rmse:3.212463+0.502699
## [4] train-rmse:2.035932+0.034793 test-rmse:2.849638+0.521042
## [5] train-rmse:1.633744+0.049469 test-rmse:2.658568+0.512480
## [6] train-rmse:1.384138+0.052795 test-rmse:2.543886+0.512309
## [7] train-rmse:1.215447+0.035256 test-rmse:2.479673+0.549347
## [8] train-rmse:1.085299+0.023828 test-rmse:2.456031+0.568558
## [9] train-rmse:0.976554+0.037289 test-rmse:2.434782+0.572216
## [10] train-rmse:0.886327+0.039378 test-rmse:2.399720+0.569817
## [11] train-rmse:0.813370+0.049617 test-rmse:2.386607+0.576436
## [12] train-rmse:0.728870+0.035646 test-rmse:2.376159+0.576722
## [13] train-rmse:0.660396+0.036183 test-rmse:2.371677+0.579821
## [14] train-rmse:0.610030+0.032332 test-rmse:2.366411+0.577984
## [15] train-rmse:0.572836+0.034055 test-rmse:2.362878+0.582540
## [16] train-rmse:0.515243+0.030936 test-rmse:2.361196+0.585628
## [17] train-rmse:0.469478+0.027029 test-rmse:2.358107+0.582375
## [18] train-rmse:0.435088+0.027602 test-rmse:2.354078+0.586954
## [19] train-rmse:0.407267+0.028170 test-rmse:2.350805+0.591906
## [20] train-rmse:0.381880+0.026200 test-rmse:2.348951+0.596132
## [21] train-rmse:0.353893+0.022598 test-rmse:2.352544+0.595746
## [22] train-rmse:0.333476+0.021562 test-rmse:2.354872+0.592300
## [23] train-rmse:0.314003+0.023557 test-rmse:2.353490+0.593715
## [24] train-rmse:0.287174+0.027463 test-rmse:2.347748+0.595234
## [25] train-rmse:0.265266+0.031738 test-rmse:2.349423+0.595472
## [26] train-rmse:0.246799+0.030090 test-rmse:2.348133+0.594093
## [27] train-rmse:0.233421+0.028157 test-rmse:2.344989+0.594008
## [28] train-rmse:0.217045+0.027757 test-rmse:2.344328+0.594163
## [29] train-rmse:0.203458+0.025243 test-rmse:2.340587+0.591717
## [30] train-rmse:0.191251+0.025357 test-rmse:2.340211+0.593050
## [31] train-rmse:0.180666+0.021568 test-rmse:2.337618+0.592289
## [32] train-rmse:0.169705+0.023677 test-rmse:2.337239+0.591845
## [33] train-rmse:0.159614+0.022816 test-rmse:2.336596+0.592128
## [34] train-rmse:0.151800+0.021396 test-rmse:2.336743+0.592107
## [35] train-rmse:0.141695+0.017709 test-rmse:2.334749+0.593656
## [36] train-rmse:0.134108+0.016544 test-rmse:2.334890+0.592276
## [37] train-rmse:0.125124+0.017679 test-rmse:2.334013+0.592388
## [38] train-rmse:0.116147+0.017471 test-rmse:2.332909+0.593439
## [39] train-rmse:0.110512+0.017452 test-rmse:2.331009+0.593117
## [40] train-rmse:0.104423+0.017739 test-rmse:2.330995+0.593557
## [41] train-rmse:0.097236+0.017614 test-rmse:2.330885+0.594962
## [42] train-rmse:0.091999+0.016947 test-rmse:2.330279+0.594459
## [43] train-rmse:0.085769+0.016462 test-rmse:2.330300+0.594119
## [44] train-rmse:0.080405+0.015696 test-rmse:2.332014+0.595283
## [45] train-rmse:0.075991+0.015196 test-rmse:2.332940+0.594114
## [46] train-rmse:0.071710+0.014324 test-rmse:2.333249+0.594230
## [47] train-rmse:0.068046+0.012908 test-rmse:2.332913+0.593527
## [48] train-rmse:0.063699+0.010624 test-rmse:2.333394+0.592966
## Stopping. Best iteration:
## [42] train-rmse:0.091999+0.016947 test-rmse:2.330279+0.594459
##
## 112## max_depth eta
## 10 3 0.12## [1] train-rmse:1.296633+0.005288 test-rmse:1.296483+0.023806
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.172200+0.004322 test-rmse:1.172116+0.022422
## [3] train-rmse:1.060667+0.003414 test-rmse:1.060655+0.021101
## [4] train-rmse:0.960789+0.002564 test-rmse:0.960914+0.019836
## [5] train-rmse:0.871446+0.001784 test-rmse:0.871740+0.018630
## [6] train-rmse:0.791639+0.001140 test-rmse:0.792117+0.017447
## [7] train-rmse:0.720471+0.000866 test-rmse:0.721139+0.016305
## [8] train-rmse:0.657130+0.001188 test-rmse:0.658775+0.015618
## [9] train-rmse:0.600742+0.001670 test-rmse:0.603127+0.014642
## [10] train-rmse:0.550441+0.002167 test-rmse:0.552952+0.013272
## [11] train-rmse:0.505859+0.002593 test-rmse:0.509293+0.012049
## [12] train-rmse:0.466247+0.002981 test-rmse:0.469968+0.010849
## [13] train-rmse:0.431255+0.003347 test-rmse:0.435189+0.011419
## [14] train-rmse:0.400296+0.003687 test-rmse:0.405147+0.011459
## [15] train-rmse:0.373125+0.003998 test-rmse:0.377762+0.012111
## [16] train-rmse:0.349219+0.004288 test-rmse:0.354480+0.012700
## [17] train-rmse:0.328382+0.004545 test-rmse:0.333978+0.013910
## [18] train-rmse:0.310156+0.004791 test-rmse:0.316315+0.015038
## [19] train-rmse:0.294381+0.004999 test-rmse:0.300607+0.015915
## [20] train-rmse:0.280668+0.005203 test-rmse:0.287067+0.017095
## [21] train-rmse:0.268889+0.005360 test-rmse:0.275309+0.017827
## [22] train-rmse:0.258685+0.005496 test-rmse:0.265244+0.019098
## [23] train-rmse:0.249976+0.005613 test-rmse:0.257027+0.019896
## [24] train-rmse:0.242465+0.005706 test-rmse:0.249720+0.021044
## [25] train-rmse:0.236091+0.005783 test-rmse:0.243379+0.021321
## [26] train-rmse:0.230588+0.005826 test-rmse:0.237950+0.022358
## [27] train-rmse:0.225944+0.005877 test-rmse:0.233352+0.022456
## [28] train-rmse:0.221935+0.005892 test-rmse:0.229361+0.023341
## [29] train-rmse:0.218477+0.005921 test-rmse:0.226935+0.023213
## [30] train-rmse:0.215479+0.005924 test-rmse:0.224339+0.024068
## [31] train-rmse:0.212866+0.005936 test-rmse:0.222786+0.024395
## [32] train-rmse:0.210564+0.005922 test-rmse:0.220520+0.025001
## [33] train-rmse:0.208522+0.005894 test-rmse:0.219730+0.024832
## [34] train-rmse:0.206683+0.005827 test-rmse:0.218487+0.024896
## [35] train-rmse:0.205047+0.005780 test-rmse:0.216878+0.025039
## [36] train-rmse:0.203561+0.005713 test-rmse:0.216046+0.025925
## [37] train-rmse:0.202211+0.005659 test-rmse:0.215390+0.025933
## [38] train-rmse:0.200979+0.005614 test-rmse:0.215198+0.025573
## [39] train-rmse:0.199853+0.005554 test-rmse:0.214357+0.025569
## [40] train-rmse:0.198796+0.005493 test-rmse:0.213645+0.026329
## [41] train-rmse:0.197809+0.005455 test-rmse:0.213048+0.026209
## [42] train-rmse:0.196883+0.005413 test-rmse:0.213310+0.026757
## [43] train-rmse:0.196024+0.005374 test-rmse:0.213188+0.026838
## [44] train-rmse:0.195208+0.005359 test-rmse:0.213260+0.026287
## [45] train-rmse:0.194446+0.005338 test-rmse:0.211850+0.026502
## [46] train-rmse:0.193698+0.005302 test-rmse:0.212265+0.026869
## [47] train-rmse:0.193001+0.005293 test-rmse:0.212228+0.026868
## [48] train-rmse:0.192325+0.005297 test-rmse:0.211900+0.027194
## [49] train-rmse:0.191693+0.005291 test-rmse:0.212405+0.027504
## [50] train-rmse:0.191075+0.005302 test-rmse:0.212307+0.026724
## [51] train-rmse:0.190486+0.005313 test-rmse:0.211759+0.026764
## [52] train-rmse:0.189911+0.005322 test-rmse:0.211669+0.026793
## [53] train-rmse:0.189356+0.005335 test-rmse:0.211809+0.027336
## [54] train-rmse:0.188808+0.005356 test-rmse:0.211729+0.027208
## [55] train-rmse:0.188289+0.005385 test-rmse:0.211670+0.027222
## [56] train-rmse:0.187801+0.005402 test-rmse:0.211121+0.027119
## [57] train-rmse:0.187323+0.005422 test-rmse:0.211076+0.026387
## [58] train-rmse:0.186833+0.005460 test-rmse:0.211596+0.026643
## [59] train-rmse:0.186378+0.005491 test-rmse:0.211474+0.026577
## [60] train-rmse:0.185926+0.005520 test-rmse:0.211044+0.026649
## [61] train-rmse:0.185492+0.005558 test-rmse:0.211631+0.026902
## [62] train-rmse:0.185073+0.005598 test-rmse:0.211645+0.026311
## [63] train-rmse:0.184664+0.005633 test-rmse:0.211719+0.026558
## [64] train-rmse:0.184267+0.005667 test-rmse:0.211394+0.026361
## [65] train-rmse:0.183882+0.005699 test-rmse:0.211020+0.026175
## [66] train-rmse:0.183491+0.005735 test-rmse:0.211437+0.026754
## [67] train-rmse:0.183109+0.005762 test-rmse:0.211440+0.026686
## [68] train-rmse:0.182742+0.005798 test-rmse:0.211061+0.026391
## [69] train-rmse:0.182388+0.005831 test-rmse:0.211219+0.026982
## [70] train-rmse:0.182047+0.005869 test-rmse:0.211473+0.026433
## [71] train-rmse:0.181711+0.005903 test-rmse:0.211223+0.026307
## Stopping. Best iteration:
## [65] train-rmse:0.183882+0.005699 test-rmse:0.211020+0.026175
##
## 1
## [1] train-rmse:1.295044+0.005591 test-rmse:1.295424+0.023218
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.168947+0.004923 test-rmse:1.169990+0.021363
## [3] train-rmse:1.055671+0.004315 test-rmse:1.057340+0.019648
## [4] train-rmse:0.953912+0.003760 test-rmse:0.956423+0.018095
## [5] train-rmse:0.862568+0.003283 test-rmse:0.866159+0.016804
## [6] train-rmse:0.780637+0.002847 test-rmse:0.785200+0.015563
## [7] train-rmse:0.707191+0.002489 test-rmse:0.712900+0.014531
## [8] train-rmse:0.641426+0.002180 test-rmse:0.648371+0.013638
## [9] train-rmse:0.582617+0.001941 test-rmse:0.590889+0.012914
## [10] train-rmse:0.530126+0.001750 test-rmse:0.540543+0.012229
## [11] train-rmse:0.483269+0.001647 test-rmse:0.495290+0.011839
## [12] train-rmse:0.441573+0.001586 test-rmse:0.455698+0.011546
## [13] train-rmse:0.404527+0.001572 test-rmse:0.420519+0.011491
## [14] train-rmse:0.371731+0.001641 test-rmse:0.390025+0.011847
## [15] train-rmse:0.342665+0.001683 test-rmse:0.363274+0.012444
## [16] train-rmse:0.317063+0.001784 test-rmse:0.339857+0.013150
## [17] train-rmse:0.294515+0.001833 test-rmse:0.319714+0.013802
## [18] train-rmse:0.274761+0.001922 test-rmse:0.302881+0.014999
## [19] train-rmse:0.257516+0.001956 test-rmse:0.287602+0.016061
## [20] train-rmse:0.242494+0.002017 test-rmse:0.275381+0.017061
## [21] train-rmse:0.229480+0.002034 test-rmse:0.264492+0.018456
## [22] train-rmse:0.218208+0.002040 test-rmse:0.255876+0.019669
## [23] train-rmse:0.207818+0.001660 test-rmse:0.247592+0.021198
## [24] train-rmse:0.199468+0.001717 test-rmse:0.241305+0.022060
## [25] train-rmse:0.191834+0.001328 test-rmse:0.235754+0.023097
## [26] train-rmse:0.185722+0.001305 test-rmse:0.231008+0.023708
## [27] train-rmse:0.180255+0.001651 test-rmse:0.226954+0.024096
## [28] train-rmse:0.175310+0.002127 test-rmse:0.223361+0.024429
## [29] train-rmse:0.171292+0.002408 test-rmse:0.220835+0.025113
## [30] train-rmse:0.167896+0.002810 test-rmse:0.218436+0.025512
## [31] train-rmse:0.164627+0.002938 test-rmse:0.217158+0.025645
## [32] train-rmse:0.161584+0.003021 test-rmse:0.215901+0.025548
## [33] train-rmse:0.159616+0.003136 test-rmse:0.214875+0.026146
## [34] train-rmse:0.157218+0.003399 test-rmse:0.214194+0.026197
## [35] train-rmse:0.155232+0.003640 test-rmse:0.213487+0.026348
## [36] train-rmse:0.153950+0.003795 test-rmse:0.212946+0.026696
## [37] train-rmse:0.152914+0.003832 test-rmse:0.212967+0.026732
## [38] train-rmse:0.151043+0.003752 test-rmse:0.212873+0.026576
## [39] train-rmse:0.149857+0.003770 test-rmse:0.212677+0.027051
## [40] train-rmse:0.148906+0.003811 test-rmse:0.212571+0.027315
## [41] train-rmse:0.147819+0.004269 test-rmse:0.212253+0.027269
## [42] train-rmse:0.147042+0.004588 test-rmse:0.212110+0.027438
## [43] train-rmse:0.145357+0.004096 test-rmse:0.212451+0.027559
## [44] train-rmse:0.144055+0.004346 test-rmse:0.211932+0.027617
## [45] train-rmse:0.143215+0.004129 test-rmse:0.212046+0.027888
## [46] train-rmse:0.142645+0.004373 test-rmse:0.211937+0.028042
## [47] train-rmse:0.141427+0.004062 test-rmse:0.211986+0.028224
## [48] train-rmse:0.140730+0.004360 test-rmse:0.211874+0.028196
## [49] train-rmse:0.139795+0.004282 test-rmse:0.211848+0.028181
## [50] train-rmse:0.139351+0.004480 test-rmse:0.211788+0.028208
## [51] train-rmse:0.138580+0.004284 test-rmse:0.211957+0.028169
## [52] train-rmse:0.137827+0.004529 test-rmse:0.211817+0.028243
## [53] train-rmse:0.137231+0.004177 test-rmse:0.212032+0.028448
## [54] train-rmse:0.136691+0.004077 test-rmse:0.212253+0.028771
## [55] train-rmse:0.136247+0.004107 test-rmse:0.212203+0.028881
## [56] train-rmse:0.135651+0.004420 test-rmse:0.211983+0.028954
## Stopping. Best iteration:
## [50] train-rmse:0.139351+0.004480 test-rmse:0.211788+0.028208
##
## 2
## [1] train-rmse:1.294731+0.005579 test-rmse:1.295658+0.022907
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.168290+0.004887 test-rmse:1.170480+0.020743
## [3] train-rmse:1.054596+0.004256 test-rmse:1.058041+0.018688
## [4] train-rmse:0.952392+0.003702 test-rmse:0.957428+0.016848
## [5] train-rmse:0.860542+0.003212 test-rmse:0.867444+0.015163
## [6] train-rmse:0.778011+0.002760 test-rmse:0.787100+0.013692
## [7] train-rmse:0.703921+0.002360 test-rmse:0.715147+0.012360
## [8] train-rmse:0.637427+0.002026 test-rmse:0.651209+0.011003
## [9] train-rmse:0.577806+0.001781 test-rmse:0.594603+0.009942
## [10] train-rmse:0.524387+0.001650 test-rmse:0.544003+0.009011
## [11] train-rmse:0.476391+0.001620 test-rmse:0.499173+0.008715
## [12] train-rmse:0.433450+0.001893 test-rmse:0.459293+0.009168
## [13] train-rmse:0.395235+0.002197 test-rmse:0.425165+0.008667
## [14] train-rmse:0.360994+0.002799 test-rmse:0.394866+0.009286
## [15] train-rmse:0.330526+0.003403 test-rmse:0.369024+0.009807
## [16] train-rmse:0.303157+0.004019 test-rmse:0.345405+0.010721
## [17] train-rmse:0.278698+0.004565 test-rmse:0.325966+0.011557
## [18] train-rmse:0.256777+0.004615 test-rmse:0.308607+0.012782
## [19] train-rmse:0.237323+0.004834 test-rmse:0.294310+0.014064
## [20] train-rmse:0.220155+0.004948 test-rmse:0.281765+0.015071
## [21] train-rmse:0.204891+0.005218 test-rmse:0.271596+0.016829
## [22] train-rmse:0.191680+0.005311 test-rmse:0.262668+0.017944
## [23] train-rmse:0.180024+0.005703 test-rmse:0.255620+0.019492
## [24] train-rmse:0.169699+0.006045 test-rmse:0.248931+0.020536
## [25] train-rmse:0.160733+0.006182 test-rmse:0.244625+0.021833
## [26] train-rmse:0.152676+0.006675 test-rmse:0.240333+0.022980
## [27] train-rmse:0.145905+0.006865 test-rmse:0.237072+0.024025
## [28] train-rmse:0.139671+0.007379 test-rmse:0.234164+0.024781
## [29] train-rmse:0.134180+0.007831 test-rmse:0.232115+0.025465
## [30] train-rmse:0.129521+0.007897 test-rmse:0.230311+0.026312
## [31] train-rmse:0.125522+0.007918 test-rmse:0.228682+0.026828
## [32] train-rmse:0.122077+0.007959 test-rmse:0.227478+0.027562
## [33] train-rmse:0.118943+0.008101 test-rmse:0.226389+0.027708
## [34] train-rmse:0.116313+0.008186 test-rmse:0.225733+0.028265
## [35] train-rmse:0.113767+0.008376 test-rmse:0.224917+0.028034
## [36] train-rmse:0.111796+0.008475 test-rmse:0.224559+0.028563
## [37] train-rmse:0.109905+0.008759 test-rmse:0.224288+0.028764
## [38] train-rmse:0.108479+0.008666 test-rmse:0.224172+0.028912
## [39] train-rmse:0.107064+0.008718 test-rmse:0.223841+0.029085
## [40] train-rmse:0.105954+0.008633 test-rmse:0.223838+0.029419
## [41] train-rmse:0.104627+0.008632 test-rmse:0.224069+0.029752
## [42] train-rmse:0.103052+0.008658 test-rmse:0.224156+0.029826
## [43] train-rmse:0.101656+0.008667 test-rmse:0.224537+0.030216
## [44] train-rmse:0.100487+0.008843 test-rmse:0.224513+0.030348
## [45] train-rmse:0.099332+0.008868 test-rmse:0.224990+0.030830
## [46] train-rmse:0.098298+0.008973 test-rmse:0.225416+0.031192
## Stopping. Best iteration:
## [40] train-rmse:0.105954+0.008633 test-rmse:0.223838+0.029419
##
## 3
## [1] train-rmse:1.294618+0.005588 test-rmse:1.295658+0.022888
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.168039+0.004903 test-rmse:1.170497+0.020703
## [3] train-rmse:1.054194+0.004286 test-rmse:1.058135+0.018641
## [4] train-rmse:0.951832+0.003713 test-rmse:0.957463+0.016740
## [5] train-rmse:0.859843+0.003190 test-rmse:0.867359+0.014987
## [6] train-rmse:0.777154+0.002772 test-rmse:0.787185+0.013524
## [7] train-rmse:0.702591+0.002129 test-rmse:0.715588+0.012312
## [8] train-rmse:0.635468+0.001671 test-rmse:0.651769+0.011118
## [9] train-rmse:0.575030+0.001330 test-rmse:0.594867+0.009898
## [10] train-rmse:0.520903+0.001261 test-rmse:0.544544+0.008825
## [11] train-rmse:0.471928+0.001043 test-rmse:0.499951+0.008066
## [12] train-rmse:0.427915+0.001093 test-rmse:0.460913+0.007630
## [13] train-rmse:0.388425+0.001178 test-rmse:0.426594+0.007923
## [14] train-rmse:0.353003+0.001467 test-rmse:0.396772+0.008532
## [15] train-rmse:0.321218+0.001806 test-rmse:0.371196+0.009463
## [16] train-rmse:0.292776+0.002189 test-rmse:0.349031+0.010566
## [17] train-rmse:0.267394+0.002520 test-rmse:0.329804+0.011814
## [18] train-rmse:0.244364+0.002933 test-rmse:0.313385+0.013219
## [19] train-rmse:0.223759+0.003396 test-rmse:0.299066+0.014754
## [20] train-rmse:0.205354+0.003755 test-rmse:0.287259+0.016365
## [21] train-rmse:0.188881+0.004042 test-rmse:0.277082+0.018180
## [22] train-rmse:0.174263+0.004374 test-rmse:0.268567+0.019749
## [23] train-rmse:0.161220+0.004681 test-rmse:0.261581+0.021382
## [24] train-rmse:0.149579+0.004892 test-rmse:0.256070+0.022911
## [25] train-rmse:0.139311+0.005163 test-rmse:0.251309+0.024471
## [26] train-rmse:0.130099+0.005544 test-rmse:0.247811+0.025696
## [27] train-rmse:0.121969+0.005912 test-rmse:0.244702+0.027016
## [28] train-rmse:0.114788+0.006289 test-rmse:0.242153+0.027804
## [29] train-rmse:0.108502+0.006607 test-rmse:0.240258+0.028716
## [30] train-rmse:0.103075+0.007025 test-rmse:0.238430+0.029577
## [31] train-rmse:0.098291+0.007442 test-rmse:0.236724+0.030044
## [32] train-rmse:0.094127+0.007836 test-rmse:0.235693+0.030534
## [33] train-rmse:0.090458+0.008150 test-rmse:0.235439+0.031372
## [34] train-rmse:0.087256+0.008196 test-rmse:0.234811+0.031839
## [35] train-rmse:0.084299+0.008148 test-rmse:0.234244+0.032219
## [36] train-rmse:0.081977+0.008297 test-rmse:0.233953+0.032558
## [37] train-rmse:0.079811+0.008365 test-rmse:0.233743+0.032955
## [38] train-rmse:0.077735+0.008585 test-rmse:0.233346+0.033041
## [39] train-rmse:0.075841+0.008768 test-rmse:0.233080+0.033165
## [40] train-rmse:0.074167+0.008965 test-rmse:0.233160+0.033500
## [41] train-rmse:0.072679+0.008934 test-rmse:0.232962+0.033652
## [42] train-rmse:0.071354+0.009060 test-rmse:0.232808+0.033721
## [43] train-rmse:0.070143+0.008799 test-rmse:0.232595+0.033774
## [44] train-rmse:0.069029+0.008670 test-rmse:0.232270+0.033721
## [45] train-rmse:0.067950+0.008592 test-rmse:0.232358+0.033860
## [46] train-rmse:0.067052+0.008553 test-rmse:0.232418+0.034031
## [47] train-rmse:0.065977+0.008527 test-rmse:0.232289+0.034004
## [48] train-rmse:0.065189+0.008550 test-rmse:0.232417+0.034122
## [49] train-rmse:0.064239+0.008665 test-rmse:0.232567+0.034221
## [50] train-rmse:0.063271+0.008489 test-rmse:0.232633+0.034243
## Stopping. Best iteration:
## [44] train-rmse:0.069029+0.008670 test-rmse:0.232270+0.033721
##
## 4
## [1] train-rmse:1.294546+0.005583 test-rmse:1.295654+0.022882
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.167897+0.004892 test-rmse:1.170503+0.020686
## [3] train-rmse:1.053953+0.004274 test-rmse:1.058156+0.018606
## [4] train-rmse:0.951495+0.003702 test-rmse:0.957579+0.016707
## [5] train-rmse:0.859398+0.003175 test-rmse:0.867532+0.014892
## [6] train-rmse:0.776614+0.002735 test-rmse:0.787417+0.013404
## [7] train-rmse:0.701862+0.002037 test-rmse:0.715712+0.012202
## [8] train-rmse:0.634457+0.001410 test-rmse:0.651849+0.011091
## [9] train-rmse:0.573558+0.001044 test-rmse:0.595315+0.009828
## [10] train-rmse:0.519092+0.000929 test-rmse:0.545144+0.008604
## [11] train-rmse:0.469819+0.000920 test-rmse:0.501404+0.007850
## [12] train-rmse:0.425461+0.000993 test-rmse:0.462439+0.007479
## [13] train-rmse:0.385615+0.001188 test-rmse:0.428245+0.008007
## [14] train-rmse:0.349621+0.001382 test-rmse:0.398909+0.008750
## [15] train-rmse:0.317266+0.001698 test-rmse:0.373398+0.009959
## [16] train-rmse:0.288167+0.001931 test-rmse:0.351494+0.011430
## [17] train-rmse:0.261860+0.002178 test-rmse:0.332677+0.013267
## [18] train-rmse:0.238190+0.002485 test-rmse:0.316693+0.015152
## [19] train-rmse:0.217210+0.002757 test-rmse:0.303243+0.017075
## [20] train-rmse:0.198040+0.002927 test-rmse:0.291675+0.019215
## [21] train-rmse:0.181022+0.003081 test-rmse:0.282155+0.021323
## [22] train-rmse:0.165649+0.003307 test-rmse:0.273957+0.023158
## [23] train-rmse:0.151748+0.003530 test-rmse:0.267380+0.025037
## [24] train-rmse:0.139384+0.003775 test-rmse:0.262305+0.027071
## [25] train-rmse:0.128332+0.003978 test-rmse:0.257838+0.028507
## [26] train-rmse:0.118494+0.004265 test-rmse:0.254038+0.029932
## [27] train-rmse:0.109636+0.004364 test-rmse:0.251017+0.031160
## [28] train-rmse:0.101800+0.004457 test-rmse:0.248809+0.032486
## [29] train-rmse:0.094560+0.004580 test-rmse:0.247266+0.033647
## [30] train-rmse:0.088449+0.004918 test-rmse:0.245963+0.034715
## [31] train-rmse:0.082961+0.005262 test-rmse:0.245050+0.035551
## [32] train-rmse:0.078013+0.005600 test-rmse:0.244595+0.036331
## [33] train-rmse:0.073559+0.005618 test-rmse:0.243739+0.036946
## [34] train-rmse:0.069710+0.005965 test-rmse:0.243398+0.037420
## [35] train-rmse:0.066348+0.006113 test-rmse:0.242995+0.037730
## [36] train-rmse:0.063359+0.006344 test-rmse:0.242454+0.038021
## [37] train-rmse:0.060644+0.006637 test-rmse:0.242321+0.038377
## [38] train-rmse:0.058238+0.006800 test-rmse:0.241965+0.038585
## [39] train-rmse:0.056020+0.006838 test-rmse:0.241768+0.038784
## [40] train-rmse:0.053912+0.006978 test-rmse:0.241662+0.038912
## [41] train-rmse:0.052160+0.006994 test-rmse:0.241696+0.039240
## [42] train-rmse:0.050524+0.007069 test-rmse:0.241583+0.039299
## [43] train-rmse:0.049160+0.007079 test-rmse:0.241516+0.039478
## [44] train-rmse:0.047906+0.007108 test-rmse:0.241614+0.039601
## [45] train-rmse:0.046874+0.007096 test-rmse:0.241542+0.039648
## [46] train-rmse:0.045868+0.007018 test-rmse:0.241603+0.039765
## [47] train-rmse:0.044889+0.006986 test-rmse:0.241611+0.039790
## [48] train-rmse:0.044035+0.006961 test-rmse:0.241874+0.039970
## [49] train-rmse:0.043237+0.006998 test-rmse:0.242036+0.040067
## Stopping. Best iteration:
## [43] train-rmse:0.049160+0.007079 test-rmse:0.241516+0.039478
##
## 5
## [1] train-rmse:1.294510+0.005599 test-rmse:1.295658+0.022870
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.167823+0.004926 test-rmse:1.170516+0.020662
## [3] train-rmse:1.053823+0.004333 test-rmse:1.058176+0.018565
## [4] train-rmse:0.951308+0.003784 test-rmse:0.957557+0.016592
## [5] train-rmse:0.859149+0.003280 test-rmse:0.867526+0.014764
## [6] train-rmse:0.776296+0.002870 test-rmse:0.787351+0.013213
## [7] train-rmse:0.701462+0.002179 test-rmse:0.715485+0.011887
## [8] train-rmse:0.633962+0.001578 test-rmse:0.651580+0.010800
## [9] train-rmse:0.573258+0.001386 test-rmse:0.594872+0.009321
## [10] train-rmse:0.518311+0.000985 test-rmse:0.544456+0.008252
## [11] train-rmse:0.468623+0.000867 test-rmse:0.500034+0.007314
## [12] train-rmse:0.424025+0.000808 test-rmse:0.460979+0.007299
## [13] train-rmse:0.383932+0.000942 test-rmse:0.426701+0.007756
## [14] train-rmse:0.347648+0.001038 test-rmse:0.397103+0.008449
## [15] train-rmse:0.315174+0.001028 test-rmse:0.371823+0.009580
## [16] train-rmse:0.285595+0.001200 test-rmse:0.349659+0.010980
## [17] train-rmse:0.258988+0.001235 test-rmse:0.331049+0.012636
## [18] train-rmse:0.235356+0.001262 test-rmse:0.315065+0.014415
## [19] train-rmse:0.213932+0.001469 test-rmse:0.301942+0.016048
## [20] train-rmse:0.194408+0.001570 test-rmse:0.290477+0.018212
## [21] train-rmse:0.176751+0.001633 test-rmse:0.280783+0.020211
## [22] train-rmse:0.161053+0.001707 test-rmse:0.272894+0.022176
## [23] train-rmse:0.146921+0.001795 test-rmse:0.266238+0.024019
## [24] train-rmse:0.134359+0.002024 test-rmse:0.260962+0.025980
## [25] train-rmse:0.123051+0.002230 test-rmse:0.256690+0.027588
## [26] train-rmse:0.112746+0.002391 test-rmse:0.253188+0.029062
## [27] train-rmse:0.103495+0.002430 test-rmse:0.250328+0.030380
## [28] train-rmse:0.095292+0.002581 test-rmse:0.248116+0.031580
## [29] train-rmse:0.087921+0.002727 test-rmse:0.246516+0.032783
## [30] train-rmse:0.081283+0.002982 test-rmse:0.245124+0.033862
## [31] train-rmse:0.075320+0.003193 test-rmse:0.244187+0.034927
## [32] train-rmse:0.069940+0.003303 test-rmse:0.243528+0.035853
## [33] train-rmse:0.065149+0.003379 test-rmse:0.242857+0.036389
## [34] train-rmse:0.060902+0.003484 test-rmse:0.242329+0.036870
## [35] train-rmse:0.057075+0.003584 test-rmse:0.241918+0.037302
## [36] train-rmse:0.053603+0.003544 test-rmse:0.241589+0.037759
## [37] train-rmse:0.050568+0.003725 test-rmse:0.241489+0.038095
## [38] train-rmse:0.047998+0.003855 test-rmse:0.241425+0.038445
## [39] train-rmse:0.045691+0.004003 test-rmse:0.241478+0.038758
## [40] train-rmse:0.043560+0.004153 test-rmse:0.241336+0.038968
## [41] train-rmse:0.041572+0.004329 test-rmse:0.241107+0.039068
## [42] train-rmse:0.039842+0.004382 test-rmse:0.241094+0.039227
## [43] train-rmse:0.038369+0.004387 test-rmse:0.241222+0.039473
## [44] train-rmse:0.037079+0.004390 test-rmse:0.241539+0.039599
## [45] train-rmse:0.035904+0.004383 test-rmse:0.241635+0.039751
## [46] train-rmse:0.034842+0.004391 test-rmse:0.241629+0.039852
## [47] train-rmse:0.033784+0.004231 test-rmse:0.241647+0.039942
## [48] train-rmse:0.032876+0.004154 test-rmse:0.241622+0.039979
## Stopping. Best iteration:
## [42] train-rmse:0.039842+0.004382 test-rmse:0.241094+0.039227
##
## 6
## [1] train-rmse:1.294494+0.005618 test-rmse:1.295659+0.022866
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.167789+0.004966 test-rmse:1.170521+0.020650
## [3] train-rmse:1.053766+0.004397 test-rmse:1.058186+0.018541
## [4] train-rmse:0.951223+0.003873 test-rmse:0.957578+0.016548
## [5] train-rmse:0.859035+0.003393 test-rmse:0.867574+0.014716
## [6] train-rmse:0.776156+0.002995 test-rmse:0.787422+0.013141
## [7] train-rmse:0.701280+0.002302 test-rmse:0.715537+0.011859
## [8] train-rmse:0.633924+0.001944 test-rmse:0.651775+0.010590
## [9] train-rmse:0.572933+0.001450 test-rmse:0.594922+0.009192
## [10] train-rmse:0.517868+0.000949 test-rmse:0.544622+0.008047
## [11] train-rmse:0.468061+0.000747 test-rmse:0.500151+0.007154
## [12] train-rmse:0.423358+0.000458 test-rmse:0.461167+0.006889
## [13] train-rmse:0.383026+0.000661 test-rmse:0.426788+0.007235
## [14] train-rmse:0.346630+0.000752 test-rmse:0.397190+0.008070
## [15] train-rmse:0.314020+0.000824 test-rmse:0.371835+0.009189
## [16] train-rmse:0.284528+0.000720 test-rmse:0.349747+0.010806
## [17] train-rmse:0.257703+0.000814 test-rmse:0.330996+0.012367
## [18] train-rmse:0.233782+0.000963 test-rmse:0.314961+0.014159
## [19] train-rmse:0.212113+0.001054 test-rmse:0.301829+0.015957
## [20] train-rmse:0.192802+0.001166 test-rmse:0.290643+0.017715
## [21] train-rmse:0.175125+0.001217 test-rmse:0.281364+0.019769
## [22] train-rmse:0.159440+0.001190 test-rmse:0.273843+0.021793
## [23] train-rmse:0.145373+0.001421 test-rmse:0.267409+0.023581
## [24] train-rmse:0.132447+0.001488 test-rmse:0.262556+0.025438
## [25] train-rmse:0.120907+0.001433 test-rmse:0.258255+0.027004
## [26] train-rmse:0.110506+0.001492 test-rmse:0.254933+0.028554
## [27] train-rmse:0.101098+0.001521 test-rmse:0.252532+0.029969
## [28] train-rmse:0.092703+0.001559 test-rmse:0.250612+0.031310
## [29] train-rmse:0.085049+0.001665 test-rmse:0.248983+0.032457
## [30] train-rmse:0.078137+0.001685 test-rmse:0.247848+0.033498
## [31] train-rmse:0.071932+0.001840 test-rmse:0.247046+0.034456
## [32] train-rmse:0.066281+0.001827 test-rmse:0.246635+0.035171
## [33] train-rmse:0.061224+0.001828 test-rmse:0.246286+0.035750
## [34] train-rmse:0.056646+0.001818 test-rmse:0.246074+0.036268
## [35] train-rmse:0.052675+0.001875 test-rmse:0.245903+0.036844
## [36] train-rmse:0.049044+0.001941 test-rmse:0.246067+0.037300
## [37] train-rmse:0.045834+0.002023 test-rmse:0.246171+0.037783
## [38] train-rmse:0.042746+0.002056 test-rmse:0.246447+0.038256
## [39] train-rmse:0.040109+0.002102 test-rmse:0.246647+0.038635
## [40] train-rmse:0.037721+0.002147 test-rmse:0.246932+0.039023
## [41] train-rmse:0.035585+0.002183 test-rmse:0.247143+0.039319
## Stopping. Best iteration:
## [35] train-rmse:0.052675+0.001875 test-rmse:0.245903+0.036844
##
## 7
## [1] train-rmse:1.268950+0.005076 test-rmse:1.268814+0.023505
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.123131+0.003928 test-rmse:1.123077+0.021852
## [3] train-rmse:0.995505+0.002865 test-rmse:0.995543+0.020281
## [4] train-rmse:0.883964+0.001890 test-rmse:0.884264+0.018823
## [5] train-rmse:0.786670+0.001107 test-rmse:0.787194+0.017386
## [6] train-rmse:0.702017+0.000902 test-rmse:0.702744+0.015994
## [7] train-rmse:0.628569+0.001469 test-rmse:0.630492+0.015211
## [8] train-rmse:0.564648+0.001990 test-rmse:0.567277+0.013743
## [9] train-rmse:0.509270+0.002609 test-rmse:0.512344+0.012318
## [10] train-rmse:0.461399+0.002990 test-rmse:0.465500+0.010664
## [11] train-rmse:0.420115+0.003515 test-rmse:0.423709+0.011850
## [12] train-rmse:0.384707+0.003829 test-rmse:0.389401+0.011004
## [13] train-rmse:0.354399+0.004264 test-rmse:0.359224+0.013495
## [14] train-rmse:0.328681+0.004513 test-rmse:0.334564+0.013432
## [15] train-rmse:0.306852+0.004872 test-rmse:0.312030+0.015667
## [16] train-rmse:0.288525+0.005058 test-rmse:0.294793+0.015863
## [17] train-rmse:0.273095+0.005337 test-rmse:0.279174+0.018521
## [18] train-rmse:0.260293+0.005457 test-rmse:0.267358+0.018754
## [19] train-rmse:0.249551+0.005638 test-rmse:0.255821+0.020689
## [20] train-rmse:0.240732+0.005703 test-rmse:0.248072+0.020709
## [21] train-rmse:0.233369+0.005829 test-rmse:0.239938+0.022373
## [22] train-rmse:0.227336+0.005842 test-rmse:0.234854+0.022281
## [23] train-rmse:0.222282+0.005900 test-rmse:0.230456+0.023653
## [24] train-rmse:0.218134+0.005907 test-rmse:0.227082+0.023262
## [25] train-rmse:0.214612+0.005939 test-rmse:0.224172+0.024456
## [26] train-rmse:0.211598+0.005922 test-rmse:0.221276+0.024757
## [27] train-rmse:0.209018+0.005906 test-rmse:0.219825+0.024446
## [28] train-rmse:0.206788+0.005862 test-rmse:0.218003+0.025314
## [29] train-rmse:0.204795+0.005763 test-rmse:0.217074+0.025452
## [30] train-rmse:0.203057+0.005697 test-rmse:0.215481+0.025748
## [31] train-rmse:0.201529+0.005642 test-rmse:0.214059+0.026309
## [32] train-rmse:0.200085+0.005568 test-rmse:0.213640+0.026367
## [33] train-rmse:0.198803+0.005505 test-rmse:0.214044+0.026239
## [34] train-rmse:0.197618+0.005455 test-rmse:0.213395+0.026088
## [35] train-rmse:0.196530+0.005388 test-rmse:0.213117+0.026257
## [36] train-rmse:0.195517+0.005363 test-rmse:0.212829+0.026681
## [37] train-rmse:0.194578+0.005344 test-rmse:0.212130+0.027091
## [38] train-rmse:0.193673+0.005303 test-rmse:0.211998+0.027098
## [39] train-rmse:0.192839+0.005287 test-rmse:0.212512+0.026702
## [40] train-rmse:0.192040+0.005291 test-rmse:0.212198+0.026496
## [41] train-rmse:0.191279+0.005283 test-rmse:0.212123+0.027032
## [42] train-rmse:0.190552+0.005302 test-rmse:0.211597+0.027155
## [43] train-rmse:0.189870+0.005319 test-rmse:0.211177+0.026954
## [44] train-rmse:0.189198+0.005350 test-rmse:0.211999+0.026708
## [45] train-rmse:0.188556+0.005371 test-rmse:0.211743+0.026527
## [46] train-rmse:0.187948+0.005385 test-rmse:0.211380+0.026801
## [47] train-rmse:0.187340+0.005432 test-rmse:0.211301+0.027032
## [48] train-rmse:0.186784+0.005464 test-rmse:0.211254+0.026882
## [49] train-rmse:0.186227+0.005504 test-rmse:0.210880+0.026626
## [50] train-rmse:0.185690+0.005542 test-rmse:0.211628+0.027050
## [51] train-rmse:0.185179+0.005588 test-rmse:0.212013+0.026443
## [52] train-rmse:0.184680+0.005636 test-rmse:0.211406+0.026456
## [53] train-rmse:0.184194+0.005663 test-rmse:0.212028+0.026806
## [54] train-rmse:0.183724+0.005712 test-rmse:0.211916+0.026842
## [55] train-rmse:0.183263+0.005749 test-rmse:0.211000+0.026498
## Stopping. Best iteration:
## [49] train-rmse:0.186227+0.005504 test-rmse:0.210880+0.026626
##
## 8
## [1] train-rmse:1.267019+0.005444 test-rmse:1.267530+0.022805
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.119151+0.004657 test-rmse:1.120495+0.020628
## [3] train-rmse:0.989335+0.003954 test-rmse:0.991580+0.018656
## [4] train-rmse:0.875398+0.003349 test-rmse:0.878748+0.016985
## [5] train-rmse:0.775548+0.002826 test-rmse:0.780073+0.015493
## [6] train-rmse:0.688093+0.002395 test-rmse:0.694003+0.014261
## [7] train-rmse:0.611668+0.002054 test-rmse:0.619077+0.013260
## [8] train-rmse:0.545008+0.001796 test-rmse:0.554810+0.012400
## [9] train-rmse:0.486907+0.001662 test-rmse:0.498623+0.011885
## [10] train-rmse:0.436444+0.001588 test-rmse:0.450192+0.011662
## [11] train-rmse:0.392814+0.001645 test-rmse:0.409033+0.012006
## [12] train-rmse:0.355124+0.001684 test-rmse:0.374670+0.012360
## [13] train-rmse:0.322669+0.001760 test-rmse:0.344854+0.012960
## [14] train-rmse:0.294887+0.001871 test-rmse:0.319631+0.014299
## [15] train-rmse:0.271232+0.001925 test-rmse:0.298865+0.015021
## [16] train-rmse:0.251118+0.002016 test-rmse:0.281633+0.016898
## [17] train-rmse:0.234221+0.002032 test-rmse:0.267991+0.018036
## [18] train-rmse:0.219985+0.002090 test-rmse:0.256234+0.019411
## [19] train-rmse:0.208138+0.002101 test-rmse:0.247267+0.021151
## [20] train-rmse:0.197400+0.001703 test-rmse:0.238878+0.022785
## [21] train-rmse:0.188670+0.001315 test-rmse:0.232370+0.023838
## [22] train-rmse:0.181631+0.001713 test-rmse:0.226787+0.024013
## [23] train-rmse:0.175846+0.002221 test-rmse:0.222773+0.024981
## [24] train-rmse:0.171126+0.002759 test-rmse:0.219383+0.025186
## [25] train-rmse:0.166883+0.002589 test-rmse:0.216841+0.025581
## [26] train-rmse:0.163199+0.003165 test-rmse:0.214502+0.025701
## [27] train-rmse:0.160238+0.003386 test-rmse:0.213063+0.025505
## [28] train-rmse:0.158093+0.003610 test-rmse:0.212216+0.026361
## [29] train-rmse:0.156087+0.003890 test-rmse:0.211386+0.026247
## [30] train-rmse:0.153949+0.003986 test-rmse:0.210824+0.026715
## [31] train-rmse:0.152476+0.003795 test-rmse:0.210815+0.027392
## [32] train-rmse:0.150912+0.003625 test-rmse:0.210860+0.027523
## [33] train-rmse:0.149612+0.003594 test-rmse:0.210919+0.027737
## [34] train-rmse:0.148676+0.003978 test-rmse:0.210506+0.027837
## [35] train-rmse:0.147583+0.003655 test-rmse:0.210665+0.028262
## [36] train-rmse:0.146234+0.004123 test-rmse:0.210505+0.028162
## [37] train-rmse:0.145287+0.004180 test-rmse:0.210439+0.028338
## [38] train-rmse:0.143964+0.004178 test-rmse:0.210315+0.028031
## [39] train-rmse:0.143394+0.003932 test-rmse:0.210521+0.028272
## [40] train-rmse:0.142681+0.004181 test-rmse:0.210386+0.028436
## [41] train-rmse:0.141342+0.003978 test-rmse:0.209967+0.028506
## [42] train-rmse:0.140601+0.004208 test-rmse:0.209745+0.028371
## [43] train-rmse:0.139837+0.003934 test-rmse:0.209745+0.028327
## [44] train-rmse:0.138665+0.004513 test-rmse:0.209741+0.028305
## [45] train-rmse:0.137809+0.004166 test-rmse:0.210070+0.028426
## [46] train-rmse:0.137236+0.004524 test-rmse:0.209941+0.028476
## [47] train-rmse:0.136747+0.004545 test-rmse:0.209886+0.028531
## [48] train-rmse:0.135611+0.004462 test-rmse:0.210351+0.027996
## [49] train-rmse:0.134977+0.004616 test-rmse:0.210463+0.027954
## [50] train-rmse:0.134346+0.004820 test-rmse:0.210524+0.027963
## Stopping. Best iteration:
## [44] train-rmse:0.138665+0.004513 test-rmse:0.209741+0.028305
##
## 9
## [1] train-rmse:1.266636+0.005428 test-rmse:1.267817+0.022428
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.118339+0.004608 test-rmse:1.121102+0.019875
## [3] train-rmse:0.988004+0.003870 test-rmse:0.992376+0.017486
## [4] train-rmse:0.873480+0.003231 test-rmse:0.880349+0.015490
## [5] train-rmse:0.772919+0.002688 test-rmse:0.782069+0.013611
## [6] train-rmse:0.684670+0.002220 test-rmse:0.696832+0.012051
## [7] train-rmse:0.607303+0.001856 test-rmse:0.622776+0.010537
## [8] train-rmse:0.539547+0.001640 test-rmse:0.558419+0.009328
## [9] train-rmse:0.480297+0.001625 test-rmse:0.503043+0.008369
## [10] train-rmse:0.428316+0.001827 test-rmse:0.454850+0.008930
## [11] train-rmse:0.383218+0.002201 test-rmse:0.414860+0.008415
## [12] train-rmse:0.343736+0.002945 test-rmse:0.380162+0.009282
## [13] train-rmse:0.309464+0.003741 test-rmse:0.351374+0.009942
## [14] train-rmse:0.279403+0.004277 test-rmse:0.326126+0.011451
## [15] train-rmse:0.253482+0.004747 test-rmse:0.306467+0.012693
## [16] train-rmse:0.230800+0.004729 test-rmse:0.289163+0.014381
## [17] train-rmse:0.211104+0.005110 test-rmse:0.275395+0.015751
## [18] train-rmse:0.194169+0.005455 test-rmse:0.264388+0.017824
## [19] train-rmse:0.179690+0.005822 test-rmse:0.255613+0.019782
## [20] train-rmse:0.167458+0.006337 test-rmse:0.247986+0.021084
## [21] train-rmse:0.157084+0.006440 test-rmse:0.242725+0.022563
## [22] train-rmse:0.148353+0.006919 test-rmse:0.238023+0.023473
## [23] train-rmse:0.140867+0.007427 test-rmse:0.234301+0.024558
## [24] train-rmse:0.134359+0.008013 test-rmse:0.231638+0.025331
## [25] train-rmse:0.129128+0.008228 test-rmse:0.229572+0.026542
## [26] train-rmse:0.124491+0.008185 test-rmse:0.227763+0.027050
## [27] train-rmse:0.120808+0.008357 test-rmse:0.226070+0.027478
## [28] train-rmse:0.117682+0.008676 test-rmse:0.225205+0.028013
## [29] train-rmse:0.114909+0.008583 test-rmse:0.224618+0.028131
## [30] train-rmse:0.112613+0.008857 test-rmse:0.223787+0.028374
## [31] train-rmse:0.110589+0.008761 test-rmse:0.223423+0.028391
## [32] train-rmse:0.108682+0.009028 test-rmse:0.223164+0.028754
## [33] train-rmse:0.107117+0.009111 test-rmse:0.223049+0.028873
## [34] train-rmse:0.105408+0.009294 test-rmse:0.223188+0.029166
## [35] train-rmse:0.103418+0.009340 test-rmse:0.223502+0.029434
## [36] train-rmse:0.101947+0.009285 test-rmse:0.224167+0.030145
## [37] train-rmse:0.100673+0.009462 test-rmse:0.224166+0.030242
## [38] train-rmse:0.099055+0.009352 test-rmse:0.224883+0.030864
## [39] train-rmse:0.098101+0.009375 test-rmse:0.225199+0.030909
## Stopping. Best iteration:
## [33] train-rmse:0.107117+0.009111 test-rmse:0.223049+0.028873
##
## 10
## [1] train-rmse:1.266498+0.005439 test-rmse:1.267819+0.022404
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.118027+0.004628 test-rmse:1.121132+0.019825
## [3] train-rmse:0.987491+0.003910 test-rmse:0.992530+0.017425
## [4] train-rmse:0.872795+0.003260 test-rmse:0.880049+0.015247
## [5] train-rmse:0.772064+0.002710 test-rmse:0.782325+0.013471
## [6] train-rmse:0.683157+0.002004 test-rmse:0.696984+0.011990
## [7] train-rmse:0.605050+0.001486 test-rmse:0.622788+0.010605
## [8] train-rmse:0.536038+0.001153 test-rmse:0.558503+0.009150
## [9] train-rmse:0.475558+0.001076 test-rmse:0.503320+0.008166
## [10] train-rmse:0.422505+0.001015 test-rmse:0.455958+0.007666
## [11] train-rmse:0.375771+0.001297 test-rmse:0.415521+0.008256
## [12] train-rmse:0.334835+0.001691 test-rmse:0.382060+0.009064
## [13] train-rmse:0.299050+0.002121 test-rmse:0.353535+0.010340
## [14] train-rmse:0.267814+0.002628 test-rmse:0.330227+0.011957
## [15] train-rmse:0.240365+0.003098 test-rmse:0.310717+0.013736
## [16] train-rmse:0.216134+0.003394 test-rmse:0.294276+0.015941
## [17] train-rmse:0.195013+0.003661 test-rmse:0.280955+0.017852
## [18] train-rmse:0.176720+0.004095 test-rmse:0.269952+0.019937
## [19] train-rmse:0.160783+0.004387 test-rmse:0.261818+0.021888
## [20] train-rmse:0.146723+0.004839 test-rmse:0.254327+0.023536
## [21] train-rmse:0.134768+0.005065 test-rmse:0.249395+0.024977
## [22] train-rmse:0.124298+0.005446 test-rmse:0.245738+0.026389
## [23] train-rmse:0.115195+0.005925 test-rmse:0.242766+0.027775
## [24] train-rmse:0.107628+0.006254 test-rmse:0.240045+0.028669
## [25] train-rmse:0.101142+0.006744 test-rmse:0.238294+0.029506
## [26] train-rmse:0.095569+0.007202 test-rmse:0.237174+0.030181
## [27] train-rmse:0.090816+0.007541 test-rmse:0.236001+0.030799
## [28] train-rmse:0.086800+0.007672 test-rmse:0.235234+0.031201
## [29] train-rmse:0.083415+0.007906 test-rmse:0.234484+0.031731
## [30] train-rmse:0.080520+0.008187 test-rmse:0.234070+0.032187
## [31] train-rmse:0.078109+0.008333 test-rmse:0.233858+0.032623
## [32] train-rmse:0.075744+0.008473 test-rmse:0.233673+0.032861
## [33] train-rmse:0.073582+0.008461 test-rmse:0.233541+0.033080
## [34] train-rmse:0.071519+0.008369 test-rmse:0.233477+0.033237
## [35] train-rmse:0.069963+0.008345 test-rmse:0.233667+0.033492
## [36] train-rmse:0.068587+0.008349 test-rmse:0.233737+0.033748
## [37] train-rmse:0.067427+0.008300 test-rmse:0.233950+0.033871
## [38] train-rmse:0.066273+0.008256 test-rmse:0.233723+0.033849
## [39] train-rmse:0.065210+0.008150 test-rmse:0.233632+0.033982
## [40] train-rmse:0.063937+0.008301 test-rmse:0.233756+0.034084
## Stopping. Best iteration:
## [34] train-rmse:0.071519+0.008369 test-rmse:0.233477+0.033237
##
## 11
## [1] train-rmse:1.266411+0.005432 test-rmse:1.267816+0.022396
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.117853+0.004614 test-rmse:1.121147+0.019802
## [3] train-rmse:0.987199+0.003892 test-rmse:0.992557+0.017377
## [4] train-rmse:0.872378+0.003239 test-rmse:0.880117+0.015174
## [5] train-rmse:0.771495+0.002693 test-rmse:0.782344+0.013305
## [6] train-rmse:0.682475+0.001826 test-rmse:0.696857+0.011905
## [7] train-rmse:0.604007+0.001154 test-rmse:0.622811+0.010574
## [8] train-rmse:0.534612+0.000867 test-rmse:0.558882+0.009030
## [9] train-rmse:0.473617+0.000961 test-rmse:0.503791+0.007979
## [10] train-rmse:0.420109+0.001138 test-rmse:0.457027+0.007429
## [11] train-rmse:0.372944+0.001411 test-rmse:0.416777+0.008239
## [12] train-rmse:0.331382+0.001681 test-rmse:0.383377+0.009233
## [13] train-rmse:0.294599+0.002011 test-rmse:0.355219+0.010902
## [14] train-rmse:0.262274+0.002329 test-rmse:0.331886+0.013027
## [15] train-rmse:0.233959+0.002598 test-rmse:0.312793+0.015267
## [16] train-rmse:0.208956+0.002845 test-rmse:0.297103+0.017321
## [17] train-rmse:0.187100+0.003134 test-rmse:0.284363+0.019810
## [18] train-rmse:0.167987+0.003220 test-rmse:0.273974+0.021871
## [19] train-rmse:0.151145+0.003430 test-rmse:0.265780+0.024164
## [20] train-rmse:0.136320+0.003807 test-rmse:0.259312+0.026029
## [21] train-rmse:0.123428+0.004077 test-rmse:0.254314+0.027608
## [22] train-rmse:0.112300+0.004261 test-rmse:0.250825+0.029324
## [23] train-rmse:0.102493+0.004461 test-rmse:0.247617+0.030525
## [24] train-rmse:0.094207+0.004847 test-rmse:0.245535+0.031813
## [25] train-rmse:0.086835+0.004982 test-rmse:0.243644+0.032707
## [26] train-rmse:0.080332+0.005190 test-rmse:0.242720+0.033743
## [27] train-rmse:0.074960+0.005687 test-rmse:0.241940+0.034384
## [28] train-rmse:0.070198+0.005927 test-rmse:0.241652+0.035100
## [29] train-rmse:0.066092+0.006375 test-rmse:0.241173+0.035450
## [30] train-rmse:0.062558+0.006569 test-rmse:0.240594+0.035782
## [31] train-rmse:0.059715+0.006786 test-rmse:0.240062+0.035975
## [32] train-rmse:0.056937+0.006964 test-rmse:0.239998+0.036311
## [33] train-rmse:0.054556+0.007280 test-rmse:0.239923+0.036549
## [34] train-rmse:0.052354+0.007240 test-rmse:0.239787+0.036596
## [35] train-rmse:0.050756+0.007359 test-rmse:0.239673+0.036801
## [36] train-rmse:0.049147+0.007378 test-rmse:0.239863+0.037067
## [37] train-rmse:0.047785+0.007399 test-rmse:0.239742+0.037151
## [38] train-rmse:0.046523+0.007367 test-rmse:0.239877+0.037256
## [39] train-rmse:0.045350+0.007341 test-rmse:0.239981+0.037428
## [40] train-rmse:0.044291+0.007308 test-rmse:0.240276+0.037655
## [41] train-rmse:0.043222+0.007147 test-rmse:0.240301+0.037717
## Stopping. Best iteration:
## [35] train-rmse:0.050756+0.007359 test-rmse:0.239673+0.036801
##
## 12
## [1] train-rmse:1.266367+0.005453 test-rmse:1.267821+0.022383
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.117762+0.004655 test-rmse:1.121165+0.019772
## [3] train-rmse:0.987040+0.003963 test-rmse:0.992590+0.017328
## [4] train-rmse:0.872147+0.003336 test-rmse:0.880202+0.015060
## [5] train-rmse:0.771181+0.002822 test-rmse:0.782470+0.013166
## [6] train-rmse:0.682024+0.002014 test-rmse:0.697014+0.011519
## [7] train-rmse:0.603404+0.001409 test-rmse:0.622900+0.010282
## [8] train-rmse:0.534277+0.001131 test-rmse:0.558783+0.008527
## [9] train-rmse:0.473063+0.000975 test-rmse:0.503593+0.007710
## [10] train-rmse:0.418897+0.001055 test-rmse:0.456524+0.007257
## [11] train-rmse:0.371486+0.001231 test-rmse:0.416463+0.008283
## [12] train-rmse:0.329344+0.001334 test-rmse:0.382767+0.009094
## [13] train-rmse:0.292293+0.001310 test-rmse:0.354858+0.010570
## [14] train-rmse:0.259898+0.001535 test-rmse:0.331485+0.012697
## [15] train-rmse:0.231297+0.001741 test-rmse:0.312826+0.014818
## [16] train-rmse:0.205800+0.001784 test-rmse:0.297049+0.017534
## [17] train-rmse:0.183990+0.002013 test-rmse:0.284552+0.019592
## [18] train-rmse:0.164228+0.002096 test-rmse:0.274246+0.021880
## [19] train-rmse:0.146999+0.002124 test-rmse:0.266219+0.024252
## [20] train-rmse:0.131793+0.002173 test-rmse:0.259943+0.026297
## [21] train-rmse:0.118608+0.002278 test-rmse:0.255061+0.028202
## [22] train-rmse:0.107018+0.002515 test-rmse:0.251462+0.029964
## [23] train-rmse:0.096749+0.002717 test-rmse:0.248904+0.031563
## [24] train-rmse:0.087854+0.002915 test-rmse:0.246840+0.032797
## [25] train-rmse:0.079846+0.003075 test-rmse:0.245436+0.033935
## [26] train-rmse:0.073006+0.003303 test-rmse:0.244399+0.035133
## [27] train-rmse:0.066916+0.003546 test-rmse:0.243691+0.036309
## [28] train-rmse:0.061707+0.003597 test-rmse:0.242926+0.036914
## [29] train-rmse:0.057110+0.003652 test-rmse:0.242523+0.037431
## [30] train-rmse:0.053063+0.003750 test-rmse:0.242133+0.037908
## [31] train-rmse:0.049549+0.003681 test-rmse:0.241744+0.038356
## [32] train-rmse:0.046657+0.003831 test-rmse:0.241623+0.038652
## [33] train-rmse:0.043959+0.004014 test-rmse:0.241423+0.038864
## [34] train-rmse:0.041624+0.004219 test-rmse:0.241368+0.039123
## [35] train-rmse:0.039625+0.004295 test-rmse:0.241193+0.039237
## [36] train-rmse:0.037882+0.004286 test-rmse:0.241536+0.039514
## [37] train-rmse:0.036406+0.004326 test-rmse:0.241754+0.039757
## [38] train-rmse:0.035046+0.004360 test-rmse:0.241837+0.039927
## [39] train-rmse:0.033743+0.004175 test-rmse:0.242021+0.040038
## [40] train-rmse:0.032534+0.004206 test-rmse:0.242023+0.040135
## [41] train-rmse:0.031485+0.004280 test-rmse:0.242203+0.040230
## Stopping. Best iteration:
## [35] train-rmse:0.039625+0.004295 test-rmse:0.241193+0.039237
##
## 13
## [1] train-rmse:1.266347+0.005475 test-rmse:1.267823+0.022377
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.117720+0.004705 test-rmse:1.121173+0.019755
## [3] train-rmse:0.986966+0.004040 test-rmse:0.992637+0.017245
## [4] train-rmse:0.872039+0.003445 test-rmse:0.880258+0.014961
## [5] train-rmse:0.771041+0.002953 test-rmse:0.782532+0.013112
## [6] train-rmse:0.681833+0.002135 test-rmse:0.697007+0.011543
## [7] train-rmse:0.603383+0.001768 test-rmse:0.623145+0.010016
## [8] train-rmse:0.533822+0.001093 test-rmse:0.558930+0.008409
## [9] train-rmse:0.472451+0.000714 test-rmse:0.503430+0.007549
## [10] train-rmse:0.418221+0.000548 test-rmse:0.456293+0.006954
## [11] train-rmse:0.370518+0.000821 test-rmse:0.416047+0.007627
## [12] train-rmse:0.328206+0.000860 test-rmse:0.382289+0.008566
## [13] train-rmse:0.291298+0.000831 test-rmse:0.354487+0.010296
## [14] train-rmse:0.258458+0.000905 test-rmse:0.330943+0.012319
## [15] train-rmse:0.229821+0.001141 test-rmse:0.312359+0.014360
## [16] train-rmse:0.204471+0.001184 test-rmse:0.296580+0.016949
## [17] train-rmse:0.182017+0.001186 test-rmse:0.284277+0.019547
## [18] train-rmse:0.162328+0.001620 test-rmse:0.274379+0.021811
## [19] train-rmse:0.145056+0.001489 test-rmse:0.266549+0.024188
## [20] train-rmse:0.129657+0.001473 test-rmse:0.260312+0.026233
## [21] train-rmse:0.116345+0.001492 test-rmse:0.255521+0.028211
## [22] train-rmse:0.104445+0.001584 test-rmse:0.252058+0.030107
## [23] train-rmse:0.093813+0.001636 test-rmse:0.249498+0.031696
## [24] train-rmse:0.084612+0.001726 test-rmse:0.247591+0.033014
## [25] train-rmse:0.076549+0.001881 test-rmse:0.246272+0.034270
## [26] train-rmse:0.069386+0.002019 test-rmse:0.245681+0.034894
## [27] train-rmse:0.063008+0.002011 test-rmse:0.245184+0.035590
## [28] train-rmse:0.057441+0.001999 test-rmse:0.244934+0.036178
## [29] train-rmse:0.052519+0.001989 test-rmse:0.244785+0.036958
## [30] train-rmse:0.048247+0.002057 test-rmse:0.244753+0.037565
## [31] train-rmse:0.044575+0.002106 test-rmse:0.244713+0.038058
## [32] train-rmse:0.041144+0.002109 test-rmse:0.244895+0.038176
## [33] train-rmse:0.038289+0.002131 test-rmse:0.245012+0.038539
## [34] train-rmse:0.035640+0.002154 test-rmse:0.245072+0.038802
## [35] train-rmse:0.033417+0.002283 test-rmse:0.245307+0.039086
## [36] train-rmse:0.031356+0.002475 test-rmse:0.245542+0.039355
## [37] train-rmse:0.029759+0.002576 test-rmse:0.245717+0.039560
## Stopping. Best iteration:
## [31] train-rmse:0.044575+0.002106 test-rmse:0.244713+0.038058
##
## 14
## [1] train-rmse:1.241290+0.004863 test-rmse:1.241168+0.023200
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.075260+0.003536 test-rmse:1.075238+0.021280
## [3] train-rmse:0.933471+0.002325 test-rmse:0.933645+0.019475
## [4] train-rmse:0.812655+0.001294 test-rmse:0.813110+0.017784
## [5] train-rmse:0.710043+0.000879 test-rmse:0.710750+0.016140
## [6] train-rmse:0.623225+0.001528 test-rmse:0.625337+0.015215
## [7] train-rmse:0.549575+0.002137 test-rmse:0.552557+0.013463
## [8] train-rmse:0.487437+0.002828 test-rmse:0.490955+0.011804
## [9] train-rmse:0.435181+0.003261 test-rmse:0.438789+0.012134
## [10] train-rmse:0.391427+0.003842 test-rmse:0.395619+0.011983
## [11] train-rmse:0.355076+0.004181 test-rmse:0.359306+0.012999
## [12] train-rmse:0.324993+0.004637 test-rmse:0.330537+0.014679
## [13] train-rmse:0.300374+0.004885 test-rmse:0.305904+0.015905
## [14] train-rmse:0.280199+0.005245 test-rmse:0.286079+0.017576
## [15] train-rmse:0.263969+0.005406 test-rmse:0.269793+0.018541
## [16] train-rmse:0.250786+0.005629 test-rmse:0.256846+0.020343
## [17] train-rmse:0.240327+0.005705 test-rmse:0.247654+0.020410
## [18] train-rmse:0.231840+0.005847 test-rmse:0.238884+0.022659
## [19] train-rmse:0.225173+0.005863 test-rmse:0.233210+0.022487
## [20] train-rmse:0.219727+0.005908 test-rmse:0.228087+0.023856
## [21] train-rmse:0.215390+0.005923 test-rmse:0.223900+0.023607
## [22] train-rmse:0.211795+0.005950 test-rmse:0.221269+0.024964
## [23] train-rmse:0.208724+0.005876 test-rmse:0.219180+0.025061
## [24] train-rmse:0.206151+0.005815 test-rmse:0.217560+0.025288
## [25] train-rmse:0.203976+0.005754 test-rmse:0.215721+0.026298
## [26] train-rmse:0.202048+0.005662 test-rmse:0.215509+0.025854
## [27] train-rmse:0.200307+0.005584 test-rmse:0.213833+0.025848
## [28] train-rmse:0.198806+0.005505 test-rmse:0.213332+0.026062
## [29] train-rmse:0.197465+0.005437 test-rmse:0.212646+0.026177
## [30] train-rmse:0.196167+0.005390 test-rmse:0.212905+0.026424
## [31] train-rmse:0.195045+0.005358 test-rmse:0.212032+0.026991
## [32] train-rmse:0.193961+0.005312 test-rmse:0.213237+0.026827
## [33] train-rmse:0.192930+0.005296 test-rmse:0.212361+0.026861
## [34] train-rmse:0.191996+0.005294 test-rmse:0.211702+0.026550
## [35] train-rmse:0.191119+0.005287 test-rmse:0.212152+0.027385
## [36] train-rmse:0.190272+0.005304 test-rmse:0.211977+0.027351
## [37] train-rmse:0.189489+0.005326 test-rmse:0.211571+0.027192
## [38] train-rmse:0.188715+0.005360 test-rmse:0.211730+0.026805
## [39] train-rmse:0.187983+0.005398 test-rmse:0.211776+0.026887
## [40] train-rmse:0.187273+0.005429 test-rmse:0.211397+0.027037
## [41] train-rmse:0.186608+0.005474 test-rmse:0.211746+0.027194
## [42] train-rmse:0.185989+0.005521 test-rmse:0.211778+0.027074
## [43] train-rmse:0.185381+0.005569 test-rmse:0.211443+0.025977
## [44] train-rmse:0.184781+0.005618 test-rmse:0.210841+0.026310
## [45] train-rmse:0.184191+0.005674 test-rmse:0.211436+0.026681
## [46] train-rmse:0.183644+0.005726 test-rmse:0.211184+0.026674
## [47] train-rmse:0.183108+0.005765 test-rmse:0.211048+0.026604
## [48] train-rmse:0.182596+0.005813 test-rmse:0.211171+0.026435
## [49] train-rmse:0.182096+0.005850 test-rmse:0.211728+0.026817
## [50] train-rmse:0.181612+0.005908 test-rmse:0.211058+0.026553
## Stopping. Best iteration:
## [44] train-rmse:0.184781+0.005618 test-rmse:0.210841+0.026310
##
## 15
## [1] train-rmse:1.239008+0.005298 test-rmse:1.239654+0.022392
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.070525+0.004395 test-rmse:1.072189+0.019908
## [3] train-rmse:0.926063+0.003605 test-rmse:0.928925+0.017699
## [4] train-rmse:0.802282+0.002965 test-rmse:0.806491+0.015891
## [5] train-rmse:0.696466+0.002429 test-rmse:0.702207+0.014350
## [6] train-rmse:0.606095+0.002029 test-rmse:0.613664+0.013157
## [7] train-rmse:0.529257+0.001764 test-rmse:0.539965+0.012236
## [8] train-rmse:0.463947+0.001616 test-rmse:0.476897+0.011614
## [9] train-rmse:0.408789+0.001602 test-rmse:0.425097+0.011483
## [10] train-rmse:0.362341+0.001713 test-rmse:0.380975+0.011989
## [11] train-rmse:0.323392+0.001764 test-rmse:0.345414+0.013028
## [12] train-rmse:0.290937+0.001859 test-rmse:0.316531+0.014035
## [13] train-rmse:0.264113+0.002005 test-rmse:0.292743+0.015869
## [14] train-rmse:0.242064+0.002035 test-rmse:0.274825+0.017395
## [15] train-rmse:0.224096+0.002098 test-rmse:0.259781+0.019158
## [16] train-rmse:0.209050+0.002366 test-rmse:0.247754+0.021147
## [17] train-rmse:0.196855+0.001706 test-rmse:0.238961+0.023088
## [18] train-rmse:0.186836+0.001336 test-rmse:0.231428+0.023815
## [19] train-rmse:0.178996+0.001724 test-rmse:0.225096+0.024119
## [20] train-rmse:0.172659+0.001996 test-rmse:0.221170+0.025379
## [21] train-rmse:0.167271+0.002777 test-rmse:0.217888+0.025666
## [22] train-rmse:0.163082+0.003436 test-rmse:0.214843+0.025838
## [23] train-rmse:0.158934+0.003773 test-rmse:0.213537+0.025737
## [24] train-rmse:0.155845+0.003691 test-rmse:0.212519+0.026383
## [25] train-rmse:0.153930+0.003806 test-rmse:0.211796+0.027357
## [26] train-rmse:0.150979+0.003657 test-rmse:0.211501+0.027105
## [27] train-rmse:0.149735+0.003676 test-rmse:0.211317+0.027622
## [28] train-rmse:0.147886+0.003347 test-rmse:0.211386+0.028266
## [29] train-rmse:0.146264+0.003497 test-rmse:0.211043+0.027992
## [30] train-rmse:0.145365+0.003382 test-rmse:0.210751+0.028431
## [31] train-rmse:0.144220+0.003384 test-rmse:0.210664+0.028560
## [32] train-rmse:0.143452+0.003692 test-rmse:0.210711+0.028798
## [33] train-rmse:0.142386+0.003919 test-rmse:0.210579+0.028478
## [34] train-rmse:0.140894+0.003843 test-rmse:0.210637+0.028276
## [35] train-rmse:0.140051+0.003797 test-rmse:0.210513+0.028469
## [36] train-rmse:0.139211+0.003852 test-rmse:0.210400+0.028455
## [37] train-rmse:0.138223+0.003915 test-rmse:0.210412+0.028486
## [38] train-rmse:0.137463+0.003472 test-rmse:0.210404+0.028709
## [39] train-rmse:0.136606+0.003663 test-rmse:0.210680+0.029094
## [40] train-rmse:0.135795+0.003256 test-rmse:0.210882+0.028861
## [41] train-rmse:0.134872+0.003198 test-rmse:0.210828+0.029019
## [42] train-rmse:0.134219+0.003439 test-rmse:0.210865+0.029014
## Stopping. Best iteration:
## [36] train-rmse:0.139211+0.003852 test-rmse:0.210400+0.028455
##
## 16
## [1] train-rmse:1.238552+0.005278 test-rmse:1.239996+0.021947
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.069550+0.004330 test-rmse:1.072919+0.019018
## [3] train-rmse:0.924471+0.003481 test-rmse:0.930130+0.016407
## [4] train-rmse:0.799918+0.002809 test-rmse:0.808494+0.014125
## [5] train-rmse:0.693148+0.002249 test-rmse:0.705157+0.012136
## [6] train-rmse:0.601725+0.001820 test-rmse:0.617151+0.010211
## [7] train-rmse:0.523561+0.001627 test-rmse:0.543511+0.008909
## [8] train-rmse:0.456606+0.001697 test-rmse:0.481285+0.008627
## [9] train-rmse:0.399718+0.002153 test-rmse:0.428701+0.009096
## [10] train-rmse:0.351440+0.002762 test-rmse:0.387153+0.009096
## [11] train-rmse:0.310029+0.003674 test-rmse:0.351686+0.010753
## [12] train-rmse:0.275099+0.004451 test-rmse:0.323663+0.012331
## [13] train-rmse:0.245198+0.004584 test-rmse:0.300174+0.013760
## [14] train-rmse:0.219884+0.004949 test-rmse:0.283055+0.015735
## [15] train-rmse:0.198697+0.005426 test-rmse:0.268093+0.018073
## [16] train-rmse:0.181459+0.005563 test-rmse:0.256981+0.019571
## [17] train-rmse:0.166930+0.006302 test-rmse:0.248132+0.021360
## [18] train-rmse:0.154830+0.006777 test-rmse:0.241550+0.022939
## [19] train-rmse:0.145261+0.007062 test-rmse:0.236781+0.023778
## [20] train-rmse:0.137038+0.007738 test-rmse:0.232768+0.024794
## [21] train-rmse:0.129907+0.007629 test-rmse:0.229658+0.025458
## [22] train-rmse:0.124519+0.007861 test-rmse:0.227904+0.026547
## [23] train-rmse:0.119931+0.008039 test-rmse:0.226262+0.026877
## [24] train-rmse:0.116270+0.008111 test-rmse:0.224898+0.027621
## [25] train-rmse:0.113018+0.008073 test-rmse:0.224320+0.027706
## [26] train-rmse:0.110162+0.008300 test-rmse:0.223971+0.027926
## [27] train-rmse:0.108079+0.008376 test-rmse:0.223164+0.027940
## [28] train-rmse:0.106153+0.008372 test-rmse:0.222918+0.028218
## [29] train-rmse:0.103874+0.008750 test-rmse:0.222998+0.028448
## [30] train-rmse:0.102228+0.009068 test-rmse:0.222797+0.028535
## [31] train-rmse:0.100670+0.008990 test-rmse:0.223738+0.029434
## [32] train-rmse:0.099167+0.008984 test-rmse:0.224251+0.029916
## [33] train-rmse:0.097791+0.009163 test-rmse:0.224522+0.030355
## [34] train-rmse:0.096563+0.008949 test-rmse:0.224238+0.030335
## [35] train-rmse:0.095379+0.008474 test-rmse:0.224528+0.030522
## [36] train-rmse:0.094295+0.008500 test-rmse:0.224874+0.030974
## Stopping. Best iteration:
## [30] train-rmse:0.102228+0.009068 test-rmse:0.222797+0.028535
##
## 17
## [1] train-rmse:1.238388+0.005291 test-rmse:1.240002+0.021919
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.069168+0.004355 test-rmse:1.072875+0.018921
## [3] train-rmse:0.923823+0.003545 test-rmse:0.929952+0.016212
## [4] train-rmse:0.799092+0.002829 test-rmse:0.808073+0.013802
## [5] train-rmse:0.691881+0.002269 test-rmse:0.704979+0.011857
## [6] train-rmse:0.599719+0.001669 test-rmse:0.617562+0.010256
## [7] train-rmse:0.520046+0.001227 test-rmse:0.543554+0.008650
## [8] train-rmse:0.451809+0.001286 test-rmse:0.481942+0.007758
## [9] train-rmse:0.393187+0.001292 test-rmse:0.430071+0.008056
## [10] train-rmse:0.343052+0.001524 test-rmse:0.387658+0.009154
## [11] train-rmse:0.300099+0.002044 test-rmse:0.353718+0.010898
## [12] train-rmse:0.263442+0.002675 test-rmse:0.326363+0.012242
## [13] train-rmse:0.232118+0.003294 test-rmse:0.304598+0.014440
## [14] train-rmse:0.205140+0.003622 test-rmse:0.286527+0.017122
## [15] train-rmse:0.182278+0.003997 test-rmse:0.272743+0.019417
## [16] train-rmse:0.163024+0.004465 test-rmse:0.262123+0.021677
## [17] train-rmse:0.146899+0.004841 test-rmse:0.254123+0.023820
## [18] train-rmse:0.132753+0.005256 test-rmse:0.247513+0.025896
## [19] train-rmse:0.121230+0.005782 test-rmse:0.243333+0.027541
## [20] train-rmse:0.111525+0.006216 test-rmse:0.240348+0.028570
## [21] train-rmse:0.103449+0.006860 test-rmse:0.237995+0.029454
## [22] train-rmse:0.097047+0.007214 test-rmse:0.236077+0.030351
## [23] train-rmse:0.091219+0.007549 test-rmse:0.234620+0.030777
## [24] train-rmse:0.086605+0.007937 test-rmse:0.233730+0.031485
## [25] train-rmse:0.082356+0.007750 test-rmse:0.232865+0.031935
## [26] train-rmse:0.079099+0.007875 test-rmse:0.232743+0.032569
## [27] train-rmse:0.076262+0.007861 test-rmse:0.232494+0.032984
## [28] train-rmse:0.073439+0.008067 test-rmse:0.232411+0.033109
## [29] train-rmse:0.071146+0.007755 test-rmse:0.232454+0.033442
## [30] train-rmse:0.069415+0.007678 test-rmse:0.232789+0.033798
## [31] train-rmse:0.068008+0.007581 test-rmse:0.233087+0.034066
## [32] train-rmse:0.066607+0.007553 test-rmse:0.232832+0.034192
## [33] train-rmse:0.065436+0.007497 test-rmse:0.233174+0.034435
## [34] train-rmse:0.064032+0.007580 test-rmse:0.233292+0.034522
## Stopping. Best iteration:
## [28] train-rmse:0.073439+0.008067 test-rmse:0.232411+0.033109
##
## 18
## [1] train-rmse:1.238285+0.005283 test-rmse:1.240000+0.021909
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.068901+0.004374 test-rmse:1.072893+0.018888
## [3] train-rmse:0.923475+0.003518 test-rmse:0.930226+0.016216
## [4] train-rmse:0.798594+0.002798 test-rmse:0.808429+0.013743
## [5] train-rmse:0.691198+0.002240 test-rmse:0.705166+0.011715
## [6] train-rmse:0.598691+0.001355 test-rmse:0.617725+0.010207
## [7] train-rmse:0.518474+0.000903 test-rmse:0.544172+0.008462
## [8] train-rmse:0.449562+0.001069 test-rmse:0.482759+0.007497
## [9] train-rmse:0.390591+0.001213 test-rmse:0.431166+0.007992
## [10] train-rmse:0.339633+0.001452 test-rmse:0.389721+0.009096
## [11] train-rmse:0.295766+0.001757 test-rmse:0.356111+0.010782
## [12] train-rmse:0.257883+0.002180 test-rmse:0.328882+0.013254
## [13] train-rmse:0.225615+0.002481 test-rmse:0.307405+0.015913
## [14] train-rmse:0.197815+0.002674 test-rmse:0.290321+0.018908
## [15] train-rmse:0.173962+0.002851 test-rmse:0.276608+0.021813
## [16] train-rmse:0.153439+0.003107 test-rmse:0.266742+0.024586
## [17] train-rmse:0.135981+0.003440 test-rmse:0.259005+0.027194
## [18] train-rmse:0.121222+0.003869 test-rmse:0.253113+0.029095
## [19] train-rmse:0.108425+0.004125 test-rmse:0.249367+0.030880
## [20] train-rmse:0.097843+0.004774 test-rmse:0.246394+0.032670
## [21] train-rmse:0.088986+0.005283 test-rmse:0.244212+0.033791
## [22] train-rmse:0.081284+0.005524 test-rmse:0.242911+0.035105
## [23] train-rmse:0.074824+0.005604 test-rmse:0.241922+0.036051
## [24] train-rmse:0.069338+0.005884 test-rmse:0.240954+0.036497
## [25] train-rmse:0.064683+0.006062 test-rmse:0.240629+0.037170
## [26] train-rmse:0.060838+0.006349 test-rmse:0.240419+0.037465
## [27] train-rmse:0.057560+0.006468 test-rmse:0.239945+0.037765
## [28] train-rmse:0.054816+0.006816 test-rmse:0.239939+0.038051
## [29] train-rmse:0.052378+0.006985 test-rmse:0.239888+0.038245
## [30] train-rmse:0.050419+0.006971 test-rmse:0.239727+0.038353
## [31] train-rmse:0.048645+0.006989 test-rmse:0.239880+0.038549
## [32] train-rmse:0.047048+0.007087 test-rmse:0.240022+0.038767
## [33] train-rmse:0.045534+0.007081 test-rmse:0.240139+0.038915
## [34] train-rmse:0.044343+0.006972 test-rmse:0.240382+0.039107
## [35] train-rmse:0.043109+0.007011 test-rmse:0.240698+0.039254
## [36] train-rmse:0.041945+0.006972 test-rmse:0.240967+0.039448
## Stopping. Best iteration:
## [30] train-rmse:0.050419+0.006971 test-rmse:0.239727+0.038353
##
## 19
## [1] train-rmse:1.238232+0.005307 test-rmse:1.240007+0.021893
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.068774+0.004433 test-rmse:1.072875+0.018835
## [3] train-rmse:0.923204+0.003655 test-rmse:0.930129+0.016055
## [4] train-rmse:0.798229+0.002987 test-rmse:0.808510+0.013536
## [5] train-rmse:0.690684+0.002521 test-rmse:0.705243+0.011558
## [6] train-rmse:0.598035+0.001655 test-rmse:0.618047+0.009755
## [7] train-rmse:0.517610+0.001060 test-rmse:0.543771+0.007991
## [8] train-rmse:0.448401+0.001196 test-rmse:0.482731+0.007088
## [9] train-rmse:0.389189+0.001350 test-rmse:0.431211+0.007545
## [10] train-rmse:0.337910+0.001565 test-rmse:0.389581+0.008577
## [11] train-rmse:0.293441+0.001716 test-rmse:0.355779+0.010368
## [12] train-rmse:0.255453+0.001774 test-rmse:0.328662+0.013029
## [13] train-rmse:0.222752+0.001814 test-rmse:0.307275+0.015667
## [14] train-rmse:0.194452+0.002074 test-rmse:0.290626+0.018576
## [15] train-rmse:0.170135+0.002649 test-rmse:0.278039+0.020759
## [16] train-rmse:0.149407+0.002815 test-rmse:0.268376+0.023691
## [17] train-rmse:0.131638+0.002982 test-rmse:0.260919+0.026065
## [18] train-rmse:0.116391+0.003160 test-rmse:0.255549+0.028298
## [19] train-rmse:0.103193+0.003291 test-rmse:0.251628+0.030242
## [20] train-rmse:0.091920+0.003513 test-rmse:0.248896+0.031977
## [21] train-rmse:0.082393+0.003920 test-rmse:0.247018+0.033636
## [22] train-rmse:0.074278+0.004287 test-rmse:0.245741+0.034783
## [23] train-rmse:0.067246+0.004596 test-rmse:0.244988+0.035831
## [24] train-rmse:0.061352+0.004632 test-rmse:0.244347+0.036545
## [25] train-rmse:0.056187+0.004529 test-rmse:0.243876+0.037215
## [26] train-rmse:0.051998+0.004784 test-rmse:0.243600+0.037738
## [27] train-rmse:0.048309+0.004953 test-rmse:0.243599+0.038178
## [28] train-rmse:0.045091+0.005143 test-rmse:0.243646+0.038450
## [29] train-rmse:0.042454+0.005217 test-rmse:0.243727+0.038793
## [30] train-rmse:0.040308+0.005306 test-rmse:0.243653+0.038991
## [31] train-rmse:0.038482+0.005373 test-rmse:0.243590+0.039139
## [32] train-rmse:0.036827+0.005268 test-rmse:0.243568+0.039324
## [33] train-rmse:0.035255+0.005398 test-rmse:0.243739+0.039474
## [34] train-rmse:0.033891+0.005240 test-rmse:0.243785+0.039623
## [35] train-rmse:0.032628+0.005081 test-rmse:0.244013+0.039912
## [36] train-rmse:0.031367+0.004694 test-rmse:0.244279+0.039969
## [37] train-rmse:0.030280+0.004509 test-rmse:0.244486+0.040229
## [38] train-rmse:0.029312+0.004184 test-rmse:0.244659+0.040358
## Stopping. Best iteration:
## [32] train-rmse:0.036827+0.005268 test-rmse:0.243568+0.039324
##
## 20
## [1] train-rmse:1.238209+0.005334 test-rmse:1.240010+0.021885
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.068722+0.004492 test-rmse:1.072887+0.018815
## [3] train-rmse:0.923118+0.003744 test-rmse:0.930161+0.016004
## [4] train-rmse:0.798110+0.003097 test-rmse:0.808591+0.013482
## [5] train-rmse:0.690514+0.002628 test-rmse:0.705333+0.011445
## [6] train-rmse:0.597785+0.001657 test-rmse:0.618080+0.009794
## [7] train-rmse:0.517161+0.000951 test-rmse:0.543687+0.007916
## [8] train-rmse:0.447708+0.000895 test-rmse:0.482528+0.006791
## [9] train-rmse:0.388085+0.000706 test-rmse:0.431602+0.006563
## [10] train-rmse:0.336738+0.001230 test-rmse:0.389496+0.007810
## [11] train-rmse:0.292467+0.001045 test-rmse:0.355975+0.010094
## [12] train-rmse:0.254200+0.001119 test-rmse:0.328962+0.012758
## [13] train-rmse:0.221008+0.001131 test-rmse:0.307862+0.015230
## [14] train-rmse:0.193033+0.001422 test-rmse:0.291554+0.017916
## [15] train-rmse:0.168341+0.001453 test-rmse:0.278634+0.020942
## [16] train-rmse:0.147240+0.001490 test-rmse:0.269128+0.023745
## [17] train-rmse:0.129080+0.001415 test-rmse:0.261860+0.026392
## [18] train-rmse:0.113395+0.001504 test-rmse:0.256634+0.028694
## [19] train-rmse:0.099810+0.001531 test-rmse:0.252804+0.030806
## [20] train-rmse:0.088259+0.001593 test-rmse:0.250473+0.032259
## [21] train-rmse:0.078201+0.001758 test-rmse:0.248796+0.033750
## [22] train-rmse:0.069692+0.001920 test-rmse:0.248150+0.034568
## [23] train-rmse:0.062177+0.001924 test-rmse:0.247725+0.035497
## [24] train-rmse:0.055791+0.001907 test-rmse:0.247627+0.036279
## [25] train-rmse:0.050314+0.001880 test-rmse:0.247663+0.037229
## [26] train-rmse:0.045486+0.001915 test-rmse:0.247683+0.037976
## [27] train-rmse:0.041345+0.001955 test-rmse:0.247910+0.038719
## [28] train-rmse:0.037921+0.002053 test-rmse:0.248149+0.039194
## [29] train-rmse:0.035012+0.002141 test-rmse:0.248441+0.039593
## [30] train-rmse:0.032483+0.002265 test-rmse:0.248744+0.039971
## Stopping. Best iteration:
## [24] train-rmse:0.055791+0.001907 test-rmse:0.247627+0.036279
##
## 21
## [1] train-rmse:1.213655+0.004647 test-rmse:1.213547+0.022892
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.028596+0.003145 test-rmse:1.028608+0.020702
## [3] train-rmse:0.874518+0.001806 test-rmse:0.874826+0.018655
## [4] train-rmse:0.746664+0.000901 test-rmse:0.747258+0.016732
## [5] train-rmse:0.641110+0.001341 test-rmse:0.642020+0.014957
## [6] train-rmse:0.554075+0.002090 test-rmse:0.555937+0.012977
## [7] train-rmse:0.482209+0.002850 test-rmse:0.485926+0.011596
## [8] train-rmse:0.423732+0.003380 test-rmse:0.427817+0.012413
## [9] train-rmse:0.375882+0.003991 test-rmse:0.380362+0.012092
## [10] train-rmse:0.337658+0.004392 test-rmse:0.342407+0.013741
## [11] train-rmse:0.306819+0.004853 test-rmse:0.312776+0.015614
## [12] train-rmse:0.282727+0.005124 test-rmse:0.288196+0.016812
## [13] train-rmse:0.263485+0.005462 test-rmse:0.269782+0.018954
## [14] train-rmse:0.248820+0.005610 test-rmse:0.255416+0.020204
## [15] train-rmse:0.237188+0.005771 test-rmse:0.244469+0.022125
## [16] train-rmse:0.228425+0.005837 test-rmse:0.237212+0.021995
## [17] train-rmse:0.221495+0.005911 test-rmse:0.229106+0.023781
## [18] train-rmse:0.216202+0.005934 test-rmse:0.225089+0.023351
## [19] train-rmse:0.211923+0.005945 test-rmse:0.221722+0.024762
## [20] train-rmse:0.208412+0.005855 test-rmse:0.219516+0.025067
## [21] train-rmse:0.205559+0.005803 test-rmse:0.217557+0.025162
## [22] train-rmse:0.203157+0.005703 test-rmse:0.215557+0.026321
## [23] train-rmse:0.201065+0.005637 test-rmse:0.214276+0.025746
## [24] train-rmse:0.199206+0.005538 test-rmse:0.214270+0.026130
## [25] train-rmse:0.197623+0.005464 test-rmse:0.213558+0.026455
## [26] train-rmse:0.196151+0.005382 test-rmse:0.213337+0.026500
## [27] train-rmse:0.194841+0.005337 test-rmse:0.212892+0.026575
## [28] train-rmse:0.193629+0.005317 test-rmse:0.210777+0.026881
## [29] train-rmse:0.192506+0.005300 test-rmse:0.212158+0.026470
## [30] train-rmse:0.191438+0.005291 test-rmse:0.212163+0.026614
## [31] train-rmse:0.190496+0.005307 test-rmse:0.211401+0.026849
## [32] train-rmse:0.189542+0.005311 test-rmse:0.212058+0.027283
## [33] train-rmse:0.188656+0.005347 test-rmse:0.212227+0.027368
## [34] train-rmse:0.187838+0.005386 test-rmse:0.210730+0.026910
## [35] train-rmse:0.187018+0.005455 test-rmse:0.211721+0.026459
## [36] train-rmse:0.186287+0.005489 test-rmse:0.211655+0.026809
## [37] train-rmse:0.185532+0.005548 test-rmse:0.211384+0.026623
## [38] train-rmse:0.184872+0.005603 test-rmse:0.212169+0.027075
## [39] train-rmse:0.184227+0.005667 test-rmse:0.211848+0.026802
## [40] train-rmse:0.183592+0.005721 test-rmse:0.211378+0.026376
## Stopping. Best iteration:
## [34] train-rmse:0.187838+0.005386 test-rmse:0.210730+0.026910
##
## 22
## [1] train-rmse:1.211010+0.005150 test-rmse:1.211798+0.021979
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.023067+0.004138 test-rmse:1.024893+0.019134
## [3] train-rmse:0.865765+0.003297 test-rmse:0.869373+0.016894
## [4] train-rmse:0.734333+0.002621 test-rmse:0.739537+0.014900
## [5] train-rmse:0.624817+0.002110 test-rmse:0.632049+0.013377
## [6] train-rmse:0.533777+0.001779 test-rmse:0.543404+0.012390
## [7] train-rmse:0.458543+0.001638 test-rmse:0.471624+0.011638
## [8] train-rmse:0.396542+0.001573 test-rmse:0.412708+0.011536
## [9] train-rmse:0.345836+0.001775 test-rmse:0.365584+0.012269
## [10] train-rmse:0.304542+0.001816 test-rmse:0.328060+0.013723
## [11] train-rmse:0.271343+0.002016 test-rmse:0.299302+0.015368
## [12] train-rmse:0.244708+0.002062 test-rmse:0.276202+0.017200
## [13] train-rmse:0.223740+0.002132 test-rmse:0.259509+0.019293
## [14] train-rmse:0.207293+0.002123 test-rmse:0.247326+0.020914
## [15] train-rmse:0.193143+0.002006 test-rmse:0.236211+0.022179
## [16] train-rmse:0.182393+0.001780 test-rmse:0.228171+0.023026
## [17] train-rmse:0.174617+0.001921 test-rmse:0.222683+0.024126
## [18] train-rmse:0.168517+0.002534 test-rmse:0.218197+0.024476
## [19] train-rmse:0.163555+0.002500 test-rmse:0.215710+0.025368
## [20] train-rmse:0.159895+0.003186 test-rmse:0.213210+0.025677
## [21] train-rmse:0.156663+0.003703 test-rmse:0.212278+0.025504
## [22] train-rmse:0.154522+0.003824 test-rmse:0.211732+0.026207
## [23] train-rmse:0.152153+0.004480 test-rmse:0.210625+0.026375
## [24] train-rmse:0.150015+0.004498 test-rmse:0.210503+0.026415
## [25] train-rmse:0.148499+0.004320 test-rmse:0.210781+0.026917
## [26] train-rmse:0.146976+0.004047 test-rmse:0.210772+0.027392
## [27] train-rmse:0.145601+0.004420 test-rmse:0.210617+0.027473
## [28] train-rmse:0.143962+0.004545 test-rmse:0.210172+0.027687
## [29] train-rmse:0.141891+0.004207 test-rmse:0.210496+0.027753
## [30] train-rmse:0.140779+0.004751 test-rmse:0.210513+0.027726
## [31] train-rmse:0.139821+0.005063 test-rmse:0.210794+0.028089
## [32] train-rmse:0.138230+0.004886 test-rmse:0.210882+0.028130
## [33] train-rmse:0.137147+0.004762 test-rmse:0.211075+0.028401
## [34] train-rmse:0.136012+0.005013 test-rmse:0.211285+0.028891
## Stopping. Best iteration:
## [28] train-rmse:0.143962+0.004545 test-rmse:0.210172+0.027687
##
## 23
## [1] train-rmse:1.210478+0.005126 test-rmse:1.212198+0.021465
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.021920+0.004053 test-rmse:1.025941+0.018170
## [3] train-rmse:0.863764+0.003199 test-rmse:0.870898+0.015306
## [4] train-rmse:0.731347+0.002477 test-rmse:0.741689+0.012815
## [5] train-rmse:0.620623+0.001910 test-rmse:0.635183+0.010600
## [6] train-rmse:0.528207+0.001627 test-rmse:0.547619+0.008828
## [7] train-rmse:0.450932+0.001679 test-rmse:0.476077+0.008851
## [8] train-rmse:0.386978+0.002244 test-rmse:0.417105+0.009577
## [9] train-rmse:0.334100+0.002966 test-rmse:0.371027+0.009782
## [10] train-rmse:0.289954+0.003994 test-rmse:0.334297+0.010966
## [11] train-rmse:0.253422+0.004961 test-rmse:0.304893+0.013002
## [12] train-rmse:0.223134+0.005362 test-rmse:0.283362+0.014913
## [13] train-rmse:0.198887+0.005547 test-rmse:0.265830+0.017120
## [14] train-rmse:0.178789+0.006008 test-rmse:0.253625+0.019749
## [15] train-rmse:0.163071+0.006211 test-rmse:0.244241+0.021285
## [16] train-rmse:0.150139+0.006827 test-rmse:0.237913+0.023279
## [17] train-rmse:0.139823+0.007355 test-rmse:0.233294+0.024908
## [18] train-rmse:0.131417+0.008004 test-rmse:0.229758+0.025519
## [19] train-rmse:0.124949+0.008060 test-rmse:0.226676+0.026004
## [20] train-rmse:0.120167+0.008357 test-rmse:0.224594+0.026655
## [21] train-rmse:0.116079+0.008605 test-rmse:0.223302+0.027455
## [22] train-rmse:0.112719+0.008736 test-rmse:0.223053+0.027673
## [23] train-rmse:0.109697+0.008745 test-rmse:0.222276+0.027963
## [24] train-rmse:0.107576+0.009033 test-rmse:0.222170+0.028597
## [25] train-rmse:0.105609+0.009700 test-rmse:0.222324+0.029010
## [26] train-rmse:0.103822+0.010013 test-rmse:0.222211+0.029255
## [27] train-rmse:0.101852+0.010058 test-rmse:0.222614+0.029719
## [28] train-rmse:0.100037+0.009999 test-rmse:0.223394+0.030106
## [29] train-rmse:0.098601+0.010423 test-rmse:0.223983+0.030485
## [30] train-rmse:0.096853+0.010850 test-rmse:0.223486+0.030299
## Stopping. Best iteration:
## [24] train-rmse:0.107576+0.009033 test-rmse:0.222170+0.028597
##
## 24
## [1] train-rmse:1.210288+0.005141 test-rmse:1.212207+0.021432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.021466+0.004084 test-rmse:1.025897+0.018057
## [3] train-rmse:0.863125+0.003192 test-rmse:0.870539+0.015056
## [4] train-rmse:0.730424+0.002532 test-rmse:0.741947+0.012658
## [5] train-rmse:0.618629+0.001699 test-rmse:0.635538+0.010731
## [6] train-rmse:0.524474+0.001119 test-rmse:0.547695+0.008985
## [7] train-rmse:0.445840+0.000955 test-rmse:0.476911+0.007783
## [8] train-rmse:0.379674+0.001181 test-rmse:0.419317+0.008175
## [9] train-rmse:0.324551+0.001661 test-rmse:0.373681+0.009692
## [10] train-rmse:0.278597+0.002118 test-rmse:0.337811+0.011932
## [11] train-rmse:0.240181+0.002820 test-rmse:0.310050+0.013878
## [12] train-rmse:0.208205+0.003314 test-rmse:0.288523+0.016822
## [13] train-rmse:0.181646+0.003706 test-rmse:0.272493+0.019362
## [14] train-rmse:0.159648+0.004177 test-rmse:0.260336+0.021510
## [15] train-rmse:0.141475+0.004715 test-rmse:0.252292+0.023584
## [16] train-rmse:0.126535+0.005267 test-rmse:0.245955+0.025360
## [17] train-rmse:0.113905+0.005562 test-rmse:0.241900+0.027097
## [18] train-rmse:0.104021+0.005692 test-rmse:0.239240+0.028644
## [19] train-rmse:0.096081+0.006145 test-rmse:0.236881+0.029522
## [20] train-rmse:0.089800+0.006591 test-rmse:0.235513+0.030306
## [21] train-rmse:0.084863+0.006560 test-rmse:0.234806+0.031137
## [22] train-rmse:0.080803+0.006796 test-rmse:0.234019+0.031640
## [23] train-rmse:0.077340+0.006935 test-rmse:0.233818+0.032078
## [24] train-rmse:0.074534+0.006862 test-rmse:0.233892+0.032613
## [25] train-rmse:0.072263+0.007155 test-rmse:0.233430+0.032586
## [26] train-rmse:0.069963+0.007284 test-rmse:0.233543+0.032918
## [27] train-rmse:0.068365+0.007224 test-rmse:0.233582+0.033272
## [28] train-rmse:0.066868+0.007280 test-rmse:0.234191+0.033643
## [29] train-rmse:0.065696+0.007352 test-rmse:0.234021+0.033618
## [30] train-rmse:0.064580+0.007326 test-rmse:0.234076+0.033771
## [31] train-rmse:0.063378+0.007653 test-rmse:0.234069+0.033792
## Stopping. Best iteration:
## [25] train-rmse:0.072263+0.007155 test-rmse:0.233430+0.032586
##
## 25
## [1] train-rmse:1.210167+0.005131 test-rmse:1.212208+0.021420
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.021152+0.004107 test-rmse:1.025930+0.018017
## [3] train-rmse:0.862642+0.003214 test-rmse:0.870780+0.015018
## [4] train-rmse:0.729801+0.002523 test-rmse:0.742311+0.012572
## [5] train-rmse:0.617564+0.001452 test-rmse:0.636009+0.010723
## [6] train-rmse:0.522959+0.000850 test-rmse:0.548684+0.008746
## [7] train-rmse:0.443636+0.001076 test-rmse:0.477738+0.007634
## [8] train-rmse:0.376990+0.001273 test-rmse:0.420607+0.008469
## [9] train-rmse:0.320776+0.001634 test-rmse:0.375828+0.009853
## [10] train-rmse:0.273542+0.001942 test-rmse:0.340536+0.012385
## [11] train-rmse:0.234063+0.002234 test-rmse:0.313700+0.015522
## [12] train-rmse:0.200844+0.002195 test-rmse:0.292913+0.019127
## [13] train-rmse:0.173055+0.002388 test-rmse:0.276947+0.022310
## [14] train-rmse:0.149820+0.002447 test-rmse:0.266073+0.025773
## [15] train-rmse:0.130400+0.002813 test-rmse:0.257798+0.028558
## [16] train-rmse:0.114437+0.003198 test-rmse:0.251965+0.031046
## [17] train-rmse:0.100898+0.003442 test-rmse:0.248024+0.033095
## [18] train-rmse:0.090249+0.003772 test-rmse:0.245260+0.034865
## [19] train-rmse:0.081312+0.004271 test-rmse:0.243208+0.035819
## [20] train-rmse:0.074141+0.004706 test-rmse:0.242050+0.036933
## [21] train-rmse:0.067919+0.005223 test-rmse:0.241385+0.037496
## [22] train-rmse:0.063056+0.005611 test-rmse:0.240547+0.037927
## [23] train-rmse:0.058941+0.006099 test-rmse:0.240848+0.038465
## [24] train-rmse:0.055522+0.006027 test-rmse:0.240698+0.039029
## [25] train-rmse:0.052721+0.006072 test-rmse:0.240343+0.039267
## [26] train-rmse:0.050211+0.006061 test-rmse:0.240025+0.039313
## [27] train-rmse:0.048121+0.005816 test-rmse:0.240080+0.039536
## [28] train-rmse:0.046258+0.005892 test-rmse:0.240215+0.039668
## [29] train-rmse:0.044819+0.005808 test-rmse:0.240226+0.039831
## [30] train-rmse:0.043408+0.005821 test-rmse:0.240191+0.039761
## [31] train-rmse:0.042213+0.005688 test-rmse:0.240535+0.039891
## [32] train-rmse:0.041079+0.005601 test-rmse:0.240663+0.040009
## Stopping. Best iteration:
## [26] train-rmse:0.050211+0.006061 test-rmse:0.240025+0.039313
##
## 26
## [1] train-rmse:1.210106+0.005159 test-rmse:1.212217+0.021400
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.021003+0.004175 test-rmse:1.025911+0.017956
## [3] train-rmse:0.862395+0.003320 test-rmse:0.870784+0.014866
## [4] train-rmse:0.729432+0.002681 test-rmse:0.742403+0.012329
## [5] train-rmse:0.617076+0.001567 test-rmse:0.635823+0.010281
## [6] train-rmse:0.522135+0.000958 test-rmse:0.548045+0.008174
## [7] train-rmse:0.442515+0.001138 test-rmse:0.477095+0.007366
## [8] train-rmse:0.375482+0.001339 test-rmse:0.419653+0.008158
## [9] train-rmse:0.318960+0.001611 test-rmse:0.374736+0.009633
## [10] train-rmse:0.271078+0.001765 test-rmse:0.339897+0.011920
## [11] train-rmse:0.231330+0.001961 test-rmse:0.312794+0.014894
## [12] train-rmse:0.197527+0.002298 test-rmse:0.292561+0.018282
## [13] train-rmse:0.169342+0.002513 test-rmse:0.277429+0.021755
## [14] train-rmse:0.145643+0.002984 test-rmse:0.266248+0.025221
## [15] train-rmse:0.126003+0.003268 test-rmse:0.258354+0.028036
## [16] train-rmse:0.109394+0.003424 test-rmse:0.252676+0.030368
## [17] train-rmse:0.095192+0.003423 test-rmse:0.248665+0.032514
## [18] train-rmse:0.083625+0.003618 test-rmse:0.245829+0.034061
## [19] train-rmse:0.074106+0.003951 test-rmse:0.244297+0.035799
## [20] train-rmse:0.066248+0.004211 test-rmse:0.242924+0.037023
## [21] train-rmse:0.059384+0.004257 test-rmse:0.242678+0.038115
## [22] train-rmse:0.053909+0.004520 test-rmse:0.242159+0.038701
## [23] train-rmse:0.049451+0.004761 test-rmse:0.241883+0.039111
## [24] train-rmse:0.045595+0.005110 test-rmse:0.241885+0.039488
## [25] train-rmse:0.042399+0.005127 test-rmse:0.241573+0.039662
## [26] train-rmse:0.039782+0.005022 test-rmse:0.241751+0.039905
## [27] train-rmse:0.037513+0.005152 test-rmse:0.241880+0.040144
## [28] train-rmse:0.035798+0.005150 test-rmse:0.242208+0.040510
## [29] train-rmse:0.034123+0.005168 test-rmse:0.242444+0.040547
## [30] train-rmse:0.032698+0.005212 test-rmse:0.242543+0.040690
## [31] train-rmse:0.031480+0.005317 test-rmse:0.242770+0.040868
## Stopping. Best iteration:
## [25] train-rmse:0.042399+0.005127 test-rmse:0.241573+0.039662
##
## 27
## [1] train-rmse:1.210079+0.005190 test-rmse:1.212221+0.021391
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.020942+0.004244 test-rmse:1.025926+0.017930
## [3] train-rmse:0.862293+0.003423 test-rmse:0.870827+0.014803
## [4] train-rmse:0.729284+0.002801 test-rmse:0.742501+0.012256
## [5] train-rmse:0.616839+0.001594 test-rmse:0.635879+0.010249
## [6] train-rmse:0.521704+0.000889 test-rmse:0.548050+0.008059
## [7] train-rmse:0.442100+0.001126 test-rmse:0.476958+0.006456
## [8] train-rmse:0.374694+0.001046 test-rmse:0.420133+0.006589
## [9] train-rmse:0.317883+0.001125 test-rmse:0.374842+0.008526
## [10] train-rmse:0.270247+0.001017 test-rmse:0.339774+0.011488
## [11] train-rmse:0.230195+0.001121 test-rmse:0.312496+0.014630
## [12] train-rmse:0.196207+0.001284 test-rmse:0.292013+0.018181
## [13] train-rmse:0.168000+0.001568 test-rmse:0.277173+0.021028
## [14] train-rmse:0.143801+0.001695 test-rmse:0.266285+0.024489
## [15] train-rmse:0.123637+0.001598 test-rmse:0.258582+0.027402
## [16] train-rmse:0.106679+0.001668 test-rmse:0.253201+0.030026
## [17] train-rmse:0.092589+0.001785 test-rmse:0.249602+0.032199
## [18] train-rmse:0.080468+0.001721 test-rmse:0.247080+0.034015
## [19] train-rmse:0.070344+0.001834 test-rmse:0.246311+0.034893
## [20] train-rmse:0.061812+0.001721 test-rmse:0.245689+0.035909
## [21] train-rmse:0.054664+0.001631 test-rmse:0.245610+0.036756
## [22] train-rmse:0.048750+0.001574 test-rmse:0.245815+0.037433
## [23] train-rmse:0.043676+0.001610 test-rmse:0.246013+0.037903
## [24] train-rmse:0.039446+0.001620 test-rmse:0.246261+0.038402
## [25] train-rmse:0.035995+0.001710 test-rmse:0.246579+0.038842
## [26] train-rmse:0.032918+0.001773 test-rmse:0.246876+0.039164
## [27] train-rmse:0.030286+0.001947 test-rmse:0.247179+0.039486
## Stopping. Best iteration:
## [21] train-rmse:0.054664+0.001631 test-rmse:0.245610+0.036756
##
## 28
## [1] train-rmse:1.186046+0.004431 test-rmse:1.185953+0.022581
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.983144+0.002759 test-rmse:0.983192+0.020120
## [3] train-rmse:0.818590+0.001334 test-rmse:0.819047+0.017885
## [4] train-rmse:0.685803+0.000980 test-rmse:0.686552+0.015707
## [5] train-rmse:0.579410+0.002060 test-rmse:0.581999+0.014647
## [6] train-rmse:0.493486+0.002693 test-rmse:0.497450+0.012060
## [7] train-rmse:0.425612+0.003535 test-rmse:0.428824+0.013350
## [8] train-rmse:0.371422+0.004002 test-rmse:0.376252+0.012264
## [9] train-rmse:0.329635+0.004646 test-rmse:0.334725+0.013857
## [10] train-rmse:0.296887+0.004953 test-rmse:0.302290+0.015551
## [11] train-rmse:0.272274+0.005415 test-rmse:0.279233+0.018644
## [12] train-rmse:0.253346+0.005583 test-rmse:0.260143+0.019731
## [13] train-rmse:0.239413+0.005790 test-rmse:0.246748+0.022104
## [14] train-rmse:0.228872+0.005836 test-rmse:0.236095+0.022333
## [15] train-rmse:0.221138+0.005938 test-rmse:0.229357+0.024570
## [16] train-rmse:0.215200+0.005921 test-rmse:0.225257+0.024128
## [17] train-rmse:0.210689+0.005939 test-rmse:0.222063+0.024655
## [18] train-rmse:0.207034+0.005850 test-rmse:0.217056+0.025664
## [19] train-rmse:0.204046+0.005753 test-rmse:0.215843+0.026081
## [20] train-rmse:0.201569+0.005652 test-rmse:0.214865+0.025347
## [21] train-rmse:0.199489+0.005546 test-rmse:0.215105+0.025882
## [22] train-rmse:0.197609+0.005457 test-rmse:0.213966+0.026803
## [23] train-rmse:0.195981+0.005373 test-rmse:0.213476+0.027020
## [24] train-rmse:0.194518+0.005360 test-rmse:0.211818+0.027179
## [25] train-rmse:0.193195+0.005332 test-rmse:0.212700+0.027644
## [26] train-rmse:0.191939+0.005331 test-rmse:0.212424+0.026316
## [27] train-rmse:0.190797+0.005330 test-rmse:0.211861+0.026903
## [28] train-rmse:0.189750+0.005311 test-rmse:0.212234+0.027021
## [29] train-rmse:0.188755+0.005347 test-rmse:0.213080+0.027585
## [30] train-rmse:0.187799+0.005390 test-rmse:0.211525+0.027219
## [31] train-rmse:0.186880+0.005471 test-rmse:0.211033+0.027600
## [32] train-rmse:0.186053+0.005518 test-rmse:0.212658+0.027233
## [33] train-rmse:0.185194+0.005580 test-rmse:0.211831+0.026600
## [34] train-rmse:0.184471+0.005645 test-rmse:0.211273+0.026544
## [35] train-rmse:0.183772+0.005707 test-rmse:0.211156+0.026415
## [36] train-rmse:0.183084+0.005763 test-rmse:0.211308+0.026705
## [37] train-rmse:0.182384+0.005830 test-rmse:0.211097+0.026951
## Stopping. Best iteration:
## [31] train-rmse:0.186880+0.005471 test-rmse:0.211033+0.027600
##
## 29
## [1] train-rmse:1.183027+0.005002 test-rmse:1.183964+0.021566
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.976783+0.003886 test-rmse:0.978946+0.018439
## [3] train-rmse:0.808415+0.003003 test-rmse:0.812447+0.015986
## [4] train-rmse:0.671352+0.002315 test-rmse:0.677432+0.014029
## [5] train-rmse:0.560167+0.001881 test-rmse:0.568715+0.012714
## [6] train-rmse:0.470461+0.001638 test-rmse:0.483175+0.011700
## [7] train-rmse:0.398472+0.001601 test-rmse:0.414376+0.011397
## [8] train-rmse:0.341144+0.001785 test-rmse:0.361265+0.012256
## [9] train-rmse:0.295819+0.001913 test-rmse:0.320419+0.014268
## [10] train-rmse:0.260531+0.002040 test-rmse:0.290389+0.015738
## [11] train-rmse:0.233267+0.002142 test-rmse:0.267123+0.017987
## [12] train-rmse:0.212521+0.002107 test-rmse:0.250823+0.020376
## [13] train-rmse:0.195678+0.001740 test-rmse:0.238499+0.023280
## [14] train-rmse:0.183634+0.001917 test-rmse:0.229209+0.024171
## [15] train-rmse:0.174532+0.002378 test-rmse:0.221910+0.024572
## [16] train-rmse:0.167823+0.003051 test-rmse:0.217231+0.025320
## [17] train-rmse:0.163172+0.003300 test-rmse:0.214873+0.026050
## [18] train-rmse:0.158003+0.003549 test-rmse:0.213215+0.025971
## [19] train-rmse:0.154824+0.003936 test-rmse:0.212386+0.026063
## [20] train-rmse:0.152358+0.003188 test-rmse:0.211906+0.026985
## [21] train-rmse:0.149856+0.003608 test-rmse:0.212079+0.027441
## [22] train-rmse:0.146691+0.003367 test-rmse:0.211930+0.026743
## [23] train-rmse:0.145743+0.003366 test-rmse:0.212029+0.027297
## [24] train-rmse:0.143786+0.003120 test-rmse:0.211913+0.027455
## [25] train-rmse:0.142369+0.002639 test-rmse:0.212722+0.027594
## [26] train-rmse:0.141170+0.003347 test-rmse:0.212480+0.027638
## Stopping. Best iteration:
## [20] train-rmse:0.152358+0.003188 test-rmse:0.211906+0.026985
##
## 30
## [1] train-rmse:1.182416+0.004972 test-rmse:1.184423+0.020980
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.975455+0.003777 test-rmse:0.980173+0.017333
## [3] train-rmse:0.806092+0.002859 test-rmse:0.814472+0.014191
## [4] train-rmse:0.667817+0.002125 test-rmse:0.680279+0.011605
## [5] train-rmse:0.555111+0.001653 test-rmse:0.573008+0.009537
## [6] train-rmse:0.463558+0.001663 test-rmse:0.487455+0.007984
## [7] train-rmse:0.389029+0.002243 test-rmse:0.418999+0.009053
## [8] train-rmse:0.328915+0.003434 test-rmse:0.367738+0.010038
## [9] train-rmse:0.280002+0.004467 test-rmse:0.326387+0.012297
## [10] train-rmse:0.240365+0.004929 test-rmse:0.297306+0.014483
## [11] train-rmse:0.209277+0.005293 test-rmse:0.275481+0.017151
## [12] train-rmse:0.184401+0.005744 test-rmse:0.258724+0.019469
## [13] train-rmse:0.165150+0.006437 test-rmse:0.248298+0.022200
## [14] train-rmse:0.150425+0.006708 test-rmse:0.239780+0.023671
## [15] train-rmse:0.138767+0.007353 test-rmse:0.234347+0.025845
## [16] train-rmse:0.129675+0.007461 test-rmse:0.230996+0.027859
## [17] train-rmse:0.122792+0.007335 test-rmse:0.227805+0.028523
## [18] train-rmse:0.117397+0.007909 test-rmse:0.225836+0.029035
## [19] train-rmse:0.113105+0.008035 test-rmse:0.225210+0.029498
## [20] train-rmse:0.109998+0.008333 test-rmse:0.224543+0.030118
## [21] train-rmse:0.106871+0.008132 test-rmse:0.224416+0.030716
## [22] train-rmse:0.104823+0.008275 test-rmse:0.224247+0.031049
## [23] train-rmse:0.102532+0.008329 test-rmse:0.224447+0.031436
## [24] train-rmse:0.100163+0.008456 test-rmse:0.225240+0.032067
## [25] train-rmse:0.098588+0.008211 test-rmse:0.225760+0.031620
## [26] train-rmse:0.097049+0.008174 test-rmse:0.226155+0.032011
## [27] train-rmse:0.095828+0.008037 test-rmse:0.226566+0.032256
## [28] train-rmse:0.094114+0.008415 test-rmse:0.226342+0.032325
## Stopping. Best iteration:
## [22] train-rmse:0.104823+0.008275 test-rmse:0.224247+0.031049
##
## 31
## [1] train-rmse:1.182198+0.004990 test-rmse:1.184438+0.020942
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.974871+0.003851 test-rmse:0.979988+0.017158
## [3] train-rmse:0.805337+0.002851 test-rmse:0.814153+0.013930
## [4] train-rmse:0.666649+0.002309 test-rmse:0.680580+0.011352
## [5] train-rmse:0.552565+0.001613 test-rmse:0.573337+0.009470
## [6] train-rmse:0.458890+0.001161 test-rmse:0.487609+0.007948
## [7] train-rmse:0.382318+0.001371 test-rmse:0.420480+0.008509
## [8] train-rmse:0.319879+0.001904 test-rmse:0.368871+0.009754
## [9] train-rmse:0.269049+0.002627 test-rmse:0.329398+0.012095
## [10] train-rmse:0.227649+0.003192 test-rmse:0.300060+0.014798
## [11] train-rmse:0.194225+0.003627 test-rmse:0.278533+0.018011
## [12] train-rmse:0.167245+0.004123 test-rmse:0.262915+0.020997
## [13] train-rmse:0.145584+0.004712 test-rmse:0.252363+0.023862
## [14] train-rmse:0.128237+0.005355 test-rmse:0.245805+0.026200
## [15] train-rmse:0.114030+0.005572 test-rmse:0.241126+0.028007
## [16] train-rmse:0.103048+0.006310 test-rmse:0.237306+0.029660
## [17] train-rmse:0.094590+0.006849 test-rmse:0.235318+0.030789
## [18] train-rmse:0.088077+0.007293 test-rmse:0.233772+0.031569
## [19] train-rmse:0.082724+0.007642 test-rmse:0.233568+0.032063
## [20] train-rmse:0.078663+0.007995 test-rmse:0.233048+0.032793
## [21] train-rmse:0.075265+0.008396 test-rmse:0.232496+0.032995
## [22] train-rmse:0.072106+0.008538 test-rmse:0.232127+0.033159
## [23] train-rmse:0.069722+0.008875 test-rmse:0.232036+0.033479
## [24] train-rmse:0.067608+0.008998 test-rmse:0.232210+0.033827
## [25] train-rmse:0.065975+0.008909 test-rmse:0.232446+0.034164
## [26] train-rmse:0.064131+0.009224 test-rmse:0.232557+0.034192
## [27] train-rmse:0.062514+0.008853 test-rmse:0.232599+0.034218
## [28] train-rmse:0.060986+0.008754 test-rmse:0.232543+0.034175
## [29] train-rmse:0.059419+0.008779 test-rmse:0.232568+0.034293
## Stopping. Best iteration:
## [23] train-rmse:0.069722+0.008875 test-rmse:0.232036+0.033479
##
## 32
## [1] train-rmse:1.182059+0.004978 test-rmse:1.184442+0.020927
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.974561+0.003841 test-rmse:0.980029+0.017108
## [3] train-rmse:0.804856+0.002807 test-rmse:0.814450+0.013798
## [4] train-rmse:0.666074+0.002130 test-rmse:0.680978+0.011170
## [5] train-rmse:0.550984+0.001149 test-rmse:0.573312+0.009014
## [6] train-rmse:0.456636+0.000592 test-rmse:0.487938+0.007507
## [7] train-rmse:0.379238+0.000856 test-rmse:0.421446+0.007605
## [8] train-rmse:0.315715+0.001462 test-rmse:0.370320+0.009566
## [9] train-rmse:0.263521+0.001984 test-rmse:0.331609+0.012670
## [10] train-rmse:0.220994+0.002485 test-rmse:0.303424+0.016176
## [11] train-rmse:0.186074+0.002946 test-rmse:0.282292+0.019800
## [12] train-rmse:0.157937+0.003093 test-rmse:0.267710+0.023436
## [13] train-rmse:0.134633+0.003674 test-rmse:0.257253+0.026562
## [14] train-rmse:0.115856+0.003959 test-rmse:0.250332+0.028868
## [15] train-rmse:0.100745+0.004213 test-rmse:0.245619+0.030859
## [16] train-rmse:0.088539+0.004340 test-rmse:0.243522+0.032794
## [17] train-rmse:0.078767+0.004766 test-rmse:0.241936+0.034327
## [18] train-rmse:0.071004+0.005303 test-rmse:0.241079+0.035656
## [19] train-rmse:0.064467+0.005845 test-rmse:0.240509+0.036226
## [20] train-rmse:0.059531+0.006096 test-rmse:0.240124+0.036620
## [21] train-rmse:0.055331+0.006326 test-rmse:0.239758+0.037231
## [22] train-rmse:0.052065+0.006666 test-rmse:0.239414+0.037534
## [23] train-rmse:0.049399+0.006982 test-rmse:0.239489+0.037705
## [24] train-rmse:0.047182+0.007241 test-rmse:0.239400+0.037838
## [25] train-rmse:0.045342+0.007301 test-rmse:0.239334+0.037899
## [26] train-rmse:0.043661+0.007382 test-rmse:0.239709+0.038234
## [27] train-rmse:0.042353+0.007371 test-rmse:0.239574+0.038141
## [28] train-rmse:0.040909+0.007544 test-rmse:0.240017+0.038412
## [29] train-rmse:0.039798+0.007505 test-rmse:0.240270+0.038693
## [30] train-rmse:0.038551+0.007368 test-rmse:0.240673+0.039139
## [31] train-rmse:0.037228+0.007118 test-rmse:0.240810+0.039218
## Stopping. Best iteration:
## [25] train-rmse:0.045342+0.007301 test-rmse:0.239334+0.037899
##
## 33
## [1] train-rmse:1.181989+0.005009 test-rmse:1.184453+0.020905
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.974388+0.003921 test-rmse:0.980164+0.017086
## [3] train-rmse:0.804487+0.003000 test-rmse:0.814351+0.013689
## [4] train-rmse:0.665573+0.002406 test-rmse:0.680991+0.010997
## [5] train-rmse:0.550786+0.001523 test-rmse:0.573834+0.008969
## [6] train-rmse:0.455859+0.001234 test-rmse:0.488081+0.007124
## [7] train-rmse:0.378107+0.001837 test-rmse:0.421777+0.007463
## [8] train-rmse:0.314315+0.001635 test-rmse:0.370190+0.009624
## [9] train-rmse:0.261610+0.002099 test-rmse:0.331764+0.012278
## [10] train-rmse:0.218214+0.002090 test-rmse:0.302964+0.016458
## [11] train-rmse:0.183320+0.002611 test-rmse:0.282884+0.019788
## [12] train-rmse:0.154184+0.002589 test-rmse:0.268190+0.023238
## [13] train-rmse:0.130584+0.002917 test-rmse:0.258213+0.026516
## [14] train-rmse:0.111427+0.003076 test-rmse:0.251568+0.029288
## [15] train-rmse:0.095712+0.003526 test-rmse:0.247021+0.031881
## [16] train-rmse:0.082621+0.003871 test-rmse:0.244513+0.033700
## [17] train-rmse:0.072343+0.004241 test-rmse:0.242905+0.035080
## [18] train-rmse:0.063953+0.004036 test-rmse:0.241767+0.036163
## [19] train-rmse:0.057026+0.004161 test-rmse:0.241184+0.036855
## [20] train-rmse:0.051576+0.004327 test-rmse:0.240924+0.037209
## [21] train-rmse:0.047091+0.004437 test-rmse:0.241219+0.037695
## [22] train-rmse:0.043603+0.004598 test-rmse:0.240922+0.037788
## [23] train-rmse:0.040599+0.004706 test-rmse:0.240991+0.038120
## [24] train-rmse:0.038227+0.004830 test-rmse:0.241267+0.038460
## [25] train-rmse:0.036235+0.005017 test-rmse:0.241670+0.038769
## [26] train-rmse:0.034155+0.005133 test-rmse:0.241631+0.038831
## [27] train-rmse:0.032478+0.005001 test-rmse:0.241766+0.038911
## [28] train-rmse:0.030679+0.005217 test-rmse:0.242101+0.039102
## Stopping. Best iteration:
## [22] train-rmse:0.043603+0.004598 test-rmse:0.240922+0.037788
##
## 34
## [1] train-rmse:1.181959+0.005046 test-rmse:1.184458+0.020894
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.974316+0.004001 test-rmse:0.980183+0.017054
## [3] train-rmse:0.804363+0.003119 test-rmse:0.814423+0.013649
## [4] train-rmse:0.665389+0.002533 test-rmse:0.681119+0.010915
## [5] train-rmse:0.550484+0.001405 test-rmse:0.573915+0.008921
## [6] train-rmse:0.455258+0.000819 test-rmse:0.488170+0.006913
## [7] train-rmse:0.377314+0.001268 test-rmse:0.421811+0.006792
## [8] train-rmse:0.313123+0.001025 test-rmse:0.370404+0.008926
## [9] train-rmse:0.260213+0.001353 test-rmse:0.331799+0.011937
## [10] train-rmse:0.217339+0.001661 test-rmse:0.303817+0.015408
## [11] train-rmse:0.181629+0.001518 test-rmse:0.283237+0.019406
## [12] train-rmse:0.152310+0.001404 test-rmse:0.268885+0.023215
## [13] train-rmse:0.128229+0.001496 test-rmse:0.258756+0.026486
## [14] train-rmse:0.108657+0.001559 test-rmse:0.252425+0.029432
## [15] train-rmse:0.092207+0.001556 test-rmse:0.248161+0.031657
## [16] train-rmse:0.079023+0.001775 test-rmse:0.245774+0.033632
## [17] train-rmse:0.067996+0.002041 test-rmse:0.244986+0.034483
## [18] train-rmse:0.058970+0.001959 test-rmse:0.244304+0.035957
## [19] train-rmse:0.051670+0.001869 test-rmse:0.244073+0.037242
## [20] train-rmse:0.045538+0.001998 test-rmse:0.244313+0.038087
## [21] train-rmse:0.040515+0.002165 test-rmse:0.244595+0.038677
## [22] train-rmse:0.036534+0.002449 test-rmse:0.245132+0.039352
## [23] train-rmse:0.033253+0.002577 test-rmse:0.245464+0.039734
## [24] train-rmse:0.030232+0.002472 test-rmse:0.245792+0.040083
## [25] train-rmse:0.027969+0.002266 test-rmse:0.245946+0.040316
## Stopping. Best iteration:
## [19] train-rmse:0.051670+0.001869 test-rmse:0.244073+0.037242
##
## 35
## [1] train-rmse:1.158465+0.004212 test-rmse:1.158388+0.022265
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.938913+0.002376 test-rmse:0.939001+0.019531
## [3] train-rmse:0.765642+0.000973 test-rmse:0.766244+0.017073
## [4] train-rmse:0.629888+0.001456 test-rmse:0.630892+0.014798
## [5] train-rmse:0.523882+0.002391 test-rmse:0.526195+0.012128
## [6] train-rmse:0.441209+0.003269 test-rmse:0.445986+0.010762
## [7] train-rmse:0.377947+0.003907 test-rmse:0.382761+0.012267
## [8] train-rmse:0.329612+0.004563 test-rmse:0.335249+0.013465
## [9] train-rmse:0.293790+0.004957 test-rmse:0.299309+0.015384
## [10] train-rmse:0.267046+0.005423 test-rmse:0.274293+0.018717
## [11] train-rmse:0.247918+0.005610 test-rmse:0.254199+0.019440
## [12] train-rmse:0.233772+0.005822 test-rmse:0.241423+0.022225
## [13] train-rmse:0.224003+0.005857 test-rmse:0.231736+0.022511
## [14] train-rmse:0.216650+0.005938 test-rmse:0.224467+0.024120
## [15] train-rmse:0.211273+0.005984 test-rmse:0.221671+0.023927
## [16] train-rmse:0.207104+0.005917 test-rmse:0.218694+0.023925
## [17] train-rmse:0.203803+0.005742 test-rmse:0.216747+0.025653
## [18] train-rmse:0.201058+0.005585 test-rmse:0.215662+0.026074
## [19] train-rmse:0.198816+0.005520 test-rmse:0.213017+0.025924
## [20] train-rmse:0.196908+0.005374 test-rmse:0.211497+0.026792
## [21] train-rmse:0.195110+0.005389 test-rmse:0.212992+0.026519
## [22] train-rmse:0.193556+0.005312 test-rmse:0.212126+0.026309
## [23] train-rmse:0.192155+0.005296 test-rmse:0.212006+0.026954
## [24] train-rmse:0.190871+0.005283 test-rmse:0.211748+0.026724
## [25] train-rmse:0.189681+0.005317 test-rmse:0.211360+0.026641
## [26] train-rmse:0.188577+0.005364 test-rmse:0.211142+0.026547
## [27] train-rmse:0.187506+0.005405 test-rmse:0.211710+0.027867
## [28] train-rmse:0.186530+0.005486 test-rmse:0.212256+0.026603
## [29] train-rmse:0.185578+0.005549 test-rmse:0.211960+0.026312
## [30] train-rmse:0.184749+0.005610 test-rmse:0.212745+0.027228
## [31] train-rmse:0.183908+0.005679 test-rmse:0.211496+0.026856
## [32] train-rmse:0.183117+0.005753 test-rmse:0.210808+0.026564
## [33] train-rmse:0.182356+0.005825 test-rmse:0.211509+0.027005
## [34] train-rmse:0.181639+0.005894 test-rmse:0.210903+0.026464
## [35] train-rmse:0.180949+0.005977 test-rmse:0.211149+0.025301
## [36] train-rmse:0.180294+0.006032 test-rmse:0.212426+0.025954
## [37] train-rmse:0.179677+0.006090 test-rmse:0.211108+0.025963
## [38] train-rmse:0.179103+0.006126 test-rmse:0.210830+0.025768
## Stopping. Best iteration:
## [32] train-rmse:0.183117+0.005753 test-rmse:0.210808+0.026564
##
## 36
## [1] train-rmse:1.155062+0.004854 test-rmse:1.156151+0.021152
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.931679+0.003639 test-rmse:0.934204+0.017760
## [3] train-rmse:0.753955+0.002729 test-rmse:0.758694+0.015183
## [4] train-rmse:0.613141+0.002057 test-rmse:0.620374+0.013271
## [5] train-rmse:0.502124+0.001728 test-rmse:0.512386+0.012200
## [6] train-rmse:0.415259+0.001627 test-rmse:0.430450+0.011532
## [7] train-rmse:0.347965+0.001852 test-rmse:0.367558+0.012453
## [8] train-rmse:0.296100+0.001932 test-rmse:0.321129+0.014013
## [9] train-rmse:0.256956+0.002047 test-rmse:0.286970+0.015912
## [10] train-rmse:0.227767+0.002157 test-rmse:0.263013+0.018674
## [11] train-rmse:0.206385+0.002162 test-rmse:0.245805+0.021039
## [12] train-rmse:0.190300+0.000983 test-rmse:0.234562+0.023689
## [13] train-rmse:0.178852+0.002033 test-rmse:0.225565+0.024203
## [14] train-rmse:0.170080+0.001900 test-rmse:0.219970+0.024900
## [15] train-rmse:0.163503+0.003185 test-rmse:0.215678+0.025538
## [16] train-rmse:0.158240+0.003044 test-rmse:0.215465+0.025058
## [17] train-rmse:0.154878+0.003815 test-rmse:0.214328+0.025269
## [18] train-rmse:0.151457+0.003962 test-rmse:0.212725+0.025520
## [19] train-rmse:0.148620+0.003575 test-rmse:0.212141+0.025368
## [20] train-rmse:0.147103+0.003416 test-rmse:0.212085+0.026280
## [21] train-rmse:0.145411+0.004056 test-rmse:0.212204+0.026257
## [22] train-rmse:0.143391+0.003571 test-rmse:0.212686+0.026506
## [23] train-rmse:0.141774+0.004431 test-rmse:0.212388+0.026729
## [24] train-rmse:0.140372+0.005095 test-rmse:0.212339+0.026692
## [25] train-rmse:0.138497+0.004497 test-rmse:0.213264+0.026665
## [26] train-rmse:0.136638+0.004687 test-rmse:0.213389+0.026855
## Stopping. Best iteration:
## [20] train-rmse:0.147103+0.003416 test-rmse:0.212085+0.026280
##
## 37
## [1] train-rmse:1.154369+0.004818 test-rmse:1.156674+0.020493
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.930089+0.003545 test-rmse:0.935199+0.016376
## [3] train-rmse:0.751230+0.002520 test-rmse:0.761124+0.013168
## [4] train-rmse:0.608933+0.001757 test-rmse:0.624317+0.010476
## [5] train-rmse:0.496029+0.001575 test-rmse:0.517628+0.008506
## [6] train-rmse:0.406453+0.002064 test-rmse:0.434961+0.008622
## [7] train-rmse:0.336003+0.003374 test-rmse:0.373662+0.010073
## [8] train-rmse:0.280138+0.004528 test-rmse:0.327086+0.011719
## [9] train-rmse:0.236548+0.004741 test-rmse:0.294392+0.014106
## [10] train-rmse:0.202837+0.005243 test-rmse:0.269986+0.016704
## [11] train-rmse:0.177210+0.005979 test-rmse:0.253452+0.019270
## [12] train-rmse:0.157482+0.006694 test-rmse:0.242780+0.021449
## [13] train-rmse:0.143145+0.007138 test-rmse:0.235300+0.023067
## [14] train-rmse:0.132132+0.007842 test-rmse:0.230831+0.024910
## [15] train-rmse:0.123836+0.007844 test-rmse:0.227602+0.026054
## [16] train-rmse:0.117933+0.008256 test-rmse:0.225798+0.027508
## [17] train-rmse:0.113282+0.008599 test-rmse:0.224386+0.027463
## [18] train-rmse:0.109469+0.008131 test-rmse:0.222965+0.027548
## [19] train-rmse:0.106618+0.007737 test-rmse:0.222938+0.028529
## [20] train-rmse:0.103994+0.007679 test-rmse:0.222912+0.028832
## [21] train-rmse:0.101885+0.007407 test-rmse:0.222745+0.029238
## [22] train-rmse:0.100191+0.007673 test-rmse:0.222788+0.029424
## [23] train-rmse:0.097895+0.007772 test-rmse:0.223767+0.029949
## [24] train-rmse:0.096113+0.007846 test-rmse:0.224406+0.030399
## [25] train-rmse:0.095091+0.007821 test-rmse:0.224003+0.030418
## [26] train-rmse:0.093548+0.007870 test-rmse:0.223904+0.030399
## [27] train-rmse:0.091739+0.007973 test-rmse:0.224452+0.030550
## Stopping. Best iteration:
## [21] train-rmse:0.101885+0.007407 test-rmse:0.222745+0.029238
##
## 38
## [1] train-rmse:1.154122+0.004838 test-rmse:1.156695+0.020450
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.929478+0.003593 test-rmse:0.935420+0.016310
## [3] train-rmse:0.750310+0.002623 test-rmse:0.760586+0.012815
## [4] train-rmse:0.606748+0.001470 test-rmse:0.623970+0.010226
## [5] train-rmse:0.491593+0.000942 test-rmse:0.517606+0.008144
## [6] train-rmse:0.399970+0.001049 test-rmse:0.436653+0.007110
## [7] train-rmse:0.326856+0.001610 test-rmse:0.375319+0.009488
## [8] train-rmse:0.268798+0.002424 test-rmse:0.330754+0.011943
## [9] train-rmse:0.222895+0.003245 test-rmse:0.298549+0.015213
## [10] train-rmse:0.186785+0.003584 test-rmse:0.275410+0.018893
## [11] train-rmse:0.158423+0.004065 test-rmse:0.259123+0.022245
## [12] train-rmse:0.136540+0.004858 test-rmse:0.249176+0.025340
## [13] train-rmse:0.119752+0.005672 test-rmse:0.241401+0.027918
## [14] train-rmse:0.106450+0.006316 test-rmse:0.237892+0.029770
## [15] train-rmse:0.096843+0.006847 test-rmse:0.234977+0.030488
## [16] train-rmse:0.089141+0.007121 test-rmse:0.233407+0.031372
## [17] train-rmse:0.083713+0.007512 test-rmse:0.232107+0.032191
## [18] train-rmse:0.079526+0.007847 test-rmse:0.231489+0.032694
## [19] train-rmse:0.075788+0.007797 test-rmse:0.231182+0.033020
## [20] train-rmse:0.072517+0.007835 test-rmse:0.231350+0.033425
## [21] train-rmse:0.070176+0.007623 test-rmse:0.231367+0.033521
## [22] train-rmse:0.068383+0.007818 test-rmse:0.231550+0.033837
## [23] train-rmse:0.066467+0.007786 test-rmse:0.231939+0.034077
## [24] train-rmse:0.064399+0.007857 test-rmse:0.232536+0.034291
## [25] train-rmse:0.062182+0.007564 test-rmse:0.232412+0.034327
## Stopping. Best iteration:
## [19] train-rmse:0.075788+0.007797 test-rmse:0.231182+0.033020
##
## 39
## [1] train-rmse:1.153965+0.004825 test-rmse:1.156703+0.020432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.929126+0.003578 test-rmse:0.935478+0.016252
## [3] train-rmse:0.749711+0.002607 test-rmse:0.760978+0.012656
## [4] train-rmse:0.605861+0.001354 test-rmse:0.624063+0.010416
## [5] train-rmse:0.490186+0.001016 test-rmse:0.518434+0.008310
## [6] train-rmse:0.397487+0.001370 test-rmse:0.437801+0.007832
## [7] train-rmse:0.323798+0.001597 test-rmse:0.377177+0.010298
## [8] train-rmse:0.264589+0.002049 test-rmse:0.332993+0.013440
## [9] train-rmse:0.217158+0.002663 test-rmse:0.301560+0.017056
## [10] train-rmse:0.179452+0.002937 test-rmse:0.279528+0.021482
## [11] train-rmse:0.149286+0.003383 test-rmse:0.264118+0.025359
## [12] train-rmse:0.125490+0.003779 test-rmse:0.254546+0.028713
## [13] train-rmse:0.106589+0.003997 test-rmse:0.247900+0.031151
## [14] train-rmse:0.091979+0.004830 test-rmse:0.243851+0.033288
## [15] train-rmse:0.080764+0.005539 test-rmse:0.242013+0.035113
## [16] train-rmse:0.071300+0.005656 test-rmse:0.241123+0.036247
## [17] train-rmse:0.064606+0.006230 test-rmse:0.240257+0.036813
## [18] train-rmse:0.059125+0.006589 test-rmse:0.240017+0.037357
## [19] train-rmse:0.055036+0.006774 test-rmse:0.239680+0.037967
## [20] train-rmse:0.051564+0.007120 test-rmse:0.239678+0.038409
## [21] train-rmse:0.048894+0.007211 test-rmse:0.239633+0.038760
## [22] train-rmse:0.046643+0.006930 test-rmse:0.239738+0.038955
## [23] train-rmse:0.044767+0.007076 test-rmse:0.240015+0.039099
## [24] train-rmse:0.043124+0.006940 test-rmse:0.240244+0.039426
## [25] train-rmse:0.041598+0.007032 test-rmse:0.240926+0.039764
## [26] train-rmse:0.040246+0.006960 test-rmse:0.240709+0.039674
## [27] train-rmse:0.039228+0.006864 test-rmse:0.240905+0.039896
## Stopping. Best iteration:
## [21] train-rmse:0.048894+0.007211 test-rmse:0.239633+0.038760
##
## 40
## [1] train-rmse:1.153886+0.004860 test-rmse:1.156717+0.020406
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.928927+0.003671 test-rmse:0.935517+0.016192
## [3] train-rmse:0.749419+0.002700 test-rmse:0.760869+0.012561
## [4] train-rmse:0.605328+0.001622 test-rmse:0.624189+0.009979
## [5] train-rmse:0.489444+0.001279 test-rmse:0.517653+0.007937
## [6] train-rmse:0.396835+0.001455 test-rmse:0.437157+0.007367
## [7] train-rmse:0.322313+0.001828 test-rmse:0.376039+0.009982
## [8] train-rmse:0.262297+0.002365 test-rmse:0.331746+0.012792
## [9] train-rmse:0.214020+0.002268 test-rmse:0.299604+0.017114
## [10] train-rmse:0.175372+0.002187 test-rmse:0.277765+0.021458
## [11] train-rmse:0.144914+0.002448 test-rmse:0.263213+0.025548
## [12] train-rmse:0.120485+0.002147 test-rmse:0.253435+0.028819
## [13] train-rmse:0.100985+0.002381 test-rmse:0.247689+0.031609
## [14] train-rmse:0.085457+0.002499 test-rmse:0.244734+0.034059
## [15] train-rmse:0.073198+0.002882 test-rmse:0.242605+0.036218
## [16] train-rmse:0.063412+0.002972 test-rmse:0.241121+0.037266
## [17] train-rmse:0.056081+0.003271 test-rmse:0.240138+0.038027
## [18] train-rmse:0.049951+0.003704 test-rmse:0.240109+0.038682
## [19] train-rmse:0.045463+0.003844 test-rmse:0.240133+0.039191
## [20] train-rmse:0.041800+0.003926 test-rmse:0.239718+0.039596
## [21] train-rmse:0.038849+0.003834 test-rmse:0.239848+0.039794
## [22] train-rmse:0.036285+0.003582 test-rmse:0.240307+0.040143
## [23] train-rmse:0.034002+0.003130 test-rmse:0.240645+0.040346
## [24] train-rmse:0.032466+0.003097 test-rmse:0.240716+0.040381
## [25] train-rmse:0.030761+0.003120 test-rmse:0.240745+0.040581
## [26] train-rmse:0.029094+0.003112 test-rmse:0.241068+0.040621
## Stopping. Best iteration:
## [20] train-rmse:0.041800+0.003926 test-rmse:0.239718+0.039596
##
## 41
## [1] train-rmse:1.153851+0.004901 test-rmse:1.156723+0.020394
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.928840+0.003765 test-rmse:0.935535+0.016152
## [3] train-rmse:0.749273+0.002831 test-rmse:0.760951+0.012517
## [4] train-rmse:0.605072+0.001663 test-rmse:0.624151+0.010039
## [5] train-rmse:0.489329+0.001755 test-rmse:0.517810+0.007562
## [6] train-rmse:0.396445+0.001553 test-rmse:0.437350+0.007307
## [7] train-rmse:0.320917+0.001405 test-rmse:0.375983+0.008983
## [8] train-rmse:0.260865+0.001332 test-rmse:0.331820+0.012497
## [9] train-rmse:0.212330+0.001386 test-rmse:0.300362+0.016335
## [10] train-rmse:0.173635+0.001481 test-rmse:0.278289+0.020651
## [11] train-rmse:0.143121+0.001739 test-rmse:0.263837+0.024816
## [12] train-rmse:0.118402+0.001588 test-rmse:0.254435+0.028226
## [13] train-rmse:0.098550+0.001744 test-rmse:0.248887+0.031271
## [14] train-rmse:0.082365+0.001643 test-rmse:0.245988+0.033980
## [15] train-rmse:0.069444+0.001618 test-rmse:0.244758+0.035296
## [16] train-rmse:0.059127+0.001548 test-rmse:0.244212+0.036544
## [17] train-rmse:0.050824+0.001499 test-rmse:0.244227+0.037536
## [18] train-rmse:0.044054+0.001504 test-rmse:0.244432+0.038452
## [19] train-rmse:0.038528+0.001537 test-rmse:0.244794+0.038992
## [20] train-rmse:0.034123+0.001690 test-rmse:0.245453+0.039722
## [21] train-rmse:0.030748+0.001808 test-rmse:0.245714+0.040034
## [22] train-rmse:0.027968+0.001874 test-rmse:0.246023+0.040436
## Stopping. Best iteration:
## [16] train-rmse:0.059127+0.001548 test-rmse:0.244212+0.036544
##
## 42
## [1] train-rmse:1.130914+0.003991 test-rmse:1.130854+0.021944
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.895912+0.001999 test-rmse:0.896043+0.018933
## [3] train-rmse:0.715628+0.000869 test-rmse:0.716344+0.016241
## [4] train-rmse:0.578718+0.002069 test-rmse:0.581739+0.014965
## [5] train-rmse:0.474361+0.002867 test-rmse:0.479179+0.011861
## [6] train-rmse:0.396449+0.003862 test-rmse:0.400312+0.013912
## [7] train-rmse:0.338454+0.004393 test-rmse:0.344397+0.013220
## [8] train-rmse:0.296704+0.005074 test-rmse:0.301249+0.017411
## [9] train-rmse:0.266615+0.005362 test-rmse:0.273110+0.017839
## [10] train-rmse:0.245711+0.005747 test-rmse:0.251828+0.022430
## [11] train-rmse:0.231075+0.005812 test-rmse:0.238929+0.022296
## [12] train-rmse:0.221027+0.005944 test-rmse:0.229464+0.024497
## [13] train-rmse:0.213931+0.005924 test-rmse:0.224620+0.023982
## [14] train-rmse:0.208791+0.005910 test-rmse:0.219330+0.024629
## [15] train-rmse:0.204807+0.005724 test-rmse:0.215580+0.025747
## [16] train-rmse:0.201579+0.005637 test-rmse:0.214376+0.026113
## [17] train-rmse:0.198996+0.005566 test-rmse:0.214398+0.025473
## [18] train-rmse:0.196908+0.005491 test-rmse:0.212983+0.026381
## [19] train-rmse:0.194932+0.005348 test-rmse:0.213765+0.027037
## [20] train-rmse:0.193256+0.005281 test-rmse:0.211758+0.026948
## [21] train-rmse:0.191719+0.005275 test-rmse:0.210906+0.026881
## [22] train-rmse:0.190321+0.005303 test-rmse:0.212268+0.027496
## [23] train-rmse:0.189073+0.005366 test-rmse:0.212189+0.026044
## [24] train-rmse:0.187874+0.005419 test-rmse:0.212983+0.026467
## [25] train-rmse:0.186790+0.005474 test-rmse:0.212028+0.027332
## [26] train-rmse:0.185686+0.005523 test-rmse:0.211025+0.026555
## [27] train-rmse:0.184730+0.005601 test-rmse:0.210917+0.026314
## Stopping. Best iteration:
## [21] train-rmse:0.191719+0.005275 test-rmse:0.210906+0.026881
##
## 43
## [1] train-rmse:1.127112+0.004705 test-rmse:1.128363+0.020738
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.887733+0.003396 test-rmse:0.890830+0.017110
## [3] train-rmse:0.702325+0.002481 test-rmse:0.707882+0.014447
## [4] train-rmse:0.559514+0.001849 test-rmse:0.568012+0.012607
## [5] train-rmse:0.450362+0.001672 test-rmse:0.463775+0.011534
## [6] train-rmse:0.367619+0.001626 test-rmse:0.385569+0.011629
## [7] train-rmse:0.305789+0.001973 test-rmse:0.329605+0.013571
## [8] train-rmse:0.260186+0.002050 test-rmse:0.289481+0.016186
## [9] train-rmse:0.227366+0.002207 test-rmse:0.261132+0.019199
## [10] train-rmse:0.204058+0.002195 test-rmse:0.244260+0.021218
## [11] train-rmse:0.187402+0.001121 test-rmse:0.231955+0.023099
## [12] train-rmse:0.175824+0.000850 test-rmse:0.224023+0.024953
## [13] train-rmse:0.166992+0.002158 test-rmse:0.218373+0.025604
## [14] train-rmse:0.160406+0.003149 test-rmse:0.214406+0.025915
## [15] train-rmse:0.155194+0.004114 test-rmse:0.212109+0.025915
## [16] train-rmse:0.151809+0.004663 test-rmse:0.211452+0.026405
## [17] train-rmse:0.149160+0.004189 test-rmse:0.211442+0.026956
## [18] train-rmse:0.146626+0.004187 test-rmse:0.212370+0.027791
## [19] train-rmse:0.144759+0.004787 test-rmse:0.211967+0.028146
## [20] train-rmse:0.142371+0.004545 test-rmse:0.212150+0.028323
## [21] train-rmse:0.140875+0.004983 test-rmse:0.211379+0.027765
## [22] train-rmse:0.139027+0.005383 test-rmse:0.211183+0.027825
## [23] train-rmse:0.138334+0.005388 test-rmse:0.211649+0.027489
## [24] train-rmse:0.136182+0.005844 test-rmse:0.212364+0.027536
## [25] train-rmse:0.134626+0.005654 test-rmse:0.213281+0.028171
## [26] train-rmse:0.133255+0.006166 test-rmse:0.212941+0.028470
## [27] train-rmse:0.132660+0.006374 test-rmse:0.213058+0.028487
## [28] train-rmse:0.131126+0.006649 test-rmse:0.213232+0.028056
## Stopping. Best iteration:
## [22] train-rmse:0.139027+0.005383 test-rmse:0.211183+0.027825
##
## 44
## [1] train-rmse:1.126333+0.004662 test-rmse:1.128952+0.020004
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.885948+0.003281 test-rmse:0.891806+0.015548
## [3] train-rmse:0.699204+0.002227 test-rmse:0.710793+0.012369
## [4] train-rmse:0.554505+0.001572 test-rmse:0.572547+0.009339
## [5] train-rmse:0.442957+0.001686 test-rmse:0.468304+0.007671
## [6] train-rmse:0.356988+0.002545 test-rmse:0.392092+0.008980
## [7] train-rmse:0.291523+0.004220 test-rmse:0.336051+0.010671
## [8] train-rmse:0.240476+0.004877 test-rmse:0.296062+0.013709
## [9] train-rmse:0.202346+0.005359 test-rmse:0.269424+0.017212
## [10] train-rmse:0.174159+0.005951 test-rmse:0.251673+0.019686
## [11] train-rmse:0.153687+0.006272 test-rmse:0.240814+0.022689
## [12] train-rmse:0.138572+0.007286 test-rmse:0.233830+0.025378
## [13] train-rmse:0.127034+0.007976 test-rmse:0.229657+0.026344
## [14] train-rmse:0.119527+0.008367 test-rmse:0.225754+0.027009
## [15] train-rmse:0.113955+0.008749 test-rmse:0.224752+0.028368
## [16] train-rmse:0.109795+0.008615 test-rmse:0.224787+0.028903
## [17] train-rmse:0.106658+0.008882 test-rmse:0.224276+0.029542
## [18] train-rmse:0.103755+0.008693 test-rmse:0.224242+0.029909
## [19] train-rmse:0.101093+0.009161 test-rmse:0.224537+0.030460
## [20] train-rmse:0.098944+0.009194 test-rmse:0.225274+0.031259
## [21] train-rmse:0.097348+0.009775 test-rmse:0.225163+0.031579
## [22] train-rmse:0.095365+0.010137 test-rmse:0.226174+0.032250
## [23] train-rmse:0.093055+0.010001 test-rmse:0.226530+0.032874
## [24] train-rmse:0.090612+0.009951 test-rmse:0.226865+0.033144
## Stopping. Best iteration:
## [18] train-rmse:0.103755+0.008693 test-rmse:0.224242+0.029909
##
## 45
## [1] train-rmse:1.126056+0.004685 test-rmse:1.128981+0.019955
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.885248+0.003340 test-rmse:0.892079+0.015471
## [3] train-rmse:0.698066+0.002365 test-rmse:0.710999+0.012092
## [4] train-rmse:0.551883+0.001279 test-rmse:0.572653+0.009599
## [5] train-rmse:0.437639+0.000962 test-rmse:0.468928+0.007810
## [6] train-rmse:0.348857+0.001584 test-rmse:0.392622+0.009597
## [7] train-rmse:0.280509+0.002218 test-rmse:0.339162+0.011443
## [8] train-rmse:0.227238+0.002852 test-rmse:0.300922+0.014694
## [9] train-rmse:0.186629+0.003634 test-rmse:0.274457+0.018419
## [10] train-rmse:0.155547+0.004711 test-rmse:0.257888+0.022272
## [11] train-rmse:0.132134+0.005428 test-rmse:0.248391+0.025100
## [12] train-rmse:0.114460+0.006360 test-rmse:0.242170+0.027450
## [13] train-rmse:0.101336+0.007260 test-rmse:0.239506+0.029328
## [14] train-rmse:0.091964+0.007958 test-rmse:0.237298+0.030538
## [15] train-rmse:0.085141+0.008499 test-rmse:0.236116+0.031714
## [16] train-rmse:0.080064+0.008645 test-rmse:0.235088+0.032390
## [17] train-rmse:0.075774+0.008796 test-rmse:0.234579+0.032709
## [18] train-rmse:0.072612+0.008996 test-rmse:0.234498+0.033184
## [19] train-rmse:0.069631+0.008431 test-rmse:0.234008+0.033353
## [20] train-rmse:0.066940+0.008280 test-rmse:0.234204+0.033639
## [21] train-rmse:0.064811+0.008528 test-rmse:0.234485+0.034093
## [22] train-rmse:0.062923+0.008422 test-rmse:0.235003+0.034483
## [23] train-rmse:0.060668+0.008036 test-rmse:0.235038+0.034526
## [24] train-rmse:0.058439+0.007962 test-rmse:0.234935+0.034361
## [25] train-rmse:0.056999+0.007867 test-rmse:0.235500+0.034609
## Stopping. Best iteration:
## [19] train-rmse:0.069631+0.008431 test-rmse:0.234008+0.033353
##
## 46
## [1] train-rmse:1.125879+0.004670 test-rmse:1.128993+0.019934
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.884853+0.003320 test-rmse:0.892149+0.015398
## [3] train-rmse:0.697448+0.002346 test-rmse:0.711229+0.011884
## [4] train-rmse:0.550486+0.001034 test-rmse:0.572559+0.009318
## [5] train-rmse:0.435467+0.001064 test-rmse:0.469685+0.007425
## [6] train-rmse:0.345797+0.001595 test-rmse:0.394132+0.009213
## [7] train-rmse:0.275781+0.002040 test-rmse:0.340826+0.012065
## [8] train-rmse:0.221081+0.002278 test-rmse:0.303787+0.017165
## [9] train-rmse:0.178514+0.002366 test-rmse:0.278828+0.021973
## [10] train-rmse:0.145536+0.002702 test-rmse:0.262884+0.026743
## [11] train-rmse:0.120396+0.003332 test-rmse:0.253403+0.030000
## [12] train-rmse:0.101041+0.004436 test-rmse:0.247136+0.032482
## [13] train-rmse:0.086126+0.005134 test-rmse:0.244298+0.034702
## [14] train-rmse:0.075411+0.005615 test-rmse:0.242208+0.036392
## [15] train-rmse:0.066625+0.005699 test-rmse:0.240930+0.036923
## [16] train-rmse:0.060331+0.006224 test-rmse:0.241111+0.037701
## [17] train-rmse:0.055437+0.006750 test-rmse:0.241040+0.038428
## [18] train-rmse:0.051720+0.006807 test-rmse:0.240848+0.039078
## [19] train-rmse:0.049065+0.007117 test-rmse:0.241489+0.039759
## [20] train-rmse:0.046692+0.007148 test-rmse:0.241585+0.039976
## [21] train-rmse:0.044531+0.007255 test-rmse:0.241577+0.040021
## [22] train-rmse:0.042676+0.007179 test-rmse:0.241777+0.040083
## [23] train-rmse:0.040836+0.006770 test-rmse:0.241891+0.040111
## [24] train-rmse:0.039376+0.006886 test-rmse:0.242030+0.040229
## Stopping. Best iteration:
## [18] train-rmse:0.051720+0.006807 test-rmse:0.240848+0.039078
##
## 47
## [1] train-rmse:1.125791+0.004708 test-rmse:1.129010+0.019905
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.884630+0.003424 test-rmse:0.892208+0.015338
## [3] train-rmse:0.697069+0.002496 test-rmse:0.711117+0.011765
## [4] train-rmse:0.549942+0.001346 test-rmse:0.572854+0.009097
## [5] train-rmse:0.434381+0.001183 test-rmse:0.468859+0.007113
## [6] train-rmse:0.344377+0.001773 test-rmse:0.393135+0.009112
## [7] train-rmse:0.273411+0.002138 test-rmse:0.339539+0.011639
## [8] train-rmse:0.217974+0.002328 test-rmse:0.303150+0.016335
## [9] train-rmse:0.174598+0.002487 test-rmse:0.278285+0.021177
## [10] train-rmse:0.141104+0.002554 test-rmse:0.262535+0.025719
## [11] train-rmse:0.115404+0.003076 test-rmse:0.253098+0.028601
## [12] train-rmse:0.095230+0.003294 test-rmse:0.247313+0.031585
## [13] train-rmse:0.079674+0.003849 test-rmse:0.244698+0.033941
## [14] train-rmse:0.067847+0.004251 test-rmse:0.243324+0.035624
## [15] train-rmse:0.058600+0.004311 test-rmse:0.242347+0.036613
## [16] train-rmse:0.051677+0.004625 test-rmse:0.241887+0.037219
## [17] train-rmse:0.046367+0.004779 test-rmse:0.241950+0.037597
## [18] train-rmse:0.042335+0.004963 test-rmse:0.241764+0.037912
## [19] train-rmse:0.038861+0.004854 test-rmse:0.242168+0.038388
## [20] train-rmse:0.035992+0.004527 test-rmse:0.242336+0.038938
## [21] train-rmse:0.033395+0.004505 test-rmse:0.242388+0.038979
## [22] train-rmse:0.031579+0.004370 test-rmse:0.242836+0.039270
## [23] train-rmse:0.029948+0.004308 test-rmse:0.242855+0.039391
## [24] train-rmse:0.028447+0.004367 test-rmse:0.242746+0.039395
## Stopping. Best iteration:
## [18] train-rmse:0.042335+0.004963 test-rmse:0.241764+0.037912
##
## 48
## [1] train-rmse:1.125752+0.004755 test-rmse:1.129018+0.019890
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.884526+0.003522 test-rmse:0.892299+0.015185
## [3] train-rmse:0.696901+0.002634 test-rmse:0.711223+0.011694
## [4] train-rmse:0.549629+0.001209 test-rmse:0.572858+0.009185
## [5] train-rmse:0.433690+0.000796 test-rmse:0.468751+0.007008
## [6] train-rmse:0.343352+0.001185 test-rmse:0.393023+0.008496
## [7] train-rmse:0.271933+0.001349 test-rmse:0.339458+0.011228
## [8] train-rmse:0.216655+0.001381 test-rmse:0.302817+0.016160
## [9] train-rmse:0.173529+0.001905 test-rmse:0.278665+0.020325
## [10] train-rmse:0.139601+0.002022 test-rmse:0.263104+0.024958
## [11] train-rmse:0.113016+0.001927 test-rmse:0.253403+0.028515
## [12] train-rmse:0.092413+0.002160 test-rmse:0.247733+0.031466
## [13] train-rmse:0.076236+0.002466 test-rmse:0.245588+0.032846
## [14] train-rmse:0.063501+0.002701 test-rmse:0.244845+0.033899
## [15] train-rmse:0.053409+0.002802 test-rmse:0.244765+0.035551
## [16] train-rmse:0.045414+0.002781 test-rmse:0.244954+0.037038
## [17] train-rmse:0.039363+0.002844 test-rmse:0.245612+0.037956
## [18] train-rmse:0.034710+0.002716 test-rmse:0.246116+0.038627
## [19] train-rmse:0.030817+0.002648 test-rmse:0.246346+0.039411
## [20] train-rmse:0.027982+0.002553 test-rmse:0.246974+0.039948
## [21] train-rmse:0.025463+0.002458 test-rmse:0.247416+0.040262
## Stopping. Best iteration:
## [15] train-rmse:0.053409+0.002802 test-rmse:0.244765+0.035551
##
## 49
## [1] train-rmse:1.103394+0.003767 test-rmse:1.103352+0.021618
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.854147+0.001633 test-rmse:0.854465+0.018376
## [3] train-rmse:0.668510+0.001097 test-rmse:0.669385+0.015450
## [4] train-rmse:0.531996+0.002560 test-rmse:0.534835+0.012880
## [5] train-rmse:0.430672+0.003319 test-rmse:0.436205+0.010684
## [6] train-rmse:0.358424+0.004313 test-rmse:0.364392+0.012908
## [7] train-rmse:0.306456+0.004830 test-rmse:0.313164+0.014628
## [8] train-rmse:0.271206+0.005418 test-rmse:0.277899+0.016862
## [9] train-rmse:0.246656+0.005691 test-rmse:0.253708+0.020006
## [10] train-rmse:0.230718+0.005884 test-rmse:0.237146+0.024328
## [11] train-rmse:0.219757+0.005900 test-rmse:0.228243+0.023637
## [12] train-rmse:0.212559+0.005977 test-rmse:0.221426+0.025710
## [13] train-rmse:0.207266+0.005928 test-rmse:0.218447+0.026179
## [14] train-rmse:0.203271+0.005714 test-rmse:0.216957+0.025312
## [15] train-rmse:0.200177+0.005537 test-rmse:0.213945+0.025766
## [16] train-rmse:0.197601+0.005438 test-rmse:0.211876+0.026639
## [17] train-rmse:0.195380+0.005340 test-rmse:0.213086+0.027546
## [18] train-rmse:0.193495+0.005363 test-rmse:0.212693+0.025954
## [19] train-rmse:0.191869+0.005306 test-rmse:0.213030+0.027117
## [20] train-rmse:0.190302+0.005323 test-rmse:0.212604+0.027019
## [21] train-rmse:0.188925+0.005363 test-rmse:0.211841+0.026336
## [22] train-rmse:0.187635+0.005393 test-rmse:0.211411+0.026175
## [23] train-rmse:0.186382+0.005460 test-rmse:0.211954+0.027596
## [24] train-rmse:0.185260+0.005541 test-rmse:0.211310+0.026903
## [25] train-rmse:0.184190+0.005691 test-rmse:0.212176+0.025722
## [26] train-rmse:0.183288+0.005761 test-rmse:0.211152+0.025831
## [27] train-rmse:0.182416+0.005823 test-rmse:0.210762+0.026195
## [28] train-rmse:0.181510+0.005899 test-rmse:0.211744+0.026876
## [29] train-rmse:0.180648+0.005980 test-rmse:0.211275+0.026146
## [30] train-rmse:0.179882+0.006052 test-rmse:0.211876+0.026435
## [31] train-rmse:0.179129+0.006135 test-rmse:0.211276+0.025943
## [32] train-rmse:0.178465+0.006208 test-rmse:0.211370+0.026349
## [33] train-rmse:0.177831+0.006258 test-rmse:0.211443+0.026329
## Stopping. Best iteration:
## [27] train-rmse:0.182416+0.005823 test-rmse:0.210762+0.026195
##
## 50
## [1] train-rmse:1.099182+0.004556 test-rmse:1.100602+0.020324
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.844994+0.003161 test-rmse:0.848531+0.016474
## [3] train-rmse:0.653477+0.002262 test-rmse:0.659892+0.013766
## [4] train-rmse:0.510237+0.001727 test-rmse:0.520358+0.012243
## [5] train-rmse:0.404248+0.001688 test-rmse:0.419883+0.011503
## [6] train-rmse:0.326835+0.001917 test-rmse:0.347218+0.013168
## [7] train-rmse:0.271154+0.002067 test-rmse:0.298410+0.015302
## [8] train-rmse:0.232075+0.002132 test-rmse:0.264873+0.018418
## [9] train-rmse:0.205344+0.002233 test-rmse:0.244857+0.020999
## [10] train-rmse:0.185400+0.002275 test-rmse:0.230574+0.023388
## [11] train-rmse:0.172316+0.003072 test-rmse:0.220876+0.023763
## [12] train-rmse:0.164470+0.003302 test-rmse:0.215731+0.025543
## [13] train-rmse:0.158144+0.004175 test-rmse:0.212683+0.025387
## [14] train-rmse:0.154505+0.004636 test-rmse:0.211152+0.025995
## [15] train-rmse:0.150838+0.004412 test-rmse:0.210375+0.027083
## [16] train-rmse:0.148143+0.004773 test-rmse:0.209794+0.027687
## [17] train-rmse:0.146414+0.004323 test-rmse:0.211086+0.026891
## [18] train-rmse:0.143761+0.005749 test-rmse:0.210711+0.027050
## [19] train-rmse:0.141736+0.006119 test-rmse:0.210221+0.027173
## [20] train-rmse:0.140456+0.005982 test-rmse:0.210291+0.027225
## [21] train-rmse:0.138103+0.005428 test-rmse:0.211277+0.026834
## [22] train-rmse:0.135268+0.005521 test-rmse:0.210458+0.026506
## Stopping. Best iteration:
## [16] train-rmse:0.148143+0.004773 test-rmse:0.209794+0.027687
##
## 51
## [1] train-rmse:1.098313+0.004505 test-rmse:1.101260+0.019511
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.842984+0.003020 test-rmse:0.849642+0.014730
## [3] train-rmse:0.649909+0.001946 test-rmse:0.663269+0.011357
## [4] train-rmse:0.504402+0.001465 test-rmse:0.525737+0.008660
## [5] train-rmse:0.395036+0.002222 test-rmse:0.423970+0.009214
## [6] train-rmse:0.314054+0.003418 test-rmse:0.355737+0.009774
## [7] train-rmse:0.253244+0.005104 test-rmse:0.306957+0.012412
## [8] train-rmse:0.208282+0.005555 test-rmse:0.275038+0.015232
## [9] train-rmse:0.176348+0.005945 test-rmse:0.254192+0.019434
## [10] train-rmse:0.153350+0.006842 test-rmse:0.241809+0.021999
## [11] train-rmse:0.137339+0.007848 test-rmse:0.235310+0.024624
## [12] train-rmse:0.125812+0.008805 test-rmse:0.229807+0.025525
## [13] train-rmse:0.118597+0.009186 test-rmse:0.227757+0.027141
## [14] train-rmse:0.112726+0.009536 test-rmse:0.226362+0.026997
## [15] train-rmse:0.107794+0.010562 test-rmse:0.225262+0.027243
## [16] train-rmse:0.104000+0.011312 test-rmse:0.225097+0.027576
## [17] train-rmse:0.100804+0.011288 test-rmse:0.225077+0.028227
## [18] train-rmse:0.098718+0.011279 test-rmse:0.225850+0.028888
## [19] train-rmse:0.096186+0.011390 test-rmse:0.226758+0.029192
## [20] train-rmse:0.094641+0.011379 test-rmse:0.227689+0.029829
## [21] train-rmse:0.092122+0.011390 test-rmse:0.226952+0.029580
## [22] train-rmse:0.090524+0.011039 test-rmse:0.227167+0.029959
## [23] train-rmse:0.088308+0.011637 test-rmse:0.227996+0.030124
## Stopping. Best iteration:
## [17] train-rmse:0.100804+0.011288 test-rmse:0.225077+0.028227
##
## 52
## [1] train-rmse:1.098005+0.004531 test-rmse:1.101298+0.019457
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.842177+0.003090 test-rmse:0.850011+0.014646
## [3] train-rmse:0.648609+0.002138 test-rmse:0.663499+0.011062
## [4] train-rmse:0.501122+0.001475 test-rmse:0.526071+0.008654
## [5] train-rmse:0.389149+0.001770 test-rmse:0.426564+0.007554
## [6] train-rmse:0.304443+0.002232 test-rmse:0.357211+0.010249
## [7] train-rmse:0.240751+0.003350 test-rmse:0.309874+0.013289
## [8] train-rmse:0.192942+0.004130 test-rmse:0.278715+0.018450
## [9] train-rmse:0.157469+0.004609 test-rmse:0.258239+0.022148
## [10] train-rmse:0.131586+0.005158 test-rmse:0.246333+0.025826
## [11] train-rmse:0.112898+0.006373 test-rmse:0.239305+0.028673
## [12] train-rmse:0.099216+0.006968 test-rmse:0.235049+0.030302
## [13] train-rmse:0.089952+0.007926 test-rmse:0.232893+0.031139
## [14] train-rmse:0.082975+0.008325 test-rmse:0.231333+0.031778
## [15] train-rmse:0.077631+0.008091 test-rmse:0.230341+0.032169
## [16] train-rmse:0.073423+0.008174 test-rmse:0.230143+0.032544
## [17] train-rmse:0.070463+0.008219 test-rmse:0.230800+0.033459
## [18] train-rmse:0.067510+0.007788 test-rmse:0.230174+0.033454
## [19] train-rmse:0.064773+0.008273 test-rmse:0.230795+0.033813
## [20] train-rmse:0.062062+0.007902 test-rmse:0.231086+0.033984
## [21] train-rmse:0.060340+0.007767 test-rmse:0.231308+0.034231
## [22] train-rmse:0.058469+0.007481 test-rmse:0.231907+0.034649
## Stopping. Best iteration:
## [16] train-rmse:0.073423+0.008174 test-rmse:0.230143+0.032544
##
## 53
## [1] train-rmse:1.097807+0.004513 test-rmse:1.101315+0.019433
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.841745+0.003067 test-rmse:0.850070+0.014559
## [3] train-rmse:0.647943+0.002138 test-rmse:0.663508+0.010845
## [4] train-rmse:0.498962+0.001004 test-rmse:0.526350+0.008150
## [5] train-rmse:0.385744+0.001322 test-rmse:0.427634+0.008404
## [6] train-rmse:0.300151+0.001935 test-rmse:0.358585+0.011673
## [7] train-rmse:0.234746+0.002478 test-rmse:0.312110+0.015763
## [8] train-rmse:0.184835+0.002521 test-rmse:0.281731+0.021070
## [9] train-rmse:0.147316+0.002639 test-rmse:0.263151+0.026142
## [10] train-rmse:0.119104+0.002701 test-rmse:0.251649+0.029965
## [11] train-rmse:0.098084+0.003423 test-rmse:0.246139+0.033174
## [12] train-rmse:0.082727+0.003532 test-rmse:0.241978+0.034891
## [13] train-rmse:0.071557+0.004016 test-rmse:0.239692+0.036350
## [14] train-rmse:0.063558+0.004676 test-rmse:0.238639+0.036900
## [15] train-rmse:0.057599+0.005025 test-rmse:0.237982+0.037680
## [16] train-rmse:0.053091+0.005334 test-rmse:0.237745+0.038214
## [17] train-rmse:0.049606+0.005255 test-rmse:0.237443+0.038388
## [18] train-rmse:0.046731+0.005078 test-rmse:0.237964+0.038917
## [19] train-rmse:0.044198+0.005627 test-rmse:0.238170+0.039233
## [20] train-rmse:0.042080+0.006052 test-rmse:0.238670+0.039357
## [21] train-rmse:0.040357+0.006190 test-rmse:0.238715+0.039789
## [22] train-rmse:0.038813+0.006355 test-rmse:0.239571+0.040260
## [23] train-rmse:0.037256+0.006221 test-rmse:0.239558+0.040123
## Stopping. Best iteration:
## [17] train-rmse:0.049606+0.005255 test-rmse:0.237443+0.038388
##
## 54
## [1] train-rmse:1.097710+0.004556 test-rmse:1.101335+0.019400
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.841497+0.003176 test-rmse:0.850205+0.014394
## [3] train-rmse:0.647516+0.002293 test-rmse:0.663750+0.010624
## [4] train-rmse:0.498545+0.001049 test-rmse:0.525817+0.007930
## [5] train-rmse:0.384479+0.000641 test-rmse:0.426174+0.007957
## [6] train-rmse:0.298059+0.000781 test-rmse:0.357170+0.011551
## [7] train-rmse:0.232195+0.001438 test-rmse:0.311216+0.014812
## [8] train-rmse:0.181860+0.001514 test-rmse:0.281029+0.020105
## [9] train-rmse:0.143587+0.001894 test-rmse:0.262474+0.024792
## [10] train-rmse:0.114570+0.002030 test-rmse:0.251697+0.028987
## [11] train-rmse:0.092860+0.002482 test-rmse:0.246262+0.032157
## [12] train-rmse:0.076355+0.002723 test-rmse:0.243560+0.035312
## [13] train-rmse:0.063816+0.002768 test-rmse:0.241769+0.036639
## [14] train-rmse:0.054686+0.003171 test-rmse:0.241149+0.037757
## [15] train-rmse:0.047980+0.003587 test-rmse:0.241478+0.038587
## [16] train-rmse:0.042918+0.003770 test-rmse:0.241202+0.038651
## [17] train-rmse:0.038490+0.003449 test-rmse:0.241749+0.039350
## [18] train-rmse:0.035207+0.003567 test-rmse:0.241852+0.039471
## [19] train-rmse:0.032887+0.003700 test-rmse:0.241941+0.039659
## [20] train-rmse:0.030957+0.003575 test-rmse:0.242121+0.039844
## Stopping. Best iteration:
## [14] train-rmse:0.054686+0.003171 test-rmse:0.241149+0.037757
##
## 55
## [1] train-rmse:1.097666+0.004607 test-rmse:1.101345+0.019383
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.841381+0.003288 test-rmse:0.850248+0.014318
## [3] train-rmse:0.647323+0.002434 test-rmse:0.663856+0.010570
## [4] train-rmse:0.498173+0.001091 test-rmse:0.525672+0.008068
## [5] train-rmse:0.383780+0.000654 test-rmse:0.425976+0.007879
## [6] train-rmse:0.297030+0.000409 test-rmse:0.356996+0.010930
## [7] train-rmse:0.230607+0.000861 test-rmse:0.311106+0.014703
## [8] train-rmse:0.180254+0.000808 test-rmse:0.281541+0.019188
## [9] train-rmse:0.142077+0.001282 test-rmse:0.263414+0.023947
## [10] train-rmse:0.112594+0.001260 test-rmse:0.252642+0.027871
## [11] train-rmse:0.090237+0.001511 test-rmse:0.246918+0.030924
## [12] train-rmse:0.073379+0.001553 test-rmse:0.244423+0.033500
## [13] train-rmse:0.060461+0.001658 test-rmse:0.243612+0.035440
## [14] train-rmse:0.050440+0.001858 test-rmse:0.243524+0.036845
## [15] train-rmse:0.043016+0.002215 test-rmse:0.244073+0.038014
## [16] train-rmse:0.037273+0.002278 test-rmse:0.244426+0.039130
## [17] train-rmse:0.032726+0.002295 test-rmse:0.245356+0.039816
## [18] train-rmse:0.029303+0.002400 test-rmse:0.246185+0.040490
## [19] train-rmse:0.026264+0.002395 test-rmse:0.246871+0.040889
## [20] train-rmse:0.024297+0.002449 test-rmse:0.247349+0.041148
## Stopping. Best iteration:
## [14] train-rmse:0.050440+0.001858 test-rmse:0.243524+0.036845
##
## 56
## [1] train-rmse:1.075910+0.003541 test-rmse:1.075886+0.021288
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.813630+0.001291 test-rmse:0.814012+0.017773
## [3] train-rmse:0.624247+0.001520 test-rmse:0.625259+0.014681
## [4] train-rmse:0.488892+0.002767 test-rmse:0.491822+0.011021
## [5] train-rmse:0.392413+0.003788 test-rmse:0.396183+0.013942
## [6] train-rmse:0.326174+0.004535 test-rmse:0.332369+0.013588
## [7] train-rmse:0.280884+0.005230 test-rmse:0.285530+0.018159
## [8] train-rmse:0.251652+0.005530 test-rmse:0.258403+0.018696
## [9] train-rmse:0.232243+0.005864 test-rmse:0.239927+0.022090
## [10] train-rmse:0.220349+0.005876 test-rmse:0.228208+0.022496
## [11] train-rmse:0.212214+0.005915 test-rmse:0.221138+0.024693
## [12] train-rmse:0.206527+0.005799 test-rmse:0.217873+0.025190
## [13] train-rmse:0.202493+0.005774 test-rmse:0.214183+0.026665
## [14] train-rmse:0.199296+0.005607 test-rmse:0.213453+0.028074
## [15] train-rmse:0.196628+0.005450 test-rmse:0.213544+0.026843
## [16] train-rmse:0.194411+0.005299 test-rmse:0.212320+0.026378
## [17] train-rmse:0.192454+0.005285 test-rmse:0.211076+0.027317
## [18] train-rmse:0.190712+0.005311 test-rmse:0.211756+0.027687
## [19] train-rmse:0.189181+0.005403 test-rmse:0.212569+0.028377
## [20] train-rmse:0.187755+0.005490 test-rmse:0.211677+0.027762
## [21] train-rmse:0.186461+0.005524 test-rmse:0.211800+0.026664
## [22] train-rmse:0.185182+0.005562 test-rmse:0.211201+0.026134
## [23] train-rmse:0.184084+0.005630 test-rmse:0.211955+0.026437
## Stopping. Best iteration:
## [17] train-rmse:0.192454+0.005285 test-rmse:0.211076+0.027317
##
## 57
## [1] train-rmse:1.071273+0.004407 test-rmse:1.072868+0.019909
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.803429+0.002934 test-rmse:0.807583+0.015840
## [3] train-rmse:0.607357+0.002075 test-rmse:0.614751+0.013160
## [4] train-rmse:0.465325+0.001670 test-rmse:0.479101+0.011742
## [5] train-rmse:0.363636+0.001712 test-rmse:0.382867+0.011463
## [6] train-rmse:0.292177+0.002047 test-rmse:0.317506+0.013972
## [7] train-rmse:0.243160+0.002186 test-rmse:0.275870+0.017025
## [8] train-rmse:0.210573+0.002204 test-rmse:0.248980+0.020460
## [9] train-rmse:0.188165+0.001380 test-rmse:0.233067+0.023128
## [10] train-rmse:0.174790+0.001375 test-rmse:0.223263+0.025475
## [11] train-rmse:0.164968+0.002403 test-rmse:0.218405+0.026157
## [12] train-rmse:0.156817+0.003678 test-rmse:0.214066+0.025852
## [13] train-rmse:0.152039+0.003870 test-rmse:0.212127+0.026081
## [14] train-rmse:0.148962+0.003916 test-rmse:0.211553+0.027925
## [15] train-rmse:0.145764+0.004727 test-rmse:0.211210+0.026020
## [16] train-rmse:0.143745+0.003951 test-rmse:0.211448+0.026994
## [17] train-rmse:0.141138+0.003991 test-rmse:0.210811+0.026837
## [18] train-rmse:0.139726+0.004234 test-rmse:0.210959+0.026595
## [19] train-rmse:0.137990+0.003435 test-rmse:0.210669+0.026890
## [20] train-rmse:0.135782+0.003638 test-rmse:0.211345+0.026453
## [21] train-rmse:0.134564+0.004017 test-rmse:0.211970+0.027122
## [22] train-rmse:0.131685+0.003885 test-rmse:0.212719+0.026997
## [23] train-rmse:0.129483+0.004142 test-rmse:0.213277+0.027185
## [24] train-rmse:0.128522+0.004160 test-rmse:0.213412+0.027154
## [25] train-rmse:0.126526+0.005055 test-rmse:0.214767+0.027544
## Stopping. Best iteration:
## [19] train-rmse:0.137990+0.003435 test-rmse:0.210669+0.026890
##
## 58
## [1] train-rmse:1.070310+0.004347 test-rmse:1.073598+0.019016
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.801237+0.002731 test-rmse:0.809365+0.014053
## [3] train-rmse:0.603250+0.001774 test-rmse:0.618619+0.010372
## [4] train-rmse:0.457823+0.001798 test-rmse:0.482345+0.009374
## [5] train-rmse:0.352624+0.003073 test-rmse:0.385984+0.010503
## [6] train-rmse:0.276043+0.005069 test-rmse:0.324114+0.012688
## [7] train-rmse:0.220614+0.005699 test-rmse:0.282524+0.016887
## [8] train-rmse:0.182350+0.006200 test-rmse:0.255781+0.020040
## [9] train-rmse:0.155596+0.007156 test-rmse:0.242225+0.024149
## [10] train-rmse:0.137752+0.008085 test-rmse:0.234061+0.026604
## [11] train-rmse:0.125750+0.008721 test-rmse:0.229488+0.028305
## [12] train-rmse:0.116793+0.008441 test-rmse:0.226389+0.028835
## [13] train-rmse:0.110613+0.008936 test-rmse:0.225200+0.030024
## [14] train-rmse:0.105362+0.008264 test-rmse:0.223692+0.030159
## [15] train-rmse:0.102714+0.008153 test-rmse:0.223245+0.031062
## [16] train-rmse:0.100373+0.008477 test-rmse:0.223903+0.031809
## [17] train-rmse:0.097181+0.009171 test-rmse:0.224301+0.031954
## [18] train-rmse:0.094664+0.009144 test-rmse:0.224671+0.032433
## [19] train-rmse:0.092032+0.009798 test-rmse:0.224898+0.032784
## [20] train-rmse:0.090326+0.010424 test-rmse:0.225926+0.033102
## [21] train-rmse:0.088086+0.010316 test-rmse:0.226074+0.033285
## Stopping. Best iteration:
## [15] train-rmse:0.102714+0.008153 test-rmse:0.223245+0.031062
##
## 59
## [1] train-rmse:1.069969+0.004376 test-rmse:1.073647+0.018956
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.800285+0.002847 test-rmse:0.809171+0.013824
## [3] train-rmse:0.601882+0.001984 test-rmse:0.618950+0.010129
## [4] train-rmse:0.454152+0.001324 test-rmse:0.482602+0.009224
## [5] train-rmse:0.345143+0.002008 test-rmse:0.388173+0.010125
## [6] train-rmse:0.265687+0.002846 test-rmse:0.326007+0.013210
## [7] train-rmse:0.207173+0.003918 test-rmse:0.285293+0.017285
## [8] train-rmse:0.165442+0.005005 test-rmse:0.260539+0.020914
## [9] train-rmse:0.135552+0.005654 test-rmse:0.247005+0.024657
## [10] train-rmse:0.113979+0.006352 test-rmse:0.238629+0.028114
## [11] train-rmse:0.099682+0.006998 test-rmse:0.234299+0.029678
## [12] train-rmse:0.089233+0.006948 test-rmse:0.232066+0.030700
## [13] train-rmse:0.082026+0.006881 test-rmse:0.230970+0.031065
## [14] train-rmse:0.076935+0.007017 test-rmse:0.231434+0.032279
## [15] train-rmse:0.072238+0.006890 test-rmse:0.231246+0.032581
## [16] train-rmse:0.069503+0.006929 test-rmse:0.231298+0.033051
## [17] train-rmse:0.066846+0.007170 test-rmse:0.232172+0.033540
## [18] train-rmse:0.064205+0.007125 test-rmse:0.232127+0.033680
## [19] train-rmse:0.061681+0.006671 test-rmse:0.232598+0.033717
## Stopping. Best iteration:
## [13] train-rmse:0.082026+0.006881 test-rmse:0.230970+0.031065
##
## 60
## [1] train-rmse:1.069750+0.004355 test-rmse:1.073670+0.018928
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.799805+0.002819 test-rmse:0.809243+0.013729
## [3] train-rmse:0.601071+0.001984 test-rmse:0.618922+0.009876
## [4] train-rmse:0.452006+0.000846 test-rmse:0.483528+0.008283
## [5] train-rmse:0.341691+0.001832 test-rmse:0.389841+0.009547
## [6] train-rmse:0.260212+0.002242 test-rmse:0.327931+0.013747
## [7] train-rmse:0.200058+0.002516 test-rmse:0.288610+0.019173
## [8] train-rmse:0.155736+0.003146 test-rmse:0.265262+0.024300
## [9] train-rmse:0.123330+0.003775 test-rmse:0.251506+0.028961
## [10] train-rmse:0.100310+0.005120 test-rmse:0.244665+0.032232
## [11] train-rmse:0.084248+0.005872 test-rmse:0.240762+0.034233
## [12] train-rmse:0.072659+0.005855 test-rmse:0.238743+0.035932
## [13] train-rmse:0.064331+0.005926 test-rmse:0.237813+0.036933
## [14] train-rmse:0.058635+0.005875 test-rmse:0.236810+0.037437
## [15] train-rmse:0.054043+0.006008 test-rmse:0.236463+0.037681
## [16] train-rmse:0.050150+0.006498 test-rmse:0.236105+0.037798
## [17] train-rmse:0.047743+0.006335 test-rmse:0.236522+0.038198
## [18] train-rmse:0.045419+0.006816 test-rmse:0.237092+0.038396
## [19] train-rmse:0.043534+0.007283 test-rmse:0.237608+0.038608
## [20] train-rmse:0.041889+0.007110 test-rmse:0.237800+0.038752
## [21] train-rmse:0.039996+0.006790 test-rmse:0.238273+0.039009
## [22] train-rmse:0.037812+0.006958 test-rmse:0.239114+0.039438
## Stopping. Best iteration:
## [16] train-rmse:0.050150+0.006498 test-rmse:0.236105+0.037798
##
## 61
## [1] train-rmse:1.069642+0.004403 test-rmse:1.073695+0.018891
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.799532+0.002934 test-rmse:0.809401+0.013547
## [3] train-rmse:0.600597+0.002133 test-rmse:0.619212+0.009646
## [4] train-rmse:0.451180+0.000930 test-rmse:0.482590+0.008424
## [5] train-rmse:0.340054+0.001776 test-rmse:0.388481+0.009376
## [6] train-rmse:0.257968+0.002073 test-rmse:0.326277+0.013036
## [7] train-rmse:0.197544+0.002709 test-rmse:0.287652+0.017850
## [8] train-rmse:0.151883+0.002702 test-rmse:0.263793+0.022957
## [9] train-rmse:0.118858+0.002719 test-rmse:0.250601+0.027834
## [10] train-rmse:0.094370+0.002728 test-rmse:0.243276+0.031280
## [11] train-rmse:0.076712+0.003334 test-rmse:0.240334+0.034279
## [12] train-rmse:0.064022+0.003730 test-rmse:0.238468+0.035772
## [13] train-rmse:0.054716+0.003982 test-rmse:0.238186+0.036988
## [14] train-rmse:0.048211+0.004123 test-rmse:0.237917+0.037688
## [15] train-rmse:0.043076+0.004588 test-rmse:0.238349+0.038344
## [16] train-rmse:0.038571+0.004404 test-rmse:0.238370+0.038358
## [17] train-rmse:0.035556+0.004636 test-rmse:0.238562+0.038648
## [18] train-rmse:0.032779+0.004416 test-rmse:0.238447+0.038679
## [19] train-rmse:0.030246+0.004488 test-rmse:0.238427+0.038835
## [20] train-rmse:0.028132+0.004365 test-rmse:0.238660+0.039092
## Stopping. Best iteration:
## [14] train-rmse:0.048211+0.004123 test-rmse:0.237917+0.037688
##
## 62
## [1] train-rmse:1.069594+0.004459 test-rmse:1.073706+0.018871
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.799401+0.003057 test-rmse:0.809452+0.013459
## [3] train-rmse:0.600366+0.002277 test-rmse:0.619342+0.009608
## [4] train-rmse:0.450677+0.001229 test-rmse:0.482603+0.008308
## [5] train-rmse:0.339294+0.000965 test-rmse:0.388903+0.009233
## [6] train-rmse:0.256858+0.000759 test-rmse:0.326916+0.013132
## [7] train-rmse:0.195506+0.001366 test-rmse:0.287922+0.017964
## [8] train-rmse:0.149930+0.001293 test-rmse:0.264539+0.023259
## [9] train-rmse:0.116429+0.001271 test-rmse:0.251919+0.028156
## [10] train-rmse:0.091302+0.001557 test-rmse:0.245199+0.032050
## [11] train-rmse:0.073170+0.001327 test-rmse:0.242693+0.033829
## [12] train-rmse:0.059476+0.001119 test-rmse:0.241319+0.036022
## [13] train-rmse:0.049320+0.001194 test-rmse:0.241588+0.037837
## [14] train-rmse:0.041835+0.001872 test-rmse:0.241962+0.038321
## [15] train-rmse:0.036294+0.002411 test-rmse:0.242527+0.039127
## [16] train-rmse:0.032082+0.002601 test-rmse:0.243233+0.039842
## [17] train-rmse:0.028700+0.002375 test-rmse:0.243427+0.040077
## [18] train-rmse:0.025325+0.002009 test-rmse:0.243842+0.040538
## Stopping. Best iteration:
## [12] train-rmse:0.059476+0.001119 test-rmse:0.241319+0.036022
##
## 63
## [1] train-rmse:1.048463+0.003312 test-rmse:1.048459+0.020951
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.774373+0.001006 test-rmse:0.774825+0.017165
## [3] train-rmse:0.582799+0.002022 test-rmse:0.583958+0.013986
## [4] train-rmse:0.449744+0.003108 test-rmse:0.453309+0.009627
## [5] train-rmse:0.359094+0.004285 test-rmse:0.363168+0.014653
## [6] train-rmse:0.299237+0.004881 test-rmse:0.306054+0.014722
## [7] train-rmse:0.260734+0.005567 test-rmse:0.265597+0.020109
## [8] train-rmse:0.236828+0.005760 test-rmse:0.244027+0.020367
## [9] train-rmse:0.221931+0.005951 test-rmse:0.228585+0.024883
## [10] train-rmse:0.212929+0.005946 test-rmse:0.222493+0.023942
## [11] train-rmse:0.206779+0.005902 test-rmse:0.217099+0.023760
## [12] train-rmse:0.202272+0.005586 test-rmse:0.214258+0.025864
## [13] train-rmse:0.198742+0.005487 test-rmse:0.213599+0.026568
## [14] train-rmse:0.196082+0.005462 test-rmse:0.211714+0.026614
## [15] train-rmse:0.193718+0.005372 test-rmse:0.211806+0.027686
## [16] train-rmse:0.191787+0.005324 test-rmse:0.212366+0.026256
## [17] train-rmse:0.190039+0.005313 test-rmse:0.212044+0.026082
## [18] train-rmse:0.188421+0.005309 test-rmse:0.210422+0.026385
## [19] train-rmse:0.186882+0.005414 test-rmse:0.211434+0.026770
## [20] train-rmse:0.185508+0.005550 test-rmse:0.211875+0.026750
## [21] train-rmse:0.184227+0.005701 test-rmse:0.213346+0.025728
## [22] train-rmse:0.183124+0.005770 test-rmse:0.211665+0.026160
## [23] train-rmse:0.182034+0.005833 test-rmse:0.211087+0.025705
## [24] train-rmse:0.181032+0.005948 test-rmse:0.210607+0.025483
## Stopping. Best iteration:
## [18] train-rmse:0.188421+0.005309 test-rmse:0.210422+0.026385
##
## 64
## [1] train-rmse:1.043383+0.004258 test-rmse:1.045165+0.019494
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.763058+0.002722 test-rmse:0.767819+0.015258
## [3] train-rmse:0.563913+0.001928 test-rmse:0.572368+0.012634
## [4] train-rmse:0.424275+0.001654 test-rmse:0.439411+0.011433
## [5] train-rmse:0.328096+0.002038 test-rmse:0.348743+0.013074
## [6] train-rmse:0.263169+0.002131 test-rmse:0.292250+0.015705
## [7] train-rmse:0.220957+0.002248 test-rmse:0.256406+0.019444
## [8] train-rmse:0.193546+0.000881 test-rmse:0.235837+0.023604
## [9] train-rmse:0.176124+0.002369 test-rmse:0.222604+0.025219
## [10] train-rmse:0.164848+0.003617 test-rmse:0.215244+0.026096
## [11] train-rmse:0.157242+0.003618 test-rmse:0.212602+0.026873
## [12] train-rmse:0.152520+0.004320 test-rmse:0.212042+0.027399
## [13] train-rmse:0.149252+0.003596 test-rmse:0.211784+0.028477
## [14] train-rmse:0.146487+0.003975 test-rmse:0.211110+0.028915
## [15] train-rmse:0.143590+0.003683 test-rmse:0.211196+0.028759
## [16] train-rmse:0.141172+0.003346 test-rmse:0.210800+0.028821
## [17] train-rmse:0.138578+0.002891 test-rmse:0.210368+0.028770
## [18] train-rmse:0.136782+0.002841 test-rmse:0.210628+0.029268
## [19] train-rmse:0.134948+0.003037 test-rmse:0.210634+0.029647
## [20] train-rmse:0.133759+0.003108 test-rmse:0.210507+0.029757
## [21] train-rmse:0.131204+0.003162 test-rmse:0.210974+0.029129
## [22] train-rmse:0.130061+0.003233 test-rmse:0.212114+0.027693
## [23] train-rmse:0.127676+0.004776 test-rmse:0.212769+0.027405
## Stopping. Best iteration:
## [17] train-rmse:0.138578+0.002891 test-rmse:0.210368+0.028770
##
## 65
## [1] train-rmse:1.042322+0.004187 test-rmse:1.045972+0.018517
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.760448+0.002674 test-rmse:0.768975+0.013101
## [3] train-rmse:0.559068+0.001713 test-rmse:0.575867+0.009572
## [4] train-rmse:0.416266+0.001916 test-rmse:0.443754+0.008252
## [5] train-rmse:0.315938+0.003081 test-rmse:0.353613+0.011190
## [6] train-rmse:0.245358+0.005185 test-rmse:0.298345+0.013815
## [7] train-rmse:0.197238+0.005405 test-rmse:0.266422+0.017246
## [8] train-rmse:0.163749+0.006938 test-rmse:0.245870+0.021043
## [9] train-rmse:0.141848+0.007836 test-rmse:0.233538+0.023736
## [10] train-rmse:0.128276+0.007665 test-rmse:0.227905+0.023737
## [11] train-rmse:0.120021+0.008100 test-rmse:0.225780+0.025895
## [12] train-rmse:0.113869+0.009014 test-rmse:0.225520+0.027116
## [13] train-rmse:0.108947+0.009734 test-rmse:0.226571+0.028948
## [14] train-rmse:0.105978+0.009469 test-rmse:0.225810+0.029225
## [15] train-rmse:0.102722+0.009381 test-rmse:0.226319+0.029838
## [16] train-rmse:0.099942+0.009692 test-rmse:0.226560+0.029569
## [17] train-rmse:0.097107+0.008869 test-rmse:0.227495+0.030439
## [18] train-rmse:0.094383+0.008857 test-rmse:0.227873+0.031219
## Stopping. Best iteration:
## [12] train-rmse:0.113869+0.009014 test-rmse:0.225520+0.027116
##
## 66
## [1] train-rmse:1.041947+0.004220 test-rmse:1.046033+0.018451
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.759570+0.002612 test-rmse:0.769586+0.013014
## [3] train-rmse:0.556372+0.001462 test-rmse:0.576481+0.009686
## [4] train-rmse:0.410497+0.001310 test-rmse:0.445175+0.007369
## [5] train-rmse:0.305931+0.002226 test-rmse:0.357606+0.009905
## [6] train-rmse:0.231715+0.003101 test-rmse:0.303255+0.014962
## [7] train-rmse:0.179012+0.003763 test-rmse:0.269811+0.020389
## [8] train-rmse:0.142647+0.004894 test-rmse:0.252198+0.024792
## [9] train-rmse:0.117685+0.005404 test-rmse:0.242457+0.028841
## [10] train-rmse:0.101369+0.006454 test-rmse:0.236240+0.030639
## [11] train-rmse:0.089851+0.006937 test-rmse:0.234649+0.031552
## [12] train-rmse:0.082131+0.006572 test-rmse:0.233941+0.033104
## [13] train-rmse:0.076567+0.006853 test-rmse:0.233157+0.033304
## [14] train-rmse:0.072635+0.006939 test-rmse:0.232840+0.033728
## [15] train-rmse:0.069102+0.006464 test-rmse:0.233540+0.034187
## [16] train-rmse:0.066666+0.006908 test-rmse:0.234180+0.034445
## [17] train-rmse:0.064122+0.007434 test-rmse:0.233361+0.034541
## [18] train-rmse:0.062107+0.007874 test-rmse:0.234099+0.034673
## [19] train-rmse:0.059651+0.007223 test-rmse:0.234762+0.034762
## [20] train-rmse:0.057264+0.006952 test-rmse:0.235333+0.035394
## Stopping. Best iteration:
## [14] train-rmse:0.072635+0.006939 test-rmse:0.232840+0.033728
##
## 67
## [1] train-rmse:1.041706+0.004196 test-rmse:1.046062+0.018419
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.759040+0.002577 test-rmse:0.769681+0.012909
## [3] train-rmse:0.555400+0.001312 test-rmse:0.576566+0.009632
## [4] train-rmse:0.408058+0.001573 test-rmse:0.445761+0.007752
## [5] train-rmse:0.302417+0.002045 test-rmse:0.359526+0.011201
## [6] train-rmse:0.225622+0.001897 test-rmse:0.305670+0.017633
## [7] train-rmse:0.170728+0.002217 test-rmse:0.273824+0.023917
## [8] train-rmse:0.131886+0.002648 test-rmse:0.256869+0.029742
## [9] train-rmse:0.104347+0.003487 test-rmse:0.247657+0.033591
## [10] train-rmse:0.085117+0.004287 test-rmse:0.243070+0.036826
## [11] train-rmse:0.072268+0.004602 test-rmse:0.240940+0.037908
## [12] train-rmse:0.063279+0.005114 test-rmse:0.239793+0.038979
## [13] train-rmse:0.056910+0.005270 test-rmse:0.239670+0.039577
## [14] train-rmse:0.052190+0.005301 test-rmse:0.239768+0.040359
## [15] train-rmse:0.048081+0.005097 test-rmse:0.240409+0.040714
## [16] train-rmse:0.045699+0.005163 test-rmse:0.240873+0.041098
## [17] train-rmse:0.043326+0.005186 test-rmse:0.241462+0.041558
## [18] train-rmse:0.041179+0.004997 test-rmse:0.241668+0.041973
## [19] train-rmse:0.039197+0.005450 test-rmse:0.242693+0.042179
## Stopping. Best iteration:
## [13] train-rmse:0.056910+0.005270 test-rmse:0.239670+0.039577
##
## 68
## [1] train-rmse:1.041588+0.004248 test-rmse:1.046092+0.018378
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.758740+0.002697 test-rmse:0.769865+0.012709
## [3] train-rmse:0.554726+0.001643 test-rmse:0.577100+0.008902
## [4] train-rmse:0.408101+0.001080 test-rmse:0.445551+0.007208
## [5] train-rmse:0.300850+0.001942 test-rmse:0.358341+0.010506
## [6] train-rmse:0.223590+0.002218 test-rmse:0.304659+0.015622
## [7] train-rmse:0.167986+0.002989 test-rmse:0.272935+0.021701
## [8] train-rmse:0.128299+0.003644 test-rmse:0.255361+0.026982
## [9] train-rmse:0.099958+0.003583 test-rmse:0.246506+0.030969
## [10] train-rmse:0.079817+0.004467 test-rmse:0.242926+0.033911
## [11] train-rmse:0.065333+0.005018 test-rmse:0.241112+0.035242
## [12] train-rmse:0.054957+0.004802 test-rmse:0.240651+0.036248
## [13] train-rmse:0.047645+0.005282 test-rmse:0.241237+0.037410
## [14] train-rmse:0.042433+0.005538 test-rmse:0.241156+0.037824
## [15] train-rmse:0.038775+0.005747 test-rmse:0.241208+0.038008
## [16] train-rmse:0.035580+0.005697 test-rmse:0.240982+0.038122
## [17] train-rmse:0.032634+0.004963 test-rmse:0.241475+0.038189
## [18] train-rmse:0.030392+0.004834 test-rmse:0.242074+0.038630
## Stopping. Best iteration:
## [12] train-rmse:0.054957+0.004802 test-rmse:0.240651+0.036248
##
## 69
## [1] train-rmse:1.041534+0.004310 test-rmse:1.046105+0.018355
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.758595+0.002830 test-rmse:0.769926+0.012611
## [3] train-rmse:0.554418+0.001599 test-rmse:0.577047+0.009068
## [4] train-rmse:0.407426+0.001222 test-rmse:0.445248+0.007365
## [5] train-rmse:0.299972+0.001163 test-rmse:0.358025+0.010487
## [6] train-rmse:0.222268+0.000699 test-rmse:0.304572+0.015841
## [7] train-rmse:0.166072+0.000810 test-rmse:0.272647+0.021754
## [8] train-rmse:0.125496+0.001042 test-rmse:0.256354+0.027336
## [9] train-rmse:0.097264+0.000995 test-rmse:0.248684+0.030325
## [10] train-rmse:0.076151+0.001011 test-rmse:0.245377+0.032312
## [11] train-rmse:0.060961+0.001148 test-rmse:0.244009+0.033861
## [12] train-rmse:0.049911+0.001760 test-rmse:0.244518+0.035527
## [13] train-rmse:0.041599+0.001879 test-rmse:0.245016+0.036691
## [14] train-rmse:0.035762+0.002348 test-rmse:0.245665+0.037429
## [15] train-rmse:0.031384+0.002559 test-rmse:0.245968+0.037699
## [16] train-rmse:0.028136+0.002399 test-rmse:0.246231+0.038159
## [17] train-rmse:0.025052+0.001677 test-rmse:0.246441+0.038349
## Stopping. Best iteration:
## [11] train-rmse:0.060961+0.001148 test-rmse:0.244009+0.033861
##
## 70
## [1] train-rmse:1.021057+0.003081 test-rmse:1.021073+0.020608
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.736387+0.000848 test-rmse:0.737026+0.016579
## [3] train-rmse:0.544127+0.002563 test-rmse:0.545473+0.013440
## [4] train-rmse:0.414513+0.003453 test-rmse:0.418788+0.008838
## [5] train-rmse:0.330497+0.004741 test-rmse:0.334840+0.015770
## [6] train-rmse:0.277205+0.005219 test-rmse:0.284498+0.016369
## [7] train-rmse:0.245141+0.005809 test-rmse:0.250048+0.022225
## [8] train-rmse:0.225916+0.005876 test-rmse:0.233385+0.021937
## [9] train-rmse:0.214703+0.005966 test-rmse:0.221462+0.025931
## [10] train-rmse:0.207424+0.005990 test-rmse:0.217781+0.024838
## [11] train-rmse:0.202592+0.005598 test-rmse:0.214798+0.024840
## [12] train-rmse:0.198952+0.005559 test-rmse:0.211928+0.026555
## [13] train-rmse:0.195889+0.005294 test-rmse:0.213197+0.027376
## [14] train-rmse:0.193327+0.005290 test-rmse:0.212928+0.027637
## [15] train-rmse:0.191300+0.005220 test-rmse:0.212537+0.027695
## [16] train-rmse:0.189436+0.005371 test-rmse:0.213086+0.026964
## [17] train-rmse:0.187803+0.005362 test-rmse:0.212586+0.026391
## [18] train-rmse:0.186202+0.005458 test-rmse:0.212646+0.026460
## Stopping. Best iteration:
## [12] train-rmse:0.198952+0.005559 test-rmse:0.211928+0.026555
##
## 71
## [1] train-rmse:1.015519+0.004109 test-rmse:1.017495+0.019079
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.723878+0.002545 test-rmse:0.729335+0.014828
## [3] train-rmse:0.523096+0.001825 test-rmse:0.532734+0.012243
## [4] train-rmse:0.387132+0.001697 test-rmse:0.404179+0.011447
## [5] train-rmse:0.297113+0.002194 test-rmse:0.321282+0.014705
## [6] train-rmse:0.239224+0.002192 test-rmse:0.272355+0.017630
## [7] train-rmse:0.203609+0.002263 test-rmse:0.242752+0.021228
## [8] train-rmse:0.181705+0.000699 test-rmse:0.227182+0.025228
## [9] train-rmse:0.168770+0.002332 test-rmse:0.218597+0.026386
## [10] train-rmse:0.160167+0.003749 test-rmse:0.213515+0.027416
## [11] train-rmse:0.154865+0.005018 test-rmse:0.211521+0.027240
## [12] train-rmse:0.150617+0.004629 test-rmse:0.212701+0.028301
## [13] train-rmse:0.145175+0.004392 test-rmse:0.211084+0.027062
## [14] train-rmse:0.143714+0.004457 test-rmse:0.211585+0.027836
## [15] train-rmse:0.141441+0.005059 test-rmse:0.211466+0.027812
## [16] train-rmse:0.139986+0.005389 test-rmse:0.211163+0.028175
## [17] train-rmse:0.138054+0.005024 test-rmse:0.212231+0.028425
## [18] train-rmse:0.136258+0.004743 test-rmse:0.211905+0.028193
## [19] train-rmse:0.133266+0.005149 test-rmse:0.213012+0.027448
## Stopping. Best iteration:
## [13] train-rmse:0.145175+0.004392 test-rmse:0.211084+0.027062
##
## 72
## [1] train-rmse:1.014355+0.004027 test-rmse:1.018382+0.018015
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.721003+0.002465 test-rmse:0.731553+0.012452
## [3] train-rmse:0.517587+0.001612 test-rmse:0.536509+0.008392
## [4] train-rmse:0.377276+0.002609 test-rmse:0.408301+0.009268
## [5] train-rmse:0.281697+0.004298 test-rmse:0.327661+0.011900
## [6] train-rmse:0.216392+0.005111 test-rmse:0.277821+0.016845
## [7] train-rmse:0.174145+0.006468 test-rmse:0.250311+0.020884
## [8] train-rmse:0.147105+0.006979 test-rmse:0.236834+0.025140
## [9] train-rmse:0.130457+0.007446 test-rmse:0.228699+0.027144
## [10] train-rmse:0.119295+0.007870 test-rmse:0.226204+0.029712
## [11] train-rmse:0.112612+0.008596 test-rmse:0.224257+0.030305
## [12] train-rmse:0.107212+0.008194 test-rmse:0.224063+0.029404
## [13] train-rmse:0.103086+0.008895 test-rmse:0.225167+0.030607
## [14] train-rmse:0.099860+0.008750 test-rmse:0.225357+0.031396
## [15] train-rmse:0.096816+0.008390 test-rmse:0.225105+0.031536
## [16] train-rmse:0.093751+0.009375 test-rmse:0.226408+0.031732
## [17] train-rmse:0.091150+0.009311 test-rmse:0.227475+0.032451
## [18] train-rmse:0.088694+0.010079 test-rmse:0.228106+0.032136
## Stopping. Best iteration:
## [12] train-rmse:0.107212+0.008194 test-rmse:0.224063+0.029404
##
## 73
## [1] train-rmse:1.013944+0.004063 test-rmse:1.018456+0.017942
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.720036+0.002386 test-rmse:0.731276+0.012216
## [3] train-rmse:0.514376+0.001453 test-rmse:0.537559+0.009046
## [4] train-rmse:0.370673+0.001532 test-rmse:0.410334+0.007976
## [5] train-rmse:0.271145+0.002537 test-rmse:0.330544+0.012446
## [6] train-rmse:0.202379+0.003243 test-rmse:0.284152+0.017503
## [7] train-rmse:0.155561+0.004185 test-rmse:0.257304+0.022600
## [8] train-rmse:0.124012+0.004756 test-rmse:0.244129+0.027969
## [9] train-rmse:0.103512+0.005589 test-rmse:0.238197+0.030246
## [10] train-rmse:0.091148+0.006245 test-rmse:0.234350+0.031395
## [11] train-rmse:0.082727+0.006820 test-rmse:0.232111+0.032248
## [12] train-rmse:0.077249+0.006558 test-rmse:0.231723+0.033464
## [13] train-rmse:0.072760+0.006224 test-rmse:0.231470+0.034063
## [14] train-rmse:0.069102+0.006513 test-rmse:0.232107+0.034374
## [15] train-rmse:0.066811+0.006224 test-rmse:0.232225+0.034543
## [16] train-rmse:0.064071+0.006836 test-rmse:0.233454+0.034852
## [17] train-rmse:0.060886+0.006182 test-rmse:0.233334+0.034480
## [18] train-rmse:0.058027+0.006681 test-rmse:0.233364+0.034562
## [19] train-rmse:0.056054+0.006239 test-rmse:0.234454+0.035183
## Stopping. Best iteration:
## [13] train-rmse:0.072760+0.006224 test-rmse:0.231470+0.034063
##
## 74
## [1] train-rmse:1.013679+0.004036 test-rmse:1.018494+0.017905
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.719453+0.002343 test-rmse:0.731398+0.012102
## [3] train-rmse:0.512921+0.001417 test-rmse:0.537538+0.008841
## [4] train-rmse:0.367434+0.001722 test-rmse:0.411301+0.007637
## [5] train-rmse:0.266435+0.002374 test-rmse:0.332903+0.012982
## [6] train-rmse:0.195126+0.002909 test-rmse:0.288225+0.019854
## [7] train-rmse:0.145847+0.003727 test-rmse:0.264057+0.027030
## [8] train-rmse:0.111375+0.004194 test-rmse:0.250454+0.031960
## [9] train-rmse:0.088234+0.005618 test-rmse:0.244846+0.035121
## [10] train-rmse:0.073220+0.006340 test-rmse:0.242037+0.037103
## [11] train-rmse:0.062701+0.006857 test-rmse:0.241563+0.038071
## [12] train-rmse:0.054986+0.006768 test-rmse:0.241496+0.038549
## [13] train-rmse:0.049851+0.007219 test-rmse:0.241420+0.039327
## [14] train-rmse:0.046168+0.007344 test-rmse:0.242593+0.040398
## [15] train-rmse:0.042982+0.007587 test-rmse:0.242477+0.040669
## [16] train-rmse:0.040705+0.007469 test-rmse:0.243777+0.041568
## [17] train-rmse:0.037854+0.006551 test-rmse:0.244486+0.042028
## [18] train-rmse:0.036131+0.006249 test-rmse:0.245143+0.042361
## [19] train-rmse:0.034504+0.006225 test-rmse:0.245682+0.042811
## Stopping. Best iteration:
## [13] train-rmse:0.049851+0.007219 test-rmse:0.241420+0.039327
##
## 75
## [1] train-rmse:1.013549+0.004093 test-rmse:1.018529+0.017860
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.719125+0.002466 test-rmse:0.731612+0.011885
## [3] train-rmse:0.512156+0.001551 test-rmse:0.538139+0.008105
## [4] train-rmse:0.366082+0.002048 test-rmse:0.410441+0.007637
## [5] train-rmse:0.264391+0.002982 test-rmse:0.331422+0.012154
## [6] train-rmse:0.192543+0.002613 test-rmse:0.285802+0.019555
## [7] train-rmse:0.141958+0.002752 test-rmse:0.260211+0.025421
## [8] train-rmse:0.107127+0.002929 test-rmse:0.247879+0.030647
## [9] train-rmse:0.082969+0.003186 test-rmse:0.242323+0.034681
## [10] train-rmse:0.066544+0.003500 test-rmse:0.239706+0.037004
## [11] train-rmse:0.055468+0.004190 test-rmse:0.239523+0.039072
## [12] train-rmse:0.047623+0.004372 test-rmse:0.240051+0.039889
## [13] train-rmse:0.041890+0.004641 test-rmse:0.240397+0.040439
## [14] train-rmse:0.037613+0.004066 test-rmse:0.240310+0.041963
## [15] train-rmse:0.034138+0.003430 test-rmse:0.240359+0.042247
## [16] train-rmse:0.031120+0.003203 test-rmse:0.240522+0.042249
## [17] train-rmse:0.028790+0.003061 test-rmse:0.240548+0.042237
## Stopping. Best iteration:
## [11] train-rmse:0.055468+0.004190 test-rmse:0.239523+0.039072
##
## 76
## [1] train-rmse:1.013491+0.004161 test-rmse:1.018544+0.017835
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.718964+0.002608 test-rmse:0.731682+0.011774
## [3] train-rmse:0.511787+0.001266 test-rmse:0.538094+0.008288
## [4] train-rmse:0.365389+0.001190 test-rmse:0.410158+0.007437
## [5] train-rmse:0.262612+0.002154 test-rmse:0.331266+0.012247
## [6] train-rmse:0.189966+0.002266 test-rmse:0.286219+0.019189
## [7] train-rmse:0.139379+0.002026 test-rmse:0.261328+0.025370
## [8] train-rmse:0.103918+0.002512 test-rmse:0.250909+0.030867
## [9] train-rmse:0.078974+0.003048 test-rmse:0.245718+0.034101
## [10] train-rmse:0.061657+0.002917 test-rmse:0.244793+0.036507
## [11] train-rmse:0.049723+0.002700 test-rmse:0.245196+0.038054
## [12] train-rmse:0.041274+0.002793 test-rmse:0.245790+0.039297
## [13] train-rmse:0.035278+0.003062 test-rmse:0.246369+0.040177
## [14] train-rmse:0.030336+0.002980 test-rmse:0.246992+0.040996
## [15] train-rmse:0.026648+0.002568 test-rmse:0.247176+0.041534
## [16] train-rmse:0.023564+0.002094 test-rmse:0.247712+0.042217
## Stopping. Best iteration:
## [10] train-rmse:0.061657+0.002917 test-rmse:0.244793+0.036507
##
## 77
## [1] train-rmse:0.993695+0.002849 test-rmse:0.993733+0.020258
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.699684+0.000902 test-rmse:0.700514+0.016041
## [3] train-rmse:0.508195+0.003128 test-rmse:0.509734+0.013059
## [4] train-rmse:0.383027+0.003828 test-rmse:0.388073+0.008995
## [5] train-rmse:0.306249+0.005140 test-rmse:0.310862+0.017428
## [6] train-rmse:0.259462+0.005482 test-rmse:0.267320+0.018310
## [7] train-rmse:0.233233+0.005958 test-rmse:0.238449+0.024080
## [8] train-rmse:0.217917+0.005942 test-rmse:0.226074+0.023216
## [9] train-rmse:0.209304+0.006063 test-rmse:0.219115+0.026518
## [10] train-rmse:0.203432+0.005811 test-rmse:0.213978+0.026799
## [11] train-rmse:0.199341+0.005618 test-rmse:0.214004+0.025845
## [12] train-rmse:0.196053+0.005317 test-rmse:0.212232+0.025642
## [13] train-rmse:0.193504+0.005262 test-rmse:0.212918+0.026573
## [14] train-rmse:0.191065+0.005311 test-rmse:0.212222+0.026035
## [15] train-rmse:0.189124+0.005447 test-rmse:0.212774+0.027914
## [16] train-rmse:0.187354+0.005471 test-rmse:0.211103+0.027814
## [17] train-rmse:0.185784+0.005504 test-rmse:0.211659+0.027927
## [18] train-rmse:0.184277+0.005657 test-rmse:0.211831+0.025604
## [19] train-rmse:0.182973+0.005719 test-rmse:0.213165+0.026369
## [20] train-rmse:0.181703+0.005914 test-rmse:0.211719+0.026278
## [21] train-rmse:0.180561+0.005988 test-rmse:0.210982+0.025913
## [22] train-rmse:0.179535+0.006079 test-rmse:0.209838+0.025387
## [23] train-rmse:0.178529+0.006202 test-rmse:0.211229+0.026057
## [24] train-rmse:0.177682+0.006295 test-rmse:0.211277+0.025806
## [25] train-rmse:0.176904+0.006327 test-rmse:0.211697+0.023948
## [26] train-rmse:0.176106+0.006461 test-rmse:0.212580+0.025380
## [27] train-rmse:0.175400+0.006433 test-rmse:0.212802+0.025159
## [28] train-rmse:0.174757+0.006488 test-rmse:0.212798+0.025090
## Stopping. Best iteration:
## [22] train-rmse:0.179535+0.006079 test-rmse:0.209838+0.025387
##
## 78
## [1] train-rmse:0.987681+0.003958 test-rmse:0.989860+0.018663
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.685926+0.002366 test-rmse:0.692013+0.014281
## [3] train-rmse:0.484870+0.001764 test-rmse:0.495766+0.011931
## [4] train-rmse:0.353658+0.001781 test-rmse:0.373067+0.011499
## [5] train-rmse:0.270505+0.002362 test-rmse:0.297625+0.015564
## [6] train-rmse:0.219738+0.002282 test-rmse:0.256696+0.019397
## [7] train-rmse:0.189633+0.001038 test-rmse:0.234230+0.023812
## [8] train-rmse:0.171878+0.002738 test-rmse:0.220907+0.025035
## [9] train-rmse:0.161190+0.004036 test-rmse:0.214497+0.025203
## [10] train-rmse:0.153388+0.004764 test-rmse:0.214426+0.022909
## [11] train-rmse:0.149133+0.005479 test-rmse:0.214332+0.024326
## [12] train-rmse:0.146649+0.005656 test-rmse:0.213920+0.025815
## [13] train-rmse:0.143480+0.004298 test-rmse:0.213391+0.025496
## [14] train-rmse:0.140066+0.005125 test-rmse:0.212985+0.025336
## [15] train-rmse:0.137039+0.005130 test-rmse:0.213177+0.025233
## [16] train-rmse:0.134554+0.004530 test-rmse:0.214780+0.024102
## [17] train-rmse:0.132149+0.004474 test-rmse:0.214846+0.024168
## [18] train-rmse:0.130080+0.004713 test-rmse:0.214467+0.024359
## [19] train-rmse:0.129196+0.004662 test-rmse:0.215109+0.024837
## [20] train-rmse:0.126901+0.004373 test-rmse:0.215322+0.024381
## Stopping. Best iteration:
## [14] train-rmse:0.140066+0.005125 test-rmse:0.212985+0.025336
##
## 79
## [1] train-rmse:0.986407+0.003864 test-rmse:0.990832+0.017508
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.682771+0.002270 test-rmse:0.694396+0.011649
## [3] train-rmse:0.478623+0.001614 test-rmse:0.499990+0.007757
## [4] train-rmse:0.342425+0.003166 test-rmse:0.376688+0.009641
## [5] train-rmse:0.251622+0.003395 test-rmse:0.305306+0.012349
## [6] train-rmse:0.193169+0.004684 test-rmse:0.264040+0.017159
## [7] train-rmse:0.157218+0.006211 test-rmse:0.243265+0.021304
## [8] train-rmse:0.135174+0.008172 test-rmse:0.233144+0.023802
## [9] train-rmse:0.122218+0.008435 test-rmse:0.227821+0.022890
## [10] train-rmse:0.113318+0.008163 test-rmse:0.225797+0.024911
## [11] train-rmse:0.107625+0.007846 test-rmse:0.224207+0.025476
## [12] train-rmse:0.104093+0.007887 test-rmse:0.224270+0.026491
## [13] train-rmse:0.100871+0.007288 test-rmse:0.224462+0.026666
## [14] train-rmse:0.097374+0.006983 test-rmse:0.226059+0.026109
## [15] train-rmse:0.094670+0.007354 test-rmse:0.226794+0.026673
## [16] train-rmse:0.092585+0.007613 test-rmse:0.226847+0.026091
## [17] train-rmse:0.089165+0.008374 test-rmse:0.227341+0.026432
## Stopping. Best iteration:
## [11] train-rmse:0.107625+0.007846 test-rmse:0.224207+0.025476
##
## 80
## [1] train-rmse:0.985959+0.003903 test-rmse:0.990922+0.017428
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.681689+0.002173 test-rmse:0.694257+0.011431
## [3] train-rmse:0.474916+0.001661 test-rmse:0.501363+0.008530
## [4] train-rmse:0.334544+0.002082 test-rmse:0.379883+0.009719
## [5] train-rmse:0.240531+0.003569 test-rmse:0.308737+0.014498
## [6] train-rmse:0.177782+0.004552 test-rmse:0.269031+0.020140
## [7] train-rmse:0.137228+0.005555 test-rmse:0.249640+0.025433
## [8] train-rmse:0.110609+0.006108 test-rmse:0.239238+0.030029
## [9] train-rmse:0.094677+0.007907 test-rmse:0.236746+0.031817
## [10] train-rmse:0.084646+0.008781 test-rmse:0.236281+0.034291
## [11] train-rmse:0.077285+0.008783 test-rmse:0.236456+0.035337
## [12] train-rmse:0.072815+0.009101 test-rmse:0.235917+0.035173
## [13] train-rmse:0.068786+0.009162 test-rmse:0.236101+0.035436
## [14] train-rmse:0.065348+0.009076 test-rmse:0.235655+0.034944
## [15] train-rmse:0.061999+0.009085 test-rmse:0.235129+0.034478
## [16] train-rmse:0.059538+0.008608 test-rmse:0.235217+0.034490
## [17] train-rmse:0.057113+0.007877 test-rmse:0.234690+0.034230
## [18] train-rmse:0.054214+0.007443 test-rmse:0.235206+0.034377
## [19] train-rmse:0.052030+0.007124 test-rmse:0.235367+0.034506
## [20] train-rmse:0.049401+0.007442 test-rmse:0.236375+0.035363
## [21] train-rmse:0.047305+0.007326 test-rmse:0.236762+0.035381
## [22] train-rmse:0.045393+0.007319 test-rmse:0.236621+0.035468
## [23] train-rmse:0.043893+0.007063 test-rmse:0.236720+0.035579
## Stopping. Best iteration:
## [17] train-rmse:0.057113+0.007877 test-rmse:0.234690+0.034230
##
## 81
## [1] train-rmse:0.985669+0.003873 test-rmse:0.990969+0.017388
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.681049+0.002120 test-rmse:0.694410+0.011310
## [3] train-rmse:0.473147+0.002034 test-rmse:0.501361+0.008207
## [4] train-rmse:0.331070+0.002297 test-rmse:0.379944+0.009890
## [5] train-rmse:0.234832+0.003212 test-rmse:0.309967+0.015469
## [6] train-rmse:0.169728+0.004017 test-rmse:0.271966+0.023031
## [7] train-rmse:0.126497+0.005107 test-rmse:0.253379+0.029030
## [8] train-rmse:0.096831+0.006021 test-rmse:0.245756+0.033770
## [9] train-rmse:0.078628+0.007615 test-rmse:0.242544+0.036085
## [10] train-rmse:0.066862+0.008616 test-rmse:0.241954+0.037808
## [11] train-rmse:0.058619+0.008484 test-rmse:0.240643+0.038547
## [12] train-rmse:0.052580+0.008683 test-rmse:0.240402+0.039107
## [13] train-rmse:0.049063+0.008892 test-rmse:0.241293+0.039914
## [14] train-rmse:0.045338+0.008621 test-rmse:0.241094+0.039983
## [15] train-rmse:0.042449+0.008512 test-rmse:0.241788+0.039987
## [16] train-rmse:0.040040+0.007655 test-rmse:0.242785+0.039486
## [17] train-rmse:0.038046+0.007596 test-rmse:0.242928+0.039824
## [18] train-rmse:0.036109+0.007232 test-rmse:0.242911+0.039881
## Stopping. Best iteration:
## [12] train-rmse:0.052580+0.008683 test-rmse:0.240402+0.039107
##
## 82
## [1] train-rmse:0.985529+0.003934 test-rmse:0.991010+0.017338
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.680693+0.002244 test-rmse:0.694656+0.011078
## [3] train-rmse:0.472491+0.002015 test-rmse:0.502141+0.007506
## [4] train-rmse:0.329348+0.002047 test-rmse:0.380318+0.009299
## [5] train-rmse:0.232359+0.002662 test-rmse:0.310716+0.014146
## [6] train-rmse:0.165629+0.002575 test-rmse:0.273727+0.021722
## [7] train-rmse:0.121332+0.002895 test-rmse:0.255122+0.028425
## [8] train-rmse:0.090891+0.002906 test-rmse:0.247193+0.033177
## [9] train-rmse:0.071272+0.003625 test-rmse:0.243947+0.035784
## [10] train-rmse:0.058324+0.004063 test-rmse:0.242543+0.037265
## [11] train-rmse:0.049030+0.004489 test-rmse:0.242208+0.038122
## [12] train-rmse:0.042881+0.004200 test-rmse:0.241873+0.040100
## [13] train-rmse:0.038716+0.003904 test-rmse:0.242033+0.040572
## [14] train-rmse:0.035424+0.003980 test-rmse:0.242166+0.040813
## [15] train-rmse:0.032535+0.003913 test-rmse:0.242391+0.041954
## [16] train-rmse:0.029986+0.003687 test-rmse:0.242537+0.042593
## [17] train-rmse:0.027351+0.003844 test-rmse:0.242518+0.043070
## [18] train-rmse:0.024849+0.003876 test-rmse:0.242954+0.043326
## Stopping. Best iteration:
## [12] train-rmse:0.042881+0.004200 test-rmse:0.241873+0.040100
##
## 83
## [1] train-rmse:0.985465+0.004009 test-rmse:0.991028+0.017309
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.680515+0.002392 test-rmse:0.694737+0.010954
## [3] train-rmse:0.472055+0.001664 test-rmse:0.501984+0.007699
## [4] train-rmse:0.328781+0.001289 test-rmse:0.380107+0.009394
## [5] train-rmse:0.230750+0.002502 test-rmse:0.310262+0.014238
## [6] train-rmse:0.163487+0.002442 test-rmse:0.273432+0.022040
## [7] train-rmse:0.118330+0.002411 test-rmse:0.254601+0.028148
## [8] train-rmse:0.087424+0.002587 test-rmse:0.247835+0.031973
## [9] train-rmse:0.066318+0.002858 test-rmse:0.244818+0.035137
## [10] train-rmse:0.052268+0.002738 test-rmse:0.244918+0.035990
## [11] train-rmse:0.042793+0.002835 test-rmse:0.245327+0.037191
## [12] train-rmse:0.036306+0.003559 test-rmse:0.245849+0.038131
## [13] train-rmse:0.031282+0.003453 test-rmse:0.246114+0.038456
## [14] train-rmse:0.027179+0.003697 test-rmse:0.246692+0.038949
## [15] train-rmse:0.023913+0.003542 test-rmse:0.247386+0.039191
## Stopping. Best iteration:
## [9] train-rmse:0.066318+0.002858 test-rmse:0.244818+0.035137
##
## 84
## [1] train-rmse:0.966378+0.002614 test-rmse:0.966441+0.019899
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.664279+0.001134 test-rmse:0.665211+0.015433
## [3] train-rmse:0.474664+0.003429 test-rmse:0.478843+0.011510
## [4] train-rmse:0.354846+0.004207 test-rmse:0.356962+0.013525
## [5] train-rmse:0.285716+0.005286 test-rmse:0.293251+0.015075
## [6] train-rmse:0.245300+0.005695 test-rmse:0.250295+0.020911
## [7] train-rmse:0.224200+0.005950 test-rmse:0.231278+0.026124
## [8] train-rmse:0.212024+0.005894 test-rmse:0.221848+0.024646
## [9] train-rmse:0.204937+0.005784 test-rmse:0.218523+0.025727
## [10] train-rmse:0.200263+0.005586 test-rmse:0.214174+0.027338
## [11] train-rmse:0.196489+0.005525 test-rmse:0.213138+0.028877
## [12] train-rmse:0.193734+0.005344 test-rmse:0.213694+0.026968
## [13] train-rmse:0.191247+0.005272 test-rmse:0.212874+0.026857
## [14] train-rmse:0.189088+0.005271 test-rmse:0.211832+0.026166
## [15] train-rmse:0.187197+0.005498 test-rmse:0.213330+0.026771
## [16] train-rmse:0.185498+0.005606 test-rmse:0.212585+0.026228
## [17] train-rmse:0.183927+0.005765 test-rmse:0.210924+0.027348
## [18] train-rmse:0.182515+0.005850 test-rmse:0.212261+0.027992
## [19] train-rmse:0.181250+0.005948 test-rmse:0.211884+0.025439
## [20] train-rmse:0.180085+0.006052 test-rmse:0.211201+0.024991
## [21] train-rmse:0.178971+0.006125 test-rmse:0.211252+0.024620
## [22] train-rmse:0.178047+0.006231 test-rmse:0.212313+0.026122
## [23] train-rmse:0.177093+0.006344 test-rmse:0.211084+0.025631
## Stopping. Best iteration:
## [17] train-rmse:0.183927+0.005765 test-rmse:0.210924+0.027348
##
## 85
## [1] train-rmse:0.959869+0.003808 test-rmse:0.962264+0.018247
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.649195+0.002205 test-rmse:0.655965+0.013764
## [3] train-rmse:0.449186+0.001752 test-rmse:0.461455+0.011746
## [4] train-rmse:0.323784+0.001825 test-rmse:0.345564+0.011895
## [5] train-rmse:0.247858+0.002393 test-rmse:0.278076+0.016723
## [6] train-rmse:0.204138+0.002392 test-rmse:0.243494+0.021802
## [7] train-rmse:0.179956+0.002865 test-rmse:0.227495+0.022696
## [8] train-rmse:0.166773+0.003780 test-rmse:0.218151+0.024092
## [9] train-rmse:0.156632+0.006061 test-rmse:0.213182+0.023968
## [10] train-rmse:0.151916+0.007395 test-rmse:0.211312+0.025014
## [11] train-rmse:0.147219+0.007062 test-rmse:0.210335+0.024163
## [12] train-rmse:0.144543+0.007196 test-rmse:0.211839+0.025445
## [13] train-rmse:0.141111+0.008327 test-rmse:0.210802+0.025618
## [14] train-rmse:0.139948+0.008372 test-rmse:0.210818+0.026552
## [15] train-rmse:0.138053+0.008322 test-rmse:0.211489+0.026977
## [16] train-rmse:0.135776+0.007967 test-rmse:0.211504+0.027309
## [17] train-rmse:0.133156+0.008093 test-rmse:0.210880+0.027849
## Stopping. Best iteration:
## [11] train-rmse:0.147219+0.007062 test-rmse:0.210335+0.024163
##
## 86
## [1] train-rmse:0.958481+0.003699 test-rmse:0.963326+0.016998
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.645729+0.002088 test-rmse:0.658581+0.010838
## [3] train-rmse:0.442126+0.001713 test-rmse:0.466596+0.007772
## [4] train-rmse:0.310314+0.003601 test-rmse:0.349589+0.011693
## [5] train-rmse:0.225725+0.004603 test-rmse:0.285419+0.014330
## [6] train-rmse:0.174524+0.005242 test-rmse:0.251771+0.017676
## [7] train-rmse:0.144206+0.006908 test-rmse:0.236010+0.022175
## [8] train-rmse:0.126522+0.008347 test-rmse:0.228317+0.025299
## [9] train-rmse:0.116042+0.008223 test-rmse:0.224821+0.026079
## [10] train-rmse:0.109115+0.008693 test-rmse:0.223883+0.026938
## [11] train-rmse:0.104055+0.008556 test-rmse:0.223799+0.027327
## [12] train-rmse:0.100302+0.008500 test-rmse:0.224339+0.028028
## [13] train-rmse:0.095558+0.010094 test-rmse:0.225232+0.028199
## [14] train-rmse:0.092410+0.011486 test-rmse:0.225576+0.028770
## [15] train-rmse:0.089733+0.011299 test-rmse:0.225656+0.029022
## [16] train-rmse:0.086320+0.011347 test-rmse:0.226199+0.028833
## [17] train-rmse:0.083963+0.012461 test-rmse:0.227857+0.029431
## Stopping. Best iteration:
## [11] train-rmse:0.104055+0.008556 test-rmse:0.223799+0.027327
##
## 87
## [1] train-rmse:0.957993+0.003743 test-rmse:0.963433+0.016911
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.644537+0.001981 test-rmse:0.658545+0.010664
## [3] train-rmse:0.437901+0.001890 test-rmse:0.467790+0.008219
## [4] train-rmse:0.301888+0.002512 test-rmse:0.352996+0.010958
## [5] train-rmse:0.214179+0.004150 test-rmse:0.292388+0.015561
## [6] train-rmse:0.157863+0.005288 test-rmse:0.261282+0.022744
## [7] train-rmse:0.121929+0.006037 test-rmse:0.244853+0.027700
## [8] train-rmse:0.101080+0.007076 test-rmse:0.237923+0.030300
## [9] train-rmse:0.088676+0.008172 test-rmse:0.235269+0.032119
## [10] train-rmse:0.081613+0.008229 test-rmse:0.235342+0.033573
## [11] train-rmse:0.076214+0.007812 test-rmse:0.235450+0.033087
## [12] train-rmse:0.072826+0.007624 test-rmse:0.235350+0.033761
## [13] train-rmse:0.069194+0.007846 test-rmse:0.235463+0.033685
## [14] train-rmse:0.065369+0.006807 test-rmse:0.235544+0.033569
## [15] train-rmse:0.061320+0.007832 test-rmse:0.236977+0.033974
## Stopping. Best iteration:
## [9] train-rmse:0.088676+0.008172 test-rmse:0.235269+0.032119
##
## 88
## [1] train-rmse:0.957677+0.003709 test-rmse:0.963491+0.016865
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.643837+0.001915 test-rmse:0.658734+0.010541
## [3] train-rmse:0.434948+0.000588 test-rmse:0.467562+0.007660
## [4] train-rmse:0.297275+0.002424 test-rmse:0.353972+0.011517
## [5] train-rmse:0.206609+0.002944 test-rmse:0.292621+0.016727
## [6] train-rmse:0.146592+0.002956 test-rmse:0.261676+0.023792
## [7] train-rmse:0.108416+0.004190 test-rmse:0.246911+0.029213
## [8] train-rmse:0.084470+0.004918 test-rmse:0.240056+0.032539
## [9] train-rmse:0.069748+0.005769 test-rmse:0.238425+0.034738
## [10] train-rmse:0.060148+0.005919 test-rmse:0.237560+0.035835
## [11] train-rmse:0.053759+0.006431 test-rmse:0.237159+0.036401
## [12] train-rmse:0.049216+0.007139 test-rmse:0.237813+0.036580
## [13] train-rmse:0.046067+0.007177 test-rmse:0.238028+0.037005
## [14] train-rmse:0.043684+0.007150 test-rmse:0.239248+0.038026
## [15] train-rmse:0.040058+0.005753 test-rmse:0.239602+0.038250
## [16] train-rmse:0.037680+0.006057 test-rmse:0.240461+0.038595
## [17] train-rmse:0.035354+0.005944 test-rmse:0.240904+0.039406
## Stopping. Best iteration:
## [11] train-rmse:0.053759+0.006431 test-rmse:0.237159+0.036401
##
## 89
## [1] train-rmse:0.957524+0.003775 test-rmse:0.963537+0.016811
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.643450+0.002036 test-rmse:0.659015+0.010294
## [3] train-rmse:0.433991+0.000859 test-rmse:0.468102+0.006997
## [4] train-rmse:0.295416+0.002136 test-rmse:0.354232+0.011053
## [5] train-rmse:0.204336+0.002970 test-rmse:0.292494+0.017106
## [6] train-rmse:0.143611+0.002971 test-rmse:0.261141+0.024343
## [7] train-rmse:0.103909+0.003016 test-rmse:0.246974+0.030126
## [8] train-rmse:0.078317+0.003528 test-rmse:0.241630+0.033685
## [9] train-rmse:0.062044+0.003282 test-rmse:0.240063+0.035906
## [10] train-rmse:0.051692+0.003428 test-rmse:0.238919+0.038707
## [11] train-rmse:0.044521+0.003792 test-rmse:0.238602+0.039181
## [12] train-rmse:0.039074+0.003668 test-rmse:0.239209+0.039577
## [13] train-rmse:0.035158+0.004247 test-rmse:0.239922+0.039895
## [14] train-rmse:0.031647+0.004020 test-rmse:0.240627+0.039875
## [15] train-rmse:0.029159+0.004199 test-rmse:0.240688+0.039630
## [16] train-rmse:0.027456+0.004257 test-rmse:0.241064+0.040042
## [17] train-rmse:0.025356+0.004231 test-rmse:0.241389+0.040159
## Stopping. Best iteration:
## [11] train-rmse:0.044521+0.003792 test-rmse:0.238602+0.039181
##
## 90
## [1] train-rmse:0.957455+0.003855 test-rmse:0.963558+0.016778
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.643244+0.002196 test-rmse:0.659103+0.010152
## [3] train-rmse:0.433325+0.001245 test-rmse:0.467809+0.007150
## [4] train-rmse:0.293955+0.001490 test-rmse:0.353958+0.010994
## [5] train-rmse:0.201872+0.001880 test-rmse:0.293340+0.017272
## [6] train-rmse:0.140444+0.001649 test-rmse:0.264399+0.025893
## [7] train-rmse:0.099639+0.002102 test-rmse:0.250130+0.031620
## [8] train-rmse:0.073445+0.002634 test-rmse:0.244959+0.035685
## [9] train-rmse:0.056090+0.002422 test-rmse:0.243816+0.038801
## [10] train-rmse:0.043734+0.002986 test-rmse:0.244482+0.040223
## [11] train-rmse:0.035717+0.003213 test-rmse:0.244994+0.041664
## [12] train-rmse:0.030469+0.003394 test-rmse:0.245332+0.042260
## [13] train-rmse:0.026106+0.003412 test-rmse:0.245979+0.043113
## [14] train-rmse:0.022425+0.003423 test-rmse:0.246528+0.043447
## [15] train-rmse:0.019162+0.003128 test-rmse:0.247145+0.043754
## Stopping. Best iteration:
## [9] train-rmse:0.056090+0.002422 test-rmse:0.243816+0.038801
##
## 91
## [1] train-rmse:0.939115+0.002375 test-rmse:0.939202+0.019533
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.630192+0.001467 test-rmse:0.631296+0.014867
## [3] train-rmse:0.443079+0.003411 test-rmse:0.447164+0.009637
## [4] train-rmse:0.329890+0.004560 test-rmse:0.335066+0.016216
## [5] train-rmse:0.268418+0.005316 test-rmse:0.275885+0.016536
## [6] train-rmse:0.234076+0.005910 test-rmse:0.240127+0.022794
## [7] train-rmse:0.217471+0.005999 test-rmse:0.226130+0.021835
## [8] train-rmse:0.207668+0.005830 test-rmse:0.217132+0.024843
## [9] train-rmse:0.201549+0.005542 test-rmse:0.215121+0.025670
## [10] train-rmse:0.197460+0.005484 test-rmse:0.214317+0.026578
## [11] train-rmse:0.194251+0.005380 test-rmse:0.210074+0.027860
## [12] train-rmse:0.191495+0.005471 test-rmse:0.212757+0.027048
## [13] train-rmse:0.189296+0.005272 test-rmse:0.211081+0.025927
## [14] train-rmse:0.187313+0.005336 test-rmse:0.212693+0.027022
## [15] train-rmse:0.185426+0.005528 test-rmse:0.211907+0.026196
## [16] train-rmse:0.183776+0.005654 test-rmse:0.210474+0.025897
## [17] train-rmse:0.182328+0.005918 test-rmse:0.211888+0.026788
## Stopping. Best iteration:
## [11] train-rmse:0.194251+0.005380 test-rmse:0.210074+0.027860
##
## 92
## [1] train-rmse:0.932088+0.003658 test-rmse:0.934710+0.017831
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.613698+0.002063 test-rmse:0.621207+0.013280
## [3] train-rmse:0.416205+0.001831 test-rmse:0.432401+0.011500
## [4] train-rmse:0.297323+0.002192 test-rmse:0.321452+0.013684
## [5] train-rmse:0.228865+0.002368 test-rmse:0.262500+0.018429
## [6] train-rmse:0.190219+0.002421 test-rmse:0.233461+0.024452
## [7] train-rmse:0.170697+0.003654 test-rmse:0.220518+0.023853
## [8] train-rmse:0.159390+0.004965 test-rmse:0.213273+0.024642
## [9] train-rmse:0.153930+0.004449 test-rmse:0.212526+0.026271
## [10] train-rmse:0.151035+0.004464 test-rmse:0.211465+0.026217
## [11] train-rmse:0.147512+0.005922 test-rmse:0.210604+0.026519
## [12] train-rmse:0.143859+0.006239 test-rmse:0.211360+0.026134
## [13] train-rmse:0.142272+0.006665 test-rmse:0.210556+0.025619
## [14] train-rmse:0.138940+0.006995 test-rmse:0.211030+0.025588
## [15] train-rmse:0.136229+0.006493 test-rmse:0.210629+0.025406
## [16] train-rmse:0.134360+0.006029 test-rmse:0.210943+0.026251
## [17] train-rmse:0.132136+0.006686 test-rmse:0.210752+0.026169
## [18] train-rmse:0.129771+0.006654 test-rmse:0.211670+0.026490
## [19] train-rmse:0.127267+0.007809 test-rmse:0.212758+0.024477
## Stopping. Best iteration:
## [13] train-rmse:0.142272+0.006665 test-rmse:0.210556+0.025619
##
## 93
## [1] train-rmse:0.930579+0.003532 test-rmse:0.935866+0.016482
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.609903+0.001927 test-rmse:0.624173+0.010171
## [3] train-rmse:0.407241+0.002685 test-rmse:0.434524+0.009603
## [4] train-rmse:0.281066+0.004509 test-rmse:0.327414+0.012102
## [5] train-rmse:0.204443+0.007424 test-rmse:0.268202+0.016103
## [6] train-rmse:0.159904+0.007867 test-rmse:0.242551+0.022362
## [7] train-rmse:0.133341+0.009794 test-rmse:0.229400+0.025603
## [8] train-rmse:0.118731+0.009828 test-rmse:0.224138+0.027326
## [9] train-rmse:0.110114+0.010871 test-rmse:0.221983+0.029238
## [10] train-rmse:0.103687+0.010518 test-rmse:0.220612+0.029742
## [11] train-rmse:0.098931+0.010731 test-rmse:0.221778+0.030410
## [12] train-rmse:0.094826+0.011595 test-rmse:0.223556+0.030622
## [13] train-rmse:0.091618+0.011996 test-rmse:0.224090+0.031116
## [14] train-rmse:0.088676+0.012072 test-rmse:0.224970+0.029697
## [15] train-rmse:0.086312+0.012638 test-rmse:0.226097+0.029897
## [16] train-rmse:0.083426+0.012154 test-rmse:0.227689+0.031384
## Stopping. Best iteration:
## [10] train-rmse:0.103687+0.010518 test-rmse:0.220612+0.029742
##
## 94
## [1] train-rmse:0.930050+0.003580 test-rmse:0.935993+0.016388
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.608587+0.001814 test-rmse:0.624159+0.009922
## [3] train-rmse:0.402954+0.002455 test-rmse:0.435755+0.009892
## [4] train-rmse:0.272354+0.003475 test-rmse:0.329278+0.013116
## [5] train-rmse:0.190883+0.005009 test-rmse:0.273782+0.018272
## [6] train-rmse:0.141332+0.006181 test-rmse:0.248447+0.024974
## [7] train-rmse:0.110371+0.006798 test-rmse:0.240174+0.029645
## [8] train-rmse:0.093633+0.008066 test-rmse:0.236576+0.030903
## [9] train-rmse:0.084345+0.008699 test-rmse:0.234596+0.032214
## [10] train-rmse:0.078266+0.009285 test-rmse:0.234227+0.032899
## [11] train-rmse:0.073737+0.008998 test-rmse:0.233644+0.033223
## [12] train-rmse:0.069739+0.009262 test-rmse:0.233626+0.033138
## [13] train-rmse:0.066081+0.009324 test-rmse:0.234122+0.033154
## [14] train-rmse:0.062844+0.009172 test-rmse:0.234392+0.033309
## [15] train-rmse:0.059815+0.009511 test-rmse:0.235285+0.033563
## [16] train-rmse:0.056934+0.009501 test-rmse:0.236331+0.033961
## [17] train-rmse:0.054551+0.009313 test-rmse:0.237025+0.034536
## [18] train-rmse:0.051205+0.009232 test-rmse:0.237846+0.035428
## Stopping. Best iteration:
## [12] train-rmse:0.069739+0.009262 test-rmse:0.233626+0.033138
##
## 95
## [1] train-rmse:0.929707+0.003542 test-rmse:0.936063+0.016337
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.607743+0.001788 test-rmse:0.624383+0.009796
## [3] train-rmse:0.399774+0.000686 test-rmse:0.436084+0.008712
## [4] train-rmse:0.267204+0.002457 test-rmse:0.329776+0.013320
## [5] train-rmse:0.181737+0.002736 test-rmse:0.276055+0.021019
## [6] train-rmse:0.127768+0.003379 test-rmse:0.250186+0.028123
## [7] train-rmse:0.095053+0.005067 test-rmse:0.239943+0.032816
## [8] train-rmse:0.075750+0.005743 test-rmse:0.236981+0.036119
## [9] train-rmse:0.063694+0.006805 test-rmse:0.236307+0.036913
## [10] train-rmse:0.055803+0.006958 test-rmse:0.236066+0.038259
## [11] train-rmse:0.051517+0.007007 test-rmse:0.236304+0.038820
## [12] train-rmse:0.047106+0.007486 test-rmse:0.237623+0.038941
## [13] train-rmse:0.043825+0.007149 test-rmse:0.237872+0.039233
## [14] train-rmse:0.040589+0.007332 test-rmse:0.238765+0.039698
## [15] train-rmse:0.038172+0.006933 test-rmse:0.239112+0.040125
## [16] train-rmse:0.036576+0.006780 test-rmse:0.239246+0.040660
## Stopping. Best iteration:
## [10] train-rmse:0.055803+0.006958 test-rmse:0.236066+0.038259
##
## 96
## [1] train-rmse:0.929541+0.003613 test-rmse:0.936116+0.016279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.607403+0.001846 test-rmse:0.624708+0.009545
## [3] train-rmse:0.398701+0.001051 test-rmse:0.436063+0.008910
## [4] train-rmse:0.265296+0.002209 test-rmse:0.330612+0.013498
## [5] train-rmse:0.178989+0.002020 test-rmse:0.276038+0.021472
## [6] train-rmse:0.123988+0.002674 test-rmse:0.251274+0.028393
## [7] train-rmse:0.088825+0.002436 test-rmse:0.242668+0.033671
## [8] train-rmse:0.067228+0.003619 test-rmse:0.239481+0.036465
## [9] train-rmse:0.053640+0.003606 test-rmse:0.238722+0.037866
## [10] train-rmse:0.045148+0.004037 test-rmse:0.238477+0.038527
## [11] train-rmse:0.039649+0.004404 test-rmse:0.238637+0.038958
## [12] train-rmse:0.035194+0.004436 test-rmse:0.239007+0.039313
## [13] train-rmse:0.031885+0.004043 test-rmse:0.238930+0.039344
## [14] train-rmse:0.027969+0.004324 test-rmse:0.239285+0.039324
## [15] train-rmse:0.025724+0.004614 test-rmse:0.239821+0.039746
## [16] train-rmse:0.023463+0.004532 test-rmse:0.240071+0.039902
## Stopping. Best iteration:
## [10] train-rmse:0.045148+0.004037 test-rmse:0.238477+0.038527
##
## 97
## [1] train-rmse:0.929466+0.003700 test-rmse:0.936141+0.016242
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.607176+0.001999 test-rmse:0.624810+0.009391
## [3] train-rmse:0.398028+0.001311 test-rmse:0.435988+0.008728
## [4] train-rmse:0.264192+0.000800 test-rmse:0.330644+0.013189
## [5] train-rmse:0.178030+0.001701 test-rmse:0.277258+0.019943
## [6] train-rmse:0.121885+0.002098 test-rmse:0.253870+0.027177
## [7] train-rmse:0.086323+0.001825 test-rmse:0.244818+0.032703
## [8] train-rmse:0.063246+0.002301 test-rmse:0.241849+0.036108
## [9] train-rmse:0.048386+0.001933 test-rmse:0.241646+0.038789
## [10] train-rmse:0.038851+0.002771 test-rmse:0.242596+0.039867
## [11] train-rmse:0.031852+0.002509 test-rmse:0.243917+0.040569
## [12] train-rmse:0.027019+0.002756 test-rmse:0.244828+0.041484
## [13] train-rmse:0.023339+0.002601 test-rmse:0.245005+0.041976
## [14] train-rmse:0.019772+0.002590 test-rmse:0.245397+0.042552
## [15] train-rmse:0.017453+0.002838 test-rmse:0.245663+0.042821
## Stopping. Best iteration:
## [9] train-rmse:0.048386+0.001933 test-rmse:0.241646+0.038789
##
## 98
## [1] train-rmse:0.911908+0.002138 test-rmse:0.912021+0.019158
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.597438+0.001852 test-rmse:0.598658+0.014306
## [3] train-rmse:0.413727+0.003594 test-rmse:0.418482+0.008492
## [4] train-rmse:0.308132+0.004896 test-rmse:0.313527+0.017250
## [5] train-rmse:0.253918+0.005506 test-rmse:0.262763+0.018400
## [6] train-rmse:0.225323+0.005981 test-rmse:0.231451+0.024117
## [7] train-rmse:0.212283+0.005904 test-rmse:0.221080+0.022783
## [8] train-rmse:0.204377+0.005879 test-rmse:0.213949+0.025096
## [9] train-rmse:0.198925+0.005368 test-rmse:0.213168+0.026203
## [10] train-rmse:0.195117+0.005340 test-rmse:0.214808+0.027554
## [11] train-rmse:0.192252+0.005314 test-rmse:0.210268+0.028368
## [12] train-rmse:0.189572+0.005381 test-rmse:0.210893+0.028729
## [13] train-rmse:0.187549+0.005416 test-rmse:0.211774+0.026205
## [14] train-rmse:0.185640+0.005505 test-rmse:0.213654+0.027429
## [15] train-rmse:0.183810+0.005700 test-rmse:0.211441+0.025713
## [16] train-rmse:0.182158+0.005750 test-rmse:0.211966+0.025807
## [17] train-rmse:0.180792+0.005964 test-rmse:0.213434+0.027082
## Stopping. Best iteration:
## [11] train-rmse:0.192252+0.005314 test-rmse:0.210268+0.028368
##
## 99
## [1] train-rmse:0.904341+0.003510 test-rmse:0.907202+0.017414
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.579447+0.001944 test-rmse:0.587757+0.012836
## [3] train-rmse:0.385437+0.001889 test-rmse:0.403270+0.011436
## [4] train-rmse:0.273777+0.002309 test-rmse:0.301408+0.015434
## [5] train-rmse:0.213141+0.002401 test-rmse:0.251148+0.020111
## [6] train-rmse:0.180946+0.001854 test-rmse:0.229591+0.021610
## [7] train-rmse:0.165062+0.002720 test-rmse:0.217531+0.022255
## [8] train-rmse:0.155913+0.002828 test-rmse:0.215755+0.024633
## [9] train-rmse:0.150936+0.003493 test-rmse:0.215034+0.025735
## [10] train-rmse:0.146477+0.005501 test-rmse:0.213729+0.025338
## [11] train-rmse:0.141620+0.004850 test-rmse:0.214441+0.025653
## [12] train-rmse:0.139981+0.005330 test-rmse:0.214588+0.026094
## [13] train-rmse:0.137572+0.005642 test-rmse:0.214711+0.026385
## [14] train-rmse:0.135302+0.006224 test-rmse:0.215864+0.023853
## [15] train-rmse:0.132625+0.005439 test-rmse:0.218463+0.024746
## [16] train-rmse:0.129878+0.006860 test-rmse:0.218051+0.024379
## Stopping. Best iteration:
## [10] train-rmse:0.146477+0.005501 test-rmse:0.213729+0.025338
##
## 100
## [1] train-rmse:0.902704+0.003364 test-rmse:0.908460+0.015961
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.575284+0.001814 test-rmse:0.590985+0.009375
## [3] train-rmse:0.375452+0.003171 test-rmse:0.405886+0.009557
## [4] train-rmse:0.255580+0.005499 test-rmse:0.306955+0.013175
## [5] train-rmse:0.185375+0.006910 test-rmse:0.257153+0.017897
## [6] train-rmse:0.146515+0.008832 test-rmse:0.235711+0.023175
## [7] train-rmse:0.124746+0.008300 test-rmse:0.225203+0.025712
## [8] train-rmse:0.114490+0.009238 test-rmse:0.221646+0.027976
## [9] train-rmse:0.108114+0.009324 test-rmse:0.221698+0.029864
## [10] train-rmse:0.104453+0.009309 test-rmse:0.221139+0.029751
## [11] train-rmse:0.099849+0.009283 test-rmse:0.221677+0.030296
## [12] train-rmse:0.096413+0.008701 test-rmse:0.221347+0.029844
## [13] train-rmse:0.093097+0.009525 test-rmse:0.223101+0.030650
## [14] train-rmse:0.089943+0.009077 test-rmse:0.223868+0.031335
## [15] train-rmse:0.087134+0.010051 test-rmse:0.224239+0.030816
## [16] train-rmse:0.083750+0.010601 test-rmse:0.225020+0.030873
## Stopping. Best iteration:
## [10] train-rmse:0.104453+0.009309 test-rmse:0.221139+0.029751
##
## 101
## [1] train-rmse:0.902131+0.003418 test-rmse:0.908608+0.015860
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.573849+0.001686 test-rmse:0.591120+0.009214
## [3] train-rmse:0.369365+0.001523 test-rmse:0.406782+0.009499
## [4] train-rmse:0.244473+0.003688 test-rmse:0.309745+0.014830
## [5] train-rmse:0.169426+0.005021 test-rmse:0.262117+0.021409
## [6] train-rmse:0.125493+0.006362 test-rmse:0.241333+0.026476
## [7] train-rmse:0.100238+0.006759 test-rmse:0.232691+0.028705
## [8] train-rmse:0.086888+0.007694 test-rmse:0.231179+0.030487
## [9] train-rmse:0.079809+0.007970 test-rmse:0.230692+0.032650
## [10] train-rmse:0.073654+0.007318 test-rmse:0.230848+0.033004
## [11] train-rmse:0.069596+0.008178 test-rmse:0.230025+0.032824
## [12] train-rmse:0.065864+0.008103 test-rmse:0.230876+0.033739
## [13] train-rmse:0.061923+0.007881 test-rmse:0.232205+0.033620
## [14] train-rmse:0.059424+0.007608 test-rmse:0.233220+0.034243
## [15] train-rmse:0.056001+0.006881 test-rmse:0.233430+0.034313
## [16] train-rmse:0.053107+0.007199 test-rmse:0.233554+0.034721
## [17] train-rmse:0.050084+0.006069 test-rmse:0.233736+0.035033
## Stopping. Best iteration:
## [11] train-rmse:0.069596+0.008178 test-rmse:0.230025+0.032824
##
## 102
## [1] train-rmse:0.901758+0.003375 test-rmse:0.908691+0.015803
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.572926+0.001627 test-rmse:0.591388+0.009101
## [3] train-rmse:0.366738+0.001212 test-rmse:0.407763+0.009012
## [4] train-rmse:0.239401+0.003341 test-rmse:0.310296+0.015042
## [5] train-rmse:0.160350+0.003010 test-rmse:0.264070+0.023930
## [6] train-rmse:0.112547+0.003933 test-rmse:0.246900+0.030609
## [7] train-rmse:0.085302+0.004784 test-rmse:0.239043+0.034005
## [8] train-rmse:0.069067+0.005778 test-rmse:0.236907+0.036170
## [9] train-rmse:0.058887+0.006008 test-rmse:0.236234+0.037088
## [10] train-rmse:0.052598+0.006991 test-rmse:0.236470+0.037534
## [11] train-rmse:0.048177+0.006983 test-rmse:0.237072+0.037865
## [12] train-rmse:0.043944+0.006596 test-rmse:0.237608+0.038291
## [13] train-rmse:0.041505+0.006666 test-rmse:0.238024+0.038868
## [14] train-rmse:0.038453+0.007119 test-rmse:0.239104+0.039460
## [15] train-rmse:0.036522+0.006427 test-rmse:0.239303+0.040291
## Stopping. Best iteration:
## [9] train-rmse:0.058887+0.006008 test-rmse:0.236234+0.037088
##
## 103
## [1] train-rmse:0.901580+0.003451 test-rmse:0.908752+0.015740
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.572541+0.001695 test-rmse:0.591751+0.008839
## [3] train-rmse:0.365582+0.001276 test-rmse:0.407908+0.009793
## [4] train-rmse:0.237240+0.002799 test-rmse:0.310730+0.015247
## [5] train-rmse:0.157411+0.002355 test-rmse:0.264599+0.024973
## [6] train-rmse:0.107575+0.002946 test-rmse:0.246539+0.031823
## [7] train-rmse:0.078149+0.003650 test-rmse:0.240529+0.036821
## [8] train-rmse:0.060084+0.004568 test-rmse:0.239230+0.038915
## [9] train-rmse:0.048700+0.005366 test-rmse:0.240320+0.040156
## [10] train-rmse:0.041013+0.005053 test-rmse:0.239974+0.040705
## [11] train-rmse:0.035820+0.005333 test-rmse:0.239732+0.040812
## [12] train-rmse:0.031931+0.005850 test-rmse:0.240340+0.041242
## [13] train-rmse:0.028784+0.006093 test-rmse:0.240896+0.041501
## [14] train-rmse:0.025840+0.006313 test-rmse:0.240995+0.041395
## Stopping. Best iteration:
## [8] train-rmse:0.060084+0.004568 test-rmse:0.239230+0.038915
##
## 104
## [1] train-rmse:0.901499+0.003545 test-rmse:0.908780+0.015699
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.572307+0.001824 test-rmse:0.591843+0.008694
## [3] train-rmse:0.364669+0.001252 test-rmse:0.407662+0.009253
## [4] train-rmse:0.236106+0.001505 test-rmse:0.310948+0.014894
## [5] train-rmse:0.155384+0.001711 test-rmse:0.265628+0.023920
## [6] train-rmse:0.105245+0.002021 test-rmse:0.247670+0.031041
## [7] train-rmse:0.074007+0.003102 test-rmse:0.243059+0.035866
## [8] train-rmse:0.054793+0.003210 test-rmse:0.243009+0.038941
## [9] train-rmse:0.042347+0.003724 test-rmse:0.243923+0.040396
## [10] train-rmse:0.034112+0.003993 test-rmse:0.244820+0.041079
## [11] train-rmse:0.028408+0.004194 test-rmse:0.245380+0.041752
## [12] train-rmse:0.024230+0.003590 test-rmse:0.245340+0.042284
## [13] train-rmse:0.020751+0.003307 test-rmse:0.245562+0.042697
## [14] train-rmse:0.018222+0.003039 test-rmse:0.246003+0.042793
## Stopping. Best iteration:
## [8] train-rmse:0.054793+0.003210 test-rmse:0.243009+0.038941
##
## 105
## [1] train-rmse:0.884763+0.001900 test-rmse:0.884903+0.018774
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.566042+0.002268 test-rmse:0.567385+0.013802
## [3] train-rmse:0.386784+0.003804 test-rmse:0.392261+0.007962
## [4] train-rmse:0.289322+0.005203 test-rmse:0.294895+0.018510
## [5] train-rmse:0.242020+0.005668 test-rmse:0.251254+0.019598
## [6] train-rmse:0.218596+0.005991 test-rmse:0.224915+0.025128
## [7] train-rmse:0.207895+0.005998 test-rmse:0.218125+0.023470
## [8] train-rmse:0.201398+0.005668 test-rmse:0.214597+0.023833
## [9] train-rmse:0.196829+0.005257 test-rmse:0.212372+0.026689
## [10] train-rmse:0.193124+0.005213 test-rmse:0.214198+0.027817
## [11] train-rmse:0.190447+0.005369 test-rmse:0.212477+0.027159
## [12] train-rmse:0.187889+0.005374 test-rmse:0.210952+0.027256
## [13] train-rmse:0.185917+0.005501 test-rmse:0.212631+0.028221
## [14] train-rmse:0.183999+0.005732 test-rmse:0.212625+0.025206
## [15] train-rmse:0.182342+0.005773 test-rmse:0.211775+0.024152
## [16] train-rmse:0.180833+0.005875 test-rmse:0.213540+0.026658
## [17] train-rmse:0.179426+0.006065 test-rmse:0.212695+0.026078
## [18] train-rmse:0.178059+0.006267 test-rmse:0.211149+0.025476
## Stopping. Best iteration:
## [12] train-rmse:0.187889+0.005374 test-rmse:0.210952+0.027256
##
## 106
## [1] train-rmse:0.876630+0.003360 test-rmse:0.879745+0.016997
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.546455+0.001852 test-rmse:0.555634+0.012438
## [3] train-rmse:0.357079+0.001972 test-rmse:0.376660+0.011550
## [4] train-rmse:0.253217+0.002412 test-rmse:0.282840+0.017046
## [5] train-rmse:0.200301+0.002465 test-rmse:0.241351+0.021613
## [6] train-rmse:0.174106+0.002009 test-rmse:0.223446+0.023222
## [7] train-rmse:0.160535+0.003444 test-rmse:0.215535+0.024316
## [8] train-rmse:0.151626+0.005279 test-rmse:0.211840+0.023755
## [9] train-rmse:0.146584+0.004406 test-rmse:0.212872+0.026075
## [10] train-rmse:0.142669+0.004957 test-rmse:0.211631+0.026430
## [11] train-rmse:0.140323+0.005089 test-rmse:0.212443+0.026139
## [12] train-rmse:0.135783+0.005267 test-rmse:0.214617+0.025945
## [13] train-rmse:0.133442+0.005411 test-rmse:0.214653+0.025686
## [14] train-rmse:0.131306+0.005860 test-rmse:0.214928+0.025397
## [15] train-rmse:0.129004+0.005161 test-rmse:0.216929+0.023870
## [16] train-rmse:0.127767+0.005664 test-rmse:0.216975+0.024225
## Stopping. Best iteration:
## [10] train-rmse:0.142669+0.004957 test-rmse:0.211631+0.026430
##
## 107
## [1] train-rmse:0.874858+0.003191 test-rmse:0.881108+0.015435
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.541890+0.001771 test-rmse:0.559520+0.009126
## [3] train-rmse:0.345951+0.003662 test-rmse:0.379462+0.009796
## [4] train-rmse:0.232214+0.004712 test-rmse:0.288663+0.016108
## [5] train-rmse:0.169295+0.006373 test-rmse:0.247963+0.021834
## [6] train-rmse:0.136403+0.007527 test-rmse:0.231074+0.026926
## [7] train-rmse:0.119513+0.008503 test-rmse:0.222466+0.027372
## [8] train-rmse:0.110353+0.008774 test-rmse:0.220950+0.028873
## [9] train-rmse:0.104763+0.009349 test-rmse:0.220277+0.029053
## [10] train-rmse:0.100068+0.008636 test-rmse:0.219888+0.030269
## [11] train-rmse:0.096382+0.008676 test-rmse:0.220615+0.030424
## [12] train-rmse:0.092120+0.010064 test-rmse:0.224897+0.031943
## [13] train-rmse:0.088920+0.009768 test-rmse:0.224708+0.031759
## [14] train-rmse:0.086216+0.010589 test-rmse:0.226868+0.032473
## [15] train-rmse:0.082469+0.010824 test-rmse:0.227007+0.032589
## [16] train-rmse:0.079797+0.011073 test-rmse:0.227301+0.032673
## Stopping. Best iteration:
## [10] train-rmse:0.100068+0.008636 test-rmse:0.219888+0.030269
##
## 108
## [1] train-rmse:0.874239+0.003251 test-rmse:0.881281+0.015326
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.540035+0.001809 test-rmse:0.560702+0.008977
## [3] train-rmse:0.338317+0.002291 test-rmse:0.382204+0.009515
## [4] train-rmse:0.219854+0.004083 test-rmse:0.292413+0.014853
## [5] train-rmse:0.151107+0.005070 test-rmse:0.253108+0.022385
## [6] train-rmse:0.112935+0.006501 test-rmse:0.237333+0.027117
## [7] train-rmse:0.093394+0.006818 test-rmse:0.231339+0.029902
## [8] train-rmse:0.082781+0.007305 test-rmse:0.230368+0.031545
## [9] train-rmse:0.076258+0.007613 test-rmse:0.231139+0.033186
## [10] train-rmse:0.071128+0.007576 test-rmse:0.231155+0.033881
## [11] train-rmse:0.066949+0.007391 test-rmse:0.231515+0.034613
## [12] train-rmse:0.062437+0.005983 test-rmse:0.232888+0.033886
## [13] train-rmse:0.058393+0.006611 test-rmse:0.234130+0.034462
## [14] train-rmse:0.054860+0.005757 test-rmse:0.235499+0.034734
## Stopping. Best iteration:
## [8] train-rmse:0.082781+0.007305 test-rmse:0.230368+0.031545
##
## 109
## [1] train-rmse:0.873835+0.003203 test-rmse:0.881379+0.015263
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.539125+0.001754 test-rmse:0.561019+0.008803
## [3] train-rmse:0.335995+0.001398 test-rmse:0.382576+0.009117
## [4] train-rmse:0.214364+0.002634 test-rmse:0.295707+0.015901
## [5] train-rmse:0.142265+0.003408 test-rmse:0.257992+0.025191
## [6] train-rmse:0.101522+0.003532 test-rmse:0.243743+0.031529
## [7] train-rmse:0.077754+0.004933 test-rmse:0.240537+0.034780
## [8] train-rmse:0.064948+0.005723 test-rmse:0.239555+0.035979
## [9] train-rmse:0.057314+0.006788 test-rmse:0.238979+0.036912
## [10] train-rmse:0.052246+0.006545 test-rmse:0.238649+0.037081
## [11] train-rmse:0.047495+0.006487 test-rmse:0.239422+0.037950
## [12] train-rmse:0.043251+0.005616 test-rmse:0.239881+0.038749
## [13] train-rmse:0.040248+0.005365 test-rmse:0.240167+0.038765
## [14] train-rmse:0.037812+0.004910 test-rmse:0.240953+0.039333
## [15] train-rmse:0.034660+0.004560 test-rmse:0.241606+0.039659
## [16] train-rmse:0.031976+0.004710 test-rmse:0.242145+0.040500
## Stopping. Best iteration:
## [10] train-rmse:0.052246+0.006545 test-rmse:0.238649+0.037081
##
## 110
## [1] train-rmse:0.873643+0.003285 test-rmse:0.881449+0.015195
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.538712+0.001808 test-rmse:0.561431+0.008482
## [3] train-rmse:0.334614+0.001448 test-rmse:0.382665+0.009060
## [4] train-rmse:0.213038+0.003169 test-rmse:0.294750+0.015837
## [5] train-rmse:0.138904+0.003428 test-rmse:0.257327+0.025098
## [6] train-rmse:0.095284+0.004352 test-rmse:0.244255+0.031801
## [7] train-rmse:0.069737+0.004788 test-rmse:0.241443+0.035461
## [8] train-rmse:0.054679+0.006246 test-rmse:0.242094+0.037503
## [9] train-rmse:0.044654+0.005358 test-rmse:0.241098+0.037653
## [10] train-rmse:0.037759+0.005510 test-rmse:0.241660+0.038063
## [11] train-rmse:0.033516+0.005764 test-rmse:0.241909+0.038311
## [12] train-rmse:0.029633+0.005554 test-rmse:0.243032+0.038759
## [13] train-rmse:0.026779+0.005584 test-rmse:0.243776+0.039295
## [14] train-rmse:0.023929+0.004884 test-rmse:0.244187+0.039491
## [15] train-rmse:0.021751+0.004530 test-rmse:0.244644+0.040208
## Stopping. Best iteration:
## [9] train-rmse:0.044654+0.005358 test-rmse:0.241098+0.037653
##
## 111
## [1] train-rmse:0.873555+0.003386 test-rmse:0.881480+0.015150
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.538453+0.001909 test-rmse:0.561536+0.008295
## [3] train-rmse:0.333392+0.001354 test-rmse:0.382619+0.008797
## [4] train-rmse:0.211629+0.001246 test-rmse:0.295465+0.016178
## [5] train-rmse:0.136559+0.001508 test-rmse:0.257723+0.025138
## [6] train-rmse:0.091947+0.001730 test-rmse:0.245336+0.031795
## [7] train-rmse:0.064945+0.002435 test-rmse:0.242621+0.036039
## [8] train-rmse:0.048540+0.002886 test-rmse:0.243805+0.037346
## [9] train-rmse:0.038455+0.003462 test-rmse:0.245263+0.038438
## [10] train-rmse:0.031950+0.003814 test-rmse:0.246339+0.039163
## [11] train-rmse:0.027653+0.004301 test-rmse:0.246630+0.039572
## [12] train-rmse:0.023864+0.004417 test-rmse:0.247611+0.039953
## [13] train-rmse:0.021320+0.004472 test-rmse:0.248509+0.040343
## Stopping. Best iteration:
## [7] train-rmse:0.064945+0.002435 test-rmse:0.242621+0.036039
##
## 112## max_depth eta
## 9 2 0.12## [1] train-rmse:0.950535+0.015707 test-rmse:0.950472+0.062012
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.904098+0.015142 test-rmse:0.906141+0.061347
## [3] train-rmse:0.864501+0.014709 test-rmse:0.868585+0.060575
## [4] train-rmse:0.830876+0.014292 test-rmse:0.836579+0.060433
## [5] train-rmse:0.802054+0.014131 test-rmse:0.813605+0.060356
## [6] train-rmse:0.777366+0.013749 test-rmse:0.789947+0.059168
## [7] train-rmse:0.756105+0.013560 test-rmse:0.772680+0.059365
## [8] train-rmse:0.737584+0.013150 test-rmse:0.755543+0.059540
## [9] train-rmse:0.721680+0.012893 test-rmse:0.740246+0.058033
## [10] train-rmse:0.707530+0.012640 test-rmse:0.726395+0.057833
## [11] train-rmse:0.695081+0.012420 test-rmse:0.715595+0.058321
## [12] train-rmse:0.684238+0.012240 test-rmse:0.703656+0.057416
## [13] train-rmse:0.674537+0.012053 test-rmse:0.696872+0.055942
## [14] train-rmse:0.665951+0.011817 test-rmse:0.688636+0.054610
## [15] train-rmse:0.658311+0.011736 test-rmse:0.681259+0.053380
## [16] train-rmse:0.651429+0.011586 test-rmse:0.675685+0.054500
## [17] train-rmse:0.645412+0.011487 test-rmse:0.669925+0.052484
## [18] train-rmse:0.639889+0.011420 test-rmse:0.665527+0.052821
## [19] train-rmse:0.634975+0.011282 test-rmse:0.661395+0.052634
## [20] train-rmse:0.630547+0.011211 test-rmse:0.655911+0.052355
## [21] train-rmse:0.626488+0.011181 test-rmse:0.653250+0.051493
## [22] train-rmse:0.622871+0.011116 test-rmse:0.650014+0.052265
## [23] train-rmse:0.619506+0.011084 test-rmse:0.646946+0.052171
## [24] train-rmse:0.616363+0.011104 test-rmse:0.645445+0.051704
## [25] train-rmse:0.613386+0.011093 test-rmse:0.642974+0.052665
## [26] train-rmse:0.610555+0.011096 test-rmse:0.640737+0.053211
## [27] train-rmse:0.607916+0.011092 test-rmse:0.639087+0.052250
## [28] train-rmse:0.605394+0.011119 test-rmse:0.637059+0.052721
## [29] train-rmse:0.602938+0.011132 test-rmse:0.634704+0.052773
## [30] train-rmse:0.600633+0.011103 test-rmse:0.632691+0.052604
## [31] train-rmse:0.598436+0.011126 test-rmse:0.631053+0.053325
## [32] train-rmse:0.596315+0.011146 test-rmse:0.628964+0.053723
## [33] train-rmse:0.594308+0.011126 test-rmse:0.627251+0.054276
## [34] train-rmse:0.592347+0.011120 test-rmse:0.624904+0.054472
## [35] train-rmse:0.590461+0.011101 test-rmse:0.623661+0.054691
## [36] train-rmse:0.588597+0.011092 test-rmse:0.622282+0.053342
## [37] train-rmse:0.586830+0.011107 test-rmse:0.620688+0.054476
## [38] train-rmse:0.585099+0.011119 test-rmse:0.619699+0.054050
## [39] train-rmse:0.583426+0.011120 test-rmse:0.618314+0.053994
## [40] train-rmse:0.581784+0.011129 test-rmse:0.616510+0.053972
## [41] train-rmse:0.580215+0.011128 test-rmse:0.615429+0.054754
## [42] train-rmse:0.578682+0.011114 test-rmse:0.613655+0.054882
## [43] train-rmse:0.577185+0.011093 test-rmse:0.613406+0.055109
## [44] train-rmse:0.575730+0.011116 test-rmse:0.612478+0.055023
## [45] train-rmse:0.574302+0.011120 test-rmse:0.611108+0.055031
## [46] train-rmse:0.572918+0.011118 test-rmse:0.610379+0.054315
## [47] train-rmse:0.571550+0.011119 test-rmse:0.609721+0.054298
## [48] train-rmse:0.570213+0.011132 test-rmse:0.608246+0.054872
## [49] train-rmse:0.568891+0.011140 test-rmse:0.606328+0.054862
## [50] train-rmse:0.567583+0.011155 test-rmse:0.604717+0.055423
## [51] train-rmse:0.566319+0.011154 test-rmse:0.603698+0.055297
## [52] train-rmse:0.565083+0.011141 test-rmse:0.602630+0.055161
## [53] train-rmse:0.563883+0.011150 test-rmse:0.601698+0.054575
## [54] train-rmse:0.562695+0.011153 test-rmse:0.601156+0.054998
## [55] train-rmse:0.561538+0.011149 test-rmse:0.600702+0.055334
## [56] train-rmse:0.560389+0.011153 test-rmse:0.600351+0.054927
## [57] train-rmse:0.559260+0.011155 test-rmse:0.599322+0.054406
## [58] train-rmse:0.558143+0.011154 test-rmse:0.598936+0.053884
## [59] train-rmse:0.557051+0.011144 test-rmse:0.597756+0.054677
## [60] train-rmse:0.555977+0.011137 test-rmse:0.596991+0.054486
## [61] train-rmse:0.554920+0.011127 test-rmse:0.595683+0.054753
## [62] train-rmse:0.553866+0.011120 test-rmse:0.594462+0.055120
## [63] train-rmse:0.552827+0.011122 test-rmse:0.593622+0.055113
## [64] train-rmse:0.551800+0.011120 test-rmse:0.592896+0.055765
## [65] train-rmse:0.550796+0.011120 test-rmse:0.591979+0.054951
## [66] train-rmse:0.549823+0.011112 test-rmse:0.591438+0.054607
## [67] train-rmse:0.548860+0.011103 test-rmse:0.591048+0.054572
## [68] train-rmse:0.547920+0.011101 test-rmse:0.590547+0.054449
## [69] train-rmse:0.546988+0.011093 test-rmse:0.589753+0.054153
## [70] train-rmse:0.546065+0.011069 test-rmse:0.588637+0.054366
## [71] train-rmse:0.545161+0.011056 test-rmse:0.588599+0.054174
## [72] train-rmse:0.544266+0.011052 test-rmse:0.588069+0.054365
## [73] train-rmse:0.543380+0.011053 test-rmse:0.587051+0.054560
## [74] train-rmse:0.542510+0.011053 test-rmse:0.586561+0.054391
## [75] train-rmse:0.541655+0.011051 test-rmse:0.585903+0.055396
## [76] train-rmse:0.540815+0.011042 test-rmse:0.585029+0.054890
## [77] train-rmse:0.539984+0.011031 test-rmse:0.584382+0.055230
## [78] train-rmse:0.539166+0.011026 test-rmse:0.583868+0.055078
## [79] train-rmse:0.538358+0.011017 test-rmse:0.583603+0.054941
## [80] train-rmse:0.537557+0.011003 test-rmse:0.583114+0.055344
## [81] train-rmse:0.536766+0.010983 test-rmse:0.582646+0.054772
## [82] train-rmse:0.535982+0.010971 test-rmse:0.581567+0.055023
## [83] train-rmse:0.535198+0.010958 test-rmse:0.580920+0.054700
## [84] train-rmse:0.534430+0.010944 test-rmse:0.580944+0.054937
## [85] train-rmse:0.533678+0.010936 test-rmse:0.580306+0.054611
## [86] train-rmse:0.532937+0.010939 test-rmse:0.580036+0.054581
## [87] train-rmse:0.532219+0.010929 test-rmse:0.579947+0.055445
## [88] train-rmse:0.531511+0.010917 test-rmse:0.579080+0.055254
## [89] train-rmse:0.530803+0.010902 test-rmse:0.578070+0.055408
## [90] train-rmse:0.530107+0.010892 test-rmse:0.577591+0.055464
## [91] train-rmse:0.529415+0.010884 test-rmse:0.577194+0.054914
## [92] train-rmse:0.528731+0.010870 test-rmse:0.576545+0.054977
## [93] train-rmse:0.528063+0.010858 test-rmse:0.576413+0.054926
## [94] train-rmse:0.527400+0.010849 test-rmse:0.575851+0.055158
## [95] train-rmse:0.526736+0.010836 test-rmse:0.575755+0.055269
## [96] train-rmse:0.526084+0.010822 test-rmse:0.575236+0.054820
## [97] train-rmse:0.525442+0.010808 test-rmse:0.575051+0.054667
## [98] train-rmse:0.524809+0.010791 test-rmse:0.574633+0.054444
## [99] train-rmse:0.524186+0.010775 test-rmse:0.573677+0.054599
## [100] train-rmse:0.523567+0.010755 test-rmse:0.572881+0.054254
## [101] train-rmse:0.522954+0.010735 test-rmse:0.572448+0.054232
## [102] train-rmse:0.522352+0.010727 test-rmse:0.571796+0.054133
## [103] train-rmse:0.521752+0.010720 test-rmse:0.571302+0.054780
## [104] train-rmse:0.521165+0.010707 test-rmse:0.570890+0.054259
## [105] train-rmse:0.520573+0.010697 test-rmse:0.570781+0.054727
## [106] train-rmse:0.519990+0.010689 test-rmse:0.570497+0.055009
## [107] train-rmse:0.519414+0.010676 test-rmse:0.570028+0.055361
## [108] train-rmse:0.518848+0.010671 test-rmse:0.569559+0.055479
## [109] train-rmse:0.518291+0.010663 test-rmse:0.569135+0.055477
## [110] train-rmse:0.517736+0.010653 test-rmse:0.568770+0.055154
## [111] train-rmse:0.517183+0.010648 test-rmse:0.568559+0.054829
## [112] train-rmse:0.516634+0.010639 test-rmse:0.567978+0.054506
## [113] train-rmse:0.516096+0.010624 test-rmse:0.567321+0.054648
## [114] train-rmse:0.515561+0.010612 test-rmse:0.566919+0.054606
## [115] train-rmse:0.515035+0.010600 test-rmse:0.566591+0.054557
## [116] train-rmse:0.514508+0.010585 test-rmse:0.566091+0.054884
## [117] train-rmse:0.513986+0.010574 test-rmse:0.566135+0.054643
## [118] train-rmse:0.513473+0.010560 test-rmse:0.565659+0.054303
## [119] train-rmse:0.512965+0.010545 test-rmse:0.565480+0.054594
## [120] train-rmse:0.512461+0.010533 test-rmse:0.564863+0.053923
## [121] train-rmse:0.511963+0.010523 test-rmse:0.564095+0.053825
## [122] train-rmse:0.511478+0.010517 test-rmse:0.563999+0.054205
## [123] train-rmse:0.510995+0.010509 test-rmse:0.563268+0.054238
## [124] train-rmse:0.510515+0.010500 test-rmse:0.562904+0.054673
## [125] train-rmse:0.510037+0.010492 test-rmse:0.562249+0.054792
## [126] train-rmse:0.509569+0.010489 test-rmse:0.561755+0.054665
## [127] train-rmse:0.509102+0.010485 test-rmse:0.561600+0.054835
## [128] train-rmse:0.508640+0.010479 test-rmse:0.561287+0.054747
## [129] train-rmse:0.508179+0.010470 test-rmse:0.561244+0.054697
## [130] train-rmse:0.507721+0.010465 test-rmse:0.560996+0.054683
## [131] train-rmse:0.507268+0.010461 test-rmse:0.560416+0.054473
## [132] train-rmse:0.506823+0.010453 test-rmse:0.560330+0.054060
## [133] train-rmse:0.506381+0.010446 test-rmse:0.560202+0.054188
## [134] train-rmse:0.505949+0.010436 test-rmse:0.560129+0.054510
## [135] train-rmse:0.505522+0.010429 test-rmse:0.559606+0.054339
## [136] train-rmse:0.505098+0.010420 test-rmse:0.559191+0.054035
## [137] train-rmse:0.504680+0.010416 test-rmse:0.558460+0.054017
## [138] train-rmse:0.504264+0.010412 test-rmse:0.557692+0.053789
## [139] train-rmse:0.503852+0.010410 test-rmse:0.557770+0.053756
## [140] train-rmse:0.503443+0.010407 test-rmse:0.557337+0.053593
## [141] train-rmse:0.503033+0.010398 test-rmse:0.557417+0.053865
## [142] train-rmse:0.502631+0.010397 test-rmse:0.557064+0.053627
## [143] train-rmse:0.502228+0.010397 test-rmse:0.556891+0.053802
## [144] train-rmse:0.501827+0.010396 test-rmse:0.556631+0.053770
## [145] train-rmse:0.501435+0.010394 test-rmse:0.556782+0.053536
## [146] train-rmse:0.501045+0.010389 test-rmse:0.556387+0.053654
## [147] train-rmse:0.500655+0.010386 test-rmse:0.556013+0.053822
## [148] train-rmse:0.500272+0.010380 test-rmse:0.555672+0.054163
## [149] train-rmse:0.499895+0.010377 test-rmse:0.555301+0.054073
## [150] train-rmse:0.499521+0.010370 test-rmse:0.554688+0.053772
## [151] train-rmse:0.499153+0.010366 test-rmse:0.554927+0.053601
## [152] train-rmse:0.498784+0.010361 test-rmse:0.554830+0.053502
## [153] train-rmse:0.498418+0.010355 test-rmse:0.554445+0.053840
## [154] train-rmse:0.498056+0.010349 test-rmse:0.554253+0.054062
## [155] train-rmse:0.497698+0.010345 test-rmse:0.553727+0.053937
## [156] train-rmse:0.497345+0.010345 test-rmse:0.553326+0.053909
## [157] train-rmse:0.496992+0.010345 test-rmse:0.552848+0.053983
## [158] train-rmse:0.496643+0.010345 test-rmse:0.552686+0.053780
## [159] train-rmse:0.496297+0.010347 test-rmse:0.552360+0.053518
## [160] train-rmse:0.495954+0.010347 test-rmse:0.551945+0.053439
## [161] train-rmse:0.495615+0.010349 test-rmse:0.551802+0.053099
## [162] train-rmse:0.495282+0.010345 test-rmse:0.551361+0.052556
## [163] train-rmse:0.494950+0.010342 test-rmse:0.551361+0.052576
## [164] train-rmse:0.494620+0.010339 test-rmse:0.551468+0.053131
## [165] train-rmse:0.494295+0.010335 test-rmse:0.551657+0.053090
## [166] train-rmse:0.493968+0.010333 test-rmse:0.551194+0.053221
## [167] train-rmse:0.493648+0.010326 test-rmse:0.551228+0.053405
## [168] train-rmse:0.493329+0.010315 test-rmse:0.550818+0.053510
## [169] train-rmse:0.493012+0.010308 test-rmse:0.550411+0.053591
## [170] train-rmse:0.492698+0.010302 test-rmse:0.550037+0.053778
## [171] train-rmse:0.492387+0.010295 test-rmse:0.550005+0.053903
## [172] train-rmse:0.492078+0.010291 test-rmse:0.549954+0.053865
## [173] train-rmse:0.491772+0.010288 test-rmse:0.549840+0.053580
## [174] train-rmse:0.491467+0.010286 test-rmse:0.549558+0.053451
## [175] train-rmse:0.491164+0.010286 test-rmse:0.549210+0.053341
## [176] train-rmse:0.490864+0.010285 test-rmse:0.548706+0.053266
## [177] train-rmse:0.490565+0.010284 test-rmse:0.548742+0.053081
## [178] train-rmse:0.490269+0.010282 test-rmse:0.548137+0.053115
## [179] train-rmse:0.489977+0.010280 test-rmse:0.547873+0.053100
## [180] train-rmse:0.489681+0.010284 test-rmse:0.547821+0.052981
## [181] train-rmse:0.489394+0.010280 test-rmse:0.547899+0.052855
## [182] train-rmse:0.489107+0.010276 test-rmse:0.547726+0.052582
## [183] train-rmse:0.488824+0.010272 test-rmse:0.547817+0.052596
## [184] train-rmse:0.488540+0.010269 test-rmse:0.547540+0.052464
## [185] train-rmse:0.488262+0.010264 test-rmse:0.547378+0.052488
## [186] train-rmse:0.487985+0.010257 test-rmse:0.547488+0.052612
## [187] train-rmse:0.487712+0.010253 test-rmse:0.547362+0.052600
## [188] train-rmse:0.487440+0.010248 test-rmse:0.547314+0.052567
## [189] train-rmse:0.487170+0.010245 test-rmse:0.547203+0.052557
## [190] train-rmse:0.486901+0.010241 test-rmse:0.546983+0.052722
## [191] train-rmse:0.486638+0.010239 test-rmse:0.547049+0.052899
## [192] train-rmse:0.486375+0.010236 test-rmse:0.546799+0.052788
## [193] train-rmse:0.486115+0.010233 test-rmse:0.546384+0.053035
## [194] train-rmse:0.485857+0.010228 test-rmse:0.546441+0.052812
## [195] train-rmse:0.485601+0.010225 test-rmse:0.546014+0.052900
## [196] train-rmse:0.485348+0.010222 test-rmse:0.546129+0.052801
## [197] train-rmse:0.485095+0.010219 test-rmse:0.545904+0.052676
## [198] train-rmse:0.484847+0.010215 test-rmse:0.545775+0.052524
## [199] train-rmse:0.484597+0.010213 test-rmse:0.545235+0.052276
## [200] train-rmse:0.484352+0.010212 test-rmse:0.544795+0.052273
## [201] train-rmse:0.484107+0.010209 test-rmse:0.544759+0.052112
## [202] train-rmse:0.483864+0.010206 test-rmse:0.544716+0.052037
## [203] train-rmse:0.483624+0.010205 test-rmse:0.544689+0.052310
## [204] train-rmse:0.483383+0.010203 test-rmse:0.544786+0.052075
## [205] train-rmse:0.483145+0.010200 test-rmse:0.544969+0.051970
## [206] train-rmse:0.482909+0.010198 test-rmse:0.544497+0.051992
## [207] train-rmse:0.482675+0.010195 test-rmse:0.544410+0.051939
## [208] train-rmse:0.482442+0.010192 test-rmse:0.544405+0.052042
## [209] train-rmse:0.482211+0.010188 test-rmse:0.544206+0.051976
## [210] train-rmse:0.481982+0.010185 test-rmse:0.543754+0.052087
## [211] train-rmse:0.481754+0.010180 test-rmse:0.543968+0.052140
## [212] train-rmse:0.481527+0.010177 test-rmse:0.543709+0.051895
## [213] train-rmse:0.481303+0.010174 test-rmse:0.543365+0.052038
## [214] train-rmse:0.481080+0.010174 test-rmse:0.543289+0.051888
## [215] train-rmse:0.480858+0.010174 test-rmse:0.543520+0.052306
## [216] train-rmse:0.480639+0.010175 test-rmse:0.543522+0.052379
## [217] train-rmse:0.480421+0.010174 test-rmse:0.543251+0.052386
## [218] train-rmse:0.480206+0.010173 test-rmse:0.542944+0.052305
## [219] train-rmse:0.479993+0.010172 test-rmse:0.542710+0.052137
## [220] train-rmse:0.479782+0.010170 test-rmse:0.542546+0.051773
## [221] train-rmse:0.479571+0.010167 test-rmse:0.542525+0.051641
## [222] train-rmse:0.479363+0.010164 test-rmse:0.542617+0.051608
## [223] train-rmse:0.479156+0.010162 test-rmse:0.542616+0.051279
## [224] train-rmse:0.478952+0.010160 test-rmse:0.542343+0.051254
## [225] train-rmse:0.478747+0.010158 test-rmse:0.542086+0.051090
## [226] train-rmse:0.478543+0.010156 test-rmse:0.541927+0.051121
## [227] train-rmse:0.478340+0.010152 test-rmse:0.541907+0.051215
## [228] train-rmse:0.478140+0.010150 test-rmse:0.541695+0.051208
## [229] train-rmse:0.477940+0.010148 test-rmse:0.541639+0.051008
## [230] train-rmse:0.477742+0.010147 test-rmse:0.541237+0.051226
## [231] train-rmse:0.477542+0.010142 test-rmse:0.541100+0.051015
## [232] train-rmse:0.477348+0.010141 test-rmse:0.540875+0.051028
## [233] train-rmse:0.477154+0.010141 test-rmse:0.540994+0.051017
## [234] train-rmse:0.476963+0.010141 test-rmse:0.540845+0.051129
## [235] train-rmse:0.476772+0.010139 test-rmse:0.540650+0.051135
## [236] train-rmse:0.476584+0.010138 test-rmse:0.540698+0.051166
## [237] train-rmse:0.476398+0.010137 test-rmse:0.540855+0.051042
## [238] train-rmse:0.476211+0.010135 test-rmse:0.540724+0.050826
## [239] train-rmse:0.476025+0.010134 test-rmse:0.540604+0.050874
## [240] train-rmse:0.475840+0.010132 test-rmse:0.540744+0.050835
## [241] train-rmse:0.475657+0.010130 test-rmse:0.540550+0.050813
## [242] train-rmse:0.475475+0.010130 test-rmse:0.540562+0.051009
## [243] train-rmse:0.475293+0.010129 test-rmse:0.540409+0.050907
## [244] train-rmse:0.475113+0.010127 test-rmse:0.540356+0.050785
## [245] train-rmse:0.474936+0.010123 test-rmse:0.540420+0.050818
## [246] train-rmse:0.474760+0.010120 test-rmse:0.540291+0.050772
## [247] train-rmse:0.474585+0.010118 test-rmse:0.540322+0.050839
## [248] train-rmse:0.474410+0.010118 test-rmse:0.540061+0.050765
## [249] train-rmse:0.474235+0.010116 test-rmse:0.539871+0.050613
## [250] train-rmse:0.474062+0.010114 test-rmse:0.539411+0.050578
## [251] train-rmse:0.473891+0.010111 test-rmse:0.539641+0.050525
## [252] train-rmse:0.473722+0.010109 test-rmse:0.539898+0.050527
## [253] train-rmse:0.473553+0.010108 test-rmse:0.539843+0.050694
## [254] train-rmse:0.473385+0.010106 test-rmse:0.539525+0.050748
## [255] train-rmse:0.473219+0.010104 test-rmse:0.539315+0.050562
## [256] train-rmse:0.473053+0.010103 test-rmse:0.539166+0.050420
## [257] train-rmse:0.472889+0.010102 test-rmse:0.538815+0.050361
## [258] train-rmse:0.472722+0.010100 test-rmse:0.538750+0.050348
## [259] train-rmse:0.472560+0.010097 test-rmse:0.538622+0.050250
## [260] train-rmse:0.472400+0.010094 test-rmse:0.538788+0.050464
## [261] train-rmse:0.472241+0.010091 test-rmse:0.538589+0.050454
## [262] train-rmse:0.472081+0.010088 test-rmse:0.538687+0.050503
## [263] train-rmse:0.471923+0.010085 test-rmse:0.538509+0.050628
## [264] train-rmse:0.471766+0.010082 test-rmse:0.538354+0.050653
## [265] train-rmse:0.471610+0.010079 test-rmse:0.538258+0.050376
## [266] train-rmse:0.471456+0.010075 test-rmse:0.538337+0.050481
## [267] train-rmse:0.471302+0.010072 test-rmse:0.538284+0.050230
## [268] train-rmse:0.471150+0.010070 test-rmse:0.538006+0.050064
## [269] train-rmse:0.470999+0.010068 test-rmse:0.537844+0.050079
## [270] train-rmse:0.470849+0.010065 test-rmse:0.537727+0.050025
## [271] train-rmse:0.470700+0.010062 test-rmse:0.537580+0.050092
## [272] train-rmse:0.470551+0.010060 test-rmse:0.537409+0.050095
## [273] train-rmse:0.470402+0.010058 test-rmse:0.537472+0.050000
## [274] train-rmse:0.470254+0.010055 test-rmse:0.537666+0.049934
## [275] train-rmse:0.470109+0.010052 test-rmse:0.537667+0.049988
## [276] train-rmse:0.469964+0.010050 test-rmse:0.537649+0.049976
## [277] train-rmse:0.469821+0.010047 test-rmse:0.537494+0.049800
## [278] train-rmse:0.469677+0.010044 test-rmse:0.537732+0.049686
## Stopping. Best iteration:
## [272] train-rmse:0.470551+0.010060 test-rmse:0.537409+0.050095
##
## 1
## [1] train-rmse:0.942777+0.015656 test-rmse:0.943340+0.061874
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.888937+0.015034 test-rmse:0.892500+0.060611
## [3] train-rmse:0.842375+0.014689 test-rmse:0.849457+0.061717
## [4] train-rmse:0.801994+0.014317 test-rmse:0.811827+0.061382
## [5] train-rmse:0.767111+0.014009 test-rmse:0.780992+0.062166
## [6] train-rmse:0.736613+0.013608 test-rmse:0.754973+0.061565
## [7] train-rmse:0.710398+0.013037 test-rmse:0.730969+0.064138
## [8] train-rmse:0.687724+0.012541 test-rmse:0.709552+0.065229
## [9] train-rmse:0.667694+0.012331 test-rmse:0.690887+0.062939
## [10] train-rmse:0.650296+0.011912 test-rmse:0.675465+0.063506
## [11] train-rmse:0.634879+0.011542 test-rmse:0.660992+0.063550
## [12] train-rmse:0.620922+0.011565 test-rmse:0.651554+0.064446
## [13] train-rmse:0.609495+0.011792 test-rmse:0.641382+0.063005
## [14] train-rmse:0.598596+0.012302 test-rmse:0.634378+0.063144
## [15] train-rmse:0.589416+0.012318 test-rmse:0.627082+0.063420
## [16] train-rmse:0.581551+0.012431 test-rmse:0.620859+0.062458
## [17] train-rmse:0.573012+0.013365 test-rmse:0.613941+0.061157
## [18] train-rmse:0.566903+0.013717 test-rmse:0.608293+0.060269
## [19] train-rmse:0.560624+0.014222 test-rmse:0.605751+0.060321
## [20] train-rmse:0.554933+0.013713 test-rmse:0.601498+0.060745
## [21] train-rmse:0.550454+0.013675 test-rmse:0.598730+0.060196
## [22] train-rmse:0.545681+0.013736 test-rmse:0.593859+0.058992
## [23] train-rmse:0.542073+0.013992 test-rmse:0.590860+0.057888
## [24] train-rmse:0.538072+0.014458 test-rmse:0.588838+0.058417
## [25] train-rmse:0.534673+0.014615 test-rmse:0.586851+0.058201
## [26] train-rmse:0.531343+0.014358 test-rmse:0.584416+0.057882
## [27] train-rmse:0.528288+0.014583 test-rmse:0.581414+0.058437
## [28] train-rmse:0.525510+0.014582 test-rmse:0.580112+0.057766
## [29] train-rmse:0.522741+0.014616 test-rmse:0.579029+0.057590
## [30] train-rmse:0.520121+0.014464 test-rmse:0.577807+0.058001
## [31] train-rmse:0.517215+0.014798 test-rmse:0.575322+0.057118
## [32] train-rmse:0.514960+0.014907 test-rmse:0.574676+0.056137
## [33] train-rmse:0.512340+0.014895 test-rmse:0.572412+0.055947
## [34] train-rmse:0.510202+0.014788 test-rmse:0.570715+0.056073
## [35] train-rmse:0.507847+0.014676 test-rmse:0.569006+0.056692
## [36] train-rmse:0.505739+0.014719 test-rmse:0.568491+0.056510
## [37] train-rmse:0.503628+0.014729 test-rmse:0.566655+0.058371
## [38] train-rmse:0.500798+0.014708 test-rmse:0.564223+0.058606
## [39] train-rmse:0.498448+0.014761 test-rmse:0.562285+0.058478
## [40] train-rmse:0.496606+0.014589 test-rmse:0.560681+0.057626
## [41] train-rmse:0.494888+0.014643 test-rmse:0.559942+0.058141
## [42] train-rmse:0.493001+0.014836 test-rmse:0.559146+0.057982
## [43] train-rmse:0.491239+0.014870 test-rmse:0.557637+0.058732
## [44] train-rmse:0.489500+0.014879 test-rmse:0.556513+0.058451
## [45] train-rmse:0.487014+0.014789 test-rmse:0.555065+0.058386
## [46] train-rmse:0.485258+0.014852 test-rmse:0.554731+0.058471
## [47] train-rmse:0.483528+0.014652 test-rmse:0.553625+0.059277
## [48] train-rmse:0.481919+0.014612 test-rmse:0.553206+0.059799
## [49] train-rmse:0.480003+0.014268 test-rmse:0.550334+0.060115
## [50] train-rmse:0.478409+0.014317 test-rmse:0.548999+0.060132
## [51] train-rmse:0.476443+0.014145 test-rmse:0.548081+0.059529
## [52] train-rmse:0.475008+0.014383 test-rmse:0.546469+0.059280
## [53] train-rmse:0.473221+0.014790 test-rmse:0.546063+0.059531
## [54] train-rmse:0.471831+0.014681 test-rmse:0.545440+0.059674
## [55] train-rmse:0.470259+0.014873 test-rmse:0.544392+0.058517
## [56] train-rmse:0.468802+0.014727 test-rmse:0.543469+0.058740
## [57] train-rmse:0.467257+0.014518 test-rmse:0.542590+0.058946
## [58] train-rmse:0.465936+0.014473 test-rmse:0.542519+0.059330
## [59] train-rmse:0.464559+0.014618 test-rmse:0.541121+0.059061
## [60] train-rmse:0.463290+0.014637 test-rmse:0.540498+0.058714
## [61] train-rmse:0.461851+0.014580 test-rmse:0.539124+0.059207
## [62] train-rmse:0.460735+0.014624 test-rmse:0.537668+0.059211
## [63] train-rmse:0.459458+0.014616 test-rmse:0.536645+0.058684
## [64] train-rmse:0.458268+0.014504 test-rmse:0.536280+0.058667
## [65] train-rmse:0.456999+0.014562 test-rmse:0.536062+0.058097
## [66] train-rmse:0.455643+0.014648 test-rmse:0.535105+0.058594
## [67] train-rmse:0.454418+0.014698 test-rmse:0.534825+0.058894
## [68] train-rmse:0.453398+0.014768 test-rmse:0.534833+0.059202
## [69] train-rmse:0.452244+0.014697 test-rmse:0.534261+0.059193
## [70] train-rmse:0.450936+0.014434 test-rmse:0.533184+0.059915
## [71] train-rmse:0.449631+0.014268 test-rmse:0.531975+0.060906
## [72] train-rmse:0.448239+0.014388 test-rmse:0.531332+0.061826
## [73] train-rmse:0.447243+0.014332 test-rmse:0.530481+0.061238
## [74] train-rmse:0.446367+0.014351 test-rmse:0.530492+0.060799
## [75] train-rmse:0.445463+0.014369 test-rmse:0.530120+0.060237
## [76] train-rmse:0.444354+0.014611 test-rmse:0.529744+0.059737
## [77] train-rmse:0.443254+0.014382 test-rmse:0.528990+0.060334
## [78] train-rmse:0.442264+0.014450 test-rmse:0.528686+0.060062
## [79] train-rmse:0.441312+0.014369 test-rmse:0.528301+0.059861
## [80] train-rmse:0.440141+0.014530 test-rmse:0.527500+0.060236
## [81] train-rmse:0.438981+0.014073 test-rmse:0.526581+0.060545
## [82] train-rmse:0.437856+0.013664 test-rmse:0.525864+0.061837
## [83] train-rmse:0.437036+0.013673 test-rmse:0.526025+0.061927
## [84] train-rmse:0.436030+0.013949 test-rmse:0.525904+0.061503
## [85] train-rmse:0.435111+0.013977 test-rmse:0.525594+0.060790
## [86] train-rmse:0.434009+0.014037 test-rmse:0.524790+0.061436
## [87] train-rmse:0.433070+0.014220 test-rmse:0.524491+0.061340
## [88] train-rmse:0.432138+0.014269 test-rmse:0.523949+0.061781
## [89] train-rmse:0.431197+0.014325 test-rmse:0.523636+0.061728
## [90] train-rmse:0.430041+0.013993 test-rmse:0.522861+0.061558
## [91] train-rmse:0.429350+0.013935 test-rmse:0.522619+0.061468
## [92] train-rmse:0.428481+0.013984 test-rmse:0.522568+0.061507
## [93] train-rmse:0.427770+0.014011 test-rmse:0.522931+0.061718
## [94] train-rmse:0.426702+0.014281 test-rmse:0.522431+0.061038
## [95] train-rmse:0.425895+0.014187 test-rmse:0.522123+0.062029
## [96] train-rmse:0.424813+0.014365 test-rmse:0.521514+0.061980
## [97] train-rmse:0.424050+0.014376 test-rmse:0.521089+0.061391
## [98] train-rmse:0.423025+0.014440 test-rmse:0.520655+0.061846
## [99] train-rmse:0.422307+0.014579 test-rmse:0.520055+0.061819
## [100] train-rmse:0.421572+0.014738 test-rmse:0.519811+0.061680
## [101] train-rmse:0.420714+0.014812 test-rmse:0.519447+0.061316
## [102] train-rmse:0.419976+0.014983 test-rmse:0.518961+0.060964
## [103] train-rmse:0.419187+0.014996 test-rmse:0.519007+0.060871
## [104] train-rmse:0.418427+0.014964 test-rmse:0.518706+0.060475
## [105] train-rmse:0.417612+0.014784 test-rmse:0.518292+0.060760
## [106] train-rmse:0.416820+0.014782 test-rmse:0.517858+0.061545
## [107] train-rmse:0.416185+0.014984 test-rmse:0.517425+0.061579
## [108] train-rmse:0.415398+0.015090 test-rmse:0.516973+0.061275
## [109] train-rmse:0.414683+0.014983 test-rmse:0.516946+0.061009
## [110] train-rmse:0.413906+0.014989 test-rmse:0.516317+0.060999
## [111] train-rmse:0.413418+0.015020 test-rmse:0.516580+0.061285
## [112] train-rmse:0.412519+0.015097 test-rmse:0.516366+0.061537
## [113] train-rmse:0.411928+0.015115 test-rmse:0.516506+0.061622
## [114] train-rmse:0.411209+0.015212 test-rmse:0.516238+0.061370
## [115] train-rmse:0.410471+0.015291 test-rmse:0.516279+0.061564
## [116] train-rmse:0.409666+0.015331 test-rmse:0.516060+0.061368
## [117] train-rmse:0.408934+0.015272 test-rmse:0.515352+0.061854
## [118] train-rmse:0.408333+0.015334 test-rmse:0.515418+0.062053
## [119] train-rmse:0.407597+0.015436 test-rmse:0.514746+0.062276
## [120] train-rmse:0.406924+0.015355 test-rmse:0.514290+0.062672
## [121] train-rmse:0.406361+0.015435 test-rmse:0.514102+0.062293
## [122] train-rmse:0.405541+0.015368 test-rmse:0.513799+0.062564
## [123] train-rmse:0.404930+0.015318 test-rmse:0.514185+0.062848
## [124] train-rmse:0.404469+0.015260 test-rmse:0.513925+0.062944
## [125] train-rmse:0.403767+0.015412 test-rmse:0.513800+0.062783
## [126] train-rmse:0.403287+0.015371 test-rmse:0.513872+0.062621
## [127] train-rmse:0.402728+0.015360 test-rmse:0.513764+0.062520
## [128] train-rmse:0.402329+0.015354 test-rmse:0.513439+0.062256
## [129] train-rmse:0.401692+0.015333 test-rmse:0.513411+0.062494
## [130] train-rmse:0.401065+0.015316 test-rmse:0.513120+0.062797
## [131] train-rmse:0.400371+0.015468 test-rmse:0.513113+0.062591
## [132] train-rmse:0.399785+0.015549 test-rmse:0.512789+0.062466
## [133] train-rmse:0.399041+0.015280 test-rmse:0.512618+0.062310
## [134] train-rmse:0.398290+0.015399 test-rmse:0.512548+0.061995
## [135] train-rmse:0.397661+0.015270 test-rmse:0.511944+0.061963
## [136] train-rmse:0.397134+0.015403 test-rmse:0.511933+0.061867
## [137] train-rmse:0.396513+0.015364 test-rmse:0.511534+0.061764
## [138] train-rmse:0.395939+0.015328 test-rmse:0.511389+0.061601
## [139] train-rmse:0.395262+0.015492 test-rmse:0.511130+0.061341
## [140] train-rmse:0.394769+0.015382 test-rmse:0.511337+0.061422
## [141] train-rmse:0.394158+0.015150 test-rmse:0.511123+0.061270
## [142] train-rmse:0.393686+0.015317 test-rmse:0.510924+0.061407
## [143] train-rmse:0.393153+0.015142 test-rmse:0.510569+0.061307
## [144] train-rmse:0.392638+0.015153 test-rmse:0.510257+0.061503
## [145] train-rmse:0.392155+0.015075 test-rmse:0.510164+0.061218
## [146] train-rmse:0.391513+0.015021 test-rmse:0.509942+0.061451
## [147] train-rmse:0.391051+0.015085 test-rmse:0.509831+0.061586
## [148] train-rmse:0.390380+0.015001 test-rmse:0.509517+0.061568
## [149] train-rmse:0.389917+0.014909 test-rmse:0.509109+0.061368
## [150] train-rmse:0.389426+0.014837 test-rmse:0.509599+0.061498
## [151] train-rmse:0.388850+0.014630 test-rmse:0.509332+0.061461
## [152] train-rmse:0.388341+0.014638 test-rmse:0.509071+0.061247
## [153] train-rmse:0.387832+0.014681 test-rmse:0.508665+0.061344
## [154] train-rmse:0.387409+0.014616 test-rmse:0.508713+0.061493
## [155] train-rmse:0.386979+0.014519 test-rmse:0.508616+0.061646
## [156] train-rmse:0.386524+0.014405 test-rmse:0.508684+0.061776
## [157] train-rmse:0.386053+0.014347 test-rmse:0.508565+0.061686
## [158] train-rmse:0.385318+0.014605 test-rmse:0.508098+0.061172
## [159] train-rmse:0.384730+0.014760 test-rmse:0.508012+0.060959
## [160] train-rmse:0.383992+0.014733 test-rmse:0.507700+0.060917
## [161] train-rmse:0.383557+0.014677 test-rmse:0.507501+0.061065
## [162] train-rmse:0.383113+0.014583 test-rmse:0.507608+0.061131
## [163] train-rmse:0.382741+0.014521 test-rmse:0.507566+0.060882
## [164] train-rmse:0.382425+0.014515 test-rmse:0.507302+0.060809
## [165] train-rmse:0.382070+0.014510 test-rmse:0.507303+0.061473
## [166] train-rmse:0.381586+0.014496 test-rmse:0.506969+0.061427
## [167] train-rmse:0.380925+0.014541 test-rmse:0.506946+0.061214
## [168] train-rmse:0.380379+0.014632 test-rmse:0.506485+0.061127
## [169] train-rmse:0.379978+0.014624 test-rmse:0.506446+0.061281
## [170] train-rmse:0.379413+0.014621 test-rmse:0.506579+0.061618
## [171] train-rmse:0.379095+0.014589 test-rmse:0.506495+0.061590
## [172] train-rmse:0.378562+0.014503 test-rmse:0.506518+0.061662
## [173] train-rmse:0.378005+0.014629 test-rmse:0.506620+0.061217
## [174] train-rmse:0.377592+0.014584 test-rmse:0.506524+0.061708
## [175] train-rmse:0.377091+0.014702 test-rmse:0.506317+0.061231
## [176] train-rmse:0.376736+0.014706 test-rmse:0.506587+0.061190
## [177] train-rmse:0.376238+0.014751 test-rmse:0.506251+0.061079
## [178] train-rmse:0.375762+0.014900 test-rmse:0.506158+0.060919
## [179] train-rmse:0.375315+0.014790 test-rmse:0.505760+0.060930
## [180] train-rmse:0.374812+0.014749 test-rmse:0.505719+0.060900
## [181] train-rmse:0.374263+0.014859 test-rmse:0.505426+0.060898
## [182] train-rmse:0.373729+0.014680 test-rmse:0.505050+0.060788
## [183] train-rmse:0.373242+0.014524 test-rmse:0.505132+0.060694
## [184] train-rmse:0.372736+0.014428 test-rmse:0.504977+0.060724
## [185] train-rmse:0.372154+0.014492 test-rmse:0.504834+0.061047
## [186] train-rmse:0.371582+0.014452 test-rmse:0.504615+0.061096
## [187] train-rmse:0.371123+0.014252 test-rmse:0.504381+0.061088
## [188] train-rmse:0.370724+0.014081 test-rmse:0.504272+0.060824
## [189] train-rmse:0.370347+0.014118 test-rmse:0.503692+0.060942
## [190] train-rmse:0.369970+0.014181 test-rmse:0.503844+0.061128
## [191] train-rmse:0.369613+0.014151 test-rmse:0.503668+0.061200
## [192] train-rmse:0.369268+0.014040 test-rmse:0.503696+0.061116
## [193] train-rmse:0.368927+0.014126 test-rmse:0.503653+0.060995
## [194] train-rmse:0.368625+0.014115 test-rmse:0.503636+0.060943
## [195] train-rmse:0.368131+0.014234 test-rmse:0.503423+0.060923
## [196] train-rmse:0.367597+0.014081 test-rmse:0.503682+0.061116
## [197] train-rmse:0.367300+0.014024 test-rmse:0.503510+0.061297
## [198] train-rmse:0.366792+0.013896 test-rmse:0.503133+0.061468
## [199] train-rmse:0.366154+0.014179 test-rmse:0.502822+0.061333
## [200] train-rmse:0.365818+0.014264 test-rmse:0.502659+0.061086
## [201] train-rmse:0.365579+0.014250 test-rmse:0.502313+0.061165
## [202] train-rmse:0.365211+0.014221 test-rmse:0.502327+0.060852
## [203] train-rmse:0.364886+0.014227 test-rmse:0.501875+0.061083
## [204] train-rmse:0.364364+0.014194 test-rmse:0.501687+0.061009
## [205] train-rmse:0.364076+0.014237 test-rmse:0.501851+0.060972
## [206] train-rmse:0.363700+0.014178 test-rmse:0.501512+0.060993
## [207] train-rmse:0.363321+0.014236 test-rmse:0.501504+0.060907
## [208] train-rmse:0.362869+0.014012 test-rmse:0.501614+0.060808
## [209] train-rmse:0.362471+0.013889 test-rmse:0.501587+0.061037
## [210] train-rmse:0.362047+0.013924 test-rmse:0.501276+0.061113
## [211] train-rmse:0.361407+0.013883 test-rmse:0.501151+0.061086
## [212] train-rmse:0.360931+0.013874 test-rmse:0.501244+0.060749
## [213] train-rmse:0.360409+0.013966 test-rmse:0.500902+0.060810
## [214] train-rmse:0.360000+0.013795 test-rmse:0.501051+0.060676
## [215] train-rmse:0.359405+0.013630 test-rmse:0.501166+0.060688
## [216] train-rmse:0.358955+0.013525 test-rmse:0.500921+0.060766
## [217] train-rmse:0.358369+0.013366 test-rmse:0.500701+0.060627
## [218] train-rmse:0.357800+0.013365 test-rmse:0.500572+0.060604
## [219] train-rmse:0.357453+0.013419 test-rmse:0.500469+0.060315
## [220] train-rmse:0.357176+0.013398 test-rmse:0.500862+0.060347
## [221] train-rmse:0.356726+0.013287 test-rmse:0.500823+0.060333
## [222] train-rmse:0.356361+0.013312 test-rmse:0.500622+0.060256
## [223] train-rmse:0.355997+0.013259 test-rmse:0.500813+0.060313
## [224] train-rmse:0.355345+0.013364 test-rmse:0.500815+0.060273
## [225] train-rmse:0.354972+0.013247 test-rmse:0.500607+0.060322
## Stopping. Best iteration:
## [219] train-rmse:0.357453+0.013419 test-rmse:0.500469+0.060315
##
## 2
## [1] train-rmse:0.939318+0.015788 test-rmse:0.941241+0.062436
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.881898+0.015193 test-rmse:0.887996+0.062277
## [3] train-rmse:0.831766+0.014944 test-rmse:0.841348+0.062224
## [4] train-rmse:0.787849+0.014567 test-rmse:0.801028+0.063325
## [5] train-rmse:0.749559+0.013968 test-rmse:0.766483+0.064071
## [6] train-rmse:0.715768+0.013623 test-rmse:0.735948+0.064151
## [7] train-rmse:0.685750+0.013214 test-rmse:0.711027+0.065893
## [8] train-rmse:0.660451+0.012924 test-rmse:0.688365+0.065751
## [9] train-rmse:0.637325+0.012661 test-rmse:0.667572+0.064223
## [10] train-rmse:0.617278+0.013116 test-rmse:0.651103+0.062967
## [11] train-rmse:0.599675+0.013056 test-rmse:0.636931+0.062729
## [12] train-rmse:0.584125+0.012509 test-rmse:0.624400+0.063180
## [13] train-rmse:0.570505+0.012161 test-rmse:0.614593+0.062446
## [14] train-rmse:0.558731+0.012361 test-rmse:0.607382+0.062143
## [15] train-rmse:0.548074+0.012477 test-rmse:0.600132+0.059703
## [16] train-rmse:0.537777+0.013118 test-rmse:0.592690+0.059010
## [17] train-rmse:0.528983+0.013018 test-rmse:0.586648+0.057456
## [18] train-rmse:0.520789+0.013619 test-rmse:0.581039+0.056024
## [19] train-rmse:0.512792+0.013930 test-rmse:0.575937+0.054192
## [20] train-rmse:0.504916+0.014387 test-rmse:0.571260+0.054148
## [21] train-rmse:0.499455+0.014721 test-rmse:0.568667+0.054538
## [22] train-rmse:0.494729+0.014571 test-rmse:0.565508+0.053173
## [23] train-rmse:0.490406+0.014552 test-rmse:0.562484+0.053560
## [24] train-rmse:0.485259+0.014844 test-rmse:0.559489+0.053154
## [25] train-rmse:0.480685+0.014284 test-rmse:0.556417+0.053529
## [26] train-rmse:0.476619+0.014458 test-rmse:0.554120+0.053853
## [27] train-rmse:0.473439+0.014431 test-rmse:0.552035+0.053067
## [28] train-rmse:0.469895+0.013951 test-rmse:0.550380+0.053247
## [29] train-rmse:0.466334+0.014260 test-rmse:0.549997+0.053908
## [30] train-rmse:0.463262+0.013831 test-rmse:0.547304+0.054628
## [31] train-rmse:0.459918+0.013694 test-rmse:0.545814+0.054678
## [32] train-rmse:0.456947+0.013798 test-rmse:0.544346+0.054499
## [33] train-rmse:0.453546+0.013460 test-rmse:0.541791+0.055051
## [34] train-rmse:0.450511+0.013500 test-rmse:0.541238+0.054889
## [35] train-rmse:0.447467+0.013568 test-rmse:0.538580+0.054527
## [36] train-rmse:0.444903+0.013678 test-rmse:0.537181+0.053772
## [37] train-rmse:0.442655+0.013775 test-rmse:0.536355+0.053686
## [38] train-rmse:0.439614+0.012571 test-rmse:0.533999+0.055530
## [39] train-rmse:0.437386+0.012728 test-rmse:0.532217+0.055480
## [40] train-rmse:0.434885+0.012605 test-rmse:0.530881+0.054917
## [41] train-rmse:0.432516+0.012215 test-rmse:0.530973+0.055376
## [42] train-rmse:0.429823+0.012683 test-rmse:0.529892+0.055470
## [43] train-rmse:0.427200+0.013269 test-rmse:0.529952+0.056267
## [44] train-rmse:0.424771+0.012981 test-rmse:0.529140+0.055623
## [45] train-rmse:0.422884+0.012730 test-rmse:0.528143+0.056175
## [46] train-rmse:0.420864+0.012659 test-rmse:0.526772+0.056428
## [47] train-rmse:0.418554+0.013369 test-rmse:0.526156+0.056048
## [48] train-rmse:0.416088+0.013730 test-rmse:0.524243+0.056327
## [49] train-rmse:0.414440+0.013821 test-rmse:0.524588+0.056539
## [50] train-rmse:0.412751+0.013789 test-rmse:0.523900+0.055785
## [51] train-rmse:0.410640+0.013842 test-rmse:0.522547+0.055649
## [52] train-rmse:0.408938+0.013930 test-rmse:0.521926+0.055834
## [53] train-rmse:0.407201+0.013928 test-rmse:0.521495+0.056285
## [54] train-rmse:0.405721+0.014105 test-rmse:0.521782+0.056353
## [55] train-rmse:0.404020+0.014325 test-rmse:0.520898+0.056566
## [56] train-rmse:0.402220+0.014752 test-rmse:0.519718+0.057122
## [57] train-rmse:0.400376+0.014249 test-rmse:0.519418+0.057064
## [58] train-rmse:0.399145+0.014270 test-rmse:0.519310+0.057383
## [59] train-rmse:0.397093+0.014448 test-rmse:0.518288+0.056786
## [60] train-rmse:0.395115+0.014295 test-rmse:0.517043+0.057214
## [61] train-rmse:0.393446+0.014404 test-rmse:0.516210+0.056955
## [62] train-rmse:0.391714+0.014495 test-rmse:0.515656+0.057193
## [63] train-rmse:0.390253+0.014850 test-rmse:0.515738+0.057280
## [64] train-rmse:0.388869+0.015089 test-rmse:0.515273+0.057103
## [65] train-rmse:0.386964+0.015127 test-rmse:0.515199+0.056844
## [66] train-rmse:0.385671+0.015154 test-rmse:0.514625+0.056219
## [67] train-rmse:0.384442+0.015034 test-rmse:0.514575+0.056401
## [68] train-rmse:0.382331+0.015251 test-rmse:0.514335+0.056444
## [69] train-rmse:0.380968+0.015428 test-rmse:0.514039+0.055969
## [70] train-rmse:0.379945+0.015409 test-rmse:0.513194+0.056023
## [71] train-rmse:0.378692+0.014990 test-rmse:0.513118+0.055882
## [72] train-rmse:0.377212+0.014989 test-rmse:0.512911+0.056391
## [73] train-rmse:0.375847+0.014864 test-rmse:0.512641+0.056175
## [74] train-rmse:0.374708+0.014826 test-rmse:0.511756+0.056502
## [75] train-rmse:0.373248+0.014951 test-rmse:0.511410+0.056543
## [76] train-rmse:0.371984+0.014598 test-rmse:0.511419+0.056792
## [77] train-rmse:0.370637+0.014534 test-rmse:0.510784+0.057254
## [78] train-rmse:0.369426+0.014480 test-rmse:0.510380+0.057253
## [79] train-rmse:0.368247+0.014473 test-rmse:0.510265+0.056931
## [80] train-rmse:0.367246+0.014374 test-rmse:0.509731+0.057025
## [81] train-rmse:0.365950+0.014307 test-rmse:0.508589+0.057281
## [82] train-rmse:0.364422+0.014503 test-rmse:0.508150+0.056983
## [83] train-rmse:0.363088+0.014685 test-rmse:0.507483+0.056458
## [84] train-rmse:0.361882+0.015094 test-rmse:0.507402+0.056510
## [85] train-rmse:0.360983+0.015133 test-rmse:0.507345+0.056514
## [86] train-rmse:0.359739+0.014856 test-rmse:0.507110+0.055885
## [87] train-rmse:0.358559+0.014592 test-rmse:0.506573+0.055731
## [88] train-rmse:0.357479+0.014700 test-rmse:0.506377+0.055625
## [89] train-rmse:0.356532+0.014656 test-rmse:0.505734+0.055301
## [90] train-rmse:0.355345+0.014489 test-rmse:0.506129+0.055501
## [91] train-rmse:0.354291+0.014349 test-rmse:0.505910+0.055602
## [92] train-rmse:0.353193+0.014152 test-rmse:0.505370+0.055689
## [93] train-rmse:0.352469+0.013925 test-rmse:0.504632+0.055513
## [94] train-rmse:0.351266+0.014222 test-rmse:0.504801+0.054928
## [95] train-rmse:0.350333+0.014287 test-rmse:0.504243+0.054807
## [96] train-rmse:0.349182+0.014413 test-rmse:0.504341+0.054741
## [97] train-rmse:0.347942+0.014399 test-rmse:0.504347+0.054749
## [98] train-rmse:0.346887+0.014428 test-rmse:0.503840+0.054575
## [99] train-rmse:0.345916+0.014279 test-rmse:0.502961+0.054455
## [100] train-rmse:0.344577+0.014404 test-rmse:0.502746+0.054754
## [101] train-rmse:0.343381+0.014367 test-rmse:0.502570+0.054449
## [102] train-rmse:0.342475+0.014028 test-rmse:0.502691+0.054484
## [103] train-rmse:0.341658+0.014026 test-rmse:0.502754+0.054490
## [104] train-rmse:0.340679+0.014106 test-rmse:0.502533+0.054466
## [105] train-rmse:0.339737+0.014038 test-rmse:0.502633+0.054240
## [106] train-rmse:0.338996+0.013830 test-rmse:0.502307+0.054548
## [107] train-rmse:0.337964+0.014024 test-rmse:0.502398+0.054167
## [108] train-rmse:0.336651+0.014661 test-rmse:0.501721+0.054033
## [109] train-rmse:0.335651+0.014356 test-rmse:0.501477+0.053993
## [110] train-rmse:0.334640+0.014104 test-rmse:0.500992+0.054162
## [111] train-rmse:0.333977+0.014060 test-rmse:0.501041+0.054354
## [112] train-rmse:0.333016+0.014269 test-rmse:0.500927+0.053954
## [113] train-rmse:0.332274+0.014160 test-rmse:0.500766+0.053921
## [114] train-rmse:0.331203+0.014172 test-rmse:0.500742+0.054039
## [115] train-rmse:0.330437+0.013999 test-rmse:0.501064+0.053579
## [116] train-rmse:0.329591+0.014132 test-rmse:0.500718+0.053507
## [117] train-rmse:0.328838+0.014115 test-rmse:0.500158+0.053335
## [118] train-rmse:0.327967+0.014109 test-rmse:0.499935+0.053484
## [119] train-rmse:0.327117+0.014226 test-rmse:0.500147+0.053385
## [120] train-rmse:0.326133+0.013807 test-rmse:0.499995+0.053239
## [121] train-rmse:0.325510+0.013728 test-rmse:0.500267+0.053102
## [122] train-rmse:0.324717+0.013700 test-rmse:0.500039+0.052724
## [123] train-rmse:0.323907+0.013442 test-rmse:0.499814+0.053023
## [124] train-rmse:0.323129+0.013433 test-rmse:0.499518+0.053060
## [125] train-rmse:0.322258+0.013485 test-rmse:0.499841+0.052833
## [126] train-rmse:0.321639+0.013570 test-rmse:0.500002+0.052827
## [127] train-rmse:0.320905+0.013898 test-rmse:0.499724+0.052711
## [128] train-rmse:0.319994+0.013735 test-rmse:0.499613+0.052695
## [129] train-rmse:0.319095+0.013846 test-rmse:0.499207+0.052507
## [130] train-rmse:0.317919+0.014042 test-rmse:0.499897+0.052250
## [131] train-rmse:0.316716+0.013835 test-rmse:0.499193+0.052661
## [132] train-rmse:0.315917+0.014035 test-rmse:0.499485+0.052570
## [133] train-rmse:0.315057+0.014312 test-rmse:0.499491+0.052613
## [134] train-rmse:0.314354+0.014401 test-rmse:0.499172+0.052332
## [135] train-rmse:0.313715+0.014407 test-rmse:0.499270+0.052619
## [136] train-rmse:0.312903+0.014616 test-rmse:0.499345+0.052315
## [137] train-rmse:0.312179+0.014417 test-rmse:0.498967+0.052519
## [138] train-rmse:0.311456+0.014230 test-rmse:0.499176+0.052656
## [139] train-rmse:0.310963+0.014234 test-rmse:0.499421+0.052720
## [140] train-rmse:0.310135+0.014576 test-rmse:0.499502+0.052508
## [141] train-rmse:0.309547+0.014518 test-rmse:0.499388+0.052537
## [142] train-rmse:0.308998+0.014470 test-rmse:0.499109+0.052211
## [143] train-rmse:0.308055+0.014570 test-rmse:0.499042+0.052256
## Stopping. Best iteration:
## [137] train-rmse:0.312179+0.014417 test-rmse:0.498967+0.052519
##
## 3
## [1] train-rmse:0.936366+0.015681 test-rmse:0.939452+0.061849
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.875983+0.014909 test-rmse:0.884276+0.061468
## [3] train-rmse:0.822956+0.014902 test-rmse:0.836286+0.061101
## [4] train-rmse:0.776023+0.014453 test-rmse:0.794012+0.062174
## [5] train-rmse:0.735251+0.014330 test-rmse:0.757502+0.062845
## [6] train-rmse:0.699066+0.014106 test-rmse:0.727364+0.063637
## [7] train-rmse:0.666978+0.013511 test-rmse:0.699669+0.063450
## [8] train-rmse:0.638071+0.013204 test-rmse:0.677362+0.064880
## [9] train-rmse:0.612587+0.013462 test-rmse:0.657617+0.065331
## [10] train-rmse:0.589363+0.013067 test-rmse:0.639791+0.064115
## [11] train-rmse:0.568952+0.012435 test-rmse:0.623917+0.062916
## [12] train-rmse:0.551051+0.013043 test-rmse:0.611108+0.060392
## [13] train-rmse:0.534921+0.012320 test-rmse:0.599946+0.059906
## [14] train-rmse:0.519816+0.012590 test-rmse:0.588166+0.059034
## [15] train-rmse:0.507458+0.012256 test-rmse:0.580151+0.058939
## [16] train-rmse:0.495910+0.012155 test-rmse:0.573062+0.058377
## [17] train-rmse:0.485157+0.011902 test-rmse:0.566306+0.059391
## [18] train-rmse:0.475677+0.012648 test-rmse:0.563097+0.057820
## [19] train-rmse:0.467093+0.012553 test-rmse:0.557989+0.056932
## [20] train-rmse:0.459190+0.012870 test-rmse:0.554545+0.055887
## [21] train-rmse:0.450796+0.013653 test-rmse:0.550242+0.055709
## [22] train-rmse:0.443826+0.013797 test-rmse:0.544849+0.053117
## [23] train-rmse:0.437916+0.013727 test-rmse:0.542070+0.053559
## [24] train-rmse:0.433307+0.013474 test-rmse:0.538645+0.053561
## [25] train-rmse:0.428998+0.013711 test-rmse:0.536137+0.053321
## [26] train-rmse:0.423858+0.012788 test-rmse:0.534156+0.052998
## [27] train-rmse:0.419027+0.013950 test-rmse:0.532831+0.053413
## [28] train-rmse:0.415083+0.013736 test-rmse:0.530388+0.052417
## [29] train-rmse:0.410789+0.013518 test-rmse:0.527665+0.054106
## [30] train-rmse:0.406992+0.013067 test-rmse:0.525419+0.055398
## [31] train-rmse:0.403225+0.013424 test-rmse:0.523265+0.054388
## [32] train-rmse:0.399851+0.013449 test-rmse:0.522346+0.054142
## [33] train-rmse:0.397195+0.013644 test-rmse:0.520928+0.055138
## [34] train-rmse:0.393948+0.013220 test-rmse:0.518218+0.055667
## [35] train-rmse:0.391024+0.012960 test-rmse:0.517505+0.055344
## [36] train-rmse:0.388464+0.012912 test-rmse:0.516833+0.054505
## [37] train-rmse:0.385728+0.013192 test-rmse:0.516151+0.054547
## [38] train-rmse:0.383341+0.013107 test-rmse:0.515809+0.054408
## [39] train-rmse:0.380572+0.012740 test-rmse:0.514125+0.054225
## [40] train-rmse:0.377877+0.012232 test-rmse:0.512977+0.053852
## [41] train-rmse:0.375424+0.011614 test-rmse:0.511687+0.054516
## [42] train-rmse:0.372822+0.011277 test-rmse:0.510148+0.055640
## [43] train-rmse:0.370751+0.011514 test-rmse:0.510056+0.055309
## [44] train-rmse:0.368876+0.011424 test-rmse:0.509210+0.054904
## [45] train-rmse:0.366475+0.011268 test-rmse:0.508077+0.054996
## [46] train-rmse:0.364513+0.011381 test-rmse:0.507716+0.054389
## [47] train-rmse:0.362668+0.010817 test-rmse:0.507270+0.054530
## [48] train-rmse:0.359918+0.011264 test-rmse:0.505914+0.053976
## [49] train-rmse:0.357618+0.011718 test-rmse:0.505258+0.054065
## [50] train-rmse:0.355856+0.012007 test-rmse:0.504909+0.054003
## [51] train-rmse:0.353572+0.012148 test-rmse:0.504399+0.054152
## [52] train-rmse:0.351272+0.012424 test-rmse:0.503685+0.054465
## [53] train-rmse:0.349027+0.011854 test-rmse:0.503091+0.054823
## [54] train-rmse:0.347346+0.011861 test-rmse:0.502669+0.054744
## [55] train-rmse:0.345604+0.012188 test-rmse:0.501675+0.054630
## [56] train-rmse:0.343669+0.011713 test-rmse:0.501332+0.054714
## [57] train-rmse:0.342193+0.011596 test-rmse:0.501090+0.054828
## [58] train-rmse:0.340446+0.011748 test-rmse:0.500664+0.054469
## [59] train-rmse:0.339051+0.011583 test-rmse:0.500355+0.054261
## [60] train-rmse:0.336676+0.012061 test-rmse:0.500348+0.054230
## [61] train-rmse:0.334635+0.012093 test-rmse:0.499431+0.054494
## [62] train-rmse:0.332750+0.011877 test-rmse:0.498875+0.054237
## [63] train-rmse:0.330535+0.011416 test-rmse:0.498654+0.054421
## [64] train-rmse:0.328932+0.011825 test-rmse:0.498079+0.054333
## [65] train-rmse:0.326997+0.012039 test-rmse:0.498052+0.054955
## [66] train-rmse:0.324961+0.012090 test-rmse:0.496982+0.055610
## [67] train-rmse:0.323129+0.011804 test-rmse:0.496508+0.055443
## [68] train-rmse:0.321099+0.011381 test-rmse:0.495942+0.055008
## [69] train-rmse:0.319326+0.011541 test-rmse:0.495936+0.054572
## [70] train-rmse:0.317091+0.011665 test-rmse:0.496143+0.054023
## [71] train-rmse:0.315274+0.011303 test-rmse:0.495697+0.054361
## [72] train-rmse:0.313932+0.011226 test-rmse:0.495842+0.054356
## [73] train-rmse:0.312406+0.011087 test-rmse:0.495903+0.053955
## [74] train-rmse:0.311015+0.011344 test-rmse:0.496199+0.053592
## [75] train-rmse:0.309557+0.011023 test-rmse:0.495607+0.053318
## [76] train-rmse:0.308299+0.010859 test-rmse:0.495252+0.053283
## [77] train-rmse:0.307264+0.010851 test-rmse:0.495135+0.053134
## [78] train-rmse:0.305743+0.010783 test-rmse:0.494846+0.053344
## [79] train-rmse:0.304288+0.010979 test-rmse:0.494837+0.053203
## [80] train-rmse:0.302988+0.010717 test-rmse:0.494668+0.052569
## [81] train-rmse:0.300830+0.010984 test-rmse:0.495034+0.052244
## [82] train-rmse:0.299359+0.011281 test-rmse:0.494992+0.052431
## [83] train-rmse:0.297693+0.011344 test-rmse:0.494811+0.051866
## [84] train-rmse:0.296554+0.011163 test-rmse:0.494998+0.051724
## [85] train-rmse:0.294956+0.011330 test-rmse:0.494709+0.051551
## [86] train-rmse:0.293930+0.011262 test-rmse:0.494992+0.051480
## Stopping. Best iteration:
## [80] train-rmse:0.302988+0.010717 test-rmse:0.494668+0.052569
##
## 4
## [1] train-rmse:0.933751+0.015431 test-rmse:0.937872+0.062279
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.870737+0.014650 test-rmse:0.880491+0.060623
## [3] train-rmse:0.815214+0.013888 test-rmse:0.831760+0.060483
## [4] train-rmse:0.765006+0.013257 test-rmse:0.788666+0.060596
## [5] train-rmse:0.721266+0.012522 test-rmse:0.751119+0.061712
## [6] train-rmse:0.682410+0.011818 test-rmse:0.719011+0.062529
## [7] train-rmse:0.648443+0.011574 test-rmse:0.691437+0.063442
## [8] train-rmse:0.616995+0.011069 test-rmse:0.666723+0.061618
## [9] train-rmse:0.589508+0.011743 test-rmse:0.646918+0.061809
## [10] train-rmse:0.564273+0.011512 test-rmse:0.627170+0.061549
## [11] train-rmse:0.542168+0.011514 test-rmse:0.611665+0.060240
## [12] train-rmse:0.522354+0.011363 test-rmse:0.597008+0.059623
## [13] train-rmse:0.504962+0.011067 test-rmse:0.586024+0.059393
## [14] train-rmse:0.488738+0.012220 test-rmse:0.576593+0.058696
## [15] train-rmse:0.475113+0.012735 test-rmse:0.569583+0.058019
## [16] train-rmse:0.461823+0.012253 test-rmse:0.562500+0.059698
## [17] train-rmse:0.449482+0.013175 test-rmse:0.555757+0.057791
## [18] train-rmse:0.438346+0.013598 test-rmse:0.549800+0.056349
## [19] train-rmse:0.427915+0.013963 test-rmse:0.546263+0.055794
## [20] train-rmse:0.419406+0.014250 test-rmse:0.542990+0.056286
## [21] train-rmse:0.411968+0.014507 test-rmse:0.541071+0.056107
## [22] train-rmse:0.404235+0.015116 test-rmse:0.538063+0.055055
## [23] train-rmse:0.397729+0.015007 test-rmse:0.534918+0.055298
## [24] train-rmse:0.392177+0.014257 test-rmse:0.531944+0.055543
## [25] train-rmse:0.386735+0.014840 test-rmse:0.529645+0.055232
## [26] train-rmse:0.380932+0.013823 test-rmse:0.525993+0.055137
## [27] train-rmse:0.376075+0.014186 test-rmse:0.523221+0.055400
## [28] train-rmse:0.371111+0.014515 test-rmse:0.521775+0.054307
## [29] train-rmse:0.366573+0.014515 test-rmse:0.520163+0.053941
## [30] train-rmse:0.360274+0.012626 test-rmse:0.516964+0.054917
## [31] train-rmse:0.357177+0.012679 test-rmse:0.516162+0.055478
## [32] train-rmse:0.353818+0.012690 test-rmse:0.514143+0.055533
## [33] train-rmse:0.350412+0.013113 test-rmse:0.512968+0.055731
## [34] train-rmse:0.347299+0.012670 test-rmse:0.511676+0.055997
## [35] train-rmse:0.343641+0.013232 test-rmse:0.511784+0.055882
## [36] train-rmse:0.340713+0.013103 test-rmse:0.510740+0.056189
## [37] train-rmse:0.336975+0.012101 test-rmse:0.509523+0.056403
## [38] train-rmse:0.334131+0.012357 test-rmse:0.508484+0.056824
## [39] train-rmse:0.331393+0.012110 test-rmse:0.507641+0.056136
## [40] train-rmse:0.328549+0.011669 test-rmse:0.507284+0.056912
## [41] train-rmse:0.325921+0.011578 test-rmse:0.506578+0.057305
## [42] train-rmse:0.323645+0.011832 test-rmse:0.505782+0.056838
## [43] train-rmse:0.321642+0.011693 test-rmse:0.505507+0.056534
## [44] train-rmse:0.319253+0.011816 test-rmse:0.504145+0.056193
## [45] train-rmse:0.316384+0.011091 test-rmse:0.503699+0.056474
## [46] train-rmse:0.314056+0.010746 test-rmse:0.503432+0.056525
## [47] train-rmse:0.311817+0.011023 test-rmse:0.502458+0.056225
## [48] train-rmse:0.309208+0.011109 test-rmse:0.502175+0.056636
## [49] train-rmse:0.306881+0.011218 test-rmse:0.501673+0.056450
## [50] train-rmse:0.304888+0.010623 test-rmse:0.501599+0.056564
## [51] train-rmse:0.302852+0.010649 test-rmse:0.501345+0.056641
## [52] train-rmse:0.300409+0.010682 test-rmse:0.501167+0.057149
## [53] train-rmse:0.298371+0.010288 test-rmse:0.500397+0.056864
## [54] train-rmse:0.296448+0.010497 test-rmse:0.500361+0.056971
## [55] train-rmse:0.294306+0.010637 test-rmse:0.500307+0.056891
## [56] train-rmse:0.292076+0.011017 test-rmse:0.499806+0.057196
## [57] train-rmse:0.289649+0.011200 test-rmse:0.498856+0.056971
## [58] train-rmse:0.287760+0.011157 test-rmse:0.498980+0.056989
## [59] train-rmse:0.286153+0.011056 test-rmse:0.498883+0.056612
## [60] train-rmse:0.283764+0.011229 test-rmse:0.498471+0.056787
## [61] train-rmse:0.281591+0.011139 test-rmse:0.498793+0.056705
## [62] train-rmse:0.279798+0.010544 test-rmse:0.498704+0.056519
## [63] train-rmse:0.278493+0.010239 test-rmse:0.498513+0.056396
## [64] train-rmse:0.277235+0.010360 test-rmse:0.498788+0.056415
## [65] train-rmse:0.275568+0.010481 test-rmse:0.497987+0.056136
## [66] train-rmse:0.273436+0.010203 test-rmse:0.497883+0.055990
## [67] train-rmse:0.272019+0.010615 test-rmse:0.497962+0.056057
## [68] train-rmse:0.269951+0.010142 test-rmse:0.498560+0.056086
## [69] train-rmse:0.268539+0.010091 test-rmse:0.498629+0.056189
## [70] train-rmse:0.266844+0.010272 test-rmse:0.499533+0.056387
## [71] train-rmse:0.265220+0.010550 test-rmse:0.499443+0.055941
## [72] train-rmse:0.263072+0.010858 test-rmse:0.499797+0.056551
## Stopping. Best iteration:
## [66] train-rmse:0.273436+0.010203 test-rmse:0.497883+0.055990
##
## 5
## [1] train-rmse:0.931377+0.015188 test-rmse:0.936786+0.062453
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.866138+0.014435 test-rmse:0.877439+0.060932
## [3] train-rmse:0.808295+0.013628 test-rmse:0.826722+0.061482
## [4] train-rmse:0.756371+0.013028 test-rmse:0.783404+0.060434
## [5] train-rmse:0.709846+0.011760 test-rmse:0.744886+0.062234
## [6] train-rmse:0.669151+0.010640 test-rmse:0.711016+0.062736
## [7] train-rmse:0.632871+0.010333 test-rmse:0.683069+0.064688
## [8] train-rmse:0.599709+0.010786 test-rmse:0.658924+0.064422
## [9] train-rmse:0.571152+0.011090 test-rmse:0.637384+0.062656
## [10] train-rmse:0.544482+0.010910 test-rmse:0.619640+0.064126
## [11] train-rmse:0.520324+0.010748 test-rmse:0.602135+0.063196
## [12] train-rmse:0.498993+0.010032 test-rmse:0.587486+0.061752
## [13] train-rmse:0.478185+0.011113 test-rmse:0.576328+0.060840
## [14] train-rmse:0.460658+0.011543 test-rmse:0.566030+0.061547
## [15] train-rmse:0.445451+0.011705 test-rmse:0.557082+0.061015
## [16] train-rmse:0.430077+0.011643 test-rmse:0.548886+0.061058
## [17] train-rmse:0.416991+0.010816 test-rmse:0.541862+0.060486
## [18] train-rmse:0.404339+0.011454 test-rmse:0.536537+0.059910
## [19] train-rmse:0.393339+0.012312 test-rmse:0.532603+0.059729
## [20] train-rmse:0.383628+0.012601 test-rmse:0.529779+0.060441
## [21] train-rmse:0.374346+0.012480 test-rmse:0.525758+0.060052
## [22] train-rmse:0.366680+0.012214 test-rmse:0.524012+0.059720
## [23] train-rmse:0.359487+0.012479 test-rmse:0.522196+0.060084
## [24] train-rmse:0.352780+0.011859 test-rmse:0.519593+0.059044
## [25] train-rmse:0.346299+0.012125 test-rmse:0.517672+0.058650
## [26] train-rmse:0.340895+0.011991 test-rmse:0.515962+0.058509
## [27] train-rmse:0.336118+0.011654 test-rmse:0.513899+0.057765
## [28] train-rmse:0.331449+0.011465 test-rmse:0.512119+0.058263
## [29] train-rmse:0.326665+0.011086 test-rmse:0.509710+0.057346
## [30] train-rmse:0.321975+0.011999 test-rmse:0.507926+0.057272
## [31] train-rmse:0.317168+0.012876 test-rmse:0.506895+0.056882
## [32] train-rmse:0.313589+0.012495 test-rmse:0.505581+0.056329
## [33] train-rmse:0.309894+0.012453 test-rmse:0.505528+0.055428
## [34] train-rmse:0.305698+0.012251 test-rmse:0.504748+0.054046
## [35] train-rmse:0.302311+0.012120 test-rmse:0.503571+0.054472
## [36] train-rmse:0.299756+0.012112 test-rmse:0.502935+0.053942
## [37] train-rmse:0.297192+0.011766 test-rmse:0.503299+0.053043
## [38] train-rmse:0.293630+0.012547 test-rmse:0.502565+0.052416
## [39] train-rmse:0.291217+0.012198 test-rmse:0.502114+0.053143
## [40] train-rmse:0.287011+0.012965 test-rmse:0.501285+0.053500
## [41] train-rmse:0.284286+0.012540 test-rmse:0.500416+0.053559
## [42] train-rmse:0.280870+0.013534 test-rmse:0.499525+0.053389
## [43] train-rmse:0.278267+0.014382 test-rmse:0.499265+0.052412
## [44] train-rmse:0.275460+0.014372 test-rmse:0.498798+0.051865
## [45] train-rmse:0.272833+0.014234 test-rmse:0.498204+0.050971
## [46] train-rmse:0.270452+0.014251 test-rmse:0.498491+0.050499
## [47] train-rmse:0.268024+0.013979 test-rmse:0.498035+0.051231
## [48] train-rmse:0.266082+0.014088 test-rmse:0.497497+0.051420
## [49] train-rmse:0.263236+0.013027 test-rmse:0.497116+0.051551
## [50] train-rmse:0.261447+0.012842 test-rmse:0.496868+0.051132
## [51] train-rmse:0.259742+0.012813 test-rmse:0.497208+0.050979
## [52] train-rmse:0.257187+0.012791 test-rmse:0.496800+0.050673
## [53] train-rmse:0.255416+0.012500 test-rmse:0.496163+0.050440
## [54] train-rmse:0.253592+0.012289 test-rmse:0.495634+0.050318
## [55] train-rmse:0.251388+0.011511 test-rmse:0.495969+0.050299
## [56] train-rmse:0.249766+0.010920 test-rmse:0.495784+0.050059
## [57] train-rmse:0.247351+0.011301 test-rmse:0.495974+0.049855
## [58] train-rmse:0.244855+0.011279 test-rmse:0.495307+0.049664
## [59] train-rmse:0.242784+0.011145 test-rmse:0.495926+0.049861
## [60] train-rmse:0.240525+0.010806 test-rmse:0.496374+0.050859
## [61] train-rmse:0.238898+0.010844 test-rmse:0.496356+0.051152
## [62] train-rmse:0.237222+0.010879 test-rmse:0.496513+0.050949
## [63] train-rmse:0.234524+0.011245 test-rmse:0.496890+0.051081
## [64] train-rmse:0.232644+0.011426 test-rmse:0.497094+0.051132
## Stopping. Best iteration:
## [58] train-rmse:0.244855+0.011279 test-rmse:0.495307+0.049664
##
## 6
## [1] train-rmse:0.929060+0.014970 test-rmse:0.935159+0.061703
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.861826+0.013902 test-rmse:0.875932+0.062111
## [3] train-rmse:0.801902+0.012559 test-rmse:0.824709+0.062944
## [4] train-rmse:0.748275+0.012009 test-rmse:0.778961+0.060760
## [5] train-rmse:0.699442+0.011264 test-rmse:0.739207+0.061100
## [6] train-rmse:0.656453+0.010784 test-rmse:0.706012+0.062182
## [7] train-rmse:0.617553+0.010674 test-rmse:0.677371+0.063698
## [8] train-rmse:0.583711+0.010209 test-rmse:0.653441+0.065398
## [9] train-rmse:0.553438+0.010641 test-rmse:0.632994+0.065476
## [10] train-rmse:0.525783+0.011272 test-rmse:0.614904+0.065181
## [11] train-rmse:0.500942+0.012434 test-rmse:0.600426+0.066588
## [12] train-rmse:0.477735+0.011868 test-rmse:0.585660+0.066499
## [13] train-rmse:0.456312+0.012619 test-rmse:0.572876+0.065226
## [14] train-rmse:0.437213+0.013139 test-rmse:0.562902+0.066767
## [15] train-rmse:0.419779+0.013871 test-rmse:0.554695+0.065761
## [16] train-rmse:0.404681+0.014177 test-rmse:0.548796+0.064861
## [17] train-rmse:0.390580+0.015052 test-rmse:0.543247+0.064381
## [18] train-rmse:0.377322+0.014911 test-rmse:0.536978+0.062248
## [19] train-rmse:0.365156+0.015557 test-rmse:0.531791+0.062908
## [20] train-rmse:0.353447+0.016038 test-rmse:0.528897+0.061356
## [21] train-rmse:0.343738+0.016362 test-rmse:0.526122+0.061388
## [22] train-rmse:0.334176+0.016674 test-rmse:0.523816+0.061606
## [23] train-rmse:0.326144+0.017143 test-rmse:0.520974+0.059852
## [24] train-rmse:0.317307+0.017591 test-rmse:0.520093+0.059411
## [25] train-rmse:0.310816+0.017880 test-rmse:0.519758+0.059055
## [26] train-rmse:0.304585+0.018851 test-rmse:0.518731+0.058274
## [27] train-rmse:0.298977+0.018751 test-rmse:0.517582+0.057416
## [28] train-rmse:0.293908+0.018862 test-rmse:0.516441+0.056571
## [29] train-rmse:0.288239+0.018596 test-rmse:0.514671+0.055169
## [30] train-rmse:0.284545+0.018724 test-rmse:0.513901+0.054379
## [31] train-rmse:0.279990+0.018690 test-rmse:0.512273+0.052958
## [32] train-rmse:0.275551+0.018526 test-rmse:0.511625+0.051973
## [33] train-rmse:0.271202+0.018265 test-rmse:0.510971+0.051347
## [34] train-rmse:0.265459+0.018199 test-rmse:0.510002+0.051292
## [35] train-rmse:0.261223+0.019259 test-rmse:0.509137+0.051137
## [36] train-rmse:0.258003+0.018933 test-rmse:0.507716+0.050579
## [37] train-rmse:0.255200+0.018210 test-rmse:0.507720+0.049952
## [38] train-rmse:0.252136+0.018757 test-rmse:0.507514+0.049469
## [39] train-rmse:0.249162+0.018386 test-rmse:0.507005+0.049888
## [40] train-rmse:0.246095+0.018783 test-rmse:0.506491+0.050070
## [41] train-rmse:0.243784+0.018932 test-rmse:0.506659+0.049855
## [42] train-rmse:0.240432+0.018846 test-rmse:0.507302+0.050360
## [43] train-rmse:0.237581+0.018790 test-rmse:0.506574+0.050670
## [44] train-rmse:0.234251+0.017756 test-rmse:0.506384+0.050186
## [45] train-rmse:0.232023+0.017313 test-rmse:0.505881+0.050448
## [46] train-rmse:0.229556+0.017514 test-rmse:0.506430+0.051208
## [47] train-rmse:0.227554+0.017654 test-rmse:0.506674+0.051258
## [48] train-rmse:0.225391+0.017465 test-rmse:0.506917+0.050818
## [49] train-rmse:0.223618+0.017140 test-rmse:0.506564+0.050916
## [50] train-rmse:0.220866+0.016605 test-rmse:0.506099+0.050814
## [51] train-rmse:0.217554+0.016482 test-rmse:0.507074+0.050744
## Stopping. Best iteration:
## [45] train-rmse:0.232023+0.017313 test-rmse:0.505881+0.050448
##
## 7
## [1] train-rmse:0.940023+0.015582 test-rmse:0.940346+0.061765
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.886440+0.014946 test-rmse:0.889287+0.061102
## [3] train-rmse:0.842376+0.014462 test-rmse:0.847455+0.060569
## [4] train-rmse:0.806133+0.014169 test-rmse:0.816460+0.061665
## [5] train-rmse:0.775931+0.013782 test-rmse:0.788190+0.059536
## [6] train-rmse:0.750755+0.013475 test-rmse:0.767247+0.060158
## [7] train-rmse:0.729628+0.012969 test-rmse:0.749284+0.059455
## [8] train-rmse:0.711624+0.012653 test-rmse:0.730728+0.059365
## [9] train-rmse:0.696120+0.012462 test-rmse:0.715891+0.058622
## [10] train-rmse:0.682989+0.012160 test-rmse:0.705552+0.056988
## [11] train-rmse:0.671548+0.012060 test-rmse:0.692833+0.055827
## [12] train-rmse:0.661670+0.011759 test-rmse:0.683458+0.054921
## [13] train-rmse:0.653138+0.011579 test-rmse:0.676868+0.055366
## [14] train-rmse:0.645567+0.011525 test-rmse:0.668302+0.054628
## [15] train-rmse:0.639039+0.011360 test-rmse:0.663984+0.054822
## [16] train-rmse:0.633213+0.011270 test-rmse:0.659411+0.053496
## [17] train-rmse:0.628112+0.011229 test-rmse:0.654489+0.052396
## [18] train-rmse:0.623593+0.011132 test-rmse:0.651348+0.052590
## [19] train-rmse:0.619494+0.011063 test-rmse:0.647020+0.052774
## [20] train-rmse:0.615646+0.011099 test-rmse:0.645344+0.052378
## [21] train-rmse:0.612098+0.011084 test-rmse:0.642376+0.053564
## [22] train-rmse:0.608816+0.011067 test-rmse:0.640144+0.053355
## [23] train-rmse:0.605749+0.011070 test-rmse:0.636830+0.053504
## [24] train-rmse:0.602855+0.011055 test-rmse:0.635027+0.054180
## [25] train-rmse:0.600065+0.011091 test-rmse:0.632039+0.053991
## [26] train-rmse:0.597396+0.011058 test-rmse:0.628991+0.053756
## [27] train-rmse:0.594883+0.011067 test-rmse:0.627156+0.054588
## [28] train-rmse:0.592498+0.011048 test-rmse:0.625977+0.054244
## [29] train-rmse:0.590215+0.011048 test-rmse:0.624487+0.054433
## [30] train-rmse:0.588023+0.011036 test-rmse:0.622664+0.054879
## [31] train-rmse:0.585889+0.011020 test-rmse:0.619769+0.054978
## [32] train-rmse:0.583861+0.011055 test-rmse:0.618632+0.054690
## [33] train-rmse:0.581879+0.011077 test-rmse:0.618051+0.054668
## [34] train-rmse:0.579972+0.011094 test-rmse:0.616703+0.054000
## [35] train-rmse:0.578155+0.011060 test-rmse:0.614157+0.053929
## [36] train-rmse:0.576366+0.011052 test-rmse:0.613346+0.054582
## [37] train-rmse:0.574599+0.011037 test-rmse:0.611563+0.055073
## [38] train-rmse:0.572879+0.011050 test-rmse:0.609997+0.055081
## [39] train-rmse:0.571238+0.011041 test-rmse:0.609026+0.054667
## [40] train-rmse:0.569647+0.011059 test-rmse:0.606965+0.054840
## [41] train-rmse:0.568068+0.011081 test-rmse:0.605645+0.054605
## [42] train-rmse:0.566519+0.011084 test-rmse:0.604299+0.054753
## [43] train-rmse:0.565010+0.011075 test-rmse:0.602701+0.055142
## [44] train-rmse:0.563572+0.011073 test-rmse:0.601066+0.055158
## [45] train-rmse:0.562141+0.011072 test-rmse:0.600199+0.056266
## [46] train-rmse:0.560740+0.011070 test-rmse:0.600203+0.056297
## [47] train-rmse:0.559375+0.011068 test-rmse:0.599663+0.054986
## [48] train-rmse:0.558050+0.011066 test-rmse:0.597752+0.055240
## [49] train-rmse:0.556731+0.011062 test-rmse:0.596363+0.054336
## [50] train-rmse:0.555432+0.011065 test-rmse:0.595370+0.054901
## [51] train-rmse:0.554142+0.011063 test-rmse:0.595121+0.054666
## [52] train-rmse:0.552878+0.011066 test-rmse:0.593938+0.053946
## [53] train-rmse:0.551638+0.011064 test-rmse:0.592685+0.053818
## [54] train-rmse:0.550439+0.011065 test-rmse:0.591815+0.054673
## [55] train-rmse:0.549257+0.011057 test-rmse:0.591498+0.055104
## [56] train-rmse:0.548110+0.011035 test-rmse:0.590962+0.054282
## [57] train-rmse:0.546993+0.011013 test-rmse:0.590351+0.054192
## [58] train-rmse:0.545888+0.010997 test-rmse:0.589787+0.054651
## [59] train-rmse:0.544803+0.010980 test-rmse:0.588227+0.054939
## [60] train-rmse:0.543728+0.010978 test-rmse:0.587383+0.054694
## [61] train-rmse:0.542681+0.010977 test-rmse:0.586764+0.054514
## [62] train-rmse:0.541641+0.010966 test-rmse:0.585765+0.054527
## [63] train-rmse:0.540602+0.010958 test-rmse:0.585212+0.054074
## [64] train-rmse:0.539587+0.010950 test-rmse:0.583811+0.053832
## [65] train-rmse:0.538594+0.010948 test-rmse:0.583236+0.054691
## [66] train-rmse:0.537610+0.010949 test-rmse:0.582951+0.054358
## [67] train-rmse:0.536660+0.010948 test-rmse:0.582818+0.055061
## [68] train-rmse:0.535718+0.010937 test-rmse:0.582513+0.054998
## [69] train-rmse:0.534788+0.010928 test-rmse:0.581407+0.055129
## [70] train-rmse:0.533878+0.010901 test-rmse:0.580643+0.054503
## [71] train-rmse:0.532987+0.010876 test-rmse:0.580532+0.053872
## [72] train-rmse:0.532104+0.010855 test-rmse:0.579760+0.054165
## [73] train-rmse:0.531247+0.010839 test-rmse:0.579015+0.054201
## [74] train-rmse:0.530389+0.010817 test-rmse:0.578068+0.054794
## [75] train-rmse:0.529557+0.010812 test-rmse:0.578080+0.054630
## [76] train-rmse:0.528728+0.010807 test-rmse:0.576994+0.054883
## [77] train-rmse:0.527917+0.010800 test-rmse:0.576008+0.055132
## [78] train-rmse:0.527108+0.010791 test-rmse:0.576234+0.055547
## [79] train-rmse:0.526316+0.010776 test-rmse:0.575574+0.054994
## [80] train-rmse:0.525537+0.010761 test-rmse:0.574908+0.055076
## [81] train-rmse:0.524774+0.010747 test-rmse:0.573920+0.055059
## [82] train-rmse:0.524025+0.010730 test-rmse:0.573164+0.055099
## [83] train-rmse:0.523281+0.010716 test-rmse:0.572262+0.055262
## [84] train-rmse:0.522538+0.010698 test-rmse:0.571499+0.055463
## [85] train-rmse:0.521811+0.010683 test-rmse:0.571475+0.054644
## [86] train-rmse:0.521099+0.010675 test-rmse:0.571586+0.054662
## [87] train-rmse:0.520385+0.010668 test-rmse:0.570793+0.054061
## [88] train-rmse:0.519686+0.010649 test-rmse:0.570240+0.054176
## [89] train-rmse:0.518990+0.010640 test-rmse:0.570449+0.054111
## [90] train-rmse:0.518311+0.010627 test-rmse:0.570127+0.054275
## [91] train-rmse:0.517639+0.010605 test-rmse:0.569390+0.054710
## [92] train-rmse:0.516980+0.010585 test-rmse:0.568768+0.054652
## [93] train-rmse:0.516327+0.010566 test-rmse:0.568716+0.054540
## [94] train-rmse:0.515679+0.010553 test-rmse:0.567807+0.054032
## [95] train-rmse:0.515042+0.010546 test-rmse:0.567317+0.054194
## [96] train-rmse:0.514408+0.010537 test-rmse:0.567150+0.054908
## [97] train-rmse:0.513787+0.010527 test-rmse:0.566647+0.054664
## [98] train-rmse:0.513172+0.010519 test-rmse:0.565719+0.054663
## [99] train-rmse:0.512562+0.010510 test-rmse:0.565024+0.054807
## [100] train-rmse:0.511958+0.010500 test-rmse:0.564283+0.055001
## [101] train-rmse:0.511365+0.010489 test-rmse:0.563488+0.054339
## [102] train-rmse:0.510775+0.010480 test-rmse:0.563185+0.054226
## [103] train-rmse:0.510189+0.010465 test-rmse:0.562070+0.054141
## [104] train-rmse:0.509623+0.010469 test-rmse:0.562174+0.054039
## [105] train-rmse:0.509059+0.010468 test-rmse:0.562002+0.054263
## [106] train-rmse:0.508503+0.010455 test-rmse:0.561631+0.054370
## [107] train-rmse:0.507956+0.010454 test-rmse:0.561385+0.053958
## [108] train-rmse:0.507415+0.010443 test-rmse:0.560823+0.053656
## [109] train-rmse:0.506877+0.010436 test-rmse:0.560456+0.053539
## [110] train-rmse:0.506347+0.010432 test-rmse:0.560353+0.054238
## [111] train-rmse:0.505819+0.010426 test-rmse:0.560092+0.054051
## [112] train-rmse:0.505298+0.010421 test-rmse:0.559658+0.054223
## [113] train-rmse:0.504787+0.010411 test-rmse:0.559273+0.054505
## [114] train-rmse:0.504275+0.010404 test-rmse:0.558824+0.054301
## [115] train-rmse:0.503770+0.010398 test-rmse:0.558757+0.054055
## [116] train-rmse:0.503270+0.010392 test-rmse:0.558305+0.054020
## [117] train-rmse:0.502786+0.010384 test-rmse:0.557970+0.054217
## [118] train-rmse:0.502303+0.010380 test-rmse:0.557528+0.054200
## [119] train-rmse:0.501827+0.010377 test-rmse:0.557203+0.053851
## [120] train-rmse:0.501356+0.010375 test-rmse:0.556828+0.054348
## [121] train-rmse:0.500886+0.010372 test-rmse:0.556625+0.053959
## [122] train-rmse:0.500423+0.010363 test-rmse:0.555728+0.053775
## [123] train-rmse:0.499965+0.010361 test-rmse:0.555395+0.053398
## [124] train-rmse:0.499515+0.010355 test-rmse:0.555257+0.053625
## [125] train-rmse:0.499067+0.010349 test-rmse:0.554578+0.053697
## [126] train-rmse:0.498626+0.010349 test-rmse:0.554353+0.053673
## [127] train-rmse:0.498188+0.010352 test-rmse:0.554487+0.053471
## [128] train-rmse:0.497752+0.010350 test-rmse:0.553840+0.053254
## [129] train-rmse:0.497319+0.010348 test-rmse:0.553562+0.053368
## [130] train-rmse:0.496896+0.010346 test-rmse:0.553574+0.053045
## [131] train-rmse:0.496479+0.010343 test-rmse:0.553468+0.053351
## [132] train-rmse:0.496066+0.010339 test-rmse:0.552929+0.053521
## [133] train-rmse:0.495654+0.010338 test-rmse:0.552538+0.053465
## [134] train-rmse:0.495250+0.010337 test-rmse:0.552361+0.053619
## [135] train-rmse:0.494852+0.010330 test-rmse:0.552013+0.053484
## [136] train-rmse:0.494458+0.010326 test-rmse:0.552144+0.053142
## [137] train-rmse:0.494067+0.010322 test-rmse:0.551735+0.052966
## [138] train-rmse:0.493674+0.010317 test-rmse:0.551267+0.053102
## [139] train-rmse:0.493284+0.010312 test-rmse:0.551040+0.053331
## [140] train-rmse:0.492901+0.010308 test-rmse:0.550810+0.053626
## [141] train-rmse:0.492523+0.010305 test-rmse:0.550614+0.053491
## [142] train-rmse:0.492148+0.010299 test-rmse:0.550315+0.053842
## [143] train-rmse:0.491776+0.010293 test-rmse:0.550171+0.053275
## [144] train-rmse:0.491408+0.010289 test-rmse:0.549819+0.053237
## [145] train-rmse:0.491043+0.010287 test-rmse:0.549521+0.053151
## [146] train-rmse:0.490685+0.010284 test-rmse:0.549334+0.053125
## [147] train-rmse:0.490330+0.010279 test-rmse:0.549283+0.052998
## [148] train-rmse:0.489979+0.010273 test-rmse:0.549241+0.052710
## [149] train-rmse:0.489631+0.010267 test-rmse:0.548850+0.052502
## [150] train-rmse:0.489287+0.010259 test-rmse:0.548622+0.052697
## [151] train-rmse:0.488943+0.010253 test-rmse:0.548409+0.052639
## [152] train-rmse:0.488602+0.010251 test-rmse:0.548378+0.052585
## [153] train-rmse:0.488264+0.010245 test-rmse:0.548239+0.052289
## [154] train-rmse:0.487929+0.010239 test-rmse:0.547680+0.052457
## [155] train-rmse:0.487599+0.010236 test-rmse:0.547680+0.052550
## [156] train-rmse:0.487272+0.010236 test-rmse:0.547840+0.052487
## [157] train-rmse:0.486947+0.010232 test-rmse:0.547295+0.052388
## [158] train-rmse:0.486627+0.010222 test-rmse:0.547403+0.052417
## [159] train-rmse:0.486309+0.010214 test-rmse:0.546985+0.052150
## [160] train-rmse:0.485995+0.010205 test-rmse:0.546603+0.052365
## [161] train-rmse:0.485676+0.010209 test-rmse:0.546341+0.052541
## [162] train-rmse:0.485372+0.010208 test-rmse:0.546594+0.052625
## [163] train-rmse:0.485070+0.010205 test-rmse:0.546014+0.052678
## [164] train-rmse:0.484770+0.010204 test-rmse:0.546251+0.052874
## [165] train-rmse:0.484469+0.010200 test-rmse:0.546324+0.052470
## [166] train-rmse:0.484174+0.010198 test-rmse:0.545898+0.052110
## [167] train-rmse:0.483879+0.010194 test-rmse:0.545640+0.052260
## [168] train-rmse:0.483588+0.010190 test-rmse:0.545351+0.052134
## [169] train-rmse:0.483298+0.010185 test-rmse:0.545137+0.052201
## [170] train-rmse:0.483010+0.010179 test-rmse:0.544707+0.052106
## [171] train-rmse:0.482729+0.010174 test-rmse:0.544582+0.051601
## [172] train-rmse:0.482444+0.010176 test-rmse:0.544621+0.052003
## [173] train-rmse:0.482166+0.010173 test-rmse:0.544611+0.051848
## [174] train-rmse:0.481893+0.010172 test-rmse:0.544253+0.052033
## [175] train-rmse:0.481622+0.010168 test-rmse:0.544296+0.051800
## [176] train-rmse:0.481354+0.010163 test-rmse:0.544198+0.051861
## [177] train-rmse:0.481085+0.010162 test-rmse:0.543906+0.051979
## [178] train-rmse:0.480819+0.010155 test-rmse:0.543524+0.051878
## [179] train-rmse:0.480556+0.010149 test-rmse:0.543397+0.051851
## [180] train-rmse:0.480287+0.010148 test-rmse:0.543504+0.051529
## [181] train-rmse:0.480026+0.010141 test-rmse:0.543062+0.051261
## [182] train-rmse:0.479774+0.010141 test-rmse:0.543098+0.051074
## [183] train-rmse:0.479522+0.010140 test-rmse:0.543109+0.051365
## [184] train-rmse:0.479273+0.010140 test-rmse:0.542958+0.051334
## [185] train-rmse:0.479024+0.010137 test-rmse:0.542661+0.051212
## [186] train-rmse:0.478778+0.010136 test-rmse:0.542343+0.051136
## [187] train-rmse:0.478534+0.010135 test-rmse:0.542065+0.051199
## [188] train-rmse:0.478290+0.010133 test-rmse:0.541949+0.051200
## [189] train-rmse:0.478050+0.010132 test-rmse:0.542045+0.051068
## [190] train-rmse:0.477810+0.010130 test-rmse:0.542026+0.051077
## [191] train-rmse:0.477570+0.010127 test-rmse:0.541910+0.051355
## [192] train-rmse:0.477330+0.010125 test-rmse:0.541636+0.051482
## [193] train-rmse:0.477096+0.010122 test-rmse:0.541502+0.051185
## [194] train-rmse:0.476863+0.010118 test-rmse:0.541358+0.051079
## [195] train-rmse:0.476632+0.010114 test-rmse:0.541296+0.051258
## [196] train-rmse:0.476406+0.010110 test-rmse:0.541073+0.051116
## [197] train-rmse:0.476179+0.010105 test-rmse:0.540985+0.050956
## [198] train-rmse:0.475957+0.010103 test-rmse:0.541132+0.051026
## [199] train-rmse:0.475736+0.010103 test-rmse:0.540615+0.051104
## [200] train-rmse:0.475518+0.010100 test-rmse:0.540284+0.050904
## [201] train-rmse:0.475302+0.010098 test-rmse:0.540316+0.050455
## [202] train-rmse:0.475087+0.010098 test-rmse:0.539912+0.050468
## [203] train-rmse:0.474872+0.010100 test-rmse:0.539713+0.050311
## [204] train-rmse:0.474660+0.010099 test-rmse:0.539856+0.050127
## [205] train-rmse:0.474449+0.010100 test-rmse:0.539814+0.050480
## [206] train-rmse:0.474242+0.010098 test-rmse:0.539572+0.050595
## [207] train-rmse:0.474034+0.010095 test-rmse:0.539201+0.050515
## [208] train-rmse:0.473831+0.010093 test-rmse:0.539329+0.050484
## [209] train-rmse:0.473628+0.010088 test-rmse:0.539030+0.050447
## [210] train-rmse:0.473426+0.010083 test-rmse:0.539153+0.050465
## [211] train-rmse:0.473227+0.010077 test-rmse:0.539037+0.050552
## [212] train-rmse:0.473027+0.010069 test-rmse:0.539100+0.050613
## [213] train-rmse:0.472827+0.010063 test-rmse:0.539061+0.050640
## [214] train-rmse:0.472628+0.010061 test-rmse:0.538885+0.050655
## [215] train-rmse:0.472431+0.010056 test-rmse:0.538642+0.050595
## [216] train-rmse:0.472240+0.010053 test-rmse:0.538639+0.050356
## [217] train-rmse:0.472050+0.010050 test-rmse:0.538366+0.050043
## [218] train-rmse:0.471854+0.010045 test-rmse:0.538506+0.050269
## [219] train-rmse:0.471665+0.010045 test-rmse:0.538430+0.050094
## [220] train-rmse:0.471477+0.010045 test-rmse:0.538473+0.050094
## [221] train-rmse:0.471292+0.010045 test-rmse:0.538498+0.049832
## [222] train-rmse:0.471108+0.010042 test-rmse:0.538552+0.049792
## [223] train-rmse:0.470928+0.010039 test-rmse:0.538460+0.049929
## Stopping. Best iteration:
## [217] train-rmse:0.472050+0.010050 test-rmse:0.538366+0.050043
##
## 8
## [1] train-rmse:0.930677+0.015519 test-rmse:0.931749+0.061626
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.868286+0.014803 test-rmse:0.872913+0.060306
## [3] train-rmse:0.816006+0.014517 test-rmse:0.825073+0.061678
## [4] train-rmse:0.772089+0.013978 test-rmse:0.784462+0.062519
## [5] train-rmse:0.735456+0.013849 test-rmse:0.752787+0.063114
## [6] train-rmse:0.704214+0.013160 test-rmse:0.725060+0.063995
## [7] train-rmse:0.677811+0.012377 test-rmse:0.699489+0.065930
## [8] train-rmse:0.655940+0.012025 test-rmse:0.679743+0.062669
## [9] train-rmse:0.636488+0.011097 test-rmse:0.660896+0.063047
## [10] train-rmse:0.619701+0.011193 test-rmse:0.648382+0.063025
## [11] train-rmse:0.605551+0.011681 test-rmse:0.638381+0.063431
## [12] train-rmse:0.594229+0.011456 test-rmse:0.629048+0.063512
## [13] train-rmse:0.583310+0.012251 test-rmse:0.621614+0.063308
## [14] train-rmse:0.574785+0.012033 test-rmse:0.614934+0.063750
## [15] train-rmse:0.567564+0.012216 test-rmse:0.609554+0.062019
## [16] train-rmse:0.560016+0.013091 test-rmse:0.604359+0.061637
## [17] train-rmse:0.554188+0.013858 test-rmse:0.600767+0.060093
## [18] train-rmse:0.548524+0.013622 test-rmse:0.596359+0.060268
## [19] train-rmse:0.543859+0.013630 test-rmse:0.592655+0.060940
## [20] train-rmse:0.539193+0.014202 test-rmse:0.588561+0.059430
## [21] train-rmse:0.534869+0.015071 test-rmse:0.587401+0.059205
## [22] train-rmse:0.530485+0.015002 test-rmse:0.583505+0.058185
## [23] train-rmse:0.526905+0.014833 test-rmse:0.581322+0.056953
## [24] train-rmse:0.523293+0.014186 test-rmse:0.579295+0.057508
## [25] train-rmse:0.519784+0.014802 test-rmse:0.576629+0.056525
## [26] train-rmse:0.516657+0.014837 test-rmse:0.575323+0.056132
## [27] train-rmse:0.513863+0.015026 test-rmse:0.573571+0.057238
## [28] train-rmse:0.511194+0.014955 test-rmse:0.572953+0.056837
## [29] train-rmse:0.508056+0.014377 test-rmse:0.570322+0.057885
## [30] train-rmse:0.505136+0.014531 test-rmse:0.568260+0.057993
## [31] train-rmse:0.502433+0.014743 test-rmse:0.565633+0.058148
## [32] train-rmse:0.499915+0.014682 test-rmse:0.565508+0.058026
## [33] train-rmse:0.497367+0.014971 test-rmse:0.563983+0.056989
## [34] train-rmse:0.494994+0.015109 test-rmse:0.561982+0.057358
## [35] train-rmse:0.492554+0.015133 test-rmse:0.560681+0.058086
## [36] train-rmse:0.490233+0.015243 test-rmse:0.558966+0.057235
## [37] train-rmse:0.487819+0.014698 test-rmse:0.556617+0.056927
## [38] train-rmse:0.485794+0.014768 test-rmse:0.555334+0.057691
## [39] train-rmse:0.483764+0.014798 test-rmse:0.553972+0.057517
## [40] train-rmse:0.481976+0.014716 test-rmse:0.553380+0.057867
## [41] train-rmse:0.479754+0.014295 test-rmse:0.552354+0.058291
## [42] train-rmse:0.477900+0.014543 test-rmse:0.550622+0.058904
## [43] train-rmse:0.476031+0.014581 test-rmse:0.550047+0.059087
## [44] train-rmse:0.474120+0.014299 test-rmse:0.548712+0.058784
## [45] train-rmse:0.472291+0.014441 test-rmse:0.546826+0.059133
## [46] train-rmse:0.470163+0.014311 test-rmse:0.543988+0.058629
## [47] train-rmse:0.468179+0.014572 test-rmse:0.542895+0.058172
## [48] train-rmse:0.466527+0.014597 test-rmse:0.542452+0.058214
## [49] train-rmse:0.464743+0.014439 test-rmse:0.541344+0.059038
## [50] train-rmse:0.463062+0.014023 test-rmse:0.541315+0.058719
## [51] train-rmse:0.461417+0.014480 test-rmse:0.540765+0.058798
## [52] train-rmse:0.460034+0.014553 test-rmse:0.539784+0.059259
## [53] train-rmse:0.458462+0.014680 test-rmse:0.537747+0.058802
## [54] train-rmse:0.456335+0.014750 test-rmse:0.536755+0.059050
## [55] train-rmse:0.454618+0.014559 test-rmse:0.536019+0.060190
## [56] train-rmse:0.453346+0.014446 test-rmse:0.534346+0.059746
## [57] train-rmse:0.451499+0.014313 test-rmse:0.533948+0.059751
## [58] train-rmse:0.450183+0.014301 test-rmse:0.534578+0.060061
## [59] train-rmse:0.448838+0.014150 test-rmse:0.533986+0.059687
## [60] train-rmse:0.447325+0.013986 test-rmse:0.532729+0.061069
## [61] train-rmse:0.445943+0.013744 test-rmse:0.531728+0.061990
## [62] train-rmse:0.444843+0.013925 test-rmse:0.530794+0.061777
## [63] train-rmse:0.443850+0.013863 test-rmse:0.530098+0.061646
## [64] train-rmse:0.442183+0.013990 test-rmse:0.529014+0.061742
## [65] train-rmse:0.441019+0.014019 test-rmse:0.528007+0.061670
## [66] train-rmse:0.439503+0.014248 test-rmse:0.527386+0.060565
## [67] train-rmse:0.438145+0.014238 test-rmse:0.527119+0.060318
## [68] train-rmse:0.436982+0.014029 test-rmse:0.526853+0.060553
## [69] train-rmse:0.435677+0.013844 test-rmse:0.526484+0.060771
## [70] train-rmse:0.434559+0.013892 test-rmse:0.525842+0.061400
## [71] train-rmse:0.433707+0.013913 test-rmse:0.525171+0.061896
## [72] train-rmse:0.432715+0.014095 test-rmse:0.524233+0.061772
## [73] train-rmse:0.431660+0.013928 test-rmse:0.523524+0.061364
## [74] train-rmse:0.430814+0.014009 test-rmse:0.523665+0.060909
## [75] train-rmse:0.429634+0.013925 test-rmse:0.522913+0.061407
## [76] train-rmse:0.428641+0.014077 test-rmse:0.522883+0.061949
## [77] train-rmse:0.427550+0.014126 test-rmse:0.522830+0.062025
## [78] train-rmse:0.426425+0.014119 test-rmse:0.522874+0.061447
## [79] train-rmse:0.425397+0.014096 test-rmse:0.522248+0.061581
## [80] train-rmse:0.424397+0.014234 test-rmse:0.521633+0.061939
## [81] train-rmse:0.423181+0.014347 test-rmse:0.521227+0.061143
## [82] train-rmse:0.422344+0.014436 test-rmse:0.521433+0.061399
## [83] train-rmse:0.421480+0.014462 test-rmse:0.521383+0.061439
## [84] train-rmse:0.420358+0.014498 test-rmse:0.520245+0.060880
## [85] train-rmse:0.419562+0.014412 test-rmse:0.519815+0.061452
## [86] train-rmse:0.418696+0.014488 test-rmse:0.519520+0.061771
## [87] train-rmse:0.417769+0.014478 test-rmse:0.518686+0.061672
## [88] train-rmse:0.416934+0.014304 test-rmse:0.519026+0.061941
## [89] train-rmse:0.416104+0.014396 test-rmse:0.518836+0.061915
## [90] train-rmse:0.414941+0.014515 test-rmse:0.517993+0.061406
## [91] train-rmse:0.414192+0.014532 test-rmse:0.518069+0.061286
## [92] train-rmse:0.413120+0.015016 test-rmse:0.517674+0.061339
## [93] train-rmse:0.412257+0.015174 test-rmse:0.517323+0.061192
## [94] train-rmse:0.411550+0.015289 test-rmse:0.516943+0.060527
## [95] train-rmse:0.410829+0.015259 test-rmse:0.516716+0.061094
## [96] train-rmse:0.410100+0.015292 test-rmse:0.516854+0.061330
## [97] train-rmse:0.409533+0.015286 test-rmse:0.516554+0.061582
## [98] train-rmse:0.408722+0.015220 test-rmse:0.515684+0.062304
## [99] train-rmse:0.407887+0.015287 test-rmse:0.515509+0.062052
## [100] train-rmse:0.407107+0.015555 test-rmse:0.515547+0.061848
## [101] train-rmse:0.406073+0.015424 test-rmse:0.515865+0.061943
## [102] train-rmse:0.405300+0.015334 test-rmse:0.515328+0.061728
## [103] train-rmse:0.404707+0.015322 test-rmse:0.515300+0.061904
## [104] train-rmse:0.403836+0.015272 test-rmse:0.515132+0.062138
## [105] train-rmse:0.403007+0.015231 test-rmse:0.514624+0.062527
## [106] train-rmse:0.402303+0.015227 test-rmse:0.514648+0.062649
## [107] train-rmse:0.401444+0.015211 test-rmse:0.514389+0.062672
## [108] train-rmse:0.400530+0.015301 test-rmse:0.514124+0.062714
## [109] train-rmse:0.399654+0.015082 test-rmse:0.513573+0.062555
## [110] train-rmse:0.399065+0.015070 test-rmse:0.512915+0.062376
## [111] train-rmse:0.398132+0.014940 test-rmse:0.513057+0.062589
## [112] train-rmse:0.397567+0.014835 test-rmse:0.512567+0.062655
## [113] train-rmse:0.396924+0.014868 test-rmse:0.513150+0.062609
## [114] train-rmse:0.396438+0.014886 test-rmse:0.513280+0.062493
## [115] train-rmse:0.395635+0.014860 test-rmse:0.513111+0.062670
## [116] train-rmse:0.394849+0.014978 test-rmse:0.513031+0.062514
## [117] train-rmse:0.394270+0.014976 test-rmse:0.513040+0.062510
## [118] train-rmse:0.393492+0.014933 test-rmse:0.512421+0.062469
## [119] train-rmse:0.392787+0.014743 test-rmse:0.512025+0.062744
## [120] train-rmse:0.392109+0.014788 test-rmse:0.511794+0.063296
## [121] train-rmse:0.391467+0.014627 test-rmse:0.511434+0.063263
## [122] train-rmse:0.391022+0.014656 test-rmse:0.511095+0.063025
## [123] train-rmse:0.390514+0.014760 test-rmse:0.510977+0.062733
## [124] train-rmse:0.389941+0.014750 test-rmse:0.510493+0.063141
## [125] train-rmse:0.389341+0.014707 test-rmse:0.510636+0.062710
## [126] train-rmse:0.388550+0.014693 test-rmse:0.510195+0.062944
## [127] train-rmse:0.387876+0.014824 test-rmse:0.509910+0.063490
## [128] train-rmse:0.387332+0.014990 test-rmse:0.509702+0.063320
## [129] train-rmse:0.386628+0.014893 test-rmse:0.509352+0.063308
## [130] train-rmse:0.386091+0.014787 test-rmse:0.509083+0.063538
## [131] train-rmse:0.385272+0.014606 test-rmse:0.508854+0.063437
## [132] train-rmse:0.384577+0.014522 test-rmse:0.508324+0.063365
## [133] train-rmse:0.383725+0.014199 test-rmse:0.507645+0.063756
## [134] train-rmse:0.383113+0.014191 test-rmse:0.507820+0.064041
## [135] train-rmse:0.382458+0.014339 test-rmse:0.507470+0.063939
## [136] train-rmse:0.381647+0.014445 test-rmse:0.507505+0.063415
## [137] train-rmse:0.381165+0.014534 test-rmse:0.507546+0.063034
## [138] train-rmse:0.380735+0.014480 test-rmse:0.507738+0.062675
## [139] train-rmse:0.380258+0.014398 test-rmse:0.507347+0.062435
## [140] train-rmse:0.379602+0.014240 test-rmse:0.507692+0.062328
## [141] train-rmse:0.379258+0.014210 test-rmse:0.507604+0.062660
## [142] train-rmse:0.378580+0.014118 test-rmse:0.507197+0.062939
## [143] train-rmse:0.378168+0.014091 test-rmse:0.506811+0.062585
## [144] train-rmse:0.377399+0.014011 test-rmse:0.506100+0.062551
## [145] train-rmse:0.376697+0.013898 test-rmse:0.505705+0.062591
## [146] train-rmse:0.375975+0.014142 test-rmse:0.505701+0.062436
## [147] train-rmse:0.375464+0.014021 test-rmse:0.505443+0.062874
## [148] train-rmse:0.374951+0.013976 test-rmse:0.505449+0.063177
## [149] train-rmse:0.374424+0.013878 test-rmse:0.505391+0.062918
## [150] train-rmse:0.373839+0.013710 test-rmse:0.505247+0.062953
## [151] train-rmse:0.373105+0.014000 test-rmse:0.505425+0.063182
## [152] train-rmse:0.372632+0.013915 test-rmse:0.505617+0.062829
## [153] train-rmse:0.372234+0.013820 test-rmse:0.505217+0.062845
## [154] train-rmse:0.371797+0.013848 test-rmse:0.505044+0.063290
## [155] train-rmse:0.371382+0.013942 test-rmse:0.504483+0.062906
## [156] train-rmse:0.370609+0.013962 test-rmse:0.504294+0.062567
## [157] train-rmse:0.370119+0.013798 test-rmse:0.504051+0.062348
## [158] train-rmse:0.369747+0.013751 test-rmse:0.503810+0.062244
## [159] train-rmse:0.369123+0.013765 test-rmse:0.502984+0.061943
## [160] train-rmse:0.368552+0.013607 test-rmse:0.502832+0.062450
## [161] train-rmse:0.368030+0.013546 test-rmse:0.502442+0.062507
## [162] train-rmse:0.367707+0.013579 test-rmse:0.502364+0.062538
## [163] train-rmse:0.367082+0.013778 test-rmse:0.502138+0.062546
## [164] train-rmse:0.366389+0.013675 test-rmse:0.501885+0.062109
## [165] train-rmse:0.365971+0.013547 test-rmse:0.501782+0.062323
## [166] train-rmse:0.365335+0.013341 test-rmse:0.501685+0.062631
## [167] train-rmse:0.364689+0.013375 test-rmse:0.501412+0.062388
## [168] train-rmse:0.364145+0.013440 test-rmse:0.501669+0.062192
## [169] train-rmse:0.363622+0.013294 test-rmse:0.501562+0.062267
## [170] train-rmse:0.362836+0.012828 test-rmse:0.501182+0.062080
## [171] train-rmse:0.362466+0.012739 test-rmse:0.501598+0.062548
## [172] train-rmse:0.362144+0.012718 test-rmse:0.501515+0.062082
## [173] train-rmse:0.361431+0.012683 test-rmse:0.501161+0.062180
## [174] train-rmse:0.360790+0.012671 test-rmse:0.500798+0.062452
## [175] train-rmse:0.360230+0.012576 test-rmse:0.500497+0.062589
## [176] train-rmse:0.359798+0.012706 test-rmse:0.500400+0.062456
## [177] train-rmse:0.359395+0.012777 test-rmse:0.500210+0.062267
## [178] train-rmse:0.359100+0.012801 test-rmse:0.500209+0.062321
## [179] train-rmse:0.358732+0.012618 test-rmse:0.499858+0.062330
## [180] train-rmse:0.357958+0.012171 test-rmse:0.500204+0.062273
## [181] train-rmse:0.357518+0.012138 test-rmse:0.500223+0.062159
## [182] train-rmse:0.356850+0.012500 test-rmse:0.500209+0.061937
## [183] train-rmse:0.356486+0.012354 test-rmse:0.499948+0.062083
## [184] train-rmse:0.355925+0.012135 test-rmse:0.500033+0.062051
## [185] train-rmse:0.355488+0.012033 test-rmse:0.500094+0.062011
## Stopping. Best iteration:
## [179] train-rmse:0.358732+0.012618 test-rmse:0.499858+0.062330
##
## 9
## [1] train-rmse:0.926509+0.015679 test-rmse:0.929240+0.062334
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.859816+0.015009 test-rmse:0.867786+0.062697
## [3] train-rmse:0.803126+0.014849 test-rmse:0.815278+0.062581
## [4] train-rmse:0.755010+0.014255 test-rmse:0.771613+0.063374
## [5] train-rmse:0.714187+0.014312 test-rmse:0.734609+0.062922
## [6] train-rmse:0.678511+0.013797 test-rmse:0.704843+0.065389
## [7] train-rmse:0.648671+0.013487 test-rmse:0.679433+0.064526
## [8] train-rmse:0.623096+0.013775 test-rmse:0.657730+0.061576
## [9] train-rmse:0.601681+0.013541 test-rmse:0.640044+0.061938
## [10] train-rmse:0.582217+0.012875 test-rmse:0.624679+0.061588
## [11] train-rmse:0.565963+0.013194 test-rmse:0.612828+0.059767
## [12] train-rmse:0.551955+0.013299 test-rmse:0.603025+0.060676
## [13] train-rmse:0.539864+0.013284 test-rmse:0.593508+0.059565
## [14] train-rmse:0.529375+0.013034 test-rmse:0.586248+0.058338
## [15] train-rmse:0.517805+0.014041 test-rmse:0.578929+0.056424
## [16] train-rmse:0.509540+0.013322 test-rmse:0.572886+0.057273
## [17] train-rmse:0.500845+0.014954 test-rmse:0.567100+0.056237
## [18] train-rmse:0.494745+0.014767 test-rmse:0.563999+0.056129
## [19] train-rmse:0.487936+0.014387 test-rmse:0.558060+0.057921
## [20] train-rmse:0.482741+0.014267 test-rmse:0.556350+0.058054
## [21] train-rmse:0.478186+0.014362 test-rmse:0.553578+0.057468
## [22] train-rmse:0.473773+0.014420 test-rmse:0.551269+0.056798
## [23] train-rmse:0.469376+0.013874 test-rmse:0.548979+0.056711
## [24] train-rmse:0.464421+0.014026 test-rmse:0.545590+0.056972
## [25] train-rmse:0.460850+0.014057 test-rmse:0.542734+0.055100
## [26] train-rmse:0.457255+0.013947 test-rmse:0.542310+0.056720
## [27] train-rmse:0.453247+0.013866 test-rmse:0.539711+0.055398
## [28] train-rmse:0.449346+0.014785 test-rmse:0.538280+0.055154
## [29] train-rmse:0.446556+0.014757 test-rmse:0.537052+0.055322
## [30] train-rmse:0.443574+0.014632 test-rmse:0.535792+0.056125
## [31] train-rmse:0.440951+0.014703 test-rmse:0.534348+0.055420
## [32] train-rmse:0.437438+0.013981 test-rmse:0.532822+0.054665
## [33] train-rmse:0.434521+0.014346 test-rmse:0.531588+0.054601
## [34] train-rmse:0.432092+0.014268 test-rmse:0.530247+0.055285
## [35] train-rmse:0.428964+0.014206 test-rmse:0.529575+0.054946
## [36] train-rmse:0.426324+0.013858 test-rmse:0.527756+0.055106
## [37] train-rmse:0.423278+0.014378 test-rmse:0.526219+0.055119
## [38] train-rmse:0.420857+0.014690 test-rmse:0.525188+0.055123
## [39] train-rmse:0.418538+0.014680 test-rmse:0.524445+0.055816
## [40] train-rmse:0.415938+0.014339 test-rmse:0.522654+0.055949
## [41] train-rmse:0.414098+0.014318 test-rmse:0.521848+0.055470
## [42] train-rmse:0.411946+0.014295 test-rmse:0.520851+0.056177
## [43] train-rmse:0.409620+0.014121 test-rmse:0.521334+0.055773
## [44] train-rmse:0.407370+0.013869 test-rmse:0.519597+0.055978
## [45] train-rmse:0.405104+0.014042 test-rmse:0.518228+0.055310
## [46] train-rmse:0.402879+0.014150 test-rmse:0.517468+0.054758
## [47] train-rmse:0.400690+0.013863 test-rmse:0.515833+0.055073
## [48] train-rmse:0.398498+0.013970 test-rmse:0.514952+0.055856
## [49] train-rmse:0.396503+0.014143 test-rmse:0.514291+0.056754
## [50] train-rmse:0.395061+0.014101 test-rmse:0.514672+0.057279
## [51] train-rmse:0.392749+0.013770 test-rmse:0.514070+0.057417
## [52] train-rmse:0.390774+0.013678 test-rmse:0.513484+0.057294
## [53] train-rmse:0.389420+0.013436 test-rmse:0.512796+0.057750
## [54] train-rmse:0.387336+0.013880 test-rmse:0.512293+0.057549
## [55] train-rmse:0.385462+0.013898 test-rmse:0.512234+0.057353
## [56] train-rmse:0.383684+0.014199 test-rmse:0.511588+0.058375
## [57] train-rmse:0.382022+0.013786 test-rmse:0.511193+0.059714
## [58] train-rmse:0.380474+0.014039 test-rmse:0.510920+0.060047
## [59] train-rmse:0.378899+0.013960 test-rmse:0.510574+0.059687
## [60] train-rmse:0.377146+0.013784 test-rmse:0.509332+0.059559
## [61] train-rmse:0.375742+0.013719 test-rmse:0.508887+0.059863
## [62] train-rmse:0.373984+0.013591 test-rmse:0.508230+0.059566
## [63] train-rmse:0.372661+0.013535 test-rmse:0.508055+0.058909
## [64] train-rmse:0.371406+0.013385 test-rmse:0.508080+0.059110
## [65] train-rmse:0.370257+0.013272 test-rmse:0.508071+0.059305
## [66] train-rmse:0.368816+0.013446 test-rmse:0.507882+0.059311
## [67] train-rmse:0.367438+0.013357 test-rmse:0.507485+0.058644
## [68] train-rmse:0.366129+0.013217 test-rmse:0.506640+0.058674
## [69] train-rmse:0.364289+0.012761 test-rmse:0.506058+0.059268
## [70] train-rmse:0.362967+0.012435 test-rmse:0.505406+0.058567
## [71] train-rmse:0.361565+0.012362 test-rmse:0.504990+0.058217
## [72] train-rmse:0.359849+0.012695 test-rmse:0.504283+0.058735
## [73] train-rmse:0.358386+0.012427 test-rmse:0.504283+0.058775
## [74] train-rmse:0.356762+0.012574 test-rmse:0.503598+0.058770
## [75] train-rmse:0.355652+0.012532 test-rmse:0.503441+0.058755
## [76] train-rmse:0.353883+0.012831 test-rmse:0.502650+0.058813
## [77] train-rmse:0.352677+0.012680 test-rmse:0.502164+0.059327
## [78] train-rmse:0.351313+0.012596 test-rmse:0.501212+0.058684
## [79] train-rmse:0.350178+0.012636 test-rmse:0.500752+0.057913
## [80] train-rmse:0.348719+0.012504 test-rmse:0.500495+0.058181
## [81] train-rmse:0.347150+0.012689 test-rmse:0.500885+0.057860
## [82] train-rmse:0.345425+0.013182 test-rmse:0.500125+0.057304
## [83] train-rmse:0.344368+0.013518 test-rmse:0.499571+0.057357
## [84] train-rmse:0.343138+0.013519 test-rmse:0.499217+0.056801
## [85] train-rmse:0.341931+0.013384 test-rmse:0.499271+0.056248
## [86] train-rmse:0.340626+0.013098 test-rmse:0.499264+0.056186
## [87] train-rmse:0.339310+0.012766 test-rmse:0.499290+0.056321
## [88] train-rmse:0.337478+0.013681 test-rmse:0.499297+0.055867
## [89] train-rmse:0.336253+0.014041 test-rmse:0.498616+0.055753
## [90] train-rmse:0.335059+0.014158 test-rmse:0.498754+0.055862
## [91] train-rmse:0.334082+0.014127 test-rmse:0.498473+0.055556
## [92] train-rmse:0.333198+0.013914 test-rmse:0.498240+0.055514
## [93] train-rmse:0.331953+0.014003 test-rmse:0.498295+0.055309
## [94] train-rmse:0.331157+0.014028 test-rmse:0.498605+0.055227
## [95] train-rmse:0.329941+0.013786 test-rmse:0.498667+0.055125
## [96] train-rmse:0.328717+0.014257 test-rmse:0.498166+0.055186
## [97] train-rmse:0.327475+0.014612 test-rmse:0.498293+0.054038
## [98] train-rmse:0.326353+0.014495 test-rmse:0.497882+0.053978
## [99] train-rmse:0.324963+0.014740 test-rmse:0.497929+0.054222
## [100] train-rmse:0.323822+0.014828 test-rmse:0.497561+0.054442
## [101] train-rmse:0.322445+0.015163 test-rmse:0.497812+0.053867
## [102] train-rmse:0.321331+0.015570 test-rmse:0.497253+0.053638
## [103] train-rmse:0.319869+0.015747 test-rmse:0.498053+0.053581
## [104] train-rmse:0.318790+0.015180 test-rmse:0.497257+0.053817
## [105] train-rmse:0.317669+0.015079 test-rmse:0.497462+0.052941
## [106] train-rmse:0.317022+0.015079 test-rmse:0.497277+0.052282
## [107] train-rmse:0.316229+0.014942 test-rmse:0.497186+0.052533
## [108] train-rmse:0.315424+0.014932 test-rmse:0.497308+0.052172
## [109] train-rmse:0.314780+0.014821 test-rmse:0.496914+0.052113
## [110] train-rmse:0.314044+0.014883 test-rmse:0.496714+0.051958
## [111] train-rmse:0.313062+0.014767 test-rmse:0.497210+0.051796
## [112] train-rmse:0.311978+0.014325 test-rmse:0.496305+0.051699
## [113] train-rmse:0.311085+0.014100 test-rmse:0.495657+0.052572
## [114] train-rmse:0.309837+0.014395 test-rmse:0.495514+0.052529
## [115] train-rmse:0.308087+0.014571 test-rmse:0.495185+0.052334
## [116] train-rmse:0.307113+0.014451 test-rmse:0.495118+0.052428
## [117] train-rmse:0.306085+0.014228 test-rmse:0.494675+0.052766
## [118] train-rmse:0.305289+0.014123 test-rmse:0.494127+0.052530
## [119] train-rmse:0.304143+0.013963 test-rmse:0.493513+0.052806
## [120] train-rmse:0.302879+0.013436 test-rmse:0.493416+0.053024
## [121] train-rmse:0.301904+0.013372 test-rmse:0.493817+0.052917
## [122] train-rmse:0.301132+0.013434 test-rmse:0.493617+0.052823
## [123] train-rmse:0.300178+0.013331 test-rmse:0.493558+0.052752
## [124] train-rmse:0.299623+0.013355 test-rmse:0.493324+0.052547
## [125] train-rmse:0.298736+0.013518 test-rmse:0.493484+0.052183
## [126] train-rmse:0.297438+0.013093 test-rmse:0.492988+0.052268
## [127] train-rmse:0.296073+0.012744 test-rmse:0.492858+0.052281
## [128] train-rmse:0.295395+0.012668 test-rmse:0.492983+0.052160
## [129] train-rmse:0.294624+0.012783 test-rmse:0.492883+0.052055
## [130] train-rmse:0.294070+0.012838 test-rmse:0.492821+0.052049
## [131] train-rmse:0.293231+0.012844 test-rmse:0.492832+0.052437
## [132] train-rmse:0.292648+0.012817 test-rmse:0.493232+0.052037
## [133] train-rmse:0.291437+0.012852 test-rmse:0.493097+0.051572
## [134] train-rmse:0.289837+0.012808 test-rmse:0.493137+0.051560
## [135] train-rmse:0.288822+0.012452 test-rmse:0.493373+0.051166
## [136] train-rmse:0.288090+0.012584 test-rmse:0.493096+0.051266
## Stopping. Best iteration:
## [130] train-rmse:0.294070+0.012838 test-rmse:0.492821+0.052049
##
## 10
## [1] train-rmse:0.922950+0.015553 test-rmse:0.927087+0.061616
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.852703+0.014659 test-rmse:0.863120+0.061397
## [3] train-rmse:0.792436+0.014403 test-rmse:0.809195+0.061264
## [4] train-rmse:0.740812+0.014232 test-rmse:0.763242+0.061656
## [5] train-rmse:0.696402+0.013656 test-rmse:0.724529+0.063647
## [6] train-rmse:0.658108+0.012565 test-rmse:0.693453+0.063708
## [7] train-rmse:0.624988+0.012679 test-rmse:0.667180+0.065838
## [8] train-rmse:0.595366+0.012354 test-rmse:0.643034+0.064233
## [9] train-rmse:0.570450+0.012919 test-rmse:0.625509+0.061815
## [10] train-rmse:0.548468+0.012702 test-rmse:0.608608+0.060865
## [11] train-rmse:0.529275+0.012498 test-rmse:0.594891+0.059977
## [12] train-rmse:0.512634+0.012337 test-rmse:0.583324+0.059258
## [13] train-rmse:0.497567+0.012738 test-rmse:0.573830+0.058662
## [14] train-rmse:0.484328+0.013660 test-rmse:0.566743+0.057470
## [15] train-rmse:0.473165+0.013426 test-rmse:0.560603+0.056813
## [16] train-rmse:0.463767+0.012879 test-rmse:0.554778+0.056726
## [17] train-rmse:0.453916+0.015111 test-rmse:0.551087+0.055889
## [18] train-rmse:0.446221+0.014896 test-rmse:0.547157+0.054284
## [19] train-rmse:0.437165+0.015224 test-rmse:0.540939+0.051877
## [20] train-rmse:0.431312+0.015563 test-rmse:0.537344+0.051864
## [21] train-rmse:0.425216+0.015321 test-rmse:0.534023+0.051225
## [22] train-rmse:0.419827+0.015488 test-rmse:0.530720+0.051732
## [23] train-rmse:0.415285+0.015590 test-rmse:0.528413+0.051354
## [24] train-rmse:0.410565+0.014899 test-rmse:0.526355+0.052229
## [25] train-rmse:0.405676+0.014439 test-rmse:0.525083+0.052587
## [26] train-rmse:0.401130+0.014669 test-rmse:0.522956+0.052662
## [27] train-rmse:0.397074+0.014279 test-rmse:0.521006+0.051661
## [28] train-rmse:0.393082+0.013883 test-rmse:0.518181+0.052668
## [29] train-rmse:0.390118+0.013959 test-rmse:0.517098+0.051711
## [30] train-rmse:0.384966+0.012765 test-rmse:0.514926+0.054220
## [31] train-rmse:0.381812+0.012845 test-rmse:0.513571+0.054408
## [32] train-rmse:0.379276+0.012787 test-rmse:0.512968+0.054145
## [33] train-rmse:0.376181+0.012205 test-rmse:0.511294+0.055026
## [34] train-rmse:0.373107+0.012020 test-rmse:0.510183+0.055358
## [35] train-rmse:0.370806+0.011878 test-rmse:0.509428+0.054861
## [36] train-rmse:0.366855+0.012849 test-rmse:0.507662+0.054538
## [37] train-rmse:0.364527+0.012565 test-rmse:0.506768+0.054960
## [38] train-rmse:0.361516+0.012798 test-rmse:0.506209+0.055030
## [39] train-rmse:0.358490+0.013916 test-rmse:0.505896+0.054867
## [40] train-rmse:0.355368+0.013521 test-rmse:0.505050+0.055469
## [41] train-rmse:0.352389+0.013499 test-rmse:0.504364+0.055669
## [42] train-rmse:0.349688+0.014373 test-rmse:0.503456+0.055628
## [43] train-rmse:0.347396+0.013814 test-rmse:0.503353+0.056254
## [44] train-rmse:0.345154+0.013354 test-rmse:0.503128+0.056580
## [45] train-rmse:0.342986+0.013880 test-rmse:0.503300+0.056827
## [46] train-rmse:0.340231+0.014235 test-rmse:0.503102+0.056447
## [47] train-rmse:0.338175+0.013826 test-rmse:0.502013+0.057302
## [48] train-rmse:0.335857+0.013808 test-rmse:0.501959+0.056589
## [49] train-rmse:0.333600+0.014006 test-rmse:0.501654+0.055953
## [50] train-rmse:0.331727+0.014127 test-rmse:0.500893+0.056445
## [51] train-rmse:0.329314+0.014401 test-rmse:0.500477+0.056819
## [52] train-rmse:0.327372+0.014201 test-rmse:0.499755+0.056186
## [53] train-rmse:0.324406+0.014879 test-rmse:0.500250+0.055572
## [54] train-rmse:0.322655+0.014601 test-rmse:0.499382+0.056112
## [55] train-rmse:0.320749+0.014761 test-rmse:0.498839+0.055985
## [56] train-rmse:0.318968+0.014762 test-rmse:0.498188+0.055658
## [57] train-rmse:0.316827+0.014484 test-rmse:0.497777+0.055638
## [58] train-rmse:0.315068+0.014446 test-rmse:0.498049+0.055562
## [59] train-rmse:0.313451+0.014103 test-rmse:0.497512+0.055478
## [60] train-rmse:0.311389+0.014248 test-rmse:0.497489+0.055563
## [61] train-rmse:0.309539+0.014432 test-rmse:0.497147+0.056015
## [62] train-rmse:0.307520+0.014572 test-rmse:0.496308+0.055688
## [63] train-rmse:0.305809+0.014718 test-rmse:0.495837+0.055053
## [64] train-rmse:0.303914+0.014408 test-rmse:0.495299+0.055447
## [65] train-rmse:0.301454+0.014151 test-rmse:0.495642+0.054662
## [66] train-rmse:0.299232+0.014087 test-rmse:0.495734+0.054437
## [67] train-rmse:0.297816+0.014299 test-rmse:0.495957+0.054551
## [68] train-rmse:0.295881+0.014301 test-rmse:0.494961+0.054767
## [69] train-rmse:0.294863+0.014022 test-rmse:0.495399+0.054867
## [70] train-rmse:0.292676+0.013830 test-rmse:0.495730+0.053844
## [71] train-rmse:0.291485+0.013893 test-rmse:0.495656+0.053434
## [72] train-rmse:0.289835+0.013798 test-rmse:0.495902+0.053385
## [73] train-rmse:0.288577+0.013515 test-rmse:0.495609+0.054305
## [74] train-rmse:0.287148+0.013510 test-rmse:0.495454+0.054540
## Stopping. Best iteration:
## [68] train-rmse:0.295881+0.014301 test-rmse:0.494961+0.054767
##
## 11
## [1] train-rmse:0.919793+0.015253 test-rmse:0.925195+0.062117
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.846332+0.014344 test-rmse:0.858437+0.060651
## [3] train-rmse:0.782855+0.012911 test-rmse:0.802885+0.061455
## [4] train-rmse:0.727553+0.012390 test-rmse:0.755104+0.062189
## [5] train-rmse:0.679748+0.011860 test-rmse:0.715971+0.062505
## [6] train-rmse:0.639625+0.011696 test-rmse:0.685200+0.062895
## [7] train-rmse:0.602706+0.010999 test-rmse:0.655731+0.061671
## [8] train-rmse:0.571413+0.011992 test-rmse:0.633861+0.062190
## [9] train-rmse:0.543849+0.012024 test-rmse:0.613514+0.060669
## [10] train-rmse:0.519920+0.012187 test-rmse:0.595314+0.059659
## [11] train-rmse:0.499050+0.012342 test-rmse:0.582498+0.058093
## [12] train-rmse:0.479912+0.013030 test-rmse:0.570600+0.056958
## [13] train-rmse:0.463430+0.013441 test-rmse:0.561910+0.055526
## [14] train-rmse:0.448554+0.013308 test-rmse:0.552645+0.055410
## [15] train-rmse:0.435090+0.013867 test-rmse:0.545435+0.056240
## [16] train-rmse:0.423599+0.013724 test-rmse:0.540190+0.055427
## [17] train-rmse:0.413347+0.014000 test-rmse:0.536605+0.054705
## [18] train-rmse:0.404597+0.013137 test-rmse:0.532221+0.055608
## [19] train-rmse:0.397239+0.013170 test-rmse:0.529368+0.054783
## [20] train-rmse:0.390082+0.012863 test-rmse:0.527358+0.054663
## [21] train-rmse:0.383488+0.013306 test-rmse:0.524742+0.055166
## [22] train-rmse:0.377605+0.012295 test-rmse:0.521648+0.055395
## [23] train-rmse:0.371349+0.014353 test-rmse:0.519156+0.054279
## [24] train-rmse:0.366367+0.014049 test-rmse:0.517786+0.053931
## [25] train-rmse:0.361860+0.014565 test-rmse:0.515717+0.052920
## [26] train-rmse:0.358133+0.015408 test-rmse:0.515012+0.053636
## [27] train-rmse:0.353784+0.014895 test-rmse:0.512776+0.054249
## [28] train-rmse:0.350108+0.014495 test-rmse:0.510789+0.054818
## [29] train-rmse:0.345362+0.014156 test-rmse:0.508626+0.056109
## [30] train-rmse:0.341696+0.014030 test-rmse:0.507216+0.055814
## [31] train-rmse:0.338151+0.013706 test-rmse:0.505876+0.054760
## [32] train-rmse:0.333439+0.014499 test-rmse:0.505371+0.055390
## [33] train-rmse:0.330389+0.014535 test-rmse:0.504043+0.054935
## [34] train-rmse:0.327659+0.014045 test-rmse:0.504199+0.055241
## [35] train-rmse:0.324156+0.014546 test-rmse:0.503965+0.055850
## [36] train-rmse:0.321511+0.014180 test-rmse:0.502959+0.055310
## [37] train-rmse:0.318245+0.013729 test-rmse:0.502623+0.055140
## [38] train-rmse:0.314782+0.013713 test-rmse:0.501434+0.055697
## [39] train-rmse:0.311823+0.013626 test-rmse:0.500978+0.056100
## [40] train-rmse:0.308894+0.014510 test-rmse:0.501501+0.056008
## [41] train-rmse:0.306679+0.014261 test-rmse:0.501355+0.055472
## [42] train-rmse:0.303858+0.013754 test-rmse:0.501068+0.055966
## [43] train-rmse:0.301236+0.013289 test-rmse:0.500905+0.055590
## [44] train-rmse:0.299003+0.013644 test-rmse:0.500619+0.056105
## [45] train-rmse:0.296855+0.013563 test-rmse:0.500525+0.056649
## [46] train-rmse:0.294555+0.014288 test-rmse:0.499860+0.056033
## [47] train-rmse:0.292356+0.013479 test-rmse:0.499859+0.055653
## [48] train-rmse:0.288923+0.013684 test-rmse:0.498853+0.055311
## [49] train-rmse:0.285909+0.013286 test-rmse:0.497884+0.056193
## [50] train-rmse:0.283452+0.013132 test-rmse:0.497587+0.056250
## [51] train-rmse:0.281131+0.013027 test-rmse:0.497039+0.056864
## [52] train-rmse:0.279210+0.013126 test-rmse:0.496760+0.057216
## [53] train-rmse:0.277195+0.013353 test-rmse:0.496653+0.056803
## [54] train-rmse:0.274302+0.013001 test-rmse:0.496183+0.056051
## [55] train-rmse:0.272521+0.012811 test-rmse:0.495828+0.055709
## [56] train-rmse:0.271037+0.013211 test-rmse:0.495826+0.055557
## [57] train-rmse:0.268223+0.012686 test-rmse:0.495054+0.055542
## [58] train-rmse:0.266417+0.012180 test-rmse:0.494939+0.055543
## [59] train-rmse:0.264409+0.012335 test-rmse:0.494453+0.054961
## [60] train-rmse:0.261240+0.013237 test-rmse:0.494751+0.055114
## [61] train-rmse:0.258959+0.012637 test-rmse:0.494660+0.055181
## [62] train-rmse:0.256782+0.012237 test-rmse:0.493735+0.054926
## [63] train-rmse:0.255440+0.012253 test-rmse:0.493587+0.054787
## [64] train-rmse:0.253208+0.013063 test-rmse:0.493025+0.054701
## [65] train-rmse:0.251139+0.013235 test-rmse:0.492664+0.054853
## [66] train-rmse:0.249013+0.013597 test-rmse:0.492967+0.054923
## [67] train-rmse:0.246959+0.013984 test-rmse:0.494147+0.055683
## [68] train-rmse:0.245013+0.014246 test-rmse:0.494423+0.056297
## [69] train-rmse:0.243708+0.014391 test-rmse:0.494176+0.055915
## [70] train-rmse:0.241102+0.014242 test-rmse:0.493588+0.056174
## [71] train-rmse:0.237980+0.014585 test-rmse:0.493817+0.055964
## Stopping. Best iteration:
## [65] train-rmse:0.251139+0.013235 test-rmse:0.492664+0.054853
##
## 12
## [1] train-rmse:0.916924+0.014959 test-rmse:0.923903+0.062312
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.840699+0.014175 test-rmse:0.854880+0.060994
## [3] train-rmse:0.774489+0.013092 test-rmse:0.798714+0.060641
## [4] train-rmse:0.716351+0.011521 test-rmse:0.749800+0.062103
## [5] train-rmse:0.665880+0.011665 test-rmse:0.708748+0.062414
## [6] train-rmse:0.623102+0.011422 test-rmse:0.676090+0.064120
## [7] train-rmse:0.585921+0.011244 test-rmse:0.648817+0.065204
## [8] train-rmse:0.552683+0.011504 test-rmse:0.626730+0.065418
## [9] train-rmse:0.522393+0.011978 test-rmse:0.606468+0.062029
## [10] train-rmse:0.495472+0.012838 test-rmse:0.590792+0.061422
## [11] train-rmse:0.472745+0.013103 test-rmse:0.575885+0.059271
## [12] train-rmse:0.452129+0.013514 test-rmse:0.564362+0.058694
## [13] train-rmse:0.434083+0.012778 test-rmse:0.554159+0.059070
## [14] train-rmse:0.418333+0.012113 test-rmse:0.547578+0.060403
## [15] train-rmse:0.403366+0.011452 test-rmse:0.539619+0.060441
## [16] train-rmse:0.391156+0.010650 test-rmse:0.534692+0.061579
## [17] train-rmse:0.380302+0.010601 test-rmse:0.530880+0.059770
## [18] train-rmse:0.369844+0.011558 test-rmse:0.527926+0.060999
## [19] train-rmse:0.362119+0.011207 test-rmse:0.525397+0.060784
## [20] train-rmse:0.354201+0.011461 test-rmse:0.522647+0.060247
## [21] train-rmse:0.347703+0.011421 test-rmse:0.521422+0.059363
## [22] train-rmse:0.341316+0.010977 test-rmse:0.518685+0.057941
## [23] train-rmse:0.335073+0.010773 test-rmse:0.517177+0.057526
## [24] train-rmse:0.330776+0.011128 test-rmse:0.516603+0.057072
## [25] train-rmse:0.324255+0.011147 test-rmse:0.515915+0.056799
## [26] train-rmse:0.317847+0.012037 test-rmse:0.514455+0.055952
## [27] train-rmse:0.313051+0.011393 test-rmse:0.512657+0.055285
## [28] train-rmse:0.308631+0.012621 test-rmse:0.511561+0.054302
## [29] train-rmse:0.304578+0.011870 test-rmse:0.510206+0.053213
## [30] train-rmse:0.299605+0.010366 test-rmse:0.507636+0.052414
## [31] train-rmse:0.294343+0.012613 test-rmse:0.506259+0.052135
## [32] train-rmse:0.291037+0.012376 test-rmse:0.505339+0.051646
## [33] train-rmse:0.287321+0.012845 test-rmse:0.504441+0.051440
## [34] train-rmse:0.284411+0.013014 test-rmse:0.504621+0.051435
## [35] train-rmse:0.279231+0.012697 test-rmse:0.504054+0.051988
## [36] train-rmse:0.276269+0.012594 test-rmse:0.502828+0.051439
## [37] train-rmse:0.273426+0.012440 test-rmse:0.502094+0.051312
## [38] train-rmse:0.269878+0.013523 test-rmse:0.501246+0.051464
## [39] train-rmse:0.267059+0.013586 test-rmse:0.501437+0.051445
## [40] train-rmse:0.264465+0.013266 test-rmse:0.501317+0.051813
## [41] train-rmse:0.261414+0.014222 test-rmse:0.501526+0.051820
## [42] train-rmse:0.257229+0.014696 test-rmse:0.501041+0.052439
## [43] train-rmse:0.255002+0.014749 test-rmse:0.500396+0.052025
## [44] train-rmse:0.251458+0.015350 test-rmse:0.500345+0.051769
## [45] train-rmse:0.249457+0.015208 test-rmse:0.500122+0.051385
## [46] train-rmse:0.246732+0.015435 test-rmse:0.499831+0.051718
## [47] train-rmse:0.244627+0.015158 test-rmse:0.500174+0.051788
## [48] train-rmse:0.242043+0.015175 test-rmse:0.500107+0.052041
## [49] train-rmse:0.239635+0.015566 test-rmse:0.499560+0.052490
## [50] train-rmse:0.237402+0.015463 test-rmse:0.499364+0.051887
## [51] train-rmse:0.234216+0.014400 test-rmse:0.499380+0.052747
## [52] train-rmse:0.232071+0.014614 test-rmse:0.499119+0.052750
## [53] train-rmse:0.229411+0.013516 test-rmse:0.499375+0.053058
## [54] train-rmse:0.226537+0.013214 test-rmse:0.500383+0.053799
## [55] train-rmse:0.223942+0.013953 test-rmse:0.499906+0.053574
## [56] train-rmse:0.222006+0.013890 test-rmse:0.499401+0.053449
## [57] train-rmse:0.220320+0.013801 test-rmse:0.499596+0.053574
## [58] train-rmse:0.218042+0.013130 test-rmse:0.499619+0.053699
## Stopping. Best iteration:
## [52] train-rmse:0.232071+0.014614 test-rmse:0.499119+0.052750
##
## 13
## [1] train-rmse:0.914122+0.014697 test-rmse:0.921960+0.061395
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.835441+0.013279 test-rmse:0.853048+0.062286
## [3] train-rmse:0.766863+0.012661 test-rmse:0.795199+0.062477
## [4] train-rmse:0.706652+0.012431 test-rmse:0.745790+0.061744
## [5] train-rmse:0.653848+0.011160 test-rmse:0.704273+0.063211
## [6] train-rmse:0.608106+0.010381 test-rmse:0.669866+0.063062
## [7] train-rmse:0.568036+0.011152 test-rmse:0.644015+0.064257
## [8] train-rmse:0.533343+0.012343 test-rmse:0.621725+0.065266
## [9] train-rmse:0.501386+0.012315 test-rmse:0.600928+0.064643
## [10] train-rmse:0.474471+0.012534 test-rmse:0.583877+0.064913
## [11] train-rmse:0.449699+0.011295 test-rmse:0.568359+0.063205
## [12] train-rmse:0.428610+0.012377 test-rmse:0.557847+0.062988
## [13] train-rmse:0.409146+0.012663 test-rmse:0.547232+0.062680
## [14] train-rmse:0.391547+0.012587 test-rmse:0.538145+0.062375
## [15] train-rmse:0.376335+0.012736 test-rmse:0.530975+0.062812
## [16] train-rmse:0.361851+0.012264 test-rmse:0.525966+0.062367
## [17] train-rmse:0.349581+0.012444 test-rmse:0.524129+0.062490
## [18] train-rmse:0.337862+0.013399 test-rmse:0.521570+0.062471
## [19] train-rmse:0.326824+0.013574 test-rmse:0.518886+0.063598
## [20] train-rmse:0.317417+0.014416 test-rmse:0.516687+0.063226
## [21] train-rmse:0.309570+0.015102 test-rmse:0.515184+0.062753
## [22] train-rmse:0.302737+0.014899 test-rmse:0.513313+0.061843
## [23] train-rmse:0.294721+0.014851 test-rmse:0.512121+0.060810
## [24] train-rmse:0.289639+0.014654 test-rmse:0.510679+0.059566
## [25] train-rmse:0.283131+0.014187 test-rmse:0.508344+0.058437
## [26] train-rmse:0.276521+0.014998 test-rmse:0.507615+0.056825
## [27] train-rmse:0.271308+0.014841 test-rmse:0.506586+0.055981
## [28] train-rmse:0.267578+0.014664 test-rmse:0.505300+0.056295
## [29] train-rmse:0.262805+0.013810 test-rmse:0.504970+0.054879
## [30] train-rmse:0.258870+0.013427 test-rmse:0.504066+0.054056
## [31] train-rmse:0.253884+0.014176 test-rmse:0.502896+0.053433
## [32] train-rmse:0.250338+0.013898 test-rmse:0.501235+0.052945
## [33] train-rmse:0.247159+0.013780 test-rmse:0.500677+0.052452
## [34] train-rmse:0.242240+0.014840 test-rmse:0.499939+0.052686
## [35] train-rmse:0.238422+0.014894 test-rmse:0.498922+0.051866
## [36] train-rmse:0.236071+0.014492 test-rmse:0.498131+0.051965
## [37] train-rmse:0.231944+0.013885 test-rmse:0.497199+0.051637
## [38] train-rmse:0.228804+0.013126 test-rmse:0.497026+0.049923
## [39] train-rmse:0.226154+0.013230 test-rmse:0.496685+0.049332
## [40] train-rmse:0.224100+0.013396 test-rmse:0.496757+0.049314
## [41] train-rmse:0.220949+0.013348 test-rmse:0.496768+0.048700
## [42] train-rmse:0.218263+0.013322 test-rmse:0.496706+0.048310
## [43] train-rmse:0.216269+0.013335 test-rmse:0.496742+0.047665
## [44] train-rmse:0.213916+0.012928 test-rmse:0.497284+0.047783
## [45] train-rmse:0.210696+0.012496 test-rmse:0.497011+0.048355
## Stopping. Best iteration:
## [39] train-rmse:0.226154+0.013230 test-rmse:0.496685+0.049332
##
## 14
## [1] train-rmse:0.929630+0.015462 test-rmse:0.930346+0.061537
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.869603+0.014764 test-rmse:0.873258+0.060906
## [3] train-rmse:0.822048+0.014210 test-rmse:0.828264+0.060508
## [4] train-rmse:0.783919+0.013994 test-rmse:0.798234+0.060355
## [5] train-rmse:0.753267+0.013489 test-rmse:0.769235+0.058183
## [6] train-rmse:0.728374+0.013052 test-rmse:0.745350+0.059582
## [7] train-rmse:0.707717+0.012619 test-rmse:0.728624+0.057513
## [8] train-rmse:0.690179+0.012440 test-rmse:0.710675+0.057446
## [9] train-rmse:0.675983+0.012090 test-rmse:0.697114+0.057045
## [10] train-rmse:0.663690+0.011827 test-rmse:0.685120+0.055089
## [11] train-rmse:0.653386+0.011719 test-rmse:0.677381+0.055831
## [12] train-rmse:0.644789+0.011525 test-rmse:0.670482+0.052474
## [13] train-rmse:0.637167+0.011400 test-rmse:0.662790+0.053638
## [14] train-rmse:0.630691+0.011232 test-rmse:0.655941+0.053156
## [15] train-rmse:0.625078+0.011169 test-rmse:0.652555+0.053006
## [16] train-rmse:0.620091+0.011103 test-rmse:0.647285+0.051275
## [17] train-rmse:0.615684+0.011191 test-rmse:0.643408+0.053120
## [18] train-rmse:0.611535+0.011230 test-rmse:0.642131+0.054325
## [19] train-rmse:0.607723+0.011207 test-rmse:0.638477+0.054618
## [20] train-rmse:0.604092+0.011181 test-rmse:0.635346+0.053557
## [21] train-rmse:0.600817+0.011182 test-rmse:0.633234+0.054130
## [22] train-rmse:0.597672+0.011226 test-rmse:0.631240+0.053335
## [23] train-rmse:0.594677+0.011177 test-rmse:0.626506+0.053705
## [24] train-rmse:0.591926+0.011165 test-rmse:0.625521+0.054013
## [25] train-rmse:0.589265+0.011146 test-rmse:0.623146+0.054799
## [26] train-rmse:0.586709+0.011182 test-rmse:0.621156+0.053904
## [27] train-rmse:0.584306+0.011184 test-rmse:0.618361+0.054742
## [28] train-rmse:0.581947+0.011225 test-rmse:0.616095+0.055679
## [29] train-rmse:0.579712+0.011238 test-rmse:0.615500+0.054260
## [30] train-rmse:0.577530+0.011180 test-rmse:0.614541+0.053792
## [31] train-rmse:0.575449+0.011128 test-rmse:0.612364+0.054356
## [32] train-rmse:0.573445+0.011156 test-rmse:0.610671+0.054991
## [33] train-rmse:0.571474+0.011167 test-rmse:0.608569+0.055719
## [34] train-rmse:0.569589+0.011177 test-rmse:0.607825+0.055748
## [35] train-rmse:0.567737+0.011203 test-rmse:0.605502+0.055390
## [36] train-rmse:0.565943+0.011194 test-rmse:0.603113+0.055423
## [37] train-rmse:0.564207+0.011189 test-rmse:0.601325+0.054800
## [38] train-rmse:0.562530+0.011188 test-rmse:0.601474+0.053705
## [39] train-rmse:0.560873+0.011208 test-rmse:0.601157+0.054192
## [40] train-rmse:0.559280+0.011225 test-rmse:0.599033+0.054403
## [41] train-rmse:0.557709+0.011231 test-rmse:0.597192+0.054565
## [42] train-rmse:0.556174+0.011216 test-rmse:0.595835+0.054995
## [43] train-rmse:0.554688+0.011194 test-rmse:0.594541+0.054237
## [44] train-rmse:0.553222+0.011186 test-rmse:0.593332+0.054479
## [45] train-rmse:0.551768+0.011185 test-rmse:0.592756+0.054160
## [46] train-rmse:0.550340+0.011185 test-rmse:0.591356+0.054351
## [47] train-rmse:0.548935+0.011193 test-rmse:0.590776+0.053479
## [48] train-rmse:0.547566+0.011202 test-rmse:0.589810+0.053652
## [49] train-rmse:0.546253+0.011191 test-rmse:0.587986+0.054491
## [50] train-rmse:0.544941+0.011173 test-rmse:0.587993+0.054048
## [51] train-rmse:0.543660+0.011146 test-rmse:0.587319+0.054319
## [52] train-rmse:0.542416+0.011146 test-rmse:0.586331+0.054662
## [53] train-rmse:0.541218+0.011117 test-rmse:0.585521+0.053967
## [54] train-rmse:0.540028+0.011074 test-rmse:0.584658+0.054088
## [55] train-rmse:0.538863+0.011068 test-rmse:0.584276+0.054822
## [56] train-rmse:0.537721+0.011068 test-rmse:0.583054+0.054486
## [57] train-rmse:0.536589+0.011076 test-rmse:0.582432+0.054739
## [58] train-rmse:0.535479+0.011080 test-rmse:0.581496+0.053970
## [59] train-rmse:0.534396+0.011082 test-rmse:0.580421+0.054887
## [60] train-rmse:0.533352+0.011078 test-rmse:0.579962+0.054646
## [61] train-rmse:0.532321+0.011067 test-rmse:0.578378+0.054274
## [62] train-rmse:0.531299+0.011054 test-rmse:0.578239+0.053596
## [63] train-rmse:0.530301+0.011040 test-rmse:0.577736+0.053399
## [64] train-rmse:0.529317+0.011023 test-rmse:0.577325+0.053036
## [65] train-rmse:0.528340+0.010996 test-rmse:0.577099+0.053013
## [66] train-rmse:0.527385+0.010970 test-rmse:0.576393+0.053931
## [67] train-rmse:0.526453+0.010942 test-rmse:0.575557+0.053983
## [68] train-rmse:0.525539+0.010907 test-rmse:0.574578+0.054113
## [69] train-rmse:0.524638+0.010885 test-rmse:0.575060+0.054867
## [70] train-rmse:0.523751+0.010877 test-rmse:0.573805+0.054902
## [71] train-rmse:0.522878+0.010860 test-rmse:0.572785+0.054335
## [72] train-rmse:0.522013+0.010839 test-rmse:0.572342+0.054860
## [73] train-rmse:0.521176+0.010840 test-rmse:0.571608+0.055528
## [74] train-rmse:0.520353+0.010832 test-rmse:0.571109+0.054793
## [75] train-rmse:0.519536+0.010828 test-rmse:0.570630+0.054034
## [76] train-rmse:0.518729+0.010817 test-rmse:0.569207+0.054216
## [77] train-rmse:0.517929+0.010797 test-rmse:0.568507+0.053773
## [78] train-rmse:0.517148+0.010786 test-rmse:0.568235+0.053607
## [79] train-rmse:0.516370+0.010774 test-rmse:0.567495+0.053359
## [80] train-rmse:0.515607+0.010761 test-rmse:0.566922+0.053977
## [81] train-rmse:0.514854+0.010752 test-rmse:0.566158+0.054354
## [82] train-rmse:0.514113+0.010745 test-rmse:0.565099+0.054239
## [83] train-rmse:0.513380+0.010735 test-rmse:0.564780+0.053609
## [84] train-rmse:0.512661+0.010725 test-rmse:0.564234+0.053584
## [85] train-rmse:0.511964+0.010712 test-rmse:0.563938+0.052908
## [86] train-rmse:0.511274+0.010702 test-rmse:0.564140+0.053265
## [87] train-rmse:0.510590+0.010687 test-rmse:0.564009+0.054221
## [88] train-rmse:0.509917+0.010666 test-rmse:0.563232+0.054424
## [89] train-rmse:0.509242+0.010645 test-rmse:0.562397+0.054207
## [90] train-rmse:0.508582+0.010636 test-rmse:0.561691+0.054094
## [91] train-rmse:0.507929+0.010638 test-rmse:0.560945+0.054027
## [92] train-rmse:0.507284+0.010638 test-rmse:0.560621+0.053753
## [93] train-rmse:0.506652+0.010632 test-rmse:0.560020+0.053345
## [94] train-rmse:0.506038+0.010620 test-rmse:0.559242+0.053389
## [95] train-rmse:0.505430+0.010613 test-rmse:0.558715+0.053436
## [96] train-rmse:0.504823+0.010600 test-rmse:0.558348+0.052814
## [97] train-rmse:0.504237+0.010597 test-rmse:0.558030+0.053194
## [98] train-rmse:0.503650+0.010589 test-rmse:0.557434+0.053306
## [99] train-rmse:0.503069+0.010582 test-rmse:0.557461+0.053626
## [100] train-rmse:0.502500+0.010576 test-rmse:0.556975+0.054255
## [101] train-rmse:0.501943+0.010569 test-rmse:0.556576+0.054230
## [102] train-rmse:0.501386+0.010556 test-rmse:0.556336+0.054034
## [103] train-rmse:0.500833+0.010546 test-rmse:0.555882+0.053698
## [104] train-rmse:0.500291+0.010538 test-rmse:0.555057+0.053789
## [105] train-rmse:0.499754+0.010544 test-rmse:0.554665+0.053768
## [106] train-rmse:0.499227+0.010548 test-rmse:0.554738+0.053027
## [107] train-rmse:0.498702+0.010549 test-rmse:0.554222+0.053243
## [108] train-rmse:0.498183+0.010551 test-rmse:0.553991+0.053481
## [109] train-rmse:0.497672+0.010555 test-rmse:0.553915+0.053199
## [110] train-rmse:0.497171+0.010558 test-rmse:0.553702+0.053037
## [111] train-rmse:0.496678+0.010560 test-rmse:0.553951+0.052354
## [112] train-rmse:0.496193+0.010559 test-rmse:0.553030+0.052278
## [113] train-rmse:0.495716+0.010558 test-rmse:0.552517+0.052194
## [114] train-rmse:0.495243+0.010553 test-rmse:0.552371+0.052019
## [115] train-rmse:0.494773+0.010548 test-rmse:0.551717+0.052376
## [116] train-rmse:0.494306+0.010536 test-rmse:0.551597+0.052396
## [117] train-rmse:0.493846+0.010520 test-rmse:0.551260+0.052144
## [118] train-rmse:0.493390+0.010508 test-rmse:0.550902+0.052510
## [119] train-rmse:0.492940+0.010503 test-rmse:0.550777+0.052200
## [120] train-rmse:0.492498+0.010499 test-rmse:0.550888+0.052672
## [121] train-rmse:0.492057+0.010496 test-rmse:0.550085+0.052599
## [122] train-rmse:0.491622+0.010494 test-rmse:0.549870+0.052721
## [123] train-rmse:0.491191+0.010491 test-rmse:0.549970+0.052693
## [124] train-rmse:0.490768+0.010488 test-rmse:0.549646+0.052612
## [125] train-rmse:0.490346+0.010478 test-rmse:0.549187+0.052731
## [126] train-rmse:0.489928+0.010474 test-rmse:0.548604+0.052778
## [127] train-rmse:0.489519+0.010470 test-rmse:0.547875+0.053003
## [128] train-rmse:0.489114+0.010461 test-rmse:0.547866+0.052251
## [129] train-rmse:0.488717+0.010455 test-rmse:0.547186+0.051956
## [130] train-rmse:0.488320+0.010453 test-rmse:0.547108+0.051987
## [131] train-rmse:0.487930+0.010451 test-rmse:0.547111+0.051717
## [132] train-rmse:0.487542+0.010445 test-rmse:0.547260+0.052172
## [133] train-rmse:0.487159+0.010446 test-rmse:0.547185+0.052201
## [134] train-rmse:0.486779+0.010450 test-rmse:0.546933+0.052057
## [135] train-rmse:0.486408+0.010451 test-rmse:0.546754+0.052068
## [136] train-rmse:0.486039+0.010451 test-rmse:0.546878+0.052078
## [137] train-rmse:0.485675+0.010448 test-rmse:0.546548+0.052022
## [138] train-rmse:0.485315+0.010442 test-rmse:0.546270+0.051838
## [139] train-rmse:0.484958+0.010435 test-rmse:0.546105+0.052106
## [140] train-rmse:0.484606+0.010427 test-rmse:0.545590+0.052028
## [141] train-rmse:0.484259+0.010419 test-rmse:0.545312+0.052369
## [142] train-rmse:0.483913+0.010411 test-rmse:0.545022+0.052423
## [143] train-rmse:0.483574+0.010404 test-rmse:0.545100+0.052081
## [144] train-rmse:0.483237+0.010397 test-rmse:0.544896+0.052168
## [145] train-rmse:0.482908+0.010388 test-rmse:0.545253+0.051980
## [146] train-rmse:0.482577+0.010383 test-rmse:0.544941+0.051994
## [147] train-rmse:0.482252+0.010379 test-rmse:0.544780+0.051901
## [148] train-rmse:0.481932+0.010380 test-rmse:0.544547+0.051893
## [149] train-rmse:0.481610+0.010382 test-rmse:0.544625+0.051621
## [150] train-rmse:0.481291+0.010374 test-rmse:0.544274+0.051354
## [151] train-rmse:0.480980+0.010372 test-rmse:0.543673+0.051356
## [152] train-rmse:0.480672+0.010373 test-rmse:0.543421+0.051042
## [153] train-rmse:0.480367+0.010375 test-rmse:0.543071+0.051294
## [154] train-rmse:0.480064+0.010373 test-rmse:0.543099+0.050929
## [155] train-rmse:0.479763+0.010374 test-rmse:0.542613+0.050653
## [156] train-rmse:0.479465+0.010373 test-rmse:0.542818+0.050964
## [157] train-rmse:0.479172+0.010370 test-rmse:0.542842+0.051252
## [158] train-rmse:0.478883+0.010366 test-rmse:0.542494+0.051115
## [159] train-rmse:0.478596+0.010362 test-rmse:0.542005+0.050832
## [160] train-rmse:0.478314+0.010363 test-rmse:0.541768+0.050564
## [161] train-rmse:0.478031+0.010363 test-rmse:0.541599+0.050789
## [162] train-rmse:0.477753+0.010368 test-rmse:0.541512+0.050868
## [163] train-rmse:0.477476+0.010367 test-rmse:0.540952+0.050778
## [164] train-rmse:0.477205+0.010365 test-rmse:0.540855+0.050960
## [165] train-rmse:0.476935+0.010367 test-rmse:0.540886+0.051039
## [166] train-rmse:0.476670+0.010368 test-rmse:0.540650+0.050839
## [167] train-rmse:0.476406+0.010367 test-rmse:0.540550+0.051156
## [168] train-rmse:0.476144+0.010365 test-rmse:0.540498+0.050972
## [169] train-rmse:0.475881+0.010364 test-rmse:0.540500+0.050590
## [170] train-rmse:0.475622+0.010360 test-rmse:0.540097+0.050589
## [171] train-rmse:0.475362+0.010352 test-rmse:0.539951+0.050280
## [172] train-rmse:0.475109+0.010348 test-rmse:0.539866+0.050430
## [173] train-rmse:0.474859+0.010343 test-rmse:0.539758+0.050277
## [174] train-rmse:0.474609+0.010338 test-rmse:0.539731+0.050079
## [175] train-rmse:0.474364+0.010335 test-rmse:0.539650+0.049813
## [176] train-rmse:0.474121+0.010332 test-rmse:0.539744+0.049597
## [177] train-rmse:0.473881+0.010328 test-rmse:0.539811+0.049920
## [178] train-rmse:0.473643+0.010325 test-rmse:0.540119+0.049970
## [179] train-rmse:0.473408+0.010319 test-rmse:0.539535+0.050011
## [180] train-rmse:0.473173+0.010314 test-rmse:0.539307+0.050056
## [181] train-rmse:0.472940+0.010306 test-rmse:0.539459+0.049715
## [182] train-rmse:0.472709+0.010300 test-rmse:0.539007+0.049699
## [183] train-rmse:0.472482+0.010298 test-rmse:0.538952+0.049564
## [184] train-rmse:0.472258+0.010298 test-rmse:0.538769+0.049406
## [185] train-rmse:0.472037+0.010297 test-rmse:0.538352+0.049538
## [186] train-rmse:0.471819+0.010293 test-rmse:0.538158+0.049546
## [187] train-rmse:0.471603+0.010290 test-rmse:0.538084+0.049676
## [188] train-rmse:0.471389+0.010286 test-rmse:0.537983+0.049727
## [189] train-rmse:0.471173+0.010282 test-rmse:0.537761+0.049548
## [190] train-rmse:0.470964+0.010277 test-rmse:0.537502+0.049489
## [191] train-rmse:0.470754+0.010274 test-rmse:0.537471+0.049674
## [192] train-rmse:0.470546+0.010272 test-rmse:0.537435+0.049646
## [193] train-rmse:0.470339+0.010272 test-rmse:0.537396+0.049498
## [194] train-rmse:0.470134+0.010267 test-rmse:0.537107+0.049531
## [195] train-rmse:0.469930+0.010264 test-rmse:0.536556+0.049257
## [196] train-rmse:0.469727+0.010259 test-rmse:0.536811+0.049338
## [197] train-rmse:0.469524+0.010256 test-rmse:0.537060+0.049235
## [198] train-rmse:0.469326+0.010251 test-rmse:0.536671+0.049164
## [199] train-rmse:0.469130+0.010247 test-rmse:0.536643+0.049050
## [200] train-rmse:0.468934+0.010243 test-rmse:0.536520+0.048931
## [201] train-rmse:0.468740+0.010237 test-rmse:0.536533+0.048860
## [202] train-rmse:0.468550+0.010233 test-rmse:0.536452+0.049130
## [203] train-rmse:0.468363+0.010227 test-rmse:0.536363+0.048990
## [204] train-rmse:0.468175+0.010222 test-rmse:0.536632+0.048750
## [205] train-rmse:0.467988+0.010218 test-rmse:0.536629+0.048589
## [206] train-rmse:0.467804+0.010211 test-rmse:0.536443+0.048603
## [207] train-rmse:0.467621+0.010204 test-rmse:0.536297+0.048726
## [208] train-rmse:0.467438+0.010200 test-rmse:0.536258+0.048322
## [209] train-rmse:0.467260+0.010195 test-rmse:0.536102+0.048479
## [210] train-rmse:0.467077+0.010190 test-rmse:0.536499+0.048459
## [211] train-rmse:0.466901+0.010186 test-rmse:0.536411+0.048663
## [212] train-rmse:0.466727+0.010182 test-rmse:0.536291+0.048582
## [213] train-rmse:0.466551+0.010173 test-rmse:0.535842+0.048569
## [214] train-rmse:0.466380+0.010166 test-rmse:0.536086+0.048557
## [215] train-rmse:0.466210+0.010161 test-rmse:0.536050+0.048497
## [216] train-rmse:0.466042+0.010155 test-rmse:0.535778+0.048449
## [217] train-rmse:0.465878+0.010150 test-rmse:0.535742+0.048145
## [218] train-rmse:0.465715+0.010143 test-rmse:0.535589+0.048311
## [219] train-rmse:0.465552+0.010137 test-rmse:0.535533+0.048289
## [220] train-rmse:0.465390+0.010132 test-rmse:0.535302+0.048077
## [221] train-rmse:0.465229+0.010126 test-rmse:0.535156+0.048118
## [222] train-rmse:0.465069+0.010121 test-rmse:0.535121+0.047988
## [223] train-rmse:0.464906+0.010109 test-rmse:0.534936+0.048095
## [224] train-rmse:0.464749+0.010101 test-rmse:0.534838+0.047981
## [225] train-rmse:0.464591+0.010094 test-rmse:0.534906+0.047737
## [226] train-rmse:0.464434+0.010088 test-rmse:0.534841+0.047825
## [227] train-rmse:0.464278+0.010082 test-rmse:0.535015+0.047662
## [228] train-rmse:0.464123+0.010078 test-rmse:0.535073+0.047629
## [229] train-rmse:0.463968+0.010073 test-rmse:0.535091+0.047499
## [230] train-rmse:0.463815+0.010068 test-rmse:0.534774+0.047502
## [231] train-rmse:0.463660+0.010061 test-rmse:0.534718+0.047479
## [232] train-rmse:0.463510+0.010054 test-rmse:0.534673+0.047596
## [233] train-rmse:0.463362+0.010045 test-rmse:0.534553+0.047453
## [234] train-rmse:0.463215+0.010038 test-rmse:0.534507+0.047459
## [235] train-rmse:0.463067+0.010030 test-rmse:0.534526+0.047514
## [236] train-rmse:0.462920+0.010024 test-rmse:0.534341+0.047432
## [237] train-rmse:0.462776+0.010017 test-rmse:0.534085+0.047372
## [238] train-rmse:0.462631+0.010007 test-rmse:0.534244+0.047356
## [239] train-rmse:0.462488+0.010000 test-rmse:0.534142+0.047306
## [240] train-rmse:0.462346+0.009995 test-rmse:0.534084+0.047314
## [241] train-rmse:0.462207+0.009989 test-rmse:0.533940+0.047306
## [242] train-rmse:0.462067+0.009981 test-rmse:0.533944+0.047437
## [243] train-rmse:0.461929+0.009976 test-rmse:0.533653+0.047610
## [244] train-rmse:0.461791+0.009970 test-rmse:0.533611+0.047354
## [245] train-rmse:0.461655+0.009961 test-rmse:0.533589+0.047360
## [246] train-rmse:0.461520+0.009953 test-rmse:0.533625+0.047353
## [247] train-rmse:0.461384+0.009946 test-rmse:0.533590+0.047136
## [248] train-rmse:0.461252+0.009939 test-rmse:0.533700+0.047261
## [249] train-rmse:0.461121+0.009932 test-rmse:0.533672+0.047246
## [250] train-rmse:0.460991+0.009925 test-rmse:0.533277+0.047482
## [251] train-rmse:0.460859+0.009918 test-rmse:0.533265+0.047236
## [252] train-rmse:0.460728+0.009911 test-rmse:0.533223+0.047306
## [253] train-rmse:0.460596+0.009903 test-rmse:0.533050+0.047133
## [254] train-rmse:0.460464+0.009890 test-rmse:0.533363+0.047049
## [255] train-rmse:0.460335+0.009884 test-rmse:0.533388+0.047141
## [256] train-rmse:0.460207+0.009877 test-rmse:0.533294+0.047183
## [257] train-rmse:0.460080+0.009869 test-rmse:0.533179+0.047129
## [258] train-rmse:0.459953+0.009861 test-rmse:0.533402+0.047069
## [259] train-rmse:0.459827+0.009854 test-rmse:0.533364+0.046856
## Stopping. Best iteration:
## [253] train-rmse:0.460596+0.009903 test-rmse:0.533050+0.047133
##
## 15
## [1] train-rmse:0.918684+0.015385 test-rmse:0.920272+0.061406
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.848454+0.014596 test-rmse:0.855857+0.061568
## [3] train-rmse:0.791507+0.014384 test-rmse:0.802442+0.061999
## [4] train-rmse:0.745111+0.013706 test-rmse:0.761539+0.063275
## [5] train-rmse:0.707041+0.013033 test-rmse:0.727695+0.064847
## [6] train-rmse:0.676141+0.012104 test-rmse:0.699621+0.066239
## [7] train-rmse:0.651050+0.012039 test-rmse:0.676100+0.063396
## [8] train-rmse:0.630253+0.011938 test-rmse:0.657060+0.062310
## [9] train-rmse:0.612043+0.012495 test-rmse:0.644429+0.061005
## [10] train-rmse:0.597482+0.012739 test-rmse:0.633882+0.061137
## [11] train-rmse:0.584814+0.013092 test-rmse:0.623683+0.060387
## [12] train-rmse:0.574596+0.013907 test-rmse:0.616722+0.060285
## [13] train-rmse:0.565783+0.013767 test-rmse:0.610710+0.059593
## [14] train-rmse:0.558011+0.013766 test-rmse:0.604115+0.059958
## [15] train-rmse:0.551507+0.014686 test-rmse:0.600712+0.058578
## [16] train-rmse:0.545197+0.014995 test-rmse:0.594631+0.057391
## [17] train-rmse:0.540002+0.014709 test-rmse:0.592122+0.059174
## [18] train-rmse:0.534626+0.015698 test-rmse:0.589815+0.057929
## [19] train-rmse:0.529968+0.015193 test-rmse:0.585114+0.057594
## [20] train-rmse:0.525884+0.015122 test-rmse:0.582562+0.057101
## [21] train-rmse:0.522296+0.015101 test-rmse:0.580262+0.057310
## [22] train-rmse:0.518150+0.014340 test-rmse:0.577975+0.059074
## [23] train-rmse:0.514746+0.014406 test-rmse:0.575782+0.058841
## [24] train-rmse:0.511496+0.014397 test-rmse:0.573862+0.059421
## [25] train-rmse:0.507452+0.014854 test-rmse:0.572229+0.060231
## [26] train-rmse:0.504763+0.014896 test-rmse:0.570821+0.060403
## [27] train-rmse:0.501322+0.015256 test-rmse:0.567366+0.060005
## [28] train-rmse:0.498372+0.015360 test-rmse:0.565159+0.058818
## [29] train-rmse:0.495747+0.015161 test-rmse:0.562546+0.060375
## [30] train-rmse:0.492674+0.015399 test-rmse:0.559396+0.060652
## [31] train-rmse:0.490366+0.015305 test-rmse:0.558794+0.060660
## [32] train-rmse:0.488003+0.015633 test-rmse:0.558054+0.060634
## [33] train-rmse:0.485485+0.015628 test-rmse:0.555863+0.060474
## [34] train-rmse:0.482557+0.015020 test-rmse:0.554053+0.060980
## [35] train-rmse:0.480381+0.015068 test-rmse:0.552285+0.060472
## [36] train-rmse:0.478181+0.015367 test-rmse:0.550882+0.061347
## [37] train-rmse:0.475935+0.015235 test-rmse:0.549733+0.060831
## [38] train-rmse:0.473411+0.015460 test-rmse:0.547086+0.059794
## [39] train-rmse:0.471604+0.015379 test-rmse:0.546427+0.060660
## [40] train-rmse:0.469665+0.015091 test-rmse:0.545711+0.061154
## [41] train-rmse:0.467406+0.015180 test-rmse:0.543791+0.060052
## [42] train-rmse:0.465206+0.014974 test-rmse:0.541887+0.061065
## [43] train-rmse:0.463162+0.015124 test-rmse:0.539546+0.061479
## [44] train-rmse:0.461521+0.015281 test-rmse:0.539078+0.061292
## [45] train-rmse:0.459550+0.015430 test-rmse:0.537866+0.061484
## [46] train-rmse:0.458137+0.015376 test-rmse:0.536752+0.061561
## [47] train-rmse:0.456305+0.014948 test-rmse:0.535296+0.061655
## [48] train-rmse:0.454923+0.015053 test-rmse:0.535778+0.061771
## [49] train-rmse:0.453312+0.015144 test-rmse:0.534814+0.060617
## [50] train-rmse:0.451822+0.015326 test-rmse:0.534496+0.060720
## [51] train-rmse:0.450217+0.015548 test-rmse:0.533610+0.060693
## [52] train-rmse:0.448738+0.015480 test-rmse:0.532378+0.060822
## [53] train-rmse:0.447138+0.015224 test-rmse:0.531465+0.061489
## [54] train-rmse:0.445451+0.015145 test-rmse:0.530568+0.061384
## [55] train-rmse:0.443789+0.014765 test-rmse:0.530095+0.061311
## [56] train-rmse:0.442439+0.014589 test-rmse:0.529175+0.061529
## [57] train-rmse:0.440818+0.014761 test-rmse:0.528133+0.060559
## [58] train-rmse:0.439625+0.014888 test-rmse:0.527390+0.060663
## [59] train-rmse:0.437956+0.015414 test-rmse:0.527002+0.060080
## [60] train-rmse:0.436721+0.015595 test-rmse:0.526988+0.060542
## [61] train-rmse:0.435455+0.015432 test-rmse:0.525800+0.061858
## [62] train-rmse:0.433877+0.015283 test-rmse:0.525653+0.062667
## [63] train-rmse:0.432758+0.015415 test-rmse:0.525316+0.062598
## [64] train-rmse:0.431265+0.015390 test-rmse:0.524784+0.062642
## [65] train-rmse:0.430161+0.015349 test-rmse:0.523576+0.062109
## [66] train-rmse:0.428854+0.015004 test-rmse:0.522610+0.062293
## [67] train-rmse:0.427720+0.015262 test-rmse:0.522434+0.062545
## [68] train-rmse:0.426565+0.015232 test-rmse:0.521514+0.062598
## [69] train-rmse:0.425659+0.015231 test-rmse:0.521358+0.062528
## [70] train-rmse:0.424193+0.015532 test-rmse:0.520379+0.062862
## [71] train-rmse:0.422931+0.015429 test-rmse:0.519233+0.064055
## [72] train-rmse:0.421876+0.015563 test-rmse:0.518787+0.063371
## [73] train-rmse:0.420781+0.015638 test-rmse:0.518355+0.063344
## [74] train-rmse:0.419583+0.015895 test-rmse:0.517868+0.062729
## [75] train-rmse:0.418464+0.015634 test-rmse:0.517967+0.063205
## [76] train-rmse:0.417168+0.015545 test-rmse:0.517451+0.063513
## [77] train-rmse:0.416092+0.015502 test-rmse:0.517471+0.063019
## [78] train-rmse:0.415289+0.015621 test-rmse:0.517079+0.063488
## [79] train-rmse:0.414387+0.015978 test-rmse:0.516611+0.063455
## [80] train-rmse:0.413166+0.015880 test-rmse:0.515953+0.064096
## [81] train-rmse:0.412001+0.015896 test-rmse:0.514994+0.064761
## [82] train-rmse:0.410952+0.015918 test-rmse:0.515248+0.066059
## [83] train-rmse:0.409941+0.015867 test-rmse:0.514820+0.065504
## [84] train-rmse:0.409203+0.015805 test-rmse:0.514444+0.064866
## [85] train-rmse:0.408219+0.015683 test-rmse:0.514380+0.064791
## [86] train-rmse:0.407276+0.015702 test-rmse:0.513853+0.064434
## [87] train-rmse:0.406298+0.015680 test-rmse:0.513454+0.064247
## [88] train-rmse:0.405340+0.015757 test-rmse:0.513701+0.064211
## [89] train-rmse:0.404522+0.015767 test-rmse:0.513659+0.064248
## [90] train-rmse:0.403660+0.015658 test-rmse:0.512839+0.064453
## [91] train-rmse:0.402931+0.015820 test-rmse:0.512988+0.064534
## [92] train-rmse:0.402195+0.015972 test-rmse:0.512551+0.064382
## [93] train-rmse:0.401420+0.015784 test-rmse:0.512326+0.064513
## [94] train-rmse:0.400843+0.015702 test-rmse:0.512151+0.064345
## [95] train-rmse:0.400074+0.015494 test-rmse:0.512471+0.064561
## [96] train-rmse:0.398975+0.015205 test-rmse:0.512275+0.064367
## [97] train-rmse:0.398210+0.015061 test-rmse:0.512272+0.065012
## [98] train-rmse:0.397267+0.014960 test-rmse:0.512143+0.064461
## [99] train-rmse:0.396327+0.015109 test-rmse:0.511832+0.064235
## [100] train-rmse:0.395567+0.015228 test-rmse:0.511736+0.063980
## [101] train-rmse:0.394791+0.014996 test-rmse:0.512182+0.063928
## [102] train-rmse:0.394098+0.015143 test-rmse:0.511608+0.063858
## [103] train-rmse:0.393099+0.014603 test-rmse:0.511263+0.063599
## [104] train-rmse:0.392093+0.014559 test-rmse:0.511009+0.063866
## [105] train-rmse:0.391585+0.014470 test-rmse:0.510719+0.063958
## [106] train-rmse:0.390794+0.014619 test-rmse:0.510295+0.064061
## [107] train-rmse:0.390135+0.014572 test-rmse:0.509722+0.064277
## [108] train-rmse:0.389250+0.014290 test-rmse:0.509336+0.064181
## [109] train-rmse:0.388266+0.014152 test-rmse:0.509516+0.064089
## [110] train-rmse:0.387724+0.014118 test-rmse:0.509410+0.063668
## [111] train-rmse:0.386842+0.013894 test-rmse:0.508714+0.064519
## [112] train-rmse:0.386024+0.013966 test-rmse:0.508186+0.064034
## [113] train-rmse:0.385422+0.013926 test-rmse:0.507992+0.064007
## [114] train-rmse:0.384943+0.013803 test-rmse:0.507857+0.064058
## [115] train-rmse:0.384265+0.013906 test-rmse:0.508053+0.064110
## [116] train-rmse:0.383338+0.013915 test-rmse:0.508042+0.063930
## [117] train-rmse:0.382565+0.013980 test-rmse:0.508340+0.063901
## [118] train-rmse:0.382057+0.013996 test-rmse:0.508321+0.063400
## [119] train-rmse:0.381614+0.013920 test-rmse:0.508324+0.063454
## [120] train-rmse:0.380997+0.013781 test-rmse:0.508273+0.063083
## Stopping. Best iteration:
## [114] train-rmse:0.384943+0.013803 test-rmse:0.507857+0.064058
##
## 16
## [1] train-rmse:0.913802+0.015576 test-rmse:0.917358+0.062270
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.838521+0.014851 test-rmse:0.848449+0.062729
## [3] train-rmse:0.776225+0.014511 test-rmse:0.791286+0.062910
## [4] train-rmse:0.725288+0.014232 test-rmse:0.745189+0.064450
## [5] train-rmse:0.682533+0.014057 test-rmse:0.708034+0.063697
## [6] train-rmse:0.647036+0.013139 test-rmse:0.677924+0.064726
## [7] train-rmse:0.617980+0.013451 test-rmse:0.652916+0.063518
## [8] train-rmse:0.593824+0.013543 test-rmse:0.633394+0.061050
## [9] train-rmse:0.572574+0.012718 test-rmse:0.618171+0.060975
## [10] train-rmse:0.555487+0.013053 test-rmse:0.603762+0.059822
## [11] train-rmse:0.540385+0.012230 test-rmse:0.594856+0.058188
## [12] train-rmse:0.527675+0.013391 test-rmse:0.585497+0.054817
## [13] train-rmse:0.516429+0.012668 test-rmse:0.576645+0.054260
## [14] train-rmse:0.505484+0.014085 test-rmse:0.570252+0.054915
## [15] train-rmse:0.497360+0.013478 test-rmse:0.565115+0.055251
## [16] train-rmse:0.490389+0.013301 test-rmse:0.561401+0.054755
## [17] train-rmse:0.483095+0.014064 test-rmse:0.558956+0.056670
## [18] train-rmse:0.477799+0.014001 test-rmse:0.555594+0.056008
## [19] train-rmse:0.472591+0.013901 test-rmse:0.553566+0.055801
## [20] train-rmse:0.467966+0.013957 test-rmse:0.549771+0.055262
## [21] train-rmse:0.463453+0.013248 test-rmse:0.547892+0.055994
## [22] train-rmse:0.459283+0.012797 test-rmse:0.545546+0.055254
## [23] train-rmse:0.454841+0.012673 test-rmse:0.542929+0.055801
## [24] train-rmse:0.450751+0.012323 test-rmse:0.539799+0.055753
## [25] train-rmse:0.446842+0.012873 test-rmse:0.537692+0.055922
## [26] train-rmse:0.442886+0.012772 test-rmse:0.536685+0.055556
## [27] train-rmse:0.439169+0.012340 test-rmse:0.534942+0.056964
## [28] train-rmse:0.435901+0.012177 test-rmse:0.534636+0.057381
## [29] train-rmse:0.432366+0.013076 test-rmse:0.532979+0.057378
## [30] train-rmse:0.429037+0.013007 test-rmse:0.530976+0.057864
## [31] train-rmse:0.426307+0.013070 test-rmse:0.529276+0.056883
## [32] train-rmse:0.422992+0.012858 test-rmse:0.528692+0.057605
## [33] train-rmse:0.420603+0.012971 test-rmse:0.528198+0.057060
## [34] train-rmse:0.416088+0.013575 test-rmse:0.525420+0.056822
## [35] train-rmse:0.413537+0.013566 test-rmse:0.524569+0.057972
## [36] train-rmse:0.410192+0.014890 test-rmse:0.523799+0.058386
## [37] train-rmse:0.407680+0.014913 test-rmse:0.523134+0.058568
## [38] train-rmse:0.405032+0.015179 test-rmse:0.522682+0.059338
## [39] train-rmse:0.402863+0.015297 test-rmse:0.522022+0.059236
## [40] train-rmse:0.400847+0.014930 test-rmse:0.521039+0.060408
## [41] train-rmse:0.398646+0.014989 test-rmse:0.520183+0.058829
## [42] train-rmse:0.396447+0.014748 test-rmse:0.519998+0.058543
## [43] train-rmse:0.394439+0.014596 test-rmse:0.518980+0.059398
## [44] train-rmse:0.391539+0.015127 test-rmse:0.518151+0.059464
## [45] train-rmse:0.389378+0.015100 test-rmse:0.516953+0.060310
## [46] train-rmse:0.387382+0.015073 test-rmse:0.516687+0.060742
## [47] train-rmse:0.385759+0.015253 test-rmse:0.515858+0.060444
## [48] train-rmse:0.383814+0.015325 test-rmse:0.514050+0.060207
## [49] train-rmse:0.381765+0.014864 test-rmse:0.513401+0.059978
## [50] train-rmse:0.380046+0.014887 test-rmse:0.513638+0.059950
## [51] train-rmse:0.378070+0.014565 test-rmse:0.512991+0.059726
## [52] train-rmse:0.376244+0.014656 test-rmse:0.512104+0.059492
## [53] train-rmse:0.374068+0.014797 test-rmse:0.511453+0.059688
## [54] train-rmse:0.371998+0.013895 test-rmse:0.511177+0.059566
## [55] train-rmse:0.369678+0.014029 test-rmse:0.511122+0.058902
## [56] train-rmse:0.367851+0.014094 test-rmse:0.509905+0.059414
## [57] train-rmse:0.366382+0.014216 test-rmse:0.509367+0.058980
## [58] train-rmse:0.364554+0.014121 test-rmse:0.509094+0.058247
## [59] train-rmse:0.362579+0.013976 test-rmse:0.507780+0.058845
## [60] train-rmse:0.360750+0.013887 test-rmse:0.507701+0.058265
## [61] train-rmse:0.359182+0.013875 test-rmse:0.508400+0.058474
## [62] train-rmse:0.358057+0.013664 test-rmse:0.508018+0.058856
## [63] train-rmse:0.356578+0.013309 test-rmse:0.507543+0.059011
## [64] train-rmse:0.355082+0.013133 test-rmse:0.506437+0.059002
## [65] train-rmse:0.353808+0.012991 test-rmse:0.505492+0.059097
## [66] train-rmse:0.352153+0.013105 test-rmse:0.505563+0.059139
## [67] train-rmse:0.350155+0.013311 test-rmse:0.504352+0.058702
## [68] train-rmse:0.348533+0.013408 test-rmse:0.503672+0.058696
## [69] train-rmse:0.346844+0.012968 test-rmse:0.502737+0.057452
## [70] train-rmse:0.345078+0.013089 test-rmse:0.502220+0.057783
## [71] train-rmse:0.343303+0.013491 test-rmse:0.501498+0.057831
## [72] train-rmse:0.342138+0.013538 test-rmse:0.501360+0.058478
## [73] train-rmse:0.340744+0.013807 test-rmse:0.501028+0.058349
## [74] train-rmse:0.339337+0.013660 test-rmse:0.500564+0.058170
## [75] train-rmse:0.337668+0.013899 test-rmse:0.499894+0.057857
## [76] train-rmse:0.336753+0.013936 test-rmse:0.500443+0.057648
## [77] train-rmse:0.335200+0.014050 test-rmse:0.499967+0.057321
## [78] train-rmse:0.333667+0.013826 test-rmse:0.500449+0.056665
## [79] train-rmse:0.331968+0.013594 test-rmse:0.499956+0.056528
## [80] train-rmse:0.330775+0.013734 test-rmse:0.499799+0.056769
## [81] train-rmse:0.328635+0.013641 test-rmse:0.498270+0.056870
## [82] train-rmse:0.327302+0.013999 test-rmse:0.498204+0.057223
## [83] train-rmse:0.326109+0.014147 test-rmse:0.498897+0.057198
## [84] train-rmse:0.324941+0.014077 test-rmse:0.498801+0.057053
## [85] train-rmse:0.323839+0.014056 test-rmse:0.499030+0.056799
## [86] train-rmse:0.322731+0.014264 test-rmse:0.498310+0.056883
## [87] train-rmse:0.321860+0.014153 test-rmse:0.498357+0.057534
## [88] train-rmse:0.320490+0.014257 test-rmse:0.497642+0.057670
## [89] train-rmse:0.318949+0.013981 test-rmse:0.497253+0.057803
## [90] train-rmse:0.317161+0.013775 test-rmse:0.497196+0.057643
## [91] train-rmse:0.315906+0.013676 test-rmse:0.497482+0.057196
## [92] train-rmse:0.314739+0.013980 test-rmse:0.497472+0.057675
## [93] train-rmse:0.313850+0.013942 test-rmse:0.497369+0.057518
## [94] train-rmse:0.312485+0.014273 test-rmse:0.496972+0.057441
## [95] train-rmse:0.310924+0.014032 test-rmse:0.497103+0.057470
## [96] train-rmse:0.309834+0.014054 test-rmse:0.497333+0.057106
## [97] train-rmse:0.308395+0.014647 test-rmse:0.497166+0.056709
## [98] train-rmse:0.307106+0.014307 test-rmse:0.496880+0.056340
## [99] train-rmse:0.305601+0.014216 test-rmse:0.496556+0.055887
## [100] train-rmse:0.304726+0.014286 test-rmse:0.496148+0.056033
## [101] train-rmse:0.303518+0.014149 test-rmse:0.496138+0.056167
## [102] train-rmse:0.302187+0.014126 test-rmse:0.495934+0.055984
## [103] train-rmse:0.301052+0.013906 test-rmse:0.495519+0.056493
## [104] train-rmse:0.300242+0.013807 test-rmse:0.495444+0.056170
## [105] train-rmse:0.298980+0.013671 test-rmse:0.495753+0.056150
## [106] train-rmse:0.298164+0.013585 test-rmse:0.495435+0.056071
## [107] train-rmse:0.297096+0.013343 test-rmse:0.494626+0.056565
## [108] train-rmse:0.296538+0.013246 test-rmse:0.495184+0.056658
## [109] train-rmse:0.295515+0.013581 test-rmse:0.495366+0.056978
## [110] train-rmse:0.294217+0.013786 test-rmse:0.494873+0.057629
## [111] train-rmse:0.293206+0.014010 test-rmse:0.495200+0.056948
## [112] train-rmse:0.292581+0.013885 test-rmse:0.495308+0.057105
## [113] train-rmse:0.291484+0.013615 test-rmse:0.494962+0.057167
## Stopping. Best iteration:
## [107] train-rmse:0.297096+0.013343 test-rmse:0.494626+0.056565
##
## 17
## [1] train-rmse:0.909629+0.015428 test-rmse:0.914837+0.061417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.830261+0.014427 test-rmse:0.843112+0.061833
## [3] train-rmse:0.764051+0.014260 test-rmse:0.783004+0.062567
## [4] train-rmse:0.708528+0.014140 test-rmse:0.733618+0.062279
## [5] train-rmse:0.662331+0.013543 test-rmse:0.696138+0.064147
## [6] train-rmse:0.622659+0.012289 test-rmse:0.665119+0.064762
## [7] train-rmse:0.589046+0.012378 test-rmse:0.638672+0.064057
## [8] train-rmse:0.561365+0.012792 test-rmse:0.619496+0.064040
## [9] train-rmse:0.536753+0.012302 test-rmse:0.602214+0.061865
## [10] train-rmse:0.516782+0.012465 test-rmse:0.588266+0.061570
## [11] train-rmse:0.498421+0.012933 test-rmse:0.576671+0.058931
## [12] train-rmse:0.483950+0.012356 test-rmse:0.567526+0.059269
## [13] train-rmse:0.470791+0.012473 test-rmse:0.560845+0.058396
## [14] train-rmse:0.460139+0.011537 test-rmse:0.555754+0.057559
## [15] train-rmse:0.449526+0.011399 test-rmse:0.551224+0.059901
## [16] train-rmse:0.440629+0.012211 test-rmse:0.544226+0.059290
## [17] train-rmse:0.430392+0.013698 test-rmse:0.540734+0.058760
## [18] train-rmse:0.423990+0.013397 test-rmse:0.538188+0.058355
## [19] train-rmse:0.417372+0.012976 test-rmse:0.533696+0.056154
## [20] train-rmse:0.411847+0.012505 test-rmse:0.529680+0.056749
## [21] train-rmse:0.405841+0.013143 test-rmse:0.525405+0.057283
## [22] train-rmse:0.401342+0.012869 test-rmse:0.524010+0.057235
## [23] train-rmse:0.395634+0.014088 test-rmse:0.522896+0.057608
## [24] train-rmse:0.391156+0.013584 test-rmse:0.520608+0.059442
## [25] train-rmse:0.386353+0.013373 test-rmse:0.518127+0.059725
## [26] train-rmse:0.381853+0.014281 test-rmse:0.517050+0.060180
## [27] train-rmse:0.377757+0.013955 test-rmse:0.515074+0.058957
## [28] train-rmse:0.374283+0.013728 test-rmse:0.514829+0.059299
## [29] train-rmse:0.370739+0.013262 test-rmse:0.511897+0.060885
## [30] train-rmse:0.366705+0.013708 test-rmse:0.510515+0.062098
## [31] train-rmse:0.363235+0.013503 test-rmse:0.509001+0.061642
## [32] train-rmse:0.360255+0.013650 test-rmse:0.508383+0.061111
## [33] train-rmse:0.357370+0.013495 test-rmse:0.505821+0.062665
## [34] train-rmse:0.353836+0.013340 test-rmse:0.504754+0.063179
## [35] train-rmse:0.351188+0.013251 test-rmse:0.504593+0.062354
## [36] train-rmse:0.348089+0.012791 test-rmse:0.501901+0.062634
## [37] train-rmse:0.345249+0.013465 test-rmse:0.500363+0.062579
## [38] train-rmse:0.342455+0.014129 test-rmse:0.500452+0.062510
## [39] train-rmse:0.340092+0.013689 test-rmse:0.499367+0.063016
## [40] train-rmse:0.338097+0.013283 test-rmse:0.499718+0.063512
## [41] train-rmse:0.335472+0.012972 test-rmse:0.499323+0.063432
## [42] train-rmse:0.332897+0.013295 test-rmse:0.499544+0.062762
## [43] train-rmse:0.330071+0.013213 test-rmse:0.498350+0.062393
## [44] train-rmse:0.328237+0.012903 test-rmse:0.498313+0.061915
## [45] train-rmse:0.326723+0.012893 test-rmse:0.498347+0.061900
## [46] train-rmse:0.324460+0.013110 test-rmse:0.497336+0.062128
## [47] train-rmse:0.322110+0.013319 test-rmse:0.497388+0.061419
## [48] train-rmse:0.320121+0.012980 test-rmse:0.496340+0.061017
## [49] train-rmse:0.316585+0.013883 test-rmse:0.495349+0.060841
## [50] train-rmse:0.314269+0.014364 test-rmse:0.494671+0.060076
## [51] train-rmse:0.311977+0.013730 test-rmse:0.494297+0.059644
## [52] train-rmse:0.309279+0.013718 test-rmse:0.493742+0.061012
## [53] train-rmse:0.307330+0.014500 test-rmse:0.493204+0.059983
## [54] train-rmse:0.305501+0.014690 test-rmse:0.493508+0.059787
## [55] train-rmse:0.303053+0.015046 test-rmse:0.493508+0.059064
## [56] train-rmse:0.300845+0.015075 test-rmse:0.493405+0.059871
## [57] train-rmse:0.298834+0.015276 test-rmse:0.493389+0.060340
## [58] train-rmse:0.296256+0.014675 test-rmse:0.493212+0.059598
## [59] train-rmse:0.294165+0.015168 test-rmse:0.493786+0.059992
## Stopping. Best iteration:
## [53] train-rmse:0.307330+0.014500 test-rmse:0.493204+0.059983
##
## 18
## [1] train-rmse:0.905925+0.015078 test-rmse:0.912635+0.061985
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.822729+0.014053 test-rmse:0.838154+0.060169
## [3] train-rmse:0.752874+0.012509 test-rmse:0.778177+0.061554
## [4] train-rmse:0.693337+0.012053 test-rmse:0.727489+0.062078
## [5] train-rmse:0.643436+0.011356 test-rmse:0.689122+0.063202
## [6] train-rmse:0.600661+0.010391 test-rmse:0.653206+0.059214
## [7] train-rmse:0.564635+0.010349 test-rmse:0.628721+0.061161
## [8] train-rmse:0.533765+0.010744 test-rmse:0.605354+0.060280
## [9] train-rmse:0.507381+0.010676 test-rmse:0.587810+0.060294
## [10] train-rmse:0.484403+0.011943 test-rmse:0.573674+0.059773
## [11] train-rmse:0.465144+0.012881 test-rmse:0.560898+0.059829
## [12] train-rmse:0.447528+0.011907 test-rmse:0.553395+0.059497
## [13] train-rmse:0.433285+0.011958 test-rmse:0.545712+0.057453
## [14] train-rmse:0.419872+0.012911 test-rmse:0.539230+0.059150
## [15] train-rmse:0.409297+0.012616 test-rmse:0.535812+0.057865
## [16] train-rmse:0.401499+0.012420 test-rmse:0.531296+0.056362
## [17] train-rmse:0.392815+0.013255 test-rmse:0.526740+0.058427
## [18] train-rmse:0.386246+0.013693 test-rmse:0.524000+0.058193
## [19] train-rmse:0.379482+0.012713 test-rmse:0.521226+0.057482
## [20] train-rmse:0.372981+0.012085 test-rmse:0.518246+0.058810
## [21] train-rmse:0.366157+0.016137 test-rmse:0.516747+0.058263
## [22] train-rmse:0.359047+0.015322 test-rmse:0.514256+0.057613
## [23] train-rmse:0.353945+0.014164 test-rmse:0.511090+0.058461
## [24] train-rmse:0.348853+0.013744 test-rmse:0.510142+0.058128
## [25] train-rmse:0.344900+0.014068 test-rmse:0.509831+0.058370
## [26] train-rmse:0.340738+0.013517 test-rmse:0.509183+0.058474
## [27] train-rmse:0.336790+0.013170 test-rmse:0.507578+0.058523
## [28] train-rmse:0.331755+0.013514 test-rmse:0.506057+0.058138
## [29] train-rmse:0.327505+0.012641 test-rmse:0.504723+0.057705
## [30] train-rmse:0.324165+0.012615 test-rmse:0.503822+0.057649
## [31] train-rmse:0.321386+0.011964 test-rmse:0.503613+0.058139
## [32] train-rmse:0.318663+0.011806 test-rmse:0.502892+0.057795
## [33] train-rmse:0.315304+0.011549 test-rmse:0.502223+0.057824
## [34] train-rmse:0.312364+0.011039 test-rmse:0.501479+0.057073
## [35] train-rmse:0.307196+0.011720 test-rmse:0.498786+0.057319
## [36] train-rmse:0.303980+0.011581 test-rmse:0.498725+0.057530
## [37] train-rmse:0.301247+0.012530 test-rmse:0.499198+0.057978
## [38] train-rmse:0.298206+0.012053 test-rmse:0.498799+0.057127
## [39] train-rmse:0.294923+0.012249 test-rmse:0.497739+0.057774
## [40] train-rmse:0.292146+0.011885 test-rmse:0.497176+0.057788
## [41] train-rmse:0.289507+0.011863 test-rmse:0.496650+0.058405
## [42] train-rmse:0.286808+0.011614 test-rmse:0.496928+0.058368
## [43] train-rmse:0.284498+0.011051 test-rmse:0.496698+0.058297
## [44] train-rmse:0.282218+0.011693 test-rmse:0.496084+0.057629
## [45] train-rmse:0.279948+0.012353 test-rmse:0.496459+0.057430
## [46] train-rmse:0.277445+0.011977 test-rmse:0.496123+0.058224
## [47] train-rmse:0.273820+0.012168 test-rmse:0.496144+0.059153
## [48] train-rmse:0.271764+0.011954 test-rmse:0.496459+0.059653
## [49] train-rmse:0.268627+0.012047 test-rmse:0.496179+0.060313
## [50] train-rmse:0.266195+0.011742 test-rmse:0.496084+0.060090
## [51] train-rmse:0.263969+0.011838 test-rmse:0.495863+0.059480
## [52] train-rmse:0.261849+0.011795 test-rmse:0.496356+0.059796
## [53] train-rmse:0.259004+0.010965 test-rmse:0.496020+0.059833
## [54] train-rmse:0.255291+0.011517 test-rmse:0.495663+0.058836
## [55] train-rmse:0.253012+0.010266 test-rmse:0.495763+0.058974
## [56] train-rmse:0.250758+0.009866 test-rmse:0.494861+0.058593
## [57] train-rmse:0.248698+0.009671 test-rmse:0.494625+0.058963
## [58] train-rmse:0.246484+0.009654 test-rmse:0.494786+0.059464
## [59] train-rmse:0.243516+0.010089 test-rmse:0.495589+0.058609
## [60] train-rmse:0.241811+0.010536 test-rmse:0.495517+0.058653
## [61] train-rmse:0.239765+0.010316 test-rmse:0.495703+0.058323
## [62] train-rmse:0.238049+0.010088 test-rmse:0.495400+0.058831
## [63] train-rmse:0.235829+0.010763 test-rmse:0.495382+0.058960
## Stopping. Best iteration:
## [57] train-rmse:0.248698+0.009671 test-rmse:0.494625+0.058963
##
## 19
## [1] train-rmse:0.902555+0.014735 test-rmse:0.911144+0.062193
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.816060+0.013850 test-rmse:0.833691+0.060646
## [3] train-rmse:0.741955+0.011860 test-rmse:0.772533+0.061414
## [4] train-rmse:0.679992+0.010572 test-rmse:0.719476+0.062598
## [5] train-rmse:0.626863+0.010791 test-rmse:0.677557+0.063431
## [6] train-rmse:0.581852+0.010260 test-rmse:0.644800+0.066486
## [7] train-rmse:0.544110+0.010326 test-rmse:0.615643+0.062908
## [8] train-rmse:0.511098+0.009607 test-rmse:0.594710+0.059758
## [9] train-rmse:0.480629+0.011386 test-rmse:0.576109+0.058331
## [10] train-rmse:0.456315+0.011382 test-rmse:0.561587+0.059839
## [11] train-rmse:0.434325+0.011803 test-rmse:0.552234+0.057818
## [12] train-rmse:0.415245+0.012198 test-rmse:0.543092+0.056559
## [13] train-rmse:0.397731+0.011404 test-rmse:0.536738+0.056063
## [14] train-rmse:0.383125+0.012549 test-rmse:0.531948+0.054075
## [15] train-rmse:0.371267+0.012528 test-rmse:0.526439+0.054678
## [16] train-rmse:0.359753+0.012557 test-rmse:0.522260+0.054089
## [17] train-rmse:0.350186+0.012533 test-rmse:0.519222+0.053353
## [18] train-rmse:0.342087+0.013317 test-rmse:0.516725+0.053023
## [19] train-rmse:0.333265+0.014910 test-rmse:0.514525+0.052276
## [20] train-rmse:0.326356+0.015062 test-rmse:0.513275+0.052121
## [21] train-rmse:0.320291+0.015040 test-rmse:0.511502+0.052115
## [22] train-rmse:0.313759+0.013806 test-rmse:0.509297+0.052218
## [23] train-rmse:0.307933+0.015433 test-rmse:0.507101+0.051002
## [24] train-rmse:0.301805+0.015522 test-rmse:0.503902+0.052349
## [25] train-rmse:0.296747+0.015457 test-rmse:0.502490+0.053227
## [26] train-rmse:0.293003+0.015222 test-rmse:0.502762+0.052877
## [27] train-rmse:0.288545+0.016124 test-rmse:0.502026+0.051788
## [28] train-rmse:0.283721+0.015970 test-rmse:0.501571+0.051614
## [29] train-rmse:0.278827+0.016152 test-rmse:0.501200+0.051978
## [30] train-rmse:0.275483+0.015562 test-rmse:0.500662+0.051664
## [31] train-rmse:0.272672+0.015038 test-rmse:0.500382+0.052126
## [32] train-rmse:0.269407+0.014768 test-rmse:0.499843+0.052261
## [33] train-rmse:0.266180+0.014385 test-rmse:0.499208+0.052982
## [34] train-rmse:0.262977+0.014323 test-rmse:0.498939+0.052517
## [35] train-rmse:0.259374+0.013516 test-rmse:0.499182+0.053025
## [36] train-rmse:0.256342+0.013377 test-rmse:0.499585+0.052905
## [37] train-rmse:0.253417+0.013652 test-rmse:0.498817+0.052987
## [38] train-rmse:0.250158+0.014271 test-rmse:0.498913+0.053472
## [39] train-rmse:0.246775+0.013779 test-rmse:0.498041+0.053863
## [40] train-rmse:0.244242+0.013448 test-rmse:0.498411+0.054198
## [41] train-rmse:0.241297+0.013748 test-rmse:0.498809+0.054913
## [42] train-rmse:0.238795+0.013839 test-rmse:0.499268+0.054689
## [43] train-rmse:0.236011+0.013982 test-rmse:0.499810+0.054359
## [44] train-rmse:0.233034+0.013384 test-rmse:0.500341+0.054282
## [45] train-rmse:0.231169+0.013594 test-rmse:0.500489+0.054226
## Stopping. Best iteration:
## [39] train-rmse:0.246775+0.013779 test-rmse:0.498041+0.053863
##
## 20
## [1] train-rmse:0.899258+0.014429 test-rmse:0.908888+0.061105
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.809918+0.012851 test-rmse:0.831511+0.062654
## [3] train-rmse:0.733415+0.012344 test-rmse:0.767685+0.062618
## [4] train-rmse:0.668761+0.011567 test-rmse:0.716598+0.062059
## [5] train-rmse:0.612240+0.011535 test-rmse:0.673251+0.062134
## [6] train-rmse:0.565139+0.011558 test-rmse:0.640239+0.065511
## [7] train-rmse:0.525239+0.012347 test-rmse:0.613735+0.066481
## [8] train-rmse:0.490127+0.012506 test-rmse:0.591477+0.065934
## [9] train-rmse:0.458880+0.013022 test-rmse:0.573747+0.064168
## [10] train-rmse:0.433413+0.012827 test-rmse:0.558641+0.063908
## [11] train-rmse:0.409796+0.014257 test-rmse:0.547456+0.064128
## [12] train-rmse:0.388202+0.013077 test-rmse:0.540481+0.064952
## [13] train-rmse:0.371645+0.013694 test-rmse:0.533465+0.065135
## [14] train-rmse:0.355769+0.013853 test-rmse:0.530119+0.065467
## [15] train-rmse:0.341692+0.015179 test-rmse:0.526224+0.065196
## [16] train-rmse:0.326794+0.016694 test-rmse:0.523187+0.062064
## [17] train-rmse:0.314307+0.016579 test-rmse:0.519829+0.060428
## [18] train-rmse:0.304181+0.017016 test-rmse:0.517330+0.060146
## [19] train-rmse:0.295962+0.016747 test-rmse:0.515101+0.059057
## [20] train-rmse:0.288156+0.015459 test-rmse:0.512900+0.058360
## [21] train-rmse:0.281541+0.014329 test-rmse:0.510568+0.057528
## [22] train-rmse:0.274482+0.016352 test-rmse:0.509058+0.055786
## [23] train-rmse:0.267816+0.015319 test-rmse:0.507103+0.056584
## [24] train-rmse:0.263702+0.014662 test-rmse:0.505483+0.056372
## [25] train-rmse:0.258977+0.015559 test-rmse:0.504665+0.055249
## [26] train-rmse:0.253100+0.017461 test-rmse:0.503591+0.054601
## [27] train-rmse:0.247876+0.016910 test-rmse:0.501927+0.054068
## [28] train-rmse:0.244376+0.016822 test-rmse:0.500853+0.052723
## [29] train-rmse:0.239175+0.017452 test-rmse:0.500298+0.052710
## [30] train-rmse:0.236635+0.017359 test-rmse:0.499743+0.052230
## [31] train-rmse:0.232824+0.016584 test-rmse:0.499348+0.051803
## [32] train-rmse:0.229998+0.015563 test-rmse:0.499523+0.051632
## [33] train-rmse:0.226202+0.014531 test-rmse:0.499564+0.050926
## [34] train-rmse:0.223007+0.014926 test-rmse:0.499386+0.050313
## [35] train-rmse:0.219787+0.014082 test-rmse:0.499107+0.050098
## [36] train-rmse:0.216254+0.013107 test-rmse:0.499612+0.050518
## [37] train-rmse:0.211450+0.015101 test-rmse:0.499189+0.050152
## [38] train-rmse:0.208382+0.014915 test-rmse:0.498987+0.049508
## [39] train-rmse:0.205739+0.013758 test-rmse:0.498663+0.049724
## [40] train-rmse:0.202752+0.013480 test-rmse:0.498465+0.049779
## [41] train-rmse:0.198464+0.013411 test-rmse:0.498060+0.049041
## [42] train-rmse:0.195837+0.013141 test-rmse:0.497690+0.048935
## [43] train-rmse:0.193178+0.013441 test-rmse:0.497782+0.049323
## [44] train-rmse:0.190312+0.013015 test-rmse:0.497963+0.049602
## [45] train-rmse:0.186588+0.012386 test-rmse:0.498296+0.049557
## [46] train-rmse:0.183527+0.011954 test-rmse:0.498733+0.049957
## [47] train-rmse:0.180476+0.011740 test-rmse:0.498489+0.049020
## [48] train-rmse:0.177736+0.010624 test-rmse:0.498233+0.048656
## Stopping. Best iteration:
## [42] train-rmse:0.195837+0.013141 test-rmse:0.497690+0.048935
##
## 21
## [1] train-rmse:0.919360+0.015345 test-rmse:0.920477+0.061327
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.853561+0.014587 test-rmse:0.858482+0.060449
## [3] train-rmse:0.803421+0.014031 test-rmse:0.812910+0.063826
## [4] train-rmse:0.764078+0.013719 test-rmse:0.778645+0.059088
## [5] train-rmse:0.733522+0.013141 test-rmse:0.753400+0.059664
## [6] train-rmse:0.709211+0.012476 test-rmse:0.729911+0.056733
## [7] train-rmse:0.688816+0.012154 test-rmse:0.710129+0.057458
## [8] train-rmse:0.672764+0.011991 test-rmse:0.693394+0.056931
## [9] train-rmse:0.659530+0.011783 test-rmse:0.682176+0.054215
## [10] train-rmse:0.648258+0.011602 test-rmse:0.672336+0.056312
## [11] train-rmse:0.639122+0.011315 test-rmse:0.664313+0.053099
## [12] train-rmse:0.631276+0.011197 test-rmse:0.656814+0.051740
## [13] train-rmse:0.624717+0.011073 test-rmse:0.651102+0.053196
## [14] train-rmse:0.619278+0.010996 test-rmse:0.645775+0.052689
## [15] train-rmse:0.614235+0.010985 test-rmse:0.645182+0.051718
## [16] train-rmse:0.609603+0.011017 test-rmse:0.640362+0.053496
## [17] train-rmse:0.605270+0.011076 test-rmse:0.636667+0.053060
## [18] train-rmse:0.601380+0.011067 test-rmse:0.632544+0.052438
## [19] train-rmse:0.597857+0.011061 test-rmse:0.630287+0.052050
## [20] train-rmse:0.594428+0.011091 test-rmse:0.627001+0.054041
## [21] train-rmse:0.591196+0.011032 test-rmse:0.623756+0.054110
## [22] train-rmse:0.588070+0.010986 test-rmse:0.621432+0.053772
## [23] train-rmse:0.585127+0.010997 test-rmse:0.620304+0.053806
## [24] train-rmse:0.582401+0.011057 test-rmse:0.618384+0.053591
## [25] train-rmse:0.579871+0.011059 test-rmse:0.615528+0.054242
## [26] train-rmse:0.577402+0.011070 test-rmse:0.613031+0.054210
## [27] train-rmse:0.575055+0.011047 test-rmse:0.611601+0.054119
## [28] train-rmse:0.572752+0.011018 test-rmse:0.609332+0.054722
## [29] train-rmse:0.570517+0.011001 test-rmse:0.607516+0.054033
## [30] train-rmse:0.568392+0.011042 test-rmse:0.605907+0.055314
## [31] train-rmse:0.566290+0.011048 test-rmse:0.604951+0.054085
## [32] train-rmse:0.564253+0.011084 test-rmse:0.602668+0.054543
## [33] train-rmse:0.562272+0.011089 test-rmse:0.601349+0.054626
## [34] train-rmse:0.560313+0.011075 test-rmse:0.600052+0.055281
## [35] train-rmse:0.558470+0.011070 test-rmse:0.598718+0.056137
## [36] train-rmse:0.556697+0.011031 test-rmse:0.596492+0.055410
## [37] train-rmse:0.554982+0.011023 test-rmse:0.595722+0.055541
## [38] train-rmse:0.553294+0.010999 test-rmse:0.594702+0.055319
## [39] train-rmse:0.551638+0.010992 test-rmse:0.593381+0.055988
## [40] train-rmse:0.550004+0.010999 test-rmse:0.592725+0.054402
## [41] train-rmse:0.548403+0.011021 test-rmse:0.590574+0.054345
## [42] train-rmse:0.546829+0.011054 test-rmse:0.589224+0.054131
## [43] train-rmse:0.545306+0.011044 test-rmse:0.587760+0.054741
## [44] train-rmse:0.543828+0.011030 test-rmse:0.586876+0.055695
## [45] train-rmse:0.542391+0.011002 test-rmse:0.586906+0.055805
## [46] train-rmse:0.540987+0.010982 test-rmse:0.585553+0.055518
## [47] train-rmse:0.539620+0.010976 test-rmse:0.585000+0.054974
## [48] train-rmse:0.538287+0.010944 test-rmse:0.584104+0.055726
## [49] train-rmse:0.536974+0.010892 test-rmse:0.582920+0.055620
## [50] train-rmse:0.535681+0.010874 test-rmse:0.581639+0.056191
## [51] train-rmse:0.534427+0.010870 test-rmse:0.581195+0.055046
## [52] train-rmse:0.533210+0.010841 test-rmse:0.579339+0.054371
## [53] train-rmse:0.532014+0.010829 test-rmse:0.579415+0.055065
## [54] train-rmse:0.530838+0.010806 test-rmse:0.579035+0.055945
## [55] train-rmse:0.529693+0.010811 test-rmse:0.578150+0.056285
## [56] train-rmse:0.528587+0.010810 test-rmse:0.577067+0.056278
## [57] train-rmse:0.527497+0.010807 test-rmse:0.575469+0.056769
## [58] train-rmse:0.526421+0.010787 test-rmse:0.574562+0.056741
## [59] train-rmse:0.525372+0.010760 test-rmse:0.574111+0.055846
## [60] train-rmse:0.524325+0.010729 test-rmse:0.573704+0.054999
## [61] train-rmse:0.523320+0.010702 test-rmse:0.572970+0.054989
## [62] train-rmse:0.522322+0.010679 test-rmse:0.572114+0.055130
## [63] train-rmse:0.521344+0.010666 test-rmse:0.571595+0.054484
## [64] train-rmse:0.520374+0.010640 test-rmse:0.570800+0.054469
## [65] train-rmse:0.519414+0.010622 test-rmse:0.570336+0.055525
## [66] train-rmse:0.518476+0.010588 test-rmse:0.570159+0.055566
## [67] train-rmse:0.517574+0.010564 test-rmse:0.569278+0.055518
## [68] train-rmse:0.516688+0.010546 test-rmse:0.567820+0.054950
## [69] train-rmse:0.515815+0.010534 test-rmse:0.567406+0.055437
## [70] train-rmse:0.514946+0.010526 test-rmse:0.567486+0.055401
## [71] train-rmse:0.514091+0.010527 test-rmse:0.566861+0.054691
## [72] train-rmse:0.513242+0.010525 test-rmse:0.565618+0.054619
## [73] train-rmse:0.512414+0.010512 test-rmse:0.565168+0.054687
## [74] train-rmse:0.511588+0.010498 test-rmse:0.564486+0.054207
## [75] train-rmse:0.510789+0.010480 test-rmse:0.563456+0.054087
## [76] train-rmse:0.510014+0.010473 test-rmse:0.562442+0.054301
## [77] train-rmse:0.509260+0.010456 test-rmse:0.561462+0.054258
## [78] train-rmse:0.508524+0.010443 test-rmse:0.561162+0.054411
## [79] train-rmse:0.507787+0.010429 test-rmse:0.560966+0.054414
## [80] train-rmse:0.507051+0.010422 test-rmse:0.561299+0.054734
## [81] train-rmse:0.506340+0.010414 test-rmse:0.561118+0.055309
## [82] train-rmse:0.505630+0.010406 test-rmse:0.560024+0.054677
## [83] train-rmse:0.504939+0.010402 test-rmse:0.559061+0.054758
## [84] train-rmse:0.504252+0.010400 test-rmse:0.558285+0.054653
## [85] train-rmse:0.503568+0.010394 test-rmse:0.558304+0.054497
## [86] train-rmse:0.502882+0.010390 test-rmse:0.558046+0.054322
## [87] train-rmse:0.502212+0.010396 test-rmse:0.557846+0.054019
## [88] train-rmse:0.501554+0.010403 test-rmse:0.557813+0.054095
## [89] train-rmse:0.500916+0.010395 test-rmse:0.557645+0.053563
## [90] train-rmse:0.500294+0.010383 test-rmse:0.556677+0.053598
## [91] train-rmse:0.499675+0.010368 test-rmse:0.556461+0.054165
## [92] train-rmse:0.499069+0.010350 test-rmse:0.555341+0.054135
## [93] train-rmse:0.498472+0.010334 test-rmse:0.554729+0.053850
## [94] train-rmse:0.497882+0.010329 test-rmse:0.554161+0.053932
## [95] train-rmse:0.497299+0.010314 test-rmse:0.553346+0.053386
## [96] train-rmse:0.496732+0.010306 test-rmse:0.553050+0.053512
## [97] train-rmse:0.496164+0.010309 test-rmse:0.552903+0.053909
## [98] train-rmse:0.495605+0.010306 test-rmse:0.552840+0.053913
## [99] train-rmse:0.495061+0.010296 test-rmse:0.552755+0.053815
## [100] train-rmse:0.494524+0.010295 test-rmse:0.552375+0.053761
## [101] train-rmse:0.493998+0.010288 test-rmse:0.552260+0.053926
## [102] train-rmse:0.493470+0.010281 test-rmse:0.551359+0.054374
## [103] train-rmse:0.492942+0.010286 test-rmse:0.552330+0.054328
## [104] train-rmse:0.492433+0.010279 test-rmse:0.551845+0.054316
## [105] train-rmse:0.491930+0.010273 test-rmse:0.551422+0.054040
## [106] train-rmse:0.491426+0.010270 test-rmse:0.550338+0.053570
## [107] train-rmse:0.490932+0.010266 test-rmse:0.549812+0.053263
## [108] train-rmse:0.490443+0.010266 test-rmse:0.549698+0.053080
## [109] train-rmse:0.489962+0.010260 test-rmse:0.549710+0.053134
## [110] train-rmse:0.489484+0.010249 test-rmse:0.548403+0.053109
## [111] train-rmse:0.489019+0.010249 test-rmse:0.548145+0.053175
## [112] train-rmse:0.488557+0.010241 test-rmse:0.548000+0.053469
## [113] train-rmse:0.488093+0.010239 test-rmse:0.548088+0.053064
## [114] train-rmse:0.487641+0.010232 test-rmse:0.547899+0.053487
## [115] train-rmse:0.487204+0.010233 test-rmse:0.547727+0.053708
## [116] train-rmse:0.486769+0.010232 test-rmse:0.548025+0.053086
## [117] train-rmse:0.486339+0.010234 test-rmse:0.547471+0.052922
## [118] train-rmse:0.485915+0.010236 test-rmse:0.547146+0.052373
## [119] train-rmse:0.485500+0.010226 test-rmse:0.546968+0.052612
## [120] train-rmse:0.485092+0.010222 test-rmse:0.546497+0.052653
## [121] train-rmse:0.484686+0.010218 test-rmse:0.546425+0.052992
## [122] train-rmse:0.484290+0.010210 test-rmse:0.546609+0.053437
## [123] train-rmse:0.483896+0.010205 test-rmse:0.545996+0.053278
## [124] train-rmse:0.483505+0.010195 test-rmse:0.545884+0.052964
## [125] train-rmse:0.483121+0.010190 test-rmse:0.545138+0.052616
## [126] train-rmse:0.482744+0.010180 test-rmse:0.544992+0.052098
## [127] train-rmse:0.482364+0.010170 test-rmse:0.544727+0.052165
## [128] train-rmse:0.481990+0.010166 test-rmse:0.544522+0.052017
## [129] train-rmse:0.481615+0.010159 test-rmse:0.544223+0.051798
## [130] train-rmse:0.481248+0.010154 test-rmse:0.544220+0.051512
## [131] train-rmse:0.480878+0.010149 test-rmse:0.543746+0.051882
## [132] train-rmse:0.480519+0.010146 test-rmse:0.543486+0.051844
## [133] train-rmse:0.480165+0.010143 test-rmse:0.543271+0.051799
## [134] train-rmse:0.479814+0.010144 test-rmse:0.543366+0.052235
## [135] train-rmse:0.479472+0.010148 test-rmse:0.543597+0.051759
## [136] train-rmse:0.479132+0.010153 test-rmse:0.543712+0.051945
## [137] train-rmse:0.478796+0.010159 test-rmse:0.543213+0.051925
## [138] train-rmse:0.478464+0.010160 test-rmse:0.543142+0.052114
## [139] train-rmse:0.478137+0.010163 test-rmse:0.542906+0.052153
## [140] train-rmse:0.477818+0.010164 test-rmse:0.543024+0.051964
## [141] train-rmse:0.477502+0.010164 test-rmse:0.542821+0.052393
## [142] train-rmse:0.477189+0.010162 test-rmse:0.542813+0.052176
## [143] train-rmse:0.476880+0.010159 test-rmse:0.542515+0.051468
## [144] train-rmse:0.476578+0.010152 test-rmse:0.542011+0.051192
## [145] train-rmse:0.476278+0.010148 test-rmse:0.541894+0.051445
## [146] train-rmse:0.475982+0.010145 test-rmse:0.541296+0.051399
## [147] train-rmse:0.475688+0.010139 test-rmse:0.541004+0.051357
## [148] train-rmse:0.475393+0.010136 test-rmse:0.541068+0.051354
## [149] train-rmse:0.475100+0.010130 test-rmse:0.541004+0.051262
## [150] train-rmse:0.474810+0.010126 test-rmse:0.541053+0.051049
## [151] train-rmse:0.474524+0.010119 test-rmse:0.540943+0.051152
## [152] train-rmse:0.474241+0.010115 test-rmse:0.540741+0.050926
## [153] train-rmse:0.473963+0.010110 test-rmse:0.540519+0.050656
## [154] train-rmse:0.473683+0.010105 test-rmse:0.540456+0.050809
## [155] train-rmse:0.473405+0.010102 test-rmse:0.540839+0.051074
## [156] train-rmse:0.473129+0.010102 test-rmse:0.540930+0.051149
## [157] train-rmse:0.472858+0.010096 test-rmse:0.540516+0.051223
## [158] train-rmse:0.472592+0.010094 test-rmse:0.540364+0.051146
## [159] train-rmse:0.472327+0.010092 test-rmse:0.540028+0.050804
## [160] train-rmse:0.472070+0.010088 test-rmse:0.540045+0.050774
## [161] train-rmse:0.471816+0.010085 test-rmse:0.539759+0.050735
## [162] train-rmse:0.471565+0.010082 test-rmse:0.539452+0.050698
## [163] train-rmse:0.471319+0.010080 test-rmse:0.539142+0.050544
## [164] train-rmse:0.471074+0.010077 test-rmse:0.538758+0.050552
## [165] train-rmse:0.470830+0.010072 test-rmse:0.538508+0.050400
## [166] train-rmse:0.470588+0.010066 test-rmse:0.538576+0.050153
## [167] train-rmse:0.470349+0.010060 test-rmse:0.538747+0.050035
## [168] train-rmse:0.470108+0.010053 test-rmse:0.538576+0.050198
## [169] train-rmse:0.469872+0.010052 test-rmse:0.538180+0.049904
## [170] train-rmse:0.469643+0.010046 test-rmse:0.537928+0.050022
## [171] train-rmse:0.469416+0.010042 test-rmse:0.538023+0.050031
## [172] train-rmse:0.469192+0.010039 test-rmse:0.538155+0.049813
## [173] train-rmse:0.468968+0.010036 test-rmse:0.537654+0.049778
## [174] train-rmse:0.468741+0.010028 test-rmse:0.537578+0.049664
## [175] train-rmse:0.468522+0.010024 test-rmse:0.537737+0.049714
## [176] train-rmse:0.468307+0.010019 test-rmse:0.537632+0.049740
## [177] train-rmse:0.468093+0.010015 test-rmse:0.537696+0.049644
## [178] train-rmse:0.467880+0.010010 test-rmse:0.537530+0.049565
## [179] train-rmse:0.467670+0.010007 test-rmse:0.537462+0.049818
## [180] train-rmse:0.467462+0.010004 test-rmse:0.537660+0.049678
## [181] train-rmse:0.467255+0.010002 test-rmse:0.537582+0.049451
## [182] train-rmse:0.467046+0.009997 test-rmse:0.537416+0.049347
## [183] train-rmse:0.466840+0.009987 test-rmse:0.537193+0.049518
## [184] train-rmse:0.466634+0.009979 test-rmse:0.537358+0.049324
## [185] train-rmse:0.466433+0.009971 test-rmse:0.537081+0.049231
## [186] train-rmse:0.466236+0.009963 test-rmse:0.537164+0.049328
## [187] train-rmse:0.466040+0.009955 test-rmse:0.537214+0.049644
## [188] train-rmse:0.465846+0.009948 test-rmse:0.537147+0.049717
## [189] train-rmse:0.465659+0.009941 test-rmse:0.536964+0.049472
## [190] train-rmse:0.465471+0.009935 test-rmse:0.536638+0.049446
## [191] train-rmse:0.465284+0.009928 test-rmse:0.536483+0.049271
## [192] train-rmse:0.465099+0.009919 test-rmse:0.536680+0.049202
## [193] train-rmse:0.464915+0.009911 test-rmse:0.536557+0.049464
## [194] train-rmse:0.464729+0.009900 test-rmse:0.536448+0.049059
## [195] train-rmse:0.464547+0.009893 test-rmse:0.536134+0.048875
## [196] train-rmse:0.464361+0.009881 test-rmse:0.535861+0.048737
## [197] train-rmse:0.464182+0.009872 test-rmse:0.535983+0.048551
## [198] train-rmse:0.464003+0.009863 test-rmse:0.535611+0.048780
## [199] train-rmse:0.463826+0.009853 test-rmse:0.535548+0.048836
## [200] train-rmse:0.463653+0.009844 test-rmse:0.535352+0.048768
## [201] train-rmse:0.463479+0.009833 test-rmse:0.535188+0.048588
## [202] train-rmse:0.463311+0.009825 test-rmse:0.535235+0.048274
## [203] train-rmse:0.463145+0.009815 test-rmse:0.534971+0.048223
## [204] train-rmse:0.462980+0.009807 test-rmse:0.534978+0.048388
## [205] train-rmse:0.462815+0.009799 test-rmse:0.534725+0.048249
## [206] train-rmse:0.462644+0.009783 test-rmse:0.534687+0.047953
## [207] train-rmse:0.462481+0.009777 test-rmse:0.534837+0.048031
## [208] train-rmse:0.462315+0.009767 test-rmse:0.534835+0.048001
## [209] train-rmse:0.462155+0.009762 test-rmse:0.534729+0.047861
## [210] train-rmse:0.461995+0.009754 test-rmse:0.534439+0.047823
## [211] train-rmse:0.461831+0.009743 test-rmse:0.534636+0.047692
## [212] train-rmse:0.461670+0.009733 test-rmse:0.534797+0.047839
## [213] train-rmse:0.461510+0.009725 test-rmse:0.534718+0.047908
## [214] train-rmse:0.461351+0.009716 test-rmse:0.534758+0.048040
## [215] train-rmse:0.461194+0.009708 test-rmse:0.534438+0.047858
## [216] train-rmse:0.461040+0.009699 test-rmse:0.534473+0.047771
## [217] train-rmse:0.460884+0.009690 test-rmse:0.534282+0.048068
## [218] train-rmse:0.460728+0.009680 test-rmse:0.534177+0.048068
## [219] train-rmse:0.460577+0.009670 test-rmse:0.533925+0.048001
## [220] train-rmse:0.460428+0.009661 test-rmse:0.533957+0.048065
## [221] train-rmse:0.460280+0.009654 test-rmse:0.534061+0.048115
## [222] train-rmse:0.460133+0.009646 test-rmse:0.534280+0.048092
## [223] train-rmse:0.459987+0.009639 test-rmse:0.534220+0.048022
## [224] train-rmse:0.459842+0.009633 test-rmse:0.534498+0.048050
## [225] train-rmse:0.459697+0.009626 test-rmse:0.534285+0.047852
## Stopping. Best iteration:
## [219] train-rmse:0.460577+0.009670 test-rmse:0.533925+0.048001
##
## 22
## [1] train-rmse:0.906803+0.015255 test-rmse:0.908912+0.061214
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.829422+0.014450 test-rmse:0.838675+0.062420
## [3] train-rmse:0.768692+0.014090 test-rmse:0.781779+0.062562
## [4] train-rmse:0.720625+0.013411 test-rmse:0.742023+0.062713
## [5] train-rmse:0.682527+0.012339 test-rmse:0.706248+0.065381
## [6] train-rmse:0.652543+0.011836 test-rmse:0.676267+0.064624
## [7] train-rmse:0.627725+0.011007 test-rmse:0.655167+0.066204
## [8] train-rmse:0.607545+0.011295 test-rmse:0.641455+0.066332
## [9] train-rmse:0.591376+0.011171 test-rmse:0.626803+0.065081
## [10] train-rmse:0.578515+0.011730 test-rmse:0.617035+0.064008
## [11] train-rmse:0.567607+0.012583 test-rmse:0.609414+0.064072
## [12] train-rmse:0.557506+0.012825 test-rmse:0.603463+0.066156
## [13] train-rmse:0.548722+0.013928 test-rmse:0.595481+0.062196
## [14] train-rmse:0.541200+0.012672 test-rmse:0.590719+0.062468
## [15] train-rmse:0.535011+0.012635 test-rmse:0.586337+0.063044
## [16] train-rmse:0.529224+0.013772 test-rmse:0.583356+0.063293
## [17] train-rmse:0.524162+0.013732 test-rmse:0.579325+0.061744
## [18] train-rmse:0.519732+0.013558 test-rmse:0.575877+0.058967
## [19] train-rmse:0.515720+0.013870 test-rmse:0.572639+0.060171
## [20] train-rmse:0.512088+0.013872 test-rmse:0.573159+0.061674
## [21] train-rmse:0.508180+0.014076 test-rmse:0.570241+0.061561
## [22] train-rmse:0.504778+0.014100 test-rmse:0.568123+0.061626
## [23] train-rmse:0.501446+0.014251 test-rmse:0.567203+0.061640
## [24] train-rmse:0.497881+0.014133 test-rmse:0.564021+0.060637
## [25] train-rmse:0.494880+0.014582 test-rmse:0.561686+0.062664
## [26] train-rmse:0.491891+0.014540 test-rmse:0.560701+0.061368
## [27] train-rmse:0.488136+0.015053 test-rmse:0.557544+0.059210
## [28] train-rmse:0.484967+0.014994 test-rmse:0.555055+0.059683
## [29] train-rmse:0.482021+0.015310 test-rmse:0.552906+0.057572
## [30] train-rmse:0.479737+0.015221 test-rmse:0.552260+0.058247
## [31] train-rmse:0.476871+0.015383 test-rmse:0.550133+0.058451
## [32] train-rmse:0.474644+0.015413 test-rmse:0.548681+0.057560
## [33] train-rmse:0.472081+0.015274 test-rmse:0.546772+0.058138
## [34] train-rmse:0.469676+0.015198 test-rmse:0.546820+0.058974
## [35] train-rmse:0.467471+0.015291 test-rmse:0.545107+0.060100
## [36] train-rmse:0.465299+0.015529 test-rmse:0.544766+0.059196
## [37] train-rmse:0.463052+0.015177 test-rmse:0.542484+0.059094
## [38] train-rmse:0.460146+0.014956 test-rmse:0.539844+0.058545
## [39] train-rmse:0.458030+0.015211 test-rmse:0.539300+0.057951
## [40] train-rmse:0.455938+0.014885 test-rmse:0.538867+0.057938
## [41] train-rmse:0.453692+0.015385 test-rmse:0.537374+0.057876
## [42] train-rmse:0.451583+0.015227 test-rmse:0.536203+0.058999
## [43] train-rmse:0.449519+0.014932 test-rmse:0.536268+0.058805
## [44] train-rmse:0.447693+0.014904 test-rmse:0.534646+0.059828
## [45] train-rmse:0.445397+0.014878 test-rmse:0.533644+0.059322
## [46] train-rmse:0.443542+0.015298 test-rmse:0.532567+0.059748
## [47] train-rmse:0.441989+0.015439 test-rmse:0.532384+0.059302
## [48] train-rmse:0.440695+0.015322 test-rmse:0.531213+0.058720
## [49] train-rmse:0.438965+0.015254 test-rmse:0.529706+0.059197
## [50] train-rmse:0.437476+0.015123 test-rmse:0.529317+0.058641
## [51] train-rmse:0.435673+0.015222 test-rmse:0.528315+0.058424
## [52] train-rmse:0.433660+0.015321 test-rmse:0.527186+0.060064
## [53] train-rmse:0.432143+0.015084 test-rmse:0.525949+0.059666
## [54] train-rmse:0.430615+0.015011 test-rmse:0.525230+0.059215
## [55] train-rmse:0.428999+0.015160 test-rmse:0.524167+0.059844
## [56] train-rmse:0.427696+0.015286 test-rmse:0.524552+0.059270
## [57] train-rmse:0.426406+0.015514 test-rmse:0.524433+0.059470
## [58] train-rmse:0.425195+0.015478 test-rmse:0.524234+0.060247
## [59] train-rmse:0.424094+0.015484 test-rmse:0.523854+0.059907
## [60] train-rmse:0.422766+0.015644 test-rmse:0.523316+0.059298
## [61] train-rmse:0.421148+0.015537 test-rmse:0.521330+0.059280
## [62] train-rmse:0.419796+0.015724 test-rmse:0.520516+0.058812
## [63] train-rmse:0.418646+0.015991 test-rmse:0.520199+0.058501
## [64] train-rmse:0.417209+0.016147 test-rmse:0.519211+0.058207
## [65] train-rmse:0.415951+0.015946 test-rmse:0.518211+0.058318
## [66] train-rmse:0.414905+0.015954 test-rmse:0.517712+0.058740
## [67] train-rmse:0.413639+0.016229 test-rmse:0.517420+0.058861
## [68] train-rmse:0.412167+0.016100 test-rmse:0.516530+0.059045
## [69] train-rmse:0.410798+0.016086 test-rmse:0.515321+0.059102
## [70] train-rmse:0.409839+0.016227 test-rmse:0.515161+0.059286
## [71] train-rmse:0.408870+0.016365 test-rmse:0.514770+0.058944
## [72] train-rmse:0.407767+0.016672 test-rmse:0.514509+0.058755
## [73] train-rmse:0.406643+0.016663 test-rmse:0.514632+0.058159
## [74] train-rmse:0.405721+0.016512 test-rmse:0.514592+0.058665
## [75] train-rmse:0.404701+0.016310 test-rmse:0.514092+0.058287
## [76] train-rmse:0.403955+0.016225 test-rmse:0.514008+0.058071
## [77] train-rmse:0.402952+0.016404 test-rmse:0.513247+0.058647
## [78] train-rmse:0.401938+0.016371 test-rmse:0.513062+0.058740
## [79] train-rmse:0.400635+0.016397 test-rmse:0.512010+0.059237
## [80] train-rmse:0.399646+0.016301 test-rmse:0.511462+0.059898
## [81] train-rmse:0.398858+0.016395 test-rmse:0.510605+0.059991
## [82] train-rmse:0.397835+0.016575 test-rmse:0.510166+0.060110
## [83] train-rmse:0.396996+0.016570 test-rmse:0.509943+0.059949
## [84] train-rmse:0.395882+0.016679 test-rmse:0.509264+0.059165
## [85] train-rmse:0.394897+0.016616 test-rmse:0.509382+0.058982
## [86] train-rmse:0.394170+0.016646 test-rmse:0.509787+0.059084
## [87] train-rmse:0.393359+0.016825 test-rmse:0.509296+0.058951
## [88] train-rmse:0.392451+0.016498 test-rmse:0.509399+0.059418
## [89] train-rmse:0.391521+0.016356 test-rmse:0.508099+0.059556
## [90] train-rmse:0.390679+0.016294 test-rmse:0.507642+0.059852
## [91] train-rmse:0.390044+0.016306 test-rmse:0.507623+0.060005
## [92] train-rmse:0.389013+0.016112 test-rmse:0.507338+0.059786
## [93] train-rmse:0.387992+0.016100 test-rmse:0.506878+0.060269
## [94] train-rmse:0.387238+0.016070 test-rmse:0.506987+0.060540
## [95] train-rmse:0.386431+0.015861 test-rmse:0.506614+0.060407
## [96] train-rmse:0.385698+0.015943 test-rmse:0.506340+0.060082
## [97] train-rmse:0.384850+0.016022 test-rmse:0.505861+0.059730
## [98] train-rmse:0.384270+0.015979 test-rmse:0.506273+0.059684
## [99] train-rmse:0.383240+0.016326 test-rmse:0.505221+0.060094
## [100] train-rmse:0.382155+0.015991 test-rmse:0.505017+0.060494
## [101] train-rmse:0.381532+0.015872 test-rmse:0.504548+0.060277
## [102] train-rmse:0.381007+0.015795 test-rmse:0.504878+0.060201
## [103] train-rmse:0.380202+0.015684 test-rmse:0.504675+0.060605
## [104] train-rmse:0.379629+0.015751 test-rmse:0.504349+0.060701
## [105] train-rmse:0.379027+0.015812 test-rmse:0.504703+0.060537
## [106] train-rmse:0.378007+0.015644 test-rmse:0.504783+0.060375
## [107] train-rmse:0.377300+0.015564 test-rmse:0.504366+0.060077
## [108] train-rmse:0.376423+0.015615 test-rmse:0.503819+0.059438
## [109] train-rmse:0.375796+0.015715 test-rmse:0.503766+0.059368
## [110] train-rmse:0.375192+0.015641 test-rmse:0.503290+0.058999
## [111] train-rmse:0.374469+0.015392 test-rmse:0.503411+0.058876
## [112] train-rmse:0.373319+0.015773 test-rmse:0.502890+0.058524
## [113] train-rmse:0.372724+0.015772 test-rmse:0.502772+0.059458
## [114] train-rmse:0.371989+0.015865 test-rmse:0.502699+0.059875
## [115] train-rmse:0.371318+0.015914 test-rmse:0.502119+0.060304
## [116] train-rmse:0.370390+0.015647 test-rmse:0.501859+0.059948
## [117] train-rmse:0.369760+0.015499 test-rmse:0.501321+0.059755
## [118] train-rmse:0.368545+0.015845 test-rmse:0.501260+0.059709
## [119] train-rmse:0.367874+0.015635 test-rmse:0.500893+0.059497
## [120] train-rmse:0.367242+0.015807 test-rmse:0.500709+0.059424
## [121] train-rmse:0.366733+0.015745 test-rmse:0.500996+0.059342
## [122] train-rmse:0.366036+0.015756 test-rmse:0.501129+0.059418
## [123] train-rmse:0.365413+0.015862 test-rmse:0.501327+0.059680
## [124] train-rmse:0.364699+0.015947 test-rmse:0.500981+0.059430
## [125] train-rmse:0.363946+0.015960 test-rmse:0.500164+0.059828
## [126] train-rmse:0.363245+0.015706 test-rmse:0.500072+0.059507
## [127] train-rmse:0.362703+0.015506 test-rmse:0.499987+0.059408
## [128] train-rmse:0.361940+0.015318 test-rmse:0.500264+0.059477
## [129] train-rmse:0.361458+0.015243 test-rmse:0.500350+0.059389
## [130] train-rmse:0.360767+0.015066 test-rmse:0.500293+0.059902
## [131] train-rmse:0.360211+0.015156 test-rmse:0.500196+0.060045
## [132] train-rmse:0.359338+0.015198 test-rmse:0.499547+0.060089
## [133] train-rmse:0.358326+0.014887 test-rmse:0.499716+0.059725
## [134] train-rmse:0.357769+0.014807 test-rmse:0.499788+0.059641
## [135] train-rmse:0.357180+0.014733 test-rmse:0.499594+0.059614
## [136] train-rmse:0.356606+0.014533 test-rmse:0.499501+0.059405
## [137] train-rmse:0.356068+0.014503 test-rmse:0.499431+0.059201
## [138] train-rmse:0.355280+0.014768 test-rmse:0.498953+0.059341
## [139] train-rmse:0.354065+0.015130 test-rmse:0.499054+0.059424
## [140] train-rmse:0.353536+0.015177 test-rmse:0.498563+0.058930
## [141] train-rmse:0.352979+0.015197 test-rmse:0.498234+0.058955
## [142] train-rmse:0.352518+0.015028 test-rmse:0.498212+0.058677
## [143] train-rmse:0.351932+0.015113 test-rmse:0.497816+0.058261
## [144] train-rmse:0.351592+0.015073 test-rmse:0.498085+0.058317
## [145] train-rmse:0.350862+0.014662 test-rmse:0.498124+0.058048
## [146] train-rmse:0.350467+0.014576 test-rmse:0.498255+0.058018
## [147] train-rmse:0.349980+0.014391 test-rmse:0.498330+0.057850
## [148] train-rmse:0.349061+0.014152 test-rmse:0.498578+0.057730
## [149] train-rmse:0.347969+0.014071 test-rmse:0.497839+0.057927
## Stopping. Best iteration:
## [143] train-rmse:0.351932+0.015113 test-rmse:0.497816+0.058261
##
## 23
## [1] train-rmse:0.901201+0.015476 test-rmse:0.905599+0.062243
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.817973+0.014871 test-rmse:0.829010+0.062305
## [3] train-rmse:0.751323+0.014372 test-rmse:0.769136+0.063694
## [4] train-rmse:0.697549+0.013517 test-rmse:0.722634+0.065467
## [5] train-rmse:0.654301+0.013699 test-rmse:0.686725+0.066611
## [6] train-rmse:0.619352+0.012970 test-rmse:0.656170+0.065738
## [7] train-rmse:0.591734+0.013389 test-rmse:0.633624+0.063978
## [8] train-rmse:0.568419+0.012651 test-rmse:0.614978+0.062727
## [9] train-rmse:0.548234+0.014161 test-rmse:0.599251+0.062221
## [10] train-rmse:0.532181+0.013992 test-rmse:0.589565+0.060115
## [11] train-rmse:0.518409+0.013103 test-rmse:0.581185+0.059375
## [12] train-rmse:0.508296+0.013457 test-rmse:0.576021+0.057659
## [13] train-rmse:0.497712+0.014431 test-rmse:0.568751+0.054966
## [14] train-rmse:0.488552+0.015157 test-rmse:0.564788+0.053346
## [15] train-rmse:0.481678+0.014655 test-rmse:0.560508+0.053579
## [16] train-rmse:0.475152+0.013853 test-rmse:0.557587+0.054640
## [17] train-rmse:0.469757+0.014404 test-rmse:0.556251+0.054534
## [18] train-rmse:0.464749+0.014387 test-rmse:0.552209+0.054838
## [19] train-rmse:0.459124+0.013392 test-rmse:0.548098+0.055908
## [20] train-rmse:0.453773+0.014436 test-rmse:0.546252+0.055730
## [21] train-rmse:0.448729+0.014487 test-rmse:0.543942+0.056855
## [22] train-rmse:0.443997+0.013582 test-rmse:0.541234+0.055685
## [23] train-rmse:0.440435+0.013567 test-rmse:0.539674+0.054919
## [24] train-rmse:0.436235+0.014113 test-rmse:0.537225+0.054273
## [25] train-rmse:0.431866+0.013666 test-rmse:0.534938+0.056656
## [26] train-rmse:0.428155+0.013659 test-rmse:0.533900+0.056301
## [27] train-rmse:0.424746+0.013697 test-rmse:0.531866+0.056256
## [28] train-rmse:0.421623+0.013489 test-rmse:0.530901+0.057220
## [29] train-rmse:0.418602+0.013956 test-rmse:0.530119+0.056158
## [30] train-rmse:0.415324+0.013661 test-rmse:0.529022+0.055803
## [31] train-rmse:0.412787+0.013565 test-rmse:0.528939+0.055526
## [32] train-rmse:0.410203+0.014107 test-rmse:0.528140+0.055180
## [33] train-rmse:0.406657+0.014259 test-rmse:0.526708+0.054411
## [34] train-rmse:0.403905+0.014685 test-rmse:0.525802+0.053873
## [35] train-rmse:0.401305+0.015050 test-rmse:0.525589+0.053618
## [36] train-rmse:0.398853+0.015313 test-rmse:0.525678+0.053089
## [37] train-rmse:0.396280+0.015319 test-rmse:0.523939+0.053565
## [38] train-rmse:0.393735+0.015430 test-rmse:0.523487+0.053464
## [39] train-rmse:0.391154+0.015205 test-rmse:0.523200+0.053527
## [40] train-rmse:0.388585+0.014844 test-rmse:0.522373+0.054361
## [41] train-rmse:0.385937+0.014778 test-rmse:0.521844+0.054682
## [42] train-rmse:0.383867+0.015113 test-rmse:0.521366+0.054880
## [43] train-rmse:0.381817+0.015204 test-rmse:0.520816+0.055274
## [44] train-rmse:0.379912+0.015387 test-rmse:0.520596+0.055284
## [45] train-rmse:0.377637+0.014855 test-rmse:0.519980+0.055102
## [46] train-rmse:0.375249+0.014605 test-rmse:0.518586+0.055660
## [47] train-rmse:0.373364+0.014646 test-rmse:0.518599+0.055529
## [48] train-rmse:0.371840+0.014196 test-rmse:0.518226+0.055850
## [49] train-rmse:0.369486+0.014191 test-rmse:0.517588+0.055412
## [50] train-rmse:0.367769+0.014097 test-rmse:0.516389+0.055992
## [51] train-rmse:0.365284+0.013334 test-rmse:0.515865+0.055470
## [52] train-rmse:0.363368+0.013191 test-rmse:0.516160+0.054691
## [53] train-rmse:0.361684+0.012829 test-rmse:0.515575+0.054947
## [54] train-rmse:0.359617+0.012897 test-rmse:0.514963+0.054484
## [55] train-rmse:0.357464+0.012314 test-rmse:0.514815+0.054783
## [56] train-rmse:0.355348+0.012192 test-rmse:0.514606+0.054154
## [57] train-rmse:0.353000+0.012070 test-rmse:0.515123+0.053337
## [58] train-rmse:0.351396+0.012448 test-rmse:0.514283+0.052401
## [59] train-rmse:0.349498+0.012960 test-rmse:0.513716+0.052684
## [60] train-rmse:0.347616+0.012573 test-rmse:0.512579+0.053077
## [61] train-rmse:0.346050+0.012676 test-rmse:0.512107+0.053157
## [62] train-rmse:0.344869+0.012452 test-rmse:0.511748+0.053309
## [63] train-rmse:0.342328+0.011950 test-rmse:0.510810+0.053563
## [64] train-rmse:0.340253+0.011618 test-rmse:0.510818+0.053574
## [65] train-rmse:0.338823+0.011436 test-rmse:0.511138+0.053614
## [66] train-rmse:0.337278+0.011737 test-rmse:0.510930+0.054006
## [67] train-rmse:0.335952+0.011991 test-rmse:0.511099+0.054104
## [68] train-rmse:0.334182+0.011639 test-rmse:0.509745+0.054890
## [69] train-rmse:0.332604+0.011361 test-rmse:0.509390+0.054764
## [70] train-rmse:0.330713+0.012004 test-rmse:0.509019+0.054350
## [71] train-rmse:0.329439+0.012310 test-rmse:0.508965+0.053643
## [72] train-rmse:0.327627+0.012294 test-rmse:0.508884+0.053412
## [73] train-rmse:0.324836+0.012359 test-rmse:0.507886+0.053140
## [74] train-rmse:0.323149+0.012325 test-rmse:0.507329+0.052432
## [75] train-rmse:0.321363+0.012198 test-rmse:0.507423+0.051515
## [76] train-rmse:0.319931+0.012156 test-rmse:0.506576+0.051113
## [77] train-rmse:0.318944+0.012098 test-rmse:0.506463+0.050727
## [78] train-rmse:0.317348+0.011951 test-rmse:0.504967+0.050762
## [79] train-rmse:0.315637+0.011995 test-rmse:0.505451+0.050235
## [80] train-rmse:0.314509+0.012264 test-rmse:0.504937+0.049962
## [81] train-rmse:0.312662+0.013213 test-rmse:0.504765+0.049841
## [82] train-rmse:0.311432+0.013327 test-rmse:0.505215+0.049425
## [83] train-rmse:0.309753+0.012898 test-rmse:0.505009+0.049616
## [84] train-rmse:0.308462+0.012825 test-rmse:0.504708+0.049600
## [85] train-rmse:0.307133+0.012680 test-rmse:0.505705+0.050095
## [86] train-rmse:0.306138+0.012669 test-rmse:0.505638+0.050145
## [87] train-rmse:0.305161+0.012552 test-rmse:0.505776+0.050127
## [88] train-rmse:0.304340+0.012563 test-rmse:0.505782+0.049864
## [89] train-rmse:0.302249+0.012814 test-rmse:0.505683+0.049337
## [90] train-rmse:0.300789+0.012551 test-rmse:0.505019+0.049408
## Stopping. Best iteration:
## [84] train-rmse:0.308462+0.012825 test-rmse:0.504708+0.049600
##
## 24
## [1] train-rmse:0.896408+0.015307 test-rmse:0.902709+0.061253
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.808636+0.014256 test-rmse:0.822852+0.060719
## [3] train-rmse:0.737302+0.013856 test-rmse:0.758518+0.060871
## [4] train-rmse:0.679151+0.012463 test-rmse:0.707432+0.063670
## [5] train-rmse:0.631686+0.012486 test-rmse:0.669759+0.066778
## [6] train-rmse:0.591935+0.012406 test-rmse:0.637494+0.063548
## [7] train-rmse:0.559208+0.013057 test-rmse:0.612711+0.060430
## [8] train-rmse:0.532904+0.012482 test-rmse:0.594018+0.058657
## [9] train-rmse:0.511136+0.013264 test-rmse:0.580277+0.057189
## [10] train-rmse:0.491277+0.014186 test-rmse:0.569283+0.055316
## [11] train-rmse:0.475590+0.014590 test-rmse:0.559585+0.055777
## [12] train-rmse:0.462545+0.014157 test-rmse:0.552739+0.054277
## [13] train-rmse:0.451100+0.013483 test-rmse:0.546316+0.052795
## [14] train-rmse:0.440990+0.014756 test-rmse:0.540606+0.052020
## [15] train-rmse:0.431174+0.014383 test-rmse:0.532672+0.052610
## [16] train-rmse:0.423806+0.014009 test-rmse:0.528982+0.052880
## [17] train-rmse:0.417843+0.014008 test-rmse:0.527538+0.053133
## [18] train-rmse:0.410640+0.015698 test-rmse:0.524292+0.053366
## [19] train-rmse:0.403608+0.015332 test-rmse:0.520277+0.053162
## [20] train-rmse:0.397831+0.015340 test-rmse:0.517495+0.055376
## [21] train-rmse:0.393310+0.015399 test-rmse:0.516355+0.055036
## [22] train-rmse:0.388786+0.015045 test-rmse:0.513865+0.054820
## [23] train-rmse:0.384642+0.014568 test-rmse:0.512724+0.054220
## [24] train-rmse:0.380043+0.015824 test-rmse:0.511151+0.053934
## [25] train-rmse:0.375592+0.015600 test-rmse:0.509507+0.053155
## [26] train-rmse:0.370923+0.015033 test-rmse:0.508193+0.054215
## [27] train-rmse:0.367996+0.015247 test-rmse:0.506431+0.053608
## [28] train-rmse:0.363603+0.014757 test-rmse:0.505202+0.053843
## [29] train-rmse:0.359547+0.015750 test-rmse:0.504005+0.053840
## [30] train-rmse:0.355186+0.014376 test-rmse:0.503581+0.055092
## [31] train-rmse:0.352329+0.014636 test-rmse:0.502826+0.054698
## [32] train-rmse:0.349359+0.014385 test-rmse:0.502360+0.054207
## [33] train-rmse:0.345816+0.014548 test-rmse:0.502628+0.054912
## [34] train-rmse:0.342805+0.015101 test-rmse:0.501768+0.054446
## [35] train-rmse:0.339231+0.014619 test-rmse:0.500364+0.054574
## [36] train-rmse:0.335708+0.015317 test-rmse:0.499033+0.054457
## [37] train-rmse:0.333211+0.014717 test-rmse:0.498804+0.053911
## [38] train-rmse:0.330424+0.014175 test-rmse:0.497579+0.053641
## [39] train-rmse:0.327086+0.014576 test-rmse:0.497225+0.053213
## [40] train-rmse:0.325044+0.014580 test-rmse:0.497600+0.052934
## [41] train-rmse:0.322144+0.014972 test-rmse:0.497106+0.052756
## [42] train-rmse:0.319346+0.014804 test-rmse:0.496639+0.053029
## [43] train-rmse:0.315680+0.015845 test-rmse:0.496228+0.053056
## [44] train-rmse:0.313129+0.015416 test-rmse:0.495765+0.053642
## [45] train-rmse:0.310373+0.014584 test-rmse:0.494905+0.053893
## [46] train-rmse:0.308429+0.014503 test-rmse:0.494149+0.054367
## [47] train-rmse:0.306239+0.014571 test-rmse:0.493813+0.054354
## [48] train-rmse:0.303324+0.014336 test-rmse:0.493635+0.054175
## [49] train-rmse:0.300852+0.014056 test-rmse:0.492957+0.054606
## [50] train-rmse:0.298413+0.014162 test-rmse:0.493064+0.054931
## [51] train-rmse:0.296573+0.014089 test-rmse:0.492864+0.055035
## [52] train-rmse:0.294882+0.014193 test-rmse:0.492519+0.055358
## [53] train-rmse:0.292242+0.014408 test-rmse:0.491445+0.055674
## [54] train-rmse:0.289825+0.014631 test-rmse:0.490892+0.055502
## [55] train-rmse:0.288011+0.015280 test-rmse:0.490701+0.055451
## [56] train-rmse:0.285942+0.015540 test-rmse:0.490673+0.055145
## [57] train-rmse:0.283854+0.015047 test-rmse:0.490962+0.054952
## [58] train-rmse:0.281936+0.015410 test-rmse:0.490572+0.053858
## [59] train-rmse:0.280458+0.015019 test-rmse:0.490977+0.054132
## [60] train-rmse:0.279005+0.014667 test-rmse:0.490754+0.054269
## [61] train-rmse:0.276668+0.014183 test-rmse:0.490918+0.054247
## [62] train-rmse:0.274776+0.014307 test-rmse:0.491246+0.054207
## [63] train-rmse:0.272840+0.014032 test-rmse:0.491133+0.053339
## [64] train-rmse:0.270804+0.014208 test-rmse:0.490668+0.053126
## Stopping. Best iteration:
## [58] train-rmse:0.281936+0.015410 test-rmse:0.490572+0.053858
##
## 25
## [1] train-rmse:0.892150+0.014908 test-rmse:0.900199+0.061882
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.800099+0.013338 test-rmse:0.819359+0.062005
## [3] train-rmse:0.723935+0.012228 test-rmse:0.753901+0.061953
## [4] train-rmse:0.662179+0.012171 test-rmse:0.700912+0.062575
## [5] train-rmse:0.609815+0.011075 test-rmse:0.662188+0.063422
## [6] train-rmse:0.567082+0.010876 test-rmse:0.630515+0.060590
## [7] train-rmse:0.531804+0.011478 test-rmse:0.603242+0.059611
## [8] train-rmse:0.502297+0.011467 test-rmse:0.584370+0.058630
## [9] train-rmse:0.477280+0.010554 test-rmse:0.572070+0.058038
## [10] train-rmse:0.456045+0.012278 test-rmse:0.560395+0.057146
## [11] train-rmse:0.437858+0.012251 test-rmse:0.551548+0.059402
## [12] train-rmse:0.422664+0.012019 test-rmse:0.544238+0.061717
## [13] train-rmse:0.409588+0.011523 test-rmse:0.539585+0.059783
## [14] train-rmse:0.398649+0.011577 test-rmse:0.534531+0.059852
## [15] train-rmse:0.389411+0.012295 test-rmse:0.530025+0.059996
## [16] train-rmse:0.381642+0.013151 test-rmse:0.526797+0.059114
## [17] train-rmse:0.372565+0.010685 test-rmse:0.524000+0.060050
## [18] train-rmse:0.364583+0.009786 test-rmse:0.519365+0.059570
## [19] train-rmse:0.359418+0.009687 test-rmse:0.516701+0.058108
## [20] train-rmse:0.351887+0.009107 test-rmse:0.512871+0.059827
## [21] train-rmse:0.346625+0.008955 test-rmse:0.511442+0.061279
## [22] train-rmse:0.342622+0.008820 test-rmse:0.510508+0.059481
## [23] train-rmse:0.336831+0.011800 test-rmse:0.509066+0.060117
## [24] train-rmse:0.332009+0.011026 test-rmse:0.506841+0.060701
## [25] train-rmse:0.327750+0.010970 test-rmse:0.505079+0.061665
## [26] train-rmse:0.323570+0.009761 test-rmse:0.503112+0.062434
## [27] train-rmse:0.319979+0.010120 test-rmse:0.502493+0.062292
## [28] train-rmse:0.315655+0.010962 test-rmse:0.501196+0.061492
## [29] train-rmse:0.311257+0.010992 test-rmse:0.499220+0.062108
## [30] train-rmse:0.307622+0.010925 test-rmse:0.498159+0.062452
## [31] train-rmse:0.303918+0.010326 test-rmse:0.498896+0.062562
## [32] train-rmse:0.299540+0.011092 test-rmse:0.497119+0.063221
## [33] train-rmse:0.296881+0.011095 test-rmse:0.496744+0.063811
## [34] train-rmse:0.293476+0.010935 test-rmse:0.495884+0.063587
## [35] train-rmse:0.290092+0.011497 test-rmse:0.495405+0.064759
## [36] train-rmse:0.286020+0.010157 test-rmse:0.495351+0.064575
## [37] train-rmse:0.283172+0.010809 test-rmse:0.495354+0.064412
## [38] train-rmse:0.279440+0.010860 test-rmse:0.495292+0.063670
## [39] train-rmse:0.275909+0.010503 test-rmse:0.493938+0.064123
## [40] train-rmse:0.272381+0.009796 test-rmse:0.493665+0.065328
## [41] train-rmse:0.269892+0.010832 test-rmse:0.494739+0.066944
## [42] train-rmse:0.267395+0.010698 test-rmse:0.494450+0.067254
## [43] train-rmse:0.263993+0.010674 test-rmse:0.493159+0.067786
## [44] train-rmse:0.261076+0.010053 test-rmse:0.493107+0.067268
## [45] train-rmse:0.257458+0.009712 test-rmse:0.492780+0.067093
## [46] train-rmse:0.254963+0.009289 test-rmse:0.492094+0.067022
## [47] train-rmse:0.252141+0.009545 test-rmse:0.491855+0.067280
## [48] train-rmse:0.249408+0.008741 test-rmse:0.491512+0.067299
## [49] train-rmse:0.245937+0.008946 test-rmse:0.491388+0.066907
## [50] train-rmse:0.242944+0.008373 test-rmse:0.491696+0.066524
## [51] train-rmse:0.240512+0.007708 test-rmse:0.491446+0.066811
## [52] train-rmse:0.238556+0.007897 test-rmse:0.491436+0.066474
## [53] train-rmse:0.235423+0.007043 test-rmse:0.490281+0.066240
## [54] train-rmse:0.232344+0.007024 test-rmse:0.490246+0.065940
## [55] train-rmse:0.229526+0.006932 test-rmse:0.489968+0.065678
## [56] train-rmse:0.227772+0.006456 test-rmse:0.489991+0.065431
## [57] train-rmse:0.225382+0.006844 test-rmse:0.490685+0.065738
## [58] train-rmse:0.222819+0.007073 test-rmse:0.491669+0.065998
## [59] train-rmse:0.218892+0.008046 test-rmse:0.491406+0.065624
## [60] train-rmse:0.216745+0.008973 test-rmse:0.490843+0.065174
## [61] train-rmse:0.214545+0.008806 test-rmse:0.491628+0.065563
## Stopping. Best iteration:
## [55] train-rmse:0.229526+0.006932 test-rmse:0.489968+0.065678
##
## 26
## [1] train-rmse:0.888270+0.014515 test-rmse:0.898512+0.062098
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.791976+0.013479 test-rmse:0.812073+0.061711
## [3] train-rmse:0.711514+0.011140 test-rmse:0.746252+0.062511
## [4] train-rmse:0.645987+0.010109 test-rmse:0.694738+0.062169
## [5] train-rmse:0.591399+0.011355 test-rmse:0.653344+0.065036
## [6] train-rmse:0.544979+0.010802 test-rmse:0.618589+0.060904
## [7] train-rmse:0.507482+0.012418 test-rmse:0.592635+0.057810
## [8] train-rmse:0.474746+0.012210 test-rmse:0.571015+0.057286
## [9] train-rmse:0.447145+0.012485 test-rmse:0.554725+0.055534
## [10] train-rmse:0.423254+0.012655 test-rmse:0.543077+0.056936
## [11] train-rmse:0.404573+0.011691 test-rmse:0.533329+0.056122
## [12] train-rmse:0.386729+0.012049 test-rmse:0.526115+0.057329
## [13] train-rmse:0.372849+0.011655 test-rmse:0.520650+0.058665
## [14] train-rmse:0.361612+0.011564 test-rmse:0.515473+0.058178
## [15] train-rmse:0.352153+0.010965 test-rmse:0.511556+0.057699
## [16] train-rmse:0.342408+0.011805 test-rmse:0.509262+0.057316
## [17] train-rmse:0.335133+0.011920 test-rmse:0.505922+0.057916
## [18] train-rmse:0.326959+0.011570 test-rmse:0.503424+0.058454
## [19] train-rmse:0.319871+0.012581 test-rmse:0.502186+0.055883
## [20] train-rmse:0.312677+0.013533 test-rmse:0.499567+0.056911
## [21] train-rmse:0.307948+0.013919 test-rmse:0.499402+0.056281
## [22] train-rmse:0.302299+0.013348 test-rmse:0.498408+0.054785
## [23] train-rmse:0.298053+0.013773 test-rmse:0.497466+0.054968
## [24] train-rmse:0.293643+0.014164 test-rmse:0.498104+0.054458
## [25] train-rmse:0.288672+0.014875 test-rmse:0.495418+0.052203
## [26] train-rmse:0.283355+0.012144 test-rmse:0.494624+0.052782
## [27] train-rmse:0.278294+0.012368 test-rmse:0.494361+0.052007
## [28] train-rmse:0.274712+0.012373 test-rmse:0.494125+0.052951
## [29] train-rmse:0.270798+0.011719 test-rmse:0.492538+0.052084
## [30] train-rmse:0.265501+0.012378 test-rmse:0.492208+0.051534
## [31] train-rmse:0.260842+0.012257 test-rmse:0.491391+0.052387
## [32] train-rmse:0.258224+0.011924 test-rmse:0.491326+0.052427
## [33] train-rmse:0.254762+0.012490 test-rmse:0.490978+0.053078
## [34] train-rmse:0.250319+0.012321 test-rmse:0.491394+0.052910
## [35] train-rmse:0.246884+0.012593 test-rmse:0.490577+0.052872
## [36] train-rmse:0.243447+0.012534 test-rmse:0.491713+0.053453
## [37] train-rmse:0.239754+0.013172 test-rmse:0.491831+0.054837
## [38] train-rmse:0.236011+0.014170 test-rmse:0.491799+0.054562
## [39] train-rmse:0.232018+0.015322 test-rmse:0.492118+0.053638
## [40] train-rmse:0.228241+0.015306 test-rmse:0.492221+0.054441
## [41] train-rmse:0.225354+0.016248 test-rmse:0.493900+0.054919
## Stopping. Best iteration:
## [35] train-rmse:0.246884+0.012593 test-rmse:0.490577+0.052872
##
## 27
## [1] train-rmse:0.884472+0.014166 test-rmse:0.895948+0.060832
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.785201+0.012367 test-rmse:0.810700+0.062904
## [3] train-rmse:0.701634+0.011080 test-rmse:0.741279+0.062773
## [4] train-rmse:0.633393+0.010128 test-rmse:0.689086+0.062593
## [5] train-rmse:0.574638+0.010760 test-rmse:0.646054+0.061324
## [6] train-rmse:0.527131+0.011766 test-rmse:0.616656+0.064756
## [7] train-rmse:0.487213+0.011560 test-rmse:0.592717+0.065144
## [8] train-rmse:0.451631+0.013285 test-rmse:0.571445+0.063475
## [9] train-rmse:0.422634+0.014164 test-rmse:0.556452+0.061189
## [10] train-rmse:0.398244+0.015201 test-rmse:0.545527+0.060554
## [11] train-rmse:0.376424+0.015203 test-rmse:0.535918+0.060819
## [12] train-rmse:0.356909+0.015042 test-rmse:0.527788+0.060320
## [13] train-rmse:0.340834+0.014732 test-rmse:0.522994+0.059737
## [14] train-rmse:0.326815+0.015257 test-rmse:0.519192+0.060198
## [15] train-rmse:0.313651+0.016238 test-rmse:0.516286+0.057642
## [16] train-rmse:0.304302+0.015862 test-rmse:0.514934+0.057070
## [17] train-rmse:0.296705+0.016621 test-rmse:0.513102+0.055820
## [18] train-rmse:0.286772+0.016714 test-rmse:0.512914+0.054881
## [19] train-rmse:0.277569+0.014935 test-rmse:0.509948+0.054049
## [20] train-rmse:0.270422+0.013034 test-rmse:0.507717+0.054034
## [21] train-rmse:0.262281+0.014121 test-rmse:0.505202+0.054622
## [22] train-rmse:0.256307+0.013682 test-rmse:0.502778+0.054117
## [23] train-rmse:0.251905+0.013560 test-rmse:0.501495+0.053905
## [24] train-rmse:0.245271+0.014396 test-rmse:0.500774+0.054177
## [25] train-rmse:0.241710+0.014108 test-rmse:0.500363+0.053399
## [26] train-rmse:0.237810+0.013659 test-rmse:0.499954+0.053742
## [27] train-rmse:0.233361+0.013492 test-rmse:0.499388+0.052346
## [28] train-rmse:0.228480+0.014192 test-rmse:0.499879+0.052101
## [29] train-rmse:0.223631+0.015334 test-rmse:0.499608+0.051591
## [30] train-rmse:0.218518+0.014118 test-rmse:0.497082+0.051754
## [31] train-rmse:0.215756+0.014243 test-rmse:0.497467+0.050841
## [32] train-rmse:0.211561+0.012719 test-rmse:0.496815+0.051279
## [33] train-rmse:0.207328+0.013177 test-rmse:0.497931+0.052787
## [34] train-rmse:0.204053+0.012582 test-rmse:0.497754+0.053107
## [35] train-rmse:0.199657+0.012291 test-rmse:0.497348+0.052111
## [36] train-rmse:0.196299+0.012838 test-rmse:0.497309+0.051337
## [37] train-rmse:0.194225+0.012911 test-rmse:0.498341+0.051152
## [38] train-rmse:0.190509+0.012434 test-rmse:0.498634+0.052328
## Stopping. Best iteration:
## [32] train-rmse:0.211561+0.012719 test-rmse:0.496815+0.051279
##
## 28
## [1] train-rmse:0.909216+0.015233 test-rmse:0.910743+0.061135
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.838320+0.014421 test-rmse:0.844083+0.060324
## [3] train-rmse:0.786199+0.014072 test-rmse:0.799262+0.062303
## [4] train-rmse:0.746369+0.013524 test-rmse:0.762957+0.058604
## [5] train-rmse:0.716401+0.012765 test-rmse:0.733642+0.059832
## [6] train-rmse:0.692415+0.012424 test-rmse:0.710091+0.056472
## [7] train-rmse:0.673271+0.012049 test-rmse:0.696385+0.056535
## [8] train-rmse:0.658563+0.011760 test-rmse:0.682017+0.054878
## [9] train-rmse:0.646023+0.011573 test-rmse:0.669714+0.052676
## [10] train-rmse:0.636255+0.011492 test-rmse:0.662508+0.053715
## [11] train-rmse:0.628177+0.011373 test-rmse:0.654488+0.050553
## [12] train-rmse:0.621166+0.011144 test-rmse:0.648361+0.050154
## [13] train-rmse:0.615394+0.011124 test-rmse:0.643776+0.051766
## [14] train-rmse:0.610155+0.011151 test-rmse:0.638658+0.052828
## [15] train-rmse:0.605375+0.011161 test-rmse:0.636848+0.053971
## [16] train-rmse:0.600905+0.011212 test-rmse:0.633701+0.053488
## [17] train-rmse:0.596754+0.011233 test-rmse:0.630241+0.051256
## [18] train-rmse:0.593093+0.011175 test-rmse:0.626916+0.052737
## [19] train-rmse:0.589502+0.011169 test-rmse:0.623911+0.054578
## [20] train-rmse:0.586168+0.011095 test-rmse:0.621001+0.055308
## [21] train-rmse:0.583017+0.011135 test-rmse:0.619426+0.056005
## [22] train-rmse:0.580066+0.011177 test-rmse:0.616834+0.054664
## [23] train-rmse:0.577238+0.011207 test-rmse:0.613869+0.053379
## [24] train-rmse:0.574527+0.011159 test-rmse:0.610447+0.054939
## [25] train-rmse:0.571941+0.011144 test-rmse:0.606917+0.054987
## [26] train-rmse:0.569441+0.011109 test-rmse:0.605649+0.053011
## [27] train-rmse:0.567024+0.011144 test-rmse:0.604203+0.053473
## [28] train-rmse:0.564678+0.011189 test-rmse:0.603108+0.054291
## [29] train-rmse:0.562481+0.011172 test-rmse:0.600885+0.054120
## [30] train-rmse:0.560331+0.011197 test-rmse:0.600518+0.054652
## [31] train-rmse:0.558222+0.011223 test-rmse:0.598513+0.055751
## [32] train-rmse:0.556173+0.011232 test-rmse:0.595656+0.056003
## [33] train-rmse:0.554199+0.011179 test-rmse:0.594306+0.056039
## [34] train-rmse:0.552283+0.011131 test-rmse:0.594107+0.055112
## [35] train-rmse:0.550443+0.011133 test-rmse:0.592755+0.054941
## [36] train-rmse:0.548644+0.011144 test-rmse:0.589888+0.054469
## [37] train-rmse:0.546883+0.011133 test-rmse:0.588481+0.054020
## [38] train-rmse:0.545196+0.011096 test-rmse:0.587635+0.053346
## [39] train-rmse:0.543532+0.011089 test-rmse:0.586224+0.054016
## [40] train-rmse:0.541892+0.011062 test-rmse:0.585443+0.053279
## [41] train-rmse:0.540295+0.011077 test-rmse:0.584201+0.055126
## [42] train-rmse:0.538756+0.011083 test-rmse:0.583150+0.054899
## [43] train-rmse:0.537286+0.011098 test-rmse:0.581326+0.055309
## [44] train-rmse:0.535843+0.011086 test-rmse:0.581209+0.055339
## [45] train-rmse:0.534415+0.011070 test-rmse:0.580021+0.054794
## [46] train-rmse:0.533016+0.011043 test-rmse:0.579213+0.053592
## [47] train-rmse:0.531666+0.010992 test-rmse:0.577953+0.054216
## [48] train-rmse:0.530341+0.010952 test-rmse:0.576570+0.054152
## [49] train-rmse:0.529066+0.010939 test-rmse:0.576405+0.054736
## [50] train-rmse:0.527800+0.010915 test-rmse:0.576823+0.054493
## [51] train-rmse:0.526598+0.010901 test-rmse:0.575526+0.054065
## [52] train-rmse:0.525421+0.010862 test-rmse:0.575151+0.053641
## [53] train-rmse:0.524259+0.010822 test-rmse:0.574143+0.053830
## [54] train-rmse:0.523109+0.010798 test-rmse:0.572588+0.053936
## [55] train-rmse:0.521994+0.010780 test-rmse:0.571520+0.054306
## [56] train-rmse:0.520892+0.010768 test-rmse:0.571238+0.055242
## [57] train-rmse:0.519816+0.010751 test-rmse:0.570730+0.054914
## [58] train-rmse:0.518757+0.010719 test-rmse:0.569764+0.054985
## [59] train-rmse:0.517706+0.010699 test-rmse:0.568386+0.055587
## [60] train-rmse:0.516691+0.010686 test-rmse:0.567375+0.055466
## [61] train-rmse:0.515701+0.010675 test-rmse:0.566652+0.055011
## [62] train-rmse:0.514709+0.010659 test-rmse:0.566319+0.055420
## [63] train-rmse:0.513721+0.010642 test-rmse:0.565471+0.054043
## [64] train-rmse:0.512746+0.010631 test-rmse:0.564605+0.054449
## [65] train-rmse:0.511801+0.010623 test-rmse:0.563701+0.055047
## [66] train-rmse:0.510891+0.010620 test-rmse:0.563532+0.054996
## [67] train-rmse:0.510004+0.010609 test-rmse:0.562868+0.054741
## [68] train-rmse:0.509148+0.010584 test-rmse:0.561937+0.054563
## [69] train-rmse:0.508314+0.010574 test-rmse:0.561039+0.054679
## [70] train-rmse:0.507491+0.010564 test-rmse:0.560444+0.053903
## [71] train-rmse:0.506668+0.010543 test-rmse:0.560440+0.054228
## [72] train-rmse:0.505855+0.010530 test-rmse:0.559635+0.053644
## [73] train-rmse:0.505058+0.010504 test-rmse:0.559364+0.053916
## [74] train-rmse:0.504272+0.010486 test-rmse:0.558483+0.053482
## [75] train-rmse:0.503509+0.010486 test-rmse:0.557720+0.053363
## [76] train-rmse:0.502759+0.010476 test-rmse:0.556892+0.052800
## [77] train-rmse:0.502028+0.010483 test-rmse:0.555458+0.052671
## [78] train-rmse:0.501310+0.010486 test-rmse:0.555329+0.052628
## [79] train-rmse:0.500597+0.010487 test-rmse:0.554616+0.052788
## [80] train-rmse:0.499897+0.010479 test-rmse:0.554357+0.052713
## [81] train-rmse:0.499203+0.010476 test-rmse:0.554145+0.053365
## [82] train-rmse:0.498508+0.010473 test-rmse:0.554057+0.053603
## [83] train-rmse:0.497828+0.010461 test-rmse:0.553482+0.053505
## [84] train-rmse:0.497165+0.010439 test-rmse:0.553745+0.052629
## [85] train-rmse:0.496510+0.010427 test-rmse:0.552980+0.053048
## [86] train-rmse:0.495870+0.010423 test-rmse:0.553246+0.052434
## [87] train-rmse:0.495234+0.010424 test-rmse:0.552840+0.052309
## [88] train-rmse:0.494599+0.010422 test-rmse:0.552373+0.052134
## [89] train-rmse:0.493980+0.010421 test-rmse:0.551626+0.052030
## [90] train-rmse:0.493385+0.010416 test-rmse:0.551154+0.051913
## [91] train-rmse:0.492795+0.010419 test-rmse:0.550320+0.051824
## [92] train-rmse:0.492214+0.010422 test-rmse:0.550042+0.052384
## [93] train-rmse:0.491648+0.010421 test-rmse:0.549889+0.052357
## [94] train-rmse:0.491093+0.010412 test-rmse:0.548931+0.052584
## [95] train-rmse:0.490545+0.010397 test-rmse:0.548613+0.052462
## [96] train-rmse:0.490009+0.010382 test-rmse:0.547909+0.051709
## [97] train-rmse:0.489479+0.010371 test-rmse:0.547821+0.051630
## [98] train-rmse:0.488952+0.010359 test-rmse:0.547318+0.051943
## [99] train-rmse:0.488432+0.010350 test-rmse:0.547389+0.052448
## [100] train-rmse:0.487922+0.010337 test-rmse:0.547243+0.051995
## [101] train-rmse:0.487417+0.010334 test-rmse:0.548138+0.052079
## [102] train-rmse:0.486919+0.010327 test-rmse:0.547815+0.052386
## [103] train-rmse:0.486432+0.010324 test-rmse:0.546858+0.052429
## [104] train-rmse:0.485953+0.010324 test-rmse:0.546393+0.052260
## [105] train-rmse:0.485476+0.010330 test-rmse:0.546169+0.052024
## [106] train-rmse:0.485007+0.010327 test-rmse:0.545572+0.052677
## [107] train-rmse:0.484542+0.010325 test-rmse:0.546053+0.052089
## [108] train-rmse:0.484092+0.010323 test-rmse:0.545421+0.052144
## [109] train-rmse:0.483644+0.010322 test-rmse:0.545211+0.051415
## [110] train-rmse:0.483202+0.010323 test-rmse:0.544607+0.051216
## [111] train-rmse:0.482762+0.010317 test-rmse:0.544225+0.050966
## [112] train-rmse:0.482333+0.010308 test-rmse:0.544347+0.051150
## [113] train-rmse:0.481906+0.010299 test-rmse:0.544086+0.051142
## [114] train-rmse:0.481484+0.010299 test-rmse:0.543942+0.050891
## [115] train-rmse:0.481070+0.010297 test-rmse:0.543843+0.050923
## [116] train-rmse:0.480662+0.010299 test-rmse:0.543387+0.051249
## [117] train-rmse:0.480261+0.010296 test-rmse:0.542934+0.050814
## [118] train-rmse:0.479861+0.010290 test-rmse:0.542937+0.051312
## [119] train-rmse:0.479474+0.010290 test-rmse:0.542490+0.051449
## [120] train-rmse:0.479092+0.010284 test-rmse:0.542094+0.051209
## [121] train-rmse:0.478715+0.010282 test-rmse:0.541601+0.051084
## [122] train-rmse:0.478339+0.010276 test-rmse:0.541223+0.050521
## [123] train-rmse:0.477965+0.010268 test-rmse:0.541468+0.050519
## [124] train-rmse:0.477599+0.010261 test-rmse:0.541487+0.049970
## [125] train-rmse:0.477237+0.010251 test-rmse:0.541528+0.050430
## [126] train-rmse:0.476878+0.010241 test-rmse:0.541069+0.050507
## [127] train-rmse:0.476527+0.010230 test-rmse:0.541032+0.050650
## [128] train-rmse:0.476184+0.010227 test-rmse:0.540707+0.050941
## [129] train-rmse:0.475845+0.010224 test-rmse:0.540769+0.050960
## [130] train-rmse:0.475509+0.010225 test-rmse:0.540595+0.050678
## [131] train-rmse:0.475175+0.010229 test-rmse:0.540383+0.050486
## [132] train-rmse:0.474842+0.010231 test-rmse:0.540106+0.050653
## [133] train-rmse:0.474519+0.010232 test-rmse:0.539559+0.050807
## [134] train-rmse:0.474204+0.010231 test-rmse:0.539511+0.050819
## [135] train-rmse:0.473890+0.010224 test-rmse:0.539214+0.050752
## [136] train-rmse:0.473573+0.010217 test-rmse:0.539122+0.050360
## [137] train-rmse:0.473261+0.010213 test-rmse:0.538725+0.050355
## [138] train-rmse:0.472955+0.010213 test-rmse:0.538715+0.050051
## [139] train-rmse:0.472657+0.010210 test-rmse:0.538372+0.049767
## [140] train-rmse:0.472359+0.010210 test-rmse:0.538499+0.049461
## [141] train-rmse:0.472068+0.010209 test-rmse:0.537972+0.049449
## [142] train-rmse:0.471780+0.010202 test-rmse:0.538045+0.048975
## [143] train-rmse:0.471492+0.010194 test-rmse:0.537510+0.049220
## [144] train-rmse:0.471210+0.010191 test-rmse:0.537825+0.049522
## [145] train-rmse:0.470932+0.010192 test-rmse:0.537901+0.049469
## [146] train-rmse:0.470657+0.010189 test-rmse:0.537868+0.049028
## [147] train-rmse:0.470385+0.010186 test-rmse:0.537791+0.048668
## [148] train-rmse:0.470119+0.010183 test-rmse:0.537808+0.048522
## [149] train-rmse:0.469857+0.010179 test-rmse:0.537646+0.048507
## Stopping. Best iteration:
## [143] train-rmse:0.471492+0.010194 test-rmse:0.537510+0.049220
##
## 29
## [1] train-rmse:0.895038+0.015127 test-rmse:0.897676+0.061050
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.811215+0.014338 test-rmse:0.821871+0.062638
## [3] train-rmse:0.747562+0.013835 test-rmse:0.763539+0.061872
## [4] train-rmse:0.699005+0.013347 test-rmse:0.723113+0.063332
## [5] train-rmse:0.661455+0.012247 test-rmse:0.687966+0.065833
## [6] train-rmse:0.632640+0.011996 test-rmse:0.662201+0.065475
## [7] train-rmse:0.608419+0.011900 test-rmse:0.643490+0.065775
## [8] train-rmse:0.590754+0.013062 test-rmse:0.630536+0.065200
## [9] train-rmse:0.576258+0.012268 test-rmse:0.616235+0.064981
## [10] train-rmse:0.562360+0.015077 test-rmse:0.608539+0.062988
## [11] train-rmse:0.552960+0.015768 test-rmse:0.602833+0.062385
## [12] train-rmse:0.544475+0.015535 test-rmse:0.594987+0.061491
## [13] train-rmse:0.537181+0.015755 test-rmse:0.589834+0.059523
## [14] train-rmse:0.531489+0.015914 test-rmse:0.586109+0.059148
## [15] train-rmse:0.525877+0.015948 test-rmse:0.583400+0.058417
## [16] train-rmse:0.521027+0.015680 test-rmse:0.579116+0.058766
## [17] train-rmse:0.515303+0.015296 test-rmse:0.575930+0.058761
## [18] train-rmse:0.510807+0.015998 test-rmse:0.572924+0.059209
## [19] train-rmse:0.506870+0.016085 test-rmse:0.570845+0.059879
## [20] train-rmse:0.503136+0.016085 test-rmse:0.569133+0.060625
## [21] train-rmse:0.499303+0.015784 test-rmse:0.566994+0.060004
## [22] train-rmse:0.495642+0.015984 test-rmse:0.564796+0.060563
## [23] train-rmse:0.491940+0.016153 test-rmse:0.563669+0.060659
## [24] train-rmse:0.488852+0.016267 test-rmse:0.562659+0.059131
## [25] train-rmse:0.485582+0.016784 test-rmse:0.560844+0.061160
## [26] train-rmse:0.482420+0.016388 test-rmse:0.558903+0.061369
## [27] train-rmse:0.478524+0.016767 test-rmse:0.554437+0.061173
## [28] train-rmse:0.475578+0.016556 test-rmse:0.553537+0.062442
## [29] train-rmse:0.472532+0.016411 test-rmse:0.550701+0.062207
## [30] train-rmse:0.469927+0.016589 test-rmse:0.550052+0.061793
## [31] train-rmse:0.467461+0.016472 test-rmse:0.548096+0.060994
## [32] train-rmse:0.465233+0.016536 test-rmse:0.547827+0.061151
## [33] train-rmse:0.462904+0.016194 test-rmse:0.546885+0.062521
## [34] train-rmse:0.460797+0.015942 test-rmse:0.545450+0.062463
## [35] train-rmse:0.458603+0.016086 test-rmse:0.543356+0.062968
## [36] train-rmse:0.456228+0.015947 test-rmse:0.540042+0.063760
## [37] train-rmse:0.453664+0.015901 test-rmse:0.538747+0.062728
## [38] train-rmse:0.451696+0.015931 test-rmse:0.537482+0.064372
## [39] train-rmse:0.449476+0.016339 test-rmse:0.536512+0.063837
## [40] train-rmse:0.446886+0.016332 test-rmse:0.534584+0.063064
## [41] train-rmse:0.444601+0.016395 test-rmse:0.532669+0.062257
## [42] train-rmse:0.442355+0.015977 test-rmse:0.531195+0.062420
## [43] train-rmse:0.440734+0.015996 test-rmse:0.530571+0.061847
## [44] train-rmse:0.438927+0.016105 test-rmse:0.529070+0.062933
## [45] train-rmse:0.437294+0.015916 test-rmse:0.528491+0.062161
## [46] train-rmse:0.435461+0.015685 test-rmse:0.528057+0.062632
## [47] train-rmse:0.433695+0.015845 test-rmse:0.528260+0.062406
## [48] train-rmse:0.431665+0.016263 test-rmse:0.527930+0.061725
## [49] train-rmse:0.429801+0.016023 test-rmse:0.527546+0.061205
## [50] train-rmse:0.428291+0.015832 test-rmse:0.527244+0.061116
## [51] train-rmse:0.426008+0.015204 test-rmse:0.525112+0.060309
## [52] train-rmse:0.424564+0.015069 test-rmse:0.523922+0.059678
## [53] train-rmse:0.422891+0.015242 test-rmse:0.522346+0.059953
## [54] train-rmse:0.421495+0.015205 test-rmse:0.521968+0.059795
## [55] train-rmse:0.419698+0.014896 test-rmse:0.521061+0.059927
## [56] train-rmse:0.418568+0.015041 test-rmse:0.520778+0.060759
## [57] train-rmse:0.416934+0.014842 test-rmse:0.520261+0.060456
## [58] train-rmse:0.415518+0.014808 test-rmse:0.519927+0.060504
## [59] train-rmse:0.414224+0.014957 test-rmse:0.519606+0.060794
## [60] train-rmse:0.412854+0.015369 test-rmse:0.518403+0.060890
## [61] train-rmse:0.411606+0.015301 test-rmse:0.518983+0.061436
## [62] train-rmse:0.410462+0.015409 test-rmse:0.519661+0.061298
## [63] train-rmse:0.408890+0.015065 test-rmse:0.519180+0.061282
## [64] train-rmse:0.407388+0.015008 test-rmse:0.519033+0.061208
## [65] train-rmse:0.406433+0.014956 test-rmse:0.518303+0.060275
## [66] train-rmse:0.405039+0.014961 test-rmse:0.517580+0.060412
## [67] train-rmse:0.403930+0.015160 test-rmse:0.517440+0.060408
## [68] train-rmse:0.402740+0.015381 test-rmse:0.516695+0.060287
## [69] train-rmse:0.401457+0.015264 test-rmse:0.515676+0.060425
## [70] train-rmse:0.400431+0.015404 test-rmse:0.515311+0.060545
## [71] train-rmse:0.399426+0.015371 test-rmse:0.515647+0.060082
## [72] train-rmse:0.398554+0.015266 test-rmse:0.515573+0.059979
## [73] train-rmse:0.397570+0.015086 test-rmse:0.515482+0.059554
## [74] train-rmse:0.396614+0.015189 test-rmse:0.515596+0.059342
## [75] train-rmse:0.395501+0.015196 test-rmse:0.515545+0.059415
## [76] train-rmse:0.394465+0.015159 test-rmse:0.515214+0.059731
## [77] train-rmse:0.393152+0.014756 test-rmse:0.514635+0.060358
## [78] train-rmse:0.392213+0.014611 test-rmse:0.514196+0.060056
## [79] train-rmse:0.391145+0.014739 test-rmse:0.513832+0.059583
## [80] train-rmse:0.390092+0.014722 test-rmse:0.513992+0.059596
## [81] train-rmse:0.388680+0.014737 test-rmse:0.513867+0.059376
## [82] train-rmse:0.387871+0.014740 test-rmse:0.513884+0.059111
## [83] train-rmse:0.387098+0.014885 test-rmse:0.513694+0.058389
## [84] train-rmse:0.386368+0.014744 test-rmse:0.513682+0.058212
## [85] train-rmse:0.384893+0.014449 test-rmse:0.512772+0.058527
## [86] train-rmse:0.383978+0.014198 test-rmse:0.512285+0.059380
## [87] train-rmse:0.383026+0.014333 test-rmse:0.511777+0.059582
## [88] train-rmse:0.382108+0.014134 test-rmse:0.511139+0.059970
## [89] train-rmse:0.381413+0.014144 test-rmse:0.510886+0.060180
## [90] train-rmse:0.380666+0.014190 test-rmse:0.510083+0.059997
## [91] train-rmse:0.379914+0.014020 test-rmse:0.509858+0.059841
## [92] train-rmse:0.378793+0.013857 test-rmse:0.510264+0.060016
## [93] train-rmse:0.377678+0.013627 test-rmse:0.510432+0.059844
## [94] train-rmse:0.376847+0.013474 test-rmse:0.510506+0.059652
## [95] train-rmse:0.375758+0.013639 test-rmse:0.509973+0.059407
## [96] train-rmse:0.374459+0.012830 test-rmse:0.509703+0.060116
## [97] train-rmse:0.373607+0.012691 test-rmse:0.508892+0.060013
## [98] train-rmse:0.372970+0.012734 test-rmse:0.508873+0.059763
## [99] train-rmse:0.372214+0.012520 test-rmse:0.508892+0.059636
## [100] train-rmse:0.371584+0.012487 test-rmse:0.508267+0.060206
## [101] train-rmse:0.370606+0.012465 test-rmse:0.507873+0.059984
## [102] train-rmse:0.368905+0.012861 test-rmse:0.506831+0.060205
## [103] train-rmse:0.368296+0.012781 test-rmse:0.506635+0.059934
## [104] train-rmse:0.367443+0.012953 test-rmse:0.506198+0.059889
## [105] train-rmse:0.366688+0.013031 test-rmse:0.505495+0.059506
## [106] train-rmse:0.366041+0.012951 test-rmse:0.504956+0.059670
## [107] train-rmse:0.365548+0.012925 test-rmse:0.505414+0.059380
## [108] train-rmse:0.364991+0.013065 test-rmse:0.505984+0.059104
## [109] train-rmse:0.364320+0.013071 test-rmse:0.505626+0.058836
## [110] train-rmse:0.363325+0.013341 test-rmse:0.505258+0.058897
## [111] train-rmse:0.362167+0.012752 test-rmse:0.505301+0.058188
## [112] train-rmse:0.361395+0.012692 test-rmse:0.504676+0.058174
## [113] train-rmse:0.360879+0.012825 test-rmse:0.504699+0.057931
## [114] train-rmse:0.359996+0.012938 test-rmse:0.504574+0.057804
## [115] train-rmse:0.359275+0.012791 test-rmse:0.504656+0.057685
## [116] train-rmse:0.358524+0.012464 test-rmse:0.504971+0.057988
## [117] train-rmse:0.357901+0.012539 test-rmse:0.504384+0.057877
## [118] train-rmse:0.357351+0.012512 test-rmse:0.504315+0.058025
## [119] train-rmse:0.356414+0.012666 test-rmse:0.504168+0.057731
## [120] train-rmse:0.355827+0.012592 test-rmse:0.504089+0.057884
## [121] train-rmse:0.354741+0.012479 test-rmse:0.504145+0.058010
## [122] train-rmse:0.353665+0.012777 test-rmse:0.504040+0.057835
## [123] train-rmse:0.352962+0.012507 test-rmse:0.503641+0.057886
## [124] train-rmse:0.351707+0.012054 test-rmse:0.503617+0.057314
## [125] train-rmse:0.350818+0.011687 test-rmse:0.503204+0.057574
## [126] train-rmse:0.350157+0.011508 test-rmse:0.503083+0.057549
## [127] train-rmse:0.349609+0.011380 test-rmse:0.503120+0.057533
## [128] train-rmse:0.348801+0.011253 test-rmse:0.503161+0.057481
## [129] train-rmse:0.347451+0.010428 test-rmse:0.502768+0.057831
## [130] train-rmse:0.346685+0.010044 test-rmse:0.502610+0.058197
## [131] train-rmse:0.346111+0.010168 test-rmse:0.502412+0.057908
## [132] train-rmse:0.345375+0.010396 test-rmse:0.502131+0.057670
## [133] train-rmse:0.344409+0.010971 test-rmse:0.501650+0.057049
## [134] train-rmse:0.343756+0.011084 test-rmse:0.501922+0.056872
## [135] train-rmse:0.342931+0.010877 test-rmse:0.501516+0.057075
## [136] train-rmse:0.342526+0.010923 test-rmse:0.501601+0.056588
## [137] train-rmse:0.342069+0.010856 test-rmse:0.501411+0.056965
## [138] train-rmse:0.341097+0.010762 test-rmse:0.500685+0.056514
## [139] train-rmse:0.340092+0.010824 test-rmse:0.500755+0.056273
## [140] train-rmse:0.339160+0.010886 test-rmse:0.500573+0.056926
## [141] train-rmse:0.338025+0.010994 test-rmse:0.500094+0.056610
## [142] train-rmse:0.337599+0.011025 test-rmse:0.499932+0.057030
## [143] train-rmse:0.336750+0.011263 test-rmse:0.500319+0.057539
## [144] train-rmse:0.335697+0.011172 test-rmse:0.500188+0.057197
## [145] train-rmse:0.335139+0.011194 test-rmse:0.500418+0.057089
## [146] train-rmse:0.334365+0.011540 test-rmse:0.500494+0.057264
## [147] train-rmse:0.333862+0.011322 test-rmse:0.500676+0.057126
## [148] train-rmse:0.333237+0.011124 test-rmse:0.500683+0.056798
## Stopping. Best iteration:
## [142] train-rmse:0.337599+0.011025 test-rmse:0.499932+0.057030
##
## 30
## [1] train-rmse:0.888710+0.015381 test-rmse:0.893968+0.062253
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.798250+0.014789 test-rmse:0.809907+0.062934
## [3] train-rmse:0.728347+0.014288 test-rmse:0.747639+0.064820
## [4] train-rmse:0.672773+0.013236 test-rmse:0.698362+0.063568
## [5] train-rmse:0.630337+0.013064 test-rmse:0.662809+0.064136
## [6] train-rmse:0.595324+0.012353 test-rmse:0.634988+0.062794
## [7] train-rmse:0.567550+0.011497 test-rmse:0.612739+0.061670
## [8] train-rmse:0.546294+0.011099 test-rmse:0.598691+0.061186
## [9] train-rmse:0.529239+0.011225 test-rmse:0.586631+0.059607
## [10] train-rmse:0.513501+0.011252 test-rmse:0.577469+0.055761
## [11] train-rmse:0.501290+0.011467 test-rmse:0.568180+0.055858
## [12] train-rmse:0.491469+0.011610 test-rmse:0.563162+0.053932
## [13] train-rmse:0.481009+0.010862 test-rmse:0.554023+0.054710
## [14] train-rmse:0.473708+0.010991 test-rmse:0.550371+0.053530
## [15] train-rmse:0.466824+0.010616 test-rmse:0.548966+0.055239
## [16] train-rmse:0.461629+0.010783 test-rmse:0.546806+0.057453
## [17] train-rmse:0.456290+0.010628 test-rmse:0.542411+0.056773
## [18] train-rmse:0.450526+0.010546 test-rmse:0.539047+0.055865
## [19] train-rmse:0.444040+0.012284 test-rmse:0.537186+0.056297
## [20] train-rmse:0.439487+0.011730 test-rmse:0.535116+0.057162
## [21] train-rmse:0.435666+0.011554 test-rmse:0.533090+0.056928
## [22] train-rmse:0.431690+0.011164 test-rmse:0.529866+0.057963
## [23] train-rmse:0.427973+0.011113 test-rmse:0.527880+0.056994
## [24] train-rmse:0.423891+0.010205 test-rmse:0.527556+0.055826
## [25] train-rmse:0.420044+0.010401 test-rmse:0.524696+0.055519
## [26] train-rmse:0.416319+0.010003 test-rmse:0.522244+0.056560
## [27] train-rmse:0.412283+0.010082 test-rmse:0.520569+0.055762
## [28] train-rmse:0.408297+0.009537 test-rmse:0.518880+0.056074
## [29] train-rmse:0.405043+0.009293 test-rmse:0.517608+0.057306
## [30] train-rmse:0.402156+0.008743 test-rmse:0.516081+0.058704
## [31] train-rmse:0.398957+0.009083 test-rmse:0.515116+0.058068
## [32] train-rmse:0.395903+0.009773 test-rmse:0.513879+0.058073
## [33] train-rmse:0.393467+0.009815 test-rmse:0.513404+0.057465
## [34] train-rmse:0.389409+0.011546 test-rmse:0.513129+0.057537
## [35] train-rmse:0.386789+0.012008 test-rmse:0.512450+0.058315
## [36] train-rmse:0.384023+0.012138 test-rmse:0.512026+0.058352
## [37] train-rmse:0.381326+0.011818 test-rmse:0.510314+0.058676
## [38] train-rmse:0.378997+0.011745 test-rmse:0.509159+0.058907
## [39] train-rmse:0.376641+0.011963 test-rmse:0.508520+0.059689
## [40] train-rmse:0.373913+0.010835 test-rmse:0.509166+0.059443
## [41] train-rmse:0.371280+0.011168 test-rmse:0.508197+0.058486
## [42] train-rmse:0.368963+0.012002 test-rmse:0.507322+0.059047
## [43] train-rmse:0.366909+0.011905 test-rmse:0.506197+0.058496
## [44] train-rmse:0.364278+0.012610 test-rmse:0.505647+0.056998
## [45] train-rmse:0.362154+0.012977 test-rmse:0.505722+0.058480
## [46] train-rmse:0.360188+0.012891 test-rmse:0.505647+0.059364
## [47] train-rmse:0.357919+0.012442 test-rmse:0.506121+0.059236
## [48] train-rmse:0.355694+0.012863 test-rmse:0.505058+0.058914
## [49] train-rmse:0.353936+0.012437 test-rmse:0.504560+0.057891
## [50] train-rmse:0.351855+0.012398 test-rmse:0.504813+0.058283
## [51] train-rmse:0.350125+0.012369 test-rmse:0.503554+0.058279
## [52] train-rmse:0.347717+0.012028 test-rmse:0.502999+0.059031
## [53] train-rmse:0.345523+0.011547 test-rmse:0.503320+0.058560
## [54] train-rmse:0.342435+0.010676 test-rmse:0.502618+0.058835
## [55] train-rmse:0.340390+0.011120 test-rmse:0.501876+0.058953
## [56] train-rmse:0.338689+0.010690 test-rmse:0.500251+0.060074
## [57] train-rmse:0.337307+0.010740 test-rmse:0.500412+0.060112
## [58] train-rmse:0.335850+0.010662 test-rmse:0.500291+0.060368
## [59] train-rmse:0.334590+0.010882 test-rmse:0.500245+0.060131
## [60] train-rmse:0.333013+0.010472 test-rmse:0.500575+0.059451
## [61] train-rmse:0.331031+0.010097 test-rmse:0.500969+0.059395
## [62] train-rmse:0.329343+0.010122 test-rmse:0.501792+0.058473
## [63] train-rmse:0.327690+0.010429 test-rmse:0.501453+0.057861
## [64] train-rmse:0.326575+0.010427 test-rmse:0.500610+0.057641
## [65] train-rmse:0.325042+0.010118 test-rmse:0.500615+0.057159
## Stopping. Best iteration:
## [59] train-rmse:0.334590+0.010882 test-rmse:0.500245+0.060131
##
## 31
## [1] train-rmse:0.883290+0.015192 test-rmse:0.890706+0.061123
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.787709+0.014195 test-rmse:0.803573+0.061011
## [3] train-rmse:0.712700+0.013792 test-rmse:0.736328+0.062959
## [4] train-rmse:0.651887+0.012731 test-rmse:0.683397+0.062988
## [5] train-rmse:0.604713+0.011574 test-rmse:0.647193+0.062883
## [6] train-rmse:0.566881+0.012279 test-rmse:0.620195+0.058593
## [7] train-rmse:0.534337+0.013780 test-rmse:0.596740+0.057875
## [8] train-rmse:0.509707+0.013846 test-rmse:0.580237+0.056280
## [9] train-rmse:0.489259+0.013984 test-rmse:0.569216+0.056800
## [10] train-rmse:0.471471+0.013895 test-rmse:0.558690+0.056129
## [11] train-rmse:0.458587+0.012972 test-rmse:0.553544+0.054012
## [12] train-rmse:0.446094+0.012049 test-rmse:0.544664+0.052639
## [13] train-rmse:0.436819+0.012343 test-rmse:0.542256+0.052461
## [14] train-rmse:0.428264+0.012361 test-rmse:0.534875+0.050967
## [15] train-rmse:0.420120+0.012316 test-rmse:0.530554+0.050017
## [16] train-rmse:0.410052+0.012226 test-rmse:0.525932+0.051790
## [17] train-rmse:0.404099+0.012933 test-rmse:0.522031+0.049901
## [18] train-rmse:0.398316+0.012286 test-rmse:0.520897+0.050334
## [19] train-rmse:0.393205+0.011401 test-rmse:0.518255+0.051113
## [20] train-rmse:0.385888+0.011458 test-rmse:0.515296+0.051584
## [21] train-rmse:0.380437+0.012268 test-rmse:0.513028+0.051771
## [22] train-rmse:0.376695+0.011948 test-rmse:0.511258+0.053263
## [23] train-rmse:0.372251+0.012161 test-rmse:0.510170+0.052152
## [24] train-rmse:0.367897+0.012903 test-rmse:0.509083+0.052124
## [25] train-rmse:0.363706+0.012497 test-rmse:0.508187+0.052909
## [26] train-rmse:0.360041+0.011887 test-rmse:0.507712+0.051723
## [27] train-rmse:0.355762+0.013009 test-rmse:0.506713+0.052011
## [28] train-rmse:0.351918+0.012458 test-rmse:0.507390+0.052245
## [29] train-rmse:0.346915+0.011283 test-rmse:0.505745+0.052981
## [30] train-rmse:0.342976+0.012093 test-rmse:0.504251+0.052885
## [31] train-rmse:0.339546+0.012167 test-rmse:0.502052+0.053804
## [32] train-rmse:0.336340+0.011898 test-rmse:0.501307+0.052543
## [33] train-rmse:0.331975+0.012092 test-rmse:0.501607+0.051951
## [34] train-rmse:0.329263+0.011536 test-rmse:0.501128+0.052588
## [35] train-rmse:0.326167+0.010650 test-rmse:0.500287+0.052053
## [36] train-rmse:0.322252+0.010944 test-rmse:0.499361+0.050893
## [37] train-rmse:0.318745+0.010053 test-rmse:0.498293+0.052285
## [38] train-rmse:0.315655+0.010056 test-rmse:0.498093+0.052328
## [39] train-rmse:0.312668+0.009984 test-rmse:0.497221+0.052119
## [40] train-rmse:0.308416+0.010793 test-rmse:0.496587+0.052566
## [41] train-rmse:0.304886+0.011459 test-rmse:0.495675+0.053907
## [42] train-rmse:0.302366+0.011980 test-rmse:0.495404+0.054053
## [43] train-rmse:0.300331+0.011989 test-rmse:0.494823+0.055068
## [44] train-rmse:0.298263+0.011677 test-rmse:0.494620+0.054805
## [45] train-rmse:0.295718+0.011697 test-rmse:0.493694+0.053849
## [46] train-rmse:0.293299+0.010798 test-rmse:0.493732+0.054078
## [47] train-rmse:0.290809+0.011184 test-rmse:0.493874+0.053928
## [48] train-rmse:0.288352+0.011073 test-rmse:0.493689+0.054997
## [49] train-rmse:0.286190+0.010460 test-rmse:0.493231+0.055184
## [50] train-rmse:0.283743+0.009407 test-rmse:0.493224+0.055705
## [51] train-rmse:0.280109+0.010187 test-rmse:0.493706+0.054965
## [52] train-rmse:0.277677+0.010193 test-rmse:0.494525+0.055288
## [53] train-rmse:0.274775+0.010910 test-rmse:0.494415+0.055771
## [54] train-rmse:0.272246+0.011443 test-rmse:0.494300+0.056220
## [55] train-rmse:0.270208+0.010779 test-rmse:0.495027+0.057077
## [56] train-rmse:0.268156+0.010743 test-rmse:0.494312+0.057481
## Stopping. Best iteration:
## [50] train-rmse:0.283743+0.009407 test-rmse:0.493224+0.055705
##
## 32
## [1] train-rmse:0.878473+0.014743 test-rmse:0.887890+0.061805
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.777812+0.013442 test-rmse:0.799791+0.060388
## [3] train-rmse:0.696852+0.012630 test-rmse:0.731348+0.061895
## [4] train-rmse:0.633893+0.012234 test-rmse:0.678858+0.063578
## [5] train-rmse:0.582224+0.010548 test-rmse:0.640818+0.065365
## [6] train-rmse:0.539959+0.011472 test-rmse:0.607231+0.063899
## [7] train-rmse:0.506062+0.012089 test-rmse:0.583805+0.061151
## [8] train-rmse:0.477241+0.013816 test-rmse:0.568143+0.059129
## [9] train-rmse:0.455574+0.013743 test-rmse:0.557393+0.058767
## [10] train-rmse:0.434991+0.015310 test-rmse:0.546045+0.057941
## [11] train-rmse:0.419189+0.014891 test-rmse:0.537181+0.057767
## [12] train-rmse:0.405000+0.017601 test-rmse:0.533699+0.055208
## [13] train-rmse:0.393641+0.016104 test-rmse:0.527868+0.057627
## [14] train-rmse:0.384323+0.017874 test-rmse:0.523815+0.058563
## [15] train-rmse:0.376179+0.018189 test-rmse:0.520674+0.056749
## [16] train-rmse:0.367734+0.016690 test-rmse:0.517507+0.058238
## [17] train-rmse:0.359608+0.018581 test-rmse:0.514638+0.058066
## [18] train-rmse:0.352878+0.018470 test-rmse:0.513238+0.060417
## [19] train-rmse:0.346983+0.018046 test-rmse:0.511757+0.059614
## [20] train-rmse:0.339702+0.017800 test-rmse:0.508615+0.059797
## [21] train-rmse:0.334143+0.016978 test-rmse:0.508141+0.059367
## [22] train-rmse:0.327141+0.018894 test-rmse:0.506841+0.059965
## [23] train-rmse:0.321174+0.017217 test-rmse:0.505815+0.061624
## [24] train-rmse:0.317159+0.017123 test-rmse:0.504226+0.062365
## [25] train-rmse:0.313507+0.016760 test-rmse:0.502561+0.061518
## [26] train-rmse:0.309091+0.016298 test-rmse:0.501478+0.061633
## [27] train-rmse:0.303942+0.017101 test-rmse:0.500579+0.061260
## [28] train-rmse:0.301048+0.016802 test-rmse:0.500467+0.060342
## [29] train-rmse:0.296904+0.017105 test-rmse:0.500363+0.061515
## [30] train-rmse:0.293307+0.016750 test-rmse:0.500240+0.061850
## [31] train-rmse:0.288752+0.017220 test-rmse:0.498937+0.061582
## [32] train-rmse:0.283268+0.017640 test-rmse:0.498855+0.061551
## [33] train-rmse:0.279439+0.016294 test-rmse:0.498137+0.061056
## [34] train-rmse:0.275741+0.015902 test-rmse:0.498093+0.059891
## [35] train-rmse:0.271303+0.015310 test-rmse:0.498554+0.059887
## [36] train-rmse:0.267148+0.014653 test-rmse:0.497988+0.059491
## [37] train-rmse:0.264559+0.014536 test-rmse:0.498686+0.059318
## [38] train-rmse:0.259432+0.015354 test-rmse:0.498307+0.059161
## [39] train-rmse:0.256102+0.014147 test-rmse:0.498391+0.059825
## [40] train-rmse:0.252471+0.015137 test-rmse:0.499493+0.059799
## [41] train-rmse:0.249537+0.013933 test-rmse:0.499629+0.059255
## [42] train-rmse:0.246881+0.013438 test-rmse:0.499426+0.059072
## Stopping. Best iteration:
## [36] train-rmse:0.267148+0.014653 test-rmse:0.497988+0.059491
##
## 33
## [1] train-rmse:0.874077+0.014300 test-rmse:0.886014+0.062025
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.769062+0.012942 test-rmse:0.792795+0.062427
## [3] train-rmse:0.683592+0.010650 test-rmse:0.722704+0.061715
## [4] train-rmse:0.614723+0.011764 test-rmse:0.670350+0.060840
## [5] train-rmse:0.560315+0.012216 test-rmse:0.631259+0.062821
## [6] train-rmse:0.514443+0.011787 test-rmse:0.599874+0.065258
## [7] train-rmse:0.477494+0.012546 test-rmse:0.575076+0.062776
## [8] train-rmse:0.448105+0.012789 test-rmse:0.560133+0.060264
## [9] train-rmse:0.421044+0.013696 test-rmse:0.545026+0.058293
## [10] train-rmse:0.399902+0.013674 test-rmse:0.533869+0.058791
## [11] train-rmse:0.381265+0.012237 test-rmse:0.526759+0.059755
## [12] train-rmse:0.366464+0.012657 test-rmse:0.518870+0.058121
## [13] train-rmse:0.353791+0.014386 test-rmse:0.513120+0.056960
## [14] train-rmse:0.343872+0.013581 test-rmse:0.508979+0.056725
## [15] train-rmse:0.334221+0.013455 test-rmse:0.508052+0.056993
## [16] train-rmse:0.323029+0.015681 test-rmse:0.505327+0.056885
## [17] train-rmse:0.317402+0.015282 test-rmse:0.504686+0.055926
## [18] train-rmse:0.306353+0.017592 test-rmse:0.501992+0.055638
## [19] train-rmse:0.299131+0.017064 test-rmse:0.499911+0.055456
## [20] train-rmse:0.293487+0.016356 test-rmse:0.497720+0.054811
## [21] train-rmse:0.288066+0.016396 test-rmse:0.498481+0.054928
## [22] train-rmse:0.282955+0.016398 test-rmse:0.496515+0.054288
## [23] train-rmse:0.275718+0.013491 test-rmse:0.494140+0.054629
## [24] train-rmse:0.269224+0.014535 test-rmse:0.493632+0.054536
## [25] train-rmse:0.264456+0.014926 test-rmse:0.493479+0.054113
## [26] train-rmse:0.260386+0.014459 test-rmse:0.493409+0.054329
## [27] train-rmse:0.254531+0.017356 test-rmse:0.492871+0.053547
## [28] train-rmse:0.250974+0.017326 test-rmse:0.493098+0.051904
## [29] train-rmse:0.246800+0.018150 test-rmse:0.493126+0.051688
## [30] train-rmse:0.242653+0.017347 test-rmse:0.493072+0.052395
## [31] train-rmse:0.239055+0.017767 test-rmse:0.493625+0.052337
## [32] train-rmse:0.235365+0.017467 test-rmse:0.493881+0.053273
## [33] train-rmse:0.231802+0.017561 test-rmse:0.493566+0.052674
## Stopping. Best iteration:
## [27] train-rmse:0.254531+0.017356 test-rmse:0.492871+0.053547
##
## 34
## [1] train-rmse:0.869768+0.013907 test-rmse:0.883145+0.060577
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.761501+0.011523 test-rmse:0.790081+0.061972
## [3] train-rmse:0.672586+0.009428 test-rmse:0.718189+0.060876
## [4] train-rmse:0.601437+0.008563 test-rmse:0.665731+0.063822
## [5] train-rmse:0.544294+0.008656 test-rmse:0.625102+0.066194
## [6] train-rmse:0.496372+0.010217 test-rmse:0.595570+0.066728
## [7] train-rmse:0.456368+0.008774 test-rmse:0.569627+0.065349
## [8] train-rmse:0.422527+0.009065 test-rmse:0.552921+0.064745
## [9] train-rmse:0.394262+0.008984 test-rmse:0.540747+0.066435
## [10] train-rmse:0.368189+0.010718 test-rmse:0.531509+0.065815
## [11] train-rmse:0.348018+0.011764 test-rmse:0.525916+0.062165
## [12] train-rmse:0.330687+0.011341 test-rmse:0.521283+0.062584
## [13] train-rmse:0.315584+0.010719 test-rmse:0.519683+0.061414
## [14] train-rmse:0.303380+0.011123 test-rmse:0.517146+0.062421
## [15] train-rmse:0.292899+0.012610 test-rmse:0.514377+0.061211
## [16] train-rmse:0.283774+0.013807 test-rmse:0.513198+0.061835
## [17] train-rmse:0.276897+0.014121 test-rmse:0.510695+0.060043
## [18] train-rmse:0.269621+0.014020 test-rmse:0.510901+0.059064
## [19] train-rmse:0.262920+0.014685 test-rmse:0.508739+0.058098
## [20] train-rmse:0.257506+0.014618 test-rmse:0.507017+0.057597
## [21] train-rmse:0.248033+0.014609 test-rmse:0.501721+0.055930
## [22] train-rmse:0.243979+0.015096 test-rmse:0.501964+0.055961
## [23] train-rmse:0.238082+0.014809 test-rmse:0.502416+0.055146
## [24] train-rmse:0.232557+0.014041 test-rmse:0.501279+0.056597
## [25] train-rmse:0.225189+0.015507 test-rmse:0.500905+0.056118
## [26] train-rmse:0.220311+0.015824 test-rmse:0.499761+0.055956
## [27] train-rmse:0.216595+0.015992 test-rmse:0.499745+0.055784
## [28] train-rmse:0.213045+0.016770 test-rmse:0.499503+0.055893
## [29] train-rmse:0.207110+0.015427 test-rmse:0.499073+0.055015
## [30] train-rmse:0.203817+0.015277 test-rmse:0.498623+0.053416
## [31] train-rmse:0.199927+0.015985 test-rmse:0.499346+0.054062
## [32] train-rmse:0.196465+0.015655 test-rmse:0.499486+0.054179
## [33] train-rmse:0.192466+0.014897 test-rmse:0.500923+0.055308
## [34] train-rmse:0.187625+0.014251 test-rmse:0.500622+0.055258
## [35] train-rmse:0.184329+0.013739 test-rmse:0.500345+0.054961
## [36] train-rmse:0.180794+0.013802 test-rmse:0.500741+0.054055
## Stopping. Best iteration:
## [30] train-rmse:0.203817+0.015277 test-rmse:0.498623+0.053416
##
## 35
## [1] train-rmse:0.899205+0.015124 test-rmse:0.901148+0.060961
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.823877+0.014266 test-rmse:0.830474+0.060235
## [3] train-rmse:0.770133+0.013875 test-rmse:0.788072+0.060306
## [4] train-rmse:0.730719+0.013284 test-rmse:0.749328+0.058321
## [5] train-rmse:0.701322+0.012489 test-rmse:0.720658+0.059597
## [6] train-rmse:0.677774+0.012243 test-rmse:0.697480+0.055812
## [7] train-rmse:0.660389+0.011918 test-rmse:0.685547+0.055902
## [8] train-rmse:0.646725+0.011788 test-rmse:0.672021+0.053610
## [9] train-rmse:0.635133+0.011354 test-rmse:0.661014+0.051990
## [10] train-rmse:0.626460+0.011330 test-rmse:0.652786+0.052156
## [11] train-rmse:0.619163+0.011278 test-rmse:0.643958+0.051676
## [12] train-rmse:0.613067+0.011319 test-rmse:0.643032+0.050057
## [13] train-rmse:0.607289+0.011282 test-rmse:0.637294+0.052928
## [14] train-rmse:0.602172+0.011370 test-rmse:0.633270+0.052476
## [15] train-rmse:0.597553+0.011339 test-rmse:0.630313+0.052403
## [16] train-rmse:0.593404+0.011345 test-rmse:0.626515+0.052222
## [17] train-rmse:0.589509+0.011294 test-rmse:0.622333+0.054379
## [18] train-rmse:0.585698+0.011263 test-rmse:0.617195+0.053789
## [19] train-rmse:0.582124+0.011189 test-rmse:0.616422+0.054005
## [20] train-rmse:0.578846+0.011218 test-rmse:0.616368+0.053960
## [21] train-rmse:0.575796+0.011186 test-rmse:0.613834+0.052307
## [22] train-rmse:0.572868+0.011154 test-rmse:0.610459+0.054223
## [23] train-rmse:0.570130+0.011180 test-rmse:0.607991+0.055251
## [24] train-rmse:0.567395+0.011229 test-rmse:0.605157+0.054850
## [25] train-rmse:0.564799+0.011269 test-rmse:0.602630+0.054576
## [26] train-rmse:0.562264+0.011306 test-rmse:0.600751+0.055008
## [27] train-rmse:0.559756+0.011328 test-rmse:0.598691+0.055436
## [28] train-rmse:0.557419+0.011376 test-rmse:0.598341+0.055706
## [29] train-rmse:0.555216+0.011366 test-rmse:0.596035+0.054778
## [30] train-rmse:0.553055+0.011334 test-rmse:0.593771+0.054224
## [31] train-rmse:0.550952+0.011310 test-rmse:0.591839+0.054050
## [32] train-rmse:0.548925+0.011254 test-rmse:0.591123+0.055368
## [33] train-rmse:0.547010+0.011203 test-rmse:0.589735+0.053762
## [34] train-rmse:0.545124+0.011151 test-rmse:0.587536+0.053922
## [35] train-rmse:0.543267+0.011104 test-rmse:0.586460+0.053062
## [36] train-rmse:0.541463+0.011107 test-rmse:0.584241+0.054344
## [37] train-rmse:0.539681+0.011110 test-rmse:0.582160+0.054485
## [38] train-rmse:0.537928+0.011094 test-rmse:0.581542+0.054157
## [39] train-rmse:0.536248+0.011110 test-rmse:0.580773+0.055529
## [40] train-rmse:0.534630+0.011109 test-rmse:0.581014+0.054762
## [41] train-rmse:0.533074+0.011116 test-rmse:0.580988+0.054859
## [42] train-rmse:0.531565+0.011086 test-rmse:0.579834+0.054400
## [43] train-rmse:0.530084+0.011086 test-rmse:0.579434+0.054101
## [44] train-rmse:0.528650+0.011050 test-rmse:0.577605+0.054899
## [45] train-rmse:0.527259+0.011022 test-rmse:0.576546+0.053922
## [46] train-rmse:0.525907+0.010989 test-rmse:0.575337+0.054160
## [47] train-rmse:0.524573+0.010971 test-rmse:0.574325+0.054858
## [48] train-rmse:0.523289+0.010930 test-rmse:0.572122+0.054645
## [49] train-rmse:0.522031+0.010899 test-rmse:0.571426+0.054031
## [50] train-rmse:0.520805+0.010851 test-rmse:0.570560+0.054701
## [51] train-rmse:0.519613+0.010837 test-rmse:0.569383+0.054669
## [52] train-rmse:0.518452+0.010811 test-rmse:0.568740+0.054010
## [53] train-rmse:0.517304+0.010785 test-rmse:0.568518+0.054873
## [54] train-rmse:0.516162+0.010743 test-rmse:0.567551+0.054087
## [55] train-rmse:0.515071+0.010732 test-rmse:0.566221+0.054029
## [56] train-rmse:0.513993+0.010724 test-rmse:0.565770+0.054939
## [57] train-rmse:0.512936+0.010704 test-rmse:0.565302+0.054461
## [58] train-rmse:0.511901+0.010684 test-rmse:0.563923+0.054474
## [59] train-rmse:0.510868+0.010653 test-rmse:0.563866+0.054354
## [60] train-rmse:0.509842+0.010601 test-rmse:0.563106+0.054245
## [61] train-rmse:0.508862+0.010584 test-rmse:0.562058+0.053687
## [62] train-rmse:0.507885+0.010584 test-rmse:0.561368+0.053639
## [63] train-rmse:0.506944+0.010597 test-rmse:0.560672+0.053235
## [64] train-rmse:0.506022+0.010618 test-rmse:0.559470+0.053640
## [65] train-rmse:0.505120+0.010623 test-rmse:0.559741+0.054501
## [66] train-rmse:0.504261+0.010618 test-rmse:0.558782+0.054134
## [67] train-rmse:0.503416+0.010605 test-rmse:0.557867+0.053914
## [68] train-rmse:0.502590+0.010597 test-rmse:0.557184+0.053880
## [69] train-rmse:0.501773+0.010592 test-rmse:0.556800+0.054278
## [70] train-rmse:0.500968+0.010577 test-rmse:0.556354+0.053769
## [71] train-rmse:0.500185+0.010574 test-rmse:0.555380+0.053176
## [72] train-rmse:0.499415+0.010571 test-rmse:0.554843+0.054031
## [73] train-rmse:0.498654+0.010559 test-rmse:0.554409+0.054230
## [74] train-rmse:0.497905+0.010556 test-rmse:0.554178+0.053292
## [75] train-rmse:0.497169+0.010546 test-rmse:0.553944+0.053487
## [76] train-rmse:0.496453+0.010540 test-rmse:0.553947+0.052489
## [77] train-rmse:0.495746+0.010539 test-rmse:0.553734+0.052250
## [78] train-rmse:0.495050+0.010541 test-rmse:0.553467+0.051495
## [79] train-rmse:0.494363+0.010527 test-rmse:0.552539+0.052120
## [80] train-rmse:0.493687+0.010518 test-rmse:0.552440+0.053047
## [81] train-rmse:0.493023+0.010510 test-rmse:0.552120+0.052663
## [82] train-rmse:0.492371+0.010503 test-rmse:0.551452+0.052450
## [83] train-rmse:0.491733+0.010503 test-rmse:0.550612+0.052750
## [84] train-rmse:0.491101+0.010497 test-rmse:0.550091+0.052695
## [85] train-rmse:0.490479+0.010493 test-rmse:0.550312+0.052866
## [86] train-rmse:0.489866+0.010487 test-rmse:0.549774+0.052554
## [87] train-rmse:0.489265+0.010477 test-rmse:0.548802+0.052829
## [88] train-rmse:0.488686+0.010475 test-rmse:0.548152+0.052651
## [89] train-rmse:0.488111+0.010472 test-rmse:0.547235+0.052526
## [90] train-rmse:0.487543+0.010462 test-rmse:0.547362+0.051781
## [91] train-rmse:0.486977+0.010447 test-rmse:0.546686+0.051390
## [92] train-rmse:0.486424+0.010444 test-rmse:0.546743+0.051417
## [93] train-rmse:0.485882+0.010437 test-rmse:0.546611+0.051904
## [94] train-rmse:0.485355+0.010424 test-rmse:0.546453+0.052153
## [95] train-rmse:0.484835+0.010419 test-rmse:0.546273+0.052197
## [96] train-rmse:0.484322+0.010425 test-rmse:0.546514+0.051747
## [97] train-rmse:0.483828+0.010422 test-rmse:0.546392+0.051419
## [98] train-rmse:0.483339+0.010425 test-rmse:0.545962+0.051090
## [99] train-rmse:0.482857+0.010424 test-rmse:0.545577+0.051212
## [100] train-rmse:0.482384+0.010416 test-rmse:0.545166+0.051110
## [101] train-rmse:0.481916+0.010414 test-rmse:0.544962+0.051721
## [102] train-rmse:0.481451+0.010410 test-rmse:0.544274+0.051230
## [103] train-rmse:0.480999+0.010405 test-rmse:0.544719+0.051719
## [104] train-rmse:0.480548+0.010394 test-rmse:0.543985+0.051567
## [105] train-rmse:0.480104+0.010387 test-rmse:0.543446+0.051394
## [106] train-rmse:0.479668+0.010389 test-rmse:0.543341+0.051153
## [107] train-rmse:0.479237+0.010388 test-rmse:0.542918+0.051113
## [108] train-rmse:0.478813+0.010394 test-rmse:0.543186+0.050743
## [109] train-rmse:0.478398+0.010403 test-rmse:0.542988+0.050233
## [110] train-rmse:0.477992+0.010401 test-rmse:0.542594+0.050032
## [111] train-rmse:0.477585+0.010393 test-rmse:0.542115+0.050277
## [112] train-rmse:0.477184+0.010377 test-rmse:0.541932+0.050332
## [113] train-rmse:0.476781+0.010362 test-rmse:0.541721+0.050345
## [114] train-rmse:0.476397+0.010353 test-rmse:0.541785+0.050499
## [115] train-rmse:0.476012+0.010356 test-rmse:0.541299+0.050174
## [116] train-rmse:0.475633+0.010350 test-rmse:0.541391+0.049493
## [117] train-rmse:0.475262+0.010345 test-rmse:0.541322+0.049344
## [118] train-rmse:0.474894+0.010340 test-rmse:0.541252+0.049948
## [119] train-rmse:0.474536+0.010345 test-rmse:0.540969+0.049746
## [120] train-rmse:0.474181+0.010345 test-rmse:0.540598+0.049807
## [121] train-rmse:0.473834+0.010343 test-rmse:0.540871+0.050376
## [122] train-rmse:0.473487+0.010344 test-rmse:0.540652+0.050489
## [123] train-rmse:0.473139+0.010345 test-rmse:0.540523+0.050365
## [124] train-rmse:0.472799+0.010339 test-rmse:0.540178+0.050442
## [125] train-rmse:0.472474+0.010333 test-rmse:0.539899+0.050424
## [126] train-rmse:0.472150+0.010326 test-rmse:0.539252+0.050378
## [127] train-rmse:0.471833+0.010320 test-rmse:0.538923+0.050202
## [128] train-rmse:0.471518+0.010314 test-rmse:0.538975+0.050079
## [129] train-rmse:0.471203+0.010309 test-rmse:0.538406+0.050261
## [130] train-rmse:0.470894+0.010302 test-rmse:0.538319+0.049968
## [131] train-rmse:0.470583+0.010298 test-rmse:0.538305+0.049916
## [132] train-rmse:0.470279+0.010298 test-rmse:0.538323+0.049888
## [133] train-rmse:0.469981+0.010298 test-rmse:0.538670+0.049822
## [134] train-rmse:0.469691+0.010291 test-rmse:0.538102+0.050019
## [135] train-rmse:0.469394+0.010272 test-rmse:0.537632+0.049863
## [136] train-rmse:0.469109+0.010264 test-rmse:0.537297+0.049122
## [137] train-rmse:0.468827+0.010254 test-rmse:0.536940+0.048800
## [138] train-rmse:0.468549+0.010244 test-rmse:0.536990+0.048535
## [139] train-rmse:0.468275+0.010238 test-rmse:0.536910+0.048752
## [140] train-rmse:0.468000+0.010230 test-rmse:0.536820+0.048631
## [141] train-rmse:0.467730+0.010227 test-rmse:0.536267+0.048616
## [142] train-rmse:0.467454+0.010218 test-rmse:0.536217+0.048299
## [143] train-rmse:0.467188+0.010209 test-rmse:0.536645+0.048063
## [144] train-rmse:0.466930+0.010202 test-rmse:0.536344+0.048216
## [145] train-rmse:0.466683+0.010197 test-rmse:0.536731+0.048364
## [146] train-rmse:0.466437+0.010192 test-rmse:0.536332+0.048301
## [147] train-rmse:0.466190+0.010183 test-rmse:0.536285+0.047814
## [148] train-rmse:0.465953+0.010174 test-rmse:0.536099+0.047451
## [149] train-rmse:0.465717+0.010164 test-rmse:0.535708+0.047378
## [150] train-rmse:0.465478+0.010157 test-rmse:0.535469+0.047297
## [151] train-rmse:0.465241+0.010147 test-rmse:0.535693+0.047122
## [152] train-rmse:0.465011+0.010142 test-rmse:0.535842+0.047105
## [153] train-rmse:0.464780+0.010131 test-rmse:0.535983+0.047102
## [154] train-rmse:0.464548+0.010118 test-rmse:0.535910+0.047056
## [155] train-rmse:0.464313+0.010099 test-rmse:0.536119+0.047094
## [156] train-rmse:0.464084+0.010089 test-rmse:0.536052+0.047072
## Stopping. Best iteration:
## [150] train-rmse:0.465478+0.010157 test-rmse:0.535469+0.047297
##
## 36
## [1] train-rmse:0.883394+0.015003 test-rmse:0.886568+0.060912
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.793891+0.014278 test-rmse:0.806241+0.062660
## [3] train-rmse:0.728390+0.013610 test-rmse:0.746023+0.062418
## [4] train-rmse:0.679701+0.013054 test-rmse:0.704097+0.065371
## [5] train-rmse:0.643096+0.011712 test-rmse:0.671287+0.065154
## [6] train-rmse:0.615213+0.011700 test-rmse:0.645675+0.063562
## [7] train-rmse:0.592756+0.012331 test-rmse:0.629606+0.063699
## [8] train-rmse:0.575698+0.011941 test-rmse:0.614792+0.064046
## [9] train-rmse:0.562786+0.011777 test-rmse:0.604449+0.062900
## [10] train-rmse:0.550832+0.013293 test-rmse:0.594957+0.061163
## [11] train-rmse:0.542785+0.013489 test-rmse:0.592471+0.059648
## [12] train-rmse:0.536038+0.013176 test-rmse:0.587326+0.059949
## [13] train-rmse:0.529509+0.013585 test-rmse:0.583747+0.058953
## [14] train-rmse:0.523559+0.013545 test-rmse:0.580406+0.056482
## [15] train-rmse:0.518206+0.013398 test-rmse:0.577180+0.055830
## [16] train-rmse:0.513098+0.013460 test-rmse:0.572541+0.056107
## [17] train-rmse:0.508550+0.013760 test-rmse:0.569502+0.057020
## [18] train-rmse:0.502586+0.013585 test-rmse:0.566809+0.059724
## [19] train-rmse:0.498341+0.013283 test-rmse:0.562935+0.059055
## [20] train-rmse:0.494379+0.013231 test-rmse:0.560553+0.060841
## [21] train-rmse:0.490092+0.013348 test-rmse:0.558329+0.061808
## [22] train-rmse:0.486960+0.013288 test-rmse:0.556305+0.062055
## [23] train-rmse:0.483631+0.013237 test-rmse:0.555578+0.061675
## [24] train-rmse:0.479953+0.013853 test-rmse:0.552812+0.061620
## [25] train-rmse:0.476864+0.014087 test-rmse:0.549850+0.062849
## [26] train-rmse:0.474348+0.014188 test-rmse:0.548329+0.061793
## [27] train-rmse:0.470812+0.014195 test-rmse:0.546489+0.060986
## [28] train-rmse:0.467010+0.013515 test-rmse:0.542003+0.061480
## [29] train-rmse:0.463936+0.013592 test-rmse:0.539832+0.062782
## [30] train-rmse:0.461242+0.013464 test-rmse:0.537883+0.063050
## [31] train-rmse:0.458676+0.013555 test-rmse:0.537679+0.062546
## [32] train-rmse:0.456031+0.012957 test-rmse:0.536984+0.061280
## [33] train-rmse:0.453700+0.013358 test-rmse:0.536373+0.061432
## [34] train-rmse:0.451747+0.013413 test-rmse:0.534929+0.060460
## [35] train-rmse:0.449208+0.013833 test-rmse:0.534035+0.060833
## [36] train-rmse:0.446865+0.013800 test-rmse:0.531423+0.061057
## [37] train-rmse:0.444592+0.014107 test-rmse:0.530328+0.060738
## [38] train-rmse:0.442414+0.013823 test-rmse:0.528753+0.061283
## [39] train-rmse:0.440318+0.014012 test-rmse:0.526914+0.061469
## [40] train-rmse:0.438589+0.014145 test-rmse:0.526116+0.062258
## [41] train-rmse:0.436760+0.014035 test-rmse:0.525091+0.062373
## [42] train-rmse:0.434736+0.014156 test-rmse:0.523961+0.063366
## [43] train-rmse:0.432902+0.014352 test-rmse:0.524429+0.063723
## [44] train-rmse:0.431184+0.014564 test-rmse:0.523040+0.063288
## [45] train-rmse:0.428770+0.014943 test-rmse:0.522711+0.062361
## [46] train-rmse:0.427323+0.015109 test-rmse:0.522506+0.062451
## [47] train-rmse:0.425690+0.015379 test-rmse:0.521820+0.061718
## [48] train-rmse:0.423948+0.015410 test-rmse:0.521125+0.060998
## [49] train-rmse:0.422443+0.015263 test-rmse:0.520325+0.059409
## [50] train-rmse:0.421099+0.015480 test-rmse:0.518498+0.059493
## [51] train-rmse:0.419364+0.015489 test-rmse:0.517333+0.061042
## [52] train-rmse:0.417255+0.015431 test-rmse:0.515373+0.061007
## [53] train-rmse:0.415986+0.015372 test-rmse:0.515217+0.061147
## [54] train-rmse:0.414862+0.015339 test-rmse:0.515214+0.061180
## [55] train-rmse:0.413435+0.015309 test-rmse:0.515333+0.061566
## [56] train-rmse:0.412064+0.015993 test-rmse:0.514798+0.060967
## [57] train-rmse:0.410684+0.015878 test-rmse:0.515179+0.060775
## [58] train-rmse:0.409029+0.015991 test-rmse:0.515448+0.060003
## [59] train-rmse:0.407671+0.016438 test-rmse:0.515105+0.059938
## [60] train-rmse:0.406465+0.016337 test-rmse:0.515065+0.060617
## [61] train-rmse:0.405163+0.015933 test-rmse:0.514291+0.059926
## [62] train-rmse:0.403889+0.015864 test-rmse:0.513266+0.060641
## [63] train-rmse:0.402283+0.015788 test-rmse:0.511753+0.060398
## [64] train-rmse:0.401212+0.015866 test-rmse:0.511831+0.060464
## [65] train-rmse:0.399402+0.015774 test-rmse:0.511158+0.060279
## [66] train-rmse:0.398517+0.015834 test-rmse:0.511664+0.059532
## [67] train-rmse:0.397494+0.015834 test-rmse:0.511259+0.059629
## [68] train-rmse:0.396432+0.015596 test-rmse:0.510749+0.059636
## [69] train-rmse:0.395181+0.015786 test-rmse:0.510789+0.059646
## [70] train-rmse:0.394165+0.015600 test-rmse:0.510551+0.059617
## [71] train-rmse:0.392702+0.015804 test-rmse:0.509943+0.059600
## [72] train-rmse:0.391795+0.015584 test-rmse:0.508936+0.059754
## [73] train-rmse:0.390309+0.015775 test-rmse:0.508354+0.059915
## [74] train-rmse:0.389428+0.015651 test-rmse:0.508232+0.060813
## [75] train-rmse:0.388533+0.015639 test-rmse:0.508030+0.061157
## [76] train-rmse:0.387302+0.015421 test-rmse:0.507519+0.061129
## [77] train-rmse:0.386399+0.015248 test-rmse:0.508144+0.060776
## [78] train-rmse:0.385470+0.015525 test-rmse:0.508174+0.060440
## [79] train-rmse:0.384345+0.015079 test-rmse:0.507618+0.060495
## [80] train-rmse:0.383036+0.014858 test-rmse:0.506653+0.060295
## [81] train-rmse:0.381654+0.015309 test-rmse:0.506535+0.060201
## [82] train-rmse:0.380565+0.015273 test-rmse:0.506582+0.060987
## [83] train-rmse:0.379692+0.015158 test-rmse:0.506323+0.061347
## [84] train-rmse:0.378670+0.014831 test-rmse:0.506349+0.060753
## [85] train-rmse:0.377685+0.014819 test-rmse:0.506462+0.061193
## [86] train-rmse:0.376177+0.015075 test-rmse:0.505332+0.061403
## [87] train-rmse:0.375174+0.014785 test-rmse:0.505203+0.061841
## [88] train-rmse:0.374311+0.014705 test-rmse:0.504230+0.061952
## [89] train-rmse:0.373478+0.014584 test-rmse:0.503764+0.061743
## [90] train-rmse:0.372334+0.015004 test-rmse:0.502921+0.062232
## [91] train-rmse:0.371312+0.014784 test-rmse:0.502927+0.062181
## [92] train-rmse:0.370291+0.014875 test-rmse:0.502471+0.061686
## [93] train-rmse:0.369644+0.014923 test-rmse:0.502981+0.061693
## [94] train-rmse:0.368966+0.014793 test-rmse:0.503099+0.061475
## [95] train-rmse:0.368167+0.014798 test-rmse:0.503126+0.061664
## [96] train-rmse:0.367089+0.015022 test-rmse:0.502654+0.061567
## [97] train-rmse:0.366196+0.014909 test-rmse:0.502220+0.060984
## [98] train-rmse:0.365090+0.014918 test-rmse:0.501969+0.060977
## [99] train-rmse:0.364121+0.014604 test-rmse:0.502395+0.061112
## [100] train-rmse:0.363454+0.014734 test-rmse:0.502171+0.061219
## [101] train-rmse:0.362171+0.014665 test-rmse:0.502068+0.060764
## [102] train-rmse:0.361189+0.014617 test-rmse:0.502029+0.060263
## [103] train-rmse:0.358828+0.014163 test-rmse:0.501375+0.059621
## [104] train-rmse:0.358036+0.013964 test-rmse:0.500738+0.059129
## [105] train-rmse:0.357219+0.013870 test-rmse:0.500607+0.058991
## [106] train-rmse:0.356121+0.013978 test-rmse:0.500662+0.059313
## [107] train-rmse:0.355301+0.014043 test-rmse:0.500121+0.058855
## [108] train-rmse:0.354197+0.013919 test-rmse:0.499683+0.058213
## [109] train-rmse:0.353448+0.014010 test-rmse:0.499671+0.057796
## [110] train-rmse:0.352490+0.013980 test-rmse:0.499814+0.057075
## [111] train-rmse:0.351869+0.013920 test-rmse:0.499497+0.057217
## [112] train-rmse:0.351440+0.013941 test-rmse:0.499662+0.057123
## [113] train-rmse:0.350605+0.013795 test-rmse:0.499478+0.057202
## [114] train-rmse:0.349765+0.014004 test-rmse:0.499097+0.057056
## [115] train-rmse:0.348979+0.014125 test-rmse:0.498792+0.056961
## [116] train-rmse:0.348122+0.013863 test-rmse:0.498649+0.057195
## [117] train-rmse:0.347458+0.013810 test-rmse:0.498443+0.056758
## [118] train-rmse:0.346904+0.013781 test-rmse:0.499015+0.056712
## [119] train-rmse:0.346321+0.013802 test-rmse:0.498272+0.056431
## [120] train-rmse:0.344332+0.015063 test-rmse:0.497392+0.056263
## [121] train-rmse:0.343187+0.015793 test-rmse:0.497507+0.055920
## [122] train-rmse:0.342562+0.015709 test-rmse:0.497083+0.056218
## [123] train-rmse:0.341760+0.015541 test-rmse:0.496288+0.056420
## [124] train-rmse:0.341105+0.015455 test-rmse:0.496312+0.056359
## [125] train-rmse:0.340375+0.015573 test-rmse:0.496353+0.056070
## [126] train-rmse:0.339862+0.015520 test-rmse:0.496630+0.056250
## [127] train-rmse:0.339286+0.015227 test-rmse:0.496632+0.055902
## [128] train-rmse:0.338642+0.015499 test-rmse:0.496696+0.055600
## [129] train-rmse:0.337698+0.015129 test-rmse:0.496134+0.055497
## [130] train-rmse:0.337248+0.015051 test-rmse:0.495935+0.055395
## [131] train-rmse:0.336559+0.014777 test-rmse:0.496216+0.055305
## [132] train-rmse:0.335901+0.014764 test-rmse:0.496426+0.055622
## [133] train-rmse:0.335234+0.014807 test-rmse:0.496381+0.055023
## [134] train-rmse:0.334294+0.015084 test-rmse:0.496827+0.054697
## [135] train-rmse:0.333807+0.014922 test-rmse:0.496237+0.054588
## [136] train-rmse:0.333095+0.014967 test-rmse:0.495852+0.053964
## [137] train-rmse:0.331956+0.015739 test-rmse:0.495845+0.053843
## [138] train-rmse:0.331140+0.015227 test-rmse:0.495511+0.053404
## [139] train-rmse:0.330688+0.015188 test-rmse:0.495285+0.053362
## [140] train-rmse:0.330284+0.015113 test-rmse:0.495067+0.053268
## [141] train-rmse:0.328992+0.015364 test-rmse:0.494859+0.053613
## [142] train-rmse:0.327952+0.015660 test-rmse:0.494849+0.053097
## [143] train-rmse:0.326401+0.016048 test-rmse:0.495385+0.052956
## [144] train-rmse:0.325523+0.016516 test-rmse:0.495099+0.052645
## [145] train-rmse:0.325015+0.016405 test-rmse:0.495586+0.052622
## [146] train-rmse:0.324311+0.015944 test-rmse:0.495151+0.052848
## [147] train-rmse:0.323527+0.015671 test-rmse:0.494474+0.052830
## [148] train-rmse:0.322752+0.015408 test-rmse:0.494900+0.052673
## [149] train-rmse:0.322006+0.015154 test-rmse:0.495168+0.052455
## [150] train-rmse:0.321407+0.015488 test-rmse:0.495192+0.052464
## [151] train-rmse:0.320822+0.015266 test-rmse:0.495383+0.052247
## [152] train-rmse:0.320045+0.015474 test-rmse:0.495397+0.052290
## [153] train-rmse:0.319391+0.015394 test-rmse:0.495227+0.052329
## Stopping. Best iteration:
## [147] train-rmse:0.323527+0.015671 test-rmse:0.494474+0.052830
##
## 37
## [1] train-rmse:0.876334+0.015290 test-rmse:0.882471+0.062297
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.779260+0.014677 test-rmse:0.793522+0.062428
## [3] train-rmse:0.706732+0.014052 test-rmse:0.730748+0.064549
## [4] train-rmse:0.651406+0.013736 test-rmse:0.682268+0.063058
## [5] train-rmse:0.609442+0.013948 test-rmse:0.647169+0.061142
## [6] train-rmse:0.577918+0.014209 test-rmse:0.623875+0.058332
## [7] train-rmse:0.551839+0.013470 test-rmse:0.607150+0.059953
## [8] train-rmse:0.532535+0.012826 test-rmse:0.592713+0.058036
## [9] train-rmse:0.515643+0.012572 test-rmse:0.583315+0.055209
## [10] train-rmse:0.503526+0.014446 test-rmse:0.573736+0.053847
## [11] train-rmse:0.491147+0.015094 test-rmse:0.568480+0.054000
## [12] train-rmse:0.482749+0.015490 test-rmse:0.564087+0.052782
## [13] train-rmse:0.474622+0.014120 test-rmse:0.558750+0.056500
## [14] train-rmse:0.466960+0.013842 test-rmse:0.554720+0.055044
## [15] train-rmse:0.460175+0.013813 test-rmse:0.551508+0.055183
## [16] train-rmse:0.454434+0.013264 test-rmse:0.547143+0.055408
## [17] train-rmse:0.448225+0.014928 test-rmse:0.544408+0.057967
## [18] train-rmse:0.442486+0.014773 test-rmse:0.541109+0.057199
## [19] train-rmse:0.437343+0.014304 test-rmse:0.539505+0.057637
## [20] train-rmse:0.431831+0.012655 test-rmse:0.538187+0.058362
## [21] train-rmse:0.426724+0.012078 test-rmse:0.533800+0.056928
## [22] train-rmse:0.422481+0.012173 test-rmse:0.532562+0.058255
## [23] train-rmse:0.418206+0.012425 test-rmse:0.530754+0.059342
## [24] train-rmse:0.414448+0.012315 test-rmse:0.530526+0.058790
## [25] train-rmse:0.410418+0.012623 test-rmse:0.529878+0.056825
## [26] train-rmse:0.406462+0.012349 test-rmse:0.527596+0.054864
## [27] train-rmse:0.402970+0.011240 test-rmse:0.526416+0.056504
## [28] train-rmse:0.398864+0.011866 test-rmse:0.525469+0.057488
## [29] train-rmse:0.395666+0.011575 test-rmse:0.523472+0.057036
## [30] train-rmse:0.392217+0.011976 test-rmse:0.523425+0.056690
## [31] train-rmse:0.388464+0.012026 test-rmse:0.522319+0.056314
## [32] train-rmse:0.385652+0.011716 test-rmse:0.521257+0.055725
## [33] train-rmse:0.382590+0.011341 test-rmse:0.520103+0.056177
## [34] train-rmse:0.379513+0.011535 test-rmse:0.520054+0.055554
## [35] train-rmse:0.377140+0.011571 test-rmse:0.519078+0.057001
## [36] train-rmse:0.374362+0.011796 test-rmse:0.518457+0.056980
## [37] train-rmse:0.371880+0.011392 test-rmse:0.517646+0.058306
## [38] train-rmse:0.370171+0.011252 test-rmse:0.517356+0.057726
## [39] train-rmse:0.367196+0.011341 test-rmse:0.516099+0.057290
## [40] train-rmse:0.364938+0.011097 test-rmse:0.515442+0.056887
## [41] train-rmse:0.361726+0.011278 test-rmse:0.514398+0.056857
## [42] train-rmse:0.358136+0.012224 test-rmse:0.513471+0.056678
## [43] train-rmse:0.355675+0.012032 test-rmse:0.512329+0.057723
## [44] train-rmse:0.353443+0.011110 test-rmse:0.512579+0.057987
## [45] train-rmse:0.351005+0.011294 test-rmse:0.512013+0.057677
## [46] train-rmse:0.348597+0.011464 test-rmse:0.512060+0.056770
## [47] train-rmse:0.346481+0.011257 test-rmse:0.511432+0.057231
## [48] train-rmse:0.344645+0.010596 test-rmse:0.511020+0.056726
## [49] train-rmse:0.342363+0.010591 test-rmse:0.511080+0.056114
## [50] train-rmse:0.339993+0.010795 test-rmse:0.510579+0.055471
## [51] train-rmse:0.337651+0.010536 test-rmse:0.510338+0.055349
## [52] train-rmse:0.335652+0.010328 test-rmse:0.508984+0.055518
## [53] train-rmse:0.334128+0.010324 test-rmse:0.508210+0.055570
## [54] train-rmse:0.331778+0.009958 test-rmse:0.507908+0.056378
## [55] train-rmse:0.329565+0.009229 test-rmse:0.506897+0.057121
## [56] train-rmse:0.327362+0.009557 test-rmse:0.506882+0.055481
## [57] train-rmse:0.325108+0.009565 test-rmse:0.506395+0.055061
## [58] train-rmse:0.322911+0.009393 test-rmse:0.506251+0.055047
## [59] train-rmse:0.321123+0.009123 test-rmse:0.506388+0.055178
## [60] train-rmse:0.319494+0.009257 test-rmse:0.506675+0.054429
## [61] train-rmse:0.317992+0.008922 test-rmse:0.507019+0.053330
## [62] train-rmse:0.316075+0.009808 test-rmse:0.507668+0.054070
## [63] train-rmse:0.314222+0.009525 test-rmse:0.506747+0.053869
## [64] train-rmse:0.311914+0.009689 test-rmse:0.505987+0.053953
## [65] train-rmse:0.309129+0.010616 test-rmse:0.506505+0.054212
## [66] train-rmse:0.307404+0.010364 test-rmse:0.506203+0.054330
## [67] train-rmse:0.305161+0.010552 test-rmse:0.506071+0.053876
## [68] train-rmse:0.303831+0.010034 test-rmse:0.506579+0.053489
## [69] train-rmse:0.301886+0.009907 test-rmse:0.506683+0.053285
## [70] train-rmse:0.300491+0.010218 test-rmse:0.506901+0.052999
## Stopping. Best iteration:
## [64] train-rmse:0.311914+0.009689 test-rmse:0.505987+0.053953
##
## 38
## [1] train-rmse:0.870282+0.015080 test-rmse:0.878834+0.061027
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.767545+0.014684 test-rmse:0.787841+0.061970
## [3] train-rmse:0.688772+0.014424 test-rmse:0.720376+0.064549
## [4] train-rmse:0.628409+0.012750 test-rmse:0.665873+0.063349
## [5] train-rmse:0.581334+0.012921 test-rmse:0.627848+0.062170
## [6] train-rmse:0.543310+0.013267 test-rmse:0.600363+0.060309
## [7] train-rmse:0.515008+0.014044 test-rmse:0.580250+0.057362
## [8] train-rmse:0.491754+0.013625 test-rmse:0.566904+0.054773
## [9] train-rmse:0.472750+0.013512 test-rmse:0.556423+0.055343
## [10] train-rmse:0.457468+0.015876 test-rmse:0.549549+0.052867
## [11] train-rmse:0.444431+0.014732 test-rmse:0.541495+0.052978
## [12] train-rmse:0.433435+0.016242 test-rmse:0.537002+0.051442
## [13] train-rmse:0.424594+0.015538 test-rmse:0.531878+0.052578
## [14] train-rmse:0.417066+0.016517 test-rmse:0.526999+0.054509
## [15] train-rmse:0.409070+0.015254 test-rmse:0.525793+0.053849
## [16] train-rmse:0.401506+0.014024 test-rmse:0.522023+0.053567
## [17] train-rmse:0.395592+0.013440 test-rmse:0.520436+0.053177
## [18] train-rmse:0.389984+0.014316 test-rmse:0.517723+0.054236
## [19] train-rmse:0.383506+0.012851 test-rmse:0.514702+0.053960
## [20] train-rmse:0.376709+0.012477 test-rmse:0.511700+0.054907
## [21] train-rmse:0.371651+0.013163 test-rmse:0.510829+0.053592
## [22] train-rmse:0.366335+0.012651 test-rmse:0.509399+0.053562
## [23] train-rmse:0.361959+0.013001 test-rmse:0.509256+0.053706
## [24] train-rmse:0.357099+0.013288 test-rmse:0.508602+0.053118
## [25] train-rmse:0.352524+0.012115 test-rmse:0.507304+0.055099
## [26] train-rmse:0.348327+0.012598 test-rmse:0.503619+0.056568
## [27] train-rmse:0.343958+0.011647 test-rmse:0.501642+0.056685
## [28] train-rmse:0.339329+0.012157 test-rmse:0.500980+0.056596
## [29] train-rmse:0.336301+0.012610 test-rmse:0.500772+0.057003
## [30] train-rmse:0.332185+0.012805 test-rmse:0.500295+0.057790
## [31] train-rmse:0.329208+0.012577 test-rmse:0.499300+0.057406
## [32] train-rmse:0.324411+0.011804 test-rmse:0.498446+0.057468
## [33] train-rmse:0.321729+0.011602 test-rmse:0.498611+0.056802
## [34] train-rmse:0.318770+0.011290 test-rmse:0.499557+0.056277
## [35] train-rmse:0.315657+0.012251 test-rmse:0.499553+0.056404
## [36] train-rmse:0.311854+0.012128 test-rmse:0.497706+0.056913
## [37] train-rmse:0.309098+0.012038 test-rmse:0.497716+0.057655
## [38] train-rmse:0.305139+0.012440 test-rmse:0.498202+0.056336
## [39] train-rmse:0.302165+0.011946 test-rmse:0.497294+0.056896
## [40] train-rmse:0.299253+0.012181 test-rmse:0.497637+0.056446
## [41] train-rmse:0.296473+0.011986 test-rmse:0.496316+0.056287
## [42] train-rmse:0.292520+0.011747 test-rmse:0.495154+0.057369
## [43] train-rmse:0.289061+0.012813 test-rmse:0.493977+0.056625
## [44] train-rmse:0.285982+0.013077 test-rmse:0.493489+0.057368
## [45] train-rmse:0.283200+0.013033 test-rmse:0.492637+0.056462
## [46] train-rmse:0.280531+0.012658 test-rmse:0.492493+0.056250
## [47] train-rmse:0.276946+0.013478 test-rmse:0.494067+0.054905
## [48] train-rmse:0.274642+0.013204 test-rmse:0.494141+0.054701
## [49] train-rmse:0.272712+0.013124 test-rmse:0.494315+0.054805
## [50] train-rmse:0.268863+0.013687 test-rmse:0.493657+0.055626
## [51] train-rmse:0.264792+0.014674 test-rmse:0.494528+0.055732
## [52] train-rmse:0.263121+0.014404 test-rmse:0.495237+0.055885
## Stopping. Best iteration:
## [46] train-rmse:0.280531+0.012658 test-rmse:0.492493+0.056250
##
## 39
## [1] train-rmse:0.864898+0.014583 test-rmse:0.875716+0.061755
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.756324+0.013266 test-rmse:0.780888+0.060708
## [3] train-rmse:0.671702+0.011987 test-rmse:0.709515+0.060778
## [4] train-rmse:0.607460+0.010911 test-rmse:0.658650+0.064485
## [5] train-rmse:0.554511+0.010447 test-rmse:0.618505+0.062058
## [6] train-rmse:0.513310+0.009951 test-rmse:0.589848+0.061913
## [7] train-rmse:0.480981+0.010593 test-rmse:0.570135+0.060713
## [8] train-rmse:0.453401+0.011888 test-rmse:0.553354+0.061212
## [9] train-rmse:0.431987+0.013225 test-rmse:0.544764+0.059331
## [10] train-rmse:0.413852+0.013365 test-rmse:0.537124+0.060786
## [11] train-rmse:0.398504+0.015026 test-rmse:0.530367+0.058663
## [12] train-rmse:0.388697+0.015681 test-rmse:0.526573+0.057200
## [13] train-rmse:0.378425+0.014864 test-rmse:0.520734+0.057536
## [14] train-rmse:0.368289+0.015549 test-rmse:0.518571+0.057157
## [15] train-rmse:0.359903+0.014301 test-rmse:0.515253+0.058946
## [16] train-rmse:0.352567+0.015822 test-rmse:0.512895+0.059691
## [17] train-rmse:0.345041+0.013955 test-rmse:0.509482+0.061779
## [18] train-rmse:0.338177+0.012361 test-rmse:0.506658+0.059708
## [19] train-rmse:0.333351+0.012447 test-rmse:0.505449+0.060461
## [20] train-rmse:0.329220+0.012656 test-rmse:0.504533+0.059169
## [21] train-rmse:0.322202+0.013546 test-rmse:0.502077+0.059350
## [22] train-rmse:0.317482+0.013595 test-rmse:0.499032+0.061069
## [23] train-rmse:0.312062+0.013905 test-rmse:0.498244+0.061642
## [24] train-rmse:0.307917+0.013541 test-rmse:0.497992+0.061662
## [25] train-rmse:0.301791+0.011559 test-rmse:0.496914+0.061634
## [26] train-rmse:0.297007+0.012095 test-rmse:0.496149+0.060730
## [27] train-rmse:0.292521+0.012447 test-rmse:0.495958+0.061420
## [28] train-rmse:0.288715+0.011783 test-rmse:0.495171+0.060429
## [29] train-rmse:0.284156+0.011506 test-rmse:0.494503+0.061244
## [30] train-rmse:0.279866+0.012170 test-rmse:0.494862+0.061364
## [31] train-rmse:0.275974+0.012319 test-rmse:0.493780+0.059558
## [32] train-rmse:0.273179+0.012017 test-rmse:0.493525+0.059435
## [33] train-rmse:0.268727+0.012149 test-rmse:0.493503+0.058617
## [34] train-rmse:0.264678+0.012718 test-rmse:0.492651+0.058046
## [35] train-rmse:0.262022+0.012098 test-rmse:0.493009+0.057445
## [36] train-rmse:0.259553+0.011641 test-rmse:0.492835+0.057351
## [37] train-rmse:0.256838+0.012611 test-rmse:0.492913+0.056891
## [38] train-rmse:0.254453+0.013238 test-rmse:0.492979+0.056720
## [39] train-rmse:0.251074+0.012687 test-rmse:0.492064+0.056789
## [40] train-rmse:0.247783+0.012436 test-rmse:0.492472+0.056870
## [41] train-rmse:0.243991+0.012860 test-rmse:0.492943+0.057164
## [42] train-rmse:0.240155+0.013859 test-rmse:0.493133+0.057481
## [43] train-rmse:0.236952+0.014032 test-rmse:0.493282+0.056394
## [44] train-rmse:0.233360+0.012964 test-rmse:0.492471+0.056891
## [45] train-rmse:0.231770+0.012549 test-rmse:0.492498+0.056704
## Stopping. Best iteration:
## [39] train-rmse:0.251074+0.012687 test-rmse:0.492064+0.056789
##
## 40
## [1] train-rmse:0.859978+0.014089 test-rmse:0.873655+0.061973
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.746628+0.012653 test-rmse:0.773269+0.061905
## [3] train-rmse:0.656349+0.011594 test-rmse:0.700390+0.063620
## [4] train-rmse:0.586671+0.011651 test-rmse:0.649035+0.064069
## [5] train-rmse:0.531570+0.012662 test-rmse:0.612658+0.063558
## [6] train-rmse:0.486911+0.013428 test-rmse:0.579947+0.063592
## [7] train-rmse:0.449590+0.014386 test-rmse:0.558671+0.063192
## [8] train-rmse:0.419475+0.013773 test-rmse:0.545227+0.062216
## [9] train-rmse:0.395951+0.013272 test-rmse:0.535467+0.062109
## [10] train-rmse:0.375342+0.014496 test-rmse:0.526667+0.060138
## [11] train-rmse:0.360190+0.015283 test-rmse:0.522843+0.059828
## [12] train-rmse:0.347033+0.015270 test-rmse:0.518310+0.057087
## [13] train-rmse:0.336486+0.015504 test-rmse:0.515119+0.056943
## [14] train-rmse:0.325711+0.014477 test-rmse:0.513216+0.058480
## [15] train-rmse:0.316416+0.015758 test-rmse:0.511835+0.057809
## [16] train-rmse:0.307594+0.015216 test-rmse:0.509067+0.057741
## [17] train-rmse:0.301516+0.014003 test-rmse:0.504935+0.059590
## [18] train-rmse:0.291703+0.013177 test-rmse:0.500549+0.061170
## [19] train-rmse:0.284300+0.014978 test-rmse:0.499986+0.059392
## [20] train-rmse:0.276566+0.014194 test-rmse:0.498997+0.056610
## [21] train-rmse:0.271113+0.013737 test-rmse:0.496672+0.056339
## [22] train-rmse:0.266696+0.013125 test-rmse:0.496608+0.055684
## [23] train-rmse:0.260834+0.010676 test-rmse:0.495303+0.057082
## [24] train-rmse:0.256094+0.010109 test-rmse:0.495701+0.056093
## [25] train-rmse:0.249907+0.011963 test-rmse:0.495546+0.054026
## [26] train-rmse:0.245006+0.011373 test-rmse:0.496836+0.053654
## [27] train-rmse:0.240687+0.012369 test-rmse:0.496202+0.054853
## [28] train-rmse:0.237589+0.011425 test-rmse:0.495598+0.054256
## [29] train-rmse:0.234003+0.010866 test-rmse:0.495821+0.053164
## Stopping. Best iteration:
## [23] train-rmse:0.260834+0.010676 test-rmse:0.495303+0.057082
##
## 41
## [1] train-rmse:0.855149+0.013654 test-rmse:0.870487+0.060339
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.737973+0.011068 test-rmse:0.771088+0.061269
## [3] train-rmse:0.643977+0.009287 test-rmse:0.694575+0.061759
## [4] train-rmse:0.571350+0.009278 test-rmse:0.642376+0.062212
## [5] train-rmse:0.512515+0.009017 test-rmse:0.600919+0.066565
## [6] train-rmse:0.465482+0.009165 test-rmse:0.572023+0.067134
## [7] train-rmse:0.429237+0.011211 test-rmse:0.552038+0.067806
## [8] train-rmse:0.396520+0.009741 test-rmse:0.541642+0.066313
## [9] train-rmse:0.369139+0.009774 test-rmse:0.529574+0.065078
## [10] train-rmse:0.349323+0.009119 test-rmse:0.521711+0.065135
## [11] train-rmse:0.331770+0.007677 test-rmse:0.518159+0.065834
## [12] train-rmse:0.316059+0.010878 test-rmse:0.514986+0.064856
## [13] train-rmse:0.302959+0.011193 test-rmse:0.510929+0.062762
## [14] train-rmse:0.291161+0.014142 test-rmse:0.507631+0.060835
## [15] train-rmse:0.283214+0.014979 test-rmse:0.505335+0.061467
## [16] train-rmse:0.273411+0.015322 test-rmse:0.502602+0.059081
## [17] train-rmse:0.262864+0.014373 test-rmse:0.500384+0.058827
## [18] train-rmse:0.255333+0.011313 test-rmse:0.498391+0.060336
## [19] train-rmse:0.248568+0.011945 test-rmse:0.497562+0.059815
## [20] train-rmse:0.243772+0.011207 test-rmse:0.496485+0.059562
## [21] train-rmse:0.236219+0.010266 test-rmse:0.495581+0.060560
## [22] train-rmse:0.232032+0.010891 test-rmse:0.495454+0.061031
## [23] train-rmse:0.226923+0.011120 test-rmse:0.495005+0.060271
## [24] train-rmse:0.222232+0.011769 test-rmse:0.494547+0.059848
## [25] train-rmse:0.216233+0.011783 test-rmse:0.492981+0.059765
## [26] train-rmse:0.211286+0.014004 test-rmse:0.493558+0.060442
## [27] train-rmse:0.205937+0.012880 test-rmse:0.492884+0.058846
## [28] train-rmse:0.200109+0.012561 test-rmse:0.494403+0.060726
## [29] train-rmse:0.196971+0.012096 test-rmse:0.494174+0.061030
## [30] train-rmse:0.192203+0.010097 test-rmse:0.494727+0.060395
## [31] train-rmse:0.188436+0.010293 test-rmse:0.495300+0.061738
## [32] train-rmse:0.183273+0.011193 test-rmse:0.496345+0.061511
## [33] train-rmse:0.178721+0.011890 test-rmse:0.496349+0.062247
## Stopping. Best iteration:
## [27] train-rmse:0.205937+0.012880 test-rmse:0.492884+0.058846
##
## 42
## [1] train-rmse:0.889329+0.015020 test-rmse:0.891698+0.060802
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.810221+0.014118 test-rmse:0.817282+0.060559
## [3] train-rmse:0.755511+0.013729 test-rmse:0.775066+0.060259
## [4] train-rmse:0.716624+0.012852 test-rmse:0.734230+0.060566
## [5] train-rmse:0.688053+0.012215 test-rmse:0.705499+0.062296
## [6] train-rmse:0.665451+0.012097 test-rmse:0.686575+0.055662
## [7] train-rmse:0.649580+0.011757 test-rmse:0.675379+0.053537
## [8] train-rmse:0.636798+0.011816 test-rmse:0.660653+0.052258
## [9] train-rmse:0.626362+0.011280 test-rmse:0.655039+0.053577
## [10] train-rmse:0.618604+0.011228 test-rmse:0.646375+0.051956
## [11] train-rmse:0.611905+0.011188 test-rmse:0.641678+0.054039
## [12] train-rmse:0.605636+0.011246 test-rmse:0.634981+0.053518
## [13] train-rmse:0.600176+0.011289 test-rmse:0.631739+0.054473
## [14] train-rmse:0.595194+0.011439 test-rmse:0.627953+0.054657
## [15] train-rmse:0.590937+0.011385 test-rmse:0.624262+0.054644
## [16] train-rmse:0.586852+0.011395 test-rmse:0.621681+0.055786
## [17] train-rmse:0.582832+0.011311 test-rmse:0.615364+0.056009
## [18] train-rmse:0.579084+0.011270 test-rmse:0.613251+0.056942
## [19] train-rmse:0.575585+0.011291 test-rmse:0.613160+0.054231
## [20] train-rmse:0.572366+0.011172 test-rmse:0.612448+0.054709
## [21] train-rmse:0.569290+0.011107 test-rmse:0.607706+0.055185
## [22] train-rmse:0.566400+0.011180 test-rmse:0.605330+0.056818
## [23] train-rmse:0.563574+0.011202 test-rmse:0.604190+0.055924
## [24] train-rmse:0.560873+0.011176 test-rmse:0.599967+0.055881
## [25] train-rmse:0.558242+0.011254 test-rmse:0.597249+0.056148
## [26] train-rmse:0.555698+0.011285 test-rmse:0.596359+0.056754
## [27] train-rmse:0.553271+0.011294 test-rmse:0.593860+0.056562
## [28] train-rmse:0.550895+0.011290 test-rmse:0.592709+0.054846
## [29] train-rmse:0.548590+0.011258 test-rmse:0.589814+0.053926
## [30] train-rmse:0.546404+0.011206 test-rmse:0.588870+0.053518
## [31] train-rmse:0.544347+0.011203 test-rmse:0.587746+0.053160
## [32] train-rmse:0.542292+0.011151 test-rmse:0.585646+0.053179
## [33] train-rmse:0.540306+0.011101 test-rmse:0.583840+0.053048
## [34] train-rmse:0.538414+0.011025 test-rmse:0.582705+0.054061
## [35] train-rmse:0.536572+0.010971 test-rmse:0.581062+0.052660
## [36] train-rmse:0.534800+0.010920 test-rmse:0.580126+0.053956
## [37] train-rmse:0.533089+0.010906 test-rmse:0.578827+0.055920
## [38] train-rmse:0.531453+0.010907 test-rmse:0.580054+0.056148
## [39] train-rmse:0.529848+0.010896 test-rmse:0.577908+0.055932
## [40] train-rmse:0.528276+0.010874 test-rmse:0.576749+0.055383
## [41] train-rmse:0.526727+0.010871 test-rmse:0.574812+0.055899
## [42] train-rmse:0.525222+0.010866 test-rmse:0.573487+0.055330
## [43] train-rmse:0.523735+0.010840 test-rmse:0.572732+0.056090
## [44] train-rmse:0.522322+0.010809 test-rmse:0.571819+0.054739
## [45] train-rmse:0.520926+0.010769 test-rmse:0.571504+0.056306
## [46] train-rmse:0.519597+0.010740 test-rmse:0.569825+0.056267
## [47] train-rmse:0.518298+0.010701 test-rmse:0.569172+0.055263
## [48] train-rmse:0.517049+0.010647 test-rmse:0.568931+0.054751
## [49] train-rmse:0.515834+0.010598 test-rmse:0.567365+0.054653
## [50] train-rmse:0.514648+0.010558 test-rmse:0.566439+0.054230
## [51] train-rmse:0.513479+0.010525 test-rmse:0.565569+0.054124
## [52] train-rmse:0.512329+0.010502 test-rmse:0.564438+0.053076
## [53] train-rmse:0.511198+0.010483 test-rmse:0.564375+0.054017
## [54] train-rmse:0.510083+0.010456 test-rmse:0.562806+0.054674
## [55] train-rmse:0.508985+0.010473 test-rmse:0.562185+0.053912
## [56] train-rmse:0.507929+0.010479 test-rmse:0.561441+0.053984
## [57] train-rmse:0.506903+0.010451 test-rmse:0.560093+0.053834
## [58] train-rmse:0.505893+0.010452 test-rmse:0.557978+0.053962
## [59] train-rmse:0.504895+0.010451 test-rmse:0.557477+0.053900
## [60] train-rmse:0.503908+0.010456 test-rmse:0.557282+0.053894
## [61] train-rmse:0.502957+0.010447 test-rmse:0.556758+0.053450
## [62] train-rmse:0.502038+0.010423 test-rmse:0.556121+0.053799
## [63] train-rmse:0.501133+0.010408 test-rmse:0.555679+0.053385
## [64] train-rmse:0.500258+0.010389 test-rmse:0.555326+0.053645
## [65] train-rmse:0.499387+0.010356 test-rmse:0.553837+0.053724
## [66] train-rmse:0.498568+0.010358 test-rmse:0.553670+0.053082
## [67] train-rmse:0.497767+0.010361 test-rmse:0.552517+0.053005
## [68] train-rmse:0.496964+0.010358 test-rmse:0.551349+0.053007
## [69] train-rmse:0.496183+0.010360 test-rmse:0.550951+0.052965
## [70] train-rmse:0.495403+0.010361 test-rmse:0.551362+0.052804
## [71] train-rmse:0.494634+0.010362 test-rmse:0.551343+0.052188
## [72] train-rmse:0.493880+0.010366 test-rmse:0.551895+0.052198
## [73] train-rmse:0.493154+0.010361 test-rmse:0.551813+0.051251
## [74] train-rmse:0.492454+0.010340 test-rmse:0.551597+0.051208
## [75] train-rmse:0.491766+0.010323 test-rmse:0.552150+0.051790
## Stopping. Best iteration:
## [69] train-rmse:0.496183+0.010360 test-rmse:0.550951+0.052965
##
## 43
## [1] train-rmse:0.871874+0.014883 test-rmse:0.875592+0.060800
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.777325+0.014219 test-rmse:0.791071+0.062952
## [3] train-rmse:0.710398+0.013414 test-rmse:0.730235+0.063237
## [4] train-rmse:0.662426+0.012410 test-rmse:0.688073+0.065082
## [5] train-rmse:0.626503+0.011220 test-rmse:0.655212+0.065779
## [6] train-rmse:0.598952+0.011911 test-rmse:0.635318+0.063951
## [7] train-rmse:0.578974+0.011591 test-rmse:0.618716+0.065636
## [8] train-rmse:0.563172+0.013148 test-rmse:0.605318+0.061807
## [9] train-rmse:0.549664+0.013051 test-rmse:0.597864+0.064347
## [10] train-rmse:0.540556+0.013244 test-rmse:0.592049+0.062021
## [11] train-rmse:0.532388+0.013240 test-rmse:0.584991+0.063132
## [12] train-rmse:0.525652+0.012795 test-rmse:0.581810+0.063654
## [13] train-rmse:0.518788+0.013940 test-rmse:0.577801+0.061742
## [14] train-rmse:0.512846+0.013818 test-rmse:0.573403+0.060743
## [15] train-rmse:0.507869+0.013817 test-rmse:0.567787+0.061273
## [16] train-rmse:0.503198+0.013999 test-rmse:0.565713+0.062793
## [17] train-rmse:0.498843+0.013923 test-rmse:0.564935+0.061732
## [18] train-rmse:0.493837+0.013898 test-rmse:0.559804+0.060618
## [19] train-rmse:0.488622+0.012822 test-rmse:0.556162+0.061803
## [20] train-rmse:0.484585+0.013120 test-rmse:0.552960+0.061166
## [21] train-rmse:0.480718+0.012927 test-rmse:0.550957+0.061739
## [22] train-rmse:0.476680+0.013888 test-rmse:0.549022+0.061102
## [23] train-rmse:0.473372+0.013594 test-rmse:0.548189+0.060572
## [24] train-rmse:0.470419+0.013474 test-rmse:0.545879+0.059067
## [25] train-rmse:0.466890+0.013870 test-rmse:0.544300+0.058831
## [26] train-rmse:0.463863+0.014092 test-rmse:0.542042+0.058861
## [27] train-rmse:0.460284+0.014152 test-rmse:0.540217+0.058766
## [28] train-rmse:0.457361+0.013771 test-rmse:0.539132+0.058797
## [29] train-rmse:0.454531+0.013821 test-rmse:0.538489+0.059107
## [30] train-rmse:0.451678+0.013799 test-rmse:0.537123+0.059749
## [31] train-rmse:0.448654+0.013718 test-rmse:0.535954+0.059007
## [32] train-rmse:0.446100+0.014035 test-rmse:0.534177+0.058508
## [33] train-rmse:0.443459+0.013921 test-rmse:0.533071+0.060220
## [34] train-rmse:0.441439+0.014251 test-rmse:0.531272+0.060393
## [35] train-rmse:0.439056+0.014336 test-rmse:0.530913+0.060648
## [36] train-rmse:0.436723+0.013903 test-rmse:0.530816+0.062022
## [37] train-rmse:0.434451+0.014307 test-rmse:0.528536+0.061476
## [38] train-rmse:0.432303+0.014168 test-rmse:0.527462+0.060975
## [39] train-rmse:0.429961+0.014758 test-rmse:0.526519+0.062152
## [40] train-rmse:0.428118+0.014958 test-rmse:0.525377+0.060737
## [41] train-rmse:0.425486+0.014999 test-rmse:0.523076+0.061276
## [42] train-rmse:0.423713+0.015433 test-rmse:0.522587+0.060862
## [43] train-rmse:0.421706+0.015247 test-rmse:0.521796+0.060088
## [44] train-rmse:0.419834+0.015343 test-rmse:0.521270+0.060854
## [45] train-rmse:0.417649+0.014750 test-rmse:0.520004+0.061444
## [46] train-rmse:0.415657+0.014413 test-rmse:0.517615+0.062389
## [47] train-rmse:0.413932+0.014517 test-rmse:0.517748+0.062983
## [48] train-rmse:0.412509+0.014371 test-rmse:0.516880+0.064195
## [49] train-rmse:0.410923+0.014461 test-rmse:0.516832+0.063415
## [50] train-rmse:0.409729+0.014577 test-rmse:0.516164+0.063342
## [51] train-rmse:0.408397+0.014737 test-rmse:0.515869+0.062114
## [52] train-rmse:0.406592+0.014444 test-rmse:0.513609+0.062868
## [53] train-rmse:0.405169+0.014470 test-rmse:0.513252+0.063327
## [54] train-rmse:0.403477+0.014722 test-rmse:0.512466+0.063017
## [55] train-rmse:0.402037+0.014832 test-rmse:0.511744+0.063661
## [56] train-rmse:0.400706+0.014971 test-rmse:0.511171+0.063692
## [57] train-rmse:0.398859+0.014216 test-rmse:0.511357+0.064550
## [58] train-rmse:0.397383+0.014134 test-rmse:0.509908+0.064938
## [59] train-rmse:0.396287+0.014271 test-rmse:0.510930+0.065466
## [60] train-rmse:0.395293+0.014511 test-rmse:0.510759+0.065362
## [61] train-rmse:0.393920+0.014514 test-rmse:0.509786+0.064852
## [62] train-rmse:0.393001+0.014573 test-rmse:0.509972+0.064825
## [63] train-rmse:0.392026+0.014577 test-rmse:0.509560+0.064147
## [64] train-rmse:0.390654+0.014529 test-rmse:0.508789+0.063573
## [65] train-rmse:0.389354+0.014230 test-rmse:0.508721+0.063947
## [66] train-rmse:0.388441+0.014123 test-rmse:0.508303+0.064592
## [67] train-rmse:0.387418+0.014047 test-rmse:0.508737+0.064698
## [68] train-rmse:0.386655+0.014199 test-rmse:0.509027+0.064636
## [69] train-rmse:0.385280+0.014412 test-rmse:0.508172+0.064309
## [70] train-rmse:0.384025+0.014546 test-rmse:0.507706+0.063896
## [71] train-rmse:0.383066+0.014427 test-rmse:0.506867+0.064656
## [72] train-rmse:0.381641+0.014644 test-rmse:0.506168+0.063980
## [73] train-rmse:0.380262+0.014576 test-rmse:0.506132+0.063890
## [74] train-rmse:0.379136+0.014744 test-rmse:0.506219+0.063355
## [75] train-rmse:0.378178+0.014527 test-rmse:0.505938+0.064119
## [76] train-rmse:0.377147+0.014373 test-rmse:0.506030+0.063652
## [77] train-rmse:0.376110+0.014386 test-rmse:0.505866+0.062865
## [78] train-rmse:0.374755+0.014605 test-rmse:0.505682+0.062423
## [79] train-rmse:0.373878+0.014557 test-rmse:0.505638+0.062161
## [80] train-rmse:0.372444+0.015156 test-rmse:0.505228+0.062157
## [81] train-rmse:0.371025+0.014764 test-rmse:0.504590+0.063239
## [82] train-rmse:0.370206+0.014722 test-rmse:0.504304+0.064022
## [83] train-rmse:0.369455+0.014878 test-rmse:0.503355+0.063328
## [84] train-rmse:0.368793+0.014819 test-rmse:0.503414+0.062941
## [85] train-rmse:0.367703+0.014945 test-rmse:0.503067+0.062325
## [86] train-rmse:0.366717+0.014555 test-rmse:0.502897+0.062035
## [87] train-rmse:0.365112+0.014230 test-rmse:0.502656+0.062108
## [88] train-rmse:0.363860+0.013910 test-rmse:0.502192+0.062478
## [89] train-rmse:0.362895+0.013908 test-rmse:0.501698+0.062072
## [90] train-rmse:0.362165+0.014013 test-rmse:0.501891+0.062142
## [91] train-rmse:0.360980+0.013865 test-rmse:0.501003+0.061820
## [92] train-rmse:0.360434+0.013884 test-rmse:0.500937+0.061297
## [93] train-rmse:0.359520+0.013836 test-rmse:0.500609+0.061071
## [94] train-rmse:0.358289+0.014045 test-rmse:0.500034+0.061409
## [95] train-rmse:0.357109+0.013800 test-rmse:0.499965+0.061685
## [96] train-rmse:0.355850+0.014081 test-rmse:0.500433+0.061538
## [97] train-rmse:0.355249+0.014200 test-rmse:0.500832+0.061317
## [98] train-rmse:0.354334+0.014006 test-rmse:0.500868+0.062048
## [99] train-rmse:0.353401+0.014399 test-rmse:0.499753+0.061413
## [100] train-rmse:0.352475+0.014347 test-rmse:0.498894+0.060963
## [101] train-rmse:0.351454+0.014123 test-rmse:0.498739+0.061098
## [102] train-rmse:0.350692+0.014120 test-rmse:0.498596+0.060440
## [103] train-rmse:0.349669+0.014153 test-rmse:0.498634+0.060530
## [104] train-rmse:0.348454+0.013664 test-rmse:0.498903+0.060034
## [105] train-rmse:0.347440+0.013768 test-rmse:0.498868+0.060004
## [106] train-rmse:0.346217+0.013529 test-rmse:0.499504+0.059676
## [107] train-rmse:0.344976+0.013439 test-rmse:0.498879+0.059997
## [108] train-rmse:0.343890+0.013485 test-rmse:0.498829+0.059850
## Stopping. Best iteration:
## [102] train-rmse:0.350692+0.014120 test-rmse:0.498596+0.060440
##
## 44
## [1] train-rmse:0.864077+0.015204 test-rmse:0.871112+0.062374
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.761195+0.014662 test-rmse:0.777223+0.062682
## [3] train-rmse:0.686763+0.014304 test-rmse:0.710511+0.063844
## [4] train-rmse:0.631556+0.013861 test-rmse:0.663813+0.064706
## [5] train-rmse:0.590376+0.013964 test-rmse:0.630353+0.062897
## [6] train-rmse:0.559433+0.013335 test-rmse:0.609579+0.059891
## [7] train-rmse:0.536613+0.013637 test-rmse:0.594126+0.057149
## [8] train-rmse:0.517645+0.014527 test-rmse:0.580301+0.054502
## [9] train-rmse:0.504224+0.014860 test-rmse:0.572413+0.054062
## [10] train-rmse:0.491726+0.014913 test-rmse:0.563477+0.051593
## [11] train-rmse:0.483237+0.015047 test-rmse:0.558553+0.050518
## [12] train-rmse:0.474141+0.012973 test-rmse:0.555110+0.054343
## [13] train-rmse:0.466856+0.012118 test-rmse:0.551968+0.056211
## [14] train-rmse:0.458942+0.012461 test-rmse:0.547573+0.055106
## [15] train-rmse:0.453150+0.012476 test-rmse:0.544777+0.056020
## [16] train-rmse:0.445925+0.012485 test-rmse:0.541398+0.054113
## [17] train-rmse:0.439712+0.011336 test-rmse:0.537803+0.055454
## [18] train-rmse:0.435043+0.011035 test-rmse:0.534440+0.055573
## [19] train-rmse:0.430681+0.011074 test-rmse:0.532068+0.055885
## [20] train-rmse:0.424871+0.010654 test-rmse:0.529395+0.057048
## [21] train-rmse:0.420624+0.011394 test-rmse:0.527640+0.056482
## [22] train-rmse:0.415652+0.011275 test-rmse:0.524098+0.055395
## [23] train-rmse:0.411762+0.011336 test-rmse:0.521898+0.058178
## [24] train-rmse:0.407829+0.010909 test-rmse:0.520563+0.058719
## [25] train-rmse:0.404418+0.010932 test-rmse:0.520939+0.058113
## [26] train-rmse:0.400721+0.010229 test-rmse:0.519314+0.056808
## [27] train-rmse:0.397011+0.010641 test-rmse:0.517164+0.056647
## [28] train-rmse:0.393573+0.011278 test-rmse:0.515045+0.057153
## [29] train-rmse:0.389711+0.011836 test-rmse:0.514090+0.056462
## [30] train-rmse:0.386190+0.011360 test-rmse:0.512379+0.057290
## [31] train-rmse:0.382974+0.011319 test-rmse:0.511869+0.056750
## [32] train-rmse:0.380426+0.011417 test-rmse:0.510911+0.056891
## [33] train-rmse:0.377476+0.011589 test-rmse:0.509751+0.056653
## [34] train-rmse:0.374696+0.011092 test-rmse:0.509758+0.057133
## [35] train-rmse:0.372043+0.010860 test-rmse:0.509093+0.057370
## [36] train-rmse:0.369513+0.011582 test-rmse:0.508507+0.057386
## [37] train-rmse:0.366509+0.011691 test-rmse:0.507990+0.056864
## [38] train-rmse:0.363268+0.011182 test-rmse:0.506291+0.057545
## [39] train-rmse:0.360784+0.011009 test-rmse:0.506385+0.057172
## [40] train-rmse:0.357066+0.011693 test-rmse:0.504931+0.056800
## [41] train-rmse:0.354882+0.011117 test-rmse:0.504490+0.057119
## [42] train-rmse:0.352437+0.010843 test-rmse:0.504162+0.055966
## [43] train-rmse:0.350133+0.010267 test-rmse:0.504012+0.055757
## [44] train-rmse:0.347688+0.009728 test-rmse:0.503006+0.054873
## [45] train-rmse:0.345238+0.009778 test-rmse:0.502546+0.054106
## [46] train-rmse:0.343031+0.010040 test-rmse:0.502030+0.054917
## [47] train-rmse:0.341076+0.009507 test-rmse:0.501279+0.055218
## [48] train-rmse:0.338580+0.009591 test-rmse:0.500817+0.054784
## [49] train-rmse:0.336678+0.009212 test-rmse:0.501910+0.053694
## [50] train-rmse:0.335132+0.009261 test-rmse:0.501599+0.052900
## [51] train-rmse:0.332775+0.009198 test-rmse:0.500882+0.053328
## [52] train-rmse:0.330524+0.008933 test-rmse:0.501830+0.053173
## [53] train-rmse:0.328078+0.008744 test-rmse:0.500691+0.054350
## [54] train-rmse:0.325334+0.009284 test-rmse:0.499377+0.054253
## [55] train-rmse:0.323766+0.008965 test-rmse:0.499015+0.054274
## [56] train-rmse:0.322239+0.009080 test-rmse:0.499135+0.054490
## [57] train-rmse:0.319674+0.009559 test-rmse:0.498382+0.054005
## [58] train-rmse:0.317296+0.009639 test-rmse:0.499284+0.053335
## [59] train-rmse:0.313823+0.011269 test-rmse:0.499583+0.053814
## [60] train-rmse:0.311713+0.010639 test-rmse:0.499721+0.053299
## [61] train-rmse:0.309905+0.011280 test-rmse:0.499646+0.053112
## [62] train-rmse:0.307930+0.011323 test-rmse:0.499880+0.052365
## [63] train-rmse:0.306107+0.011614 test-rmse:0.499485+0.052118
## Stopping. Best iteration:
## [57] train-rmse:0.319674+0.009559 test-rmse:0.498382+0.054005
##
## 45
## [1] train-rmse:0.857388+0.014974 test-rmse:0.867099+0.060963
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.747853+0.013948 test-rmse:0.770470+0.061352
## [3] train-rmse:0.665472+0.013055 test-rmse:0.698917+0.063377
## [4] train-rmse:0.606642+0.012589 test-rmse:0.651457+0.068215
## [5] train-rmse:0.558833+0.013882 test-rmse:0.613285+0.061543
## [6] train-rmse:0.520938+0.014301 test-rmse:0.587787+0.057387
## [7] train-rmse:0.493039+0.015195 test-rmse:0.569702+0.054441
## [8] train-rmse:0.472053+0.014476 test-rmse:0.558285+0.055137
## [9] train-rmse:0.456181+0.015405 test-rmse:0.551276+0.050496
## [10] train-rmse:0.440612+0.014814 test-rmse:0.543874+0.051752
## [11] train-rmse:0.428005+0.015965 test-rmse:0.537260+0.047726
## [12] train-rmse:0.417368+0.014097 test-rmse:0.532273+0.050430
## [13] train-rmse:0.408870+0.013836 test-rmse:0.527022+0.052305
## [14] train-rmse:0.401145+0.013253 test-rmse:0.522744+0.053147
## [15] train-rmse:0.392516+0.011950 test-rmse:0.517236+0.054977
## [16] train-rmse:0.386405+0.011102 test-rmse:0.513325+0.055025
## [17] train-rmse:0.380839+0.011030 test-rmse:0.512735+0.055392
## [18] train-rmse:0.374197+0.009776 test-rmse:0.510404+0.056300
## [19] train-rmse:0.368435+0.010332 test-rmse:0.508676+0.055396
## [20] train-rmse:0.361838+0.011203 test-rmse:0.509210+0.053864
## [21] train-rmse:0.355046+0.012280 test-rmse:0.506928+0.053668
## [22] train-rmse:0.350792+0.012728 test-rmse:0.505539+0.054031
## [23] train-rmse:0.345316+0.013765 test-rmse:0.505468+0.054443
## [24] train-rmse:0.340818+0.012635 test-rmse:0.504435+0.056191
## [25] train-rmse:0.337278+0.012360 test-rmse:0.503725+0.055788
## [26] train-rmse:0.333005+0.012303 test-rmse:0.503931+0.054973
## [27] train-rmse:0.329425+0.012777 test-rmse:0.503172+0.055285
## [28] train-rmse:0.324988+0.013140 test-rmse:0.501902+0.055223
## [29] train-rmse:0.319868+0.012790 test-rmse:0.500758+0.056518
## [30] train-rmse:0.316650+0.013070 test-rmse:0.499285+0.057303
## [31] train-rmse:0.312424+0.012938 test-rmse:0.498839+0.055762
## [32] train-rmse:0.309771+0.012698 test-rmse:0.499036+0.055425
## [33] train-rmse:0.305373+0.011942 test-rmse:0.499403+0.055445
## [34] train-rmse:0.300221+0.012879 test-rmse:0.499263+0.056381
## [35] train-rmse:0.298372+0.012486 test-rmse:0.499451+0.056267
## [36] train-rmse:0.295340+0.012240 test-rmse:0.498954+0.056018
## [37] train-rmse:0.292569+0.012675 test-rmse:0.498932+0.055176
## Stopping. Best iteration:
## [31] train-rmse:0.312424+0.012938 test-rmse:0.498839+0.055762
##
## 46
## [1] train-rmse:0.851431+0.014428 test-rmse:0.863680+0.061730
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.735948+0.013384 test-rmse:0.763291+0.060418
## [3] train-rmse:0.649030+0.012607 test-rmse:0.690905+0.061700
## [4] train-rmse:0.582802+0.010468 test-rmse:0.635669+0.057028
## [5] train-rmse:0.532043+0.009810 test-rmse:0.598532+0.055558
## [6] train-rmse:0.492377+0.009852 test-rmse:0.571410+0.053985
## [7] train-rmse:0.460222+0.010960 test-rmse:0.551810+0.055405
## [8] train-rmse:0.435894+0.010250 test-rmse:0.539398+0.054761
## [9] train-rmse:0.416430+0.009993 test-rmse:0.529485+0.052533
## [10] train-rmse:0.398348+0.013251 test-rmse:0.525185+0.050280
## [11] train-rmse:0.385636+0.011699 test-rmse:0.517889+0.050866
## [12] train-rmse:0.374467+0.010910 test-rmse:0.513332+0.050849
## [13] train-rmse:0.364383+0.010591 test-rmse:0.508521+0.051322
## [14] train-rmse:0.356307+0.009760 test-rmse:0.506170+0.051375
## [15] train-rmse:0.348254+0.008796 test-rmse:0.504142+0.050895
## [16] train-rmse:0.339230+0.009353 test-rmse:0.500692+0.051285
## [17] train-rmse:0.334495+0.009479 test-rmse:0.499738+0.052048
## [18] train-rmse:0.328392+0.008891 test-rmse:0.497168+0.052786
## [19] train-rmse:0.321298+0.012496 test-rmse:0.496917+0.053534
## [20] train-rmse:0.315891+0.011387 test-rmse:0.494826+0.053947
## [21] train-rmse:0.311890+0.011109 test-rmse:0.495954+0.053456
## [22] train-rmse:0.305144+0.011173 test-rmse:0.494470+0.052672
## [23] train-rmse:0.297874+0.010913 test-rmse:0.492811+0.051923
## [24] train-rmse:0.293000+0.010451 test-rmse:0.491324+0.051187
## [25] train-rmse:0.287802+0.010330 test-rmse:0.490931+0.052266
## [26] train-rmse:0.283259+0.010338 test-rmse:0.489137+0.049570
## [27] train-rmse:0.280321+0.010256 test-rmse:0.489166+0.050227
## [28] train-rmse:0.276037+0.010386 test-rmse:0.487948+0.051071
## [29] train-rmse:0.272072+0.008463 test-rmse:0.486335+0.051904
## [30] train-rmse:0.267456+0.007538 test-rmse:0.486428+0.051466
## [31] train-rmse:0.263877+0.007207 test-rmse:0.487037+0.051773
## [32] train-rmse:0.259311+0.006819 test-rmse:0.488322+0.051619
## [33] train-rmse:0.255505+0.008714 test-rmse:0.487832+0.052345
## [34] train-rmse:0.251010+0.008136 test-rmse:0.487918+0.052048
## [35] train-rmse:0.245722+0.007981 test-rmse:0.487692+0.052709
## Stopping. Best iteration:
## [29] train-rmse:0.272072+0.008463 test-rmse:0.486335+0.051904
##
## 47
## [1] train-rmse:0.845978+0.013885 test-rmse:0.861442+0.061941
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.724792+0.012240 test-rmse:0.756510+0.060742
## [3] train-rmse:0.630968+0.011791 test-rmse:0.681634+0.060706
## [4] train-rmse:0.560797+0.012422 test-rmse:0.631831+0.063696
## [5] train-rmse:0.504689+0.012828 test-rmse:0.591551+0.061727
## [6] train-rmse:0.462327+0.014089 test-rmse:0.562954+0.060813
## [7] train-rmse:0.426849+0.014500 test-rmse:0.540976+0.059602
## [8] train-rmse:0.398714+0.015244 test-rmse:0.530779+0.056860
## [9] train-rmse:0.375887+0.013564 test-rmse:0.523243+0.055823
## [10] train-rmse:0.355044+0.018489 test-rmse:0.519746+0.057026
## [11] train-rmse:0.342615+0.018549 test-rmse:0.514141+0.054730
## [12] train-rmse:0.330822+0.019028 test-rmse:0.509622+0.056208
## [13] train-rmse:0.319929+0.018074 test-rmse:0.508439+0.055173
## [14] train-rmse:0.310025+0.019740 test-rmse:0.505967+0.055046
## [15] train-rmse:0.301795+0.018789 test-rmse:0.500982+0.053620
## [16] train-rmse:0.294856+0.018657 test-rmse:0.500310+0.053216
## [17] train-rmse:0.288495+0.017595 test-rmse:0.499374+0.051051
## [18] train-rmse:0.280936+0.017503 test-rmse:0.498861+0.051138
## [19] train-rmse:0.276223+0.019082 test-rmse:0.499175+0.050758
## [20] train-rmse:0.269506+0.018175 test-rmse:0.496257+0.051229
## [21] train-rmse:0.264640+0.017568 test-rmse:0.496844+0.049822
## [22] train-rmse:0.257577+0.015165 test-rmse:0.496003+0.050974
## [23] train-rmse:0.253507+0.014154 test-rmse:0.493731+0.051739
## [24] train-rmse:0.248065+0.014540 test-rmse:0.493958+0.051192
## [25] train-rmse:0.244749+0.014494 test-rmse:0.493809+0.050321
## [26] train-rmse:0.239480+0.014430 test-rmse:0.492973+0.050479
## [27] train-rmse:0.235736+0.013918 test-rmse:0.492766+0.050152
## [28] train-rmse:0.229803+0.012954 test-rmse:0.492771+0.049327
## [29] train-rmse:0.222678+0.011850 test-rmse:0.494413+0.051153
## [30] train-rmse:0.219518+0.011401 test-rmse:0.494995+0.051580
## [31] train-rmse:0.215596+0.010637 test-rmse:0.495867+0.052105
## [32] train-rmse:0.211560+0.011508 test-rmse:0.495839+0.051303
## [33] train-rmse:0.207922+0.011812 test-rmse:0.495227+0.050300
## Stopping. Best iteration:
## [27] train-rmse:0.235736+0.013918 test-rmse:0.492766+0.050152
##
## 48
## [1] train-rmse:0.840622+0.013406 test-rmse:0.857979+0.060116
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.715628+0.010753 test-rmse:0.754138+0.060101
## [3] train-rmse:0.618197+0.009126 test-rmse:0.675485+0.061546
## [4] train-rmse:0.543358+0.009676 test-rmse:0.623861+0.059534
## [5] train-rmse:0.484314+0.008697 test-rmse:0.586134+0.058760
## [6] train-rmse:0.439965+0.009375 test-rmse:0.560089+0.058910
## [7] train-rmse:0.401467+0.007883 test-rmse:0.542401+0.059588
## [8] train-rmse:0.371122+0.009076 test-rmse:0.530426+0.059615
## [9] train-rmse:0.346392+0.009655 test-rmse:0.526038+0.057803
## [10] train-rmse:0.325875+0.008613 test-rmse:0.522018+0.058378
## [11] train-rmse:0.311265+0.008985 test-rmse:0.520231+0.056353
## [12] train-rmse:0.298421+0.010040 test-rmse:0.518783+0.057599
## [13] train-rmse:0.287655+0.011040 test-rmse:0.516722+0.058516
## [14] train-rmse:0.275842+0.015876 test-rmse:0.514677+0.058994
## [15] train-rmse:0.264246+0.017031 test-rmse:0.510007+0.056346
## [16] train-rmse:0.255649+0.014780 test-rmse:0.508288+0.056764
## [17] train-rmse:0.245120+0.013186 test-rmse:0.507063+0.055764
## [18] train-rmse:0.240115+0.014355 test-rmse:0.505604+0.054271
## [19] train-rmse:0.232693+0.011519 test-rmse:0.502395+0.055770
## [20] train-rmse:0.225004+0.012081 test-rmse:0.501798+0.056804
## [21] train-rmse:0.219232+0.011350 test-rmse:0.500954+0.055078
## [22] train-rmse:0.215105+0.011258 test-rmse:0.499946+0.056205
## [23] train-rmse:0.211392+0.011248 test-rmse:0.499260+0.057347
## [24] train-rmse:0.205737+0.009634 test-rmse:0.499134+0.057046
## [25] train-rmse:0.201520+0.009514 test-rmse:0.499097+0.057345
## [26] train-rmse:0.196488+0.009101 test-rmse:0.498250+0.057157
## [27] train-rmse:0.190830+0.008840 test-rmse:0.498783+0.058645
## [28] train-rmse:0.185866+0.009561 test-rmse:0.498960+0.058486
## [29] train-rmse:0.178997+0.009362 test-rmse:0.499520+0.056439
## [30] train-rmse:0.172248+0.010218 test-rmse:0.499149+0.056816
## [31] train-rmse:0.167490+0.010945 test-rmse:0.499849+0.056280
## [32] train-rmse:0.162269+0.011603 test-rmse:0.500028+0.056462
## Stopping. Best iteration:
## [26] train-rmse:0.196488+0.009101 test-rmse:0.498250+0.057157
##
## 49
## [1] train-rmse:0.879593+0.014920 test-rmse:0.882397+0.060659
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.797308+0.013945 test-rmse:0.805163+0.060589
## [3] train-rmse:0.742045+0.013707 test-rmse:0.761250+0.057251
## [4] train-rmse:0.704265+0.012303 test-rmse:0.727531+0.061672
## [5] train-rmse:0.676222+0.012064 test-rmse:0.696299+0.059138
## [6] train-rmse:0.655092+0.012005 test-rmse:0.680119+0.054017
## [7] train-rmse:0.640317+0.011695 test-rmse:0.667588+0.055899
## [8] train-rmse:0.628236+0.011649 test-rmse:0.656010+0.053608
## [9] train-rmse:0.619117+0.011256 test-rmse:0.649536+0.052806
## [10] train-rmse:0.611887+0.011316 test-rmse:0.644026+0.055799
## [11] train-rmse:0.605266+0.011370 test-rmse:0.637836+0.053602
## [12] train-rmse:0.599168+0.011353 test-rmse:0.630156+0.053110
## [13] train-rmse:0.593650+0.011417 test-rmse:0.627401+0.054033
## [14] train-rmse:0.588984+0.011459 test-rmse:0.624696+0.054295
## [15] train-rmse:0.584562+0.011404 test-rmse:0.620741+0.055631
## [16] train-rmse:0.580297+0.011373 test-rmse:0.616118+0.057168
## [17] train-rmse:0.576481+0.011405 test-rmse:0.612977+0.056411
## [18] train-rmse:0.572916+0.011348 test-rmse:0.611110+0.056100
## [19] train-rmse:0.569519+0.011305 test-rmse:0.607585+0.056433
## [20] train-rmse:0.566316+0.011257 test-rmse:0.603501+0.057941
## [21] train-rmse:0.563239+0.011363 test-rmse:0.600936+0.054938
## [22] train-rmse:0.560299+0.011295 test-rmse:0.598190+0.056360
## [23] train-rmse:0.557405+0.011270 test-rmse:0.597225+0.057369
## [24] train-rmse:0.554636+0.011302 test-rmse:0.595881+0.057215
## [25] train-rmse:0.551947+0.011433 test-rmse:0.594284+0.058468
## [26] train-rmse:0.549423+0.011451 test-rmse:0.593262+0.056986
## [27] train-rmse:0.546968+0.011455 test-rmse:0.590352+0.055493
## [28] train-rmse:0.544611+0.011437 test-rmse:0.589827+0.054126
## [29] train-rmse:0.542345+0.011374 test-rmse:0.588041+0.053099
## [30] train-rmse:0.540271+0.011340 test-rmse:0.585743+0.053408
## [31] train-rmse:0.538183+0.011293 test-rmse:0.583949+0.053168
## [32] train-rmse:0.536212+0.011264 test-rmse:0.583659+0.053049
## [33] train-rmse:0.534315+0.011202 test-rmse:0.583923+0.054630
## [34] train-rmse:0.532457+0.011121 test-rmse:0.580890+0.054570
## [35] train-rmse:0.530621+0.011116 test-rmse:0.580157+0.054786
## [36] train-rmse:0.528859+0.011048 test-rmse:0.577325+0.055534
## [37] train-rmse:0.527144+0.011012 test-rmse:0.576043+0.055665
## [38] train-rmse:0.525452+0.010981 test-rmse:0.574340+0.055566
## [39] train-rmse:0.523820+0.010936 test-rmse:0.573320+0.056624
## [40] train-rmse:0.522273+0.010877 test-rmse:0.572401+0.055930
## [41] train-rmse:0.520758+0.010864 test-rmse:0.571729+0.055092
## [42] train-rmse:0.519309+0.010850 test-rmse:0.570554+0.054678
## [43] train-rmse:0.517905+0.010825 test-rmse:0.570119+0.055884
## [44] train-rmse:0.516527+0.010813 test-rmse:0.569920+0.054836
## [45] train-rmse:0.515155+0.010790 test-rmse:0.567936+0.053770
## [46] train-rmse:0.513805+0.010743 test-rmse:0.566873+0.055059
## [47] train-rmse:0.512531+0.010712 test-rmse:0.565205+0.054460
## [48] train-rmse:0.511316+0.010651 test-rmse:0.564037+0.054749
## [49] train-rmse:0.510137+0.010626 test-rmse:0.562642+0.054686
## [50] train-rmse:0.508995+0.010576 test-rmse:0.562975+0.056164
## [51] train-rmse:0.507862+0.010527 test-rmse:0.561856+0.056470
## [52] train-rmse:0.506747+0.010508 test-rmse:0.560645+0.055161
## [53] train-rmse:0.505651+0.010497 test-rmse:0.561040+0.054928
## [54] train-rmse:0.504597+0.010509 test-rmse:0.561102+0.054412
## [55] train-rmse:0.503568+0.010532 test-rmse:0.560024+0.054537
## [56] train-rmse:0.502578+0.010529 test-rmse:0.559386+0.054110
## [57] train-rmse:0.501576+0.010520 test-rmse:0.557499+0.054001
## [58] train-rmse:0.500602+0.010516 test-rmse:0.556984+0.053899
## [59] train-rmse:0.499628+0.010520 test-rmse:0.555603+0.054266
## [60] train-rmse:0.498671+0.010502 test-rmse:0.554921+0.054439
## [61] train-rmse:0.497748+0.010505 test-rmse:0.554135+0.054454
## [62] train-rmse:0.496848+0.010480 test-rmse:0.552844+0.054432
## [63] train-rmse:0.495986+0.010435 test-rmse:0.551969+0.054055
## [64] train-rmse:0.495146+0.010417 test-rmse:0.551484+0.053592
## [65] train-rmse:0.494334+0.010411 test-rmse:0.551338+0.054267
## [66] train-rmse:0.493532+0.010401 test-rmse:0.551368+0.053549
## [67] train-rmse:0.492742+0.010391 test-rmse:0.550476+0.053491
## [68] train-rmse:0.491992+0.010397 test-rmse:0.551205+0.052862
## [69] train-rmse:0.491252+0.010406 test-rmse:0.550769+0.052419
## [70] train-rmse:0.490524+0.010398 test-rmse:0.550709+0.052398
## [71] train-rmse:0.489813+0.010398 test-rmse:0.549650+0.052296
## [72] train-rmse:0.489115+0.010395 test-rmse:0.549447+0.052382
## [73] train-rmse:0.488438+0.010377 test-rmse:0.548307+0.052640
## [74] train-rmse:0.487762+0.010363 test-rmse:0.548137+0.052072
## [75] train-rmse:0.487076+0.010375 test-rmse:0.547927+0.052578
## [76] train-rmse:0.486419+0.010370 test-rmse:0.547666+0.052599
## [77] train-rmse:0.485774+0.010360 test-rmse:0.547431+0.052161
## [78] train-rmse:0.485148+0.010341 test-rmse:0.547085+0.052463
## [79] train-rmse:0.484537+0.010333 test-rmse:0.545963+0.052569
## [80] train-rmse:0.483918+0.010322 test-rmse:0.545684+0.052844
## [81] train-rmse:0.483312+0.010310 test-rmse:0.546395+0.052606
## [82] train-rmse:0.482722+0.010290 test-rmse:0.546508+0.053700
## [83] train-rmse:0.482148+0.010283 test-rmse:0.546409+0.053881
## [84] train-rmse:0.481593+0.010286 test-rmse:0.546000+0.053713
## [85] train-rmse:0.481058+0.010284 test-rmse:0.544972+0.053230
## [86] train-rmse:0.480529+0.010284 test-rmse:0.544235+0.053207
## [87] train-rmse:0.480013+0.010277 test-rmse:0.544003+0.052751
## [88] train-rmse:0.479484+0.010255 test-rmse:0.542919+0.052209
## [89] train-rmse:0.478989+0.010246 test-rmse:0.542182+0.052564
## [90] train-rmse:0.478473+0.010224 test-rmse:0.542377+0.052223
## [91] train-rmse:0.477986+0.010225 test-rmse:0.542025+0.051570
## [92] train-rmse:0.477510+0.010224 test-rmse:0.542588+0.051580
## [93] train-rmse:0.477033+0.010229 test-rmse:0.542207+0.051343
## [94] train-rmse:0.476573+0.010240 test-rmse:0.541847+0.051229
## [95] train-rmse:0.476114+0.010244 test-rmse:0.541215+0.050948
## [96] train-rmse:0.475667+0.010238 test-rmse:0.541186+0.050506
## [97] train-rmse:0.475233+0.010235 test-rmse:0.541248+0.050421
## [98] train-rmse:0.474804+0.010227 test-rmse:0.541195+0.050368
## [99] train-rmse:0.474381+0.010235 test-rmse:0.540790+0.050368
## [100] train-rmse:0.473971+0.010241 test-rmse:0.540819+0.050514
## [101] train-rmse:0.473569+0.010236 test-rmse:0.540640+0.050699
## [102] train-rmse:0.473162+0.010236 test-rmse:0.540192+0.051235
## [103] train-rmse:0.472767+0.010221 test-rmse:0.540132+0.051336
## [104] train-rmse:0.472373+0.010209 test-rmse:0.539884+0.052250
## [105] train-rmse:0.471988+0.010196 test-rmse:0.539718+0.051577
## [106] train-rmse:0.471606+0.010171 test-rmse:0.539396+0.050889
## [107] train-rmse:0.471222+0.010154 test-rmse:0.538877+0.050322
## [108] train-rmse:0.470850+0.010131 test-rmse:0.538190+0.050245
## [109] train-rmse:0.470488+0.010113 test-rmse:0.537778+0.050206
## [110] train-rmse:0.470141+0.010095 test-rmse:0.537507+0.050149
## [111] train-rmse:0.469799+0.010084 test-rmse:0.537475+0.050459
## [112] train-rmse:0.469465+0.010078 test-rmse:0.537020+0.050405
## [113] train-rmse:0.469135+0.010066 test-rmse:0.536445+0.050187
## [114] train-rmse:0.468809+0.010059 test-rmse:0.536557+0.049989
## [115] train-rmse:0.468487+0.010051 test-rmse:0.536689+0.049656
## [116] train-rmse:0.468172+0.010044 test-rmse:0.536727+0.049606
## [117] train-rmse:0.467852+0.010039 test-rmse:0.536677+0.049610
## [118] train-rmse:0.467542+0.010027 test-rmse:0.536482+0.049712
## [119] train-rmse:0.467220+0.010017 test-rmse:0.536758+0.049485
## Stopping. Best iteration:
## [113] train-rmse:0.469135+0.010066 test-rmse:0.536445+0.050187
##
## 50
## [1] train-rmse:0.860486+0.014766 test-rmse:0.864754+0.060712
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.761515+0.014150 test-rmse:0.776586+0.063324
## [3] train-rmse:0.693869+0.013184 test-rmse:0.714958+0.064139
## [4] train-rmse:0.646752+0.012176 test-rmse:0.675296+0.066420
## [5] train-rmse:0.612037+0.011190 test-rmse:0.644288+0.066395
## [6] train-rmse:0.586941+0.011901 test-rmse:0.627227+0.066273
## [7] train-rmse:0.568162+0.011760 test-rmse:0.612731+0.068944
## [8] train-rmse:0.555781+0.011727 test-rmse:0.603013+0.069735
## [9] train-rmse:0.543510+0.014344 test-rmse:0.595191+0.065773
## [10] train-rmse:0.534023+0.013089 test-rmse:0.584613+0.067682
## [11] train-rmse:0.525559+0.012879 test-rmse:0.579931+0.065437
## [12] train-rmse:0.518659+0.012868 test-rmse:0.577296+0.063971
## [13] train-rmse:0.512153+0.013300 test-rmse:0.572233+0.061522
## [14] train-rmse:0.506486+0.013601 test-rmse:0.569865+0.062043
## [15] train-rmse:0.501460+0.013585 test-rmse:0.568508+0.063432
## [16] train-rmse:0.495659+0.014165 test-rmse:0.561547+0.061986
## [17] train-rmse:0.490763+0.014015 test-rmse:0.557932+0.060011
## [18] train-rmse:0.485876+0.014129 test-rmse:0.556284+0.061231
## [19] train-rmse:0.481043+0.013885 test-rmse:0.552800+0.059350
## [20] train-rmse:0.476396+0.014126 test-rmse:0.550933+0.059542
## [21] train-rmse:0.472550+0.014624 test-rmse:0.550454+0.059030
## [22] train-rmse:0.468960+0.014390 test-rmse:0.547068+0.061151
## [23] train-rmse:0.465394+0.014208 test-rmse:0.544477+0.059577
## [24] train-rmse:0.462085+0.013656 test-rmse:0.542304+0.058840
## [25] train-rmse:0.458821+0.013072 test-rmse:0.539935+0.062077
## [26] train-rmse:0.455572+0.013593 test-rmse:0.538078+0.060317
## [27] train-rmse:0.452383+0.013078 test-rmse:0.536700+0.061859
## [28] train-rmse:0.449871+0.013212 test-rmse:0.537221+0.062782
## [29] train-rmse:0.447466+0.013500 test-rmse:0.536532+0.061787
## [30] train-rmse:0.444948+0.013726 test-rmse:0.534426+0.061488
## [31] train-rmse:0.441620+0.014194 test-rmse:0.532467+0.062020
## [32] train-rmse:0.439094+0.013607 test-rmse:0.532123+0.061254
## [33] train-rmse:0.436818+0.013373 test-rmse:0.530580+0.060552
## [34] train-rmse:0.434351+0.013095 test-rmse:0.529877+0.059531
## [35] train-rmse:0.431955+0.013900 test-rmse:0.529794+0.060065
## [36] train-rmse:0.429170+0.013578 test-rmse:0.529479+0.060957
## [37] train-rmse:0.427145+0.013383 test-rmse:0.529399+0.059660
## [38] train-rmse:0.424493+0.013684 test-rmse:0.528474+0.059015
## [39] train-rmse:0.422418+0.013845 test-rmse:0.528316+0.058667
## [40] train-rmse:0.420338+0.014341 test-rmse:0.526990+0.057401
## [41] train-rmse:0.418397+0.014637 test-rmse:0.524622+0.058494
## [42] train-rmse:0.416154+0.014487 test-rmse:0.523672+0.058957
## [43] train-rmse:0.414285+0.014047 test-rmse:0.522051+0.061175
## [44] train-rmse:0.412147+0.014044 test-rmse:0.521343+0.061349
## [45] train-rmse:0.410542+0.014128 test-rmse:0.521172+0.061036
## [46] train-rmse:0.408262+0.014346 test-rmse:0.520310+0.060976
## [47] train-rmse:0.405745+0.013626 test-rmse:0.519900+0.060461
## [48] train-rmse:0.404076+0.013476 test-rmse:0.518841+0.059352
## [49] train-rmse:0.402127+0.013241 test-rmse:0.518728+0.060079
## [50] train-rmse:0.399956+0.013728 test-rmse:0.518231+0.061103
## [51] train-rmse:0.398748+0.013602 test-rmse:0.517818+0.060638
## [52] train-rmse:0.397150+0.014078 test-rmse:0.516183+0.060584
## [53] train-rmse:0.395464+0.014222 test-rmse:0.516132+0.060657
## [54] train-rmse:0.393779+0.014425 test-rmse:0.515412+0.061518
## [55] train-rmse:0.391994+0.014266 test-rmse:0.514185+0.062231
## [56] train-rmse:0.390666+0.014025 test-rmse:0.514208+0.062662
## [57] train-rmse:0.389464+0.013974 test-rmse:0.514433+0.062301
## [58] train-rmse:0.388518+0.014100 test-rmse:0.513790+0.062680
## [59] train-rmse:0.387537+0.014053 test-rmse:0.513556+0.061721
## [60] train-rmse:0.386398+0.013926 test-rmse:0.512708+0.061061
## [61] train-rmse:0.384593+0.013863 test-rmse:0.512649+0.060089
## [62] train-rmse:0.382578+0.013873 test-rmse:0.511295+0.060287
## [63] train-rmse:0.380981+0.014093 test-rmse:0.510786+0.059939
## [64] train-rmse:0.379308+0.013952 test-rmse:0.511408+0.060388
## [65] train-rmse:0.377959+0.013847 test-rmse:0.512338+0.060106
## [66] train-rmse:0.376395+0.013220 test-rmse:0.510226+0.061607
## [67] train-rmse:0.375400+0.013460 test-rmse:0.510395+0.061290
## [68] train-rmse:0.374315+0.013382 test-rmse:0.510088+0.061002
## [69] train-rmse:0.373278+0.013366 test-rmse:0.510853+0.060168
## [70] train-rmse:0.372280+0.013230 test-rmse:0.510285+0.060707
## [71] train-rmse:0.371139+0.013552 test-rmse:0.510310+0.060202
## [72] train-rmse:0.370398+0.013614 test-rmse:0.509953+0.059878
## [73] train-rmse:0.369066+0.014005 test-rmse:0.509565+0.059093
## [74] train-rmse:0.368012+0.013858 test-rmse:0.508715+0.058826
## [75] train-rmse:0.366548+0.014554 test-rmse:0.507557+0.058886
## [76] train-rmse:0.364698+0.015251 test-rmse:0.507029+0.058920
## [77] train-rmse:0.363429+0.015059 test-rmse:0.506434+0.059546
## [78] train-rmse:0.362475+0.014984 test-rmse:0.506010+0.059736
## [79] train-rmse:0.361420+0.015058 test-rmse:0.506671+0.060022
## [80] train-rmse:0.360015+0.015652 test-rmse:0.507099+0.058490
## [81] train-rmse:0.358710+0.014684 test-rmse:0.507423+0.057680
## [82] train-rmse:0.357882+0.014557 test-rmse:0.507175+0.056827
## [83] train-rmse:0.356268+0.015322 test-rmse:0.506311+0.055995
## [84] train-rmse:0.355135+0.014957 test-rmse:0.505648+0.055888
## [85] train-rmse:0.354493+0.014961 test-rmse:0.505440+0.055560
## [86] train-rmse:0.353553+0.015066 test-rmse:0.505664+0.055391
## [87] train-rmse:0.352383+0.015054 test-rmse:0.505368+0.055731
## [88] train-rmse:0.351182+0.014522 test-rmse:0.504050+0.055700
## [89] train-rmse:0.349461+0.014250 test-rmse:0.504678+0.055446
## [90] train-rmse:0.348189+0.013697 test-rmse:0.504143+0.055890
## [91] train-rmse:0.347396+0.013578 test-rmse:0.504018+0.055563
## [92] train-rmse:0.346464+0.013589 test-rmse:0.503921+0.054542
## [93] train-rmse:0.345430+0.013477 test-rmse:0.503367+0.054886
## [94] train-rmse:0.344715+0.013236 test-rmse:0.502823+0.054056
## [95] train-rmse:0.343972+0.013026 test-rmse:0.502645+0.053934
## [96] train-rmse:0.342481+0.013644 test-rmse:0.502633+0.054456
## [97] train-rmse:0.341791+0.013493 test-rmse:0.502385+0.054552
## [98] train-rmse:0.340330+0.013519 test-rmse:0.502239+0.054199
## [99] train-rmse:0.338645+0.013675 test-rmse:0.502431+0.053540
## [100] train-rmse:0.337733+0.013690 test-rmse:0.501999+0.053408
## [101] train-rmse:0.336869+0.013379 test-rmse:0.502305+0.053903
## [102] train-rmse:0.336182+0.013145 test-rmse:0.502095+0.054567
## [103] train-rmse:0.335620+0.012938 test-rmse:0.502243+0.054541
## [104] train-rmse:0.333821+0.012184 test-rmse:0.502387+0.054801
## [105] train-rmse:0.332804+0.012526 test-rmse:0.502261+0.054639
## [106] train-rmse:0.331809+0.012908 test-rmse:0.502712+0.054525
## Stopping. Best iteration:
## [100] train-rmse:0.337733+0.013690 test-rmse:0.501999+0.053408
##
## 51
## [1] train-rmse:0.851946+0.015123 test-rmse:0.859898+0.062482
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.743928+0.014671 test-rmse:0.762900+0.062243
## [3] train-rmse:0.667582+0.014085 test-rmse:0.692543+0.064229
## [4] train-rmse:0.611739+0.013795 test-rmse:0.647505+0.064270
## [5] train-rmse:0.571522+0.013749 test-rmse:0.616073+0.060140
## [6] train-rmse:0.543243+0.013690 test-rmse:0.597855+0.055587
## [7] train-rmse:0.521356+0.014272 test-rmse:0.578729+0.054712
## [8] train-rmse:0.505652+0.014531 test-rmse:0.566828+0.051684
## [9] train-rmse:0.492029+0.013798 test-rmse:0.560339+0.051018
## [10] train-rmse:0.480578+0.012421 test-rmse:0.554641+0.054588
## [11] train-rmse:0.470459+0.014030 test-rmse:0.552287+0.056850
## [12] train-rmse:0.461985+0.013755 test-rmse:0.546514+0.055523
## [13] train-rmse:0.454535+0.014819 test-rmse:0.542899+0.056531
## [14] train-rmse:0.447208+0.015382 test-rmse:0.539345+0.056862
## [15] train-rmse:0.440514+0.014643 test-rmse:0.536319+0.054768
## [16] train-rmse:0.433415+0.013457 test-rmse:0.532000+0.053348
## [17] train-rmse:0.427691+0.013979 test-rmse:0.529272+0.054207
## [18] train-rmse:0.423001+0.013686 test-rmse:0.525327+0.054928
## [19] train-rmse:0.417946+0.015137 test-rmse:0.523111+0.053766
## [20] train-rmse:0.413313+0.015055 test-rmse:0.523097+0.053264
## [21] train-rmse:0.408849+0.014504 test-rmse:0.521620+0.052844
## [22] train-rmse:0.403640+0.014664 test-rmse:0.517496+0.054432
## [23] train-rmse:0.400012+0.014469 test-rmse:0.516803+0.056067
## [24] train-rmse:0.396416+0.014563 test-rmse:0.514173+0.054871
## [25] train-rmse:0.392246+0.014168 test-rmse:0.511907+0.055244
## [26] train-rmse:0.387869+0.013727 test-rmse:0.510687+0.056384
## [27] train-rmse:0.384473+0.013077 test-rmse:0.509548+0.057579
## [28] train-rmse:0.380206+0.012797 test-rmse:0.506388+0.058415
## [29] train-rmse:0.377051+0.013076 test-rmse:0.505813+0.057464
## [30] train-rmse:0.373531+0.012246 test-rmse:0.505581+0.057728
## [31] train-rmse:0.370685+0.012314 test-rmse:0.505919+0.058512
## [32] train-rmse:0.368023+0.012712 test-rmse:0.506034+0.058293
## [33] train-rmse:0.364475+0.013455 test-rmse:0.504500+0.057400
## [34] train-rmse:0.362121+0.013609 test-rmse:0.505397+0.056361
## [35] train-rmse:0.359091+0.013047 test-rmse:0.503402+0.057891
## [36] train-rmse:0.356801+0.012538 test-rmse:0.503753+0.057213
## [37] train-rmse:0.352567+0.013570 test-rmse:0.502693+0.057986
## [38] train-rmse:0.349747+0.013042 test-rmse:0.500979+0.057597
## [39] train-rmse:0.347458+0.012921 test-rmse:0.501499+0.058447
## [40] train-rmse:0.344481+0.013613 test-rmse:0.500773+0.058796
## [41] train-rmse:0.341616+0.013538 test-rmse:0.501047+0.059084
## [42] train-rmse:0.339711+0.013030 test-rmse:0.499856+0.060019
## [43] train-rmse:0.336912+0.013154 test-rmse:0.499011+0.059354
## [44] train-rmse:0.334299+0.012344 test-rmse:0.498517+0.059141
## [45] train-rmse:0.331097+0.011833 test-rmse:0.496615+0.059638
## [46] train-rmse:0.328872+0.011697 test-rmse:0.495655+0.059226
## [47] train-rmse:0.326429+0.011565 test-rmse:0.495393+0.058634
## [48] train-rmse:0.323710+0.010916 test-rmse:0.493957+0.058566
## [49] train-rmse:0.320790+0.010237 test-rmse:0.493877+0.057304
## [50] train-rmse:0.318314+0.010732 test-rmse:0.494960+0.057387
## [51] train-rmse:0.316308+0.010291 test-rmse:0.495881+0.056487
## [52] train-rmse:0.313592+0.010094 test-rmse:0.495330+0.056638
## [53] train-rmse:0.311268+0.010680 test-rmse:0.495110+0.056223
## [54] train-rmse:0.308623+0.011069 test-rmse:0.493731+0.055862
## [55] train-rmse:0.306283+0.010825 test-rmse:0.493793+0.056295
## [56] train-rmse:0.304471+0.010936 test-rmse:0.493504+0.056796
## [57] train-rmse:0.303062+0.011077 test-rmse:0.493251+0.056473
## [58] train-rmse:0.300350+0.011907 test-rmse:0.493298+0.057295
## [59] train-rmse:0.297638+0.011036 test-rmse:0.492384+0.056854
## [60] train-rmse:0.295258+0.010715 test-rmse:0.492746+0.055101
## [61] train-rmse:0.292872+0.010953 test-rmse:0.493219+0.053786
## [62] train-rmse:0.291374+0.010467 test-rmse:0.493515+0.052615
## [63] train-rmse:0.288639+0.010649 test-rmse:0.493303+0.051869
## [64] train-rmse:0.286982+0.010716 test-rmse:0.492551+0.051389
## [65] train-rmse:0.284783+0.011268 test-rmse:0.492379+0.050344
## [66] train-rmse:0.282689+0.012038 test-rmse:0.493100+0.050215
## [67] train-rmse:0.280112+0.011645 test-rmse:0.493038+0.050447
## [68] train-rmse:0.277464+0.011928 test-rmse:0.492906+0.050284
## [69] train-rmse:0.275569+0.010786 test-rmse:0.492374+0.049943
## [70] train-rmse:0.274233+0.010712 test-rmse:0.492452+0.050215
## [71] train-rmse:0.272924+0.010467 test-rmse:0.492296+0.050493
## [72] train-rmse:0.271482+0.010471 test-rmse:0.491778+0.049911
## [73] train-rmse:0.269462+0.010588 test-rmse:0.491332+0.049343
## [74] train-rmse:0.267859+0.010478 test-rmse:0.491050+0.049116
## [75] train-rmse:0.266112+0.010543 test-rmse:0.490836+0.049503
## [76] train-rmse:0.263071+0.011346 test-rmse:0.491475+0.049035
## [77] train-rmse:0.261799+0.011256 test-rmse:0.492099+0.047963
## [78] train-rmse:0.259672+0.011724 test-rmse:0.491932+0.048215
## [79] train-rmse:0.257700+0.011791 test-rmse:0.490814+0.048201
## [80] train-rmse:0.255871+0.011965 test-rmse:0.490968+0.049605
## [81] train-rmse:0.254005+0.012395 test-rmse:0.490960+0.049359
## [82] train-rmse:0.252210+0.011891 test-rmse:0.491667+0.048533
## [83] train-rmse:0.250811+0.011999 test-rmse:0.491147+0.048372
## [84] train-rmse:0.249471+0.012206 test-rmse:0.490537+0.048692
## [85] train-rmse:0.247540+0.011878 test-rmse:0.490662+0.048934
## [86] train-rmse:0.245180+0.011470 test-rmse:0.489763+0.048861
## [87] train-rmse:0.243628+0.012168 test-rmse:0.489497+0.047601
## [88] train-rmse:0.242791+0.012138 test-rmse:0.489360+0.047532
## [89] train-rmse:0.240964+0.013274 test-rmse:0.489641+0.047576
## [90] train-rmse:0.239441+0.013243 test-rmse:0.489538+0.047364
## [91] train-rmse:0.238123+0.013704 test-rmse:0.488886+0.047277
## [92] train-rmse:0.237076+0.013671 test-rmse:0.488479+0.047223
## [93] train-rmse:0.235319+0.013449 test-rmse:0.487820+0.046595
## [94] train-rmse:0.233781+0.013131 test-rmse:0.487480+0.046761
## [95] train-rmse:0.231702+0.012849 test-rmse:0.487487+0.047281
## [96] train-rmse:0.229825+0.012591 test-rmse:0.487541+0.046846
## [97] train-rmse:0.228855+0.012472 test-rmse:0.487779+0.046618
## [98] train-rmse:0.227829+0.012650 test-rmse:0.488828+0.047644
## [99] train-rmse:0.226865+0.012520 test-rmse:0.488996+0.047272
## [100] train-rmse:0.225235+0.012463 test-rmse:0.488707+0.047754
## Stopping. Best iteration:
## [94] train-rmse:0.233781+0.013131 test-rmse:0.487480+0.046761
##
## 52
## [1] train-rmse:0.844613+0.014873 test-rmse:0.855506+0.060931
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.729408+0.013858 test-rmse:0.753988+0.061850
## [3] train-rmse:0.645135+0.012445 test-rmse:0.682335+0.063728
## [4] train-rmse:0.587801+0.012787 test-rmse:0.636252+0.064860
## [5] train-rmse:0.538456+0.015221 test-rmse:0.597047+0.057233
## [6] train-rmse:0.504955+0.015780 test-rmse:0.578062+0.057877
## [7] train-rmse:0.480670+0.014314 test-rmse:0.565297+0.057891
## [8] train-rmse:0.459299+0.015193 test-rmse:0.552934+0.057717
## [9] train-rmse:0.443870+0.016082 test-rmse:0.543374+0.052526
## [10] train-rmse:0.430510+0.017373 test-rmse:0.534849+0.053857
## [11] train-rmse:0.420303+0.015580 test-rmse:0.529471+0.055197
## [12] train-rmse:0.409652+0.016412 test-rmse:0.525328+0.054381
## [13] train-rmse:0.401856+0.017090 test-rmse:0.521608+0.055078
## [14] train-rmse:0.394494+0.016727 test-rmse:0.518634+0.055295
## [15] train-rmse:0.387123+0.014803 test-rmse:0.514837+0.054855
## [16] train-rmse:0.381076+0.015038 test-rmse:0.512937+0.053797
## [17] train-rmse:0.373987+0.015810 test-rmse:0.512121+0.053128
## [18] train-rmse:0.369168+0.014787 test-rmse:0.511828+0.051990
## [19] train-rmse:0.362885+0.014126 test-rmse:0.512034+0.052608
## [20] train-rmse:0.356127+0.013885 test-rmse:0.510545+0.053421
## [21] train-rmse:0.351299+0.014439 test-rmse:0.510052+0.052895
## [22] train-rmse:0.346601+0.013694 test-rmse:0.507056+0.053797
## [23] train-rmse:0.341644+0.013586 test-rmse:0.504017+0.053507
## [24] train-rmse:0.338312+0.013447 test-rmse:0.505317+0.053269
## [25] train-rmse:0.334205+0.013149 test-rmse:0.503711+0.051807
## [26] train-rmse:0.329194+0.013481 test-rmse:0.501099+0.053881
## [27] train-rmse:0.324657+0.014737 test-rmse:0.500358+0.054310
## [28] train-rmse:0.320218+0.015105 test-rmse:0.498073+0.054588
## [29] train-rmse:0.316018+0.014880 test-rmse:0.497338+0.053131
## [30] train-rmse:0.311336+0.013458 test-rmse:0.495786+0.055407
## [31] train-rmse:0.308191+0.013652 test-rmse:0.494529+0.053439
## [32] train-rmse:0.304634+0.013396 test-rmse:0.494707+0.052412
## [33] train-rmse:0.301045+0.012604 test-rmse:0.492768+0.053458
## [34] train-rmse:0.296927+0.012931 test-rmse:0.492646+0.051953
## [35] train-rmse:0.292397+0.012387 test-rmse:0.492616+0.051738
## [36] train-rmse:0.287336+0.014267 test-rmse:0.492517+0.051284
## [37] train-rmse:0.283486+0.014593 test-rmse:0.493005+0.051167
## [38] train-rmse:0.279941+0.013490 test-rmse:0.493745+0.050799
## [39] train-rmse:0.275760+0.014264 test-rmse:0.493086+0.047444
## [40] train-rmse:0.272738+0.013428 test-rmse:0.493998+0.046940
## [41] train-rmse:0.269635+0.012838 test-rmse:0.493643+0.047124
## [42] train-rmse:0.265875+0.013512 test-rmse:0.493667+0.047070
## Stopping. Best iteration:
## [36] train-rmse:0.287336+0.014267 test-rmse:0.492517+0.051284
##
## 53
## [1] train-rmse:0.838076+0.014280 test-rmse:0.851791+0.061729
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.715451+0.011992 test-rmse:0.743849+0.063197
## [3] train-rmse:0.625217+0.012478 test-rmse:0.670229+0.063508
## [4] train-rmse:0.557164+0.010947 test-rmse:0.619570+0.058938
## [5] train-rmse:0.507851+0.011329 test-rmse:0.583156+0.056536
## [6] train-rmse:0.469859+0.009973 test-rmse:0.562983+0.057264
## [7] train-rmse:0.439634+0.010623 test-rmse:0.544200+0.055171
## [8] train-rmse:0.416391+0.010664 test-rmse:0.535256+0.054764
## [9] train-rmse:0.399288+0.011072 test-rmse:0.531262+0.053100
## [10] train-rmse:0.385672+0.012425 test-rmse:0.527530+0.052193
## [11] train-rmse:0.371817+0.010036 test-rmse:0.520633+0.054948
## [12] train-rmse:0.362819+0.010194 test-rmse:0.514770+0.057612
## [13] train-rmse:0.351050+0.012045 test-rmse:0.509819+0.059050
## [14] train-rmse:0.342101+0.010281 test-rmse:0.505776+0.059697
## [15] train-rmse:0.333279+0.008434 test-rmse:0.504098+0.058453
## [16] train-rmse:0.325953+0.010502 test-rmse:0.502211+0.056720
## [17] train-rmse:0.319443+0.009181 test-rmse:0.500411+0.056507
## [18] train-rmse:0.311676+0.007747 test-rmse:0.497411+0.059646
## [19] train-rmse:0.306837+0.007541 test-rmse:0.495944+0.059428
## [20] train-rmse:0.300164+0.009753 test-rmse:0.496306+0.058898
## [21] train-rmse:0.294757+0.009581 test-rmse:0.494994+0.058425
## [22] train-rmse:0.290207+0.009449 test-rmse:0.493564+0.058159
## [23] train-rmse:0.284914+0.009485 test-rmse:0.492218+0.059136
## [24] train-rmse:0.281268+0.010525 test-rmse:0.491150+0.058451
## [25] train-rmse:0.275929+0.009265 test-rmse:0.491778+0.058362
## [26] train-rmse:0.270666+0.009449 test-rmse:0.490702+0.058818
## [27] train-rmse:0.265844+0.010322 test-rmse:0.491254+0.059801
## [28] train-rmse:0.259310+0.010354 test-rmse:0.490992+0.059346
## [29] train-rmse:0.254124+0.012776 test-rmse:0.490676+0.059175
## [30] train-rmse:0.249435+0.012041 test-rmse:0.490424+0.058945
## [31] train-rmse:0.244535+0.010880 test-rmse:0.490882+0.058494
## [32] train-rmse:0.240772+0.010749 test-rmse:0.490943+0.058318
## [33] train-rmse:0.236503+0.012397 test-rmse:0.490093+0.057913
## [34] train-rmse:0.233331+0.013355 test-rmse:0.490478+0.057934
## [35] train-rmse:0.228750+0.014617 test-rmse:0.491286+0.058590
## [36] train-rmse:0.223606+0.013501 test-rmse:0.491303+0.061335
## [37] train-rmse:0.219921+0.014145 test-rmse:0.491868+0.061282
## [38] train-rmse:0.213563+0.014622 test-rmse:0.493390+0.060955
## [39] train-rmse:0.209126+0.013901 test-rmse:0.492952+0.061011
## Stopping. Best iteration:
## [33] train-rmse:0.236503+0.012397 test-rmse:0.490093+0.057913
##
## 54
## [1] train-rmse:0.832084+0.013686 test-rmse:0.849380+0.061929
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.702200+0.010678 test-rmse:0.739139+0.062265
## [3] train-rmse:0.606798+0.010986 test-rmse:0.663288+0.063283
## [4] train-rmse:0.534979+0.014022 test-rmse:0.612313+0.059275
## [5] train-rmse:0.480925+0.014536 test-rmse:0.575066+0.058156
## [6] train-rmse:0.438578+0.014122 test-rmse:0.553692+0.056349
## [7] train-rmse:0.407474+0.014745 test-rmse:0.536344+0.057959
## [8] train-rmse:0.383566+0.015513 test-rmse:0.526759+0.059004
## [9] train-rmse:0.361388+0.014330 test-rmse:0.516933+0.058247
## [10] train-rmse:0.347410+0.015278 test-rmse:0.513088+0.057746
## [11] train-rmse:0.335638+0.017995 test-rmse:0.511558+0.055357
## [12] train-rmse:0.322332+0.014179 test-rmse:0.508889+0.053671
## [13] train-rmse:0.310975+0.016031 test-rmse:0.504734+0.053108
## [14] train-rmse:0.301326+0.013115 test-rmse:0.501565+0.055749
## [15] train-rmse:0.291570+0.015876 test-rmse:0.499456+0.057123
## [16] train-rmse:0.284542+0.014172 test-rmse:0.496102+0.054574
## [17] train-rmse:0.277008+0.012861 test-rmse:0.493996+0.053783
## [18] train-rmse:0.270996+0.013534 test-rmse:0.493467+0.052208
## [19] train-rmse:0.264225+0.012619 test-rmse:0.491380+0.051109
## [20] train-rmse:0.258727+0.012680 test-rmse:0.490632+0.051908
## [21] train-rmse:0.250579+0.013663 test-rmse:0.489435+0.051928
## [22] train-rmse:0.246154+0.013709 test-rmse:0.489560+0.052642
## [23] train-rmse:0.241680+0.012434 test-rmse:0.489588+0.051659
## [24] train-rmse:0.236680+0.013933 test-rmse:0.491241+0.051958
## [25] train-rmse:0.232390+0.013825 test-rmse:0.491470+0.050991
## [26] train-rmse:0.227729+0.013935 test-rmse:0.491880+0.049787
## [27] train-rmse:0.222348+0.013051 test-rmse:0.492471+0.047566
## Stopping. Best iteration:
## [21] train-rmse:0.250579+0.013663 test-rmse:0.489435+0.051928
##
## 55
## [1] train-rmse:0.826189+0.013165 test-rmse:0.845628+0.059907
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.692883+0.012080 test-rmse:0.738116+0.060657
## [3] train-rmse:0.592191+0.010949 test-rmse:0.660227+0.061868
## [4] train-rmse:0.516996+0.011083 test-rmse:0.610140+0.061096
## [5] train-rmse:0.461497+0.011603 test-rmse:0.578689+0.060432
## [6] train-rmse:0.415959+0.011691 test-rmse:0.553622+0.063040
## [7] train-rmse:0.380435+0.014417 test-rmse:0.541029+0.063310
## [8] train-rmse:0.351527+0.012092 test-rmse:0.529385+0.060639
## [9] train-rmse:0.328702+0.014519 test-rmse:0.525328+0.061103
## [10] train-rmse:0.309474+0.014335 test-rmse:0.522044+0.060547
## [11] train-rmse:0.296800+0.014529 test-rmse:0.520534+0.059842
## [12] train-rmse:0.281759+0.014669 test-rmse:0.517763+0.058432
## [13] train-rmse:0.272043+0.015925 test-rmse:0.516433+0.058169
## [14] train-rmse:0.261880+0.015636 test-rmse:0.514463+0.056671
## [15] train-rmse:0.251933+0.012991 test-rmse:0.510551+0.059025
## [16] train-rmse:0.239990+0.015205 test-rmse:0.508344+0.056087
## [17] train-rmse:0.233098+0.013976 test-rmse:0.507312+0.054991
## [18] train-rmse:0.226542+0.015680 test-rmse:0.506310+0.054434
## [19] train-rmse:0.221469+0.015927 test-rmse:0.506120+0.054243
## [20] train-rmse:0.216479+0.014922 test-rmse:0.503407+0.054397
## [21] train-rmse:0.211658+0.014847 test-rmse:0.502811+0.055959
## [22] train-rmse:0.204636+0.012811 test-rmse:0.501468+0.053979
## [23] train-rmse:0.199597+0.012371 test-rmse:0.502219+0.054665
## [24] train-rmse:0.193143+0.013784 test-rmse:0.502082+0.054463
## [25] train-rmse:0.188322+0.015192 test-rmse:0.501834+0.054051
## [26] train-rmse:0.181883+0.017116 test-rmse:0.502218+0.054337
## [27] train-rmse:0.176832+0.017401 test-rmse:0.504031+0.056920
## [28] train-rmse:0.172004+0.017298 test-rmse:0.503503+0.058120
## Stopping. Best iteration:
## [22] train-rmse:0.204636+0.012811 test-rmse:0.501468+0.053979
##
## 56
## [1] train-rmse:0.870003+0.014825 test-rmse:0.873249+0.060532
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.785158+0.013776 test-rmse:0.793799+0.060642
## [3] train-rmse:0.729647+0.013608 test-rmse:0.750328+0.057082
## [4] train-rmse:0.692899+0.012433 test-rmse:0.713306+0.059447
## [5] train-rmse:0.665710+0.012407 test-rmse:0.684232+0.056165
## [6] train-rmse:0.646057+0.011692 test-rmse:0.672047+0.050999
## [7] train-rmse:0.632082+0.011397 test-rmse:0.658146+0.049616
## [8] train-rmse:0.620880+0.011315 test-rmse:0.649707+0.050396
## [9] train-rmse:0.612918+0.011229 test-rmse:0.643819+0.048901
## [10] train-rmse:0.605401+0.011201 test-rmse:0.639413+0.051049
## [11] train-rmse:0.599013+0.011336 test-rmse:0.634192+0.052572
## [12] train-rmse:0.593051+0.011462 test-rmse:0.625930+0.052507
## [13] train-rmse:0.587761+0.011495 test-rmse:0.619666+0.052810
## [14] train-rmse:0.582976+0.011405 test-rmse:0.617050+0.053357
## [15] train-rmse:0.578696+0.011300 test-rmse:0.613288+0.054255
## [16] train-rmse:0.574344+0.011196 test-rmse:0.612095+0.054963
## [17] train-rmse:0.570615+0.011133 test-rmse:0.611197+0.053114
## [18] train-rmse:0.567190+0.011028 test-rmse:0.607039+0.055020
## [19] train-rmse:0.563726+0.011004 test-rmse:0.602715+0.055271
## [20] train-rmse:0.560493+0.010971 test-rmse:0.600261+0.055823
## [21] train-rmse:0.557301+0.011006 test-rmse:0.596251+0.056517
## [22] train-rmse:0.554321+0.011094 test-rmse:0.593322+0.058644
## [23] train-rmse:0.551475+0.011142 test-rmse:0.592634+0.058615
## [24] train-rmse:0.548714+0.011155 test-rmse:0.591113+0.058153
## [25] train-rmse:0.546034+0.011229 test-rmse:0.590542+0.056707
## [26] train-rmse:0.543496+0.011255 test-rmse:0.588609+0.053652
## [27] train-rmse:0.541070+0.011298 test-rmse:0.588180+0.053576
## [28] train-rmse:0.538770+0.011290 test-rmse:0.585127+0.053813
## [29] train-rmse:0.536599+0.011163 test-rmse:0.582957+0.054184
## [30] train-rmse:0.534470+0.011055 test-rmse:0.581941+0.053138
## [31] train-rmse:0.532422+0.010995 test-rmse:0.582470+0.053427
## [32] train-rmse:0.530432+0.011000 test-rmse:0.581762+0.056403
## [33] train-rmse:0.528504+0.011004 test-rmse:0.578567+0.055927
## [34] train-rmse:0.526652+0.010952 test-rmse:0.577473+0.054264
## [35] train-rmse:0.524859+0.010926 test-rmse:0.574662+0.054699
## [36] train-rmse:0.523142+0.010882 test-rmse:0.572761+0.055287
## [37] train-rmse:0.521452+0.010883 test-rmse:0.571192+0.056083
## [38] train-rmse:0.519808+0.010842 test-rmse:0.571087+0.055467
## [39] train-rmse:0.518287+0.010848 test-rmse:0.569927+0.054398
## [40] train-rmse:0.516771+0.010832 test-rmse:0.569669+0.054896
## [41] train-rmse:0.515286+0.010793 test-rmse:0.567649+0.054630
## [42] train-rmse:0.513841+0.010727 test-rmse:0.566925+0.054213
## [43] train-rmse:0.512434+0.010711 test-rmse:0.565158+0.053950
## [44] train-rmse:0.511051+0.010688 test-rmse:0.563451+0.053563
## [45] train-rmse:0.509722+0.010664 test-rmse:0.563302+0.054803
## [46] train-rmse:0.508408+0.010656 test-rmse:0.562476+0.054408
## [47] train-rmse:0.507154+0.010617 test-rmse:0.562994+0.054752
## [48] train-rmse:0.505936+0.010633 test-rmse:0.562408+0.053975
## [49] train-rmse:0.504776+0.010588 test-rmse:0.561902+0.054064
## [50] train-rmse:0.503631+0.010590 test-rmse:0.560184+0.053697
## [51] train-rmse:0.502508+0.010576 test-rmse:0.560119+0.054184
## [52] train-rmse:0.501430+0.010587 test-rmse:0.559829+0.054536
## [53] train-rmse:0.500387+0.010610 test-rmse:0.558572+0.054973
## [54] train-rmse:0.499361+0.010624 test-rmse:0.557061+0.055596
## [55] train-rmse:0.498351+0.010659 test-rmse:0.556259+0.054891
## [56] train-rmse:0.497367+0.010678 test-rmse:0.554882+0.054177
## [57] train-rmse:0.496415+0.010691 test-rmse:0.554641+0.052486
## [58] train-rmse:0.495465+0.010683 test-rmse:0.554285+0.053029
## [59] train-rmse:0.494532+0.010673 test-rmse:0.553210+0.052974
## [60] train-rmse:0.493622+0.010665 test-rmse:0.552413+0.052298
## [61] train-rmse:0.492744+0.010626 test-rmse:0.551737+0.051944
## [62] train-rmse:0.491865+0.010612 test-rmse:0.551889+0.051819
## [63] train-rmse:0.490995+0.010580 test-rmse:0.550092+0.052013
## [64] train-rmse:0.490187+0.010576 test-rmse:0.550820+0.052655
## [65] train-rmse:0.489399+0.010578 test-rmse:0.550133+0.052446
## [66] train-rmse:0.488636+0.010549 test-rmse:0.549677+0.052258
## [67] train-rmse:0.487905+0.010522 test-rmse:0.548871+0.051874
## [68] train-rmse:0.487179+0.010527 test-rmse:0.548490+0.051548
## [69] train-rmse:0.486465+0.010518 test-rmse:0.547581+0.051619
## [70] train-rmse:0.485766+0.010511 test-rmse:0.547338+0.051050
## [71] train-rmse:0.485080+0.010510 test-rmse:0.547678+0.051394
## [72] train-rmse:0.484404+0.010492 test-rmse:0.547474+0.051539
## [73] train-rmse:0.483737+0.010485 test-rmse:0.546582+0.051490
## [74] train-rmse:0.483082+0.010484 test-rmse:0.546527+0.051991
## [75] train-rmse:0.482432+0.010473 test-rmse:0.546612+0.052065
## [76] train-rmse:0.481798+0.010480 test-rmse:0.546657+0.052607
## [77] train-rmse:0.481187+0.010473 test-rmse:0.546354+0.051975
## [78] train-rmse:0.480590+0.010461 test-rmse:0.546600+0.051576
## [79] train-rmse:0.480007+0.010452 test-rmse:0.545833+0.051535
## [80] train-rmse:0.479445+0.010445 test-rmse:0.545755+0.051355
## [81] train-rmse:0.478885+0.010437 test-rmse:0.545351+0.051611
## [82] train-rmse:0.478328+0.010424 test-rmse:0.545167+0.052419
## [83] train-rmse:0.477790+0.010412 test-rmse:0.544336+0.052102
## [84] train-rmse:0.477258+0.010401 test-rmse:0.543707+0.052554
## [85] train-rmse:0.476756+0.010412 test-rmse:0.543691+0.052399
## [86] train-rmse:0.476259+0.010416 test-rmse:0.543253+0.052402
## [87] train-rmse:0.475774+0.010419 test-rmse:0.543060+0.051833
## [88] train-rmse:0.475287+0.010420 test-rmse:0.542777+0.051599
## [89] train-rmse:0.474810+0.010421 test-rmse:0.542554+0.051590
## [90] train-rmse:0.474334+0.010412 test-rmse:0.542040+0.051456
## [91] train-rmse:0.473861+0.010414 test-rmse:0.541914+0.050856
## [92] train-rmse:0.473403+0.010417 test-rmse:0.541327+0.050089
## [93] train-rmse:0.472949+0.010416 test-rmse:0.540942+0.049304
## [94] train-rmse:0.472504+0.010417 test-rmse:0.540852+0.048588
## [95] train-rmse:0.472059+0.010414 test-rmse:0.540736+0.049102
## [96] train-rmse:0.471636+0.010411 test-rmse:0.539609+0.049220
## [97] train-rmse:0.471223+0.010404 test-rmse:0.539431+0.049159
## [98] train-rmse:0.470805+0.010399 test-rmse:0.539713+0.049131
## [99] train-rmse:0.470410+0.010381 test-rmse:0.539682+0.048799
## [100] train-rmse:0.470014+0.010360 test-rmse:0.539324+0.049235
## [101] train-rmse:0.469644+0.010351 test-rmse:0.539598+0.048830
## [102] train-rmse:0.469277+0.010339 test-rmse:0.539170+0.048730
## [103] train-rmse:0.468918+0.010334 test-rmse:0.539016+0.048487
## [104] train-rmse:0.468551+0.010322 test-rmse:0.538951+0.049337
## [105] train-rmse:0.468202+0.010314 test-rmse:0.538651+0.049443
## [106] train-rmse:0.467847+0.010307 test-rmse:0.538351+0.049095
## [107] train-rmse:0.467492+0.010295 test-rmse:0.538459+0.049541
## [108] train-rmse:0.467150+0.010279 test-rmse:0.537482+0.049654
## [109] train-rmse:0.466803+0.010247 test-rmse:0.537415+0.049615
## [110] train-rmse:0.466463+0.010219 test-rmse:0.537618+0.049168
## [111] train-rmse:0.466129+0.010194 test-rmse:0.537549+0.049131
## [112] train-rmse:0.465809+0.010171 test-rmse:0.537679+0.049178
## [113] train-rmse:0.465490+0.010145 test-rmse:0.537806+0.048327
## [114] train-rmse:0.465180+0.010126 test-rmse:0.538162+0.048402
## [115] train-rmse:0.464873+0.010109 test-rmse:0.537631+0.048376
## Stopping. Best iteration:
## [109] train-rmse:0.466803+0.010247 test-rmse:0.537415+0.049615
##
## 57
## [1] train-rmse:0.849232+0.014653 test-rmse:0.854059+0.060649
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.746461+0.014038 test-rmse:0.762971+0.063659
## [3] train-rmse:0.678635+0.012757 test-rmse:0.701965+0.064495
## [4] train-rmse:0.632580+0.010753 test-rmse:0.661462+0.065841
## [5] train-rmse:0.600467+0.011113 test-rmse:0.635500+0.065476
## [6] train-rmse:0.576857+0.012796 test-rmse:0.619197+0.063124
## [7] train-rmse:0.558250+0.014978 test-rmse:0.600433+0.061305
## [8] train-rmse:0.547086+0.014993 test-rmse:0.593488+0.059787
## [9] train-rmse:0.537519+0.014636 test-rmse:0.589278+0.059442
## [10] train-rmse:0.529646+0.014444 test-rmse:0.583538+0.057621
## [11] train-rmse:0.521750+0.015143 test-rmse:0.581329+0.057768
## [12] train-rmse:0.514023+0.014256 test-rmse:0.574993+0.055048
## [13] train-rmse:0.506678+0.015462 test-rmse:0.570011+0.054016
## [14] train-rmse:0.501676+0.015767 test-rmse:0.570913+0.056647
## [15] train-rmse:0.496442+0.016145 test-rmse:0.566837+0.058014
## [16] train-rmse:0.490956+0.015843 test-rmse:0.561369+0.060220
## [17] train-rmse:0.486441+0.015843 test-rmse:0.558533+0.058956
## [18] train-rmse:0.482194+0.015935 test-rmse:0.557531+0.058214
## [19] train-rmse:0.477489+0.017126 test-rmse:0.554284+0.057053
## [20] train-rmse:0.472714+0.015809 test-rmse:0.551344+0.059435
## [21] train-rmse:0.468839+0.016190 test-rmse:0.549999+0.060731
## [22] train-rmse:0.464800+0.015962 test-rmse:0.544983+0.060185
## [23] train-rmse:0.460659+0.016547 test-rmse:0.543629+0.060496
## [24] train-rmse:0.457455+0.016316 test-rmse:0.541748+0.060071
## [25] train-rmse:0.454411+0.016712 test-rmse:0.539986+0.061586
## [26] train-rmse:0.450850+0.016541 test-rmse:0.538233+0.062355
## [27] train-rmse:0.447283+0.015430 test-rmse:0.537409+0.062937
## [28] train-rmse:0.444561+0.015292 test-rmse:0.535517+0.062997
## [29] train-rmse:0.441956+0.015381 test-rmse:0.533571+0.062247
## [30] train-rmse:0.438768+0.014908 test-rmse:0.532898+0.062591
## [31] train-rmse:0.436225+0.014844 test-rmse:0.531383+0.064474
## [32] train-rmse:0.434021+0.014601 test-rmse:0.529972+0.063825
## [33] train-rmse:0.431958+0.014945 test-rmse:0.529982+0.062980
## [34] train-rmse:0.429441+0.014426 test-rmse:0.528047+0.061631
## [35] train-rmse:0.426997+0.014380 test-rmse:0.526758+0.062052
## [36] train-rmse:0.423756+0.014557 test-rmse:0.525305+0.063317
## [37] train-rmse:0.421793+0.014561 test-rmse:0.524964+0.063167
## [38] train-rmse:0.419761+0.014971 test-rmse:0.523878+0.062705
## [39] train-rmse:0.417865+0.015135 test-rmse:0.522390+0.063070
## [40] train-rmse:0.415845+0.014900 test-rmse:0.520537+0.064360
## [41] train-rmse:0.414400+0.014922 test-rmse:0.519511+0.063254
## [42] train-rmse:0.412553+0.015013 test-rmse:0.519960+0.062423
## [43] train-rmse:0.410964+0.014943 test-rmse:0.520477+0.062220
## [44] train-rmse:0.408840+0.014528 test-rmse:0.518446+0.063464
## [45] train-rmse:0.407503+0.014321 test-rmse:0.519087+0.063721
## [46] train-rmse:0.405697+0.014048 test-rmse:0.519361+0.063856
## [47] train-rmse:0.404056+0.013898 test-rmse:0.519774+0.063394
## [48] train-rmse:0.401892+0.014070 test-rmse:0.518651+0.063206
## [49] train-rmse:0.400041+0.014247 test-rmse:0.517732+0.062984
## [50] train-rmse:0.398700+0.014488 test-rmse:0.517704+0.063099
## [51] train-rmse:0.396751+0.014120 test-rmse:0.518118+0.063497
## [52] train-rmse:0.394820+0.014655 test-rmse:0.516945+0.062329
## [53] train-rmse:0.393406+0.014653 test-rmse:0.515282+0.062387
## [54] train-rmse:0.391989+0.014481 test-rmse:0.515320+0.063207
## [55] train-rmse:0.390275+0.014258 test-rmse:0.515878+0.062934
## [56] train-rmse:0.388309+0.013859 test-rmse:0.516695+0.062324
## [57] train-rmse:0.386641+0.014244 test-rmse:0.515921+0.062121
## [58] train-rmse:0.385066+0.014010 test-rmse:0.516455+0.062081
## [59] train-rmse:0.384159+0.013860 test-rmse:0.516259+0.062683
## Stopping. Best iteration:
## [53] train-rmse:0.393406+0.014653 test-rmse:0.515282+0.062387
##
## 58
## [1] train-rmse:0.839946+0.015046 test-rmse:0.848834+0.062620
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.727010+0.014133 test-rmse:0.748779+0.062153
## [3] train-rmse:0.650108+0.013800 test-rmse:0.680336+0.062405
## [4] train-rmse:0.595550+0.014548 test-rmse:0.635581+0.060806
## [5] train-rmse:0.557781+0.013836 test-rmse:0.607040+0.057656
## [6] train-rmse:0.531161+0.013694 test-rmse:0.584248+0.057954
## [7] train-rmse:0.509859+0.015285 test-rmse:0.570138+0.053716
## [8] train-rmse:0.494304+0.016840 test-rmse:0.562246+0.050508
## [9] train-rmse:0.482652+0.016727 test-rmse:0.556482+0.049966
## [10] train-rmse:0.473184+0.017284 test-rmse:0.552708+0.050352
## [11] train-rmse:0.462096+0.016279 test-rmse:0.549869+0.048545
## [12] train-rmse:0.451632+0.013552 test-rmse:0.542784+0.047135
## [13] train-rmse:0.445005+0.013437 test-rmse:0.538972+0.048304
## [14] train-rmse:0.437295+0.014745 test-rmse:0.536912+0.050041
## [15] train-rmse:0.430676+0.014311 test-rmse:0.533543+0.049720
## [16] train-rmse:0.423567+0.013496 test-rmse:0.530652+0.050041
## [17] train-rmse:0.418737+0.013922 test-rmse:0.529858+0.048814
## [18] train-rmse:0.413149+0.012964 test-rmse:0.526681+0.051319
## [19] train-rmse:0.408372+0.012999 test-rmse:0.523975+0.052136
## [20] train-rmse:0.403790+0.012883 test-rmse:0.522498+0.051524
## [21] train-rmse:0.399583+0.013196 test-rmse:0.521339+0.052680
## [22] train-rmse:0.396004+0.013245 test-rmse:0.518569+0.051601
## [23] train-rmse:0.392064+0.012959 test-rmse:0.517586+0.051575
## [24] train-rmse:0.388404+0.012221 test-rmse:0.516551+0.050847
## [25] train-rmse:0.383776+0.011380 test-rmse:0.514447+0.051873
## [26] train-rmse:0.380427+0.011397 test-rmse:0.512860+0.050436
## [27] train-rmse:0.376161+0.011590 test-rmse:0.511591+0.050595
## [28] train-rmse:0.373254+0.011834 test-rmse:0.510920+0.049690
## [29] train-rmse:0.369662+0.012474 test-rmse:0.509038+0.049775
## [30] train-rmse:0.364936+0.012957 test-rmse:0.506313+0.050884
## [31] train-rmse:0.361918+0.012598 test-rmse:0.506217+0.051757
## [32] train-rmse:0.358631+0.013148 test-rmse:0.505277+0.050856
## [33] train-rmse:0.355018+0.012897 test-rmse:0.504809+0.050437
## [34] train-rmse:0.351400+0.012568 test-rmse:0.502375+0.050160
## [35] train-rmse:0.347524+0.012965 test-rmse:0.502782+0.050658
## [36] train-rmse:0.344003+0.014347 test-rmse:0.503262+0.049602
## [37] train-rmse:0.341668+0.014103 test-rmse:0.504205+0.049951
## [38] train-rmse:0.339742+0.013850 test-rmse:0.504260+0.049691
## [39] train-rmse:0.336684+0.014101 test-rmse:0.501886+0.051058
## [40] train-rmse:0.332766+0.012834 test-rmse:0.501646+0.050371
## [41] train-rmse:0.329356+0.012766 test-rmse:0.501009+0.049844
## [42] train-rmse:0.326220+0.012512 test-rmse:0.498932+0.049319
## [43] train-rmse:0.323088+0.012478 test-rmse:0.498875+0.048755
## [44] train-rmse:0.320278+0.012628 test-rmse:0.499428+0.047857
## [45] train-rmse:0.316382+0.012106 test-rmse:0.499445+0.047702
## [46] train-rmse:0.313649+0.011329 test-rmse:0.498659+0.047389
## [47] train-rmse:0.311490+0.011006 test-rmse:0.498681+0.046537
## [48] train-rmse:0.307398+0.013450 test-rmse:0.498031+0.045434
## [49] train-rmse:0.303599+0.012165 test-rmse:0.496024+0.044343
## [50] train-rmse:0.301958+0.011630 test-rmse:0.495009+0.044632
## [51] train-rmse:0.299963+0.011613 test-rmse:0.495335+0.046267
## [52] train-rmse:0.297646+0.011296 test-rmse:0.494523+0.046352
## [53] train-rmse:0.294950+0.011278 test-rmse:0.495321+0.045184
## [54] train-rmse:0.292073+0.011742 test-rmse:0.494055+0.045110
## [55] train-rmse:0.290723+0.011481 test-rmse:0.493514+0.044657
## [56] train-rmse:0.289032+0.011350 test-rmse:0.493798+0.043826
## [57] train-rmse:0.286729+0.011506 test-rmse:0.493016+0.043545
## [58] train-rmse:0.284488+0.012465 test-rmse:0.491817+0.042232
## [59] train-rmse:0.282664+0.012514 test-rmse:0.491230+0.042289
## [60] train-rmse:0.279766+0.013060 test-rmse:0.491858+0.042898
## [61] train-rmse:0.277787+0.012277 test-rmse:0.492499+0.043131
## [62] train-rmse:0.274426+0.012924 test-rmse:0.491606+0.043258
## [63] train-rmse:0.271304+0.013700 test-rmse:0.490667+0.042337
## [64] train-rmse:0.269107+0.013934 test-rmse:0.490318+0.042633
## [65] train-rmse:0.266659+0.014189 test-rmse:0.489182+0.042195
## [66] train-rmse:0.264656+0.014335 test-rmse:0.488657+0.041566
## [67] train-rmse:0.261756+0.013624 test-rmse:0.488484+0.042093
## [68] train-rmse:0.259345+0.012676 test-rmse:0.487569+0.042679
## [69] train-rmse:0.258151+0.012812 test-rmse:0.487474+0.042076
## [70] train-rmse:0.256552+0.013030 test-rmse:0.486568+0.041813
## [71] train-rmse:0.254856+0.012398 test-rmse:0.487904+0.042023
## [72] train-rmse:0.252671+0.012223 test-rmse:0.487352+0.042081
## [73] train-rmse:0.250632+0.012510 test-rmse:0.487367+0.041694
## [74] train-rmse:0.249356+0.012615 test-rmse:0.487119+0.041118
## [75] train-rmse:0.247976+0.012513 test-rmse:0.487506+0.040713
## [76] train-rmse:0.246090+0.012830 test-rmse:0.488877+0.040234
## Stopping. Best iteration:
## [70] train-rmse:0.256552+0.013030 test-rmse:0.486568+0.041813
##
## 59
## [1] train-rmse:0.831962+0.014776 test-rmse:0.844061+0.060930
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.711769+0.013811 test-rmse:0.738197+0.064485
## [3] train-rmse:0.626535+0.011723 test-rmse:0.663425+0.063690
## [4] train-rmse:0.568232+0.010936 test-rmse:0.619424+0.065515
## [5] train-rmse:0.522834+0.012622 test-rmse:0.586078+0.063385
## [6] train-rmse:0.489720+0.015077 test-rmse:0.564424+0.057603
## [7] train-rmse:0.465654+0.013402 test-rmse:0.552976+0.057306
## [8] train-rmse:0.444748+0.014123 test-rmse:0.542036+0.053829
## [9] train-rmse:0.432459+0.014635 test-rmse:0.535664+0.053289
## [10] train-rmse:0.417666+0.017062 test-rmse:0.528243+0.049610
## [11] train-rmse:0.405686+0.016272 test-rmse:0.520238+0.051371
## [12] train-rmse:0.397222+0.016492 test-rmse:0.517850+0.052870
## [13] train-rmse:0.389372+0.016817 test-rmse:0.513489+0.049634
## [14] train-rmse:0.382734+0.017218 test-rmse:0.512935+0.051290
## [15] train-rmse:0.375712+0.017125 test-rmse:0.510060+0.051031
## [16] train-rmse:0.369633+0.017631 test-rmse:0.507542+0.051104
## [17] train-rmse:0.363812+0.017788 test-rmse:0.504480+0.050589
## [18] train-rmse:0.357999+0.018091 test-rmse:0.501820+0.052738
## [19] train-rmse:0.349567+0.017712 test-rmse:0.499803+0.053081
## [20] train-rmse:0.342723+0.017415 test-rmse:0.497718+0.052922
## [21] train-rmse:0.338738+0.018069 test-rmse:0.496628+0.052790
## [22] train-rmse:0.333245+0.016627 test-rmse:0.496629+0.052606
## [23] train-rmse:0.328103+0.016568 test-rmse:0.494596+0.051886
## [24] train-rmse:0.323691+0.015668 test-rmse:0.494934+0.051790
## [25] train-rmse:0.318797+0.015423 test-rmse:0.495215+0.053704
## [26] train-rmse:0.314571+0.015420 test-rmse:0.495696+0.052704
## [27] train-rmse:0.310423+0.015069 test-rmse:0.495237+0.051395
## [28] train-rmse:0.306287+0.015280 test-rmse:0.494969+0.051782
## [29] train-rmse:0.301058+0.013795 test-rmse:0.494042+0.050749
## [30] train-rmse:0.297311+0.013385 test-rmse:0.494044+0.050397
## [31] train-rmse:0.293174+0.011817 test-rmse:0.492122+0.051808
## [32] train-rmse:0.289159+0.012535 test-rmse:0.490547+0.050697
## [33] train-rmse:0.285052+0.012445 test-rmse:0.491199+0.050733
## [34] train-rmse:0.281164+0.013541 test-rmse:0.489730+0.050598
## [35] train-rmse:0.277299+0.013378 test-rmse:0.488871+0.050530
## [36] train-rmse:0.273545+0.014109 test-rmse:0.489070+0.050974
## [37] train-rmse:0.268945+0.014191 test-rmse:0.489490+0.051420
## [38] train-rmse:0.265000+0.013974 test-rmse:0.490140+0.051944
## [39] train-rmse:0.261376+0.015083 test-rmse:0.489589+0.052207
## [40] train-rmse:0.258659+0.014973 test-rmse:0.490520+0.051911
## [41] train-rmse:0.255300+0.014470 test-rmse:0.491300+0.050930
## Stopping. Best iteration:
## [35] train-rmse:0.277299+0.013378 test-rmse:0.488871+0.050530
##
## 60
## [1] train-rmse:0.824840+0.014137 test-rmse:0.840053+0.061749
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.696366+0.011590 test-rmse:0.729001+0.062850
## [3] train-rmse:0.603919+0.012552 test-rmse:0.656182+0.061727
## [4] train-rmse:0.538388+0.011602 test-rmse:0.608575+0.055225
## [5] train-rmse:0.490184+0.010022 test-rmse:0.574740+0.053405
## [6] train-rmse:0.453658+0.009548 test-rmse:0.556566+0.052830
## [7] train-rmse:0.428205+0.008077 test-rmse:0.540938+0.057663
## [8] train-rmse:0.408073+0.008925 test-rmse:0.533880+0.056139
## [9] train-rmse:0.389516+0.010081 test-rmse:0.526966+0.057418
## [10] train-rmse:0.378083+0.009446 test-rmse:0.519688+0.056605
## [11] train-rmse:0.367155+0.009450 test-rmse:0.515807+0.057952
## [12] train-rmse:0.356060+0.010880 test-rmse:0.509997+0.058226
## [13] train-rmse:0.347263+0.010732 test-rmse:0.506632+0.056284
## [14] train-rmse:0.334290+0.012457 test-rmse:0.503369+0.056880
## [15] train-rmse:0.329771+0.012861 test-rmse:0.502339+0.056924
## [16] train-rmse:0.321291+0.010105 test-rmse:0.499971+0.057599
## [17] train-rmse:0.314913+0.009650 test-rmse:0.497902+0.057390
## [18] train-rmse:0.308073+0.010630 test-rmse:0.497150+0.059228
## [19] train-rmse:0.301952+0.009155 test-rmse:0.496652+0.058047
## [20] train-rmse:0.297943+0.009949 test-rmse:0.496739+0.057549
## [21] train-rmse:0.290767+0.010663 test-rmse:0.494034+0.056224
## [22] train-rmse:0.284928+0.010004 test-rmse:0.492723+0.056733
## [23] train-rmse:0.278308+0.010945 test-rmse:0.492235+0.055816
## [24] train-rmse:0.270944+0.008979 test-rmse:0.492028+0.055771
## [25] train-rmse:0.266839+0.008960 test-rmse:0.493094+0.054577
## [26] train-rmse:0.261377+0.009923 test-rmse:0.492222+0.053902
## [27] train-rmse:0.253909+0.013470 test-rmse:0.491357+0.052521
## [28] train-rmse:0.251312+0.013841 test-rmse:0.491209+0.052174
## [29] train-rmse:0.247053+0.012356 test-rmse:0.491273+0.050700
## [30] train-rmse:0.240760+0.011171 test-rmse:0.490816+0.051494
## [31] train-rmse:0.235525+0.012014 test-rmse:0.491807+0.050326
## [32] train-rmse:0.231262+0.010819 test-rmse:0.491274+0.050448
## [33] train-rmse:0.227627+0.011363 test-rmse:0.492067+0.051123
## [34] train-rmse:0.222663+0.010700 test-rmse:0.493090+0.049595
## [35] train-rmse:0.217632+0.010940 test-rmse:0.493349+0.049603
## [36] train-rmse:0.213347+0.008812 test-rmse:0.493543+0.048843
## Stopping. Best iteration:
## [30] train-rmse:0.240760+0.011171 test-rmse:0.490816+0.051494
##
## 61
## [1] train-rmse:0.818299+0.013493 test-rmse:0.837477+0.061934
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.682390+0.010611 test-rmse:0.722901+0.062642
## [3] train-rmse:0.585841+0.011997 test-rmse:0.646950+0.062208
## [4] train-rmse:0.511660+0.013512 test-rmse:0.595398+0.056997
## [5] train-rmse:0.460108+0.013856 test-rmse:0.563481+0.052128
## [6] train-rmse:0.418687+0.013108 test-rmse:0.542120+0.050638
## [7] train-rmse:0.388356+0.013990 test-rmse:0.532410+0.049236
## [8] train-rmse:0.365121+0.012472 test-rmse:0.522733+0.052564
## [9] train-rmse:0.348507+0.013319 test-rmse:0.514307+0.047890
## [10] train-rmse:0.334952+0.012105 test-rmse:0.509123+0.049093
## [11] train-rmse:0.319554+0.010539 test-rmse:0.505099+0.048467
## [12] train-rmse:0.309874+0.010590 test-rmse:0.502016+0.050476
## [13] train-rmse:0.300653+0.008216 test-rmse:0.500811+0.048928
## [14] train-rmse:0.293271+0.007042 test-rmse:0.498575+0.049812
## [15] train-rmse:0.286710+0.006713 test-rmse:0.498036+0.050323
## [16] train-rmse:0.279408+0.007513 test-rmse:0.497398+0.050414
## [17] train-rmse:0.272914+0.006871 test-rmse:0.498644+0.049271
## [18] train-rmse:0.265917+0.007614 test-rmse:0.497855+0.049480
## [19] train-rmse:0.259076+0.009085 test-rmse:0.496537+0.049510
## [20] train-rmse:0.252829+0.008040 test-rmse:0.497692+0.050924
## [21] train-rmse:0.246715+0.008476 test-rmse:0.496483+0.050456
## [22] train-rmse:0.240674+0.010165 test-rmse:0.496400+0.050270
## [23] train-rmse:0.234694+0.013299 test-rmse:0.495190+0.050214
## [24] train-rmse:0.226726+0.011373 test-rmse:0.496476+0.048760
## [25] train-rmse:0.218278+0.011540 test-rmse:0.496279+0.047463
## [26] train-rmse:0.211356+0.012241 test-rmse:0.498930+0.048730
## [27] train-rmse:0.205574+0.009685 test-rmse:0.498786+0.048945
## [28] train-rmse:0.202038+0.008628 test-rmse:0.498046+0.048516
## [29] train-rmse:0.196272+0.007899 test-rmse:0.497813+0.048858
## Stopping. Best iteration:
## [23] train-rmse:0.234694+0.013299 test-rmse:0.495190+0.050214
##
## 62
## [1] train-rmse:0.811857+0.012932 test-rmse:0.833441+0.059712
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.672109+0.011890 test-rmse:0.721639+0.060942
## [3] train-rmse:0.568846+0.010857 test-rmse:0.642445+0.060857
## [4] train-rmse:0.495628+0.011943 test-rmse:0.596647+0.061305
## [5] train-rmse:0.437896+0.014321 test-rmse:0.566277+0.058210
## [6] train-rmse:0.394251+0.015624 test-rmse:0.546869+0.062388
## [7] train-rmse:0.357876+0.013920 test-rmse:0.535245+0.060499
## [8] train-rmse:0.331036+0.014741 test-rmse:0.528795+0.061632
## [9] train-rmse:0.309149+0.014889 test-rmse:0.524854+0.061134
## [10] train-rmse:0.292196+0.014688 test-rmse:0.523196+0.061874
## [11] train-rmse:0.279352+0.014102 test-rmse:0.519812+0.062183
## [12] train-rmse:0.265191+0.011147 test-rmse:0.517437+0.062740
## [13] train-rmse:0.254540+0.011089 test-rmse:0.515620+0.061119
## [14] train-rmse:0.246294+0.010602 test-rmse:0.514590+0.060681
## [15] train-rmse:0.236974+0.011324 test-rmse:0.513392+0.060407
## [16] train-rmse:0.230046+0.011828 test-rmse:0.513840+0.060774
## [17] train-rmse:0.222409+0.013672 test-rmse:0.514681+0.058610
## [18] train-rmse:0.215800+0.012107 test-rmse:0.514167+0.059810
## [19] train-rmse:0.207756+0.012964 test-rmse:0.513705+0.059068
## [20] train-rmse:0.202529+0.013511 test-rmse:0.514860+0.059498
## [21] train-rmse:0.197928+0.013331 test-rmse:0.515414+0.059112
## Stopping. Best iteration:
## [15] train-rmse:0.236974+0.011324 test-rmse:0.513392+0.060407
##
## 63
## [1] train-rmse:0.860562+0.014734 test-rmse:0.864260+0.060418
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.773753+0.013612 test-rmse:0.785582+0.059119
## [3] train-rmse:0.718804+0.013609 test-rmse:0.743482+0.055230
## [4] train-rmse:0.682526+0.012621 test-rmse:0.703809+0.053265
## [5] train-rmse:0.656202+0.012307 test-rmse:0.680444+0.053777
## [6] train-rmse:0.638457+0.011604 test-rmse:0.666800+0.050216
## [7] train-rmse:0.625227+0.011362 test-rmse:0.653732+0.049014
## [8] train-rmse:0.614935+0.011209 test-rmse:0.646150+0.049820
## [9] train-rmse:0.606848+0.011237 test-rmse:0.639992+0.053502
## [10] train-rmse:0.599909+0.011339 test-rmse:0.634302+0.053220
## [11] train-rmse:0.593542+0.011471 test-rmse:0.632405+0.051708
## [12] train-rmse:0.587860+0.011561 test-rmse:0.625598+0.051121
## [13] train-rmse:0.582733+0.011598 test-rmse:0.618655+0.051925
## [14] train-rmse:0.577761+0.011606 test-rmse:0.614651+0.052976
## [15] train-rmse:0.573173+0.011495 test-rmse:0.610221+0.054647
## [16] train-rmse:0.569158+0.011346 test-rmse:0.609483+0.052545
## [17] train-rmse:0.565453+0.011205 test-rmse:0.607549+0.051739
## [18] train-rmse:0.561837+0.011094 test-rmse:0.603117+0.054240
## [19] train-rmse:0.558435+0.011122 test-rmse:0.599406+0.055642
## [20] train-rmse:0.555175+0.011176 test-rmse:0.598253+0.057714
## [21] train-rmse:0.552051+0.011195 test-rmse:0.595487+0.057294
## [22] train-rmse:0.549117+0.011172 test-rmse:0.594126+0.057799
## [23] train-rmse:0.546268+0.011172 test-rmse:0.589708+0.057714
## [24] train-rmse:0.543440+0.011241 test-rmse:0.585726+0.056280
## [25] train-rmse:0.540690+0.011330 test-rmse:0.584319+0.055780
## [26] train-rmse:0.538194+0.011321 test-rmse:0.583168+0.055160
## [27] train-rmse:0.535777+0.011358 test-rmse:0.581037+0.055357
## [28] train-rmse:0.533542+0.011259 test-rmse:0.580208+0.057129
## [29] train-rmse:0.531297+0.011127 test-rmse:0.581471+0.056564
## [30] train-rmse:0.529213+0.011073 test-rmse:0.580388+0.055836
## [31] train-rmse:0.527196+0.011009 test-rmse:0.579284+0.054533
## [32] train-rmse:0.525243+0.010969 test-rmse:0.577724+0.055493
## [33] train-rmse:0.523317+0.010945 test-rmse:0.574792+0.056562
## [34] train-rmse:0.521461+0.010945 test-rmse:0.572620+0.056434
## [35] train-rmse:0.519701+0.010943 test-rmse:0.571287+0.056201
## [36] train-rmse:0.517999+0.010947 test-rmse:0.569752+0.056200
## [37] train-rmse:0.516378+0.010933 test-rmse:0.567223+0.056474
## [38] train-rmse:0.514779+0.010892 test-rmse:0.565813+0.056070
## [39] train-rmse:0.513232+0.010848 test-rmse:0.564478+0.055801
## [40] train-rmse:0.511711+0.010796 test-rmse:0.562242+0.055163
## [41] train-rmse:0.510263+0.010737 test-rmse:0.559926+0.054785
## [42] train-rmse:0.508851+0.010687 test-rmse:0.560398+0.055427
## [43] train-rmse:0.507497+0.010639 test-rmse:0.561049+0.053902
## [44] train-rmse:0.506191+0.010608 test-rmse:0.560246+0.053116
## [45] train-rmse:0.504949+0.010600 test-rmse:0.559671+0.054188
## [46] train-rmse:0.503728+0.010625 test-rmse:0.560171+0.054301
## [47] train-rmse:0.502528+0.010637 test-rmse:0.560306+0.053793
## [48] train-rmse:0.501322+0.010646 test-rmse:0.560142+0.054534
## [49] train-rmse:0.500165+0.010664 test-rmse:0.559329+0.054863
## [50] train-rmse:0.499028+0.010692 test-rmse:0.558029+0.054673
## [51] train-rmse:0.497943+0.010736 test-rmse:0.556974+0.054610
## [52] train-rmse:0.496920+0.010752 test-rmse:0.556527+0.054344
## [53] train-rmse:0.495913+0.010756 test-rmse:0.555307+0.054822
## [54] train-rmse:0.494922+0.010770 test-rmse:0.553302+0.055153
## [55] train-rmse:0.493938+0.010762 test-rmse:0.553042+0.054082
## [56] train-rmse:0.492983+0.010769 test-rmse:0.552735+0.053477
## [57] train-rmse:0.492063+0.010744 test-rmse:0.551798+0.054034
## [58] train-rmse:0.491148+0.010737 test-rmse:0.551180+0.053503
## [59] train-rmse:0.490268+0.010700 test-rmse:0.549702+0.053226
## [60] train-rmse:0.489395+0.010712 test-rmse:0.549928+0.053547
## [61] train-rmse:0.488546+0.010716 test-rmse:0.549550+0.052857
## [62] train-rmse:0.487741+0.010706 test-rmse:0.549000+0.053868
## [63] train-rmse:0.486942+0.010680 test-rmse:0.548443+0.053440
## [64] train-rmse:0.486159+0.010658 test-rmse:0.548462+0.052140
## [65] train-rmse:0.485374+0.010631 test-rmse:0.548327+0.052861
## [66] train-rmse:0.484613+0.010577 test-rmse:0.547567+0.053305
## [67] train-rmse:0.483905+0.010572 test-rmse:0.546569+0.053103
## [68] train-rmse:0.483223+0.010565 test-rmse:0.545725+0.052987
## [69] train-rmse:0.482555+0.010574 test-rmse:0.545557+0.052411
## [70] train-rmse:0.481895+0.010575 test-rmse:0.545042+0.051333
## [71] train-rmse:0.481246+0.010576 test-rmse:0.545267+0.052492
## [72] train-rmse:0.480608+0.010589 test-rmse:0.544619+0.051256
## [73] train-rmse:0.479987+0.010596 test-rmse:0.544132+0.050973
## [74] train-rmse:0.479372+0.010600 test-rmse:0.543783+0.051078
## [75] train-rmse:0.478730+0.010624 test-rmse:0.544001+0.051401
## [76] train-rmse:0.478143+0.010620 test-rmse:0.543891+0.051279
## [77] train-rmse:0.477565+0.010610 test-rmse:0.543493+0.050790
## [78] train-rmse:0.477000+0.010581 test-rmse:0.543333+0.051125
## [79] train-rmse:0.476449+0.010559 test-rmse:0.542770+0.050826
## [80] train-rmse:0.475907+0.010547 test-rmse:0.541746+0.050911
## [81] train-rmse:0.475370+0.010534 test-rmse:0.541973+0.051000
## [82] train-rmse:0.474864+0.010531 test-rmse:0.541215+0.051306
## [83] train-rmse:0.474356+0.010525 test-rmse:0.540956+0.051311
## [84] train-rmse:0.473864+0.010524 test-rmse:0.541915+0.051589
## [85] train-rmse:0.473380+0.010521 test-rmse:0.541548+0.051346
## [86] train-rmse:0.472914+0.010519 test-rmse:0.541021+0.051711
## [87] train-rmse:0.472422+0.010533 test-rmse:0.541158+0.051313
## [88] train-rmse:0.471967+0.010529 test-rmse:0.540864+0.051241
## [89] train-rmse:0.471524+0.010523 test-rmse:0.540552+0.051076
## [90] train-rmse:0.471087+0.010514 test-rmse:0.539914+0.051511
## [91] train-rmse:0.470662+0.010505 test-rmse:0.540032+0.050721
## [92] train-rmse:0.470236+0.010504 test-rmse:0.539916+0.050563
## [93] train-rmse:0.469816+0.010498 test-rmse:0.539661+0.049754
## [94] train-rmse:0.469396+0.010484 test-rmse:0.539045+0.049646
## [95] train-rmse:0.468980+0.010480 test-rmse:0.539122+0.049751
## [96] train-rmse:0.468584+0.010463 test-rmse:0.538515+0.049508
## [97] train-rmse:0.468203+0.010454 test-rmse:0.538053+0.049951
## [98] train-rmse:0.467824+0.010438 test-rmse:0.538236+0.050024
## [99] train-rmse:0.467462+0.010424 test-rmse:0.537452+0.050079
## [100] train-rmse:0.467098+0.010403 test-rmse:0.537263+0.049438
## [101] train-rmse:0.466746+0.010388 test-rmse:0.537142+0.049289
## [102] train-rmse:0.466399+0.010381 test-rmse:0.536937+0.049532
## [103] train-rmse:0.466055+0.010374 test-rmse:0.537572+0.049916
## [104] train-rmse:0.465717+0.010371 test-rmse:0.537246+0.049454
## [105] train-rmse:0.465393+0.010354 test-rmse:0.537163+0.049182
## [106] train-rmse:0.465049+0.010321 test-rmse:0.537184+0.049893
## [107] train-rmse:0.464720+0.010302 test-rmse:0.537436+0.049430
## [108] train-rmse:0.464397+0.010284 test-rmse:0.537480+0.049235
## Stopping. Best iteration:
## [102] train-rmse:0.466399+0.010381 test-rmse:0.536937+0.049532
##
## 64
## [1] train-rmse:0.838120+0.014544 test-rmse:0.843513+0.060609
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.732185+0.013921 test-rmse:0.750047+0.064114
## [3] train-rmse:0.664425+0.011689 test-rmse:0.688328+0.068453
## [4] train-rmse:0.620951+0.011757 test-rmse:0.651423+0.064469
## [5] train-rmse:0.590642+0.011458 test-rmse:0.622212+0.064871
## [6] train-rmse:0.568826+0.013148 test-rmse:0.608221+0.064849
## [7] train-rmse:0.552021+0.015769 test-rmse:0.594758+0.059594
## [8] train-rmse:0.540875+0.014871 test-rmse:0.586631+0.061177
## [9] train-rmse:0.530429+0.014269 test-rmse:0.578793+0.065937
## [10] train-rmse:0.520749+0.016407 test-rmse:0.576454+0.062492
## [11] train-rmse:0.513354+0.016949 test-rmse:0.573267+0.060292
## [12] train-rmse:0.505457+0.015519 test-rmse:0.564882+0.058419
## [13] train-rmse:0.499462+0.015841 test-rmse:0.561408+0.058933
## [14] train-rmse:0.494035+0.016238 test-rmse:0.561108+0.061020
## [15] train-rmse:0.488424+0.017017 test-rmse:0.555589+0.061106
## [16] train-rmse:0.483415+0.016788 test-rmse:0.552250+0.061830
## [17] train-rmse:0.478712+0.016960 test-rmse:0.551631+0.061829
## [18] train-rmse:0.474736+0.017451 test-rmse:0.548188+0.058990
## [19] train-rmse:0.470842+0.017313 test-rmse:0.546111+0.060290
## [20] train-rmse:0.465518+0.016948 test-rmse:0.543830+0.058592
## [21] train-rmse:0.461548+0.017631 test-rmse:0.540882+0.058389
## [22] train-rmse:0.457496+0.017475 test-rmse:0.540294+0.060974
## [23] train-rmse:0.453504+0.017455 test-rmse:0.536346+0.060309
## [24] train-rmse:0.449703+0.016982 test-rmse:0.533103+0.061597
## [25] train-rmse:0.446665+0.017171 test-rmse:0.529674+0.061169
## [26] train-rmse:0.443700+0.017045 test-rmse:0.529258+0.059835
## [27] train-rmse:0.440507+0.016634 test-rmse:0.529316+0.061210
## [28] train-rmse:0.437224+0.016952 test-rmse:0.529994+0.060110
## [29] train-rmse:0.434240+0.016842 test-rmse:0.528624+0.058961
## [30] train-rmse:0.432022+0.016841 test-rmse:0.527110+0.061394
## [31] train-rmse:0.429750+0.017050 test-rmse:0.526348+0.061188
## [32] train-rmse:0.426997+0.017014 test-rmse:0.524838+0.059904
## [33] train-rmse:0.424762+0.017127 test-rmse:0.525286+0.059033
## [34] train-rmse:0.422010+0.017845 test-rmse:0.524290+0.058688
## [35] train-rmse:0.419310+0.017942 test-rmse:0.523245+0.058967
## [36] train-rmse:0.416647+0.018388 test-rmse:0.521175+0.059420
## [37] train-rmse:0.414016+0.017535 test-rmse:0.520240+0.057925
## [38] train-rmse:0.412087+0.017548 test-rmse:0.520965+0.057808
## [39] train-rmse:0.410329+0.017443 test-rmse:0.520791+0.058405
## [40] train-rmse:0.408600+0.017606 test-rmse:0.519232+0.058812
## [41] train-rmse:0.406892+0.017614 test-rmse:0.516527+0.059718
## [42] train-rmse:0.404815+0.017514 test-rmse:0.515663+0.060050
## [43] train-rmse:0.402976+0.017515 test-rmse:0.515393+0.059208
## [44] train-rmse:0.401320+0.017410 test-rmse:0.516318+0.059630
## [45] train-rmse:0.399415+0.017645 test-rmse:0.516469+0.059461
## [46] train-rmse:0.397430+0.017409 test-rmse:0.515805+0.059758
## [47] train-rmse:0.396104+0.017525 test-rmse:0.515299+0.059831
## [48] train-rmse:0.393804+0.016458 test-rmse:0.515385+0.059945
## [49] train-rmse:0.391537+0.016422 test-rmse:0.514118+0.059891
## [50] train-rmse:0.390069+0.016715 test-rmse:0.512259+0.059802
## [51] train-rmse:0.388565+0.016859 test-rmse:0.511450+0.058618
## [52] train-rmse:0.386493+0.016962 test-rmse:0.510903+0.058010
## [53] train-rmse:0.385083+0.017341 test-rmse:0.510174+0.057995
## [54] train-rmse:0.383791+0.017354 test-rmse:0.508995+0.056364
## [55] train-rmse:0.382365+0.017303 test-rmse:0.509201+0.055729
## [56] train-rmse:0.381098+0.017381 test-rmse:0.508518+0.055183
## [57] train-rmse:0.379135+0.017437 test-rmse:0.507699+0.054803
## [58] train-rmse:0.378189+0.017191 test-rmse:0.506786+0.054205
## [59] train-rmse:0.376290+0.016754 test-rmse:0.506932+0.055157
## [60] train-rmse:0.374688+0.016557 test-rmse:0.506054+0.055175
## [61] train-rmse:0.373411+0.016778 test-rmse:0.505575+0.055731
## [62] train-rmse:0.371751+0.016310 test-rmse:0.505618+0.055009
## [63] train-rmse:0.369427+0.016984 test-rmse:0.504490+0.055446
## [64] train-rmse:0.368646+0.016950 test-rmse:0.504462+0.055906
## [65] train-rmse:0.366825+0.016337 test-rmse:0.504339+0.055459
## [66] train-rmse:0.365585+0.016140 test-rmse:0.503320+0.055795
## [67] train-rmse:0.364272+0.016301 test-rmse:0.503487+0.055179
## [68] train-rmse:0.363258+0.016052 test-rmse:0.504572+0.054681
## [69] train-rmse:0.362234+0.016063 test-rmse:0.503798+0.054409
## [70] train-rmse:0.360674+0.015655 test-rmse:0.502706+0.055306
## [71] train-rmse:0.359965+0.015515 test-rmse:0.501381+0.054915
## [72] train-rmse:0.358732+0.016068 test-rmse:0.501100+0.054486
## [73] train-rmse:0.357483+0.015833 test-rmse:0.501263+0.054161
## [74] train-rmse:0.355847+0.015170 test-rmse:0.498823+0.055380
## [75] train-rmse:0.355256+0.015139 test-rmse:0.499171+0.054474
## [76] train-rmse:0.353611+0.014475 test-rmse:0.500984+0.054695
## [77] train-rmse:0.352424+0.014158 test-rmse:0.500489+0.054645
## [78] train-rmse:0.351658+0.014363 test-rmse:0.500501+0.055619
## [79] train-rmse:0.349884+0.014107 test-rmse:0.498604+0.056030
## [80] train-rmse:0.348678+0.013898 test-rmse:0.498331+0.055152
## [81] train-rmse:0.347602+0.013267 test-rmse:0.498911+0.055276
## [82] train-rmse:0.346085+0.012506 test-rmse:0.498829+0.054136
## [83] train-rmse:0.345185+0.012262 test-rmse:0.498803+0.053754
## [84] train-rmse:0.344128+0.012350 test-rmse:0.498452+0.053536
## [85] train-rmse:0.343132+0.012565 test-rmse:0.497996+0.053247
## [86] train-rmse:0.342079+0.012640 test-rmse:0.498295+0.053437
## [87] train-rmse:0.340498+0.012070 test-rmse:0.497278+0.053694
## [88] train-rmse:0.339597+0.011722 test-rmse:0.497970+0.053647
## [89] train-rmse:0.338806+0.011885 test-rmse:0.497399+0.053130
## [90] train-rmse:0.337204+0.012369 test-rmse:0.495775+0.051237
## [91] train-rmse:0.336053+0.012278 test-rmse:0.495886+0.050711
## [92] train-rmse:0.334584+0.013228 test-rmse:0.496218+0.050627
## [93] train-rmse:0.333214+0.013636 test-rmse:0.497201+0.050860
## [94] train-rmse:0.332288+0.013236 test-rmse:0.498012+0.051030
## [95] train-rmse:0.331282+0.013189 test-rmse:0.497001+0.051468
## [96] train-rmse:0.328666+0.013727 test-rmse:0.496016+0.052000
## Stopping. Best iteration:
## [90] train-rmse:0.337204+0.012369 test-rmse:0.495775+0.051237
##
## 65
## [1] train-rmse:0.828081+0.014975 test-rmse:0.837926+0.062786
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.711245+0.014043 test-rmse:0.734970+0.062525
## [3] train-rmse:0.633517+0.014088 test-rmse:0.669581+0.062509
## [4] train-rmse:0.581571+0.014031 test-rmse:0.628038+0.062310
## [5] train-rmse:0.545360+0.013164 test-rmse:0.598489+0.056978
## [6] train-rmse:0.518640+0.015421 test-rmse:0.584873+0.056221
## [7] train-rmse:0.500044+0.016975 test-rmse:0.570975+0.053686
## [8] train-rmse:0.484856+0.015964 test-rmse:0.562260+0.049288
## [9] train-rmse:0.472552+0.015166 test-rmse:0.553233+0.047770
## [10] train-rmse:0.462732+0.015407 test-rmse:0.550877+0.047690
## [11] train-rmse:0.454277+0.014789 test-rmse:0.545968+0.046989
## [12] train-rmse:0.444456+0.012980 test-rmse:0.541355+0.046269
## [13] train-rmse:0.437300+0.012704 test-rmse:0.537736+0.047080
## [14] train-rmse:0.430467+0.013128 test-rmse:0.535010+0.050198
## [15] train-rmse:0.424255+0.011870 test-rmse:0.531521+0.052513
## [16] train-rmse:0.417923+0.011798 test-rmse:0.527927+0.052408
## [17] train-rmse:0.412217+0.011085 test-rmse:0.525796+0.053341
## [18] train-rmse:0.407420+0.011363 test-rmse:0.524909+0.055036
## [19] train-rmse:0.402704+0.011469 test-rmse:0.524381+0.054552
## [20] train-rmse:0.397224+0.011933 test-rmse:0.521789+0.054579
## [21] train-rmse:0.391719+0.013105 test-rmse:0.521401+0.055031
## [22] train-rmse:0.385535+0.013813 test-rmse:0.519887+0.053369
## [23] train-rmse:0.381755+0.013090 test-rmse:0.518538+0.054928
## [24] train-rmse:0.377944+0.012495 test-rmse:0.518376+0.052649
## [25] train-rmse:0.375304+0.012722 test-rmse:0.516667+0.052826
## [26] train-rmse:0.372194+0.013126 test-rmse:0.514667+0.052164
## [27] train-rmse:0.367877+0.012808 test-rmse:0.513699+0.051861
## [28] train-rmse:0.363882+0.011605 test-rmse:0.513162+0.051913
## [29] train-rmse:0.359135+0.012511 test-rmse:0.513524+0.051448
## [30] train-rmse:0.354474+0.012772 test-rmse:0.513190+0.051219
## [31] train-rmse:0.351527+0.012648 test-rmse:0.513414+0.050470
## [32] train-rmse:0.348222+0.012297 test-rmse:0.511043+0.049619
## [33] train-rmse:0.345560+0.012460 test-rmse:0.510007+0.050402
## [34] train-rmse:0.342899+0.012061 test-rmse:0.509106+0.050645
## [35] train-rmse:0.339652+0.012127 test-rmse:0.509100+0.049346
## [36] train-rmse:0.335869+0.010958 test-rmse:0.507757+0.050252
## [37] train-rmse:0.332450+0.011761 test-rmse:0.507027+0.049772
## [38] train-rmse:0.329899+0.011305 test-rmse:0.507628+0.049282
## [39] train-rmse:0.326662+0.012103 test-rmse:0.507627+0.049812
## [40] train-rmse:0.323537+0.011976 test-rmse:0.509441+0.049128
## [41] train-rmse:0.321196+0.011617 test-rmse:0.507959+0.049080
## [42] train-rmse:0.317861+0.010678 test-rmse:0.508935+0.048297
## [43] train-rmse:0.314909+0.012313 test-rmse:0.508375+0.049325
## Stopping. Best iteration:
## [37] train-rmse:0.332450+0.011761 test-rmse:0.507027+0.049772
##
## 66
## [1] train-rmse:0.819441+0.014685 test-rmse:0.832770+0.060959
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.694665+0.013581 test-rmse:0.720940+0.065231
## [3] train-rmse:0.609348+0.012249 test-rmse:0.653547+0.067749
## [4] train-rmse:0.549083+0.013178 test-rmse:0.604369+0.059374
## [5] train-rmse:0.504340+0.013624 test-rmse:0.572296+0.057190
## [6] train-rmse:0.474738+0.014661 test-rmse:0.553737+0.052769
## [7] train-rmse:0.450637+0.017145 test-rmse:0.538486+0.053956
## [8] train-rmse:0.435342+0.017936 test-rmse:0.530604+0.051394
## [9] train-rmse:0.420574+0.016847 test-rmse:0.525419+0.056141
## [10] train-rmse:0.408161+0.015763 test-rmse:0.520305+0.055110
## [11] train-rmse:0.397075+0.013903 test-rmse:0.514817+0.057999
## [12] train-rmse:0.388493+0.013589 test-rmse:0.508546+0.059665
## [13] train-rmse:0.381143+0.013569 test-rmse:0.507019+0.059333
## [14] train-rmse:0.373295+0.012482 test-rmse:0.502477+0.058493
## [15] train-rmse:0.366322+0.013297 test-rmse:0.504086+0.059422
## [16] train-rmse:0.358749+0.013646 test-rmse:0.501626+0.059607
## [17] train-rmse:0.351812+0.015867 test-rmse:0.499095+0.057982
## [18] train-rmse:0.345099+0.015839 test-rmse:0.498607+0.058756
## [19] train-rmse:0.337855+0.016134 test-rmse:0.499112+0.058527
## [20] train-rmse:0.331856+0.014833 test-rmse:0.499783+0.057949
## [21] train-rmse:0.327463+0.014722 test-rmse:0.500363+0.057869
## [22] train-rmse:0.323178+0.014416 test-rmse:0.499817+0.056377
## [23] train-rmse:0.319162+0.013522 test-rmse:0.499763+0.056638
## [24] train-rmse:0.314438+0.014210 test-rmse:0.500868+0.055515
## Stopping. Best iteration:
## [18] train-rmse:0.345099+0.015839 test-rmse:0.498607+0.058756
##
## 67
## [1] train-rmse:0.811726+0.013999 test-rmse:0.828473+0.061791
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.678290+0.011330 test-rmse:0.714800+0.062399
## [3] train-rmse:0.583642+0.010787 test-rmse:0.640410+0.063570
## [4] train-rmse:0.518860+0.012693 test-rmse:0.594138+0.059191
## [5] train-rmse:0.473694+0.012724 test-rmse:0.564118+0.056322
## [6] train-rmse:0.438421+0.012150 test-rmse:0.546732+0.053725
## [7] train-rmse:0.415609+0.012551 test-rmse:0.538522+0.055447
## [8] train-rmse:0.397615+0.011855 test-rmse:0.531390+0.053573
## [9] train-rmse:0.381127+0.016143 test-rmse:0.523589+0.055404
## [10] train-rmse:0.368867+0.016104 test-rmse:0.515480+0.053297
## [11] train-rmse:0.356930+0.014385 test-rmse:0.511756+0.054777
## [12] train-rmse:0.343642+0.018432 test-rmse:0.509075+0.051704
## [13] train-rmse:0.334929+0.017093 test-rmse:0.506250+0.052144
## [14] train-rmse:0.326985+0.014847 test-rmse:0.502929+0.052632
## [15] train-rmse:0.317441+0.014382 test-rmse:0.499318+0.055407
## [16] train-rmse:0.311271+0.014945 test-rmse:0.497513+0.055460
## [17] train-rmse:0.304457+0.014593 test-rmse:0.496490+0.057822
## [18] train-rmse:0.296859+0.013544 test-rmse:0.494729+0.057389
## [19] train-rmse:0.288668+0.014016 test-rmse:0.492023+0.056936
## [20] train-rmse:0.282620+0.012881 test-rmse:0.491896+0.058648
## [21] train-rmse:0.277985+0.013199 test-rmse:0.490137+0.058656
## [22] train-rmse:0.272118+0.013907 test-rmse:0.490294+0.059731
## [23] train-rmse:0.264611+0.014223 test-rmse:0.490652+0.060773
## [24] train-rmse:0.257582+0.017032 test-rmse:0.490598+0.060469
## [25] train-rmse:0.252224+0.017421 test-rmse:0.489360+0.060720
## [26] train-rmse:0.245529+0.016923 test-rmse:0.488344+0.061988
## [27] train-rmse:0.240710+0.015882 test-rmse:0.489899+0.063415
## [28] train-rmse:0.235577+0.013258 test-rmse:0.492518+0.064342
## [29] train-rmse:0.231507+0.013529 test-rmse:0.491782+0.064201
## [30] train-rmse:0.227378+0.012673 test-rmse:0.491574+0.064113
## [31] train-rmse:0.222765+0.015642 test-rmse:0.491576+0.063750
## [32] train-rmse:0.218601+0.015558 test-rmse:0.492253+0.062327
## Stopping. Best iteration:
## [26] train-rmse:0.245529+0.016923 test-rmse:0.488344+0.061988
##
## 68
## [1] train-rmse:0.804630+0.013307 test-rmse:0.825739+0.061956
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.662732+0.010509 test-rmse:0.707200+0.062612
## [3] train-rmse:0.564880+0.012642 test-rmse:0.631900+0.063395
## [4] train-rmse:0.493177+0.013954 test-rmse:0.584289+0.056583
## [5] train-rmse:0.443573+0.012351 test-rmse:0.551608+0.054362
## [6] train-rmse:0.404988+0.012083 test-rmse:0.530721+0.051088
## [7] train-rmse:0.374805+0.011372 test-rmse:0.521177+0.054550
## [8] train-rmse:0.353875+0.010748 test-rmse:0.514091+0.053793
## [9] train-rmse:0.338671+0.011175 test-rmse:0.510367+0.052600
## [10] train-rmse:0.319048+0.009398 test-rmse:0.505590+0.049756
## [11] train-rmse:0.307544+0.012573 test-rmse:0.503286+0.048602
## [12] train-rmse:0.299066+0.011381 test-rmse:0.499328+0.048541
## [13] train-rmse:0.287724+0.011890 test-rmse:0.494999+0.051851
## [14] train-rmse:0.280479+0.010021 test-rmse:0.495325+0.050932
## [15] train-rmse:0.272905+0.010392 test-rmse:0.493883+0.052613
## [16] train-rmse:0.266030+0.012313 test-rmse:0.492831+0.052111
## [17] train-rmse:0.259254+0.011067 test-rmse:0.491810+0.052241
## [18] train-rmse:0.252518+0.014009 test-rmse:0.491369+0.052809
## [19] train-rmse:0.246398+0.013961 test-rmse:0.491498+0.053392
## [20] train-rmse:0.238775+0.013639 test-rmse:0.491478+0.052473
## [21] train-rmse:0.232492+0.012459 test-rmse:0.494677+0.053238
## [22] train-rmse:0.225430+0.014922 test-rmse:0.492332+0.051642
## [23] train-rmse:0.218570+0.015529 test-rmse:0.492198+0.051738
## [24] train-rmse:0.211431+0.016167 test-rmse:0.492723+0.049671
## Stopping. Best iteration:
## [18] train-rmse:0.252518+0.014009 test-rmse:0.491369+0.052809
##
## 69
## [1] train-rmse:0.797631+0.012704 test-rmse:0.821427+0.059529
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.649896+0.010060 test-rmse:0.702971+0.061650
## [3] train-rmse:0.548629+0.009022 test-rmse:0.625251+0.062967
## [4] train-rmse:0.475848+0.008859 test-rmse:0.582559+0.059503
## [5] train-rmse:0.417331+0.013371 test-rmse:0.547996+0.062324
## [6] train-rmse:0.375375+0.013124 test-rmse:0.528764+0.061055
## [7] train-rmse:0.344524+0.012790 test-rmse:0.518175+0.056203
## [8] train-rmse:0.317653+0.017361 test-rmse:0.512937+0.058287
## [9] train-rmse:0.302338+0.017736 test-rmse:0.511007+0.057391
## [10] train-rmse:0.288101+0.017920 test-rmse:0.507654+0.053762
## [11] train-rmse:0.276314+0.016278 test-rmse:0.506125+0.053418
## [12] train-rmse:0.264469+0.013749 test-rmse:0.503927+0.054071
## [13] train-rmse:0.251783+0.015102 test-rmse:0.502626+0.052296
## [14] train-rmse:0.241683+0.014651 test-rmse:0.501523+0.050410
## [15] train-rmse:0.234712+0.013208 test-rmse:0.499048+0.049457
## [16] train-rmse:0.226416+0.011000 test-rmse:0.498036+0.049781
## [17] train-rmse:0.219975+0.012051 test-rmse:0.498765+0.050619
## [18] train-rmse:0.214304+0.011230 test-rmse:0.498455+0.050450
## [19] train-rmse:0.208532+0.009770 test-rmse:0.499420+0.050480
## [20] train-rmse:0.197507+0.010983 test-rmse:0.499703+0.052823
## [21] train-rmse:0.191383+0.011555 test-rmse:0.501155+0.052338
## [22] train-rmse:0.186155+0.011138 test-rmse:0.501643+0.051789
## Stopping. Best iteration:
## [16] train-rmse:0.226416+0.011000 test-rmse:0.498036+0.049781
##
## 70
## [1] train-rmse:0.851277+0.014647 test-rmse:0.855434+0.060317
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.762903+0.013586 test-rmse:0.779237+0.064284
## [3] train-rmse:0.708413+0.013379 test-rmse:0.731249+0.059236
## [4] train-rmse:0.672401+0.012276 test-rmse:0.691567+0.057835
## [5] train-rmse:0.648010+0.012022 test-rmse:0.673332+0.054964
## [6] train-rmse:0.631771+0.011468 test-rmse:0.661088+0.051150
## [7] train-rmse:0.619350+0.011292 test-rmse:0.648963+0.049969
## [8] train-rmse:0.609882+0.011102 test-rmse:0.641361+0.049664
## [9] train-rmse:0.601671+0.011070 test-rmse:0.637053+0.052725
## [10] train-rmse:0.594655+0.011016 test-rmse:0.630337+0.054826
## [11] train-rmse:0.588467+0.011138 test-rmse:0.621926+0.055472
## [12] train-rmse:0.582979+0.011022 test-rmse:0.616404+0.055640
## [13] train-rmse:0.577839+0.011017 test-rmse:0.613343+0.056176
## [14] train-rmse:0.572999+0.011214 test-rmse:0.608236+0.057899
## [15] train-rmse:0.568576+0.011301 test-rmse:0.604193+0.059038
## [16] train-rmse:0.564551+0.011130 test-rmse:0.600966+0.057145
## [17] train-rmse:0.560713+0.011127 test-rmse:0.596944+0.056700
## [18] train-rmse:0.557091+0.011120 test-rmse:0.594944+0.059289
## [19] train-rmse:0.553581+0.011078 test-rmse:0.595851+0.057519
## [20] train-rmse:0.550226+0.010883 test-rmse:0.593670+0.058797
## [21] train-rmse:0.547177+0.010876 test-rmse:0.591993+0.056378
## [22] train-rmse:0.544214+0.010894 test-rmse:0.590580+0.056284
## [23] train-rmse:0.541416+0.010961 test-rmse:0.587823+0.056792
## [24] train-rmse:0.538670+0.011023 test-rmse:0.585268+0.057040
## [25] train-rmse:0.536009+0.011061 test-rmse:0.582177+0.056090
## [26] train-rmse:0.533468+0.011082 test-rmse:0.579919+0.055656
## [27] train-rmse:0.531105+0.010998 test-rmse:0.581935+0.055635
## [28] train-rmse:0.528792+0.010914 test-rmse:0.579626+0.057523
## [29] train-rmse:0.526620+0.010884 test-rmse:0.579754+0.057500
## [30] train-rmse:0.524576+0.010804 test-rmse:0.577899+0.056340
## [31] train-rmse:0.522578+0.010731 test-rmse:0.576244+0.055434
## [32] train-rmse:0.520675+0.010670 test-rmse:0.574792+0.055937
## [33] train-rmse:0.518842+0.010660 test-rmse:0.572164+0.056686
## [34] train-rmse:0.517046+0.010654 test-rmse:0.570743+0.057169
## [35] train-rmse:0.515298+0.010629 test-rmse:0.567866+0.056668
## [36] train-rmse:0.513595+0.010558 test-rmse:0.566111+0.056908
## [37] train-rmse:0.511957+0.010492 test-rmse:0.564036+0.056606
## [38] train-rmse:0.510383+0.010471 test-rmse:0.562288+0.055328
## [39] train-rmse:0.508815+0.010437 test-rmse:0.560585+0.055781
## [40] train-rmse:0.507345+0.010355 test-rmse:0.559306+0.055685
## [41] train-rmse:0.505925+0.010305 test-rmse:0.557319+0.056043
## [42] train-rmse:0.504553+0.010273 test-rmse:0.556202+0.056162
## [43] train-rmse:0.503244+0.010234 test-rmse:0.557619+0.056001
## [44] train-rmse:0.501969+0.010191 test-rmse:0.557368+0.054787
## [45] train-rmse:0.500750+0.010239 test-rmse:0.556916+0.055070
## [46] train-rmse:0.499563+0.010256 test-rmse:0.558456+0.055811
## [47] train-rmse:0.498403+0.010294 test-rmse:0.557787+0.055099
## [48] train-rmse:0.497244+0.010336 test-rmse:0.556786+0.055299
## Stopping. Best iteration:
## [42] train-rmse:0.504553+0.010273 test-rmse:0.556202+0.056162
##
## 71
## [1] train-rmse:0.827155+0.014439 test-rmse:0.833121+0.060589
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.718581+0.013679 test-rmse:0.737013+0.065688
## [3] train-rmse:0.652180+0.012414 test-rmse:0.680361+0.063820
## [4] train-rmse:0.610260+0.012049 test-rmse:0.644727+0.061006
## [5] train-rmse:0.582667+0.010908 test-rmse:0.625894+0.065368
## [6] train-rmse:0.560776+0.012784 test-rmse:0.610263+0.060827
## [7] train-rmse:0.547130+0.013102 test-rmse:0.599643+0.058236
## [8] train-rmse:0.535256+0.014019 test-rmse:0.590662+0.056670
## [9] train-rmse:0.526394+0.014301 test-rmse:0.582556+0.060126
## [10] train-rmse:0.517911+0.014214 test-rmse:0.576738+0.059241
## [11] train-rmse:0.509579+0.013619 test-rmse:0.571919+0.058979
## [12] train-rmse:0.502240+0.013224 test-rmse:0.569486+0.057994
## [13] train-rmse:0.494432+0.014076 test-rmse:0.561483+0.055752
## [14] train-rmse:0.487889+0.014809 test-rmse:0.560239+0.057336
## [15] train-rmse:0.482956+0.014844 test-rmse:0.556931+0.057537
## [16] train-rmse:0.477521+0.014970 test-rmse:0.552730+0.055338
## [17] train-rmse:0.472686+0.015564 test-rmse:0.549954+0.056966
## [18] train-rmse:0.467554+0.015446 test-rmse:0.545709+0.055528
## [19] train-rmse:0.463268+0.015229 test-rmse:0.543843+0.055857
## [20] train-rmse:0.459138+0.015251 test-rmse:0.543281+0.055755
## [21] train-rmse:0.454519+0.016068 test-rmse:0.540152+0.055778
## [22] train-rmse:0.450936+0.015365 test-rmse:0.537306+0.056316
## [23] train-rmse:0.446999+0.015311 test-rmse:0.534914+0.059467
## [24] train-rmse:0.443894+0.015539 test-rmse:0.533292+0.060199
## [25] train-rmse:0.440375+0.015106 test-rmse:0.530739+0.058333
## [26] train-rmse:0.437073+0.015363 test-rmse:0.528596+0.058813
## [27] train-rmse:0.434527+0.015484 test-rmse:0.529301+0.058391
## [28] train-rmse:0.431337+0.014874 test-rmse:0.526574+0.060734
## [29] train-rmse:0.427471+0.015397 test-rmse:0.525315+0.061494
## [30] train-rmse:0.424701+0.015340 test-rmse:0.525113+0.062238
## [31] train-rmse:0.422141+0.014970 test-rmse:0.523372+0.062604
## [32] train-rmse:0.420050+0.014599 test-rmse:0.521597+0.063111
## [33] train-rmse:0.417227+0.013820 test-rmse:0.521647+0.063722
## [34] train-rmse:0.414708+0.014094 test-rmse:0.521112+0.063878
## [35] train-rmse:0.412732+0.013619 test-rmse:0.519430+0.064909
## [36] train-rmse:0.410299+0.013893 test-rmse:0.517904+0.064875
## [37] train-rmse:0.408207+0.014126 test-rmse:0.517436+0.063580
## [38] train-rmse:0.406079+0.014062 test-rmse:0.516958+0.062165
## [39] train-rmse:0.403854+0.014309 test-rmse:0.517268+0.061965
## [40] train-rmse:0.402139+0.014287 test-rmse:0.516611+0.061104
## [41] train-rmse:0.400561+0.014326 test-rmse:0.517044+0.060989
## [42] train-rmse:0.398307+0.013583 test-rmse:0.515534+0.060470
## [43] train-rmse:0.396634+0.013730 test-rmse:0.514646+0.060286
## [44] train-rmse:0.394554+0.013510 test-rmse:0.514578+0.059599
## [45] train-rmse:0.392897+0.013762 test-rmse:0.514079+0.059779
## [46] train-rmse:0.391150+0.013630 test-rmse:0.512040+0.061723
## [47] train-rmse:0.389680+0.013632 test-rmse:0.511594+0.060427
## [48] train-rmse:0.388113+0.013721 test-rmse:0.511735+0.061000
## [49] train-rmse:0.386748+0.013476 test-rmse:0.511147+0.061491
## [50] train-rmse:0.385013+0.012990 test-rmse:0.509936+0.061835
## [51] train-rmse:0.383558+0.013102 test-rmse:0.510827+0.060898
## [52] train-rmse:0.382285+0.012856 test-rmse:0.510947+0.060397
## [53] train-rmse:0.380910+0.013238 test-rmse:0.510485+0.060692
## [54] train-rmse:0.379162+0.013256 test-rmse:0.509190+0.061699
## [55] train-rmse:0.378039+0.013182 test-rmse:0.508485+0.061609
## [56] train-rmse:0.376364+0.012651 test-rmse:0.507324+0.061965
## [57] train-rmse:0.375123+0.012226 test-rmse:0.507041+0.062918
## [58] train-rmse:0.373983+0.012200 test-rmse:0.507181+0.062541
## [59] train-rmse:0.372390+0.012781 test-rmse:0.506612+0.062578
## [60] train-rmse:0.370367+0.012567 test-rmse:0.506446+0.063758
## [61] train-rmse:0.369269+0.012340 test-rmse:0.506227+0.064481
## [62] train-rmse:0.368010+0.012204 test-rmse:0.506191+0.063447
## [63] train-rmse:0.365741+0.011988 test-rmse:0.504206+0.065294
## [64] train-rmse:0.364900+0.011908 test-rmse:0.503372+0.065517
## [65] train-rmse:0.362793+0.012463 test-rmse:0.501725+0.065451
## [66] train-rmse:0.361412+0.013020 test-rmse:0.502218+0.065807
## [67] train-rmse:0.360240+0.012931 test-rmse:0.501922+0.065905
## [68] train-rmse:0.359221+0.013041 test-rmse:0.500247+0.065841
## [69] train-rmse:0.357730+0.013689 test-rmse:0.499737+0.066273
## [70] train-rmse:0.355714+0.013503 test-rmse:0.498635+0.064974
## [71] train-rmse:0.354295+0.013423 test-rmse:0.498975+0.064335
## [72] train-rmse:0.352131+0.012377 test-rmse:0.498630+0.064168
## [73] train-rmse:0.349937+0.011981 test-rmse:0.498549+0.063255
## [74] train-rmse:0.348403+0.012439 test-rmse:0.498739+0.063591
## [75] train-rmse:0.346629+0.012296 test-rmse:0.498893+0.062191
## [76] train-rmse:0.345682+0.012106 test-rmse:0.498440+0.061654
## [77] train-rmse:0.344996+0.012141 test-rmse:0.498651+0.062212
## [78] train-rmse:0.343934+0.012463 test-rmse:0.498504+0.062265
## [79] train-rmse:0.343031+0.012348 test-rmse:0.499189+0.061503
## [80] train-rmse:0.341716+0.013048 test-rmse:0.498501+0.061152
## [81] train-rmse:0.340019+0.013053 test-rmse:0.498966+0.061864
## [82] train-rmse:0.338391+0.012611 test-rmse:0.497532+0.060793
## [83] train-rmse:0.337096+0.012656 test-rmse:0.497437+0.060371
## [84] train-rmse:0.336309+0.012589 test-rmse:0.497634+0.059528
## [85] train-rmse:0.334846+0.012475 test-rmse:0.498184+0.058717
## [86] train-rmse:0.333296+0.012844 test-rmse:0.497523+0.058146
## [87] train-rmse:0.332245+0.012796 test-rmse:0.497531+0.058199
## [88] train-rmse:0.331408+0.012769 test-rmse:0.496531+0.058043
## [89] train-rmse:0.330592+0.012746 test-rmse:0.496372+0.058360
## [90] train-rmse:0.329121+0.012526 test-rmse:0.495588+0.058298
## [91] train-rmse:0.327356+0.012948 test-rmse:0.495832+0.058411
## [92] train-rmse:0.326391+0.013167 test-rmse:0.495469+0.058745
## [93] train-rmse:0.325026+0.014082 test-rmse:0.494822+0.057974
## [94] train-rmse:0.323605+0.014273 test-rmse:0.494689+0.058380
## [95] train-rmse:0.322552+0.014128 test-rmse:0.495466+0.058597
## [96] train-rmse:0.320932+0.012825 test-rmse:0.495617+0.057922
## [97] train-rmse:0.319008+0.012655 test-rmse:0.495696+0.057931
## [98] train-rmse:0.317026+0.012129 test-rmse:0.495339+0.057786
## [99] train-rmse:0.315828+0.011682 test-rmse:0.494652+0.058263
## [100] train-rmse:0.314679+0.011540 test-rmse:0.496064+0.056987
## [101] train-rmse:0.313558+0.011902 test-rmse:0.496396+0.056832
## [102] train-rmse:0.312412+0.012210 test-rmse:0.496595+0.056429
## [103] train-rmse:0.311558+0.012855 test-rmse:0.497577+0.057593
## [104] train-rmse:0.310547+0.013102 test-rmse:0.497861+0.057851
## [105] train-rmse:0.308823+0.012888 test-rmse:0.496883+0.058453
## Stopping. Best iteration:
## [99] train-rmse:0.315828+0.011682 test-rmse:0.494652+0.058263
##
## 72
## [1] train-rmse:0.816359+0.014908 test-rmse:0.827181+0.062976
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.696234+0.013941 test-rmse:0.722136+0.062807
## [3] train-rmse:0.619414+0.014513 test-rmse:0.655841+0.059839
## [4] train-rmse:0.568968+0.014356 test-rmse:0.615593+0.059154
## [5] train-rmse:0.533701+0.013635 test-rmse:0.591393+0.053214
## [6] train-rmse:0.511347+0.012534 test-rmse:0.573743+0.056424
## [7] train-rmse:0.492970+0.015236 test-rmse:0.563117+0.049775
## [8] train-rmse:0.480670+0.014862 test-rmse:0.557357+0.048569
## [9] train-rmse:0.469118+0.016228 test-rmse:0.552132+0.049622
## [10] train-rmse:0.460008+0.016739 test-rmse:0.546610+0.048120
## [11] train-rmse:0.449853+0.014722 test-rmse:0.542960+0.050222
## [12] train-rmse:0.441152+0.012663 test-rmse:0.535461+0.053170
## [13] train-rmse:0.432471+0.012129 test-rmse:0.533077+0.054311
## [14] train-rmse:0.425952+0.013055 test-rmse:0.529055+0.053799
## [15] train-rmse:0.418741+0.013784 test-rmse:0.527116+0.054923
## [16] train-rmse:0.413063+0.013551 test-rmse:0.525326+0.057021
## [17] train-rmse:0.405181+0.013441 test-rmse:0.521892+0.056356
## [18] train-rmse:0.400472+0.013308 test-rmse:0.522169+0.056372
## [19] train-rmse:0.394863+0.013775 test-rmse:0.519698+0.056747
## [20] train-rmse:0.390335+0.013641 test-rmse:0.518228+0.058422
## [21] train-rmse:0.387194+0.013253 test-rmse:0.518440+0.057691
## [22] train-rmse:0.381933+0.012509 test-rmse:0.516953+0.058002
## [23] train-rmse:0.377772+0.013486 test-rmse:0.513584+0.057799
## [24] train-rmse:0.373369+0.013155 test-rmse:0.513225+0.057089
## [25] train-rmse:0.368800+0.012823 test-rmse:0.513286+0.056800
## [26] train-rmse:0.362511+0.015122 test-rmse:0.511323+0.058464
## [27] train-rmse:0.358939+0.014770 test-rmse:0.510046+0.059812
## [28] train-rmse:0.355214+0.014915 test-rmse:0.507938+0.060353
## [29] train-rmse:0.351776+0.013967 test-rmse:0.507938+0.059387
## [30] train-rmse:0.347965+0.013170 test-rmse:0.508063+0.058429
## [31] train-rmse:0.344330+0.013463 test-rmse:0.506690+0.058808
## [32] train-rmse:0.340839+0.012989 test-rmse:0.504961+0.058131
## [33] train-rmse:0.337270+0.012994 test-rmse:0.504227+0.060195
## [34] train-rmse:0.335029+0.012754 test-rmse:0.503940+0.059125
## [35] train-rmse:0.331884+0.013064 test-rmse:0.504485+0.059617
## [36] train-rmse:0.329242+0.012775 test-rmse:0.504892+0.058429
## [37] train-rmse:0.326711+0.012557 test-rmse:0.503848+0.058383
## [38] train-rmse:0.323630+0.013565 test-rmse:0.505350+0.056769
## [39] train-rmse:0.319110+0.015074 test-rmse:0.505082+0.055336
## [40] train-rmse:0.316206+0.015488 test-rmse:0.504719+0.056450
## [41] train-rmse:0.312844+0.014694 test-rmse:0.503458+0.057259
## [42] train-rmse:0.308582+0.014125 test-rmse:0.502363+0.057243
## [43] train-rmse:0.305493+0.014129 test-rmse:0.501350+0.054836
## [44] train-rmse:0.302944+0.015009 test-rmse:0.500463+0.052953
## [45] train-rmse:0.299412+0.015440 test-rmse:0.500898+0.052459
## [46] train-rmse:0.296652+0.015044 test-rmse:0.499940+0.052137
## [47] train-rmse:0.294390+0.015533 test-rmse:0.500186+0.053709
## [48] train-rmse:0.291988+0.016454 test-rmse:0.499799+0.054607
## [49] train-rmse:0.289085+0.015241 test-rmse:0.499881+0.054247
## [50] train-rmse:0.286857+0.015360 test-rmse:0.500763+0.052545
## [51] train-rmse:0.283287+0.015119 test-rmse:0.500561+0.053353
## [52] train-rmse:0.281180+0.015449 test-rmse:0.500761+0.053999
## [53] train-rmse:0.278603+0.015245 test-rmse:0.500242+0.053976
## [54] train-rmse:0.275754+0.016583 test-rmse:0.499394+0.054014
## [55] train-rmse:0.273651+0.016365 test-rmse:0.499623+0.053547
## [56] train-rmse:0.270874+0.015877 test-rmse:0.500377+0.053445
## [57] train-rmse:0.268453+0.015217 test-rmse:0.499953+0.053657
## [58] train-rmse:0.266829+0.015022 test-rmse:0.500628+0.053538
## [59] train-rmse:0.264998+0.015029 test-rmse:0.500392+0.053505
## [60] train-rmse:0.263642+0.014908 test-rmse:0.499478+0.053973
## Stopping. Best iteration:
## [54] train-rmse:0.275754+0.016583 test-rmse:0.499394+0.054014
##
## 73
## [1] train-rmse:0.807057+0.014600 test-rmse:0.821640+0.061016
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.678475+0.013422 test-rmse:0.707165+0.065401
## [3] train-rmse:0.593705+0.012368 test-rmse:0.641087+0.061203
## [4] train-rmse:0.533152+0.013501 test-rmse:0.597362+0.052002
## [5] train-rmse:0.492895+0.015720 test-rmse:0.570084+0.048848
## [6] train-rmse:0.465821+0.016339 test-rmse:0.556258+0.046803
## [7] train-rmse:0.440623+0.017576 test-rmse:0.543738+0.051314
## [8] train-rmse:0.426849+0.018093 test-rmse:0.536189+0.051577
## [9] train-rmse:0.415120+0.018017 test-rmse:0.528208+0.052295
## [10] train-rmse:0.405440+0.016393 test-rmse:0.523300+0.051726
## [11] train-rmse:0.394682+0.016437 test-rmse:0.520461+0.053215
## [12] train-rmse:0.384508+0.016256 test-rmse:0.514686+0.053801
## [13] train-rmse:0.375650+0.016534 test-rmse:0.514700+0.052807
## [14] train-rmse:0.366342+0.017075 test-rmse:0.509129+0.053355
## [15] train-rmse:0.360924+0.016016 test-rmse:0.507525+0.054650
## [16] train-rmse:0.354420+0.016239 test-rmse:0.507928+0.053954
## [17] train-rmse:0.349218+0.016370 test-rmse:0.508858+0.054445
## [18] train-rmse:0.343472+0.015661 test-rmse:0.509410+0.054683
## [19] train-rmse:0.336151+0.014184 test-rmse:0.505211+0.056945
## [20] train-rmse:0.330950+0.013112 test-rmse:0.502480+0.055256
## [21] train-rmse:0.323408+0.012050 test-rmse:0.502327+0.055055
## [22] train-rmse:0.317111+0.013423 test-rmse:0.503542+0.057044
## [23] train-rmse:0.310602+0.012449 test-rmse:0.502622+0.056116
## [24] train-rmse:0.305690+0.012102 test-rmse:0.502670+0.056293
## [25] train-rmse:0.301296+0.012840 test-rmse:0.501560+0.055082
## [26] train-rmse:0.296677+0.013107 test-rmse:0.498976+0.054220
## [27] train-rmse:0.292267+0.013388 test-rmse:0.499324+0.053643
## [28] train-rmse:0.287962+0.013103 test-rmse:0.499756+0.053923
## [29] train-rmse:0.282739+0.011404 test-rmse:0.499420+0.055664
## [30] train-rmse:0.278699+0.010967 test-rmse:0.498820+0.053955
## [31] train-rmse:0.275543+0.011478 test-rmse:0.498105+0.053274
## [32] train-rmse:0.270640+0.011754 test-rmse:0.498614+0.052845
## [33] train-rmse:0.266444+0.012404 test-rmse:0.498275+0.051873
## [34] train-rmse:0.261436+0.012874 test-rmse:0.497450+0.051079
## [35] train-rmse:0.258439+0.012009 test-rmse:0.497518+0.051326
## [36] train-rmse:0.254549+0.011705 test-rmse:0.498128+0.051528
## [37] train-rmse:0.249728+0.011987 test-rmse:0.500305+0.051752
## [38] train-rmse:0.245180+0.010763 test-rmse:0.500143+0.053203
## [39] train-rmse:0.240926+0.011096 test-rmse:0.501521+0.050878
## [40] train-rmse:0.236699+0.011703 test-rmse:0.502426+0.051656
## Stopping. Best iteration:
## [34] train-rmse:0.261436+0.012874 test-rmse:0.497450+0.051079
##
## 74
## [1] train-rmse:0.798743+0.013868 test-rmse:0.817058+0.061851
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.660980+0.011075 test-rmse:0.700722+0.062722
## [3] train-rmse:0.565182+0.010739 test-rmse:0.623347+0.057973
## [4] train-rmse:0.502400+0.012518 test-rmse:0.582197+0.055908
## [5] train-rmse:0.458196+0.012330 test-rmse:0.556860+0.054747
## [6] train-rmse:0.424255+0.012363 test-rmse:0.545099+0.055594
## [7] train-rmse:0.402661+0.011810 test-rmse:0.534513+0.054352
## [8] train-rmse:0.382550+0.011896 test-rmse:0.525279+0.052740
## [9] train-rmse:0.366361+0.012204 test-rmse:0.521367+0.051377
## [10] train-rmse:0.353166+0.009270 test-rmse:0.516605+0.047895
## [11] train-rmse:0.341353+0.013648 test-rmse:0.515472+0.047375
## [12] train-rmse:0.330672+0.013360 test-rmse:0.512097+0.046505
## [13] train-rmse:0.322185+0.012515 test-rmse:0.510007+0.049828
## [14] train-rmse:0.312727+0.012707 test-rmse:0.508277+0.050344
## [15] train-rmse:0.305354+0.012033 test-rmse:0.508413+0.049832
## [16] train-rmse:0.296197+0.013883 test-rmse:0.504875+0.050498
## [17] train-rmse:0.289238+0.013962 test-rmse:0.505045+0.048106
## [18] train-rmse:0.282866+0.013742 test-rmse:0.503638+0.049491
## [19] train-rmse:0.276776+0.012475 test-rmse:0.502212+0.049712
## [20] train-rmse:0.270108+0.013795 test-rmse:0.500835+0.046672
## [21] train-rmse:0.265064+0.013598 test-rmse:0.500837+0.046894
## [22] train-rmse:0.259599+0.013783 test-rmse:0.500516+0.045765
## [23] train-rmse:0.252000+0.013746 test-rmse:0.499998+0.045947
## [24] train-rmse:0.247720+0.013763 test-rmse:0.497969+0.045791
## [25] train-rmse:0.242237+0.013666 test-rmse:0.499662+0.043936
## [26] train-rmse:0.234511+0.012247 test-rmse:0.501404+0.043594
## [27] train-rmse:0.228732+0.012261 test-rmse:0.502023+0.042798
## [28] train-rmse:0.223918+0.013579 test-rmse:0.503154+0.042997
## [29] train-rmse:0.218202+0.011567 test-rmse:0.502799+0.042756
## [30] train-rmse:0.213418+0.011440 test-rmse:0.503359+0.041549
## Stopping. Best iteration:
## [24] train-rmse:0.247720+0.013763 test-rmse:0.497969+0.045791
##
## 75
## [1] train-rmse:0.791083+0.013128 test-rmse:0.814174+0.061993
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.644200+0.010318 test-rmse:0.692631+0.062932
## [3] train-rmse:0.541870+0.012471 test-rmse:0.614305+0.057112
## [4] train-rmse:0.472131+0.014132 test-rmse:0.565407+0.054548
## [5] train-rmse:0.425058+0.014678 test-rmse:0.541100+0.054129
## [6] train-rmse:0.389032+0.014042 test-rmse:0.525849+0.061480
## [7] train-rmse:0.363002+0.012011 test-rmse:0.517615+0.065455
## [8] train-rmse:0.343249+0.011197 test-rmse:0.509640+0.064189
## [9] train-rmse:0.324875+0.009953 test-rmse:0.505676+0.058993
## [10] train-rmse:0.314259+0.008450 test-rmse:0.501950+0.057814
## [11] train-rmse:0.301155+0.015225 test-rmse:0.497390+0.056097
## [12] train-rmse:0.290118+0.013299 test-rmse:0.496241+0.054358
## [13] train-rmse:0.280252+0.013741 test-rmse:0.492986+0.056328
## [14] train-rmse:0.272833+0.012350 test-rmse:0.492806+0.055816
## [15] train-rmse:0.262970+0.012905 test-rmse:0.490399+0.056398
## [16] train-rmse:0.254688+0.014247 test-rmse:0.492052+0.055893
## [17] train-rmse:0.246366+0.013253 test-rmse:0.492524+0.055625
## [18] train-rmse:0.240318+0.014248 test-rmse:0.491264+0.053726
## [19] train-rmse:0.233235+0.015940 test-rmse:0.492093+0.053490
## [20] train-rmse:0.225207+0.014123 test-rmse:0.489713+0.053075
## [21] train-rmse:0.218006+0.012348 test-rmse:0.491503+0.053555
## [22] train-rmse:0.210317+0.012461 test-rmse:0.492173+0.052939
## [23] train-rmse:0.205422+0.012553 test-rmse:0.492256+0.053215
## [24] train-rmse:0.200763+0.012509 test-rmse:0.492336+0.053285
## [25] train-rmse:0.195689+0.012364 test-rmse:0.493631+0.053649
## [26] train-rmse:0.188544+0.011375 test-rmse:0.495827+0.054958
## Stopping. Best iteration:
## [20] train-rmse:0.225207+0.014123 test-rmse:0.489713+0.053075
##
## 76
## [1] train-rmse:0.783517+0.012486 test-rmse:0.809591+0.059358
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.629781+0.009612 test-rmse:0.688438+0.061649
## [3] train-rmse:0.528181+0.009002 test-rmse:0.614232+0.061480
## [4] train-rmse:0.453978+0.009717 test-rmse:0.567129+0.061051
## [5] train-rmse:0.401338+0.011712 test-rmse:0.541311+0.059700
## [6] train-rmse:0.356401+0.010399 test-rmse:0.524129+0.057749
## [7] train-rmse:0.326802+0.014464 test-rmse:0.514363+0.056722
## [8] train-rmse:0.300909+0.014038 test-rmse:0.506012+0.048669
## [9] train-rmse:0.283580+0.012303 test-rmse:0.504361+0.042984
## [10] train-rmse:0.266709+0.012012 test-rmse:0.497721+0.043221
## [11] train-rmse:0.255761+0.012824 test-rmse:0.496664+0.044170
## [12] train-rmse:0.245650+0.009803 test-rmse:0.491999+0.043926
## [13] train-rmse:0.237632+0.008204 test-rmse:0.491088+0.042652
## [14] train-rmse:0.230179+0.009524 test-rmse:0.487915+0.040372
## [15] train-rmse:0.219153+0.012137 test-rmse:0.489344+0.041675
## [16] train-rmse:0.210623+0.011981 test-rmse:0.490778+0.041525
## [17] train-rmse:0.204151+0.012234 test-rmse:0.490939+0.040742
## [18] train-rmse:0.198981+0.012366 test-rmse:0.491248+0.040781
## [19] train-rmse:0.189926+0.013199 test-rmse:0.492638+0.038169
## [20] train-rmse:0.183026+0.012868 test-rmse:0.492851+0.039293
## Stopping. Best iteration:
## [14] train-rmse:0.230179+0.009524 test-rmse:0.487915+0.040372
##
## 77
## [1] train-rmse:0.842152+0.014565 test-rmse:0.846778+0.060229
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.752670+0.013708 test-rmse:0.770034+0.064207
## [3] train-rmse:0.698969+0.013111 test-rmse:0.723345+0.059289
## [4] train-rmse:0.663647+0.012005 test-rmse:0.684078+0.057812
## [5] train-rmse:0.640807+0.011974 test-rmse:0.668890+0.054358
## [6] train-rmse:0.625957+0.011595 test-rmse:0.654008+0.048660
## [7] train-rmse:0.614492+0.011346 test-rmse:0.643344+0.048010
## [8] train-rmse:0.605400+0.011254 test-rmse:0.638235+0.049995
## [9] train-rmse:0.596966+0.011163 test-rmse:0.633589+0.052769
## [10] train-rmse:0.589826+0.011162 test-rmse:0.628899+0.055414
## [11] train-rmse:0.583885+0.011008 test-rmse:0.623521+0.055665
## [12] train-rmse:0.578323+0.011043 test-rmse:0.617395+0.057526
## [13] train-rmse:0.573237+0.010917 test-rmse:0.613502+0.058043
## [14] train-rmse:0.568551+0.010968 test-rmse:0.607265+0.058386
## [15] train-rmse:0.564103+0.011099 test-rmse:0.603687+0.057531
## [16] train-rmse:0.560075+0.011110 test-rmse:0.598598+0.056055
## [17] train-rmse:0.556167+0.011089 test-rmse:0.593032+0.057063
## [18] train-rmse:0.552422+0.011102 test-rmse:0.592686+0.057371
## [19] train-rmse:0.548838+0.011017 test-rmse:0.588503+0.059422
## [20] train-rmse:0.545568+0.010929 test-rmse:0.588650+0.058816
## [21] train-rmse:0.542408+0.010808 test-rmse:0.588483+0.057185
## [22] train-rmse:0.539489+0.010910 test-rmse:0.584913+0.057806
## [23] train-rmse:0.536707+0.010974 test-rmse:0.582859+0.057995
## [24] train-rmse:0.534059+0.010949 test-rmse:0.580998+0.055476
## [25] train-rmse:0.531511+0.010957 test-rmse:0.577310+0.056311
## [26] train-rmse:0.528965+0.010972 test-rmse:0.573665+0.056839
## [27] train-rmse:0.526590+0.010965 test-rmse:0.575055+0.058172
## [28] train-rmse:0.524338+0.010934 test-rmse:0.574550+0.059134
## [29] train-rmse:0.522236+0.010883 test-rmse:0.571652+0.059175
## [30] train-rmse:0.520180+0.010738 test-rmse:0.570318+0.058017
## [31] train-rmse:0.518279+0.010712 test-rmse:0.570420+0.057104
## [32] train-rmse:0.516420+0.010712 test-rmse:0.569379+0.055598
## [33] train-rmse:0.514658+0.010709 test-rmse:0.568152+0.054690
## [34] train-rmse:0.512904+0.010658 test-rmse:0.565412+0.054329
## [35] train-rmse:0.511196+0.010598 test-rmse:0.564065+0.055716
## [36] train-rmse:0.509471+0.010605 test-rmse:0.562044+0.056071
## [37] train-rmse:0.507827+0.010600 test-rmse:0.561559+0.056489
## [38] train-rmse:0.506246+0.010561 test-rmse:0.558816+0.055849
## [39] train-rmse:0.504742+0.010540 test-rmse:0.557435+0.055250
## [40] train-rmse:0.503296+0.010453 test-rmse:0.555795+0.054715
## [41] train-rmse:0.501954+0.010420 test-rmse:0.555440+0.054725
## [42] train-rmse:0.500633+0.010366 test-rmse:0.554271+0.054674
## [43] train-rmse:0.499336+0.010384 test-rmse:0.553627+0.054050
## [44] train-rmse:0.498114+0.010379 test-rmse:0.553384+0.053322
## [45] train-rmse:0.496918+0.010414 test-rmse:0.553823+0.054707
## [46] train-rmse:0.495760+0.010454 test-rmse:0.553986+0.056041
## [47] train-rmse:0.494620+0.010482 test-rmse:0.552950+0.056202
## [48] train-rmse:0.493532+0.010519 test-rmse:0.552928+0.054505
## [49] train-rmse:0.492471+0.010511 test-rmse:0.552431+0.054830
## [50] train-rmse:0.491399+0.010566 test-rmse:0.552142+0.055183
## [51] train-rmse:0.490383+0.010592 test-rmse:0.550550+0.054678
## [52] train-rmse:0.489378+0.010587 test-rmse:0.549896+0.054403
## [53] train-rmse:0.488402+0.010576 test-rmse:0.548769+0.054133
## [54] train-rmse:0.487491+0.010557 test-rmse:0.547971+0.053778
## [55] train-rmse:0.486588+0.010538 test-rmse:0.547457+0.053378
## [56] train-rmse:0.485746+0.010513 test-rmse:0.546566+0.053009
## [57] train-rmse:0.484907+0.010494 test-rmse:0.546423+0.053262
## [58] train-rmse:0.484070+0.010473 test-rmse:0.545364+0.053963
## [59] train-rmse:0.483257+0.010464 test-rmse:0.545582+0.054016
## [60] train-rmse:0.482460+0.010419 test-rmse:0.544937+0.053948
## [61] train-rmse:0.481683+0.010390 test-rmse:0.544846+0.053032
## [62] train-rmse:0.480926+0.010389 test-rmse:0.544352+0.052423
## [63] train-rmse:0.480196+0.010387 test-rmse:0.543821+0.051876
## [64] train-rmse:0.479484+0.010393 test-rmse:0.542913+0.051923
## [65] train-rmse:0.478782+0.010406 test-rmse:0.542036+0.052002
## [66] train-rmse:0.478098+0.010417 test-rmse:0.541407+0.051761
## [67] train-rmse:0.477427+0.010442 test-rmse:0.540824+0.051455
## [68] train-rmse:0.476776+0.010465 test-rmse:0.540629+0.051540
## [69] train-rmse:0.476125+0.010486 test-rmse:0.540827+0.052525
## [70] train-rmse:0.475496+0.010484 test-rmse:0.539431+0.052118
## [71] train-rmse:0.474883+0.010480 test-rmse:0.540232+0.050530
## [72] train-rmse:0.474287+0.010486 test-rmse:0.540797+0.051544
## [73] train-rmse:0.473712+0.010480 test-rmse:0.541170+0.051284
## [74] train-rmse:0.473162+0.010467 test-rmse:0.541112+0.051655
## [75] train-rmse:0.472638+0.010459 test-rmse:0.540655+0.051513
## [76] train-rmse:0.472119+0.010452 test-rmse:0.540579+0.051774
## Stopping. Best iteration:
## [70] train-rmse:0.475496+0.010484 test-rmse:0.539431+0.052118
##
## 78
## [1] train-rmse:0.816342+0.014337 test-rmse:0.822889+0.060591
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.705705+0.013489 test-rmse:0.725489+0.066285
## [3] train-rmse:0.640308+0.012367 test-rmse:0.668346+0.061907
## [4] train-rmse:0.600021+0.012627 test-rmse:0.638002+0.061183
## [5] train-rmse:0.571549+0.013352 test-rmse:0.615545+0.064352
## [6] train-rmse:0.552567+0.011999 test-rmse:0.599766+0.067497
## [7] train-rmse:0.539373+0.013830 test-rmse:0.589231+0.058917
## [8] train-rmse:0.529673+0.013680 test-rmse:0.582397+0.059930
## [9] train-rmse:0.520040+0.014079 test-rmse:0.569559+0.061724
## [10] train-rmse:0.512133+0.014669 test-rmse:0.565595+0.057992
## [11] train-rmse:0.505406+0.014680 test-rmse:0.563224+0.058009
## [12] train-rmse:0.498501+0.015022 test-rmse:0.558542+0.063695
## [13] train-rmse:0.491852+0.015811 test-rmse:0.556786+0.061860
## [14] train-rmse:0.485459+0.016161 test-rmse:0.552128+0.063086
## [15] train-rmse:0.479384+0.015940 test-rmse:0.550365+0.064688
## [16] train-rmse:0.474317+0.015138 test-rmse:0.544786+0.064548
## [17] train-rmse:0.469411+0.014831 test-rmse:0.541186+0.063809
## [18] train-rmse:0.464469+0.014322 test-rmse:0.537179+0.062068
## [19] train-rmse:0.460595+0.014253 test-rmse:0.538331+0.064079
## [20] train-rmse:0.456800+0.014513 test-rmse:0.536514+0.065563
## [21] train-rmse:0.453320+0.015085 test-rmse:0.535766+0.065559
## [22] train-rmse:0.449618+0.015018 test-rmse:0.534548+0.063907
## [23] train-rmse:0.445665+0.015066 test-rmse:0.531377+0.065369
## [24] train-rmse:0.442253+0.015407 test-rmse:0.529888+0.065399
## [25] train-rmse:0.438595+0.015020 test-rmse:0.530396+0.065356
## [26] train-rmse:0.435600+0.014688 test-rmse:0.530445+0.063716
## [27] train-rmse:0.432335+0.015107 test-rmse:0.529452+0.064172
## [28] train-rmse:0.429544+0.014844 test-rmse:0.529420+0.065352
## [29] train-rmse:0.427017+0.014583 test-rmse:0.527856+0.065998
## [30] train-rmse:0.423948+0.013845 test-rmse:0.527021+0.067912
## [31] train-rmse:0.421386+0.013993 test-rmse:0.526566+0.066556
## [32] train-rmse:0.418674+0.014291 test-rmse:0.524972+0.065791
## [33] train-rmse:0.414886+0.014958 test-rmse:0.523282+0.065873
## [34] train-rmse:0.412521+0.014677 test-rmse:0.521215+0.066201
## [35] train-rmse:0.410480+0.015001 test-rmse:0.522660+0.066599
## [36] train-rmse:0.408319+0.014901 test-rmse:0.521249+0.066116
## [37] train-rmse:0.405165+0.013405 test-rmse:0.519971+0.066365
## [38] train-rmse:0.403099+0.013381 test-rmse:0.519255+0.064928
## [39] train-rmse:0.401043+0.012961 test-rmse:0.518518+0.065131
## [40] train-rmse:0.398931+0.013476 test-rmse:0.517330+0.065327
## [41] train-rmse:0.397379+0.013195 test-rmse:0.516883+0.065905
## [42] train-rmse:0.395153+0.013456 test-rmse:0.516307+0.065083
## [43] train-rmse:0.393446+0.013495 test-rmse:0.516529+0.065138
## [44] train-rmse:0.391023+0.013464 test-rmse:0.515406+0.064252
## [45] train-rmse:0.388899+0.012975 test-rmse:0.514907+0.064283
## [46] train-rmse:0.387328+0.012435 test-rmse:0.515649+0.063988
## [47] train-rmse:0.385524+0.011886 test-rmse:0.515516+0.063308
## [48] train-rmse:0.383579+0.012165 test-rmse:0.514224+0.062984
## [49] train-rmse:0.382339+0.011953 test-rmse:0.513525+0.063670
## [50] train-rmse:0.379649+0.011836 test-rmse:0.512621+0.063197
## [51] train-rmse:0.377953+0.011802 test-rmse:0.510603+0.063564
## [52] train-rmse:0.375732+0.011838 test-rmse:0.508919+0.063709
## [53] train-rmse:0.374395+0.011648 test-rmse:0.509810+0.063274
## [54] train-rmse:0.372382+0.011937 test-rmse:0.509211+0.062469
## [55] train-rmse:0.371064+0.011926 test-rmse:0.509557+0.062637
## [56] train-rmse:0.369535+0.012157 test-rmse:0.508652+0.062334
## [57] train-rmse:0.368486+0.011838 test-rmse:0.508201+0.062311
## [58] train-rmse:0.366760+0.012058 test-rmse:0.508257+0.061052
## [59] train-rmse:0.365860+0.011970 test-rmse:0.508330+0.061351
## [60] train-rmse:0.364485+0.011895 test-rmse:0.508073+0.061401
## [61] train-rmse:0.362921+0.012018 test-rmse:0.507839+0.059973
## [62] train-rmse:0.361469+0.011858 test-rmse:0.506896+0.059267
## [63] train-rmse:0.360151+0.011765 test-rmse:0.506734+0.058619
## [64] train-rmse:0.358678+0.012054 test-rmse:0.506177+0.058644
## [65] train-rmse:0.357431+0.011928 test-rmse:0.506281+0.058408
## [66] train-rmse:0.355866+0.012971 test-rmse:0.505690+0.057562
## [67] train-rmse:0.353942+0.011516 test-rmse:0.504903+0.057157
## [68] train-rmse:0.351506+0.012331 test-rmse:0.504227+0.055928
## [69] train-rmse:0.350279+0.012031 test-rmse:0.503650+0.056154
## [70] train-rmse:0.349381+0.011979 test-rmse:0.503332+0.055589
## [71] train-rmse:0.347091+0.011057 test-rmse:0.501455+0.054729
## [72] train-rmse:0.346154+0.011069 test-rmse:0.500877+0.053876
## [73] train-rmse:0.344622+0.010421 test-rmse:0.500843+0.054207
## [74] train-rmse:0.343120+0.010158 test-rmse:0.500230+0.053874
## [75] train-rmse:0.341173+0.010439 test-rmse:0.500045+0.052850
## [76] train-rmse:0.339880+0.011023 test-rmse:0.499268+0.052104
## [77] train-rmse:0.339140+0.011251 test-rmse:0.499626+0.052868
## [78] train-rmse:0.338272+0.010817 test-rmse:0.499815+0.053142
## [79] train-rmse:0.336731+0.011340 test-rmse:0.497866+0.053763
## [80] train-rmse:0.335064+0.011920 test-rmse:0.496850+0.053722
## [81] train-rmse:0.334250+0.011939 test-rmse:0.496514+0.053287
## [82] train-rmse:0.332438+0.010686 test-rmse:0.495616+0.054168
## [83] train-rmse:0.331222+0.010960 test-rmse:0.495360+0.054356
## [84] train-rmse:0.330567+0.010736 test-rmse:0.495122+0.054572
## [85] train-rmse:0.328603+0.009514 test-rmse:0.494882+0.054269
## [86] train-rmse:0.326705+0.010492 test-rmse:0.495299+0.054457
## [87] train-rmse:0.324925+0.010064 test-rmse:0.495977+0.052763
## [88] train-rmse:0.323151+0.008915 test-rmse:0.494220+0.053083
## [89] train-rmse:0.321862+0.009074 test-rmse:0.494790+0.053233
## [90] train-rmse:0.320424+0.009326 test-rmse:0.494299+0.053286
## [91] train-rmse:0.318925+0.009666 test-rmse:0.493321+0.052949
## [92] train-rmse:0.317820+0.010000 test-rmse:0.493643+0.052909
## [93] train-rmse:0.316928+0.010196 test-rmse:0.493050+0.052478
## [94] train-rmse:0.315402+0.010651 test-rmse:0.493306+0.054384
## [95] train-rmse:0.314357+0.010446 test-rmse:0.493572+0.053975
## [96] train-rmse:0.313461+0.010704 test-rmse:0.493423+0.053650
## [97] train-rmse:0.312318+0.010528 test-rmse:0.492790+0.054330
## [98] train-rmse:0.311398+0.011035 test-rmse:0.492420+0.054077
## [99] train-rmse:0.309552+0.012193 test-rmse:0.492444+0.054326
## [100] train-rmse:0.307955+0.012437 test-rmse:0.492522+0.054695
## [101] train-rmse:0.306781+0.013015 test-rmse:0.492374+0.055921
## [102] train-rmse:0.304871+0.013755 test-rmse:0.491702+0.054797
## [103] train-rmse:0.302942+0.014025 test-rmse:0.492701+0.053849
## [104] train-rmse:0.301679+0.014139 test-rmse:0.492239+0.054005
## [105] train-rmse:0.300249+0.014838 test-rmse:0.492573+0.054578
## [106] train-rmse:0.298938+0.014314 test-rmse:0.492621+0.055063
## [107] train-rmse:0.297489+0.013355 test-rmse:0.492499+0.055383
## [108] train-rmse:0.296333+0.012705 test-rmse:0.492299+0.055430
## Stopping. Best iteration:
## [102] train-rmse:0.304871+0.013755 test-rmse:0.491702+0.054797
##
## 79
## [1] train-rmse:0.804785+0.014847 test-rmse:0.816605+0.063189
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.681967+0.013819 test-rmse:0.707786+0.064209
## [3] train-rmse:0.606060+0.014675 test-rmse:0.643084+0.061256
## [4] train-rmse:0.556836+0.013956 test-rmse:0.605621+0.059956
## [5] train-rmse:0.525198+0.015209 test-rmse:0.584844+0.054795
## [6] train-rmse:0.502074+0.015500 test-rmse:0.572270+0.055336
## [7] train-rmse:0.487088+0.015869 test-rmse:0.564769+0.052286
## [8] train-rmse:0.474083+0.018067 test-rmse:0.557464+0.049532
## [9] train-rmse:0.463816+0.019679 test-rmse:0.553961+0.049032
## [10] train-rmse:0.452692+0.020568 test-rmse:0.550552+0.048554
## [11] train-rmse:0.444045+0.020695 test-rmse:0.544795+0.047316
## [12] train-rmse:0.434418+0.020740 test-rmse:0.541055+0.048917
## [13] train-rmse:0.425594+0.020055 test-rmse:0.540486+0.048845
## [14] train-rmse:0.418863+0.019174 test-rmse:0.533680+0.052477
## [15] train-rmse:0.412115+0.020977 test-rmse:0.530318+0.050844
## [16] train-rmse:0.405235+0.020584 test-rmse:0.527452+0.049182
## [17] train-rmse:0.400359+0.019793 test-rmse:0.525498+0.048459
## [18] train-rmse:0.394177+0.018894 test-rmse:0.524155+0.048416
## [19] train-rmse:0.387717+0.020822 test-rmse:0.523602+0.048660
## [20] train-rmse:0.382790+0.019601 test-rmse:0.521603+0.048663
## [21] train-rmse:0.377362+0.019098 test-rmse:0.521374+0.050042
## [22] train-rmse:0.373250+0.018228 test-rmse:0.520868+0.048932
## [23] train-rmse:0.369090+0.017569 test-rmse:0.517901+0.049094
## [24] train-rmse:0.364708+0.018004 test-rmse:0.514949+0.049006
## [25] train-rmse:0.360434+0.018267 test-rmse:0.514296+0.049748
## [26] train-rmse:0.356574+0.018499 test-rmse:0.512934+0.050911
## [27] train-rmse:0.349695+0.019409 test-rmse:0.513456+0.050469
## [28] train-rmse:0.346771+0.019076 test-rmse:0.512344+0.050469
## [29] train-rmse:0.342953+0.018129 test-rmse:0.512922+0.050668
## [30] train-rmse:0.339086+0.018745 test-rmse:0.512141+0.049779
## [31] train-rmse:0.336142+0.018025 test-rmse:0.512019+0.049493
## [32] train-rmse:0.332496+0.018515 test-rmse:0.512356+0.049274
## [33] train-rmse:0.329116+0.017342 test-rmse:0.511782+0.049018
## [34] train-rmse:0.326075+0.017098 test-rmse:0.510815+0.050092
## [35] train-rmse:0.323426+0.017520 test-rmse:0.510750+0.050036
## [36] train-rmse:0.320222+0.017531 test-rmse:0.510408+0.050927
## [37] train-rmse:0.317887+0.017960 test-rmse:0.510474+0.050543
## [38] train-rmse:0.314564+0.016020 test-rmse:0.510254+0.050141
## [39] train-rmse:0.312328+0.016463 test-rmse:0.512201+0.049325
## [40] train-rmse:0.308865+0.016824 test-rmse:0.511337+0.049970
## [41] train-rmse:0.305306+0.015948 test-rmse:0.513885+0.048562
## [42] train-rmse:0.302213+0.016044 test-rmse:0.515241+0.047506
## [43] train-rmse:0.298843+0.016426 test-rmse:0.514762+0.045867
## [44] train-rmse:0.294187+0.018715 test-rmse:0.513389+0.044603
## Stopping. Best iteration:
## [38] train-rmse:0.314564+0.016020 test-rmse:0.510254+0.050141
##
## 80
## [1] train-rmse:0.794816+0.014520 test-rmse:0.810678+0.061101
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.663265+0.013405 test-rmse:0.695174+0.064685
## [3] train-rmse:0.578163+0.012281 test-rmse:0.626935+0.059956
## [4] train-rmse:0.522927+0.012518 test-rmse:0.587295+0.060098
## [5] train-rmse:0.482878+0.016858 test-rmse:0.564566+0.060490
## [6] train-rmse:0.459254+0.015814 test-rmse:0.555320+0.056935
## [7] train-rmse:0.439319+0.016931 test-rmse:0.540754+0.060203
## [8] train-rmse:0.420828+0.016119 test-rmse:0.533265+0.057472
## [9] train-rmse:0.409978+0.016609 test-rmse:0.524811+0.056996
## [10] train-rmse:0.399561+0.015552 test-rmse:0.517424+0.061138
## [11] train-rmse:0.389189+0.015132 test-rmse:0.513732+0.063068
## [12] train-rmse:0.378766+0.014075 test-rmse:0.509767+0.062889
## [13] train-rmse:0.369570+0.014144 test-rmse:0.503547+0.060344
## [14] train-rmse:0.363353+0.015054 test-rmse:0.500930+0.059672
## [15] train-rmse:0.356395+0.013330 test-rmse:0.500654+0.060208
## [16] train-rmse:0.349378+0.011681 test-rmse:0.497828+0.062087
## [17] train-rmse:0.343460+0.010594 test-rmse:0.498187+0.060485
## [18] train-rmse:0.336997+0.012342 test-rmse:0.497025+0.058716
## [19] train-rmse:0.331744+0.011657 test-rmse:0.496056+0.059926
## [20] train-rmse:0.327172+0.010967 test-rmse:0.496044+0.061539
## [21] train-rmse:0.321435+0.010945 test-rmse:0.494029+0.059197
## [22] train-rmse:0.314657+0.011318 test-rmse:0.493738+0.056877
## [23] train-rmse:0.308315+0.009997 test-rmse:0.494342+0.054525
## [24] train-rmse:0.300457+0.012241 test-rmse:0.494972+0.053833
## [25] train-rmse:0.295277+0.011822 test-rmse:0.493631+0.052931
## [26] train-rmse:0.290874+0.012781 test-rmse:0.493862+0.053859
## [27] train-rmse:0.287090+0.013061 test-rmse:0.493703+0.054817
## [28] train-rmse:0.280265+0.011167 test-rmse:0.492262+0.055767
## [29] train-rmse:0.276147+0.010788 test-rmse:0.491959+0.055583
## [30] train-rmse:0.271446+0.009679 test-rmse:0.491968+0.054190
## [31] train-rmse:0.267982+0.008791 test-rmse:0.493657+0.053494
## [32] train-rmse:0.261111+0.008905 test-rmse:0.493207+0.053230
## [33] train-rmse:0.258050+0.008310 test-rmse:0.492693+0.053980
## [34] train-rmse:0.252462+0.009340 test-rmse:0.491841+0.052130
## [35] train-rmse:0.249189+0.009484 test-rmse:0.492965+0.050996
## [36] train-rmse:0.245810+0.008803 test-rmse:0.492506+0.051171
## [37] train-rmse:0.242584+0.008578 test-rmse:0.490920+0.049732
## [38] train-rmse:0.237393+0.008519 test-rmse:0.492348+0.048844
## [39] train-rmse:0.232238+0.008042 test-rmse:0.493576+0.047442
## [40] train-rmse:0.228168+0.008961 test-rmse:0.495313+0.048409
## [41] train-rmse:0.225098+0.008737 test-rmse:0.495855+0.047588
## [42] train-rmse:0.218122+0.009087 test-rmse:0.497172+0.046710
## [43] train-rmse:0.213899+0.008538 test-rmse:0.496298+0.045244
## Stopping. Best iteration:
## [37] train-rmse:0.242584+0.008578 test-rmse:0.490920+0.049732
##
## 81
## [1] train-rmse:0.785896+0.013743 test-rmse:0.805816+0.061928
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.644559+0.010752 test-rmse:0.687860+0.063327
## [3] train-rmse:0.547974+0.011866 test-rmse:0.611168+0.057270
## [4] train-rmse:0.487199+0.014256 test-rmse:0.574677+0.055612
## [5] train-rmse:0.445943+0.013801 test-rmse:0.553267+0.059191
## [6] train-rmse:0.415932+0.012495 test-rmse:0.542386+0.056677
## [7] train-rmse:0.393890+0.012015 test-rmse:0.533558+0.059309
## [8] train-rmse:0.377937+0.011544 test-rmse:0.526345+0.058395
## [9] train-rmse:0.362547+0.019199 test-rmse:0.523627+0.058779
## [10] train-rmse:0.347199+0.015922 test-rmse:0.516632+0.056161
## [11] train-rmse:0.333567+0.017017 test-rmse:0.511229+0.058098
## [12] train-rmse:0.321546+0.014967 test-rmse:0.504854+0.060256
## [13] train-rmse:0.314101+0.014654 test-rmse:0.502533+0.059570
## [14] train-rmse:0.305901+0.015255 test-rmse:0.500047+0.058455
## [15] train-rmse:0.297679+0.015532 test-rmse:0.497463+0.057282
## [16] train-rmse:0.290764+0.014800 test-rmse:0.495281+0.059534
## [17] train-rmse:0.283085+0.012372 test-rmse:0.494066+0.059676
## [18] train-rmse:0.276920+0.010799 test-rmse:0.493666+0.058364
## [19] train-rmse:0.269472+0.014974 test-rmse:0.489606+0.057225
## [20] train-rmse:0.260812+0.015052 test-rmse:0.491199+0.058238
## [21] train-rmse:0.252279+0.014267 test-rmse:0.493531+0.058306
## [22] train-rmse:0.246287+0.016061 test-rmse:0.495252+0.059064
## [23] train-rmse:0.238177+0.018294 test-rmse:0.495943+0.060204
## [24] train-rmse:0.233302+0.017349 test-rmse:0.495957+0.059919
## [25] train-rmse:0.229563+0.017766 test-rmse:0.496662+0.059077
## Stopping. Best iteration:
## [19] train-rmse:0.269472+0.014974 test-rmse:0.489606+0.057225
##
## 82
## [1] train-rmse:0.777663+0.012957 test-rmse:0.802789+0.062044
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.626199+0.009776 test-rmse:0.678669+0.063281
## [3] train-rmse:0.524353+0.012553 test-rmse:0.603113+0.055227
## [4] train-rmse:0.459456+0.012103 test-rmse:0.555831+0.056411
## [5] train-rmse:0.413099+0.012129 test-rmse:0.527943+0.059266
## [6] train-rmse:0.379674+0.011110 test-rmse:0.520106+0.057586
## [7] train-rmse:0.356388+0.010905 test-rmse:0.514007+0.053920
## [8] train-rmse:0.337822+0.012405 test-rmse:0.509272+0.052734
## [9] train-rmse:0.321287+0.015286 test-rmse:0.500979+0.052073
## [10] train-rmse:0.305052+0.013876 test-rmse:0.499657+0.055459
## [11] train-rmse:0.294745+0.014389 test-rmse:0.499280+0.053675
## [12] train-rmse:0.284406+0.013847 test-rmse:0.497210+0.051536
## [13] train-rmse:0.274852+0.010191 test-rmse:0.497906+0.048846
## [14] train-rmse:0.265464+0.012931 test-rmse:0.496583+0.050210
## [15] train-rmse:0.257189+0.012314 test-rmse:0.493928+0.052385
## [16] train-rmse:0.248105+0.013840 test-rmse:0.492574+0.053513
## [17] train-rmse:0.239824+0.014207 test-rmse:0.494266+0.054124
## [18] train-rmse:0.234001+0.013744 test-rmse:0.495768+0.053052
## [19] train-rmse:0.228460+0.011570 test-rmse:0.496471+0.053734
## [20] train-rmse:0.220011+0.011843 test-rmse:0.495605+0.054933
## [21] train-rmse:0.212863+0.011040 test-rmse:0.496085+0.056308
## [22] train-rmse:0.204745+0.013738 test-rmse:0.497693+0.057384
## Stopping. Best iteration:
## [16] train-rmse:0.248105+0.013840 test-rmse:0.492574+0.053513
##
## 83
## [1] train-rmse:0.769520+0.012276 test-rmse:0.797942+0.059197
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.611052+0.009374 test-rmse:0.675532+0.061973
## [3] train-rmse:0.509405+0.011474 test-rmse:0.605700+0.061039
## [4] train-rmse:0.436092+0.013114 test-rmse:0.562092+0.061387
## [5] train-rmse:0.383215+0.010303 test-rmse:0.541091+0.060386
## [6] train-rmse:0.344379+0.013174 test-rmse:0.531217+0.059534
## [7] train-rmse:0.316437+0.013625 test-rmse:0.522120+0.061012
## [8] train-rmse:0.299313+0.014240 test-rmse:0.516319+0.061156
## [9] train-rmse:0.281383+0.011793 test-rmse:0.509913+0.057876
## [10] train-rmse:0.260903+0.012509 test-rmse:0.505321+0.053455
## [11] train-rmse:0.249857+0.013742 test-rmse:0.507899+0.052203
## [12] train-rmse:0.235672+0.012085 test-rmse:0.508384+0.051017
## [13] train-rmse:0.227180+0.013458 test-rmse:0.507849+0.051838
## [14] train-rmse:0.216353+0.013083 test-rmse:0.503775+0.049207
## [15] train-rmse:0.209526+0.013095 test-rmse:0.503969+0.048872
## [16] train-rmse:0.198476+0.012704 test-rmse:0.505658+0.050419
## [17] train-rmse:0.190518+0.013509 test-rmse:0.507136+0.050119
## [18] train-rmse:0.182997+0.013863 test-rmse:0.506989+0.052020
## [19] train-rmse:0.176640+0.012785 test-rmse:0.505264+0.051773
## [20] train-rmse:0.169703+0.011407 test-rmse:0.505407+0.050988
## Stopping. Best iteration:
## [14] train-rmse:0.216353+0.013083 test-rmse:0.503775+0.049207
##
## 84
## [1] train-rmse:0.833191+0.014487 test-rmse:0.838295+0.060151
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.743021+0.013835 test-rmse:0.762316+0.063636
## [3] train-rmse:0.690266+0.012789 test-rmse:0.712962+0.060743
## [4] train-rmse:0.655852+0.011737 test-rmse:0.674196+0.058553
## [5] train-rmse:0.634118+0.011827 test-rmse:0.660228+0.054809
## [6] train-rmse:0.620130+0.011600 test-rmse:0.646669+0.053096
## [7] train-rmse:0.609825+0.011391 test-rmse:0.634423+0.051284
## [8] train-rmse:0.601027+0.011318 test-rmse:0.629515+0.054998
## [9] train-rmse:0.593203+0.011300 test-rmse:0.621401+0.056270
## [10] train-rmse:0.585934+0.011414 test-rmse:0.620964+0.053661
## [11] train-rmse:0.580143+0.011568 test-rmse:0.615548+0.053856
## [12] train-rmse:0.574709+0.011622 test-rmse:0.611589+0.053470
## [13] train-rmse:0.569710+0.011391 test-rmse:0.605130+0.053216
## [14] train-rmse:0.565051+0.011234 test-rmse:0.599111+0.053189
## [15] train-rmse:0.560702+0.011179 test-rmse:0.594071+0.055264
## [16] train-rmse:0.556435+0.011067 test-rmse:0.591368+0.054854
## [17] train-rmse:0.552281+0.011160 test-rmse:0.587494+0.056574
## [18] train-rmse:0.548532+0.010969 test-rmse:0.585754+0.057205
## [19] train-rmse:0.544921+0.010860 test-rmse:0.582610+0.057454
## [20] train-rmse:0.541611+0.010651 test-rmse:0.581899+0.056110
## [21] train-rmse:0.538487+0.010536 test-rmse:0.582391+0.055206
## [22] train-rmse:0.535604+0.010597 test-rmse:0.581911+0.054686
## [23] train-rmse:0.532801+0.010727 test-rmse:0.578161+0.054381
## [24] train-rmse:0.530144+0.010808 test-rmse:0.576425+0.054412
## [25] train-rmse:0.527631+0.010751 test-rmse:0.572298+0.054687
## [26] train-rmse:0.525216+0.010689 test-rmse:0.571675+0.057433
## [27] train-rmse:0.522925+0.010630 test-rmse:0.570365+0.056447
## [28] train-rmse:0.520732+0.010544 test-rmse:0.567854+0.055494
## [29] train-rmse:0.518618+0.010515 test-rmse:0.567500+0.056428
## [30] train-rmse:0.516543+0.010455 test-rmse:0.564643+0.056071
## [31] train-rmse:0.514651+0.010404 test-rmse:0.564031+0.055021
## [32] train-rmse:0.512784+0.010351 test-rmse:0.562693+0.055905
## [33] train-rmse:0.510907+0.010301 test-rmse:0.560766+0.054710
## [34] train-rmse:0.509161+0.010257 test-rmse:0.557666+0.054373
## [35] train-rmse:0.507419+0.010234 test-rmse:0.555345+0.052599
## [36] train-rmse:0.505747+0.010267 test-rmse:0.553485+0.053608
## [37] train-rmse:0.504171+0.010208 test-rmse:0.553347+0.054394
## [38] train-rmse:0.502636+0.010174 test-rmse:0.553125+0.055127
## [39] train-rmse:0.501197+0.010140 test-rmse:0.552092+0.055041
## [40] train-rmse:0.499782+0.010094 test-rmse:0.551615+0.054074
## [41] train-rmse:0.498419+0.010025 test-rmse:0.549861+0.053995
## [42] train-rmse:0.497173+0.010006 test-rmse:0.548531+0.053372
## [43] train-rmse:0.495953+0.009995 test-rmse:0.548916+0.052988
## [44] train-rmse:0.494778+0.009990 test-rmse:0.547641+0.053233
## [45] train-rmse:0.493642+0.010007 test-rmse:0.547548+0.053606
## [46] train-rmse:0.492505+0.010034 test-rmse:0.547736+0.054860
## [47] train-rmse:0.491364+0.010003 test-rmse:0.547630+0.052757
## [48] train-rmse:0.490275+0.010026 test-rmse:0.546843+0.052255
## [49] train-rmse:0.489203+0.010046 test-rmse:0.545547+0.052756
## [50] train-rmse:0.488173+0.010054 test-rmse:0.545187+0.052202
## [51] train-rmse:0.487176+0.010075 test-rmse:0.544847+0.052218
## [52] train-rmse:0.486200+0.010103 test-rmse:0.543935+0.051683
## [53] train-rmse:0.485273+0.010096 test-rmse:0.543563+0.052146
## [54] train-rmse:0.484391+0.010065 test-rmse:0.542906+0.051509
## [55] train-rmse:0.483553+0.010037 test-rmse:0.541566+0.051035
## [56] train-rmse:0.482714+0.009976 test-rmse:0.541591+0.051051
## [57] train-rmse:0.481910+0.009928 test-rmse:0.540960+0.051049
## [58] train-rmse:0.481142+0.009915 test-rmse:0.540550+0.051309
## [59] train-rmse:0.480369+0.009910 test-rmse:0.539589+0.051361
## [60] train-rmse:0.479634+0.009914 test-rmse:0.539027+0.050814
## [61] train-rmse:0.478914+0.009907 test-rmse:0.538926+0.050963
## [62] train-rmse:0.478197+0.009909 test-rmse:0.538160+0.051027
## [63] train-rmse:0.477480+0.009911 test-rmse:0.537404+0.050463
## [64] train-rmse:0.476793+0.009920 test-rmse:0.535763+0.050058
## [65] train-rmse:0.476117+0.009953 test-rmse:0.536152+0.049691
## [66] train-rmse:0.475483+0.009971 test-rmse:0.536288+0.049280
## [67] train-rmse:0.474854+0.009977 test-rmse:0.535663+0.049283
## [68] train-rmse:0.474241+0.009990 test-rmse:0.534464+0.048898
## [69] train-rmse:0.473625+0.010003 test-rmse:0.534901+0.049714
## [70] train-rmse:0.473024+0.010019 test-rmse:0.535592+0.049314
## [71] train-rmse:0.472455+0.010010 test-rmse:0.535576+0.049711
## [72] train-rmse:0.471902+0.009982 test-rmse:0.535845+0.049864
## [73] train-rmse:0.471374+0.009983 test-rmse:0.536442+0.049648
## [74] train-rmse:0.470872+0.009975 test-rmse:0.536656+0.050256
## Stopping. Best iteration:
## [68] train-rmse:0.474241+0.009990 test-rmse:0.534464+0.048898
##
## 85
## [1] train-rmse:0.805688+0.014240 test-rmse:0.812823+0.060611
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.693661+0.013420 test-rmse:0.716673+0.064414
## [3] train-rmse:0.630268+0.012126 test-rmse:0.659808+0.063470
## [4] train-rmse:0.591624+0.012570 test-rmse:0.633679+0.065150
## [5] train-rmse:0.565989+0.016134 test-rmse:0.615566+0.064777
## [6] train-rmse:0.547023+0.015171 test-rmse:0.596593+0.063958
## [7] train-rmse:0.534407+0.015362 test-rmse:0.589488+0.061383
## [8] train-rmse:0.523967+0.015402 test-rmse:0.580703+0.059996
## [9] train-rmse:0.515704+0.015420 test-rmse:0.570292+0.064578
## [10] train-rmse:0.507779+0.015173 test-rmse:0.566286+0.062953
## [11] train-rmse:0.498474+0.012775 test-rmse:0.558495+0.063807
## [12] train-rmse:0.491845+0.013418 test-rmse:0.559401+0.066653
## [13] train-rmse:0.485290+0.013345 test-rmse:0.557577+0.064037
## [14] train-rmse:0.479040+0.013171 test-rmse:0.549401+0.062890
## [15] train-rmse:0.473738+0.012963 test-rmse:0.544941+0.064168
## [16] train-rmse:0.468826+0.013908 test-rmse:0.543962+0.063371
## [17] train-rmse:0.462842+0.013156 test-rmse:0.543554+0.062556
## [18] train-rmse:0.458218+0.012918 test-rmse:0.538105+0.064954
## [19] train-rmse:0.454294+0.012194 test-rmse:0.537348+0.065143
## [20] train-rmse:0.450284+0.012599 test-rmse:0.533287+0.066861
## [21] train-rmse:0.446742+0.013285 test-rmse:0.529976+0.064251
## [22] train-rmse:0.442408+0.012223 test-rmse:0.527260+0.063231
## [23] train-rmse:0.438758+0.012226 test-rmse:0.525730+0.065915
## [24] train-rmse:0.435446+0.013059 test-rmse:0.524912+0.066145
## [25] train-rmse:0.431269+0.013075 test-rmse:0.524089+0.062973
## [26] train-rmse:0.428614+0.013172 test-rmse:0.523826+0.062698
## [27] train-rmse:0.425184+0.013454 test-rmse:0.522812+0.061439
## [28] train-rmse:0.422402+0.013971 test-rmse:0.522906+0.060216
## [29] train-rmse:0.419104+0.013979 test-rmse:0.521535+0.060779
## [30] train-rmse:0.416121+0.013909 test-rmse:0.518915+0.061364
## [31] train-rmse:0.411841+0.014439 test-rmse:0.515408+0.060984
## [32] train-rmse:0.409389+0.014528 test-rmse:0.513668+0.060176
## [33] train-rmse:0.407564+0.014208 test-rmse:0.513056+0.059317
## [34] train-rmse:0.405089+0.014584 test-rmse:0.510230+0.059714
## [35] train-rmse:0.403033+0.014587 test-rmse:0.509930+0.059102
## [36] train-rmse:0.400878+0.014501 test-rmse:0.509412+0.059061
## [37] train-rmse:0.398894+0.014711 test-rmse:0.508696+0.059411
## [38] train-rmse:0.396930+0.014159 test-rmse:0.508221+0.061246
## [39] train-rmse:0.394722+0.013690 test-rmse:0.507410+0.061637
## [40] train-rmse:0.392058+0.013397 test-rmse:0.507018+0.060713
## [41] train-rmse:0.390487+0.013484 test-rmse:0.507393+0.059578
## [42] train-rmse:0.388464+0.013547 test-rmse:0.506130+0.059091
## [43] train-rmse:0.386842+0.013817 test-rmse:0.505619+0.058681
## [44] train-rmse:0.384832+0.013736 test-rmse:0.504891+0.059609
## [45] train-rmse:0.383433+0.013800 test-rmse:0.505160+0.059574
## [46] train-rmse:0.381250+0.012890 test-rmse:0.505701+0.056988
## [47] train-rmse:0.379440+0.013045 test-rmse:0.505121+0.055750
## [48] train-rmse:0.377130+0.012739 test-rmse:0.502463+0.055140
## [49] train-rmse:0.375545+0.013017 test-rmse:0.502613+0.056574
## [50] train-rmse:0.373623+0.012542 test-rmse:0.502725+0.056939
## [51] train-rmse:0.371750+0.013462 test-rmse:0.502650+0.057517
## [52] train-rmse:0.368975+0.014133 test-rmse:0.501649+0.058287
## [53] train-rmse:0.366685+0.014675 test-rmse:0.501555+0.058596
## [54] train-rmse:0.364589+0.013774 test-rmse:0.501550+0.057796
## [55] train-rmse:0.362128+0.012872 test-rmse:0.499451+0.057621
## [56] train-rmse:0.360174+0.012911 test-rmse:0.498936+0.057133
## [57] train-rmse:0.357714+0.013240 test-rmse:0.498701+0.056077
## [58] train-rmse:0.356449+0.012943 test-rmse:0.499026+0.057706
## [59] train-rmse:0.355444+0.012987 test-rmse:0.498130+0.057114
## [60] train-rmse:0.352724+0.013963 test-rmse:0.497147+0.055436
## [61] train-rmse:0.350684+0.013619 test-rmse:0.495299+0.055578
## [62] train-rmse:0.347898+0.013606 test-rmse:0.496395+0.054544
## [63] train-rmse:0.345935+0.012238 test-rmse:0.496784+0.053721
## [64] train-rmse:0.344697+0.012260 test-rmse:0.496157+0.054474
## [65] train-rmse:0.342699+0.011324 test-rmse:0.496057+0.055046
## [66] train-rmse:0.340411+0.011257 test-rmse:0.495981+0.055263
## [67] train-rmse:0.338119+0.010117 test-rmse:0.495840+0.054317
## Stopping. Best iteration:
## [61] train-rmse:0.350684+0.013619 test-rmse:0.495299+0.055578
##
## 86
## [1] train-rmse:0.793366+0.014790 test-rmse:0.806205+0.063424
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.668474+0.013762 test-rmse:0.696139+0.064678
## [3] train-rmse:0.593694+0.014804 test-rmse:0.633295+0.061541
## [4] train-rmse:0.544940+0.014228 test-rmse:0.594063+0.054827
## [5] train-rmse:0.512878+0.019287 test-rmse:0.581318+0.048615
## [6] train-rmse:0.491308+0.017334 test-rmse:0.569132+0.046276
## [7] train-rmse:0.477106+0.016910 test-rmse:0.559198+0.043224
## [8] train-rmse:0.461767+0.013811 test-rmse:0.550355+0.045743
## [9] train-rmse:0.451143+0.014315 test-rmse:0.549198+0.049773
## [10] train-rmse:0.443220+0.013917 test-rmse:0.540937+0.050630
## [11] train-rmse:0.433151+0.012992 test-rmse:0.533875+0.048442
## [12] train-rmse:0.426031+0.013677 test-rmse:0.531151+0.050472
## [13] train-rmse:0.418303+0.014640 test-rmse:0.527937+0.049488
## [14] train-rmse:0.412792+0.015143 test-rmse:0.524813+0.049228
## [15] train-rmse:0.406577+0.015791 test-rmse:0.522178+0.048002
## [16] train-rmse:0.400261+0.015220 test-rmse:0.520569+0.047169
## [17] train-rmse:0.393040+0.014556 test-rmse:0.519721+0.045914
## [18] train-rmse:0.387228+0.014782 test-rmse:0.518016+0.048641
## [19] train-rmse:0.380988+0.014042 test-rmse:0.513196+0.049878
## [20] train-rmse:0.376940+0.014818 test-rmse:0.513541+0.049577
## [21] train-rmse:0.373203+0.015278 test-rmse:0.511536+0.051679
## [22] train-rmse:0.369248+0.014744 test-rmse:0.510244+0.051320
## [23] train-rmse:0.364457+0.015584 test-rmse:0.508228+0.050456
## [24] train-rmse:0.360717+0.015603 test-rmse:0.507385+0.050976
## [25] train-rmse:0.356281+0.017122 test-rmse:0.507062+0.050274
## [26] train-rmse:0.352475+0.016536 test-rmse:0.508473+0.049319
## [27] train-rmse:0.348209+0.017075 test-rmse:0.505796+0.048298
## [28] train-rmse:0.343765+0.017869 test-rmse:0.505633+0.048673
## [29] train-rmse:0.339563+0.017279 test-rmse:0.503623+0.051147
## [30] train-rmse:0.336042+0.016703 test-rmse:0.503147+0.050928
## [31] train-rmse:0.332266+0.017562 test-rmse:0.501340+0.049439
## [32] train-rmse:0.330015+0.017899 test-rmse:0.501683+0.047907
## [33] train-rmse:0.327224+0.017119 test-rmse:0.500365+0.048546
## [34] train-rmse:0.324112+0.017241 test-rmse:0.500715+0.047964
## [35] train-rmse:0.320027+0.015888 test-rmse:0.501093+0.047558
## [36] train-rmse:0.317333+0.016067 test-rmse:0.502853+0.046125
## [37] train-rmse:0.312848+0.013972 test-rmse:0.501157+0.046378
## [38] train-rmse:0.309449+0.013104 test-rmse:0.500697+0.047674
## [39] train-rmse:0.305249+0.013645 test-rmse:0.502110+0.046549
## Stopping. Best iteration:
## [33] train-rmse:0.327224+0.017119 test-rmse:0.500365+0.048546
##
## 87
## [1] train-rmse:0.782724+0.014445 test-rmse:0.799890+0.061211
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.648730+0.013412 test-rmse:0.681875+0.063862
## [3] train-rmse:0.565034+0.012554 test-rmse:0.619460+0.055431
## [4] train-rmse:0.510178+0.012701 test-rmse:0.580889+0.053872
## [5] train-rmse:0.472679+0.016417 test-rmse:0.559354+0.052042
## [6] train-rmse:0.450041+0.015446 test-rmse:0.546497+0.051574
## [7] train-rmse:0.432654+0.017129 test-rmse:0.541530+0.053888
## [8] train-rmse:0.419276+0.015225 test-rmse:0.535959+0.049155
## [9] train-rmse:0.402812+0.013351 test-rmse:0.525426+0.051808
## [10] train-rmse:0.393440+0.015007 test-rmse:0.521956+0.051315
## [11] train-rmse:0.382845+0.013906 test-rmse:0.518546+0.055260
## [12] train-rmse:0.371507+0.016547 test-rmse:0.516949+0.054825
## [13] train-rmse:0.362613+0.015230 test-rmse:0.512721+0.056463
## [14] train-rmse:0.355643+0.014225 test-rmse:0.512138+0.057051
## [15] train-rmse:0.347539+0.015166 test-rmse:0.509206+0.056999
## [16] train-rmse:0.340660+0.015470 test-rmse:0.506043+0.054154
## [17] train-rmse:0.333445+0.015367 test-rmse:0.504207+0.055987
## [18] train-rmse:0.327958+0.015841 test-rmse:0.502121+0.055875
## [19] train-rmse:0.322767+0.015699 test-rmse:0.504128+0.056023
## [20] train-rmse:0.316492+0.016059 test-rmse:0.504238+0.056594
## [21] train-rmse:0.310742+0.017401 test-rmse:0.503832+0.057434
## [22] train-rmse:0.302836+0.016939 test-rmse:0.502401+0.055956
## [23] train-rmse:0.297248+0.014968 test-rmse:0.498761+0.054703
## [24] train-rmse:0.293091+0.015772 test-rmse:0.501063+0.054955
## [25] train-rmse:0.288215+0.017535 test-rmse:0.500638+0.055745
## [26] train-rmse:0.282768+0.015636 test-rmse:0.499612+0.055088
## [27] train-rmse:0.279103+0.015129 test-rmse:0.500608+0.056913
## [28] train-rmse:0.272405+0.015658 test-rmse:0.500443+0.057390
## [29] train-rmse:0.268313+0.014831 test-rmse:0.500446+0.057129
## Stopping. Best iteration:
## [23] train-rmse:0.297248+0.014968 test-rmse:0.498761+0.054703
##
## 88
## [1] train-rmse:0.773192+0.013625 test-rmse:0.794753+0.062020
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.628594+0.010415 test-rmse:0.674695+0.062670
## [3] train-rmse:0.534982+0.013324 test-rmse:0.598702+0.058952
## [4] train-rmse:0.474872+0.015709 test-rmse:0.565293+0.058050
## [5] train-rmse:0.434435+0.014421 test-rmse:0.540730+0.056301
## [6] train-rmse:0.408939+0.013129 test-rmse:0.526852+0.052749
## [7] train-rmse:0.389454+0.014575 test-rmse:0.520004+0.053227
## [8] train-rmse:0.369767+0.021199 test-rmse:0.517113+0.061090
## [9] train-rmse:0.357722+0.020455 test-rmse:0.512357+0.060638
## [10] train-rmse:0.346220+0.019033 test-rmse:0.510118+0.058777
## [11] train-rmse:0.336395+0.017625 test-rmse:0.512375+0.057794
## [12] train-rmse:0.328762+0.016194 test-rmse:0.508698+0.062346
## [13] train-rmse:0.318022+0.017233 test-rmse:0.505554+0.061123
## [14] train-rmse:0.308667+0.015280 test-rmse:0.507249+0.060283
## [15] train-rmse:0.299750+0.016030 test-rmse:0.509026+0.061685
## [16] train-rmse:0.292956+0.014327 test-rmse:0.507313+0.061023
## [17] train-rmse:0.287149+0.013469 test-rmse:0.506640+0.059907
## [18] train-rmse:0.279683+0.012148 test-rmse:0.506666+0.061865
## [19] train-rmse:0.273351+0.011160 test-rmse:0.505630+0.062402
## Stopping. Best iteration:
## [13] train-rmse:0.318022+0.017233 test-rmse:0.505554+0.061123
##
## 89
## [1] train-rmse:0.764378+0.012794 test-rmse:0.791591+0.062106
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.609210+0.009910 test-rmse:0.665074+0.063365
## [3] train-rmse:0.508265+0.012537 test-rmse:0.594777+0.060348
## [4] train-rmse:0.445781+0.013173 test-rmse:0.557763+0.059349
## [5] train-rmse:0.400942+0.015074 test-rmse:0.536072+0.059675
## [6] train-rmse:0.370979+0.013408 test-rmse:0.521277+0.061840
## [7] train-rmse:0.349932+0.013727 test-rmse:0.519454+0.064081
## [8] train-rmse:0.328410+0.010351 test-rmse:0.511186+0.064085
## [9] train-rmse:0.313581+0.010390 test-rmse:0.501441+0.063099
## [10] train-rmse:0.299729+0.009643 test-rmse:0.498345+0.060569
## [11] train-rmse:0.286114+0.012597 test-rmse:0.496013+0.062673
## [12] train-rmse:0.276430+0.010356 test-rmse:0.493022+0.064349
## [13] train-rmse:0.264269+0.012787 test-rmse:0.494618+0.066462
## [14] train-rmse:0.255584+0.015560 test-rmse:0.492205+0.064215
## [15] train-rmse:0.246693+0.015353 test-rmse:0.492314+0.065256
## [16] train-rmse:0.237770+0.014633 test-rmse:0.492821+0.067661
## [17] train-rmse:0.227351+0.013089 test-rmse:0.490657+0.066950
## [18] train-rmse:0.221141+0.015317 test-rmse:0.490075+0.066045
## [19] train-rmse:0.213633+0.015476 test-rmse:0.490362+0.065667
## [20] train-rmse:0.207484+0.015060 test-rmse:0.491113+0.065160
## [21] train-rmse:0.200046+0.014201 test-rmse:0.492725+0.067217
## [22] train-rmse:0.193067+0.014714 test-rmse:0.493016+0.066284
## [23] train-rmse:0.185891+0.013200 test-rmse:0.493991+0.064790
## [24] train-rmse:0.180478+0.014676 test-rmse:0.494601+0.065155
## Stopping. Best iteration:
## [18] train-rmse:0.221141+0.015317 test-rmse:0.490075+0.066045
##
## 90
## [1] train-rmse:0.755647+0.012075 test-rmse:0.786490+0.059045
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.594194+0.007239 test-rmse:0.659861+0.060596
## [3] train-rmse:0.491670+0.010297 test-rmse:0.594609+0.064603
## [4] train-rmse:0.419016+0.011965 test-rmse:0.550797+0.063542
## [5] train-rmse:0.367043+0.006428 test-rmse:0.532116+0.062758
## [6] train-rmse:0.330678+0.006828 test-rmse:0.520525+0.059324
## [7] train-rmse:0.304489+0.012174 test-rmse:0.511756+0.048967
## [8] train-rmse:0.289053+0.013941 test-rmse:0.505879+0.045186
## [9] train-rmse:0.268641+0.014450 test-rmse:0.506652+0.048532
## [10] train-rmse:0.251422+0.017627 test-rmse:0.501688+0.043462
## [11] train-rmse:0.241139+0.017781 test-rmse:0.502211+0.045519
## [12] train-rmse:0.231363+0.015196 test-rmse:0.496560+0.047313
## [13] train-rmse:0.220172+0.015025 test-rmse:0.493504+0.047893
## [14] train-rmse:0.209484+0.018580 test-rmse:0.493721+0.049417
## [15] train-rmse:0.198726+0.017023 test-rmse:0.497204+0.056455
## [16] train-rmse:0.191171+0.016813 test-rmse:0.497275+0.055998
## [17] train-rmse:0.183162+0.014266 test-rmse:0.498094+0.056292
## [18] train-rmse:0.176094+0.014293 test-rmse:0.498130+0.056393
## [19] train-rmse:0.169860+0.012933 test-rmse:0.498066+0.055315
## Stopping. Best iteration:
## [13] train-rmse:0.220172+0.015025 test-rmse:0.493504+0.047893
##
## 91
## [1] train-rmse:0.824402+0.014414 test-rmse:0.829993+0.060083
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.734014+0.013995 test-rmse:0.754311+0.063563
## [3] train-rmse:0.682694+0.012539 test-rmse:0.706753+0.060760
## [4] train-rmse:0.649009+0.011502 test-rmse:0.668430+0.058475
## [5] train-rmse:0.628593+0.011771 test-rmse:0.656117+0.054106
## [6] train-rmse:0.615384+0.011645 test-rmse:0.643404+0.052398
## [7] train-rmse:0.605637+0.011566 test-rmse:0.634720+0.055231
## [8] train-rmse:0.596724+0.011371 test-rmse:0.632076+0.053962
## [9] train-rmse:0.588854+0.011290 test-rmse:0.624764+0.057396
## [10] train-rmse:0.581930+0.011096 test-rmse:0.618328+0.058167
## [11] train-rmse:0.575969+0.011240 test-rmse:0.615958+0.058099
## [12] train-rmse:0.570646+0.010960 test-rmse:0.611110+0.056225
## [13] train-rmse:0.565656+0.010840 test-rmse:0.606075+0.057854
## [14] train-rmse:0.560980+0.010715 test-rmse:0.602220+0.055485
## [15] train-rmse:0.556564+0.010762 test-rmse:0.595731+0.055103
## [16] train-rmse:0.552277+0.010819 test-rmse:0.592883+0.056812
## [17] train-rmse:0.548156+0.010953 test-rmse:0.587867+0.055977
## [18] train-rmse:0.544271+0.010958 test-rmse:0.583028+0.056079
## [19] train-rmse:0.540617+0.011006 test-rmse:0.583359+0.055773
## [20] train-rmse:0.537406+0.010921 test-rmse:0.579507+0.053909
## [21] train-rmse:0.534336+0.010866 test-rmse:0.577530+0.055489
## [22] train-rmse:0.531525+0.010878 test-rmse:0.575870+0.056036
## [23] train-rmse:0.528815+0.010973 test-rmse:0.575033+0.055961
## [24] train-rmse:0.526210+0.010996 test-rmse:0.575447+0.055255
## [25] train-rmse:0.523737+0.010925 test-rmse:0.575393+0.056507
## [26] train-rmse:0.521376+0.010913 test-rmse:0.572823+0.056338
## [27] train-rmse:0.518985+0.010875 test-rmse:0.570783+0.056554
## [28] train-rmse:0.516768+0.010774 test-rmse:0.568523+0.055661
## [29] train-rmse:0.514684+0.010705 test-rmse:0.567137+0.056891
## [30] train-rmse:0.512704+0.010698 test-rmse:0.564454+0.056421
## [31] train-rmse:0.510801+0.010683 test-rmse:0.562944+0.054893
## [32] train-rmse:0.508939+0.010657 test-rmse:0.559554+0.054232
## [33] train-rmse:0.507072+0.010668 test-rmse:0.557304+0.053785
## [34] train-rmse:0.505331+0.010646 test-rmse:0.555029+0.053185
## [35] train-rmse:0.503665+0.010569 test-rmse:0.553679+0.052293
## [36] train-rmse:0.502071+0.010546 test-rmse:0.552860+0.051964
## [37] train-rmse:0.500517+0.010523 test-rmse:0.550533+0.053275
## [38] train-rmse:0.499016+0.010555 test-rmse:0.550337+0.053941
## [39] train-rmse:0.497606+0.010555 test-rmse:0.551087+0.053684
## [40] train-rmse:0.496267+0.010494 test-rmse:0.550256+0.052887
## [41] train-rmse:0.494991+0.010475 test-rmse:0.549973+0.052644
## [42] train-rmse:0.493705+0.010489 test-rmse:0.550473+0.052040
## [43] train-rmse:0.492498+0.010444 test-rmse:0.549141+0.051529
## [44] train-rmse:0.491356+0.010440 test-rmse:0.547956+0.051603
## [45] train-rmse:0.490266+0.010461 test-rmse:0.545969+0.051503
## [46] train-rmse:0.489188+0.010464 test-rmse:0.545473+0.051860
## [47] train-rmse:0.488133+0.010484 test-rmse:0.545134+0.051547
## [48] train-rmse:0.487071+0.010529 test-rmse:0.545478+0.052201
## [49] train-rmse:0.486057+0.010575 test-rmse:0.545205+0.051107
## [50] train-rmse:0.485082+0.010598 test-rmse:0.544077+0.050901
## [51] train-rmse:0.484133+0.010588 test-rmse:0.543254+0.051763
## [52] train-rmse:0.483189+0.010569 test-rmse:0.543394+0.052061
## [53] train-rmse:0.482296+0.010527 test-rmse:0.543573+0.050578
## [54] train-rmse:0.481420+0.010495 test-rmse:0.542934+0.049462
## [55] train-rmse:0.480595+0.010469 test-rmse:0.542180+0.050231
## [56] train-rmse:0.479820+0.010428 test-rmse:0.541755+0.051101
## [57] train-rmse:0.479038+0.010369 test-rmse:0.540617+0.050768
## [58] train-rmse:0.478272+0.010371 test-rmse:0.539751+0.050634
## [59] train-rmse:0.477552+0.010370 test-rmse:0.539184+0.050317
## [60] train-rmse:0.476849+0.010364 test-rmse:0.539109+0.050098
## [61] train-rmse:0.476175+0.010367 test-rmse:0.539046+0.049840
## [62] train-rmse:0.475515+0.010369 test-rmse:0.538210+0.049288
## [63] train-rmse:0.474882+0.010373 test-rmse:0.536681+0.049805
## [64] train-rmse:0.474242+0.010364 test-rmse:0.536974+0.049780
## [65] train-rmse:0.473626+0.010366 test-rmse:0.536629+0.049117
## [66] train-rmse:0.473021+0.010379 test-rmse:0.536425+0.049200
## [67] train-rmse:0.472431+0.010370 test-rmse:0.535710+0.049325
## [68] train-rmse:0.471836+0.010402 test-rmse:0.535696+0.048527
## [69] train-rmse:0.471270+0.010400 test-rmse:0.534980+0.048285
## [70] train-rmse:0.470705+0.010388 test-rmse:0.534830+0.049319
## [71] train-rmse:0.470159+0.010383 test-rmse:0.535185+0.049381
## [72] train-rmse:0.469626+0.010375 test-rmse:0.535581+0.049481
## [73] train-rmse:0.469110+0.010359 test-rmse:0.535545+0.049916
## [74] train-rmse:0.468595+0.010311 test-rmse:0.536890+0.049200
## [75] train-rmse:0.468122+0.010290 test-rmse:0.536227+0.048721
## [76] train-rmse:0.467673+0.010274 test-rmse:0.536273+0.048276
## Stopping. Best iteration:
## [70] train-rmse:0.470705+0.010388 test-rmse:0.534830+0.049319
##
## 92
## [1] train-rmse:0.795200+0.014147 test-rmse:0.802931+0.060649
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.682208+0.013164 test-rmse:0.706643+0.064943
## [3] train-rmse:0.620541+0.012079 test-rmse:0.651882+0.063906
## [4] train-rmse:0.582296+0.014234 test-rmse:0.627466+0.063756
## [5] train-rmse:0.559315+0.017242 test-rmse:0.610527+0.063750
## [6] train-rmse:0.543886+0.017375 test-rmse:0.593578+0.059280
## [7] train-rmse:0.532217+0.018027 test-rmse:0.588399+0.063239
## [8] train-rmse:0.519973+0.016137 test-rmse:0.574245+0.065775
## [9] train-rmse:0.511324+0.016038 test-rmse:0.570623+0.066840
## [10] train-rmse:0.503503+0.016793 test-rmse:0.565191+0.066291
## [11] train-rmse:0.494098+0.014965 test-rmse:0.559649+0.063783
## [12] train-rmse:0.486735+0.014676 test-rmse:0.555096+0.060759
## [13] train-rmse:0.479739+0.014973 test-rmse:0.551776+0.064625
## [14] train-rmse:0.473942+0.015365 test-rmse:0.547979+0.062867
## [15] train-rmse:0.468067+0.014541 test-rmse:0.547085+0.061668
## [16] train-rmse:0.463254+0.014456 test-rmse:0.544532+0.064568
## [17] train-rmse:0.457695+0.015433 test-rmse:0.538926+0.063042
## [18] train-rmse:0.453237+0.015462 test-rmse:0.534111+0.061194
## [19] train-rmse:0.449053+0.014252 test-rmse:0.533172+0.060565
## [20] train-rmse:0.445028+0.013529 test-rmse:0.529175+0.061303
## [21] train-rmse:0.440876+0.013328 test-rmse:0.527800+0.059213
## [22] train-rmse:0.436955+0.014156 test-rmse:0.527221+0.059997
## [23] train-rmse:0.432634+0.014665 test-rmse:0.526159+0.059070
## [24] train-rmse:0.429067+0.015633 test-rmse:0.523740+0.059825
## [25] train-rmse:0.425918+0.015329 test-rmse:0.521920+0.057637
## [26] train-rmse:0.422428+0.015583 test-rmse:0.519519+0.055454
## [27] train-rmse:0.419283+0.015658 test-rmse:0.518319+0.056348
## [28] train-rmse:0.416767+0.015316 test-rmse:0.517517+0.056901
## [29] train-rmse:0.414044+0.015336 test-rmse:0.515562+0.057071
## [30] train-rmse:0.409331+0.016269 test-rmse:0.513797+0.055847
## [31] train-rmse:0.406828+0.015993 test-rmse:0.513316+0.055632
## [32] train-rmse:0.405153+0.016043 test-rmse:0.513219+0.053631
## [33] train-rmse:0.402504+0.015919 test-rmse:0.511124+0.054309
## [34] train-rmse:0.400376+0.015452 test-rmse:0.510927+0.054060
## [35] train-rmse:0.398385+0.015669 test-rmse:0.510958+0.054766
## [36] train-rmse:0.395605+0.015622 test-rmse:0.509568+0.057705
## [37] train-rmse:0.393642+0.015333 test-rmse:0.508177+0.059793
## [38] train-rmse:0.391711+0.015579 test-rmse:0.508774+0.058200
## [39] train-rmse:0.390038+0.015743 test-rmse:0.509151+0.057528
## [40] train-rmse:0.388175+0.015828 test-rmse:0.508225+0.057081
## [41] train-rmse:0.385661+0.016222 test-rmse:0.507756+0.058855
## [42] train-rmse:0.384149+0.015870 test-rmse:0.507219+0.057315
## [43] train-rmse:0.382113+0.016094 test-rmse:0.507127+0.055688
## [44] train-rmse:0.378987+0.016018 test-rmse:0.504850+0.055732
## [45] train-rmse:0.376878+0.015796 test-rmse:0.505512+0.055566
## [46] train-rmse:0.374273+0.016245 test-rmse:0.503825+0.054455
## [47] train-rmse:0.372230+0.016155 test-rmse:0.503186+0.053291
## [48] train-rmse:0.370350+0.015973 test-rmse:0.502670+0.052777
## [49] train-rmse:0.369016+0.016112 test-rmse:0.503509+0.052426
## [50] train-rmse:0.366393+0.017916 test-rmse:0.502242+0.051837
## [51] train-rmse:0.364166+0.017003 test-rmse:0.502783+0.051264
## [52] train-rmse:0.362543+0.016831 test-rmse:0.502584+0.051426
## [53] train-rmse:0.361229+0.016596 test-rmse:0.501184+0.051284
## [54] train-rmse:0.359487+0.015784 test-rmse:0.500555+0.051681
## [55] train-rmse:0.356933+0.014889 test-rmse:0.501195+0.050259
## [56] train-rmse:0.354788+0.015635 test-rmse:0.499779+0.050990
## [57] train-rmse:0.352832+0.015182 test-rmse:0.500320+0.051950
## [58] train-rmse:0.351583+0.015067 test-rmse:0.500613+0.052322
## [59] train-rmse:0.348691+0.014552 test-rmse:0.497954+0.053456
## [60] train-rmse:0.346540+0.014721 test-rmse:0.496769+0.054659
## [61] train-rmse:0.345240+0.014688 test-rmse:0.496513+0.053298
## [62] train-rmse:0.344047+0.014650 test-rmse:0.496410+0.052861
## [63] train-rmse:0.341631+0.016167 test-rmse:0.495410+0.051287
## [64] train-rmse:0.339923+0.016230 test-rmse:0.494919+0.051641
## [65] train-rmse:0.338611+0.016152 test-rmse:0.493810+0.051457
## [66] train-rmse:0.337501+0.016038 test-rmse:0.494745+0.051431
## [67] train-rmse:0.336065+0.016723 test-rmse:0.495912+0.052369
## [68] train-rmse:0.334737+0.016548 test-rmse:0.495931+0.053004
## [69] train-rmse:0.333770+0.016867 test-rmse:0.494417+0.053388
## [70] train-rmse:0.332394+0.017383 test-rmse:0.494302+0.052023
## [71] train-rmse:0.330677+0.018204 test-rmse:0.494319+0.050917
## Stopping. Best iteration:
## [65] train-rmse:0.338611+0.016152 test-rmse:0.493810+0.051457
##
## 93
## [1] train-rmse:0.782108+0.014739 test-rmse:0.795988+0.063676
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.655360+0.013727 test-rmse:0.684134+0.065668
## [3] train-rmse:0.583106+0.014773 test-rmse:0.624744+0.061855
## [4] train-rmse:0.535598+0.014808 test-rmse:0.589187+0.056820
## [5] train-rmse:0.508361+0.017169 test-rmse:0.572777+0.048847
## [6] train-rmse:0.485882+0.017406 test-rmse:0.562017+0.043733
## [7] train-rmse:0.471757+0.016491 test-rmse:0.555010+0.044540
## [8] train-rmse:0.458579+0.015814 test-rmse:0.547587+0.047599
## [9] train-rmse:0.446327+0.016542 test-rmse:0.543065+0.043372
## [10] train-rmse:0.436259+0.015866 test-rmse:0.537764+0.042217
## [11] train-rmse:0.427497+0.014225 test-rmse:0.534403+0.041522
## [12] train-rmse:0.419863+0.013529 test-rmse:0.534385+0.041184
## [13] train-rmse:0.412289+0.013429 test-rmse:0.531026+0.040882
## [14] train-rmse:0.405550+0.012876 test-rmse:0.527436+0.042851
## [15] train-rmse:0.398817+0.011739 test-rmse:0.525116+0.041962
## [16] train-rmse:0.393338+0.012050 test-rmse:0.522412+0.043576
## [17] train-rmse:0.387691+0.012898 test-rmse:0.522534+0.041897
## [18] train-rmse:0.381282+0.012854 test-rmse:0.521499+0.040483
## [19] train-rmse:0.375613+0.010788 test-rmse:0.518265+0.043945
## [20] train-rmse:0.370094+0.011621 test-rmse:0.514360+0.044814
## [21] train-rmse:0.364394+0.013280 test-rmse:0.511483+0.044409
## [22] train-rmse:0.359813+0.012889 test-rmse:0.509345+0.045400
## [23] train-rmse:0.355785+0.013785 test-rmse:0.506877+0.045698
## [24] train-rmse:0.352075+0.013071 test-rmse:0.507056+0.045757
## [25] train-rmse:0.346465+0.013790 test-rmse:0.507410+0.046131
## [26] train-rmse:0.341958+0.014030 test-rmse:0.506180+0.048476
## [27] train-rmse:0.337863+0.012994 test-rmse:0.504426+0.049980
## [28] train-rmse:0.333706+0.012560 test-rmse:0.502438+0.047003
## [29] train-rmse:0.329748+0.012190 test-rmse:0.503123+0.044811
## [30] train-rmse:0.324771+0.012995 test-rmse:0.503047+0.046349
## [31] train-rmse:0.321668+0.012230 test-rmse:0.502727+0.045759
## [32] train-rmse:0.316233+0.010192 test-rmse:0.502914+0.045897
## [33] train-rmse:0.311558+0.011933 test-rmse:0.503588+0.041767
## [34] train-rmse:0.309148+0.012529 test-rmse:0.504892+0.042610
## Stopping. Best iteration:
## [28] train-rmse:0.333706+0.012560 test-rmse:0.502438+0.047003
##
## 94
## [1] train-rmse:0.770788+0.014377 test-rmse:0.789284+0.061346
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.635003+0.013420 test-rmse:0.668914+0.061639
## [3] train-rmse:0.552819+0.012608 test-rmse:0.614816+0.059395
## [4] train-rmse:0.498482+0.015406 test-rmse:0.575557+0.056739
## [5] train-rmse:0.467326+0.014902 test-rmse:0.554362+0.054671
## [6] train-rmse:0.446244+0.014782 test-rmse:0.541752+0.055596
## [7] train-rmse:0.424321+0.016974 test-rmse:0.530091+0.056788
## [8] train-rmse:0.407364+0.018047 test-rmse:0.524746+0.058498
## [9] train-rmse:0.394852+0.016644 test-rmse:0.520176+0.057139
## [10] train-rmse:0.385002+0.015363 test-rmse:0.516045+0.059236
## [11] train-rmse:0.375820+0.016282 test-rmse:0.514786+0.059212
## [12] train-rmse:0.365341+0.017935 test-rmse:0.511395+0.060717
## [13] train-rmse:0.353290+0.018534 test-rmse:0.505812+0.061544
## [14] train-rmse:0.346567+0.017990 test-rmse:0.506516+0.060932
## [15] train-rmse:0.337338+0.019738 test-rmse:0.503647+0.060914
## [16] train-rmse:0.329929+0.020691 test-rmse:0.499656+0.061185
## [17] train-rmse:0.323394+0.020076 test-rmse:0.497467+0.059875
## [18] train-rmse:0.313472+0.018295 test-rmse:0.497912+0.060421
## [19] train-rmse:0.309457+0.017786 test-rmse:0.498473+0.059727
## [20] train-rmse:0.303937+0.017022 test-rmse:0.498457+0.060027
## [21] train-rmse:0.295781+0.014553 test-rmse:0.497733+0.059295
## [22] train-rmse:0.288867+0.015719 test-rmse:0.496152+0.054107
## [23] train-rmse:0.281059+0.014937 test-rmse:0.497632+0.054221
## [24] train-rmse:0.275787+0.015582 test-rmse:0.496766+0.052054
## [25] train-rmse:0.271346+0.014802 test-rmse:0.496726+0.053452
## [26] train-rmse:0.267612+0.013856 test-rmse:0.496639+0.051441
## [27] train-rmse:0.263175+0.015313 test-rmse:0.497568+0.050777
## [28] train-rmse:0.258050+0.017504 test-rmse:0.498187+0.052248
## Stopping. Best iteration:
## [22] train-rmse:0.288867+0.015719 test-rmse:0.496152+0.054107
##
## 95
## [1] train-rmse:0.760638+0.013514 test-rmse:0.783878+0.062125
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.612383+0.010833 test-rmse:0.661360+0.062640
## [3] train-rmse:0.520642+0.013622 test-rmse:0.589110+0.055828
## [4] train-rmse:0.463170+0.013179 test-rmse:0.554718+0.054038
## [5] train-rmse:0.423270+0.012722 test-rmse:0.536801+0.052975
## [6] train-rmse:0.398246+0.013718 test-rmse:0.523868+0.048773
## [7] train-rmse:0.379174+0.014229 test-rmse:0.518206+0.054472
## [8] train-rmse:0.365012+0.014927 test-rmse:0.511207+0.056839
## [9] train-rmse:0.352560+0.015973 test-rmse:0.501993+0.057970
## [10] train-rmse:0.337511+0.010417 test-rmse:0.500444+0.058218
## [11] train-rmse:0.327551+0.009821 test-rmse:0.496608+0.060090
## [12] train-rmse:0.317855+0.009581 test-rmse:0.495256+0.063859
## [13] train-rmse:0.308065+0.009487 test-rmse:0.492307+0.064523
## [14] train-rmse:0.298724+0.008253 test-rmse:0.491332+0.063568
## [15] train-rmse:0.291923+0.007615 test-rmse:0.490071+0.063750
## [16] train-rmse:0.283160+0.008046 test-rmse:0.488727+0.063264
## [17] train-rmse:0.274244+0.008280 test-rmse:0.488291+0.063327
## [18] train-rmse:0.266462+0.007645 test-rmse:0.488074+0.060409
## [19] train-rmse:0.258723+0.009078 test-rmse:0.487401+0.058785
## [20] train-rmse:0.251189+0.008693 test-rmse:0.486687+0.058384
## [21] train-rmse:0.243344+0.006992 test-rmse:0.490052+0.060464
## [22] train-rmse:0.234005+0.008771 test-rmse:0.490572+0.057443
## [23] train-rmse:0.229508+0.009711 test-rmse:0.491198+0.055478
## [24] train-rmse:0.222833+0.012079 test-rmse:0.490696+0.055621
## [25] train-rmse:0.218080+0.011177 test-rmse:0.490727+0.055831
## [26] train-rmse:0.211966+0.010037 test-rmse:0.492223+0.054030
## Stopping. Best iteration:
## [20] train-rmse:0.251189+0.008693 test-rmse:0.486687+0.058384
##
## 96
## [1] train-rmse:0.751235+0.012639 test-rmse:0.780591+0.062179
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.592926+0.009722 test-rmse:0.654456+0.065147
## [3] train-rmse:0.492699+0.012491 test-rmse:0.586868+0.063260
## [4] train-rmse:0.433404+0.011339 test-rmse:0.552356+0.054501
## [5] train-rmse:0.392374+0.009704 test-rmse:0.533512+0.053369
## [6] train-rmse:0.362426+0.012331 test-rmse:0.521923+0.051038
## [7] train-rmse:0.340441+0.011806 test-rmse:0.515050+0.051036
## [8] train-rmse:0.315497+0.011798 test-rmse:0.503534+0.060218
## [9] train-rmse:0.300265+0.011141 test-rmse:0.503324+0.060186
## [10] train-rmse:0.287775+0.010692 test-rmse:0.499526+0.059559
## [11] train-rmse:0.275782+0.011477 test-rmse:0.499021+0.060796
## [12] train-rmse:0.264322+0.010029 test-rmse:0.496289+0.062819
## [13] train-rmse:0.252166+0.010877 test-rmse:0.495492+0.066109
## [14] train-rmse:0.244805+0.011537 test-rmse:0.499074+0.068294
## [15] train-rmse:0.235046+0.010454 test-rmse:0.497743+0.068434
## [16] train-rmse:0.225860+0.008831 test-rmse:0.498873+0.067740
## [17] train-rmse:0.217061+0.011606 test-rmse:0.499608+0.066810
## [18] train-rmse:0.211206+0.010802 test-rmse:0.502424+0.067578
## [19] train-rmse:0.205941+0.012071 test-rmse:0.503441+0.067792
## Stopping. Best iteration:
## [13] train-rmse:0.252166+0.010877 test-rmse:0.495492+0.066109
##
## 97
## [1] train-rmse:0.741906+0.011885 test-rmse:0.775242+0.058900
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.577092+0.007029 test-rmse:0.649824+0.063617
## [3] train-rmse:0.474742+0.007853 test-rmse:0.581782+0.065174
## [4] train-rmse:0.403616+0.012140 test-rmse:0.540460+0.063915
## [5] train-rmse:0.355476+0.010114 test-rmse:0.516958+0.065412
## [6] train-rmse:0.317574+0.013790 test-rmse:0.506607+0.064901
## [7] train-rmse:0.297645+0.016740 test-rmse:0.500614+0.064392
## [8] train-rmse:0.281007+0.014682 test-rmse:0.496918+0.062414
## [9] train-rmse:0.264577+0.012087 test-rmse:0.495063+0.058653
## [10] train-rmse:0.251226+0.011032 test-rmse:0.492052+0.061000
## [11] train-rmse:0.238668+0.011539 test-rmse:0.489665+0.054994
## [12] train-rmse:0.229936+0.011920 test-rmse:0.489271+0.054798
## [13] train-rmse:0.216966+0.012463 test-rmse:0.487688+0.053543
## [14] train-rmse:0.206891+0.014214 test-rmse:0.484978+0.051632
## [15] train-rmse:0.194539+0.012458 test-rmse:0.488520+0.054346
## [16] train-rmse:0.186835+0.012687 test-rmse:0.487561+0.051768
## [17] train-rmse:0.179792+0.011235 test-rmse:0.487815+0.052421
## [18] train-rmse:0.171203+0.011334 test-rmse:0.489175+0.050631
## [19] train-rmse:0.163585+0.009586 test-rmse:0.489385+0.052406
## [20] train-rmse:0.156901+0.011162 test-rmse:0.488652+0.051103
## Stopping. Best iteration:
## [14] train-rmse:0.206891+0.014214 test-rmse:0.484978+0.051632
##
## 98
## [1] train-rmse:0.815789+0.014346 test-rmse:0.821875+0.060024
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.725158+0.013734 test-rmse:0.752851+0.060067
## [3] train-rmse:0.675931+0.012586 test-rmse:0.702077+0.059746
## [4] train-rmse:0.643008+0.011447 test-rmse:0.664612+0.057021
## [5] train-rmse:0.623515+0.011283 test-rmse:0.654376+0.051346
## [6] train-rmse:0.611122+0.011172 test-rmse:0.642865+0.049053
## [7] train-rmse:0.601318+0.011081 test-rmse:0.635457+0.052616
## [8] train-rmse:0.592373+0.010831 test-rmse:0.628902+0.055127
## [9] train-rmse:0.584445+0.010789 test-rmse:0.619494+0.055946
## [10] train-rmse:0.577787+0.010729 test-rmse:0.614217+0.057220
## [11] train-rmse:0.571825+0.010789 test-rmse:0.610910+0.057601
## [12] train-rmse:0.566608+0.010936 test-rmse:0.604086+0.057240
## [13] train-rmse:0.561787+0.010900 test-rmse:0.602471+0.054135
## [14] train-rmse:0.557233+0.010949 test-rmse:0.599062+0.052942
## [15] train-rmse:0.552678+0.010796 test-rmse:0.593741+0.052809
## [16] train-rmse:0.548374+0.010788 test-rmse:0.588806+0.054322
## [17] train-rmse:0.544305+0.010794 test-rmse:0.584610+0.053965
## [18] train-rmse:0.540425+0.010663 test-rmse:0.581493+0.055390
## [19] train-rmse:0.536786+0.010582 test-rmse:0.581274+0.054460
## [20] train-rmse:0.533588+0.010538 test-rmse:0.578865+0.052672
## [21] train-rmse:0.530595+0.010601 test-rmse:0.577125+0.051896
## [22] train-rmse:0.527756+0.010565 test-rmse:0.575040+0.052194
## [23] train-rmse:0.525084+0.010562 test-rmse:0.574087+0.052518
## [24] train-rmse:0.522559+0.010593 test-rmse:0.573313+0.053648
## [25] train-rmse:0.520090+0.010571 test-rmse:0.572761+0.056063
## [26] train-rmse:0.517719+0.010611 test-rmse:0.570038+0.055942
## [27] train-rmse:0.515501+0.010494 test-rmse:0.569517+0.055354
## [28] train-rmse:0.513419+0.010507 test-rmse:0.567515+0.054823
## [29] train-rmse:0.511421+0.010437 test-rmse:0.565889+0.054901
## [30] train-rmse:0.509467+0.010395 test-rmse:0.562440+0.054684
## [31] train-rmse:0.507567+0.010346 test-rmse:0.559826+0.053781
## [32] train-rmse:0.505687+0.010314 test-rmse:0.557148+0.052314
## [33] train-rmse:0.503908+0.010256 test-rmse:0.555763+0.053341
## [34] train-rmse:0.502200+0.010167 test-rmse:0.553829+0.051996
## [35] train-rmse:0.500556+0.010100 test-rmse:0.551713+0.051024
## [36] train-rmse:0.498991+0.010113 test-rmse:0.552156+0.051531
## [37] train-rmse:0.497507+0.010101 test-rmse:0.551940+0.051056
## [38] train-rmse:0.496055+0.010113 test-rmse:0.550347+0.052119
## [39] train-rmse:0.494675+0.010125 test-rmse:0.549459+0.052373
## [40] train-rmse:0.493380+0.010141 test-rmse:0.548768+0.052857
## [41] train-rmse:0.492160+0.010136 test-rmse:0.547591+0.053519
## [42] train-rmse:0.490983+0.010138 test-rmse:0.547322+0.053523
## [43] train-rmse:0.489827+0.010172 test-rmse:0.547252+0.052685
## [44] train-rmse:0.488703+0.010146 test-rmse:0.546422+0.052360
## [45] train-rmse:0.487586+0.010108 test-rmse:0.547423+0.051726
## [46] train-rmse:0.486515+0.010102 test-rmse:0.547123+0.051937
## [47] train-rmse:0.485479+0.010113 test-rmse:0.545711+0.051523
## [48] train-rmse:0.484488+0.010093 test-rmse:0.545892+0.052292
## [49] train-rmse:0.483535+0.010098 test-rmse:0.546138+0.052497
## [50] train-rmse:0.482582+0.010117 test-rmse:0.543952+0.053257
## [51] train-rmse:0.481669+0.010127 test-rmse:0.543913+0.052210
## [52] train-rmse:0.480793+0.010161 test-rmse:0.543275+0.051518
## [53] train-rmse:0.479948+0.010158 test-rmse:0.542658+0.051387
## [54] train-rmse:0.479123+0.010141 test-rmse:0.542426+0.050307
## [55] train-rmse:0.478347+0.010102 test-rmse:0.540896+0.050197
## [56] train-rmse:0.477590+0.010070 test-rmse:0.539808+0.050435
## [57] train-rmse:0.476835+0.010069 test-rmse:0.540115+0.049722
## [58] train-rmse:0.476104+0.010060 test-rmse:0.539637+0.049596
## [59] train-rmse:0.475400+0.010050 test-rmse:0.538761+0.048569
## [60] train-rmse:0.474725+0.010021 test-rmse:0.537671+0.047336
## [61] train-rmse:0.474069+0.009987 test-rmse:0.537428+0.046903
## [62] train-rmse:0.473441+0.009960 test-rmse:0.537528+0.047186
## [63] train-rmse:0.472827+0.009941 test-rmse:0.537372+0.047978
## [64] train-rmse:0.472238+0.009936 test-rmse:0.537255+0.046760
## [65] train-rmse:0.471656+0.009923 test-rmse:0.537540+0.047563
## [66] train-rmse:0.471106+0.009914 test-rmse:0.537197+0.047388
## [67] train-rmse:0.470571+0.009913 test-rmse:0.537210+0.047423
## [68] train-rmse:0.470028+0.009909 test-rmse:0.537319+0.046863
## [69] train-rmse:0.469459+0.009854 test-rmse:0.537136+0.047603
## [70] train-rmse:0.468928+0.009856 test-rmse:0.536700+0.046471
## [71] train-rmse:0.468423+0.009844 test-rmse:0.536709+0.047302
## [72] train-rmse:0.467934+0.009848 test-rmse:0.537542+0.047687
## [73] train-rmse:0.467461+0.009818 test-rmse:0.537262+0.047747
## [74] train-rmse:0.466970+0.009808 test-rmse:0.536860+0.048181
## [75] train-rmse:0.466475+0.009753 test-rmse:0.535500+0.048361
## [76] train-rmse:0.466037+0.009741 test-rmse:0.535486+0.048107
## [77] train-rmse:0.465621+0.009728 test-rmse:0.535156+0.047299
## [78] train-rmse:0.465202+0.009727 test-rmse:0.535569+0.047071
## [79] train-rmse:0.464776+0.009737 test-rmse:0.534735+0.046792
## [80] train-rmse:0.464346+0.009711 test-rmse:0.534309+0.046918
## [81] train-rmse:0.463889+0.009709 test-rmse:0.534271+0.046820
## [82] train-rmse:0.463459+0.009712 test-rmse:0.533912+0.046718
## [83] train-rmse:0.463047+0.009706 test-rmse:0.533222+0.046645
## [84] train-rmse:0.462655+0.009687 test-rmse:0.533697+0.046701
## [85] train-rmse:0.462225+0.009667 test-rmse:0.533056+0.046500
## [86] train-rmse:0.461852+0.009658 test-rmse:0.533666+0.045587
## [87] train-rmse:0.461481+0.009640 test-rmse:0.533226+0.045093
## [88] train-rmse:0.461114+0.009631 test-rmse:0.533421+0.045063
## [89] train-rmse:0.460759+0.009620 test-rmse:0.533779+0.044597
## [90] train-rmse:0.460414+0.009611 test-rmse:0.533270+0.044293
## [91] train-rmse:0.460075+0.009594 test-rmse:0.533028+0.044711
## [92] train-rmse:0.459725+0.009571 test-rmse:0.533163+0.045077
## [93] train-rmse:0.459398+0.009549 test-rmse:0.533007+0.044243
## [94] train-rmse:0.459051+0.009570 test-rmse:0.532595+0.044957
## [95] train-rmse:0.458723+0.009545 test-rmse:0.532508+0.045071
## [96] train-rmse:0.458405+0.009533 test-rmse:0.532318+0.045448
## [97] train-rmse:0.458080+0.009528 test-rmse:0.532694+0.045869
## [98] train-rmse:0.457762+0.009520 test-rmse:0.532659+0.045440
## [99] train-rmse:0.457441+0.009509 test-rmse:0.533320+0.045490
## [100] train-rmse:0.457117+0.009485 test-rmse:0.532993+0.045409
## [101] train-rmse:0.456818+0.009490 test-rmse:0.532974+0.045478
## [102] train-rmse:0.456533+0.009481 test-rmse:0.532648+0.045545
## Stopping. Best iteration:
## [96] train-rmse:0.458405+0.009533 test-rmse:0.532318+0.045448
##
## 99
## [1] train-rmse:0.784883+0.014058 test-rmse:0.793218+0.060702
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.671373+0.013039 test-rmse:0.696318+0.064368
## [3] train-rmse:0.611127+0.012739 test-rmse:0.641752+0.066116
## [4] train-rmse:0.574491+0.013719 test-rmse:0.617014+0.066322
## [5] train-rmse:0.552766+0.016618 test-rmse:0.602430+0.065888
## [6] train-rmse:0.536869+0.017332 test-rmse:0.588183+0.060915
## [7] train-rmse:0.525677+0.018304 test-rmse:0.577541+0.060430
## [8] train-rmse:0.515780+0.017452 test-rmse:0.573002+0.059353
## [9] train-rmse:0.506394+0.017795 test-rmse:0.563607+0.057823
## [10] train-rmse:0.497146+0.017663 test-rmse:0.560895+0.059546
## [11] train-rmse:0.489807+0.017591 test-rmse:0.553091+0.061420
## [12] train-rmse:0.483843+0.017809 test-rmse:0.547931+0.061404
## [13] train-rmse:0.476543+0.017315 test-rmse:0.545131+0.063741
## [14] train-rmse:0.470598+0.017649 test-rmse:0.543107+0.062905
## [15] train-rmse:0.464994+0.017010 test-rmse:0.540602+0.063876
## [16] train-rmse:0.459751+0.016362 test-rmse:0.537890+0.063958
## [17] train-rmse:0.455347+0.017033 test-rmse:0.537391+0.061873
## [18] train-rmse:0.450160+0.018164 test-rmse:0.532364+0.064046
## [19] train-rmse:0.446015+0.017493 test-rmse:0.529165+0.062636
## [20] train-rmse:0.442573+0.017142 test-rmse:0.526793+0.061547
## [21] train-rmse:0.438962+0.017275 test-rmse:0.526235+0.060558
## [22] train-rmse:0.435399+0.016699 test-rmse:0.524963+0.061951
## [23] train-rmse:0.430941+0.017193 test-rmse:0.523114+0.060379
## [24] train-rmse:0.428068+0.016943 test-rmse:0.520432+0.060601
## [25] train-rmse:0.424680+0.017581 test-rmse:0.521195+0.059583
## [26] train-rmse:0.421189+0.018189 test-rmse:0.516851+0.059659
## [27] train-rmse:0.418557+0.018633 test-rmse:0.516829+0.059761
## [28] train-rmse:0.415929+0.018709 test-rmse:0.515614+0.059303
## [29] train-rmse:0.413494+0.018437 test-rmse:0.513934+0.059653
## [30] train-rmse:0.409994+0.018199 test-rmse:0.512969+0.059391
## [31] train-rmse:0.407155+0.017864 test-rmse:0.510873+0.059202
## [32] train-rmse:0.403744+0.017473 test-rmse:0.511777+0.059126
## [33] train-rmse:0.401306+0.017911 test-rmse:0.511713+0.059793
## [34] train-rmse:0.399282+0.018179 test-rmse:0.512505+0.058025
## [35] train-rmse:0.396710+0.017035 test-rmse:0.512857+0.058254
## [36] train-rmse:0.394341+0.016814 test-rmse:0.512081+0.058291
## [37] train-rmse:0.391580+0.017372 test-rmse:0.511509+0.058476
## Stopping. Best iteration:
## [31] train-rmse:0.407155+0.017864 test-rmse:0.510873+0.059202
##
## 100
## [1] train-rmse:0.771020+0.014694 test-rmse:0.785961+0.063944
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.643289+0.013648 test-rmse:0.675108+0.065527
## [3] train-rmse:0.573236+0.014885 test-rmse:0.619489+0.062162
## [4] train-rmse:0.529824+0.013013 test-rmse:0.586999+0.058637
## [5] train-rmse:0.500688+0.018435 test-rmse:0.571772+0.050510
## [6] train-rmse:0.481870+0.016438 test-rmse:0.556956+0.052123
## [7] train-rmse:0.467773+0.016412 test-rmse:0.554775+0.052065
## [8] train-rmse:0.453727+0.016422 test-rmse:0.547967+0.053937
## [9] train-rmse:0.441218+0.016125 test-rmse:0.540175+0.055694
## [10] train-rmse:0.431360+0.015924 test-rmse:0.535945+0.058894
## [11] train-rmse:0.421703+0.017441 test-rmse:0.533677+0.059703
## [12] train-rmse:0.414709+0.017188 test-rmse:0.529336+0.059385
## [13] train-rmse:0.406265+0.016467 test-rmse:0.525024+0.061162
## [14] train-rmse:0.399266+0.017178 test-rmse:0.520522+0.061438
## [15] train-rmse:0.392070+0.016763 test-rmse:0.516944+0.058997
## [16] train-rmse:0.385984+0.017676 test-rmse:0.517192+0.058725
## [17] train-rmse:0.381661+0.017567 test-rmse:0.517975+0.059422
## [18] train-rmse:0.376569+0.017419 test-rmse:0.517971+0.058589
## [19] train-rmse:0.369859+0.017094 test-rmse:0.516667+0.058201
## [20] train-rmse:0.364891+0.015989 test-rmse:0.514530+0.059906
## [21] train-rmse:0.361002+0.016046 test-rmse:0.517084+0.057829
## [22] train-rmse:0.354117+0.015387 test-rmse:0.513368+0.056520
## [23] train-rmse:0.347250+0.015117 test-rmse:0.511972+0.055233
## [24] train-rmse:0.344098+0.014621 test-rmse:0.511249+0.055121
## [25] train-rmse:0.338269+0.015415 test-rmse:0.512498+0.054364
## [26] train-rmse:0.334805+0.016107 test-rmse:0.510623+0.053255
## [27] train-rmse:0.331861+0.015909 test-rmse:0.510093+0.051840
## [28] train-rmse:0.327420+0.015190 test-rmse:0.510070+0.052019
## [29] train-rmse:0.321430+0.017262 test-rmse:0.506392+0.053845
## [30] train-rmse:0.317531+0.017328 test-rmse:0.506353+0.055440
## [31] train-rmse:0.312825+0.017414 test-rmse:0.507895+0.054117
## [32] train-rmse:0.310529+0.017639 test-rmse:0.509311+0.053150
## [33] train-rmse:0.306475+0.014833 test-rmse:0.508560+0.052174
## [34] train-rmse:0.303125+0.013557 test-rmse:0.509064+0.052957
## [35] train-rmse:0.299693+0.014094 test-rmse:0.508440+0.054418
## [36] train-rmse:0.294787+0.015070 test-rmse:0.506959+0.054548
## Stopping. Best iteration:
## [30] train-rmse:0.317531+0.017328 test-rmse:0.506353+0.055440
##
## 101
## [1] train-rmse:0.759017+0.014314 test-rmse:0.778867+0.061503
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.621372+0.013069 test-rmse:0.658333+0.063686
## [3] train-rmse:0.541201+0.014276 test-rmse:0.602441+0.058611
## [4] train-rmse:0.490081+0.016503 test-rmse:0.568169+0.053792
## [5] train-rmse:0.457781+0.017631 test-rmse:0.552053+0.054633
## [6] train-rmse:0.439903+0.018301 test-rmse:0.540068+0.049844
## [7] train-rmse:0.418235+0.015726 test-rmse:0.527924+0.049203
## [8] train-rmse:0.403032+0.017448 test-rmse:0.523957+0.047863
## [9] train-rmse:0.392272+0.016813 test-rmse:0.519280+0.049310
## [10] train-rmse:0.379346+0.017510 test-rmse:0.513821+0.054764
## [11] train-rmse:0.367663+0.016943 test-rmse:0.511191+0.055740
## [12] train-rmse:0.359942+0.016650 test-rmse:0.509392+0.056957
## [13] train-rmse:0.351778+0.016334 test-rmse:0.505466+0.059682
## [14] train-rmse:0.343628+0.014990 test-rmse:0.505141+0.059618
## [15] train-rmse:0.335221+0.014791 test-rmse:0.504052+0.058935
## [16] train-rmse:0.328210+0.015005 test-rmse:0.503035+0.060944
## [17] train-rmse:0.321142+0.012500 test-rmse:0.504959+0.059306
## [18] train-rmse:0.315654+0.012565 test-rmse:0.503228+0.060162
## [19] train-rmse:0.308086+0.013074 test-rmse:0.502507+0.058036
## [20] train-rmse:0.302555+0.013632 test-rmse:0.500583+0.056165
## [21] train-rmse:0.295190+0.012466 test-rmse:0.499118+0.058753
## [22] train-rmse:0.287844+0.014276 test-rmse:0.499915+0.058479
## [23] train-rmse:0.281954+0.014356 test-rmse:0.501046+0.058479
## [24] train-rmse:0.274409+0.013992 test-rmse:0.496501+0.057647
## [25] train-rmse:0.269795+0.013684 test-rmse:0.495663+0.058551
## [26] train-rmse:0.263907+0.013746 test-rmse:0.491481+0.060143
## [27] train-rmse:0.257785+0.013120 test-rmse:0.490280+0.060713
## [28] train-rmse:0.253240+0.011312 test-rmse:0.490139+0.059468
## [29] train-rmse:0.246398+0.010656 test-rmse:0.491654+0.060318
## [30] train-rmse:0.241826+0.007823 test-rmse:0.491643+0.060747
## [31] train-rmse:0.237439+0.008026 test-rmse:0.492020+0.061484
## [32] train-rmse:0.234058+0.008575 test-rmse:0.492787+0.060095
## [33] train-rmse:0.229039+0.009153 test-rmse:0.492860+0.059752
## [34] train-rmse:0.223158+0.009097 test-rmse:0.491740+0.059074
## Stopping. Best iteration:
## [28] train-rmse:0.253240+0.011312 test-rmse:0.490139+0.059468
##
## 102
## [1] train-rmse:0.748242+0.013411 test-rmse:0.773198+0.062241
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.597605+0.011310 test-rmse:0.650481+0.062549
## [3] train-rmse:0.507203+0.014242 test-rmse:0.585064+0.050113
## [4] train-rmse:0.451491+0.012362 test-rmse:0.547656+0.053354
## [5] train-rmse:0.413643+0.011652 test-rmse:0.529532+0.056146
## [6] train-rmse:0.383909+0.017866 test-rmse:0.517601+0.055648
## [7] train-rmse:0.369217+0.018294 test-rmse:0.514586+0.053233
## [8] train-rmse:0.355404+0.016258 test-rmse:0.508341+0.054392
## [9] train-rmse:0.341437+0.019041 test-rmse:0.509187+0.055584
## [10] train-rmse:0.328634+0.016451 test-rmse:0.503280+0.059470
## [11] train-rmse:0.317396+0.016714 test-rmse:0.501633+0.058440
## [12] train-rmse:0.306634+0.016821 test-rmse:0.501609+0.058121
## [13] train-rmse:0.297289+0.015509 test-rmse:0.500232+0.059637
## [14] train-rmse:0.287011+0.015725 test-rmse:0.499899+0.058667
## [15] train-rmse:0.276783+0.013410 test-rmse:0.500391+0.058408
## [16] train-rmse:0.269117+0.013124 test-rmse:0.500636+0.055924
## [17] train-rmse:0.258633+0.015163 test-rmse:0.497109+0.056792
## [18] train-rmse:0.251633+0.016066 test-rmse:0.497299+0.055273
## [19] train-rmse:0.244411+0.015766 test-rmse:0.495184+0.055177
## [20] train-rmse:0.237180+0.012486 test-rmse:0.496070+0.054016
## [21] train-rmse:0.229717+0.011527 test-rmse:0.496142+0.055956
## [22] train-rmse:0.222255+0.009198 test-rmse:0.496599+0.055535
## [23] train-rmse:0.215755+0.009870 test-rmse:0.497040+0.053421
## [24] train-rmse:0.209120+0.010619 test-rmse:0.496919+0.051931
## [25] train-rmse:0.204805+0.010670 test-rmse:0.497605+0.051337
## Stopping. Best iteration:
## [19] train-rmse:0.244411+0.015766 test-rmse:0.495184+0.055177
##
## 103
## [1] train-rmse:0.738241+0.012492 test-rmse:0.769795+0.062261
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.577245+0.009675 test-rmse:0.643638+0.061818
## [3] train-rmse:0.475754+0.011709 test-rmse:0.572813+0.056134
## [4] train-rmse:0.415930+0.010550 test-rmse:0.539342+0.052929
## [5] train-rmse:0.373070+0.010181 test-rmse:0.522005+0.053298
## [6] train-rmse:0.348700+0.012163 test-rmse:0.514098+0.051143
## [7] train-rmse:0.329080+0.011178 test-rmse:0.508062+0.047895
## [8] train-rmse:0.313356+0.012558 test-rmse:0.506485+0.047215
## [9] train-rmse:0.295777+0.012635 test-rmse:0.502137+0.049423
## [10] train-rmse:0.286193+0.013174 test-rmse:0.499487+0.049764
## [11] train-rmse:0.272700+0.011458 test-rmse:0.495090+0.049246
## [12] train-rmse:0.264396+0.013477 test-rmse:0.495786+0.050205
## [13] train-rmse:0.250393+0.010210 test-rmse:0.493573+0.048797
## [14] train-rmse:0.242408+0.009887 test-rmse:0.493621+0.049159
## [15] train-rmse:0.231693+0.010025 test-rmse:0.494261+0.051475
## [16] train-rmse:0.223447+0.008934 test-rmse:0.494226+0.051381
## [17] train-rmse:0.216083+0.007482 test-rmse:0.496396+0.052494
## [18] train-rmse:0.210852+0.006789 test-rmse:0.496940+0.053024
## [19] train-rmse:0.200118+0.005711 test-rmse:0.498875+0.050813
## Stopping. Best iteration:
## [13] train-rmse:0.250393+0.010210 test-rmse:0.493573+0.048797
##
## 104
## [1] train-rmse:0.728303+0.011705 test-rmse:0.764208+0.058762
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.561099+0.006742 test-rmse:0.635151+0.061308
## [3] train-rmse:0.455936+0.006970 test-rmse:0.570704+0.063714
## [4] train-rmse:0.388361+0.011775 test-rmse:0.537328+0.068286
## [5] train-rmse:0.346618+0.009342 test-rmse:0.518241+0.065829
## [6] train-rmse:0.316056+0.012775 test-rmse:0.512460+0.064588
## [7] train-rmse:0.292904+0.016588 test-rmse:0.509843+0.061292
## [8] train-rmse:0.271723+0.015630 test-rmse:0.504341+0.054756
## [9] train-rmse:0.254461+0.016639 test-rmse:0.500291+0.053771
## [10] train-rmse:0.240463+0.018322 test-rmse:0.496192+0.051869
## [11] train-rmse:0.228330+0.017438 test-rmse:0.493912+0.049871
## [12] train-rmse:0.220121+0.016141 test-rmse:0.493638+0.048915
## [13] train-rmse:0.209676+0.014608 test-rmse:0.490508+0.049486
## [14] train-rmse:0.198408+0.014019 test-rmse:0.491847+0.053301
## [15] train-rmse:0.190607+0.014106 test-rmse:0.490461+0.053450
## [16] train-rmse:0.181244+0.012206 test-rmse:0.492591+0.050425
## [17] train-rmse:0.168961+0.015336 test-rmse:0.495342+0.051985
## [18] train-rmse:0.163697+0.016149 test-rmse:0.494793+0.051227
## [19] train-rmse:0.153302+0.015322 test-rmse:0.496000+0.051516
## [20] train-rmse:0.146364+0.012828 test-rmse:0.497097+0.053283
## [21] train-rmse:0.140313+0.013302 test-rmse:0.497576+0.055744
## Stopping. Best iteration:
## [15] train-rmse:0.190607+0.014106 test-rmse:0.490461+0.053450
##
## 105
## [1] train-rmse:0.807358+0.014282 test-rmse:0.813948+0.059973
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.717000+0.013574 test-rmse:0.741531+0.056261
## [3] train-rmse:0.669187+0.012352 test-rmse:0.689499+0.051873
## [4] train-rmse:0.638328+0.010567 test-rmse:0.660489+0.056998
## [5] train-rmse:0.619244+0.011279 test-rmse:0.650353+0.050186
## [6] train-rmse:0.607596+0.011059 test-rmse:0.639807+0.048223
## [7] train-rmse:0.597497+0.010899 test-rmse:0.634665+0.052003
## [8] train-rmse:0.588708+0.010971 test-rmse:0.625840+0.054412
## [9] train-rmse:0.580851+0.010704 test-rmse:0.616334+0.055373
## [10] train-rmse:0.574190+0.010708 test-rmse:0.611617+0.057207
## [11] train-rmse:0.568160+0.010802 test-rmse:0.606680+0.055226
## [12] train-rmse:0.562951+0.010833 test-rmse:0.602057+0.052967
## [13] train-rmse:0.558073+0.010832 test-rmse:0.597988+0.052948
## [14] train-rmse:0.553337+0.010915 test-rmse:0.591759+0.053048
## [15] train-rmse:0.548864+0.010967 test-rmse:0.588548+0.054716
## [16] train-rmse:0.544708+0.010945 test-rmse:0.587455+0.053273
## [17] train-rmse:0.540656+0.010837 test-rmse:0.584324+0.054125
## [18] train-rmse:0.536856+0.010785 test-rmse:0.578912+0.054502
## [19] train-rmse:0.533249+0.010657 test-rmse:0.578044+0.053082
## [20] train-rmse:0.530116+0.010589 test-rmse:0.576390+0.051936
## [21] train-rmse:0.527046+0.010640 test-rmse:0.576781+0.050339
## [22] train-rmse:0.524168+0.010607 test-rmse:0.573269+0.051703
## [23] train-rmse:0.521609+0.010679 test-rmse:0.570792+0.052302
## [24] train-rmse:0.519046+0.010699 test-rmse:0.570833+0.054832
## [25] train-rmse:0.516607+0.010716 test-rmse:0.570190+0.054859
## [26] train-rmse:0.514215+0.010779 test-rmse:0.568278+0.053469
## [27] train-rmse:0.512052+0.010768 test-rmse:0.566153+0.053539
## [28] train-rmse:0.509961+0.010705 test-rmse:0.563868+0.051981
## [29] train-rmse:0.507962+0.010670 test-rmse:0.561906+0.053229
## [30] train-rmse:0.506009+0.010614 test-rmse:0.560436+0.051606
## [31] train-rmse:0.504111+0.010506 test-rmse:0.558167+0.051716
## [32] train-rmse:0.502336+0.010447 test-rmse:0.555350+0.052189
## [33] train-rmse:0.500567+0.010381 test-rmse:0.554004+0.051694
## [34] train-rmse:0.498866+0.010350 test-rmse:0.551915+0.051524
## [35] train-rmse:0.497193+0.010325 test-rmse:0.550503+0.051557
## [36] train-rmse:0.495686+0.010321 test-rmse:0.548941+0.050315
## [37] train-rmse:0.494248+0.010280 test-rmse:0.550022+0.048955
## [38] train-rmse:0.492869+0.010232 test-rmse:0.549148+0.049547
## [39] train-rmse:0.491491+0.010215 test-rmse:0.547147+0.050857
## [40] train-rmse:0.490182+0.010224 test-rmse:0.547081+0.050671
## [41] train-rmse:0.488906+0.010266 test-rmse:0.545050+0.051036
## [42] train-rmse:0.487739+0.010230 test-rmse:0.544172+0.051548
## [43] train-rmse:0.486602+0.010242 test-rmse:0.544280+0.051650
## [44] train-rmse:0.485455+0.010229 test-rmse:0.544368+0.049914
## [45] train-rmse:0.484412+0.010234 test-rmse:0.545740+0.049730
## [46] train-rmse:0.483376+0.010228 test-rmse:0.544055+0.049355
## [47] train-rmse:0.482376+0.010214 test-rmse:0.544565+0.049230
## [48] train-rmse:0.481430+0.010199 test-rmse:0.543899+0.049793
## [49] train-rmse:0.480502+0.010181 test-rmse:0.543779+0.051283
## [50] train-rmse:0.479580+0.010154 test-rmse:0.543801+0.050602
## [51] train-rmse:0.478714+0.010151 test-rmse:0.542078+0.050168
## [52] train-rmse:0.477888+0.010171 test-rmse:0.541598+0.050235
## [53] train-rmse:0.477086+0.010173 test-rmse:0.541016+0.049421
## [54] train-rmse:0.476317+0.010165 test-rmse:0.540013+0.048832
## [55] train-rmse:0.475545+0.010142 test-rmse:0.540797+0.048708
## [56] train-rmse:0.474808+0.010130 test-rmse:0.539860+0.048627
## [57] train-rmse:0.474101+0.010116 test-rmse:0.539996+0.048286
## [58] train-rmse:0.473395+0.010096 test-rmse:0.539270+0.047353
## [59] train-rmse:0.472716+0.010051 test-rmse:0.538260+0.046882
## [60] train-rmse:0.472055+0.010008 test-rmse:0.536968+0.045814
## [61] train-rmse:0.471412+0.010007 test-rmse:0.536524+0.045217
## [62] train-rmse:0.470800+0.009997 test-rmse:0.536518+0.045245
## [63] train-rmse:0.470203+0.009965 test-rmse:0.536492+0.044703
## [64] train-rmse:0.469629+0.009943 test-rmse:0.535316+0.046106
## [65] train-rmse:0.469059+0.009913 test-rmse:0.535742+0.046308
## [66] train-rmse:0.468509+0.009879 test-rmse:0.535446+0.046041
## [67] train-rmse:0.467976+0.009867 test-rmse:0.535703+0.045176
## [68] train-rmse:0.467462+0.009849 test-rmse:0.534994+0.045763
## [69] train-rmse:0.466965+0.009819 test-rmse:0.535180+0.045440
## [70] train-rmse:0.466440+0.009816 test-rmse:0.535465+0.045398
## [71] train-rmse:0.465957+0.009781 test-rmse:0.535203+0.045501
## [72] train-rmse:0.465462+0.009734 test-rmse:0.535151+0.045877
## [73] train-rmse:0.465002+0.009714 test-rmse:0.535394+0.046129
## [74] train-rmse:0.464537+0.009698 test-rmse:0.534591+0.045856
## [75] train-rmse:0.464093+0.009678 test-rmse:0.534621+0.045183
## [76] train-rmse:0.463651+0.009663 test-rmse:0.534477+0.045193
## [77] train-rmse:0.463218+0.009647 test-rmse:0.535111+0.045737
## [78] train-rmse:0.462793+0.009636 test-rmse:0.534661+0.044963
## [79] train-rmse:0.462379+0.009635 test-rmse:0.533139+0.045473
## [80] train-rmse:0.461962+0.009632 test-rmse:0.532965+0.045472
## [81] train-rmse:0.461554+0.009642 test-rmse:0.532757+0.045499
## [82] train-rmse:0.461153+0.009632 test-rmse:0.532397+0.045533
## [83] train-rmse:0.460758+0.009619 test-rmse:0.532152+0.045553
## [84] train-rmse:0.460371+0.009606 test-rmse:0.532318+0.045848
## [85] train-rmse:0.459988+0.009603 test-rmse:0.532136+0.045474
## [86] train-rmse:0.459597+0.009599 test-rmse:0.532817+0.044852
## [87] train-rmse:0.459252+0.009583 test-rmse:0.532444+0.044699
## [88] train-rmse:0.458875+0.009573 test-rmse:0.532577+0.044295
## [89] train-rmse:0.458526+0.009546 test-rmse:0.532136+0.044890
## [90] train-rmse:0.458194+0.009529 test-rmse:0.531819+0.044751
## [91] train-rmse:0.457862+0.009506 test-rmse:0.531156+0.044188
## [92] train-rmse:0.457536+0.009488 test-rmse:0.531826+0.044420
## [93] train-rmse:0.457204+0.009479 test-rmse:0.530914+0.044912
## [94] train-rmse:0.456871+0.009452 test-rmse:0.530858+0.044551
## [95] train-rmse:0.456551+0.009418 test-rmse:0.531067+0.044412
## [96] train-rmse:0.456232+0.009419 test-rmse:0.531355+0.044633
## [97] train-rmse:0.455930+0.009400 test-rmse:0.530894+0.045740
## [98] train-rmse:0.455624+0.009386 test-rmse:0.530924+0.045232
## [99] train-rmse:0.455301+0.009363 test-rmse:0.530458+0.045317
## [100] train-rmse:0.454961+0.009344 test-rmse:0.531148+0.045008
## [101] train-rmse:0.454668+0.009337 test-rmse:0.530315+0.044712
## [102] train-rmse:0.454365+0.009326 test-rmse:0.529863+0.045271
## [103] train-rmse:0.454056+0.009325 test-rmse:0.529556+0.045449
## [104] train-rmse:0.453757+0.009314 test-rmse:0.529472+0.045606
## [105] train-rmse:0.453468+0.009297 test-rmse:0.529721+0.045688
## [106] train-rmse:0.453184+0.009286 test-rmse:0.529586+0.045244
## [107] train-rmse:0.452875+0.009241 test-rmse:0.529169+0.045021
## [108] train-rmse:0.452592+0.009234 test-rmse:0.529685+0.045037
## [109] train-rmse:0.452317+0.009227 test-rmse:0.529259+0.044742
## [110] train-rmse:0.452042+0.009219 test-rmse:0.529022+0.045009
## [111] train-rmse:0.451744+0.009225 test-rmse:0.528823+0.044996
## [112] train-rmse:0.451458+0.009223 test-rmse:0.529181+0.044612
## [113] train-rmse:0.451189+0.009215 test-rmse:0.528897+0.044125
## [114] train-rmse:0.450933+0.009209 test-rmse:0.528615+0.043119
## [115] train-rmse:0.450646+0.009173 test-rmse:0.528583+0.043619
## [116] train-rmse:0.450376+0.009174 test-rmse:0.529284+0.043875
## [117] train-rmse:0.450119+0.009157 test-rmse:0.528843+0.044439
## [118] train-rmse:0.449862+0.009145 test-rmse:0.528862+0.044357
## [119] train-rmse:0.449597+0.009137 test-rmse:0.529422+0.044190
## [120] train-rmse:0.449337+0.009114 test-rmse:0.529813+0.044260
## [121] train-rmse:0.449081+0.009122 test-rmse:0.529725+0.044801
## Stopping. Best iteration:
## [115] train-rmse:0.450646+0.009173 test-rmse:0.528583+0.043619
##
## 106
## [1] train-rmse:0.774745+0.013973 test-rmse:0.783691+0.060769
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.661252+0.012883 test-rmse:0.687540+0.064771
## [3] train-rmse:0.602165+0.011712 test-rmse:0.632278+0.069915
## [4] train-rmse:0.568067+0.014390 test-rmse:0.609976+0.067620
## [5] train-rmse:0.546730+0.018214 test-rmse:0.591453+0.061900
## [6] train-rmse:0.532945+0.017802 test-rmse:0.579766+0.059109
## [7] train-rmse:0.521159+0.017898 test-rmse:0.570561+0.058894
## [8] train-rmse:0.511141+0.016874 test-rmse:0.565441+0.058778
## [9] train-rmse:0.501410+0.016948 test-rmse:0.556358+0.057255
## [10] train-rmse:0.492686+0.017786 test-rmse:0.554302+0.054931
## [11] train-rmse:0.486234+0.017383 test-rmse:0.547806+0.060994
## [12] train-rmse:0.479502+0.017794 test-rmse:0.540470+0.057236
## [13] train-rmse:0.473267+0.016447 test-rmse:0.538424+0.061450
## [14] train-rmse:0.465405+0.018810 test-rmse:0.535117+0.057752
## [15] train-rmse:0.460273+0.018659 test-rmse:0.533180+0.055534
## [16] train-rmse:0.455117+0.018239 test-rmse:0.531119+0.056980
## [17] train-rmse:0.449025+0.017634 test-rmse:0.529569+0.059146
## [18] train-rmse:0.443642+0.018064 test-rmse:0.528216+0.059147
## [19] train-rmse:0.440152+0.017964 test-rmse:0.527673+0.056878
## [20] train-rmse:0.436141+0.017697 test-rmse:0.528425+0.057835
## [21] train-rmse:0.431736+0.016970 test-rmse:0.527278+0.056613
## [22] train-rmse:0.427673+0.017544 test-rmse:0.525005+0.058078
## [23] train-rmse:0.423212+0.018960 test-rmse:0.524421+0.058542
## [24] train-rmse:0.419957+0.018947 test-rmse:0.523346+0.055424
## [25] train-rmse:0.416199+0.018461 test-rmse:0.521145+0.055377
## [26] train-rmse:0.413110+0.018505 test-rmse:0.518792+0.055340
## [27] train-rmse:0.409910+0.018408 test-rmse:0.518627+0.055403
## [28] train-rmse:0.406752+0.019500 test-rmse:0.517452+0.055699
## [29] train-rmse:0.403942+0.018850 test-rmse:0.514108+0.057282
## [30] train-rmse:0.401932+0.018568 test-rmse:0.512547+0.057348
## [31] train-rmse:0.399879+0.018306 test-rmse:0.514013+0.057679
## [32] train-rmse:0.396590+0.018274 test-rmse:0.512426+0.059777
## [33] train-rmse:0.394077+0.017679 test-rmse:0.511806+0.059850
## [34] train-rmse:0.390916+0.016826 test-rmse:0.509187+0.060115
## [35] train-rmse:0.389048+0.016271 test-rmse:0.507517+0.061503
## [36] train-rmse:0.387054+0.016131 test-rmse:0.507005+0.062036
## [37] train-rmse:0.384418+0.016559 test-rmse:0.506822+0.061725
## [38] train-rmse:0.382438+0.016952 test-rmse:0.505758+0.060195
## [39] train-rmse:0.380187+0.017308 test-rmse:0.506704+0.059841
## [40] train-rmse:0.377902+0.017024 test-rmse:0.505175+0.059503
## [41] train-rmse:0.376227+0.016983 test-rmse:0.504955+0.060040
## [42] train-rmse:0.374779+0.017136 test-rmse:0.503492+0.058008
## [43] train-rmse:0.372405+0.018293 test-rmse:0.503176+0.056719
## [44] train-rmse:0.369645+0.018184 test-rmse:0.502470+0.058132
## [45] train-rmse:0.367936+0.018051 test-rmse:0.503138+0.057137
## [46] train-rmse:0.366233+0.017521 test-rmse:0.504306+0.057238
## [47] train-rmse:0.363922+0.017807 test-rmse:0.504128+0.057805
## [48] train-rmse:0.361651+0.018293 test-rmse:0.504086+0.058196
## [49] train-rmse:0.360052+0.018353 test-rmse:0.504530+0.056820
## [50] train-rmse:0.357465+0.017308 test-rmse:0.503614+0.059021
## Stopping. Best iteration:
## [44] train-rmse:0.369645+0.018184 test-rmse:0.502470+0.058132
##
## 107
## [1] train-rmse:0.760107+0.014654 test-rmse:0.776133+0.064224
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.631484+0.013748 test-rmse:0.664209+0.064834
## [3] train-rmse:0.563744+0.015189 test-rmse:0.611069+0.060250
## [4] train-rmse:0.524491+0.017984 test-rmse:0.586396+0.057102
## [5] train-rmse:0.497345+0.017912 test-rmse:0.566613+0.054297
## [6] train-rmse:0.480632+0.016800 test-rmse:0.555208+0.053106
## [7] train-rmse:0.465744+0.016592 test-rmse:0.549702+0.052537
## [8] train-rmse:0.454871+0.016495 test-rmse:0.540533+0.054564
## [9] train-rmse:0.443255+0.017674 test-rmse:0.536084+0.051719
## [10] train-rmse:0.429844+0.018243 test-rmse:0.531929+0.056362
## [11] train-rmse:0.421248+0.018823 test-rmse:0.529701+0.057236
## [12] train-rmse:0.412225+0.016297 test-rmse:0.527001+0.055087
## [13] train-rmse:0.404254+0.018456 test-rmse:0.526509+0.054810
## [14] train-rmse:0.395408+0.020393 test-rmse:0.523167+0.058533
## [15] train-rmse:0.389489+0.019169 test-rmse:0.520708+0.058037
## [16] train-rmse:0.382256+0.018660 test-rmse:0.518677+0.059534
## [17] train-rmse:0.376710+0.018676 test-rmse:0.516101+0.062359
## [18] train-rmse:0.371029+0.017647 test-rmse:0.515025+0.062245
## [19] train-rmse:0.364426+0.019903 test-rmse:0.516414+0.060513
## [20] train-rmse:0.359787+0.019563 test-rmse:0.514301+0.060440
## [21] train-rmse:0.353532+0.017726 test-rmse:0.513670+0.058521
## [22] train-rmse:0.348734+0.018120 test-rmse:0.511007+0.059043
## [23] train-rmse:0.343144+0.017007 test-rmse:0.507402+0.061068
## [24] train-rmse:0.335983+0.018562 test-rmse:0.506448+0.059024
## [25] train-rmse:0.330158+0.019627 test-rmse:0.508040+0.058280
## [26] train-rmse:0.325089+0.018415 test-rmse:0.507581+0.057122
## [27] train-rmse:0.320950+0.018153 test-rmse:0.507132+0.055044
## [28] train-rmse:0.316217+0.017661 test-rmse:0.506412+0.053318
## [29] train-rmse:0.311769+0.018312 test-rmse:0.505613+0.051399
## [30] train-rmse:0.308002+0.019300 test-rmse:0.503558+0.050739
## [31] train-rmse:0.305056+0.019058 test-rmse:0.502978+0.049950
## [32] train-rmse:0.301490+0.018755 test-rmse:0.502261+0.048568
## [33] train-rmse:0.297553+0.019283 test-rmse:0.503861+0.048106
## [34] train-rmse:0.294893+0.018587 test-rmse:0.503641+0.047850
## [35] train-rmse:0.291152+0.018142 test-rmse:0.504126+0.049190
## [36] train-rmse:0.285663+0.018607 test-rmse:0.500058+0.047218
## [37] train-rmse:0.281443+0.019100 test-rmse:0.501098+0.046230
## [38] train-rmse:0.277369+0.017895 test-rmse:0.500640+0.046047
## [39] train-rmse:0.274520+0.018154 test-rmse:0.499980+0.045069
## [40] train-rmse:0.271505+0.017242 test-rmse:0.500023+0.045770
## [41] train-rmse:0.267655+0.016144 test-rmse:0.499460+0.048013
## [42] train-rmse:0.264730+0.015588 test-rmse:0.500732+0.049296
## [43] train-rmse:0.260698+0.015840 test-rmse:0.499965+0.048324
## [44] train-rmse:0.257029+0.016893 test-rmse:0.500914+0.047501
## [45] train-rmse:0.253657+0.016299 test-rmse:0.501599+0.047087
## [46] train-rmse:0.250333+0.016375 test-rmse:0.500072+0.045949
## [47] train-rmse:0.247992+0.016429 test-rmse:0.500107+0.044377
## Stopping. Best iteration:
## [41] train-rmse:0.267655+0.016144 test-rmse:0.499460+0.048013
##
## 108
## [1] train-rmse:0.747417+0.014258 test-rmse:0.768647+0.061680
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.607708+0.013616 test-rmse:0.648085+0.063261
## [3] train-rmse:0.531868+0.013512 test-rmse:0.598520+0.057230
## [4] train-rmse:0.484321+0.011137 test-rmse:0.563595+0.052729
## [5] train-rmse:0.454923+0.011283 test-rmse:0.546047+0.051032
## [6] train-rmse:0.431791+0.014196 test-rmse:0.540327+0.049538
## [7] train-rmse:0.413085+0.012689 test-rmse:0.534298+0.054971
## [8] train-rmse:0.397872+0.012688 test-rmse:0.527981+0.053665
## [9] train-rmse:0.386273+0.012912 test-rmse:0.520782+0.055120
## [10] train-rmse:0.375072+0.011914 test-rmse:0.521259+0.053757
## [11] train-rmse:0.364454+0.012761 test-rmse:0.520291+0.056176
## [12] train-rmse:0.355116+0.014874 test-rmse:0.515656+0.056813
## [13] train-rmse:0.346744+0.014351 test-rmse:0.515805+0.053247
## [14] train-rmse:0.338428+0.012798 test-rmse:0.514828+0.055010
## [15] train-rmse:0.330940+0.010816 test-rmse:0.514387+0.053484
## [16] train-rmse:0.323223+0.010597 test-rmse:0.513721+0.057008
## [17] train-rmse:0.316450+0.010169 test-rmse:0.515849+0.057234
## [18] train-rmse:0.309638+0.009651 test-rmse:0.514476+0.056068
## [19] train-rmse:0.302640+0.010673 test-rmse:0.512567+0.054906
## [20] train-rmse:0.293238+0.008915 test-rmse:0.513083+0.055418
## [21] train-rmse:0.289099+0.007897 test-rmse:0.512720+0.057370
## [22] train-rmse:0.279719+0.009457 test-rmse:0.508859+0.059338
## [23] train-rmse:0.275351+0.010064 test-rmse:0.508407+0.061266
## [24] train-rmse:0.269540+0.012442 test-rmse:0.506651+0.060075
## [25] train-rmse:0.263636+0.013311 test-rmse:0.507180+0.058506
## [26] train-rmse:0.258227+0.012504 test-rmse:0.507671+0.058840
## [27] train-rmse:0.252399+0.013131 test-rmse:0.507624+0.057534
## [28] train-rmse:0.247872+0.013078 test-rmse:0.508029+0.058558
## [29] train-rmse:0.242672+0.013683 test-rmse:0.508869+0.059156
## [30] train-rmse:0.237804+0.014018 test-rmse:0.507876+0.057581
## Stopping. Best iteration:
## [24] train-rmse:0.269540+0.012442 test-rmse:0.506651+0.060075
##
## 109
## [1] train-rmse:0.736012+0.013314 test-rmse:0.762723+0.062366
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.584481+0.011053 test-rmse:0.640493+0.062654
## [3] train-rmse:0.495923+0.014250 test-rmse:0.577449+0.049557
## [4] train-rmse:0.442165+0.011673 test-rmse:0.552155+0.059202
## [5] train-rmse:0.412143+0.011641 test-rmse:0.536789+0.059107
## [6] train-rmse:0.382917+0.017960 test-rmse:0.523955+0.062226
## [7] train-rmse:0.364416+0.016649 test-rmse:0.519657+0.061509
## [8] train-rmse:0.349759+0.014350 test-rmse:0.513488+0.057357
## [9] train-rmse:0.334141+0.017649 test-rmse:0.515783+0.058429
## [10] train-rmse:0.322535+0.018272 test-rmse:0.513030+0.059651
## [11] train-rmse:0.312042+0.017135 test-rmse:0.508074+0.063153
## [12] train-rmse:0.300917+0.017550 test-rmse:0.505062+0.064625
## [13] train-rmse:0.291643+0.016353 test-rmse:0.501638+0.064581
## [14] train-rmse:0.280009+0.017369 test-rmse:0.499444+0.063494
## [15] train-rmse:0.268485+0.019036 test-rmse:0.496816+0.063079
## [16] train-rmse:0.255182+0.017848 test-rmse:0.497210+0.060489
## [17] train-rmse:0.245220+0.017241 test-rmse:0.498355+0.059437
## [18] train-rmse:0.236643+0.017515 test-rmse:0.499378+0.059359
## [19] train-rmse:0.230104+0.018002 test-rmse:0.497640+0.059124
## [20] train-rmse:0.225609+0.018983 test-rmse:0.497309+0.058550
## [21] train-rmse:0.218740+0.018617 test-rmse:0.496598+0.055432
## [22] train-rmse:0.211488+0.020765 test-rmse:0.498367+0.056222
## [23] train-rmse:0.203882+0.021420 test-rmse:0.500040+0.057153
## [24] train-rmse:0.196681+0.021972 test-rmse:0.504156+0.058110
## [25] train-rmse:0.191542+0.018576 test-rmse:0.504186+0.057582
## [26] train-rmse:0.183551+0.016618 test-rmse:0.505099+0.058257
## [27] train-rmse:0.178865+0.017364 test-rmse:0.506403+0.059355
## Stopping. Best iteration:
## [21] train-rmse:0.218740+0.018617 test-rmse:0.496598+0.055432
##
## 110
## [1] train-rmse:0.725404+0.012356 test-rmse:0.759212+0.062350
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.562336+0.009455 test-rmse:0.632981+0.062164
## [3] train-rmse:0.462422+0.011152 test-rmse:0.563447+0.057108
## [4] train-rmse:0.405655+0.006801 test-rmse:0.534809+0.053303
## [5] train-rmse:0.367629+0.013082 test-rmse:0.526878+0.050095
## [6] train-rmse:0.334774+0.014231 test-rmse:0.520510+0.049920
## [7] train-rmse:0.318643+0.013230 test-rmse:0.512006+0.049932
## [8] train-rmse:0.303200+0.011799 test-rmse:0.506564+0.047211
## [9] train-rmse:0.283598+0.019149 test-rmse:0.502054+0.045804
## [10] train-rmse:0.270475+0.016091 test-rmse:0.496954+0.048231
## [11] train-rmse:0.257924+0.017602 test-rmse:0.494418+0.048467
## [12] train-rmse:0.246025+0.015111 test-rmse:0.493681+0.046280
## [13] train-rmse:0.236575+0.014088 test-rmse:0.492414+0.046150
## [14] train-rmse:0.228030+0.013550 test-rmse:0.493839+0.046514
## [15] train-rmse:0.216027+0.012430 test-rmse:0.492237+0.044778
## [16] train-rmse:0.207453+0.009605 test-rmse:0.491457+0.044249
## [17] train-rmse:0.198460+0.009608 test-rmse:0.490600+0.042333
## [18] train-rmse:0.188311+0.010890 test-rmse:0.489445+0.042172
## [19] train-rmse:0.181472+0.013088 test-rmse:0.490780+0.044653
## [20] train-rmse:0.175106+0.013595 test-rmse:0.492062+0.045477
## [21] train-rmse:0.169468+0.012751 test-rmse:0.492959+0.044378
## [22] train-rmse:0.163491+0.010647 test-rmse:0.494192+0.044579
## [23] train-rmse:0.156610+0.010752 test-rmse:0.495349+0.044211
## [24] train-rmse:0.148900+0.008083 test-rmse:0.497846+0.044066
## Stopping. Best iteration:
## [18] train-rmse:0.188311+0.010890 test-rmse:0.489445+0.042172
##
## 111
## [1] train-rmse:0.714847+0.011536 test-rmse:0.753398+0.058629
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.545134+0.006454 test-rmse:0.624695+0.060813
## [3] train-rmse:0.442349+0.006937 test-rmse:0.555810+0.060216
## [4] train-rmse:0.378426+0.007206 test-rmse:0.522271+0.062161
## [5] train-rmse:0.334287+0.009469 test-rmse:0.508678+0.064733
## [6] train-rmse:0.303208+0.005725 test-rmse:0.502667+0.060561
## [7] train-rmse:0.287526+0.007787 test-rmse:0.499740+0.057848
## [8] train-rmse:0.266179+0.007373 test-rmse:0.491349+0.055074
## [9] train-rmse:0.246272+0.012374 test-rmse:0.492561+0.055719
## [10] train-rmse:0.236381+0.011172 test-rmse:0.492984+0.054388
## [11] train-rmse:0.226088+0.009980 test-rmse:0.493491+0.057585
## [12] train-rmse:0.216157+0.009118 test-rmse:0.490955+0.055857
## [13] train-rmse:0.203148+0.006435 test-rmse:0.493673+0.058777
## [14] train-rmse:0.191692+0.007088 test-rmse:0.493223+0.058484
## [15] train-rmse:0.180719+0.007475 test-rmse:0.494607+0.054568
## [16] train-rmse:0.170878+0.007199 test-rmse:0.495601+0.056192
## [17] train-rmse:0.163430+0.008751 test-rmse:0.496427+0.057354
## [18] train-rmse:0.151682+0.006470 test-rmse:0.495096+0.056308
## Stopping. Best iteration:
## [12] train-rmse:0.216157+0.009118 test-rmse:0.490955+0.055857
##
## 112## max_depth eta
## 98 7 0.36## [1] train-rmse:1.482418+0.015245 test-rmse:1.482465+0.068789
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.374356+0.013741 test-rmse:1.375676+0.069946
## [3] train-rmse:1.280008+0.012388 test-rmse:1.282001+0.069717
## [4] train-rmse:1.198026+0.011212 test-rmse:1.201141+0.070062
## [5] train-rmse:1.127156+0.010179 test-rmse:1.131071+0.069015
## [6] train-rmse:1.066209+0.009309 test-rmse:1.071277+0.068499
## [7] train-rmse:1.013879+0.008670 test-rmse:1.022203+0.067685
## [8] train-rmse:0.969057+0.008182 test-rmse:0.978466+0.065470
## [9] train-rmse:0.930692+0.007743 test-rmse:0.941915+0.063792
## [10] train-rmse:0.898027+0.007377 test-rmse:0.911898+0.061915
## [11] train-rmse:0.870189+0.007106 test-rmse:0.885571+0.059447
## [12] train-rmse:0.846570+0.006837 test-rmse:0.863350+0.058010
## [13] train-rmse:0.826545+0.006701 test-rmse:0.845984+0.055831
## [14] train-rmse:0.809521+0.006525 test-rmse:0.830524+0.052735
## [15] train-rmse:0.795133+0.006381 test-rmse:0.818341+0.051176
## [16] train-rmse:0.782907+0.006151 test-rmse:0.807748+0.048804
## [17] train-rmse:0.772489+0.006018 test-rmse:0.799427+0.048186
## [18] train-rmse:0.763529+0.005886 test-rmse:0.790461+0.047713
## [19] train-rmse:0.755869+0.005749 test-rmse:0.783401+0.044856
## [20] train-rmse:0.749183+0.005625 test-rmse:0.778109+0.042128
## [21] train-rmse:0.743326+0.005507 test-rmse:0.773552+0.041227
## [22] train-rmse:0.738198+0.005430 test-rmse:0.768730+0.040003
## [23] train-rmse:0.733656+0.005341 test-rmse:0.764679+0.040312
## [24] train-rmse:0.729642+0.005262 test-rmse:0.761408+0.038615
## [25] train-rmse:0.726026+0.005168 test-rmse:0.759667+0.037969
## [26] train-rmse:0.722769+0.005083 test-rmse:0.756612+0.036371
## [27] train-rmse:0.719797+0.005077 test-rmse:0.754036+0.036529
## [28] train-rmse:0.717092+0.005037 test-rmse:0.752788+0.035814
## [29] train-rmse:0.714596+0.004987 test-rmse:0.751555+0.034642
## [30] train-rmse:0.712254+0.004952 test-rmse:0.749818+0.033563
## [31] train-rmse:0.710079+0.004937 test-rmse:0.748380+0.032951
## [32] train-rmse:0.708026+0.004954 test-rmse:0.746792+0.032111
## [33] train-rmse:0.706116+0.004970 test-rmse:0.746924+0.031737
## [34] train-rmse:0.704304+0.004953 test-rmse:0.745932+0.031324
## [35] train-rmse:0.702550+0.004927 test-rmse:0.744233+0.031532
## [36] train-rmse:0.700847+0.004917 test-rmse:0.743097+0.031108
## [37] train-rmse:0.699275+0.004940 test-rmse:0.741147+0.030288
## [38] train-rmse:0.697738+0.004962 test-rmse:0.740188+0.030271
## [39] train-rmse:0.696241+0.005006 test-rmse:0.739675+0.029143
## [40] train-rmse:0.694813+0.005017 test-rmse:0.739267+0.028584
## [41] train-rmse:0.693425+0.005029 test-rmse:0.738753+0.028647
## [42] train-rmse:0.692062+0.005037 test-rmse:0.739394+0.028981
## [43] train-rmse:0.690728+0.005033 test-rmse:0.738671+0.029026
## [44] train-rmse:0.689439+0.005036 test-rmse:0.737592+0.029116
## [45] train-rmse:0.688181+0.005027 test-rmse:0.736704+0.029802
## [46] train-rmse:0.686963+0.005054 test-rmse:0.735733+0.029934
## [47] train-rmse:0.685762+0.005073 test-rmse:0.734294+0.029646
## [48] train-rmse:0.684578+0.005092 test-rmse:0.733705+0.029457
## [49] train-rmse:0.683428+0.005095 test-rmse:0.733659+0.028622
## [50] train-rmse:0.682303+0.005095 test-rmse:0.733913+0.028909
## [51] train-rmse:0.681203+0.005100 test-rmse:0.733560+0.029376
## [52] train-rmse:0.680123+0.005103 test-rmse:0.732729+0.029160
## [53] train-rmse:0.679055+0.005107 test-rmse:0.732224+0.029342
## [54] train-rmse:0.677996+0.005115 test-rmse:0.731975+0.030007
## [55] train-rmse:0.676960+0.005129 test-rmse:0.731584+0.029939
## [56] train-rmse:0.675940+0.005129 test-rmse:0.730798+0.029958
## [57] train-rmse:0.674949+0.005137 test-rmse:0.730371+0.030331
## [58] train-rmse:0.673975+0.005138 test-rmse:0.730091+0.030803
## [59] train-rmse:0.673004+0.005132 test-rmse:0.729252+0.030832
## [60] train-rmse:0.672043+0.005142 test-rmse:0.728785+0.030767
## [61] train-rmse:0.671105+0.005137 test-rmse:0.728798+0.030609
## [62] train-rmse:0.670171+0.005136 test-rmse:0.729493+0.031122
## [63] train-rmse:0.669252+0.005152 test-rmse:0.728175+0.030877
## [64] train-rmse:0.668357+0.005172 test-rmse:0.728558+0.031679
## [65] train-rmse:0.667479+0.005194 test-rmse:0.728095+0.031597
## [66] train-rmse:0.666609+0.005219 test-rmse:0.727415+0.031792
## [67] train-rmse:0.665755+0.005234 test-rmse:0.727009+0.031839
## [68] train-rmse:0.664909+0.005245 test-rmse:0.726239+0.031315
## [69] train-rmse:0.664081+0.005253 test-rmse:0.725806+0.031313
## [70] train-rmse:0.663260+0.005267 test-rmse:0.725020+0.031174
## [71] train-rmse:0.662446+0.005285 test-rmse:0.724144+0.031504
## [72] train-rmse:0.661641+0.005295 test-rmse:0.724342+0.032235
## [73] train-rmse:0.660848+0.005304 test-rmse:0.724578+0.032355
## [74] train-rmse:0.660069+0.005317 test-rmse:0.724287+0.032768
## [75] train-rmse:0.659292+0.005331 test-rmse:0.723248+0.031600
## [76] train-rmse:0.658523+0.005340 test-rmse:0.723551+0.031909
## [77] train-rmse:0.657763+0.005352 test-rmse:0.723691+0.032455
## [78] train-rmse:0.657012+0.005369 test-rmse:0.723627+0.032371
## [79] train-rmse:0.656267+0.005382 test-rmse:0.723142+0.032313
## [80] train-rmse:0.655535+0.005405 test-rmse:0.722174+0.032507
## [81] train-rmse:0.654823+0.005426 test-rmse:0.722324+0.032765
## [82] train-rmse:0.654125+0.005437 test-rmse:0.721766+0.032425
## [83] train-rmse:0.653427+0.005445 test-rmse:0.721396+0.032214
## [84] train-rmse:0.652735+0.005454 test-rmse:0.721206+0.032106
## [85] train-rmse:0.652049+0.005469 test-rmse:0.720449+0.032096
## [86] train-rmse:0.651371+0.005494 test-rmse:0.720269+0.032813
## [87] train-rmse:0.650698+0.005514 test-rmse:0.720686+0.033392
## [88] train-rmse:0.650039+0.005525 test-rmse:0.720577+0.033524
## [89] train-rmse:0.649388+0.005535 test-rmse:0.719993+0.032947
## [90] train-rmse:0.648743+0.005548 test-rmse:0.720115+0.033208
## [91] train-rmse:0.648107+0.005556 test-rmse:0.719892+0.033058
## [92] train-rmse:0.647478+0.005564 test-rmse:0.719639+0.033355
## [93] train-rmse:0.646860+0.005573 test-rmse:0.719840+0.033704
## [94] train-rmse:0.646244+0.005582 test-rmse:0.719264+0.033997
## [95] train-rmse:0.645636+0.005586 test-rmse:0.719811+0.034406
## [96] train-rmse:0.645031+0.005596 test-rmse:0.719133+0.034266
## [97] train-rmse:0.644433+0.005610 test-rmse:0.718825+0.033648
## [98] train-rmse:0.643839+0.005623 test-rmse:0.717923+0.033680
## [99] train-rmse:0.643254+0.005632 test-rmse:0.718344+0.034070
## [100] train-rmse:0.642675+0.005640 test-rmse:0.718010+0.033924
## [101] train-rmse:0.642100+0.005650 test-rmse:0.717897+0.034437
## [102] train-rmse:0.641535+0.005659 test-rmse:0.717701+0.034369
## [103] train-rmse:0.640964+0.005661 test-rmse:0.717403+0.034543
## [104] train-rmse:0.640408+0.005672 test-rmse:0.717367+0.034426
## [105] train-rmse:0.639850+0.005684 test-rmse:0.717023+0.034404
## [106] train-rmse:0.639304+0.005691 test-rmse:0.717201+0.034608
## [107] train-rmse:0.638764+0.005695 test-rmse:0.716801+0.034552
## [108] train-rmse:0.638224+0.005701 test-rmse:0.716468+0.034388
## [109] train-rmse:0.637690+0.005705 test-rmse:0.716596+0.034662
## [110] train-rmse:0.637166+0.005712 test-rmse:0.715903+0.034834
## [111] train-rmse:0.636643+0.005721 test-rmse:0.715479+0.033962
## [112] train-rmse:0.636125+0.005728 test-rmse:0.715124+0.033992
## [113] train-rmse:0.635603+0.005730 test-rmse:0.715112+0.034386
## [114] train-rmse:0.635084+0.005739 test-rmse:0.714305+0.034743
## [115] train-rmse:0.634566+0.005748 test-rmse:0.713790+0.034475
## [116] train-rmse:0.634063+0.005751 test-rmse:0.713361+0.034325
## [117] train-rmse:0.633565+0.005755 test-rmse:0.713251+0.034486
## [118] train-rmse:0.633072+0.005759 test-rmse:0.712753+0.034383
## [119] train-rmse:0.632576+0.005759 test-rmse:0.712313+0.034195
## [120] train-rmse:0.632086+0.005765 test-rmse:0.711942+0.034006
## [121] train-rmse:0.631604+0.005765 test-rmse:0.711917+0.033909
## [122] train-rmse:0.631123+0.005768 test-rmse:0.711552+0.034248
## [123] train-rmse:0.630645+0.005772 test-rmse:0.711656+0.034267
## [124] train-rmse:0.630172+0.005779 test-rmse:0.711445+0.034001
## [125] train-rmse:0.629699+0.005777 test-rmse:0.711710+0.034395
## [126] train-rmse:0.629225+0.005780 test-rmse:0.711579+0.034808
## [127] train-rmse:0.628754+0.005790 test-rmse:0.711508+0.034547
## [128] train-rmse:0.628287+0.005799 test-rmse:0.711326+0.034157
## [129] train-rmse:0.627829+0.005806 test-rmse:0.710510+0.033833
## [130] train-rmse:0.627369+0.005814 test-rmse:0.711337+0.034253
## [131] train-rmse:0.626918+0.005817 test-rmse:0.710666+0.034047
## [132] train-rmse:0.626464+0.005830 test-rmse:0.710514+0.033992
## [133] train-rmse:0.626018+0.005839 test-rmse:0.710415+0.033728
## [134] train-rmse:0.625578+0.005844 test-rmse:0.710708+0.034182
## [135] train-rmse:0.625141+0.005847 test-rmse:0.709925+0.034490
## [136] train-rmse:0.624704+0.005851 test-rmse:0.709281+0.034421
## [137] train-rmse:0.624270+0.005855 test-rmse:0.709171+0.034051
## [138] train-rmse:0.623842+0.005863 test-rmse:0.708861+0.034015
## [139] train-rmse:0.623412+0.005869 test-rmse:0.708493+0.033658
## [140] train-rmse:0.622988+0.005875 test-rmse:0.707873+0.033600
## [141] train-rmse:0.622573+0.005880 test-rmse:0.707627+0.033774
## [142] train-rmse:0.622160+0.005887 test-rmse:0.707588+0.033557
## [143] train-rmse:0.621750+0.005896 test-rmse:0.707744+0.033964
## [144] train-rmse:0.621344+0.005906 test-rmse:0.707200+0.033363
## [145] train-rmse:0.620936+0.005913 test-rmse:0.706826+0.033328
## [146] train-rmse:0.620528+0.005918 test-rmse:0.706694+0.033381
## [147] train-rmse:0.620127+0.005923 test-rmse:0.706607+0.033719
## [148] train-rmse:0.619730+0.005924 test-rmse:0.705990+0.033621
## [149] train-rmse:0.619337+0.005929 test-rmse:0.705978+0.033253
## [150] train-rmse:0.618946+0.005931 test-rmse:0.705601+0.033399
## [151] train-rmse:0.618553+0.005932 test-rmse:0.705779+0.033690
## [152] train-rmse:0.618161+0.005935 test-rmse:0.705344+0.033414
## [153] train-rmse:0.617769+0.005942 test-rmse:0.704796+0.033742
## [154] train-rmse:0.617379+0.005954 test-rmse:0.704981+0.033504
## [155] train-rmse:0.616989+0.005969 test-rmse:0.705289+0.033770
## [156] train-rmse:0.616613+0.005970 test-rmse:0.705249+0.033825
## [157] train-rmse:0.616237+0.005973 test-rmse:0.705212+0.033852
## [158] train-rmse:0.615860+0.005976 test-rmse:0.704738+0.033888
## [159] train-rmse:0.615490+0.005983 test-rmse:0.704283+0.033853
## [160] train-rmse:0.615119+0.005985 test-rmse:0.703776+0.034526
## [161] train-rmse:0.614756+0.005987 test-rmse:0.703968+0.034922
## [162] train-rmse:0.614391+0.005988 test-rmse:0.703865+0.034844
## [163] train-rmse:0.614027+0.005990 test-rmse:0.704042+0.035186
## [164] train-rmse:0.613667+0.005996 test-rmse:0.703907+0.034892
## [165] train-rmse:0.613309+0.006001 test-rmse:0.703653+0.034522
## [166] train-rmse:0.612952+0.006004 test-rmse:0.703373+0.034191
## [167] train-rmse:0.612596+0.006007 test-rmse:0.703404+0.034137
## [168] train-rmse:0.612242+0.006011 test-rmse:0.702951+0.034048
## [169] train-rmse:0.611889+0.006017 test-rmse:0.702617+0.033746
## [170] train-rmse:0.611540+0.006023 test-rmse:0.702859+0.033853
## [171] train-rmse:0.611194+0.006027 test-rmse:0.702650+0.034637
## [172] train-rmse:0.610851+0.006032 test-rmse:0.702260+0.034557
## [173] train-rmse:0.610510+0.006037 test-rmse:0.701713+0.034147
## [174] train-rmse:0.610170+0.006040 test-rmse:0.701237+0.034363
## [175] train-rmse:0.609833+0.006043 test-rmse:0.700950+0.034304
## [176] train-rmse:0.609497+0.006046 test-rmse:0.700537+0.033961
## [177] train-rmse:0.609156+0.006053 test-rmse:0.700054+0.033993
## [178] train-rmse:0.608820+0.006056 test-rmse:0.700414+0.034078
## [179] train-rmse:0.608486+0.006056 test-rmse:0.700665+0.034386
## [180] train-rmse:0.608155+0.006060 test-rmse:0.700502+0.034342
## [181] train-rmse:0.607828+0.006061 test-rmse:0.700312+0.034464
## [182] train-rmse:0.607500+0.006066 test-rmse:0.700482+0.033925
## [183] train-rmse:0.607175+0.006069 test-rmse:0.700417+0.033451
## Stopping. Best iteration:
## [177] train-rmse:0.609156+0.006053 test-rmse:0.700054+0.033993
##
## 1
## [1] train-rmse:1.479685+0.015198 test-rmse:1.481571+0.068923
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.368784+0.013576 test-rmse:1.373915+0.070725
## [3] train-rmse:1.271519+0.012119 test-rmse:1.278585+0.071907
## [4] train-rmse:1.186707+0.010866 test-rmse:1.197442+0.072464
## [5] train-rmse:1.112669+0.009693 test-rmse:1.126912+0.073130
## [6] train-rmse:1.048541+0.008693 test-rmse:1.065686+0.071902
## [7] train-rmse:0.993063+0.007802 test-rmse:1.013915+0.070707
## [8] train-rmse:0.945319+0.007080 test-rmse:0.969956+0.070714
## [9] train-rmse:0.904526+0.006539 test-rmse:0.932153+0.069411
## [10] train-rmse:0.869111+0.006341 test-rmse:0.900941+0.067870
## [11] train-rmse:0.838590+0.005075 test-rmse:0.872467+0.065895
## [12] train-rmse:0.812626+0.004419 test-rmse:0.850278+0.064940
## [13] train-rmse:0.788775+0.004230 test-rmse:0.828218+0.063840
## [14] train-rmse:0.768873+0.005632 test-rmse:0.810572+0.060179
## [15] train-rmse:0.751635+0.004593 test-rmse:0.795195+0.059122
## [16] train-rmse:0.736810+0.004741 test-rmse:0.782233+0.058887
## [17] train-rmse:0.724463+0.005031 test-rmse:0.771719+0.055766
## [18] train-rmse:0.713217+0.004218 test-rmse:0.763380+0.053985
## [19] train-rmse:0.703986+0.004239 test-rmse:0.756479+0.052146
## [20] train-rmse:0.696030+0.004434 test-rmse:0.751822+0.051691
## [21] train-rmse:0.687665+0.004464 test-rmse:0.745001+0.051101
## [22] train-rmse:0.681389+0.004570 test-rmse:0.740452+0.050736
## [23] train-rmse:0.675757+0.004879 test-rmse:0.735618+0.049451
## [24] train-rmse:0.669935+0.005557 test-rmse:0.731952+0.047809
## [25] train-rmse:0.663495+0.005897 test-rmse:0.726743+0.044259
## [26] train-rmse:0.659495+0.006095 test-rmse:0.724575+0.044400
## [27] train-rmse:0.655708+0.006191 test-rmse:0.722636+0.042961
## [28] train-rmse:0.651948+0.005808 test-rmse:0.719426+0.041305
## [29] train-rmse:0.648651+0.005879 test-rmse:0.718836+0.042053
## [30] train-rmse:0.645768+0.005917 test-rmse:0.717744+0.040485
## [31] train-rmse:0.642618+0.005889 test-rmse:0.715631+0.043251
## [32] train-rmse:0.639725+0.006379 test-rmse:0.714525+0.042347
## [33] train-rmse:0.637095+0.006460 test-rmse:0.713837+0.042250
## [34] train-rmse:0.633722+0.005262 test-rmse:0.711510+0.043178
## [35] train-rmse:0.630515+0.005421 test-rmse:0.710701+0.043988
## [36] train-rmse:0.627941+0.005980 test-rmse:0.709224+0.043823
## [37] train-rmse:0.625634+0.005974 test-rmse:0.707634+0.043462
## [38] train-rmse:0.622975+0.006170 test-rmse:0.705447+0.042076
## [39] train-rmse:0.620811+0.005793 test-rmse:0.705437+0.042137
## [40] train-rmse:0.618897+0.005816 test-rmse:0.705663+0.042616
## [41] train-rmse:0.616968+0.005792 test-rmse:0.704930+0.042363
## [42] train-rmse:0.615127+0.005855 test-rmse:0.703002+0.041528
## [43] train-rmse:0.613052+0.006439 test-rmse:0.702140+0.041247
## [44] train-rmse:0.611206+0.006563 test-rmse:0.700838+0.041632
## [45] train-rmse:0.608758+0.005709 test-rmse:0.699436+0.042275
## [46] train-rmse:0.606827+0.005610 test-rmse:0.697797+0.042022
## [47] train-rmse:0.604899+0.005429 test-rmse:0.697875+0.042931
## [48] train-rmse:0.602448+0.005968 test-rmse:0.696513+0.042526
## [49] train-rmse:0.600271+0.005295 test-rmse:0.696222+0.041408
## [50] train-rmse:0.598930+0.005156 test-rmse:0.695240+0.041237
## [51] train-rmse:0.597260+0.005042 test-rmse:0.695679+0.041861
## [52] train-rmse:0.595815+0.004873 test-rmse:0.694606+0.042175
## [53] train-rmse:0.594154+0.005238 test-rmse:0.693847+0.042341
## [54] train-rmse:0.592878+0.005266 test-rmse:0.695038+0.043308
## [55] train-rmse:0.590411+0.005837 test-rmse:0.692556+0.042556
## [56] train-rmse:0.588545+0.005473 test-rmse:0.692383+0.043072
## [57] train-rmse:0.587228+0.005420 test-rmse:0.693187+0.042705
## [58] train-rmse:0.585638+0.005266 test-rmse:0.691957+0.041845
## [59] train-rmse:0.584207+0.005148 test-rmse:0.691444+0.041913
## [60] train-rmse:0.581627+0.005732 test-rmse:0.690548+0.040637
## [61] train-rmse:0.580249+0.005665 test-rmse:0.689931+0.040775
## [62] train-rmse:0.579085+0.005525 test-rmse:0.691003+0.041760
## [63] train-rmse:0.577562+0.005822 test-rmse:0.690443+0.042011
## [64] train-rmse:0.576500+0.005811 test-rmse:0.689992+0.042213
## [65] train-rmse:0.574739+0.005736 test-rmse:0.688755+0.041788
## [66] train-rmse:0.573135+0.005536 test-rmse:0.686764+0.042346
## [67] train-rmse:0.571894+0.005456 test-rmse:0.686153+0.041617
## [68] train-rmse:0.570594+0.005401 test-rmse:0.685824+0.041809
## [69] train-rmse:0.569050+0.005100 test-rmse:0.685694+0.042695
## [70] train-rmse:0.567830+0.005138 test-rmse:0.684980+0.042233
## [71] train-rmse:0.566356+0.005038 test-rmse:0.684785+0.042945
## [72] train-rmse:0.565117+0.004931 test-rmse:0.684448+0.042624
## [73] train-rmse:0.563595+0.005591 test-rmse:0.683854+0.042607
## [74] train-rmse:0.561807+0.005059 test-rmse:0.682416+0.040978
## [75] train-rmse:0.560649+0.005119 test-rmse:0.681711+0.041205
## [76] train-rmse:0.559212+0.005278 test-rmse:0.681095+0.040773
## [77] train-rmse:0.557608+0.005891 test-rmse:0.679871+0.040887
## [78] train-rmse:0.556163+0.006084 test-rmse:0.679026+0.041130
## [79] train-rmse:0.555224+0.006091 test-rmse:0.679127+0.041264
## [80] train-rmse:0.554122+0.005993 test-rmse:0.678982+0.041206
## [81] train-rmse:0.552802+0.006230 test-rmse:0.678937+0.041125
## [82] train-rmse:0.551753+0.006108 test-rmse:0.678485+0.040856
## [83] train-rmse:0.550676+0.005919 test-rmse:0.678683+0.041149
## [84] train-rmse:0.549170+0.006357 test-rmse:0.677442+0.041053
## [85] train-rmse:0.548287+0.006265 test-rmse:0.677568+0.041425
## [86] train-rmse:0.547315+0.006341 test-rmse:0.676727+0.040999
## [87] train-rmse:0.544279+0.006445 test-rmse:0.675486+0.041641
## [88] train-rmse:0.543256+0.006608 test-rmse:0.675112+0.041546
## [89] train-rmse:0.541538+0.006301 test-rmse:0.673737+0.040416
## [90] train-rmse:0.540260+0.005965 test-rmse:0.673072+0.040606
## [91] train-rmse:0.539373+0.005922 test-rmse:0.672809+0.040770
## [92] train-rmse:0.537319+0.005511 test-rmse:0.671171+0.040944
## [93] train-rmse:0.535962+0.005515 test-rmse:0.670272+0.042477
## [94] train-rmse:0.534668+0.005989 test-rmse:0.670049+0.042654
## [95] train-rmse:0.533317+0.006038 test-rmse:0.669785+0.042191
## [96] train-rmse:0.531698+0.006708 test-rmse:0.668312+0.042188
## [97] train-rmse:0.530222+0.006383 test-rmse:0.667974+0.042105
## [98] train-rmse:0.528753+0.007162 test-rmse:0.667270+0.042411
## [99] train-rmse:0.527805+0.007143 test-rmse:0.666835+0.042338
## [100] train-rmse:0.526733+0.006966 test-rmse:0.667101+0.042505
## [101] train-rmse:0.525885+0.006962 test-rmse:0.666749+0.042322
## [102] train-rmse:0.524808+0.007086 test-rmse:0.666561+0.042757
## [103] train-rmse:0.523959+0.006957 test-rmse:0.666422+0.042348
## [104] train-rmse:0.523166+0.006863 test-rmse:0.666415+0.043226
## [105] train-rmse:0.521576+0.007778 test-rmse:0.665998+0.043944
## [106] train-rmse:0.520693+0.007987 test-rmse:0.665323+0.044402
## [107] train-rmse:0.519426+0.008256 test-rmse:0.664415+0.043257
## [108] train-rmse:0.518660+0.008221 test-rmse:0.664233+0.043725
## [109] train-rmse:0.517952+0.008242 test-rmse:0.663831+0.043468
## [110] train-rmse:0.517175+0.008324 test-rmse:0.663849+0.043807
## [111] train-rmse:0.516123+0.008468 test-rmse:0.663553+0.043821
## [112] train-rmse:0.514707+0.007958 test-rmse:0.662524+0.043162
## [113] train-rmse:0.513556+0.008303 test-rmse:0.662315+0.042911
## [114] train-rmse:0.512083+0.008783 test-rmse:0.660624+0.042987
## [115] train-rmse:0.511138+0.008580 test-rmse:0.660741+0.043369
## [116] train-rmse:0.510021+0.008148 test-rmse:0.660690+0.043564
## [117] train-rmse:0.509282+0.008076 test-rmse:0.660554+0.043339
## [118] train-rmse:0.508083+0.007586 test-rmse:0.659898+0.043652
## [119] train-rmse:0.507354+0.007646 test-rmse:0.659690+0.043369
## [120] train-rmse:0.506479+0.007749 test-rmse:0.659377+0.044052
## [121] train-rmse:0.505273+0.008502 test-rmse:0.658771+0.043561
## [122] train-rmse:0.504278+0.008806 test-rmse:0.658345+0.043602
## [123] train-rmse:0.503212+0.009160 test-rmse:0.657362+0.043051
## [124] train-rmse:0.502539+0.009098 test-rmse:0.656779+0.043460
## [125] train-rmse:0.501730+0.009091 test-rmse:0.657062+0.043638
## [126] train-rmse:0.500950+0.009458 test-rmse:0.657126+0.043685
## [127] train-rmse:0.500216+0.009385 test-rmse:0.656189+0.043615
## [128] train-rmse:0.499504+0.009461 test-rmse:0.655550+0.043311
## [129] train-rmse:0.498944+0.009493 test-rmse:0.655541+0.043016
## [130] train-rmse:0.498269+0.009504 test-rmse:0.655314+0.042664
## [131] train-rmse:0.497590+0.009453 test-rmse:0.655457+0.042586
## [132] train-rmse:0.496998+0.009372 test-rmse:0.655641+0.042907
## [133] train-rmse:0.495880+0.010053 test-rmse:0.655416+0.043538
## [134] train-rmse:0.495206+0.009899 test-rmse:0.655252+0.043105
## [135] train-rmse:0.494156+0.009992 test-rmse:0.655160+0.043083
## [136] train-rmse:0.492903+0.010041 test-rmse:0.654866+0.042756
## [137] train-rmse:0.491953+0.009907 test-rmse:0.654925+0.043000
## [138] train-rmse:0.491300+0.010074 test-rmse:0.653732+0.042985
## [139] train-rmse:0.490705+0.010084 test-rmse:0.653274+0.043318
## [140] train-rmse:0.490118+0.010089 test-rmse:0.653523+0.043970
## [141] train-rmse:0.489598+0.010108 test-rmse:0.653200+0.044168
## [142] train-rmse:0.488706+0.010033 test-rmse:0.652901+0.044181
## [143] train-rmse:0.487974+0.010036 test-rmse:0.653127+0.044271
## [144] train-rmse:0.486664+0.009806 test-rmse:0.652740+0.045334
## [145] train-rmse:0.485873+0.009928 test-rmse:0.652283+0.045464
## [146] train-rmse:0.485312+0.010057 test-rmse:0.651544+0.045148
## [147] train-rmse:0.484578+0.010102 test-rmse:0.651576+0.044885
## [148] train-rmse:0.483749+0.010320 test-rmse:0.651614+0.044717
## [149] train-rmse:0.482739+0.009860 test-rmse:0.651418+0.045713
## [150] train-rmse:0.482044+0.009785 test-rmse:0.651251+0.045364
## [151] train-rmse:0.481251+0.010025 test-rmse:0.651258+0.045192
## [152] train-rmse:0.480600+0.009887 test-rmse:0.651318+0.046093
## [153] train-rmse:0.479618+0.009359 test-rmse:0.650839+0.044781
## [154] train-rmse:0.478809+0.009402 test-rmse:0.651294+0.044695
## [155] train-rmse:0.478094+0.009260 test-rmse:0.651501+0.045070
## [156] train-rmse:0.476906+0.009066 test-rmse:0.650216+0.045466
## [157] train-rmse:0.476328+0.009112 test-rmse:0.649426+0.045354
## [158] train-rmse:0.475435+0.009142 test-rmse:0.648846+0.045129
## [159] train-rmse:0.474814+0.009033 test-rmse:0.648637+0.045092
## [160] train-rmse:0.473888+0.009708 test-rmse:0.648832+0.045415
## [161] train-rmse:0.473320+0.009611 test-rmse:0.648329+0.045068
## [162] train-rmse:0.472366+0.009366 test-rmse:0.648168+0.045523
## [163] train-rmse:0.471899+0.009319 test-rmse:0.648411+0.045623
## [164] train-rmse:0.471430+0.009236 test-rmse:0.648151+0.045648
## [165] train-rmse:0.470938+0.009222 test-rmse:0.648165+0.045791
## [166] train-rmse:0.469760+0.008788 test-rmse:0.648220+0.045919
## [167] train-rmse:0.468978+0.008706 test-rmse:0.647638+0.045604
## [168] train-rmse:0.468355+0.008866 test-rmse:0.647157+0.045615
## [169] train-rmse:0.467734+0.008982 test-rmse:0.646791+0.045920
## [170] train-rmse:0.467287+0.008975 test-rmse:0.646740+0.045943
## [171] train-rmse:0.466264+0.008889 test-rmse:0.646331+0.045332
## [172] train-rmse:0.465578+0.008929 test-rmse:0.645677+0.044928
## [173] train-rmse:0.465012+0.008812 test-rmse:0.645393+0.045400
## [174] train-rmse:0.464546+0.008809 test-rmse:0.645793+0.045658
## [175] train-rmse:0.463995+0.008939 test-rmse:0.645796+0.045704
## [176] train-rmse:0.463241+0.009627 test-rmse:0.645591+0.045806
## [177] train-rmse:0.462693+0.009576 test-rmse:0.645649+0.045915
## [178] train-rmse:0.462158+0.009544 test-rmse:0.645978+0.045880
## [179] train-rmse:0.461424+0.009596 test-rmse:0.645569+0.045786
## Stopping. Best iteration:
## [173] train-rmse:0.465012+0.008812 test-rmse:0.645393+0.045400
##
## 2
## [1] train-rmse:1.477838+0.015045 test-rmse:1.480565+0.068366
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.364903+0.013404 test-rmse:1.371795+0.068488
## [3] train-rmse:1.265014+0.011982 test-rmse:1.276348+0.069526
## [4] train-rmse:1.177354+0.010747 test-rmse:1.191375+0.071495
## [5] train-rmse:1.100984+0.009428 test-rmse:1.119790+0.071356
## [6] train-rmse:1.034451+0.008599 test-rmse:1.058333+0.071913
## [7] train-rmse:0.976362+0.007934 test-rmse:1.004832+0.070978
## [8] train-rmse:0.926041+0.007733 test-rmse:0.958081+0.069040
## [9] train-rmse:0.881791+0.006648 test-rmse:0.916375+0.069758
## [10] train-rmse:0.843975+0.005932 test-rmse:0.883271+0.067584
## [11] train-rmse:0.811574+0.005128 test-rmse:0.852873+0.067122
## [12] train-rmse:0.783457+0.005248 test-rmse:0.829048+0.066565
## [13] train-rmse:0.758840+0.005392 test-rmse:0.808258+0.063521
## [14] train-rmse:0.737375+0.005879 test-rmse:0.792207+0.061621
## [15] train-rmse:0.715929+0.005855 test-rmse:0.773560+0.059155
## [16] train-rmse:0.698053+0.005477 test-rmse:0.758322+0.057111
## [17] train-rmse:0.683264+0.006097 test-rmse:0.747651+0.054988
## [18] train-rmse:0.668553+0.005810 test-rmse:0.734128+0.053599
## [19] train-rmse:0.657257+0.005901 test-rmse:0.726707+0.053389
## [20] train-rmse:0.646828+0.005844 test-rmse:0.719769+0.051488
## [21] train-rmse:0.638128+0.006234 test-rmse:0.714366+0.049038
## [22] train-rmse:0.630802+0.006419 test-rmse:0.711958+0.049151
## [23] train-rmse:0.624023+0.006208 test-rmse:0.707576+0.050942
## [24] train-rmse:0.618048+0.005894 test-rmse:0.703605+0.050406
## [25] train-rmse:0.611302+0.005872 test-rmse:0.699734+0.049640
## [26] train-rmse:0.605721+0.005898 test-rmse:0.696318+0.048031
## [27] train-rmse:0.601348+0.005857 test-rmse:0.694151+0.048902
## [28] train-rmse:0.596772+0.006389 test-rmse:0.693419+0.049521
## [29] train-rmse:0.592168+0.005906 test-rmse:0.692125+0.050358
## [30] train-rmse:0.588150+0.005692 test-rmse:0.690435+0.049975
## [31] train-rmse:0.584106+0.005951 test-rmse:0.688250+0.050654
## [32] train-rmse:0.579581+0.005308 test-rmse:0.685638+0.048511
## [33] train-rmse:0.576203+0.005299 test-rmse:0.684903+0.048168
## [34] train-rmse:0.573212+0.005407 test-rmse:0.684676+0.049255
## [35] train-rmse:0.569559+0.005713 test-rmse:0.680819+0.048294
## [36] train-rmse:0.566014+0.006511 test-rmse:0.680832+0.048990
## [37] train-rmse:0.562080+0.004961 test-rmse:0.680347+0.050207
## [38] train-rmse:0.558621+0.005061 test-rmse:0.678762+0.050644
## [39] train-rmse:0.555532+0.005336 test-rmse:0.676627+0.049211
## [40] train-rmse:0.553222+0.005419 test-rmse:0.676433+0.049517
## [41] train-rmse:0.550705+0.005214 test-rmse:0.675986+0.050581
## [42] train-rmse:0.548143+0.005746 test-rmse:0.673969+0.050364
## [43] train-rmse:0.545470+0.005831 test-rmse:0.672619+0.050920
## [44] train-rmse:0.542050+0.005515 test-rmse:0.669949+0.049794
## [45] train-rmse:0.538560+0.005520 test-rmse:0.668560+0.049798
## [46] train-rmse:0.535871+0.004966 test-rmse:0.668439+0.050912
## [47] train-rmse:0.533827+0.004702 test-rmse:0.667715+0.051337
## [48] train-rmse:0.530755+0.005345 test-rmse:0.666967+0.051185
## [49] train-rmse:0.528235+0.005001 test-rmse:0.665798+0.052953
## [50] train-rmse:0.525626+0.005327 test-rmse:0.664087+0.053450
## [51] train-rmse:0.522598+0.006282 test-rmse:0.663531+0.051770
## [52] train-rmse:0.519590+0.005646 test-rmse:0.661914+0.051884
## [53] train-rmse:0.517697+0.005373 test-rmse:0.662267+0.052038
## [54] train-rmse:0.515817+0.005406 test-rmse:0.661613+0.051845
## [55] train-rmse:0.513900+0.005781 test-rmse:0.660838+0.052478
## [56] train-rmse:0.511312+0.006548 test-rmse:0.659271+0.051585
## [57] train-rmse:0.509464+0.006714 test-rmse:0.658629+0.052303
## [58] train-rmse:0.506023+0.006224 test-rmse:0.655817+0.050722
## [59] train-rmse:0.504020+0.006681 test-rmse:0.655636+0.050481
## [60] train-rmse:0.501551+0.007075 test-rmse:0.655333+0.050876
## [61] train-rmse:0.498255+0.006677 test-rmse:0.652690+0.050418
## [62] train-rmse:0.496598+0.006497 test-rmse:0.652432+0.051068
## [63] train-rmse:0.494658+0.006406 test-rmse:0.652531+0.050945
## [64] train-rmse:0.493007+0.006402 test-rmse:0.651927+0.052677
## [65] train-rmse:0.490914+0.006597 test-rmse:0.650198+0.051818
## [66] train-rmse:0.488631+0.006993 test-rmse:0.649357+0.051146
## [67] train-rmse:0.485998+0.007634 test-rmse:0.648951+0.051329
## [68] train-rmse:0.483665+0.005818 test-rmse:0.648708+0.052674
## [69] train-rmse:0.482441+0.005853 test-rmse:0.648836+0.052809
## [70] train-rmse:0.480341+0.005861 test-rmse:0.649174+0.052969
## [71] train-rmse:0.478063+0.005984 test-rmse:0.647540+0.053460
## [72] train-rmse:0.476437+0.005753 test-rmse:0.646747+0.053118
## [73] train-rmse:0.474509+0.006078 test-rmse:0.644981+0.053116
## [74] train-rmse:0.472742+0.006283 test-rmse:0.644996+0.052870
## [75] train-rmse:0.471407+0.006178 test-rmse:0.644130+0.053282
## [76] train-rmse:0.469006+0.005405 test-rmse:0.644632+0.054617
## [77] train-rmse:0.467408+0.005093 test-rmse:0.645057+0.054101
## [78] train-rmse:0.465721+0.005241 test-rmse:0.646008+0.054408
## [79] train-rmse:0.463887+0.005582 test-rmse:0.644432+0.054389
## [80] train-rmse:0.461471+0.006215 test-rmse:0.642628+0.054409
## [81] train-rmse:0.460173+0.006333 test-rmse:0.642815+0.054967
## [82] train-rmse:0.457255+0.006792 test-rmse:0.641243+0.053549
## [83] train-rmse:0.455639+0.006993 test-rmse:0.641279+0.053664
## [84] train-rmse:0.454375+0.006624 test-rmse:0.640792+0.053669
## [85] train-rmse:0.452954+0.006765 test-rmse:0.640676+0.054000
## [86] train-rmse:0.451389+0.006845 test-rmse:0.640459+0.054582
## [87] train-rmse:0.449280+0.006186 test-rmse:0.639702+0.053725
## [88] train-rmse:0.447209+0.006557 test-rmse:0.639360+0.053178
## [89] train-rmse:0.445522+0.006765 test-rmse:0.638228+0.052696
## [90] train-rmse:0.444103+0.006689 test-rmse:0.637463+0.053207
## [91] train-rmse:0.442953+0.006878 test-rmse:0.637607+0.053107
## [92] train-rmse:0.441539+0.006950 test-rmse:0.637744+0.052293
## [93] train-rmse:0.439975+0.006478 test-rmse:0.637484+0.052097
## [94] train-rmse:0.438594+0.006843 test-rmse:0.636601+0.052068
## [95] train-rmse:0.436685+0.007311 test-rmse:0.636623+0.052108
## [96] train-rmse:0.434753+0.007823 test-rmse:0.636621+0.052017
## [97] train-rmse:0.433436+0.007634 test-rmse:0.636171+0.052447
## [98] train-rmse:0.431503+0.008256 test-rmse:0.636132+0.053228
## [99] train-rmse:0.430409+0.008546 test-rmse:0.636477+0.053315
## [100] train-rmse:0.428432+0.008765 test-rmse:0.635593+0.052511
## [101] train-rmse:0.427229+0.008826 test-rmse:0.635763+0.052483
## [102] train-rmse:0.425522+0.008460 test-rmse:0.635205+0.052034
## [103] train-rmse:0.424082+0.008694 test-rmse:0.634499+0.052661
## [104] train-rmse:0.423142+0.008917 test-rmse:0.634288+0.052470
## [105] train-rmse:0.422069+0.009041 test-rmse:0.633968+0.052130
## [106] train-rmse:0.420574+0.009304 test-rmse:0.634408+0.052261
## [107] train-rmse:0.419331+0.009265 test-rmse:0.634125+0.052079
## [108] train-rmse:0.417963+0.009257 test-rmse:0.634180+0.052512
## [109] train-rmse:0.417083+0.009310 test-rmse:0.634338+0.052488
## [110] train-rmse:0.415544+0.009574 test-rmse:0.633727+0.052889
## [111] train-rmse:0.414562+0.009440 test-rmse:0.633034+0.052484
## [112] train-rmse:0.413225+0.009575 test-rmse:0.633658+0.053255
## [113] train-rmse:0.412020+0.009922 test-rmse:0.633337+0.052988
## [114] train-rmse:0.410491+0.010690 test-rmse:0.633141+0.052367
## [115] train-rmse:0.408966+0.009649 test-rmse:0.633183+0.053131
## [116] train-rmse:0.407324+0.009570 test-rmse:0.632895+0.052474
## [117] train-rmse:0.406322+0.009391 test-rmse:0.632539+0.053002
## [118] train-rmse:0.404979+0.009633 test-rmse:0.632326+0.053487
## [119] train-rmse:0.403801+0.009804 test-rmse:0.632403+0.053602
## [120] train-rmse:0.402849+0.009461 test-rmse:0.632925+0.053311
## [121] train-rmse:0.401421+0.009837 test-rmse:0.632356+0.052858
## [122] train-rmse:0.400359+0.009470 test-rmse:0.631898+0.052762
## [123] train-rmse:0.399273+0.009890 test-rmse:0.631847+0.052698
## [124] train-rmse:0.398411+0.010032 test-rmse:0.631954+0.052700
## [125] train-rmse:0.397405+0.009803 test-rmse:0.632101+0.052764
## [126] train-rmse:0.396627+0.010043 test-rmse:0.632071+0.052943
## [127] train-rmse:0.394562+0.009467 test-rmse:0.631750+0.052879
## [128] train-rmse:0.392833+0.009740 test-rmse:0.630881+0.053012
## [129] train-rmse:0.391762+0.010035 test-rmse:0.630827+0.052888
## [130] train-rmse:0.390541+0.010104 test-rmse:0.630716+0.053245
## [131] train-rmse:0.389903+0.010240 test-rmse:0.630643+0.053131
## [132] train-rmse:0.388500+0.010490 test-rmse:0.631418+0.053619
## [133] train-rmse:0.387785+0.010727 test-rmse:0.631686+0.053704
## [134] train-rmse:0.387039+0.010816 test-rmse:0.631294+0.053625
## [135] train-rmse:0.385732+0.011357 test-rmse:0.630248+0.053535
## [136] train-rmse:0.384484+0.012233 test-rmse:0.629771+0.053251
## [137] train-rmse:0.383638+0.012340 test-rmse:0.629502+0.053147
## [138] train-rmse:0.382964+0.012500 test-rmse:0.629991+0.053529
## [139] train-rmse:0.381445+0.012958 test-rmse:0.629038+0.052769
## [140] train-rmse:0.379622+0.013446 test-rmse:0.628603+0.053777
## [141] train-rmse:0.378179+0.013432 test-rmse:0.628298+0.053505
## [142] train-rmse:0.377117+0.013378 test-rmse:0.628219+0.053628
## [143] train-rmse:0.376100+0.013276 test-rmse:0.628518+0.054543
## [144] train-rmse:0.375061+0.013224 test-rmse:0.628470+0.054754
## [145] train-rmse:0.373945+0.013627 test-rmse:0.627963+0.054294
## [146] train-rmse:0.372613+0.013494 test-rmse:0.627847+0.053649
## [147] train-rmse:0.371672+0.013401 test-rmse:0.626759+0.053120
## [148] train-rmse:0.370683+0.013838 test-rmse:0.626872+0.052940
## [149] train-rmse:0.369608+0.013542 test-rmse:0.626697+0.053227
## [150] train-rmse:0.368524+0.013723 test-rmse:0.626674+0.053131
## [151] train-rmse:0.367614+0.013621 test-rmse:0.626974+0.053129
## [152] train-rmse:0.366073+0.013450 test-rmse:0.626799+0.053338
## [153] train-rmse:0.364875+0.013684 test-rmse:0.627311+0.053740
## [154] train-rmse:0.364236+0.013765 test-rmse:0.627454+0.053935
## [155] train-rmse:0.363613+0.013567 test-rmse:0.627293+0.054581
## [156] train-rmse:0.362591+0.013499 test-rmse:0.626845+0.054785
## Stopping. Best iteration:
## [150] train-rmse:0.368524+0.013723 test-rmse:0.626674+0.053131
##
## 3
## [1] train-rmse:1.475959+0.015073 test-rmse:1.481760+0.068506
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.360566+0.013445 test-rmse:1.371688+0.068621
## [3] train-rmse:1.258160+0.011471 test-rmse:1.274546+0.069832
## [4] train-rmse:1.167672+0.009986 test-rmse:1.189086+0.070415
## [5] train-rmse:1.088055+0.008741 test-rmse:1.117199+0.071140
## [6] train-rmse:1.018042+0.007774 test-rmse:1.052780+0.072685
## [7] train-rmse:0.957543+0.006860 test-rmse:0.999625+0.072386
## [8] train-rmse:0.903907+0.005862 test-rmse:0.953168+0.071655
## [9] train-rmse:0.858069+0.005441 test-rmse:0.911469+0.072088
## [10] train-rmse:0.818247+0.005144 test-rmse:0.878222+0.070380
## [11] train-rmse:0.783059+0.005396 test-rmse:0.846990+0.068551
## [12] train-rmse:0.753221+0.005337 test-rmse:0.822690+0.068414
## [13] train-rmse:0.726923+0.004495 test-rmse:0.801361+0.067490
## [14] train-rmse:0.704042+0.004318 test-rmse:0.784560+0.066335
## [15] train-rmse:0.684053+0.004580 test-rmse:0.768247+0.064906
## [16] train-rmse:0.666253+0.004157 test-rmse:0.755479+0.063359
## [17] train-rmse:0.646167+0.005146 test-rmse:0.741225+0.060119
## [18] train-rmse:0.630331+0.008077 test-rmse:0.731875+0.060923
## [19] train-rmse:0.616810+0.007159 test-rmse:0.724018+0.059981
## [20] train-rmse:0.603682+0.005583 test-rmse:0.716489+0.056668
## [21] train-rmse:0.594255+0.005403 test-rmse:0.710505+0.054733
## [22] train-rmse:0.584600+0.005897 test-rmse:0.705552+0.056834
## [23] train-rmse:0.576323+0.006064 test-rmse:0.701968+0.058514
## [24] train-rmse:0.567968+0.006684 test-rmse:0.696982+0.056485
## [25] train-rmse:0.561304+0.007048 test-rmse:0.694540+0.056842
## [26] train-rmse:0.554723+0.006121 test-rmse:0.691036+0.056072
## [27] train-rmse:0.549153+0.007417 test-rmse:0.687435+0.057259
## [28] train-rmse:0.544584+0.007278 test-rmse:0.685768+0.057106
## [29] train-rmse:0.538853+0.006701 test-rmse:0.684325+0.057375
## [30] train-rmse:0.533808+0.006674 test-rmse:0.681166+0.058042
## [31] train-rmse:0.529030+0.007276 test-rmse:0.678057+0.056681
## [32] train-rmse:0.523183+0.006724 test-rmse:0.675760+0.057107
## [33] train-rmse:0.518801+0.006695 test-rmse:0.675568+0.057697
## [34] train-rmse:0.515519+0.007135 test-rmse:0.675111+0.057473
## [35] train-rmse:0.511278+0.006679 test-rmse:0.674230+0.057918
## [36] train-rmse:0.507416+0.006344 test-rmse:0.672689+0.057677
## [37] train-rmse:0.502076+0.004866 test-rmse:0.668117+0.057571
## [38] train-rmse:0.497667+0.005705 test-rmse:0.667903+0.058450
## [39] train-rmse:0.494229+0.005709 test-rmse:0.666881+0.058683
## [40] train-rmse:0.489933+0.005467 test-rmse:0.665418+0.058960
## [41] train-rmse:0.486773+0.005519 test-rmse:0.663938+0.057989
## [42] train-rmse:0.484135+0.005652 test-rmse:0.663306+0.059064
## [43] train-rmse:0.480899+0.005983 test-rmse:0.661854+0.060030
## [44] train-rmse:0.478054+0.006203 test-rmse:0.660646+0.061527
## [45] train-rmse:0.475094+0.006386 test-rmse:0.660815+0.061262
## [46] train-rmse:0.471731+0.007257 test-rmse:0.661509+0.060545
## [47] train-rmse:0.468694+0.007174 test-rmse:0.660241+0.060699
## [48] train-rmse:0.465598+0.007241 test-rmse:0.659360+0.062012
## [49] train-rmse:0.462781+0.008021 test-rmse:0.659075+0.061762
## [50] train-rmse:0.459902+0.008308 test-rmse:0.659961+0.062036
## [51] train-rmse:0.455672+0.008693 test-rmse:0.658501+0.061751
## [52] train-rmse:0.452587+0.009380 test-rmse:0.657564+0.062815
## [53] train-rmse:0.450088+0.009558 test-rmse:0.657055+0.063331
## [54] train-rmse:0.446767+0.009332 test-rmse:0.655675+0.063281
## [55] train-rmse:0.443124+0.008694 test-rmse:0.654056+0.063643
## [56] train-rmse:0.440973+0.008967 test-rmse:0.654469+0.064621
## [57] train-rmse:0.436852+0.007709 test-rmse:0.651911+0.062419
## [58] train-rmse:0.433745+0.007157 test-rmse:0.651801+0.061585
## [59] train-rmse:0.431487+0.007027 test-rmse:0.651105+0.062091
## [60] train-rmse:0.428683+0.007586 test-rmse:0.650798+0.062410
## [61] train-rmse:0.425522+0.008853 test-rmse:0.649916+0.061871
## [62] train-rmse:0.423309+0.009400 test-rmse:0.649277+0.062127
## [63] train-rmse:0.420858+0.009720 test-rmse:0.647273+0.061145
## [64] train-rmse:0.418603+0.010157 test-rmse:0.646836+0.062185
## [65] train-rmse:0.416515+0.009799 test-rmse:0.645996+0.061964
## [66] train-rmse:0.414602+0.010070 test-rmse:0.646426+0.060697
## [67] train-rmse:0.412673+0.009836 test-rmse:0.646385+0.061447
## [68] train-rmse:0.410940+0.009679 test-rmse:0.645940+0.062076
## [69] train-rmse:0.408809+0.010645 test-rmse:0.646019+0.062777
## [70] train-rmse:0.406084+0.010118 test-rmse:0.645370+0.062619
## [71] train-rmse:0.403943+0.009260 test-rmse:0.643260+0.062176
## [72] train-rmse:0.401235+0.010627 test-rmse:0.643081+0.062387
## [73] train-rmse:0.399367+0.010866 test-rmse:0.642930+0.062378
## [74] train-rmse:0.396337+0.011540 test-rmse:0.641883+0.061559
## [75] train-rmse:0.394715+0.011429 test-rmse:0.642028+0.062292
## [76] train-rmse:0.392428+0.012378 test-rmse:0.641138+0.061770
## [77] train-rmse:0.389528+0.012080 test-rmse:0.638961+0.061284
## [78] train-rmse:0.387774+0.012248 test-rmse:0.637809+0.060869
## [79] train-rmse:0.386078+0.012175 test-rmse:0.637786+0.060764
## [80] train-rmse:0.383882+0.012701 test-rmse:0.637453+0.060674
## [81] train-rmse:0.381525+0.013282 test-rmse:0.637243+0.060623
## [82] train-rmse:0.379682+0.013605 test-rmse:0.637064+0.060712
## [83] train-rmse:0.378055+0.013696 test-rmse:0.637619+0.061328
## [84] train-rmse:0.375908+0.013726 test-rmse:0.637294+0.060789
## [85] train-rmse:0.373389+0.014300 test-rmse:0.637040+0.060914
## [86] train-rmse:0.371371+0.014864 test-rmse:0.637011+0.061154
## [87] train-rmse:0.369395+0.014759 test-rmse:0.636675+0.061397
## [88] train-rmse:0.367695+0.014068 test-rmse:0.635793+0.062613
## [89] train-rmse:0.365843+0.013725 test-rmse:0.636182+0.062480
## [90] train-rmse:0.364401+0.013654 test-rmse:0.635810+0.062114
## [91] train-rmse:0.362571+0.014076 test-rmse:0.634949+0.061992
## [92] train-rmse:0.360601+0.013998 test-rmse:0.633792+0.061684
## [93] train-rmse:0.358773+0.013933 test-rmse:0.633982+0.062058
## [94] train-rmse:0.357108+0.013591 test-rmse:0.633980+0.062407
## [95] train-rmse:0.354548+0.012646 test-rmse:0.633440+0.062558
## [96] train-rmse:0.352061+0.012143 test-rmse:0.633091+0.062736
## [97] train-rmse:0.350232+0.012522 test-rmse:0.633044+0.062665
## [98] train-rmse:0.348588+0.012077 test-rmse:0.632677+0.062699
## [99] train-rmse:0.347103+0.012408 test-rmse:0.632321+0.062169
## [100] train-rmse:0.345612+0.012285 test-rmse:0.632751+0.062981
## [101] train-rmse:0.343224+0.012430 test-rmse:0.632022+0.062067
## [102] train-rmse:0.341499+0.012400 test-rmse:0.631803+0.061378
## [103] train-rmse:0.340053+0.011977 test-rmse:0.631187+0.061041
## [104] train-rmse:0.338050+0.011839 test-rmse:0.632093+0.061184
## [105] train-rmse:0.336593+0.012014 test-rmse:0.632072+0.061228
## [106] train-rmse:0.334359+0.012288 test-rmse:0.630813+0.061222
## [107] train-rmse:0.332774+0.012152 test-rmse:0.629771+0.060812
## [108] train-rmse:0.330914+0.011734 test-rmse:0.629994+0.060379
## [109] train-rmse:0.329610+0.011511 test-rmse:0.629309+0.060025
## [110] train-rmse:0.328148+0.012403 test-rmse:0.629070+0.059391
## [111] train-rmse:0.326500+0.012999 test-rmse:0.629632+0.059453
## [112] train-rmse:0.324861+0.012768 test-rmse:0.629788+0.059267
## [113] train-rmse:0.322667+0.012556 test-rmse:0.629483+0.060265
## [114] train-rmse:0.321318+0.012356 test-rmse:0.629254+0.060195
## [115] train-rmse:0.320332+0.012462 test-rmse:0.629695+0.060479
## [116] train-rmse:0.318571+0.012549 test-rmse:0.629798+0.060356
## Stopping. Best iteration:
## [110] train-rmse:0.328148+0.012403 test-rmse:0.629070+0.059391
##
## 4
## [1] train-rmse:1.474693+0.015160 test-rmse:1.479880+0.069533
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.357830+0.013637 test-rmse:1.371845+0.070502
## [3] train-rmse:1.254204+0.012017 test-rmse:1.271961+0.071076
## [4] train-rmse:1.161403+0.010222 test-rmse:1.186564+0.070605
## [5] train-rmse:1.079184+0.008756 test-rmse:1.112165+0.072367
## [6] train-rmse:1.006218+0.007643 test-rmse:1.046624+0.070856
## [7] train-rmse:0.942274+0.006419 test-rmse:0.988097+0.071675
## [8] train-rmse:0.886460+0.006317 test-rmse:0.938545+0.070425
## [9] train-rmse:0.836848+0.006308 test-rmse:0.898174+0.069954
## [10] train-rmse:0.793648+0.006201 test-rmse:0.862833+0.069420
## [11] train-rmse:0.756745+0.006443 test-rmse:0.832617+0.067531
## [12] train-rmse:0.724249+0.006720 test-rmse:0.807463+0.065972
## [13] train-rmse:0.696292+0.006917 test-rmse:0.785355+0.063938
## [14] train-rmse:0.671562+0.007574 test-rmse:0.768567+0.063315
## [15] train-rmse:0.649342+0.007350 test-rmse:0.753828+0.060878
## [16] train-rmse:0.630873+0.007964 test-rmse:0.741109+0.059899
## [17] train-rmse:0.613419+0.007159 test-rmse:0.729103+0.056637
## [18] train-rmse:0.595064+0.010531 test-rmse:0.720988+0.053809
## [19] train-rmse:0.579941+0.009089 test-rmse:0.712062+0.051492
## [20] train-rmse:0.564439+0.011210 test-rmse:0.701710+0.047749
## [21] train-rmse:0.553980+0.010990 test-rmse:0.696224+0.045784
## [22] train-rmse:0.544206+0.011320 test-rmse:0.690232+0.044731
## [23] train-rmse:0.532686+0.010764 test-rmse:0.685838+0.045226
## [24] train-rmse:0.522102+0.013094 test-rmse:0.681571+0.044415
## [25] train-rmse:0.514768+0.013219 test-rmse:0.678887+0.044906
## [26] train-rmse:0.506580+0.013242 test-rmse:0.677020+0.045641
## [27] train-rmse:0.500492+0.013384 test-rmse:0.674445+0.045466
## [28] train-rmse:0.493968+0.014223 test-rmse:0.673669+0.046140
## [29] train-rmse:0.486169+0.012435 test-rmse:0.669909+0.046827
## [30] train-rmse:0.480631+0.012752 test-rmse:0.667619+0.047927
## [31] train-rmse:0.475167+0.013580 test-rmse:0.666130+0.049193
## [32] train-rmse:0.469095+0.014168 test-rmse:0.664240+0.047846
## [33] train-rmse:0.463247+0.015368 test-rmse:0.661756+0.047239
## [34] train-rmse:0.458392+0.013157 test-rmse:0.658130+0.047405
## [35] train-rmse:0.454092+0.012963 test-rmse:0.658805+0.048297
## [36] train-rmse:0.449799+0.013553 test-rmse:0.657968+0.048789
## [37] train-rmse:0.445349+0.012075 test-rmse:0.656415+0.050090
## [38] train-rmse:0.442199+0.011778 test-rmse:0.656543+0.050597
## [39] train-rmse:0.437175+0.013068 test-rmse:0.654093+0.050428
## [40] train-rmse:0.433505+0.012628 test-rmse:0.653978+0.051874
## [41] train-rmse:0.429117+0.013874 test-rmse:0.653670+0.051600
## [42] train-rmse:0.422294+0.013590 test-rmse:0.651807+0.051323
## [43] train-rmse:0.418518+0.013507 test-rmse:0.651561+0.051755
## [44] train-rmse:0.415229+0.014235 test-rmse:0.650525+0.053440
## [45] train-rmse:0.412502+0.014480 test-rmse:0.650332+0.054094
## [46] train-rmse:0.408379+0.014705 test-rmse:0.650027+0.053836
## [47] train-rmse:0.405146+0.014817 test-rmse:0.650818+0.054901
## [48] train-rmse:0.401346+0.013425 test-rmse:0.649355+0.054043
## [49] train-rmse:0.397211+0.012796 test-rmse:0.645272+0.053931
## [50] train-rmse:0.393192+0.013560 test-rmse:0.645645+0.054344
## [51] train-rmse:0.389494+0.014467 test-rmse:0.644293+0.054690
## [52] train-rmse:0.386472+0.015253 test-rmse:0.644004+0.054713
## [53] train-rmse:0.383240+0.015968 test-rmse:0.642504+0.055065
## [54] train-rmse:0.379676+0.015920 test-rmse:0.641823+0.055601
## [55] train-rmse:0.376556+0.016498 test-rmse:0.642416+0.055434
## [56] train-rmse:0.373049+0.015654 test-rmse:0.641968+0.058153
## [57] train-rmse:0.370213+0.016083 test-rmse:0.641628+0.058303
## [58] train-rmse:0.367422+0.016258 test-rmse:0.641430+0.058588
## [59] train-rmse:0.365043+0.015736 test-rmse:0.641606+0.058649
## [60] train-rmse:0.361732+0.015273 test-rmse:0.640682+0.057721
## [61] train-rmse:0.358375+0.014903 test-rmse:0.640683+0.058290
## [62] train-rmse:0.355514+0.015148 test-rmse:0.640535+0.058159
## [63] train-rmse:0.352943+0.015344 test-rmse:0.640796+0.058632
## [64] train-rmse:0.349059+0.016469 test-rmse:0.639910+0.058500
## [65] train-rmse:0.346367+0.017292 test-rmse:0.640090+0.058664
## [66] train-rmse:0.343646+0.016907 test-rmse:0.640182+0.059232
## [67] train-rmse:0.340468+0.016735 test-rmse:0.637817+0.058777
## [68] train-rmse:0.337354+0.015809 test-rmse:0.636331+0.058733
## [69] train-rmse:0.335287+0.016019 test-rmse:0.636163+0.059540
## [70] train-rmse:0.333255+0.015480 test-rmse:0.636247+0.059704
## [71] train-rmse:0.331398+0.015357 test-rmse:0.636614+0.059880
## [72] train-rmse:0.328666+0.014955 test-rmse:0.634958+0.059058
## [73] train-rmse:0.326740+0.015279 test-rmse:0.634323+0.059114
## [74] train-rmse:0.324272+0.015420 test-rmse:0.634906+0.059658
## [75] train-rmse:0.321264+0.014944 test-rmse:0.635521+0.059322
## [76] train-rmse:0.319299+0.015211 test-rmse:0.634987+0.058854
## [77] train-rmse:0.317351+0.015401 test-rmse:0.635149+0.058373
## [78] train-rmse:0.314885+0.015232 test-rmse:0.634827+0.058716
## [79] train-rmse:0.312805+0.015589 test-rmse:0.634442+0.058430
## Stopping. Best iteration:
## [73] train-rmse:0.326740+0.015279 test-rmse:0.634323+0.059114
##
## 5
## [1] train-rmse:1.473412+0.014950 test-rmse:1.480683+0.070400
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.355648+0.013614 test-rmse:1.371093+0.072067
## [3] train-rmse:1.250746+0.011873 test-rmse:1.272139+0.071626
## [4] train-rmse:1.156885+0.010454 test-rmse:1.183928+0.071858
## [5] train-rmse:1.072888+0.008526 test-rmse:1.108442+0.073613
## [6] train-rmse:0.997724+0.007514 test-rmse:1.041720+0.073965
## [7] train-rmse:0.931169+0.006927 test-rmse:0.985631+0.072026
## [8] train-rmse:0.872457+0.006433 test-rmse:0.936231+0.070728
## [9] train-rmse:0.820188+0.005368 test-rmse:0.894130+0.070628
## [10] train-rmse:0.774216+0.005171 test-rmse:0.856151+0.070249
## [11] train-rmse:0.733701+0.005324 test-rmse:0.825362+0.072126
## [12] train-rmse:0.698553+0.005975 test-rmse:0.799095+0.070839
## [13] train-rmse:0.667706+0.007281 test-rmse:0.777789+0.068324
## [14] train-rmse:0.641024+0.007878 test-rmse:0.757834+0.066357
## [15] train-rmse:0.618253+0.008461 test-rmse:0.743060+0.064791
## [16] train-rmse:0.597179+0.008879 test-rmse:0.728071+0.062603
## [17] train-rmse:0.577592+0.008422 test-rmse:0.717502+0.061897
## [18] train-rmse:0.562023+0.007900 test-rmse:0.706822+0.060496
## [19] train-rmse:0.543089+0.010186 test-rmse:0.699328+0.059031
## [20] train-rmse:0.528861+0.009791 test-rmse:0.691194+0.057416
## [21] train-rmse:0.513387+0.012689 test-rmse:0.686657+0.055096
## [22] train-rmse:0.500587+0.014200 test-rmse:0.681941+0.053083
## [23] train-rmse:0.487327+0.012249 test-rmse:0.676330+0.052356
## [24] train-rmse:0.477682+0.012781 test-rmse:0.672960+0.052602
## [25] train-rmse:0.467927+0.010941 test-rmse:0.670545+0.051928
## [26] train-rmse:0.459454+0.011704 test-rmse:0.668921+0.052407
## [27] train-rmse:0.451665+0.013252 test-rmse:0.665778+0.053055
## [28] train-rmse:0.444344+0.014015 test-rmse:0.664242+0.051959
## [29] train-rmse:0.437548+0.015592 test-rmse:0.663451+0.051334
## [30] train-rmse:0.431002+0.015859 test-rmse:0.661879+0.050349
## [31] train-rmse:0.425570+0.015809 test-rmse:0.661203+0.049633
## [32] train-rmse:0.419088+0.016304 test-rmse:0.660515+0.049274
## [33] train-rmse:0.414462+0.016578 test-rmse:0.659466+0.050997
## [34] train-rmse:0.408800+0.016460 test-rmse:0.659448+0.051039
## [35] train-rmse:0.403598+0.016294 test-rmse:0.657592+0.053137
## [36] train-rmse:0.397840+0.016532 test-rmse:0.656874+0.054649
## [37] train-rmse:0.393562+0.016345 test-rmse:0.655950+0.053466
## [38] train-rmse:0.388609+0.016197 test-rmse:0.652695+0.053064
## [39] train-rmse:0.383666+0.016190 test-rmse:0.651695+0.053010
## [40] train-rmse:0.379574+0.016847 test-rmse:0.651619+0.053745
## [41] train-rmse:0.375947+0.016838 test-rmse:0.651660+0.054407
## [42] train-rmse:0.371198+0.015601 test-rmse:0.649622+0.054908
## [43] train-rmse:0.366610+0.016465 test-rmse:0.648306+0.055682
## [44] train-rmse:0.363137+0.016676 test-rmse:0.648143+0.054816
## [45] train-rmse:0.358855+0.016641 test-rmse:0.647553+0.054846
## [46] train-rmse:0.355750+0.015630 test-rmse:0.647746+0.055223
## [47] train-rmse:0.350783+0.016252 test-rmse:0.645926+0.054299
## [48] train-rmse:0.347453+0.016419 test-rmse:0.646042+0.054300
## [49] train-rmse:0.341930+0.017362 test-rmse:0.645641+0.055461
## [50] train-rmse:0.339186+0.017322 test-rmse:0.645354+0.055427
## [51] train-rmse:0.336133+0.016410 test-rmse:0.644872+0.055397
## [52] train-rmse:0.331635+0.017437 test-rmse:0.644672+0.056095
## [53] train-rmse:0.328450+0.017428 test-rmse:0.645519+0.057106
## [54] train-rmse:0.325051+0.017936 test-rmse:0.645517+0.057448
## [55] train-rmse:0.320961+0.016590 test-rmse:0.643514+0.056765
## [56] train-rmse:0.318035+0.016364 test-rmse:0.643003+0.056671
## [57] train-rmse:0.313882+0.016675 test-rmse:0.643443+0.055793
## [58] train-rmse:0.310412+0.016997 test-rmse:0.642065+0.055961
## [59] train-rmse:0.306545+0.015801 test-rmse:0.641885+0.055963
## [60] train-rmse:0.304383+0.015903 test-rmse:0.641727+0.056220
## [61] train-rmse:0.300758+0.016191 test-rmse:0.640614+0.056414
## [62] train-rmse:0.298002+0.016650 test-rmse:0.641313+0.057264
## [63] train-rmse:0.294839+0.016384 test-rmse:0.641226+0.057320
## [64] train-rmse:0.292526+0.016378 test-rmse:0.641030+0.057396
## [65] train-rmse:0.289378+0.017013 test-rmse:0.640919+0.057793
## [66] train-rmse:0.284635+0.016006 test-rmse:0.638189+0.058289
## [67] train-rmse:0.282340+0.015626 test-rmse:0.637993+0.058004
## [68] train-rmse:0.280238+0.015565 test-rmse:0.638369+0.057686
## [69] train-rmse:0.277299+0.016520 test-rmse:0.637325+0.057406
## [70] train-rmse:0.274591+0.017105 test-rmse:0.637059+0.057047
## [71] train-rmse:0.271891+0.016426 test-rmse:0.636445+0.056589
## [72] train-rmse:0.269631+0.015941 test-rmse:0.636395+0.056746
## [73] train-rmse:0.266560+0.016024 test-rmse:0.635532+0.056885
## [74] train-rmse:0.264125+0.016283 test-rmse:0.635658+0.057061
## [75] train-rmse:0.261633+0.017097 test-rmse:0.635659+0.056923
## [76] train-rmse:0.258834+0.016649 test-rmse:0.635478+0.056978
## [77] train-rmse:0.255626+0.016526 test-rmse:0.634799+0.055775
## [78] train-rmse:0.253282+0.016353 test-rmse:0.634914+0.056402
## [79] train-rmse:0.250895+0.016538 test-rmse:0.634981+0.056816
## [80] train-rmse:0.249146+0.017315 test-rmse:0.635531+0.057214
## [81] train-rmse:0.246291+0.017148 test-rmse:0.635148+0.056967
## [82] train-rmse:0.244322+0.016962 test-rmse:0.635636+0.056576
## [83] train-rmse:0.242713+0.017157 test-rmse:0.635635+0.057140
## Stopping. Best iteration:
## [77] train-rmse:0.255626+0.016526 test-rmse:0.634799+0.055775
##
## 6
## [1] train-rmse:1.472470+0.014761 test-rmse:1.480154+0.070464
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.353573+0.013280 test-rmse:1.371050+0.070484
## [3] train-rmse:1.247286+0.011732 test-rmse:1.270536+0.071970
## [4] train-rmse:1.151638+0.009662 test-rmse:1.182283+0.070489
## [5] train-rmse:1.066852+0.008036 test-rmse:1.105216+0.072041
## [6] train-rmse:0.991054+0.006484 test-rmse:1.038717+0.073000
## [7] train-rmse:0.922815+0.005949 test-rmse:0.983394+0.071043
## [8] train-rmse:0.861822+0.004916 test-rmse:0.933361+0.071659
## [9] train-rmse:0.807729+0.005120 test-rmse:0.891929+0.072728
## [10] train-rmse:0.760797+0.005920 test-rmse:0.856557+0.072640
## [11] train-rmse:0.717136+0.005258 test-rmse:0.823264+0.070803
## [12] train-rmse:0.679615+0.005644 test-rmse:0.797979+0.071502
## [13] train-rmse:0.646551+0.007105 test-rmse:0.775471+0.071513
## [14] train-rmse:0.618156+0.007902 test-rmse:0.756045+0.070078
## [15] train-rmse:0.592762+0.008337 test-rmse:0.738041+0.067871
## [16] train-rmse:0.570533+0.010710 test-rmse:0.726103+0.066948
## [17] train-rmse:0.548371+0.009818 test-rmse:0.712657+0.065220
## [18] train-rmse:0.530692+0.010378 test-rmse:0.702610+0.064160
## [19] train-rmse:0.512268+0.009235 test-rmse:0.694686+0.062164
## [20] train-rmse:0.499388+0.009433 test-rmse:0.688775+0.062127
## [21] train-rmse:0.483648+0.012703 test-rmse:0.683043+0.061434
## [22] train-rmse:0.468281+0.015185 test-rmse:0.676955+0.059335
## [23] train-rmse:0.458478+0.015253 test-rmse:0.671048+0.056724
## [24] train-rmse:0.443779+0.015050 test-rmse:0.663256+0.053698
## [25] train-rmse:0.433233+0.016148 test-rmse:0.660892+0.051706
## [26] train-rmse:0.422788+0.014978 test-rmse:0.657560+0.051365
## [27] train-rmse:0.412353+0.015909 test-rmse:0.655739+0.051289
## [28] train-rmse:0.402551+0.014192 test-rmse:0.653078+0.049253
## [29] train-rmse:0.395122+0.013910 test-rmse:0.651437+0.049341
## [30] train-rmse:0.387429+0.015500 test-rmse:0.649395+0.050225
## [31] train-rmse:0.380094+0.015325 test-rmse:0.649034+0.049682
## [32] train-rmse:0.373416+0.014364 test-rmse:0.647913+0.048483
## [33] train-rmse:0.367771+0.015074 test-rmse:0.646499+0.048031
## [34] train-rmse:0.362135+0.015765 test-rmse:0.644622+0.048295
## [35] train-rmse:0.355726+0.016127 test-rmse:0.644609+0.048911
## [36] train-rmse:0.350681+0.016782 test-rmse:0.643537+0.048406
## [37] train-rmse:0.346115+0.017490 test-rmse:0.643153+0.049520
## [38] train-rmse:0.341523+0.017145 test-rmse:0.643051+0.048798
## [39] train-rmse:0.337245+0.017174 test-rmse:0.643833+0.049388
## [40] train-rmse:0.333031+0.017772 test-rmse:0.644396+0.049905
## [41] train-rmse:0.328734+0.016890 test-rmse:0.642370+0.050275
## [42] train-rmse:0.324580+0.016919 test-rmse:0.641693+0.050241
## [43] train-rmse:0.319984+0.017245 test-rmse:0.640787+0.049693
## [44] train-rmse:0.315962+0.017469 test-rmse:0.641211+0.049707
## [45] train-rmse:0.312197+0.018098 test-rmse:0.641170+0.049925
## [46] train-rmse:0.308225+0.016996 test-rmse:0.639454+0.048888
## [47] train-rmse:0.304593+0.017110 test-rmse:0.638836+0.050171
## [48] train-rmse:0.298216+0.015422 test-rmse:0.637810+0.050875
## [49] train-rmse:0.293521+0.016724 test-rmse:0.637248+0.050075
## [50] train-rmse:0.289231+0.015230 test-rmse:0.635286+0.050615
## [51] train-rmse:0.285055+0.013845 test-rmse:0.635028+0.050465
## [52] train-rmse:0.280884+0.013774 test-rmse:0.634692+0.051158
## [53] train-rmse:0.277061+0.012487 test-rmse:0.634960+0.051579
## [54] train-rmse:0.272405+0.012840 test-rmse:0.634568+0.051244
## [55] train-rmse:0.268524+0.014009 test-rmse:0.634370+0.050576
## [56] train-rmse:0.264668+0.014363 test-rmse:0.634507+0.050177
## [57] train-rmse:0.261595+0.014489 test-rmse:0.635511+0.050972
## [58] train-rmse:0.258017+0.014307 test-rmse:0.634845+0.050685
## [59] train-rmse:0.253864+0.014349 test-rmse:0.635042+0.051325
## [60] train-rmse:0.250446+0.014769 test-rmse:0.634998+0.050665
## [61] train-rmse:0.246977+0.015023 test-rmse:0.634412+0.051922
## Stopping. Best iteration:
## [55] train-rmse:0.268524+0.014009 test-rmse:0.634370+0.050576
##
## 7
## [1] train-rmse:1.458151+0.014904 test-rmse:1.458404+0.069082
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.332529+0.013149 test-rmse:1.334732+0.070086
## [3] train-rmse:1.226210+0.011606 test-rmse:1.228638+0.069914
## [4] train-rmse:1.136934+0.010329 test-rmse:1.141404+0.069568
## [5] train-rmse:1.062490+0.009248 test-rmse:1.067398+0.068302
## [6] train-rmse:1.000671+0.008521 test-rmse:1.009153+0.067369
## [7] train-rmse:0.949414+0.007956 test-rmse:0.959168+0.064551
## [8] train-rmse:0.907145+0.007518 test-rmse:0.921295+0.063375
## [9] train-rmse:0.872331+0.007094 test-rmse:0.887450+0.059497
## [10] train-rmse:0.843833+0.006866 test-rmse:0.861182+0.056848
## [11] train-rmse:0.820407+0.006595 test-rmse:0.839329+0.055092
## [12] train-rmse:0.801227+0.006491 test-rmse:0.823234+0.052088
## [13] train-rmse:0.785623+0.006280 test-rmse:0.809090+0.048626
## [14] train-rmse:0.772702+0.006029 test-rmse:0.798513+0.047984
## [15] train-rmse:0.761940+0.005892 test-rmse:0.788457+0.046928
## [16] train-rmse:0.753019+0.005690 test-rmse:0.780546+0.043494
## [17] train-rmse:0.745452+0.005553 test-rmse:0.773813+0.041241
## [18] train-rmse:0.739064+0.005396 test-rmse:0.769350+0.039236
## [19] train-rmse:0.733500+0.005324 test-rmse:0.765403+0.039358
## [20] train-rmse:0.728671+0.005265 test-rmse:0.760222+0.038855
## [21] train-rmse:0.724484+0.005181 test-rmse:0.757599+0.037676
## [22] train-rmse:0.720762+0.005038 test-rmse:0.754377+0.035606
## [23] train-rmse:0.717429+0.005002 test-rmse:0.751904+0.034225
## [24] train-rmse:0.714401+0.004976 test-rmse:0.750288+0.032709
## [25] train-rmse:0.711596+0.004959 test-rmse:0.749326+0.032451
## [26] train-rmse:0.709010+0.004928 test-rmse:0.747401+0.033115
## [27] train-rmse:0.706623+0.004934 test-rmse:0.746121+0.032431
## [28] train-rmse:0.704380+0.004943 test-rmse:0.744304+0.031569
## [29] train-rmse:0.702273+0.004958 test-rmse:0.742924+0.030955
## [30] train-rmse:0.700260+0.004939 test-rmse:0.742033+0.030081
## [31] train-rmse:0.698396+0.004934 test-rmse:0.742083+0.030231
## [32] train-rmse:0.696581+0.004936 test-rmse:0.742063+0.030313
## [33] train-rmse:0.694821+0.004948 test-rmse:0.739794+0.029508
## [34] train-rmse:0.693138+0.004963 test-rmse:0.738309+0.029439
## [35] train-rmse:0.691508+0.004987 test-rmse:0.737102+0.029675
## [36] train-rmse:0.689921+0.005021 test-rmse:0.736981+0.029053
## [37] train-rmse:0.688396+0.005028 test-rmse:0.736541+0.029001
## [38] train-rmse:0.686894+0.005039 test-rmse:0.735494+0.029138
## [39] train-rmse:0.685436+0.005065 test-rmse:0.734326+0.029189
## [40] train-rmse:0.684016+0.005074 test-rmse:0.733589+0.029588
## [41] train-rmse:0.682642+0.005079 test-rmse:0.733636+0.029645
## [42] train-rmse:0.681309+0.005082 test-rmse:0.732603+0.029161
## [43] train-rmse:0.679996+0.005087 test-rmse:0.731689+0.028728
## [44] train-rmse:0.678712+0.005095 test-rmse:0.731327+0.029386
## [45] train-rmse:0.677455+0.005110 test-rmse:0.730357+0.030169
## [46] train-rmse:0.676210+0.005125 test-rmse:0.729999+0.030233
## [47] train-rmse:0.674995+0.005126 test-rmse:0.729565+0.030169
## [48] train-rmse:0.673808+0.005114 test-rmse:0.729168+0.029575
## [49] train-rmse:0.672642+0.005114 test-rmse:0.728808+0.029689
## [50] train-rmse:0.671492+0.005120 test-rmse:0.728953+0.030049
## [51] train-rmse:0.670380+0.005119 test-rmse:0.728254+0.031159
## [52] train-rmse:0.669278+0.005124 test-rmse:0.728038+0.031224
## [53] train-rmse:0.668187+0.005142 test-rmse:0.727747+0.031584
## [54] train-rmse:0.667111+0.005155 test-rmse:0.727241+0.031915
## [55] train-rmse:0.666044+0.005184 test-rmse:0.727575+0.032260
## [56] train-rmse:0.665025+0.005190 test-rmse:0.727017+0.032553
## [57] train-rmse:0.664015+0.005202 test-rmse:0.726225+0.032312
## [58] train-rmse:0.663023+0.005203 test-rmse:0.725696+0.032356
## [59] train-rmse:0.662044+0.005216 test-rmse:0.725209+0.032967
## [60] train-rmse:0.661067+0.005231 test-rmse:0.724358+0.032317
## [61] train-rmse:0.660107+0.005259 test-rmse:0.724495+0.031624
## [62] train-rmse:0.659164+0.005274 test-rmse:0.723618+0.031888
## [63] train-rmse:0.658250+0.005298 test-rmse:0.723647+0.031456
## [64] train-rmse:0.657333+0.005323 test-rmse:0.722669+0.032006
## [65] train-rmse:0.656431+0.005337 test-rmse:0.722883+0.032280
## [66] train-rmse:0.655547+0.005355 test-rmse:0.722726+0.033011
## [67] train-rmse:0.654669+0.005380 test-rmse:0.721574+0.032688
## [68] train-rmse:0.653793+0.005407 test-rmse:0.720850+0.032932
## [69] train-rmse:0.652947+0.005423 test-rmse:0.720374+0.032596
## [70] train-rmse:0.652126+0.005439 test-rmse:0.720449+0.033035
## [71] train-rmse:0.651306+0.005455 test-rmse:0.720239+0.033680
## [72] train-rmse:0.650503+0.005462 test-rmse:0.720394+0.033766
## [73] train-rmse:0.649711+0.005474 test-rmse:0.719905+0.033525
## [74] train-rmse:0.648925+0.005491 test-rmse:0.718909+0.033071
## [75] train-rmse:0.648150+0.005506 test-rmse:0.719230+0.033292
## [76] train-rmse:0.647380+0.005518 test-rmse:0.719504+0.033419
## [77] train-rmse:0.646627+0.005534 test-rmse:0.719826+0.033276
## [78] train-rmse:0.645886+0.005546 test-rmse:0.719552+0.033080
## [79] train-rmse:0.645158+0.005554 test-rmse:0.720106+0.034049
## [80] train-rmse:0.644424+0.005561 test-rmse:0.720015+0.034235
## Stopping. Best iteration:
## [74] train-rmse:0.648925+0.005491 test-rmse:0.718909+0.033071
##
## 8
## [1] train-rmse:1.454841+0.014847 test-rmse:1.457328+0.069239
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.325743+0.012935 test-rmse:1.332190+0.071220
## [3] train-rmse:1.216019+0.011333 test-rmse:1.226377+0.072718
## [4] train-rmse:1.123029+0.009843 test-rmse:1.137426+0.073119
## [5] train-rmse:1.044643+0.008593 test-rmse:1.062095+0.071928
## [6] train-rmse:0.979128+0.007596 test-rmse:1.000799+0.071046
## [7] train-rmse:0.924493+0.006726 test-rmse:0.950839+0.069385
## [8] train-rmse:0.879325+0.006134 test-rmse:0.909391+0.068816
## [9] train-rmse:0.841572+0.006058 test-rmse:0.875245+0.068399
## [10] train-rmse:0.809516+0.005205 test-rmse:0.844920+0.066359
## [11] train-rmse:0.782143+0.004883 test-rmse:0.820941+0.063673
## [12] train-rmse:0.758017+0.004133 test-rmse:0.799435+0.061719
## [13] train-rmse:0.740643+0.004105 test-rmse:0.784443+0.058454
## [14] train-rmse:0.724990+0.004486 test-rmse:0.772037+0.056234
## [15] train-rmse:0.711711+0.004274 test-rmse:0.762540+0.053903
## [16] train-rmse:0.701080+0.004266 test-rmse:0.754133+0.052426
## [17] train-rmse:0.691621+0.004232 test-rmse:0.748184+0.052390
## [18] train-rmse:0.683695+0.004493 test-rmse:0.743578+0.050799
## [19] train-rmse:0.674992+0.003973 test-rmse:0.737437+0.048803
## [20] train-rmse:0.668840+0.003920 test-rmse:0.734787+0.047361
## [21] train-rmse:0.663368+0.003616 test-rmse:0.729655+0.045423
## [22] train-rmse:0.657644+0.004865 test-rmse:0.726736+0.042929
## [23] train-rmse:0.651903+0.004524 test-rmse:0.724665+0.043268
## [24] train-rmse:0.648047+0.004453 test-rmse:0.723068+0.043441
## [25] train-rmse:0.643120+0.004794 test-rmse:0.718659+0.040461
## [26] train-rmse:0.639698+0.004758 test-rmse:0.715185+0.039284
## [27] train-rmse:0.636531+0.004396 test-rmse:0.714368+0.040133
## [28] train-rmse:0.633102+0.004455 test-rmse:0.713676+0.039822
## [29] train-rmse:0.629144+0.004940 test-rmse:0.711789+0.039154
## [30] train-rmse:0.625417+0.005639 test-rmse:0.710514+0.038109
## [31] train-rmse:0.622586+0.005628 test-rmse:0.708484+0.039661
## [32] train-rmse:0.620015+0.005453 test-rmse:0.707528+0.039653
## [33] train-rmse:0.617668+0.005673 test-rmse:0.705769+0.038623
## [34] train-rmse:0.615184+0.005691 test-rmse:0.704113+0.038950
## [35] train-rmse:0.612771+0.005991 test-rmse:0.704205+0.038357
## [36] train-rmse:0.610516+0.006080 test-rmse:0.704196+0.039102
## [37] train-rmse:0.608408+0.005955 test-rmse:0.703807+0.038549
## [38] train-rmse:0.606210+0.005997 test-rmse:0.703767+0.039081
## [39] train-rmse:0.604190+0.006181 test-rmse:0.701572+0.038722
## [40] train-rmse:0.602156+0.005817 test-rmse:0.700683+0.039388
## [41] train-rmse:0.599589+0.005671 test-rmse:0.699621+0.038454
## [42] train-rmse:0.597085+0.005755 test-rmse:0.699401+0.038452
## [43] train-rmse:0.595072+0.005619 test-rmse:0.698480+0.037953
## [44] train-rmse:0.593407+0.005418 test-rmse:0.699078+0.039314
## [45] train-rmse:0.591443+0.005585 test-rmse:0.697384+0.038641
## [46] train-rmse:0.589432+0.005495 test-rmse:0.696942+0.038972
## [47] train-rmse:0.587451+0.005668 test-rmse:0.697747+0.039543
## [48] train-rmse:0.585584+0.005599 test-rmse:0.696853+0.039503
## [49] train-rmse:0.583423+0.005611 test-rmse:0.696136+0.038230
## [50] train-rmse:0.581764+0.005819 test-rmse:0.695893+0.036604
## [51] train-rmse:0.580238+0.005662 test-rmse:0.695536+0.037658
## [52] train-rmse:0.578616+0.005733 test-rmse:0.694898+0.036884
## [53] train-rmse:0.576876+0.005882 test-rmse:0.693762+0.036712
## [54] train-rmse:0.575066+0.005781 test-rmse:0.693389+0.036790
## [55] train-rmse:0.573534+0.005248 test-rmse:0.694604+0.038246
## [56] train-rmse:0.571221+0.005954 test-rmse:0.693342+0.038724
## [57] train-rmse:0.569541+0.006034 test-rmse:0.692810+0.038635
## [58] train-rmse:0.567586+0.006307 test-rmse:0.691400+0.038524
## [59] train-rmse:0.566198+0.006376 test-rmse:0.691435+0.038157
## [60] train-rmse:0.564668+0.006628 test-rmse:0.690437+0.037522
## [61] train-rmse:0.563102+0.006770 test-rmse:0.688947+0.036115
## [62] train-rmse:0.561581+0.006873 test-rmse:0.688635+0.036261
## [63] train-rmse:0.560324+0.006636 test-rmse:0.688290+0.037878
## [64] train-rmse:0.559122+0.006645 test-rmse:0.687414+0.038058
## [65] train-rmse:0.558008+0.006467 test-rmse:0.687094+0.038907
## [66] train-rmse:0.556489+0.006608 test-rmse:0.686101+0.039120
## [67] train-rmse:0.554352+0.007179 test-rmse:0.685738+0.039091
## [68] train-rmse:0.552004+0.005617 test-rmse:0.683708+0.039205
## [69] train-rmse:0.550540+0.005386 test-rmse:0.684065+0.038910
## [70] train-rmse:0.549280+0.005560 test-rmse:0.684080+0.040670
## [71] train-rmse:0.547211+0.005671 test-rmse:0.682119+0.040042
## [72] train-rmse:0.545877+0.005891 test-rmse:0.682002+0.039325
## [73] train-rmse:0.544246+0.005869 test-rmse:0.681571+0.038839
## [74] train-rmse:0.543036+0.006046 test-rmse:0.681428+0.038646
## [75] train-rmse:0.541583+0.006381 test-rmse:0.682228+0.038458
## [76] train-rmse:0.539999+0.006206 test-rmse:0.681960+0.039212
## [77] train-rmse:0.538047+0.004945 test-rmse:0.680499+0.040084
## [78] train-rmse:0.536821+0.005019 test-rmse:0.678701+0.039479
## [79] train-rmse:0.535907+0.005098 test-rmse:0.678578+0.040224
## [80] train-rmse:0.534647+0.005357 test-rmse:0.677951+0.039721
## [81] train-rmse:0.532986+0.005448 test-rmse:0.676489+0.038900
## [82] train-rmse:0.531706+0.005528 test-rmse:0.676193+0.038714
## [83] train-rmse:0.529749+0.005052 test-rmse:0.675019+0.038779
## [84] train-rmse:0.528554+0.004969 test-rmse:0.674548+0.038984
## [85] train-rmse:0.526832+0.005187 test-rmse:0.673379+0.039130
## [86] train-rmse:0.525442+0.005231 test-rmse:0.673177+0.039900
## [87] train-rmse:0.524388+0.005472 test-rmse:0.673713+0.039622
## [88] train-rmse:0.523443+0.005527 test-rmse:0.673611+0.039594
## [89] train-rmse:0.522035+0.005744 test-rmse:0.673677+0.039254
## [90] train-rmse:0.520412+0.006607 test-rmse:0.672786+0.038561
## [91] train-rmse:0.519283+0.006923 test-rmse:0.672654+0.038656
## [92] train-rmse:0.517575+0.006803 test-rmse:0.672033+0.038610
## [93] train-rmse:0.516910+0.006769 test-rmse:0.671814+0.038418
## [94] train-rmse:0.516152+0.006813 test-rmse:0.671342+0.038221
## [95] train-rmse:0.514642+0.006994 test-rmse:0.669351+0.037704
## [96] train-rmse:0.512928+0.005938 test-rmse:0.668651+0.038281
## [97] train-rmse:0.511777+0.006143 test-rmse:0.667832+0.037817
## [98] train-rmse:0.510662+0.006341 test-rmse:0.667059+0.037702
## [99] train-rmse:0.509503+0.006129 test-rmse:0.666039+0.037336
## [100] train-rmse:0.508388+0.006465 test-rmse:0.665618+0.037196
## [101] train-rmse:0.507092+0.006413 test-rmse:0.664954+0.036447
## [102] train-rmse:0.505571+0.007222 test-rmse:0.664627+0.036701
## [103] train-rmse:0.504871+0.007293 test-rmse:0.664864+0.037848
## [104] train-rmse:0.503949+0.007330 test-rmse:0.665160+0.038191
## [105] train-rmse:0.502519+0.007614 test-rmse:0.664525+0.038066
## [106] train-rmse:0.501595+0.007537 test-rmse:0.664317+0.038551
## [107] train-rmse:0.500224+0.007158 test-rmse:0.663950+0.038990
## [108] train-rmse:0.499422+0.007252 test-rmse:0.663680+0.038596
## [109] train-rmse:0.498104+0.007106 test-rmse:0.662403+0.037284
## [110] train-rmse:0.497317+0.007130 test-rmse:0.662205+0.036961
## [111] train-rmse:0.496190+0.007366 test-rmse:0.661522+0.037329
## [112] train-rmse:0.495239+0.007675 test-rmse:0.660934+0.037419
## [113] train-rmse:0.494177+0.008089 test-rmse:0.661136+0.036928
## [114] train-rmse:0.492853+0.007409 test-rmse:0.659986+0.038204
## [115] train-rmse:0.492127+0.007374 test-rmse:0.659504+0.038542
## [116] train-rmse:0.490930+0.007458 test-rmse:0.658389+0.038018
## [117] train-rmse:0.489711+0.007540 test-rmse:0.657771+0.037815
## [118] train-rmse:0.488548+0.007244 test-rmse:0.657148+0.037432
## [119] train-rmse:0.487602+0.007388 test-rmse:0.656803+0.036877
## [120] train-rmse:0.486941+0.007459 test-rmse:0.657034+0.037293
## [121] train-rmse:0.485841+0.007503 test-rmse:0.656494+0.037228
## [122] train-rmse:0.484950+0.007673 test-rmse:0.656418+0.037570
## [123] train-rmse:0.484092+0.007854 test-rmse:0.656166+0.037333
## [124] train-rmse:0.482981+0.007623 test-rmse:0.655751+0.036983
## [125] train-rmse:0.481874+0.007415 test-rmse:0.655050+0.037299
## [126] train-rmse:0.481106+0.007466 test-rmse:0.655738+0.037670
## [127] train-rmse:0.480461+0.007660 test-rmse:0.655526+0.037247
## [128] train-rmse:0.479974+0.007687 test-rmse:0.655302+0.037658
## [129] train-rmse:0.479257+0.007704 test-rmse:0.654412+0.037320
## [130] train-rmse:0.477959+0.007174 test-rmse:0.654105+0.037665
## [131] train-rmse:0.477294+0.007221 test-rmse:0.653571+0.037873
## [132] train-rmse:0.476413+0.007446 test-rmse:0.653592+0.037675
## [133] train-rmse:0.475240+0.007312 test-rmse:0.653034+0.037034
## [134] train-rmse:0.473943+0.008062 test-rmse:0.652473+0.036420
## [135] train-rmse:0.473155+0.008354 test-rmse:0.652764+0.035752
## [136] train-rmse:0.472476+0.008115 test-rmse:0.653105+0.036784
## [137] train-rmse:0.471403+0.007967 test-rmse:0.652485+0.036619
## [138] train-rmse:0.470846+0.008094 test-rmse:0.651861+0.036205
## [139] train-rmse:0.469831+0.008371 test-rmse:0.652885+0.036432
## [140] train-rmse:0.468700+0.007816 test-rmse:0.651811+0.037140
## [141] train-rmse:0.468114+0.007767 test-rmse:0.651803+0.037152
## [142] train-rmse:0.467022+0.007393 test-rmse:0.651690+0.037156
## [143] train-rmse:0.465639+0.007951 test-rmse:0.651358+0.037723
## [144] train-rmse:0.464873+0.008128 test-rmse:0.651774+0.037820
## [145] train-rmse:0.464022+0.008170 test-rmse:0.651056+0.037788
## [146] train-rmse:0.463475+0.008092 test-rmse:0.650748+0.037766
## [147] train-rmse:0.462901+0.008314 test-rmse:0.650391+0.037848
## [148] train-rmse:0.461642+0.008754 test-rmse:0.650149+0.037940
## [149] train-rmse:0.460823+0.008904 test-rmse:0.649931+0.037868
## [150] train-rmse:0.460223+0.008963 test-rmse:0.650157+0.037974
## [151] train-rmse:0.459532+0.009135 test-rmse:0.650317+0.038425
## [152] train-rmse:0.458976+0.009139 test-rmse:0.649785+0.038023
## [153] train-rmse:0.458316+0.009125 test-rmse:0.648982+0.037867
## [154] train-rmse:0.457796+0.009147 test-rmse:0.649286+0.037883
## [155] train-rmse:0.457090+0.009437 test-rmse:0.649236+0.037877
## [156] train-rmse:0.456377+0.009594 test-rmse:0.648940+0.037769
## [157] train-rmse:0.455732+0.009648 test-rmse:0.649480+0.037643
## [158] train-rmse:0.454982+0.009785 test-rmse:0.649586+0.038099
## [159] train-rmse:0.454049+0.010505 test-rmse:0.649066+0.038351
## [160] train-rmse:0.453453+0.010518 test-rmse:0.648977+0.038458
## [161] train-rmse:0.452353+0.009907 test-rmse:0.648672+0.038086
## [162] train-rmse:0.451477+0.009764 test-rmse:0.648246+0.037945
## [163] train-rmse:0.451014+0.009796 test-rmse:0.648645+0.038237
## [164] train-rmse:0.450369+0.009932 test-rmse:0.648505+0.038282
## [165] train-rmse:0.449622+0.009837 test-rmse:0.648691+0.038391
## [166] train-rmse:0.448966+0.009643 test-rmse:0.648609+0.038750
## [167] train-rmse:0.448472+0.009673 test-rmse:0.648734+0.038944
## [168] train-rmse:0.447440+0.010469 test-rmse:0.648940+0.038803
## Stopping. Best iteration:
## [162] train-rmse:0.451477+0.009764 test-rmse:0.648246+0.037945
##
## 9
## [1] train-rmse:1.452603+0.014661 test-rmse:1.456115+0.068570
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.320775+0.012586 test-rmse:1.328903+0.068973
## [3] train-rmse:1.207949+0.011201 test-rmse:1.222013+0.069931
## [4] train-rmse:1.111988+0.009743 test-rmse:1.131049+0.069322
## [5] train-rmse:1.030540+0.008833 test-rmse:1.054890+0.068710
## [6] train-rmse:0.961654+0.007909 test-rmse:0.992380+0.067755
## [7] train-rmse:0.903635+0.006435 test-rmse:0.936948+0.068541
## [8] train-rmse:0.854588+0.006316 test-rmse:0.890370+0.067342
## [9] train-rmse:0.814065+0.005334 test-rmse:0.855588+0.065427
## [10] train-rmse:0.779932+0.005941 test-rmse:0.827359+0.063855
## [11] train-rmse:0.751761+0.005779 test-rmse:0.802066+0.063653
## [12] train-rmse:0.726117+0.006487 test-rmse:0.784010+0.061548
## [13] train-rmse:0.702819+0.006029 test-rmse:0.765548+0.060173
## [14] train-rmse:0.685034+0.005351 test-rmse:0.751233+0.058027
## [15] train-rmse:0.668581+0.005914 test-rmse:0.736470+0.053317
## [16] train-rmse:0.655504+0.005789 test-rmse:0.726184+0.050597
## [17] train-rmse:0.644061+0.005689 test-rmse:0.719562+0.049296
## [18] train-rmse:0.633669+0.006161 test-rmse:0.714110+0.050575
## [19] train-rmse:0.625218+0.006121 test-rmse:0.710834+0.050790
## [20] train-rmse:0.617658+0.006615 test-rmse:0.707673+0.050463
## [21] train-rmse:0.610851+0.006404 test-rmse:0.702416+0.050025
## [22] train-rmse:0.604652+0.006439 test-rmse:0.700624+0.049473
## [23] train-rmse:0.599222+0.007592 test-rmse:0.698889+0.050044
## [24] train-rmse:0.594356+0.008225 test-rmse:0.698174+0.049495
## [25] train-rmse:0.589129+0.007863 test-rmse:0.695401+0.050943
## [26] train-rmse:0.584343+0.007854 test-rmse:0.694023+0.051571
## [27] train-rmse:0.579144+0.007162 test-rmse:0.691065+0.049615
## [28] train-rmse:0.573802+0.007845 test-rmse:0.689857+0.050179
## [29] train-rmse:0.570111+0.007676 test-rmse:0.687704+0.051176
## [30] train-rmse:0.566179+0.008171 test-rmse:0.686891+0.051535
## [31] train-rmse:0.562139+0.007912 test-rmse:0.686057+0.051886
## [32] train-rmse:0.557850+0.008289 test-rmse:0.685081+0.051182
## [33] train-rmse:0.554385+0.008924 test-rmse:0.684609+0.050635
## [34] train-rmse:0.551409+0.008562 test-rmse:0.682548+0.051488
## [35] train-rmse:0.548100+0.009040 test-rmse:0.681617+0.051568
## [36] train-rmse:0.545563+0.009115 test-rmse:0.680917+0.052527
## [37] train-rmse:0.543152+0.009566 test-rmse:0.681260+0.052364
## [38] train-rmse:0.539084+0.010330 test-rmse:0.680231+0.052980
## [39] train-rmse:0.535222+0.009118 test-rmse:0.678376+0.054293
## [40] train-rmse:0.530369+0.008053 test-rmse:0.675915+0.054505
## [41] train-rmse:0.527458+0.008177 test-rmse:0.675264+0.054040
## [42] train-rmse:0.524401+0.008416 test-rmse:0.675817+0.055242
## [43] train-rmse:0.521059+0.008809 test-rmse:0.675343+0.055870
## [44] train-rmse:0.518588+0.008503 test-rmse:0.674249+0.056954
## [45] train-rmse:0.515981+0.008108 test-rmse:0.674563+0.057989
## [46] train-rmse:0.512957+0.008346 test-rmse:0.673932+0.059940
## [47] train-rmse:0.509579+0.009695 test-rmse:0.672380+0.057622
## [48] train-rmse:0.507296+0.010243 test-rmse:0.672179+0.058267
## [49] train-rmse:0.504655+0.010960 test-rmse:0.671930+0.059567
## [50] train-rmse:0.501698+0.011430 test-rmse:0.670397+0.058013
## [51] train-rmse:0.498503+0.010243 test-rmse:0.668395+0.057977
## [52] train-rmse:0.495805+0.010600 test-rmse:0.667824+0.059391
## [53] train-rmse:0.493499+0.009679 test-rmse:0.666889+0.060193
## [54] train-rmse:0.491601+0.009778 test-rmse:0.666603+0.061844
## [55] train-rmse:0.488345+0.010626 test-rmse:0.665389+0.061426
## [56] train-rmse:0.485722+0.011101 test-rmse:0.665428+0.061616
## [57] train-rmse:0.482679+0.010518 test-rmse:0.664198+0.060883
## [58] train-rmse:0.480820+0.010338 test-rmse:0.663646+0.061408
## [59] train-rmse:0.478763+0.010087 test-rmse:0.663498+0.062295
## [60] train-rmse:0.476270+0.010553 test-rmse:0.662575+0.062515
## [61] train-rmse:0.474469+0.010281 test-rmse:0.662664+0.062915
## [62] train-rmse:0.471900+0.010861 test-rmse:0.660414+0.061870
## [63] train-rmse:0.469086+0.009532 test-rmse:0.658686+0.060899
## [64] train-rmse:0.466965+0.009985 test-rmse:0.658492+0.061384
## [65] train-rmse:0.464592+0.009555 test-rmse:0.658263+0.061895
## [66] train-rmse:0.462469+0.009524 test-rmse:0.657367+0.060897
## [67] train-rmse:0.460449+0.009741 test-rmse:0.658163+0.061259
## [68] train-rmse:0.458173+0.010797 test-rmse:0.657208+0.061575
## [69] train-rmse:0.455703+0.009601 test-rmse:0.655135+0.062714
## [70] train-rmse:0.453558+0.009523 test-rmse:0.655061+0.062588
## [71] train-rmse:0.451178+0.009785 test-rmse:0.654929+0.062602
## [72] train-rmse:0.449300+0.009648 test-rmse:0.655152+0.063177
## [73] train-rmse:0.446481+0.009500 test-rmse:0.653627+0.063017
## [74] train-rmse:0.444584+0.009442 test-rmse:0.652577+0.062434
## [75] train-rmse:0.442714+0.009627 test-rmse:0.651846+0.062079
## [76] train-rmse:0.441340+0.009510 test-rmse:0.651112+0.062154
## [77] train-rmse:0.440040+0.009820 test-rmse:0.651029+0.062549
## [78] train-rmse:0.438703+0.009782 test-rmse:0.650732+0.062995
## [79] train-rmse:0.437122+0.010159 test-rmse:0.650920+0.063202
## [80] train-rmse:0.434919+0.010795 test-rmse:0.651212+0.063296
## [81] train-rmse:0.432511+0.010936 test-rmse:0.650285+0.063852
## [82] train-rmse:0.430387+0.012001 test-rmse:0.649754+0.063202
## [83] train-rmse:0.428199+0.011960 test-rmse:0.648495+0.062890
## [84] train-rmse:0.425931+0.011012 test-rmse:0.648779+0.063479
## [85] train-rmse:0.424694+0.011144 test-rmse:0.647770+0.063529
## [86] train-rmse:0.423148+0.011861 test-rmse:0.647271+0.063268
## [87] train-rmse:0.421067+0.012373 test-rmse:0.646810+0.062768
## [88] train-rmse:0.418824+0.011680 test-rmse:0.646262+0.062552
## [89] train-rmse:0.416575+0.012080 test-rmse:0.644633+0.062615
## [90] train-rmse:0.414345+0.012146 test-rmse:0.644293+0.062281
## [91] train-rmse:0.412717+0.012364 test-rmse:0.644701+0.062839
## [92] train-rmse:0.410629+0.011794 test-rmse:0.643834+0.062912
## [93] train-rmse:0.409125+0.012105 test-rmse:0.643563+0.063045
## [94] train-rmse:0.407842+0.012279 test-rmse:0.643901+0.062786
## [95] train-rmse:0.406338+0.012132 test-rmse:0.644046+0.063603
## [96] train-rmse:0.404942+0.012217 test-rmse:0.643952+0.063206
## [97] train-rmse:0.403140+0.011381 test-rmse:0.643241+0.064273
## [98] train-rmse:0.400288+0.012302 test-rmse:0.641952+0.064398
## [99] train-rmse:0.398718+0.012440 test-rmse:0.641352+0.063578
## [100] train-rmse:0.396955+0.012868 test-rmse:0.640915+0.063098
## [101] train-rmse:0.395774+0.013033 test-rmse:0.641311+0.062911
## [102] train-rmse:0.394738+0.013010 test-rmse:0.640956+0.062920
## [103] train-rmse:0.392980+0.013766 test-rmse:0.640474+0.062660
## [104] train-rmse:0.391495+0.014471 test-rmse:0.640501+0.063245
## [105] train-rmse:0.390511+0.014594 test-rmse:0.640178+0.062635
## [106] train-rmse:0.388770+0.014575 test-rmse:0.639552+0.062869
## [107] train-rmse:0.387194+0.014032 test-rmse:0.639842+0.062553
## [108] train-rmse:0.385164+0.013690 test-rmse:0.638254+0.061577
## [109] train-rmse:0.383421+0.013571 test-rmse:0.637345+0.061058
## [110] train-rmse:0.382099+0.013549 test-rmse:0.637262+0.061480
## [111] train-rmse:0.380640+0.013568 test-rmse:0.636955+0.060778
## [112] train-rmse:0.379334+0.013942 test-rmse:0.636611+0.060028
## [113] train-rmse:0.377881+0.013739 test-rmse:0.636477+0.060205
## [114] train-rmse:0.376948+0.014062 test-rmse:0.636790+0.060371
## [115] train-rmse:0.375795+0.013867 test-rmse:0.636573+0.060097
## [116] train-rmse:0.374371+0.013997 test-rmse:0.636686+0.060657
## [117] train-rmse:0.372813+0.013681 test-rmse:0.636556+0.060255
## [118] train-rmse:0.371458+0.013638 test-rmse:0.636120+0.060742
## [119] train-rmse:0.369883+0.014195 test-rmse:0.635576+0.060675
## [120] train-rmse:0.368825+0.014378 test-rmse:0.635286+0.060447
## [121] train-rmse:0.367878+0.014783 test-rmse:0.635488+0.060350
## [122] train-rmse:0.366396+0.015044 test-rmse:0.634787+0.060188
## [123] train-rmse:0.365241+0.015056 test-rmse:0.635550+0.061064
## [124] train-rmse:0.364210+0.015205 test-rmse:0.635347+0.060609
## [125] train-rmse:0.363094+0.015235 test-rmse:0.635142+0.060551
## [126] train-rmse:0.361914+0.015453 test-rmse:0.634497+0.059756
## [127] train-rmse:0.360832+0.015207 test-rmse:0.634477+0.059788
## [128] train-rmse:0.359992+0.015329 test-rmse:0.634427+0.059846
## [129] train-rmse:0.358481+0.015958 test-rmse:0.634562+0.059930
## [130] train-rmse:0.357390+0.015780 test-rmse:0.634663+0.060167
## [131] train-rmse:0.356487+0.015646 test-rmse:0.634907+0.060368
## [132] train-rmse:0.355292+0.015152 test-rmse:0.635034+0.060533
## [133] train-rmse:0.354335+0.014890 test-rmse:0.635475+0.060538
## [134] train-rmse:0.352776+0.014881 test-rmse:0.634887+0.061262
## Stopping. Best iteration:
## [128] train-rmse:0.359992+0.015329 test-rmse:0.634427+0.059846
##
## 10
## [1] train-rmse:1.450321+0.014694 test-rmse:1.457560+0.068773
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.315655+0.012777 test-rmse:1.329399+0.068835
## [3] train-rmse:1.198966+0.010584 test-rmse:1.219167+0.068962
## [4] train-rmse:1.099360+0.008995 test-rmse:1.129506+0.071686
## [5] train-rmse:1.014129+0.007778 test-rmse:1.049510+0.072084
## [6] train-rmse:0.941377+0.006258 test-rmse:0.983879+0.071088
## [7] train-rmse:0.880894+0.005112 test-rmse:0.933500+0.070333
## [8] train-rmse:0.829402+0.004360 test-rmse:0.886623+0.070100
## [9] train-rmse:0.786383+0.003835 test-rmse:0.850676+0.067456
## [10] train-rmse:0.750424+0.004089 test-rmse:0.821285+0.066342
## [11] train-rmse:0.718797+0.003148 test-rmse:0.795662+0.063897
## [12] train-rmse:0.693397+0.003800 test-rmse:0.775636+0.062368
## [13] train-rmse:0.667039+0.006970 test-rmse:0.756267+0.062397
## [14] train-rmse:0.647627+0.006379 test-rmse:0.742379+0.060586
## [15] train-rmse:0.631762+0.006018 test-rmse:0.730307+0.058981
## [16] train-rmse:0.612910+0.008465 test-rmse:0.718736+0.058282
## [17] train-rmse:0.599361+0.009998 test-rmse:0.710009+0.056610
## [18] train-rmse:0.585629+0.011709 test-rmse:0.700639+0.051577
## [19] train-rmse:0.575175+0.011823 test-rmse:0.696257+0.051829
## [20] train-rmse:0.566186+0.011538 test-rmse:0.693033+0.052783
## [21] train-rmse:0.558112+0.011404 test-rmse:0.690993+0.053816
## [22] train-rmse:0.550451+0.011560 test-rmse:0.686955+0.055401
## [23] train-rmse:0.544156+0.011644 test-rmse:0.685424+0.054444
## [24] train-rmse:0.536955+0.012022 test-rmse:0.682314+0.053807
## [25] train-rmse:0.530697+0.011311 test-rmse:0.679718+0.053422
## [26] train-rmse:0.523563+0.012468 test-rmse:0.676595+0.054285
## [27] train-rmse:0.518476+0.012706 test-rmse:0.676853+0.054857
## [28] train-rmse:0.513330+0.013099 test-rmse:0.675758+0.053680
## [29] train-rmse:0.509158+0.012958 test-rmse:0.673274+0.053768
## [30] train-rmse:0.504030+0.014466 test-rmse:0.670173+0.052904
## [31] train-rmse:0.499012+0.015436 test-rmse:0.670334+0.055134
## [32] train-rmse:0.495630+0.015217 test-rmse:0.670337+0.055392
## [33] train-rmse:0.491879+0.015960 test-rmse:0.669816+0.056414
## [34] train-rmse:0.487031+0.016879 test-rmse:0.667590+0.057336
## [35] train-rmse:0.483231+0.015309 test-rmse:0.666336+0.056620
## [36] train-rmse:0.478370+0.015219 test-rmse:0.666079+0.055187
## [37] train-rmse:0.473707+0.015986 test-rmse:0.663979+0.056774
## [38] train-rmse:0.468061+0.014637 test-rmse:0.658986+0.054895
## [39] train-rmse:0.463734+0.014625 test-rmse:0.657229+0.054930
## [40] train-rmse:0.460464+0.014520 test-rmse:0.657369+0.055147
## [41] train-rmse:0.455338+0.014770 test-rmse:0.655203+0.055070
## [42] train-rmse:0.452255+0.015326 test-rmse:0.655681+0.055631
## [43] train-rmse:0.448535+0.015173 test-rmse:0.654777+0.055268
## [44] train-rmse:0.445154+0.015621 test-rmse:0.654916+0.055952
## [45] train-rmse:0.441881+0.015480 test-rmse:0.653092+0.055870
## [46] train-rmse:0.438886+0.015885 test-rmse:0.651980+0.056432
## [47] train-rmse:0.435558+0.015945 test-rmse:0.650608+0.056228
## [48] train-rmse:0.432004+0.016826 test-rmse:0.650315+0.055874
## [49] train-rmse:0.428402+0.016131 test-rmse:0.650036+0.056293
## [50] train-rmse:0.426033+0.016055 test-rmse:0.650017+0.057268
## [51] train-rmse:0.423621+0.016378 test-rmse:0.649892+0.058585
## [52] train-rmse:0.420930+0.016035 test-rmse:0.648996+0.058711
## [53] train-rmse:0.418078+0.015813 test-rmse:0.649312+0.058936
## [54] train-rmse:0.414875+0.015981 test-rmse:0.649469+0.058620
## [55] train-rmse:0.411209+0.015864 test-rmse:0.648743+0.058621
## [56] train-rmse:0.407723+0.016985 test-rmse:0.649262+0.058926
## [57] train-rmse:0.405169+0.016696 test-rmse:0.649004+0.059925
## [58] train-rmse:0.402933+0.016666 test-rmse:0.648219+0.059601
## [59] train-rmse:0.400144+0.017474 test-rmse:0.648346+0.060210
## [60] train-rmse:0.397697+0.017068 test-rmse:0.647078+0.059882
## [61] train-rmse:0.394525+0.016934 test-rmse:0.646334+0.060447
## [62] train-rmse:0.391458+0.017511 test-rmse:0.643805+0.060175
## [63] train-rmse:0.389047+0.017803 test-rmse:0.642737+0.060713
## [64] train-rmse:0.386781+0.018139 test-rmse:0.642884+0.061729
## [65] train-rmse:0.384380+0.018598 test-rmse:0.643027+0.062503
## [66] train-rmse:0.381761+0.018965 test-rmse:0.643457+0.062476
## [67] train-rmse:0.379449+0.018832 test-rmse:0.642808+0.063693
## [68] train-rmse:0.376791+0.018550 test-rmse:0.641069+0.063021
## [69] train-rmse:0.374536+0.017917 test-rmse:0.640948+0.063259
## [70] train-rmse:0.371740+0.017871 test-rmse:0.640842+0.063914
## [71] train-rmse:0.369903+0.018195 test-rmse:0.641341+0.064403
## [72] train-rmse:0.366143+0.017502 test-rmse:0.641721+0.064230
## [73] train-rmse:0.363599+0.017245 test-rmse:0.641978+0.064112
## [74] train-rmse:0.361161+0.017206 test-rmse:0.642085+0.064154
## [75] train-rmse:0.359083+0.016969 test-rmse:0.642185+0.064355
## [76] train-rmse:0.356744+0.016662 test-rmse:0.642500+0.065113
## Stopping. Best iteration:
## [70] train-rmse:0.371740+0.017871 test-rmse:0.640842+0.063914
##
## 11
## [1] train-rmse:1.448782+0.014799 test-rmse:1.455295+0.070036
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.312288+0.013083 test-rmse:1.328540+0.072409
## [3] train-rmse:1.194099+0.010974 test-rmse:1.214616+0.072760
## [4] train-rmse:1.091303+0.009035 test-rmse:1.120256+0.072043
## [5] train-rmse:1.003214+0.007059 test-rmse:1.040349+0.072379
## [6] train-rmse:0.927138+0.006345 test-rmse:0.972610+0.073375
## [7] train-rmse:0.861778+0.005902 test-rmse:0.914440+0.072396
## [8] train-rmse:0.806364+0.006632 test-rmse:0.870092+0.070951
## [9] train-rmse:0.759400+0.006862 test-rmse:0.830341+0.067563
## [10] train-rmse:0.719366+0.006806 test-rmse:0.799134+0.066491
## [11] train-rmse:0.686168+0.007855 test-rmse:0.774965+0.064168
## [12] train-rmse:0.658040+0.008254 test-rmse:0.755928+0.063147
## [13] train-rmse:0.634327+0.008904 test-rmse:0.738095+0.059529
## [14] train-rmse:0.613101+0.008351 test-rmse:0.721599+0.055764
## [15] train-rmse:0.591529+0.012335 test-rmse:0.711972+0.054729
## [16] train-rmse:0.575582+0.011325 test-rmse:0.702247+0.053657
## [17] train-rmse:0.560968+0.009990 test-rmse:0.693421+0.052115
## [18] train-rmse:0.545858+0.013643 test-rmse:0.685691+0.050426
## [19] train-rmse:0.534734+0.013243 test-rmse:0.683040+0.051172
## [20] train-rmse:0.524113+0.014275 test-rmse:0.679898+0.050321
## [21] train-rmse:0.514062+0.012221 test-rmse:0.675753+0.051524
## [22] train-rmse:0.504431+0.010976 test-rmse:0.671514+0.051997
## [23] train-rmse:0.496956+0.009723 test-rmse:0.667256+0.054074
## [24] train-rmse:0.489964+0.009115 test-rmse:0.663757+0.052916
## [25] train-rmse:0.482856+0.010586 test-rmse:0.662075+0.054720
## [26] train-rmse:0.478045+0.009936 test-rmse:0.660025+0.056080
## [27] train-rmse:0.471648+0.010884 test-rmse:0.658792+0.058327
## [28] train-rmse:0.464309+0.013132 test-rmse:0.657692+0.056418
## [29] train-rmse:0.457041+0.014835 test-rmse:0.655109+0.054916
## [30] train-rmse:0.449849+0.014053 test-rmse:0.652454+0.054564
## [31] train-rmse:0.444424+0.016139 test-rmse:0.652627+0.055647
## [32] train-rmse:0.438890+0.016916 test-rmse:0.651947+0.057326
## [33] train-rmse:0.433276+0.015128 test-rmse:0.648687+0.059060
## [34] train-rmse:0.428998+0.015030 test-rmse:0.647754+0.058433
## [35] train-rmse:0.423691+0.013957 test-rmse:0.645797+0.058256
## [36] train-rmse:0.418650+0.014485 test-rmse:0.644382+0.058230
## [37] train-rmse:0.414386+0.015200 test-rmse:0.642630+0.059158
## [38] train-rmse:0.409097+0.014863 test-rmse:0.641586+0.059116
## [39] train-rmse:0.404872+0.014706 test-rmse:0.641179+0.060125
## [40] train-rmse:0.399778+0.014770 test-rmse:0.641031+0.060332
## [41] train-rmse:0.395474+0.014246 test-rmse:0.639625+0.059816
## [42] train-rmse:0.391313+0.014193 test-rmse:0.640506+0.058432
## [43] train-rmse:0.386066+0.014720 test-rmse:0.637802+0.058063
## [44] train-rmse:0.380509+0.013310 test-rmse:0.636265+0.058348
## [45] train-rmse:0.377117+0.013073 test-rmse:0.635664+0.060023
## [46] train-rmse:0.373875+0.013290 test-rmse:0.635099+0.059989
## [47] train-rmse:0.369974+0.014014 test-rmse:0.633806+0.059944
## [48] train-rmse:0.366703+0.014393 test-rmse:0.633957+0.060439
## [49] train-rmse:0.362575+0.014677 test-rmse:0.632886+0.060361
## [50] train-rmse:0.358899+0.014635 test-rmse:0.631754+0.059808
## [51] train-rmse:0.355298+0.015381 test-rmse:0.631506+0.060993
## [52] train-rmse:0.352344+0.015706 test-rmse:0.631587+0.060459
## [53] train-rmse:0.349084+0.015903 test-rmse:0.631497+0.060545
## [54] train-rmse:0.344838+0.016471 test-rmse:0.629899+0.060985
## [55] train-rmse:0.341964+0.016518 test-rmse:0.629553+0.061143
## [56] train-rmse:0.339201+0.016157 test-rmse:0.629065+0.060973
## [57] train-rmse:0.334095+0.015056 test-rmse:0.626140+0.062518
## [58] train-rmse:0.331873+0.014964 test-rmse:0.626397+0.062707
## [59] train-rmse:0.328489+0.015212 test-rmse:0.626149+0.063984
## [60] train-rmse:0.325371+0.015555 test-rmse:0.626053+0.064172
## [61] train-rmse:0.322393+0.015533 test-rmse:0.627205+0.064881
## [62] train-rmse:0.319881+0.015785 test-rmse:0.627110+0.064692
## [63] train-rmse:0.316985+0.016641 test-rmse:0.626227+0.065130
## [64] train-rmse:0.314906+0.016576 test-rmse:0.626071+0.065070
## [65] train-rmse:0.312063+0.015539 test-rmse:0.624946+0.065103
## [66] train-rmse:0.309671+0.015650 test-rmse:0.625469+0.065211
## [67] train-rmse:0.307040+0.016428 test-rmse:0.625387+0.065209
## [68] train-rmse:0.304882+0.016018 test-rmse:0.624194+0.065234
## [69] train-rmse:0.302275+0.016223 test-rmse:0.623963+0.065759
## [70] train-rmse:0.299456+0.016479 test-rmse:0.623888+0.066013
## [71] train-rmse:0.297157+0.017262 test-rmse:0.623598+0.065232
## [72] train-rmse:0.294543+0.017819 test-rmse:0.623695+0.065844
## [73] train-rmse:0.291452+0.017639 test-rmse:0.622605+0.065880
## [74] train-rmse:0.288113+0.016686 test-rmse:0.623126+0.066272
## [75] train-rmse:0.286087+0.016958 test-rmse:0.623534+0.066724
## [76] train-rmse:0.282770+0.017052 test-rmse:0.623750+0.066517
## [77] train-rmse:0.280774+0.017498 test-rmse:0.623622+0.066306
## [78] train-rmse:0.278175+0.017018 test-rmse:0.622767+0.065946
## [79] train-rmse:0.276208+0.016374 test-rmse:0.622470+0.066149
## [80] train-rmse:0.274162+0.017033 test-rmse:0.622245+0.065639
## [81] train-rmse:0.272218+0.016803 test-rmse:0.622375+0.065490
## [82] train-rmse:0.270448+0.016784 test-rmse:0.622532+0.065532
## [83] train-rmse:0.268431+0.017142 test-rmse:0.622711+0.065511
## [84] train-rmse:0.266266+0.017484 test-rmse:0.623407+0.065633
## [85] train-rmse:0.263753+0.017529 test-rmse:0.623449+0.065057
## [86] train-rmse:0.260870+0.017182 test-rmse:0.624006+0.065168
## Stopping. Best iteration:
## [80] train-rmse:0.274162+0.017033 test-rmse:0.622245+0.065639
##
## 12
## [1] train-rmse:1.447224+0.014544 test-rmse:1.456261+0.071061
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.309635+0.013071 test-rmse:1.329631+0.073465
## [3] train-rmse:1.189639+0.010827 test-rmse:1.217443+0.073480
## [4] train-rmse:1.084639+0.008437 test-rmse:1.121012+0.075846
## [5] train-rmse:0.993806+0.006487 test-rmse:1.040973+0.076313
## [6] train-rmse:0.915628+0.006040 test-rmse:0.973552+0.075247
## [7] train-rmse:0.848037+0.005887 test-rmse:0.918842+0.071594
## [8] train-rmse:0.789052+0.005158 test-rmse:0.871154+0.072138
## [9] train-rmse:0.738827+0.006097 test-rmse:0.831196+0.068338
## [10] train-rmse:0.696198+0.006617 test-rmse:0.797760+0.069558
## [11] train-rmse:0.660215+0.006839 test-rmse:0.770433+0.068632
## [12] train-rmse:0.627822+0.009878 test-rmse:0.750015+0.064999
## [13] train-rmse:0.602360+0.010681 test-rmse:0.732608+0.062751
## [14] train-rmse:0.577757+0.010137 test-rmse:0.717095+0.060662
## [15] train-rmse:0.557687+0.010220 test-rmse:0.707714+0.061948
## [16] train-rmse:0.539469+0.009980 test-rmse:0.698661+0.060871
## [17] train-rmse:0.522755+0.010305 test-rmse:0.689500+0.057161
## [18] train-rmse:0.505546+0.013780 test-rmse:0.684204+0.057367
## [19] train-rmse:0.490235+0.014331 test-rmse:0.679498+0.057775
## [20] train-rmse:0.476064+0.014326 test-rmse:0.674051+0.057152
## [21] train-rmse:0.465379+0.016279 test-rmse:0.670496+0.059293
## [22] train-rmse:0.456541+0.017087 test-rmse:0.666080+0.060234
## [23] train-rmse:0.447205+0.016804 test-rmse:0.662925+0.058945
## [24] train-rmse:0.439796+0.016375 test-rmse:0.661079+0.057890
## [25] train-rmse:0.431755+0.016465 test-rmse:0.659277+0.058501
## [26] train-rmse:0.424821+0.015949 test-rmse:0.657776+0.059540
## [27] train-rmse:0.419169+0.016583 test-rmse:0.654854+0.060001
## [28] train-rmse:0.412161+0.018780 test-rmse:0.653966+0.060510
## [29] train-rmse:0.406682+0.019771 test-rmse:0.654697+0.061928
## [30] train-rmse:0.401159+0.019329 test-rmse:0.653934+0.062669
## [31] train-rmse:0.395663+0.019929 test-rmse:0.651673+0.064034
## [32] train-rmse:0.389760+0.018656 test-rmse:0.650970+0.064843
## [33] train-rmse:0.383815+0.020459 test-rmse:0.650127+0.064827
## [34] train-rmse:0.378811+0.020728 test-rmse:0.648874+0.063456
## [35] train-rmse:0.374175+0.021977 test-rmse:0.647720+0.064175
## [36] train-rmse:0.368080+0.021582 test-rmse:0.647325+0.064834
## [37] train-rmse:0.363876+0.021424 test-rmse:0.647478+0.065065
## [38] train-rmse:0.358135+0.021757 test-rmse:0.646323+0.065883
## [39] train-rmse:0.352702+0.020567 test-rmse:0.644009+0.067886
## [40] train-rmse:0.347044+0.020374 test-rmse:0.644128+0.068816
## [41] train-rmse:0.340659+0.019105 test-rmse:0.642577+0.066595
## [42] train-rmse:0.336886+0.019181 test-rmse:0.641852+0.065642
## [43] train-rmse:0.332744+0.019683 test-rmse:0.641909+0.065907
## [44] train-rmse:0.328889+0.019612 test-rmse:0.640855+0.064631
## [45] train-rmse:0.323404+0.019380 test-rmse:0.640442+0.065477
## [46] train-rmse:0.320181+0.019016 test-rmse:0.640502+0.064669
## [47] train-rmse:0.315242+0.018798 test-rmse:0.639315+0.064326
## [48] train-rmse:0.309927+0.016961 test-rmse:0.637836+0.065141
## [49] train-rmse:0.307098+0.016932 test-rmse:0.637759+0.066378
## [50] train-rmse:0.302331+0.016570 test-rmse:0.636416+0.065704
## [51] train-rmse:0.299422+0.016399 test-rmse:0.636655+0.066078
## [52] train-rmse:0.296163+0.016113 test-rmse:0.636953+0.066931
## [53] train-rmse:0.292638+0.015464 test-rmse:0.636913+0.067003
## [54] train-rmse:0.289391+0.015931 test-rmse:0.636032+0.066271
## [55] train-rmse:0.284805+0.015632 test-rmse:0.634461+0.066783
## [56] train-rmse:0.281216+0.015001 test-rmse:0.633489+0.066571
## [57] train-rmse:0.277494+0.014782 test-rmse:0.632822+0.066332
## [58] train-rmse:0.273180+0.014857 test-rmse:0.632511+0.067238
## [59] train-rmse:0.270667+0.014506 test-rmse:0.632541+0.067004
## [60] train-rmse:0.267599+0.014215 test-rmse:0.632163+0.065930
## [61] train-rmse:0.264312+0.014113 test-rmse:0.631914+0.065973
## [62] train-rmse:0.261167+0.014507 test-rmse:0.632257+0.065568
## [63] train-rmse:0.257260+0.014950 test-rmse:0.630467+0.064741
## [64] train-rmse:0.254975+0.015123 test-rmse:0.630841+0.064887
## [65] train-rmse:0.251603+0.016302 test-rmse:0.630922+0.064632
## [66] train-rmse:0.248666+0.015585 test-rmse:0.630406+0.064124
## [67] train-rmse:0.246597+0.015994 test-rmse:0.629746+0.064575
## [68] train-rmse:0.243711+0.016001 test-rmse:0.629641+0.064331
## [69] train-rmse:0.241292+0.015419 test-rmse:0.629340+0.064181
## [70] train-rmse:0.239701+0.015262 test-rmse:0.629455+0.064891
## [71] train-rmse:0.236743+0.014944 test-rmse:0.628938+0.065131
## [72] train-rmse:0.233351+0.014124 test-rmse:0.628410+0.064327
## [73] train-rmse:0.230786+0.014070 test-rmse:0.628378+0.064950
## [74] train-rmse:0.228474+0.014557 test-rmse:0.628943+0.064887
## [75] train-rmse:0.226065+0.014029 test-rmse:0.627622+0.064055
## [76] train-rmse:0.223471+0.012613 test-rmse:0.627519+0.064040
## [77] train-rmse:0.221387+0.013590 test-rmse:0.628085+0.064424
## [78] train-rmse:0.219365+0.014257 test-rmse:0.628580+0.065180
## [79] train-rmse:0.216661+0.012746 test-rmse:0.627825+0.064964
## [80] train-rmse:0.214055+0.011754 test-rmse:0.627840+0.065476
## [81] train-rmse:0.211646+0.011989 test-rmse:0.627828+0.065145
## [82] train-rmse:0.208492+0.011763 test-rmse:0.627794+0.065175
## Stopping. Best iteration:
## [76] train-rmse:0.223471+0.012613 test-rmse:0.627519+0.064040
##
## 13
## [1] train-rmse:1.446079+0.014316 test-rmse:1.455624+0.071141
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.307152+0.012644 test-rmse:1.327866+0.071313
## [3] train-rmse:1.184869+0.010313 test-rmse:1.216100+0.071942
## [4] train-rmse:1.079294+0.008509 test-rmse:1.117726+0.070914
## [5] train-rmse:0.986488+0.006404 test-rmse:1.036011+0.073947
## [6] train-rmse:0.905415+0.006081 test-rmse:0.968326+0.072681
## [7] train-rmse:0.835077+0.005267 test-rmse:0.910939+0.072865
## [8] train-rmse:0.774059+0.005637 test-rmse:0.864107+0.073042
## [9] train-rmse:0.720984+0.005702 test-rmse:0.822060+0.070466
## [10] train-rmse:0.676115+0.006540 test-rmse:0.790402+0.071596
## [11] train-rmse:0.637913+0.008510 test-rmse:0.764782+0.071462
## [12] train-rmse:0.606089+0.009256 test-rmse:0.743006+0.070380
## [13] train-rmse:0.575478+0.010345 test-rmse:0.726937+0.066735
## [14] train-rmse:0.551108+0.010826 test-rmse:0.711938+0.064832
## [15] train-rmse:0.530403+0.011265 test-rmse:0.700781+0.063203
## [16] train-rmse:0.509162+0.009893 test-rmse:0.689484+0.063583
## [17] train-rmse:0.491369+0.009333 test-rmse:0.681698+0.062785
## [18] train-rmse:0.472630+0.012208 test-rmse:0.671187+0.060535
## [19] train-rmse:0.454677+0.012781 test-rmse:0.665202+0.059314
## [20] train-rmse:0.440471+0.014310 test-rmse:0.660113+0.059569
## [21] train-rmse:0.428069+0.015327 test-rmse:0.655724+0.059710
## [22] train-rmse:0.413599+0.015035 test-rmse:0.652890+0.059405
## [23] train-rmse:0.402703+0.016080 test-rmse:0.650225+0.058307
## [24] train-rmse:0.393752+0.016209 test-rmse:0.649262+0.056788
## [25] train-rmse:0.385598+0.017546 test-rmse:0.647965+0.057337
## [26] train-rmse:0.376808+0.017645 test-rmse:0.646067+0.056203
## [27] train-rmse:0.368807+0.018136 test-rmse:0.644978+0.056120
## [28] train-rmse:0.362383+0.018162 test-rmse:0.644986+0.055682
## [29] train-rmse:0.356042+0.017155 test-rmse:0.643017+0.054982
## [30] train-rmse:0.348267+0.017273 test-rmse:0.642366+0.056276
## [31] train-rmse:0.342188+0.018763 test-rmse:0.641474+0.055728
## [32] train-rmse:0.336615+0.018595 test-rmse:0.640663+0.057114
## [33] train-rmse:0.331075+0.019358 test-rmse:0.641788+0.057029
## [34] train-rmse:0.326725+0.019264 test-rmse:0.642176+0.056769
## [35] train-rmse:0.320188+0.020147 test-rmse:0.641639+0.056795
## [36] train-rmse:0.315126+0.021199 test-rmse:0.641998+0.057086
## [37] train-rmse:0.309914+0.019464 test-rmse:0.639649+0.056752
## [38] train-rmse:0.305046+0.019898 test-rmse:0.639086+0.055804
## [39] train-rmse:0.300166+0.018975 test-rmse:0.638916+0.056232
## [40] train-rmse:0.296688+0.018666 test-rmse:0.638293+0.055691
## [41] train-rmse:0.291949+0.018521 test-rmse:0.638313+0.055962
## [42] train-rmse:0.286412+0.017214 test-rmse:0.637445+0.056658
## [43] train-rmse:0.281733+0.016453 test-rmse:0.636387+0.056259
## [44] train-rmse:0.277323+0.015678 test-rmse:0.635698+0.054701
## [45] train-rmse:0.272139+0.015018 test-rmse:0.635417+0.055690
## [46] train-rmse:0.267264+0.015523 test-rmse:0.636004+0.055230
## [47] train-rmse:0.263477+0.015825 test-rmse:0.637102+0.055666
## [48] train-rmse:0.259305+0.016532 test-rmse:0.636612+0.054998
## [49] train-rmse:0.256134+0.016599 test-rmse:0.635991+0.055449
## [50] train-rmse:0.251389+0.016146 test-rmse:0.634742+0.055368
## [51] train-rmse:0.246935+0.015879 test-rmse:0.635205+0.056126
## [52] train-rmse:0.242916+0.016819 test-rmse:0.634636+0.056028
## [53] train-rmse:0.238662+0.016512 test-rmse:0.634227+0.056285
## [54] train-rmse:0.234217+0.015584 test-rmse:0.634631+0.056179
## [55] train-rmse:0.229977+0.016304 test-rmse:0.635069+0.055396
## [56] train-rmse:0.226141+0.016312 test-rmse:0.634548+0.055598
## [57] train-rmse:0.222100+0.016595 test-rmse:0.634486+0.056320
## [58] train-rmse:0.219421+0.015959 test-rmse:0.634280+0.056626
## [59] train-rmse:0.214980+0.014544 test-rmse:0.632798+0.057261
## [60] train-rmse:0.211893+0.014698 test-rmse:0.632080+0.056993
## [61] train-rmse:0.208996+0.014945 test-rmse:0.631730+0.056932
## [62] train-rmse:0.204269+0.015190 test-rmse:0.630300+0.055977
## [63] train-rmse:0.201925+0.015432 test-rmse:0.630483+0.056377
## [64] train-rmse:0.198762+0.015730 test-rmse:0.630517+0.056443
## [65] train-rmse:0.195204+0.015495 test-rmse:0.630029+0.055655
## [66] train-rmse:0.191734+0.014431 test-rmse:0.630317+0.055351
## [67] train-rmse:0.189198+0.014733 test-rmse:0.629909+0.055352
## [68] train-rmse:0.185680+0.015044 test-rmse:0.630515+0.055816
## [69] train-rmse:0.182527+0.014077 test-rmse:0.629892+0.055081
## [70] train-rmse:0.179851+0.013259 test-rmse:0.630373+0.055644
## [71] train-rmse:0.177059+0.013139 test-rmse:0.630744+0.055584
## [72] train-rmse:0.174125+0.012180 test-rmse:0.630388+0.056192
## [73] train-rmse:0.171446+0.012544 test-rmse:0.630746+0.056539
## [74] train-rmse:0.168727+0.012384 test-rmse:0.630185+0.056620
## [75] train-rmse:0.166063+0.012141 test-rmse:0.630084+0.056698
## Stopping. Best iteration:
## [69] train-rmse:0.182527+0.014077 test-rmse:0.629892+0.055081
##
## 14
## [1] train-rmse:1.434029+0.014566 test-rmse:1.434496+0.069361
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.292215+0.012571 test-rmse:1.295052+0.070304
## [3] train-rmse:1.176080+0.010880 test-rmse:1.179201+0.069804
## [4] train-rmse:1.082032+0.009534 test-rmse:1.087562+0.069004
## [5] train-rmse:1.006514+0.008544 test-rmse:1.014525+0.067222
## [6] train-rmse:0.945823+0.007959 test-rmse:0.956609+0.064803
## [7] train-rmse:0.897587+0.007370 test-rmse:0.910627+0.062740
## [8] train-rmse:0.859124+0.006999 test-rmse:0.874055+0.057534
## [9] train-rmse:0.828912+0.006696 test-rmse:0.846519+0.055630
## [10] train-rmse:0.804885+0.006480 test-rmse:0.825001+0.052501
## [11] train-rmse:0.785922+0.006209 test-rmse:0.810099+0.050162
## [12] train-rmse:0.771016+0.005995 test-rmse:0.797051+0.048192
## [13] train-rmse:0.758941+0.005877 test-rmse:0.787997+0.046154
## [14] train-rmse:0.749089+0.005633 test-rmse:0.778010+0.044378
## [15] train-rmse:0.740981+0.005440 test-rmse:0.770927+0.041148
## [16] train-rmse:0.734205+0.005382 test-rmse:0.765072+0.039197
## [17] train-rmse:0.728521+0.005201 test-rmse:0.761274+0.038650
## [18] train-rmse:0.723642+0.005147 test-rmse:0.757023+0.037964
## [19] train-rmse:0.719397+0.005136 test-rmse:0.753558+0.037125
## [20] train-rmse:0.715648+0.005023 test-rmse:0.751649+0.036143
## [21] train-rmse:0.712305+0.004960 test-rmse:0.749725+0.035518
## [22] train-rmse:0.709212+0.004869 test-rmse:0.747385+0.033305
## [23] train-rmse:0.706388+0.004898 test-rmse:0.745073+0.030985
## [24] train-rmse:0.703809+0.004920 test-rmse:0.743756+0.031976
## [25] train-rmse:0.701383+0.004940 test-rmse:0.742347+0.031632
## [26] train-rmse:0.699082+0.004950 test-rmse:0.741341+0.030150
## [27] train-rmse:0.696888+0.004975 test-rmse:0.740613+0.030247
## [28] train-rmse:0.694831+0.005005 test-rmse:0.739403+0.030170
## [29] train-rmse:0.692853+0.004987 test-rmse:0.737898+0.029419
## [30] train-rmse:0.690965+0.005022 test-rmse:0.736322+0.028085
## [31] train-rmse:0.689163+0.005050 test-rmse:0.736251+0.028329
## [32] train-rmse:0.687395+0.005087 test-rmse:0.735840+0.028758
## [33] train-rmse:0.685676+0.005136 test-rmse:0.735127+0.028600
## [34] train-rmse:0.684009+0.005155 test-rmse:0.734194+0.028441
## [35] train-rmse:0.682389+0.005171 test-rmse:0.733861+0.028669
## [36] train-rmse:0.680818+0.005182 test-rmse:0.732506+0.029445
## [37] train-rmse:0.679278+0.005183 test-rmse:0.731467+0.028626
## [38] train-rmse:0.677779+0.005159 test-rmse:0.730726+0.029047
## [39] train-rmse:0.676315+0.005119 test-rmse:0.730565+0.029921
## [40] train-rmse:0.674883+0.005104 test-rmse:0.729420+0.029775
## [41] train-rmse:0.673486+0.005122 test-rmse:0.728887+0.029885
## [42] train-rmse:0.672118+0.005161 test-rmse:0.729152+0.030456
## [43] train-rmse:0.670771+0.005183 test-rmse:0.728882+0.030880
## [44] train-rmse:0.669474+0.005198 test-rmse:0.728652+0.031291
## [45] train-rmse:0.668184+0.005207 test-rmse:0.727626+0.031056
## [46] train-rmse:0.666937+0.005232 test-rmse:0.727821+0.031521
## [47] train-rmse:0.665709+0.005282 test-rmse:0.728223+0.031890
## [48] train-rmse:0.664491+0.005319 test-rmse:0.727207+0.031383
## [49] train-rmse:0.663302+0.005319 test-rmse:0.726487+0.032327
## [50] train-rmse:0.662142+0.005315 test-rmse:0.725621+0.032276
## [51] train-rmse:0.660994+0.005302 test-rmse:0.724530+0.032471
## [52] train-rmse:0.659869+0.005295 test-rmse:0.723289+0.031839
## [53] train-rmse:0.658773+0.005297 test-rmse:0.722773+0.031462
## [54] train-rmse:0.657684+0.005301 test-rmse:0.721900+0.031881
## [55] train-rmse:0.656640+0.005327 test-rmse:0.721792+0.032768
## [56] train-rmse:0.655614+0.005354 test-rmse:0.722332+0.033855
## [57] train-rmse:0.654591+0.005385 test-rmse:0.721588+0.033280
## [58] train-rmse:0.653590+0.005406 test-rmse:0.720618+0.032896
## [59] train-rmse:0.652612+0.005439 test-rmse:0.720660+0.033526
## [60] train-rmse:0.651639+0.005464 test-rmse:0.720113+0.034052
## [61] train-rmse:0.650677+0.005500 test-rmse:0.720111+0.033756
## [62] train-rmse:0.649725+0.005529 test-rmse:0.719689+0.033889
## [63] train-rmse:0.648794+0.005552 test-rmse:0.719056+0.034241
## [64] train-rmse:0.647884+0.005579 test-rmse:0.718893+0.033794
## [65] train-rmse:0.646987+0.005592 test-rmse:0.718515+0.032890
## [66] train-rmse:0.646089+0.005591 test-rmse:0.718333+0.032973
## [67] train-rmse:0.645221+0.005590 test-rmse:0.718087+0.032963
## [68] train-rmse:0.644349+0.005597 test-rmse:0.719193+0.033894
## [69] train-rmse:0.643500+0.005608 test-rmse:0.719543+0.034740
## [70] train-rmse:0.642640+0.005609 test-rmse:0.719467+0.034769
## [71] train-rmse:0.641824+0.005623 test-rmse:0.718506+0.034361
## [72] train-rmse:0.641015+0.005645 test-rmse:0.716550+0.034389
## [73] train-rmse:0.640225+0.005656 test-rmse:0.716625+0.034222
## [74] train-rmse:0.639446+0.005673 test-rmse:0.715301+0.033900
## [75] train-rmse:0.638683+0.005689 test-rmse:0.713801+0.033996
## [76] train-rmse:0.637928+0.005711 test-rmse:0.713205+0.033962
## [77] train-rmse:0.637177+0.005724 test-rmse:0.713038+0.033782
## [78] train-rmse:0.636423+0.005731 test-rmse:0.712658+0.033530
## [79] train-rmse:0.635685+0.005739 test-rmse:0.713241+0.034230
## [80] train-rmse:0.634962+0.005744 test-rmse:0.712475+0.034089
## [81] train-rmse:0.634236+0.005754 test-rmse:0.712405+0.033768
## [82] train-rmse:0.633521+0.005755 test-rmse:0.712523+0.034175
## [83] train-rmse:0.632814+0.005760 test-rmse:0.711779+0.033559
## [84] train-rmse:0.632110+0.005761 test-rmse:0.711308+0.033432
## [85] train-rmse:0.631408+0.005769 test-rmse:0.711665+0.033556
## [86] train-rmse:0.630709+0.005778 test-rmse:0.711077+0.032944
## [87] train-rmse:0.630021+0.005787 test-rmse:0.710669+0.033231
## [88] train-rmse:0.629334+0.005795 test-rmse:0.711163+0.033853
## [89] train-rmse:0.628665+0.005805 test-rmse:0.710524+0.034238
## [90] train-rmse:0.628011+0.005816 test-rmse:0.709210+0.034286
## [91] train-rmse:0.627362+0.005821 test-rmse:0.709736+0.034394
## [92] train-rmse:0.626709+0.005823 test-rmse:0.710352+0.034230
## [93] train-rmse:0.626073+0.005825 test-rmse:0.709671+0.033817
## [94] train-rmse:0.625435+0.005827 test-rmse:0.709573+0.033495
## [95] train-rmse:0.624811+0.005839 test-rmse:0.708187+0.033483
## [96] train-rmse:0.624202+0.005856 test-rmse:0.707660+0.033144
## [97] train-rmse:0.623595+0.005856 test-rmse:0.708199+0.034099
## [98] train-rmse:0.622983+0.005859 test-rmse:0.708350+0.034296
## [99] train-rmse:0.622382+0.005870 test-rmse:0.708483+0.033991
## [100] train-rmse:0.621783+0.005899 test-rmse:0.707472+0.033666
## [101] train-rmse:0.621194+0.005906 test-rmse:0.706624+0.033797
## [102] train-rmse:0.620616+0.005914 test-rmse:0.706680+0.033853
## [103] train-rmse:0.620044+0.005922 test-rmse:0.705809+0.032993
## [104] train-rmse:0.619474+0.005930 test-rmse:0.705532+0.033214
## [105] train-rmse:0.618913+0.005933 test-rmse:0.705573+0.032926
## [106] train-rmse:0.618359+0.005944 test-rmse:0.704976+0.033080
## [107] train-rmse:0.617808+0.005951 test-rmse:0.705816+0.034090
## [108] train-rmse:0.617252+0.005969 test-rmse:0.705382+0.034403
## [109] train-rmse:0.616706+0.005972 test-rmse:0.705044+0.034347
## [110] train-rmse:0.616174+0.005973 test-rmse:0.704395+0.034120
## [111] train-rmse:0.615643+0.005966 test-rmse:0.704916+0.033786
## [112] train-rmse:0.615112+0.005953 test-rmse:0.704406+0.033723
## [113] train-rmse:0.614591+0.005959 test-rmse:0.704534+0.033438
## [114] train-rmse:0.614066+0.005963 test-rmse:0.703918+0.033735
## [115] train-rmse:0.613553+0.005967 test-rmse:0.704438+0.034386
## [116] train-rmse:0.613043+0.005977 test-rmse:0.704068+0.033942
## [117] train-rmse:0.612541+0.005986 test-rmse:0.703080+0.034618
## [118] train-rmse:0.612038+0.005991 test-rmse:0.702725+0.034966
## [119] train-rmse:0.611529+0.005985 test-rmse:0.702828+0.034793
## [120] train-rmse:0.611028+0.005984 test-rmse:0.702128+0.034422
## [121] train-rmse:0.610544+0.005993 test-rmse:0.701462+0.034194
## [122] train-rmse:0.610066+0.005996 test-rmse:0.700716+0.034239
## [123] train-rmse:0.609588+0.006006 test-rmse:0.699873+0.034295
## [124] train-rmse:0.609115+0.006015 test-rmse:0.699568+0.034312
## [125] train-rmse:0.608640+0.006024 test-rmse:0.698884+0.034794
## [126] train-rmse:0.608168+0.006022 test-rmse:0.699176+0.034504
## [127] train-rmse:0.607706+0.006030 test-rmse:0.698954+0.034718
## [128] train-rmse:0.607249+0.006033 test-rmse:0.698522+0.034140
## [129] train-rmse:0.606790+0.006036 test-rmse:0.698621+0.034288
## [130] train-rmse:0.606333+0.006037 test-rmse:0.698839+0.033993
## [131] train-rmse:0.605878+0.006044 test-rmse:0.698660+0.034137
## [132] train-rmse:0.605415+0.006053 test-rmse:0.698927+0.034357
## [133] train-rmse:0.604967+0.006060 test-rmse:0.698853+0.034651
## [134] train-rmse:0.604522+0.006064 test-rmse:0.699209+0.034416
## Stopping. Best iteration:
## [128] train-rmse:0.607249+0.006033 test-rmse:0.698522+0.034140
##
## 15
## [1] train-rmse:1.430133+0.014496 test-rmse:1.433238+0.069539
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.284193+0.012304 test-rmse:1.292016+0.071648
## [3] train-rmse:1.164001+0.010509 test-rmse:1.177016+0.072750
## [4] train-rmse:1.065366+0.008973 test-rmse:1.082791+0.073108
## [5] train-rmse:0.985188+0.007678 test-rmse:1.005894+0.071462
## [6] train-rmse:0.920739+0.006709 test-rmse:0.946769+0.069044
## [7] train-rmse:0.869109+0.005969 test-rmse:0.900903+0.068170
## [8] train-rmse:0.827456+0.005961 test-rmse:0.864033+0.065318
## [9] train-rmse:0.792050+0.005295 test-rmse:0.829641+0.063358
## [10] train-rmse:0.763687+0.005274 test-rmse:0.802660+0.060145
## [11] train-rmse:0.740869+0.004794 test-rmse:0.782272+0.059854
## [12] train-rmse:0.723360+0.004670 test-rmse:0.770694+0.056179
## [13] train-rmse:0.709491+0.004756 test-rmse:0.760662+0.051749
## [14] train-rmse:0.695870+0.005085 test-rmse:0.750514+0.050664
## [15] train-rmse:0.686180+0.005044 test-rmse:0.744865+0.050152
## [16] train-rmse:0.677437+0.004566 test-rmse:0.739574+0.047655
## [17] train-rmse:0.669535+0.005938 test-rmse:0.733582+0.044829
## [18] train-rmse:0.662969+0.005248 test-rmse:0.729472+0.043316
## [19] train-rmse:0.656139+0.006028 test-rmse:0.726339+0.042927
## [20] train-rmse:0.650765+0.005914 test-rmse:0.723308+0.043087
## [21] train-rmse:0.646337+0.005884 test-rmse:0.722376+0.043363
## [22] train-rmse:0.642204+0.005801 test-rmse:0.719789+0.042324
## [23] train-rmse:0.638361+0.005601 test-rmse:0.717726+0.040448
## [24] train-rmse:0.633552+0.006132 test-rmse:0.716351+0.039388
## [25] train-rmse:0.628890+0.006764 test-rmse:0.713432+0.040395
## [26] train-rmse:0.625351+0.006328 test-rmse:0.711992+0.038948
## [27] train-rmse:0.622408+0.006380 test-rmse:0.711366+0.040337
## [28] train-rmse:0.618816+0.005775 test-rmse:0.708957+0.044016
## [29] train-rmse:0.615317+0.005783 test-rmse:0.707948+0.042945
## [30] train-rmse:0.612136+0.005952 test-rmse:0.705697+0.041318
## [31] train-rmse:0.609447+0.006007 test-rmse:0.705657+0.042053
## [32] train-rmse:0.607049+0.005904 test-rmse:0.703897+0.041356
## [33] train-rmse:0.603479+0.005784 test-rmse:0.704439+0.040887
## [34] train-rmse:0.600038+0.005293 test-rmse:0.702398+0.041696
## [35] train-rmse:0.597799+0.005432 test-rmse:0.702226+0.042665
## [36] train-rmse:0.595438+0.005492 test-rmse:0.700262+0.042850
## [37] train-rmse:0.593507+0.005487 test-rmse:0.700049+0.042613
## [38] train-rmse:0.590162+0.004653 test-rmse:0.699202+0.044812
## [39] train-rmse:0.586903+0.004844 test-rmse:0.696549+0.046209
## [40] train-rmse:0.585077+0.004759 test-rmse:0.696488+0.046067
## [41] train-rmse:0.582856+0.004862 test-rmse:0.694601+0.044163
## [42] train-rmse:0.580379+0.004872 test-rmse:0.693635+0.043269
## [43] train-rmse:0.578509+0.004998 test-rmse:0.693064+0.043110
## [44] train-rmse:0.576335+0.005068 test-rmse:0.692563+0.042568
## [45] train-rmse:0.574313+0.005013 test-rmse:0.692182+0.041216
## [46] train-rmse:0.572250+0.005495 test-rmse:0.691620+0.041003
## [47] train-rmse:0.570711+0.005522 test-rmse:0.691465+0.040429
## [48] train-rmse:0.568531+0.005433 test-rmse:0.689388+0.044036
## [49] train-rmse:0.566683+0.005017 test-rmse:0.687457+0.044684
## [50] train-rmse:0.564768+0.005320 test-rmse:0.686544+0.044403
## [51] train-rmse:0.562537+0.005953 test-rmse:0.684872+0.043259
## [52] train-rmse:0.560593+0.006470 test-rmse:0.684524+0.043157
## [53] train-rmse:0.558011+0.007613 test-rmse:0.683120+0.042572
## [54] train-rmse:0.556338+0.007906 test-rmse:0.682644+0.042021
## [55] train-rmse:0.554491+0.007986 test-rmse:0.682865+0.043041
## [56] train-rmse:0.552857+0.007919 test-rmse:0.683147+0.043445
## [57] train-rmse:0.551368+0.007951 test-rmse:0.681637+0.043163
## [58] train-rmse:0.549619+0.007918 test-rmse:0.679518+0.042972
## [59] train-rmse:0.547530+0.008301 test-rmse:0.678638+0.042478
## [60] train-rmse:0.545855+0.008756 test-rmse:0.677910+0.042647
## [61] train-rmse:0.544252+0.009174 test-rmse:0.678684+0.042953
## [62] train-rmse:0.542121+0.010397 test-rmse:0.677846+0.042635
## [63] train-rmse:0.540269+0.009877 test-rmse:0.677362+0.042350
## [64] train-rmse:0.538936+0.009830 test-rmse:0.676441+0.043031
## [65] train-rmse:0.537593+0.009795 test-rmse:0.676754+0.043211
## [66] train-rmse:0.536300+0.009676 test-rmse:0.676052+0.044071
## [67] train-rmse:0.534025+0.010810 test-rmse:0.674535+0.042090
## [68] train-rmse:0.532755+0.011022 test-rmse:0.674208+0.041908
## [69] train-rmse:0.531522+0.011026 test-rmse:0.673254+0.042460
## [70] train-rmse:0.528950+0.010118 test-rmse:0.671441+0.043914
## [71] train-rmse:0.527337+0.010613 test-rmse:0.671512+0.044155
## [72] train-rmse:0.525929+0.010451 test-rmse:0.671121+0.044435
## [73] train-rmse:0.524183+0.010018 test-rmse:0.671306+0.044108
## [74] train-rmse:0.523034+0.009943 test-rmse:0.671170+0.044373
## [75] train-rmse:0.521027+0.011196 test-rmse:0.671278+0.044870
## [76] train-rmse:0.519007+0.011801 test-rmse:0.669680+0.043894
## [77] train-rmse:0.517633+0.011893 test-rmse:0.669333+0.043853
## [78] train-rmse:0.516250+0.012117 test-rmse:0.667263+0.042514
## [79] train-rmse:0.515177+0.012004 test-rmse:0.668104+0.043632
## [80] train-rmse:0.514050+0.012188 test-rmse:0.667442+0.044459
## [81] train-rmse:0.512336+0.012139 test-rmse:0.667143+0.044279
## [82] train-rmse:0.511086+0.011898 test-rmse:0.666220+0.043854
## [83] train-rmse:0.509769+0.012360 test-rmse:0.665375+0.043592
## [84] train-rmse:0.508006+0.013449 test-rmse:0.664394+0.043351
## [85] train-rmse:0.507084+0.013485 test-rmse:0.664142+0.043812
## [86] train-rmse:0.505445+0.014007 test-rmse:0.665229+0.043920
## [87] train-rmse:0.504241+0.013728 test-rmse:0.665387+0.044346
## [88] train-rmse:0.503347+0.013662 test-rmse:0.664873+0.044035
## [89] train-rmse:0.502102+0.013893 test-rmse:0.663578+0.044509
## [90] train-rmse:0.501022+0.013816 test-rmse:0.663089+0.045076
## [91] train-rmse:0.499081+0.014449 test-rmse:0.662655+0.044733
## [92] train-rmse:0.497954+0.014327 test-rmse:0.662327+0.045430
## [93] train-rmse:0.496693+0.014678 test-rmse:0.661621+0.045189
## [94] train-rmse:0.495713+0.014726 test-rmse:0.661226+0.045577
## [95] train-rmse:0.494628+0.014867 test-rmse:0.660616+0.046374
## [96] train-rmse:0.492797+0.013784 test-rmse:0.659245+0.044642
## [97] train-rmse:0.491753+0.014108 test-rmse:0.658442+0.044725
## [98] train-rmse:0.490455+0.013936 test-rmse:0.657177+0.043021
## [99] train-rmse:0.489070+0.013683 test-rmse:0.657064+0.043062
## [100] train-rmse:0.488106+0.013638 test-rmse:0.657058+0.043580
## [101] train-rmse:0.486848+0.014524 test-rmse:0.656353+0.043406
## [102] train-rmse:0.485948+0.014560 test-rmse:0.656171+0.043082
## [103] train-rmse:0.484025+0.013994 test-rmse:0.655621+0.043860
## [104] train-rmse:0.483342+0.014117 test-rmse:0.655492+0.044589
## [105] train-rmse:0.481682+0.014374 test-rmse:0.654729+0.044699
## [106] train-rmse:0.480952+0.014300 test-rmse:0.654830+0.044755
## [107] train-rmse:0.480238+0.014548 test-rmse:0.654357+0.044019
## [108] train-rmse:0.479417+0.014407 test-rmse:0.653496+0.043958
## [109] train-rmse:0.478719+0.014349 test-rmse:0.653563+0.044310
## [110] train-rmse:0.477667+0.014230 test-rmse:0.653100+0.044410
## [111] train-rmse:0.476251+0.013889 test-rmse:0.652269+0.045014
## [112] train-rmse:0.474699+0.014701 test-rmse:0.651390+0.045564
## [113] train-rmse:0.473569+0.014828 test-rmse:0.650934+0.045496
## [114] train-rmse:0.472935+0.014865 test-rmse:0.650845+0.045450
## [115] train-rmse:0.471461+0.014983 test-rmse:0.651113+0.046100
## [116] train-rmse:0.470743+0.014943 test-rmse:0.651698+0.046360
## [117] train-rmse:0.469137+0.014657 test-rmse:0.650525+0.046531
## [118] train-rmse:0.468376+0.014904 test-rmse:0.650431+0.046248
## [119] train-rmse:0.467443+0.015350 test-rmse:0.651348+0.046944
## [120] train-rmse:0.466406+0.015239 test-rmse:0.651824+0.046425
## [121] train-rmse:0.465614+0.015537 test-rmse:0.651320+0.046219
## [122] train-rmse:0.464810+0.015619 test-rmse:0.650260+0.045974
## [123] train-rmse:0.463743+0.015327 test-rmse:0.650151+0.046227
## [124] train-rmse:0.462448+0.015027 test-rmse:0.650849+0.046173
## [125] train-rmse:0.460914+0.014249 test-rmse:0.649812+0.044978
## [126] train-rmse:0.459653+0.014306 test-rmse:0.648889+0.044829
## [127] train-rmse:0.458337+0.014914 test-rmse:0.648738+0.045341
## [128] train-rmse:0.456874+0.014289 test-rmse:0.647637+0.044219
## [129] train-rmse:0.456004+0.014474 test-rmse:0.647033+0.044211
## [130] train-rmse:0.455408+0.014452 test-rmse:0.646539+0.044614
## [131] train-rmse:0.454401+0.014516 test-rmse:0.646070+0.044504
## [132] train-rmse:0.453572+0.014388 test-rmse:0.646020+0.045223
## [133] train-rmse:0.452979+0.014506 test-rmse:0.645505+0.045570
## [134] train-rmse:0.452413+0.014481 test-rmse:0.645797+0.045752
## [135] train-rmse:0.451325+0.014089 test-rmse:0.646445+0.045671
## [136] train-rmse:0.450511+0.014260 test-rmse:0.645786+0.045410
## [137] train-rmse:0.449787+0.014288 test-rmse:0.646053+0.045853
## [138] train-rmse:0.448669+0.014489 test-rmse:0.646325+0.045801
## [139] train-rmse:0.448177+0.014462 test-rmse:0.646060+0.045514
## Stopping. Best iteration:
## [133] train-rmse:0.452979+0.014506 test-rmse:0.645505+0.045570
##
## 16
## [1] train-rmse:1.427495+0.014276 test-rmse:1.431815+0.068759
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.277798+0.012117 test-rmse:1.288845+0.069458
## [3] train-rmse:1.154346+0.010416 test-rmse:1.171697+0.070063
## [4] train-rmse:1.052043+0.008823 test-rmse:1.075647+0.069606
## [5] train-rmse:0.967552+0.007405 test-rmse:0.995347+0.070068
## [6] train-rmse:0.899671+0.006630 test-rmse:0.934931+0.070384
## [7] train-rmse:0.843250+0.005448 test-rmse:0.883334+0.070721
## [8] train-rmse:0.798537+0.005704 test-rmse:0.843942+0.067432
## [9] train-rmse:0.761767+0.006642 test-rmse:0.813953+0.067187
## [10] train-rmse:0.728960+0.008198 test-rmse:0.784351+0.061006
## [11] train-rmse:0.702963+0.006382 test-rmse:0.762875+0.061425
## [12] train-rmse:0.679357+0.006379 test-rmse:0.746147+0.057077
## [13] train-rmse:0.660712+0.006001 test-rmse:0.733313+0.052972
## [14] train-rmse:0.646383+0.006262 test-rmse:0.724449+0.052503
## [15] train-rmse:0.634191+0.006264 test-rmse:0.716431+0.052152
## [16] train-rmse:0.622973+0.005878 test-rmse:0.713046+0.049785
## [17] train-rmse:0.614804+0.006106 test-rmse:0.708196+0.048796
## [18] train-rmse:0.604580+0.006333 test-rmse:0.700508+0.046112
## [19] train-rmse:0.597770+0.006276 test-rmse:0.698683+0.047863
## [20] train-rmse:0.591624+0.006559 test-rmse:0.695302+0.048437
## [21] train-rmse:0.585863+0.006676 test-rmse:0.692470+0.048768
## [22] train-rmse:0.581129+0.006755 test-rmse:0.692206+0.048175
## [23] train-rmse:0.576346+0.006358 test-rmse:0.688547+0.047131
## [24] train-rmse:0.571905+0.007140 test-rmse:0.686554+0.047869
## [25] train-rmse:0.566897+0.006203 test-rmse:0.684787+0.049800
## [26] train-rmse:0.560854+0.006828 test-rmse:0.681955+0.050650
## [27] train-rmse:0.556467+0.006370 test-rmse:0.681324+0.052101
## [28] train-rmse:0.552730+0.006581 test-rmse:0.680272+0.052176
## [29] train-rmse:0.547578+0.006776 test-rmse:0.679489+0.053736
## [30] train-rmse:0.544144+0.007192 test-rmse:0.677876+0.054571
## [31] train-rmse:0.540494+0.006924 test-rmse:0.676709+0.055538
## [32] train-rmse:0.537457+0.007118 test-rmse:0.675996+0.055006
## [33] train-rmse:0.534550+0.007406 test-rmse:0.674386+0.055143
## [34] train-rmse:0.530212+0.006640 test-rmse:0.672224+0.056317
## [35] train-rmse:0.527363+0.006095 test-rmse:0.672479+0.056556
## [36] train-rmse:0.523190+0.005296 test-rmse:0.670152+0.058557
## [37] train-rmse:0.519492+0.003487 test-rmse:0.670491+0.058303
## [38] train-rmse:0.517126+0.003686 test-rmse:0.671481+0.058747
## [39] train-rmse:0.514133+0.003769 test-rmse:0.670701+0.058437
## [40] train-rmse:0.510521+0.005046 test-rmse:0.668744+0.057778
## [41] train-rmse:0.507612+0.005078 test-rmse:0.667387+0.058414
## [42] train-rmse:0.503248+0.005977 test-rmse:0.665592+0.057869
## [43] train-rmse:0.500380+0.006053 test-rmse:0.664868+0.059172
## [44] train-rmse:0.497399+0.005981 test-rmse:0.664725+0.059161
## [45] train-rmse:0.493568+0.006696 test-rmse:0.664972+0.059750
## [46] train-rmse:0.490554+0.007411 test-rmse:0.664549+0.059815
## [47] train-rmse:0.487140+0.007820 test-rmse:0.661625+0.057494
## [48] train-rmse:0.483049+0.005794 test-rmse:0.659640+0.057219
## [49] train-rmse:0.480660+0.006738 test-rmse:0.658040+0.058246
## [50] train-rmse:0.477312+0.006453 test-rmse:0.657864+0.058931
## [51] train-rmse:0.474752+0.006738 test-rmse:0.656731+0.059106
## [52] train-rmse:0.471403+0.008127 test-rmse:0.654703+0.058501
## [53] train-rmse:0.469004+0.008011 test-rmse:0.655268+0.059012
## [54] train-rmse:0.466154+0.007449 test-rmse:0.653337+0.057888
## [55] train-rmse:0.464047+0.007256 test-rmse:0.653551+0.057867
## [56] train-rmse:0.461711+0.006492 test-rmse:0.651449+0.058179
## [57] train-rmse:0.459117+0.006733 test-rmse:0.649598+0.057829
## [58] train-rmse:0.457082+0.006875 test-rmse:0.649615+0.058594
## [59] train-rmse:0.455097+0.006672 test-rmse:0.648905+0.058308
## [60] train-rmse:0.451256+0.008074 test-rmse:0.648268+0.058586
## [61] train-rmse:0.449049+0.008829 test-rmse:0.648031+0.058148
## [62] train-rmse:0.445918+0.009713 test-rmse:0.648122+0.057965
## [63] train-rmse:0.443436+0.009369 test-rmse:0.647719+0.057997
## [64] train-rmse:0.440877+0.009102 test-rmse:0.648083+0.058212
## [65] train-rmse:0.438720+0.009015 test-rmse:0.646596+0.057950
## [66] train-rmse:0.437245+0.009035 test-rmse:0.645548+0.058098
## [67] train-rmse:0.435541+0.009640 test-rmse:0.646069+0.058943
## [68] train-rmse:0.433411+0.009732 test-rmse:0.645301+0.058975
## [69] train-rmse:0.430953+0.009971 test-rmse:0.644362+0.058778
## [70] train-rmse:0.428372+0.011393 test-rmse:0.644721+0.058943
## [71] train-rmse:0.426485+0.012124 test-rmse:0.644495+0.059293
## [72] train-rmse:0.424431+0.012751 test-rmse:0.644753+0.059182
## [73] train-rmse:0.422538+0.012631 test-rmse:0.644796+0.059130
## [74] train-rmse:0.419990+0.012727 test-rmse:0.643069+0.059688
## [75] train-rmse:0.418477+0.012470 test-rmse:0.643490+0.059335
## [76] train-rmse:0.416709+0.013189 test-rmse:0.642691+0.058875
## [77] train-rmse:0.414696+0.013674 test-rmse:0.641936+0.058023
## [78] train-rmse:0.413357+0.013759 test-rmse:0.642496+0.058135
## [79] train-rmse:0.411507+0.013512 test-rmse:0.641681+0.058771
## [80] train-rmse:0.409417+0.012774 test-rmse:0.640307+0.057665
## [81] train-rmse:0.407022+0.012334 test-rmse:0.639935+0.057921
## [82] train-rmse:0.405119+0.011932 test-rmse:0.639905+0.057970
## [83] train-rmse:0.403288+0.012042 test-rmse:0.639520+0.058567
## [84] train-rmse:0.401167+0.012662 test-rmse:0.639148+0.058680
## [85] train-rmse:0.399608+0.012943 test-rmse:0.638863+0.058338
## [86] train-rmse:0.398203+0.013042 test-rmse:0.639637+0.058765
## [87] train-rmse:0.395438+0.012509 test-rmse:0.638848+0.058956
## [88] train-rmse:0.393534+0.012698 test-rmse:0.638351+0.059300
## [89] train-rmse:0.392454+0.012684 test-rmse:0.638173+0.059446
## [90] train-rmse:0.391038+0.012761 test-rmse:0.637059+0.059201
## [91] train-rmse:0.388821+0.013284 test-rmse:0.635696+0.058209
## [92] train-rmse:0.387264+0.013626 test-rmse:0.635232+0.058420
## [93] train-rmse:0.385553+0.012552 test-rmse:0.634891+0.059099
## [94] train-rmse:0.383687+0.012137 test-rmse:0.635679+0.059047
## [95] train-rmse:0.381821+0.012200 test-rmse:0.635076+0.059326
## [96] train-rmse:0.380333+0.012749 test-rmse:0.634913+0.058920
## [97] train-rmse:0.379079+0.013159 test-rmse:0.634985+0.058889
## [98] train-rmse:0.378105+0.013300 test-rmse:0.635685+0.059341
## [99] train-rmse:0.375761+0.013783 test-rmse:0.635717+0.058836
## Stopping. Best iteration:
## [93] train-rmse:0.385553+0.012552 test-rmse:0.634891+0.059099
##
## 17
## [1] train-rmse:1.424802+0.014316 test-rmse:1.433513+0.069038
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.272152+0.012118 test-rmse:1.288329+0.068584
## [3] train-rmse:1.143628+0.009722 test-rmse:1.167931+0.068865
## [4] train-rmse:1.037414+0.007238 test-rmse:1.071376+0.068430
## [5] train-rmse:0.949057+0.006510 test-rmse:0.988004+0.066948
## [6] train-rmse:0.875453+0.004853 test-rmse:0.922911+0.068083
## [7] train-rmse:0.816772+0.004825 test-rmse:0.873417+0.066923
## [8] train-rmse:0.768370+0.005059 test-rmse:0.832370+0.065407
## [9] train-rmse:0.729384+0.005515 test-rmse:0.798619+0.063468
## [10] train-rmse:0.697463+0.004692 test-rmse:0.774174+0.062394
## [11] train-rmse:0.668471+0.009815 test-rmse:0.754481+0.060817
## [12] train-rmse:0.645513+0.008779 test-rmse:0.735934+0.059782
## [13] train-rmse:0.623439+0.011195 test-rmse:0.720320+0.058797
## [14] train-rmse:0.605865+0.009785 test-rmse:0.709207+0.053796
## [15] train-rmse:0.587695+0.009738 test-rmse:0.700147+0.054691
## [16] train-rmse:0.575210+0.009021 test-rmse:0.694668+0.055003
## [17] train-rmse:0.565654+0.009009 test-rmse:0.692738+0.055198
## [18] train-rmse:0.556960+0.009397 test-rmse:0.686994+0.053311
## [19] train-rmse:0.548465+0.009506 test-rmse:0.683373+0.053921
## [20] train-rmse:0.539562+0.010234 test-rmse:0.681078+0.051814
## [21] train-rmse:0.532816+0.010312 test-rmse:0.679101+0.054881
## [22] train-rmse:0.526959+0.010012 test-rmse:0.675811+0.056050
## [23] train-rmse:0.520707+0.009440 test-rmse:0.674176+0.057470
## [24] train-rmse:0.515134+0.010764 test-rmse:0.674359+0.057487
## [25] train-rmse:0.509397+0.012364 test-rmse:0.672635+0.055954
## [26] train-rmse:0.504811+0.012771 test-rmse:0.671135+0.056602
## [27] train-rmse:0.497010+0.012073 test-rmse:0.667761+0.055853
## [28] train-rmse:0.491311+0.013081 test-rmse:0.666268+0.056585
## [29] train-rmse:0.485331+0.013708 test-rmse:0.665990+0.058252
## [30] train-rmse:0.480558+0.015581 test-rmse:0.664209+0.060269
## [31] train-rmse:0.476087+0.015688 test-rmse:0.661962+0.061924
## [32] train-rmse:0.471275+0.015618 test-rmse:0.662324+0.061601
## [33] train-rmse:0.466472+0.014667 test-rmse:0.661525+0.062916
## [34] train-rmse:0.461821+0.014110 test-rmse:0.660981+0.062383
## [35] train-rmse:0.456981+0.014697 test-rmse:0.659051+0.062569
## [36] train-rmse:0.452690+0.015649 test-rmse:0.657350+0.064339
## [37] train-rmse:0.447537+0.014476 test-rmse:0.654831+0.063479
## [38] train-rmse:0.443599+0.014593 test-rmse:0.654474+0.065094
## [39] train-rmse:0.439814+0.014413 test-rmse:0.654830+0.065348
## [40] train-rmse:0.436259+0.014869 test-rmse:0.653592+0.066187
## [41] train-rmse:0.432592+0.015374 test-rmse:0.653650+0.065271
## [42] train-rmse:0.428722+0.015224 test-rmse:0.652681+0.065988
## [43] train-rmse:0.424731+0.014208 test-rmse:0.651681+0.067984
## [44] train-rmse:0.421676+0.014054 test-rmse:0.651041+0.068796
## [45] train-rmse:0.418012+0.014988 test-rmse:0.649545+0.070151
## [46] train-rmse:0.414870+0.014953 test-rmse:0.649289+0.070561
## [47] train-rmse:0.411980+0.015447 test-rmse:0.649981+0.069817
## [48] train-rmse:0.409301+0.015529 test-rmse:0.648627+0.070294
## [49] train-rmse:0.406425+0.015590 test-rmse:0.648358+0.069461
## [50] train-rmse:0.402306+0.016550 test-rmse:0.647419+0.070449
## [51] train-rmse:0.397585+0.017022 test-rmse:0.646726+0.069386
## [52] train-rmse:0.394740+0.017078 test-rmse:0.646453+0.069761
## [53] train-rmse:0.392671+0.016966 test-rmse:0.646570+0.071084
## [54] train-rmse:0.389998+0.016648 test-rmse:0.645439+0.071665
## [55] train-rmse:0.385936+0.016545 test-rmse:0.643787+0.071112
## [56] train-rmse:0.382671+0.016978 test-rmse:0.644198+0.070204
## [57] train-rmse:0.378778+0.017096 test-rmse:0.643545+0.071125
## [58] train-rmse:0.375099+0.016692 test-rmse:0.643639+0.071421
## [59] train-rmse:0.372332+0.016086 test-rmse:0.642611+0.070787
## [60] train-rmse:0.370342+0.016187 test-rmse:0.642871+0.071497
## [61] train-rmse:0.368448+0.016126 test-rmse:0.642421+0.071581
## [62] train-rmse:0.366185+0.016182 test-rmse:0.642602+0.071649
## [63] train-rmse:0.363422+0.016023 test-rmse:0.641509+0.071041
## [64] train-rmse:0.359466+0.014935 test-rmse:0.641723+0.070868
## [65] train-rmse:0.356853+0.015603 test-rmse:0.641910+0.071555
## [66] train-rmse:0.354067+0.015315 test-rmse:0.641061+0.070174
## [67] train-rmse:0.351992+0.015354 test-rmse:0.640074+0.069727
## [68] train-rmse:0.349187+0.015690 test-rmse:0.639032+0.070175
## [69] train-rmse:0.346263+0.015565 test-rmse:0.638903+0.069803
## [70] train-rmse:0.343460+0.015562 test-rmse:0.637682+0.069715
## [71] train-rmse:0.340938+0.016091 test-rmse:0.636796+0.070574
## [72] train-rmse:0.338912+0.016281 test-rmse:0.637183+0.070104
## [73] train-rmse:0.337109+0.016407 test-rmse:0.637103+0.070205
## [74] train-rmse:0.335305+0.016416 test-rmse:0.636220+0.070495
## [75] train-rmse:0.333059+0.016492 test-rmse:0.635821+0.069843
## [76] train-rmse:0.331239+0.016359 test-rmse:0.635295+0.070226
## [77] train-rmse:0.327678+0.014797 test-rmse:0.634166+0.069571
## [78] train-rmse:0.325730+0.014980 test-rmse:0.633440+0.069517
## [79] train-rmse:0.323462+0.015552 test-rmse:0.634532+0.069636
## [80] train-rmse:0.321148+0.015729 test-rmse:0.634787+0.069743
## [81] train-rmse:0.318382+0.015554 test-rmse:0.634348+0.070398
## [82] train-rmse:0.315358+0.016210 test-rmse:0.633350+0.070778
## [83] train-rmse:0.312423+0.015139 test-rmse:0.632245+0.071150
## [84] train-rmse:0.310211+0.015290 test-rmse:0.632733+0.071069
## [85] train-rmse:0.308349+0.015564 test-rmse:0.632834+0.071436
## [86] train-rmse:0.306458+0.015848 test-rmse:0.632926+0.071651
## [87] train-rmse:0.304298+0.016350 test-rmse:0.632804+0.070584
## [88] train-rmse:0.302290+0.016825 test-rmse:0.633079+0.071331
## [89] train-rmse:0.300880+0.016833 test-rmse:0.633069+0.071202
## Stopping. Best iteration:
## [83] train-rmse:0.312423+0.015139 test-rmse:0.632245+0.071150
##
## 18
## [1] train-rmse:1.422982+0.014439 test-rmse:1.430862+0.070547
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.267872+0.012282 test-rmse:1.286119+0.073797
## [3] train-rmse:1.136801+0.009734 test-rmse:1.164443+0.074193
## [4] train-rmse:1.026257+0.008009 test-rmse:1.062840+0.075054
## [5] train-rmse:0.933549+0.007073 test-rmse:0.979713+0.074090
## [6] train-rmse:0.857473+0.006765 test-rmse:0.912963+0.073471
## [7] train-rmse:0.794908+0.006381 test-rmse:0.860844+0.071100
## [8] train-rmse:0.742086+0.005360 test-rmse:0.819291+0.072942
## [9] train-rmse:0.699498+0.004919 test-rmse:0.785670+0.070980
## [10] train-rmse:0.663626+0.004637 test-rmse:0.760243+0.069325
## [11] train-rmse:0.634938+0.005198 test-rmse:0.739115+0.065902
## [12] train-rmse:0.608782+0.006145 test-rmse:0.724461+0.063385
## [13] train-rmse:0.585415+0.009045 test-rmse:0.710455+0.060046
## [14] train-rmse:0.565647+0.008941 test-rmse:0.697918+0.060750
## [15] train-rmse:0.546575+0.012604 test-rmse:0.690580+0.055292
## [16] train-rmse:0.534436+0.013054 test-rmse:0.684338+0.054502
## [17] train-rmse:0.523325+0.012929 test-rmse:0.680685+0.056215
## [18] train-rmse:0.511670+0.012699 test-rmse:0.675253+0.056461
## [19] train-rmse:0.501296+0.013612 test-rmse:0.671689+0.057300
## [20] train-rmse:0.491982+0.014056 test-rmse:0.669439+0.059250
## [21] train-rmse:0.482735+0.014759 test-rmse:0.664449+0.058345
## [22] train-rmse:0.475275+0.014942 test-rmse:0.661841+0.058684
## [23] train-rmse:0.467400+0.016124 test-rmse:0.658699+0.055681
## [24] train-rmse:0.458654+0.013618 test-rmse:0.654823+0.053430
## [25] train-rmse:0.453399+0.013876 test-rmse:0.653487+0.053482
## [26] train-rmse:0.446443+0.014573 test-rmse:0.651748+0.054428
## [27] train-rmse:0.439138+0.013559 test-rmse:0.650569+0.055648
## [28] train-rmse:0.431489+0.015175 test-rmse:0.648110+0.052954
## [29] train-rmse:0.425537+0.014990 test-rmse:0.647040+0.054738
## [30] train-rmse:0.419525+0.013328 test-rmse:0.646236+0.054452
## [31] train-rmse:0.414690+0.014925 test-rmse:0.645151+0.054082
## [32] train-rmse:0.408820+0.017133 test-rmse:0.643962+0.054144
## [33] train-rmse:0.403719+0.015446 test-rmse:0.640555+0.055391
## [34] train-rmse:0.398381+0.014699 test-rmse:0.642255+0.054772
## [35] train-rmse:0.391461+0.014743 test-rmse:0.639176+0.054490
## [36] train-rmse:0.387107+0.013944 test-rmse:0.638060+0.055815
## [37] train-rmse:0.383109+0.014930 test-rmse:0.638943+0.056462
## [38] train-rmse:0.378461+0.014756 test-rmse:0.637295+0.056647
## [39] train-rmse:0.373804+0.015189 test-rmse:0.636212+0.055453
## [40] train-rmse:0.368552+0.014701 test-rmse:0.633211+0.056078
## [41] train-rmse:0.363446+0.014279 test-rmse:0.634517+0.056817
## [42] train-rmse:0.359300+0.013931 test-rmse:0.633236+0.058335
## [43] train-rmse:0.355688+0.014167 test-rmse:0.632336+0.058958
## [44] train-rmse:0.351848+0.014917 test-rmse:0.632940+0.059070
## [45] train-rmse:0.346971+0.015495 test-rmse:0.633786+0.057731
## [46] train-rmse:0.343299+0.016003 test-rmse:0.633142+0.057477
## [47] train-rmse:0.339296+0.017126 test-rmse:0.632582+0.056875
## [48] train-rmse:0.335744+0.018059 test-rmse:0.632564+0.057452
## [49] train-rmse:0.331367+0.019115 test-rmse:0.632923+0.057123
## Stopping. Best iteration:
## [43] train-rmse:0.355688+0.014167 test-rmse:0.632336+0.058958
##
## 19
## [1] train-rmse:1.421139+0.014137 test-rmse:1.431990+0.071723
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.264685+0.012239 test-rmse:1.287568+0.071484
## [3] train-rmse:1.131523+0.009313 test-rmse:1.165222+0.073741
## [4] train-rmse:1.018267+0.006964 test-rmse:1.065529+0.074472
## [5] train-rmse:0.923204+0.005982 test-rmse:0.984606+0.074278
## [6] train-rmse:0.842479+0.004805 test-rmse:0.917508+0.072175
## [7] train-rmse:0.776190+0.004924 test-rmse:0.861885+0.070319
## [8] train-rmse:0.720507+0.005125 test-rmse:0.817948+0.068115
## [9] train-rmse:0.672958+0.006693 test-rmse:0.784232+0.066292
## [10] train-rmse:0.634158+0.006518 test-rmse:0.753545+0.061800
## [11] train-rmse:0.602732+0.006011 test-rmse:0.734396+0.061564
## [12] train-rmse:0.577503+0.005483 test-rmse:0.719634+0.059425
## [13] train-rmse:0.551475+0.003249 test-rmse:0.704960+0.058697
## [14] train-rmse:0.528240+0.008824 test-rmse:0.696738+0.056780
## [15] train-rmse:0.510561+0.008396 test-rmse:0.688060+0.055377
## [16] train-rmse:0.493101+0.011012 test-rmse:0.681963+0.055694
## [17] train-rmse:0.477933+0.012060 test-rmse:0.676039+0.056550
## [18] train-rmse:0.462052+0.015515 test-rmse:0.670789+0.057261
## [19] train-rmse:0.450913+0.014936 test-rmse:0.667767+0.057505
## [20] train-rmse:0.442443+0.014385 test-rmse:0.664278+0.058192
## [21] train-rmse:0.430656+0.015927 test-rmse:0.660898+0.058403
## [22] train-rmse:0.423118+0.015438 test-rmse:0.659349+0.058348
## [23] train-rmse:0.414811+0.016905 test-rmse:0.656659+0.057758
## [24] train-rmse:0.407431+0.019115 test-rmse:0.655394+0.056937
## [25] train-rmse:0.399810+0.019476 test-rmse:0.655010+0.056210
## [26] train-rmse:0.391917+0.019961 test-rmse:0.653404+0.057373
## [27] train-rmse:0.382002+0.016962 test-rmse:0.647781+0.054720
## [28] train-rmse:0.375673+0.017109 test-rmse:0.646093+0.054329
## [29] train-rmse:0.370170+0.019137 test-rmse:0.644167+0.054004
## [30] train-rmse:0.365506+0.019663 test-rmse:0.642520+0.054740
## [31] train-rmse:0.358728+0.020751 test-rmse:0.640126+0.054763
## [32] train-rmse:0.351359+0.019015 test-rmse:0.639559+0.056994
## [33] train-rmse:0.347357+0.018954 test-rmse:0.638547+0.057470
## [34] train-rmse:0.342753+0.019905 test-rmse:0.637660+0.056884
## [35] train-rmse:0.334926+0.019773 test-rmse:0.637382+0.056982
## [36] train-rmse:0.329343+0.020295 test-rmse:0.637807+0.058288
## [37] train-rmse:0.323598+0.018460 test-rmse:0.636479+0.057201
## [38] train-rmse:0.318966+0.019203 test-rmse:0.635869+0.057940
## [39] train-rmse:0.313544+0.019152 test-rmse:0.634883+0.057002
## [40] train-rmse:0.309553+0.019314 test-rmse:0.633573+0.057243
## [41] train-rmse:0.305472+0.018339 test-rmse:0.632735+0.056803
## [42] train-rmse:0.300987+0.018737 test-rmse:0.632445+0.056125
## [43] train-rmse:0.296216+0.019476 test-rmse:0.632772+0.057225
## [44] train-rmse:0.292188+0.018671 test-rmse:0.631075+0.058013
## [45] train-rmse:0.288629+0.019127 test-rmse:0.631141+0.058368
## [46] train-rmse:0.286228+0.019610 test-rmse:0.631108+0.058355
## [47] train-rmse:0.281498+0.019596 test-rmse:0.629626+0.056087
## [48] train-rmse:0.277079+0.019029 test-rmse:0.628992+0.057315
## [49] train-rmse:0.272218+0.017792 test-rmse:0.629996+0.057248
## [50] train-rmse:0.268595+0.016370 test-rmse:0.630867+0.056760
## [51] train-rmse:0.264079+0.015679 test-rmse:0.629176+0.056013
## [52] train-rmse:0.260098+0.015866 test-rmse:0.629097+0.057333
## [53] train-rmse:0.256235+0.015223 test-rmse:0.628221+0.056764
## [54] train-rmse:0.252483+0.015329 test-rmse:0.628152+0.056210
## [55] train-rmse:0.250169+0.015310 test-rmse:0.627898+0.056071
## [56] train-rmse:0.246795+0.015409 test-rmse:0.628318+0.056105
## [57] train-rmse:0.243053+0.015685 test-rmse:0.627943+0.056754
## [58] train-rmse:0.239915+0.015414 test-rmse:0.627166+0.056462
## [59] train-rmse:0.236257+0.014003 test-rmse:0.627458+0.056867
## [60] train-rmse:0.232289+0.015046 test-rmse:0.626616+0.056190
## [61] train-rmse:0.229319+0.014900 test-rmse:0.626710+0.056679
## [62] train-rmse:0.226082+0.014915 test-rmse:0.626612+0.058070
## [63] train-rmse:0.224022+0.014606 test-rmse:0.626858+0.058186
## [64] train-rmse:0.220137+0.014842 test-rmse:0.624795+0.057247
## [65] train-rmse:0.217003+0.014394 test-rmse:0.625651+0.057440
## [66] train-rmse:0.213839+0.014033 test-rmse:0.626146+0.057900
## [67] train-rmse:0.210639+0.012733 test-rmse:0.625285+0.057685
## [68] train-rmse:0.208217+0.012684 test-rmse:0.625548+0.058077
## [69] train-rmse:0.204166+0.011685 test-rmse:0.625820+0.057211
## [70] train-rmse:0.202046+0.011481 test-rmse:0.626160+0.056912
## Stopping. Best iteration:
## [64] train-rmse:0.220137+0.014842 test-rmse:0.624795+0.057247
##
## 20
## [1] train-rmse:1.419784+0.013869 test-rmse:1.431245+0.071819
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.261420+0.011744 test-rmse:1.284268+0.071821
## [3] train-rmse:1.126356+0.009235 test-rmse:1.159489+0.070161
## [4] train-rmse:1.011705+0.007220 test-rmse:1.059072+0.072533
## [5] train-rmse:0.913295+0.006339 test-rmse:0.976391+0.071057
## [6] train-rmse:0.830142+0.004993 test-rmse:0.908875+0.071232
## [7] train-rmse:0.759353+0.004191 test-rmse:0.856440+0.070111
## [8] train-rmse:0.699755+0.004384 test-rmse:0.811470+0.070372
## [9] train-rmse:0.651362+0.005258 test-rmse:0.782570+0.073585
## [10] train-rmse:0.609956+0.005563 test-rmse:0.755234+0.071336
## [11] train-rmse:0.574887+0.008809 test-rmse:0.735878+0.068717
## [12] train-rmse:0.545869+0.009327 test-rmse:0.719770+0.068132
## [13] train-rmse:0.518522+0.009505 test-rmse:0.704055+0.065163
## [14] train-rmse:0.495447+0.010416 test-rmse:0.693934+0.063190
## [15] train-rmse:0.474145+0.011210 test-rmse:0.681858+0.061932
## [16] train-rmse:0.456934+0.011178 test-rmse:0.674614+0.057931
## [17] train-rmse:0.437673+0.013403 test-rmse:0.669627+0.056766
## [18] train-rmse:0.421554+0.015063 test-rmse:0.664951+0.055719
## [19] train-rmse:0.409988+0.015220 test-rmse:0.662984+0.055238
## [20] train-rmse:0.397849+0.017545 test-rmse:0.662055+0.055283
## [21] train-rmse:0.388255+0.018492 test-rmse:0.659030+0.054760
## [22] train-rmse:0.376522+0.016946 test-rmse:0.657987+0.054856
## [23] train-rmse:0.368810+0.015319 test-rmse:0.656789+0.055569
## [24] train-rmse:0.359514+0.015541 test-rmse:0.654985+0.054852
## [25] train-rmse:0.351416+0.013734 test-rmse:0.654405+0.054202
## [26] train-rmse:0.343732+0.014247 test-rmse:0.652749+0.051571
## [27] train-rmse:0.337680+0.014867 test-rmse:0.651984+0.051076
## [28] train-rmse:0.331703+0.012611 test-rmse:0.649714+0.052248
## [29] train-rmse:0.326636+0.012252 test-rmse:0.650290+0.052244
## [30] train-rmse:0.319490+0.013907 test-rmse:0.650216+0.052121
## [31] train-rmse:0.313576+0.015860 test-rmse:0.648709+0.052529
## [32] train-rmse:0.307764+0.015444 test-rmse:0.648233+0.052887
## [33] train-rmse:0.301357+0.016505 test-rmse:0.647657+0.051616
## [34] train-rmse:0.296942+0.016993 test-rmse:0.648166+0.053001
## [35] train-rmse:0.291235+0.017400 test-rmse:0.648139+0.054197
## [36] train-rmse:0.284366+0.016485 test-rmse:0.645648+0.054176
## [37] train-rmse:0.279817+0.016101 test-rmse:0.646102+0.055650
## [38] train-rmse:0.275625+0.016150 test-rmse:0.646473+0.054351
## [39] train-rmse:0.269445+0.015912 test-rmse:0.646043+0.052959
## [40] train-rmse:0.263118+0.017498 test-rmse:0.645806+0.055135
## [41] train-rmse:0.259649+0.017063 test-rmse:0.645740+0.055612
## [42] train-rmse:0.253036+0.016298 test-rmse:0.644794+0.056731
## [43] train-rmse:0.248197+0.015538 test-rmse:0.643578+0.057011
## [44] train-rmse:0.243856+0.016432 test-rmse:0.643416+0.057347
## [45] train-rmse:0.240138+0.017244 test-rmse:0.643404+0.057603
## [46] train-rmse:0.234917+0.016926 test-rmse:0.643894+0.058373
## [47] train-rmse:0.230582+0.015541 test-rmse:0.644686+0.058731
## [48] train-rmse:0.227149+0.015935 test-rmse:0.645333+0.058925
## [49] train-rmse:0.222433+0.015662 test-rmse:0.645415+0.058683
## [50] train-rmse:0.218628+0.015512 test-rmse:0.644330+0.058418
## [51] train-rmse:0.214603+0.015365 test-rmse:0.644289+0.057247
## Stopping. Best iteration:
## [45] train-rmse:0.240138+0.017244 test-rmse:0.643404+0.057603
##
## 21
## [1] train-rmse:1.410061+0.014228 test-rmse:1.410749+0.069625
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.253427+0.012009 test-rmse:1.256917+0.070470
## [3] train-rmse:1.129539+0.010207 test-rmse:1.133471+0.069642
## [4] train-rmse:1.033016+0.008840 test-rmse:1.038305+0.067174
## [5] train-rmse:0.958134+0.008147 test-rmse:0.967865+0.065539
## [6] train-rmse:0.900358+0.007363 test-rmse:0.911929+0.061670
## [7] train-rmse:0.856175+0.007032 test-rmse:0.871101+0.056623
## [8] train-rmse:0.822456+0.006607 test-rmse:0.840072+0.053525
## [9] train-rmse:0.796715+0.006443 test-rmse:0.817753+0.051085
## [10] train-rmse:0.777195+0.006067 test-rmse:0.802232+0.047744
## [11] train-rmse:0.762195+0.005840 test-rmse:0.789382+0.046763
## [12] train-rmse:0.750342+0.005696 test-rmse:0.778796+0.045886
## [13] train-rmse:0.740881+0.005507 test-rmse:0.770462+0.042207
## [14] train-rmse:0.733148+0.005313 test-rmse:0.762698+0.038735
## [15] train-rmse:0.726806+0.005222 test-rmse:0.757703+0.035892
## [16] train-rmse:0.721590+0.005168 test-rmse:0.754962+0.035772
## [17] train-rmse:0.717037+0.005108 test-rmse:0.750810+0.035497
## [18] train-rmse:0.713061+0.005051 test-rmse:0.750123+0.033420
## [19] train-rmse:0.709459+0.004966 test-rmse:0.748192+0.033568
## [20] train-rmse:0.706195+0.004913 test-rmse:0.746070+0.033290
## [21] train-rmse:0.703228+0.004912 test-rmse:0.743811+0.032300
## [22] train-rmse:0.700462+0.004961 test-rmse:0.740842+0.030098
## [23] train-rmse:0.697922+0.004987 test-rmse:0.739180+0.030838
## [24] train-rmse:0.695484+0.005013 test-rmse:0.738402+0.029396
## [25] train-rmse:0.693164+0.005025 test-rmse:0.738479+0.030039
## [26] train-rmse:0.690942+0.005073 test-rmse:0.736313+0.028583
## [27] train-rmse:0.688850+0.005084 test-rmse:0.735775+0.029681
## [28] train-rmse:0.686831+0.005109 test-rmse:0.735135+0.030058
## [29] train-rmse:0.684885+0.005105 test-rmse:0.734278+0.030410
## [30] train-rmse:0.683001+0.005116 test-rmse:0.732667+0.030384
## [31] train-rmse:0.681168+0.005115 test-rmse:0.731513+0.029705
## [32] train-rmse:0.679383+0.005108 test-rmse:0.730653+0.030238
## [33] train-rmse:0.677676+0.005118 test-rmse:0.730457+0.030241
## [34] train-rmse:0.675994+0.005124 test-rmse:0.728729+0.029616
## [35] train-rmse:0.674338+0.005158 test-rmse:0.729537+0.030797
## [36] train-rmse:0.672719+0.005178 test-rmse:0.729496+0.030467
## [37] train-rmse:0.671148+0.005164 test-rmse:0.728911+0.031418
## [38] train-rmse:0.669625+0.005155 test-rmse:0.727243+0.031068
## [39] train-rmse:0.668140+0.005167 test-rmse:0.728147+0.032295
## [40] train-rmse:0.666717+0.005192 test-rmse:0.726693+0.031649
## [41] train-rmse:0.665324+0.005203 test-rmse:0.725726+0.031492
## [42] train-rmse:0.663962+0.005212 test-rmse:0.725172+0.032836
## [43] train-rmse:0.662612+0.005225 test-rmse:0.725372+0.034308
## [44] train-rmse:0.661286+0.005231 test-rmse:0.724482+0.034164
## [45] train-rmse:0.659986+0.005262 test-rmse:0.723064+0.033799
## [46] train-rmse:0.658721+0.005316 test-rmse:0.723335+0.033529
## [47] train-rmse:0.657474+0.005372 test-rmse:0.722775+0.033247
## [48] train-rmse:0.656253+0.005430 test-rmse:0.721891+0.032726
## [49] train-rmse:0.655055+0.005451 test-rmse:0.721142+0.032445
## [50] train-rmse:0.653870+0.005443 test-rmse:0.722092+0.033308
## [51] train-rmse:0.652735+0.005445 test-rmse:0.722409+0.033156
## [52] train-rmse:0.651607+0.005452 test-rmse:0.720613+0.032889
## [53] train-rmse:0.650490+0.005462 test-rmse:0.720589+0.033677
## [54] train-rmse:0.649400+0.005487 test-rmse:0.720254+0.033261
## [55] train-rmse:0.648356+0.005504 test-rmse:0.719726+0.032970
## [56] train-rmse:0.647331+0.005520 test-rmse:0.718592+0.033556
## [57] train-rmse:0.646330+0.005543 test-rmse:0.717439+0.033010
## [58] train-rmse:0.645336+0.005561 test-rmse:0.717919+0.033994
## [59] train-rmse:0.644347+0.005578 test-rmse:0.717623+0.033588
## [60] train-rmse:0.643374+0.005575 test-rmse:0.717372+0.033983
## [61] train-rmse:0.642409+0.005573 test-rmse:0.718211+0.034291
## [62] train-rmse:0.641462+0.005591 test-rmse:0.717721+0.034014
## [63] train-rmse:0.640535+0.005624 test-rmse:0.716208+0.034471
## [64] train-rmse:0.639612+0.005664 test-rmse:0.715563+0.034423
## [65] train-rmse:0.638719+0.005693 test-rmse:0.716158+0.035563
## [66] train-rmse:0.637832+0.005715 test-rmse:0.715093+0.035124
## [67] train-rmse:0.636952+0.005702 test-rmse:0.714803+0.035220
## [68] train-rmse:0.636078+0.005689 test-rmse:0.714202+0.034362
## [69] train-rmse:0.635220+0.005691 test-rmse:0.713911+0.034641
## [70] train-rmse:0.634374+0.005697 test-rmse:0.713211+0.034421
## [71] train-rmse:0.633553+0.005715 test-rmse:0.712268+0.034204
## [72] train-rmse:0.632743+0.005737 test-rmse:0.711441+0.033975
## [73] train-rmse:0.631950+0.005739 test-rmse:0.711512+0.034407
## [74] train-rmse:0.631168+0.005752 test-rmse:0.711037+0.033914
## [75] train-rmse:0.630384+0.005760 test-rmse:0.710122+0.032750
## [76] train-rmse:0.629589+0.005751 test-rmse:0.709856+0.033086
## [77] train-rmse:0.628812+0.005763 test-rmse:0.709591+0.033297
## [78] train-rmse:0.628060+0.005772 test-rmse:0.707586+0.033734
## [79] train-rmse:0.627312+0.005783 test-rmse:0.707307+0.034062
## [80] train-rmse:0.626571+0.005799 test-rmse:0.708116+0.034623
## [81] train-rmse:0.625848+0.005803 test-rmse:0.708409+0.035074
## [82] train-rmse:0.625129+0.005818 test-rmse:0.708513+0.035125
## [83] train-rmse:0.624410+0.005832 test-rmse:0.707942+0.035035
## [84] train-rmse:0.623703+0.005847 test-rmse:0.707855+0.034880
## [85] train-rmse:0.622984+0.005858 test-rmse:0.707586+0.035629
## Stopping. Best iteration:
## [79] train-rmse:0.627312+0.005783 test-rmse:0.707307+0.034062
##
## 22
## [1] train-rmse:1.405568+0.014145 test-rmse:1.409307+0.069823
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.244094+0.011707 test-rmse:1.251897+0.071075
## [3] train-rmse:1.115494+0.009676 test-rmse:1.129172+0.071833
## [4] train-rmse:1.013484+0.008091 test-rmse:1.032714+0.072667
## [5] train-rmse:0.933734+0.006908 test-rmse:0.957179+0.069902
## [6] train-rmse:0.872274+0.006028 test-rmse:0.901326+0.066919
## [7] train-rmse:0.824359+0.005993 test-rmse:0.859582+0.067902
## [8] train-rmse:0.785574+0.004460 test-rmse:0.824878+0.064137
## [9] train-rmse:0.755086+0.003850 test-rmse:0.795336+0.062594
## [10] train-rmse:0.731164+0.004909 test-rmse:0.774452+0.058513
## [11] train-rmse:0.713368+0.004934 test-rmse:0.761381+0.053792
## [12] train-rmse:0.697864+0.003272 test-rmse:0.750332+0.051433
## [13] train-rmse:0.685183+0.004839 test-rmse:0.741831+0.046834
## [14] train-rmse:0.675699+0.004886 test-rmse:0.736884+0.046431
## [15] train-rmse:0.667072+0.005110 test-rmse:0.729192+0.042571
## [16] train-rmse:0.660426+0.004982 test-rmse:0.725779+0.042208
## [17] train-rmse:0.654272+0.004687 test-rmse:0.721968+0.039473
## [18] train-rmse:0.647853+0.004717 test-rmse:0.718018+0.042746
## [19] train-rmse:0.641393+0.005168 test-rmse:0.715360+0.043001
## [20] train-rmse:0.636673+0.004920 test-rmse:0.715942+0.043331
## [21] train-rmse:0.632695+0.004668 test-rmse:0.713037+0.042250
## [22] train-rmse:0.628723+0.004220 test-rmse:0.712436+0.042011
## [23] train-rmse:0.624319+0.004264 test-rmse:0.708629+0.045044
## [24] train-rmse:0.619984+0.005428 test-rmse:0.705973+0.043313
## [25] train-rmse:0.616397+0.005437 test-rmse:0.705983+0.043735
## [26] train-rmse:0.613637+0.005737 test-rmse:0.703905+0.044010
## [27] train-rmse:0.610457+0.006162 test-rmse:0.702798+0.044372
## [28] train-rmse:0.607102+0.005644 test-rmse:0.699123+0.045415
## [29] train-rmse:0.604378+0.005309 test-rmse:0.697164+0.045306
## [30] train-rmse:0.600845+0.004737 test-rmse:0.697561+0.044663
## [31] train-rmse:0.598132+0.004458 test-rmse:0.696965+0.044147
## [32] train-rmse:0.595328+0.005154 test-rmse:0.696757+0.044899
## [33] train-rmse:0.591575+0.005040 test-rmse:0.695011+0.046539
## [34] train-rmse:0.588950+0.005588 test-rmse:0.694483+0.045943
## [35] train-rmse:0.585722+0.006129 test-rmse:0.693395+0.046036
## [36] train-rmse:0.583217+0.006087 test-rmse:0.692974+0.045955
## [37] train-rmse:0.580501+0.005722 test-rmse:0.689854+0.045671
## [38] train-rmse:0.578149+0.005401 test-rmse:0.689350+0.044497
## [39] train-rmse:0.576181+0.005230 test-rmse:0.689040+0.044715
## [40] train-rmse:0.573828+0.005258 test-rmse:0.688532+0.044236
## [41] train-rmse:0.570966+0.006540 test-rmse:0.687318+0.043509
## [42] train-rmse:0.568788+0.007062 test-rmse:0.687143+0.043492
## [43] train-rmse:0.566946+0.007079 test-rmse:0.686473+0.043605
## [44] train-rmse:0.564864+0.007081 test-rmse:0.684261+0.043812
## [45] train-rmse:0.561696+0.007528 test-rmse:0.683210+0.043787
## [46] train-rmse:0.558268+0.006064 test-rmse:0.682068+0.043031
## [47] train-rmse:0.556271+0.006129 test-rmse:0.681808+0.042837
## [48] train-rmse:0.554264+0.006528 test-rmse:0.680102+0.042993
## [49] train-rmse:0.552326+0.006214 test-rmse:0.679407+0.043254
## [50] train-rmse:0.550503+0.006630 test-rmse:0.678736+0.043357
## [51] train-rmse:0.548491+0.006928 test-rmse:0.679175+0.043748
## [52] train-rmse:0.545699+0.007847 test-rmse:0.678723+0.043949
## [53] train-rmse:0.543824+0.008497 test-rmse:0.677644+0.043871
## [54] train-rmse:0.542272+0.008613 test-rmse:0.676808+0.043469
## [55] train-rmse:0.537523+0.006703 test-rmse:0.672448+0.042351
## [56] train-rmse:0.536139+0.006953 test-rmse:0.672538+0.042088
## [57] train-rmse:0.533500+0.006989 test-rmse:0.672833+0.042258
## [58] train-rmse:0.531279+0.007246 test-rmse:0.672182+0.043209
## [59] train-rmse:0.529800+0.007441 test-rmse:0.670517+0.043525
## [60] train-rmse:0.527279+0.008232 test-rmse:0.668578+0.043319
## [61] train-rmse:0.525784+0.008365 test-rmse:0.667993+0.043525
## [62] train-rmse:0.523966+0.008739 test-rmse:0.668217+0.044166
## [63] train-rmse:0.522491+0.008624 test-rmse:0.668941+0.045854
## [64] train-rmse:0.520819+0.008737 test-rmse:0.667590+0.045304
## [65] train-rmse:0.519027+0.009344 test-rmse:0.666890+0.044124
## [66] train-rmse:0.517570+0.009459 test-rmse:0.665745+0.043805
## [67] train-rmse:0.516112+0.009663 test-rmse:0.665633+0.044245
## [68] train-rmse:0.514101+0.008843 test-rmse:0.666251+0.043513
## [69] train-rmse:0.511798+0.008565 test-rmse:0.663740+0.041980
## [70] train-rmse:0.510595+0.008304 test-rmse:0.662519+0.042393
## [71] train-rmse:0.508480+0.008674 test-rmse:0.661707+0.041798
## [72] train-rmse:0.507061+0.008646 test-rmse:0.661228+0.041893
## [73] train-rmse:0.505597+0.008622 test-rmse:0.661186+0.041864
## [74] train-rmse:0.504389+0.008383 test-rmse:0.660848+0.042467
## [75] train-rmse:0.502206+0.008467 test-rmse:0.659270+0.042402
## [76] train-rmse:0.500268+0.009595 test-rmse:0.659266+0.042439
## [77] train-rmse:0.498632+0.010079 test-rmse:0.658122+0.041638
## [78] train-rmse:0.497242+0.010337 test-rmse:0.656592+0.041288
## [79] train-rmse:0.496243+0.010330 test-rmse:0.655876+0.040990
## [80] train-rmse:0.493874+0.008918 test-rmse:0.653869+0.042614
## [81] train-rmse:0.492113+0.009749 test-rmse:0.653363+0.042387
## [82] train-rmse:0.490777+0.009858 test-rmse:0.652674+0.042723
## [83] train-rmse:0.488311+0.009151 test-rmse:0.651260+0.040452
## [84] train-rmse:0.487129+0.009020 test-rmse:0.650893+0.040012
## [85] train-rmse:0.486077+0.009087 test-rmse:0.649696+0.040337
## [86] train-rmse:0.484738+0.009183 test-rmse:0.649953+0.040451
## [87] train-rmse:0.483439+0.008959 test-rmse:0.650455+0.040216
## [88] train-rmse:0.482635+0.008923 test-rmse:0.650615+0.040561
## [89] train-rmse:0.481926+0.008934 test-rmse:0.650822+0.040119
## [90] train-rmse:0.480810+0.009178 test-rmse:0.650635+0.041047
## [91] train-rmse:0.479475+0.009284 test-rmse:0.650293+0.041271
## Stopping. Best iteration:
## [85] train-rmse:0.486077+0.009087 test-rmse:0.649696+0.040337
##
## 23
## [1] train-rmse:1.402522+0.013891 test-rmse:1.407672+0.068930
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.236641+0.011495 test-rmse:1.249426+0.069343
## [3] train-rmse:1.104095+0.009660 test-rmse:1.124998+0.069702
## [4] train-rmse:0.998325+0.008020 test-rmse:1.026557+0.068917
## [5] train-rmse:0.912818+0.006156 test-rmse:0.945636+0.069942
## [6] train-rmse:0.847401+0.005187 test-rmse:0.886261+0.067967
## [7] train-rmse:0.795121+0.004272 test-rmse:0.840111+0.069459
## [8] train-rmse:0.754311+0.005218 test-rmse:0.807721+0.065587
## [9] train-rmse:0.721080+0.005612 test-rmse:0.779147+0.064504
## [10] train-rmse:0.691478+0.004964 test-rmse:0.754979+0.057352
## [11] train-rmse:0.666203+0.005917 test-rmse:0.736061+0.051734
## [12] train-rmse:0.649607+0.006604 test-rmse:0.725889+0.051535
## [13] train-rmse:0.636079+0.006748 test-rmse:0.715768+0.049476
## [14] train-rmse:0.624336+0.007259 test-rmse:0.710851+0.047017
## [15] train-rmse:0.614286+0.007249 test-rmse:0.705178+0.046807
## [16] train-rmse:0.605259+0.008340 test-rmse:0.698684+0.047621
## [17] train-rmse:0.598004+0.008307 test-rmse:0.695589+0.047826
## [18] train-rmse:0.590735+0.007771 test-rmse:0.693539+0.050632
## [19] train-rmse:0.583163+0.007449 test-rmse:0.690040+0.049716
## [20] train-rmse:0.577001+0.006780 test-rmse:0.688993+0.049096
## [21] train-rmse:0.571057+0.007015 test-rmse:0.688165+0.048498
## [22] train-rmse:0.566976+0.006706 test-rmse:0.686881+0.050122
## [23] train-rmse:0.562823+0.006965 test-rmse:0.684545+0.050796
## [24] train-rmse:0.557837+0.006496 test-rmse:0.683500+0.050755
## [25] train-rmse:0.553577+0.006799 test-rmse:0.684154+0.050594
## [26] train-rmse:0.546256+0.007765 test-rmse:0.679826+0.048903
## [27] train-rmse:0.541123+0.007702 test-rmse:0.677986+0.049776
## [28] train-rmse:0.536824+0.006942 test-rmse:0.676249+0.050864
## [29] train-rmse:0.532015+0.007812 test-rmse:0.673664+0.051465
## [30] train-rmse:0.527140+0.007503 test-rmse:0.672856+0.049879
## [31] train-rmse:0.522889+0.008383 test-rmse:0.671504+0.052248
## [32] train-rmse:0.519332+0.009259 test-rmse:0.670055+0.050714
## [33] train-rmse:0.514393+0.009402 test-rmse:0.669597+0.051595
## [34] train-rmse:0.511309+0.009301 test-rmse:0.668798+0.051049
## [35] train-rmse:0.507248+0.009852 test-rmse:0.668412+0.050555
## [36] train-rmse:0.504777+0.009915 test-rmse:0.667798+0.051897
## [37] train-rmse:0.498093+0.009935 test-rmse:0.663048+0.048849
## [38] train-rmse:0.495076+0.009871 test-rmse:0.662531+0.048784
## [39] train-rmse:0.491880+0.010463 test-rmse:0.660892+0.048433
## [40] train-rmse:0.487183+0.007343 test-rmse:0.661019+0.051233
## [41] train-rmse:0.482066+0.006548 test-rmse:0.657648+0.052381
## [42] train-rmse:0.479480+0.006849 test-rmse:0.658149+0.052210
## [43] train-rmse:0.476486+0.007272 test-rmse:0.657729+0.053786
## [44] train-rmse:0.472636+0.008091 test-rmse:0.655618+0.053061
## [45] train-rmse:0.470181+0.008964 test-rmse:0.655359+0.054525
## [46] train-rmse:0.467839+0.009211 test-rmse:0.655429+0.056313
## [47] train-rmse:0.463226+0.007716 test-rmse:0.653387+0.056489
## [48] train-rmse:0.460247+0.007955 test-rmse:0.653309+0.056187
## [49] train-rmse:0.457641+0.007870 test-rmse:0.650816+0.055155
## [50] train-rmse:0.454778+0.007384 test-rmse:0.649784+0.055916
## [51] train-rmse:0.452863+0.007250 test-rmse:0.649325+0.055631
## [52] train-rmse:0.449772+0.007017 test-rmse:0.647880+0.055705
## [53] train-rmse:0.447049+0.007629 test-rmse:0.648710+0.056436
## [54] train-rmse:0.445031+0.007563 test-rmse:0.647903+0.056802
## [55] train-rmse:0.442984+0.008171 test-rmse:0.648155+0.057494
## [56] train-rmse:0.440438+0.008223 test-rmse:0.648602+0.057673
## [57] train-rmse:0.438356+0.008663 test-rmse:0.647616+0.056504
## [58] train-rmse:0.435272+0.007327 test-rmse:0.646951+0.059231
## [59] train-rmse:0.432996+0.007758 test-rmse:0.645861+0.059531
## [60] train-rmse:0.430530+0.007589 test-rmse:0.644130+0.060638
## [61] train-rmse:0.428417+0.008199 test-rmse:0.644799+0.061574
## [62] train-rmse:0.425266+0.007735 test-rmse:0.644909+0.062035
## [63] train-rmse:0.423192+0.007937 test-rmse:0.643746+0.062661
## [64] train-rmse:0.420835+0.009152 test-rmse:0.643158+0.062131
## [65] train-rmse:0.418344+0.009098 test-rmse:0.642644+0.062575
## [66] train-rmse:0.416070+0.009406 test-rmse:0.641600+0.061291
## [67] train-rmse:0.414460+0.009626 test-rmse:0.641374+0.061527
## [68] train-rmse:0.411391+0.009051 test-rmse:0.640725+0.062137
## [69] train-rmse:0.408830+0.009685 test-rmse:0.640382+0.060634
## [70] train-rmse:0.407192+0.009406 test-rmse:0.641156+0.060903
## [71] train-rmse:0.405285+0.009852 test-rmse:0.641433+0.061169
## [72] train-rmse:0.402298+0.009457 test-rmse:0.641320+0.060387
## [73] train-rmse:0.400251+0.010149 test-rmse:0.641155+0.060399
## [74] train-rmse:0.398675+0.010348 test-rmse:0.641059+0.060960
## [75] train-rmse:0.396247+0.009907 test-rmse:0.638938+0.060291
## [76] train-rmse:0.394401+0.010111 test-rmse:0.638405+0.059985
## [77] train-rmse:0.392826+0.010629 test-rmse:0.637658+0.059382
## [78] train-rmse:0.391063+0.010785 test-rmse:0.637543+0.060214
## [79] train-rmse:0.388967+0.011270 test-rmse:0.637437+0.059579
## [80] train-rmse:0.387336+0.011376 test-rmse:0.636615+0.060412
## [81] train-rmse:0.385777+0.010869 test-rmse:0.636325+0.060205
## [82] train-rmse:0.383632+0.009680 test-rmse:0.635574+0.058963
## [83] train-rmse:0.381425+0.010179 test-rmse:0.636731+0.059522
## [84] train-rmse:0.379762+0.011156 test-rmse:0.636537+0.059503
## [85] train-rmse:0.377680+0.011266 test-rmse:0.636254+0.059705
## [86] train-rmse:0.376293+0.011369 test-rmse:0.635841+0.059501
## [87] train-rmse:0.374871+0.011863 test-rmse:0.634985+0.058095
## [88] train-rmse:0.373383+0.012240 test-rmse:0.635581+0.058225
## [89] train-rmse:0.371390+0.011752 test-rmse:0.635257+0.057941
## [90] train-rmse:0.368897+0.011787 test-rmse:0.635336+0.057768
## [91] train-rmse:0.366865+0.011802 test-rmse:0.634767+0.057419
## [92] train-rmse:0.365874+0.011775 test-rmse:0.635351+0.057908
## [93] train-rmse:0.364173+0.012279 test-rmse:0.634418+0.057397
## [94] train-rmse:0.362237+0.011667 test-rmse:0.634603+0.057625
## [95] train-rmse:0.360002+0.012106 test-rmse:0.634683+0.057412
## [96] train-rmse:0.358743+0.012294 test-rmse:0.634802+0.057454
## [97] train-rmse:0.357291+0.012623 test-rmse:0.634353+0.057716
## [98] train-rmse:0.356134+0.012634 test-rmse:0.634425+0.057649
## [99] train-rmse:0.354677+0.012781 test-rmse:0.634243+0.057253
## [100] train-rmse:0.352753+0.012335 test-rmse:0.633299+0.056767
## [101] train-rmse:0.351637+0.012540 test-rmse:0.633052+0.056694
## [102] train-rmse:0.349496+0.013124 test-rmse:0.632740+0.056763
## [103] train-rmse:0.348787+0.013153 test-rmse:0.632905+0.056630
## [104] train-rmse:0.346476+0.012790 test-rmse:0.630580+0.056374
## [105] train-rmse:0.344508+0.012537 test-rmse:0.629714+0.057357
## [106] train-rmse:0.343597+0.012546 test-rmse:0.629154+0.057281
## [107] train-rmse:0.342752+0.012549 test-rmse:0.628913+0.057646
## [108] train-rmse:0.341362+0.012234 test-rmse:0.628554+0.057557
## [109] train-rmse:0.339933+0.012701 test-rmse:0.629120+0.057886
## [110] train-rmse:0.338563+0.012661 test-rmse:0.628505+0.057616
## [111] train-rmse:0.336106+0.012599 test-rmse:0.627711+0.056959
## [112] train-rmse:0.333851+0.012302 test-rmse:0.626202+0.057136
## [113] train-rmse:0.332184+0.012149 test-rmse:0.626050+0.056870
## [114] train-rmse:0.330187+0.012428 test-rmse:0.627108+0.057039
## [115] train-rmse:0.328121+0.011705 test-rmse:0.626754+0.056970
## [116] train-rmse:0.326423+0.011414 test-rmse:0.626957+0.056614
## [117] train-rmse:0.325187+0.011496 test-rmse:0.626312+0.056300
## [118] train-rmse:0.324314+0.011686 test-rmse:0.626194+0.056292
## [119] train-rmse:0.323275+0.011564 test-rmse:0.626227+0.056337
## Stopping. Best iteration:
## [113] train-rmse:0.332184+0.012149 test-rmse:0.626050+0.056870
##
## 24
## [1] train-rmse:1.399406+0.013937 test-rmse:1.409628+0.069298
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.229500+0.010848 test-rmse:1.248725+0.070128
## [3] train-rmse:1.091599+0.008706 test-rmse:1.117608+0.070936
## [4] train-rmse:0.980335+0.006229 test-rmse:1.016304+0.070508
## [5] train-rmse:0.892466+0.004627 test-rmse:0.938576+0.074474
## [6] train-rmse:0.819198+0.003670 test-rmse:0.874619+0.070120
## [7] train-rmse:0.763801+0.003461 test-rmse:0.828527+0.066701
## [8] train-rmse:0.718665+0.004298 test-rmse:0.792227+0.067962
## [9] train-rmse:0.685064+0.003968 test-rmse:0.766333+0.065128
## [10] train-rmse:0.655555+0.004383 test-rmse:0.741869+0.062623
## [11] train-rmse:0.627703+0.005829 test-rmse:0.725432+0.061146
## [12] train-rmse:0.607278+0.006801 test-rmse:0.710307+0.059114
## [13] train-rmse:0.591283+0.008678 test-rmse:0.701054+0.056903
## [14] train-rmse:0.577654+0.008339 test-rmse:0.695819+0.057396
## [15] train-rmse:0.565767+0.009279 test-rmse:0.691829+0.058398
## [16] train-rmse:0.556792+0.010097 test-rmse:0.685564+0.057865
## [17] train-rmse:0.545756+0.009014 test-rmse:0.681836+0.057676
## [18] train-rmse:0.535744+0.006766 test-rmse:0.679126+0.056627
## [19] train-rmse:0.529216+0.006723 test-rmse:0.678960+0.059235
## [20] train-rmse:0.522881+0.006891 test-rmse:0.675792+0.061724
## [21] train-rmse:0.517001+0.007659 test-rmse:0.674111+0.060550
## [22] train-rmse:0.510421+0.007970 test-rmse:0.672259+0.060228
## [23] train-rmse:0.503934+0.007792 test-rmse:0.667171+0.059195
## [24] train-rmse:0.499042+0.008173 test-rmse:0.663254+0.060038
## [25] train-rmse:0.493604+0.009398 test-rmse:0.661122+0.061745
## [26] train-rmse:0.486241+0.006170 test-rmse:0.657307+0.061680
## [27] train-rmse:0.480556+0.005718 test-rmse:0.655897+0.063315
## [28] train-rmse:0.474731+0.004511 test-rmse:0.654248+0.064538
## [29] train-rmse:0.469797+0.003216 test-rmse:0.654927+0.064481
## [30] train-rmse:0.463114+0.005974 test-rmse:0.654790+0.063901
## [31] train-rmse:0.459274+0.006305 test-rmse:0.651669+0.064651
## [32] train-rmse:0.455723+0.006040 test-rmse:0.652110+0.064257
## [33] train-rmse:0.451530+0.006040 test-rmse:0.651523+0.067045
## [34] train-rmse:0.446700+0.006143 test-rmse:0.650618+0.068082
## [35] train-rmse:0.442576+0.005990 test-rmse:0.651692+0.068660
## [36] train-rmse:0.434714+0.008450 test-rmse:0.649327+0.066924
## [37] train-rmse:0.429579+0.009107 test-rmse:0.647086+0.066120
## [38] train-rmse:0.424914+0.009022 test-rmse:0.646559+0.067218
## [39] train-rmse:0.420276+0.010766 test-rmse:0.645151+0.065787
## [40] train-rmse:0.416045+0.010395 test-rmse:0.644430+0.066795
## [41] train-rmse:0.413039+0.011126 test-rmse:0.644540+0.067099
## [42] train-rmse:0.408394+0.011420 test-rmse:0.642598+0.066766
## [43] train-rmse:0.405011+0.011238 test-rmse:0.642333+0.069004
## [44] train-rmse:0.401977+0.011615 test-rmse:0.640730+0.069511
## [45] train-rmse:0.397507+0.011517 test-rmse:0.638417+0.068239
## [46] train-rmse:0.393334+0.010751 test-rmse:0.637964+0.068339
## [47] train-rmse:0.389239+0.010979 test-rmse:0.637053+0.066925
## [48] train-rmse:0.385502+0.011537 test-rmse:0.637038+0.067179
## [49] train-rmse:0.383067+0.010831 test-rmse:0.635105+0.068350
## [50] train-rmse:0.379703+0.010835 test-rmse:0.634459+0.068715
## [51] train-rmse:0.376357+0.010158 test-rmse:0.633509+0.068566
## [52] train-rmse:0.371760+0.011049 test-rmse:0.632401+0.067038
## [53] train-rmse:0.368531+0.012030 test-rmse:0.632355+0.067696
## [54] train-rmse:0.365806+0.012392 test-rmse:0.632768+0.067892
## [55] train-rmse:0.361023+0.012553 test-rmse:0.632707+0.068749
## [56] train-rmse:0.358299+0.013985 test-rmse:0.632504+0.070013
## [57] train-rmse:0.355720+0.013543 test-rmse:0.632830+0.069349
## [58] train-rmse:0.352821+0.013322 test-rmse:0.631569+0.068311
## [59] train-rmse:0.349735+0.013880 test-rmse:0.632069+0.067539
## [60] train-rmse:0.346360+0.013554 test-rmse:0.631056+0.067205
## [61] train-rmse:0.344096+0.013001 test-rmse:0.630567+0.067797
## [62] train-rmse:0.340375+0.013157 test-rmse:0.628887+0.068185
## [63] train-rmse:0.337784+0.012826 test-rmse:0.628470+0.067765
## [64] train-rmse:0.334258+0.011537 test-rmse:0.629807+0.067855
## [65] train-rmse:0.331267+0.012566 test-rmse:0.630513+0.068566
## [66] train-rmse:0.326903+0.012322 test-rmse:0.629856+0.067164
## [67] train-rmse:0.323894+0.012301 test-rmse:0.629522+0.068109
## [68] train-rmse:0.321701+0.012678 test-rmse:0.629542+0.068588
## [69] train-rmse:0.318831+0.013492 test-rmse:0.628977+0.069559
## Stopping. Best iteration:
## [63] train-rmse:0.337784+0.012826 test-rmse:0.628470+0.067765
##
## 25
## [1] train-rmse:1.397298+0.014078 test-rmse:1.406588+0.071064
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.224898+0.011115 test-rmse:1.246934+0.076237
## [3] train-rmse:1.082947+0.008956 test-rmse:1.111611+0.074477
## [4] train-rmse:0.966795+0.006856 test-rmse:1.007595+0.074487
## [5] train-rmse:0.873620+0.007455 test-rmse:0.926386+0.071355
## [6] train-rmse:0.798559+0.006658 test-rmse:0.865619+0.070943
## [7] train-rmse:0.738960+0.005642 test-rmse:0.817784+0.067687
## [8] train-rmse:0.690320+0.005299 test-rmse:0.780703+0.067657
## [9] train-rmse:0.651571+0.004086 test-rmse:0.751829+0.064759
## [10] train-rmse:0.620556+0.005042 test-rmse:0.731499+0.063968
## [11] train-rmse:0.594921+0.004583 test-rmse:0.715066+0.057624
## [12] train-rmse:0.569317+0.007718 test-rmse:0.701181+0.055194
## [13] train-rmse:0.550500+0.006306 test-rmse:0.691554+0.054635
## [14] train-rmse:0.535416+0.008347 test-rmse:0.687520+0.051822
## [15] train-rmse:0.520033+0.009617 test-rmse:0.682312+0.052614
## [16] train-rmse:0.508222+0.010746 test-rmse:0.677147+0.051713
## [17] train-rmse:0.497271+0.013563 test-rmse:0.672993+0.050311
## [18] train-rmse:0.484959+0.012911 test-rmse:0.666203+0.047001
## [19] train-rmse:0.475322+0.014590 test-rmse:0.661184+0.047972
## [20] train-rmse:0.465257+0.011732 test-rmse:0.654734+0.047418
## [21] train-rmse:0.458061+0.013531 test-rmse:0.656205+0.046993
## [22] train-rmse:0.449745+0.013940 test-rmse:0.655719+0.046639
## [23] train-rmse:0.443126+0.013180 test-rmse:0.655243+0.046912
## [24] train-rmse:0.434722+0.013972 test-rmse:0.652203+0.046237
## [25] train-rmse:0.427762+0.011369 test-rmse:0.647870+0.046978
## [26] train-rmse:0.421102+0.013331 test-rmse:0.645596+0.044715
## [27] train-rmse:0.415408+0.013927 test-rmse:0.645697+0.047157
## [28] train-rmse:0.409708+0.015501 test-rmse:0.646284+0.047723
## [29] train-rmse:0.403960+0.016421 test-rmse:0.645035+0.047322
## [30] train-rmse:0.397280+0.015840 test-rmse:0.642707+0.048524
## [31] train-rmse:0.391341+0.016300 test-rmse:0.643350+0.049762
## [32] train-rmse:0.385865+0.017729 test-rmse:0.641803+0.049661
## [33] train-rmse:0.380306+0.016872 test-rmse:0.642468+0.049715
## [34] train-rmse:0.374738+0.018214 test-rmse:0.642051+0.049141
## [35] train-rmse:0.367420+0.018921 test-rmse:0.639096+0.049199
## [36] train-rmse:0.362445+0.018718 test-rmse:0.636929+0.050295
## [37] train-rmse:0.358546+0.018450 test-rmse:0.636619+0.050064
## [38] train-rmse:0.352856+0.017397 test-rmse:0.635002+0.047976
## [39] train-rmse:0.347347+0.017022 test-rmse:0.633663+0.048856
## [40] train-rmse:0.343260+0.016194 test-rmse:0.633175+0.049593
## [41] train-rmse:0.337617+0.015915 test-rmse:0.629957+0.049275
## [42] train-rmse:0.333505+0.015313 test-rmse:0.628559+0.046720
## [43] train-rmse:0.330456+0.016267 test-rmse:0.629070+0.046781
## [44] train-rmse:0.326344+0.017060 test-rmse:0.629066+0.047135
## [45] train-rmse:0.320982+0.015880 test-rmse:0.627885+0.047455
## [46] train-rmse:0.316490+0.015918 test-rmse:0.627430+0.046397
## [47] train-rmse:0.313111+0.016132 test-rmse:0.628175+0.046488
## [48] train-rmse:0.309584+0.015243 test-rmse:0.627937+0.047391
## [49] train-rmse:0.304993+0.015841 test-rmse:0.627268+0.046710
## [50] train-rmse:0.299277+0.014563 test-rmse:0.627730+0.047840
## [51] train-rmse:0.296108+0.014885 test-rmse:0.628507+0.048174
## [52] train-rmse:0.291970+0.013987 test-rmse:0.627207+0.049089
## [53] train-rmse:0.288392+0.015254 test-rmse:0.626658+0.047424
## [54] train-rmse:0.285915+0.015407 test-rmse:0.627264+0.047533
## [55] train-rmse:0.282347+0.016058 test-rmse:0.627586+0.048788
## [56] train-rmse:0.278848+0.016058 test-rmse:0.628295+0.048939
## [57] train-rmse:0.274467+0.015367 test-rmse:0.628484+0.049275
## [58] train-rmse:0.270019+0.015014 test-rmse:0.628577+0.048087
## [59] train-rmse:0.266117+0.013800 test-rmse:0.628378+0.047616
## Stopping. Best iteration:
## [53] train-rmse:0.288392+0.015254 test-rmse:0.626658+0.047424
##
## 26
## [1] train-rmse:1.395162+0.013729 test-rmse:1.407879+0.072383
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.220971+0.010717 test-rmse:1.246479+0.073746
## [3] train-rmse:1.077040+0.008235 test-rmse:1.116410+0.072808
## [4] train-rmse:0.957725+0.006109 test-rmse:1.012360+0.072264
## [5] train-rmse:0.859981+0.004881 test-rmse:0.933101+0.069176
## [6] train-rmse:0.781442+0.004871 test-rmse:0.868817+0.067286
## [7] train-rmse:0.717303+0.003039 test-rmse:0.816589+0.065690
## [8] train-rmse:0.665024+0.003749 test-rmse:0.781578+0.065321
## [9] train-rmse:0.622640+0.009030 test-rmse:0.753571+0.063797
## [10] train-rmse:0.589253+0.010481 test-rmse:0.733380+0.062900
## [11] train-rmse:0.560728+0.009522 test-rmse:0.714798+0.058987
## [12] train-rmse:0.539343+0.010564 test-rmse:0.702139+0.058141
## [13] train-rmse:0.512629+0.012726 test-rmse:0.689362+0.054208
## [14] train-rmse:0.491397+0.017862 test-rmse:0.682735+0.055044
## [15] train-rmse:0.475678+0.017109 test-rmse:0.676123+0.054435
## [16] train-rmse:0.460075+0.017145 test-rmse:0.669233+0.055590
## [17] train-rmse:0.445971+0.020611 test-rmse:0.667167+0.057557
## [18] train-rmse:0.433859+0.020328 test-rmse:0.663415+0.058191
## [19] train-rmse:0.426295+0.020241 test-rmse:0.658984+0.057283
## [20] train-rmse:0.415954+0.021339 test-rmse:0.657027+0.057535
## [21] train-rmse:0.409595+0.021509 test-rmse:0.654682+0.058912
## [22] train-rmse:0.399954+0.023864 test-rmse:0.653627+0.059201
## [23] train-rmse:0.394179+0.022162 test-rmse:0.651297+0.059960
## [24] train-rmse:0.383972+0.018627 test-rmse:0.643857+0.057344
## [25] train-rmse:0.375578+0.016399 test-rmse:0.642204+0.058193
## [26] train-rmse:0.367376+0.018714 test-rmse:0.640506+0.059484
## [27] train-rmse:0.359583+0.019196 test-rmse:0.638736+0.059462
## [28] train-rmse:0.354700+0.018605 test-rmse:0.638165+0.059419
## [29] train-rmse:0.348070+0.019844 test-rmse:0.638561+0.059356
## [30] train-rmse:0.343654+0.021210 test-rmse:0.638465+0.059444
## [31] train-rmse:0.335669+0.018507 test-rmse:0.637086+0.060285
## [32] train-rmse:0.328121+0.018965 test-rmse:0.634418+0.058697
## [33] train-rmse:0.320323+0.018923 test-rmse:0.633706+0.058539
## [34] train-rmse:0.314590+0.018510 test-rmse:0.633227+0.058045
## [35] train-rmse:0.309490+0.018482 test-rmse:0.633442+0.057953
## [36] train-rmse:0.303975+0.017895 test-rmse:0.634050+0.058779
## [37] train-rmse:0.296875+0.015486 test-rmse:0.632113+0.055868
## [38] train-rmse:0.291925+0.015651 test-rmse:0.631153+0.055632
## [39] train-rmse:0.286209+0.014942 test-rmse:0.631223+0.057161
## [40] train-rmse:0.281331+0.013138 test-rmse:0.631782+0.058725
## [41] train-rmse:0.274952+0.012878 test-rmse:0.629513+0.057295
## [42] train-rmse:0.270235+0.013739 test-rmse:0.627885+0.055551
## [43] train-rmse:0.264505+0.012731 test-rmse:0.626514+0.055707
## [44] train-rmse:0.259462+0.012295 test-rmse:0.625197+0.054837
## [45] train-rmse:0.255314+0.013216 test-rmse:0.624635+0.053582
## [46] train-rmse:0.251029+0.012370 test-rmse:0.624364+0.053897
## [47] train-rmse:0.247416+0.012906 test-rmse:0.624507+0.054923
## [48] train-rmse:0.244861+0.013163 test-rmse:0.624906+0.055139
## [49] train-rmse:0.240742+0.013273 test-rmse:0.625219+0.056273
## [50] train-rmse:0.236423+0.012530 test-rmse:0.625151+0.054945
## [51] train-rmse:0.232833+0.012084 test-rmse:0.624747+0.054394
## [52] train-rmse:0.228977+0.012396 test-rmse:0.624869+0.055021
## Stopping. Best iteration:
## [46] train-rmse:0.251029+0.012370 test-rmse:0.624364+0.053897
##
## 27
## [1] train-rmse:1.393592+0.013421 test-rmse:1.407025+0.072496
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.217450+0.011069 test-rmse:1.244622+0.072097
## [3] train-rmse:1.070584+0.007859 test-rmse:1.107442+0.070160
## [4] train-rmse:0.949626+0.005864 test-rmse:1.003642+0.073205
## [5] train-rmse:0.847605+0.004381 test-rmse:0.918806+0.073670
## [6] train-rmse:0.764955+0.005275 test-rmse:0.855522+0.072682
## [7] train-rmse:0.697635+0.006103 test-rmse:0.805025+0.071753
## [8] train-rmse:0.643761+0.007689 test-rmse:0.767905+0.070282
## [9] train-rmse:0.598798+0.009797 test-rmse:0.738930+0.068658
## [10] train-rmse:0.562308+0.011138 test-rmse:0.715797+0.064932
## [11] train-rmse:0.529939+0.008076 test-rmse:0.696913+0.058894
## [12] train-rmse:0.502961+0.007014 test-rmse:0.682108+0.055392
## [13] train-rmse:0.480272+0.012402 test-rmse:0.676954+0.055713
## [14] train-rmse:0.456788+0.016193 test-rmse:0.669097+0.054061
## [15] train-rmse:0.439501+0.015870 test-rmse:0.658949+0.051397
## [16] train-rmse:0.422519+0.018152 test-rmse:0.656095+0.052616
## [17] train-rmse:0.405353+0.017786 test-rmse:0.649950+0.054029
## [18] train-rmse:0.389291+0.017281 test-rmse:0.645281+0.053840
## [19] train-rmse:0.375539+0.018087 test-rmse:0.645357+0.054690
## [20] train-rmse:0.365502+0.016010 test-rmse:0.642319+0.051630
## [21] train-rmse:0.356461+0.014603 test-rmse:0.639528+0.049003
## [22] train-rmse:0.348522+0.013379 test-rmse:0.638855+0.049842
## [23] train-rmse:0.341075+0.013919 test-rmse:0.637261+0.051737
## [24] train-rmse:0.331914+0.013623 test-rmse:0.637766+0.052130
## [25] train-rmse:0.322718+0.012720 test-rmse:0.637076+0.051922
## [26] train-rmse:0.312983+0.014543 test-rmse:0.636345+0.049798
## [27] train-rmse:0.306305+0.014729 test-rmse:0.634600+0.048781
## [28] train-rmse:0.297833+0.013578 test-rmse:0.632596+0.049481
## [29] train-rmse:0.289577+0.014195 test-rmse:0.630363+0.050877
## [30] train-rmse:0.284043+0.015184 test-rmse:0.631836+0.052099
## [31] train-rmse:0.277895+0.015386 test-rmse:0.631870+0.053316
## [32] train-rmse:0.271804+0.015261 test-rmse:0.630715+0.054354
## [33] train-rmse:0.265073+0.015726 test-rmse:0.630551+0.053589
## [34] train-rmse:0.259868+0.014093 test-rmse:0.629749+0.053552
## [35] train-rmse:0.254741+0.013644 test-rmse:0.627941+0.053625
## [36] train-rmse:0.250274+0.013524 test-rmse:0.628588+0.053166
## [37] train-rmse:0.245466+0.013455 test-rmse:0.627096+0.053050
## [38] train-rmse:0.241907+0.013158 test-rmse:0.627387+0.052597
## [39] train-rmse:0.237542+0.013572 test-rmse:0.628887+0.051942
## [40] train-rmse:0.231679+0.013689 test-rmse:0.628274+0.053182
## [41] train-rmse:0.225927+0.013314 test-rmse:0.626771+0.052030
## [42] train-rmse:0.222452+0.012674 test-rmse:0.627001+0.053402
## [43] train-rmse:0.217188+0.013125 test-rmse:0.626413+0.052920
## [44] train-rmse:0.212496+0.013276 test-rmse:0.625373+0.052872
## [45] train-rmse:0.209174+0.012899 test-rmse:0.625863+0.053421
## [46] train-rmse:0.204770+0.013139 test-rmse:0.625829+0.052756
## [47] train-rmse:0.202028+0.013099 test-rmse:0.625933+0.053414
## [48] train-rmse:0.197399+0.013184 test-rmse:0.626336+0.053326
## [49] train-rmse:0.194447+0.013436 test-rmse:0.626101+0.052779
## [50] train-rmse:0.190124+0.012548 test-rmse:0.625262+0.052810
## [51] train-rmse:0.184348+0.011791 test-rmse:0.625243+0.053851
## [52] train-rmse:0.180245+0.012017 test-rmse:0.625884+0.053607
## [53] train-rmse:0.176334+0.011416 test-rmse:0.626518+0.052614
## [54] train-rmse:0.173294+0.011809 test-rmse:0.626566+0.053037
## [55] train-rmse:0.168944+0.012006 test-rmse:0.626717+0.053765
## [56] train-rmse:0.165889+0.012245 test-rmse:0.627353+0.053864
## [57] train-rmse:0.162842+0.012306 test-rmse:0.628050+0.053288
## Stopping. Best iteration:
## [51] train-rmse:0.184348+0.011791 test-rmse:0.625243+0.053851
##
## 28
## [1] train-rmse:1.386254+0.013890 test-rmse:1.387172+0.069872
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.216168+0.011470 test-rmse:1.218427+0.069355
## [3] train-rmse:1.086512+0.009589 test-rmse:1.090883+0.068957
## [4] train-rmse:0.989465+0.008291 test-rmse:0.998564+0.068221
## [5] train-rmse:0.916574+0.007675 test-rmse:0.928967+0.062798
## [6] train-rmse:0.863109+0.007065 test-rmse:0.876894+0.059258
## [7] train-rmse:0.823445+0.006652 test-rmse:0.842076+0.054188
## [8] train-rmse:0.794657+0.006343 test-rmse:0.819324+0.051636
## [9] train-rmse:0.773300+0.006141 test-rmse:0.800514+0.047242
## [10] train-rmse:0.757361+0.005769 test-rmse:0.786547+0.045825
## [11] train-rmse:0.745263+0.005512 test-rmse:0.775279+0.043841
## [12] train-rmse:0.735782+0.005301 test-rmse:0.767505+0.040253
## [13] train-rmse:0.728136+0.005196 test-rmse:0.761116+0.037856
## [14] train-rmse:0.721942+0.005120 test-rmse:0.755740+0.035158
## [15] train-rmse:0.716737+0.005073 test-rmse:0.750453+0.033912
## [16] train-rmse:0.712294+0.005100 test-rmse:0.748685+0.033387
## [17] train-rmse:0.708342+0.005047 test-rmse:0.747839+0.031177
## [18] train-rmse:0.704706+0.004947 test-rmse:0.745026+0.032503
## [19] train-rmse:0.701522+0.004885 test-rmse:0.743263+0.031816
## [20] train-rmse:0.698570+0.004981 test-rmse:0.742122+0.031182
## [21] train-rmse:0.695811+0.005013 test-rmse:0.739517+0.029702
## [22] train-rmse:0.693203+0.005067 test-rmse:0.736836+0.028270
## [23] train-rmse:0.690720+0.005122 test-rmse:0.735573+0.026990
## [24] train-rmse:0.688337+0.005147 test-rmse:0.735010+0.028163
## [25] train-rmse:0.686041+0.005184 test-rmse:0.734282+0.029038
## [26] train-rmse:0.683835+0.005229 test-rmse:0.733126+0.029456
## [27] train-rmse:0.681693+0.005218 test-rmse:0.732648+0.029103
## [28] train-rmse:0.679706+0.005181 test-rmse:0.731229+0.028511
## [29] train-rmse:0.677772+0.005142 test-rmse:0.729950+0.028617
## [30] train-rmse:0.675877+0.005122 test-rmse:0.729291+0.028439
## [31] train-rmse:0.673989+0.005099 test-rmse:0.728808+0.030725
## [32] train-rmse:0.672204+0.005100 test-rmse:0.727263+0.031030
## [33] train-rmse:0.670445+0.005104 test-rmse:0.726812+0.030081
## [34] train-rmse:0.668713+0.005122 test-rmse:0.726932+0.031033
## [35] train-rmse:0.667040+0.005140 test-rmse:0.727690+0.031673
## [36] train-rmse:0.665434+0.005196 test-rmse:0.727941+0.033779
## [37] train-rmse:0.663880+0.005247 test-rmse:0.726393+0.033144
## [38] train-rmse:0.662375+0.005320 test-rmse:0.724751+0.032068
## [39] train-rmse:0.660889+0.005367 test-rmse:0.724453+0.032007
## [40] train-rmse:0.659436+0.005382 test-rmse:0.723734+0.031627
## [41] train-rmse:0.658001+0.005396 test-rmse:0.723228+0.031026
## [42] train-rmse:0.656589+0.005433 test-rmse:0.723000+0.032523
## [43] train-rmse:0.655220+0.005456 test-rmse:0.721882+0.032543
## [44] train-rmse:0.653879+0.005437 test-rmse:0.721372+0.033002
## [45] train-rmse:0.652575+0.005441 test-rmse:0.720096+0.032962
## [46] train-rmse:0.651292+0.005469 test-rmse:0.719258+0.032951
## [47] train-rmse:0.650068+0.005498 test-rmse:0.719818+0.032513
## [48] train-rmse:0.648851+0.005530 test-rmse:0.720158+0.033933
## [49] train-rmse:0.647664+0.005553 test-rmse:0.720759+0.034892
## [50] train-rmse:0.646516+0.005577 test-rmse:0.720412+0.035151
## [51] train-rmse:0.645374+0.005603 test-rmse:0.718786+0.034140
## [52] train-rmse:0.644239+0.005626 test-rmse:0.717744+0.034202
## [53] train-rmse:0.643136+0.005644 test-rmse:0.717968+0.034042
## [54] train-rmse:0.642059+0.005633 test-rmse:0.715889+0.033958
## [55] train-rmse:0.641014+0.005646 test-rmse:0.715932+0.035664
## [56] train-rmse:0.639993+0.005669 test-rmse:0.715540+0.035597
## [57] train-rmse:0.638968+0.005675 test-rmse:0.716411+0.036466
## [58] train-rmse:0.637959+0.005687 test-rmse:0.715665+0.035613
## [59] train-rmse:0.636976+0.005695 test-rmse:0.715017+0.034822
## [60] train-rmse:0.635982+0.005728 test-rmse:0.715719+0.035052
## [61] train-rmse:0.634993+0.005718 test-rmse:0.714681+0.035500
## [62] train-rmse:0.634042+0.005720 test-rmse:0.714550+0.035866
## [63] train-rmse:0.633111+0.005724 test-rmse:0.714076+0.035978
## [64] train-rmse:0.632200+0.005720 test-rmse:0.713151+0.035917
## [65] train-rmse:0.631296+0.005755 test-rmse:0.711778+0.035484
## [66] train-rmse:0.630395+0.005788 test-rmse:0.711601+0.035119
## [67] train-rmse:0.629503+0.005801 test-rmse:0.711868+0.035408
## [68] train-rmse:0.628628+0.005809 test-rmse:0.712657+0.035950
## [69] train-rmse:0.627760+0.005829 test-rmse:0.712150+0.036128
## [70] train-rmse:0.626901+0.005830 test-rmse:0.711869+0.035423
## [71] train-rmse:0.626059+0.005824 test-rmse:0.711035+0.033936
## [72] train-rmse:0.625238+0.005826 test-rmse:0.710714+0.032817
## [73] train-rmse:0.624421+0.005847 test-rmse:0.709653+0.033400
## [74] train-rmse:0.623628+0.005864 test-rmse:0.708539+0.032721
## [75] train-rmse:0.622847+0.005863 test-rmse:0.707703+0.032734
## [76] train-rmse:0.622058+0.005859 test-rmse:0.707193+0.032807
## [77] train-rmse:0.621292+0.005863 test-rmse:0.706839+0.032292
## [78] train-rmse:0.620537+0.005863 test-rmse:0.706051+0.032474
## [79] train-rmse:0.619795+0.005857 test-rmse:0.705820+0.033348
## [80] train-rmse:0.619069+0.005872 test-rmse:0.705437+0.033496
## [81] train-rmse:0.618346+0.005899 test-rmse:0.704932+0.033750
## [82] train-rmse:0.617614+0.005943 test-rmse:0.702929+0.033821
## [83] train-rmse:0.616892+0.005962 test-rmse:0.702020+0.033611
## [84] train-rmse:0.616182+0.005966 test-rmse:0.703292+0.033732
## [85] train-rmse:0.615479+0.005971 test-rmse:0.703141+0.033931
## [86] train-rmse:0.614769+0.005971 test-rmse:0.702747+0.033885
## [87] train-rmse:0.614075+0.005973 test-rmse:0.703666+0.034455
## [88] train-rmse:0.613387+0.005967 test-rmse:0.701904+0.034528
## [89] train-rmse:0.612719+0.005969 test-rmse:0.702169+0.035440
## [90] train-rmse:0.612062+0.005973 test-rmse:0.701745+0.035258
## [91] train-rmse:0.611420+0.005992 test-rmse:0.701699+0.035146
## [92] train-rmse:0.610797+0.006009 test-rmse:0.701403+0.035232
## [93] train-rmse:0.610178+0.006018 test-rmse:0.701847+0.035258
## [94] train-rmse:0.609562+0.006024 test-rmse:0.701704+0.035102
## [95] train-rmse:0.608946+0.006026 test-rmse:0.700213+0.035006
## [96] train-rmse:0.608321+0.006025 test-rmse:0.699378+0.034811
## [97] train-rmse:0.607713+0.006033 test-rmse:0.699348+0.034937
## [98] train-rmse:0.607094+0.006054 test-rmse:0.699364+0.035213
## [99] train-rmse:0.606483+0.006051 test-rmse:0.699241+0.035088
## [100] train-rmse:0.605881+0.006056 test-rmse:0.699096+0.034797
## [101] train-rmse:0.605294+0.006073 test-rmse:0.699064+0.034712
## [102] train-rmse:0.604716+0.006077 test-rmse:0.698852+0.034818
## [103] train-rmse:0.604141+0.006089 test-rmse:0.698445+0.034651
## [104] train-rmse:0.603561+0.006079 test-rmse:0.698568+0.034368
## [105] train-rmse:0.602989+0.006077 test-rmse:0.698331+0.035074
## [106] train-rmse:0.602426+0.006092 test-rmse:0.698404+0.034735
## [107] train-rmse:0.601868+0.006103 test-rmse:0.698027+0.034732
## [108] train-rmse:0.601326+0.006113 test-rmse:0.696931+0.034822
## [109] train-rmse:0.600794+0.006125 test-rmse:0.696293+0.036232
## [110] train-rmse:0.600262+0.006137 test-rmse:0.696492+0.036413
## [111] train-rmse:0.599736+0.006148 test-rmse:0.695943+0.035746
## [112] train-rmse:0.599198+0.006157 test-rmse:0.695242+0.035929
## [113] train-rmse:0.598668+0.006164 test-rmse:0.695176+0.034966
## [114] train-rmse:0.598147+0.006162 test-rmse:0.695062+0.034514
## [115] train-rmse:0.597644+0.006164 test-rmse:0.694483+0.034345
## [116] train-rmse:0.597149+0.006168 test-rmse:0.693429+0.034010
## [117] train-rmse:0.596662+0.006173 test-rmse:0.693735+0.034815
## [118] train-rmse:0.596162+0.006171 test-rmse:0.693557+0.034445
## [119] train-rmse:0.595661+0.006172 test-rmse:0.693282+0.034549
## [120] train-rmse:0.595165+0.006179 test-rmse:0.693085+0.034979
## [121] train-rmse:0.594668+0.006201 test-rmse:0.693266+0.035327
## [122] train-rmse:0.594187+0.006227 test-rmse:0.692464+0.035036
## [123] train-rmse:0.593707+0.006229 test-rmse:0.693157+0.034668
## [124] train-rmse:0.593238+0.006240 test-rmse:0.692885+0.034857
## [125] train-rmse:0.592771+0.006252 test-rmse:0.692599+0.034899
## [126] train-rmse:0.592304+0.006259 test-rmse:0.691296+0.034455
## [127] train-rmse:0.591850+0.006267 test-rmse:0.691446+0.034482
## [128] train-rmse:0.591398+0.006276 test-rmse:0.691240+0.034880
## [129] train-rmse:0.590949+0.006291 test-rmse:0.691619+0.034896
## [130] train-rmse:0.590500+0.006306 test-rmse:0.691300+0.034942
## [131] train-rmse:0.590045+0.006332 test-rmse:0.690502+0.035015
## [132] train-rmse:0.589596+0.006344 test-rmse:0.690436+0.034774
## [133] train-rmse:0.589156+0.006359 test-rmse:0.690155+0.034389
## [134] train-rmse:0.588716+0.006374 test-rmse:0.690363+0.033846
## [135] train-rmse:0.588273+0.006383 test-rmse:0.690404+0.034608
## [136] train-rmse:0.587849+0.006400 test-rmse:0.689864+0.034712
## [137] train-rmse:0.587428+0.006404 test-rmse:0.689933+0.034698
## [138] train-rmse:0.587011+0.006407 test-rmse:0.689830+0.034778
## [139] train-rmse:0.586605+0.006414 test-rmse:0.689027+0.035465
## [140] train-rmse:0.586197+0.006426 test-rmse:0.688852+0.035568
## [141] train-rmse:0.585786+0.006432 test-rmse:0.688562+0.036033
## [142] train-rmse:0.585377+0.006438 test-rmse:0.688852+0.035966
## [143] train-rmse:0.584984+0.006454 test-rmse:0.688303+0.036058
## [144] train-rmse:0.584592+0.006469 test-rmse:0.687963+0.036383
## [145] train-rmse:0.584201+0.006483 test-rmse:0.688647+0.036717
## [146] train-rmse:0.583808+0.006494 test-rmse:0.688575+0.036570
## [147] train-rmse:0.583414+0.006519 test-rmse:0.687991+0.036486
## [148] train-rmse:0.583021+0.006529 test-rmse:0.688394+0.036054
## [149] train-rmse:0.582632+0.006544 test-rmse:0.688149+0.036072
## [150] train-rmse:0.582248+0.006556 test-rmse:0.688487+0.035893
## Stopping. Best iteration:
## [144] train-rmse:0.584592+0.006469 test-rmse:0.687963+0.036383
##
## 29
## [1] train-rmse:1.381152+0.013794 test-rmse:1.385545+0.070089
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.205546+0.011106 test-rmse:1.214618+0.071177
## [3] train-rmse:1.070370+0.008999 test-rmse:1.086819+0.072655
## [4] train-rmse:0.967096+0.007298 test-rmse:0.988236+0.070515
## [5] train-rmse:0.889777+0.006269 test-rmse:0.917055+0.068005
## [6] train-rmse:0.830374+0.004944 test-rmse:0.865415+0.067411
## [7] train-rmse:0.786727+0.004515 test-rmse:0.823906+0.062739
## [8] train-rmse:0.752379+0.005424 test-rmse:0.792678+0.059628
## [9] train-rmse:0.725312+0.002875 test-rmse:0.771246+0.059403
## [10] train-rmse:0.706320+0.003313 test-rmse:0.756835+0.055024
## [11] train-rmse:0.690827+0.005467 test-rmse:0.744696+0.051441
## [12] train-rmse:0.678368+0.004819 test-rmse:0.738996+0.050089
## [13] train-rmse:0.668778+0.004373 test-rmse:0.732669+0.046767
## [14] train-rmse:0.661203+0.004135 test-rmse:0.727145+0.042348
## [15] train-rmse:0.654784+0.004035 test-rmse:0.723435+0.042446
## [16] train-rmse:0.648016+0.004088 test-rmse:0.717961+0.045922
## [17] train-rmse:0.642913+0.004134 test-rmse:0.715700+0.044575
## [18] train-rmse:0.637134+0.005851 test-rmse:0.713817+0.043296
## [19] train-rmse:0.632994+0.006354 test-rmse:0.712612+0.043865
## [20] train-rmse:0.627287+0.005095 test-rmse:0.709299+0.043454
## [21] train-rmse:0.623194+0.004763 test-rmse:0.707318+0.042584
## [22] train-rmse:0.618689+0.006066 test-rmse:0.704645+0.043437
## [23] train-rmse:0.615181+0.006158 test-rmse:0.702498+0.042934
## [24] train-rmse:0.611689+0.005660 test-rmse:0.702274+0.043369
## [25] train-rmse:0.608516+0.005633 test-rmse:0.700738+0.042341
## [26] train-rmse:0.604494+0.005305 test-rmse:0.699777+0.042496
## [27] train-rmse:0.601011+0.004562 test-rmse:0.699997+0.043271
## [28] train-rmse:0.598160+0.004687 test-rmse:0.700299+0.043993
## [29] train-rmse:0.594562+0.004534 test-rmse:0.697993+0.042587
## [30] train-rmse:0.590728+0.005017 test-rmse:0.697783+0.044498
## [31] train-rmse:0.587521+0.005793 test-rmse:0.697504+0.044488
## [32] train-rmse:0.584953+0.005921 test-rmse:0.697248+0.043401
## [33] train-rmse:0.582346+0.005526 test-rmse:0.695976+0.042695
## [34] train-rmse:0.578594+0.004415 test-rmse:0.694102+0.041517
## [35] train-rmse:0.576103+0.004165 test-rmse:0.695033+0.043180
## [36] train-rmse:0.572771+0.005000 test-rmse:0.691752+0.043293
## [37] train-rmse:0.570438+0.005418 test-rmse:0.689661+0.043278
## [38] train-rmse:0.566458+0.004903 test-rmse:0.687791+0.040593
## [39] train-rmse:0.564179+0.005037 test-rmse:0.687049+0.041009
## [40] train-rmse:0.559871+0.004843 test-rmse:0.684979+0.040844
## [41] train-rmse:0.556697+0.005037 test-rmse:0.682767+0.040466
## [42] train-rmse:0.554724+0.004973 test-rmse:0.681667+0.042077
## [43] train-rmse:0.552855+0.005194 test-rmse:0.681252+0.040983
## [44] train-rmse:0.550355+0.005236 test-rmse:0.680396+0.041330
## [45] train-rmse:0.547745+0.005439 test-rmse:0.679157+0.040883
## [46] train-rmse:0.544175+0.003673 test-rmse:0.677037+0.041713
## [47] train-rmse:0.542435+0.003524 test-rmse:0.677541+0.042440
## [48] train-rmse:0.539075+0.004918 test-rmse:0.676432+0.042537
## [49] train-rmse:0.537355+0.004901 test-rmse:0.676721+0.043074
## [50] train-rmse:0.535595+0.005103 test-rmse:0.675703+0.041712
## [51] train-rmse:0.533000+0.004696 test-rmse:0.673199+0.041084
## [52] train-rmse:0.530137+0.003742 test-rmse:0.671255+0.040879
## [53] train-rmse:0.527750+0.003751 test-rmse:0.669868+0.044059
## [54] train-rmse:0.524692+0.005300 test-rmse:0.669369+0.043711
## [55] train-rmse:0.522590+0.005064 test-rmse:0.669095+0.043031
## [56] train-rmse:0.521021+0.004948 test-rmse:0.669094+0.043434
## [57] train-rmse:0.518889+0.006033 test-rmse:0.668772+0.042817
## [58] train-rmse:0.517499+0.006124 test-rmse:0.668738+0.043662
## [59] train-rmse:0.515891+0.006407 test-rmse:0.666942+0.043333
## [60] train-rmse:0.514625+0.006539 test-rmse:0.666467+0.043914
## [61] train-rmse:0.512952+0.006500 test-rmse:0.664941+0.043964
## [62] train-rmse:0.510572+0.007904 test-rmse:0.665344+0.043631
## [63] train-rmse:0.508968+0.007813 test-rmse:0.665755+0.043692
## [64] train-rmse:0.507492+0.008420 test-rmse:0.665305+0.043645
## [65] train-rmse:0.506048+0.008213 test-rmse:0.665448+0.044466
## [66] train-rmse:0.503193+0.008770 test-rmse:0.662994+0.042446
## [67] train-rmse:0.501717+0.008599 test-rmse:0.661510+0.042643
## [68] train-rmse:0.500096+0.009169 test-rmse:0.660838+0.042311
## [69] train-rmse:0.498601+0.009167 test-rmse:0.660233+0.042876
## [70] train-rmse:0.496605+0.009142 test-rmse:0.658681+0.042878
## [71] train-rmse:0.495387+0.009051 test-rmse:0.659409+0.042725
## [72] train-rmse:0.493136+0.009480 test-rmse:0.657787+0.041955
## [73] train-rmse:0.491563+0.009193 test-rmse:0.656972+0.042401
## [74] train-rmse:0.489832+0.008805 test-rmse:0.656140+0.043518
## [75] train-rmse:0.487744+0.009393 test-rmse:0.654178+0.041762
## [76] train-rmse:0.486014+0.009298 test-rmse:0.653260+0.042161
## [77] train-rmse:0.484672+0.009749 test-rmse:0.651855+0.041505
## [78] train-rmse:0.483490+0.009862 test-rmse:0.650946+0.041476
## [79] train-rmse:0.481782+0.009490 test-rmse:0.650423+0.040993
## [80] train-rmse:0.479950+0.008795 test-rmse:0.649715+0.040595
## [81] train-rmse:0.478767+0.009187 test-rmse:0.648408+0.040322
## [82] train-rmse:0.477814+0.009180 test-rmse:0.647894+0.040163
## [83] train-rmse:0.476336+0.008973 test-rmse:0.647929+0.040395
## [84] train-rmse:0.474426+0.008681 test-rmse:0.647780+0.040663
## [85] train-rmse:0.473503+0.008640 test-rmse:0.647856+0.040856
## [86] train-rmse:0.472595+0.008700 test-rmse:0.648709+0.040961
## [87] train-rmse:0.471782+0.008760 test-rmse:0.649093+0.041462
## [88] train-rmse:0.470762+0.008888 test-rmse:0.649824+0.042191
## [89] train-rmse:0.469704+0.008904 test-rmse:0.648775+0.042019
## [90] train-rmse:0.468241+0.008523 test-rmse:0.646478+0.041458
## [91] train-rmse:0.466348+0.009685 test-rmse:0.646263+0.042014
## [92] train-rmse:0.464751+0.009538 test-rmse:0.645191+0.042281
## [93] train-rmse:0.463306+0.009886 test-rmse:0.645527+0.041400
## [94] train-rmse:0.462196+0.010130 test-rmse:0.644410+0.040499
## [95] train-rmse:0.460361+0.010351 test-rmse:0.643281+0.039760
## [96] train-rmse:0.459493+0.010335 test-rmse:0.642911+0.039778
## [97] train-rmse:0.457975+0.009714 test-rmse:0.642624+0.041013
## [98] train-rmse:0.456905+0.010253 test-rmse:0.642600+0.041527
## [99] train-rmse:0.455754+0.009935 test-rmse:0.642848+0.041551
## [100] train-rmse:0.455004+0.010049 test-rmse:0.642186+0.041289
## [101] train-rmse:0.453723+0.010382 test-rmse:0.641875+0.041602
## [102] train-rmse:0.452093+0.010608 test-rmse:0.641259+0.040711
## [103] train-rmse:0.449737+0.010891 test-rmse:0.640597+0.040998
## [104] train-rmse:0.447937+0.011277 test-rmse:0.640544+0.040691
## [105] train-rmse:0.446599+0.010890 test-rmse:0.640005+0.041288
## [106] train-rmse:0.445230+0.011505 test-rmse:0.639909+0.041170
## [107] train-rmse:0.444375+0.011595 test-rmse:0.639396+0.041663
## [108] train-rmse:0.443162+0.011569 test-rmse:0.638341+0.040901
## [109] train-rmse:0.442235+0.011887 test-rmse:0.637447+0.041069
## [110] train-rmse:0.441526+0.011884 test-rmse:0.638646+0.041126
## [111] train-rmse:0.440876+0.011840 test-rmse:0.639135+0.041754
## [112] train-rmse:0.440197+0.011936 test-rmse:0.639794+0.042115
## [113] train-rmse:0.439367+0.012206 test-rmse:0.640127+0.042390
## [114] train-rmse:0.438178+0.011820 test-rmse:0.639944+0.042429
## [115] train-rmse:0.437096+0.011883 test-rmse:0.638592+0.043672
## Stopping. Best iteration:
## [109] train-rmse:0.442235+0.011887 test-rmse:0.637447+0.041069
##
## 30
## [1] train-rmse:1.377689+0.013505 test-rmse:1.383696+0.069084
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.196955+0.010890 test-rmse:1.211703+0.069233
## [3] train-rmse:1.057188+0.008910 test-rmse:1.080950+0.069180
## [4] train-rmse:0.949260+0.007848 test-rmse:0.981128+0.067817
## [5] train-rmse:0.865711+0.005727 test-rmse:0.901350+0.068007
## [6] train-rmse:0.804283+0.004988 test-rmse:0.849308+0.066754
## [7] train-rmse:0.755389+0.004763 test-rmse:0.807006+0.067751
## [8] train-rmse:0.717349+0.006294 test-rmse:0.775613+0.060277
## [9] train-rmse:0.684490+0.004345 test-rmse:0.752724+0.060496
## [10] train-rmse:0.659674+0.004608 test-rmse:0.734018+0.054336
## [11] train-rmse:0.641251+0.004593 test-rmse:0.721917+0.053745
## [12] train-rmse:0.627161+0.005627 test-rmse:0.712229+0.052537
## [13] train-rmse:0.615472+0.006454 test-rmse:0.708165+0.053020
## [14] train-rmse:0.604697+0.005190 test-rmse:0.701570+0.052975
## [15] train-rmse:0.595836+0.006018 test-rmse:0.697212+0.049975
## [16] train-rmse:0.588803+0.005803 test-rmse:0.695068+0.054905
## [17] train-rmse:0.582087+0.006586 test-rmse:0.691844+0.054201
## [18] train-rmse:0.574600+0.005666 test-rmse:0.690409+0.052098
## [19] train-rmse:0.567517+0.006011 test-rmse:0.685341+0.051740
## [20] train-rmse:0.559710+0.005847 test-rmse:0.680551+0.051985
## [21] train-rmse:0.551681+0.005761 test-rmse:0.678494+0.053722
## [22] train-rmse:0.546622+0.005142 test-rmse:0.677212+0.055172
## [23] train-rmse:0.542335+0.005035 test-rmse:0.675585+0.054924
## [24] train-rmse:0.537238+0.005613 test-rmse:0.675267+0.053796
## [25] train-rmse:0.530844+0.002332 test-rmse:0.673708+0.055517
## [26] train-rmse:0.526897+0.002317 test-rmse:0.669051+0.057532
## [27] train-rmse:0.521344+0.002385 test-rmse:0.668508+0.057055
## [28] train-rmse:0.516885+0.002386 test-rmse:0.667070+0.054223
## [29] train-rmse:0.510896+0.002144 test-rmse:0.666412+0.055325
## [30] train-rmse:0.507661+0.002408 test-rmse:0.664615+0.056703
## [31] train-rmse:0.503688+0.003448 test-rmse:0.665651+0.056609
## [32] train-rmse:0.499886+0.003245 test-rmse:0.663934+0.056922
## [33] train-rmse:0.495424+0.002552 test-rmse:0.661493+0.057234
## [34] train-rmse:0.491305+0.003867 test-rmse:0.659735+0.057035
## [35] train-rmse:0.487567+0.004994 test-rmse:0.658760+0.056682
## [36] train-rmse:0.482773+0.004181 test-rmse:0.656536+0.055673
## [37] train-rmse:0.478964+0.003648 test-rmse:0.656562+0.055020
## [38] train-rmse:0.475311+0.003707 test-rmse:0.655973+0.055075
## [39] train-rmse:0.470998+0.004235 test-rmse:0.655620+0.055714
## [40] train-rmse:0.467651+0.005391 test-rmse:0.654725+0.058920
## [41] train-rmse:0.463389+0.005202 test-rmse:0.652133+0.057947
## [42] train-rmse:0.460566+0.005605 test-rmse:0.651532+0.058015
## [43] train-rmse:0.458499+0.005785 test-rmse:0.651623+0.058521
## [44] train-rmse:0.455107+0.005110 test-rmse:0.651286+0.059391
## [45] train-rmse:0.449961+0.005796 test-rmse:0.648788+0.057543
## [46] train-rmse:0.447185+0.005541 test-rmse:0.647899+0.057186
## [47] train-rmse:0.444272+0.005631 test-rmse:0.648217+0.058244
## [48] train-rmse:0.442560+0.005808 test-rmse:0.646672+0.057749
## [49] train-rmse:0.440059+0.006056 test-rmse:0.645924+0.058248
## [50] train-rmse:0.435990+0.005665 test-rmse:0.645116+0.059402
## [51] train-rmse:0.433789+0.005492 test-rmse:0.645592+0.059262
## [52] train-rmse:0.431527+0.005375 test-rmse:0.644916+0.058384
## [53] train-rmse:0.428256+0.006475 test-rmse:0.644604+0.058317
## [54] train-rmse:0.426060+0.006601 test-rmse:0.644415+0.059294
## [55] train-rmse:0.424228+0.006196 test-rmse:0.643309+0.059425
## [56] train-rmse:0.421406+0.006717 test-rmse:0.642671+0.059876
## [57] train-rmse:0.418043+0.007923 test-rmse:0.642604+0.058548
## [58] train-rmse:0.416234+0.008309 test-rmse:0.642483+0.058775
## [59] train-rmse:0.410913+0.007329 test-rmse:0.639091+0.057384
## [60] train-rmse:0.408938+0.008470 test-rmse:0.639250+0.057453
## [61] train-rmse:0.406954+0.008554 test-rmse:0.639399+0.057180
## [62] train-rmse:0.405414+0.008822 test-rmse:0.639140+0.057018
## [63] train-rmse:0.403392+0.008605 test-rmse:0.640857+0.057661
## [64] train-rmse:0.399817+0.007513 test-rmse:0.638527+0.056725
## [65] train-rmse:0.397755+0.007278 test-rmse:0.636585+0.056991
## [66] train-rmse:0.393746+0.006660 test-rmse:0.636644+0.056797
## [67] train-rmse:0.390815+0.007144 test-rmse:0.636564+0.056882
## [68] train-rmse:0.388395+0.007244 test-rmse:0.636082+0.057967
## [69] train-rmse:0.385942+0.007891 test-rmse:0.637157+0.058299
## [70] train-rmse:0.383760+0.008863 test-rmse:0.638445+0.058479
## [71] train-rmse:0.381746+0.009583 test-rmse:0.638472+0.058447
## [72] train-rmse:0.380036+0.009568 test-rmse:0.637697+0.058878
## [73] train-rmse:0.376688+0.009897 test-rmse:0.635977+0.057916
## [74] train-rmse:0.374820+0.010302 test-rmse:0.636674+0.059067
## [75] train-rmse:0.373227+0.010446 test-rmse:0.635886+0.058843
## [76] train-rmse:0.371546+0.011413 test-rmse:0.634814+0.057871
## [77] train-rmse:0.369672+0.011753 test-rmse:0.635472+0.058511
## [78] train-rmse:0.368488+0.012002 test-rmse:0.635570+0.058686
## [79] train-rmse:0.365714+0.012327 test-rmse:0.634646+0.058606
## [80] train-rmse:0.363539+0.012451 test-rmse:0.635519+0.058697
## [81] train-rmse:0.361989+0.013308 test-rmse:0.634660+0.058489
## [82] train-rmse:0.359874+0.013607 test-rmse:0.634355+0.059217
## [83] train-rmse:0.356694+0.013263 test-rmse:0.631489+0.057770
## [84] train-rmse:0.354205+0.011399 test-rmse:0.631486+0.058245
## [85] train-rmse:0.351628+0.010556 test-rmse:0.629813+0.057917
## [86] train-rmse:0.349725+0.010145 test-rmse:0.630127+0.058503
## [87] train-rmse:0.348152+0.010730 test-rmse:0.630763+0.058683
## [88] train-rmse:0.345999+0.011348 test-rmse:0.631369+0.058778
## [89] train-rmse:0.343922+0.011421 test-rmse:0.631355+0.057896
## [90] train-rmse:0.342914+0.011463 test-rmse:0.631891+0.058483
## [91] train-rmse:0.341803+0.011510 test-rmse:0.632389+0.059210
## Stopping. Best iteration:
## [85] train-rmse:0.351628+0.010556 test-rmse:0.629813+0.057917
##
## 31
## [1] train-rmse:1.374140+0.013558 test-rmse:1.385912+0.069554
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.188718+0.010164 test-rmse:1.210333+0.070713
## [3] train-rmse:1.042281+0.007696 test-rmse:1.072393+0.071886
## [4] train-rmse:0.929049+0.005695 test-rmse:0.972185+0.073727
## [5] train-rmse:0.842377+0.004845 test-rmse:0.896957+0.073317
## [6] train-rmse:0.775246+0.005049 test-rmse:0.836303+0.072514
## [7] train-rmse:0.724517+0.004479 test-rmse:0.793059+0.072289
## [8] train-rmse:0.684845+0.005078 test-rmse:0.764155+0.068944
## [9] train-rmse:0.652293+0.005858 test-rmse:0.744665+0.069025
## [10] train-rmse:0.623760+0.009413 test-rmse:0.727359+0.067284
## [11] train-rmse:0.601540+0.008411 test-rmse:0.711324+0.064888
## [12] train-rmse:0.584144+0.007282 test-rmse:0.700986+0.061470
## [13] train-rmse:0.570866+0.007720 test-rmse:0.696907+0.061795
## [14] train-rmse:0.557208+0.004032 test-rmse:0.687709+0.061509
## [15] train-rmse:0.546925+0.003963 test-rmse:0.684691+0.064238
## [16] train-rmse:0.539361+0.003805 test-rmse:0.681121+0.064887
## [17] train-rmse:0.528390+0.005729 test-rmse:0.675703+0.059815
## [18] train-rmse:0.521994+0.005455 test-rmse:0.677699+0.061597
## [19] train-rmse:0.511626+0.006696 test-rmse:0.673847+0.063449
## [20] train-rmse:0.504913+0.007794 test-rmse:0.672306+0.063992
## [21] train-rmse:0.497965+0.008132 test-rmse:0.670806+0.061571
## [22] train-rmse:0.490490+0.008719 test-rmse:0.669223+0.063025
## [23] train-rmse:0.483388+0.008804 test-rmse:0.664858+0.063086
## [24] train-rmse:0.475947+0.011473 test-rmse:0.665131+0.063838
## [25] train-rmse:0.470878+0.011239 test-rmse:0.665010+0.064709
## [26] train-rmse:0.465056+0.013001 test-rmse:0.665033+0.064601
## [27] train-rmse:0.457351+0.012252 test-rmse:0.662420+0.064474
## [28] train-rmse:0.451477+0.011691 test-rmse:0.659641+0.063008
## [29] train-rmse:0.445987+0.012168 test-rmse:0.656363+0.063122
## [30] train-rmse:0.441604+0.012725 test-rmse:0.654328+0.064195
## [31] train-rmse:0.436571+0.012837 test-rmse:0.654702+0.065466
## [32] train-rmse:0.430653+0.014293 test-rmse:0.654931+0.065674
## [33] train-rmse:0.426434+0.013631 test-rmse:0.653207+0.066948
## [34] train-rmse:0.422087+0.014593 test-rmse:0.651638+0.066108
## [35] train-rmse:0.417386+0.015324 test-rmse:0.650708+0.064304
## [36] train-rmse:0.410227+0.012043 test-rmse:0.645464+0.063680
## [37] train-rmse:0.407283+0.012427 test-rmse:0.645264+0.062202
## [38] train-rmse:0.401626+0.010892 test-rmse:0.643265+0.061582
## [39] train-rmse:0.396583+0.011729 test-rmse:0.641825+0.060999
## [40] train-rmse:0.392175+0.010871 test-rmse:0.642463+0.061703
## [41] train-rmse:0.387745+0.011531 test-rmse:0.640945+0.061034
## [42] train-rmse:0.383739+0.012606 test-rmse:0.639412+0.062458
## [43] train-rmse:0.380912+0.012810 test-rmse:0.639562+0.063157
## [44] train-rmse:0.376963+0.012620 test-rmse:0.638993+0.063228
## [45] train-rmse:0.373420+0.011834 test-rmse:0.638380+0.062470
## [46] train-rmse:0.371016+0.012128 test-rmse:0.638118+0.062686
## [47] train-rmse:0.366327+0.012006 test-rmse:0.636172+0.061978
## [48] train-rmse:0.361457+0.012686 test-rmse:0.635203+0.061146
## [49] train-rmse:0.357562+0.013299 test-rmse:0.634971+0.062134
## [50] train-rmse:0.354347+0.013591 test-rmse:0.633323+0.061135
## [51] train-rmse:0.351554+0.013825 test-rmse:0.633324+0.061417
## [52] train-rmse:0.348981+0.014386 test-rmse:0.632570+0.062233
## [53] train-rmse:0.345340+0.013872 test-rmse:0.629997+0.061840
## [54] train-rmse:0.340726+0.013302 test-rmse:0.629607+0.061441
## [55] train-rmse:0.338107+0.012473 test-rmse:0.628619+0.062336
## [56] train-rmse:0.335217+0.011675 test-rmse:0.629485+0.063422
## [57] train-rmse:0.332360+0.013322 test-rmse:0.629641+0.064510
## [58] train-rmse:0.328627+0.014894 test-rmse:0.631894+0.064639
## [59] train-rmse:0.324957+0.015297 test-rmse:0.630870+0.064693
## [60] train-rmse:0.322270+0.015805 test-rmse:0.631598+0.064941
## [61] train-rmse:0.320012+0.015228 test-rmse:0.632042+0.065564
## Stopping. Best iteration:
## [55] train-rmse:0.338107+0.012473 test-rmse:0.628619+0.062336
##
## 32
## [1] train-rmse:1.371737+0.013717 test-rmse:1.382481+0.071587
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.183367+0.010510 test-rmse:1.207745+0.074495
## [3] train-rmse:1.032495+0.007790 test-rmse:1.066764+0.074552
## [4] train-rmse:0.912958+0.006297 test-rmse:0.961372+0.073879
## [5] train-rmse:0.821172+0.007355 test-rmse:0.884816+0.071666
## [6] train-rmse:0.747688+0.007194 test-rmse:0.825300+0.069340
## [7] train-rmse:0.693169+0.006934 test-rmse:0.780451+0.061799
## [8] train-rmse:0.648722+0.005008 test-rmse:0.747707+0.058756
## [9] train-rmse:0.612444+0.004479 test-rmse:0.726888+0.061119
## [10] train-rmse:0.584197+0.004008 test-rmse:0.710457+0.058063
## [11] train-rmse:0.557037+0.007411 test-rmse:0.696638+0.052394
## [12] train-rmse:0.534293+0.005528 test-rmse:0.685138+0.049363
## [13] train-rmse:0.515706+0.008566 test-rmse:0.678902+0.050943
## [14] train-rmse:0.501525+0.007629 test-rmse:0.670562+0.049742
## [15] train-rmse:0.489179+0.006250 test-rmse:0.667505+0.051957
## [16] train-rmse:0.479152+0.006768 test-rmse:0.666465+0.050580
## [17] train-rmse:0.467723+0.004642 test-rmse:0.661479+0.049330
## [18] train-rmse:0.460187+0.005378 test-rmse:0.659784+0.049302
## [19] train-rmse:0.449206+0.006335 test-rmse:0.656405+0.049220
## [20] train-rmse:0.440679+0.007968 test-rmse:0.655979+0.051583
## [21] train-rmse:0.430767+0.009980 test-rmse:0.654517+0.049805
## [22] train-rmse:0.422652+0.011403 test-rmse:0.652675+0.049463
## [23] train-rmse:0.415193+0.011557 test-rmse:0.652215+0.049935
## [24] train-rmse:0.409285+0.012574 test-rmse:0.652541+0.050850
## [25] train-rmse:0.402894+0.012842 test-rmse:0.651906+0.050225
## [26] train-rmse:0.394552+0.014502 test-rmse:0.650712+0.053811
## [27] train-rmse:0.389485+0.014033 test-rmse:0.650481+0.053728
## [28] train-rmse:0.379188+0.015438 test-rmse:0.646474+0.054554
## [29] train-rmse:0.372778+0.015101 test-rmse:0.643435+0.054544
## [30] train-rmse:0.365566+0.014217 test-rmse:0.643744+0.055029
## [31] train-rmse:0.360492+0.014257 test-rmse:0.643133+0.056445
## [32] train-rmse:0.354554+0.015952 test-rmse:0.642396+0.054888
## [33] train-rmse:0.349701+0.013523 test-rmse:0.640813+0.053216
## [34] train-rmse:0.346310+0.013729 test-rmse:0.641401+0.053543
## [35] train-rmse:0.341144+0.013063 test-rmse:0.640131+0.055245
## [36] train-rmse:0.335960+0.014085 test-rmse:0.640243+0.053939
## [37] train-rmse:0.329935+0.015815 test-rmse:0.639653+0.053145
## [38] train-rmse:0.325517+0.015624 test-rmse:0.640412+0.053748
## [39] train-rmse:0.319122+0.015240 test-rmse:0.636577+0.051406
## [40] train-rmse:0.316782+0.015275 test-rmse:0.636310+0.051786
## [41] train-rmse:0.310148+0.014387 test-rmse:0.635039+0.053372
## [42] train-rmse:0.305117+0.015544 test-rmse:0.634478+0.054274
## [43] train-rmse:0.301945+0.015328 test-rmse:0.634364+0.054558
## [44] train-rmse:0.298224+0.015972 test-rmse:0.633512+0.053698
## [45] train-rmse:0.293601+0.016383 test-rmse:0.633899+0.053881
## [46] train-rmse:0.289560+0.016025 test-rmse:0.633460+0.054676
## [47] train-rmse:0.285736+0.016592 test-rmse:0.634294+0.055193
## [48] train-rmse:0.280758+0.016205 test-rmse:0.632801+0.055237
## [49] train-rmse:0.276395+0.016907 test-rmse:0.632937+0.055948
## [50] train-rmse:0.271796+0.016081 test-rmse:0.632371+0.056472
## [51] train-rmse:0.268237+0.015480 test-rmse:0.631903+0.056473
## [52] train-rmse:0.264742+0.015862 test-rmse:0.632877+0.055720
## [53] train-rmse:0.262415+0.015455 test-rmse:0.631892+0.056135
## [54] train-rmse:0.258875+0.015382 test-rmse:0.632812+0.056227
## [55] train-rmse:0.255568+0.016314 test-rmse:0.632165+0.055800
## [56] train-rmse:0.252056+0.016297 test-rmse:0.631189+0.055437
## [57] train-rmse:0.248848+0.015936 test-rmse:0.630166+0.054508
## [58] train-rmse:0.244375+0.015694 test-rmse:0.630487+0.055080
## [59] train-rmse:0.241701+0.015122 test-rmse:0.629913+0.055957
## [60] train-rmse:0.238718+0.016491 test-rmse:0.631690+0.056014
## [61] train-rmse:0.235604+0.015872 test-rmse:0.632148+0.056278
## [62] train-rmse:0.232156+0.015727 test-rmse:0.631975+0.055947
## [63] train-rmse:0.230021+0.015377 test-rmse:0.632156+0.056369
## [64] train-rmse:0.227286+0.016396 test-rmse:0.632138+0.054912
## [65] train-rmse:0.224661+0.015789 test-rmse:0.631243+0.054896
## Stopping. Best iteration:
## [59] train-rmse:0.241701+0.015122 test-rmse:0.629913+0.055957
##
## 33
## [1] train-rmse:1.369299+0.013319 test-rmse:1.383935+0.073041
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.178851+0.010051 test-rmse:1.208476+0.074081
## [3] train-rmse:1.026902+0.008393 test-rmse:1.076419+0.072766
## [4] train-rmse:0.902467+0.007175 test-rmse:0.967013+0.072959
## [5] train-rmse:0.804315+0.006455 test-rmse:0.885117+0.073808
## [6] train-rmse:0.727730+0.008016 test-rmse:0.825039+0.072557
## [7] train-rmse:0.666528+0.008146 test-rmse:0.780462+0.073575
## [8] train-rmse:0.619954+0.010243 test-rmse:0.748307+0.072161
## [9] train-rmse:0.583766+0.011088 test-rmse:0.722945+0.068779
## [10] train-rmse:0.552749+0.009930 test-rmse:0.704124+0.065431
## [11] train-rmse:0.521366+0.015466 test-rmse:0.693212+0.065520
## [12] train-rmse:0.497657+0.018374 test-rmse:0.681722+0.066955
## [13] train-rmse:0.474107+0.016365 test-rmse:0.672163+0.065281
## [14] train-rmse:0.455821+0.017481 test-rmse:0.663661+0.064288
## [15] train-rmse:0.440288+0.015399 test-rmse:0.658535+0.063045
## [16] train-rmse:0.429912+0.014612 test-rmse:0.656167+0.062215
## [17] train-rmse:0.416841+0.015496 test-rmse:0.652886+0.061560
## [18] train-rmse:0.405797+0.016383 test-rmse:0.651818+0.060676
## [19] train-rmse:0.397221+0.017985 test-rmse:0.648600+0.062021
## [20] train-rmse:0.389143+0.017539 test-rmse:0.647018+0.061777
## [21] train-rmse:0.380174+0.016638 test-rmse:0.644117+0.060432
## [22] train-rmse:0.368289+0.016985 test-rmse:0.641106+0.059281
## [23] train-rmse:0.361678+0.016819 test-rmse:0.638180+0.059360
## [24] train-rmse:0.355966+0.016096 test-rmse:0.638778+0.060102
## [25] train-rmse:0.349219+0.016293 test-rmse:0.639196+0.061899
## [26] train-rmse:0.342142+0.015904 test-rmse:0.637909+0.063006
## [27] train-rmse:0.336531+0.015815 test-rmse:0.636201+0.061839
## [28] train-rmse:0.330706+0.016099 test-rmse:0.637369+0.062835
## [29] train-rmse:0.323052+0.015276 test-rmse:0.636075+0.062369
## [30] train-rmse:0.316004+0.014719 test-rmse:0.633884+0.061514
## [31] train-rmse:0.309232+0.015304 test-rmse:0.632045+0.062732
## [32] train-rmse:0.304256+0.015534 test-rmse:0.631714+0.062613
## [33] train-rmse:0.298424+0.016621 test-rmse:0.632398+0.062708
## [34] train-rmse:0.293360+0.017264 test-rmse:0.631649+0.063608
## [35] train-rmse:0.288013+0.016833 test-rmse:0.631635+0.064272
## [36] train-rmse:0.282974+0.016649 test-rmse:0.631968+0.062668
## [37] train-rmse:0.277625+0.016213 test-rmse:0.631444+0.062743
## [38] train-rmse:0.273908+0.016830 test-rmse:0.632122+0.062879
## [39] train-rmse:0.268171+0.017098 test-rmse:0.631465+0.062164
## [40] train-rmse:0.262965+0.016163 test-rmse:0.630774+0.061318
## [41] train-rmse:0.259204+0.016268 test-rmse:0.629965+0.061029
## [42] train-rmse:0.253680+0.017570 test-rmse:0.627604+0.059937
## [43] train-rmse:0.249258+0.018379 test-rmse:0.628720+0.059700
## [44] train-rmse:0.243864+0.019265 test-rmse:0.628863+0.059117
## [45] train-rmse:0.238500+0.018627 test-rmse:0.627106+0.058563
## [46] train-rmse:0.235347+0.018646 test-rmse:0.626499+0.059253
## [47] train-rmse:0.230570+0.018190 test-rmse:0.626704+0.059787
## [48] train-rmse:0.224877+0.016785 test-rmse:0.625881+0.058474
## [49] train-rmse:0.220649+0.015456 test-rmse:0.625600+0.058211
## [50] train-rmse:0.216888+0.015571 test-rmse:0.624922+0.057607
## [51] train-rmse:0.212282+0.013998 test-rmse:0.625286+0.056536
## [52] train-rmse:0.208674+0.014599 test-rmse:0.624914+0.056100
## [53] train-rmse:0.205358+0.014579 test-rmse:0.624765+0.055750
## [54] train-rmse:0.202831+0.013845 test-rmse:0.624363+0.055615
## [55] train-rmse:0.198349+0.014129 test-rmse:0.625510+0.056523
## [56] train-rmse:0.194406+0.013398 test-rmse:0.624571+0.056977
## [57] train-rmse:0.190231+0.013389 test-rmse:0.624544+0.057701
## [58] train-rmse:0.187534+0.013805 test-rmse:0.624954+0.057558
## [59] train-rmse:0.185145+0.014186 test-rmse:0.624524+0.057107
## [60] train-rmse:0.181940+0.014133 test-rmse:0.623582+0.056850
## [61] train-rmse:0.179003+0.013914 test-rmse:0.623995+0.056889
## [62] train-rmse:0.175315+0.013276 test-rmse:0.624975+0.056433
## [63] train-rmse:0.171953+0.012572 test-rmse:0.625553+0.055630
## [64] train-rmse:0.168803+0.013079 test-rmse:0.625687+0.055942
## [65] train-rmse:0.165987+0.013212 test-rmse:0.625522+0.056613
## [66] train-rmse:0.163609+0.013671 test-rmse:0.625341+0.057017
## Stopping. Best iteration:
## [60] train-rmse:0.181940+0.014133 test-rmse:0.623582+0.056850
##
## 34
## [1] train-rmse:1.367508+0.012971 test-rmse:1.382972+0.073171
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.174925+0.010492 test-rmse:1.205867+0.072856
## [3] train-rmse:1.018375+0.006844 test-rmse:1.063852+0.072783
## [4] train-rmse:0.891502+0.006216 test-rmse:0.956367+0.073611
## [5] train-rmse:0.788993+0.004389 test-rmse:0.874497+0.074849
## [6] train-rmse:0.706740+0.003714 test-rmse:0.810637+0.068332
## [7] train-rmse:0.643878+0.006598 test-rmse:0.763809+0.064901
## [8] train-rmse:0.590340+0.009281 test-rmse:0.732095+0.063686
## [9] train-rmse:0.549580+0.009640 test-rmse:0.708588+0.060282
## [10] train-rmse:0.514029+0.007851 test-rmse:0.691000+0.055955
## [11] train-rmse:0.487112+0.007434 test-rmse:0.676693+0.053916
## [12] train-rmse:0.463121+0.007039 test-rmse:0.669117+0.053909
## [13] train-rmse:0.436171+0.005407 test-rmse:0.655606+0.049824
## [14] train-rmse:0.418980+0.008763 test-rmse:0.650225+0.049764
## [15] train-rmse:0.402616+0.012518 test-rmse:0.646746+0.049313
## [16] train-rmse:0.391259+0.012018 test-rmse:0.643775+0.048931
## [17] train-rmse:0.380675+0.010168 test-rmse:0.642887+0.049582
## [18] train-rmse:0.365791+0.013522 test-rmse:0.641708+0.052245
## [19] train-rmse:0.355346+0.017153 test-rmse:0.640823+0.052548
## [20] train-rmse:0.346446+0.018281 test-rmse:0.640830+0.053732
## [21] train-rmse:0.336795+0.018207 test-rmse:0.639939+0.052044
## [22] train-rmse:0.326902+0.018084 test-rmse:0.640433+0.053178
## [23] train-rmse:0.319370+0.018371 test-rmse:0.641315+0.053130
## [24] train-rmse:0.307753+0.018352 test-rmse:0.641528+0.051347
## [25] train-rmse:0.299336+0.017028 test-rmse:0.641650+0.053532
## [26] train-rmse:0.291170+0.017519 test-rmse:0.641668+0.052333
## [27] train-rmse:0.282842+0.017983 test-rmse:0.638759+0.052555
## [28] train-rmse:0.277609+0.019193 test-rmse:0.639485+0.053123
## [29] train-rmse:0.268603+0.016627 test-rmse:0.638162+0.052858
## [30] train-rmse:0.261750+0.016500 test-rmse:0.639315+0.053694
## [31] train-rmse:0.255907+0.017319 test-rmse:0.640741+0.052999
## [32] train-rmse:0.247124+0.016348 test-rmse:0.637399+0.051426
## [33] train-rmse:0.240828+0.017095 test-rmse:0.635835+0.051582
## [34] train-rmse:0.234026+0.016034 test-rmse:0.634394+0.052542
## [35] train-rmse:0.229210+0.016209 test-rmse:0.633535+0.051958
## [36] train-rmse:0.225336+0.015688 test-rmse:0.634296+0.051681
## [37] train-rmse:0.220409+0.016659 test-rmse:0.635244+0.053069
## [38] train-rmse:0.215420+0.015577 test-rmse:0.635134+0.053546
## [39] train-rmse:0.211252+0.017188 test-rmse:0.634491+0.053165
## [40] train-rmse:0.205625+0.017471 test-rmse:0.634556+0.053617
## [41] train-rmse:0.200205+0.017932 test-rmse:0.634921+0.055068
## Stopping. Best iteration:
## [35] train-rmse:0.229210+0.016209 test-rmse:0.633535+0.051958
##
## 35
## [1] train-rmse:1.362617+0.013555 test-rmse:1.363772+0.070103
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.180459+0.010953 test-rmse:1.183166+0.069207
## [3] train-rmse:1.046911+0.009026 test-rmse:1.052008+0.068259
## [4] train-rmse:0.950642+0.008004 test-rmse:0.962382+0.065886
## [5] train-rmse:0.881354+0.007228 test-rmse:0.895477+0.060690
## [6] train-rmse:0.832616+0.006823 test-rmse:0.850384+0.054310
## [7] train-rmse:0.797906+0.006443 test-rmse:0.818925+0.050598
## [8] train-rmse:0.773390+0.006098 test-rmse:0.799472+0.045724
## [9] train-rmse:0.755905+0.005820 test-rmse:0.786088+0.045563
## [10] train-rmse:0.742987+0.005520 test-rmse:0.774645+0.044245
## [11] train-rmse:0.733081+0.005351 test-rmse:0.765991+0.040916
## [12] train-rmse:0.725167+0.005159 test-rmse:0.759541+0.038007
## [13] train-rmse:0.718873+0.005059 test-rmse:0.753248+0.034833
## [14] train-rmse:0.713443+0.005034 test-rmse:0.750018+0.033610
## [15] train-rmse:0.708960+0.005083 test-rmse:0.748903+0.031344
## [16] train-rmse:0.704923+0.005073 test-rmse:0.746701+0.032115
## [17] train-rmse:0.701241+0.004995 test-rmse:0.744931+0.031933
## [18] train-rmse:0.698020+0.004965 test-rmse:0.743561+0.032007
## [19] train-rmse:0.695037+0.004990 test-rmse:0.741521+0.031176
## [20] train-rmse:0.692198+0.005031 test-rmse:0.737136+0.029274
## [21] train-rmse:0.689457+0.005073 test-rmse:0.735294+0.028794
## [22] train-rmse:0.686842+0.005134 test-rmse:0.734353+0.027562
## [23] train-rmse:0.684349+0.005181 test-rmse:0.734801+0.027177
## [24] train-rmse:0.681978+0.005249 test-rmse:0.734829+0.027513
## [25] train-rmse:0.679676+0.005316 test-rmse:0.733585+0.028375
## [26] train-rmse:0.677454+0.005319 test-rmse:0.732493+0.028827
## [27] train-rmse:0.675373+0.005266 test-rmse:0.730182+0.029997
## [28] train-rmse:0.673314+0.005215 test-rmse:0.729966+0.031968
## [29] train-rmse:0.671299+0.005141 test-rmse:0.728548+0.031389
## [30] train-rmse:0.669364+0.005059 test-rmse:0.726978+0.030843
## [31] train-rmse:0.667534+0.005061 test-rmse:0.727751+0.032130
## [32] train-rmse:0.665735+0.005079 test-rmse:0.727583+0.031872
## [33] train-rmse:0.663995+0.005083 test-rmse:0.726769+0.032039
## [34] train-rmse:0.662269+0.005136 test-rmse:0.725206+0.032113
## [35] train-rmse:0.660579+0.005237 test-rmse:0.723371+0.031954
## [36] train-rmse:0.658947+0.005283 test-rmse:0.722558+0.033314
## [37] train-rmse:0.657347+0.005357 test-rmse:0.723701+0.034127
## [38] train-rmse:0.655798+0.005437 test-rmse:0.722952+0.034210
## [39] train-rmse:0.654280+0.005517 test-rmse:0.722908+0.035598
## [40] train-rmse:0.652807+0.005550 test-rmse:0.721836+0.034977
## [41] train-rmse:0.651363+0.005568 test-rmse:0.720318+0.034812
## [42] train-rmse:0.649945+0.005580 test-rmse:0.719700+0.034589
## [43] train-rmse:0.648577+0.005564 test-rmse:0.719488+0.033934
## [44] train-rmse:0.647242+0.005577 test-rmse:0.719895+0.034609
## [45] train-rmse:0.645932+0.005615 test-rmse:0.718373+0.034652
## [46] train-rmse:0.644685+0.005638 test-rmse:0.717453+0.033839
## [47] train-rmse:0.643452+0.005638 test-rmse:0.717911+0.035222
## [48] train-rmse:0.642231+0.005630 test-rmse:0.718082+0.035928
## [49] train-rmse:0.641055+0.005656 test-rmse:0.717401+0.033992
## [50] train-rmse:0.639903+0.005710 test-rmse:0.717387+0.034149
## [51] train-rmse:0.638755+0.005711 test-rmse:0.717362+0.033238
## [52] train-rmse:0.637627+0.005723 test-rmse:0.716634+0.033113
## [53] train-rmse:0.636528+0.005743 test-rmse:0.714618+0.033827
## [54] train-rmse:0.635459+0.005762 test-rmse:0.714444+0.034733
## [55] train-rmse:0.634410+0.005781 test-rmse:0.715445+0.036700
## [56] train-rmse:0.633377+0.005777 test-rmse:0.713632+0.036719
## [57] train-rmse:0.632307+0.005775 test-rmse:0.713589+0.036656
## [58] train-rmse:0.631299+0.005787 test-rmse:0.713303+0.036130
## [59] train-rmse:0.630297+0.005787 test-rmse:0.713170+0.036320
## [60] train-rmse:0.629299+0.005811 test-rmse:0.712390+0.036240
## [61] train-rmse:0.628328+0.005832 test-rmse:0.711922+0.035254
## [62] train-rmse:0.627371+0.005849 test-rmse:0.711459+0.034949
## [63] train-rmse:0.626423+0.005869 test-rmse:0.710996+0.035546
## [64] train-rmse:0.625474+0.005882 test-rmse:0.709829+0.034742
## [65] train-rmse:0.624541+0.005901 test-rmse:0.709124+0.033965
## [66] train-rmse:0.623625+0.005933 test-rmse:0.708903+0.034277
## [67] train-rmse:0.622728+0.005946 test-rmse:0.708111+0.034288
## [68] train-rmse:0.621863+0.005961 test-rmse:0.707995+0.034256
## [69] train-rmse:0.621003+0.005971 test-rmse:0.708211+0.033875
## [70] train-rmse:0.620142+0.005987 test-rmse:0.707775+0.033295
## [71] train-rmse:0.619283+0.005990 test-rmse:0.706943+0.032853
## [72] train-rmse:0.618439+0.005992 test-rmse:0.706426+0.033682
## [73] train-rmse:0.617631+0.005992 test-rmse:0.706466+0.033761
## [74] train-rmse:0.616840+0.006002 test-rmse:0.706114+0.034493
## [75] train-rmse:0.616058+0.006007 test-rmse:0.705270+0.034266
## [76] train-rmse:0.615279+0.006002 test-rmse:0.705361+0.034413
## [77] train-rmse:0.614493+0.005994 test-rmse:0.703667+0.034093
## [78] train-rmse:0.613719+0.006027 test-rmse:0.702894+0.033886
## [79] train-rmse:0.612970+0.006049 test-rmse:0.703203+0.034180
## [80] train-rmse:0.612244+0.006052 test-rmse:0.703633+0.034259
## [81] train-rmse:0.611523+0.006059 test-rmse:0.703764+0.034028
## [82] train-rmse:0.610800+0.006068 test-rmse:0.703258+0.033782
## [83] train-rmse:0.610098+0.006072 test-rmse:0.703171+0.034058
## [84] train-rmse:0.609384+0.006086 test-rmse:0.701750+0.033403
## [85] train-rmse:0.608668+0.006096 test-rmse:0.701623+0.033263
## [86] train-rmse:0.607988+0.006103 test-rmse:0.700208+0.034287
## [87] train-rmse:0.607311+0.006111 test-rmse:0.699940+0.033864
## [88] train-rmse:0.606638+0.006119 test-rmse:0.699379+0.034689
## [89] train-rmse:0.605975+0.006131 test-rmse:0.699583+0.035895
## [90] train-rmse:0.605317+0.006144 test-rmse:0.698581+0.035611
## [91] train-rmse:0.604647+0.006154 test-rmse:0.697929+0.035417
## [92] train-rmse:0.603996+0.006159 test-rmse:0.697070+0.034855
## [93] train-rmse:0.603356+0.006173 test-rmse:0.697903+0.034881
## [94] train-rmse:0.602716+0.006177 test-rmse:0.697059+0.034721
## [95] train-rmse:0.602074+0.006198 test-rmse:0.696783+0.034057
## [96] train-rmse:0.601446+0.006208 test-rmse:0.696721+0.033653
## [97] train-rmse:0.600833+0.006220 test-rmse:0.696257+0.033832
## [98] train-rmse:0.600222+0.006234 test-rmse:0.696107+0.034200
## [99] train-rmse:0.599619+0.006258 test-rmse:0.696199+0.034947
## [100] train-rmse:0.599036+0.006262 test-rmse:0.695891+0.034869
## [101] train-rmse:0.598455+0.006284 test-rmse:0.695418+0.033717
## [102] train-rmse:0.597872+0.006299 test-rmse:0.694826+0.033499
## [103] train-rmse:0.597305+0.006310 test-rmse:0.694741+0.033150
## [104] train-rmse:0.596739+0.006324 test-rmse:0.694465+0.032948
## [105] train-rmse:0.596161+0.006332 test-rmse:0.694505+0.034143
## [106] train-rmse:0.595602+0.006336 test-rmse:0.694231+0.034480
## [107] train-rmse:0.595058+0.006337 test-rmse:0.694941+0.035392
## [108] train-rmse:0.594526+0.006351 test-rmse:0.695226+0.035699
## [109] train-rmse:0.594002+0.006355 test-rmse:0.695505+0.036142
## [110] train-rmse:0.593476+0.006364 test-rmse:0.694839+0.036088
## [111] train-rmse:0.592956+0.006377 test-rmse:0.694415+0.036659
## [112] train-rmse:0.592439+0.006388 test-rmse:0.694421+0.036068
## Stopping. Best iteration:
## [106] train-rmse:0.595602+0.006336 test-rmse:0.694231+0.034480
##
## 36
## [1] train-rmse:1.356895+0.013444 test-rmse:1.361960+0.070337
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.168812+0.010668 test-rmse:1.179462+0.070859
## [3] train-rmse:1.028553+0.008334 test-rmse:1.047919+0.072059
## [4] train-rmse:0.925826+0.006647 test-rmse:0.950084+0.069312
## [5] train-rmse:0.852024+0.005829 test-rmse:0.882045+0.065858
## [6] train-rmse:0.797778+0.007197 test-rmse:0.836036+0.060099
## [7] train-rmse:0.757156+0.005364 test-rmse:0.795247+0.060279
## [8] train-rmse:0.727446+0.006050 test-rmse:0.768911+0.057203
## [9] train-rmse:0.706721+0.006222 test-rmse:0.755506+0.050408
## [10] train-rmse:0.688975+0.007868 test-rmse:0.742860+0.048673
## [11] train-rmse:0.676671+0.007819 test-rmse:0.735128+0.047361
## [12] train-rmse:0.666616+0.006772 test-rmse:0.728686+0.045920
## [13] train-rmse:0.656952+0.008243 test-rmse:0.724118+0.042794
## [14] train-rmse:0.648417+0.005655 test-rmse:0.721368+0.044232
## [15] train-rmse:0.642279+0.005330 test-rmse:0.720075+0.042853
## [16] train-rmse:0.636598+0.005281 test-rmse:0.716039+0.042560
## [17] train-rmse:0.629596+0.006128 test-rmse:0.709645+0.039164
## [18] train-rmse:0.624735+0.006608 test-rmse:0.706144+0.038503
## [19] train-rmse:0.619820+0.006248 test-rmse:0.704424+0.044180
## [20] train-rmse:0.614534+0.006249 test-rmse:0.702813+0.042590
## [21] train-rmse:0.609488+0.006259 test-rmse:0.698812+0.041762
## [22] train-rmse:0.605593+0.005582 test-rmse:0.697463+0.041070
## [23] train-rmse:0.600903+0.005615 test-rmse:0.693960+0.042165
## [24] train-rmse:0.596656+0.005460 test-rmse:0.693569+0.042937
## [25] train-rmse:0.592993+0.005838 test-rmse:0.690266+0.041874
## [26] train-rmse:0.589129+0.003949 test-rmse:0.690027+0.041876
## [27] train-rmse:0.586188+0.003873 test-rmse:0.692281+0.044517
## [28] train-rmse:0.581515+0.002523 test-rmse:0.690558+0.045559
## [29] train-rmse:0.578997+0.002642 test-rmse:0.688642+0.045434
## [30] train-rmse:0.574965+0.003222 test-rmse:0.686962+0.043292
## [31] train-rmse:0.571334+0.003682 test-rmse:0.685720+0.042234
## [32] train-rmse:0.567777+0.004934 test-rmse:0.682468+0.041381
## [33] train-rmse:0.565250+0.005432 test-rmse:0.681899+0.040991
## [34] train-rmse:0.562890+0.005574 test-rmse:0.681328+0.041291
## [35] train-rmse:0.559321+0.005831 test-rmse:0.680531+0.040260
## [36] train-rmse:0.557064+0.005783 test-rmse:0.681389+0.041128
## [37] train-rmse:0.551611+0.003759 test-rmse:0.676911+0.043476
## [38] train-rmse:0.549016+0.004295 test-rmse:0.676189+0.042222
## [39] train-rmse:0.546473+0.004133 test-rmse:0.675397+0.042506
## [40] train-rmse:0.544114+0.004186 test-rmse:0.673719+0.041451
## [41] train-rmse:0.540767+0.005316 test-rmse:0.670424+0.041379
## [42] train-rmse:0.538344+0.004946 test-rmse:0.669696+0.041786
## [43] train-rmse:0.534851+0.006262 test-rmse:0.669957+0.041765
## [44] train-rmse:0.532181+0.007607 test-rmse:0.669315+0.041124
## [45] train-rmse:0.530057+0.007882 test-rmse:0.669019+0.041885
## [46] train-rmse:0.527074+0.007445 test-rmse:0.668121+0.041980
## [47] train-rmse:0.525009+0.007079 test-rmse:0.665175+0.041377
## [48] train-rmse:0.522889+0.007136 test-rmse:0.663888+0.040424
## [49] train-rmse:0.521438+0.007195 test-rmse:0.664307+0.040954
## [50] train-rmse:0.518259+0.006328 test-rmse:0.663774+0.039101
## [51] train-rmse:0.516244+0.006822 test-rmse:0.662091+0.041013
## [52] train-rmse:0.513941+0.007184 test-rmse:0.661583+0.040154
## [53] train-rmse:0.512339+0.006877 test-rmse:0.660755+0.040733
## [54] train-rmse:0.510507+0.007207 test-rmse:0.659601+0.040836
## [55] train-rmse:0.508006+0.007362 test-rmse:0.658825+0.039742
## [56] train-rmse:0.505199+0.009091 test-rmse:0.657361+0.039560
## [57] train-rmse:0.502599+0.008844 test-rmse:0.654198+0.038264
## [58] train-rmse:0.500852+0.009619 test-rmse:0.654557+0.038412
## [59] train-rmse:0.498808+0.010035 test-rmse:0.654118+0.039040
## [60] train-rmse:0.497094+0.010346 test-rmse:0.653654+0.039306
## [61] train-rmse:0.495506+0.010492 test-rmse:0.653658+0.039833
## [62] train-rmse:0.493584+0.010451 test-rmse:0.652222+0.038120
## [63] train-rmse:0.492051+0.010541 test-rmse:0.650630+0.036388
## [64] train-rmse:0.490965+0.010447 test-rmse:0.650511+0.037215
## [65] train-rmse:0.489294+0.010612 test-rmse:0.649922+0.037036
## [66] train-rmse:0.487566+0.010015 test-rmse:0.648864+0.036512
## [67] train-rmse:0.485611+0.011014 test-rmse:0.648197+0.035969
## [68] train-rmse:0.483815+0.011242 test-rmse:0.648306+0.036147
## [69] train-rmse:0.482182+0.011287 test-rmse:0.647998+0.037491
## [70] train-rmse:0.481083+0.011275 test-rmse:0.648314+0.037277
## [71] train-rmse:0.479923+0.011111 test-rmse:0.649399+0.037818
## [72] train-rmse:0.477688+0.011998 test-rmse:0.649450+0.037641
## [73] train-rmse:0.475584+0.012463 test-rmse:0.647355+0.035357
## [74] train-rmse:0.472958+0.010899 test-rmse:0.646019+0.037127
## [75] train-rmse:0.472104+0.010870 test-rmse:0.645842+0.037231
## [76] train-rmse:0.470803+0.010936 test-rmse:0.645446+0.037848
## [77] train-rmse:0.469210+0.011688 test-rmse:0.645938+0.038034
## [78] train-rmse:0.467885+0.011525 test-rmse:0.645779+0.038207
## [79] train-rmse:0.465149+0.010798 test-rmse:0.643995+0.038966
## [80] train-rmse:0.463786+0.010962 test-rmse:0.642777+0.038548
## [81] train-rmse:0.462506+0.010933 test-rmse:0.641978+0.040359
## [82] train-rmse:0.461300+0.010788 test-rmse:0.641586+0.040800
## [83] train-rmse:0.460406+0.010686 test-rmse:0.642220+0.040367
## [84] train-rmse:0.458874+0.011933 test-rmse:0.643366+0.040879
## [85] train-rmse:0.457402+0.011871 test-rmse:0.642937+0.041397
## [86] train-rmse:0.455740+0.011748 test-rmse:0.642343+0.040618
## [87] train-rmse:0.454801+0.011844 test-rmse:0.641111+0.040498
## [88] train-rmse:0.453348+0.011269 test-rmse:0.641320+0.040494
## [89] train-rmse:0.452153+0.011099 test-rmse:0.641091+0.040829
## [90] train-rmse:0.450651+0.012104 test-rmse:0.640792+0.040892
## [91] train-rmse:0.449735+0.012146 test-rmse:0.641094+0.041508
## [92] train-rmse:0.448706+0.012320 test-rmse:0.639670+0.041256
## [93] train-rmse:0.447770+0.012513 test-rmse:0.639973+0.041013
## [94] train-rmse:0.446365+0.012419 test-rmse:0.640801+0.041378
## [95] train-rmse:0.445474+0.012367 test-rmse:0.638969+0.041086
## [96] train-rmse:0.444209+0.013021 test-rmse:0.639559+0.041383
## [97] train-rmse:0.443126+0.012957 test-rmse:0.639195+0.042188
## [98] train-rmse:0.442227+0.013023 test-rmse:0.639022+0.042006
## [99] train-rmse:0.440581+0.013593 test-rmse:0.639341+0.042114
## [100] train-rmse:0.439378+0.013532 test-rmse:0.638559+0.041808
## [101] train-rmse:0.438477+0.013731 test-rmse:0.638658+0.041861
## [102] train-rmse:0.436786+0.013275 test-rmse:0.637610+0.040445
## [103] train-rmse:0.435004+0.012828 test-rmse:0.636885+0.040749
## [104] train-rmse:0.433646+0.013245 test-rmse:0.636053+0.040127
## [105] train-rmse:0.432396+0.013480 test-rmse:0.635199+0.039760
## [106] train-rmse:0.431611+0.013879 test-rmse:0.635507+0.039988
## [107] train-rmse:0.430249+0.013977 test-rmse:0.635278+0.040126
## [108] train-rmse:0.429179+0.014296 test-rmse:0.634966+0.039686
## [109] train-rmse:0.427906+0.013739 test-rmse:0.634441+0.040968
## [110] train-rmse:0.427008+0.013910 test-rmse:0.633566+0.040268
## [111] train-rmse:0.425833+0.014174 test-rmse:0.633310+0.040246
## [112] train-rmse:0.424952+0.014443 test-rmse:0.633285+0.041149
## [113] train-rmse:0.423872+0.015304 test-rmse:0.633364+0.041190
## [114] train-rmse:0.422700+0.015293 test-rmse:0.632258+0.042431
## [115] train-rmse:0.421399+0.015213 test-rmse:0.632249+0.042484
## [116] train-rmse:0.419971+0.015294 test-rmse:0.632178+0.042514
## [117] train-rmse:0.419223+0.015270 test-rmse:0.631793+0.042594
## [118] train-rmse:0.418483+0.015350 test-rmse:0.631998+0.042647
## [119] train-rmse:0.417670+0.015638 test-rmse:0.632178+0.042840
## [120] train-rmse:0.415928+0.015893 test-rmse:0.630641+0.043304
## [121] train-rmse:0.414879+0.015838 test-rmse:0.631138+0.043658
## [122] train-rmse:0.414040+0.015830 test-rmse:0.631658+0.043900
## [123] train-rmse:0.412729+0.015652 test-rmse:0.632136+0.044020
## [124] train-rmse:0.411957+0.015674 test-rmse:0.632374+0.044588
## [125] train-rmse:0.410179+0.016240 test-rmse:0.631984+0.044203
## [126] train-rmse:0.409558+0.016312 test-rmse:0.632468+0.044345
## Stopping. Best iteration:
## [120] train-rmse:0.415928+0.015893 test-rmse:0.630641+0.043304
##
## 37
## [1] train-rmse:1.353005+0.013120 test-rmse:1.359893+0.069217
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.158752+0.010306 test-rmse:1.175521+0.069019
## [3] train-rmse:1.013746+0.008100 test-rmse:1.041091+0.069593
## [4] train-rmse:0.905462+0.007325 test-rmse:0.940201+0.068773
## [5] train-rmse:0.826923+0.006751 test-rmse:0.870292+0.064649
## [6] train-rmse:0.766591+0.007055 test-rmse:0.816185+0.066928
## [7] train-rmse:0.722433+0.008429 test-rmse:0.782924+0.059899
## [8] train-rmse:0.685903+0.007468 test-rmse:0.755817+0.060718
## [9] train-rmse:0.658260+0.008350 test-rmse:0.733118+0.056240
## [10] train-rmse:0.638997+0.006550 test-rmse:0.718215+0.052800
## [11] train-rmse:0.623382+0.007200 test-rmse:0.707900+0.051047
## [12] train-rmse:0.611479+0.007757 test-rmse:0.703996+0.053923
## [13] train-rmse:0.600599+0.007678 test-rmse:0.697805+0.051350
## [14] train-rmse:0.592934+0.007989 test-rmse:0.695374+0.050866
## [15] train-rmse:0.584350+0.008376 test-rmse:0.690627+0.052049
## [16] train-rmse:0.575266+0.009486 test-rmse:0.687921+0.050085
## [17] train-rmse:0.569173+0.009407 test-rmse:0.684856+0.049200
## [18] train-rmse:0.562285+0.009021 test-rmse:0.682928+0.049403
## [19] train-rmse:0.556630+0.009136 test-rmse:0.682763+0.049688
## [20] train-rmse:0.548013+0.007629 test-rmse:0.680675+0.051786
## [21] train-rmse:0.543504+0.007716 test-rmse:0.679087+0.052241
## [22] train-rmse:0.537463+0.007051 test-rmse:0.677084+0.053502
## [23] train-rmse:0.532650+0.007197 test-rmse:0.678689+0.054174
## [24] train-rmse:0.528685+0.007439 test-rmse:0.677295+0.055041
## [25] train-rmse:0.523521+0.008614 test-rmse:0.675542+0.053920
## [26] train-rmse:0.518398+0.008078 test-rmse:0.672802+0.054432
## [27] train-rmse:0.512241+0.005385 test-rmse:0.673265+0.058202
## [28] train-rmse:0.506293+0.006880 test-rmse:0.668098+0.060038
## [29] train-rmse:0.502296+0.006878 test-rmse:0.667977+0.060926
## [30] train-rmse:0.495684+0.006598 test-rmse:0.662390+0.058974
## [31] train-rmse:0.491811+0.007388 test-rmse:0.661106+0.058077
## [32] train-rmse:0.488249+0.007964 test-rmse:0.661432+0.055571
## [33] train-rmse:0.485067+0.007471 test-rmse:0.658976+0.058291
## [34] train-rmse:0.481929+0.007793 test-rmse:0.659129+0.058698
## [35] train-rmse:0.478148+0.008660 test-rmse:0.659405+0.060083
## [36] train-rmse:0.473965+0.009483 test-rmse:0.657356+0.059051
## [37] train-rmse:0.469219+0.009125 test-rmse:0.653236+0.058156
## [38] train-rmse:0.465177+0.010023 test-rmse:0.649984+0.057848
## [39] train-rmse:0.461802+0.011123 test-rmse:0.651148+0.058879
## [40] train-rmse:0.456875+0.009486 test-rmse:0.651347+0.060789
## [41] train-rmse:0.453721+0.009928 test-rmse:0.651852+0.060513
## [42] train-rmse:0.450667+0.010237 test-rmse:0.650024+0.061932
## [43] train-rmse:0.447808+0.010482 test-rmse:0.650665+0.062492
## [44] train-rmse:0.444607+0.010838 test-rmse:0.650604+0.062993
## Stopping. Best iteration:
## [38] train-rmse:0.465177+0.010023 test-rmse:0.649984+0.057848
##
## 38
## [1] train-rmse:1.349013+0.013180 test-rmse:1.362375+0.069803
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.149346+0.009499 test-rmse:1.173852+0.071047
## [3] train-rmse:0.997080+0.008023 test-rmse:1.034565+0.070884
## [4] train-rmse:0.882568+0.005508 test-rmse:0.934415+0.074274
## [5] train-rmse:0.798535+0.005619 test-rmse:0.860470+0.070500
## [6] train-rmse:0.734612+0.005211 test-rmse:0.807380+0.069358
## [7] train-rmse:0.688838+0.004958 test-rmse:0.766739+0.069017
## [8] train-rmse:0.652915+0.007757 test-rmse:0.738971+0.063894
## [9] train-rmse:0.621246+0.008627 test-rmse:0.722909+0.061109
## [10] train-rmse:0.597341+0.013343 test-rmse:0.710545+0.060890
## [11] train-rmse:0.577193+0.010275 test-rmse:0.697454+0.057700
## [12] train-rmse:0.561870+0.009444 test-rmse:0.688337+0.057817
## [13] train-rmse:0.548695+0.009080 test-rmse:0.679248+0.057339
## [14] train-rmse:0.537885+0.008713 test-rmse:0.679036+0.058374
## [15] train-rmse:0.528269+0.010198 test-rmse:0.672722+0.056376
## [16] train-rmse:0.518954+0.009587 test-rmse:0.669290+0.058652
## [17] train-rmse:0.511248+0.009730 test-rmse:0.667987+0.058931
## [18] train-rmse:0.502427+0.010785 test-rmse:0.666146+0.058823
## [19] train-rmse:0.494224+0.010732 test-rmse:0.663037+0.058847
## [20] train-rmse:0.486602+0.008653 test-rmse:0.658235+0.060266
## [21] train-rmse:0.480344+0.009425 test-rmse:0.656564+0.063477
## [22] train-rmse:0.470415+0.011806 test-rmse:0.653383+0.059318
## [23] train-rmse:0.463175+0.013040 test-rmse:0.651863+0.058593
## [24] train-rmse:0.455980+0.014487 test-rmse:0.652389+0.057350
## [25] train-rmse:0.449284+0.015883 test-rmse:0.651627+0.057875
## [26] train-rmse:0.444296+0.015859 test-rmse:0.650569+0.060641
## [27] train-rmse:0.435953+0.013537 test-rmse:0.645203+0.058928
## [28] train-rmse:0.429981+0.015008 test-rmse:0.641550+0.059648
## [29] train-rmse:0.424230+0.014086 test-rmse:0.637301+0.059050
## [30] train-rmse:0.420038+0.013569 test-rmse:0.636374+0.060488
## [31] train-rmse:0.414354+0.014814 test-rmse:0.636589+0.061522
## [32] train-rmse:0.408719+0.014981 test-rmse:0.634707+0.063367
## [33] train-rmse:0.403487+0.013803 test-rmse:0.635379+0.062299
## [34] train-rmse:0.398358+0.013800 test-rmse:0.635889+0.062174
## [35] train-rmse:0.393688+0.014621 test-rmse:0.633942+0.061053
## [36] train-rmse:0.390005+0.015026 test-rmse:0.632081+0.062588
## [37] train-rmse:0.384824+0.017602 test-rmse:0.630636+0.063684
## [38] train-rmse:0.380376+0.018366 test-rmse:0.631554+0.063244
## [39] train-rmse:0.374774+0.016397 test-rmse:0.630483+0.062563
## [40] train-rmse:0.370646+0.015682 test-rmse:0.630281+0.062858
## [41] train-rmse:0.366435+0.015660 test-rmse:0.628500+0.063141
## [42] train-rmse:0.362904+0.015286 test-rmse:0.628328+0.062784
## [43] train-rmse:0.358062+0.015654 test-rmse:0.626991+0.061785
## [44] train-rmse:0.354934+0.015827 test-rmse:0.625799+0.062568
## [45] train-rmse:0.352004+0.015846 test-rmse:0.625838+0.063308
## [46] train-rmse:0.349421+0.015525 test-rmse:0.626685+0.063830
## [47] train-rmse:0.343982+0.013870 test-rmse:0.626333+0.064346
## [48] train-rmse:0.341179+0.014454 test-rmse:0.627929+0.064729
## [49] train-rmse:0.337157+0.014663 test-rmse:0.627966+0.066121
## [50] train-rmse:0.333894+0.016023 test-rmse:0.627851+0.066274
## Stopping. Best iteration:
## [44] train-rmse:0.354934+0.015827 test-rmse:0.625799+0.062568
##
## 39
## [1] train-rmse:1.346306+0.013357 test-rmse:1.358549+0.072114
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.143226+0.009909 test-rmse:1.168804+0.073706
## [3] train-rmse:0.986725+0.007708 test-rmse:1.028688+0.070100
## [4] train-rmse:0.864947+0.005471 test-rmse:0.920941+0.070762
## [5] train-rmse:0.773996+0.005952 test-rmse:0.846102+0.066445
## [6] train-rmse:0.706742+0.006369 test-rmse:0.792736+0.062144
## [7] train-rmse:0.653524+0.007165 test-rmse:0.758426+0.058845
## [8] train-rmse:0.615501+0.006865 test-rmse:0.734933+0.059671
## [9] train-rmse:0.585027+0.005866 test-rmse:0.718423+0.056692
## [10] train-rmse:0.554404+0.011815 test-rmse:0.701059+0.051029
## [11] train-rmse:0.530556+0.006959 test-rmse:0.687881+0.051058
## [12] train-rmse:0.515361+0.007200 test-rmse:0.680591+0.050674
## [13] train-rmse:0.501979+0.008065 test-rmse:0.674489+0.052837
## [14] train-rmse:0.485256+0.008458 test-rmse:0.668150+0.050973
## [15] train-rmse:0.471606+0.011109 test-rmse:0.665906+0.052930
## [16] train-rmse:0.462062+0.013278 test-rmse:0.662850+0.054317
## [17] train-rmse:0.450953+0.014645 test-rmse:0.659492+0.055941
## [18] train-rmse:0.441357+0.015077 test-rmse:0.657958+0.053216
## [19] train-rmse:0.433232+0.015799 test-rmse:0.657773+0.056152
## [20] train-rmse:0.425284+0.016172 test-rmse:0.656454+0.056080
## [21] train-rmse:0.417885+0.017205 test-rmse:0.653881+0.055755
## [22] train-rmse:0.406824+0.017733 test-rmse:0.651870+0.052825
## [23] train-rmse:0.400607+0.017466 test-rmse:0.650058+0.053688
## [24] train-rmse:0.395539+0.017531 test-rmse:0.647038+0.051835
## [25] train-rmse:0.387046+0.015381 test-rmse:0.643293+0.050085
## [26] train-rmse:0.382058+0.015369 test-rmse:0.645240+0.051779
## [27] train-rmse:0.374475+0.017239 test-rmse:0.641384+0.052136
## [28] train-rmse:0.365086+0.016543 test-rmse:0.639399+0.052195
## [29] train-rmse:0.360978+0.016220 test-rmse:0.637893+0.052159
## [30] train-rmse:0.353736+0.015885 test-rmse:0.638161+0.053135
## [31] train-rmse:0.346140+0.014639 test-rmse:0.636649+0.051852
## [32] train-rmse:0.341608+0.015052 test-rmse:0.637159+0.051985
## [33] train-rmse:0.333756+0.015257 test-rmse:0.636885+0.051162
## [34] train-rmse:0.328062+0.014578 test-rmse:0.635010+0.051033
## [35] train-rmse:0.322880+0.013886 test-rmse:0.634970+0.050956
## [36] train-rmse:0.316822+0.014163 test-rmse:0.633817+0.050694
## [37] train-rmse:0.311599+0.015275 test-rmse:0.633604+0.050581
## [38] train-rmse:0.306197+0.014092 test-rmse:0.632100+0.050576
## [39] train-rmse:0.302324+0.015345 test-rmse:0.632596+0.051776
## [40] train-rmse:0.298545+0.014236 test-rmse:0.632722+0.049965
## [41] train-rmse:0.293335+0.014908 test-rmse:0.634703+0.050401
## [42] train-rmse:0.288853+0.015121 test-rmse:0.634262+0.050778
## [43] train-rmse:0.285774+0.015186 test-rmse:0.633746+0.050781
## [44] train-rmse:0.278162+0.014347 test-rmse:0.631620+0.048789
## [45] train-rmse:0.273310+0.014656 test-rmse:0.631537+0.047802
## [46] train-rmse:0.268844+0.014733 test-rmse:0.631867+0.047366
## [47] train-rmse:0.265052+0.016015 test-rmse:0.630339+0.047498
## [48] train-rmse:0.261519+0.016044 test-rmse:0.631469+0.047917
## [49] train-rmse:0.259119+0.015915 test-rmse:0.631326+0.048172
## [50] train-rmse:0.254984+0.015760 test-rmse:0.632404+0.048132
## [51] train-rmse:0.251294+0.016936 test-rmse:0.633098+0.048859
## [52] train-rmse:0.246836+0.016095 test-rmse:0.633692+0.048796
## [53] train-rmse:0.242594+0.015344 test-rmse:0.632766+0.050251
## Stopping. Best iteration:
## [47] train-rmse:0.265052+0.016015 test-rmse:0.630339+0.047498
##
## 40
## [1] train-rmse:1.343557+0.012909 test-rmse:1.360168+0.073694
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.137865+0.009688 test-rmse:1.168087+0.074641
## [3] train-rmse:0.976928+0.006893 test-rmse:1.029855+0.076886
## [4] train-rmse:0.850436+0.006659 test-rmse:0.918278+0.071821
## [5] train-rmse:0.753048+0.004697 test-rmse:0.841593+0.072101
## [6] train-rmse:0.680636+0.003383 test-rmse:0.783278+0.068707
## [7] train-rmse:0.626051+0.005521 test-rmse:0.745083+0.066422
## [8] train-rmse:0.583577+0.005127 test-rmse:0.716307+0.058961
## [9] train-rmse:0.550321+0.008440 test-rmse:0.696883+0.057336
## [10] train-rmse:0.522362+0.007935 test-rmse:0.680822+0.054622
## [11] train-rmse:0.495414+0.012316 test-rmse:0.674820+0.051521
## [12] train-rmse:0.474096+0.010820 test-rmse:0.670351+0.052945
## [13] train-rmse:0.457894+0.013645 test-rmse:0.667681+0.053017
## [14] train-rmse:0.441044+0.012290 test-rmse:0.664061+0.056418
## [15] train-rmse:0.426288+0.011360 test-rmse:0.658692+0.057420
## [16] train-rmse:0.415122+0.014605 test-rmse:0.657633+0.058449
## [17] train-rmse:0.403243+0.014958 test-rmse:0.656134+0.058574
## [18] train-rmse:0.391118+0.013580 test-rmse:0.651733+0.058648
## [19] train-rmse:0.381162+0.013612 test-rmse:0.646583+0.058463
## [20] train-rmse:0.374038+0.012892 test-rmse:0.643301+0.059700
## [21] train-rmse:0.362945+0.010350 test-rmse:0.642320+0.059320
## [22] train-rmse:0.355972+0.010541 test-rmse:0.640288+0.059987
## [23] train-rmse:0.348958+0.011621 test-rmse:0.639572+0.063040
## [24] train-rmse:0.341701+0.010183 test-rmse:0.636934+0.062849
## [25] train-rmse:0.333925+0.010513 test-rmse:0.636020+0.063410
## [26] train-rmse:0.329098+0.010614 test-rmse:0.635285+0.065259
## [27] train-rmse:0.322430+0.010459 test-rmse:0.634641+0.066131
## [28] train-rmse:0.314635+0.011235 test-rmse:0.632656+0.067025
## [29] train-rmse:0.305813+0.014296 test-rmse:0.632455+0.065398
## [30] train-rmse:0.299257+0.013892 test-rmse:0.632586+0.064805
## [31] train-rmse:0.293324+0.016134 test-rmse:0.631544+0.065846
## [32] train-rmse:0.286567+0.015430 test-rmse:0.632366+0.063426
## [33] train-rmse:0.281733+0.016203 test-rmse:0.632514+0.062861
## [34] train-rmse:0.273878+0.014820 test-rmse:0.634292+0.064188
## [35] train-rmse:0.266138+0.013336 test-rmse:0.631935+0.064790
## [36] train-rmse:0.259552+0.013404 test-rmse:0.631924+0.064588
## [37] train-rmse:0.254281+0.013784 test-rmse:0.630393+0.064757
## [38] train-rmse:0.249418+0.014452 test-rmse:0.630632+0.065345
## [39] train-rmse:0.245345+0.014691 test-rmse:0.630090+0.065808
## [40] train-rmse:0.237929+0.012275 test-rmse:0.627049+0.066010
## [41] train-rmse:0.232795+0.013409 test-rmse:0.626761+0.065341
## [42] train-rmse:0.228011+0.012790 test-rmse:0.627818+0.065264
## [43] train-rmse:0.223443+0.012943 test-rmse:0.626591+0.064496
## [44] train-rmse:0.219952+0.011755 test-rmse:0.626664+0.063996
## [45] train-rmse:0.215231+0.011863 test-rmse:0.626189+0.062255
## [46] train-rmse:0.211370+0.011610 test-rmse:0.626690+0.062009
## [47] train-rmse:0.207213+0.011828 test-rmse:0.627194+0.061175
## [48] train-rmse:0.201664+0.011770 test-rmse:0.625347+0.059370
## [49] train-rmse:0.198732+0.011525 test-rmse:0.625872+0.059167
## [50] train-rmse:0.194147+0.011721 test-rmse:0.626947+0.059687
## [51] train-rmse:0.189488+0.011342 test-rmse:0.626397+0.060234
## [52] train-rmse:0.186261+0.010845 test-rmse:0.626686+0.059441
## [53] train-rmse:0.183425+0.011722 test-rmse:0.626928+0.059220
## [54] train-rmse:0.180347+0.011936 test-rmse:0.626902+0.059164
## Stopping. Best iteration:
## [48] train-rmse:0.201664+0.011770 test-rmse:0.625347+0.059370
##
## 41
## [1] train-rmse:1.341538+0.012519 test-rmse:1.359095+0.073842
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.133456+0.009374 test-rmse:1.170481+0.075899
## [3] train-rmse:0.968743+0.005510 test-rmse:1.022548+0.079154
## [4] train-rmse:0.839134+0.005908 test-rmse:0.921145+0.076643
## [5] train-rmse:0.738168+0.004778 test-rmse:0.844240+0.079017
## [6] train-rmse:0.660717+0.006936 test-rmse:0.788713+0.078503
## [7] train-rmse:0.602067+0.010283 test-rmse:0.750838+0.076877
## [8] train-rmse:0.557536+0.009932 test-rmse:0.725070+0.078065
## [9] train-rmse:0.521446+0.010668 test-rmse:0.704565+0.074782
## [10] train-rmse:0.490626+0.009476 test-rmse:0.690530+0.072403
## [11] train-rmse:0.461882+0.014007 test-rmse:0.675730+0.069459
## [12] train-rmse:0.437611+0.011617 test-rmse:0.667737+0.067812
## [13] train-rmse:0.421114+0.011706 test-rmse:0.663000+0.067476
## [14] train-rmse:0.398992+0.014851 test-rmse:0.660371+0.065655
## [15] train-rmse:0.383303+0.018139 test-rmse:0.659098+0.065622
## [16] train-rmse:0.371801+0.018935 test-rmse:0.659402+0.064015
## [17] train-rmse:0.355599+0.021266 test-rmse:0.657420+0.063709
## [18] train-rmse:0.343270+0.022671 test-rmse:0.656732+0.066046
## [19] train-rmse:0.334344+0.021612 test-rmse:0.655458+0.063849
## [20] train-rmse:0.325037+0.021874 test-rmse:0.656170+0.063466
## [21] train-rmse:0.317311+0.021253 test-rmse:0.654266+0.061698
## [22] train-rmse:0.310207+0.021123 test-rmse:0.653151+0.060702
## [23] train-rmse:0.300019+0.023097 test-rmse:0.655027+0.060834
## [24] train-rmse:0.293283+0.022886 test-rmse:0.654673+0.061637
## [25] train-rmse:0.286022+0.021998 test-rmse:0.653316+0.058417
## [26] train-rmse:0.277531+0.019773 test-rmse:0.651919+0.058798
## [27] train-rmse:0.268808+0.019988 test-rmse:0.650608+0.057987
## [28] train-rmse:0.260570+0.019184 test-rmse:0.649486+0.058840
## [29] train-rmse:0.251545+0.016102 test-rmse:0.648275+0.058021
## [30] train-rmse:0.243959+0.015102 test-rmse:0.646806+0.056612
## [31] train-rmse:0.236871+0.015617 test-rmse:0.649029+0.057005
## [32] train-rmse:0.231976+0.014348 test-rmse:0.646958+0.057642
## [33] train-rmse:0.227019+0.012422 test-rmse:0.645757+0.057687
## [34] train-rmse:0.222841+0.011527 test-rmse:0.645800+0.057893
## [35] train-rmse:0.215564+0.012043 test-rmse:0.642408+0.055584
## [36] train-rmse:0.209871+0.012944 test-rmse:0.642819+0.055574
## [37] train-rmse:0.205016+0.014473 test-rmse:0.643018+0.055728
## [38] train-rmse:0.199893+0.015622 test-rmse:0.643082+0.055487
## [39] train-rmse:0.195712+0.016178 test-rmse:0.644033+0.055476
## [40] train-rmse:0.190403+0.015956 test-rmse:0.643551+0.055384
## [41] train-rmse:0.185367+0.014837 test-rmse:0.643594+0.054846
## Stopping. Best iteration:
## [35] train-rmse:0.215564+0.012043 test-rmse:0.642408+0.055584
##
## 42
## [1] train-rmse:1.339159+0.013221 test-rmse:1.340559+0.070314
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.146312+0.010458 test-rmse:1.149488+0.068966
## [3] train-rmse:1.010638+0.008521 test-rmse:1.016484+0.067374
## [4] train-rmse:0.916494+0.007828 test-rmse:0.928994+0.064439
## [5] train-rmse:0.851763+0.006866 test-rmse:0.865127+0.059213
## [6] train-rmse:0.807980+0.006748 test-rmse:0.826024+0.053601
## [7] train-rmse:0.778225+0.006385 test-rmse:0.802375+0.047665
## [8] train-rmse:0.757552+0.005994 test-rmse:0.786002+0.045208
## [9] train-rmse:0.742920+0.005716 test-rmse:0.773832+0.041695
## [10] train-rmse:0.732005+0.005420 test-rmse:0.762776+0.040952
## [11] train-rmse:0.723562+0.005150 test-rmse:0.756143+0.037642
## [12] train-rmse:0.716928+0.005179 test-rmse:0.751076+0.035302
## [13] train-rmse:0.711568+0.005077 test-rmse:0.750611+0.033449
## [14] train-rmse:0.706913+0.005093 test-rmse:0.747481+0.032073
## [15] train-rmse:0.702756+0.005099 test-rmse:0.743469+0.032353
## [16] train-rmse:0.698839+0.005152 test-rmse:0.742108+0.031597
## [17] train-rmse:0.695321+0.005073 test-rmse:0.739102+0.031576
## [18] train-rmse:0.692190+0.005098 test-rmse:0.737904+0.031925
## [19] train-rmse:0.689309+0.005156 test-rmse:0.737285+0.029914
## [20] train-rmse:0.686487+0.005161 test-rmse:0.735317+0.030335
## [21] train-rmse:0.683731+0.005198 test-rmse:0.733710+0.029059
## [22] train-rmse:0.681203+0.005233 test-rmse:0.732467+0.028242
## [23] train-rmse:0.678713+0.005301 test-rmse:0.731548+0.027877
## [24] train-rmse:0.676303+0.005346 test-rmse:0.731611+0.028176
## [25] train-rmse:0.673939+0.005367 test-rmse:0.730142+0.029075
## [26] train-rmse:0.671749+0.005343 test-rmse:0.729715+0.031467
## [27] train-rmse:0.669612+0.005284 test-rmse:0.728206+0.032141
## [28] train-rmse:0.667505+0.005246 test-rmse:0.728107+0.033908
## [29] train-rmse:0.665484+0.005196 test-rmse:0.727928+0.035677
## [30] train-rmse:0.663531+0.005217 test-rmse:0.727592+0.035421
## [31] train-rmse:0.661623+0.005259 test-rmse:0.725622+0.034366
## [32] train-rmse:0.659797+0.005294 test-rmse:0.725116+0.033324
## [33] train-rmse:0.658033+0.005300 test-rmse:0.724251+0.033150
## [34] train-rmse:0.656294+0.005327 test-rmse:0.722852+0.031972
## [35] train-rmse:0.654630+0.005401 test-rmse:0.721874+0.032910
## [36] train-rmse:0.652957+0.005440 test-rmse:0.721649+0.031978
## [37] train-rmse:0.651382+0.005528 test-rmse:0.721803+0.034078
## [38] train-rmse:0.649851+0.005593 test-rmse:0.721308+0.034733
## [39] train-rmse:0.648347+0.005643 test-rmse:0.720635+0.035152
## [40] train-rmse:0.646896+0.005659 test-rmse:0.720319+0.035608
## [41] train-rmse:0.645464+0.005646 test-rmse:0.720322+0.036666
## [42] train-rmse:0.644062+0.005666 test-rmse:0.719256+0.037192
## [43] train-rmse:0.642724+0.005697 test-rmse:0.717881+0.036456
## [44] train-rmse:0.641386+0.005718 test-rmse:0.716691+0.036211
## [45] train-rmse:0.640029+0.005710 test-rmse:0.716218+0.035431
## [46] train-rmse:0.638726+0.005736 test-rmse:0.715457+0.035239
## [47] train-rmse:0.637460+0.005751 test-rmse:0.714345+0.035324
## [48] train-rmse:0.636234+0.005751 test-rmse:0.713326+0.034757
## [49] train-rmse:0.635054+0.005767 test-rmse:0.713325+0.034619
## [50] train-rmse:0.633908+0.005761 test-rmse:0.712790+0.035929
## [51] train-rmse:0.632760+0.005787 test-rmse:0.712232+0.035421
## [52] train-rmse:0.631657+0.005794 test-rmse:0.711986+0.034085
## [53] train-rmse:0.630552+0.005816 test-rmse:0.712177+0.034327
## [54] train-rmse:0.629471+0.005824 test-rmse:0.712343+0.034498
## [55] train-rmse:0.628394+0.005849 test-rmse:0.713505+0.035176
## [56] train-rmse:0.627308+0.005828 test-rmse:0.712243+0.035801
## [57] train-rmse:0.626235+0.005824 test-rmse:0.712087+0.035856
## [58] train-rmse:0.625224+0.005822 test-rmse:0.711861+0.035069
## [59] train-rmse:0.624205+0.005855 test-rmse:0.710800+0.036343
## [60] train-rmse:0.623213+0.005887 test-rmse:0.710531+0.037039
## [61] train-rmse:0.622235+0.005919 test-rmse:0.710788+0.037820
## [62] train-rmse:0.621256+0.005959 test-rmse:0.708564+0.037312
## [63] train-rmse:0.620313+0.005972 test-rmse:0.708251+0.036638
## [64] train-rmse:0.619369+0.005983 test-rmse:0.706466+0.036407
## [65] train-rmse:0.618442+0.006004 test-rmse:0.705581+0.036025
## [66] train-rmse:0.617530+0.006021 test-rmse:0.705710+0.035621
## [67] train-rmse:0.616642+0.006036 test-rmse:0.705669+0.035409
## [68] train-rmse:0.615748+0.006049 test-rmse:0.704690+0.034847
## [69] train-rmse:0.614860+0.006055 test-rmse:0.704104+0.036090
## [70] train-rmse:0.614007+0.006054 test-rmse:0.703276+0.035491
## [71] train-rmse:0.613147+0.006068 test-rmse:0.703046+0.034591
## [72] train-rmse:0.612300+0.006074 test-rmse:0.701785+0.034081
## [73] train-rmse:0.611485+0.006072 test-rmse:0.701064+0.033744
## [74] train-rmse:0.610696+0.006072 test-rmse:0.701458+0.035106
## [75] train-rmse:0.609899+0.006073 test-rmse:0.701430+0.035778
## [76] train-rmse:0.609124+0.006062 test-rmse:0.700643+0.036132
## [77] train-rmse:0.608368+0.006055 test-rmse:0.701786+0.036790
## [78] train-rmse:0.607621+0.006048 test-rmse:0.700565+0.036814
## [79] train-rmse:0.606875+0.006052 test-rmse:0.699979+0.037865
## [80] train-rmse:0.606144+0.006062 test-rmse:0.699227+0.036817
## [81] train-rmse:0.605395+0.006078 test-rmse:0.699246+0.037565
## [82] train-rmse:0.604672+0.006091 test-rmse:0.698414+0.037293
## [83] train-rmse:0.603962+0.006102 test-rmse:0.698226+0.038293
## [84] train-rmse:0.603246+0.006132 test-rmse:0.697313+0.037467
## [85] train-rmse:0.602537+0.006159 test-rmse:0.697230+0.036978
## [86] train-rmse:0.601849+0.006168 test-rmse:0.696808+0.036609
## [87] train-rmse:0.601172+0.006195 test-rmse:0.697190+0.036343
## [88] train-rmse:0.600512+0.006218 test-rmse:0.696555+0.036130
## [89] train-rmse:0.599846+0.006209 test-rmse:0.696737+0.035231
## [90] train-rmse:0.599174+0.006192 test-rmse:0.696203+0.034920
## [91] train-rmse:0.598507+0.006192 test-rmse:0.694763+0.034634
## [92] train-rmse:0.597852+0.006221 test-rmse:0.695897+0.035532
## [93] train-rmse:0.597201+0.006246 test-rmse:0.696018+0.035991
## [94] train-rmse:0.596567+0.006261 test-rmse:0.694864+0.035778
## [95] train-rmse:0.595940+0.006286 test-rmse:0.694027+0.036354
## [96] train-rmse:0.595320+0.006309 test-rmse:0.693803+0.036304
## [97] train-rmse:0.594711+0.006318 test-rmse:0.693589+0.035498
## [98] train-rmse:0.594103+0.006308 test-rmse:0.692501+0.035414
## [99] train-rmse:0.593500+0.006320 test-rmse:0.691121+0.035865
## [100] train-rmse:0.592918+0.006342 test-rmse:0.692130+0.035710
## [101] train-rmse:0.592342+0.006357 test-rmse:0.692020+0.035945
## [102] train-rmse:0.591781+0.006375 test-rmse:0.691927+0.036562
## [103] train-rmse:0.591222+0.006396 test-rmse:0.691864+0.036655
## [104] train-rmse:0.590662+0.006416 test-rmse:0.692191+0.036321
## [105] train-rmse:0.590099+0.006421 test-rmse:0.690179+0.035891
## [106] train-rmse:0.589541+0.006438 test-rmse:0.689962+0.035996
## [107] train-rmse:0.588985+0.006448 test-rmse:0.689759+0.035484
## [108] train-rmse:0.588440+0.006471 test-rmse:0.689638+0.036080
## [109] train-rmse:0.587908+0.006490 test-rmse:0.689444+0.035549
## [110] train-rmse:0.587374+0.006511 test-rmse:0.689910+0.034972
## [111] train-rmse:0.586848+0.006529 test-rmse:0.689956+0.034683
## [112] train-rmse:0.586328+0.006554 test-rmse:0.689707+0.035243
## [113] train-rmse:0.585811+0.006572 test-rmse:0.689695+0.035764
## [114] train-rmse:0.585293+0.006586 test-rmse:0.689074+0.035835
## [115] train-rmse:0.584786+0.006608 test-rmse:0.688788+0.035267
## [116] train-rmse:0.584287+0.006619 test-rmse:0.687930+0.035377
## [117] train-rmse:0.583793+0.006627 test-rmse:0.688271+0.035112
## [118] train-rmse:0.583313+0.006647 test-rmse:0.688145+0.034230
## [119] train-rmse:0.582836+0.006656 test-rmse:0.687979+0.034742
## [120] train-rmse:0.582368+0.006661 test-rmse:0.688185+0.035456
## [121] train-rmse:0.581909+0.006671 test-rmse:0.688035+0.035471
## [122] train-rmse:0.581443+0.006676 test-rmse:0.688203+0.035657
## Stopping. Best iteration:
## [116] train-rmse:0.584287+0.006619 test-rmse:0.687930+0.035377
##
## 43
## [1] train-rmse:1.332805+0.013095 test-rmse:1.338561+0.070564
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.133350+0.010097 test-rmse:1.145376+0.070734
## [3] train-rmse:0.990041+0.007729 test-rmse:1.011774+0.071559
## [4] train-rmse:0.889367+0.006062 test-rmse:0.916441+0.068034
## [5] train-rmse:0.819589+0.006101 test-rmse:0.856042+0.065251
## [6] train-rmse:0.767072+0.004264 test-rmse:0.804954+0.063929
## [7] train-rmse:0.732624+0.006318 test-rmse:0.774164+0.056579
## [8] train-rmse:0.706018+0.005975 test-rmse:0.754923+0.048448
## [9] train-rmse:0.686676+0.005694 test-rmse:0.737073+0.049691
## [10] train-rmse:0.671096+0.005735 test-rmse:0.729938+0.048902
## [11] train-rmse:0.660404+0.005660 test-rmse:0.723987+0.045002
## [12] train-rmse:0.652329+0.005407 test-rmse:0.719536+0.044486
## [13] train-rmse:0.644881+0.005386 test-rmse:0.717727+0.047337
## [14] train-rmse:0.638240+0.004794 test-rmse:0.713359+0.048901
## [15] train-rmse:0.632149+0.004265 test-rmse:0.710314+0.046550
## [16] train-rmse:0.626035+0.004042 test-rmse:0.709458+0.045757
## [17] train-rmse:0.620915+0.003482 test-rmse:0.707688+0.045511
## [18] train-rmse:0.616368+0.003645 test-rmse:0.705341+0.046457
## [19] train-rmse:0.611063+0.003476 test-rmse:0.705743+0.046465
## [20] train-rmse:0.606495+0.004629 test-rmse:0.705545+0.046111
## [21] train-rmse:0.601745+0.005417 test-rmse:0.703140+0.045820
## [22] train-rmse:0.596998+0.005208 test-rmse:0.701720+0.045989
## [23] train-rmse:0.593831+0.005403 test-rmse:0.699451+0.046837
## [24] train-rmse:0.589898+0.004985 test-rmse:0.697346+0.046895
## [25] train-rmse:0.586137+0.006144 test-rmse:0.695680+0.046468
## [26] train-rmse:0.582812+0.005539 test-rmse:0.695153+0.048458
## [27] train-rmse:0.578483+0.006901 test-rmse:0.694389+0.046050
## [28] train-rmse:0.573272+0.008123 test-rmse:0.691640+0.045337
## [29] train-rmse:0.570053+0.007851 test-rmse:0.689392+0.047479
## [30] train-rmse:0.567579+0.007770 test-rmse:0.688484+0.047185
## [31] train-rmse:0.564663+0.008264 test-rmse:0.689167+0.046638
## [32] train-rmse:0.561879+0.008085 test-rmse:0.686757+0.045088
## [33] train-rmse:0.559264+0.008142 test-rmse:0.686375+0.044608
## [34] train-rmse:0.556231+0.008285 test-rmse:0.685103+0.046161
## [35] train-rmse:0.553508+0.007795 test-rmse:0.682041+0.046403
## [36] train-rmse:0.549664+0.007894 test-rmse:0.681598+0.047207
## [37] train-rmse:0.546654+0.008592 test-rmse:0.680907+0.045283
## [38] train-rmse:0.542459+0.010078 test-rmse:0.677293+0.043506
## [39] train-rmse:0.539630+0.010288 test-rmse:0.677054+0.045185
## [40] train-rmse:0.535416+0.008329 test-rmse:0.676100+0.048223
## [41] train-rmse:0.532597+0.008330 test-rmse:0.675465+0.049305
## [42] train-rmse:0.529416+0.007826 test-rmse:0.676437+0.049421
## [43] train-rmse:0.527188+0.007313 test-rmse:0.675340+0.049546
## [44] train-rmse:0.523594+0.008247 test-rmse:0.674977+0.047655
## [45] train-rmse:0.521555+0.008504 test-rmse:0.674593+0.047368
## [46] train-rmse:0.519884+0.008583 test-rmse:0.674693+0.048174
## [47] train-rmse:0.517510+0.009094 test-rmse:0.674028+0.048067
## [48] train-rmse:0.514923+0.008759 test-rmse:0.671532+0.047143
## [49] train-rmse:0.512764+0.008083 test-rmse:0.671207+0.046856
## [50] train-rmse:0.510241+0.008659 test-rmse:0.669421+0.046086
## [51] train-rmse:0.507293+0.007908 test-rmse:0.667362+0.047203
## [52] train-rmse:0.505498+0.008128 test-rmse:0.666200+0.046538
## [53] train-rmse:0.503516+0.008559 test-rmse:0.664897+0.046542
## [54] train-rmse:0.501869+0.008512 test-rmse:0.663187+0.047664
## [55] train-rmse:0.499091+0.009386 test-rmse:0.664149+0.048500
## [56] train-rmse:0.496725+0.009131 test-rmse:0.663659+0.048572
## [57] train-rmse:0.494174+0.008935 test-rmse:0.662136+0.047121
## [58] train-rmse:0.492347+0.008592 test-rmse:0.661378+0.047760
## [59] train-rmse:0.490286+0.008714 test-rmse:0.660755+0.048717
## [60] train-rmse:0.488966+0.008823 test-rmse:0.660290+0.048985
## [61] train-rmse:0.487458+0.009051 test-rmse:0.660857+0.048697
## [62] train-rmse:0.486019+0.009017 test-rmse:0.659799+0.048540
## [63] train-rmse:0.484180+0.009310 test-rmse:0.659746+0.048328
## [64] train-rmse:0.482767+0.009430 test-rmse:0.659896+0.048662
## [65] train-rmse:0.481231+0.009032 test-rmse:0.660291+0.049219
## [66] train-rmse:0.479744+0.008958 test-rmse:0.659140+0.049724
## [67] train-rmse:0.478168+0.008829 test-rmse:0.658276+0.050560
## [68] train-rmse:0.475001+0.006501 test-rmse:0.656797+0.047383
## [69] train-rmse:0.472558+0.005183 test-rmse:0.656331+0.046126
## [70] train-rmse:0.470648+0.005876 test-rmse:0.654690+0.045753
## [71] train-rmse:0.468953+0.006173 test-rmse:0.654345+0.045954
## [72] train-rmse:0.467558+0.006206 test-rmse:0.653970+0.046101
## [73] train-rmse:0.466494+0.006302 test-rmse:0.653844+0.045300
## [74] train-rmse:0.465253+0.006043 test-rmse:0.653056+0.043919
## [75] train-rmse:0.463897+0.006035 test-rmse:0.653375+0.044870
## [76] train-rmse:0.462745+0.006125 test-rmse:0.652648+0.044884
## [77] train-rmse:0.460986+0.005266 test-rmse:0.651124+0.045812
## [78] train-rmse:0.459022+0.006156 test-rmse:0.650970+0.046981
## [79] train-rmse:0.457273+0.007331 test-rmse:0.651286+0.048116
## [80] train-rmse:0.455477+0.006729 test-rmse:0.650216+0.046867
## [81] train-rmse:0.453569+0.006887 test-rmse:0.650823+0.046900
## [82] train-rmse:0.452623+0.006759 test-rmse:0.650221+0.046241
## [83] train-rmse:0.451374+0.006994 test-rmse:0.649788+0.046837
## [84] train-rmse:0.450145+0.006904 test-rmse:0.650111+0.047062
## [85] train-rmse:0.448923+0.007038 test-rmse:0.650422+0.046936
## [86] train-rmse:0.447823+0.007350 test-rmse:0.650559+0.046983
## [87] train-rmse:0.445837+0.006813 test-rmse:0.649164+0.047259
## [88] train-rmse:0.444866+0.007048 test-rmse:0.648710+0.047165
## [89] train-rmse:0.443663+0.007105 test-rmse:0.648311+0.047972
## [90] train-rmse:0.441687+0.006742 test-rmse:0.648979+0.048270
## [91] train-rmse:0.440524+0.006507 test-rmse:0.649708+0.048515
## [92] train-rmse:0.438778+0.006522 test-rmse:0.650325+0.049410
## [93] train-rmse:0.437929+0.006506 test-rmse:0.649150+0.049843
## [94] train-rmse:0.436996+0.006369 test-rmse:0.650233+0.049028
## [95] train-rmse:0.435133+0.007915 test-rmse:0.649796+0.049232
## Stopping. Best iteration:
## [89] train-rmse:0.443663+0.007105 test-rmse:0.648311+0.047972
##
## 44
## [1] train-rmse:1.328479+0.012733 test-rmse:1.336276+0.069330
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.122337+0.009889 test-rmse:1.141824+0.068217
## [3] train-rmse:0.973411+0.007476 test-rmse:1.003928+0.068650
## [4] train-rmse:0.866238+0.005224 test-rmse:0.903406+0.070091
## [5] train-rmse:0.790640+0.006177 test-rmse:0.838866+0.065803
## [6] train-rmse:0.734798+0.006672 test-rmse:0.795028+0.061360
## [7] train-rmse:0.691310+0.006285 test-rmse:0.761727+0.055548
## [8] train-rmse:0.663040+0.004912 test-rmse:0.737865+0.053375
## [9] train-rmse:0.636982+0.005972 test-rmse:0.719731+0.044142
## [10] train-rmse:0.620287+0.006304 test-rmse:0.707200+0.040536
## [11] train-rmse:0.606054+0.005540 test-rmse:0.703512+0.040852
## [12] train-rmse:0.595100+0.004873 test-rmse:0.697128+0.046911
## [13] train-rmse:0.585834+0.006583 test-rmse:0.696019+0.047096
## [14] train-rmse:0.576729+0.007236 test-rmse:0.692437+0.045935
## [15] train-rmse:0.569579+0.006895 test-rmse:0.689894+0.049524
## [16] train-rmse:0.562781+0.007528 test-rmse:0.685213+0.049053
## [17] train-rmse:0.555906+0.006385 test-rmse:0.680895+0.050714
## [18] train-rmse:0.547469+0.006342 test-rmse:0.677277+0.050195
## [19] train-rmse:0.541811+0.006538 test-rmse:0.675160+0.049567
## [20] train-rmse:0.536156+0.006964 test-rmse:0.673587+0.047578
## [21] train-rmse:0.531673+0.007624 test-rmse:0.673465+0.048073
## [22] train-rmse:0.526978+0.007798 test-rmse:0.673017+0.047432
## [23] train-rmse:0.522468+0.008040 test-rmse:0.673296+0.048857
## [24] train-rmse:0.516473+0.009339 test-rmse:0.673402+0.050862
## [25] train-rmse:0.512119+0.008716 test-rmse:0.672861+0.053923
## [26] train-rmse:0.507328+0.009793 test-rmse:0.668654+0.051908
## [27] train-rmse:0.501038+0.010820 test-rmse:0.661989+0.048654
## [28] train-rmse:0.493316+0.008237 test-rmse:0.657014+0.048142
## [29] train-rmse:0.488657+0.007694 test-rmse:0.657202+0.049256
## [30] train-rmse:0.481952+0.005912 test-rmse:0.653507+0.052033
## [31] train-rmse:0.478029+0.005978 test-rmse:0.652639+0.053529
## [32] train-rmse:0.472155+0.007829 test-rmse:0.650643+0.053472
## [33] train-rmse:0.468733+0.006993 test-rmse:0.652113+0.053437
## [34] train-rmse:0.464656+0.006269 test-rmse:0.651010+0.055082
## [35] train-rmse:0.458346+0.008768 test-rmse:0.649506+0.054059
## [36] train-rmse:0.455004+0.009040 test-rmse:0.649507+0.054166
## [37] train-rmse:0.451758+0.008459 test-rmse:0.650670+0.057056
## [38] train-rmse:0.447885+0.008465 test-rmse:0.650301+0.056160
## [39] train-rmse:0.445055+0.009220 test-rmse:0.649811+0.055169
## [40] train-rmse:0.440783+0.009092 test-rmse:0.647378+0.055943
## [41] train-rmse:0.437531+0.009471 test-rmse:0.646870+0.058622
## [42] train-rmse:0.433862+0.010628 test-rmse:0.644020+0.057140
## [43] train-rmse:0.429613+0.010848 test-rmse:0.643392+0.058189
## [44] train-rmse:0.425972+0.011350 test-rmse:0.644910+0.058806
## [45] train-rmse:0.421001+0.010436 test-rmse:0.641814+0.055946
## [46] train-rmse:0.416867+0.008576 test-rmse:0.642055+0.056549
## [47] train-rmse:0.413155+0.009033 test-rmse:0.638496+0.054483
## [48] train-rmse:0.410090+0.008791 test-rmse:0.636508+0.054502
## [49] train-rmse:0.408040+0.008879 test-rmse:0.636427+0.054633
## [50] train-rmse:0.405587+0.008477 test-rmse:0.636208+0.056297
## [51] train-rmse:0.400641+0.008633 test-rmse:0.635890+0.054577
## [52] train-rmse:0.398718+0.008731 test-rmse:0.634312+0.053679
## [53] train-rmse:0.396295+0.008984 test-rmse:0.632659+0.053459
## [54] train-rmse:0.393663+0.008261 test-rmse:0.631197+0.052942
## [55] train-rmse:0.391391+0.009646 test-rmse:0.631870+0.053082
## [56] train-rmse:0.388634+0.009227 test-rmse:0.632320+0.054082
## [57] train-rmse:0.386227+0.009847 test-rmse:0.632550+0.054084
## [58] train-rmse:0.384021+0.009740 test-rmse:0.631366+0.054799
## [59] train-rmse:0.382025+0.010180 test-rmse:0.630878+0.054998
## [60] train-rmse:0.379285+0.009154 test-rmse:0.630390+0.055406
## [61] train-rmse:0.376623+0.008820 test-rmse:0.629389+0.057350
## [62] train-rmse:0.374723+0.009399 test-rmse:0.629252+0.057001
## [63] train-rmse:0.372028+0.010226 test-rmse:0.628619+0.056285
## [64] train-rmse:0.368786+0.010054 test-rmse:0.628279+0.055794
## [65] train-rmse:0.366094+0.010455 test-rmse:0.628985+0.056026
## [66] train-rmse:0.364099+0.009932 test-rmse:0.628672+0.056041
## [67] train-rmse:0.362177+0.010645 test-rmse:0.628407+0.055705
## [68] train-rmse:0.358636+0.011573 test-rmse:0.626353+0.056593
## [69] train-rmse:0.357033+0.012060 test-rmse:0.626483+0.056828
## [70] train-rmse:0.354255+0.013206 test-rmse:0.625141+0.056761
## [71] train-rmse:0.351311+0.012979 test-rmse:0.626329+0.058697
## [72] train-rmse:0.347233+0.013770 test-rmse:0.626545+0.059565
## [73] train-rmse:0.345414+0.014470 test-rmse:0.627910+0.059716
## [74] train-rmse:0.343179+0.013855 test-rmse:0.628552+0.060029
## [75] train-rmse:0.340799+0.014079 test-rmse:0.628510+0.061067
## [76] train-rmse:0.337502+0.012176 test-rmse:0.628419+0.061621
## Stopping. Best iteration:
## [70] train-rmse:0.354255+0.013206 test-rmse:0.625141+0.056761
##
## 45
## [1] train-rmse:1.324034+0.012802 test-rmse:1.339027+0.070047
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.110725+0.008923 test-rmse:1.139776+0.071979
## [3] train-rmse:0.955540+0.005997 test-rmse:0.999421+0.072971
## [4] train-rmse:0.841470+0.003345 test-rmse:0.897967+0.076816
## [5] train-rmse:0.760174+0.002623 test-rmse:0.828080+0.071403
## [6] train-rmse:0.700785+0.002915 test-rmse:0.777174+0.062826
## [7] train-rmse:0.656179+0.004774 test-rmse:0.746205+0.063712
## [8] train-rmse:0.619707+0.006703 test-rmse:0.721205+0.061022
## [9] train-rmse:0.593708+0.008156 test-rmse:0.705547+0.059075
## [10] train-rmse:0.576491+0.008247 test-rmse:0.696748+0.058211
## [11] train-rmse:0.560841+0.007863 test-rmse:0.686649+0.056780
## [12] train-rmse:0.547045+0.006754 test-rmse:0.680454+0.057583
## [13] train-rmse:0.534365+0.005859 test-rmse:0.675490+0.058411
## [14] train-rmse:0.523426+0.007769 test-rmse:0.671412+0.055789
## [15] train-rmse:0.514224+0.007851 test-rmse:0.670373+0.056083
## [16] train-rmse:0.504588+0.010989 test-rmse:0.667550+0.056900
## [17] train-rmse:0.496798+0.011308 test-rmse:0.664896+0.054848
## [18] train-rmse:0.486761+0.011132 test-rmse:0.661333+0.056647
## [19] train-rmse:0.480007+0.010629 test-rmse:0.657692+0.060423
## [20] train-rmse:0.470371+0.008134 test-rmse:0.655489+0.059446
## [21] train-rmse:0.461304+0.010256 test-rmse:0.656326+0.056185
## [22] train-rmse:0.455430+0.011850 test-rmse:0.653420+0.053216
## [23] train-rmse:0.448356+0.010925 test-rmse:0.654141+0.052966
## [24] train-rmse:0.440684+0.013228 test-rmse:0.650600+0.051726
## [25] train-rmse:0.432166+0.012721 test-rmse:0.647061+0.050681
## [26] train-rmse:0.427703+0.012215 test-rmse:0.647429+0.049357
## [27] train-rmse:0.421985+0.011092 test-rmse:0.645477+0.050728
## [28] train-rmse:0.416346+0.011074 test-rmse:0.644581+0.051428
## [29] train-rmse:0.411927+0.012797 test-rmse:0.645079+0.051681
## [30] train-rmse:0.405803+0.014651 test-rmse:0.646339+0.053444
## [31] train-rmse:0.398415+0.015726 test-rmse:0.641734+0.048987
## [32] train-rmse:0.393080+0.015542 test-rmse:0.641485+0.048878
## [33] train-rmse:0.389247+0.016057 test-rmse:0.639184+0.049330
## [34] train-rmse:0.385776+0.015781 test-rmse:0.639756+0.049127
## [35] train-rmse:0.381980+0.015684 test-rmse:0.640218+0.051269
## [36] train-rmse:0.376329+0.017002 test-rmse:0.639701+0.051980
## [37] train-rmse:0.371354+0.014874 test-rmse:0.638465+0.052789
## [38] train-rmse:0.366591+0.016512 test-rmse:0.636579+0.051209
## [39] train-rmse:0.360895+0.016158 test-rmse:0.634489+0.052782
## [40] train-rmse:0.356948+0.015599 test-rmse:0.634320+0.053388
## [41] train-rmse:0.351137+0.015913 test-rmse:0.635814+0.056551
## [42] train-rmse:0.347768+0.016372 test-rmse:0.634476+0.056008
## [43] train-rmse:0.345196+0.016810 test-rmse:0.634045+0.056085
## [44] train-rmse:0.341369+0.017394 test-rmse:0.632932+0.054932
## [45] train-rmse:0.336254+0.016931 test-rmse:0.632036+0.053980
## [46] train-rmse:0.331819+0.017093 test-rmse:0.631116+0.053722
## [47] train-rmse:0.328311+0.017448 test-rmse:0.631910+0.054442
## [48] train-rmse:0.323996+0.015825 test-rmse:0.632836+0.053265
## [49] train-rmse:0.320724+0.015539 test-rmse:0.633746+0.053466
## [50] train-rmse:0.316593+0.014761 test-rmse:0.633062+0.053029
## [51] train-rmse:0.314544+0.015232 test-rmse:0.632385+0.053389
## [52] train-rmse:0.312312+0.015007 test-rmse:0.632863+0.053311
## Stopping. Best iteration:
## [46] train-rmse:0.331819+0.017093 test-rmse:0.631116+0.053722
##
## 46
## [1] train-rmse:1.321013+0.012997 test-rmse:1.334804+0.072645
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.104558+0.009486 test-rmse:1.133973+0.073323
## [3] train-rmse:0.941711+0.007697 test-rmse:0.987567+0.074588
## [4] train-rmse:0.821931+0.005514 test-rmse:0.885955+0.071940
## [5] train-rmse:0.736701+0.005671 test-rmse:0.815098+0.068405
## [6] train-rmse:0.674366+0.005624 test-rmse:0.769987+0.066216
## [7] train-rmse:0.627899+0.005798 test-rmse:0.736623+0.055029
## [8] train-rmse:0.591736+0.007180 test-rmse:0.716595+0.051536
## [9] train-rmse:0.557310+0.012373 test-rmse:0.697833+0.049037
## [10] train-rmse:0.528937+0.009698 test-rmse:0.688985+0.047545
## [11] train-rmse:0.508450+0.010817 test-rmse:0.682907+0.047890
## [12] train-rmse:0.492965+0.010823 test-rmse:0.677908+0.047662
## [13] train-rmse:0.478542+0.011662 test-rmse:0.673647+0.045185
## [14] train-rmse:0.466691+0.013676 test-rmse:0.672391+0.047412
## [15] train-rmse:0.453516+0.013176 test-rmse:0.665392+0.050221
## [16] train-rmse:0.441482+0.009543 test-rmse:0.657967+0.044493
## [17] train-rmse:0.432847+0.011824 test-rmse:0.655672+0.045974
## [18] train-rmse:0.421617+0.007546 test-rmse:0.650429+0.044169
## [19] train-rmse:0.409299+0.011572 test-rmse:0.646645+0.046677
## [20] train-rmse:0.402458+0.013085 test-rmse:0.647454+0.046264
## [21] train-rmse:0.395239+0.011630 test-rmse:0.646800+0.047104
## [22] train-rmse:0.388595+0.011484 test-rmse:0.648198+0.048110
## [23] train-rmse:0.381642+0.010842 test-rmse:0.646943+0.048134
## [24] train-rmse:0.374861+0.011739 test-rmse:0.646858+0.048262
## [25] train-rmse:0.367049+0.010466 test-rmse:0.645647+0.048207
## [26] train-rmse:0.361574+0.010921 test-rmse:0.646119+0.047858
## [27] train-rmse:0.353624+0.012015 test-rmse:0.642597+0.047995
## [28] train-rmse:0.346033+0.011177 test-rmse:0.639349+0.048146
## [29] train-rmse:0.341865+0.011332 test-rmse:0.638527+0.047726
## [30] train-rmse:0.336057+0.012133 test-rmse:0.637614+0.047629
## [31] train-rmse:0.331266+0.010452 test-rmse:0.636997+0.046942
## [32] train-rmse:0.326916+0.010621 test-rmse:0.636598+0.048484
## [33] train-rmse:0.322575+0.009485 test-rmse:0.636424+0.050197
## [34] train-rmse:0.316788+0.009041 test-rmse:0.636178+0.046457
## [35] train-rmse:0.312093+0.010395 test-rmse:0.636644+0.046009
## [36] train-rmse:0.306417+0.010929 test-rmse:0.637188+0.047410
## [37] train-rmse:0.301003+0.008511 test-rmse:0.635585+0.045686
## [38] train-rmse:0.295687+0.010028 test-rmse:0.633909+0.045314
## [39] train-rmse:0.291950+0.010321 test-rmse:0.633245+0.044582
## [40] train-rmse:0.286823+0.008677 test-rmse:0.633113+0.045267
## [41] train-rmse:0.277945+0.009105 test-rmse:0.632245+0.047206
## [42] train-rmse:0.273968+0.009045 test-rmse:0.631878+0.047209
## [43] train-rmse:0.269150+0.009086 test-rmse:0.630639+0.046585
## [44] train-rmse:0.265555+0.009229 test-rmse:0.627756+0.045799
## [45] train-rmse:0.260260+0.008619 test-rmse:0.626368+0.045334
## [46] train-rmse:0.255822+0.007681 test-rmse:0.625817+0.045803
## [47] train-rmse:0.250292+0.006595 test-rmse:0.625717+0.046187
## [48] train-rmse:0.245716+0.005551 test-rmse:0.625302+0.045709
## [49] train-rmse:0.241876+0.005766 test-rmse:0.624884+0.046168
## [50] train-rmse:0.237862+0.006068 test-rmse:0.624138+0.045504
## [51] train-rmse:0.233970+0.005351 test-rmse:0.625471+0.046005
## [52] train-rmse:0.231855+0.005663 test-rmse:0.625182+0.046865
## [53] train-rmse:0.229404+0.005995 test-rmse:0.625934+0.047896
## [54] train-rmse:0.226027+0.005709 test-rmse:0.625878+0.048259
## [55] train-rmse:0.222643+0.006765 test-rmse:0.626317+0.048414
## [56] train-rmse:0.219135+0.007240 test-rmse:0.626766+0.048889
## Stopping. Best iteration:
## [50] train-rmse:0.237862+0.006068 test-rmse:0.624138+0.045504
##
## 47
## [1] train-rmse:1.317944+0.012497 test-rmse:1.336587+0.074340
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.098281+0.008934 test-rmse:1.132505+0.075169
## [3] train-rmse:0.930867+0.007295 test-rmse:0.988027+0.073821
## [4] train-rmse:0.804201+0.005482 test-rmse:0.880658+0.071867
## [5] train-rmse:0.710858+0.004608 test-rmse:0.810543+0.068446
## [6] train-rmse:0.642871+0.006091 test-rmse:0.760514+0.066678
## [7] train-rmse:0.588211+0.011186 test-rmse:0.727960+0.065535
## [8] train-rmse:0.547800+0.010122 test-rmse:0.703771+0.060452
## [9] train-rmse:0.513527+0.011205 test-rmse:0.688430+0.057508
## [10] train-rmse:0.486395+0.016089 test-rmse:0.676893+0.051462
## [11] train-rmse:0.468160+0.015267 test-rmse:0.671071+0.052424
## [12] train-rmse:0.451389+0.014986 test-rmse:0.664576+0.054352
## [13] train-rmse:0.432510+0.015729 test-rmse:0.659781+0.057334
## [14] train-rmse:0.416647+0.018229 test-rmse:0.657461+0.056543
## [15] train-rmse:0.404887+0.019073 test-rmse:0.654504+0.059353
## [16] train-rmse:0.388829+0.016875 test-rmse:0.647769+0.054785
## [17] train-rmse:0.377940+0.018996 test-rmse:0.646580+0.055976
## [18] train-rmse:0.366659+0.018637 test-rmse:0.645293+0.053352
## [19] train-rmse:0.355531+0.015164 test-rmse:0.641625+0.054068
## [20] train-rmse:0.344956+0.015833 test-rmse:0.640443+0.053853
## [21] train-rmse:0.335075+0.013613 test-rmse:0.638759+0.055436
## [22] train-rmse:0.325445+0.017677 test-rmse:0.636628+0.053234
## [23] train-rmse:0.316744+0.017236 test-rmse:0.639304+0.056066
## [24] train-rmse:0.311144+0.017046 test-rmse:0.638590+0.057517
## [25] train-rmse:0.302271+0.018840 test-rmse:0.639017+0.056079
## [26] train-rmse:0.293284+0.017934 test-rmse:0.634636+0.053703
## [27] train-rmse:0.286631+0.017592 test-rmse:0.635144+0.054414
## [28] train-rmse:0.280189+0.015677 test-rmse:0.636264+0.054786
## [29] train-rmse:0.274117+0.015739 test-rmse:0.636396+0.055556
## [30] train-rmse:0.268008+0.015752 test-rmse:0.635330+0.054169
## [31] train-rmse:0.260725+0.014871 test-rmse:0.631975+0.054684
## [32] train-rmse:0.253846+0.014308 test-rmse:0.631967+0.055470
## [33] train-rmse:0.247225+0.015294 test-rmse:0.632816+0.056412
## [34] train-rmse:0.241374+0.015932 test-rmse:0.632464+0.056075
## [35] train-rmse:0.236018+0.016362 test-rmse:0.631827+0.055155
## [36] train-rmse:0.230450+0.013282 test-rmse:0.631808+0.054301
## [37] train-rmse:0.225938+0.013178 test-rmse:0.632402+0.053283
## [38] train-rmse:0.220227+0.013459 test-rmse:0.633171+0.053016
## [39] train-rmse:0.216364+0.013119 test-rmse:0.632166+0.052969
## [40] train-rmse:0.212494+0.012929 test-rmse:0.632538+0.052855
## [41] train-rmse:0.208687+0.013198 test-rmse:0.632292+0.053460
## [42] train-rmse:0.205489+0.013654 test-rmse:0.632736+0.053946
## Stopping. Best iteration:
## [36] train-rmse:0.230450+0.013282 test-rmse:0.631808+0.054301
##
## 48
## [1] train-rmse:1.315690+0.012066 test-rmse:1.335404+0.074507
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.093464+0.008860 test-rmse:1.137688+0.074651
## [3] train-rmse:0.922206+0.005430 test-rmse:0.986358+0.078090
## [4] train-rmse:0.790585+0.007182 test-rmse:0.882324+0.073716
## [5] train-rmse:0.690562+0.008215 test-rmse:0.809352+0.074680
## [6] train-rmse:0.618371+0.009671 test-rmse:0.757248+0.075679
## [7] train-rmse:0.565353+0.012214 test-rmse:0.727695+0.071640
## [8] train-rmse:0.517674+0.009632 test-rmse:0.701196+0.066266
## [9] train-rmse:0.486159+0.010349 test-rmse:0.686549+0.065421
## [10] train-rmse:0.456609+0.008854 test-rmse:0.677191+0.060940
## [11] train-rmse:0.424670+0.010258 test-rmse:0.665901+0.057431
## [12] train-rmse:0.404125+0.012237 test-rmse:0.660419+0.055360
## [13] train-rmse:0.385914+0.015309 test-rmse:0.656881+0.053402
## [14] train-rmse:0.371024+0.014201 test-rmse:0.653014+0.052325
## [15] train-rmse:0.354482+0.015050 test-rmse:0.648035+0.050890
## [16] train-rmse:0.344776+0.014845 test-rmse:0.643851+0.049730
## [17] train-rmse:0.331724+0.015272 test-rmse:0.638750+0.050696
## [18] train-rmse:0.319699+0.013720 test-rmse:0.641084+0.049825
## [19] train-rmse:0.308349+0.015809 test-rmse:0.640784+0.050022
## [20] train-rmse:0.298180+0.015012 test-rmse:0.641627+0.048185
## [21] train-rmse:0.289827+0.014562 test-rmse:0.640311+0.047134
## [22] train-rmse:0.277730+0.013108 test-rmse:0.640452+0.049829
## [23] train-rmse:0.271568+0.013341 test-rmse:0.641022+0.050579
## Stopping. Best iteration:
## [17] train-rmse:0.331724+0.015272 test-rmse:0.638750+0.050696
##
## 49
## [1] train-rmse:1.315890+0.012887 test-rmse:1.317544+0.070505
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.113738+0.009987 test-rmse:1.117403+0.068629
## [3] train-rmse:0.977582+0.008073 test-rmse:0.984194+0.066309
## [4] train-rmse:0.886404+0.007556 test-rmse:0.898710+0.060480
## [5] train-rmse:0.827035+0.006755 test-rmse:0.841880+0.054926
## [6] train-rmse:0.787837+0.006393 test-rmse:0.808665+0.048167
## [7] train-rmse:0.762346+0.005764 test-rmse:0.789827+0.043885
## [8] train-rmse:0.745203+0.005531 test-rmse:0.774005+0.043564
## [9] train-rmse:0.732934+0.005344 test-rmse:0.764308+0.041103
## [10] train-rmse:0.723546+0.005191 test-rmse:0.755119+0.037148
## [11] train-rmse:0.716179+0.004925 test-rmse:0.749276+0.035410
## [12] train-rmse:0.710251+0.005041 test-rmse:0.746066+0.030572
## [13] train-rmse:0.705450+0.005036 test-rmse:0.744156+0.030675
## [14] train-rmse:0.701107+0.005107 test-rmse:0.741423+0.029062
## [15] train-rmse:0.697018+0.005158 test-rmse:0.739630+0.029941
## [16] train-rmse:0.693313+0.005096 test-rmse:0.738294+0.030407
## [17] train-rmse:0.690021+0.005099 test-rmse:0.734282+0.028824
## [18] train-rmse:0.686880+0.005117 test-rmse:0.732968+0.029171
## [19] train-rmse:0.683869+0.005154 test-rmse:0.733849+0.029645
## [20] train-rmse:0.681045+0.005180 test-rmse:0.733042+0.027571
## [21] train-rmse:0.678290+0.005225 test-rmse:0.731145+0.027424
## [22] train-rmse:0.675654+0.005345 test-rmse:0.729012+0.027272
## [23] train-rmse:0.673142+0.005350 test-rmse:0.727682+0.029730
## [24] train-rmse:0.670686+0.005329 test-rmse:0.728091+0.032107
## [25] train-rmse:0.668315+0.005329 test-rmse:0.727423+0.032723
## [26] train-rmse:0.666054+0.005304 test-rmse:0.725774+0.032572
## [27] train-rmse:0.663843+0.005324 test-rmse:0.726022+0.033202
## [28] train-rmse:0.661734+0.005351 test-rmse:0.723649+0.032418
## [29] train-rmse:0.659696+0.005381 test-rmse:0.722493+0.032527
## [30] train-rmse:0.657758+0.005432 test-rmse:0.720233+0.030230
## [31] train-rmse:0.655892+0.005431 test-rmse:0.720589+0.031472
## [32] train-rmse:0.654095+0.005441 test-rmse:0.720500+0.032130
## [33] train-rmse:0.652323+0.005490 test-rmse:0.721934+0.032150
## [34] train-rmse:0.650538+0.005526 test-rmse:0.719780+0.030809
## [35] train-rmse:0.648882+0.005591 test-rmse:0.719030+0.031080
## [36] train-rmse:0.647246+0.005643 test-rmse:0.719881+0.032829
## [37] train-rmse:0.645664+0.005692 test-rmse:0.718505+0.034148
## [38] train-rmse:0.644113+0.005716 test-rmse:0.718282+0.034845
## [39] train-rmse:0.642588+0.005762 test-rmse:0.718812+0.034863
## [40] train-rmse:0.641122+0.005789 test-rmse:0.718681+0.035429
## [41] train-rmse:0.639714+0.005787 test-rmse:0.716537+0.034656
## [42] train-rmse:0.638327+0.005785 test-rmse:0.715975+0.035061
## [43] train-rmse:0.636965+0.005773 test-rmse:0.714461+0.035018
## [44] train-rmse:0.635625+0.005800 test-rmse:0.713615+0.035479
## [45] train-rmse:0.634290+0.005808 test-rmse:0.712902+0.033717
## [46] train-rmse:0.632990+0.005811 test-rmse:0.712070+0.033845
## [47] train-rmse:0.631717+0.005863 test-rmse:0.710856+0.035066
## [48] train-rmse:0.630481+0.005862 test-rmse:0.709024+0.034507
## [49] train-rmse:0.629282+0.005852 test-rmse:0.709020+0.035572
## [50] train-rmse:0.628091+0.005839 test-rmse:0.709851+0.036211
## [51] train-rmse:0.626913+0.005860 test-rmse:0.709295+0.035355
## [52] train-rmse:0.625757+0.005854 test-rmse:0.707974+0.035399
## [53] train-rmse:0.624637+0.005865 test-rmse:0.709099+0.035644
## [54] train-rmse:0.623543+0.005867 test-rmse:0.710186+0.035704
## [55] train-rmse:0.622465+0.005869 test-rmse:0.708644+0.034881
## [56] train-rmse:0.621382+0.005909 test-rmse:0.707593+0.033037
## [57] train-rmse:0.620335+0.005936 test-rmse:0.708005+0.032742
## [58] train-rmse:0.619304+0.005972 test-rmse:0.708598+0.033803
## [59] train-rmse:0.618303+0.005996 test-rmse:0.708244+0.033255
## [60] train-rmse:0.617320+0.006006 test-rmse:0.706907+0.033224
## [61] train-rmse:0.616339+0.006037 test-rmse:0.705922+0.033191
## [62] train-rmse:0.615373+0.006065 test-rmse:0.707497+0.033493
## [63] train-rmse:0.614399+0.006060 test-rmse:0.705368+0.035124
## [64] train-rmse:0.613456+0.006065 test-rmse:0.704429+0.034680
## [65] train-rmse:0.612543+0.006073 test-rmse:0.703026+0.033562
## [66] train-rmse:0.611633+0.006101 test-rmse:0.702797+0.033605
## [67] train-rmse:0.610740+0.006097 test-rmse:0.701457+0.033767
## [68] train-rmse:0.609853+0.006087 test-rmse:0.701979+0.033623
## [69] train-rmse:0.608995+0.006075 test-rmse:0.700926+0.033811
## [70] train-rmse:0.608153+0.006086 test-rmse:0.700877+0.034137
## [71] train-rmse:0.607318+0.006085 test-rmse:0.700580+0.034196
## [72] train-rmse:0.606499+0.006096 test-rmse:0.699602+0.034537
## [73] train-rmse:0.605681+0.006105 test-rmse:0.699012+0.034097
## [74] train-rmse:0.604888+0.006099 test-rmse:0.697652+0.035379
## [75] train-rmse:0.604095+0.006115 test-rmse:0.697307+0.035055
## [76] train-rmse:0.603300+0.006145 test-rmse:0.696267+0.034624
## [77] train-rmse:0.602517+0.006173 test-rmse:0.695468+0.033740
## [78] train-rmse:0.601741+0.006197 test-rmse:0.695443+0.034028
## [79] train-rmse:0.600998+0.006202 test-rmse:0.695903+0.034948
## [80] train-rmse:0.600260+0.006224 test-rmse:0.695642+0.034393
## [81] train-rmse:0.599528+0.006225 test-rmse:0.696506+0.033802
## [82] train-rmse:0.598797+0.006225 test-rmse:0.697504+0.035064
## [83] train-rmse:0.598079+0.006217 test-rmse:0.696632+0.035505
## [84] train-rmse:0.597365+0.006240 test-rmse:0.695724+0.036397
## Stopping. Best iteration:
## [78] train-rmse:0.601741+0.006197 test-rmse:0.695443+0.034028
##
## 50
## [1] train-rmse:1.308891+0.012745 test-rmse:1.315359+0.070771
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.099438+0.009538 test-rmse:1.112881+0.070501
## [3] train-rmse:0.954720+0.007183 test-rmse:0.979794+0.069721
## [4] train-rmse:0.857385+0.005544 test-rmse:0.888279+0.065288
## [5] train-rmse:0.792684+0.005931 test-rmse:0.830967+0.063979
## [6] train-rmse:0.744643+0.003650 test-rmse:0.787513+0.057729
## [7] train-rmse:0.714367+0.004621 test-rmse:0.762643+0.051285
## [8] train-rmse:0.690852+0.002215 test-rmse:0.742088+0.051339
## [9] train-rmse:0.676178+0.002027 test-rmse:0.733439+0.050555
## [10] train-rmse:0.664697+0.002169 test-rmse:0.728237+0.046007
## [11] train-rmse:0.655548+0.002276 test-rmse:0.723305+0.043110
## [12] train-rmse:0.648329+0.002299 test-rmse:0.719667+0.042217
## [13] train-rmse:0.640115+0.003327 test-rmse:0.714165+0.046875
## [14] train-rmse:0.633258+0.003652 test-rmse:0.706544+0.045266
## [15] train-rmse:0.627669+0.004468 test-rmse:0.703882+0.043811
## [16] train-rmse:0.619173+0.002961 test-rmse:0.699901+0.044356
## [17] train-rmse:0.614003+0.003948 test-rmse:0.698425+0.044095
## [18] train-rmse:0.609294+0.003495 test-rmse:0.700569+0.047441
## [19] train-rmse:0.605010+0.003176 test-rmse:0.699961+0.048113
## [20] train-rmse:0.600404+0.002830 test-rmse:0.699307+0.047939
## [21] train-rmse:0.595444+0.002669 test-rmse:0.700035+0.046338
## [22] train-rmse:0.591000+0.002345 test-rmse:0.699611+0.047818
## [23] train-rmse:0.585698+0.003946 test-rmse:0.693954+0.046012
## [24] train-rmse:0.581240+0.003295 test-rmse:0.692549+0.045058
## [25] train-rmse:0.576542+0.003578 test-rmse:0.691857+0.043641
## [26] train-rmse:0.573364+0.003088 test-rmse:0.690311+0.042875
## [27] train-rmse:0.570387+0.003543 test-rmse:0.690308+0.044879
## [28] train-rmse:0.564411+0.005547 test-rmse:0.685045+0.042418
## [29] train-rmse:0.560902+0.005461 test-rmse:0.681812+0.040520
## [30] train-rmse:0.557504+0.004777 test-rmse:0.680443+0.041768
## [31] train-rmse:0.554712+0.005221 test-rmse:0.679122+0.040924
## [32] train-rmse:0.550416+0.006021 test-rmse:0.678497+0.041594
## [33] train-rmse:0.546889+0.006803 test-rmse:0.677563+0.040994
## [34] train-rmse:0.544013+0.006700 test-rmse:0.676324+0.041237
## [35] train-rmse:0.541775+0.007146 test-rmse:0.677040+0.043103
## [36] train-rmse:0.538438+0.008070 test-rmse:0.675065+0.042416
## [37] train-rmse:0.535729+0.007704 test-rmse:0.675646+0.041556
## [38] train-rmse:0.533621+0.007697 test-rmse:0.675660+0.041784
## [39] train-rmse:0.530035+0.008029 test-rmse:0.672426+0.039578
## [40] train-rmse:0.526726+0.007125 test-rmse:0.669518+0.036557
## [41] train-rmse:0.523710+0.006969 test-rmse:0.668896+0.034861
## [42] train-rmse:0.521088+0.006843 test-rmse:0.668053+0.036785
## [43] train-rmse:0.518827+0.007312 test-rmse:0.667572+0.038270
## [44] train-rmse:0.516668+0.007597 test-rmse:0.666731+0.039089
## [45] train-rmse:0.514805+0.007726 test-rmse:0.666742+0.039692
## [46] train-rmse:0.511995+0.007256 test-rmse:0.667268+0.040201
## [47] train-rmse:0.508533+0.004231 test-rmse:0.666380+0.044274
## [48] train-rmse:0.506426+0.003843 test-rmse:0.666131+0.043681
## [49] train-rmse:0.504240+0.003859 test-rmse:0.666207+0.044824
## [50] train-rmse:0.502373+0.003247 test-rmse:0.665497+0.047074
## [51] train-rmse:0.499495+0.005639 test-rmse:0.664057+0.047834
## [52] train-rmse:0.497090+0.006437 test-rmse:0.662959+0.046977
## [53] train-rmse:0.494744+0.006014 test-rmse:0.662019+0.046642
## [54] train-rmse:0.491025+0.005628 test-rmse:0.661295+0.045194
## [55] train-rmse:0.488860+0.004913 test-rmse:0.660985+0.043987
## [56] train-rmse:0.486283+0.006771 test-rmse:0.660171+0.043889
## [57] train-rmse:0.483359+0.008308 test-rmse:0.660496+0.044047
## [58] train-rmse:0.481354+0.009075 test-rmse:0.661120+0.043203
## [59] train-rmse:0.479414+0.008985 test-rmse:0.660116+0.042740
## [60] train-rmse:0.478063+0.008975 test-rmse:0.657139+0.043137
## [61] train-rmse:0.476530+0.009191 test-rmse:0.655444+0.043520
## [62] train-rmse:0.474808+0.009288 test-rmse:0.656462+0.043707
## [63] train-rmse:0.473304+0.009232 test-rmse:0.656427+0.044202
## [64] train-rmse:0.470580+0.008430 test-rmse:0.655425+0.046025
## [65] train-rmse:0.468946+0.008216 test-rmse:0.655180+0.046413
## [66] train-rmse:0.467099+0.008201 test-rmse:0.653242+0.047363
## [67] train-rmse:0.465974+0.008363 test-rmse:0.652861+0.047368
## [68] train-rmse:0.464659+0.008990 test-rmse:0.654497+0.049383
## [69] train-rmse:0.462126+0.009754 test-rmse:0.654385+0.049566
## [70] train-rmse:0.460456+0.010052 test-rmse:0.652600+0.049460
## [71] train-rmse:0.459181+0.009869 test-rmse:0.653095+0.049160
## [72] train-rmse:0.457345+0.010399 test-rmse:0.650842+0.048962
## [73] train-rmse:0.454802+0.009965 test-rmse:0.648046+0.047633
## [74] train-rmse:0.452965+0.010180 test-rmse:0.647660+0.047953
## [75] train-rmse:0.451485+0.010462 test-rmse:0.647409+0.047898
## [76] train-rmse:0.450265+0.010417 test-rmse:0.646753+0.047608
## [77] train-rmse:0.449162+0.010524 test-rmse:0.645606+0.046658
## [78] train-rmse:0.447865+0.010628 test-rmse:0.645655+0.047295
## [79] train-rmse:0.446700+0.010683 test-rmse:0.645296+0.047053
## [80] train-rmse:0.443881+0.009786 test-rmse:0.644353+0.047147
## [81] train-rmse:0.441565+0.009512 test-rmse:0.644335+0.046901
## [82] train-rmse:0.439483+0.009153 test-rmse:0.642401+0.046573
## [83] train-rmse:0.437979+0.009524 test-rmse:0.643598+0.045457
## [84] train-rmse:0.436921+0.009829 test-rmse:0.642604+0.044830
## [85] train-rmse:0.435143+0.011350 test-rmse:0.642658+0.044542
## [86] train-rmse:0.433860+0.012004 test-rmse:0.642765+0.044018
## [87] train-rmse:0.432922+0.011963 test-rmse:0.642730+0.044991
## [88] train-rmse:0.431555+0.011693 test-rmse:0.641454+0.044141
## [89] train-rmse:0.430303+0.011294 test-rmse:0.640935+0.043907
## [90] train-rmse:0.428775+0.011621 test-rmse:0.641567+0.044410
## [91] train-rmse:0.427893+0.011632 test-rmse:0.640431+0.043774
## [92] train-rmse:0.427106+0.011690 test-rmse:0.640452+0.043701
## [93] train-rmse:0.425693+0.011795 test-rmse:0.640815+0.043974
## [94] train-rmse:0.424620+0.011920 test-rmse:0.640631+0.044034
## [95] train-rmse:0.423434+0.012557 test-rmse:0.640632+0.044824
## [96] train-rmse:0.422251+0.012707 test-rmse:0.641897+0.044295
## [97] train-rmse:0.420619+0.012591 test-rmse:0.642099+0.044204
## Stopping. Best iteration:
## [91] train-rmse:0.427893+0.011632 test-rmse:0.640431+0.043774
##
## 51
## [1] train-rmse:1.304120+0.012347 test-rmse:1.312853+0.069419
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.087154+0.009351 test-rmse:1.108824+0.067761
## [3] train-rmse:0.936170+0.006935 test-rmse:0.970044+0.067728
## [4] train-rmse:0.831798+0.004655 test-rmse:0.873167+0.069957
## [5] train-rmse:0.759816+0.005479 test-rmse:0.815723+0.066027
## [6] train-rmse:0.711141+0.008698 test-rmse:0.777318+0.058962
## [7] train-rmse:0.668828+0.005733 test-rmse:0.744899+0.055307
## [8] train-rmse:0.644714+0.004454 test-rmse:0.724089+0.052169
## [9] train-rmse:0.623914+0.005132 test-rmse:0.714400+0.050054
## [10] train-rmse:0.610059+0.006081 test-rmse:0.708183+0.048631
## [11] train-rmse:0.598453+0.006705 test-rmse:0.704158+0.048846
## [12] train-rmse:0.583973+0.006754 test-rmse:0.699228+0.051301
## [13] train-rmse:0.575541+0.007046 test-rmse:0.696404+0.054779
## [14] train-rmse:0.567714+0.007427 test-rmse:0.695596+0.052239
## [15] train-rmse:0.560784+0.008264 test-rmse:0.692382+0.051879
## [16] train-rmse:0.551976+0.006425 test-rmse:0.688311+0.051296
## [17] train-rmse:0.546747+0.006380 test-rmse:0.685555+0.055060
## [18] train-rmse:0.539581+0.006561 test-rmse:0.686185+0.056954
## [19] train-rmse:0.533007+0.008329 test-rmse:0.687186+0.057675
## [20] train-rmse:0.527569+0.009281 test-rmse:0.683450+0.056366
## [21] train-rmse:0.519548+0.010959 test-rmse:0.682719+0.058287
## [22] train-rmse:0.513001+0.011540 test-rmse:0.680401+0.057713
## [23] train-rmse:0.506406+0.012588 test-rmse:0.673925+0.053050
## [24] train-rmse:0.499358+0.009191 test-rmse:0.666846+0.051472
## [25] train-rmse:0.493308+0.007849 test-rmse:0.666480+0.054425
## [26] train-rmse:0.488235+0.008140 test-rmse:0.667279+0.056584
## [27] train-rmse:0.482714+0.007960 test-rmse:0.662412+0.054412
## [28] train-rmse:0.478566+0.007655 test-rmse:0.661542+0.055745
## [29] train-rmse:0.474130+0.009102 test-rmse:0.657887+0.054909
## [30] train-rmse:0.468862+0.010596 test-rmse:0.654666+0.053880
## [31] train-rmse:0.463433+0.009999 test-rmse:0.653953+0.053355
## [32] train-rmse:0.459136+0.010346 test-rmse:0.651980+0.052891
## [33] train-rmse:0.455318+0.010889 test-rmse:0.650611+0.052642
## [34] train-rmse:0.451554+0.010512 test-rmse:0.650017+0.052033
## [35] train-rmse:0.446789+0.010031 test-rmse:0.650763+0.054916
## [36] train-rmse:0.443576+0.009943 test-rmse:0.649908+0.056052
## [37] train-rmse:0.440628+0.010562 test-rmse:0.648594+0.056974
## [38] train-rmse:0.436306+0.009717 test-rmse:0.646367+0.056862
## [39] train-rmse:0.432387+0.010894 test-rmse:0.647777+0.055568
## [40] train-rmse:0.427887+0.010286 test-rmse:0.645537+0.058844
## [41] train-rmse:0.423775+0.010312 test-rmse:0.643856+0.059097
## [42] train-rmse:0.418944+0.012601 test-rmse:0.642842+0.061340
## [43] train-rmse:0.415968+0.013304 test-rmse:0.643515+0.062567
## [44] train-rmse:0.413025+0.013410 test-rmse:0.642769+0.063912
## [45] train-rmse:0.410151+0.013600 test-rmse:0.641911+0.061756
## [46] train-rmse:0.407861+0.013679 test-rmse:0.642480+0.061750
## [47] train-rmse:0.404865+0.013681 test-rmse:0.641617+0.061111
## [48] train-rmse:0.401538+0.013915 test-rmse:0.641290+0.061141
## [49] train-rmse:0.399181+0.014540 test-rmse:0.640240+0.063177
## [50] train-rmse:0.396354+0.015151 test-rmse:0.639629+0.064148
## [51] train-rmse:0.392263+0.013831 test-rmse:0.640265+0.065031
## [52] train-rmse:0.388678+0.013494 test-rmse:0.639806+0.065410
## [53] train-rmse:0.386321+0.013486 test-rmse:0.640314+0.066043
## [54] train-rmse:0.382987+0.013503 test-rmse:0.641362+0.066513
## [55] train-rmse:0.380991+0.013194 test-rmse:0.640919+0.066116
## [56] train-rmse:0.378957+0.013205 test-rmse:0.641069+0.066855
## Stopping. Best iteration:
## [50] train-rmse:0.396354+0.015151 test-rmse:0.639629+0.064148
##
## 52
## [1] train-rmse:1.299208+0.012425 test-rmse:1.315877+0.070282
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.074396+0.008521 test-rmse:1.105152+0.073050
## [3] train-rmse:0.916632+0.006306 test-rmse:0.958205+0.077652
## [4] train-rmse:0.805732+0.005516 test-rmse:0.863112+0.073598
## [5] train-rmse:0.727958+0.006274 test-rmse:0.798766+0.069591
## [6] train-rmse:0.671201+0.003486 test-rmse:0.748758+0.064084
## [7] train-rmse:0.633052+0.004583 test-rmse:0.727520+0.061274
## [8] train-rmse:0.600431+0.004748 test-rmse:0.706210+0.055031
## [9] train-rmse:0.576697+0.003846 test-rmse:0.693092+0.052566
## [10] train-rmse:0.556749+0.007249 test-rmse:0.685811+0.050714
## [11] train-rmse:0.541989+0.007708 test-rmse:0.678157+0.053105
## [12] train-rmse:0.527802+0.008859 test-rmse:0.675938+0.052960
## [13] train-rmse:0.517404+0.008900 test-rmse:0.669168+0.051326
## [14] train-rmse:0.506640+0.008506 test-rmse:0.664779+0.054373
## [15] train-rmse:0.497208+0.010722 test-rmse:0.666246+0.054434
## [16] train-rmse:0.487844+0.010777 test-rmse:0.662103+0.049815
## [17] train-rmse:0.476818+0.005119 test-rmse:0.657487+0.046980
## [18] train-rmse:0.467282+0.007295 test-rmse:0.654529+0.046424
## [19] train-rmse:0.457942+0.007830 test-rmse:0.654951+0.048055
## [20] train-rmse:0.449692+0.006577 test-rmse:0.648639+0.047403
## [21] train-rmse:0.443932+0.007503 test-rmse:0.649939+0.050405
## [22] train-rmse:0.437171+0.008173 test-rmse:0.648047+0.051553
## [23] train-rmse:0.428368+0.008518 test-rmse:0.644121+0.053851
## [24] train-rmse:0.418810+0.007039 test-rmse:0.642360+0.053299
## [25] train-rmse:0.411797+0.004669 test-rmse:0.642063+0.055161
## [26] train-rmse:0.406471+0.005979 test-rmse:0.639404+0.055509
## [27] train-rmse:0.402270+0.006381 test-rmse:0.640429+0.055618
## [28] train-rmse:0.397564+0.007121 test-rmse:0.641459+0.055855
## [29] train-rmse:0.391000+0.006919 test-rmse:0.639579+0.055170
## [30] train-rmse:0.383807+0.008511 test-rmse:0.641087+0.056179
## [31] train-rmse:0.379149+0.009932 test-rmse:0.642194+0.056193
## [32] train-rmse:0.374163+0.010043 test-rmse:0.642630+0.056257
## Stopping. Best iteration:
## [26] train-rmse:0.406471+0.005979 test-rmse:0.639404+0.055509
##
## 53
## [1] train-rmse:1.295865+0.012639 test-rmse:1.311254+0.073177
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.067190+0.008991 test-rmse:1.100080+0.073706
## [3] train-rmse:0.900889+0.007038 test-rmse:0.949939+0.072493
## [4] train-rmse:0.783862+0.005836 test-rmse:0.854883+0.072168
## [5] train-rmse:0.701708+0.006952 test-rmse:0.786549+0.064615
## [6] train-rmse:0.642902+0.005385 test-rmse:0.745342+0.058394
## [7] train-rmse:0.600939+0.004035 test-rmse:0.720495+0.056126
## [8] train-rmse:0.566994+0.004436 test-rmse:0.699419+0.052353
## [9] train-rmse:0.539384+0.004753 test-rmse:0.686298+0.052769
## [10] train-rmse:0.514008+0.007387 test-rmse:0.679085+0.052521
## [11] train-rmse:0.489076+0.008561 test-rmse:0.668363+0.054331
## [12] train-rmse:0.473043+0.010061 test-rmse:0.667935+0.056635
## [13] train-rmse:0.461754+0.007917 test-rmse:0.663239+0.056992
## [14] train-rmse:0.450012+0.007361 test-rmse:0.659458+0.055075
## [15] train-rmse:0.434873+0.003459 test-rmse:0.653941+0.050531
## [16] train-rmse:0.424217+0.005809 test-rmse:0.654167+0.049771
## [17] train-rmse:0.415079+0.006298 test-rmse:0.651595+0.048821
## [18] train-rmse:0.406636+0.007853 test-rmse:0.650329+0.049715
## [19] train-rmse:0.397028+0.010620 test-rmse:0.648515+0.050847
## [20] train-rmse:0.386773+0.011015 test-rmse:0.645666+0.050635
## [21] train-rmse:0.378823+0.009133 test-rmse:0.643729+0.052515
## [22] train-rmse:0.370106+0.007388 test-rmse:0.642516+0.053840
## [23] train-rmse:0.361833+0.008677 test-rmse:0.641523+0.052784
## [24] train-rmse:0.355551+0.009173 test-rmse:0.641616+0.056775
## [25] train-rmse:0.347486+0.008513 test-rmse:0.638688+0.055683
## [26] train-rmse:0.339945+0.010391 test-rmse:0.637531+0.053733
## [27] train-rmse:0.332639+0.010923 test-rmse:0.633689+0.052288
## [28] train-rmse:0.327260+0.010291 test-rmse:0.634932+0.051002
## [29] train-rmse:0.321862+0.009706 test-rmse:0.635939+0.049698
## [30] train-rmse:0.315415+0.009724 test-rmse:0.634441+0.049262
## [31] train-rmse:0.308442+0.008244 test-rmse:0.632919+0.049967
## [32] train-rmse:0.301480+0.009107 test-rmse:0.633212+0.052224
## [33] train-rmse:0.295859+0.009453 test-rmse:0.634256+0.052586
## [34] train-rmse:0.289589+0.007739 test-rmse:0.632575+0.052113
## [35] train-rmse:0.284771+0.009117 test-rmse:0.632762+0.053811
## [36] train-rmse:0.279541+0.009461 test-rmse:0.631652+0.054123
## [37] train-rmse:0.273966+0.009528 test-rmse:0.631047+0.054571
## [38] train-rmse:0.268529+0.010314 test-rmse:0.631401+0.054205
## [39] train-rmse:0.264987+0.010397 test-rmse:0.632747+0.053293
## [40] train-rmse:0.259673+0.009999 test-rmse:0.633931+0.055018
## [41] train-rmse:0.255042+0.011841 test-rmse:0.634917+0.053694
## [42] train-rmse:0.250153+0.012062 test-rmse:0.634960+0.053622
## [43] train-rmse:0.244135+0.011471 test-rmse:0.633761+0.056965
## Stopping. Best iteration:
## [37] train-rmse:0.273966+0.009528 test-rmse:0.631047+0.054571
##
## 54
## [1] train-rmse:1.292466+0.012085 test-rmse:1.313203+0.074978
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.060121+0.008388 test-rmse:1.098735+0.075871
## [3] train-rmse:0.886153+0.006848 test-rmse:0.944895+0.074737
## [4] train-rmse:0.761714+0.005996 test-rmse:0.847918+0.073385
## [5] train-rmse:0.672224+0.007791 test-rmse:0.776482+0.065922
## [6] train-rmse:0.609005+0.007774 test-rmse:0.730398+0.068054
## [7] train-rmse:0.561921+0.008619 test-rmse:0.701820+0.063026
## [8] train-rmse:0.526592+0.008590 test-rmse:0.687631+0.061092
## [9] train-rmse:0.493657+0.014657 test-rmse:0.677120+0.060821
## [10] train-rmse:0.471492+0.016544 test-rmse:0.669495+0.059920
## [11] train-rmse:0.449980+0.018965 test-rmse:0.661253+0.057637
## [12] train-rmse:0.430423+0.018050 test-rmse:0.653383+0.055406
## [13] train-rmse:0.415448+0.019605 test-rmse:0.649652+0.056139
## [14] train-rmse:0.402963+0.019946 test-rmse:0.646089+0.054992
## [15] train-rmse:0.386874+0.017783 test-rmse:0.640588+0.049639
## [16] train-rmse:0.376590+0.018264 test-rmse:0.637080+0.048119
## [17] train-rmse:0.365339+0.017965 test-rmse:0.636889+0.046935
## [18] train-rmse:0.358570+0.019029 test-rmse:0.636333+0.047503
## [19] train-rmse:0.349709+0.016047 test-rmse:0.635539+0.045799
## [20] train-rmse:0.338226+0.015453 test-rmse:0.633137+0.047065
## [21] train-rmse:0.328678+0.018655 test-rmse:0.633112+0.047584
## [22] train-rmse:0.319053+0.018250 test-rmse:0.630058+0.048874
## [23] train-rmse:0.307343+0.016340 test-rmse:0.628689+0.048993
## [24] train-rmse:0.298621+0.015930 test-rmse:0.627749+0.050099
## [25] train-rmse:0.291140+0.016892 test-rmse:0.627502+0.048175
## [26] train-rmse:0.286043+0.016557 test-rmse:0.626121+0.047911
## [27] train-rmse:0.278124+0.016927 test-rmse:0.625853+0.047879
## [28] train-rmse:0.272625+0.016940 test-rmse:0.626830+0.048415
## [29] train-rmse:0.266049+0.017105 test-rmse:0.626590+0.048207
## [30] train-rmse:0.258153+0.016194 test-rmse:0.623741+0.048768
## [31] train-rmse:0.250575+0.015947 test-rmse:0.622282+0.049536
## [32] train-rmse:0.244645+0.013651 test-rmse:0.622498+0.051126
## [33] train-rmse:0.238266+0.014914 test-rmse:0.622710+0.048415
## [34] train-rmse:0.232589+0.016093 test-rmse:0.621727+0.049399
## [35] train-rmse:0.226171+0.014826 test-rmse:0.620258+0.048819
## [36] train-rmse:0.220483+0.016258 test-rmse:0.621012+0.048479
## [37] train-rmse:0.214752+0.015284 test-rmse:0.620588+0.048308
## [38] train-rmse:0.209968+0.015194 test-rmse:0.620609+0.049588
## [39] train-rmse:0.204286+0.014515 test-rmse:0.620807+0.048856
## [40] train-rmse:0.199429+0.015267 test-rmse:0.621428+0.049127
## [41] train-rmse:0.193931+0.014958 test-rmse:0.620452+0.048798
## Stopping. Best iteration:
## [35] train-rmse:0.226171+0.014826 test-rmse:0.620258+0.048819
##
## 55
## [1] train-rmse:1.289971+0.011612 test-rmse:1.311908+0.075164
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.054713+0.008133 test-rmse:1.101450+0.077715
## [3] train-rmse:0.877898+0.006587 test-rmse:0.949771+0.083471
## [4] train-rmse:0.746734+0.007289 test-rmse:0.847587+0.079723
## [5] train-rmse:0.650482+0.010656 test-rmse:0.778157+0.079093
## [6] train-rmse:0.582575+0.012331 test-rmse:0.732293+0.076890
## [7] train-rmse:0.532380+0.015982 test-rmse:0.705280+0.072358
## [8] train-rmse:0.491438+0.015792 test-rmse:0.682534+0.066567
## [9] train-rmse:0.457605+0.018602 test-rmse:0.668848+0.059853
## [10] train-rmse:0.431108+0.019719 test-rmse:0.660890+0.060661
## [11] train-rmse:0.409445+0.019133 test-rmse:0.657681+0.061602
## [12] train-rmse:0.391589+0.017806 test-rmse:0.654951+0.062243
## [13] train-rmse:0.374981+0.017874 test-rmse:0.652577+0.059942
## [14] train-rmse:0.357962+0.018813 test-rmse:0.652929+0.058631
## [15] train-rmse:0.342389+0.017829 test-rmse:0.654247+0.050525
## [16] train-rmse:0.329752+0.016987 test-rmse:0.656011+0.051014
## [17] train-rmse:0.320235+0.017753 test-rmse:0.654141+0.052451
## [18] train-rmse:0.303670+0.017638 test-rmse:0.649618+0.048111
## [19] train-rmse:0.295075+0.018038 test-rmse:0.650583+0.047567
## [20] train-rmse:0.282293+0.017628 test-rmse:0.652574+0.048927
## [21] train-rmse:0.273560+0.015914 test-rmse:0.650263+0.047163
## [22] train-rmse:0.265742+0.014784 test-rmse:0.651609+0.047862
## [23] train-rmse:0.258557+0.016085 test-rmse:0.653012+0.049396
## [24] train-rmse:0.248550+0.016803 test-rmse:0.653415+0.048401
## Stopping. Best iteration:
## [18] train-rmse:0.303670+0.017638 test-rmse:0.649618+0.048111
##
## 56
## [1] train-rmse:1.292819+0.012557 test-rmse:1.294737+0.070674
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.082743+0.009540 test-rmse:1.086919+0.068193
## [3] train-rmse:0.947429+0.007836 test-rmse:0.957209+0.067019
## [4] train-rmse:0.860153+0.007180 test-rmse:0.874910+0.058684
## [5] train-rmse:0.806376+0.006581 test-rmse:0.824667+0.051093
## [6] train-rmse:0.771956+0.006217 test-rmse:0.795951+0.045363
## [7] train-rmse:0.749900+0.005599 test-rmse:0.779459+0.041713
## [8] train-rmse:0.735327+0.005199 test-rmse:0.765939+0.039113
## [9] train-rmse:0.724634+0.004908 test-rmse:0.756581+0.035902
## [10] train-rmse:0.716488+0.004762 test-rmse:0.751133+0.033433
## [11] train-rmse:0.710061+0.004771 test-rmse:0.747695+0.031567
## [12] train-rmse:0.704771+0.004787 test-rmse:0.745696+0.029557
## [13] train-rmse:0.700148+0.004851 test-rmse:0.743440+0.029557
## [14] train-rmse:0.695895+0.004929 test-rmse:0.740220+0.027284
## [15] train-rmse:0.691773+0.004964 test-rmse:0.737950+0.029368
## [16] train-rmse:0.688123+0.005024 test-rmse:0.734510+0.028762
## [17] train-rmse:0.684850+0.004999 test-rmse:0.733333+0.027744
## [18] train-rmse:0.681797+0.005025 test-rmse:0.732427+0.027959
## [19] train-rmse:0.678902+0.005097 test-rmse:0.731670+0.028545
## [20] train-rmse:0.676081+0.005159 test-rmse:0.729283+0.027531
## [21] train-rmse:0.673297+0.005245 test-rmse:0.728580+0.028652
## [22] train-rmse:0.670686+0.005185 test-rmse:0.730024+0.030088
## [23] train-rmse:0.668154+0.005142 test-rmse:0.728039+0.030232
## [24] train-rmse:0.665658+0.005111 test-rmse:0.726383+0.030413
## [25] train-rmse:0.663313+0.005160 test-rmse:0.727488+0.031418
## [26] train-rmse:0.661010+0.005246 test-rmse:0.725895+0.032636
## [27] train-rmse:0.658785+0.005326 test-rmse:0.724120+0.031216
## [28] train-rmse:0.656597+0.005370 test-rmse:0.724082+0.032746
## [29] train-rmse:0.654528+0.005390 test-rmse:0.722657+0.033345
## [30] train-rmse:0.652576+0.005451 test-rmse:0.719933+0.031874
## [31] train-rmse:0.650683+0.005426 test-rmse:0.718367+0.031102
## [32] train-rmse:0.648849+0.005450 test-rmse:0.717011+0.032714
## [33] train-rmse:0.647103+0.005509 test-rmse:0.716622+0.031121
## [34] train-rmse:0.645400+0.005547 test-rmse:0.715780+0.030948
## [35] train-rmse:0.643738+0.005597 test-rmse:0.716613+0.032446
## [36] train-rmse:0.642109+0.005642 test-rmse:0.715657+0.032697
## [37] train-rmse:0.640545+0.005656 test-rmse:0.715308+0.031452
## [38] train-rmse:0.639046+0.005705 test-rmse:0.715679+0.032383
## [39] train-rmse:0.637523+0.005741 test-rmse:0.716066+0.033263
## [40] train-rmse:0.636070+0.005792 test-rmse:0.717457+0.033481
## [41] train-rmse:0.634613+0.005791 test-rmse:0.715892+0.034895
## [42] train-rmse:0.633199+0.005816 test-rmse:0.713584+0.035297
## [43] train-rmse:0.631817+0.005863 test-rmse:0.711588+0.034681
## [44] train-rmse:0.630464+0.005930 test-rmse:0.709398+0.033974
## [45] train-rmse:0.629115+0.005966 test-rmse:0.708841+0.033640
## [46] train-rmse:0.627802+0.005947 test-rmse:0.708189+0.033736
## [47] train-rmse:0.626530+0.005920 test-rmse:0.707033+0.034136
## [48] train-rmse:0.625309+0.005904 test-rmse:0.706969+0.033096
## [49] train-rmse:0.624116+0.005890 test-rmse:0.707473+0.033854
## [50] train-rmse:0.622951+0.005866 test-rmse:0.706300+0.033781
## [51] train-rmse:0.621773+0.005855 test-rmse:0.706015+0.034067
## [52] train-rmse:0.620581+0.005860 test-rmse:0.706530+0.034052
## [53] train-rmse:0.619442+0.005873 test-rmse:0.707480+0.033973
## [54] train-rmse:0.618337+0.005886 test-rmse:0.706475+0.034567
## [55] train-rmse:0.617283+0.005924 test-rmse:0.707219+0.034466
## [56] train-rmse:0.616248+0.005952 test-rmse:0.705010+0.034863
## [57] train-rmse:0.615177+0.005983 test-rmse:0.703701+0.033447
## [58] train-rmse:0.614159+0.005997 test-rmse:0.702368+0.032550
## [59] train-rmse:0.613156+0.006006 test-rmse:0.704126+0.033584
## [60] train-rmse:0.612157+0.006025 test-rmse:0.704614+0.033005
## [61] train-rmse:0.611166+0.006042 test-rmse:0.702557+0.033691
## [62] train-rmse:0.610196+0.006059 test-rmse:0.701860+0.033958
## [63] train-rmse:0.609244+0.006070 test-rmse:0.700985+0.034240
## [64] train-rmse:0.608294+0.006062 test-rmse:0.700514+0.035604
## [65] train-rmse:0.607369+0.006068 test-rmse:0.700285+0.035191
## [66] train-rmse:0.606453+0.006068 test-rmse:0.699065+0.035112
## [67] train-rmse:0.605571+0.006089 test-rmse:0.699055+0.034976
## [68] train-rmse:0.604684+0.006104 test-rmse:0.697310+0.034811
## [69] train-rmse:0.603801+0.006123 test-rmse:0.696845+0.034767
## [70] train-rmse:0.602920+0.006166 test-rmse:0.696099+0.035288
## [71] train-rmse:0.602096+0.006176 test-rmse:0.695791+0.035687
## [72] train-rmse:0.601295+0.006171 test-rmse:0.695929+0.035593
## [73] train-rmse:0.600491+0.006171 test-rmse:0.694780+0.035232
## [74] train-rmse:0.599656+0.006173 test-rmse:0.694337+0.035253
## [75] train-rmse:0.598854+0.006175 test-rmse:0.693103+0.034276
## [76] train-rmse:0.598071+0.006182 test-rmse:0.692575+0.034851
## [77] train-rmse:0.597294+0.006198 test-rmse:0.692503+0.035873
## [78] train-rmse:0.596542+0.006198 test-rmse:0.693534+0.035761
## [79] train-rmse:0.595788+0.006198 test-rmse:0.693147+0.035556
## [80] train-rmse:0.595070+0.006214 test-rmse:0.693187+0.035798
## [81] train-rmse:0.594362+0.006226 test-rmse:0.692603+0.034427
## [82] train-rmse:0.593643+0.006250 test-rmse:0.690993+0.034614
## [83] train-rmse:0.592943+0.006291 test-rmse:0.691165+0.034081
## [84] train-rmse:0.592252+0.006322 test-rmse:0.691090+0.034143
## [85] train-rmse:0.591554+0.006338 test-rmse:0.691547+0.033642
## [86] train-rmse:0.590854+0.006335 test-rmse:0.689854+0.033863
## [87] train-rmse:0.590161+0.006359 test-rmse:0.689966+0.033658
## [88] train-rmse:0.589500+0.006380 test-rmse:0.688691+0.034426
## [89] train-rmse:0.588849+0.006390 test-rmse:0.687409+0.033934
## [90] train-rmse:0.588208+0.006393 test-rmse:0.687673+0.033913
## [91] train-rmse:0.587550+0.006452 test-rmse:0.687149+0.032869
## [92] train-rmse:0.586916+0.006484 test-rmse:0.687369+0.032485
## [93] train-rmse:0.586307+0.006503 test-rmse:0.687242+0.032501
## [94] train-rmse:0.585703+0.006504 test-rmse:0.687062+0.032528
## [95] train-rmse:0.585107+0.006520 test-rmse:0.687478+0.033611
## [96] train-rmse:0.584514+0.006540 test-rmse:0.686972+0.033109
## [97] train-rmse:0.583922+0.006574 test-rmse:0.687040+0.033356
## [98] train-rmse:0.583321+0.006589 test-rmse:0.687322+0.033549
## [99] train-rmse:0.582760+0.006600 test-rmse:0.686040+0.034101
## [100] train-rmse:0.582197+0.006619 test-rmse:0.685861+0.034264
## [101] train-rmse:0.581627+0.006625 test-rmse:0.685605+0.035059
## [102] train-rmse:0.581057+0.006638 test-rmse:0.684815+0.035086
## [103] train-rmse:0.580478+0.006669 test-rmse:0.684626+0.034909
## [104] train-rmse:0.579922+0.006694 test-rmse:0.684436+0.034523
## [105] train-rmse:0.579372+0.006687 test-rmse:0.684793+0.034458
## [106] train-rmse:0.578814+0.006673 test-rmse:0.684594+0.034890
## [107] train-rmse:0.578282+0.006671 test-rmse:0.684362+0.035847
## [108] train-rmse:0.577782+0.006685 test-rmse:0.683502+0.035899
## [109] train-rmse:0.577272+0.006701 test-rmse:0.683877+0.035728
## [110] train-rmse:0.576760+0.006724 test-rmse:0.683182+0.035931
## [111] train-rmse:0.576243+0.006734 test-rmse:0.682719+0.035939
## [112] train-rmse:0.575741+0.006760 test-rmse:0.683369+0.035971
## [113] train-rmse:0.575247+0.006771 test-rmse:0.683466+0.035862
## [114] train-rmse:0.574746+0.006787 test-rmse:0.682331+0.035355
## [115] train-rmse:0.574265+0.006809 test-rmse:0.682596+0.035169
## [116] train-rmse:0.573776+0.006823 test-rmse:0.682820+0.035647
## [117] train-rmse:0.573299+0.006835 test-rmse:0.682713+0.035645
## [118] train-rmse:0.572823+0.006854 test-rmse:0.682011+0.036173
## [119] train-rmse:0.572352+0.006855 test-rmse:0.682702+0.036341
## [120] train-rmse:0.571888+0.006862 test-rmse:0.682993+0.036540
## [121] train-rmse:0.571429+0.006871 test-rmse:0.683008+0.036310
## [122] train-rmse:0.570966+0.006863 test-rmse:0.682134+0.036032
## [123] train-rmse:0.570531+0.006877 test-rmse:0.682539+0.035448
## [124] train-rmse:0.570098+0.006882 test-rmse:0.681192+0.035197
## [125] train-rmse:0.569659+0.006898 test-rmse:0.681035+0.035053
## [126] train-rmse:0.569215+0.006920 test-rmse:0.681558+0.034695
## [127] train-rmse:0.568783+0.006944 test-rmse:0.681407+0.034807
## [128] train-rmse:0.568345+0.006957 test-rmse:0.681944+0.035767
## [129] train-rmse:0.567919+0.006982 test-rmse:0.681341+0.035547
## [130] train-rmse:0.567504+0.006990 test-rmse:0.681062+0.035259
## [131] train-rmse:0.567091+0.007015 test-rmse:0.680793+0.034999
## [132] train-rmse:0.566694+0.007038 test-rmse:0.680713+0.035262
## [133] train-rmse:0.566293+0.007051 test-rmse:0.680055+0.035772
## [134] train-rmse:0.565870+0.007070 test-rmse:0.679626+0.035722
## [135] train-rmse:0.565469+0.007082 test-rmse:0.679482+0.036013
## [136] train-rmse:0.565073+0.007093 test-rmse:0.678733+0.036058
## [137] train-rmse:0.564677+0.007100 test-rmse:0.678235+0.036129
## [138] train-rmse:0.564280+0.007111 test-rmse:0.678794+0.036226
## [139] train-rmse:0.563881+0.007126 test-rmse:0.678563+0.035143
## [140] train-rmse:0.563502+0.007140 test-rmse:0.678161+0.035524
## [141] train-rmse:0.563131+0.007150 test-rmse:0.677281+0.035386
## [142] train-rmse:0.562758+0.007160 test-rmse:0.677770+0.035700
## [143] train-rmse:0.562390+0.007170 test-rmse:0.677587+0.035265
## [144] train-rmse:0.562008+0.007190 test-rmse:0.676470+0.035256
## [145] train-rmse:0.561634+0.007207 test-rmse:0.677187+0.035529
## [146] train-rmse:0.561261+0.007218 test-rmse:0.676168+0.035750
## [147] train-rmse:0.560891+0.007223 test-rmse:0.675865+0.035784
## [148] train-rmse:0.560540+0.007238 test-rmse:0.675991+0.035659
## [149] train-rmse:0.560203+0.007250 test-rmse:0.675746+0.035331
## [150] train-rmse:0.559856+0.007251 test-rmse:0.675935+0.035422
## [151] train-rmse:0.559517+0.007275 test-rmse:0.675673+0.035421
## [152] train-rmse:0.559172+0.007286 test-rmse:0.675855+0.035829
## [153] train-rmse:0.558829+0.007299 test-rmse:0.676601+0.036407
## [154] train-rmse:0.558502+0.007306 test-rmse:0.676830+0.036189
## [155] train-rmse:0.558172+0.007322 test-rmse:0.676792+0.035711
## [156] train-rmse:0.557844+0.007344 test-rmse:0.676735+0.035761
## [157] train-rmse:0.557516+0.007370 test-rmse:0.676664+0.035746
## Stopping. Best iteration:
## [151] train-rmse:0.559517+0.007275 test-rmse:0.675673+0.035421
##
## 57
## [1] train-rmse:1.285162+0.012397 test-rmse:1.292365+0.070954
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.067065+0.008981 test-rmse:1.082941+0.069582
## [3] train-rmse:0.922388+0.006565 test-rmse:0.950040+0.068722
## [4] train-rmse:0.827948+0.003420 test-rmse:0.861524+0.065691
## [5] train-rmse:0.766932+0.004576 test-rmse:0.808533+0.062406
## [6] train-rmse:0.727324+0.005671 test-rmse:0.774157+0.054102
## [7] train-rmse:0.698856+0.004299 test-rmse:0.751730+0.047366
## [8] train-rmse:0.679305+0.002780 test-rmse:0.736007+0.049159
## [9] train-rmse:0.665499+0.001514 test-rmse:0.731056+0.046763
## [10] train-rmse:0.654850+0.002203 test-rmse:0.725033+0.044979
## [11] train-rmse:0.646643+0.002083 test-rmse:0.719656+0.044452
## [12] train-rmse:0.638853+0.002546 test-rmse:0.716802+0.046239
## [13] train-rmse:0.631855+0.003736 test-rmse:0.716102+0.045036
## [14] train-rmse:0.625554+0.003864 test-rmse:0.711578+0.042995
## [15] train-rmse:0.619925+0.004501 test-rmse:0.708427+0.042113
## [16] train-rmse:0.613559+0.003826 test-rmse:0.710050+0.046288
## [17] train-rmse:0.607940+0.004327 test-rmse:0.705982+0.044917
## [18] train-rmse:0.599502+0.003258 test-rmse:0.701512+0.043747
## [19] train-rmse:0.593401+0.000877 test-rmse:0.698175+0.045997
## [20] train-rmse:0.589106+0.001378 test-rmse:0.697791+0.045878
## [21] train-rmse:0.583647+0.002828 test-rmse:0.694229+0.043298
## [22] train-rmse:0.579657+0.003006 test-rmse:0.692152+0.042179
## [23] train-rmse:0.574740+0.003635 test-rmse:0.684190+0.040620
## [24] train-rmse:0.570926+0.003591 test-rmse:0.683341+0.040712
## [25] train-rmse:0.567266+0.003917 test-rmse:0.686238+0.040115
## [26] train-rmse:0.563915+0.003782 test-rmse:0.683011+0.041813
## [27] train-rmse:0.558703+0.005507 test-rmse:0.679194+0.040388
## [28] train-rmse:0.555042+0.005058 test-rmse:0.677926+0.040479
## [29] train-rmse:0.551035+0.005660 test-rmse:0.676896+0.039980
## [30] train-rmse:0.547462+0.004597 test-rmse:0.674926+0.044156
## [31] train-rmse:0.543789+0.005970 test-rmse:0.671900+0.041552
## [32] train-rmse:0.540469+0.006097 test-rmse:0.671171+0.040638
## [33] train-rmse:0.537503+0.006224 test-rmse:0.669570+0.039026
## [34] train-rmse:0.534876+0.006116 test-rmse:0.668396+0.040348
## [35] train-rmse:0.530623+0.006377 test-rmse:0.668908+0.041044
## [36] train-rmse:0.526010+0.009237 test-rmse:0.665992+0.038801
## [37] train-rmse:0.522828+0.007721 test-rmse:0.666546+0.038316
## [38] train-rmse:0.520617+0.007640 test-rmse:0.664192+0.039832
## [39] train-rmse:0.518081+0.007388 test-rmse:0.664900+0.041130
## [40] train-rmse:0.515849+0.006922 test-rmse:0.664147+0.042270
## [41] train-rmse:0.513514+0.006834 test-rmse:0.663500+0.044249
## [42] train-rmse:0.510677+0.006804 test-rmse:0.663066+0.044749
## [43] train-rmse:0.508372+0.006971 test-rmse:0.662038+0.044937
## [44] train-rmse:0.504690+0.007433 test-rmse:0.660188+0.042769
## [45] train-rmse:0.501939+0.008071 test-rmse:0.659436+0.042442
## [46] train-rmse:0.497903+0.004747 test-rmse:0.657691+0.046044
## [47] train-rmse:0.495853+0.003985 test-rmse:0.657452+0.046676
## [48] train-rmse:0.492534+0.007204 test-rmse:0.656079+0.045411
## [49] train-rmse:0.490999+0.007314 test-rmse:0.654754+0.046272
## [50] train-rmse:0.488026+0.007553 test-rmse:0.655334+0.046626
## [51] train-rmse:0.486351+0.007724 test-rmse:0.654305+0.047205
## [52] train-rmse:0.483014+0.006669 test-rmse:0.654064+0.049264
## [53] train-rmse:0.481447+0.006803 test-rmse:0.651887+0.047655
## [54] train-rmse:0.478077+0.007609 test-rmse:0.649750+0.047914
## [55] train-rmse:0.476534+0.007502 test-rmse:0.649510+0.048359
## [56] train-rmse:0.474241+0.008460 test-rmse:0.649695+0.048150
## [57] train-rmse:0.471563+0.008470 test-rmse:0.647466+0.046843
## [58] train-rmse:0.468997+0.009557 test-rmse:0.646965+0.046854
## [59] train-rmse:0.466962+0.008880 test-rmse:0.646525+0.047038
## [60] train-rmse:0.464446+0.008143 test-rmse:0.646602+0.046871
## [61] train-rmse:0.462179+0.007659 test-rmse:0.647059+0.046697
## [62] train-rmse:0.460693+0.007444 test-rmse:0.647021+0.047525
## [63] train-rmse:0.458787+0.008060 test-rmse:0.646791+0.048582
## [64] train-rmse:0.457406+0.008295 test-rmse:0.646595+0.049232
## [65] train-rmse:0.455797+0.008330 test-rmse:0.645318+0.049047
## [66] train-rmse:0.453624+0.009777 test-rmse:0.644042+0.049083
## [67] train-rmse:0.452457+0.009809 test-rmse:0.643854+0.049086
## [68] train-rmse:0.450522+0.011343 test-rmse:0.644705+0.049048
## [69] train-rmse:0.449581+0.011486 test-rmse:0.644640+0.048584
## [70] train-rmse:0.447657+0.012465 test-rmse:0.643780+0.048327
## [71] train-rmse:0.446492+0.012518 test-rmse:0.643190+0.048320
## [72] train-rmse:0.444447+0.012240 test-rmse:0.642383+0.049439
## [73] train-rmse:0.442520+0.011626 test-rmse:0.641110+0.049935
## [74] train-rmse:0.441172+0.012208 test-rmse:0.640966+0.049742
## [75] train-rmse:0.439789+0.012791 test-rmse:0.641345+0.049352
## [76] train-rmse:0.438141+0.012153 test-rmse:0.639632+0.048740
## [77] train-rmse:0.436328+0.012434 test-rmse:0.641200+0.049564
## [78] train-rmse:0.434802+0.013123 test-rmse:0.641559+0.049740
## [79] train-rmse:0.432435+0.013403 test-rmse:0.640717+0.050545
## [80] train-rmse:0.431385+0.013861 test-rmse:0.640388+0.050411
## [81] train-rmse:0.430007+0.013703 test-rmse:0.641598+0.051463
## [82] train-rmse:0.428992+0.013660 test-rmse:0.642347+0.051830
## Stopping. Best iteration:
## [76] train-rmse:0.438141+0.012153 test-rmse:0.639632+0.048740
##
## 58
## [1] train-rmse:1.279937+0.011961 test-rmse:1.289635+0.069484
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.053490+0.008836 test-rmse:1.077395+0.067191
## [3] train-rmse:0.902000+0.006452 test-rmse:0.939725+0.066992
## [4] train-rmse:0.801675+0.003911 test-rmse:0.847964+0.069695
## [5] train-rmse:0.735052+0.004844 test-rmse:0.797103+0.065940
## [6] train-rmse:0.681336+0.005591 test-rmse:0.755190+0.055914
## [7] train-rmse:0.646672+0.006064 test-rmse:0.731862+0.046981
## [8] train-rmse:0.622226+0.008366 test-rmse:0.718981+0.044741
## [9] train-rmse:0.605289+0.007701 test-rmse:0.709337+0.043387
## [10] train-rmse:0.592736+0.008747 test-rmse:0.702680+0.042100
## [11] train-rmse:0.583163+0.008956 test-rmse:0.698455+0.044195
## [12] train-rmse:0.570613+0.009896 test-rmse:0.693062+0.044768
## [13] train-rmse:0.561874+0.010896 test-rmse:0.686431+0.046695
## [14] train-rmse:0.553069+0.010145 test-rmse:0.690428+0.046318
## [15] train-rmse:0.544663+0.008307 test-rmse:0.689333+0.047618
## [16] train-rmse:0.534912+0.004874 test-rmse:0.684903+0.047518
## [17] train-rmse:0.529371+0.004529 test-rmse:0.682864+0.048003
## [18] train-rmse:0.523726+0.004160 test-rmse:0.681241+0.050091
## [19] train-rmse:0.516498+0.005640 test-rmse:0.673620+0.047216
## [20] train-rmse:0.510769+0.005042 test-rmse:0.672392+0.049221
## [21] train-rmse:0.505939+0.005362 test-rmse:0.671880+0.051689
## [22] train-rmse:0.500182+0.007614 test-rmse:0.672733+0.052740
## [23] train-rmse:0.494400+0.005540 test-rmse:0.669706+0.051302
## [24] train-rmse:0.489454+0.005417 test-rmse:0.667568+0.050799
## [25] train-rmse:0.485495+0.005300 test-rmse:0.666822+0.050781
## [26] train-rmse:0.478735+0.007010 test-rmse:0.664961+0.052916
## [27] train-rmse:0.472040+0.007716 test-rmse:0.664719+0.052741
## [28] train-rmse:0.466119+0.006952 test-rmse:0.661129+0.054676
## [29] train-rmse:0.460651+0.010366 test-rmse:0.657050+0.054361
## [30] train-rmse:0.454420+0.011432 test-rmse:0.654550+0.053271
## [31] train-rmse:0.450275+0.010419 test-rmse:0.653496+0.056672
## [32] train-rmse:0.446641+0.010927 test-rmse:0.653939+0.055253
## [33] train-rmse:0.442432+0.011975 test-rmse:0.653148+0.053121
## [34] train-rmse:0.437499+0.012615 test-rmse:0.653661+0.054697
## [35] train-rmse:0.433057+0.013019 test-rmse:0.654014+0.054690
## [36] train-rmse:0.428269+0.012303 test-rmse:0.652628+0.055475
## [37] train-rmse:0.424943+0.012427 test-rmse:0.651021+0.055155
## [38] train-rmse:0.419387+0.010631 test-rmse:0.648408+0.055251
## [39] train-rmse:0.415234+0.011338 test-rmse:0.645742+0.055192
## [40] train-rmse:0.412590+0.012151 test-rmse:0.646418+0.054197
## [41] train-rmse:0.408139+0.009812 test-rmse:0.643876+0.053796
## [42] train-rmse:0.403341+0.008998 test-rmse:0.641158+0.052750
## [43] train-rmse:0.397880+0.010801 test-rmse:0.638318+0.051639
## [44] train-rmse:0.394832+0.009663 test-rmse:0.635073+0.051311
## [45] train-rmse:0.390746+0.009828 test-rmse:0.633854+0.051076
## [46] train-rmse:0.386723+0.011309 test-rmse:0.631153+0.049072
## [47] train-rmse:0.381700+0.010174 test-rmse:0.631406+0.049413
## [48] train-rmse:0.377638+0.010919 test-rmse:0.634358+0.050770
## [49] train-rmse:0.374787+0.010718 test-rmse:0.635009+0.051249
## [50] train-rmse:0.371312+0.010118 test-rmse:0.633944+0.051719
## [51] train-rmse:0.367275+0.009622 test-rmse:0.631571+0.051861
## [52] train-rmse:0.364657+0.009527 test-rmse:0.631464+0.051543
## Stopping. Best iteration:
## [46] train-rmse:0.386723+0.011309 test-rmse:0.631153+0.049072
##
## 59
## [1] train-rmse:1.274546+0.012050 test-rmse:1.292936+0.070508
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.040457+0.008088 test-rmse:1.075544+0.073882
## [3] train-rmse:0.880630+0.006340 test-rmse:0.924848+0.075285
## [4] train-rmse:0.775143+0.006166 test-rmse:0.836574+0.071266
## [5] train-rmse:0.703875+0.006124 test-rmse:0.778657+0.066156
## [6] train-rmse:0.649337+0.004853 test-rmse:0.738067+0.062245
## [7] train-rmse:0.612905+0.007848 test-rmse:0.713832+0.060478
## [8] train-rmse:0.581570+0.008690 test-rmse:0.699279+0.055048
## [9] train-rmse:0.562936+0.008338 test-rmse:0.690308+0.055089
## [10] train-rmse:0.547619+0.011365 test-rmse:0.684610+0.057147
## [11] train-rmse:0.532653+0.011736 test-rmse:0.678956+0.065678
## [12] train-rmse:0.519325+0.008833 test-rmse:0.675235+0.064872
## [13] train-rmse:0.508820+0.010163 test-rmse:0.673069+0.061752
## [14] train-rmse:0.495149+0.006670 test-rmse:0.665119+0.057792
## [15] train-rmse:0.484078+0.009209 test-rmse:0.662706+0.057284
## [16] train-rmse:0.475997+0.009486 test-rmse:0.662742+0.057574
## [17] train-rmse:0.463396+0.009876 test-rmse:0.655133+0.058835
## [18] train-rmse:0.456605+0.010919 test-rmse:0.654997+0.058103
## [19] train-rmse:0.446290+0.012465 test-rmse:0.653126+0.057727
## [20] train-rmse:0.439634+0.013491 test-rmse:0.650271+0.060169
## [21] train-rmse:0.431233+0.010166 test-rmse:0.646526+0.061309
## [22] train-rmse:0.423919+0.010836 test-rmse:0.646401+0.061031
## [23] train-rmse:0.415672+0.011322 test-rmse:0.641053+0.064614
## [24] train-rmse:0.408650+0.014062 test-rmse:0.639843+0.061511
## [25] train-rmse:0.400936+0.012316 test-rmse:0.634603+0.064280
## [26] train-rmse:0.394963+0.013526 test-rmse:0.634885+0.063732
## [27] train-rmse:0.390479+0.013474 test-rmse:0.634339+0.064767
## [28] train-rmse:0.383261+0.015862 test-rmse:0.631216+0.062553
## [29] train-rmse:0.378840+0.016327 test-rmse:0.630638+0.062908
## [30] train-rmse:0.373364+0.017562 test-rmse:0.630777+0.062803
## [31] train-rmse:0.369488+0.017894 test-rmse:0.630925+0.063557
## [32] train-rmse:0.363834+0.019735 test-rmse:0.633299+0.064306
## [33] train-rmse:0.357710+0.019995 test-rmse:0.632082+0.063501
## [34] train-rmse:0.352962+0.019606 test-rmse:0.630380+0.063643
## [35] train-rmse:0.348055+0.019115 test-rmse:0.631016+0.065230
## [36] train-rmse:0.344971+0.019255 test-rmse:0.631391+0.064107
## [37] train-rmse:0.340539+0.019636 test-rmse:0.629584+0.064004
## [38] train-rmse:0.335709+0.018834 test-rmse:0.628827+0.061979
## [39] train-rmse:0.329985+0.020015 test-rmse:0.629346+0.062481
## [40] train-rmse:0.326973+0.020152 test-rmse:0.629627+0.061976
## [41] train-rmse:0.321994+0.018469 test-rmse:0.628832+0.061502
## [42] train-rmse:0.319171+0.018666 test-rmse:0.630149+0.062030
## [43] train-rmse:0.314081+0.017772 test-rmse:0.630527+0.062335
## [44] train-rmse:0.309788+0.017344 test-rmse:0.629775+0.061936
## Stopping. Best iteration:
## [38] train-rmse:0.335709+0.018834 test-rmse:0.628827+0.061979
##
## 60
## [1] train-rmse:1.270872+0.012282 test-rmse:1.287911+0.073711
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.031544+0.008460 test-rmse:1.071309+0.074769
## [3] train-rmse:0.863498+0.006121 test-rmse:0.919478+0.072177
## [4] train-rmse:0.750644+0.006189 test-rmse:0.824068+0.065419
## [5] train-rmse:0.673297+0.006049 test-rmse:0.759542+0.058545
## [6] train-rmse:0.618618+0.002928 test-rmse:0.726542+0.050823
## [7] train-rmse:0.576234+0.005695 test-rmse:0.700913+0.051820
## [8] train-rmse:0.542264+0.010973 test-rmse:0.686594+0.051289
## [9] train-rmse:0.518633+0.013490 test-rmse:0.681766+0.054743
## [10] train-rmse:0.501625+0.013486 test-rmse:0.676952+0.052600
## [11] train-rmse:0.485352+0.013386 test-rmse:0.670099+0.052438
## [12] train-rmse:0.467590+0.006687 test-rmse:0.663324+0.053943
## [13] train-rmse:0.453703+0.011157 test-rmse:0.660245+0.055548
## [14] train-rmse:0.440159+0.011556 test-rmse:0.655302+0.056136
## [15] train-rmse:0.421668+0.008747 test-rmse:0.649523+0.054022
## [16] train-rmse:0.409293+0.007549 test-rmse:0.648205+0.058413
## [17] train-rmse:0.400814+0.007358 test-rmse:0.648748+0.056539
## [18] train-rmse:0.390773+0.006746 test-rmse:0.646277+0.060452
## [19] train-rmse:0.380892+0.005984 test-rmse:0.641931+0.062701
## [20] train-rmse:0.371814+0.005158 test-rmse:0.639818+0.063361
## [21] train-rmse:0.359864+0.004494 test-rmse:0.637435+0.064768
## [22] train-rmse:0.352617+0.005790 test-rmse:0.633496+0.063414
## [23] train-rmse:0.343868+0.003089 test-rmse:0.631220+0.063759
## [24] train-rmse:0.336881+0.003007 test-rmse:0.631639+0.064521
## [25] train-rmse:0.328689+0.002709 test-rmse:0.628647+0.064643
## [26] train-rmse:0.322341+0.001522 test-rmse:0.628830+0.063880
## [27] train-rmse:0.315250+0.003311 test-rmse:0.631723+0.064203
## [28] train-rmse:0.306266+0.004458 test-rmse:0.626495+0.065279
## [29] train-rmse:0.300342+0.005435 test-rmse:0.624108+0.063552
## [30] train-rmse:0.294549+0.006266 test-rmse:0.623331+0.064723
## [31] train-rmse:0.285277+0.006523 test-rmse:0.621533+0.063374
## [32] train-rmse:0.278321+0.005796 test-rmse:0.622994+0.063876
## [33] train-rmse:0.274338+0.006058 test-rmse:0.622862+0.063751
## [34] train-rmse:0.268580+0.006472 test-rmse:0.623521+0.062953
## [35] train-rmse:0.263206+0.006335 test-rmse:0.623168+0.061052
## [36] train-rmse:0.257436+0.006298 test-rmse:0.624131+0.061266
## [37] train-rmse:0.250580+0.005420 test-rmse:0.623280+0.062866
## Stopping. Best iteration:
## [31] train-rmse:0.285277+0.006523 test-rmse:0.621533+0.063374
##
## 61
## [1] train-rmse:1.267132+0.011673 test-rmse:1.290025+0.075605
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.023211+0.007329 test-rmse:1.066440+0.075720
## [3] train-rmse:0.847898+0.005751 test-rmse:0.917303+0.070521
## [4] train-rmse:0.724438+0.006862 test-rmse:0.819546+0.062867
## [5] train-rmse:0.641834+0.006354 test-rmse:0.759137+0.056453
## [6] train-rmse:0.581862+0.006937 test-rmse:0.720747+0.055950
## [7] train-rmse:0.538357+0.012507 test-rmse:0.697015+0.056790
## [8] train-rmse:0.500064+0.017870 test-rmse:0.678350+0.055315
## [9] train-rmse:0.468901+0.014555 test-rmse:0.667149+0.051166
## [10] train-rmse:0.448178+0.013200 test-rmse:0.661283+0.052297
## [11] train-rmse:0.433118+0.012665 test-rmse:0.654254+0.050713
## [12] train-rmse:0.417859+0.009687 test-rmse:0.652208+0.052648
## [13] train-rmse:0.399394+0.011271 test-rmse:0.641467+0.052516
## [14] train-rmse:0.383436+0.015634 test-rmse:0.643605+0.056401
## [15] train-rmse:0.370834+0.015720 test-rmse:0.639916+0.054375
## [16] train-rmse:0.355565+0.014682 test-rmse:0.635143+0.055782
## [17] train-rmse:0.340875+0.017617 test-rmse:0.632547+0.051548
## [18] train-rmse:0.329479+0.014638 test-rmse:0.633337+0.051942
## [19] train-rmse:0.318597+0.015866 test-rmse:0.634415+0.051474
## [20] train-rmse:0.308529+0.016138 test-rmse:0.631635+0.048963
## [21] train-rmse:0.301245+0.016177 test-rmse:0.632195+0.048212
## [22] train-rmse:0.293395+0.017256 test-rmse:0.633778+0.051972
## [23] train-rmse:0.286314+0.018372 test-rmse:0.633345+0.052072
## [24] train-rmse:0.279935+0.018577 test-rmse:0.633871+0.051317
## [25] train-rmse:0.270137+0.016242 test-rmse:0.633278+0.052648
## [26] train-rmse:0.262206+0.017211 test-rmse:0.630743+0.052637
## [27] train-rmse:0.252732+0.019643 test-rmse:0.631023+0.050542
## [28] train-rmse:0.242024+0.017432 test-rmse:0.630473+0.054364
## [29] train-rmse:0.237004+0.018106 test-rmse:0.630665+0.054040
## [30] train-rmse:0.232437+0.019509 test-rmse:0.632141+0.054211
## [31] train-rmse:0.225962+0.015914 test-rmse:0.632768+0.053829
## [32] train-rmse:0.219424+0.014605 test-rmse:0.630530+0.052540
## [33] train-rmse:0.214927+0.015466 test-rmse:0.631454+0.052719
## [34] train-rmse:0.208534+0.015702 test-rmse:0.631520+0.052389
## Stopping. Best iteration:
## [28] train-rmse:0.242024+0.017432 test-rmse:0.630473+0.054364
##
## 62
## [1] train-rmse:1.264388+0.011158 test-rmse:1.288620+0.075810
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.017094+0.007475 test-rmse:1.065270+0.075639
## [3] train-rmse:0.835856+0.006245 test-rmse:0.919040+0.082174
## [4] train-rmse:0.705900+0.008903 test-rmse:0.819859+0.077021
## [5] train-rmse:0.618293+0.012947 test-rmse:0.763338+0.072046
## [6] train-rmse:0.551936+0.015207 test-rmse:0.719438+0.071767
## [7] train-rmse:0.502914+0.014463 test-rmse:0.692313+0.068573
## [8] train-rmse:0.459679+0.010365 test-rmse:0.673249+0.059177
## [9] train-rmse:0.423507+0.014152 test-rmse:0.662686+0.054649
## [10] train-rmse:0.402807+0.015819 test-rmse:0.657716+0.057929
## [11] train-rmse:0.380424+0.016003 test-rmse:0.654129+0.056140
## [12] train-rmse:0.364785+0.013924 test-rmse:0.648662+0.053468
## [13] train-rmse:0.350446+0.016417 test-rmse:0.643594+0.051328
## [14] train-rmse:0.334036+0.011540 test-rmse:0.636998+0.048480
## [15] train-rmse:0.320596+0.012362 test-rmse:0.631840+0.047357
## [16] train-rmse:0.310072+0.012106 test-rmse:0.630296+0.043146
## [17] train-rmse:0.302344+0.012992 test-rmse:0.627997+0.044449
## [18] train-rmse:0.293072+0.015603 test-rmse:0.627732+0.043480
## [19] train-rmse:0.278981+0.014193 test-rmse:0.628222+0.044172
## [20] train-rmse:0.268234+0.015606 test-rmse:0.630346+0.040541
## [21] train-rmse:0.257671+0.015100 test-rmse:0.628896+0.042540
## [22] train-rmse:0.247742+0.012885 test-rmse:0.628406+0.039343
## [23] train-rmse:0.239240+0.014763 test-rmse:0.627814+0.041106
## [24] train-rmse:0.231799+0.016167 test-rmse:0.624527+0.043545
## [25] train-rmse:0.223529+0.015599 test-rmse:0.623120+0.044067
## [26] train-rmse:0.215589+0.017245 test-rmse:0.621506+0.042271
## [27] train-rmse:0.206617+0.017762 test-rmse:0.621849+0.041386
## [28] train-rmse:0.199626+0.017591 test-rmse:0.622555+0.043013
## [29] train-rmse:0.191606+0.015922 test-rmse:0.623013+0.043756
## [30] train-rmse:0.185336+0.015336 test-rmse:0.624633+0.043953
## [31] train-rmse:0.177988+0.012820 test-rmse:0.624734+0.045763
## [32] train-rmse:0.172449+0.014407 test-rmse:0.625940+0.044882
## Stopping. Best iteration:
## [26] train-rmse:0.215589+0.017245 test-rmse:0.621506+0.042271
##
## 63
## [1] train-rmse:1.269958+0.012227 test-rmse:1.272150+0.070819
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.053331+0.009121 test-rmse:1.058038+0.067655
## [3] train-rmse:0.919766+0.007692 test-rmse:0.933246+0.064084
## [4] train-rmse:0.837551+0.006956 test-rmse:0.851627+0.057362
## [5] train-rmse:0.789412+0.006697 test-rmse:0.809949+0.049673
## [6] train-rmse:0.759632+0.006364 test-rmse:0.785392+0.043792
## [7] train-rmse:0.740605+0.005953 test-rmse:0.770959+0.042089
## [8] train-rmse:0.727895+0.005746 test-rmse:0.758315+0.040559
## [9] train-rmse:0.718426+0.005400 test-rmse:0.750674+0.035113
## [10] train-rmse:0.710972+0.004961 test-rmse:0.745606+0.034598
## [11] train-rmse:0.705064+0.004947 test-rmse:0.745634+0.032231
## [12] train-rmse:0.700218+0.005146 test-rmse:0.742828+0.030191
## [13] train-rmse:0.695846+0.005257 test-rmse:0.739280+0.031824
## [14] train-rmse:0.691692+0.005285 test-rmse:0.737006+0.031054
## [15] train-rmse:0.687791+0.005357 test-rmse:0.736528+0.032506
## [16] train-rmse:0.684094+0.005420 test-rmse:0.732024+0.031783
## [17] train-rmse:0.680637+0.005308 test-rmse:0.731003+0.030692
## [18] train-rmse:0.677403+0.005242 test-rmse:0.730446+0.031638
## [19] train-rmse:0.674416+0.005250 test-rmse:0.729460+0.031330
## [20] train-rmse:0.671513+0.005243 test-rmse:0.730235+0.032527
## [21] train-rmse:0.668709+0.005279 test-rmse:0.729757+0.030515
## [22] train-rmse:0.666078+0.005207 test-rmse:0.728196+0.031761
## [23] train-rmse:0.663522+0.005146 test-rmse:0.724339+0.031768
## [24] train-rmse:0.661060+0.005130 test-rmse:0.725068+0.033955
## [25] train-rmse:0.658640+0.005188 test-rmse:0.722772+0.033334
## [26] train-rmse:0.656375+0.005366 test-rmse:0.722361+0.034613
## [27] train-rmse:0.654185+0.005485 test-rmse:0.722849+0.036389
## [28] train-rmse:0.652099+0.005595 test-rmse:0.721710+0.034826
## [29] train-rmse:0.650113+0.005594 test-rmse:0.719901+0.034262
## [30] train-rmse:0.648131+0.005549 test-rmse:0.719909+0.033354
## [31] train-rmse:0.646265+0.005552 test-rmse:0.716620+0.033695
## [32] train-rmse:0.644445+0.005595 test-rmse:0.716316+0.034519
## [33] train-rmse:0.642677+0.005633 test-rmse:0.715147+0.034382
## [34] train-rmse:0.640938+0.005634 test-rmse:0.714377+0.033121
## [35] train-rmse:0.639234+0.005669 test-rmse:0.714870+0.034327
## [36] train-rmse:0.637591+0.005726 test-rmse:0.713214+0.033559
## [37] train-rmse:0.635944+0.005755 test-rmse:0.711674+0.034247
## [38] train-rmse:0.634382+0.005810 test-rmse:0.712590+0.034398
## [39] train-rmse:0.632862+0.005874 test-rmse:0.711919+0.035011
## [40] train-rmse:0.631360+0.005863 test-rmse:0.712673+0.035126
## [41] train-rmse:0.629840+0.005862 test-rmse:0.711530+0.035229
## [42] train-rmse:0.628343+0.005936 test-rmse:0.708999+0.036190
## [43] train-rmse:0.626919+0.005982 test-rmse:0.707103+0.035247
## [44] train-rmse:0.625537+0.005992 test-rmse:0.707942+0.035829
## [45] train-rmse:0.624224+0.005989 test-rmse:0.707970+0.036681
## [46] train-rmse:0.622965+0.005978 test-rmse:0.706661+0.036547
## [47] train-rmse:0.621714+0.005980 test-rmse:0.707402+0.035415
## [48] train-rmse:0.620512+0.005965 test-rmse:0.706774+0.035562
## [49] train-rmse:0.619287+0.005933 test-rmse:0.706162+0.034469
## [50] train-rmse:0.618099+0.005933 test-rmse:0.706920+0.034672
## [51] train-rmse:0.616890+0.005920 test-rmse:0.707457+0.035657
## [52] train-rmse:0.615741+0.005963 test-rmse:0.706651+0.035712
## [53] train-rmse:0.614609+0.005992 test-rmse:0.705857+0.034895
## [54] train-rmse:0.613520+0.006037 test-rmse:0.702747+0.034843
## [55] train-rmse:0.612414+0.006052 test-rmse:0.703639+0.034682
## [56] train-rmse:0.611341+0.006058 test-rmse:0.703339+0.034864
## [57] train-rmse:0.610302+0.006057 test-rmse:0.702220+0.035753
## [58] train-rmse:0.609274+0.006066 test-rmse:0.702616+0.036250
## [59] train-rmse:0.608280+0.006067 test-rmse:0.701769+0.036692
## [60] train-rmse:0.607266+0.006075 test-rmse:0.699809+0.037031
## [61] train-rmse:0.606276+0.006107 test-rmse:0.699971+0.035558
## [62] train-rmse:0.605286+0.006141 test-rmse:0.699136+0.036161
## [63] train-rmse:0.604328+0.006163 test-rmse:0.698405+0.035019
## [64] train-rmse:0.603385+0.006187 test-rmse:0.698792+0.036224
## [65] train-rmse:0.602458+0.006206 test-rmse:0.698736+0.036553
## [66] train-rmse:0.601564+0.006227 test-rmse:0.698317+0.036679
## [67] train-rmse:0.600672+0.006248 test-rmse:0.697011+0.036755
## [68] train-rmse:0.599779+0.006302 test-rmse:0.695005+0.036026
## [69] train-rmse:0.598911+0.006331 test-rmse:0.695295+0.036270
## [70] train-rmse:0.598061+0.006360 test-rmse:0.694892+0.036478
## [71] train-rmse:0.597221+0.006386 test-rmse:0.694512+0.035910
## [72] train-rmse:0.596410+0.006398 test-rmse:0.694483+0.035301
## [73] train-rmse:0.595591+0.006405 test-rmse:0.695288+0.036652
## [74] train-rmse:0.594800+0.006394 test-rmse:0.695266+0.036587
## [75] train-rmse:0.594034+0.006393 test-rmse:0.695912+0.037750
## [76] train-rmse:0.593290+0.006395 test-rmse:0.695235+0.037502
## [77] train-rmse:0.592537+0.006385 test-rmse:0.695092+0.036027
## [78] train-rmse:0.591789+0.006389 test-rmse:0.693238+0.036783
## [79] train-rmse:0.591055+0.006407 test-rmse:0.692145+0.036953
## [80] train-rmse:0.590289+0.006427 test-rmse:0.690494+0.035841
## [81] train-rmse:0.589544+0.006464 test-rmse:0.689880+0.037899
## [82] train-rmse:0.588821+0.006485 test-rmse:0.690448+0.037355
## [83] train-rmse:0.588105+0.006515 test-rmse:0.690411+0.037437
## [84] train-rmse:0.587402+0.006541 test-rmse:0.689711+0.037127
## [85] train-rmse:0.586716+0.006562 test-rmse:0.688952+0.036901
## [86] train-rmse:0.586031+0.006576 test-rmse:0.688239+0.036824
## [87] train-rmse:0.585377+0.006578 test-rmse:0.687548+0.036541
## [88] train-rmse:0.584708+0.006619 test-rmse:0.687943+0.036082
## [89] train-rmse:0.584043+0.006608 test-rmse:0.687857+0.036606
## [90] train-rmse:0.583407+0.006620 test-rmse:0.687124+0.036961
## [91] train-rmse:0.582780+0.006647 test-rmse:0.686483+0.036629
## [92] train-rmse:0.582162+0.006674 test-rmse:0.686651+0.035711
## [93] train-rmse:0.581562+0.006687 test-rmse:0.687349+0.034817
## [94] train-rmse:0.580960+0.006695 test-rmse:0.687808+0.035449
## [95] train-rmse:0.580362+0.006706 test-rmse:0.687801+0.035730
## [96] train-rmse:0.579785+0.006718 test-rmse:0.687115+0.035576
## [97] train-rmse:0.579203+0.006718 test-rmse:0.686959+0.035218
## Stopping. Best iteration:
## [91] train-rmse:0.582780+0.006647 test-rmse:0.686483+0.036629
##
## 64
## [1] train-rmse:1.261631+0.012050 test-rmse:1.269588+0.071112
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.036213+0.008412 test-rmse:1.053695+0.069092
## [3] train-rmse:0.893056+0.006046 test-rmse:0.922176+0.068303
## [4] train-rmse:0.801789+0.002580 test-rmse:0.841895+0.065073
## [5] train-rmse:0.744965+0.003050 test-rmse:0.788383+0.057309
## [6] train-rmse:0.709678+0.003524 test-rmse:0.757919+0.055499
## [7] train-rmse:0.684247+0.004385 test-rmse:0.739151+0.046765
## [8] train-rmse:0.667572+0.003645 test-rmse:0.729000+0.042767
## [9] train-rmse:0.655706+0.004144 test-rmse:0.724427+0.041154
## [10] train-rmse:0.644192+0.005253 test-rmse:0.715573+0.031477
## [11] train-rmse:0.636132+0.005332 test-rmse:0.712340+0.031556
## [12] train-rmse:0.628115+0.005627 test-rmse:0.710566+0.032841
## [13] train-rmse:0.621916+0.006341 test-rmse:0.708893+0.030824
## [14] train-rmse:0.615857+0.006548 test-rmse:0.705485+0.030980
## [15] train-rmse:0.609533+0.006939 test-rmse:0.705225+0.028660
## [16] train-rmse:0.604453+0.006406 test-rmse:0.704557+0.030719
## [17] train-rmse:0.600142+0.006636 test-rmse:0.705012+0.033129
## [18] train-rmse:0.595155+0.007310 test-rmse:0.700844+0.033110
## [19] train-rmse:0.589456+0.008388 test-rmse:0.696489+0.031211
## [20] train-rmse:0.581378+0.006267 test-rmse:0.692171+0.033586
## [21] train-rmse:0.576793+0.005320 test-rmse:0.690564+0.034584
## [22] train-rmse:0.571487+0.006473 test-rmse:0.685645+0.030933
## [23] train-rmse:0.568424+0.006021 test-rmse:0.683553+0.035942
## [24] train-rmse:0.563169+0.006481 test-rmse:0.681326+0.036359
## [25] train-rmse:0.558330+0.007230 test-rmse:0.683658+0.037452
## [26] train-rmse:0.552353+0.009098 test-rmse:0.677708+0.032068
## [27] train-rmse:0.549218+0.009586 test-rmse:0.675678+0.032007
## [28] train-rmse:0.546430+0.008973 test-rmse:0.674384+0.030877
## [29] train-rmse:0.543861+0.009255 test-rmse:0.674256+0.031073
## [30] train-rmse:0.540100+0.008108 test-rmse:0.673116+0.030714
## [31] train-rmse:0.536755+0.008336 test-rmse:0.671620+0.031000
## [32] train-rmse:0.534050+0.007960 test-rmse:0.671840+0.031707
## [33] train-rmse:0.529657+0.007540 test-rmse:0.669789+0.029605
## [34] train-rmse:0.527104+0.007563 test-rmse:0.670735+0.030517
## [35] train-rmse:0.522920+0.008116 test-rmse:0.668152+0.030452
## [36] train-rmse:0.518994+0.006994 test-rmse:0.666795+0.032770
## [37] train-rmse:0.516130+0.006353 test-rmse:0.665222+0.032435
## [38] train-rmse:0.511560+0.007019 test-rmse:0.663099+0.032455
## [39] train-rmse:0.509634+0.006687 test-rmse:0.662573+0.034748
## [40] train-rmse:0.506488+0.007172 test-rmse:0.660177+0.034944
## [41] train-rmse:0.503547+0.007910 test-rmse:0.661309+0.036374
## [42] train-rmse:0.501509+0.008066 test-rmse:0.660148+0.035524
## [43] train-rmse:0.498460+0.007806 test-rmse:0.659455+0.033313
## [44] train-rmse:0.495334+0.007787 test-rmse:0.657523+0.033446
## [45] train-rmse:0.492083+0.008604 test-rmse:0.656812+0.033230
## [46] train-rmse:0.489332+0.007431 test-rmse:0.654410+0.037340
## [47] train-rmse:0.487651+0.007469 test-rmse:0.655583+0.037300
## [48] train-rmse:0.485614+0.007599 test-rmse:0.656737+0.038163
## [49] train-rmse:0.483289+0.008118 test-rmse:0.655391+0.038535
## [50] train-rmse:0.480970+0.008142 test-rmse:0.655618+0.038479
## [51] train-rmse:0.478047+0.007895 test-rmse:0.653632+0.037451
## [52] train-rmse:0.476361+0.007632 test-rmse:0.651522+0.036909
## [53] train-rmse:0.473221+0.008918 test-rmse:0.649086+0.037281
## [54] train-rmse:0.470789+0.010184 test-rmse:0.650211+0.036409
## [55] train-rmse:0.469294+0.009999 test-rmse:0.650567+0.036930
## [56] train-rmse:0.467175+0.009903 test-rmse:0.649870+0.036994
## [57] train-rmse:0.465181+0.009055 test-rmse:0.651174+0.037730
## [58] train-rmse:0.463773+0.008551 test-rmse:0.652269+0.038175
## [59] train-rmse:0.462141+0.008616 test-rmse:0.652801+0.037716
## Stopping. Best iteration:
## [53] train-rmse:0.473221+0.008918 test-rmse:0.649086+0.037281
##
## 65
## [1] train-rmse:1.255940+0.011576 test-rmse:1.266633+0.069522
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.021237+0.008278 test-rmse:1.045885+0.067538
## [3] train-rmse:0.869783+0.005820 test-rmse:0.909758+0.067308
## [4] train-rmse:0.773087+0.004890 test-rmse:0.822430+0.069589
## [5] train-rmse:0.707903+0.008242 test-rmse:0.771993+0.061441
## [6] train-rmse:0.664410+0.005751 test-rmse:0.741426+0.061788
## [7] train-rmse:0.636669+0.006952 test-rmse:0.719709+0.056060
## [8] train-rmse:0.618082+0.006653 test-rmse:0.709866+0.054730
## [9] train-rmse:0.600706+0.007023 test-rmse:0.700044+0.053111
## [10] train-rmse:0.586036+0.008242 test-rmse:0.692987+0.054967
## [11] train-rmse:0.574590+0.009455 test-rmse:0.687379+0.053292
## [12] train-rmse:0.565619+0.008506 test-rmse:0.681826+0.053542
## [13] train-rmse:0.558341+0.008114 test-rmse:0.682958+0.055012
## [14] train-rmse:0.551227+0.007617 test-rmse:0.678492+0.058116
## [15] train-rmse:0.541765+0.009266 test-rmse:0.677855+0.056876
## [16] train-rmse:0.532381+0.008854 test-rmse:0.675681+0.053837
## [17] train-rmse:0.525948+0.008818 test-rmse:0.673903+0.055212
## [18] train-rmse:0.519248+0.009193 test-rmse:0.671794+0.053803
## [19] train-rmse:0.511706+0.012266 test-rmse:0.669207+0.055061
## [20] train-rmse:0.505571+0.011402 test-rmse:0.667235+0.054917
## [21] train-rmse:0.501454+0.011677 test-rmse:0.663941+0.057351
## [22] train-rmse:0.495811+0.012653 test-rmse:0.662299+0.057087
## [23] train-rmse:0.484952+0.013290 test-rmse:0.659570+0.056402
## [24] train-rmse:0.479999+0.012623 test-rmse:0.657321+0.057333
## [25] train-rmse:0.473259+0.014470 test-rmse:0.653942+0.055943
## [26] train-rmse:0.466094+0.016519 test-rmse:0.653416+0.056369
## [27] train-rmse:0.460662+0.015479 test-rmse:0.651645+0.056801
## [28] train-rmse:0.456253+0.015921 test-rmse:0.653922+0.056261
## [29] train-rmse:0.450819+0.016673 test-rmse:0.648134+0.055739
## [30] train-rmse:0.446570+0.018528 test-rmse:0.647525+0.055440
## [31] train-rmse:0.440568+0.018274 test-rmse:0.647231+0.054911
## [32] train-rmse:0.434616+0.015416 test-rmse:0.643025+0.052032
## [33] train-rmse:0.429083+0.014955 test-rmse:0.639293+0.050477
## [34] train-rmse:0.424922+0.014640 test-rmse:0.637415+0.050586
## [35] train-rmse:0.418710+0.015042 test-rmse:0.639159+0.049771
## [36] train-rmse:0.415232+0.014734 test-rmse:0.639846+0.052669
## [37] train-rmse:0.409800+0.014777 test-rmse:0.638221+0.050829
## [38] train-rmse:0.405514+0.015010 test-rmse:0.635858+0.051171
## [39] train-rmse:0.401512+0.015100 test-rmse:0.635409+0.052939
## [40] train-rmse:0.397870+0.014661 test-rmse:0.636402+0.052244
## [41] train-rmse:0.394450+0.014241 test-rmse:0.635916+0.051183
## [42] train-rmse:0.391127+0.013506 test-rmse:0.635347+0.051630
## [43] train-rmse:0.387536+0.013603 test-rmse:0.635960+0.051002
## [44] train-rmse:0.384441+0.014000 test-rmse:0.636331+0.050658
## [45] train-rmse:0.380808+0.015042 test-rmse:0.635257+0.050092
## [46] train-rmse:0.377694+0.016178 test-rmse:0.635795+0.049821
## [47] train-rmse:0.374565+0.016141 test-rmse:0.635061+0.051033
## [48] train-rmse:0.371555+0.017541 test-rmse:0.633938+0.050308
## [49] train-rmse:0.365849+0.016289 test-rmse:0.634476+0.048157
## [50] train-rmse:0.361907+0.016410 test-rmse:0.633982+0.046522
## [51] train-rmse:0.359745+0.016287 test-rmse:0.635017+0.047231
## [52] train-rmse:0.355793+0.017480 test-rmse:0.631844+0.046409
## [53] train-rmse:0.352524+0.016833 test-rmse:0.633510+0.044312
## [54] train-rmse:0.347948+0.016380 test-rmse:0.629822+0.045579
## [55] train-rmse:0.345717+0.016467 test-rmse:0.630412+0.045192
## [56] train-rmse:0.342467+0.015191 test-rmse:0.629997+0.046874
## [57] train-rmse:0.339384+0.015334 test-rmse:0.629369+0.047076
## [58] train-rmse:0.335433+0.014406 test-rmse:0.629902+0.046374
## [59] train-rmse:0.332072+0.015451 test-rmse:0.629334+0.045288
## [60] train-rmse:0.328638+0.017032 test-rmse:0.629729+0.046122
## [61] train-rmse:0.324989+0.017086 test-rmse:0.630427+0.045756
## [62] train-rmse:0.322879+0.017063 test-rmse:0.630395+0.045418
## [63] train-rmse:0.320021+0.016684 test-rmse:0.630214+0.044849
## [64] train-rmse:0.317972+0.017391 test-rmse:0.630557+0.044766
## [65] train-rmse:0.315074+0.017600 test-rmse:0.630909+0.043614
## Stopping. Best iteration:
## [59] train-rmse:0.332072+0.015451 test-rmse:0.629334+0.045288
##
## 66
## [1] train-rmse:1.250057+0.011678 test-rmse:1.270215+0.070724
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.006952+0.007657 test-rmse:1.045207+0.074528
## [3] train-rmse:0.845568+0.005991 test-rmse:0.900196+0.070338
## [4] train-rmse:0.742601+0.005169 test-rmse:0.809974+0.072330
## [5] train-rmse:0.674831+0.005335 test-rmse:0.756998+0.068317
## [6] train-rmse:0.630077+0.008669 test-rmse:0.729986+0.067793
## [7] train-rmse:0.592013+0.010513 test-rmse:0.708272+0.062404
## [8] train-rmse:0.570014+0.009379 test-rmse:0.697579+0.059812
## [9] train-rmse:0.545636+0.013776 test-rmse:0.691457+0.057690
## [10] train-rmse:0.531710+0.013926 test-rmse:0.686867+0.060085
## [11] train-rmse:0.517804+0.012620 test-rmse:0.679328+0.057609
## [12] train-rmse:0.504987+0.014406 test-rmse:0.673677+0.057248
## [13] train-rmse:0.493941+0.015335 test-rmse:0.675245+0.056575
## [14] train-rmse:0.486589+0.015641 test-rmse:0.676930+0.060526
## [15] train-rmse:0.473047+0.014892 test-rmse:0.668221+0.058332
## [16] train-rmse:0.460031+0.012304 test-rmse:0.662422+0.058311
## [17] train-rmse:0.451022+0.013242 test-rmse:0.657735+0.057216
## [18] train-rmse:0.440238+0.011730 test-rmse:0.653633+0.057952
## [19] train-rmse:0.432508+0.012798 test-rmse:0.651461+0.057294
## [20] train-rmse:0.425027+0.013149 test-rmse:0.651029+0.058533
## [21] train-rmse:0.416419+0.010194 test-rmse:0.651348+0.063078
## [22] train-rmse:0.407861+0.011359 test-rmse:0.648428+0.064159
## [23] train-rmse:0.403007+0.011833 test-rmse:0.648975+0.065587
## [24] train-rmse:0.393315+0.013545 test-rmse:0.647403+0.065073
## [25] train-rmse:0.384939+0.014462 test-rmse:0.644535+0.065283
## [26] train-rmse:0.381249+0.014336 test-rmse:0.642225+0.064532
## [27] train-rmse:0.373729+0.014289 test-rmse:0.637534+0.064406
## [28] train-rmse:0.364253+0.011329 test-rmse:0.634583+0.064359
## [29] train-rmse:0.358026+0.013553 test-rmse:0.634968+0.064052
## [30] train-rmse:0.351231+0.012299 test-rmse:0.630882+0.062591
## [31] train-rmse:0.344729+0.010349 test-rmse:0.630871+0.062187
## [32] train-rmse:0.339042+0.008800 test-rmse:0.630588+0.062439
## [33] train-rmse:0.333700+0.011584 test-rmse:0.631361+0.062600
## [34] train-rmse:0.328776+0.011244 test-rmse:0.629592+0.062813
## [35] train-rmse:0.324649+0.012599 test-rmse:0.630706+0.063755
## [36] train-rmse:0.319480+0.012819 test-rmse:0.630514+0.064210
## [37] train-rmse:0.315601+0.012422 test-rmse:0.628936+0.063737
## [38] train-rmse:0.309281+0.012366 test-rmse:0.627326+0.061669
## [39] train-rmse:0.303782+0.014456 test-rmse:0.627903+0.061441
## [40] train-rmse:0.299875+0.013914 test-rmse:0.628000+0.061064
## [41] train-rmse:0.296955+0.013960 test-rmse:0.628243+0.061062
## [42] train-rmse:0.292627+0.013663 test-rmse:0.630327+0.062788
## [43] train-rmse:0.285960+0.011598 test-rmse:0.632524+0.061955
## [44] train-rmse:0.281942+0.012036 test-rmse:0.634418+0.063472
## Stopping. Best iteration:
## [38] train-rmse:0.309281+0.012366 test-rmse:0.627326+0.061669
##
## 67
## [1] train-rmse:1.246041+0.011926 test-rmse:1.264787+0.074243
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.996652+0.007515 test-rmse:1.040071+0.075990
## [3] train-rmse:0.827052+0.005146 test-rmse:0.887840+0.073590
## [4] train-rmse:0.716928+0.006765 test-rmse:0.800933+0.066496
## [5] train-rmse:0.644560+0.008431 test-rmse:0.745464+0.058647
## [6] train-rmse:0.594591+0.008576 test-rmse:0.716082+0.048716
## [7] train-rmse:0.556336+0.007140 test-rmse:0.698079+0.044285
## [8] train-rmse:0.529516+0.003931 test-rmse:0.680245+0.048183
## [9] train-rmse:0.506181+0.008200 test-rmse:0.674895+0.047751
## [10] train-rmse:0.483676+0.013679 test-rmse:0.661806+0.047251
## [11] train-rmse:0.469840+0.014340 test-rmse:0.660669+0.048345
## [12] train-rmse:0.454912+0.012620 test-rmse:0.656664+0.050442
## [13] train-rmse:0.442957+0.013658 test-rmse:0.649216+0.053611
## [14] train-rmse:0.429484+0.012438 test-rmse:0.645558+0.056224
## [15] train-rmse:0.417963+0.010897 test-rmse:0.641357+0.053478
## [16] train-rmse:0.405575+0.013853 test-rmse:0.641582+0.055859
## [17] train-rmse:0.391898+0.021540 test-rmse:0.638620+0.054809
## [18] train-rmse:0.380494+0.016301 test-rmse:0.633752+0.055362
## [19] train-rmse:0.370674+0.021568 test-rmse:0.632592+0.051617
## [20] train-rmse:0.361975+0.023229 test-rmse:0.630165+0.050053
## [21] train-rmse:0.350152+0.023367 test-rmse:0.630763+0.053727
## [22] train-rmse:0.337745+0.018078 test-rmse:0.625804+0.053331
## [23] train-rmse:0.332292+0.017895 test-rmse:0.624191+0.053486
## [24] train-rmse:0.324833+0.014871 test-rmse:0.618843+0.052876
## [25] train-rmse:0.316252+0.015295 test-rmse:0.617814+0.055108
## [26] train-rmse:0.307106+0.016182 test-rmse:0.616124+0.056186
## [27] train-rmse:0.298966+0.017888 test-rmse:0.615464+0.055516
## [28] train-rmse:0.291503+0.014762 test-rmse:0.614762+0.055972
## [29] train-rmse:0.285058+0.016907 test-rmse:0.615199+0.055815
## [30] train-rmse:0.278736+0.016325 test-rmse:0.613282+0.056940
## [31] train-rmse:0.271863+0.017917 test-rmse:0.612309+0.057981
## [32] train-rmse:0.264732+0.019710 test-rmse:0.612100+0.057525
## [33] train-rmse:0.258566+0.018302 test-rmse:0.613636+0.057726
## [34] train-rmse:0.252455+0.017331 test-rmse:0.615272+0.058428
## [35] train-rmse:0.246038+0.014519 test-rmse:0.614911+0.058323
## [36] train-rmse:0.241573+0.013542 test-rmse:0.614581+0.059238
## [37] train-rmse:0.236347+0.012277 test-rmse:0.611415+0.059004
## [38] train-rmse:0.231763+0.013297 test-rmse:0.611646+0.060249
## [39] train-rmse:0.228420+0.014128 test-rmse:0.610945+0.059405
## [40] train-rmse:0.223693+0.015169 test-rmse:0.612521+0.060605
## [41] train-rmse:0.219599+0.014708 test-rmse:0.613198+0.060470
## [42] train-rmse:0.212668+0.012454 test-rmse:0.612093+0.059665
## [43] train-rmse:0.209517+0.012840 test-rmse:0.610817+0.059092
## [44] train-rmse:0.204227+0.013476 test-rmse:0.611516+0.059583
## [45] train-rmse:0.199779+0.014664 test-rmse:0.612522+0.060719
## [46] train-rmse:0.196188+0.015138 test-rmse:0.614054+0.062095
## [47] train-rmse:0.192800+0.014056 test-rmse:0.614771+0.062639
## [48] train-rmse:0.189342+0.014780 test-rmse:0.614901+0.064530
## [49] train-rmse:0.185260+0.013903 test-rmse:0.613470+0.064765
## Stopping. Best iteration:
## [43] train-rmse:0.209517+0.012840 test-rmse:0.610817+0.059092
##
## 68
## [1] train-rmse:1.241950+0.011262 test-rmse:1.267066+0.076219
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.987731+0.006668 test-rmse:1.035023+0.076188
## [3] train-rmse:0.811042+0.005537 test-rmse:0.888337+0.069253
## [4] train-rmse:0.692345+0.006329 test-rmse:0.798672+0.068520
## [5] train-rmse:0.613622+0.005712 test-rmse:0.740762+0.059388
## [6] train-rmse:0.559684+0.007565 test-rmse:0.710319+0.057424
## [7] train-rmse:0.511308+0.013996 test-rmse:0.693168+0.051688
## [8] train-rmse:0.474145+0.009185 test-rmse:0.674826+0.049209
## [9] train-rmse:0.452077+0.008635 test-rmse:0.660375+0.045657
## [10] train-rmse:0.426990+0.012683 test-rmse:0.659560+0.043996
## [11] train-rmse:0.411366+0.014160 test-rmse:0.658097+0.045211
## [12] train-rmse:0.396275+0.013308 test-rmse:0.658609+0.046281
## [13] train-rmse:0.381030+0.016521 test-rmse:0.660238+0.046284
## [14] train-rmse:0.359675+0.009719 test-rmse:0.647937+0.043446
## [15] train-rmse:0.350112+0.009894 test-rmse:0.645163+0.041823
## [16] train-rmse:0.337733+0.011803 test-rmse:0.647246+0.042274
## [17] train-rmse:0.325315+0.013994 test-rmse:0.643738+0.042138
## [18] train-rmse:0.316361+0.012914 test-rmse:0.642359+0.045407
## [19] train-rmse:0.305352+0.012772 test-rmse:0.635745+0.045741
## [20] train-rmse:0.295477+0.011086 test-rmse:0.633317+0.043800
## [21] train-rmse:0.284630+0.011446 test-rmse:0.635022+0.044812
## [22] train-rmse:0.276945+0.010877 test-rmse:0.634007+0.046352
## [23] train-rmse:0.268586+0.010907 test-rmse:0.633485+0.046885
## [24] train-rmse:0.259169+0.009143 test-rmse:0.632175+0.046295
## [25] train-rmse:0.251638+0.008516 test-rmse:0.631670+0.047425
## [26] train-rmse:0.242844+0.007269 test-rmse:0.629991+0.046555
## [27] train-rmse:0.233638+0.005579 test-rmse:0.629860+0.047958
## [28] train-rmse:0.226614+0.005867 test-rmse:0.631646+0.048258
## [29] train-rmse:0.218024+0.006640 test-rmse:0.631525+0.049491
## [30] train-rmse:0.211922+0.008292 test-rmse:0.631956+0.050555
## [31] train-rmse:0.205918+0.006633 test-rmse:0.632907+0.050108
## [32] train-rmse:0.202029+0.006565 test-rmse:0.632829+0.051872
## [33] train-rmse:0.197863+0.007330 test-rmse:0.631912+0.051543
## Stopping. Best iteration:
## [27] train-rmse:0.233638+0.005579 test-rmse:0.629860+0.047958
##
## 69
## [1] train-rmse:1.238950+0.010704 test-rmse:1.265550+0.076443
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.981544+0.006885 test-rmse:1.029839+0.077037
## [3] train-rmse:0.799180+0.007153 test-rmse:0.884443+0.081521
## [4] train-rmse:0.677705+0.008302 test-rmse:0.798316+0.076444
## [5] train-rmse:0.586886+0.011915 test-rmse:0.744175+0.066196
## [6] train-rmse:0.525934+0.013337 test-rmse:0.710444+0.065448
## [7] train-rmse:0.480974+0.010293 test-rmse:0.684653+0.052727
## [8] train-rmse:0.439238+0.016176 test-rmse:0.670366+0.046795
## [9] train-rmse:0.412847+0.015127 test-rmse:0.663829+0.047126
## [10] train-rmse:0.387020+0.014341 test-rmse:0.658864+0.049519
## [11] train-rmse:0.368405+0.015710 test-rmse:0.654749+0.052713
## [12] train-rmse:0.350500+0.016872 test-rmse:0.650021+0.052672
## [13] train-rmse:0.332955+0.014151 test-rmse:0.645156+0.048197
## [14] train-rmse:0.318821+0.012918 test-rmse:0.644193+0.049540
## [15] train-rmse:0.307339+0.012754 test-rmse:0.641759+0.051487
## [16] train-rmse:0.295108+0.012166 test-rmse:0.644498+0.051856
## [17] train-rmse:0.281827+0.011172 test-rmse:0.639950+0.051402
## [18] train-rmse:0.272129+0.010158 test-rmse:0.640630+0.052994
## [19] train-rmse:0.258280+0.014428 test-rmse:0.642166+0.053409
## [20] train-rmse:0.247844+0.016581 test-rmse:0.644366+0.053877
## [21] train-rmse:0.234890+0.013918 test-rmse:0.642168+0.051552
## [22] train-rmse:0.227136+0.013593 test-rmse:0.642207+0.049986
## [23] train-rmse:0.219464+0.012719 test-rmse:0.642726+0.053358
## Stopping. Best iteration:
## [17] train-rmse:0.281827+0.011172 test-rmse:0.639950+0.051402
##
## 70
## [1] train-rmse:1.247318+0.011900 test-rmse:1.249793+0.070938
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.025503+0.008729 test-rmse:1.030761+0.067016
## [3] train-rmse:0.894820+0.007647 test-rmse:0.909440+0.062506
## [4] train-rmse:0.818060+0.006764 test-rmse:0.834111+0.055296
## [5] train-rmse:0.774856+0.006485 test-rmse:0.800010+0.048643
## [6] train-rmse:0.749346+0.006009 test-rmse:0.779585+0.043449
## [7] train-rmse:0.733097+0.005805 test-rmse:0.764840+0.040107
## [8] train-rmse:0.721699+0.005637 test-rmse:0.753281+0.039253
## [9] train-rmse:0.713050+0.005181 test-rmse:0.746075+0.034895
## [10] train-rmse:0.706244+0.004937 test-rmse:0.741949+0.034017
## [11] train-rmse:0.700570+0.004765 test-rmse:0.742663+0.032019
## [12] train-rmse:0.695728+0.005004 test-rmse:0.739934+0.030091
## [13] train-rmse:0.691344+0.005187 test-rmse:0.736807+0.031495
## [14] train-rmse:0.687344+0.005333 test-rmse:0.736019+0.031410
## [15] train-rmse:0.683581+0.005401 test-rmse:0.734050+0.030519
## [16] train-rmse:0.680035+0.005298 test-rmse:0.732386+0.030865
## [17] train-rmse:0.676669+0.005207 test-rmse:0.731374+0.032810
## [18] train-rmse:0.673397+0.005218 test-rmse:0.728201+0.032407
## [19] train-rmse:0.670287+0.005177 test-rmse:0.726523+0.031644
## [20] train-rmse:0.667310+0.005246 test-rmse:0.728846+0.032580
## [21] train-rmse:0.664479+0.005310 test-rmse:0.725802+0.032484
## [22] train-rmse:0.661759+0.005321 test-rmse:0.723960+0.032854
## [23] train-rmse:0.659100+0.005352 test-rmse:0.724871+0.034468
## [24] train-rmse:0.656548+0.005366 test-rmse:0.722025+0.032912
## [25] train-rmse:0.654171+0.005372 test-rmse:0.720646+0.033854
## [26] train-rmse:0.651890+0.005446 test-rmse:0.721010+0.035004
## [27] train-rmse:0.649748+0.005519 test-rmse:0.720132+0.035191
## [28] train-rmse:0.647667+0.005644 test-rmse:0.720375+0.034369
## [29] train-rmse:0.645660+0.005596 test-rmse:0.719354+0.034580
## [30] train-rmse:0.643749+0.005593 test-rmse:0.717931+0.034664
## [31] train-rmse:0.641804+0.005566 test-rmse:0.716118+0.033983
## [32] train-rmse:0.639992+0.005603 test-rmse:0.713909+0.032494
## [33] train-rmse:0.638226+0.005633 test-rmse:0.713680+0.033109
## [34] train-rmse:0.636505+0.005676 test-rmse:0.713290+0.033608
## [35] train-rmse:0.634813+0.005707 test-rmse:0.713166+0.032985
## [36] train-rmse:0.633140+0.005729 test-rmse:0.711730+0.032208
## [37] train-rmse:0.631465+0.005710 test-rmse:0.711328+0.034877
## [38] train-rmse:0.629885+0.005696 test-rmse:0.710861+0.035041
## [39] train-rmse:0.628348+0.005667 test-rmse:0.708346+0.033883
## [40] train-rmse:0.626796+0.005762 test-rmse:0.710018+0.033736
## [41] train-rmse:0.625199+0.005860 test-rmse:0.709952+0.032355
## [42] train-rmse:0.623682+0.005974 test-rmse:0.710139+0.034317
## [43] train-rmse:0.622288+0.005946 test-rmse:0.708513+0.035191
## [44] train-rmse:0.620947+0.005963 test-rmse:0.708805+0.035076
## [45] train-rmse:0.619623+0.005939 test-rmse:0.708494+0.035863
## Stopping. Best iteration:
## [39] train-rmse:0.628348+0.005667 test-rmse:0.708346+0.033883
##
## 71
## [1] train-rmse:1.238307+0.011705 test-rmse:1.247043+0.071244
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.006899+0.007845 test-rmse:1.026014+0.068470
## [3] train-rmse:0.866435+0.005549 test-rmse:0.898007+0.066934
## [4] train-rmse:0.780319+0.002415 test-rmse:0.823037+0.063289
## [5] train-rmse:0.726876+0.005122 test-rmse:0.775286+0.051731
## [6] train-rmse:0.694929+0.004964 test-rmse:0.747115+0.047719
## [7] train-rmse:0.673382+0.002982 test-rmse:0.733001+0.042036
## [8] train-rmse:0.658645+0.002505 test-rmse:0.724350+0.041348
## [9] train-rmse:0.645858+0.005360 test-rmse:0.717274+0.036028
## [10] train-rmse:0.637457+0.005599 test-rmse:0.714433+0.032522
## [11] train-rmse:0.629868+0.005827 test-rmse:0.713339+0.038061
## [12] train-rmse:0.620472+0.004639 test-rmse:0.708074+0.037685
## [13] train-rmse:0.613633+0.005430 test-rmse:0.707580+0.037787
## [14] train-rmse:0.607550+0.004495 test-rmse:0.701524+0.034585
## [15] train-rmse:0.598179+0.004908 test-rmse:0.697402+0.034215
## [16] train-rmse:0.593737+0.004438 test-rmse:0.698742+0.039790
## [17] train-rmse:0.587452+0.003187 test-rmse:0.694971+0.036751
## [18] train-rmse:0.582307+0.004156 test-rmse:0.692072+0.038490
## [19] train-rmse:0.577654+0.003666 test-rmse:0.688709+0.039485
## [20] train-rmse:0.573099+0.003295 test-rmse:0.687000+0.039763
## [21] train-rmse:0.568118+0.005319 test-rmse:0.682659+0.036258
## [22] train-rmse:0.562742+0.003702 test-rmse:0.675867+0.039158
## [23] train-rmse:0.558335+0.004460 test-rmse:0.673764+0.038121
## [24] train-rmse:0.555048+0.004550 test-rmse:0.672125+0.036908
## [25] train-rmse:0.552079+0.004489 test-rmse:0.672673+0.038571
## [26] train-rmse:0.548180+0.004707 test-rmse:0.673096+0.039835
## [27] train-rmse:0.543138+0.003829 test-rmse:0.669839+0.044047
## [28] train-rmse:0.538716+0.003976 test-rmse:0.666381+0.042871
## [29] train-rmse:0.534266+0.004147 test-rmse:0.663358+0.042622
## [30] train-rmse:0.531170+0.003701 test-rmse:0.662227+0.043837
## [31] train-rmse:0.527937+0.003896 test-rmse:0.659536+0.043078
## [32] train-rmse:0.523993+0.004455 test-rmse:0.657410+0.042943
## [33] train-rmse:0.519058+0.004199 test-rmse:0.657809+0.041463
## [34] train-rmse:0.515975+0.004599 test-rmse:0.658085+0.043086
## [35] train-rmse:0.511813+0.005135 test-rmse:0.658665+0.043102
## [36] train-rmse:0.509492+0.005278 test-rmse:0.659105+0.040705
## [37] train-rmse:0.506850+0.005161 test-rmse:0.656631+0.043618
## [38] train-rmse:0.504552+0.005098 test-rmse:0.656138+0.044768
## [39] train-rmse:0.502191+0.005307 test-rmse:0.657627+0.045367
## [40] train-rmse:0.497878+0.006613 test-rmse:0.655002+0.043955
## [41] train-rmse:0.495227+0.006624 test-rmse:0.655538+0.045167
## [42] train-rmse:0.492921+0.006875 test-rmse:0.654835+0.044972
## [43] train-rmse:0.490297+0.006962 test-rmse:0.654878+0.045343
## [44] train-rmse:0.487535+0.006348 test-rmse:0.654130+0.044777
## [45] train-rmse:0.485379+0.006815 test-rmse:0.653361+0.045892
## [46] train-rmse:0.483197+0.007523 test-rmse:0.653345+0.047977
## [47] train-rmse:0.480664+0.006661 test-rmse:0.650715+0.046420
## [48] train-rmse:0.476416+0.005440 test-rmse:0.647867+0.042850
## [49] train-rmse:0.473816+0.004829 test-rmse:0.646107+0.041968
## [50] train-rmse:0.470491+0.004878 test-rmse:0.645900+0.044027
## [51] train-rmse:0.468857+0.005415 test-rmse:0.646249+0.045618
## [52] train-rmse:0.466097+0.006215 test-rmse:0.646555+0.043806
## [53] train-rmse:0.464330+0.006025 test-rmse:0.646272+0.043738
## [54] train-rmse:0.462360+0.006684 test-rmse:0.644219+0.043292
## [55] train-rmse:0.459572+0.008001 test-rmse:0.644671+0.042739
## [56] train-rmse:0.456721+0.007131 test-rmse:0.642022+0.042431
## [57] train-rmse:0.454584+0.006815 test-rmse:0.643188+0.044279
## [58] train-rmse:0.453000+0.006269 test-rmse:0.643273+0.043544
## [59] train-rmse:0.451274+0.006362 test-rmse:0.643351+0.043687
## [60] train-rmse:0.448860+0.006659 test-rmse:0.643445+0.044115
## [61] train-rmse:0.446944+0.006327 test-rmse:0.642716+0.042535
## [62] train-rmse:0.443402+0.008792 test-rmse:0.641945+0.042429
## [63] train-rmse:0.442014+0.008821 test-rmse:0.641461+0.042677
## [64] train-rmse:0.440187+0.008918 test-rmse:0.642718+0.042985
## [65] train-rmse:0.439128+0.009069 test-rmse:0.642714+0.043225
## [66] train-rmse:0.437918+0.009547 test-rmse:0.642401+0.042811
## [67] train-rmse:0.435556+0.011321 test-rmse:0.641658+0.043518
## [68] train-rmse:0.434103+0.011486 test-rmse:0.641489+0.044072
## [69] train-rmse:0.431589+0.013001 test-rmse:0.641692+0.043711
## Stopping. Best iteration:
## [63] train-rmse:0.442014+0.008821 test-rmse:0.641461+0.042677
##
## 72
## [1] train-rmse:1.232139+0.011191 test-rmse:1.243859+0.069532
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.990580+0.007784 test-rmse:1.017445+0.066840
## [3] train-rmse:0.840968+0.005562 test-rmse:0.878514+0.068448
## [4] train-rmse:0.750661+0.006005 test-rmse:0.800758+0.068332
## [5] train-rmse:0.688566+0.008020 test-rmse:0.755380+0.058774
## [6] train-rmse:0.651283+0.009270 test-rmse:0.726875+0.048282
## [7] train-rmse:0.624975+0.009466 test-rmse:0.713498+0.041135
## [8] train-rmse:0.608526+0.008401 test-rmse:0.704858+0.039529
## [9] train-rmse:0.592254+0.009505 test-rmse:0.694793+0.038216
## [10] train-rmse:0.580189+0.010698 test-rmse:0.691216+0.040127
## [11] train-rmse:0.569788+0.011536 test-rmse:0.688577+0.038685
## [12] train-rmse:0.560547+0.012304 test-rmse:0.687909+0.039857
## [13] train-rmse:0.548455+0.011206 test-rmse:0.683302+0.041301
## [14] train-rmse:0.537858+0.013593 test-rmse:0.682815+0.041221
## [15] train-rmse:0.525874+0.010936 test-rmse:0.676194+0.045641
## [16] train-rmse:0.516787+0.008682 test-rmse:0.671233+0.046435
## [17] train-rmse:0.510646+0.009116 test-rmse:0.668508+0.048479
## [18] train-rmse:0.505747+0.009600 test-rmse:0.667255+0.048390
## [19] train-rmse:0.500183+0.008924 test-rmse:0.664931+0.051234
## [20] train-rmse:0.495614+0.008929 test-rmse:0.664782+0.054150
## [21] train-rmse:0.488573+0.008079 test-rmse:0.660992+0.051446
## [22] train-rmse:0.482525+0.009667 test-rmse:0.656039+0.048188
## [23] train-rmse:0.474588+0.010599 test-rmse:0.653794+0.051060
## [24] train-rmse:0.468837+0.012118 test-rmse:0.652445+0.050041
## [25] train-rmse:0.462130+0.012989 test-rmse:0.650328+0.051544
## [26] train-rmse:0.454857+0.012796 test-rmse:0.651920+0.052386
## [27] train-rmse:0.450660+0.012720 test-rmse:0.650944+0.054530
## [28] train-rmse:0.442439+0.009462 test-rmse:0.644579+0.051558
## [29] train-rmse:0.438206+0.009894 test-rmse:0.642572+0.052529
## [30] train-rmse:0.434441+0.011444 test-rmse:0.644841+0.052720
## [31] train-rmse:0.429685+0.010619 test-rmse:0.645879+0.053818
## [32] train-rmse:0.423515+0.012803 test-rmse:0.642725+0.054428
## [33] train-rmse:0.419889+0.013608 test-rmse:0.640674+0.051644
## [34] train-rmse:0.416237+0.013793 test-rmse:0.639182+0.050295
## [35] train-rmse:0.409637+0.012544 test-rmse:0.640628+0.051831
## [36] train-rmse:0.405369+0.014731 test-rmse:0.640029+0.050523
## [37] train-rmse:0.400954+0.015599 test-rmse:0.639564+0.050429
## [38] train-rmse:0.397607+0.015501 test-rmse:0.638250+0.051633
## [39] train-rmse:0.393014+0.014366 test-rmse:0.635268+0.053134
## [40] train-rmse:0.388951+0.014723 test-rmse:0.634587+0.055215
## [41] train-rmse:0.385215+0.015086 test-rmse:0.635222+0.054865
## [42] train-rmse:0.379990+0.012659 test-rmse:0.633836+0.053856
## [43] train-rmse:0.374773+0.011143 test-rmse:0.632544+0.053614
## [44] train-rmse:0.369147+0.011526 test-rmse:0.630019+0.051317
## [45] train-rmse:0.364364+0.010676 test-rmse:0.631418+0.047953
## [46] train-rmse:0.362028+0.011353 test-rmse:0.631229+0.046675
## [47] train-rmse:0.358997+0.011291 test-rmse:0.631288+0.046497
## [48] train-rmse:0.354573+0.012254 test-rmse:0.630088+0.045155
## [49] train-rmse:0.352042+0.012435 test-rmse:0.628292+0.046340
## [50] train-rmse:0.349509+0.012845 test-rmse:0.629323+0.046435
## [51] train-rmse:0.346832+0.013797 test-rmse:0.628834+0.046390
## [52] train-rmse:0.345379+0.013969 test-rmse:0.629047+0.046967
## [53] train-rmse:0.341930+0.015996 test-rmse:0.630372+0.048316
## [54] train-rmse:0.338320+0.014952 test-rmse:0.629913+0.047679
## [55] train-rmse:0.335703+0.015526 test-rmse:0.629931+0.047731
## Stopping. Best iteration:
## [49] train-rmse:0.352042+0.012435 test-rmse:0.628292+0.046340
##
## 73
## [1] train-rmse:1.225752+0.011308 test-rmse:1.247727+0.070928
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.974896+0.007277 test-rmse:1.016078+0.074301
## [3] train-rmse:0.816407+0.004887 test-rmse:0.877666+0.070632
## [4] train-rmse:0.718130+0.004160 test-rmse:0.792177+0.072065
## [5] train-rmse:0.650806+0.011164 test-rmse:0.748778+0.064923
## [6] train-rmse:0.604551+0.008507 test-rmse:0.718601+0.063335
## [7] train-rmse:0.575215+0.008209 test-rmse:0.700500+0.065137
## [8] train-rmse:0.555480+0.007385 test-rmse:0.693002+0.064499
## [9] train-rmse:0.536284+0.006856 test-rmse:0.678456+0.061007
## [10] train-rmse:0.522379+0.007619 test-rmse:0.670624+0.059063
## [11] train-rmse:0.506516+0.010003 test-rmse:0.665297+0.060746
## [12] train-rmse:0.493731+0.011062 test-rmse:0.661809+0.062710
## [13] train-rmse:0.480576+0.014456 test-rmse:0.666374+0.066278
## [14] train-rmse:0.471301+0.016958 test-rmse:0.662436+0.064579
## [15] train-rmse:0.461260+0.016968 test-rmse:0.656243+0.060740
## [16] train-rmse:0.451457+0.015848 test-rmse:0.654997+0.058888
## [17] train-rmse:0.442101+0.014457 test-rmse:0.653756+0.058792
## [18] train-rmse:0.428198+0.011272 test-rmse:0.650498+0.061838
## [19] train-rmse:0.421189+0.014517 test-rmse:0.650256+0.059249
## [20] train-rmse:0.411616+0.012156 test-rmse:0.648810+0.059635
## [21] train-rmse:0.403668+0.010218 test-rmse:0.643702+0.062739
## [22] train-rmse:0.392263+0.011913 test-rmse:0.640883+0.060739
## [23] train-rmse:0.386671+0.013492 test-rmse:0.641770+0.061807
## [24] train-rmse:0.378668+0.015320 test-rmse:0.640306+0.060926
## [25] train-rmse:0.372115+0.015205 test-rmse:0.640086+0.061688
## [26] train-rmse:0.363619+0.015327 test-rmse:0.642142+0.064265
## [27] train-rmse:0.355594+0.013163 test-rmse:0.640997+0.064230
## [28] train-rmse:0.349573+0.013590 test-rmse:0.640471+0.062875
## [29] train-rmse:0.342121+0.014208 test-rmse:0.640296+0.062057
## [30] train-rmse:0.336226+0.013250 test-rmse:0.636102+0.061543
## [31] train-rmse:0.330615+0.014615 test-rmse:0.636066+0.061674
## [32] train-rmse:0.324148+0.015411 test-rmse:0.633727+0.062620
## [33] train-rmse:0.317241+0.017480 test-rmse:0.632740+0.062151
## [34] train-rmse:0.312034+0.016768 test-rmse:0.633022+0.062486
## [35] train-rmse:0.308591+0.017170 test-rmse:0.633520+0.062244
## [36] train-rmse:0.303674+0.018738 test-rmse:0.631449+0.063475
## [37] train-rmse:0.300892+0.018246 test-rmse:0.631518+0.062642
## [38] train-rmse:0.296578+0.016323 test-rmse:0.631004+0.062420
## [39] train-rmse:0.289874+0.016494 test-rmse:0.631305+0.062561
## [40] train-rmse:0.284932+0.015603 test-rmse:0.631959+0.063337
## [41] train-rmse:0.281360+0.016669 test-rmse:0.631083+0.062610
## [42] train-rmse:0.276711+0.015478 test-rmse:0.631618+0.062193
## [43] train-rmse:0.271214+0.016287 test-rmse:0.633795+0.062583
## [44] train-rmse:0.265470+0.016895 test-rmse:0.636258+0.062775
## Stopping. Best iteration:
## [38] train-rmse:0.296578+0.016323 test-rmse:0.631004+0.062420
##
## 74
## [1] train-rmse:1.221384+0.011574 test-rmse:1.241892+0.074774
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.963560+0.007063 test-rmse:1.008906+0.078872
## [3] train-rmse:0.794513+0.005675 test-rmse:0.859339+0.075442
## [4] train-rmse:0.688333+0.007434 test-rmse:0.775365+0.068892
## [5] train-rmse:0.622346+0.009313 test-rmse:0.733780+0.066110
## [6] train-rmse:0.570984+0.007659 test-rmse:0.705610+0.058161
## [7] train-rmse:0.531032+0.005286 test-rmse:0.682980+0.053197
## [8] train-rmse:0.502840+0.010663 test-rmse:0.677197+0.050302
## [9] train-rmse:0.482398+0.007954 test-rmse:0.666801+0.051112
## [10] train-rmse:0.468898+0.009314 test-rmse:0.666270+0.053249
## [11] train-rmse:0.452821+0.014493 test-rmse:0.664455+0.059903
## [12] train-rmse:0.439282+0.015452 test-rmse:0.658306+0.053139
## [13] train-rmse:0.421581+0.012664 test-rmse:0.649481+0.052320
## [14] train-rmse:0.406286+0.006916 test-rmse:0.641477+0.052669
## [15] train-rmse:0.396612+0.006393 test-rmse:0.644449+0.054893
## [16] train-rmse:0.378923+0.011767 test-rmse:0.640330+0.054327
## [17] train-rmse:0.366524+0.009879 test-rmse:0.634292+0.050834
## [18] train-rmse:0.358320+0.011426 test-rmse:0.634028+0.052998
## [19] train-rmse:0.348317+0.013590 test-rmse:0.632228+0.052354
## [20] train-rmse:0.340415+0.013737 test-rmse:0.633412+0.055206
## [21] train-rmse:0.332551+0.015995 test-rmse:0.630939+0.054268
## [22] train-rmse:0.323099+0.013728 test-rmse:0.631157+0.058136
## [23] train-rmse:0.316332+0.012935 test-rmse:0.631769+0.058557
## [24] train-rmse:0.309649+0.012658 test-rmse:0.631172+0.057945
## [25] train-rmse:0.302539+0.013311 test-rmse:0.632223+0.057965
## [26] train-rmse:0.292445+0.012822 test-rmse:0.634689+0.057611
## [27] train-rmse:0.284758+0.014672 test-rmse:0.631452+0.054498
## Stopping. Best iteration:
## [21] train-rmse:0.332551+0.015995 test-rmse:0.630939+0.054268
##
## 75
## [1] train-rmse:1.216931+0.010852 test-rmse:1.244339+0.076818
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.953690+0.005933 test-rmse:1.007138+0.075607
## [3] train-rmse:0.778111+0.004295 test-rmse:0.861624+0.070449
## [4] train-rmse:0.662877+0.005609 test-rmse:0.777028+0.069318
## [5] train-rmse:0.591393+0.006690 test-rmse:0.726199+0.059994
## [6] train-rmse:0.534517+0.016639 test-rmse:0.701302+0.058290
## [7] train-rmse:0.492200+0.010443 test-rmse:0.684029+0.048531
## [8] train-rmse:0.462980+0.012731 test-rmse:0.668651+0.048849
## [9] train-rmse:0.436588+0.009159 test-rmse:0.663854+0.056053
## [10] train-rmse:0.414958+0.014340 test-rmse:0.662054+0.054715
## [11] train-rmse:0.398048+0.016915 test-rmse:0.654520+0.061773
## [12] train-rmse:0.383777+0.016741 test-rmse:0.652457+0.062726
## [13] train-rmse:0.364689+0.013750 test-rmse:0.642353+0.063541
## [14] train-rmse:0.347154+0.013596 test-rmse:0.641076+0.062577
## [15] train-rmse:0.338429+0.015005 test-rmse:0.640903+0.062860
## [16] train-rmse:0.325977+0.012218 test-rmse:0.638406+0.061000
## [17] train-rmse:0.315393+0.010030 test-rmse:0.638751+0.062068
## [18] train-rmse:0.306262+0.010009 test-rmse:0.639420+0.061630
## [19] train-rmse:0.295558+0.010436 test-rmse:0.640070+0.059926
## [20] train-rmse:0.287800+0.011570 test-rmse:0.641616+0.061636
## [21] train-rmse:0.278955+0.013193 test-rmse:0.644459+0.063259
## [22] train-rmse:0.269993+0.014392 test-rmse:0.643968+0.064863
## Stopping. Best iteration:
## [16] train-rmse:0.325977+0.012218 test-rmse:0.638406+0.061000
##
## 76
## [1] train-rmse:1.213666+0.010250 test-rmse:1.242711+0.077061
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.945476+0.005893 test-rmse:1.000468+0.075469
## [3] train-rmse:0.762549+0.006224 test-rmse:0.858699+0.081059
## [4] train-rmse:0.638715+0.006704 test-rmse:0.771773+0.077423
## [5] train-rmse:0.552305+0.010093 test-rmse:0.723148+0.067201
## [6] train-rmse:0.494989+0.013107 test-rmse:0.688106+0.061032
## [7] train-rmse:0.446898+0.021899 test-rmse:0.671482+0.058891
## [8] train-rmse:0.415077+0.023276 test-rmse:0.663815+0.060483
## [9] train-rmse:0.396076+0.021812 test-rmse:0.661583+0.061493
## [10] train-rmse:0.373044+0.018777 test-rmse:0.649961+0.059835
## [11] train-rmse:0.354813+0.023100 test-rmse:0.651473+0.056669
## [12] train-rmse:0.337981+0.026010 test-rmse:0.643391+0.053330
## [13] train-rmse:0.323009+0.023790 test-rmse:0.639461+0.054091
## [14] train-rmse:0.307083+0.017154 test-rmse:0.639173+0.053542
## [15] train-rmse:0.295422+0.017765 test-rmse:0.635465+0.051521
## [16] train-rmse:0.284005+0.017246 test-rmse:0.634122+0.051894
## [17] train-rmse:0.272977+0.019482 test-rmse:0.633049+0.051579
## [18] train-rmse:0.261221+0.016859 test-rmse:0.631334+0.046165
## [19] train-rmse:0.247930+0.015683 test-rmse:0.626399+0.042990
## [20] train-rmse:0.235347+0.016691 test-rmse:0.628175+0.044672
## [21] train-rmse:0.226783+0.017287 test-rmse:0.628438+0.042420
## [22] train-rmse:0.216136+0.015388 test-rmse:0.627359+0.041326
## [23] train-rmse:0.205217+0.013734 test-rmse:0.626296+0.041307
## [24] train-rmse:0.195474+0.015500 test-rmse:0.627381+0.042342
## [25] train-rmse:0.189454+0.015653 test-rmse:0.627019+0.041998
## [26] train-rmse:0.182360+0.017287 test-rmse:0.627739+0.043149
## [27] train-rmse:0.174764+0.015167 test-rmse:0.626054+0.045582
## [28] train-rmse:0.166379+0.014698 test-rmse:0.626067+0.046211
## [29] train-rmse:0.158795+0.014243 test-rmse:0.625882+0.045006
## [30] train-rmse:0.152496+0.012800 test-rmse:0.626046+0.045366
## [31] train-rmse:0.148162+0.011825 test-rmse:0.625551+0.044742
## [32] train-rmse:0.143160+0.011345 test-rmse:0.625712+0.045623
## [33] train-rmse:0.137308+0.010098 test-rmse:0.626236+0.046387
## [34] train-rmse:0.130737+0.009758 test-rmse:0.627631+0.046410
## [35] train-rmse:0.125791+0.009659 test-rmse:0.628853+0.045808
## [36] train-rmse:0.120201+0.009817 test-rmse:0.628896+0.046062
## [37] train-rmse:0.115792+0.010942 test-rmse:0.629180+0.045656
## Stopping. Best iteration:
## [31] train-rmse:0.148162+0.011825 test-rmse:0.625551+0.044742
##
## 77
## [1] train-rmse:1.224911+0.011575 test-rmse:1.227681+0.071031
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.999257+0.008367 test-rmse:1.005085+0.066272
## [3] train-rmse:0.872193+0.007607 test-rmse:0.888733+0.060056
## [4] train-rmse:0.801572+0.006780 test-rmse:0.820629+0.050630
## [5] train-rmse:0.762759+0.006048 test-rmse:0.791248+0.044080
## [6] train-rmse:0.740432+0.005397 test-rmse:0.775399+0.039885
## [7] train-rmse:0.726328+0.004866 test-rmse:0.761034+0.038489
## [8] train-rmse:0.716014+0.004639 test-rmse:0.750998+0.032435
## [9] train-rmse:0.708146+0.004488 test-rmse:0.744082+0.030282
## [10] train-rmse:0.701875+0.004599 test-rmse:0.742240+0.028122
## [11] train-rmse:0.696437+0.004526 test-rmse:0.742029+0.027985
## [12] train-rmse:0.691661+0.004684 test-rmse:0.740261+0.028292
## [13] train-rmse:0.687204+0.004883 test-rmse:0.737328+0.027629
## [14] train-rmse:0.683174+0.004974 test-rmse:0.734985+0.025703
## [15] train-rmse:0.679415+0.004990 test-rmse:0.736171+0.029206
## [16] train-rmse:0.675890+0.004980 test-rmse:0.734417+0.029303
## [17] train-rmse:0.672460+0.005024 test-rmse:0.730684+0.030715
## [18] train-rmse:0.669186+0.005021 test-rmse:0.728536+0.030618
## [19] train-rmse:0.666125+0.005000 test-rmse:0.725984+0.029613
## [20] train-rmse:0.663053+0.004987 test-rmse:0.725254+0.031280
## [21] train-rmse:0.660263+0.005032 test-rmse:0.726148+0.031793
## [22] train-rmse:0.657454+0.005105 test-rmse:0.724630+0.033261
## [23] train-rmse:0.654852+0.005264 test-rmse:0.722726+0.032287
## [24] train-rmse:0.652347+0.005306 test-rmse:0.720755+0.031269
## [25] train-rmse:0.650015+0.005409 test-rmse:0.722470+0.031125
## [26] train-rmse:0.647763+0.005509 test-rmse:0.722376+0.032808
## [27] train-rmse:0.645600+0.005482 test-rmse:0.720632+0.032072
## [28] train-rmse:0.643525+0.005551 test-rmse:0.720201+0.033913
## [29] train-rmse:0.641525+0.005600 test-rmse:0.718356+0.033780
## [30] train-rmse:0.639606+0.005577 test-rmse:0.716729+0.035016
## [31] train-rmse:0.637702+0.005569 test-rmse:0.715753+0.033454
## [32] train-rmse:0.635886+0.005575 test-rmse:0.713530+0.033472
## [33] train-rmse:0.634181+0.005608 test-rmse:0.712419+0.032564
## [34] train-rmse:0.632494+0.005671 test-rmse:0.713521+0.034355
## [35] train-rmse:0.630809+0.005727 test-rmse:0.713107+0.033533
## [36] train-rmse:0.629120+0.005715 test-rmse:0.712981+0.034682
## [37] train-rmse:0.627485+0.005678 test-rmse:0.713429+0.033756
## [38] train-rmse:0.625799+0.005764 test-rmse:0.709984+0.034270
## [39] train-rmse:0.624149+0.005825 test-rmse:0.708371+0.032875
## [40] train-rmse:0.622548+0.005865 test-rmse:0.707073+0.033858
## [41] train-rmse:0.621024+0.005918 test-rmse:0.707909+0.033555
## [42] train-rmse:0.619523+0.005970 test-rmse:0.709801+0.033241
## [43] train-rmse:0.618118+0.005994 test-rmse:0.708361+0.033920
## [44] train-rmse:0.616758+0.006018 test-rmse:0.707616+0.034411
## [45] train-rmse:0.615461+0.006027 test-rmse:0.707142+0.034724
## [46] train-rmse:0.614158+0.006029 test-rmse:0.705915+0.036829
## [47] train-rmse:0.612894+0.006056 test-rmse:0.704843+0.034694
## [48] train-rmse:0.611682+0.006079 test-rmse:0.703724+0.034610
## [49] train-rmse:0.610461+0.006075 test-rmse:0.702732+0.033411
## [50] train-rmse:0.609223+0.006051 test-rmse:0.702368+0.033304
## [51] train-rmse:0.608075+0.006064 test-rmse:0.700877+0.032807
## [52] train-rmse:0.606954+0.006073 test-rmse:0.698938+0.034601
## [53] train-rmse:0.605838+0.006101 test-rmse:0.699437+0.034634
## [54] train-rmse:0.604745+0.006107 test-rmse:0.699400+0.033925
## [55] train-rmse:0.603683+0.006177 test-rmse:0.698649+0.033163
## [56] train-rmse:0.602660+0.006211 test-rmse:0.698295+0.033585
## [57] train-rmse:0.601616+0.006246 test-rmse:0.696639+0.034264
## [58] train-rmse:0.600569+0.006304 test-rmse:0.695508+0.033384
## [59] train-rmse:0.599539+0.006327 test-rmse:0.696059+0.033893
## [60] train-rmse:0.598554+0.006332 test-rmse:0.696057+0.034415
## [61] train-rmse:0.597598+0.006334 test-rmse:0.696207+0.033232
## [62] train-rmse:0.596605+0.006304 test-rmse:0.696205+0.035326
## [63] train-rmse:0.595657+0.006312 test-rmse:0.696468+0.035345
## [64] train-rmse:0.594712+0.006355 test-rmse:0.695775+0.036187
## Stopping. Best iteration:
## [58] train-rmse:0.600569+0.006304 test-rmse:0.695508+0.033384
##
## 78
## [1] train-rmse:1.215203+0.011360 test-rmse:1.224742+0.071347
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.979037+0.007506 test-rmse:1.001241+0.069918
## [3] train-rmse:0.842539+0.005036 test-rmse:0.874872+0.063138
## [4] train-rmse:0.760371+0.003855 test-rmse:0.804098+0.057562
## [5] train-rmse:0.714742+0.003188 test-rmse:0.766875+0.051847
## [6] train-rmse:0.687507+0.002904 test-rmse:0.749213+0.044523
## [7] train-rmse:0.666865+0.004430 test-rmse:0.731598+0.045792
## [8] train-rmse:0.653729+0.005352 test-rmse:0.720839+0.043849
## [9] train-rmse:0.643137+0.006817 test-rmse:0.714897+0.043540
## [10] train-rmse:0.632528+0.004667 test-rmse:0.714696+0.044384
## [11] train-rmse:0.623583+0.005077 test-rmse:0.708548+0.044601
## [12] train-rmse:0.615737+0.004360 test-rmse:0.706495+0.044210
## [13] train-rmse:0.609764+0.004501 test-rmse:0.702873+0.045954
## [14] train-rmse:0.603009+0.005710 test-rmse:0.702588+0.044407
## [15] train-rmse:0.597320+0.004476 test-rmse:0.702994+0.047320
## [16] train-rmse:0.590057+0.004307 test-rmse:0.701045+0.044181
## [17] train-rmse:0.584453+0.005389 test-rmse:0.696592+0.042675
## [18] train-rmse:0.579627+0.004870 test-rmse:0.693441+0.043110
## [19] train-rmse:0.573099+0.007580 test-rmse:0.687560+0.040917
## [20] train-rmse:0.567861+0.006533 test-rmse:0.684118+0.041236
## [21] train-rmse:0.560679+0.005482 test-rmse:0.680897+0.035593
## [22] train-rmse:0.556726+0.005968 test-rmse:0.679565+0.037171
## [23] train-rmse:0.552306+0.005166 test-rmse:0.676190+0.038215
## [24] train-rmse:0.548632+0.005214 test-rmse:0.676596+0.038319
## [25] train-rmse:0.544951+0.005318 test-rmse:0.676740+0.038015
## [26] train-rmse:0.541005+0.005489 test-rmse:0.676410+0.041827
## [27] train-rmse:0.537801+0.005871 test-rmse:0.675964+0.042938
## [28] train-rmse:0.534650+0.006621 test-rmse:0.674595+0.041317
## [29] train-rmse:0.529778+0.008363 test-rmse:0.671121+0.039140
## [30] train-rmse:0.527440+0.008216 test-rmse:0.668426+0.039675
## [31] train-rmse:0.524959+0.008114 test-rmse:0.667888+0.040037
## [32] train-rmse:0.521776+0.008049 test-rmse:0.667401+0.041763
## [33] train-rmse:0.518485+0.008450 test-rmse:0.666524+0.042878
## [34] train-rmse:0.514430+0.009274 test-rmse:0.665133+0.040903
## [35] train-rmse:0.510972+0.007546 test-rmse:0.664747+0.041160
## [36] train-rmse:0.506107+0.007790 test-rmse:0.664255+0.040303
## [37] train-rmse:0.503138+0.008369 test-rmse:0.662754+0.040361
## [38] train-rmse:0.500704+0.008130 test-rmse:0.661187+0.038765
## [39] train-rmse:0.498068+0.007986 test-rmse:0.661143+0.039609
## [40] train-rmse:0.495094+0.007811 test-rmse:0.659260+0.040827
## [41] train-rmse:0.492828+0.008135 test-rmse:0.660341+0.042115
## [42] train-rmse:0.488827+0.004670 test-rmse:0.658179+0.044748
## [43] train-rmse:0.486929+0.004660 test-rmse:0.656973+0.044337
## [44] train-rmse:0.483813+0.005189 test-rmse:0.655774+0.044033
## [45] train-rmse:0.481088+0.005421 test-rmse:0.654119+0.046074
## [46] train-rmse:0.478824+0.005536 test-rmse:0.652858+0.045234
## [47] train-rmse:0.474862+0.003672 test-rmse:0.649097+0.042005
## [48] train-rmse:0.472154+0.003118 test-rmse:0.649306+0.041460
## [49] train-rmse:0.469976+0.003147 test-rmse:0.649686+0.042343
## [50] train-rmse:0.467251+0.003975 test-rmse:0.649216+0.042634
## [51] train-rmse:0.464800+0.003783 test-rmse:0.647135+0.043501
## [52] train-rmse:0.461757+0.005235 test-rmse:0.645820+0.042336
## [53] train-rmse:0.459506+0.006077 test-rmse:0.645028+0.041153
## [54] train-rmse:0.454990+0.009886 test-rmse:0.644172+0.042505
## [55] train-rmse:0.451328+0.009142 test-rmse:0.643349+0.042096
## [56] train-rmse:0.447709+0.008134 test-rmse:0.642159+0.042961
## [57] train-rmse:0.445406+0.008197 test-rmse:0.640182+0.043143
## [58] train-rmse:0.443829+0.008189 test-rmse:0.639615+0.043421
## [59] train-rmse:0.441630+0.008646 test-rmse:0.640041+0.042969
## [60] train-rmse:0.440146+0.008887 test-rmse:0.640718+0.043588
## [61] train-rmse:0.439088+0.009122 test-rmse:0.640008+0.042760
## [62] train-rmse:0.436423+0.009165 test-rmse:0.639894+0.042359
## [63] train-rmse:0.434833+0.008814 test-rmse:0.639342+0.043162
## [64] train-rmse:0.433077+0.009478 test-rmse:0.638030+0.042817
## [65] train-rmse:0.431290+0.009050 test-rmse:0.637566+0.045202
## [66] train-rmse:0.429908+0.008952 test-rmse:0.637252+0.045276
## [67] train-rmse:0.427627+0.007919 test-rmse:0.638848+0.045771
## [68] train-rmse:0.425619+0.008887 test-rmse:0.638299+0.045942
## [69] train-rmse:0.422489+0.011430 test-rmse:0.638588+0.045896
## [70] train-rmse:0.420245+0.011744 test-rmse:0.638201+0.045422
## [71] train-rmse:0.418905+0.012072 test-rmse:0.637910+0.045341
## [72] train-rmse:0.417781+0.011836 test-rmse:0.637170+0.045655
## [73] train-rmse:0.414932+0.011840 test-rmse:0.637559+0.046246
## [74] train-rmse:0.412262+0.011160 test-rmse:0.635745+0.047093
## [75] train-rmse:0.410182+0.011218 test-rmse:0.634680+0.046370
## [76] train-rmse:0.408194+0.011050 test-rmse:0.635160+0.046440
## [77] train-rmse:0.406233+0.010552 test-rmse:0.633765+0.047315
## [78] train-rmse:0.402895+0.011501 test-rmse:0.633787+0.045665
## [79] train-rmse:0.401766+0.012086 test-rmse:0.633619+0.045566
## [80] train-rmse:0.398793+0.012762 test-rmse:0.633256+0.044620
## [81] train-rmse:0.396366+0.012240 test-rmse:0.630999+0.044012
## [82] train-rmse:0.394036+0.012717 test-rmse:0.632171+0.045287
## [83] train-rmse:0.393100+0.012892 test-rmse:0.632423+0.046142
## [84] train-rmse:0.391474+0.013385 test-rmse:0.632352+0.046547
## [85] train-rmse:0.389390+0.012372 test-rmse:0.632170+0.047948
## [86] train-rmse:0.387264+0.012315 test-rmse:0.632039+0.048360
## [87] train-rmse:0.384559+0.013920 test-rmse:0.631371+0.049215
## Stopping. Best iteration:
## [81] train-rmse:0.396366+0.012240 test-rmse:0.630999+0.044012
##
## 79
## [1] train-rmse:1.208547+0.010806 test-rmse:1.221327+0.069510
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.961455+0.007329 test-rmse:0.991333+0.067181
## [3] train-rmse:0.816597+0.005870 test-rmse:0.859455+0.066370
## [4] train-rmse:0.732017+0.007259 test-rmse:0.785254+0.062107
## [5] train-rmse:0.673225+0.011056 test-rmse:0.744053+0.050712
## [6] train-rmse:0.637818+0.010147 test-rmse:0.720904+0.040441
## [7] train-rmse:0.617135+0.010229 test-rmse:0.716945+0.039095
## [8] train-rmse:0.600265+0.008837 test-rmse:0.705971+0.038790
## [9] train-rmse:0.585236+0.013115 test-rmse:0.695074+0.037533
## [10] train-rmse:0.574559+0.014568 test-rmse:0.689172+0.035230
## [11] train-rmse:0.563738+0.014160 test-rmse:0.687641+0.037934
## [12] train-rmse:0.552607+0.012886 test-rmse:0.687940+0.040070
## [13] train-rmse:0.545569+0.011957 test-rmse:0.685973+0.043408
## [14] train-rmse:0.535803+0.010255 test-rmse:0.682547+0.046407
## [15] train-rmse:0.525946+0.011593 test-rmse:0.679124+0.052481
## [16] train-rmse:0.515750+0.013448 test-rmse:0.672273+0.055288
## [17] train-rmse:0.505009+0.014607 test-rmse:0.661535+0.049249
## [18] train-rmse:0.498966+0.014026 test-rmse:0.662144+0.049329
## [19] train-rmse:0.493164+0.014156 test-rmse:0.659736+0.047839
## [20] train-rmse:0.483788+0.010967 test-rmse:0.651952+0.051187
## [21] train-rmse:0.476986+0.009741 test-rmse:0.649574+0.050674
## [22] train-rmse:0.469260+0.014092 test-rmse:0.649428+0.049311
## [23] train-rmse:0.462818+0.013459 test-rmse:0.647865+0.047906
## [24] train-rmse:0.456020+0.015156 test-rmse:0.646885+0.046972
## [25] train-rmse:0.449217+0.016438 test-rmse:0.645341+0.049004
## [26] train-rmse:0.444001+0.016816 test-rmse:0.642763+0.050894
## [27] train-rmse:0.437798+0.018494 test-rmse:0.640413+0.053071
## [28] train-rmse:0.431180+0.015421 test-rmse:0.635449+0.050360
## [29] train-rmse:0.424159+0.013709 test-rmse:0.635423+0.049364
## [30] train-rmse:0.417916+0.012251 test-rmse:0.633899+0.049068
## [31] train-rmse:0.413331+0.012390 test-rmse:0.635589+0.050232
## [32] train-rmse:0.408650+0.012348 test-rmse:0.631068+0.047844
## [33] train-rmse:0.405503+0.012605 test-rmse:0.631210+0.049956
## [34] train-rmse:0.399579+0.014562 test-rmse:0.630575+0.048160
## [35] train-rmse:0.394918+0.014315 test-rmse:0.631416+0.048266
## [36] train-rmse:0.390154+0.016749 test-rmse:0.628718+0.044162
## [37] train-rmse:0.386066+0.017925 test-rmse:0.627107+0.044406
## [38] train-rmse:0.380592+0.017955 test-rmse:0.626009+0.044276
## [39] train-rmse:0.375134+0.015224 test-rmse:0.624281+0.045021
## [40] train-rmse:0.372136+0.014782 test-rmse:0.624234+0.045377
## [41] train-rmse:0.369148+0.014914 test-rmse:0.625338+0.044820
## [42] train-rmse:0.366204+0.014831 test-rmse:0.625419+0.045092
## [43] train-rmse:0.363420+0.015518 test-rmse:0.622442+0.042821
## [44] train-rmse:0.358691+0.014727 test-rmse:0.621497+0.042469
## [45] train-rmse:0.355001+0.015157 test-rmse:0.623887+0.044872
## [46] train-rmse:0.352186+0.016060 test-rmse:0.624482+0.045975
## [47] train-rmse:0.349567+0.016281 test-rmse:0.624210+0.045836
## [48] train-rmse:0.346330+0.015140 test-rmse:0.624630+0.046193
## [49] train-rmse:0.341284+0.013271 test-rmse:0.624946+0.045249
## [50] train-rmse:0.338711+0.013171 test-rmse:0.627457+0.045481
## Stopping. Best iteration:
## [44] train-rmse:0.358691+0.014727 test-rmse:0.621497+0.042469
##
## 80
## [1] train-rmse:1.201642+0.010942 test-rmse:1.225484+0.071119
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.944295+0.006957 test-rmse:0.988934+0.074505
## [3] train-rmse:0.789173+0.004804 test-rmse:0.854556+0.064366
## [4] train-rmse:0.696583+0.004473 test-rmse:0.775889+0.066369
## [5] train-rmse:0.629049+0.013666 test-rmse:0.727839+0.061540
## [6] train-rmse:0.590773+0.011998 test-rmse:0.706825+0.061919
## [7] train-rmse:0.563625+0.010544 test-rmse:0.687562+0.056172
## [8] train-rmse:0.543821+0.008952 test-rmse:0.678301+0.057225
## [9] train-rmse:0.527582+0.012682 test-rmse:0.677774+0.058416
## [10] train-rmse:0.512850+0.009881 test-rmse:0.674542+0.056931
## [11] train-rmse:0.498805+0.013280 test-rmse:0.668585+0.056323
## [12] train-rmse:0.483120+0.016473 test-rmse:0.661843+0.056725
## [13] train-rmse:0.471654+0.015751 test-rmse:0.654919+0.053601
## [14] train-rmse:0.457973+0.016699 test-rmse:0.647934+0.049824
## [15] train-rmse:0.449176+0.015502 test-rmse:0.651751+0.051952
## [16] train-rmse:0.439440+0.016566 test-rmse:0.651180+0.049040
## [17] train-rmse:0.431842+0.016845 test-rmse:0.648671+0.050512
## [18] train-rmse:0.421317+0.017730 test-rmse:0.640538+0.046473
## [19] train-rmse:0.414012+0.018205 test-rmse:0.641398+0.046188
## [20] train-rmse:0.400539+0.016713 test-rmse:0.641122+0.049630
## [21] train-rmse:0.390942+0.016037 test-rmse:0.638605+0.049047
## [22] train-rmse:0.383297+0.015918 test-rmse:0.636050+0.047668
## [23] train-rmse:0.372935+0.013794 test-rmse:0.634423+0.051045
## [24] train-rmse:0.366979+0.012851 test-rmse:0.632729+0.049314
## [25] train-rmse:0.360979+0.013274 test-rmse:0.632310+0.048487
## [26] train-rmse:0.356747+0.013876 test-rmse:0.632661+0.048734
## [27] train-rmse:0.349334+0.014214 test-rmse:0.629456+0.048328
## [28] train-rmse:0.342272+0.013653 test-rmse:0.627561+0.050270
## [29] train-rmse:0.334494+0.015239 test-rmse:0.629312+0.049784
## [30] train-rmse:0.326183+0.014508 test-rmse:0.629186+0.049868
## [31] train-rmse:0.321572+0.014501 test-rmse:0.631230+0.051275
## [32] train-rmse:0.315204+0.014046 test-rmse:0.631867+0.053047
## [33] train-rmse:0.308892+0.013319 test-rmse:0.633726+0.052734
## [34] train-rmse:0.303474+0.014566 test-rmse:0.633340+0.052908
## Stopping. Best iteration:
## [28] train-rmse:0.342272+0.013653 test-rmse:0.627561+0.050270
##
## 81
## [1] train-rmse:1.196912+0.011224 test-rmse:1.219242+0.075301
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.931711+0.006585 test-rmse:0.978321+0.077461
## [3] train-rmse:0.765170+0.004949 test-rmse:0.834745+0.075001
## [4] train-rmse:0.665482+0.005393 test-rmse:0.758767+0.069172
## [5] train-rmse:0.604071+0.008493 test-rmse:0.716749+0.063887
## [6] train-rmse:0.554395+0.007420 test-rmse:0.691515+0.056297
## [7] train-rmse:0.518259+0.004857 test-rmse:0.670767+0.053453
## [8] train-rmse:0.492660+0.010677 test-rmse:0.661940+0.049007
## [9] train-rmse:0.472698+0.011062 test-rmse:0.656513+0.053052
## [10] train-rmse:0.456896+0.013632 test-rmse:0.655319+0.051439
## [11] train-rmse:0.443219+0.014622 test-rmse:0.657574+0.055707
## [12] train-rmse:0.428151+0.011511 test-rmse:0.650540+0.054396
## [13] train-rmse:0.410237+0.013794 test-rmse:0.641570+0.056302
## [14] train-rmse:0.394427+0.013312 test-rmse:0.646061+0.057077
## [15] train-rmse:0.381540+0.011427 test-rmse:0.642830+0.058876
## [16] train-rmse:0.371536+0.010540 test-rmse:0.642346+0.062670
## [17] train-rmse:0.361437+0.011318 test-rmse:0.640248+0.059589
## [18] train-rmse:0.352592+0.013068 test-rmse:0.638226+0.060552
## [19] train-rmse:0.344361+0.014501 test-rmse:0.637639+0.062367
## [20] train-rmse:0.333354+0.011734 test-rmse:0.637329+0.064342
## [21] train-rmse:0.324178+0.010868 test-rmse:0.633968+0.065491
## [22] train-rmse:0.315329+0.011910 test-rmse:0.631778+0.067766
## [23] train-rmse:0.305001+0.012002 test-rmse:0.630537+0.062923
## [24] train-rmse:0.295487+0.012364 test-rmse:0.635065+0.061479
## [25] train-rmse:0.288543+0.012304 test-rmse:0.633969+0.061004
## [26] train-rmse:0.277854+0.012069 test-rmse:0.632067+0.061005
## [27] train-rmse:0.272463+0.012624 test-rmse:0.632477+0.060489
## [28] train-rmse:0.266303+0.012941 test-rmse:0.636473+0.060381
## [29] train-rmse:0.262228+0.013752 test-rmse:0.635050+0.059949
## Stopping. Best iteration:
## [23] train-rmse:0.305001+0.012002 test-rmse:0.630537+0.062923
##
## 82
## [1] train-rmse:1.192084+0.010443 test-rmse:1.221855+0.077398
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.921271+0.004606 test-rmse:0.976819+0.078668
## [3] train-rmse:0.745324+0.006481 test-rmse:0.828613+0.074070
## [4] train-rmse:0.636278+0.005944 test-rmse:0.743867+0.065121
## [5] train-rmse:0.563163+0.012661 test-rmse:0.704597+0.066435
## [6] train-rmse:0.514961+0.008555 test-rmse:0.683807+0.060195
## [7] train-rmse:0.475061+0.013880 test-rmse:0.667141+0.054949
## [8] train-rmse:0.448053+0.014817 test-rmse:0.656833+0.052916
## [9] train-rmse:0.428029+0.013643 test-rmse:0.655392+0.054451
## [10] train-rmse:0.409720+0.016760 test-rmse:0.648807+0.056295
## [11] train-rmse:0.396751+0.018275 test-rmse:0.649153+0.056024
## [12] train-rmse:0.378855+0.016646 test-rmse:0.641005+0.053026
## [13] train-rmse:0.367356+0.014945 test-rmse:0.638040+0.052169
## [14] train-rmse:0.354400+0.020897 test-rmse:0.635087+0.052633
## [15] train-rmse:0.345891+0.021535 test-rmse:0.637922+0.054469
## [16] train-rmse:0.326453+0.014340 test-rmse:0.631768+0.053521
## [17] train-rmse:0.316862+0.015932 test-rmse:0.631500+0.057397
## [18] train-rmse:0.301826+0.013082 test-rmse:0.629663+0.056118
## [19] train-rmse:0.293836+0.013731 test-rmse:0.628038+0.052160
## [20] train-rmse:0.284868+0.013319 test-rmse:0.630469+0.052210
## [21] train-rmse:0.272086+0.011846 test-rmse:0.627654+0.050879
## [22] train-rmse:0.256893+0.007396 test-rmse:0.624728+0.048389
## [23] train-rmse:0.249274+0.008096 test-rmse:0.624907+0.046594
## [24] train-rmse:0.242059+0.007883 test-rmse:0.621964+0.045033
## [25] train-rmse:0.235913+0.007351 test-rmse:0.622387+0.045595
## [26] train-rmse:0.225161+0.008746 test-rmse:0.622991+0.048057
## [27] train-rmse:0.218384+0.008424 test-rmse:0.623126+0.048203
## [28] train-rmse:0.212353+0.008042 test-rmse:0.623395+0.045801
## [29] train-rmse:0.205606+0.010164 test-rmse:0.623278+0.046681
## [30] train-rmse:0.197720+0.007984 test-rmse:0.621613+0.046781
## [31] train-rmse:0.189026+0.007137 test-rmse:0.623085+0.046507
## [32] train-rmse:0.182856+0.006004 test-rmse:0.623282+0.047546
## [33] train-rmse:0.176624+0.006990 test-rmse:0.625107+0.046580
## [34] train-rmse:0.170909+0.006160 test-rmse:0.628123+0.046974
## [35] train-rmse:0.164542+0.005858 test-rmse:0.629927+0.046372
## [36] train-rmse:0.158718+0.006866 test-rmse:0.632297+0.046747
## Stopping. Best iteration:
## [30] train-rmse:0.197720+0.007984 test-rmse:0.621613+0.046781
##
## 83
## [1] train-rmse:1.188546+0.009797 test-rmse:1.220116+0.077660
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.911726+0.005586 test-rmse:0.971695+0.075686
## [3] train-rmse:0.729763+0.006303 test-rmse:0.835916+0.081433
## [4] train-rmse:0.610206+0.007351 test-rmse:0.750202+0.076030
## [5] train-rmse:0.539705+0.010728 test-rmse:0.708628+0.074859
## [6] train-rmse:0.490652+0.009634 test-rmse:0.688398+0.071730
## [7] train-rmse:0.439815+0.012295 test-rmse:0.665511+0.058797
## [8] train-rmse:0.407406+0.015117 test-rmse:0.656910+0.059521
## [9] train-rmse:0.379619+0.007462 test-rmse:0.650206+0.058292
## [10] train-rmse:0.360736+0.010625 test-rmse:0.645516+0.058349
## [11] train-rmse:0.345279+0.011395 test-rmse:0.643584+0.056875
## [12] train-rmse:0.327153+0.009638 test-rmse:0.644045+0.058901
## [13] train-rmse:0.315923+0.010787 test-rmse:0.642929+0.058435
## [14] train-rmse:0.301537+0.014125 test-rmse:0.635651+0.058555
## [15] train-rmse:0.287191+0.012526 test-rmse:0.631322+0.058440
## [16] train-rmse:0.277931+0.008391 test-rmse:0.629971+0.058307
## [17] train-rmse:0.264501+0.011482 test-rmse:0.632173+0.057382
## [18] train-rmse:0.250262+0.010412 test-rmse:0.631159+0.059893
## [19] train-rmse:0.240232+0.013070 test-rmse:0.631546+0.060569
## [20] train-rmse:0.229336+0.015507 test-rmse:0.632814+0.060357
## [21] train-rmse:0.217075+0.013198 test-rmse:0.630399+0.062435
## [22] train-rmse:0.209454+0.012820 test-rmse:0.630515+0.065374
## Stopping. Best iteration:
## [16] train-rmse:0.277931+0.008391 test-rmse:0.629971+0.058307
##
## 84
## [1] train-rmse:1.202750+0.011254 test-rmse:1.205826+0.071093
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.974587+0.008035 test-rmse:0.981001+0.065426
## [3] train-rmse:0.851867+0.007455 test-rmse:0.869654+0.058540
## [4] train-rmse:0.787618+0.006743 test-rmse:0.807863+0.048492
## [5] train-rmse:0.753090+0.005933 test-rmse:0.782915+0.042199
## [6] train-rmse:0.733409+0.005311 test-rmse:0.769899+0.038279
## [7] train-rmse:0.720607+0.004804 test-rmse:0.756835+0.037349
## [8] train-rmse:0.711282+0.004384 test-rmse:0.748537+0.032320
## [9] train-rmse:0.703934+0.004521 test-rmse:0.743386+0.027278
## [10] train-rmse:0.697935+0.004579 test-rmse:0.742265+0.026408
## [11] train-rmse:0.692802+0.004578 test-rmse:0.740595+0.027079
## [12] train-rmse:0.688210+0.004659 test-rmse:0.737922+0.027100
## [13] train-rmse:0.683698+0.004834 test-rmse:0.735920+0.027790
## [14] train-rmse:0.679564+0.004949 test-rmse:0.734904+0.027198
## [15] train-rmse:0.675744+0.004902 test-rmse:0.736167+0.027565
## [16] train-rmse:0.672209+0.004935 test-rmse:0.732778+0.029322
## [17] train-rmse:0.668768+0.004946 test-rmse:0.729501+0.031323
## [18] train-rmse:0.665440+0.004932 test-rmse:0.727348+0.031288
## [19] train-rmse:0.662245+0.004896 test-rmse:0.726277+0.032629
## [20] train-rmse:0.659183+0.004787 test-rmse:0.723568+0.031753
## [21] train-rmse:0.656360+0.004846 test-rmse:0.722475+0.030292
## [22] train-rmse:0.653623+0.005040 test-rmse:0.723369+0.032574
## [23] train-rmse:0.650991+0.005319 test-rmse:0.724158+0.032305
## [24] train-rmse:0.648487+0.005453 test-rmse:0.722790+0.032724
## [25] train-rmse:0.646133+0.005540 test-rmse:0.723771+0.032737
## [26] train-rmse:0.643899+0.005600 test-rmse:0.720973+0.031014
## [27] train-rmse:0.641798+0.005594 test-rmse:0.719058+0.031663
## [28] train-rmse:0.639755+0.005580 test-rmse:0.717636+0.031542
## [29] train-rmse:0.637805+0.005564 test-rmse:0.717693+0.032232
## [30] train-rmse:0.635857+0.005530 test-rmse:0.715667+0.031410
## [31] train-rmse:0.633930+0.005546 test-rmse:0.714396+0.033202
## [32] train-rmse:0.632079+0.005657 test-rmse:0.711490+0.034395
## [33] train-rmse:0.630293+0.005712 test-rmse:0.709657+0.032838
## [34] train-rmse:0.628501+0.005667 test-rmse:0.709293+0.033273
## [35] train-rmse:0.626756+0.005658 test-rmse:0.708668+0.033930
## [36] train-rmse:0.625052+0.005667 test-rmse:0.706253+0.032916
## [37] train-rmse:0.623364+0.005734 test-rmse:0.706589+0.033557
## [38] train-rmse:0.621746+0.005820 test-rmse:0.705800+0.034434
## [39] train-rmse:0.620170+0.005917 test-rmse:0.708540+0.034514
## [40] train-rmse:0.618614+0.006010 test-rmse:0.707725+0.034486
## [41] train-rmse:0.617058+0.005995 test-rmse:0.707657+0.033405
## [42] train-rmse:0.615638+0.006047 test-rmse:0.708283+0.033224
## [43] train-rmse:0.614230+0.006068 test-rmse:0.706169+0.035239
## [44] train-rmse:0.612826+0.006119 test-rmse:0.705010+0.035386
## [45] train-rmse:0.611468+0.006122 test-rmse:0.704672+0.036477
## [46] train-rmse:0.610166+0.006137 test-rmse:0.703787+0.035426
## [47] train-rmse:0.608851+0.006152 test-rmse:0.703817+0.034477
## [48] train-rmse:0.607589+0.006151 test-rmse:0.702408+0.033641
## [49] train-rmse:0.606352+0.006153 test-rmse:0.699882+0.033782
## [50] train-rmse:0.605163+0.006194 test-rmse:0.700003+0.032993
## [51] train-rmse:0.603984+0.006213 test-rmse:0.697004+0.034223
## [52] train-rmse:0.602858+0.006273 test-rmse:0.697194+0.034893
## [53] train-rmse:0.601724+0.006256 test-rmse:0.696230+0.035766
## [54] train-rmse:0.600627+0.006323 test-rmse:0.696228+0.036302
## [55] train-rmse:0.599537+0.006342 test-rmse:0.694556+0.036124
## [56] train-rmse:0.598511+0.006346 test-rmse:0.694381+0.035028
## [57] train-rmse:0.597514+0.006389 test-rmse:0.694763+0.034697
## [58] train-rmse:0.596509+0.006397 test-rmse:0.694324+0.035575
## [59] train-rmse:0.595508+0.006425 test-rmse:0.692730+0.034994
## [60] train-rmse:0.594506+0.006455 test-rmse:0.692320+0.034102
## [61] train-rmse:0.593530+0.006508 test-rmse:0.692694+0.034238
## [62] train-rmse:0.592605+0.006530 test-rmse:0.692470+0.034229
## [63] train-rmse:0.591683+0.006543 test-rmse:0.693817+0.035154
## [64] train-rmse:0.590785+0.006576 test-rmse:0.694076+0.035611
## [65] train-rmse:0.589882+0.006615 test-rmse:0.693417+0.035956
## [66] train-rmse:0.589003+0.006657 test-rmse:0.691163+0.035848
## [67] train-rmse:0.588119+0.006703 test-rmse:0.690574+0.035707
## [68] train-rmse:0.587275+0.006726 test-rmse:0.690923+0.036023
## [69] train-rmse:0.586446+0.006738 test-rmse:0.689611+0.036405
## [70] train-rmse:0.585621+0.006750 test-rmse:0.689525+0.035556
## [71] train-rmse:0.584791+0.006756 test-rmse:0.688923+0.035069
## [72] train-rmse:0.584017+0.006778 test-rmse:0.688588+0.033544
## [73] train-rmse:0.583206+0.006773 test-rmse:0.689133+0.034356
## [74] train-rmse:0.582405+0.006781 test-rmse:0.688098+0.034608
## [75] train-rmse:0.581627+0.006780 test-rmse:0.687110+0.034578
## [76] train-rmse:0.580844+0.006806 test-rmse:0.686508+0.035271
## [77] train-rmse:0.580075+0.006843 test-rmse:0.685534+0.035804
## [78] train-rmse:0.579323+0.006860 test-rmse:0.685477+0.036296
## [79] train-rmse:0.578602+0.006884 test-rmse:0.684675+0.036327
## [80] train-rmse:0.577871+0.006925 test-rmse:0.685669+0.036036
## [81] train-rmse:0.577152+0.006918 test-rmse:0.685454+0.036127
## [82] train-rmse:0.576459+0.006918 test-rmse:0.685758+0.035396
## [83] train-rmse:0.575758+0.006961 test-rmse:0.684794+0.036731
## [84] train-rmse:0.575095+0.007006 test-rmse:0.685164+0.036417
## [85] train-rmse:0.574436+0.007023 test-rmse:0.685765+0.037530
## Stopping. Best iteration:
## [79] train-rmse:0.578602+0.006884 test-rmse:0.684675+0.036327
##
## 85
## [1] train-rmse:1.192331+0.011018 test-rmse:1.202697+0.071420
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.952776+0.007114 test-rmse:0.976813+0.069206
## [3] train-rmse:0.821237+0.004940 test-rmse:0.855858+0.061255
## [4] train-rmse:0.741383+0.005345 test-rmse:0.786829+0.053473
## [5] train-rmse:0.700498+0.005463 test-rmse:0.757934+0.046089
## [6] train-rmse:0.674058+0.005777 test-rmse:0.734604+0.048854
## [7] train-rmse:0.657088+0.004323 test-rmse:0.725640+0.046804
## [8] train-rmse:0.644330+0.004194 test-rmse:0.720109+0.045336
## [9] train-rmse:0.631492+0.004321 test-rmse:0.713272+0.042617
## [10] train-rmse:0.623778+0.004964 test-rmse:0.708803+0.042819
## [11] train-rmse:0.616722+0.005577 test-rmse:0.703949+0.044649
## [12] train-rmse:0.607900+0.005498 test-rmse:0.700720+0.044642
## [13] train-rmse:0.598213+0.003709 test-rmse:0.696688+0.046676
## [14] train-rmse:0.590993+0.002383 test-rmse:0.695017+0.044731
## [15] train-rmse:0.583451+0.004305 test-rmse:0.694783+0.043866
## [16] train-rmse:0.577457+0.005808 test-rmse:0.691717+0.039388
## [17] train-rmse:0.572520+0.006821 test-rmse:0.689072+0.042640
## [18] train-rmse:0.568779+0.006876 test-rmse:0.687347+0.040501
## [19] train-rmse:0.562219+0.006400 test-rmse:0.679831+0.040599
## [20] train-rmse:0.557476+0.006711 test-rmse:0.679024+0.043189
## [21] train-rmse:0.550177+0.007024 test-rmse:0.674486+0.043216
## [22] train-rmse:0.542800+0.004861 test-rmse:0.668495+0.043650
## [23] train-rmse:0.538751+0.004697 test-rmse:0.664713+0.047746
## [24] train-rmse:0.533610+0.004547 test-rmse:0.661798+0.043747
## [25] train-rmse:0.529514+0.004973 test-rmse:0.658723+0.043487
## [26] train-rmse:0.525709+0.005761 test-rmse:0.656422+0.042253
## [27] train-rmse:0.521084+0.006392 test-rmse:0.655891+0.042535
## [28] train-rmse:0.517727+0.006872 test-rmse:0.653868+0.041190
## [29] train-rmse:0.514432+0.007201 test-rmse:0.652583+0.043674
## [30] train-rmse:0.511535+0.007340 test-rmse:0.654226+0.044727
## [31] train-rmse:0.507978+0.008027 test-rmse:0.654572+0.043003
## [32] train-rmse:0.504200+0.007275 test-rmse:0.655521+0.043195
## [33] train-rmse:0.501094+0.007896 test-rmse:0.655078+0.044321
## [34] train-rmse:0.496606+0.009817 test-rmse:0.654320+0.043674
## [35] train-rmse:0.492333+0.009221 test-rmse:0.652338+0.042424
## [36] train-rmse:0.488503+0.008446 test-rmse:0.650786+0.040669
## [37] train-rmse:0.485828+0.007629 test-rmse:0.649319+0.041743
## [38] train-rmse:0.483000+0.008026 test-rmse:0.648079+0.041236
## [39] train-rmse:0.480693+0.008079 test-rmse:0.644940+0.043901
## [40] train-rmse:0.477417+0.007945 test-rmse:0.644062+0.045893
## [41] train-rmse:0.474783+0.008088 test-rmse:0.640107+0.044902
## [42] train-rmse:0.471727+0.008341 test-rmse:0.641299+0.045766
## [43] train-rmse:0.469439+0.008545 test-rmse:0.640945+0.045547
## [44] train-rmse:0.465442+0.007929 test-rmse:0.640274+0.044867
## [45] train-rmse:0.463486+0.008185 test-rmse:0.640811+0.045688
## [46] train-rmse:0.461291+0.008275 test-rmse:0.641414+0.047537
## [47] train-rmse:0.459662+0.008576 test-rmse:0.640343+0.046933
## Stopping. Best iteration:
## [41] train-rmse:0.474783+0.008088 test-rmse:0.640107+0.044902
##
## 86
## [1] train-rmse:1.185176+0.010424 test-rmse:1.199050+0.069456
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.933863+0.006922 test-rmse:0.966147+0.066270
## [3] train-rmse:0.793275+0.005662 test-rmse:0.839433+0.064754
## [4] train-rmse:0.715440+0.007843 test-rmse:0.775695+0.053634
## [5] train-rmse:0.656518+0.005772 test-rmse:0.733687+0.051771
## [6] train-rmse:0.625757+0.007523 test-rmse:0.715374+0.043838
## [7] train-rmse:0.605005+0.006626 test-rmse:0.707840+0.046260
## [8] train-rmse:0.590442+0.006898 test-rmse:0.699305+0.046369
## [9] train-rmse:0.572864+0.007955 test-rmse:0.687823+0.051441
## [10] train-rmse:0.554614+0.004313 test-rmse:0.681451+0.052093
## [11] train-rmse:0.543107+0.004524 test-rmse:0.680546+0.054967
## [12] train-rmse:0.535294+0.004851 test-rmse:0.679135+0.053981
## [13] train-rmse:0.526550+0.002145 test-rmse:0.678173+0.058107
## [14] train-rmse:0.519811+0.003290 test-rmse:0.676167+0.059472
## [15] train-rmse:0.511227+0.003038 test-rmse:0.673772+0.058650
## [16] train-rmse:0.504901+0.003239 test-rmse:0.672919+0.062091
## [17] train-rmse:0.496613+0.004317 test-rmse:0.669044+0.061252
## [18] train-rmse:0.490192+0.004652 test-rmse:0.666731+0.061583
## [19] train-rmse:0.485003+0.003936 test-rmse:0.665847+0.060205
## [20] train-rmse:0.479441+0.006040 test-rmse:0.663760+0.060631
## [21] train-rmse:0.470167+0.007593 test-rmse:0.659945+0.061142
## [22] train-rmse:0.464876+0.006987 test-rmse:0.658976+0.060941
## [23] train-rmse:0.458007+0.009244 test-rmse:0.656990+0.060969
## [24] train-rmse:0.449868+0.006318 test-rmse:0.654235+0.059985
## [25] train-rmse:0.444367+0.006178 test-rmse:0.654493+0.058582
## [26] train-rmse:0.439184+0.005210 test-rmse:0.653507+0.062875
## [27] train-rmse:0.433660+0.005325 test-rmse:0.651821+0.063748
## [28] train-rmse:0.428813+0.006227 test-rmse:0.649486+0.060464
## [29] train-rmse:0.421658+0.006039 test-rmse:0.644383+0.060483
## [30] train-rmse:0.416661+0.006531 test-rmse:0.642189+0.059993
## [31] train-rmse:0.411851+0.008615 test-rmse:0.642719+0.060703
## [32] train-rmse:0.406088+0.008761 test-rmse:0.640226+0.059806
## [33] train-rmse:0.399280+0.005351 test-rmse:0.640074+0.063447
## [34] train-rmse:0.395319+0.005090 test-rmse:0.638028+0.063900
## [35] train-rmse:0.392013+0.005278 test-rmse:0.638079+0.064086
## [36] train-rmse:0.387236+0.007662 test-rmse:0.635458+0.061172
## [37] train-rmse:0.382194+0.007442 test-rmse:0.634617+0.060396
## [38] train-rmse:0.378528+0.008000 test-rmse:0.631659+0.059500
## [39] train-rmse:0.374637+0.008672 test-rmse:0.632741+0.058424
## [40] train-rmse:0.369990+0.007895 test-rmse:0.630798+0.059904
## [41] train-rmse:0.365835+0.007055 test-rmse:0.629675+0.059338
## [42] train-rmse:0.362390+0.006143 test-rmse:0.629278+0.059176
## [43] train-rmse:0.356633+0.008358 test-rmse:0.630663+0.061846
## [44] train-rmse:0.353215+0.008752 test-rmse:0.631089+0.062794
## [45] train-rmse:0.349052+0.009295 test-rmse:0.629135+0.063082
## [46] train-rmse:0.346019+0.009941 test-rmse:0.629642+0.064223
## [47] train-rmse:0.341502+0.013211 test-rmse:0.627936+0.060944
## [48] train-rmse:0.337000+0.013197 test-rmse:0.626998+0.061893
## [49] train-rmse:0.333025+0.013754 test-rmse:0.627225+0.061454
## [50] train-rmse:0.329260+0.013727 test-rmse:0.625729+0.061768
## [51] train-rmse:0.325566+0.013699 test-rmse:0.624490+0.063147
## [52] train-rmse:0.321971+0.014028 test-rmse:0.625223+0.063665
## [53] train-rmse:0.317704+0.012065 test-rmse:0.625333+0.062986
## [54] train-rmse:0.313278+0.013912 test-rmse:0.625215+0.064877
## [55] train-rmse:0.309580+0.012153 test-rmse:0.625281+0.065902
## [56] train-rmse:0.305678+0.012660 test-rmse:0.626135+0.063223
## [57] train-rmse:0.302858+0.014808 test-rmse:0.627982+0.065382
## Stopping. Best iteration:
## [51] train-rmse:0.325566+0.013699 test-rmse:0.624490+0.063147
##
## 87
## [1] train-rmse:1.177738+0.010581 test-rmse:1.203501+0.071294
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.915124+0.006672 test-rmse:0.963648+0.076145
## [3] train-rmse:0.763627+0.004932 test-rmse:0.835232+0.066032
## [4] train-rmse:0.677385+0.004579 test-rmse:0.762333+0.067373
## [5] train-rmse:0.611640+0.011787 test-rmse:0.719537+0.063542
## [6] train-rmse:0.574763+0.010322 test-rmse:0.701343+0.059990
## [7] train-rmse:0.551986+0.012077 test-rmse:0.690241+0.058416
## [8] train-rmse:0.533811+0.011241 test-rmse:0.682977+0.062125
## [9] train-rmse:0.516557+0.015188 test-rmse:0.675434+0.054971
## [10] train-rmse:0.496354+0.015798 test-rmse:0.668911+0.057930
## [11] train-rmse:0.479120+0.014950 test-rmse:0.660481+0.053392
## [12] train-rmse:0.469131+0.014972 test-rmse:0.663170+0.052038
## [13] train-rmse:0.459411+0.015612 test-rmse:0.662614+0.051193
## [14] train-rmse:0.450780+0.016843 test-rmse:0.663739+0.054264
## [15] train-rmse:0.437913+0.015504 test-rmse:0.658728+0.050834
## [16] train-rmse:0.426506+0.016576 test-rmse:0.659139+0.048254
## [17] train-rmse:0.417438+0.018769 test-rmse:0.657029+0.051054
## [18] train-rmse:0.407860+0.017536 test-rmse:0.653648+0.051917
## [19] train-rmse:0.399757+0.014933 test-rmse:0.648582+0.050671
## [20] train-rmse:0.389290+0.013821 test-rmse:0.645755+0.049711
## [21] train-rmse:0.380635+0.016005 test-rmse:0.643080+0.047684
## [22] train-rmse:0.374434+0.015495 test-rmse:0.642714+0.044385
## [23] train-rmse:0.364321+0.012329 test-rmse:0.640468+0.045359
## [24] train-rmse:0.356035+0.009926 test-rmse:0.637793+0.048686
## [25] train-rmse:0.347043+0.012957 test-rmse:0.639011+0.049482
## [26] train-rmse:0.342326+0.012244 test-rmse:0.640254+0.049694
## [27] train-rmse:0.336585+0.011557 test-rmse:0.641664+0.048541
## [28] train-rmse:0.330085+0.014441 test-rmse:0.641392+0.047116
## [29] train-rmse:0.325522+0.014516 test-rmse:0.642705+0.048416
## [30] train-rmse:0.317211+0.014462 test-rmse:0.640538+0.049140
## Stopping. Best iteration:
## [24] train-rmse:0.356035+0.009926 test-rmse:0.637793+0.048686
##
## 88
## [1] train-rmse:1.172636+0.010879 test-rmse:1.196848+0.075821
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.901654+0.006329 test-rmse:0.951344+0.077339
## [3] train-rmse:0.737262+0.006817 test-rmse:0.811099+0.070566
## [4] train-rmse:0.642810+0.008498 test-rmse:0.744389+0.065857
## [5] train-rmse:0.585823+0.009710 test-rmse:0.710546+0.061771
## [6] train-rmse:0.541013+0.009239 test-rmse:0.692912+0.052184
## [7] train-rmse:0.509878+0.009678 test-rmse:0.680029+0.053159
## [8] train-rmse:0.489658+0.011385 test-rmse:0.670719+0.053790
## [9] train-rmse:0.472371+0.013263 test-rmse:0.667263+0.049062
## [10] train-rmse:0.451671+0.008437 test-rmse:0.655773+0.050145
## [11] train-rmse:0.435804+0.010876 test-rmse:0.655989+0.054004
## [12] train-rmse:0.419371+0.010199 test-rmse:0.650758+0.052239
## [13] train-rmse:0.406458+0.010397 test-rmse:0.648828+0.057147
## [14] train-rmse:0.392820+0.010589 test-rmse:0.651007+0.058407
## [15] train-rmse:0.381357+0.014027 test-rmse:0.652865+0.059078
## [16] train-rmse:0.369135+0.015106 test-rmse:0.649504+0.060019
## [17] train-rmse:0.358492+0.013857 test-rmse:0.646842+0.062262
## [18] train-rmse:0.347592+0.011339 test-rmse:0.642112+0.058833
## [19] train-rmse:0.337222+0.013009 test-rmse:0.641251+0.059039
## [20] train-rmse:0.324477+0.012142 test-rmse:0.640265+0.060580
## [21] train-rmse:0.316294+0.013192 test-rmse:0.639396+0.060987
## [22] train-rmse:0.307731+0.013264 test-rmse:0.635755+0.061553
## [23] train-rmse:0.296391+0.011066 test-rmse:0.635095+0.058984
## [24] train-rmse:0.287513+0.010888 test-rmse:0.634484+0.056835
## [25] train-rmse:0.279869+0.014075 test-rmse:0.632220+0.055029
## [26] train-rmse:0.272347+0.012062 test-rmse:0.630446+0.056566
## [27] train-rmse:0.264737+0.011527 test-rmse:0.631149+0.054951
## [28] train-rmse:0.259560+0.009988 test-rmse:0.632067+0.055069
## [29] train-rmse:0.251049+0.009932 test-rmse:0.630784+0.055272
## [30] train-rmse:0.243077+0.008889 test-rmse:0.628596+0.053572
## [31] train-rmse:0.236833+0.010168 test-rmse:0.629863+0.050853
## [32] train-rmse:0.229777+0.011505 test-rmse:0.631717+0.052903
## [33] train-rmse:0.220602+0.012496 test-rmse:0.631716+0.052712
## [34] train-rmse:0.216049+0.014082 test-rmse:0.631568+0.052801
## [35] train-rmse:0.207935+0.012348 test-rmse:0.632555+0.053195
## [36] train-rmse:0.202708+0.011062 test-rmse:0.630647+0.052311
## Stopping. Best iteration:
## [30] train-rmse:0.243077+0.008889 test-rmse:0.628596+0.053572
##
## 89
## [1] train-rmse:1.167420+0.010038 test-rmse:1.199629+0.077957
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.889239+0.003961 test-rmse:0.953308+0.076666
## [3] train-rmse:0.716779+0.007119 test-rmse:0.812701+0.064692
## [4] train-rmse:0.612190+0.006634 test-rmse:0.738068+0.062703
## [5] train-rmse:0.547035+0.013413 test-rmse:0.700565+0.054466
## [6] train-rmse:0.497876+0.009786 test-rmse:0.673802+0.046805
## [7] train-rmse:0.469850+0.008153 test-rmse:0.665121+0.040741
## [8] train-rmse:0.441578+0.010821 test-rmse:0.664137+0.042068
## [9] train-rmse:0.416863+0.014853 test-rmse:0.661175+0.039919
## [10] train-rmse:0.397186+0.020184 test-rmse:0.664085+0.041776
## [11] train-rmse:0.381848+0.020638 test-rmse:0.664011+0.044445
## [12] train-rmse:0.368983+0.021155 test-rmse:0.660721+0.045074
## [13] train-rmse:0.347694+0.015765 test-rmse:0.647232+0.041347
## [14] train-rmse:0.334060+0.018190 test-rmse:0.648036+0.043474
## [15] train-rmse:0.322077+0.016277 test-rmse:0.649212+0.043172
## [16] train-rmse:0.308782+0.017600 test-rmse:0.641723+0.040485
## [17] train-rmse:0.297252+0.015199 test-rmse:0.643052+0.040700
## [18] train-rmse:0.287240+0.019249 test-rmse:0.642286+0.038748
## [19] train-rmse:0.273986+0.017845 test-rmse:0.638331+0.043274
## [20] train-rmse:0.264897+0.019521 test-rmse:0.640349+0.043352
## [21] train-rmse:0.256073+0.020276 test-rmse:0.642200+0.042562
## [22] train-rmse:0.244839+0.020411 test-rmse:0.643961+0.043283
## [23] train-rmse:0.236975+0.019681 test-rmse:0.640566+0.043079
## [24] train-rmse:0.226021+0.021566 test-rmse:0.641513+0.042552
## [25] train-rmse:0.218075+0.019765 test-rmse:0.642612+0.044156
## Stopping. Best iteration:
## [19] train-rmse:0.273986+0.017845 test-rmse:0.638331+0.043274
##
## 90
## [1] train-rmse:1.163601+0.009347 test-rmse:1.197778+0.078238
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.880491+0.005279 test-rmse:0.943386+0.080964
## [3] train-rmse:0.698230+0.009141 test-rmse:0.806197+0.083512
## [4] train-rmse:0.587622+0.008720 test-rmse:0.728642+0.083227
## [5] train-rmse:0.516640+0.017894 test-rmse:0.691629+0.079465
## [6] train-rmse:0.470873+0.011251 test-rmse:0.677468+0.071789
## [7] train-rmse:0.432158+0.012619 test-rmse:0.666507+0.071894
## [8] train-rmse:0.399261+0.015801 test-rmse:0.658199+0.069667
## [9] train-rmse:0.373745+0.015403 test-rmse:0.655883+0.070705
## [10] train-rmse:0.351943+0.016104 test-rmse:0.652874+0.068117
## [11] train-rmse:0.340171+0.016777 test-rmse:0.650856+0.069377
## [12] train-rmse:0.315672+0.018968 test-rmse:0.643457+0.060955
## [13] train-rmse:0.297897+0.015664 test-rmse:0.640857+0.060127
## [14] train-rmse:0.284574+0.016591 test-rmse:0.643686+0.059280
## [15] train-rmse:0.271342+0.018049 test-rmse:0.647377+0.055670
## [16] train-rmse:0.261467+0.014993 test-rmse:0.648910+0.054821
## [17] train-rmse:0.249029+0.010079 test-rmse:0.647453+0.055216
## [18] train-rmse:0.236416+0.007440 test-rmse:0.646986+0.048907
## [19] train-rmse:0.225624+0.007847 test-rmse:0.651205+0.052564
## Stopping. Best iteration:
## [13] train-rmse:0.297897+0.015664 test-rmse:0.640857+0.060127
##
## 91
## [1] train-rmse:1.180850+0.010934 test-rmse:1.184243+0.071123
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.951481+0.007734 test-rmse:0.958497+0.064479
## [3] train-rmse:0.833773+0.007396 test-rmse:0.854391+0.058424
## [4] train-rmse:0.775941+0.006745 test-rmse:0.802822+0.051610
## [5] train-rmse:0.745101+0.005838 test-rmse:0.780436+0.044376
## [6] train-rmse:0.727554+0.005225 test-rmse:0.768436+0.038465
## [7] train-rmse:0.715509+0.004688 test-rmse:0.755007+0.037848
## [8] train-rmse:0.706507+0.004216 test-rmse:0.747236+0.034837
## [9] train-rmse:0.699686+0.004461 test-rmse:0.744104+0.031281
## [10] train-rmse:0.693860+0.004664 test-rmse:0.741002+0.028606
## [11] train-rmse:0.688974+0.004791 test-rmse:0.739450+0.029057
## [12] train-rmse:0.684344+0.004917 test-rmse:0.737557+0.029501
## [13] train-rmse:0.680006+0.005054 test-rmse:0.735910+0.028995
## [14] train-rmse:0.675912+0.005120 test-rmse:0.731292+0.026508
## [15] train-rmse:0.672026+0.005222 test-rmse:0.731198+0.028217
## [16] train-rmse:0.668294+0.005351 test-rmse:0.728713+0.029431
## [17] train-rmse:0.664774+0.005252 test-rmse:0.726050+0.029545
## [18] train-rmse:0.661418+0.005271 test-rmse:0.722739+0.030715
## [19] train-rmse:0.658123+0.005211 test-rmse:0.722319+0.034294
## [20] train-rmse:0.655076+0.005165 test-rmse:0.721506+0.035274
## [21] train-rmse:0.652262+0.005267 test-rmse:0.722212+0.037962
## [22] train-rmse:0.649435+0.005462 test-rmse:0.721562+0.034169
## [23] train-rmse:0.646665+0.005666 test-rmse:0.721316+0.034452
## [24] train-rmse:0.644151+0.005742 test-rmse:0.721507+0.035556
## [25] train-rmse:0.641802+0.005824 test-rmse:0.719951+0.034504
## [26] train-rmse:0.639604+0.005816 test-rmse:0.716686+0.033297
## [27] train-rmse:0.637542+0.005776 test-rmse:0.717742+0.035464
## [28] train-rmse:0.635463+0.005763 test-rmse:0.716294+0.033423
## [29] train-rmse:0.633484+0.005717 test-rmse:0.716050+0.034482
## [30] train-rmse:0.631579+0.005691 test-rmse:0.713385+0.033810
## [31] train-rmse:0.629664+0.005747 test-rmse:0.710555+0.033001
## [32] train-rmse:0.627839+0.005787 test-rmse:0.709101+0.034770
## [33] train-rmse:0.626002+0.005750 test-rmse:0.707629+0.033247
## [34] train-rmse:0.624168+0.005737 test-rmse:0.705807+0.034373
## [35] train-rmse:0.622389+0.005744 test-rmse:0.706684+0.034917
## [36] train-rmse:0.620667+0.005752 test-rmse:0.704405+0.034166
## [37] train-rmse:0.619001+0.005766 test-rmse:0.702789+0.033777
## [38] train-rmse:0.617391+0.005768 test-rmse:0.705231+0.033403
## [39] train-rmse:0.615849+0.005810 test-rmse:0.704067+0.033124
## [40] train-rmse:0.614329+0.005881 test-rmse:0.702445+0.032752
## [41] train-rmse:0.612862+0.005969 test-rmse:0.699736+0.031816
## [42] train-rmse:0.611396+0.005996 test-rmse:0.698976+0.032169
## [43] train-rmse:0.609992+0.006002 test-rmse:0.698270+0.031900
## [44] train-rmse:0.608626+0.006036 test-rmse:0.698805+0.031550
## [45] train-rmse:0.607315+0.006046 test-rmse:0.699717+0.031936
## [46] train-rmse:0.606027+0.006004 test-rmse:0.699601+0.033263
## [47] train-rmse:0.604781+0.005987 test-rmse:0.698376+0.034161
## [48] train-rmse:0.603528+0.005993 test-rmse:0.697174+0.033654
## [49] train-rmse:0.602310+0.005994 test-rmse:0.696716+0.035247
## [50] train-rmse:0.601048+0.006053 test-rmse:0.697263+0.034702
## [51] train-rmse:0.599822+0.006097 test-rmse:0.695621+0.035528
## [52] train-rmse:0.598631+0.006128 test-rmse:0.695970+0.036250
## [53] train-rmse:0.597493+0.006171 test-rmse:0.694828+0.034688
## [54] train-rmse:0.596372+0.006215 test-rmse:0.694717+0.033894
## [55] train-rmse:0.595246+0.006219 test-rmse:0.692229+0.033797
## [56] train-rmse:0.594142+0.006299 test-rmse:0.692720+0.034682
## [57] train-rmse:0.593149+0.006295 test-rmse:0.692089+0.034933
## [58] train-rmse:0.592153+0.006293 test-rmse:0.692523+0.033764
## [59] train-rmse:0.591173+0.006299 test-rmse:0.691768+0.033960
## [60] train-rmse:0.590217+0.006344 test-rmse:0.690469+0.034212
## [61] train-rmse:0.589237+0.006386 test-rmse:0.690723+0.034522
## [62] train-rmse:0.588321+0.006389 test-rmse:0.689598+0.034892
## [63] train-rmse:0.587388+0.006397 test-rmse:0.688929+0.035121
## [64] train-rmse:0.586494+0.006420 test-rmse:0.689662+0.035873
## [65] train-rmse:0.585614+0.006470 test-rmse:0.689866+0.034869
## [66] train-rmse:0.584766+0.006494 test-rmse:0.687236+0.033223
## [67] train-rmse:0.583922+0.006537 test-rmse:0.685652+0.034058
## [68] train-rmse:0.583083+0.006572 test-rmse:0.686702+0.034090
## [69] train-rmse:0.582250+0.006584 test-rmse:0.686098+0.033781
## [70] train-rmse:0.581434+0.006576 test-rmse:0.685931+0.035502
## [71] train-rmse:0.580632+0.006589 test-rmse:0.684575+0.033992
## [72] train-rmse:0.579825+0.006617 test-rmse:0.684397+0.033102
## [73] train-rmse:0.579004+0.006654 test-rmse:0.683873+0.033321
## [74] train-rmse:0.578232+0.006663 test-rmse:0.683259+0.034215
## [75] train-rmse:0.577474+0.006692 test-rmse:0.682142+0.033970
## [76] train-rmse:0.576740+0.006716 test-rmse:0.682882+0.034476
## [77] train-rmse:0.575995+0.006755 test-rmse:0.681924+0.034627
## [78] train-rmse:0.575254+0.006771 test-rmse:0.681583+0.033828
## [79] train-rmse:0.574532+0.006800 test-rmse:0.681164+0.033697
## [80] train-rmse:0.573816+0.006803 test-rmse:0.682191+0.033558
## [81] train-rmse:0.573075+0.006828 test-rmse:0.683411+0.033494
## [82] train-rmse:0.572380+0.006827 test-rmse:0.682473+0.033931
## [83] train-rmse:0.571701+0.006831 test-rmse:0.681492+0.034464
## [84] train-rmse:0.571021+0.006829 test-rmse:0.679235+0.035582
## [85] train-rmse:0.570360+0.006821 test-rmse:0.679954+0.036665
## [86] train-rmse:0.569725+0.006828 test-rmse:0.680238+0.036609
## [87] train-rmse:0.569114+0.006870 test-rmse:0.680484+0.036445
## [88] train-rmse:0.568513+0.006899 test-rmse:0.680950+0.035655
## [89] train-rmse:0.567896+0.006970 test-rmse:0.681398+0.034557
## [90] train-rmse:0.567313+0.006997 test-rmse:0.681209+0.034576
## Stopping. Best iteration:
## [84] train-rmse:0.571021+0.006829 test-rmse:0.679235+0.035582
##
## 92
## [1] train-rmse:1.169705+0.010677 test-rmse:1.180925+0.071460
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.928064+0.006750 test-rmse:0.953963+0.068377
## [3] train-rmse:0.800367+0.002813 test-rmse:0.837189+0.061785
## [4] train-rmse:0.727359+0.005467 test-rmse:0.775684+0.050923
## [5] train-rmse:0.690589+0.004383 test-rmse:0.748916+0.045187
## [6] train-rmse:0.667754+0.004114 test-rmse:0.736096+0.041228
## [7] train-rmse:0.651797+0.006575 test-rmse:0.725988+0.037430
## [8] train-rmse:0.640163+0.006874 test-rmse:0.720426+0.039318
## [9] train-rmse:0.627389+0.007578 test-rmse:0.714468+0.039301
## [10] train-rmse:0.619247+0.008206 test-rmse:0.710385+0.039276
## [11] train-rmse:0.611635+0.008238 test-rmse:0.705830+0.039022
## [12] train-rmse:0.603688+0.009672 test-rmse:0.696626+0.039294
## [13] train-rmse:0.594334+0.008800 test-rmse:0.690659+0.039367
## [14] train-rmse:0.586634+0.010749 test-rmse:0.690014+0.042294
## [15] train-rmse:0.579380+0.008773 test-rmse:0.693762+0.043321
## [16] train-rmse:0.573513+0.010179 test-rmse:0.689030+0.042745
## [17] train-rmse:0.568090+0.010011 test-rmse:0.688449+0.044331
## [18] train-rmse:0.563160+0.010141 test-rmse:0.687690+0.042202
## [19] train-rmse:0.558776+0.010076 test-rmse:0.682763+0.040021
## [20] train-rmse:0.554162+0.010069 test-rmse:0.680081+0.039652
## [21] train-rmse:0.547143+0.010838 test-rmse:0.679268+0.048217
## [22] train-rmse:0.542577+0.011532 test-rmse:0.677984+0.046568
## [23] train-rmse:0.537553+0.011988 test-rmse:0.674427+0.049638
## [24] train-rmse:0.533151+0.011463 test-rmse:0.673236+0.052504
## [25] train-rmse:0.527952+0.010561 test-rmse:0.672509+0.049075
## [26] train-rmse:0.522889+0.011179 test-rmse:0.666682+0.050529
## [27] train-rmse:0.518990+0.010728 test-rmse:0.665939+0.049780
## [28] train-rmse:0.515580+0.010020 test-rmse:0.663718+0.049293
## [29] train-rmse:0.510735+0.009580 test-rmse:0.663231+0.046358
## [30] train-rmse:0.507312+0.009148 test-rmse:0.662971+0.047288
## [31] train-rmse:0.504708+0.009161 test-rmse:0.665465+0.048041
## [32] train-rmse:0.498770+0.009869 test-rmse:0.664604+0.047726
## [33] train-rmse:0.494994+0.009708 test-rmse:0.664516+0.049725
## [34] train-rmse:0.490101+0.009248 test-rmse:0.664980+0.050398
## [35] train-rmse:0.486723+0.007942 test-rmse:0.664140+0.051812
## [36] train-rmse:0.483206+0.007358 test-rmse:0.663092+0.049258
## Stopping. Best iteration:
## [30] train-rmse:0.507312+0.009148 test-rmse:0.662971+0.047288
##
## 93
## [1] train-rmse:1.162040+0.010042 test-rmse:1.177042+0.069366
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.908451+0.005908 test-rmse:0.945794+0.070830
## [3] train-rmse:0.772634+0.004819 test-rmse:0.823205+0.065791
## [4] train-rmse:0.696816+0.010182 test-rmse:0.760061+0.049218
## [5] train-rmse:0.644202+0.004842 test-rmse:0.724354+0.057725
## [6] train-rmse:0.616311+0.007927 test-rmse:0.705118+0.051829
## [7] train-rmse:0.597064+0.007492 test-rmse:0.702435+0.050080
## [8] train-rmse:0.580183+0.010528 test-rmse:0.696576+0.053563
## [9] train-rmse:0.568590+0.011871 test-rmse:0.686495+0.050738
## [10] train-rmse:0.554662+0.012235 test-rmse:0.676163+0.054644
## [11] train-rmse:0.540746+0.011310 test-rmse:0.672420+0.055893
## [12] train-rmse:0.526969+0.005749 test-rmse:0.669290+0.051892
## [13] train-rmse:0.517797+0.006208 test-rmse:0.669274+0.053594
## [14] train-rmse:0.511610+0.007225 test-rmse:0.668702+0.056709
## [15] train-rmse:0.504011+0.006827 test-rmse:0.665307+0.060191
## [16] train-rmse:0.497167+0.008540 test-rmse:0.659307+0.056438
## [17] train-rmse:0.486366+0.009273 test-rmse:0.654771+0.053848
## [18] train-rmse:0.474590+0.009527 test-rmse:0.651965+0.054210
## [19] train-rmse:0.467855+0.008930 test-rmse:0.652160+0.054133
## [20] train-rmse:0.460293+0.008483 test-rmse:0.649565+0.055339
## [21] train-rmse:0.451011+0.004997 test-rmse:0.644635+0.058644
## [22] train-rmse:0.444004+0.003862 test-rmse:0.639103+0.056451
## [23] train-rmse:0.438965+0.004144 test-rmse:0.637412+0.058263
## [24] train-rmse:0.430005+0.004538 test-rmse:0.635044+0.059415
## [25] train-rmse:0.424241+0.006084 test-rmse:0.630936+0.058360
## [26] train-rmse:0.418794+0.005334 test-rmse:0.630849+0.059586
## [27] train-rmse:0.410195+0.007362 test-rmse:0.630711+0.058823
## [28] train-rmse:0.406003+0.007343 test-rmse:0.632005+0.058311
## [29] train-rmse:0.400902+0.006684 test-rmse:0.628423+0.059750
## [30] train-rmse:0.395436+0.007052 test-rmse:0.629140+0.062741
## [31] train-rmse:0.388546+0.010396 test-rmse:0.626675+0.061241
## [32] train-rmse:0.383343+0.011976 test-rmse:0.624211+0.058659
## [33] train-rmse:0.379100+0.011988 test-rmse:0.623415+0.057921
## [34] train-rmse:0.374912+0.012478 test-rmse:0.621603+0.060264
## [35] train-rmse:0.370359+0.011663 test-rmse:0.621956+0.060894
## [36] train-rmse:0.368293+0.011691 test-rmse:0.623422+0.060885
## [37] train-rmse:0.363635+0.009490 test-rmse:0.621694+0.061270
## [38] train-rmse:0.358098+0.008664 test-rmse:0.620390+0.063440
## [39] train-rmse:0.355012+0.008807 test-rmse:0.620426+0.064697
## [40] train-rmse:0.352038+0.009557 test-rmse:0.618218+0.063996
## [41] train-rmse:0.347370+0.007368 test-rmse:0.617982+0.062916
## [42] train-rmse:0.344052+0.007347 test-rmse:0.618631+0.062331
## [43] train-rmse:0.338970+0.009241 test-rmse:0.620158+0.065030
## [44] train-rmse:0.335405+0.008578 test-rmse:0.619368+0.067006
## [45] train-rmse:0.331379+0.008343 test-rmse:0.619394+0.066735
## [46] train-rmse:0.326899+0.009964 test-rmse:0.618555+0.066033
## [47] train-rmse:0.323509+0.009827 test-rmse:0.617872+0.066424
## [48] train-rmse:0.319666+0.009652 test-rmse:0.616925+0.066085
## [49] train-rmse:0.316903+0.010077 test-rmse:0.617572+0.065098
## [50] train-rmse:0.314070+0.011815 test-rmse:0.617706+0.064601
## [51] train-rmse:0.308978+0.009823 test-rmse:0.616890+0.065995
## [52] train-rmse:0.306112+0.009002 test-rmse:0.617824+0.065791
## [53] train-rmse:0.300747+0.008695 test-rmse:0.614749+0.065311
## [54] train-rmse:0.297259+0.007813 test-rmse:0.613994+0.062615
## [55] train-rmse:0.293262+0.010517 test-rmse:0.613719+0.064027
## [56] train-rmse:0.290862+0.010120 test-rmse:0.612233+0.066280
## [57] train-rmse:0.288437+0.010839 test-rmse:0.612794+0.066940
## [58] train-rmse:0.286023+0.011509 test-rmse:0.613799+0.067907
## [59] train-rmse:0.283928+0.011225 test-rmse:0.614924+0.067099
## [60] train-rmse:0.280892+0.012130 test-rmse:0.616406+0.067205
## [61] train-rmse:0.277261+0.012309 test-rmse:0.615957+0.066556
## [62] train-rmse:0.275440+0.012049 test-rmse:0.616528+0.067367
## Stopping. Best iteration:
## [56] train-rmse:0.290862+0.010120 test-rmse:0.612233+0.066280
##
## 94
## [1] train-rmse:1.154054+0.010226 test-rmse:1.181791+0.071452
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.887382+0.006659 test-rmse:0.937732+0.074292
## [3] train-rmse:0.740624+0.005997 test-rmse:0.812792+0.063658
## [4] train-rmse:0.659004+0.002085 test-rmse:0.744461+0.068915
## [5] train-rmse:0.605174+0.008844 test-rmse:0.710057+0.070006
## [6] train-rmse:0.574714+0.009230 test-rmse:0.691848+0.059151
## [7] train-rmse:0.549386+0.006944 test-rmse:0.683010+0.062103
## [8] train-rmse:0.522727+0.011336 test-rmse:0.672708+0.064981
## [9] train-rmse:0.504382+0.007954 test-rmse:0.664235+0.057494
## [10] train-rmse:0.487332+0.008763 test-rmse:0.661023+0.059671
## [11] train-rmse:0.473757+0.007840 test-rmse:0.652813+0.058610
## [12] train-rmse:0.463335+0.008314 test-rmse:0.649916+0.060575
## [13] train-rmse:0.451273+0.011916 test-rmse:0.650268+0.059394
## [14] train-rmse:0.440135+0.012371 test-rmse:0.646265+0.054692
## [15] train-rmse:0.428296+0.011515 test-rmse:0.641814+0.056743
## [16] train-rmse:0.420273+0.011578 test-rmse:0.637230+0.057032
## [17] train-rmse:0.410704+0.009669 test-rmse:0.634693+0.058996
## [18] train-rmse:0.400512+0.013248 test-rmse:0.632016+0.058157
## [19] train-rmse:0.394112+0.012779 test-rmse:0.629366+0.056337
## [20] train-rmse:0.385533+0.012036 test-rmse:0.627499+0.059631
## [21] train-rmse:0.378905+0.012628 test-rmse:0.626466+0.061452
## [22] train-rmse:0.370033+0.014421 test-rmse:0.621795+0.066473
## [23] train-rmse:0.360843+0.011269 test-rmse:0.614017+0.062897
## [24] train-rmse:0.346941+0.009060 test-rmse:0.610525+0.060226
## [25] train-rmse:0.338885+0.008193 test-rmse:0.611010+0.059786
## [26] train-rmse:0.332448+0.009482 test-rmse:0.607684+0.058018
## [27] train-rmse:0.323865+0.006684 test-rmse:0.607582+0.060297
## [28] train-rmse:0.317732+0.006781 test-rmse:0.605617+0.059856
## [29] train-rmse:0.310470+0.005932 test-rmse:0.604549+0.058008
## [30] train-rmse:0.303650+0.005519 test-rmse:0.606800+0.058740
## [31] train-rmse:0.300181+0.005451 test-rmse:0.604529+0.059818
## [32] train-rmse:0.292800+0.008180 test-rmse:0.604804+0.060433
## [33] train-rmse:0.285267+0.008914 test-rmse:0.603685+0.060727
## [34] train-rmse:0.279393+0.009120 test-rmse:0.604978+0.061530
## [35] train-rmse:0.275048+0.010397 test-rmse:0.605371+0.061015
## [36] train-rmse:0.269698+0.006890 test-rmse:0.605165+0.061167
## [37] train-rmse:0.264648+0.006898 test-rmse:0.607265+0.062233
## [38] train-rmse:0.258210+0.009008 test-rmse:0.608184+0.061919
## [39] train-rmse:0.254844+0.008592 test-rmse:0.607658+0.062251
## Stopping. Best iteration:
## [33] train-rmse:0.285267+0.008914 test-rmse:0.603685+0.060727
##
## 95
## [1] train-rmse:1.148567+0.010538 test-rmse:1.174728+0.076333
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.872923+0.006138 test-rmse:0.926502+0.077592
## [3] train-rmse:0.712600+0.007517 test-rmse:0.795198+0.065471
## [4] train-rmse:0.624293+0.008709 test-rmse:0.730263+0.063340
## [5] train-rmse:0.569914+0.011830 test-rmse:0.707678+0.061927
## [6] train-rmse:0.526906+0.016128 test-rmse:0.686876+0.067656
## [7] train-rmse:0.496230+0.011555 test-rmse:0.670315+0.067246
## [8] train-rmse:0.478124+0.010322 test-rmse:0.670362+0.068206
## [9] train-rmse:0.453435+0.017264 test-rmse:0.662543+0.072224
## [10] train-rmse:0.430736+0.012976 test-rmse:0.654826+0.067628
## [11] train-rmse:0.418772+0.013744 test-rmse:0.654646+0.068358
## [12] train-rmse:0.399510+0.009259 test-rmse:0.648870+0.060370
## [13] train-rmse:0.388639+0.009712 test-rmse:0.642465+0.062598
## [14] train-rmse:0.369920+0.010312 test-rmse:0.645960+0.062401
## [15] train-rmse:0.357109+0.011105 test-rmse:0.642149+0.067677
## [16] train-rmse:0.344574+0.011026 test-rmse:0.640066+0.071375
## [17] train-rmse:0.335917+0.009916 test-rmse:0.636709+0.071548
## [18] train-rmse:0.325530+0.010631 test-rmse:0.631832+0.070702
## [19] train-rmse:0.313539+0.009000 test-rmse:0.630575+0.072122
## [20] train-rmse:0.305465+0.012397 test-rmse:0.630490+0.073233
## [21] train-rmse:0.297513+0.013699 test-rmse:0.631805+0.073427
## [22] train-rmse:0.289916+0.012839 test-rmse:0.629969+0.072360
## [23] train-rmse:0.282854+0.012134 test-rmse:0.629437+0.072560
## [24] train-rmse:0.273445+0.009297 test-rmse:0.630101+0.073936
## [25] train-rmse:0.263119+0.009235 test-rmse:0.629071+0.069356
## [26] train-rmse:0.255302+0.013241 test-rmse:0.630295+0.071394
## [27] train-rmse:0.246362+0.013755 test-rmse:0.629399+0.071313
## [28] train-rmse:0.241136+0.013761 test-rmse:0.630004+0.071510
## [29] train-rmse:0.233200+0.014084 test-rmse:0.631153+0.072483
## [30] train-rmse:0.228259+0.014309 test-rmse:0.631651+0.071589
## [31] train-rmse:0.220998+0.011988 test-rmse:0.632265+0.073825
## Stopping. Best iteration:
## [25] train-rmse:0.263119+0.009235 test-rmse:0.629071+0.069356
##
## 96
## [1] train-rmse:1.142951+0.009636 test-rmse:1.177677+0.078490
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.859946+0.005297 test-rmse:0.930324+0.074857
## [3] train-rmse:0.689654+0.007636 test-rmse:0.796079+0.064208
## [4] train-rmse:0.589351+0.007289 test-rmse:0.721769+0.066753
## [5] train-rmse:0.523897+0.015303 test-rmse:0.683813+0.054784
## [6] train-rmse:0.476430+0.015477 test-rmse:0.662670+0.047013
## [7] train-rmse:0.443852+0.015401 test-rmse:0.658825+0.049731
## [8] train-rmse:0.423827+0.017498 test-rmse:0.651742+0.047531
## [9] train-rmse:0.403327+0.015195 test-rmse:0.647087+0.048963
## [10] train-rmse:0.377834+0.016646 test-rmse:0.638002+0.046580
## [11] train-rmse:0.366651+0.018739 test-rmse:0.634815+0.045461
## [12] train-rmse:0.348749+0.015707 test-rmse:0.636033+0.049738
## [13] train-rmse:0.327077+0.020291 test-rmse:0.634327+0.047227
## [14] train-rmse:0.314002+0.021052 test-rmse:0.632302+0.054608
## [15] train-rmse:0.298949+0.017075 test-rmse:0.630513+0.054193
## [16] train-rmse:0.286289+0.016370 test-rmse:0.631039+0.057014
## [17] train-rmse:0.274143+0.016743 test-rmse:0.630475+0.057473
## [18] train-rmse:0.265850+0.018293 test-rmse:0.632211+0.055583
## [19] train-rmse:0.251996+0.015407 test-rmse:0.632743+0.054433
## [20] train-rmse:0.238683+0.014875 test-rmse:0.632868+0.052802
## [21] train-rmse:0.227785+0.015876 test-rmse:0.634241+0.053976
## [22] train-rmse:0.219239+0.014215 test-rmse:0.636158+0.053383
## [23] train-rmse:0.208829+0.011861 test-rmse:0.635371+0.054524
## Stopping. Best iteration:
## [17] train-rmse:0.274143+0.016743 test-rmse:0.630475+0.057473
##
## 97
## [1] train-rmse:1.138841+0.008902 test-rmse:1.175713+0.078792
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.848966+0.004322 test-rmse:0.919333+0.079747
## [3] train-rmse:0.671054+0.008911 test-rmse:0.787631+0.076845
## [4] train-rmse:0.567541+0.009715 test-rmse:0.719698+0.074194
## [5] train-rmse:0.496115+0.008483 test-rmse:0.686713+0.067192
## [6] train-rmse:0.445812+0.007164 test-rmse:0.666244+0.065977
## [7] train-rmse:0.410817+0.006289 test-rmse:0.652382+0.058402
## [8] train-rmse:0.382273+0.011729 test-rmse:0.652607+0.054009
## [9] train-rmse:0.358643+0.009663 test-rmse:0.647890+0.054780
## [10] train-rmse:0.342443+0.011291 test-rmse:0.643806+0.054730
## [11] train-rmse:0.316431+0.011331 test-rmse:0.638300+0.055204
## [12] train-rmse:0.302707+0.013820 test-rmse:0.639416+0.053234
## [13] train-rmse:0.289240+0.012501 test-rmse:0.639485+0.050900
## [14] train-rmse:0.274033+0.012320 test-rmse:0.634531+0.051847
## [15] train-rmse:0.257338+0.014579 test-rmse:0.638362+0.057250
## [16] train-rmse:0.243009+0.014498 test-rmse:0.636674+0.061256
## [17] train-rmse:0.226176+0.011246 test-rmse:0.633667+0.057954
## [18] train-rmse:0.212198+0.011222 test-rmse:0.633015+0.062292
## [19] train-rmse:0.200220+0.010453 test-rmse:0.633508+0.062951
## [20] train-rmse:0.189696+0.010337 test-rmse:0.635293+0.065212
## [21] train-rmse:0.180697+0.011551 test-rmse:0.636368+0.067131
## [22] train-rmse:0.173923+0.012089 test-rmse:0.637791+0.066110
## [23] train-rmse:0.164067+0.014186 test-rmse:0.635612+0.064781
## [24] train-rmse:0.157210+0.012074 test-rmse:0.635587+0.064223
## Stopping. Best iteration:
## [18] train-rmse:0.212198+0.011222 test-rmse:0.633015+0.062292
##
## 98
## [1] train-rmse:1.159224+0.010618 test-rmse:1.162948+0.071119
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.929923+0.007465 test-rmse:0.937554+0.063435
## [3] train-rmse:0.817639+0.007335 test-rmse:0.830631+0.054426
## [4] train-rmse:0.765605+0.006356 test-rmse:0.788185+0.046016
## [5] train-rmse:0.738815+0.005807 test-rmse:0.765819+0.039468
## [6] train-rmse:0.723271+0.005715 test-rmse:0.757156+0.036899
## [7] train-rmse:0.712117+0.005445 test-rmse:0.745265+0.037015
## [8] train-rmse:0.703439+0.004943 test-rmse:0.737591+0.034197
## [9] train-rmse:0.696497+0.004860 test-rmse:0.736672+0.031667
## [10] train-rmse:0.690795+0.005047 test-rmse:0.734433+0.031633
## [11] train-rmse:0.685854+0.005179 test-rmse:0.731899+0.029986
## [12] train-rmse:0.681177+0.005329 test-rmse:0.730232+0.029115
## [13] train-rmse:0.676829+0.005380 test-rmse:0.728949+0.027722
## [14] train-rmse:0.672720+0.005410 test-rmse:0.726937+0.028524
## [15] train-rmse:0.668802+0.005388 test-rmse:0.726672+0.030426
## [16] train-rmse:0.665189+0.005341 test-rmse:0.722357+0.029122
## [17] train-rmse:0.661780+0.005367 test-rmse:0.723282+0.031004
## [18] train-rmse:0.658363+0.005355 test-rmse:0.720724+0.032207
## [19] train-rmse:0.655069+0.005304 test-rmse:0.718096+0.032763
## [20] train-rmse:0.651974+0.005376 test-rmse:0.717928+0.037679
## [21] train-rmse:0.648963+0.005512 test-rmse:0.718162+0.037391
## [22] train-rmse:0.646035+0.005545 test-rmse:0.713917+0.036956
## [23] train-rmse:0.643290+0.005718 test-rmse:0.716427+0.036265
## [24] train-rmse:0.640674+0.005814 test-rmse:0.715680+0.036086
## [25] train-rmse:0.638218+0.005813 test-rmse:0.714719+0.034835
## [26] train-rmse:0.635965+0.005883 test-rmse:0.713719+0.035556
## [27] train-rmse:0.633843+0.005816 test-rmse:0.710424+0.033301
## [28] train-rmse:0.631754+0.005766 test-rmse:0.711726+0.035408
## [29] train-rmse:0.629834+0.005714 test-rmse:0.709460+0.033759
## [30] train-rmse:0.627854+0.005750 test-rmse:0.707332+0.032833
## [31] train-rmse:0.625916+0.005824 test-rmse:0.707031+0.033173
## [32] train-rmse:0.624051+0.005849 test-rmse:0.704477+0.034558
## [33] train-rmse:0.622205+0.005791 test-rmse:0.706663+0.037323
## [34] train-rmse:0.620345+0.005755 test-rmse:0.704193+0.037654
## [35] train-rmse:0.618578+0.005690 test-rmse:0.702817+0.036260
## [36] train-rmse:0.616910+0.005695 test-rmse:0.701322+0.036073
## [37] train-rmse:0.615294+0.005673 test-rmse:0.699914+0.033532
## [38] train-rmse:0.613659+0.005688 test-rmse:0.701271+0.034715
## [39] train-rmse:0.612137+0.005735 test-rmse:0.700709+0.033595
## [40] train-rmse:0.610612+0.005826 test-rmse:0.699456+0.033640
## [41] train-rmse:0.609135+0.005851 test-rmse:0.697724+0.034237
## [42] train-rmse:0.607704+0.005844 test-rmse:0.697302+0.034555
## [43] train-rmse:0.606271+0.005884 test-rmse:0.696988+0.034864
## [44] train-rmse:0.604873+0.005907 test-rmse:0.697504+0.034000
## [45] train-rmse:0.603544+0.005929 test-rmse:0.695619+0.035009
## [46] train-rmse:0.602255+0.005958 test-rmse:0.695414+0.034193
## [47] train-rmse:0.600943+0.005983 test-rmse:0.692693+0.033302
## [48] train-rmse:0.599668+0.006008 test-rmse:0.693212+0.033728
## [49] train-rmse:0.598433+0.006011 test-rmse:0.691931+0.034169
## [50] train-rmse:0.597222+0.006046 test-rmse:0.691116+0.035349
## [51] train-rmse:0.595986+0.006088 test-rmse:0.691738+0.035873
## [52] train-rmse:0.594800+0.006154 test-rmse:0.691273+0.036075
## [53] train-rmse:0.593697+0.006202 test-rmse:0.690001+0.036685
## [54] train-rmse:0.592576+0.006196 test-rmse:0.688346+0.035468
## [55] train-rmse:0.591507+0.006223 test-rmse:0.689080+0.035465
## [56] train-rmse:0.590493+0.006209 test-rmse:0.690051+0.036262
## [57] train-rmse:0.589462+0.006215 test-rmse:0.689543+0.035681
## [58] train-rmse:0.588485+0.006246 test-rmse:0.688882+0.036400
## [59] train-rmse:0.587496+0.006274 test-rmse:0.687146+0.034376
## [60] train-rmse:0.586525+0.006352 test-rmse:0.686682+0.035070
## [61] train-rmse:0.585573+0.006392 test-rmse:0.686550+0.033903
## [62] train-rmse:0.584600+0.006389 test-rmse:0.685875+0.035192
## [63] train-rmse:0.583675+0.006451 test-rmse:0.686553+0.035434
## [64] train-rmse:0.582791+0.006490 test-rmse:0.686202+0.035727
## [65] train-rmse:0.581911+0.006499 test-rmse:0.686900+0.036396
## [66] train-rmse:0.581069+0.006520 test-rmse:0.686482+0.035882
## [67] train-rmse:0.580200+0.006551 test-rmse:0.686295+0.035023
## [68] train-rmse:0.579394+0.006542 test-rmse:0.685738+0.034687
## [69] train-rmse:0.578574+0.006571 test-rmse:0.683694+0.035562
## [70] train-rmse:0.577766+0.006583 test-rmse:0.682237+0.035325
## [71] train-rmse:0.576979+0.006597 test-rmse:0.682072+0.034502
## [72] train-rmse:0.576190+0.006595 test-rmse:0.679360+0.034572
## [73] train-rmse:0.575420+0.006596 test-rmse:0.679062+0.034521
## [74] train-rmse:0.574655+0.006608 test-rmse:0.679625+0.035273
## [75] train-rmse:0.573913+0.006642 test-rmse:0.680697+0.035269
## [76] train-rmse:0.573168+0.006664 test-rmse:0.680025+0.036040
## [77] train-rmse:0.572404+0.006734 test-rmse:0.680240+0.035622
## [78] train-rmse:0.571692+0.006758 test-rmse:0.678822+0.035108
## [79] train-rmse:0.570990+0.006773 test-rmse:0.678773+0.034419
## [80] train-rmse:0.570298+0.006796 test-rmse:0.678365+0.034325
## [81] train-rmse:0.569654+0.006801 test-rmse:0.679026+0.034592
## [82] train-rmse:0.568985+0.006781 test-rmse:0.678975+0.035771
## [83] train-rmse:0.568348+0.006803 test-rmse:0.678780+0.037113
## [84] train-rmse:0.567717+0.006812 test-rmse:0.677611+0.037682
## [85] train-rmse:0.567067+0.006828 test-rmse:0.676532+0.038078
## [86] train-rmse:0.566437+0.006875 test-rmse:0.677643+0.038170
## [87] train-rmse:0.565842+0.006902 test-rmse:0.677883+0.037401
## [88] train-rmse:0.565254+0.006928 test-rmse:0.678775+0.037324
## [89] train-rmse:0.564659+0.006927 test-rmse:0.677767+0.037057
## [90] train-rmse:0.564049+0.006954 test-rmse:0.678446+0.036572
## [91] train-rmse:0.563446+0.006965 test-rmse:0.679209+0.036831
## Stopping. Best iteration:
## [85] train-rmse:0.567067+0.006828 test-rmse:0.676532+0.038078
##
## 99
## [1] train-rmse:1.147340+0.010339 test-rmse:1.159439+0.071465
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.904889+0.006413 test-rmse:0.932673+0.067430
## [3] train-rmse:0.783195+0.002625 test-rmse:0.822104+0.060404
## [4] train-rmse:0.715445+0.005616 test-rmse:0.766805+0.049821
## [5] train-rmse:0.681550+0.005660 test-rmse:0.747099+0.043488
## [6] train-rmse:0.660973+0.004572 test-rmse:0.733297+0.038191
## [7] train-rmse:0.645977+0.006969 test-rmse:0.722703+0.036200
## [8] train-rmse:0.631467+0.008783 test-rmse:0.710495+0.041354
## [9] train-rmse:0.622342+0.008942 test-rmse:0.713228+0.049334
## [10] train-rmse:0.614548+0.009414 test-rmse:0.709177+0.046094
## [11] train-rmse:0.600323+0.007938 test-rmse:0.702145+0.045354
## [12] train-rmse:0.592726+0.007167 test-rmse:0.700988+0.045623
## [13] train-rmse:0.585653+0.008277 test-rmse:0.696135+0.048862
## [14] train-rmse:0.578479+0.009253 test-rmse:0.695242+0.050293
## [15] train-rmse:0.572158+0.008316 test-rmse:0.695677+0.053735
## [16] train-rmse:0.564170+0.013040 test-rmse:0.692587+0.052502
## [17] train-rmse:0.559215+0.013232 test-rmse:0.693209+0.052316
## [18] train-rmse:0.554835+0.013081 test-rmse:0.692128+0.052940
## [19] train-rmse:0.545843+0.011579 test-rmse:0.684227+0.051323
## [20] train-rmse:0.540274+0.010965 test-rmse:0.681386+0.048663
## [21] train-rmse:0.536192+0.010577 test-rmse:0.678037+0.049279
## [22] train-rmse:0.532561+0.011254 test-rmse:0.676756+0.048693
## [23] train-rmse:0.526784+0.009685 test-rmse:0.672711+0.054146
## [24] train-rmse:0.521817+0.009238 test-rmse:0.672631+0.054356
## [25] train-rmse:0.517935+0.009420 test-rmse:0.669525+0.055082
## [26] train-rmse:0.512785+0.010633 test-rmse:0.665903+0.052139
## [27] train-rmse:0.508989+0.011321 test-rmse:0.662999+0.053732
## [28] train-rmse:0.505720+0.011047 test-rmse:0.662428+0.052663
## [29] train-rmse:0.502079+0.011340 test-rmse:0.662862+0.052592
## [30] train-rmse:0.497312+0.010103 test-rmse:0.659679+0.050931
## [31] train-rmse:0.493330+0.009447 test-rmse:0.658603+0.050690
## [32] train-rmse:0.489490+0.009161 test-rmse:0.656383+0.054369
## [33] train-rmse:0.487054+0.009016 test-rmse:0.656483+0.053880
## [34] train-rmse:0.483350+0.010437 test-rmse:0.657195+0.055079
## [35] train-rmse:0.476523+0.013133 test-rmse:0.655446+0.055121
## [36] train-rmse:0.472517+0.012878 test-rmse:0.655924+0.054099
## [37] train-rmse:0.469260+0.012653 test-rmse:0.653082+0.055907
## [38] train-rmse:0.465394+0.013183 test-rmse:0.651581+0.054114
## [39] train-rmse:0.462538+0.012424 test-rmse:0.651535+0.053237
## [40] train-rmse:0.458431+0.015482 test-rmse:0.647887+0.053255
## [41] train-rmse:0.455432+0.014791 test-rmse:0.647005+0.053014
## [42] train-rmse:0.451748+0.013675 test-rmse:0.643798+0.050950
## [43] train-rmse:0.449465+0.013346 test-rmse:0.642433+0.050547
## [44] train-rmse:0.447658+0.013475 test-rmse:0.641844+0.050349
## [45] train-rmse:0.444525+0.014261 test-rmse:0.643688+0.050215
## [46] train-rmse:0.442230+0.014215 test-rmse:0.645760+0.050395
## [47] train-rmse:0.438414+0.014161 test-rmse:0.646059+0.050759
## [48] train-rmse:0.435179+0.016701 test-rmse:0.647405+0.050972
## [49] train-rmse:0.433449+0.016687 test-rmse:0.648241+0.050988
## [50] train-rmse:0.431147+0.015897 test-rmse:0.647926+0.052009
## Stopping. Best iteration:
## [44] train-rmse:0.447658+0.013475 test-rmse:0.641844+0.050349
##
## 100
## [1] train-rmse:1.139152+0.009663 test-rmse:1.155321+0.069237
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.883940+0.005666 test-rmse:0.924018+0.070036
## [3] train-rmse:0.753953+0.004868 test-rmse:0.807407+0.063585
## [4] train-rmse:0.683460+0.010345 test-rmse:0.751470+0.044580
## [5] train-rmse:0.638911+0.009762 test-rmse:0.723436+0.046575
## [6] train-rmse:0.610272+0.006809 test-rmse:0.706459+0.041451
## [7] train-rmse:0.591928+0.005428 test-rmse:0.701855+0.041355
## [8] train-rmse:0.577970+0.008167 test-rmse:0.693914+0.038931
## [9] train-rmse:0.565544+0.007226 test-rmse:0.691262+0.043737
## [10] train-rmse:0.555229+0.007434 test-rmse:0.691917+0.045790
## [11] train-rmse:0.540303+0.010019 test-rmse:0.684606+0.046797
## [12] train-rmse:0.529792+0.007554 test-rmse:0.681433+0.048186
## [13] train-rmse:0.523236+0.006650 test-rmse:0.686754+0.049752
## [14] train-rmse:0.510031+0.011683 test-rmse:0.678097+0.043676
## [15] train-rmse:0.501084+0.014535 test-rmse:0.674925+0.043300
## [16] train-rmse:0.491066+0.010458 test-rmse:0.662749+0.048007
## [17] train-rmse:0.484290+0.010943 test-rmse:0.658870+0.050752
## [18] train-rmse:0.476566+0.010282 test-rmse:0.655791+0.051566
## [19] train-rmse:0.468126+0.010406 test-rmse:0.648682+0.046484
## [20] train-rmse:0.457312+0.008704 test-rmse:0.642686+0.045236
## [21] train-rmse:0.449539+0.011116 test-rmse:0.645032+0.046206
## [22] train-rmse:0.444047+0.011480 test-rmse:0.647801+0.049048
## [23] train-rmse:0.438486+0.010642 test-rmse:0.645883+0.048413
## [24] train-rmse:0.430846+0.010288 test-rmse:0.643804+0.045350
## [25] train-rmse:0.426844+0.011038 test-rmse:0.641554+0.044699
## [26] train-rmse:0.421209+0.010875 test-rmse:0.642250+0.045507
## [27] train-rmse:0.415585+0.012458 test-rmse:0.643017+0.046679
## [28] train-rmse:0.407641+0.012772 test-rmse:0.640252+0.047900
## [29] train-rmse:0.402354+0.014253 test-rmse:0.638444+0.046595
## [30] train-rmse:0.396657+0.013959 test-rmse:0.637164+0.050682
## [31] train-rmse:0.390964+0.015616 test-rmse:0.638961+0.051412
## [32] train-rmse:0.384873+0.014308 test-rmse:0.639345+0.050109
## [33] train-rmse:0.379057+0.014435 test-rmse:0.635585+0.050770
## [34] train-rmse:0.373845+0.014687 test-rmse:0.639244+0.047387
## [35] train-rmse:0.367208+0.012065 test-rmse:0.634856+0.048049
## [36] train-rmse:0.362848+0.012096 test-rmse:0.635916+0.050197
## [37] train-rmse:0.359149+0.011163 test-rmse:0.635898+0.048987
## [38] train-rmse:0.355568+0.011821 test-rmse:0.635455+0.048501
## [39] train-rmse:0.350734+0.011462 test-rmse:0.632311+0.048301
## [40] train-rmse:0.347023+0.011496 test-rmse:0.631974+0.050317
## [41] train-rmse:0.343477+0.012583 test-rmse:0.631218+0.050974
## [42] train-rmse:0.339230+0.013228 test-rmse:0.632230+0.050600
## [43] train-rmse:0.334297+0.013691 test-rmse:0.630671+0.051931
## [44] train-rmse:0.328770+0.012423 test-rmse:0.629238+0.051165
## [45] train-rmse:0.324700+0.013361 test-rmse:0.628148+0.049710
## [46] train-rmse:0.320512+0.014422 test-rmse:0.629653+0.051443
## [47] train-rmse:0.317126+0.013633 test-rmse:0.629572+0.051799
## [48] train-rmse:0.313639+0.012396 test-rmse:0.628790+0.051111
## [49] train-rmse:0.309529+0.012394 test-rmse:0.625916+0.049037
## [50] train-rmse:0.306614+0.011127 test-rmse:0.625129+0.049023
## [51] train-rmse:0.303777+0.009652 test-rmse:0.625131+0.049745
## [52] train-rmse:0.300200+0.008437 test-rmse:0.625093+0.049054
## [53] train-rmse:0.296610+0.009665 test-rmse:0.627599+0.050998
## [54] train-rmse:0.293022+0.010209 test-rmse:0.628442+0.052588
## [55] train-rmse:0.289271+0.010844 test-rmse:0.628401+0.051846
## [56] train-rmse:0.286280+0.010926 test-rmse:0.626718+0.050994
## [57] train-rmse:0.283046+0.010814 test-rmse:0.625134+0.052269
## [58] train-rmse:0.280907+0.010511 test-rmse:0.626333+0.052236
## Stopping. Best iteration:
## [52] train-rmse:0.300200+0.008437 test-rmse:0.625093+0.049054
##
## 101
## [1] train-rmse:1.130604+0.009879 test-rmse:1.160370+0.071592
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.863881+0.004001 test-rmse:0.921574+0.080451
## [3] train-rmse:0.720167+0.006467 test-rmse:0.794825+0.064997
## [4] train-rmse:0.636515+0.014832 test-rmse:0.735490+0.059986
## [5] train-rmse:0.586154+0.012206 test-rmse:0.708981+0.065223
## [6] train-rmse:0.557285+0.011467 test-rmse:0.695229+0.057910
## [7] train-rmse:0.532848+0.010162 test-rmse:0.689244+0.068722
## [8] train-rmse:0.509049+0.008959 test-rmse:0.677568+0.056783
## [9] train-rmse:0.488585+0.013554 test-rmse:0.667868+0.055834
## [10] train-rmse:0.474541+0.012971 test-rmse:0.662321+0.052789
## [11] train-rmse:0.465212+0.013390 test-rmse:0.663417+0.052322
## [12] train-rmse:0.446442+0.009359 test-rmse:0.656421+0.056572
## [13] train-rmse:0.434500+0.010632 test-rmse:0.651664+0.056797
## [14] train-rmse:0.423119+0.011794 test-rmse:0.647771+0.057175
## [15] train-rmse:0.411577+0.011122 test-rmse:0.644224+0.058200
## [16] train-rmse:0.404231+0.010943 test-rmse:0.646549+0.059966
## [17] train-rmse:0.391368+0.011988 test-rmse:0.641293+0.056350
## [18] train-rmse:0.378854+0.012055 test-rmse:0.638488+0.057117
## [19] train-rmse:0.370807+0.014694 test-rmse:0.638185+0.057562
## [20] train-rmse:0.363152+0.013558 test-rmse:0.636705+0.057950
## [21] train-rmse:0.354768+0.011765 test-rmse:0.634932+0.057777
## [22] train-rmse:0.347586+0.012844 test-rmse:0.634520+0.058952
## [23] train-rmse:0.339192+0.013641 test-rmse:0.632384+0.060187
## [24] train-rmse:0.330461+0.010980 test-rmse:0.630624+0.059582
## [25] train-rmse:0.323119+0.009498 test-rmse:0.627269+0.057819
## [26] train-rmse:0.315952+0.011871 test-rmse:0.627596+0.058509
## [27] train-rmse:0.310003+0.010982 test-rmse:0.629586+0.059953
## [28] train-rmse:0.304795+0.012954 test-rmse:0.630830+0.062108
## [29] train-rmse:0.299398+0.011796 test-rmse:0.634410+0.060795
## [30] train-rmse:0.292207+0.013303 test-rmse:0.637007+0.059986
## [31] train-rmse:0.285828+0.012772 test-rmse:0.635896+0.059036
## Stopping. Best iteration:
## [25] train-rmse:0.323119+0.009498 test-rmse:0.627269+0.057819
##
## 102
## [1] train-rmse:1.124719+0.010204 test-rmse:1.152895+0.076835
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.845856+0.006188 test-rmse:0.904480+0.076691
## [3] train-rmse:0.691793+0.009325 test-rmse:0.782846+0.059127
## [4] train-rmse:0.608295+0.008312 test-rmse:0.719482+0.055110
## [5] train-rmse:0.553512+0.006450 test-rmse:0.697102+0.050127
## [6] train-rmse:0.519410+0.009653 test-rmse:0.687762+0.051045
## [7] train-rmse:0.490330+0.013679 test-rmse:0.683216+0.052764
## [8] train-rmse:0.466598+0.013005 test-rmse:0.680976+0.055874
## [9] train-rmse:0.453490+0.013300 test-rmse:0.681007+0.051919
## [10] train-rmse:0.426482+0.016455 test-rmse:0.660673+0.053986
## [11] train-rmse:0.408757+0.022756 test-rmse:0.662568+0.053844
## [12] train-rmse:0.397323+0.020599 test-rmse:0.663246+0.056399
## [13] train-rmse:0.383756+0.022350 test-rmse:0.659335+0.057251
## [14] train-rmse:0.363744+0.016650 test-rmse:0.647961+0.056728
## [15] train-rmse:0.351482+0.017857 test-rmse:0.648459+0.054089
## [16] train-rmse:0.339898+0.014785 test-rmse:0.649836+0.053311
## [17] train-rmse:0.329044+0.012582 test-rmse:0.646306+0.055073
## [18] train-rmse:0.319500+0.012841 test-rmse:0.644232+0.054183
## [19] train-rmse:0.307125+0.009673 test-rmse:0.638847+0.051942
## [20] train-rmse:0.295888+0.012190 test-rmse:0.638358+0.048550
## [21] train-rmse:0.284468+0.011536 test-rmse:0.634823+0.049876
## [22] train-rmse:0.272525+0.010293 test-rmse:0.632859+0.054104
## [23] train-rmse:0.264676+0.008536 test-rmse:0.635226+0.054235
## [24] train-rmse:0.258268+0.008079 test-rmse:0.636390+0.053393
## [25] train-rmse:0.248057+0.009507 test-rmse:0.639175+0.054036
## [26] train-rmse:0.240618+0.007634 test-rmse:0.635821+0.053218
## [27] train-rmse:0.233289+0.009956 test-rmse:0.635922+0.054472
## [28] train-rmse:0.228210+0.010283 test-rmse:0.636532+0.057128
## Stopping. Best iteration:
## [22] train-rmse:0.272525+0.010293 test-rmse:0.632859+0.054104
##
## 103
## [1] train-rmse:1.118691+0.009240 test-rmse:1.156013+0.078996
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.831471+0.005272 test-rmse:0.907313+0.074154
## [3] train-rmse:0.665469+0.008149 test-rmse:0.772461+0.061542
## [4] train-rmse:0.574299+0.010038 test-rmse:0.706097+0.066375
## [5] train-rmse:0.506552+0.017148 test-rmse:0.672225+0.050186
## [6] train-rmse:0.467238+0.019790 test-rmse:0.659664+0.048454
## [7] train-rmse:0.436542+0.018336 test-rmse:0.648358+0.047896
## [8] train-rmse:0.413379+0.013172 test-rmse:0.639189+0.042840
## [9] train-rmse:0.395121+0.011999 test-rmse:0.637081+0.039260
## [10] train-rmse:0.377825+0.014368 test-rmse:0.635064+0.041726
## [11] train-rmse:0.353871+0.018978 test-rmse:0.635458+0.038568
## [12] train-rmse:0.339833+0.021106 test-rmse:0.631641+0.039332
## [13] train-rmse:0.322278+0.021321 test-rmse:0.629950+0.040386
## [14] train-rmse:0.305591+0.018842 test-rmse:0.621748+0.037514
## [15] train-rmse:0.293184+0.020120 test-rmse:0.621926+0.034581
## [16] train-rmse:0.279480+0.023740 test-rmse:0.625939+0.034600
## [17] train-rmse:0.267438+0.019029 test-rmse:0.624638+0.034850
## [18] train-rmse:0.255754+0.018496 test-rmse:0.627378+0.035574
## [19] train-rmse:0.242758+0.020735 test-rmse:0.624147+0.039557
## [20] train-rmse:0.233738+0.021741 test-rmse:0.625708+0.041879
## Stopping. Best iteration:
## [14] train-rmse:0.305591+0.018842 test-rmse:0.621748+0.037514
##
## 104
## [1] train-rmse:1.114279+0.008461 test-rmse:1.153937+0.079318
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.819521+0.004283 test-rmse:0.896927+0.079361
## [3] train-rmse:0.645414+0.011754 test-rmse:0.774283+0.078419
## [4] train-rmse:0.546569+0.011368 test-rmse:0.709228+0.073107
## [5] train-rmse:0.476384+0.014302 test-rmse:0.680108+0.065330
## [6] train-rmse:0.431078+0.015423 test-rmse:0.665788+0.067483
## [7] train-rmse:0.397273+0.010146 test-rmse:0.662054+0.068278
## [8] train-rmse:0.372269+0.013870 test-rmse:0.660888+0.063599
## [9] train-rmse:0.346527+0.015257 test-rmse:0.648971+0.059960
## [10] train-rmse:0.328330+0.017225 test-rmse:0.649755+0.058117
## [11] train-rmse:0.312274+0.014294 test-rmse:0.648170+0.063229
## [12] train-rmse:0.293591+0.018863 test-rmse:0.647898+0.063144
## [13] train-rmse:0.275663+0.021295 test-rmse:0.652905+0.065469
## [14] train-rmse:0.264973+0.023397 test-rmse:0.650995+0.066699
## [15] train-rmse:0.248952+0.022305 test-rmse:0.649718+0.067219
## [16] train-rmse:0.235888+0.021856 test-rmse:0.648648+0.067210
## [17] train-rmse:0.220782+0.017729 test-rmse:0.647639+0.066719
## [18] train-rmse:0.208683+0.016252 test-rmse:0.647656+0.068475
## [19] train-rmse:0.194509+0.018062 test-rmse:0.641633+0.064465
## [20] train-rmse:0.186326+0.015413 test-rmse:0.644042+0.063400
## [21] train-rmse:0.177238+0.016183 test-rmse:0.644535+0.062972
## [22] train-rmse:0.168093+0.014699 test-rmse:0.645397+0.065040
## [23] train-rmse:0.157820+0.013280 test-rmse:0.646591+0.063855
## [24] train-rmse:0.148554+0.013249 test-rmse:0.645832+0.062311
## [25] train-rmse:0.140028+0.013145 test-rmse:0.643234+0.058210
## Stopping. Best iteration:
## [19] train-rmse:0.194509+0.018062 test-rmse:0.641633+0.064465
##
## 105
## [1] train-rmse:1.137889+0.010305 test-rmse:1.141956+0.071077
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.909891+0.007227 test-rmse:0.918147+0.062296
## [3] train-rmse:0.803389+0.007312 test-rmse:0.817333+0.052701
## [4] train-rmse:0.756969+0.006375 test-rmse:0.781706+0.045874
## [5] train-rmse:0.733560+0.005674 test-rmse:0.763888+0.041392
## [6] train-rmse:0.719463+0.005615 test-rmse:0.754543+0.036102
## [7] train-rmse:0.708825+0.005360 test-rmse:0.743155+0.036667
## [8] train-rmse:0.700204+0.004832 test-rmse:0.736009+0.034514
## [9] train-rmse:0.693328+0.004636 test-rmse:0.735418+0.031612
## [10] train-rmse:0.687662+0.005010 test-rmse:0.730869+0.029066
## [11] train-rmse:0.682550+0.005130 test-rmse:0.729710+0.029028
## [12] train-rmse:0.677775+0.005257 test-rmse:0.730578+0.029889
## [13] train-rmse:0.673495+0.005265 test-rmse:0.728413+0.027947
## [14] train-rmse:0.669329+0.005222 test-rmse:0.725170+0.028965
## [15] train-rmse:0.665444+0.005182 test-rmse:0.724523+0.030842
## [16] train-rmse:0.661825+0.005249 test-rmse:0.722944+0.032191
## [17] train-rmse:0.658420+0.005226 test-rmse:0.721850+0.035113
## [18] train-rmse:0.655102+0.005225 test-rmse:0.719965+0.034695
## [19] train-rmse:0.651844+0.005293 test-rmse:0.718267+0.033889
## [20] train-rmse:0.648687+0.005396 test-rmse:0.718209+0.034692
## [21] train-rmse:0.645688+0.005480 test-rmse:0.715872+0.034937
## [22] train-rmse:0.642731+0.005543 test-rmse:0.714221+0.036246
## [23] train-rmse:0.639946+0.005660 test-rmse:0.714760+0.036783
## [24] train-rmse:0.637300+0.005692 test-rmse:0.713040+0.035918
## [25] train-rmse:0.634853+0.005754 test-rmse:0.712451+0.034592
## [26] train-rmse:0.632535+0.005845 test-rmse:0.711525+0.034452
## [27] train-rmse:0.630344+0.005820 test-rmse:0.707246+0.031917
## [28] train-rmse:0.628220+0.005827 test-rmse:0.704693+0.034003
## [29] train-rmse:0.626139+0.005838 test-rmse:0.705017+0.035017
## [30] train-rmse:0.624189+0.005835 test-rmse:0.704638+0.036155
## [31] train-rmse:0.622242+0.005843 test-rmse:0.701978+0.035221
## [32] train-rmse:0.620334+0.005766 test-rmse:0.702101+0.034678
## [33] train-rmse:0.618435+0.005744 test-rmse:0.703120+0.038380
## [34] train-rmse:0.616637+0.005679 test-rmse:0.700859+0.036722
## [35] train-rmse:0.614917+0.005717 test-rmse:0.700045+0.035877
## [36] train-rmse:0.613255+0.005730 test-rmse:0.699135+0.033331
## [37] train-rmse:0.611614+0.005764 test-rmse:0.697835+0.032960
## [38] train-rmse:0.610023+0.005802 test-rmse:0.699804+0.034501
## [39] train-rmse:0.608470+0.005854 test-rmse:0.698950+0.033552
## [40] train-rmse:0.607002+0.005875 test-rmse:0.696807+0.034119
## [41] train-rmse:0.605477+0.005865 test-rmse:0.696689+0.034081
## [42] train-rmse:0.604030+0.005882 test-rmse:0.696640+0.034917
## [43] train-rmse:0.602707+0.005900 test-rmse:0.696007+0.035193
## [44] train-rmse:0.601385+0.005914 test-rmse:0.695332+0.035034
## [45] train-rmse:0.600076+0.005954 test-rmse:0.694770+0.036642
## [46] train-rmse:0.598779+0.005998 test-rmse:0.691054+0.036287
## [47] train-rmse:0.597450+0.006080 test-rmse:0.689202+0.035176
## [48] train-rmse:0.596161+0.006149 test-rmse:0.690875+0.036085
## [49] train-rmse:0.594909+0.006198 test-rmse:0.691091+0.035397
## [50] train-rmse:0.593674+0.006234 test-rmse:0.690814+0.035237
## [51] train-rmse:0.592474+0.006257 test-rmse:0.690805+0.035279
## [52] train-rmse:0.591286+0.006341 test-rmse:0.689992+0.035845
## [53] train-rmse:0.590175+0.006327 test-rmse:0.689003+0.037933
## [54] train-rmse:0.589108+0.006350 test-rmse:0.688588+0.036903
## [55] train-rmse:0.588063+0.006363 test-rmse:0.686115+0.036062
## [56] train-rmse:0.587058+0.006361 test-rmse:0.686801+0.035692
## [57] train-rmse:0.586048+0.006362 test-rmse:0.685393+0.034831
## [58] train-rmse:0.585024+0.006390 test-rmse:0.686235+0.035161
## [59] train-rmse:0.584055+0.006389 test-rmse:0.686139+0.034133
## [60] train-rmse:0.583093+0.006415 test-rmse:0.685064+0.035026
## [61] train-rmse:0.582130+0.006472 test-rmse:0.685638+0.035534
## [62] train-rmse:0.581234+0.006498 test-rmse:0.684195+0.035217
## [63] train-rmse:0.580362+0.006556 test-rmse:0.683697+0.033886
## [64] train-rmse:0.579470+0.006603 test-rmse:0.684260+0.033653
## [65] train-rmse:0.578597+0.006621 test-rmse:0.684083+0.033339
## [66] train-rmse:0.577755+0.006664 test-rmse:0.681636+0.033779
## [67] train-rmse:0.576949+0.006722 test-rmse:0.680994+0.034691
## [68] train-rmse:0.576139+0.006747 test-rmse:0.679667+0.034441
## [69] train-rmse:0.575353+0.006785 test-rmse:0.678659+0.034972
## [70] train-rmse:0.574541+0.006780 test-rmse:0.679879+0.034710
## [71] train-rmse:0.573762+0.006771 test-rmse:0.678200+0.035743
## [72] train-rmse:0.572960+0.006777 test-rmse:0.677483+0.035836
## [73] train-rmse:0.572206+0.006816 test-rmse:0.678304+0.034811
## [74] train-rmse:0.571464+0.006838 test-rmse:0.677266+0.035239
## [75] train-rmse:0.570743+0.006861 test-rmse:0.676590+0.035031
## [76] train-rmse:0.570038+0.006888 test-rmse:0.677464+0.035460
## [77] train-rmse:0.569318+0.006892 test-rmse:0.677400+0.035434
## [78] train-rmse:0.568639+0.006923 test-rmse:0.677311+0.036001
## [79] train-rmse:0.567942+0.006976 test-rmse:0.677254+0.035460
## [80] train-rmse:0.567238+0.006983 test-rmse:0.676925+0.036006
## [81] train-rmse:0.566553+0.007033 test-rmse:0.676974+0.036763
## Stopping. Best iteration:
## [75] train-rmse:0.570743+0.006861 test-rmse:0.676590+0.035031
##
## 106
## [1] train-rmse:1.125250+0.010004 test-rmse:1.138258+0.071432
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.883240+0.006103 test-rmse:0.912922+0.066369
## [3] train-rmse:0.765622+0.006626 test-rmse:0.805164+0.056228
## [4] train-rmse:0.703367+0.006457 test-rmse:0.753912+0.044165
## [5] train-rmse:0.673194+0.007244 test-rmse:0.735502+0.037436
## [6] train-rmse:0.653694+0.006358 test-rmse:0.720581+0.030648
## [7] train-rmse:0.639779+0.008421 test-rmse:0.711312+0.029127
## [8] train-rmse:0.628359+0.010009 test-rmse:0.710725+0.030296
## [9] train-rmse:0.618772+0.009709 test-rmse:0.706006+0.026298
## [10] train-rmse:0.609721+0.009722 test-rmse:0.699671+0.029875
## [11] train-rmse:0.598975+0.008975 test-rmse:0.690874+0.029723
## [12] train-rmse:0.592331+0.008611 test-rmse:0.688887+0.031265
## [13] train-rmse:0.582357+0.006624 test-rmse:0.683253+0.029679
## [14] train-rmse:0.576595+0.006912 test-rmse:0.683505+0.035525
## [15] train-rmse:0.568604+0.007158 test-rmse:0.682689+0.034838
## [16] train-rmse:0.562631+0.006756 test-rmse:0.681206+0.035263
## [17] train-rmse:0.556096+0.009308 test-rmse:0.680962+0.031114
## [18] train-rmse:0.551365+0.008537 test-rmse:0.676615+0.033562
## [19] train-rmse:0.546048+0.008354 test-rmse:0.672741+0.033722
## [20] train-rmse:0.539870+0.009805 test-rmse:0.672131+0.036980
## [21] train-rmse:0.533911+0.011863 test-rmse:0.670404+0.034344
## [22] train-rmse:0.530330+0.011764 test-rmse:0.669389+0.034894
## [23] train-rmse:0.524413+0.012353 test-rmse:0.666225+0.031738
## [24] train-rmse:0.520461+0.013332 test-rmse:0.664493+0.030374
## [25] train-rmse:0.516148+0.012281 test-rmse:0.662757+0.033915
## [26] train-rmse:0.509875+0.007248 test-rmse:0.658829+0.036921
## [27] train-rmse:0.504962+0.008149 test-rmse:0.654372+0.038076
## [28] train-rmse:0.501608+0.008154 test-rmse:0.653616+0.036610
## [29] train-rmse:0.495299+0.008434 test-rmse:0.651787+0.035786
## [30] train-rmse:0.492128+0.008717 test-rmse:0.651614+0.036826
## [31] train-rmse:0.487134+0.007600 test-rmse:0.652155+0.035949
## [32] train-rmse:0.483632+0.007273 test-rmse:0.653114+0.037715
## [33] train-rmse:0.477846+0.009897 test-rmse:0.649808+0.035993
## [34] train-rmse:0.475173+0.010135 test-rmse:0.647323+0.037121
## [35] train-rmse:0.469132+0.010382 test-rmse:0.645344+0.037484
## [36] train-rmse:0.465827+0.009843 test-rmse:0.646021+0.037354
## [37] train-rmse:0.462341+0.008837 test-rmse:0.642233+0.040325
## [38] train-rmse:0.458203+0.010972 test-rmse:0.640428+0.038847
## [39] train-rmse:0.455008+0.011664 test-rmse:0.639403+0.038101
## [40] train-rmse:0.450853+0.012720 test-rmse:0.639701+0.037698
## [41] train-rmse:0.445627+0.013976 test-rmse:0.639740+0.039194
## [42] train-rmse:0.442203+0.013401 test-rmse:0.641810+0.040109
## [43] train-rmse:0.439139+0.013468 test-rmse:0.643422+0.040834
## [44] train-rmse:0.437255+0.013869 test-rmse:0.642383+0.040514
## [45] train-rmse:0.435253+0.013655 test-rmse:0.643373+0.041933
## Stopping. Best iteration:
## [39] train-rmse:0.455008+0.011664 test-rmse:0.639403+0.038101
##
## 107
## [1] train-rmse:1.116528+0.009287 test-rmse:1.133900+0.069066
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.860870+0.005420 test-rmse:0.902886+0.070068
## [3] train-rmse:0.736170+0.007497 test-rmse:0.794610+0.067950
## [4] train-rmse:0.660055+0.004531 test-rmse:0.738090+0.052632
## [5] train-rmse:0.623339+0.005443 test-rmse:0.715385+0.045960
## [6] train-rmse:0.600942+0.004098 test-rmse:0.706948+0.043408
## [7] train-rmse:0.584355+0.006108 test-rmse:0.695202+0.041051
## [8] train-rmse:0.568997+0.007232 test-rmse:0.689193+0.046742
## [9] train-rmse:0.557661+0.007508 test-rmse:0.686965+0.045799
## [10] train-rmse:0.546101+0.009928 test-rmse:0.682391+0.043199
## [11] train-rmse:0.530813+0.010764 test-rmse:0.680755+0.052988
## [12] train-rmse:0.515261+0.015509 test-rmse:0.674891+0.056492
## [13] train-rmse:0.504581+0.015762 test-rmse:0.675340+0.053717
## [14] train-rmse:0.491702+0.017030 test-rmse:0.664770+0.050032
## [15] train-rmse:0.478857+0.014239 test-rmse:0.662669+0.054075
## [16] train-rmse:0.469484+0.010675 test-rmse:0.657223+0.056600
## [17] train-rmse:0.461220+0.011943 test-rmse:0.654808+0.054619
## [18] train-rmse:0.453804+0.011047 test-rmse:0.647980+0.056649
## [19] train-rmse:0.445281+0.012002 test-rmse:0.644722+0.052866
## [20] train-rmse:0.435558+0.013505 test-rmse:0.643854+0.049227
## [21] train-rmse:0.428253+0.014269 test-rmse:0.642903+0.048696
## [22] train-rmse:0.422924+0.015054 test-rmse:0.643705+0.052316
## [23] train-rmse:0.416221+0.010987 test-rmse:0.644600+0.054276
## [24] train-rmse:0.410647+0.012158 test-rmse:0.645877+0.052765
## [25] train-rmse:0.403488+0.015412 test-rmse:0.642764+0.052069
## [26] train-rmse:0.395127+0.014457 test-rmse:0.642539+0.049039
## [27] train-rmse:0.388754+0.015928 test-rmse:0.643284+0.049112
## [28] train-rmse:0.384551+0.016131 test-rmse:0.643060+0.048047
## [29] train-rmse:0.379425+0.016481 test-rmse:0.642365+0.048982
## [30] train-rmse:0.373115+0.017776 test-rmse:0.638287+0.048937
## [31] train-rmse:0.369676+0.017786 test-rmse:0.637381+0.049985
## [32] train-rmse:0.365094+0.019671 test-rmse:0.635294+0.049334
## [33] train-rmse:0.358660+0.019665 test-rmse:0.635311+0.045657
## [34] train-rmse:0.354831+0.020335 test-rmse:0.634045+0.045162
## [35] train-rmse:0.350092+0.020685 test-rmse:0.631623+0.045644
## [36] train-rmse:0.344845+0.019170 test-rmse:0.630249+0.044634
## [37] train-rmse:0.337327+0.016458 test-rmse:0.626409+0.043513
## [38] train-rmse:0.334142+0.016042 test-rmse:0.626363+0.043035
## [39] train-rmse:0.329689+0.015658 test-rmse:0.626299+0.041524
## [40] train-rmse:0.324957+0.016575 test-rmse:0.624609+0.042001
## [41] train-rmse:0.320992+0.015853 test-rmse:0.625008+0.042578
## [42] train-rmse:0.315202+0.014504 test-rmse:0.626090+0.042185
## [43] train-rmse:0.310262+0.016530 test-rmse:0.626218+0.042220
## [44] train-rmse:0.307059+0.016022 test-rmse:0.625455+0.042098
## [45] train-rmse:0.304035+0.015454 test-rmse:0.626096+0.043051
## [46] train-rmse:0.300869+0.014978 test-rmse:0.626321+0.042133
## Stopping. Best iteration:
## [40] train-rmse:0.324957+0.016575 test-rmse:0.624609+0.042001
##
## 108
## [1] train-rmse:1.107402+0.009540 test-rmse:1.139256+0.071710
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.841793+0.002259 test-rmse:0.902552+0.080630
## [3] train-rmse:0.701316+0.006871 test-rmse:0.779535+0.067386
## [4] train-rmse:0.617313+0.010353 test-rmse:0.719611+0.056776
## [5] train-rmse:0.568080+0.009571 test-rmse:0.701654+0.055935
## [6] train-rmse:0.540026+0.011749 test-rmse:0.692892+0.058982
## [7] train-rmse:0.521386+0.011956 test-rmse:0.679744+0.058545
## [8] train-rmse:0.507819+0.012423 test-rmse:0.675706+0.060363
## [9] train-rmse:0.492778+0.015299 test-rmse:0.673445+0.062540
## [10] train-rmse:0.477001+0.017311 test-rmse:0.671027+0.065993
## [11] train-rmse:0.455615+0.014307 test-rmse:0.663010+0.063438
## [12] train-rmse:0.440597+0.012633 test-rmse:0.667025+0.060962
## [13] train-rmse:0.426416+0.011121 test-rmse:0.663631+0.068033
## [14] train-rmse:0.411563+0.015292 test-rmse:0.652863+0.069745
## [15] train-rmse:0.399738+0.013429 test-rmse:0.654070+0.071401
## [16] train-rmse:0.389094+0.013736 test-rmse:0.651396+0.070779
## [17] train-rmse:0.378779+0.017916 test-rmse:0.646520+0.069983
## [18] train-rmse:0.370555+0.019525 test-rmse:0.647011+0.069575
## [19] train-rmse:0.363000+0.019174 test-rmse:0.643544+0.070223
## [20] train-rmse:0.353758+0.018833 test-rmse:0.643759+0.071034
## [21] train-rmse:0.346943+0.019108 test-rmse:0.643950+0.069048
## [22] train-rmse:0.339014+0.021289 test-rmse:0.643929+0.068014
## [23] train-rmse:0.332428+0.024338 test-rmse:0.645459+0.067456
## [24] train-rmse:0.324171+0.025564 test-rmse:0.647049+0.067466
## [25] train-rmse:0.315888+0.025252 test-rmse:0.642112+0.069029
## [26] train-rmse:0.308984+0.022416 test-rmse:0.643418+0.069550
## [27] train-rmse:0.300180+0.020820 test-rmse:0.641003+0.065240
## [28] train-rmse:0.292703+0.018470 test-rmse:0.637397+0.063650
## [29] train-rmse:0.283465+0.020199 test-rmse:0.640374+0.062588
## [30] train-rmse:0.278042+0.020810 test-rmse:0.638602+0.061229
## [31] train-rmse:0.271259+0.021271 test-rmse:0.637177+0.062847
## [32] train-rmse:0.265107+0.019124 test-rmse:0.636164+0.062353
## [33] train-rmse:0.258771+0.017835 test-rmse:0.637813+0.060988
## [34] train-rmse:0.252637+0.019174 test-rmse:0.639465+0.062695
## [35] train-rmse:0.248400+0.018355 test-rmse:0.641109+0.064203
## [36] train-rmse:0.243987+0.018275 test-rmse:0.640794+0.062051
## [37] train-rmse:0.239046+0.017229 test-rmse:0.640690+0.061344
## [38] train-rmse:0.233673+0.016964 test-rmse:0.640274+0.061842
## Stopping. Best iteration:
## [32] train-rmse:0.265107+0.019124 test-rmse:0.636164+0.062353
##
## 109
## [1] train-rmse:1.101108+0.009877 test-rmse:1.131368+0.077323
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.822123+0.003784 test-rmse:0.889789+0.083825
## [3] train-rmse:0.675481+0.009744 test-rmse:0.777048+0.067673
## [4] train-rmse:0.596342+0.007671 test-rmse:0.714403+0.057210
## [5] train-rmse:0.541939+0.007653 test-rmse:0.691896+0.052506
## [6] train-rmse:0.510407+0.006879 test-rmse:0.682071+0.049497
## [7] train-rmse:0.479089+0.008284 test-rmse:0.668994+0.039412
## [8] train-rmse:0.462904+0.012321 test-rmse:0.665892+0.042472
## [9] train-rmse:0.444021+0.012692 test-rmse:0.663770+0.044382
## [10] train-rmse:0.426620+0.014162 test-rmse:0.659442+0.049646
## [11] train-rmse:0.409630+0.015040 test-rmse:0.658292+0.058579
## [12] train-rmse:0.394748+0.010059 test-rmse:0.655882+0.055540
## [13] train-rmse:0.384158+0.011055 test-rmse:0.654891+0.054599
## [14] train-rmse:0.371339+0.014638 test-rmse:0.656578+0.057894
## [15] train-rmse:0.358378+0.013453 test-rmse:0.658064+0.062644
## [16] train-rmse:0.337802+0.005542 test-rmse:0.646813+0.062525
## [17] train-rmse:0.326693+0.009551 test-rmse:0.646639+0.065628
## [18] train-rmse:0.312980+0.010052 test-rmse:0.641954+0.062571
## [19] train-rmse:0.300223+0.010258 test-rmse:0.643004+0.066170
## [20] train-rmse:0.287713+0.014157 test-rmse:0.646452+0.070057
## [21] train-rmse:0.275310+0.011072 test-rmse:0.645159+0.066622
## [22] train-rmse:0.267700+0.012316 test-rmse:0.644634+0.068153
## [23] train-rmse:0.259493+0.014712 test-rmse:0.646929+0.068312
## [24] train-rmse:0.251913+0.016504 test-rmse:0.645235+0.070609
## Stopping. Best iteration:
## [18] train-rmse:0.312980+0.010052 test-rmse:0.641954+0.062571
##
## 110
## [1] train-rmse:1.094652+0.008852 test-rmse:1.134654+0.079470
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.804496+0.005276 test-rmse:0.886164+0.073556
## [3] train-rmse:0.644203+0.002630 test-rmse:0.760510+0.063040
## [4] train-rmse:0.561336+0.009665 test-rmse:0.709227+0.057457
## [5] train-rmse:0.491504+0.016855 test-rmse:0.677647+0.050417
## [6] train-rmse:0.451712+0.016586 test-rmse:0.662462+0.053653
## [7] train-rmse:0.425016+0.018294 test-rmse:0.658844+0.055853
## [8] train-rmse:0.398434+0.018877 test-rmse:0.652424+0.049818
## [9] train-rmse:0.371773+0.011175 test-rmse:0.646001+0.051372
## [10] train-rmse:0.351797+0.009637 test-rmse:0.642373+0.052019
## [11] train-rmse:0.336826+0.012365 test-rmse:0.635174+0.053028
## [12] train-rmse:0.320827+0.010079 test-rmse:0.635800+0.050646
## [13] train-rmse:0.306068+0.008242 test-rmse:0.634445+0.053032
## [14] train-rmse:0.292260+0.006719 test-rmse:0.629111+0.058381
## [15] train-rmse:0.278815+0.005926 test-rmse:0.631151+0.059977
## [16] train-rmse:0.265408+0.006057 test-rmse:0.633875+0.062215
## [17] train-rmse:0.253513+0.005742 test-rmse:0.633090+0.065646
## [18] train-rmse:0.242803+0.009596 test-rmse:0.633944+0.064358
## [19] train-rmse:0.229519+0.009076 test-rmse:0.626845+0.058633
## [20] train-rmse:0.217799+0.006955 test-rmse:0.630115+0.058906
## [21] train-rmse:0.206416+0.008288 test-rmse:0.631103+0.060162
## [22] train-rmse:0.197638+0.005909 test-rmse:0.629865+0.061898
## [23] train-rmse:0.189048+0.006602 test-rmse:0.630128+0.061472
## [24] train-rmse:0.180712+0.006183 test-rmse:0.630762+0.061856
## [25] train-rmse:0.171941+0.008154 test-rmse:0.630747+0.061490
## Stopping. Best iteration:
## [19] train-rmse:0.229519+0.009076 test-rmse:0.626845+0.058633
##
## 111
## [1] train-rmse:1.089930+0.008029 test-rmse:1.132467+0.079812
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.791356+0.003859 test-rmse:0.877350+0.077553
## [3] train-rmse:0.620705+0.010970 test-rmse:0.756413+0.067745
## [4] train-rmse:0.527305+0.008367 test-rmse:0.697432+0.064205
## [5] train-rmse:0.454802+0.013130 test-rmse:0.673726+0.061110
## [6] train-rmse:0.409249+0.006082 test-rmse:0.658893+0.060048
## [7] train-rmse:0.380328+0.010308 test-rmse:0.649553+0.058158
## [8] train-rmse:0.357052+0.007869 test-rmse:0.647155+0.059929
## [9] train-rmse:0.331043+0.016141 test-rmse:0.647631+0.061141
## [10] train-rmse:0.307030+0.013239 test-rmse:0.641875+0.060480
## [11] train-rmse:0.292692+0.013698 test-rmse:0.643807+0.063412
## [12] train-rmse:0.275619+0.017352 test-rmse:0.636685+0.056419
## [13] train-rmse:0.263014+0.021565 test-rmse:0.639270+0.055559
## [14] train-rmse:0.251282+0.022806 test-rmse:0.641956+0.054414
## [15] train-rmse:0.236014+0.019625 test-rmse:0.640598+0.055303
## [16] train-rmse:0.221415+0.019372 test-rmse:0.640139+0.055551
## [17] train-rmse:0.208907+0.018275 test-rmse:0.638767+0.057710
## [18] train-rmse:0.197404+0.016557 test-rmse:0.640901+0.055710
## Stopping. Best iteration:
## [12] train-rmse:0.275619+0.017352 test-rmse:0.636685+0.056419
##
## 112## max_depth eta
## 95 4 0.36## [1] train-rmse:1.501674+0.027852 test-rmse:1.497786+0.114244
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.381749+0.025744 test-rmse:1.377894+0.111510
## [3] train-rmse:1.276150+0.023952 test-rmse:1.272327+0.109023
## [4] train-rmse:1.183530+0.022457 test-rmse:1.179745+0.106728
## [5] train-rmse:1.102645+0.021236 test-rmse:1.098908+0.104584
## [6] train-rmse:1.032343+0.020263 test-rmse:1.028666+0.102563
## [7] train-rmse:0.971547+0.019511 test-rmse:0.967945+0.100650
## [8] train-rmse:0.919246+0.018950 test-rmse:0.915733+0.098841
## [9] train-rmse:0.874499+0.018549 test-rmse:0.871087+0.097138
## [10] train-rmse:0.836418+0.018278 test-rmse:0.833117+0.095544
## [11] train-rmse:0.804184+0.018107 test-rmse:0.801001+0.094067
## [12] train-rmse:0.777035+0.018011 test-rmse:0.773976+0.092713
## [13] train-rmse:0.754282+0.017968 test-rmse:0.751344+0.091480
## [14] train-rmse:0.735295+0.017961 test-rmse:0.732477+0.090370
## [15] train-rmse:0.719515+0.017978 test-rmse:0.716812+0.089377
## [16] train-rmse:0.706255+0.017832 test-rmse:0.706275+0.089838
## [17] train-rmse:0.694626+0.017617 test-rmse:0.693863+0.088839
## [18] train-rmse:0.684511+0.017342 test-rmse:0.684643+0.088817
## [19] train-rmse:0.675249+0.017212 test-rmse:0.674817+0.088021
## [20] train-rmse:0.667191+0.017017 test-rmse:0.667513+0.087701
## [21] train-rmse:0.659732+0.016920 test-rmse:0.659791+0.087041
## [22] train-rmse:0.653036+0.016569 test-rmse:0.654694+0.085777
## [23] train-rmse:0.646911+0.016295 test-rmse:0.648503+0.085372
## [24] train-rmse:0.641229+0.015866 test-rmse:0.643040+0.084774
## [25] train-rmse:0.635839+0.015413 test-rmse:0.638526+0.082516
## [26] train-rmse:0.630818+0.015128 test-rmse:0.632551+0.082432
## [27] train-rmse:0.626081+0.014745 test-rmse:0.628082+0.079971
## [28] train-rmse:0.621672+0.014521 test-rmse:0.624243+0.080229
## [29] train-rmse:0.617528+0.014205 test-rmse:0.620302+0.079542
## [30] train-rmse:0.613571+0.013857 test-rmse:0.617389+0.078024
## [31] train-rmse:0.609873+0.013630 test-rmse:0.613235+0.077484
## [32] train-rmse:0.606292+0.013463 test-rmse:0.609775+0.075632
## [33] train-rmse:0.603013+0.013283 test-rmse:0.607743+0.075041
## [34] train-rmse:0.599869+0.013091 test-rmse:0.604385+0.074442
## [35] train-rmse:0.596787+0.012950 test-rmse:0.601342+0.074457
## [36] train-rmse:0.593878+0.012748 test-rmse:0.599248+0.072901
## [37] train-rmse:0.591204+0.012588 test-rmse:0.596015+0.072406
## [38] train-rmse:0.588584+0.012479 test-rmse:0.594354+0.071091
## [39] train-rmse:0.586052+0.012347 test-rmse:0.591865+0.070428
## [40] train-rmse:0.583612+0.012189 test-rmse:0.591140+0.071049
## [41] train-rmse:0.581337+0.012096 test-rmse:0.588361+0.070395
## [42] train-rmse:0.579078+0.012039 test-rmse:0.587168+0.069159
## [43] train-rmse:0.576923+0.011926 test-rmse:0.584905+0.068118
## [44] train-rmse:0.574857+0.011827 test-rmse:0.584288+0.067946
## [45] train-rmse:0.572840+0.011741 test-rmse:0.582638+0.067333
## [46] train-rmse:0.570895+0.011713 test-rmse:0.580303+0.067006
## [47] train-rmse:0.569065+0.011616 test-rmse:0.579441+0.066334
## [48] train-rmse:0.567294+0.011485 test-rmse:0.577758+0.065956
## [49] train-rmse:0.565551+0.011396 test-rmse:0.577155+0.065325
## [50] train-rmse:0.563825+0.011345 test-rmse:0.575271+0.065777
## [51] train-rmse:0.562211+0.011256 test-rmse:0.573861+0.065407
## [52] train-rmse:0.560629+0.011183 test-rmse:0.572172+0.065273
## [53] train-rmse:0.559110+0.011109 test-rmse:0.570921+0.064813
## [54] train-rmse:0.557616+0.011076 test-rmse:0.570297+0.063863
## [55] train-rmse:0.556192+0.011026 test-rmse:0.569023+0.063662
## [56] train-rmse:0.554816+0.010957 test-rmse:0.567993+0.062471
## [57] train-rmse:0.553457+0.010886 test-rmse:0.566673+0.062185
## [58] train-rmse:0.552160+0.010843 test-rmse:0.565863+0.062725
## [59] train-rmse:0.550870+0.010791 test-rmse:0.565109+0.063204
## [60] train-rmse:0.549616+0.010734 test-rmse:0.563775+0.061883
## [61] train-rmse:0.548411+0.010706 test-rmse:0.563351+0.061461
## [62] train-rmse:0.547252+0.010641 test-rmse:0.562518+0.061388
## [63] train-rmse:0.546114+0.010596 test-rmse:0.562271+0.061834
## [64] train-rmse:0.545005+0.010529 test-rmse:0.561586+0.062113
## [65] train-rmse:0.543917+0.010490 test-rmse:0.560558+0.061002
## [66] train-rmse:0.542851+0.010475 test-rmse:0.559514+0.060779
## [67] train-rmse:0.541807+0.010441 test-rmse:0.558870+0.060549
## [68] train-rmse:0.540794+0.010409 test-rmse:0.558284+0.059904
## [69] train-rmse:0.539810+0.010357 test-rmse:0.557946+0.060075
## [70] train-rmse:0.538831+0.010303 test-rmse:0.556547+0.059687
## [71] train-rmse:0.537851+0.010257 test-rmse:0.556497+0.059748
## [72] train-rmse:0.536896+0.010215 test-rmse:0.555785+0.060067
## [73] train-rmse:0.535962+0.010181 test-rmse:0.555414+0.059255
## [74] train-rmse:0.535065+0.010137 test-rmse:0.554980+0.058811
## [75] train-rmse:0.534181+0.010098 test-rmse:0.554625+0.058944
## [76] train-rmse:0.533307+0.010051 test-rmse:0.553541+0.058385
## [77] train-rmse:0.532431+0.010006 test-rmse:0.553070+0.058637
## [78] train-rmse:0.531591+0.009973 test-rmse:0.552734+0.058744
## [79] train-rmse:0.530757+0.009943 test-rmse:0.552435+0.059283
## [80] train-rmse:0.529924+0.009908 test-rmse:0.551406+0.058901
## [81] train-rmse:0.529096+0.009869 test-rmse:0.550848+0.058825
## [82] train-rmse:0.528284+0.009798 test-rmse:0.550588+0.058149
## [83] train-rmse:0.527498+0.009761 test-rmse:0.549555+0.058133
## [84] train-rmse:0.526726+0.009720 test-rmse:0.549344+0.058142
## [85] train-rmse:0.525973+0.009672 test-rmse:0.549373+0.057951
## [86] train-rmse:0.525209+0.009625 test-rmse:0.548452+0.057761
## [87] train-rmse:0.524458+0.009583 test-rmse:0.547987+0.057658
## [88] train-rmse:0.523707+0.009540 test-rmse:0.547738+0.057354
## [89] train-rmse:0.522975+0.009501 test-rmse:0.547842+0.057729
## [90] train-rmse:0.522276+0.009459 test-rmse:0.547347+0.057874
## [91] train-rmse:0.521576+0.009413 test-rmse:0.545997+0.057699
## [92] train-rmse:0.520876+0.009384 test-rmse:0.545359+0.057063
## [93] train-rmse:0.520203+0.009347 test-rmse:0.545032+0.057017
## [94] train-rmse:0.519530+0.009316 test-rmse:0.544523+0.056961
## [95] train-rmse:0.518846+0.009271 test-rmse:0.543361+0.056509
## [96] train-rmse:0.518170+0.009226 test-rmse:0.543282+0.056313
## [97] train-rmse:0.517524+0.009209 test-rmse:0.543359+0.056426
## [98] train-rmse:0.516882+0.009178 test-rmse:0.543228+0.056519
## [99] train-rmse:0.516238+0.009143 test-rmse:0.542524+0.056321
## [100] train-rmse:0.515614+0.009119 test-rmse:0.542351+0.055560
## [101] train-rmse:0.514991+0.009093 test-rmse:0.541402+0.055698
## [102] train-rmse:0.514364+0.009061 test-rmse:0.540767+0.055925
## [103] train-rmse:0.513753+0.009032 test-rmse:0.540330+0.056176
## [104] train-rmse:0.513149+0.009006 test-rmse:0.540051+0.056116
## [105] train-rmse:0.512550+0.008982 test-rmse:0.540017+0.055939
## [106] train-rmse:0.511956+0.008966 test-rmse:0.538989+0.055485
## [107] train-rmse:0.511356+0.008945 test-rmse:0.538356+0.055551
## [108] train-rmse:0.510771+0.008923 test-rmse:0.538096+0.055602
## [109] train-rmse:0.510191+0.008907 test-rmse:0.537858+0.055592
## [110] train-rmse:0.509624+0.008880 test-rmse:0.537366+0.055930
## [111] train-rmse:0.509050+0.008850 test-rmse:0.536686+0.055546
## [112] train-rmse:0.508479+0.008814 test-rmse:0.536584+0.054903
## [113] train-rmse:0.507918+0.008804 test-rmse:0.536359+0.055164
## [114] train-rmse:0.507374+0.008783 test-rmse:0.535651+0.055332
## [115] train-rmse:0.506828+0.008759 test-rmse:0.535175+0.055277
## [116] train-rmse:0.506280+0.008730 test-rmse:0.534784+0.055056
## [117] train-rmse:0.505744+0.008712 test-rmse:0.534548+0.054445
## [118] train-rmse:0.505216+0.008697 test-rmse:0.534424+0.054668
## [119] train-rmse:0.504688+0.008679 test-rmse:0.533675+0.054397
## [120] train-rmse:0.504172+0.008659 test-rmse:0.533621+0.054637
## [121] train-rmse:0.503657+0.008636 test-rmse:0.533332+0.054763
## [122] train-rmse:0.503138+0.008614 test-rmse:0.532999+0.054956
## [123] train-rmse:0.502632+0.008602 test-rmse:0.533038+0.054250
## [124] train-rmse:0.502132+0.008588 test-rmse:0.532734+0.054484
## [125] train-rmse:0.501624+0.008571 test-rmse:0.532424+0.054382
## [126] train-rmse:0.501116+0.008546 test-rmse:0.531343+0.054474
## [127] train-rmse:0.500628+0.008538 test-rmse:0.530734+0.054336
## [128] train-rmse:0.500145+0.008526 test-rmse:0.530624+0.054272
## [129] train-rmse:0.499665+0.008511 test-rmse:0.530308+0.054298
## [130] train-rmse:0.499187+0.008497 test-rmse:0.529790+0.053962
## [131] train-rmse:0.498702+0.008490 test-rmse:0.529665+0.054164
## [132] train-rmse:0.498222+0.008480 test-rmse:0.529596+0.053819
## [133] train-rmse:0.497758+0.008455 test-rmse:0.529064+0.053880
## [134] train-rmse:0.497294+0.008430 test-rmse:0.528365+0.053817
## [135] train-rmse:0.496836+0.008412 test-rmse:0.528174+0.053348
## [136] train-rmse:0.496388+0.008405 test-rmse:0.528233+0.053528
## [137] train-rmse:0.495924+0.008401 test-rmse:0.528010+0.053611
## [138] train-rmse:0.495485+0.008385 test-rmse:0.527799+0.053357
## [139] train-rmse:0.495041+0.008365 test-rmse:0.527580+0.053362
## [140] train-rmse:0.494607+0.008349 test-rmse:0.526925+0.053541
## [141] train-rmse:0.494179+0.008332 test-rmse:0.526158+0.053149
## [142] train-rmse:0.493741+0.008317 test-rmse:0.525560+0.053041
## [143] train-rmse:0.493310+0.008307 test-rmse:0.525692+0.053132
## [144] train-rmse:0.492879+0.008304 test-rmse:0.525311+0.052979
## [145] train-rmse:0.492454+0.008295 test-rmse:0.525092+0.053164
## [146] train-rmse:0.492035+0.008285 test-rmse:0.524809+0.053122
## [147] train-rmse:0.491622+0.008274 test-rmse:0.524975+0.053074
## [148] train-rmse:0.491204+0.008257 test-rmse:0.524422+0.052686
## [149] train-rmse:0.490790+0.008247 test-rmse:0.524491+0.052512
## [150] train-rmse:0.490384+0.008236 test-rmse:0.524575+0.052907
## [151] train-rmse:0.489980+0.008230 test-rmse:0.523878+0.052770
## [152] train-rmse:0.489581+0.008224 test-rmse:0.522938+0.052693
## [153] train-rmse:0.489189+0.008213 test-rmse:0.523041+0.052228
## [154] train-rmse:0.488802+0.008202 test-rmse:0.522739+0.052531
## [155] train-rmse:0.488411+0.008192 test-rmse:0.522622+0.052611
## [156] train-rmse:0.488021+0.008176 test-rmse:0.522598+0.052759
## [157] train-rmse:0.487626+0.008163 test-rmse:0.522444+0.052413
## [158] train-rmse:0.487243+0.008151 test-rmse:0.522037+0.052401
## [159] train-rmse:0.486868+0.008148 test-rmse:0.521810+0.051996
## [160] train-rmse:0.486502+0.008138 test-rmse:0.521660+0.052190
## [161] train-rmse:0.486132+0.008130 test-rmse:0.521561+0.052073
## [162] train-rmse:0.485765+0.008123 test-rmse:0.521740+0.052174
## [163] train-rmse:0.485393+0.008112 test-rmse:0.521338+0.052124
## [164] train-rmse:0.485028+0.008096 test-rmse:0.521287+0.051901
## [165] train-rmse:0.484664+0.008085 test-rmse:0.520790+0.051772
## [166] train-rmse:0.484306+0.008078 test-rmse:0.520698+0.051355
## [167] train-rmse:0.483951+0.008070 test-rmse:0.520535+0.051315
## [168] train-rmse:0.483598+0.008060 test-rmse:0.520214+0.051374
## [169] train-rmse:0.483248+0.008051 test-rmse:0.520109+0.051741
## [170] train-rmse:0.482900+0.008040 test-rmse:0.519719+0.051689
## [171] train-rmse:0.482561+0.008031 test-rmse:0.519547+0.051677
## [172] train-rmse:0.482222+0.008019 test-rmse:0.519569+0.051368
## [173] train-rmse:0.481882+0.008016 test-rmse:0.519077+0.051076
## [174] train-rmse:0.481543+0.008012 test-rmse:0.518951+0.051147
## [175] train-rmse:0.481213+0.008005 test-rmse:0.519101+0.051094
## [176] train-rmse:0.480883+0.007997 test-rmse:0.519094+0.051130
## [177] train-rmse:0.480550+0.007987 test-rmse:0.519289+0.051088
## [178] train-rmse:0.480225+0.007980 test-rmse:0.519382+0.051246
## [179] train-rmse:0.479904+0.007976 test-rmse:0.518820+0.051113
## [180] train-rmse:0.479577+0.007974 test-rmse:0.518401+0.051030
## [181] train-rmse:0.479260+0.007966 test-rmse:0.517971+0.050823
## [182] train-rmse:0.478943+0.007960 test-rmse:0.518015+0.050603
## [183] train-rmse:0.478623+0.007954 test-rmse:0.517294+0.049395
## [184] train-rmse:0.478300+0.007947 test-rmse:0.516874+0.049376
## [185] train-rmse:0.477989+0.007943 test-rmse:0.516332+0.049222
## [186] train-rmse:0.477683+0.007942 test-rmse:0.516526+0.049382
## [187] train-rmse:0.477375+0.007937 test-rmse:0.516539+0.049325
## [188] train-rmse:0.477072+0.007932 test-rmse:0.516122+0.049260
## [189] train-rmse:0.476769+0.007922 test-rmse:0.515660+0.049363
## [190] train-rmse:0.476468+0.007914 test-rmse:0.515514+0.049229
## [191] train-rmse:0.476170+0.007908 test-rmse:0.515474+0.048921
## [192] train-rmse:0.475872+0.007906 test-rmse:0.515263+0.049230
## [193] train-rmse:0.475579+0.007903 test-rmse:0.515285+0.049122
## [194] train-rmse:0.475287+0.007899 test-rmse:0.515236+0.049309
## [195] train-rmse:0.474996+0.007893 test-rmse:0.515015+0.049085
## [196] train-rmse:0.474710+0.007886 test-rmse:0.514844+0.049307
## [197] train-rmse:0.474426+0.007879 test-rmse:0.514996+0.049006
## [198] train-rmse:0.474137+0.007875 test-rmse:0.514569+0.049007
## [199] train-rmse:0.473853+0.007866 test-rmse:0.514445+0.049238
## [200] train-rmse:0.473571+0.007859 test-rmse:0.514257+0.049068
## [201] train-rmse:0.473293+0.007851 test-rmse:0.513989+0.048937
## [202] train-rmse:0.473011+0.007849 test-rmse:0.514011+0.049095
## [203] train-rmse:0.472735+0.007841 test-rmse:0.513169+0.048313
## [204] train-rmse:0.472459+0.007831 test-rmse:0.513076+0.048114
## [205] train-rmse:0.472185+0.007823 test-rmse:0.512621+0.047878
## [206] train-rmse:0.471912+0.007820 test-rmse:0.512404+0.047700
## [207] train-rmse:0.471642+0.007816 test-rmse:0.512592+0.047927
## [208] train-rmse:0.471370+0.007808 test-rmse:0.512361+0.047832
## [209] train-rmse:0.471101+0.007803 test-rmse:0.512630+0.047954
## [210] train-rmse:0.470835+0.007796 test-rmse:0.512351+0.047536
## [211] train-rmse:0.470572+0.007792 test-rmse:0.512058+0.047298
## [212] train-rmse:0.470309+0.007787 test-rmse:0.512088+0.047305
## [213] train-rmse:0.470044+0.007781 test-rmse:0.511952+0.047686
## [214] train-rmse:0.469789+0.007779 test-rmse:0.511753+0.047794
## [215] train-rmse:0.469532+0.007774 test-rmse:0.511352+0.047632
## [216] train-rmse:0.469277+0.007769 test-rmse:0.511203+0.047587
## [217] train-rmse:0.469022+0.007761 test-rmse:0.510723+0.047771
## [218] train-rmse:0.468767+0.007753 test-rmse:0.510612+0.047348
## [219] train-rmse:0.468518+0.007749 test-rmse:0.510669+0.047608
## [220] train-rmse:0.468269+0.007751 test-rmse:0.510485+0.047576
## [221] train-rmse:0.468023+0.007746 test-rmse:0.510821+0.047686
## [222] train-rmse:0.467774+0.007741 test-rmse:0.510475+0.047510
## [223] train-rmse:0.467529+0.007731 test-rmse:0.510031+0.047781
## [224] train-rmse:0.467286+0.007722 test-rmse:0.510240+0.047675
## [225] train-rmse:0.467046+0.007720 test-rmse:0.510349+0.047687
## [226] train-rmse:0.466802+0.007713 test-rmse:0.510028+0.047558
## [227] train-rmse:0.466558+0.007707 test-rmse:0.509926+0.047792
## [228] train-rmse:0.466320+0.007704 test-rmse:0.510046+0.047478
## [229] train-rmse:0.466082+0.007699 test-rmse:0.509954+0.047381
## [230] train-rmse:0.465847+0.007694 test-rmse:0.509639+0.047410
## [231] train-rmse:0.465616+0.007690 test-rmse:0.508759+0.046645
## [232] train-rmse:0.465381+0.007682 test-rmse:0.508681+0.046434
## [233] train-rmse:0.465149+0.007679 test-rmse:0.508637+0.046303
## [234] train-rmse:0.464920+0.007672 test-rmse:0.508678+0.046353
## [235] train-rmse:0.464688+0.007665 test-rmse:0.508502+0.046369
## [236] train-rmse:0.464460+0.007661 test-rmse:0.508404+0.046303
## [237] train-rmse:0.464235+0.007659 test-rmse:0.508212+0.046398
## [238] train-rmse:0.464013+0.007654 test-rmse:0.508137+0.046361
## [239] train-rmse:0.463788+0.007644 test-rmse:0.508275+0.046259
## [240] train-rmse:0.463564+0.007636 test-rmse:0.508441+0.046377
## [241] train-rmse:0.463342+0.007629 test-rmse:0.508424+0.046100
## [242] train-rmse:0.463120+0.007626 test-rmse:0.508256+0.046060
## [243] train-rmse:0.462902+0.007621 test-rmse:0.508008+0.046278
## [244] train-rmse:0.462682+0.007618 test-rmse:0.508038+0.046106
## [245] train-rmse:0.462465+0.007613 test-rmse:0.507544+0.046267
## [246] train-rmse:0.462247+0.007608 test-rmse:0.507371+0.046439
## [247] train-rmse:0.462031+0.007604 test-rmse:0.507136+0.045976
## [248] train-rmse:0.461816+0.007601 test-rmse:0.506931+0.045755
## [249] train-rmse:0.461601+0.007595 test-rmse:0.507122+0.046008
## [250] train-rmse:0.461392+0.007592 test-rmse:0.506748+0.045987
## [251] train-rmse:0.461176+0.007586 test-rmse:0.506828+0.046097
## [252] train-rmse:0.460968+0.007581 test-rmse:0.506782+0.045889
## [253] train-rmse:0.460761+0.007577 test-rmse:0.506609+0.045741
## [254] train-rmse:0.460554+0.007573 test-rmse:0.506200+0.045978
## [255] train-rmse:0.460347+0.007570 test-rmse:0.505883+0.045837
## [256] train-rmse:0.460140+0.007566 test-rmse:0.506023+0.045704
## [257] train-rmse:0.459934+0.007562 test-rmse:0.505899+0.045658
## [258] train-rmse:0.459730+0.007559 test-rmse:0.505856+0.045682
## [259] train-rmse:0.459528+0.007554 test-rmse:0.505839+0.045576
## [260] train-rmse:0.459327+0.007549 test-rmse:0.505822+0.045682
## [261] train-rmse:0.459125+0.007544 test-rmse:0.505255+0.044922
## [262] train-rmse:0.458922+0.007535 test-rmse:0.505470+0.044914
## [263] train-rmse:0.458728+0.007532 test-rmse:0.505655+0.045160
## [264] train-rmse:0.458534+0.007529 test-rmse:0.505541+0.045256
## [265] train-rmse:0.458337+0.007525 test-rmse:0.505537+0.045236
## [266] train-rmse:0.458145+0.007521 test-rmse:0.505288+0.045130
## [267] train-rmse:0.457951+0.007515 test-rmse:0.505215+0.044959
## [268] train-rmse:0.457755+0.007513 test-rmse:0.505007+0.044899
## [269] train-rmse:0.457563+0.007512 test-rmse:0.505082+0.045013
## [270] train-rmse:0.457368+0.007513 test-rmse:0.505035+0.044861
## [271] train-rmse:0.457177+0.007511 test-rmse:0.504857+0.045002
## [272] train-rmse:0.456990+0.007509 test-rmse:0.504807+0.044990
## [273] train-rmse:0.456804+0.007506 test-rmse:0.504710+0.044541
## [274] train-rmse:0.456618+0.007504 test-rmse:0.504596+0.044353
## [275] train-rmse:0.456434+0.007501 test-rmse:0.504416+0.044565
## [276] train-rmse:0.456248+0.007498 test-rmse:0.504183+0.044524
## [277] train-rmse:0.456061+0.007496 test-rmse:0.504066+0.044612
## [278] train-rmse:0.455875+0.007493 test-rmse:0.504102+0.044391
## [279] train-rmse:0.455691+0.007490 test-rmse:0.504169+0.044427
## [280] train-rmse:0.455508+0.007488 test-rmse:0.504029+0.044547
## [281] train-rmse:0.455324+0.007481 test-rmse:0.504200+0.044528
## [282] train-rmse:0.455145+0.007480 test-rmse:0.503886+0.044385
## [283] train-rmse:0.454966+0.007480 test-rmse:0.503978+0.044173
## [284] train-rmse:0.454788+0.007479 test-rmse:0.504073+0.044067
## [285] train-rmse:0.454614+0.007476 test-rmse:0.503699+0.044217
## [286] train-rmse:0.454439+0.007472 test-rmse:0.503525+0.044309
## [287] train-rmse:0.454265+0.007469 test-rmse:0.503452+0.044158
## [288] train-rmse:0.454091+0.007466 test-rmse:0.503399+0.044271
## [289] train-rmse:0.453916+0.007465 test-rmse:0.503565+0.044494
## [290] train-rmse:0.453744+0.007464 test-rmse:0.503155+0.044307
## [291] train-rmse:0.453571+0.007466 test-rmse:0.503055+0.044136
## [292] train-rmse:0.453402+0.007465 test-rmse:0.503185+0.044079
## [293] train-rmse:0.453231+0.007463 test-rmse:0.502713+0.043581
## [294] train-rmse:0.453064+0.007460 test-rmse:0.502842+0.043536
## [295] train-rmse:0.452898+0.007458 test-rmse:0.502864+0.043435
## [296] train-rmse:0.452729+0.007457 test-rmse:0.502741+0.043598
## [297] train-rmse:0.452558+0.007454 test-rmse:0.502857+0.043681
## [298] train-rmse:0.452391+0.007452 test-rmse:0.502471+0.043558
## [299] train-rmse:0.452227+0.007451 test-rmse:0.502612+0.043381
## [300] train-rmse:0.452064+0.007451 test-rmse:0.502603+0.043491
## [301] train-rmse:0.451901+0.007451 test-rmse:0.502515+0.043369
## [302] train-rmse:0.451736+0.007452 test-rmse:0.502544+0.043227
## [303] train-rmse:0.451572+0.007453 test-rmse:0.502235+0.043241
## [304] train-rmse:0.451412+0.007453 test-rmse:0.502407+0.043290
## [305] train-rmse:0.451252+0.007453 test-rmse:0.502138+0.043090
## [306] train-rmse:0.451093+0.007455 test-rmse:0.502215+0.043268
## [307] train-rmse:0.450937+0.007456 test-rmse:0.502264+0.043040
## [308] train-rmse:0.450779+0.007456 test-rmse:0.502293+0.042997
## [309] train-rmse:0.450621+0.007454 test-rmse:0.502145+0.043024
## [310] train-rmse:0.450463+0.007450 test-rmse:0.502127+0.042960
## [311] train-rmse:0.450303+0.007450 test-rmse:0.501931+0.043189
## [312] train-rmse:0.450147+0.007453 test-rmse:0.501640+0.043037
## [313] train-rmse:0.449995+0.007454 test-rmse:0.501579+0.042986
## [314] train-rmse:0.449841+0.007451 test-rmse:0.501683+0.043058
## [315] train-rmse:0.449690+0.007449 test-rmse:0.501569+0.042979
## [316] train-rmse:0.449537+0.007448 test-rmse:0.501854+0.042965
## [317] train-rmse:0.449382+0.007447 test-rmse:0.501599+0.042709
## [318] train-rmse:0.449233+0.007446 test-rmse:0.501330+0.042842
## [319] train-rmse:0.449079+0.007446 test-rmse:0.501469+0.042802
## [320] train-rmse:0.448931+0.007444 test-rmse:0.501297+0.042955
## [321] train-rmse:0.448784+0.007443 test-rmse:0.501262+0.042776
## [322] train-rmse:0.448639+0.007442 test-rmse:0.501270+0.042751
## [323] train-rmse:0.448492+0.007441 test-rmse:0.501206+0.042718
## [324] train-rmse:0.448345+0.007441 test-rmse:0.500968+0.042626
## [325] train-rmse:0.448199+0.007440 test-rmse:0.501072+0.042427
## [326] train-rmse:0.448051+0.007442 test-rmse:0.501214+0.042453
## [327] train-rmse:0.447905+0.007441 test-rmse:0.500901+0.042658
## [328] train-rmse:0.447759+0.007440 test-rmse:0.500903+0.042693
## [329] train-rmse:0.447616+0.007441 test-rmse:0.500820+0.042598
## [330] train-rmse:0.447474+0.007443 test-rmse:0.500581+0.042601
## [331] train-rmse:0.447335+0.007441 test-rmse:0.500668+0.042737
## [332] train-rmse:0.447190+0.007442 test-rmse:0.500658+0.042612
## [333] train-rmse:0.447049+0.007439 test-rmse:0.500820+0.042562
## [334] train-rmse:0.446906+0.007439 test-rmse:0.500310+0.042101
## [335] train-rmse:0.446768+0.007439 test-rmse:0.500414+0.042263
## [336] train-rmse:0.446630+0.007439 test-rmse:0.500468+0.042361
## [337] train-rmse:0.446491+0.007440 test-rmse:0.500350+0.042245
## [338] train-rmse:0.446354+0.007443 test-rmse:0.500451+0.042259
## [339] train-rmse:0.446217+0.007443 test-rmse:0.500035+0.042143
## [340] train-rmse:0.446079+0.007443 test-rmse:0.499963+0.042062
## [341] train-rmse:0.445944+0.007442 test-rmse:0.500075+0.041877
## [342] train-rmse:0.445810+0.007442 test-rmse:0.499897+0.042048
## [343] train-rmse:0.445675+0.007442 test-rmse:0.499770+0.042017
## [344] train-rmse:0.445541+0.007442 test-rmse:0.499651+0.041820
## [345] train-rmse:0.445404+0.007442 test-rmse:0.499703+0.041915
## [346] train-rmse:0.445270+0.007443 test-rmse:0.499728+0.041826
## [347] train-rmse:0.445138+0.007443 test-rmse:0.499675+0.041917
## [348] train-rmse:0.445004+0.007443 test-rmse:0.499781+0.041756
## [349] train-rmse:0.444870+0.007443 test-rmse:0.499682+0.041959
## [350] train-rmse:0.444737+0.007442 test-rmse:0.499666+0.041996
## Stopping. Best iteration:
## [344] train-rmse:0.445541+0.007442 test-rmse:0.499651+0.041820
##
## 1
## [1] train-rmse:1.495649+0.027857 test-rmse:1.492989+0.115930
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.369327+0.025766 test-rmse:1.369565+0.113754
## [3] train-rmse:1.256752+0.023766 test-rmse:1.258817+0.114193
## [4] train-rmse:1.156618+0.022182 test-rmse:1.159723+0.109250
## [5] train-rmse:1.067682+0.020722 test-rmse:1.070428+0.109585
## [6] train-rmse:0.989023+0.019443 test-rmse:0.992355+0.106310
## [7] train-rmse:0.919542+0.018394 test-rmse:0.923034+0.106429
## [8] train-rmse:0.858236+0.017341 test-rmse:0.861613+0.103341
## [9] train-rmse:0.804365+0.016509 test-rmse:0.808984+0.104038
## [10] train-rmse:0.757203+0.015733 test-rmse:0.761758+0.102224
## [11] train-rmse:0.715963+0.015118 test-rmse:0.720239+0.100630
## [12] train-rmse:0.679826+0.014683 test-rmse:0.685548+0.098336
## [13] train-rmse:0.648510+0.014289 test-rmse:0.655632+0.098085
## [14] train-rmse:0.621231+0.013840 test-rmse:0.629861+0.096104
## [15] train-rmse:0.597557+0.013530 test-rmse:0.607509+0.093841
## [16] train-rmse:0.576888+0.013254 test-rmse:0.586417+0.092024
## [17] train-rmse:0.558962+0.012988 test-rmse:0.571776+0.089292
## [18] train-rmse:0.543317+0.012764 test-rmse:0.560583+0.088610
## [19] train-rmse:0.529599+0.012474 test-rmse:0.547762+0.085080
## [20] train-rmse:0.517434+0.012208 test-rmse:0.536972+0.083001
## [21] train-rmse:0.506831+0.011955 test-rmse:0.528328+0.081198
## [22] train-rmse:0.497461+0.011683 test-rmse:0.519582+0.077797
## [23] train-rmse:0.489117+0.011402 test-rmse:0.512895+0.077052
## [24] train-rmse:0.481707+0.011141 test-rmse:0.505391+0.075178
## [25] train-rmse:0.474577+0.010919 test-rmse:0.500512+0.072286
## [26] train-rmse:0.467454+0.010394 test-rmse:0.494790+0.071335
## [27] train-rmse:0.460855+0.009830 test-rmse:0.488990+0.070131
## [28] train-rmse:0.454921+0.009161 test-rmse:0.484312+0.068977
## [29] train-rmse:0.449636+0.009016 test-rmse:0.480537+0.067432
## [30] train-rmse:0.444787+0.008506 test-rmse:0.477200+0.067124
## [31] train-rmse:0.440251+0.008172 test-rmse:0.475414+0.066647
## [32] train-rmse:0.436136+0.007919 test-rmse:0.473096+0.066403
## [33] train-rmse:0.431807+0.007738 test-rmse:0.470405+0.065993
## [34] train-rmse:0.428328+0.007512 test-rmse:0.469714+0.064371
## [35] train-rmse:0.425066+0.007183 test-rmse:0.467389+0.063950
## [36] train-rmse:0.421454+0.007032 test-rmse:0.465488+0.064086
## [37] train-rmse:0.418363+0.006873 test-rmse:0.464194+0.063309
## [38] train-rmse:0.415348+0.006870 test-rmse:0.463177+0.062267
## [39] train-rmse:0.412455+0.006542 test-rmse:0.461690+0.061278
## [40] train-rmse:0.409735+0.006315 test-rmse:0.460059+0.059504
## [41] train-rmse:0.407289+0.006228 test-rmse:0.459108+0.059182
## [42] train-rmse:0.404885+0.006180 test-rmse:0.457478+0.057713
## [43] train-rmse:0.402515+0.005866 test-rmse:0.456440+0.057698
## [44] train-rmse:0.399601+0.005319 test-rmse:0.456371+0.057966
## [45] train-rmse:0.397525+0.005342 test-rmse:0.455320+0.057881
## [46] train-rmse:0.395319+0.005364 test-rmse:0.454714+0.058062
## [47] train-rmse:0.393309+0.005330 test-rmse:0.453500+0.058014
## [48] train-rmse:0.391483+0.005202 test-rmse:0.453536+0.057286
## [49] train-rmse:0.389533+0.005087 test-rmse:0.453177+0.057222
## [50] train-rmse:0.387759+0.004974 test-rmse:0.452624+0.057156
## [51] train-rmse:0.385630+0.004832 test-rmse:0.451149+0.056738
## [52] train-rmse:0.383748+0.004524 test-rmse:0.450527+0.058022
## [53] train-rmse:0.382028+0.004446 test-rmse:0.449867+0.057314
## [54] train-rmse:0.380414+0.004309 test-rmse:0.449927+0.056966
## [55] train-rmse:0.378706+0.004393 test-rmse:0.449043+0.056025
## [56] train-rmse:0.376960+0.004159 test-rmse:0.448081+0.056103
## [57] train-rmse:0.375406+0.004098 test-rmse:0.447461+0.055946
## [58] train-rmse:0.373932+0.004058 test-rmse:0.447458+0.055583
## [59] train-rmse:0.372454+0.004166 test-rmse:0.447408+0.055637
## [60] train-rmse:0.371001+0.004010 test-rmse:0.446940+0.055574
## [61] train-rmse:0.369546+0.003964 test-rmse:0.446929+0.055079
## [62] train-rmse:0.368065+0.003669 test-rmse:0.446468+0.054846
## [63] train-rmse:0.366734+0.003761 test-rmse:0.446018+0.054787
## [64] train-rmse:0.365370+0.003693 test-rmse:0.445515+0.054656
## [65] train-rmse:0.363793+0.003730 test-rmse:0.445613+0.054218
## [66] train-rmse:0.362450+0.003801 test-rmse:0.445343+0.053906
## [67] train-rmse:0.361368+0.003732 test-rmse:0.445197+0.053840
## [68] train-rmse:0.359946+0.003675 test-rmse:0.445080+0.053514
## [69] train-rmse:0.358788+0.003730 test-rmse:0.444448+0.053109
## [70] train-rmse:0.357638+0.003831 test-rmse:0.444037+0.052398
## [71] train-rmse:0.356390+0.004000 test-rmse:0.443997+0.052356
## [72] train-rmse:0.355216+0.004080 test-rmse:0.444200+0.052167
## [73] train-rmse:0.353891+0.003914 test-rmse:0.443533+0.051674
## [74] train-rmse:0.352729+0.003892 test-rmse:0.443091+0.051702
## [75] train-rmse:0.351784+0.003810 test-rmse:0.442435+0.052347
## [76] train-rmse:0.350781+0.003826 test-rmse:0.441920+0.052166
## [77] train-rmse:0.349633+0.004065 test-rmse:0.441971+0.052056
## [78] train-rmse:0.348295+0.003910 test-rmse:0.441875+0.052320
## [79] train-rmse:0.347200+0.003994 test-rmse:0.440846+0.052588
## [80] train-rmse:0.345983+0.004057 test-rmse:0.440489+0.052432
## [81] train-rmse:0.344778+0.004020 test-rmse:0.440372+0.051681
## [82] train-rmse:0.343664+0.003966 test-rmse:0.440900+0.051750
## [83] train-rmse:0.342362+0.004120 test-rmse:0.441244+0.051685
## [84] train-rmse:0.341285+0.004025 test-rmse:0.440948+0.052337
## [85] train-rmse:0.339874+0.003852 test-rmse:0.440594+0.052153
## [86] train-rmse:0.338597+0.003853 test-rmse:0.440382+0.052530
## [87] train-rmse:0.337700+0.003883 test-rmse:0.440357+0.052919
## [88] train-rmse:0.336823+0.003918 test-rmse:0.440790+0.052486
## [89] train-rmse:0.335682+0.003885 test-rmse:0.440543+0.052411
## [90] train-rmse:0.334522+0.003888 test-rmse:0.440440+0.052851
## [91] train-rmse:0.333714+0.003962 test-rmse:0.439662+0.052735
## [92] train-rmse:0.332690+0.003944 test-rmse:0.439446+0.052356
## [93] train-rmse:0.331668+0.003940 test-rmse:0.439494+0.052430
## [94] train-rmse:0.330765+0.004063 test-rmse:0.439978+0.051943
## [95] train-rmse:0.329952+0.004041 test-rmse:0.439824+0.052380
## [96] train-rmse:0.329129+0.003965 test-rmse:0.439752+0.052740
## [97] train-rmse:0.328016+0.003800 test-rmse:0.439105+0.052713
## [98] train-rmse:0.327209+0.003841 test-rmse:0.439270+0.053147
## [99] train-rmse:0.326334+0.004040 test-rmse:0.439444+0.053124
## [100] train-rmse:0.325582+0.004058 test-rmse:0.439197+0.053386
## [101] train-rmse:0.324782+0.004123 test-rmse:0.439416+0.053419
## [102] train-rmse:0.323808+0.004022 test-rmse:0.438483+0.053379
## [103] train-rmse:0.322891+0.003878 test-rmse:0.438184+0.053584
## [104] train-rmse:0.322025+0.004197 test-rmse:0.437994+0.052865
## [105] train-rmse:0.321256+0.004218 test-rmse:0.437375+0.053167
## [106] train-rmse:0.320563+0.004084 test-rmse:0.437456+0.052989
## [107] train-rmse:0.319730+0.004261 test-rmse:0.437383+0.053232
## [108] train-rmse:0.319030+0.004509 test-rmse:0.437250+0.053275
## [109] train-rmse:0.317839+0.004489 test-rmse:0.436809+0.053147
## [110] train-rmse:0.317303+0.004607 test-rmse:0.436448+0.053195
## [111] train-rmse:0.316489+0.004518 test-rmse:0.436068+0.053558
## [112] train-rmse:0.315718+0.004575 test-rmse:0.436029+0.053799
## [113] train-rmse:0.315068+0.004523 test-rmse:0.435812+0.054137
## [114] train-rmse:0.314414+0.004657 test-rmse:0.435653+0.054071
## [115] train-rmse:0.313686+0.004469 test-rmse:0.435663+0.054354
## [116] train-rmse:0.312979+0.004459 test-rmse:0.435736+0.054004
## [117] train-rmse:0.312195+0.004352 test-rmse:0.434876+0.054459
## [118] train-rmse:0.311426+0.004458 test-rmse:0.434831+0.055261
## [119] train-rmse:0.310775+0.004445 test-rmse:0.434232+0.055155
## [120] train-rmse:0.310220+0.004479 test-rmse:0.434288+0.054672
## [121] train-rmse:0.309743+0.004507 test-rmse:0.434428+0.054511
## [122] train-rmse:0.309246+0.004581 test-rmse:0.434523+0.054955
## [123] train-rmse:0.308405+0.004637 test-rmse:0.434355+0.055129
## [124] train-rmse:0.307636+0.004722 test-rmse:0.434428+0.055036
## [125] train-rmse:0.307104+0.004773 test-rmse:0.434414+0.054765
## Stopping. Best iteration:
## [119] train-rmse:0.310775+0.004445 test-rmse:0.434232+0.055155
##
## 2
## [1] train-rmse:1.492531+0.027516 test-rmse:1.491496+0.117086
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.362988+0.025200 test-rmse:1.363089+0.113933
## [3] train-rmse:1.246662+0.023116 test-rmse:1.250322+0.113222
## [4] train-rmse:1.142918+0.021177 test-rmse:1.145982+0.112986
## [5] train-rmse:1.049960+0.019459 test-rmse:1.054743+0.113132
## [6] train-rmse:0.967309+0.017959 test-rmse:0.971429+0.114033
## [7] train-rmse:0.892797+0.017352 test-rmse:0.898165+0.110112
## [8] train-rmse:0.827046+0.015993 test-rmse:0.835777+0.108774
## [9] train-rmse:0.768151+0.015444 test-rmse:0.779961+0.104217
## [10] train-rmse:0.716096+0.014408 test-rmse:0.728352+0.102250
## [11] train-rmse:0.670075+0.013490 test-rmse:0.687412+0.100827
## [12] train-rmse:0.629247+0.012651 test-rmse:0.650819+0.097903
## [13] train-rmse:0.593375+0.011833 test-rmse:0.619651+0.096878
## [14] train-rmse:0.561608+0.011782 test-rmse:0.589265+0.095234
## [15] train-rmse:0.533638+0.011340 test-rmse:0.566721+0.093243
## [16] train-rmse:0.509176+0.010829 test-rmse:0.545329+0.090165
## [17] train-rmse:0.487196+0.010541 test-rmse:0.528427+0.086228
## [18] train-rmse:0.468531+0.010204 test-rmse:0.513523+0.085538
## [19] train-rmse:0.451760+0.010390 test-rmse:0.502846+0.082485
## [20] train-rmse:0.436527+0.010057 test-rmse:0.489956+0.080374
## [21] train-rmse:0.423718+0.009607 test-rmse:0.480650+0.079689
## [22] train-rmse:0.412156+0.008835 test-rmse:0.473032+0.078220
## [23] train-rmse:0.402243+0.008938 test-rmse:0.466519+0.075304
## [24] train-rmse:0.393449+0.008792 test-rmse:0.461229+0.074587
## [25] train-rmse:0.385289+0.008760 test-rmse:0.456333+0.073386
## [26] train-rmse:0.377952+0.009085 test-rmse:0.452481+0.071309
## [27] train-rmse:0.371725+0.009009 test-rmse:0.449739+0.069793
## [28] train-rmse:0.365984+0.009108 test-rmse:0.447366+0.068558
## [29] train-rmse:0.360969+0.008920 test-rmse:0.445374+0.067827
## [30] train-rmse:0.356391+0.009005 test-rmse:0.443646+0.065392
## [31] train-rmse:0.352363+0.009065 test-rmse:0.442885+0.064241
## [32] train-rmse:0.348387+0.008671 test-rmse:0.442164+0.063120
## [33] train-rmse:0.344086+0.008283 test-rmse:0.439405+0.062899
## [34] train-rmse:0.339957+0.008472 test-rmse:0.437760+0.062399
## [35] train-rmse:0.336345+0.008649 test-rmse:0.436643+0.062513
## [36] train-rmse:0.332878+0.008357 test-rmse:0.435728+0.061794
## [37] train-rmse:0.329864+0.008380 test-rmse:0.434333+0.060952
## [38] train-rmse:0.327113+0.008369 test-rmse:0.434142+0.060707
## [39] train-rmse:0.324533+0.008082 test-rmse:0.433857+0.060002
## [40] train-rmse:0.321732+0.008114 test-rmse:0.433207+0.060432
## [41] train-rmse:0.319251+0.007679 test-rmse:0.433423+0.059786
## [42] train-rmse:0.316571+0.007920 test-rmse:0.433546+0.059600
## [43] train-rmse:0.314157+0.007909 test-rmse:0.432981+0.059457
## [44] train-rmse:0.311912+0.007647 test-rmse:0.433217+0.059327
## [45] train-rmse:0.310007+0.007616 test-rmse:0.432881+0.058557
## [46] train-rmse:0.307703+0.007570 test-rmse:0.430558+0.058108
## [47] train-rmse:0.305879+0.007105 test-rmse:0.430855+0.057758
## [48] train-rmse:0.303999+0.007156 test-rmse:0.430210+0.058093
## [49] train-rmse:0.301929+0.007143 test-rmse:0.430123+0.058049
## [50] train-rmse:0.299681+0.006930 test-rmse:0.429654+0.058253
## [51] train-rmse:0.297703+0.006245 test-rmse:0.429404+0.058904
## [52] train-rmse:0.295642+0.005804 test-rmse:0.428482+0.059321
## [53] train-rmse:0.293860+0.006051 test-rmse:0.428561+0.059413
## [54] train-rmse:0.292033+0.005499 test-rmse:0.427846+0.059122
## [55] train-rmse:0.290443+0.005504 test-rmse:0.427166+0.059062
## [56] train-rmse:0.288755+0.005593 test-rmse:0.427503+0.059158
## [57] train-rmse:0.286880+0.005214 test-rmse:0.427069+0.059076
## [58] train-rmse:0.284867+0.004586 test-rmse:0.426866+0.059626
## [59] train-rmse:0.283286+0.004493 test-rmse:0.427231+0.059499
## [60] train-rmse:0.281846+0.004552 test-rmse:0.426826+0.059528
## [61] train-rmse:0.280021+0.004242 test-rmse:0.426455+0.059751
## [62] train-rmse:0.278085+0.003850 test-rmse:0.426743+0.059873
## [63] train-rmse:0.276563+0.003481 test-rmse:0.426150+0.060206
## [64] train-rmse:0.275335+0.003584 test-rmse:0.425758+0.060464
## [65] train-rmse:0.273638+0.003513 test-rmse:0.425086+0.060434
## [66] train-rmse:0.272293+0.003411 test-rmse:0.425080+0.060742
## [67] train-rmse:0.270849+0.003202 test-rmse:0.424232+0.061456
## [68] train-rmse:0.269448+0.003024 test-rmse:0.424340+0.061686
## [69] train-rmse:0.268130+0.003074 test-rmse:0.424200+0.061102
## [70] train-rmse:0.266447+0.002842 test-rmse:0.424064+0.061684
## [71] train-rmse:0.265171+0.002586 test-rmse:0.423217+0.061512
## [72] train-rmse:0.263802+0.002674 test-rmse:0.423099+0.061434
## [73] train-rmse:0.262288+0.002940 test-rmse:0.422694+0.061633
## [74] train-rmse:0.260789+0.002674 test-rmse:0.422089+0.061308
## [75] train-rmse:0.259472+0.002642 test-rmse:0.421689+0.061407
## [76] train-rmse:0.258291+0.002423 test-rmse:0.421755+0.061111
## [77] train-rmse:0.256989+0.002345 test-rmse:0.421253+0.061042
## [78] train-rmse:0.256030+0.002391 test-rmse:0.420589+0.060879
## [79] train-rmse:0.254941+0.002341 test-rmse:0.420499+0.060865
## [80] train-rmse:0.253892+0.002439 test-rmse:0.420391+0.061464
## [81] train-rmse:0.252803+0.002476 test-rmse:0.420409+0.061634
## [82] train-rmse:0.251721+0.002340 test-rmse:0.419759+0.061640
## [83] train-rmse:0.250476+0.001988 test-rmse:0.419371+0.062204
## [84] train-rmse:0.249509+0.001949 test-rmse:0.419563+0.062572
## [85] train-rmse:0.248386+0.001770 test-rmse:0.419966+0.062678
## [86] train-rmse:0.247568+0.001794 test-rmse:0.419922+0.062444
## [87] train-rmse:0.246723+0.001760 test-rmse:0.419774+0.062373
## [88] train-rmse:0.245755+0.001681 test-rmse:0.419627+0.062041
## [89] train-rmse:0.244639+0.001615 test-rmse:0.419608+0.062770
## Stopping. Best iteration:
## [83] train-rmse:0.250476+0.001988 test-rmse:0.419371+0.062204
##
## 3
## [1] train-rmse:1.491164+0.027305 test-rmse:1.489894+0.117157
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.359962+0.024670 test-rmse:1.361481+0.117481
## [3] train-rmse:1.242160+0.022622 test-rmse:1.243389+0.114675
## [4] train-rmse:1.136243+0.020446 test-rmse:1.137909+0.114265
## [5] train-rmse:1.041663+0.019037 test-rmse:1.044401+0.111680
## [6] train-rmse:0.956310+0.017657 test-rmse:0.961740+0.108485
## [7] train-rmse:0.880540+0.016130 test-rmse:0.889853+0.107264
## [8] train-rmse:0.812188+0.014868 test-rmse:0.825019+0.104876
## [9] train-rmse:0.751073+0.013167 test-rmse:0.767189+0.104177
## [10] train-rmse:0.696415+0.012250 test-rmse:0.716444+0.100555
## [11] train-rmse:0.647968+0.011087 test-rmse:0.671142+0.100071
## [12] train-rmse:0.604884+0.010136 test-rmse:0.633938+0.096994
## [13] train-rmse:0.566374+0.009385 test-rmse:0.599638+0.092762
## [14] train-rmse:0.532762+0.008584 test-rmse:0.573116+0.091365
## [15] train-rmse:0.502480+0.008155 test-rmse:0.549782+0.087618
## [16] train-rmse:0.475869+0.007856 test-rmse:0.527970+0.083927
## [17] train-rmse:0.452470+0.007455 test-rmse:0.510767+0.083364
## [18] train-rmse:0.431850+0.007077 test-rmse:0.496139+0.079762
## [19] train-rmse:0.413913+0.007138 test-rmse:0.483547+0.076319
## [20] train-rmse:0.396765+0.006840 test-rmse:0.472554+0.076178
## [21] train-rmse:0.382312+0.006519 test-rmse:0.462962+0.073162
## [22] train-rmse:0.368968+0.005407 test-rmse:0.456371+0.071068
## [23] train-rmse:0.357836+0.004895 test-rmse:0.451192+0.069949
## [24] train-rmse:0.348265+0.004867 test-rmse:0.445944+0.067739
## [25] train-rmse:0.338626+0.004599 test-rmse:0.440078+0.067126
## [26] train-rmse:0.331067+0.004385 test-rmse:0.436919+0.066508
## [27] train-rmse:0.322955+0.003888 test-rmse:0.434186+0.065028
## [28] train-rmse:0.316536+0.003301 test-rmse:0.431798+0.065143
## [29] train-rmse:0.310540+0.003448 test-rmse:0.429981+0.063051
## [30] train-rmse:0.305793+0.003520 test-rmse:0.428098+0.060869
## [31] train-rmse:0.299895+0.004382 test-rmse:0.426597+0.059852
## [32] train-rmse:0.294803+0.003564 test-rmse:0.425263+0.058507
## [33] train-rmse:0.291039+0.003402 test-rmse:0.424333+0.057439
## [34] train-rmse:0.287427+0.003243 test-rmse:0.423094+0.057208
## [35] train-rmse:0.284202+0.003126 test-rmse:0.421665+0.057752
## [36] train-rmse:0.280832+0.002720 test-rmse:0.420423+0.056637
## [37] train-rmse:0.277881+0.002751 test-rmse:0.418728+0.055919
## [38] train-rmse:0.274368+0.003336 test-rmse:0.418775+0.055486
## [39] train-rmse:0.271524+0.003613 test-rmse:0.418811+0.055418
## [40] train-rmse:0.268160+0.003219 test-rmse:0.418428+0.055466
## [41] train-rmse:0.265475+0.002864 test-rmse:0.416999+0.056103
## [42] train-rmse:0.262544+0.003266 test-rmse:0.416654+0.056254
## [43] train-rmse:0.259631+0.003357 test-rmse:0.415592+0.055265
## [44] train-rmse:0.256990+0.003625 test-rmse:0.414655+0.055675
## [45] train-rmse:0.254467+0.003127 test-rmse:0.413033+0.056071
## [46] train-rmse:0.252170+0.002954 test-rmse:0.413682+0.056113
## [47] train-rmse:0.249778+0.003006 test-rmse:0.412731+0.055990
## [48] train-rmse:0.247531+0.003008 test-rmse:0.411961+0.056804
## [49] train-rmse:0.245484+0.002903 test-rmse:0.411299+0.057071
## [50] train-rmse:0.243703+0.002669 test-rmse:0.411186+0.057600
## [51] train-rmse:0.241363+0.002436 test-rmse:0.411618+0.057515
## [52] train-rmse:0.239231+0.002434 test-rmse:0.410385+0.057729
## [53] train-rmse:0.237035+0.002203 test-rmse:0.410146+0.057708
## [54] train-rmse:0.234716+0.001649 test-rmse:0.409335+0.056948
## [55] train-rmse:0.232500+0.001326 test-rmse:0.408658+0.056864
## [56] train-rmse:0.230699+0.001448 test-rmse:0.407909+0.058187
## [57] train-rmse:0.228723+0.001332 test-rmse:0.407634+0.057751
## [58] train-rmse:0.226971+0.001466 test-rmse:0.407126+0.057405
## [59] train-rmse:0.225147+0.001478 test-rmse:0.407115+0.057150
## [60] train-rmse:0.223260+0.001655 test-rmse:0.406544+0.057709
## [61] train-rmse:0.221761+0.001595 test-rmse:0.406468+0.057798
## [62] train-rmse:0.219909+0.001350 test-rmse:0.405818+0.057940
## [63] train-rmse:0.218535+0.001354 test-rmse:0.405547+0.057788
## [64] train-rmse:0.216952+0.001507 test-rmse:0.404922+0.057798
## [65] train-rmse:0.215741+0.001475 test-rmse:0.404677+0.057850
## [66] train-rmse:0.214064+0.001473 test-rmse:0.403817+0.058034
## [67] train-rmse:0.212456+0.001900 test-rmse:0.402847+0.058849
## [68] train-rmse:0.211003+0.001998 test-rmse:0.402996+0.058587
## [69] train-rmse:0.209268+0.001952 test-rmse:0.402676+0.058219
## [70] train-rmse:0.207488+0.002177 test-rmse:0.402476+0.058310
## [71] train-rmse:0.206012+0.002164 test-rmse:0.402449+0.058295
## [72] train-rmse:0.204339+0.001612 test-rmse:0.402285+0.058360
## [73] train-rmse:0.202886+0.002160 test-rmse:0.402125+0.058260
## [74] train-rmse:0.200916+0.002205 test-rmse:0.401820+0.058601
## [75] train-rmse:0.199376+0.002401 test-rmse:0.400543+0.059318
## [76] train-rmse:0.198183+0.002698 test-rmse:0.400055+0.059438
## [77] train-rmse:0.197059+0.002758 test-rmse:0.399831+0.059587
## [78] train-rmse:0.195246+0.003513 test-rmse:0.399455+0.059804
## [79] train-rmse:0.194094+0.003662 test-rmse:0.398996+0.060443
## [80] train-rmse:0.192892+0.003738 test-rmse:0.398360+0.060252
## [81] train-rmse:0.191369+0.003516 test-rmse:0.398289+0.060586
## [82] train-rmse:0.190129+0.003698 test-rmse:0.398298+0.060304
## [83] train-rmse:0.188871+0.003642 test-rmse:0.398079+0.060648
## [84] train-rmse:0.187763+0.003681 test-rmse:0.397718+0.060451
## [85] train-rmse:0.186620+0.003725 test-rmse:0.397150+0.061013
## [86] train-rmse:0.184940+0.003714 test-rmse:0.396703+0.061490
## [87] train-rmse:0.183913+0.003800 test-rmse:0.396516+0.061240
## [88] train-rmse:0.182682+0.003972 test-rmse:0.395743+0.060817
## [89] train-rmse:0.181562+0.004000 test-rmse:0.395411+0.061370
## [90] train-rmse:0.180400+0.004067 test-rmse:0.395096+0.061099
## [91] train-rmse:0.179094+0.004099 test-rmse:0.395111+0.061027
## [92] train-rmse:0.178148+0.003927 test-rmse:0.395125+0.060983
## [93] train-rmse:0.177327+0.003783 test-rmse:0.395156+0.061138
## [94] train-rmse:0.175738+0.003982 test-rmse:0.394811+0.061588
## [95] train-rmse:0.174525+0.003735 test-rmse:0.394690+0.061350
## [96] train-rmse:0.173436+0.003425 test-rmse:0.394980+0.061231
## [97] train-rmse:0.172398+0.003673 test-rmse:0.394720+0.061374
## [98] train-rmse:0.171352+0.003530 test-rmse:0.394738+0.061505
## [99] train-rmse:0.170286+0.003491 test-rmse:0.394685+0.061885
## [100] train-rmse:0.169122+0.003045 test-rmse:0.394477+0.062160
## [101] train-rmse:0.167831+0.003282 test-rmse:0.394549+0.062326
## [102] train-rmse:0.166504+0.003363 test-rmse:0.394248+0.062405
## [103] train-rmse:0.165616+0.003205 test-rmse:0.394076+0.062530
## [104] train-rmse:0.164731+0.003280 test-rmse:0.394070+0.062680
## [105] train-rmse:0.163694+0.003385 test-rmse:0.393965+0.062040
## [106] train-rmse:0.162691+0.003709 test-rmse:0.393887+0.062313
## [107] train-rmse:0.161678+0.003767 test-rmse:0.393661+0.062368
## [108] train-rmse:0.160557+0.003696 test-rmse:0.393875+0.062156
## [109] train-rmse:0.160011+0.003633 test-rmse:0.393907+0.062013
## [110] train-rmse:0.159319+0.003612 test-rmse:0.393821+0.061928
## [111] train-rmse:0.158619+0.003495 test-rmse:0.393922+0.061833
## [112] train-rmse:0.157619+0.003629 test-rmse:0.393979+0.061643
## [113] train-rmse:0.156601+0.003722 test-rmse:0.394074+0.061360
## Stopping. Best iteration:
## [107] train-rmse:0.161678+0.003767 test-rmse:0.393661+0.062368
##
## 4
## [1] train-rmse:1.490338+0.027452 test-rmse:1.488948+0.115796
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.358379+0.024906 test-rmse:1.359169+0.114591
## [3] train-rmse:1.239907+0.022400 test-rmse:1.241768+0.112558
## [4] train-rmse:1.133531+0.020967 test-rmse:1.135300+0.108876
## [5] train-rmse:1.037633+0.019024 test-rmse:1.040672+0.107692
## [6] train-rmse:0.951627+0.017406 test-rmse:0.958024+0.105887
## [7] train-rmse:0.874483+0.015730 test-rmse:0.883383+0.103906
## [8] train-rmse:0.804386+0.014539 test-rmse:0.817301+0.101173
## [9] train-rmse:0.742346+0.013063 test-rmse:0.759231+0.100363
## [10] train-rmse:0.686361+0.012282 test-rmse:0.707235+0.098265
## [11] train-rmse:0.636381+0.011862 test-rmse:0.661891+0.094230
## [12] train-rmse:0.591384+0.011127 test-rmse:0.621884+0.091242
## [13] train-rmse:0.551379+0.010127 test-rmse:0.588125+0.088211
## [14] train-rmse:0.515760+0.009427 test-rmse:0.559932+0.087093
## [15] train-rmse:0.483687+0.008425 test-rmse:0.534564+0.084976
## [16] train-rmse:0.454679+0.007733 test-rmse:0.513516+0.084379
## [17] train-rmse:0.428885+0.007388 test-rmse:0.495330+0.081492
## [18] train-rmse:0.406079+0.006935 test-rmse:0.478974+0.079442
## [19] train-rmse:0.384702+0.006449 test-rmse:0.467736+0.078788
## [20] train-rmse:0.366458+0.006491 test-rmse:0.456530+0.076299
## [21] train-rmse:0.349579+0.006586 test-rmse:0.448485+0.075939
## [22] train-rmse:0.335088+0.006471 test-rmse:0.442397+0.074726
## [23] train-rmse:0.321927+0.006775 test-rmse:0.436419+0.072217
## [24] train-rmse:0.310518+0.007034 test-rmse:0.430463+0.072128
## [25] train-rmse:0.300349+0.007258 test-rmse:0.426372+0.070011
## [26] train-rmse:0.291292+0.007780 test-rmse:0.423222+0.069774
## [27] train-rmse:0.283412+0.008331 test-rmse:0.420009+0.067919
## [28] train-rmse:0.273704+0.006919 test-rmse:0.417022+0.066057
## [29] train-rmse:0.266316+0.006899 test-rmse:0.414865+0.064684
## [30] train-rmse:0.260769+0.007108 test-rmse:0.413170+0.064235
## [31] train-rmse:0.253243+0.006542 test-rmse:0.411203+0.063264
## [32] train-rmse:0.248238+0.006860 test-rmse:0.410397+0.062351
## [33] train-rmse:0.244023+0.007312 test-rmse:0.409517+0.061263
## [34] train-rmse:0.238823+0.007633 test-rmse:0.408846+0.060115
## [35] train-rmse:0.235402+0.007646 test-rmse:0.407939+0.060370
## [36] train-rmse:0.231714+0.008014 test-rmse:0.406814+0.059177
## [37] train-rmse:0.227528+0.007252 test-rmse:0.406547+0.059559
## [38] train-rmse:0.224018+0.007414 test-rmse:0.405504+0.058721
## [39] train-rmse:0.221096+0.007133 test-rmse:0.404521+0.057687
## [40] train-rmse:0.218155+0.007496 test-rmse:0.404806+0.057269
## [41] train-rmse:0.215203+0.006515 test-rmse:0.404413+0.057198
## [42] train-rmse:0.212181+0.006951 test-rmse:0.404107+0.056883
## [43] train-rmse:0.209592+0.006787 test-rmse:0.404021+0.056659
## [44] train-rmse:0.207404+0.006579 test-rmse:0.402230+0.056172
## [45] train-rmse:0.204866+0.006783 test-rmse:0.402805+0.055418
## [46] train-rmse:0.201947+0.006793 test-rmse:0.402342+0.055598
## [47] train-rmse:0.199003+0.006929 test-rmse:0.402619+0.055441
## [48] train-rmse:0.196293+0.007166 test-rmse:0.403128+0.055034
## [49] train-rmse:0.192767+0.006551 test-rmse:0.403264+0.055019
## [50] train-rmse:0.189698+0.006290 test-rmse:0.403697+0.054577
## Stopping. Best iteration:
## [44] train-rmse:0.207404+0.006579 test-rmse:0.402230+0.056172
##
## 5
## [1] train-rmse:1.490099+0.027479 test-rmse:1.488300+0.115261
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.357755+0.024981 test-rmse:1.357604+0.112461
## [3] train-rmse:1.238789+0.022621 test-rmse:1.239209+0.110106
## [4] train-rmse:1.131739+0.021195 test-rmse:1.132655+0.105295
## [5] train-rmse:1.035617+0.019462 test-rmse:1.038766+0.103016
## [6] train-rmse:0.948562+0.018370 test-rmse:0.954051+0.099406
## [7] train-rmse:0.870432+0.016949 test-rmse:0.878252+0.098304
## [8] train-rmse:0.800264+0.015698 test-rmse:0.812681+0.096608
## [9] train-rmse:0.737157+0.014432 test-rmse:0.754358+0.095524
## [10] train-rmse:0.680086+0.013385 test-rmse:0.701486+0.094080
## [11] train-rmse:0.628939+0.013032 test-rmse:0.656886+0.094124
## [12] train-rmse:0.582958+0.012445 test-rmse:0.615370+0.091056
## [13] train-rmse:0.541719+0.011732 test-rmse:0.583416+0.090316
## [14] train-rmse:0.504737+0.010846 test-rmse:0.555282+0.088350
## [15] train-rmse:0.470631+0.010467 test-rmse:0.529651+0.086248
## [16] train-rmse:0.440268+0.009816 test-rmse:0.509077+0.084135
## [17] train-rmse:0.412910+0.009184 test-rmse:0.491107+0.083086
## [18] train-rmse:0.388317+0.009324 test-rmse:0.476403+0.082271
## [19] train-rmse:0.366320+0.009205 test-rmse:0.464394+0.079896
## [20] train-rmse:0.346803+0.008995 test-rmse:0.454652+0.078535
## [21] train-rmse:0.329349+0.008510 test-rmse:0.446905+0.077319
## [22] train-rmse:0.313264+0.008556 test-rmse:0.438402+0.075333
## [23] train-rmse:0.299072+0.008844 test-rmse:0.433033+0.072338
## [24] train-rmse:0.286681+0.008698 test-rmse:0.428923+0.072047
## [25] train-rmse:0.275413+0.009059 test-rmse:0.425057+0.069842
## [26] train-rmse:0.263344+0.007930 test-rmse:0.420757+0.068856
## [27] train-rmse:0.253422+0.007739 test-rmse:0.419007+0.067906
## [28] train-rmse:0.244671+0.007419 test-rmse:0.416616+0.067858
## [29] train-rmse:0.236791+0.007567 test-rmse:0.415860+0.066411
## [30] train-rmse:0.228690+0.006532 test-rmse:0.412651+0.065524
## [31] train-rmse:0.222770+0.006569 test-rmse:0.411818+0.065316
## [32] train-rmse:0.215783+0.005686 test-rmse:0.409456+0.064421
## [33] train-rmse:0.210197+0.005391 test-rmse:0.409375+0.063694
## [34] train-rmse:0.205504+0.005658 test-rmse:0.407463+0.063522
## [35] train-rmse:0.201151+0.005879 test-rmse:0.406989+0.062758
## [36] train-rmse:0.196391+0.006792 test-rmse:0.405216+0.063242
## [37] train-rmse:0.192525+0.006108 test-rmse:0.404721+0.062678
## [38] train-rmse:0.188160+0.005560 test-rmse:0.404098+0.062868
## [39] train-rmse:0.184725+0.005738 test-rmse:0.403443+0.063412
## [40] train-rmse:0.181584+0.006357 test-rmse:0.402667+0.062398
## [41] train-rmse:0.178723+0.006668 test-rmse:0.402438+0.062758
## [42] train-rmse:0.176134+0.006793 test-rmse:0.401725+0.062095
## [43] train-rmse:0.172704+0.004843 test-rmse:0.400981+0.061691
## [44] train-rmse:0.170356+0.004442 test-rmse:0.400652+0.061107
## [45] train-rmse:0.166886+0.003981 test-rmse:0.399829+0.060660
## [46] train-rmse:0.164717+0.004597 test-rmse:0.399408+0.060687
## [47] train-rmse:0.161978+0.004372 test-rmse:0.399160+0.061929
## [48] train-rmse:0.159200+0.003756 test-rmse:0.399742+0.061982
## [49] train-rmse:0.156680+0.003954 test-rmse:0.399377+0.062018
## [50] train-rmse:0.154188+0.003924 test-rmse:0.399184+0.061800
## [51] train-rmse:0.152095+0.004058 test-rmse:0.399098+0.061726
## [52] train-rmse:0.149347+0.004690 test-rmse:0.399447+0.061106
## [53] train-rmse:0.146487+0.004557 test-rmse:0.400270+0.060681
## [54] train-rmse:0.143089+0.005175 test-rmse:0.399795+0.060488
## [55] train-rmse:0.140436+0.005642 test-rmse:0.399259+0.060281
## [56] train-rmse:0.137687+0.005916 test-rmse:0.399312+0.059205
## [57] train-rmse:0.135405+0.006306 test-rmse:0.398538+0.059328
## [58] train-rmse:0.132973+0.006195 test-rmse:0.398494+0.059479
## [59] train-rmse:0.130928+0.005824 test-rmse:0.398239+0.059986
## [60] train-rmse:0.128928+0.006025 test-rmse:0.398186+0.059753
## [61] train-rmse:0.127218+0.005942 test-rmse:0.397559+0.060863
## [62] train-rmse:0.125121+0.005465 test-rmse:0.397873+0.060850
## [63] train-rmse:0.123564+0.005227 test-rmse:0.397348+0.060499
## [64] train-rmse:0.121821+0.005426 test-rmse:0.397263+0.060186
## [65] train-rmse:0.119546+0.005795 test-rmse:0.396913+0.060543
## [66] train-rmse:0.117940+0.005482 test-rmse:0.397254+0.060535
## [67] train-rmse:0.116369+0.006107 test-rmse:0.397148+0.060690
## [68] train-rmse:0.114707+0.006371 test-rmse:0.397485+0.060967
## [69] train-rmse:0.113004+0.006141 test-rmse:0.397023+0.061427
## [70] train-rmse:0.111848+0.006078 test-rmse:0.397035+0.061639
## [71] train-rmse:0.110295+0.005871 test-rmse:0.396756+0.061380
## [72] train-rmse:0.108852+0.005755 test-rmse:0.396415+0.061780
## [73] train-rmse:0.107647+0.005802 test-rmse:0.396418+0.061521
## [74] train-rmse:0.106145+0.005918 test-rmse:0.396375+0.061752
## [75] train-rmse:0.105063+0.006008 test-rmse:0.396163+0.061776
## [76] train-rmse:0.103145+0.006201 test-rmse:0.395881+0.061842
## [77] train-rmse:0.101926+0.005955 test-rmse:0.395698+0.061786
## [78] train-rmse:0.100405+0.005455 test-rmse:0.395500+0.061671
## [79] train-rmse:0.099328+0.005197 test-rmse:0.395626+0.061672
## [80] train-rmse:0.097941+0.004949 test-rmse:0.395474+0.061879
## [81] train-rmse:0.096782+0.004633 test-rmse:0.395298+0.061998
## [82] train-rmse:0.095616+0.004432 test-rmse:0.394976+0.062348
## [83] train-rmse:0.094371+0.004259 test-rmse:0.394617+0.062360
## [84] train-rmse:0.093154+0.004158 test-rmse:0.394522+0.062035
## [85] train-rmse:0.092326+0.004081 test-rmse:0.394406+0.061806
## [86] train-rmse:0.090909+0.004166 test-rmse:0.394307+0.061745
## [87] train-rmse:0.089764+0.004278 test-rmse:0.394335+0.061742
## [88] train-rmse:0.088578+0.004143 test-rmse:0.393941+0.061842
## [89] train-rmse:0.087642+0.003825 test-rmse:0.393840+0.062001
## [90] train-rmse:0.086813+0.003679 test-rmse:0.394026+0.061877
## [91] train-rmse:0.085785+0.003398 test-rmse:0.394144+0.062036
## [92] train-rmse:0.084898+0.003382 test-rmse:0.394071+0.062076
## [93] train-rmse:0.083759+0.003359 test-rmse:0.393946+0.062343
## [94] train-rmse:0.082867+0.003352 test-rmse:0.393911+0.062648
## [95] train-rmse:0.081811+0.003169 test-rmse:0.394025+0.062575
## Stopping. Best iteration:
## [89] train-rmse:0.087642+0.003825 test-rmse:0.393840+0.062001
##
## 6
## [1] train-rmse:1.489976+0.027481 test-rmse:1.488741+0.114911
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.357402+0.024997 test-rmse:1.356362+0.112124
## [3] train-rmse:1.238071+0.022738 test-rmse:1.236223+0.110244
## [4] train-rmse:1.130634+0.021313 test-rmse:1.128950+0.106722
## [5] train-rmse:1.034130+0.019661 test-rmse:1.034525+0.104369
## [6] train-rmse:0.946655+0.018597 test-rmse:0.953410+0.102742
## [7] train-rmse:0.868202+0.017277 test-rmse:0.877942+0.101276
## [8] train-rmse:0.797435+0.016023 test-rmse:0.812660+0.099496
## [9] train-rmse:0.733894+0.014844 test-rmse:0.754855+0.097937
## [10] train-rmse:0.676610+0.014106 test-rmse:0.703855+0.097880
## [11] train-rmse:0.624134+0.013379 test-rmse:0.658081+0.095616
## [12] train-rmse:0.577391+0.012428 test-rmse:0.619274+0.093966
## [13] train-rmse:0.535232+0.012191 test-rmse:0.586960+0.092016
## [14] train-rmse:0.497059+0.011273 test-rmse:0.556280+0.090614
## [15] train-rmse:0.462581+0.010233 test-rmse:0.530197+0.089476
## [16] train-rmse:0.430801+0.009987 test-rmse:0.508856+0.086972
## [17] train-rmse:0.403049+0.009838 test-rmse:0.491939+0.086135
## [18] train-rmse:0.377898+0.009593 test-rmse:0.478086+0.084898
## [19] train-rmse:0.354601+0.010098 test-rmse:0.466244+0.083149
## [20] train-rmse:0.333054+0.009868 test-rmse:0.456150+0.080164
## [21] train-rmse:0.314019+0.009719 test-rmse:0.448303+0.077898
## [22] train-rmse:0.297110+0.009497 test-rmse:0.442883+0.076347
## [23] train-rmse:0.281234+0.009308 test-rmse:0.437801+0.076342
## [24] train-rmse:0.267019+0.009576 test-rmse:0.432078+0.073793
## [25] train-rmse:0.254184+0.009201 test-rmse:0.429381+0.073501
## [26] train-rmse:0.242351+0.009067 test-rmse:0.427199+0.073758
## [27] train-rmse:0.231118+0.009142 test-rmse:0.423401+0.071987
## [28] train-rmse:0.221763+0.009175 test-rmse:0.421490+0.070826
## [29] train-rmse:0.212148+0.009232 test-rmse:0.419629+0.069943
## [30] train-rmse:0.204305+0.008740 test-rmse:0.418509+0.069144
## [31] train-rmse:0.195924+0.008654 test-rmse:0.416854+0.069053
## [32] train-rmse:0.187945+0.009138 test-rmse:0.415260+0.068543
## [33] train-rmse:0.180468+0.007359 test-rmse:0.413407+0.068202
## [34] train-rmse:0.174781+0.007452 test-rmse:0.412495+0.067280
## [35] train-rmse:0.168710+0.006198 test-rmse:0.411360+0.067171
## [36] train-rmse:0.163356+0.005921 test-rmse:0.410902+0.066295
## [37] train-rmse:0.158726+0.006005 test-rmse:0.410112+0.066192
## [38] train-rmse:0.153628+0.006333 test-rmse:0.410058+0.065037
## [39] train-rmse:0.148851+0.006801 test-rmse:0.409014+0.064462
## [40] train-rmse:0.145435+0.006750 test-rmse:0.408978+0.063014
## [41] train-rmse:0.142015+0.006336 test-rmse:0.408272+0.062457
## [42] train-rmse:0.139001+0.006656 test-rmse:0.407376+0.061223
## [43] train-rmse:0.136033+0.006741 test-rmse:0.407446+0.060723
## [44] train-rmse:0.133158+0.006662 test-rmse:0.406911+0.059920
## [45] train-rmse:0.129600+0.005338 test-rmse:0.406373+0.059168
## [46] train-rmse:0.127455+0.005255 test-rmse:0.405297+0.058845
## [47] train-rmse:0.125585+0.005001 test-rmse:0.405496+0.057827
## [48] train-rmse:0.123016+0.004867 test-rmse:0.405157+0.057088
## [49] train-rmse:0.120759+0.004403 test-rmse:0.405040+0.056917
## [50] train-rmse:0.117668+0.004647 test-rmse:0.405010+0.056918
## [51] train-rmse:0.115397+0.004690 test-rmse:0.404688+0.057184
## [52] train-rmse:0.112931+0.004438 test-rmse:0.404635+0.056337
## [53] train-rmse:0.110562+0.004035 test-rmse:0.403999+0.056688
## [54] train-rmse:0.109131+0.004129 test-rmse:0.403620+0.056314
## [55] train-rmse:0.106236+0.003746 test-rmse:0.403649+0.056348
## [56] train-rmse:0.104064+0.003742 test-rmse:0.404048+0.055592
## [57] train-rmse:0.102437+0.003830 test-rmse:0.404130+0.054764
## [58] train-rmse:0.100342+0.004208 test-rmse:0.404553+0.054336
## [59] train-rmse:0.097538+0.004103 test-rmse:0.404784+0.054810
## [60] train-rmse:0.095476+0.003781 test-rmse:0.404651+0.054793
## Stopping. Best iteration:
## [54] train-rmse:0.109131+0.004129 test-rmse:0.403620+0.056314
##
## 7
## [1] train-rmse:1.474838+0.027375 test-rmse:1.470957+0.113637
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.335059+0.024942 test-rmse:1.331218+0.110424
## [3] train-rmse:1.215506+0.022964 test-rmse:1.211706+0.107537
## [4] train-rmse:1.113873+0.021399 test-rmse:1.110129+0.104893
## [5] train-rmse:1.028063+0.020206 test-rmse:1.024390+0.102435
## [6] train-rmse:0.956145+0.019336 test-rmse:0.952566+0.100137
## [7] train-rmse:0.896338+0.018734 test-rmse:0.892874+0.097990
## [8] train-rmse:0.846999+0.018345 test-rmse:0.843664+0.096003
## [9] train-rmse:0.806613+0.018117 test-rmse:0.803421+0.094185
## [10] train-rmse:0.773805+0.018002 test-rmse:0.770761+0.092544
## [11] train-rmse:0.747340+0.017962 test-rmse:0.744445+0.091085
## [12] train-rmse:0.726129+0.017968 test-rmse:0.723376+0.089803
## [13] train-rmse:0.709223+0.017999 test-rmse:0.706605+0.088688
## [14] train-rmse:0.694854+0.017582 test-rmse:0.694461+0.089036
## [15] train-rmse:0.682760+0.017395 test-rmse:0.680425+0.087952
## [16] train-rmse:0.671828+0.017114 test-rmse:0.671823+0.087933
## [17] train-rmse:0.662625+0.017010 test-rmse:0.661428+0.086848
## [18] train-rmse:0.654194+0.016787 test-rmse:0.654777+0.086643
## [19] train-rmse:0.646682+0.016136 test-rmse:0.647718+0.083288
## [20] train-rmse:0.639872+0.015736 test-rmse:0.642342+0.084858
## [21] train-rmse:0.633426+0.015269 test-rmse:0.636094+0.082274
## [22] train-rmse:0.627674+0.014928 test-rmse:0.630649+0.080208
## [23] train-rmse:0.622168+0.014534 test-rmse:0.624002+0.080165
## [24] train-rmse:0.617076+0.014077 test-rmse:0.620370+0.078895
## [25] train-rmse:0.612417+0.013829 test-rmse:0.615769+0.078402
## [26] train-rmse:0.607944+0.013534 test-rmse:0.611554+0.076109
## [27] train-rmse:0.603857+0.013336 test-rmse:0.607827+0.074714
## [28] train-rmse:0.600011+0.013143 test-rmse:0.603331+0.073993
## [29] train-rmse:0.596358+0.012909 test-rmse:0.601337+0.072954
## [30] train-rmse:0.592825+0.012675 test-rmse:0.598518+0.072719
## [31] train-rmse:0.589662+0.012525 test-rmse:0.595021+0.072721
## [32] train-rmse:0.586575+0.012353 test-rmse:0.591543+0.070391
## [33] train-rmse:0.583646+0.012194 test-rmse:0.589726+0.069176
## [34] train-rmse:0.580789+0.012082 test-rmse:0.588923+0.069882
## [35] train-rmse:0.578110+0.011997 test-rmse:0.586281+0.067982
## [36] train-rmse:0.575583+0.011882 test-rmse:0.583537+0.068372
## [37] train-rmse:0.573138+0.011742 test-rmse:0.581927+0.067030
## [38] train-rmse:0.570782+0.011678 test-rmse:0.580859+0.066597
## [39] train-rmse:0.568536+0.011623 test-rmse:0.578863+0.065921
## [40] train-rmse:0.566378+0.011551 test-rmse:0.577142+0.065770
## [41] train-rmse:0.564345+0.011420 test-rmse:0.575096+0.065635
## [42] train-rmse:0.562362+0.011288 test-rmse:0.573246+0.065026
## [43] train-rmse:0.560411+0.011173 test-rmse:0.571870+0.063625
## [44] train-rmse:0.558589+0.011122 test-rmse:0.570958+0.064481
## [45] train-rmse:0.556797+0.011040 test-rmse:0.568759+0.064324
## [46] train-rmse:0.555077+0.010943 test-rmse:0.568502+0.063628
## [47] train-rmse:0.553478+0.010876 test-rmse:0.566218+0.063008
## [48] train-rmse:0.551893+0.010829 test-rmse:0.565187+0.062875
## [49] train-rmse:0.550340+0.010784 test-rmse:0.563987+0.062615
## [50] train-rmse:0.548862+0.010723 test-rmse:0.564276+0.062462
## [51] train-rmse:0.547454+0.010661 test-rmse:0.563527+0.061804
## [52] train-rmse:0.546094+0.010591 test-rmse:0.561425+0.061217
## [53] train-rmse:0.544739+0.010559 test-rmse:0.560482+0.061516
## [54] train-rmse:0.543434+0.010489 test-rmse:0.560279+0.061197
## [55] train-rmse:0.542145+0.010458 test-rmse:0.559245+0.060231
## [56] train-rmse:0.540906+0.010409 test-rmse:0.558347+0.060674
## [57] train-rmse:0.539697+0.010367 test-rmse:0.556723+0.059579
## [58] train-rmse:0.538532+0.010300 test-rmse:0.556523+0.059456
## [59] train-rmse:0.537375+0.010235 test-rmse:0.555776+0.059849
## [60] train-rmse:0.536220+0.010166 test-rmse:0.555285+0.059030
## [61] train-rmse:0.535098+0.010133 test-rmse:0.555247+0.058655
## [62] train-rmse:0.534025+0.010101 test-rmse:0.553851+0.058857
## [63] train-rmse:0.532965+0.010048 test-rmse:0.553931+0.058954
## [64] train-rmse:0.531936+0.010003 test-rmse:0.553301+0.059232
## [65] train-rmse:0.530919+0.009946 test-rmse:0.552328+0.059294
## [66] train-rmse:0.529916+0.009894 test-rmse:0.551494+0.058726
## [67] train-rmse:0.528926+0.009824 test-rmse:0.551085+0.059036
## [68] train-rmse:0.527931+0.009763 test-rmse:0.550428+0.058343
## [69] train-rmse:0.526982+0.009741 test-rmse:0.549852+0.057706
## [70] train-rmse:0.526067+0.009715 test-rmse:0.548677+0.057962
## [71] train-rmse:0.525160+0.009645 test-rmse:0.548706+0.057738
## [72] train-rmse:0.524268+0.009583 test-rmse:0.547492+0.057182
## [73] train-rmse:0.523390+0.009551 test-rmse:0.547155+0.057143
## [74] train-rmse:0.522528+0.009505 test-rmse:0.546772+0.057092
## [75] train-rmse:0.521674+0.009448 test-rmse:0.546354+0.057306
## [76] train-rmse:0.520821+0.009394 test-rmse:0.545788+0.056480
## [77] train-rmse:0.519980+0.009360 test-rmse:0.545181+0.056050
## [78] train-rmse:0.519156+0.009314 test-rmse:0.544849+0.055875
## [79] train-rmse:0.518356+0.009277 test-rmse:0.544229+0.055710
## [80] train-rmse:0.517552+0.009241 test-rmse:0.543490+0.055960
## [81] train-rmse:0.516756+0.009176 test-rmse:0.543111+0.056074
## [82] train-rmse:0.515977+0.009131 test-rmse:0.542025+0.055656
## [83] train-rmse:0.515235+0.009114 test-rmse:0.541283+0.056118
## [84] train-rmse:0.514494+0.009082 test-rmse:0.541001+0.056017
## [85] train-rmse:0.513739+0.009058 test-rmse:0.540333+0.056012
## [86] train-rmse:0.513005+0.009029 test-rmse:0.540388+0.055794
## [87] train-rmse:0.512274+0.008980 test-rmse:0.539590+0.055114
## [88] train-rmse:0.511556+0.008962 test-rmse:0.538815+0.054747
## [89] train-rmse:0.510838+0.008945 test-rmse:0.538031+0.055054
## [90] train-rmse:0.510138+0.008920 test-rmse:0.537916+0.055309
## [91] train-rmse:0.509453+0.008889 test-rmse:0.537826+0.055491
## [92] train-rmse:0.508777+0.008843 test-rmse:0.536625+0.055232
## [93] train-rmse:0.508102+0.008813 test-rmse:0.536517+0.054559
## [94] train-rmse:0.507416+0.008793 test-rmse:0.535639+0.054518
## [95] train-rmse:0.506747+0.008779 test-rmse:0.535524+0.054687
## [96] train-rmse:0.506099+0.008740 test-rmse:0.535193+0.054997
## [97] train-rmse:0.505448+0.008710 test-rmse:0.534629+0.054340
## [98] train-rmse:0.504804+0.008689 test-rmse:0.533839+0.054564
## [99] train-rmse:0.504177+0.008669 test-rmse:0.533255+0.054218
## [100] train-rmse:0.503561+0.008646 test-rmse:0.532770+0.054599
## [101] train-rmse:0.502945+0.008633 test-rmse:0.532262+0.054401
## [102] train-rmse:0.502329+0.008622 test-rmse:0.531319+0.053839
## [103] train-rmse:0.501701+0.008584 test-rmse:0.531220+0.053495
## [104] train-rmse:0.501090+0.008561 test-rmse:0.531357+0.054193
## [105] train-rmse:0.500498+0.008547 test-rmse:0.530602+0.054074
## [106] train-rmse:0.499919+0.008531 test-rmse:0.529951+0.054056
## [107] train-rmse:0.499339+0.008506 test-rmse:0.529878+0.053430
## [108] train-rmse:0.498764+0.008485 test-rmse:0.530025+0.053835
## [109] train-rmse:0.498192+0.008481 test-rmse:0.529377+0.053273
## [110] train-rmse:0.497621+0.008471 test-rmse:0.529547+0.053568
## [111] train-rmse:0.497060+0.008457 test-rmse:0.529291+0.053886
## [112] train-rmse:0.496502+0.008430 test-rmse:0.528340+0.053658
## [113] train-rmse:0.495956+0.008411 test-rmse:0.528471+0.053782
## [114] train-rmse:0.495403+0.008393 test-rmse:0.527907+0.053415
## [115] train-rmse:0.494872+0.008370 test-rmse:0.527353+0.052824
## [116] train-rmse:0.494345+0.008353 test-rmse:0.526841+0.052254
## [117] train-rmse:0.493816+0.008343 test-rmse:0.526481+0.052834
## [118] train-rmse:0.493292+0.008335 test-rmse:0.525962+0.052586
## [119] train-rmse:0.492777+0.008321 test-rmse:0.525464+0.052468
## [120] train-rmse:0.492275+0.008306 test-rmse:0.525193+0.052698
## [121] train-rmse:0.491776+0.008292 test-rmse:0.524835+0.052568
## [122] train-rmse:0.491278+0.008277 test-rmse:0.524343+0.052146
## [123] train-rmse:0.490775+0.008267 test-rmse:0.524221+0.052023
## [124] train-rmse:0.490278+0.008254 test-rmse:0.523835+0.052281
## [125] train-rmse:0.489780+0.008240 test-rmse:0.523707+0.052079
## [126] train-rmse:0.489298+0.008220 test-rmse:0.524105+0.051589
## [127] train-rmse:0.488818+0.008206 test-rmse:0.523781+0.051682
## [128] train-rmse:0.488343+0.008198 test-rmse:0.523278+0.051443
## [129] train-rmse:0.487882+0.008186 test-rmse:0.523011+0.051832
## [130] train-rmse:0.487421+0.008176 test-rmse:0.522374+0.052358
## [131] train-rmse:0.486955+0.008154 test-rmse:0.521770+0.052090
## [132] train-rmse:0.486503+0.008141 test-rmse:0.521430+0.052010
## [133] train-rmse:0.486051+0.008124 test-rmse:0.521865+0.051929
## [134] train-rmse:0.485601+0.008112 test-rmse:0.521865+0.051602
## [135] train-rmse:0.485162+0.008104 test-rmse:0.521151+0.051843
## [136] train-rmse:0.484719+0.008098 test-rmse:0.520662+0.051654
## [137] train-rmse:0.484281+0.008090 test-rmse:0.520763+0.051822
## [138] train-rmse:0.483858+0.008082 test-rmse:0.520140+0.051800
## [139] train-rmse:0.483437+0.008072 test-rmse:0.519595+0.051586
## [140] train-rmse:0.483009+0.008065 test-rmse:0.519231+0.051200
## [141] train-rmse:0.482587+0.008058 test-rmse:0.519008+0.050576
## [142] train-rmse:0.482176+0.008050 test-rmse:0.519328+0.050482
## [143] train-rmse:0.481769+0.008041 test-rmse:0.519559+0.050466
## [144] train-rmse:0.481369+0.008033 test-rmse:0.519007+0.050392
## [145] train-rmse:0.480963+0.008023 test-rmse:0.519440+0.050674
## [146] train-rmse:0.480568+0.008007 test-rmse:0.519541+0.050458
## [147] train-rmse:0.480173+0.008007 test-rmse:0.519214+0.050422
## [148] train-rmse:0.479769+0.007987 test-rmse:0.518934+0.050326
## [149] train-rmse:0.479381+0.007978 test-rmse:0.518703+0.050362
## [150] train-rmse:0.478995+0.007966 test-rmse:0.518524+0.050263
## [151] train-rmse:0.478607+0.007963 test-rmse:0.518562+0.050280
## [152] train-rmse:0.478237+0.007958 test-rmse:0.517229+0.049301
## [153] train-rmse:0.477865+0.007945 test-rmse:0.516773+0.049216
## [154] train-rmse:0.477495+0.007941 test-rmse:0.516593+0.049017
## [155] train-rmse:0.477121+0.007942 test-rmse:0.516512+0.048568
## [156] train-rmse:0.476752+0.007943 test-rmse:0.515972+0.048135
## [157] train-rmse:0.476394+0.007945 test-rmse:0.515941+0.048267
## [158] train-rmse:0.476033+0.007936 test-rmse:0.515738+0.048565
## [159] train-rmse:0.475667+0.007916 test-rmse:0.515471+0.048793
## [160] train-rmse:0.475317+0.007909 test-rmse:0.515350+0.048198
## [161] train-rmse:0.474963+0.007909 test-rmse:0.514937+0.048110
## [162] train-rmse:0.474618+0.007905 test-rmse:0.514802+0.048156
## [163] train-rmse:0.474277+0.007898 test-rmse:0.514858+0.048585
## [164] train-rmse:0.473938+0.007895 test-rmse:0.514556+0.048556
## [165] train-rmse:0.473595+0.007884 test-rmse:0.514152+0.048263
## [166] train-rmse:0.473256+0.007877 test-rmse:0.514115+0.048063
## [167] train-rmse:0.472916+0.007872 test-rmse:0.514030+0.048347
## [168] train-rmse:0.472582+0.007865 test-rmse:0.513716+0.048601
## [169] train-rmse:0.472247+0.007860 test-rmse:0.513067+0.048945
## [170] train-rmse:0.471916+0.007857 test-rmse:0.513126+0.048600
## [171] train-rmse:0.471589+0.007851 test-rmse:0.513000+0.048526
## [172] train-rmse:0.471268+0.007847 test-rmse:0.512454+0.048239
## [173] train-rmse:0.470945+0.007840 test-rmse:0.512519+0.048113
## [174] train-rmse:0.470622+0.007828 test-rmse:0.512450+0.048142
## [175] train-rmse:0.470301+0.007818 test-rmse:0.512254+0.048102
## [176] train-rmse:0.469985+0.007807 test-rmse:0.512080+0.048221
## [177] train-rmse:0.469671+0.007805 test-rmse:0.511087+0.047302
## [178] train-rmse:0.469365+0.007797 test-rmse:0.511442+0.047323
## [179] train-rmse:0.469059+0.007787 test-rmse:0.511250+0.047224
## [180] train-rmse:0.468751+0.007785 test-rmse:0.511145+0.047003
## [181] train-rmse:0.468438+0.007773 test-rmse:0.510877+0.046457
## [182] train-rmse:0.468135+0.007775 test-rmse:0.510647+0.046243
## [183] train-rmse:0.467834+0.007766 test-rmse:0.510539+0.046666
## [184] train-rmse:0.467541+0.007765 test-rmse:0.510290+0.046451
## [185] train-rmse:0.467248+0.007760 test-rmse:0.510050+0.046612
## [186] train-rmse:0.466952+0.007754 test-rmse:0.510425+0.046569
## [187] train-rmse:0.466667+0.007749 test-rmse:0.510347+0.046337
## [188] train-rmse:0.466378+0.007741 test-rmse:0.509867+0.046349
## [189] train-rmse:0.466088+0.007737 test-rmse:0.509501+0.046455
## [190] train-rmse:0.465796+0.007732 test-rmse:0.509274+0.046413
## [191] train-rmse:0.465517+0.007726 test-rmse:0.509058+0.046408
## [192] train-rmse:0.465243+0.007722 test-rmse:0.509076+0.046529
## [193] train-rmse:0.464956+0.007708 test-rmse:0.509222+0.046990
## [194] train-rmse:0.464677+0.007695 test-rmse:0.508837+0.046799
## [195] train-rmse:0.464404+0.007690 test-rmse:0.508416+0.046490
## [196] train-rmse:0.464134+0.007689 test-rmse:0.508596+0.046301
## [197] train-rmse:0.463865+0.007684 test-rmse:0.508169+0.046023
## [198] train-rmse:0.463594+0.007680 test-rmse:0.508166+0.045919
## [199] train-rmse:0.463327+0.007670 test-rmse:0.508123+0.045705
## [200] train-rmse:0.463064+0.007663 test-rmse:0.508149+0.045650
## [201] train-rmse:0.462803+0.007656 test-rmse:0.507514+0.045555
## [202] train-rmse:0.462539+0.007650 test-rmse:0.507406+0.045693
## [203] train-rmse:0.462275+0.007644 test-rmse:0.507421+0.045336
## [204] train-rmse:0.462012+0.007639 test-rmse:0.507571+0.045501
## [205] train-rmse:0.461757+0.007636 test-rmse:0.507577+0.045492
## [206] train-rmse:0.461494+0.007635 test-rmse:0.507554+0.045591
## [207] train-rmse:0.461241+0.007632 test-rmse:0.506736+0.044639
## [208] train-rmse:0.460990+0.007627 test-rmse:0.507391+0.044535
## [209] train-rmse:0.460738+0.007620 test-rmse:0.507110+0.044492
## [210] train-rmse:0.460486+0.007614 test-rmse:0.506813+0.044525
## [211] train-rmse:0.460227+0.007609 test-rmse:0.506691+0.044420
## [212] train-rmse:0.459978+0.007607 test-rmse:0.506208+0.044389
## [213] train-rmse:0.459731+0.007605 test-rmse:0.506082+0.044664
## [214] train-rmse:0.459487+0.007601 test-rmse:0.505765+0.044893
## [215] train-rmse:0.459245+0.007597 test-rmse:0.505964+0.045005
## [216] train-rmse:0.459000+0.007596 test-rmse:0.505922+0.045239
## [217] train-rmse:0.458763+0.007594 test-rmse:0.506203+0.045132
## [218] train-rmse:0.458521+0.007582 test-rmse:0.505957+0.045109
## [219] train-rmse:0.458284+0.007577 test-rmse:0.506129+0.045069
## [220] train-rmse:0.458052+0.007569 test-rmse:0.506017+0.045230
## Stopping. Best iteration:
## [214] train-rmse:0.459487+0.007601 test-rmse:0.505765+0.044893
##
## 8
## [1] train-rmse:1.467532+0.027385 test-rmse:1.465154+0.115699
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.319817+0.024919 test-rmse:1.320852+0.113315
## [3] train-rmse:1.191424+0.022662 test-rmse:1.192273+0.109494
## [4] train-rmse:1.080227+0.020920 test-rmse:1.083222+0.108658
## [5] train-rmse:0.984331+0.019393 test-rmse:0.988383+0.108640
## [6] train-rmse:0.901629+0.018060 test-rmse:0.905521+0.105267
## [7] train-rmse:0.831016+0.016969 test-rmse:0.835631+0.105466
## [8] train-rmse:0.770671+0.015957 test-rmse:0.773420+0.102256
## [9] train-rmse:0.719158+0.015112 test-rmse:0.725085+0.100547
## [10] train-rmse:0.675595+0.014647 test-rmse:0.680981+0.098810
## [11] train-rmse:0.638829+0.014190 test-rmse:0.646603+0.098745
## [12] train-rmse:0.607726+0.013662 test-rmse:0.616204+0.095482
## [13] train-rmse:0.581533+0.013376 test-rmse:0.591842+0.093579
## [14] train-rmse:0.559420+0.013067 test-rmse:0.571270+0.090513
## [15] train-rmse:0.540608+0.012750 test-rmse:0.553744+0.087964
## [16] train-rmse:0.524582+0.012409 test-rmse:0.543302+0.085675
## [17] train-rmse:0.510871+0.012141 test-rmse:0.529675+0.082941
## [18] train-rmse:0.499002+0.011716 test-rmse:0.520044+0.080688
## [19] train-rmse:0.488769+0.011378 test-rmse:0.512254+0.076748
## [20] train-rmse:0.479959+0.011153 test-rmse:0.503840+0.075114
## [21] train-rmse:0.471227+0.010027 test-rmse:0.496888+0.075011
## [22] train-rmse:0.463443+0.009794 test-rmse:0.491912+0.073057
## [23] train-rmse:0.455913+0.009737 test-rmse:0.485196+0.070684
## [24] train-rmse:0.449586+0.009213 test-rmse:0.479223+0.069538
## [25] train-rmse:0.443995+0.009078 test-rmse:0.476622+0.068992
## [26] train-rmse:0.438494+0.008299 test-rmse:0.473246+0.068890
## [27] train-rmse:0.433628+0.007891 test-rmse:0.471481+0.068513
## [28] train-rmse:0.428964+0.007718 test-rmse:0.470128+0.067823
## [29] train-rmse:0.424658+0.007300 test-rmse:0.467167+0.066179
## [30] train-rmse:0.421012+0.007048 test-rmse:0.464413+0.065269
## [31] train-rmse:0.417242+0.006799 test-rmse:0.462433+0.064696
## [32] train-rmse:0.413672+0.006272 test-rmse:0.461441+0.063556
## [33] train-rmse:0.410425+0.005973 test-rmse:0.459137+0.061449
## [34] train-rmse:0.407318+0.005904 test-rmse:0.457544+0.060755
## [35] train-rmse:0.404439+0.005626 test-rmse:0.456659+0.059515
## [36] train-rmse:0.401776+0.005466 test-rmse:0.456183+0.058860
## [37] train-rmse:0.399218+0.005321 test-rmse:0.454771+0.059047
## [38] train-rmse:0.396324+0.004778 test-rmse:0.452781+0.059193
## [39] train-rmse:0.393913+0.004514 test-rmse:0.452261+0.059229
## [40] train-rmse:0.391476+0.004321 test-rmse:0.452340+0.059176
## [41] train-rmse:0.389488+0.004249 test-rmse:0.450520+0.058805
## [42] train-rmse:0.386702+0.004080 test-rmse:0.448571+0.058807
## [43] train-rmse:0.384673+0.003819 test-rmse:0.447877+0.058948
## [44] train-rmse:0.382790+0.003761 test-rmse:0.448232+0.059059
## [45] train-rmse:0.380399+0.003711 test-rmse:0.448039+0.058141
## [46] train-rmse:0.378370+0.003479 test-rmse:0.448217+0.057655
## [47] train-rmse:0.376500+0.003537 test-rmse:0.448060+0.057159
## [48] train-rmse:0.374671+0.003704 test-rmse:0.447545+0.057201
## [49] train-rmse:0.372730+0.003447 test-rmse:0.446246+0.057302
## [50] train-rmse:0.370786+0.003477 test-rmse:0.445644+0.057702
## [51] train-rmse:0.369148+0.003433 test-rmse:0.445877+0.057698
## [52] train-rmse:0.367432+0.003263 test-rmse:0.445393+0.057996
## [53] train-rmse:0.365742+0.003401 test-rmse:0.444844+0.057216
## [54] train-rmse:0.363802+0.003495 test-rmse:0.444776+0.056926
## [55] train-rmse:0.362267+0.003418 test-rmse:0.444331+0.056704
## [56] train-rmse:0.360594+0.003359 test-rmse:0.444098+0.056428
## [57] train-rmse:0.358941+0.003270 test-rmse:0.444290+0.056200
## [58] train-rmse:0.357289+0.003197 test-rmse:0.444416+0.056600
## [59] train-rmse:0.355901+0.003280 test-rmse:0.443280+0.056144
## [60] train-rmse:0.354518+0.003337 test-rmse:0.442982+0.056104
## [61] train-rmse:0.353365+0.003275 test-rmse:0.443127+0.055650
## [62] train-rmse:0.351657+0.003146 test-rmse:0.442710+0.055746
## [63] train-rmse:0.350472+0.003179 test-rmse:0.442296+0.056431
## [64] train-rmse:0.349145+0.003179 test-rmse:0.442343+0.056811
## [65] train-rmse:0.347501+0.002862 test-rmse:0.440925+0.056854
## [66] train-rmse:0.346171+0.002862 test-rmse:0.440608+0.057153
## [67] train-rmse:0.345156+0.002956 test-rmse:0.440665+0.057592
## [68] train-rmse:0.344071+0.002980 test-rmse:0.440687+0.056833
## [69] train-rmse:0.342985+0.002904 test-rmse:0.440397+0.056515
## [70] train-rmse:0.340987+0.002730 test-rmse:0.440404+0.056754
## [71] train-rmse:0.339440+0.002444 test-rmse:0.440030+0.056580
## [72] train-rmse:0.338151+0.002387 test-rmse:0.439849+0.056193
## [73] train-rmse:0.337060+0.002327 test-rmse:0.440490+0.056249
## [74] train-rmse:0.335858+0.002326 test-rmse:0.440526+0.055812
## [75] train-rmse:0.334001+0.002546 test-rmse:0.439943+0.055463
## [76] train-rmse:0.332989+0.002457 test-rmse:0.439691+0.055870
## [77] train-rmse:0.331561+0.002808 test-rmse:0.439835+0.055646
## [78] train-rmse:0.330161+0.002504 test-rmse:0.439457+0.055775
## [79] train-rmse:0.328755+0.002386 test-rmse:0.439306+0.055661
## [80] train-rmse:0.327844+0.002384 test-rmse:0.439451+0.056130
## [81] train-rmse:0.327047+0.002389 test-rmse:0.439011+0.055779
## [82] train-rmse:0.326185+0.002362 test-rmse:0.438837+0.056175
## [83] train-rmse:0.324908+0.002706 test-rmse:0.438032+0.056132
## [84] train-rmse:0.324070+0.002613 test-rmse:0.438323+0.055898
## [85] train-rmse:0.323033+0.002669 test-rmse:0.437405+0.055872
## [86] train-rmse:0.321810+0.002845 test-rmse:0.437589+0.055565
## [87] train-rmse:0.320706+0.002878 test-rmse:0.437074+0.055760
## [88] train-rmse:0.319672+0.003123 test-rmse:0.436390+0.056242
## [89] train-rmse:0.318873+0.003086 test-rmse:0.436285+0.056597
## [90] train-rmse:0.318092+0.003092 test-rmse:0.436006+0.057557
## [91] train-rmse:0.317046+0.003124 test-rmse:0.435609+0.057838
## [92] train-rmse:0.315666+0.002944 test-rmse:0.435672+0.058304
## [93] train-rmse:0.314915+0.002902 test-rmse:0.435560+0.058335
## [94] train-rmse:0.314096+0.002802 test-rmse:0.434644+0.057970
## [95] train-rmse:0.312995+0.002835 test-rmse:0.434159+0.058734
## [96] train-rmse:0.311823+0.002776 test-rmse:0.433534+0.059362
## [97] train-rmse:0.311069+0.002798 test-rmse:0.433462+0.059401
## [98] train-rmse:0.310111+0.002759 test-rmse:0.433690+0.059376
## [99] train-rmse:0.309186+0.002721 test-rmse:0.433151+0.059281
## [100] train-rmse:0.308318+0.002492 test-rmse:0.433107+0.059444
## [101] train-rmse:0.307540+0.002394 test-rmse:0.432926+0.059377
## [102] train-rmse:0.306421+0.002877 test-rmse:0.432652+0.059526
## [103] train-rmse:0.305792+0.002963 test-rmse:0.432344+0.059973
## [104] train-rmse:0.304952+0.003065 test-rmse:0.432436+0.060016
## [105] train-rmse:0.304145+0.003123 test-rmse:0.432342+0.059916
## [106] train-rmse:0.303264+0.003136 test-rmse:0.431827+0.059607
## [107] train-rmse:0.302292+0.003089 test-rmse:0.431573+0.059483
## [108] train-rmse:0.301258+0.003117 test-rmse:0.431404+0.059518
## [109] train-rmse:0.300474+0.003359 test-rmse:0.431685+0.059467
## [110] train-rmse:0.299893+0.003382 test-rmse:0.431612+0.059894
## [111] train-rmse:0.299172+0.003394 test-rmse:0.431842+0.059565
## [112] train-rmse:0.298440+0.003510 test-rmse:0.431947+0.059664
## [113] train-rmse:0.297698+0.003587 test-rmse:0.431283+0.059615
## [114] train-rmse:0.297007+0.003865 test-rmse:0.430925+0.059372
## [115] train-rmse:0.296417+0.003943 test-rmse:0.431036+0.059523
## [116] train-rmse:0.295975+0.003930 test-rmse:0.430638+0.059796
## [117] train-rmse:0.295257+0.004077 test-rmse:0.430009+0.060455
## [118] train-rmse:0.294645+0.004083 test-rmse:0.430126+0.060451
## [119] train-rmse:0.293963+0.004006 test-rmse:0.429795+0.060142
## [120] train-rmse:0.293505+0.004086 test-rmse:0.429601+0.060051
## [121] train-rmse:0.292567+0.003948 test-rmse:0.429427+0.059772
## [122] train-rmse:0.291914+0.004094 test-rmse:0.428899+0.059774
## [123] train-rmse:0.291278+0.004040 test-rmse:0.428898+0.059590
## [124] train-rmse:0.290491+0.004118 test-rmse:0.428759+0.059739
## [125] train-rmse:0.289876+0.004035 test-rmse:0.428503+0.059958
## [126] train-rmse:0.289212+0.003759 test-rmse:0.428201+0.060345
## [127] train-rmse:0.288666+0.003754 test-rmse:0.428107+0.060517
## [128] train-rmse:0.288193+0.003678 test-rmse:0.427611+0.060941
## [129] train-rmse:0.287405+0.003837 test-rmse:0.427612+0.060462
## [130] train-rmse:0.286737+0.004081 test-rmse:0.427599+0.060569
## [131] train-rmse:0.285861+0.004130 test-rmse:0.427123+0.060597
## [132] train-rmse:0.285075+0.004190 test-rmse:0.426768+0.060284
## [133] train-rmse:0.284229+0.004170 test-rmse:0.426608+0.060225
## [134] train-rmse:0.283773+0.004136 test-rmse:0.426148+0.060195
## [135] train-rmse:0.283319+0.004225 test-rmse:0.426056+0.060388
## [136] train-rmse:0.282845+0.004215 test-rmse:0.425832+0.060400
## [137] train-rmse:0.282314+0.004218 test-rmse:0.425325+0.060952
## [138] train-rmse:0.281341+0.004652 test-rmse:0.425178+0.060708
## [139] train-rmse:0.280826+0.004656 test-rmse:0.425782+0.060411
## [140] train-rmse:0.280311+0.004591 test-rmse:0.425698+0.060368
## [141] train-rmse:0.279378+0.005316 test-rmse:0.425476+0.060202
## [142] train-rmse:0.279013+0.005303 test-rmse:0.425361+0.060169
## [143] train-rmse:0.278537+0.005226 test-rmse:0.425207+0.060307
## [144] train-rmse:0.277982+0.005176 test-rmse:0.425260+0.060652
## Stopping. Best iteration:
## [138] train-rmse:0.281341+0.004652 test-rmse:0.425178+0.060708
##
## 9
## [1] train-rmse:1.463740+0.026972 test-rmse:1.463298+0.117089
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.312091+0.024319 test-rmse:1.312985+0.113461
## [3] train-rmse:1.179107+0.021922 test-rmse:1.184224+0.112879
## [4] train-rmse:1.063315+0.019719 test-rmse:1.066695+0.112701
## [5] train-rmse:0.962292+0.017875 test-rmse:0.969047+0.112863
## [6] train-rmse:0.874545+0.016071 test-rmse:0.881695+0.113416
## [7] train-rmse:0.797845+0.015725 test-rmse:0.808982+0.107020
## [8] train-rmse:0.731494+0.014426 test-rmse:0.744284+0.105982
## [9] train-rmse:0.674202+0.014156 test-rmse:0.689802+0.102297
## [10] train-rmse:0.624970+0.013147 test-rmse:0.646152+0.100588
## [11] train-rmse:0.582510+0.012323 test-rmse:0.607072+0.098524
## [12] train-rmse:0.546034+0.011267 test-rmse:0.578618+0.096334
## [13] train-rmse:0.515328+0.010799 test-rmse:0.551403+0.093337
## [14] train-rmse:0.488204+0.010569 test-rmse:0.528550+0.088921
## [15] train-rmse:0.465495+0.009967 test-rmse:0.511140+0.086969
## [16] train-rmse:0.446244+0.009868 test-rmse:0.498462+0.084023
## [17] train-rmse:0.429566+0.009109 test-rmse:0.485672+0.083208
## [18] train-rmse:0.415187+0.009103 test-rmse:0.474786+0.079322
## [19] train-rmse:0.402732+0.009260 test-rmse:0.467159+0.078151
## [20] train-rmse:0.391947+0.008965 test-rmse:0.459854+0.075393
## [21] train-rmse:0.382728+0.008780 test-rmse:0.455426+0.073470
## [22] train-rmse:0.374653+0.008909 test-rmse:0.451630+0.070899
## [23] train-rmse:0.367794+0.009053 test-rmse:0.447529+0.068040
## [24] train-rmse:0.361379+0.008737 test-rmse:0.443350+0.066704
## [25] train-rmse:0.355865+0.008763 test-rmse:0.441484+0.065309
## [26] train-rmse:0.351140+0.008760 test-rmse:0.440738+0.064487
## [27] train-rmse:0.346530+0.008366 test-rmse:0.438830+0.063183
## [28] train-rmse:0.342032+0.008404 test-rmse:0.437402+0.062091
## [29] train-rmse:0.337524+0.008614 test-rmse:0.435707+0.061021
## [30] train-rmse:0.333458+0.008926 test-rmse:0.434772+0.060845
## [31] train-rmse:0.329927+0.009053 test-rmse:0.434716+0.061009
## [32] train-rmse:0.326606+0.008949 test-rmse:0.434172+0.059959
## [33] train-rmse:0.323100+0.009163 test-rmse:0.434783+0.060089
## [34] train-rmse:0.319569+0.008396 test-rmse:0.432670+0.060519
## [35] train-rmse:0.316547+0.008616 test-rmse:0.432022+0.060136
## [36] train-rmse:0.313970+0.008422 test-rmse:0.432193+0.059797
## [37] train-rmse:0.311340+0.008594 test-rmse:0.431588+0.059717
## [38] train-rmse:0.308625+0.008555 test-rmse:0.430245+0.059986
## [39] train-rmse:0.306220+0.008419 test-rmse:0.430511+0.059977
## [40] train-rmse:0.303911+0.008548 test-rmse:0.430810+0.060115
## [41] train-rmse:0.301145+0.008103 test-rmse:0.430411+0.060967
## [42] train-rmse:0.298890+0.007993 test-rmse:0.429756+0.061025
## [43] train-rmse:0.296738+0.007887 test-rmse:0.430345+0.060922
## [44] train-rmse:0.294526+0.008055 test-rmse:0.429880+0.060497
## [45] train-rmse:0.291950+0.007718 test-rmse:0.429588+0.060121
## [46] train-rmse:0.289989+0.007470 test-rmse:0.429000+0.060055
## [47] train-rmse:0.288061+0.007400 test-rmse:0.428317+0.060620
## [48] train-rmse:0.286243+0.007177 test-rmse:0.428557+0.060400
## [49] train-rmse:0.284097+0.007044 test-rmse:0.428463+0.060419
## [50] train-rmse:0.282603+0.006979 test-rmse:0.428412+0.060545
## [51] train-rmse:0.280508+0.006705 test-rmse:0.428206+0.060979
## [52] train-rmse:0.278321+0.006143 test-rmse:0.427516+0.060791
## [53] train-rmse:0.276771+0.006033 test-rmse:0.427497+0.060791
## [54] train-rmse:0.274690+0.005650 test-rmse:0.427694+0.060430
## [55] train-rmse:0.272730+0.005533 test-rmse:0.427759+0.061354
## [56] train-rmse:0.270962+0.005322 test-rmse:0.427285+0.062094
## [57] train-rmse:0.269345+0.005063 test-rmse:0.426412+0.061846
## [58] train-rmse:0.267813+0.005008 test-rmse:0.426184+0.062099
## [59] train-rmse:0.266259+0.005158 test-rmse:0.426199+0.061569
## [60] train-rmse:0.264591+0.005092 test-rmse:0.425614+0.061605
## [61] train-rmse:0.263196+0.005055 test-rmse:0.424197+0.062727
## [62] train-rmse:0.261778+0.005027 test-rmse:0.424089+0.062886
## [63] train-rmse:0.260372+0.004952 test-rmse:0.423953+0.062654
## [64] train-rmse:0.259289+0.005142 test-rmse:0.423936+0.062814
## [65] train-rmse:0.257462+0.004678 test-rmse:0.422781+0.063145
## [66] train-rmse:0.256391+0.004578 test-rmse:0.422823+0.063207
## [67] train-rmse:0.255007+0.004534 test-rmse:0.422727+0.063858
## [68] train-rmse:0.253453+0.004222 test-rmse:0.422489+0.064583
## [69] train-rmse:0.251836+0.004621 test-rmse:0.422141+0.063772
## [70] train-rmse:0.250654+0.004614 test-rmse:0.422268+0.063637
## [71] train-rmse:0.249387+0.004517 test-rmse:0.422092+0.063587
## [72] train-rmse:0.247878+0.004659 test-rmse:0.422748+0.063346
## [73] train-rmse:0.246437+0.004117 test-rmse:0.422170+0.063378
## [74] train-rmse:0.245396+0.004124 test-rmse:0.421949+0.063496
## [75] train-rmse:0.244031+0.004096 test-rmse:0.421839+0.063038
## [76] train-rmse:0.242474+0.003864 test-rmse:0.421821+0.063320
## [77] train-rmse:0.241477+0.003754 test-rmse:0.421431+0.064043
## [78] train-rmse:0.240001+0.003348 test-rmse:0.420956+0.063303
## [79] train-rmse:0.238933+0.003068 test-rmse:0.421071+0.063427
## [80] train-rmse:0.237554+0.003413 test-rmse:0.419737+0.064110
## [81] train-rmse:0.236345+0.003827 test-rmse:0.419385+0.064058
## [82] train-rmse:0.235287+0.004009 test-rmse:0.419149+0.064090
## [83] train-rmse:0.234278+0.003882 test-rmse:0.419190+0.063889
## [84] train-rmse:0.233282+0.003688 test-rmse:0.418879+0.063734
## [85] train-rmse:0.232094+0.003520 test-rmse:0.418457+0.064122
## [86] train-rmse:0.230856+0.003697 test-rmse:0.417824+0.064113
## [87] train-rmse:0.229776+0.003712 test-rmse:0.417422+0.064689
## [88] train-rmse:0.228419+0.004003 test-rmse:0.416806+0.064924
## [89] train-rmse:0.227500+0.003941 test-rmse:0.416332+0.064944
## [90] train-rmse:0.226431+0.003909 test-rmse:0.416310+0.064945
## [91] train-rmse:0.225648+0.003835 test-rmse:0.416613+0.064903
## [92] train-rmse:0.224473+0.003717 test-rmse:0.416003+0.065176
## [93] train-rmse:0.223251+0.003993 test-rmse:0.415705+0.065985
## [94] train-rmse:0.222410+0.004209 test-rmse:0.415144+0.066022
## [95] train-rmse:0.221480+0.004152 test-rmse:0.415266+0.065646
## [96] train-rmse:0.220737+0.004132 test-rmse:0.415324+0.065765
## [97] train-rmse:0.219626+0.004303 test-rmse:0.415575+0.065625
## [98] train-rmse:0.218804+0.004240 test-rmse:0.415226+0.065351
## [99] train-rmse:0.217817+0.004008 test-rmse:0.414977+0.066028
## [100] train-rmse:0.216822+0.004353 test-rmse:0.415016+0.066171
## [101] train-rmse:0.215958+0.004543 test-rmse:0.415347+0.065931
## [102] train-rmse:0.214410+0.004709 test-rmse:0.415222+0.066143
## [103] train-rmse:0.213261+0.005193 test-rmse:0.415341+0.066208
## [104] train-rmse:0.212501+0.005063 test-rmse:0.415436+0.066350
## [105] train-rmse:0.211565+0.005016 test-rmse:0.415420+0.066354
## Stopping. Best iteration:
## [99] train-rmse:0.217817+0.004008 test-rmse:0.414977+0.066028
##
## 10
## [1] train-rmse:1.462069+0.026714 test-rmse:1.461352+0.117207
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.308726+0.023659 test-rmse:1.308665+0.116812
## [3] train-rmse:1.174263+0.021557 test-rmse:1.174839+0.112924
## [4] train-rmse:1.056279+0.019281 test-rmse:1.058996+0.112829
## [5] train-rmse:0.952896+0.017836 test-rmse:0.960407+0.108622
## [6] train-rmse:0.862361+0.016157 test-rmse:0.871645+0.106904
## [7] train-rmse:0.783418+0.014375 test-rmse:0.796428+0.105631
## [8] train-rmse:0.712920+0.013175 test-rmse:0.730375+0.102184
## [9] train-rmse:0.653156+0.011714 test-rmse:0.675637+0.099702
## [10] train-rmse:0.601021+0.010555 test-rmse:0.628105+0.096170
## [11] train-rmse:0.555378+0.009554 test-rmse:0.590307+0.093224
## [12] train-rmse:0.516542+0.008924 test-rmse:0.557248+0.089422
## [13] train-rmse:0.482956+0.008391 test-rmse:0.532759+0.087515
## [14] train-rmse:0.452604+0.007927 test-rmse:0.508519+0.086305
## [15] train-rmse:0.428171+0.007947 test-rmse:0.491363+0.082190
## [16] train-rmse:0.406120+0.006757 test-rmse:0.475954+0.079855
## [17] train-rmse:0.387724+0.006537 test-rmse:0.464457+0.077912
## [18] train-rmse:0.371857+0.005773 test-rmse:0.454659+0.078373
## [19] train-rmse:0.357801+0.005005 test-rmse:0.448020+0.075457
## [20] train-rmse:0.345883+0.004605 test-rmse:0.442566+0.073817
## [21] train-rmse:0.334486+0.005173 test-rmse:0.436773+0.070567
## [22] train-rmse:0.324978+0.004744 test-rmse:0.432109+0.069229
## [23] train-rmse:0.317640+0.004429 test-rmse:0.429693+0.067901
## [24] train-rmse:0.310267+0.004032 test-rmse:0.427509+0.067066
## [25] train-rmse:0.303839+0.002932 test-rmse:0.426359+0.066719
## [26] train-rmse:0.298337+0.002976 test-rmse:0.424636+0.065996
## [27] train-rmse:0.292950+0.002777 test-rmse:0.422359+0.064218
## [28] train-rmse:0.288267+0.002659 test-rmse:0.421380+0.062467
## [29] train-rmse:0.284157+0.002773 test-rmse:0.421146+0.062586
## [30] train-rmse:0.280431+0.002726 test-rmse:0.420529+0.062209
## [31] train-rmse:0.277237+0.002748 test-rmse:0.419136+0.060699
## [32] train-rmse:0.273075+0.002374 test-rmse:0.417914+0.060360
## [33] train-rmse:0.269215+0.002334 test-rmse:0.416966+0.060285
## [34] train-rmse:0.264973+0.002455 test-rmse:0.416559+0.060862
## [35] train-rmse:0.261259+0.002447 test-rmse:0.415987+0.060329
## [36] train-rmse:0.257767+0.002652 test-rmse:0.415368+0.060651
## [37] train-rmse:0.254761+0.002715 test-rmse:0.414674+0.060949
## [38] train-rmse:0.251575+0.003314 test-rmse:0.413783+0.062488
## [39] train-rmse:0.249200+0.002896 test-rmse:0.414006+0.062448
## [40] train-rmse:0.246727+0.003194 test-rmse:0.413019+0.062999
## [41] train-rmse:0.242917+0.003020 test-rmse:0.412039+0.063930
## [42] train-rmse:0.240193+0.002863 test-rmse:0.411636+0.063374
## [43] train-rmse:0.237771+0.002857 test-rmse:0.411198+0.062928
## [44] train-rmse:0.235652+0.002686 test-rmse:0.411265+0.062895
## [45] train-rmse:0.232398+0.002216 test-rmse:0.410620+0.063086
## [46] train-rmse:0.229409+0.002236 test-rmse:0.410799+0.062669
## [47] train-rmse:0.226905+0.001969 test-rmse:0.410334+0.063242
## [48] train-rmse:0.224055+0.002216 test-rmse:0.410150+0.063232
## [49] train-rmse:0.222153+0.002431 test-rmse:0.409627+0.063646
## [50] train-rmse:0.219775+0.002577 test-rmse:0.409373+0.063585
## [51] train-rmse:0.217502+0.003094 test-rmse:0.409083+0.063590
## [52] train-rmse:0.215234+0.003390 test-rmse:0.408940+0.063547
## [53] train-rmse:0.213132+0.003764 test-rmse:0.408857+0.063908
## [54] train-rmse:0.211494+0.003883 test-rmse:0.407901+0.063674
## [55] train-rmse:0.209290+0.004118 test-rmse:0.407418+0.063423
## [56] train-rmse:0.207618+0.004315 test-rmse:0.406613+0.063850
## [57] train-rmse:0.205862+0.004173 test-rmse:0.406714+0.063704
## [58] train-rmse:0.204304+0.003960 test-rmse:0.406207+0.063785
## [59] train-rmse:0.202800+0.004140 test-rmse:0.406302+0.063741
## [60] train-rmse:0.201318+0.004092 test-rmse:0.405979+0.064013
## [61] train-rmse:0.199496+0.004477 test-rmse:0.404943+0.063438
## [62] train-rmse:0.197526+0.004677 test-rmse:0.404934+0.063485
## [63] train-rmse:0.196179+0.004976 test-rmse:0.404882+0.063507
## [64] train-rmse:0.194150+0.003759 test-rmse:0.404499+0.063406
## [65] train-rmse:0.192658+0.003863 test-rmse:0.404470+0.063620
## [66] train-rmse:0.190980+0.004274 test-rmse:0.404184+0.064041
## [67] train-rmse:0.189506+0.004182 test-rmse:0.404334+0.064039
## [68] train-rmse:0.188056+0.003806 test-rmse:0.404412+0.063846
## [69] train-rmse:0.186764+0.004052 test-rmse:0.403564+0.063860
## [70] train-rmse:0.185308+0.004245 test-rmse:0.403667+0.064007
## [71] train-rmse:0.183961+0.003681 test-rmse:0.403401+0.064598
## [72] train-rmse:0.182455+0.004109 test-rmse:0.403369+0.064562
## [73] train-rmse:0.181313+0.004055 test-rmse:0.402954+0.064710
## [74] train-rmse:0.180184+0.003904 test-rmse:0.402636+0.064852
## [75] train-rmse:0.178725+0.003896 test-rmse:0.402753+0.064230
## [76] train-rmse:0.177360+0.003467 test-rmse:0.402248+0.063455
## [77] train-rmse:0.176230+0.002958 test-rmse:0.401854+0.064055
## [78] train-rmse:0.174824+0.003034 test-rmse:0.401894+0.063951
## [79] train-rmse:0.173714+0.003119 test-rmse:0.402074+0.063985
## [80] train-rmse:0.172323+0.002743 test-rmse:0.401854+0.064223
## [81] train-rmse:0.171005+0.003072 test-rmse:0.401613+0.064224
## [82] train-rmse:0.169568+0.003384 test-rmse:0.401492+0.063999
## [83] train-rmse:0.168180+0.003255 test-rmse:0.401587+0.064169
## [84] train-rmse:0.166826+0.002997 test-rmse:0.400918+0.064356
## [85] train-rmse:0.165725+0.002911 test-rmse:0.400935+0.064875
## [86] train-rmse:0.164665+0.002941 test-rmse:0.401168+0.064764
## [87] train-rmse:0.163736+0.002634 test-rmse:0.400466+0.065562
## [88] train-rmse:0.162824+0.002610 test-rmse:0.400417+0.066031
## [89] train-rmse:0.161733+0.002975 test-rmse:0.399920+0.065633
## [90] train-rmse:0.160716+0.002813 test-rmse:0.399666+0.065898
## [91] train-rmse:0.159641+0.002872 test-rmse:0.399432+0.065928
## [92] train-rmse:0.158598+0.003147 test-rmse:0.399252+0.066138
## [93] train-rmse:0.157699+0.002917 test-rmse:0.399532+0.065874
## [94] train-rmse:0.156853+0.002766 test-rmse:0.399362+0.065766
## [95] train-rmse:0.155967+0.002779 test-rmse:0.399186+0.065671
## [96] train-rmse:0.154615+0.003246 test-rmse:0.399451+0.065475
## [97] train-rmse:0.153574+0.003216 test-rmse:0.399287+0.065681
## [98] train-rmse:0.152829+0.003243 test-rmse:0.398841+0.065546
## [99] train-rmse:0.151600+0.003564 test-rmse:0.398824+0.065490
## [100] train-rmse:0.150827+0.003611 test-rmse:0.398721+0.065348
## [101] train-rmse:0.149594+0.003500 test-rmse:0.398564+0.065636
## [102] train-rmse:0.148754+0.003821 test-rmse:0.398835+0.065341
## [103] train-rmse:0.147819+0.003919 test-rmse:0.398664+0.065384
## [104] train-rmse:0.146952+0.003659 test-rmse:0.398818+0.065131
## [105] train-rmse:0.146259+0.003509 test-rmse:0.398659+0.065404
## [106] train-rmse:0.144899+0.003979 test-rmse:0.398068+0.065863
## [107] train-rmse:0.144158+0.004101 test-rmse:0.398142+0.065836
## [108] train-rmse:0.142960+0.004484 test-rmse:0.397999+0.065695
## [109] train-rmse:0.142259+0.004583 test-rmse:0.398076+0.065774
## [110] train-rmse:0.141149+0.004844 test-rmse:0.397817+0.065463
## [111] train-rmse:0.140471+0.005174 test-rmse:0.397841+0.065435
## [112] train-rmse:0.139648+0.005141 test-rmse:0.398033+0.065086
## [113] train-rmse:0.138524+0.004904 test-rmse:0.397754+0.065521
## [114] train-rmse:0.137554+0.005064 test-rmse:0.397876+0.065536
## [115] train-rmse:0.136866+0.005071 test-rmse:0.398087+0.065343
## [116] train-rmse:0.135976+0.005467 test-rmse:0.397877+0.064992
## [117] train-rmse:0.135258+0.005679 test-rmse:0.397615+0.065122
## [118] train-rmse:0.134717+0.005649 test-rmse:0.397790+0.064948
## [119] train-rmse:0.133708+0.005930 test-rmse:0.397643+0.064958
## [120] train-rmse:0.132723+0.005667 test-rmse:0.397316+0.065191
## [121] train-rmse:0.132099+0.005888 test-rmse:0.397298+0.065212
## [122] train-rmse:0.131623+0.005907 test-rmse:0.397352+0.065414
## [123] train-rmse:0.130869+0.005927 test-rmse:0.397296+0.065395
## [124] train-rmse:0.129969+0.006000 test-rmse:0.397374+0.065290
## [125] train-rmse:0.129237+0.005962 test-rmse:0.397346+0.065678
## [126] train-rmse:0.128437+0.006039 test-rmse:0.397175+0.065622
## [127] train-rmse:0.127513+0.005613 test-rmse:0.397317+0.065718
## [128] train-rmse:0.126860+0.005813 test-rmse:0.397382+0.065516
## [129] train-rmse:0.126039+0.005520 test-rmse:0.397368+0.065236
## [130] train-rmse:0.125009+0.005459 test-rmse:0.397212+0.065718
## [131] train-rmse:0.124431+0.005498 test-rmse:0.397166+0.065797
## [132] train-rmse:0.123701+0.005296 test-rmse:0.396959+0.066130
## [133] train-rmse:0.122759+0.005148 test-rmse:0.397090+0.066101
## [134] train-rmse:0.122111+0.005030 test-rmse:0.397084+0.066110
## [135] train-rmse:0.121367+0.005022 test-rmse:0.396670+0.065702
## [136] train-rmse:0.120329+0.005048 test-rmse:0.396546+0.066185
## [137] train-rmse:0.119826+0.005137 test-rmse:0.396609+0.066110
## [138] train-rmse:0.118973+0.005230 test-rmse:0.395956+0.066081
## [139] train-rmse:0.118269+0.005177 test-rmse:0.395963+0.066217
## [140] train-rmse:0.117702+0.005201 test-rmse:0.395464+0.066729
## [141] train-rmse:0.117168+0.005305 test-rmse:0.395657+0.066592
## [142] train-rmse:0.116621+0.005355 test-rmse:0.395287+0.066819
## [143] train-rmse:0.115767+0.005409 test-rmse:0.395092+0.066632
## [144] train-rmse:0.114964+0.005392 test-rmse:0.394891+0.067255
## [145] train-rmse:0.114076+0.005276 test-rmse:0.395246+0.066701
## [146] train-rmse:0.113575+0.005395 test-rmse:0.395205+0.066749
## [147] train-rmse:0.112867+0.005508 test-rmse:0.395056+0.067115
## [148] train-rmse:0.112316+0.005461 test-rmse:0.395772+0.066595
## [149] train-rmse:0.111938+0.005502 test-rmse:0.396047+0.066495
## [150] train-rmse:0.111445+0.005660 test-rmse:0.395898+0.066659
## Stopping. Best iteration:
## [144] train-rmse:0.114964+0.005392 test-rmse:0.394891+0.067255
##
## 11
## [1] train-rmse:1.461063+0.026892 test-rmse:1.460223+0.115551
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.306537+0.023872 test-rmse:1.306286+0.113780
## [3] train-rmse:1.171174+0.021135 test-rmse:1.171847+0.111596
## [4] train-rmse:1.051438+0.019368 test-rmse:1.053673+0.107405
## [5] train-rmse:0.946547+0.017198 test-rmse:0.952784+0.105951
## [6] train-rmse:0.854580+0.015376 test-rmse:0.863730+0.103483
## [7] train-rmse:0.773752+0.014387 test-rmse:0.786386+0.100443
## [8] train-rmse:0.703471+0.012843 test-rmse:0.721083+0.099450
## [9] train-rmse:0.640368+0.011822 test-rmse:0.665820+0.094998
## [10] train-rmse:0.586240+0.011489 test-rmse:0.618029+0.090873
## [11] train-rmse:0.538564+0.010273 test-rmse:0.579628+0.089451
## [12] train-rmse:0.496399+0.009822 test-rmse:0.546305+0.087063
## [13] train-rmse:0.460067+0.008965 test-rmse:0.518823+0.086057
## [14] train-rmse:0.428191+0.008106 test-rmse:0.495687+0.084145
## [15] train-rmse:0.400938+0.007266 test-rmse:0.478037+0.081080
## [16] train-rmse:0.376756+0.007097 test-rmse:0.463762+0.080691
## [17] train-rmse:0.355904+0.007180 test-rmse:0.452955+0.078940
## [18] train-rmse:0.336597+0.005774 test-rmse:0.443588+0.078256
## [19] train-rmse:0.320775+0.005989 test-rmse:0.435969+0.075525
## [20] train-rmse:0.305929+0.006219 test-rmse:0.429895+0.075773
## [21] train-rmse:0.293715+0.006590 test-rmse:0.426122+0.075225
## [22] train-rmse:0.282900+0.007531 test-rmse:0.422136+0.073193
## [23] train-rmse:0.273714+0.007746 test-rmse:0.419853+0.071665
## [24] train-rmse:0.264787+0.006001 test-rmse:0.416446+0.069614
## [25] train-rmse:0.257789+0.006729 test-rmse:0.414274+0.067701
## [26] train-rmse:0.249899+0.005703 test-rmse:0.412287+0.067750
## [27] train-rmse:0.243778+0.006502 test-rmse:0.410453+0.065856
## [28] train-rmse:0.238828+0.007156 test-rmse:0.408283+0.065962
## [29] train-rmse:0.234383+0.007576 test-rmse:0.408450+0.065545
## [30] train-rmse:0.228456+0.005812 test-rmse:0.408331+0.065186
## [31] train-rmse:0.224716+0.005955 test-rmse:0.407769+0.064016
## [32] train-rmse:0.221127+0.006308 test-rmse:0.407808+0.063814
## [33] train-rmse:0.217853+0.006194 test-rmse:0.407481+0.063533
## [34] train-rmse:0.213857+0.006015 test-rmse:0.404934+0.062812
## [35] train-rmse:0.209801+0.006109 test-rmse:0.403403+0.062906
## [36] train-rmse:0.206955+0.006123 test-rmse:0.402930+0.061850
## [37] train-rmse:0.203685+0.006048 test-rmse:0.403022+0.061088
## [38] train-rmse:0.199586+0.005436 test-rmse:0.402798+0.060862
## [39] train-rmse:0.196879+0.005213 test-rmse:0.402383+0.061233
## [40] train-rmse:0.193557+0.005965 test-rmse:0.402169+0.060299
## [41] train-rmse:0.190560+0.006548 test-rmse:0.402012+0.060195
## [42] train-rmse:0.188057+0.006903 test-rmse:0.401681+0.060961
## [43] train-rmse:0.185535+0.007340 test-rmse:0.401358+0.060752
## [44] train-rmse:0.182709+0.007621 test-rmse:0.401325+0.060790
## [45] train-rmse:0.180038+0.008138 test-rmse:0.401002+0.061291
## [46] train-rmse:0.177271+0.007621 test-rmse:0.401267+0.060783
## [47] train-rmse:0.175116+0.007685 test-rmse:0.401055+0.060368
## [48] train-rmse:0.172964+0.007339 test-rmse:0.400211+0.060235
## [49] train-rmse:0.170386+0.006924 test-rmse:0.400101+0.059977
## [50] train-rmse:0.168817+0.007184 test-rmse:0.399853+0.059902
## [51] train-rmse:0.166930+0.006984 test-rmse:0.399793+0.059675
## [52] train-rmse:0.165207+0.006289 test-rmse:0.399579+0.060069
## [53] train-rmse:0.163123+0.005953 test-rmse:0.399617+0.060093
## [54] train-rmse:0.161497+0.005406 test-rmse:0.399763+0.059741
## [55] train-rmse:0.159796+0.005057 test-rmse:0.400152+0.059531
## [56] train-rmse:0.157628+0.004719 test-rmse:0.400301+0.059121
## [57] train-rmse:0.155443+0.004831 test-rmse:0.399281+0.059799
## [58] train-rmse:0.153460+0.005058 test-rmse:0.399242+0.060342
## [59] train-rmse:0.151212+0.004803 test-rmse:0.399611+0.059704
## [60] train-rmse:0.149241+0.004684 test-rmse:0.398348+0.059194
## [61] train-rmse:0.147276+0.004426 test-rmse:0.397776+0.059216
## [62] train-rmse:0.145776+0.003882 test-rmse:0.397725+0.059158
## [63] train-rmse:0.144127+0.003960 test-rmse:0.397334+0.058823
## [64] train-rmse:0.142992+0.004006 test-rmse:0.396888+0.058650
## [65] train-rmse:0.141546+0.004237 test-rmse:0.396477+0.058307
## [66] train-rmse:0.140172+0.003977 test-rmse:0.396381+0.058045
## [67] train-rmse:0.138421+0.004083 test-rmse:0.396188+0.057669
## [68] train-rmse:0.136422+0.004060 test-rmse:0.396479+0.057664
## [69] train-rmse:0.135249+0.004149 test-rmse:0.396213+0.057893
## [70] train-rmse:0.134478+0.004255 test-rmse:0.396250+0.058021
## [71] train-rmse:0.132928+0.004653 test-rmse:0.395767+0.057633
## [72] train-rmse:0.131492+0.004665 test-rmse:0.395609+0.057774
## [73] train-rmse:0.130277+0.005001 test-rmse:0.395170+0.058355
## [74] train-rmse:0.128945+0.004603 test-rmse:0.394773+0.058294
## [75] train-rmse:0.127253+0.004438 test-rmse:0.394237+0.058454
## [76] train-rmse:0.125967+0.004484 test-rmse:0.393780+0.058786
## [77] train-rmse:0.124813+0.004598 test-rmse:0.393660+0.058558
## [78] train-rmse:0.123704+0.004381 test-rmse:0.393291+0.058275
## [79] train-rmse:0.122838+0.004481 test-rmse:0.393152+0.057883
## [80] train-rmse:0.121703+0.004333 test-rmse:0.393341+0.057566
## [81] train-rmse:0.120103+0.003849 test-rmse:0.393453+0.057581
## [82] train-rmse:0.118730+0.004266 test-rmse:0.393436+0.057895
## [83] train-rmse:0.117433+0.004285 test-rmse:0.393376+0.057890
## [84] train-rmse:0.116223+0.004293 test-rmse:0.393761+0.057834
## [85] train-rmse:0.115067+0.004341 test-rmse:0.393327+0.057942
## Stopping. Best iteration:
## [79] train-rmse:0.122838+0.004481 test-rmse:0.393152+0.057883
##
## 12
## [1] train-rmse:1.460769+0.026923 test-rmse:1.459447+0.114903
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.305657+0.023981 test-rmse:1.303380+0.111464
## [3] train-rmse:1.169410+0.021266 test-rmse:1.167843+0.108728
## [4] train-rmse:1.049210+0.019766 test-rmse:1.050096+0.103219
## [5] train-rmse:0.943842+0.017840 test-rmse:0.949257+0.101299
## [6] train-rmse:0.850420+0.016640 test-rmse:0.860167+0.096954
## [7] train-rmse:0.768583+0.015113 test-rmse:0.783093+0.095870
## [8] train-rmse:0.696733+0.013517 test-rmse:0.717914+0.094908
## [9] train-rmse:0.633534+0.012830 test-rmse:0.661997+0.092729
## [10] train-rmse:0.577568+0.012068 test-rmse:0.615147+0.089848
## [11] train-rmse:0.528275+0.011342 test-rmse:0.573327+0.086587
## [12] train-rmse:0.485383+0.010793 test-rmse:0.541400+0.085185
## [13] train-rmse:0.447927+0.009989 test-rmse:0.513034+0.082855
## [14] train-rmse:0.414495+0.009400 test-rmse:0.492744+0.079502
## [15] train-rmse:0.385266+0.009129 test-rmse:0.473641+0.077232
## [16] train-rmse:0.359785+0.008660 test-rmse:0.461169+0.074856
## [17] train-rmse:0.337247+0.008573 test-rmse:0.450243+0.073829
## [18] train-rmse:0.317020+0.008755 test-rmse:0.442047+0.072329
## [19] train-rmse:0.299361+0.008368 test-rmse:0.435473+0.071828
## [20] train-rmse:0.284006+0.008361 test-rmse:0.429309+0.069281
## [21] train-rmse:0.270393+0.008979 test-rmse:0.425009+0.068368
## [22] train-rmse:0.257627+0.009614 test-rmse:0.421074+0.067222
## [23] train-rmse:0.247121+0.009370 test-rmse:0.417061+0.065282
## [24] train-rmse:0.237095+0.008948 test-rmse:0.412911+0.065789
## [25] train-rmse:0.228357+0.009722 test-rmse:0.412087+0.063593
## [26] train-rmse:0.220543+0.010053 test-rmse:0.409907+0.061244
## [27] train-rmse:0.212114+0.009156 test-rmse:0.408810+0.061740
## [28] train-rmse:0.205766+0.009224 test-rmse:0.406289+0.060916
## [29] train-rmse:0.200651+0.009130 test-rmse:0.405505+0.060333
## [30] train-rmse:0.195851+0.009347 test-rmse:0.404104+0.060145
## [31] train-rmse:0.190874+0.009722 test-rmse:0.403853+0.059285
## [32] train-rmse:0.186861+0.010338 test-rmse:0.404100+0.058651
## [33] train-rmse:0.182450+0.009904 test-rmse:0.403596+0.058073
## [34] train-rmse:0.179088+0.009889 test-rmse:0.402587+0.056755
## [35] train-rmse:0.175186+0.008688 test-rmse:0.402799+0.055907
## [36] train-rmse:0.171080+0.009476 test-rmse:0.402127+0.054880
## [37] train-rmse:0.168451+0.009551 test-rmse:0.401443+0.053810
## [38] train-rmse:0.164791+0.008629 test-rmse:0.400404+0.051998
## [39] train-rmse:0.160753+0.008895 test-rmse:0.400016+0.051317
## [40] train-rmse:0.158178+0.009513 test-rmse:0.399009+0.051615
## [41] train-rmse:0.155193+0.010273 test-rmse:0.398596+0.051083
## [42] train-rmse:0.151782+0.010275 test-rmse:0.398103+0.051579
## [43] train-rmse:0.149297+0.010546 test-rmse:0.397663+0.050823
## [44] train-rmse:0.145488+0.010334 test-rmse:0.397447+0.050684
## [45] train-rmse:0.142241+0.009796 test-rmse:0.397182+0.050165
## [46] train-rmse:0.138875+0.009540 test-rmse:0.396854+0.050693
## [47] train-rmse:0.136072+0.009411 test-rmse:0.397378+0.050060
## [48] train-rmse:0.133745+0.009194 test-rmse:0.397490+0.050004
## [49] train-rmse:0.131251+0.008897 test-rmse:0.397654+0.050180
## [50] train-rmse:0.128565+0.009217 test-rmse:0.397995+0.050344
## [51] train-rmse:0.126134+0.008760 test-rmse:0.397842+0.050303
## [52] train-rmse:0.123489+0.008815 test-rmse:0.398290+0.050518
## Stopping. Best iteration:
## [46] train-rmse:0.138875+0.009540 test-rmse:0.396854+0.050693
##
## 13
## [1] train-rmse:1.460620+0.026925 test-rmse:1.459981+0.114504
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.305176+0.024016 test-rmse:1.302791+0.110760
## [3] train-rmse:1.168546+0.021455 test-rmse:1.166150+0.108320
## [4] train-rmse:1.047938+0.019981 test-rmse:1.048352+0.103918
## [5] train-rmse:0.941849+0.018084 test-rmse:0.945950+0.102741
## [6] train-rmse:0.847786+0.016975 test-rmse:0.858587+0.100672
## [7] train-rmse:0.765325+0.015451 test-rmse:0.781969+0.099330
## [8] train-rmse:0.692796+0.014417 test-rmse:0.716043+0.099909
## [9] train-rmse:0.628715+0.013789 test-rmse:0.659697+0.096814
## [10] train-rmse:0.572222+0.013119 test-rmse:0.613255+0.094727
## [11] train-rmse:0.522052+0.012167 test-rmse:0.573136+0.093952
## [12] train-rmse:0.477503+0.010811 test-rmse:0.538800+0.093235
## [13] train-rmse:0.438108+0.010405 test-rmse:0.513512+0.090419
## [14] train-rmse:0.403494+0.009805 test-rmse:0.492028+0.089526
## [15] train-rmse:0.372847+0.010246 test-rmse:0.473821+0.087683
## [16] train-rmse:0.345874+0.010292 test-rmse:0.459503+0.087290
## [17] train-rmse:0.321869+0.010197 test-rmse:0.449939+0.084914
## [18] train-rmse:0.300212+0.009739 test-rmse:0.441538+0.084523
## [19] train-rmse:0.280628+0.010173 test-rmse:0.435053+0.082467
## [20] train-rmse:0.263633+0.009988 test-rmse:0.429524+0.080286
## [21] train-rmse:0.248588+0.010129 test-rmse:0.426034+0.079599
## [22] train-rmse:0.235467+0.009833 test-rmse:0.422548+0.078155
## [23] train-rmse:0.223301+0.009316 test-rmse:0.420599+0.076625
## [24] train-rmse:0.211698+0.009014 test-rmse:0.417889+0.074864
## [25] train-rmse:0.201566+0.008806 test-rmse:0.416042+0.072470
## [26] train-rmse:0.192639+0.008766 test-rmse:0.413945+0.072314
## [27] train-rmse:0.185707+0.009154 test-rmse:0.412922+0.071176
## [28] train-rmse:0.178481+0.009468 test-rmse:0.412185+0.070731
## [29] train-rmse:0.170898+0.010124 test-rmse:0.411077+0.070379
## [30] train-rmse:0.164551+0.010876 test-rmse:0.411001+0.069015
## [31] train-rmse:0.159132+0.011118 test-rmse:0.410860+0.068412
## [32] train-rmse:0.153617+0.011262 test-rmse:0.411538+0.067003
## [33] train-rmse:0.148929+0.011061 test-rmse:0.410536+0.066366
## [34] train-rmse:0.143801+0.011726 test-rmse:0.410814+0.066181
## [35] train-rmse:0.139725+0.010841 test-rmse:0.409832+0.065985
## [36] train-rmse:0.134111+0.010107 test-rmse:0.408793+0.064962
## [37] train-rmse:0.130679+0.010223 test-rmse:0.408567+0.063594
## [38] train-rmse:0.127511+0.010357 test-rmse:0.408280+0.064106
## [39] train-rmse:0.124686+0.010300 test-rmse:0.408112+0.063469
## [40] train-rmse:0.121800+0.010136 test-rmse:0.408724+0.063249
## [41] train-rmse:0.119097+0.009974 test-rmse:0.408089+0.063016
## [42] train-rmse:0.116237+0.008635 test-rmse:0.407809+0.062479
## [43] train-rmse:0.113711+0.007982 test-rmse:0.407742+0.062563
## [44] train-rmse:0.111024+0.008477 test-rmse:0.407608+0.062174
## [45] train-rmse:0.108388+0.007565 test-rmse:0.406833+0.062009
## [46] train-rmse:0.106524+0.007209 test-rmse:0.407224+0.061454
## [47] train-rmse:0.104176+0.007261 test-rmse:0.407196+0.061602
## [48] train-rmse:0.102518+0.007240 test-rmse:0.406782+0.061841
## [49] train-rmse:0.099928+0.007431 test-rmse:0.406508+0.061633
## [50] train-rmse:0.096979+0.006809 test-rmse:0.406203+0.061560
## [51] train-rmse:0.094555+0.006435 test-rmse:0.406227+0.061454
## [52] train-rmse:0.092749+0.005929 test-rmse:0.406261+0.061687
## [53] train-rmse:0.090525+0.005885 test-rmse:0.406114+0.061615
## [54] train-rmse:0.087806+0.005694 test-rmse:0.405992+0.060951
## [55] train-rmse:0.085451+0.005469 test-rmse:0.406214+0.060671
## [56] train-rmse:0.083178+0.005724 test-rmse:0.406223+0.060611
## [57] train-rmse:0.080844+0.005629 test-rmse:0.406469+0.060758
## [58] train-rmse:0.079224+0.005228 test-rmse:0.406321+0.060605
## [59] train-rmse:0.077199+0.005188 test-rmse:0.406511+0.060727
## [60] train-rmse:0.075800+0.005037 test-rmse:0.406179+0.060587
## Stopping. Best iteration:
## [54] train-rmse:0.087806+0.005694 test-rmse:0.405992+0.060951
##
## 14
## [1] train-rmse:1.448119+0.026902 test-rmse:1.444245+0.113031
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.289888+0.024180 test-rmse:1.286061+0.109354
## [3] train-rmse:1.158626+0.022071 test-rmse:1.154855+0.106087
## [4] train-rmse:1.050708+0.020506 test-rmse:1.047013+0.103110
## [5] train-rmse:0.962863+0.019410 test-rmse:0.959274+0.100363
## [6] train-rmse:0.892122+0.018696 test-rmse:0.888668+0.097830
## [7] train-rmse:0.835784+0.018272 test-rmse:0.832485+0.095518
## [8] train-rmse:0.791399+0.018054 test-rmse:0.788271+0.093445
## [9] train-rmse:0.756791+0.017970 test-rmse:0.753839+0.091623
## [10] train-rmse:0.730057+0.017963 test-rmse:0.727276+0.090050
## [11] train-rmse:0.709574+0.017998 test-rmse:0.706953+0.088713
## [12] train-rmse:0.692913+0.017523 test-rmse:0.692939+0.089075
## [13] train-rmse:0.678983+0.017311 test-rmse:0.676718+0.087812
## [14] train-rmse:0.666927+0.017015 test-rmse:0.667430+0.087682
## [15] train-rmse:0.656718+0.016882 test-rmse:0.655852+0.086450
## [16] train-rmse:0.647643+0.016334 test-rmse:0.649579+0.085140
## [17] train-rmse:0.639615+0.015685 test-rmse:0.640566+0.082148
## [18] train-rmse:0.632114+0.015302 test-rmse:0.633837+0.082698
## [19] train-rmse:0.625348+0.014646 test-rmse:0.628955+0.081127
## [20] train-rmse:0.619183+0.014368 test-rmse:0.620915+0.080018
## [21] train-rmse:0.613446+0.013924 test-rmse:0.616654+0.077865
## [22] train-rmse:0.608238+0.013528 test-rmse:0.611492+0.075920
## [23] train-rmse:0.603471+0.013302 test-rmse:0.607143+0.076756
## [24] train-rmse:0.598860+0.012987 test-rmse:0.603035+0.073773
## [25] train-rmse:0.594624+0.012842 test-rmse:0.600980+0.073670
## [26] train-rmse:0.590733+0.012604 test-rmse:0.596117+0.071263
## [27] train-rmse:0.587117+0.012428 test-rmse:0.592575+0.071351
## [28] train-rmse:0.583613+0.012147 test-rmse:0.590615+0.069573
## [29] train-rmse:0.580311+0.012075 test-rmse:0.587290+0.069577
## [30] train-rmse:0.577202+0.011983 test-rmse:0.586510+0.069196
## [31] train-rmse:0.574229+0.011858 test-rmse:0.582789+0.068559
## [32] train-rmse:0.571426+0.011701 test-rmse:0.581117+0.066935
## [33] train-rmse:0.568747+0.011580 test-rmse:0.579724+0.066329
## [34] train-rmse:0.566203+0.011503 test-rmse:0.576558+0.065532
## [35] train-rmse:0.563784+0.011413 test-rmse:0.575686+0.065635
## [36] train-rmse:0.561520+0.011222 test-rmse:0.574536+0.065145
## [37] train-rmse:0.559306+0.011109 test-rmse:0.571638+0.064049
## [38] train-rmse:0.557168+0.011024 test-rmse:0.569510+0.064404
## [39] train-rmse:0.555168+0.010988 test-rmse:0.567728+0.063670
## [40] train-rmse:0.553219+0.010914 test-rmse:0.566665+0.063763
## [41] train-rmse:0.551380+0.010780 test-rmse:0.566551+0.062965
## [42] train-rmse:0.549673+0.010721 test-rmse:0.565033+0.063520
## [43] train-rmse:0.547967+0.010655 test-rmse:0.562659+0.062143
## [44] train-rmse:0.546276+0.010618 test-rmse:0.562242+0.062057
## [45] train-rmse:0.544679+0.010567 test-rmse:0.561061+0.062060
## [46] train-rmse:0.543119+0.010456 test-rmse:0.561155+0.061664
## [47] train-rmse:0.541656+0.010406 test-rmse:0.560193+0.060113
## [48] train-rmse:0.540251+0.010363 test-rmse:0.558118+0.059702
## [49] train-rmse:0.538836+0.010301 test-rmse:0.557061+0.060184
## [50] train-rmse:0.537469+0.010249 test-rmse:0.556862+0.060590
## [51] train-rmse:0.536127+0.010194 test-rmse:0.555343+0.059997
## [52] train-rmse:0.534833+0.010127 test-rmse:0.554027+0.058487
## [53] train-rmse:0.533529+0.010074 test-rmse:0.553533+0.058941
## [54] train-rmse:0.532297+0.009993 test-rmse:0.554073+0.058608
## [55] train-rmse:0.531084+0.009931 test-rmse:0.553150+0.058303
## [56] train-rmse:0.529909+0.009872 test-rmse:0.552668+0.058679
## [57] train-rmse:0.528744+0.009809 test-rmse:0.551376+0.058610
## [58] train-rmse:0.527618+0.009779 test-rmse:0.550510+0.058173
## [59] train-rmse:0.526523+0.009737 test-rmse:0.550125+0.057799
## [60] train-rmse:0.525416+0.009636 test-rmse:0.548938+0.058174
## [61] train-rmse:0.524342+0.009551 test-rmse:0.548584+0.058267
## [62] train-rmse:0.523284+0.009500 test-rmse:0.548041+0.058302
## [63] train-rmse:0.522261+0.009465 test-rmse:0.546670+0.057747
## [64] train-rmse:0.521266+0.009392 test-rmse:0.545775+0.057495
## [65] train-rmse:0.520297+0.009332 test-rmse:0.545077+0.057463
## [66] train-rmse:0.519346+0.009266 test-rmse:0.544461+0.055998
## [67] train-rmse:0.518399+0.009230 test-rmse:0.544379+0.056309
## [68] train-rmse:0.517476+0.009194 test-rmse:0.543617+0.055954
## [69] train-rmse:0.516548+0.009155 test-rmse:0.543154+0.055720
## [70] train-rmse:0.515613+0.009081 test-rmse:0.542715+0.056634
## [71] train-rmse:0.514719+0.009046 test-rmse:0.542168+0.056341
## [72] train-rmse:0.513851+0.009010 test-rmse:0.541207+0.056381
## [73] train-rmse:0.512991+0.008976 test-rmse:0.540441+0.056694
## [74] train-rmse:0.512149+0.008939 test-rmse:0.539937+0.056286
## [75] train-rmse:0.511304+0.008888 test-rmse:0.539315+0.055721
## [76] train-rmse:0.510440+0.008858 test-rmse:0.538888+0.055676
## [77] train-rmse:0.509610+0.008837 test-rmse:0.536690+0.055624
## [78] train-rmse:0.508815+0.008818 test-rmse:0.536412+0.055588
## [79] train-rmse:0.508035+0.008797 test-rmse:0.536158+0.055626
## [80] train-rmse:0.507271+0.008774 test-rmse:0.535345+0.054607
## [81] train-rmse:0.506480+0.008754 test-rmse:0.535103+0.054457
## [82] train-rmse:0.505692+0.008726 test-rmse:0.535279+0.054462
## [83] train-rmse:0.504943+0.008690 test-rmse:0.534719+0.054662
## [84] train-rmse:0.504196+0.008645 test-rmse:0.533817+0.054435
## [85] train-rmse:0.503461+0.008628 test-rmse:0.532992+0.053919
## [86] train-rmse:0.502698+0.008604 test-rmse:0.532664+0.054491
## [87] train-rmse:0.501986+0.008577 test-rmse:0.531779+0.054426
## [88] train-rmse:0.501280+0.008550 test-rmse:0.532150+0.054363
## [89] train-rmse:0.500585+0.008515 test-rmse:0.530983+0.054507
## [90] train-rmse:0.499908+0.008480 test-rmse:0.530582+0.054647
## [91] train-rmse:0.499230+0.008461 test-rmse:0.529919+0.054731
## [92] train-rmse:0.498534+0.008453 test-rmse:0.529380+0.054164
## [93] train-rmse:0.497864+0.008435 test-rmse:0.528780+0.054315
## [94] train-rmse:0.497199+0.008427 test-rmse:0.528563+0.053757
## [95] train-rmse:0.496539+0.008405 test-rmse:0.528234+0.053905
## [96] train-rmse:0.495889+0.008382 test-rmse:0.528197+0.053824
## [97] train-rmse:0.495239+0.008362 test-rmse:0.527216+0.053301
## [98] train-rmse:0.494609+0.008340 test-rmse:0.527041+0.053159
## [99] train-rmse:0.493991+0.008336 test-rmse:0.527162+0.053181
## [100] train-rmse:0.493383+0.008336 test-rmse:0.526457+0.053590
## [101] train-rmse:0.492776+0.008310 test-rmse:0.526051+0.053714
## [102] train-rmse:0.492165+0.008285 test-rmse:0.525484+0.053477
## [103] train-rmse:0.491555+0.008262 test-rmse:0.525190+0.052601
## [104] train-rmse:0.490970+0.008254 test-rmse:0.525345+0.051990
## [105] train-rmse:0.490403+0.008242 test-rmse:0.525131+0.052218
## [106] train-rmse:0.489832+0.008233 test-rmse:0.524698+0.052109
## [107] train-rmse:0.489259+0.008218 test-rmse:0.524283+0.052512
## [108] train-rmse:0.488699+0.008196 test-rmse:0.523818+0.052075
## [109] train-rmse:0.488134+0.008181 test-rmse:0.522575+0.051946
## [110] train-rmse:0.487582+0.008174 test-rmse:0.522054+0.051817
## [111] train-rmse:0.487048+0.008148 test-rmse:0.521600+0.052399
## [112] train-rmse:0.486506+0.008119 test-rmse:0.521818+0.052179
## [113] train-rmse:0.485967+0.008097 test-rmse:0.521854+0.051998
## [114] train-rmse:0.485441+0.008079 test-rmse:0.521240+0.052006
## [115] train-rmse:0.484918+0.008079 test-rmse:0.520858+0.051538
## [116] train-rmse:0.484406+0.008069 test-rmse:0.521024+0.051807
## [117] train-rmse:0.483899+0.008054 test-rmse:0.520928+0.052063
## [118] train-rmse:0.483407+0.008046 test-rmse:0.521157+0.051819
## [119] train-rmse:0.482917+0.008035 test-rmse:0.520100+0.051730
## [120] train-rmse:0.482416+0.008022 test-rmse:0.520336+0.051934
## [121] train-rmse:0.481926+0.008002 test-rmse:0.519772+0.051811
## [122] train-rmse:0.481439+0.008007 test-rmse:0.519587+0.051131
## [123] train-rmse:0.480973+0.007996 test-rmse:0.519597+0.050969
## [124] train-rmse:0.480510+0.007981 test-rmse:0.518896+0.050851
## [125] train-rmse:0.480054+0.007973 test-rmse:0.518708+0.051113
## [126] train-rmse:0.479598+0.007962 test-rmse:0.518416+0.050618
## [127] train-rmse:0.479137+0.007963 test-rmse:0.517636+0.051078
## [128] train-rmse:0.478679+0.007949 test-rmse:0.517612+0.050917
## [129] train-rmse:0.478231+0.007929 test-rmse:0.517472+0.050589
## [130] train-rmse:0.477787+0.007919 test-rmse:0.517161+0.050627
## [131] train-rmse:0.477355+0.007910 test-rmse:0.516973+0.050243
## [132] train-rmse:0.476918+0.007899 test-rmse:0.516439+0.050116
## [133] train-rmse:0.476484+0.007894 test-rmse:0.515667+0.048837
## [134] train-rmse:0.476052+0.007879 test-rmse:0.515478+0.048859
## [135] train-rmse:0.475625+0.007875 test-rmse:0.515253+0.049011
## [136] train-rmse:0.475214+0.007866 test-rmse:0.515085+0.048567
## [137] train-rmse:0.474800+0.007869 test-rmse:0.514762+0.048849
## [138] train-rmse:0.474389+0.007864 test-rmse:0.514503+0.048626
## [139] train-rmse:0.473983+0.007854 test-rmse:0.514543+0.048744
## [140] train-rmse:0.473580+0.007840 test-rmse:0.514438+0.048284
## [141] train-rmse:0.473175+0.007834 test-rmse:0.514404+0.048357
## [142] train-rmse:0.472774+0.007824 test-rmse:0.513645+0.048373
## [143] train-rmse:0.472392+0.007814 test-rmse:0.512918+0.048118
## [144] train-rmse:0.472008+0.007804 test-rmse:0.513271+0.048607
## [145] train-rmse:0.471629+0.007795 test-rmse:0.513013+0.048692
## [146] train-rmse:0.471253+0.007785 test-rmse:0.512688+0.048787
## [147] train-rmse:0.470867+0.007776 test-rmse:0.512435+0.049121
## [148] train-rmse:0.470496+0.007771 test-rmse:0.512039+0.048756
## [149] train-rmse:0.470111+0.007767 test-rmse:0.512345+0.048199
## [150] train-rmse:0.469737+0.007761 test-rmse:0.511672+0.048465
## [151] train-rmse:0.469376+0.007753 test-rmse:0.511776+0.048602
## [152] train-rmse:0.469023+0.007742 test-rmse:0.512106+0.048407
## [153] train-rmse:0.468659+0.007735 test-rmse:0.511951+0.048856
## [154] train-rmse:0.468310+0.007732 test-rmse:0.511230+0.048557
## [155] train-rmse:0.467952+0.007727 test-rmse:0.511225+0.048079
## [156] train-rmse:0.467599+0.007714 test-rmse:0.510637+0.048207
## [157] train-rmse:0.467251+0.007708 test-rmse:0.509737+0.047211
## [158] train-rmse:0.466906+0.007693 test-rmse:0.509558+0.047235
## [159] train-rmse:0.466558+0.007690 test-rmse:0.509291+0.047238
## [160] train-rmse:0.466219+0.007683 test-rmse:0.508762+0.046953
## [161] train-rmse:0.465887+0.007675 test-rmse:0.508839+0.046904
## [162] train-rmse:0.465554+0.007662 test-rmse:0.508749+0.046863
## [163] train-rmse:0.465216+0.007660 test-rmse:0.508610+0.046916
## [164] train-rmse:0.464885+0.007646 test-rmse:0.508447+0.046851
## [165] train-rmse:0.464560+0.007637 test-rmse:0.508904+0.046697
## [166] train-rmse:0.464242+0.007630 test-rmse:0.508957+0.046711
## [167] train-rmse:0.463923+0.007619 test-rmse:0.508866+0.046012
## [168] train-rmse:0.463604+0.007609 test-rmse:0.508653+0.046173
## [169] train-rmse:0.463286+0.007601 test-rmse:0.507914+0.046039
## [170] train-rmse:0.462968+0.007599 test-rmse:0.508348+0.046223
## [171] train-rmse:0.462647+0.007587 test-rmse:0.508439+0.046358
## [172] train-rmse:0.462339+0.007580 test-rmse:0.507976+0.046542
## [173] train-rmse:0.462025+0.007573 test-rmse:0.507704+0.046506
## [174] train-rmse:0.461723+0.007567 test-rmse:0.507751+0.046655
## [175] train-rmse:0.461423+0.007566 test-rmse:0.507564+0.046492
## [176] train-rmse:0.461125+0.007556 test-rmse:0.507691+0.046476
## [177] train-rmse:0.460833+0.007545 test-rmse:0.507642+0.046539
## [178] train-rmse:0.460534+0.007531 test-rmse:0.507367+0.046194
## [179] train-rmse:0.460243+0.007525 test-rmse:0.507251+0.045654
## [180] train-rmse:0.459950+0.007524 test-rmse:0.507290+0.045992
## [181] train-rmse:0.459662+0.007520 test-rmse:0.506903+0.046044
## [182] train-rmse:0.459378+0.007523 test-rmse:0.506961+0.046085
## [183] train-rmse:0.459088+0.007522 test-rmse:0.506890+0.046291
## [184] train-rmse:0.458810+0.007519 test-rmse:0.506465+0.046157
## [185] train-rmse:0.458526+0.007504 test-rmse:0.506415+0.046019
## [186] train-rmse:0.458245+0.007496 test-rmse:0.506407+0.045842
## [187] train-rmse:0.457966+0.007498 test-rmse:0.506268+0.045532
## [188] train-rmse:0.457692+0.007497 test-rmse:0.506108+0.045054
## [189] train-rmse:0.457417+0.007493 test-rmse:0.504966+0.044152
## [190] train-rmse:0.457149+0.007485 test-rmse:0.504905+0.044440
## [191] train-rmse:0.456884+0.007480 test-rmse:0.504671+0.044742
## [192] train-rmse:0.456621+0.007476 test-rmse:0.504767+0.044577
## [193] train-rmse:0.456358+0.007475 test-rmse:0.504540+0.044322
## [194] train-rmse:0.456092+0.007471 test-rmse:0.504573+0.044562
## [195] train-rmse:0.455832+0.007466 test-rmse:0.504356+0.044563
## [196] train-rmse:0.455573+0.007460 test-rmse:0.504190+0.044116
## [197] train-rmse:0.455315+0.007452 test-rmse:0.504092+0.044296
## [198] train-rmse:0.455047+0.007446 test-rmse:0.504105+0.044530
## [199] train-rmse:0.454792+0.007440 test-rmse:0.503833+0.044940
## [200] train-rmse:0.454537+0.007440 test-rmse:0.503610+0.044947
## [201] train-rmse:0.454280+0.007431 test-rmse:0.503825+0.044760
## [202] train-rmse:0.454024+0.007431 test-rmse:0.503498+0.044516
## [203] train-rmse:0.453781+0.007433 test-rmse:0.503399+0.044770
## [204] train-rmse:0.453533+0.007432 test-rmse:0.503461+0.044407
## [205] train-rmse:0.453286+0.007430 test-rmse:0.503675+0.044243
## [206] train-rmse:0.453044+0.007427 test-rmse:0.503579+0.044305
## [207] train-rmse:0.452803+0.007424 test-rmse:0.503066+0.043981
## [208] train-rmse:0.452569+0.007422 test-rmse:0.503026+0.043918
## [209] train-rmse:0.452341+0.007422 test-rmse:0.502831+0.043999
## [210] train-rmse:0.452110+0.007422 test-rmse:0.502986+0.043917
## [211] train-rmse:0.451876+0.007422 test-rmse:0.502734+0.043722
## [212] train-rmse:0.451642+0.007426 test-rmse:0.502788+0.043335
## [213] train-rmse:0.451414+0.007424 test-rmse:0.502453+0.043349
## [214] train-rmse:0.451182+0.007423 test-rmse:0.502613+0.043595
## [215] train-rmse:0.450960+0.007425 test-rmse:0.502445+0.043556
## [216] train-rmse:0.450740+0.007426 test-rmse:0.502098+0.043637
## [217] train-rmse:0.450520+0.007428 test-rmse:0.502450+0.043405
## [218] train-rmse:0.450298+0.007424 test-rmse:0.502213+0.043076
## [219] train-rmse:0.450076+0.007419 test-rmse:0.501973+0.042978
## [220] train-rmse:0.449858+0.007418 test-rmse:0.501955+0.042957
## [221] train-rmse:0.449638+0.007418 test-rmse:0.502166+0.043118
## [222] train-rmse:0.449427+0.007417 test-rmse:0.502226+0.043016
## [223] train-rmse:0.449213+0.007417 test-rmse:0.502394+0.043006
## [224] train-rmse:0.448994+0.007420 test-rmse:0.502372+0.043154
## [225] train-rmse:0.448781+0.007421 test-rmse:0.502290+0.043326
## [226] train-rmse:0.448572+0.007425 test-rmse:0.501779+0.042644
## [227] train-rmse:0.448365+0.007425 test-rmse:0.501743+0.042667
## [228] train-rmse:0.448158+0.007419 test-rmse:0.501801+0.042291
## [229] train-rmse:0.447947+0.007415 test-rmse:0.501392+0.041980
## [230] train-rmse:0.447741+0.007415 test-rmse:0.501291+0.042144
## [231] train-rmse:0.447533+0.007410 test-rmse:0.501160+0.042246
## [232] train-rmse:0.447331+0.007410 test-rmse:0.501176+0.042370
## [233] train-rmse:0.447132+0.007409 test-rmse:0.501057+0.042360
## [234] train-rmse:0.446933+0.007408 test-rmse:0.501033+0.042301
## [235] train-rmse:0.446730+0.007406 test-rmse:0.501165+0.042725
## [236] train-rmse:0.446528+0.007401 test-rmse:0.501325+0.042855
## [237] train-rmse:0.446330+0.007401 test-rmse:0.500782+0.042701
## [238] train-rmse:0.446135+0.007402 test-rmse:0.500595+0.042945
## [239] train-rmse:0.445943+0.007402 test-rmse:0.500701+0.042822
## [240] train-rmse:0.445743+0.007404 test-rmse:0.500673+0.042837
## [241] train-rmse:0.445550+0.007411 test-rmse:0.500363+0.042606
## [242] train-rmse:0.445360+0.007412 test-rmse:0.500217+0.042310
## [243] train-rmse:0.445168+0.007409 test-rmse:0.500258+0.042328
## [244] train-rmse:0.444977+0.007405 test-rmse:0.500074+0.042406
## [245] train-rmse:0.444790+0.007405 test-rmse:0.500195+0.042687
## [246] train-rmse:0.444607+0.007404 test-rmse:0.499846+0.042701
## [247] train-rmse:0.444424+0.007407 test-rmse:0.499432+0.042466
## [248] train-rmse:0.444244+0.007405 test-rmse:0.499315+0.042455
## [249] train-rmse:0.444060+0.007404 test-rmse:0.499372+0.042413
## [250] train-rmse:0.443876+0.007403 test-rmse:0.499440+0.042448
## [251] train-rmse:0.443691+0.007411 test-rmse:0.499023+0.042126
## [252] train-rmse:0.443513+0.007410 test-rmse:0.498809+0.042132
## [253] train-rmse:0.443334+0.007408 test-rmse:0.499034+0.042367
## [254] train-rmse:0.443149+0.007408 test-rmse:0.499068+0.042630
## [255] train-rmse:0.442973+0.007410 test-rmse:0.498970+0.042712
## [256] train-rmse:0.442790+0.007407 test-rmse:0.499068+0.042690
## [257] train-rmse:0.442612+0.007407 test-rmse:0.499056+0.042911
## [258] train-rmse:0.442433+0.007412 test-rmse:0.498616+0.042641
## [259] train-rmse:0.442263+0.007412 test-rmse:0.498675+0.042659
## [260] train-rmse:0.442088+0.007413 test-rmse:0.498738+0.042431
## [261] train-rmse:0.441912+0.007412 test-rmse:0.498562+0.042665
## [262] train-rmse:0.441740+0.007412 test-rmse:0.498517+0.042425
## [263] train-rmse:0.441569+0.007410 test-rmse:0.498736+0.042362
## [264] train-rmse:0.441393+0.007412 test-rmse:0.498472+0.042307
## [265] train-rmse:0.441222+0.007414 test-rmse:0.498564+0.042057
## [266] train-rmse:0.441054+0.007415 test-rmse:0.498081+0.041252
## [267] train-rmse:0.440880+0.007415 test-rmse:0.498056+0.041147
## [268] train-rmse:0.440712+0.007418 test-rmse:0.498103+0.040883
## [269] train-rmse:0.440544+0.007421 test-rmse:0.498261+0.040931
## [270] train-rmse:0.440380+0.007419 test-rmse:0.498271+0.040900
## [271] train-rmse:0.440216+0.007420 test-rmse:0.498028+0.040768
## [272] train-rmse:0.440050+0.007422 test-rmse:0.498034+0.040814
## [273] train-rmse:0.439886+0.007422 test-rmse:0.497646+0.040974
## [274] train-rmse:0.439722+0.007421 test-rmse:0.497729+0.041081
## [275] train-rmse:0.439560+0.007421 test-rmse:0.497664+0.041105
## [276] train-rmse:0.439396+0.007422 test-rmse:0.497654+0.041166
## [277] train-rmse:0.439236+0.007426 test-rmse:0.497666+0.041222
## [278] train-rmse:0.439072+0.007429 test-rmse:0.497538+0.041246
## [279] train-rmse:0.438911+0.007430 test-rmse:0.497471+0.040960
## [280] train-rmse:0.438749+0.007431 test-rmse:0.497624+0.040873
## [281] train-rmse:0.438589+0.007430 test-rmse:0.497331+0.040850
## [282] train-rmse:0.438434+0.007432 test-rmse:0.497309+0.041094
## [283] train-rmse:0.438279+0.007435 test-rmse:0.497222+0.040899
## [284] train-rmse:0.438123+0.007437 test-rmse:0.497081+0.040943
## [285] train-rmse:0.437965+0.007433 test-rmse:0.496701+0.041005
## [286] train-rmse:0.437812+0.007432 test-rmse:0.496602+0.041038
## [287] train-rmse:0.437658+0.007432 test-rmse:0.496895+0.041231
## [288] train-rmse:0.437500+0.007438 test-rmse:0.496701+0.041185
## [289] train-rmse:0.437346+0.007439 test-rmse:0.496736+0.041212
## [290] train-rmse:0.437194+0.007442 test-rmse:0.496423+0.040892
## [291] train-rmse:0.437040+0.007444 test-rmse:0.496149+0.040577
## [292] train-rmse:0.436884+0.007445 test-rmse:0.496497+0.040740
## [293] train-rmse:0.436731+0.007445 test-rmse:0.496366+0.040693
## [294] train-rmse:0.436582+0.007447 test-rmse:0.496803+0.040646
## [295] train-rmse:0.436433+0.007450 test-rmse:0.496756+0.040624
## [296] train-rmse:0.436285+0.007454 test-rmse:0.496666+0.040667
## [297] train-rmse:0.436140+0.007452 test-rmse:0.496943+0.040946
## Stopping. Best iteration:
## [291] train-rmse:0.437040+0.007444 test-rmse:0.496149+0.040577
##
## 15
## [1] train-rmse:1.439504+0.026920 test-rmse:1.437417+0.115482
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.271640+0.024051 test-rmse:1.271576+0.113815
## [3] train-rmse:1.129551+0.021700 test-rmse:1.131199+0.108565
## [4] train-rmse:1.009940+0.019778 test-rmse:1.013998+0.107805
## [5] train-rmse:0.909803+0.018165 test-rmse:0.909524+0.103966
## [6] train-rmse:0.825924+0.016901 test-rmse:0.831223+0.103737
## [7] train-rmse:0.756725+0.015635 test-rmse:0.762474+0.104311
## [8] train-rmse:0.699484+0.014805 test-rmse:0.706836+0.101950
## [9] train-rmse:0.652415+0.014209 test-rmse:0.661603+0.099653
## [10] train-rmse:0.613842+0.013916 test-rmse:0.623590+0.096835
## [11] train-rmse:0.582283+0.013417 test-rmse:0.593004+0.093128
## [12] train-rmse:0.556504+0.012982 test-rmse:0.567724+0.091049
## [13] train-rmse:0.535108+0.012576 test-rmse:0.549754+0.088943
## [14] train-rmse:0.517411+0.012293 test-rmse:0.536188+0.084368
## [15] train-rmse:0.502689+0.011864 test-rmse:0.523860+0.082740
## [16] train-rmse:0.490213+0.011504 test-rmse:0.509897+0.079111
## [17] train-rmse:0.479794+0.011122 test-rmse:0.504376+0.075335
## [18] train-rmse:0.469257+0.009670 test-rmse:0.494674+0.074633
## [19] train-rmse:0.459964+0.009540 test-rmse:0.487562+0.072507
## [20] train-rmse:0.451956+0.009333 test-rmse:0.480188+0.070559
## [21] train-rmse:0.445183+0.008926 test-rmse:0.475955+0.069890
## [22] train-rmse:0.438824+0.008113 test-rmse:0.472266+0.069622
## [23] train-rmse:0.433525+0.007889 test-rmse:0.469613+0.067390
## [24] train-rmse:0.428284+0.007553 test-rmse:0.466888+0.067734
## [25] train-rmse:0.423215+0.007184 test-rmse:0.465645+0.066090
## [26] train-rmse:0.418730+0.007040 test-rmse:0.464123+0.062884
## [27] train-rmse:0.414762+0.006833 test-rmse:0.461947+0.063026
## [28] train-rmse:0.410621+0.006769 test-rmse:0.458097+0.061821
## [29] train-rmse:0.407030+0.006276 test-rmse:0.456084+0.062191
## [30] train-rmse:0.403513+0.005498 test-rmse:0.455528+0.062253
## [31] train-rmse:0.399981+0.005283 test-rmse:0.454703+0.061213
## [32] train-rmse:0.397168+0.005191 test-rmse:0.454315+0.060476
## [33] train-rmse:0.394136+0.004777 test-rmse:0.453358+0.059761
## [34] train-rmse:0.391468+0.004537 test-rmse:0.450537+0.059653
## [35] train-rmse:0.388518+0.004447 test-rmse:0.448984+0.057344
## [36] train-rmse:0.385969+0.004512 test-rmse:0.447729+0.056615
## [37] train-rmse:0.383550+0.004439 test-rmse:0.446790+0.056474
## [38] train-rmse:0.380761+0.004158 test-rmse:0.447461+0.056994
## [39] train-rmse:0.378275+0.004180 test-rmse:0.446774+0.056466
## [40] train-rmse:0.375997+0.004237 test-rmse:0.447009+0.055452
## [41] train-rmse:0.373951+0.004205 test-rmse:0.446461+0.054823
## [42] train-rmse:0.371725+0.004126 test-rmse:0.446491+0.054400
## [43] train-rmse:0.369522+0.003957 test-rmse:0.446210+0.055227
## [44] train-rmse:0.367659+0.004064 test-rmse:0.445295+0.055560
## [45] train-rmse:0.365612+0.003765 test-rmse:0.444550+0.055474
## [46] train-rmse:0.362680+0.003658 test-rmse:0.443681+0.055073
## [47] train-rmse:0.360578+0.003629 test-rmse:0.441979+0.053893
## [48] train-rmse:0.358790+0.003524 test-rmse:0.442192+0.054167
## [49] train-rmse:0.357114+0.003423 test-rmse:0.442074+0.054525
## [50] train-rmse:0.354960+0.003291 test-rmse:0.441637+0.054020
## [51] train-rmse:0.353278+0.003298 test-rmse:0.441994+0.053844
## [52] train-rmse:0.351772+0.003306 test-rmse:0.442505+0.054297
## [53] train-rmse:0.349721+0.003574 test-rmse:0.442538+0.054585
## [54] train-rmse:0.348159+0.003319 test-rmse:0.441102+0.055583
## [55] train-rmse:0.346719+0.003117 test-rmse:0.441001+0.055422
## [56] train-rmse:0.344983+0.003089 test-rmse:0.440853+0.055103
## [57] train-rmse:0.343286+0.002976 test-rmse:0.439721+0.055031
## [58] train-rmse:0.342048+0.002980 test-rmse:0.439308+0.055519
## [59] train-rmse:0.340643+0.003038 test-rmse:0.439036+0.055238
## [60] train-rmse:0.339421+0.002910 test-rmse:0.438464+0.055957
## [61] train-rmse:0.338021+0.003327 test-rmse:0.437616+0.055820
## [62] train-rmse:0.336659+0.003272 test-rmse:0.438674+0.055231
## [63] train-rmse:0.335329+0.003485 test-rmse:0.438884+0.054864
## [64] train-rmse:0.334076+0.003386 test-rmse:0.438978+0.055088
## [65] train-rmse:0.332766+0.003309 test-rmse:0.438668+0.055074
## [66] train-rmse:0.331155+0.003315 test-rmse:0.438835+0.054801
## [67] train-rmse:0.329953+0.003479 test-rmse:0.438015+0.055124
## Stopping. Best iteration:
## [61] train-rmse:0.338021+0.003327 test-rmse:0.437616+0.055820
##
## 16
## [1] train-rmse:1.435017+0.026435 test-rmse:1.435173+0.117104
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.262508+0.023445 test-rmse:1.264194+0.113085
## [3] train-rmse:1.114929+0.020758 test-rmse:1.121497+0.112691
## [4] train-rmse:0.989723+0.018394 test-rmse:0.994484+0.112424
## [5] train-rmse:0.883168+0.016351 test-rmse:0.891676+0.112650
## [6] train-rmse:0.792264+0.015862 test-rmse:0.800073+0.107866
## [7] train-rmse:0.716142+0.014332 test-rmse:0.729609+0.103769
## [8] train-rmse:0.652776+0.013370 test-rmse:0.671523+0.103923
## [9] train-rmse:0.598742+0.012978 test-rmse:0.622368+0.098067
## [10] train-rmse:0.554277+0.012241 test-rmse:0.584493+0.094313
## [11] train-rmse:0.516693+0.011389 test-rmse:0.553307+0.094242
## [12] train-rmse:0.485540+0.011554 test-rmse:0.528157+0.088092
## [13] train-rmse:0.459521+0.010906 test-rmse:0.508161+0.085229
## [14] train-rmse:0.437904+0.010400 test-rmse:0.491598+0.082890
## [15] train-rmse:0.419972+0.009925 test-rmse:0.477207+0.080949
## [16] train-rmse:0.405182+0.010321 test-rmse:0.468258+0.076904
## [17] train-rmse:0.391723+0.009679 test-rmse:0.461740+0.075359
## [18] train-rmse:0.381323+0.009934 test-rmse:0.454414+0.072252
## [19] train-rmse:0.372217+0.009811 test-rmse:0.449299+0.070434
## [20] train-rmse:0.364720+0.009952 test-rmse:0.446644+0.069491
## [21] train-rmse:0.357855+0.010014 test-rmse:0.444515+0.066482
## [22] train-rmse:0.351951+0.009691 test-rmse:0.441016+0.066934
## [23] train-rmse:0.346425+0.009813 test-rmse:0.439723+0.065771
## [24] train-rmse:0.340624+0.009998 test-rmse:0.437671+0.065560
## [25] train-rmse:0.335759+0.010022 test-rmse:0.435984+0.063762
## [26] train-rmse:0.331384+0.010247 test-rmse:0.434210+0.063011
## [27] train-rmse:0.327432+0.010188 test-rmse:0.434088+0.062441
## [28] train-rmse:0.323291+0.009776 test-rmse:0.434054+0.062030
## [29] train-rmse:0.319291+0.009856 test-rmse:0.432435+0.061734
## [30] train-rmse:0.316144+0.009437 test-rmse:0.431930+0.060690
## [31] train-rmse:0.313313+0.009307 test-rmse:0.432296+0.060827
## [32] train-rmse:0.310020+0.009312 test-rmse:0.430845+0.061976
## [33] train-rmse:0.306900+0.008662 test-rmse:0.429859+0.062063
## [34] train-rmse:0.303712+0.008335 test-rmse:0.429667+0.061385
## [35] train-rmse:0.301103+0.008023 test-rmse:0.430233+0.060911
## [36] train-rmse:0.298126+0.008126 test-rmse:0.430284+0.061314
## [37] train-rmse:0.295855+0.007815 test-rmse:0.429534+0.061856
## [38] train-rmse:0.293779+0.007682 test-rmse:0.428671+0.061358
## [39] train-rmse:0.290642+0.007364 test-rmse:0.427962+0.061471
## [40] train-rmse:0.288413+0.007217 test-rmse:0.427479+0.061770
## [41] train-rmse:0.286161+0.006777 test-rmse:0.427489+0.062764
## [42] train-rmse:0.283459+0.007219 test-rmse:0.427039+0.062478
## [43] train-rmse:0.281385+0.006784 test-rmse:0.426468+0.062294
## [44] train-rmse:0.278772+0.006674 test-rmse:0.425451+0.062773
## [45] train-rmse:0.276495+0.006662 test-rmse:0.426177+0.062834
## [46] train-rmse:0.274568+0.006528 test-rmse:0.425738+0.065126
## [47] train-rmse:0.272756+0.006320 test-rmse:0.424803+0.065512
## [48] train-rmse:0.270473+0.006645 test-rmse:0.424322+0.065966
## [49] train-rmse:0.268492+0.006411 test-rmse:0.423851+0.066036
## [50] train-rmse:0.266805+0.006318 test-rmse:0.424329+0.066909
## [51] train-rmse:0.265064+0.006346 test-rmse:0.424128+0.066554
## [52] train-rmse:0.263140+0.006170 test-rmse:0.423117+0.066918
## [53] train-rmse:0.260837+0.006173 test-rmse:0.422554+0.067235
## [54] train-rmse:0.258867+0.006371 test-rmse:0.422149+0.066913
## [55] train-rmse:0.256985+0.005749 test-rmse:0.421635+0.067483
## [56] train-rmse:0.254961+0.005818 test-rmse:0.421504+0.066788
## [57] train-rmse:0.253283+0.005948 test-rmse:0.421023+0.067109
## [58] train-rmse:0.251644+0.005948 test-rmse:0.420931+0.067912
## [59] train-rmse:0.249983+0.005792 test-rmse:0.420906+0.067446
## [60] train-rmse:0.248118+0.005795 test-rmse:0.419824+0.067904
## [61] train-rmse:0.246519+0.005502 test-rmse:0.419829+0.068299
## [62] train-rmse:0.245316+0.005540 test-rmse:0.419672+0.068369
## [63] train-rmse:0.243921+0.005391 test-rmse:0.419892+0.067991
## [64] train-rmse:0.242143+0.005103 test-rmse:0.418731+0.068689
## [65] train-rmse:0.240666+0.004668 test-rmse:0.418573+0.068424
## [66] train-rmse:0.239334+0.004646 test-rmse:0.418904+0.068353
## [67] train-rmse:0.237778+0.004448 test-rmse:0.419064+0.068254
## [68] train-rmse:0.236163+0.004112 test-rmse:0.418254+0.068608
## [69] train-rmse:0.234806+0.003849 test-rmse:0.417782+0.069674
## [70] train-rmse:0.233796+0.003602 test-rmse:0.417307+0.069834
## [71] train-rmse:0.232446+0.003613 test-rmse:0.416503+0.069528
## [72] train-rmse:0.231394+0.003564 test-rmse:0.416135+0.069221
## [73] train-rmse:0.229852+0.003868 test-rmse:0.416025+0.068605
## [74] train-rmse:0.228752+0.003523 test-rmse:0.415607+0.068444
## [75] train-rmse:0.227377+0.003629 test-rmse:0.414984+0.069417
## [76] train-rmse:0.225865+0.003807 test-rmse:0.414999+0.069240
## [77] train-rmse:0.224507+0.004406 test-rmse:0.414832+0.069507
## [78] train-rmse:0.223135+0.004233 test-rmse:0.415008+0.069305
## [79] train-rmse:0.221836+0.004180 test-rmse:0.415547+0.069805
## [80] train-rmse:0.220751+0.003927 test-rmse:0.415643+0.069941
## [81] train-rmse:0.219466+0.003808 test-rmse:0.415355+0.070278
## [82] train-rmse:0.218327+0.003985 test-rmse:0.415569+0.069989
## [83] train-rmse:0.217007+0.003772 test-rmse:0.415246+0.070028
## Stopping. Best iteration:
## [77] train-rmse:0.224507+0.004406 test-rmse:0.414832+0.069507
##
## 17
## [1] train-rmse:1.433031+0.026127 test-rmse:1.432875+0.117281
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.258632+0.022814 test-rmse:1.259251+0.116819
## [3] train-rmse:1.109265+0.020637 test-rmse:1.111780+0.112531
## [4] train-rmse:0.980701+0.018546 test-rmse:0.986232+0.108329
## [5] train-rmse:0.871193+0.016173 test-rmse:0.879937+0.108903
## [6] train-rmse:0.777637+0.014242 test-rmse:0.789814+0.105442
## [7] train-rmse:0.697122+0.012521 test-rmse:0.714606+0.101087
## [8] train-rmse:0.629909+0.011000 test-rmse:0.655902+0.098942
## [9] train-rmse:0.572223+0.009497 test-rmse:0.605440+0.094905
## [10] train-rmse:0.524520+0.008283 test-rmse:0.565833+0.092060
## [11] train-rmse:0.484122+0.007980 test-rmse:0.532301+0.087884
## [12] train-rmse:0.450038+0.007074 test-rmse:0.506790+0.083252
## [13] train-rmse:0.419571+0.005775 test-rmse:0.486037+0.082571
## [14] train-rmse:0.395710+0.006084 test-rmse:0.470341+0.078820
## [15] train-rmse:0.376085+0.006096 test-rmse:0.457925+0.075955
## [16] train-rmse:0.359752+0.006136 test-rmse:0.448376+0.072554
## [17] train-rmse:0.345471+0.004986 test-rmse:0.440975+0.074470
## [18] train-rmse:0.332107+0.006910 test-rmse:0.434878+0.070902
## [19] train-rmse:0.321461+0.005970 test-rmse:0.430792+0.068541
## [20] train-rmse:0.313190+0.005996 test-rmse:0.427460+0.065533
## [21] train-rmse:0.306096+0.005806 test-rmse:0.425085+0.064591
## [22] train-rmse:0.297782+0.006050 test-rmse:0.423192+0.062830
## [23] train-rmse:0.291679+0.005768 test-rmse:0.422093+0.062320
## [24] train-rmse:0.286051+0.006127 test-rmse:0.421099+0.062793
## [25] train-rmse:0.281620+0.006024 test-rmse:0.419843+0.061218
## [26] train-rmse:0.276744+0.005432 test-rmse:0.417126+0.062147
## [27] train-rmse:0.272951+0.005314 test-rmse:0.415704+0.061687
## [28] train-rmse:0.268523+0.003879 test-rmse:0.414153+0.060024
## [29] train-rmse:0.264141+0.003533 test-rmse:0.413954+0.060031
## [30] train-rmse:0.260609+0.003132 test-rmse:0.413524+0.060168
## [31] train-rmse:0.256097+0.003098 test-rmse:0.412115+0.061741
## [32] train-rmse:0.252440+0.002786 test-rmse:0.411357+0.060218
## [33] train-rmse:0.249051+0.001874 test-rmse:0.409554+0.060941
## [34] train-rmse:0.245328+0.002388 test-rmse:0.409281+0.060468
## [35] train-rmse:0.242775+0.002309 test-rmse:0.409093+0.060226
## [36] train-rmse:0.239956+0.002215 test-rmse:0.408366+0.060041
## [37] train-rmse:0.237328+0.002127 test-rmse:0.408083+0.059486
## [38] train-rmse:0.234569+0.001519 test-rmse:0.407199+0.058965
## [39] train-rmse:0.231918+0.001146 test-rmse:0.407447+0.059177
## [40] train-rmse:0.228699+0.001220 test-rmse:0.407051+0.058696
## [41] train-rmse:0.226677+0.001150 test-rmse:0.405547+0.059730
## [42] train-rmse:0.223924+0.001699 test-rmse:0.405221+0.059177
## [43] train-rmse:0.221650+0.001799 test-rmse:0.404779+0.059168
## [44] train-rmse:0.219576+0.001704 test-rmse:0.403614+0.058996
## [45] train-rmse:0.216674+0.002190 test-rmse:0.403691+0.060119
## [46] train-rmse:0.214500+0.002368 test-rmse:0.403333+0.059671
## [47] train-rmse:0.212069+0.002279 test-rmse:0.402523+0.059657
## [48] train-rmse:0.209593+0.002660 test-rmse:0.401418+0.060187
## [49] train-rmse:0.207912+0.002518 test-rmse:0.401321+0.059964
## [50] train-rmse:0.205267+0.002263 test-rmse:0.401213+0.060222
## [51] train-rmse:0.203466+0.002309 test-rmse:0.401182+0.059458
## [52] train-rmse:0.201206+0.002549 test-rmse:0.401186+0.059101
## [53] train-rmse:0.199117+0.002975 test-rmse:0.400480+0.059250
## [54] train-rmse:0.197469+0.003136 test-rmse:0.400209+0.059462
## [55] train-rmse:0.195116+0.003646 test-rmse:0.399746+0.059945
## [56] train-rmse:0.193196+0.003644 test-rmse:0.399785+0.060212
## [57] train-rmse:0.191249+0.003339 test-rmse:0.399341+0.059955
## [58] train-rmse:0.189433+0.003147 test-rmse:0.398978+0.059383
## [59] train-rmse:0.187465+0.003663 test-rmse:0.398438+0.059911
## [60] train-rmse:0.185468+0.003753 test-rmse:0.398196+0.059668
## [61] train-rmse:0.184298+0.003612 test-rmse:0.398221+0.059656
## [62] train-rmse:0.182616+0.003272 test-rmse:0.397767+0.059621
## [63] train-rmse:0.180539+0.003384 test-rmse:0.397548+0.059376
## [64] train-rmse:0.179435+0.003477 test-rmse:0.396837+0.060370
## [65] train-rmse:0.178135+0.003549 test-rmse:0.396606+0.060682
## [66] train-rmse:0.176277+0.003399 test-rmse:0.396287+0.060824
## [67] train-rmse:0.175204+0.003286 test-rmse:0.396226+0.060658
## [68] train-rmse:0.173870+0.003240 test-rmse:0.396105+0.060451
## [69] train-rmse:0.171991+0.003160 test-rmse:0.395941+0.060422
## [70] train-rmse:0.170657+0.003121 test-rmse:0.395780+0.060204
## [71] train-rmse:0.169564+0.003228 test-rmse:0.396068+0.060566
## [72] train-rmse:0.167908+0.003820 test-rmse:0.396207+0.061035
## [73] train-rmse:0.166432+0.003510 test-rmse:0.395637+0.061436
## [74] train-rmse:0.165154+0.003586 test-rmse:0.395042+0.060931
## [75] train-rmse:0.164035+0.003393 test-rmse:0.394872+0.061271
## [76] train-rmse:0.162794+0.003590 test-rmse:0.394792+0.061238
## [77] train-rmse:0.161227+0.004077 test-rmse:0.394874+0.061418
## [78] train-rmse:0.159797+0.004506 test-rmse:0.395045+0.061433
## [79] train-rmse:0.158686+0.004608 test-rmse:0.395013+0.061587
## [80] train-rmse:0.157483+0.004774 test-rmse:0.394883+0.061559
## [81] train-rmse:0.156170+0.004821 test-rmse:0.394870+0.061263
## [82] train-rmse:0.154772+0.005003 test-rmse:0.394674+0.061009
## [83] train-rmse:0.153680+0.005124 test-rmse:0.394365+0.061525
## [84] train-rmse:0.152332+0.004954 test-rmse:0.394439+0.061487
## [85] train-rmse:0.151114+0.005188 test-rmse:0.394194+0.061420
## [86] train-rmse:0.150111+0.004892 test-rmse:0.393931+0.061706
## [87] train-rmse:0.149216+0.005015 test-rmse:0.393744+0.062124
## [88] train-rmse:0.148248+0.005228 test-rmse:0.393945+0.062186
## [89] train-rmse:0.147161+0.005378 test-rmse:0.393718+0.062481
## [90] train-rmse:0.145999+0.005682 test-rmse:0.393745+0.062434
## [91] train-rmse:0.144863+0.005955 test-rmse:0.393298+0.062100
## [92] train-rmse:0.144073+0.006232 test-rmse:0.393100+0.062225
## [93] train-rmse:0.142942+0.006343 test-rmse:0.393107+0.062079
## [94] train-rmse:0.141886+0.006409 test-rmse:0.393260+0.061968
## [95] train-rmse:0.141245+0.006431 test-rmse:0.393478+0.062306
## [96] train-rmse:0.140057+0.006317 test-rmse:0.393362+0.062543
## [97] train-rmse:0.138723+0.006232 test-rmse:0.392671+0.062869
## [98] train-rmse:0.137510+0.005925 test-rmse:0.392528+0.062604
## [99] train-rmse:0.136670+0.006033 test-rmse:0.392323+0.062883
## [100] train-rmse:0.135279+0.006126 test-rmse:0.391917+0.063306
## [101] train-rmse:0.134253+0.006176 test-rmse:0.391295+0.063944
## [102] train-rmse:0.133188+0.006038 test-rmse:0.391065+0.064389
## [103] train-rmse:0.132437+0.006238 test-rmse:0.390967+0.064170
## [104] train-rmse:0.131154+0.006454 test-rmse:0.391050+0.064427
## [105] train-rmse:0.130115+0.006561 test-rmse:0.390916+0.064288
## [106] train-rmse:0.129139+0.006781 test-rmse:0.390867+0.064488
## [107] train-rmse:0.127930+0.006508 test-rmse:0.390934+0.064384
## [108] train-rmse:0.127013+0.006747 test-rmse:0.390833+0.064431
## [109] train-rmse:0.126058+0.007097 test-rmse:0.390803+0.064262
## [110] train-rmse:0.124900+0.006957 test-rmse:0.390465+0.064635
## [111] train-rmse:0.123899+0.006786 test-rmse:0.390566+0.064735
## [112] train-rmse:0.122957+0.006721 test-rmse:0.390600+0.064797
## [113] train-rmse:0.122309+0.006916 test-rmse:0.390824+0.064880
## [114] train-rmse:0.121515+0.006957 test-rmse:0.390866+0.065043
## [115] train-rmse:0.120609+0.006995 test-rmse:0.390769+0.065083
## [116] train-rmse:0.119799+0.007257 test-rmse:0.390187+0.065583
## [117] train-rmse:0.118744+0.007324 test-rmse:0.390428+0.065343
## [118] train-rmse:0.118128+0.007343 test-rmse:0.390094+0.064963
## [119] train-rmse:0.117369+0.007428 test-rmse:0.390237+0.065110
## [120] train-rmse:0.116431+0.007466 test-rmse:0.390102+0.065665
## [121] train-rmse:0.115494+0.007661 test-rmse:0.390400+0.065811
## [122] train-rmse:0.114638+0.007973 test-rmse:0.390411+0.065820
## [123] train-rmse:0.113953+0.008004 test-rmse:0.390327+0.065573
## [124] train-rmse:0.113047+0.007938 test-rmse:0.389961+0.066012
## [125] train-rmse:0.112055+0.008027 test-rmse:0.389844+0.065479
## [126] train-rmse:0.111223+0.008122 test-rmse:0.389825+0.065499
## [127] train-rmse:0.110605+0.008124 test-rmse:0.390072+0.065578
## [128] train-rmse:0.110036+0.008070 test-rmse:0.389784+0.065598
## [129] train-rmse:0.109543+0.008178 test-rmse:0.389974+0.065587
## [130] train-rmse:0.108838+0.008082 test-rmse:0.390118+0.065647
## [131] train-rmse:0.108314+0.008343 test-rmse:0.390535+0.065888
## [132] train-rmse:0.107778+0.008421 test-rmse:0.390473+0.065772
## [133] train-rmse:0.107182+0.008263 test-rmse:0.390310+0.065882
## [134] train-rmse:0.106127+0.008178 test-rmse:0.390416+0.066330
## Stopping. Best iteration:
## [128] train-rmse:0.110036+0.008070 test-rmse:0.389784+0.065598
##
## 18
## [1] train-rmse:1.431841+0.026335 test-rmse:1.431565+0.115322
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.256000+0.022820 test-rmse:1.256197+0.112955
## [3] train-rmse:1.104969+0.020731 test-rmse:1.105254+0.107922
## [4] train-rmse:0.975356+0.018250 test-rmse:0.980318+0.106521
## [5] train-rmse:0.863908+0.016557 test-rmse:0.873814+0.101772
## [6] train-rmse:0.768417+0.014541 test-rmse:0.782908+0.100377
## [7] train-rmse:0.686769+0.013301 test-rmse:0.705551+0.097365
## [8] train-rmse:0.616162+0.011832 test-rmse:0.645118+0.092946
## [9] train-rmse:0.556529+0.010817 test-rmse:0.593099+0.089019
## [10] train-rmse:0.505201+0.009543 test-rmse:0.552357+0.088171
## [11] train-rmse:0.461781+0.008111 test-rmse:0.519523+0.083997
## [12] train-rmse:0.425022+0.007987 test-rmse:0.495195+0.081138
## [13] train-rmse:0.393305+0.007254 test-rmse:0.473179+0.082626
## [14] train-rmse:0.366467+0.006971 test-rmse:0.458235+0.080939
## [15] train-rmse:0.344173+0.006524 test-rmse:0.445897+0.078494
## [16] train-rmse:0.323167+0.004970 test-rmse:0.435779+0.075526
## [17] train-rmse:0.305598+0.005299 test-rmse:0.426462+0.074160
## [18] train-rmse:0.292397+0.005609 test-rmse:0.421585+0.073827
## [19] train-rmse:0.280278+0.006169 test-rmse:0.418622+0.071139
## [20] train-rmse:0.267706+0.005174 test-rmse:0.413549+0.071308
## [21] train-rmse:0.258198+0.004937 test-rmse:0.409778+0.068553
## [22] train-rmse:0.250758+0.004931 test-rmse:0.408997+0.067543
## [23] train-rmse:0.242930+0.005218 test-rmse:0.408250+0.066942
## [24] train-rmse:0.236084+0.005213 test-rmse:0.405936+0.063692
## [25] train-rmse:0.230515+0.005881 test-rmse:0.405341+0.062489
## [26] train-rmse:0.225288+0.005392 test-rmse:0.406251+0.061609
## [27] train-rmse:0.219975+0.006281 test-rmse:0.405490+0.063093
## [28] train-rmse:0.215915+0.006290 test-rmse:0.404571+0.061278
## [29] train-rmse:0.211784+0.006341 test-rmse:0.403194+0.060241
## [30] train-rmse:0.208137+0.006026 test-rmse:0.403013+0.060006
## [31] train-rmse:0.203119+0.004965 test-rmse:0.403375+0.059436
## [32] train-rmse:0.200076+0.005040 test-rmse:0.403181+0.059439
## [33] train-rmse:0.196054+0.004495 test-rmse:0.401996+0.057952
## [34] train-rmse:0.192850+0.004241 test-rmse:0.402205+0.057622
## [35] train-rmse:0.189819+0.004259 test-rmse:0.401807+0.057116
## [36] train-rmse:0.186159+0.004802 test-rmse:0.401650+0.057960
## [37] train-rmse:0.183486+0.004920 test-rmse:0.401224+0.058260
## [38] train-rmse:0.180865+0.004868 test-rmse:0.400876+0.058023
## [39] train-rmse:0.178680+0.004649 test-rmse:0.400800+0.057828
## [40] train-rmse:0.175392+0.004226 test-rmse:0.400669+0.057022
## [41] train-rmse:0.171986+0.004065 test-rmse:0.399709+0.057167
## [42] train-rmse:0.169439+0.003864 test-rmse:0.399348+0.056752
## [43] train-rmse:0.167063+0.003715 test-rmse:0.399188+0.056477
## [44] train-rmse:0.164635+0.003602 test-rmse:0.399385+0.056483
## [45] train-rmse:0.161063+0.004395 test-rmse:0.398798+0.056882
## [46] train-rmse:0.158513+0.004282 test-rmse:0.398092+0.056433
## [47] train-rmse:0.155878+0.003831 test-rmse:0.397370+0.056382
## [48] train-rmse:0.153933+0.003978 test-rmse:0.397201+0.056869
## [49] train-rmse:0.151468+0.003281 test-rmse:0.396354+0.057680
## [50] train-rmse:0.149280+0.002367 test-rmse:0.395736+0.057137
## [51] train-rmse:0.147373+0.002599 test-rmse:0.395007+0.056679
## [52] train-rmse:0.145805+0.002285 test-rmse:0.394819+0.055857
## [53] train-rmse:0.143745+0.002670 test-rmse:0.394414+0.055947
## [54] train-rmse:0.142174+0.003601 test-rmse:0.394123+0.056579
## [55] train-rmse:0.140473+0.003232 test-rmse:0.394398+0.056713
## [56] train-rmse:0.139055+0.003251 test-rmse:0.394707+0.056401
## [57] train-rmse:0.137237+0.003490 test-rmse:0.394154+0.056714
## [58] train-rmse:0.136105+0.003442 test-rmse:0.394435+0.056804
## [59] train-rmse:0.134428+0.003894 test-rmse:0.394577+0.056135
## [60] train-rmse:0.132229+0.003893 test-rmse:0.394232+0.056192
## Stopping. Best iteration:
## [54] train-rmse:0.142174+0.003601 test-rmse:0.394123+0.056579
##
## 19
## [1] train-rmse:1.431489+0.026371 test-rmse:1.430662+0.114558
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.254864+0.022878 test-rmse:1.252409+0.112188
## [3] train-rmse:1.102886+0.020879 test-rmse:1.100524+0.105676
## [4] train-rmse:0.972221+0.018510 test-rmse:0.974227+0.102307
## [5] train-rmse:0.860098+0.016640 test-rmse:0.868964+0.100259
## [6] train-rmse:0.763325+0.015378 test-rmse:0.777345+0.095527
## [7] train-rmse:0.680491+0.013431 test-rmse:0.700934+0.094443
## [8] train-rmse:0.609193+0.012489 test-rmse:0.640677+0.095452
## [9] train-rmse:0.547843+0.011927 test-rmse:0.589762+0.091596
## [10] train-rmse:0.495334+0.011106 test-rmse:0.550788+0.090041
## [11] train-rmse:0.449956+0.010820 test-rmse:0.518103+0.088037
## [12] train-rmse:0.411459+0.010588 test-rmse:0.495475+0.084804
## [13] train-rmse:0.377795+0.010184 test-rmse:0.474059+0.082894
## [14] train-rmse:0.348873+0.009547 test-rmse:0.459235+0.082115
## [15] train-rmse:0.324215+0.009386 test-rmse:0.447942+0.080936
## [16] train-rmse:0.302536+0.008514 test-rmse:0.440047+0.079516
## [17] train-rmse:0.284063+0.008693 test-rmse:0.433590+0.076414
## [18] train-rmse:0.267258+0.008685 test-rmse:0.427049+0.074045
## [19] train-rmse:0.254348+0.008716 test-rmse:0.422177+0.071926
## [20] train-rmse:0.242267+0.008748 test-rmse:0.420126+0.071495
## [21] train-rmse:0.232250+0.007809 test-rmse:0.417634+0.069996
## [22] train-rmse:0.222514+0.008954 test-rmse:0.415121+0.068074
## [23] train-rmse:0.214827+0.008490 test-rmse:0.413344+0.068345
## [24] train-rmse:0.208177+0.008115 test-rmse:0.411940+0.068389
## [25] train-rmse:0.201794+0.009504 test-rmse:0.410464+0.067087
## [26] train-rmse:0.193924+0.008153 test-rmse:0.409913+0.066106
## [27] train-rmse:0.188835+0.008055 test-rmse:0.408544+0.065247
## [28] train-rmse:0.184481+0.007774 test-rmse:0.407248+0.064042
## [29] train-rmse:0.178732+0.008630 test-rmse:0.406831+0.065137
## [30] train-rmse:0.174221+0.008112 test-rmse:0.406265+0.064680
## [31] train-rmse:0.169889+0.007554 test-rmse:0.406450+0.064132
## [32] train-rmse:0.166531+0.006703 test-rmse:0.406229+0.062635
## [33] train-rmse:0.163655+0.006644 test-rmse:0.405874+0.062154
## [34] train-rmse:0.160574+0.006836 test-rmse:0.404683+0.062564
## [35] train-rmse:0.156838+0.007236 test-rmse:0.405152+0.062426
## [36] train-rmse:0.152902+0.007176 test-rmse:0.404798+0.062595
## [37] train-rmse:0.149678+0.007228 test-rmse:0.404974+0.062281
## [38] train-rmse:0.146675+0.007168 test-rmse:0.405186+0.061080
## [39] train-rmse:0.141089+0.006825 test-rmse:0.404426+0.061727
## [40] train-rmse:0.137800+0.006839 test-rmse:0.403897+0.061801
## [41] train-rmse:0.133860+0.007241 test-rmse:0.404301+0.059825
## [42] train-rmse:0.130648+0.007620 test-rmse:0.403596+0.059644
## [43] train-rmse:0.127564+0.008042 test-rmse:0.403335+0.060404
## [44] train-rmse:0.125059+0.008464 test-rmse:0.403523+0.060280
## [45] train-rmse:0.122919+0.008576 test-rmse:0.403178+0.059850
## [46] train-rmse:0.119954+0.007915 test-rmse:0.402245+0.060094
## [47] train-rmse:0.117568+0.008072 test-rmse:0.401897+0.059831
## [48] train-rmse:0.115324+0.007925 test-rmse:0.401954+0.060056
## [49] train-rmse:0.112894+0.007868 test-rmse:0.402284+0.059839
## [50] train-rmse:0.110818+0.007713 test-rmse:0.401964+0.060113
## [51] train-rmse:0.109003+0.007785 test-rmse:0.402082+0.059509
## [52] train-rmse:0.106476+0.006612 test-rmse:0.401822+0.059649
## [53] train-rmse:0.104448+0.006429 test-rmse:0.401458+0.060196
## [54] train-rmse:0.102337+0.006853 test-rmse:0.401299+0.059840
## [55] train-rmse:0.099638+0.006664 test-rmse:0.400870+0.059121
## [56] train-rmse:0.098188+0.007018 test-rmse:0.400485+0.059208
## [57] train-rmse:0.096364+0.007095 test-rmse:0.399776+0.059813
## [58] train-rmse:0.094091+0.006705 test-rmse:0.399760+0.059531
## [59] train-rmse:0.092821+0.006684 test-rmse:0.399930+0.059583
## [60] train-rmse:0.091594+0.006742 test-rmse:0.399940+0.060161
## [61] train-rmse:0.090193+0.006655 test-rmse:0.399707+0.060314
## [62] train-rmse:0.089010+0.006522 test-rmse:0.399967+0.060293
## [63] train-rmse:0.087721+0.006380 test-rmse:0.400195+0.060166
## [64] train-rmse:0.086768+0.006548 test-rmse:0.399971+0.060575
## [65] train-rmse:0.085110+0.006218 test-rmse:0.400160+0.060524
## [66] train-rmse:0.083992+0.006225 test-rmse:0.400208+0.060552
## [67] train-rmse:0.082837+0.006249 test-rmse:0.400133+0.060532
## Stopping. Best iteration:
## [61] train-rmse:0.090193+0.006655 test-rmse:0.399707+0.060314
##
## 20
## [1] train-rmse:1.431311+0.026373 test-rmse:1.431290+0.114119
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.254247+0.022994 test-rmse:1.253084+0.111212
## [3] train-rmse:1.101840+0.021041 test-rmse:1.099563+0.104565
## [4] train-rmse:0.970610+0.018621 test-rmse:0.973054+0.101683
## [5] train-rmse:0.857314+0.017254 test-rmse:0.868374+0.102051
## [6] train-rmse:0.760002+0.015432 test-rmse:0.779161+0.099524
## [7] train-rmse:0.675295+0.013758 test-rmse:0.703200+0.096560
## [8] train-rmse:0.602618+0.012779 test-rmse:0.640433+0.095330
## [9] train-rmse:0.539930+0.011843 test-rmse:0.591064+0.093534
## [10] train-rmse:0.486067+0.011829 test-rmse:0.550802+0.090252
## [11] train-rmse:0.439396+0.011002 test-rmse:0.518690+0.089152
## [12] train-rmse:0.398487+0.010565 test-rmse:0.490283+0.087083
## [13] train-rmse:0.363605+0.009689 test-rmse:0.469764+0.084158
## [14] train-rmse:0.333556+0.008925 test-rmse:0.455831+0.083634
## [15] train-rmse:0.307676+0.008429 test-rmse:0.446245+0.083119
## [16] train-rmse:0.283976+0.009958 test-rmse:0.435997+0.080999
## [17] train-rmse:0.263034+0.011097 test-rmse:0.430197+0.078781
## [18] train-rmse:0.244514+0.011349 test-rmse:0.424942+0.075993
## [19] train-rmse:0.229248+0.011475 test-rmse:0.420971+0.073177
## [20] train-rmse:0.215853+0.011898 test-rmse:0.417582+0.071782
## [21] train-rmse:0.204128+0.011536 test-rmse:0.417158+0.071330
## [22] train-rmse:0.192884+0.011462 test-rmse:0.414751+0.069277
## [23] train-rmse:0.183663+0.012424 test-rmse:0.412747+0.068785
## [24] train-rmse:0.175140+0.012697 test-rmse:0.411745+0.066784
## [25] train-rmse:0.168435+0.013125 test-rmse:0.411014+0.066067
## [26] train-rmse:0.161211+0.012029 test-rmse:0.410914+0.064612
## [27] train-rmse:0.156039+0.011758 test-rmse:0.410695+0.062523
## [28] train-rmse:0.150499+0.010912 test-rmse:0.410382+0.062046
## [29] train-rmse:0.145779+0.011028 test-rmse:0.409482+0.061135
## [30] train-rmse:0.141601+0.011890 test-rmse:0.408959+0.060014
## [31] train-rmse:0.137313+0.011712 test-rmse:0.408420+0.059059
## [32] train-rmse:0.132871+0.011039 test-rmse:0.407237+0.058651
## [33] train-rmse:0.129407+0.010921 test-rmse:0.406192+0.058002
## [34] train-rmse:0.126611+0.010647 test-rmse:0.405170+0.057176
## [35] train-rmse:0.122443+0.008999 test-rmse:0.404393+0.056788
## [36] train-rmse:0.119703+0.008218 test-rmse:0.403272+0.056571
## [37] train-rmse:0.116427+0.009080 test-rmse:0.402711+0.056619
## [38] train-rmse:0.112660+0.008871 test-rmse:0.402404+0.055849
## [39] train-rmse:0.110695+0.008990 test-rmse:0.401791+0.055447
## [40] train-rmse:0.107521+0.008307 test-rmse:0.401204+0.055775
## [41] train-rmse:0.104205+0.008578 test-rmse:0.400706+0.054390
## [42] train-rmse:0.100827+0.009062 test-rmse:0.400980+0.053866
## [43] train-rmse:0.098095+0.009759 test-rmse:0.400566+0.053588
## [44] train-rmse:0.094997+0.009653 test-rmse:0.400612+0.053436
## [45] train-rmse:0.091303+0.009229 test-rmse:0.400192+0.053287
## [46] train-rmse:0.089194+0.009762 test-rmse:0.399361+0.052802
## [47] train-rmse:0.087078+0.009507 test-rmse:0.399277+0.052090
## [48] train-rmse:0.084594+0.009300 test-rmse:0.399561+0.051603
## [49] train-rmse:0.082696+0.009004 test-rmse:0.399519+0.051379
## [50] train-rmse:0.080039+0.009191 test-rmse:0.399742+0.051306
## [51] train-rmse:0.077887+0.008979 test-rmse:0.399885+0.051507
## [52] train-rmse:0.075839+0.009322 test-rmse:0.399579+0.051349
## [53] train-rmse:0.073765+0.009063 test-rmse:0.399404+0.051148
## Stopping. Best iteration:
## [47] train-rmse:0.087078+0.009507 test-rmse:0.399277+0.052090
##
## 21
## [1] train-rmse:1.421522+0.026434 test-rmse:1.417655+0.112425
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.246253+0.023459 test-rmse:1.242440+0.108299
## [3] train-rmse:1.105462+0.021274 test-rmse:1.101724+0.104665
## [4] train-rmse:0.993775+0.019772 test-rmse:0.990143+0.101374
## [5] train-rmse:0.906402+0.018825 test-rmse:0.902915+0.098372
## [6] train-rmse:0.839045+0.018292 test-rmse:0.835736+0.095662
## [7] train-rmse:0.787872+0.018041 test-rmse:0.784760+0.093270
## [8] train-rmse:0.749523+0.017962 test-rmse:0.746614+0.091212
## [9] train-rmse:0.721135+0.017974 test-rmse:0.718420+0.089485
## [10] train-rmse:0.700168+0.017803 test-rmse:0.701925+0.090005
## [11] train-rmse:0.682801+0.017308 test-rmse:0.682587+0.088698
## [12] train-rmse:0.668579+0.017098 test-rmse:0.670424+0.087985
## [13] train-rmse:0.656445+0.016804 test-rmse:0.657788+0.086971
## [14] train-rmse:0.646201+0.016130 test-rmse:0.650020+0.084986
## [15] train-rmse:0.637042+0.015557 test-rmse:0.637290+0.083881
## [16] train-rmse:0.628824+0.015104 test-rmse:0.631945+0.081023
## [17] train-rmse:0.621342+0.014461 test-rmse:0.623602+0.077779
## [18] train-rmse:0.614638+0.013908 test-rmse:0.616229+0.077988
## [19] train-rmse:0.608516+0.013516 test-rmse:0.613477+0.076654
## [20] train-rmse:0.602947+0.013234 test-rmse:0.605901+0.076392
## [21] train-rmse:0.597829+0.013064 test-rmse:0.602040+0.072923
## [22] train-rmse:0.593053+0.012806 test-rmse:0.598575+0.071168
## [23] train-rmse:0.588582+0.012427 test-rmse:0.594824+0.070268
## [24] train-rmse:0.584586+0.012192 test-rmse:0.589927+0.070942
## [25] train-rmse:0.580743+0.012033 test-rmse:0.586878+0.068777
## [26] train-rmse:0.577181+0.011942 test-rmse:0.585547+0.068349
## [27] train-rmse:0.573714+0.011872 test-rmse:0.582818+0.066379
## [28] train-rmse:0.570454+0.011691 test-rmse:0.580706+0.065641
## [29] train-rmse:0.567519+0.011515 test-rmse:0.576771+0.065461
## [30] train-rmse:0.564655+0.011410 test-rmse:0.574292+0.066246
## [31] train-rmse:0.561908+0.011249 test-rmse:0.572339+0.063938
## [32] train-rmse:0.559359+0.011110 test-rmse:0.571258+0.065007
## [33] train-rmse:0.556933+0.011018 test-rmse:0.568846+0.063839
## [34] train-rmse:0.554589+0.010970 test-rmse:0.569023+0.063231
## [35] train-rmse:0.552385+0.010859 test-rmse:0.565853+0.062470
## [36] train-rmse:0.550315+0.010766 test-rmse:0.563638+0.062785
## [37] train-rmse:0.548383+0.010722 test-rmse:0.562058+0.062259
## [38] train-rmse:0.546485+0.010624 test-rmse:0.562040+0.061159
## [39] train-rmse:0.544645+0.010555 test-rmse:0.561527+0.061309
## [40] train-rmse:0.542827+0.010482 test-rmse:0.560495+0.060700
## [41] train-rmse:0.541113+0.010411 test-rmse:0.559286+0.059364
## [42] train-rmse:0.539460+0.010373 test-rmse:0.558161+0.059944
## [43] train-rmse:0.537869+0.010308 test-rmse:0.555956+0.059542
## [44] train-rmse:0.536347+0.010182 test-rmse:0.553727+0.058647
## [45] train-rmse:0.534889+0.010111 test-rmse:0.553843+0.058867
## [46] train-rmse:0.533392+0.010035 test-rmse:0.553929+0.058387
## [47] train-rmse:0.531955+0.009963 test-rmse:0.553160+0.059037
## [48] train-rmse:0.530569+0.009893 test-rmse:0.552380+0.058684
## [49] train-rmse:0.529215+0.009837 test-rmse:0.551527+0.059111
## [50] train-rmse:0.527920+0.009793 test-rmse:0.549997+0.058440
## [51] train-rmse:0.526618+0.009762 test-rmse:0.549412+0.058318
## [52] train-rmse:0.525362+0.009655 test-rmse:0.548759+0.057734
## [53] train-rmse:0.524132+0.009581 test-rmse:0.547555+0.057741
## [54] train-rmse:0.522941+0.009500 test-rmse:0.546858+0.057213
## [55] train-rmse:0.521780+0.009419 test-rmse:0.546308+0.057510
## [56] train-rmse:0.520670+0.009342 test-rmse:0.544675+0.057468
## [57] train-rmse:0.519516+0.009287 test-rmse:0.544436+0.056928
## [58] train-rmse:0.518402+0.009225 test-rmse:0.543858+0.057330
## [59] train-rmse:0.517318+0.009175 test-rmse:0.543780+0.056885
## [60] train-rmse:0.516272+0.009139 test-rmse:0.543462+0.056155
## [61] train-rmse:0.515246+0.009100 test-rmse:0.542140+0.055941
## [62] train-rmse:0.514212+0.009057 test-rmse:0.541128+0.056214
## [63] train-rmse:0.513208+0.008994 test-rmse:0.540361+0.055956
## [64] train-rmse:0.512218+0.008948 test-rmse:0.539710+0.056339
## [65] train-rmse:0.511233+0.008907 test-rmse:0.539531+0.056383
## [66] train-rmse:0.510262+0.008873 test-rmse:0.539070+0.055917
## [67] train-rmse:0.509303+0.008854 test-rmse:0.536709+0.055188
## [68] train-rmse:0.508375+0.008813 test-rmse:0.536405+0.055069
## [69] train-rmse:0.507462+0.008765 test-rmse:0.535353+0.054413
## [70] train-rmse:0.506565+0.008728 test-rmse:0.534961+0.055166
## [71] train-rmse:0.505637+0.008668 test-rmse:0.534284+0.054909
## [72] train-rmse:0.504780+0.008657 test-rmse:0.533698+0.054837
## [73] train-rmse:0.503937+0.008635 test-rmse:0.532340+0.054629
## [74] train-rmse:0.503095+0.008612 test-rmse:0.532244+0.054368
## [75] train-rmse:0.502272+0.008589 test-rmse:0.530818+0.054445
## [76] train-rmse:0.501429+0.008552 test-rmse:0.531156+0.054770
## [77] train-rmse:0.500620+0.008526 test-rmse:0.531204+0.054740
## [78] train-rmse:0.499806+0.008498 test-rmse:0.531041+0.054023
## [79] train-rmse:0.499017+0.008483 test-rmse:0.529706+0.053569
## [80] train-rmse:0.498239+0.008461 test-rmse:0.529346+0.053878
## [81] train-rmse:0.497475+0.008458 test-rmse:0.528906+0.054090
## [82] train-rmse:0.496734+0.008443 test-rmse:0.529023+0.053941
## [83] train-rmse:0.495996+0.008420 test-rmse:0.528739+0.053681
## [84] train-rmse:0.495257+0.008385 test-rmse:0.527798+0.053258
## [85] train-rmse:0.494520+0.008364 test-rmse:0.527034+0.052889
## [86] train-rmse:0.493763+0.008335 test-rmse:0.525757+0.053189
## [87] train-rmse:0.493086+0.008325 test-rmse:0.525782+0.053388
## [88] train-rmse:0.492395+0.008303 test-rmse:0.525879+0.053577
## [89] train-rmse:0.491711+0.008288 test-rmse:0.525199+0.053230
## [90] train-rmse:0.491032+0.008269 test-rmse:0.524546+0.053157
## [91] train-rmse:0.490374+0.008265 test-rmse:0.524052+0.052838
## [92] train-rmse:0.489708+0.008260 test-rmse:0.523899+0.052930
## [93] train-rmse:0.489069+0.008227 test-rmse:0.523041+0.051984
## [94] train-rmse:0.488433+0.008204 test-rmse:0.522829+0.051727
## [95] train-rmse:0.487790+0.008194 test-rmse:0.522568+0.050827
## [96] train-rmse:0.487138+0.008191 test-rmse:0.522835+0.051208
## [97] train-rmse:0.486523+0.008181 test-rmse:0.523079+0.051443
## [98] train-rmse:0.485911+0.008166 test-rmse:0.522874+0.051459
## [99] train-rmse:0.485320+0.008146 test-rmse:0.521869+0.051480
## [100] train-rmse:0.484735+0.008121 test-rmse:0.521466+0.051179
## [101] train-rmse:0.484140+0.008106 test-rmse:0.520826+0.051333
## [102] train-rmse:0.483544+0.008085 test-rmse:0.520248+0.051680
## [103] train-rmse:0.482958+0.008088 test-rmse:0.519859+0.050781
## [104] train-rmse:0.482396+0.008086 test-rmse:0.519698+0.051378
## [105] train-rmse:0.481836+0.008048 test-rmse:0.518866+0.051722
## [106] train-rmse:0.481286+0.008018 test-rmse:0.518762+0.051435
## [107] train-rmse:0.480752+0.008009 test-rmse:0.518621+0.051489
## [108] train-rmse:0.480218+0.008001 test-rmse:0.518355+0.050878
## [109] train-rmse:0.479679+0.008002 test-rmse:0.517901+0.050141
## [110] train-rmse:0.479146+0.007992 test-rmse:0.517606+0.050288
## [111] train-rmse:0.478633+0.007983 test-rmse:0.517769+0.050516
## [112] train-rmse:0.478128+0.007975 test-rmse:0.517929+0.050317
## [113] train-rmse:0.477615+0.007960 test-rmse:0.517572+0.050213
## [114] train-rmse:0.477119+0.007940 test-rmse:0.517493+0.049712
## [115] train-rmse:0.476624+0.007927 test-rmse:0.516595+0.049435
## [116] train-rmse:0.476121+0.007914 test-rmse:0.516352+0.049553
## [117] train-rmse:0.475632+0.007916 test-rmse:0.516523+0.049631
## [118] train-rmse:0.475151+0.007915 test-rmse:0.515089+0.048469
## [119] train-rmse:0.474661+0.007896 test-rmse:0.514612+0.048274
## [120] train-rmse:0.474183+0.007888 test-rmse:0.515501+0.048052
## [121] train-rmse:0.473725+0.007880 test-rmse:0.514953+0.047847
## [122] train-rmse:0.473266+0.007879 test-rmse:0.514253+0.047690
## [123] train-rmse:0.472806+0.007873 test-rmse:0.513809+0.047923
## [124] train-rmse:0.472349+0.007867 test-rmse:0.513681+0.047983
## [125] train-rmse:0.471904+0.007862 test-rmse:0.513276+0.048433
## [126] train-rmse:0.471471+0.007859 test-rmse:0.512375+0.048244
## [127] train-rmse:0.471056+0.007848 test-rmse:0.511866+0.048106
## [128] train-rmse:0.470616+0.007831 test-rmse:0.511614+0.047937
## [129] train-rmse:0.470161+0.007832 test-rmse:0.512392+0.047807
## [130] train-rmse:0.469740+0.007822 test-rmse:0.512018+0.047743
## [131] train-rmse:0.469319+0.007807 test-rmse:0.511769+0.047706
## [132] train-rmse:0.468906+0.007791 test-rmse:0.510971+0.047475
## [133] train-rmse:0.468494+0.007775 test-rmse:0.510763+0.047237
## [134] train-rmse:0.468085+0.007757 test-rmse:0.510373+0.047348
## [135] train-rmse:0.467692+0.007749 test-rmse:0.510153+0.046860
## [136] train-rmse:0.467288+0.007746 test-rmse:0.510136+0.047078
## [137] train-rmse:0.466886+0.007740 test-rmse:0.509822+0.047372
## [138] train-rmse:0.466490+0.007743 test-rmse:0.509502+0.047265
## [139] train-rmse:0.466095+0.007731 test-rmse:0.509379+0.047653
## [140] train-rmse:0.465712+0.007720 test-rmse:0.509289+0.047282
## [141] train-rmse:0.465329+0.007706 test-rmse:0.508494+0.045856
## [142] train-rmse:0.464947+0.007700 test-rmse:0.508533+0.046211
## [143] train-rmse:0.464569+0.007696 test-rmse:0.507964+0.046515
## [144] train-rmse:0.464197+0.007693 test-rmse:0.507420+0.046201
## [145] train-rmse:0.463821+0.007692 test-rmse:0.507358+0.046201
## [146] train-rmse:0.463460+0.007684 test-rmse:0.507297+0.046388
## [147] train-rmse:0.463102+0.007677 test-rmse:0.507141+0.045910
## [148] train-rmse:0.462743+0.007674 test-rmse:0.507489+0.046039
## [149] train-rmse:0.462390+0.007663 test-rmse:0.507550+0.046520
## [150] train-rmse:0.462037+0.007654 test-rmse:0.507320+0.046399
## [151] train-rmse:0.461679+0.007643 test-rmse:0.507117+0.045703
## [152] train-rmse:0.461323+0.007633 test-rmse:0.506633+0.045636
## [153] train-rmse:0.460980+0.007625 test-rmse:0.506925+0.045340
## [154] train-rmse:0.460640+0.007620 test-rmse:0.507081+0.045073
## [155] train-rmse:0.460307+0.007616 test-rmse:0.506672+0.045256
## [156] train-rmse:0.459970+0.007608 test-rmse:0.506500+0.045194
## [157] train-rmse:0.459631+0.007600 test-rmse:0.506138+0.045417
## [158] train-rmse:0.459297+0.007597 test-rmse:0.506370+0.045077
## [159] train-rmse:0.458965+0.007583 test-rmse:0.506090+0.044986
## [160] train-rmse:0.458643+0.007572 test-rmse:0.505865+0.044568
## [161] train-rmse:0.458314+0.007569 test-rmse:0.505865+0.044584
## [162] train-rmse:0.458000+0.007561 test-rmse:0.505520+0.044468
## [163] train-rmse:0.457686+0.007560 test-rmse:0.505207+0.044461
## [164] train-rmse:0.457374+0.007555 test-rmse:0.505123+0.044342
## [165] train-rmse:0.457058+0.007556 test-rmse:0.505179+0.044878
## [166] train-rmse:0.456753+0.007554 test-rmse:0.504783+0.044743
## [167] train-rmse:0.456437+0.007552 test-rmse:0.505032+0.044832
## [168] train-rmse:0.456129+0.007549 test-rmse:0.504729+0.044778
## [169] train-rmse:0.455824+0.007547 test-rmse:0.504086+0.043600
## [170] train-rmse:0.455530+0.007541 test-rmse:0.504295+0.043498
## [171] train-rmse:0.455236+0.007538 test-rmse:0.504496+0.043509
## [172] train-rmse:0.454934+0.007533 test-rmse:0.503928+0.043605
## [173] train-rmse:0.454650+0.007527 test-rmse:0.503667+0.043919
## [174] train-rmse:0.454366+0.007523 test-rmse:0.503695+0.043754
## [175] train-rmse:0.454084+0.007519 test-rmse:0.503415+0.043802
## [176] train-rmse:0.453804+0.007512 test-rmse:0.503678+0.043498
## [177] train-rmse:0.453520+0.007509 test-rmse:0.503310+0.043150
## [178] train-rmse:0.453242+0.007508 test-rmse:0.503539+0.042597
## [179] train-rmse:0.452967+0.007506 test-rmse:0.503537+0.042951
## [180] train-rmse:0.452681+0.007509 test-rmse:0.503114+0.042965
## [181] train-rmse:0.452413+0.007505 test-rmse:0.503283+0.043200
## [182] train-rmse:0.452138+0.007502 test-rmse:0.503260+0.043290
## [183] train-rmse:0.451871+0.007503 test-rmse:0.502497+0.043036
## [184] train-rmse:0.451605+0.007507 test-rmse:0.502819+0.043304
## [185] train-rmse:0.451341+0.007506 test-rmse:0.502143+0.043132
## [186] train-rmse:0.451079+0.007507 test-rmse:0.502156+0.043198
## [187] train-rmse:0.450817+0.007508 test-rmse:0.502323+0.042815
## [188] train-rmse:0.450556+0.007508 test-rmse:0.502191+0.043165
## [189] train-rmse:0.450298+0.007510 test-rmse:0.502463+0.043107
## [190] train-rmse:0.450049+0.007514 test-rmse:0.502162+0.042992
## [191] train-rmse:0.449795+0.007518 test-rmse:0.502038+0.043020
## [192] train-rmse:0.449543+0.007519 test-rmse:0.501871+0.042884
## [193] train-rmse:0.449299+0.007520 test-rmse:0.501977+0.042679
## [194] train-rmse:0.449052+0.007521 test-rmse:0.501781+0.042494
## [195] train-rmse:0.448805+0.007519 test-rmse:0.501946+0.042734
## [196] train-rmse:0.448558+0.007516 test-rmse:0.501621+0.042776
## [197] train-rmse:0.448319+0.007517 test-rmse:0.501597+0.042511
## [198] train-rmse:0.448081+0.007518 test-rmse:0.501671+0.042723
## [199] train-rmse:0.447836+0.007520 test-rmse:0.501559+0.042624
## [200] train-rmse:0.447603+0.007515 test-rmse:0.501185+0.042591
## [201] train-rmse:0.447368+0.007517 test-rmse:0.501221+0.042215
## [202] train-rmse:0.447135+0.007520 test-rmse:0.500935+0.042085
## [203] train-rmse:0.446906+0.007517 test-rmse:0.500745+0.042093
## [204] train-rmse:0.446684+0.007518 test-rmse:0.500575+0.041847
## [205] train-rmse:0.446453+0.007519 test-rmse:0.500557+0.041923
## [206] train-rmse:0.446229+0.007517 test-rmse:0.500596+0.042026
## [207] train-rmse:0.446001+0.007516 test-rmse:0.500681+0.042363
## [208] train-rmse:0.445779+0.007515 test-rmse:0.499801+0.041721
## [209] train-rmse:0.445558+0.007512 test-rmse:0.499984+0.041494
## [210] train-rmse:0.445342+0.007511 test-rmse:0.500141+0.041309
## [211] train-rmse:0.445125+0.007511 test-rmse:0.499820+0.041384
## [212] train-rmse:0.444904+0.007509 test-rmse:0.499910+0.041460
## [213] train-rmse:0.444690+0.007509 test-rmse:0.499440+0.041304
## [214] train-rmse:0.444471+0.007515 test-rmse:0.499563+0.040807
## [215] train-rmse:0.444257+0.007513 test-rmse:0.499984+0.040789
## [216] train-rmse:0.444045+0.007511 test-rmse:0.500019+0.041293
## [217] train-rmse:0.443827+0.007512 test-rmse:0.499543+0.040906
## [218] train-rmse:0.443619+0.007516 test-rmse:0.499592+0.040930
## [219] train-rmse:0.443412+0.007521 test-rmse:0.499485+0.041221
## Stopping. Best iteration:
## [213] train-rmse:0.444690+0.007509 test-rmse:0.499440+0.041304
##
## 22
## [1] train-rmse:1.411569+0.026462 test-rmse:1.409784+0.115276
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.224842+0.023183 test-rmse:1.225312+0.113611
## [3] train-rmse:1.071115+0.020821 test-rmse:1.073652+0.107674
## [4] train-rmse:0.945519+0.018734 test-rmse:0.950754+0.106992
## [5] train-rmse:0.843417+0.017133 test-rmse:0.844741+0.102446
## [6] train-rmse:0.760933+0.015962 test-rmse:0.768008+0.102190
## [7] train-rmse:0.694914+0.014703 test-rmse:0.699622+0.100256
## [8] train-rmse:0.642271+0.014225 test-rmse:0.650545+0.097856
## [9] train-rmse:0.600369+0.013670 test-rmse:0.607730+0.093223
## [10] train-rmse:0.567384+0.013042 test-rmse:0.578122+0.088307
## [11] train-rmse:0.540970+0.012834 test-rmse:0.553166+0.088412
## [12] train-rmse:0.519762+0.012378 test-rmse:0.536757+0.083691
## [13] train-rmse:0.502575+0.011808 test-rmse:0.522718+0.079479
## [14] train-rmse:0.488455+0.011454 test-rmse:0.507337+0.077299
## [15] train-rmse:0.476809+0.011028 test-rmse:0.499629+0.074176
## [16] train-rmse:0.465645+0.009632 test-rmse:0.492685+0.073183
## [17] train-rmse:0.454936+0.009308 test-rmse:0.484031+0.071390
## [18] train-rmse:0.446587+0.008598 test-rmse:0.476900+0.068773
## [19] train-rmse:0.438712+0.007525 test-rmse:0.473142+0.068491
## [20] train-rmse:0.432225+0.007181 test-rmse:0.471047+0.067254
## [21] train-rmse:0.426337+0.006618 test-rmse:0.469397+0.066655
## [22] train-rmse:0.420559+0.006235 test-rmse:0.465604+0.065855
## [23] train-rmse:0.415387+0.006106 test-rmse:0.462461+0.064016
## [24] train-rmse:0.411026+0.005723 test-rmse:0.458810+0.061598
## [25] train-rmse:0.406793+0.005572 test-rmse:0.456263+0.060968
## [26] train-rmse:0.402827+0.005307 test-rmse:0.454869+0.060735
## [27] train-rmse:0.399357+0.005210 test-rmse:0.453352+0.060266
## [28] train-rmse:0.395988+0.005197 test-rmse:0.452172+0.059613
## [29] train-rmse:0.392874+0.005048 test-rmse:0.450866+0.058498
## [30] train-rmse:0.389097+0.004369 test-rmse:0.450087+0.059778
## [31] train-rmse:0.385736+0.004255 test-rmse:0.447947+0.059428
## [32] train-rmse:0.382963+0.004094 test-rmse:0.446381+0.059316
## [33] train-rmse:0.380018+0.004020 test-rmse:0.446735+0.058524
## [34] train-rmse:0.377198+0.004151 test-rmse:0.445740+0.057091
## [35] train-rmse:0.374899+0.004162 test-rmse:0.444422+0.056766
## [36] train-rmse:0.371837+0.003672 test-rmse:0.444236+0.057896
## [37] train-rmse:0.369645+0.003599 test-rmse:0.444873+0.056776
## [38] train-rmse:0.367321+0.003650 test-rmse:0.443744+0.056866
## [39] train-rmse:0.365323+0.003757 test-rmse:0.443785+0.055936
## [40] train-rmse:0.362456+0.003585 test-rmse:0.443844+0.055626
## [41] train-rmse:0.360678+0.003615 test-rmse:0.443649+0.055262
## [42] train-rmse:0.358653+0.003454 test-rmse:0.443288+0.055225
## [43] train-rmse:0.356604+0.003833 test-rmse:0.443493+0.054556
## [44] train-rmse:0.354545+0.003857 test-rmse:0.444057+0.055454
## [45] train-rmse:0.352795+0.004112 test-rmse:0.443244+0.054620
## [46] train-rmse:0.350924+0.004067 test-rmse:0.442233+0.054820
## [47] train-rmse:0.348843+0.004187 test-rmse:0.441415+0.054329
## [48] train-rmse:0.347094+0.004188 test-rmse:0.441712+0.054700
## [49] train-rmse:0.345103+0.003693 test-rmse:0.441684+0.055013
## [50] train-rmse:0.343567+0.003552 test-rmse:0.441081+0.054734
## [51] train-rmse:0.342041+0.003505 test-rmse:0.441475+0.054966
## [52] train-rmse:0.340653+0.003766 test-rmse:0.440689+0.054805
## [53] train-rmse:0.339366+0.003969 test-rmse:0.440117+0.054862
## [54] train-rmse:0.337420+0.004099 test-rmse:0.440204+0.053808
## [55] train-rmse:0.335931+0.004124 test-rmse:0.440278+0.053552
## [56] train-rmse:0.334307+0.004037 test-rmse:0.439953+0.054293
## [57] train-rmse:0.332317+0.003504 test-rmse:0.439167+0.054663
## [58] train-rmse:0.331104+0.003353 test-rmse:0.438640+0.054379
## [59] train-rmse:0.329530+0.003709 test-rmse:0.438390+0.054503
## [60] train-rmse:0.327997+0.004029 test-rmse:0.438120+0.053574
## [61] train-rmse:0.326790+0.004042 test-rmse:0.437966+0.054385
## [62] train-rmse:0.325589+0.004176 test-rmse:0.437866+0.055203
## [63] train-rmse:0.323619+0.004046 test-rmse:0.437399+0.054797
## [64] train-rmse:0.322256+0.004050 test-rmse:0.436898+0.054762
## [65] train-rmse:0.320818+0.003814 test-rmse:0.436008+0.054870
## [66] train-rmse:0.319614+0.004184 test-rmse:0.436890+0.054626
## [67] train-rmse:0.318555+0.004129 test-rmse:0.435757+0.056248
## [68] train-rmse:0.317083+0.004481 test-rmse:0.434785+0.054440
## [69] train-rmse:0.315958+0.004607 test-rmse:0.434572+0.054807
## [70] train-rmse:0.314863+0.004907 test-rmse:0.433474+0.054966
## [71] train-rmse:0.313789+0.005261 test-rmse:0.432906+0.054840
## [72] train-rmse:0.312684+0.005239 test-rmse:0.432774+0.054484
## [73] train-rmse:0.311560+0.005564 test-rmse:0.432514+0.054271
## [74] train-rmse:0.310237+0.005232 test-rmse:0.432668+0.054606
## [75] train-rmse:0.309335+0.005329 test-rmse:0.432524+0.054781
## [76] train-rmse:0.307605+0.005405 test-rmse:0.432229+0.055261
## [77] train-rmse:0.306629+0.005599 test-rmse:0.432379+0.054784
## [78] train-rmse:0.304628+0.005182 test-rmse:0.431624+0.054810
## [79] train-rmse:0.303808+0.005144 test-rmse:0.430853+0.055365
## [80] train-rmse:0.302778+0.005159 test-rmse:0.430612+0.055664
## [81] train-rmse:0.301612+0.005010 test-rmse:0.430794+0.055802
## [82] train-rmse:0.300384+0.005145 test-rmse:0.430389+0.055557
## [83] train-rmse:0.299566+0.004923 test-rmse:0.430169+0.055605
## [84] train-rmse:0.298403+0.004801 test-rmse:0.429619+0.056269
## [85] train-rmse:0.297277+0.005422 test-rmse:0.429848+0.056587
## [86] train-rmse:0.296473+0.005733 test-rmse:0.429290+0.056464
## [87] train-rmse:0.295823+0.005787 test-rmse:0.429143+0.056742
## [88] train-rmse:0.295001+0.005702 test-rmse:0.429046+0.057070
## [89] train-rmse:0.293968+0.006436 test-rmse:0.429195+0.057288
## [90] train-rmse:0.292847+0.006415 test-rmse:0.428609+0.057481
## [91] train-rmse:0.292139+0.006738 test-rmse:0.428170+0.057461
## [92] train-rmse:0.291372+0.006628 test-rmse:0.427976+0.057127
## [93] train-rmse:0.290772+0.006541 test-rmse:0.427782+0.057249
## [94] train-rmse:0.289090+0.005872 test-rmse:0.427881+0.056937
## [95] train-rmse:0.287844+0.005681 test-rmse:0.427158+0.057061
## [96] train-rmse:0.286843+0.005905 test-rmse:0.427253+0.057106
## [97] train-rmse:0.286208+0.005931 test-rmse:0.426474+0.056937
## [98] train-rmse:0.285406+0.006092 test-rmse:0.426425+0.056904
## [99] train-rmse:0.284804+0.006021 test-rmse:0.425713+0.057764
## [100] train-rmse:0.284035+0.006114 test-rmse:0.425551+0.057746
## [101] train-rmse:0.283349+0.006136 test-rmse:0.425709+0.057690
## [102] train-rmse:0.282259+0.006453 test-rmse:0.425789+0.057972
## [103] train-rmse:0.281135+0.006306 test-rmse:0.425683+0.057973
## [104] train-rmse:0.280413+0.006592 test-rmse:0.425898+0.057675
## [105] train-rmse:0.279892+0.006632 test-rmse:0.425931+0.057703
## [106] train-rmse:0.279280+0.006566 test-rmse:0.425243+0.058113
## [107] train-rmse:0.278624+0.006512 test-rmse:0.425401+0.058448
## [108] train-rmse:0.278070+0.006591 test-rmse:0.424936+0.058483
## [109] train-rmse:0.277612+0.006618 test-rmse:0.424082+0.058402
## [110] train-rmse:0.276589+0.006490 test-rmse:0.423572+0.058104
## [111] train-rmse:0.275751+0.006296 test-rmse:0.423582+0.057948
## [112] train-rmse:0.274894+0.006352 test-rmse:0.423440+0.057961
## [113] train-rmse:0.274100+0.006378 test-rmse:0.423292+0.058149
## [114] train-rmse:0.272638+0.007469 test-rmse:0.422943+0.058046
## [115] train-rmse:0.272058+0.007345 test-rmse:0.422829+0.058360
## [116] train-rmse:0.271371+0.007335 test-rmse:0.422941+0.058228
## [117] train-rmse:0.270637+0.007437 test-rmse:0.422684+0.057884
## [118] train-rmse:0.268994+0.007291 test-rmse:0.422852+0.058127
## [119] train-rmse:0.268103+0.007390 test-rmse:0.422287+0.058223
## [120] train-rmse:0.267305+0.007314 test-rmse:0.422031+0.058928
## [121] train-rmse:0.266491+0.006962 test-rmse:0.421591+0.058921
## [122] train-rmse:0.265597+0.007453 test-rmse:0.421135+0.058416
## [123] train-rmse:0.264644+0.007154 test-rmse:0.420607+0.058780
## [124] train-rmse:0.264118+0.007323 test-rmse:0.420912+0.058529
## [125] train-rmse:0.263408+0.007228 test-rmse:0.420563+0.059180
## [126] train-rmse:0.262874+0.007105 test-rmse:0.420586+0.059014
## [127] train-rmse:0.262108+0.007081 test-rmse:0.420325+0.059367
## [128] train-rmse:0.261080+0.007459 test-rmse:0.419884+0.059054
## [129] train-rmse:0.260362+0.007362 test-rmse:0.418999+0.059467
## [130] train-rmse:0.259569+0.007314 test-rmse:0.419100+0.059638
## [131] train-rmse:0.258599+0.008154 test-rmse:0.419124+0.059965
## [132] train-rmse:0.258063+0.008449 test-rmse:0.419129+0.059872
## [133] train-rmse:0.257205+0.008279 test-rmse:0.419311+0.059821
## [134] train-rmse:0.256752+0.008219 test-rmse:0.419269+0.059668
## [135] train-rmse:0.256115+0.008151 test-rmse:0.419551+0.059602
## Stopping. Best iteration:
## [129] train-rmse:0.260362+0.007362 test-rmse:0.418999+0.059467
##
## 23
## [1] train-rmse:1.406367+0.025903 test-rmse:1.407126+0.117133
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.214237+0.022612 test-rmse:1.218105+0.112915
## [3] train-rmse:1.053782+0.019637 test-rmse:1.060026+0.113289
## [4] train-rmse:0.921221+0.017093 test-rmse:0.926332+0.112516
## [5] train-rmse:0.811697+0.014771 test-rmse:0.821568+0.112665
## [6] train-rmse:0.720812+0.014516 test-rmse:0.732544+0.107288
## [7] train-rmse:0.646157+0.012868 test-rmse:0.664830+0.102356
## [8] train-rmse:0.586628+0.011605 test-rmse:0.613767+0.099424
## [9] train-rmse:0.537642+0.010277 test-rmse:0.569479+0.095038
## [10] train-rmse:0.497205+0.010840 test-rmse:0.537109+0.089081
## [11] train-rmse:0.465308+0.010184 test-rmse:0.511094+0.085936
## [12] train-rmse:0.439044+0.010209 test-rmse:0.490042+0.080312
## [13] train-rmse:0.418375+0.009452 test-rmse:0.474058+0.079261
## [14] train-rmse:0.401163+0.008867 test-rmse:0.465752+0.076684
## [15] train-rmse:0.386606+0.008642 test-rmse:0.458778+0.074868
## [16] train-rmse:0.375367+0.009033 test-rmse:0.452822+0.070538
## [17] train-rmse:0.365959+0.009382 test-rmse:0.447828+0.068326
## [18] train-rmse:0.357585+0.009554 test-rmse:0.444542+0.067320
## [19] train-rmse:0.350635+0.009391 test-rmse:0.441807+0.064276
## [20] train-rmse:0.344868+0.009375 test-rmse:0.438760+0.064341
## [21] train-rmse:0.338300+0.009383 test-rmse:0.436427+0.063985
## [22] train-rmse:0.332959+0.009535 test-rmse:0.436253+0.063450
## [23] train-rmse:0.327985+0.009730 test-rmse:0.435236+0.061987
## [24] train-rmse:0.323718+0.009563 test-rmse:0.433784+0.063224
## [25] train-rmse:0.319507+0.008469 test-rmse:0.433366+0.061327
## [26] train-rmse:0.315435+0.008673 test-rmse:0.432936+0.061036
## [27] train-rmse:0.311664+0.008882 test-rmse:0.433028+0.060547
## [28] train-rmse:0.308353+0.008145 test-rmse:0.432474+0.060519
## [29] train-rmse:0.303960+0.007287 test-rmse:0.431448+0.060339
## [30] train-rmse:0.300710+0.007074 test-rmse:0.431273+0.060004
## [31] train-rmse:0.297504+0.006877 test-rmse:0.430791+0.060605
## [32] train-rmse:0.294458+0.006335 test-rmse:0.430144+0.060950
## [33] train-rmse:0.291623+0.006482 test-rmse:0.429826+0.059857
## [34] train-rmse:0.289445+0.006409 test-rmse:0.430048+0.059883
## [35] train-rmse:0.285762+0.006713 test-rmse:0.429591+0.059440
## [36] train-rmse:0.283398+0.006636 test-rmse:0.428732+0.058668
## [37] train-rmse:0.280879+0.006191 test-rmse:0.428823+0.058748
## [38] train-rmse:0.277981+0.005983 test-rmse:0.428705+0.057783
## [39] train-rmse:0.275709+0.005683 test-rmse:0.428066+0.058584
## [40] train-rmse:0.273392+0.005692 test-rmse:0.427989+0.059283
## [41] train-rmse:0.270475+0.005786 test-rmse:0.427835+0.059432
## [42] train-rmse:0.268100+0.005729 test-rmse:0.427330+0.059934
## [43] train-rmse:0.265990+0.005439 test-rmse:0.426434+0.059739
## [44] train-rmse:0.264184+0.005256 test-rmse:0.425669+0.060084
## [45] train-rmse:0.262541+0.005237 test-rmse:0.425073+0.060287
## [46] train-rmse:0.260636+0.005247 test-rmse:0.423475+0.060747
## [47] train-rmse:0.258727+0.004975 test-rmse:0.423078+0.061670
## [48] train-rmse:0.256405+0.005091 test-rmse:0.424002+0.061839
## [49] train-rmse:0.254142+0.004913 test-rmse:0.423346+0.062753
## [50] train-rmse:0.252414+0.004857 test-rmse:0.422135+0.063040
## [51] train-rmse:0.250902+0.004755 test-rmse:0.422372+0.063203
## [52] train-rmse:0.249109+0.004673 test-rmse:0.421859+0.063914
## [53] train-rmse:0.246947+0.003950 test-rmse:0.421716+0.064636
## [54] train-rmse:0.244720+0.004480 test-rmse:0.422074+0.064467
## [55] train-rmse:0.242763+0.004760 test-rmse:0.421887+0.064950
## [56] train-rmse:0.241412+0.004704 test-rmse:0.422248+0.064711
## [57] train-rmse:0.239919+0.004606 test-rmse:0.422297+0.065063
## [58] train-rmse:0.237549+0.005245 test-rmse:0.421035+0.064521
## [59] train-rmse:0.235628+0.005748 test-rmse:0.421070+0.064491
## [60] train-rmse:0.234105+0.005657 test-rmse:0.419550+0.066001
## [61] train-rmse:0.232620+0.005457 test-rmse:0.419058+0.065545
## [62] train-rmse:0.231408+0.005320 test-rmse:0.418545+0.065678
## [63] train-rmse:0.229917+0.005559 test-rmse:0.417923+0.066136
## [64] train-rmse:0.228758+0.005272 test-rmse:0.418411+0.066273
## [65] train-rmse:0.227698+0.005165 test-rmse:0.417517+0.066693
## [66] train-rmse:0.225673+0.005029 test-rmse:0.416841+0.066504
## [67] train-rmse:0.224487+0.004925 test-rmse:0.416497+0.066178
## [68] train-rmse:0.223111+0.004632 test-rmse:0.416633+0.066564
## [69] train-rmse:0.221435+0.005003 test-rmse:0.416110+0.065890
## [70] train-rmse:0.219933+0.005092 test-rmse:0.415933+0.065599
## [71] train-rmse:0.218969+0.005211 test-rmse:0.415776+0.065603
## [72] train-rmse:0.217472+0.005825 test-rmse:0.414561+0.065685
## [73] train-rmse:0.215927+0.005250 test-rmse:0.414379+0.065679
## [74] train-rmse:0.214289+0.005500 test-rmse:0.414448+0.065735
## [75] train-rmse:0.212972+0.005215 test-rmse:0.414193+0.066074
## [76] train-rmse:0.211568+0.005494 test-rmse:0.414297+0.066100
## [77] train-rmse:0.210429+0.005476 test-rmse:0.413962+0.066370
## [78] train-rmse:0.209084+0.005477 test-rmse:0.414113+0.065961
## [79] train-rmse:0.207784+0.005217 test-rmse:0.414463+0.065746
## [80] train-rmse:0.206601+0.005551 test-rmse:0.414448+0.065634
## [81] train-rmse:0.205280+0.005691 test-rmse:0.414152+0.065583
## [82] train-rmse:0.203761+0.005831 test-rmse:0.414674+0.065388
## [83] train-rmse:0.202753+0.005744 test-rmse:0.414520+0.065876
## Stopping. Best iteration:
## [77] train-rmse:0.210429+0.005476 test-rmse:0.413962+0.066370
##
## 24
## [1] train-rmse:1.404054+0.025543 test-rmse:1.404469+0.117377
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.209775+0.021932 test-rmse:1.212667+0.115287
## [3] train-rmse:1.046088+0.019451 test-rmse:1.049437+0.111208
## [4] train-rmse:0.910284+0.017328 test-rmse:0.918566+0.106605
## [5] train-rmse:0.796397+0.014331 test-rmse:0.810566+0.105889
## [6] train-rmse:0.702308+0.012185 test-rmse:0.721744+0.102439
## [7] train-rmse:0.624798+0.010688 test-rmse:0.648464+0.099965
## [8] train-rmse:0.561554+0.009054 test-rmse:0.593924+0.094905
## [9] train-rmse:0.508909+0.008494 test-rmse:0.553120+0.091156
## [10] train-rmse:0.464716+0.007271 test-rmse:0.518925+0.086692
## [11] train-rmse:0.429170+0.006408 test-rmse:0.493479+0.082292
## [12] train-rmse:0.400586+0.007377 test-rmse:0.474513+0.075354
## [13] train-rmse:0.377584+0.007511 test-rmse:0.463450+0.072034
## [14] train-rmse:0.357013+0.008005 test-rmse:0.450009+0.072676
## [15] train-rmse:0.342144+0.007798 test-rmse:0.443061+0.070397
## [16] train-rmse:0.329022+0.006609 test-rmse:0.438513+0.068793
## [17] train-rmse:0.316757+0.006454 test-rmse:0.433378+0.065080
## [18] train-rmse:0.307562+0.005769 test-rmse:0.430073+0.063508
## [19] train-rmse:0.299028+0.006861 test-rmse:0.428664+0.063124
## [20] train-rmse:0.291806+0.005990 test-rmse:0.425638+0.061258
## [21] train-rmse:0.285648+0.006483 test-rmse:0.424585+0.059855
## [22] train-rmse:0.279834+0.007065 test-rmse:0.423585+0.058716
## [23] train-rmse:0.274777+0.006631 test-rmse:0.420610+0.060208
## [24] train-rmse:0.269881+0.005988 test-rmse:0.418580+0.059170
## [25] train-rmse:0.265380+0.005997 test-rmse:0.417035+0.059039
## [26] train-rmse:0.260719+0.005726 test-rmse:0.416400+0.059481
## [27] train-rmse:0.257016+0.006091 test-rmse:0.415726+0.059999
## [28] train-rmse:0.252383+0.006547 test-rmse:0.415406+0.060364
## [29] train-rmse:0.248312+0.004676 test-rmse:0.414105+0.059662
## [30] train-rmse:0.244843+0.005453 test-rmse:0.412081+0.060693
## [31] train-rmse:0.240288+0.004395 test-rmse:0.411476+0.061720
## [32] train-rmse:0.237285+0.004313 test-rmse:0.411145+0.061294
## [33] train-rmse:0.233303+0.003479 test-rmse:0.410272+0.061470
## [34] train-rmse:0.229550+0.002861 test-rmse:0.410389+0.061703
## [35] train-rmse:0.227144+0.002482 test-rmse:0.409356+0.061566
## [36] train-rmse:0.224098+0.002014 test-rmse:0.408127+0.062390
## [37] train-rmse:0.220873+0.002121 test-rmse:0.406724+0.061060
## [38] train-rmse:0.218424+0.002287 test-rmse:0.406826+0.060805
## [39] train-rmse:0.215894+0.002401 test-rmse:0.406180+0.060751
## [40] train-rmse:0.213546+0.002409 test-rmse:0.405966+0.060847
## [41] train-rmse:0.211363+0.002139 test-rmse:0.404493+0.061967
## [42] train-rmse:0.209488+0.002290 test-rmse:0.404084+0.062472
## [43] train-rmse:0.207015+0.001859 test-rmse:0.404393+0.062008
## [44] train-rmse:0.204978+0.002009 test-rmse:0.403957+0.061568
## [45] train-rmse:0.201769+0.002078 test-rmse:0.404322+0.060688
## [46] train-rmse:0.199513+0.002138 test-rmse:0.404344+0.061929
## [47] train-rmse:0.196647+0.002399 test-rmse:0.404041+0.061073
## [48] train-rmse:0.194594+0.002320 test-rmse:0.403875+0.061101
## [49] train-rmse:0.192807+0.002521 test-rmse:0.403248+0.061216
## [50] train-rmse:0.190691+0.001453 test-rmse:0.402755+0.061410
## [51] train-rmse:0.187805+0.001917 test-rmse:0.401736+0.061479
## [52] train-rmse:0.186187+0.001942 test-rmse:0.401364+0.061157
## [53] train-rmse:0.184308+0.001776 test-rmse:0.400401+0.060852
## [54] train-rmse:0.182423+0.002177 test-rmse:0.400797+0.060905
## [55] train-rmse:0.181118+0.002146 test-rmse:0.400031+0.061598
## [56] train-rmse:0.179487+0.002283 test-rmse:0.400056+0.061624
## [57] train-rmse:0.177962+0.002526 test-rmse:0.400293+0.061137
## [58] train-rmse:0.175283+0.002441 test-rmse:0.400222+0.060625
## [59] train-rmse:0.172812+0.002410 test-rmse:0.398767+0.062005
## [60] train-rmse:0.171152+0.002687 test-rmse:0.398750+0.061203
## [61] train-rmse:0.169903+0.002506 test-rmse:0.398046+0.062146
## [62] train-rmse:0.167624+0.002876 test-rmse:0.398078+0.061235
## [63] train-rmse:0.165491+0.002540 test-rmse:0.397764+0.061875
## [64] train-rmse:0.164093+0.002331 test-rmse:0.397783+0.061856
## [65] train-rmse:0.162878+0.002303 test-rmse:0.397647+0.062301
## [66] train-rmse:0.161008+0.002070 test-rmse:0.397639+0.062429
## [67] train-rmse:0.159681+0.001947 test-rmse:0.397663+0.062737
## [68] train-rmse:0.158368+0.001602 test-rmse:0.397234+0.063078
## [69] train-rmse:0.156823+0.002040 test-rmse:0.397416+0.063166
## [70] train-rmse:0.155416+0.001561 test-rmse:0.397476+0.062750
## [71] train-rmse:0.153735+0.002096 test-rmse:0.396758+0.063116
## [72] train-rmse:0.151870+0.001938 test-rmse:0.396634+0.063169
## [73] train-rmse:0.150763+0.001747 test-rmse:0.395829+0.063752
## [74] train-rmse:0.149739+0.001849 test-rmse:0.395894+0.063447
## [75] train-rmse:0.148426+0.001565 test-rmse:0.395705+0.063677
## [76] train-rmse:0.147052+0.001620 test-rmse:0.395809+0.063881
## [77] train-rmse:0.145788+0.002141 test-rmse:0.395895+0.063696
## [78] train-rmse:0.144370+0.002533 test-rmse:0.396532+0.063798
## [79] train-rmse:0.143134+0.002862 test-rmse:0.396259+0.064346
## [80] train-rmse:0.141967+0.003307 test-rmse:0.395847+0.064538
## [81] train-rmse:0.140262+0.002484 test-rmse:0.395736+0.064464
## Stopping. Best iteration:
## [75] train-rmse:0.148426+0.001565 test-rmse:0.395705+0.063677
##
## 25
## [1] train-rmse:1.402672+0.025782 test-rmse:1.402980+0.115106
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.206753+0.021964 test-rmse:1.207992+0.112095
## [3] train-rmse:1.042323+0.019763 test-rmse:1.044087+0.107376
## [4] train-rmse:0.904286+0.017025 test-rmse:0.911831+0.105617
## [5] train-rmse:0.788651+0.015410 test-rmse:0.802552+0.100231
## [6] train-rmse:0.691650+0.013154 test-rmse:0.714497+0.097961
## [7] train-rmse:0.610257+0.012107 test-rmse:0.641045+0.092677
## [8] train-rmse:0.542138+0.010632 test-rmse:0.586636+0.087433
## [9] train-rmse:0.485171+0.008771 test-rmse:0.542019+0.086179
## [10] train-rmse:0.439037+0.007548 test-rmse:0.506959+0.081701
## [11] train-rmse:0.400189+0.006387 test-rmse:0.480541+0.081092
## [12] train-rmse:0.367059+0.004962 test-rmse:0.459271+0.078060
## [13] train-rmse:0.340444+0.004517 test-rmse:0.445007+0.076683
## [14] train-rmse:0.318393+0.004476 test-rmse:0.434698+0.073910
## [15] train-rmse:0.301064+0.004875 test-rmse:0.428268+0.073222
## [16] train-rmse:0.282372+0.004326 test-rmse:0.422449+0.070492
## [17] train-rmse:0.269376+0.004590 test-rmse:0.418349+0.066252
## [18] train-rmse:0.259469+0.004241 test-rmse:0.416534+0.064778
## [19] train-rmse:0.250334+0.005252 test-rmse:0.413461+0.065259
## [20] train-rmse:0.239739+0.003813 test-rmse:0.411861+0.063208
## [21] train-rmse:0.233953+0.004039 test-rmse:0.410620+0.062384
## [22] train-rmse:0.227790+0.004752 test-rmse:0.408824+0.063569
## [23] train-rmse:0.221380+0.005128 test-rmse:0.407716+0.062237
## [24] train-rmse:0.215313+0.003732 test-rmse:0.406224+0.061037
## [25] train-rmse:0.209431+0.003713 test-rmse:0.406523+0.060642
## [26] train-rmse:0.205644+0.003261 test-rmse:0.405008+0.061960
## [27] train-rmse:0.201559+0.002826 test-rmse:0.404518+0.061284
## [28] train-rmse:0.197358+0.001650 test-rmse:0.404568+0.060243
## [29] train-rmse:0.192835+0.002170 test-rmse:0.404721+0.059851
## [30] train-rmse:0.188702+0.003499 test-rmse:0.405105+0.059521
## [31] train-rmse:0.184328+0.004516 test-rmse:0.404889+0.059379
## [32] train-rmse:0.180468+0.004990 test-rmse:0.403861+0.058055
## [33] train-rmse:0.177527+0.005053 test-rmse:0.403602+0.057320
## [34] train-rmse:0.175144+0.005447 test-rmse:0.404439+0.057337
## [35] train-rmse:0.171941+0.005720 test-rmse:0.402463+0.057930
## [36] train-rmse:0.168310+0.005120 test-rmse:0.402877+0.057865
## [37] train-rmse:0.165880+0.005455 test-rmse:0.403097+0.057459
## [38] train-rmse:0.163717+0.004983 test-rmse:0.402720+0.057010
## [39] train-rmse:0.160889+0.005046 test-rmse:0.403252+0.056899
## [40] train-rmse:0.158293+0.004920 test-rmse:0.402068+0.057106
## [41] train-rmse:0.155895+0.003959 test-rmse:0.401397+0.057890
## [42] train-rmse:0.153903+0.003666 test-rmse:0.401652+0.057072
## [43] train-rmse:0.151680+0.003244 test-rmse:0.401367+0.056960
## [44] train-rmse:0.149242+0.003120 test-rmse:0.400753+0.057557
## [45] train-rmse:0.146833+0.002454 test-rmse:0.400743+0.057976
## [46] train-rmse:0.144594+0.002042 test-rmse:0.400914+0.057067
## [47] train-rmse:0.141947+0.001528 test-rmse:0.400139+0.057186
## [48] train-rmse:0.139655+0.001197 test-rmse:0.399434+0.057240
## [49] train-rmse:0.138202+0.000947 test-rmse:0.399341+0.057011
## [50] train-rmse:0.136492+0.001017 test-rmse:0.398675+0.057484
## [51] train-rmse:0.134647+0.001130 test-rmse:0.398590+0.057531
## [52] train-rmse:0.132583+0.000549 test-rmse:0.397856+0.057546
## [53] train-rmse:0.130855+0.000805 test-rmse:0.398260+0.057449
## [54] train-rmse:0.129186+0.001359 test-rmse:0.397667+0.057339
## [55] train-rmse:0.127631+0.001336 test-rmse:0.396903+0.057506
## [56] train-rmse:0.125799+0.001710 test-rmse:0.396722+0.057448
## [57] train-rmse:0.124419+0.001447 test-rmse:0.396212+0.057539
## [58] train-rmse:0.121788+0.001234 test-rmse:0.395954+0.057788
## [59] train-rmse:0.119995+0.001299 test-rmse:0.395823+0.056978
## [60] train-rmse:0.118499+0.001810 test-rmse:0.395977+0.056948
## [61] train-rmse:0.116709+0.001623 test-rmse:0.395495+0.057221
## [62] train-rmse:0.115098+0.001930 test-rmse:0.394962+0.057745
## [63] train-rmse:0.113756+0.001839 test-rmse:0.394875+0.057931
## [64] train-rmse:0.112136+0.001973 test-rmse:0.394819+0.058136
## [65] train-rmse:0.110726+0.001938 test-rmse:0.394835+0.058530
## [66] train-rmse:0.108313+0.001624 test-rmse:0.394703+0.058016
## [67] train-rmse:0.106825+0.001537 test-rmse:0.394207+0.058223
## [68] train-rmse:0.105658+0.001690 test-rmse:0.393727+0.057689
## [69] train-rmse:0.104580+0.001403 test-rmse:0.393827+0.057510
## [70] train-rmse:0.103028+0.001678 test-rmse:0.393432+0.057798
## [71] train-rmse:0.100921+0.001989 test-rmse:0.393556+0.057619
## [72] train-rmse:0.099882+0.001895 test-rmse:0.393497+0.056959
## [73] train-rmse:0.098418+0.002180 test-rmse:0.393484+0.056950
## [74] train-rmse:0.097276+0.001872 test-rmse:0.392998+0.056830
## [75] train-rmse:0.096308+0.001649 test-rmse:0.392939+0.056897
## [76] train-rmse:0.095041+0.001600 test-rmse:0.392788+0.056964
## [77] train-rmse:0.094122+0.001504 test-rmse:0.393454+0.056584
## [78] train-rmse:0.093375+0.001462 test-rmse:0.393548+0.056398
## [79] train-rmse:0.092220+0.001602 test-rmse:0.393649+0.056519
## [80] train-rmse:0.090936+0.001606 test-rmse:0.393545+0.056347
## [81] train-rmse:0.089565+0.002422 test-rmse:0.393353+0.056076
## [82] train-rmse:0.088455+0.002521 test-rmse:0.393388+0.056019
## Stopping. Best iteration:
## [76] train-rmse:0.095041+0.001600 test-rmse:0.392788+0.056964
##
## 26
## [1] train-rmse:1.402260+0.025821 test-rmse:1.401950+0.114226
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.205393+0.022039 test-rmse:1.204374+0.110422
## [3] train-rmse:1.039785+0.019952 test-rmse:1.038220+0.104466
## [4] train-rmse:0.901022+0.017651 test-rmse:0.906020+0.101935
## [5] train-rmse:0.784102+0.016257 test-rmse:0.797099+0.097961
## [6] train-rmse:0.686663+0.014144 test-rmse:0.709787+0.096337
## [7] train-rmse:0.604194+0.013209 test-rmse:0.637828+0.092675
## [8] train-rmse:0.535055+0.012227 test-rmse:0.580784+0.088573
## [9] train-rmse:0.476676+0.011019 test-rmse:0.537404+0.087232
## [10] train-rmse:0.428736+0.010446 test-rmse:0.505558+0.085565
## [11] train-rmse:0.388289+0.009990 test-rmse:0.480127+0.079577
## [12] train-rmse:0.354199+0.009921 test-rmse:0.462116+0.077918
## [13] train-rmse:0.325523+0.009149 test-rmse:0.451506+0.078077
## [14] train-rmse:0.301873+0.008318 test-rmse:0.441639+0.076052
## [15] train-rmse:0.280032+0.007782 test-rmse:0.432692+0.072307
## [16] train-rmse:0.260856+0.008137 test-rmse:0.426398+0.071119
## [17] train-rmse:0.246750+0.007869 test-rmse:0.421504+0.071089
## [18] train-rmse:0.231052+0.004827 test-rmse:0.417397+0.069036
## [19] train-rmse:0.219256+0.002617 test-rmse:0.415000+0.069355
## [20] train-rmse:0.209757+0.003222 test-rmse:0.413710+0.067448
## [21] train-rmse:0.201432+0.003102 test-rmse:0.411496+0.066479
## [22] train-rmse:0.195554+0.003287 test-rmse:0.411244+0.065925
## [23] train-rmse:0.189895+0.003526 test-rmse:0.410546+0.066418
## [24] train-rmse:0.182637+0.002697 test-rmse:0.410311+0.066357
## [25] train-rmse:0.176821+0.002606 test-rmse:0.408743+0.066965
## [26] train-rmse:0.172573+0.001972 test-rmse:0.408376+0.065681
## [27] train-rmse:0.167435+0.002964 test-rmse:0.407979+0.064206
## [28] train-rmse:0.163016+0.003383 test-rmse:0.407545+0.062181
## [29] train-rmse:0.158865+0.003589 test-rmse:0.406635+0.059495
## [30] train-rmse:0.153573+0.004153 test-rmse:0.406794+0.058924
## [31] train-rmse:0.149657+0.004832 test-rmse:0.406820+0.059463
## [32] train-rmse:0.146139+0.005224 test-rmse:0.407657+0.058888
## [33] train-rmse:0.140944+0.004946 test-rmse:0.407574+0.059185
## [34] train-rmse:0.136025+0.004647 test-rmse:0.407747+0.058685
## [35] train-rmse:0.132257+0.005318 test-rmse:0.407707+0.057551
## Stopping. Best iteration:
## [29] train-rmse:0.158865+0.003589 test-rmse:0.406635+0.059495
##
## 27
## [1] train-rmse:1.402053+0.025823 test-rmse:1.402675+0.113754
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.204707+0.022250 test-rmse:1.204279+0.110463
## [3] train-rmse:1.038515+0.020159 test-rmse:1.038237+0.105220
## [4] train-rmse:0.898749+0.017654 test-rmse:0.904783+0.102439
## [5] train-rmse:0.780893+0.016345 test-rmse:0.796854+0.100132
## [6] train-rmse:0.682196+0.014240 test-rmse:0.708027+0.098142
## [7] train-rmse:0.598272+0.013440 test-rmse:0.635367+0.093812
## [8] train-rmse:0.527666+0.011996 test-rmse:0.578629+0.092060
## [9] train-rmse:0.467888+0.011660 test-rmse:0.533151+0.086500
## [10] train-rmse:0.417453+0.010462 test-rmse:0.498880+0.082707
## [11] train-rmse:0.374869+0.009936 test-rmse:0.474808+0.082143
## [12] train-rmse:0.338784+0.010548 test-rmse:0.457659+0.078899
## [13] train-rmse:0.307604+0.010369 test-rmse:0.444487+0.077359
## [14] train-rmse:0.281057+0.010267 test-rmse:0.434598+0.075832
## [15] train-rmse:0.259078+0.009515 test-rmse:0.426394+0.074095
## [16] train-rmse:0.239476+0.010491 test-rmse:0.422020+0.071633
## [17] train-rmse:0.222210+0.010525 test-rmse:0.418614+0.071252
## [18] train-rmse:0.208632+0.009854 test-rmse:0.414863+0.069426
## [19] train-rmse:0.196015+0.008619 test-rmse:0.413381+0.068475
## [20] train-rmse:0.184751+0.009949 test-rmse:0.412120+0.066396
## [21] train-rmse:0.176670+0.010332 test-rmse:0.410466+0.065439
## [22] train-rmse:0.167688+0.010280 test-rmse:0.410056+0.063638
## [23] train-rmse:0.160698+0.009354 test-rmse:0.409009+0.062531
## [24] train-rmse:0.154552+0.010759 test-rmse:0.407267+0.061652
## [25] train-rmse:0.148298+0.010401 test-rmse:0.406972+0.060754
## [26] train-rmse:0.141207+0.007727 test-rmse:0.405286+0.061507
## [27] train-rmse:0.136225+0.007305 test-rmse:0.404065+0.061610
## [28] train-rmse:0.130704+0.005219 test-rmse:0.404091+0.060485
## [29] train-rmse:0.126897+0.005367 test-rmse:0.402958+0.060807
## [30] train-rmse:0.123672+0.005179 test-rmse:0.402432+0.060028
## [31] train-rmse:0.118517+0.005325 test-rmse:0.401744+0.060291
## [32] train-rmse:0.115184+0.004935 test-rmse:0.401372+0.060892
## [33] train-rmse:0.112235+0.004318 test-rmse:0.400511+0.060675
## [34] train-rmse:0.107891+0.004870 test-rmse:0.400067+0.061232
## [35] train-rmse:0.103866+0.004964 test-rmse:0.399340+0.060883
## [36] train-rmse:0.100513+0.004801 test-rmse:0.399232+0.060793
## [37] train-rmse:0.097607+0.005424 test-rmse:0.399476+0.061210
## [38] train-rmse:0.092941+0.005425 test-rmse:0.398706+0.061518
## [39] train-rmse:0.090043+0.005205 test-rmse:0.399147+0.061314
## [40] train-rmse:0.086672+0.005680 test-rmse:0.399557+0.061149
## [41] train-rmse:0.083194+0.005934 test-rmse:0.399108+0.061361
## [42] train-rmse:0.079869+0.005796 test-rmse:0.399029+0.060802
## [43] train-rmse:0.077513+0.005530 test-rmse:0.399114+0.060739
## [44] train-rmse:0.074867+0.005115 test-rmse:0.399755+0.059991
## Stopping. Best iteration:
## [38] train-rmse:0.092941+0.005425 test-rmse:0.398706+0.061518
##
## 28
## [1] train-rmse:1.395054+0.025971 test-rmse:1.391195+0.111819
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.204171+0.022780 test-rmse:1.200376+0.107255
## [3] train-rmse:1.055957+0.020575 test-rmse:1.052257+0.103266
## [4] train-rmse:0.942802+0.019188 test-rmse:0.939245+0.099682
## [5] train-rmse:0.858003+0.018420 test-rmse:0.854636+0.096470
## [6] train-rmse:0.795654+0.018069 test-rmse:0.792508+0.093658
## [7] train-rmse:0.750633+0.017963 test-rmse:0.747718+0.091277
## [8] train-rmse:0.718643+0.017977 test-rmse:0.715947+0.089323
## [9] train-rmse:0.695784+0.017691 test-rmse:0.698104+0.089873
## [10] train-rmse:0.677126+0.017126 test-rmse:0.675006+0.087924
## [11] train-rmse:0.662240+0.016977 test-rmse:0.664572+0.087502
## [12] train-rmse:0.649654+0.016630 test-rmse:0.649537+0.085864
## [13] train-rmse:0.639151+0.015443 test-rmse:0.643703+0.084454
## [14] train-rmse:0.629543+0.015009 test-rmse:0.630103+0.083118
## [15] train-rmse:0.620864+0.014452 test-rmse:0.624688+0.079914
## [16] train-rmse:0.613496+0.013830 test-rmse:0.616635+0.076403
## [17] train-rmse:0.606744+0.013517 test-rmse:0.609183+0.076278
## [18] train-rmse:0.600450+0.013017 test-rmse:0.603918+0.073385
## [19] train-rmse:0.594935+0.012882 test-rmse:0.601525+0.074417
## [20] train-rmse:0.589788+0.012596 test-rmse:0.595020+0.073226
## [21] train-rmse:0.585091+0.012347 test-rmse:0.590423+0.070441
## [22] train-rmse:0.580671+0.012004 test-rmse:0.588268+0.069790
## [23] train-rmse:0.576677+0.011907 test-rmse:0.585910+0.067302
## [24] train-rmse:0.572784+0.011828 test-rmse:0.582702+0.067200
## [25] train-rmse:0.569220+0.011626 test-rmse:0.579928+0.065156
## [26] train-rmse:0.565950+0.011468 test-rmse:0.577382+0.065767
## [27] train-rmse:0.562805+0.011273 test-rmse:0.575169+0.066525
## [28] train-rmse:0.559848+0.011142 test-rmse:0.569926+0.065516
## [29] train-rmse:0.557057+0.011059 test-rmse:0.568654+0.063469
## [30] train-rmse:0.554376+0.010971 test-rmse:0.567529+0.062925
## [31] train-rmse:0.551879+0.010798 test-rmse:0.566936+0.063036
## [32] train-rmse:0.549547+0.010734 test-rmse:0.564662+0.062230
## [33] train-rmse:0.547327+0.010670 test-rmse:0.562715+0.061858
## [34] train-rmse:0.545290+0.010544 test-rmse:0.561591+0.061440
## [35] train-rmse:0.543243+0.010429 test-rmse:0.560397+0.060401
## [36] train-rmse:0.541264+0.010360 test-rmse:0.559337+0.060898
## [37] train-rmse:0.539419+0.010335 test-rmse:0.558312+0.059529
## [38] train-rmse:0.537614+0.010290 test-rmse:0.556288+0.059156
## [39] train-rmse:0.535867+0.010202 test-rmse:0.554568+0.059703
## [40] train-rmse:0.534194+0.010081 test-rmse:0.552616+0.059901
## [41] train-rmse:0.532587+0.010002 test-rmse:0.552204+0.057977
## [42] train-rmse:0.530990+0.009921 test-rmse:0.552554+0.057953
## [43] train-rmse:0.529421+0.009837 test-rmse:0.551897+0.058185
## [44] train-rmse:0.527915+0.009720 test-rmse:0.550249+0.059256
## [45] train-rmse:0.526461+0.009659 test-rmse:0.549020+0.058732
## [46] train-rmse:0.525042+0.009597 test-rmse:0.547644+0.058617
## [47] train-rmse:0.523670+0.009551 test-rmse:0.546844+0.058083
## [48] train-rmse:0.522263+0.009485 test-rmse:0.547280+0.057710
## [49] train-rmse:0.520986+0.009378 test-rmse:0.545866+0.058369
## [50] train-rmse:0.519721+0.009283 test-rmse:0.544314+0.057569
## [51] train-rmse:0.518471+0.009216 test-rmse:0.543616+0.056441
## [52] train-rmse:0.517256+0.009188 test-rmse:0.542879+0.055973
## [53] train-rmse:0.516062+0.009116 test-rmse:0.542404+0.056358
## [54] train-rmse:0.514880+0.009052 test-rmse:0.540557+0.056207
## [55] train-rmse:0.513707+0.009013 test-rmse:0.539565+0.056606
## [56] train-rmse:0.512534+0.008973 test-rmse:0.539656+0.056286
## [57] train-rmse:0.511443+0.008905 test-rmse:0.539276+0.056210
## [58] train-rmse:0.510356+0.008844 test-rmse:0.538000+0.056036
## [59] train-rmse:0.509288+0.008797 test-rmse:0.536968+0.055641
## [60] train-rmse:0.508244+0.008775 test-rmse:0.536191+0.055403
## [61] train-rmse:0.507192+0.008759 test-rmse:0.535513+0.055283
## [62] train-rmse:0.506182+0.008734 test-rmse:0.535835+0.054832
## [63] train-rmse:0.505190+0.008694 test-rmse:0.534157+0.055439
## [64] train-rmse:0.504186+0.008673 test-rmse:0.533036+0.054735
## [65] train-rmse:0.503221+0.008630 test-rmse:0.532292+0.054497
## [66] train-rmse:0.502253+0.008564 test-rmse:0.532318+0.053823
## [67] train-rmse:0.501320+0.008527 test-rmse:0.533023+0.054198
## [68] train-rmse:0.500418+0.008504 test-rmse:0.532221+0.053859
## [69] train-rmse:0.499514+0.008451 test-rmse:0.530424+0.054152
## [70] train-rmse:0.498609+0.008446 test-rmse:0.529788+0.054635
## [71] train-rmse:0.497701+0.008452 test-rmse:0.529084+0.054126
## [72] train-rmse:0.496855+0.008413 test-rmse:0.529271+0.053686
## [73] train-rmse:0.496007+0.008375 test-rmse:0.529023+0.053555
## [74] train-rmse:0.495190+0.008345 test-rmse:0.527959+0.053113
## [75] train-rmse:0.494372+0.008343 test-rmse:0.526708+0.052282
## [76] train-rmse:0.493538+0.008333 test-rmse:0.526950+0.052436
## [77] train-rmse:0.492712+0.008282 test-rmse:0.525914+0.053116
## [78] train-rmse:0.491920+0.008261 test-rmse:0.525280+0.053082
## [79] train-rmse:0.491149+0.008251 test-rmse:0.525357+0.052450
## [80] train-rmse:0.490387+0.008230 test-rmse:0.524388+0.052342
## [81] train-rmse:0.489646+0.008227 test-rmse:0.524033+0.051725
## [82] train-rmse:0.488917+0.008217 test-rmse:0.523458+0.052044
## [83] train-rmse:0.488187+0.008187 test-rmse:0.523189+0.052156
## [84] train-rmse:0.487467+0.008181 test-rmse:0.522907+0.051796
## [85] train-rmse:0.486751+0.008158 test-rmse:0.521649+0.052180
## [86] train-rmse:0.486055+0.008125 test-rmse:0.522021+0.052516
## [87] train-rmse:0.485376+0.008107 test-rmse:0.521074+0.051967
## [88] train-rmse:0.484687+0.008103 test-rmse:0.521301+0.051057
## [89] train-rmse:0.484031+0.008084 test-rmse:0.520429+0.050641
## [90] train-rmse:0.483373+0.008073 test-rmse:0.520375+0.050624
## [91] train-rmse:0.482720+0.008060 test-rmse:0.520636+0.051051
## [92] train-rmse:0.482086+0.008040 test-rmse:0.520047+0.050321
## [93] train-rmse:0.481459+0.008019 test-rmse:0.519715+0.051178
## [94] train-rmse:0.480835+0.007997 test-rmse:0.519166+0.051064
## [95] train-rmse:0.480206+0.007962 test-rmse:0.518574+0.050933
## [96] train-rmse:0.479609+0.007934 test-rmse:0.518280+0.050897
## [97] train-rmse:0.479022+0.007917 test-rmse:0.518641+0.050195
## [98] train-rmse:0.478422+0.007892 test-rmse:0.518431+0.050471
## [99] train-rmse:0.477842+0.007909 test-rmse:0.518148+0.050795
## [100] train-rmse:0.477268+0.007914 test-rmse:0.517328+0.049751
## [101] train-rmse:0.476712+0.007899 test-rmse:0.517072+0.050053
## [102] train-rmse:0.476139+0.007882 test-rmse:0.516684+0.050140
## [103] train-rmse:0.475586+0.007864 test-rmse:0.515305+0.048653
## [104] train-rmse:0.475036+0.007854 test-rmse:0.514985+0.048640
## [105] train-rmse:0.474499+0.007851 test-rmse:0.514890+0.048578
## [106] train-rmse:0.473976+0.007846 test-rmse:0.514295+0.048576
## [107] train-rmse:0.473464+0.007836 test-rmse:0.513773+0.048214
## [108] train-rmse:0.472932+0.007829 test-rmse:0.514032+0.047383
## [109] train-rmse:0.472422+0.007824 test-rmse:0.513891+0.047476
## [110] train-rmse:0.471887+0.007846 test-rmse:0.513633+0.047532
## [111] train-rmse:0.471387+0.007839 test-rmse:0.513276+0.047562
## [112] train-rmse:0.470886+0.007832 test-rmse:0.512623+0.048024
## [113] train-rmse:0.470397+0.007810 test-rmse:0.512143+0.048491
## [114] train-rmse:0.469911+0.007792 test-rmse:0.512012+0.048424
## [115] train-rmse:0.469436+0.007794 test-rmse:0.511581+0.048332
## [116] train-rmse:0.468960+0.007808 test-rmse:0.511178+0.047701
## [117] train-rmse:0.468494+0.007796 test-rmse:0.511282+0.047470
## [118] train-rmse:0.468029+0.007773 test-rmse:0.510698+0.047691
## [119] train-rmse:0.467560+0.007747 test-rmse:0.511015+0.046967
## [120] train-rmse:0.467099+0.007728 test-rmse:0.510435+0.046845
## [121] train-rmse:0.466648+0.007723 test-rmse:0.510581+0.046352
## [122] train-rmse:0.466212+0.007711 test-rmse:0.509996+0.046483
## [123] train-rmse:0.465781+0.007705 test-rmse:0.509777+0.046590
## [124] train-rmse:0.465347+0.007688 test-rmse:0.508988+0.046793
## [125] train-rmse:0.464913+0.007677 test-rmse:0.508852+0.046965
## [126] train-rmse:0.464482+0.007666 test-rmse:0.508125+0.045235
## [127] train-rmse:0.464059+0.007651 test-rmse:0.508688+0.045282
## [128] train-rmse:0.463637+0.007637 test-rmse:0.508016+0.044636
## [129] train-rmse:0.463225+0.007628 test-rmse:0.508337+0.045016
## [130] train-rmse:0.462811+0.007619 test-rmse:0.507296+0.045112
## [131] train-rmse:0.462414+0.007623 test-rmse:0.507479+0.045105
## [132] train-rmse:0.462018+0.007612 test-rmse:0.507048+0.044974
## [133] train-rmse:0.461621+0.007598 test-rmse:0.507332+0.045200
## [134] train-rmse:0.461233+0.007591 test-rmse:0.507593+0.045461
## [135] train-rmse:0.460848+0.007586 test-rmse:0.506954+0.045745
## [136] train-rmse:0.460460+0.007579 test-rmse:0.507158+0.046105
## [137] train-rmse:0.460080+0.007569 test-rmse:0.506382+0.046437
## [138] train-rmse:0.459704+0.007557 test-rmse:0.506318+0.046033
## [139] train-rmse:0.459324+0.007545 test-rmse:0.506161+0.045498
## [140] train-rmse:0.458940+0.007535 test-rmse:0.505962+0.045305
## [141] train-rmse:0.458568+0.007536 test-rmse:0.506231+0.045457
## [142] train-rmse:0.458196+0.007534 test-rmse:0.505899+0.045426
## [143] train-rmse:0.457834+0.007535 test-rmse:0.505855+0.045036
## [144] train-rmse:0.457473+0.007532 test-rmse:0.505723+0.044937
## [145] train-rmse:0.457112+0.007532 test-rmse:0.505337+0.044480
## [146] train-rmse:0.456770+0.007530 test-rmse:0.505020+0.044391
## [147] train-rmse:0.456419+0.007525 test-rmse:0.505005+0.044414
## [148] train-rmse:0.456079+0.007520 test-rmse:0.504643+0.044147
## [149] train-rmse:0.455727+0.007505 test-rmse:0.504586+0.044200
## [150] train-rmse:0.455394+0.007506 test-rmse:0.504630+0.044557
## [151] train-rmse:0.455064+0.007507 test-rmse:0.504582+0.044426
## [152] train-rmse:0.454725+0.007508 test-rmse:0.504592+0.044581
## [153] train-rmse:0.454402+0.007506 test-rmse:0.504113+0.044401
## [154] train-rmse:0.454079+0.007503 test-rmse:0.504268+0.044232
## [155] train-rmse:0.453757+0.007500 test-rmse:0.504287+0.044010
## [156] train-rmse:0.453428+0.007495 test-rmse:0.504313+0.043610
## [157] train-rmse:0.453110+0.007488 test-rmse:0.504269+0.043730
## [158] train-rmse:0.452802+0.007480 test-rmse:0.503974+0.044087
## [159] train-rmse:0.452483+0.007476 test-rmse:0.504070+0.044050
## [160] train-rmse:0.452184+0.007479 test-rmse:0.503015+0.043555
## [161] train-rmse:0.451883+0.007478 test-rmse:0.503395+0.043855
## [162] train-rmse:0.451578+0.007475 test-rmse:0.503034+0.043876
## [163] train-rmse:0.451280+0.007473 test-rmse:0.503350+0.043596
## [164] train-rmse:0.450980+0.007480 test-rmse:0.502839+0.043626
## [165] train-rmse:0.450686+0.007489 test-rmse:0.502434+0.043202
## [166] train-rmse:0.450393+0.007489 test-rmse:0.502248+0.043456
## [167] train-rmse:0.450094+0.007487 test-rmse:0.501792+0.042322
## [168] train-rmse:0.449797+0.007484 test-rmse:0.501792+0.042069
## [169] train-rmse:0.449516+0.007476 test-rmse:0.501657+0.041758
## [170] train-rmse:0.449239+0.007472 test-rmse:0.501610+0.042111
## [171] train-rmse:0.448956+0.007471 test-rmse:0.501636+0.041830
## [172] train-rmse:0.448678+0.007474 test-rmse:0.501211+0.041870
## [173] train-rmse:0.448405+0.007480 test-rmse:0.500897+0.042061
## [174] train-rmse:0.448133+0.007481 test-rmse:0.501239+0.042073
## [175] train-rmse:0.447869+0.007483 test-rmse:0.501306+0.042246
## [176] train-rmse:0.447600+0.007485 test-rmse:0.501645+0.042235
## [177] train-rmse:0.447332+0.007481 test-rmse:0.501606+0.042086
## [178] train-rmse:0.447072+0.007479 test-rmse:0.501640+0.042306
## [179] train-rmse:0.446814+0.007476 test-rmse:0.501013+0.042157
## Stopping. Best iteration:
## [173] train-rmse:0.448405+0.007480 test-rmse:0.500897+0.042061
##
## 29
## [1] train-rmse:1.383731+0.026011 test-rmse:1.382262+0.115083
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.179447+0.022319 test-rmse:1.180448+0.113474
## [3] train-rmse:1.016068+0.020026 test-rmse:1.019675+0.106774
## [4] train-rmse:0.886748+0.017786 test-rmse:0.893265+0.106532
## [5] train-rmse:0.784760+0.016174 test-rmse:0.786519+0.102247
## [6] train-rmse:0.705441+0.014921 test-rmse:0.712635+0.099314
## [7] train-rmse:0.644070+0.014109 test-rmse:0.652852+0.096291
## [8] train-rmse:0.596799+0.013572 test-rmse:0.608930+0.094962
## [9] train-rmse:0.560680+0.013163 test-rmse:0.572666+0.089691
## [10] train-rmse:0.532580+0.012732 test-rmse:0.547033+0.084092
## [11] train-rmse:0.510544+0.012183 test-rmse:0.529087+0.084628
## [12] train-rmse:0.492914+0.011438 test-rmse:0.516327+0.078717
## [13] train-rmse:0.478934+0.010998 test-rmse:0.502063+0.076483
## [14] train-rmse:0.466325+0.010463 test-rmse:0.494126+0.073409
## [15] train-rmse:0.454292+0.009168 test-rmse:0.484118+0.072793
## [16] train-rmse:0.445266+0.009011 test-rmse:0.478436+0.069438
## [17] train-rmse:0.437200+0.008430 test-rmse:0.473575+0.067324
## [18] train-rmse:0.429715+0.008052 test-rmse:0.468232+0.063666
## [19] train-rmse:0.423783+0.007636 test-rmse:0.467045+0.063725
## [20] train-rmse:0.418036+0.007224 test-rmse:0.464144+0.062130
## [21] train-rmse:0.413026+0.006937 test-rmse:0.460422+0.058494
## [22] train-rmse:0.407687+0.006493 test-rmse:0.460340+0.056971
## [23] train-rmse:0.403163+0.005932 test-rmse:0.458738+0.056787
## [24] train-rmse:0.398543+0.005145 test-rmse:0.456929+0.057054
## [25] train-rmse:0.394674+0.005151 test-rmse:0.457379+0.056700
## [26] train-rmse:0.390923+0.005072 test-rmse:0.454383+0.056706
## [27] train-rmse:0.387231+0.004729 test-rmse:0.452418+0.057458
## [28] train-rmse:0.383984+0.004634 test-rmse:0.452399+0.057256
## [29] train-rmse:0.380826+0.004435 test-rmse:0.449738+0.057016
## [30] train-rmse:0.377658+0.004466 test-rmse:0.446841+0.055450
## [31] train-rmse:0.375033+0.004499 test-rmse:0.446361+0.055611
## [32] train-rmse:0.371879+0.004347 test-rmse:0.446275+0.055761
## [33] train-rmse:0.369020+0.004258 test-rmse:0.445663+0.054572
## [34] train-rmse:0.366548+0.004277 test-rmse:0.446847+0.055375
## [35] train-rmse:0.363332+0.004375 test-rmse:0.445474+0.055589
## [36] train-rmse:0.361084+0.004670 test-rmse:0.444916+0.054881
## [37] train-rmse:0.358776+0.004555 test-rmse:0.444463+0.054745
## [38] train-rmse:0.356492+0.004690 test-rmse:0.444027+0.054643
## [39] train-rmse:0.353696+0.005258 test-rmse:0.442659+0.052359
## [40] train-rmse:0.351657+0.005260 test-rmse:0.442226+0.052745
## [41] train-rmse:0.349590+0.005358 test-rmse:0.441972+0.052264
## [42] train-rmse:0.347562+0.005423 test-rmse:0.440539+0.053003
## [43] train-rmse:0.344395+0.005022 test-rmse:0.440441+0.053754
## [44] train-rmse:0.342508+0.004895 test-rmse:0.440734+0.053085
## [45] train-rmse:0.340137+0.004718 test-rmse:0.440599+0.052839
## [46] train-rmse:0.338351+0.004918 test-rmse:0.440328+0.052878
## [47] train-rmse:0.336544+0.005020 test-rmse:0.439943+0.053577
## [48] train-rmse:0.335027+0.004483 test-rmse:0.438356+0.054341
## [49] train-rmse:0.332638+0.004284 test-rmse:0.438553+0.054621
## [50] train-rmse:0.331261+0.004341 test-rmse:0.438680+0.054560
## [51] train-rmse:0.329636+0.004331 test-rmse:0.438374+0.053580
## [52] train-rmse:0.327839+0.004617 test-rmse:0.439295+0.053952
## [53] train-rmse:0.326069+0.004861 test-rmse:0.439176+0.053880
## [54] train-rmse:0.324617+0.004728 test-rmse:0.439595+0.053806
## Stopping. Best iteration:
## [48] train-rmse:0.335027+0.004483 test-rmse:0.438356+0.054341
##
## 30
## [1] train-rmse:1.377794+0.025377 test-rmse:1.379164+0.117173
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.167253+0.021730 test-rmse:1.172147+0.112609
## [3] train-rmse:0.996020+0.018508 test-rmse:1.003855+0.112873
## [4] train-rmse:0.857574+0.017365 test-rmse:0.863914+0.106327
## [5] train-rmse:0.746991+0.015245 test-rmse:0.759508+0.107231
## [6] train-rmse:0.658554+0.013342 test-rmse:0.675100+0.100152
## [7] train-rmse:0.588395+0.011875 test-rmse:0.612523+0.099611
## [8] train-rmse:0.532864+0.010691 test-rmse:0.566065+0.094791
## [9] train-rmse:0.489901+0.011176 test-rmse:0.532272+0.087320
## [10] train-rmse:0.456051+0.010386 test-rmse:0.506732+0.081147
## [11] train-rmse:0.429233+0.009512 test-rmse:0.485024+0.080914
## [12] train-rmse:0.408642+0.008778 test-rmse:0.471583+0.079803
## [13] train-rmse:0.391183+0.008017 test-rmse:0.460528+0.075792
## [14] train-rmse:0.377906+0.007536 test-rmse:0.452589+0.072123
## [15] train-rmse:0.366853+0.007790 test-rmse:0.450556+0.070552
## [16] train-rmse:0.358022+0.007630 test-rmse:0.447886+0.068298
## [17] train-rmse:0.350077+0.007757 test-rmse:0.443192+0.065184
## [18] train-rmse:0.343563+0.007421 test-rmse:0.441598+0.064021
## [19] train-rmse:0.336381+0.007591 test-rmse:0.439423+0.063480
## [20] train-rmse:0.330743+0.007324 test-rmse:0.438345+0.062619
## [21] train-rmse:0.325413+0.007227 test-rmse:0.436203+0.061334
## [22] train-rmse:0.321356+0.007014 test-rmse:0.434846+0.061245
## [23] train-rmse:0.316865+0.007294 test-rmse:0.434764+0.060954
## [24] train-rmse:0.312633+0.007148 test-rmse:0.434133+0.060145
## [25] train-rmse:0.309083+0.006740 test-rmse:0.433483+0.059805
## [26] train-rmse:0.305137+0.006016 test-rmse:0.432670+0.058703
## [27] train-rmse:0.300911+0.005869 test-rmse:0.432256+0.057682
## [28] train-rmse:0.297688+0.005307 test-rmse:0.430750+0.057849
## [29] train-rmse:0.293854+0.005998 test-rmse:0.430181+0.058832
## [30] train-rmse:0.290811+0.005729 test-rmse:0.429767+0.059302
## [31] train-rmse:0.287532+0.006069 test-rmse:0.429120+0.060046
## [32] train-rmse:0.283867+0.006198 test-rmse:0.428522+0.060634
## [33] train-rmse:0.281530+0.006027 test-rmse:0.427996+0.060438
## [34] train-rmse:0.277947+0.005667 test-rmse:0.427368+0.059416
## [35] train-rmse:0.275313+0.005758 test-rmse:0.426627+0.059703
## [36] train-rmse:0.272806+0.005617 test-rmse:0.426494+0.061000
## [37] train-rmse:0.270008+0.005991 test-rmse:0.425704+0.060938
## [38] train-rmse:0.267537+0.005988 test-rmse:0.425082+0.060973
## [39] train-rmse:0.264975+0.006062 test-rmse:0.425822+0.061590
## [40] train-rmse:0.262594+0.005566 test-rmse:0.425185+0.062216
## [41] train-rmse:0.260157+0.005427 test-rmse:0.424934+0.061867
## [42] train-rmse:0.258093+0.005478 test-rmse:0.424868+0.061048
## [43] train-rmse:0.256144+0.005416 test-rmse:0.424225+0.062178
## [44] train-rmse:0.254063+0.005163 test-rmse:0.424008+0.062552
## [45] train-rmse:0.251626+0.005083 test-rmse:0.423631+0.062453
## [46] train-rmse:0.249116+0.004293 test-rmse:0.422851+0.062980
## [47] train-rmse:0.246250+0.003022 test-rmse:0.422441+0.064048
## [48] train-rmse:0.244822+0.003056 test-rmse:0.422140+0.064135
## [49] train-rmse:0.242873+0.003385 test-rmse:0.421674+0.063815
## [50] train-rmse:0.240771+0.002814 test-rmse:0.420416+0.064000
## [51] train-rmse:0.238678+0.002705 test-rmse:0.420068+0.064754
## [52] train-rmse:0.236919+0.002919 test-rmse:0.419160+0.064702
## [53] train-rmse:0.235335+0.003083 test-rmse:0.419658+0.065633
## [54] train-rmse:0.232690+0.003623 test-rmse:0.418966+0.066847
## [55] train-rmse:0.231066+0.003719 test-rmse:0.419320+0.066829
## [56] train-rmse:0.229047+0.003904 test-rmse:0.418486+0.066763
## [57] train-rmse:0.227049+0.004158 test-rmse:0.417449+0.066968
## [58] train-rmse:0.225439+0.003801 test-rmse:0.416882+0.066878
## [59] train-rmse:0.223892+0.003325 test-rmse:0.416880+0.067402
## [60] train-rmse:0.222311+0.004273 test-rmse:0.416945+0.067778
## [61] train-rmse:0.220721+0.004230 test-rmse:0.416693+0.067531
## [62] train-rmse:0.218782+0.004552 test-rmse:0.416595+0.067324
## [63] train-rmse:0.217420+0.004118 test-rmse:0.416034+0.068225
## [64] train-rmse:0.216128+0.003751 test-rmse:0.415722+0.068252
## [65] train-rmse:0.214505+0.003902 test-rmse:0.415590+0.067688
## [66] train-rmse:0.213300+0.003878 test-rmse:0.415240+0.067533
## [67] train-rmse:0.212002+0.003734 test-rmse:0.415202+0.067515
## [68] train-rmse:0.210032+0.003404 test-rmse:0.415166+0.066793
## [69] train-rmse:0.209159+0.003408 test-rmse:0.415121+0.066627
## [70] train-rmse:0.207304+0.004078 test-rmse:0.415195+0.066330
## [71] train-rmse:0.205095+0.003832 test-rmse:0.415186+0.066239
## [72] train-rmse:0.203429+0.003482 test-rmse:0.415186+0.066250
## [73] train-rmse:0.202043+0.003674 test-rmse:0.415085+0.066570
## [74] train-rmse:0.201057+0.003594 test-rmse:0.414600+0.067157
## [75] train-rmse:0.199511+0.004131 test-rmse:0.414570+0.067005
## [76] train-rmse:0.198237+0.004398 test-rmse:0.414313+0.067340
## [77] train-rmse:0.197373+0.004604 test-rmse:0.414042+0.066958
## [78] train-rmse:0.196175+0.004635 test-rmse:0.413965+0.066723
## [79] train-rmse:0.194075+0.005093 test-rmse:0.414092+0.067100
## [80] train-rmse:0.193058+0.004956 test-rmse:0.414400+0.067154
## [81] train-rmse:0.191546+0.004325 test-rmse:0.413187+0.068430
## [82] train-rmse:0.190565+0.004685 test-rmse:0.413381+0.068471
## [83] train-rmse:0.189618+0.004982 test-rmse:0.413304+0.067998
## [84] train-rmse:0.188661+0.005037 test-rmse:0.413157+0.068089
## [85] train-rmse:0.187285+0.004775 test-rmse:0.413119+0.068320
## [86] train-rmse:0.186181+0.004866 test-rmse:0.413078+0.068281
## [87] train-rmse:0.185414+0.004728 test-rmse:0.412937+0.068061
## [88] train-rmse:0.184190+0.004622 test-rmse:0.412971+0.068614
## [89] train-rmse:0.182706+0.005279 test-rmse:0.413093+0.068460
## [90] train-rmse:0.181865+0.005311 test-rmse:0.412976+0.068557
## [91] train-rmse:0.180959+0.005278 test-rmse:0.413317+0.068364
## [92] train-rmse:0.179762+0.005491 test-rmse:0.413179+0.068463
## [93] train-rmse:0.178054+0.005788 test-rmse:0.413080+0.068720
## Stopping. Best iteration:
## [87] train-rmse:0.185414+0.004728 test-rmse:0.412937+0.068061
##
## 31
## [1] train-rmse:1.375140+0.024963 test-rmse:1.376136+0.117493
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.162181+0.021006 test-rmse:1.165998+0.115345
## [3] train-rmse:0.986989+0.018336 test-rmse:0.992931+0.109406
## [4] train-rmse:0.844750+0.015139 test-rmse:0.855736+0.106117
## [5] train-rmse:0.730475+0.012841 test-rmse:0.749326+0.104940
## [6] train-rmse:0.637431+0.011054 test-rmse:0.662131+0.100811
## [7] train-rmse:0.563016+0.008809 test-rmse:0.597543+0.098090
## [8] train-rmse:0.503234+0.007121 test-rmse:0.549201+0.091719
## [9] train-rmse:0.454863+0.005177 test-rmse:0.513802+0.085446
## [10] train-rmse:0.417321+0.005545 test-rmse:0.486265+0.080529
## [11] train-rmse:0.386643+0.002953 test-rmse:0.465888+0.080914
## [12] train-rmse:0.362741+0.002783 test-rmse:0.455002+0.078453
## [13] train-rmse:0.343753+0.002858 test-rmse:0.443720+0.073189
## [14] train-rmse:0.327739+0.002487 test-rmse:0.436776+0.068180
## [15] train-rmse:0.314634+0.002235 test-rmse:0.432591+0.066965
## [16] train-rmse:0.305104+0.002319 test-rmse:0.429171+0.063664
## [17] train-rmse:0.296553+0.002804 test-rmse:0.426608+0.063111
## [18] train-rmse:0.287498+0.002430 test-rmse:0.424214+0.060710
## [19] train-rmse:0.280954+0.003298 test-rmse:0.421740+0.061349
## [20] train-rmse:0.275398+0.003887 test-rmse:0.420368+0.060749
## [21] train-rmse:0.268622+0.003668 test-rmse:0.419496+0.060748
## [22] train-rmse:0.262237+0.002298 test-rmse:0.419782+0.061443
## [23] train-rmse:0.257097+0.003159 test-rmse:0.418826+0.061353
## [24] train-rmse:0.251670+0.003544 test-rmse:0.416578+0.059512
## [25] train-rmse:0.247009+0.004644 test-rmse:0.414971+0.061534
## [26] train-rmse:0.242330+0.005717 test-rmse:0.413392+0.061747
## [27] train-rmse:0.239297+0.005640 test-rmse:0.413331+0.060787
## [28] train-rmse:0.235284+0.005905 test-rmse:0.413386+0.060135
## [29] train-rmse:0.230657+0.005605 test-rmse:0.413112+0.059598
## [30] train-rmse:0.227206+0.005519 test-rmse:0.411795+0.059358
## [31] train-rmse:0.223294+0.005247 test-rmse:0.411264+0.057983
## [32] train-rmse:0.220301+0.005248 test-rmse:0.410118+0.057965
## [33] train-rmse:0.217044+0.004705 test-rmse:0.409182+0.058080
## [34] train-rmse:0.213925+0.004287 test-rmse:0.408091+0.058689
## [35] train-rmse:0.211104+0.004747 test-rmse:0.407309+0.057931
## [36] train-rmse:0.208289+0.005312 test-rmse:0.407121+0.057822
## [37] train-rmse:0.204987+0.005151 test-rmse:0.407348+0.057930
## [38] train-rmse:0.202761+0.005220 test-rmse:0.405761+0.058416
## [39] train-rmse:0.200587+0.004994 test-rmse:0.405486+0.057777
## [40] train-rmse:0.198653+0.004957 test-rmse:0.405136+0.057734
## [41] train-rmse:0.196287+0.004673 test-rmse:0.404808+0.057932
## [42] train-rmse:0.193810+0.005718 test-rmse:0.404636+0.058285
## [43] train-rmse:0.191886+0.005519 test-rmse:0.403608+0.057141
## [44] train-rmse:0.189752+0.005396 test-rmse:0.403589+0.057743
## [45] train-rmse:0.187300+0.005840 test-rmse:0.403766+0.058013
## [46] train-rmse:0.184603+0.005589 test-rmse:0.402795+0.057511
## [47] train-rmse:0.182478+0.005059 test-rmse:0.401736+0.057854
## [48] train-rmse:0.180727+0.005082 test-rmse:0.401013+0.057537
## [49] train-rmse:0.178852+0.004815 test-rmse:0.400812+0.057240
## [50] train-rmse:0.176384+0.005073 test-rmse:0.400141+0.056917
## [51] train-rmse:0.174514+0.005135 test-rmse:0.399316+0.057303
## [52] train-rmse:0.172512+0.004890 test-rmse:0.398886+0.056588
## [53] train-rmse:0.170497+0.005119 test-rmse:0.398309+0.056609
## [54] train-rmse:0.167781+0.005432 test-rmse:0.396553+0.057445
## [55] train-rmse:0.166187+0.005539 test-rmse:0.397026+0.057293
## [56] train-rmse:0.164854+0.005594 test-rmse:0.396971+0.057398
## [57] train-rmse:0.163351+0.005601 test-rmse:0.395975+0.057126
## [58] train-rmse:0.161408+0.005733 test-rmse:0.396059+0.058183
## [59] train-rmse:0.159472+0.005208 test-rmse:0.395996+0.058114
## [60] train-rmse:0.157750+0.005498 test-rmse:0.395913+0.057835
## [61] train-rmse:0.156165+0.006020 test-rmse:0.395994+0.058045
## [62] train-rmse:0.154670+0.006061 test-rmse:0.396062+0.058381
## [63] train-rmse:0.153111+0.006032 test-rmse:0.395937+0.058159
## [64] train-rmse:0.151745+0.006347 test-rmse:0.395542+0.058466
## [65] train-rmse:0.149288+0.005806 test-rmse:0.395230+0.058475
## [66] train-rmse:0.147675+0.005775 test-rmse:0.394464+0.059042
## [67] train-rmse:0.146380+0.005606 test-rmse:0.394593+0.058644
## [68] train-rmse:0.144646+0.005619 test-rmse:0.394121+0.059067
## [69] train-rmse:0.142953+0.005305 test-rmse:0.393985+0.058257
## [70] train-rmse:0.141888+0.005199 test-rmse:0.394311+0.058275
## [71] train-rmse:0.140504+0.004857 test-rmse:0.393400+0.058520
## [72] train-rmse:0.138628+0.005353 test-rmse:0.393124+0.059070
## [73] train-rmse:0.137375+0.005579 test-rmse:0.393229+0.058785
## [74] train-rmse:0.136006+0.005698 test-rmse:0.393156+0.058432
## [75] train-rmse:0.135056+0.005738 test-rmse:0.393126+0.058346
## [76] train-rmse:0.133892+0.006236 test-rmse:0.392753+0.057565
## [77] train-rmse:0.132752+0.006126 test-rmse:0.392774+0.057808
## [78] train-rmse:0.131883+0.005875 test-rmse:0.392681+0.057724
## [79] train-rmse:0.130617+0.006077 test-rmse:0.392623+0.057546
## [80] train-rmse:0.129366+0.005881 test-rmse:0.393062+0.057806
## [81] train-rmse:0.127494+0.005614 test-rmse:0.393244+0.058246
## [82] train-rmse:0.126293+0.005586 test-rmse:0.392780+0.057695
## [83] train-rmse:0.125389+0.005547 test-rmse:0.392159+0.056979
## [84] train-rmse:0.124653+0.005302 test-rmse:0.392163+0.057072
## [85] train-rmse:0.123630+0.005064 test-rmse:0.392234+0.056941
## [86] train-rmse:0.122399+0.005502 test-rmse:0.392355+0.056949
## [87] train-rmse:0.121164+0.005210 test-rmse:0.392459+0.056538
## [88] train-rmse:0.119848+0.005005 test-rmse:0.392609+0.056556
## [89] train-rmse:0.118787+0.005264 test-rmse:0.392375+0.056234
## Stopping. Best iteration:
## [83] train-rmse:0.125389+0.005547 test-rmse:0.392159+0.056979
##
## 32
## [1] train-rmse:1.373562+0.025233 test-rmse:1.374472+0.114904
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.158914+0.021138 test-rmse:1.161735+0.111411
## [3] train-rmse:0.982863+0.018933 test-rmse:0.987564+0.106221
## [4] train-rmse:0.838839+0.015785 test-rmse:0.850672+0.103965
## [5] train-rmse:0.721004+0.014289 test-rmse:0.740002+0.099165
## [6] train-rmse:0.624825+0.012895 test-rmse:0.649874+0.093291
## [7] train-rmse:0.547287+0.011143 test-rmse:0.583438+0.091649
## [8] train-rmse:0.484202+0.009226 test-rmse:0.533573+0.087025
## [9] train-rmse:0.432752+0.008022 test-rmse:0.495544+0.086106
## [10] train-rmse:0.390280+0.006024 test-rmse:0.469593+0.083441
## [11] train-rmse:0.358004+0.005953 test-rmse:0.450785+0.081686
## [12] train-rmse:0.330385+0.005919 test-rmse:0.438521+0.079720
## [13] train-rmse:0.307866+0.005197 test-rmse:0.425968+0.078378
## [14] train-rmse:0.290828+0.004685 test-rmse:0.418639+0.076754
## [15] train-rmse:0.274930+0.006030 test-rmse:0.416366+0.076380
## [16] train-rmse:0.261584+0.005541 test-rmse:0.413831+0.074957
## [17] train-rmse:0.252457+0.005219 test-rmse:0.411397+0.074003
## [18] train-rmse:0.243253+0.004814 test-rmse:0.408313+0.072525
## [19] train-rmse:0.235879+0.005672 test-rmse:0.406928+0.070768
## [20] train-rmse:0.228360+0.003960 test-rmse:0.404580+0.069500
## [21] train-rmse:0.221825+0.003672 test-rmse:0.401842+0.066493
## [22] train-rmse:0.215928+0.002496 test-rmse:0.400746+0.066285
## [23] train-rmse:0.210857+0.003039 test-rmse:0.401216+0.065888
## [24] train-rmse:0.206319+0.002987 test-rmse:0.399251+0.067461
## [25] train-rmse:0.199810+0.001787 test-rmse:0.399304+0.066981
## [26] train-rmse:0.194997+0.001454 test-rmse:0.399177+0.066545
## [27] train-rmse:0.190172+0.000791 test-rmse:0.399033+0.066851
## [28] train-rmse:0.184836+0.002129 test-rmse:0.399652+0.066814
## [29] train-rmse:0.179997+0.001973 test-rmse:0.399387+0.066461
## [30] train-rmse:0.177099+0.002380 test-rmse:0.399280+0.066105
## [31] train-rmse:0.172887+0.003086 test-rmse:0.399166+0.066773
## [32] train-rmse:0.169835+0.002315 test-rmse:0.399215+0.067186
## [33] train-rmse:0.167298+0.001966 test-rmse:0.398843+0.067632
## [34] train-rmse:0.164569+0.002378 test-rmse:0.398541+0.067567
## [35] train-rmse:0.161195+0.002604 test-rmse:0.398644+0.067729
## [36] train-rmse:0.158051+0.002276 test-rmse:0.397725+0.067993
## [37] train-rmse:0.155461+0.002674 test-rmse:0.396405+0.068083
## [38] train-rmse:0.152558+0.002464 test-rmse:0.396385+0.066913
## [39] train-rmse:0.149090+0.002272 test-rmse:0.396615+0.066823
## [40] train-rmse:0.146747+0.001897 test-rmse:0.396257+0.066922
## [41] train-rmse:0.143986+0.002025 test-rmse:0.395774+0.067370
## [42] train-rmse:0.141759+0.002552 test-rmse:0.395577+0.067250
## [43] train-rmse:0.140305+0.002662 test-rmse:0.394739+0.067257
## [44] train-rmse:0.138301+0.002493 test-rmse:0.394189+0.067276
## [45] train-rmse:0.136256+0.002811 test-rmse:0.393990+0.067167
## [46] train-rmse:0.134018+0.003332 test-rmse:0.394117+0.067149
## [47] train-rmse:0.131969+0.003044 test-rmse:0.393394+0.067141
## [48] train-rmse:0.130835+0.003106 test-rmse:0.393124+0.067767
## [49] train-rmse:0.128929+0.003524 test-rmse:0.392760+0.068312
## [50] train-rmse:0.126894+0.003431 test-rmse:0.392659+0.067844
## [51] train-rmse:0.125284+0.003358 test-rmse:0.392584+0.067832
## [52] train-rmse:0.122494+0.003351 test-rmse:0.392521+0.068107
## [53] train-rmse:0.120075+0.003456 test-rmse:0.392938+0.067696
## [54] train-rmse:0.117596+0.003467 test-rmse:0.392874+0.068885
## [55] train-rmse:0.115828+0.003306 test-rmse:0.392565+0.068620
## [56] train-rmse:0.113928+0.003509 test-rmse:0.392741+0.068847
## [57] train-rmse:0.111950+0.003194 test-rmse:0.392871+0.068656
## [58] train-rmse:0.110706+0.003122 test-rmse:0.393683+0.067546
## Stopping. Best iteration:
## [52] train-rmse:0.122494+0.003351 test-rmse:0.392521+0.068107
##
## 33
## [1] train-rmse:1.373086+0.025275 test-rmse:1.373315+0.113906
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.157387+0.021173 test-rmse:1.159048+0.109143
## [3] train-rmse:0.980061+0.019160 test-rmse:0.984567+0.102363
## [4] train-rmse:0.834343+0.016440 test-rmse:0.846670+0.099616
## [5] train-rmse:0.715165+0.014867 test-rmse:0.735832+0.096121
## [6] train-rmse:0.617799+0.012574 test-rmse:0.648289+0.092263
## [7] train-rmse:0.536966+0.012043 test-rmse:0.582072+0.089990
## [8] train-rmse:0.470290+0.010662 test-rmse:0.528698+0.086986
## [9] train-rmse:0.416347+0.010015 test-rmse:0.491305+0.086323
## [10] train-rmse:0.371539+0.009465 test-rmse:0.461749+0.085843
## [11] train-rmse:0.335810+0.009439 test-rmse:0.443107+0.083266
## [12] train-rmse:0.305457+0.008852 test-rmse:0.429420+0.082246
## [13] train-rmse:0.281188+0.007791 test-rmse:0.419426+0.079866
## [14] train-rmse:0.260687+0.007164 test-rmse:0.413091+0.079096
## [15] train-rmse:0.242676+0.006225 test-rmse:0.408496+0.078384
## [16] train-rmse:0.228502+0.006623 test-rmse:0.403873+0.075478
## [17] train-rmse:0.214982+0.004499 test-rmse:0.401844+0.076330
## [18] train-rmse:0.205808+0.004184 test-rmse:0.399048+0.074080
## [19] train-rmse:0.196446+0.005400 test-rmse:0.396961+0.075553
## [20] train-rmse:0.188853+0.005432 test-rmse:0.396806+0.074207
## [21] train-rmse:0.182680+0.004767 test-rmse:0.396822+0.072414
## [22] train-rmse:0.176624+0.003351 test-rmse:0.396834+0.073506
## [23] train-rmse:0.171946+0.003778 test-rmse:0.395537+0.072451
## [24] train-rmse:0.166422+0.002955 test-rmse:0.394710+0.071530
## [25] train-rmse:0.161858+0.004178 test-rmse:0.393783+0.071411
## [26] train-rmse:0.157410+0.004909 test-rmse:0.393921+0.070536
## [27] train-rmse:0.153769+0.005501 test-rmse:0.393596+0.069914
## [28] train-rmse:0.148851+0.006571 test-rmse:0.392964+0.069603
## [29] train-rmse:0.144922+0.007530 test-rmse:0.392354+0.068176
## [30] train-rmse:0.140206+0.006741 test-rmse:0.393712+0.066810
## [31] train-rmse:0.135660+0.006632 test-rmse:0.393799+0.066752
## [32] train-rmse:0.132077+0.006842 test-rmse:0.393105+0.065978
## [33] train-rmse:0.128565+0.007268 test-rmse:0.392246+0.066220
## [34] train-rmse:0.124401+0.006683 test-rmse:0.390947+0.065784
## [35] train-rmse:0.121773+0.006763 test-rmse:0.390681+0.065263
## [36] train-rmse:0.119521+0.006692 test-rmse:0.390601+0.064609
## [37] train-rmse:0.116001+0.006487 test-rmse:0.389881+0.064624
## [38] train-rmse:0.113375+0.006653 test-rmse:0.389672+0.064094
## [39] train-rmse:0.111596+0.006504 test-rmse:0.389283+0.063318
## [40] train-rmse:0.109126+0.006095 test-rmse:0.389232+0.062928
## [41] train-rmse:0.107404+0.006754 test-rmse:0.388777+0.063190
## [42] train-rmse:0.104941+0.007208 test-rmse:0.388305+0.063278
## [43] train-rmse:0.102633+0.007319 test-rmse:0.388394+0.062763
## [44] train-rmse:0.100178+0.007276 test-rmse:0.388340+0.062689
## [45] train-rmse:0.097652+0.006914 test-rmse:0.387541+0.063255
## [46] train-rmse:0.095141+0.006830 test-rmse:0.387348+0.064144
## [47] train-rmse:0.092364+0.006448 test-rmse:0.387576+0.064523
## [48] train-rmse:0.090387+0.006482 test-rmse:0.387261+0.064407
## [49] train-rmse:0.088674+0.006834 test-rmse:0.386825+0.063954
## [50] train-rmse:0.086854+0.006751 test-rmse:0.386837+0.063634
## [51] train-rmse:0.084880+0.006436 test-rmse:0.386973+0.063707
## [52] train-rmse:0.083231+0.006163 test-rmse:0.386668+0.064030
## [53] train-rmse:0.081554+0.006263 test-rmse:0.386540+0.063797
## [54] train-rmse:0.079454+0.006139 test-rmse:0.386484+0.063400
## [55] train-rmse:0.077892+0.006012 test-rmse:0.386188+0.063667
## [56] train-rmse:0.075734+0.006600 test-rmse:0.386146+0.063229
## [57] train-rmse:0.074668+0.006595 test-rmse:0.385926+0.063664
## [58] train-rmse:0.073189+0.006689 test-rmse:0.386014+0.063760
## [59] train-rmse:0.071653+0.005950 test-rmse:0.386312+0.064115
## [60] train-rmse:0.070092+0.005875 test-rmse:0.386354+0.064260
## [61] train-rmse:0.068790+0.005730 test-rmse:0.386214+0.064345
## [62] train-rmse:0.067146+0.005992 test-rmse:0.386143+0.064377
## [63] train-rmse:0.066111+0.005888 test-rmse:0.386301+0.064127
## Stopping. Best iteration:
## [57] train-rmse:0.074668+0.006595 test-rmse:0.385926+0.063664
##
## 34
## [1] train-rmse:1.372849+0.025278 test-rmse:1.374139+0.113409
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.156291+0.021373 test-rmse:1.156623+0.109983
## [3] train-rmse:0.978177+0.019337 test-rmse:0.980156+0.103992
## [4] train-rmse:0.831664+0.016621 test-rmse:0.845254+0.100139
## [5] train-rmse:0.711287+0.015181 test-rmse:0.734611+0.097219
## [6] train-rmse:0.612423+0.013114 test-rmse:0.648331+0.093816
## [7] train-rmse:0.531158+0.013162 test-rmse:0.580125+0.092127
## [8] train-rmse:0.463325+0.012223 test-rmse:0.530940+0.089251
## [9] train-rmse:0.406696+0.010752 test-rmse:0.493700+0.086413
## [10] train-rmse:0.359625+0.011177 test-rmse:0.468152+0.083729
## [11] train-rmse:0.321057+0.011007 test-rmse:0.447857+0.082646
## [12] train-rmse:0.289259+0.010357 test-rmse:0.436741+0.083683
## [13] train-rmse:0.261665+0.010420 test-rmse:0.428965+0.080729
## [14] train-rmse:0.239174+0.010745 test-rmse:0.422553+0.079421
## [15] train-rmse:0.221171+0.011272 test-rmse:0.418152+0.077684
## [16] train-rmse:0.204662+0.010312 test-rmse:0.412701+0.074640
## [17] train-rmse:0.191721+0.011250 test-rmse:0.410540+0.073515
## [18] train-rmse:0.180539+0.012131 test-rmse:0.408520+0.070987
## [19] train-rmse:0.167936+0.008601 test-rmse:0.407881+0.068085
## [20] train-rmse:0.159259+0.010777 test-rmse:0.407537+0.066621
## [21] train-rmse:0.150228+0.008830 test-rmse:0.409164+0.065316
## [22] train-rmse:0.142606+0.009472 test-rmse:0.407977+0.062708
## [23] train-rmse:0.135273+0.007902 test-rmse:0.405930+0.061354
## [24] train-rmse:0.129264+0.008132 test-rmse:0.405892+0.058413
## [25] train-rmse:0.124278+0.008385 test-rmse:0.405095+0.056370
## [26] train-rmse:0.119595+0.008723 test-rmse:0.403389+0.055491
## [27] train-rmse:0.114897+0.007703 test-rmse:0.402410+0.055372
## [28] train-rmse:0.111693+0.007270 test-rmse:0.402847+0.055086
## [29] train-rmse:0.107534+0.005904 test-rmse:0.402559+0.055351
## [30] train-rmse:0.104474+0.005013 test-rmse:0.402072+0.054632
## [31] train-rmse:0.101326+0.004995 test-rmse:0.402286+0.053003
## [32] train-rmse:0.097996+0.005791 test-rmse:0.401594+0.053732
## [33] train-rmse:0.094312+0.006376 test-rmse:0.401353+0.053540
## [34] train-rmse:0.091190+0.006858 test-rmse:0.400979+0.052833
## [35] train-rmse:0.088721+0.006724 test-rmse:0.400757+0.052754
## [36] train-rmse:0.085477+0.006267 test-rmse:0.400790+0.052296
## [37] train-rmse:0.082552+0.006054 test-rmse:0.400793+0.052084
## [38] train-rmse:0.079434+0.006239 test-rmse:0.401124+0.051939
## [39] train-rmse:0.076677+0.005557 test-rmse:0.400570+0.051978
## [40] train-rmse:0.074184+0.004820 test-rmse:0.400505+0.052304
## [41] train-rmse:0.071595+0.004739 test-rmse:0.400700+0.052008
## [42] train-rmse:0.069166+0.004604 test-rmse:0.400613+0.051757
## [43] train-rmse:0.066950+0.004651 test-rmse:0.400499+0.051909
## [44] train-rmse:0.065110+0.004609 test-rmse:0.400334+0.051912
## [45] train-rmse:0.063369+0.004381 test-rmse:0.400145+0.051809
## [46] train-rmse:0.061579+0.004042 test-rmse:0.400088+0.051715
## [47] train-rmse:0.059850+0.004234 test-rmse:0.399638+0.051253
## [48] train-rmse:0.058557+0.004024 test-rmse:0.399757+0.051281
## [49] train-rmse:0.057185+0.003789 test-rmse:0.399714+0.051457
## [50] train-rmse:0.055754+0.003759 test-rmse:0.399672+0.051212
## [51] train-rmse:0.054321+0.003903 test-rmse:0.399815+0.050587
## [52] train-rmse:0.052814+0.004450 test-rmse:0.399667+0.051138
## [53] train-rmse:0.051678+0.004603 test-rmse:0.399483+0.051221
## [54] train-rmse:0.050435+0.004397 test-rmse:0.399518+0.050770
## [55] train-rmse:0.048899+0.003998 test-rmse:0.399389+0.050475
## [56] train-rmse:0.047441+0.004100 test-rmse:0.399530+0.049995
## [57] train-rmse:0.046177+0.004225 test-rmse:0.399436+0.050083
## [58] train-rmse:0.044488+0.004195 test-rmse:0.399493+0.049814
## [59] train-rmse:0.043457+0.003806 test-rmse:0.399734+0.049290
## [60] train-rmse:0.042531+0.003665 test-rmse:0.399597+0.049191
## [61] train-rmse:0.041490+0.003697 test-rmse:0.399688+0.049264
## Stopping. Best iteration:
## [55] train-rmse:0.048899+0.003998 test-rmse:0.399389+0.050475
##
## 35
## [1] train-rmse:1.368724+0.025516 test-rmse:1.364872+0.111211
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.163659+0.022145 test-rmse:1.159885+0.106220
## [3] train-rmse:1.010046+0.019972 test-rmse:1.006394+0.101887
## [4] train-rmse:0.897489+0.018741 test-rmse:0.894022+0.098039
## [5] train-rmse:0.816963+0.018164 test-rmse:0.813730+0.094674
## [6] train-rmse:0.760692+0.017973 test-rmse:0.757718+0.091842
## [7] train-rmse:0.722197+0.017971 test-rmse:0.719473+0.089555
## [8] train-rmse:0.696203+0.017822 test-rmse:0.695537+0.084837
## [9] train-rmse:0.675307+0.017042 test-rmse:0.675674+0.088440
## [10] train-rmse:0.659240+0.017013 test-rmse:0.662135+0.087059
## [11] train-rmse:0.645726+0.016529 test-rmse:0.645992+0.085359
## [12] train-rmse:0.634599+0.014962 test-rmse:0.639640+0.083891
## [13] train-rmse:0.624400+0.014486 test-rmse:0.626695+0.082360
## [14] train-rmse:0.615374+0.013930 test-rmse:0.619981+0.078919
## [15] train-rmse:0.607736+0.013536 test-rmse:0.612320+0.075855
## [16] train-rmse:0.600929+0.013056 test-rmse:0.603965+0.074715
## [17] train-rmse:0.594308+0.012821 test-rmse:0.598514+0.071449
## [18] train-rmse:0.588686+0.012421 test-rmse:0.596225+0.072750
## [19] train-rmse:0.583584+0.012126 test-rmse:0.589264+0.071337
## [20] train-rmse:0.578659+0.011974 test-rmse:0.585822+0.068625
## [21] train-rmse:0.574314+0.011894 test-rmse:0.585073+0.067792
## [22] train-rmse:0.570222+0.011629 test-rmse:0.581038+0.065842
## [23] train-rmse:0.566497+0.011442 test-rmse:0.578066+0.064887
## [24] train-rmse:0.562938+0.011405 test-rmse:0.573506+0.064382
## [25] train-rmse:0.559662+0.011180 test-rmse:0.571084+0.065536
## [26] train-rmse:0.556491+0.010940 test-rmse:0.569455+0.064970
## [27] train-rmse:0.553608+0.010866 test-rmse:0.565577+0.063838
## [28] train-rmse:0.550817+0.010847 test-rmse:0.565397+0.062648
## [29] train-rmse:0.548296+0.010742 test-rmse:0.563631+0.062900
## [30] train-rmse:0.545922+0.010544 test-rmse:0.562932+0.062267
## [31] train-rmse:0.543650+0.010410 test-rmse:0.559156+0.060571
## [32] train-rmse:0.541435+0.010351 test-rmse:0.558022+0.061252
## [33] train-rmse:0.539308+0.010336 test-rmse:0.557778+0.059561
## [34] train-rmse:0.537274+0.010300 test-rmse:0.556640+0.060179
## [35] train-rmse:0.535362+0.010253 test-rmse:0.555107+0.059798
## [36] train-rmse:0.533502+0.010112 test-rmse:0.553702+0.059183
## [37] train-rmse:0.531700+0.010012 test-rmse:0.552935+0.058783
## [38] train-rmse:0.529924+0.009917 test-rmse:0.551222+0.058244
## [39] train-rmse:0.528230+0.009809 test-rmse:0.551783+0.057762
## [40] train-rmse:0.526599+0.009678 test-rmse:0.550773+0.057616
## [41] train-rmse:0.524928+0.009567 test-rmse:0.548578+0.057847
## [42] train-rmse:0.523337+0.009508 test-rmse:0.546510+0.057164
## [43] train-rmse:0.521875+0.009487 test-rmse:0.545628+0.057712
## [44] train-rmse:0.520434+0.009331 test-rmse:0.544932+0.056251
## [45] train-rmse:0.519009+0.009241 test-rmse:0.544414+0.056785
## [46] train-rmse:0.517605+0.009178 test-rmse:0.543902+0.057157
## [47] train-rmse:0.516247+0.009170 test-rmse:0.541937+0.055922
## [48] train-rmse:0.514944+0.009111 test-rmse:0.542168+0.056030
## [49] train-rmse:0.513656+0.009062 test-rmse:0.541141+0.055826
## [50] train-rmse:0.512407+0.008985 test-rmse:0.540426+0.055485
## [51] train-rmse:0.511176+0.008927 test-rmse:0.539859+0.055153
## [52] train-rmse:0.509944+0.008872 test-rmse:0.538908+0.053758
## [53] train-rmse:0.508698+0.008832 test-rmse:0.537870+0.054293
## [54] train-rmse:0.507482+0.008764 test-rmse:0.536104+0.054082
## [55] train-rmse:0.506348+0.008716 test-rmse:0.535692+0.054959
## [56] train-rmse:0.505244+0.008702 test-rmse:0.535249+0.055196
## [57] train-rmse:0.504170+0.008676 test-rmse:0.534674+0.054441
## [58] train-rmse:0.503096+0.008662 test-rmse:0.532866+0.055374
## [59] train-rmse:0.502035+0.008659 test-rmse:0.531639+0.054734
## [60] train-rmse:0.500997+0.008614 test-rmse:0.531036+0.054650
## [61] train-rmse:0.499939+0.008539 test-rmse:0.530551+0.054022
## [62] train-rmse:0.498906+0.008506 test-rmse:0.530040+0.053101
## [63] train-rmse:0.497925+0.008486 test-rmse:0.528904+0.053306
## [64] train-rmse:0.496971+0.008463 test-rmse:0.528512+0.053435
## [65] train-rmse:0.496020+0.008462 test-rmse:0.527881+0.053155
## [66] train-rmse:0.495076+0.008431 test-rmse:0.526863+0.054248
## [67] train-rmse:0.494137+0.008370 test-rmse:0.525961+0.053848
## [68] train-rmse:0.493209+0.008324 test-rmse:0.525612+0.052651
## [69] train-rmse:0.492306+0.008328 test-rmse:0.525509+0.052180
## [70] train-rmse:0.491454+0.008311 test-rmse:0.524645+0.052822
## [71] train-rmse:0.490601+0.008282 test-rmse:0.525096+0.052742
## [72] train-rmse:0.489748+0.008244 test-rmse:0.524043+0.052640
## [73] train-rmse:0.488931+0.008210 test-rmse:0.523611+0.052256
## [74] train-rmse:0.488115+0.008195 test-rmse:0.522178+0.051487
## [75] train-rmse:0.487309+0.008185 test-rmse:0.521624+0.050770
## [76] train-rmse:0.486503+0.008181 test-rmse:0.521491+0.051034
## [77] train-rmse:0.485692+0.008174 test-rmse:0.521371+0.050599
## [78] train-rmse:0.484922+0.008123 test-rmse:0.521613+0.050982
## [79] train-rmse:0.484187+0.008101 test-rmse:0.521153+0.051457
## [80] train-rmse:0.483467+0.008080 test-rmse:0.521036+0.051866
## [81] train-rmse:0.482750+0.008060 test-rmse:0.520930+0.051946
## [82] train-rmse:0.482021+0.008053 test-rmse:0.519885+0.051272
## [83] train-rmse:0.481306+0.008061 test-rmse:0.519094+0.051442
## [84] train-rmse:0.480621+0.008036 test-rmse:0.518300+0.050679
## [85] train-rmse:0.479910+0.008027 test-rmse:0.517983+0.050892
## [86] train-rmse:0.479227+0.008008 test-rmse:0.516845+0.050202
## [87] train-rmse:0.478548+0.007968 test-rmse:0.516199+0.049960
## [88] train-rmse:0.477912+0.007958 test-rmse:0.516117+0.050382
## [89] train-rmse:0.477270+0.007929 test-rmse:0.516294+0.050564
## [90] train-rmse:0.476644+0.007915 test-rmse:0.516284+0.050419
## [91] train-rmse:0.476029+0.007905 test-rmse:0.515560+0.047927
## [92] train-rmse:0.475415+0.007886 test-rmse:0.514936+0.047929
## [93] train-rmse:0.474794+0.007875 test-rmse:0.514720+0.046879
## [94] train-rmse:0.474193+0.007877 test-rmse:0.513997+0.047458
## [95] train-rmse:0.473607+0.007875 test-rmse:0.513689+0.047344
## [96] train-rmse:0.473029+0.007874 test-rmse:0.513520+0.047197
## [97] train-rmse:0.472443+0.007862 test-rmse:0.514204+0.047058
## [98] train-rmse:0.471876+0.007861 test-rmse:0.513717+0.046583
## [99] train-rmse:0.471311+0.007843 test-rmse:0.513790+0.047021
## [100] train-rmse:0.470741+0.007829 test-rmse:0.513005+0.047098
## [101] train-rmse:0.470196+0.007825 test-rmse:0.511836+0.046953
## [102] train-rmse:0.469653+0.007821 test-rmse:0.511112+0.047455
## [103] train-rmse:0.469110+0.007810 test-rmse:0.511428+0.047143
## [104] train-rmse:0.468579+0.007795 test-rmse:0.510964+0.046992
## [105] train-rmse:0.468048+0.007808 test-rmse:0.510013+0.047444
## [106] train-rmse:0.467529+0.007780 test-rmse:0.509707+0.047720
## [107] train-rmse:0.467017+0.007772 test-rmse:0.509627+0.047696
## [108] train-rmse:0.466484+0.007745 test-rmse:0.508958+0.047174
## [109] train-rmse:0.465983+0.007735 test-rmse:0.509293+0.047184
## [110] train-rmse:0.465497+0.007736 test-rmse:0.509280+0.046660
## [111] train-rmse:0.465005+0.007732 test-rmse:0.509353+0.046847
## [112] train-rmse:0.464533+0.007716 test-rmse:0.508781+0.046966
## [113] train-rmse:0.464066+0.007696 test-rmse:0.508091+0.047006
## [114] train-rmse:0.463586+0.007683 test-rmse:0.508399+0.046097
## [115] train-rmse:0.463122+0.007676 test-rmse:0.507758+0.045666
## [116] train-rmse:0.462663+0.007667 test-rmse:0.507335+0.045767
## [117] train-rmse:0.462200+0.007668 test-rmse:0.507384+0.046063
## [118] train-rmse:0.461751+0.007664 test-rmse:0.505945+0.044732
## [119] train-rmse:0.461312+0.007650 test-rmse:0.506611+0.044849
## [120] train-rmse:0.460887+0.007630 test-rmse:0.505952+0.045506
## [121] train-rmse:0.460447+0.007607 test-rmse:0.505648+0.045209
## [122] train-rmse:0.460004+0.007601 test-rmse:0.506085+0.045225
## [123] train-rmse:0.459589+0.007595 test-rmse:0.505899+0.045461
## [124] train-rmse:0.459160+0.007583 test-rmse:0.505251+0.045143
## [125] train-rmse:0.458750+0.007575 test-rmse:0.504959+0.044641
## [126] train-rmse:0.458340+0.007569 test-rmse:0.505572+0.044471
## [127] train-rmse:0.457939+0.007561 test-rmse:0.505356+0.044900
## [128] train-rmse:0.457534+0.007555 test-rmse:0.505586+0.045103
## [129] train-rmse:0.457118+0.007540 test-rmse:0.505459+0.044265
## [130] train-rmse:0.456732+0.007537 test-rmse:0.505046+0.044353
## [131] train-rmse:0.456326+0.007536 test-rmse:0.504910+0.044378
## [132] train-rmse:0.455943+0.007533 test-rmse:0.504647+0.044356
## [133] train-rmse:0.455561+0.007526 test-rmse:0.504119+0.044142
## [134] train-rmse:0.455178+0.007536 test-rmse:0.503749+0.043727
## [135] train-rmse:0.454799+0.007553 test-rmse:0.504072+0.043849
## [136] train-rmse:0.454428+0.007547 test-rmse:0.503786+0.044076
## [137] train-rmse:0.454054+0.007532 test-rmse:0.504230+0.044164
## [138] train-rmse:0.453683+0.007519 test-rmse:0.503611+0.044389
## [139] train-rmse:0.453323+0.007511 test-rmse:0.503577+0.043858
## [140] train-rmse:0.452973+0.007508 test-rmse:0.503147+0.043658
## [141] train-rmse:0.452625+0.007515 test-rmse:0.502960+0.043350
## [142] train-rmse:0.452276+0.007512 test-rmse:0.502954+0.043750
## [143] train-rmse:0.451926+0.007516 test-rmse:0.502752+0.043243
## [144] train-rmse:0.451581+0.007503 test-rmse:0.503049+0.043517
## [145] train-rmse:0.451226+0.007503 test-rmse:0.503264+0.043409
## [146] train-rmse:0.450898+0.007502 test-rmse:0.503237+0.043580
## [147] train-rmse:0.450575+0.007501 test-rmse:0.503228+0.043771
## [148] train-rmse:0.450262+0.007503 test-rmse:0.503084+0.043858
## [149] train-rmse:0.449928+0.007512 test-rmse:0.502981+0.043488
## Stopping. Best iteration:
## [143] train-rmse:0.451926+0.007516 test-rmse:0.502752+0.043243
##
## 36
## [1] train-rmse:1.356000+0.025568 test-rmse:1.354857+0.114901
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.135532+0.021344 test-rmse:1.132845+0.105841
## [3] train-rmse:0.964354+0.019318 test-rmse:0.968752+0.105626
## [4] train-rmse:0.833096+0.016963 test-rmse:0.836522+0.106068
## [5] train-rmse:0.733261+0.015229 test-rmse:0.735726+0.101027
## [6] train-rmse:0.658213+0.014378 test-rmse:0.663534+0.098042
## [7] train-rmse:0.602461+0.013733 test-rmse:0.612945+0.094358
## [8] train-rmse:0.561234+0.013097 test-rmse:0.573353+0.087577
## [9] train-rmse:0.530369+0.012435 test-rmse:0.544514+0.087645
## [10] train-rmse:0.506262+0.012099 test-rmse:0.527554+0.082007
## [11] train-rmse:0.487867+0.011399 test-rmse:0.512850+0.077326
## [12] train-rmse:0.472402+0.009659 test-rmse:0.499857+0.074359
## [13] train-rmse:0.458717+0.010164 test-rmse:0.487112+0.069529
## [14] train-rmse:0.447313+0.008957 test-rmse:0.477378+0.067104
## [15] train-rmse:0.437994+0.007384 test-rmse:0.471752+0.067385
## [16] train-rmse:0.429789+0.007540 test-rmse:0.468066+0.065324
## [17] train-rmse:0.422635+0.007664 test-rmse:0.465868+0.065628
## [18] train-rmse:0.416638+0.007481 test-rmse:0.463823+0.061346
## [19] train-rmse:0.410975+0.007402 test-rmse:0.460871+0.060963
## [20] train-rmse:0.405480+0.007242 test-rmse:0.459816+0.058949
## [21] train-rmse:0.400481+0.006391 test-rmse:0.455615+0.056624
## [22] train-rmse:0.396086+0.005913 test-rmse:0.452582+0.058808
## [23] train-rmse:0.391266+0.005517 test-rmse:0.451540+0.058690
## [24] train-rmse:0.386252+0.005328 test-rmse:0.451465+0.058192
## [25] train-rmse:0.382730+0.004764 test-rmse:0.448636+0.058493
## [26] train-rmse:0.378746+0.004400 test-rmse:0.448439+0.057724
## [27] train-rmse:0.375641+0.004646 test-rmse:0.448729+0.057823
## [28] train-rmse:0.372600+0.004401 test-rmse:0.447158+0.057200
## [29] train-rmse:0.369296+0.003971 test-rmse:0.446100+0.057127
## [30] train-rmse:0.366366+0.003700 test-rmse:0.446104+0.056074
## [31] train-rmse:0.363320+0.003468 test-rmse:0.444771+0.056470
## [32] train-rmse:0.360531+0.003483 test-rmse:0.445036+0.055360
## [33] train-rmse:0.357897+0.003329 test-rmse:0.444078+0.055378
## [34] train-rmse:0.355402+0.002991 test-rmse:0.443707+0.055304
## [35] train-rmse:0.353212+0.003507 test-rmse:0.443930+0.054294
## [36] train-rmse:0.350930+0.003490 test-rmse:0.443824+0.053876
## [37] train-rmse:0.348506+0.003192 test-rmse:0.443816+0.054519
## [38] train-rmse:0.346258+0.003295 test-rmse:0.443647+0.054539
## [39] train-rmse:0.343941+0.002996 test-rmse:0.440703+0.055863
## [40] train-rmse:0.341048+0.003729 test-rmse:0.439798+0.056332
## [41] train-rmse:0.339037+0.004015 test-rmse:0.439291+0.055812
## [42] train-rmse:0.336949+0.003736 test-rmse:0.439355+0.054859
## [43] train-rmse:0.335282+0.003806 test-rmse:0.439443+0.055190
## [44] train-rmse:0.333103+0.003640 test-rmse:0.437515+0.055009
## [45] train-rmse:0.331104+0.003761 test-rmse:0.436667+0.055308
## [46] train-rmse:0.329349+0.003922 test-rmse:0.437288+0.056032
## [47] train-rmse:0.327792+0.003840 test-rmse:0.436976+0.055668
## [48] train-rmse:0.326227+0.003653 test-rmse:0.436329+0.055262
## [49] train-rmse:0.323942+0.003874 test-rmse:0.436786+0.055430
## [50] train-rmse:0.322196+0.003704 test-rmse:0.437242+0.055152
## [51] train-rmse:0.320349+0.003279 test-rmse:0.437160+0.055318
## [52] train-rmse:0.318628+0.003309 test-rmse:0.437603+0.055554
## [53] train-rmse:0.317042+0.003126 test-rmse:0.435783+0.056620
## [54] train-rmse:0.315598+0.003306 test-rmse:0.434799+0.056970
## [55] train-rmse:0.314234+0.003335 test-rmse:0.435140+0.057825
## [56] train-rmse:0.312926+0.003254 test-rmse:0.435076+0.058016
## [57] train-rmse:0.311960+0.003365 test-rmse:0.434486+0.058304
## [58] train-rmse:0.310034+0.003252 test-rmse:0.433294+0.058900
## [59] train-rmse:0.308192+0.003710 test-rmse:0.434026+0.058889
## [60] train-rmse:0.306298+0.004046 test-rmse:0.433677+0.059034
## [61] train-rmse:0.305046+0.004387 test-rmse:0.433437+0.058230
## [62] train-rmse:0.303883+0.004835 test-rmse:0.433070+0.058284
## [63] train-rmse:0.301869+0.004019 test-rmse:0.432695+0.058330
## [64] train-rmse:0.300291+0.004205 test-rmse:0.432402+0.058955
## [65] train-rmse:0.299219+0.004301 test-rmse:0.432170+0.059207
## [66] train-rmse:0.297503+0.004478 test-rmse:0.431077+0.059162
## [67] train-rmse:0.295818+0.004743 test-rmse:0.430754+0.058378
## [68] train-rmse:0.293976+0.005110 test-rmse:0.430809+0.057721
## [69] train-rmse:0.292713+0.004995 test-rmse:0.430201+0.057868
## [70] train-rmse:0.291342+0.004910 test-rmse:0.430558+0.058413
## [71] train-rmse:0.289735+0.005352 test-rmse:0.430583+0.057152
## [72] train-rmse:0.288464+0.004975 test-rmse:0.430203+0.057558
## [73] train-rmse:0.287321+0.004784 test-rmse:0.429923+0.058254
## [74] train-rmse:0.285519+0.006082 test-rmse:0.430230+0.058716
## [75] train-rmse:0.284347+0.005846 test-rmse:0.430085+0.058471
## [76] train-rmse:0.283162+0.005668 test-rmse:0.430184+0.058244
## [77] train-rmse:0.282177+0.005485 test-rmse:0.430082+0.058842
## [78] train-rmse:0.281215+0.005377 test-rmse:0.429695+0.058793
## [79] train-rmse:0.280269+0.005614 test-rmse:0.429437+0.058816
## [80] train-rmse:0.279054+0.006083 test-rmse:0.429170+0.058861
## [81] train-rmse:0.278102+0.005876 test-rmse:0.428184+0.059672
## [82] train-rmse:0.277008+0.005515 test-rmse:0.427669+0.060254
## [83] train-rmse:0.276321+0.005397 test-rmse:0.427465+0.059903
## [84] train-rmse:0.275529+0.005487 test-rmse:0.426654+0.060230
## [85] train-rmse:0.274375+0.006079 test-rmse:0.425780+0.060199
## [86] train-rmse:0.273524+0.006166 test-rmse:0.425698+0.059653
## [87] train-rmse:0.272268+0.005724 test-rmse:0.425795+0.059519
## [88] train-rmse:0.271261+0.005682 test-rmse:0.425641+0.059029
## [89] train-rmse:0.270289+0.005319 test-rmse:0.425225+0.059552
## [90] train-rmse:0.269102+0.004979 test-rmse:0.425784+0.059798
## [91] train-rmse:0.267872+0.005225 test-rmse:0.425167+0.059804
## [92] train-rmse:0.267316+0.005154 test-rmse:0.424926+0.060069
## [93] train-rmse:0.266441+0.005371 test-rmse:0.425266+0.059761
## [94] train-rmse:0.265510+0.005936 test-rmse:0.424218+0.060198
## [95] train-rmse:0.264318+0.006302 test-rmse:0.424926+0.059939
## [96] train-rmse:0.263668+0.006368 test-rmse:0.424971+0.060125
## [97] train-rmse:0.263078+0.006429 test-rmse:0.424584+0.059841
## [98] train-rmse:0.262404+0.006429 test-rmse:0.423533+0.059851
## [99] train-rmse:0.261680+0.006369 test-rmse:0.422977+0.059671
## [100] train-rmse:0.260988+0.006298 test-rmse:0.422483+0.060020
## [101] train-rmse:0.260438+0.006220 test-rmse:0.422689+0.059966
## [102] train-rmse:0.259437+0.005943 test-rmse:0.422467+0.060898
## [103] train-rmse:0.258506+0.006359 test-rmse:0.421966+0.060662
## [104] train-rmse:0.257953+0.006285 test-rmse:0.421495+0.061377
## [105] train-rmse:0.256429+0.006824 test-rmse:0.421599+0.061700
## [106] train-rmse:0.255988+0.006822 test-rmse:0.421379+0.061877
## [107] train-rmse:0.255477+0.006824 test-rmse:0.421494+0.062055
## [108] train-rmse:0.254597+0.007489 test-rmse:0.422068+0.062268
## [109] train-rmse:0.254099+0.007468 test-rmse:0.421655+0.062508
## [110] train-rmse:0.253582+0.007459 test-rmse:0.421676+0.062320
## [111] train-rmse:0.252270+0.007931 test-rmse:0.421130+0.061650
## [112] train-rmse:0.251291+0.008252 test-rmse:0.421049+0.062284
## [113] train-rmse:0.250521+0.008222 test-rmse:0.420896+0.062319
## [114] train-rmse:0.249796+0.008456 test-rmse:0.421126+0.062608
## [115] train-rmse:0.248529+0.008259 test-rmse:0.419833+0.062628
## [116] train-rmse:0.248008+0.008127 test-rmse:0.419684+0.062610
## [117] train-rmse:0.247348+0.008247 test-rmse:0.419572+0.062558
## [118] train-rmse:0.246305+0.007929 test-rmse:0.419821+0.062296
## [119] train-rmse:0.245506+0.007793 test-rmse:0.419215+0.062047
## [120] train-rmse:0.244815+0.007579 test-rmse:0.418384+0.061992
## [121] train-rmse:0.244176+0.007421 test-rmse:0.418600+0.062043
## [122] train-rmse:0.243485+0.007641 test-rmse:0.418030+0.062642
## [123] train-rmse:0.242589+0.008157 test-rmse:0.417536+0.062313
## [124] train-rmse:0.242121+0.008168 test-rmse:0.417253+0.062334
## [125] train-rmse:0.241356+0.007882 test-rmse:0.417183+0.062548
## [126] train-rmse:0.240182+0.007878 test-rmse:0.416656+0.062263
## [127] train-rmse:0.239397+0.007822 test-rmse:0.416501+0.062237
## [128] train-rmse:0.238261+0.007934 test-rmse:0.416650+0.061850
## [129] train-rmse:0.237176+0.008195 test-rmse:0.416326+0.062106
## [130] train-rmse:0.236488+0.008298 test-rmse:0.415985+0.061985
## [131] train-rmse:0.235955+0.008356 test-rmse:0.415663+0.061914
## [132] train-rmse:0.235313+0.008125 test-rmse:0.415909+0.062089
## [133] train-rmse:0.234614+0.007969 test-rmse:0.416446+0.062251
## [134] train-rmse:0.233937+0.007916 test-rmse:0.416866+0.062386
## [135] train-rmse:0.233131+0.008147 test-rmse:0.416895+0.061456
## [136] train-rmse:0.232457+0.007977 test-rmse:0.416548+0.061721
## [137] train-rmse:0.231770+0.007747 test-rmse:0.415532+0.062494
## [138] train-rmse:0.231238+0.008082 test-rmse:0.415689+0.062811
## [139] train-rmse:0.230598+0.008191 test-rmse:0.415536+0.062832
## [140] train-rmse:0.229743+0.009021 test-rmse:0.415604+0.063053
## [141] train-rmse:0.229049+0.008859 test-rmse:0.415179+0.063232
## [142] train-rmse:0.228391+0.009056 test-rmse:0.415014+0.062992
## [143] train-rmse:0.227772+0.009467 test-rmse:0.414501+0.062692
## [144] train-rmse:0.227316+0.009449 test-rmse:0.414548+0.063001
## [145] train-rmse:0.226495+0.010279 test-rmse:0.414743+0.063128
## [146] train-rmse:0.226018+0.010228 test-rmse:0.414360+0.063255
## [147] train-rmse:0.225521+0.010364 test-rmse:0.413991+0.063333
## [148] train-rmse:0.224941+0.010385 test-rmse:0.413936+0.063360
## [149] train-rmse:0.224209+0.010137 test-rmse:0.413642+0.063295
## [150] train-rmse:0.223821+0.010079 test-rmse:0.414068+0.063108
## [151] train-rmse:0.222990+0.009844 test-rmse:0.414104+0.063134
## [152] train-rmse:0.222583+0.009813 test-rmse:0.414302+0.063109
## [153] train-rmse:0.221955+0.009912 test-rmse:0.413606+0.063232
## [154] train-rmse:0.221179+0.010121 test-rmse:0.413092+0.063449
## [155] train-rmse:0.220701+0.010020 test-rmse:0.413428+0.063473
## [156] train-rmse:0.220070+0.009991 test-rmse:0.414095+0.063563
## [157] train-rmse:0.219419+0.010235 test-rmse:0.413655+0.063735
## [158] train-rmse:0.218811+0.010108 test-rmse:0.413768+0.063907
## [159] train-rmse:0.217990+0.010083 test-rmse:0.413698+0.063537
## [160] train-rmse:0.217324+0.009891 test-rmse:0.413988+0.063589
## Stopping. Best iteration:
## [154] train-rmse:0.221179+0.010121 test-rmse:0.413092+0.063449
##
## 37
## [1] train-rmse:1.349303+0.024857 test-rmse:1.351289+0.117226
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.121657+0.020905 test-rmse:1.127461+0.112458
## [3] train-rmse:0.941300+0.017351 test-rmse:0.950938+0.112709
## [4] train-rmse:0.800317+0.016706 test-rmse:0.812759+0.105865
## [5] train-rmse:0.690022+0.014409 test-rmse:0.707483+0.106109
## [6] train-rmse:0.605854+0.013099 test-rmse:0.627896+0.097719
## [7] train-rmse:0.541281+0.011384 test-rmse:0.571297+0.092159
## [8] train-rmse:0.491772+0.010713 test-rmse:0.535229+0.087691
## [9] train-rmse:0.453469+0.011221 test-rmse:0.505050+0.081337
## [10] train-rmse:0.424598+0.009889 test-rmse:0.483788+0.082016
## [11] train-rmse:0.402697+0.010171 test-rmse:0.467061+0.076344
## [12] train-rmse:0.384706+0.008891 test-rmse:0.456700+0.071813
## [13] train-rmse:0.371918+0.008831 test-rmse:0.450639+0.069038
## [14] train-rmse:0.361525+0.009152 test-rmse:0.445855+0.068255
## [15] train-rmse:0.353044+0.008998 test-rmse:0.442980+0.065916
## [16] train-rmse:0.345089+0.008645 test-rmse:0.441523+0.064183
## [17] train-rmse:0.337210+0.008827 test-rmse:0.437576+0.061720
## [18] train-rmse:0.331152+0.009084 test-rmse:0.435385+0.060888
## [19] train-rmse:0.325500+0.008713 test-rmse:0.435197+0.059993
## [20] train-rmse:0.320465+0.008487 test-rmse:0.433064+0.059274
## [21] train-rmse:0.315757+0.008542 test-rmse:0.431541+0.061200
## [22] train-rmse:0.311115+0.008619 test-rmse:0.431509+0.062331
## [23] train-rmse:0.307267+0.008187 test-rmse:0.431470+0.061410
## [24] train-rmse:0.302170+0.006502 test-rmse:0.427855+0.063720
## [25] train-rmse:0.298849+0.006408 test-rmse:0.426493+0.062223
## [26] train-rmse:0.294871+0.006997 test-rmse:0.425822+0.061721
## [27] train-rmse:0.291384+0.006366 test-rmse:0.426326+0.062291
## [28] train-rmse:0.287830+0.005464 test-rmse:0.423974+0.061699
## [29] train-rmse:0.283986+0.005646 test-rmse:0.422350+0.061240
## [30] train-rmse:0.280424+0.004636 test-rmse:0.422242+0.062215
## [31] train-rmse:0.277061+0.004470 test-rmse:0.423039+0.062032
## [32] train-rmse:0.274146+0.004186 test-rmse:0.423830+0.061768
## [33] train-rmse:0.270915+0.005486 test-rmse:0.423626+0.062011
## [34] train-rmse:0.267974+0.004710 test-rmse:0.421430+0.062191
## [35] train-rmse:0.265327+0.004564 test-rmse:0.419298+0.064246
## [36] train-rmse:0.262200+0.004455 test-rmse:0.418102+0.065172
## [37] train-rmse:0.259813+0.004246 test-rmse:0.418396+0.065187
## [38] train-rmse:0.256907+0.004178 test-rmse:0.418275+0.065821
## [39] train-rmse:0.255090+0.004119 test-rmse:0.417332+0.066863
## [40] train-rmse:0.251868+0.003034 test-rmse:0.417495+0.067821
## [41] train-rmse:0.249766+0.002912 test-rmse:0.416468+0.067872
## [42] train-rmse:0.246620+0.004252 test-rmse:0.415726+0.066549
## [43] train-rmse:0.244244+0.003927 test-rmse:0.415849+0.066503
## [44] train-rmse:0.241214+0.004210 test-rmse:0.414987+0.066808
## [45] train-rmse:0.239208+0.003986 test-rmse:0.415400+0.065798
## [46] train-rmse:0.237171+0.004827 test-rmse:0.414673+0.066045
## [47] train-rmse:0.235329+0.004644 test-rmse:0.413951+0.066986
## [48] train-rmse:0.233328+0.004390 test-rmse:0.412915+0.066598
## [49] train-rmse:0.231465+0.004600 test-rmse:0.413136+0.066663
## [50] train-rmse:0.229180+0.004288 test-rmse:0.411807+0.067266
## [51] train-rmse:0.227391+0.004449 test-rmse:0.411704+0.067281
## [52] train-rmse:0.224842+0.004866 test-rmse:0.412504+0.066837
## [53] train-rmse:0.222456+0.004886 test-rmse:0.410955+0.066122
## [54] train-rmse:0.220768+0.004884 test-rmse:0.410070+0.066037
## [55] train-rmse:0.218823+0.004006 test-rmse:0.409915+0.066169
## [56] train-rmse:0.217183+0.004105 test-rmse:0.410288+0.065724
## [57] train-rmse:0.215326+0.004324 test-rmse:0.409995+0.065948
## [58] train-rmse:0.213573+0.004270 test-rmse:0.410256+0.066258
## [59] train-rmse:0.211729+0.004095 test-rmse:0.409192+0.066868
## [60] train-rmse:0.210171+0.004367 test-rmse:0.409170+0.066732
## [61] train-rmse:0.208427+0.004482 test-rmse:0.408766+0.067140
## [62] train-rmse:0.206644+0.004440 test-rmse:0.409047+0.066956
## [63] train-rmse:0.205399+0.004535 test-rmse:0.409104+0.066503
## [64] train-rmse:0.203630+0.005458 test-rmse:0.409353+0.066956
## [65] train-rmse:0.201948+0.005492 test-rmse:0.408586+0.067708
## [66] train-rmse:0.199884+0.005671 test-rmse:0.408993+0.067050
## [67] train-rmse:0.198552+0.005950 test-rmse:0.409365+0.067031
## [68] train-rmse:0.197451+0.006057 test-rmse:0.409124+0.067441
## [69] train-rmse:0.195788+0.006361 test-rmse:0.408918+0.067796
## [70] train-rmse:0.194545+0.005955 test-rmse:0.408792+0.067527
## [71] train-rmse:0.193074+0.006219 test-rmse:0.408820+0.067495
## Stopping. Best iteration:
## [65] train-rmse:0.201948+0.005492 test-rmse:0.408586+0.067708
##
## 38
## [1] train-rmse:1.346296+0.024386 test-rmse:1.347883+0.117628
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.115896+0.019858 test-rmse:1.122419+0.116495
## [3] train-rmse:0.930781+0.017171 test-rmse:0.939329+0.108755
## [4] train-rmse:0.785414+0.014380 test-rmse:0.799529+0.103257
## [5] train-rmse:0.670632+0.011802 test-rmse:0.688824+0.099921
## [6] train-rmse:0.580823+0.010046 test-rmse:0.610487+0.095448
## [7] train-rmse:0.510805+0.008127 test-rmse:0.552787+0.088891
## [8] train-rmse:0.456568+0.006463 test-rmse:0.507878+0.087140
## [9] train-rmse:0.414699+0.008084 test-rmse:0.480753+0.081907
## [10] train-rmse:0.381537+0.007815 test-rmse:0.459118+0.079186
## [11] train-rmse:0.356083+0.008266 test-rmse:0.443884+0.072457
## [12] train-rmse:0.335742+0.006322 test-rmse:0.434823+0.072169
## [13] train-rmse:0.320668+0.005835 test-rmse:0.430174+0.068238
## [14] train-rmse:0.307121+0.005060 test-rmse:0.423221+0.065265
## [15] train-rmse:0.298041+0.004723 test-rmse:0.421651+0.063705
## [16] train-rmse:0.288290+0.005266 test-rmse:0.418161+0.061399
## [17] train-rmse:0.281372+0.004509 test-rmse:0.416962+0.062470
## [18] train-rmse:0.275206+0.003608 test-rmse:0.413405+0.060778
## [19] train-rmse:0.268391+0.003666 test-rmse:0.411979+0.059123
## [20] train-rmse:0.262318+0.003527 test-rmse:0.411722+0.059125
## [21] train-rmse:0.257170+0.003618 test-rmse:0.411103+0.058903
## [22] train-rmse:0.252197+0.004400 test-rmse:0.409697+0.060729
## [23] train-rmse:0.247153+0.004225 test-rmse:0.408193+0.059733
## [24] train-rmse:0.243584+0.004082 test-rmse:0.407605+0.058763
## [25] train-rmse:0.238194+0.002770 test-rmse:0.406475+0.059567
## [26] train-rmse:0.233928+0.002667 test-rmse:0.406905+0.058793
## [27] train-rmse:0.229956+0.001681 test-rmse:0.407007+0.058535
## [28] train-rmse:0.226043+0.002157 test-rmse:0.406344+0.057431
## [29] train-rmse:0.221842+0.002587 test-rmse:0.406329+0.057389
## [30] train-rmse:0.218368+0.002788 test-rmse:0.405587+0.057595
## [31] train-rmse:0.214477+0.003438 test-rmse:0.403835+0.057258
## [32] train-rmse:0.211289+0.003583 test-rmse:0.403387+0.056620
## [33] train-rmse:0.208545+0.003792 test-rmse:0.402800+0.057350
## [34] train-rmse:0.204846+0.004050 test-rmse:0.402978+0.056703
## [35] train-rmse:0.200755+0.004032 test-rmse:0.402092+0.057436
## [36] train-rmse:0.197874+0.003224 test-rmse:0.402101+0.057487
## [37] train-rmse:0.195293+0.003034 test-rmse:0.401914+0.057495
## [38] train-rmse:0.192544+0.003339 test-rmse:0.400798+0.057011
## [39] train-rmse:0.190079+0.003709 test-rmse:0.399346+0.057035
## [40] train-rmse:0.187363+0.003767 test-rmse:0.398307+0.056693
## [41] train-rmse:0.184799+0.003334 test-rmse:0.397642+0.056493
## [42] train-rmse:0.182419+0.002796 test-rmse:0.397417+0.057093
## [43] train-rmse:0.179542+0.002669 test-rmse:0.397030+0.057169
## [44] train-rmse:0.177943+0.002483 test-rmse:0.396296+0.057375
## [45] train-rmse:0.175633+0.002035 test-rmse:0.394992+0.057683
## [46] train-rmse:0.173728+0.002044 test-rmse:0.394662+0.058131
## [47] train-rmse:0.171513+0.002122 test-rmse:0.395043+0.057675
## [48] train-rmse:0.169483+0.002495 test-rmse:0.394796+0.058370
## [49] train-rmse:0.167051+0.002595 test-rmse:0.394274+0.058830
## [50] train-rmse:0.164384+0.003303 test-rmse:0.393527+0.057862
## [51] train-rmse:0.162644+0.003447 test-rmse:0.393726+0.057902
## [52] train-rmse:0.160659+0.003967 test-rmse:0.394357+0.058104
## [53] train-rmse:0.159099+0.003972 test-rmse:0.392669+0.059422
## [54] train-rmse:0.157044+0.004039 test-rmse:0.392656+0.059430
## [55] train-rmse:0.155597+0.003879 test-rmse:0.392535+0.059331
## [56] train-rmse:0.154189+0.003935 test-rmse:0.392288+0.059581
## [57] train-rmse:0.152498+0.004753 test-rmse:0.392959+0.059114
## [58] train-rmse:0.150893+0.004938 test-rmse:0.392045+0.060451
## [59] train-rmse:0.148566+0.005489 test-rmse:0.392133+0.060673
## [60] train-rmse:0.146808+0.005720 test-rmse:0.393154+0.059977
## [61] train-rmse:0.144859+0.005567 test-rmse:0.393094+0.059780
## [62] train-rmse:0.143260+0.006276 test-rmse:0.392446+0.059186
## [63] train-rmse:0.140509+0.006569 test-rmse:0.392149+0.059306
## [64] train-rmse:0.138949+0.005976 test-rmse:0.392337+0.059187
## Stopping. Best iteration:
## [58] train-rmse:0.150893+0.004938 test-rmse:0.392045+0.060451
##
## 39
## [1] train-rmse:1.344515+0.024688 test-rmse:1.346046+0.114712
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.112177+0.020266 test-rmse:1.114815+0.111016
## [3] train-rmse:0.925776+0.017946 test-rmse:0.932016+0.103441
## [4] train-rmse:0.777994+0.014803 test-rmse:0.792661+0.100473
## [5] train-rmse:0.659102+0.012898 test-rmse:0.686104+0.095731
## [6] train-rmse:0.565399+0.011627 test-rmse:0.604316+0.089491
## [7] train-rmse:0.491853+0.009219 test-rmse:0.545027+0.084397
## [8] train-rmse:0.434305+0.008372 test-rmse:0.502127+0.083218
## [9] train-rmse:0.387417+0.006879 test-rmse:0.472571+0.084328
## [10] train-rmse:0.352353+0.006284 test-rmse:0.454168+0.084210
## [11] train-rmse:0.325218+0.006906 test-rmse:0.438600+0.079984
## [12] train-rmse:0.301178+0.009324 test-rmse:0.428576+0.077461
## [13] train-rmse:0.284684+0.010332 test-rmse:0.423862+0.076604
## [14] train-rmse:0.270132+0.010160 test-rmse:0.418588+0.074838
## [15] train-rmse:0.257523+0.011168 test-rmse:0.416467+0.071729
## [16] train-rmse:0.246373+0.011032 test-rmse:0.413987+0.070623
## [17] train-rmse:0.238177+0.010684 test-rmse:0.410631+0.071353
## [18] train-rmse:0.230625+0.011403 test-rmse:0.410433+0.070357
## [19] train-rmse:0.222949+0.010438 test-rmse:0.409811+0.069718
## [20] train-rmse:0.215367+0.007100 test-rmse:0.407038+0.071059
## [21] train-rmse:0.209406+0.007649 test-rmse:0.407824+0.069303
## [22] train-rmse:0.204324+0.007861 test-rmse:0.407194+0.067605
## [23] train-rmse:0.199150+0.005708 test-rmse:0.405973+0.067067
## [24] train-rmse:0.194140+0.005560 test-rmse:0.404878+0.066863
## [25] train-rmse:0.188010+0.006309 test-rmse:0.404023+0.064896
## [26] train-rmse:0.182818+0.007608 test-rmse:0.403282+0.065938
## [27] train-rmse:0.179535+0.007561 test-rmse:0.403208+0.065911
## [28] train-rmse:0.174580+0.007472 test-rmse:0.403734+0.065218
## [29] train-rmse:0.169950+0.006527 test-rmse:0.403097+0.065939
## [30] train-rmse:0.163693+0.004870 test-rmse:0.403033+0.066932
## [31] train-rmse:0.159947+0.004775 test-rmse:0.402904+0.066265
## [32] train-rmse:0.155705+0.005278 test-rmse:0.402841+0.066299
## [33] train-rmse:0.152230+0.005694 test-rmse:0.402384+0.066378
## [34] train-rmse:0.149233+0.005732 test-rmse:0.401638+0.065967
## [35] train-rmse:0.145790+0.005317 test-rmse:0.400096+0.066382
## [36] train-rmse:0.143204+0.004990 test-rmse:0.401162+0.066071
## [37] train-rmse:0.140646+0.004279 test-rmse:0.400987+0.066548
## [38] train-rmse:0.138216+0.004114 test-rmse:0.401102+0.067330
## [39] train-rmse:0.136079+0.003952 test-rmse:0.401033+0.067131
## [40] train-rmse:0.134019+0.003636 test-rmse:0.401194+0.066922
## [41] train-rmse:0.131712+0.003589 test-rmse:0.400663+0.066290
## Stopping. Best iteration:
## [35] train-rmse:0.145790+0.005317 test-rmse:0.400096+0.066382
##
## 40
## [1] train-rmse:1.343971+0.024734 test-rmse:1.344762+0.113596
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.110474+0.020298 test-rmse:1.112438+0.108652
## [3] train-rmse:0.922551+0.018112 test-rmse:0.927979+0.099771
## [4] train-rmse:0.772836+0.015385 test-rmse:0.786611+0.097423
## [5] train-rmse:0.653415+0.014169 test-rmse:0.679351+0.093389
## [6] train-rmse:0.556691+0.012902 test-rmse:0.595899+0.087546
## [7] train-rmse:0.480613+0.011482 test-rmse:0.535298+0.085734
## [8] train-rmse:0.419830+0.010483 test-rmse:0.492504+0.084637
## [9] train-rmse:0.371526+0.010358 test-rmse:0.464180+0.080985
## [10] train-rmse:0.332169+0.009621 test-rmse:0.445482+0.080389
## [11] train-rmse:0.301774+0.008908 test-rmse:0.432914+0.078741
## [12] train-rmse:0.276131+0.009873 test-rmse:0.422634+0.075863
## [13] train-rmse:0.254472+0.011051 test-rmse:0.415067+0.073499
## [14] train-rmse:0.238178+0.010553 test-rmse:0.411488+0.073177
## [15] train-rmse:0.222073+0.005569 test-rmse:0.408166+0.072754
## [16] train-rmse:0.207857+0.004203 test-rmse:0.404842+0.071192
## [17] train-rmse:0.196257+0.003993 test-rmse:0.401165+0.071455
## [18] train-rmse:0.187287+0.003450 test-rmse:0.398720+0.070905
## [19] train-rmse:0.179081+0.005460 test-rmse:0.398025+0.068381
## [20] train-rmse:0.172226+0.006439 test-rmse:0.396895+0.065845
## [21] train-rmse:0.165491+0.004819 test-rmse:0.397144+0.067003
## [22] train-rmse:0.160313+0.004943 test-rmse:0.397689+0.066648
## [23] train-rmse:0.155126+0.004550 test-rmse:0.397847+0.063529
## [24] train-rmse:0.150053+0.004933 test-rmse:0.397974+0.063673
## [25] train-rmse:0.145658+0.005660 test-rmse:0.397539+0.062982
## [26] train-rmse:0.141308+0.006019 test-rmse:0.398688+0.061492
## Stopping. Best iteration:
## [20] train-rmse:0.172226+0.006439 test-rmse:0.396895+0.065845
##
## 41
## [1] train-rmse:1.343703+0.024735 test-rmse:1.345687+0.113081
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.109460+0.020579 test-rmse:1.111858+0.108635
## [3] train-rmse:0.920717+0.018522 test-rmse:0.931229+0.100422
## [4] train-rmse:0.769940+0.015777 test-rmse:0.787991+0.098432
## [5] train-rmse:0.648383+0.014548 test-rmse:0.679991+0.096047
## [6] train-rmse:0.549788+0.013289 test-rmse:0.598333+0.093432
## [7] train-rmse:0.470716+0.012210 test-rmse:0.537912+0.085931
## [8] train-rmse:0.406679+0.011396 test-rmse:0.499322+0.084391
## [9] train-rmse:0.355068+0.010799 test-rmse:0.468069+0.080129
## [10] train-rmse:0.313191+0.011127 test-rmse:0.447609+0.078018
## [11] train-rmse:0.279207+0.010346 test-rmse:0.436061+0.076079
## [12] train-rmse:0.251884+0.010595 test-rmse:0.429494+0.075570
## [13] train-rmse:0.227941+0.011902 test-rmse:0.423405+0.073190
## [14] train-rmse:0.209864+0.011793 test-rmse:0.419065+0.071037
## [15] train-rmse:0.192658+0.012090 test-rmse:0.415951+0.070185
## [16] train-rmse:0.178971+0.011459 test-rmse:0.415139+0.070003
## [17] train-rmse:0.164224+0.009076 test-rmse:0.412455+0.067240
## [18] train-rmse:0.154691+0.008793 test-rmse:0.410552+0.066094
## [19] train-rmse:0.147044+0.008754 test-rmse:0.410225+0.065927
## [20] train-rmse:0.140122+0.009565 test-rmse:0.409739+0.065000
## [21] train-rmse:0.133177+0.007586 test-rmse:0.409537+0.064614
## [22] train-rmse:0.128144+0.008518 test-rmse:0.409303+0.064711
## [23] train-rmse:0.122874+0.008029 test-rmse:0.409049+0.065370
## [24] train-rmse:0.116853+0.007912 test-rmse:0.408540+0.064144
## [25] train-rmse:0.111363+0.008558 test-rmse:0.408969+0.063905
## [26] train-rmse:0.106922+0.008532 test-rmse:0.407171+0.062773
## [27] train-rmse:0.103541+0.009048 test-rmse:0.406822+0.062907
## [28] train-rmse:0.100694+0.008369 test-rmse:0.405778+0.061704
## [29] train-rmse:0.097053+0.008043 test-rmse:0.406562+0.060251
## [30] train-rmse:0.092843+0.007331 test-rmse:0.405838+0.059411
## [31] train-rmse:0.089438+0.006907 test-rmse:0.405875+0.059248
## [32] train-rmse:0.085485+0.006240 test-rmse:0.406732+0.058424
## [33] train-rmse:0.081826+0.005880 test-rmse:0.406476+0.058054
## [34] train-rmse:0.078815+0.005293 test-rmse:0.406433+0.057751
## Stopping. Best iteration:
## [28] train-rmse:0.100694+0.008369 test-rmse:0.405778+0.061704
##
## 42
## [1] train-rmse:1.342539+0.025066 test-rmse:1.338695+0.110602
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.124737+0.021556 test-rmse:1.120984+0.105192
## [3] train-rmse:0.967659+0.019461 test-rmse:0.964062+0.100529
## [4] train-rmse:0.857519+0.018415 test-rmse:0.854154+0.096452
## [5] train-rmse:0.782551+0.018023 test-rmse:0.779464+0.093003
## [6] train-rmse:0.732927+0.017959 test-rmse:0.730126+0.090231
## [7] train-rmse:0.700846+0.018022 test-rmse:0.698303+0.088100
## [8] train-rmse:0.677317+0.017109 test-rmse:0.679031+0.088552
## [9] train-rmse:0.658842+0.017079 test-rmse:0.657004+0.086601
## [10] train-rmse:0.644274+0.016363 test-rmse:0.648260+0.084177
## [11] train-rmse:0.632104+0.014873 test-rmse:0.634133+0.079797
## [12] train-rmse:0.620850+0.014538 test-rmse:0.622347+0.079810
## [13] train-rmse:0.611782+0.013606 test-rmse:0.617346+0.078157
## [14] train-rmse:0.603683+0.013254 test-rmse:0.607143+0.077619
## [15] train-rmse:0.596657+0.012803 test-rmse:0.601489+0.072846
## [16] train-rmse:0.589793+0.012683 test-rmse:0.595188+0.069832
## [17] train-rmse:0.584001+0.012096 test-rmse:0.592222+0.071404
## [18] train-rmse:0.578804+0.011780 test-rmse:0.584148+0.069954
## [19] train-rmse:0.573794+0.011741 test-rmse:0.581605+0.066979
## [20] train-rmse:0.569288+0.011631 test-rmse:0.580816+0.066394
## [21] train-rmse:0.565217+0.011428 test-rmse:0.577092+0.064092
## [22] train-rmse:0.561453+0.011232 test-rmse:0.575037+0.063210
## [23] train-rmse:0.557921+0.011069 test-rmse:0.570511+0.062983
## [24] train-rmse:0.554549+0.010870 test-rmse:0.566509+0.063889
## [25] train-rmse:0.551367+0.010823 test-rmse:0.565354+0.063201
## [26] train-rmse:0.548502+0.010693 test-rmse:0.563592+0.061192
## [27] train-rmse:0.545796+0.010687 test-rmse:0.561020+0.060527
## [28] train-rmse:0.543291+0.010482 test-rmse:0.560155+0.061453
## [29] train-rmse:0.540867+0.010314 test-rmse:0.558291+0.061459
## [30] train-rmse:0.538499+0.010250 test-rmse:0.557352+0.059119
## [31] train-rmse:0.536351+0.010200 test-rmse:0.556493+0.059956
## [32] train-rmse:0.534156+0.010107 test-rmse:0.554246+0.059507
## [33] train-rmse:0.532212+0.009982 test-rmse:0.552835+0.059555
## [34] train-rmse:0.530285+0.009877 test-rmse:0.551544+0.059990
## [35] train-rmse:0.528465+0.009784 test-rmse:0.549434+0.059155
## [36] train-rmse:0.526653+0.009665 test-rmse:0.549310+0.058411
## [37] train-rmse:0.524833+0.009501 test-rmse:0.547349+0.057719
## [38] train-rmse:0.523105+0.009435 test-rmse:0.547113+0.057126
## [39] train-rmse:0.521465+0.009362 test-rmse:0.546824+0.057847
## [40] train-rmse:0.519853+0.009244 test-rmse:0.545608+0.058326
## [41] train-rmse:0.518263+0.009171 test-rmse:0.545090+0.056778
## [42] train-rmse:0.516785+0.009165 test-rmse:0.541885+0.056011
## [43] train-rmse:0.515334+0.009111 test-rmse:0.541277+0.056007
## [44] train-rmse:0.513799+0.009049 test-rmse:0.540887+0.056508
## [45] train-rmse:0.512423+0.008969 test-rmse:0.541345+0.055838
## [46] train-rmse:0.511046+0.008883 test-rmse:0.538626+0.056137
## [47] train-rmse:0.509684+0.008855 test-rmse:0.537299+0.054812
## [48] train-rmse:0.508335+0.008839 test-rmse:0.536119+0.054296
## [49] train-rmse:0.507039+0.008798 test-rmse:0.534438+0.054367
## [50] train-rmse:0.505764+0.008729 test-rmse:0.534456+0.054587
## [51] train-rmse:0.504540+0.008639 test-rmse:0.533100+0.055003
## [52] train-rmse:0.503326+0.008602 test-rmse:0.532136+0.054427
## [53] train-rmse:0.502124+0.008567 test-rmse:0.531123+0.053325
## [54] train-rmse:0.500943+0.008537 test-rmse:0.531636+0.052764
## [55] train-rmse:0.499837+0.008506 test-rmse:0.531360+0.053794
## [56] train-rmse:0.498735+0.008464 test-rmse:0.531176+0.053741
## [57] train-rmse:0.497635+0.008427 test-rmse:0.529598+0.054719
## [58] train-rmse:0.496566+0.008403 test-rmse:0.528884+0.053945
## [59] train-rmse:0.495514+0.008406 test-rmse:0.528359+0.053952
## [60] train-rmse:0.494479+0.008387 test-rmse:0.527763+0.053574
## [61] train-rmse:0.493451+0.008351 test-rmse:0.528454+0.053365
## [62] train-rmse:0.492447+0.008292 test-rmse:0.527046+0.052526
## [63] train-rmse:0.491456+0.008247 test-rmse:0.525232+0.052247
## [64] train-rmse:0.490489+0.008203 test-rmse:0.523719+0.052171
## [65] train-rmse:0.489550+0.008174 test-rmse:0.523041+0.052064
## [66] train-rmse:0.488625+0.008190 test-rmse:0.522752+0.052972
## [67] train-rmse:0.487719+0.008167 test-rmse:0.522009+0.052221
## [68] train-rmse:0.486824+0.008149 test-rmse:0.522609+0.051583
## [69] train-rmse:0.485973+0.008129 test-rmse:0.522814+0.051005
## [70] train-rmse:0.485135+0.008113 test-rmse:0.521465+0.050518
## [71] train-rmse:0.484314+0.008108 test-rmse:0.521568+0.050675
## [72] train-rmse:0.483466+0.008081 test-rmse:0.520959+0.050468
## [73] train-rmse:0.482626+0.008070 test-rmse:0.520556+0.051199
## [74] train-rmse:0.481825+0.008064 test-rmse:0.519939+0.051225
## [75] train-rmse:0.481049+0.008035 test-rmse:0.519839+0.051117
## [76] train-rmse:0.480283+0.007994 test-rmse:0.519676+0.050248
## [77] train-rmse:0.479532+0.007997 test-rmse:0.518364+0.050277
## [78] train-rmse:0.478792+0.008002 test-rmse:0.517889+0.050061
## [79] train-rmse:0.478083+0.008005 test-rmse:0.517394+0.050700
## [80] train-rmse:0.477391+0.007978 test-rmse:0.517750+0.050514
## [81] train-rmse:0.476702+0.007947 test-rmse:0.516453+0.050098
## [82] train-rmse:0.476007+0.007928 test-rmse:0.516072+0.049541
## [83] train-rmse:0.475317+0.007916 test-rmse:0.515721+0.049548
## [84] train-rmse:0.474627+0.007897 test-rmse:0.515407+0.049419
## [85] train-rmse:0.473963+0.007885 test-rmse:0.512541+0.047839
## [86] train-rmse:0.473322+0.007863 test-rmse:0.513201+0.048335
## [87] train-rmse:0.472684+0.007844 test-rmse:0.513348+0.047630
## [88] train-rmse:0.472034+0.007830 test-rmse:0.512986+0.047961
## [89] train-rmse:0.471379+0.007802 test-rmse:0.513458+0.047635
## [90] train-rmse:0.470779+0.007797 test-rmse:0.512859+0.047062
## [91] train-rmse:0.470175+0.007801 test-rmse:0.512180+0.047534
## [92] train-rmse:0.469561+0.007786 test-rmse:0.512353+0.047584
## [93] train-rmse:0.468978+0.007792 test-rmse:0.512203+0.047908
## [94] train-rmse:0.468397+0.007781 test-rmse:0.511822+0.047714
## [95] train-rmse:0.467816+0.007770 test-rmse:0.511580+0.047028
## [96] train-rmse:0.467255+0.007758 test-rmse:0.511766+0.046857
## [97] train-rmse:0.466700+0.007751 test-rmse:0.510048+0.046575
## [98] train-rmse:0.466156+0.007722 test-rmse:0.510259+0.046553
## [99] train-rmse:0.465600+0.007702 test-rmse:0.510232+0.046621
## [100] train-rmse:0.465061+0.007692 test-rmse:0.510013+0.046936
## [101] train-rmse:0.464503+0.007710 test-rmse:0.509632+0.046714
## [102] train-rmse:0.463973+0.007668 test-rmse:0.508726+0.046708
## [103] train-rmse:0.463469+0.007658 test-rmse:0.508540+0.046070
## [104] train-rmse:0.462957+0.007647 test-rmse:0.508425+0.046454
## [105] train-rmse:0.462453+0.007658 test-rmse:0.507620+0.046593
## [106] train-rmse:0.461956+0.007644 test-rmse:0.508130+0.046393
## [107] train-rmse:0.461462+0.007629 test-rmse:0.508073+0.046391
## [108] train-rmse:0.460978+0.007621 test-rmse:0.507844+0.045853
## [109] train-rmse:0.460496+0.007603 test-rmse:0.506542+0.044173
## [110] train-rmse:0.460020+0.007581 test-rmse:0.506855+0.044488
## [111] train-rmse:0.459544+0.007575 test-rmse:0.506762+0.044574
## [112] train-rmse:0.459066+0.007569 test-rmse:0.505951+0.043926
## [113] train-rmse:0.458609+0.007565 test-rmse:0.505275+0.044056
## [114] train-rmse:0.458163+0.007559 test-rmse:0.505374+0.043566
## [115] train-rmse:0.457709+0.007554 test-rmse:0.505112+0.043404
## [116] train-rmse:0.457261+0.007544 test-rmse:0.504991+0.043412
## [117] train-rmse:0.456822+0.007537 test-rmse:0.505568+0.043296
## [118] train-rmse:0.456387+0.007538 test-rmse:0.505311+0.043424
## [119] train-rmse:0.455947+0.007513 test-rmse:0.505087+0.043954
## [120] train-rmse:0.455504+0.007522 test-rmse:0.504874+0.044240
## [121] train-rmse:0.455064+0.007516 test-rmse:0.504973+0.043957
## [122] train-rmse:0.454658+0.007510 test-rmse:0.504519+0.044324
## [123] train-rmse:0.454245+0.007492 test-rmse:0.504253+0.044205
## [124] train-rmse:0.453838+0.007498 test-rmse:0.503780+0.043869
## [125] train-rmse:0.453445+0.007503 test-rmse:0.504793+0.043596
## [126] train-rmse:0.453065+0.007497 test-rmse:0.504471+0.043336
## [127] train-rmse:0.452690+0.007492 test-rmse:0.504263+0.043134
## [128] train-rmse:0.452307+0.007492 test-rmse:0.503877+0.043325
## [129] train-rmse:0.451919+0.007500 test-rmse:0.503604+0.043077
## [130] train-rmse:0.451536+0.007522 test-rmse:0.503419+0.042874
## [131] train-rmse:0.451177+0.007522 test-rmse:0.502987+0.042796
## [132] train-rmse:0.450813+0.007513 test-rmse:0.502703+0.043047
## [133] train-rmse:0.450445+0.007513 test-rmse:0.503093+0.043222
## [134] train-rmse:0.450083+0.007513 test-rmse:0.503854+0.043296
## [135] train-rmse:0.449733+0.007510 test-rmse:0.503084+0.043433
## [136] train-rmse:0.449386+0.007508 test-rmse:0.503182+0.043555
## [137] train-rmse:0.449040+0.007500 test-rmse:0.502787+0.043577
## [138] train-rmse:0.448706+0.007494 test-rmse:0.502102+0.043218
## [139] train-rmse:0.448378+0.007490 test-rmse:0.501925+0.043277
## [140] train-rmse:0.448047+0.007493 test-rmse:0.501722+0.043429
## [141] train-rmse:0.447712+0.007498 test-rmse:0.501846+0.042977
## [142] train-rmse:0.447372+0.007513 test-rmse:0.501265+0.042436
## [143] train-rmse:0.447045+0.007514 test-rmse:0.501776+0.042356
## [144] train-rmse:0.446709+0.007497 test-rmse:0.501620+0.042835
## [145] train-rmse:0.446389+0.007490 test-rmse:0.501143+0.042970
## [146] train-rmse:0.446069+0.007486 test-rmse:0.500939+0.043081
## [147] train-rmse:0.445755+0.007483 test-rmse:0.501170+0.043578
## [148] train-rmse:0.445433+0.007486 test-rmse:0.501090+0.043595
## [149] train-rmse:0.445119+0.007487 test-rmse:0.501131+0.042999
## [150] train-rmse:0.444810+0.007492 test-rmse:0.501070+0.043040
## [151] train-rmse:0.444515+0.007495 test-rmse:0.500576+0.042959
## [152] train-rmse:0.444216+0.007499 test-rmse:0.500622+0.042582
## [153] train-rmse:0.443914+0.007502 test-rmse:0.500020+0.042522
## [154] train-rmse:0.443625+0.007509 test-rmse:0.499782+0.042428
## [155] train-rmse:0.443327+0.007513 test-rmse:0.499558+0.042200
## [156] train-rmse:0.443014+0.007510 test-rmse:0.499797+0.042527
## [157] train-rmse:0.442712+0.007514 test-rmse:0.498989+0.041289
## [158] train-rmse:0.442428+0.007514 test-rmse:0.499423+0.041302
## [159] train-rmse:0.442136+0.007521 test-rmse:0.499098+0.040891
## [160] train-rmse:0.441854+0.007531 test-rmse:0.499138+0.041348
## [161] train-rmse:0.441565+0.007523 test-rmse:0.498347+0.040872
## [162] train-rmse:0.441291+0.007529 test-rmse:0.497714+0.040644
## [163] train-rmse:0.441005+0.007535 test-rmse:0.498022+0.040736
## [164] train-rmse:0.440739+0.007540 test-rmse:0.497770+0.040928
## [165] train-rmse:0.440461+0.007548 test-rmse:0.498007+0.040616
## [166] train-rmse:0.440189+0.007546 test-rmse:0.498067+0.040605
## [167] train-rmse:0.439928+0.007548 test-rmse:0.497710+0.040278
## [168] train-rmse:0.439668+0.007552 test-rmse:0.498173+0.040512
## [169] train-rmse:0.439410+0.007559 test-rmse:0.497875+0.040690
## [170] train-rmse:0.439155+0.007561 test-rmse:0.498331+0.040837
## [171] train-rmse:0.438886+0.007570 test-rmse:0.498469+0.040513
## [172] train-rmse:0.438632+0.007566 test-rmse:0.498685+0.040732
## [173] train-rmse:0.438382+0.007569 test-rmse:0.498958+0.040865
## Stopping. Best iteration:
## [167] train-rmse:0.439928+0.007548 test-rmse:0.497710+0.040278
##
## 43
## [1] train-rmse:1.328379+0.025135 test-rmse:1.327577+0.114729
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.092918+0.020613 test-rmse:1.090374+0.104998
## [3] train-rmse:0.915933+0.018686 test-rmse:0.921362+0.104727
## [4] train-rmse:0.784651+0.016176 test-rmse:0.789144+0.105113
## [5] train-rmse:0.688453+0.014358 test-rmse:0.691891+0.099912
## [6] train-rmse:0.618682+0.013951 test-rmse:0.625895+0.096326
## [7] train-rmse:0.568581+0.013195 test-rmse:0.579755+0.091701
## [8] train-rmse:0.532768+0.012721 test-rmse:0.544563+0.085015
## [9] train-rmse:0.506112+0.012313 test-rmse:0.527841+0.080795
## [10] train-rmse:0.486341+0.011591 test-rmse:0.511903+0.078718
## [11] train-rmse:0.470281+0.009741 test-rmse:0.494022+0.075445
## [12] train-rmse:0.454977+0.009930 test-rmse:0.481701+0.071829
## [13] train-rmse:0.443309+0.008664 test-rmse:0.472256+0.066458
## [14] train-rmse:0.434201+0.008272 test-rmse:0.469379+0.066572
## [15] train-rmse:0.425152+0.007191 test-rmse:0.465936+0.064340
## [16] train-rmse:0.418329+0.007799 test-rmse:0.462944+0.061804
## [17] train-rmse:0.411320+0.006439 test-rmse:0.458212+0.059978
## [18] train-rmse:0.405710+0.006108 test-rmse:0.455991+0.057484
## [19] train-rmse:0.400139+0.005466 test-rmse:0.455554+0.057429
## [20] train-rmse:0.395571+0.005421 test-rmse:0.452989+0.057006
## [21] train-rmse:0.390797+0.004626 test-rmse:0.449523+0.057139
## [22] train-rmse:0.385933+0.004427 test-rmse:0.448087+0.055959
## [23] train-rmse:0.382238+0.004234 test-rmse:0.447747+0.056366
## [24] train-rmse:0.378402+0.003868 test-rmse:0.445477+0.057253
## [25] train-rmse:0.374811+0.003816 test-rmse:0.446714+0.056037
## [26] train-rmse:0.370200+0.003917 test-rmse:0.444521+0.055121
## [27] train-rmse:0.366511+0.003728 test-rmse:0.443856+0.055387
## [28] train-rmse:0.363724+0.003848 test-rmse:0.443080+0.054583
## [29] train-rmse:0.360553+0.003795 test-rmse:0.442604+0.052871
## [30] train-rmse:0.357269+0.003691 test-rmse:0.443826+0.053168
## [31] train-rmse:0.354336+0.003339 test-rmse:0.443348+0.053014
## [32] train-rmse:0.351570+0.003323 test-rmse:0.441944+0.054209
## [33] train-rmse:0.348632+0.003090 test-rmse:0.441605+0.052645
## [34] train-rmse:0.346179+0.003383 test-rmse:0.441170+0.052471
## [35] train-rmse:0.343814+0.003154 test-rmse:0.440288+0.049192
## [36] train-rmse:0.340680+0.003497 test-rmse:0.439476+0.048457
## [37] train-rmse:0.338145+0.003367 test-rmse:0.438705+0.048419
## [38] train-rmse:0.335825+0.003373 test-rmse:0.438339+0.048231
## [39] train-rmse:0.333554+0.003980 test-rmse:0.437641+0.048396
## [40] train-rmse:0.331495+0.004080 test-rmse:0.437688+0.049405
## [41] train-rmse:0.329010+0.004176 test-rmse:0.436569+0.050809
## [42] train-rmse:0.326957+0.004480 test-rmse:0.436577+0.049438
## [43] train-rmse:0.325328+0.004161 test-rmse:0.435706+0.049286
## [44] train-rmse:0.323558+0.004109 test-rmse:0.435650+0.049575
## [45] train-rmse:0.321724+0.004169 test-rmse:0.435811+0.050053
## [46] train-rmse:0.320041+0.003669 test-rmse:0.434254+0.052384
## [47] train-rmse:0.317978+0.003735 test-rmse:0.434543+0.053103
## [48] train-rmse:0.316188+0.004072 test-rmse:0.434323+0.053012
## [49] train-rmse:0.314188+0.004487 test-rmse:0.433762+0.052474
## [50] train-rmse:0.312267+0.004900 test-rmse:0.434485+0.053064
## [51] train-rmse:0.310703+0.004972 test-rmse:0.433821+0.053694
## [52] train-rmse:0.308572+0.004833 test-rmse:0.432610+0.054492
## [53] train-rmse:0.307169+0.005049 test-rmse:0.432388+0.054471
## [54] train-rmse:0.306097+0.005322 test-rmse:0.432317+0.055016
## [55] train-rmse:0.304512+0.005458 test-rmse:0.431238+0.055499
## [56] train-rmse:0.303059+0.005281 test-rmse:0.432071+0.054866
## [57] train-rmse:0.301756+0.005171 test-rmse:0.431406+0.054959
## [58] train-rmse:0.300020+0.004918 test-rmse:0.430988+0.054891
## [59] train-rmse:0.298572+0.004115 test-rmse:0.430715+0.055352
## [60] train-rmse:0.297477+0.004055 test-rmse:0.430643+0.054749
## [61] train-rmse:0.296168+0.003812 test-rmse:0.429577+0.055038
## [62] train-rmse:0.294913+0.003751 test-rmse:0.429471+0.054887
## [63] train-rmse:0.293777+0.003890 test-rmse:0.429386+0.055455
## [64] train-rmse:0.292346+0.003902 test-rmse:0.429264+0.055774
## [65] train-rmse:0.291438+0.003819 test-rmse:0.428065+0.057982
## [66] train-rmse:0.290025+0.004597 test-rmse:0.427292+0.057537
## [67] train-rmse:0.288952+0.004575 test-rmse:0.426672+0.057018
## [68] train-rmse:0.287030+0.004066 test-rmse:0.426491+0.057314
## [69] train-rmse:0.285935+0.004103 test-rmse:0.426860+0.057202
## [70] train-rmse:0.284762+0.004723 test-rmse:0.427463+0.057403
## [71] train-rmse:0.283741+0.004538 test-rmse:0.426683+0.057150
## [72] train-rmse:0.282408+0.004390 test-rmse:0.426658+0.057402
## [73] train-rmse:0.280356+0.005902 test-rmse:0.426596+0.058157
## [74] train-rmse:0.279355+0.005851 test-rmse:0.426393+0.058338
## [75] train-rmse:0.278561+0.005882 test-rmse:0.426182+0.058168
## [76] train-rmse:0.277641+0.005758 test-rmse:0.425507+0.058535
## [77] train-rmse:0.276608+0.005469 test-rmse:0.425307+0.058547
## [78] train-rmse:0.275750+0.005490 test-rmse:0.426395+0.058284
## [79] train-rmse:0.274388+0.006827 test-rmse:0.425536+0.057884
## [80] train-rmse:0.273002+0.007364 test-rmse:0.424969+0.057802
## [81] train-rmse:0.271646+0.007715 test-rmse:0.424459+0.057771
## [82] train-rmse:0.270299+0.007280 test-rmse:0.424330+0.057825
## [83] train-rmse:0.269385+0.007299 test-rmse:0.423944+0.058116
## [84] train-rmse:0.268551+0.007051 test-rmse:0.423413+0.058358
## [85] train-rmse:0.267822+0.007129 test-rmse:0.423649+0.058664
## [86] train-rmse:0.266685+0.008156 test-rmse:0.423954+0.059003
## [87] train-rmse:0.265555+0.008391 test-rmse:0.423494+0.058880
## [88] train-rmse:0.264751+0.008231 test-rmse:0.423346+0.058888
## [89] train-rmse:0.263746+0.008057 test-rmse:0.423454+0.058855
## [90] train-rmse:0.262796+0.008325 test-rmse:0.423349+0.058956
## [91] train-rmse:0.261965+0.008244 test-rmse:0.422619+0.059844
## [92] train-rmse:0.261117+0.008680 test-rmse:0.422236+0.059588
## [93] train-rmse:0.260350+0.008624 test-rmse:0.421398+0.060391
## [94] train-rmse:0.259101+0.008829 test-rmse:0.421847+0.060627
## [95] train-rmse:0.258555+0.008782 test-rmse:0.421470+0.060459
## [96] train-rmse:0.257714+0.008856 test-rmse:0.421881+0.059900
## [97] train-rmse:0.256773+0.008745 test-rmse:0.421678+0.059949
## [98] train-rmse:0.256009+0.008780 test-rmse:0.421477+0.059890
## [99] train-rmse:0.255216+0.008659 test-rmse:0.420432+0.060241
## [100] train-rmse:0.254213+0.009077 test-rmse:0.419751+0.059733
## [101] train-rmse:0.253604+0.009010 test-rmse:0.420013+0.060013
## [102] train-rmse:0.252443+0.008627 test-rmse:0.419433+0.060075
## [103] train-rmse:0.251373+0.008331 test-rmse:0.419809+0.059788
## [104] train-rmse:0.249450+0.008037 test-rmse:0.418769+0.059136
## [105] train-rmse:0.248627+0.008116 test-rmse:0.418159+0.059728
## [106] train-rmse:0.247404+0.008515 test-rmse:0.418404+0.059518
## [107] train-rmse:0.246584+0.008443 test-rmse:0.417950+0.059731
## [108] train-rmse:0.245522+0.008863 test-rmse:0.417624+0.059614
## [109] train-rmse:0.244694+0.009250 test-rmse:0.417460+0.059423
## [110] train-rmse:0.244217+0.009311 test-rmse:0.417736+0.059590
## [111] train-rmse:0.243306+0.009089 test-rmse:0.416660+0.059706
## [112] train-rmse:0.242706+0.009024 test-rmse:0.416723+0.059570
## [113] train-rmse:0.241983+0.009208 test-rmse:0.416653+0.059452
## [114] train-rmse:0.241277+0.008953 test-rmse:0.416733+0.059521
## [115] train-rmse:0.240810+0.008859 test-rmse:0.416194+0.059811
## [116] train-rmse:0.240247+0.008771 test-rmse:0.415941+0.059602
## [117] train-rmse:0.239454+0.008567 test-rmse:0.415885+0.059578
## [118] train-rmse:0.238742+0.008797 test-rmse:0.415857+0.059255
## [119] train-rmse:0.238057+0.008767 test-rmse:0.416320+0.059245
## [120] train-rmse:0.237303+0.009165 test-rmse:0.416703+0.059084
## [121] train-rmse:0.236694+0.009271 test-rmse:0.417135+0.058915
## [122] train-rmse:0.236109+0.009083 test-rmse:0.417379+0.058885
## [123] train-rmse:0.235319+0.009397 test-rmse:0.417522+0.058647
## [124] train-rmse:0.234593+0.009261 test-rmse:0.416726+0.059278
## Stopping. Best iteration:
## [118] train-rmse:0.238742+0.008797 test-rmse:0.415857+0.059255
##
## 44
## [1] train-rmse:1.320899+0.024346 test-rmse:1.323509+0.117289
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.077437+0.020107 test-rmse:1.084134+0.112356
## [3] train-rmse:0.889951+0.016348 test-rmse:0.901027+0.112584
## [4] train-rmse:0.747757+0.015876 test-rmse:0.762349+0.105342
## [5] train-rmse:0.639643+0.013475 test-rmse:0.660416+0.098787
## [6] train-rmse:0.560453+0.012291 test-rmse:0.590950+0.094274
## [7] train-rmse:0.500843+0.011272 test-rmse:0.540840+0.086747
## [8] train-rmse:0.456236+0.010682 test-rmse:0.503117+0.079930
## [9] train-rmse:0.424626+0.010630 test-rmse:0.481733+0.074732
## [10] train-rmse:0.401204+0.010661 test-rmse:0.466660+0.073222
## [11] train-rmse:0.382617+0.009833 test-rmse:0.453549+0.074316
## [12] train-rmse:0.368158+0.009162 test-rmse:0.445633+0.071637
## [13] train-rmse:0.357741+0.009149 test-rmse:0.442246+0.068514
## [14] train-rmse:0.348064+0.010017 test-rmse:0.440804+0.062446
## [15] train-rmse:0.339271+0.009661 test-rmse:0.436063+0.059471
## [16] train-rmse:0.332224+0.009950 test-rmse:0.435358+0.059863
## [17] train-rmse:0.326509+0.009498 test-rmse:0.434560+0.058773
## [18] train-rmse:0.319978+0.009485 test-rmse:0.432882+0.057281
## [19] train-rmse:0.313979+0.009750 test-rmse:0.433541+0.057321
## [20] train-rmse:0.308850+0.009245 test-rmse:0.430854+0.056438
## [21] train-rmse:0.303702+0.008108 test-rmse:0.429292+0.056624
## [22] train-rmse:0.298912+0.008023 test-rmse:0.429234+0.057125
## [23] train-rmse:0.294624+0.007180 test-rmse:0.429224+0.056609
## [24] train-rmse:0.290107+0.007468 test-rmse:0.428741+0.055339
## [25] train-rmse:0.285349+0.006107 test-rmse:0.428450+0.055639
## [26] train-rmse:0.281071+0.005271 test-rmse:0.426568+0.056258
## [27] train-rmse:0.277014+0.005698 test-rmse:0.424915+0.057557
## [28] train-rmse:0.273762+0.005244 test-rmse:0.425297+0.058180
## [29] train-rmse:0.270482+0.005259 test-rmse:0.424905+0.060782
## [30] train-rmse:0.267392+0.005227 test-rmse:0.424975+0.061187
## [31] train-rmse:0.263726+0.005557 test-rmse:0.425925+0.061031
## [32] train-rmse:0.259799+0.005716 test-rmse:0.426982+0.061629
## [33] train-rmse:0.257210+0.004853 test-rmse:0.425615+0.061817
## [34] train-rmse:0.254490+0.004697 test-rmse:0.425281+0.062339
## [35] train-rmse:0.250908+0.004964 test-rmse:0.424943+0.062333
## Stopping. Best iteration:
## [29] train-rmse:0.270482+0.005259 test-rmse:0.424905+0.060782
##
## 45
## [1] train-rmse:1.317524+0.023815 test-rmse:1.319714+0.117780
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.070903+0.018929 test-rmse:1.078618+0.116649
## [3] train-rmse:0.879097+0.016416 test-rmse:0.888212+0.108196
## [4] train-rmse:0.730560+0.013156 test-rmse:0.746191+0.102703
## [5] train-rmse:0.618240+0.010074 test-rmse:0.640492+0.100524
## [6] train-rmse:0.532338+0.008694 test-rmse:0.565385+0.095522
## [7] train-rmse:0.467953+0.006930 test-rmse:0.514551+0.086510
## [8] train-rmse:0.417860+0.007674 test-rmse:0.483322+0.083203
## [9] train-rmse:0.382214+0.008483 test-rmse:0.460212+0.076598
## [10] train-rmse:0.355950+0.008108 test-rmse:0.444678+0.074141
## [11] train-rmse:0.335299+0.009120 test-rmse:0.434581+0.066842
## [12] train-rmse:0.318734+0.008348 test-rmse:0.427419+0.069660
## [13] train-rmse:0.304987+0.006491 test-rmse:0.424325+0.064992
## [14] train-rmse:0.294357+0.007753 test-rmse:0.422628+0.064634
## [15] train-rmse:0.284488+0.006600 test-rmse:0.418736+0.066087
## [16] train-rmse:0.278004+0.005927 test-rmse:0.416571+0.064694
## [17] train-rmse:0.271379+0.006074 test-rmse:0.414667+0.062754
## [18] train-rmse:0.265303+0.005030 test-rmse:0.413472+0.061485
## [19] train-rmse:0.258962+0.005264 test-rmse:0.411957+0.063140
## [20] train-rmse:0.253300+0.004298 test-rmse:0.412507+0.063248
## [21] train-rmse:0.249555+0.003995 test-rmse:0.409986+0.065613
## [22] train-rmse:0.241440+0.004389 test-rmse:0.409759+0.065433
## [23] train-rmse:0.237135+0.004355 test-rmse:0.407809+0.064722
## [24] train-rmse:0.231434+0.004361 test-rmse:0.406412+0.065022
## [25] train-rmse:0.226874+0.004348 test-rmse:0.407617+0.062980
## [26] train-rmse:0.222771+0.004683 test-rmse:0.406489+0.062602
## [27] train-rmse:0.218288+0.005338 test-rmse:0.404741+0.062436
## [28] train-rmse:0.215081+0.004483 test-rmse:0.403503+0.063415
## [29] train-rmse:0.211818+0.004362 test-rmse:0.402598+0.062925
## [30] train-rmse:0.208793+0.003715 test-rmse:0.402660+0.062909
## [31] train-rmse:0.205104+0.004605 test-rmse:0.400245+0.062230
## [32] train-rmse:0.202499+0.004873 test-rmse:0.400180+0.062392
## [33] train-rmse:0.199222+0.004883 test-rmse:0.400455+0.062445
## [34] train-rmse:0.195964+0.004347 test-rmse:0.400291+0.062747
## [35] train-rmse:0.193370+0.004719 test-rmse:0.399249+0.062063
## [36] train-rmse:0.190370+0.005238 test-rmse:0.398861+0.060734
## [37] train-rmse:0.187576+0.006165 test-rmse:0.398356+0.060439
## [38] train-rmse:0.184708+0.005602 test-rmse:0.398681+0.059837
## [39] train-rmse:0.181858+0.005858 test-rmse:0.397988+0.060611
## [40] train-rmse:0.178897+0.006348 test-rmse:0.398055+0.060017
## [41] train-rmse:0.176911+0.006577 test-rmse:0.397269+0.059788
## [42] train-rmse:0.174699+0.006207 test-rmse:0.396794+0.059341
## [43] train-rmse:0.171700+0.006170 test-rmse:0.396347+0.059938
## [44] train-rmse:0.168917+0.006098 test-rmse:0.394684+0.060073
## [45] train-rmse:0.166647+0.005831 test-rmse:0.394784+0.060141
## [46] train-rmse:0.163699+0.005102 test-rmse:0.394666+0.059637
## [47] train-rmse:0.161966+0.004804 test-rmse:0.394550+0.059446
## [48] train-rmse:0.159393+0.005461 test-rmse:0.394903+0.059611
## [49] train-rmse:0.157408+0.004962 test-rmse:0.395035+0.060656
## [50] train-rmse:0.155876+0.005209 test-rmse:0.394893+0.060874
## [51] train-rmse:0.153750+0.005134 test-rmse:0.395826+0.060195
## [52] train-rmse:0.151489+0.004918 test-rmse:0.395670+0.060670
## [53] train-rmse:0.149538+0.005389 test-rmse:0.395986+0.060889
## Stopping. Best iteration:
## [47] train-rmse:0.161966+0.004804 test-rmse:0.394550+0.059446
##
## 46
## [1] train-rmse:1.315533+0.024148 test-rmse:1.317707+0.114530
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.066540+0.019212 test-rmse:1.071005+0.110531
## [3] train-rmse:0.871859+0.017002 test-rmse:0.880571+0.102330
## [4] train-rmse:0.720163+0.013318 test-rmse:0.743540+0.098575
## [5] train-rmse:0.602931+0.010847 test-rmse:0.638073+0.094883
## [6] train-rmse:0.513033+0.009990 test-rmse:0.560712+0.086068
## [7] train-rmse:0.443792+0.007591 test-rmse:0.506912+0.084953
## [8] train-rmse:0.390857+0.006529 test-rmse:0.470808+0.081311
## [9] train-rmse:0.351715+0.005550 test-rmse:0.449237+0.081829
## [10] train-rmse:0.321661+0.005263 test-rmse:0.432681+0.077751
## [11] train-rmse:0.298905+0.005718 test-rmse:0.422496+0.072992
## [12] train-rmse:0.277461+0.007740 test-rmse:0.415364+0.069102
## [13] train-rmse:0.262600+0.007090 test-rmse:0.410215+0.068470
## [14] train-rmse:0.250838+0.007709 test-rmse:0.403570+0.068368
## [15] train-rmse:0.242127+0.007423 test-rmse:0.403301+0.067638
## [16] train-rmse:0.231699+0.008203 test-rmse:0.402264+0.066349
## [17] train-rmse:0.223953+0.005908 test-rmse:0.403372+0.064547
## [18] train-rmse:0.216750+0.004235 test-rmse:0.403428+0.064742
## [19] train-rmse:0.209200+0.004738 test-rmse:0.402315+0.064202
## [20] train-rmse:0.203230+0.004789 test-rmse:0.400679+0.063425
## [21] train-rmse:0.196385+0.006789 test-rmse:0.401746+0.063438
## [22] train-rmse:0.190922+0.007070 test-rmse:0.399900+0.064044
## [23] train-rmse:0.186401+0.007150 test-rmse:0.399735+0.063356
## [24] train-rmse:0.181389+0.007108 test-rmse:0.398146+0.063421
## [25] train-rmse:0.175324+0.005759 test-rmse:0.398089+0.063884
## [26] train-rmse:0.171759+0.005609 test-rmse:0.397642+0.064914
## [27] train-rmse:0.167435+0.004671 test-rmse:0.396681+0.064573
## [28] train-rmse:0.164192+0.005009 test-rmse:0.397110+0.064905
## [29] train-rmse:0.160506+0.003849 test-rmse:0.397081+0.062941
## [30] train-rmse:0.157274+0.004028 test-rmse:0.398251+0.063101
## [31] train-rmse:0.153811+0.005527 test-rmse:0.397998+0.062384
## [32] train-rmse:0.150665+0.006348 test-rmse:0.397934+0.062306
## [33] train-rmse:0.146354+0.006141 test-rmse:0.397801+0.062948
## Stopping. Best iteration:
## [27] train-rmse:0.167435+0.004671 test-rmse:0.396681+0.064573
##
## 47
## [1] train-rmse:1.314918+0.024196 test-rmse:1.316298+0.113294
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.064597+0.019290 test-rmse:1.068215+0.108025
## [3] train-rmse:0.868114+0.017240 test-rmse:0.879135+0.097433
## [4] train-rmse:0.715357+0.014398 test-rmse:0.736785+0.094985
## [5] train-rmse:0.596167+0.011874 test-rmse:0.631436+0.098548
## [6] train-rmse:0.503602+0.011491 test-rmse:0.553214+0.090915
## [7] train-rmse:0.431463+0.009547 test-rmse:0.503133+0.085168
## [8] train-rmse:0.374891+0.009833 test-rmse:0.464454+0.080323
## [9] train-rmse:0.331114+0.009904 test-rmse:0.442892+0.075550
## [10] train-rmse:0.297450+0.009574 test-rmse:0.428691+0.074164
## [11] train-rmse:0.271677+0.009318 test-rmse:0.418479+0.070175
## [12] train-rmse:0.248990+0.009096 test-rmse:0.413037+0.069608
## [13] train-rmse:0.232778+0.009789 test-rmse:0.408533+0.066071
## [14] train-rmse:0.216507+0.010781 test-rmse:0.405061+0.064819
## [15] train-rmse:0.203515+0.007404 test-rmse:0.402939+0.065928
## [16] train-rmse:0.194302+0.007924 test-rmse:0.402280+0.062716
## [17] train-rmse:0.185895+0.008816 test-rmse:0.401411+0.061149
## [18] train-rmse:0.176973+0.006791 test-rmse:0.398793+0.058594
## [19] train-rmse:0.169923+0.006335 test-rmse:0.397733+0.059000
## [20] train-rmse:0.164229+0.005969 test-rmse:0.397236+0.057013
## [21] train-rmse:0.159209+0.006678 test-rmse:0.398514+0.055968
## [22] train-rmse:0.152304+0.007204 test-rmse:0.400076+0.053948
## [23] train-rmse:0.146740+0.007472 test-rmse:0.400711+0.051524
## [24] train-rmse:0.140365+0.007598 test-rmse:0.400329+0.051603
## [25] train-rmse:0.137057+0.007855 test-rmse:0.400067+0.051839
## [26] train-rmse:0.132201+0.006962 test-rmse:0.400883+0.050738
## Stopping. Best iteration:
## [20] train-rmse:0.164229+0.005969 test-rmse:0.397236+0.057013
##
## 48
## [1] train-rmse:1.314618+0.024198 test-rmse:1.317326+0.112770
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.063432+0.019587 test-rmse:1.067330+0.108139
## [3] train-rmse:0.865746+0.017639 test-rmse:0.875114+0.100183
## [4] train-rmse:0.710773+0.014388 test-rmse:0.737736+0.096495
## [5] train-rmse:0.588776+0.011811 test-rmse:0.631430+0.096425
## [6] train-rmse:0.493258+0.011408 test-rmse:0.556922+0.091543
## [7] train-rmse:0.418354+0.012032 test-rmse:0.506310+0.085206
## [8] train-rmse:0.359617+0.012067 test-rmse:0.475568+0.079441
## [9] train-rmse:0.313938+0.012730 test-rmse:0.457102+0.074919
## [10] train-rmse:0.276312+0.012090 test-rmse:0.444040+0.074232
## [11] train-rmse:0.246513+0.011739 test-rmse:0.435038+0.070545
## [12] train-rmse:0.223332+0.012547 test-rmse:0.428385+0.066719
## [13] train-rmse:0.203728+0.013644 test-rmse:0.427555+0.064166
## [14] train-rmse:0.189262+0.014593 test-rmse:0.427489+0.062224
## [15] train-rmse:0.172312+0.012474 test-rmse:0.425193+0.060723
## [16] train-rmse:0.160765+0.013020 test-rmse:0.425544+0.059243
## [17] train-rmse:0.149927+0.013613 test-rmse:0.423151+0.059341
## [18] train-rmse:0.141135+0.010296 test-rmse:0.423012+0.057901
## [19] train-rmse:0.133523+0.009057 test-rmse:0.421764+0.053909
## [20] train-rmse:0.127955+0.009475 test-rmse:0.420009+0.053011
## [21] train-rmse:0.123367+0.009317 test-rmse:0.419516+0.051880
## [22] train-rmse:0.118261+0.008876 test-rmse:0.418709+0.049913
## [23] train-rmse:0.114345+0.008408 test-rmse:0.418355+0.049673
## [24] train-rmse:0.109903+0.008870 test-rmse:0.418008+0.049249
## [25] train-rmse:0.104084+0.008458 test-rmse:0.417563+0.049522
## [26] train-rmse:0.100418+0.008022 test-rmse:0.417909+0.050104
## [27] train-rmse:0.095487+0.008122 test-rmse:0.417213+0.050017
## [28] train-rmse:0.090812+0.007944 test-rmse:0.416564+0.048323
## [29] train-rmse:0.085591+0.007722 test-rmse:0.415913+0.048027
## [30] train-rmse:0.082635+0.007080 test-rmse:0.415790+0.047905
## [31] train-rmse:0.079460+0.007103 test-rmse:0.415140+0.047369
## [32] train-rmse:0.077061+0.007111 test-rmse:0.414318+0.047494
## [33] train-rmse:0.073278+0.005109 test-rmse:0.414096+0.047913
## [34] train-rmse:0.070857+0.004713 test-rmse:0.414070+0.047860
## [35] train-rmse:0.067905+0.003401 test-rmse:0.413965+0.047776
## [36] train-rmse:0.064820+0.003816 test-rmse:0.413680+0.048648
## [37] train-rmse:0.063035+0.004072 test-rmse:0.413166+0.048977
## [38] train-rmse:0.060309+0.003808 test-rmse:0.413419+0.049062
## [39] train-rmse:0.059117+0.003689 test-rmse:0.413386+0.048994
## [40] train-rmse:0.056850+0.003962 test-rmse:0.412668+0.048791
## [41] train-rmse:0.054766+0.003600 test-rmse:0.412439+0.048510
## [42] train-rmse:0.052779+0.003145 test-rmse:0.412020+0.048580
## [43] train-rmse:0.051189+0.002783 test-rmse:0.412149+0.048509
## [44] train-rmse:0.049092+0.002672 test-rmse:0.412303+0.047973
## [45] train-rmse:0.047605+0.002737 test-rmse:0.412145+0.047911
## [46] train-rmse:0.045995+0.002409 test-rmse:0.412052+0.048418
## [47] train-rmse:0.044659+0.002549 test-rmse:0.411828+0.048578
## [48] train-rmse:0.043076+0.002036 test-rmse:0.411745+0.048358
## [49] train-rmse:0.042116+0.002118 test-rmse:0.411683+0.048590
## [50] train-rmse:0.040387+0.001953 test-rmse:0.411305+0.048615
## [51] train-rmse:0.039229+0.001910 test-rmse:0.411175+0.048556
## [52] train-rmse:0.038225+0.002145 test-rmse:0.411034+0.048421
## [53] train-rmse:0.036782+0.002190 test-rmse:0.410930+0.048441
## [54] train-rmse:0.035558+0.001995 test-rmse:0.410629+0.048243
## [55] train-rmse:0.034325+0.001959 test-rmse:0.410451+0.048356
## [56] train-rmse:0.033048+0.001938 test-rmse:0.410462+0.048485
## [57] train-rmse:0.032008+0.001814 test-rmse:0.410512+0.048708
## [58] train-rmse:0.031051+0.001459 test-rmse:0.410414+0.048518
## [59] train-rmse:0.030173+0.001375 test-rmse:0.410200+0.048395
## [60] train-rmse:0.029101+0.001467 test-rmse:0.409903+0.048240
## [61] train-rmse:0.028283+0.001733 test-rmse:0.409805+0.048254
## [62] train-rmse:0.027557+0.001678 test-rmse:0.409772+0.048157
## [63] train-rmse:0.026681+0.001817 test-rmse:0.409795+0.048082
## [64] train-rmse:0.025866+0.001886 test-rmse:0.409817+0.048100
## [65] train-rmse:0.025289+0.001922 test-rmse:0.409967+0.048134
## [66] train-rmse:0.024853+0.002029 test-rmse:0.410024+0.048176
## [67] train-rmse:0.024112+0.001994 test-rmse:0.410023+0.048153
## [68] train-rmse:0.023378+0.001562 test-rmse:0.409941+0.048196
## Stopping. Best iteration:
## [62] train-rmse:0.027557+0.001678 test-rmse:0.409772+0.048157
##
## 49
## [1] train-rmse:1.316507+0.024624 test-rmse:1.312671+0.109991
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.087418+0.021011 test-rmse:1.083693+0.104168
## [3] train-rmse:0.928710+0.019039 test-rmse:0.925179+0.099190
## [4] train-rmse:0.822549+0.018192 test-rmse:0.819297+0.094932
## [5] train-rmse:0.754031+0.017964 test-rmse:0.751095+0.091473
## [6] train-rmse:0.711192+0.017992 test-rmse:0.708557+0.088827
## [7] train-rmse:0.683585+0.017269 test-rmse:0.687554+0.089446
## [8] train-rmse:0.661277+0.016973 test-rmse:0.659536+0.086896
## [9] train-rmse:0.645585+0.016479 test-rmse:0.650253+0.084161
## [10] train-rmse:0.631648+0.015315 test-rmse:0.632435+0.082585
## [11] train-rmse:0.619583+0.014324 test-rmse:0.621443+0.079489
## [12] train-rmse:0.609838+0.013561 test-rmse:0.614426+0.074299
## [13] train-rmse:0.600931+0.012903 test-rmse:0.602054+0.074179
## [14] train-rmse:0.593566+0.012712 test-rmse:0.598888+0.071896
## [15] train-rmse:0.586713+0.012711 test-rmse:0.592494+0.068385
## [16] train-rmse:0.580513+0.012043 test-rmse:0.588108+0.067707
## [17] train-rmse:0.574992+0.011725 test-rmse:0.581609+0.069060
## [18] train-rmse:0.569733+0.011689 test-rmse:0.578184+0.066385
## [19] train-rmse:0.565298+0.011472 test-rmse:0.576074+0.062824
## [20] train-rmse:0.561168+0.011149 test-rmse:0.573337+0.064520
## [21] train-rmse:0.557164+0.010921 test-rmse:0.568709+0.064432
## [22] train-rmse:0.553695+0.010883 test-rmse:0.567732+0.065348
## [23] train-rmse:0.550433+0.010721 test-rmse:0.565220+0.063958
## [24] train-rmse:0.547244+0.010779 test-rmse:0.563762+0.061410
## [25] train-rmse:0.544386+0.010642 test-rmse:0.561493+0.059489
## [26] train-rmse:0.541718+0.010497 test-rmse:0.560510+0.059448
## [27] train-rmse:0.539177+0.010365 test-rmse:0.558600+0.059273
## [28] train-rmse:0.536676+0.010176 test-rmse:0.556516+0.060316
## [29] train-rmse:0.534265+0.010099 test-rmse:0.553274+0.058968
## [30] train-rmse:0.531972+0.010037 test-rmse:0.552875+0.058368
## [31] train-rmse:0.529789+0.009880 test-rmse:0.551799+0.057857
## [32] train-rmse:0.527744+0.009770 test-rmse:0.550902+0.058445
## [33] train-rmse:0.525795+0.009605 test-rmse:0.548829+0.058187
## [34] train-rmse:0.523883+0.009514 test-rmse:0.546267+0.056432
## [35] train-rmse:0.522012+0.009487 test-rmse:0.546130+0.056181
## [36] train-rmse:0.520239+0.009444 test-rmse:0.545409+0.057015
## [37] train-rmse:0.518564+0.009294 test-rmse:0.545005+0.057892
## [38] train-rmse:0.516919+0.009163 test-rmse:0.542498+0.057161
## [39] train-rmse:0.515242+0.009016 test-rmse:0.541012+0.056046
## [40] train-rmse:0.513670+0.008946 test-rmse:0.539858+0.056163
## [41] train-rmse:0.512145+0.008970 test-rmse:0.538432+0.055027
## [42] train-rmse:0.510625+0.008904 test-rmse:0.538376+0.055361
## [43] train-rmse:0.509083+0.008799 test-rmse:0.537018+0.055804
## [44] train-rmse:0.507653+0.008743 test-rmse:0.536850+0.055133
## [45] train-rmse:0.506244+0.008680 test-rmse:0.535191+0.053833
## [46] train-rmse:0.504851+0.008652 test-rmse:0.532956+0.053891
## [47] train-rmse:0.503530+0.008609 test-rmse:0.533517+0.054016
## [48] train-rmse:0.502274+0.008574 test-rmse:0.532915+0.053613
## [49] train-rmse:0.500912+0.008501 test-rmse:0.530264+0.053833
## [50] train-rmse:0.499660+0.008466 test-rmse:0.529222+0.054635
## [51] train-rmse:0.498424+0.008460 test-rmse:0.528545+0.053385
## [52] train-rmse:0.497261+0.008470 test-rmse:0.527749+0.052978
## [53] train-rmse:0.496099+0.008414 test-rmse:0.527440+0.051612
## [54] train-rmse:0.494962+0.008392 test-rmse:0.527699+0.052211
## [55] train-rmse:0.493819+0.008354 test-rmse:0.526878+0.052424
## [56] train-rmse:0.492701+0.008262 test-rmse:0.526818+0.053577
## [57] train-rmse:0.491657+0.008232 test-rmse:0.526304+0.053486
## [58] train-rmse:0.490598+0.008212 test-rmse:0.524402+0.052464
## [59] train-rmse:0.489558+0.008186 test-rmse:0.523127+0.053239
## [60] train-rmse:0.488524+0.008130 test-rmse:0.522788+0.052730
## [61] train-rmse:0.487518+0.008102 test-rmse:0.523770+0.052487
## [62] train-rmse:0.486538+0.008057 test-rmse:0.523716+0.051853
## [63] train-rmse:0.485565+0.008012 test-rmse:0.521884+0.051768
## [64] train-rmse:0.484658+0.007990 test-rmse:0.520927+0.051585
## [65] train-rmse:0.483725+0.007951 test-rmse:0.520353+0.051536
## [66] train-rmse:0.482809+0.007967 test-rmse:0.519425+0.050938
## [67] train-rmse:0.481938+0.007981 test-rmse:0.519449+0.051359
## [68] train-rmse:0.481071+0.007978 test-rmse:0.519320+0.052192
## [69] train-rmse:0.480225+0.007974 test-rmse:0.518930+0.051307
## [70] train-rmse:0.479417+0.007954 test-rmse:0.518203+0.051461
## [71] train-rmse:0.478609+0.007924 test-rmse:0.517883+0.051002
## [72] train-rmse:0.477792+0.007888 test-rmse:0.517650+0.050475
## [73] train-rmse:0.476988+0.007897 test-rmse:0.518048+0.050837
## [74] train-rmse:0.476224+0.007874 test-rmse:0.517889+0.050426
## [75] train-rmse:0.475490+0.007863 test-rmse:0.516302+0.050841
## [76] train-rmse:0.474772+0.007865 test-rmse:0.516001+0.050534
## [77] train-rmse:0.474024+0.007845 test-rmse:0.513916+0.047561
## [78] train-rmse:0.473319+0.007826 test-rmse:0.513274+0.047069
## [79] train-rmse:0.472615+0.007826 test-rmse:0.511567+0.047003
## [80] train-rmse:0.471902+0.007813 test-rmse:0.511548+0.046767
## [81] train-rmse:0.471171+0.007789 test-rmse:0.512115+0.046436
## [82] train-rmse:0.470483+0.007746 test-rmse:0.512211+0.047038
## [83] train-rmse:0.469820+0.007722 test-rmse:0.511524+0.047449
## [84] train-rmse:0.469164+0.007702 test-rmse:0.511281+0.047643
## [85] train-rmse:0.468525+0.007700 test-rmse:0.510045+0.047120
## [86] train-rmse:0.467892+0.007682 test-rmse:0.509959+0.047587
## [87] train-rmse:0.467258+0.007650 test-rmse:0.508715+0.047218
## [88] train-rmse:0.466620+0.007632 test-rmse:0.509193+0.046545
## [89] train-rmse:0.466013+0.007613 test-rmse:0.509010+0.046769
## [90] train-rmse:0.465413+0.007614 test-rmse:0.509393+0.047429
## [91] train-rmse:0.464825+0.007590 test-rmse:0.509614+0.047470
## [92] train-rmse:0.464255+0.007563 test-rmse:0.509472+0.047626
## [93] train-rmse:0.463692+0.007546 test-rmse:0.509421+0.047225
## Stopping. Best iteration:
## [87] train-rmse:0.467258+0.007650 test-rmse:0.508715+0.047218
##
## 50
## [1] train-rmse:1.300877+0.024709 test-rmse:1.300430+0.114566
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.051738+0.019914 test-rmse:1.049344+0.104196
## [3] train-rmse:0.870650+0.018004 test-rmse:0.877749+0.103053
## [4] train-rmse:0.741120+0.015489 test-rmse:0.746921+0.104029
## [5] train-rmse:0.649718+0.013618 test-rmse:0.658148+0.098840
## [6] train-rmse:0.585676+0.013285 test-rmse:0.595185+0.094872
## [7] train-rmse:0.541416+0.012828 test-rmse:0.555299+0.089148
## [8] train-rmse:0.510155+0.012439 test-rmse:0.530818+0.080902
## [9] train-rmse:0.487456+0.011829 test-rmse:0.511381+0.080839
## [10] train-rmse:0.470696+0.010897 test-rmse:0.494019+0.075470
## [11] train-rmse:0.454045+0.009156 test-rmse:0.477144+0.071403
## [12] train-rmse:0.441402+0.008536 test-rmse:0.470337+0.067351
## [13] train-rmse:0.430645+0.007610 test-rmse:0.466199+0.065643
## [14] train-rmse:0.422278+0.006981 test-rmse:0.462180+0.062909
## [15] train-rmse:0.415189+0.006980 test-rmse:0.460196+0.059758
## [16] train-rmse:0.407995+0.005761 test-rmse:0.455668+0.057778
## [17] train-rmse:0.402348+0.005624 test-rmse:0.455058+0.058065
## [18] train-rmse:0.397235+0.005357 test-rmse:0.453097+0.056254
## [19] train-rmse:0.391918+0.005968 test-rmse:0.449978+0.056447
## [20] train-rmse:0.386248+0.005023 test-rmse:0.444183+0.056377
## [21] train-rmse:0.381419+0.004643 test-rmse:0.442935+0.058625
## [22] train-rmse:0.377673+0.004248 test-rmse:0.442794+0.058535
## [23] train-rmse:0.373829+0.004173 test-rmse:0.442503+0.056872
## [24] train-rmse:0.370054+0.003777 test-rmse:0.442318+0.056727
## [25] train-rmse:0.365881+0.003921 test-rmse:0.443379+0.057302
## [26] train-rmse:0.362822+0.004038 test-rmse:0.443355+0.057885
## [27] train-rmse:0.359588+0.003801 test-rmse:0.442192+0.056502
## [28] train-rmse:0.356458+0.003938 test-rmse:0.440477+0.057117
## [29] train-rmse:0.353633+0.003771 test-rmse:0.437468+0.054637
## [30] train-rmse:0.350088+0.002931 test-rmse:0.438194+0.053014
## [31] train-rmse:0.346741+0.002433 test-rmse:0.439199+0.054265
## [32] train-rmse:0.344124+0.002617 test-rmse:0.438625+0.053909
## [33] train-rmse:0.340129+0.003588 test-rmse:0.438450+0.055491
## [34] train-rmse:0.337360+0.003587 test-rmse:0.437594+0.054464
## [35] train-rmse:0.334725+0.002965 test-rmse:0.436278+0.054770
## [36] train-rmse:0.332846+0.002606 test-rmse:0.435841+0.055242
## [37] train-rmse:0.330869+0.002713 test-rmse:0.436761+0.056479
## [38] train-rmse:0.328733+0.003355 test-rmse:0.436535+0.055401
## [39] train-rmse:0.326843+0.003083 test-rmse:0.436812+0.057091
## [40] train-rmse:0.323856+0.003197 test-rmse:0.436696+0.057271
## [41] train-rmse:0.321815+0.003351 test-rmse:0.436331+0.057704
## [42] train-rmse:0.319866+0.003612 test-rmse:0.435120+0.057418
## [43] train-rmse:0.318360+0.003571 test-rmse:0.435870+0.058061
## [44] train-rmse:0.316051+0.003224 test-rmse:0.435440+0.055784
## [45] train-rmse:0.313945+0.003626 test-rmse:0.435153+0.056266
## [46] train-rmse:0.312491+0.003494 test-rmse:0.434374+0.057143
## [47] train-rmse:0.311089+0.003441 test-rmse:0.434115+0.057982
## [48] train-rmse:0.309626+0.003406 test-rmse:0.435355+0.057952
## [49] train-rmse:0.307147+0.004149 test-rmse:0.434814+0.057080
## [50] train-rmse:0.304243+0.005845 test-rmse:0.435209+0.058178
## [51] train-rmse:0.302814+0.005590 test-rmse:0.434334+0.057660
## [52] train-rmse:0.300975+0.005311 test-rmse:0.433896+0.056887
## [53] train-rmse:0.298901+0.004268 test-rmse:0.433335+0.057281
## [54] train-rmse:0.297075+0.004322 test-rmse:0.432555+0.056834
## [55] train-rmse:0.296099+0.004396 test-rmse:0.432471+0.057116
## [56] train-rmse:0.294470+0.004868 test-rmse:0.431899+0.056650
## [57] train-rmse:0.293051+0.004975 test-rmse:0.431802+0.056982
## [58] train-rmse:0.291169+0.004929 test-rmse:0.430568+0.057583
## [59] train-rmse:0.288950+0.005163 test-rmse:0.430840+0.057233
## [60] train-rmse:0.287662+0.004773 test-rmse:0.430662+0.056979
## [61] train-rmse:0.286421+0.004850 test-rmse:0.428585+0.058165
## [62] train-rmse:0.284804+0.005394 test-rmse:0.428504+0.057543
## [63] train-rmse:0.283351+0.005540 test-rmse:0.428371+0.057662
## [64] train-rmse:0.281998+0.005734 test-rmse:0.427612+0.058284
## [65] train-rmse:0.281192+0.005727 test-rmse:0.427524+0.058367
## [66] train-rmse:0.279461+0.006737 test-rmse:0.427326+0.057844
## [67] train-rmse:0.277816+0.006351 test-rmse:0.427094+0.057895
## [68] train-rmse:0.276662+0.006268 test-rmse:0.426715+0.058368
## [69] train-rmse:0.275475+0.005881 test-rmse:0.426962+0.058354
## [70] train-rmse:0.274414+0.006085 test-rmse:0.426543+0.058054
## [71] train-rmse:0.272711+0.006229 test-rmse:0.426372+0.058068
## [72] train-rmse:0.271673+0.006206 test-rmse:0.426257+0.057741
## [73] train-rmse:0.270338+0.005925 test-rmse:0.424688+0.057873
## [74] train-rmse:0.268916+0.005536 test-rmse:0.424884+0.058094
## [75] train-rmse:0.267794+0.005386 test-rmse:0.424260+0.058566
## [76] train-rmse:0.266577+0.005065 test-rmse:0.423299+0.059744
## [77] train-rmse:0.265238+0.004669 test-rmse:0.424269+0.059334
## [78] train-rmse:0.263470+0.005373 test-rmse:0.423138+0.058872
## [79] train-rmse:0.262673+0.005416 test-rmse:0.423380+0.058852
## [80] train-rmse:0.261318+0.004878 test-rmse:0.423488+0.059047
## [81] train-rmse:0.260711+0.004999 test-rmse:0.423238+0.058687
## [82] train-rmse:0.259869+0.005220 test-rmse:0.422286+0.058964
## [83] train-rmse:0.259077+0.005142 test-rmse:0.422174+0.059228
## [84] train-rmse:0.257333+0.004681 test-rmse:0.422291+0.059817
## [85] train-rmse:0.256574+0.004722 test-rmse:0.422951+0.059856
## [86] train-rmse:0.255705+0.004498 test-rmse:0.423328+0.059451
## [87] train-rmse:0.254645+0.004376 test-rmse:0.422544+0.058864
## [88] train-rmse:0.253867+0.004267 test-rmse:0.422719+0.058149
## [89] train-rmse:0.253018+0.004785 test-rmse:0.422107+0.058165
## [90] train-rmse:0.252087+0.005248 test-rmse:0.422196+0.058418
## [91] train-rmse:0.250856+0.005782 test-rmse:0.421685+0.057695
## [92] train-rmse:0.249435+0.005300 test-rmse:0.421435+0.058172
## [93] train-rmse:0.248473+0.005156 test-rmse:0.421525+0.058387
## [94] train-rmse:0.247192+0.004801 test-rmse:0.419858+0.058270
## [95] train-rmse:0.246351+0.004922 test-rmse:0.419315+0.058398
## [96] train-rmse:0.245640+0.004862 test-rmse:0.419892+0.058152
## [97] train-rmse:0.245020+0.004875 test-rmse:0.419256+0.058333
## [98] train-rmse:0.244380+0.004740 test-rmse:0.419441+0.058386
## [99] train-rmse:0.243628+0.005000 test-rmse:0.419399+0.057762
## [100] train-rmse:0.242282+0.005008 test-rmse:0.418637+0.057648
## [101] train-rmse:0.241301+0.004821 test-rmse:0.418145+0.057137
## [102] train-rmse:0.240112+0.005715 test-rmse:0.417757+0.056910
## [103] train-rmse:0.239407+0.005627 test-rmse:0.417588+0.057532
## [104] train-rmse:0.238403+0.005340 test-rmse:0.417365+0.057487
## [105] train-rmse:0.237839+0.005390 test-rmse:0.417189+0.057095
## [106] train-rmse:0.236946+0.005241 test-rmse:0.416501+0.057915
## [107] train-rmse:0.236099+0.005063 test-rmse:0.416201+0.058338
## [108] train-rmse:0.235649+0.005040 test-rmse:0.416133+0.058353
## [109] train-rmse:0.234685+0.005379 test-rmse:0.416122+0.058622
## [110] train-rmse:0.234336+0.005347 test-rmse:0.416187+0.058733
## [111] train-rmse:0.233562+0.005220 test-rmse:0.415780+0.058617
## [112] train-rmse:0.232836+0.005402 test-rmse:0.415457+0.058852
## [113] train-rmse:0.232122+0.005197 test-rmse:0.415113+0.059228
## [114] train-rmse:0.231141+0.005896 test-rmse:0.414786+0.059451
## [115] train-rmse:0.230305+0.005444 test-rmse:0.415052+0.059560
## [116] train-rmse:0.229578+0.005796 test-rmse:0.414403+0.059539
## [117] train-rmse:0.229026+0.006132 test-rmse:0.414353+0.059482
## [118] train-rmse:0.228399+0.006223 test-rmse:0.414222+0.058805
## [119] train-rmse:0.227905+0.006214 test-rmse:0.414224+0.058712
## [120] train-rmse:0.227301+0.006062 test-rmse:0.413554+0.058887
## [121] train-rmse:0.226286+0.006342 test-rmse:0.413159+0.058443
## [122] train-rmse:0.225291+0.006231 test-rmse:0.413277+0.058033
## [123] train-rmse:0.224053+0.006180 test-rmse:0.413857+0.057621
## [124] train-rmse:0.223230+0.005929 test-rmse:0.413530+0.057501
## [125] train-rmse:0.222576+0.005836 test-rmse:0.413375+0.057835
## [126] train-rmse:0.221744+0.005568 test-rmse:0.413104+0.058268
## [127] train-rmse:0.221329+0.005538 test-rmse:0.413445+0.057919
## [128] train-rmse:0.220810+0.005421 test-rmse:0.413799+0.058594
## [129] train-rmse:0.219938+0.004963 test-rmse:0.413979+0.058969
## [130] train-rmse:0.219132+0.005040 test-rmse:0.413592+0.058983
## [131] train-rmse:0.218642+0.005301 test-rmse:0.413832+0.059233
## [132] train-rmse:0.218030+0.005176 test-rmse:0.413787+0.059026
## Stopping. Best iteration:
## [126] train-rmse:0.221744+0.005568 test-rmse:0.413104+0.058268
##
## 51
## [1] train-rmse:1.292589+0.023841 test-rmse:1.295830+0.117360
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.034609+0.019337 test-rmse:1.042184+0.112293
## [3] train-rmse:0.842459+0.015523 test-rmse:0.854187+0.113320
## [4] train-rmse:0.699229+0.014278 test-rmse:0.717760+0.102850
## [5] train-rmse:0.594498+0.011744 test-rmse:0.618848+0.098401
## [6] train-rmse:0.520839+0.009982 test-rmse:0.555639+0.094137
## [7] train-rmse:0.467417+0.009743 test-rmse:0.515586+0.084309
## [8] train-rmse:0.429037+0.009653 test-rmse:0.488649+0.079074
## [9] train-rmse:0.402155+0.009203 test-rmse:0.471443+0.078454
## [10] train-rmse:0.381294+0.009219 test-rmse:0.457434+0.070942
## [11] train-rmse:0.365912+0.007744 test-rmse:0.447000+0.072641
## [12] train-rmse:0.354380+0.007899 test-rmse:0.441632+0.070270
## [13] train-rmse:0.344497+0.007923 test-rmse:0.437587+0.068334
## [14] train-rmse:0.335686+0.007250 test-rmse:0.434160+0.064800
## [15] train-rmse:0.328581+0.006733 test-rmse:0.433094+0.064219
## [16] train-rmse:0.321685+0.005869 test-rmse:0.429987+0.065005
## [17] train-rmse:0.314945+0.005322 test-rmse:0.430215+0.064839
## [18] train-rmse:0.308179+0.005738 test-rmse:0.429323+0.065330
## [19] train-rmse:0.303442+0.004814 test-rmse:0.429000+0.064649
## [20] train-rmse:0.297345+0.004640 test-rmse:0.426219+0.062261
## [21] train-rmse:0.293269+0.004541 test-rmse:0.425866+0.063930
## [22] train-rmse:0.289494+0.004455 test-rmse:0.424251+0.066180
## [23] train-rmse:0.285073+0.004530 test-rmse:0.423470+0.064016
## [24] train-rmse:0.279702+0.005543 test-rmse:0.423103+0.064883
## [25] train-rmse:0.275982+0.005550 test-rmse:0.421792+0.064947
## [26] train-rmse:0.272881+0.005817 test-rmse:0.421761+0.065466
## [27] train-rmse:0.269558+0.005873 test-rmse:0.421963+0.065479
## [28] train-rmse:0.266408+0.005853 test-rmse:0.419570+0.067800
## [29] train-rmse:0.262915+0.006008 test-rmse:0.419850+0.067742
## [30] train-rmse:0.258788+0.005823 test-rmse:0.419525+0.067243
## [31] train-rmse:0.256328+0.006038 test-rmse:0.418893+0.068946
## [32] train-rmse:0.253638+0.005990 test-rmse:0.417135+0.067580
## [33] train-rmse:0.250106+0.006341 test-rmse:0.416478+0.067649
## [34] train-rmse:0.247108+0.006760 test-rmse:0.415302+0.068825
## [35] train-rmse:0.244346+0.006482 test-rmse:0.415916+0.068709
## [36] train-rmse:0.240655+0.006428 test-rmse:0.415853+0.069508
## [37] train-rmse:0.237458+0.005652 test-rmse:0.414512+0.071273
## [38] train-rmse:0.234656+0.005056 test-rmse:0.414872+0.070843
## [39] train-rmse:0.232719+0.005339 test-rmse:0.414663+0.070880
## [40] train-rmse:0.230200+0.005985 test-rmse:0.412505+0.072501
## [41] train-rmse:0.227789+0.006051 test-rmse:0.412390+0.072126
## [42] train-rmse:0.226094+0.006177 test-rmse:0.411079+0.072123
## [43] train-rmse:0.223624+0.005528 test-rmse:0.412607+0.071134
## [44] train-rmse:0.221588+0.005508 test-rmse:0.412937+0.070513
## [45] train-rmse:0.219304+0.005569 test-rmse:0.412485+0.071273
## [46] train-rmse:0.217366+0.005620 test-rmse:0.411964+0.071660
## [47] train-rmse:0.214318+0.006492 test-rmse:0.411419+0.071699
## [48] train-rmse:0.212601+0.006216 test-rmse:0.411987+0.070828
## Stopping. Best iteration:
## [42] train-rmse:0.226094+0.006177 test-rmse:0.411079+0.072123
##
## 52
## [1] train-rmse:1.288830+0.023247 test-rmse:1.291635+0.117945
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.027226+0.018045 test-rmse:1.035652+0.116298
## [3] train-rmse:0.829072+0.015515 test-rmse:0.840007+0.107077
## [4] train-rmse:0.680214+0.011533 test-rmse:0.698388+0.104663
## [5] train-rmse:0.571833+0.009135 test-rmse:0.600736+0.097602
## [6] train-rmse:0.490651+0.007002 test-rmse:0.532613+0.092741
## [7] train-rmse:0.432211+0.005813 test-rmse:0.489379+0.085514
## [8] train-rmse:0.389954+0.005016 test-rmse:0.456397+0.083784
## [9] train-rmse:0.357759+0.007031 test-rmse:0.439841+0.081030
## [10] train-rmse:0.334002+0.005946 test-rmse:0.430508+0.076920
## [11] train-rmse:0.316162+0.004882 test-rmse:0.425064+0.071127
## [12] train-rmse:0.302167+0.004328 test-rmse:0.417369+0.067029
## [13] train-rmse:0.291226+0.006377 test-rmse:0.416864+0.064465
## [14] train-rmse:0.282122+0.006580 test-rmse:0.413494+0.062604
## [15] train-rmse:0.272883+0.004957 test-rmse:0.412794+0.062270
## [16] train-rmse:0.265069+0.003236 test-rmse:0.411645+0.061683
## [17] train-rmse:0.258734+0.003633 test-rmse:0.409688+0.058677
## [18] train-rmse:0.252455+0.003771 test-rmse:0.408937+0.058462
## [19] train-rmse:0.246682+0.003920 test-rmse:0.406564+0.060378
## [20] train-rmse:0.239333+0.004737 test-rmse:0.406230+0.059160
## [21] train-rmse:0.234336+0.005322 test-rmse:0.405102+0.057920
## [22] train-rmse:0.230540+0.004714 test-rmse:0.402991+0.059809
## [23] train-rmse:0.226019+0.003510 test-rmse:0.403682+0.058776
## [24] train-rmse:0.221843+0.003512 test-rmse:0.400986+0.058157
## [25] train-rmse:0.217165+0.003727 test-rmse:0.400812+0.057843
## [26] train-rmse:0.212561+0.004301 test-rmse:0.400732+0.057517
## [27] train-rmse:0.208104+0.005678 test-rmse:0.401309+0.056532
## [28] train-rmse:0.204442+0.005216 test-rmse:0.401428+0.056579
## [29] train-rmse:0.201229+0.005595 test-rmse:0.400911+0.056146
## [30] train-rmse:0.197657+0.004602 test-rmse:0.399319+0.057039
## [31] train-rmse:0.194187+0.005362 test-rmse:0.397288+0.057688
## [32] train-rmse:0.189774+0.006290 test-rmse:0.396699+0.058087
## [33] train-rmse:0.187190+0.006208 test-rmse:0.396060+0.057748
## [34] train-rmse:0.183768+0.006501 test-rmse:0.395740+0.058690
## [35] train-rmse:0.181201+0.006676 test-rmse:0.395756+0.058425
## [36] train-rmse:0.178538+0.006658 test-rmse:0.395318+0.058074
## [37] train-rmse:0.174913+0.005990 test-rmse:0.394785+0.058223
## [38] train-rmse:0.172002+0.006104 test-rmse:0.395346+0.057878
## [39] train-rmse:0.169062+0.006986 test-rmse:0.395203+0.057681
## [40] train-rmse:0.165457+0.007303 test-rmse:0.396177+0.058032
## [41] train-rmse:0.163129+0.006922 test-rmse:0.395907+0.057460
## [42] train-rmse:0.160547+0.007186 test-rmse:0.395044+0.058009
## [43] train-rmse:0.158679+0.006933 test-rmse:0.394762+0.058453
## [44] train-rmse:0.156657+0.006678 test-rmse:0.393089+0.058823
## [45] train-rmse:0.154385+0.007133 test-rmse:0.392090+0.059878
## [46] train-rmse:0.152529+0.006916 test-rmse:0.392695+0.059461
## [47] train-rmse:0.150315+0.006350 test-rmse:0.392669+0.059191
## [48] train-rmse:0.148479+0.006776 test-rmse:0.392170+0.059322
## [49] train-rmse:0.146814+0.006682 test-rmse:0.392443+0.059846
## [50] train-rmse:0.144913+0.007260 test-rmse:0.391219+0.059714
## [51] train-rmse:0.141961+0.007238 test-rmse:0.389677+0.059455
## [52] train-rmse:0.140295+0.007378 test-rmse:0.388855+0.059872
## [53] train-rmse:0.138280+0.007695 test-rmse:0.387786+0.059502
## [54] train-rmse:0.135799+0.007994 test-rmse:0.387792+0.059919
## [55] train-rmse:0.134504+0.007930 test-rmse:0.387630+0.060336
## [56] train-rmse:0.132482+0.008291 test-rmse:0.387051+0.060204
## [57] train-rmse:0.130450+0.008764 test-rmse:0.387192+0.060497
## [58] train-rmse:0.128503+0.008774 test-rmse:0.387315+0.060387
## [59] train-rmse:0.127334+0.009076 test-rmse:0.387206+0.060765
## [60] train-rmse:0.125949+0.009055 test-rmse:0.387357+0.061156
## [61] train-rmse:0.124837+0.009036 test-rmse:0.387256+0.061172
## [62] train-rmse:0.123403+0.009321 test-rmse:0.387157+0.061217
## Stopping. Best iteration:
## [56] train-rmse:0.132482+0.008291 test-rmse:0.387051+0.060204
##
## 53
## [1] train-rmse:1.286622+0.023611 test-rmse:1.289462+0.114354
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.022372+0.018406 test-rmse:1.027396+0.109698
## [3] train-rmse:0.821209+0.016247 test-rmse:0.831386+0.100246
## [4] train-rmse:0.667878+0.011701 test-rmse:0.692837+0.099029
## [5] train-rmse:0.553790+0.011000 test-rmse:0.590354+0.089515
## [6] train-rmse:0.468294+0.008186 test-rmse:0.523898+0.085140
## [7] train-rmse:0.405049+0.007499 test-rmse:0.478093+0.081768
## [8] train-rmse:0.357603+0.008361 test-rmse:0.448051+0.079458
## [9] train-rmse:0.321451+0.009203 test-rmse:0.429292+0.075184
## [10] train-rmse:0.294553+0.010613 test-rmse:0.416381+0.075123
## [11] train-rmse:0.275534+0.011555 test-rmse:0.412037+0.074346
## [12] train-rmse:0.256752+0.008722 test-rmse:0.406128+0.071576
## [13] train-rmse:0.245377+0.010088 test-rmse:0.402436+0.069106
## [14] train-rmse:0.233318+0.011631 test-rmse:0.401256+0.068430
## [15] train-rmse:0.224007+0.009952 test-rmse:0.401239+0.067762
## [16] train-rmse:0.215555+0.007791 test-rmse:0.399739+0.067150
## [17] train-rmse:0.208544+0.007686 test-rmse:0.397623+0.063978
## [18] train-rmse:0.202333+0.007647 test-rmse:0.397325+0.063757
## [19] train-rmse:0.195494+0.006774 test-rmse:0.398770+0.062273
## [20] train-rmse:0.188961+0.007521 test-rmse:0.398452+0.061891
## [21] train-rmse:0.182554+0.008154 test-rmse:0.398650+0.060797
## [22] train-rmse:0.176848+0.009709 test-rmse:0.398118+0.061422
## [23] train-rmse:0.171347+0.010709 test-rmse:0.397037+0.061242
## [24] train-rmse:0.166779+0.009823 test-rmse:0.395898+0.059700
## [25] train-rmse:0.162566+0.010059 test-rmse:0.395093+0.061158
## [26] train-rmse:0.158780+0.010061 test-rmse:0.395275+0.060978
## [27] train-rmse:0.155701+0.010110 test-rmse:0.395065+0.059611
## [28] train-rmse:0.152176+0.008705 test-rmse:0.394814+0.060116
## [29] train-rmse:0.148884+0.008122 test-rmse:0.395204+0.059683
## [30] train-rmse:0.144777+0.007193 test-rmse:0.395025+0.059532
## [31] train-rmse:0.141331+0.007688 test-rmse:0.394330+0.059859
## [32] train-rmse:0.137798+0.006222 test-rmse:0.395058+0.059349
## [33] train-rmse:0.134448+0.005984 test-rmse:0.395245+0.060163
## [34] train-rmse:0.130742+0.005150 test-rmse:0.393738+0.060463
## [35] train-rmse:0.128619+0.005466 test-rmse:0.393289+0.060762
## [36] train-rmse:0.126113+0.005571 test-rmse:0.393569+0.061039
## [37] train-rmse:0.124248+0.005146 test-rmse:0.392954+0.061086
## [38] train-rmse:0.121000+0.005707 test-rmse:0.392565+0.060581
## [39] train-rmse:0.117884+0.005593 test-rmse:0.392496+0.060734
## [40] train-rmse:0.115971+0.004980 test-rmse:0.392027+0.061191
## [41] train-rmse:0.113127+0.004694 test-rmse:0.392681+0.060548
## [42] train-rmse:0.110564+0.005244 test-rmse:0.392200+0.060993
## [43] train-rmse:0.107780+0.004786 test-rmse:0.391609+0.061900
## [44] train-rmse:0.105516+0.005428 test-rmse:0.391508+0.062540
## [45] train-rmse:0.103310+0.004736 test-rmse:0.391651+0.061400
## [46] train-rmse:0.100881+0.004780 test-rmse:0.391892+0.061284
## [47] train-rmse:0.098936+0.004700 test-rmse:0.392233+0.061581
## [48] train-rmse:0.097348+0.004752 test-rmse:0.391318+0.060612
## [49] train-rmse:0.095684+0.005112 test-rmse:0.391107+0.060441
## [50] train-rmse:0.093426+0.005451 test-rmse:0.391604+0.059489
## [51] train-rmse:0.091022+0.005186 test-rmse:0.391286+0.059213
## [52] train-rmse:0.089242+0.004812 test-rmse:0.391048+0.059363
## [53] train-rmse:0.087008+0.004972 test-rmse:0.391412+0.059090
## [54] train-rmse:0.085116+0.005561 test-rmse:0.390780+0.058713
## [55] train-rmse:0.083845+0.005513 test-rmse:0.390946+0.058754
## [56] train-rmse:0.081903+0.005984 test-rmse:0.391151+0.058432
## [57] train-rmse:0.080046+0.005748 test-rmse:0.391078+0.058509
## [58] train-rmse:0.078901+0.005193 test-rmse:0.390979+0.058550
## [59] train-rmse:0.076786+0.004626 test-rmse:0.391290+0.058333
## [60] train-rmse:0.075364+0.004221 test-rmse:0.391545+0.058023
## Stopping. Best iteration:
## [54] train-rmse:0.085116+0.005561 test-rmse:0.390780+0.058713
##
## 54
## [1] train-rmse:1.285934+0.023663 test-rmse:1.287928+0.112999
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.020171+0.018479 test-rmse:1.024554+0.107012
## [3] train-rmse:0.816953+0.016566 test-rmse:0.830495+0.095986
## [4] train-rmse:0.662689+0.013708 test-rmse:0.689222+0.093283
## [5] train-rmse:0.544284+0.012361 test-rmse:0.585814+0.095405
## [6] train-rmse:0.456878+0.009943 test-rmse:0.519246+0.092295
## [7] train-rmse:0.389520+0.008469 test-rmse:0.474051+0.089846
## [8] train-rmse:0.338902+0.008849 test-rmse:0.445280+0.084624
## [9] train-rmse:0.300443+0.008412 test-rmse:0.430296+0.083998
## [10] train-rmse:0.270478+0.009490 test-rmse:0.419840+0.079496
## [11] train-rmse:0.245401+0.008332 test-rmse:0.411750+0.077772
## [12] train-rmse:0.223623+0.010734 test-rmse:0.405503+0.074110
## [13] train-rmse:0.210208+0.011776 test-rmse:0.405270+0.073237
## [14] train-rmse:0.195407+0.011995 test-rmse:0.402621+0.072893
## [15] train-rmse:0.184381+0.014012 test-rmse:0.401943+0.071037
## [16] train-rmse:0.175337+0.015375 test-rmse:0.400425+0.069262
## [17] train-rmse:0.167672+0.013926 test-rmse:0.400239+0.069134
## [18] train-rmse:0.161334+0.014592 test-rmse:0.400205+0.069681
## [19] train-rmse:0.154817+0.013794 test-rmse:0.399134+0.068649
## [20] train-rmse:0.147938+0.012999 test-rmse:0.399970+0.065770
## [21] train-rmse:0.141362+0.013178 test-rmse:0.399143+0.065013
## [22] train-rmse:0.135407+0.010549 test-rmse:0.398939+0.065837
## [23] train-rmse:0.131143+0.010631 test-rmse:0.398602+0.064927
## [24] train-rmse:0.126934+0.011060 test-rmse:0.398585+0.064753
## [25] train-rmse:0.121157+0.009609 test-rmse:0.397222+0.063363
## [26] train-rmse:0.117170+0.008965 test-rmse:0.396998+0.063788
## [27] train-rmse:0.113019+0.008148 test-rmse:0.397199+0.063696
## [28] train-rmse:0.108602+0.008588 test-rmse:0.396931+0.062982
## [29] train-rmse:0.104653+0.008398 test-rmse:0.397395+0.062287
## [30] train-rmse:0.101699+0.007764 test-rmse:0.397380+0.062570
## [31] train-rmse:0.099226+0.007614 test-rmse:0.397628+0.062762
## [32] train-rmse:0.096642+0.007536 test-rmse:0.397665+0.063060
## [33] train-rmse:0.093732+0.006657 test-rmse:0.397737+0.063487
## [34] train-rmse:0.090705+0.005805 test-rmse:0.398250+0.063515
## Stopping. Best iteration:
## [28] train-rmse:0.108602+0.008588 test-rmse:0.396931+0.062982
##
## 55
## [1] train-rmse:1.285599+0.023664 test-rmse:1.289060+0.112473
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.018851+0.018807 test-rmse:1.023601+0.107182
## [3] train-rmse:0.814028+0.016864 test-rmse:0.825915+0.098888
## [4] train-rmse:0.658081+0.013729 test-rmse:0.683573+0.096978
## [5] train-rmse:0.537629+0.012760 test-rmse:0.584650+0.091762
## [6] train-rmse:0.446620+0.011313 test-rmse:0.520214+0.088276
## [7] train-rmse:0.375400+0.011693 test-rmse:0.479516+0.081608
## [8] train-rmse:0.321129+0.010734 test-rmse:0.454688+0.080536
## [9] train-rmse:0.278579+0.010584 test-rmse:0.440147+0.076049
## [10] train-rmse:0.245728+0.010141 test-rmse:0.430012+0.073936
## [11] train-rmse:0.221008+0.010099 test-rmse:0.422073+0.069819
## [12] train-rmse:0.199580+0.012543 test-rmse:0.420631+0.066306
## [13] train-rmse:0.182060+0.012337 test-rmse:0.417099+0.062107
## [14] train-rmse:0.166081+0.009604 test-rmse:0.416156+0.059904
## [15] train-rmse:0.155099+0.010178 test-rmse:0.414706+0.056783
## [16] train-rmse:0.144734+0.008431 test-rmse:0.414312+0.056628
## [17] train-rmse:0.137498+0.008294 test-rmse:0.413743+0.054276
## [18] train-rmse:0.129866+0.007483 test-rmse:0.413597+0.052631
## [19] train-rmse:0.122461+0.006514 test-rmse:0.410924+0.051782
## [20] train-rmse:0.116699+0.006023 test-rmse:0.409669+0.050058
## [21] train-rmse:0.110179+0.007259 test-rmse:0.408593+0.048381
## [22] train-rmse:0.105616+0.007167 test-rmse:0.406599+0.047771
## [23] train-rmse:0.101179+0.007006 test-rmse:0.407382+0.045896
## [24] train-rmse:0.096130+0.007620 test-rmse:0.407524+0.044836
## [25] train-rmse:0.091135+0.008742 test-rmse:0.407657+0.044962
## [26] train-rmse:0.086359+0.009965 test-rmse:0.407173+0.044424
## [27] train-rmse:0.083028+0.009735 test-rmse:0.407386+0.043717
## [28] train-rmse:0.079900+0.010012 test-rmse:0.407142+0.043866
## Stopping. Best iteration:
## [22] train-rmse:0.105616+0.007167 test-rmse:0.406599+0.047771
##
## 56
## [1] train-rmse:1.290638+0.024188 test-rmse:1.286811+0.109377
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.051722+0.020515 test-rmse:1.048027+0.103147
## [3] train-rmse:0.893110+0.018699 test-rmse:0.889652+0.097875
## [4] train-rmse:0.792216+0.018053 test-rmse:0.789085+0.093493
## [5] train-rmse:0.730668+0.017960 test-rmse:0.727882+0.090093
## [6] train-rmse:0.694411+0.018045 test-rmse:0.691932+0.087626
## [7] train-rmse:0.668028+0.016841 test-rmse:0.666056+0.087481
## [8] train-rmse:0.649221+0.017005 test-rmse:0.647512+0.085819
## [9] train-rmse:0.633231+0.015227 test-rmse:0.634353+0.082201
## [10] train-rmse:0.620268+0.014245 test-rmse:0.624549+0.079351
## [11] train-rmse:0.609194+0.014047 test-rmse:0.611745+0.077383
## [12] train-rmse:0.599822+0.012687 test-rmse:0.602427+0.073105
## [13] train-rmse:0.591966+0.012570 test-rmse:0.595296+0.072149
## [14] train-rmse:0.584549+0.012447 test-rmse:0.593535+0.070682
## [15] train-rmse:0.578206+0.012238 test-rmse:0.586414+0.069455
## [16] train-rmse:0.572202+0.011745 test-rmse:0.581319+0.067593
## [17] train-rmse:0.566892+0.011529 test-rmse:0.576687+0.064725
## [18] train-rmse:0.562275+0.011495 test-rmse:0.572271+0.063374
## [19] train-rmse:0.558019+0.011093 test-rmse:0.570345+0.065245
## [20] train-rmse:0.553965+0.010673 test-rmse:0.566477+0.062503
## [21] train-rmse:0.550303+0.010744 test-rmse:0.564877+0.063617
## [22] train-rmse:0.547035+0.010762 test-rmse:0.561711+0.061960
## [23] train-rmse:0.543936+0.010722 test-rmse:0.559454+0.060599
## [24] train-rmse:0.541039+0.010535 test-rmse:0.559976+0.060137
## [25] train-rmse:0.538212+0.010439 test-rmse:0.558962+0.059235
## [26] train-rmse:0.535555+0.010139 test-rmse:0.556742+0.059170
## [27] train-rmse:0.533090+0.010087 test-rmse:0.554612+0.059842
## [28] train-rmse:0.530655+0.009899 test-rmse:0.551870+0.060461
## [29] train-rmse:0.528248+0.009817 test-rmse:0.549412+0.058013
## [30] train-rmse:0.526048+0.009704 test-rmse:0.548795+0.058199
## [31] train-rmse:0.523974+0.009630 test-rmse:0.547898+0.058356
## [32] train-rmse:0.522001+0.009543 test-rmse:0.547490+0.057092
## [33] train-rmse:0.520109+0.009390 test-rmse:0.545859+0.056550
## [34] train-rmse:0.518203+0.009199 test-rmse:0.544635+0.055961
## [35] train-rmse:0.516355+0.009118 test-rmse:0.542031+0.054921
## [36] train-rmse:0.514578+0.009021 test-rmse:0.540855+0.054519
## [37] train-rmse:0.512851+0.009017 test-rmse:0.539558+0.055004
## [38] train-rmse:0.511218+0.008948 test-rmse:0.539231+0.055620
## [39] train-rmse:0.509623+0.008887 test-rmse:0.538538+0.056178
## [40] train-rmse:0.508025+0.008804 test-rmse:0.536542+0.056125
## [41] train-rmse:0.506464+0.008746 test-rmse:0.536357+0.054038
## [42] train-rmse:0.504948+0.008620 test-rmse:0.534434+0.053682
## [43] train-rmse:0.503475+0.008600 test-rmse:0.533253+0.053525
## [44] train-rmse:0.502055+0.008572 test-rmse:0.532915+0.054198
## [45] train-rmse:0.500654+0.008541 test-rmse:0.533571+0.054223
## [46] train-rmse:0.499305+0.008477 test-rmse:0.530818+0.053624
## [47] train-rmse:0.497940+0.008480 test-rmse:0.529579+0.053343
## [48] train-rmse:0.496548+0.008427 test-rmse:0.529097+0.051911
## [49] train-rmse:0.495289+0.008411 test-rmse:0.527460+0.052849
## [50] train-rmse:0.494058+0.008358 test-rmse:0.527931+0.053625
## [51] train-rmse:0.492847+0.008324 test-rmse:0.527183+0.053191
## [52] train-rmse:0.491665+0.008325 test-rmse:0.525687+0.052913
## [53] train-rmse:0.490527+0.008280 test-rmse:0.524510+0.052240
## [54] train-rmse:0.489418+0.008245 test-rmse:0.523550+0.052909
## [55] train-rmse:0.488331+0.008229 test-rmse:0.523031+0.052288
## [56] train-rmse:0.487271+0.008210 test-rmse:0.522622+0.051331
## [57] train-rmse:0.486192+0.008149 test-rmse:0.522943+0.050813
## [58] train-rmse:0.485130+0.008114 test-rmse:0.522729+0.051073
## [59] train-rmse:0.484088+0.008073 test-rmse:0.521942+0.050415
## [60] train-rmse:0.483076+0.008014 test-rmse:0.520875+0.049584
## [61] train-rmse:0.482142+0.008017 test-rmse:0.518360+0.050189
## [62] train-rmse:0.481189+0.007984 test-rmse:0.518393+0.050514
## [63] train-rmse:0.480269+0.007946 test-rmse:0.517942+0.049879
## [64] train-rmse:0.479391+0.007931 test-rmse:0.517394+0.048395
## [65] train-rmse:0.478474+0.007937 test-rmse:0.518010+0.048942
## [66] train-rmse:0.477601+0.007946 test-rmse:0.516587+0.049635
## [67] train-rmse:0.476731+0.007947 test-rmse:0.517216+0.049765
## [68] train-rmse:0.475885+0.007916 test-rmse:0.516614+0.049580
## [69] train-rmse:0.475085+0.007897 test-rmse:0.516710+0.050556
## [70] train-rmse:0.474282+0.007877 test-rmse:0.516302+0.050012
## [71] train-rmse:0.473467+0.007882 test-rmse:0.516189+0.049717
## [72] train-rmse:0.472699+0.007849 test-rmse:0.515143+0.049811
## [73] train-rmse:0.471941+0.007834 test-rmse:0.514681+0.049739
## [74] train-rmse:0.471184+0.007821 test-rmse:0.512478+0.046563
## [75] train-rmse:0.470459+0.007811 test-rmse:0.512683+0.046345
## [76] train-rmse:0.469741+0.007801 test-rmse:0.513023+0.046019
## [77] train-rmse:0.469040+0.007788 test-rmse:0.512287+0.046026
## [78] train-rmse:0.468318+0.007762 test-rmse:0.511187+0.045112
## [79] train-rmse:0.467620+0.007751 test-rmse:0.510701+0.045814
## [80] train-rmse:0.466922+0.007756 test-rmse:0.509240+0.045717
## [81] train-rmse:0.466251+0.007760 test-rmse:0.508918+0.046449
## [82] train-rmse:0.465599+0.007751 test-rmse:0.507728+0.046955
## [83] train-rmse:0.464934+0.007719 test-rmse:0.507760+0.047124
## [84] train-rmse:0.464325+0.007696 test-rmse:0.508060+0.046548
## [85] train-rmse:0.463688+0.007652 test-rmse:0.507778+0.045866
## [86] train-rmse:0.463040+0.007634 test-rmse:0.507754+0.046348
## [87] train-rmse:0.462421+0.007617 test-rmse:0.507204+0.046077
## [88] train-rmse:0.461812+0.007593 test-rmse:0.507385+0.045852
## [89] train-rmse:0.461219+0.007584 test-rmse:0.506947+0.046685
## [90] train-rmse:0.460646+0.007564 test-rmse:0.506707+0.046920
## [91] train-rmse:0.460081+0.007546 test-rmse:0.507051+0.047111
## [92] train-rmse:0.459534+0.007536 test-rmse:0.506508+0.046716
## [93] train-rmse:0.458963+0.007519 test-rmse:0.507045+0.046479
## [94] train-rmse:0.458405+0.007508 test-rmse:0.506382+0.046066
## [95] train-rmse:0.457872+0.007503 test-rmse:0.506241+0.046527
## [96] train-rmse:0.457356+0.007499 test-rmse:0.505779+0.046215
## [97] train-rmse:0.456815+0.007495 test-rmse:0.506017+0.045745
## [98] train-rmse:0.456311+0.007491 test-rmse:0.506179+0.045717
## [99] train-rmse:0.455802+0.007486 test-rmse:0.505614+0.044722
## [100] train-rmse:0.455298+0.007479 test-rmse:0.504732+0.044941
## [101] train-rmse:0.454792+0.007468 test-rmse:0.505383+0.044762
## [102] train-rmse:0.454297+0.007465 test-rmse:0.504598+0.044845
## [103] train-rmse:0.453818+0.007464 test-rmse:0.504474+0.043838
## [104] train-rmse:0.453328+0.007464 test-rmse:0.504170+0.043390
## [105] train-rmse:0.452847+0.007459 test-rmse:0.504717+0.044297
## [106] train-rmse:0.452376+0.007469 test-rmse:0.504317+0.044313
## [107] train-rmse:0.451916+0.007481 test-rmse:0.503205+0.042994
## [108] train-rmse:0.451429+0.007467 test-rmse:0.502727+0.042999
## [109] train-rmse:0.450988+0.007463 test-rmse:0.502556+0.042772
## [110] train-rmse:0.450555+0.007466 test-rmse:0.502172+0.042880
## [111] train-rmse:0.450105+0.007465 test-rmse:0.502058+0.043262
## [112] train-rmse:0.449690+0.007469 test-rmse:0.502093+0.042596
## [113] train-rmse:0.449268+0.007495 test-rmse:0.501724+0.042524
## [114] train-rmse:0.448846+0.007505 test-rmse:0.501039+0.042596
## [115] train-rmse:0.448449+0.007510 test-rmse:0.500928+0.042796
## [116] train-rmse:0.448035+0.007519 test-rmse:0.500484+0.042861
## [117] train-rmse:0.447607+0.007504 test-rmse:0.500477+0.043021
## [118] train-rmse:0.447216+0.007501 test-rmse:0.500505+0.042684
## [119] train-rmse:0.446837+0.007505 test-rmse:0.500699+0.042698
## [120] train-rmse:0.446453+0.007502 test-rmse:0.500542+0.042824
## [121] train-rmse:0.446071+0.007498 test-rmse:0.500447+0.042880
## [122] train-rmse:0.445689+0.007515 test-rmse:0.500228+0.043174
## [123] train-rmse:0.445315+0.007508 test-rmse:0.500144+0.043093
## [124] train-rmse:0.444948+0.007510 test-rmse:0.500039+0.043033
## [125] train-rmse:0.444585+0.007509 test-rmse:0.499808+0.042829
## [126] train-rmse:0.444233+0.007510 test-rmse:0.499817+0.043035
## [127] train-rmse:0.443881+0.007502 test-rmse:0.499472+0.042277
## [128] train-rmse:0.443502+0.007523 test-rmse:0.499970+0.043000
## [129] train-rmse:0.443138+0.007496 test-rmse:0.499674+0.042503
## [130] train-rmse:0.442792+0.007486 test-rmse:0.499324+0.041579
## [131] train-rmse:0.442449+0.007493 test-rmse:0.499507+0.041693
## [132] train-rmse:0.442109+0.007494 test-rmse:0.499500+0.041133
## [133] train-rmse:0.441762+0.007502 test-rmse:0.498831+0.041153
## [134] train-rmse:0.441420+0.007517 test-rmse:0.498239+0.041727
## [135] train-rmse:0.441084+0.007519 test-rmse:0.498553+0.041500
## [136] train-rmse:0.440756+0.007524 test-rmse:0.498603+0.041858
## [137] train-rmse:0.440435+0.007527 test-rmse:0.498608+0.041891
## [138] train-rmse:0.440118+0.007534 test-rmse:0.498504+0.041893
## [139] train-rmse:0.439790+0.007537 test-rmse:0.498333+0.041772
## [140] train-rmse:0.439479+0.007534 test-rmse:0.498379+0.041947
## Stopping. Best iteration:
## [134] train-rmse:0.441420+0.007517 test-rmse:0.498239+0.041727
##
## 57
## [1] train-rmse:1.273501+0.024293 test-rmse:1.273425+0.114412
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.012006+0.019249 test-rmse:1.009773+0.103431
## [3] train-rmse:0.828289+0.017327 test-rmse:0.829905+0.104039
## [4] train-rmse:0.702251+0.014896 test-rmse:0.709157+0.102856
## [5] train-rmse:0.616418+0.013190 test-rmse:0.627819+0.098881
## [6] train-rmse:0.558415+0.012664 test-rmse:0.569612+0.092360
## [7] train-rmse:0.519820+0.012130 test-rmse:0.535611+0.087870
## [8] train-rmse:0.492168+0.011859 test-rmse:0.514735+0.077455
## [9] train-rmse:0.472499+0.010893 test-rmse:0.496989+0.074742
## [10] train-rmse:0.454999+0.009539 test-rmse:0.479012+0.070401
## [11] train-rmse:0.441197+0.008294 test-rmse:0.469701+0.066854
## [12] train-rmse:0.431361+0.008211 test-rmse:0.464123+0.060711
## [13] train-rmse:0.421776+0.008230 test-rmse:0.460403+0.058889
## [14] train-rmse:0.413516+0.007895 test-rmse:0.456755+0.057838
## [15] train-rmse:0.406712+0.007402 test-rmse:0.455752+0.058488
## [16] train-rmse:0.400640+0.006862 test-rmse:0.454594+0.057619
## [17] train-rmse:0.393989+0.005973 test-rmse:0.451188+0.056217
## [18] train-rmse:0.388426+0.005362 test-rmse:0.448520+0.055779
## [19] train-rmse:0.383384+0.005230 test-rmse:0.446305+0.055253
## [20] train-rmse:0.378574+0.005175 test-rmse:0.446569+0.052762
## [21] train-rmse:0.374478+0.005385 test-rmse:0.443845+0.051871
## [22] train-rmse:0.370326+0.005768 test-rmse:0.444964+0.052659
## [23] train-rmse:0.366358+0.005366 test-rmse:0.443842+0.053161
## [24] train-rmse:0.362307+0.005695 test-rmse:0.441460+0.052281
## [25] train-rmse:0.358823+0.005821 test-rmse:0.441916+0.051820
## [26] train-rmse:0.355808+0.005716 test-rmse:0.442262+0.051458
## [27] train-rmse:0.351440+0.006404 test-rmse:0.440346+0.051259
## [28] train-rmse:0.346731+0.006052 test-rmse:0.439187+0.052060
## [29] train-rmse:0.343437+0.005913 test-rmse:0.438779+0.053481
## [30] train-rmse:0.340088+0.005552 test-rmse:0.436566+0.052323
## [31] train-rmse:0.337715+0.005592 test-rmse:0.438373+0.052826
## [32] train-rmse:0.335456+0.005087 test-rmse:0.438704+0.053328
## [33] train-rmse:0.332302+0.005891 test-rmse:0.439381+0.053506
## [34] train-rmse:0.329282+0.005939 test-rmse:0.437810+0.053239
## [35] train-rmse:0.326251+0.006398 test-rmse:0.437957+0.053752
## [36] train-rmse:0.324196+0.006556 test-rmse:0.437554+0.052928
## Stopping. Best iteration:
## [30] train-rmse:0.340088+0.005552 test-rmse:0.436566+0.052323
##
## 58
## [1] train-rmse:1.264377+0.023346 test-rmse:1.268258+0.117439
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.993186+0.018597 test-rmse:1.001626+0.112258
## [3] train-rmse:0.797211+0.014502 test-rmse:0.811850+0.111347
## [4] train-rmse:0.655881+0.013266 test-rmse:0.678732+0.099810
## [5] train-rmse:0.555936+0.010861 test-rmse:0.586359+0.093908
## [6] train-rmse:0.488614+0.009369 test-rmse:0.529851+0.087761
## [7] train-rmse:0.440447+0.010091 test-rmse:0.487711+0.083192
## [8] train-rmse:0.407935+0.009511 test-rmse:0.468720+0.077088
## [9] train-rmse:0.384566+0.008780 test-rmse:0.455917+0.080624
## [10] train-rmse:0.366433+0.007434 test-rmse:0.448856+0.077330
## [11] train-rmse:0.352796+0.007753 test-rmse:0.439959+0.071585
## [12] train-rmse:0.342433+0.006976 test-rmse:0.439582+0.070700
## [13] train-rmse:0.332323+0.007268 test-rmse:0.435446+0.068281
## [14] train-rmse:0.324301+0.007751 test-rmse:0.435002+0.066583
## [15] train-rmse:0.316884+0.006897 test-rmse:0.432057+0.068074
## [16] train-rmse:0.310338+0.005774 test-rmse:0.430887+0.068561
## [17] train-rmse:0.304826+0.005642 test-rmse:0.430429+0.068400
## [18] train-rmse:0.299847+0.005431 test-rmse:0.428855+0.065145
## [19] train-rmse:0.294847+0.005188 test-rmse:0.428950+0.064821
## [20] train-rmse:0.290207+0.004509 test-rmse:0.427925+0.065547
## [21] train-rmse:0.285284+0.004249 test-rmse:0.426919+0.065038
## [22] train-rmse:0.280327+0.003154 test-rmse:0.427404+0.064512
## [23] train-rmse:0.276089+0.002845 test-rmse:0.427222+0.065337
## [24] train-rmse:0.270569+0.003064 test-rmse:0.426535+0.065603
## [25] train-rmse:0.266893+0.002804 test-rmse:0.423499+0.067365
## [26] train-rmse:0.262964+0.003120 test-rmse:0.423648+0.069821
## [27] train-rmse:0.259834+0.003414 test-rmse:0.421938+0.068703
## [28] train-rmse:0.256501+0.002766 test-rmse:0.421022+0.068538
## [29] train-rmse:0.253664+0.002759 test-rmse:0.420006+0.068606
## [30] train-rmse:0.250547+0.002797 test-rmse:0.419156+0.067798
## [31] train-rmse:0.247721+0.002791 test-rmse:0.417954+0.069878
## [32] train-rmse:0.244267+0.003308 test-rmse:0.417465+0.069256
## [33] train-rmse:0.240648+0.003873 test-rmse:0.418558+0.069491
## [34] train-rmse:0.238311+0.004005 test-rmse:0.418558+0.069990
## [35] train-rmse:0.235505+0.003374 test-rmse:0.416899+0.069796
## [36] train-rmse:0.233204+0.003909 test-rmse:0.415360+0.070024
## [37] train-rmse:0.229471+0.004529 test-rmse:0.413363+0.069642
## [38] train-rmse:0.227349+0.004791 test-rmse:0.413359+0.069934
## [39] train-rmse:0.224181+0.004820 test-rmse:0.413363+0.069821
## [40] train-rmse:0.221068+0.005648 test-rmse:0.412218+0.070462
## [41] train-rmse:0.218792+0.005594 test-rmse:0.411337+0.071263
## [42] train-rmse:0.216278+0.005442 test-rmse:0.412244+0.070865
## [43] train-rmse:0.213955+0.005514 test-rmse:0.412143+0.070811
## [44] train-rmse:0.211270+0.005555 test-rmse:0.410765+0.070892
## [45] train-rmse:0.209372+0.005846 test-rmse:0.409573+0.071037
## [46] train-rmse:0.206992+0.006141 test-rmse:0.408032+0.072077
## [47] train-rmse:0.205420+0.006463 test-rmse:0.407773+0.071786
## [48] train-rmse:0.203083+0.005825 test-rmse:0.407928+0.071296
## [49] train-rmse:0.201172+0.005955 test-rmse:0.407916+0.071823
## [50] train-rmse:0.198987+0.005675 test-rmse:0.407137+0.072131
## [51] train-rmse:0.196453+0.004593 test-rmse:0.407861+0.071882
## [52] train-rmse:0.194301+0.005133 test-rmse:0.408434+0.071396
## [53] train-rmse:0.192673+0.005265 test-rmse:0.407749+0.071214
## [54] train-rmse:0.190533+0.004905 test-rmse:0.407107+0.072003
## [55] train-rmse:0.189252+0.005312 test-rmse:0.407573+0.072034
## [56] train-rmse:0.187121+0.004888 test-rmse:0.406554+0.071612
## [57] train-rmse:0.185049+0.004734 test-rmse:0.406126+0.072124
## [58] train-rmse:0.183481+0.004410 test-rmse:0.406378+0.071758
## [59] train-rmse:0.182132+0.004642 test-rmse:0.406428+0.072475
## [60] train-rmse:0.180481+0.005105 test-rmse:0.406671+0.072600
## [61] train-rmse:0.178057+0.005062 test-rmse:0.407033+0.071662
## [62] train-rmse:0.175764+0.005781 test-rmse:0.406708+0.072335
## [63] train-rmse:0.174364+0.006037 test-rmse:0.406103+0.073386
## [64] train-rmse:0.173178+0.005923 test-rmse:0.406174+0.073199
## [65] train-rmse:0.171752+0.005977 test-rmse:0.406345+0.073013
## [66] train-rmse:0.170494+0.005720 test-rmse:0.406335+0.073033
## [67] train-rmse:0.169301+0.005533 test-rmse:0.406273+0.073035
## [68] train-rmse:0.168400+0.005255 test-rmse:0.406679+0.072932
## [69] train-rmse:0.166590+0.005622 test-rmse:0.407124+0.073103
## Stopping. Best iteration:
## [63] train-rmse:0.174364+0.006037 test-rmse:0.406103+0.073386
##
## 59
## [1] train-rmse:1.260220+0.022684 test-rmse:1.263652+0.118122
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.984897+0.017185 test-rmse:0.994215+0.116345
## [3] train-rmse:0.781704+0.015504 test-rmse:0.792637+0.102030
## [4] train-rmse:0.634056+0.011533 test-rmse:0.655226+0.101511
## [5] train-rmse:0.529731+0.008961 test-rmse:0.563160+0.091716
## [6] train-rmse:0.454173+0.008333 test-rmse:0.507594+0.086070
## [7] train-rmse:0.400245+0.007216 test-rmse:0.468063+0.080619
## [8] train-rmse:0.362617+0.006959 test-rmse:0.447400+0.074558
## [9] train-rmse:0.336869+0.006324 test-rmse:0.439349+0.072317
## [10] train-rmse:0.317935+0.005032 test-rmse:0.429076+0.072872
## [11] train-rmse:0.302429+0.003538 test-rmse:0.423694+0.068549
## [12] train-rmse:0.291345+0.003413 test-rmse:0.420271+0.062883
## [13] train-rmse:0.282525+0.003262 test-rmse:0.419292+0.062437
## [14] train-rmse:0.274121+0.003810 test-rmse:0.417973+0.061397
## [15] train-rmse:0.265611+0.003717 test-rmse:0.416799+0.061199
## [16] train-rmse:0.258176+0.004590 test-rmse:0.416427+0.058739
## [17] train-rmse:0.252214+0.003921 test-rmse:0.414597+0.061183
## [18] train-rmse:0.244819+0.002716 test-rmse:0.415385+0.061449
## [19] train-rmse:0.240035+0.002485 test-rmse:0.413573+0.062521
## [20] train-rmse:0.233899+0.003219 test-rmse:0.415208+0.061490
## [21] train-rmse:0.229197+0.002547 test-rmse:0.415866+0.061058
## [22] train-rmse:0.225758+0.002469 test-rmse:0.415127+0.061157
## [23] train-rmse:0.220703+0.004275 test-rmse:0.414074+0.061358
## [24] train-rmse:0.214588+0.003798 test-rmse:0.414773+0.061402
## [25] train-rmse:0.210933+0.003734 test-rmse:0.415875+0.061656
## Stopping. Best iteration:
## [19] train-rmse:0.240035+0.002485 test-rmse:0.413573+0.062521
##
## 60
## [1] train-rmse:1.257788+0.023081 test-rmse:1.261317+0.114183
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.979532+0.017641 test-rmse:0.985460+0.109339
## [3] train-rmse:0.773310+0.015477 test-rmse:0.785541+0.099700
## [4] train-rmse:0.621601+0.011259 test-rmse:0.647040+0.098720
## [5] train-rmse:0.511223+0.010874 test-rmse:0.554128+0.087673
## [6] train-rmse:0.430537+0.009195 test-rmse:0.492367+0.083844
## [7] train-rmse:0.373943+0.007790 test-rmse:0.455960+0.083266
## [8] train-rmse:0.332778+0.006707 test-rmse:0.432778+0.079553
## [9] train-rmse:0.300029+0.005722 test-rmse:0.417910+0.075475
## [10] train-rmse:0.275896+0.004235 test-rmse:0.414126+0.073181
## [11] train-rmse:0.259698+0.005154 test-rmse:0.410991+0.071772
## [12] train-rmse:0.245978+0.002762 test-rmse:0.408437+0.067502
## [13] train-rmse:0.235139+0.004508 test-rmse:0.406389+0.070065
## [14] train-rmse:0.225160+0.003076 test-rmse:0.405147+0.069657
## [15] train-rmse:0.216296+0.002818 test-rmse:0.404579+0.068821
## [16] train-rmse:0.208393+0.004475 test-rmse:0.404572+0.067920
## [17] train-rmse:0.201733+0.003809 test-rmse:0.406544+0.064891
## [18] train-rmse:0.194001+0.002783 test-rmse:0.406274+0.064838
## [19] train-rmse:0.185596+0.001513 test-rmse:0.405046+0.066390
## [20] train-rmse:0.179841+0.003047 test-rmse:0.405080+0.065675
## [21] train-rmse:0.173988+0.003910 test-rmse:0.405298+0.066587
## [22] train-rmse:0.168952+0.003474 test-rmse:0.402883+0.065374
## [23] train-rmse:0.163172+0.005072 test-rmse:0.399330+0.066011
## [24] train-rmse:0.157601+0.005112 test-rmse:0.399587+0.064998
## [25] train-rmse:0.153475+0.006910 test-rmse:0.398661+0.064412
## [26] train-rmse:0.147920+0.006196 test-rmse:0.398798+0.063982
## [27] train-rmse:0.145050+0.006094 test-rmse:0.398376+0.065217
## [28] train-rmse:0.141714+0.006950 test-rmse:0.397269+0.065736
## [29] train-rmse:0.136567+0.007673 test-rmse:0.397160+0.066063
## [30] train-rmse:0.133138+0.008372 test-rmse:0.396894+0.064499
## [31] train-rmse:0.130646+0.008191 test-rmse:0.396443+0.063640
## [32] train-rmse:0.127537+0.007310 test-rmse:0.394890+0.065800
## [33] train-rmse:0.124549+0.006822 test-rmse:0.394594+0.066166
## [34] train-rmse:0.121029+0.007104 test-rmse:0.394318+0.065619
## [35] train-rmse:0.118524+0.006881 test-rmse:0.394726+0.064537
## [36] train-rmse:0.115306+0.007638 test-rmse:0.394975+0.064782
## [37] train-rmse:0.112678+0.007351 test-rmse:0.394729+0.065232
## [38] train-rmse:0.110541+0.007449 test-rmse:0.395181+0.065159
## [39] train-rmse:0.108032+0.007300 test-rmse:0.394784+0.065544
## [40] train-rmse:0.104929+0.007274 test-rmse:0.393662+0.065117
## [41] train-rmse:0.102402+0.007882 test-rmse:0.394104+0.065043
## [42] train-rmse:0.100063+0.007350 test-rmse:0.394038+0.063382
## [43] train-rmse:0.098488+0.007240 test-rmse:0.393956+0.063298
## [44] train-rmse:0.096197+0.007478 test-rmse:0.392889+0.063578
## [45] train-rmse:0.093764+0.007811 test-rmse:0.392782+0.062929
## [46] train-rmse:0.092042+0.007170 test-rmse:0.392184+0.063508
## [47] train-rmse:0.089777+0.007300 test-rmse:0.392297+0.063638
## [48] train-rmse:0.088005+0.007497 test-rmse:0.392529+0.063540
## [49] train-rmse:0.086203+0.006893 test-rmse:0.392184+0.064064
## [50] train-rmse:0.084414+0.006855 test-rmse:0.391951+0.064551
## [51] train-rmse:0.082711+0.007037 test-rmse:0.391667+0.064725
## [52] train-rmse:0.081238+0.006463 test-rmse:0.391629+0.064627
## [53] train-rmse:0.079246+0.006818 test-rmse:0.391486+0.064032
## [54] train-rmse:0.078059+0.006994 test-rmse:0.391337+0.064375
## [55] train-rmse:0.076778+0.006939 test-rmse:0.391128+0.064736
## [56] train-rmse:0.075110+0.006480 test-rmse:0.391422+0.064748
## [57] train-rmse:0.072942+0.005773 test-rmse:0.390644+0.064646
## [58] train-rmse:0.071458+0.005501 test-rmse:0.389959+0.064650
## [59] train-rmse:0.070288+0.005543 test-rmse:0.389861+0.064456
## [60] train-rmse:0.068805+0.005202 test-rmse:0.390305+0.064096
## [61] train-rmse:0.068043+0.005183 test-rmse:0.390424+0.064381
## [62] train-rmse:0.066758+0.005444 test-rmse:0.390416+0.064396
## [63] train-rmse:0.065600+0.005335 test-rmse:0.390424+0.064462
## [64] train-rmse:0.064554+0.005449 test-rmse:0.390398+0.064211
## [65] train-rmse:0.063033+0.005469 test-rmse:0.390671+0.064353
## Stopping. Best iteration:
## [59] train-rmse:0.070288+0.005543 test-rmse:0.389861+0.064456
##
## 61
## [1] train-rmse:1.257020+0.023134 test-rmse:1.259659+0.112708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.977027+0.017719 test-rmse:0.981526+0.107675
## [3] train-rmse:0.768594+0.015963 test-rmse:0.784991+0.095287
## [4] train-rmse:0.614746+0.012363 test-rmse:0.644509+0.092556
## [5] train-rmse:0.500935+0.012254 test-rmse:0.551436+0.091175
## [6] train-rmse:0.416733+0.010110 test-rmse:0.494366+0.088594
## [7] train-rmse:0.353498+0.007871 test-rmse:0.453728+0.082528
## [8] train-rmse:0.306199+0.007950 test-rmse:0.428516+0.080416
## [9] train-rmse:0.271369+0.008351 test-rmse:0.417207+0.077456
## [10] train-rmse:0.244185+0.009302 test-rmse:0.410750+0.075530
## [11] train-rmse:0.223879+0.009983 test-rmse:0.407094+0.070765
## [12] train-rmse:0.209152+0.010089 test-rmse:0.403523+0.068350
## [13] train-rmse:0.193572+0.006978 test-rmse:0.400565+0.069434
## [14] train-rmse:0.183991+0.005660 test-rmse:0.400668+0.068643
## [15] train-rmse:0.176860+0.005386 test-rmse:0.398155+0.069601
## [16] train-rmse:0.169486+0.005924 test-rmse:0.396256+0.067160
## [17] train-rmse:0.161642+0.008043 test-rmse:0.393946+0.065899
## [18] train-rmse:0.154494+0.006885 test-rmse:0.393498+0.065774
## [19] train-rmse:0.146934+0.007447 test-rmse:0.393917+0.063630
## [20] train-rmse:0.141228+0.006309 test-rmse:0.392745+0.063384
## [21] train-rmse:0.134641+0.005068 test-rmse:0.393780+0.063053
## [22] train-rmse:0.129202+0.004126 test-rmse:0.393105+0.064082
## [23] train-rmse:0.122416+0.004284 test-rmse:0.395150+0.062781
## [24] train-rmse:0.118213+0.005388 test-rmse:0.394441+0.063514
## [25] train-rmse:0.113340+0.005306 test-rmse:0.393404+0.062286
## [26] train-rmse:0.109414+0.004832 test-rmse:0.393201+0.061851
## Stopping. Best iteration:
## [20] train-rmse:0.141228+0.006309 test-rmse:0.392745+0.063384
##
## 62
## [1] train-rmse:1.256650+0.023135 test-rmse:1.260899+0.112189
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.975548+0.018089 test-rmse:0.981359+0.106755
## [3] train-rmse:0.765160+0.016049 test-rmse:0.778702+0.095825
## [4] train-rmse:0.608990+0.012370 test-rmse:0.641018+0.091673
## [5] train-rmse:0.491147+0.011755 test-rmse:0.551490+0.088761
## [6] train-rmse:0.404792+0.010359 test-rmse:0.496259+0.083248
## [7] train-rmse:0.339492+0.011209 test-rmse:0.458593+0.076939
## [8] train-rmse:0.290617+0.011379 test-rmse:0.439604+0.075439
## [9] train-rmse:0.252692+0.012038 test-rmse:0.427886+0.069240
## [10] train-rmse:0.223882+0.011406 test-rmse:0.420864+0.066394
## [11] train-rmse:0.202918+0.012809 test-rmse:0.416845+0.062784
## [12] train-rmse:0.185627+0.013583 test-rmse:0.416903+0.059481
## [13] train-rmse:0.167241+0.008873 test-rmse:0.415070+0.059313
## [14] train-rmse:0.153866+0.007663 test-rmse:0.414485+0.058764
## [15] train-rmse:0.143015+0.008584 test-rmse:0.413432+0.056973
## [16] train-rmse:0.134329+0.009556 test-rmse:0.413088+0.055495
## [17] train-rmse:0.127024+0.008782 test-rmse:0.412721+0.053539
## [18] train-rmse:0.121250+0.009190 test-rmse:0.412223+0.050701
## [19] train-rmse:0.114494+0.009817 test-rmse:0.409776+0.049580
## [20] train-rmse:0.108687+0.009608 test-rmse:0.408611+0.049868
## [21] train-rmse:0.103829+0.009172 test-rmse:0.409077+0.048148
## [22] train-rmse:0.097376+0.009155 test-rmse:0.410267+0.046996
## [23] train-rmse:0.090402+0.010892 test-rmse:0.411313+0.046500
## [24] train-rmse:0.084176+0.010017 test-rmse:0.410730+0.046231
## [25] train-rmse:0.079455+0.008376 test-rmse:0.411512+0.045923
## [26] train-rmse:0.076038+0.008020 test-rmse:0.410906+0.046228
## Stopping. Best iteration:
## [20] train-rmse:0.108687+0.009608 test-rmse:0.408611+0.049868
##
## 63
## [1] train-rmse:1.264943+0.023761 test-rmse:1.261124+0.108760
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:1.017660+0.020065 test-rmse:1.014000+0.102128
## [3] train-rmse:0.860747+0.018436 test-rmse:0.857372+0.096590
## [4] train-rmse:0.766138+0.017980 test-rmse:0.763134+0.092144
## [5] train-rmse:0.711752+0.017989 test-rmse:0.709112+0.088867
## [6] train-rmse:0.679995+0.017193 test-rmse:0.685174+0.089330
## [7] train-rmse:0.654763+0.016870 test-rmse:0.653287+0.086413
## [8] train-rmse:0.637909+0.015517 test-rmse:0.643960+0.083722
## [9] train-rmse:0.623161+0.014295 test-rmse:0.624642+0.081895
## [10] train-rmse:0.610150+0.013875 test-rmse:0.612981+0.077496
## [11] train-rmse:0.600186+0.012888 test-rmse:0.607397+0.073293
## [12] train-rmse:0.591471+0.012250 test-rmse:0.594214+0.071652
## [13] train-rmse:0.583435+0.012178 test-rmse:0.586848+0.070338
## [14] train-rmse:0.576582+0.012029 test-rmse:0.585238+0.068499
## [15] train-rmse:0.570538+0.011728 test-rmse:0.583416+0.067503
## [16] train-rmse:0.565019+0.011405 test-rmse:0.577830+0.063457
## [17] train-rmse:0.559888+0.011326 test-rmse:0.570332+0.062704
## [18] train-rmse:0.555564+0.010897 test-rmse:0.568633+0.064648
## [19] train-rmse:0.551398+0.010623 test-rmse:0.567552+0.064022
## [20] train-rmse:0.547602+0.010614 test-rmse:0.562137+0.062313
## [21] train-rmse:0.544210+0.010651 test-rmse:0.560562+0.061500
## [22] train-rmse:0.541146+0.010472 test-rmse:0.557960+0.060060
## [23] train-rmse:0.538150+0.010362 test-rmse:0.557198+0.059469
## [24] train-rmse:0.535375+0.010182 test-rmse:0.554654+0.058504
## [25] train-rmse:0.532671+0.009972 test-rmse:0.553991+0.057767
## [26] train-rmse:0.530087+0.009840 test-rmse:0.553795+0.059037
## [27] train-rmse:0.527433+0.009667 test-rmse:0.550336+0.058355
## [28] train-rmse:0.525037+0.009476 test-rmse:0.547287+0.059159
## [29] train-rmse:0.522806+0.009477 test-rmse:0.545849+0.056692
## [30] train-rmse:0.520722+0.009426 test-rmse:0.543962+0.057497
## [31] train-rmse:0.518686+0.009354 test-rmse:0.544413+0.056389
## [32] train-rmse:0.516616+0.009130 test-rmse:0.543203+0.056566
## [33] train-rmse:0.514703+0.008961 test-rmse:0.540708+0.056339
## [34] train-rmse:0.512887+0.008805 test-rmse:0.539828+0.056765
## [35] train-rmse:0.511064+0.008801 test-rmse:0.540053+0.056904
## [36] train-rmse:0.509363+0.008801 test-rmse:0.538688+0.056064
## [37] train-rmse:0.507625+0.008831 test-rmse:0.536613+0.055480
## [38] train-rmse:0.505938+0.008661 test-rmse:0.535671+0.055839
## [39] train-rmse:0.504276+0.008539 test-rmse:0.534593+0.055544
## [40] train-rmse:0.502689+0.008466 test-rmse:0.533331+0.054512
## [41] train-rmse:0.501223+0.008438 test-rmse:0.532722+0.055082
## [42] train-rmse:0.499760+0.008385 test-rmse:0.530685+0.055447
## [43] train-rmse:0.498323+0.008272 test-rmse:0.529785+0.056030
## [44] train-rmse:0.496899+0.008183 test-rmse:0.529308+0.054831
## [45] train-rmse:0.495559+0.008154 test-rmse:0.528455+0.055101
## [46] train-rmse:0.494181+0.008205 test-rmse:0.527860+0.055421
## [47] train-rmse:0.492839+0.008269 test-rmse:0.526383+0.053856
## [48] train-rmse:0.491503+0.008156 test-rmse:0.525468+0.053524
## [49] train-rmse:0.490161+0.008119 test-rmse:0.523800+0.053292
## [50] train-rmse:0.488914+0.008105 test-rmse:0.523920+0.053815
## [51] train-rmse:0.487770+0.008106 test-rmse:0.524155+0.052546
## [52] train-rmse:0.486692+0.008092 test-rmse:0.523169+0.052579
## [53] train-rmse:0.485578+0.008053 test-rmse:0.523438+0.052497
## [54] train-rmse:0.484481+0.007990 test-rmse:0.521340+0.051802
## [55] train-rmse:0.483325+0.007948 test-rmse:0.521521+0.051325
## [56] train-rmse:0.482257+0.007919 test-rmse:0.521177+0.050910
## [57] train-rmse:0.481241+0.007907 test-rmse:0.520369+0.050438
## [58] train-rmse:0.480244+0.007876 test-rmse:0.519102+0.051268
## [59] train-rmse:0.479273+0.007838 test-rmse:0.518599+0.050785
## [60] train-rmse:0.478327+0.007815 test-rmse:0.517346+0.050863
## [61] train-rmse:0.477343+0.007815 test-rmse:0.515839+0.049999
## [62] train-rmse:0.476392+0.007806 test-rmse:0.515208+0.050104
## [63] train-rmse:0.475475+0.007775 test-rmse:0.514403+0.050479
## [64] train-rmse:0.474624+0.007751 test-rmse:0.514862+0.050287
## [65] train-rmse:0.473731+0.007715 test-rmse:0.514533+0.050212
## [66] train-rmse:0.472902+0.007693 test-rmse:0.513484+0.050439
## [67] train-rmse:0.472079+0.007728 test-rmse:0.513806+0.050534
## [68] train-rmse:0.471246+0.007720 test-rmse:0.513324+0.050084
## [69] train-rmse:0.470459+0.007743 test-rmse:0.512677+0.049077
## [70] train-rmse:0.469682+0.007724 test-rmse:0.512154+0.046850
## [71] train-rmse:0.468893+0.007696 test-rmse:0.511511+0.046692
## [72] train-rmse:0.468138+0.007678 test-rmse:0.511846+0.046242
## [73] train-rmse:0.467399+0.007663 test-rmse:0.511706+0.046927
## [74] train-rmse:0.466696+0.007638 test-rmse:0.511087+0.046837
## [75] train-rmse:0.465965+0.007585 test-rmse:0.510595+0.046811
## [76] train-rmse:0.465211+0.007576 test-rmse:0.509926+0.046425
## [77] train-rmse:0.464484+0.007577 test-rmse:0.509852+0.045259
## [78] train-rmse:0.463790+0.007561 test-rmse:0.509087+0.045620
## [79] train-rmse:0.463120+0.007549 test-rmse:0.508670+0.046065
## [80] train-rmse:0.462450+0.007513 test-rmse:0.507894+0.046099
## [81] train-rmse:0.461783+0.007479 test-rmse:0.508329+0.045973
## [82] train-rmse:0.461150+0.007473 test-rmse:0.507271+0.046658
## [83] train-rmse:0.460529+0.007469 test-rmse:0.506236+0.046198
## [84] train-rmse:0.459921+0.007470 test-rmse:0.505865+0.045902
## [85] train-rmse:0.459312+0.007472 test-rmse:0.505880+0.046020
## [86] train-rmse:0.458710+0.007428 test-rmse:0.505983+0.045664
## [87] train-rmse:0.458106+0.007400 test-rmse:0.505653+0.045946
## [88] train-rmse:0.457533+0.007382 test-rmse:0.505514+0.045744
## [89] train-rmse:0.456961+0.007380 test-rmse:0.505274+0.046115
## [90] train-rmse:0.456413+0.007370 test-rmse:0.505780+0.046106
## [91] train-rmse:0.455848+0.007364 test-rmse:0.505866+0.046034
## [92] train-rmse:0.455284+0.007355 test-rmse:0.505083+0.046546
## [93] train-rmse:0.454736+0.007352 test-rmse:0.504652+0.045719
## [94] train-rmse:0.454198+0.007352 test-rmse:0.505807+0.045344
## [95] train-rmse:0.453675+0.007343 test-rmse:0.505076+0.045257
## [96] train-rmse:0.453153+0.007335 test-rmse:0.504646+0.045937
## [97] train-rmse:0.452638+0.007320 test-rmse:0.504142+0.045844
## [98] train-rmse:0.452144+0.007326 test-rmse:0.502825+0.043866
## [99] train-rmse:0.451630+0.007316 test-rmse:0.503343+0.043542
## [100] train-rmse:0.451154+0.007309 test-rmse:0.503387+0.043410
## [101] train-rmse:0.450683+0.007296 test-rmse:0.502629+0.042756
## [102] train-rmse:0.450213+0.007292 test-rmse:0.502463+0.042096
## [103] train-rmse:0.449758+0.007303 test-rmse:0.502447+0.041333
## [104] train-rmse:0.449287+0.007326 test-rmse:0.502463+0.041733
## [105] train-rmse:0.448842+0.007328 test-rmse:0.501673+0.042029
## [106] train-rmse:0.448373+0.007340 test-rmse:0.501395+0.042842
## [107] train-rmse:0.447934+0.007350 test-rmse:0.500945+0.043162
## [108] train-rmse:0.447512+0.007365 test-rmse:0.501058+0.043262
## [109] train-rmse:0.447070+0.007361 test-rmse:0.501263+0.043096
## [110] train-rmse:0.446649+0.007352 test-rmse:0.501179+0.042603
## [111] train-rmse:0.446225+0.007348 test-rmse:0.501492+0.042985
## [112] train-rmse:0.445825+0.007343 test-rmse:0.501467+0.042935
## [113] train-rmse:0.445410+0.007336 test-rmse:0.500917+0.042758
## [114] train-rmse:0.445006+0.007349 test-rmse:0.500936+0.042512
## [115] train-rmse:0.444606+0.007347 test-rmse:0.501106+0.042412
## [116] train-rmse:0.444222+0.007351 test-rmse:0.500270+0.042982
## [117] train-rmse:0.443843+0.007348 test-rmse:0.499522+0.043397
## [118] train-rmse:0.443469+0.007342 test-rmse:0.499402+0.043550
## [119] train-rmse:0.443083+0.007330 test-rmse:0.499464+0.042898
## [120] train-rmse:0.442693+0.007350 test-rmse:0.499385+0.043152
## [121] train-rmse:0.442332+0.007350 test-rmse:0.499386+0.042543
## [122] train-rmse:0.441938+0.007364 test-rmse:0.499348+0.043074
## [123] train-rmse:0.441574+0.007369 test-rmse:0.499001+0.043257
## [124] train-rmse:0.441212+0.007378 test-rmse:0.499302+0.042622
## [125] train-rmse:0.440857+0.007387 test-rmse:0.499193+0.042448
## [126] train-rmse:0.440513+0.007394 test-rmse:0.498849+0.042155
## [127] train-rmse:0.440176+0.007388 test-rmse:0.498879+0.041935
## [128] train-rmse:0.439839+0.007393 test-rmse:0.499033+0.041389
## [129] train-rmse:0.439501+0.007390 test-rmse:0.499207+0.041471
## [130] train-rmse:0.439147+0.007379 test-rmse:0.498390+0.040934
## [131] train-rmse:0.438805+0.007405 test-rmse:0.498841+0.041041
## [132] train-rmse:0.438452+0.007394 test-rmse:0.498418+0.041107
## [133] train-rmse:0.438117+0.007407 test-rmse:0.498806+0.041483
## [134] train-rmse:0.437793+0.007392 test-rmse:0.498520+0.041422
## [135] train-rmse:0.437464+0.007396 test-rmse:0.498108+0.041038
## [136] train-rmse:0.437127+0.007407 test-rmse:0.498111+0.041353
## [137] train-rmse:0.436802+0.007426 test-rmse:0.498057+0.041837
## [138] train-rmse:0.436486+0.007411 test-rmse:0.497645+0.042023
## [139] train-rmse:0.436173+0.007405 test-rmse:0.497168+0.042513
## [140] train-rmse:0.435873+0.007408 test-rmse:0.497205+0.042691
## [141] train-rmse:0.435563+0.007419 test-rmse:0.496506+0.042289
## [142] train-rmse:0.435250+0.007410 test-rmse:0.496773+0.042438
## [143] train-rmse:0.434922+0.007431 test-rmse:0.496405+0.042054
## [144] train-rmse:0.434626+0.007440 test-rmse:0.496369+0.042212
## [145] train-rmse:0.434322+0.007437 test-rmse:0.496500+0.042222
## [146] train-rmse:0.434033+0.007431 test-rmse:0.496543+0.042208
## [147] train-rmse:0.433736+0.007433 test-rmse:0.495843+0.040564
## [148] train-rmse:0.433446+0.007433 test-rmse:0.495532+0.040349
## [149] train-rmse:0.433156+0.007429 test-rmse:0.495318+0.039929
## [150] train-rmse:0.432847+0.007427 test-rmse:0.495092+0.040308
## [151] train-rmse:0.432553+0.007423 test-rmse:0.495383+0.040151
## [152] train-rmse:0.432261+0.007433 test-rmse:0.495471+0.039859
## [153] train-rmse:0.431991+0.007432 test-rmse:0.495243+0.039571
## [154] train-rmse:0.431724+0.007439 test-rmse:0.494757+0.039558
## [155] train-rmse:0.431445+0.007442 test-rmse:0.494607+0.039751
## [156] train-rmse:0.431166+0.007449 test-rmse:0.494622+0.039648
## [157] train-rmse:0.430898+0.007451 test-rmse:0.494601+0.039962
## [158] train-rmse:0.430635+0.007451 test-rmse:0.494654+0.039918
## [159] train-rmse:0.430377+0.007447 test-rmse:0.494879+0.040251
## [160] train-rmse:0.430105+0.007461 test-rmse:0.494571+0.039900
## [161] train-rmse:0.429839+0.007458 test-rmse:0.495186+0.040456
## [162] train-rmse:0.429571+0.007465 test-rmse:0.495001+0.040166
## [163] train-rmse:0.429303+0.007468 test-rmse:0.494816+0.040577
## [164] train-rmse:0.429044+0.007464 test-rmse:0.494915+0.040438
## [165] train-rmse:0.428792+0.007459 test-rmse:0.494866+0.040724
## [166] train-rmse:0.428543+0.007463 test-rmse:0.494807+0.040305
## Stopping. Best iteration:
## [160] train-rmse:0.430105+0.007461 test-rmse:0.494571+0.039900
##
## 64
## [1] train-rmse:1.246260+0.023887 test-rmse:1.246569+0.114264
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.973742+0.018616 test-rmse:0.971677+0.102694
## [3] train-rmse:0.788997+0.016740 test-rmse:0.792712+0.102762
## [4] train-rmse:0.667599+0.014639 test-rmse:0.673044+0.096215
## [5] train-rmse:0.587785+0.013113 test-rmse:0.599007+0.093459
## [6] train-rmse:0.535634+0.012640 test-rmse:0.546208+0.084977
## [7] train-rmse:0.501860+0.011787 test-rmse:0.518048+0.084475
## [8] train-rmse:0.477532+0.011601 test-rmse:0.503098+0.075265
## [9] train-rmse:0.456183+0.010370 test-rmse:0.483848+0.072733
## [10] train-rmse:0.441919+0.008766 test-rmse:0.472812+0.069383
## [11] train-rmse:0.429344+0.007699 test-rmse:0.465378+0.068088
## [12] train-rmse:0.419406+0.007853 test-rmse:0.465519+0.066080
## [13] train-rmse:0.411062+0.007395 test-rmse:0.461032+0.061603
## [14] train-rmse:0.403427+0.006429 test-rmse:0.456272+0.061394
## [15] train-rmse:0.397359+0.006300 test-rmse:0.453066+0.060187
## [16] train-rmse:0.390552+0.006351 test-rmse:0.449726+0.058518
## [17] train-rmse:0.384742+0.005939 test-rmse:0.450087+0.057942
## [18] train-rmse:0.379748+0.005811 test-rmse:0.449504+0.058777
## [19] train-rmse:0.374177+0.005198 test-rmse:0.448119+0.056335
## [20] train-rmse:0.369026+0.005298 test-rmse:0.445458+0.054214
## [21] train-rmse:0.364431+0.004480 test-rmse:0.444410+0.054451
## [22] train-rmse:0.360221+0.004884 test-rmse:0.444768+0.052933
## [23] train-rmse:0.355452+0.004795 test-rmse:0.443681+0.052768
## [24] train-rmse:0.352065+0.004634 test-rmse:0.443187+0.053270
## [25] train-rmse:0.348724+0.004533 test-rmse:0.443193+0.052693
## [26] train-rmse:0.344311+0.005130 test-rmse:0.441347+0.054058
## [27] train-rmse:0.340698+0.005646 test-rmse:0.439140+0.054587
## [28] train-rmse:0.338085+0.005729 test-rmse:0.439520+0.053957
## [29] train-rmse:0.335014+0.005878 test-rmse:0.438667+0.051693
## [30] train-rmse:0.331954+0.005313 test-rmse:0.438977+0.052848
## [31] train-rmse:0.329718+0.004936 test-rmse:0.438113+0.052651
## [32] train-rmse:0.327216+0.004971 test-rmse:0.436789+0.052260
## [33] train-rmse:0.324808+0.005404 test-rmse:0.437216+0.052826
## [34] train-rmse:0.322809+0.005386 test-rmse:0.436151+0.052234
## [35] train-rmse:0.320187+0.005272 test-rmse:0.434849+0.052010
## [36] train-rmse:0.317745+0.005155 test-rmse:0.434746+0.053291
## [37] train-rmse:0.315674+0.005320 test-rmse:0.434036+0.054643
## [38] train-rmse:0.312360+0.005536 test-rmse:0.432538+0.054380
## [39] train-rmse:0.309851+0.006750 test-rmse:0.432259+0.055381
## [40] train-rmse:0.307685+0.006525 test-rmse:0.431889+0.055300
## [41] train-rmse:0.305515+0.006066 test-rmse:0.432586+0.056126
## [42] train-rmse:0.302861+0.005625 test-rmse:0.430996+0.057711
## [43] train-rmse:0.301142+0.005841 test-rmse:0.431708+0.058491
## [44] train-rmse:0.298620+0.004900 test-rmse:0.429839+0.059711
## [45] train-rmse:0.296762+0.004899 test-rmse:0.429232+0.060184
## [46] train-rmse:0.295114+0.005312 test-rmse:0.429918+0.059411
## [47] train-rmse:0.293001+0.005210 test-rmse:0.429612+0.059483
## [48] train-rmse:0.291784+0.005010 test-rmse:0.429585+0.058258
## [49] train-rmse:0.290143+0.004975 test-rmse:0.429272+0.059060
## [50] train-rmse:0.288667+0.004613 test-rmse:0.428616+0.059708
## [51] train-rmse:0.286949+0.004578 test-rmse:0.428410+0.058945
## [52] train-rmse:0.283617+0.005449 test-rmse:0.428177+0.058875
## [53] train-rmse:0.281544+0.005417 test-rmse:0.426913+0.059575
## [54] train-rmse:0.279656+0.005033 test-rmse:0.426319+0.060040
## [55] train-rmse:0.278229+0.005137 test-rmse:0.426680+0.060155
## [56] train-rmse:0.276880+0.005143 test-rmse:0.427207+0.059232
## [57] train-rmse:0.275911+0.005222 test-rmse:0.426851+0.059021
## [58] train-rmse:0.273755+0.004711 test-rmse:0.427058+0.059332
## [59] train-rmse:0.272061+0.005574 test-rmse:0.427356+0.059784
## [60] train-rmse:0.270362+0.006038 test-rmse:0.426991+0.059682
## Stopping. Best iteration:
## [54] train-rmse:0.279656+0.005033 test-rmse:0.426319+0.060040
##
## 65
## [1] train-rmse:1.236273+0.022859 test-rmse:1.240800+0.117526
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.952329+0.019536 test-rmse:0.957256+0.103409
## [3] train-rmse:0.753782+0.016474 test-rmse:0.768019+0.104185
## [4] train-rmse:0.617317+0.014939 test-rmse:0.636342+0.098284
## [5] train-rmse:0.523398+0.013947 test-rmse:0.554329+0.092689
## [6] train-rmse:0.461931+0.011622 test-rmse:0.509854+0.088119
## [7] train-rmse:0.419378+0.011743 test-rmse:0.478382+0.081601
## [8] train-rmse:0.389587+0.011336 test-rmse:0.460583+0.072357
## [9] train-rmse:0.368892+0.009463 test-rmse:0.446461+0.074620
## [10] train-rmse:0.354657+0.009349 test-rmse:0.439425+0.069085
## [11] train-rmse:0.342692+0.008351 test-rmse:0.433804+0.066463
## [12] train-rmse:0.332239+0.008745 test-rmse:0.430405+0.065678
## [13] train-rmse:0.323413+0.007884 test-rmse:0.429909+0.064114
## [14] train-rmse:0.315417+0.008073 test-rmse:0.429495+0.063798
## [15] train-rmse:0.308899+0.007984 test-rmse:0.427648+0.064413
## [16] train-rmse:0.302566+0.007783 test-rmse:0.427145+0.064217
## [17] train-rmse:0.296558+0.007583 test-rmse:0.427517+0.063496
## [18] train-rmse:0.289973+0.008106 test-rmse:0.427149+0.065364
## [19] train-rmse:0.284874+0.007214 test-rmse:0.426826+0.064793
## [20] train-rmse:0.279370+0.006922 test-rmse:0.425225+0.065650
## [21] train-rmse:0.275107+0.006204 test-rmse:0.425735+0.063747
## [22] train-rmse:0.271740+0.005639 test-rmse:0.425245+0.065128
## [23] train-rmse:0.268326+0.005224 test-rmse:0.424365+0.063844
## [24] train-rmse:0.265287+0.005163 test-rmse:0.423093+0.063805
## [25] train-rmse:0.260910+0.005056 test-rmse:0.421890+0.064629
## [26] train-rmse:0.257336+0.004904 test-rmse:0.420876+0.065909
## [27] train-rmse:0.253624+0.005488 test-rmse:0.420121+0.065530
## [28] train-rmse:0.250671+0.005648 test-rmse:0.418992+0.065874
## [29] train-rmse:0.248195+0.005203 test-rmse:0.418345+0.066312
## [30] train-rmse:0.244659+0.005037 test-rmse:0.418364+0.066200
## [31] train-rmse:0.241632+0.005129 test-rmse:0.419180+0.067448
## [32] train-rmse:0.237953+0.005923 test-rmse:0.416872+0.067446
## [33] train-rmse:0.235046+0.004648 test-rmse:0.415499+0.068236
## [34] train-rmse:0.231878+0.004996 test-rmse:0.415282+0.068245
## [35] train-rmse:0.227640+0.006664 test-rmse:0.414124+0.068100
## [36] train-rmse:0.225182+0.005903 test-rmse:0.414025+0.066727
## [37] train-rmse:0.222638+0.006169 test-rmse:0.412654+0.068304
## [38] train-rmse:0.219711+0.006186 test-rmse:0.413115+0.067454
## [39] train-rmse:0.217018+0.006145 test-rmse:0.413028+0.066593
## [40] train-rmse:0.214861+0.005878 test-rmse:0.412144+0.067559
## [41] train-rmse:0.212333+0.006013 test-rmse:0.411192+0.066646
## [42] train-rmse:0.209081+0.005347 test-rmse:0.411239+0.065836
## [43] train-rmse:0.206267+0.005870 test-rmse:0.408668+0.064930
## [44] train-rmse:0.204141+0.005529 test-rmse:0.407811+0.064925
## [45] train-rmse:0.202200+0.005454 test-rmse:0.407854+0.064370
## [46] train-rmse:0.199836+0.004646 test-rmse:0.407732+0.065726
## [47] train-rmse:0.198079+0.004784 test-rmse:0.408007+0.066120
## [48] train-rmse:0.195829+0.005252 test-rmse:0.406866+0.065336
## [49] train-rmse:0.193256+0.004082 test-rmse:0.407441+0.065391
## [50] train-rmse:0.190692+0.003513 test-rmse:0.407436+0.065355
## [51] train-rmse:0.189393+0.003351 test-rmse:0.407514+0.064875
## [52] train-rmse:0.186788+0.004450 test-rmse:0.407806+0.065142
## [53] train-rmse:0.185479+0.004522 test-rmse:0.407262+0.064990
## [54] train-rmse:0.183571+0.005028 test-rmse:0.407419+0.065359
## Stopping. Best iteration:
## [48] train-rmse:0.195829+0.005252 test-rmse:0.406866+0.065336
##
## 66
## [1] train-rmse:1.231699+0.022126 test-rmse:1.235772+0.118308
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.943951+0.016348 test-rmse:0.954554+0.116417
## [3] train-rmse:0.737746+0.014656 test-rmse:0.750125+0.101262
## [4] train-rmse:0.592662+0.010483 test-rmse:0.621517+0.098830
## [5] train-rmse:0.493180+0.009166 test-rmse:0.536641+0.087336
## [6] train-rmse:0.423079+0.008328 test-rmse:0.483964+0.079755
## [7] train-rmse:0.377009+0.008111 test-rmse:0.455759+0.075186
## [8] train-rmse:0.341474+0.006013 test-rmse:0.435409+0.075798
## [9] train-rmse:0.320539+0.004860 test-rmse:0.429893+0.074068
## [10] train-rmse:0.303379+0.005679 test-rmse:0.426026+0.070760
## [11] train-rmse:0.289285+0.003975 test-rmse:0.420647+0.067784
## [12] train-rmse:0.279069+0.004248 test-rmse:0.417297+0.064231
## [13] train-rmse:0.270260+0.004741 test-rmse:0.415597+0.063377
## [14] train-rmse:0.261124+0.004637 test-rmse:0.414439+0.063739
## [15] train-rmse:0.252144+0.003857 test-rmse:0.414194+0.063192
## [16] train-rmse:0.246343+0.003029 test-rmse:0.412671+0.063558
## [17] train-rmse:0.240176+0.003586 test-rmse:0.412203+0.063835
## [18] train-rmse:0.233613+0.003110 test-rmse:0.409922+0.065839
## [19] train-rmse:0.226643+0.002348 test-rmse:0.410019+0.065777
## [20] train-rmse:0.221824+0.002635 test-rmse:0.409949+0.065241
## [21] train-rmse:0.214899+0.004673 test-rmse:0.411311+0.064507
## [22] train-rmse:0.209927+0.005080 test-rmse:0.410303+0.065301
## [23] train-rmse:0.205646+0.005157 test-rmse:0.408830+0.064380
## [24] train-rmse:0.201004+0.004963 test-rmse:0.405513+0.064691
## [25] train-rmse:0.197700+0.004487 test-rmse:0.406073+0.063961
## [26] train-rmse:0.192627+0.005095 test-rmse:0.404309+0.063795
## [27] train-rmse:0.188807+0.005768 test-rmse:0.403945+0.064119
## [28] train-rmse:0.185970+0.005932 test-rmse:0.402712+0.063481
## [29] train-rmse:0.182129+0.005201 test-rmse:0.401397+0.064212
## [30] train-rmse:0.178731+0.005041 test-rmse:0.399014+0.063891
## [31] train-rmse:0.173616+0.004891 test-rmse:0.398660+0.064487
## [32] train-rmse:0.171089+0.005222 test-rmse:0.398502+0.064409
## [33] train-rmse:0.168479+0.005298 test-rmse:0.398820+0.063735
## [34] train-rmse:0.165072+0.005616 test-rmse:0.398531+0.064441
## [35] train-rmse:0.162708+0.006136 test-rmse:0.397830+0.063806
## [36] train-rmse:0.158940+0.006066 test-rmse:0.394922+0.063087
## [37] train-rmse:0.156082+0.006672 test-rmse:0.394346+0.062731
## [38] train-rmse:0.153407+0.005632 test-rmse:0.394351+0.062421
## [39] train-rmse:0.151431+0.005753 test-rmse:0.394027+0.062668
## [40] train-rmse:0.148389+0.006219 test-rmse:0.394101+0.062766
## [41] train-rmse:0.145858+0.005427 test-rmse:0.393667+0.063226
## [42] train-rmse:0.143114+0.004882 test-rmse:0.392187+0.063700
## [43] train-rmse:0.140862+0.004676 test-rmse:0.392448+0.062883
## [44] train-rmse:0.138470+0.005221 test-rmse:0.391984+0.063344
## [45] train-rmse:0.136265+0.005389 test-rmse:0.391516+0.062901
## [46] train-rmse:0.133735+0.005944 test-rmse:0.391052+0.063182
## [47] train-rmse:0.131056+0.005510 test-rmse:0.391057+0.063022
## [48] train-rmse:0.129440+0.005587 test-rmse:0.390513+0.063434
## [49] train-rmse:0.127426+0.005254 test-rmse:0.390579+0.063178
## [50] train-rmse:0.125899+0.005669 test-rmse:0.390446+0.063284
## [51] train-rmse:0.124198+0.006125 test-rmse:0.390398+0.063366
## [52] train-rmse:0.122290+0.006445 test-rmse:0.390148+0.064028
## [53] train-rmse:0.119725+0.006096 test-rmse:0.390792+0.063346
## [54] train-rmse:0.117952+0.005942 test-rmse:0.389246+0.062769
## [55] train-rmse:0.115379+0.005027 test-rmse:0.388893+0.063344
## [56] train-rmse:0.113579+0.005535 test-rmse:0.388708+0.063416
## [57] train-rmse:0.112081+0.005109 test-rmse:0.388790+0.063783
## [58] train-rmse:0.110897+0.004910 test-rmse:0.388853+0.063103
## [59] train-rmse:0.109203+0.004386 test-rmse:0.388886+0.062773
## [60] train-rmse:0.107339+0.003829 test-rmse:0.388638+0.062626
## [61] train-rmse:0.105890+0.004417 test-rmse:0.388151+0.062029
## [62] train-rmse:0.104464+0.004526 test-rmse:0.387940+0.062141
## [63] train-rmse:0.102980+0.004561 test-rmse:0.387880+0.062087
## [64] train-rmse:0.101763+0.004836 test-rmse:0.387316+0.061915
## [65] train-rmse:0.101213+0.004812 test-rmse:0.387192+0.061858
## [66] train-rmse:0.099845+0.004750 test-rmse:0.387155+0.062088
## [67] train-rmse:0.098758+0.004467 test-rmse:0.387099+0.062399
## [68] train-rmse:0.097309+0.004978 test-rmse:0.386482+0.062886
## [69] train-rmse:0.095776+0.004387 test-rmse:0.386215+0.062355
## [70] train-rmse:0.094466+0.004498 test-rmse:0.386143+0.062431
## [71] train-rmse:0.093278+0.004716 test-rmse:0.386212+0.062566
## [72] train-rmse:0.092262+0.004517 test-rmse:0.386256+0.061715
## [73] train-rmse:0.090689+0.004833 test-rmse:0.386551+0.061306
## [74] train-rmse:0.089247+0.004030 test-rmse:0.386361+0.061575
## [75] train-rmse:0.088297+0.003822 test-rmse:0.386528+0.061697
## [76] train-rmse:0.086712+0.003731 test-rmse:0.386902+0.061494
## Stopping. Best iteration:
## [70] train-rmse:0.094466+0.004498 test-rmse:0.386143+0.062431
##
## 67
## [1] train-rmse:1.229034+0.022556 test-rmse:1.233278+0.114014
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.938066+0.016916 test-rmse:0.945978+0.108766
## [3] train-rmse:0.727873+0.014754 test-rmse:0.743333+0.098749
## [4] train-rmse:0.577500+0.010023 test-rmse:0.610492+0.092855
## [5] train-rmse:0.473428+0.007816 test-rmse:0.529726+0.090318
## [6] train-rmse:0.398583+0.006980 test-rmse:0.472724+0.083294
## [7] train-rmse:0.343312+0.004089 test-rmse:0.447790+0.082474
## [8] train-rmse:0.306881+0.004545 test-rmse:0.427046+0.074863
## [9] train-rmse:0.281059+0.004760 test-rmse:0.416877+0.069478
## [10] train-rmse:0.260429+0.005281 test-rmse:0.412484+0.069651
## [11] train-rmse:0.245479+0.006573 test-rmse:0.411799+0.064240
## [12] train-rmse:0.232738+0.007137 test-rmse:0.407892+0.065174
## [13] train-rmse:0.222311+0.004993 test-rmse:0.406691+0.063992
## [14] train-rmse:0.213536+0.004746 test-rmse:0.405622+0.062513
## [15] train-rmse:0.206077+0.004330 test-rmse:0.404048+0.060618
## [16] train-rmse:0.198514+0.004888 test-rmse:0.403441+0.058898
## [17] train-rmse:0.190450+0.004050 test-rmse:0.401811+0.057205
## [18] train-rmse:0.181020+0.004972 test-rmse:0.401107+0.060973
## [19] train-rmse:0.174050+0.005829 test-rmse:0.399111+0.058756
## [20] train-rmse:0.169416+0.006915 test-rmse:0.399887+0.058740
## [21] train-rmse:0.164630+0.006710 test-rmse:0.398818+0.060195
## [22] train-rmse:0.158728+0.005915 test-rmse:0.398307+0.060003
## [23] train-rmse:0.154874+0.006345 test-rmse:0.397724+0.058968
## [24] train-rmse:0.149838+0.005811 test-rmse:0.394412+0.059828
## [25] train-rmse:0.145755+0.004806 test-rmse:0.393773+0.060040
## [26] train-rmse:0.142036+0.005371 test-rmse:0.394951+0.059224
## [27] train-rmse:0.138722+0.004659 test-rmse:0.394643+0.058497
## [28] train-rmse:0.134960+0.004306 test-rmse:0.394500+0.057606
## [29] train-rmse:0.131548+0.004413 test-rmse:0.393615+0.057252
## [30] train-rmse:0.127203+0.004072 test-rmse:0.394139+0.056638
## [31] train-rmse:0.124396+0.004488 test-rmse:0.394561+0.056321
## [32] train-rmse:0.120499+0.003868 test-rmse:0.395085+0.056520
## [33] train-rmse:0.118133+0.003969 test-rmse:0.394387+0.055918
## [34] train-rmse:0.114970+0.004613 test-rmse:0.394842+0.056675
## [35] train-rmse:0.111430+0.005017 test-rmse:0.394785+0.056450
## Stopping. Best iteration:
## [29] train-rmse:0.131548+0.004413 test-rmse:0.393615+0.057252
##
## 68
## [1] train-rmse:1.228184+0.022611 test-rmse:1.231497+0.112420
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.934927+0.016603 test-rmse:0.945662+0.104553
## [3] train-rmse:0.722602+0.014887 test-rmse:0.744247+0.093420
## [4] train-rmse:0.569989+0.011745 test-rmse:0.609390+0.090564
## [5] train-rmse:0.459943+0.011799 test-rmse:0.526059+0.090926
## [6] train-rmse:0.381640+0.010756 test-rmse:0.473012+0.084535
## [7] train-rmse:0.324675+0.011639 test-rmse:0.442186+0.077105
## [8] train-rmse:0.282968+0.010433 test-rmse:0.424297+0.073545
## [9] train-rmse:0.253219+0.012159 test-rmse:0.417470+0.074136
## [10] train-rmse:0.231071+0.013111 test-rmse:0.412308+0.070700
## [11] train-rmse:0.215043+0.014847 test-rmse:0.406305+0.070479
## [12] train-rmse:0.200668+0.012783 test-rmse:0.402863+0.066360
## [13] train-rmse:0.186930+0.011835 test-rmse:0.398770+0.064363
## [14] train-rmse:0.179375+0.012519 test-rmse:0.399493+0.065399
## [15] train-rmse:0.171570+0.010722 test-rmse:0.399026+0.065467
## [16] train-rmse:0.162481+0.007108 test-rmse:0.396536+0.061667
## [17] train-rmse:0.154469+0.006006 test-rmse:0.398803+0.058734
## [18] train-rmse:0.145859+0.005822 test-rmse:0.399191+0.058739
## [19] train-rmse:0.139620+0.006479 test-rmse:0.398328+0.057031
## [20] train-rmse:0.134303+0.004920 test-rmse:0.397831+0.056859
## [21] train-rmse:0.129321+0.004524 test-rmse:0.397413+0.056638
## [22] train-rmse:0.123708+0.003779 test-rmse:0.397304+0.055809
## Stopping. Best iteration:
## [16] train-rmse:0.162481+0.007108 test-rmse:0.396536+0.061667
##
## 69
## [1] train-rmse:1.227777+0.022611 test-rmse:1.232847+0.111915
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.933307+0.017052 test-rmse:0.943470+0.105127
## [3] train-rmse:0.718920+0.015089 test-rmse:0.738021+0.093511
## [4] train-rmse:0.565371+0.011860 test-rmse:0.605769+0.090246
## [5] train-rmse:0.450331+0.011688 test-rmse:0.523451+0.085906
## [6] train-rmse:0.368869+0.011870 test-rmse:0.471364+0.081245
## [7] train-rmse:0.307300+0.013830 test-rmse:0.441143+0.075086
## [8] train-rmse:0.262675+0.014587 test-rmse:0.425175+0.070467
## [9] train-rmse:0.225478+0.010793 test-rmse:0.417094+0.070879
## [10] train-rmse:0.201396+0.011902 test-rmse:0.413057+0.068401
## [11] train-rmse:0.178268+0.009935 test-rmse:0.409561+0.065197
## [12] train-rmse:0.163126+0.011867 test-rmse:0.407318+0.063793
## [13] train-rmse:0.152017+0.012295 test-rmse:0.405992+0.064622
## [14] train-rmse:0.142116+0.012199 test-rmse:0.404600+0.061305
## [15] train-rmse:0.133684+0.011429 test-rmse:0.405171+0.061004
## [16] train-rmse:0.122561+0.011402 test-rmse:0.405744+0.057829
## [17] train-rmse:0.114250+0.008182 test-rmse:0.403034+0.057683
## [18] train-rmse:0.108738+0.008495 test-rmse:0.402580+0.056406
## [19] train-rmse:0.102707+0.009130 test-rmse:0.402126+0.056051
## [20] train-rmse:0.097398+0.007694 test-rmse:0.403052+0.055670
## [21] train-rmse:0.090312+0.008917 test-rmse:0.402038+0.055068
## [22] train-rmse:0.085684+0.008052 test-rmse:0.402205+0.052324
## [23] train-rmse:0.081175+0.008136 test-rmse:0.401962+0.051619
## [24] train-rmse:0.077068+0.007563 test-rmse:0.401742+0.052473
## [25] train-rmse:0.073989+0.007784 test-rmse:0.400749+0.052019
## [26] train-rmse:0.070743+0.007366 test-rmse:0.399964+0.052113
## [27] train-rmse:0.067410+0.007654 test-rmse:0.399654+0.052105
## [28] train-rmse:0.064649+0.006963 test-rmse:0.399159+0.052293
## [29] train-rmse:0.062483+0.006667 test-rmse:0.398923+0.052113
## [30] train-rmse:0.059790+0.006037 test-rmse:0.398713+0.052500
## [31] train-rmse:0.057276+0.005839 test-rmse:0.398555+0.052129
## [32] train-rmse:0.054214+0.006286 test-rmse:0.398600+0.051853
## [33] train-rmse:0.052508+0.005939 test-rmse:0.398675+0.051341
## [34] train-rmse:0.050123+0.006275 test-rmse:0.398599+0.051428
## [35] train-rmse:0.047675+0.005677 test-rmse:0.398806+0.051136
## [36] train-rmse:0.045483+0.005709 test-rmse:0.398401+0.050940
## [37] train-rmse:0.043357+0.005601 test-rmse:0.398541+0.050505
## [38] train-rmse:0.041776+0.005184 test-rmse:0.398605+0.050479
## [39] train-rmse:0.039879+0.005165 test-rmse:0.398538+0.050331
## [40] train-rmse:0.038036+0.005250 test-rmse:0.398575+0.051145
## [41] train-rmse:0.036722+0.005463 test-rmse:0.398672+0.051000
## [42] train-rmse:0.034719+0.004722 test-rmse:0.398631+0.050807
## Stopping. Best iteration:
## [36] train-rmse:0.045483+0.005709 test-rmse:0.398401+0.050940
##
## 70
## [1] train-rmse:1.239431+0.023343 test-rmse:1.235622+0.108139
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.985247+0.019663 test-rmse:0.981626+0.101110
## [3] train-rmse:0.831502+0.018240 test-rmse:0.828218+0.095339
## [4] train-rmse:0.743924+0.017954 test-rmse:0.741050+0.090895
## [5] train-rmse:0.696613+0.018036 test-rmse:0.694111+0.087792
## [6] train-rmse:0.666539+0.016706 test-rmse:0.668566+0.088529
## [7] train-rmse:0.644645+0.016901 test-rmse:0.643261+0.085475
## [8] train-rmse:0.626972+0.014462 test-rmse:0.628719+0.081551
## [9] train-rmse:0.613387+0.013910 test-rmse:0.619262+0.077668
## [10] train-rmse:0.601682+0.013634 test-rmse:0.605331+0.075403
## [11] train-rmse:0.591806+0.012409 test-rmse:0.597561+0.070808
## [12] train-rmse:0.583486+0.012087 test-rmse:0.586775+0.069298
## [13] train-rmse:0.576219+0.011967 test-rmse:0.584869+0.071083
## [14] train-rmse:0.569638+0.011573 test-rmse:0.579777+0.066141
## [15] train-rmse:0.563547+0.011497 test-rmse:0.576147+0.064959
## [16] train-rmse:0.558492+0.011184 test-rmse:0.571895+0.061852
## [17] train-rmse:0.553866+0.010836 test-rmse:0.565359+0.060379
## [18] train-rmse:0.549581+0.010677 test-rmse:0.562992+0.062708
## [19] train-rmse:0.545570+0.010546 test-rmse:0.561651+0.061396
## [20] train-rmse:0.542079+0.010597 test-rmse:0.558755+0.060458
## [21] train-rmse:0.538854+0.010368 test-rmse:0.557873+0.059137
## [22] train-rmse:0.535815+0.010278 test-rmse:0.556795+0.058641
## [23] train-rmse:0.532712+0.010240 test-rmse:0.553156+0.056904
## [24] train-rmse:0.529843+0.010015 test-rmse:0.551357+0.058018
## [25] train-rmse:0.527233+0.009658 test-rmse:0.548387+0.058349
## [26] train-rmse:0.524711+0.009515 test-rmse:0.548216+0.059918
## [27] train-rmse:0.522319+0.009386 test-rmse:0.545882+0.057594
## [28] train-rmse:0.519959+0.009321 test-rmse:0.544012+0.056357
## [29] train-rmse:0.517705+0.009216 test-rmse:0.544761+0.056313
## [30] train-rmse:0.515572+0.009120 test-rmse:0.543790+0.056495
## [31] train-rmse:0.513540+0.009033 test-rmse:0.541202+0.056310
## [32] train-rmse:0.511620+0.008941 test-rmse:0.539819+0.057767
## [33] train-rmse:0.509651+0.008865 test-rmse:0.537770+0.054782
## [34] train-rmse:0.507650+0.008790 test-rmse:0.535913+0.054425
## [35] train-rmse:0.505855+0.008715 test-rmse:0.534100+0.054978
## [36] train-rmse:0.504122+0.008587 test-rmse:0.532717+0.054641
## [37] train-rmse:0.502485+0.008532 test-rmse:0.531707+0.054260
## [38] train-rmse:0.500916+0.008515 test-rmse:0.532553+0.054842
## [39] train-rmse:0.499229+0.008492 test-rmse:0.530350+0.054480
## [40] train-rmse:0.497673+0.008427 test-rmse:0.529388+0.052820
## [41] train-rmse:0.496149+0.008394 test-rmse:0.526910+0.053402
## [42] train-rmse:0.494661+0.008379 test-rmse:0.526735+0.052649
## [43] train-rmse:0.493204+0.008357 test-rmse:0.526336+0.052520
## [44] train-rmse:0.491822+0.008322 test-rmse:0.525255+0.051495
## [45] train-rmse:0.490428+0.008218 test-rmse:0.524438+0.052761
## [46] train-rmse:0.489031+0.008162 test-rmse:0.523485+0.052306
## [47] train-rmse:0.487691+0.008153 test-rmse:0.523694+0.053743
## [48] train-rmse:0.486434+0.008121 test-rmse:0.523842+0.052219
## [49] train-rmse:0.485230+0.008099 test-rmse:0.522198+0.051490
## [50] train-rmse:0.484070+0.008066 test-rmse:0.521659+0.050463
## [51] train-rmse:0.482887+0.008030 test-rmse:0.520468+0.050079
## [52] train-rmse:0.481758+0.008007 test-rmse:0.520101+0.050963
## [53] train-rmse:0.480642+0.007987 test-rmse:0.520273+0.051146
## [54] train-rmse:0.479592+0.007983 test-rmse:0.519854+0.051023
## [55] train-rmse:0.478517+0.007954 test-rmse:0.518736+0.051391
## [56] train-rmse:0.477517+0.007960 test-rmse:0.518201+0.050484
## [57] train-rmse:0.476537+0.008000 test-rmse:0.516217+0.049480
## [58] train-rmse:0.475561+0.007977 test-rmse:0.517049+0.049045
## [59] train-rmse:0.474596+0.007954 test-rmse:0.514778+0.049646
## [60] train-rmse:0.473662+0.007911 test-rmse:0.515298+0.049850
## [61] train-rmse:0.472754+0.007894 test-rmse:0.515077+0.049870
## [62] train-rmse:0.471826+0.007886 test-rmse:0.512611+0.046686
## [63] train-rmse:0.470978+0.007861 test-rmse:0.511823+0.046735
## [64] train-rmse:0.470126+0.007890 test-rmse:0.510906+0.047102
## [65] train-rmse:0.469229+0.007857 test-rmse:0.509899+0.046784
## [66] train-rmse:0.468385+0.007883 test-rmse:0.508938+0.046347
## [67] train-rmse:0.467553+0.007872 test-rmse:0.509768+0.046120
## [68] train-rmse:0.466766+0.007852 test-rmse:0.509724+0.046828
## [69] train-rmse:0.465991+0.007812 test-rmse:0.509752+0.046574
## [70] train-rmse:0.465193+0.007777 test-rmse:0.510210+0.047528
## [71] train-rmse:0.464472+0.007764 test-rmse:0.510435+0.047186
## [72] train-rmse:0.463725+0.007730 test-rmse:0.509404+0.046484
## Stopping. Best iteration:
## [66] train-rmse:0.468385+0.007883 test-rmse:0.508938+0.046347
##
## 71
## [1] train-rmse:1.219162+0.023494 test-rmse:1.219875+0.114121
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.936960+0.018018 test-rmse:0.935077+0.101980
## [3] train-rmse:0.752643+0.016210 test-rmse:0.757184+0.101802
## [4] train-rmse:0.636928+0.014213 test-rmse:0.650326+0.099191
## [5] train-rmse:0.563542+0.013073 test-rmse:0.576805+0.089942
## [6] train-rmse:0.517087+0.012503 test-rmse:0.530263+0.082530
## [7] train-rmse:0.487080+0.011744 test-rmse:0.503427+0.077751
## [8] train-rmse:0.463150+0.009877 test-rmse:0.486402+0.074773
## [9] train-rmse:0.444228+0.009098 test-rmse:0.473753+0.069760
## [10] train-rmse:0.430758+0.007288 test-rmse:0.468703+0.068026
## [11] train-rmse:0.419215+0.006669 test-rmse:0.464024+0.067353
## [12] train-rmse:0.410609+0.006466 test-rmse:0.461127+0.063683
## [13] train-rmse:0.402923+0.006013 test-rmse:0.459832+0.062544
## [14] train-rmse:0.394961+0.004738 test-rmse:0.454611+0.062165
## [15] train-rmse:0.388289+0.005137 test-rmse:0.451610+0.059148
## [16] train-rmse:0.382663+0.004140 test-rmse:0.447975+0.058726
## [17] train-rmse:0.377108+0.003907 test-rmse:0.446116+0.054651
## [18] train-rmse:0.372410+0.003679 test-rmse:0.446083+0.055406
## [19] train-rmse:0.367751+0.003260 test-rmse:0.443896+0.051511
## [20] train-rmse:0.363259+0.002640 test-rmse:0.443089+0.053184
## [21] train-rmse:0.358375+0.002618 test-rmse:0.441249+0.050920
## [22] train-rmse:0.354632+0.002651 test-rmse:0.439339+0.049807
## [23] train-rmse:0.350672+0.003123 test-rmse:0.438675+0.048944
## [24] train-rmse:0.347656+0.003402 test-rmse:0.436715+0.050412
## [25] train-rmse:0.343606+0.002890 test-rmse:0.435911+0.050969
## [26] train-rmse:0.340324+0.002560 test-rmse:0.435246+0.051977
## [27] train-rmse:0.336332+0.002627 test-rmse:0.437002+0.052101
## [28] train-rmse:0.333224+0.003095 test-rmse:0.436037+0.050725
## [29] train-rmse:0.330174+0.003264 test-rmse:0.434883+0.050936
## [30] train-rmse:0.326951+0.003368 test-rmse:0.433202+0.048608
## [31] train-rmse:0.323549+0.003125 test-rmse:0.432294+0.050528
## [32] train-rmse:0.321193+0.003594 test-rmse:0.430418+0.050740
## [33] train-rmse:0.317583+0.004562 test-rmse:0.430251+0.050590
## [34] train-rmse:0.315063+0.004575 test-rmse:0.429185+0.051459
## [35] train-rmse:0.312482+0.004221 test-rmse:0.428979+0.052033
## [36] train-rmse:0.310022+0.004275 test-rmse:0.427127+0.052936
## [37] train-rmse:0.307600+0.003994 test-rmse:0.427024+0.051876
## [38] train-rmse:0.305314+0.003703 test-rmse:0.426688+0.052798
## [39] train-rmse:0.302020+0.004796 test-rmse:0.426393+0.052531
## [40] train-rmse:0.299545+0.005228 test-rmse:0.426341+0.052647
## [41] train-rmse:0.297728+0.005594 test-rmse:0.427646+0.052719
## [42] train-rmse:0.294749+0.005381 test-rmse:0.426915+0.052343
## [43] train-rmse:0.293329+0.005373 test-rmse:0.425569+0.053261
## [44] train-rmse:0.291682+0.004894 test-rmse:0.425689+0.053128
## [45] train-rmse:0.289914+0.005072 test-rmse:0.423839+0.053915
## [46] train-rmse:0.287961+0.004410 test-rmse:0.423323+0.054472
## [47] train-rmse:0.286017+0.003767 test-rmse:0.422516+0.055087
## [48] train-rmse:0.283996+0.004022 test-rmse:0.422467+0.055895
## [49] train-rmse:0.282651+0.004295 test-rmse:0.421993+0.055278
## [50] train-rmse:0.280646+0.004430 test-rmse:0.422041+0.055520
## [51] train-rmse:0.279252+0.004202 test-rmse:0.422155+0.055864
## [52] train-rmse:0.278198+0.004160 test-rmse:0.421311+0.055999
## [53] train-rmse:0.276396+0.004256 test-rmse:0.419791+0.055445
## [54] train-rmse:0.275032+0.003931 test-rmse:0.420098+0.056132
## [55] train-rmse:0.273129+0.004683 test-rmse:0.419395+0.056824
## [56] train-rmse:0.271526+0.004555 test-rmse:0.418300+0.057003
## [57] train-rmse:0.269635+0.005392 test-rmse:0.418812+0.057772
## [58] train-rmse:0.268203+0.005781 test-rmse:0.419566+0.056882
## [59] train-rmse:0.266449+0.005619 test-rmse:0.419850+0.057072
## [60] train-rmse:0.265090+0.005415 test-rmse:0.419859+0.057032
## [61] train-rmse:0.264191+0.005326 test-rmse:0.419866+0.056706
## [62] train-rmse:0.262864+0.006011 test-rmse:0.419372+0.056217
## Stopping. Best iteration:
## [56] train-rmse:0.271526+0.004555 test-rmse:0.418300+0.057003
##
## 72
## [1] train-rmse:1.208282+0.022383 test-rmse:1.213465+0.117616
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.913621+0.019087 test-rmse:0.918747+0.103167
## [3] train-rmse:0.713867+0.015744 test-rmse:0.732430+0.099741
## [4] train-rmse:0.580728+0.013317 test-rmse:0.608790+0.089104
## [5] train-rmse:0.492919+0.011766 test-rmse:0.537222+0.086886
## [6] train-rmse:0.436747+0.009890 test-rmse:0.493730+0.078484
## [7] train-rmse:0.400768+0.009708 test-rmse:0.465489+0.073577
## [8] train-rmse:0.376253+0.010222 test-rmse:0.448883+0.069499
## [9] train-rmse:0.357706+0.009580 test-rmse:0.443950+0.064960
## [10] train-rmse:0.345344+0.009719 test-rmse:0.441571+0.061688
## [11] train-rmse:0.333265+0.009019 test-rmse:0.436999+0.058967
## [12] train-rmse:0.323335+0.009670 test-rmse:0.430564+0.057069
## [13] train-rmse:0.314291+0.007605 test-rmse:0.430759+0.057655
## [14] train-rmse:0.307344+0.006937 test-rmse:0.429969+0.058369
## [15] train-rmse:0.300206+0.007083 test-rmse:0.430568+0.058687
## [16] train-rmse:0.293470+0.005674 test-rmse:0.428496+0.061250
## [17] train-rmse:0.287367+0.005919 test-rmse:0.427362+0.060118
## [18] train-rmse:0.282296+0.005798 test-rmse:0.427473+0.060594
## [19] train-rmse:0.276999+0.005701 test-rmse:0.425248+0.061442
## [20] train-rmse:0.272751+0.006096 test-rmse:0.424723+0.062454
## [21] train-rmse:0.268111+0.005586 test-rmse:0.425698+0.062502
## [22] train-rmse:0.264099+0.005262 test-rmse:0.423729+0.064614
## [23] train-rmse:0.259872+0.004579 test-rmse:0.421730+0.064198
## [24] train-rmse:0.256318+0.004790 test-rmse:0.419639+0.063699
## [25] train-rmse:0.250803+0.005786 test-rmse:0.417757+0.064269
## [26] train-rmse:0.246997+0.005033 test-rmse:0.416878+0.063161
## [27] train-rmse:0.242575+0.005598 test-rmse:0.418140+0.062719
## [28] train-rmse:0.238527+0.004775 test-rmse:0.417120+0.064403
## [29] train-rmse:0.235632+0.004495 test-rmse:0.414695+0.064723
## [30] train-rmse:0.232283+0.005076 test-rmse:0.412524+0.064366
## [31] train-rmse:0.229525+0.004739 test-rmse:0.414909+0.064835
## [32] train-rmse:0.226114+0.004730 test-rmse:0.415546+0.064146
## [33] train-rmse:0.222891+0.005066 test-rmse:0.416397+0.064395
## [34] train-rmse:0.219888+0.005820 test-rmse:0.415446+0.063840
## [35] train-rmse:0.217419+0.005795 test-rmse:0.414312+0.063904
## [36] train-rmse:0.214639+0.006185 test-rmse:0.414012+0.062972
## Stopping. Best iteration:
## [30] train-rmse:0.232283+0.005076 test-rmse:0.412524+0.064366
##
## 73
## [1] train-rmse:1.203273+0.021575 test-rmse:1.208003+0.118500
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.904180+0.015727 test-rmse:0.914552+0.115624
## [3] train-rmse:0.696818+0.015497 test-rmse:0.707217+0.105257
## [4] train-rmse:0.554900+0.010239 test-rmse:0.584858+0.102180
## [5] train-rmse:0.460539+0.010536 test-rmse:0.511941+0.090519
## [6] train-rmse:0.396476+0.010094 test-rmse:0.465741+0.082940
## [7] train-rmse:0.355601+0.009004 test-rmse:0.442006+0.075509
## [8] train-rmse:0.327221+0.008376 test-rmse:0.432205+0.069434
## [9] train-rmse:0.306852+0.007231 test-rmse:0.424894+0.066285
## [10] train-rmse:0.292704+0.005954 test-rmse:0.419971+0.063495
## [11] train-rmse:0.282685+0.006528 test-rmse:0.418220+0.064338
## [12] train-rmse:0.270636+0.006927 test-rmse:0.417892+0.062084
## [13] train-rmse:0.259986+0.006950 test-rmse:0.418079+0.061962
## [14] train-rmse:0.250809+0.007405 test-rmse:0.417453+0.061840
## [15] train-rmse:0.242887+0.006397 test-rmse:0.416029+0.065105
## [16] train-rmse:0.235318+0.007357 test-rmse:0.415258+0.065449
## [17] train-rmse:0.228110+0.006847 test-rmse:0.416167+0.064463
## [18] train-rmse:0.222814+0.007320 test-rmse:0.415449+0.064938
## [19] train-rmse:0.216528+0.008609 test-rmse:0.413718+0.064440
## [20] train-rmse:0.211738+0.008414 test-rmse:0.411347+0.066166
## [21] train-rmse:0.206132+0.007675 test-rmse:0.409578+0.064383
## [22] train-rmse:0.201883+0.007639 test-rmse:0.408064+0.065491
## [23] train-rmse:0.197993+0.009157 test-rmse:0.406922+0.064593
## [24] train-rmse:0.192117+0.009089 test-rmse:0.403908+0.067600
## [25] train-rmse:0.188092+0.008893 test-rmse:0.402799+0.067388
## [26] train-rmse:0.184043+0.008867 test-rmse:0.402084+0.068923
## [27] train-rmse:0.179398+0.010211 test-rmse:0.403309+0.067516
## [28] train-rmse:0.175436+0.010015 test-rmse:0.402826+0.066996
## [29] train-rmse:0.172395+0.010050 test-rmse:0.402947+0.067961
## [30] train-rmse:0.169149+0.010076 test-rmse:0.402107+0.068008
## [31] train-rmse:0.165454+0.010087 test-rmse:0.402676+0.067021
## [32] train-rmse:0.162443+0.009596 test-rmse:0.402233+0.068214
## Stopping. Best iteration:
## [26] train-rmse:0.184043+0.008867 test-rmse:0.402084+0.068923
##
## 74
## [1] train-rmse:1.200366+0.022037 test-rmse:1.205354+0.113843
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.897596+0.016301 test-rmse:0.907203+0.109367
## [3] train-rmse:0.685256+0.014107 test-rmse:0.703721+0.098505
## [4] train-rmse:0.538206+0.009217 test-rmse:0.577530+0.091311
## [5] train-rmse:0.438657+0.006189 test-rmse:0.500574+0.091659
## [6] train-rmse:0.368971+0.005243 test-rmse:0.450650+0.081162
## [7] train-rmse:0.323114+0.003265 test-rmse:0.432309+0.077536
## [8] train-rmse:0.288892+0.003899 test-rmse:0.419496+0.073425
## [9] train-rmse:0.264849+0.001095 test-rmse:0.411973+0.068008
## [10] train-rmse:0.249068+0.003178 test-rmse:0.407526+0.064484
## [11] train-rmse:0.234233+0.005349 test-rmse:0.406386+0.063532
## [12] train-rmse:0.223955+0.004354 test-rmse:0.404090+0.062879
## [13] train-rmse:0.213300+0.005002 test-rmse:0.399205+0.063079
## [14] train-rmse:0.205624+0.003387 test-rmse:0.399142+0.062111
## [15] train-rmse:0.196678+0.006113 test-rmse:0.398220+0.061704
## [16] train-rmse:0.189273+0.006808 test-rmse:0.397390+0.063947
## [17] train-rmse:0.181666+0.008668 test-rmse:0.395938+0.064764
## [18] train-rmse:0.175615+0.008918 test-rmse:0.395878+0.064989
## [19] train-rmse:0.169557+0.009248 test-rmse:0.394710+0.062846
## [20] train-rmse:0.164866+0.009681 test-rmse:0.394190+0.063164
## [21] train-rmse:0.159529+0.007637 test-rmse:0.393311+0.063148
## [22] train-rmse:0.153809+0.006956 test-rmse:0.392747+0.063806
## [23] train-rmse:0.149100+0.005998 test-rmse:0.391307+0.062383
## [24] train-rmse:0.142999+0.006806 test-rmse:0.389564+0.064250
## [25] train-rmse:0.138555+0.006250 test-rmse:0.390180+0.060834
## [26] train-rmse:0.135605+0.005685 test-rmse:0.389651+0.060342
## [27] train-rmse:0.131744+0.006431 test-rmse:0.389851+0.060376
## [28] train-rmse:0.128222+0.005726 test-rmse:0.389127+0.060260
## [29] train-rmse:0.124655+0.004334 test-rmse:0.388043+0.059032
## [30] train-rmse:0.121063+0.003132 test-rmse:0.386076+0.059332
## [31] train-rmse:0.118269+0.002744 test-rmse:0.385699+0.060063
## [32] train-rmse:0.115180+0.003056 test-rmse:0.386138+0.059485
## [33] train-rmse:0.112308+0.002620 test-rmse:0.386394+0.058502
## [34] train-rmse:0.108455+0.003662 test-rmse:0.386873+0.059153
## [35] train-rmse:0.106661+0.003000 test-rmse:0.387127+0.060103
## [36] train-rmse:0.104434+0.003023 test-rmse:0.387280+0.060584
## [37] train-rmse:0.101496+0.003198 test-rmse:0.386557+0.060487
## Stopping. Best iteration:
## [31] train-rmse:0.118269+0.002744 test-rmse:0.385699+0.060063
##
## 75
## [1] train-rmse:1.199429+0.022093 test-rmse:1.203451+0.112131
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.894044+0.015975 test-rmse:0.904495+0.103786
## [3] train-rmse:0.679296+0.014207 test-rmse:0.703302+0.092884
## [4] train-rmse:0.528789+0.011873 test-rmse:0.580091+0.089136
## [5] train-rmse:0.423692+0.009932 test-rmse:0.499992+0.084993
## [6] train-rmse:0.352906+0.009336 test-rmse:0.456372+0.076634
## [7] train-rmse:0.302582+0.010089 test-rmse:0.433265+0.065449
## [8] train-rmse:0.266881+0.008572 test-rmse:0.421718+0.065253
## [9] train-rmse:0.240654+0.008370 test-rmse:0.415231+0.062691
## [10] train-rmse:0.223964+0.008910 test-rmse:0.411699+0.059094
## [11] train-rmse:0.204337+0.009680 test-rmse:0.408299+0.055060
## [12] train-rmse:0.189936+0.012125 test-rmse:0.404768+0.055682
## [13] train-rmse:0.179419+0.013662 test-rmse:0.405718+0.053558
## [14] train-rmse:0.170250+0.013103 test-rmse:0.403240+0.052426
## [15] train-rmse:0.157100+0.011012 test-rmse:0.404297+0.049663
## [16] train-rmse:0.150662+0.010849 test-rmse:0.404592+0.049831
## [17] train-rmse:0.140075+0.009877 test-rmse:0.405079+0.049044
## [18] train-rmse:0.132952+0.010026 test-rmse:0.401992+0.048178
## [19] train-rmse:0.126931+0.010511 test-rmse:0.402411+0.047799
## [20] train-rmse:0.121255+0.009112 test-rmse:0.403345+0.047970
## [21] train-rmse:0.116386+0.008755 test-rmse:0.401520+0.046953
## [22] train-rmse:0.112941+0.008139 test-rmse:0.401070+0.046213
## [23] train-rmse:0.108529+0.008488 test-rmse:0.402451+0.045721
## [24] train-rmse:0.102484+0.008869 test-rmse:0.402880+0.046336
## [25] train-rmse:0.098621+0.009312 test-rmse:0.402898+0.046863
## [26] train-rmse:0.095293+0.009445 test-rmse:0.403232+0.046271
## [27] train-rmse:0.090855+0.007466 test-rmse:0.402868+0.046674
## [28] train-rmse:0.087815+0.006903 test-rmse:0.401200+0.047986
## Stopping. Best iteration:
## [22] train-rmse:0.112941+0.008139 test-rmse:0.401070+0.046213
##
## 76
## [1] train-rmse:1.198984+0.022093 test-rmse:1.204914+0.111648
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.892190+0.016458 test-rmse:0.903795+0.103902
## [3] train-rmse:0.674604+0.014236 test-rmse:0.698218+0.092698
## [4] train-rmse:0.521118+0.012999 test-rmse:0.573919+0.091363
## [5] train-rmse:0.412520+0.011986 test-rmse:0.492586+0.088673
## [6] train-rmse:0.335983+0.012843 test-rmse:0.451714+0.080714
## [7] train-rmse:0.280907+0.013575 test-rmse:0.433980+0.072789
## [8] train-rmse:0.241235+0.013749 test-rmse:0.423587+0.069865
## [9] train-rmse:0.209376+0.012638 test-rmse:0.415875+0.066404
## [10] train-rmse:0.186106+0.010745 test-rmse:0.412158+0.066175
## [11] train-rmse:0.168515+0.012741 test-rmse:0.409227+0.061960
## [12] train-rmse:0.154002+0.009217 test-rmse:0.408803+0.058922
## [13] train-rmse:0.145324+0.009714 test-rmse:0.407351+0.055398
## [14] train-rmse:0.135716+0.009863 test-rmse:0.408462+0.052064
## [15] train-rmse:0.126039+0.009862 test-rmse:0.407509+0.051403
## [16] train-rmse:0.118461+0.010963 test-rmse:0.407775+0.049933
## [17] train-rmse:0.111465+0.010054 test-rmse:0.409225+0.048140
## [18] train-rmse:0.105523+0.009396 test-rmse:0.408480+0.049066
## [19] train-rmse:0.099155+0.010411 test-rmse:0.407009+0.048497
## [20] train-rmse:0.094252+0.009476 test-rmse:0.406553+0.047546
## [21] train-rmse:0.086234+0.009404 test-rmse:0.406888+0.046297
## [22] train-rmse:0.081447+0.008336 test-rmse:0.406681+0.045110
## [23] train-rmse:0.075620+0.006843 test-rmse:0.406304+0.045123
## [24] train-rmse:0.072865+0.006135 test-rmse:0.406327+0.045224
## [25] train-rmse:0.068707+0.004936 test-rmse:0.406112+0.046084
## [26] train-rmse:0.065097+0.005762 test-rmse:0.406158+0.046093
## [27] train-rmse:0.061643+0.004925 test-rmse:0.405969+0.045510
## [28] train-rmse:0.058722+0.004687 test-rmse:0.406068+0.045425
## [29] train-rmse:0.057123+0.004386 test-rmse:0.405858+0.045454
## [30] train-rmse:0.054246+0.004081 test-rmse:0.405778+0.045863
## [31] train-rmse:0.052063+0.004441 test-rmse:0.405097+0.046387
## [32] train-rmse:0.049523+0.003660 test-rmse:0.405512+0.045641
## [33] train-rmse:0.047079+0.003910 test-rmse:0.405802+0.045555
## [34] train-rmse:0.044817+0.003587 test-rmse:0.405124+0.045816
## [35] train-rmse:0.042835+0.003074 test-rmse:0.405288+0.045924
## [36] train-rmse:0.040578+0.003247 test-rmse:0.404820+0.046409
## [37] train-rmse:0.038916+0.003166 test-rmse:0.404605+0.046545
## [38] train-rmse:0.037001+0.003203 test-rmse:0.404694+0.046173
## [39] train-rmse:0.035323+0.003474 test-rmse:0.404793+0.046521
## [40] train-rmse:0.034243+0.003705 test-rmse:0.405023+0.046470
## [41] train-rmse:0.032915+0.003384 test-rmse:0.404910+0.046597
## [42] train-rmse:0.031845+0.003563 test-rmse:0.404935+0.046680
## [43] train-rmse:0.030221+0.003355 test-rmse:0.404592+0.047230
## [44] train-rmse:0.028831+0.003558 test-rmse:0.404617+0.047001
## [45] train-rmse:0.027675+0.003095 test-rmse:0.404601+0.046867
## [46] train-rmse:0.026409+0.002950 test-rmse:0.404457+0.046944
## [47] train-rmse:0.024958+0.002801 test-rmse:0.404396+0.047205
## [48] train-rmse:0.023837+0.002524 test-rmse:0.404469+0.047186
## [49] train-rmse:0.022794+0.002534 test-rmse:0.404305+0.047298
## [50] train-rmse:0.021845+0.002479 test-rmse:0.404387+0.047366
## [51] train-rmse:0.020912+0.002455 test-rmse:0.404459+0.047339
## [52] train-rmse:0.020004+0.002097 test-rmse:0.404546+0.047205
## [53] train-rmse:0.018914+0.002289 test-rmse:0.404482+0.047061
## [54] train-rmse:0.018059+0.001917 test-rmse:0.404584+0.047066
## [55] train-rmse:0.017418+0.001947 test-rmse:0.404458+0.047132
## Stopping. Best iteration:
## [49] train-rmse:0.022794+0.002534 test-rmse:0.404305+0.047298
##
## 77
## [1] train-rmse:1.214115+0.022932 test-rmse:1.210316+0.107513
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.954492+0.019308 test-rmse:0.950917+0.100093
## [3] train-rmse:0.805239+0.018103 test-rmse:0.802054+0.094131
## [4] train-rmse:0.725179+0.017964 test-rmse:0.722433+0.089752
## [5] train-rmse:0.684635+0.018087 test-rmse:0.682260+0.086860
## [6] train-rmse:0.654201+0.016732 test-rmse:0.652560+0.086378
## [7] train-rmse:0.635469+0.015249 test-rmse:0.642909+0.083383
## [8] train-rmse:0.617731+0.013669 test-rmse:0.619357+0.081322
## [9] train-rmse:0.604812+0.013482 test-rmse:0.611128+0.076734
## [10] train-rmse:0.593949+0.012787 test-rmse:0.602306+0.071110
## [11] train-rmse:0.584765+0.012084 test-rmse:0.590711+0.070046
## [12] train-rmse:0.576372+0.011739 test-rmse:0.582207+0.069068
## [13] train-rmse:0.569250+0.011804 test-rmse:0.579561+0.066642
## [14] train-rmse:0.563094+0.011465 test-rmse:0.576435+0.062713
## [15] train-rmse:0.557627+0.011240 test-rmse:0.569417+0.062100
## [16] train-rmse:0.552656+0.010671 test-rmse:0.562728+0.063063
## [17] train-rmse:0.548173+0.010605 test-rmse:0.561789+0.062027
## [18] train-rmse:0.544170+0.010523 test-rmse:0.562160+0.061462
## [19] train-rmse:0.540548+0.010479 test-rmse:0.558898+0.058622
## [20] train-rmse:0.537170+0.010327 test-rmse:0.556555+0.057719
## [21] train-rmse:0.533879+0.010345 test-rmse:0.556566+0.057632
## [22] train-rmse:0.530852+0.010175 test-rmse:0.552457+0.056858
## [23] train-rmse:0.527840+0.009801 test-rmse:0.550037+0.057976
## [24] train-rmse:0.525126+0.009512 test-rmse:0.548760+0.056635
## [25] train-rmse:0.522499+0.009417 test-rmse:0.545192+0.057056
## [26] train-rmse:0.520065+0.009389 test-rmse:0.544907+0.056794
## [27] train-rmse:0.517492+0.009125 test-rmse:0.543628+0.057286
## [28] train-rmse:0.515163+0.008987 test-rmse:0.541807+0.056639
## [29] train-rmse:0.513061+0.008852 test-rmse:0.541082+0.056051
## [30] train-rmse:0.511069+0.008903 test-rmse:0.537787+0.054182
## [31] train-rmse:0.508954+0.008844 test-rmse:0.536059+0.054892
## [32] train-rmse:0.506910+0.008751 test-rmse:0.536170+0.055732
## [33] train-rmse:0.504945+0.008594 test-rmse:0.534873+0.054257
## [34] train-rmse:0.503060+0.008569 test-rmse:0.533530+0.055240
## [35] train-rmse:0.501358+0.008477 test-rmse:0.532456+0.054524
## [36] train-rmse:0.499530+0.008483 test-rmse:0.529206+0.053427
## [37] train-rmse:0.497845+0.008495 test-rmse:0.527579+0.053860
## [38] train-rmse:0.496199+0.008451 test-rmse:0.528228+0.053856
## [39] train-rmse:0.494638+0.008349 test-rmse:0.527422+0.054956
## [40] train-rmse:0.493051+0.008323 test-rmse:0.527044+0.053542
## [41] train-rmse:0.491541+0.008291 test-rmse:0.523748+0.052196
## [42] train-rmse:0.490112+0.008308 test-rmse:0.522381+0.052849
## [43] train-rmse:0.488631+0.008257 test-rmse:0.522438+0.051919
## [44] train-rmse:0.487195+0.008179 test-rmse:0.522420+0.052156
## [45] train-rmse:0.485773+0.008089 test-rmse:0.520935+0.051746
## [46] train-rmse:0.484436+0.007994 test-rmse:0.519895+0.052493
## [47] train-rmse:0.483213+0.007949 test-rmse:0.521381+0.052140
## [48] train-rmse:0.482032+0.008010 test-rmse:0.520820+0.050178
## [49] train-rmse:0.480805+0.007995 test-rmse:0.519372+0.049682
## [50] train-rmse:0.479663+0.007962 test-rmse:0.518383+0.049254
## [51] train-rmse:0.478532+0.007909 test-rmse:0.519356+0.049675
## [52] train-rmse:0.477457+0.007899 test-rmse:0.518330+0.050193
## [53] train-rmse:0.476405+0.007886 test-rmse:0.516541+0.049409
## [54] train-rmse:0.475328+0.007902 test-rmse:0.515361+0.049691
## [55] train-rmse:0.474306+0.007909 test-rmse:0.515157+0.049911
## [56] train-rmse:0.473357+0.007909 test-rmse:0.515530+0.049832
## [57] train-rmse:0.472345+0.007857 test-rmse:0.515385+0.050775
## [58] train-rmse:0.471377+0.007811 test-rmse:0.514325+0.049164
## [59] train-rmse:0.470417+0.007794 test-rmse:0.513081+0.048118
## [60] train-rmse:0.469478+0.007772 test-rmse:0.511795+0.047831
## [61] train-rmse:0.468556+0.007767 test-rmse:0.511428+0.048544
## [62] train-rmse:0.467644+0.007776 test-rmse:0.507920+0.046034
## [63] train-rmse:0.466800+0.007760 test-rmse:0.507566+0.046225
## [64] train-rmse:0.465899+0.007780 test-rmse:0.507385+0.046753
## [65] train-rmse:0.465075+0.007763 test-rmse:0.506704+0.046884
## [66] train-rmse:0.464313+0.007731 test-rmse:0.507900+0.046446
## [67] train-rmse:0.463542+0.007688 test-rmse:0.506684+0.046615
## [68] train-rmse:0.462752+0.007634 test-rmse:0.506504+0.045852
## [69] train-rmse:0.461972+0.007619 test-rmse:0.506141+0.045293
## [70] train-rmse:0.461214+0.007610 test-rmse:0.506818+0.045036
## [71] train-rmse:0.460448+0.007631 test-rmse:0.506801+0.045026
## [72] train-rmse:0.459712+0.007583 test-rmse:0.505592+0.045100
## [73] train-rmse:0.458967+0.007518 test-rmse:0.506241+0.045139
## [74] train-rmse:0.458253+0.007493 test-rmse:0.505479+0.045421
## [75] train-rmse:0.457575+0.007484 test-rmse:0.506049+0.045474
## [76] train-rmse:0.456929+0.007492 test-rmse:0.505956+0.045488
## [77] train-rmse:0.456284+0.007510 test-rmse:0.505770+0.045994
## [78] train-rmse:0.455632+0.007492 test-rmse:0.505692+0.044597
## [79] train-rmse:0.454964+0.007499 test-rmse:0.505085+0.044123
## [80] train-rmse:0.454308+0.007488 test-rmse:0.504694+0.044074
## [81] train-rmse:0.453633+0.007495 test-rmse:0.503646+0.043245
## [82] train-rmse:0.453011+0.007480 test-rmse:0.503423+0.042779
## [83] train-rmse:0.452419+0.007478 test-rmse:0.503161+0.043480
## [84] train-rmse:0.451833+0.007464 test-rmse:0.502370+0.043607
## [85] train-rmse:0.451269+0.007454 test-rmse:0.501736+0.043435
## [86] train-rmse:0.450724+0.007464 test-rmse:0.501825+0.043269
## [87] train-rmse:0.450133+0.007501 test-rmse:0.501838+0.042696
## [88] train-rmse:0.449595+0.007479 test-rmse:0.499186+0.041063
## [89] train-rmse:0.449083+0.007448 test-rmse:0.499126+0.040546
## [90] train-rmse:0.448555+0.007431 test-rmse:0.499471+0.041110
## [91] train-rmse:0.448048+0.007426 test-rmse:0.500074+0.040838
## [92] train-rmse:0.447539+0.007418 test-rmse:0.499539+0.041239
## [93] train-rmse:0.447025+0.007416 test-rmse:0.499988+0.041160
## [94] train-rmse:0.446548+0.007413 test-rmse:0.499376+0.041495
## [95] train-rmse:0.446067+0.007400 test-rmse:0.499520+0.041396
## Stopping. Best iteration:
## [89] train-rmse:0.449083+0.007448 test-rmse:0.499126+0.040546
##
## 78
## [1] train-rmse:1.192217+0.023110 test-rmse:1.193352+0.113982
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.901677+0.017455 test-rmse:0.900281+0.101204
## [3] train-rmse:0.719190+0.015745 test-rmse:0.724640+0.100761
## [4] train-rmse:0.609587+0.013907 test-rmse:0.624302+0.098286
## [5] train-rmse:0.542581+0.012576 test-rmse:0.557149+0.091555
## [6] train-rmse:0.501569+0.012295 test-rmse:0.520804+0.080609
## [7] train-rmse:0.474594+0.011753 test-rmse:0.500193+0.076296
## [8] train-rmse:0.450451+0.010674 test-rmse:0.480762+0.070328
## [9] train-rmse:0.435785+0.009189 test-rmse:0.477351+0.070350
## [10] train-rmse:0.423578+0.008457 test-rmse:0.468165+0.065944
## [11] train-rmse:0.413460+0.008176 test-rmse:0.465986+0.061531
## [12] train-rmse:0.405887+0.007897 test-rmse:0.461941+0.060670
## [13] train-rmse:0.398814+0.007985 test-rmse:0.461050+0.056556
## [14] train-rmse:0.391651+0.007657 test-rmse:0.452451+0.057519
## [15] train-rmse:0.386231+0.008082 test-rmse:0.450658+0.053687
## [16] train-rmse:0.381099+0.008097 test-rmse:0.450943+0.051702
## [17] train-rmse:0.375838+0.007234 test-rmse:0.451540+0.051813
## [18] train-rmse:0.369316+0.006484 test-rmse:0.445967+0.049195
## [19] train-rmse:0.363920+0.007327 test-rmse:0.444412+0.049516
## [20] train-rmse:0.358550+0.008139 test-rmse:0.445226+0.050387
## [21] train-rmse:0.354786+0.007928 test-rmse:0.442456+0.049070
## [22] train-rmse:0.349413+0.008159 test-rmse:0.440759+0.047637
## [23] train-rmse:0.345986+0.008629 test-rmse:0.440169+0.046989
## [24] train-rmse:0.342219+0.007319 test-rmse:0.438217+0.048678
## [25] train-rmse:0.338933+0.007566 test-rmse:0.436562+0.049847
## [26] train-rmse:0.335528+0.007385 test-rmse:0.436668+0.052304
## [27] train-rmse:0.331902+0.007231 test-rmse:0.436802+0.052744
## [28] train-rmse:0.327562+0.008368 test-rmse:0.436825+0.052244
## [29] train-rmse:0.325062+0.008637 test-rmse:0.435359+0.052005
## [30] train-rmse:0.322321+0.008633 test-rmse:0.436449+0.051881
## [31] train-rmse:0.319541+0.008741 test-rmse:0.437896+0.053040
## [32] train-rmse:0.316831+0.009269 test-rmse:0.437678+0.052335
## [33] train-rmse:0.314039+0.009954 test-rmse:0.435969+0.051837
## [34] train-rmse:0.311035+0.008803 test-rmse:0.435503+0.050527
## [35] train-rmse:0.308070+0.006924 test-rmse:0.435316+0.051822
## [36] train-rmse:0.306079+0.006369 test-rmse:0.434313+0.050789
## [37] train-rmse:0.304430+0.006287 test-rmse:0.432659+0.050891
## [38] train-rmse:0.301655+0.006270 test-rmse:0.432799+0.051323
## [39] train-rmse:0.299201+0.006988 test-rmse:0.432594+0.051843
## [40] train-rmse:0.296947+0.006637 test-rmse:0.433062+0.051644
## [41] train-rmse:0.295225+0.007035 test-rmse:0.433096+0.050580
## [42] train-rmse:0.292984+0.006616 test-rmse:0.432016+0.051396
## [43] train-rmse:0.291404+0.007032 test-rmse:0.431382+0.050995
## [44] train-rmse:0.289230+0.005929 test-rmse:0.431817+0.050543
## [45] train-rmse:0.287362+0.005565 test-rmse:0.431971+0.050748
## [46] train-rmse:0.285196+0.005943 test-rmse:0.430340+0.051186
## [47] train-rmse:0.283438+0.005953 test-rmse:0.430727+0.051531
## [48] train-rmse:0.281115+0.005541 test-rmse:0.430966+0.051487
## [49] train-rmse:0.279115+0.005825 test-rmse:0.431297+0.052281
## [50] train-rmse:0.277317+0.006961 test-rmse:0.430559+0.050771
## [51] train-rmse:0.275138+0.006186 test-rmse:0.428000+0.050490
## [52] train-rmse:0.273642+0.006255 test-rmse:0.427268+0.050552
## [53] train-rmse:0.272290+0.006362 test-rmse:0.425346+0.050111
## [54] train-rmse:0.270272+0.005992 test-rmse:0.424632+0.050645
## [55] train-rmse:0.269082+0.005531 test-rmse:0.423563+0.050679
## [56] train-rmse:0.267015+0.005867 test-rmse:0.422320+0.050808
## [57] train-rmse:0.264797+0.007159 test-rmse:0.421781+0.052682
## [58] train-rmse:0.263697+0.007492 test-rmse:0.421172+0.052342
## [59] train-rmse:0.262105+0.007929 test-rmse:0.422358+0.050644
## [60] train-rmse:0.260699+0.008499 test-rmse:0.421575+0.050756
## [61] train-rmse:0.258591+0.008332 test-rmse:0.421153+0.051032
## [62] train-rmse:0.257135+0.008020 test-rmse:0.418479+0.052968
## [63] train-rmse:0.255955+0.007876 test-rmse:0.416894+0.052952
## [64] train-rmse:0.254472+0.007687 test-rmse:0.416627+0.052760
## [65] train-rmse:0.253392+0.007989 test-rmse:0.416833+0.052801
## [66] train-rmse:0.252407+0.008161 test-rmse:0.416761+0.052854
## [67] train-rmse:0.251356+0.007824 test-rmse:0.416796+0.052997
## [68] train-rmse:0.250229+0.007398 test-rmse:0.417336+0.052805
## [69] train-rmse:0.249336+0.007285 test-rmse:0.416780+0.054021
## [70] train-rmse:0.247969+0.007058 test-rmse:0.414385+0.053999
## [71] train-rmse:0.245697+0.007036 test-rmse:0.414705+0.054082
## [72] train-rmse:0.244122+0.007029 test-rmse:0.414261+0.054183
## [73] train-rmse:0.243127+0.007090 test-rmse:0.414870+0.053705
## [74] train-rmse:0.241329+0.007418 test-rmse:0.415692+0.054523
## [75] train-rmse:0.240111+0.008151 test-rmse:0.416231+0.054893
## [76] train-rmse:0.239221+0.007784 test-rmse:0.416528+0.054638
## [77] train-rmse:0.237586+0.007680 test-rmse:0.415440+0.053963
## [78] train-rmse:0.236900+0.007645 test-rmse:0.415103+0.054165
## Stopping. Best iteration:
## [72] train-rmse:0.244122+0.007029 test-rmse:0.414261+0.054183
##
## 79
## [1] train-rmse:1.180412+0.021917 test-rmse:1.186260+0.117710
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.876460+0.018698 test-rmse:0.878288+0.102681
## [3] train-rmse:0.676054+0.014844 test-rmse:0.693752+0.099057
## [4] train-rmse:0.549507+0.012403 test-rmse:0.581667+0.085586
## [5] train-rmse:0.469458+0.010853 test-rmse:0.510392+0.086523
## [6] train-rmse:0.420305+0.009965 test-rmse:0.477113+0.077910
## [7] train-rmse:0.388756+0.008589 test-rmse:0.457748+0.074979
## [8] train-rmse:0.366646+0.006787 test-rmse:0.447488+0.069080
## [9] train-rmse:0.350322+0.006905 test-rmse:0.443662+0.064781
## [10] train-rmse:0.337924+0.005660 test-rmse:0.441681+0.063426
## [11] train-rmse:0.326842+0.005710 test-rmse:0.435885+0.060767
## [12] train-rmse:0.316304+0.006295 test-rmse:0.433846+0.060689
## [13] train-rmse:0.307751+0.006368 test-rmse:0.433403+0.060842
## [14] train-rmse:0.300082+0.005511 test-rmse:0.433620+0.057743
## [15] train-rmse:0.293981+0.004715 test-rmse:0.433358+0.059573
## [16] train-rmse:0.288068+0.005242 test-rmse:0.432560+0.059508
## [17] train-rmse:0.282730+0.005005 test-rmse:0.430731+0.059269
## [18] train-rmse:0.276086+0.006135 test-rmse:0.430514+0.059777
## [19] train-rmse:0.269102+0.005848 test-rmse:0.429242+0.061312
## [20] train-rmse:0.264598+0.005709 test-rmse:0.428767+0.061830
## [21] train-rmse:0.259471+0.005221 test-rmse:0.427301+0.063756
## [22] train-rmse:0.254773+0.005091 test-rmse:0.426818+0.063404
## [23] train-rmse:0.248635+0.004640 test-rmse:0.423139+0.065629
## [24] train-rmse:0.245040+0.004455 test-rmse:0.423028+0.065575
## [25] train-rmse:0.242213+0.004252 test-rmse:0.421521+0.065765
## [26] train-rmse:0.238679+0.004487 test-rmse:0.419919+0.066331
## [27] train-rmse:0.234332+0.003725 test-rmse:0.418542+0.067033
## [28] train-rmse:0.230904+0.002863 test-rmse:0.418177+0.069021
## [29] train-rmse:0.227064+0.003822 test-rmse:0.416422+0.071245
## [30] train-rmse:0.223367+0.003273 test-rmse:0.416206+0.070846
## [31] train-rmse:0.220687+0.002968 test-rmse:0.416101+0.070123
## [32] train-rmse:0.217845+0.003141 test-rmse:0.415491+0.070523
## [33] train-rmse:0.215004+0.002890 test-rmse:0.414646+0.070658
## [34] train-rmse:0.211129+0.003237 test-rmse:0.415386+0.070424
## [35] train-rmse:0.207513+0.004386 test-rmse:0.412094+0.068973
## [36] train-rmse:0.203524+0.004470 test-rmse:0.412077+0.070604
## [37] train-rmse:0.201474+0.004446 test-rmse:0.412172+0.070109
## [38] train-rmse:0.198179+0.004769 test-rmse:0.412126+0.068956
## [39] train-rmse:0.195766+0.004456 test-rmse:0.411300+0.069057
## [40] train-rmse:0.193929+0.004529 test-rmse:0.412010+0.068591
## [41] train-rmse:0.191484+0.004369 test-rmse:0.412445+0.068790
## [42] train-rmse:0.189121+0.002999 test-rmse:0.412460+0.067796
## [43] train-rmse:0.187154+0.002837 test-rmse:0.412799+0.067805
## [44] train-rmse:0.185038+0.002801 test-rmse:0.412087+0.067240
## [45] train-rmse:0.182832+0.002645 test-rmse:0.412032+0.066324
## Stopping. Best iteration:
## [39] train-rmse:0.195766+0.004456 test-rmse:0.411300+0.069057
##
## 80
## [1] train-rmse:1.174950+0.021028 test-rmse:1.180353+0.118694
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.865770+0.014877 test-rmse:0.876716+0.117022
## [3] train-rmse:0.657485+0.013988 test-rmse:0.675423+0.099529
## [4] train-rmse:0.522374+0.009912 test-rmse:0.562238+0.096608
## [5] train-rmse:0.436012+0.009300 test-rmse:0.493311+0.082543
## [6] train-rmse:0.377091+0.009595 test-rmse:0.452279+0.082309
## [7] train-rmse:0.337667+0.007565 test-rmse:0.430619+0.078165
## [8] train-rmse:0.313409+0.006597 test-rmse:0.423469+0.072805
## [9] train-rmse:0.296211+0.006248 test-rmse:0.418206+0.071124
## [10] train-rmse:0.283799+0.005149 test-rmse:0.415498+0.070289
## [11] train-rmse:0.273521+0.005226 test-rmse:0.411580+0.070331
## [12] train-rmse:0.263378+0.005914 test-rmse:0.412116+0.070573
## [13] train-rmse:0.253523+0.005772 test-rmse:0.409949+0.074081
## [14] train-rmse:0.245942+0.003682 test-rmse:0.409622+0.072467
## [15] train-rmse:0.237256+0.003042 test-rmse:0.410259+0.073804
## [16] train-rmse:0.229381+0.003315 test-rmse:0.409293+0.071210
## [17] train-rmse:0.222866+0.002530 test-rmse:0.407727+0.072200
## [18] train-rmse:0.216460+0.004988 test-rmse:0.406674+0.072280
## [19] train-rmse:0.211329+0.005242 test-rmse:0.403260+0.071856
## [20] train-rmse:0.207244+0.004821 test-rmse:0.402125+0.072243
## [21] train-rmse:0.202667+0.005290 test-rmse:0.402293+0.071470
## [22] train-rmse:0.199666+0.004984 test-rmse:0.402425+0.071974
## [23] train-rmse:0.193877+0.004339 test-rmse:0.398693+0.073381
## [24] train-rmse:0.187793+0.003594 test-rmse:0.397096+0.072476
## [25] train-rmse:0.183222+0.003154 test-rmse:0.397575+0.070837
## [26] train-rmse:0.179964+0.003628 test-rmse:0.397692+0.070145
## [27] train-rmse:0.175730+0.002740 test-rmse:0.398513+0.071431
## [28] train-rmse:0.171051+0.002544 test-rmse:0.397015+0.070009
## [29] train-rmse:0.167814+0.003123 test-rmse:0.397270+0.071062
## [30] train-rmse:0.163862+0.003540 test-rmse:0.398007+0.070686
## [31] train-rmse:0.161054+0.004124 test-rmse:0.397307+0.071466
## [32] train-rmse:0.157632+0.003901 test-rmse:0.395716+0.072582
## [33] train-rmse:0.153493+0.005608 test-rmse:0.396014+0.072362
## [34] train-rmse:0.151324+0.006223 test-rmse:0.395065+0.072776
## [35] train-rmse:0.148938+0.006320 test-rmse:0.394355+0.072792
## [36] train-rmse:0.146684+0.006437 test-rmse:0.394880+0.072385
## [37] train-rmse:0.144056+0.006301 test-rmse:0.394374+0.072778
## [38] train-rmse:0.140789+0.005671 test-rmse:0.392842+0.072994
## [39] train-rmse:0.137769+0.006471 test-rmse:0.392767+0.072913
## [40] train-rmse:0.135173+0.006952 test-rmse:0.392084+0.072221
## [41] train-rmse:0.132134+0.007197 test-rmse:0.391317+0.073141
## [42] train-rmse:0.129675+0.007237 test-rmse:0.391223+0.072368
## [43] train-rmse:0.127320+0.006456 test-rmse:0.390770+0.072009
## [44] train-rmse:0.125172+0.006309 test-rmse:0.389998+0.071730
## [45] train-rmse:0.122268+0.005581 test-rmse:0.390238+0.072007
## [46] train-rmse:0.120214+0.005118 test-rmse:0.390493+0.072188
## [47] train-rmse:0.118289+0.005402 test-rmse:0.390416+0.072484
## [48] train-rmse:0.115756+0.006245 test-rmse:0.389511+0.072002
## [49] train-rmse:0.114283+0.005945 test-rmse:0.389502+0.072001
## [50] train-rmse:0.112145+0.006198 test-rmse:0.389561+0.072049
## [51] train-rmse:0.110214+0.005699 test-rmse:0.389739+0.072725
## [52] train-rmse:0.108491+0.005434 test-rmse:0.389914+0.071983
## [53] train-rmse:0.106518+0.005905 test-rmse:0.389986+0.072412
## [54] train-rmse:0.104851+0.005968 test-rmse:0.389496+0.071492
## [55] train-rmse:0.102847+0.005886 test-rmse:0.389349+0.071583
## [56] train-rmse:0.101417+0.005994 test-rmse:0.389659+0.071527
## [57] train-rmse:0.099827+0.005661 test-rmse:0.389591+0.071479
## [58] train-rmse:0.097921+0.005524 test-rmse:0.389556+0.071866
## [59] train-rmse:0.096505+0.005362 test-rmse:0.389805+0.072442
## [60] train-rmse:0.094312+0.005013 test-rmse:0.389916+0.071934
## [61] train-rmse:0.092670+0.004638 test-rmse:0.390037+0.071758
## Stopping. Best iteration:
## [55] train-rmse:0.102847+0.005886 test-rmse:0.389349+0.071583
##
## 81
## [1] train-rmse:1.171793+0.021524 test-rmse:1.177553+0.113668
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.858079+0.015238 test-rmse:0.872004+0.105915
## [3] train-rmse:0.645670+0.013156 test-rmse:0.669238+0.096044
## [4] train-rmse:0.503122+0.008359 test-rmse:0.554238+0.086606
## [5] train-rmse:0.408060+0.006218 test-rmse:0.484078+0.079919
## [6] train-rmse:0.346613+0.007223 test-rmse:0.448330+0.072310
## [7] train-rmse:0.302345+0.004770 test-rmse:0.428328+0.074052
## [8] train-rmse:0.273912+0.006154 test-rmse:0.423796+0.071504
## [9] train-rmse:0.253467+0.005427 test-rmse:0.415501+0.068393
## [10] train-rmse:0.240136+0.005799 test-rmse:0.411930+0.064355
## [11] train-rmse:0.227101+0.007307 test-rmse:0.410483+0.064401
## [12] train-rmse:0.217444+0.006801 test-rmse:0.402598+0.059336
## [13] train-rmse:0.207308+0.005103 test-rmse:0.402785+0.059069
## [14] train-rmse:0.197492+0.005275 test-rmse:0.400872+0.058820
## [15] train-rmse:0.190604+0.004855 test-rmse:0.400262+0.057449
## [16] train-rmse:0.182736+0.006048 test-rmse:0.397917+0.058144
## [17] train-rmse:0.174884+0.005564 test-rmse:0.398213+0.059822
## [18] train-rmse:0.169583+0.006990 test-rmse:0.398404+0.058792
## [19] train-rmse:0.164465+0.006806 test-rmse:0.397352+0.057662
## [20] train-rmse:0.158470+0.007149 test-rmse:0.398342+0.058229
## [21] train-rmse:0.153857+0.007095 test-rmse:0.397146+0.057486
## [22] train-rmse:0.149995+0.007220 test-rmse:0.397746+0.056272
## [23] train-rmse:0.143297+0.008257 test-rmse:0.397703+0.055543
## [24] train-rmse:0.139880+0.008665 test-rmse:0.398078+0.055823
## [25] train-rmse:0.136146+0.008880 test-rmse:0.398050+0.056288
## [26] train-rmse:0.131252+0.008615 test-rmse:0.398497+0.055476
## [27] train-rmse:0.126065+0.010298 test-rmse:0.398721+0.054553
## Stopping. Best iteration:
## [21] train-rmse:0.153857+0.007095 test-rmse:0.397146+0.057486
##
## 82
## [1] train-rmse:1.170764+0.021580 test-rmse:1.175529+0.111837
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.854586+0.015283 test-rmse:0.868034+0.104741
## [3] train-rmse:0.638807+0.013748 test-rmse:0.668059+0.092240
## [4] train-rmse:0.489387+0.010319 test-rmse:0.543656+0.089578
## [5] train-rmse:0.388966+0.008062 test-rmse:0.469824+0.081810
## [6] train-rmse:0.319578+0.008136 test-rmse:0.435181+0.073548
## [7] train-rmse:0.273457+0.008673 test-rmse:0.415852+0.067212
## [8] train-rmse:0.240546+0.007314 test-rmse:0.407086+0.060226
## [9] train-rmse:0.216664+0.008908 test-rmse:0.403454+0.057919
## [10] train-rmse:0.198752+0.009023 test-rmse:0.402789+0.056557
## [11] train-rmse:0.182800+0.006996 test-rmse:0.398754+0.056964
## [12] train-rmse:0.172396+0.006572 test-rmse:0.398073+0.054474
## [13] train-rmse:0.163247+0.006967 test-rmse:0.398208+0.054604
## [14] train-rmse:0.153602+0.003298 test-rmse:0.397096+0.054346
## [15] train-rmse:0.144373+0.002576 test-rmse:0.397013+0.050069
## [16] train-rmse:0.137171+0.002340 test-rmse:0.396980+0.049667
## [17] train-rmse:0.128844+0.003310 test-rmse:0.397544+0.048232
## [18] train-rmse:0.122734+0.001653 test-rmse:0.396714+0.048600
## [19] train-rmse:0.116516+0.002934 test-rmse:0.398168+0.048868
## [20] train-rmse:0.111523+0.003257 test-rmse:0.397404+0.048420
## [21] train-rmse:0.107039+0.003535 test-rmse:0.396831+0.049085
## [22] train-rmse:0.101441+0.003968 test-rmse:0.394851+0.050579
## [23] train-rmse:0.097018+0.004017 test-rmse:0.394395+0.050588
## [24] train-rmse:0.092495+0.004483 test-rmse:0.393753+0.049762
## [25] train-rmse:0.089228+0.004857 test-rmse:0.392618+0.050680
## [26] train-rmse:0.084995+0.004864 test-rmse:0.391983+0.050496
## [27] train-rmse:0.080179+0.005418 test-rmse:0.392138+0.051134
## [28] train-rmse:0.077172+0.004855 test-rmse:0.392914+0.050661
## [29] train-rmse:0.074472+0.004816 test-rmse:0.392696+0.050680
## [30] train-rmse:0.070901+0.004579 test-rmse:0.393279+0.050792
## [31] train-rmse:0.067513+0.004312 test-rmse:0.393523+0.050490
## [32] train-rmse:0.064796+0.004363 test-rmse:0.393480+0.050606
## Stopping. Best iteration:
## [26] train-rmse:0.084995+0.004864 test-rmse:0.391983+0.050496
##
## 83
## [1] train-rmse:1.170278+0.021579 test-rmse:1.177108+0.111386
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.852531+0.015798 test-rmse:0.866838+0.103685
## [3] train-rmse:0.633664+0.013618 test-rmse:0.661978+0.092300
## [4] train-rmse:0.482781+0.012448 test-rmse:0.544638+0.090343
## [5] train-rmse:0.379943+0.011630 test-rmse:0.472929+0.084553
## [6] train-rmse:0.307612+0.010998 test-rmse:0.440034+0.076443
## [7] train-rmse:0.259585+0.010474 test-rmse:0.427690+0.073424
## [8] train-rmse:0.225221+0.011379 test-rmse:0.418507+0.068620
## [9] train-rmse:0.194422+0.008599 test-rmse:0.414616+0.064140
## [10] train-rmse:0.171548+0.006697 test-rmse:0.414770+0.066153
## [11] train-rmse:0.155746+0.008897 test-rmse:0.415126+0.062233
## [12] train-rmse:0.144318+0.008526 test-rmse:0.412679+0.061602
## [13] train-rmse:0.133225+0.010756 test-rmse:0.411707+0.060380
## [14] train-rmse:0.125258+0.009482 test-rmse:0.412055+0.056530
## [15] train-rmse:0.117916+0.009200 test-rmse:0.411411+0.057100
## [16] train-rmse:0.109492+0.006938 test-rmse:0.410470+0.053261
## [17] train-rmse:0.101090+0.005808 test-rmse:0.409754+0.053231
## [18] train-rmse:0.094600+0.006242 test-rmse:0.410686+0.052488
## [19] train-rmse:0.088537+0.006578 test-rmse:0.410704+0.050999
## [20] train-rmse:0.081344+0.004813 test-rmse:0.410089+0.051616
## [21] train-rmse:0.076675+0.005307 test-rmse:0.409670+0.052365
## [22] train-rmse:0.072980+0.005600 test-rmse:0.409338+0.052907
## [23] train-rmse:0.069640+0.005449 test-rmse:0.410293+0.053535
## [24] train-rmse:0.065591+0.005488 test-rmse:0.409539+0.052893
## [25] train-rmse:0.062875+0.006184 test-rmse:0.408880+0.052224
## [26] train-rmse:0.058897+0.005848 test-rmse:0.409173+0.052502
## [27] train-rmse:0.055955+0.005201 test-rmse:0.408706+0.051899
## [28] train-rmse:0.052730+0.004265 test-rmse:0.407833+0.051148
## [29] train-rmse:0.049694+0.004497 test-rmse:0.407825+0.050946
## [30] train-rmse:0.046251+0.003633 test-rmse:0.407279+0.051233
## [31] train-rmse:0.044174+0.003541 test-rmse:0.407006+0.050960
## [32] train-rmse:0.042744+0.003154 test-rmse:0.406548+0.051367
## [33] train-rmse:0.040935+0.003148 test-rmse:0.406669+0.051029
## [34] train-rmse:0.038575+0.003871 test-rmse:0.406635+0.050937
## [35] train-rmse:0.037139+0.004295 test-rmse:0.406707+0.051518
## [36] train-rmse:0.035912+0.004269 test-rmse:0.406704+0.051417
## [37] train-rmse:0.034600+0.004366 test-rmse:0.406473+0.051305
## [38] train-rmse:0.032844+0.004080 test-rmse:0.406194+0.051165
## [39] train-rmse:0.030936+0.003529 test-rmse:0.406098+0.051601
## [40] train-rmse:0.029404+0.003906 test-rmse:0.406041+0.051252
## [41] train-rmse:0.027777+0.003549 test-rmse:0.406008+0.051437
## [42] train-rmse:0.026652+0.003850 test-rmse:0.405895+0.051365
## [43] train-rmse:0.025525+0.003876 test-rmse:0.405813+0.051409
## [44] train-rmse:0.024824+0.003924 test-rmse:0.405690+0.051399
## [45] train-rmse:0.023842+0.003680 test-rmse:0.405769+0.051540
## [46] train-rmse:0.022156+0.003505 test-rmse:0.405632+0.051278
## [47] train-rmse:0.021308+0.003602 test-rmse:0.405650+0.050875
## [48] train-rmse:0.020241+0.003432 test-rmse:0.405624+0.050833
## [49] train-rmse:0.019408+0.003302 test-rmse:0.405580+0.050758
## [50] train-rmse:0.018564+0.002858 test-rmse:0.405665+0.050583
## [51] train-rmse:0.017848+0.002790 test-rmse:0.405675+0.050738
## [52] train-rmse:0.017018+0.002606 test-rmse:0.405806+0.050775
## [53] train-rmse:0.016423+0.002493 test-rmse:0.405792+0.051036
## [54] train-rmse:0.015658+0.002333 test-rmse:0.405750+0.051156
## [55] train-rmse:0.015143+0.002159 test-rmse:0.405799+0.051210
## Stopping. Best iteration:
## [49] train-rmse:0.019408+0.003302 test-rmse:0.405580+0.050758
##
## 84
## [1] train-rmse:1.189006+0.022533 test-rmse:1.185219+0.106881
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.925403+0.018999 test-rmse:0.921878+0.099079
## [3] train-rmse:0.781810+0.018015 test-rmse:0.778727+0.092973
## [4] train-rmse:0.709510+0.017993 test-rmse:0.706889+0.088717
## [5] train-rmse:0.672778+0.016967 test-rmse:0.677757+0.088485
## [6] train-rmse:0.644437+0.016761 test-rmse:0.643387+0.085433
## [7] train-rmse:0.625770+0.013895 test-rmse:0.628164+0.081377
## [8] train-rmse:0.609760+0.013257 test-rmse:0.613994+0.082100
## [9] train-rmse:0.597319+0.013157 test-rmse:0.602789+0.072809
## [10] train-rmse:0.586794+0.012457 test-rmse:0.593613+0.068735
## [11] train-rmse:0.577946+0.011808 test-rmse:0.585512+0.068501
## [12] train-rmse:0.570132+0.011527 test-rmse:0.576527+0.066379
## [13] train-rmse:0.562934+0.011598 test-rmse:0.574379+0.065275
## [14] train-rmse:0.557233+0.011197 test-rmse:0.569769+0.061132
## [15] train-rmse:0.551975+0.010651 test-rmse:0.562141+0.059850
## [16] train-rmse:0.547252+0.010462 test-rmse:0.559952+0.061772
## [17] train-rmse:0.543089+0.010502 test-rmse:0.562203+0.061159
## [18] train-rmse:0.539338+0.010445 test-rmse:0.558769+0.059387
## [19] train-rmse:0.535793+0.010230 test-rmse:0.559110+0.057667
## [20] train-rmse:0.532513+0.010201 test-rmse:0.557775+0.058649
## [21] train-rmse:0.529283+0.010195 test-rmse:0.552572+0.056582
## [22] train-rmse:0.526291+0.009920 test-rmse:0.549004+0.055808
## [23] train-rmse:0.523203+0.009626 test-rmse:0.546523+0.055392
## [24] train-rmse:0.520339+0.009420 test-rmse:0.543441+0.057588
## [25] train-rmse:0.517798+0.009080 test-rmse:0.542522+0.057932
## [26] train-rmse:0.515394+0.008997 test-rmse:0.541376+0.056378
## [27] train-rmse:0.513139+0.008869 test-rmse:0.541749+0.057447
## [28] train-rmse:0.510970+0.008837 test-rmse:0.540068+0.056156
## [29] train-rmse:0.508740+0.008725 test-rmse:0.538594+0.053544
## [30] train-rmse:0.506536+0.008767 test-rmse:0.535367+0.054047
## [31] train-rmse:0.504444+0.008554 test-rmse:0.534685+0.054065
## [32] train-rmse:0.502552+0.008528 test-rmse:0.534770+0.054475
## [33] train-rmse:0.500484+0.008426 test-rmse:0.533272+0.055943
## [34] train-rmse:0.498636+0.008401 test-rmse:0.531283+0.053749
## [35] train-rmse:0.496931+0.008457 test-rmse:0.529597+0.053227
## [36] train-rmse:0.495124+0.008453 test-rmse:0.526436+0.053384
## [37] train-rmse:0.493421+0.008379 test-rmse:0.525837+0.054045
## [38] train-rmse:0.491857+0.008289 test-rmse:0.525157+0.054848
## [39] train-rmse:0.490314+0.008240 test-rmse:0.525481+0.053073
## [40] train-rmse:0.488773+0.008272 test-rmse:0.525100+0.052406
## [41] train-rmse:0.487235+0.008229 test-rmse:0.524097+0.052775
## [42] train-rmse:0.485757+0.008127 test-rmse:0.523124+0.052256
## [43] train-rmse:0.484304+0.008047 test-rmse:0.521881+0.051059
## [44] train-rmse:0.482986+0.008058 test-rmse:0.521027+0.049627
## [45] train-rmse:0.481597+0.008054 test-rmse:0.519714+0.048258
## [46] train-rmse:0.480336+0.008036 test-rmse:0.519714+0.048864
## [47] train-rmse:0.479028+0.007896 test-rmse:0.518694+0.050382
## [48] train-rmse:0.477846+0.007897 test-rmse:0.518497+0.050256
## [49] train-rmse:0.476702+0.007868 test-rmse:0.517431+0.051553
## [50] train-rmse:0.475616+0.007882 test-rmse:0.516278+0.050557
## [51] train-rmse:0.474572+0.007921 test-rmse:0.514898+0.049437
## [52] train-rmse:0.473530+0.007958 test-rmse:0.514677+0.049062
## [53] train-rmse:0.472477+0.007899 test-rmse:0.513766+0.048529
## [54] train-rmse:0.471472+0.007860 test-rmse:0.514038+0.048911
## [55] train-rmse:0.470466+0.007798 test-rmse:0.514513+0.049052
## [56] train-rmse:0.469414+0.007746 test-rmse:0.511425+0.046501
## [57] train-rmse:0.468435+0.007747 test-rmse:0.510702+0.047248
## [58] train-rmse:0.467511+0.007757 test-rmse:0.508725+0.045823
## [59] train-rmse:0.466606+0.007767 test-rmse:0.508045+0.046000
## [60] train-rmse:0.465683+0.007747 test-rmse:0.507878+0.045429
## [61] train-rmse:0.464794+0.007737 test-rmse:0.507250+0.045382
## [62] train-rmse:0.463820+0.007713 test-rmse:0.506615+0.044776
## [63] train-rmse:0.462990+0.007732 test-rmse:0.506159+0.045390
## [64] train-rmse:0.462149+0.007694 test-rmse:0.506176+0.046139
## [65] train-rmse:0.461357+0.007673 test-rmse:0.504383+0.046090
## [66] train-rmse:0.460562+0.007641 test-rmse:0.505756+0.046035
## [67] train-rmse:0.459815+0.007611 test-rmse:0.505944+0.044819
## [68] train-rmse:0.459015+0.007574 test-rmse:0.505599+0.044601
## [69] train-rmse:0.458273+0.007556 test-rmse:0.506148+0.044656
## [70] train-rmse:0.457564+0.007527 test-rmse:0.505165+0.045290
## [71] train-rmse:0.456863+0.007521 test-rmse:0.505701+0.044891
## Stopping. Best iteration:
## [65] train-rmse:0.461357+0.007673 test-rmse:0.504383+0.046090
##
## 85
## [1] train-rmse:1.165436+0.022741 test-rmse:1.167013+0.113843
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.867899+0.016927 test-rmse:0.866738+0.100504
## [3] train-rmse:0.688368+0.015071 test-rmse:0.693104+0.102464
## [4] train-rmse:0.585486+0.013657 test-rmse:0.598380+0.097938
## [5] train-rmse:0.525170+0.012217 test-rmse:0.540923+0.090036
## [6] train-rmse:0.488813+0.012168 test-rmse:0.513840+0.077962
## [7] train-rmse:0.462193+0.013769 test-rmse:0.490644+0.074406
## [8] train-rmse:0.441760+0.010054 test-rmse:0.471395+0.069256
## [9] train-rmse:0.427944+0.009394 test-rmse:0.466407+0.065107
## [10] train-rmse:0.416079+0.009095 test-rmse:0.462713+0.061309
## [11] train-rmse:0.407579+0.008353 test-rmse:0.460308+0.058619
## [12] train-rmse:0.399519+0.007554 test-rmse:0.458106+0.057443
## [13] train-rmse:0.390937+0.007119 test-rmse:0.453215+0.059347
## [14] train-rmse:0.384370+0.007056 test-rmse:0.452473+0.058941
## [15] train-rmse:0.378316+0.005651 test-rmse:0.447994+0.058453
## [16] train-rmse:0.372606+0.005508 test-rmse:0.447556+0.053726
## [17] train-rmse:0.367423+0.006247 test-rmse:0.446214+0.055290
## [18] train-rmse:0.361396+0.006217 test-rmse:0.442162+0.053652
## [19] train-rmse:0.355950+0.006423 test-rmse:0.442627+0.053637
## [20] train-rmse:0.350719+0.006935 test-rmse:0.441555+0.049261
## [21] train-rmse:0.346202+0.007292 test-rmse:0.438184+0.049170
## [22] train-rmse:0.341409+0.007744 test-rmse:0.435622+0.050668
## [23] train-rmse:0.336646+0.008329 test-rmse:0.435947+0.050447
## [24] train-rmse:0.332643+0.007900 test-rmse:0.435427+0.051988
## [25] train-rmse:0.330081+0.007969 test-rmse:0.434445+0.052171
## [26] train-rmse:0.327260+0.008425 test-rmse:0.435147+0.052320
## [27] train-rmse:0.324687+0.008139 test-rmse:0.433186+0.055000
## [28] train-rmse:0.321980+0.008571 test-rmse:0.432544+0.055253
## [29] train-rmse:0.318491+0.008749 test-rmse:0.432147+0.056431
## [30] train-rmse:0.314770+0.009508 test-rmse:0.431952+0.054478
## [31] train-rmse:0.312388+0.009347 test-rmse:0.431699+0.053157
## [32] train-rmse:0.309569+0.008737 test-rmse:0.430127+0.054613
## [33] train-rmse:0.306339+0.006905 test-rmse:0.430191+0.054691
## [34] train-rmse:0.303992+0.006844 test-rmse:0.427397+0.052387
## [35] train-rmse:0.301919+0.007170 test-rmse:0.427688+0.052246
## [36] train-rmse:0.298133+0.008294 test-rmse:0.427626+0.053740
## [37] train-rmse:0.296135+0.007660 test-rmse:0.425902+0.055436
## [38] train-rmse:0.293994+0.007265 test-rmse:0.426001+0.053849
## [39] train-rmse:0.291967+0.007526 test-rmse:0.424064+0.054155
## [40] train-rmse:0.290043+0.007629 test-rmse:0.424209+0.054471
## [41] train-rmse:0.286734+0.007155 test-rmse:0.423518+0.055102
## [42] train-rmse:0.284949+0.007070 test-rmse:0.422073+0.056831
## [43] train-rmse:0.282932+0.007491 test-rmse:0.421314+0.056952
## [44] train-rmse:0.280433+0.006875 test-rmse:0.420617+0.056802
## [45] train-rmse:0.279046+0.006610 test-rmse:0.421227+0.055181
## [46] train-rmse:0.277431+0.006493 test-rmse:0.420870+0.055726
## [47] train-rmse:0.275222+0.005881 test-rmse:0.419567+0.055417
## [48] train-rmse:0.273804+0.005899 test-rmse:0.418658+0.054490
## [49] train-rmse:0.271988+0.005823 test-rmse:0.418356+0.054630
## [50] train-rmse:0.270599+0.005772 test-rmse:0.418023+0.054072
## [51] train-rmse:0.268368+0.005411 test-rmse:0.416734+0.054992
## [52] train-rmse:0.266472+0.005015 test-rmse:0.417843+0.054885
## [53] train-rmse:0.264579+0.005522 test-rmse:0.416871+0.055771
## [54] train-rmse:0.262589+0.005723 test-rmse:0.416774+0.056904
## [55] train-rmse:0.260759+0.005500 test-rmse:0.416194+0.056875
## [56] train-rmse:0.259187+0.005228 test-rmse:0.416109+0.057845
## [57] train-rmse:0.257561+0.005399 test-rmse:0.416205+0.058248
## [58] train-rmse:0.256089+0.006242 test-rmse:0.416435+0.059191
## [59] train-rmse:0.255043+0.005990 test-rmse:0.416850+0.058604
## [60] train-rmse:0.253963+0.006185 test-rmse:0.417224+0.058139
## [61] train-rmse:0.252417+0.006159 test-rmse:0.417637+0.058552
## [62] train-rmse:0.251311+0.006209 test-rmse:0.417871+0.058176
## Stopping. Best iteration:
## [56] train-rmse:0.259187+0.005228 test-rmse:0.416109+0.057845
##
## 86
## [1] train-rmse:1.152674+0.021464 test-rmse:1.159197+0.117803
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.840502+0.018259 test-rmse:0.842779+0.102820
## [3] train-rmse:0.641521+0.013917 test-rmse:0.661618+0.098683
## [4] train-rmse:0.520688+0.011383 test-rmse:0.557184+0.083899
## [5] train-rmse:0.448515+0.010066 test-rmse:0.494629+0.085020
## [6] train-rmse:0.403815+0.008819 test-rmse:0.466398+0.081151
## [7] train-rmse:0.374624+0.007733 test-rmse:0.449416+0.073048
## [8] train-rmse:0.354977+0.007213 test-rmse:0.438601+0.064910
## [9] train-rmse:0.339765+0.008046 test-rmse:0.434341+0.060783
## [10] train-rmse:0.328070+0.008196 test-rmse:0.431534+0.059785
## [11] train-rmse:0.316989+0.007375 test-rmse:0.429401+0.057373
## [12] train-rmse:0.307730+0.007308 test-rmse:0.430546+0.057944
## [13] train-rmse:0.300516+0.005286 test-rmse:0.427668+0.059013
## [14] train-rmse:0.293937+0.005288 test-rmse:0.426728+0.059346
## [15] train-rmse:0.286753+0.005865 test-rmse:0.428854+0.059152
## [16] train-rmse:0.280192+0.005904 test-rmse:0.429294+0.058304
## [17] train-rmse:0.275125+0.005861 test-rmse:0.431264+0.057796
## [18] train-rmse:0.270802+0.005802 test-rmse:0.431021+0.057320
## [19] train-rmse:0.265327+0.004770 test-rmse:0.427457+0.059705
## [20] train-rmse:0.261510+0.004877 test-rmse:0.428133+0.060483
## Stopping. Best iteration:
## [14] train-rmse:0.293937+0.005288 test-rmse:0.426728+0.059346
##
## 87
## [1] train-rmse:1.146736+0.020489 test-rmse:1.152830+0.118885
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.827991+0.013938 test-rmse:0.840792+0.114570
## [3] train-rmse:0.620212+0.013304 test-rmse:0.641182+0.104460
## [4] train-rmse:0.489496+0.010528 test-rmse:0.532169+0.093901
## [5] train-rmse:0.408787+0.008957 test-rmse:0.475242+0.087668
## [6] train-rmse:0.356448+0.010057 test-rmse:0.445216+0.076930
## [7] train-rmse:0.325945+0.008339 test-rmse:0.432115+0.075046
## [8] train-rmse:0.303812+0.011055 test-rmse:0.423580+0.065436
## [9] train-rmse:0.287401+0.007950 test-rmse:0.417341+0.070020
## [10] train-rmse:0.275704+0.009435 test-rmse:0.413369+0.068219
## [11] train-rmse:0.265140+0.007456 test-rmse:0.411198+0.066814
## [12] train-rmse:0.255338+0.006343 test-rmse:0.406204+0.068553
## [13] train-rmse:0.246290+0.006202 test-rmse:0.406044+0.068376
## [14] train-rmse:0.239864+0.006073 test-rmse:0.404395+0.067467
## [15] train-rmse:0.231258+0.005867 test-rmse:0.402418+0.067260
## [16] train-rmse:0.224498+0.005938 test-rmse:0.397565+0.066317
## [17] train-rmse:0.216534+0.005123 test-rmse:0.395176+0.067143
## [18] train-rmse:0.210371+0.005315 test-rmse:0.394463+0.066606
## [19] train-rmse:0.205612+0.005417 test-rmse:0.395786+0.064655
## [20] train-rmse:0.198443+0.005130 test-rmse:0.394772+0.061831
## [21] train-rmse:0.193041+0.004103 test-rmse:0.393789+0.062661
## [22] train-rmse:0.189127+0.004260 test-rmse:0.393750+0.062877
## [23] train-rmse:0.184097+0.004542 test-rmse:0.392773+0.062444
## [24] train-rmse:0.180502+0.003904 test-rmse:0.391656+0.063827
## [25] train-rmse:0.174874+0.003296 test-rmse:0.393021+0.064329
## [26] train-rmse:0.171366+0.002916 test-rmse:0.389529+0.065455
## [27] train-rmse:0.166046+0.003523 test-rmse:0.387910+0.063199
## [28] train-rmse:0.162973+0.003465 test-rmse:0.387910+0.063563
## [29] train-rmse:0.159596+0.004280 test-rmse:0.387823+0.064062
## [30] train-rmse:0.155866+0.003655 test-rmse:0.388123+0.064661
## [31] train-rmse:0.150219+0.002526 test-rmse:0.387449+0.063256
## [32] train-rmse:0.147929+0.002331 test-rmse:0.388200+0.062423
## [33] train-rmse:0.145016+0.003028 test-rmse:0.386733+0.062508
## [34] train-rmse:0.141974+0.003281 test-rmse:0.387127+0.062542
## [35] train-rmse:0.139394+0.003644 test-rmse:0.386537+0.061551
## [36] train-rmse:0.136544+0.003331 test-rmse:0.386469+0.060978
## [37] train-rmse:0.132488+0.003888 test-rmse:0.386888+0.060756
## [38] train-rmse:0.129171+0.004035 test-rmse:0.386104+0.062740
## [39] train-rmse:0.126610+0.004894 test-rmse:0.386287+0.061902
## [40] train-rmse:0.124356+0.005436 test-rmse:0.385828+0.061974
## [41] train-rmse:0.121600+0.005083 test-rmse:0.384939+0.062167
## [42] train-rmse:0.119703+0.005317 test-rmse:0.386292+0.062586
## [43] train-rmse:0.117400+0.005735 test-rmse:0.385589+0.062896
## [44] train-rmse:0.115368+0.005619 test-rmse:0.386184+0.062715
## [45] train-rmse:0.113032+0.005732 test-rmse:0.385779+0.062367
## [46] train-rmse:0.110781+0.005970 test-rmse:0.385989+0.062230
## [47] train-rmse:0.108822+0.006409 test-rmse:0.385674+0.061441
## Stopping. Best iteration:
## [41] train-rmse:0.121600+0.005083 test-rmse:0.384939+0.062167
##
## 88
## [1] train-rmse:1.143318+0.021018 test-rmse:1.149886+0.113484
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.820222+0.014525 test-rmse:0.834828+0.105377
## [3] train-rmse:0.608786+0.012568 test-rmse:0.637830+0.095555
## [4] train-rmse:0.469679+0.006836 test-rmse:0.530343+0.089179
## [5] train-rmse:0.381391+0.006280 test-rmse:0.472264+0.082840
## [6] train-rmse:0.323318+0.007667 test-rmse:0.440573+0.076373
## [7] train-rmse:0.284622+0.008514 test-rmse:0.426456+0.070749
## [8] train-rmse:0.260477+0.010102 test-rmse:0.420110+0.069997
## [9] train-rmse:0.245091+0.010343 test-rmse:0.417963+0.066676
## [10] train-rmse:0.232345+0.010448 test-rmse:0.413673+0.068882
## [11] train-rmse:0.216841+0.005258 test-rmse:0.410460+0.072623
## [12] train-rmse:0.206086+0.006439 test-rmse:0.410135+0.069973
## [13] train-rmse:0.194431+0.005636 test-rmse:0.412018+0.070202
## [14] train-rmse:0.186163+0.004431 test-rmse:0.411815+0.069325
## [15] train-rmse:0.178914+0.004358 test-rmse:0.409879+0.070922
## [16] train-rmse:0.173221+0.004494 test-rmse:0.409176+0.071860
## [17] train-rmse:0.167549+0.004852 test-rmse:0.409494+0.072780
## [18] train-rmse:0.162483+0.004964 test-rmse:0.409056+0.072793
## [19] train-rmse:0.158239+0.004306 test-rmse:0.408018+0.070797
## [20] train-rmse:0.153245+0.004757 test-rmse:0.406523+0.070830
## [21] train-rmse:0.146815+0.006072 test-rmse:0.405876+0.072581
## [22] train-rmse:0.141344+0.007153 test-rmse:0.405872+0.073840
## [23] train-rmse:0.135540+0.006270 test-rmse:0.406625+0.072879
## [24] train-rmse:0.129351+0.008267 test-rmse:0.406552+0.071098
## [25] train-rmse:0.125738+0.008616 test-rmse:0.406589+0.071641
## [26] train-rmse:0.122347+0.008911 test-rmse:0.407118+0.071230
## [27] train-rmse:0.118217+0.009624 test-rmse:0.406822+0.069950
## [28] train-rmse:0.113670+0.009972 test-rmse:0.406657+0.070282
## Stopping. Best iteration:
## [22] train-rmse:0.141344+0.007153 test-rmse:0.405872+0.073840
##
## 89
## [1] train-rmse:1.142192+0.021075 test-rmse:1.147741+0.111537
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.816428+0.014663 test-rmse:0.831961+0.104140
## [3] train-rmse:0.600774+0.013163 test-rmse:0.633311+0.091961
## [4] train-rmse:0.456976+0.010782 test-rmse:0.514786+0.089895
## [5] train-rmse:0.363254+0.009524 test-rmse:0.456077+0.085055
## [6] train-rmse:0.304019+0.011776 test-rmse:0.425943+0.070704
## [7] train-rmse:0.263930+0.011019 test-rmse:0.408336+0.064188
## [8] train-rmse:0.234317+0.014361 test-rmse:0.403357+0.064258
## [9] train-rmse:0.214139+0.013342 test-rmse:0.399960+0.060237
## [10] train-rmse:0.198621+0.012356 test-rmse:0.398558+0.058756
## [11] train-rmse:0.180578+0.008793 test-rmse:0.394350+0.057018
## [12] train-rmse:0.169102+0.009210 test-rmse:0.394065+0.052148
## [13] train-rmse:0.159291+0.008109 test-rmse:0.392981+0.050430
## [14] train-rmse:0.147404+0.006948 test-rmse:0.392898+0.051038
## [15] train-rmse:0.139546+0.009293 test-rmse:0.391509+0.052432
## [16] train-rmse:0.131822+0.010041 test-rmse:0.392548+0.049193
## [17] train-rmse:0.125019+0.009792 test-rmse:0.390190+0.049868
## [18] train-rmse:0.116712+0.010285 test-rmse:0.389397+0.050165
## [19] train-rmse:0.111215+0.010153 test-rmse:0.389020+0.049748
## [20] train-rmse:0.106791+0.010490 test-rmse:0.388688+0.050297
## [21] train-rmse:0.102489+0.010654 test-rmse:0.388331+0.050334
## [22] train-rmse:0.098025+0.010672 test-rmse:0.387554+0.050125
## [23] train-rmse:0.093361+0.009624 test-rmse:0.388332+0.049260
## [24] train-rmse:0.089260+0.009410 test-rmse:0.386818+0.049044
## [25] train-rmse:0.084582+0.007768 test-rmse:0.387284+0.049750
## [26] train-rmse:0.079739+0.006456 test-rmse:0.387353+0.049493
## [27] train-rmse:0.076189+0.006282 test-rmse:0.387772+0.048757
## [28] train-rmse:0.072860+0.005246 test-rmse:0.387766+0.049183
## [29] train-rmse:0.069951+0.003971 test-rmse:0.388908+0.049893
## [30] train-rmse:0.066594+0.003984 test-rmse:0.389065+0.049230
## Stopping. Best iteration:
## [24] train-rmse:0.089260+0.009410 test-rmse:0.386818+0.049044
##
## 90
## [1] train-rmse:1.141665+0.021072 test-rmse:1.149439+0.111123
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.814171+0.015196 test-rmse:0.830822+0.103121
## [3] train-rmse:0.595372+0.013113 test-rmse:0.627192+0.092592
## [4] train-rmse:0.449203+0.012462 test-rmse:0.515959+0.091198
## [5] train-rmse:0.349190+0.009331 test-rmse:0.455008+0.086091
## [6] train-rmse:0.282531+0.007210 test-rmse:0.432805+0.080843
## [7] train-rmse:0.234336+0.007950 test-rmse:0.419401+0.071512
## [8] train-rmse:0.200949+0.008667 test-rmse:0.411296+0.068495
## [9] train-rmse:0.177771+0.010805 test-rmse:0.404666+0.064063
## [10] train-rmse:0.159978+0.008291 test-rmse:0.401319+0.062705
## [11] train-rmse:0.147124+0.009840 test-rmse:0.401538+0.059459
## [12] train-rmse:0.135628+0.007961 test-rmse:0.401265+0.056839
## [13] train-rmse:0.125943+0.008434 test-rmse:0.399211+0.055312
## [14] train-rmse:0.117414+0.008448 test-rmse:0.399647+0.054492
## [15] train-rmse:0.108278+0.006965 test-rmse:0.401026+0.054700
## [16] train-rmse:0.099847+0.005374 test-rmse:0.401465+0.055870
## [17] train-rmse:0.091192+0.003863 test-rmse:0.401269+0.054845
## [18] train-rmse:0.083133+0.004517 test-rmse:0.400196+0.055123
## [19] train-rmse:0.079036+0.004795 test-rmse:0.399809+0.054949
## Stopping. Best iteration:
## [13] train-rmse:0.125943+0.008434 test-rmse:0.399211+0.055312
##
## 91
## [1] train-rmse:1.164118+0.022144 test-rmse:1.160344+0.106242
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.897983+0.018737 test-rmse:0.894514+0.098069
## [3] train-rmse:0.761054+0.017968 test-rmse:0.758077+0.091872
## [4] train-rmse:0.696538+0.018034 test-rmse:0.694036+0.087789
## [5] train-rmse:0.661569+0.016538 test-rmse:0.664732+0.088353
## [6] train-rmse:0.636768+0.016790 test-rmse:0.635814+0.084627
## [7] train-rmse:0.616249+0.013578 test-rmse:0.616834+0.076918
## [8] train-rmse:0.602691+0.013488 test-rmse:0.609905+0.074515
## [9] train-rmse:0.591151+0.013127 test-rmse:0.596252+0.071958
## [10] train-rmse:0.580638+0.012213 test-rmse:0.587902+0.066303
## [11] train-rmse:0.571544+0.011498 test-rmse:0.579113+0.066132
## [12] train-rmse:0.563962+0.011548 test-rmse:0.575916+0.064607
## [13] train-rmse:0.557642+0.011408 test-rmse:0.569780+0.062673
## [14] train-rmse:0.551932+0.010963 test-rmse:0.563144+0.062057
## [15] train-rmse:0.546673+0.010432 test-rmse:0.560252+0.060870
## [16] train-rmse:0.542532+0.010388 test-rmse:0.559446+0.062537
## [17] train-rmse:0.538496+0.010359 test-rmse:0.557884+0.059536
## [18] train-rmse:0.534826+0.010285 test-rmse:0.558076+0.058111
## [19] train-rmse:0.531259+0.010109 test-rmse:0.556313+0.058793
## [20] train-rmse:0.527945+0.010010 test-rmse:0.552853+0.057858
## [21] train-rmse:0.524878+0.009772 test-rmse:0.549549+0.058030
## [22] train-rmse:0.521751+0.009273 test-rmse:0.545350+0.058719
## [23] train-rmse:0.518899+0.009004 test-rmse:0.546427+0.058396
## [24] train-rmse:0.516159+0.008917 test-rmse:0.542886+0.058037
## [25] train-rmse:0.513546+0.008846 test-rmse:0.540840+0.054594
## [26] train-rmse:0.511202+0.008789 test-rmse:0.538324+0.054780
## [27] train-rmse:0.508939+0.008683 test-rmse:0.538547+0.055201
## [28] train-rmse:0.506638+0.008748 test-rmse:0.537885+0.056163
## [29] train-rmse:0.504431+0.008659 test-rmse:0.535752+0.055384
## [30] train-rmse:0.502266+0.008643 test-rmse:0.535116+0.052496
## [31] train-rmse:0.500109+0.008443 test-rmse:0.532762+0.052332
## [32] train-rmse:0.498207+0.008374 test-rmse:0.529716+0.053727
## [33] train-rmse:0.496427+0.008415 test-rmse:0.529746+0.054099
## [34] train-rmse:0.494584+0.008571 test-rmse:0.527954+0.053957
## [35] train-rmse:0.492848+0.008511 test-rmse:0.527565+0.054910
## [36] train-rmse:0.491085+0.008415 test-rmse:0.526405+0.054552
## [37] train-rmse:0.489416+0.008465 test-rmse:0.523733+0.054099
## [38] train-rmse:0.487754+0.008326 test-rmse:0.522592+0.053499
## [39] train-rmse:0.486209+0.008236 test-rmse:0.523165+0.052809
## [40] train-rmse:0.484714+0.008166 test-rmse:0.521086+0.052686
## [41] train-rmse:0.483224+0.008126 test-rmse:0.521455+0.051832
## [42] train-rmse:0.481790+0.008142 test-rmse:0.519512+0.050337
## [43] train-rmse:0.480426+0.008085 test-rmse:0.520227+0.049389
## [44] train-rmse:0.479044+0.008029 test-rmse:0.518455+0.049550
## [45] train-rmse:0.477703+0.007923 test-rmse:0.518981+0.049890
## [46] train-rmse:0.476508+0.007868 test-rmse:0.518836+0.049378
## [47] train-rmse:0.475252+0.007872 test-rmse:0.516885+0.049672
## [48] train-rmse:0.474109+0.007849 test-rmse:0.517214+0.048396
## [49] train-rmse:0.472990+0.007830 test-rmse:0.515559+0.049035
## [50] train-rmse:0.471885+0.007809 test-rmse:0.514974+0.048386
## [51] train-rmse:0.470847+0.007774 test-rmse:0.515537+0.048861
## [52] train-rmse:0.469829+0.007747 test-rmse:0.515360+0.050044
## [53] train-rmse:0.468791+0.007736 test-rmse:0.514226+0.050287
## [54] train-rmse:0.467793+0.007715 test-rmse:0.514093+0.050396
## [55] train-rmse:0.466821+0.007696 test-rmse:0.513233+0.050188
## [56] train-rmse:0.465809+0.007626 test-rmse:0.510642+0.045840
## [57] train-rmse:0.464865+0.007602 test-rmse:0.510057+0.045881
## [58] train-rmse:0.463911+0.007597 test-rmse:0.508888+0.045538
## [59] train-rmse:0.463001+0.007609 test-rmse:0.507974+0.045726
## [60] train-rmse:0.462075+0.007575 test-rmse:0.506011+0.045958
## [61] train-rmse:0.461218+0.007616 test-rmse:0.505693+0.045731
## [62] train-rmse:0.460397+0.007603 test-rmse:0.506136+0.044672
## [63] train-rmse:0.459625+0.007605 test-rmse:0.505285+0.044073
## [64] train-rmse:0.458832+0.007641 test-rmse:0.505371+0.044504
## [65] train-rmse:0.458044+0.007613 test-rmse:0.504884+0.044476
## [66] train-rmse:0.457219+0.007529 test-rmse:0.503558+0.045824
## [67] train-rmse:0.456435+0.007456 test-rmse:0.504061+0.045265
## [68] train-rmse:0.455732+0.007453 test-rmse:0.505034+0.045550
## [69] train-rmse:0.455030+0.007494 test-rmse:0.505048+0.045666
## [70] train-rmse:0.454301+0.007500 test-rmse:0.504572+0.045591
## [71] train-rmse:0.453563+0.007479 test-rmse:0.505126+0.045440
## [72] train-rmse:0.452854+0.007445 test-rmse:0.504319+0.044487
## Stopping. Best iteration:
## [66] train-rmse:0.457219+0.007529 test-rmse:0.503558+0.045824
##
## 92
## [1] train-rmse:1.138830+0.022385 test-rmse:1.140870+0.113704
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.835653+0.016434 test-rmse:0.834746+0.099799
## [3] train-rmse:0.660263+0.014450 test-rmse:0.665793+0.101798
## [4] train-rmse:0.564595+0.013460 test-rmse:0.579043+0.096733
## [5] train-rmse:0.510172+0.011834 test-rmse:0.525142+0.088834
## [6] train-rmse:0.477026+0.011621 test-rmse:0.502091+0.076444
## [7] train-rmse:0.450879+0.010567 test-rmse:0.477891+0.074339
## [8] train-rmse:0.433075+0.010240 test-rmse:0.467331+0.065583
## [9] train-rmse:0.418984+0.008977 test-rmse:0.460763+0.064404
## [10] train-rmse:0.407555+0.007605 test-rmse:0.458863+0.063483
## [11] train-rmse:0.398467+0.006429 test-rmse:0.456384+0.062714
## [12] train-rmse:0.391011+0.005913 test-rmse:0.452625+0.063080
## [13] train-rmse:0.383819+0.005556 test-rmse:0.452785+0.062886
## [14] train-rmse:0.377290+0.005086 test-rmse:0.445748+0.066546
## [15] train-rmse:0.371713+0.004896 test-rmse:0.445632+0.062161
## [16] train-rmse:0.365759+0.004734 test-rmse:0.444222+0.063769
## [17] train-rmse:0.360756+0.004525 test-rmse:0.444311+0.061135
## [18] train-rmse:0.356160+0.004555 test-rmse:0.441995+0.061285
## [19] train-rmse:0.352058+0.004385 test-rmse:0.440253+0.059539
## [20] train-rmse:0.346174+0.004035 test-rmse:0.441303+0.061128
## [21] train-rmse:0.341710+0.005412 test-rmse:0.441383+0.058924
## [22] train-rmse:0.338116+0.005480 test-rmse:0.441844+0.059772
## [23] train-rmse:0.335121+0.005394 test-rmse:0.438895+0.060830
## [24] train-rmse:0.331489+0.005137 test-rmse:0.439844+0.061153
## [25] train-rmse:0.328683+0.004675 test-rmse:0.440004+0.061935
## [26] train-rmse:0.325423+0.004712 test-rmse:0.439356+0.060790
## [27] train-rmse:0.321674+0.004740 test-rmse:0.439305+0.060343
## [28] train-rmse:0.317934+0.005016 test-rmse:0.438480+0.060066
## [29] train-rmse:0.313775+0.005229 test-rmse:0.436516+0.061117
## [30] train-rmse:0.310708+0.004887 test-rmse:0.432851+0.059983
## [31] train-rmse:0.308529+0.004730 test-rmse:0.433175+0.059386
## [32] train-rmse:0.306149+0.004610 test-rmse:0.433146+0.060098
## [33] train-rmse:0.303114+0.004538 test-rmse:0.432485+0.060604
## [34] train-rmse:0.301179+0.005183 test-rmse:0.432303+0.060947
## [35] train-rmse:0.298376+0.005488 test-rmse:0.431564+0.060850
## [36] train-rmse:0.295761+0.005892 test-rmse:0.429833+0.060843
## [37] train-rmse:0.294112+0.006279 test-rmse:0.430020+0.061361
## [38] train-rmse:0.292675+0.006101 test-rmse:0.427466+0.063955
## [39] train-rmse:0.290168+0.005059 test-rmse:0.426624+0.064652
## [40] train-rmse:0.287649+0.005691 test-rmse:0.426225+0.065494
## [41] train-rmse:0.285152+0.005648 test-rmse:0.426388+0.065888
## [42] train-rmse:0.283487+0.005862 test-rmse:0.426218+0.065360
## [43] train-rmse:0.281740+0.005956 test-rmse:0.426133+0.065512
## [44] train-rmse:0.278587+0.006834 test-rmse:0.426117+0.065580
## [45] train-rmse:0.276671+0.006940 test-rmse:0.425514+0.065153
## [46] train-rmse:0.275586+0.006855 test-rmse:0.426225+0.064298
## [47] train-rmse:0.273279+0.006752 test-rmse:0.425192+0.063999
## [48] train-rmse:0.271799+0.006400 test-rmse:0.424753+0.064406
## [49] train-rmse:0.270456+0.006611 test-rmse:0.424214+0.064531
## [50] train-rmse:0.268312+0.007371 test-rmse:0.421746+0.064376
## [51] train-rmse:0.266826+0.007614 test-rmse:0.422323+0.064298
## [52] train-rmse:0.265630+0.007507 test-rmse:0.421272+0.064308
## [53] train-rmse:0.262874+0.007925 test-rmse:0.420274+0.064540
## [54] train-rmse:0.261461+0.007729 test-rmse:0.419908+0.065193
## [55] train-rmse:0.259693+0.007167 test-rmse:0.419866+0.065533
## [56] train-rmse:0.257344+0.007672 test-rmse:0.420557+0.065683
## [57] train-rmse:0.256022+0.007454 test-rmse:0.421089+0.065203
## [58] train-rmse:0.254160+0.007944 test-rmse:0.420967+0.064367
## [59] train-rmse:0.251750+0.007694 test-rmse:0.420915+0.064723
## [60] train-rmse:0.250700+0.007323 test-rmse:0.420719+0.064849
## [61] train-rmse:0.249721+0.007672 test-rmse:0.419816+0.063053
## [62] train-rmse:0.248390+0.007452 test-rmse:0.419271+0.062957
## [63] train-rmse:0.246942+0.006649 test-rmse:0.419812+0.062667
## [64] train-rmse:0.245197+0.006116 test-rmse:0.420181+0.062890
## [65] train-rmse:0.244050+0.006820 test-rmse:0.419240+0.061878
## [66] train-rmse:0.242980+0.006771 test-rmse:0.418190+0.062181
## [67] train-rmse:0.241508+0.007105 test-rmse:0.418226+0.061543
## [68] train-rmse:0.240209+0.006569 test-rmse:0.417862+0.062023
## [69] train-rmse:0.238713+0.007216 test-rmse:0.418436+0.061998
## [70] train-rmse:0.237466+0.007204 test-rmse:0.417445+0.061688
## [71] train-rmse:0.236032+0.007059 test-rmse:0.416930+0.062154
## [72] train-rmse:0.234597+0.007039 test-rmse:0.416367+0.061926
## [73] train-rmse:0.233356+0.007431 test-rmse:0.416592+0.061818
## [74] train-rmse:0.231888+0.006908 test-rmse:0.416407+0.061794
## [75] train-rmse:0.231010+0.006831 test-rmse:0.415889+0.061731
## [76] train-rmse:0.229831+0.006978 test-rmse:0.416430+0.061929
## [77] train-rmse:0.228801+0.006600 test-rmse:0.416653+0.062255
## [78] train-rmse:0.227480+0.007347 test-rmse:0.415252+0.062971
## [79] train-rmse:0.225930+0.007516 test-rmse:0.415898+0.062354
## [80] train-rmse:0.224280+0.007114 test-rmse:0.415298+0.063425
## [81] train-rmse:0.223527+0.007028 test-rmse:0.415068+0.063749
## [82] train-rmse:0.222696+0.007005 test-rmse:0.415925+0.063512
## [83] train-rmse:0.221621+0.007115 test-rmse:0.415720+0.063107
## [84] train-rmse:0.220781+0.006924 test-rmse:0.415554+0.062982
## [85] train-rmse:0.219354+0.007390 test-rmse:0.413770+0.063543
## [86] train-rmse:0.218126+0.007120 test-rmse:0.414462+0.063765
## [87] train-rmse:0.217175+0.006929 test-rmse:0.413708+0.064074
## [88] train-rmse:0.216669+0.006936 test-rmse:0.413741+0.063695
## [89] train-rmse:0.215118+0.006710 test-rmse:0.414944+0.062728
## [90] train-rmse:0.213608+0.006432 test-rmse:0.414215+0.062518
## [91] train-rmse:0.212500+0.006311 test-rmse:0.414247+0.062533
## [92] train-rmse:0.211272+0.006097 test-rmse:0.415140+0.062532
## [93] train-rmse:0.209738+0.005954 test-rmse:0.415513+0.062016
## Stopping. Best iteration:
## [87] train-rmse:0.217175+0.006929 test-rmse:0.413708+0.064074
##
## 93
## [1] train-rmse:1.125075+0.021024 test-rmse:1.132284+0.117895
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.806048+0.017895 test-rmse:0.809299+0.102263
## [3] train-rmse:0.611059+0.014241 test-rmse:0.628801+0.103500
## [4] train-rmse:0.495985+0.011070 test-rmse:0.532747+0.089062
## [5] train-rmse:0.431186+0.009968 test-rmse:0.481897+0.085821
## [6] train-rmse:0.392889+0.008685 test-rmse:0.456307+0.079818
## [7] train-rmse:0.368838+0.008076 test-rmse:0.442248+0.080822
## [8] train-rmse:0.350129+0.006461 test-rmse:0.434508+0.077453
## [9] train-rmse:0.334812+0.005703 test-rmse:0.430081+0.073520
## [10] train-rmse:0.322066+0.005031 test-rmse:0.429677+0.072215
## [11] train-rmse:0.312467+0.004424 test-rmse:0.430385+0.070878
## [12] train-rmse:0.303174+0.005169 test-rmse:0.429120+0.070965
## [13] train-rmse:0.296247+0.005403 test-rmse:0.427362+0.068772
## [14] train-rmse:0.286795+0.006053 test-rmse:0.426081+0.070372
## [15] train-rmse:0.280874+0.005929 test-rmse:0.426742+0.069431
## [16] train-rmse:0.275102+0.005684 test-rmse:0.424684+0.071282
## [17] train-rmse:0.270644+0.005793 test-rmse:0.422273+0.073651
## [18] train-rmse:0.264311+0.005948 test-rmse:0.422857+0.072671
## [19] train-rmse:0.257973+0.004787 test-rmse:0.418555+0.071236
## [20] train-rmse:0.253047+0.004345 test-rmse:0.418204+0.070645
## [21] train-rmse:0.248881+0.004431 test-rmse:0.416642+0.070901
## [22] train-rmse:0.243308+0.004660 test-rmse:0.414029+0.072631
## [23] train-rmse:0.238862+0.004960 test-rmse:0.414504+0.072107
## [24] train-rmse:0.235512+0.005076 test-rmse:0.413698+0.073459
## [25] train-rmse:0.231263+0.005077 test-rmse:0.413172+0.074342
## [26] train-rmse:0.227709+0.004829 test-rmse:0.412914+0.074773
## [27] train-rmse:0.222265+0.005470 test-rmse:0.412657+0.076094
## [28] train-rmse:0.218428+0.006585 test-rmse:0.412446+0.077850
## [29] train-rmse:0.215814+0.005766 test-rmse:0.413626+0.078262
## [30] train-rmse:0.212656+0.005729 test-rmse:0.411889+0.077354
## [31] train-rmse:0.210166+0.006222 test-rmse:0.410447+0.078705
## [32] train-rmse:0.207502+0.006356 test-rmse:0.409496+0.078472
## [33] train-rmse:0.205135+0.006098 test-rmse:0.409441+0.078207
## [34] train-rmse:0.201556+0.004749 test-rmse:0.409172+0.077921
## [35] train-rmse:0.198572+0.003618 test-rmse:0.408379+0.078596
## [36] train-rmse:0.196045+0.002699 test-rmse:0.408763+0.078901
## [37] train-rmse:0.193136+0.002172 test-rmse:0.408406+0.078444
## [38] train-rmse:0.191524+0.002699 test-rmse:0.409076+0.078281
## [39] train-rmse:0.187134+0.004969 test-rmse:0.409822+0.077253
## [40] train-rmse:0.184293+0.006081 test-rmse:0.409281+0.077010
## [41] train-rmse:0.181005+0.006193 test-rmse:0.409542+0.075875
## Stopping. Best iteration:
## [35] train-rmse:0.198572+0.003618 test-rmse:0.408379+0.078596
##
## 94
## [1] train-rmse:1.118641+0.019956 test-rmse:1.125444+0.119070
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.792291+0.013286 test-rmse:0.804649+0.110809
## [3] train-rmse:0.587799+0.011686 test-rmse:0.614330+0.095292
## [4] train-rmse:0.461926+0.007996 test-rmse:0.510770+0.084042
## [5] train-rmse:0.389042+0.009804 test-rmse:0.467546+0.079944
## [6] train-rmse:0.340995+0.006364 test-rmse:0.442930+0.071987
## [7] train-rmse:0.312874+0.005131 test-rmse:0.427747+0.078167
## [8] train-rmse:0.295122+0.006150 test-rmse:0.422868+0.073620
## [9] train-rmse:0.282240+0.006913 test-rmse:0.417019+0.071482
## [10] train-rmse:0.267771+0.003054 test-rmse:0.417231+0.066023
## [11] train-rmse:0.253300+0.005113 test-rmse:0.416216+0.066551
## [12] train-rmse:0.242777+0.005620 test-rmse:0.410249+0.067022
## [13] train-rmse:0.235864+0.005452 test-rmse:0.409166+0.067358
## [14] train-rmse:0.227253+0.004445 test-rmse:0.409309+0.067724
## [15] train-rmse:0.218550+0.002866 test-rmse:0.410244+0.067649
## [16] train-rmse:0.212958+0.002337 test-rmse:0.408865+0.067563
## [17] train-rmse:0.205536+0.003727 test-rmse:0.406653+0.063611
## [18] train-rmse:0.200405+0.003588 test-rmse:0.408073+0.062928
## [19] train-rmse:0.195182+0.003338 test-rmse:0.406176+0.063095
## [20] train-rmse:0.188381+0.001470 test-rmse:0.402820+0.062996
## [21] train-rmse:0.182324+0.003308 test-rmse:0.401943+0.065747
## [22] train-rmse:0.177162+0.003672 test-rmse:0.401856+0.066633
## [23] train-rmse:0.172955+0.004819 test-rmse:0.401046+0.067004
## [24] train-rmse:0.168882+0.005314 test-rmse:0.402683+0.067221
## [25] train-rmse:0.162791+0.004305 test-rmse:0.404193+0.066550
## [26] train-rmse:0.159233+0.004256 test-rmse:0.403301+0.067741
## [27] train-rmse:0.155512+0.004819 test-rmse:0.402981+0.067741
## [28] train-rmse:0.152451+0.005438 test-rmse:0.401906+0.068725
## [29] train-rmse:0.148472+0.005054 test-rmse:0.399605+0.068342
## [30] train-rmse:0.145960+0.005027 test-rmse:0.398797+0.068489
## [31] train-rmse:0.143663+0.005145 test-rmse:0.399123+0.069032
## [32] train-rmse:0.139465+0.005145 test-rmse:0.399940+0.069114
## [33] train-rmse:0.135850+0.005989 test-rmse:0.398910+0.070085
## [34] train-rmse:0.132572+0.006969 test-rmse:0.399349+0.069291
## [35] train-rmse:0.129078+0.007955 test-rmse:0.398319+0.069877
## [36] train-rmse:0.126571+0.007437 test-rmse:0.398671+0.070150
## [37] train-rmse:0.124157+0.007387 test-rmse:0.399073+0.070196
## [38] train-rmse:0.122031+0.006598 test-rmse:0.398591+0.069523
## [39] train-rmse:0.118735+0.006366 test-rmse:0.399096+0.070082
## [40] train-rmse:0.116683+0.007050 test-rmse:0.399113+0.069998
## [41] train-rmse:0.114333+0.007559 test-rmse:0.398837+0.069710
## Stopping. Best iteration:
## [35] train-rmse:0.129078+0.007955 test-rmse:0.398319+0.069877
##
## 95
## [1] train-rmse:1.114953+0.020520 test-rmse:1.122361+0.113287
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.783700+0.013829 test-rmse:0.802021+0.104113
## [3] train-rmse:0.573991+0.011722 test-rmse:0.610362+0.093443
## [4] train-rmse:0.439958+0.006625 test-rmse:0.505720+0.087350
## [5] train-rmse:0.357442+0.007685 test-rmse:0.447773+0.079896
## [6] train-rmse:0.308135+0.008045 test-rmse:0.428855+0.077217
## [7] train-rmse:0.273966+0.005343 test-rmse:0.419472+0.075607
## [8] train-rmse:0.252802+0.005805 test-rmse:0.411678+0.075517
## [9] train-rmse:0.237742+0.009206 test-rmse:0.409757+0.077192
## [10] train-rmse:0.225795+0.008234 test-rmse:0.409443+0.073384
## [11] train-rmse:0.212374+0.004973 test-rmse:0.406936+0.072342
## [12] train-rmse:0.202637+0.003983 test-rmse:0.406385+0.070515
## [13] train-rmse:0.190635+0.006230 test-rmse:0.407967+0.071335
## [14] train-rmse:0.181750+0.006354 test-rmse:0.405672+0.071816
## [15] train-rmse:0.172711+0.006257 test-rmse:0.406063+0.068618
## [16] train-rmse:0.166024+0.005948 test-rmse:0.407088+0.068425
## [17] train-rmse:0.159003+0.005766 test-rmse:0.402447+0.068501
## [18] train-rmse:0.151418+0.005299 test-rmse:0.403026+0.069690
## [19] train-rmse:0.147050+0.005590 test-rmse:0.402589+0.069474
## [20] train-rmse:0.142265+0.004849 test-rmse:0.403818+0.069665
## [21] train-rmse:0.136212+0.006344 test-rmse:0.401919+0.068891
## [22] train-rmse:0.131856+0.006399 test-rmse:0.402283+0.069099
## [23] train-rmse:0.127766+0.006673 test-rmse:0.402209+0.068588
## [24] train-rmse:0.123266+0.007931 test-rmse:0.401697+0.069518
## [25] train-rmse:0.119346+0.009938 test-rmse:0.402105+0.069743
## [26] train-rmse:0.114712+0.010948 test-rmse:0.401828+0.071375
## [27] train-rmse:0.111095+0.011265 test-rmse:0.403582+0.071361
## [28] train-rmse:0.107544+0.010569 test-rmse:0.404590+0.070670
## [29] train-rmse:0.103260+0.010475 test-rmse:0.404593+0.071957
## [30] train-rmse:0.100138+0.010655 test-rmse:0.404841+0.071798
## Stopping. Best iteration:
## [24] train-rmse:0.123266+0.007931 test-rmse:0.401697+0.069518
##
## 96
## [1] train-rmse:1.113724+0.020575 test-rmse:1.120096+0.111226
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.779504+0.014067 test-rmse:0.798096+0.103144
## [3] train-rmse:0.566189+0.012725 test-rmse:0.601694+0.092635
## [4] train-rmse:0.428202+0.010060 test-rmse:0.498309+0.089722
## [5] train-rmse:0.341115+0.011363 test-rmse:0.449378+0.082528
## [6] train-rmse:0.285353+0.011502 test-rmse:0.428535+0.078384
## [7] train-rmse:0.251145+0.012774 test-rmse:0.418036+0.073839
## [8] train-rmse:0.223337+0.015774 test-rmse:0.409574+0.068341
## [9] train-rmse:0.200536+0.014418 test-rmse:0.406969+0.062542
## [10] train-rmse:0.183566+0.017419 test-rmse:0.402748+0.057436
## [11] train-rmse:0.173647+0.015429 test-rmse:0.402133+0.057438
## [12] train-rmse:0.160726+0.016401 test-rmse:0.399761+0.057704
## [13] train-rmse:0.151090+0.017602 test-rmse:0.399445+0.060345
## [14] train-rmse:0.140852+0.015222 test-rmse:0.398966+0.057806
## [15] train-rmse:0.132658+0.014915 test-rmse:0.397131+0.057710
## [16] train-rmse:0.125809+0.014183 test-rmse:0.399099+0.054088
## [17] train-rmse:0.120152+0.012045 test-rmse:0.398560+0.055712
## [18] train-rmse:0.114045+0.011246 test-rmse:0.398841+0.054776
## [19] train-rmse:0.105848+0.012089 test-rmse:0.396927+0.055982
## [20] train-rmse:0.099018+0.010174 test-rmse:0.397116+0.055555
## [21] train-rmse:0.094191+0.008345 test-rmse:0.396883+0.055545
## [22] train-rmse:0.090574+0.009354 test-rmse:0.395564+0.055631
## [23] train-rmse:0.086073+0.007761 test-rmse:0.396142+0.055521
## [24] train-rmse:0.081565+0.008466 test-rmse:0.396245+0.054662
## [25] train-rmse:0.077734+0.008605 test-rmse:0.395964+0.054820
## [26] train-rmse:0.073942+0.008070 test-rmse:0.396583+0.055378
## [27] train-rmse:0.071141+0.008197 test-rmse:0.397621+0.056151
## [28] train-rmse:0.068281+0.007705 test-rmse:0.397714+0.056233
## Stopping. Best iteration:
## [22] train-rmse:0.090574+0.009354 test-rmse:0.395564+0.055631
##
## 97
## [1] train-rmse:1.113152+0.020573 test-rmse:1.121916+0.110858
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.776726+0.014542 test-rmse:0.797210+0.102040
## [3] train-rmse:0.559565+0.012472 test-rmse:0.596585+0.092984
## [4] train-rmse:0.415741+0.011880 test-rmse:0.500117+0.090903
## [5] train-rmse:0.322919+0.011216 test-rmse:0.448697+0.084185
## [6] train-rmse:0.263059+0.010844 test-rmse:0.429038+0.081054
## [7] train-rmse:0.220132+0.009485 test-rmse:0.418527+0.074767
## [8] train-rmse:0.188517+0.009649 test-rmse:0.411108+0.073817
## [9] train-rmse:0.170534+0.011444 test-rmse:0.408848+0.070283
## [10] train-rmse:0.154781+0.009342 test-rmse:0.406768+0.068613
## [11] train-rmse:0.138426+0.012535 test-rmse:0.404778+0.066620
## [12] train-rmse:0.126554+0.014239 test-rmse:0.403744+0.066400
## [13] train-rmse:0.116877+0.012889 test-rmse:0.402030+0.066056
## [14] train-rmse:0.109531+0.013980 test-rmse:0.401466+0.065190
## [15] train-rmse:0.100930+0.012177 test-rmse:0.402141+0.065591
## [16] train-rmse:0.094899+0.011327 test-rmse:0.400801+0.065479
## [17] train-rmse:0.088614+0.011436 test-rmse:0.400418+0.064620
## [18] train-rmse:0.082952+0.012020 test-rmse:0.401063+0.064322
## [19] train-rmse:0.078483+0.012017 test-rmse:0.401329+0.064767
## [20] train-rmse:0.071996+0.010700 test-rmse:0.401409+0.064449
## [21] train-rmse:0.067185+0.009244 test-rmse:0.400282+0.064599
## [22] train-rmse:0.062367+0.007843 test-rmse:0.400243+0.064649
## [23] train-rmse:0.058791+0.008060 test-rmse:0.400975+0.064011
## [24] train-rmse:0.054564+0.008940 test-rmse:0.401084+0.063023
## [25] train-rmse:0.051668+0.008793 test-rmse:0.399952+0.063375
## [26] train-rmse:0.048698+0.009543 test-rmse:0.399640+0.062977
## [27] train-rmse:0.045582+0.008945 test-rmse:0.399203+0.063210
## [28] train-rmse:0.042898+0.007728 test-rmse:0.399097+0.063397
## [29] train-rmse:0.040417+0.007385 test-rmse:0.399253+0.062780
## [30] train-rmse:0.037925+0.006654 test-rmse:0.399007+0.062482
## [31] train-rmse:0.036076+0.006037 test-rmse:0.398881+0.062457
## [32] train-rmse:0.033941+0.005387 test-rmse:0.399250+0.061981
## [33] train-rmse:0.031864+0.005001 test-rmse:0.399498+0.061695
## [34] train-rmse:0.029563+0.005200 test-rmse:0.399616+0.061820
## [35] train-rmse:0.028077+0.005491 test-rmse:0.399652+0.061708
## [36] train-rmse:0.026548+0.005206 test-rmse:0.399758+0.061349
## [37] train-rmse:0.024738+0.004877 test-rmse:0.399969+0.061239
## Stopping. Best iteration:
## [31] train-rmse:0.036076+0.006037 test-rmse:0.398881+0.062457
##
## 98
## [1] train-rmse:1.139467+0.021767 test-rmse:1.135707+0.105597
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.872231+0.018517 test-rmse:0.868825+0.097066
## [3] train-rmse:0.742796+0.017951 test-rmse:0.739929+0.090834
## [4] train-rmse:0.685902+0.018080 test-rmse:0.683512+0.086966
## [5] train-rmse:0.650716+0.016571 test-rmse:0.649363+0.085964
## [6] train-rmse:0.629412+0.015138 test-rmse:0.634459+0.076045
## [7] train-rmse:0.608285+0.013316 test-rmse:0.608349+0.076219
## [8] train-rmse:0.596032+0.013064 test-rmse:0.604351+0.072569
## [9] train-rmse:0.584457+0.012798 test-rmse:0.590536+0.071400
## [10] train-rmse:0.575055+0.012018 test-rmse:0.584383+0.066563
## [11] train-rmse:0.566373+0.011178 test-rmse:0.575591+0.065809
## [12] train-rmse:0.558288+0.011297 test-rmse:0.570286+0.063953
## [13] train-rmse:0.552508+0.010907 test-rmse:0.565311+0.062340
## [14] train-rmse:0.547135+0.010434 test-rmse:0.560709+0.060945
## [15] train-rmse:0.542082+0.010273 test-rmse:0.555826+0.059863
## [16] train-rmse:0.538028+0.010214 test-rmse:0.557280+0.060270
## [17] train-rmse:0.534359+0.010157 test-rmse:0.554961+0.058552
## [18] train-rmse:0.530689+0.010234 test-rmse:0.554654+0.057280
## [19] train-rmse:0.527106+0.009983 test-rmse:0.551241+0.056727
## [20] train-rmse:0.523743+0.009708 test-rmse:0.550015+0.055949
## [21] train-rmse:0.520733+0.009568 test-rmse:0.549621+0.053043
## [22] train-rmse:0.517832+0.009416 test-rmse:0.546356+0.054430
## [23] train-rmse:0.515039+0.009179 test-rmse:0.543630+0.053793
## [24] train-rmse:0.512187+0.008738 test-rmse:0.541375+0.055163
## [25] train-rmse:0.509821+0.008647 test-rmse:0.537779+0.053005
## [26] train-rmse:0.507366+0.008678 test-rmse:0.536666+0.053042
## [27] train-rmse:0.505026+0.008628 test-rmse:0.534728+0.053765
## [28] train-rmse:0.502683+0.008722 test-rmse:0.533765+0.054521
## [29] train-rmse:0.500484+0.008548 test-rmse:0.532341+0.055041
## [30] train-rmse:0.498312+0.008291 test-rmse:0.533073+0.054328
## [31] train-rmse:0.496322+0.008232 test-rmse:0.529095+0.054426
## [32] train-rmse:0.494368+0.008357 test-rmse:0.527899+0.052553
## [33] train-rmse:0.492446+0.008324 test-rmse:0.526691+0.052073
## [34] train-rmse:0.490583+0.008370 test-rmse:0.525063+0.053411
## [35] train-rmse:0.488813+0.008289 test-rmse:0.525087+0.054131
## [36] train-rmse:0.487165+0.008171 test-rmse:0.524410+0.052320
## [37] train-rmse:0.485561+0.008217 test-rmse:0.523136+0.051080
## [38] train-rmse:0.483941+0.008239 test-rmse:0.520048+0.051222
## [39] train-rmse:0.482351+0.008149 test-rmse:0.519940+0.052013
## [40] train-rmse:0.480954+0.008116 test-rmse:0.521108+0.050524
## [41] train-rmse:0.479569+0.008067 test-rmse:0.518646+0.050935
## [42] train-rmse:0.478191+0.008038 test-rmse:0.519679+0.050974
## [43] train-rmse:0.476760+0.008000 test-rmse:0.517518+0.049732
## [44] train-rmse:0.475451+0.007932 test-rmse:0.517195+0.048551
## [45] train-rmse:0.474271+0.007893 test-rmse:0.516560+0.048304
## [46] train-rmse:0.473060+0.007802 test-rmse:0.517344+0.047980
## [47] train-rmse:0.471824+0.007747 test-rmse:0.516797+0.048085
## [48] train-rmse:0.470719+0.007708 test-rmse:0.514815+0.048413
## [49] train-rmse:0.469622+0.007656 test-rmse:0.513562+0.047598
## [50] train-rmse:0.468529+0.007621 test-rmse:0.512910+0.049138
## [51] train-rmse:0.467490+0.007703 test-rmse:0.512066+0.048675
## [52] train-rmse:0.466496+0.007729 test-rmse:0.512556+0.048923
## [53] train-rmse:0.465435+0.007724 test-rmse:0.512854+0.047175
## [54] train-rmse:0.464408+0.007728 test-rmse:0.509633+0.044099
## [55] train-rmse:0.463418+0.007688 test-rmse:0.508278+0.044000
## [56] train-rmse:0.462460+0.007645 test-rmse:0.508046+0.044096
## [57] train-rmse:0.461573+0.007595 test-rmse:0.505995+0.043933
## [58] train-rmse:0.460689+0.007601 test-rmse:0.506007+0.044544
## [59] train-rmse:0.459802+0.007567 test-rmse:0.504672+0.044708
## [60] train-rmse:0.458923+0.007486 test-rmse:0.505300+0.045041
## [61] train-rmse:0.458035+0.007548 test-rmse:0.504863+0.044189
## [62] train-rmse:0.457203+0.007535 test-rmse:0.504175+0.044867
## [63] train-rmse:0.456420+0.007495 test-rmse:0.504647+0.044350
## [64] train-rmse:0.455655+0.007466 test-rmse:0.502974+0.044130
## [65] train-rmse:0.454868+0.007408 test-rmse:0.502021+0.044123
## [66] train-rmse:0.454079+0.007383 test-rmse:0.502113+0.043143
## [67] train-rmse:0.453346+0.007364 test-rmse:0.503066+0.043785
## [68] train-rmse:0.452615+0.007389 test-rmse:0.503051+0.042925
## [69] train-rmse:0.451913+0.007389 test-rmse:0.503398+0.042942
## [70] train-rmse:0.451224+0.007402 test-rmse:0.503009+0.043178
## [71] train-rmse:0.450541+0.007414 test-rmse:0.502471+0.043485
## Stopping. Best iteration:
## [65] train-rmse:0.454868+0.007408 test-rmse:0.502021+0.044123
##
## 99
## [1] train-rmse:1.112413+0.022043 test-rmse:1.114938+0.113561
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.804953+0.015979 test-rmse:0.804321+0.099076
## [3] train-rmse:0.634750+0.013856 test-rmse:0.641117+0.101062
## [4] train-rmse:0.546626+0.013298 test-rmse:0.559159+0.094880
## [5] train-rmse:0.497399+0.011476 test-rmse:0.508906+0.085734
## [6] train-rmse:0.466211+0.012751 test-rmse:0.494299+0.075113
## [7] train-rmse:0.442503+0.010725 test-rmse:0.468695+0.066973
## [8] train-rmse:0.425886+0.009748 test-rmse:0.463309+0.065317
## [9] train-rmse:0.413124+0.009717 test-rmse:0.460070+0.060231
## [10] train-rmse:0.402649+0.007794 test-rmse:0.457887+0.060243
## [11] train-rmse:0.393659+0.006694 test-rmse:0.454980+0.056510
## [12] train-rmse:0.385568+0.006381 test-rmse:0.449474+0.060050
## [13] train-rmse:0.377987+0.006496 test-rmse:0.448500+0.060137
## [14] train-rmse:0.371626+0.006634 test-rmse:0.444162+0.060154
## [15] train-rmse:0.365926+0.006460 test-rmse:0.443582+0.060781
## [16] train-rmse:0.360983+0.006274 test-rmse:0.444765+0.060395
## [17] train-rmse:0.355267+0.006569 test-rmse:0.443755+0.058907
## [18] train-rmse:0.348856+0.006839 test-rmse:0.441489+0.058114
## [19] train-rmse:0.343775+0.005583 test-rmse:0.442007+0.057563
## [20] train-rmse:0.340371+0.005011 test-rmse:0.441089+0.059071
## [21] train-rmse:0.336334+0.004121 test-rmse:0.440766+0.057771
## [22] train-rmse:0.333321+0.004136 test-rmse:0.441421+0.058578
## [23] train-rmse:0.329304+0.003715 test-rmse:0.438489+0.057839
## [24] train-rmse:0.325745+0.003972 test-rmse:0.436507+0.058844
## [25] train-rmse:0.321188+0.004137 test-rmse:0.434460+0.060098
## [26] train-rmse:0.318451+0.004338 test-rmse:0.431309+0.058262
## [27] train-rmse:0.315359+0.004664 test-rmse:0.430567+0.058539
## [28] train-rmse:0.312066+0.004940 test-rmse:0.430670+0.058593
## [29] train-rmse:0.308589+0.005874 test-rmse:0.430675+0.058444
## [30] train-rmse:0.305669+0.005276 test-rmse:0.429259+0.058562
## [31] train-rmse:0.302349+0.005842 test-rmse:0.427912+0.058338
## [32] train-rmse:0.299047+0.004908 test-rmse:0.425260+0.060164
## [33] train-rmse:0.295958+0.004427 test-rmse:0.424830+0.061380
## [34] train-rmse:0.292358+0.003077 test-rmse:0.425460+0.060659
## [35] train-rmse:0.289317+0.003402 test-rmse:0.425862+0.058534
## [36] train-rmse:0.286787+0.004067 test-rmse:0.422180+0.057843
## [37] train-rmse:0.283563+0.003967 test-rmse:0.420483+0.059751
## [38] train-rmse:0.281566+0.003996 test-rmse:0.420357+0.059944
## [39] train-rmse:0.279341+0.004066 test-rmse:0.420218+0.059348
## [40] train-rmse:0.277757+0.004655 test-rmse:0.419466+0.059866
## [41] train-rmse:0.275018+0.005925 test-rmse:0.419998+0.059254
## [42] train-rmse:0.272833+0.006536 test-rmse:0.419879+0.058165
## [43] train-rmse:0.270491+0.007153 test-rmse:0.418181+0.058484
## [44] train-rmse:0.268314+0.006439 test-rmse:0.418406+0.058442
## [45] train-rmse:0.266924+0.006430 test-rmse:0.418236+0.058665
## [46] train-rmse:0.265317+0.007080 test-rmse:0.418760+0.059197
## [47] train-rmse:0.263666+0.006787 test-rmse:0.417885+0.059533
## [48] train-rmse:0.262455+0.006874 test-rmse:0.417555+0.059048
## [49] train-rmse:0.260548+0.008543 test-rmse:0.417733+0.059280
## [50] train-rmse:0.258445+0.008640 test-rmse:0.418726+0.058930
## [51] train-rmse:0.256835+0.008147 test-rmse:0.417513+0.058900
## [52] train-rmse:0.255491+0.007709 test-rmse:0.418435+0.058595
## [53] train-rmse:0.254244+0.007477 test-rmse:0.417923+0.059231
## [54] train-rmse:0.252683+0.007503 test-rmse:0.418739+0.059411
## [55] train-rmse:0.250397+0.007999 test-rmse:0.417309+0.058236
## [56] train-rmse:0.249459+0.007931 test-rmse:0.416502+0.059200
## [57] train-rmse:0.248055+0.007738 test-rmse:0.417164+0.059332
## [58] train-rmse:0.246191+0.007250 test-rmse:0.416654+0.059806
## [59] train-rmse:0.243955+0.007387 test-rmse:0.415106+0.058853
## [60] train-rmse:0.242469+0.007709 test-rmse:0.415268+0.058894
## [61] train-rmse:0.239999+0.008583 test-rmse:0.414885+0.059473
## [62] train-rmse:0.237697+0.007661 test-rmse:0.414234+0.060072
## [63] train-rmse:0.235762+0.008494 test-rmse:0.415316+0.059749
## [64] train-rmse:0.234046+0.008383 test-rmse:0.415289+0.060090
## [65] train-rmse:0.232835+0.008187 test-rmse:0.414886+0.060353
## [66] train-rmse:0.231371+0.008012 test-rmse:0.415088+0.059849
## [67] train-rmse:0.229719+0.009138 test-rmse:0.414891+0.060068
## [68] train-rmse:0.228137+0.008649 test-rmse:0.412891+0.059286
## [69] train-rmse:0.226614+0.008642 test-rmse:0.411443+0.060585
## [70] train-rmse:0.224802+0.008724 test-rmse:0.412032+0.060423
## [71] train-rmse:0.222953+0.010324 test-rmse:0.410515+0.059761
## [72] train-rmse:0.222169+0.010116 test-rmse:0.410156+0.059660
## [73] train-rmse:0.220856+0.009610 test-rmse:0.409586+0.059688
## [74] train-rmse:0.219341+0.010160 test-rmse:0.409607+0.059943
## [75] train-rmse:0.218391+0.009939 test-rmse:0.409656+0.059925
## [76] train-rmse:0.217220+0.010080 test-rmse:0.408674+0.059362
## [77] train-rmse:0.215785+0.009802 test-rmse:0.408282+0.059723
## [78] train-rmse:0.214283+0.009703 test-rmse:0.407625+0.060124
## [79] train-rmse:0.212815+0.010179 test-rmse:0.407803+0.059985
## [80] train-rmse:0.211846+0.010528 test-rmse:0.407680+0.059717
## [81] train-rmse:0.210997+0.010313 test-rmse:0.407284+0.060227
## [82] train-rmse:0.210047+0.010251 test-rmse:0.406228+0.061113
## [83] train-rmse:0.208971+0.010084 test-rmse:0.406623+0.061504
## [84] train-rmse:0.207503+0.010466 test-rmse:0.406227+0.061037
## [85] train-rmse:0.206685+0.010851 test-rmse:0.405255+0.059212
## [86] train-rmse:0.205653+0.010515 test-rmse:0.405286+0.058785
## [87] train-rmse:0.204252+0.010766 test-rmse:0.404546+0.058264
## [88] train-rmse:0.202514+0.011041 test-rmse:0.404563+0.058334
## [89] train-rmse:0.201716+0.010828 test-rmse:0.404269+0.058135
## [90] train-rmse:0.200782+0.010933 test-rmse:0.403974+0.058425
## [91] train-rmse:0.199950+0.011109 test-rmse:0.404976+0.057850
## [92] train-rmse:0.198627+0.011515 test-rmse:0.404711+0.057663
## [93] train-rmse:0.197640+0.011047 test-rmse:0.404391+0.057153
## [94] train-rmse:0.196579+0.010847 test-rmse:0.403411+0.057489
## [95] train-rmse:0.195299+0.010403 test-rmse:0.403297+0.057164
## [96] train-rmse:0.194068+0.010380 test-rmse:0.403614+0.057028
## [97] train-rmse:0.193288+0.010631 test-rmse:0.402689+0.057896
## [98] train-rmse:0.192318+0.010386 test-rmse:0.402268+0.057385
## [99] train-rmse:0.191806+0.010394 test-rmse:0.402191+0.056964
## [100] train-rmse:0.190638+0.010704 test-rmse:0.402022+0.056598
## [101] train-rmse:0.189814+0.010759 test-rmse:0.402124+0.056080
## [102] train-rmse:0.189142+0.010643 test-rmse:0.401954+0.056960
## [103] train-rmse:0.188473+0.010614 test-rmse:0.402318+0.056219
## [104] train-rmse:0.187869+0.010411 test-rmse:0.402386+0.056485
## [105] train-rmse:0.186631+0.010373 test-rmse:0.402253+0.056649
## [106] train-rmse:0.185415+0.009776 test-rmse:0.401963+0.056751
## [107] train-rmse:0.184731+0.009855 test-rmse:0.402981+0.056722
## [108] train-rmse:0.183729+0.009226 test-rmse:0.403488+0.056140
## Stopping. Best iteration:
## [102] train-rmse:0.189142+0.010643 test-rmse:0.401954+0.056960
##
## 100
## [1] train-rmse:1.097627+0.020598 test-rmse:1.105533+0.117982
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.773008+0.017553 test-rmse:0.776553+0.101621
## [3] train-rmse:0.582240+0.013827 test-rmse:0.602088+0.102045
## [4] train-rmse:0.473891+0.010332 test-rmse:0.514621+0.086494
## [5] train-rmse:0.415399+0.009139 test-rmse:0.467399+0.078035
## [6] train-rmse:0.381307+0.008324 test-rmse:0.445561+0.071258
## [7] train-rmse:0.360001+0.007420 test-rmse:0.432498+0.079096
## [8] train-rmse:0.341179+0.005181 test-rmse:0.424724+0.074020
## [9] train-rmse:0.325967+0.005901 test-rmse:0.427751+0.068824
## [10] train-rmse:0.312827+0.005224 test-rmse:0.426009+0.066545
## [11] train-rmse:0.303091+0.005081 test-rmse:0.426119+0.067925
## [12] train-rmse:0.295094+0.005799 test-rmse:0.423485+0.069259
## [13] train-rmse:0.288217+0.005954 test-rmse:0.420332+0.066290
## [14] train-rmse:0.281794+0.006281 test-rmse:0.418356+0.069509
## [15] train-rmse:0.275371+0.006083 test-rmse:0.418260+0.070340
## [16] train-rmse:0.270129+0.005884 test-rmse:0.415800+0.071148
## [17] train-rmse:0.264700+0.005781 test-rmse:0.413921+0.073794
## [18] train-rmse:0.259490+0.006537 test-rmse:0.414017+0.075106
## [19] train-rmse:0.255015+0.006841 test-rmse:0.411677+0.074245
## [20] train-rmse:0.249318+0.006015 test-rmse:0.411768+0.076316
## [21] train-rmse:0.242504+0.004490 test-rmse:0.410649+0.076259
## [22] train-rmse:0.236368+0.006350 test-rmse:0.409128+0.075614
## [23] train-rmse:0.231824+0.007363 test-rmse:0.408137+0.074134
## [24] train-rmse:0.226896+0.006664 test-rmse:0.407503+0.076620
## [25] train-rmse:0.222588+0.006222 test-rmse:0.405329+0.077083
## [26] train-rmse:0.219389+0.005530 test-rmse:0.406087+0.076551
## [27] train-rmse:0.216273+0.005933 test-rmse:0.405262+0.076719
## [28] train-rmse:0.213580+0.006026 test-rmse:0.404887+0.077283
## [29] train-rmse:0.209996+0.005880 test-rmse:0.407162+0.076620
## [30] train-rmse:0.206618+0.005955 test-rmse:0.405214+0.076009
## [31] train-rmse:0.203445+0.006021 test-rmse:0.405446+0.076941
## [32] train-rmse:0.199426+0.005712 test-rmse:0.406375+0.077135
## [33] train-rmse:0.196971+0.005207 test-rmse:0.405806+0.077804
## [34] train-rmse:0.193767+0.004391 test-rmse:0.404392+0.078082
## [35] train-rmse:0.190289+0.004810 test-rmse:0.403908+0.078757
## [36] train-rmse:0.187876+0.004965 test-rmse:0.403972+0.078187
## [37] train-rmse:0.185515+0.005470 test-rmse:0.404518+0.077737
## [38] train-rmse:0.183136+0.004988 test-rmse:0.404307+0.077761
## [39] train-rmse:0.179879+0.005525 test-rmse:0.405008+0.077392
## [40] train-rmse:0.177781+0.005915 test-rmse:0.404888+0.078075
## [41] train-rmse:0.175524+0.005939 test-rmse:0.405979+0.078052
## Stopping. Best iteration:
## [35] train-rmse:0.190289+0.004810 test-rmse:0.403908+0.078757
##
## 101
## [1] train-rmse:1.090673+0.019432 test-rmse:1.098207+0.119244
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.758039+0.012608 test-rmse:0.771045+0.110931
## [3] train-rmse:0.555033+0.009199 test-rmse:0.586087+0.093111
## [4] train-rmse:0.438262+0.005360 test-rmse:0.494647+0.079004
## [5] train-rmse:0.367898+0.003585 test-rmse:0.453872+0.077755
## [6] train-rmse:0.328363+0.004301 test-rmse:0.436902+0.071606
## [7] train-rmse:0.302753+0.004233 test-rmse:0.429250+0.061304
## [8] train-rmse:0.284162+0.002147 test-rmse:0.422548+0.057220
## [9] train-rmse:0.271477+0.002645 test-rmse:0.421019+0.054508
## [10] train-rmse:0.257599+0.001980 test-rmse:0.420226+0.054265
## [11] train-rmse:0.247447+0.002418 test-rmse:0.416920+0.049351
## [12] train-rmse:0.240140+0.003523 test-rmse:0.418129+0.050104
## [13] train-rmse:0.231535+0.005185 test-rmse:0.418344+0.049873
## [14] train-rmse:0.225081+0.005124 test-rmse:0.417073+0.047330
## [15] train-rmse:0.216091+0.006157 test-rmse:0.415750+0.048380
## [16] train-rmse:0.208924+0.004753 test-rmse:0.414507+0.049346
## [17] train-rmse:0.202209+0.005021 test-rmse:0.413140+0.049682
## [18] train-rmse:0.196615+0.003638 test-rmse:0.412605+0.050293
## [19] train-rmse:0.190341+0.006255 test-rmse:0.411942+0.050954
## [20] train-rmse:0.185532+0.005711 test-rmse:0.410695+0.053526
## [21] train-rmse:0.180504+0.005761 test-rmse:0.410474+0.054749
## [22] train-rmse:0.176836+0.004994 test-rmse:0.410429+0.054982
## [23] train-rmse:0.172045+0.005335 test-rmse:0.409629+0.055608
## [24] train-rmse:0.166756+0.006276 test-rmse:0.406161+0.055740
## [25] train-rmse:0.161360+0.006774 test-rmse:0.405281+0.057270
## [26] train-rmse:0.157554+0.006326 test-rmse:0.405158+0.055747
## [27] train-rmse:0.154291+0.006643 test-rmse:0.405157+0.054969
## [28] train-rmse:0.151251+0.005886 test-rmse:0.403349+0.054241
## [29] train-rmse:0.147347+0.005963 test-rmse:0.403324+0.055145
## [30] train-rmse:0.144830+0.006696 test-rmse:0.403121+0.055726
## [31] train-rmse:0.141897+0.006525 test-rmse:0.402520+0.054698
## [32] train-rmse:0.137769+0.006876 test-rmse:0.402491+0.056029
## [33] train-rmse:0.135705+0.007722 test-rmse:0.401517+0.056021
## [34] train-rmse:0.131303+0.007774 test-rmse:0.401436+0.056750
## [35] train-rmse:0.128217+0.008564 test-rmse:0.399367+0.058076
## [36] train-rmse:0.125843+0.009010 test-rmse:0.399374+0.057181
## [37] train-rmse:0.122949+0.008520 test-rmse:0.399995+0.057670
## [38] train-rmse:0.120524+0.007689 test-rmse:0.399534+0.058037
## [39] train-rmse:0.117655+0.008144 test-rmse:0.400583+0.059077
## [40] train-rmse:0.115116+0.009334 test-rmse:0.400170+0.060230
## [41] train-rmse:0.111385+0.007899 test-rmse:0.399955+0.059719
## Stopping. Best iteration:
## [35] train-rmse:0.128217+0.008564 test-rmse:0.399367+0.058076
##
## 102
## [1] train-rmse:1.086703+0.020030 test-rmse:1.094990+0.113071
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.748617+0.013195 test-rmse:0.769160+0.103151
## [3] train-rmse:0.542108+0.011149 test-rmse:0.589771+0.090952
## [4] train-rmse:0.412221+0.006846 test-rmse:0.491158+0.082439
## [5] train-rmse:0.335977+0.007217 test-rmse:0.438890+0.080460
## [6] train-rmse:0.292596+0.007682 test-rmse:0.418618+0.073981
## [7] train-rmse:0.261272+0.003792 test-rmse:0.411155+0.072078
## [8] train-rmse:0.240045+0.006342 test-rmse:0.409822+0.068773
## [9] train-rmse:0.225620+0.008560 test-rmse:0.409209+0.068640
## [10] train-rmse:0.211462+0.008481 test-rmse:0.407379+0.071104
## [11] train-rmse:0.198647+0.010177 test-rmse:0.407175+0.068503
## [12] train-rmse:0.190487+0.009130 test-rmse:0.403107+0.064061
## [13] train-rmse:0.179604+0.008627 test-rmse:0.402331+0.063188
## [14] train-rmse:0.171415+0.009067 test-rmse:0.401767+0.062470
## [15] train-rmse:0.162848+0.008467 test-rmse:0.403066+0.061387
## [16] train-rmse:0.156133+0.009987 test-rmse:0.401438+0.061192
## [17] train-rmse:0.150368+0.009808 test-rmse:0.401678+0.059997
## [18] train-rmse:0.146128+0.009413 test-rmse:0.399967+0.062189
## [19] train-rmse:0.140261+0.008451 test-rmse:0.399163+0.062637
## [20] train-rmse:0.135047+0.009266 test-rmse:0.399074+0.063015
## [21] train-rmse:0.129725+0.008977 test-rmse:0.400142+0.063143
## [22] train-rmse:0.124793+0.009299 test-rmse:0.399545+0.062087
## [23] train-rmse:0.120562+0.008879 test-rmse:0.399416+0.062551
## [24] train-rmse:0.114867+0.008294 test-rmse:0.399430+0.063435
## [25] train-rmse:0.111047+0.007439 test-rmse:0.398503+0.063489
## [26] train-rmse:0.107478+0.006707 test-rmse:0.399341+0.062902
## [27] train-rmse:0.103636+0.006072 test-rmse:0.396781+0.063517
## [28] train-rmse:0.100619+0.005959 test-rmse:0.396566+0.063923
## [29] train-rmse:0.097116+0.005173 test-rmse:0.396268+0.064955
## [30] train-rmse:0.093217+0.004855 test-rmse:0.395181+0.064117
## [31] train-rmse:0.089884+0.005544 test-rmse:0.394737+0.063315
## [32] train-rmse:0.086524+0.005751 test-rmse:0.394248+0.064243
## [33] train-rmse:0.083441+0.005043 test-rmse:0.393600+0.065526
## [34] train-rmse:0.080481+0.004153 test-rmse:0.394051+0.065852
## [35] train-rmse:0.077262+0.003378 test-rmse:0.394349+0.066594
## [36] train-rmse:0.074469+0.003236 test-rmse:0.394544+0.065713
## [37] train-rmse:0.072859+0.002955 test-rmse:0.394380+0.066126
## [38] train-rmse:0.070260+0.002454 test-rmse:0.393976+0.066397
## [39] train-rmse:0.068537+0.002612 test-rmse:0.393777+0.066344
## Stopping. Best iteration:
## [33] train-rmse:0.083441+0.005043 test-rmse:0.393600+0.065526
##
## 103
## [1] train-rmse:1.085366+0.020084 test-rmse:1.092607+0.110899
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.744117+0.013547 test-rmse:0.764524+0.102467
## [3] train-rmse:0.532548+0.011943 test-rmse:0.574790+0.092386
## [4] train-rmse:0.400058+0.008995 test-rmse:0.480969+0.078008
## [5] train-rmse:0.320634+0.005960 test-rmse:0.439133+0.076217
## [6] train-rmse:0.270475+0.008590 test-rmse:0.422205+0.068197
## [7] train-rmse:0.236506+0.007288 test-rmse:0.413921+0.063226
## [8] train-rmse:0.215632+0.009619 test-rmse:0.409985+0.061830
## [9] train-rmse:0.197509+0.006813 test-rmse:0.407187+0.061832
## [10] train-rmse:0.184660+0.009407 test-rmse:0.404855+0.058229
## [11] train-rmse:0.169735+0.012112 test-rmse:0.406387+0.060342
## [12] train-rmse:0.155966+0.009798 test-rmse:0.406100+0.059754
## [13] train-rmse:0.144843+0.006300 test-rmse:0.404420+0.059848
## [14] train-rmse:0.135787+0.007133 test-rmse:0.402683+0.057933
## [15] train-rmse:0.125154+0.009162 test-rmse:0.404574+0.056159
## [16] train-rmse:0.118622+0.009319 test-rmse:0.405380+0.056380
## [17] train-rmse:0.112091+0.010851 test-rmse:0.405356+0.056375
## [18] train-rmse:0.105679+0.009576 test-rmse:0.405627+0.056397
## [19] train-rmse:0.101265+0.008908 test-rmse:0.406265+0.056487
## [20] train-rmse:0.096951+0.009244 test-rmse:0.405510+0.056070
## Stopping. Best iteration:
## [14] train-rmse:0.135787+0.007133 test-rmse:0.402683+0.057933
##
## 104
## [1] train-rmse:1.084748+0.020080 test-rmse:1.094552+0.110585
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.740992+0.013996 test-rmse:0.764200+0.101273
## [3] train-rmse:0.525094+0.011480 test-rmse:0.571343+0.091152
## [4] train-rmse:0.386716+0.010616 test-rmse:0.485479+0.080138
## [5] train-rmse:0.299974+0.009324 test-rmse:0.438194+0.073776
## [6] train-rmse:0.245953+0.010766 test-rmse:0.421561+0.069390
## [7] train-rmse:0.208708+0.012089 test-rmse:0.413471+0.061622
## [8] train-rmse:0.184904+0.013608 test-rmse:0.412521+0.058611
## [9] train-rmse:0.165552+0.015630 test-rmse:0.408058+0.058292
## [10] train-rmse:0.146760+0.007769 test-rmse:0.407563+0.053820
## [11] train-rmse:0.131795+0.008450 test-rmse:0.410340+0.050457
## [12] train-rmse:0.119623+0.008781 test-rmse:0.409383+0.047844
## [13] train-rmse:0.108832+0.008672 test-rmse:0.411381+0.044735
## [14] train-rmse:0.099199+0.009074 test-rmse:0.412588+0.045061
## [15] train-rmse:0.090232+0.009127 test-rmse:0.412962+0.045760
## [16] train-rmse:0.081061+0.007856 test-rmse:0.412221+0.046434
## Stopping. Best iteration:
## [10] train-rmse:0.146760+0.007769 test-rmse:0.407563+0.053820
##
## 105
## [1] train-rmse:1.115068+0.021402 test-rmse:1.111322+0.104943
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.848141+0.018340 test-rmse:0.844804+0.096071
## [3] train-rmse:0.726857+0.017959 test-rmse:0.724099+0.089863
## [4] train-rmse:0.677266+0.018126 test-rmse:0.674980+0.086240
## [5] train-rmse:0.641855+0.016606 test-rmse:0.640680+0.085078
## [6] train-rmse:0.621238+0.013231 test-rmse:0.628053+0.078922
## [7] train-rmse:0.601573+0.013059 test-rmse:0.601852+0.074406
## [8] train-rmse:0.589549+0.012446 test-rmse:0.597127+0.070446
## [9] train-rmse:0.578602+0.012443 test-rmse:0.586940+0.064536
## [10] train-rmse:0.569579+0.011429 test-rmse:0.582247+0.065318
## [11] train-rmse:0.561303+0.011288 test-rmse:0.571566+0.066673
## [12] train-rmse:0.553480+0.011129 test-rmse:0.566070+0.061137
## [13] train-rmse:0.547777+0.010629 test-rmse:0.564103+0.063632
## [14] train-rmse:0.542524+0.010321 test-rmse:0.556638+0.059386
## [15] train-rmse:0.538206+0.010085 test-rmse:0.556184+0.061315
## [16] train-rmse:0.534007+0.009971 test-rmse:0.552689+0.057822
## [17] train-rmse:0.529780+0.010028 test-rmse:0.551304+0.059105
## [18] train-rmse:0.526329+0.009847 test-rmse:0.548310+0.059308
## [19] train-rmse:0.523043+0.009485 test-rmse:0.547735+0.057918
## [20] train-rmse:0.519798+0.009445 test-rmse:0.543704+0.056691
## [21] train-rmse:0.516447+0.008998 test-rmse:0.542017+0.054483
## [22] train-rmse:0.513439+0.008870 test-rmse:0.540894+0.054723
## [23] train-rmse:0.510774+0.008841 test-rmse:0.540761+0.056617
## [24] train-rmse:0.508211+0.008833 test-rmse:0.538166+0.056659
## [25] train-rmse:0.505759+0.008632 test-rmse:0.534348+0.054496
## [26] train-rmse:0.503365+0.008603 test-rmse:0.534317+0.052009
## [27] train-rmse:0.501002+0.008490 test-rmse:0.532681+0.051645
## [28] train-rmse:0.498682+0.008424 test-rmse:0.529970+0.051545
## [29] train-rmse:0.496529+0.008272 test-rmse:0.528764+0.053076
## [30] train-rmse:0.494472+0.008172 test-rmse:0.526991+0.054372
## [31] train-rmse:0.492475+0.008297 test-rmse:0.526124+0.053752
## [32] train-rmse:0.490445+0.008358 test-rmse:0.522830+0.052905
## [33] train-rmse:0.488540+0.008182 test-rmse:0.522933+0.052091
## [34] train-rmse:0.486758+0.008106 test-rmse:0.522283+0.052158
## [35] train-rmse:0.485020+0.008012 test-rmse:0.520598+0.053105
## [36] train-rmse:0.483410+0.008028 test-rmse:0.518791+0.052928
## [37] train-rmse:0.481761+0.008013 test-rmse:0.517207+0.050941
## [38] train-rmse:0.480226+0.007981 test-rmse:0.518201+0.050459
## [39] train-rmse:0.478607+0.007981 test-rmse:0.517967+0.048499
## [40] train-rmse:0.477152+0.008033 test-rmse:0.516982+0.050392
## [41] train-rmse:0.475768+0.007993 test-rmse:0.517642+0.050630
## [42] train-rmse:0.474267+0.007950 test-rmse:0.517598+0.049876
## [43] train-rmse:0.473038+0.007936 test-rmse:0.517342+0.048366
## [44] train-rmse:0.471787+0.007958 test-rmse:0.515329+0.049305
## [45] train-rmse:0.470578+0.007935 test-rmse:0.514697+0.049091
## [46] train-rmse:0.469367+0.007932 test-rmse:0.512081+0.045479
## [47] train-rmse:0.468245+0.007880 test-rmse:0.512144+0.044379
## [48] train-rmse:0.467109+0.007819 test-rmse:0.511317+0.045384
## [49] train-rmse:0.465992+0.007785 test-rmse:0.509938+0.045417
## [50] train-rmse:0.464884+0.007727 test-rmse:0.508894+0.045361
## [51] train-rmse:0.463857+0.007656 test-rmse:0.508873+0.046486
## [52] train-rmse:0.462816+0.007629 test-rmse:0.507020+0.045195
## [53] train-rmse:0.461824+0.007704 test-rmse:0.505193+0.044856
## [54] train-rmse:0.460820+0.007664 test-rmse:0.503286+0.044185
## [55] train-rmse:0.459809+0.007649 test-rmse:0.503263+0.044402
## [56] train-rmse:0.458908+0.007635 test-rmse:0.503527+0.043218
## [57] train-rmse:0.457990+0.007628 test-rmse:0.503313+0.043630
## [58] train-rmse:0.457105+0.007559 test-rmse:0.502577+0.044427
## [59] train-rmse:0.456259+0.007524 test-rmse:0.502485+0.044359
## [60] train-rmse:0.455420+0.007492 test-rmse:0.501305+0.044033
## [61] train-rmse:0.454544+0.007474 test-rmse:0.502257+0.043333
## [62] train-rmse:0.453707+0.007444 test-rmse:0.502630+0.043813
## [63] train-rmse:0.452869+0.007382 test-rmse:0.501376+0.043349
## [64] train-rmse:0.452054+0.007432 test-rmse:0.501252+0.043688
## [65] train-rmse:0.451283+0.007453 test-rmse:0.501134+0.042603
## [66] train-rmse:0.450554+0.007465 test-rmse:0.501558+0.041422
## [67] train-rmse:0.449836+0.007485 test-rmse:0.501719+0.040580
## [68] train-rmse:0.449099+0.007479 test-rmse:0.500857+0.041280
## [69] train-rmse:0.448380+0.007480 test-rmse:0.500812+0.041102
## [70] train-rmse:0.447689+0.007488 test-rmse:0.501545+0.041659
## [71] train-rmse:0.447024+0.007529 test-rmse:0.500872+0.041237
## [72] train-rmse:0.446334+0.007592 test-rmse:0.500706+0.041874
## [73] train-rmse:0.445700+0.007610 test-rmse:0.501713+0.042628
## [74] train-rmse:0.445083+0.007604 test-rmse:0.501114+0.042568
## [75] train-rmse:0.444470+0.007580 test-rmse:0.500234+0.042688
## [76] train-rmse:0.443864+0.007547 test-rmse:0.500869+0.042333
## [77] train-rmse:0.443266+0.007548 test-rmse:0.500879+0.040935
## [78] train-rmse:0.442716+0.007531 test-rmse:0.500047+0.041551
## [79] train-rmse:0.442156+0.007510 test-rmse:0.500192+0.041551
## [80] train-rmse:0.441594+0.007511 test-rmse:0.500820+0.041140
## [81] train-rmse:0.441046+0.007506 test-rmse:0.501040+0.042189
## [82] train-rmse:0.440473+0.007524 test-rmse:0.499511+0.041593
## [83] train-rmse:0.439955+0.007539 test-rmse:0.498576+0.041736
## [84] train-rmse:0.439405+0.007559 test-rmse:0.498028+0.041764
## [85] train-rmse:0.438846+0.007548 test-rmse:0.498587+0.042081
## [86] train-rmse:0.438340+0.007565 test-rmse:0.496964+0.040617
## [87] train-rmse:0.437801+0.007573 test-rmse:0.496737+0.040385
## [88] train-rmse:0.437288+0.007577 test-rmse:0.495927+0.039792
## [89] train-rmse:0.436756+0.007599 test-rmse:0.496516+0.040860
## [90] train-rmse:0.436221+0.007600 test-rmse:0.496340+0.040840
## [91] train-rmse:0.435733+0.007599 test-rmse:0.496337+0.041390
## [92] train-rmse:0.435248+0.007574 test-rmse:0.496279+0.041474
## [93] train-rmse:0.434792+0.007571 test-rmse:0.496745+0.042038
## [94] train-rmse:0.434335+0.007607 test-rmse:0.495648+0.041356
## [95] train-rmse:0.433875+0.007598 test-rmse:0.495030+0.040842
## [96] train-rmse:0.433420+0.007600 test-rmse:0.495272+0.039458
## [97] train-rmse:0.432957+0.007592 test-rmse:0.494704+0.039451
## [98] train-rmse:0.432527+0.007599 test-rmse:0.494505+0.039278
## [99] train-rmse:0.432096+0.007618 test-rmse:0.494226+0.039104
## [100] train-rmse:0.431668+0.007615 test-rmse:0.494784+0.039713
## [101] train-rmse:0.431249+0.007621 test-rmse:0.495710+0.039771
## [102] train-rmse:0.430846+0.007617 test-rmse:0.495739+0.039851
## [103] train-rmse:0.430420+0.007633 test-rmse:0.495564+0.039418
## [104] train-rmse:0.430013+0.007622 test-rmse:0.495183+0.039102
## [105] train-rmse:0.429584+0.007652 test-rmse:0.495041+0.039732
## Stopping. Best iteration:
## [99] train-rmse:0.432096+0.007618 test-rmse:0.494226+0.039104
##
## 106
## [1] train-rmse:1.086197+0.021718 test-rmse:1.089233+0.113411
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.775802+0.015559 test-rmse:0.775615+0.098324
## [3] train-rmse:0.611712+0.013294 test-rmse:0.618969+0.100239
## [4] train-rmse:0.530554+0.012657 test-rmse:0.545278+0.092545
## [5] train-rmse:0.486362+0.011113 test-rmse:0.509678+0.084928
## [6] train-rmse:0.454873+0.011251 test-rmse:0.486502+0.072268
## [7] train-rmse:0.434491+0.009539 test-rmse:0.474802+0.067879
## [8] train-rmse:0.419738+0.009304 test-rmse:0.466007+0.058931
## [9] train-rmse:0.406655+0.008005 test-rmse:0.457605+0.060118
## [10] train-rmse:0.396054+0.006996 test-rmse:0.454793+0.055361
## [11] train-rmse:0.388443+0.007227 test-rmse:0.453647+0.053639
## [12] train-rmse:0.381514+0.007917 test-rmse:0.452504+0.056594
## [13] train-rmse:0.375015+0.008446 test-rmse:0.447634+0.053308
## [14] train-rmse:0.368962+0.008253 test-rmse:0.445039+0.053348
## [15] train-rmse:0.364219+0.008015 test-rmse:0.447271+0.053780
## [16] train-rmse:0.357207+0.007570 test-rmse:0.444960+0.055605
## [17] train-rmse:0.351164+0.007264 test-rmse:0.445957+0.057208
## [18] train-rmse:0.346687+0.007066 test-rmse:0.445319+0.054962
## [19] train-rmse:0.342302+0.006667 test-rmse:0.442830+0.051239
## [20] train-rmse:0.338322+0.006586 test-rmse:0.442678+0.052124
## [21] train-rmse:0.334244+0.006935 test-rmse:0.441224+0.049447
## [22] train-rmse:0.329799+0.006979 test-rmse:0.439298+0.050836
## [23] train-rmse:0.324833+0.007002 test-rmse:0.438993+0.050221
## [24] train-rmse:0.321167+0.006823 test-rmse:0.437730+0.050798
## [25] train-rmse:0.317348+0.006952 test-rmse:0.433123+0.047257
## [26] train-rmse:0.313923+0.007205 test-rmse:0.432884+0.048902
## [27] train-rmse:0.308620+0.008966 test-rmse:0.432417+0.050494
## [28] train-rmse:0.305729+0.008897 test-rmse:0.430410+0.050960
## [29] train-rmse:0.302290+0.008878 test-rmse:0.430013+0.051764
## [30] train-rmse:0.299946+0.009309 test-rmse:0.431239+0.051775
## [31] train-rmse:0.297457+0.009294 test-rmse:0.430862+0.052029
## [32] train-rmse:0.295300+0.008964 test-rmse:0.430532+0.051790
## [33] train-rmse:0.293005+0.009018 test-rmse:0.430062+0.051562
## [34] train-rmse:0.289872+0.007849 test-rmse:0.429133+0.051496
## [35] train-rmse:0.286685+0.008107 test-rmse:0.428500+0.050809
## [36] train-rmse:0.284013+0.008028 test-rmse:0.426893+0.052711
## [37] train-rmse:0.281498+0.007857 test-rmse:0.426649+0.053320
## [38] train-rmse:0.279123+0.008107 test-rmse:0.424525+0.055169
## [39] train-rmse:0.276218+0.008063 test-rmse:0.424239+0.056281
## [40] train-rmse:0.274517+0.008268 test-rmse:0.423767+0.055980
## [41] train-rmse:0.272549+0.008446 test-rmse:0.423831+0.057180
## [42] train-rmse:0.270567+0.007363 test-rmse:0.423373+0.057667
## [43] train-rmse:0.269290+0.007712 test-rmse:0.423031+0.057903
## [44] train-rmse:0.267884+0.007682 test-rmse:0.423278+0.058023
## [45] train-rmse:0.266421+0.007662 test-rmse:0.422723+0.059487
## [46] train-rmse:0.265072+0.007645 test-rmse:0.422434+0.059584
## [47] train-rmse:0.263696+0.007512 test-rmse:0.421744+0.059966
## [48] train-rmse:0.260363+0.008075 test-rmse:0.421017+0.060246
## [49] train-rmse:0.258721+0.008169 test-rmse:0.421753+0.060470
## [50] train-rmse:0.256465+0.009232 test-rmse:0.421181+0.059298
## [51] train-rmse:0.255219+0.008887 test-rmse:0.422459+0.058948
## [52] train-rmse:0.252979+0.008875 test-rmse:0.422470+0.058108
## [53] train-rmse:0.250580+0.008565 test-rmse:0.421585+0.058544
## [54] train-rmse:0.247577+0.009716 test-rmse:0.420452+0.058951
## [55] train-rmse:0.245879+0.009577 test-rmse:0.419055+0.060444
## [56] train-rmse:0.244291+0.009491 test-rmse:0.418815+0.060714
## [57] train-rmse:0.242086+0.010457 test-rmse:0.418751+0.061791
## [58] train-rmse:0.240349+0.010241 test-rmse:0.418572+0.061140
## [59] train-rmse:0.238838+0.010063 test-rmse:0.417999+0.060607
## [60] train-rmse:0.237312+0.010407 test-rmse:0.417103+0.060763
## [61] train-rmse:0.234311+0.009295 test-rmse:0.418020+0.060344
## [62] train-rmse:0.232379+0.008967 test-rmse:0.417441+0.060848
## [63] train-rmse:0.230468+0.009113 test-rmse:0.416821+0.061122
## [64] train-rmse:0.228460+0.008253 test-rmse:0.417424+0.061671
## [65] train-rmse:0.227631+0.008216 test-rmse:0.417734+0.061756
## [66] train-rmse:0.225845+0.007552 test-rmse:0.417420+0.062007
## [67] train-rmse:0.224285+0.007002 test-rmse:0.417354+0.062211
## [68] train-rmse:0.221872+0.006452 test-rmse:0.416846+0.061848
## [69] train-rmse:0.220247+0.007403 test-rmse:0.416364+0.062139
## [70] train-rmse:0.218021+0.007534 test-rmse:0.416677+0.061745
## [71] train-rmse:0.217416+0.007555 test-rmse:0.416532+0.062135
## [72] train-rmse:0.216330+0.008064 test-rmse:0.416108+0.062245
## [73] train-rmse:0.215207+0.008093 test-rmse:0.415561+0.061802
## [74] train-rmse:0.213360+0.008666 test-rmse:0.415652+0.062174
## [75] train-rmse:0.212052+0.008441 test-rmse:0.414416+0.062733
## [76] train-rmse:0.211148+0.008523 test-rmse:0.415386+0.062933
## [77] train-rmse:0.209978+0.008310 test-rmse:0.416246+0.063083
## [78] train-rmse:0.208692+0.008334 test-rmse:0.415682+0.062586
## [79] train-rmse:0.208124+0.008291 test-rmse:0.416619+0.062769
## [80] train-rmse:0.206995+0.007926 test-rmse:0.416349+0.063068
## [81] train-rmse:0.205943+0.008434 test-rmse:0.416966+0.063358
## Stopping. Best iteration:
## [75] train-rmse:0.212052+0.008441 test-rmse:0.414416+0.062733
##
## 107
## [1] train-rmse:1.070342+0.020187 test-rmse:1.078957+0.118060
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.741515+0.017267 test-rmse:0.746221+0.101011
## [3] train-rmse:0.556247+0.013510 test-rmse:0.578741+0.100714
## [4] train-rmse:0.455397+0.009972 test-rmse:0.500139+0.084029
## [5] train-rmse:0.402796+0.008011 test-rmse:0.460626+0.080655
## [6] train-rmse:0.373354+0.008525 test-rmse:0.449378+0.075390
## [7] train-rmse:0.353882+0.006877 test-rmse:0.432832+0.078974
## [8] train-rmse:0.337301+0.005900 test-rmse:0.426409+0.075362
## [9] train-rmse:0.322972+0.007354 test-rmse:0.426029+0.073952
## [10] train-rmse:0.309687+0.005575 test-rmse:0.426633+0.073435
## [11] train-rmse:0.300194+0.006304 test-rmse:0.423398+0.072168
## [12] train-rmse:0.293629+0.006176 test-rmse:0.423028+0.071411
## [13] train-rmse:0.282882+0.006994 test-rmse:0.421170+0.072089
## [14] train-rmse:0.275883+0.006515 test-rmse:0.419546+0.072062
## [15] train-rmse:0.269722+0.007214 test-rmse:0.417227+0.072300
## [16] train-rmse:0.263703+0.007974 test-rmse:0.416620+0.071540
## [17] train-rmse:0.257256+0.009513 test-rmse:0.415791+0.074832
## [18] train-rmse:0.252608+0.009120 test-rmse:0.416389+0.074205
## [19] train-rmse:0.247761+0.009561 test-rmse:0.415723+0.075841
## [20] train-rmse:0.241899+0.008837 test-rmse:0.416016+0.073786
## [21] train-rmse:0.234401+0.008445 test-rmse:0.413812+0.075321
## [22] train-rmse:0.228491+0.006213 test-rmse:0.412956+0.074810
## [23] train-rmse:0.224857+0.005222 test-rmse:0.413773+0.074806
## [24] train-rmse:0.220853+0.005680 test-rmse:0.413213+0.074317
## [25] train-rmse:0.216725+0.005661 test-rmse:0.414673+0.074966
## [26] train-rmse:0.212803+0.005590 test-rmse:0.413389+0.074235
## [27] train-rmse:0.209468+0.006004 test-rmse:0.412654+0.074009
## [28] train-rmse:0.205573+0.006382 test-rmse:0.411253+0.075001
## [29] train-rmse:0.202121+0.007344 test-rmse:0.410659+0.075543
## [30] train-rmse:0.199082+0.006415 test-rmse:0.410162+0.078108
## [31] train-rmse:0.196071+0.007489 test-rmse:0.410046+0.078883
## [32] train-rmse:0.193671+0.007663 test-rmse:0.410422+0.078945
## [33] train-rmse:0.190300+0.007898 test-rmse:0.409130+0.077932
## [34] train-rmse:0.187131+0.008124 test-rmse:0.409114+0.077961
## [35] train-rmse:0.183808+0.006136 test-rmse:0.410192+0.077005
## [36] train-rmse:0.181407+0.006367 test-rmse:0.410492+0.076968
## [37] train-rmse:0.178832+0.007163 test-rmse:0.410453+0.075584
## [38] train-rmse:0.177089+0.007433 test-rmse:0.410398+0.075497
## [39] train-rmse:0.173736+0.007538 test-rmse:0.411411+0.074523
## [40] train-rmse:0.170794+0.006891 test-rmse:0.410399+0.074940
## Stopping. Best iteration:
## [34] train-rmse:0.187131+0.008124 test-rmse:0.409114+0.077961
##
## 108
## [1] train-rmse:1.062843+0.018914 test-rmse:1.071130+0.119400
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.725266+0.011769 test-rmse:0.740164+0.110433
## [3] train-rmse:0.527359+0.008854 test-rmse:0.564044+0.089993
## [4] train-rmse:0.417857+0.005408 test-rmse:0.480431+0.074166
## [5] train-rmse:0.354359+0.005251 test-rmse:0.439585+0.078038
## [6] train-rmse:0.319172+0.005625 test-rmse:0.425551+0.072725
## [7] train-rmse:0.296834+0.004102 test-rmse:0.418632+0.071180
## [8] train-rmse:0.281228+0.004630 test-rmse:0.414980+0.066453
## [9] train-rmse:0.266711+0.005231 test-rmse:0.414920+0.065887
## [10] train-rmse:0.254590+0.008965 test-rmse:0.404176+0.068142
## [11] train-rmse:0.243510+0.012126 test-rmse:0.404438+0.065607
## [12] train-rmse:0.233724+0.012279 test-rmse:0.402509+0.065925
## [13] train-rmse:0.223075+0.013009 test-rmse:0.398545+0.067159
## [14] train-rmse:0.215722+0.010712 test-rmse:0.397117+0.066141
## [15] train-rmse:0.208985+0.011431 test-rmse:0.396124+0.067478
## [16] train-rmse:0.201618+0.009491 test-rmse:0.398160+0.068377
## [17] train-rmse:0.195884+0.009305 test-rmse:0.397406+0.069161
## [18] train-rmse:0.188557+0.008431 test-rmse:0.394330+0.067795
## [19] train-rmse:0.183885+0.009402 test-rmse:0.394000+0.068626
## [20] train-rmse:0.179659+0.009854 test-rmse:0.394457+0.066450
## [21] train-rmse:0.175109+0.008437 test-rmse:0.393947+0.065616
## [22] train-rmse:0.171029+0.009633 test-rmse:0.392511+0.064525
## [23] train-rmse:0.164192+0.009512 test-rmse:0.392147+0.064615
## [24] train-rmse:0.159434+0.008673 test-rmse:0.391567+0.063048
## [25] train-rmse:0.155648+0.008602 test-rmse:0.391161+0.064202
## [26] train-rmse:0.152490+0.008357 test-rmse:0.391850+0.063511
## [27] train-rmse:0.148245+0.008297 test-rmse:0.390782+0.063115
## [28] train-rmse:0.144514+0.007501 test-rmse:0.389687+0.061416
## [29] train-rmse:0.140690+0.009104 test-rmse:0.389163+0.061801
## [30] train-rmse:0.136979+0.009944 test-rmse:0.387856+0.061510
## [31] train-rmse:0.133069+0.008292 test-rmse:0.387551+0.061093
## [32] train-rmse:0.130144+0.007662 test-rmse:0.388370+0.061377
## [33] train-rmse:0.126178+0.007659 test-rmse:0.389847+0.061813
## [34] train-rmse:0.123267+0.007612 test-rmse:0.388287+0.061825
## [35] train-rmse:0.119832+0.007029 test-rmse:0.388136+0.062152
## [36] train-rmse:0.116815+0.007709 test-rmse:0.389041+0.062608
## [37] train-rmse:0.114465+0.006666 test-rmse:0.389340+0.062994
## Stopping. Best iteration:
## [31] train-rmse:0.133069+0.008292 test-rmse:0.387551+0.061093
##
## 109
## [1] train-rmse:1.058579+0.019548 test-rmse:1.067785+0.112832
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.715473+0.013053 test-rmse:0.735913+0.101725
## [3] train-rmse:0.509467+0.009982 test-rmse:0.553814+0.086850
## [4] train-rmse:0.389390+0.005493 test-rmse:0.467725+0.082341
## [5] train-rmse:0.320172+0.005869 test-rmse:0.426575+0.076004
## [6] train-rmse:0.282349+0.007561 test-rmse:0.410647+0.071099
## [7] train-rmse:0.257710+0.008118 test-rmse:0.405411+0.069559
## [8] train-rmse:0.239565+0.008920 test-rmse:0.401631+0.068095
## [9] train-rmse:0.227844+0.007908 test-rmse:0.399327+0.070633
## [10] train-rmse:0.214681+0.010817 test-rmse:0.398972+0.068046
## [11] train-rmse:0.201179+0.009291 test-rmse:0.398086+0.067622
## [12] train-rmse:0.187995+0.004800 test-rmse:0.395386+0.068587
## [13] train-rmse:0.175763+0.004378 test-rmse:0.395013+0.064898
## [14] train-rmse:0.168655+0.002699 test-rmse:0.393059+0.063942
## [15] train-rmse:0.162168+0.002878 test-rmse:0.393338+0.065121
## [16] train-rmse:0.156030+0.003136 test-rmse:0.393521+0.066832
## [17] train-rmse:0.149970+0.001882 test-rmse:0.392387+0.068215
## [18] train-rmse:0.142577+0.002912 test-rmse:0.390642+0.069078
## [19] train-rmse:0.138101+0.004210 test-rmse:0.389491+0.071263
## [20] train-rmse:0.130240+0.005673 test-rmse:0.388866+0.071107
## [21] train-rmse:0.124327+0.005397 test-rmse:0.390498+0.069915
## [22] train-rmse:0.117273+0.006044 test-rmse:0.391518+0.072049
## [23] train-rmse:0.112704+0.006625 test-rmse:0.390702+0.070811
## [24] train-rmse:0.108400+0.007894 test-rmse:0.392082+0.069111
## [25] train-rmse:0.105051+0.008162 test-rmse:0.392862+0.068759
## [26] train-rmse:0.100445+0.007968 test-rmse:0.393294+0.069087
## Stopping. Best iteration:
## [20] train-rmse:0.130240+0.005673 test-rmse:0.388866+0.071107
##
## 110
## [1] train-rmse:1.057127+0.019600 test-rmse:1.065285+0.110552
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.710004+0.013043 test-rmse:0.733101+0.101644
## [3] train-rmse:0.500453+0.012132 test-rmse:0.544470+0.084558
## [4] train-rmse:0.375373+0.009881 test-rmse:0.464034+0.077242
## [5] train-rmse:0.299261+0.008480 test-rmse:0.425951+0.073163
## [6] train-rmse:0.254815+0.007264 test-rmse:0.414461+0.071745
## [7] train-rmse:0.223680+0.008626 test-rmse:0.402506+0.066819
## [8] train-rmse:0.200636+0.012267 test-rmse:0.398057+0.059127
## [9] train-rmse:0.181212+0.006620 test-rmse:0.395553+0.059668
## [10] train-rmse:0.170920+0.006891 test-rmse:0.396262+0.057235
## [11] train-rmse:0.157048+0.004678 test-rmse:0.400091+0.053171
## [12] train-rmse:0.146553+0.001863 test-rmse:0.398454+0.051924
## [13] train-rmse:0.137420+0.005044 test-rmse:0.398886+0.051373
## [14] train-rmse:0.125672+0.004795 test-rmse:0.398158+0.052993
## [15] train-rmse:0.118475+0.003781 test-rmse:0.397109+0.054929
## Stopping. Best iteration:
## [9] train-rmse:0.181212+0.006620 test-rmse:0.395553+0.059668
##
## 111
## [1] train-rmse:1.056461+0.019595 test-rmse:1.067360+0.110299
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:0.706778+0.013587 test-rmse:0.733248+0.100276
## [3] train-rmse:0.493695+0.012709 test-rmse:0.549352+0.082384
## [4] train-rmse:0.362441+0.012037 test-rmse:0.465365+0.073881
## [5] train-rmse:0.281333+0.013469 test-rmse:0.422677+0.069231
## [6] train-rmse:0.230052+0.014199 test-rmse:0.408418+0.062697
## [7] train-rmse:0.199616+0.013957 test-rmse:0.404749+0.057401
## [8] train-rmse:0.174315+0.012524 test-rmse:0.404333+0.056640
## [9] train-rmse:0.156863+0.012423 test-rmse:0.403803+0.053178
## [10] train-rmse:0.136455+0.006254 test-rmse:0.402040+0.049421
## [11] train-rmse:0.125165+0.006882 test-rmse:0.401563+0.047451
## [12] train-rmse:0.113026+0.008085 test-rmse:0.402449+0.043666
## [13] train-rmse:0.105160+0.007852 test-rmse:0.403136+0.043630
## [14] train-rmse:0.097666+0.007516 test-rmse:0.400886+0.044206
## [15] train-rmse:0.091116+0.009313 test-rmse:0.401317+0.042666
## [16] train-rmse:0.084066+0.008681 test-rmse:0.402610+0.042224
## [17] train-rmse:0.076500+0.009760 test-rmse:0.402478+0.042456
## [18] train-rmse:0.071516+0.008747 test-rmse:0.402851+0.042653
## [19] train-rmse:0.065477+0.007691 test-rmse:0.403201+0.041633
## [20] train-rmse:0.061847+0.007456 test-rmse:0.403595+0.041923
## Stopping. Best iteration:
## [14] train-rmse:0.097666+0.007516 test-rmse:0.400886+0.044206
##
## 112## max_depth eta
## 88 4 0.34## [1] 1.22## [1] 0.125## [1] 32## [1] train-rmse:8.801410+0.063773 test-rmse:8.797373+0.276559
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.996855+0.056656 test-rmse:7.989608+0.274703
## [3] train-rmse:7.278528+0.049931 test-rmse:7.272552+0.269181
## [4] train-rmse:6.638549+0.043868 test-rmse:6.633544+0.268302
## [5] train-rmse:6.069083+0.038534 test-rmse:6.063704+0.261787
## [6] train-rmse:5.563678+0.033672 test-rmse:5.559228+0.258198
## [7] train-rmse:5.116204+0.029358 test-rmse:5.115140+0.248005
## [8] train-rmse:4.721193+0.025636 test-rmse:4.723271+0.241130
## [9] train-rmse:4.373668+0.022102 test-rmse:4.375931+0.232943
## [10] train-rmse:4.069049+0.019086 test-rmse:4.076793+0.221844
## [11] train-rmse:3.803135+0.016653 test-rmse:3.815533+0.211051
## [12] train-rmse:3.571802+0.014668 test-rmse:3.589126+0.194586
## [13] train-rmse:3.371021+0.013296 test-rmse:3.389444+0.183583
## [14] train-rmse:3.197823+0.012415 test-rmse:3.214830+0.169173
## [15] train-rmse:3.048821+0.011963 test-rmse:3.068779+0.157202
## [16] train-rmse:2.920928+0.011896 test-rmse:2.942512+0.139706
## [17] train-rmse:2.811491+0.012036 test-rmse:2.838470+0.126802
## [18] train-rmse:2.718158+0.012204 test-rmse:2.745614+0.114746
## [19] train-rmse:2.638732+0.012649 test-rmse:2.669374+0.103178
## [20] train-rmse:2.571121+0.013307 test-rmse:2.603682+0.087958
## [21] train-rmse:2.513557+0.013840 test-rmse:2.546997+0.079999
## [22] train-rmse:2.464540+0.014197 test-rmse:2.499386+0.070405
## [23] train-rmse:2.422579+0.014552 test-rmse:2.459368+0.064334
## [24] train-rmse:2.387059+0.014931 test-rmse:2.426760+0.058062
## [25] train-rmse:2.356525+0.015394 test-rmse:2.398854+0.051602
## [26] train-rmse:2.330121+0.015750 test-rmse:2.373342+0.049059
## [27] train-rmse:2.307214+0.016097 test-rmse:2.351414+0.047126
## [28] train-rmse:2.287261+0.016553 test-rmse:2.331835+0.048614
## [29] train-rmse:2.269726+0.016960 test-rmse:2.318372+0.049404
## [30] train-rmse:2.254295+0.017330 test-rmse:2.299190+0.051618
## [31] train-rmse:2.240519+0.017613 test-rmse:2.282832+0.054081
## [32] train-rmse:2.228073+0.017954 test-rmse:2.273255+0.058416
## [33] train-rmse:2.216764+0.018350 test-rmse:2.261358+0.058326
## [34] train-rmse:2.206411+0.018821 test-rmse:2.253755+0.061330
## [35] train-rmse:2.196807+0.019102 test-rmse:2.247709+0.061817
## [36] train-rmse:2.188017+0.019272 test-rmse:2.238772+0.062839
## [37] train-rmse:2.179795+0.019658 test-rmse:2.228281+0.064032
## [38] train-rmse:2.172055+0.019983 test-rmse:2.219967+0.069714
## [39] train-rmse:2.164723+0.020325 test-rmse:2.215204+0.069977
## [40] train-rmse:2.157729+0.020651 test-rmse:2.210421+0.069968
## [41] train-rmse:2.150941+0.021003 test-rmse:2.204343+0.073018
## [42] train-rmse:2.144343+0.021289 test-rmse:2.201669+0.074435
## [43] train-rmse:2.137950+0.021482 test-rmse:2.190643+0.075139
## [44] train-rmse:2.131818+0.021644 test-rmse:2.187295+0.075681
## [45] train-rmse:2.125911+0.021816 test-rmse:2.182742+0.076662
## [46] train-rmse:2.120160+0.022003 test-rmse:2.176844+0.080089
## [47] train-rmse:2.114567+0.022220 test-rmse:2.172236+0.080136
## [48] train-rmse:2.108995+0.022466 test-rmse:2.165274+0.081577
## [49] train-rmse:2.103602+0.022712 test-rmse:2.161130+0.080989
## [50] train-rmse:2.098310+0.022958 test-rmse:2.157396+0.082878
## [51] train-rmse:2.093086+0.023206 test-rmse:2.154564+0.083290
## [52] train-rmse:2.087891+0.023469 test-rmse:2.150352+0.083454
## [53] train-rmse:2.082753+0.023664 test-rmse:2.143669+0.085480
## [54] train-rmse:2.077750+0.023913 test-rmse:2.142922+0.084870
## [55] train-rmse:2.072812+0.024162 test-rmse:2.136422+0.087012
## [56] train-rmse:2.067922+0.024420 test-rmse:2.128346+0.087267
## [57] train-rmse:2.063124+0.024664 test-rmse:2.125789+0.088538
## [58] train-rmse:2.058320+0.024926 test-rmse:2.121199+0.086628
## [59] train-rmse:2.053624+0.025146 test-rmse:2.117615+0.087937
## [60] train-rmse:2.048973+0.025313 test-rmse:2.114638+0.088425
## [61] train-rmse:2.044454+0.025488 test-rmse:2.109907+0.090288
## [62] train-rmse:2.039949+0.025730 test-rmse:2.105870+0.087282
## [63] train-rmse:2.035496+0.026013 test-rmse:2.101151+0.087077
## [64] train-rmse:2.031092+0.026257 test-rmse:2.099773+0.089878
## [65] train-rmse:2.026756+0.026520 test-rmse:2.093617+0.089762
## [66] train-rmse:2.022457+0.026725 test-rmse:2.090098+0.090139
## [67] train-rmse:2.018176+0.026900 test-rmse:2.087204+0.090027
## [68] train-rmse:2.013953+0.027145 test-rmse:2.083629+0.090077
## [69] train-rmse:2.009750+0.027351 test-rmse:2.077344+0.088818
## [70] train-rmse:2.005613+0.027562 test-rmse:2.073614+0.088181
## [71] train-rmse:2.001512+0.027726 test-rmse:2.071082+0.088317
## [72] train-rmse:1.997443+0.027867 test-rmse:2.069893+0.088980
## [73] train-rmse:1.993435+0.028054 test-rmse:2.064396+0.091087
## [74] train-rmse:1.989492+0.028247 test-rmse:2.062507+0.092278
## [75] train-rmse:1.985613+0.028451 test-rmse:2.060399+0.094583
## [76] train-rmse:1.981753+0.028631 test-rmse:2.056383+0.093867
## [77] train-rmse:1.977923+0.028808 test-rmse:2.052069+0.093253
## [78] train-rmse:1.974084+0.028971 test-rmse:2.049236+0.093656
## [79] train-rmse:1.970274+0.029088 test-rmse:2.046252+0.093230
## [80] train-rmse:1.966517+0.029248 test-rmse:2.040168+0.093957
## [81] train-rmse:1.962767+0.029419 test-rmse:2.037784+0.093786
## [82] train-rmse:1.959120+0.029630 test-rmse:2.034057+0.091003
## [83] train-rmse:1.955484+0.029829 test-rmse:2.029274+0.089237
## [84] train-rmse:1.951893+0.030013 test-rmse:2.028684+0.087961
## [85] train-rmse:1.948308+0.030258 test-rmse:2.024581+0.085697
## [86] train-rmse:1.944800+0.030473 test-rmse:2.021054+0.086572
## [87] train-rmse:1.941316+0.030681 test-rmse:2.016672+0.086987
## [88] train-rmse:1.937816+0.030850 test-rmse:2.014531+0.089025
## [89] train-rmse:1.934350+0.030997 test-rmse:2.011894+0.088445
## [90] train-rmse:1.930949+0.031163 test-rmse:2.010199+0.088552
## [91] train-rmse:1.927554+0.031331 test-rmse:2.007725+0.088822
## [92] train-rmse:1.924176+0.031529 test-rmse:2.003015+0.088466
## [93] train-rmse:1.920829+0.031705 test-rmse:2.000966+0.089898
## [94] train-rmse:1.917523+0.031881 test-rmse:1.997571+0.092148
## [95] train-rmse:1.914226+0.032082 test-rmse:1.994675+0.092251
## [96] train-rmse:1.910965+0.032287 test-rmse:1.991232+0.090533
## [97] train-rmse:1.907715+0.032445 test-rmse:1.986547+0.088253
## [98] train-rmse:1.904520+0.032619 test-rmse:1.983774+0.090132
## [99] train-rmse:1.901362+0.032807 test-rmse:1.980518+0.089991
## [100] train-rmse:1.898215+0.033009 test-rmse:1.976046+0.090049
## [101] train-rmse:1.895080+0.033205 test-rmse:1.974794+0.087696
## [102] train-rmse:1.891955+0.033418 test-rmse:1.972175+0.087066
## [103] train-rmse:1.888873+0.033629 test-rmse:1.969172+0.087689
## [104] train-rmse:1.885832+0.033804 test-rmse:1.966943+0.087176
## [105] train-rmse:1.882807+0.034000 test-rmse:1.964977+0.087646
## [106] train-rmse:1.879810+0.034199 test-rmse:1.963345+0.088764
## [107] train-rmse:1.876833+0.034361 test-rmse:1.960922+0.088566
## [108] train-rmse:1.873870+0.034549 test-rmse:1.960909+0.088715
## [109] train-rmse:1.870941+0.034728 test-rmse:1.958506+0.086750
## [110] train-rmse:1.868025+0.034898 test-rmse:1.958311+0.088988
## [111] train-rmse:1.865137+0.035067 test-rmse:1.956107+0.089134
## [112] train-rmse:1.862290+0.035230 test-rmse:1.953913+0.089508
## [113] train-rmse:1.859436+0.035405 test-rmse:1.949821+0.089122
## [114] train-rmse:1.856600+0.035556 test-rmse:1.947286+0.089338
## [115] train-rmse:1.853769+0.035711 test-rmse:1.944039+0.091244
## [116] train-rmse:1.850958+0.035888 test-rmse:1.940574+0.092552
## [117] train-rmse:1.848157+0.036048 test-rmse:1.938081+0.092482
## [118] train-rmse:1.845377+0.036231 test-rmse:1.934308+0.091089
## [119] train-rmse:1.842627+0.036405 test-rmse:1.932912+0.091233
## [120] train-rmse:1.839881+0.036587 test-rmse:1.932233+0.091012
## [121] train-rmse:1.837138+0.036760 test-rmse:1.927705+0.090583
## [122] train-rmse:1.834430+0.036938 test-rmse:1.925406+0.091190
## [123] train-rmse:1.831744+0.037118 test-rmse:1.922611+0.091266
## [124] train-rmse:1.829109+0.037282 test-rmse:1.920747+0.090437
## [125] train-rmse:1.826483+0.037413 test-rmse:1.920648+0.091913
## [126] train-rmse:1.823841+0.037559 test-rmse:1.917835+0.090869
## [127] train-rmse:1.821269+0.037695 test-rmse:1.915892+0.090410
## [128] train-rmse:1.818708+0.037854 test-rmse:1.913579+0.090550
## [129] train-rmse:1.816170+0.037994 test-rmse:1.911426+0.089903
## [130] train-rmse:1.813624+0.038134 test-rmse:1.910208+0.090360
## [131] train-rmse:1.811127+0.038284 test-rmse:1.907703+0.091489
## [132] train-rmse:1.808618+0.038426 test-rmse:1.905098+0.092253
## [133] train-rmse:1.806124+0.038563 test-rmse:1.903520+0.091326
## [134] train-rmse:1.803641+0.038687 test-rmse:1.901314+0.091954
## [135] train-rmse:1.801195+0.038832 test-rmse:1.900485+0.093203
## [136] train-rmse:1.798738+0.039004 test-rmse:1.897657+0.092196
## [137] train-rmse:1.796286+0.039146 test-rmse:1.896751+0.094725
## [138] train-rmse:1.793836+0.039299 test-rmse:1.895267+0.094344
## [139] train-rmse:1.791407+0.039447 test-rmse:1.891239+0.096639
## [140] train-rmse:1.789009+0.039575 test-rmse:1.888162+0.094676
## [141] train-rmse:1.786623+0.039710 test-rmse:1.887738+0.094134
## [142] train-rmse:1.784267+0.039853 test-rmse:1.885569+0.093800
## [143] train-rmse:1.781926+0.039980 test-rmse:1.883001+0.092138
## [144] train-rmse:1.779604+0.040129 test-rmse:1.879752+0.093841
## [145] train-rmse:1.777301+0.040271 test-rmse:1.877325+0.095165
## [146] train-rmse:1.775001+0.040381 test-rmse:1.874680+0.094611
## [147] train-rmse:1.772697+0.040516 test-rmse:1.873305+0.093467
## [148] train-rmse:1.770386+0.040675 test-rmse:1.870893+0.094286
## [149] train-rmse:1.768089+0.040842 test-rmse:1.868068+0.094901
## [150] train-rmse:1.765858+0.040995 test-rmse:1.866268+0.094320
## [151] train-rmse:1.763651+0.041118 test-rmse:1.864738+0.094599
## [152] train-rmse:1.761457+0.041238 test-rmse:1.864303+0.094020
## [153] train-rmse:1.759266+0.041360 test-rmse:1.861790+0.094407
## [154] train-rmse:1.757075+0.041481 test-rmse:1.858935+0.094161
## [155] train-rmse:1.754911+0.041589 test-rmse:1.857130+0.093483
## [156] train-rmse:1.752747+0.041703 test-rmse:1.855888+0.094635
## [157] train-rmse:1.750625+0.041825 test-rmse:1.855588+0.095235
## [158] train-rmse:1.748468+0.041945 test-rmse:1.854581+0.095784
## [159] train-rmse:1.746360+0.042081 test-rmse:1.852966+0.095383
## [160] train-rmse:1.744264+0.042211 test-rmse:1.851403+0.095347
## [161] train-rmse:1.742178+0.042334 test-rmse:1.849066+0.095376
## [162] train-rmse:1.740096+0.042448 test-rmse:1.848030+0.097960
## [163] train-rmse:1.738027+0.042573 test-rmse:1.845435+0.098492
## [164] train-rmse:1.735972+0.042718 test-rmse:1.843582+0.099315
## [165] train-rmse:1.733916+0.042856 test-rmse:1.840373+0.098740
## [166] train-rmse:1.731872+0.042990 test-rmse:1.837670+0.099395
## [167] train-rmse:1.729837+0.043105 test-rmse:1.835301+0.098508
## [168] train-rmse:1.727804+0.043194 test-rmse:1.832205+0.097973
## [169] train-rmse:1.725797+0.043264 test-rmse:1.829824+0.098273
## [170] train-rmse:1.723799+0.043344 test-rmse:1.829197+0.098630
## [171] train-rmse:1.721812+0.043443 test-rmse:1.829861+0.098692
## [172] train-rmse:1.719835+0.043558 test-rmse:1.827214+0.098830
## [173] train-rmse:1.717882+0.043667 test-rmse:1.825272+0.098738
## [174] train-rmse:1.715955+0.043773 test-rmse:1.824033+0.098616
## [175] train-rmse:1.714020+0.043882 test-rmse:1.821977+0.098978
## [176] train-rmse:1.712125+0.044005 test-rmse:1.820450+0.098963
## [177] train-rmse:1.710228+0.044129 test-rmse:1.820585+0.099018
## [178] train-rmse:1.708343+0.044224 test-rmse:1.818354+0.099071
## [179] train-rmse:1.706456+0.044324 test-rmse:1.816808+0.100726
## [180] train-rmse:1.704588+0.044433 test-rmse:1.815544+0.100274
## [181] train-rmse:1.702731+0.044525 test-rmse:1.814978+0.100598
## [182] train-rmse:1.700899+0.044632 test-rmse:1.813974+0.099409
## [183] train-rmse:1.699080+0.044729 test-rmse:1.813087+0.100953
## [184] train-rmse:1.697276+0.044826 test-rmse:1.811500+0.100168
## [185] train-rmse:1.695476+0.044913 test-rmse:1.810378+0.100521
## [186] train-rmse:1.693691+0.045000 test-rmse:1.809102+0.101702
## [187] train-rmse:1.691911+0.045091 test-rmse:1.806910+0.103091
## [188] train-rmse:1.690138+0.045184 test-rmse:1.805791+0.104841
## [189] train-rmse:1.688373+0.045285 test-rmse:1.803091+0.104113
## [190] train-rmse:1.686605+0.045375 test-rmse:1.801299+0.104226
## [191] train-rmse:1.684856+0.045455 test-rmse:1.801108+0.103638
## [192] train-rmse:1.683093+0.045564 test-rmse:1.799439+0.104329
## [193] train-rmse:1.681350+0.045650 test-rmse:1.798624+0.104130
## [194] train-rmse:1.679609+0.045749 test-rmse:1.796819+0.104172
## [195] train-rmse:1.677895+0.045843 test-rmse:1.795368+0.103988
## [196] train-rmse:1.676192+0.045924 test-rmse:1.793765+0.105111
## [197] train-rmse:1.674496+0.045990 test-rmse:1.791606+0.105307
## [198] train-rmse:1.672814+0.046067 test-rmse:1.788886+0.104521
## [199] train-rmse:1.671149+0.046127 test-rmse:1.787339+0.104241
## [200] train-rmse:1.669461+0.046187 test-rmse:1.784911+0.104779
## [201] train-rmse:1.667803+0.046245 test-rmse:1.784505+0.104365
## [202] train-rmse:1.666147+0.046309 test-rmse:1.783058+0.104682
## [203] train-rmse:1.664513+0.046370 test-rmse:1.781363+0.106235
## [204] train-rmse:1.662880+0.046466 test-rmse:1.781054+0.106984
## [205] train-rmse:1.661278+0.046545 test-rmse:1.777626+0.108625
## [206] train-rmse:1.659682+0.046617 test-rmse:1.776916+0.108893
## [207] train-rmse:1.658091+0.046696 test-rmse:1.775137+0.107841
## [208] train-rmse:1.656507+0.046776 test-rmse:1.776008+0.108119
## [209] train-rmse:1.654911+0.046829 test-rmse:1.774416+0.108358
## [210] train-rmse:1.653347+0.046904 test-rmse:1.773216+0.108251
## [211] train-rmse:1.651802+0.046977 test-rmse:1.772501+0.108432
## [212] train-rmse:1.650262+0.047053 test-rmse:1.771471+0.108149
## [213] train-rmse:1.648735+0.047120 test-rmse:1.770744+0.109032
## [214] train-rmse:1.647204+0.047203 test-rmse:1.768190+0.108127
## [215] train-rmse:1.645685+0.047275 test-rmse:1.767009+0.110147
## [216] train-rmse:1.644155+0.047361 test-rmse:1.765823+0.110344
## [217] train-rmse:1.642612+0.047458 test-rmse:1.763801+0.111577
## [218] train-rmse:1.641101+0.047533 test-rmse:1.762751+0.111129
## [219] train-rmse:1.639589+0.047587 test-rmse:1.762097+0.110974
## [220] train-rmse:1.638093+0.047646 test-rmse:1.761492+0.112516
## [221] train-rmse:1.636596+0.047697 test-rmse:1.759853+0.111970
## [222] train-rmse:1.635091+0.047752 test-rmse:1.757772+0.112399
## [223] train-rmse:1.633595+0.047812 test-rmse:1.755573+0.113070
## [224] train-rmse:1.632130+0.047875 test-rmse:1.754451+0.113176
## [225] train-rmse:1.630676+0.047936 test-rmse:1.752426+0.112740
## [226] train-rmse:1.629229+0.047989 test-rmse:1.751220+0.112194
## [227] train-rmse:1.627783+0.048055 test-rmse:1.750040+0.112662
## [228] train-rmse:1.626359+0.048113 test-rmse:1.749188+0.114329
## [229] train-rmse:1.624942+0.048177 test-rmse:1.747369+0.114549
## [230] train-rmse:1.623519+0.048238 test-rmse:1.745930+0.114044
## [231] train-rmse:1.622093+0.048276 test-rmse:1.745361+0.113764
## [232] train-rmse:1.620682+0.048337 test-rmse:1.743726+0.115198
## [233] train-rmse:1.619286+0.048392 test-rmse:1.743067+0.115519
## [234] train-rmse:1.617905+0.048451 test-rmse:1.742373+0.115733
## [235] train-rmse:1.616532+0.048515 test-rmse:1.741744+0.115633
## [236] train-rmse:1.615166+0.048574 test-rmse:1.740111+0.115779
## [237] train-rmse:1.613799+0.048632 test-rmse:1.738601+0.115622
## [238] train-rmse:1.612461+0.048694 test-rmse:1.738101+0.115930
## [239] train-rmse:1.611107+0.048763 test-rmse:1.737320+0.117267
## [240] train-rmse:1.609756+0.048808 test-rmse:1.734763+0.117400
## [241] train-rmse:1.608419+0.048838 test-rmse:1.733121+0.118824
## [242] train-rmse:1.607090+0.048886 test-rmse:1.732293+0.118512
## [243] train-rmse:1.605757+0.048942 test-rmse:1.731484+0.118824
## [244] train-rmse:1.604429+0.049007 test-rmse:1.729403+0.121568
## [245] train-rmse:1.603099+0.049060 test-rmse:1.728358+0.121140
## [246] train-rmse:1.601782+0.049106 test-rmse:1.726089+0.121578
## [247] train-rmse:1.600475+0.049150 test-rmse:1.725338+0.121593
## [248] train-rmse:1.599177+0.049190 test-rmse:1.724077+0.122098
## [249] train-rmse:1.597884+0.049236 test-rmse:1.722447+0.121967
## [250] train-rmse:1.596601+0.049277 test-rmse:1.721763+0.121959
## [251] train-rmse:1.595336+0.049314 test-rmse:1.720591+0.121686
## [252] train-rmse:1.594075+0.049345 test-rmse:1.718702+0.121008
## [253] train-rmse:1.592814+0.049383 test-rmse:1.717139+0.120459
## [254] train-rmse:1.591566+0.049423 test-rmse:1.715726+0.121554
## [255] train-rmse:1.590319+0.049465 test-rmse:1.713739+0.122178
## [256] train-rmse:1.589075+0.049503 test-rmse:1.712782+0.122463
## [257] train-rmse:1.587831+0.049541 test-rmse:1.712867+0.123711
## [258] train-rmse:1.586598+0.049585 test-rmse:1.711155+0.124550
## [259] train-rmse:1.585374+0.049636 test-rmse:1.711501+0.124229
## [260] train-rmse:1.584146+0.049683 test-rmse:1.710597+0.124331
## [261] train-rmse:1.582933+0.049737 test-rmse:1.708915+0.124663
## [262] train-rmse:1.581726+0.049782 test-rmse:1.709554+0.125530
## [263] train-rmse:1.580526+0.049826 test-rmse:1.707482+0.126361
## [264] train-rmse:1.579324+0.049861 test-rmse:1.706819+0.126352
## [265] train-rmse:1.578123+0.049913 test-rmse:1.706203+0.126032
## [266] train-rmse:1.576930+0.049953 test-rmse:1.705635+0.125961
## [267] train-rmse:1.575746+0.050002 test-rmse:1.705491+0.126357
## [268] train-rmse:1.574568+0.050048 test-rmse:1.703590+0.125744
## [269] train-rmse:1.573397+0.050088 test-rmse:1.702914+0.125512
## [270] train-rmse:1.572230+0.050127 test-rmse:1.700834+0.127836
## [271] train-rmse:1.571080+0.050165 test-rmse:1.700068+0.128206
## [272] train-rmse:1.569938+0.050202 test-rmse:1.698882+0.128411
## [273] train-rmse:1.568806+0.050229 test-rmse:1.697621+0.128479
## [274] train-rmse:1.567671+0.050249 test-rmse:1.697282+0.129034
## [275] train-rmse:1.566540+0.050274 test-rmse:1.696314+0.129264
## [276] train-rmse:1.565408+0.050307 test-rmse:1.694964+0.128580
## [277] train-rmse:1.564290+0.050328 test-rmse:1.693123+0.128358
## [278] train-rmse:1.563171+0.050353 test-rmse:1.691866+0.129010
## [279] train-rmse:1.562058+0.050376 test-rmse:1.691303+0.129037
## [280] train-rmse:1.560953+0.050396 test-rmse:1.690880+0.129076
## [281] train-rmse:1.559853+0.050418 test-rmse:1.688670+0.128558
## [282] train-rmse:1.558752+0.050448 test-rmse:1.687581+0.129048
## [283] train-rmse:1.557667+0.050471 test-rmse:1.686326+0.129566
## [284] train-rmse:1.556595+0.050509 test-rmse:1.684727+0.129827
## [285] train-rmse:1.555518+0.050547 test-rmse:1.682428+0.131679
## [286] train-rmse:1.554449+0.050579 test-rmse:1.682386+0.132524
## [287] train-rmse:1.553384+0.050609 test-rmse:1.680636+0.132565
## [288] train-rmse:1.552326+0.050637 test-rmse:1.680169+0.133094
## [289] train-rmse:1.551271+0.050669 test-rmse:1.680340+0.133214
## [290] train-rmse:1.550218+0.050694 test-rmse:1.678537+0.134182
## [291] train-rmse:1.549168+0.050726 test-rmse:1.678769+0.132823
## [292] train-rmse:1.548121+0.050750 test-rmse:1.678168+0.132792
## [293] train-rmse:1.547080+0.050776 test-rmse:1.676671+0.133330
## [294] train-rmse:1.546043+0.050808 test-rmse:1.676977+0.133980
## [295] train-rmse:1.545015+0.050841 test-rmse:1.677138+0.134385
## [296] train-rmse:1.543984+0.050881 test-rmse:1.676177+0.134613
## [297] train-rmse:1.542970+0.050909 test-rmse:1.675825+0.135094
## [298] train-rmse:1.541960+0.050933 test-rmse:1.674196+0.134759
## [299] train-rmse:1.540954+0.050959 test-rmse:1.673318+0.135402
## [300] train-rmse:1.539961+0.050980 test-rmse:1.671407+0.136919
## [301] train-rmse:1.538966+0.050998 test-rmse:1.670807+0.136819
## [302] train-rmse:1.537985+0.051018 test-rmse:1.669588+0.136187
## [303] train-rmse:1.537006+0.051032 test-rmse:1.669455+0.136564
## [304] train-rmse:1.536022+0.051064 test-rmse:1.669061+0.136826
## [305] train-rmse:1.535042+0.051081 test-rmse:1.667806+0.137628
## [306] train-rmse:1.534067+0.051104 test-rmse:1.667102+0.137897
## [307] train-rmse:1.533093+0.051136 test-rmse:1.666410+0.136994
## [308] train-rmse:1.532129+0.051168 test-rmse:1.665880+0.136647
## [309] train-rmse:1.531177+0.051190 test-rmse:1.664070+0.136418
## [310] train-rmse:1.530230+0.051209 test-rmse:1.662523+0.137012
## [311] train-rmse:1.529290+0.051226 test-rmse:1.661642+0.136878
## [312] train-rmse:1.528340+0.051246 test-rmse:1.660837+0.137287
## [313] train-rmse:1.527396+0.051273 test-rmse:1.660147+0.137782
## [314] train-rmse:1.526460+0.051289 test-rmse:1.659423+0.139855
## [315] train-rmse:1.525537+0.051307 test-rmse:1.658807+0.139941
## [316] train-rmse:1.524611+0.051326 test-rmse:1.658895+0.139502
## [317] train-rmse:1.523682+0.051343 test-rmse:1.656598+0.138813
## [318] train-rmse:1.522757+0.051348 test-rmse:1.655652+0.139466
## [319] train-rmse:1.521834+0.051365 test-rmse:1.654887+0.140473
## [320] train-rmse:1.520921+0.051392 test-rmse:1.653927+0.140002
## [321] train-rmse:1.520014+0.051412 test-rmse:1.652256+0.141138
## [322] train-rmse:1.519117+0.051435 test-rmse:1.651981+0.141505
## [323] train-rmse:1.518228+0.051451 test-rmse:1.651956+0.141659
## [324] train-rmse:1.517333+0.051467 test-rmse:1.651565+0.141625
## [325] train-rmse:1.516440+0.051487 test-rmse:1.650498+0.141504
## [326] train-rmse:1.515561+0.051508 test-rmse:1.649735+0.141696
## [327] train-rmse:1.514691+0.051529 test-rmse:1.649662+0.141042
## [328] train-rmse:1.513811+0.051544 test-rmse:1.648284+0.141614
## [329] train-rmse:1.512947+0.051562 test-rmse:1.647731+0.141974
## [330] train-rmse:1.512085+0.051585 test-rmse:1.647190+0.141727
## [331] train-rmse:1.511225+0.051611 test-rmse:1.646211+0.141484
## [332] train-rmse:1.510367+0.051632 test-rmse:1.645692+0.140959
## [333] train-rmse:1.509507+0.051661 test-rmse:1.643947+0.141165
## [334] train-rmse:1.508661+0.051693 test-rmse:1.643727+0.141762
## [335] train-rmse:1.507814+0.051722 test-rmse:1.642432+0.141744
## [336] train-rmse:1.506972+0.051744 test-rmse:1.641265+0.142138
## [337] train-rmse:1.506129+0.051764 test-rmse:1.640414+0.142987
## [338] train-rmse:1.505294+0.051774 test-rmse:1.639595+0.144634
## [339] train-rmse:1.504455+0.051784 test-rmse:1.637854+0.143973
## [340] train-rmse:1.503629+0.051801 test-rmse:1.636323+0.143411
## [341] train-rmse:1.502806+0.051818 test-rmse:1.635201+0.143765
## [342] train-rmse:1.501989+0.051839 test-rmse:1.634003+0.143645
## [343] train-rmse:1.501174+0.051868 test-rmse:1.633881+0.143152
## [344] train-rmse:1.500360+0.051904 test-rmse:1.632261+0.143428
## [345] train-rmse:1.499544+0.051932 test-rmse:1.632348+0.143093
## [346] train-rmse:1.498734+0.051953 test-rmse:1.632588+0.144620
## [347] train-rmse:1.497934+0.051976 test-rmse:1.632095+0.144694
## [348] train-rmse:1.497137+0.051991 test-rmse:1.632182+0.145631
## [349] train-rmse:1.496346+0.052008 test-rmse:1.631962+0.145708
## [350] train-rmse:1.495553+0.052023 test-rmse:1.631171+0.145070
## [351] train-rmse:1.494767+0.052034 test-rmse:1.629231+0.146328
## [352] train-rmse:1.493982+0.052046 test-rmse:1.628135+0.146519
## [353] train-rmse:1.493205+0.052063 test-rmse:1.627903+0.146558
## [354] train-rmse:1.492427+0.052085 test-rmse:1.627761+0.146484
## [355] train-rmse:1.491651+0.052110 test-rmse:1.626770+0.145622
## [356] train-rmse:1.490879+0.052139 test-rmse:1.625903+0.145538
## [357] train-rmse:1.490117+0.052164 test-rmse:1.626056+0.146290
## [358] train-rmse:1.489358+0.052189 test-rmse:1.625566+0.146191
## [359] train-rmse:1.488599+0.052205 test-rmse:1.625270+0.146931
## [360] train-rmse:1.487847+0.052224 test-rmse:1.624878+0.147126
## [361] train-rmse:1.487101+0.052246 test-rmse:1.623302+0.146487
## [362] train-rmse:1.486358+0.052266 test-rmse:1.622648+0.147638
## [363] train-rmse:1.485616+0.052276 test-rmse:1.622192+0.147917
## [364] train-rmse:1.484875+0.052286 test-rmse:1.621199+0.148497
## [365] train-rmse:1.484134+0.052302 test-rmse:1.620395+0.148886
## [366] train-rmse:1.483407+0.052322 test-rmse:1.619103+0.149440
## [367] train-rmse:1.482682+0.052341 test-rmse:1.617537+0.147624
## [368] train-rmse:1.481961+0.052363 test-rmse:1.616001+0.148070
## [369] train-rmse:1.481241+0.052385 test-rmse:1.615828+0.148373
## [370] train-rmse:1.480519+0.052400 test-rmse:1.615995+0.149109
## [371] train-rmse:1.479802+0.052412 test-rmse:1.616184+0.148814
## [372] train-rmse:1.479084+0.052425 test-rmse:1.616020+0.149590
## [373] train-rmse:1.478370+0.052435 test-rmse:1.615291+0.149439
## [374] train-rmse:1.477662+0.052452 test-rmse:1.614085+0.149914
## [375] train-rmse:1.476951+0.052466 test-rmse:1.613555+0.150029
## [376] train-rmse:1.476245+0.052482 test-rmse:1.613475+0.149416
## [377] train-rmse:1.475540+0.052502 test-rmse:1.612850+0.149144
## [378] train-rmse:1.474839+0.052521 test-rmse:1.611998+0.151141
## [379] train-rmse:1.474136+0.052541 test-rmse:1.611477+0.151359
## [380] train-rmse:1.473445+0.052561 test-rmse:1.610806+0.151477
## [381] train-rmse:1.472748+0.052574 test-rmse:1.610348+0.151406
## [382] train-rmse:1.472062+0.052591 test-rmse:1.609110+0.151177
## [383] train-rmse:1.471381+0.052600 test-rmse:1.608141+0.151771
## [384] train-rmse:1.470704+0.052617 test-rmse:1.607832+0.151797
## [385] train-rmse:1.470030+0.052632 test-rmse:1.606800+0.152172
## [386] train-rmse:1.469361+0.052651 test-rmse:1.605931+0.151967
## [387] train-rmse:1.468694+0.052664 test-rmse:1.604745+0.152670
## [388] train-rmse:1.468031+0.052675 test-rmse:1.603663+0.153463
## [389] train-rmse:1.467374+0.052686 test-rmse:1.603521+0.152557
## [390] train-rmse:1.466720+0.052699 test-rmse:1.602119+0.152078
## [391] train-rmse:1.466062+0.052714 test-rmse:1.601561+0.152397
## [392] train-rmse:1.465415+0.052727 test-rmse:1.601225+0.152975
## [393] train-rmse:1.464765+0.052739 test-rmse:1.600162+0.152747
## [394] train-rmse:1.464117+0.052760 test-rmse:1.599993+0.152684
## [395] train-rmse:1.463468+0.052780 test-rmse:1.599843+0.153100
## [396] train-rmse:1.462813+0.052806 test-rmse:1.598948+0.152468
## [397] train-rmse:1.462170+0.052818 test-rmse:1.598138+0.153823
## [398] train-rmse:1.461530+0.052832 test-rmse:1.597936+0.154632
## [399] train-rmse:1.460895+0.052838 test-rmse:1.596208+0.154319
## [400] train-rmse:1.460266+0.052846 test-rmse:1.595975+0.154677
## [401] train-rmse:1.459637+0.052854 test-rmse:1.594846+0.154903
## [402] train-rmse:1.459009+0.052862 test-rmse:1.595230+0.155157
## [403] train-rmse:1.458381+0.052873 test-rmse:1.594467+0.155205
## [404] train-rmse:1.457756+0.052885 test-rmse:1.594963+0.155114
## [405] train-rmse:1.457129+0.052898 test-rmse:1.593684+0.154987
## [406] train-rmse:1.456505+0.052910 test-rmse:1.593787+0.156058
## [407] train-rmse:1.455888+0.052920 test-rmse:1.592951+0.156249
## [408] train-rmse:1.455280+0.052928 test-rmse:1.592970+0.156392
## [409] train-rmse:1.454673+0.052936 test-rmse:1.592291+0.155858
## [410] train-rmse:1.454066+0.052950 test-rmse:1.591202+0.155175
## [411] train-rmse:1.453463+0.052956 test-rmse:1.591027+0.155127
## [412] train-rmse:1.452861+0.052963 test-rmse:1.590381+0.155296
## [413] train-rmse:1.452262+0.052968 test-rmse:1.589622+0.155321
## [414] train-rmse:1.451663+0.052979 test-rmse:1.589376+0.155851
## [415] train-rmse:1.451065+0.052989 test-rmse:1.588302+0.155766
## [416] train-rmse:1.450475+0.052995 test-rmse:1.587761+0.156586
## [417] train-rmse:1.449894+0.052999 test-rmse:1.587459+0.157210
## [418] train-rmse:1.449307+0.053008 test-rmse:1.586822+0.157398
## [419] train-rmse:1.448730+0.053016 test-rmse:1.586318+0.157471
## [420] train-rmse:1.448158+0.053023 test-rmse:1.585829+0.157211
## [421] train-rmse:1.447587+0.053029 test-rmse:1.585098+0.157615
## [422] train-rmse:1.447015+0.053038 test-rmse:1.584364+0.158444
## [423] train-rmse:1.446446+0.053046 test-rmse:1.583752+0.157603
## [424] train-rmse:1.445879+0.053056 test-rmse:1.583964+0.158121
## [425] train-rmse:1.445307+0.053061 test-rmse:1.583467+0.158692
## [426] train-rmse:1.444738+0.053066 test-rmse:1.583301+0.158104
## [427] train-rmse:1.444167+0.053076 test-rmse:1.582410+0.158611
## [428] train-rmse:1.443602+0.053086 test-rmse:1.581686+0.158327
## [429] train-rmse:1.443047+0.053093 test-rmse:1.580501+0.159637
## [430] train-rmse:1.442493+0.053101 test-rmse:1.580914+0.160278
## [431] train-rmse:1.441941+0.053110 test-rmse:1.580305+0.158972
## [432] train-rmse:1.441384+0.053121 test-rmse:1.579458+0.158172
## [433] train-rmse:1.440831+0.053134 test-rmse:1.579108+0.159145
## [434] train-rmse:1.440283+0.053141 test-rmse:1.578294+0.158416
## [435] train-rmse:1.439738+0.053151 test-rmse:1.579132+0.159619
## [436] train-rmse:1.439196+0.053158 test-rmse:1.578231+0.159272
## [437] train-rmse:1.438654+0.053161 test-rmse:1.577835+0.159370
## [438] train-rmse:1.438112+0.053159 test-rmse:1.577225+0.159757
## [439] train-rmse:1.437581+0.053163 test-rmse:1.576724+0.159670
## [440] train-rmse:1.437053+0.053168 test-rmse:1.577151+0.159787
## [441] train-rmse:1.436530+0.053170 test-rmse:1.576267+0.159740
## [442] train-rmse:1.436005+0.053176 test-rmse:1.576287+0.159999
## [443] train-rmse:1.435483+0.053184 test-rmse:1.575331+0.160681
## [444] train-rmse:1.434964+0.053189 test-rmse:1.574632+0.160516
## [445] train-rmse:1.434446+0.053194 test-rmse:1.574448+0.160591
## [446] train-rmse:1.433932+0.053197 test-rmse:1.574206+0.160322
## [447] train-rmse:1.433418+0.053202 test-rmse:1.574012+0.160602
## [448] train-rmse:1.432901+0.053206 test-rmse:1.573282+0.160931
## [449] train-rmse:1.432391+0.053207 test-rmse:1.573202+0.160426
## [450] train-rmse:1.431880+0.053206 test-rmse:1.572480+0.160302
## [451] train-rmse:1.431372+0.053208 test-rmse:1.571126+0.160999
## [452] train-rmse:1.430864+0.053212 test-rmse:1.570982+0.161099
## [453] train-rmse:1.430365+0.053214 test-rmse:1.570140+0.160755
## [454] train-rmse:1.429870+0.053216 test-rmse:1.569367+0.161160
## [455] train-rmse:1.429375+0.053225 test-rmse:1.569687+0.161831
## [456] train-rmse:1.428879+0.053234 test-rmse:1.568663+0.161673
## [457] train-rmse:1.428382+0.053232 test-rmse:1.568224+0.162360
## [458] train-rmse:1.427889+0.053237 test-rmse:1.568337+0.161837
## [459] train-rmse:1.427397+0.053246 test-rmse:1.567927+0.162400
## [460] train-rmse:1.426911+0.053248 test-rmse:1.567084+0.163126
## [461] train-rmse:1.426421+0.053249 test-rmse:1.566596+0.163406
## [462] train-rmse:1.425937+0.053250 test-rmse:1.565638+0.163549
## [463] train-rmse:1.425455+0.053254 test-rmse:1.565537+0.163915
## [464] train-rmse:1.424972+0.053257 test-rmse:1.565042+0.164346
## [465] train-rmse:1.424492+0.053258 test-rmse:1.564174+0.163829
## [466] train-rmse:1.424016+0.053263 test-rmse:1.564222+0.163918
## [467] train-rmse:1.423544+0.053269 test-rmse:1.564108+0.164781
## [468] train-rmse:1.423072+0.053272 test-rmse:1.562724+0.164311
## [469] train-rmse:1.422605+0.053278 test-rmse:1.562454+0.165413
## [470] train-rmse:1.422140+0.053280 test-rmse:1.562589+0.166290
## [471] train-rmse:1.421668+0.053289 test-rmse:1.561857+0.166020
## [472] train-rmse:1.421203+0.053295 test-rmse:1.561243+0.166017
## [473] train-rmse:1.420743+0.053300 test-rmse:1.560968+0.166004
## [474] train-rmse:1.420282+0.053303 test-rmse:1.560504+0.165744
## [475] train-rmse:1.419820+0.053304 test-rmse:1.559797+0.165631
## [476] train-rmse:1.419364+0.053309 test-rmse:1.559159+0.165070
## [477] train-rmse:1.418913+0.053311 test-rmse:1.559068+0.165443
## [478] train-rmse:1.418466+0.053317 test-rmse:1.558677+0.165844
## [479] train-rmse:1.418021+0.053323 test-rmse:1.558331+0.165729
## [480] train-rmse:1.417575+0.053326 test-rmse:1.558263+0.165226
## [481] train-rmse:1.417128+0.053336 test-rmse:1.558442+0.165873
## [482] train-rmse:1.416685+0.053338 test-rmse:1.557589+0.167075
## [483] train-rmse:1.416244+0.053337 test-rmse:1.557441+0.167582
## [484] train-rmse:1.415807+0.053340 test-rmse:1.557103+0.167576
## [485] train-rmse:1.415374+0.053344 test-rmse:1.556972+0.167539
## [486] train-rmse:1.414939+0.053347 test-rmse:1.556428+0.167830
## [487] train-rmse:1.414505+0.053350 test-rmse:1.556389+0.167723
## [488] train-rmse:1.414074+0.053357 test-rmse:1.555861+0.167733
## [489] train-rmse:1.413649+0.053358 test-rmse:1.554965+0.167682
## [490] train-rmse:1.413223+0.053359 test-rmse:1.554671+0.167627
## [491] train-rmse:1.412796+0.053359 test-rmse:1.553681+0.167373
## [492] train-rmse:1.412368+0.053360 test-rmse:1.553524+0.167494
## [493] train-rmse:1.411939+0.053358 test-rmse:1.553382+0.167799
## [494] train-rmse:1.411521+0.053357 test-rmse:1.553300+0.168036
## [495] train-rmse:1.411097+0.053362 test-rmse:1.552946+0.168226
## [496] train-rmse:1.410673+0.053367 test-rmse:1.551725+0.167674
## [497] train-rmse:1.410255+0.053371 test-rmse:1.550840+0.167523
## [498] train-rmse:1.409840+0.053372 test-rmse:1.550526+0.168158
## [499] train-rmse:1.409429+0.053374 test-rmse:1.550036+0.168773
## [500] train-rmse:1.409020+0.053376 test-rmse:1.549814+0.168834
## [501] train-rmse:1.408613+0.053378 test-rmse:1.549216+0.168462
## [502] train-rmse:1.408203+0.053376 test-rmse:1.549945+0.168814
## [503] train-rmse:1.407795+0.053376 test-rmse:1.549503+0.168967
## [504] train-rmse:1.407389+0.053377 test-rmse:1.548558+0.168864
## [505] train-rmse:1.406982+0.053380 test-rmse:1.548233+0.168790
## [506] train-rmse:1.406580+0.053382 test-rmse:1.547286+0.169179
## [507] train-rmse:1.406178+0.053389 test-rmse:1.547141+0.169278
## [508] train-rmse:1.405779+0.053394 test-rmse:1.546568+0.169644
## [509] train-rmse:1.405385+0.053395 test-rmse:1.546712+0.169889
## [510] train-rmse:1.404998+0.053398 test-rmse:1.546611+0.170292
## [511] train-rmse:1.404608+0.053399 test-rmse:1.546407+0.170630
## [512] train-rmse:1.404219+0.053401 test-rmse:1.546103+0.170504
## [513] train-rmse:1.403834+0.053405 test-rmse:1.545365+0.170437
## [514] train-rmse:1.403452+0.053404 test-rmse:1.545066+0.170362
## [515] train-rmse:1.403068+0.053402 test-rmse:1.544504+0.170428
## [516] train-rmse:1.402681+0.053402 test-rmse:1.544951+0.171303
## [517] train-rmse:1.402296+0.053408 test-rmse:1.544326+0.171394
## [518] train-rmse:1.401910+0.053408 test-rmse:1.543652+0.171039
## [519] train-rmse:1.401531+0.053410 test-rmse:1.543170+0.171503
## [520] train-rmse:1.401154+0.053411 test-rmse:1.542495+0.171238
## [521] train-rmse:1.400778+0.053413 test-rmse:1.542600+0.171877
## [522] train-rmse:1.400405+0.053414 test-rmse:1.542565+0.171231
## [523] train-rmse:1.400034+0.053417 test-rmse:1.542573+0.170856
## [524] train-rmse:1.399664+0.053417 test-rmse:1.541927+0.170500
## [525] train-rmse:1.399296+0.053416 test-rmse:1.542052+0.170338
## [526] train-rmse:1.398929+0.053415 test-rmse:1.542460+0.171207
## [527] train-rmse:1.398563+0.053414 test-rmse:1.542443+0.171425
## [528] train-rmse:1.398199+0.053414 test-rmse:1.542885+0.171446
## [529] train-rmse:1.397837+0.053414 test-rmse:1.542145+0.171857
## [530] train-rmse:1.397474+0.053415 test-rmse:1.541190+0.171222
## [531] train-rmse:1.397114+0.053416 test-rmse:1.540002+0.172091
## [532] train-rmse:1.396754+0.053417 test-rmse:1.539466+0.172013
## [533] train-rmse:1.396396+0.053420 test-rmse:1.539067+0.172386
## [534] train-rmse:1.396042+0.053419 test-rmse:1.538487+0.172215
## [535] train-rmse:1.395685+0.053411 test-rmse:1.538261+0.172227
## [536] train-rmse:1.395334+0.053410 test-rmse:1.537629+0.172059
## [537] train-rmse:1.394983+0.053408 test-rmse:1.536591+0.172180
## [538] train-rmse:1.394633+0.053404 test-rmse:1.536778+0.172651
## [539] train-rmse:1.394282+0.053402 test-rmse:1.536792+0.172644
## [540] train-rmse:1.393932+0.053401 test-rmse:1.535993+0.172897
## [541] train-rmse:1.393586+0.053400 test-rmse:1.536007+0.173168
## [542] train-rmse:1.393240+0.053399 test-rmse:1.535694+0.172916
## [543] train-rmse:1.392897+0.053400 test-rmse:1.535224+0.173282
## [544] train-rmse:1.392557+0.053401 test-rmse:1.535203+0.173749
## [545] train-rmse:1.392222+0.053402 test-rmse:1.535033+0.173836
## [546] train-rmse:1.391887+0.053404 test-rmse:1.534492+0.173926
## [547] train-rmse:1.391551+0.053404 test-rmse:1.534303+0.173903
## [548] train-rmse:1.391213+0.053402 test-rmse:1.534578+0.173787
## [549] train-rmse:1.390880+0.053404 test-rmse:1.534309+0.174132
## [550] train-rmse:1.390545+0.053406 test-rmse:1.533756+0.174050
## [551] train-rmse:1.390211+0.053411 test-rmse:1.533552+0.174225
## [552] train-rmse:1.389881+0.053411 test-rmse:1.533153+0.174494
## [553] train-rmse:1.389551+0.053412 test-rmse:1.532679+0.174891
## [554] train-rmse:1.389223+0.053411 test-rmse:1.532845+0.175097
## [555] train-rmse:1.388896+0.053408 test-rmse:1.532495+0.175355
## [556] train-rmse:1.388572+0.053406 test-rmse:1.532278+0.175451
## [557] train-rmse:1.388253+0.053405 test-rmse:1.532220+0.175226
## [558] train-rmse:1.387937+0.053407 test-rmse:1.532198+0.176149
## [559] train-rmse:1.387620+0.053409 test-rmse:1.531943+0.175977
## [560] train-rmse:1.387302+0.053410 test-rmse:1.530710+0.176543
## [561] train-rmse:1.386984+0.053411 test-rmse:1.530610+0.176583
## [562] train-rmse:1.386665+0.053413 test-rmse:1.530406+0.176187
## [563] train-rmse:1.386349+0.053411 test-rmse:1.530086+0.176329
## [564] train-rmse:1.386033+0.053412 test-rmse:1.530282+0.176814
## [565] train-rmse:1.385716+0.053412 test-rmse:1.530849+0.175730
## [566] train-rmse:1.385406+0.053411 test-rmse:1.530846+0.176152
## [567] train-rmse:1.385095+0.053410 test-rmse:1.530605+0.175923
## [568] train-rmse:1.384786+0.053409 test-rmse:1.528948+0.175948
## [569] train-rmse:1.384479+0.053408 test-rmse:1.528444+0.176005
## [570] train-rmse:1.384173+0.053409 test-rmse:1.528144+0.176092
## [571] train-rmse:1.383868+0.053409 test-rmse:1.528034+0.176262
## [572] train-rmse:1.383558+0.053403 test-rmse:1.527931+0.176620
## [573] train-rmse:1.383254+0.053403 test-rmse:1.527341+0.176754
## [574] train-rmse:1.382953+0.053404 test-rmse:1.527173+0.176904
## [575] train-rmse:1.382655+0.053404 test-rmse:1.526979+0.177103
## [576] train-rmse:1.382360+0.053403 test-rmse:1.526989+0.177125
## [577] train-rmse:1.382065+0.053402 test-rmse:1.526828+0.177123
## [578] train-rmse:1.381770+0.053399 test-rmse:1.526254+0.176755
## [579] train-rmse:1.381473+0.053397 test-rmse:1.525951+0.177093
## [580] train-rmse:1.381182+0.053396 test-rmse:1.525413+0.177079
## [581] train-rmse:1.380890+0.053394 test-rmse:1.525397+0.177035
## [582] train-rmse:1.380598+0.053392 test-rmse:1.524736+0.177846
## [583] train-rmse:1.380306+0.053391 test-rmse:1.524378+0.178304
## [584] train-rmse:1.380017+0.053391 test-rmse:1.524442+0.178363
## [585] train-rmse:1.379729+0.053388 test-rmse:1.524744+0.178772
## [586] train-rmse:1.379442+0.053386 test-rmse:1.524574+0.178698
## [587] train-rmse:1.379156+0.053387 test-rmse:1.524471+0.179107
## [588] train-rmse:1.378874+0.053386 test-rmse:1.524317+0.179294
## [589] train-rmse:1.378594+0.053386 test-rmse:1.524179+0.178810
## [590] train-rmse:1.378314+0.053384 test-rmse:1.523407+0.178769
## [591] train-rmse:1.378034+0.053378 test-rmse:1.523288+0.179030
## [592] train-rmse:1.377753+0.053378 test-rmse:1.522595+0.178769
## [593] train-rmse:1.377475+0.053376 test-rmse:1.522226+0.178780
## [594] train-rmse:1.377200+0.053374 test-rmse:1.521794+0.178956
## [595] train-rmse:1.376924+0.053373 test-rmse:1.521575+0.178501
## [596] train-rmse:1.376651+0.053372 test-rmse:1.521772+0.179386
## [597] train-rmse:1.376378+0.053370 test-rmse:1.521134+0.179998
## [598] train-rmse:1.376107+0.053369 test-rmse:1.521073+0.180628
## [599] train-rmse:1.375837+0.053367 test-rmse:1.521351+0.181124
## [600] train-rmse:1.375570+0.053366 test-rmse:1.521681+0.180885
## [601] train-rmse:1.375301+0.053364 test-rmse:1.521557+0.180760
## [602] train-rmse:1.375032+0.053362 test-rmse:1.522010+0.180743
## [603] train-rmse:1.374764+0.053360 test-rmse:1.522048+0.180410
## [604] train-rmse:1.374498+0.053359 test-rmse:1.521771+0.180501
## Stopping. Best iteration:
## [598] train-rmse:1.376107+0.053369 test-rmse:1.521073+0.180628
##
## 1
## [1] train-rmse:8.789440+0.063950 test-rmse:8.785614+0.274244
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.973733+0.058414 test-rmse:7.969443+0.266366
## [3] train-rmse:7.245054+0.053821 test-rmse:7.238426+0.258899
## [4] train-rmse:6.593414+0.047359 test-rmse:6.587706+0.251795
## [5] train-rmse:6.011371+0.041493 test-rmse:6.005166+0.247941
## [6] train-rmse:5.493581+0.036832 test-rmse:5.493338+0.239819
## [7] train-rmse:5.033976+0.033897 test-rmse:5.036446+0.231822
## [8] train-rmse:4.626179+0.030294 test-rmse:4.632743+0.221405
## [9] train-rmse:4.265695+0.028100 test-rmse:4.275876+0.207858
## [10] train-rmse:3.945516+0.026091 test-rmse:3.961258+0.194472
## [11] train-rmse:3.659155+0.019991 test-rmse:3.681070+0.187429
## [12] train-rmse:3.412688+0.019013 test-rmse:3.438998+0.172137
## [13] train-rmse:3.194715+0.017853 test-rmse:3.232044+0.156321
## [14] train-rmse:3.004899+0.017085 test-rmse:3.048188+0.143495
## [15] train-rmse:2.840541+0.015698 test-rmse:2.891989+0.131423
## [16] train-rmse:2.697824+0.015652 test-rmse:2.753195+0.115386
## [17] train-rmse:2.571741+0.013227 test-rmse:2.631053+0.105848
## [18] train-rmse:2.466009+0.012430 test-rmse:2.533699+0.092956
## [19] train-rmse:2.372024+0.012673 test-rmse:2.444969+0.085975
## [20] train-rmse:2.292583+0.012831 test-rmse:2.370657+0.072239
## [21] train-rmse:2.223290+0.013660 test-rmse:2.310521+0.067247
## [22] train-rmse:2.164191+0.012647 test-rmse:2.256828+0.061961
## [23] train-rmse:2.110912+0.015075 test-rmse:2.207143+0.055104
## [24] train-rmse:2.067049+0.014750 test-rmse:2.169404+0.050468
## [25] train-rmse:2.027909+0.015005 test-rmse:2.138279+0.050620
## [26] train-rmse:1.992941+0.014680 test-rmse:2.108110+0.056745
## [27] train-rmse:1.963419+0.014969 test-rmse:2.081645+0.057166
## [28] train-rmse:1.937721+0.015419 test-rmse:2.057528+0.060502
## [29] train-rmse:1.914648+0.016066 test-rmse:2.035725+0.062348
## [30] train-rmse:1.894045+0.016011 test-rmse:2.018113+0.063086
## [31] train-rmse:1.874803+0.016099 test-rmse:2.008082+0.070723
## [32] train-rmse:1.857602+0.015710 test-rmse:1.991403+0.073072
## [33] train-rmse:1.842550+0.016328 test-rmse:1.977142+0.076635
## [34] train-rmse:1.828589+0.016741 test-rmse:1.963488+0.077517
## [35] train-rmse:1.814223+0.019934 test-rmse:1.950846+0.081300
## [36] train-rmse:1.801308+0.020961 test-rmse:1.940979+0.080977
## [37] train-rmse:1.789437+0.021225 test-rmse:1.932796+0.085100
## [38] train-rmse:1.778466+0.021586 test-rmse:1.928760+0.089244
## [39] train-rmse:1.767346+0.021853 test-rmse:1.916452+0.091471
## [40] train-rmse:1.757277+0.022165 test-rmse:1.908181+0.089533
## [41] train-rmse:1.747603+0.022769 test-rmse:1.899221+0.091723
## [42] train-rmse:1.738553+0.022858 test-rmse:1.890477+0.092348
## [43] train-rmse:1.729682+0.023765 test-rmse:1.883310+0.094436
## [44] train-rmse:1.721260+0.024052 test-rmse:1.875505+0.095056
## [45] train-rmse:1.712836+0.024185 test-rmse:1.866528+0.094957
## [46] train-rmse:1.703535+0.026760 test-rmse:1.861743+0.101486
## [47] train-rmse:1.695044+0.026973 test-rmse:1.854693+0.100780
## [48] train-rmse:1.686351+0.027058 test-rmse:1.848715+0.101528
## [49] train-rmse:1.678502+0.027796 test-rmse:1.842067+0.099097
## [50] train-rmse:1.670313+0.027484 test-rmse:1.838141+0.100431
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## [52] train-rmse:1.652818+0.028849 test-rmse:1.826236+0.100443
## [53] train-rmse:1.645339+0.029047 test-rmse:1.821675+0.103131
## [54] train-rmse:1.638368+0.029462 test-rmse:1.816624+0.108231
## [55] train-rmse:1.629949+0.030266 test-rmse:1.808059+0.108299
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## [57] train-rmse:1.616274+0.030267 test-rmse:1.796715+0.107617
## [58] train-rmse:1.609399+0.030371 test-rmse:1.792574+0.105187
## [59] train-rmse:1.602857+0.030987 test-rmse:1.789024+0.104568
## [60] train-rmse:1.596244+0.030558 test-rmse:1.782346+0.103990
## [61] train-rmse:1.589562+0.030618 test-rmse:1.777576+0.104477
## [62] train-rmse:1.581436+0.032196 test-rmse:1.770020+0.106327
## [63] train-rmse:1.575024+0.033393 test-rmse:1.767470+0.107170
## [64] train-rmse:1.568186+0.033608 test-rmse:1.763823+0.110115
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## [66] train-rmse:1.556136+0.033755 test-rmse:1.757946+0.110292
## [67] train-rmse:1.550234+0.033611 test-rmse:1.753279+0.112770
## [68] train-rmse:1.544501+0.033859 test-rmse:1.748956+0.112649
## [69] train-rmse:1.538632+0.033290 test-rmse:1.743388+0.114226
## [70] train-rmse:1.533603+0.033476 test-rmse:1.736859+0.113210
## [71] train-rmse:1.527971+0.033849 test-rmse:1.731062+0.112349
## [72] train-rmse:1.522824+0.033869 test-rmse:1.727647+0.112839
## [73] train-rmse:1.517553+0.034289 test-rmse:1.725868+0.110601
## [74] train-rmse:1.512016+0.034825 test-rmse:1.720627+0.112096
## [75] train-rmse:1.506744+0.034960 test-rmse:1.717496+0.115259
## [76] train-rmse:1.498740+0.035275 test-rmse:1.709360+0.118247
## [77] train-rmse:1.492910+0.035481 test-rmse:1.707086+0.117422
## [78] train-rmse:1.488189+0.035861 test-rmse:1.703898+0.117114
## [79] train-rmse:1.483249+0.036619 test-rmse:1.698317+0.118521
## [80] train-rmse:1.478358+0.037151 test-rmse:1.694394+0.117640
## [81] train-rmse:1.473265+0.037566 test-rmse:1.689151+0.118764
## [82] train-rmse:1.468197+0.037370 test-rmse:1.684193+0.117742
## [83] train-rmse:1.463669+0.037612 test-rmse:1.681392+0.118698
## [84] train-rmse:1.458865+0.037559 test-rmse:1.679087+0.119367
## [85] train-rmse:1.454120+0.038014 test-rmse:1.674491+0.119980
## [86] train-rmse:1.449036+0.038565 test-rmse:1.673564+0.121178
## [87] train-rmse:1.444497+0.038750 test-rmse:1.671056+0.122433
## [88] train-rmse:1.439427+0.038626 test-rmse:1.667833+0.121719
## [89] train-rmse:1.431727+0.039066 test-rmse:1.658806+0.107909
## [90] train-rmse:1.427514+0.039388 test-rmse:1.655350+0.107768
## [91] train-rmse:1.423216+0.040095 test-rmse:1.652229+0.108358
## [92] train-rmse:1.418818+0.040306 test-rmse:1.646241+0.108443
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## [94] train-rmse:1.409848+0.041142 test-rmse:1.640209+0.107059
## [95] train-rmse:1.402485+0.045425 test-rmse:1.638776+0.107139
## [96] train-rmse:1.398065+0.046196 test-rmse:1.635755+0.106666
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## [98] train-rmse:1.387456+0.047308 test-rmse:1.627927+0.108311
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## [100] train-rmse:1.376836+0.047672 test-rmse:1.620209+0.111379
## [101] train-rmse:1.373219+0.047853 test-rmse:1.617311+0.111684
## [102] train-rmse:1.369698+0.048087 test-rmse:1.614153+0.110650
## [103] train-rmse:1.365451+0.048125 test-rmse:1.612356+0.111094
## [104] train-rmse:1.359727+0.045184 test-rmse:1.608760+0.113094
## [105] train-rmse:1.352520+0.048438 test-rmse:1.607190+0.115950
## [106] train-rmse:1.347776+0.048741 test-rmse:1.600513+0.118691
## [107] train-rmse:1.344183+0.048990 test-rmse:1.597780+0.119991
## [108] train-rmse:1.340447+0.049619 test-rmse:1.594161+0.119478
## [109] train-rmse:1.336490+0.050009 test-rmse:1.593021+0.121450
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## [112] train-rmse:1.324265+0.050001 test-rmse:1.579054+0.124606
## [113] train-rmse:1.319448+0.050937 test-rmse:1.575261+0.125509
## [114] train-rmse:1.315887+0.050705 test-rmse:1.572707+0.125682
## [115] train-rmse:1.310030+0.054502 test-rmse:1.571362+0.126065
## [116] train-rmse:1.306500+0.054555 test-rmse:1.569383+0.128729
## [117] train-rmse:1.302487+0.054327 test-rmse:1.566693+0.130740
## [118] train-rmse:1.298858+0.054500 test-rmse:1.565166+0.132538
## [119] train-rmse:1.295669+0.054622 test-rmse:1.562973+0.133476
## [120] train-rmse:1.292314+0.054848 test-rmse:1.561787+0.133712
## [121] train-rmse:1.289344+0.055025 test-rmse:1.559358+0.133527
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## [123] train-rmse:1.282116+0.056009 test-rmse:1.553852+0.132841
## [124] train-rmse:1.279063+0.056104 test-rmse:1.550571+0.133103
## [125] train-rmse:1.273437+0.056749 test-rmse:1.546701+0.134229
## [126] train-rmse:1.269929+0.056389 test-rmse:1.544898+0.133971
## [127] train-rmse:1.266929+0.056512 test-rmse:1.543026+0.133924
## [128] train-rmse:1.263787+0.056255 test-rmse:1.539932+0.136153
## [129] train-rmse:1.260927+0.056290 test-rmse:1.538467+0.136137
## [130] train-rmse:1.258159+0.056479 test-rmse:1.540147+0.137413
## [131] train-rmse:1.255076+0.056434 test-rmse:1.538058+0.138781
## [132] train-rmse:1.251758+0.056781 test-rmse:1.535539+0.138911
## [133] train-rmse:1.248569+0.057184 test-rmse:1.532569+0.138306
## [134] train-rmse:1.246019+0.057335 test-rmse:1.531297+0.138195
## [135] train-rmse:1.243417+0.057521 test-rmse:1.529761+0.138927
## [136] train-rmse:1.240607+0.057893 test-rmse:1.528059+0.138736
## [137] train-rmse:1.237981+0.058148 test-rmse:1.526300+0.139757
## [138] train-rmse:1.234350+0.059525 test-rmse:1.523776+0.138390
## [139] train-rmse:1.231087+0.058908 test-rmse:1.519414+0.137593
## [140] train-rmse:1.228141+0.058573 test-rmse:1.517540+0.137015
## [141] train-rmse:1.225370+0.058901 test-rmse:1.516886+0.136910
## [142] train-rmse:1.222290+0.058915 test-rmse:1.515159+0.136699
## [143] train-rmse:1.219650+0.058706 test-rmse:1.511562+0.138391
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## [145] train-rmse:1.213949+0.058772 test-rmse:1.509254+0.140299
## [146] train-rmse:1.207515+0.053308 test-rmse:1.504968+0.145020
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## [148] train-rmse:1.201813+0.054592 test-rmse:1.502914+0.144301
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## [150] train-rmse:1.196872+0.054673 test-rmse:1.496978+0.146593
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## [156] train-rmse:1.178194+0.050587 test-rmse:1.483802+0.148644
## [157] train-rmse:1.175339+0.050612 test-rmse:1.482436+0.147176
## [158] train-rmse:1.172895+0.050465 test-rmse:1.481087+0.149283
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## [160] train-rmse:1.166919+0.050643 test-rmse:1.477845+0.152002
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## [162] train-rmse:1.162393+0.051194 test-rmse:1.476339+0.153160
## [163] train-rmse:1.160134+0.051349 test-rmse:1.474602+0.153221
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## [165] train-rmse:1.152227+0.049285 test-rmse:1.466644+0.141734
## [166] train-rmse:1.148000+0.048468 test-rmse:1.463797+0.138498
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## [168] train-rmse:1.141238+0.047305 test-rmse:1.455636+0.140320
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## [170] train-rmse:1.136608+0.047381 test-rmse:1.454068+0.141655
## [171] train-rmse:1.134559+0.047588 test-rmse:1.452136+0.141862
## [172] train-rmse:1.132577+0.047868 test-rmse:1.449546+0.141906
## [173] train-rmse:1.130674+0.048146 test-rmse:1.447298+0.143309
## [174] train-rmse:1.128059+0.048073 test-rmse:1.444476+0.141981
## [175] train-rmse:1.125202+0.048605 test-rmse:1.443314+0.142414
## [176] train-rmse:1.123180+0.048681 test-rmse:1.443547+0.143329
## [177] train-rmse:1.121332+0.048950 test-rmse:1.443023+0.142655
## [178] train-rmse:1.119361+0.048983 test-rmse:1.441667+0.142779
## [179] train-rmse:1.114789+0.048164 test-rmse:1.434935+0.134803
## [180] train-rmse:1.111412+0.048175 test-rmse:1.432889+0.132073
## [181] train-rmse:1.109316+0.047807 test-rmse:1.430846+0.132868
## [182] train-rmse:1.107053+0.047445 test-rmse:1.429411+0.133427
## [183] train-rmse:1.105071+0.047434 test-rmse:1.427390+0.133385
## [184] train-rmse:1.102833+0.047660 test-rmse:1.426650+0.134531
## [185] train-rmse:1.101003+0.047609 test-rmse:1.425750+0.134530
## [186] train-rmse:1.096036+0.043064 test-rmse:1.423430+0.137535
## [187] train-rmse:1.094009+0.043008 test-rmse:1.423552+0.138362
## [188] train-rmse:1.092212+0.043184 test-rmse:1.421849+0.138070
## [189] train-rmse:1.090214+0.043230 test-rmse:1.420400+0.139209
## [190] train-rmse:1.086369+0.044207 test-rmse:1.417780+0.141022
## [191] train-rmse:1.084221+0.043960 test-rmse:1.417531+0.141740
## [192] train-rmse:1.079837+0.040464 test-rmse:1.415398+0.143707
## [193] train-rmse:1.077361+0.039974 test-rmse:1.414271+0.143740
## [194] train-rmse:1.075660+0.039911 test-rmse:1.413695+0.144459
## [195] train-rmse:1.073935+0.039953 test-rmse:1.413205+0.145312
## [196] train-rmse:1.071732+0.039687 test-rmse:1.412008+0.144824
## [197] train-rmse:1.070031+0.039869 test-rmse:1.410743+0.145452
## [198] train-rmse:1.068554+0.039917 test-rmse:1.409476+0.145922
## [199] train-rmse:1.066720+0.039748 test-rmse:1.408481+0.146861
## [200] train-rmse:1.064990+0.039729 test-rmse:1.410180+0.148283
## [201] train-rmse:1.062946+0.039388 test-rmse:1.410753+0.147798
## [202] train-rmse:1.061262+0.039485 test-rmse:1.409282+0.146011
## [203] train-rmse:1.059788+0.039518 test-rmse:1.409427+0.145283
## [204] train-rmse:1.057672+0.040560 test-rmse:1.408345+0.145870
## [205] train-rmse:1.054090+0.039626 test-rmse:1.401850+0.148325
## [206] train-rmse:1.049740+0.039957 test-rmse:1.396236+0.142279
## [207] train-rmse:1.048036+0.039909 test-rmse:1.394142+0.143143
## [208] train-rmse:1.045115+0.040017 test-rmse:1.391527+0.142141
## [209] train-rmse:1.043256+0.039989 test-rmse:1.390098+0.142003
## [210] train-rmse:1.041569+0.039790 test-rmse:1.388476+0.143589
## [211] train-rmse:1.039883+0.039709 test-rmse:1.388130+0.143553
## [212] train-rmse:1.038290+0.039536 test-rmse:1.387576+0.142946
## [213] train-rmse:1.034432+0.036247 test-rmse:1.386317+0.144470
## [214] train-rmse:1.030085+0.034911 test-rmse:1.383399+0.146961
## [215] train-rmse:1.027982+0.035761 test-rmse:1.381813+0.148492
## [216] train-rmse:1.025469+0.034680 test-rmse:1.379915+0.150501
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## [218] train-rmse:1.022646+0.034744 test-rmse:1.379407+0.148657
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## [220] train-rmse:1.019487+0.034412 test-rmse:1.378448+0.152108
## [221] train-rmse:1.018054+0.034606 test-rmse:1.377593+0.153045
## [222] train-rmse:1.016698+0.034635 test-rmse:1.377867+0.153137
## [223] train-rmse:1.015078+0.034363 test-rmse:1.377272+0.153181
## [224] train-rmse:1.013366+0.033941 test-rmse:1.376582+0.153014
## [225] train-rmse:1.012026+0.034034 test-rmse:1.375726+0.153451
## [226] train-rmse:1.009688+0.033977 test-rmse:1.374706+0.153618
## [227] train-rmse:1.008381+0.034074 test-rmse:1.373837+0.153014
## [228] train-rmse:1.007097+0.034126 test-rmse:1.374821+0.153708
## [229] train-rmse:1.005160+0.034886 test-rmse:1.373710+0.153612
## [230] train-rmse:1.003865+0.034851 test-rmse:1.372574+0.153731
## [231] train-rmse:1.002327+0.034863 test-rmse:1.371723+0.152618
## [232] train-rmse:1.000986+0.035111 test-rmse:1.370320+0.151935
## [233] train-rmse:0.999686+0.035087 test-rmse:1.370173+0.152806
## [234] train-rmse:0.998084+0.035321 test-rmse:1.369909+0.153295
## [235] train-rmse:0.996716+0.035308 test-rmse:1.369737+0.153587
## [236] train-rmse:0.994982+0.034875 test-rmse:1.368066+0.153705
## [237] train-rmse:0.993683+0.034788 test-rmse:1.367588+0.153797
## [238] train-rmse:0.991229+0.034117 test-rmse:1.364581+0.153920
## [239] train-rmse:0.990041+0.034200 test-rmse:1.364785+0.155287
## [240] train-rmse:0.987941+0.034172 test-rmse:1.363270+0.154649
## [241] train-rmse:0.986836+0.034212 test-rmse:1.363068+0.154533
## [242] train-rmse:0.985746+0.034101 test-rmse:1.361672+0.154984
## [243] train-rmse:0.984619+0.034185 test-rmse:1.361232+0.155248
## [244] train-rmse:0.983310+0.034329 test-rmse:1.360023+0.155310
## [245] train-rmse:0.982025+0.034387 test-rmse:1.360776+0.154887
## [246] train-rmse:0.980706+0.034664 test-rmse:1.360075+0.153405
## [247] train-rmse:0.977018+0.031707 test-rmse:1.358258+0.155378
## [248] train-rmse:0.975059+0.031712 test-rmse:1.357156+0.155238
## [249] train-rmse:0.973611+0.031550 test-rmse:1.356437+0.154977
## [250] train-rmse:0.972483+0.031611 test-rmse:1.357200+0.157695
## [251] train-rmse:0.971149+0.032255 test-rmse:1.357666+0.157590
## [252] train-rmse:0.970147+0.032264 test-rmse:1.358307+0.157079
## [253] train-rmse:0.969073+0.032303 test-rmse:1.357852+0.156828
## [254] train-rmse:0.966556+0.032472 test-rmse:1.355627+0.157135
## [255] train-rmse:0.965457+0.032498 test-rmse:1.355187+0.157280
## [256] train-rmse:0.964381+0.032549 test-rmse:1.353894+0.156857
## [257] train-rmse:0.963330+0.032732 test-rmse:1.354057+0.157150
## [258] train-rmse:0.962256+0.032804 test-rmse:1.353111+0.157043
## [259] train-rmse:0.960941+0.032838 test-rmse:1.353773+0.156803
## [260] train-rmse:0.957872+0.036699 test-rmse:1.353494+0.156493
## [261] train-rmse:0.956888+0.036666 test-rmse:1.352620+0.156994
## [262] train-rmse:0.955605+0.036833 test-rmse:1.352041+0.157845
## [263] train-rmse:0.952674+0.034791 test-rmse:1.351036+0.159926
## [264] train-rmse:0.951563+0.034776 test-rmse:1.349868+0.159654
## [265] train-rmse:0.950508+0.034758 test-rmse:1.349375+0.159271
## [266] train-rmse:0.949356+0.034556 test-rmse:1.348536+0.160246
## [267] train-rmse:0.948402+0.034540 test-rmse:1.347587+0.160123
## [268] train-rmse:0.947439+0.034503 test-rmse:1.347119+0.160596
## [269] train-rmse:0.945354+0.034650 test-rmse:1.347070+0.160576
## [270] train-rmse:0.944304+0.034828 test-rmse:1.346117+0.160606
## [271] train-rmse:0.939998+0.033248 test-rmse:1.343503+0.162203
## [272] train-rmse:0.939079+0.033238 test-rmse:1.344049+0.163583
## [273] train-rmse:0.938145+0.033328 test-rmse:1.342977+0.163877
## [274] train-rmse:0.937148+0.033425 test-rmse:1.342633+0.163899
## [275] train-rmse:0.936080+0.033591 test-rmse:1.343143+0.164078
## [276] train-rmse:0.935136+0.033474 test-rmse:1.342957+0.163864
## [277] train-rmse:0.933912+0.033473 test-rmse:1.342805+0.164404
## [278] train-rmse:0.932842+0.033347 test-rmse:1.342030+0.163469
## [279] train-rmse:0.930638+0.033614 test-rmse:1.339977+0.164197
## [280] train-rmse:0.929760+0.033587 test-rmse:1.340014+0.164449
## [281] train-rmse:0.928773+0.033650 test-rmse:1.339712+0.164310
## [282] train-rmse:0.927685+0.034106 test-rmse:1.339068+0.164593
## [283] train-rmse:0.926620+0.033996 test-rmse:1.339640+0.164727
## [284] train-rmse:0.924520+0.033075 test-rmse:1.337982+0.164991
## [285] train-rmse:0.923728+0.033030 test-rmse:1.338094+0.165620
## [286] train-rmse:0.922961+0.033025 test-rmse:1.338142+0.165698
## [287] train-rmse:0.922229+0.033016 test-rmse:1.337874+0.165453
## [288] train-rmse:0.921233+0.032936 test-rmse:1.337294+0.165914
## [289] train-rmse:0.920435+0.032972 test-rmse:1.338140+0.166427
## [290] train-rmse:0.919388+0.033235 test-rmse:1.338086+0.167120
## [291] train-rmse:0.918359+0.033532 test-rmse:1.337506+0.167182
## [292] train-rmse:0.917573+0.033629 test-rmse:1.336934+0.166222
## [293] train-rmse:0.916720+0.033638 test-rmse:1.336753+0.166608
## [294] train-rmse:0.915441+0.033568 test-rmse:1.336025+0.166214
## [295] train-rmse:0.912202+0.036576 test-rmse:1.335324+0.166621
## [296] train-rmse:0.911368+0.036579 test-rmse:1.335523+0.166401
## [297] train-rmse:0.910614+0.036626 test-rmse:1.334959+0.166487
## [298] train-rmse:0.909869+0.036676 test-rmse:1.335133+0.166588
## [299] train-rmse:0.908787+0.036856 test-rmse:1.334588+0.167945
## [300] train-rmse:0.908041+0.036997 test-rmse:1.333965+0.168088
## [301] train-rmse:0.907290+0.036967 test-rmse:1.333366+0.168405
## [302] train-rmse:0.906462+0.036874 test-rmse:1.333187+0.168297
## [303] train-rmse:0.905589+0.036761 test-rmse:1.333341+0.168930
## [304] train-rmse:0.904336+0.036459 test-rmse:1.332749+0.168648
## [305] train-rmse:0.903368+0.036605 test-rmse:1.332565+0.168620
## [306] train-rmse:0.902629+0.036573 test-rmse:1.332120+0.168252
## [307] train-rmse:0.900298+0.035380 test-rmse:1.330959+0.168757
## [308] train-rmse:0.899444+0.035528 test-rmse:1.331091+0.168741
## [309] train-rmse:0.898730+0.035534 test-rmse:1.331189+0.169021
## [310] train-rmse:0.898048+0.035543 test-rmse:1.331599+0.168642
## [311] train-rmse:0.897121+0.035495 test-rmse:1.331758+0.169211
## [312] train-rmse:0.895707+0.036690 test-rmse:1.331189+0.169009
## [313] train-rmse:0.895098+0.036713 test-rmse:1.331087+0.169018
## Stopping. Best iteration:
## [307] train-rmse:0.900298+0.035380 test-rmse:1.330959+0.168757
##
## 2
## [1] train-rmse:8.784801+0.064460 test-rmse:8.781121+0.271381
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.962677+0.057657 test-rmse:7.956898+0.264117
## [3] train-rmse:7.226935+0.051559 test-rmse:7.220718+0.257179
## [4] train-rmse:6.568486+0.045933 test-rmse:6.564711+0.251924
## [5] train-rmse:5.980547+0.041228 test-rmse:5.978368+0.243856
## [6] train-rmse:5.456164+0.036954 test-rmse:5.456378+0.237199
## [7] train-rmse:4.988096+0.032861 test-rmse:4.996288+0.227499
## [8] train-rmse:4.570705+0.030001 test-rmse:4.587146+0.209648
## [9] train-rmse:4.200109+0.027517 test-rmse:4.218981+0.197555
## [10] train-rmse:3.870079+0.025852 test-rmse:3.899702+0.190762
## [11] train-rmse:3.577693+0.024741 test-rmse:3.611828+0.176786
## [12] train-rmse:3.318644+0.022165 test-rmse:3.358112+0.166289
## [13] train-rmse:3.091135+0.021382 test-rmse:3.139308+0.154590
## [14] train-rmse:2.889369+0.018685 test-rmse:2.947466+0.146213
## [15] train-rmse:2.713538+0.018548 test-rmse:2.776654+0.132555
## [16] train-rmse:2.559030+0.017414 test-rmse:2.631263+0.121422
## [17] train-rmse:2.423600+0.015584 test-rmse:2.506089+0.116927
## [18] train-rmse:2.304550+0.014589 test-rmse:2.399764+0.108358
## [19] train-rmse:2.201847+0.014770 test-rmse:2.309068+0.098250
## [20] train-rmse:2.113020+0.014640 test-rmse:2.231966+0.095529
## [21] train-rmse:2.034095+0.013383 test-rmse:2.162986+0.091503
## [22] train-rmse:1.966365+0.013685 test-rmse:2.106573+0.088609
## [23] train-rmse:1.905046+0.012498 test-rmse:2.054669+0.083624
## [24] train-rmse:1.852915+0.013439 test-rmse:2.010761+0.076744
## [25] train-rmse:1.805612+0.012695 test-rmse:1.963946+0.074000
## [26] train-rmse:1.764273+0.012549 test-rmse:1.935866+0.074512
## [27] train-rmse:1.726453+0.014500 test-rmse:1.909210+0.075833
## [28] train-rmse:1.694534+0.014963 test-rmse:1.879751+0.071318
## [29] train-rmse:1.665436+0.015869 test-rmse:1.858082+0.070711
## [30] train-rmse:1.639985+0.016421 test-rmse:1.836434+0.068805
## [31] train-rmse:1.617384+0.017179 test-rmse:1.817008+0.068962
## [32] train-rmse:1.595337+0.016761 test-rmse:1.802905+0.068976
## [33] train-rmse:1.575571+0.016863 test-rmse:1.790758+0.066570
## [34] train-rmse:1.556048+0.018497 test-rmse:1.773774+0.066485
## [35] train-rmse:1.538201+0.018490 test-rmse:1.761849+0.065064
## [36] train-rmse:1.522899+0.018744 test-rmse:1.748968+0.066248
## [37] train-rmse:1.508633+0.018833 test-rmse:1.739041+0.064685
## [38] train-rmse:1.494375+0.019405 test-rmse:1.731302+0.063170
## [39] train-rmse:1.480408+0.019662 test-rmse:1.720444+0.064177
## [40] train-rmse:1.467464+0.019762 test-rmse:1.712953+0.064105
## [41] train-rmse:1.454362+0.020354 test-rmse:1.704951+0.067786
## [42] train-rmse:1.441964+0.019190 test-rmse:1.696095+0.069982
## [43] train-rmse:1.429148+0.019134 test-rmse:1.686065+0.072167
## [44] train-rmse:1.418592+0.019548 test-rmse:1.678544+0.072095
## [45] train-rmse:1.406359+0.021835 test-rmse:1.669278+0.071742
## [46] train-rmse:1.395302+0.021093 test-rmse:1.661811+0.075251
## [47] train-rmse:1.383826+0.021344 test-rmse:1.656612+0.077590
## [48] train-rmse:1.373966+0.021664 test-rmse:1.649923+0.080077
## [49] train-rmse:1.365076+0.021790 test-rmse:1.645395+0.080809
## [50] train-rmse:1.355124+0.021861 test-rmse:1.639554+0.081479
## [51] train-rmse:1.346033+0.022088 test-rmse:1.632524+0.082959
## [52] train-rmse:1.337228+0.022010 test-rmse:1.624491+0.082213
## [53] train-rmse:1.328546+0.022086 test-rmse:1.615893+0.086210
## [54] train-rmse:1.319179+0.022725 test-rmse:1.610082+0.086002
## [55] train-rmse:1.309287+0.023323 test-rmse:1.604476+0.087836
## [56] train-rmse:1.300825+0.021952 test-rmse:1.598662+0.089762
## [57] train-rmse:1.292369+0.021560 test-rmse:1.591497+0.089857
## [58] train-rmse:1.284758+0.021715 test-rmse:1.586667+0.090921
## [59] train-rmse:1.276569+0.022709 test-rmse:1.583775+0.093506
## [60] train-rmse:1.267906+0.023245 test-rmse:1.577669+0.094868
## [61] train-rmse:1.258531+0.024875 test-rmse:1.571389+0.097986
## [62] train-rmse:1.251005+0.024171 test-rmse:1.564689+0.097463
## [63] train-rmse:1.243201+0.024676 test-rmse:1.560482+0.097438
## [64] train-rmse:1.235057+0.025798 test-rmse:1.552309+0.096414
## [65] train-rmse:1.226997+0.026653 test-rmse:1.545246+0.099951
## [66] train-rmse:1.219530+0.027459 test-rmse:1.542418+0.101922
## [67] train-rmse:1.212364+0.027009 test-rmse:1.539413+0.104783
## [68] train-rmse:1.204741+0.025570 test-rmse:1.534683+0.105789
## [69] train-rmse:1.198691+0.025113 test-rmse:1.530774+0.108501
## [70] train-rmse:1.191461+0.025380 test-rmse:1.527809+0.106697
## [71] train-rmse:1.182754+0.025780 test-rmse:1.522663+0.106870
## [72] train-rmse:1.176839+0.025716 test-rmse:1.519217+0.107572
## [73] train-rmse:1.168352+0.025676 test-rmse:1.509055+0.103684
## [74] train-rmse:1.160777+0.026686 test-rmse:1.506030+0.105411
## [75] train-rmse:1.154272+0.027375 test-rmse:1.503049+0.106708
## [76] train-rmse:1.147737+0.028302 test-rmse:1.499952+0.107227
## [77] train-rmse:1.141740+0.027616 test-rmse:1.493818+0.110962
## [78] train-rmse:1.134664+0.028420 test-rmse:1.490047+0.111325
## [79] train-rmse:1.129202+0.028475 test-rmse:1.487715+0.110816
## [80] train-rmse:1.122667+0.029315 test-rmse:1.481985+0.107118
## [81] train-rmse:1.116568+0.030111 test-rmse:1.480140+0.108491
## [82] train-rmse:1.111173+0.030262 test-rmse:1.478764+0.110673
## [83] train-rmse:1.105982+0.030557 test-rmse:1.475923+0.111378
## [84] train-rmse:1.099528+0.030242 test-rmse:1.468782+0.112648
## [85] train-rmse:1.094797+0.029921 test-rmse:1.463776+0.114228
## [86] train-rmse:1.086442+0.027551 test-rmse:1.456322+0.113799
## [87] train-rmse:1.081164+0.027689 test-rmse:1.455581+0.114502
## [88] train-rmse:1.075620+0.028507 test-rmse:1.453662+0.115067
## [89] train-rmse:1.069863+0.027610 test-rmse:1.451141+0.115332
## [90] train-rmse:1.064254+0.027070 test-rmse:1.447624+0.117224
## [91] train-rmse:1.057492+0.026432 test-rmse:1.443611+0.118751
## [92] train-rmse:1.052507+0.026230 test-rmse:1.444172+0.120662
## [93] train-rmse:1.046942+0.025709 test-rmse:1.441974+0.123573
## [94] train-rmse:1.042051+0.026179 test-rmse:1.438141+0.126892
## [95] train-rmse:1.037164+0.026429 test-rmse:1.436064+0.126028
## [96] train-rmse:1.031537+0.026413 test-rmse:1.431670+0.126905
## [97] train-rmse:1.027651+0.026435 test-rmse:1.428819+0.127702
## [98] train-rmse:1.023171+0.026834 test-rmse:1.423452+0.128521
## [99] train-rmse:1.017367+0.026438 test-rmse:1.419511+0.130399
## [100] train-rmse:1.011999+0.026962 test-rmse:1.413740+0.130531
## [101] train-rmse:1.008210+0.026838 test-rmse:1.411741+0.131511
## [102] train-rmse:1.002891+0.026763 test-rmse:1.408339+0.131478
## [103] train-rmse:0.997833+0.026017 test-rmse:1.407477+0.132597
## [104] train-rmse:0.992619+0.026285 test-rmse:1.402514+0.131991
## [105] train-rmse:0.988469+0.026235 test-rmse:1.402022+0.133929
## [106] train-rmse:0.984503+0.025954 test-rmse:1.400435+0.137156
## [107] train-rmse:0.979535+0.024988 test-rmse:1.398479+0.139810
## [108] train-rmse:0.974466+0.025303 test-rmse:1.394282+0.138413
## [109] train-rmse:0.971249+0.025379 test-rmse:1.392818+0.138688
## [110] train-rmse:0.967021+0.024683 test-rmse:1.391504+0.138677
## [111] train-rmse:0.963400+0.024645 test-rmse:1.388929+0.138908
## [112] train-rmse:0.959909+0.025228 test-rmse:1.388319+0.140471
## [113] train-rmse:0.955405+0.024633 test-rmse:1.385393+0.140052
## [114] train-rmse:0.950791+0.024829 test-rmse:1.383438+0.141245
## [115] train-rmse:0.943853+0.023359 test-rmse:1.381117+0.142756
## [116] train-rmse:0.939900+0.023733 test-rmse:1.380493+0.143990
## [117] train-rmse:0.936394+0.023747 test-rmse:1.379227+0.142601
## [118] train-rmse:0.932347+0.025126 test-rmse:1.375701+0.142094
## [119] train-rmse:0.928280+0.025425 test-rmse:1.373592+0.143205
## [120] train-rmse:0.925257+0.025278 test-rmse:1.372114+0.144705
## [121] train-rmse:0.921407+0.024419 test-rmse:1.370271+0.144877
## [122] train-rmse:0.918036+0.024378 test-rmse:1.367851+0.146088
## [123] train-rmse:0.913022+0.023751 test-rmse:1.366979+0.148252
## [124] train-rmse:0.908876+0.024896 test-rmse:1.366148+0.148548
## [125] train-rmse:0.905725+0.024905 test-rmse:1.364945+0.149022
## [126] train-rmse:0.900384+0.022797 test-rmse:1.362784+0.149986
## [127] train-rmse:0.897509+0.022609 test-rmse:1.361207+0.151383
## [128] train-rmse:0.893478+0.023554 test-rmse:1.360086+0.152150
## [129] train-rmse:0.890021+0.022754 test-rmse:1.357926+0.153657
## [130] train-rmse:0.886311+0.023322 test-rmse:1.355732+0.155923
## [131] train-rmse:0.882314+0.022531 test-rmse:1.354148+0.155607
## [132] train-rmse:0.879345+0.023148 test-rmse:1.353003+0.155406
## [133] train-rmse:0.876175+0.023701 test-rmse:1.352708+0.156261
## [134] train-rmse:0.872008+0.024546 test-rmse:1.352187+0.156907
## [135] train-rmse:0.868331+0.025862 test-rmse:1.349192+0.156752
## [136] train-rmse:0.866002+0.025590 test-rmse:1.348284+0.157290
## [137] train-rmse:0.862887+0.024846 test-rmse:1.347012+0.157786
## [138] train-rmse:0.860302+0.024733 test-rmse:1.345177+0.157675
## [139] train-rmse:0.857140+0.025147 test-rmse:1.343398+0.158249
## [140] train-rmse:0.854311+0.025135 test-rmse:1.342036+0.157071
## [141] train-rmse:0.850743+0.024672 test-rmse:1.339527+0.157879
## [142] train-rmse:0.848274+0.024719 test-rmse:1.338723+0.158537
## [143] train-rmse:0.843852+0.024753 test-rmse:1.335502+0.157689
## [144] train-rmse:0.841364+0.024849 test-rmse:1.335908+0.157573
## [145] train-rmse:0.838419+0.024926 test-rmse:1.334270+0.157488
## [146] train-rmse:0.835881+0.024724 test-rmse:1.334997+0.157917
## [147] train-rmse:0.833057+0.024349 test-rmse:1.333517+0.159192
## [148] train-rmse:0.830781+0.024549 test-rmse:1.331602+0.159201
## [149] train-rmse:0.828066+0.025081 test-rmse:1.331482+0.159420
## [150] train-rmse:0.824859+0.024700 test-rmse:1.330710+0.159851
## [151] train-rmse:0.821867+0.024224 test-rmse:1.330202+0.160638
## [152] train-rmse:0.818670+0.022773 test-rmse:1.329539+0.160941
## [153] train-rmse:0.816017+0.022972 test-rmse:1.328940+0.162206
## [154] train-rmse:0.813393+0.023631 test-rmse:1.327305+0.162750
## [155] train-rmse:0.810501+0.023065 test-rmse:1.327550+0.164622
## [156] train-rmse:0.807810+0.022755 test-rmse:1.326265+0.164661
## [157] train-rmse:0.805386+0.023127 test-rmse:1.324309+0.165056
## [158] train-rmse:0.803264+0.023193 test-rmse:1.324594+0.164950
## [159] train-rmse:0.801515+0.023289 test-rmse:1.325051+0.166070
## [160] train-rmse:0.798451+0.022030 test-rmse:1.323702+0.166989
## [161] train-rmse:0.796225+0.022132 test-rmse:1.323762+0.167991
## [162] train-rmse:0.792951+0.022921 test-rmse:1.322373+0.168440
## [163] train-rmse:0.789462+0.023491 test-rmse:1.321320+0.169035
## [164] train-rmse:0.786699+0.023641 test-rmse:1.318065+0.169442
## [165] train-rmse:0.784189+0.023296 test-rmse:1.316402+0.169819
## [166] train-rmse:0.782001+0.024126 test-rmse:1.315661+0.169163
## [167] train-rmse:0.779647+0.024264 test-rmse:1.314599+0.169049
## [168] train-rmse:0.776670+0.024769 test-rmse:1.312867+0.168153
## [169] train-rmse:0.773960+0.024905 test-rmse:1.311306+0.168196
## [170] train-rmse:0.771960+0.024703 test-rmse:1.310683+0.167647
## [171] train-rmse:0.769798+0.024276 test-rmse:1.309430+0.167520
## [172] train-rmse:0.767660+0.024022 test-rmse:1.309744+0.169093
## [173] train-rmse:0.764825+0.025297 test-rmse:1.307865+0.169300
## [174] train-rmse:0.763036+0.025065 test-rmse:1.308099+0.169953
## [175] train-rmse:0.760242+0.025738 test-rmse:1.307470+0.169725
## [176] train-rmse:0.757519+0.025343 test-rmse:1.305768+0.169408
## [177] train-rmse:0.755746+0.025340 test-rmse:1.306106+0.169495
## [178] train-rmse:0.753933+0.025355 test-rmse:1.306066+0.169303
## [179] train-rmse:0.752316+0.025374 test-rmse:1.304927+0.170108
## [180] train-rmse:0.749771+0.025499 test-rmse:1.304277+0.170329
## [181] train-rmse:0.748021+0.025526 test-rmse:1.304734+0.170152
## [182] train-rmse:0.745797+0.026260 test-rmse:1.304239+0.171452
## [183] train-rmse:0.743767+0.025656 test-rmse:1.303158+0.172030
## [184] train-rmse:0.741926+0.025484 test-rmse:1.304036+0.171849
## [185] train-rmse:0.740035+0.025748 test-rmse:1.302790+0.171697
## [186] train-rmse:0.737599+0.025332 test-rmse:1.303240+0.170085
## [187] train-rmse:0.735502+0.026151 test-rmse:1.302752+0.170255
## [188] train-rmse:0.733848+0.026206 test-rmse:1.301377+0.170364
## [189] train-rmse:0.731388+0.025614 test-rmse:1.301443+0.171644
## [190] train-rmse:0.729928+0.025570 test-rmse:1.300919+0.171631
## [191] train-rmse:0.728039+0.025587 test-rmse:1.301202+0.170943
## [192] train-rmse:0.726418+0.025527 test-rmse:1.300086+0.171342
## [193] train-rmse:0.725189+0.025680 test-rmse:1.301011+0.171101
## [194] train-rmse:0.722745+0.026046 test-rmse:1.300190+0.170665
## [195] train-rmse:0.720767+0.025485 test-rmse:1.299578+0.171069
## [196] train-rmse:0.719331+0.025849 test-rmse:1.299057+0.171145
## [197] train-rmse:0.717077+0.024797 test-rmse:1.298728+0.172507
## [198] train-rmse:0.715515+0.024898 test-rmse:1.298926+0.172284
## [199] train-rmse:0.713458+0.024698 test-rmse:1.296748+0.172175
## [200] train-rmse:0.711756+0.025143 test-rmse:1.296696+0.173204
## [201] train-rmse:0.709819+0.025631 test-rmse:1.295470+0.173984
## [202] train-rmse:0.708558+0.025731 test-rmse:1.294880+0.174153
## [203] train-rmse:0.707063+0.025728 test-rmse:1.295021+0.174977
## [204] train-rmse:0.705284+0.025581 test-rmse:1.295015+0.175231
## [205] train-rmse:0.703899+0.025685 test-rmse:1.295748+0.174307
## [206] train-rmse:0.702333+0.025969 test-rmse:1.295007+0.174281
## [207] train-rmse:0.699819+0.026289 test-rmse:1.293572+0.173571
## [208] train-rmse:0.698239+0.026463 test-rmse:1.292249+0.174080
## [209] train-rmse:0.696450+0.026993 test-rmse:1.291978+0.174589
## [210] train-rmse:0.694206+0.027476 test-rmse:1.292500+0.174783
## [211] train-rmse:0.692479+0.028172 test-rmse:1.291688+0.174640
## [212] train-rmse:0.691138+0.028320 test-rmse:1.291621+0.173981
## [213] train-rmse:0.689631+0.028210 test-rmse:1.291581+0.174246
## [214] train-rmse:0.687838+0.028315 test-rmse:1.291226+0.174731
## [215] train-rmse:0.686493+0.028741 test-rmse:1.290929+0.175047
## [216] train-rmse:0.685180+0.028673 test-rmse:1.291749+0.175051
## [217] train-rmse:0.683730+0.028848 test-rmse:1.290914+0.175045
## [218] train-rmse:0.682138+0.028785 test-rmse:1.289047+0.175098
## [219] train-rmse:0.681019+0.029202 test-rmse:1.288547+0.175579
## [220] train-rmse:0.679788+0.029136 test-rmse:1.287660+0.177273
## [221] train-rmse:0.678617+0.029058 test-rmse:1.286905+0.177045
## [222] train-rmse:0.676625+0.029174 test-rmse:1.287648+0.176344
## [223] train-rmse:0.675033+0.029035 test-rmse:1.287653+0.176017
## [224] train-rmse:0.674061+0.029245 test-rmse:1.287320+0.176445
## [225] train-rmse:0.672594+0.029660 test-rmse:1.287015+0.176492
## [226] train-rmse:0.671137+0.030457 test-rmse:1.285992+0.175790
## [227] train-rmse:0.669217+0.030068 test-rmse:1.285439+0.176009
## [228] train-rmse:0.667576+0.030477 test-rmse:1.285177+0.176591
## [229] train-rmse:0.665675+0.030024 test-rmse:1.284692+0.176267
## [230] train-rmse:0.664550+0.030135 test-rmse:1.284402+0.176742
## [231] train-rmse:0.663272+0.030152 test-rmse:1.284643+0.176395
## [232] train-rmse:0.662184+0.030118 test-rmse:1.285320+0.175818
## [233] train-rmse:0.661205+0.030136 test-rmse:1.285512+0.175539
## [234] train-rmse:0.660045+0.030067 test-rmse:1.285619+0.175303
## [235] train-rmse:0.657795+0.029999 test-rmse:1.283706+0.174293
## [236] train-rmse:0.656526+0.030214 test-rmse:1.283731+0.174752
## [237] train-rmse:0.654924+0.030674 test-rmse:1.282454+0.174838
## [238] train-rmse:0.653601+0.031240 test-rmse:1.282084+0.174330
## [239] train-rmse:0.651112+0.030399 test-rmse:1.281481+0.174608
## [240] train-rmse:0.650137+0.030704 test-rmse:1.280803+0.174841
## [241] train-rmse:0.648869+0.031143 test-rmse:1.281248+0.174907
## [242] train-rmse:0.646781+0.031831 test-rmse:1.279402+0.173771
## [243] train-rmse:0.645566+0.032052 test-rmse:1.278445+0.173451
## [244] train-rmse:0.643667+0.031894 test-rmse:1.276390+0.173462
## [245] train-rmse:0.642791+0.032080 test-rmse:1.276084+0.173131
## [246] train-rmse:0.640711+0.031443 test-rmse:1.276010+0.172964
## [247] train-rmse:0.639697+0.031531 test-rmse:1.275723+0.173291
## [248] train-rmse:0.638407+0.031931 test-rmse:1.276258+0.172756
## [249] train-rmse:0.636975+0.031717 test-rmse:1.277123+0.172752
## [250] train-rmse:0.636128+0.031655 test-rmse:1.276896+0.172708
## [251] train-rmse:0.634694+0.032021 test-rmse:1.275115+0.172291
## [252] train-rmse:0.633564+0.031878 test-rmse:1.275471+0.172030
## [253] train-rmse:0.632321+0.031848 test-rmse:1.275168+0.172549
## [254] train-rmse:0.631228+0.031841 test-rmse:1.275438+0.172842
## [255] train-rmse:0.629739+0.031577 test-rmse:1.274520+0.172853
## [256] train-rmse:0.628533+0.031943 test-rmse:1.274763+0.172316
## [257] train-rmse:0.627748+0.032232 test-rmse:1.274467+0.172382
## [258] train-rmse:0.626757+0.032193 test-rmse:1.274300+0.172488
## [259] train-rmse:0.625700+0.032246 test-rmse:1.275056+0.173099
## [260] train-rmse:0.623610+0.032005 test-rmse:1.274778+0.173315
## [261] train-rmse:0.622486+0.031874 test-rmse:1.274843+0.173204
## [262] train-rmse:0.621533+0.031926 test-rmse:1.275031+0.173611
## [263] train-rmse:0.620689+0.031905 test-rmse:1.275189+0.173253
## [264] train-rmse:0.619512+0.031823 test-rmse:1.275044+0.173541
## Stopping. Best iteration:
## [258] train-rmse:0.626757+0.032193 test-rmse:1.274300+0.172488
##
## 3
## [1] train-rmse:8.782948+0.064675 test-rmse:8.779056+0.272155
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.959312+0.058467 test-rmse:7.956778+0.263932
## [3] train-rmse:7.221282+0.052739 test-rmse:7.222496+0.255251
## [4] train-rmse:6.560292+0.047560 test-rmse:6.566499+0.248318
## [5] train-rmse:5.968593+0.042588 test-rmse:5.975348+0.244310
## [6] train-rmse:5.439521+0.037597 test-rmse:5.448422+0.232807
## [7] train-rmse:4.968710+0.033677 test-rmse:4.982271+0.220672
## [8] train-rmse:4.546313+0.030751 test-rmse:4.562564+0.210810
## [9] train-rmse:4.168490+0.027293 test-rmse:4.191473+0.201352
## [10] train-rmse:3.832638+0.024767 test-rmse:3.862432+0.183189
## [11] train-rmse:3.533442+0.022334 test-rmse:3.570166+0.177272
## [12] train-rmse:3.266563+0.019023 test-rmse:3.310650+0.164082
## [13] train-rmse:3.031070+0.015962 test-rmse:3.083351+0.153975
## [14] train-rmse:2.821377+0.013250 test-rmse:2.880742+0.144580
## [15] train-rmse:2.636625+0.012331 test-rmse:2.713098+0.137006
## [16] train-rmse:2.473081+0.011870 test-rmse:2.563221+0.123092
## [17] train-rmse:2.329521+0.010273 test-rmse:2.431183+0.113047
## [18] train-rmse:2.202832+0.010554 test-rmse:2.320206+0.109369
## [19] train-rmse:2.093414+0.009577 test-rmse:2.224594+0.102637
## [20] train-rmse:1.993275+0.008515 test-rmse:2.132109+0.097120
## [21] train-rmse:1.906262+0.010239 test-rmse:2.056611+0.093923
## [22] train-rmse:1.829491+0.009113 test-rmse:1.994075+0.090036
## [23] train-rmse:1.761652+0.009467 test-rmse:1.936723+0.080085
## [24] train-rmse:1.701732+0.010702 test-rmse:1.891571+0.079998
## [25] train-rmse:1.648942+0.012204 test-rmse:1.850042+0.071210
## [26] train-rmse:1.601715+0.013213 test-rmse:1.813823+0.071059
## [27] train-rmse:1.561124+0.013887 test-rmse:1.778690+0.067348
## [28] train-rmse:1.519646+0.014462 test-rmse:1.750862+0.065750
## [29] train-rmse:1.487867+0.013968 test-rmse:1.727296+0.064335
## [30] train-rmse:1.457132+0.013803 test-rmse:1.703157+0.066787
## [31] train-rmse:1.426999+0.016407 test-rmse:1.681531+0.066938
## [32] train-rmse:1.399246+0.017818 test-rmse:1.666096+0.063837
## [33] train-rmse:1.375824+0.019249 test-rmse:1.648348+0.061530
## [34] train-rmse:1.354428+0.020057 test-rmse:1.633871+0.065025
## [35] train-rmse:1.333889+0.021321 test-rmse:1.618910+0.064368
## [36] train-rmse:1.315716+0.021375 test-rmse:1.603557+0.064244
## [37] train-rmse:1.296270+0.021323 test-rmse:1.592916+0.066059
## [38] train-rmse:1.279482+0.021753 test-rmse:1.584826+0.069621
## [39] train-rmse:1.262751+0.021053 test-rmse:1.573477+0.076198
## [40] train-rmse:1.248542+0.020197 test-rmse:1.559713+0.076494
## [41] train-rmse:1.233741+0.019848 test-rmse:1.552195+0.079773
## [42] train-rmse:1.219032+0.020129 test-rmse:1.545548+0.079342
## [43] train-rmse:1.206888+0.020482 test-rmse:1.538599+0.080028
## [44] train-rmse:1.194613+0.020532 test-rmse:1.531124+0.082063
## [45] train-rmse:1.182169+0.020527 test-rmse:1.522198+0.084721
## [46] train-rmse:1.170673+0.020183 test-rmse:1.516292+0.084289
## [47] train-rmse:1.157946+0.019360 test-rmse:1.509202+0.088085
## [48] train-rmse:1.146629+0.019473 test-rmse:1.500727+0.091748
## [49] train-rmse:1.134974+0.020211 test-rmse:1.496099+0.092347
## [50] train-rmse:1.125306+0.020013 test-rmse:1.488223+0.094140
## [51] train-rmse:1.114619+0.020945 test-rmse:1.485087+0.096930
## [52] train-rmse:1.102910+0.021644 test-rmse:1.476466+0.093993
## [53] train-rmse:1.092599+0.020712 test-rmse:1.471426+0.096264
## [54] train-rmse:1.083556+0.020443 test-rmse:1.465713+0.096992
## [55] train-rmse:1.074441+0.018242 test-rmse:1.457016+0.102096
## [56] train-rmse:1.064889+0.017811 test-rmse:1.451267+0.106582
## [57] train-rmse:1.056747+0.017863 test-rmse:1.448101+0.109744
## [58] train-rmse:1.047080+0.018856 test-rmse:1.442758+0.111671
## [59] train-rmse:1.038045+0.019978 test-rmse:1.437136+0.112386
## [60] train-rmse:1.029510+0.018011 test-rmse:1.430727+0.112985
## [61] train-rmse:1.021446+0.018236 test-rmse:1.426695+0.111107
## [62] train-rmse:1.012837+0.016933 test-rmse:1.422885+0.111505
## [63] train-rmse:1.005278+0.017560 test-rmse:1.420126+0.116867
## [64] train-rmse:0.997024+0.017128 test-rmse:1.415179+0.119202
## [65] train-rmse:0.989510+0.017384 test-rmse:1.412777+0.120793
## [66] train-rmse:0.982228+0.015826 test-rmse:1.408260+0.122153
## [67] train-rmse:0.974862+0.014731 test-rmse:1.403135+0.123504
## [68] train-rmse:0.966773+0.014933 test-rmse:1.399939+0.126362
## [69] train-rmse:0.958709+0.014904 test-rmse:1.397998+0.129339
## [70] train-rmse:0.952537+0.014192 test-rmse:1.398338+0.130031
## [71] train-rmse:0.944711+0.015928 test-rmse:1.393523+0.129939
## [72] train-rmse:0.937513+0.016545 test-rmse:1.389859+0.130687
## [73] train-rmse:0.931247+0.016229 test-rmse:1.385961+0.130595
## [74] train-rmse:0.924930+0.015168 test-rmse:1.382831+0.133485
## [75] train-rmse:0.916848+0.016198 test-rmse:1.375761+0.133493
## [76] train-rmse:0.910349+0.016503 test-rmse:1.375218+0.134968
## [77] train-rmse:0.902561+0.017776 test-rmse:1.370138+0.135525
## [78] train-rmse:0.896956+0.017871 test-rmse:1.369508+0.136268
## [79] train-rmse:0.891464+0.017154 test-rmse:1.367060+0.136999
## [80] train-rmse:0.886048+0.017211 test-rmse:1.364213+0.137634
## [81] train-rmse:0.879156+0.017315 test-rmse:1.359014+0.141859
## [82] train-rmse:0.873663+0.017219 test-rmse:1.355538+0.142838
## [83] train-rmse:0.867811+0.017549 test-rmse:1.353520+0.143831
## [84] train-rmse:0.862508+0.017169 test-rmse:1.351539+0.145436
## [85] train-rmse:0.857167+0.017621 test-rmse:1.348936+0.147677
## [86] train-rmse:0.850082+0.017105 test-rmse:1.345270+0.147324
## [87] train-rmse:0.844448+0.017578 test-rmse:1.342722+0.146500
## [88] train-rmse:0.838631+0.018205 test-rmse:1.339008+0.146784
## [89] train-rmse:0.832909+0.017076 test-rmse:1.336064+0.146756
## [90] train-rmse:0.827630+0.016544 test-rmse:1.335247+0.149048
## [91] train-rmse:0.821832+0.016381 test-rmse:1.333206+0.148713
## [92] train-rmse:0.817525+0.016586 test-rmse:1.331176+0.149731
## [93] train-rmse:0.813024+0.016901 test-rmse:1.329392+0.149319
## [94] train-rmse:0.807769+0.017440 test-rmse:1.327897+0.149475
## [95] train-rmse:0.803149+0.017453 test-rmse:1.326279+0.150602
## [96] train-rmse:0.798682+0.016783 test-rmse:1.323877+0.150897
## [97] train-rmse:0.793942+0.017054 test-rmse:1.321789+0.151629
## [98] train-rmse:0.788760+0.017751 test-rmse:1.320404+0.153034
## [99] train-rmse:0.783517+0.016763 test-rmse:1.319784+0.155453
## [100] train-rmse:0.778792+0.016003 test-rmse:1.319646+0.156825
## [101] train-rmse:0.773717+0.016480 test-rmse:1.316534+0.155461
## [102] train-rmse:0.769227+0.017329 test-rmse:1.313941+0.154802
## [103] train-rmse:0.764527+0.016446 test-rmse:1.310530+0.155875
## [104] train-rmse:0.760851+0.016726 test-rmse:1.309797+0.155891
## [105] train-rmse:0.757130+0.016557 test-rmse:1.308460+0.156532
## [106] train-rmse:0.753640+0.017111 test-rmse:1.306815+0.157040
## [107] train-rmse:0.748800+0.018123 test-rmse:1.306545+0.157767
## [108] train-rmse:0.744907+0.018646 test-rmse:1.304714+0.157528
## [109] train-rmse:0.740948+0.018554 test-rmse:1.303175+0.158677
## [110] train-rmse:0.736376+0.019799 test-rmse:1.303029+0.158821
## [111] train-rmse:0.732181+0.018910 test-rmse:1.300158+0.158124
## [112] train-rmse:0.728166+0.018695 test-rmse:1.299002+0.159096
## [113] train-rmse:0.724234+0.018645 test-rmse:1.298839+0.160116
## [114] train-rmse:0.721324+0.018584 test-rmse:1.297443+0.160860
## [115] train-rmse:0.718019+0.018072 test-rmse:1.297639+0.160922
## [116] train-rmse:0.715055+0.017770 test-rmse:1.296704+0.160897
## [117] train-rmse:0.711020+0.018568 test-rmse:1.294224+0.159452
## [118] train-rmse:0.707247+0.018693 test-rmse:1.291651+0.158925
## [119] train-rmse:0.702921+0.018851 test-rmse:1.290070+0.159511
## [120] train-rmse:0.698373+0.018699 test-rmse:1.288048+0.160228
## [121] train-rmse:0.694855+0.018684 test-rmse:1.288471+0.160354
## [122] train-rmse:0.690959+0.018310 test-rmse:1.287147+0.162610
## [123] train-rmse:0.687483+0.018624 test-rmse:1.284804+0.162945
## [124] train-rmse:0.684201+0.019059 test-rmse:1.285208+0.161869
## [125] train-rmse:0.680889+0.017652 test-rmse:1.284506+0.162370
## [126] train-rmse:0.677579+0.016753 test-rmse:1.284904+0.163034
## [127] train-rmse:0.674088+0.018143 test-rmse:1.282564+0.162369
## [128] train-rmse:0.670503+0.018870 test-rmse:1.282139+0.162083
## [129] train-rmse:0.668117+0.018420 test-rmse:1.281514+0.162665
## [130] train-rmse:0.665142+0.017884 test-rmse:1.280870+0.162558
## [131] train-rmse:0.661982+0.017967 test-rmse:1.279539+0.162882
## [132] train-rmse:0.658742+0.016933 test-rmse:1.277987+0.164648
## [133] train-rmse:0.656186+0.017179 test-rmse:1.276989+0.164436
## [134] train-rmse:0.653295+0.017877 test-rmse:1.276055+0.164667
## [135] train-rmse:0.649321+0.017536 test-rmse:1.276214+0.164270
## [136] train-rmse:0.646785+0.017551 test-rmse:1.275650+0.165098
## [137] train-rmse:0.644766+0.017296 test-rmse:1.276151+0.165993
## [138] train-rmse:0.641695+0.017511 test-rmse:1.274610+0.166668
## [139] train-rmse:0.638297+0.017206 test-rmse:1.272197+0.167494
## [140] train-rmse:0.635933+0.016940 test-rmse:1.271956+0.166824
## [141] train-rmse:0.633630+0.017229 test-rmse:1.271609+0.167489
## [142] train-rmse:0.630734+0.017534 test-rmse:1.272062+0.167557
## [143] train-rmse:0.628514+0.017302 test-rmse:1.273705+0.168475
## [144] train-rmse:0.625744+0.017632 test-rmse:1.272895+0.167954
## [145] train-rmse:0.623406+0.017118 test-rmse:1.273477+0.167715
## [146] train-rmse:0.620881+0.017028 test-rmse:1.273510+0.168164
## [147] train-rmse:0.618176+0.016684 test-rmse:1.272488+0.167416
## Stopping. Best iteration:
## [141] train-rmse:0.633630+0.017229 test-rmse:1.271609+0.167489
##
## 4
## [1] train-rmse:8.781437+0.064833 test-rmse:8.779204+0.272339
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.955775+0.058724 test-rmse:7.956083+0.264611
## [3] train-rmse:7.215436+0.053317 test-rmse:7.220302+0.254016
## [4] train-rmse:6.551793+0.047957 test-rmse:6.560112+0.244548
## [5] train-rmse:5.957146+0.043440 test-rmse:5.968863+0.237982
## [6] train-rmse:5.424753+0.039108 test-rmse:5.439298+0.223282
## [7] train-rmse:4.948888+0.035043 test-rmse:4.965037+0.211486
## [8] train-rmse:4.522894+0.032563 test-rmse:4.544291+0.197525
## [9] train-rmse:4.141407+0.029840 test-rmse:4.170567+0.186225
## [10] train-rmse:3.800104+0.027069 test-rmse:3.835774+0.171860
## [11] train-rmse:3.496263+0.024697 test-rmse:3.537075+0.162836
## [12] train-rmse:3.224674+0.021953 test-rmse:3.278072+0.155369
## [13] train-rmse:2.982878+0.021621 test-rmse:3.042374+0.145489
## [14] train-rmse:2.767962+0.019401 test-rmse:2.843447+0.142189
## [15] train-rmse:2.576908+0.019139 test-rmse:2.662956+0.128711
## [16] train-rmse:2.407958+0.019172 test-rmse:2.508312+0.126365
## [17] train-rmse:2.258498+0.017966 test-rmse:2.379107+0.122434
## [18] train-rmse:2.125302+0.015900 test-rmse:2.262296+0.116106
## [19] train-rmse:2.004834+0.018863 test-rmse:2.157174+0.103868
## [20] train-rmse:1.899500+0.017048 test-rmse:2.072201+0.106341
## [21] train-rmse:1.806534+0.018657 test-rmse:1.993217+0.089402
## [22] train-rmse:1.721830+0.016572 test-rmse:1.926842+0.092293
## [23] train-rmse:1.649412+0.017518 test-rmse:1.869215+0.090030
## [24] train-rmse:1.582194+0.017890 test-rmse:1.812510+0.086397
## [25] train-rmse:1.523179+0.016278 test-rmse:1.770948+0.086138
## [26] train-rmse:1.472191+0.016424 test-rmse:1.732272+0.083832
## [27] train-rmse:1.425524+0.016995 test-rmse:1.695624+0.081610
## [28] train-rmse:1.382386+0.016586 test-rmse:1.663660+0.082822
## [29] train-rmse:1.346024+0.017153 test-rmse:1.641384+0.081174
## [30] train-rmse:1.312515+0.016006 test-rmse:1.617753+0.082498
## [31] train-rmse:1.280340+0.017748 test-rmse:1.593553+0.079507
## [32] train-rmse:1.250712+0.018318 test-rmse:1.574211+0.081542
## [33] train-rmse:1.224855+0.018304 test-rmse:1.560779+0.081178
## [34] train-rmse:1.200266+0.019791 test-rmse:1.544223+0.080009
## [35] train-rmse:1.177194+0.020957 test-rmse:1.530916+0.082153
## [36] train-rmse:1.157745+0.022066 test-rmse:1.520445+0.083672
## [37] train-rmse:1.137442+0.022412 test-rmse:1.506477+0.086220
## [38] train-rmse:1.118516+0.022319 test-rmse:1.495572+0.089081
## [39] train-rmse:1.101799+0.022198 test-rmse:1.486226+0.088702
## [40] train-rmse:1.083872+0.022210 test-rmse:1.477993+0.088865
## [41] train-rmse:1.066962+0.023876 test-rmse:1.468089+0.090825
## [42] train-rmse:1.052740+0.024007 test-rmse:1.460385+0.091559
## [43] train-rmse:1.037239+0.022225 test-rmse:1.456184+0.097356
## [44] train-rmse:1.022614+0.023287 test-rmse:1.446433+0.101574
## [45] train-rmse:1.008958+0.022594 test-rmse:1.439999+0.102940
## [46] train-rmse:0.994591+0.024091 test-rmse:1.433911+0.104291
## [47] train-rmse:0.978443+0.023789 test-rmse:1.427447+0.103526
## [48] train-rmse:0.966076+0.023106 test-rmse:1.419783+0.107053
## [49] train-rmse:0.956184+0.023058 test-rmse:1.411292+0.108498
## [50] train-rmse:0.945198+0.023803 test-rmse:1.407161+0.111449
## [51] train-rmse:0.933912+0.024051 test-rmse:1.404806+0.112996
## [52] train-rmse:0.922860+0.022641 test-rmse:1.401502+0.115743
## [53] train-rmse:0.912574+0.022573 test-rmse:1.397764+0.119119
## [54] train-rmse:0.902502+0.023282 test-rmse:1.391595+0.121801
## [55] train-rmse:0.893672+0.024451 test-rmse:1.386073+0.121949
## [56] train-rmse:0.885305+0.024644 test-rmse:1.381967+0.123052
## [57] train-rmse:0.875410+0.023620 test-rmse:1.377818+0.125343
## [58] train-rmse:0.865404+0.022225 test-rmse:1.374338+0.128245
## [59] train-rmse:0.856125+0.022567 test-rmse:1.369550+0.127412
## [60] train-rmse:0.848226+0.022116 test-rmse:1.367713+0.129662
## [61] train-rmse:0.840792+0.021723 test-rmse:1.364028+0.130699
## [62] train-rmse:0.831708+0.022444 test-rmse:1.360879+0.130646
## [63] train-rmse:0.823909+0.021984 test-rmse:1.356193+0.132519
## [64] train-rmse:0.816920+0.021891 test-rmse:1.353147+0.133168
## [65] train-rmse:0.810137+0.022094 test-rmse:1.350295+0.134840
## [66] train-rmse:0.801765+0.022922 test-rmse:1.348211+0.137081
## [67] train-rmse:0.793987+0.022072 test-rmse:1.346317+0.139343
## [68] train-rmse:0.785933+0.021641 test-rmse:1.344723+0.138366
## [69] train-rmse:0.779713+0.021081 test-rmse:1.340894+0.142786
## [70] train-rmse:0.772634+0.021571 test-rmse:1.339173+0.142855
## [71] train-rmse:0.765943+0.021418 test-rmse:1.336411+0.143175
## [72] train-rmse:0.758395+0.021277 test-rmse:1.333855+0.143707
## [73] train-rmse:0.752656+0.021857 test-rmse:1.332537+0.146598
## [74] train-rmse:0.745636+0.021192 test-rmse:1.326973+0.145564
## [75] train-rmse:0.739478+0.021038 test-rmse:1.326202+0.145147
## [76] train-rmse:0.733492+0.022064 test-rmse:1.324593+0.144125
## [77] train-rmse:0.726297+0.021229 test-rmse:1.321219+0.146446
## [78] train-rmse:0.721283+0.021626 test-rmse:1.318605+0.147566
## [79] train-rmse:0.715033+0.020957 test-rmse:1.315125+0.149010
## [80] train-rmse:0.709788+0.020642 test-rmse:1.313878+0.149381
## [81] train-rmse:0.703102+0.020984 test-rmse:1.310687+0.149392
## [82] train-rmse:0.698232+0.020240 test-rmse:1.307928+0.149349
## [83] train-rmse:0.692773+0.019917 test-rmse:1.308765+0.150598
## [84] train-rmse:0.688286+0.020999 test-rmse:1.306199+0.150764
## [85] train-rmse:0.683367+0.020516 test-rmse:1.304360+0.153546
## [86] train-rmse:0.677724+0.020382 test-rmse:1.302617+0.153465
## [87] train-rmse:0.672083+0.020358 test-rmse:1.300675+0.154096
## [88] train-rmse:0.666744+0.019980 test-rmse:1.297690+0.156228
## [89] train-rmse:0.661244+0.018752 test-rmse:1.297108+0.158480
## [90] train-rmse:0.657490+0.019095 test-rmse:1.295495+0.159796
## [91] train-rmse:0.652647+0.019063 test-rmse:1.293783+0.161499
## [92] train-rmse:0.648481+0.019574 test-rmse:1.295320+0.162234
## [93] train-rmse:0.644330+0.020139 test-rmse:1.293345+0.161247
## [94] train-rmse:0.640218+0.019675 test-rmse:1.291422+0.162215
## [95] train-rmse:0.636442+0.019636 test-rmse:1.291248+0.161612
## [96] train-rmse:0.631796+0.019743 test-rmse:1.290175+0.162255
## [97] train-rmse:0.625866+0.019058 test-rmse:1.289581+0.162562
## [98] train-rmse:0.621746+0.019090 test-rmse:1.289047+0.163289
## [99] train-rmse:0.617737+0.019240 test-rmse:1.286401+0.165010
## [100] train-rmse:0.613113+0.019563 test-rmse:1.285298+0.165382
## [101] train-rmse:0.608940+0.019931 test-rmse:1.286296+0.165029
## [102] train-rmse:0.605215+0.019529 test-rmse:1.285349+0.165597
## [103] train-rmse:0.600936+0.019006 test-rmse:1.285394+0.165841
## [104] train-rmse:0.597329+0.019073 test-rmse:1.285464+0.166177
## [105] train-rmse:0.593729+0.019653 test-rmse:1.284185+0.165391
## [106] train-rmse:0.590663+0.019960 test-rmse:1.283268+0.166471
## [107] train-rmse:0.587108+0.020242 test-rmse:1.282489+0.166987
## [108] train-rmse:0.583913+0.019934 test-rmse:1.281411+0.167631
## [109] train-rmse:0.579799+0.019748 test-rmse:1.280913+0.165296
## [110] train-rmse:0.576888+0.020483 test-rmse:1.280835+0.164910
## [111] train-rmse:0.572932+0.021633 test-rmse:1.279847+0.165510
## [112] train-rmse:0.568427+0.019917 test-rmse:1.279001+0.167036
## [113] train-rmse:0.564730+0.020119 test-rmse:1.278187+0.167249
## [114] train-rmse:0.561543+0.020223 test-rmse:1.278356+0.166726
## [115] train-rmse:0.558271+0.020528 test-rmse:1.276950+0.166230
## [116] train-rmse:0.555390+0.021063 test-rmse:1.276733+0.166575
## [117] train-rmse:0.552836+0.021208 test-rmse:1.276425+0.168278
## [118] train-rmse:0.547816+0.020146 test-rmse:1.275030+0.170866
## [119] train-rmse:0.545203+0.019833 test-rmse:1.275371+0.171815
## [120] train-rmse:0.541725+0.019790 test-rmse:1.274748+0.171817
## [121] train-rmse:0.538264+0.019287 test-rmse:1.272540+0.172125
## [122] train-rmse:0.534955+0.019499 test-rmse:1.272448+0.171960
## [123] train-rmse:0.531974+0.020189 test-rmse:1.271205+0.171393
## [124] train-rmse:0.529191+0.019493 test-rmse:1.270401+0.172140
## [125] train-rmse:0.526790+0.019531 test-rmse:1.269630+0.172811
## [126] train-rmse:0.524480+0.019901 test-rmse:1.269158+0.172418
## [127] train-rmse:0.522111+0.020837 test-rmse:1.267776+0.172663
## [128] train-rmse:0.517389+0.020038 test-rmse:1.266850+0.173009
## [129] train-rmse:0.514672+0.019957 test-rmse:1.268077+0.173000
## [130] train-rmse:0.511700+0.020121 test-rmse:1.265894+0.172946
## [131] train-rmse:0.509670+0.020381 test-rmse:1.265301+0.172431
## [132] train-rmse:0.507049+0.020371 test-rmse:1.263906+0.171096
## [133] train-rmse:0.504449+0.019721 test-rmse:1.263395+0.171920
## [134] train-rmse:0.501481+0.019428 test-rmse:1.262503+0.172844
## [135] train-rmse:0.499224+0.020102 test-rmse:1.262100+0.172885
## [136] train-rmse:0.497225+0.020224 test-rmse:1.262055+0.173360
## [137] train-rmse:0.494330+0.020388 test-rmse:1.261270+0.174853
## [138] train-rmse:0.492224+0.021103 test-rmse:1.260978+0.174836
## [139] train-rmse:0.489469+0.021524 test-rmse:1.260619+0.174789
## [140] train-rmse:0.487197+0.021889 test-rmse:1.260826+0.174123
## [141] train-rmse:0.484149+0.021729 test-rmse:1.259133+0.173977
## [142] train-rmse:0.481428+0.021356 test-rmse:1.259151+0.172802
## [143] train-rmse:0.479084+0.021500 test-rmse:1.258430+0.173006
## [144] train-rmse:0.476439+0.020727 test-rmse:1.258479+0.172797
## [145] train-rmse:0.474426+0.020834 test-rmse:1.258441+0.173156
## [146] train-rmse:0.472441+0.021597 test-rmse:1.258074+0.173195
## [147] train-rmse:0.469609+0.021661 test-rmse:1.258202+0.172777
## [148] train-rmse:0.467937+0.021660 test-rmse:1.258724+0.171987
## [149] train-rmse:0.465826+0.021651 test-rmse:1.257404+0.172401
## [150] train-rmse:0.463498+0.021866 test-rmse:1.257077+0.172821
## [151] train-rmse:0.461594+0.022310 test-rmse:1.257515+0.173526
## [152] train-rmse:0.460135+0.022441 test-rmse:1.258267+0.174144
## [153] train-rmse:0.457879+0.021961 test-rmse:1.258498+0.173709
## [154] train-rmse:0.455763+0.022165 test-rmse:1.257865+0.173322
## [155] train-rmse:0.453437+0.022211 test-rmse:1.257156+0.172996
## [156] train-rmse:0.451544+0.022211 test-rmse:1.257612+0.173039
## Stopping. Best iteration:
## [150] train-rmse:0.463498+0.021866 test-rmse:1.257077+0.172821
##
## 5
## [1] train-rmse:8.781147+0.064908 test-rmse:8.779598+0.269073
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.954948+0.058862 test-rmse:7.959590+0.259149
## [3] train-rmse:7.213000+0.053363 test-rmse:7.222035+0.247410
## [4] train-rmse:6.547675+0.048468 test-rmse:6.561636+0.239535
## [5] train-rmse:5.950918+0.043756 test-rmse:5.968801+0.225135
## [6] train-rmse:5.415611+0.039766 test-rmse:5.438809+0.211575
## [7] train-rmse:4.936452+0.035680 test-rmse:4.963846+0.198924
## [8] train-rmse:4.507307+0.033458 test-rmse:4.536331+0.186452
## [9] train-rmse:4.122302+0.031176 test-rmse:4.156437+0.171012
## [10] train-rmse:3.776337+0.028419 test-rmse:3.817662+0.164566
## [11] train-rmse:3.466959+0.025565 test-rmse:3.515659+0.150774
## [12] train-rmse:3.191727+0.023390 test-rmse:3.249179+0.137632
## [13] train-rmse:2.945365+0.021344 test-rmse:3.015992+0.135334
## [14] train-rmse:2.726227+0.019670 test-rmse:2.810164+0.125819
## [15] train-rmse:2.531188+0.016974 test-rmse:2.630820+0.126586
## [16] train-rmse:2.357657+0.016451 test-rmse:2.478692+0.123263
## [17] train-rmse:2.200434+0.014429 test-rmse:2.335784+0.117451
## [18] train-rmse:2.060202+0.013227 test-rmse:2.212831+0.113346
## [19] train-rmse:1.936649+0.012836 test-rmse:2.110905+0.110809
## [20] train-rmse:1.826503+0.015414 test-rmse:2.012550+0.101894
## [21] train-rmse:1.728572+0.014159 test-rmse:1.926954+0.100373
## [22] train-rmse:1.638422+0.015670 test-rmse:1.854747+0.095603
## [23] train-rmse:1.559423+0.013483 test-rmse:1.792426+0.094306
## [24] train-rmse:1.489762+0.014470 test-rmse:1.741234+0.090130
## [25] train-rmse:1.428092+0.015486 test-rmse:1.697805+0.087485
## [26] train-rmse:1.373205+0.015234 test-rmse:1.653623+0.090300
## [27] train-rmse:1.322016+0.015442 test-rmse:1.619037+0.089276
## [28] train-rmse:1.275875+0.018027 test-rmse:1.584045+0.088789
## [29] train-rmse:1.234283+0.016563 test-rmse:1.557922+0.088346
## [30] train-rmse:1.196819+0.018189 test-rmse:1.528751+0.089080
## [31] train-rmse:1.162968+0.018890 test-rmse:1.505674+0.088275
## [32] train-rmse:1.133722+0.018672 test-rmse:1.490532+0.091072
## [33] train-rmse:1.104885+0.019126 test-rmse:1.471961+0.093699
## [34] train-rmse:1.076453+0.018800 test-rmse:1.456310+0.095703
## [35] train-rmse:1.052211+0.019693 test-rmse:1.445157+0.098115
## [36] train-rmse:1.027628+0.019020 test-rmse:1.433209+0.099462
## [37] train-rmse:1.006428+0.018627 test-rmse:1.424161+0.099531
## [38] train-rmse:0.985086+0.018828 test-rmse:1.412391+0.104662
## [39] train-rmse:0.966206+0.020122 test-rmse:1.402028+0.106927
## [40] train-rmse:0.946487+0.018759 test-rmse:1.393222+0.108510
## [41] train-rmse:0.931110+0.018196 test-rmse:1.386520+0.110872
## [42] train-rmse:0.914350+0.018448 test-rmse:1.383537+0.113542
## [43] train-rmse:0.899034+0.018977 test-rmse:1.375932+0.114139
## [44] train-rmse:0.883465+0.018131 test-rmse:1.371797+0.115333
## [45] train-rmse:0.870797+0.018628 test-rmse:1.364702+0.118016
## [46] train-rmse:0.855685+0.020531 test-rmse:1.358864+0.118990
## [47] train-rmse:0.843265+0.020069 test-rmse:1.352268+0.123794
## [48] train-rmse:0.828805+0.019671 test-rmse:1.346536+0.124187
## [49] train-rmse:0.818070+0.020742 test-rmse:1.343719+0.125451
## [50] train-rmse:0.805893+0.020900 test-rmse:1.342571+0.125529
## [51] train-rmse:0.795681+0.022305 test-rmse:1.338921+0.127317
## [52] train-rmse:0.783976+0.022520 test-rmse:1.332331+0.129076
## [53] train-rmse:0.774225+0.022241 test-rmse:1.331016+0.132029
## [54] train-rmse:0.763345+0.021444 test-rmse:1.325435+0.134330
## [55] train-rmse:0.754203+0.021258 test-rmse:1.320020+0.134401
## [56] train-rmse:0.744578+0.020033 test-rmse:1.317599+0.136231
## [57] train-rmse:0.735311+0.021686 test-rmse:1.314925+0.135693
## [58] train-rmse:0.726485+0.022135 test-rmse:1.311339+0.133599
## [59] train-rmse:0.717109+0.022659 test-rmse:1.307003+0.134572
## [60] train-rmse:0.709883+0.022489 test-rmse:1.301355+0.136883
## [61] train-rmse:0.701740+0.021038 test-rmse:1.299646+0.139098
## [62] train-rmse:0.694175+0.021912 test-rmse:1.297459+0.139183
## [63] train-rmse:0.684951+0.020876 test-rmse:1.293213+0.143128
## [64] train-rmse:0.677021+0.020532 test-rmse:1.288422+0.142164
## [65] train-rmse:0.669873+0.019849 test-rmse:1.285271+0.142828
## [66] train-rmse:0.663460+0.020478 test-rmse:1.282739+0.143089
## [67] train-rmse:0.655529+0.020322 test-rmse:1.281314+0.142840
## [68] train-rmse:0.648707+0.020379 test-rmse:1.280626+0.143742
## [69] train-rmse:0.641093+0.019501 test-rmse:1.277998+0.145536
## [70] train-rmse:0.634576+0.019648 test-rmse:1.276320+0.145991
## [71] train-rmse:0.628586+0.019004 test-rmse:1.274135+0.146482
## [72] train-rmse:0.622654+0.019158 test-rmse:1.273071+0.148283
## [73] train-rmse:0.616214+0.018744 test-rmse:1.271384+0.148265
## [74] train-rmse:0.610495+0.018982 test-rmse:1.268388+0.147122
## [75] train-rmse:0.603647+0.018599 test-rmse:1.267872+0.146801
## [76] train-rmse:0.597561+0.017960 test-rmse:1.265904+0.149045
## [77] train-rmse:0.591667+0.016672 test-rmse:1.265662+0.149419
## [78] train-rmse:0.585537+0.015565 test-rmse:1.263081+0.151160
## [79] train-rmse:0.578945+0.015686 test-rmse:1.262943+0.150805
## [80] train-rmse:0.573938+0.015656 test-rmse:1.260701+0.152069
## [81] train-rmse:0.568912+0.016523 test-rmse:1.258804+0.152215
## [82] train-rmse:0.563945+0.016230 test-rmse:1.255637+0.151989
## [83] train-rmse:0.557910+0.016335 test-rmse:1.256086+0.151663
## [84] train-rmse:0.553795+0.016986 test-rmse:1.256698+0.152315
## [85] train-rmse:0.549519+0.016346 test-rmse:1.256001+0.152474
## [86] train-rmse:0.544464+0.016224 test-rmse:1.255244+0.152115
## [87] train-rmse:0.539880+0.015630 test-rmse:1.256463+0.153077
## [88] train-rmse:0.535128+0.015859 test-rmse:1.254280+0.152154
## [89] train-rmse:0.530194+0.014800 test-rmse:1.254310+0.152916
## [90] train-rmse:0.525844+0.015644 test-rmse:1.252559+0.153132
## [91] train-rmse:0.521106+0.015326 test-rmse:1.253415+0.153620
## [92] train-rmse:0.516398+0.015039 test-rmse:1.252549+0.153694
## [93] train-rmse:0.510804+0.014990 test-rmse:1.252519+0.152644
## [94] train-rmse:0.506700+0.015433 test-rmse:1.252348+0.152154
## [95] train-rmse:0.501482+0.014647 test-rmse:1.252384+0.154307
## [96] train-rmse:0.497463+0.014513 test-rmse:1.251327+0.155239
## [97] train-rmse:0.493821+0.014551 test-rmse:1.252140+0.155902
## [98] train-rmse:0.489550+0.013823 test-rmse:1.252280+0.156251
## [99] train-rmse:0.485827+0.014754 test-rmse:1.250221+0.156830
## [100] train-rmse:0.481591+0.014279 test-rmse:1.248984+0.157554
## [101] train-rmse:0.478317+0.013711 test-rmse:1.247803+0.159351
## [102] train-rmse:0.475172+0.012947 test-rmse:1.247062+0.161171
## [103] train-rmse:0.471179+0.013311 test-rmse:1.246014+0.161348
## [104] train-rmse:0.466735+0.013618 test-rmse:1.244991+0.162276
## [105] train-rmse:0.463587+0.013201 test-rmse:1.244169+0.163780
## [106] train-rmse:0.460202+0.013570 test-rmse:1.243687+0.163389
## [107] train-rmse:0.456305+0.012927 test-rmse:1.243744+0.163453
## [108] train-rmse:0.452838+0.013801 test-rmse:1.242567+0.163871
## [109] train-rmse:0.449943+0.014165 test-rmse:1.241721+0.164042
## [110] train-rmse:0.446318+0.014309 test-rmse:1.241251+0.164627
## [111] train-rmse:0.442586+0.014672 test-rmse:1.240038+0.165890
## [112] train-rmse:0.439185+0.013967 test-rmse:1.239920+0.166092
## [113] train-rmse:0.436628+0.014434 test-rmse:1.239831+0.167562
## [114] train-rmse:0.434344+0.014428 test-rmse:1.239043+0.167802
## [115] train-rmse:0.431534+0.013869 test-rmse:1.238891+0.168704
## [116] train-rmse:0.429196+0.013966 test-rmse:1.238170+0.168424
## [117] train-rmse:0.425551+0.014629 test-rmse:1.237166+0.169122
## [118] train-rmse:0.422053+0.014277 test-rmse:1.236582+0.169133
## [119] train-rmse:0.418796+0.014106 test-rmse:1.236420+0.169909
## [120] train-rmse:0.415643+0.014023 test-rmse:1.235787+0.169357
## [121] train-rmse:0.413286+0.013878 test-rmse:1.235268+0.169174
## [122] train-rmse:0.410782+0.013707 test-rmse:1.235771+0.170072
## [123] train-rmse:0.407847+0.014089 test-rmse:1.234884+0.170637
## [124] train-rmse:0.405421+0.014366 test-rmse:1.234458+0.170968
## [125] train-rmse:0.402994+0.014617 test-rmse:1.235227+0.172061
## [126] train-rmse:0.400909+0.014507 test-rmse:1.235214+0.172713
## [127] train-rmse:0.398289+0.015395 test-rmse:1.234892+0.172646
## [128] train-rmse:0.396353+0.015318 test-rmse:1.234787+0.173613
## [129] train-rmse:0.393925+0.015103 test-rmse:1.234304+0.173558
## [130] train-rmse:0.390904+0.015844 test-rmse:1.234168+0.173658
## [131] train-rmse:0.388153+0.015872 test-rmse:1.234707+0.173866
## [132] train-rmse:0.385297+0.015276 test-rmse:1.234580+0.173480
## [133] train-rmse:0.382460+0.015342 test-rmse:1.233175+0.174037
## [134] train-rmse:0.379242+0.014722 test-rmse:1.232777+0.174240
## [135] train-rmse:0.376850+0.014422 test-rmse:1.233130+0.173674
## [136] train-rmse:0.374378+0.014463 test-rmse:1.233845+0.173174
## [137] train-rmse:0.372202+0.013772 test-rmse:1.233382+0.173600
## [138] train-rmse:0.369426+0.013422 test-rmse:1.233177+0.172634
## [139] train-rmse:0.367076+0.013644 test-rmse:1.233059+0.172966
## [140] train-rmse:0.365428+0.013471 test-rmse:1.233405+0.173815
## Stopping. Best iteration:
## [134] train-rmse:0.379242+0.014722 test-rmse:1.232777+0.174240
##
## 6
## [1] train-rmse:8.781147+0.064908 test-rmse:8.779598+0.269073
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.954747+0.058941 test-rmse:7.957469+0.257884
## [3] train-rmse:7.212286+0.053602 test-rmse:7.219896+0.243580
## [4] train-rmse:6.545748+0.048620 test-rmse:6.556030+0.235052
## [5] train-rmse:5.947111+0.043858 test-rmse:5.961826+0.219783
## [6] train-rmse:5.409957+0.039574 test-rmse:5.421333+0.207070
## [7] train-rmse:4.927937+0.036003 test-rmse:4.945133+0.194158
## [8] train-rmse:4.495938+0.032398 test-rmse:4.518853+0.179084
## [9] train-rmse:4.108970+0.030627 test-rmse:4.137652+0.166274
## [10] train-rmse:3.760750+0.028316 test-rmse:3.797024+0.156411
## [11] train-rmse:3.447695+0.025538 test-rmse:3.489306+0.145241
## [12] train-rmse:3.168867+0.024070 test-rmse:3.218906+0.135496
## [13] train-rmse:2.918985+0.022545 test-rmse:2.984145+0.128550
## [14] train-rmse:2.695654+0.020622 test-rmse:2.771615+0.130469
## [15] train-rmse:2.494766+0.016815 test-rmse:2.585344+0.132204
## [16] train-rmse:2.315890+0.016183 test-rmse:2.429323+0.120208
## [17] train-rmse:2.155639+0.016379 test-rmse:2.287847+0.113461
## [18] train-rmse:2.007957+0.016466 test-rmse:2.158687+0.107883
## [19] train-rmse:1.878739+0.016138 test-rmse:2.050769+0.100474
## [20] train-rmse:1.761938+0.017261 test-rmse:1.948485+0.093990
## [21] train-rmse:1.659425+0.018129 test-rmse:1.866008+0.089219
## [22] train-rmse:1.563532+0.018607 test-rmse:1.792708+0.086973
## [23] train-rmse:1.480064+0.018558 test-rmse:1.727693+0.084001
## [24] train-rmse:1.404389+0.017819 test-rmse:1.672911+0.082166
## [25] train-rmse:1.337176+0.019605 test-rmse:1.621478+0.084116
## [26] train-rmse:1.273831+0.018969 test-rmse:1.581596+0.080620
## [27] train-rmse:1.219560+0.020465 test-rmse:1.546328+0.084237
## [28] train-rmse:1.170204+0.019904 test-rmse:1.516046+0.085767
## [29] train-rmse:1.125639+0.020308 test-rmse:1.487811+0.087501
## [30] train-rmse:1.086345+0.020733 test-rmse:1.462924+0.089508
## [31] train-rmse:1.051758+0.021284 test-rmse:1.444011+0.090416
## [32] train-rmse:1.016954+0.020077 test-rmse:1.426010+0.097095
## [33] train-rmse:0.986883+0.019850 test-rmse:1.411146+0.096582
## [34] train-rmse:0.957155+0.018668 test-rmse:1.394745+0.100589
## [35] train-rmse:0.930766+0.018333 test-rmse:1.383581+0.101234
## [36] train-rmse:0.906364+0.018670 test-rmse:1.371389+0.101560
## [37] train-rmse:0.882561+0.015991 test-rmse:1.361019+0.105672
## [38] train-rmse:0.861268+0.016760 test-rmse:1.349316+0.106029
## [39] train-rmse:0.841862+0.016847 test-rmse:1.341899+0.110760
## [40] train-rmse:0.822637+0.015985 test-rmse:1.333602+0.114366
## [41] train-rmse:0.804477+0.016104 test-rmse:1.327791+0.117122
## [42] train-rmse:0.787566+0.016687 test-rmse:1.318468+0.118012
## [43] train-rmse:0.771400+0.016335 test-rmse:1.312746+0.119878
## [44] train-rmse:0.759033+0.015784 test-rmse:1.308771+0.118745
## [45] train-rmse:0.744713+0.014626 test-rmse:1.303725+0.120872
## [46] train-rmse:0.731211+0.014119 test-rmse:1.299963+0.122197
## [47] train-rmse:0.717480+0.015507 test-rmse:1.292763+0.121992
## [48] train-rmse:0.706037+0.014613 test-rmse:1.290015+0.123977
## [49] train-rmse:0.692231+0.014755 test-rmse:1.282414+0.124304
## [50] train-rmse:0.681561+0.013952 test-rmse:1.276993+0.128723
## [51] train-rmse:0.669866+0.014552 test-rmse:1.274070+0.126608
## [52] train-rmse:0.661247+0.013943 test-rmse:1.273871+0.128566
## [53] train-rmse:0.651896+0.013860 test-rmse:1.269568+0.129460
## [54] train-rmse:0.642377+0.014460 test-rmse:1.265600+0.131264
## [55] train-rmse:0.632444+0.013882 test-rmse:1.264069+0.130146
## [56] train-rmse:0.624179+0.014048 test-rmse:1.264860+0.132125
## [57] train-rmse:0.615484+0.014246 test-rmse:1.263152+0.132052
## [58] train-rmse:0.605938+0.014790 test-rmse:1.261437+0.132096
## [59] train-rmse:0.597521+0.014803 test-rmse:1.257300+0.134040
## [60] train-rmse:0.588871+0.014978 test-rmse:1.255338+0.136198
## [61] train-rmse:0.581567+0.015590 test-rmse:1.252784+0.136719
## [62] train-rmse:0.575678+0.014963 test-rmse:1.252263+0.137658
## [63] train-rmse:0.569266+0.014778 test-rmse:1.249845+0.140089
## [64] train-rmse:0.561504+0.014748 test-rmse:1.245953+0.141461
## [65] train-rmse:0.555078+0.014434 test-rmse:1.245489+0.142724
## [66] train-rmse:0.549259+0.014428 test-rmse:1.244172+0.144619
## [67] train-rmse:0.542238+0.015083 test-rmse:1.240748+0.142450
## [68] train-rmse:0.534545+0.013817 test-rmse:1.238107+0.143588
## [69] train-rmse:0.527970+0.012956 test-rmse:1.237882+0.143180
## [70] train-rmse:0.521904+0.013374 test-rmse:1.237194+0.143921
## [71] train-rmse:0.515759+0.012798 test-rmse:1.236502+0.143406
## [72] train-rmse:0.509441+0.013818 test-rmse:1.234623+0.143864
## [73] train-rmse:0.503372+0.014755 test-rmse:1.233518+0.144153
## [74] train-rmse:0.498261+0.015167 test-rmse:1.232819+0.145789
## [75] train-rmse:0.494199+0.015314 test-rmse:1.230894+0.146556
## [76] train-rmse:0.489350+0.015040 test-rmse:1.230766+0.147325
## [77] train-rmse:0.483631+0.015464 test-rmse:1.229086+0.149961
## [78] train-rmse:0.477648+0.013984 test-rmse:1.228265+0.151072
## [79] train-rmse:0.472767+0.013271 test-rmse:1.227470+0.151288
## [80] train-rmse:0.467651+0.013213 test-rmse:1.227179+0.152280
## [81] train-rmse:0.463080+0.013952 test-rmse:1.224173+0.152735
## [82] train-rmse:0.458189+0.012665 test-rmse:1.223560+0.153459
## [83] train-rmse:0.453918+0.013216 test-rmse:1.222702+0.153648
## [84] train-rmse:0.449976+0.012868 test-rmse:1.221962+0.154386
## [85] train-rmse:0.444361+0.013525 test-rmse:1.221809+0.155385
## [86] train-rmse:0.440198+0.013735 test-rmse:1.221443+0.154511
## [87] train-rmse:0.434762+0.011328 test-rmse:1.221494+0.154984
## [88] train-rmse:0.431318+0.011954 test-rmse:1.220681+0.155388
## [89] train-rmse:0.426739+0.012212 test-rmse:1.221341+0.156622
## [90] train-rmse:0.422482+0.012246 test-rmse:1.220726+0.156348
## [91] train-rmse:0.419001+0.013014 test-rmse:1.220926+0.156490
## [92] train-rmse:0.415502+0.014103 test-rmse:1.220674+0.156658
## [93] train-rmse:0.410603+0.014386 test-rmse:1.219156+0.156446
## [94] train-rmse:0.406982+0.014170 test-rmse:1.218287+0.157470
## [95] train-rmse:0.403920+0.014901 test-rmse:1.217566+0.157511
## [96] train-rmse:0.400406+0.014790 test-rmse:1.216395+0.158107
## [97] train-rmse:0.397778+0.014745 test-rmse:1.216229+0.158751
## [98] train-rmse:0.393871+0.014964 test-rmse:1.215168+0.159120
## [99] train-rmse:0.390255+0.015227 test-rmse:1.214456+0.159087
## [100] train-rmse:0.387087+0.016143 test-rmse:1.214297+0.159634
## [101] train-rmse:0.382787+0.013796 test-rmse:1.215182+0.160653
## [102] train-rmse:0.379338+0.014254 test-rmse:1.215052+0.160976
## [103] train-rmse:0.376011+0.015462 test-rmse:1.214227+0.161253
## [104] train-rmse:0.371705+0.014775 test-rmse:1.214687+0.160716
## [105] train-rmse:0.368620+0.015604 test-rmse:1.214253+0.160501
## [106] train-rmse:0.365039+0.015355 test-rmse:1.213701+0.160495
## [107] train-rmse:0.361461+0.014939 test-rmse:1.214065+0.160008
## [108] train-rmse:0.358953+0.014911 test-rmse:1.214617+0.159796
## [109] train-rmse:0.356301+0.015542 test-rmse:1.214368+0.160474
## [110] train-rmse:0.354416+0.015769 test-rmse:1.214252+0.160511
## [111] train-rmse:0.351752+0.016006 test-rmse:1.213926+0.160064
## [112] train-rmse:0.349402+0.015015 test-rmse:1.213230+0.161084
## [113] train-rmse:0.345906+0.014950 test-rmse:1.213077+0.161166
## [114] train-rmse:0.342871+0.014592 test-rmse:1.213060+0.161528
## [115] train-rmse:0.339690+0.014722 test-rmse:1.212369+0.162635
## [116] train-rmse:0.336905+0.014570 test-rmse:1.213104+0.163177
## [117] train-rmse:0.333231+0.013458 test-rmse:1.212167+0.163510
## [118] train-rmse:0.329227+0.012382 test-rmse:1.211693+0.163222
## [119] train-rmse:0.326856+0.012806 test-rmse:1.211232+0.164270
## [120] train-rmse:0.323766+0.012280 test-rmse:1.211299+0.164860
## [121] train-rmse:0.321657+0.012817 test-rmse:1.210869+0.165473
## [122] train-rmse:0.319137+0.012209 test-rmse:1.210752+0.165381
## [123] train-rmse:0.317321+0.012564 test-rmse:1.210584+0.166085
## [124] train-rmse:0.314276+0.012312 test-rmse:1.210521+0.166189
## [125] train-rmse:0.312514+0.012483 test-rmse:1.210231+0.166156
## [126] train-rmse:0.309879+0.012150 test-rmse:1.210020+0.166049
## [127] train-rmse:0.307801+0.013010 test-rmse:1.210191+0.165764
## [128] train-rmse:0.304594+0.012091 test-rmse:1.210820+0.165876
## [129] train-rmse:0.302752+0.012100 test-rmse:1.210550+0.167501
## [130] train-rmse:0.300180+0.013312 test-rmse:1.210744+0.167367
## [131] train-rmse:0.298128+0.013206 test-rmse:1.210819+0.167813
## [132] train-rmse:0.296086+0.012495 test-rmse:1.211311+0.168268
## Stopping. Best iteration:
## [126] train-rmse:0.309879+0.012150 test-rmse:1.210020+0.166049
##
## 7
## [1] train-rmse:8.622191+0.062206 test-rmse:8.618123+0.275463
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.680580+0.053765 test-rmse:7.672911+0.273038
## [3] train-rmse:6.860691+0.045917 test-rmse:6.854712+0.266215
## [4] train-rmse:6.148834+0.039280 test-rmse:6.142053+0.264393
## [5] train-rmse:5.532528+0.033311 test-rmse:5.528606+0.255469
## [6] train-rmse:5.000972+0.028270 test-rmse:4.999558+0.245476
## [7] train-rmse:4.544460+0.023828 test-rmse:4.547617+0.237755
## [8] train-rmse:4.154864+0.020301 test-rmse:4.161322+0.226229
## [9] train-rmse:3.823647+0.016975 test-rmse:3.830972+0.215107
## [10] train-rmse:3.543768+0.014101 test-rmse:3.560156+0.198097
## [11] train-rmse:3.308699+0.012701 test-rmse:3.331081+0.176971
## [12] train-rmse:3.112170+0.011862 test-rmse:3.135893+0.163541
## [13] train-rmse:2.948649+0.011766 test-rmse:2.973790+0.144853
## [14] train-rmse:2.813402+0.012261 test-rmse:2.838214+0.125632
## [15] train-rmse:2.701751+0.012706 test-rmse:2.730942+0.109554
## [16] train-rmse:2.609745+0.013021 test-rmse:2.638827+0.096109
## [17] train-rmse:2.534149+0.013647 test-rmse:2.566783+0.084159
## [18] train-rmse:2.472306+0.014137 test-rmse:2.506587+0.069961
## [19] train-rmse:2.421181+0.014634 test-rmse:2.460810+0.060812
## [20] train-rmse:2.378858+0.015090 test-rmse:2.415976+0.057163
## [21] train-rmse:2.343420+0.015434 test-rmse:2.385544+0.053285
## [22] train-rmse:2.314114+0.016093 test-rmse:2.360776+0.049444
## [23] train-rmse:2.289416+0.016549 test-rmse:2.335205+0.047592
## [24] train-rmse:2.268250+0.016979 test-rmse:2.315313+0.049302
## [25] train-rmse:2.249857+0.017454 test-rmse:2.296416+0.049316
## [26] train-rmse:2.233760+0.017852 test-rmse:2.279193+0.053782
## [27] train-rmse:2.219737+0.018331 test-rmse:2.266317+0.058626
## [28] train-rmse:2.207102+0.018797 test-rmse:2.257577+0.062296
## [29] train-rmse:2.195662+0.019097 test-rmse:2.247601+0.063779
## [30] train-rmse:2.185103+0.019420 test-rmse:2.236349+0.065554
## [31] train-rmse:2.175138+0.019842 test-rmse:2.223420+0.065551
## [32] train-rmse:2.166034+0.020247 test-rmse:2.215501+0.069825
## [33] train-rmse:2.157401+0.020625 test-rmse:2.208560+0.073281
## [34] train-rmse:2.149352+0.020983 test-rmse:2.204358+0.073075
## [35] train-rmse:2.141686+0.021280 test-rmse:2.197805+0.077301
## [36] train-rmse:2.134211+0.021461 test-rmse:2.189442+0.075641
## [37] train-rmse:2.126963+0.021800 test-rmse:2.185513+0.075824
## [38] train-rmse:2.119969+0.022115 test-rmse:2.179768+0.078284
## [39] train-rmse:2.113207+0.022440 test-rmse:2.176037+0.078623
## [40] train-rmse:2.106597+0.022676 test-rmse:2.169063+0.081680
## [41] train-rmse:2.100074+0.022911 test-rmse:2.163180+0.080821
## [42] train-rmse:2.093634+0.023231 test-rmse:2.151774+0.084152
## [43] train-rmse:2.087301+0.023508 test-rmse:2.145290+0.084421
## [44] train-rmse:2.081097+0.023791 test-rmse:2.141227+0.085373
## [45] train-rmse:2.075086+0.024080 test-rmse:2.134619+0.084634
## [46] train-rmse:2.069240+0.024348 test-rmse:2.132387+0.085728
## [47] train-rmse:2.063442+0.024610 test-rmse:2.127683+0.088101
## [48] train-rmse:2.057604+0.024810 test-rmse:2.119239+0.087262
## [49] train-rmse:2.051963+0.025102 test-rmse:2.114610+0.084761
## [50] train-rmse:2.046328+0.025410 test-rmse:2.108785+0.086434
## [51] train-rmse:2.040773+0.025756 test-rmse:2.107007+0.086893
## [52] train-rmse:2.035375+0.026116 test-rmse:2.100433+0.090810
## [53] train-rmse:2.030056+0.026413 test-rmse:2.097298+0.092651
## [54] train-rmse:2.024885+0.026674 test-rmse:2.093624+0.090845
## [55] train-rmse:2.019717+0.026881 test-rmse:2.089012+0.090180
## [56] train-rmse:2.014568+0.027059 test-rmse:2.085436+0.087890
## [57] train-rmse:2.009569+0.027321 test-rmse:2.079082+0.090303
## [58] train-rmse:2.004577+0.027545 test-rmse:2.074610+0.088623
## [59] train-rmse:1.999632+0.027756 test-rmse:2.069781+0.089035
## [60] train-rmse:1.994751+0.027972 test-rmse:2.066259+0.091531
## [61] train-rmse:1.989926+0.028187 test-rmse:2.059884+0.089437
## [62] train-rmse:1.985152+0.028464 test-rmse:2.056736+0.090154
## [63] train-rmse:1.980441+0.028710 test-rmse:2.053814+0.090301
## [64] train-rmse:1.975804+0.028917 test-rmse:2.050227+0.092257
## [65] train-rmse:1.971247+0.029126 test-rmse:2.048022+0.093674
## [66] train-rmse:1.966721+0.029320 test-rmse:2.044138+0.090789
## [67] train-rmse:1.962222+0.029462 test-rmse:2.036831+0.091421
## [68] train-rmse:1.957760+0.029668 test-rmse:2.034281+0.093309
## [69] train-rmse:1.953364+0.029903 test-rmse:2.029301+0.093320
## [70] train-rmse:1.948985+0.030154 test-rmse:2.024067+0.088795
## [71] train-rmse:1.944651+0.030384 test-rmse:2.020708+0.088902
## [72] train-rmse:1.940330+0.030673 test-rmse:2.015397+0.086509
## [73] train-rmse:1.936085+0.030942 test-rmse:2.010028+0.087751
## [74] train-rmse:1.931926+0.031126 test-rmse:2.006394+0.087441
## [75] train-rmse:1.927860+0.031358 test-rmse:2.004426+0.088522
## [76] train-rmse:1.923826+0.031547 test-rmse:2.003795+0.090455
## [77] train-rmse:1.919795+0.031758 test-rmse:1.999624+0.090240
## [78] train-rmse:1.915850+0.031974 test-rmse:1.997332+0.087528
## [79] train-rmse:1.911901+0.032178 test-rmse:1.992807+0.089565
## [80] train-rmse:1.908009+0.032394 test-rmse:1.990718+0.090398
## [81] train-rmse:1.904143+0.032631 test-rmse:1.985245+0.093041
## [82] train-rmse:1.900320+0.032854 test-rmse:1.982951+0.092371
## [83] train-rmse:1.896541+0.033107 test-rmse:1.979249+0.090538
## [84] train-rmse:1.892764+0.033374 test-rmse:1.977084+0.090307
## [85] train-rmse:1.889018+0.033561 test-rmse:1.971437+0.088480
## [86] train-rmse:1.885300+0.033783 test-rmse:1.969672+0.088602
## [87] train-rmse:1.881620+0.034022 test-rmse:1.965735+0.089148
## [88] train-rmse:1.877954+0.034306 test-rmse:1.960952+0.088525
## [89] train-rmse:1.874324+0.034596 test-rmse:1.955950+0.087119
## [90] train-rmse:1.870715+0.034824 test-rmse:1.954842+0.088046
## [91] train-rmse:1.867169+0.035002 test-rmse:1.951871+0.087731
## [92] train-rmse:1.863647+0.035176 test-rmse:1.950385+0.087867
## [93] train-rmse:1.860222+0.035383 test-rmse:1.948436+0.091593
## [94] train-rmse:1.856811+0.035597 test-rmse:1.947697+0.093132
## [95] train-rmse:1.853393+0.035809 test-rmse:1.944151+0.093108
## [96] train-rmse:1.849997+0.036003 test-rmse:1.940834+0.091495
## [97] train-rmse:1.846619+0.036212 test-rmse:1.937995+0.090876
## [98] train-rmse:1.843286+0.036366 test-rmse:1.935306+0.088340
## [99] train-rmse:1.840003+0.036535 test-rmse:1.933170+0.089389
## [100] train-rmse:1.836734+0.036718 test-rmse:1.930848+0.088850
## [101] train-rmse:1.833498+0.036928 test-rmse:1.926691+0.089209
## [102] train-rmse:1.830285+0.037120 test-rmse:1.922908+0.089725
## [103] train-rmse:1.827068+0.037338 test-rmse:1.918533+0.092046
## [104] train-rmse:1.823890+0.037547 test-rmse:1.917129+0.093977
## [105] train-rmse:1.820747+0.037733 test-rmse:1.914318+0.092717
## [106] train-rmse:1.817620+0.037937 test-rmse:1.911320+0.092526
## [107] train-rmse:1.814519+0.038129 test-rmse:1.908135+0.089879
## [108] train-rmse:1.811442+0.038322 test-rmse:1.905254+0.091493
## [109] train-rmse:1.808395+0.038486 test-rmse:1.903393+0.092898
## [110] train-rmse:1.805369+0.038640 test-rmse:1.902056+0.092611
## [111] train-rmse:1.802378+0.038827 test-rmse:1.899883+0.093109
## [112] train-rmse:1.799371+0.039003 test-rmse:1.896973+0.094262
## [113] train-rmse:1.796389+0.039160 test-rmse:1.894382+0.094578
## [114] train-rmse:1.793435+0.039378 test-rmse:1.892000+0.093109
## [115] train-rmse:1.790542+0.039529 test-rmse:1.889125+0.094455
## [116] train-rmse:1.787634+0.039684 test-rmse:1.886314+0.093415
## [117] train-rmse:1.784760+0.039849 test-rmse:1.883697+0.094062
## [118] train-rmse:1.781903+0.040016 test-rmse:1.880878+0.092692
## [119] train-rmse:1.779085+0.040156 test-rmse:1.878753+0.092455
## [120] train-rmse:1.776309+0.040286 test-rmse:1.876226+0.091501
## [121] train-rmse:1.773544+0.040429 test-rmse:1.875328+0.094115
## [122] train-rmse:1.770791+0.040569 test-rmse:1.871808+0.093859
## [123] train-rmse:1.768062+0.040751 test-rmse:1.872116+0.093169
## [124] train-rmse:1.765347+0.040923 test-rmse:1.868227+0.094349
## [125] train-rmse:1.762616+0.041114 test-rmse:1.865109+0.093215
## [126] train-rmse:1.759940+0.041259 test-rmse:1.861851+0.094174
## [127] train-rmse:1.757268+0.041409 test-rmse:1.857981+0.094236
## [128] train-rmse:1.754617+0.041590 test-rmse:1.855872+0.095462
## [129] train-rmse:1.752003+0.041766 test-rmse:1.852163+0.096562
## [130] train-rmse:1.749399+0.041922 test-rmse:1.851031+0.096054
## [131] train-rmse:1.746835+0.042059 test-rmse:1.849454+0.094557
## [132] train-rmse:1.744313+0.042217 test-rmse:1.846709+0.096193
## [133] train-rmse:1.741803+0.042357 test-rmse:1.845350+0.098390
## [134] train-rmse:1.739295+0.042496 test-rmse:1.843165+0.097378
## [135] train-rmse:1.736813+0.042593 test-rmse:1.842474+0.097972
## [136] train-rmse:1.734327+0.042718 test-rmse:1.840889+0.098386
## [137] train-rmse:1.731872+0.042897 test-rmse:1.838347+0.098562
## [138] train-rmse:1.729409+0.043075 test-rmse:1.836266+0.099429
## [139] train-rmse:1.726973+0.043207 test-rmse:1.833621+0.099794
## [140] train-rmse:1.724576+0.043330 test-rmse:1.830880+0.099454
## [141] train-rmse:1.722175+0.043452 test-rmse:1.830256+0.099453
## [142] train-rmse:1.719808+0.043581 test-rmse:1.828279+0.098720
## [143] train-rmse:1.717427+0.043758 test-rmse:1.826805+0.100435
## [144] train-rmse:1.715066+0.043894 test-rmse:1.824620+0.100920
## [145] train-rmse:1.712729+0.044021 test-rmse:1.823278+0.100922
## [146] train-rmse:1.710426+0.044128 test-rmse:1.820939+0.100984
## [147] train-rmse:1.708141+0.044250 test-rmse:1.817878+0.099692
## [148] train-rmse:1.705882+0.044377 test-rmse:1.816319+0.100239
## [149] train-rmse:1.703620+0.044508 test-rmse:1.815360+0.100466
## [150] train-rmse:1.701371+0.044636 test-rmse:1.813370+0.099564
## [151] train-rmse:1.699146+0.044762 test-rmse:1.811594+0.101464
## [152] train-rmse:1.696906+0.044886 test-rmse:1.809246+0.101287
## [153] train-rmse:1.694668+0.044999 test-rmse:1.807670+0.100851
## [154] train-rmse:1.692523+0.045125 test-rmse:1.805480+0.101039
## [155] train-rmse:1.690378+0.045226 test-rmse:1.803247+0.102163
## [156] train-rmse:1.688228+0.045308 test-rmse:1.803337+0.103575
## [157] train-rmse:1.686103+0.045407 test-rmse:1.799744+0.103712
## [158] train-rmse:1.684009+0.045524 test-rmse:1.798584+0.104236
## [159] train-rmse:1.681900+0.045644 test-rmse:1.796741+0.104908
## [160] train-rmse:1.679804+0.045753 test-rmse:1.796861+0.104264
## [161] train-rmse:1.677715+0.045850 test-rmse:1.796442+0.105106
## [162] train-rmse:1.675653+0.045954 test-rmse:1.794208+0.103484
## [163] train-rmse:1.673611+0.046055 test-rmse:1.789524+0.104744
## [164] train-rmse:1.671585+0.046162 test-rmse:1.788505+0.105794
## [165] train-rmse:1.669564+0.046254 test-rmse:1.786721+0.106916
## [166] train-rmse:1.667578+0.046332 test-rmse:1.785351+0.105559
## [167] train-rmse:1.665590+0.046404 test-rmse:1.782749+0.107289
## [168] train-rmse:1.663619+0.046471 test-rmse:1.781785+0.108098
## [169] train-rmse:1.661660+0.046538 test-rmse:1.778092+0.106925
## [170] train-rmse:1.659696+0.046638 test-rmse:1.774269+0.108275
## [171] train-rmse:1.657750+0.046738 test-rmse:1.773263+0.108423
## [172] train-rmse:1.655838+0.046836 test-rmse:1.772470+0.109696
## [173] train-rmse:1.653934+0.046917 test-rmse:1.771226+0.110151
## [174] train-rmse:1.652051+0.046997 test-rmse:1.769725+0.109827
## [175] train-rmse:1.650180+0.047073 test-rmse:1.767893+0.110128
## [176] train-rmse:1.648290+0.047134 test-rmse:1.766094+0.109388
## [177] train-rmse:1.646416+0.047204 test-rmse:1.764829+0.109901
## [178] train-rmse:1.644558+0.047285 test-rmse:1.763200+0.110750
## [179] train-rmse:1.642705+0.047371 test-rmse:1.763212+0.108582
## [180] train-rmse:1.640872+0.047458 test-rmse:1.760769+0.109228
## [181] train-rmse:1.639056+0.047524 test-rmse:1.759574+0.110090
## [182] train-rmse:1.637231+0.047613 test-rmse:1.759026+0.110732
## [183] train-rmse:1.635416+0.047713 test-rmse:1.757523+0.110561
## [184] train-rmse:1.633625+0.047800 test-rmse:1.756636+0.110551
## [185] train-rmse:1.631865+0.047880 test-rmse:1.755733+0.112864
## [186] train-rmse:1.630114+0.047968 test-rmse:1.752788+0.113595
## [187] train-rmse:1.628348+0.048059 test-rmse:1.751629+0.113893
## [188] train-rmse:1.626606+0.048140 test-rmse:1.749740+0.115445
## [189] train-rmse:1.624849+0.048192 test-rmse:1.749172+0.116590
## [190] train-rmse:1.623131+0.048258 test-rmse:1.748099+0.116450
## [191] train-rmse:1.621423+0.048345 test-rmse:1.746197+0.115859
## [192] train-rmse:1.619747+0.048409 test-rmse:1.742414+0.118190
## [193] train-rmse:1.618077+0.048479 test-rmse:1.739785+0.117350
## [194] train-rmse:1.616403+0.048552 test-rmse:1.739709+0.117081
## [195] train-rmse:1.614746+0.048606 test-rmse:1.737210+0.116421
## [196] train-rmse:1.613117+0.048670 test-rmse:1.735316+0.115637
## [197] train-rmse:1.611478+0.048733 test-rmse:1.734238+0.116676
## [198] train-rmse:1.609838+0.048770 test-rmse:1.733289+0.116596
## [199] train-rmse:1.608215+0.048820 test-rmse:1.731971+0.115802
## [200] train-rmse:1.606602+0.048879 test-rmse:1.730310+0.116529
## [201] train-rmse:1.604993+0.048933 test-rmse:1.729790+0.117849
## [202] train-rmse:1.603403+0.048993 test-rmse:1.728030+0.117855
## [203] train-rmse:1.601810+0.049056 test-rmse:1.726760+0.118104
## [204] train-rmse:1.600225+0.049120 test-rmse:1.724730+0.119247
## [205] train-rmse:1.598659+0.049204 test-rmse:1.725132+0.118231
## [206] train-rmse:1.597094+0.049274 test-rmse:1.723465+0.118615
## [207] train-rmse:1.595553+0.049337 test-rmse:1.721829+0.119439
## [208] train-rmse:1.594033+0.049401 test-rmse:1.720213+0.120751
## [209] train-rmse:1.592499+0.049476 test-rmse:1.718991+0.121037
## [210] train-rmse:1.590978+0.049521 test-rmse:1.716820+0.122465
## [211] train-rmse:1.589473+0.049576 test-rmse:1.715340+0.123664
## [212] train-rmse:1.587967+0.049629 test-rmse:1.714048+0.123157
## [213] train-rmse:1.586451+0.049677 test-rmse:1.713562+0.124513
## [214] train-rmse:1.584943+0.049718 test-rmse:1.711275+0.125097
## [215] train-rmse:1.583459+0.049759 test-rmse:1.710511+0.125815
## [216] train-rmse:1.581990+0.049819 test-rmse:1.710005+0.126128
## [217] train-rmse:1.580510+0.049887 test-rmse:1.708752+0.127170
## [218] train-rmse:1.579058+0.049930 test-rmse:1.706933+0.126471
## [219] train-rmse:1.577624+0.049975 test-rmse:1.705835+0.126841
## [220] train-rmse:1.576189+0.050018 test-rmse:1.704255+0.125957
## [221] train-rmse:1.574758+0.050059 test-rmse:1.700194+0.128096
## [222] train-rmse:1.573345+0.050093 test-rmse:1.699552+0.128143
## [223] train-rmse:1.571939+0.050125 test-rmse:1.699810+0.128519
## [224] train-rmse:1.570545+0.050158 test-rmse:1.697872+0.128062
## [225] train-rmse:1.569159+0.050196 test-rmse:1.696083+0.127520
## [226] train-rmse:1.567779+0.050232 test-rmse:1.695460+0.127339
## [227] train-rmse:1.566420+0.050259 test-rmse:1.694708+0.127325
## [228] train-rmse:1.565069+0.050283 test-rmse:1.694815+0.127350
## [229] train-rmse:1.563720+0.050313 test-rmse:1.693963+0.127450
## [230] train-rmse:1.562369+0.050350 test-rmse:1.692007+0.127016
## [231] train-rmse:1.561034+0.050403 test-rmse:1.691327+0.128500
## [232] train-rmse:1.559700+0.050437 test-rmse:1.689447+0.129053
## [233] train-rmse:1.558379+0.050474 test-rmse:1.688456+0.128928
## [234] train-rmse:1.557050+0.050512 test-rmse:1.687302+0.128348
## [235] train-rmse:1.555727+0.050556 test-rmse:1.685458+0.131302
## [236] train-rmse:1.554407+0.050592 test-rmse:1.683536+0.131888
## [237] train-rmse:1.553123+0.050627 test-rmse:1.682393+0.131571
## [238] train-rmse:1.551856+0.050666 test-rmse:1.681077+0.131628
## [239] train-rmse:1.550583+0.050700 test-rmse:1.679318+0.131439
## [240] train-rmse:1.549312+0.050739 test-rmse:1.678835+0.131291
## [241] train-rmse:1.548049+0.050780 test-rmse:1.678836+0.132958
## [242] train-rmse:1.546797+0.050826 test-rmse:1.679623+0.134267
## [243] train-rmse:1.545563+0.050869 test-rmse:1.677728+0.134274
## [244] train-rmse:1.544332+0.050900 test-rmse:1.676155+0.135243
## [245] train-rmse:1.543103+0.050933 test-rmse:1.675843+0.134446
## [246] train-rmse:1.541868+0.050979 test-rmse:1.674362+0.135716
## [247] train-rmse:1.540617+0.051025 test-rmse:1.673060+0.135240
## [248] train-rmse:1.539406+0.051066 test-rmse:1.670789+0.134814
## [249] train-rmse:1.538192+0.051075 test-rmse:1.670022+0.135779
## [250] train-rmse:1.536987+0.051086 test-rmse:1.669697+0.136125
## [251] train-rmse:1.535796+0.051100 test-rmse:1.667898+0.137505
## [252] train-rmse:1.534632+0.051121 test-rmse:1.667932+0.136615
## [253] train-rmse:1.533473+0.051148 test-rmse:1.666768+0.136789
## [254] train-rmse:1.532318+0.051166 test-rmse:1.666916+0.137055
## [255] train-rmse:1.531158+0.051198 test-rmse:1.665395+0.137578
## [256] train-rmse:1.529996+0.051224 test-rmse:1.663140+0.138328
## [257] train-rmse:1.528839+0.051250 test-rmse:1.661393+0.138269
## [258] train-rmse:1.527679+0.051263 test-rmse:1.657744+0.137538
## [259] train-rmse:1.526548+0.051286 test-rmse:1.656921+0.137448
## [260] train-rmse:1.525419+0.051302 test-rmse:1.655961+0.138360
## [261] train-rmse:1.524304+0.051308 test-rmse:1.655618+0.138873
## [262] train-rmse:1.523194+0.051322 test-rmse:1.654170+0.138078
## [263] train-rmse:1.522094+0.051333 test-rmse:1.653806+0.138162
## [264] train-rmse:1.521006+0.051351 test-rmse:1.654002+0.139019
## [265] train-rmse:1.519920+0.051383 test-rmse:1.651785+0.138579
## [266] train-rmse:1.518827+0.051419 test-rmse:1.649398+0.140787
## [267] train-rmse:1.517739+0.051456 test-rmse:1.648990+0.141002
## [268] train-rmse:1.516671+0.051487 test-rmse:1.647745+0.140522
## [269] train-rmse:1.515603+0.051517 test-rmse:1.646851+0.139673
## [270] train-rmse:1.514526+0.051551 test-rmse:1.645744+0.138831
## [271] train-rmse:1.513475+0.051590 test-rmse:1.646102+0.140512
## [272] train-rmse:1.512423+0.051627 test-rmse:1.644960+0.140514
## [273] train-rmse:1.511370+0.051654 test-rmse:1.643261+0.141209
## [274] train-rmse:1.510324+0.051682 test-rmse:1.643034+0.140254
## [275] train-rmse:1.509286+0.051697 test-rmse:1.642793+0.141729
## [276] train-rmse:1.508241+0.051709 test-rmse:1.641717+0.141411
## [277] train-rmse:1.507210+0.051733 test-rmse:1.641574+0.142664
## [278] train-rmse:1.506184+0.051758 test-rmse:1.641714+0.142799
## [279] train-rmse:1.505171+0.051781 test-rmse:1.641149+0.142276
## [280] train-rmse:1.504171+0.051809 test-rmse:1.639442+0.142887
## [281] train-rmse:1.503174+0.051838 test-rmse:1.637940+0.143690
## [282] train-rmse:1.502169+0.051875 test-rmse:1.636536+0.143381
## [283] train-rmse:1.501172+0.051910 test-rmse:1.635563+0.143077
## [284] train-rmse:1.500189+0.051935 test-rmse:1.634951+0.143464
## [285] train-rmse:1.499205+0.051954 test-rmse:1.635254+0.144610
## [286] train-rmse:1.498227+0.051977 test-rmse:1.634015+0.144279
## [287] train-rmse:1.497254+0.051997 test-rmse:1.632962+0.144603
## [288] train-rmse:1.496297+0.052011 test-rmse:1.631880+0.145813
## [289] train-rmse:1.495349+0.052033 test-rmse:1.631664+0.145620
## [290] train-rmse:1.494400+0.052065 test-rmse:1.629500+0.147606
## [291] train-rmse:1.493456+0.052089 test-rmse:1.628179+0.148657
## [292] train-rmse:1.492522+0.052099 test-rmse:1.626557+0.148973
## [293] train-rmse:1.491586+0.052122 test-rmse:1.624710+0.147876
## [294] train-rmse:1.490654+0.052143 test-rmse:1.624872+0.149368
## [295] train-rmse:1.489723+0.052161 test-rmse:1.623116+0.148317
## [296] train-rmse:1.488796+0.052180 test-rmse:1.622496+0.148320
## [297] train-rmse:1.487885+0.052201 test-rmse:1.621733+0.148154
## [298] train-rmse:1.486979+0.052228 test-rmse:1.621714+0.147098
## [299] train-rmse:1.486082+0.052251 test-rmse:1.620943+0.147056
## [300] train-rmse:1.485187+0.052274 test-rmse:1.619807+0.147185
## [301] train-rmse:1.484299+0.052296 test-rmse:1.619486+0.147378
## [302] train-rmse:1.483418+0.052325 test-rmse:1.618293+0.148321
## [303] train-rmse:1.482523+0.052350 test-rmse:1.617211+0.148741
## [304] train-rmse:1.481626+0.052355 test-rmse:1.616264+0.148370
## [305] train-rmse:1.480746+0.052374 test-rmse:1.615516+0.148008
## [306] train-rmse:1.479867+0.052403 test-rmse:1.614867+0.149306
## [307] train-rmse:1.478984+0.052452 test-rmse:1.613579+0.149870
## [308] train-rmse:1.478122+0.052475 test-rmse:1.613395+0.150356
## [309] train-rmse:1.477263+0.052500 test-rmse:1.613361+0.150261
## [310] train-rmse:1.476417+0.052521 test-rmse:1.613911+0.150371
## [311] train-rmse:1.475575+0.052541 test-rmse:1.613896+0.150092
## [312] train-rmse:1.474740+0.052561 test-rmse:1.612860+0.149989
## [313] train-rmse:1.473909+0.052579 test-rmse:1.612042+0.151318
## [314] train-rmse:1.473078+0.052590 test-rmse:1.609609+0.149723
## [315] train-rmse:1.472252+0.052600 test-rmse:1.608303+0.150112
## [316] train-rmse:1.471420+0.052606 test-rmse:1.607377+0.149995
## [317] train-rmse:1.470604+0.052618 test-rmse:1.607246+0.150122
## [318] train-rmse:1.469786+0.052629 test-rmse:1.606908+0.151138
## [319] train-rmse:1.468973+0.052647 test-rmse:1.605960+0.151326
## [320] train-rmse:1.468162+0.052660 test-rmse:1.606280+0.151609
## [321] train-rmse:1.467351+0.052668 test-rmse:1.604010+0.153273
## [322] train-rmse:1.466553+0.052680 test-rmse:1.603377+0.154626
## [323] train-rmse:1.465747+0.052700 test-rmse:1.602103+0.153965
## [324] train-rmse:1.464960+0.052729 test-rmse:1.601153+0.154422
## [325] train-rmse:1.464169+0.052758 test-rmse:1.599747+0.153621
## [326] train-rmse:1.463384+0.052767 test-rmse:1.599107+0.154046
## [327] train-rmse:1.462602+0.052780 test-rmse:1.597995+0.153670
## [328] train-rmse:1.461831+0.052797 test-rmse:1.598097+0.153183
## [329] train-rmse:1.461061+0.052811 test-rmse:1.598633+0.154352
## [330] train-rmse:1.460297+0.052823 test-rmse:1.598078+0.154358
## [331] train-rmse:1.459539+0.052833 test-rmse:1.597473+0.154352
## [332] train-rmse:1.458783+0.052843 test-rmse:1.596982+0.154039
## [333] train-rmse:1.458033+0.052873 test-rmse:1.596978+0.154899
## [334] train-rmse:1.457292+0.052892 test-rmse:1.595546+0.154887
## [335] train-rmse:1.456543+0.052911 test-rmse:1.594723+0.154985
## [336] train-rmse:1.455790+0.052924 test-rmse:1.594683+0.155042
## [337] train-rmse:1.455052+0.052944 test-rmse:1.594471+0.155104
## [338] train-rmse:1.454311+0.052958 test-rmse:1.593662+0.155170
## [339] train-rmse:1.453576+0.052976 test-rmse:1.592409+0.154955
## [340] train-rmse:1.452834+0.052997 test-rmse:1.591785+0.155442
## [341] train-rmse:1.452105+0.053006 test-rmse:1.591389+0.155551
## [342] train-rmse:1.451386+0.053015 test-rmse:1.590393+0.155049
## [343] train-rmse:1.450669+0.053018 test-rmse:1.590123+0.156073
## [344] train-rmse:1.449948+0.053028 test-rmse:1.590172+0.155625
## [345] train-rmse:1.449242+0.053035 test-rmse:1.589164+0.155743
## [346] train-rmse:1.448539+0.053040 test-rmse:1.588374+0.156137
## [347] train-rmse:1.447844+0.053044 test-rmse:1.586592+0.157891
## [348] train-rmse:1.447147+0.053050 test-rmse:1.585870+0.158113
## [349] train-rmse:1.446452+0.053058 test-rmse:1.584983+0.158280
## [350] train-rmse:1.445761+0.053066 test-rmse:1.584209+0.159042
## [351] train-rmse:1.445067+0.053064 test-rmse:1.583489+0.158800
## [352] train-rmse:1.444376+0.053066 test-rmse:1.582860+0.158763
## [353] train-rmse:1.443691+0.053076 test-rmse:1.581848+0.159020
## [354] train-rmse:1.443011+0.053085 test-rmse:1.580646+0.159549
## [355] train-rmse:1.442341+0.053089 test-rmse:1.580590+0.160655
## [356] train-rmse:1.441679+0.053098 test-rmse:1.579865+0.160115
## [357] train-rmse:1.441029+0.053109 test-rmse:1.579431+0.160163
## [358] train-rmse:1.440374+0.053128 test-rmse:1.578569+0.160005
## [359] train-rmse:1.439724+0.053144 test-rmse:1.577254+0.159874
## [360] train-rmse:1.439077+0.053158 test-rmse:1.578085+0.160737
## [361] train-rmse:1.438430+0.053170 test-rmse:1.577478+0.160641
## [362] train-rmse:1.437785+0.053177 test-rmse:1.576342+0.159797
## [363] train-rmse:1.437139+0.053184 test-rmse:1.575564+0.160081
## [364] train-rmse:1.436489+0.053187 test-rmse:1.575249+0.160276
## [365] train-rmse:1.435852+0.053194 test-rmse:1.574624+0.160702
## [366] train-rmse:1.435211+0.053209 test-rmse:1.574322+0.161038
## [367] train-rmse:1.434575+0.053221 test-rmse:1.574009+0.160462
## [368] train-rmse:1.433947+0.053230 test-rmse:1.572599+0.160481
## [369] train-rmse:1.433319+0.053237 test-rmse:1.572450+0.160586
## [370] train-rmse:1.432699+0.053245 test-rmse:1.572767+0.160449
## [371] train-rmse:1.432079+0.053258 test-rmse:1.573035+0.160801
## [372] train-rmse:1.431467+0.053259 test-rmse:1.572438+0.161057
## [373] train-rmse:1.430848+0.053246 test-rmse:1.571542+0.160491
## [374] train-rmse:1.430233+0.053244 test-rmse:1.571254+0.160927
## [375] train-rmse:1.429627+0.053241 test-rmse:1.571269+0.161602
## [376] train-rmse:1.429018+0.053247 test-rmse:1.570100+0.163160
## [377] train-rmse:1.428414+0.053255 test-rmse:1.569434+0.163354
## [378] train-rmse:1.427812+0.053262 test-rmse:1.568437+0.163451
## [379] train-rmse:1.427218+0.053265 test-rmse:1.567784+0.163280
## [380] train-rmse:1.426629+0.053273 test-rmse:1.566769+0.162290
## [381] train-rmse:1.426044+0.053281 test-rmse:1.566515+0.162199
## [382] train-rmse:1.425462+0.053286 test-rmse:1.566757+0.163387
## [383] train-rmse:1.424873+0.053282 test-rmse:1.565927+0.163650
## [384] train-rmse:1.424295+0.053283 test-rmse:1.564498+0.163754
## [385] train-rmse:1.423720+0.053281 test-rmse:1.563449+0.163368
## [386] train-rmse:1.423145+0.053285 test-rmse:1.562742+0.164086
## [387] train-rmse:1.422572+0.053289 test-rmse:1.561957+0.164575
## [388] train-rmse:1.422002+0.053291 test-rmse:1.561480+0.164405
## [389] train-rmse:1.421433+0.053293 test-rmse:1.560866+0.164044
## [390] train-rmse:1.420878+0.053304 test-rmse:1.559921+0.164331
## [391] train-rmse:1.420331+0.053307 test-rmse:1.559989+0.164648
## [392] train-rmse:1.419788+0.053311 test-rmse:1.558311+0.165929
## [393] train-rmse:1.419243+0.053314 test-rmse:1.557613+0.165824
## [394] train-rmse:1.418698+0.053318 test-rmse:1.557791+0.166819
## [395] train-rmse:1.418155+0.053319 test-rmse:1.557243+0.166628
## [396] train-rmse:1.417607+0.053320 test-rmse:1.556619+0.167415
## [397] train-rmse:1.417067+0.053325 test-rmse:1.556580+0.167392
## [398] train-rmse:1.416531+0.053327 test-rmse:1.556353+0.167577
## [399] train-rmse:1.415994+0.053332 test-rmse:1.556136+0.168111
## [400] train-rmse:1.415462+0.053335 test-rmse:1.555940+0.167458
## [401] train-rmse:1.414935+0.053342 test-rmse:1.555596+0.168236
## [402] train-rmse:1.414413+0.053347 test-rmse:1.555473+0.168720
## [403] train-rmse:1.413894+0.053356 test-rmse:1.555267+0.168474
## [404] train-rmse:1.413377+0.053367 test-rmse:1.553887+0.167748
## [405] train-rmse:1.412857+0.053362 test-rmse:1.553734+0.167659
## [406] train-rmse:1.412344+0.053366 test-rmse:1.552896+0.168205
## [407] train-rmse:1.411832+0.053370 test-rmse:1.552244+0.167217
## [408] train-rmse:1.411322+0.053383 test-rmse:1.551784+0.167212
## [409] train-rmse:1.410817+0.053390 test-rmse:1.551792+0.167907
## [410] train-rmse:1.410315+0.053392 test-rmse:1.551194+0.168493
## [411] train-rmse:1.409818+0.053396 test-rmse:1.551729+0.169949
## [412] train-rmse:1.409321+0.053398 test-rmse:1.550951+0.170118
## [413] train-rmse:1.408824+0.053401 test-rmse:1.549926+0.169783
## [414] train-rmse:1.408327+0.053399 test-rmse:1.549720+0.169250
## [415] train-rmse:1.407830+0.053400 test-rmse:1.549000+0.169677
## [416] train-rmse:1.407333+0.053404 test-rmse:1.548236+0.169570
## [417] train-rmse:1.406845+0.053397 test-rmse:1.547928+0.169898
## [418] train-rmse:1.406358+0.053395 test-rmse:1.547935+0.169372
## [419] train-rmse:1.405872+0.053398 test-rmse:1.547736+0.169138
## [420] train-rmse:1.405390+0.053403 test-rmse:1.548198+0.169030
## [421] train-rmse:1.404914+0.053406 test-rmse:1.546860+0.168366
## [422] train-rmse:1.404437+0.053409 test-rmse:1.546278+0.169127
## [423] train-rmse:1.403962+0.053419 test-rmse:1.546596+0.169346
## [424] train-rmse:1.403492+0.053426 test-rmse:1.545807+0.169366
## [425] train-rmse:1.403026+0.053428 test-rmse:1.545438+0.168850
## [426] train-rmse:1.402562+0.053429 test-rmse:1.543863+0.170068
## [427] train-rmse:1.402099+0.053430 test-rmse:1.542990+0.170051
## [428] train-rmse:1.401638+0.053430 test-rmse:1.542899+0.169863
## [429] train-rmse:1.401181+0.053427 test-rmse:1.542871+0.169774
## [430] train-rmse:1.400725+0.053424 test-rmse:1.542641+0.170137
## [431] train-rmse:1.400264+0.053415 test-rmse:1.542421+0.170470
## [432] train-rmse:1.399811+0.053414 test-rmse:1.542011+0.171068
## [433] train-rmse:1.399364+0.053411 test-rmse:1.541283+0.171720
## [434] train-rmse:1.398919+0.053415 test-rmse:1.541577+0.172023
## [435] train-rmse:1.398476+0.053419 test-rmse:1.541362+0.172362
## [436] train-rmse:1.398035+0.053418 test-rmse:1.540031+0.172159
## [437] train-rmse:1.397596+0.053418 test-rmse:1.540044+0.171956
## [438] train-rmse:1.397161+0.053418 test-rmse:1.540351+0.172385
## [439] train-rmse:1.396724+0.053417 test-rmse:1.539861+0.172516
## [440] train-rmse:1.396290+0.053419 test-rmse:1.539807+0.172288
## [441] train-rmse:1.395861+0.053416 test-rmse:1.538995+0.172916
## [442] train-rmse:1.395437+0.053417 test-rmse:1.538482+0.172889
## [443] train-rmse:1.395014+0.053414 test-rmse:1.538593+0.173901
## [444] train-rmse:1.394592+0.053412 test-rmse:1.538354+0.174098
## [445] train-rmse:1.394172+0.053412 test-rmse:1.537704+0.173428
## [446] train-rmse:1.393755+0.053410 test-rmse:1.539192+0.173599
## [447] train-rmse:1.393336+0.053417 test-rmse:1.538798+0.173594
## [448] train-rmse:1.392921+0.053424 test-rmse:1.537705+0.173745
## [449] train-rmse:1.392504+0.053424 test-rmse:1.536827+0.173969
## [450] train-rmse:1.392094+0.053429 test-rmse:1.535842+0.174398
## [451] train-rmse:1.391685+0.053431 test-rmse:1.534583+0.174270
## [452] train-rmse:1.391282+0.053435 test-rmse:1.534177+0.173964
## [453] train-rmse:1.390874+0.053444 test-rmse:1.532819+0.175269
## [454] train-rmse:1.390466+0.053446 test-rmse:1.532893+0.174873
## [455] train-rmse:1.390068+0.053443 test-rmse:1.532667+0.174873
## [456] train-rmse:1.389670+0.053441 test-rmse:1.532783+0.175357
## [457] train-rmse:1.389275+0.053445 test-rmse:1.532343+0.175461
## [458] train-rmse:1.388885+0.053446 test-rmse:1.531932+0.175102
## [459] train-rmse:1.388494+0.053448 test-rmse:1.532056+0.175284
## [460] train-rmse:1.388102+0.053448 test-rmse:1.531964+0.174998
## [461] train-rmse:1.387714+0.053443 test-rmse:1.531037+0.174849
## [462] train-rmse:1.387325+0.053433 test-rmse:1.530814+0.175118
## [463] train-rmse:1.386939+0.053426 test-rmse:1.529868+0.175080
## [464] train-rmse:1.386553+0.053417 test-rmse:1.529937+0.176380
## [465] train-rmse:1.386174+0.053411 test-rmse:1.530005+0.175575
## [466] train-rmse:1.385796+0.053408 test-rmse:1.529689+0.176082
## [467] train-rmse:1.385418+0.053407 test-rmse:1.529423+0.176247
## [468] train-rmse:1.385039+0.053402 test-rmse:1.529888+0.176138
## [469] train-rmse:1.384666+0.053399 test-rmse:1.529968+0.176307
## [470] train-rmse:1.384299+0.053399 test-rmse:1.529880+0.176163
## [471] train-rmse:1.383932+0.053399 test-rmse:1.529932+0.177060
## [472] train-rmse:1.383566+0.053398 test-rmse:1.529036+0.177087
## [473] train-rmse:1.383198+0.053396 test-rmse:1.528372+0.177136
## [474] train-rmse:1.382829+0.053392 test-rmse:1.528124+0.177245
## [475] train-rmse:1.382465+0.053391 test-rmse:1.528529+0.177001
## [476] train-rmse:1.382103+0.053389 test-rmse:1.527808+0.177456
## [477] train-rmse:1.381746+0.053390 test-rmse:1.527248+0.177428
## [478] train-rmse:1.381393+0.053390 test-rmse:1.526986+0.177487
## [479] train-rmse:1.381042+0.053388 test-rmse:1.526223+0.178353
## [480] train-rmse:1.380689+0.053387 test-rmse:1.526033+0.178560
## [481] train-rmse:1.380331+0.053388 test-rmse:1.525367+0.179012
## [482] train-rmse:1.379976+0.053382 test-rmse:1.525302+0.179124
## [483] train-rmse:1.379625+0.053380 test-rmse:1.525556+0.179535
## [484] train-rmse:1.379272+0.053376 test-rmse:1.525121+0.179384
## [485] train-rmse:1.378924+0.053374 test-rmse:1.524553+0.179478
## [486] train-rmse:1.378580+0.053377 test-rmse:1.523591+0.178806
## [487] train-rmse:1.378238+0.053378 test-rmse:1.523001+0.179314
## [488] train-rmse:1.377899+0.053382 test-rmse:1.522765+0.179358
## [489] train-rmse:1.377564+0.053386 test-rmse:1.522792+0.179106
## [490] train-rmse:1.377230+0.053391 test-rmse:1.522800+0.178225
## [491] train-rmse:1.376900+0.053389 test-rmse:1.521992+0.178131
## [492] train-rmse:1.376571+0.053386 test-rmse:1.522192+0.178753
## [493] train-rmse:1.376243+0.053383 test-rmse:1.521990+0.178355
## [494] train-rmse:1.375914+0.053383 test-rmse:1.522056+0.178154
## [495] train-rmse:1.375586+0.053380 test-rmse:1.521292+0.178341
## [496] train-rmse:1.375254+0.053375 test-rmse:1.520699+0.178565
## [497] train-rmse:1.374928+0.053373 test-rmse:1.521360+0.178602
## [498] train-rmse:1.374605+0.053372 test-rmse:1.520376+0.179812
## [499] train-rmse:1.374283+0.053369 test-rmse:1.519338+0.180027
## [500] train-rmse:1.373965+0.053368 test-rmse:1.519840+0.179898
## [501] train-rmse:1.373649+0.053367 test-rmse:1.519462+0.179797
## [502] train-rmse:1.373334+0.053364 test-rmse:1.519582+0.179427
## [503] train-rmse:1.373022+0.053365 test-rmse:1.519011+0.179672
## [504] train-rmse:1.372711+0.053367 test-rmse:1.518431+0.179786
## [505] train-rmse:1.372400+0.053366 test-rmse:1.518356+0.179989
## [506] train-rmse:1.372086+0.053363 test-rmse:1.518151+0.180228
## [507] train-rmse:1.371775+0.053359 test-rmse:1.517614+0.180330
## [508] train-rmse:1.371460+0.053359 test-rmse:1.517423+0.180104
## [509] train-rmse:1.371150+0.053356 test-rmse:1.516999+0.181278
## [510] train-rmse:1.370845+0.053354 test-rmse:1.516772+0.181972
## [511] train-rmse:1.370542+0.053352 test-rmse:1.516485+0.182099
## [512] train-rmse:1.370241+0.053350 test-rmse:1.517377+0.181833
## [513] train-rmse:1.369944+0.053348 test-rmse:1.516836+0.181985
## [514] train-rmse:1.369648+0.053349 test-rmse:1.516789+0.182327
## [515] train-rmse:1.369356+0.053349 test-rmse:1.516568+0.182508
## [516] train-rmse:1.369062+0.053349 test-rmse:1.515646+0.182512
## [517] train-rmse:1.368772+0.053347 test-rmse:1.516011+0.182408
## [518] train-rmse:1.368478+0.053342 test-rmse:1.516136+0.182988
## [519] train-rmse:1.368188+0.053340 test-rmse:1.515993+0.183159
## [520] train-rmse:1.367900+0.053341 test-rmse:1.515848+0.183427
## [521] train-rmse:1.367610+0.053342 test-rmse:1.514677+0.183108
## [522] train-rmse:1.367320+0.053343 test-rmse:1.514322+0.183620
## [523] train-rmse:1.367036+0.053342 test-rmse:1.514767+0.184033
## [524] train-rmse:1.366752+0.053342 test-rmse:1.514470+0.183906
## [525] train-rmse:1.366471+0.053340 test-rmse:1.513985+0.183680
## [526] train-rmse:1.366192+0.053339 test-rmse:1.513054+0.184565
## [527] train-rmse:1.365915+0.053335 test-rmse:1.512685+0.184062
## [528] train-rmse:1.365639+0.053333 test-rmse:1.513068+0.184046
## [529] train-rmse:1.365365+0.053331 test-rmse:1.512891+0.183949
## [530] train-rmse:1.365092+0.053330 test-rmse:1.512029+0.183515
## [531] train-rmse:1.364820+0.053327 test-rmse:1.511890+0.183589
## [532] train-rmse:1.364548+0.053324 test-rmse:1.511913+0.183780
## [533] train-rmse:1.364273+0.053324 test-rmse:1.511586+0.184742
## [534] train-rmse:1.364001+0.053325 test-rmse:1.510967+0.184076
## [535] train-rmse:1.363733+0.053322 test-rmse:1.511235+0.183760
## [536] train-rmse:1.363468+0.053320 test-rmse:1.511498+0.184264
## [537] train-rmse:1.363202+0.053319 test-rmse:1.511506+0.184445
## [538] train-rmse:1.362937+0.053319 test-rmse:1.511444+0.184583
## [539] train-rmse:1.362676+0.053317 test-rmse:1.511247+0.184536
## [540] train-rmse:1.362412+0.053312 test-rmse:1.510583+0.184054
## [541] train-rmse:1.362151+0.053306 test-rmse:1.510243+0.184688
## [542] train-rmse:1.361892+0.053303 test-rmse:1.510182+0.184903
## [543] train-rmse:1.361635+0.053298 test-rmse:1.509717+0.184989
## [544] train-rmse:1.361379+0.053296 test-rmse:1.509367+0.185490
## [545] train-rmse:1.361127+0.053294 test-rmse:1.509404+0.185931
## [546] train-rmse:1.360875+0.053291 test-rmse:1.509124+0.185832
## [547] train-rmse:1.360625+0.053289 test-rmse:1.508223+0.185383
## [548] train-rmse:1.360374+0.053285 test-rmse:1.508237+0.185741
## [549] train-rmse:1.360122+0.053282 test-rmse:1.507640+0.186455
## [550] train-rmse:1.359871+0.053280 test-rmse:1.507456+0.186441
## [551] train-rmse:1.359622+0.053279 test-rmse:1.506998+0.186521
## [552] train-rmse:1.359376+0.053277 test-rmse:1.506687+0.186233
## [553] train-rmse:1.359133+0.053276 test-rmse:1.506798+0.185937
## [554] train-rmse:1.358887+0.053275 test-rmse:1.506354+0.186533
## [555] train-rmse:1.358643+0.053274 test-rmse:1.506270+0.186685
## [556] train-rmse:1.358401+0.053273 test-rmse:1.506194+0.186765
## [557] train-rmse:1.358163+0.053272 test-rmse:1.506510+0.187117
## [558] train-rmse:1.357925+0.053273 test-rmse:1.506744+0.187241
## [559] train-rmse:1.357687+0.053273 test-rmse:1.506551+0.187436
## [560] train-rmse:1.357448+0.053273 test-rmse:1.505970+0.187035
## [561] train-rmse:1.357210+0.053268 test-rmse:1.505782+0.187122
## [562] train-rmse:1.356977+0.053265 test-rmse:1.505900+0.188083
## [563] train-rmse:1.356743+0.053261 test-rmse:1.505781+0.187923
## [564] train-rmse:1.356512+0.053260 test-rmse:1.505819+0.187921
## [565] train-rmse:1.356281+0.053257 test-rmse:1.505645+0.187870
## [566] train-rmse:1.356051+0.053253 test-rmse:1.504961+0.188362
## [567] train-rmse:1.355821+0.053249 test-rmse:1.504768+0.188098
## [568] train-rmse:1.355593+0.053246 test-rmse:1.505264+0.188369
## [569] train-rmse:1.355367+0.053242 test-rmse:1.505210+0.188141
## [570] train-rmse:1.355142+0.053242 test-rmse:1.505085+0.187824
## [571] train-rmse:1.354916+0.053240 test-rmse:1.504583+0.187989
## [572] train-rmse:1.354694+0.053240 test-rmse:1.504364+0.188011
## [573] train-rmse:1.354472+0.053239 test-rmse:1.503968+0.187995
## [574] train-rmse:1.354249+0.053241 test-rmse:1.504189+0.188216
## [575] train-rmse:1.354029+0.053240 test-rmse:1.503787+0.188250
## [576] train-rmse:1.353812+0.053236 test-rmse:1.503788+0.188663
## [577] train-rmse:1.353593+0.053234 test-rmse:1.503929+0.187979
## [578] train-rmse:1.353376+0.053229 test-rmse:1.503747+0.187945
## [579] train-rmse:1.353159+0.053226 test-rmse:1.502918+0.188618
## [580] train-rmse:1.352944+0.053223 test-rmse:1.502705+0.188635
## [581] train-rmse:1.352727+0.053218 test-rmse:1.502360+0.188969
## [582] train-rmse:1.352514+0.053213 test-rmse:1.502111+0.189137
## [583] train-rmse:1.352301+0.053208 test-rmse:1.501036+0.188664
## [584] train-rmse:1.352089+0.053204 test-rmse:1.501476+0.188665
## [585] train-rmse:1.351880+0.053199 test-rmse:1.501733+0.189407
## [586] train-rmse:1.351672+0.053197 test-rmse:1.501580+0.189546
## [587] train-rmse:1.351464+0.053193 test-rmse:1.501790+0.189227
## [588] train-rmse:1.351261+0.053190 test-rmse:1.502422+0.189275
## [589] train-rmse:1.351057+0.053187 test-rmse:1.502471+0.190266
## Stopping. Best iteration:
## [583] train-rmse:1.352301+0.053208 test-rmse:1.501036+0.188664
##
## 8
## [1] train-rmse:8.607669+0.062417 test-rmse:8.603851+0.272725
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.653456+0.056176 test-rmse:7.646251+0.263750
## [3] train-rmse:6.819654+0.050579 test-rmse:6.813798+0.254260
## [4] train-rmse:6.095678+0.046127 test-rmse:6.092175+0.240744
## [5] train-rmse:5.462803+0.037736 test-rmse:5.462488+0.236042
## [6] train-rmse:4.915407+0.032147 test-rmse:4.923890+0.229098
## [7] train-rmse:4.441358+0.025397 test-rmse:4.453839+0.215999
## [8] train-rmse:4.032191+0.021548 test-rmse:4.048703+0.200958
## [9] train-rmse:3.681272+0.019229 test-rmse:3.704442+0.185357
## [10] train-rmse:3.381431+0.016596 test-rmse:3.408252+0.172759
## [11] train-rmse:3.126108+0.015864 test-rmse:3.163614+0.154985
## [12] train-rmse:2.910810+0.014495 test-rmse:2.953770+0.141556
## [13] train-rmse:2.727200+0.011264 test-rmse:2.780382+0.127195
## [14] train-rmse:2.574499+0.010981 test-rmse:2.636684+0.106031
## [15] train-rmse:2.446396+0.010757 test-rmse:2.519173+0.093810
## [16] train-rmse:2.336410+0.012934 test-rmse:2.415900+0.084777
## [17] train-rmse:2.247028+0.012301 test-rmse:2.337683+0.067278
## [18] train-rmse:2.172614+0.012389 test-rmse:2.267142+0.059631
## [19] train-rmse:2.110398+0.013219 test-rmse:2.209251+0.054013
## [20] train-rmse:2.058116+0.012813 test-rmse:2.166790+0.049097
## [21] train-rmse:2.013301+0.011759 test-rmse:2.130560+0.054067
## [22] train-rmse:1.974598+0.012743 test-rmse:2.098561+0.055750
## [23] train-rmse:1.939721+0.015430 test-rmse:2.066327+0.059478
## [24] train-rmse:1.912584+0.014900 test-rmse:2.043560+0.059213
## [25] train-rmse:1.888210+0.014955 test-rmse:2.022143+0.062963
## [26] train-rmse:1.866879+0.015564 test-rmse:2.008803+0.072205
## [27] train-rmse:1.845306+0.019505 test-rmse:1.988794+0.075050
## [28] train-rmse:1.825520+0.021388 test-rmse:1.967573+0.077662
## [29] train-rmse:1.809191+0.022226 test-rmse:1.954555+0.080769
## [30] train-rmse:1.794751+0.022230 test-rmse:1.937892+0.080178
## [31] train-rmse:1.780445+0.022254 test-rmse:1.927903+0.083376
## [32] train-rmse:1.767876+0.022638 test-rmse:1.918840+0.087673
## [33] train-rmse:1.754573+0.023253 test-rmse:1.909171+0.092842
## [34] train-rmse:1.740696+0.023288 test-rmse:1.897272+0.097059
## [35] train-rmse:1.729365+0.022002 test-rmse:1.885245+0.097089
## [36] train-rmse:1.718386+0.022446 test-rmse:1.879526+0.096896
## [37] train-rmse:1.706679+0.024554 test-rmse:1.870105+0.097792
## [38] train-rmse:1.697136+0.024874 test-rmse:1.858882+0.098525
## [39] train-rmse:1.685337+0.020817 test-rmse:1.849024+0.097884
## [40] train-rmse:1.676004+0.021202 test-rmse:1.845621+0.099205
## [41] train-rmse:1.666737+0.021320 test-rmse:1.837869+0.103475
## [42] train-rmse:1.654757+0.023496 test-rmse:1.827213+0.107855
## [43] train-rmse:1.645586+0.024377 test-rmse:1.821177+0.107677
## [44] train-rmse:1.636689+0.023901 test-rmse:1.813818+0.108373
## [45] train-rmse:1.627906+0.024246 test-rmse:1.810067+0.112260
## [46] train-rmse:1.618088+0.025952 test-rmse:1.796216+0.113201
## [47] train-rmse:1.608219+0.028366 test-rmse:1.790285+0.112648
## [48] train-rmse:1.600110+0.028323 test-rmse:1.783206+0.112820
## [49] train-rmse:1.592304+0.028371 test-rmse:1.778743+0.112196
## [50] train-rmse:1.583848+0.029289 test-rmse:1.774908+0.114579
## [51] train-rmse:1.572833+0.028194 test-rmse:1.761417+0.099733
## [52] train-rmse:1.560853+0.029652 test-rmse:1.757310+0.101768
## [53] train-rmse:1.553022+0.029186 test-rmse:1.749312+0.104102
## [54] train-rmse:1.546144+0.029336 test-rmse:1.744516+0.103496
## [55] train-rmse:1.539095+0.028970 test-rmse:1.738452+0.102557
## [56] train-rmse:1.531946+0.029665 test-rmse:1.734991+0.102242
## [57] train-rmse:1.523457+0.029857 test-rmse:1.728678+0.107349
## [58] train-rmse:1.516645+0.029958 test-rmse:1.725491+0.106341
## [59] train-rmse:1.509712+0.030295 test-rmse:1.721335+0.103370
## [60] train-rmse:1.502160+0.029605 test-rmse:1.716350+0.103764
## [61] train-rmse:1.495453+0.029412 test-rmse:1.710434+0.104561
## [62] train-rmse:1.488072+0.030511 test-rmse:1.705980+0.107898
## [63] train-rmse:1.479812+0.027336 test-rmse:1.700095+0.111327
## [64] train-rmse:1.473869+0.027534 test-rmse:1.695360+0.112745
## [65] train-rmse:1.467402+0.027389 test-rmse:1.690988+0.113306
## [66] train-rmse:1.461470+0.027958 test-rmse:1.686098+0.112662
## [67] train-rmse:1.455311+0.027621 test-rmse:1.683026+0.111357
## [68] train-rmse:1.449837+0.027695 test-rmse:1.678295+0.109864
## [69] train-rmse:1.444059+0.027868 test-rmse:1.675446+0.107461
## [70] train-rmse:1.438165+0.028289 test-rmse:1.669200+0.107904
## [71] train-rmse:1.432146+0.028900 test-rmse:1.661660+0.109323
## [72] train-rmse:1.426563+0.029671 test-rmse:1.658643+0.111313
## [73] train-rmse:1.418342+0.032125 test-rmse:1.653498+0.113952
## [74] train-rmse:1.412152+0.031314 test-rmse:1.648520+0.112968
## [75] train-rmse:1.406992+0.031691 test-rmse:1.646302+0.111446
## [76] train-rmse:1.401177+0.031064 test-rmse:1.642540+0.115806
## [77] train-rmse:1.394653+0.028605 test-rmse:1.639586+0.117728
## [78] train-rmse:1.388375+0.029010 test-rmse:1.637155+0.117535
## [79] train-rmse:1.383356+0.029589 test-rmse:1.633037+0.118082
## [80] train-rmse:1.378355+0.029747 test-rmse:1.625463+0.119077
## [81] train-rmse:1.373670+0.029905 test-rmse:1.621785+0.118345
## [82] train-rmse:1.368956+0.029868 test-rmse:1.618307+0.120525
## [83] train-rmse:1.359550+0.033484 test-rmse:1.608601+0.128349
## [84] train-rmse:1.354271+0.033878 test-rmse:1.605486+0.129402
## [85] train-rmse:1.349312+0.033857 test-rmse:1.601156+0.129947
## [86] train-rmse:1.344594+0.033529 test-rmse:1.595314+0.131551
## [87] train-rmse:1.338703+0.032196 test-rmse:1.590718+0.131633
## [88] train-rmse:1.333386+0.031814 test-rmse:1.589545+0.133241
## [89] train-rmse:1.329173+0.032200 test-rmse:1.589053+0.132008
## [90] train-rmse:1.324060+0.031772 test-rmse:1.585581+0.132428
## [91] train-rmse:1.318274+0.032841 test-rmse:1.581087+0.130658
## [92] train-rmse:1.313844+0.032940 test-rmse:1.578396+0.131094
## [93] train-rmse:1.309436+0.033004 test-rmse:1.576200+0.132536
## [94] train-rmse:1.304849+0.033218 test-rmse:1.572827+0.132877
## [95] train-rmse:1.300602+0.033613 test-rmse:1.571535+0.132456
## [96] train-rmse:1.296857+0.033855 test-rmse:1.567494+0.134284
## [97] train-rmse:1.292489+0.034482 test-rmse:1.561948+0.138142
## [98] train-rmse:1.285076+0.037386 test-rmse:1.559406+0.138349
## [99] train-rmse:1.275223+0.030869 test-rmse:1.551296+0.140637
## [100] train-rmse:1.271048+0.030793 test-rmse:1.546297+0.140586
## [101] train-rmse:1.266916+0.030678 test-rmse:1.545080+0.142129
## [102] train-rmse:1.262761+0.030191 test-rmse:1.540524+0.144449
## [103] train-rmse:1.258859+0.030760 test-rmse:1.538884+0.143692
## [104] train-rmse:1.254855+0.030594 test-rmse:1.536476+0.143919
## [105] train-rmse:1.251276+0.030801 test-rmse:1.532592+0.143388
## [106] train-rmse:1.245613+0.032257 test-rmse:1.525061+0.148634
## [107] train-rmse:1.241981+0.032662 test-rmse:1.521306+0.151529
## [108] train-rmse:1.237099+0.033782 test-rmse:1.520709+0.153485
## [109] train-rmse:1.232390+0.034788 test-rmse:1.517193+0.154077
## [110] train-rmse:1.228257+0.034299 test-rmse:1.514142+0.153062
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## [112] train-rmse:1.221521+0.034263 test-rmse:1.510027+0.154364
## [113] train-rmse:1.218373+0.034338 test-rmse:1.507987+0.153400
## [114] train-rmse:1.214491+0.034447 test-rmse:1.505857+0.155312
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## [116] train-rmse:1.206934+0.035408 test-rmse:1.499882+0.152805
## [117] train-rmse:1.203324+0.035516 test-rmse:1.497486+0.152981
## [118] train-rmse:1.196492+0.033126 test-rmse:1.486051+0.142105
## [119] train-rmse:1.190701+0.032117 test-rmse:1.482672+0.138843
## [120] train-rmse:1.187404+0.032174 test-rmse:1.481074+0.139088
## [121] train-rmse:1.184107+0.032515 test-rmse:1.479407+0.139220
## [122] train-rmse:1.180660+0.032890 test-rmse:1.476137+0.140358
## [123] train-rmse:1.177843+0.033194 test-rmse:1.474902+0.141028
## [124] train-rmse:1.174567+0.032725 test-rmse:1.473194+0.141137
## [125] train-rmse:1.167281+0.025693 test-rmse:1.468216+0.143561
## [126] train-rmse:1.164148+0.025463 test-rmse:1.465584+0.143388
## [127] train-rmse:1.161073+0.025557 test-rmse:1.462244+0.145671
## [128] train-rmse:1.158307+0.025785 test-rmse:1.462507+0.146075
## [129] train-rmse:1.154836+0.025439 test-rmse:1.460279+0.148424
## [130] train-rmse:1.152119+0.025441 test-rmse:1.457805+0.149020
## [131] train-rmse:1.148751+0.026353 test-rmse:1.455784+0.148879
## [132] train-rmse:1.143747+0.026620 test-rmse:1.452760+0.150827
## [133] train-rmse:1.140052+0.027477 test-rmse:1.449575+0.149272
## [134] train-rmse:1.137388+0.027809 test-rmse:1.449423+0.149776
## [135] train-rmse:1.133910+0.027847 test-rmse:1.446626+0.151636
## [136] train-rmse:1.131434+0.028046 test-rmse:1.446750+0.152939
## [137] train-rmse:1.126660+0.026989 test-rmse:1.442308+0.149736
## [138] train-rmse:1.123948+0.027383 test-rmse:1.440677+0.149638
## [139] train-rmse:1.121054+0.027490 test-rmse:1.436262+0.148838
## [140] train-rmse:1.118295+0.027196 test-rmse:1.435465+0.148281
## [141] train-rmse:1.116011+0.027318 test-rmse:1.432580+0.148802
## [142] train-rmse:1.113184+0.027578 test-rmse:1.431739+0.149372
## [143] train-rmse:1.110926+0.027577 test-rmse:1.430482+0.150362
## [144] train-rmse:1.107531+0.028415 test-rmse:1.426679+0.151922
## [145] train-rmse:1.104861+0.028351 test-rmse:1.425309+0.152020
## [146] train-rmse:1.102747+0.028668 test-rmse:1.423373+0.151751
## [147] train-rmse:1.099469+0.029103 test-rmse:1.422037+0.152235
## [148] train-rmse:1.096908+0.028997 test-rmse:1.421084+0.152448
## [149] train-rmse:1.094450+0.028667 test-rmse:1.419451+0.153970
## [150] train-rmse:1.089968+0.029080 test-rmse:1.417323+0.155000
## [151] train-rmse:1.087197+0.028939 test-rmse:1.416245+0.155160
## [152] train-rmse:1.085025+0.029104 test-rmse:1.414350+0.155273
## [153] train-rmse:1.082370+0.029077 test-rmse:1.413464+0.155147
## [154] train-rmse:1.079722+0.029274 test-rmse:1.412879+0.155925
## [155] train-rmse:1.077627+0.029188 test-rmse:1.410295+0.154967
## [156] train-rmse:1.075406+0.029428 test-rmse:1.409751+0.156441
## [157] train-rmse:1.073206+0.029583 test-rmse:1.410152+0.158061
## [158] train-rmse:1.071234+0.029845 test-rmse:1.409681+0.159494
## [159] train-rmse:1.069061+0.029648 test-rmse:1.407443+0.159222
## [160] train-rmse:1.067135+0.029898 test-rmse:1.405617+0.158568
## [161] train-rmse:1.064689+0.029733 test-rmse:1.403288+0.160471
## [162] train-rmse:1.062160+0.029478 test-rmse:1.402401+0.160943
## [163] train-rmse:1.056092+0.022921 test-rmse:1.399854+0.163100
## [164] train-rmse:1.054076+0.022733 test-rmse:1.397923+0.164491
## [165] train-rmse:1.051429+0.022466 test-rmse:1.395752+0.165336
## [166] train-rmse:1.049646+0.022479 test-rmse:1.394057+0.165259
## [167] train-rmse:1.046614+0.023358 test-rmse:1.392871+0.165584
## [168] train-rmse:1.043223+0.024313 test-rmse:1.389608+0.165402
## [169] train-rmse:1.041013+0.023971 test-rmse:1.388659+0.165361
## [170] train-rmse:1.038853+0.023856 test-rmse:1.388755+0.167089
## [171] train-rmse:1.036982+0.024004 test-rmse:1.388075+0.167274
## [172] train-rmse:1.035278+0.024291 test-rmse:1.385809+0.168323
## [173] train-rmse:1.033116+0.024629 test-rmse:1.384150+0.169210
## [174] train-rmse:1.031345+0.024682 test-rmse:1.382292+0.169485
## [175] train-rmse:1.029096+0.024807 test-rmse:1.381982+0.169193
## [176] train-rmse:1.027289+0.024522 test-rmse:1.380245+0.169527
## [177] train-rmse:1.025662+0.024536 test-rmse:1.378201+0.169198
## [178] train-rmse:1.023749+0.024398 test-rmse:1.378611+0.169261
## [179] train-rmse:1.021723+0.024027 test-rmse:1.378575+0.169318
## [180] train-rmse:1.020220+0.024202 test-rmse:1.377442+0.168963
## [181] train-rmse:1.018434+0.024275 test-rmse:1.376431+0.169127
## [182] train-rmse:1.016655+0.024222 test-rmse:1.375535+0.167878
## [183] train-rmse:1.015073+0.024327 test-rmse:1.375392+0.169097
## [184] train-rmse:1.013462+0.024546 test-rmse:1.374413+0.169452
## [185] train-rmse:1.011617+0.024653 test-rmse:1.372566+0.170054
## [186] train-rmse:1.010072+0.024760 test-rmse:1.370103+0.170492
## [187] train-rmse:1.007725+0.025314 test-rmse:1.369101+0.170965
## [188] train-rmse:1.006115+0.025537 test-rmse:1.367095+0.171340
## [189] train-rmse:1.003486+0.026493 test-rmse:1.366794+0.172012
## [190] train-rmse:1.001963+0.026521 test-rmse:1.366143+0.171355
## [191] train-rmse:1.000474+0.026784 test-rmse:1.366359+0.171361
## [192] train-rmse:0.995792+0.025416 test-rmse:1.358846+0.163948
## [193] train-rmse:0.992370+0.024685 test-rmse:1.358322+0.164274
## [194] train-rmse:0.990397+0.024510 test-rmse:1.357831+0.164953
## [195] train-rmse:0.987696+0.024134 test-rmse:1.355916+0.164916
## [196] train-rmse:0.986235+0.024216 test-rmse:1.354807+0.164001
## [197] train-rmse:0.984886+0.024369 test-rmse:1.355131+0.162340
## [198] train-rmse:0.983534+0.024392 test-rmse:1.355914+0.163393
## [199] train-rmse:0.978870+0.018361 test-rmse:1.355466+0.165552
## [200] train-rmse:0.977275+0.018173 test-rmse:1.353992+0.166301
## [201] train-rmse:0.975778+0.018276 test-rmse:1.353842+0.165621
## [202] train-rmse:0.974248+0.018165 test-rmse:1.351147+0.166265
## [203] train-rmse:0.972594+0.018053 test-rmse:1.351043+0.166853
## [204] train-rmse:0.971292+0.018257 test-rmse:1.351319+0.166739
## [205] train-rmse:0.967678+0.020156 test-rmse:1.348790+0.167571
## [206] train-rmse:0.966041+0.020127 test-rmse:1.347605+0.167822
## [207] train-rmse:0.961928+0.015705 test-rmse:1.344834+0.169413
## [208] train-rmse:0.959954+0.015967 test-rmse:1.344837+0.170448
## [209] train-rmse:0.958565+0.016323 test-rmse:1.343718+0.170554
## [210] train-rmse:0.957171+0.016449 test-rmse:1.342239+0.171164
## [211] train-rmse:0.955659+0.016705 test-rmse:1.342511+0.171188
## [212] train-rmse:0.954203+0.016617 test-rmse:1.342859+0.172071
## [213] train-rmse:0.952980+0.016659 test-rmse:1.342415+0.171899
## [214] train-rmse:0.950504+0.017716 test-rmse:1.340950+0.172107
## [215] train-rmse:0.949132+0.017616 test-rmse:1.339155+0.173051
## [216] train-rmse:0.947602+0.017425 test-rmse:1.339135+0.174578
## [217] train-rmse:0.946425+0.017533 test-rmse:1.338511+0.174735
## [218] train-rmse:0.945000+0.017380 test-rmse:1.337501+0.173970
## [219] train-rmse:0.943548+0.017251 test-rmse:1.337692+0.174154
## [220] train-rmse:0.940227+0.014341 test-rmse:1.336218+0.177149
## [221] train-rmse:0.938897+0.014370 test-rmse:1.335159+0.176770
## [222] train-rmse:0.937689+0.014488 test-rmse:1.335572+0.176748
## [223] train-rmse:0.935696+0.015366 test-rmse:1.335678+0.176608
## [224] train-rmse:0.934370+0.015156 test-rmse:1.335554+0.176811
## [225] train-rmse:0.933257+0.015094 test-rmse:1.335034+0.177110
## [226] train-rmse:0.931907+0.015097 test-rmse:1.334488+0.177509
## [227] train-rmse:0.929068+0.013561 test-rmse:1.332013+0.179590
## [228] train-rmse:0.927196+0.013998 test-rmse:1.332533+0.180940
## [229] train-rmse:0.926136+0.014047 test-rmse:1.330740+0.180257
## [230] train-rmse:0.924882+0.013956 test-rmse:1.330372+0.180471
## [231] train-rmse:0.921218+0.014982 test-rmse:1.324827+0.182638
## [232] train-rmse:0.920046+0.014947 test-rmse:1.324432+0.183445
## [233] train-rmse:0.919096+0.015062 test-rmse:1.325989+0.184038
## [234] train-rmse:0.917711+0.015046 test-rmse:1.325267+0.184227
## [235] train-rmse:0.916563+0.015039 test-rmse:1.325127+0.183975
## [236] train-rmse:0.915314+0.015200 test-rmse:1.323661+0.183431
## [237] train-rmse:0.913975+0.015646 test-rmse:1.323055+0.183955
## [238] train-rmse:0.912773+0.015583 test-rmse:1.323255+0.183960
## [239] train-rmse:0.910868+0.015659 test-rmse:1.322320+0.184684
## [240] train-rmse:0.909683+0.015546 test-rmse:1.322126+0.184719
## [241] train-rmse:0.908405+0.015674 test-rmse:1.321636+0.185017
## [242] train-rmse:0.907488+0.015691 test-rmse:1.320828+0.186154
## [243] train-rmse:0.906566+0.015657 test-rmse:1.320809+0.186263
## [244] train-rmse:0.905555+0.015779 test-rmse:1.321750+0.185761
## [245] train-rmse:0.904658+0.015814 test-rmse:1.321894+0.184560
## [246] train-rmse:0.900802+0.013658 test-rmse:1.316752+0.179187
## [247] train-rmse:0.899653+0.013612 test-rmse:1.316134+0.179259
## [248] train-rmse:0.895865+0.011489 test-rmse:1.315438+0.180128
## [249] train-rmse:0.894922+0.011519 test-rmse:1.315043+0.181424
## [250] train-rmse:0.893808+0.011525 test-rmse:1.314641+0.181785
## [251] train-rmse:0.892995+0.011553 test-rmse:1.313854+0.182152
## [252] train-rmse:0.891605+0.012487 test-rmse:1.313726+0.182410
## [253] train-rmse:0.889040+0.013216 test-rmse:1.308718+0.184852
## [254] train-rmse:0.887886+0.013106 test-rmse:1.308813+0.184973
## [255] train-rmse:0.886644+0.012974 test-rmse:1.308596+0.184947
## [256] train-rmse:0.885839+0.012969 test-rmse:1.308939+0.184711
## [257] train-rmse:0.884826+0.012897 test-rmse:1.308845+0.184771
## [258] train-rmse:0.883716+0.013185 test-rmse:1.309344+0.184576
## [259] train-rmse:0.882254+0.013890 test-rmse:1.309594+0.184659
## [260] train-rmse:0.881201+0.013946 test-rmse:1.308682+0.184630
## [261] train-rmse:0.880349+0.013905 test-rmse:1.308588+0.184659
## [262] train-rmse:0.879462+0.014009 test-rmse:1.307891+0.184105
## [263] train-rmse:0.878575+0.014143 test-rmse:1.306943+0.184022
## [264] train-rmse:0.877784+0.014080 test-rmse:1.307174+0.183807
## [265] train-rmse:0.876744+0.014023 test-rmse:1.307408+0.184430
## [266] train-rmse:0.875697+0.014217 test-rmse:1.306926+0.184338
## [267] train-rmse:0.871548+0.019005 test-rmse:1.305108+0.182575
## [268] train-rmse:0.870487+0.019381 test-rmse:1.305843+0.183889
## [269] train-rmse:0.869591+0.019477 test-rmse:1.305277+0.184046
## [270] train-rmse:0.868724+0.019469 test-rmse:1.305754+0.184542
## [271] train-rmse:0.867187+0.018799 test-rmse:1.304769+0.183700
## [272] train-rmse:0.866339+0.018765 test-rmse:1.305548+0.183171
## [273] train-rmse:0.865565+0.018848 test-rmse:1.304751+0.183445
## [274] train-rmse:0.864919+0.018937 test-rmse:1.304414+0.182623
## [275] train-rmse:0.864145+0.018899 test-rmse:1.304466+0.182500
## [276] train-rmse:0.863217+0.018590 test-rmse:1.303424+0.182480
## [277] train-rmse:0.862337+0.018980 test-rmse:1.304476+0.182891
## [278] train-rmse:0.860122+0.017961 test-rmse:1.304136+0.183264
## [279] train-rmse:0.858467+0.018605 test-rmse:1.302950+0.184286
## [280] train-rmse:0.856331+0.019163 test-rmse:1.298574+0.185879
## [281] train-rmse:0.855201+0.019311 test-rmse:1.298940+0.186216
## [282] train-rmse:0.854369+0.019450 test-rmse:1.298946+0.186368
## [283] train-rmse:0.853644+0.019462 test-rmse:1.298016+0.186587
## [284] train-rmse:0.853079+0.019532 test-rmse:1.297791+0.186319
## [285] train-rmse:0.851684+0.020375 test-rmse:1.297953+0.186664
## [286] train-rmse:0.851020+0.020344 test-rmse:1.299181+0.186470
## [287] train-rmse:0.849988+0.020947 test-rmse:1.298810+0.185742
## [288] train-rmse:0.849353+0.020933 test-rmse:1.298315+0.185315
## [289] train-rmse:0.848556+0.020800 test-rmse:1.297764+0.185857
## [290] train-rmse:0.847582+0.020645 test-rmse:1.298477+0.186320
## [291] train-rmse:0.846827+0.020529 test-rmse:1.298807+0.186373
## [292] train-rmse:0.845805+0.020664 test-rmse:1.299346+0.186311
## [293] train-rmse:0.844870+0.020693 test-rmse:1.299748+0.187095
## [294] train-rmse:0.844012+0.020804 test-rmse:1.299491+0.187147
## [295] train-rmse:0.842094+0.020130 test-rmse:1.298681+0.187115
## Stopping. Best iteration:
## [289] train-rmse:0.848556+0.020800 test-rmse:1.297764+0.185857
##
## 9
## [1] train-rmse:8.602038+0.063038 test-rmse:8.598392+0.269333
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.638928+0.054970 test-rmse:7.632945+0.260851
## [3] train-rmse:6.797607+0.047864 test-rmse:6.791304+0.252814
## [4] train-rmse:6.063359+0.042153 test-rmse:6.061784+0.243236
## [5] train-rmse:5.424092+0.037057 test-rmse:5.424835+0.233342
## [6] train-rmse:4.866962+0.033502 test-rmse:4.877109+0.222512
## [7] train-rmse:4.383603+0.029610 test-rmse:4.401899+0.201847
## [8] train-rmse:3.964169+0.026793 test-rmse:3.985774+0.189885
## [9] train-rmse:3.600896+0.024690 test-rmse:3.640523+0.180605
## [10] train-rmse:3.287172+0.021365 test-rmse:3.328875+0.163171
## [11] train-rmse:3.018218+0.020240 test-rmse:3.075841+0.150619
## [12] train-rmse:2.788148+0.018258 test-rmse:2.855221+0.132929
## [13] train-rmse:2.592052+0.016771 test-rmse:2.667986+0.119925
## [14] train-rmse:2.426551+0.016418 test-rmse:2.521083+0.114009
## [15] train-rmse:2.284662+0.015648 test-rmse:2.394612+0.102504
## [16] train-rmse:2.164839+0.013545 test-rmse:2.277553+0.096830
## [17] train-rmse:2.063256+0.014581 test-rmse:2.196047+0.088665
## [18] train-rmse:1.976327+0.013204 test-rmse:2.117674+0.081778
## [19] train-rmse:1.903386+0.012227 test-rmse:2.048476+0.075582
## [20] train-rmse:1.842334+0.012522 test-rmse:1.998090+0.074710
## [21] train-rmse:1.787912+0.012901 test-rmse:1.956050+0.072151
## [22] train-rmse:1.742584+0.013486 test-rmse:1.915275+0.071214
## [23] train-rmse:1.699969+0.010853 test-rmse:1.888341+0.069871
## [24] train-rmse:1.664604+0.011308 test-rmse:1.854534+0.069742
## [25] train-rmse:1.633540+0.012091 test-rmse:1.837158+0.067486
## [26] train-rmse:1.606159+0.012393 test-rmse:1.816219+0.064074
## [27] train-rmse:1.581172+0.013785 test-rmse:1.797731+0.066196
## [28] train-rmse:1.559484+0.015088 test-rmse:1.779746+0.067474
## [29] train-rmse:1.538488+0.017221 test-rmse:1.763355+0.067759
## [30] train-rmse:1.519858+0.018026 test-rmse:1.752915+0.067910
## [31] train-rmse:1.501629+0.017052 test-rmse:1.743224+0.070320
## [32] train-rmse:1.484191+0.017453 test-rmse:1.732403+0.075145
## [33] train-rmse:1.465937+0.018646 test-rmse:1.720823+0.072823
## [34] train-rmse:1.449253+0.021350 test-rmse:1.714049+0.074693
## [35] train-rmse:1.434387+0.021997 test-rmse:1.703640+0.075380
## [36] train-rmse:1.418793+0.020425 test-rmse:1.692535+0.076407
## [37] train-rmse:1.404701+0.021340 test-rmse:1.678602+0.072940
## [38] train-rmse:1.390935+0.022273 test-rmse:1.660960+0.067564
## [39] train-rmse:1.378173+0.022511 test-rmse:1.654562+0.070373
## [40] train-rmse:1.366515+0.023374 test-rmse:1.645102+0.069610
## [41] train-rmse:1.354185+0.024649 test-rmse:1.636400+0.071916
## [42] train-rmse:1.341953+0.025015 test-rmse:1.628040+0.072752
## [43] train-rmse:1.330823+0.025133 test-rmse:1.619594+0.073024
## [44] train-rmse:1.319187+0.025727 test-rmse:1.613517+0.073925
## [45] train-rmse:1.307765+0.026415 test-rmse:1.605895+0.072003
## [46] train-rmse:1.297472+0.026776 test-rmse:1.599565+0.074123
## [47] train-rmse:1.286099+0.027389 test-rmse:1.590260+0.078961
## [48] train-rmse:1.277050+0.027332 test-rmse:1.582729+0.077668
## [49] train-rmse:1.267573+0.027626 test-rmse:1.577371+0.080164
## [50] train-rmse:1.256048+0.026668 test-rmse:1.569875+0.080584
## [51] train-rmse:1.245291+0.027961 test-rmse:1.565962+0.082015
## [52] train-rmse:1.235575+0.028992 test-rmse:1.555600+0.081981
## [53] train-rmse:1.226796+0.029750 test-rmse:1.551246+0.083803
## [54] train-rmse:1.218887+0.029832 test-rmse:1.544650+0.085655
## [55] train-rmse:1.210095+0.029922 test-rmse:1.539323+0.083750
## [56] train-rmse:1.200965+0.031228 test-rmse:1.533123+0.085062
## [57] train-rmse:1.192397+0.031662 test-rmse:1.529262+0.087346
## [58] train-rmse:1.182504+0.032459 test-rmse:1.520670+0.086755
## [59] train-rmse:1.173941+0.033590 test-rmse:1.515272+0.088009
## [60] train-rmse:1.166606+0.033849 test-rmse:1.510555+0.086674
## [61] train-rmse:1.158798+0.034892 test-rmse:1.503448+0.084175
## [62] train-rmse:1.151245+0.035191 test-rmse:1.496603+0.085164
## [63] train-rmse:1.144361+0.035847 test-rmse:1.492973+0.084911
## [64] train-rmse:1.136485+0.034728 test-rmse:1.486881+0.088917
## [65] train-rmse:1.128747+0.034665 test-rmse:1.484321+0.091222
## [66] train-rmse:1.121407+0.035092 test-rmse:1.480818+0.090974
## [67] train-rmse:1.114502+0.034465 test-rmse:1.475870+0.090490
## [68] train-rmse:1.107097+0.035613 test-rmse:1.473067+0.090427
## [69] train-rmse:1.099475+0.034393 test-rmse:1.468430+0.095966
## [70] train-rmse:1.091718+0.034791 test-rmse:1.462360+0.099440
## [71] train-rmse:1.086167+0.034868 test-rmse:1.460504+0.099947
## [72] train-rmse:1.079154+0.034674 test-rmse:1.453590+0.098618
## [73] train-rmse:1.073672+0.034889 test-rmse:1.449694+0.099867
## [74] train-rmse:1.067009+0.034165 test-rmse:1.445441+0.105102
## [75] train-rmse:1.060367+0.035274 test-rmse:1.441440+0.106556
## [76] train-rmse:1.052969+0.035854 test-rmse:1.437609+0.107684
## [77] train-rmse:1.047023+0.036117 test-rmse:1.436550+0.109026
## [78] train-rmse:1.041050+0.036539 test-rmse:1.431491+0.109054
## [79] train-rmse:1.035029+0.034436 test-rmse:1.429139+0.112233
## [80] train-rmse:1.028805+0.033885 test-rmse:1.423501+0.115572
## [81] train-rmse:1.022846+0.033968 test-rmse:1.420754+0.115652
## [82] train-rmse:1.017152+0.033767 test-rmse:1.414899+0.117050
## [83] train-rmse:1.011887+0.033019 test-rmse:1.411901+0.116981
## [84] train-rmse:1.006000+0.034653 test-rmse:1.407694+0.118446
## [85] train-rmse:1.000946+0.034355 test-rmse:1.405132+0.119058
## [86] train-rmse:0.995939+0.033771 test-rmse:1.401547+0.120328
## [87] train-rmse:0.990628+0.033507 test-rmse:1.398695+0.121473
## [88] train-rmse:0.984538+0.031565 test-rmse:1.394899+0.122826
## [89] train-rmse:0.979547+0.031816 test-rmse:1.392326+0.123877
## [90] train-rmse:0.974206+0.032805 test-rmse:1.389018+0.123704
## [91] train-rmse:0.967045+0.031463 test-rmse:1.385890+0.124651
## [92] train-rmse:0.962294+0.031282 test-rmse:1.384798+0.125656
## [93] train-rmse:0.957226+0.031275 test-rmse:1.381209+0.127977
## [94] train-rmse:0.952617+0.029772 test-rmse:1.378235+0.129661
## [95] train-rmse:0.947292+0.030956 test-rmse:1.377005+0.129764
## [96] train-rmse:0.943726+0.030828 test-rmse:1.376412+0.130706
## [97] train-rmse:0.939658+0.031014 test-rmse:1.374050+0.130768
## [98] train-rmse:0.935439+0.031065 test-rmse:1.372859+0.130318
## [99] train-rmse:0.930171+0.030416 test-rmse:1.370681+0.130675
## [100] train-rmse:0.925103+0.031037 test-rmse:1.367349+0.132241
## [101] train-rmse:0.919592+0.031302 test-rmse:1.364128+0.136204
## [102] train-rmse:0.915089+0.030797 test-rmse:1.363978+0.136935
## [103] train-rmse:0.910754+0.030995 test-rmse:1.362764+0.138249
## [104] train-rmse:0.904418+0.027693 test-rmse:1.359878+0.140290
## [105] train-rmse:0.899371+0.028835 test-rmse:1.355737+0.140535
## [106] train-rmse:0.894691+0.029703 test-rmse:1.351545+0.139664
## [107] train-rmse:0.890175+0.030303 test-rmse:1.350592+0.139024
## [108] train-rmse:0.886409+0.031008 test-rmse:1.348863+0.139256
## [109] train-rmse:0.882373+0.031454 test-rmse:1.347098+0.139755
## [110] train-rmse:0.878890+0.031210 test-rmse:1.345296+0.142017
## [111] train-rmse:0.874944+0.031355 test-rmse:1.343628+0.141667
## [112] train-rmse:0.871720+0.031806 test-rmse:1.343337+0.140520
## [113] train-rmse:0.867030+0.030476 test-rmse:1.340435+0.141492
## [114] train-rmse:0.862172+0.028909 test-rmse:1.337087+0.141795
## [115] train-rmse:0.858337+0.029636 test-rmse:1.334957+0.142927
## [116] train-rmse:0.854921+0.029786 test-rmse:1.334404+0.144271
## [117] train-rmse:0.851520+0.028992 test-rmse:1.332533+0.144363
## [118] train-rmse:0.848735+0.029127 test-rmse:1.330838+0.144860
## [119] train-rmse:0.846011+0.028852 test-rmse:1.329725+0.145614
## [120] train-rmse:0.842245+0.028941 test-rmse:1.328814+0.146526
## [121] train-rmse:0.837611+0.030027 test-rmse:1.325457+0.145292
## [122] train-rmse:0.834528+0.029052 test-rmse:1.324554+0.146207
## [123] train-rmse:0.830955+0.028515 test-rmse:1.324577+0.145692
## [124] train-rmse:0.828271+0.028775 test-rmse:1.323375+0.144966
## [125] train-rmse:0.825384+0.028329 test-rmse:1.322062+0.144304
## [126] train-rmse:0.822203+0.028684 test-rmse:1.320459+0.142691
## [127] train-rmse:0.817028+0.027417 test-rmse:1.318352+0.143339
## [128] train-rmse:0.813849+0.027697 test-rmse:1.318293+0.145469
## [129] train-rmse:0.811043+0.028014 test-rmse:1.317619+0.145455
## [130] train-rmse:0.807658+0.028179 test-rmse:1.315819+0.144749
## [131] train-rmse:0.803602+0.027658 test-rmse:1.310866+0.147146
## [132] train-rmse:0.801038+0.027806 test-rmse:1.309310+0.146000
## [133] train-rmse:0.798651+0.028302 test-rmse:1.307739+0.146591
## [134] train-rmse:0.795823+0.028120 test-rmse:1.307199+0.147729
## [135] train-rmse:0.793480+0.028194 test-rmse:1.308347+0.147303
## [136] train-rmse:0.790929+0.028136 test-rmse:1.307632+0.147709
## [137] train-rmse:0.787667+0.027640 test-rmse:1.307193+0.146990
## [138] train-rmse:0.783258+0.024474 test-rmse:1.305975+0.148449
## [139] train-rmse:0.780488+0.025242 test-rmse:1.304661+0.149122
## [140] train-rmse:0.777818+0.025182 test-rmse:1.303410+0.149103
## [141] train-rmse:0.775445+0.025024 test-rmse:1.300608+0.148987
## [142] train-rmse:0.771360+0.025665 test-rmse:1.299322+0.150029
## [143] train-rmse:0.768995+0.025331 test-rmse:1.299701+0.150737
## [144] train-rmse:0.766043+0.025460 test-rmse:1.299025+0.149956
## [145] train-rmse:0.762320+0.026703 test-rmse:1.295380+0.148270
## [146] train-rmse:0.760485+0.026609 test-rmse:1.294615+0.148239
## [147] train-rmse:0.756598+0.028027 test-rmse:1.294271+0.148590
## [148] train-rmse:0.753573+0.026946 test-rmse:1.293375+0.149495
## [149] train-rmse:0.751277+0.026766 test-rmse:1.293533+0.149211
## [150] train-rmse:0.749158+0.026578 test-rmse:1.293292+0.148941
## [151] train-rmse:0.747339+0.026899 test-rmse:1.292358+0.149709
## [152] train-rmse:0.744610+0.027139 test-rmse:1.291144+0.150255
## [153] train-rmse:0.742394+0.027457 test-rmse:1.291327+0.149758
## [154] train-rmse:0.740351+0.027334 test-rmse:1.290834+0.150603
## [155] train-rmse:0.737519+0.028115 test-rmse:1.288049+0.149298
## [156] train-rmse:0.735548+0.027805 test-rmse:1.287212+0.150023
## [157] train-rmse:0.733694+0.027323 test-rmse:1.288299+0.152147
## [158] train-rmse:0.731941+0.027542 test-rmse:1.287884+0.151987
## [159] train-rmse:0.729893+0.027610 test-rmse:1.288282+0.150192
## [160] train-rmse:0.726532+0.027523 test-rmse:1.286593+0.149611
## [161] train-rmse:0.724488+0.027922 test-rmse:1.285683+0.150155
## [162] train-rmse:0.722753+0.027782 test-rmse:1.285083+0.150161
## [163] train-rmse:0.719187+0.027155 test-rmse:1.283078+0.149940
## [164] train-rmse:0.717665+0.027160 test-rmse:1.282171+0.150057
## [165] train-rmse:0.716100+0.027235 test-rmse:1.282188+0.150946
## [166] train-rmse:0.714636+0.027025 test-rmse:1.282049+0.150923
## [167] train-rmse:0.712542+0.027222 test-rmse:1.283138+0.151613
## [168] train-rmse:0.710878+0.027348 test-rmse:1.283991+0.151551
## [169] train-rmse:0.708741+0.026290 test-rmse:1.283525+0.150810
## [170] train-rmse:0.706502+0.026219 test-rmse:1.283015+0.151077
## [171] train-rmse:0.704947+0.026466 test-rmse:1.281681+0.150655
## [172] train-rmse:0.703246+0.026576 test-rmse:1.282202+0.150109
## [173] train-rmse:0.701477+0.026383 test-rmse:1.283630+0.150072
## [174] train-rmse:0.699476+0.026132 test-rmse:1.282649+0.150749
## [175] train-rmse:0.697777+0.026163 test-rmse:1.282845+0.150789
## [176] train-rmse:0.695458+0.026522 test-rmse:1.281632+0.149815
## [177] train-rmse:0.694129+0.026457 test-rmse:1.282313+0.150664
## [178] train-rmse:0.692704+0.026237 test-rmse:1.282017+0.150626
## [179] train-rmse:0.690474+0.027575 test-rmse:1.281669+0.150267
## [180] train-rmse:0.689254+0.027627 test-rmse:1.281259+0.149105
## [181] train-rmse:0.687744+0.027651 test-rmse:1.281186+0.149337
## [182] train-rmse:0.685022+0.026601 test-rmse:1.280965+0.149143
## [183] train-rmse:0.683672+0.026804 test-rmse:1.281045+0.150565
## [184] train-rmse:0.681846+0.026577 test-rmse:1.281447+0.149630
## [185] train-rmse:0.679478+0.026694 test-rmse:1.279412+0.149919
## [186] train-rmse:0.676700+0.024425 test-rmse:1.279036+0.150438
## [187] train-rmse:0.675190+0.024365 test-rmse:1.279034+0.151180
## [188] train-rmse:0.673818+0.024779 test-rmse:1.278782+0.151912
## [189] train-rmse:0.672482+0.024776 test-rmse:1.278892+0.152162
## [190] train-rmse:0.669944+0.024992 test-rmse:1.277366+0.151848
## [191] train-rmse:0.667997+0.025323 test-rmse:1.277108+0.151586
## [192] train-rmse:0.666347+0.024855 test-rmse:1.276937+0.151724
## [193] train-rmse:0.665171+0.024667 test-rmse:1.276871+0.152079
## [194] train-rmse:0.663290+0.024421 test-rmse:1.276310+0.152437
## [195] train-rmse:0.661145+0.024661 test-rmse:1.274527+0.151239
## [196] train-rmse:0.659970+0.024704 test-rmse:1.273897+0.151153
## [197] train-rmse:0.659010+0.024613 test-rmse:1.273374+0.151447
## [198] train-rmse:0.656672+0.023939 test-rmse:1.271891+0.150940
## [199] train-rmse:0.654224+0.025589 test-rmse:1.272521+0.150300
## [200] train-rmse:0.652831+0.025503 test-rmse:1.272252+0.150070
## [201] train-rmse:0.651200+0.025745 test-rmse:1.273822+0.150670
## [202] train-rmse:0.650003+0.025776 test-rmse:1.274141+0.150820
## [203] train-rmse:0.648660+0.025971 test-rmse:1.274220+0.151083
## [204] train-rmse:0.647157+0.026345 test-rmse:1.273363+0.150042
## Stopping. Best iteration:
## [198] train-rmse:0.656672+0.023939 test-rmse:1.271891+0.150940
##
## 10
## [1] train-rmse:8.599779+0.063296 test-rmse:8.595917+0.270248
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.634780+0.056010 test-rmse:7.632757+0.260612
## [3] train-rmse:6.790282+0.049380 test-rmse:6.794255+0.250304
## [4] train-rmse:6.051739+0.043207 test-rmse:6.056684+0.243030
## [5] train-rmse:5.407077+0.037773 test-rmse:5.412808+0.229712
## [6] train-rmse:4.844985+0.033884 test-rmse:4.851709+0.214091
## [7] train-rmse:4.353955+0.029553 test-rmse:4.366780+0.198254
## [8] train-rmse:3.926916+0.025198 test-rmse:3.943761+0.184198
## [9] train-rmse:3.557206+0.021737 test-rmse:3.581915+0.178976
## [10] train-rmse:3.234822+0.016710 test-rmse:3.277683+0.170330
## [11] train-rmse:2.957219+0.015395 test-rmse:3.004378+0.150801
## [12] train-rmse:2.716783+0.013267 test-rmse:2.779840+0.139679
## [13] train-rmse:2.511181+0.012144 test-rmse:2.592742+0.127930
## [14] train-rmse:2.331681+0.011262 test-rmse:2.419291+0.113733
## [15] train-rmse:2.182248+0.011274 test-rmse:2.296311+0.108459
## [16] train-rmse:2.049637+0.009764 test-rmse:2.175168+0.098132
## [17] train-rmse:1.937822+0.011320 test-rmse:2.081710+0.091378
## [18] train-rmse:1.843288+0.011266 test-rmse:2.009150+0.087920
## [19] train-rmse:1.760281+0.013285 test-rmse:1.941480+0.078440
## [20] train-rmse:1.687311+0.015126 test-rmse:1.884844+0.078158
## [21] train-rmse:1.623367+0.017249 test-rmse:1.838693+0.072546
## [22] train-rmse:1.571348+0.017570 test-rmse:1.793063+0.073784
## [23] train-rmse:1.524880+0.017536 test-rmse:1.755986+0.072016
## [24] train-rmse:1.484512+0.016991 test-rmse:1.729674+0.069982
## [25] train-rmse:1.447568+0.018705 test-rmse:1.704223+0.067854
## [26] train-rmse:1.414924+0.016918 test-rmse:1.688964+0.070289
## [27] train-rmse:1.385078+0.017782 test-rmse:1.667487+0.071009
## [28] train-rmse:1.357993+0.019030 test-rmse:1.642010+0.075684
## [29] train-rmse:1.332832+0.020545 test-rmse:1.630203+0.075408
## [30] train-rmse:1.310916+0.019897 test-rmse:1.615856+0.083245
## [31] train-rmse:1.290154+0.019070 test-rmse:1.601561+0.082595
## [32] train-rmse:1.270377+0.019823 test-rmse:1.586734+0.085004
## [33] train-rmse:1.249520+0.020755 test-rmse:1.575241+0.090223
## [34] train-rmse:1.233618+0.019754 test-rmse:1.563585+0.088987
## [35] train-rmse:1.217844+0.019462 test-rmse:1.551669+0.091491
## [36] train-rmse:1.201071+0.020960 test-rmse:1.546821+0.097752
## [37] train-rmse:1.186570+0.020777 test-rmse:1.539623+0.097766
## [38] train-rmse:1.172131+0.020030 test-rmse:1.527964+0.104177
## [39] train-rmse:1.158978+0.020721 test-rmse:1.519934+0.107804
## [40] train-rmse:1.144815+0.020842 test-rmse:1.510955+0.110470
## [41] train-rmse:1.130779+0.020979 test-rmse:1.505450+0.108591
## [42] train-rmse:1.117904+0.021654 test-rmse:1.493792+0.108635
## [43] train-rmse:1.105216+0.022937 test-rmse:1.487385+0.113029
## [44] train-rmse:1.093327+0.023734 test-rmse:1.481253+0.116169
## [45] train-rmse:1.080766+0.024695 test-rmse:1.474643+0.119509
## [46] train-rmse:1.069289+0.022773 test-rmse:1.465272+0.121692
## [47] train-rmse:1.057697+0.023277 test-rmse:1.461505+0.121124
## [48] train-rmse:1.046387+0.025002 test-rmse:1.455310+0.123913
## [49] train-rmse:1.036394+0.024858 test-rmse:1.453585+0.125725
## [50] train-rmse:1.025369+0.022645 test-rmse:1.447213+0.126903
## [51] train-rmse:1.013990+0.023158 test-rmse:1.439926+0.132082
## [52] train-rmse:1.004504+0.022986 test-rmse:1.435100+0.134591
## [53] train-rmse:0.994861+0.022315 test-rmse:1.429977+0.133975
## [54] train-rmse:0.984242+0.022334 test-rmse:1.424890+0.137667
## [55] train-rmse:0.976329+0.021865 test-rmse:1.419268+0.141685
## [56] train-rmse:0.967569+0.021314 test-rmse:1.413723+0.139956
## [57] train-rmse:0.959317+0.021276 test-rmse:1.410952+0.140813
## [58] train-rmse:0.949979+0.022181 test-rmse:1.406253+0.141000
## [59] train-rmse:0.941962+0.022541 test-rmse:1.402803+0.143517
## [60] train-rmse:0.934150+0.021399 test-rmse:1.399659+0.144643
## [61] train-rmse:0.925079+0.020841 test-rmse:1.393792+0.148117
## [62] train-rmse:0.917297+0.020649 test-rmse:1.387282+0.153205
## [63] train-rmse:0.909453+0.020457 test-rmse:1.383006+0.152814
## [64] train-rmse:0.900887+0.021290 test-rmse:1.379487+0.153399
## [65] train-rmse:0.892196+0.018529 test-rmse:1.376356+0.154646
## [66] train-rmse:0.884146+0.019051 test-rmse:1.372422+0.155535
## [67] train-rmse:0.876863+0.019472 test-rmse:1.368584+0.158119
## [68] train-rmse:0.869395+0.018970 test-rmse:1.364951+0.158396
## [69] train-rmse:0.861145+0.019945 test-rmse:1.362111+0.159813
## [70] train-rmse:0.853960+0.018473 test-rmse:1.357710+0.160398
## [71] train-rmse:0.846678+0.018413 test-rmse:1.358395+0.162952
## [72] train-rmse:0.841466+0.018427 test-rmse:1.354786+0.162666
## [73] train-rmse:0.835211+0.018936 test-rmse:1.351272+0.162582
## [74] train-rmse:0.827716+0.018978 test-rmse:1.349488+0.162784
## [75] train-rmse:0.822531+0.018539 test-rmse:1.346769+0.163953
## [76] train-rmse:0.815491+0.019656 test-rmse:1.342723+0.165683
## [77] train-rmse:0.809914+0.019675 test-rmse:1.339943+0.164299
## [78] train-rmse:0.804387+0.019598 test-rmse:1.338968+0.166209
## [79] train-rmse:0.797575+0.020012 test-rmse:1.337598+0.166619
## [80] train-rmse:0.791666+0.019167 test-rmse:1.336688+0.170990
## [81] train-rmse:0.787149+0.019065 test-rmse:1.333245+0.171702
## [82] train-rmse:0.781527+0.019591 test-rmse:1.329855+0.171848
## [83] train-rmse:0.776546+0.019854 test-rmse:1.327887+0.173096
## [84] train-rmse:0.770131+0.020385 test-rmse:1.325202+0.174477
## [85] train-rmse:0.764723+0.021577 test-rmse:1.322983+0.173292
## [86] train-rmse:0.759439+0.021924 test-rmse:1.322506+0.174239
## [87] train-rmse:0.754714+0.021871 test-rmse:1.321889+0.175159
## [88] train-rmse:0.749871+0.021216 test-rmse:1.319741+0.173491
## [89] train-rmse:0.744701+0.021732 test-rmse:1.316645+0.173345
## [90] train-rmse:0.740511+0.021810 test-rmse:1.316040+0.174208
## [91] train-rmse:0.735343+0.022302 test-rmse:1.313182+0.174002
## [92] train-rmse:0.730793+0.020784 test-rmse:1.311803+0.175082
## [93] train-rmse:0.726336+0.021011 test-rmse:1.311318+0.175146
## [94] train-rmse:0.720410+0.021323 test-rmse:1.309979+0.174490
## [95] train-rmse:0.714425+0.021318 test-rmse:1.307557+0.175716
## [96] train-rmse:0.710733+0.020835 test-rmse:1.306766+0.176052
## [97] train-rmse:0.705133+0.020599 test-rmse:1.304973+0.176929
## [98] train-rmse:0.701159+0.020052 test-rmse:1.304891+0.177746
## [99] train-rmse:0.696164+0.020505 test-rmse:1.303278+0.177528
## [100] train-rmse:0.691963+0.019913 test-rmse:1.299632+0.177754
## [101] train-rmse:0.688519+0.020260 test-rmse:1.300368+0.178226
## [102] train-rmse:0.684823+0.020268 test-rmse:1.298370+0.177997
## [103] train-rmse:0.680698+0.019996 test-rmse:1.296502+0.177913
## [104] train-rmse:0.676888+0.020045 test-rmse:1.296612+0.178974
## [105] train-rmse:0.672653+0.020827 test-rmse:1.294605+0.180257
## [106] train-rmse:0.668530+0.021430 test-rmse:1.292869+0.179473
## [107] train-rmse:0.665099+0.020708 test-rmse:1.289947+0.181057
## [108] train-rmse:0.661784+0.020968 test-rmse:1.289866+0.181698
## [109] train-rmse:0.657826+0.021912 test-rmse:1.288462+0.180151
## [110] train-rmse:0.653447+0.021365 test-rmse:1.287473+0.180295
## [111] train-rmse:0.650397+0.021481 test-rmse:1.287416+0.181099
## [112] train-rmse:0.646535+0.020804 test-rmse:1.286474+0.181905
## [113] train-rmse:0.643080+0.021583 test-rmse:1.284999+0.181770
## [114] train-rmse:0.639087+0.021563 test-rmse:1.283614+0.181912
## [115] train-rmse:0.636429+0.021005 test-rmse:1.282986+0.182609
## [116] train-rmse:0.633403+0.020533 test-rmse:1.282308+0.183244
## [117] train-rmse:0.630332+0.021352 test-rmse:1.283087+0.184526
## [118] train-rmse:0.626541+0.021966 test-rmse:1.281900+0.183647
## [119] train-rmse:0.622956+0.022385 test-rmse:1.282654+0.183876
## [120] train-rmse:0.619720+0.022047 test-rmse:1.283228+0.184478
## [121] train-rmse:0.617196+0.022071 test-rmse:1.283817+0.184436
## [122] train-rmse:0.614354+0.021802 test-rmse:1.281317+0.185753
## [123] train-rmse:0.611352+0.022436 test-rmse:1.279711+0.185352
## [124] train-rmse:0.608342+0.021691 test-rmse:1.278312+0.186960
## [125] train-rmse:0.604709+0.021543 test-rmse:1.274677+0.187013
## [126] train-rmse:0.602149+0.021263 test-rmse:1.274694+0.186993
## [127] train-rmse:0.598848+0.020481 test-rmse:1.274889+0.186692
## [128] train-rmse:0.596446+0.020208 test-rmse:1.274481+0.188097
## [129] train-rmse:0.592827+0.020263 test-rmse:1.272656+0.187985
## [130] train-rmse:0.589915+0.020262 test-rmse:1.274839+0.188131
## [131] train-rmse:0.586492+0.019912 test-rmse:1.274197+0.188945
## [132] train-rmse:0.584370+0.019723 test-rmse:1.272823+0.189175
## [133] train-rmse:0.582287+0.020168 test-rmse:1.272967+0.189260
## [134] train-rmse:0.580109+0.020130 test-rmse:1.273097+0.188970
## [135] train-rmse:0.577781+0.020442 test-rmse:1.271392+0.188547
## [136] train-rmse:0.575136+0.021198 test-rmse:1.268909+0.186390
## [137] train-rmse:0.571814+0.021607 test-rmse:1.268051+0.185518
## [138] train-rmse:0.569882+0.021335 test-rmse:1.268762+0.186271
## [139] train-rmse:0.567992+0.021188 test-rmse:1.269297+0.188362
## [140] train-rmse:0.565286+0.021396 test-rmse:1.267871+0.188267
## [141] train-rmse:0.562258+0.021283 test-rmse:1.267181+0.188161
## [142] train-rmse:0.560045+0.021362 test-rmse:1.267155+0.188819
## [143] train-rmse:0.557053+0.021406 test-rmse:1.267639+0.190176
## [144] train-rmse:0.554372+0.021257 test-rmse:1.267711+0.189615
## [145] train-rmse:0.552349+0.022044 test-rmse:1.267664+0.189663
## [146] train-rmse:0.549859+0.022166 test-rmse:1.265358+0.189657
## [147] train-rmse:0.547734+0.022240 test-rmse:1.264633+0.189162
## [148] train-rmse:0.545853+0.022132 test-rmse:1.265938+0.190796
## [149] train-rmse:0.544091+0.022168 test-rmse:1.265489+0.190632
## [150] train-rmse:0.542249+0.022687 test-rmse:1.265918+0.190684
## [151] train-rmse:0.538710+0.022543 test-rmse:1.263143+0.188675
## [152] train-rmse:0.536725+0.022040 test-rmse:1.262780+0.189171
## [153] train-rmse:0.534447+0.022673 test-rmse:1.262198+0.188672
## [154] train-rmse:0.531514+0.022520 test-rmse:1.263106+0.186295
## [155] train-rmse:0.529698+0.022116 test-rmse:1.264127+0.187144
## [156] train-rmse:0.527036+0.021598 test-rmse:1.264211+0.187418
## [157] train-rmse:0.524861+0.021494 test-rmse:1.263567+0.187226
## [158] train-rmse:0.523334+0.021488 test-rmse:1.263680+0.186858
## [159] train-rmse:0.521341+0.021817 test-rmse:1.263249+0.185352
## Stopping. Best iteration:
## [153] train-rmse:0.534447+0.022673 test-rmse:1.262198+0.188672
##
## 11
## [1] train-rmse:8.597928+0.063487 test-rmse:8.596086+0.270479
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.630484+0.056269 test-rmse:7.634652+0.255911
## [3] train-rmse:6.783073+0.049842 test-rmse:6.790280+0.249988
## [4] train-rmse:6.041616+0.044428 test-rmse:6.051096+0.238762
## [5] train-rmse:5.393189+0.039341 test-rmse:5.408186+0.220414
## [6] train-rmse:4.828324+0.035163 test-rmse:4.843369+0.205496
## [7] train-rmse:4.331585+0.031334 test-rmse:4.352436+0.191724
## [8] train-rmse:3.898417+0.028031 test-rmse:3.924614+0.170213
## [9] train-rmse:3.521510+0.024843 test-rmse:3.561430+0.160333
## [10] train-rmse:3.192155+0.021502 test-rmse:3.244208+0.141725
## [11] train-rmse:2.906508+0.018013 test-rmse:2.967277+0.136053
## [12] train-rmse:2.658924+0.017814 test-rmse:2.735506+0.118731
## [13] train-rmse:2.445923+0.015565 test-rmse:2.550449+0.117140
## [14] train-rmse:2.260320+0.015020 test-rmse:2.378616+0.114734
## [15] train-rmse:2.101847+0.016864 test-rmse:2.235878+0.096956
## [16] train-rmse:1.961547+0.013783 test-rmse:2.111174+0.094648
## [17] train-rmse:1.839642+0.014036 test-rmse:2.013723+0.090603
## [18] train-rmse:1.738296+0.014948 test-rmse:1.930241+0.086958
## [19] train-rmse:1.647412+0.014404 test-rmse:1.858539+0.085062
## [20] train-rmse:1.571449+0.013499 test-rmse:1.800997+0.079213
## [21] train-rmse:1.502253+0.015845 test-rmse:1.751944+0.073366
## [22] train-rmse:1.444658+0.016228 test-rmse:1.707123+0.069245
## [23] train-rmse:1.388751+0.020495 test-rmse:1.658491+0.071634
## [24] train-rmse:1.343752+0.020940 test-rmse:1.627157+0.072696
## [25] train-rmse:1.301431+0.021042 test-rmse:1.604395+0.066465
## [26] train-rmse:1.265239+0.021970 test-rmse:1.580557+0.077842
## [27] train-rmse:1.231468+0.021852 test-rmse:1.561871+0.079832
## [28] train-rmse:1.200839+0.021658 test-rmse:1.543597+0.080690
## [29] train-rmse:1.171483+0.021482 test-rmse:1.521509+0.081260
## [30] train-rmse:1.144831+0.023370 test-rmse:1.504227+0.084714
## [31] train-rmse:1.120095+0.024222 test-rmse:1.497167+0.082442
## [32] train-rmse:1.097461+0.024809 test-rmse:1.482050+0.089009
## [33] train-rmse:1.077682+0.023340 test-rmse:1.471011+0.093645
## [34] train-rmse:1.056171+0.022803 test-rmse:1.455813+0.099114
## [35] train-rmse:1.038940+0.022371 test-rmse:1.444525+0.098269
## [36] train-rmse:1.022225+0.021874 test-rmse:1.437153+0.099017
## [37] train-rmse:1.004451+0.021610 test-rmse:1.431231+0.101996
## [38] train-rmse:0.989636+0.021523 test-rmse:1.421940+0.104611
## [39] train-rmse:0.974530+0.021626 test-rmse:1.413521+0.104990
## [40] train-rmse:0.961434+0.022050 test-rmse:1.404791+0.106325
## [41] train-rmse:0.947349+0.022435 test-rmse:1.399933+0.108848
## [42] train-rmse:0.933441+0.021721 test-rmse:1.398314+0.112156
## [43] train-rmse:0.920750+0.022578 test-rmse:1.392278+0.113858
## [44] train-rmse:0.909593+0.021970 test-rmse:1.385985+0.118479
## [45] train-rmse:0.898884+0.021545 test-rmse:1.382001+0.116692
## [46] train-rmse:0.885554+0.021567 test-rmse:1.374576+0.116249
## [47] train-rmse:0.873574+0.020897 test-rmse:1.369933+0.119365
## [48] train-rmse:0.862144+0.021298 test-rmse:1.365174+0.120564
## [49] train-rmse:0.849929+0.022207 test-rmse:1.361871+0.126968
## [50] train-rmse:0.839650+0.020578 test-rmse:1.356081+0.128551
## [51] train-rmse:0.830982+0.020262 test-rmse:1.353116+0.129334
## [52] train-rmse:0.820646+0.021157 test-rmse:1.349626+0.128475
## [53] train-rmse:0.810484+0.021671 test-rmse:1.348051+0.128367
## [54] train-rmse:0.799846+0.021323 test-rmse:1.346618+0.131345
## [55] train-rmse:0.791045+0.021132 test-rmse:1.343601+0.132562
## [56] train-rmse:0.781906+0.020855 test-rmse:1.340835+0.134361
## [57] train-rmse:0.771496+0.019754 test-rmse:1.333062+0.135718
## [58] train-rmse:0.764196+0.018573 test-rmse:1.326842+0.140139
## [59] train-rmse:0.756667+0.018683 test-rmse:1.324863+0.142775
## [60] train-rmse:0.749289+0.019043 test-rmse:1.321937+0.144229
## [61] train-rmse:0.741863+0.018508 test-rmse:1.317597+0.146414
## [62] train-rmse:0.732884+0.018616 test-rmse:1.312337+0.146144
## [63] train-rmse:0.724718+0.018449 test-rmse:1.311165+0.149694
## [64] train-rmse:0.717715+0.017921 test-rmse:1.307300+0.150962
## [65] train-rmse:0.710428+0.019300 test-rmse:1.300349+0.148393
## [66] train-rmse:0.703065+0.019496 test-rmse:1.299211+0.151208
## [67] train-rmse:0.696937+0.019814 test-rmse:1.300763+0.150680
## [68] train-rmse:0.690842+0.019969 test-rmse:1.298538+0.152814
## [69] train-rmse:0.685172+0.020040 test-rmse:1.296456+0.153111
## [70] train-rmse:0.679129+0.019761 test-rmse:1.292321+0.152952
## [71] train-rmse:0.672346+0.019230 test-rmse:1.289207+0.153075
## [72] train-rmse:0.666154+0.019478 test-rmse:1.288955+0.152478
## [73] train-rmse:0.658685+0.019722 test-rmse:1.289012+0.153512
## [74] train-rmse:0.653924+0.018973 test-rmse:1.285977+0.154816
## [75] train-rmse:0.648359+0.020173 test-rmse:1.284303+0.154400
## [76] train-rmse:0.642798+0.020431 test-rmse:1.282060+0.155593
## [77] train-rmse:0.636881+0.020084 test-rmse:1.278838+0.156676
## [78] train-rmse:0.631165+0.019256 test-rmse:1.279965+0.156984
## [79] train-rmse:0.625256+0.019774 test-rmse:1.276483+0.157988
## [80] train-rmse:0.620340+0.019424 test-rmse:1.276920+0.158360
## [81] train-rmse:0.615555+0.018973 test-rmse:1.275369+0.159863
## [82] train-rmse:0.611351+0.019259 test-rmse:1.273998+0.159837
## [83] train-rmse:0.606428+0.019212 test-rmse:1.272021+0.159584
## [84] train-rmse:0.601478+0.019222 test-rmse:1.273147+0.159476
## [85] train-rmse:0.596249+0.019684 test-rmse:1.272600+0.160061
## [86] train-rmse:0.592144+0.019506 test-rmse:1.272312+0.159006
## [87] train-rmse:0.587592+0.019020 test-rmse:1.271062+0.160077
## [88] train-rmse:0.583340+0.018360 test-rmse:1.268661+0.161686
## [89] train-rmse:0.578199+0.018683 test-rmse:1.266004+0.163204
## [90] train-rmse:0.573976+0.019214 test-rmse:1.263885+0.165044
## [91] train-rmse:0.569473+0.018119 test-rmse:1.262912+0.166036
## [92] train-rmse:0.565584+0.017909 test-rmse:1.265118+0.166903
## [93] train-rmse:0.562418+0.017653 test-rmse:1.264855+0.167697
## [94] train-rmse:0.558424+0.018355 test-rmse:1.261651+0.168728
## [95] train-rmse:0.554241+0.018716 test-rmse:1.260812+0.168092
## [96] train-rmse:0.550118+0.018693 test-rmse:1.260075+0.168633
## [97] train-rmse:0.547153+0.018519 test-rmse:1.259183+0.169092
## [98] train-rmse:0.543038+0.018441 test-rmse:1.257640+0.169457
## [99] train-rmse:0.538676+0.018979 test-rmse:1.256447+0.168964
## [100] train-rmse:0.534708+0.018221 test-rmse:1.256282+0.169338
## [101] train-rmse:0.531033+0.017874 test-rmse:1.257299+0.168053
## [102] train-rmse:0.527637+0.018286 test-rmse:1.256659+0.168121
## [103] train-rmse:0.524631+0.018347 test-rmse:1.257667+0.170125
## [104] train-rmse:0.521745+0.017685 test-rmse:1.256684+0.170654
## [105] train-rmse:0.518452+0.018482 test-rmse:1.256129+0.170119
## [106] train-rmse:0.515304+0.018841 test-rmse:1.254387+0.170076
## [107] train-rmse:0.511210+0.019153 test-rmse:1.253863+0.169907
## [108] train-rmse:0.508014+0.019254 test-rmse:1.252861+0.169554
## [109] train-rmse:0.503774+0.018381 test-rmse:1.253104+0.171140
## [110] train-rmse:0.500438+0.018691 test-rmse:1.252360+0.172998
## [111] train-rmse:0.498070+0.018341 test-rmse:1.253161+0.172278
## [112] train-rmse:0.495079+0.018196 test-rmse:1.252622+0.172509
## [113] train-rmse:0.491388+0.018887 test-rmse:1.252569+0.173077
## [114] train-rmse:0.488397+0.018500 test-rmse:1.252293+0.174877
## [115] train-rmse:0.484270+0.018567 test-rmse:1.251475+0.174815
## [116] train-rmse:0.481559+0.019126 test-rmse:1.251890+0.174735
## [117] train-rmse:0.478150+0.018606 test-rmse:1.250772+0.175286
## [118] train-rmse:0.475358+0.018107 test-rmse:1.251216+0.175468
## [119] train-rmse:0.473050+0.018121 test-rmse:1.252135+0.176998
## [120] train-rmse:0.470806+0.017620 test-rmse:1.252136+0.177729
## [121] train-rmse:0.467668+0.017413 test-rmse:1.252421+0.177075
## [122] train-rmse:0.465246+0.017248 test-rmse:1.251304+0.176844
## [123] train-rmse:0.463299+0.017431 test-rmse:1.250721+0.177154
## [124] train-rmse:0.459872+0.016962 test-rmse:1.249427+0.177944
## [125] train-rmse:0.456532+0.016808 test-rmse:1.247848+0.176920
## [126] train-rmse:0.452374+0.016365 test-rmse:1.246167+0.176586
## [127] train-rmse:0.450121+0.015364 test-rmse:1.246641+0.176211
## [128] train-rmse:0.447024+0.014522 test-rmse:1.246855+0.175651
## [129] train-rmse:0.445384+0.015076 test-rmse:1.245940+0.175461
## [130] train-rmse:0.442567+0.014217 test-rmse:1.247004+0.175021
## [131] train-rmse:0.439519+0.013523 test-rmse:1.247565+0.174640
## [132] train-rmse:0.436973+0.012939 test-rmse:1.246794+0.173909
## [133] train-rmse:0.433606+0.012755 test-rmse:1.246469+0.173425
## [134] train-rmse:0.430682+0.012040 test-rmse:1.246384+0.173604
## [135] train-rmse:0.428731+0.011484 test-rmse:1.246334+0.175068
## Stopping. Best iteration:
## [129] train-rmse:0.445384+0.015076 test-rmse:1.245940+0.175461
##
## 12
## [1] train-rmse:8.597563+0.063580 test-rmse:8.596548+0.266574
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.629038+0.056336 test-rmse:7.634638+0.256595
## [3] train-rmse:6.779605+0.050272 test-rmse:6.793583+0.243870
## [4] train-rmse:6.035081+0.044259 test-rmse:6.046747+0.230798
## [5] train-rmse:5.382682+0.039354 test-rmse:5.399235+0.214991
## [6] train-rmse:4.812152+0.034632 test-rmse:4.835240+0.194444
## [7] train-rmse:4.312985+0.030448 test-rmse:4.337753+0.184257
## [8] train-rmse:3.876792+0.027630 test-rmse:3.911385+0.170607
## [9] train-rmse:3.492692+0.024660 test-rmse:3.535052+0.154773
## [10] train-rmse:3.158581+0.021955 test-rmse:3.207781+0.143279
## [11] train-rmse:2.867908+0.018784 test-rmse:2.939927+0.138909
## [12] train-rmse:2.615902+0.015168 test-rmse:2.713372+0.134971
## [13] train-rmse:2.397146+0.014557 test-rmse:2.510076+0.127629
## [14] train-rmse:2.207406+0.014271 test-rmse:2.340386+0.122811
## [15] train-rmse:2.038252+0.013560 test-rmse:2.193979+0.114881
## [16] train-rmse:1.891882+0.011787 test-rmse:2.069323+0.106639
## [17] train-rmse:1.762297+0.014110 test-rmse:1.962547+0.096321
## [18] train-rmse:1.654061+0.012540 test-rmse:1.874255+0.092439
## [19] train-rmse:1.555523+0.014218 test-rmse:1.798393+0.083880
## [20] train-rmse:1.470764+0.014922 test-rmse:1.735292+0.080408
## [21] train-rmse:1.399382+0.015384 test-rmse:1.679112+0.084830
## [22] train-rmse:1.335576+0.016326 test-rmse:1.634074+0.078346
## [23] train-rmse:1.277527+0.016704 test-rmse:1.592956+0.081787
## [24] train-rmse:1.228059+0.018685 test-rmse:1.561278+0.079234
## [25] train-rmse:1.184356+0.020642 test-rmse:1.533181+0.081314
## [26] train-rmse:1.142828+0.020003 test-rmse:1.503904+0.077928
## [27] train-rmse:1.109900+0.020068 test-rmse:1.480043+0.085872
## [28] train-rmse:1.076726+0.020599 test-rmse:1.461120+0.086127
## [29] train-rmse:1.047814+0.020682 test-rmse:1.443730+0.091237
## [30] train-rmse:1.020055+0.016923 test-rmse:1.433091+0.096320
## [31] train-rmse:0.992986+0.019313 test-rmse:1.416245+0.097043
## [32] train-rmse:0.970530+0.018002 test-rmse:1.403074+0.099518
## [33] train-rmse:0.946180+0.021416 test-rmse:1.391938+0.102432
## [34] train-rmse:0.925859+0.019257 test-rmse:1.385358+0.106552
## [35] train-rmse:0.906707+0.018605 test-rmse:1.376856+0.106526
## [36] train-rmse:0.887217+0.019189 test-rmse:1.366545+0.110871
## [37] train-rmse:0.869434+0.020135 test-rmse:1.357369+0.113496
## [38] train-rmse:0.853439+0.018962 test-rmse:1.349885+0.116152
## [39] train-rmse:0.838189+0.019403 test-rmse:1.345880+0.119183
## [40] train-rmse:0.823249+0.019013 test-rmse:1.337773+0.117755
## [41] train-rmse:0.807705+0.016780 test-rmse:1.330408+0.123439
## [42] train-rmse:0.793829+0.019310 test-rmse:1.323680+0.124111
## [43] train-rmse:0.782384+0.019404 test-rmse:1.316383+0.124731
## [44] train-rmse:0.769129+0.018913 test-rmse:1.310070+0.128415
## [45] train-rmse:0.756209+0.019260 test-rmse:1.302433+0.126419
## [46] train-rmse:0.746055+0.019277 test-rmse:1.298905+0.128684
## [47] train-rmse:0.734790+0.020121 test-rmse:1.296326+0.130038
## [48] train-rmse:0.723986+0.017928 test-rmse:1.291737+0.131453
## [49] train-rmse:0.714965+0.017808 test-rmse:1.291626+0.132910
## [50] train-rmse:0.702312+0.018869 test-rmse:1.285911+0.133630
## [51] train-rmse:0.692613+0.018677 test-rmse:1.283260+0.134568
## [52] train-rmse:0.682387+0.019259 test-rmse:1.280422+0.137243
## [53] train-rmse:0.672981+0.021119 test-rmse:1.276597+0.137077
## [54] train-rmse:0.663412+0.021159 test-rmse:1.273223+0.139669
## [55] train-rmse:0.654104+0.019863 test-rmse:1.267090+0.141046
## [56] train-rmse:0.645259+0.019229 test-rmse:1.266388+0.140588
## [57] train-rmse:0.636316+0.019685 test-rmse:1.266850+0.143094
## [58] train-rmse:0.629292+0.020421 test-rmse:1.265773+0.142827
## [59] train-rmse:0.621834+0.020405 test-rmse:1.264118+0.144348
## [60] train-rmse:0.614520+0.019889 test-rmse:1.263346+0.145093
## [61] train-rmse:0.606122+0.019047 test-rmse:1.259258+0.145341
## [62] train-rmse:0.598401+0.018259 test-rmse:1.257856+0.147195
## [63] train-rmse:0.591525+0.017232 test-rmse:1.255167+0.145424
## [64] train-rmse:0.584242+0.018313 test-rmse:1.254029+0.146499
## [65] train-rmse:0.576440+0.017135 test-rmse:1.251371+0.147783
## [66] train-rmse:0.569379+0.018363 test-rmse:1.249429+0.148853
## [67] train-rmse:0.563198+0.017905 test-rmse:1.249017+0.149607
## [68] train-rmse:0.557063+0.017711 test-rmse:1.248239+0.150502
## [69] train-rmse:0.550910+0.017056 test-rmse:1.248269+0.151831
## [70] train-rmse:0.544182+0.016922 test-rmse:1.246766+0.153907
## [71] train-rmse:0.539090+0.016430 test-rmse:1.246597+0.154783
## [72] train-rmse:0.533776+0.016923 test-rmse:1.247064+0.156058
## [73] train-rmse:0.529147+0.016171 test-rmse:1.246587+0.157176
## [74] train-rmse:0.523855+0.016296 test-rmse:1.245897+0.158017
## [75] train-rmse:0.517239+0.017320 test-rmse:1.243937+0.156575
## [76] train-rmse:0.511743+0.018143 test-rmse:1.244405+0.156132
## [77] train-rmse:0.505814+0.018228 test-rmse:1.242744+0.156366
## [78] train-rmse:0.501038+0.017873 test-rmse:1.241266+0.156263
## [79] train-rmse:0.494308+0.016344 test-rmse:1.241031+0.157919
## [80] train-rmse:0.490607+0.015973 test-rmse:1.240104+0.157133
## [81] train-rmse:0.486254+0.015129 test-rmse:1.241456+0.158192
## [82] train-rmse:0.481520+0.014883 test-rmse:1.241148+0.159079
## [83] train-rmse:0.477430+0.014618 test-rmse:1.240998+0.160368
## [84] train-rmse:0.472131+0.013949 test-rmse:1.240500+0.160312
## [85] train-rmse:0.467778+0.013925 test-rmse:1.242175+0.159172
## [86] train-rmse:0.463707+0.013033 test-rmse:1.241619+0.160611
## Stopping. Best iteration:
## [80] train-rmse:0.490607+0.015973 test-rmse:1.240104+0.157133
##
## 13
## [1] train-rmse:8.597563+0.063580 test-rmse:8.596548+0.266574
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.628717+0.056478 test-rmse:7.635774+0.249370
## [3] train-rmse:6.778183+0.050495 test-rmse:6.791447+0.235434
## [4] train-rmse:6.032119+0.044869 test-rmse:6.041971+0.220321
## [5] train-rmse:5.377560+0.039929 test-rmse:5.395916+0.203687
## [6] train-rmse:4.804059+0.035711 test-rmse:4.825172+0.189196
## [7] train-rmse:4.302120+0.032945 test-rmse:4.328937+0.171354
## [8] train-rmse:3.861901+0.031035 test-rmse:3.898374+0.162019
## [9] train-rmse:3.473919+0.027170 test-rmse:3.517812+0.146915
## [10] train-rmse:3.137834+0.025039 test-rmse:3.194822+0.136385
## [11] train-rmse:2.842460+0.022931 test-rmse:2.911778+0.137199
## [12] train-rmse:2.584506+0.021677 test-rmse:2.671828+0.128889
## [13] train-rmse:2.359836+0.021057 test-rmse:2.475039+0.122723
## [14] train-rmse:2.161302+0.018495 test-rmse:2.293902+0.117755
## [15] train-rmse:1.986451+0.020700 test-rmse:2.138600+0.106371
## [16] train-rmse:1.835670+0.019105 test-rmse:2.018050+0.103680
## [17] train-rmse:1.702261+0.020293 test-rmse:1.905465+0.094652
## [18] train-rmse:1.585549+0.019426 test-rmse:1.808003+0.093648
## [19] train-rmse:1.482277+0.019197 test-rmse:1.730486+0.093518
## [20] train-rmse:1.393727+0.020243 test-rmse:1.665277+0.093010
## [21] train-rmse:1.314213+0.023989 test-rmse:1.607631+0.087748
## [22] train-rmse:1.246432+0.024304 test-rmse:1.564967+0.088136
## [23] train-rmse:1.186041+0.023749 test-rmse:1.525865+0.091742
## [24] train-rmse:1.132056+0.024587 test-rmse:1.491073+0.089291
## [25] train-rmse:1.083662+0.022811 test-rmse:1.464491+0.094008
## [26] train-rmse:1.040740+0.023039 test-rmse:1.437969+0.100479
## [27] train-rmse:1.003460+0.021928 test-rmse:1.418219+0.102794
## [28] train-rmse:0.966295+0.023065 test-rmse:1.396428+0.105612
## [29] train-rmse:0.936162+0.024024 test-rmse:1.380013+0.111082
## [30] train-rmse:0.905452+0.022148 test-rmse:1.364935+0.111726
## [31] train-rmse:0.879214+0.022014 test-rmse:1.353010+0.111476
## [32] train-rmse:0.853449+0.022084 test-rmse:1.342673+0.116062
## [33] train-rmse:0.830554+0.020353 test-rmse:1.332493+0.118834
## [34] train-rmse:0.810007+0.019896 test-rmse:1.324758+0.123033
## [35] train-rmse:0.792431+0.020190 test-rmse:1.317770+0.125377
## [36] train-rmse:0.772498+0.019282 test-rmse:1.311548+0.132635
## [37] train-rmse:0.754056+0.018790 test-rmse:1.302301+0.131216
## [38] train-rmse:0.735762+0.015188 test-rmse:1.295922+0.137038
## [39] train-rmse:0.720666+0.015867 test-rmse:1.289711+0.136149
## [40] train-rmse:0.704742+0.018469 test-rmse:1.285788+0.137614
## [41] train-rmse:0.692143+0.017126 test-rmse:1.283286+0.138538
## [42] train-rmse:0.680694+0.016559 test-rmse:1.278953+0.142665
## [43] train-rmse:0.666941+0.017203 test-rmse:1.272201+0.140868
## [44] train-rmse:0.655439+0.014570 test-rmse:1.268262+0.144408
## [45] train-rmse:0.643515+0.015513 test-rmse:1.263535+0.139543
## [46] train-rmse:0.628945+0.017509 test-rmse:1.261490+0.140328
## [47] train-rmse:0.619304+0.016493 test-rmse:1.260094+0.140421
## [48] train-rmse:0.607857+0.014392 test-rmse:1.257780+0.141874
## [49] train-rmse:0.597730+0.015005 test-rmse:1.255487+0.141430
## [50] train-rmse:0.587059+0.014763 test-rmse:1.253016+0.142049
## [51] train-rmse:0.576606+0.015414 test-rmse:1.251055+0.140476
## [52] train-rmse:0.568549+0.014919 test-rmse:1.249903+0.143571
## [53] train-rmse:0.559951+0.015076 test-rmse:1.248709+0.143479
## [54] train-rmse:0.550491+0.014688 test-rmse:1.247155+0.143840
## [55] train-rmse:0.544232+0.013880 test-rmse:1.245404+0.142931
## [56] train-rmse:0.536022+0.015040 test-rmse:1.242329+0.142897
## [57] train-rmse:0.526620+0.014702 test-rmse:1.240366+0.143250
## [58] train-rmse:0.519004+0.013858 test-rmse:1.238807+0.142734
## [59] train-rmse:0.511187+0.013957 test-rmse:1.236938+0.141485
## [60] train-rmse:0.504145+0.014249 test-rmse:1.236101+0.143675
## [61] train-rmse:0.497891+0.014042 test-rmse:1.236174+0.143278
## [62] train-rmse:0.489963+0.013020 test-rmse:1.235241+0.144679
## [63] train-rmse:0.483798+0.012071 test-rmse:1.234754+0.142570
## [64] train-rmse:0.476819+0.012492 test-rmse:1.234776+0.142442
## [65] train-rmse:0.471294+0.012113 test-rmse:1.235223+0.143091
## [66] train-rmse:0.465911+0.012636 test-rmse:1.233338+0.143099
## [67] train-rmse:0.460466+0.012042 test-rmse:1.232508+0.142468
## [68] train-rmse:0.454162+0.014302 test-rmse:1.229937+0.143261
## [69] train-rmse:0.448454+0.012591 test-rmse:1.228861+0.142633
## [70] train-rmse:0.443107+0.012596 test-rmse:1.227058+0.142444
## [71] train-rmse:0.436804+0.012853 test-rmse:1.225726+0.143936
## [72] train-rmse:0.430302+0.012875 test-rmse:1.226425+0.144304
## [73] train-rmse:0.426025+0.013182 test-rmse:1.226352+0.143742
## [74] train-rmse:0.419820+0.013088 test-rmse:1.225402+0.143338
## [75] train-rmse:0.415359+0.013483 test-rmse:1.224675+0.144318
## [76] train-rmse:0.411000+0.012770 test-rmse:1.223409+0.145609
## [77] train-rmse:0.407249+0.013282 test-rmse:1.223424+0.146557
## [78] train-rmse:0.401157+0.012441 test-rmse:1.221218+0.147525
## [79] train-rmse:0.396615+0.012125 test-rmse:1.220443+0.150017
## [80] train-rmse:0.391985+0.011599 test-rmse:1.219105+0.149727
## [81] train-rmse:0.387866+0.011991 test-rmse:1.218949+0.149914
## [82] train-rmse:0.382967+0.010816 test-rmse:1.217126+0.149709
## [83] train-rmse:0.378417+0.009691 test-rmse:1.217186+0.150703
## [84] train-rmse:0.375017+0.010159 test-rmse:1.216888+0.151724
## [85] train-rmse:0.371500+0.010174 test-rmse:1.217165+0.151381
## [86] train-rmse:0.365770+0.009532 test-rmse:1.216935+0.150784
## [87] train-rmse:0.361977+0.010160 test-rmse:1.217476+0.150539
## [88] train-rmse:0.357527+0.008989 test-rmse:1.216706+0.150362
## [89] train-rmse:0.354059+0.009792 test-rmse:1.216451+0.149796
## [90] train-rmse:0.349832+0.009297 test-rmse:1.216227+0.150981
## [91] train-rmse:0.346294+0.009766 test-rmse:1.216115+0.150355
## [92] train-rmse:0.343913+0.009771 test-rmse:1.216183+0.151526
## [93] train-rmse:0.340270+0.010615 test-rmse:1.216126+0.151564
## [94] train-rmse:0.337056+0.011399 test-rmse:1.215448+0.150653
## [95] train-rmse:0.333168+0.011113 test-rmse:1.215745+0.150959
## [96] train-rmse:0.330602+0.011412 test-rmse:1.216201+0.151660
## [97] train-rmse:0.327957+0.010819 test-rmse:1.215932+0.152072
## [98] train-rmse:0.325070+0.010995 test-rmse:1.215783+0.152600
## [99] train-rmse:0.321310+0.010997 test-rmse:1.215471+0.152576
## [100] train-rmse:0.317303+0.009246 test-rmse:1.215217+0.151820
## [101] train-rmse:0.314931+0.009096 test-rmse:1.215115+0.151031
## [102] train-rmse:0.312564+0.009537 test-rmse:1.215783+0.150901
## [103] train-rmse:0.310109+0.009998 test-rmse:1.215870+0.151253
## [104] train-rmse:0.307256+0.010354 test-rmse:1.216828+0.150874
## [105] train-rmse:0.304236+0.010128 test-rmse:1.216699+0.151316
## [106] train-rmse:0.299733+0.008775 test-rmse:1.217465+0.150909
## [107] train-rmse:0.297718+0.008862 test-rmse:1.217640+0.150879
## Stopping. Best iteration:
## [101] train-rmse:0.314931+0.009096 test-rmse:1.215115+0.151031
##
## 14
## [1] train-rmse:8.443288+0.060639 test-rmse:8.439194+0.274341
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.372684+0.050903 test-rmse:7.364692+0.271260
## [3] train-rmse:6.464429+0.042016 test-rmse:6.461018+0.267420
## [4] train-rmse:5.696782+0.034954 test-rmse:5.691126+0.259328
## [5] train-rmse:5.051332+0.028809 test-rmse:5.052022+0.247100
## [6] train-rmse:4.511761+0.023437 test-rmse:4.515635+0.233996
## [7] train-rmse:4.063932+0.019376 test-rmse:4.070827+0.223138
## [8] train-rmse:3.695430+0.015977 test-rmse:3.712601+0.202397
## [9] train-rmse:3.394037+0.013570 test-rmse:3.410422+0.183754
## [10] train-rmse:3.149194+0.012193 test-rmse:3.168328+0.163636
## [11] train-rmse:2.952112+0.011796 test-rmse:2.974429+0.141996
## [12] train-rmse:2.794768+0.012050 test-rmse:2.823919+0.122094
## [13] train-rmse:2.669218+0.012465 test-rmse:2.701736+0.109245
## [14] train-rmse:2.569327+0.012990 test-rmse:2.599641+0.093567
## [15] train-rmse:2.490093+0.013930 test-rmse:2.525011+0.075100
## [16] train-rmse:2.427408+0.014780 test-rmse:2.464465+0.065245
## [17] train-rmse:2.377352+0.015205 test-rmse:2.419437+0.053463
## [18] train-rmse:2.336738+0.015825 test-rmse:2.376679+0.052098
## [19] train-rmse:2.303793+0.016267 test-rmse:2.347152+0.048280
## [20] train-rmse:2.276567+0.016574 test-rmse:2.319084+0.050025
## [21] train-rmse:2.254241+0.017108 test-rmse:2.302153+0.049529
## [22] train-rmse:2.235158+0.017527 test-rmse:2.282358+0.055546
## [23] train-rmse:2.218471+0.017868 test-rmse:2.264809+0.059256
## [24] train-rmse:2.203793+0.018777 test-rmse:2.251461+0.064263
## [25] train-rmse:2.190690+0.019392 test-rmse:2.242351+0.066367
## [26] train-rmse:2.178821+0.019675 test-rmse:2.233175+0.069687
## [27] train-rmse:2.167952+0.020046 test-rmse:2.219595+0.068629
## [28] train-rmse:2.157858+0.020524 test-rmse:2.209878+0.070352
## [29] train-rmse:2.148263+0.020930 test-rmse:2.197292+0.073576
## [30] train-rmse:2.139292+0.021337 test-rmse:2.191646+0.074951
## [31] train-rmse:2.130665+0.021717 test-rmse:2.184220+0.081722
## [32] train-rmse:2.122394+0.021983 test-rmse:2.179163+0.085036
## [33] train-rmse:2.114513+0.022267 test-rmse:2.172116+0.085854
## [34] train-rmse:2.106540+0.022650 test-rmse:2.165186+0.081060
## [35] train-rmse:2.098703+0.023027 test-rmse:2.158733+0.082738
## [36] train-rmse:2.091172+0.023358 test-rmse:2.151164+0.083289
## [37] train-rmse:2.083820+0.023710 test-rmse:2.147877+0.083445
## [38] train-rmse:2.076602+0.024021 test-rmse:2.141284+0.087459
## [39] train-rmse:2.069447+0.024344 test-rmse:2.135507+0.085480
## [40] train-rmse:2.062580+0.024602 test-rmse:2.125214+0.084515
## [41] train-rmse:2.055834+0.024913 test-rmse:2.120856+0.087674
## [42] train-rmse:2.049313+0.025329 test-rmse:2.113840+0.088392
## [43] train-rmse:2.042917+0.025609 test-rmse:2.106444+0.089110
## [44] train-rmse:2.036557+0.025911 test-rmse:2.101484+0.088525
## [45] train-rmse:2.030311+0.026291 test-rmse:2.094989+0.085030
## [46] train-rmse:2.023961+0.026643 test-rmse:2.091078+0.086487
## [47] train-rmse:2.017846+0.026925 test-rmse:2.083320+0.089284
## [48] train-rmse:2.011890+0.027229 test-rmse:2.079522+0.090188
## [49] train-rmse:2.006008+0.027471 test-rmse:2.073692+0.093444
## [50] train-rmse:2.000163+0.027767 test-rmse:2.071433+0.092788
## [51] train-rmse:1.994397+0.028111 test-rmse:2.066952+0.092502
## [52] train-rmse:1.988792+0.028281 test-rmse:2.061604+0.095244
## [53] train-rmse:1.983228+0.028458 test-rmse:2.055282+0.091014
## [54] train-rmse:1.977705+0.028746 test-rmse:2.051653+0.090085
## [55] train-rmse:1.972256+0.029062 test-rmse:2.047799+0.091008
## [56] train-rmse:1.966850+0.029340 test-rmse:2.045379+0.092825
## [57] train-rmse:1.961554+0.029527 test-rmse:2.038619+0.091397
## [58] train-rmse:1.956305+0.029650 test-rmse:2.035787+0.093374
## [59] train-rmse:1.951200+0.029969 test-rmse:2.031773+0.091031
## [60] train-rmse:1.946122+0.030250 test-rmse:2.023492+0.089562
## [61] train-rmse:1.941061+0.030572 test-rmse:2.015479+0.087021
## [62] train-rmse:1.936049+0.030867 test-rmse:2.010870+0.089310
## [63] train-rmse:1.931198+0.031180 test-rmse:2.006415+0.087774
## [64] train-rmse:1.926392+0.031490 test-rmse:2.002371+0.086860
## [65] train-rmse:1.921663+0.031769 test-rmse:1.999777+0.088380
## [66] train-rmse:1.916957+0.032007 test-rmse:1.994413+0.088408
## [67] train-rmse:1.912250+0.032250 test-rmse:1.989060+0.089215
## [68] train-rmse:1.907686+0.032431 test-rmse:1.985595+0.090640
## [69] train-rmse:1.903085+0.032658 test-rmse:1.980588+0.088244
## [70] train-rmse:1.898589+0.032873 test-rmse:1.977189+0.087874
## [71] train-rmse:1.894098+0.033055 test-rmse:1.974745+0.090607
## [72] train-rmse:1.889627+0.033354 test-rmse:1.971476+0.090941
## [73] train-rmse:1.885222+0.033648 test-rmse:1.968692+0.094055
## [74] train-rmse:1.880946+0.033940 test-rmse:1.966745+0.092364
## [75] train-rmse:1.876762+0.034263 test-rmse:1.963335+0.093946
## [76] train-rmse:1.872573+0.034618 test-rmse:1.958403+0.090993
## [77] train-rmse:1.868448+0.034903 test-rmse:1.955405+0.089531
## [78] train-rmse:1.864340+0.035167 test-rmse:1.951998+0.090143
## [79] train-rmse:1.860243+0.035409 test-rmse:1.949969+0.091061
## [80] train-rmse:1.856240+0.035639 test-rmse:1.946708+0.090845
## [81] train-rmse:1.852219+0.035926 test-rmse:1.942768+0.088085
## [82] train-rmse:1.848159+0.036221 test-rmse:1.939029+0.087089
## [83] train-rmse:1.844250+0.036419 test-rmse:1.936721+0.089717
## [84] train-rmse:1.840276+0.036693 test-rmse:1.936046+0.090611
## [85] train-rmse:1.836350+0.036936 test-rmse:1.930593+0.094304
## [86] train-rmse:1.832495+0.037187 test-rmse:1.925171+0.096257
## [87] train-rmse:1.828746+0.037364 test-rmse:1.920774+0.095915
## [88] train-rmse:1.825041+0.037512 test-rmse:1.916381+0.093881
## [89] train-rmse:1.821259+0.037673 test-rmse:1.913441+0.095034
## [90] train-rmse:1.817538+0.037904 test-rmse:1.908636+0.096221
## [91] train-rmse:1.813875+0.038085 test-rmse:1.905527+0.093943
## [92] train-rmse:1.810266+0.038283 test-rmse:1.901668+0.093752
## [93] train-rmse:1.806672+0.038481 test-rmse:1.899761+0.093003
## [94] train-rmse:1.803093+0.038642 test-rmse:1.898543+0.092377
## [95] train-rmse:1.799524+0.038886 test-rmse:1.894438+0.090723
## [96] train-rmse:1.796075+0.039103 test-rmse:1.893329+0.093039
## [97] train-rmse:1.792613+0.039298 test-rmse:1.889956+0.092708
## [98] train-rmse:1.789247+0.039508 test-rmse:1.887977+0.094657
## [99] train-rmse:1.785895+0.039695 test-rmse:1.883690+0.093058
## [100] train-rmse:1.782553+0.039897 test-rmse:1.880913+0.091603
## [101] train-rmse:1.779281+0.040126 test-rmse:1.878440+0.091096
## [102] train-rmse:1.776014+0.040364 test-rmse:1.877433+0.092082
## [103] train-rmse:1.772774+0.040566 test-rmse:1.875010+0.093066
## [104] train-rmse:1.769543+0.040780 test-rmse:1.871539+0.091768
## [105] train-rmse:1.766312+0.040989 test-rmse:1.868089+0.092683
## [106] train-rmse:1.763056+0.041232 test-rmse:1.866557+0.090064
## [107] train-rmse:1.759858+0.041393 test-rmse:1.861816+0.090712
## [108] train-rmse:1.756754+0.041565 test-rmse:1.858752+0.091028
## [109] train-rmse:1.753637+0.041713 test-rmse:1.857338+0.093263
## [110] train-rmse:1.750560+0.041833 test-rmse:1.852794+0.094633
## [111] train-rmse:1.747507+0.041963 test-rmse:1.849186+0.096075
## [112] train-rmse:1.744486+0.042089 test-rmse:1.846126+0.096733
## [113] train-rmse:1.741512+0.042243 test-rmse:1.845229+0.098801
## [114] train-rmse:1.738571+0.042414 test-rmse:1.842878+0.098352
## [115] train-rmse:1.735618+0.042592 test-rmse:1.841856+0.096542
## [116] train-rmse:1.732674+0.042791 test-rmse:1.841248+0.096673
## [117] train-rmse:1.729755+0.043004 test-rmse:1.837756+0.098997
## [118] train-rmse:1.726887+0.043225 test-rmse:1.836015+0.098913
## [119] train-rmse:1.724056+0.043401 test-rmse:1.833349+0.098552
## [120] train-rmse:1.721241+0.043547 test-rmse:1.832165+0.100932
## [121] train-rmse:1.718427+0.043677 test-rmse:1.827745+0.100662
## [122] train-rmse:1.715675+0.043827 test-rmse:1.825557+0.101074
## [123] train-rmse:1.712968+0.044008 test-rmse:1.823190+0.100182
## [124] train-rmse:1.710262+0.044170 test-rmse:1.821317+0.099845
## [125] train-rmse:1.707563+0.044320 test-rmse:1.818812+0.101059
## [126] train-rmse:1.704880+0.044460 test-rmse:1.816503+0.101094
## [127] train-rmse:1.702228+0.044607 test-rmse:1.813959+0.101700
## [128] train-rmse:1.699588+0.044732 test-rmse:1.809839+0.101386
## [129] train-rmse:1.696971+0.044879 test-rmse:1.807097+0.099212
## [130] train-rmse:1.694331+0.044997 test-rmse:1.804697+0.100962
## [131] train-rmse:1.691769+0.045143 test-rmse:1.802134+0.102938
## [132] train-rmse:1.689274+0.045259 test-rmse:1.801110+0.101271
## [133] train-rmse:1.686756+0.045394 test-rmse:1.799860+0.104127
## [134] train-rmse:1.684259+0.045509 test-rmse:1.797965+0.104386
## [135] train-rmse:1.681764+0.045588 test-rmse:1.795028+0.106323
## [136] train-rmse:1.679327+0.045714 test-rmse:1.793907+0.106079
## [137] train-rmse:1.676890+0.045841 test-rmse:1.792631+0.107412
## [138] train-rmse:1.674497+0.045945 test-rmse:1.791392+0.106729
## [139] train-rmse:1.672105+0.046060 test-rmse:1.788761+0.107706
## [140] train-rmse:1.669738+0.046155 test-rmse:1.786909+0.106633
## [141] train-rmse:1.667354+0.046257 test-rmse:1.783999+0.106614
## [142] train-rmse:1.664983+0.046350 test-rmse:1.782054+0.105634
## [143] train-rmse:1.662659+0.046458 test-rmse:1.780096+0.106692
## [144] train-rmse:1.660372+0.046561 test-rmse:1.779639+0.106618
## [145] train-rmse:1.658095+0.046666 test-rmse:1.779355+0.108549
## [146] train-rmse:1.655836+0.046786 test-rmse:1.776027+0.108044
## [147] train-rmse:1.653586+0.046885 test-rmse:1.772703+0.112129
## [148] train-rmse:1.651364+0.046988 test-rmse:1.769399+0.112269
## [149] train-rmse:1.649161+0.047079 test-rmse:1.768203+0.111374
## [150] train-rmse:1.646949+0.047218 test-rmse:1.766883+0.111257
## [151] train-rmse:1.644731+0.047321 test-rmse:1.765639+0.112050
## [152] train-rmse:1.642557+0.047401 test-rmse:1.762129+0.111724
## [153] train-rmse:1.640406+0.047479 test-rmse:1.759955+0.112787
## [154] train-rmse:1.638255+0.047579 test-rmse:1.758477+0.114282
## [155] train-rmse:1.636145+0.047692 test-rmse:1.754957+0.114890
## [156] train-rmse:1.634033+0.047786 test-rmse:1.753273+0.115203
## [157] train-rmse:1.631949+0.047875 test-rmse:1.752396+0.115149
## [158] train-rmse:1.629880+0.047961 test-rmse:1.750034+0.113405
## [159] train-rmse:1.627851+0.048048 test-rmse:1.748156+0.112190
## [160] train-rmse:1.625813+0.048156 test-rmse:1.746549+0.113342
## [161] train-rmse:1.623765+0.048227 test-rmse:1.745475+0.113208
## [162] train-rmse:1.621708+0.048293 test-rmse:1.743413+0.113493
## [163] train-rmse:1.619678+0.048389 test-rmse:1.744473+0.113316
## [164] train-rmse:1.617652+0.048508 test-rmse:1.741428+0.114183
## [165] train-rmse:1.615682+0.048599 test-rmse:1.739862+0.115299
## [166] train-rmse:1.613757+0.048693 test-rmse:1.739667+0.114609
## [167] train-rmse:1.611849+0.048757 test-rmse:1.737976+0.116083
## [168] train-rmse:1.609950+0.048831 test-rmse:1.736000+0.115865
## [169] train-rmse:1.608009+0.048880 test-rmse:1.734515+0.117068
## [170] train-rmse:1.606105+0.048930 test-rmse:1.730789+0.119531
## [171] train-rmse:1.604225+0.048986 test-rmse:1.729781+0.119693
## [172] train-rmse:1.602357+0.049032 test-rmse:1.729030+0.120420
## [173] train-rmse:1.600495+0.049063 test-rmse:1.727384+0.120930
## [174] train-rmse:1.598648+0.049138 test-rmse:1.726338+0.121323
## [175] train-rmse:1.596809+0.049210 test-rmse:1.723139+0.122042
## [176] train-rmse:1.594997+0.049298 test-rmse:1.720190+0.120488
## [177] train-rmse:1.593169+0.049407 test-rmse:1.718268+0.119709
## [178] train-rmse:1.591369+0.049500 test-rmse:1.716068+0.120651
## [179] train-rmse:1.589580+0.049555 test-rmse:1.715116+0.120247
## [180] train-rmse:1.587808+0.049619 test-rmse:1.713748+0.122451
## [181] train-rmse:1.586057+0.049694 test-rmse:1.711568+0.124074
## [182] train-rmse:1.584335+0.049760 test-rmse:1.711312+0.124132
## [183] train-rmse:1.582628+0.049816 test-rmse:1.710107+0.124912
## [184] train-rmse:1.580921+0.049869 test-rmse:1.707870+0.124742
## [185] train-rmse:1.579207+0.049931 test-rmse:1.705326+0.125991
## [186] train-rmse:1.577515+0.049968 test-rmse:1.705185+0.127210
## [187] train-rmse:1.575809+0.050007 test-rmse:1.704227+0.128910
## [188] train-rmse:1.574104+0.050037 test-rmse:1.703858+0.128004
## [189] train-rmse:1.572441+0.050094 test-rmse:1.700994+0.129713
## [190] train-rmse:1.570785+0.050158 test-rmse:1.700191+0.128491
## [191] train-rmse:1.569148+0.050201 test-rmse:1.699495+0.127768
## [192] train-rmse:1.567499+0.050223 test-rmse:1.698566+0.127500
## [193] train-rmse:1.565876+0.050268 test-rmse:1.697437+0.127487
## [194] train-rmse:1.564278+0.050320 test-rmse:1.695870+0.128303
## [195] train-rmse:1.562679+0.050354 test-rmse:1.694084+0.128483
## [196] train-rmse:1.561073+0.050402 test-rmse:1.693090+0.129069
## [197] train-rmse:1.559479+0.050422 test-rmse:1.689502+0.132118
## [198] train-rmse:1.557934+0.050456 test-rmse:1.689083+0.132637
## [199] train-rmse:1.556403+0.050510 test-rmse:1.687104+0.132147
## [200] train-rmse:1.554897+0.050556 test-rmse:1.686004+0.132940
## [201] train-rmse:1.553376+0.050613 test-rmse:1.683651+0.132653
## [202] train-rmse:1.551868+0.050650 test-rmse:1.681187+0.131920
## [203] train-rmse:1.550369+0.050699 test-rmse:1.680458+0.132950
## [204] train-rmse:1.548880+0.050749 test-rmse:1.679464+0.131706
## [205] train-rmse:1.547414+0.050797 test-rmse:1.677917+0.131950
## [206] train-rmse:1.545945+0.050849 test-rmse:1.676039+0.134387
## [207] train-rmse:1.544454+0.050876 test-rmse:1.673313+0.134331
## [208] train-rmse:1.542962+0.050930 test-rmse:1.671949+0.134728
## [209] train-rmse:1.541514+0.050985 test-rmse:1.670260+0.134687
## [210] train-rmse:1.540080+0.051038 test-rmse:1.668861+0.136261
## [211] train-rmse:1.538654+0.051083 test-rmse:1.667530+0.136579
## [212] train-rmse:1.537257+0.051119 test-rmse:1.668799+0.137464
## [213] train-rmse:1.535855+0.051131 test-rmse:1.667962+0.138467
## [214] train-rmse:1.534460+0.051149 test-rmse:1.667374+0.138573
## [215] train-rmse:1.533076+0.051173 test-rmse:1.665206+0.138125
## [216] train-rmse:1.531716+0.051196 test-rmse:1.664302+0.139277
## [217] train-rmse:1.530365+0.051223 test-rmse:1.664158+0.141103
## [218] train-rmse:1.529034+0.051249 test-rmse:1.663701+0.141204
## [219] train-rmse:1.527702+0.051278 test-rmse:1.661964+0.142056
## [220] train-rmse:1.526378+0.051309 test-rmse:1.658268+0.139422
## [221] train-rmse:1.525058+0.051331 test-rmse:1.657269+0.140061
## [222] train-rmse:1.523747+0.051350 test-rmse:1.656083+0.140604
## [223] train-rmse:1.522449+0.051365 test-rmse:1.653645+0.140625
## [224] train-rmse:1.521130+0.051360 test-rmse:1.652965+0.140494
## [225] train-rmse:1.519821+0.051398 test-rmse:1.651310+0.141829
## [226] train-rmse:1.518523+0.051415 test-rmse:1.650815+0.141676
## [227] train-rmse:1.517243+0.051433 test-rmse:1.650997+0.140908
## [228] train-rmse:1.515974+0.051452 test-rmse:1.649357+0.141657
## [229] train-rmse:1.514702+0.051484 test-rmse:1.647750+0.140677
## [230] train-rmse:1.513443+0.051515 test-rmse:1.646399+0.139971
## [231] train-rmse:1.512196+0.051550 test-rmse:1.644863+0.140003
## [232] train-rmse:1.510964+0.051590 test-rmse:1.643088+0.141303
## [233] train-rmse:1.509739+0.051642 test-rmse:1.642219+0.141541
## [234] train-rmse:1.508514+0.051675 test-rmse:1.640956+0.141960
## [235] train-rmse:1.507302+0.051690 test-rmse:1.638953+0.142163
## [236] train-rmse:1.506109+0.051730 test-rmse:1.637627+0.142439
## [237] train-rmse:1.504910+0.051788 test-rmse:1.637689+0.143875
## [238] train-rmse:1.503716+0.051824 test-rmse:1.637101+0.144547
## [239] train-rmse:1.502547+0.051861 test-rmse:1.635603+0.144868
## [240] train-rmse:1.501382+0.051894 test-rmse:1.634061+0.144192
## [241] train-rmse:1.500216+0.051936 test-rmse:1.632878+0.143655
## [242] train-rmse:1.499080+0.051961 test-rmse:1.632866+0.144689
## [243] train-rmse:1.497946+0.051980 test-rmse:1.630593+0.146946
## [244] train-rmse:1.496820+0.052001 test-rmse:1.630135+0.146770
## [245] train-rmse:1.495692+0.052042 test-rmse:1.628493+0.146316
## [246] train-rmse:1.494566+0.052094 test-rmse:1.628094+0.146535
## [247] train-rmse:1.493453+0.052116 test-rmse:1.627607+0.147246
## [248] train-rmse:1.492341+0.052142 test-rmse:1.628041+0.148346
## [249] train-rmse:1.491231+0.052181 test-rmse:1.627533+0.148915
## [250] train-rmse:1.490139+0.052202 test-rmse:1.625917+0.148415
## [251] train-rmse:1.489057+0.052229 test-rmse:1.624928+0.148442
## [252] train-rmse:1.487988+0.052250 test-rmse:1.623835+0.148279
## [253] train-rmse:1.486906+0.052267 test-rmse:1.623333+0.148195
## [254] train-rmse:1.485844+0.052287 test-rmse:1.621855+0.148756
## [255] train-rmse:1.484785+0.052302 test-rmse:1.619638+0.148862
## [256] train-rmse:1.483726+0.052328 test-rmse:1.619095+0.147765
## [257] train-rmse:1.482681+0.052344 test-rmse:1.618838+0.148325
## [258] train-rmse:1.481638+0.052353 test-rmse:1.617539+0.147998
## [259] train-rmse:1.480605+0.052365 test-rmse:1.617138+0.148854
## [260] train-rmse:1.479584+0.052373 test-rmse:1.614880+0.148154
## [261] train-rmse:1.478563+0.052379 test-rmse:1.614759+0.148829
## [262] train-rmse:1.477556+0.052400 test-rmse:1.613402+0.149334
## [263] train-rmse:1.476551+0.052407 test-rmse:1.611853+0.149385
## [264] train-rmse:1.475553+0.052430 test-rmse:1.612104+0.149153
## [265] train-rmse:1.474562+0.052466 test-rmse:1.610851+0.148053
## [266] train-rmse:1.473559+0.052513 test-rmse:1.610296+0.147955
## [267] train-rmse:1.472573+0.052572 test-rmse:1.610577+0.149611
## [268] train-rmse:1.471604+0.052611 test-rmse:1.609012+0.150319
## [269] train-rmse:1.470628+0.052648 test-rmse:1.608354+0.150041
## [270] train-rmse:1.469649+0.052666 test-rmse:1.607318+0.150711
## [271] train-rmse:1.468681+0.052671 test-rmse:1.606779+0.150949
## [272] train-rmse:1.467737+0.052678 test-rmse:1.606276+0.151211
## [273] train-rmse:1.466802+0.052692 test-rmse:1.603859+0.153081
## [274] train-rmse:1.465872+0.052706 test-rmse:1.602864+0.153082
## [275] train-rmse:1.464951+0.052722 test-rmse:1.602539+0.155406
## [276] train-rmse:1.464039+0.052743 test-rmse:1.602224+0.154962
## [277] train-rmse:1.463117+0.052772 test-rmse:1.600204+0.155216
## [278] train-rmse:1.462208+0.052789 test-rmse:1.599709+0.154562
## [279] train-rmse:1.461302+0.052800 test-rmse:1.597963+0.154404
## [280] train-rmse:1.460393+0.052817 test-rmse:1.596835+0.154166
## [281] train-rmse:1.459489+0.052849 test-rmse:1.597451+0.154121
## [282] train-rmse:1.458596+0.052878 test-rmse:1.596825+0.154585
## [283] train-rmse:1.457714+0.052895 test-rmse:1.596225+0.154304
## [284] train-rmse:1.456817+0.052912 test-rmse:1.594365+0.153888
## [285] train-rmse:1.455930+0.052931 test-rmse:1.593666+0.154169
## [286] train-rmse:1.455039+0.052941 test-rmse:1.592578+0.156149
## [287] train-rmse:1.454152+0.052943 test-rmse:1.590882+0.156433
## [288] train-rmse:1.453292+0.052959 test-rmse:1.589387+0.156572
## [289] train-rmse:1.452434+0.052980 test-rmse:1.589498+0.158311
## [290] train-rmse:1.451588+0.052996 test-rmse:1.587862+0.158711
## [291] train-rmse:1.450737+0.053009 test-rmse:1.587931+0.158386
## [292] train-rmse:1.449886+0.053028 test-rmse:1.587556+0.157962
## [293] train-rmse:1.449054+0.053043 test-rmse:1.587647+0.157247
## [294] train-rmse:1.448235+0.053052 test-rmse:1.586503+0.156738
## [295] train-rmse:1.447413+0.053055 test-rmse:1.585391+0.155848
## [296] train-rmse:1.446590+0.053060 test-rmse:1.585015+0.156186
## [297] train-rmse:1.445781+0.053068 test-rmse:1.585631+0.156513
## [298] train-rmse:1.444970+0.053063 test-rmse:1.584495+0.156513
## [299] train-rmse:1.444156+0.053071 test-rmse:1.582070+0.158312
## [300] train-rmse:1.443355+0.053079 test-rmse:1.581331+0.158847
## [301] train-rmse:1.442552+0.053087 test-rmse:1.579797+0.158991
## [302] train-rmse:1.441757+0.053102 test-rmse:1.580458+0.159500
## [303] train-rmse:1.440975+0.053123 test-rmse:1.580660+0.159252
## [304] train-rmse:1.440194+0.053143 test-rmse:1.581148+0.160886
## [305] train-rmse:1.439427+0.053154 test-rmse:1.580185+0.161505
## [306] train-rmse:1.438669+0.053162 test-rmse:1.580442+0.162038
## [307] train-rmse:1.437915+0.053175 test-rmse:1.578819+0.160945
## [308] train-rmse:1.437153+0.053190 test-rmse:1.577894+0.160981
## [309] train-rmse:1.436388+0.053202 test-rmse:1.578048+0.161602
## [310] train-rmse:1.435634+0.053206 test-rmse:1.576473+0.160880
## [311] train-rmse:1.434892+0.053214 test-rmse:1.575042+0.160833
## [312] train-rmse:1.434147+0.053208 test-rmse:1.574916+0.161451
## [313] train-rmse:1.433412+0.053203 test-rmse:1.573252+0.160356
## [314] train-rmse:1.432684+0.053207 test-rmse:1.572286+0.160641
## [315] train-rmse:1.431958+0.053206 test-rmse:1.572236+0.162624
## [316] train-rmse:1.431227+0.053222 test-rmse:1.571898+0.162277
## [317] train-rmse:1.430502+0.053226 test-rmse:1.571394+0.162898
## [318] train-rmse:1.429784+0.053231 test-rmse:1.570576+0.162799
## [319] train-rmse:1.429068+0.053243 test-rmse:1.569819+0.162636
## [320] train-rmse:1.428373+0.053255 test-rmse:1.569895+0.162266
## [321] train-rmse:1.427677+0.053268 test-rmse:1.570462+0.162278
## [322] train-rmse:1.426982+0.053273 test-rmse:1.568769+0.164280
## [323] train-rmse:1.426295+0.053281 test-rmse:1.567645+0.164678
## [324] train-rmse:1.425609+0.053281 test-rmse:1.566301+0.163923
## [325] train-rmse:1.424916+0.053281 test-rmse:1.565471+0.163828
## [326] train-rmse:1.424237+0.053278 test-rmse:1.564925+0.164318
## [327] train-rmse:1.423547+0.053279 test-rmse:1.565174+0.164205
## [328] train-rmse:1.422862+0.053283 test-rmse:1.564769+0.164154
## [329] train-rmse:1.422198+0.053291 test-rmse:1.564297+0.163982
## [330] train-rmse:1.421536+0.053298 test-rmse:1.563492+0.163835
## [331] train-rmse:1.420875+0.053305 test-rmse:1.563940+0.165089
## [332] train-rmse:1.420219+0.053317 test-rmse:1.562645+0.164725
## [333] train-rmse:1.419570+0.053322 test-rmse:1.561793+0.165441
## [334] train-rmse:1.418924+0.053330 test-rmse:1.561202+0.164911
## [335] train-rmse:1.418281+0.053332 test-rmse:1.559885+0.164446
## [336] train-rmse:1.417637+0.053331 test-rmse:1.559940+0.165419
## [337] train-rmse:1.416996+0.053336 test-rmse:1.557448+0.165828
## [338] train-rmse:1.416362+0.053341 test-rmse:1.557437+0.165534
## [339] train-rmse:1.415733+0.053349 test-rmse:1.556931+0.165417
## [340] train-rmse:1.415110+0.053355 test-rmse:1.556925+0.165919
## [341] train-rmse:1.414480+0.053360 test-rmse:1.556390+0.166197
## [342] train-rmse:1.413860+0.053363 test-rmse:1.554940+0.166008
## [343] train-rmse:1.413248+0.053366 test-rmse:1.554929+0.166797
## [344] train-rmse:1.412638+0.053374 test-rmse:1.554806+0.166873
## [345] train-rmse:1.412041+0.053380 test-rmse:1.554386+0.167649
## [346] train-rmse:1.411446+0.053383 test-rmse:1.553090+0.167338
## [347] train-rmse:1.410855+0.053380 test-rmse:1.552689+0.167728
## [348] train-rmse:1.410267+0.053380 test-rmse:1.551819+0.168018
## [349] train-rmse:1.409684+0.053379 test-rmse:1.551498+0.168378
## [350] train-rmse:1.409099+0.053373 test-rmse:1.549985+0.169993
## [351] train-rmse:1.408520+0.053374 test-rmse:1.549508+0.169536
## [352] train-rmse:1.407939+0.053384 test-rmse:1.549300+0.170277
## [353] train-rmse:1.407362+0.053385 test-rmse:1.549704+0.170889
## [354] train-rmse:1.406783+0.053390 test-rmse:1.548760+0.169990
## [355] train-rmse:1.406206+0.053386 test-rmse:1.548210+0.170041
## [356] train-rmse:1.405632+0.053388 test-rmse:1.547703+0.169700
## [357] train-rmse:1.405068+0.053399 test-rmse:1.547159+0.169840
## [358] train-rmse:1.404511+0.053401 test-rmse:1.547680+0.171351
## [359] train-rmse:1.403953+0.053402 test-rmse:1.546161+0.171434
## [360] train-rmse:1.403392+0.053404 test-rmse:1.546686+0.171263
## [361] train-rmse:1.402840+0.053410 test-rmse:1.545702+0.171295
## [362] train-rmse:1.402292+0.053409 test-rmse:1.544874+0.171274
## [363] train-rmse:1.401746+0.053404 test-rmse:1.543281+0.171730
## [364] train-rmse:1.401215+0.053399 test-rmse:1.543887+0.171247
## [365] train-rmse:1.400681+0.053394 test-rmse:1.543154+0.171331
## [366] train-rmse:1.400140+0.053391 test-rmse:1.542345+0.171785
## [367] train-rmse:1.399603+0.053389 test-rmse:1.542585+0.171787
## [368] train-rmse:1.399062+0.053393 test-rmse:1.541379+0.170776
## [369] train-rmse:1.398534+0.053400 test-rmse:1.541233+0.170339
## [370] train-rmse:1.398003+0.053414 test-rmse:1.541769+0.169529
## [371] train-rmse:1.397481+0.053417 test-rmse:1.543027+0.170669
## [372] train-rmse:1.396970+0.053423 test-rmse:1.541348+0.170535
## [373] train-rmse:1.396461+0.053421 test-rmse:1.539609+0.171764
## [374] train-rmse:1.395957+0.053416 test-rmse:1.538986+0.172071
## [375] train-rmse:1.395461+0.053408 test-rmse:1.538686+0.171953
## [376] train-rmse:1.394967+0.053408 test-rmse:1.538418+0.172336
## [377] train-rmse:1.394469+0.053414 test-rmse:1.537661+0.172292
## [378] train-rmse:1.393977+0.053425 test-rmse:1.537532+0.172098
## [379] train-rmse:1.393493+0.053430 test-rmse:1.536961+0.172824
## [380] train-rmse:1.393011+0.053433 test-rmse:1.536132+0.173245
## [381] train-rmse:1.392530+0.053433 test-rmse:1.535490+0.173065
## [382] train-rmse:1.392053+0.053430 test-rmse:1.536200+0.172723
## [383] train-rmse:1.391577+0.053422 test-rmse:1.535865+0.172972
## [384] train-rmse:1.391094+0.053419 test-rmse:1.535430+0.172702
## [385] train-rmse:1.390614+0.053414 test-rmse:1.535394+0.172623
## [386] train-rmse:1.390140+0.053415 test-rmse:1.534998+0.173030
## [387] train-rmse:1.389671+0.053415 test-rmse:1.534281+0.173408
## [388] train-rmse:1.389208+0.053412 test-rmse:1.532759+0.173653
## [389] train-rmse:1.388745+0.053408 test-rmse:1.532302+0.174329
## [390] train-rmse:1.388283+0.053400 test-rmse:1.531788+0.174623
## [391] train-rmse:1.387826+0.053392 test-rmse:1.531501+0.174537
## [392] train-rmse:1.387372+0.053382 test-rmse:1.530659+0.174118
## [393] train-rmse:1.386915+0.053377 test-rmse:1.530403+0.174024
## [394] train-rmse:1.386454+0.053376 test-rmse:1.530566+0.174824
## [395] train-rmse:1.385998+0.053384 test-rmse:1.530056+0.174982
## [396] train-rmse:1.385543+0.053390 test-rmse:1.529302+0.175705
## [397] train-rmse:1.385099+0.053389 test-rmse:1.530080+0.176847
## [398] train-rmse:1.384665+0.053394 test-rmse:1.530003+0.176684
## [399] train-rmse:1.384235+0.053399 test-rmse:1.528947+0.176529
## [400] train-rmse:1.383799+0.053400 test-rmse:1.528511+0.176880
## [401] train-rmse:1.383362+0.053400 test-rmse:1.528299+0.176990
## [402] train-rmse:1.382931+0.053397 test-rmse:1.527344+0.178367
## [403] train-rmse:1.382500+0.053394 test-rmse:1.526715+0.178632
## [404] train-rmse:1.382074+0.053390 test-rmse:1.527730+0.178564
## [405] train-rmse:1.381654+0.053385 test-rmse:1.526580+0.178843
## [406] train-rmse:1.381234+0.053387 test-rmse:1.526194+0.178805
## [407] train-rmse:1.380815+0.053393 test-rmse:1.525834+0.178576
## [408] train-rmse:1.380402+0.053401 test-rmse:1.525716+0.178886
## [409] train-rmse:1.379985+0.053410 test-rmse:1.524456+0.178618
## [410] train-rmse:1.379579+0.053406 test-rmse:1.524254+0.179766
## [411] train-rmse:1.379178+0.053402 test-rmse:1.523543+0.179585
## [412] train-rmse:1.378775+0.053398 test-rmse:1.523658+0.180329
## [413] train-rmse:1.378366+0.053395 test-rmse:1.523885+0.180145
## [414] train-rmse:1.377963+0.053389 test-rmse:1.523406+0.180444
## [415] train-rmse:1.377560+0.053383 test-rmse:1.523225+0.179196
## [416] train-rmse:1.377161+0.053385 test-rmse:1.522886+0.179398
## [417] train-rmse:1.376759+0.053390 test-rmse:1.522574+0.179162
## [418] train-rmse:1.376364+0.053390 test-rmse:1.521763+0.179416
## [419] train-rmse:1.375974+0.053387 test-rmse:1.521130+0.179181
## [420] train-rmse:1.375586+0.053382 test-rmse:1.520030+0.178562
## [421] train-rmse:1.375206+0.053380 test-rmse:1.519719+0.178461
## [422] train-rmse:1.374825+0.053377 test-rmse:1.519099+0.178272
## [423] train-rmse:1.374445+0.053374 test-rmse:1.519319+0.178867
## [424] train-rmse:1.374068+0.053369 test-rmse:1.518528+0.178397
## [425] train-rmse:1.373691+0.053368 test-rmse:1.519363+0.179267
## [426] train-rmse:1.373320+0.053365 test-rmse:1.519668+0.179659
## [427] train-rmse:1.372950+0.053363 test-rmse:1.518511+0.180928
## [428] train-rmse:1.372583+0.053362 test-rmse:1.518940+0.181281
## [429] train-rmse:1.372219+0.053358 test-rmse:1.519595+0.181171
## [430] train-rmse:1.371855+0.053361 test-rmse:1.518492+0.180743
## [431] train-rmse:1.371490+0.053365 test-rmse:1.517562+0.180996
## [432] train-rmse:1.371132+0.053366 test-rmse:1.516580+0.181424
## [433] train-rmse:1.370774+0.053364 test-rmse:1.516024+0.181913
## [434] train-rmse:1.370418+0.053366 test-rmse:1.516530+0.182497
## [435] train-rmse:1.370056+0.053360 test-rmse:1.516120+0.182609
## [436] train-rmse:1.369704+0.053352 test-rmse:1.515910+0.182685
## [437] train-rmse:1.369353+0.053344 test-rmse:1.516227+0.182559
## [438] train-rmse:1.369007+0.053341 test-rmse:1.515710+0.182901
## [439] train-rmse:1.368664+0.053336 test-rmse:1.516247+0.182883
## [440] train-rmse:1.368322+0.053334 test-rmse:1.516034+0.183143
## [441] train-rmse:1.367984+0.053329 test-rmse:1.516180+0.183592
## [442] train-rmse:1.367651+0.053327 test-rmse:1.516188+0.183614
## [443] train-rmse:1.367315+0.053323 test-rmse:1.515638+0.183754
## [444] train-rmse:1.366983+0.053322 test-rmse:1.515308+0.183454
## [445] train-rmse:1.366650+0.053321 test-rmse:1.514728+0.183487
## [446] train-rmse:1.366319+0.053320 test-rmse:1.514408+0.183944
## [447] train-rmse:1.365987+0.053322 test-rmse:1.513890+0.184136
## [448] train-rmse:1.365656+0.053323 test-rmse:1.513933+0.184238
## [449] train-rmse:1.365333+0.053323 test-rmse:1.512881+0.183700
## [450] train-rmse:1.365012+0.053321 test-rmse:1.511893+0.184761
## [451] train-rmse:1.364692+0.053319 test-rmse:1.511203+0.185099
## [452] train-rmse:1.364375+0.053317 test-rmse:1.511475+0.186087
## [453] train-rmse:1.364059+0.053317 test-rmse:1.510367+0.185761
## [454] train-rmse:1.363741+0.053317 test-rmse:1.510162+0.184632
## [455] train-rmse:1.363425+0.053317 test-rmse:1.509981+0.184825
## [456] train-rmse:1.363108+0.053314 test-rmse:1.510536+0.185827
## [457] train-rmse:1.362795+0.053313 test-rmse:1.510405+0.185775
## [458] train-rmse:1.362483+0.053310 test-rmse:1.510376+0.185986
## [459] train-rmse:1.362178+0.053309 test-rmse:1.509692+0.185679
## [460] train-rmse:1.361870+0.053309 test-rmse:1.509824+0.186228
## [461] train-rmse:1.361563+0.053308 test-rmse:1.508903+0.186363
## [462] train-rmse:1.361256+0.053306 test-rmse:1.508914+0.186559
## [463] train-rmse:1.360952+0.053306 test-rmse:1.508890+0.186574
## [464] train-rmse:1.360653+0.053301 test-rmse:1.508982+0.186130
## [465] train-rmse:1.360359+0.053300 test-rmse:1.509154+0.186043
## [466] train-rmse:1.360070+0.053300 test-rmse:1.509430+0.185995
## [467] train-rmse:1.359776+0.053297 test-rmse:1.508717+0.185746
## [468] train-rmse:1.359484+0.053293 test-rmse:1.508650+0.185610
## [469] train-rmse:1.359196+0.053288 test-rmse:1.508422+0.185554
## [470] train-rmse:1.358909+0.053284 test-rmse:1.507675+0.186176
## [471] train-rmse:1.358624+0.053280 test-rmse:1.507263+0.186311
## [472] train-rmse:1.358337+0.053276 test-rmse:1.506731+0.187600
## [473] train-rmse:1.358052+0.053273 test-rmse:1.506388+0.187896
## [474] train-rmse:1.357772+0.053271 test-rmse:1.506345+0.187438
## [475] train-rmse:1.357495+0.053270 test-rmse:1.506205+0.187021
## [476] train-rmse:1.357220+0.053268 test-rmse:1.505590+0.187263
## [477] train-rmse:1.356946+0.053267 test-rmse:1.505028+0.187311
## [478] train-rmse:1.356672+0.053265 test-rmse:1.505024+0.187467
## [479] train-rmse:1.356400+0.053263 test-rmse:1.504810+0.187906
## [480] train-rmse:1.356129+0.053260 test-rmse:1.505204+0.188537
## [481] train-rmse:1.355860+0.053256 test-rmse:1.504644+0.188607
## [482] train-rmse:1.355592+0.053254 test-rmse:1.504401+0.188270
## [483] train-rmse:1.355326+0.053253 test-rmse:1.503989+0.187923
## [484] train-rmse:1.355062+0.053251 test-rmse:1.504197+0.188480
## [485] train-rmse:1.354803+0.053247 test-rmse:1.503910+0.188757
## [486] train-rmse:1.354542+0.053242 test-rmse:1.504001+0.188436
## [487] train-rmse:1.354277+0.053238 test-rmse:1.503352+0.188317
## [488] train-rmse:1.354018+0.053237 test-rmse:1.503217+0.188420
## [489] train-rmse:1.353761+0.053234 test-rmse:1.503144+0.188251
## [490] train-rmse:1.353504+0.053229 test-rmse:1.503816+0.188847
## [491] train-rmse:1.353245+0.053221 test-rmse:1.502928+0.189050
## [492] train-rmse:1.352991+0.053213 test-rmse:1.502770+0.189344
## [493] train-rmse:1.352740+0.053208 test-rmse:1.502411+0.189059
## [494] train-rmse:1.352490+0.053203 test-rmse:1.502040+0.188727
## [495] train-rmse:1.352242+0.053197 test-rmse:1.502908+0.188983
## [496] train-rmse:1.351997+0.053194 test-rmse:1.503328+0.189155
## [497] train-rmse:1.351753+0.053192 test-rmse:1.502707+0.188967
## [498] train-rmse:1.351507+0.053188 test-rmse:1.502146+0.189063
## [499] train-rmse:1.351264+0.053187 test-rmse:1.501286+0.189091
## [500] train-rmse:1.351018+0.053184 test-rmse:1.500769+0.190577
## [501] train-rmse:1.350772+0.053181 test-rmse:1.500815+0.190566
## [502] train-rmse:1.350531+0.053178 test-rmse:1.500383+0.190508
## [503] train-rmse:1.350290+0.053175 test-rmse:1.500342+0.190811
## [504] train-rmse:1.350052+0.053171 test-rmse:1.500854+0.190708
## [505] train-rmse:1.349815+0.053166 test-rmse:1.500788+0.190128
## [506] train-rmse:1.349579+0.053167 test-rmse:1.500421+0.190312
## [507] train-rmse:1.349345+0.053166 test-rmse:1.499918+0.190426
## [508] train-rmse:1.349113+0.053164 test-rmse:1.499632+0.190639
## [509] train-rmse:1.348885+0.053162 test-rmse:1.500057+0.190481
## [510] train-rmse:1.348658+0.053158 test-rmse:1.499727+0.190109
## [511] train-rmse:1.348432+0.053154 test-rmse:1.499633+0.190519
## [512] train-rmse:1.348205+0.053149 test-rmse:1.499074+0.190583
## [513] train-rmse:1.347983+0.053143 test-rmse:1.499038+0.190759
## [514] train-rmse:1.347761+0.053139 test-rmse:1.498410+0.190826
## [515] train-rmse:1.347542+0.053136 test-rmse:1.498295+0.190769
## [516] train-rmse:1.347320+0.053136 test-rmse:1.497831+0.190772
## [517] train-rmse:1.347100+0.053131 test-rmse:1.497617+0.190723
## [518] train-rmse:1.346882+0.053125 test-rmse:1.497129+0.190935
## [519] train-rmse:1.346664+0.053121 test-rmse:1.496344+0.190955
## [520] train-rmse:1.346450+0.053119 test-rmse:1.496422+0.191095
## [521] train-rmse:1.346235+0.053114 test-rmse:1.496538+0.190800
## [522] train-rmse:1.346020+0.053108 test-rmse:1.497119+0.191557
## [523] train-rmse:1.345804+0.053102 test-rmse:1.496634+0.191721
## [524] train-rmse:1.345589+0.053096 test-rmse:1.497066+0.191661
## [525] train-rmse:1.345377+0.053092 test-rmse:1.497307+0.192106
## Stopping. Best iteration:
## [519] train-rmse:1.346664+0.053121 test-rmse:1.496344+0.190955
##
## 15
## [1] train-rmse:8.426154+0.060886 test-rmse:8.422350+0.271196
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.340416+0.053681 test-rmse:7.332899+0.260787
## [3] train-rmse:6.417331+0.047955 test-rmse:6.408774+0.249889
## [4] train-rmse:5.632658+0.042763 test-rmse:5.631033+0.236644
## [5] train-rmse:4.967715+0.033523 test-rmse:4.973947+0.229546
## [6] train-rmse:4.409512+0.029325 test-rmse:4.421727+0.215721
## [7] train-rmse:3.940521+0.026409 test-rmse:3.955519+0.196409
## [8] train-rmse:3.546830+0.024623 test-rmse:3.572406+0.177733
## [9] train-rmse:3.219021+0.017544 test-rmse:3.251741+0.165657
## [10] train-rmse:2.952903+0.016825 test-rmse:3.001071+0.140641
## [11] train-rmse:2.732952+0.015660 test-rmse:2.789129+0.126051
## [12] train-rmse:2.551957+0.018589 test-rmse:2.619256+0.107490
## [13] train-rmse:2.407324+0.019597 test-rmse:2.484748+0.084593
## [14] train-rmse:2.287754+0.021607 test-rmse:2.374636+0.064561
## [15] train-rmse:2.194326+0.020749 test-rmse:2.293487+0.050880
## [16] train-rmse:2.117694+0.021987 test-rmse:2.221231+0.051110
## [17] train-rmse:2.056944+0.023107 test-rmse:2.164427+0.047480
## [18] train-rmse:2.001974+0.025703 test-rmse:2.118392+0.044590
## [19] train-rmse:1.956925+0.026556 test-rmse:2.080728+0.049080
## [20] train-rmse:1.921323+0.027638 test-rmse:2.053427+0.056768
## [21] train-rmse:1.890609+0.029065 test-rmse:2.027732+0.063355
## [22] train-rmse:1.860987+0.030920 test-rmse:2.005190+0.057501
## [23] train-rmse:1.838742+0.030825 test-rmse:1.987221+0.064220
## [24] train-rmse:1.818909+0.031305 test-rmse:1.971921+0.064096
## [25] train-rmse:1.797932+0.032678 test-rmse:1.954299+0.070696
## [26] train-rmse:1.781115+0.032803 test-rmse:1.939667+0.072427
## [27] train-rmse:1.765955+0.033175 test-rmse:1.926029+0.075535
## [28] train-rmse:1.747551+0.035630 test-rmse:1.908701+0.079646
## [29] train-rmse:1.733936+0.036370 test-rmse:1.900837+0.085870
## [30] train-rmse:1.720155+0.037383 test-rmse:1.888804+0.086175
## [31] train-rmse:1.704788+0.039562 test-rmse:1.869263+0.070918
## [32] train-rmse:1.692656+0.040060 test-rmse:1.853388+0.074577
## [33] train-rmse:1.681486+0.040536 test-rmse:1.843121+0.074360
## [34] train-rmse:1.667008+0.040403 test-rmse:1.833079+0.079619
## [35] train-rmse:1.656152+0.040927 test-rmse:1.824977+0.080206
## [36] train-rmse:1.644398+0.042114 test-rmse:1.818749+0.082491
## [37] train-rmse:1.633452+0.042329 test-rmse:1.812465+0.081182
## [38] train-rmse:1.622873+0.042806 test-rmse:1.800764+0.079789
## [39] train-rmse:1.612818+0.043676 test-rmse:1.795635+0.077308
## [40] train-rmse:1.603713+0.044277 test-rmse:1.790541+0.083117
## [41] train-rmse:1.593883+0.045000 test-rmse:1.783823+0.082922
## [42] train-rmse:1.584912+0.045902 test-rmse:1.776881+0.084354
## [43] train-rmse:1.576312+0.046256 test-rmse:1.768604+0.085340
## [44] train-rmse:1.564668+0.047433 test-rmse:1.754712+0.087176
## [45] train-rmse:1.555560+0.046861 test-rmse:1.747108+0.087643
## [46] train-rmse:1.545901+0.046281 test-rmse:1.741894+0.091151
## [47] train-rmse:1.538023+0.046580 test-rmse:1.736290+0.092095
## [48] train-rmse:1.529482+0.047543 test-rmse:1.732134+0.095943
## [49] train-rmse:1.521737+0.048114 test-rmse:1.725148+0.097813
## [50] train-rmse:1.511303+0.051115 test-rmse:1.713467+0.082219
## [51] train-rmse:1.500433+0.051951 test-rmse:1.707828+0.082923
## [52] train-rmse:1.492511+0.052303 test-rmse:1.703271+0.084172
## [53] train-rmse:1.485371+0.052483 test-rmse:1.696555+0.083762
## [54] train-rmse:1.477947+0.052065 test-rmse:1.689733+0.083346
## [55] train-rmse:1.468295+0.054569 test-rmse:1.682877+0.077437
## [56] train-rmse:1.461255+0.054344 test-rmse:1.678492+0.076657
## [57] train-rmse:1.453615+0.054823 test-rmse:1.671302+0.076078
## [58] train-rmse:1.445978+0.055128 test-rmse:1.666693+0.079287
## [59] train-rmse:1.438805+0.054710 test-rmse:1.659363+0.082083
## [60] train-rmse:1.431663+0.055660 test-rmse:1.652158+0.084351
## [61] train-rmse:1.425058+0.055847 test-rmse:1.648320+0.086359
## [62] train-rmse:1.418648+0.056086 test-rmse:1.645337+0.086160
## [63] train-rmse:1.411821+0.055781 test-rmse:1.639063+0.085930
## [64] train-rmse:1.405884+0.056036 test-rmse:1.638056+0.084450
## [65] train-rmse:1.397000+0.056943 test-rmse:1.631258+0.081649
## [66] train-rmse:1.390017+0.056761 test-rmse:1.626487+0.081129
## [67] train-rmse:1.383029+0.057441 test-rmse:1.621167+0.084036
## [68] train-rmse:1.377622+0.057534 test-rmse:1.614926+0.085161
## [69] train-rmse:1.370092+0.057340 test-rmse:1.608135+0.089010
## [70] train-rmse:1.364104+0.056891 test-rmse:1.604880+0.089934
## [71] train-rmse:1.356164+0.058133 test-rmse:1.600876+0.090384
## [72] train-rmse:1.342990+0.051545 test-rmse:1.587752+0.080829
## [73] train-rmse:1.336945+0.051184 test-rmse:1.582539+0.082580
## [74] train-rmse:1.331212+0.051061 test-rmse:1.577437+0.084031
## [75] train-rmse:1.325144+0.050886 test-rmse:1.572165+0.091174
## [76] train-rmse:1.320257+0.050987 test-rmse:1.570112+0.090516
## [77] train-rmse:1.314954+0.050712 test-rmse:1.565586+0.090757
## [78] train-rmse:1.305172+0.052906 test-rmse:1.550640+0.088893
## [79] train-rmse:1.299784+0.053392 test-rmse:1.547593+0.088941
## [80] train-rmse:1.294554+0.053864 test-rmse:1.540483+0.088077
## [81] train-rmse:1.289350+0.054009 test-rmse:1.538623+0.089174
## [82] train-rmse:1.284539+0.054252 test-rmse:1.534282+0.089245
## [83] train-rmse:1.273277+0.056550 test-rmse:1.525489+0.097042
## [84] train-rmse:1.267580+0.057132 test-rmse:1.523497+0.097197
## [85] train-rmse:1.262717+0.057791 test-rmse:1.520371+0.097538
## [86] train-rmse:1.258294+0.057902 test-rmse:1.517801+0.098518
## [87] train-rmse:1.254177+0.057954 test-rmse:1.513390+0.100803
## [88] train-rmse:1.250065+0.058248 test-rmse:1.510796+0.100776
## [89] train-rmse:1.244916+0.058272 test-rmse:1.505471+0.105845
## [90] train-rmse:1.238910+0.060274 test-rmse:1.501452+0.102561
## [91] train-rmse:1.234660+0.060367 test-rmse:1.498242+0.101931
## [92] train-rmse:1.230239+0.060396 test-rmse:1.494090+0.102698
## [93] train-rmse:1.226435+0.060497 test-rmse:1.491645+0.101946
## [94] train-rmse:1.222130+0.060458 test-rmse:1.492462+0.101832
## [95] train-rmse:1.215801+0.061424 test-rmse:1.485755+0.093275
## [96] train-rmse:1.210049+0.061967 test-rmse:1.479520+0.095327
## [97] train-rmse:1.206270+0.062215 test-rmse:1.477013+0.093725
## [98] train-rmse:1.202173+0.062750 test-rmse:1.475585+0.095765
## [99] train-rmse:1.195319+0.065249 test-rmse:1.471580+0.096026
## [100] train-rmse:1.191591+0.065599 test-rmse:1.468402+0.096152
## [101] train-rmse:1.187650+0.065180 test-rmse:1.464388+0.097568
## [102] train-rmse:1.178892+0.057115 test-rmse:1.458720+0.098550
## [103] train-rmse:1.169631+0.051817 test-rmse:1.452137+0.100968
## [104] train-rmse:1.163650+0.051227 test-rmse:1.444940+0.105919
## [105] train-rmse:1.157352+0.046923 test-rmse:1.439404+0.105712
## [106] train-rmse:1.152905+0.047682 test-rmse:1.439026+0.106040
## [107] train-rmse:1.149531+0.047781 test-rmse:1.434886+0.108677
## [108] train-rmse:1.146086+0.047895 test-rmse:1.432842+0.108663
## [109] train-rmse:1.142359+0.047327 test-rmse:1.429245+0.111161
## [110] train-rmse:1.138922+0.046994 test-rmse:1.426629+0.113384
## [111] train-rmse:1.135638+0.046874 test-rmse:1.424900+0.113382
## [112] train-rmse:1.132164+0.047341 test-rmse:1.422876+0.113145
## [113] train-rmse:1.128349+0.046173 test-rmse:1.420200+0.114769
## [114] train-rmse:1.125091+0.046508 test-rmse:1.418935+0.114111
## [115] train-rmse:1.121783+0.046888 test-rmse:1.418961+0.114328
## [116] train-rmse:1.118643+0.046670 test-rmse:1.414499+0.113669
## [117] train-rmse:1.115396+0.046479 test-rmse:1.413763+0.115227
## [118] train-rmse:1.112081+0.046055 test-rmse:1.413006+0.115307
## [119] train-rmse:1.108706+0.046451 test-rmse:1.409959+0.115194
## [120] train-rmse:1.105178+0.046000 test-rmse:1.408501+0.114846
## [121] train-rmse:1.101891+0.046035 test-rmse:1.407799+0.115930
## [122] train-rmse:1.098494+0.046759 test-rmse:1.404892+0.117826
## [123] train-rmse:1.094465+0.048340 test-rmse:1.399697+0.117022
## [124] train-rmse:1.091292+0.048232 test-rmse:1.398382+0.118151
## [125] train-rmse:1.088336+0.048227 test-rmse:1.395823+0.118901
## [126] train-rmse:1.085575+0.048180 test-rmse:1.392680+0.118960
## [127] train-rmse:1.082042+0.047734 test-rmse:1.393293+0.120334
## [128] train-rmse:1.074047+0.042648 test-rmse:1.385647+0.117234
## [129] train-rmse:1.071212+0.042615 test-rmse:1.384089+0.119804
## [130] train-rmse:1.066992+0.044396 test-rmse:1.382315+0.119430
## [131] train-rmse:1.064174+0.044136 test-rmse:1.380383+0.118888
## [132] train-rmse:1.061282+0.044255 test-rmse:1.378678+0.118785
## [133] train-rmse:1.058854+0.044457 test-rmse:1.376638+0.118967
## [134] train-rmse:1.056315+0.044673 test-rmse:1.376824+0.117560
## [135] train-rmse:1.053192+0.044707 test-rmse:1.375104+0.119927
## [136] train-rmse:1.050664+0.044712 test-rmse:1.372023+0.120321
## [137] train-rmse:1.047524+0.044640 test-rmse:1.371521+0.121771
## [138] train-rmse:1.044796+0.045048 test-rmse:1.370389+0.122913
## [139] train-rmse:1.042334+0.045077 test-rmse:1.368896+0.123766
## [140] train-rmse:1.036920+0.039897 test-rmse:1.368385+0.124898
## [141] train-rmse:1.032675+0.036935 test-rmse:1.364329+0.126461
## [142] train-rmse:1.030269+0.036735 test-rmse:1.364299+0.126772
## [143] train-rmse:1.028260+0.036618 test-rmse:1.361793+0.128850
## [144] train-rmse:1.026220+0.036687 test-rmse:1.358682+0.129559
## [145] train-rmse:1.022870+0.035966 test-rmse:1.357924+0.130305
## [146] train-rmse:1.020864+0.036021 test-rmse:1.358293+0.130505
## [147] train-rmse:1.017845+0.035479 test-rmse:1.358636+0.129995
## [148] train-rmse:1.015655+0.035311 test-rmse:1.356691+0.130497
## [149] train-rmse:1.013434+0.035502 test-rmse:1.356807+0.129927
## [150] train-rmse:1.011330+0.035831 test-rmse:1.355517+0.130719
## [151] train-rmse:1.008858+0.036037 test-rmse:1.352918+0.129920
## [152] train-rmse:1.004521+0.035372 test-rmse:1.349784+0.131568
## [153] train-rmse:1.002223+0.035038 test-rmse:1.347603+0.133895
## [154] train-rmse:1.000117+0.034777 test-rmse:1.344920+0.133382
## [155] train-rmse:0.997813+0.034594 test-rmse:1.344503+0.133535
## [156] train-rmse:0.995685+0.034561 test-rmse:1.345165+0.135041
## [157] train-rmse:0.993576+0.034717 test-rmse:1.345215+0.136083
## [158] train-rmse:0.991730+0.034742 test-rmse:1.345256+0.135557
## [159] train-rmse:0.987196+0.030487 test-rmse:1.343629+0.137038
## [160] train-rmse:0.983726+0.032404 test-rmse:1.338363+0.133402
## [161] train-rmse:0.981765+0.032311 test-rmse:1.337308+0.134728
## [162] train-rmse:0.980074+0.032221 test-rmse:1.334775+0.135460
## [163] train-rmse:0.977066+0.033380 test-rmse:1.335038+0.135156
## [164] train-rmse:0.975446+0.033327 test-rmse:1.334717+0.134486
## [165] train-rmse:0.971131+0.032156 test-rmse:1.327887+0.139301
## [166] train-rmse:0.968727+0.031716 test-rmse:1.329717+0.140530
## [167] train-rmse:0.966881+0.031978 test-rmse:1.327516+0.140132
## [168] train-rmse:0.963800+0.033524 test-rmse:1.323407+0.138261
## [169] train-rmse:0.961524+0.033467 test-rmse:1.321932+0.139218
## [170] train-rmse:0.958875+0.035153 test-rmse:1.319310+0.139048
## [171] train-rmse:0.956784+0.034470 test-rmse:1.318590+0.140367
## [172] train-rmse:0.953806+0.035086 test-rmse:1.318390+0.139761
## [173] train-rmse:0.952105+0.034784 test-rmse:1.316803+0.140304
## [174] train-rmse:0.950462+0.034733 test-rmse:1.315057+0.140872
## [175] train-rmse:0.948305+0.035080 test-rmse:1.314121+0.140558
## [176] train-rmse:0.946613+0.034946 test-rmse:1.313626+0.140282
## [177] train-rmse:0.945065+0.034809 test-rmse:1.312789+0.140693
## [178] train-rmse:0.942919+0.034974 test-rmse:1.313152+0.141590
## [179] train-rmse:0.940841+0.034976 test-rmse:1.311535+0.142139
## [180] train-rmse:0.938409+0.035794 test-rmse:1.311180+0.141320
## [181] train-rmse:0.935784+0.037011 test-rmse:1.310064+0.142038
## [182] train-rmse:0.934212+0.036716 test-rmse:1.309498+0.141907
## [183] train-rmse:0.932788+0.036496 test-rmse:1.309876+0.143123
## [184] train-rmse:0.931351+0.036517 test-rmse:1.308590+0.143234
## [185] train-rmse:0.929831+0.036786 test-rmse:1.308238+0.143937
## [186] train-rmse:0.928098+0.036720 test-rmse:1.307142+0.144131
## [187] train-rmse:0.926685+0.036407 test-rmse:1.306863+0.143969
## [188] train-rmse:0.924917+0.036004 test-rmse:1.306594+0.144996
## [189] train-rmse:0.921014+0.032632 test-rmse:1.304389+0.146708
## [190] train-rmse:0.919694+0.032659 test-rmse:1.301992+0.147749
## [191] train-rmse:0.917172+0.032981 test-rmse:1.301010+0.147874
## [192] train-rmse:0.915217+0.033410 test-rmse:1.301624+0.149259
## [193] train-rmse:0.913900+0.033447 test-rmse:1.300997+0.149724
## [194] train-rmse:0.912542+0.033406 test-rmse:1.301227+0.149531
## [195] train-rmse:0.908348+0.036261 test-rmse:1.301687+0.149261
## [196] train-rmse:0.906820+0.036490 test-rmse:1.300057+0.149270
## [197] train-rmse:0.905553+0.036377 test-rmse:1.300898+0.148904
## [198] train-rmse:0.904286+0.036480 test-rmse:1.300626+0.148391
## [199] train-rmse:0.903120+0.036663 test-rmse:1.298880+0.148925
## [200] train-rmse:0.901721+0.036442 test-rmse:1.298393+0.149055
## [201] train-rmse:0.899657+0.037369 test-rmse:1.296683+0.149251
## [202] train-rmse:0.898346+0.037299 test-rmse:1.295045+0.149429
## [203] train-rmse:0.896696+0.037251 test-rmse:1.295501+0.149179
## [204] train-rmse:0.893639+0.037045 test-rmse:1.291048+0.152988
## [205] train-rmse:0.892310+0.037220 test-rmse:1.290150+0.153475
## [206] train-rmse:0.891275+0.037298 test-rmse:1.290127+0.152790
## [207] train-rmse:0.890164+0.037277 test-rmse:1.290262+0.152675
## [208] train-rmse:0.888861+0.037304 test-rmse:1.289530+0.152092
## [209] train-rmse:0.887635+0.037234 test-rmse:1.288951+0.152148
## [210] train-rmse:0.885875+0.036746 test-rmse:1.287732+0.152940
## [211] train-rmse:0.884672+0.036915 test-rmse:1.288383+0.153174
## [212] train-rmse:0.883379+0.036755 test-rmse:1.288247+0.154050
## [213] train-rmse:0.879833+0.036198 test-rmse:1.286865+0.154340
## [214] train-rmse:0.877923+0.036718 test-rmse:1.285793+0.153850
## [215] train-rmse:0.876530+0.036729 test-rmse:1.285529+0.154259
## [216] train-rmse:0.875240+0.036590 test-rmse:1.284467+0.154501
## [217] train-rmse:0.874218+0.036690 test-rmse:1.281868+0.154576
## [218] train-rmse:0.873228+0.036663 test-rmse:1.281750+0.153652
## [219] train-rmse:0.872248+0.036573 test-rmse:1.282169+0.153402
## [220] train-rmse:0.871320+0.036642 test-rmse:1.282115+0.152824
## [221] train-rmse:0.870171+0.036770 test-rmse:1.281307+0.153047
## [222] train-rmse:0.868883+0.036839 test-rmse:1.282395+0.153392
## [223] train-rmse:0.867750+0.037071 test-rmse:1.282609+0.155134
## [224] train-rmse:0.866358+0.037119 test-rmse:1.282390+0.155642
## [225] train-rmse:0.862927+0.040260 test-rmse:1.282066+0.156215
## [226] train-rmse:0.861647+0.040660 test-rmse:1.281680+0.157110
## [227] train-rmse:0.859690+0.041771 test-rmse:1.280279+0.154860
## [228] train-rmse:0.856552+0.042113 test-rmse:1.275810+0.153308
## [229] train-rmse:0.855664+0.042289 test-rmse:1.275328+0.152763
## [230] train-rmse:0.852736+0.042003 test-rmse:1.271129+0.154935
## [231] train-rmse:0.851689+0.041967 test-rmse:1.270667+0.155172
## [232] train-rmse:0.850669+0.041915 test-rmse:1.269878+0.155511
## [233] train-rmse:0.847256+0.038739 test-rmse:1.269607+0.155593
## [234] train-rmse:0.844942+0.037096 test-rmse:1.268290+0.157493
## [235] train-rmse:0.843885+0.037178 test-rmse:1.267647+0.157081
## [236] train-rmse:0.843008+0.037261 test-rmse:1.266710+0.157490
## [237] train-rmse:0.841923+0.037170 test-rmse:1.266062+0.157907
## [238] train-rmse:0.840665+0.037058 test-rmse:1.265819+0.158077
## [239] train-rmse:0.839576+0.036974 test-rmse:1.265668+0.158015
## [240] train-rmse:0.838704+0.036980 test-rmse:1.265523+0.158401
## [241] train-rmse:0.837634+0.037007 test-rmse:1.265075+0.158612
## [242] train-rmse:0.836671+0.037367 test-rmse:1.264838+0.159549
## [243] train-rmse:0.835764+0.037502 test-rmse:1.265905+0.161357
## [244] train-rmse:0.834901+0.037479 test-rmse:1.265805+0.161627
## [245] train-rmse:0.834132+0.037425 test-rmse:1.265588+0.161633
## [246] train-rmse:0.833350+0.037387 test-rmse:1.265061+0.161969
## [247] train-rmse:0.832641+0.037320 test-rmse:1.264931+0.162019
## [248] train-rmse:0.831795+0.037353 test-rmse:1.265499+0.161594
## Stopping. Best iteration:
## [242] train-rmse:0.836671+0.037367 test-rmse:1.264838+0.159549
##
## 16
## [1] train-rmse:8.419507+0.061618 test-rmse:8.415899+0.267291
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.323309+0.052313 test-rmse:7.317192+0.257656
## [3] train-rmse:6.389040+0.044369 test-rmse:6.384067+0.251838
## [4] train-rmse:5.594691+0.038073 test-rmse:5.594532+0.238608
## [5] train-rmse:4.922178+0.032879 test-rmse:4.927424+0.222727
## [6] train-rmse:4.349643+0.029661 test-rmse:4.360761+0.205170
## [7] train-rmse:3.866741+0.025534 test-rmse:3.892117+0.190174
## [8] train-rmse:3.459728+0.023375 test-rmse:3.490501+0.167287
## [9] train-rmse:3.117987+0.021451 test-rmse:3.163814+0.149571
## [10] train-rmse:2.833312+0.020950 test-rmse:2.889908+0.132570
## [11] train-rmse:2.597596+0.017161 test-rmse:2.668512+0.127576
## [12] train-rmse:2.402193+0.014086 test-rmse:2.476099+0.115050
## [13] train-rmse:2.242039+0.013311 test-rmse:2.335210+0.099923
## [14] train-rmse:2.109415+0.012728 test-rmse:2.218513+0.089162
## [15] train-rmse:2.000228+0.011630 test-rmse:2.128857+0.086752
## [16] train-rmse:1.912136+0.011069 test-rmse:2.049637+0.079734
## [17] train-rmse:1.837423+0.010862 test-rmse:1.990361+0.076711
## [18] train-rmse:1.774628+0.010052 test-rmse:1.936604+0.079675
## [19] train-rmse:1.721425+0.011877 test-rmse:1.898623+0.080111
## [20] train-rmse:1.675910+0.014143 test-rmse:1.864497+0.077892
## [21] train-rmse:1.637247+0.013215 test-rmse:1.836863+0.069633
## [22] train-rmse:1.604533+0.012800 test-rmse:1.807922+0.073423
## [23] train-rmse:1.576344+0.013137 test-rmse:1.789617+0.077168
## [24] train-rmse:1.550734+0.013090 test-rmse:1.765610+0.076781
## [25] train-rmse:1.526337+0.013492 test-rmse:1.752549+0.078442
## [26] train-rmse:1.505947+0.013512 test-rmse:1.737207+0.071204
## [27] train-rmse:1.485237+0.014183 test-rmse:1.718563+0.069529
## [28] train-rmse:1.465946+0.015009 test-rmse:1.712505+0.074042
## [29] train-rmse:1.448483+0.017178 test-rmse:1.698494+0.071903
## [30] train-rmse:1.431782+0.018223 test-rmse:1.684744+0.075590
## [31] train-rmse:1.414760+0.016719 test-rmse:1.666907+0.078852
## [32] train-rmse:1.399411+0.016307 test-rmse:1.657282+0.076444
## [33] train-rmse:1.380848+0.015451 test-rmse:1.648099+0.082778
## [34] train-rmse:1.366769+0.015311 test-rmse:1.644479+0.085370
## [35] train-rmse:1.353086+0.015480 test-rmse:1.632775+0.092368
## [36] train-rmse:1.338857+0.016565 test-rmse:1.618626+0.083139
## [37] train-rmse:1.324962+0.015454 test-rmse:1.612952+0.087023
## [38] train-rmse:1.312225+0.014958 test-rmse:1.604134+0.089878
## [39] train-rmse:1.299572+0.015418 test-rmse:1.595395+0.091283
## [40] train-rmse:1.287479+0.016726 test-rmse:1.580116+0.086986
## [41] train-rmse:1.274247+0.016481 test-rmse:1.569439+0.091159
## [42] train-rmse:1.261800+0.016447 test-rmse:1.561920+0.088868
## [43] train-rmse:1.251926+0.016291 test-rmse:1.554961+0.085614
## [44] train-rmse:1.239408+0.017852 test-rmse:1.542270+0.085332
## [45] train-rmse:1.229658+0.018426 test-rmse:1.538899+0.089117
## [46] train-rmse:1.218642+0.018942 test-rmse:1.534722+0.087847
## [47] train-rmse:1.208281+0.018913 test-rmse:1.530400+0.086951
## [48] train-rmse:1.198635+0.018787 test-rmse:1.527480+0.088763
## [49] train-rmse:1.188736+0.019502 test-rmse:1.519351+0.094578
## [50] train-rmse:1.178391+0.021458 test-rmse:1.511968+0.095473
## [51] train-rmse:1.167159+0.022755 test-rmse:1.505804+0.095570
## [52] train-rmse:1.155991+0.017979 test-rmse:1.496594+0.098362
## [53] train-rmse:1.146601+0.018533 test-rmse:1.489944+0.099281
## [54] train-rmse:1.138810+0.018244 test-rmse:1.485705+0.099669
## [55] train-rmse:1.129879+0.018534 test-rmse:1.482151+0.104372
## [56] train-rmse:1.119592+0.020596 test-rmse:1.473939+0.104547
## [57] train-rmse:1.110408+0.019364 test-rmse:1.470257+0.106495
## [58] train-rmse:1.101890+0.018196 test-rmse:1.466114+0.108926
## [59] train-rmse:1.093377+0.017826 test-rmse:1.460351+0.109612
## [60] train-rmse:1.085109+0.018744 test-rmse:1.454365+0.107418
## [61] train-rmse:1.076041+0.019049 test-rmse:1.448788+0.106612
## [62] train-rmse:1.067875+0.021031 test-rmse:1.441965+0.108498
## [63] train-rmse:1.059136+0.018771 test-rmse:1.433671+0.110369
## [64] train-rmse:1.050322+0.020794 test-rmse:1.426904+0.110371
## [65] train-rmse:1.042756+0.020700 test-rmse:1.424120+0.113717
## [66] train-rmse:1.036146+0.019810 test-rmse:1.420384+0.115042
## [67] train-rmse:1.029485+0.019346 test-rmse:1.416917+0.113353
## [68] train-rmse:1.022578+0.019844 test-rmse:1.414682+0.116589
## [69] train-rmse:1.016813+0.020001 test-rmse:1.410267+0.114549
## [70] train-rmse:1.009213+0.021127 test-rmse:1.402462+0.112153
## [71] train-rmse:1.001066+0.023587 test-rmse:1.398752+0.113100
## [72] train-rmse:0.994477+0.023087 test-rmse:1.393065+0.115743
## [73] train-rmse:0.987064+0.022093 test-rmse:1.390704+0.117231
## [74] train-rmse:0.980922+0.021049 test-rmse:1.389905+0.119847
## [75] train-rmse:0.975769+0.020895 test-rmse:1.387510+0.119992
## [76] train-rmse:0.969457+0.019957 test-rmse:1.383614+0.120436
## [77] train-rmse:0.963555+0.020288 test-rmse:1.380688+0.120782
## [78] train-rmse:0.958432+0.019940 test-rmse:1.379214+0.123510
## [79] train-rmse:0.952588+0.019796 test-rmse:1.372398+0.124454
## [80] train-rmse:0.945341+0.020410 test-rmse:1.367866+0.126739
## [81] train-rmse:0.939179+0.019877 test-rmse:1.365280+0.129122
## [82] train-rmse:0.933207+0.021718 test-rmse:1.363345+0.129025
## [83] train-rmse:0.928875+0.021388 test-rmse:1.361944+0.129134
## [84] train-rmse:0.922759+0.021377 test-rmse:1.358449+0.127828
## [85] train-rmse:0.916680+0.020297 test-rmse:1.355448+0.128272
## [86] train-rmse:0.911747+0.019795 test-rmse:1.353067+0.130701
## [87] train-rmse:0.906975+0.019833 test-rmse:1.351182+0.132762
## [88] train-rmse:0.901574+0.019979 test-rmse:1.348645+0.131639
## [89] train-rmse:0.896360+0.019595 test-rmse:1.346215+0.132025
## [90] train-rmse:0.890734+0.019522 test-rmse:1.345142+0.131257
## [91] train-rmse:0.887005+0.019212 test-rmse:1.343514+0.130932
## [92] train-rmse:0.881429+0.018688 test-rmse:1.341294+0.131035
## [93] train-rmse:0.877434+0.019041 test-rmse:1.338374+0.131964
## [94] train-rmse:0.873484+0.019204 test-rmse:1.336434+0.131134
## [95] train-rmse:0.868568+0.019659 test-rmse:1.332152+0.129597
## [96] train-rmse:0.864622+0.019506 test-rmse:1.333607+0.131994
## [97] train-rmse:0.859341+0.018934 test-rmse:1.330240+0.131574
## [98] train-rmse:0.855504+0.019469 test-rmse:1.330729+0.131131
## [99] train-rmse:0.851425+0.018713 test-rmse:1.328797+0.131095
## [100] train-rmse:0.846760+0.017427 test-rmse:1.327311+0.132012
## [101] train-rmse:0.843023+0.017335 test-rmse:1.325087+0.131398
## [102] train-rmse:0.838756+0.017943 test-rmse:1.323006+0.132935
## [103] train-rmse:0.835034+0.017945 test-rmse:1.321669+0.134632
## [104] train-rmse:0.831142+0.018328 test-rmse:1.321696+0.133884
## [105] train-rmse:0.827434+0.018802 test-rmse:1.320124+0.134490
## [106] train-rmse:0.822083+0.021047 test-rmse:1.316766+0.134745
## [107] train-rmse:0.818957+0.020671 test-rmse:1.316133+0.134675
## [108] train-rmse:0.814135+0.022940 test-rmse:1.315756+0.136685
## [109] train-rmse:0.809866+0.021525 test-rmse:1.315205+0.137343
## [110] train-rmse:0.805368+0.020827 test-rmse:1.313797+0.137546
## [111] train-rmse:0.802350+0.020877 test-rmse:1.313030+0.137717
## [112] train-rmse:0.798805+0.021457 test-rmse:1.312486+0.137714
## [113] train-rmse:0.795689+0.021388 test-rmse:1.312315+0.137177
## [114] train-rmse:0.791951+0.022556 test-rmse:1.310402+0.137509
## [115] train-rmse:0.788914+0.022006 test-rmse:1.310447+0.139112
## [116] train-rmse:0.785411+0.022190 test-rmse:1.309884+0.138543
## [117] train-rmse:0.782362+0.021982 test-rmse:1.308413+0.139194
## [118] train-rmse:0.779458+0.022461 test-rmse:1.308717+0.141765
## [119] train-rmse:0.776518+0.022361 test-rmse:1.306681+0.141738
## [120] train-rmse:0.772444+0.021547 test-rmse:1.304509+0.143898
## [121] train-rmse:0.770033+0.021680 test-rmse:1.302470+0.144653
## [122] train-rmse:0.766081+0.022219 test-rmse:1.302278+0.145100
## [123] train-rmse:0.763704+0.022532 test-rmse:1.302327+0.144385
## [124] train-rmse:0.760460+0.023384 test-rmse:1.301457+0.144375
## [125] train-rmse:0.758183+0.023236 test-rmse:1.301269+0.145170
## [126] train-rmse:0.756133+0.023480 test-rmse:1.301367+0.145144
## [127] train-rmse:0.752545+0.023599 test-rmse:1.299502+0.143847
## [128] train-rmse:0.747787+0.023679 test-rmse:1.296023+0.141533
## [129] train-rmse:0.744471+0.022422 test-rmse:1.294718+0.142240
## [130] train-rmse:0.741976+0.022305 test-rmse:1.293738+0.142187
## [131] train-rmse:0.738835+0.021602 test-rmse:1.294754+0.144917
## [132] train-rmse:0.736842+0.021607 test-rmse:1.294198+0.144712
## [133] train-rmse:0.734223+0.021792 test-rmse:1.293616+0.143482
## [134] train-rmse:0.731249+0.021206 test-rmse:1.292680+0.143600
## [135] train-rmse:0.728340+0.020319 test-rmse:1.291481+0.144487
## [136] train-rmse:0.726108+0.020960 test-rmse:1.290517+0.144828
## [137] train-rmse:0.723500+0.021386 test-rmse:1.291531+0.144459
## [138] train-rmse:0.720463+0.021242 test-rmse:1.290057+0.143506
## [139] train-rmse:0.718126+0.021533 test-rmse:1.289882+0.144151
## [140] train-rmse:0.715331+0.021500 test-rmse:1.290453+0.144923
## [141] train-rmse:0.712652+0.021252 test-rmse:1.289445+0.145036
## [142] train-rmse:0.710948+0.020959 test-rmse:1.289663+0.146371
## [143] train-rmse:0.708499+0.020316 test-rmse:1.289365+0.147336
## [144] train-rmse:0.706043+0.020728 test-rmse:1.288852+0.145322
## [145] train-rmse:0.703236+0.021785 test-rmse:1.285628+0.144794
## [146] train-rmse:0.700707+0.021997 test-rmse:1.285276+0.145397
## [147] train-rmse:0.698931+0.022020 test-rmse:1.285317+0.146611
## [148] train-rmse:0.695473+0.020855 test-rmse:1.282514+0.145011
## [149] train-rmse:0.693690+0.020553 test-rmse:1.282085+0.145417
## [150] train-rmse:0.691407+0.020095 test-rmse:1.280974+0.145511
## [151] train-rmse:0.688997+0.019049 test-rmse:1.280843+0.146242
## [152] train-rmse:0.686922+0.019688 test-rmse:1.279613+0.145334
## [153] train-rmse:0.685142+0.020055 test-rmse:1.279394+0.146286
## [154] train-rmse:0.682271+0.020932 test-rmse:1.278085+0.145921
## [155] train-rmse:0.680330+0.020739 test-rmse:1.277634+0.145791
## [156] train-rmse:0.678725+0.020560 test-rmse:1.277754+0.144715
## [157] train-rmse:0.676876+0.020468 test-rmse:1.278210+0.145538
## [158] train-rmse:0.675244+0.020290 test-rmse:1.278679+0.144329
## [159] train-rmse:0.673783+0.020732 test-rmse:1.279040+0.145960
## [160] train-rmse:0.672169+0.020838 test-rmse:1.278517+0.145659
## [161] train-rmse:0.670540+0.021139 test-rmse:1.277054+0.143728
## [162] train-rmse:0.668594+0.021312 test-rmse:1.277392+0.144238
## [163] train-rmse:0.666512+0.021479 test-rmse:1.277035+0.142741
## [164] train-rmse:0.664491+0.020580 test-rmse:1.275490+0.142854
## [165] train-rmse:0.662945+0.020960 test-rmse:1.275621+0.143832
## [166] train-rmse:0.660933+0.021450 test-rmse:1.272530+0.144463
## [167] train-rmse:0.658802+0.021418 test-rmse:1.272999+0.143969
## [168] train-rmse:0.655716+0.019928 test-rmse:1.272782+0.144378
## [169] train-rmse:0.653803+0.020371 test-rmse:1.275463+0.145188
## [170] train-rmse:0.651921+0.019620 test-rmse:1.273416+0.146116
## [171] train-rmse:0.650708+0.019726 test-rmse:1.273116+0.146484
## [172] train-rmse:0.649150+0.019759 test-rmse:1.270910+0.147645
## [173] train-rmse:0.647990+0.019799 test-rmse:1.270776+0.147610
## [174] train-rmse:0.646520+0.019755 test-rmse:1.271309+0.148662
## [175] train-rmse:0.644586+0.019211 test-rmse:1.270926+0.149220
## [176] train-rmse:0.641760+0.017810 test-rmse:1.271800+0.149424
## [177] train-rmse:0.640249+0.017683 test-rmse:1.271650+0.150214
## [178] train-rmse:0.638654+0.017527 test-rmse:1.272157+0.150642
## [179] train-rmse:0.637007+0.016919 test-rmse:1.272700+0.149878
## Stopping. Best iteration:
## [173] train-rmse:0.647990+0.019799 test-rmse:1.270776+0.147610
##
## 17
## [1] train-rmse:8.416828+0.061919 test-rmse:8.413012+0.268342
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.318333+0.053608 test-rmse:7.316918+0.257360
## [3] train-rmse:6.380129+0.046126 test-rmse:6.383760+0.247489
## [4] train-rmse:5.580331+0.039350 test-rmse:5.588299+0.240614
## [5] train-rmse:4.901447+0.034111 test-rmse:4.912928+0.226484
## [6] train-rmse:4.322437+0.029544 test-rmse:4.345181+0.205264
## [7] train-rmse:3.828304+0.025730 test-rmse:3.861348+0.180067
## [8] train-rmse:3.412123+0.021696 test-rmse:3.456998+0.171092
## [9] train-rmse:3.059439+0.016547 test-rmse:3.110062+0.154282
## [10] train-rmse:2.765167+0.015229 test-rmse:2.839674+0.140314
## [11] train-rmse:2.517787+0.013031 test-rmse:2.600276+0.128245
## [12] train-rmse:2.309920+0.012179 test-rmse:2.413734+0.108831
## [13] train-rmse:2.138373+0.011958 test-rmse:2.264182+0.100889
## [14] train-rmse:1.995462+0.013678 test-rmse:2.137424+0.087112
## [15] train-rmse:1.874818+0.012697 test-rmse:2.034254+0.081013
## [16] train-rmse:1.773403+0.012220 test-rmse:1.947897+0.081212
## [17] train-rmse:1.690898+0.014686 test-rmse:1.890429+0.069252
## [18] train-rmse:1.620602+0.015206 test-rmse:1.835329+0.065340
## [19] train-rmse:1.560693+0.018876 test-rmse:1.790249+0.066389
## [20] train-rmse:1.508119+0.020612 test-rmse:1.755047+0.063812
## [21] train-rmse:1.461310+0.019084 test-rmse:1.723517+0.061786
## [22] train-rmse:1.424926+0.019744 test-rmse:1.701036+0.064037
## [23] train-rmse:1.393953+0.019846 test-rmse:1.674384+0.064418
## [24] train-rmse:1.359915+0.020221 test-rmse:1.647625+0.071859
## [25] train-rmse:1.332550+0.020036 test-rmse:1.632281+0.078843
## [26] train-rmse:1.306947+0.020541 test-rmse:1.610842+0.084322
## [27] train-rmse:1.282041+0.021657 test-rmse:1.590676+0.080723
## [28] train-rmse:1.258338+0.020001 test-rmse:1.577930+0.082371
## [29] train-rmse:1.236215+0.021741 test-rmse:1.562357+0.078040
## [30] train-rmse:1.217590+0.021561 test-rmse:1.554705+0.079944
## [31] train-rmse:1.199108+0.022200 test-rmse:1.544549+0.087056
## [32] train-rmse:1.180722+0.020394 test-rmse:1.536055+0.092598
## [33] train-rmse:1.164506+0.018985 test-rmse:1.526236+0.097907
## [34] train-rmse:1.147022+0.021502 test-rmse:1.513469+0.097021
## [35] train-rmse:1.129561+0.020263 test-rmse:1.501029+0.101504
## [36] train-rmse:1.114825+0.021452 test-rmse:1.492466+0.106905
## [37] train-rmse:1.100560+0.021149 test-rmse:1.484638+0.107825
## [38] train-rmse:1.086757+0.021065 test-rmse:1.477748+0.106480
## [39] train-rmse:1.074242+0.021320 test-rmse:1.471389+0.107612
## [40] train-rmse:1.061975+0.020722 test-rmse:1.463865+0.109709
## [41] train-rmse:1.047400+0.021366 test-rmse:1.456155+0.114202
## [42] train-rmse:1.036897+0.020894 test-rmse:1.448488+0.117881
## [43] train-rmse:1.024401+0.021807 test-rmse:1.439636+0.119849
## [44] train-rmse:1.012012+0.022753 test-rmse:1.428683+0.122241
## [45] train-rmse:0.999447+0.022058 test-rmse:1.425143+0.123910
## [46] train-rmse:0.988330+0.022168 test-rmse:1.420657+0.124474
## [47] train-rmse:0.978139+0.023521 test-rmse:1.412627+0.122898
## [48] train-rmse:0.968455+0.022462 test-rmse:1.406658+0.124935
## [49] train-rmse:0.958416+0.022466 test-rmse:1.405107+0.123930
## [50] train-rmse:0.948785+0.022390 test-rmse:1.401688+0.127874
## [51] train-rmse:0.938171+0.023598 test-rmse:1.395166+0.127779
## [52] train-rmse:0.927260+0.023179 test-rmse:1.391835+0.130477
## [53] train-rmse:0.919077+0.022672 test-rmse:1.385415+0.132986
## [54] train-rmse:0.909109+0.022160 test-rmse:1.379149+0.130650
## [55] train-rmse:0.899743+0.022241 test-rmse:1.376421+0.130612
## [56] train-rmse:0.891326+0.024088 test-rmse:1.375426+0.132373
## [57] train-rmse:0.882967+0.024501 test-rmse:1.372212+0.133943
## [58] train-rmse:0.874538+0.024347 test-rmse:1.369716+0.133859
## [59] train-rmse:0.866500+0.024092 test-rmse:1.367234+0.137642
## [60] train-rmse:0.859623+0.023986 test-rmse:1.363558+0.137685
## [61] train-rmse:0.852113+0.024626 test-rmse:1.360684+0.139556
## [62] train-rmse:0.845015+0.023527 test-rmse:1.357445+0.140927
## [63] train-rmse:0.837568+0.023764 test-rmse:1.354858+0.142278
## [64] train-rmse:0.829559+0.023264 test-rmse:1.352611+0.143195
## [65] train-rmse:0.822217+0.022429 test-rmse:1.346156+0.145259
## [66] train-rmse:0.815772+0.023301 test-rmse:1.343782+0.143805
## [67] train-rmse:0.808518+0.021933 test-rmse:1.341820+0.144984
## [68] train-rmse:0.802385+0.021105 test-rmse:1.338162+0.146046
## [69] train-rmse:0.797085+0.021970 test-rmse:1.334569+0.145106
## [70] train-rmse:0.789678+0.022733 test-rmse:1.329309+0.146054
## [71] train-rmse:0.784253+0.021830 test-rmse:1.330012+0.147822
## [72] train-rmse:0.778911+0.021264 test-rmse:1.330472+0.150278
## [73] train-rmse:0.773555+0.020383 test-rmse:1.328124+0.150430
## [74] train-rmse:0.766471+0.020502 test-rmse:1.326376+0.151578
## [75] train-rmse:0.760256+0.021335 test-rmse:1.322408+0.149543
## [76] train-rmse:0.754961+0.021502 test-rmse:1.318967+0.150646
## [77] train-rmse:0.749871+0.021101 test-rmse:1.317584+0.151376
## [78] train-rmse:0.743436+0.019853 test-rmse:1.317032+0.152448
## [79] train-rmse:0.738321+0.019863 test-rmse:1.315247+0.151839
## [80] train-rmse:0.732889+0.018580 test-rmse:1.315368+0.154182
## [81] train-rmse:0.727124+0.018599 test-rmse:1.315382+0.155110
## [82] train-rmse:0.721997+0.017538 test-rmse:1.312612+0.155308
## [83] train-rmse:0.716878+0.018298 test-rmse:1.311238+0.157342
## [84] train-rmse:0.710726+0.017086 test-rmse:1.308448+0.158478
## [85] train-rmse:0.705193+0.019134 test-rmse:1.306187+0.158261
## [86] train-rmse:0.699791+0.018855 test-rmse:1.305980+0.158259
## [87] train-rmse:0.692421+0.018193 test-rmse:1.304685+0.157587
## [88] train-rmse:0.688109+0.018873 test-rmse:1.303972+0.158508
## [89] train-rmse:0.683643+0.018552 test-rmse:1.302874+0.160312
## [90] train-rmse:0.679886+0.017645 test-rmse:1.301482+0.160789
## [91] train-rmse:0.674846+0.018561 test-rmse:1.302505+0.162350
## [92] train-rmse:0.669884+0.018280 test-rmse:1.302696+0.163500
## [93] train-rmse:0.665553+0.017683 test-rmse:1.300783+0.163068
## [94] train-rmse:0.661275+0.017951 test-rmse:1.299757+0.164120
## [95] train-rmse:0.656877+0.018862 test-rmse:1.298605+0.164294
## [96] train-rmse:0.652492+0.018482 test-rmse:1.297167+0.163366
## [97] train-rmse:0.649013+0.019145 test-rmse:1.296022+0.164049
## [98] train-rmse:0.644898+0.018185 test-rmse:1.295142+0.165250
## [99] train-rmse:0.641863+0.017454 test-rmse:1.293260+0.166552
## [100] train-rmse:0.636859+0.017090 test-rmse:1.291931+0.166767
## [101] train-rmse:0.633038+0.018353 test-rmse:1.291963+0.166706
## [102] train-rmse:0.628334+0.016965 test-rmse:1.293320+0.168630
## [103] train-rmse:0.624964+0.016540 test-rmse:1.291545+0.168053
## [104] train-rmse:0.621833+0.016160 test-rmse:1.291053+0.168432
## [105] train-rmse:0.618748+0.016824 test-rmse:1.291832+0.168430
## [106] train-rmse:0.616108+0.016383 test-rmse:1.292908+0.168163
## [107] train-rmse:0.612543+0.015449 test-rmse:1.294024+0.168941
## [108] train-rmse:0.609650+0.016019 test-rmse:1.294213+0.170361
## [109] train-rmse:0.605979+0.017179 test-rmse:1.292810+0.169301
## [110] train-rmse:0.602667+0.018066 test-rmse:1.292528+0.169364
## Stopping. Best iteration:
## [104] train-rmse:0.621833+0.016160 test-rmse:1.291053+0.168432
##
## 18
## [1] train-rmse:8.414624+0.062147 test-rmse:8.413201+0.268625
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.312891+0.053867 test-rmse:7.317948+0.252161
## [3] train-rmse:6.371178+0.046648 test-rmse:6.385164+0.242531
## [4] train-rmse:5.567149+0.040337 test-rmse:5.579473+0.229377
## [5] train-rmse:4.880200+0.034274 test-rmse:4.900421+0.209184
## [6] train-rmse:4.294722+0.030302 test-rmse:4.321350+0.183302
## [7] train-rmse:3.796572+0.026260 test-rmse:3.831689+0.169194
## [8] train-rmse:3.371763+0.023754 test-rmse:3.418629+0.149520
## [9] train-rmse:3.013335+0.021513 test-rmse:3.078430+0.142601
## [10] train-rmse:2.709109+0.017869 test-rmse:2.794117+0.130515
## [11] train-rmse:2.451383+0.016747 test-rmse:2.545826+0.124288
## [12] train-rmse:2.238483+0.014606 test-rmse:2.358674+0.114788
## [13] train-rmse:2.057362+0.014147 test-rmse:2.201004+0.110816
## [14] train-rmse:1.900003+0.016081 test-rmse:2.067742+0.095564
## [15] train-rmse:1.772854+0.016209 test-rmse:1.953323+0.095891
## [16] train-rmse:1.661489+0.019014 test-rmse:1.868981+0.081988
## [17] train-rmse:1.569428+0.019523 test-rmse:1.800112+0.077452
## [18] train-rmse:1.490798+0.019250 test-rmse:1.741685+0.076168
## [19] train-rmse:1.423295+0.022212 test-rmse:1.689650+0.072578
## [20] train-rmse:1.365154+0.018286 test-rmse:1.649220+0.076409
## [21] train-rmse:1.314326+0.020317 test-rmse:1.609086+0.085600
## [22] train-rmse:1.271581+0.019642 test-rmse:1.577484+0.083840
## [23] train-rmse:1.231813+0.023956 test-rmse:1.555582+0.082246
## [24] train-rmse:1.196538+0.023110 test-rmse:1.536045+0.083293
## [25] train-rmse:1.165923+0.024796 test-rmse:1.523978+0.090014
## [26] train-rmse:1.137126+0.026524 test-rmse:1.505001+0.093423
## [27] train-rmse:1.110239+0.025021 test-rmse:1.488917+0.095775
## [28] train-rmse:1.082508+0.026163 test-rmse:1.475355+0.101036
## [29] train-rmse:1.060546+0.027003 test-rmse:1.462243+0.104432
## [30] train-rmse:1.041830+0.026225 test-rmse:1.456059+0.109052
## [31] train-rmse:1.023188+0.027554 test-rmse:1.441863+0.108757
## [32] train-rmse:1.003068+0.030271 test-rmse:1.429790+0.110139
## [33] train-rmse:0.987344+0.030021 test-rmse:1.421909+0.112556
## [34] train-rmse:0.969714+0.027034 test-rmse:1.412256+0.114098
## [35] train-rmse:0.953889+0.027402 test-rmse:1.397695+0.116568
## [36] train-rmse:0.938330+0.028070 test-rmse:1.386909+0.118929
## [37] train-rmse:0.922209+0.024715 test-rmse:1.376590+0.126007
## [38] train-rmse:0.907320+0.025327 test-rmse:1.370794+0.131283
## [39] train-rmse:0.893355+0.023025 test-rmse:1.365074+0.130859
## [40] train-rmse:0.878329+0.023511 test-rmse:1.357448+0.132546
## [41] train-rmse:0.864158+0.024785 test-rmse:1.347791+0.135919
## [42] train-rmse:0.851770+0.024368 test-rmse:1.344452+0.135346
## [43] train-rmse:0.839052+0.021563 test-rmse:1.338446+0.137941
## [44] train-rmse:0.827425+0.020918 test-rmse:1.334008+0.136420
## [45] train-rmse:0.816292+0.019609 test-rmse:1.328255+0.139137
## [46] train-rmse:0.804167+0.019314 test-rmse:1.325693+0.138950
## [47] train-rmse:0.792972+0.020436 test-rmse:1.317654+0.138400
## [48] train-rmse:0.782867+0.018721 test-rmse:1.315198+0.138603
## [49] train-rmse:0.772227+0.017537 test-rmse:1.310198+0.139360
## [50] train-rmse:0.762210+0.018798 test-rmse:1.303721+0.140570
## [51] train-rmse:0.752283+0.019185 test-rmse:1.303832+0.141395
## [52] train-rmse:0.743911+0.018722 test-rmse:1.301252+0.143460
## [53] train-rmse:0.734748+0.019818 test-rmse:1.297769+0.143246
## [54] train-rmse:0.727016+0.018433 test-rmse:1.295352+0.145921
## [55] train-rmse:0.717325+0.020063 test-rmse:1.291481+0.146653
## [56] train-rmse:0.708149+0.020407 test-rmse:1.287226+0.147825
## [57] train-rmse:0.699636+0.021462 test-rmse:1.282561+0.148027
## [58] train-rmse:0.692180+0.020296 test-rmse:1.280539+0.149132
## [59] train-rmse:0.684501+0.020577 test-rmse:1.276164+0.150521
## [60] train-rmse:0.677525+0.022383 test-rmse:1.273473+0.150130
## [61] train-rmse:0.669800+0.022012 test-rmse:1.271754+0.150047
## [62] train-rmse:0.661912+0.020763 test-rmse:1.267418+0.152081
## [63] train-rmse:0.654509+0.022024 test-rmse:1.264510+0.154086
## [64] train-rmse:0.648362+0.020671 test-rmse:1.265409+0.153726
## [65] train-rmse:0.642200+0.021567 test-rmse:1.264188+0.153164
## [66] train-rmse:0.635775+0.021754 test-rmse:1.262595+0.154154
## [67] train-rmse:0.629232+0.020640 test-rmse:1.259623+0.154466
## [68] train-rmse:0.623318+0.021369 test-rmse:1.257707+0.153164
## [69] train-rmse:0.617825+0.020546 test-rmse:1.257477+0.154724
## [70] train-rmse:0.610889+0.019486 test-rmse:1.256126+0.156510
## [71] train-rmse:0.605443+0.019737 test-rmse:1.256445+0.155934
## [72] train-rmse:0.600476+0.020493 test-rmse:1.257087+0.156708
## [73] train-rmse:0.595484+0.019509 test-rmse:1.256122+0.157506
## [74] train-rmse:0.589560+0.019403 test-rmse:1.255030+0.159323
## [75] train-rmse:0.585704+0.019510 test-rmse:1.253169+0.160525
## [76] train-rmse:0.580777+0.018941 test-rmse:1.253923+0.159874
## [77] train-rmse:0.574683+0.017873 test-rmse:1.251893+0.161409
## [78] train-rmse:0.570227+0.018102 test-rmse:1.251857+0.161435
## [79] train-rmse:0.565009+0.017375 test-rmse:1.251190+0.161344
## [80] train-rmse:0.558388+0.017833 test-rmse:1.248906+0.160567
## [81] train-rmse:0.554996+0.017689 test-rmse:1.249772+0.161782
## [82] train-rmse:0.550982+0.016848 test-rmse:1.249030+0.162938
## [83] train-rmse:0.546226+0.018144 test-rmse:1.246708+0.162571
## [84] train-rmse:0.541772+0.017690 test-rmse:1.247864+0.162592
## [85] train-rmse:0.537409+0.017455 test-rmse:1.247829+0.163017
## [86] train-rmse:0.533447+0.017666 test-rmse:1.248915+0.164468
## [87] train-rmse:0.529648+0.017830 test-rmse:1.250041+0.164951
## [88] train-rmse:0.524600+0.018295 test-rmse:1.247853+0.162819
## [89] train-rmse:0.520001+0.018204 test-rmse:1.246236+0.164910
## [90] train-rmse:0.516189+0.018992 test-rmse:1.244499+0.165214
## [91] train-rmse:0.512814+0.018469 test-rmse:1.242846+0.166554
## [92] train-rmse:0.509619+0.018265 test-rmse:1.242814+0.165895
## [93] train-rmse:0.506042+0.019497 test-rmse:1.243277+0.165567
## [94] train-rmse:0.503270+0.019004 test-rmse:1.242717+0.165740
## [95] train-rmse:0.499994+0.019407 test-rmse:1.242565+0.166940
## [96] train-rmse:0.496811+0.019437 test-rmse:1.242106+0.167804
## [97] train-rmse:0.494142+0.019327 test-rmse:1.242442+0.168623
## [98] train-rmse:0.489796+0.019365 test-rmse:1.241015+0.166832
## [99] train-rmse:0.485038+0.019573 test-rmse:1.239092+0.166142
## [100] train-rmse:0.479881+0.018824 test-rmse:1.239578+0.167712
## [101] train-rmse:0.476040+0.018827 test-rmse:1.238387+0.167396
## [102] train-rmse:0.473241+0.018271 test-rmse:1.237445+0.166725
## [103] train-rmse:0.468369+0.019173 test-rmse:1.237242+0.166793
## [104] train-rmse:0.466329+0.019106 test-rmse:1.236478+0.167162
## [105] train-rmse:0.462312+0.018928 test-rmse:1.236280+0.166567
## [106] train-rmse:0.459704+0.018997 test-rmse:1.236242+0.166912
## [107] train-rmse:0.456081+0.017349 test-rmse:1.235351+0.166659
## [108] train-rmse:0.454321+0.018052 test-rmse:1.236350+0.166510
## [109] train-rmse:0.451535+0.017693 test-rmse:1.235596+0.166612
## [110] train-rmse:0.447781+0.017479 test-rmse:1.233345+0.166988
## [111] train-rmse:0.444630+0.017531 test-rmse:1.231977+0.166406
## [112] train-rmse:0.441661+0.017312 test-rmse:1.232375+0.166098
## [113] train-rmse:0.439984+0.016992 test-rmse:1.232126+0.166096
## [114] train-rmse:0.436820+0.017611 test-rmse:1.232053+0.165847
## [115] train-rmse:0.433188+0.017363 test-rmse:1.230910+0.164731
## [116] train-rmse:0.431451+0.017265 test-rmse:1.230932+0.165244
## [117] train-rmse:0.428886+0.017482 test-rmse:1.230697+0.165563
## [118] train-rmse:0.425915+0.018097 test-rmse:1.230761+0.165714
## [119] train-rmse:0.423545+0.018580 test-rmse:1.230018+0.166093
## [120] train-rmse:0.420498+0.019179 test-rmse:1.229320+0.165806
## [121] train-rmse:0.417715+0.019941 test-rmse:1.227194+0.165347
## [122] train-rmse:0.415660+0.019864 test-rmse:1.227919+0.164674
## [123] train-rmse:0.412787+0.018725 test-rmse:1.228084+0.165716
## [124] train-rmse:0.410798+0.018546 test-rmse:1.228379+0.165376
## [125] train-rmse:0.408098+0.018089 test-rmse:1.228340+0.166150
## [126] train-rmse:0.405968+0.018446 test-rmse:1.227850+0.165961
## [127] train-rmse:0.402719+0.018543 test-rmse:1.226088+0.165343
## [128] train-rmse:0.400201+0.019200 test-rmse:1.225509+0.165399
## [129] train-rmse:0.398225+0.019680 test-rmse:1.226362+0.165032
## [130] train-rmse:0.394172+0.019119 test-rmse:1.224614+0.163178
## [131] train-rmse:0.392355+0.019315 test-rmse:1.224450+0.162819
## [132] train-rmse:0.389419+0.018172 test-rmse:1.224961+0.163560
## [133] train-rmse:0.387449+0.018187 test-rmse:1.224904+0.164032
## [134] train-rmse:0.385142+0.017796 test-rmse:1.225952+0.164596
## [135] train-rmse:0.381288+0.018252 test-rmse:1.225589+0.164197
## [136] train-rmse:0.379229+0.018299 test-rmse:1.226303+0.164803
## [137] train-rmse:0.377496+0.018323 test-rmse:1.225817+0.164381
## Stopping. Best iteration:
## [131] train-rmse:0.392355+0.019315 test-rmse:1.224450+0.162819
##
## 19
## [1] train-rmse:8.414179+0.062258 test-rmse:8.413727+0.264087
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.311039+0.053910 test-rmse:7.317775+0.253045
## [3] train-rmse:6.366366+0.047261 test-rmse:6.380102+0.239374
## [4] train-rmse:5.557974+0.040698 test-rmse:5.577010+0.221682
## [5] train-rmse:4.866925+0.035157 test-rmse:4.892786+0.203985
## [6] train-rmse:4.277521+0.032288 test-rmse:4.302446+0.185683
## [7] train-rmse:3.771630+0.028563 test-rmse:3.803053+0.166036
## [8] train-rmse:3.340733+0.025083 test-rmse:3.378763+0.155826
## [9] train-rmse:2.972933+0.021480 test-rmse:3.030739+0.145295
## [10] train-rmse:2.661926+0.020537 test-rmse:2.743451+0.133460
## [11] train-rmse:2.400121+0.017454 test-rmse:2.507819+0.134047
## [12] train-rmse:2.177309+0.016694 test-rmse:2.312062+0.121412
## [13] train-rmse:1.984105+0.020667 test-rmse:2.135511+0.108814
## [14] train-rmse:1.823970+0.018382 test-rmse:2.006811+0.101919
## [15] train-rmse:1.684839+0.020101 test-rmse:1.896297+0.091000
## [16] train-rmse:1.568612+0.022016 test-rmse:1.807902+0.085174
## [17] train-rmse:1.465174+0.022450 test-rmse:1.716051+0.090570
## [18] train-rmse:1.380137+0.023206 test-rmse:1.663865+0.087279
## [19] train-rmse:1.307878+0.022994 test-rmse:1.605508+0.084902
## [20] train-rmse:1.244362+0.025078 test-rmse:1.562654+0.086776
## [21] train-rmse:1.189738+0.025064 test-rmse:1.523393+0.095693
## [22] train-rmse:1.141550+0.022049 test-rmse:1.494726+0.097736
## [23] train-rmse:1.100841+0.020360 test-rmse:1.469120+0.101798
## [24] train-rmse:1.061746+0.020614 test-rmse:1.441236+0.102476
## [25] train-rmse:1.026191+0.020321 test-rmse:1.423386+0.108321
## [26] train-rmse:0.993858+0.021077 test-rmse:1.409273+0.109656
## [27] train-rmse:0.966348+0.022273 test-rmse:1.392891+0.108385
## [28] train-rmse:0.940357+0.022071 test-rmse:1.380293+0.115457
## [29] train-rmse:0.915322+0.025170 test-rmse:1.362902+0.116032
## [30] train-rmse:0.893017+0.026641 test-rmse:1.356433+0.118880
## [31] train-rmse:0.872469+0.027948 test-rmse:1.344851+0.118646
## [32] train-rmse:0.853398+0.024940 test-rmse:1.336648+0.124969
## [33] train-rmse:0.833792+0.024107 test-rmse:1.329833+0.127852
## [34] train-rmse:0.818120+0.024525 test-rmse:1.324705+0.130308
## [35] train-rmse:0.799941+0.023262 test-rmse:1.317740+0.132055
## [36] train-rmse:0.784924+0.023757 test-rmse:1.312324+0.134410
## [37] train-rmse:0.770751+0.021135 test-rmse:1.306425+0.136899
## [38] train-rmse:0.754451+0.022244 test-rmse:1.305694+0.136235
## [39] train-rmse:0.741377+0.021531 test-rmse:1.300569+0.139316
## [40] train-rmse:0.727835+0.019959 test-rmse:1.292134+0.139750
## [41] train-rmse:0.716396+0.021659 test-rmse:1.286243+0.138455
## [42] train-rmse:0.703855+0.020257 test-rmse:1.283171+0.142960
## [43] train-rmse:0.691647+0.020202 test-rmse:1.279404+0.144768
## [44] train-rmse:0.680789+0.020816 test-rmse:1.276532+0.144346
## [45] train-rmse:0.668289+0.019440 test-rmse:1.269009+0.147158
## [46] train-rmse:0.659408+0.019547 test-rmse:1.268501+0.152908
## [47] train-rmse:0.648594+0.019551 test-rmse:1.263309+0.151490
## [48] train-rmse:0.637445+0.019622 test-rmse:1.260308+0.152584
## [49] train-rmse:0.626714+0.019546 test-rmse:1.259647+0.150448
## [50] train-rmse:0.618443+0.018349 test-rmse:1.257110+0.152713
## [51] train-rmse:0.610051+0.018053 test-rmse:1.256355+0.151588
## [52] train-rmse:0.600949+0.018672 test-rmse:1.252469+0.150060
## [53] train-rmse:0.592615+0.018873 test-rmse:1.247980+0.152188
## [54] train-rmse:0.584498+0.019361 test-rmse:1.243617+0.152549
## [55] train-rmse:0.576963+0.018096 test-rmse:1.240295+0.153840
## [56] train-rmse:0.569254+0.019926 test-rmse:1.241236+0.152958
## [57] train-rmse:0.560835+0.018595 test-rmse:1.237912+0.156153
## [58] train-rmse:0.553796+0.019021 test-rmse:1.237270+0.157742
## [59] train-rmse:0.547441+0.019851 test-rmse:1.236506+0.158232
## [60] train-rmse:0.539505+0.019143 test-rmse:1.235861+0.156644
## [61] train-rmse:0.533814+0.018833 test-rmse:1.234412+0.158072
## [62] train-rmse:0.528000+0.018918 test-rmse:1.234238+0.159946
## [63] train-rmse:0.520939+0.017977 test-rmse:1.234206+0.160535
## [64] train-rmse:0.513465+0.017425 test-rmse:1.231272+0.161638
## [65] train-rmse:0.507863+0.017235 test-rmse:1.231625+0.162621
## [66] train-rmse:0.502428+0.016971 test-rmse:1.230136+0.165856
## [67] train-rmse:0.498091+0.017080 test-rmse:1.230252+0.165287
## [68] train-rmse:0.492232+0.016430 test-rmse:1.230867+0.165981
## [69] train-rmse:0.483460+0.018189 test-rmse:1.226859+0.164949
## [70] train-rmse:0.478477+0.018099 test-rmse:1.226602+0.166093
## [71] train-rmse:0.473300+0.018248 test-rmse:1.226973+0.163576
## [72] train-rmse:0.467966+0.017317 test-rmse:1.224742+0.164344
## [73] train-rmse:0.462794+0.017645 test-rmse:1.224163+0.165288
## [74] train-rmse:0.457448+0.017149 test-rmse:1.224731+0.164709
## [75] train-rmse:0.453087+0.017268 test-rmse:1.223376+0.164433
## [76] train-rmse:0.449168+0.017974 test-rmse:1.221622+0.163487
## [77] train-rmse:0.444888+0.018332 test-rmse:1.220378+0.164468
## [78] train-rmse:0.439679+0.018174 test-rmse:1.218810+0.165159
## [79] train-rmse:0.433913+0.016556 test-rmse:1.219326+0.167528
## [80] train-rmse:0.430521+0.016772 test-rmse:1.218812+0.167599
## [81] train-rmse:0.426986+0.016778 test-rmse:1.218159+0.167615
## [82] train-rmse:0.421453+0.014511 test-rmse:1.217332+0.166968
## [83] train-rmse:0.417333+0.014603 test-rmse:1.216910+0.166205
## [84] train-rmse:0.413404+0.015138 test-rmse:1.217507+0.167406
## [85] train-rmse:0.409182+0.016983 test-rmse:1.216721+0.167343
## [86] train-rmse:0.403115+0.015975 test-rmse:1.215134+0.167870
## [87] train-rmse:0.400011+0.015483 test-rmse:1.214757+0.169264
## [88] train-rmse:0.397100+0.015918 test-rmse:1.214066+0.169942
## [89] train-rmse:0.393883+0.015651 test-rmse:1.212891+0.170834
## [90] train-rmse:0.389906+0.016535 test-rmse:1.212799+0.171312
## [91] train-rmse:0.387584+0.016486 test-rmse:1.213238+0.170749
## [92] train-rmse:0.384569+0.015979 test-rmse:1.212357+0.171268
## [93] train-rmse:0.382219+0.016714 test-rmse:1.211219+0.170860
## [94] train-rmse:0.378953+0.017211 test-rmse:1.211619+0.171834
## [95] train-rmse:0.374581+0.018467 test-rmse:1.211043+0.171387
## [96] train-rmse:0.371974+0.018901 test-rmse:1.211253+0.170945
## [97] train-rmse:0.367068+0.015954 test-rmse:1.211269+0.170460
## [98] train-rmse:0.363591+0.017196 test-rmse:1.210391+0.170580
## [99] train-rmse:0.360618+0.018005 test-rmse:1.210949+0.170882
## [100] train-rmse:0.357496+0.019115 test-rmse:1.210754+0.170550
## [101] train-rmse:0.354919+0.018927 test-rmse:1.210939+0.170397
## [102] train-rmse:0.351576+0.018649 test-rmse:1.210633+0.170322
## [103] train-rmse:0.347630+0.018262 test-rmse:1.211858+0.169600
## [104] train-rmse:0.344469+0.019406 test-rmse:1.210888+0.169387
## Stopping. Best iteration:
## [98] train-rmse:0.363591+0.017196 test-rmse:1.210391+0.170580
##
## 20
## [1] train-rmse:8.414179+0.062258 test-rmse:8.413727+0.264087
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.310448+0.054060 test-rmse:7.318706+0.244898
## [3] train-rmse:6.364224+0.047489 test-rmse:6.378444+0.229779
## [4] train-rmse:5.553561+0.040829 test-rmse:5.576745+0.206176
## [5] train-rmse:4.859259+0.035335 test-rmse:4.884487+0.181970
## [6] train-rmse:4.266186+0.032149 test-rmse:4.297749+0.168526
## [7] train-rmse:3.756373+0.028485 test-rmse:3.791426+0.154260
## [8] train-rmse:3.320130+0.025043 test-rmse:3.365415+0.137886
## [9] train-rmse:2.948978+0.021485 test-rmse:3.015403+0.133706
## [10] train-rmse:2.632114+0.019620 test-rmse:2.716818+0.124584
## [11] train-rmse:2.362404+0.017878 test-rmse:2.468000+0.125506
## [12] train-rmse:2.129447+0.017855 test-rmse:2.262105+0.111506
## [13] train-rmse:1.932257+0.013908 test-rmse:2.089486+0.102481
## [14] train-rmse:1.764641+0.014945 test-rmse:1.961517+0.094123
## [15] train-rmse:1.618898+0.012549 test-rmse:1.847736+0.094925
## [16] train-rmse:1.494984+0.011345 test-rmse:1.745650+0.097021
## [17] train-rmse:1.390483+0.009728 test-rmse:1.668308+0.096533
## [18] train-rmse:1.298247+0.011791 test-rmse:1.601517+0.093919
## [19] train-rmse:1.219993+0.010326 test-rmse:1.550861+0.099146
## [20] train-rmse:1.150732+0.013796 test-rmse:1.507418+0.099382
## [21] train-rmse:1.092776+0.014865 test-rmse:1.473864+0.100823
## [22] train-rmse:1.039909+0.013203 test-rmse:1.439047+0.103995
## [23] train-rmse:0.995620+0.013194 test-rmse:1.419598+0.110780
## [24] train-rmse:0.953185+0.012394 test-rmse:1.396197+0.118549
## [25] train-rmse:0.919630+0.011657 test-rmse:1.378795+0.121368
## [26] train-rmse:0.887917+0.012548 test-rmse:1.362907+0.126992
## [27] train-rmse:0.857377+0.014645 test-rmse:1.345683+0.128475
## [28] train-rmse:0.833363+0.013379 test-rmse:1.337742+0.135024
## [29] train-rmse:0.811003+0.015274 test-rmse:1.328627+0.133162
## [30] train-rmse:0.787503+0.012880 test-rmse:1.315849+0.142159
## [31] train-rmse:0.767306+0.011875 test-rmse:1.309229+0.145822
## [32] train-rmse:0.746065+0.011953 test-rmse:1.299689+0.147819
## [33] train-rmse:0.728846+0.011140 test-rmse:1.293610+0.150138
## [34] train-rmse:0.711847+0.014825 test-rmse:1.285563+0.146613
## [35] train-rmse:0.695543+0.015943 test-rmse:1.281210+0.147931
## [36] train-rmse:0.677764+0.013958 test-rmse:1.280532+0.150357
## [37] train-rmse:0.663430+0.013163 test-rmse:1.277573+0.152396
## [38] train-rmse:0.648010+0.011891 test-rmse:1.269640+0.154868
## [39] train-rmse:0.634794+0.010882 test-rmse:1.268570+0.153811
## [40] train-rmse:0.623167+0.009069 test-rmse:1.267542+0.155162
## [41] train-rmse:0.611400+0.009617 test-rmse:1.267957+0.160374
## [42] train-rmse:0.596585+0.010469 test-rmse:1.262590+0.160751
## [43] train-rmse:0.585683+0.010007 test-rmse:1.262184+0.162119
## [44] train-rmse:0.575189+0.010075 test-rmse:1.257694+0.161788
## [45] train-rmse:0.564348+0.008112 test-rmse:1.257065+0.164403
## [46] train-rmse:0.554545+0.008109 test-rmse:1.256026+0.164397
## [47] train-rmse:0.545586+0.008260 test-rmse:1.252613+0.166058
## [48] train-rmse:0.536439+0.007486 test-rmse:1.247115+0.165360
## [49] train-rmse:0.526525+0.008390 test-rmse:1.245377+0.163692
## [50] train-rmse:0.518150+0.008652 test-rmse:1.244155+0.165463
## [51] train-rmse:0.509584+0.009236 test-rmse:1.240024+0.164711
## [52] train-rmse:0.501455+0.010175 test-rmse:1.241762+0.164627
## [53] train-rmse:0.494802+0.010036 test-rmse:1.240426+0.165772
## [54] train-rmse:0.487609+0.009416 test-rmse:1.240988+0.167518
## [55] train-rmse:0.480372+0.009344 test-rmse:1.241107+0.167082
## [56] train-rmse:0.472295+0.009087 test-rmse:1.240355+0.166997
## [57] train-rmse:0.465350+0.009652 test-rmse:1.237666+0.167500
## [58] train-rmse:0.458543+0.010001 test-rmse:1.236220+0.169282
## [59] train-rmse:0.453765+0.011220 test-rmse:1.234326+0.169952
## [60] train-rmse:0.445930+0.009088 test-rmse:1.231038+0.170528
## [61] train-rmse:0.440213+0.009268 test-rmse:1.230305+0.172146
## [62] train-rmse:0.433562+0.009963 test-rmse:1.227487+0.172470
## [63] train-rmse:0.428354+0.009402 test-rmse:1.227016+0.172594
## [64] train-rmse:0.419193+0.007776 test-rmse:1.228034+0.171754
## [65] train-rmse:0.414466+0.007231 test-rmse:1.228592+0.172572
## [66] train-rmse:0.410959+0.007495 test-rmse:1.228467+0.173638
## [67] train-rmse:0.405520+0.008442 test-rmse:1.228304+0.172042
## [68] train-rmse:0.401661+0.009378 test-rmse:1.228071+0.173468
## [69] train-rmse:0.397746+0.010251 test-rmse:1.227397+0.173552
## Stopping. Best iteration:
## [63] train-rmse:0.428354+0.009402 test-rmse:1.227016+0.172594
##
## 21
## [1] train-rmse:8.264722+0.059069 test-rmse:8.260608+0.273191
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.073244+0.048071 test-rmse:7.065033+0.269355
## [3] train-rmse:6.089241+0.038642 test-rmse:6.086336+0.263827
## [4] train-rmse:5.281084+0.030897 test-rmse:5.276163+0.253816
## [5] train-rmse:4.622433+0.024635 test-rmse:4.625573+0.237674
## [6] train-rmse:4.090524+0.019187 test-rmse:4.097756+0.221690
## [7] train-rmse:3.665491+0.015760 test-rmse:3.677271+0.207129
## [8] train-rmse:3.328571+0.013045 test-rmse:3.348549+0.177905
## [9] train-rmse:3.064749+0.012045 test-rmse:3.086331+0.153614
## [10] train-rmse:2.860173+0.012327 test-rmse:2.881776+0.135343
## [11] train-rmse:2.702459+0.012789 test-rmse:2.724357+0.115209
## [12] train-rmse:2.581458+0.013442 test-rmse:2.614429+0.087619
## [13] train-rmse:2.488857+0.013831 test-rmse:2.523697+0.075512
## [14] train-rmse:2.417808+0.014537 test-rmse:2.450856+0.061864
## [15] train-rmse:2.362590+0.015222 test-rmse:2.398023+0.053872
## [16] train-rmse:2.319879+0.016052 test-rmse:2.360780+0.049052
## [17] train-rmse:2.285959+0.016506 test-rmse:2.330815+0.049435
## [18] train-rmse:2.258737+0.017193 test-rmse:2.304313+0.051389
## [19] train-rmse:2.236241+0.017807 test-rmse:2.283692+0.054849
## [20] train-rmse:2.217015+0.018401 test-rmse:2.260762+0.059002
## [21] train-rmse:2.200428+0.019060 test-rmse:2.247334+0.063171
## [22] train-rmse:2.186011+0.019781 test-rmse:2.235395+0.066213
## [23] train-rmse:2.173058+0.020157 test-rmse:2.223333+0.063974
## [24] train-rmse:2.160904+0.020298 test-rmse:2.218050+0.070247
## [25] train-rmse:2.149581+0.020710 test-rmse:2.206257+0.072115
## [26] train-rmse:2.138919+0.021323 test-rmse:2.195226+0.075771
## [27] train-rmse:2.128977+0.021749 test-rmse:2.184594+0.080796
## [28] train-rmse:2.119537+0.022119 test-rmse:2.174098+0.078879
## [29] train-rmse:2.110436+0.022677 test-rmse:2.168091+0.080038
## [30] train-rmse:2.101678+0.022974 test-rmse:2.158993+0.082488
## [31] train-rmse:2.092921+0.023281 test-rmse:2.151039+0.080619
## [32] train-rmse:2.084494+0.023570 test-rmse:2.144335+0.086325
## [33] train-rmse:2.076189+0.023948 test-rmse:2.138327+0.087443
## [34] train-rmse:2.067982+0.024346 test-rmse:2.132627+0.087681
## [35] train-rmse:2.060092+0.024690 test-rmse:2.129590+0.088139
## [36] train-rmse:2.052474+0.025078 test-rmse:2.115748+0.087040
## [37] train-rmse:2.044850+0.025429 test-rmse:2.112136+0.086950
## [38] train-rmse:2.037422+0.025915 test-rmse:2.102116+0.091175
## [39] train-rmse:2.030179+0.026268 test-rmse:2.093215+0.091172
## [40] train-rmse:2.023009+0.026581 test-rmse:2.087288+0.089762
## [41] train-rmse:2.016003+0.026984 test-rmse:2.079663+0.085365
## [42] train-rmse:2.009112+0.027399 test-rmse:2.077659+0.087542
## [43] train-rmse:2.002338+0.027866 test-rmse:2.073677+0.089709
## [44] train-rmse:1.995810+0.028093 test-rmse:2.069704+0.087023
## [45] train-rmse:1.989383+0.028392 test-rmse:2.064668+0.089512
## [46] train-rmse:1.982907+0.028713 test-rmse:2.058481+0.090973
## [47] train-rmse:1.976580+0.028914 test-rmse:2.052641+0.091776
## [48] train-rmse:1.970269+0.029202 test-rmse:2.046775+0.090574
## [49] train-rmse:1.964110+0.029397 test-rmse:2.043572+0.095602
## [50] train-rmse:1.958012+0.029700 test-rmse:2.036933+0.093751
## [51] train-rmse:1.951946+0.029992 test-rmse:2.032458+0.093219
## [52] train-rmse:1.945968+0.030231 test-rmse:2.027351+0.093095
## [53] train-rmse:1.940118+0.030545 test-rmse:2.017442+0.094538
## [54] train-rmse:1.934389+0.030834 test-rmse:2.009981+0.090070
## [55] train-rmse:1.928812+0.031092 test-rmse:2.006818+0.088871
## [56] train-rmse:1.923319+0.031415 test-rmse:2.001433+0.088290
## [57] train-rmse:1.917961+0.031670 test-rmse:1.995854+0.085489
## [58] train-rmse:1.912599+0.032022 test-rmse:1.990740+0.084580
## [59] train-rmse:1.907341+0.032358 test-rmse:1.987162+0.084432
## [60] train-rmse:1.902148+0.032724 test-rmse:1.987528+0.084393
## [61] train-rmse:1.896949+0.033063 test-rmse:1.983179+0.083870
## [62] train-rmse:1.891784+0.033355 test-rmse:1.979562+0.083793
## [63] train-rmse:1.886679+0.033709 test-rmse:1.975408+0.085908
## [64] train-rmse:1.881625+0.034030 test-rmse:1.969264+0.085632
## [65] train-rmse:1.876624+0.034371 test-rmse:1.960637+0.090361
## [66] train-rmse:1.871800+0.034701 test-rmse:1.953299+0.089043
## [67] train-rmse:1.866957+0.035018 test-rmse:1.946616+0.089891
## [68] train-rmse:1.862198+0.035320 test-rmse:1.946501+0.092710
## [69] train-rmse:1.857512+0.035598 test-rmse:1.943232+0.092972
## [70] train-rmse:1.852875+0.035930 test-rmse:1.936031+0.090991
## [71] train-rmse:1.848315+0.036208 test-rmse:1.933142+0.089643
## [72] train-rmse:1.843831+0.036470 test-rmse:1.930841+0.089743
## [73] train-rmse:1.839335+0.036756 test-rmse:1.929619+0.090595
## [74] train-rmse:1.834947+0.037005 test-rmse:1.926084+0.090933
## [75] train-rmse:1.830583+0.037291 test-rmse:1.922068+0.088743
## [76] train-rmse:1.826205+0.037439 test-rmse:1.919749+0.089955
## [77] train-rmse:1.821833+0.037594 test-rmse:1.917304+0.092330
## [78] train-rmse:1.817508+0.037814 test-rmse:1.915797+0.090691
## [79] train-rmse:1.813256+0.038033 test-rmse:1.911429+0.090759
## [80] train-rmse:1.809020+0.038212 test-rmse:1.906931+0.091295
## [81] train-rmse:1.804906+0.038462 test-rmse:1.902069+0.092549
## [82] train-rmse:1.800864+0.038761 test-rmse:1.898151+0.091817
## [83] train-rmse:1.796863+0.039018 test-rmse:1.892679+0.091244
## [84] train-rmse:1.792904+0.039314 test-rmse:1.889353+0.090750
## [85] train-rmse:1.788975+0.039555 test-rmse:1.886968+0.092844
## [86] train-rmse:1.785127+0.039779 test-rmse:1.883282+0.091907
## [87] train-rmse:1.781289+0.040010 test-rmse:1.881928+0.093500
## [88] train-rmse:1.777480+0.040271 test-rmse:1.880356+0.095518
## [89] train-rmse:1.773711+0.040526 test-rmse:1.876281+0.096241
## [90] train-rmse:1.769966+0.040768 test-rmse:1.874539+0.096554
## [91] train-rmse:1.766282+0.040960 test-rmse:1.872409+0.099748
## [92] train-rmse:1.762580+0.041195 test-rmse:1.866753+0.099879
## [93] train-rmse:1.758906+0.041436 test-rmse:1.861405+0.098459
## [94] train-rmse:1.755299+0.041655 test-rmse:1.859276+0.099091
## [95] train-rmse:1.751714+0.041869 test-rmse:1.856872+0.099147
## [96] train-rmse:1.748142+0.042117 test-rmse:1.853598+0.097988
## [97] train-rmse:1.744594+0.042288 test-rmse:1.849428+0.099403
## [98] train-rmse:1.741100+0.042451 test-rmse:1.845982+0.099791
## [99] train-rmse:1.737654+0.042640 test-rmse:1.841178+0.097094
## [100] train-rmse:1.734265+0.042783 test-rmse:1.837716+0.096936
## [101] train-rmse:1.730922+0.042976 test-rmse:1.834829+0.097762
## [102] train-rmse:1.727625+0.043103 test-rmse:1.832243+0.099777
## [103] train-rmse:1.724305+0.043245 test-rmse:1.829156+0.096531
## [104] train-rmse:1.721010+0.043434 test-rmse:1.827193+0.097498
## [105] train-rmse:1.717784+0.043623 test-rmse:1.825417+0.098193
## [106] train-rmse:1.714638+0.043801 test-rmse:1.823299+0.097736
## [107] train-rmse:1.711495+0.043941 test-rmse:1.820217+0.100153
## [108] train-rmse:1.708409+0.044097 test-rmse:1.816125+0.100667
## [109] train-rmse:1.705353+0.044299 test-rmse:1.814123+0.101343
## [110] train-rmse:1.702342+0.044465 test-rmse:1.813581+0.103212
## [111] train-rmse:1.699358+0.044610 test-rmse:1.810838+0.103349
## [112] train-rmse:1.696398+0.044811 test-rmse:1.808102+0.105173
## [113] train-rmse:1.693423+0.045026 test-rmse:1.805855+0.105342
## [114] train-rmse:1.690447+0.045252 test-rmse:1.803578+0.104929
## [115] train-rmse:1.687532+0.045403 test-rmse:1.800143+0.105966
## [116] train-rmse:1.684580+0.045532 test-rmse:1.799065+0.108688
## [117] train-rmse:1.681682+0.045691 test-rmse:1.796441+0.105290
## [118] train-rmse:1.678854+0.045853 test-rmse:1.796513+0.105650
## [119] train-rmse:1.676033+0.045994 test-rmse:1.795090+0.107062
## [120] train-rmse:1.673280+0.046159 test-rmse:1.790858+0.107987
## [121] train-rmse:1.670536+0.046300 test-rmse:1.789771+0.108150
## [122] train-rmse:1.667830+0.046403 test-rmse:1.787203+0.110009
## [123] train-rmse:1.665158+0.046492 test-rmse:1.784445+0.109843
## [124] train-rmse:1.662500+0.046592 test-rmse:1.781741+0.111427
## [125] train-rmse:1.659795+0.046719 test-rmse:1.778905+0.109861
## [126] train-rmse:1.657120+0.046804 test-rmse:1.773436+0.112949
## [127] train-rmse:1.654438+0.046902 test-rmse:1.770895+0.112341
## [128] train-rmse:1.651818+0.047002 test-rmse:1.766503+0.111628
## [129] train-rmse:1.649240+0.047076 test-rmse:1.764671+0.110292
## [130] train-rmse:1.646673+0.047165 test-rmse:1.762485+0.109896
## [131] train-rmse:1.644167+0.047274 test-rmse:1.761154+0.109960
## [132] train-rmse:1.641669+0.047410 test-rmse:1.758551+0.110052
## [133] train-rmse:1.639199+0.047523 test-rmse:1.756480+0.110531
## [134] train-rmse:1.636763+0.047635 test-rmse:1.755934+0.110329
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## [136] train-rmse:1.632022+0.047837 test-rmse:1.751370+0.113416
## [137] train-rmse:1.629654+0.047948 test-rmse:1.749403+0.114360
## [138] train-rmse:1.627284+0.048008 test-rmse:1.746556+0.115590
## [139] train-rmse:1.624920+0.048090 test-rmse:1.744253+0.116094
## [140] train-rmse:1.622552+0.048156 test-rmse:1.743139+0.115322
## [141] train-rmse:1.620200+0.048315 test-rmse:1.740819+0.117357
## [142] train-rmse:1.617867+0.048494 test-rmse:1.739197+0.116533
## [143] train-rmse:1.615595+0.048610 test-rmse:1.739095+0.113607
## [144] train-rmse:1.613357+0.048739 test-rmse:1.737704+0.114690
## [145] train-rmse:1.611144+0.048855 test-rmse:1.737124+0.117499
## [146] train-rmse:1.608954+0.048954 test-rmse:1.735220+0.119975
## [147] train-rmse:1.606750+0.049013 test-rmse:1.735200+0.120878
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## [149] train-rmse:1.602387+0.049139 test-rmse:1.729425+0.122542
## [150] train-rmse:1.600252+0.049213 test-rmse:1.727241+0.122954
## [151] train-rmse:1.598124+0.049302 test-rmse:1.723535+0.125816
## [152] train-rmse:1.595975+0.049398 test-rmse:1.722027+0.126663
## [153] train-rmse:1.593884+0.049501 test-rmse:1.719865+0.127187
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## [155] train-rmse:1.589778+0.049667 test-rmse:1.715033+0.124613
## [156] train-rmse:1.587754+0.049722 test-rmse:1.712025+0.124692
## [157] train-rmse:1.585738+0.049771 test-rmse:1.711561+0.124333
## [158] train-rmse:1.583745+0.049824 test-rmse:1.709891+0.123480
## [159] train-rmse:1.581748+0.049882 test-rmse:1.707959+0.122197
## [160] train-rmse:1.579763+0.049917 test-rmse:1.706544+0.122476
## [161] train-rmse:1.577792+0.049955 test-rmse:1.704668+0.121648
## [162] train-rmse:1.575807+0.049977 test-rmse:1.704463+0.124096
## [163] train-rmse:1.573888+0.050034 test-rmse:1.704789+0.124895
## [164] train-rmse:1.571971+0.050087 test-rmse:1.701943+0.125501
## [165] train-rmse:1.570025+0.050135 test-rmse:1.699328+0.126849
## [166] train-rmse:1.568101+0.050219 test-rmse:1.698090+0.127837
## [167] train-rmse:1.566207+0.050270 test-rmse:1.697007+0.126766
## [168] train-rmse:1.564352+0.050323 test-rmse:1.695876+0.127756
## [169] train-rmse:1.562527+0.050379 test-rmse:1.695003+0.127763
## [170] train-rmse:1.560717+0.050406 test-rmse:1.692791+0.130670
## [171] train-rmse:1.558933+0.050445 test-rmse:1.691237+0.131493
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## [173] train-rmse:1.555361+0.050532 test-rmse:1.685955+0.133520
## [174] train-rmse:1.553572+0.050550 test-rmse:1.682821+0.134718
## [175] train-rmse:1.551834+0.050588 test-rmse:1.680762+0.134113
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## [177] train-rmse:1.548358+0.050745 test-rmse:1.677266+0.135069
## [178] train-rmse:1.546649+0.050841 test-rmse:1.675574+0.135772
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## [180] train-rmse:1.543281+0.050958 test-rmse:1.670162+0.134632
## [181] train-rmse:1.541614+0.051008 test-rmse:1.668887+0.136628
## [182] train-rmse:1.539963+0.051036 test-rmse:1.668736+0.137701
## [183] train-rmse:1.538328+0.051078 test-rmse:1.667692+0.137414
## [184] train-rmse:1.536707+0.051095 test-rmse:1.666965+0.137314
## [185] train-rmse:1.535103+0.051115 test-rmse:1.666183+0.138155
## [186] train-rmse:1.533491+0.051141 test-rmse:1.667758+0.140366
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## [188] train-rmse:1.530273+0.051216 test-rmse:1.664152+0.139930
## [189] train-rmse:1.528703+0.051268 test-rmse:1.662401+0.140927
## [190] train-rmse:1.527168+0.051306 test-rmse:1.661162+0.140634
## [191] train-rmse:1.525633+0.051340 test-rmse:1.658758+0.140823
## [192] train-rmse:1.524119+0.051361 test-rmse:1.656657+0.140674
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## [194] train-rmse:1.521108+0.051385 test-rmse:1.653867+0.139654
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## [196] train-rmse:1.518154+0.051430 test-rmse:1.651659+0.142395
## [197] train-rmse:1.516697+0.051453 test-rmse:1.650782+0.142840
## [198] train-rmse:1.515226+0.051474 test-rmse:1.649402+0.142887
## [199] train-rmse:1.513781+0.051518 test-rmse:1.647814+0.142976
## [200] train-rmse:1.512329+0.051549 test-rmse:1.648445+0.140453
## [201] train-rmse:1.510896+0.051568 test-rmse:1.643974+0.143004
## [202] train-rmse:1.509495+0.051588 test-rmse:1.641831+0.143345
## [203] train-rmse:1.508090+0.051638 test-rmse:1.640092+0.142768
## [204] train-rmse:1.506690+0.051670 test-rmse:1.638606+0.142857
## [205] train-rmse:1.505318+0.051712 test-rmse:1.636732+0.143114
## [206] train-rmse:1.503961+0.051760 test-rmse:1.635573+0.142479
## [207] train-rmse:1.502618+0.051806 test-rmse:1.635525+0.143393
## [208] train-rmse:1.501271+0.051853 test-rmse:1.633488+0.143395
## [209] train-rmse:1.499953+0.051905 test-rmse:1.630714+0.143813
## [210] train-rmse:1.498651+0.051955 test-rmse:1.629857+0.144814
## [211] train-rmse:1.497369+0.051991 test-rmse:1.628823+0.145554
## [212] train-rmse:1.496082+0.052045 test-rmse:1.626639+0.144128
## [213] train-rmse:1.494788+0.052097 test-rmse:1.626336+0.145952
## [214] train-rmse:1.493495+0.052140 test-rmse:1.625536+0.148073
## [215] train-rmse:1.492196+0.052182 test-rmse:1.625929+0.147589
## [216] train-rmse:1.490922+0.052202 test-rmse:1.624245+0.146764
## [217] train-rmse:1.489646+0.052228 test-rmse:1.625014+0.148791
## [218] train-rmse:1.488416+0.052259 test-rmse:1.624287+0.148587
## [219] train-rmse:1.487189+0.052298 test-rmse:1.623735+0.148822
## [220] train-rmse:1.485952+0.052321 test-rmse:1.623247+0.148645
## [221] train-rmse:1.484716+0.052333 test-rmse:1.621764+0.148773
## [222] train-rmse:1.483496+0.052346 test-rmse:1.620990+0.148934
## [223] train-rmse:1.482282+0.052366 test-rmse:1.621179+0.148412
## [224] train-rmse:1.481073+0.052378 test-rmse:1.617489+0.151220
## [225] train-rmse:1.479884+0.052390 test-rmse:1.616485+0.153046
## [226] train-rmse:1.478698+0.052397 test-rmse:1.616529+0.153499
## [227] train-rmse:1.477519+0.052420 test-rmse:1.613611+0.150790
## [228] train-rmse:1.476360+0.052472 test-rmse:1.611864+0.151626
## [229] train-rmse:1.475209+0.052523 test-rmse:1.610303+0.151689
## [230] train-rmse:1.474050+0.052575 test-rmse:1.608127+0.152394
## [231] train-rmse:1.472884+0.052620 test-rmse:1.606880+0.153081
## [232] train-rmse:1.471714+0.052663 test-rmse:1.606551+0.154234
## [233] train-rmse:1.470595+0.052687 test-rmse:1.605576+0.153841
## [234] train-rmse:1.469482+0.052696 test-rmse:1.605641+0.154201
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## [236] train-rmse:1.467289+0.052707 test-rmse:1.604911+0.153808
## [237] train-rmse:1.466224+0.052727 test-rmse:1.603294+0.153783
## [238] train-rmse:1.465151+0.052757 test-rmse:1.602494+0.155056
## [239] train-rmse:1.464081+0.052791 test-rmse:1.600703+0.154661
## [240] train-rmse:1.463007+0.052823 test-rmse:1.599731+0.153737
## [241] train-rmse:1.461952+0.052845 test-rmse:1.598841+0.153238
## [242] train-rmse:1.460912+0.052868 test-rmse:1.599369+0.152852
## [243] train-rmse:1.459865+0.052884 test-rmse:1.598645+0.151474
## [244] train-rmse:1.458833+0.052894 test-rmse:1.597206+0.150947
## [245] train-rmse:1.457816+0.052905 test-rmse:1.596329+0.153487
## [246] train-rmse:1.456790+0.052925 test-rmse:1.596474+0.153587
## [247] train-rmse:1.455765+0.052927 test-rmse:1.595831+0.154465
## [248] train-rmse:1.454753+0.052937 test-rmse:1.594863+0.154573
## [249] train-rmse:1.453744+0.052954 test-rmse:1.595070+0.155928
## [250] train-rmse:1.452764+0.052964 test-rmse:1.593515+0.154539
## [251] train-rmse:1.451791+0.052975 test-rmse:1.592305+0.154758
## [252] train-rmse:1.450830+0.052990 test-rmse:1.590951+0.155338
## [253] train-rmse:1.449859+0.053002 test-rmse:1.589046+0.158282
## [254] train-rmse:1.448894+0.053014 test-rmse:1.588999+0.157829
## [255] train-rmse:1.447950+0.053019 test-rmse:1.588486+0.157755
## [256] train-rmse:1.447007+0.053029 test-rmse:1.587099+0.158375
## [257] train-rmse:1.446070+0.053049 test-rmse:1.585999+0.158109
## [258] train-rmse:1.445115+0.053067 test-rmse:1.584727+0.157238
## [259] train-rmse:1.444176+0.053074 test-rmse:1.584303+0.158974
## [260] train-rmse:1.443247+0.053079 test-rmse:1.582594+0.158058
## [261] train-rmse:1.442345+0.053091 test-rmse:1.581779+0.157814
## [262] train-rmse:1.441432+0.053103 test-rmse:1.580788+0.158339
## [263] train-rmse:1.440530+0.053113 test-rmse:1.579744+0.157800
## [264] train-rmse:1.439622+0.053116 test-rmse:1.578819+0.159110
## [265] train-rmse:1.438734+0.053124 test-rmse:1.578381+0.159760
## [266] train-rmse:1.437847+0.053124 test-rmse:1.578038+0.160835
## [267] train-rmse:1.436970+0.053132 test-rmse:1.577363+0.160961
## [268] train-rmse:1.436084+0.053155 test-rmse:1.577456+0.162259
## [269] train-rmse:1.435218+0.053164 test-rmse:1.575652+0.161808
## [270] train-rmse:1.434356+0.053174 test-rmse:1.574426+0.161421
## [271] train-rmse:1.433492+0.053187 test-rmse:1.572347+0.161122
## [272] train-rmse:1.432641+0.053207 test-rmse:1.571575+0.161709
## [273] train-rmse:1.431801+0.053224 test-rmse:1.571740+0.162053
## [274] train-rmse:1.430952+0.053240 test-rmse:1.571275+0.162531
## [275] train-rmse:1.430124+0.053268 test-rmse:1.570454+0.161914
## [276] train-rmse:1.429310+0.053280 test-rmse:1.569562+0.161617
## [277] train-rmse:1.428501+0.053292 test-rmse:1.569867+0.160442
## [278] train-rmse:1.427707+0.053305 test-rmse:1.569819+0.162066
## [279] train-rmse:1.426910+0.053303 test-rmse:1.568192+0.162676
## [280] train-rmse:1.426107+0.053306 test-rmse:1.567591+0.162892
## [281] train-rmse:1.425299+0.053318 test-rmse:1.568032+0.164603
## [282] train-rmse:1.424523+0.053322 test-rmse:1.564875+0.165796
## [283] train-rmse:1.423741+0.053317 test-rmse:1.564168+0.165580
## [284] train-rmse:1.422953+0.053297 test-rmse:1.562395+0.166502
## [285] train-rmse:1.422167+0.053281 test-rmse:1.561312+0.167299
## [286] train-rmse:1.421410+0.053280 test-rmse:1.560687+0.166239
## [287] train-rmse:1.420646+0.053282 test-rmse:1.559937+0.166293
## [288] train-rmse:1.419888+0.053294 test-rmse:1.559122+0.166642
## [289] train-rmse:1.419130+0.053312 test-rmse:1.558603+0.166931
## [290] train-rmse:1.418378+0.053311 test-rmse:1.558261+0.166994
## [291] train-rmse:1.417631+0.053312 test-rmse:1.557187+0.166618
## [292] train-rmse:1.416899+0.053306 test-rmse:1.557447+0.167309
## [293] train-rmse:1.416171+0.053311 test-rmse:1.557420+0.167960
## [294] train-rmse:1.415449+0.053319 test-rmse:1.557148+0.167975
## [295] train-rmse:1.414740+0.053329 test-rmse:1.556578+0.167586
## [296] train-rmse:1.414030+0.053337 test-rmse:1.556530+0.166926
## [297] train-rmse:1.413334+0.053339 test-rmse:1.557301+0.166842
## [298] train-rmse:1.412641+0.053347 test-rmse:1.555854+0.166227
## [299] train-rmse:1.411948+0.053354 test-rmse:1.556122+0.167511
## [300] train-rmse:1.411256+0.053355 test-rmse:1.554168+0.166741
## [301] train-rmse:1.410564+0.053350 test-rmse:1.552603+0.167317
## [302] train-rmse:1.409883+0.053348 test-rmse:1.551231+0.167671
## [303] train-rmse:1.409201+0.053344 test-rmse:1.550317+0.167768
## [304] train-rmse:1.408525+0.053343 test-rmse:1.550144+0.168217
## [305] train-rmse:1.407857+0.053349 test-rmse:1.547470+0.169254
## [306] train-rmse:1.407191+0.053355 test-rmse:1.547124+0.169430
## [307] train-rmse:1.406523+0.053359 test-rmse:1.547269+0.169023
## [308] train-rmse:1.405863+0.053370 test-rmse:1.544802+0.168779
## [309] train-rmse:1.405212+0.053380 test-rmse:1.544672+0.168002
## [310] train-rmse:1.404566+0.053393 test-rmse:1.543760+0.168056
## [311] train-rmse:1.403920+0.053396 test-rmse:1.544684+0.169702
## [312] train-rmse:1.403289+0.053408 test-rmse:1.544874+0.170524
## [313] train-rmse:1.402658+0.053411 test-rmse:1.544669+0.170454
## [314] train-rmse:1.402041+0.053412 test-rmse:1.543038+0.171323
## [315] train-rmse:1.401418+0.053410 test-rmse:1.543210+0.171745
## [316] train-rmse:1.400800+0.053409 test-rmse:1.542501+0.171849
## [317] train-rmse:1.400179+0.053411 test-rmse:1.541933+0.171801
## [318] train-rmse:1.399567+0.053411 test-rmse:1.542251+0.171348
## [319] train-rmse:1.398965+0.053413 test-rmse:1.541524+0.170443
## [320] train-rmse:1.398358+0.053421 test-rmse:1.541291+0.171489
## [321] train-rmse:1.397757+0.053418 test-rmse:1.540853+0.171751
## [322] train-rmse:1.397164+0.053424 test-rmse:1.540169+0.171675
## [323] train-rmse:1.396573+0.053429 test-rmse:1.539497+0.171924
## [324] train-rmse:1.395979+0.053435 test-rmse:1.539029+0.172422
## [325] train-rmse:1.395380+0.053430 test-rmse:1.538067+0.172256
## [326] train-rmse:1.394789+0.053429 test-rmse:1.537118+0.173222
## [327] train-rmse:1.394215+0.053431 test-rmse:1.536981+0.173078
## [328] train-rmse:1.393647+0.053427 test-rmse:1.535669+0.173040
## [329] train-rmse:1.393076+0.053427 test-rmse:1.536162+0.173295
## [330] train-rmse:1.392503+0.053420 test-rmse:1.535554+0.173451
## [331] train-rmse:1.391934+0.053419 test-rmse:1.535199+0.173575
## [332] train-rmse:1.391372+0.053417 test-rmse:1.535201+0.174421
## [333] train-rmse:1.390817+0.053409 test-rmse:1.534508+0.174262
## [334] train-rmse:1.390276+0.053411 test-rmse:1.534593+0.174488
## [335] train-rmse:1.389733+0.053410 test-rmse:1.532700+0.175313
## [336] train-rmse:1.389197+0.053409 test-rmse:1.532443+0.174021
## [337] train-rmse:1.388667+0.053404 test-rmse:1.531705+0.173948
## [338] train-rmse:1.388141+0.053398 test-rmse:1.530566+0.174051
## [339] train-rmse:1.387614+0.053385 test-rmse:1.530593+0.174227
## [340] train-rmse:1.387097+0.053383 test-rmse:1.530149+0.174713
## [341] train-rmse:1.386580+0.053382 test-rmse:1.528152+0.174609
## [342] train-rmse:1.386070+0.053382 test-rmse:1.529942+0.175770
## [343] train-rmse:1.385564+0.053388 test-rmse:1.529897+0.175730
## [344] train-rmse:1.385043+0.053389 test-rmse:1.528808+0.175366
## [345] train-rmse:1.384531+0.053388 test-rmse:1.528483+0.175320
## [346] train-rmse:1.384024+0.053386 test-rmse:1.527453+0.175455
## [347] train-rmse:1.383524+0.053389 test-rmse:1.526994+0.175549
## [348] train-rmse:1.383021+0.053392 test-rmse:1.526663+0.175467
## [349] train-rmse:1.382526+0.053392 test-rmse:1.525971+0.176012
## [350] train-rmse:1.382040+0.053397 test-rmse:1.524978+0.175593
## [351] train-rmse:1.381552+0.053402 test-rmse:1.524508+0.175488
## [352] train-rmse:1.381072+0.053406 test-rmse:1.524636+0.175620
## [353] train-rmse:1.380596+0.053405 test-rmse:1.523886+0.177818
## [354] train-rmse:1.380112+0.053418 test-rmse:1.524190+0.178008
## [355] train-rmse:1.379633+0.053418 test-rmse:1.523504+0.178229
## [356] train-rmse:1.379159+0.053416 test-rmse:1.522855+0.178480
## [357] train-rmse:1.378693+0.053411 test-rmse:1.523107+0.179356
## [358] train-rmse:1.378229+0.053407 test-rmse:1.522536+0.178859
## [359] train-rmse:1.377757+0.053390 test-rmse:1.521801+0.178995
## [360] train-rmse:1.377292+0.053380 test-rmse:1.522068+0.179286
## [361] train-rmse:1.376840+0.053376 test-rmse:1.521809+0.179288
## [362] train-rmse:1.376392+0.053374 test-rmse:1.521743+0.180862
## [363] train-rmse:1.375949+0.053369 test-rmse:1.521056+0.181034
## [364] train-rmse:1.375506+0.053368 test-rmse:1.520632+0.181288
## [365] train-rmse:1.375061+0.053367 test-rmse:1.520661+0.180839
## [366] train-rmse:1.374618+0.053368 test-rmse:1.519727+0.180741
## [367] train-rmse:1.374173+0.053369 test-rmse:1.520196+0.180565
## [368] train-rmse:1.373735+0.053363 test-rmse:1.520100+0.180474
## [369] train-rmse:1.373307+0.053360 test-rmse:1.519660+0.180127
## [370] train-rmse:1.372882+0.053357 test-rmse:1.520012+0.180408
## [371] train-rmse:1.372453+0.053354 test-rmse:1.519434+0.181115
## [372] train-rmse:1.372028+0.053348 test-rmse:1.519769+0.180887
## [373] train-rmse:1.371606+0.053345 test-rmse:1.518854+0.180670
## [374] train-rmse:1.371191+0.053342 test-rmse:1.519198+0.181083
## [375] train-rmse:1.370780+0.053341 test-rmse:1.518888+0.181256
## [376] train-rmse:1.370372+0.053339 test-rmse:1.518677+0.181150
## [377] train-rmse:1.369969+0.053337 test-rmse:1.517771+0.180497
## [378] train-rmse:1.369565+0.053337 test-rmse:1.517623+0.180699
## [379] train-rmse:1.369162+0.053334 test-rmse:1.517324+0.181254
## [380] train-rmse:1.368764+0.053330 test-rmse:1.515887+0.181076
## [381] train-rmse:1.368367+0.053325 test-rmse:1.515421+0.181023
## [382] train-rmse:1.367969+0.053319 test-rmse:1.515759+0.180826
## [383] train-rmse:1.367581+0.053318 test-rmse:1.515232+0.180990
## [384] train-rmse:1.367191+0.053320 test-rmse:1.515104+0.180438
## [385] train-rmse:1.366805+0.053321 test-rmse:1.513516+0.181735
## [386] train-rmse:1.366420+0.053326 test-rmse:1.513119+0.182721
## [387] train-rmse:1.366036+0.053333 test-rmse:1.513386+0.182436
## [388] train-rmse:1.365656+0.053337 test-rmse:1.512345+0.182600
## [389] train-rmse:1.365286+0.053338 test-rmse:1.512778+0.182806
## [390] train-rmse:1.364914+0.053336 test-rmse:1.512649+0.183566
## [391] train-rmse:1.364543+0.053328 test-rmse:1.511485+0.183822
## [392] train-rmse:1.364179+0.053324 test-rmse:1.510443+0.183518
## [393] train-rmse:1.363815+0.053323 test-rmse:1.511953+0.184339
## [394] train-rmse:1.363453+0.053321 test-rmse:1.511356+0.184586
## [395] train-rmse:1.363096+0.053318 test-rmse:1.511352+0.184277
## [396] train-rmse:1.362735+0.053314 test-rmse:1.510914+0.184402
## [397] train-rmse:1.362374+0.053311 test-rmse:1.510574+0.184374
## [398] train-rmse:1.362017+0.053309 test-rmse:1.511159+0.185584
## Stopping. Best iteration:
## [392] train-rmse:1.364179+0.053324 test-rmse:1.510443+0.183518
##
## 22
## [1] train-rmse:8.244913+0.059351 test-rmse:8.241126+0.269653
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.035620+0.051202 test-rmse:7.027877+0.257810
## [3] train-rmse:6.035524+0.044837 test-rmse:6.029809+0.242337
## [4] train-rmse:5.203720+0.035496 test-rmse:5.206150+0.236013
## [5] train-rmse:4.524140+0.030788 test-rmse:4.536401+0.217767
## [6] train-rmse:3.967544+0.028415 test-rmse:3.987373+0.195860
## [7] train-rmse:3.516766+0.026908 test-rmse:3.542907+0.173206
## [8] train-rmse:3.151321+0.026916 test-rmse:3.190709+0.148479
## [9] train-rmse:2.862866+0.026947 test-rmse:2.909006+0.128938
## [10] train-rmse:2.626852+0.024349 test-rmse:2.687727+0.103315
## [11] train-rmse:2.446084+0.023842 test-rmse:2.520274+0.085670
## [12] train-rmse:2.305069+0.023005 test-rmse:2.387446+0.058306
## [13] train-rmse:2.189657+0.025504 test-rmse:2.284608+0.038529
## [14] train-rmse:2.101989+0.025849 test-rmse:2.204603+0.041235
## [15] train-rmse:2.033881+0.026303 test-rmse:2.144431+0.036720
## [16] train-rmse:1.979362+0.024889 test-rmse:2.104565+0.046184
## [17] train-rmse:1.935504+0.024742 test-rmse:2.063782+0.054559
## [18] train-rmse:1.897989+0.025119 test-rmse:2.030834+0.063647
## [19] train-rmse:1.866939+0.025561 test-rmse:2.010768+0.071269
## [20] train-rmse:1.840646+0.024967 test-rmse:1.988763+0.080368
## [21] train-rmse:1.817213+0.025655 test-rmse:1.963680+0.085561
## [22] train-rmse:1.797164+0.025965 test-rmse:1.950771+0.087138
## [23] train-rmse:1.778781+0.026275 test-rmse:1.933408+0.087786
## [24] train-rmse:1.761267+0.026149 test-rmse:1.921777+0.092046
## [25] train-rmse:1.745465+0.026466 test-rmse:1.913347+0.095318
## [26] train-rmse:1.726892+0.029797 test-rmse:1.891117+0.101540
## [27] train-rmse:1.711932+0.031733 test-rmse:1.879219+0.112709
## [28] train-rmse:1.697667+0.032378 test-rmse:1.869772+0.109784
## [29] train-rmse:1.684519+0.031716 test-rmse:1.857961+0.108340
## [30] train-rmse:1.672027+0.032603 test-rmse:1.848446+0.106907
## [31] train-rmse:1.659395+0.032070 test-rmse:1.837046+0.103699
## [32] train-rmse:1.647190+0.032555 test-rmse:1.833760+0.105083
## [33] train-rmse:1.636231+0.032968 test-rmse:1.823906+0.104789
## [34] train-rmse:1.623823+0.033963 test-rmse:1.813132+0.111665
## [35] train-rmse:1.613050+0.035076 test-rmse:1.807043+0.114256
## [36] train-rmse:1.602904+0.035136 test-rmse:1.798872+0.112015
## [37] train-rmse:1.587056+0.029379 test-rmse:1.788132+0.118683
## [38] train-rmse:1.572101+0.027338 test-rmse:1.772804+0.113339
## [39] train-rmse:1.561483+0.029040 test-rmse:1.766643+0.112371
## [40] train-rmse:1.547745+0.027862 test-rmse:1.750718+0.111786
## [41] train-rmse:1.536426+0.027195 test-rmse:1.741032+0.114139
## [42] train-rmse:1.527705+0.027444 test-rmse:1.734368+0.113591
## [43] train-rmse:1.519003+0.027548 test-rmse:1.726312+0.114488
## [44] train-rmse:1.509774+0.027957 test-rmse:1.719074+0.111210
## [45] train-rmse:1.498560+0.029590 test-rmse:1.710231+0.117297
## [46] train-rmse:1.488781+0.029670 test-rmse:1.709934+0.119249
## [47] train-rmse:1.479790+0.029993 test-rmse:1.707410+0.118559
## [48] train-rmse:1.471765+0.030446 test-rmse:1.700035+0.120671
## [49] train-rmse:1.463384+0.031014 test-rmse:1.691578+0.123395
## [50] train-rmse:1.455783+0.031564 test-rmse:1.685287+0.121706
## [51] train-rmse:1.447579+0.030747 test-rmse:1.675477+0.124033
## [52] train-rmse:1.440231+0.030828 test-rmse:1.667994+0.125263
## [53] train-rmse:1.431913+0.031192 test-rmse:1.664982+0.123121
## [54] train-rmse:1.423948+0.030521 test-rmse:1.661303+0.123177
## [55] train-rmse:1.416435+0.030467 test-rmse:1.654117+0.122841
## [56] train-rmse:1.408075+0.030379 test-rmse:1.647459+0.126807
## [57] train-rmse:1.400898+0.030204 test-rmse:1.645434+0.130241
## [58] train-rmse:1.391078+0.033719 test-rmse:1.639945+0.130566
## [59] train-rmse:1.384003+0.034521 test-rmse:1.633149+0.130376
## [60] train-rmse:1.377184+0.035038 test-rmse:1.630291+0.128870
## [61] train-rmse:1.369854+0.034767 test-rmse:1.625167+0.130247
## [62] train-rmse:1.363057+0.035096 test-rmse:1.619193+0.130873
## [63] train-rmse:1.355958+0.035120 test-rmse:1.614199+0.131728
## [64] train-rmse:1.344856+0.040862 test-rmse:1.608526+0.131980
## [65] train-rmse:1.339074+0.041275 test-rmse:1.603278+0.130496
## [66] train-rmse:1.332701+0.041223 test-rmse:1.601036+0.131060
## [67] train-rmse:1.327149+0.041367 test-rmse:1.594023+0.131437
## [68] train-rmse:1.319846+0.043308 test-rmse:1.589037+0.132854
## [69] train-rmse:1.313484+0.043008 test-rmse:1.582383+0.134173
## [70] train-rmse:1.307203+0.043027 test-rmse:1.579309+0.133892
## [71] train-rmse:1.301281+0.043393 test-rmse:1.575482+0.133857
## [72] train-rmse:1.295794+0.043818 test-rmse:1.573343+0.133319
## [73] train-rmse:1.290841+0.043947 test-rmse:1.572512+0.135342
## [74] train-rmse:1.285473+0.043743 test-rmse:1.569506+0.135705
## [75] train-rmse:1.279964+0.043554 test-rmse:1.566753+0.138216
## [76] train-rmse:1.270602+0.037483 test-rmse:1.559092+0.142037
## [77] train-rmse:1.265331+0.037307 test-rmse:1.553254+0.143833
## [78] train-rmse:1.260201+0.036979 test-rmse:1.550105+0.144287
## [79] train-rmse:1.250373+0.033837 test-rmse:1.537623+0.124773
## [80] train-rmse:1.243934+0.034168 test-rmse:1.535447+0.127775
## [81] train-rmse:1.238845+0.034004 test-rmse:1.533640+0.126072
## [82] train-rmse:1.234317+0.034430 test-rmse:1.530334+0.127276
## [83] train-rmse:1.226144+0.033941 test-rmse:1.520021+0.112376
## [84] train-rmse:1.221677+0.034305 test-rmse:1.512378+0.113865
## [85] train-rmse:1.216679+0.034928 test-rmse:1.508636+0.113967
## [86] train-rmse:1.208487+0.036258 test-rmse:1.501804+0.109098
## [87] train-rmse:1.200696+0.040725 test-rmse:1.498975+0.108924
## [88] train-rmse:1.195494+0.040257 test-rmse:1.494727+0.108737
## [89] train-rmse:1.191055+0.039908 test-rmse:1.491436+0.109595
## [90] train-rmse:1.186923+0.040006 test-rmse:1.489478+0.112096
## [91] train-rmse:1.182588+0.041000 test-rmse:1.487049+0.110812
## [92] train-rmse:1.178105+0.040413 test-rmse:1.483043+0.112539
## [93] train-rmse:1.171136+0.035220 test-rmse:1.477220+0.117373
## [94] train-rmse:1.166875+0.035061 test-rmse:1.474670+0.118748
## [95] train-rmse:1.163069+0.035154 test-rmse:1.470227+0.115411
## [96] train-rmse:1.158899+0.035628 test-rmse:1.465589+0.115621
## [97] train-rmse:1.154866+0.035692 test-rmse:1.464247+0.117417
## [98] train-rmse:1.148524+0.036778 test-rmse:1.460752+0.114722
## [99] train-rmse:1.144043+0.037202 test-rmse:1.459496+0.114815
## [100] train-rmse:1.139990+0.037986 test-rmse:1.459271+0.115967
## [101] train-rmse:1.136115+0.038431 test-rmse:1.455716+0.118215
## [102] train-rmse:1.132651+0.037893 test-rmse:1.450506+0.120501
## [103] train-rmse:1.129302+0.037611 test-rmse:1.448009+0.121692
## [104] train-rmse:1.126036+0.037360 test-rmse:1.448270+0.120855
## [105] train-rmse:1.121663+0.038159 test-rmse:1.446244+0.121394
## [106] train-rmse:1.116372+0.040192 test-rmse:1.440040+0.117472
## [107] train-rmse:1.108476+0.041507 test-rmse:1.431546+0.107076
## [108] train-rmse:1.104983+0.041702 test-rmse:1.428035+0.106256
## [109] train-rmse:1.099755+0.043077 test-rmse:1.423239+0.103950
## [110] train-rmse:1.096410+0.043346 test-rmse:1.422236+0.103385
## [111] train-rmse:1.093597+0.043284 test-rmse:1.422121+0.102578
## [112] train-rmse:1.090592+0.043544 test-rmse:1.419867+0.102364
## [113] train-rmse:1.083517+0.037422 test-rmse:1.418266+0.106817
## [114] train-rmse:1.080542+0.037017 test-rmse:1.416057+0.108880
## [115] train-rmse:1.075547+0.034109 test-rmse:1.413710+0.112587
## [116] train-rmse:1.072432+0.034359 test-rmse:1.411339+0.113401
## [117] train-rmse:1.068020+0.035634 test-rmse:1.408856+0.111757
## [118] train-rmse:1.065268+0.035938 test-rmse:1.403921+0.114944
## [119] train-rmse:1.062430+0.035964 test-rmse:1.401960+0.114476
## [120] train-rmse:1.059569+0.035410 test-rmse:1.399783+0.114119
## [121] train-rmse:1.056248+0.035319 test-rmse:1.396888+0.115573
## [122] train-rmse:1.053597+0.034954 test-rmse:1.395224+0.118600
## [123] train-rmse:1.051141+0.035050 test-rmse:1.397165+0.118693
## [124] train-rmse:1.047028+0.033030 test-rmse:1.392815+0.120007
## [125] train-rmse:1.044347+0.032725 test-rmse:1.390890+0.120850
## [126] train-rmse:1.039562+0.031098 test-rmse:1.388484+0.121588
## [127] train-rmse:1.037091+0.031266 test-rmse:1.389385+0.123235
## [128] train-rmse:1.034543+0.031012 test-rmse:1.388375+0.126981
## [129] train-rmse:1.031931+0.031338 test-rmse:1.385366+0.129105
## [130] train-rmse:1.026411+0.032587 test-rmse:1.382476+0.129142
## [131] train-rmse:1.022935+0.033465 test-rmse:1.379408+0.127833
## [132] train-rmse:1.020376+0.033847 test-rmse:1.378378+0.128822
## [133] train-rmse:1.018243+0.033723 test-rmse:1.376703+0.129674
## [134] train-rmse:1.016228+0.033788 test-rmse:1.374526+0.129414
## [135] train-rmse:1.013727+0.034234 test-rmse:1.375353+0.129341
## [136] train-rmse:1.011763+0.034033 test-rmse:1.374630+0.130245
## [137] train-rmse:1.009361+0.033719 test-rmse:1.375007+0.130906
## [138] train-rmse:1.003850+0.037332 test-rmse:1.370361+0.129085
## [139] train-rmse:1.001684+0.037589 test-rmse:1.367710+0.129347
## [140] train-rmse:0.999495+0.037840 test-rmse:1.365562+0.127929
## [141] train-rmse:0.997095+0.037983 test-rmse:1.364655+0.128998
## [142] train-rmse:0.994947+0.038000 test-rmse:1.363590+0.128934
## [143] train-rmse:0.993027+0.037865 test-rmse:1.360403+0.130840
## [144] train-rmse:0.988414+0.039763 test-rmse:1.353732+0.126473
## [145] train-rmse:0.985788+0.040031 test-rmse:1.354420+0.129492
## [146] train-rmse:0.981232+0.041903 test-rmse:1.352994+0.129965
## [147] train-rmse:0.975699+0.037679 test-rmse:1.350571+0.133162
## [148] train-rmse:0.973817+0.037323 test-rmse:1.350560+0.135197
## [149] train-rmse:0.971685+0.037402 test-rmse:1.350320+0.135304
## [150] train-rmse:0.969651+0.037111 test-rmse:1.348568+0.134668
## [151] train-rmse:0.967179+0.037462 test-rmse:1.345973+0.135976
## [152] train-rmse:0.965437+0.037351 test-rmse:1.345781+0.135862
## [153] train-rmse:0.963864+0.037178 test-rmse:1.345607+0.135155
## [154] train-rmse:0.959675+0.037879 test-rmse:1.343740+0.135728
## [155] train-rmse:0.956879+0.039341 test-rmse:1.340489+0.133884
## [156] train-rmse:0.954635+0.039027 test-rmse:1.339822+0.134378
## [157] train-rmse:0.952482+0.038191 test-rmse:1.338417+0.134728
## [158] train-rmse:0.950070+0.037959 test-rmse:1.338536+0.134546
## [159] train-rmse:0.948289+0.037944 test-rmse:1.337839+0.135847
## [160] train-rmse:0.946279+0.038020 test-rmse:1.337035+0.134876
## [161] train-rmse:0.944166+0.038390 test-rmse:1.338617+0.136221
## [162] train-rmse:0.942402+0.038280 test-rmse:1.338247+0.135882
## [163] train-rmse:0.940728+0.038213 test-rmse:1.337157+0.136426
## [164] train-rmse:0.939000+0.038628 test-rmse:1.336167+0.137704
## [165] train-rmse:0.937118+0.038526 test-rmse:1.336304+0.139299
## [166] train-rmse:0.935526+0.038252 test-rmse:1.336058+0.138336
## [167] train-rmse:0.933892+0.038358 test-rmse:1.334220+0.138747
## [168] train-rmse:0.932239+0.038045 test-rmse:1.332767+0.139595
## [169] train-rmse:0.930580+0.038232 test-rmse:1.331871+0.140352
## [170] train-rmse:0.929337+0.038126 test-rmse:1.332024+0.140224
## [171] train-rmse:0.927984+0.038230 test-rmse:1.332332+0.141556
## [172] train-rmse:0.926359+0.038394 test-rmse:1.332580+0.141741
## [173] train-rmse:0.924169+0.037649 test-rmse:1.331555+0.140514
## [174] train-rmse:0.922599+0.037440 test-rmse:1.329902+0.140779
## [175] train-rmse:0.921319+0.037449 test-rmse:1.331148+0.141387
## [176] train-rmse:0.920108+0.037366 test-rmse:1.331126+0.141071
## [177] train-rmse:0.914776+0.034869 test-rmse:1.328290+0.142221
## [178] train-rmse:0.913303+0.034839 test-rmse:1.327070+0.143866
## [179] train-rmse:0.911652+0.034736 test-rmse:1.325345+0.144948
## [180] train-rmse:0.910294+0.034759 test-rmse:1.326059+0.146777
## [181] train-rmse:0.908817+0.034728 test-rmse:1.326974+0.147979
## [182] train-rmse:0.907500+0.034679 test-rmse:1.325691+0.147186
## [183] train-rmse:0.905698+0.034922 test-rmse:1.324555+0.147244
## [184] train-rmse:0.902048+0.032674 test-rmse:1.322765+0.147707
## [185] train-rmse:0.900814+0.032587 test-rmse:1.322221+0.148488
## [186] train-rmse:0.899312+0.032490 test-rmse:1.321103+0.149930
## [187] train-rmse:0.898255+0.032406 test-rmse:1.320406+0.150304
## [188] train-rmse:0.896407+0.033381 test-rmse:1.320170+0.150298
## [189] train-rmse:0.891206+0.028182 test-rmse:1.310443+0.150496
## [190] train-rmse:0.889476+0.027796 test-rmse:1.309880+0.150105
## [191] train-rmse:0.887698+0.027566 test-rmse:1.309544+0.150217
## [192] train-rmse:0.885982+0.028027 test-rmse:1.309053+0.149659
## [193] train-rmse:0.884555+0.027991 test-rmse:1.309528+0.150998
## [194] train-rmse:0.883251+0.027941 test-rmse:1.311512+0.152621
## [195] train-rmse:0.882003+0.028133 test-rmse:1.313144+0.152287
## [196] train-rmse:0.880892+0.027871 test-rmse:1.313765+0.153600
## [197] train-rmse:0.879642+0.027955 test-rmse:1.314194+0.155141
## [198] train-rmse:0.878477+0.027788 test-rmse:1.313122+0.156062
## Stopping. Best iteration:
## [192] train-rmse:0.885982+0.028027 test-rmse:1.309053+0.149659
##
## 23
## [1] train-rmse:8.237223+0.060198 test-rmse:8.233657+0.265253
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.015877+0.049685 test-rmse:7.009700+0.254524
## [3] train-rmse:6.002563+0.042775 test-rmse:6.000282+0.241801
## [4] train-rmse:5.162975+0.035612 test-rmse:5.169400+0.224466
## [5] train-rmse:4.468253+0.031624 test-rmse:4.487491+0.200933
## [6] train-rmse:3.895492+0.026479 test-rmse:3.926548+0.192199
## [7] train-rmse:3.424670+0.022201 test-rmse:3.462898+0.171764
## [8] train-rmse:3.043152+0.019490 test-rmse:3.098740+0.144362
## [9] train-rmse:2.734006+0.019089 test-rmse:2.800809+0.126570
## [10] train-rmse:2.486555+0.015045 test-rmse:2.571553+0.117360
## [11] train-rmse:2.286755+0.014639 test-rmse:2.395572+0.101031
## [12] train-rmse:2.129410+0.015159 test-rmse:2.257554+0.095458
## [13] train-rmse:1.999719+0.011459 test-rmse:2.146611+0.094227
## [14] train-rmse:1.897595+0.012755 test-rmse:2.050355+0.081606
## [15] train-rmse:1.814720+0.012574 test-rmse:1.984999+0.079124
## [16] train-rmse:1.750224+0.013609 test-rmse:1.931688+0.075458
## [17] train-rmse:1.696036+0.013906 test-rmse:1.880255+0.076738
## [18] train-rmse:1.651916+0.015005 test-rmse:1.846397+0.072444
## [19] train-rmse:1.611415+0.016486 test-rmse:1.821654+0.068479
## [20] train-rmse:1.577610+0.015977 test-rmse:1.797700+0.068962
## [21] train-rmse:1.548058+0.016568 test-rmse:1.780707+0.069496
## [22] train-rmse:1.520287+0.019144 test-rmse:1.759792+0.074763
## [23] train-rmse:1.497046+0.021702 test-rmse:1.739408+0.074413
## [24] train-rmse:1.475385+0.022709 test-rmse:1.726617+0.067708
## [25] train-rmse:1.451601+0.021571 test-rmse:1.713803+0.072255
## [26] train-rmse:1.432005+0.021118 test-rmse:1.696622+0.070120
## [27] train-rmse:1.414408+0.022212 test-rmse:1.686549+0.075435
## [28] train-rmse:1.394716+0.023599 test-rmse:1.671433+0.072548
## [29] train-rmse:1.379155+0.023290 test-rmse:1.660173+0.080257
## [30] train-rmse:1.363050+0.024373 test-rmse:1.649585+0.085871
## [31] train-rmse:1.347248+0.022431 test-rmse:1.640591+0.084765
## [32] train-rmse:1.331456+0.021292 test-rmse:1.625235+0.084638
## [33] train-rmse:1.317407+0.021015 test-rmse:1.620210+0.083630
## [34] train-rmse:1.301519+0.024727 test-rmse:1.609565+0.083448
## [35] train-rmse:1.287816+0.024461 test-rmse:1.601067+0.084562
## [36] train-rmse:1.273277+0.026812 test-rmse:1.588077+0.076918
## [37] train-rmse:1.262000+0.026285 test-rmse:1.578553+0.080619
## [38] train-rmse:1.249475+0.025516 test-rmse:1.574318+0.082828
## [39] train-rmse:1.232807+0.022698 test-rmse:1.565823+0.089446
## [40] train-rmse:1.221008+0.022615 test-rmse:1.557725+0.089899
## [41] train-rmse:1.210180+0.021570 test-rmse:1.551132+0.092368
## [42] train-rmse:1.198839+0.021638 test-rmse:1.542736+0.092844
## [43] train-rmse:1.187253+0.020261 test-rmse:1.530517+0.094409
## [44] train-rmse:1.174444+0.022292 test-rmse:1.521955+0.098291
## [45] train-rmse:1.164617+0.023083 test-rmse:1.521017+0.098831
## [46] train-rmse:1.153407+0.021349 test-rmse:1.513447+0.101783
## [47] train-rmse:1.142339+0.019939 test-rmse:1.502157+0.102818
## [48] train-rmse:1.133477+0.019740 test-rmse:1.497159+0.102831
## [49] train-rmse:1.123336+0.019497 test-rmse:1.486754+0.108869
## [50] train-rmse:1.114514+0.019650 test-rmse:1.480960+0.107639
## [51] train-rmse:1.105196+0.020808 test-rmse:1.478508+0.110172
## [52] train-rmse:1.094813+0.020668 test-rmse:1.474861+0.113922
## [53] train-rmse:1.086762+0.021464 test-rmse:1.471107+0.111966
## [54] train-rmse:1.077770+0.021680 test-rmse:1.467388+0.114872
## [55] train-rmse:1.068063+0.023306 test-rmse:1.461111+0.113316
## [56] train-rmse:1.058692+0.021981 test-rmse:1.457833+0.113924
## [57] train-rmse:1.051150+0.021958 test-rmse:1.455177+0.114590
## [58] train-rmse:1.041999+0.021303 test-rmse:1.448540+0.116204
## [59] train-rmse:1.034133+0.021009 test-rmse:1.442551+0.118229
## [60] train-rmse:1.025291+0.021517 test-rmse:1.430582+0.117237
## [61] train-rmse:1.017813+0.021651 test-rmse:1.425746+0.115526
## [62] train-rmse:1.009847+0.022650 test-rmse:1.425233+0.120061
## [63] train-rmse:1.001782+0.023558 test-rmse:1.420133+0.117723
## [64] train-rmse:0.993215+0.023900 test-rmse:1.420139+0.118654
## [65] train-rmse:0.982641+0.027315 test-rmse:1.413475+0.120529
## [66] train-rmse:0.974186+0.026187 test-rmse:1.408681+0.119491
## [67] train-rmse:0.966396+0.027029 test-rmse:1.401887+0.120971
## [68] train-rmse:0.959966+0.027076 test-rmse:1.400041+0.123324
## [69] train-rmse:0.952662+0.027123 test-rmse:1.396057+0.127112
## [70] train-rmse:0.945152+0.027216 test-rmse:1.394386+0.131058
## [71] train-rmse:0.938570+0.026183 test-rmse:1.389538+0.132791
## [72] train-rmse:0.932154+0.024918 test-rmse:1.386184+0.132663
## [73] train-rmse:0.926205+0.024883 test-rmse:1.379625+0.136988
## [74] train-rmse:0.921026+0.024116 test-rmse:1.378005+0.136241
## [75] train-rmse:0.914865+0.023208 test-rmse:1.376110+0.139116
## [76] train-rmse:0.909918+0.022391 test-rmse:1.374263+0.140359
## [77] train-rmse:0.904843+0.022196 test-rmse:1.370173+0.143213
## [78] train-rmse:0.898499+0.023019 test-rmse:1.364871+0.143641
## [79] train-rmse:0.893785+0.023434 test-rmse:1.361860+0.144752
## [80] train-rmse:0.887251+0.024023 test-rmse:1.359649+0.146208
## [81] train-rmse:0.881533+0.023281 test-rmse:1.358342+0.145887
## [82] train-rmse:0.874427+0.024318 test-rmse:1.352537+0.145039
## [83] train-rmse:0.869206+0.025437 test-rmse:1.351831+0.145407
## [84] train-rmse:0.863393+0.025704 test-rmse:1.346927+0.149328
## [85] train-rmse:0.858876+0.025432 test-rmse:1.345301+0.150220
## [86] train-rmse:0.854279+0.025679 test-rmse:1.345249+0.151582
## [87] train-rmse:0.850097+0.025637 test-rmse:1.341128+0.152994
## [88] train-rmse:0.846194+0.025909 test-rmse:1.340377+0.151712
## [89] train-rmse:0.842293+0.025389 test-rmse:1.338176+0.151776
## [90] train-rmse:0.838460+0.025080 test-rmse:1.337043+0.152498
## [91] train-rmse:0.833596+0.025621 test-rmse:1.335825+0.151995
## [92] train-rmse:0.829786+0.025720 test-rmse:1.335608+0.152306
## [93] train-rmse:0.825702+0.025015 test-rmse:1.334968+0.154409
## [94] train-rmse:0.820284+0.027979 test-rmse:1.333946+0.153905
## [95] train-rmse:0.815524+0.027604 test-rmse:1.333239+0.155639
## [96] train-rmse:0.811965+0.027066 test-rmse:1.334388+0.155324
## [97] train-rmse:0.805357+0.027798 test-rmse:1.327034+0.155244
## [98] train-rmse:0.801215+0.027526 test-rmse:1.324860+0.156236
## [99] train-rmse:0.798046+0.027502 test-rmse:1.323724+0.156999
## [100] train-rmse:0.795209+0.027905 test-rmse:1.322707+0.156727
## [101] train-rmse:0.790090+0.027141 test-rmse:1.321112+0.157900
## [102] train-rmse:0.785271+0.027493 test-rmse:1.318889+0.158228
## [103] train-rmse:0.780030+0.029343 test-rmse:1.317074+0.158009
## [104] train-rmse:0.776300+0.029440 test-rmse:1.317143+0.159331
## [105] train-rmse:0.773253+0.029305 test-rmse:1.318162+0.160234
## [106] train-rmse:0.770197+0.029010 test-rmse:1.318026+0.160137
## [107] train-rmse:0.768015+0.029083 test-rmse:1.317102+0.160232
## [108] train-rmse:0.764352+0.028836 test-rmse:1.316137+0.159743
## [109] train-rmse:0.761459+0.028579 test-rmse:1.317451+0.159028
## [110] train-rmse:0.758171+0.028621 test-rmse:1.316051+0.158806
## [111] train-rmse:0.754747+0.030419 test-rmse:1.316318+0.160159
## [112] train-rmse:0.752012+0.030225 test-rmse:1.315037+0.159207
## [113] train-rmse:0.748506+0.029953 test-rmse:1.314146+0.161586
## [114] train-rmse:0.745552+0.030716 test-rmse:1.313912+0.161223
## [115] train-rmse:0.739963+0.028965 test-rmse:1.311526+0.163862
## [116] train-rmse:0.736359+0.027917 test-rmse:1.309351+0.165014
## [117] train-rmse:0.733206+0.027737 test-rmse:1.310054+0.164403
## [118] train-rmse:0.728992+0.027310 test-rmse:1.305024+0.161700
## [119] train-rmse:0.724250+0.026604 test-rmse:1.303409+0.161432
## [120] train-rmse:0.721753+0.026823 test-rmse:1.303167+0.161829
## [121] train-rmse:0.719001+0.026865 test-rmse:1.304854+0.162211
## [122] train-rmse:0.716507+0.026954 test-rmse:1.304886+0.163879
## [123] train-rmse:0.713701+0.028356 test-rmse:1.304843+0.163423
## [124] train-rmse:0.711138+0.027695 test-rmse:1.303969+0.163080
## [125] train-rmse:0.707760+0.028349 test-rmse:1.303812+0.163750
## [126] train-rmse:0.705005+0.028769 test-rmse:1.301778+0.163209
## [127] train-rmse:0.702872+0.028720 test-rmse:1.301083+0.163610
## [128] train-rmse:0.700388+0.028431 test-rmse:1.301858+0.165436
## [129] train-rmse:0.698558+0.028491 test-rmse:1.302187+0.164648
## [130] train-rmse:0.695088+0.028679 test-rmse:1.297159+0.162223
## [131] train-rmse:0.693021+0.028781 test-rmse:1.296969+0.162404
## [132] train-rmse:0.690372+0.028488 test-rmse:1.296593+0.163301
## [133] train-rmse:0.688607+0.028341 test-rmse:1.296540+0.163883
## [134] train-rmse:0.685574+0.028136 test-rmse:1.292828+0.164853
## [135] train-rmse:0.683470+0.028396 test-rmse:1.292841+0.163995
## [136] train-rmse:0.679547+0.029367 test-rmse:1.291839+0.162448
## [137] train-rmse:0.677873+0.029558 test-rmse:1.291567+0.162039
## [138] train-rmse:0.676422+0.029920 test-rmse:1.291476+0.162067
## [139] train-rmse:0.673943+0.031449 test-rmse:1.291603+0.162841
## [140] train-rmse:0.672182+0.031452 test-rmse:1.291892+0.164682
## [141] train-rmse:0.670274+0.031420 test-rmse:1.290617+0.164577
## [142] train-rmse:0.668242+0.031428 test-rmse:1.290520+0.164809
## [143] train-rmse:0.665559+0.031484 test-rmse:1.289378+0.164364
## [144] train-rmse:0.662934+0.031467 test-rmse:1.289366+0.164683
## [145] train-rmse:0.661377+0.031393 test-rmse:1.289241+0.163498
## [146] train-rmse:0.657556+0.033038 test-rmse:1.286196+0.161864
## [147] train-rmse:0.655022+0.034457 test-rmse:1.286117+0.162278
## [148] train-rmse:0.652766+0.034829 test-rmse:1.285594+0.162069
## [149] train-rmse:0.650262+0.034641 test-rmse:1.287155+0.164378
## [150] train-rmse:0.648843+0.034411 test-rmse:1.288123+0.163223
## [151] train-rmse:0.646915+0.034554 test-rmse:1.288388+0.163414
## [152] train-rmse:0.645182+0.034396 test-rmse:1.288387+0.163572
## [153] train-rmse:0.643536+0.034473 test-rmse:1.288824+0.165394
## [154] train-rmse:0.641143+0.034316 test-rmse:1.289091+0.166560
## Stopping. Best iteration:
## [148] train-rmse:0.652766+0.034829 test-rmse:1.285594+0.162069
##
## 24
## [1] train-rmse:8.234109+0.060544 test-rmse:8.230359+0.266434
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.010027+0.051262 test-rmse:7.009323+0.254171
## [3] train-rmse:5.990892+0.043398 test-rmse:5.997430+0.241636
## [4] train-rmse:5.145758+0.036528 test-rmse:5.153261+0.224212
## [5] train-rmse:4.442205+0.030874 test-rmse:4.466286+0.196596
## [6] train-rmse:3.858761+0.025808 test-rmse:3.884766+0.176223
## [7] train-rmse:3.378160+0.021666 test-rmse:3.417301+0.166546
## [8] train-rmse:2.984764+0.017639 test-rmse:3.038289+0.158509
## [9] train-rmse:2.660158+0.015363 test-rmse:2.737354+0.134220
## [10] train-rmse:2.400778+0.013013 test-rmse:2.503119+0.126598
## [11] train-rmse:2.188191+0.011592 test-rmse:2.310908+0.127579
## [12] train-rmse:2.013807+0.014149 test-rmse:2.156808+0.109427
## [13] train-rmse:1.875196+0.015573 test-rmse:2.030558+0.097970
## [14] train-rmse:1.761799+0.014873 test-rmse:1.940458+0.097564
## [15] train-rmse:1.668052+0.017593 test-rmse:1.863679+0.086881
## [16] train-rmse:1.593250+0.019288 test-rmse:1.806442+0.086347
## [17] train-rmse:1.532785+0.018774 test-rmse:1.756729+0.080519
## [18] train-rmse:1.476970+0.020212 test-rmse:1.721154+0.077878
## [19] train-rmse:1.429179+0.020460 test-rmse:1.695705+0.073771
## [20] train-rmse:1.387120+0.021222 test-rmse:1.665439+0.076036
## [21] train-rmse:1.352137+0.023302 test-rmse:1.639721+0.082687
## [22] train-rmse:1.322887+0.023602 test-rmse:1.618292+0.085638
## [23] train-rmse:1.293614+0.024177 test-rmse:1.596098+0.086262
## [24] train-rmse:1.268876+0.024191 test-rmse:1.584332+0.090930
## [25] train-rmse:1.245902+0.027369 test-rmse:1.567141+0.091218
## [26] train-rmse:1.224432+0.027596 test-rmse:1.552565+0.094020
## [27] train-rmse:1.203484+0.028075 test-rmse:1.535409+0.099215
## [28] train-rmse:1.181894+0.023321 test-rmse:1.520665+0.103012
## [29] train-rmse:1.162933+0.023963 test-rmse:1.511679+0.110462
## [30] train-rmse:1.144113+0.023415 test-rmse:1.504061+0.113192
## [31] train-rmse:1.127790+0.022558 test-rmse:1.497226+0.117054
## [32] train-rmse:1.112502+0.022868 test-rmse:1.483087+0.116677
## [33] train-rmse:1.094259+0.023896 test-rmse:1.475533+0.117860
## [34] train-rmse:1.077084+0.022647 test-rmse:1.469844+0.125197
## [35] train-rmse:1.061654+0.022490 test-rmse:1.459315+0.129014
## [36] train-rmse:1.047600+0.021252 test-rmse:1.453585+0.127058
## [37] train-rmse:1.033283+0.022618 test-rmse:1.438769+0.124979
## [38] train-rmse:1.020031+0.023464 test-rmse:1.430739+0.126483
## [39] train-rmse:1.005924+0.024162 test-rmse:1.419859+0.128684
## [40] train-rmse:0.992677+0.023416 test-rmse:1.413856+0.130923
## [41] train-rmse:0.982571+0.022130 test-rmse:1.404994+0.128910
## [42] train-rmse:0.970706+0.022449 test-rmse:1.398016+0.134177
## [43] train-rmse:0.958705+0.023937 test-rmse:1.397406+0.134784
## [44] train-rmse:0.946780+0.026161 test-rmse:1.388340+0.135209
## [45] train-rmse:0.934964+0.025179 test-rmse:1.383757+0.138118
## [46] train-rmse:0.924580+0.026869 test-rmse:1.380886+0.138446
## [47] train-rmse:0.912961+0.026335 test-rmse:1.374557+0.142751
## [48] train-rmse:0.901451+0.026733 test-rmse:1.373100+0.145002
## [49] train-rmse:0.891928+0.025535 test-rmse:1.369268+0.143513
## [50] train-rmse:0.882041+0.024470 test-rmse:1.366982+0.150362
## [51] train-rmse:0.872925+0.022785 test-rmse:1.361130+0.150592
## [52] train-rmse:0.864460+0.024704 test-rmse:1.357715+0.151049
## [53] train-rmse:0.854811+0.025974 test-rmse:1.354952+0.151639
## [54] train-rmse:0.847726+0.025658 test-rmse:1.349861+0.154547
## [55] train-rmse:0.837130+0.024594 test-rmse:1.345017+0.150622
## [56] train-rmse:0.829539+0.024022 test-rmse:1.339450+0.148609
## [57] train-rmse:0.821223+0.023150 test-rmse:1.338002+0.149537
## [58] train-rmse:0.812509+0.023377 test-rmse:1.331319+0.149526
## [59] train-rmse:0.805165+0.023698 test-rmse:1.328133+0.150214
## [60] train-rmse:0.797871+0.024303 test-rmse:1.324812+0.151497
## [61] train-rmse:0.790990+0.024608 test-rmse:1.320649+0.152133
## [62] train-rmse:0.782645+0.024672 test-rmse:1.318381+0.154870
## [63] train-rmse:0.775051+0.022625 test-rmse:1.316900+0.155468
## [64] train-rmse:0.766744+0.024276 test-rmse:1.313535+0.155591
## [65] train-rmse:0.759802+0.024120 test-rmse:1.313458+0.153968
## [66] train-rmse:0.752960+0.023032 test-rmse:1.309150+0.155456
## [67] train-rmse:0.746598+0.023693 test-rmse:1.307771+0.156198
## [68] train-rmse:0.738928+0.024934 test-rmse:1.303859+0.158181
## [69] train-rmse:0.732349+0.025862 test-rmse:1.301513+0.156063
## [70] train-rmse:0.725857+0.025118 test-rmse:1.298601+0.155928
## [71] train-rmse:0.721078+0.025451 test-rmse:1.296002+0.156427
## [72] train-rmse:0.715398+0.026543 test-rmse:1.295456+0.157696
## [73] train-rmse:0.710497+0.026121 test-rmse:1.294765+0.160268
## [74] train-rmse:0.705438+0.025328 test-rmse:1.292153+0.160058
## [75] train-rmse:0.700261+0.024866 test-rmse:1.291044+0.160832
## [76] train-rmse:0.695145+0.024102 test-rmse:1.288735+0.161012
## [77] train-rmse:0.686942+0.023183 test-rmse:1.286813+0.160983
## [78] train-rmse:0.680565+0.024052 test-rmse:1.285132+0.162195
## [79] train-rmse:0.673480+0.023279 test-rmse:1.283329+0.161044
## [80] train-rmse:0.668739+0.022354 test-rmse:1.284470+0.163445
## [81] train-rmse:0.663216+0.022130 test-rmse:1.285220+0.164513
## [82] train-rmse:0.658695+0.021711 test-rmse:1.283676+0.163480
## [83] train-rmse:0.655113+0.021660 test-rmse:1.283646+0.164297
## [84] train-rmse:0.650498+0.022139 test-rmse:1.283326+0.166435
## [85] train-rmse:0.646518+0.022483 test-rmse:1.283596+0.165357
## [86] train-rmse:0.641973+0.024015 test-rmse:1.284977+0.165145
## [87] train-rmse:0.638008+0.023757 test-rmse:1.283301+0.164121
## [88] train-rmse:0.634283+0.023518 test-rmse:1.284227+0.165409
## [89] train-rmse:0.628901+0.022963 test-rmse:1.282704+0.165102
## [90] train-rmse:0.624000+0.024408 test-rmse:1.280156+0.165854
## [91] train-rmse:0.618842+0.022285 test-rmse:1.278767+0.166102
## [92] train-rmse:0.614770+0.022519 test-rmse:1.280232+0.164615
## [93] train-rmse:0.611209+0.021967 test-rmse:1.280740+0.165635
## [94] train-rmse:0.607988+0.022078 test-rmse:1.279608+0.165254
## [95] train-rmse:0.603918+0.021887 test-rmse:1.280369+0.165269
## [96] train-rmse:0.600580+0.021696 test-rmse:1.280202+0.165039
## [97] train-rmse:0.595023+0.023154 test-rmse:1.280059+0.164677
## Stopping. Best iteration:
## [91] train-rmse:0.618842+0.022285 test-rmse:1.278767+0.166102
##
## 25
## [1] train-rmse:8.231537+0.060807 test-rmse:8.230565+0.266774
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.003653+0.051618 test-rmse:7.010138+0.248406
## [3] train-rmse:5.979616+0.043543 test-rmse:5.993730+0.231142
## [4] train-rmse:5.125632+0.036160 test-rmse:5.143746+0.208866
## [5] train-rmse:4.416896+0.031925 test-rmse:4.437808+0.183755
## [6] train-rmse:3.828164+0.028870 test-rmse:3.856725+0.169541
## [7] train-rmse:3.337892+0.024262 test-rmse:3.389434+0.156901
## [8] train-rmse:2.933526+0.023179 test-rmse:2.998724+0.140470
## [9] train-rmse:2.600211+0.020245 test-rmse:2.693919+0.126673
## [10] train-rmse:2.325259+0.018766 test-rmse:2.440370+0.120575
## [11] train-rmse:2.103418+0.016775 test-rmse:2.249832+0.115117
## [12] train-rmse:1.923222+0.017360 test-rmse:2.102292+0.108405
## [13] train-rmse:1.770320+0.020059 test-rmse:1.970658+0.094011
## [14] train-rmse:1.644259+0.020994 test-rmse:1.865858+0.089410
## [15] train-rmse:1.542744+0.019289 test-rmse:1.790901+0.089061
## [16] train-rmse:1.457769+0.021553 test-rmse:1.730284+0.075638
## [17] train-rmse:1.385734+0.022356 test-rmse:1.668968+0.080400
## [18] train-rmse:1.326639+0.022948 test-rmse:1.630677+0.084850
## [19] train-rmse:1.277860+0.025514 test-rmse:1.597005+0.081214
## [20] train-rmse:1.232849+0.023816 test-rmse:1.560254+0.089655
## [21] train-rmse:1.194270+0.025532 test-rmse:1.533063+0.091092
## [22] train-rmse:1.156956+0.025924 test-rmse:1.505424+0.090450
## [23] train-rmse:1.124575+0.027087 test-rmse:1.489053+0.091190
## [24] train-rmse:1.095549+0.025600 test-rmse:1.472166+0.090059
## [25] train-rmse:1.067385+0.027760 test-rmse:1.462545+0.094438
## [26] train-rmse:1.044655+0.028013 test-rmse:1.454602+0.100929
## [27] train-rmse:1.017391+0.029719 test-rmse:1.444806+0.107982
## [28] train-rmse:0.997732+0.030150 test-rmse:1.429051+0.104729
## [29] train-rmse:0.975409+0.027840 test-rmse:1.416382+0.106841
## [30] train-rmse:0.953336+0.027401 test-rmse:1.401853+0.116664
## [31] train-rmse:0.938536+0.026701 test-rmse:1.395586+0.125542
## [32] train-rmse:0.922677+0.024498 test-rmse:1.389227+0.125699
## [33] train-rmse:0.904545+0.022090 test-rmse:1.377038+0.132850
## [34] train-rmse:0.890990+0.021610 test-rmse:1.371186+0.134460
## [35] train-rmse:0.875467+0.022423 test-rmse:1.364304+0.139340
## [36] train-rmse:0.860161+0.022482 test-rmse:1.359256+0.140555
## [37] train-rmse:0.847245+0.022322 test-rmse:1.353481+0.144151
## [38] train-rmse:0.834644+0.022499 test-rmse:1.350736+0.148671
## [39] train-rmse:0.819845+0.024117 test-rmse:1.342051+0.144236
## [40] train-rmse:0.804977+0.022744 test-rmse:1.331766+0.148496
## [41] train-rmse:0.792071+0.023609 test-rmse:1.327369+0.149958
## [42] train-rmse:0.782028+0.023540 test-rmse:1.319575+0.152476
## [43] train-rmse:0.773219+0.023373 test-rmse:1.316082+0.156067
## [44] train-rmse:0.762161+0.023056 test-rmse:1.313555+0.157574
## [45] train-rmse:0.751341+0.022732 test-rmse:1.307613+0.159578
## [46] train-rmse:0.741710+0.023569 test-rmse:1.303868+0.158512
## [47] train-rmse:0.732671+0.023379 test-rmse:1.298469+0.158191
## [48] train-rmse:0.723752+0.023993 test-rmse:1.295996+0.158889
## [49] train-rmse:0.712721+0.020857 test-rmse:1.293104+0.163804
## [50] train-rmse:0.705961+0.020404 test-rmse:1.290717+0.163240
## [51] train-rmse:0.695513+0.020647 test-rmse:1.286072+0.164892
## [52] train-rmse:0.687519+0.019353 test-rmse:1.283307+0.163749
## [53] train-rmse:0.679596+0.019024 test-rmse:1.280580+0.165390
## [54] train-rmse:0.671906+0.018868 test-rmse:1.277492+0.166212
## [55] train-rmse:0.661913+0.018407 test-rmse:1.273817+0.165476
## [56] train-rmse:0.653844+0.016737 test-rmse:1.271188+0.168287
## [57] train-rmse:0.647562+0.016441 test-rmse:1.268718+0.169550
## [58] train-rmse:0.640415+0.016890 test-rmse:1.266402+0.167026
## [59] train-rmse:0.632208+0.017730 test-rmse:1.266977+0.167401
## [60] train-rmse:0.625452+0.017094 test-rmse:1.266591+0.170800
## [61] train-rmse:0.618242+0.017517 test-rmse:1.266427+0.172142
## [62] train-rmse:0.611064+0.017997 test-rmse:1.263588+0.170796
## [63] train-rmse:0.604493+0.015822 test-rmse:1.264827+0.173427
## [64] train-rmse:0.598027+0.014880 test-rmse:1.264018+0.175928
## [65] train-rmse:0.593049+0.015689 test-rmse:1.261812+0.175145
## [66] train-rmse:0.586740+0.016981 test-rmse:1.258125+0.175462
## [67] train-rmse:0.580725+0.015996 test-rmse:1.258036+0.175859
## [68] train-rmse:0.575994+0.015943 test-rmse:1.256403+0.178368
## [69] train-rmse:0.570794+0.016579 test-rmse:1.254892+0.178779
## [70] train-rmse:0.564947+0.017409 test-rmse:1.254664+0.176826
## [71] train-rmse:0.558153+0.016000 test-rmse:1.252250+0.176923
## [72] train-rmse:0.552616+0.017461 test-rmse:1.249919+0.175258
## [73] train-rmse:0.546179+0.016775 test-rmse:1.249452+0.174744
## [74] train-rmse:0.541407+0.017571 test-rmse:1.248817+0.175787
## [75] train-rmse:0.535761+0.015599 test-rmse:1.248319+0.175689
## [76] train-rmse:0.531778+0.015416 test-rmse:1.247742+0.176299
## [77] train-rmse:0.527244+0.016108 test-rmse:1.247624+0.176889
## [78] train-rmse:0.523079+0.015897 test-rmse:1.246683+0.179538
## [79] train-rmse:0.516942+0.015393 test-rmse:1.246841+0.179476
## [80] train-rmse:0.512299+0.016227 test-rmse:1.247844+0.180224
## [81] train-rmse:0.508096+0.014835 test-rmse:1.247049+0.179903
## [82] train-rmse:0.504458+0.013970 test-rmse:1.247016+0.179925
## [83] train-rmse:0.500958+0.014565 test-rmse:1.245888+0.179580
## [84] train-rmse:0.495509+0.016351 test-rmse:1.244862+0.179462
## [85] train-rmse:0.490637+0.017362 test-rmse:1.244057+0.179593
## [86] train-rmse:0.487140+0.017454 test-rmse:1.244060+0.180247
## [87] train-rmse:0.483685+0.017915 test-rmse:1.245640+0.179099
## [88] train-rmse:0.479268+0.017911 test-rmse:1.246201+0.178891
## [89] train-rmse:0.475542+0.018524 test-rmse:1.245223+0.177634
## [90] train-rmse:0.470441+0.018052 test-rmse:1.242385+0.179161
## [91] train-rmse:0.467363+0.017290 test-rmse:1.243019+0.179507
## [92] train-rmse:0.464681+0.017342 test-rmse:1.242070+0.179101
## [93] train-rmse:0.461623+0.018472 test-rmse:1.241275+0.177755
## [94] train-rmse:0.458534+0.018092 test-rmse:1.242455+0.175496
## [95] train-rmse:0.455104+0.017200 test-rmse:1.242297+0.175138
## [96] train-rmse:0.451590+0.016546 test-rmse:1.241840+0.175244
## [97] train-rmse:0.448685+0.016024 test-rmse:1.241365+0.174702
## [98] train-rmse:0.445513+0.016423 test-rmse:1.241053+0.173508
## [99] train-rmse:0.442104+0.018107 test-rmse:1.240280+0.173532
## [100] train-rmse:0.439546+0.017841 test-rmse:1.240958+0.175080
## [101] train-rmse:0.435784+0.017369 test-rmse:1.240940+0.174234
## [102] train-rmse:0.433020+0.018444 test-rmse:1.240862+0.174325
## [103] train-rmse:0.429072+0.018689 test-rmse:1.239387+0.175740
## [104] train-rmse:0.426209+0.017811 test-rmse:1.238531+0.175145
## [105] train-rmse:0.424588+0.017830 test-rmse:1.238268+0.175582
## [106] train-rmse:0.421054+0.017461 test-rmse:1.237929+0.176521
## [107] train-rmse:0.418228+0.018607 test-rmse:1.239099+0.175455
## [108] train-rmse:0.416042+0.018038 test-rmse:1.239786+0.175091
## [109] train-rmse:0.413757+0.018983 test-rmse:1.239026+0.175544
## [110] train-rmse:0.411074+0.017946 test-rmse:1.237823+0.177542
## [111] train-rmse:0.409248+0.017723 test-rmse:1.238825+0.178444
## [112] train-rmse:0.406235+0.017546 test-rmse:1.239575+0.177589
## [113] train-rmse:0.403034+0.017656 test-rmse:1.238434+0.178169
## [114] train-rmse:0.398735+0.017295 test-rmse:1.238129+0.178013
## [115] train-rmse:0.395357+0.019014 test-rmse:1.237874+0.177555
## [116] train-rmse:0.392176+0.020298 test-rmse:1.237711+0.177480
## [117] train-rmse:0.390172+0.020183 test-rmse:1.237555+0.178750
## [118] train-rmse:0.388081+0.020691 test-rmse:1.237344+0.177691
## [119] train-rmse:0.384192+0.020914 test-rmse:1.236741+0.177000
## [120] train-rmse:0.382152+0.020902 test-rmse:1.237696+0.177987
## [121] train-rmse:0.379076+0.020075 test-rmse:1.238085+0.177263
## [122] train-rmse:0.377247+0.020882 test-rmse:1.238301+0.177480
## [123] train-rmse:0.374765+0.022211 test-rmse:1.238456+0.176569
## [124] train-rmse:0.372828+0.022061 test-rmse:1.238678+0.176153
## [125] train-rmse:0.370368+0.022501 test-rmse:1.239499+0.174654
## Stopping. Best iteration:
## [119] train-rmse:0.384192+0.020914 test-rmse:1.236741+0.177000
##
## 26
## [1] train-rmse:8.231005+0.060939 test-rmse:8.231149+0.261610
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:7.000695+0.051762 test-rmse:7.003591+0.246565
## [3] train-rmse:5.972831+0.044148 test-rmse:5.986918+0.232337
## [4] train-rmse:5.115268+0.037435 test-rmse:5.136525+0.202723
## [5] train-rmse:4.401219+0.031307 test-rmse:4.433035+0.183241
## [6] train-rmse:3.807012+0.029391 test-rmse:3.842613+0.162788
## [7] train-rmse:3.308208+0.024073 test-rmse:3.358036+0.142263
## [8] train-rmse:2.895613+0.021015 test-rmse:2.969897+0.135730
## [9] train-rmse:2.555317+0.019742 test-rmse:2.649359+0.119839
## [10] train-rmse:2.276611+0.018461 test-rmse:2.398003+0.112067
## [11] train-rmse:2.040174+0.015729 test-rmse:2.190715+0.109826
## [12] train-rmse:1.849169+0.017427 test-rmse:2.033729+0.103007
## [13] train-rmse:1.688907+0.017642 test-rmse:1.904561+0.102020
## [14] train-rmse:1.555578+0.017730 test-rmse:1.792721+0.086633
## [15] train-rmse:1.445778+0.019703 test-rmse:1.711229+0.088090
## [16] train-rmse:1.352115+0.017931 test-rmse:1.648718+0.087348
## [17] train-rmse:1.275051+0.021702 test-rmse:1.593062+0.082424
## [18] train-rmse:1.209729+0.022477 test-rmse:1.542320+0.077727
## [19] train-rmse:1.154728+0.018323 test-rmse:1.516001+0.077403
## [20] train-rmse:1.105423+0.020335 test-rmse:1.484817+0.078753
## [21] train-rmse:1.059765+0.020956 test-rmse:1.458036+0.081498
## [22] train-rmse:1.020979+0.020746 test-rmse:1.440783+0.084531
## [23] train-rmse:0.989505+0.018321 test-rmse:1.421650+0.092120
## [24] train-rmse:0.956988+0.019973 test-rmse:1.409923+0.091014
## [25] train-rmse:0.929803+0.017259 test-rmse:1.393240+0.100913
## [26] train-rmse:0.903505+0.017832 test-rmse:1.380071+0.104369
## [27] train-rmse:0.880457+0.019840 test-rmse:1.368222+0.103350
## [28] train-rmse:0.856826+0.018205 test-rmse:1.360839+0.111776
## [29] train-rmse:0.836818+0.017863 test-rmse:1.351088+0.113595
## [30] train-rmse:0.816358+0.016825 test-rmse:1.338154+0.118674
## [31] train-rmse:0.797354+0.015970 test-rmse:1.327610+0.121119
## [32] train-rmse:0.782260+0.015423 test-rmse:1.322698+0.122850
## [33] train-rmse:0.766704+0.016825 test-rmse:1.314558+0.125069
## [34] train-rmse:0.750544+0.017120 test-rmse:1.310333+0.131218
## [35] train-rmse:0.735505+0.017936 test-rmse:1.303957+0.130864
## [36] train-rmse:0.721191+0.018634 test-rmse:1.297047+0.134948
## [37] train-rmse:0.707234+0.018484 test-rmse:1.290682+0.137362
## [38] train-rmse:0.693485+0.020110 test-rmse:1.290443+0.135496
## [39] train-rmse:0.678766+0.020891 test-rmse:1.286261+0.137655
## [40] train-rmse:0.667414+0.017932 test-rmse:1.281809+0.139873
## [41] train-rmse:0.655488+0.016443 test-rmse:1.280552+0.143138
## [42] train-rmse:0.645158+0.017316 test-rmse:1.276368+0.144617
## [43] train-rmse:0.633099+0.016496 test-rmse:1.270741+0.145421
## [44] train-rmse:0.624656+0.015194 test-rmse:1.270282+0.147958
## [45] train-rmse:0.615151+0.015881 test-rmse:1.266500+0.146788
## [46] train-rmse:0.603797+0.014046 test-rmse:1.260943+0.149696
## [47] train-rmse:0.591840+0.012950 test-rmse:1.257658+0.150460
## [48] train-rmse:0.584908+0.012587 test-rmse:1.256165+0.152448
## [49] train-rmse:0.575615+0.011908 test-rmse:1.250775+0.153263
## [50] train-rmse:0.567607+0.013090 test-rmse:1.249454+0.153040
## [51] train-rmse:0.558839+0.014531 test-rmse:1.247484+0.153313
## [52] train-rmse:0.549939+0.014244 test-rmse:1.245268+0.157348
## [53] train-rmse:0.541873+0.013049 test-rmse:1.244203+0.158573
## [54] train-rmse:0.534264+0.013168 test-rmse:1.241720+0.161163
## [55] train-rmse:0.527399+0.013999 test-rmse:1.239893+0.159704
## [56] train-rmse:0.520926+0.014519 test-rmse:1.240084+0.160643
## [57] train-rmse:0.513459+0.014964 test-rmse:1.239187+0.161408
## [58] train-rmse:0.506332+0.013865 test-rmse:1.237082+0.163309
## [59] train-rmse:0.500942+0.013872 test-rmse:1.234939+0.162957
## [60] train-rmse:0.493782+0.014451 test-rmse:1.234853+0.162520
## [61] train-rmse:0.489492+0.014510 test-rmse:1.234519+0.163620
## [62] train-rmse:0.483852+0.014577 test-rmse:1.235108+0.163140
## [63] train-rmse:0.477737+0.016766 test-rmse:1.236220+0.163135
## [64] train-rmse:0.472136+0.016203 test-rmse:1.235969+0.164486
## [65] train-rmse:0.465007+0.013862 test-rmse:1.236375+0.164955
## [66] train-rmse:0.460693+0.014767 test-rmse:1.234880+0.165655
## [67] train-rmse:0.455657+0.013547 test-rmse:1.236174+0.166931
## Stopping. Best iteration:
## [61] train-rmse:0.489492+0.014510 test-rmse:1.234519+0.163620
##
## 27
## [1] train-rmse:8.231005+0.060939 test-rmse:8.231149+0.261610
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.999801+0.051870 test-rmse:7.007982+0.240368
## [3] train-rmse:5.969970+0.044430 test-rmse:5.985404+0.225188
## [4] train-rmse:5.109511+0.037562 test-rmse:5.130418+0.193858
## [5] train-rmse:4.391281+0.031631 test-rmse:4.418208+0.171087
## [6] train-rmse:3.793175+0.030053 test-rmse:3.820819+0.152880
## [7] train-rmse:3.288494+0.024205 test-rmse:3.329165+0.137237
## [8] train-rmse:2.870816+0.022088 test-rmse:2.934286+0.127474
## [9] train-rmse:2.523999+0.020599 test-rmse:2.620834+0.117849
## [10] train-rmse:2.237465+0.017824 test-rmse:2.369893+0.110878
## [11] train-rmse:1.997417+0.020703 test-rmse:2.144259+0.096124
## [12] train-rmse:1.794873+0.019730 test-rmse:1.981540+0.092909
## [13] train-rmse:1.627733+0.022853 test-rmse:1.852886+0.091455
## [14] train-rmse:1.483734+0.021094 test-rmse:1.737903+0.082535
## [15] train-rmse:1.365705+0.018435 test-rmse:1.657876+0.085940
## [16] train-rmse:1.267661+0.020817 test-rmse:1.578165+0.083384
## [17] train-rmse:1.180209+0.019583 test-rmse:1.525937+0.089854
## [18] train-rmse:1.109294+0.021424 test-rmse:1.470143+0.093833
## [19] train-rmse:1.050319+0.022189 test-rmse:1.440908+0.102769
## [20] train-rmse:0.994942+0.025264 test-rmse:1.406506+0.101484
## [21] train-rmse:0.951110+0.023312 test-rmse:1.386963+0.100215
## [22] train-rmse:0.911109+0.023435 test-rmse:1.377899+0.100861
## [23] train-rmse:0.873802+0.023376 test-rmse:1.356521+0.111546
## [24] train-rmse:0.841555+0.024989 test-rmse:1.342724+0.112104
## [25] train-rmse:0.812134+0.025554 test-rmse:1.330445+0.119381
## [26] train-rmse:0.786007+0.025663 test-rmse:1.316480+0.122056
## [27] train-rmse:0.761693+0.025500 test-rmse:1.299650+0.122842
## [28] train-rmse:0.737113+0.022847 test-rmse:1.291524+0.127671
## [29] train-rmse:0.717077+0.019864 test-rmse:1.283871+0.134050
## [30] train-rmse:0.698532+0.022245 test-rmse:1.277153+0.137438
## [31] train-rmse:0.679227+0.023707 test-rmse:1.269321+0.138169
## [32] train-rmse:0.661152+0.021916 test-rmse:1.264764+0.138542
## [33] train-rmse:0.646076+0.021762 test-rmse:1.263056+0.143307
## [34] train-rmse:0.631060+0.022176 test-rmse:1.257986+0.145519
## [35] train-rmse:0.616952+0.022752 test-rmse:1.255614+0.147151
## [36] train-rmse:0.601458+0.022292 test-rmse:1.254768+0.147748
## [37] train-rmse:0.589481+0.019654 test-rmse:1.252199+0.149467
## [38] train-rmse:0.576348+0.018745 test-rmse:1.249532+0.153660
## [39] train-rmse:0.564351+0.019705 test-rmse:1.245230+0.153013
## [40] train-rmse:0.553797+0.019745 test-rmse:1.242749+0.154231
## [41] train-rmse:0.540491+0.018362 test-rmse:1.240811+0.155051
## [42] train-rmse:0.528475+0.019875 test-rmse:1.236822+0.156314
## [43] train-rmse:0.520292+0.018867 test-rmse:1.236202+0.159129
## [44] train-rmse:0.510401+0.020574 test-rmse:1.233865+0.159269
## [45] train-rmse:0.501721+0.020122 test-rmse:1.233940+0.158203
## [46] train-rmse:0.491969+0.020806 test-rmse:1.234836+0.159625
## [47] train-rmse:0.482728+0.020641 test-rmse:1.232552+0.160445
## [48] train-rmse:0.475634+0.020222 test-rmse:1.233399+0.161174
## [49] train-rmse:0.466477+0.019836 test-rmse:1.231715+0.161115
## [50] train-rmse:0.457797+0.018418 test-rmse:1.230107+0.160396
## [51] train-rmse:0.450171+0.018885 test-rmse:1.227904+0.159999
## [52] train-rmse:0.444102+0.018535 test-rmse:1.226551+0.160660
## [53] train-rmse:0.436974+0.018837 test-rmse:1.226340+0.160542
## [54] train-rmse:0.431001+0.019186 test-rmse:1.223900+0.161091
## [55] train-rmse:0.424438+0.019601 test-rmse:1.222358+0.160419
## [56] train-rmse:0.415643+0.019539 test-rmse:1.220239+0.160889
## [57] train-rmse:0.409516+0.017334 test-rmse:1.220092+0.160997
## [58] train-rmse:0.401897+0.017537 test-rmse:1.219907+0.158522
## [59] train-rmse:0.396526+0.018333 test-rmse:1.219788+0.159724
## [60] train-rmse:0.387829+0.018876 test-rmse:1.219809+0.159312
## [61] train-rmse:0.382998+0.019052 test-rmse:1.219700+0.159437
## [62] train-rmse:0.378703+0.018816 test-rmse:1.220392+0.158820
## [63] train-rmse:0.373962+0.018372 test-rmse:1.219470+0.158831
## [64] train-rmse:0.368201+0.018681 test-rmse:1.217084+0.160593
## [65] train-rmse:0.364282+0.018825 test-rmse:1.217540+0.161506
## [66] train-rmse:0.358322+0.019176 test-rmse:1.217658+0.161694
## [67] train-rmse:0.354187+0.018873 test-rmse:1.218611+0.162021
## [68] train-rmse:0.349779+0.018522 test-rmse:1.218378+0.161245
## [69] train-rmse:0.345322+0.018578 test-rmse:1.218523+0.159955
## [70] train-rmse:0.341391+0.018571 test-rmse:1.218224+0.160667
## Stopping. Best iteration:
## [64] train-rmse:0.368201+0.018681 test-rmse:1.217084+0.160593
##
## 28
## [1] train-rmse:8.086516+0.057499 test-rmse:8.082386+0.272011
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.782281+0.045261 test-rmse:6.775442+0.267096
## [3] train-rmse:5.734850+0.035447 test-rmse:5.729225+0.260300
## [4] train-rmse:4.900583+0.027143 test-rmse:4.900642+0.246714
## [5] train-rmse:4.243027+0.020943 test-rmse:4.247597+0.226863
## [6] train-rmse:3.731424+0.015924 test-rmse:3.743665+0.209512
## [7] train-rmse:3.339081+0.012866 test-rmse:3.363427+0.181001
## [8] train-rmse:3.041262+0.011578 test-rmse:3.068478+0.155138
## [9] train-rmse:2.817789+0.011816 test-rmse:2.842160+0.127514
## [10] train-rmse:2.652577+0.012941 test-rmse:2.685164+0.102631
## [11] train-rmse:2.530764+0.013812 test-rmse:2.566154+0.086979
## [12] train-rmse:2.440349+0.014649 test-rmse:2.478745+0.065004
## [13] train-rmse:2.372938+0.015203 test-rmse:2.409546+0.054385
## [14] train-rmse:2.322455+0.015582 test-rmse:2.365133+0.047526
## [15] train-rmse:2.283918+0.016524 test-rmse:2.325681+0.054725
## [16] train-rmse:2.253765+0.016794 test-rmse:2.298030+0.051726
## [17] train-rmse:2.229320+0.017635 test-rmse:2.277243+0.056589
## [18] train-rmse:2.208945+0.018445 test-rmse:2.253268+0.065027
## [19] train-rmse:2.191646+0.019070 test-rmse:2.241209+0.067588
## [20] train-rmse:2.176366+0.019744 test-rmse:2.226802+0.067194
## [21] train-rmse:2.162145+0.020373 test-rmse:2.214168+0.070701
## [22] train-rmse:2.149013+0.020970 test-rmse:2.202673+0.072144
## [23] train-rmse:2.137307+0.021467 test-rmse:2.195626+0.074522
## [24] train-rmse:2.126561+0.021705 test-rmse:2.186961+0.073843
## [25] train-rmse:2.116067+0.021988 test-rmse:2.170145+0.074287
## [26] train-rmse:2.105964+0.022489 test-rmse:2.160117+0.081612
## [27] train-rmse:2.095966+0.022954 test-rmse:2.150444+0.084296
## [28] train-rmse:2.086153+0.023393 test-rmse:2.147575+0.086872
## [29] train-rmse:2.076704+0.023897 test-rmse:2.142536+0.085862
## [30] train-rmse:2.067312+0.024468 test-rmse:2.134294+0.089536
## [31] train-rmse:2.058339+0.024918 test-rmse:2.126950+0.086511
## [32] train-rmse:2.049555+0.025384 test-rmse:2.119110+0.086322
## [33] train-rmse:2.041015+0.025824 test-rmse:2.106161+0.083217
## [34] train-rmse:2.032560+0.026195 test-rmse:2.097641+0.089363
## [35] train-rmse:2.024400+0.026377 test-rmse:2.091875+0.090788
## [36] train-rmse:2.016464+0.026743 test-rmse:2.084319+0.088764
## [37] train-rmse:2.008521+0.027119 test-rmse:2.078700+0.087793
## [38] train-rmse:2.000837+0.027646 test-rmse:2.067320+0.086739
## [39] train-rmse:1.993499+0.028161 test-rmse:2.065263+0.090020
## [40] train-rmse:1.986193+0.028695 test-rmse:2.059764+0.091661
## [41] train-rmse:1.979116+0.028951 test-rmse:2.052382+0.090940
## [42] train-rmse:1.972140+0.029175 test-rmse:2.047821+0.094085
## [43] train-rmse:1.965026+0.029412 test-rmse:2.038632+0.093798
## [44] train-rmse:1.958187+0.029641 test-rmse:2.031414+0.096001
## [45] train-rmse:1.951474+0.029915 test-rmse:2.025615+0.093532
## [46] train-rmse:1.944840+0.030164 test-rmse:2.018669+0.089582
## [47] train-rmse:1.938222+0.030378 test-rmse:2.015581+0.090670
## [48] train-rmse:1.931708+0.030665 test-rmse:2.008903+0.088009
## [49] train-rmse:1.925359+0.031106 test-rmse:2.007681+0.086403
## [50] train-rmse:1.919081+0.031526 test-rmse:2.003287+0.085575
## [51] train-rmse:1.912848+0.032000 test-rmse:1.996254+0.091380
## [52] train-rmse:1.906671+0.032465 test-rmse:1.991506+0.087897
## [53] train-rmse:1.900531+0.032764 test-rmse:1.984589+0.090013
## [54] train-rmse:1.894673+0.033041 test-rmse:1.974611+0.090116
## [55] train-rmse:1.888953+0.033465 test-rmse:1.972006+0.093112
## [56] train-rmse:1.883362+0.033903 test-rmse:1.968146+0.092650
## [57] train-rmse:1.877789+0.034349 test-rmse:1.960550+0.093131
## [58] train-rmse:1.872322+0.034668 test-rmse:1.954309+0.093048
## [59] train-rmse:1.866815+0.035024 test-rmse:1.950698+0.095722
## [60] train-rmse:1.861519+0.035421 test-rmse:1.945270+0.094543
## [61] train-rmse:1.856173+0.035761 test-rmse:1.942203+0.095300
## [62] train-rmse:1.850971+0.036096 test-rmse:1.937539+0.091321
## [63] train-rmse:1.845801+0.036421 test-rmse:1.933097+0.089527
## [64] train-rmse:1.840702+0.036685 test-rmse:1.928963+0.091684
## [65] train-rmse:1.835608+0.036935 test-rmse:1.925000+0.090655
## [66] train-rmse:1.830569+0.037150 test-rmse:1.923129+0.091253
## [67] train-rmse:1.825621+0.037347 test-rmse:1.918712+0.087912
## [68] train-rmse:1.820691+0.037541 test-rmse:1.915435+0.086942
## [69] train-rmse:1.815792+0.037809 test-rmse:1.912685+0.088087
## [70] train-rmse:1.810892+0.038180 test-rmse:1.909520+0.090444
## [71] train-rmse:1.806242+0.038426 test-rmse:1.905150+0.091311
## [72] train-rmse:1.801598+0.038684 test-rmse:1.905062+0.092066
## [73] train-rmse:1.797068+0.038962 test-rmse:1.898535+0.091630
## [74] train-rmse:1.792474+0.039204 test-rmse:1.890896+0.094637
## [75] train-rmse:1.787971+0.039466 test-rmse:1.884991+0.097331
## [76] train-rmse:1.783494+0.039823 test-rmse:1.883212+0.095943
## [77] train-rmse:1.779146+0.040024 test-rmse:1.880537+0.096215
## [78] train-rmse:1.774842+0.040314 test-rmse:1.875118+0.096782
## [79] train-rmse:1.770573+0.040600 test-rmse:1.872543+0.096744
## [80] train-rmse:1.766366+0.040925 test-rmse:1.868534+0.095552
## [81] train-rmse:1.762199+0.041232 test-rmse:1.861580+0.094943
## [82] train-rmse:1.758142+0.041510 test-rmse:1.857102+0.096675
## [83] train-rmse:1.754084+0.041725 test-rmse:1.855125+0.100951
## [84] train-rmse:1.750092+0.041919 test-rmse:1.853563+0.098902
## [85] train-rmse:1.746192+0.042127 test-rmse:1.851991+0.101569
## [86] train-rmse:1.742291+0.042360 test-rmse:1.848537+0.100144
## [87] train-rmse:1.738440+0.042570 test-rmse:1.845087+0.101068
## [88] train-rmse:1.734524+0.042817 test-rmse:1.842146+0.101345
## [89] train-rmse:1.730668+0.043037 test-rmse:1.839210+0.101765
## [90] train-rmse:1.726919+0.043180 test-rmse:1.835695+0.101466
## [91] train-rmse:1.723134+0.043374 test-rmse:1.832043+0.099414
## [92] train-rmse:1.719425+0.043631 test-rmse:1.830999+0.098500
## [93] train-rmse:1.715769+0.043836 test-rmse:1.827765+0.099483
## [94] train-rmse:1.712116+0.044063 test-rmse:1.825608+0.099054
## [95] train-rmse:1.708466+0.044182 test-rmse:1.822506+0.101286
## [96] train-rmse:1.704882+0.044368 test-rmse:1.818078+0.099158
## [97] train-rmse:1.701335+0.044521 test-rmse:1.815076+0.099533
## [98] train-rmse:1.697949+0.044651 test-rmse:1.810184+0.099768
## [99] train-rmse:1.694586+0.044775 test-rmse:1.807851+0.098963
## [100] train-rmse:1.691239+0.044887 test-rmse:1.803988+0.100821
## [101] train-rmse:1.687998+0.045075 test-rmse:1.799035+0.102569
## [102] train-rmse:1.684745+0.045276 test-rmse:1.794492+0.103340
## [103] train-rmse:1.681526+0.045431 test-rmse:1.794198+0.104823
## [104] train-rmse:1.678276+0.045719 test-rmse:1.794650+0.105407
## [105] train-rmse:1.675111+0.045965 test-rmse:1.791263+0.105984
## [106] train-rmse:1.671942+0.046149 test-rmse:1.787233+0.102040
## [107] train-rmse:1.668812+0.046301 test-rmse:1.784890+0.105061
## [108] train-rmse:1.665754+0.046411 test-rmse:1.783652+0.106402
## [109] train-rmse:1.662735+0.046528 test-rmse:1.783327+0.105787
## [110] train-rmse:1.659695+0.046709 test-rmse:1.780836+0.105262
## [111] train-rmse:1.656740+0.046911 test-rmse:1.777781+0.105524
## [112] train-rmse:1.653829+0.047095 test-rmse:1.776040+0.106196
## [113] train-rmse:1.650864+0.047291 test-rmse:1.773300+0.107558
## [114] train-rmse:1.647970+0.047391 test-rmse:1.767927+0.107011
## [115] train-rmse:1.645104+0.047504 test-rmse:1.766500+0.110156
## [116] train-rmse:1.642267+0.047583 test-rmse:1.760807+0.114948
## [117] train-rmse:1.639408+0.047679 test-rmse:1.760892+0.116041
## [118] train-rmse:1.636569+0.047760 test-rmse:1.759247+0.115764
## [119] train-rmse:1.633798+0.047799 test-rmse:1.756769+0.116076
## [120] train-rmse:1.631090+0.047845 test-rmse:1.754189+0.116532
## [121] train-rmse:1.628390+0.047924 test-rmse:1.749731+0.117174
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## [123] train-rmse:1.623061+0.048115 test-rmse:1.745132+0.116342
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## [125] train-rmse:1.617809+0.048341 test-rmse:1.740746+0.118210
## [126] train-rmse:1.615187+0.048415 test-rmse:1.738463+0.117432
## [127] train-rmse:1.612621+0.048523 test-rmse:1.735198+0.115278
## [128] train-rmse:1.610087+0.048658 test-rmse:1.733700+0.116061
## [129] train-rmse:1.607574+0.048815 test-rmse:1.732488+0.116621
## [130] train-rmse:1.605102+0.048930 test-rmse:1.731266+0.117672
## [131] train-rmse:1.602664+0.049036 test-rmse:1.730999+0.116528
## [132] train-rmse:1.600209+0.049123 test-rmse:1.726892+0.117100
## [133] train-rmse:1.597798+0.049216 test-rmse:1.724357+0.115759
## [134] train-rmse:1.595416+0.049291 test-rmse:1.722538+0.116295
## [135] train-rmse:1.593049+0.049388 test-rmse:1.721758+0.114817
## [136] train-rmse:1.590719+0.049476 test-rmse:1.718922+0.115317
## [137] train-rmse:1.588395+0.049551 test-rmse:1.719045+0.117187
## [138] train-rmse:1.586083+0.049613 test-rmse:1.716007+0.119374
## [139] train-rmse:1.583772+0.049708 test-rmse:1.716709+0.121478
## [140] train-rmse:1.581448+0.049863 test-rmse:1.711716+0.125487
## [141] train-rmse:1.579178+0.049976 test-rmse:1.707825+0.126165
## [142] train-rmse:1.576922+0.050088 test-rmse:1.704040+0.123886
## [143] train-rmse:1.574705+0.050130 test-rmse:1.704214+0.125348
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## [145] train-rmse:1.570368+0.050201 test-rmse:1.702752+0.127452
## [146] train-rmse:1.568220+0.050245 test-rmse:1.700013+0.127425
## [147] train-rmse:1.566090+0.050312 test-rmse:1.698468+0.129015
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## [154] train-rmse:1.551743+0.050644 test-rmse:1.682621+0.133953
## [155] train-rmse:1.549768+0.050707 test-rmse:1.681480+0.134044
## [156] train-rmse:1.547800+0.050755 test-rmse:1.681557+0.133923
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## [162] train-rmse:1.536598+0.050982 test-rmse:1.668904+0.138308
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## [166] train-rmse:1.529365+0.051189 test-rmse:1.660141+0.137553
## [167] train-rmse:1.527597+0.051267 test-rmse:1.658212+0.137528
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## [170] train-rmse:1.522351+0.051424 test-rmse:1.654721+0.138873
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## [172] train-rmse:1.518979+0.051477 test-rmse:1.651798+0.139665
## [173] train-rmse:1.517305+0.051478 test-rmse:1.650352+0.141257
## [174] train-rmse:1.515651+0.051483 test-rmse:1.647333+0.141390
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## [182] train-rmse:1.502799+0.051869 test-rmse:1.640595+0.144986
## [183] train-rmse:1.501241+0.051934 test-rmse:1.637202+0.143285
## [184] train-rmse:1.499719+0.051971 test-rmse:1.636487+0.144156
## [185] train-rmse:1.498213+0.051989 test-rmse:1.633820+0.142582
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## [187] train-rmse:1.495209+0.051977 test-rmse:1.630210+0.145872
## [188] train-rmse:1.493719+0.052000 test-rmse:1.628114+0.148438
## [189] train-rmse:1.492223+0.052067 test-rmse:1.625678+0.147939
## [190] train-rmse:1.490793+0.052108 test-rmse:1.623886+0.147912
## [191] train-rmse:1.489357+0.052115 test-rmse:1.624236+0.150588
## [192] train-rmse:1.487940+0.052144 test-rmse:1.621618+0.150221
## [193] train-rmse:1.486563+0.052201 test-rmse:1.619742+0.151672
## [194] train-rmse:1.485204+0.052243 test-rmse:1.617554+0.150761
## [195] train-rmse:1.483833+0.052289 test-rmse:1.616609+0.150305
## [196] train-rmse:1.482474+0.052336 test-rmse:1.616161+0.151566
## [197] train-rmse:1.481110+0.052374 test-rmse:1.615369+0.151093
## [198] train-rmse:1.479769+0.052388 test-rmse:1.615329+0.150936
## [199] train-rmse:1.478450+0.052400 test-rmse:1.614274+0.152054
## [200] train-rmse:1.477123+0.052393 test-rmse:1.611106+0.150637
## [201] train-rmse:1.475816+0.052403 test-rmse:1.610195+0.151316
## [202] train-rmse:1.474505+0.052440 test-rmse:1.608113+0.151910
## [203] train-rmse:1.473217+0.052454 test-rmse:1.607924+0.152916
## [204] train-rmse:1.471947+0.052473 test-rmse:1.605590+0.153208
## [205] train-rmse:1.470708+0.052493 test-rmse:1.605930+0.153065
## [206] train-rmse:1.469456+0.052527 test-rmse:1.603844+0.152051
## [207] train-rmse:1.468214+0.052574 test-rmse:1.604198+0.151147
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## [209] train-rmse:1.465760+0.052641 test-rmse:1.605187+0.149537
## [210] train-rmse:1.464523+0.052658 test-rmse:1.603631+0.149329
## [211] train-rmse:1.463324+0.052688 test-rmse:1.603245+0.149261
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## [214] train-rmse:1.459792+0.052823 test-rmse:1.600228+0.149712
## [215] train-rmse:1.458637+0.052867 test-rmse:1.598782+0.149205
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## [217] train-rmse:1.456305+0.052955 test-rmse:1.597366+0.151310
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## [219] train-rmse:1.453983+0.053018 test-rmse:1.595494+0.151769
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## [224] train-rmse:1.448270+0.053059 test-rmse:1.586983+0.157742
## [225] train-rmse:1.447185+0.053055 test-rmse:1.585796+0.158136
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## [227] train-rmse:1.445049+0.053074 test-rmse:1.585204+0.157740
## [228] train-rmse:1.443972+0.053119 test-rmse:1.583753+0.157577
## [229] train-rmse:1.442919+0.053152 test-rmse:1.581801+0.158558
## [230] train-rmse:1.441874+0.053169 test-rmse:1.580939+0.160406
## [231] train-rmse:1.440852+0.053184 test-rmse:1.579029+0.160258
## [232] train-rmse:1.439834+0.053198 test-rmse:1.578354+0.160561
## [233] train-rmse:1.438821+0.053199 test-rmse:1.578052+0.161531
## [234] train-rmse:1.437818+0.053193 test-rmse:1.577725+0.161577
## [235] train-rmse:1.436826+0.053179 test-rmse:1.577321+0.162101
## [236] train-rmse:1.435848+0.053158 test-rmse:1.576249+0.162318
## [237] train-rmse:1.434874+0.053155 test-rmse:1.573616+0.161910
## [238] train-rmse:1.433905+0.053157 test-rmse:1.572764+0.164100
## [239] train-rmse:1.432954+0.053161 test-rmse:1.572124+0.164216
## [240] train-rmse:1.432007+0.053163 test-rmse:1.570181+0.163081
## [241] train-rmse:1.431074+0.053168 test-rmse:1.569933+0.163320
## [242] train-rmse:1.430144+0.053170 test-rmse:1.568180+0.163658
## [243] train-rmse:1.429217+0.053169 test-rmse:1.568324+0.163047
## [244] train-rmse:1.428288+0.053161 test-rmse:1.567608+0.162704
## [245] train-rmse:1.427342+0.053138 test-rmse:1.568046+0.163137
## [246] train-rmse:1.426413+0.053160 test-rmse:1.566094+0.161571
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## [248] train-rmse:1.424560+0.053229 test-rmse:1.566117+0.163797
## [249] train-rmse:1.423678+0.053231 test-rmse:1.565527+0.163995
## [250] train-rmse:1.422814+0.053234 test-rmse:1.562618+0.163610
## [251] train-rmse:1.421950+0.053236 test-rmse:1.561358+0.163572
## [252] train-rmse:1.421082+0.053245 test-rmse:1.562089+0.164815
## [253] train-rmse:1.420229+0.053257 test-rmse:1.561329+0.165451
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## [255] train-rmse:1.418542+0.053291 test-rmse:1.560204+0.165662
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## [258] train-rmse:1.416009+0.053349 test-rmse:1.557599+0.165261
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## [260] train-rmse:1.414374+0.053401 test-rmse:1.555564+0.165244
## [261] train-rmse:1.413570+0.053403 test-rmse:1.555352+0.164726
## [262] train-rmse:1.412775+0.053396 test-rmse:1.554581+0.165638
## [263] train-rmse:1.411992+0.053402 test-rmse:1.553762+0.167798
## [264] train-rmse:1.411217+0.053406 test-rmse:1.552338+0.169781
## [265] train-rmse:1.410443+0.053410 test-rmse:1.552263+0.169365
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## [267] train-rmse:1.408882+0.053407 test-rmse:1.549601+0.168729
## [268] train-rmse:1.408124+0.053401 test-rmse:1.547468+0.169433
## [269] train-rmse:1.407360+0.053391 test-rmse:1.546345+0.169892
## [270] train-rmse:1.406607+0.053378 test-rmse:1.546068+0.170198
## [271] train-rmse:1.405862+0.053375 test-rmse:1.545214+0.169767
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## [273] train-rmse:1.404390+0.053362 test-rmse:1.543305+0.171165
## [274] train-rmse:1.403646+0.053362 test-rmse:1.543105+0.171517
## [275] train-rmse:1.402922+0.053360 test-rmse:1.542276+0.171859
## [276] train-rmse:1.402195+0.053368 test-rmse:1.542505+0.171288
## [277] train-rmse:1.401480+0.053379 test-rmse:1.541918+0.172080
## [278] train-rmse:1.400770+0.053378 test-rmse:1.541822+0.171801
## [279] train-rmse:1.400062+0.053371 test-rmse:1.541940+0.170609
## [280] train-rmse:1.399358+0.053355 test-rmse:1.541751+0.170729
## [281] train-rmse:1.398668+0.053350 test-rmse:1.542448+0.170447
## [282] train-rmse:1.397983+0.053346 test-rmse:1.542710+0.172814
## [283] train-rmse:1.397310+0.053351 test-rmse:1.541146+0.171524
## [284] train-rmse:1.396644+0.053356 test-rmse:1.540149+0.170716
## [285] train-rmse:1.395973+0.053378 test-rmse:1.539548+0.170777
## [286] train-rmse:1.395310+0.053393 test-rmse:1.539111+0.170695
## [287] train-rmse:1.394653+0.053410 test-rmse:1.537964+0.170775
## [288] train-rmse:1.393995+0.053415 test-rmse:1.538268+0.171765
## [289] train-rmse:1.393339+0.053439 test-rmse:1.537852+0.171999
## [290] train-rmse:1.392697+0.053442 test-rmse:1.536774+0.173664
## [291] train-rmse:1.392060+0.053442 test-rmse:1.536107+0.173765
## [292] train-rmse:1.391425+0.053442 test-rmse:1.535834+0.174253
## [293] train-rmse:1.390798+0.053436 test-rmse:1.535205+0.175313
## [294] train-rmse:1.390172+0.053433 test-rmse:1.535305+0.175509
## [295] train-rmse:1.389558+0.053431 test-rmse:1.535406+0.174724
## [296] train-rmse:1.388948+0.053431 test-rmse:1.533089+0.175960
## [297] train-rmse:1.388352+0.053424 test-rmse:1.532373+0.176480
## [298] train-rmse:1.387755+0.053415 test-rmse:1.531955+0.176092
## [299] train-rmse:1.387157+0.053408 test-rmse:1.531959+0.176206
## [300] train-rmse:1.386570+0.053401 test-rmse:1.531414+0.176023
## [301] train-rmse:1.385988+0.053391 test-rmse:1.531416+0.175905
## [302] train-rmse:1.385404+0.053387 test-rmse:1.530782+0.174998
## [303] train-rmse:1.384811+0.053386 test-rmse:1.530494+0.176609
## [304] train-rmse:1.384224+0.053392 test-rmse:1.528815+0.175719
## [305] train-rmse:1.383655+0.053390 test-rmse:1.527726+0.176399
## [306] train-rmse:1.383096+0.053385 test-rmse:1.527202+0.176240
## [307] train-rmse:1.382537+0.053381 test-rmse:1.526953+0.176744
## [308] train-rmse:1.381982+0.053381 test-rmse:1.526483+0.176354
## [309] train-rmse:1.381436+0.053383 test-rmse:1.526365+0.176638
## [310] train-rmse:1.380904+0.053378 test-rmse:1.525159+0.176745
## [311] train-rmse:1.380368+0.053376 test-rmse:1.524028+0.177360
## [312] train-rmse:1.379838+0.053368 test-rmse:1.523972+0.177326
## [313] train-rmse:1.379307+0.053362 test-rmse:1.523613+0.177052
## [314] train-rmse:1.378776+0.053356 test-rmse:1.522830+0.177078
## [315] train-rmse:1.378250+0.053351 test-rmse:1.522335+0.177021
## [316] train-rmse:1.377731+0.053354 test-rmse:1.521715+0.176380
## [317] train-rmse:1.377212+0.053355 test-rmse:1.521988+0.178079
## [318] train-rmse:1.376702+0.053355 test-rmse:1.521272+0.178836
## [319] train-rmse:1.376190+0.053356 test-rmse:1.521814+0.178357
## [320] train-rmse:1.375682+0.053366 test-rmse:1.521733+0.179964
## [321] train-rmse:1.375177+0.053370 test-rmse:1.522201+0.179406
## [322] train-rmse:1.374668+0.053374 test-rmse:1.521173+0.180025
## [323] train-rmse:1.374160+0.053378 test-rmse:1.520212+0.180556
## [324] train-rmse:1.373658+0.053377 test-rmse:1.520098+0.182041
## [325] train-rmse:1.373156+0.053372 test-rmse:1.519343+0.181843
## [326] train-rmse:1.372667+0.053363 test-rmse:1.519183+0.181669
## [327] train-rmse:1.372180+0.053360 test-rmse:1.518027+0.182416
## [328] train-rmse:1.371694+0.053366 test-rmse:1.518421+0.182971
## [329] train-rmse:1.371204+0.053357 test-rmse:1.516796+0.182928
## [330] train-rmse:1.370728+0.053354 test-rmse:1.516672+0.182461
## [331] train-rmse:1.370256+0.053349 test-rmse:1.516457+0.182403
## [332] train-rmse:1.369795+0.053349 test-rmse:1.515229+0.181871
## [333] train-rmse:1.369344+0.053348 test-rmse:1.515544+0.181383
## [334] train-rmse:1.368901+0.053352 test-rmse:1.515318+0.181098
## [335] train-rmse:1.368460+0.053356 test-rmse:1.514722+0.181373
## [336] train-rmse:1.368016+0.053355 test-rmse:1.513961+0.182214
## [337] train-rmse:1.367569+0.053352 test-rmse:1.514694+0.182315
## [338] train-rmse:1.367122+0.053349 test-rmse:1.514972+0.183328
## [339] train-rmse:1.366686+0.053343 test-rmse:1.514244+0.183273
## [340] train-rmse:1.366248+0.053337 test-rmse:1.513380+0.184694
## [341] train-rmse:1.365813+0.053331 test-rmse:1.512588+0.185089
## [342] train-rmse:1.365386+0.053326 test-rmse:1.512667+0.184499
## [343] train-rmse:1.364964+0.053324 test-rmse:1.512435+0.184529
## [344] train-rmse:1.364546+0.053319 test-rmse:1.512257+0.184292
## [345] train-rmse:1.364134+0.053312 test-rmse:1.512033+0.184798
## [346] train-rmse:1.363720+0.053301 test-rmse:1.510851+0.184745
## [347] train-rmse:1.363308+0.053289 test-rmse:1.511375+0.183970
## [348] train-rmse:1.362900+0.053278 test-rmse:1.510964+0.183883
## [349] train-rmse:1.362496+0.053270 test-rmse:1.510051+0.183780
## [350] train-rmse:1.362095+0.053264 test-rmse:1.509828+0.184685
## [351] train-rmse:1.361695+0.053260 test-rmse:1.509732+0.185425
## [352] train-rmse:1.361303+0.053257 test-rmse:1.509226+0.185310
## [353] train-rmse:1.360912+0.053258 test-rmse:1.509087+0.184994
## [354] train-rmse:1.360522+0.053267 test-rmse:1.508003+0.184332
## [355] train-rmse:1.360127+0.053269 test-rmse:1.508244+0.185628
## [356] train-rmse:1.359738+0.053274 test-rmse:1.508084+0.186239
## [357] train-rmse:1.359353+0.053275 test-rmse:1.507398+0.186195
## [358] train-rmse:1.358963+0.053270 test-rmse:1.507120+0.185802
## [359] train-rmse:1.358588+0.053274 test-rmse:1.506680+0.186016
## [360] train-rmse:1.358212+0.053277 test-rmse:1.505111+0.186649
## [361] train-rmse:1.357847+0.053278 test-rmse:1.504822+0.187337
## [362] train-rmse:1.357486+0.053277 test-rmse:1.504662+0.187664
## [363] train-rmse:1.357125+0.053274 test-rmse:1.504593+0.188824
## [364] train-rmse:1.356763+0.053270 test-rmse:1.504512+0.188465
## [365] train-rmse:1.356398+0.053273 test-rmse:1.504565+0.187740
## [366] train-rmse:1.356036+0.053277 test-rmse:1.504035+0.188367
## [367] train-rmse:1.355682+0.053272 test-rmse:1.504128+0.188091
## [368] train-rmse:1.355329+0.053264 test-rmse:1.504034+0.187997
## [369] train-rmse:1.354981+0.053256 test-rmse:1.503526+0.187863
## [370] train-rmse:1.354638+0.053249 test-rmse:1.503782+0.187899
## [371] train-rmse:1.354298+0.053242 test-rmse:1.504322+0.187948
## [372] train-rmse:1.353963+0.053234 test-rmse:1.503485+0.187410
## [373] train-rmse:1.353632+0.053223 test-rmse:1.502941+0.187910
## [374] train-rmse:1.353296+0.053213 test-rmse:1.502477+0.187070
## [375] train-rmse:1.352958+0.053201 test-rmse:1.503042+0.187281
## [376] train-rmse:1.352629+0.053188 test-rmse:1.502832+0.187565
## [377] train-rmse:1.352301+0.053178 test-rmse:1.502009+0.187621
## [378] train-rmse:1.351971+0.053179 test-rmse:1.502043+0.188417
## [379] train-rmse:1.351647+0.053175 test-rmse:1.501215+0.187825
## [380] train-rmse:1.351325+0.053173 test-rmse:1.501062+0.188114
## [381] train-rmse:1.351005+0.053165 test-rmse:1.500452+0.188409
## [382] train-rmse:1.350685+0.053160 test-rmse:1.501406+0.189720
## [383] train-rmse:1.350368+0.053160 test-rmse:1.500895+0.189762
## [384] train-rmse:1.350057+0.053156 test-rmse:1.500176+0.189476
## [385] train-rmse:1.349753+0.053155 test-rmse:1.500115+0.189356
## [386] train-rmse:1.349450+0.053152 test-rmse:1.499791+0.189914
## [387] train-rmse:1.349148+0.053153 test-rmse:1.499482+0.190385
## [388] train-rmse:1.348847+0.053152 test-rmse:1.498389+0.190598
## [389] train-rmse:1.348543+0.053147 test-rmse:1.497784+0.190507
## [390] train-rmse:1.348246+0.053139 test-rmse:1.497452+0.190775
## [391] train-rmse:1.347950+0.053128 test-rmse:1.496794+0.191583
## [392] train-rmse:1.347650+0.053125 test-rmse:1.497416+0.191200
## [393] train-rmse:1.347359+0.053118 test-rmse:1.498200+0.191734
## [394] train-rmse:1.347068+0.053111 test-rmse:1.497681+0.191237
## [395] train-rmse:1.346780+0.053105 test-rmse:1.496669+0.192345
## [396] train-rmse:1.346493+0.053099 test-rmse:1.496816+0.192256
## [397] train-rmse:1.346209+0.053093 test-rmse:1.496020+0.192419
## [398] train-rmse:1.345927+0.053085 test-rmse:1.495482+0.192761
## [399] train-rmse:1.345650+0.053078 test-rmse:1.495665+0.192705
## [400] train-rmse:1.345378+0.053071 test-rmse:1.495445+0.192662
## [401] train-rmse:1.345106+0.053066 test-rmse:1.495519+0.193003
## [402] train-rmse:1.344831+0.053055 test-rmse:1.496006+0.193452
## [403] train-rmse:1.344557+0.053056 test-rmse:1.495671+0.193777
## [404] train-rmse:1.344288+0.053054 test-rmse:1.495059+0.194555
## [405] train-rmse:1.344018+0.053052 test-rmse:1.495009+0.194162
## [406] train-rmse:1.343756+0.053051 test-rmse:1.495133+0.194214
## [407] train-rmse:1.343495+0.053050 test-rmse:1.495193+0.193288
## [408] train-rmse:1.343241+0.053048 test-rmse:1.494708+0.193016
## [409] train-rmse:1.342983+0.053039 test-rmse:1.494587+0.192814
## [410] train-rmse:1.342726+0.053032 test-rmse:1.494325+0.192852
## [411] train-rmse:1.342476+0.053024 test-rmse:1.494073+0.192564
## [412] train-rmse:1.342227+0.053018 test-rmse:1.495511+0.193293
## [413] train-rmse:1.341979+0.053012 test-rmse:1.494975+0.193241
## [414] train-rmse:1.341727+0.053002 test-rmse:1.494752+0.193115
## [415] train-rmse:1.341478+0.052999 test-rmse:1.493899+0.194185
## [416] train-rmse:1.341229+0.052998 test-rmse:1.493531+0.194551
## [417] train-rmse:1.340985+0.052991 test-rmse:1.492744+0.194786
## [418] train-rmse:1.340745+0.052985 test-rmse:1.492253+0.195036
## [419] train-rmse:1.340504+0.052980 test-rmse:1.491609+0.194708
## [420] train-rmse:1.340260+0.052974 test-rmse:1.491095+0.195002
## [421] train-rmse:1.340018+0.052966 test-rmse:1.491318+0.194577
## [422] train-rmse:1.339778+0.052961 test-rmse:1.490848+0.194273
## [423] train-rmse:1.339547+0.052958 test-rmse:1.490943+0.194535
## [424] train-rmse:1.339313+0.052952 test-rmse:1.490843+0.194534
## [425] train-rmse:1.339081+0.052944 test-rmse:1.490823+0.194345
## [426] train-rmse:1.338853+0.052936 test-rmse:1.490383+0.194221
## [427] train-rmse:1.338629+0.052938 test-rmse:1.490597+0.194440
## [428] train-rmse:1.338404+0.052935 test-rmse:1.490161+0.194585
## [429] train-rmse:1.338180+0.052929 test-rmse:1.490363+0.194991
## [430] train-rmse:1.337960+0.052924 test-rmse:1.490762+0.194939
## [431] train-rmse:1.337742+0.052920 test-rmse:1.490076+0.194782
## [432] train-rmse:1.337525+0.052911 test-rmse:1.489757+0.194516
## [433] train-rmse:1.337312+0.052907 test-rmse:1.488886+0.195261
## [434] train-rmse:1.337100+0.052904 test-rmse:1.488950+0.195355
## [435] train-rmse:1.336889+0.052901 test-rmse:1.489425+0.194704
## [436] train-rmse:1.336682+0.052895 test-rmse:1.489361+0.195709
## [437] train-rmse:1.336475+0.052889 test-rmse:1.489176+0.195888
## [438] train-rmse:1.336271+0.052884 test-rmse:1.489208+0.195787
## [439] train-rmse:1.336065+0.052881 test-rmse:1.488640+0.195981
## [440] train-rmse:1.335862+0.052875 test-rmse:1.488482+0.195525
## [441] train-rmse:1.335658+0.052870 test-rmse:1.488638+0.195327
## [442] train-rmse:1.335458+0.052865 test-rmse:1.488902+0.195278
## [443] train-rmse:1.335261+0.052861 test-rmse:1.489292+0.195409
## [444] train-rmse:1.335066+0.052856 test-rmse:1.488843+0.195241
## [445] train-rmse:1.334861+0.052844 test-rmse:1.488398+0.195471
## [446] train-rmse:1.334663+0.052838 test-rmse:1.488201+0.195494
## [447] train-rmse:1.334467+0.052833 test-rmse:1.487855+0.197121
## [448] train-rmse:1.334272+0.052829 test-rmse:1.487898+0.196984
## [449] train-rmse:1.334077+0.052824 test-rmse:1.488578+0.197502
## [450] train-rmse:1.333886+0.052820 test-rmse:1.488122+0.197576
## [451] train-rmse:1.333695+0.052816 test-rmse:1.487246+0.196720
## [452] train-rmse:1.333503+0.052814 test-rmse:1.487319+0.196680
## [453] train-rmse:1.333315+0.052807 test-rmse:1.487184+0.196639
## [454] train-rmse:1.333127+0.052800 test-rmse:1.486636+0.197018
## [455] train-rmse:1.332941+0.052795 test-rmse:1.486113+0.196964
## [456] train-rmse:1.332757+0.052787 test-rmse:1.485934+0.197326
## [457] train-rmse:1.332576+0.052781 test-rmse:1.485432+0.196843
## [458] train-rmse:1.332395+0.052776 test-rmse:1.485684+0.196054
## [459] train-rmse:1.332213+0.052771 test-rmse:1.485328+0.196642
## [460] train-rmse:1.332035+0.052767 test-rmse:1.485322+0.196737
## [461] train-rmse:1.331858+0.052760 test-rmse:1.485196+0.197091
## [462] train-rmse:1.331681+0.052754 test-rmse:1.485854+0.197008
## [463] train-rmse:1.331509+0.052748 test-rmse:1.485443+0.197396
## [464] train-rmse:1.331333+0.052742 test-rmse:1.484583+0.197244
## [465] train-rmse:1.331165+0.052736 test-rmse:1.484787+0.197052
## [466] train-rmse:1.330999+0.052735 test-rmse:1.484801+0.197035
## [467] train-rmse:1.330833+0.052732 test-rmse:1.484284+0.197782
## [468] train-rmse:1.330669+0.052726 test-rmse:1.484410+0.198007
## [469] train-rmse:1.330503+0.052721 test-rmse:1.484492+0.198548
## [470] train-rmse:1.330334+0.052713 test-rmse:1.485207+0.198561
## [471] train-rmse:1.330166+0.052704 test-rmse:1.485050+0.198028
## [472] train-rmse:1.330004+0.052697 test-rmse:1.484792+0.197813
## [473] train-rmse:1.329844+0.052690 test-rmse:1.484975+0.197499
## Stopping. Best iteration:
## [467] train-rmse:1.330833+0.052732 test-rmse:1.484284+0.197782
##
## 29
## [1] train-rmse:8.063964+0.057815 test-rmse:8.060198+0.268096
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.739139+0.048735 test-rmse:6.731255+0.254806
## [3] train-rmse:5.673326+0.041966 test-rmse:5.672520+0.235858
## [4] train-rmse:4.814800+0.034749 test-rmse:4.826408+0.226860
## [5] train-rmse:4.134450+0.030936 test-rmse:4.156872+0.203363
## [6] train-rmse:3.591443+0.028773 test-rmse:3.621099+0.178670
## [7] train-rmse:3.167096+0.026393 test-rmse:3.202528+0.155171
## [8] train-rmse:2.839798+0.025919 test-rmse:2.897377+0.126661
## [9] train-rmse:2.590214+0.025021 test-rmse:2.651126+0.112862
## [10] train-rmse:2.397884+0.026045 test-rmse:2.473041+0.088013
## [11] train-rmse:2.247410+0.014202 test-rmse:2.331845+0.075781
## [12] train-rmse:2.138160+0.013016 test-rmse:2.236067+0.063180
## [13] train-rmse:2.054915+0.015172 test-rmse:2.165431+0.056045
## [14] train-rmse:1.991430+0.015181 test-rmse:2.112904+0.057279
## [15] train-rmse:1.936457+0.017806 test-rmse:2.071226+0.072547
## [16] train-rmse:1.887353+0.015781 test-rmse:2.030867+0.084158
## [17] train-rmse:1.853557+0.014656 test-rmse:2.001971+0.088084
## [18] train-rmse:1.820923+0.013400 test-rmse:1.977793+0.074258
## [19] train-rmse:1.797172+0.013893 test-rmse:1.955236+0.073473
## [20] train-rmse:1.776136+0.014950 test-rmse:1.931434+0.074739
## [21] train-rmse:1.756693+0.014962 test-rmse:1.915310+0.080931
## [22] train-rmse:1.733042+0.018348 test-rmse:1.899714+0.086923
## [23] train-rmse:1.715886+0.019815 test-rmse:1.886629+0.092787
## [24] train-rmse:1.700902+0.020260 test-rmse:1.876104+0.096740
## [25] train-rmse:1.686778+0.020774 test-rmse:1.860290+0.093738
## [26] train-rmse:1.672271+0.021487 test-rmse:1.846753+0.093200
## [27] train-rmse:1.655301+0.023452 test-rmse:1.830757+0.091312
## [28] train-rmse:1.638969+0.024791 test-rmse:1.817324+0.096689
## [29] train-rmse:1.626242+0.025468 test-rmse:1.811292+0.097020
## [30] train-rmse:1.612704+0.024461 test-rmse:1.803071+0.100233
## [31] train-rmse:1.600183+0.025194 test-rmse:1.793160+0.105468
## [32] train-rmse:1.587323+0.025540 test-rmse:1.785402+0.104813
## [33] train-rmse:1.567702+0.032065 test-rmse:1.763460+0.084317
## [34] train-rmse:1.555257+0.033291 test-rmse:1.759957+0.087892
## [35] train-rmse:1.544746+0.033880 test-rmse:1.752649+0.085953
## [36] train-rmse:1.532972+0.034260 test-rmse:1.744660+0.084819
## [37] train-rmse:1.521876+0.035207 test-rmse:1.735866+0.085508
## [38] train-rmse:1.498727+0.027419 test-rmse:1.708661+0.074592
## [39] train-rmse:1.487571+0.026885 test-rmse:1.703154+0.077674
## [40] train-rmse:1.477417+0.027035 test-rmse:1.696561+0.073338
## [41] train-rmse:1.467219+0.027406 test-rmse:1.689523+0.077097
## [42] train-rmse:1.456663+0.029491 test-rmse:1.689278+0.079025
## [43] train-rmse:1.447437+0.029593 test-rmse:1.678551+0.079689
## [44] train-rmse:1.437468+0.030518 test-rmse:1.675657+0.082867
## [45] train-rmse:1.425469+0.035346 test-rmse:1.666746+0.077471
## [46] train-rmse:1.416463+0.035485 test-rmse:1.658490+0.074328
## [47] train-rmse:1.407376+0.034812 test-rmse:1.650324+0.075334
## [48] train-rmse:1.398031+0.035187 test-rmse:1.645493+0.077031
## [49] train-rmse:1.388787+0.034678 test-rmse:1.634391+0.079613
## [50] train-rmse:1.380857+0.035190 test-rmse:1.627229+0.081555
## [51] train-rmse:1.373115+0.034389 test-rmse:1.619361+0.080372
## [52] train-rmse:1.364540+0.034239 test-rmse:1.614142+0.081303
## [53] train-rmse:1.356117+0.034528 test-rmse:1.606651+0.082050
## [54] train-rmse:1.343868+0.035425 test-rmse:1.600593+0.086332
## [55] train-rmse:1.335083+0.036386 test-rmse:1.599124+0.090193
## [56] train-rmse:1.328516+0.036322 test-rmse:1.594915+0.088965
## [57] train-rmse:1.321735+0.035796 test-rmse:1.589543+0.091365
## [58] train-rmse:1.314518+0.035493 test-rmse:1.584737+0.093038
## [59] train-rmse:1.307851+0.035544 test-rmse:1.576414+0.097809
## [60] train-rmse:1.301330+0.035992 test-rmse:1.573168+0.098349
## [61] train-rmse:1.294147+0.035146 test-rmse:1.569091+0.100324
## [62] train-rmse:1.284859+0.038742 test-rmse:1.556683+0.090478
## [63] train-rmse:1.278739+0.038819 test-rmse:1.548935+0.094390
## [64] train-rmse:1.270913+0.040891 test-rmse:1.546693+0.095910
## [65] train-rmse:1.263871+0.041143 test-rmse:1.542406+0.095748
## [66] train-rmse:1.252405+0.043620 test-rmse:1.536850+0.095916
## [67] train-rmse:1.243893+0.047168 test-rmse:1.531587+0.091221
## [68] train-rmse:1.237338+0.047252 test-rmse:1.526099+0.089951
## [69] train-rmse:1.232075+0.047241 test-rmse:1.525695+0.088993
## [70] train-rmse:1.226355+0.046814 test-rmse:1.523616+0.089302
## [71] train-rmse:1.219736+0.046649 test-rmse:1.518310+0.093046
## [72] train-rmse:1.214309+0.046506 test-rmse:1.516473+0.094984
## [73] train-rmse:1.203487+0.039438 test-rmse:1.508516+0.099259
## [74] train-rmse:1.197712+0.039359 test-rmse:1.503948+0.098660
## [75] train-rmse:1.192482+0.039785 test-rmse:1.501251+0.100643
## [76] train-rmse:1.186330+0.039505 test-rmse:1.496036+0.102246
## [77] train-rmse:1.180760+0.038913 test-rmse:1.490656+0.105725
## [78] train-rmse:1.174628+0.038713 test-rmse:1.482758+0.110560
## [79] train-rmse:1.169009+0.039588 test-rmse:1.480898+0.108590
## [80] train-rmse:1.160689+0.035564 test-rmse:1.475927+0.114987
## [81] train-rmse:1.153465+0.038638 test-rmse:1.471841+0.114696
## [82] train-rmse:1.148914+0.039204 test-rmse:1.469026+0.116156
## [83] train-rmse:1.144220+0.038632 test-rmse:1.464986+0.117792
## [84] train-rmse:1.139265+0.039213 test-rmse:1.459839+0.118190
## [85] train-rmse:1.131409+0.039225 test-rmse:1.454888+0.122180
## [86] train-rmse:1.126519+0.039648 test-rmse:1.448495+0.118759
## [87] train-rmse:1.122395+0.039939 test-rmse:1.449326+0.119330
## [88] train-rmse:1.118118+0.040066 test-rmse:1.445686+0.117404
## [89] train-rmse:1.113624+0.039625 test-rmse:1.443188+0.121117
## [90] train-rmse:1.108642+0.039671 test-rmse:1.438379+0.125980
## [91] train-rmse:1.103487+0.041139 test-rmse:1.436500+0.123793
## [92] train-rmse:1.099558+0.041491 test-rmse:1.436136+0.126798
## [93] train-rmse:1.095111+0.042645 test-rmse:1.434677+0.125638
## [94] train-rmse:1.091323+0.042735 test-rmse:1.431857+0.123997
## [95] train-rmse:1.087605+0.042543 test-rmse:1.431638+0.124003
## [96] train-rmse:1.084020+0.042566 test-rmse:1.430538+0.125776
## [97] train-rmse:1.080394+0.042709 test-rmse:1.427742+0.127013
## [98] train-rmse:1.076897+0.042912 test-rmse:1.424520+0.128227
## [99] train-rmse:1.073475+0.043019 test-rmse:1.420007+0.126792
## [100] train-rmse:1.070097+0.043343 test-rmse:1.416528+0.127657
## [101] train-rmse:1.066948+0.043219 test-rmse:1.416638+0.127482
## [102] train-rmse:1.063685+0.043350 test-rmse:1.414511+0.126344
## [103] train-rmse:1.057223+0.043774 test-rmse:1.409214+0.132316
## [104] train-rmse:1.053470+0.043909 test-rmse:1.405849+0.134133
## [105] train-rmse:1.048773+0.045162 test-rmse:1.404584+0.133362
## [106] train-rmse:1.044991+0.045802 test-rmse:1.403749+0.133243
## [107] train-rmse:1.041622+0.046388 test-rmse:1.402388+0.134566
## [108] train-rmse:1.036843+0.044688 test-rmse:1.398351+0.137354
## [109] train-rmse:1.033922+0.044824 test-rmse:1.397578+0.138773
## [110] train-rmse:1.031211+0.044679 test-rmse:1.397059+0.139175
## [111] train-rmse:1.028312+0.044737 test-rmse:1.395624+0.139123
## [112] train-rmse:1.025369+0.045051 test-rmse:1.395330+0.139949
## [113] train-rmse:1.022951+0.045011 test-rmse:1.394755+0.141125
## [114] train-rmse:1.020150+0.044835 test-rmse:1.393753+0.142610
## [115] train-rmse:1.016985+0.045590 test-rmse:1.390409+0.144839
## [116] train-rmse:1.014377+0.045770 test-rmse:1.386805+0.146208
## [117] train-rmse:1.011522+0.045296 test-rmse:1.384233+0.145569
## [118] train-rmse:1.003159+0.038213 test-rmse:1.369314+0.147825
## [119] train-rmse:1.000785+0.038239 test-rmse:1.368320+0.148431
## [120] train-rmse:0.997954+0.038909 test-rmse:1.367569+0.147833
## [121] train-rmse:0.995488+0.039061 test-rmse:1.366245+0.146122
## [122] train-rmse:0.993233+0.039315 test-rmse:1.365198+0.146239
## [123] train-rmse:0.989909+0.038522 test-rmse:1.364345+0.146549
## [124] train-rmse:0.987534+0.038786 test-rmse:1.362551+0.147096
## [125] train-rmse:0.981752+0.038580 test-rmse:1.356743+0.148587
## [126] train-rmse:0.979637+0.038293 test-rmse:1.356186+0.151185
## [127] train-rmse:0.974889+0.041400 test-rmse:1.351787+0.147159
## [128] train-rmse:0.972657+0.041591 test-rmse:1.350481+0.147724
## [129] train-rmse:0.970408+0.041816 test-rmse:1.348722+0.148100
## [130] train-rmse:0.967763+0.041957 test-rmse:1.348396+0.147045
## [131] train-rmse:0.965464+0.041668 test-rmse:1.345354+0.148408
## [132] train-rmse:0.963518+0.041711 test-rmse:1.345909+0.147837
## [133] train-rmse:0.961061+0.042465 test-rmse:1.346533+0.148009
## [134] train-rmse:0.958570+0.042653 test-rmse:1.345966+0.150083
## [135] train-rmse:0.956093+0.043165 test-rmse:1.346415+0.150184
## [136] train-rmse:0.954039+0.043151 test-rmse:1.346070+0.150596
## [137] train-rmse:0.952146+0.043464 test-rmse:1.347459+0.150732
## Stopping. Best iteration:
## [131] train-rmse:0.965464+0.041668 test-rmse:1.345354+0.148408
##
## 30
## [1] train-rmse:8.055202+0.058780 test-rmse:8.051680+0.263219
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.716698+0.047086 test-rmse:6.710539+0.251448
## [3] train-rmse:5.634388+0.038559 test-rmse:5.632943+0.238753
## [4] train-rmse:4.763912+0.031910 test-rmse:4.780465+0.221536
## [5] train-rmse:4.060938+0.025719 test-rmse:4.071797+0.204217
## [6] train-rmse:3.501352+0.023238 test-rmse:3.529014+0.180714
## [7] train-rmse:3.057267+0.018617 test-rmse:3.096828+0.165721
## [8] train-rmse:2.706157+0.013839 test-rmse:2.762432+0.141163
## [9] train-rmse:2.432698+0.008433 test-rmse:2.505938+0.130309
## [10] train-rmse:2.223498+0.008139 test-rmse:2.317193+0.115002
## [11] train-rmse:2.057852+0.007372 test-rmse:2.182255+0.107725
## [12] train-rmse:1.931677+0.008992 test-rmse:2.072202+0.091830
## [13] train-rmse:1.833002+0.011030 test-rmse:1.991188+0.082920
## [14] train-rmse:1.753803+0.015677 test-rmse:1.932685+0.079621
## [15] train-rmse:1.692052+0.015872 test-rmse:1.874607+0.073046
## [16] train-rmse:1.643188+0.017092 test-rmse:1.836745+0.063812
## [17] train-rmse:1.598543+0.016981 test-rmse:1.807567+0.064911
## [18] train-rmse:1.559154+0.017025 test-rmse:1.782556+0.060016
## [19] train-rmse:1.529615+0.017257 test-rmse:1.755135+0.058230
## [20] train-rmse:1.502301+0.017708 test-rmse:1.738562+0.070015
## [21] train-rmse:1.476299+0.019207 test-rmse:1.717223+0.066656
## [22] train-rmse:1.451413+0.020252 test-rmse:1.701829+0.065008
## [23] train-rmse:1.426216+0.019711 test-rmse:1.688326+0.072779
## [24] train-rmse:1.404624+0.018350 test-rmse:1.671849+0.075120
## [25] train-rmse:1.382876+0.017987 test-rmse:1.657803+0.079134
## [26] train-rmse:1.364772+0.017718 test-rmse:1.647052+0.077330
## [27] train-rmse:1.345895+0.020526 test-rmse:1.629022+0.081275
## [28] train-rmse:1.328809+0.021570 test-rmse:1.612755+0.081802
## [29] train-rmse:1.310351+0.020666 test-rmse:1.598413+0.081820
## [30] train-rmse:1.291978+0.019654 test-rmse:1.589864+0.082679
## [31] train-rmse:1.277894+0.019246 test-rmse:1.579224+0.082172
## [32] train-rmse:1.261015+0.020414 test-rmse:1.569713+0.078728
## [33] train-rmse:1.246435+0.022066 test-rmse:1.561460+0.083465
## [34] train-rmse:1.228822+0.023493 test-rmse:1.551412+0.085474
## [35] train-rmse:1.214760+0.024014 test-rmse:1.542447+0.089347
## [36] train-rmse:1.201888+0.024449 test-rmse:1.532270+0.095513
## [37] train-rmse:1.189542+0.024000 test-rmse:1.524258+0.094232
## [38] train-rmse:1.176022+0.021751 test-rmse:1.514745+0.097761
## [39] train-rmse:1.163403+0.022120 test-rmse:1.505487+0.098566
## [40] train-rmse:1.151790+0.021874 test-rmse:1.500834+0.099304
## [41] train-rmse:1.139980+0.022546 test-rmse:1.496681+0.099502
## [42] train-rmse:1.126696+0.024291 test-rmse:1.491490+0.102703
## [43] train-rmse:1.116634+0.024467 test-rmse:1.481625+0.108664
## [44] train-rmse:1.105757+0.024512 test-rmse:1.476213+0.110361
## [45] train-rmse:1.094771+0.025093 test-rmse:1.468923+0.113959
## [46] train-rmse:1.084698+0.024573 test-rmse:1.462526+0.112514
## [47] train-rmse:1.075902+0.024885 test-rmse:1.456712+0.113371
## [48] train-rmse:1.066518+0.025473 test-rmse:1.449836+0.111933
## [49] train-rmse:1.057761+0.025850 test-rmse:1.444264+0.112892
## [50] train-rmse:1.047707+0.025521 test-rmse:1.437042+0.117314
## [51] train-rmse:1.038276+0.025642 test-rmse:1.433840+0.118068
## [52] train-rmse:1.029536+0.025322 test-rmse:1.427522+0.119196
## [53] train-rmse:1.019226+0.025016 test-rmse:1.421658+0.124073
## [54] train-rmse:1.010375+0.025439 test-rmse:1.416204+0.124663
## [55] train-rmse:1.002477+0.026295 test-rmse:1.412296+0.125420
## [56] train-rmse:0.993450+0.024180 test-rmse:1.410872+0.129139
## [57] train-rmse:0.985324+0.023726 test-rmse:1.402333+0.132567
## [58] train-rmse:0.978928+0.023423 test-rmse:1.399319+0.133370
## [59] train-rmse:0.970649+0.023808 test-rmse:1.396850+0.135576
## [60] train-rmse:0.963304+0.024462 test-rmse:1.393496+0.133210
## [61] train-rmse:0.956282+0.025663 test-rmse:1.387678+0.134667
## [62] train-rmse:0.946766+0.026054 test-rmse:1.383252+0.137779
## [63] train-rmse:0.935934+0.030094 test-rmse:1.374335+0.139422
## [64] train-rmse:0.927304+0.030633 test-rmse:1.365206+0.133518
## [65] train-rmse:0.920691+0.029884 test-rmse:1.361928+0.139313
## [66] train-rmse:0.913999+0.030063 test-rmse:1.359916+0.138169
## [67] train-rmse:0.907833+0.029709 test-rmse:1.354442+0.141159
## [68] train-rmse:0.901251+0.028965 test-rmse:1.347892+0.140636
## [69] train-rmse:0.895149+0.029861 test-rmse:1.346940+0.142185
## [70] train-rmse:0.889295+0.028927 test-rmse:1.343718+0.146683
## [71] train-rmse:0.882114+0.029242 test-rmse:1.338765+0.146298
## [72] train-rmse:0.877362+0.029207 test-rmse:1.335681+0.147290
## [73] train-rmse:0.870444+0.028080 test-rmse:1.336788+0.150576
## [74] train-rmse:0.860747+0.031300 test-rmse:1.330286+0.147841
## [75] train-rmse:0.854911+0.032577 test-rmse:1.328086+0.147434
## [76] train-rmse:0.850508+0.033190 test-rmse:1.322714+0.146910
## [77] train-rmse:0.842805+0.028280 test-rmse:1.320663+0.148534
## [78] train-rmse:0.835855+0.029146 test-rmse:1.315062+0.145446
## [79] train-rmse:0.829078+0.025853 test-rmse:1.311022+0.149375
## [80] train-rmse:0.821086+0.027675 test-rmse:1.305812+0.148882
## [81] train-rmse:0.816601+0.027222 test-rmse:1.305547+0.149902
## [82] train-rmse:0.810708+0.027066 test-rmse:1.303893+0.147893
## [83] train-rmse:0.805555+0.027912 test-rmse:1.302637+0.148763
## [84] train-rmse:0.800594+0.028864 test-rmse:1.301854+0.149825
## [85] train-rmse:0.795765+0.030041 test-rmse:1.299822+0.149325
## [86] train-rmse:0.790475+0.028305 test-rmse:1.298739+0.151013
## [87] train-rmse:0.785613+0.028051 test-rmse:1.297472+0.151532
## [88] train-rmse:0.781229+0.028766 test-rmse:1.297162+0.151372
## [89] train-rmse:0.777587+0.028203 test-rmse:1.296462+0.151769
## [90] train-rmse:0.774013+0.027903 test-rmse:1.296430+0.152011
## [91] train-rmse:0.768147+0.027880 test-rmse:1.288414+0.151489
## [92] train-rmse:0.763469+0.027673 test-rmse:1.285843+0.152970
## [93] train-rmse:0.759981+0.027561 test-rmse:1.285337+0.153801
## [94] train-rmse:0.756460+0.028322 test-rmse:1.283379+0.153048
## [95] train-rmse:0.752217+0.028011 test-rmse:1.283438+0.153302
## [96] train-rmse:0.747722+0.028939 test-rmse:1.283379+0.155217
## [97] train-rmse:0.743574+0.028511 test-rmse:1.281515+0.154906
## [98] train-rmse:0.739115+0.028201 test-rmse:1.281356+0.158440
## [99] train-rmse:0.735632+0.029249 test-rmse:1.284441+0.158627
## [100] train-rmse:0.732410+0.030332 test-rmse:1.281288+0.157329
## [101] train-rmse:0.728603+0.031301 test-rmse:1.277176+0.155386
## [102] train-rmse:0.723546+0.031785 test-rmse:1.271145+0.154047
## [103] train-rmse:0.720594+0.031728 test-rmse:1.270474+0.154015
## [104] train-rmse:0.716606+0.032040 test-rmse:1.270012+0.153138
## [105] train-rmse:0.713603+0.031832 test-rmse:1.266373+0.155289
## [106] train-rmse:0.708103+0.028830 test-rmse:1.266701+0.157375
## [107] train-rmse:0.704829+0.028643 test-rmse:1.264777+0.157866
## [108] train-rmse:0.702636+0.028471 test-rmse:1.264440+0.157681
## [109] train-rmse:0.699606+0.028480 test-rmse:1.264284+0.158008
## [110] train-rmse:0.696698+0.028881 test-rmse:1.264303+0.158717
## [111] train-rmse:0.694029+0.029099 test-rmse:1.264692+0.159676
## [112] train-rmse:0.691181+0.029386 test-rmse:1.263285+0.159870
## [113] train-rmse:0.688852+0.029223 test-rmse:1.264619+0.161884
## [114] train-rmse:0.686009+0.028795 test-rmse:1.262987+0.162984
## [115] train-rmse:0.683868+0.028867 test-rmse:1.261119+0.163208
## [116] train-rmse:0.681703+0.028950 test-rmse:1.261707+0.162676
## [117] train-rmse:0.677756+0.027010 test-rmse:1.260702+0.163296
## [118] train-rmse:0.674127+0.028237 test-rmse:1.261189+0.162826
## [119] train-rmse:0.670046+0.028109 test-rmse:1.257030+0.163000
## [120] train-rmse:0.667177+0.029035 test-rmse:1.257579+0.165295
## [121] train-rmse:0.664996+0.028907 test-rmse:1.256205+0.166319
## [122] train-rmse:0.662438+0.028567 test-rmse:1.255714+0.166519
## [123] train-rmse:0.660122+0.027896 test-rmse:1.255843+0.167103
## [124] train-rmse:0.657970+0.028004 test-rmse:1.255693+0.166114
## [125] train-rmse:0.655141+0.028981 test-rmse:1.256309+0.164864
## [126] train-rmse:0.653533+0.029164 test-rmse:1.255218+0.164822
## [127] train-rmse:0.650837+0.029817 test-rmse:1.253169+0.163879
## [128] train-rmse:0.646537+0.029621 test-rmse:1.252638+0.164978
## [129] train-rmse:0.643516+0.029439 test-rmse:1.252431+0.165348
## [130] train-rmse:0.640892+0.029545 test-rmse:1.252385+0.165760
## [131] train-rmse:0.638847+0.029905 test-rmse:1.251287+0.165585
## [132] train-rmse:0.637236+0.029800 test-rmse:1.251613+0.166393
## [133] train-rmse:0.635151+0.029368 test-rmse:1.250916+0.167302
## [134] train-rmse:0.633323+0.029347 test-rmse:1.254617+0.166274
## [135] train-rmse:0.631198+0.029911 test-rmse:1.255457+0.166654
## [136] train-rmse:0.629528+0.029939 test-rmse:1.255541+0.166882
## [137] train-rmse:0.626643+0.028586 test-rmse:1.256061+0.167305
## [138] train-rmse:0.624877+0.029525 test-rmse:1.255819+0.168092
## [139] train-rmse:0.623111+0.029804 test-rmse:1.255681+0.168005
## Stopping. Best iteration:
## [133] train-rmse:0.635151+0.029368 test-rmse:1.250916+0.167302
##
## 31
## [1] train-rmse:8.051639+0.059171 test-rmse:8.047974+0.264527
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.709657+0.048883 test-rmse:6.713727+0.250042
## [3] train-rmse:5.620473+0.040109 test-rmse:5.628791+0.237195
## [4] train-rmse:4.742311+0.032259 test-rmse:4.752261+0.217757
## [5] train-rmse:4.031851+0.027926 test-rmse:4.046914+0.196448
## [6] train-rmse:3.455110+0.021131 test-rmse:3.487097+0.164906
## [7] train-rmse:2.998732+0.020287 test-rmse:3.045015+0.145351
## [8] train-rmse:2.633209+0.015403 test-rmse:2.702101+0.133137
## [9] train-rmse:2.347488+0.012946 test-rmse:2.438699+0.125320
## [10] train-rmse:2.116730+0.017157 test-rmse:2.223952+0.101473
## [11] train-rmse:1.941189+0.018004 test-rmse:2.083587+0.097187
## [12] train-rmse:1.800603+0.019644 test-rmse:1.968230+0.087943
## [13] train-rmse:1.688005+0.020786 test-rmse:1.867522+0.077941
## [14] train-rmse:1.599409+0.020591 test-rmse:1.806271+0.075225
## [15] train-rmse:1.525658+0.025435 test-rmse:1.754457+0.071071
## [16] train-rmse:1.463410+0.026138 test-rmse:1.712461+0.068862
## [17] train-rmse:1.414598+0.027209 test-rmse:1.672631+0.070638
## [18] train-rmse:1.367259+0.021924 test-rmse:1.644887+0.072646
## [19] train-rmse:1.326212+0.023970 test-rmse:1.616509+0.076446
## [20] train-rmse:1.294224+0.023674 test-rmse:1.600475+0.082283
## [21] train-rmse:1.263280+0.024277 test-rmse:1.577651+0.091711
## [22] train-rmse:1.237750+0.025508 test-rmse:1.557091+0.088425
## [23] train-rmse:1.212482+0.025506 test-rmse:1.539823+0.094591
## [24] train-rmse:1.188931+0.027298 test-rmse:1.533209+0.098685
## [25] train-rmse:1.164855+0.026372 test-rmse:1.518625+0.101803
## [26] train-rmse:1.144693+0.026654 test-rmse:1.508090+0.102378
## [27] train-rmse:1.121953+0.028316 test-rmse:1.488218+0.106899
## [28] train-rmse:1.103744+0.029070 test-rmse:1.479934+0.114979
## [29] train-rmse:1.085257+0.030927 test-rmse:1.468030+0.116189
## [30] train-rmse:1.067954+0.031013 test-rmse:1.460532+0.120077
## [31] train-rmse:1.051353+0.033046 test-rmse:1.448549+0.122082
## [32] train-rmse:1.034952+0.031535 test-rmse:1.444938+0.125373
## [33] train-rmse:1.016801+0.032663 test-rmse:1.434418+0.133204
## [34] train-rmse:1.002195+0.034116 test-rmse:1.422709+0.127410
## [35] train-rmse:0.988340+0.034990 test-rmse:1.411763+0.127022
## [36] train-rmse:0.974358+0.034280 test-rmse:1.398568+0.128680
## [37] train-rmse:0.960794+0.034307 test-rmse:1.386560+0.129553
## [38] train-rmse:0.946116+0.035489 test-rmse:1.384018+0.126643
## [39] train-rmse:0.932994+0.034504 test-rmse:1.373266+0.137071
## [40] train-rmse:0.920907+0.034603 test-rmse:1.366745+0.142643
## [41] train-rmse:0.909753+0.034925 test-rmse:1.366104+0.143914
## [42] train-rmse:0.897346+0.036748 test-rmse:1.360698+0.141643
## [43] train-rmse:0.885216+0.038570 test-rmse:1.354190+0.145255
## [44] train-rmse:0.873740+0.039325 test-rmse:1.351478+0.146763
## [45] train-rmse:0.864252+0.039696 test-rmse:1.348417+0.149278
## [46] train-rmse:0.854609+0.040800 test-rmse:1.341919+0.149877
## [47] train-rmse:0.844990+0.041306 test-rmse:1.338252+0.154010
## [48] train-rmse:0.834860+0.041455 test-rmse:1.332317+0.154104
## [49] train-rmse:0.824187+0.039638 test-rmse:1.327596+0.154956
## [50] train-rmse:0.813314+0.036870 test-rmse:1.327276+0.157061
## [51] train-rmse:0.806486+0.037373 test-rmse:1.326625+0.154256
## [52] train-rmse:0.798464+0.037492 test-rmse:1.323726+0.155104
## [53] train-rmse:0.789345+0.038106 test-rmse:1.325096+0.156050
## [54] train-rmse:0.781241+0.037149 test-rmse:1.322293+0.156852
## [55] train-rmse:0.772880+0.037508 test-rmse:1.317367+0.157618
## [56] train-rmse:0.762802+0.035692 test-rmse:1.314735+0.161517
## [57] train-rmse:0.754377+0.035859 test-rmse:1.310656+0.158969
## [58] train-rmse:0.747165+0.036157 test-rmse:1.303473+0.160893
## [59] train-rmse:0.739431+0.036284 test-rmse:1.299890+0.163189
## [60] train-rmse:0.732911+0.037236 test-rmse:1.297248+0.164065
## [61] train-rmse:0.726570+0.038276 test-rmse:1.295125+0.161349
## [62] train-rmse:0.718867+0.035283 test-rmse:1.296380+0.162201
## [63] train-rmse:0.713729+0.035776 test-rmse:1.294823+0.161911
## [64] train-rmse:0.706692+0.034837 test-rmse:1.294239+0.164470
## [65] train-rmse:0.699561+0.035560 test-rmse:1.291071+0.164801
## [66] train-rmse:0.693317+0.032382 test-rmse:1.289742+0.166173
## [67] train-rmse:0.686488+0.033585 test-rmse:1.291169+0.165259
## [68] train-rmse:0.681098+0.033866 test-rmse:1.288960+0.166039
## [69] train-rmse:0.675207+0.034093 test-rmse:1.289840+0.166866
## [70] train-rmse:0.668500+0.034105 test-rmse:1.287200+0.165573
## [71] train-rmse:0.662800+0.034546 test-rmse:1.284957+0.166030
## [72] train-rmse:0.657608+0.034120 test-rmse:1.284448+0.164579
## [73] train-rmse:0.652074+0.034784 test-rmse:1.281231+0.164133
## [74] train-rmse:0.647343+0.035037 test-rmse:1.280251+0.164089
## [75] train-rmse:0.642058+0.035617 test-rmse:1.281652+0.163729
## [76] train-rmse:0.636064+0.036886 test-rmse:1.281403+0.163856
## [77] train-rmse:0.632048+0.036703 test-rmse:1.280267+0.164044
## [78] train-rmse:0.628208+0.037251 test-rmse:1.278495+0.165192
## [79] train-rmse:0.622350+0.035995 test-rmse:1.274204+0.164876
## [80] train-rmse:0.618511+0.035985 test-rmse:1.274400+0.167149
## [81] train-rmse:0.611753+0.035301 test-rmse:1.271783+0.168889
## [82] train-rmse:0.607642+0.035601 test-rmse:1.270893+0.168066
## [83] train-rmse:0.603773+0.035102 test-rmse:1.271642+0.166869
## [84] train-rmse:0.598448+0.034902 test-rmse:1.273074+0.167104
## [85] train-rmse:0.594828+0.035211 test-rmse:1.271012+0.166917
## [86] train-rmse:0.590983+0.033962 test-rmse:1.269807+0.167713
## [87] train-rmse:0.587292+0.034350 test-rmse:1.268535+0.169021
## [88] train-rmse:0.582965+0.031767 test-rmse:1.268153+0.168581
## [89] train-rmse:0.577429+0.032445 test-rmse:1.264686+0.167194
## [90] train-rmse:0.572215+0.030777 test-rmse:1.264438+0.166711
## [91] train-rmse:0.566883+0.029871 test-rmse:1.263281+0.166408
## [92] train-rmse:0.564222+0.030259 test-rmse:1.263434+0.166138
## [93] train-rmse:0.560490+0.029927 test-rmse:1.263316+0.166490
## [94] train-rmse:0.557317+0.029738 test-rmse:1.266681+0.168448
## [95] train-rmse:0.554861+0.030591 test-rmse:1.266155+0.168291
## [96] train-rmse:0.551828+0.031234 test-rmse:1.266005+0.168036
## [97] train-rmse:0.546166+0.031342 test-rmse:1.263322+0.166269
## Stopping. Best iteration:
## [91] train-rmse:0.566883+0.029871 test-rmse:1.263281+0.166408
##
## 32
## [1] train-rmse:8.048684+0.059471 test-rmse:8.048192+0.264928
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.701828+0.048963 test-rmse:6.710370+0.244974
## [3] train-rmse:5.607077+0.040321 test-rmse:5.623191+0.226189
## [4] train-rmse:4.717904+0.032310 test-rmse:4.739982+0.201719
## [5] train-rmse:4.000863+0.029206 test-rmse:4.030558+0.171346
## [6] train-rmse:3.417561+0.024777 test-rmse:3.460864+0.156806
## [7] train-rmse:2.947424+0.021225 test-rmse:3.013527+0.146671
## [8] train-rmse:2.569899+0.021631 test-rmse:2.652586+0.120636
## [9] train-rmse:2.272408+0.018630 test-rmse:2.394267+0.121980
## [10] train-rmse:2.036626+0.019397 test-rmse:2.186568+0.110765
## [11] train-rmse:1.839504+0.022559 test-rmse:2.012298+0.086628
## [12] train-rmse:1.687291+0.020093 test-rmse:1.903142+0.084341
## [13] train-rmse:1.566431+0.021400 test-rmse:1.805189+0.079498
## [14] train-rmse:1.468962+0.022098 test-rmse:1.720096+0.071333
## [15] train-rmse:1.392148+0.020935 test-rmse:1.668989+0.072436
## [16] train-rmse:1.319635+0.023283 test-rmse:1.621040+0.070941
## [17] train-rmse:1.264118+0.020240 test-rmse:1.582644+0.076130
## [18] train-rmse:1.216497+0.019730 test-rmse:1.549974+0.082661
## [19] train-rmse:1.174090+0.016633 test-rmse:1.524408+0.083967
## [20] train-rmse:1.137955+0.019605 test-rmse:1.508328+0.086363
## [21] train-rmse:1.097020+0.015527 test-rmse:1.488799+0.091007
## [22] train-rmse:1.067394+0.019012 test-rmse:1.467980+0.092846
## [23] train-rmse:1.040219+0.019580 test-rmse:1.454862+0.097211
## [24] train-rmse:1.016322+0.018823 test-rmse:1.442629+0.102755
## [25] train-rmse:0.992692+0.017571 test-rmse:1.430953+0.107217
## [26] train-rmse:0.970758+0.018403 test-rmse:1.419923+0.113531
## [27] train-rmse:0.947722+0.019844 test-rmse:1.406891+0.113680
## [28] train-rmse:0.928984+0.020304 test-rmse:1.402396+0.115562
## [29] train-rmse:0.911065+0.018002 test-rmse:1.390292+0.119489
## [30] train-rmse:0.892703+0.016628 test-rmse:1.389932+0.122514
## [31] train-rmse:0.878842+0.017114 test-rmse:1.384042+0.127247
## [32] train-rmse:0.861020+0.019974 test-rmse:1.373728+0.128693
## [33] train-rmse:0.845933+0.017926 test-rmse:1.366427+0.131431
## [34] train-rmse:0.829924+0.018583 test-rmse:1.357066+0.138250
## [35] train-rmse:0.814636+0.019543 test-rmse:1.348987+0.138781
## [36] train-rmse:0.800153+0.017331 test-rmse:1.343314+0.141566
## [37] train-rmse:0.787800+0.016658 test-rmse:1.339150+0.143117
## [38] train-rmse:0.774529+0.016572 test-rmse:1.335140+0.146423
## [39] train-rmse:0.764287+0.014629 test-rmse:1.334303+0.146899
## [40] train-rmse:0.753514+0.015004 test-rmse:1.329695+0.143855
## [41] train-rmse:0.740518+0.015418 test-rmse:1.322074+0.146594
## [42] train-rmse:0.729449+0.015045 test-rmse:1.317343+0.151019
## [43] train-rmse:0.716787+0.014899 test-rmse:1.311539+0.152808
## [44] train-rmse:0.706481+0.017645 test-rmse:1.307810+0.152770
## [45] train-rmse:0.697665+0.017058 test-rmse:1.303644+0.154082
## [46] train-rmse:0.689061+0.017459 test-rmse:1.299362+0.155470
## [47] train-rmse:0.680896+0.016576 test-rmse:1.297948+0.157718
## [48] train-rmse:0.669721+0.014989 test-rmse:1.292856+0.157214
## [49] train-rmse:0.661430+0.014409 test-rmse:1.293845+0.159094
## [50] train-rmse:0.651862+0.014976 test-rmse:1.291555+0.160141
## [51] train-rmse:0.643804+0.015391 test-rmse:1.290724+0.162698
## [52] train-rmse:0.634317+0.012741 test-rmse:1.287243+0.162881
## [53] train-rmse:0.624508+0.014669 test-rmse:1.282067+0.161392
## [54] train-rmse:0.617815+0.014791 test-rmse:1.281306+0.159866
## [55] train-rmse:0.608852+0.015682 test-rmse:1.274849+0.159647
## [56] train-rmse:0.600922+0.013784 test-rmse:1.270823+0.159609
## [57] train-rmse:0.593648+0.013577 test-rmse:1.270491+0.160687
## [58] train-rmse:0.587902+0.013907 test-rmse:1.268385+0.161786
## [59] train-rmse:0.582783+0.014239 test-rmse:1.268119+0.161659
## [60] train-rmse:0.576567+0.014892 test-rmse:1.268109+0.161149
## [61] train-rmse:0.569877+0.014527 test-rmse:1.267657+0.163703
## [62] train-rmse:0.564054+0.013645 test-rmse:1.265733+0.164140
## [63] train-rmse:0.557957+0.012949 test-rmse:1.262946+0.165859
## [64] train-rmse:0.551057+0.014691 test-rmse:1.262085+0.168669
## [65] train-rmse:0.546084+0.013746 test-rmse:1.261124+0.169101
## [66] train-rmse:0.541292+0.013143 test-rmse:1.261842+0.168895
## [67] train-rmse:0.535869+0.013856 test-rmse:1.258568+0.169075
## [68] train-rmse:0.531458+0.013830 test-rmse:1.257394+0.170430
## [69] train-rmse:0.525918+0.014552 test-rmse:1.254270+0.169294
## [70] train-rmse:0.521374+0.015161 test-rmse:1.254101+0.167479
## [71] train-rmse:0.516192+0.014712 test-rmse:1.254304+0.167561
## [72] train-rmse:0.511441+0.014522 test-rmse:1.255208+0.169170
## [73] train-rmse:0.505836+0.013961 test-rmse:1.254341+0.169776
## [74] train-rmse:0.500925+0.012994 test-rmse:1.253551+0.171561
## [75] train-rmse:0.497102+0.012315 test-rmse:1.254159+0.170176
## [76] train-rmse:0.489789+0.014176 test-rmse:1.253524+0.170206
## [77] train-rmse:0.485751+0.013618 test-rmse:1.252004+0.170892
## [78] train-rmse:0.480131+0.012248 test-rmse:1.254251+0.169680
## [79] train-rmse:0.476829+0.011514 test-rmse:1.254662+0.170031
## [80] train-rmse:0.471807+0.010562 test-rmse:1.254767+0.169967
## [81] train-rmse:0.468584+0.010718 test-rmse:1.254847+0.169838
## [82] train-rmse:0.465127+0.010454 test-rmse:1.254435+0.168701
## [83] train-rmse:0.459991+0.012341 test-rmse:1.253268+0.167181
## Stopping. Best iteration:
## [77] train-rmse:0.485751+0.013618 test-rmse:1.252004+0.170892
##
## 33
## [1] train-rmse:8.048058+0.059624 test-rmse:8.048831+0.259144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.698121+0.049205 test-rmse:6.702773+0.242866
## [3] train-rmse:5.599459+0.040379 test-rmse:5.608384+0.217884
## [4] train-rmse:4.705984+0.033831 test-rmse:4.731683+0.191969
## [5] train-rmse:3.980292+0.031045 test-rmse:4.009253+0.168640
## [6] train-rmse:3.388506+0.024443 test-rmse:3.424835+0.151770
## [7] train-rmse:2.908644+0.020875 test-rmse:2.957974+0.132744
## [8] train-rmse:2.522658+0.018842 test-rmse:2.608712+0.124723
## [9] train-rmse:2.213458+0.017914 test-rmse:2.336957+0.111889
## [10] train-rmse:1.961833+0.021504 test-rmse:2.099274+0.099366
## [11] train-rmse:1.757826+0.019705 test-rmse:1.941364+0.094725
## [12] train-rmse:1.598899+0.020517 test-rmse:1.810774+0.092887
## [13] train-rmse:1.465618+0.021870 test-rmse:1.703053+0.082725
## [14] train-rmse:1.357483+0.020849 test-rmse:1.627396+0.084094
## [15] train-rmse:1.267852+0.025613 test-rmse:1.564430+0.080489
## [16] train-rmse:1.193185+0.027180 test-rmse:1.511918+0.082142
## [17] train-rmse:1.131547+0.027334 test-rmse:1.474721+0.082840
## [18] train-rmse:1.080284+0.027973 test-rmse:1.445450+0.094105
## [19] train-rmse:1.035452+0.028166 test-rmse:1.415144+0.098256
## [20] train-rmse:0.995844+0.029710 test-rmse:1.394824+0.099662
## [21] train-rmse:0.960088+0.032038 test-rmse:1.380783+0.103977
## [22] train-rmse:0.927584+0.025566 test-rmse:1.360846+0.118953
## [23] train-rmse:0.897757+0.029396 test-rmse:1.353399+0.113093
## [24] train-rmse:0.871516+0.028200 test-rmse:1.337372+0.125417
## [25] train-rmse:0.846770+0.027243 test-rmse:1.331709+0.129313
## [26] train-rmse:0.824262+0.029031 test-rmse:1.321064+0.127782
## [27] train-rmse:0.805130+0.029578 test-rmse:1.312761+0.130346
## [28] train-rmse:0.784412+0.028923 test-rmse:1.302518+0.132002
## [29] train-rmse:0.764619+0.028493 test-rmse:1.292487+0.135849
## [30] train-rmse:0.746001+0.029044 test-rmse:1.282101+0.133242
## [31] train-rmse:0.730584+0.028938 test-rmse:1.275170+0.138075
## [32] train-rmse:0.711935+0.028661 test-rmse:1.269548+0.138436
## [33] train-rmse:0.695201+0.025229 test-rmse:1.260030+0.146548
## [34] train-rmse:0.682560+0.025649 test-rmse:1.257051+0.146320
## [35] train-rmse:0.669803+0.026970 test-rmse:1.251503+0.148333
## [36] train-rmse:0.655740+0.026036 test-rmse:1.247678+0.145715
## [37] train-rmse:0.643260+0.024133 test-rmse:1.245929+0.150002
## [38] train-rmse:0.630352+0.022120 test-rmse:1.242902+0.150430
## [39] train-rmse:0.617335+0.021668 test-rmse:1.241701+0.150734
## [40] train-rmse:0.606894+0.019784 test-rmse:1.235717+0.152280
## [41] train-rmse:0.597037+0.018933 test-rmse:1.236151+0.151788
## [42] train-rmse:0.586076+0.019701 test-rmse:1.234908+0.151318
## [43] train-rmse:0.577886+0.020185 test-rmse:1.231481+0.153597
## [44] train-rmse:0.568354+0.021965 test-rmse:1.230698+0.154726
## [45] train-rmse:0.559804+0.019878 test-rmse:1.227064+0.154046
## [46] train-rmse:0.549475+0.020977 test-rmse:1.225743+0.156425
## [47] train-rmse:0.540287+0.020832 test-rmse:1.225226+0.159672
## [48] train-rmse:0.531520+0.021031 test-rmse:1.223907+0.160844
## [49] train-rmse:0.523831+0.021065 test-rmse:1.221397+0.161209
## [50] train-rmse:0.514375+0.019266 test-rmse:1.221600+0.162002
## [51] train-rmse:0.506420+0.019183 test-rmse:1.220423+0.160933
## [52] train-rmse:0.498254+0.017101 test-rmse:1.221379+0.160201
## [53] train-rmse:0.490415+0.016622 test-rmse:1.221023+0.161314
## [54] train-rmse:0.482433+0.016873 test-rmse:1.219217+0.161004
## [55] train-rmse:0.475398+0.016952 test-rmse:1.218345+0.162578
## [56] train-rmse:0.469829+0.017067 test-rmse:1.216242+0.162411
## [57] train-rmse:0.463879+0.017537 test-rmse:1.215877+0.162117
## [58] train-rmse:0.455404+0.016945 test-rmse:1.215363+0.161275
## [59] train-rmse:0.448377+0.018266 test-rmse:1.214879+0.159185
## [60] train-rmse:0.443214+0.017745 test-rmse:1.213595+0.159198
## [61] train-rmse:0.437899+0.017412 test-rmse:1.212775+0.157478
## [62] train-rmse:0.432770+0.016878 test-rmse:1.212362+0.158087
## [63] train-rmse:0.425045+0.017243 test-rmse:1.208783+0.158175
## [64] train-rmse:0.419957+0.018051 test-rmse:1.208320+0.159756
## [65] train-rmse:0.415283+0.017056 test-rmse:1.209096+0.159363
## [66] train-rmse:0.410153+0.017019 test-rmse:1.209107+0.159069
## [67] train-rmse:0.405833+0.017034 test-rmse:1.210317+0.159126
## [68] train-rmse:0.400257+0.017471 test-rmse:1.211525+0.157680
## [69] train-rmse:0.394367+0.018456 test-rmse:1.210247+0.158316
## [70] train-rmse:0.388656+0.017824 test-rmse:1.210349+0.158936
## Stopping. Best iteration:
## [64] train-rmse:0.419957+0.018051 test-rmse:1.208320+0.159756
##
## 34
## [1] train-rmse:8.048058+0.059624 test-rmse:8.048831+0.259144
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.696936+0.049378 test-rmse:6.707031+0.235249
## [3] train-rmse:5.594928+0.041310 test-rmse:5.609382+0.212385
## [4] train-rmse:4.697124+0.034570 test-rmse:4.725340+0.188819
## [5] train-rmse:3.966086+0.032032 test-rmse:3.994053+0.167230
## [6] train-rmse:3.369063+0.026354 test-rmse:3.403156+0.146011
## [7] train-rmse:2.881973+0.021380 test-rmse:2.933326+0.131634
## [8] train-rmse:2.489678+0.017722 test-rmse:2.576904+0.123083
## [9] train-rmse:2.172998+0.014233 test-rmse:2.311825+0.120416
## [10] train-rmse:1.911575+0.013043 test-rmse:2.079269+0.116722
## [11] train-rmse:1.703359+0.012902 test-rmse:1.905113+0.115181
## [12] train-rmse:1.527475+0.015239 test-rmse:1.766558+0.101122
## [13] train-rmse:1.385922+0.014925 test-rmse:1.652194+0.100808
## [14] train-rmse:1.271266+0.011684 test-rmse:1.584714+0.098494
## [15] train-rmse:1.173409+0.015776 test-rmse:1.515788+0.099587
## [16] train-rmse:1.095086+0.013003 test-rmse:1.475110+0.100514
## [17] train-rmse:1.025670+0.016957 test-rmse:1.434524+0.108255
## [18] train-rmse:0.971611+0.014213 test-rmse:1.407723+0.108054
## [19] train-rmse:0.922011+0.014762 test-rmse:1.376751+0.118849
## [20] train-rmse:0.880617+0.013956 test-rmse:1.356433+0.124780
## [21] train-rmse:0.841518+0.017966 test-rmse:1.338949+0.135148
## [22] train-rmse:0.806544+0.016639 test-rmse:1.317264+0.139545
## [23] train-rmse:0.777648+0.016905 test-rmse:1.311609+0.141530
## [24] train-rmse:0.751735+0.016553 test-rmse:1.299031+0.148133
## [25] train-rmse:0.727488+0.015969 test-rmse:1.289104+0.151199
## [26] train-rmse:0.705210+0.017048 test-rmse:1.276417+0.154234
## [27] train-rmse:0.683446+0.014110 test-rmse:1.271205+0.160207
## [28] train-rmse:0.664587+0.009101 test-rmse:1.262650+0.165574
## [29] train-rmse:0.646865+0.010997 test-rmse:1.257023+0.164945
## [30] train-rmse:0.629019+0.010134 test-rmse:1.253079+0.167220
## [31] train-rmse:0.611591+0.011864 test-rmse:1.251659+0.165956
## [32] train-rmse:0.598894+0.010367 test-rmse:1.248266+0.165873
## [33] train-rmse:0.581355+0.008162 test-rmse:1.247038+0.170884
## [34] train-rmse:0.566383+0.008637 test-rmse:1.242752+0.168195
## [35] train-rmse:0.554174+0.009177 test-rmse:1.238101+0.169587
## [36] train-rmse:0.537589+0.009203 test-rmse:1.230161+0.167615
## [37] train-rmse:0.525411+0.008686 test-rmse:1.225773+0.168156
## [38] train-rmse:0.514490+0.008194 test-rmse:1.225185+0.169773
## [39] train-rmse:0.504374+0.008320 test-rmse:1.223980+0.170855
## [40] train-rmse:0.492919+0.007791 test-rmse:1.223137+0.171140
## [41] train-rmse:0.482837+0.007784 test-rmse:1.221463+0.167547
## [42] train-rmse:0.473957+0.008023 test-rmse:1.220910+0.170320
## [43] train-rmse:0.464175+0.008154 test-rmse:1.218963+0.171683
## [44] train-rmse:0.455165+0.006924 test-rmse:1.218850+0.171145
## [45] train-rmse:0.444995+0.008365 test-rmse:1.218914+0.171862
## [46] train-rmse:0.434755+0.007898 test-rmse:1.219658+0.173080
## [47] train-rmse:0.426396+0.008068 test-rmse:1.220308+0.172450
## [48] train-rmse:0.419205+0.008113 test-rmse:1.218234+0.172448
## [49] train-rmse:0.412313+0.008535 test-rmse:1.217899+0.173163
## [50] train-rmse:0.404718+0.009361 test-rmse:1.218142+0.172402
## [51] train-rmse:0.397931+0.007967 test-rmse:1.218417+0.173353
## [52] train-rmse:0.390664+0.007632 test-rmse:1.215940+0.173978
## [53] train-rmse:0.384368+0.006663 test-rmse:1.215450+0.174359
## [54] train-rmse:0.378045+0.009239 test-rmse:1.216336+0.174454
## [55] train-rmse:0.371557+0.008444 test-rmse:1.215686+0.173436
## [56] train-rmse:0.364659+0.009453 test-rmse:1.216011+0.174479
## [57] train-rmse:0.359423+0.009834 test-rmse:1.216239+0.174257
## [58] train-rmse:0.354192+0.010626 test-rmse:1.213480+0.174923
## [59] train-rmse:0.349075+0.009867 test-rmse:1.212521+0.175716
## [60] train-rmse:0.342785+0.010970 test-rmse:1.213467+0.175065
## [61] train-rmse:0.338156+0.011595 test-rmse:1.213983+0.175962
## [62] train-rmse:0.331750+0.012223 test-rmse:1.213972+0.174739
## [63] train-rmse:0.327683+0.011753 test-rmse:1.212798+0.174420
## [64] train-rmse:0.321247+0.010459 test-rmse:1.212388+0.174881
## [65] train-rmse:0.317110+0.010196 test-rmse:1.214045+0.174453
## [66] train-rmse:0.312814+0.009783 test-rmse:1.214254+0.175737
## [67] train-rmse:0.308515+0.009226 test-rmse:1.214270+0.175624
## [68] train-rmse:0.304343+0.009290 test-rmse:1.215024+0.175649
## [69] train-rmse:0.300650+0.009285 test-rmse:1.215975+0.175031
## [70] train-rmse:0.295409+0.010225 test-rmse:1.214701+0.174439
## Stopping. Best iteration:
## [64] train-rmse:0.321247+0.010459 test-rmse:1.212388+0.174881
##
## 35
## [1] train-rmse:7.908694+0.055927 test-rmse:7.904554+0.270795
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.499872+0.042468 test-rmse:6.493187+0.264877
## [3] train-rmse:5.401059+0.032435 test-rmse:5.393868+0.258347
## [4] train-rmse:4.553801+0.023800 test-rmse:4.556607+0.241748
## [5] train-rmse:3.909760+0.017683 test-rmse:3.917387+0.215083
## [6] train-rmse:3.429148+0.013615 test-rmse:3.445671+0.193907
## [7] train-rmse:3.075590+0.011617 test-rmse:3.103803+0.155243
## [8] train-rmse:2.819733+0.011351 test-rmse:2.847101+0.133034
## [9] train-rmse:2.636003+0.012090 test-rmse:2.667122+0.102228
## [10] train-rmse:2.505960+0.013803 test-rmse:2.548212+0.077211
## [11] train-rmse:2.413852+0.014895 test-rmse:2.451644+0.061787
## [12] train-rmse:2.347449+0.015603 test-rmse:2.389828+0.052558
## [13] train-rmse:2.297732+0.016535 test-rmse:2.339413+0.050086
## [14] train-rmse:2.260791+0.017248 test-rmse:2.302412+0.052104
## [15] train-rmse:2.232923+0.018189 test-rmse:2.274973+0.049947
## [16] train-rmse:2.210149+0.018670 test-rmse:2.251243+0.060015
## [17] train-rmse:2.190625+0.019232 test-rmse:2.240160+0.063967
## [18] train-rmse:2.173529+0.019827 test-rmse:2.222867+0.068958
## [19] train-rmse:2.158041+0.020454 test-rmse:2.213869+0.072131
## [20] train-rmse:2.143982+0.021131 test-rmse:2.199867+0.079433
## [21] train-rmse:2.131239+0.021744 test-rmse:2.185245+0.079158
## [22] train-rmse:2.118928+0.022364 test-rmse:2.175479+0.078438
## [23] train-rmse:2.107237+0.023079 test-rmse:2.167625+0.080254
## [24] train-rmse:2.096118+0.023250 test-rmse:2.162558+0.081300
## [25] train-rmse:2.085434+0.023685 test-rmse:2.146629+0.089313
## [26] train-rmse:2.075182+0.023992 test-rmse:2.135087+0.090148
## [27] train-rmse:2.065051+0.024423 test-rmse:2.131286+0.090865
## [28] train-rmse:2.055027+0.024957 test-rmse:2.119543+0.084767
## [29] train-rmse:2.045475+0.025723 test-rmse:2.110724+0.084074
## [30] train-rmse:2.035977+0.026236 test-rmse:2.100217+0.083610
## [31] train-rmse:2.026489+0.026635 test-rmse:2.095214+0.087592
## [32] train-rmse:2.017221+0.027181 test-rmse:2.087445+0.085090
## [33] train-rmse:2.008419+0.027383 test-rmse:2.075809+0.083737
## [34] train-rmse:1.999866+0.028060 test-rmse:2.066728+0.086748
## [35] train-rmse:1.991441+0.028577 test-rmse:2.058616+0.090320
## [36] train-rmse:1.983256+0.029077 test-rmse:2.054627+0.092437
## [37] train-rmse:1.975283+0.029322 test-rmse:2.050732+0.090120
## [38] train-rmse:1.967526+0.029652 test-rmse:2.043598+0.092729
## [39] train-rmse:1.959774+0.029837 test-rmse:2.035053+0.093409
## [40] train-rmse:1.952251+0.030148 test-rmse:2.025928+0.092793
## [41] train-rmse:1.944778+0.030446 test-rmse:2.017866+0.090247
## [42] train-rmse:1.937359+0.030693 test-rmse:2.015134+0.094255
## [43] train-rmse:1.930079+0.031012 test-rmse:2.007810+0.088793
## [44] train-rmse:1.922960+0.031364 test-rmse:1.999202+0.085149
## [45] train-rmse:1.915998+0.031761 test-rmse:1.991083+0.087355
## [46] train-rmse:1.909153+0.032228 test-rmse:1.988143+0.084382
## [47] train-rmse:1.902357+0.032583 test-rmse:1.986069+0.089321
## [48] train-rmse:1.895766+0.033056 test-rmse:1.979416+0.089842
## [49] train-rmse:1.889215+0.033369 test-rmse:1.975444+0.088869
## [50] train-rmse:1.882939+0.033714 test-rmse:1.967805+0.087463
## [51] train-rmse:1.876644+0.034203 test-rmse:1.959363+0.093553
## [52] train-rmse:1.870379+0.034758 test-rmse:1.953962+0.093753
## [53] train-rmse:1.864247+0.035289 test-rmse:1.948815+0.091811
## [54] train-rmse:1.858238+0.035771 test-rmse:1.947221+0.094254
## [55] train-rmse:1.852346+0.036261 test-rmse:1.942703+0.091980
## [56] train-rmse:1.846599+0.036662 test-rmse:1.936581+0.090103
## [57] train-rmse:1.840954+0.037056 test-rmse:1.929292+0.087030
## [58] train-rmse:1.835346+0.037335 test-rmse:1.926896+0.088473
## [59] train-rmse:1.829743+0.037634 test-rmse:1.922463+0.090870
## [60] train-rmse:1.824314+0.037799 test-rmse:1.912388+0.090894
## [61] train-rmse:1.818789+0.038047 test-rmse:1.907505+0.091087
## [62] train-rmse:1.813315+0.038285 test-rmse:1.903224+0.090096
## [63] train-rmse:1.807840+0.038582 test-rmse:1.897959+0.090427
## [64] train-rmse:1.802566+0.038859 test-rmse:1.897363+0.089144
## [65] train-rmse:1.797339+0.039087 test-rmse:1.894985+0.090927
## [66] train-rmse:1.792177+0.039406 test-rmse:1.891239+0.088926
## [67] train-rmse:1.786981+0.039678 test-rmse:1.887119+0.088469
## [68] train-rmse:1.782011+0.039984 test-rmse:1.884054+0.092744
## [69] train-rmse:1.777132+0.040259 test-rmse:1.881102+0.092461
## [70] train-rmse:1.772355+0.040544 test-rmse:1.875704+0.092713
## [71] train-rmse:1.767532+0.040739 test-rmse:1.870451+0.094246
## [72] train-rmse:1.762833+0.041019 test-rmse:1.867975+0.095245
## [73] train-rmse:1.758231+0.041304 test-rmse:1.859815+0.095841
## [74] train-rmse:1.753752+0.041543 test-rmse:1.857089+0.098282
## [75] train-rmse:1.749319+0.041823 test-rmse:1.851253+0.097501
## [76] train-rmse:1.744906+0.042132 test-rmse:1.845395+0.098931
## [77] train-rmse:1.740665+0.042436 test-rmse:1.843470+0.098322
## [78] train-rmse:1.736373+0.042728 test-rmse:1.839694+0.096712
## [79] train-rmse:1.732093+0.042964 test-rmse:1.833524+0.099121
## [80] train-rmse:1.727857+0.043194 test-rmse:1.831116+0.100284
## [81] train-rmse:1.723631+0.043417 test-rmse:1.826554+0.099373
## [82] train-rmse:1.719477+0.043671 test-rmse:1.825015+0.098268
## [83] train-rmse:1.715373+0.044012 test-rmse:1.822614+0.101019
## [84] train-rmse:1.711319+0.044291 test-rmse:1.820413+0.102608
## [85] train-rmse:1.707348+0.044551 test-rmse:1.814247+0.102054
## [86] train-rmse:1.703356+0.044864 test-rmse:1.811225+0.104025
## [87] train-rmse:1.699422+0.045130 test-rmse:1.808761+0.105123
## [88] train-rmse:1.695564+0.045320 test-rmse:1.806911+0.107262
## [89] train-rmse:1.691750+0.045457 test-rmse:1.807584+0.108589
## [90] train-rmse:1.688034+0.045510 test-rmse:1.802568+0.107979
## [91] train-rmse:1.684387+0.045586 test-rmse:1.799253+0.106549
## [92] train-rmse:1.680727+0.045626 test-rmse:1.796012+0.106768
## [93] train-rmse:1.677184+0.045760 test-rmse:1.793537+0.106909
## [94] train-rmse:1.673679+0.045841 test-rmse:1.791397+0.106542
## [95] train-rmse:1.670205+0.045986 test-rmse:1.790134+0.102368
## [96] train-rmse:1.666736+0.046156 test-rmse:1.788894+0.102664
## [97] train-rmse:1.663307+0.046312 test-rmse:1.786374+0.104703
## [98] train-rmse:1.659870+0.046520 test-rmse:1.781521+0.104080
## [99] train-rmse:1.656514+0.046732 test-rmse:1.781155+0.106050
## [100] train-rmse:1.653167+0.046920 test-rmse:1.778737+0.107248
## [101] train-rmse:1.649820+0.047135 test-rmse:1.774607+0.108491
## [102] train-rmse:1.646554+0.047259 test-rmse:1.772090+0.107095
## [103] train-rmse:1.643281+0.047444 test-rmse:1.765374+0.111131
## [104] train-rmse:1.640100+0.047559 test-rmse:1.757665+0.110390
## [105] train-rmse:1.636929+0.047708 test-rmse:1.753575+0.110719
## [106] train-rmse:1.633818+0.047845 test-rmse:1.752455+0.111849
## [107] train-rmse:1.630779+0.047990 test-rmse:1.748382+0.113555
## [108] train-rmse:1.627726+0.048089 test-rmse:1.745656+0.114230
## [109] train-rmse:1.624714+0.048250 test-rmse:1.745389+0.115707
## [110] train-rmse:1.621785+0.048387 test-rmse:1.743628+0.117744
## [111] train-rmse:1.618816+0.048517 test-rmse:1.741493+0.119497
## [112] train-rmse:1.615858+0.048577 test-rmse:1.742230+0.121328
## [113] train-rmse:1.612974+0.048665 test-rmse:1.740251+0.120773
## [114] train-rmse:1.610131+0.048814 test-rmse:1.738712+0.120873
## [115] train-rmse:1.607341+0.048958 test-rmse:1.734055+0.119830
## [116] train-rmse:1.604625+0.049054 test-rmse:1.730686+0.119551
## [117] train-rmse:1.601889+0.049162 test-rmse:1.728422+0.120353
## [118] train-rmse:1.599197+0.049252 test-rmse:1.726087+0.119625
## [119] train-rmse:1.596515+0.049358 test-rmse:1.723181+0.120778
## [120] train-rmse:1.593877+0.049450 test-rmse:1.719334+0.120957
## [121] train-rmse:1.591231+0.049582 test-rmse:1.716415+0.122880
## [122] train-rmse:1.588610+0.049728 test-rmse:1.712400+0.123579
## [123] train-rmse:1.586015+0.049824 test-rmse:1.709975+0.125068
## [124] train-rmse:1.583404+0.049919 test-rmse:1.707345+0.124230
## [125] train-rmse:1.580827+0.050000 test-rmse:1.706202+0.125392
## [126] train-rmse:1.578261+0.050048 test-rmse:1.707422+0.121748
## [127] train-rmse:1.575713+0.050106 test-rmse:1.707104+0.127469
## [128] train-rmse:1.573217+0.050169 test-rmse:1.701934+0.131214
## [129] train-rmse:1.570801+0.050269 test-rmse:1.697637+0.128740
## [130] train-rmse:1.568403+0.050345 test-rmse:1.695845+0.127917
## [131] train-rmse:1.566008+0.050415 test-rmse:1.693038+0.128099
## [132] train-rmse:1.563643+0.050469 test-rmse:1.691061+0.129261
## [133] train-rmse:1.561287+0.050486 test-rmse:1.690000+0.129582
## [134] train-rmse:1.558969+0.050527 test-rmse:1.687671+0.129556
## [135] train-rmse:1.556695+0.050573 test-rmse:1.684989+0.130426
## [136] train-rmse:1.554405+0.050620 test-rmse:1.682612+0.132014
## [137] train-rmse:1.552156+0.050656 test-rmse:1.682695+0.130826
## [138] train-rmse:1.549960+0.050694 test-rmse:1.679255+0.131728
## [139] train-rmse:1.547780+0.050753 test-rmse:1.679026+0.133492
## [140] train-rmse:1.545609+0.050831 test-rmse:1.677250+0.133219
## [141] train-rmse:1.543493+0.050895 test-rmse:1.673410+0.132937
## [142] train-rmse:1.541398+0.050963 test-rmse:1.671071+0.133421
## [143] train-rmse:1.539298+0.051023 test-rmse:1.672043+0.134559
## [144] train-rmse:1.537214+0.051114 test-rmse:1.668002+0.134598
## [145] train-rmse:1.535143+0.051186 test-rmse:1.666941+0.135171
## [146] train-rmse:1.533133+0.051232 test-rmse:1.664923+0.136993
## [147] train-rmse:1.531140+0.051253 test-rmse:1.663029+0.137220
## [148] train-rmse:1.529136+0.051283 test-rmse:1.661281+0.137706
## [149] train-rmse:1.527179+0.051330 test-rmse:1.662968+0.138120
## [150] train-rmse:1.525207+0.051361 test-rmse:1.660463+0.137615
## [151] train-rmse:1.523240+0.051400 test-rmse:1.656717+0.142245
## [152] train-rmse:1.521265+0.051427 test-rmse:1.653123+0.142542
## [153] train-rmse:1.519352+0.051456 test-rmse:1.651968+0.141641
## [154] train-rmse:1.517480+0.051519 test-rmse:1.650640+0.142772
## [155] train-rmse:1.515585+0.051515 test-rmse:1.651272+0.143679
## [156] train-rmse:1.513722+0.051528 test-rmse:1.647284+0.144597
## [157] train-rmse:1.511913+0.051580 test-rmse:1.645722+0.145837
## [158] train-rmse:1.510079+0.051634 test-rmse:1.644891+0.144733
## [159] train-rmse:1.508282+0.051668 test-rmse:1.641912+0.142803
## [160] train-rmse:1.506473+0.051734 test-rmse:1.639993+0.142841
## [161] train-rmse:1.504668+0.051836 test-rmse:1.636585+0.143166
## [162] train-rmse:1.502900+0.051868 test-rmse:1.635153+0.144504
## [163] train-rmse:1.501179+0.051889 test-rmse:1.633175+0.144547
## [164] train-rmse:1.499496+0.051965 test-rmse:1.631249+0.145784
## [165] train-rmse:1.497793+0.051984 test-rmse:1.629460+0.144935
## [166] train-rmse:1.496146+0.052036 test-rmse:1.630525+0.146091
## [167] train-rmse:1.494530+0.052077 test-rmse:1.629450+0.145371
## [168] train-rmse:1.492918+0.052117 test-rmse:1.626424+0.142721
## [169] train-rmse:1.491333+0.052149 test-rmse:1.626932+0.144891
## [170] train-rmse:1.489757+0.052186 test-rmse:1.626285+0.144868
## [171] train-rmse:1.488154+0.052239 test-rmse:1.625093+0.145561
## [172] train-rmse:1.486545+0.052297 test-rmse:1.624895+0.146417
## [173] train-rmse:1.484952+0.052362 test-rmse:1.620871+0.150252
## [174] train-rmse:1.483411+0.052424 test-rmse:1.618331+0.150125
## [175] train-rmse:1.481870+0.052497 test-rmse:1.617136+0.149483
## [176] train-rmse:1.480363+0.052520 test-rmse:1.614268+0.150063
## [177] train-rmse:1.478887+0.052566 test-rmse:1.613132+0.150573
## [178] train-rmse:1.477409+0.052611 test-rmse:1.611225+0.149510
## [179] train-rmse:1.475916+0.052620 test-rmse:1.610869+0.149548
## [180] train-rmse:1.474463+0.052650 test-rmse:1.610906+0.149872
## [181] train-rmse:1.473014+0.052692 test-rmse:1.611533+0.151346
## [182] train-rmse:1.471569+0.052716 test-rmse:1.610178+0.150298
## [183] train-rmse:1.470149+0.052733 test-rmse:1.608655+0.150281
## [184] train-rmse:1.468735+0.052742 test-rmse:1.606365+0.151407
## [185] train-rmse:1.467313+0.052792 test-rmse:1.602905+0.150323
## [186] train-rmse:1.465932+0.052818 test-rmse:1.601663+0.151934
## [187] train-rmse:1.464559+0.052841 test-rmse:1.599913+0.152120
## [188] train-rmse:1.463204+0.052846 test-rmse:1.599816+0.152873
## [189] train-rmse:1.461821+0.052834 test-rmse:1.599196+0.153158
## [190] train-rmse:1.460486+0.052836 test-rmse:1.597278+0.153419
## [191] train-rmse:1.459140+0.052834 test-rmse:1.597482+0.153655
## [192] train-rmse:1.457811+0.052826 test-rmse:1.595375+0.153423
## [193] train-rmse:1.456506+0.052851 test-rmse:1.595075+0.153294
## [194] train-rmse:1.455237+0.052868 test-rmse:1.592961+0.154446
## [195] train-rmse:1.453968+0.052877 test-rmse:1.590133+0.155691
## [196] train-rmse:1.452726+0.052903 test-rmse:1.589497+0.156015
## [197] train-rmse:1.451484+0.052946 test-rmse:1.589516+0.156031
## [198] train-rmse:1.450221+0.052982 test-rmse:1.590816+0.157086
## [199] train-rmse:1.448966+0.053037 test-rmse:1.587789+0.156924
## [200] train-rmse:1.447728+0.053067 test-rmse:1.588240+0.159175
## [201] train-rmse:1.446515+0.053078 test-rmse:1.585473+0.158834
## [202] train-rmse:1.445297+0.053083 test-rmse:1.582341+0.162322
## [203] train-rmse:1.444105+0.053106 test-rmse:1.580676+0.161933
## [204] train-rmse:1.442910+0.053140 test-rmse:1.579938+0.161480
## [205] train-rmse:1.441727+0.053184 test-rmse:1.579743+0.161659
## [206] train-rmse:1.440543+0.053226 test-rmse:1.576758+0.161568
## [207] train-rmse:1.439402+0.053228 test-rmse:1.576156+0.160310
## [208] train-rmse:1.438285+0.053206 test-rmse:1.575374+0.160355
## [209] train-rmse:1.437185+0.053208 test-rmse:1.574333+0.159963
## [210] train-rmse:1.436097+0.053213 test-rmse:1.575077+0.160485
## [211] train-rmse:1.435002+0.053202 test-rmse:1.575087+0.160346
## [212] train-rmse:1.433915+0.053202 test-rmse:1.575254+0.160249
## [213] train-rmse:1.432840+0.053201 test-rmse:1.574031+0.161346
## [214] train-rmse:1.431781+0.053198 test-rmse:1.573825+0.161799
## [215] train-rmse:1.430715+0.053195 test-rmse:1.572788+0.161354
## [216] train-rmse:1.429669+0.053202 test-rmse:1.572699+0.161648
## [217] train-rmse:1.428619+0.053208 test-rmse:1.571462+0.163244
## [218] train-rmse:1.427576+0.053210 test-rmse:1.570013+0.163893
## [219] train-rmse:1.426556+0.053221 test-rmse:1.566879+0.162367
## [220] train-rmse:1.425528+0.053232 test-rmse:1.565667+0.162707
## [221] train-rmse:1.424527+0.053244 test-rmse:1.563540+0.162538
## [222] train-rmse:1.423505+0.053284 test-rmse:1.562829+0.163500
## [223] train-rmse:1.422502+0.053303 test-rmse:1.562072+0.164221
## [224] train-rmse:1.421526+0.053310 test-rmse:1.560792+0.163276
## [225] train-rmse:1.420561+0.053321 test-rmse:1.559355+0.163457
## [226] train-rmse:1.419591+0.053334 test-rmse:1.558535+0.163656
## [227] train-rmse:1.418635+0.053350 test-rmse:1.557123+0.163509
## [228] train-rmse:1.417699+0.053356 test-rmse:1.555390+0.165525
## [229] train-rmse:1.416755+0.053358 test-rmse:1.554536+0.165185
## [230] train-rmse:1.415838+0.053349 test-rmse:1.554391+0.166565
## [231] train-rmse:1.414930+0.053363 test-rmse:1.554900+0.169065
## [232] train-rmse:1.414016+0.053375 test-rmse:1.554577+0.169665
## [233] train-rmse:1.413126+0.053378 test-rmse:1.553157+0.169545
## [234] train-rmse:1.412236+0.053389 test-rmse:1.553152+0.169627
## [235] train-rmse:1.411345+0.053385 test-rmse:1.552424+0.171107
## [236] train-rmse:1.410454+0.053382 test-rmse:1.550848+0.171208
## [237] train-rmse:1.409557+0.053375 test-rmse:1.550111+0.171599
## [238] train-rmse:1.408672+0.053369 test-rmse:1.546991+0.170749
## [239] train-rmse:1.407809+0.053367 test-rmse:1.546679+0.169952
## [240] train-rmse:1.406962+0.053364 test-rmse:1.546056+0.170048
## [241] train-rmse:1.406128+0.053360 test-rmse:1.545271+0.169031
## [242] train-rmse:1.405293+0.053369 test-rmse:1.543490+0.170433
## [243] train-rmse:1.404465+0.053372 test-rmse:1.543021+0.169365
## [244] train-rmse:1.403621+0.053370 test-rmse:1.544797+0.170537
## [245] train-rmse:1.402796+0.053374 test-rmse:1.544636+0.171293
## [246] train-rmse:1.401990+0.053382 test-rmse:1.545221+0.171370
## [247] train-rmse:1.401197+0.053403 test-rmse:1.544409+0.170696
## [248] train-rmse:1.400421+0.053425 test-rmse:1.543308+0.170252
## [249] train-rmse:1.399654+0.053443 test-rmse:1.543635+0.170873
## Stopping. Best iteration:
## [243] train-rmse:1.404465+0.053372 test-rmse:1.543021+0.169365
##
## 36
## [1] train-rmse:7.883326+0.056278 test-rmse:7.879588+0.266523
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.451047+0.046276 test-rmse:6.443117+0.251766
## [3] train-rmse:5.330540+0.039397 test-rmse:5.329892+0.230513
## [4] train-rmse:4.454277+0.032458 test-rmse:4.468604+0.215713
## [5] train-rmse:3.778179+0.031574 test-rmse:3.797720+0.187139
## [6] train-rmse:3.268291+0.029637 test-rmse:3.303118+0.155267
## [7] train-rmse:2.879840+0.031544 test-rmse:2.928340+0.122437
## [8] train-rmse:2.591149+0.031057 test-rmse:2.650773+0.098799
## [9] train-rmse:2.376743+0.028077 test-rmse:2.453404+0.081184
## [10] train-rmse:2.217132+0.030884 test-rmse:2.309200+0.059946
## [11] train-rmse:2.103930+0.030493 test-rmse:2.207655+0.056962
## [12] train-rmse:2.016484+0.021992 test-rmse:2.124534+0.051926
## [13] train-rmse:1.954148+0.023996 test-rmse:2.074005+0.063185
## [14] train-rmse:1.904324+0.024793 test-rmse:2.027444+0.068874
## [15] train-rmse:1.862565+0.023684 test-rmse:2.000318+0.075099
## [16] train-rmse:1.829702+0.025872 test-rmse:1.970879+0.081300
## [17] train-rmse:1.802832+0.026726 test-rmse:1.948092+0.082674
## [18] train-rmse:1.775315+0.032053 test-rmse:1.918669+0.085925
## [19] train-rmse:1.754568+0.032884 test-rmse:1.904042+0.089119
## [20] train-rmse:1.734938+0.033472 test-rmse:1.888059+0.104008
## [21] train-rmse:1.717727+0.033119 test-rmse:1.871893+0.107462
## [22] train-rmse:1.697822+0.032389 test-rmse:1.860110+0.106692
## [23] train-rmse:1.676182+0.039169 test-rmse:1.848898+0.111884
## [24] train-rmse:1.656328+0.039227 test-rmse:1.819272+0.091775
## [25] train-rmse:1.641173+0.040689 test-rmse:1.806062+0.093834
## [26] train-rmse:1.626772+0.041965 test-rmse:1.795921+0.089255
## [27] train-rmse:1.611241+0.042891 test-rmse:1.783930+0.085488
## [28] train-rmse:1.595273+0.041832 test-rmse:1.775773+0.085111
## [29] train-rmse:1.582226+0.043375 test-rmse:1.765178+0.081686
## [30] train-rmse:1.565692+0.046580 test-rmse:1.754889+0.091879
## [31] train-rmse:1.552916+0.047743 test-rmse:1.745748+0.091053
## [32] train-rmse:1.539908+0.048236 test-rmse:1.736575+0.090945
## [33] train-rmse:1.527080+0.047318 test-rmse:1.724363+0.089879
## [34] train-rmse:1.514736+0.048182 test-rmse:1.716526+0.094046
## [35] train-rmse:1.503416+0.048543 test-rmse:1.705170+0.090685
## [36] train-rmse:1.488588+0.054488 test-rmse:1.699062+0.090116
## [37] train-rmse:1.478142+0.054864 test-rmse:1.693368+0.091108
## [38] train-rmse:1.466605+0.054586 test-rmse:1.687209+0.096012
## [39] train-rmse:1.452328+0.057769 test-rmse:1.674354+0.093179
## [40] train-rmse:1.442359+0.057853 test-rmse:1.668239+0.092770
## [41] train-rmse:1.432862+0.058890 test-rmse:1.663276+0.100092
## [42] train-rmse:1.422708+0.060073 test-rmse:1.658539+0.099489
## [43] train-rmse:1.411229+0.058964 test-rmse:1.647759+0.101950
## [44] train-rmse:1.401487+0.059838 test-rmse:1.641373+0.101432
## [45] train-rmse:1.392481+0.060058 test-rmse:1.635563+0.098363
## [46] train-rmse:1.379997+0.058618 test-rmse:1.625377+0.098760
## [47] train-rmse:1.370891+0.059926 test-rmse:1.618819+0.101039
## [48] train-rmse:1.362914+0.060455 test-rmse:1.613266+0.100492
## [49] train-rmse:1.354239+0.060020 test-rmse:1.607117+0.099630
## [50] train-rmse:1.346678+0.060796 test-rmse:1.601316+0.098616
## [51] train-rmse:1.337494+0.061575 test-rmse:1.590206+0.104317
## [52] train-rmse:1.329312+0.062285 test-rmse:1.582754+0.107004
## [53] train-rmse:1.321423+0.062391 test-rmse:1.581909+0.112749
## [54] train-rmse:1.310038+0.060224 test-rmse:1.572781+0.114875
## [55] train-rmse:1.301747+0.059066 test-rmse:1.567663+0.115308
## [56] train-rmse:1.294896+0.059437 test-rmse:1.560306+0.113641
## [57] train-rmse:1.288296+0.059841 test-rmse:1.551447+0.114476
## [58] train-rmse:1.281220+0.060714 test-rmse:1.549417+0.114677
## [59] train-rmse:1.275268+0.061324 test-rmse:1.546884+0.115463
## [60] train-rmse:1.268465+0.061608 test-rmse:1.543479+0.118437
## [61] train-rmse:1.262105+0.062290 test-rmse:1.540481+0.119390
## [62] train-rmse:1.256199+0.063024 test-rmse:1.540996+0.118464
## [63] train-rmse:1.248764+0.062849 test-rmse:1.539225+0.120672
## [64] train-rmse:1.242431+0.062807 test-rmse:1.534634+0.122542
## [65] train-rmse:1.236404+0.062825 test-rmse:1.529628+0.128136
## [66] train-rmse:1.222850+0.054677 test-rmse:1.517336+0.134181
## [67] train-rmse:1.211741+0.053246 test-rmse:1.501816+0.114877
## [68] train-rmse:1.200702+0.053576 test-rmse:1.489804+0.113064
## [69] train-rmse:1.194086+0.054336 test-rmse:1.483851+0.114949
## [70] train-rmse:1.189222+0.054734 test-rmse:1.478178+0.116567
## [71] train-rmse:1.184178+0.054994 test-rmse:1.472989+0.116698
## [72] train-rmse:1.179186+0.055404 test-rmse:1.473615+0.116918
## [73] train-rmse:1.174074+0.055513 test-rmse:1.470947+0.116446
## [74] train-rmse:1.168945+0.056074 test-rmse:1.470763+0.116525
## [75] train-rmse:1.162674+0.056347 test-rmse:1.467334+0.118447
## [76] train-rmse:1.157311+0.056258 test-rmse:1.464351+0.118724
## [77] train-rmse:1.152057+0.057505 test-rmse:1.466149+0.117001
## [78] train-rmse:1.147607+0.057601 test-rmse:1.459890+0.117712
## [79] train-rmse:1.143260+0.057663 test-rmse:1.453394+0.116915
## [80] train-rmse:1.138802+0.057314 test-rmse:1.449767+0.118196
## [81] train-rmse:1.132653+0.056303 test-rmse:1.446985+0.118417
## [82] train-rmse:1.127700+0.056207 test-rmse:1.441326+0.120408
## [83] train-rmse:1.117825+0.050475 test-rmse:1.437550+0.124369
## [84] train-rmse:1.112374+0.050885 test-rmse:1.436446+0.125799
## [85] train-rmse:1.108408+0.050768 test-rmse:1.435446+0.125387
## [86] train-rmse:1.103375+0.052982 test-rmse:1.433503+0.129247
## [87] train-rmse:1.099087+0.052730 test-rmse:1.432530+0.130862
## [88] train-rmse:1.095880+0.053013 test-rmse:1.431949+0.130500
## [89] train-rmse:1.085890+0.047871 test-rmse:1.417051+0.130852
## [90] train-rmse:1.077528+0.047858 test-rmse:1.404517+0.118004
## [91] train-rmse:1.071065+0.049242 test-rmse:1.399685+0.114313
## [92] train-rmse:1.065837+0.047446 test-rmse:1.399226+0.114481
## [93] train-rmse:1.062380+0.047737 test-rmse:1.397063+0.115462
## [94] train-rmse:1.058721+0.047606 test-rmse:1.398025+0.115171
## [95] train-rmse:1.055215+0.047612 test-rmse:1.396657+0.115764
## [96] train-rmse:1.051444+0.048178 test-rmse:1.392062+0.117344
## [97] train-rmse:1.047136+0.047957 test-rmse:1.389152+0.119089
## [98] train-rmse:1.043999+0.048159 test-rmse:1.387458+0.118376
## [99] train-rmse:1.038762+0.047297 test-rmse:1.384518+0.121466
## [100] train-rmse:1.035084+0.046339 test-rmse:1.382294+0.119519
## [101] train-rmse:1.031866+0.046117 test-rmse:1.380201+0.123851
## [102] train-rmse:1.028748+0.046183 test-rmse:1.378638+0.124022
## [103] train-rmse:1.025154+0.046695 test-rmse:1.376181+0.124107
## [104] train-rmse:1.022549+0.046968 test-rmse:1.376453+0.126410
## [105] train-rmse:1.019821+0.047172 test-rmse:1.376232+0.125600
## [106] train-rmse:1.017283+0.047447 test-rmse:1.373640+0.123691
## [107] train-rmse:1.014269+0.047702 test-rmse:1.374456+0.123685
## [108] train-rmse:1.011453+0.047990 test-rmse:1.375284+0.123467
## [109] train-rmse:1.008043+0.048404 test-rmse:1.372941+0.125157
## [110] train-rmse:0.997710+0.045260 test-rmse:1.360066+0.122775
## [111] train-rmse:0.994700+0.044828 test-rmse:1.358346+0.123833
## [112] train-rmse:0.990322+0.046438 test-rmse:1.356471+0.120966
## [113] train-rmse:0.987325+0.046729 test-rmse:1.354959+0.121860
## [114] train-rmse:0.985056+0.046934 test-rmse:1.355566+0.122114
## [115] train-rmse:0.982594+0.046962 test-rmse:1.355891+0.121921
## [116] train-rmse:0.977960+0.047908 test-rmse:1.351941+0.122454
## [117] train-rmse:0.975702+0.048016 test-rmse:1.351628+0.123833
## [118] train-rmse:0.968779+0.044483 test-rmse:1.337004+0.125465
## [119] train-rmse:0.965869+0.043889 test-rmse:1.335167+0.126975
## [120] train-rmse:0.961702+0.042675 test-rmse:1.331518+0.128713
## [121] train-rmse:0.959112+0.042745 test-rmse:1.329846+0.129750
## [122] train-rmse:0.956443+0.042815 test-rmse:1.329044+0.129652
## [123] train-rmse:0.953908+0.043627 test-rmse:1.329587+0.128358
## [124] train-rmse:0.947101+0.041575 test-rmse:1.326020+0.131621
## [125] train-rmse:0.944425+0.041546 test-rmse:1.324067+0.132659
## [126] train-rmse:0.942239+0.041365 test-rmse:1.323389+0.133020
## [127] train-rmse:0.939989+0.041501 test-rmse:1.322163+0.133839
## [128] train-rmse:0.938116+0.041285 test-rmse:1.321587+0.135348
## [129] train-rmse:0.935336+0.041124 test-rmse:1.318798+0.132585
## [130] train-rmse:0.933411+0.041417 test-rmse:1.319193+0.133380
## [131] train-rmse:0.928417+0.046581 test-rmse:1.318189+0.134019
## [132] train-rmse:0.925679+0.048153 test-rmse:1.317032+0.132830
## [133] train-rmse:0.923497+0.048051 test-rmse:1.316946+0.133024
## [134] train-rmse:0.921453+0.048023 test-rmse:1.314490+0.132614
## [135] train-rmse:0.918589+0.048725 test-rmse:1.314381+0.131409
## [136] train-rmse:0.916057+0.048648 test-rmse:1.312812+0.131708
## [137] train-rmse:0.913319+0.049212 test-rmse:1.314204+0.131894
## [138] train-rmse:0.911035+0.049483 test-rmse:1.312840+0.134560
## [139] train-rmse:0.907268+0.049264 test-rmse:1.314433+0.133743
## [140] train-rmse:0.901871+0.044896 test-rmse:1.311962+0.135686
## [141] train-rmse:0.899779+0.044488 test-rmse:1.311524+0.136214
## [142] train-rmse:0.897765+0.044351 test-rmse:1.310480+0.134982
## [143] train-rmse:0.896081+0.044280 test-rmse:1.310433+0.135433
## [144] train-rmse:0.894349+0.044105 test-rmse:1.308777+0.135782
## [145] train-rmse:0.891775+0.043659 test-rmse:1.306717+0.136054
## [146] train-rmse:0.889211+0.043692 test-rmse:1.305458+0.136183
## [147] train-rmse:0.887591+0.043684 test-rmse:1.304532+0.135936
## [148] train-rmse:0.886217+0.043867 test-rmse:1.304044+0.134866
## [149] train-rmse:0.884619+0.043945 test-rmse:1.304966+0.136858
## [150] train-rmse:0.882688+0.043792 test-rmse:1.304532+0.135539
## [151] train-rmse:0.881123+0.043782 test-rmse:1.303990+0.136761
## [152] train-rmse:0.879735+0.043901 test-rmse:1.303752+0.137830
## [153] train-rmse:0.878298+0.043660 test-rmse:1.302487+0.137912
## [154] train-rmse:0.876988+0.043594 test-rmse:1.302620+0.137531
## [155] train-rmse:0.875715+0.043650 test-rmse:1.301061+0.137514
## [156] train-rmse:0.868411+0.040046 test-rmse:1.295275+0.140774
## [157] train-rmse:0.867145+0.040070 test-rmse:1.294354+0.139106
## [158] train-rmse:0.865551+0.040281 test-rmse:1.292820+0.137925
## [159] train-rmse:0.863645+0.040152 test-rmse:1.293273+0.137684
## [160] train-rmse:0.861897+0.040444 test-rmse:1.292980+0.138976
## [161] train-rmse:0.860111+0.040633 test-rmse:1.292621+0.140087
## [162] train-rmse:0.858056+0.039710 test-rmse:1.292664+0.142073
## [163] train-rmse:0.856592+0.039395 test-rmse:1.292602+0.142459
## [164] train-rmse:0.855132+0.039061 test-rmse:1.292573+0.141836
## [165] train-rmse:0.853715+0.039174 test-rmse:1.292770+0.141280
## [166] train-rmse:0.852465+0.039196 test-rmse:1.292658+0.141911
## [167] train-rmse:0.851051+0.039200 test-rmse:1.293245+0.141691
## [168] train-rmse:0.849996+0.039436 test-rmse:1.293130+0.142587
## [169] train-rmse:0.847868+0.040273 test-rmse:1.291988+0.142046
## [170] train-rmse:0.846792+0.040307 test-rmse:1.291758+0.142165
## [171] train-rmse:0.845690+0.040372 test-rmse:1.290342+0.142241
## [172] train-rmse:0.844560+0.040425 test-rmse:1.289282+0.141138
## [173] train-rmse:0.840373+0.040315 test-rmse:1.286222+0.142587
## [174] train-rmse:0.838875+0.040151 test-rmse:1.285838+0.144319
## [175] train-rmse:0.837149+0.040350 test-rmse:1.285375+0.143872
## [176] train-rmse:0.835433+0.039603 test-rmse:1.284427+0.143617
## [177] train-rmse:0.834386+0.039776 test-rmse:1.285222+0.143744
## [178] train-rmse:0.833395+0.039528 test-rmse:1.285120+0.144113
## [179] train-rmse:0.832176+0.039393 test-rmse:1.284940+0.143890
## [180] train-rmse:0.830807+0.039545 test-rmse:1.286401+0.144033
## [181] train-rmse:0.829379+0.039284 test-rmse:1.286498+0.143057
## [182] train-rmse:0.826004+0.039297 test-rmse:1.284213+0.143410
## [183] train-rmse:0.823076+0.037522 test-rmse:1.283583+0.143124
## [184] train-rmse:0.821990+0.037394 test-rmse:1.284165+0.142714
## [185] train-rmse:0.821196+0.037235 test-rmse:1.283656+0.143075
## [186] train-rmse:0.819985+0.037950 test-rmse:1.281742+0.144760
## [187] train-rmse:0.818945+0.037954 test-rmse:1.281527+0.144821
## [188] train-rmse:0.817749+0.037807 test-rmse:1.281763+0.144839
## [189] train-rmse:0.816583+0.037778 test-rmse:1.281424+0.144219
## [190] train-rmse:0.815692+0.037910 test-rmse:1.281064+0.144199
## [191] train-rmse:0.814834+0.037873 test-rmse:1.280277+0.144548
## [192] train-rmse:0.813804+0.037674 test-rmse:1.280346+0.144847
## [193] train-rmse:0.812808+0.037591 test-rmse:1.280874+0.144325
## [194] train-rmse:0.811878+0.038111 test-rmse:1.280725+0.144556
## [195] train-rmse:0.810564+0.038183 test-rmse:1.280319+0.144889
## [196] train-rmse:0.809501+0.038145 test-rmse:1.280872+0.145569
## [197] train-rmse:0.808797+0.038142 test-rmse:1.281617+0.144936
## Stopping. Best iteration:
## [191] train-rmse:0.814834+0.037873 test-rmse:1.280277+0.144548
##
## 37
## [1] train-rmse:7.873465+0.057363 test-rmse:7.869991+0.261188
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.425839+0.044514 test-rmse:6.419784+0.248417
## [3] train-rmse:5.286801+0.035511 test-rmse:5.287199+0.235940
## [4] train-rmse:4.393763+0.029067 test-rmse:4.412989+0.208787
## [5] train-rmse:3.699592+0.021289 test-rmse:3.723711+0.186281
## [6] train-rmse:3.159953+0.016613 test-rmse:3.191618+0.158472
## [7] train-rmse:2.747214+0.012700 test-rmse:2.805559+0.138654
## [8] train-rmse:2.434140+0.009403 test-rmse:2.516879+0.119720
## [9] train-rmse:2.202183+0.010112 test-rmse:2.298130+0.101323
## [10] train-rmse:2.023656+0.009767 test-rmse:2.154928+0.090014
## [11] train-rmse:1.894163+0.011016 test-rmse:2.042802+0.088379
## [12] train-rmse:1.796791+0.012670 test-rmse:1.958164+0.078034
## [13] train-rmse:1.719642+0.014921 test-rmse:1.895159+0.070310
## [14] train-rmse:1.656983+0.013916 test-rmse:1.850015+0.070297
## [15] train-rmse:1.607059+0.014370 test-rmse:1.810169+0.065429
## [16] train-rmse:1.562062+0.015631 test-rmse:1.782149+0.060896
## [17] train-rmse:1.528293+0.016211 test-rmse:1.763412+0.067753
## [18] train-rmse:1.497425+0.016869 test-rmse:1.747069+0.064148
## [19] train-rmse:1.470645+0.017909 test-rmse:1.720718+0.068203
## [20] train-rmse:1.443410+0.020935 test-rmse:1.701799+0.073062
## [21] train-rmse:1.415169+0.018384 test-rmse:1.679631+0.082002
## [22] train-rmse:1.390184+0.020230 test-rmse:1.660948+0.083473
## [23] train-rmse:1.368715+0.019567 test-rmse:1.652373+0.081319
## [24] train-rmse:1.348162+0.020127 test-rmse:1.636396+0.086182
## [25] train-rmse:1.329902+0.021226 test-rmse:1.624108+0.089160
## [26] train-rmse:1.312290+0.021491 test-rmse:1.608211+0.089344
## [27] train-rmse:1.291828+0.023867 test-rmse:1.592255+0.089849
## [28] train-rmse:1.275707+0.025349 test-rmse:1.583514+0.090361
## [29] train-rmse:1.258851+0.025977 test-rmse:1.568078+0.091998
## [30] train-rmse:1.241581+0.027508 test-rmse:1.556102+0.100468
## [31] train-rmse:1.224740+0.027207 test-rmse:1.546635+0.101451
## [32] train-rmse:1.209258+0.029584 test-rmse:1.533595+0.101548
## [33] train-rmse:1.193343+0.028232 test-rmse:1.523963+0.104245
## [34] train-rmse:1.181572+0.028483 test-rmse:1.517182+0.102361
## [35] train-rmse:1.167639+0.025957 test-rmse:1.509152+0.105393
## [36] train-rmse:1.151873+0.025686 test-rmse:1.495636+0.113308
## [37] train-rmse:1.138681+0.026120 test-rmse:1.489077+0.114260
## [38] train-rmse:1.126732+0.026966 test-rmse:1.477593+0.117520
## [39] train-rmse:1.112019+0.025822 test-rmse:1.470841+0.120423
## [40] train-rmse:1.100823+0.026195 test-rmse:1.465437+0.119645
## [41] train-rmse:1.090113+0.026339 test-rmse:1.454107+0.123039
## [42] train-rmse:1.072938+0.022054 test-rmse:1.442168+0.132177
## [43] train-rmse:1.060100+0.024730 test-rmse:1.435945+0.136158
## [44] train-rmse:1.046496+0.022477 test-rmse:1.422158+0.127858
## [45] train-rmse:1.035404+0.022316 test-rmse:1.415270+0.126107
## [46] train-rmse:1.024755+0.021282 test-rmse:1.409508+0.128372
## [47] train-rmse:1.013993+0.019849 test-rmse:1.409599+0.132753
## [48] train-rmse:1.003244+0.022355 test-rmse:1.398759+0.135190
## [49] train-rmse:0.994114+0.022528 test-rmse:1.392634+0.134626
## [50] train-rmse:0.985427+0.023321 test-rmse:1.387848+0.135318
## [51] train-rmse:0.976550+0.023526 test-rmse:1.384961+0.135538
## [52] train-rmse:0.967479+0.022211 test-rmse:1.379131+0.135852
## [53] train-rmse:0.957753+0.022065 test-rmse:1.375218+0.138257
## [54] train-rmse:0.949481+0.023235 test-rmse:1.371940+0.139846
## [55] train-rmse:0.941783+0.022475 test-rmse:1.368375+0.142783
## [56] train-rmse:0.933782+0.024628 test-rmse:1.365576+0.144493
## [57] train-rmse:0.926021+0.024985 test-rmse:1.358812+0.145442
## [58] train-rmse:0.918225+0.026281 test-rmse:1.353274+0.144086
## [59] train-rmse:0.912006+0.026540 test-rmse:1.351071+0.146961
## [60] train-rmse:0.903087+0.024418 test-rmse:1.347302+0.149526
## [61] train-rmse:0.896227+0.023926 test-rmse:1.346481+0.148946
## [62] train-rmse:0.889633+0.024121 test-rmse:1.344397+0.151024
## [63] train-rmse:0.879891+0.021793 test-rmse:1.333003+0.140471
## [64] train-rmse:0.873901+0.022096 test-rmse:1.329009+0.145122
## [65] train-rmse:0.865045+0.019959 test-rmse:1.323107+0.146947
## [66] train-rmse:0.857462+0.021990 test-rmse:1.320722+0.146479
## [67] train-rmse:0.850250+0.020395 test-rmse:1.318099+0.149100
## [68] train-rmse:0.843288+0.020781 test-rmse:1.315626+0.151912
## [69] train-rmse:0.837340+0.020046 test-rmse:1.310845+0.151492
## [70] train-rmse:0.831636+0.020063 test-rmse:1.304714+0.153745
## [71] train-rmse:0.827146+0.020375 test-rmse:1.303134+0.154531
## [72] train-rmse:0.821487+0.019132 test-rmse:1.303281+0.153582
## [73] train-rmse:0.814488+0.018275 test-rmse:1.300932+0.155568
## [74] train-rmse:0.809209+0.019089 test-rmse:1.298580+0.155806
## [75] train-rmse:0.803778+0.018857 test-rmse:1.299294+0.159166
## [76] train-rmse:0.798291+0.018836 test-rmse:1.297952+0.160941
## [77] train-rmse:0.793862+0.018597 test-rmse:1.297152+0.161608
## [78] train-rmse:0.789369+0.018424 test-rmse:1.291341+0.162187
## [79] train-rmse:0.781841+0.020791 test-rmse:1.289176+0.159549
## [80] train-rmse:0.777103+0.021751 test-rmse:1.288593+0.159523
## [81] train-rmse:0.772580+0.022093 test-rmse:1.288319+0.160257
## [82] train-rmse:0.766930+0.023397 test-rmse:1.286819+0.161372
## [83] train-rmse:0.762826+0.023920 test-rmse:1.290118+0.161263
## [84] train-rmse:0.758454+0.022997 test-rmse:1.288810+0.161682
## [85] train-rmse:0.754978+0.023697 test-rmse:1.288710+0.162410
## [86] train-rmse:0.749172+0.020870 test-rmse:1.286375+0.159749
## [87] train-rmse:0.745293+0.021471 test-rmse:1.282585+0.161803
## [88] train-rmse:0.740807+0.024136 test-rmse:1.280831+0.162326
## [89] train-rmse:0.735257+0.023891 test-rmse:1.277239+0.162924
## [90] train-rmse:0.731580+0.023965 test-rmse:1.278837+0.161751
## [91] train-rmse:0.727482+0.023834 test-rmse:1.279107+0.162361
## [92] train-rmse:0.724318+0.023827 test-rmse:1.278913+0.163181
## [93] train-rmse:0.720109+0.022730 test-rmse:1.277048+0.162858
## [94] train-rmse:0.713434+0.023055 test-rmse:1.271566+0.157756
## [95] train-rmse:0.710425+0.023270 test-rmse:1.270702+0.157871
## [96] train-rmse:0.706025+0.022383 test-rmse:1.271041+0.158958
## [97] train-rmse:0.702925+0.023949 test-rmse:1.269842+0.159501
## [98] train-rmse:0.700311+0.023819 test-rmse:1.267080+0.160927
## [99] train-rmse:0.696372+0.023571 test-rmse:1.265438+0.160195
## [100] train-rmse:0.693024+0.024068 test-rmse:1.265860+0.162074
## [101] train-rmse:0.688380+0.024248 test-rmse:1.265803+0.163070
## [102] train-rmse:0.685940+0.024395 test-rmse:1.264922+0.162586
## [103] train-rmse:0.682266+0.024751 test-rmse:1.263352+0.163173
## [104] train-rmse:0.679381+0.025010 test-rmse:1.263657+0.160992
## [105] train-rmse:0.676981+0.025137 test-rmse:1.262314+0.160162
## [106] train-rmse:0.671909+0.024067 test-rmse:1.257196+0.158849
## [107] train-rmse:0.669195+0.024993 test-rmse:1.256037+0.157320
## [108] train-rmse:0.666241+0.025052 test-rmse:1.256264+0.158195
## [109] train-rmse:0.663231+0.025876 test-rmse:1.255868+0.158271
## [110] train-rmse:0.661066+0.026141 test-rmse:1.258346+0.157524
## [111] train-rmse:0.658530+0.025927 test-rmse:1.257851+0.157463
## [112] train-rmse:0.656561+0.025859 test-rmse:1.258253+0.158124
## [113] train-rmse:0.652381+0.024496 test-rmse:1.256332+0.158825
## [114] train-rmse:0.650737+0.024497 test-rmse:1.256341+0.158831
## [115] train-rmse:0.648362+0.024675 test-rmse:1.256278+0.160365
## Stopping. Best iteration:
## [109] train-rmse:0.663231+0.025876 test-rmse:1.255868+0.158271
##
## 38
## [1] train-rmse:7.869436+0.057797 test-rmse:7.865875+0.262617
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.416680+0.045604 test-rmse:6.422274+0.248946
## [3] train-rmse:5.269623+0.035819 test-rmse:5.280480+0.236620
## [4] train-rmse:4.367523+0.029752 test-rmse:4.385335+0.198018
## [5] train-rmse:3.658163+0.024741 test-rmse:3.694937+0.181263
## [6] train-rmse:3.099882+0.020468 test-rmse:3.154237+0.153087
## [7] train-rmse:2.672176+0.016558 test-rmse:2.738559+0.136974
## [8] train-rmse:2.344504+0.016816 test-rmse:2.443586+0.118354
## [9] train-rmse:2.092104+0.015359 test-rmse:2.213811+0.097263
## [10] train-rmse:1.900306+0.016672 test-rmse:2.050636+0.096064
## [11] train-rmse:1.752877+0.017601 test-rmse:1.933752+0.081781
## [12] train-rmse:1.640969+0.017274 test-rmse:1.845037+0.074634
## [13] train-rmse:1.549068+0.021528 test-rmse:1.778657+0.068722
## [14] train-rmse:1.478628+0.023312 test-rmse:1.735563+0.067270
## [15] train-rmse:1.419456+0.023983 test-rmse:1.697618+0.071639
## [16] train-rmse:1.369841+0.025294 test-rmse:1.653473+0.070547
## [17] train-rmse:1.327419+0.024585 test-rmse:1.626986+0.080345
## [18] train-rmse:1.288439+0.020007 test-rmse:1.600936+0.087244
## [19] train-rmse:1.257727+0.021454 test-rmse:1.577151+0.080357
## [20] train-rmse:1.230691+0.021206 test-rmse:1.558713+0.078549
## [21] train-rmse:1.203615+0.019380 test-rmse:1.540173+0.090829
## [22] train-rmse:1.172649+0.019377 test-rmse:1.523607+0.083068
## [23] train-rmse:1.149208+0.020171 test-rmse:1.509821+0.086594
## [24] train-rmse:1.123851+0.017322 test-rmse:1.493305+0.090245
## [25] train-rmse:1.101182+0.016202 test-rmse:1.487922+0.096513
## [26] train-rmse:1.081733+0.018816 test-rmse:1.469693+0.097010
## [27] train-rmse:1.063223+0.019616 test-rmse:1.458515+0.103263
## [28] train-rmse:1.044803+0.019325 test-rmse:1.451006+0.104708
## [29] train-rmse:1.025120+0.021371 test-rmse:1.439330+0.108537
## [30] train-rmse:1.008054+0.023029 test-rmse:1.427182+0.110306
## [31] train-rmse:0.993515+0.022750 test-rmse:1.420817+0.114638
## [32] train-rmse:0.979837+0.022868 test-rmse:1.411981+0.120188
## [33] train-rmse:0.964403+0.024907 test-rmse:1.410282+0.122526
## [34] train-rmse:0.949305+0.025955 test-rmse:1.400945+0.120818
## [35] train-rmse:0.930351+0.025253 test-rmse:1.384571+0.121029
## [36] train-rmse:0.914729+0.023605 test-rmse:1.372894+0.121757
## [37] train-rmse:0.901426+0.021185 test-rmse:1.371179+0.119036
## [38] train-rmse:0.889675+0.023663 test-rmse:1.368267+0.119691
## [39] train-rmse:0.877001+0.023342 test-rmse:1.359360+0.123823
## [40] train-rmse:0.864672+0.022652 test-rmse:1.350141+0.133175
## [41] train-rmse:0.853428+0.021961 test-rmse:1.349241+0.134546
## [42] train-rmse:0.840539+0.021041 test-rmse:1.343157+0.136946
## [43] train-rmse:0.827983+0.020896 test-rmse:1.338525+0.137779
## [44] train-rmse:0.819480+0.021193 test-rmse:1.331370+0.139791
## [45] train-rmse:0.807448+0.022204 test-rmse:1.324937+0.142280
## [46] train-rmse:0.799026+0.021235 test-rmse:1.321734+0.141560
## [47] train-rmse:0.789596+0.022031 test-rmse:1.314453+0.140653
## [48] train-rmse:0.780628+0.021791 test-rmse:1.310893+0.144567
## [49] train-rmse:0.771828+0.022069 test-rmse:1.307331+0.147988
## [50] train-rmse:0.761640+0.023780 test-rmse:1.306277+0.146580
## [51] train-rmse:0.751743+0.022932 test-rmse:1.304010+0.147071
## [52] train-rmse:0.743591+0.022036 test-rmse:1.303697+0.149045
## [53] train-rmse:0.735964+0.021114 test-rmse:1.303850+0.147890
## [54] train-rmse:0.728106+0.019931 test-rmse:1.304209+0.149099
## [55] train-rmse:0.720896+0.020884 test-rmse:1.303705+0.152869
## [56] train-rmse:0.713861+0.020021 test-rmse:1.298396+0.155597
## [57] train-rmse:0.706036+0.021255 test-rmse:1.294804+0.157728
## [58] train-rmse:0.699213+0.020234 test-rmse:1.292726+0.158915
## [59] train-rmse:0.692866+0.021547 test-rmse:1.291833+0.157376
## [60] train-rmse:0.685363+0.020476 test-rmse:1.291473+0.159927
## [61] train-rmse:0.678457+0.019775 test-rmse:1.290500+0.159649
## [62] train-rmse:0.672563+0.021770 test-rmse:1.290660+0.162467
## [63] train-rmse:0.664325+0.022780 test-rmse:1.287037+0.160961
## [64] train-rmse:0.656631+0.022144 test-rmse:1.286032+0.162245
## [65] train-rmse:0.650546+0.020474 test-rmse:1.286232+0.161880
## [66] train-rmse:0.645894+0.020484 test-rmse:1.283717+0.162473
## [67] train-rmse:0.639344+0.021617 test-rmse:1.283563+0.163243
## [68] train-rmse:0.631417+0.021373 test-rmse:1.278670+0.160191
## [69] train-rmse:0.626682+0.021296 test-rmse:1.278403+0.158359
## [70] train-rmse:0.621447+0.022545 test-rmse:1.278375+0.158491
## [71] train-rmse:0.616341+0.022451 test-rmse:1.277744+0.159003
## [72] train-rmse:0.611341+0.021586 test-rmse:1.275648+0.160690
## [73] train-rmse:0.605852+0.022574 test-rmse:1.270895+0.160817
## [74] train-rmse:0.600871+0.024599 test-rmse:1.268262+0.159536
## [75] train-rmse:0.594773+0.022851 test-rmse:1.267128+0.159904
## [76] train-rmse:0.591667+0.023200 test-rmse:1.267924+0.160518
## [77] train-rmse:0.585883+0.023388 test-rmse:1.268944+0.158806
## [78] train-rmse:0.580041+0.021771 test-rmse:1.266339+0.161038
## [79] train-rmse:0.575262+0.020180 test-rmse:1.267202+0.161487
## [80] train-rmse:0.571112+0.019982 test-rmse:1.267497+0.162565
## [81] train-rmse:0.567723+0.019972 test-rmse:1.267593+0.162778
## [82] train-rmse:0.563179+0.020026 test-rmse:1.266034+0.163512
## [83] train-rmse:0.559009+0.020543 test-rmse:1.264346+0.165386
## [84] train-rmse:0.553637+0.020617 test-rmse:1.264956+0.165444
## [85] train-rmse:0.551134+0.020651 test-rmse:1.264882+0.165673
## [86] train-rmse:0.547682+0.020226 test-rmse:1.262515+0.165162
## [87] train-rmse:0.543757+0.020675 test-rmse:1.261129+0.164161
## [88] train-rmse:0.540589+0.020979 test-rmse:1.259741+0.164733
## [89] train-rmse:0.536817+0.019801 test-rmse:1.259184+0.166782
## [90] train-rmse:0.534433+0.019275 test-rmse:1.258566+0.167719
## [91] train-rmse:0.530576+0.018549 test-rmse:1.261010+0.166393
## [92] train-rmse:0.528285+0.019067 test-rmse:1.263504+0.167815
## [93] train-rmse:0.524493+0.018551 test-rmse:1.263326+0.169138
## [94] train-rmse:0.521833+0.018406 test-rmse:1.264148+0.168791
## [95] train-rmse:0.519357+0.018908 test-rmse:1.263653+0.168265
## [96] train-rmse:0.516387+0.019193 test-rmse:1.262103+0.167465
## Stopping. Best iteration:
## [90] train-rmse:0.534433+0.019275 test-rmse:1.258566+0.167719
##
## 39
## [1] train-rmse:7.866079+0.058136 test-rmse:7.866103+0.263086
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.408394+0.046670 test-rmse:6.419045+0.241432
## [3] train-rmse:5.254581+0.037942 test-rmse:5.271280+0.226953
## [4] train-rmse:4.343115+0.033961 test-rmse:4.351040+0.199979
## [5] train-rmse:3.624815+0.027362 test-rmse:3.648356+0.167888
## [6] train-rmse:3.056779+0.020287 test-rmse:3.105241+0.149587
## [7] train-rmse:2.614993+0.020013 test-rmse:2.683284+0.120398
## [8] train-rmse:2.271914+0.015833 test-rmse:2.379263+0.109672
## [9] train-rmse:2.007939+0.015616 test-rmse:2.155759+0.103213
## [10] train-rmse:1.797039+0.018044 test-rmse:1.973490+0.082299
## [11] train-rmse:1.640072+0.019729 test-rmse:1.842152+0.083930
## [12] train-rmse:1.514959+0.023887 test-rmse:1.755314+0.082550
## [13] train-rmse:1.407391+0.022544 test-rmse:1.674200+0.074040
## [14] train-rmse:1.329159+0.022984 test-rmse:1.628751+0.077064
## [15] train-rmse:1.265866+0.021937 test-rmse:1.586460+0.087267
## [16] train-rmse:1.208618+0.025132 test-rmse:1.551175+0.092730
## [17] train-rmse:1.161259+0.026329 test-rmse:1.513731+0.094131
## [18] train-rmse:1.123137+0.024511 test-rmse:1.495245+0.100593
## [19] train-rmse:1.084648+0.024754 test-rmse:1.471002+0.104075
## [20] train-rmse:1.048386+0.022960 test-rmse:1.453681+0.110957
## [21] train-rmse:1.020232+0.025345 test-rmse:1.440768+0.115813
## [22] train-rmse:0.992556+0.027552 test-rmse:1.426038+0.116793
## [23] train-rmse:0.966473+0.025733 test-rmse:1.410055+0.118986
## [24] train-rmse:0.943569+0.025914 test-rmse:1.397980+0.129112
## [25] train-rmse:0.920384+0.027308 test-rmse:1.384527+0.132830
## [26] train-rmse:0.898351+0.025912 test-rmse:1.376256+0.140889
## [27] train-rmse:0.876015+0.023765 test-rmse:1.365282+0.142974
## [28] train-rmse:0.857429+0.025761 test-rmse:1.357403+0.141342
## [29] train-rmse:0.838128+0.020605 test-rmse:1.349723+0.152409
## [30] train-rmse:0.821076+0.018779 test-rmse:1.340087+0.152150
## [31] train-rmse:0.803726+0.018756 test-rmse:1.331190+0.154989
## [32] train-rmse:0.788096+0.021380 test-rmse:1.322388+0.154889
## [33] train-rmse:0.771390+0.021932 test-rmse:1.316704+0.158156
## [34] train-rmse:0.758304+0.022232 test-rmse:1.312812+0.158559
## [35] train-rmse:0.743197+0.021984 test-rmse:1.311423+0.161827
## [36] train-rmse:0.731307+0.021373 test-rmse:1.306263+0.163130
## [37] train-rmse:0.718531+0.021385 test-rmse:1.302573+0.167370
## [38] train-rmse:0.705476+0.020794 test-rmse:1.295581+0.166791
## [39] train-rmse:0.694805+0.020031 test-rmse:1.291793+0.166977
## [40] train-rmse:0.682216+0.019706 test-rmse:1.287026+0.171382
## [41] train-rmse:0.672549+0.019031 test-rmse:1.284133+0.174303
## [42] train-rmse:0.662463+0.018643 test-rmse:1.285651+0.174609
## [43] train-rmse:0.653146+0.018667 test-rmse:1.282835+0.176682
## [44] train-rmse:0.643682+0.018800 test-rmse:1.279225+0.175565
## [45] train-rmse:0.632899+0.020318 test-rmse:1.274905+0.172819
## [46] train-rmse:0.624410+0.019660 test-rmse:1.273861+0.175518
## [47] train-rmse:0.615924+0.019510 test-rmse:1.271981+0.177819
## [48] train-rmse:0.607050+0.017707 test-rmse:1.267997+0.178143
## [49] train-rmse:0.599591+0.018431 test-rmse:1.268774+0.180748
## [50] train-rmse:0.593023+0.018114 test-rmse:1.268441+0.180663
## [51] train-rmse:0.584749+0.018171 test-rmse:1.264699+0.184212
## [52] train-rmse:0.575433+0.016169 test-rmse:1.263259+0.186639
## [53] train-rmse:0.569338+0.015405 test-rmse:1.263340+0.186465
## [54] train-rmse:0.562745+0.015006 test-rmse:1.262424+0.187698
## [55] train-rmse:0.558071+0.015017 test-rmse:1.263667+0.189613
## [56] train-rmse:0.548926+0.016634 test-rmse:1.258059+0.190889
## [57] train-rmse:0.542364+0.018174 test-rmse:1.256271+0.190452
## [58] train-rmse:0.536999+0.017189 test-rmse:1.257781+0.192430
## [59] train-rmse:0.528824+0.016433 test-rmse:1.254473+0.194467
## [60] train-rmse:0.523460+0.017527 test-rmse:1.254046+0.195182
## [61] train-rmse:0.518591+0.017629 test-rmse:1.253403+0.194645
## [62] train-rmse:0.513441+0.017465 test-rmse:1.251023+0.195071
## [63] train-rmse:0.506601+0.015859 test-rmse:1.250861+0.195609
## [64] train-rmse:0.501821+0.016361 test-rmse:1.250328+0.195327
## [65] train-rmse:0.497026+0.017633 test-rmse:1.252831+0.195287
## [66] train-rmse:0.491694+0.017578 test-rmse:1.250698+0.195711
## [67] train-rmse:0.488187+0.017347 test-rmse:1.249528+0.195853
## [68] train-rmse:0.483746+0.017542 test-rmse:1.248552+0.196159
## [69] train-rmse:0.476559+0.017711 test-rmse:1.247504+0.196614
## [70] train-rmse:0.472764+0.017525 test-rmse:1.246325+0.196076
## [71] train-rmse:0.467925+0.016542 test-rmse:1.246614+0.195963
## [72] train-rmse:0.463328+0.015342 test-rmse:1.247247+0.198005
## [73] train-rmse:0.458226+0.016620 test-rmse:1.246819+0.195582
## [74] train-rmse:0.454118+0.015909 test-rmse:1.246816+0.195298
## [75] train-rmse:0.449156+0.015758 test-rmse:1.244814+0.194156
## [76] train-rmse:0.446164+0.016379 test-rmse:1.243979+0.193749
## [77] train-rmse:0.441956+0.016205 test-rmse:1.243549+0.194900
## [78] train-rmse:0.437027+0.017089 test-rmse:1.243433+0.194574
## [79] train-rmse:0.430838+0.016676 test-rmse:1.241802+0.194111
## [80] train-rmse:0.426767+0.016394 test-rmse:1.243454+0.194727
## [81] train-rmse:0.424228+0.016994 test-rmse:1.242585+0.195081
## [82] train-rmse:0.421025+0.016175 test-rmse:1.242319+0.195615
## [83] train-rmse:0.418190+0.016321 test-rmse:1.240277+0.196006
## [84] train-rmse:0.413644+0.015987 test-rmse:1.241159+0.197383
## [85] train-rmse:0.410999+0.016797 test-rmse:1.242525+0.198500
## [86] train-rmse:0.406898+0.017279 test-rmse:1.242109+0.198278
## [87] train-rmse:0.403024+0.018380 test-rmse:1.241539+0.196597
## [88] train-rmse:0.399961+0.018493 test-rmse:1.241469+0.196175
## [89] train-rmse:0.397347+0.019153 test-rmse:1.241942+0.195465
## Stopping. Best iteration:
## [83] train-rmse:0.418190+0.016321 test-rmse:1.240277+0.196006
##
## 40
## [1] train-rmse:7.865351+0.058314 test-rmse:7.866791+0.256691
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.404043+0.047049 test-rmse:6.415631+0.238084
## [3] train-rmse:5.245533+0.038382 test-rmse:5.263406+0.211601
## [4] train-rmse:4.327564+0.033936 test-rmse:4.359995+0.186368
## [5] train-rmse:3.600377+0.031230 test-rmse:3.636383+0.164022
## [6] train-rmse:3.025014+0.023672 test-rmse:3.090946+0.151250
## [7] train-rmse:2.570874+0.022275 test-rmse:2.664360+0.121498
## [8] train-rmse:2.219717+0.020119 test-rmse:2.346648+0.121247
## [9] train-rmse:1.945001+0.019649 test-rmse:2.110638+0.104075
## [10] train-rmse:1.722830+0.020138 test-rmse:1.923815+0.097614
## [11] train-rmse:1.555568+0.026089 test-rmse:1.793579+0.095781
## [12] train-rmse:1.416003+0.029867 test-rmse:1.677639+0.091357
## [13] train-rmse:1.303697+0.024517 test-rmse:1.609619+0.097638
## [14] train-rmse:1.217846+0.024190 test-rmse:1.543336+0.103856
## [15] train-rmse:1.147162+0.024229 test-rmse:1.497481+0.097484
## [16] train-rmse:1.089425+0.023189 test-rmse:1.461611+0.102845
## [17] train-rmse:1.036443+0.019260 test-rmse:1.429359+0.107693
## [18] train-rmse:0.994143+0.019702 test-rmse:1.412642+0.111336
## [19] train-rmse:0.954884+0.022071 test-rmse:1.391903+0.113334
## [20] train-rmse:0.917723+0.022716 test-rmse:1.378386+0.121123
## [21] train-rmse:0.889594+0.022200 test-rmse:1.359258+0.126557
## [22] train-rmse:0.862027+0.022157 test-rmse:1.352030+0.134981
## [23] train-rmse:0.837730+0.022108 test-rmse:1.348666+0.138236
## [24] train-rmse:0.813259+0.023085 test-rmse:1.331056+0.138652
## [25] train-rmse:0.786706+0.019575 test-rmse:1.321282+0.145080
## [26] train-rmse:0.767984+0.020372 test-rmse:1.316868+0.148079
## [27] train-rmse:0.748979+0.020441 test-rmse:1.312393+0.148589
## [28] train-rmse:0.728766+0.017616 test-rmse:1.312321+0.154165
## [29] train-rmse:0.710521+0.015842 test-rmse:1.299340+0.156956
## [30] train-rmse:0.688600+0.014747 test-rmse:1.285530+0.158424
## [31] train-rmse:0.674684+0.013146 test-rmse:1.280336+0.164416
## [32] train-rmse:0.658788+0.013540 test-rmse:1.271996+0.170067
## [33] train-rmse:0.643018+0.015443 test-rmse:1.268383+0.165019
## [34] train-rmse:0.630369+0.014166 test-rmse:1.262978+0.169591
## [35] train-rmse:0.618430+0.013765 test-rmse:1.256598+0.168042
## [36] train-rmse:0.603663+0.013794 test-rmse:1.253479+0.167210
## [37] train-rmse:0.592445+0.015145 test-rmse:1.252444+0.170645
## [38] train-rmse:0.580095+0.015420 test-rmse:1.248240+0.174694
## [39] train-rmse:0.569987+0.014655 test-rmse:1.245631+0.176469
## [40] train-rmse:0.560383+0.013646 test-rmse:1.243087+0.176097
## [41] train-rmse:0.550640+0.013082 test-rmse:1.241316+0.178699
## [42] train-rmse:0.539290+0.013240 test-rmse:1.240427+0.178244
## [43] train-rmse:0.531230+0.013225 test-rmse:1.239674+0.178774
## [44] train-rmse:0.522875+0.014770 test-rmse:1.238725+0.178592
## [45] train-rmse:0.516179+0.014190 test-rmse:1.236754+0.179339
## [46] train-rmse:0.506993+0.012128 test-rmse:1.235852+0.179111
## [47] train-rmse:0.499832+0.011858 test-rmse:1.236876+0.179064
## [48] train-rmse:0.490733+0.011538 test-rmse:1.236390+0.178786
## [49] train-rmse:0.483503+0.011282 test-rmse:1.233049+0.177937
## [50] train-rmse:0.472808+0.008872 test-rmse:1.233763+0.177961
## [51] train-rmse:0.464512+0.010861 test-rmse:1.230205+0.176824
## [52] train-rmse:0.457873+0.009978 test-rmse:1.228713+0.178786
## [53] train-rmse:0.451217+0.009997 test-rmse:1.228353+0.177698
## [54] train-rmse:0.444592+0.011230 test-rmse:1.227438+0.178397
## [55] train-rmse:0.435292+0.011636 test-rmse:1.226473+0.176696
## [56] train-rmse:0.429345+0.012538 test-rmse:1.225197+0.178556
## [57] train-rmse:0.421803+0.009731 test-rmse:1.225711+0.180453
## [58] train-rmse:0.415742+0.009898 test-rmse:1.227557+0.179814
## [59] train-rmse:0.408722+0.011092 test-rmse:1.224275+0.180749
## [60] train-rmse:0.404682+0.011417 test-rmse:1.224481+0.179669
## [61] train-rmse:0.400418+0.010976 test-rmse:1.223041+0.180093
## [62] train-rmse:0.396198+0.010667 test-rmse:1.224455+0.180877
## [63] train-rmse:0.391208+0.010363 test-rmse:1.224427+0.181354
## [64] train-rmse:0.385373+0.011731 test-rmse:1.224972+0.179017
## [65] train-rmse:0.380588+0.010270 test-rmse:1.224801+0.177713
## [66] train-rmse:0.375682+0.011925 test-rmse:1.224653+0.178029
## [67] train-rmse:0.371141+0.011366 test-rmse:1.224842+0.178923
## Stopping. Best iteration:
## [61] train-rmse:0.400418+0.010976 test-rmse:1.223041+0.180093
##
## 41
## [1] train-rmse:7.865351+0.058314 test-rmse:7.866791+0.256691
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.402262+0.047257 test-rmse:6.418812+0.229866
## [3] train-rmse:5.239467+0.038119 test-rmse:5.256364+0.197088
## [4] train-rmse:4.316681+0.031402 test-rmse:4.351797+0.172700
## [5] train-rmse:3.582371+0.027554 test-rmse:3.630455+0.147249
## [6] train-rmse:3.002419+0.023576 test-rmse:3.055434+0.128827
## [7] train-rmse:2.540638+0.017429 test-rmse:2.633321+0.130114
## [8] train-rmse:2.178462+0.017355 test-rmse:2.319204+0.117738
## [9] train-rmse:1.888482+0.016521 test-rmse:2.067030+0.104777
## [10] train-rmse:1.658069+0.013334 test-rmse:1.876897+0.095930
## [11] train-rmse:1.478924+0.019502 test-rmse:1.743829+0.083880
## [12] train-rmse:1.331492+0.020174 test-rmse:1.634301+0.086899
## [13] train-rmse:1.212519+0.015620 test-rmse:1.545614+0.091811
## [14] train-rmse:1.122272+0.020452 test-rmse:1.494239+0.088993
## [15] train-rmse:1.043006+0.022556 test-rmse:1.455579+0.097138
## [16] train-rmse:0.978034+0.019497 test-rmse:1.427200+0.105053
## [17] train-rmse:0.922171+0.021293 test-rmse:1.401687+0.117181
## [18] train-rmse:0.872842+0.020159 test-rmse:1.369899+0.116121
## [19] train-rmse:0.832902+0.018863 test-rmse:1.352164+0.118979
## [20] train-rmse:0.797528+0.018990 test-rmse:1.339241+0.123942
## [21] train-rmse:0.765872+0.019180 test-rmse:1.326626+0.128639
## [22] train-rmse:0.740233+0.017122 test-rmse:1.315595+0.131476
## [23] train-rmse:0.710292+0.015129 test-rmse:1.303142+0.143036
## [24] train-rmse:0.685813+0.014753 test-rmse:1.296019+0.144265
## [25] train-rmse:0.663195+0.012751 test-rmse:1.283146+0.151851
## [26] train-rmse:0.643857+0.009284 test-rmse:1.276854+0.157359
## [27] train-rmse:0.625895+0.007357 test-rmse:1.272723+0.157256
## [28] train-rmse:0.606334+0.008883 test-rmse:1.267890+0.155244
## [29] train-rmse:0.590331+0.007083 test-rmse:1.268333+0.157610
## [30] train-rmse:0.573890+0.009519 test-rmse:1.262614+0.159102
## [31] train-rmse:0.559708+0.008940 test-rmse:1.259852+0.162809
## [32] train-rmse:0.546201+0.009624 test-rmse:1.258794+0.164864
## [33] train-rmse:0.532701+0.008409 test-rmse:1.254972+0.168084
## [34] train-rmse:0.521320+0.007714 test-rmse:1.253749+0.168946
## [35] train-rmse:0.509391+0.009729 test-rmse:1.251029+0.169032
## [36] train-rmse:0.496769+0.009980 test-rmse:1.248874+0.169013
## [37] train-rmse:0.488149+0.009847 test-rmse:1.250134+0.168015
## [38] train-rmse:0.477886+0.010387 test-rmse:1.247662+0.167881
## [39] train-rmse:0.468638+0.009500 test-rmse:1.243379+0.167384
## [40] train-rmse:0.459232+0.008844 test-rmse:1.242575+0.168975
## [41] train-rmse:0.448678+0.009301 test-rmse:1.241836+0.167695
## [42] train-rmse:0.441289+0.008701 test-rmse:1.239075+0.166367
## [43] train-rmse:0.430954+0.012597 test-rmse:1.237653+0.164575
## [44] train-rmse:0.423765+0.013980 test-rmse:1.236958+0.165771
## [45] train-rmse:0.415181+0.011497 test-rmse:1.236497+0.167167
## [46] train-rmse:0.406554+0.012843 test-rmse:1.235997+0.169232
## [47] train-rmse:0.397938+0.012545 test-rmse:1.235523+0.169849
## [48] train-rmse:0.390012+0.010170 test-rmse:1.233048+0.168913
## [49] train-rmse:0.383961+0.009725 test-rmse:1.232536+0.169486
## [50] train-rmse:0.376793+0.012244 test-rmse:1.232017+0.170217
## [51] train-rmse:0.368688+0.010974 test-rmse:1.230290+0.169176
## [52] train-rmse:0.363212+0.011417 test-rmse:1.230977+0.169215
## [53] train-rmse:0.358025+0.012416 test-rmse:1.232823+0.170425
## [54] train-rmse:0.350656+0.011977 test-rmse:1.232034+0.171240
## [55] train-rmse:0.344103+0.009918 test-rmse:1.231433+0.169433
## [56] train-rmse:0.336377+0.009967 test-rmse:1.229625+0.168072
## [57] train-rmse:0.329900+0.010825 test-rmse:1.228545+0.167991
## [58] train-rmse:0.324366+0.009050 test-rmse:1.228081+0.168362
## [59] train-rmse:0.319715+0.009483 test-rmse:1.227826+0.169010
## [60] train-rmse:0.313802+0.008064 test-rmse:1.225976+0.168103
## [61] train-rmse:0.306982+0.008446 test-rmse:1.226261+0.167080
## [62] train-rmse:0.303716+0.008183 test-rmse:1.227272+0.167865
## [63] train-rmse:0.297516+0.006131 test-rmse:1.226335+0.168282
## [64] train-rmse:0.291555+0.006074 test-rmse:1.226264+0.166330
## [65] train-rmse:0.287607+0.005720 test-rmse:1.226947+0.165805
## [66] train-rmse:0.283361+0.004694 test-rmse:1.227842+0.164600
## Stopping. Best iteration:
## [60] train-rmse:0.313802+0.008064 test-rmse:1.225976+0.168103
##
## 42
## [1] train-rmse:7.731281+0.054356 test-rmse:7.727139+0.269544
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.226154+0.039700 test-rmse:6.219747+0.262488
## [3] train-rmse:5.087537+0.029467 test-rmse:5.081068+0.253417
## [4] train-rmse:4.239465+0.021167 test-rmse:4.243872+0.233231
## [5] train-rmse:3.619914+0.015045 test-rmse:3.630991+0.202211
## [6] train-rmse:3.177763+0.012237 test-rmse:3.199426+0.169698
## [7] train-rmse:2.866652+0.011526 test-rmse:2.900409+0.133846
## [8] train-rmse:2.651440+0.012051 test-rmse:2.679247+0.104475
## [9] train-rmse:2.503948+0.013084 test-rmse:2.540381+0.083313
## [10] train-rmse:2.403840+0.014622 test-rmse:2.441061+0.059369
## [11] train-rmse:2.334536+0.015409 test-rmse:2.375684+0.051684
## [12] train-rmse:2.284308+0.016273 test-rmse:2.322693+0.050936
## [13] train-rmse:2.247313+0.017281 test-rmse:2.289681+0.056451
## [14] train-rmse:2.218642+0.018484 test-rmse:2.265193+0.056601
## [15] train-rmse:2.196237+0.019377 test-rmse:2.244285+0.061578
## [16] train-rmse:2.177446+0.020193 test-rmse:2.225437+0.062057
## [17] train-rmse:2.160535+0.021046 test-rmse:2.212566+0.068816
## [18] train-rmse:2.144936+0.021120 test-rmse:2.201895+0.073788
## [19] train-rmse:2.130203+0.021354 test-rmse:2.189201+0.079707
## [20] train-rmse:2.116496+0.022225 test-rmse:2.180550+0.085728
## [21] train-rmse:2.103748+0.022724 test-rmse:2.166541+0.085539
## [22] train-rmse:2.091310+0.023433 test-rmse:2.148302+0.082948
## [23] train-rmse:2.079679+0.024172 test-rmse:2.134210+0.094308
## [24] train-rmse:2.068473+0.024896 test-rmse:2.125662+0.095084
## [25] train-rmse:2.057530+0.025451 test-rmse:2.117968+0.094658
## [26] train-rmse:2.046563+0.025986 test-rmse:2.110161+0.092734
## [27] train-rmse:2.035821+0.026151 test-rmse:2.103101+0.093421
## [28] train-rmse:2.024950+0.026386 test-rmse:2.097231+0.092901
## [29] train-rmse:2.015185+0.026986 test-rmse:2.083320+0.087398
## [30] train-rmse:2.005461+0.027972 test-rmse:2.077561+0.083783
## [31] train-rmse:1.996217+0.028361 test-rmse:2.068903+0.085108
## [32] train-rmse:1.987316+0.028782 test-rmse:2.060990+0.090766
## [33] train-rmse:1.978251+0.029122 test-rmse:2.055403+0.091279
## [34] train-rmse:1.969663+0.029324 test-rmse:2.045495+0.086390
## [35] train-rmse:1.961067+0.029737 test-rmse:2.036417+0.090090
## [36] train-rmse:1.952683+0.030109 test-rmse:2.027068+0.095758
## [37] train-rmse:1.944432+0.030515 test-rmse:2.017417+0.092707
## [38] train-rmse:1.936299+0.030854 test-rmse:2.013130+0.093158
## [39] train-rmse:1.928120+0.031241 test-rmse:2.009760+0.091356
## [40] train-rmse:1.920309+0.031654 test-rmse:2.001632+0.096130
## [41] train-rmse:1.912501+0.032231 test-rmse:1.992438+0.093856
## [42] train-rmse:1.904724+0.032700 test-rmse:1.983368+0.087156
## [43] train-rmse:1.897198+0.033044 test-rmse:1.978930+0.087975
## [44] train-rmse:1.890057+0.033499 test-rmse:1.972596+0.091891
## [45] train-rmse:1.882859+0.033880 test-rmse:1.965350+0.086923
## [46] train-rmse:1.875708+0.034377 test-rmse:1.957820+0.088191
## [47] train-rmse:1.868772+0.034888 test-rmse:1.954775+0.088789
## [48] train-rmse:1.861837+0.035380 test-rmse:1.948744+0.086855
## [49] train-rmse:1.855318+0.035731 test-rmse:1.945706+0.092863
## [50] train-rmse:1.848890+0.036201 test-rmse:1.940611+0.093780
## [51] train-rmse:1.842544+0.036661 test-rmse:1.935087+0.094628
## [52] train-rmse:1.836274+0.037055 test-rmse:1.931652+0.097292
## [53] train-rmse:1.830162+0.037448 test-rmse:1.925674+0.100708
## [54] train-rmse:1.824014+0.037959 test-rmse:1.920797+0.095723
## [55] train-rmse:1.818020+0.038220 test-rmse:1.915334+0.095377
## [56] train-rmse:1.812194+0.038493 test-rmse:1.909449+0.093786
## [57] train-rmse:1.806337+0.038715 test-rmse:1.903937+0.094580
## [58] train-rmse:1.800575+0.038970 test-rmse:1.897084+0.093606
## [59] train-rmse:1.794958+0.039267 test-rmse:1.893658+0.095199
## [60] train-rmse:1.789252+0.039639 test-rmse:1.887594+0.092729
## [61] train-rmse:1.783624+0.039984 test-rmse:1.884355+0.094107
## [62] train-rmse:1.778197+0.040238 test-rmse:1.882329+0.093461
## [63] train-rmse:1.772733+0.040617 test-rmse:1.875425+0.091971
## [64] train-rmse:1.767292+0.041063 test-rmse:1.868722+0.094535
## [65] train-rmse:1.762032+0.041345 test-rmse:1.866069+0.094784
## [66] train-rmse:1.756720+0.041443 test-rmse:1.859669+0.094920
## [67] train-rmse:1.751667+0.041781 test-rmse:1.853399+0.094813
## [68] train-rmse:1.746633+0.042082 test-rmse:1.849111+0.094801
## [69] train-rmse:1.741747+0.042311 test-rmse:1.842889+0.094344
## [70] train-rmse:1.736909+0.042725 test-rmse:1.841203+0.093763
## [71] train-rmse:1.732234+0.043102 test-rmse:1.838068+0.095626
## [72] train-rmse:1.727664+0.043458 test-rmse:1.833200+0.093947
## [73] train-rmse:1.723107+0.043646 test-rmse:1.828931+0.095060
## [74] train-rmse:1.718577+0.043893 test-rmse:1.829291+0.095175
## [75] train-rmse:1.714003+0.044093 test-rmse:1.824169+0.097398
## [76] train-rmse:1.709495+0.044364 test-rmse:1.817864+0.096816
## [77] train-rmse:1.705130+0.044593 test-rmse:1.816920+0.100134
## [78] train-rmse:1.700814+0.044870 test-rmse:1.814116+0.101303
## [79] train-rmse:1.696624+0.045103 test-rmse:1.811701+0.100935
## [80] train-rmse:1.692500+0.045301 test-rmse:1.809251+0.097737
## [81] train-rmse:1.688332+0.045566 test-rmse:1.805995+0.099609
## [82] train-rmse:1.684142+0.045878 test-rmse:1.802314+0.100649
## [83] train-rmse:1.679971+0.046130 test-rmse:1.799209+0.102003
## [84] train-rmse:1.675983+0.046286 test-rmse:1.794460+0.104227
## [85] train-rmse:1.672040+0.046477 test-rmse:1.793750+0.109340
## [86] train-rmse:1.668123+0.046565 test-rmse:1.792078+0.110528
## [87] train-rmse:1.664286+0.046713 test-rmse:1.788037+0.109888
## [88] train-rmse:1.660528+0.046805 test-rmse:1.782736+0.113550
## [89] train-rmse:1.656725+0.046916 test-rmse:1.776008+0.111052
## [90] train-rmse:1.653081+0.047001 test-rmse:1.773098+0.109702
## [91] train-rmse:1.649284+0.047232 test-rmse:1.773246+0.107170
## [92] train-rmse:1.645650+0.047357 test-rmse:1.763998+0.107361
## [93] train-rmse:1.642060+0.047447 test-rmse:1.757034+0.113966
## [94] train-rmse:1.638570+0.047600 test-rmse:1.754838+0.113765
## [95] train-rmse:1.635142+0.047778 test-rmse:1.751877+0.113828
## [96] train-rmse:1.631699+0.047905 test-rmse:1.748655+0.114946
## [97] train-rmse:1.628265+0.048030 test-rmse:1.747051+0.114459
## [98] train-rmse:1.624904+0.048157 test-rmse:1.745454+0.114286
## [99] train-rmse:1.621629+0.048262 test-rmse:1.744257+0.115213
## [100] train-rmse:1.618366+0.048395 test-rmse:1.740075+0.115464
## [101] train-rmse:1.615112+0.048516 test-rmse:1.737228+0.116093
## [102] train-rmse:1.611968+0.048680 test-rmse:1.735550+0.117215
## [103] train-rmse:1.608903+0.048863 test-rmse:1.731359+0.118642
## [104] train-rmse:1.605763+0.048979 test-rmse:1.730656+0.121168
## [105] train-rmse:1.602724+0.049096 test-rmse:1.727088+0.121781
## [106] train-rmse:1.599690+0.049183 test-rmse:1.728573+0.121475
## [107] train-rmse:1.596721+0.049348 test-rmse:1.727087+0.121128
## [108] train-rmse:1.593772+0.049482 test-rmse:1.722632+0.118752
## [109] train-rmse:1.590878+0.049584 test-rmse:1.722839+0.118168
## [110] train-rmse:1.588010+0.049723 test-rmse:1.723587+0.120785
## [111] train-rmse:1.585142+0.049820 test-rmse:1.719054+0.120301
## [112] train-rmse:1.582243+0.049907 test-rmse:1.714559+0.122329
## [113] train-rmse:1.579413+0.050023 test-rmse:1.711049+0.123835
## [114] train-rmse:1.576595+0.050164 test-rmse:1.709227+0.125851
## [115] train-rmse:1.573884+0.050208 test-rmse:1.706233+0.127347
## [116] train-rmse:1.571204+0.050275 test-rmse:1.704414+0.128265
## [117] train-rmse:1.568551+0.050297 test-rmse:1.703642+0.129168
## [118] train-rmse:1.565954+0.050339 test-rmse:1.698904+0.129989
## [119] train-rmse:1.563317+0.050475 test-rmse:1.699901+0.131242
## [120] train-rmse:1.560686+0.050515 test-rmse:1.693061+0.133457
## [121] train-rmse:1.558037+0.050590 test-rmse:1.691080+0.134241
## [122] train-rmse:1.555394+0.050618 test-rmse:1.689316+0.135905
## [123] train-rmse:1.552903+0.050733 test-rmse:1.688240+0.137807
## [124] train-rmse:1.550455+0.050832 test-rmse:1.684347+0.136153
## [125] train-rmse:1.547994+0.050936 test-rmse:1.680386+0.136023
## [126] train-rmse:1.545514+0.050992 test-rmse:1.675475+0.137904
## [127] train-rmse:1.543049+0.051079 test-rmse:1.672876+0.138627
## [128] train-rmse:1.540674+0.051094 test-rmse:1.669155+0.137379
## [129] train-rmse:1.538378+0.051110 test-rmse:1.667884+0.138164
## [130] train-rmse:1.536083+0.051145 test-rmse:1.666961+0.135412
## [131] train-rmse:1.533841+0.051167 test-rmse:1.664436+0.135391
## [132] train-rmse:1.531602+0.051254 test-rmse:1.663545+0.136442
## [133] train-rmse:1.529394+0.051307 test-rmse:1.660975+0.134962
## [134] train-rmse:1.527205+0.051368 test-rmse:1.657249+0.136922
## [135] train-rmse:1.525022+0.051387 test-rmse:1.657875+0.137951
## [136] train-rmse:1.522853+0.051417 test-rmse:1.655351+0.137770
## [137] train-rmse:1.520688+0.051443 test-rmse:1.653524+0.142352
## [138] train-rmse:1.518615+0.051466 test-rmse:1.653978+0.142607
## [139] train-rmse:1.516554+0.051498 test-rmse:1.650430+0.141231
## [140] train-rmse:1.514518+0.051539 test-rmse:1.648518+0.141734
## [141] train-rmse:1.512494+0.051607 test-rmse:1.645369+0.139412
## [142] train-rmse:1.510489+0.051639 test-rmse:1.640120+0.142685
## [143] train-rmse:1.508502+0.051691 test-rmse:1.638048+0.142113
## [144] train-rmse:1.506548+0.051752 test-rmse:1.635384+0.141278
## [145] train-rmse:1.504601+0.051788 test-rmse:1.636525+0.143721
## [146] train-rmse:1.502727+0.051821 test-rmse:1.635279+0.143905
## [147] train-rmse:1.500844+0.051850 test-rmse:1.635059+0.144506
## [148] train-rmse:1.498940+0.051909 test-rmse:1.632368+0.144959
## [149] train-rmse:1.497007+0.051954 test-rmse:1.629833+0.145444
## [150] train-rmse:1.495127+0.051984 test-rmse:1.628889+0.145187
## [151] train-rmse:1.493257+0.052019 test-rmse:1.627538+0.146906
## [152] train-rmse:1.491441+0.052090 test-rmse:1.627720+0.148104
## [153] train-rmse:1.489646+0.052160 test-rmse:1.626985+0.148007
## [154] train-rmse:1.487858+0.052202 test-rmse:1.625987+0.148050
## [155] train-rmse:1.486096+0.052248 test-rmse:1.624454+0.149375
## [156] train-rmse:1.484363+0.052320 test-rmse:1.623914+0.149877
## [157] train-rmse:1.482667+0.052380 test-rmse:1.622564+0.148511
## [158] train-rmse:1.480926+0.052473 test-rmse:1.619713+0.149191
## [159] train-rmse:1.479169+0.052540 test-rmse:1.617651+0.149300
## [160] train-rmse:1.477451+0.052593 test-rmse:1.615796+0.149970
## [161] train-rmse:1.475799+0.052634 test-rmse:1.614274+0.149387
## [162] train-rmse:1.474162+0.052673 test-rmse:1.610869+0.150276
## [163] train-rmse:1.472556+0.052718 test-rmse:1.609053+0.150807
## [164] train-rmse:1.470969+0.052735 test-rmse:1.607453+0.151819
## [165] train-rmse:1.469420+0.052748 test-rmse:1.606789+0.153747
## [166] train-rmse:1.467876+0.052763 test-rmse:1.606058+0.153541
## [167] train-rmse:1.466330+0.052817 test-rmse:1.603844+0.152597
## [168] train-rmse:1.464799+0.052865 test-rmse:1.602982+0.151395
## [169] train-rmse:1.463330+0.052894 test-rmse:1.601333+0.151813
## [170] train-rmse:1.461854+0.052930 test-rmse:1.602180+0.154305
## [171] train-rmse:1.460385+0.052952 test-rmse:1.600057+0.154112
## [172] train-rmse:1.458925+0.052969 test-rmse:1.594971+0.157882
## [173] train-rmse:1.457475+0.052963 test-rmse:1.593706+0.156491
## [174] train-rmse:1.456043+0.052985 test-rmse:1.590478+0.157525
## [175] train-rmse:1.454610+0.053031 test-rmse:1.589042+0.156841
## [176] train-rmse:1.453192+0.053068 test-rmse:1.590823+0.157309
## [177] train-rmse:1.451800+0.053076 test-rmse:1.590228+0.158228
## [178] train-rmse:1.450426+0.053105 test-rmse:1.587654+0.156293
## [179] train-rmse:1.449055+0.053097 test-rmse:1.585909+0.156191
## [180] train-rmse:1.447681+0.053077 test-rmse:1.585660+0.154656
## [181] train-rmse:1.446319+0.053035 test-rmse:1.582782+0.153944
## [182] train-rmse:1.445011+0.053044 test-rmse:1.582036+0.153849
## [183] train-rmse:1.443720+0.053044 test-rmse:1.581936+0.153849
## [184] train-rmse:1.442430+0.053061 test-rmse:1.579991+0.154636
## [185] train-rmse:1.441147+0.053064 test-rmse:1.577859+0.155382
## [186] train-rmse:1.439873+0.053085 test-rmse:1.575380+0.156783
## [187] train-rmse:1.438600+0.053108 test-rmse:1.575373+0.158040
## [188] train-rmse:1.437335+0.053140 test-rmse:1.574843+0.158624
## [189] train-rmse:1.436093+0.053171 test-rmse:1.574301+0.160576
## [190] train-rmse:1.434864+0.053199 test-rmse:1.573716+0.161511
## [191] train-rmse:1.433657+0.053218 test-rmse:1.572276+0.161830
## [192] train-rmse:1.432432+0.053253 test-rmse:1.571029+0.161670
## [193] train-rmse:1.431222+0.053276 test-rmse:1.571745+0.163061
## [194] train-rmse:1.430026+0.053298 test-rmse:1.568861+0.162491
## [195] train-rmse:1.428831+0.053273 test-rmse:1.567003+0.163082
## [196] train-rmse:1.427625+0.053280 test-rmse:1.565961+0.163310
## [197] train-rmse:1.426481+0.053307 test-rmse:1.565507+0.164473
## [198] train-rmse:1.425364+0.053298 test-rmse:1.566435+0.164201
## [199] train-rmse:1.424245+0.053300 test-rmse:1.566620+0.163556
## [200] train-rmse:1.423131+0.053296 test-rmse:1.566009+0.164010
## [201] train-rmse:1.422018+0.053290 test-rmse:1.565310+0.168377
## [202] train-rmse:1.420934+0.053296 test-rmse:1.563652+0.167631
## [203] train-rmse:1.419875+0.053311 test-rmse:1.560428+0.166423
## [204] train-rmse:1.418811+0.053306 test-rmse:1.559765+0.166537
## [205] train-rmse:1.417766+0.053313 test-rmse:1.558822+0.165716
## [206] train-rmse:1.416726+0.053328 test-rmse:1.557391+0.165603
## [207] train-rmse:1.415675+0.053351 test-rmse:1.555207+0.165207
## [208] train-rmse:1.414666+0.053363 test-rmse:1.554580+0.165426
## [209] train-rmse:1.413629+0.053378 test-rmse:1.554222+0.165933
## [210] train-rmse:1.412605+0.053392 test-rmse:1.554214+0.164824
## [211] train-rmse:1.411618+0.053397 test-rmse:1.553888+0.164474
## [212] train-rmse:1.410651+0.053397 test-rmse:1.553353+0.164172
## [213] train-rmse:1.409667+0.053408 test-rmse:1.553754+0.166282
## [214] train-rmse:1.408718+0.053404 test-rmse:1.551328+0.166604
## [215] train-rmse:1.407771+0.053414 test-rmse:1.550961+0.167413
## [216] train-rmse:1.406843+0.053421 test-rmse:1.549503+0.168365
## [217] train-rmse:1.405932+0.053405 test-rmse:1.548177+0.168117
## [218] train-rmse:1.405016+0.053410 test-rmse:1.546506+0.168384
## [219] train-rmse:1.404111+0.053410 test-rmse:1.545660+0.168758
## [220] train-rmse:1.403210+0.053416 test-rmse:1.544745+0.168662
## [221] train-rmse:1.402316+0.053406 test-rmse:1.541308+0.170837
## [222] train-rmse:1.401414+0.053373 test-rmse:1.541097+0.170738
## [223] train-rmse:1.400514+0.053337 test-rmse:1.539670+0.170785
## [224] train-rmse:1.399642+0.053306 test-rmse:1.539111+0.171371
## [225] train-rmse:1.398793+0.053306 test-rmse:1.538460+0.171267
## [226] train-rmse:1.397926+0.053305 test-rmse:1.538788+0.172159
## [227] train-rmse:1.397094+0.053319 test-rmse:1.539328+0.171825
## [228] train-rmse:1.396268+0.053337 test-rmse:1.538887+0.171462
## [229] train-rmse:1.395450+0.053369 test-rmse:1.538963+0.170990
## [230] train-rmse:1.394644+0.053383 test-rmse:1.539492+0.172731
## [231] train-rmse:1.393837+0.053403 test-rmse:1.539464+0.172563
## Stopping. Best iteration:
## [225] train-rmse:1.398793+0.053306 test-rmse:1.538460+0.171267
##
## 43
## [1] train-rmse:7.703024+0.054739 test-rmse:7.699317+0.264931
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.171347+0.043823 test-rmse:6.165040+0.248365
## [3] train-rmse:5.008519+0.035339 test-rmse:5.012505+0.228441
## [4] train-rmse:4.128750+0.028987 test-rmse:4.143591+0.203237
## [5] train-rmse:3.474198+0.024079 test-rmse:3.499772+0.168047
## [6] train-rmse:2.998160+0.021413 test-rmse:3.031620+0.145838
## [7] train-rmse:2.651412+0.022097 test-rmse:2.707955+0.113921
## [8] train-rmse:2.406729+0.019387 test-rmse:2.470054+0.078161
## [9] train-rmse:2.235851+0.017990 test-rmse:2.303046+0.066208
## [10] train-rmse:2.112971+0.019865 test-rmse:2.204879+0.064479
## [11] train-rmse:2.018624+0.026561 test-rmse:2.128414+0.061176
## [12] train-rmse:1.952312+0.023018 test-rmse:2.068860+0.068328
## [13] train-rmse:1.902318+0.022521 test-rmse:2.023933+0.077100
## [14] train-rmse:1.862185+0.021748 test-rmse:2.007046+0.089816
## [15] train-rmse:1.826348+0.027839 test-rmse:1.968621+0.091891
## [16] train-rmse:1.799382+0.027666 test-rmse:1.945418+0.099348
## [17] train-rmse:1.776001+0.027922 test-rmse:1.925342+0.103655
## [18] train-rmse:1.754658+0.029015 test-rmse:1.902883+0.101715
## [19] train-rmse:1.732981+0.029638 test-rmse:1.883194+0.105932
## [20] train-rmse:1.708165+0.033049 test-rmse:1.861597+0.120248
## [21] train-rmse:1.689679+0.033205 test-rmse:1.847015+0.115225
## [22] train-rmse:1.666130+0.038192 test-rmse:1.834188+0.119601
## [23] train-rmse:1.648567+0.037845 test-rmse:1.821788+0.118228
## [24] train-rmse:1.631953+0.038529 test-rmse:1.818361+0.123939
## [25] train-rmse:1.608082+0.036742 test-rmse:1.801782+0.108362
## [26] train-rmse:1.593660+0.037921 test-rmse:1.786699+0.105891
## [27] train-rmse:1.577451+0.038107 test-rmse:1.772968+0.105492
## [28] train-rmse:1.563847+0.038875 test-rmse:1.765526+0.105268
## [29] train-rmse:1.549011+0.040079 test-rmse:1.750110+0.108443
## [30] train-rmse:1.534751+0.041388 test-rmse:1.742546+0.119166
## [31] train-rmse:1.522334+0.042265 test-rmse:1.736626+0.119832
## [32] train-rmse:1.504449+0.045088 test-rmse:1.712088+0.095203
## [33] train-rmse:1.490611+0.044293 test-rmse:1.703099+0.098319
## [34] train-rmse:1.478821+0.045303 test-rmse:1.694909+0.098317
## [35] train-rmse:1.466648+0.043782 test-rmse:1.685041+0.097763
## [36] train-rmse:1.450840+0.048237 test-rmse:1.676049+0.088005
## [37] train-rmse:1.437783+0.048279 test-rmse:1.670364+0.095190
## [38] train-rmse:1.426161+0.048968 test-rmse:1.657469+0.093256
## [39] train-rmse:1.411483+0.049934 test-rmse:1.647682+0.097846
## [40] train-rmse:1.395114+0.052168 test-rmse:1.626116+0.108266
## [41] train-rmse:1.384066+0.051242 test-rmse:1.614175+0.112469
## [42] train-rmse:1.370845+0.054896 test-rmse:1.599147+0.096838
## [43] train-rmse:1.359461+0.054563 test-rmse:1.590931+0.104521
## [44] train-rmse:1.350215+0.055254 test-rmse:1.586535+0.100449
## [45] train-rmse:1.341343+0.056040 test-rmse:1.577715+0.102207
## [46] train-rmse:1.332974+0.056588 test-rmse:1.570405+0.105331
## [47] train-rmse:1.324399+0.057386 test-rmse:1.565831+0.106399
## [48] train-rmse:1.316128+0.056902 test-rmse:1.558150+0.103842
## [49] train-rmse:1.308387+0.057271 test-rmse:1.555403+0.108887
## [50] train-rmse:1.295160+0.061824 test-rmse:1.547845+0.107297
## [51] train-rmse:1.286413+0.062748 test-rmse:1.543639+0.108545
## [52] train-rmse:1.277447+0.063231 test-rmse:1.536813+0.107726
## [53] train-rmse:1.269146+0.062322 test-rmse:1.532451+0.111368
## [54] train-rmse:1.261002+0.061938 test-rmse:1.525818+0.114827
## [55] train-rmse:1.253810+0.062841 test-rmse:1.523319+0.117072
## [56] train-rmse:1.247354+0.063204 test-rmse:1.518811+0.118995
## [57] train-rmse:1.237244+0.064222 test-rmse:1.510573+0.120078
## [58] train-rmse:1.230599+0.064975 test-rmse:1.509387+0.120745
## [59] train-rmse:1.223285+0.065770 test-rmse:1.505422+0.124482
## [60] train-rmse:1.208919+0.050901 test-rmse:1.493993+0.127278
## [61] train-rmse:1.192248+0.046588 test-rmse:1.478500+0.139590
## [62] train-rmse:1.181478+0.050978 test-rmse:1.472519+0.137204
## [63] train-rmse:1.175492+0.051266 test-rmse:1.465443+0.135904
## [64] train-rmse:1.168901+0.051063 test-rmse:1.461039+0.138043
## [65] train-rmse:1.162949+0.051650 test-rmse:1.457911+0.134231
## [66] train-rmse:1.155499+0.050738 test-rmse:1.453961+0.135149
## [67] train-rmse:1.149865+0.050648 test-rmse:1.449052+0.135352
## [68] train-rmse:1.140408+0.051538 test-rmse:1.441096+0.140248
## [69] train-rmse:1.134863+0.052109 test-rmse:1.438175+0.138306
## [70] train-rmse:1.127464+0.052074 test-rmse:1.432698+0.139096
## [71] train-rmse:1.121722+0.051345 test-rmse:1.425748+0.136170
## [72] train-rmse:1.115732+0.052348 test-rmse:1.418664+0.139856
## [73] train-rmse:1.111121+0.052436 test-rmse:1.415121+0.139650
## [74] train-rmse:1.105874+0.052666 test-rmse:1.414079+0.137778
## [75] train-rmse:1.101266+0.053043 test-rmse:1.413689+0.141552
## [76] train-rmse:1.096322+0.053221 test-rmse:1.413300+0.142867
## [77] train-rmse:1.092326+0.053302 test-rmse:1.410481+0.142721
## [78] train-rmse:1.081020+0.042907 test-rmse:1.405635+0.147843
## [79] train-rmse:1.076517+0.042434 test-rmse:1.405102+0.147100
## [80] train-rmse:1.072171+0.042144 test-rmse:1.403677+0.151349
## [81] train-rmse:1.063344+0.041090 test-rmse:1.391641+0.140980
## [82] train-rmse:1.055296+0.041193 test-rmse:1.385493+0.137556
## [83] train-rmse:1.050891+0.041105 test-rmse:1.382149+0.136043
## [84] train-rmse:1.047245+0.041554 test-rmse:1.379406+0.137855
## [85] train-rmse:1.042702+0.040939 test-rmse:1.377381+0.141098
## [86] train-rmse:1.033350+0.034589 test-rmse:1.371193+0.143633
## [87] train-rmse:1.029019+0.034554 test-rmse:1.366965+0.146714
## [88] train-rmse:1.024612+0.034293 test-rmse:1.365870+0.144992
## [89] train-rmse:1.019496+0.033507 test-rmse:1.363681+0.146043
## [90] train-rmse:1.015856+0.033611 test-rmse:1.359452+0.144576
## [91] train-rmse:1.012507+0.033655 test-rmse:1.358525+0.142754
## [92] train-rmse:1.008339+0.033619 test-rmse:1.357396+0.147079
## [93] train-rmse:1.004667+0.033164 test-rmse:1.354552+0.148390
## [94] train-rmse:0.997567+0.037305 test-rmse:1.352288+0.145247
## [95] train-rmse:0.993684+0.037544 test-rmse:1.350843+0.144419
## [96] train-rmse:0.987695+0.039017 test-rmse:1.341837+0.138554
## [97] train-rmse:0.984876+0.039168 test-rmse:1.338212+0.140433
## [98] train-rmse:0.981574+0.038795 test-rmse:1.336704+0.141325
## [99] train-rmse:0.975464+0.039425 test-rmse:1.332700+0.141517
## [100] train-rmse:0.969492+0.039135 test-rmse:1.327120+0.145485
## [101] train-rmse:0.964368+0.042433 test-rmse:1.325496+0.145658
## [102] train-rmse:0.959193+0.041247 test-rmse:1.314438+0.150056
## [103] train-rmse:0.955348+0.041054 test-rmse:1.315463+0.149248
## [104] train-rmse:0.952566+0.041093 test-rmse:1.315620+0.149341
## [105] train-rmse:0.949600+0.040586 test-rmse:1.313118+0.150935
## [106] train-rmse:0.946719+0.040081 test-rmse:1.313729+0.150633
## [107] train-rmse:0.943972+0.039969 test-rmse:1.312621+0.148993
## [108] train-rmse:0.941150+0.039438 test-rmse:1.312200+0.148634
## [109] train-rmse:0.938671+0.039598 test-rmse:1.311071+0.147857
## [110] train-rmse:0.936290+0.039843 test-rmse:1.309381+0.148325
## [111] train-rmse:0.933740+0.039975 test-rmse:1.309004+0.150840
## [112] train-rmse:0.930771+0.040184 test-rmse:1.307778+0.151942
## [113] train-rmse:0.928718+0.040139 test-rmse:1.306606+0.152397
## [114] train-rmse:0.926336+0.039628 test-rmse:1.307763+0.152616
## [115] train-rmse:0.920149+0.033974 test-rmse:1.306133+0.154464
## [116] train-rmse:0.914856+0.031777 test-rmse:1.304264+0.156639
## [117] train-rmse:0.912465+0.031915 test-rmse:1.304029+0.158889
## [118] train-rmse:0.910198+0.032377 test-rmse:1.301288+0.158933
## [119] train-rmse:0.907523+0.032105 test-rmse:1.301793+0.159388
## [120] train-rmse:0.902601+0.033194 test-rmse:1.294685+0.154833
## [121] train-rmse:0.899545+0.032394 test-rmse:1.292524+0.156559
## [122] train-rmse:0.897235+0.032430 test-rmse:1.289956+0.156528
## [123] train-rmse:0.893768+0.034629 test-rmse:1.289440+0.155622
## [124] train-rmse:0.892014+0.034683 test-rmse:1.287809+0.156350
## [125] train-rmse:0.890046+0.034312 test-rmse:1.286334+0.158006
## [126] train-rmse:0.887879+0.034688 test-rmse:1.287535+0.158599
## [127] train-rmse:0.884999+0.034746 test-rmse:1.286944+0.157498
## [128] train-rmse:0.881617+0.037338 test-rmse:1.286138+0.156948
## [129] train-rmse:0.880018+0.037348 test-rmse:1.286299+0.157127
## [130] train-rmse:0.876065+0.041078 test-rmse:1.281779+0.150808
## [131] train-rmse:0.874154+0.040984 test-rmse:1.281076+0.152586
## [132] train-rmse:0.870545+0.039449 test-rmse:1.279407+0.155066
## [133] train-rmse:0.868729+0.039698 test-rmse:1.279045+0.155428
## [134] train-rmse:0.864320+0.038051 test-rmse:1.276639+0.156672
## [135] train-rmse:0.862476+0.038082 test-rmse:1.275520+0.157432
## [136] train-rmse:0.858880+0.038111 test-rmse:1.273103+0.157074
## [137] train-rmse:0.855946+0.037406 test-rmse:1.273764+0.156281
## [138] train-rmse:0.854213+0.037575 test-rmse:1.273215+0.157105
## [139] train-rmse:0.852777+0.037546 test-rmse:1.274257+0.156132
## [140] train-rmse:0.851420+0.037576 test-rmse:1.273406+0.155812
## [141] train-rmse:0.849579+0.037714 test-rmse:1.274373+0.157148
## [142] train-rmse:0.848066+0.037677 test-rmse:1.273489+0.158273
## Stopping. Best iteration:
## [136] train-rmse:0.858880+0.038111 test-rmse:1.273103+0.157074
##
## 44
## [1] train-rmse:7.692030+0.055948 test-rmse:7.688607+0.259158
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.143300+0.041962 test-rmse:6.139013+0.245130
## [3] train-rmse:4.958204+0.033934 test-rmse:4.961825+0.229426
## [4] train-rmse:4.055561+0.028603 test-rmse:4.064797+0.199284
## [5] train-rmse:3.379000+0.026274 test-rmse:3.414383+0.181632
## [6] train-rmse:2.870117+0.020099 test-rmse:2.913240+0.152835
## [7] train-rmse:2.493488+0.015763 test-rmse:2.566059+0.118866
## [8] train-rmse:2.222850+0.015093 test-rmse:2.318615+0.096409
## [9] train-rmse:2.023179+0.013593 test-rmse:2.151534+0.089392
## [10] train-rmse:1.881256+0.015625 test-rmse:2.028942+0.093390
## [11] train-rmse:1.774667+0.017457 test-rmse:1.938239+0.091133
## [12] train-rmse:1.695079+0.018401 test-rmse:1.880289+0.083098
## [13] train-rmse:1.634157+0.017765 test-rmse:1.839751+0.082886
## [14] train-rmse:1.584708+0.020196 test-rmse:1.799173+0.074788
## [15] train-rmse:1.540129+0.020844 test-rmse:1.776246+0.070880
## [16] train-rmse:1.505098+0.023358 test-rmse:1.745131+0.074428
## [17] train-rmse:1.473128+0.024615 test-rmse:1.722887+0.079555
## [18] train-rmse:1.445472+0.025370 test-rmse:1.704534+0.079766
## [19] train-rmse:1.420218+0.027353 test-rmse:1.690063+0.077661
## [20] train-rmse:1.394634+0.026282 test-rmse:1.664939+0.077473
## [21] train-rmse:1.371069+0.028546 test-rmse:1.652158+0.071944
## [22] train-rmse:1.349121+0.027691 test-rmse:1.634980+0.081828
## [23] train-rmse:1.328317+0.026572 test-rmse:1.626410+0.085379
## [24] train-rmse:1.308833+0.026355 test-rmse:1.612435+0.094387
## [25] train-rmse:1.291656+0.027470 test-rmse:1.599768+0.095777
## [26] train-rmse:1.273709+0.026220 test-rmse:1.589589+0.094965
## [27] train-rmse:1.256029+0.026774 test-rmse:1.579442+0.096504
## [28] train-rmse:1.237160+0.029074 test-rmse:1.559739+0.099306
## [29] train-rmse:1.219046+0.029821 test-rmse:1.540902+0.084039
## [30] train-rmse:1.200916+0.027802 test-rmse:1.535139+0.090372
## [31] train-rmse:1.184587+0.026470 test-rmse:1.522659+0.099611
## [32] train-rmse:1.168094+0.026565 test-rmse:1.513946+0.099286
## [33] train-rmse:1.151271+0.024535 test-rmse:1.506172+0.100635
## [34] train-rmse:1.135221+0.019743 test-rmse:1.495455+0.102784
## [35] train-rmse:1.122976+0.020439 test-rmse:1.484945+0.106651
## [36] train-rmse:1.109123+0.020674 test-rmse:1.471607+0.107357
## [37] train-rmse:1.095945+0.019917 test-rmse:1.465030+0.108558
## [38] train-rmse:1.078820+0.020051 test-rmse:1.454108+0.112559
## [39] train-rmse:1.064705+0.023666 test-rmse:1.448565+0.118896
## [40] train-rmse:1.054084+0.025431 test-rmse:1.441064+0.120128
## [41] train-rmse:1.041830+0.023495 test-rmse:1.437406+0.121823
## [42] train-rmse:1.029745+0.022840 test-rmse:1.432676+0.123811
## [43] train-rmse:1.018544+0.022114 test-rmse:1.428467+0.121204
## [44] train-rmse:1.008446+0.022855 test-rmse:1.421818+0.123673
## [45] train-rmse:0.998727+0.022991 test-rmse:1.416381+0.126315
## [46] train-rmse:0.988007+0.022071 test-rmse:1.412279+0.128813
## [47] train-rmse:0.977583+0.022447 test-rmse:1.405294+0.133648
## [48] train-rmse:0.967128+0.022882 test-rmse:1.399677+0.136785
## [49] train-rmse:0.960120+0.022900 test-rmse:1.397744+0.136378
## [50] train-rmse:0.950929+0.023226 test-rmse:1.388564+0.139666
## [51] train-rmse:0.941283+0.022201 test-rmse:1.383424+0.137874
## [52] train-rmse:0.933206+0.021630 test-rmse:1.381107+0.138824
## [53] train-rmse:0.924919+0.021396 test-rmse:1.372570+0.139077
## [54] train-rmse:0.914909+0.020259 test-rmse:1.373894+0.139265
## [55] train-rmse:0.908997+0.020376 test-rmse:1.372420+0.142422
## [56] train-rmse:0.898456+0.025730 test-rmse:1.365840+0.142898
## [57] train-rmse:0.890923+0.028222 test-rmse:1.361198+0.143230
## [58] train-rmse:0.884080+0.028347 test-rmse:1.354797+0.144918
## [59] train-rmse:0.876793+0.029254 test-rmse:1.353147+0.147734
## [60] train-rmse:0.869645+0.028343 test-rmse:1.354688+0.146729
## [61] train-rmse:0.859685+0.026515 test-rmse:1.348343+0.146392
## [62] train-rmse:0.853558+0.026228 test-rmse:1.347572+0.146800
## [63] train-rmse:0.846506+0.025099 test-rmse:1.346548+0.150448
## [64] train-rmse:0.838361+0.024644 test-rmse:1.344746+0.149290
## [65] train-rmse:0.832384+0.024885 test-rmse:1.342827+0.151402
## [66] train-rmse:0.826187+0.025051 test-rmse:1.340452+0.152784
## [67] train-rmse:0.821123+0.025086 test-rmse:1.338326+0.152318
## [68] train-rmse:0.814593+0.022977 test-rmse:1.336505+0.154949
## [69] train-rmse:0.804980+0.025330 test-rmse:1.334753+0.155704
## [70] train-rmse:0.797952+0.027528 test-rmse:1.329108+0.151921
## [71] train-rmse:0.792553+0.027981 test-rmse:1.327564+0.152046
## [72] train-rmse:0.787496+0.027216 test-rmse:1.325853+0.151646
## [73] train-rmse:0.782977+0.027876 test-rmse:1.323391+0.151784
## [74] train-rmse:0.779371+0.027792 test-rmse:1.322843+0.152676
## [75] train-rmse:0.774457+0.027674 test-rmse:1.320533+0.153561
## [76] train-rmse:0.768464+0.028596 test-rmse:1.320174+0.154796
## [77] train-rmse:0.763416+0.028725 test-rmse:1.321828+0.157922
## [78] train-rmse:0.758239+0.030995 test-rmse:1.319799+0.156776
## [79] train-rmse:0.753414+0.031191 test-rmse:1.319840+0.155406
## [80] train-rmse:0.750235+0.031519 test-rmse:1.319125+0.156293
## [81] train-rmse:0.746532+0.030912 test-rmse:1.318570+0.157850
## [82] train-rmse:0.741374+0.035268 test-rmse:1.316463+0.158482
## [83] train-rmse:0.737597+0.034929 test-rmse:1.317344+0.159664
## [84] train-rmse:0.734132+0.035015 test-rmse:1.317117+0.160618
## [85] train-rmse:0.727466+0.032554 test-rmse:1.310365+0.154935
## [86] train-rmse:0.722888+0.031658 test-rmse:1.310904+0.154127
## [87] train-rmse:0.719137+0.032190 test-rmse:1.307205+0.153648
## [88] train-rmse:0.715192+0.032629 test-rmse:1.306422+0.154108
## [89] train-rmse:0.708191+0.031834 test-rmse:1.306035+0.153935
## [90] train-rmse:0.705334+0.032397 test-rmse:1.307942+0.154877
## [91] train-rmse:0.702204+0.032342 test-rmse:1.308506+0.155020
## [92] train-rmse:0.698376+0.032092 test-rmse:1.308700+0.155564
## [93] train-rmse:0.695256+0.033301 test-rmse:1.307766+0.155213
## [94] train-rmse:0.692014+0.033314 test-rmse:1.308353+0.154720
## [95] train-rmse:0.688639+0.035016 test-rmse:1.307413+0.154449
## Stopping. Best iteration:
## [89] train-rmse:0.708191+0.031834 test-rmse:1.306035+0.153935
##
## 45
## [1] train-rmse:7.687516+0.056427 test-rmse:7.684084+0.260706
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.132698+0.043409 test-rmse:6.141381+0.245607
## [3] train-rmse:4.938933+0.032580 test-rmse:4.946127+0.222311
## [4] train-rmse:4.027196+0.027880 test-rmse:4.056770+0.194184
## [5] train-rmse:3.328452+0.019849 test-rmse:3.380152+0.172477
## [6] train-rmse:2.805357+0.016531 test-rmse:2.877283+0.150335
## [7] train-rmse:2.413657+0.019260 test-rmse:2.507635+0.123033
## [8] train-rmse:2.118629+0.015574 test-rmse:2.237319+0.114479
## [9] train-rmse:1.905025+0.012696 test-rmse:2.059239+0.098461
## [10] train-rmse:1.746832+0.013933 test-rmse:1.922743+0.095107
## [11] train-rmse:1.625115+0.014779 test-rmse:1.829396+0.090194
## [12] train-rmse:1.532288+0.019077 test-rmse:1.769274+0.079815
## [13] train-rmse:1.459724+0.023076 test-rmse:1.727130+0.075369
## [14] train-rmse:1.404335+0.025822 test-rmse:1.692266+0.071078
## [15] train-rmse:1.354087+0.026766 test-rmse:1.654612+0.079749
## [16] train-rmse:1.309221+0.030797 test-rmse:1.620144+0.082499
## [17] train-rmse:1.274007+0.031895 test-rmse:1.596008+0.083512
## [18] train-rmse:1.240788+0.032966 test-rmse:1.574254+0.098709
## [19] train-rmse:1.208260+0.028287 test-rmse:1.549089+0.104512
## [20] train-rmse:1.178163+0.030634 test-rmse:1.533650+0.108621
## [21] train-rmse:1.150222+0.030854 test-rmse:1.515889+0.111190
## [22] train-rmse:1.127846+0.028415 test-rmse:1.497100+0.116467
## [23] train-rmse:1.102317+0.028485 test-rmse:1.486899+0.127721
## [24] train-rmse:1.078366+0.026175 test-rmse:1.471140+0.126318
## [25] train-rmse:1.055042+0.027127 test-rmse:1.456622+0.124693
## [26] train-rmse:1.035031+0.026508 test-rmse:1.447845+0.131531
## [27] train-rmse:1.017005+0.027292 test-rmse:1.434391+0.134772
## [28] train-rmse:0.996509+0.030659 test-rmse:1.424342+0.134595
## [29] train-rmse:0.979604+0.032525 test-rmse:1.412862+0.137675
## [30] train-rmse:0.962387+0.029104 test-rmse:1.408998+0.141296
## [31] train-rmse:0.945152+0.029888 test-rmse:1.402947+0.146611
## [32] train-rmse:0.928619+0.027276 test-rmse:1.397829+0.152012
## [33] train-rmse:0.915637+0.026384 test-rmse:1.388780+0.157522
## [34] train-rmse:0.898764+0.025426 test-rmse:1.378566+0.160955
## [35] train-rmse:0.885154+0.026622 test-rmse:1.371819+0.167398
## [36] train-rmse:0.869296+0.024665 test-rmse:1.358975+0.161478
## [37] train-rmse:0.857500+0.024260 test-rmse:1.354535+0.166957
## [38] train-rmse:0.846904+0.024365 test-rmse:1.344779+0.170436
## [39] train-rmse:0.835943+0.024960 test-rmse:1.349132+0.174675
## [40] train-rmse:0.826381+0.025081 test-rmse:1.346824+0.173558
## [41] train-rmse:0.815871+0.026532 test-rmse:1.343515+0.176440
## [42] train-rmse:0.803851+0.024970 test-rmse:1.337550+0.179274
## [43] train-rmse:0.791263+0.023272 test-rmse:1.328975+0.183000
## [44] train-rmse:0.781405+0.024475 test-rmse:1.323099+0.182205
## [45] train-rmse:0.772409+0.023381 test-rmse:1.320596+0.185120
## [46] train-rmse:0.759778+0.021389 test-rmse:1.318560+0.187469
## [47] train-rmse:0.750138+0.021748 test-rmse:1.314014+0.185587
## [48] train-rmse:0.739631+0.022650 test-rmse:1.311914+0.182404
## [49] train-rmse:0.730907+0.021336 test-rmse:1.307676+0.183277
## [50] train-rmse:0.722534+0.019563 test-rmse:1.304008+0.183411
## [51] train-rmse:0.713452+0.018370 test-rmse:1.303587+0.182307
## [52] train-rmse:0.704462+0.018811 test-rmse:1.304244+0.181942
## [53] train-rmse:0.697311+0.016428 test-rmse:1.303024+0.181743
## [54] train-rmse:0.688458+0.017997 test-rmse:1.304331+0.183252
## [55] train-rmse:0.680909+0.018287 test-rmse:1.299389+0.188025
## [56] train-rmse:0.674341+0.017891 test-rmse:1.298374+0.189749
## [57] train-rmse:0.668169+0.019101 test-rmse:1.297828+0.190764
## [58] train-rmse:0.661189+0.019535 test-rmse:1.296919+0.193447
## [59] train-rmse:0.654986+0.018646 test-rmse:1.294289+0.196536
## [60] train-rmse:0.647496+0.020076 test-rmse:1.292535+0.198078
## [61] train-rmse:0.642082+0.020126 test-rmse:1.293818+0.197729
## [62] train-rmse:0.634971+0.021223 test-rmse:1.290405+0.199241
## [63] train-rmse:0.629496+0.021889 test-rmse:1.290338+0.196154
## [64] train-rmse:0.624189+0.021519 test-rmse:1.291174+0.197509
## [65] train-rmse:0.618731+0.022645 test-rmse:1.292297+0.195919
## [66] train-rmse:0.614120+0.021673 test-rmse:1.287403+0.195338
## [67] train-rmse:0.607774+0.020772 test-rmse:1.283513+0.198328
## [68] train-rmse:0.601880+0.023613 test-rmse:1.284079+0.196236
## [69] train-rmse:0.597687+0.023298 test-rmse:1.283755+0.196519
## [70] train-rmse:0.590885+0.020800 test-rmse:1.281130+0.193370
## [71] train-rmse:0.585095+0.023008 test-rmse:1.281126+0.193535
## [72] train-rmse:0.580345+0.022173 test-rmse:1.280637+0.192267
## [73] train-rmse:0.576830+0.023366 test-rmse:1.281902+0.193656
## [74] train-rmse:0.572938+0.023290 test-rmse:1.281562+0.193789
## [75] train-rmse:0.568455+0.024261 test-rmse:1.280652+0.195334
## [76] train-rmse:0.562862+0.023813 test-rmse:1.278593+0.193150
## [77] train-rmse:0.556613+0.024339 test-rmse:1.272420+0.192318
## [78] train-rmse:0.552431+0.023956 test-rmse:1.271516+0.191624
## [79] train-rmse:0.546817+0.025197 test-rmse:1.268521+0.191113
## [80] train-rmse:0.543147+0.025504 test-rmse:1.270011+0.190513
## [81] train-rmse:0.538832+0.025241 test-rmse:1.271529+0.190061
## [82] train-rmse:0.535267+0.025669 test-rmse:1.271494+0.187938
## [83] train-rmse:0.532528+0.025653 test-rmse:1.271399+0.188297
## [84] train-rmse:0.527957+0.027697 test-rmse:1.270602+0.188760
## [85] train-rmse:0.523616+0.028190 test-rmse:1.268878+0.187513
## Stopping. Best iteration:
## [79] train-rmse:0.546817+0.025197 test-rmse:1.268521+0.191113
##
## 46
## [1] train-rmse:7.683741+0.056806 test-rmse:7.684318+0.261247
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.123049+0.044409 test-rmse:6.137570+0.237632
## [3] train-rmse:4.921186+0.034497 test-rmse:4.940935+0.213195
## [4] train-rmse:3.999049+0.028976 test-rmse:4.039040+0.175680
## [5] train-rmse:3.291077+0.027009 test-rmse:3.335051+0.149643
## [6] train-rmse:2.744199+0.018622 test-rmse:2.809741+0.136629
## [7] train-rmse:2.336310+0.015428 test-rmse:2.440036+0.123379
## [8] train-rmse:2.028182+0.014498 test-rmse:2.179662+0.102030
## [9] train-rmse:1.798593+0.014614 test-rmse:1.980756+0.107286
## [10] train-rmse:1.624269+0.015874 test-rmse:1.840292+0.090520
## [11] train-rmse:1.495209+0.020475 test-rmse:1.737619+0.085997
## [12] train-rmse:1.393985+0.019521 test-rmse:1.684419+0.071335
## [13] train-rmse:1.311168+0.024528 test-rmse:1.619724+0.075001
## [14] train-rmse:1.246151+0.026066 test-rmse:1.569822+0.067849
## [15] train-rmse:1.189597+0.020934 test-rmse:1.541094+0.081192
## [16] train-rmse:1.140481+0.023190 test-rmse:1.504171+0.088204
## [17] train-rmse:1.100841+0.024947 test-rmse:1.480592+0.080215
## [18] train-rmse:1.067816+0.024172 test-rmse:1.463039+0.087893
## [19] train-rmse:1.032700+0.025464 test-rmse:1.439979+0.095750
## [20] train-rmse:1.004372+0.026420 test-rmse:1.421944+0.096166
## [21] train-rmse:0.976366+0.023654 test-rmse:1.413497+0.105827
## [22] train-rmse:0.949906+0.024967 test-rmse:1.401173+0.112587
## [23] train-rmse:0.922983+0.023167 test-rmse:1.391765+0.119575
## [24] train-rmse:0.898892+0.026152 test-rmse:1.381453+0.117908
## [25] train-rmse:0.876807+0.023058 test-rmse:1.366877+0.123121
## [26] train-rmse:0.857374+0.024236 test-rmse:1.361216+0.123849
## [27] train-rmse:0.837584+0.022092 test-rmse:1.346830+0.130466
## [28] train-rmse:0.819093+0.019577 test-rmse:1.344101+0.130582
## [29] train-rmse:0.797924+0.020733 test-rmse:1.335730+0.136845
## [30] train-rmse:0.782442+0.021568 test-rmse:1.326490+0.137543
## [31] train-rmse:0.766970+0.021221 test-rmse:1.322619+0.136922
## [32] train-rmse:0.750832+0.018825 test-rmse:1.317333+0.140936
## [33] train-rmse:0.738895+0.018214 test-rmse:1.312829+0.140869
## [34] train-rmse:0.723829+0.017194 test-rmse:1.306440+0.141756
## [35] train-rmse:0.708411+0.019610 test-rmse:1.297099+0.143395
## [36] train-rmse:0.696048+0.019614 test-rmse:1.292647+0.147639
## [37] train-rmse:0.683796+0.019154 test-rmse:1.287603+0.154361
## [38] train-rmse:0.669724+0.019137 test-rmse:1.280416+0.155819
## [39] train-rmse:0.656393+0.019444 test-rmse:1.273332+0.151484
## [40] train-rmse:0.645906+0.018110 test-rmse:1.272505+0.153168
## [41] train-rmse:0.635382+0.017179 test-rmse:1.269427+0.158786
## [42] train-rmse:0.626967+0.018665 test-rmse:1.267630+0.157701
## [43] train-rmse:0.618428+0.018871 test-rmse:1.264583+0.158256
## [44] train-rmse:0.608991+0.018912 test-rmse:1.259403+0.159271
## [45] train-rmse:0.598279+0.019131 test-rmse:1.257275+0.159020
## [46] train-rmse:0.590506+0.019108 test-rmse:1.255629+0.159484
## [47] train-rmse:0.581217+0.021013 test-rmse:1.254956+0.160712
## [48] train-rmse:0.573860+0.021524 test-rmse:1.257701+0.158388
## [49] train-rmse:0.564169+0.022465 test-rmse:1.254156+0.161588
## [50] train-rmse:0.554826+0.022478 test-rmse:1.252123+0.165692
## [51] train-rmse:0.547643+0.021524 test-rmse:1.253650+0.166856
## [52] train-rmse:0.540772+0.023216 test-rmse:1.249745+0.168264
## [53] train-rmse:0.532892+0.022725 test-rmse:1.248214+0.168757
## [54] train-rmse:0.526402+0.021379 test-rmse:1.244763+0.171458
## [55] train-rmse:0.517163+0.020610 test-rmse:1.246405+0.169353
## [56] train-rmse:0.511957+0.021409 test-rmse:1.247726+0.169035
## [57] train-rmse:0.504485+0.019572 test-rmse:1.248644+0.169555
## [58] train-rmse:0.498298+0.018686 test-rmse:1.245596+0.170771
## [59] train-rmse:0.493682+0.019208 test-rmse:1.244627+0.171350
## [60] train-rmse:0.488402+0.018905 test-rmse:1.244556+0.174257
## [61] train-rmse:0.482179+0.019057 test-rmse:1.243824+0.174569
## [62] train-rmse:0.475708+0.018351 test-rmse:1.243934+0.172098
## [63] train-rmse:0.469747+0.017294 test-rmse:1.245930+0.172576
## [64] train-rmse:0.463930+0.018978 test-rmse:1.243794+0.173301
## [65] train-rmse:0.458365+0.017890 test-rmse:1.242412+0.172314
## [66] train-rmse:0.453955+0.017113 test-rmse:1.241520+0.171238
## [67] train-rmse:0.447048+0.015016 test-rmse:1.239486+0.172712
## [68] train-rmse:0.442420+0.014466 test-rmse:1.239626+0.171133
## [69] train-rmse:0.439013+0.014380 test-rmse:1.239917+0.171912
## [70] train-rmse:0.433706+0.014675 test-rmse:1.239613+0.172467
## [71] train-rmse:0.427587+0.015886 test-rmse:1.238516+0.173025
## [72] train-rmse:0.424827+0.016163 test-rmse:1.238627+0.172049
## [73] train-rmse:0.419601+0.016125 test-rmse:1.238589+0.171864
## [74] train-rmse:0.414681+0.015534 test-rmse:1.238981+0.173196
## [75] train-rmse:0.410232+0.016847 test-rmse:1.238318+0.173495
## [76] train-rmse:0.406372+0.016257 test-rmse:1.239679+0.175160
## [77] train-rmse:0.402556+0.015687 test-rmse:1.239175+0.174749
## [78] train-rmse:0.399048+0.017146 test-rmse:1.238737+0.172421
## [79] train-rmse:0.393840+0.016318 test-rmse:1.235702+0.171355
## [80] train-rmse:0.390910+0.016317 test-rmse:1.235485+0.171001
## [81] train-rmse:0.385288+0.015360 test-rmse:1.235539+0.171207
## [82] train-rmse:0.380250+0.015417 test-rmse:1.235680+0.172469
## [83] train-rmse:0.376526+0.016559 test-rmse:1.235529+0.171740
## [84] train-rmse:0.372437+0.016398 test-rmse:1.236132+0.171496
## [85] train-rmse:0.369195+0.016186 test-rmse:1.235999+0.170243
## [86] train-rmse:0.366713+0.016894 test-rmse:1.235718+0.169461
## Stopping. Best iteration:
## [80] train-rmse:0.390910+0.016317 test-rmse:1.235485+0.171001
##
## 47
## [1] train-rmse:7.682904+0.057008 test-rmse:7.685049+0.254248
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.117735+0.044927 test-rmse:6.133432+0.233998
## [3] train-rmse:4.909099+0.035664 test-rmse:4.930035+0.191705
## [4] train-rmse:3.981423+0.028736 test-rmse:4.021556+0.169466
## [5] train-rmse:3.260318+0.024109 test-rmse:3.305261+0.151856
## [6] train-rmse:2.705827+0.015735 test-rmse:2.777445+0.147689
## [7] train-rmse:2.283029+0.017601 test-rmse:2.394513+0.115966
## [8] train-rmse:1.965492+0.014779 test-rmse:2.125365+0.107224
## [9] train-rmse:1.718708+0.016769 test-rmse:1.909055+0.105872
## [10] train-rmse:1.527333+0.023822 test-rmse:1.749064+0.093374
## [11] train-rmse:1.384785+0.022270 test-rmse:1.640983+0.083748
## [12] train-rmse:1.271087+0.023864 test-rmse:1.560045+0.077635
## [13] train-rmse:1.181841+0.018811 test-rmse:1.499723+0.090713
## [14] train-rmse:1.109848+0.020247 test-rmse:1.462590+0.089518
## [15] train-rmse:1.050077+0.022357 test-rmse:1.427534+0.092897
## [16] train-rmse:1.003081+0.021917 test-rmse:1.404281+0.106486
## [17] train-rmse:0.956956+0.024536 test-rmse:1.379620+0.107533
## [18] train-rmse:0.918960+0.023852 test-rmse:1.354788+0.116121
## [19] train-rmse:0.883841+0.027029 test-rmse:1.335987+0.117794
## [20] train-rmse:0.852732+0.023720 test-rmse:1.328131+0.118647
## [21] train-rmse:0.827165+0.024619 test-rmse:1.318677+0.125257
## [22] train-rmse:0.799338+0.024996 test-rmse:1.308221+0.127316
## [23] train-rmse:0.777713+0.024995 test-rmse:1.296024+0.133796
## [24] train-rmse:0.756595+0.026098 test-rmse:1.285111+0.136125
## [25] train-rmse:0.731962+0.024763 test-rmse:1.274442+0.133724
## [26] train-rmse:0.713125+0.024063 test-rmse:1.271920+0.138173
## [27] train-rmse:0.693962+0.020139 test-rmse:1.270282+0.147478
## [28] train-rmse:0.675258+0.019339 test-rmse:1.264551+0.145312
## [29] train-rmse:0.658410+0.020930 test-rmse:1.254169+0.145022
## [30] train-rmse:0.642354+0.020436 test-rmse:1.245785+0.146262
## [31] train-rmse:0.630706+0.019658 test-rmse:1.249473+0.145682
## [32] train-rmse:0.613616+0.017848 test-rmse:1.245947+0.146612
## [33] train-rmse:0.599348+0.018859 test-rmse:1.243592+0.143598
## [34] train-rmse:0.584677+0.019550 test-rmse:1.237813+0.145805
## [35] train-rmse:0.574232+0.020790 test-rmse:1.234378+0.149450
## [36] train-rmse:0.563758+0.017551 test-rmse:1.233326+0.149090
## [37] train-rmse:0.552254+0.018478 test-rmse:1.227009+0.150994
## [38] train-rmse:0.541396+0.019612 test-rmse:1.225039+0.153922
## [39] train-rmse:0.532269+0.021050 test-rmse:1.224603+0.153895
## [40] train-rmse:0.520591+0.020648 test-rmse:1.223043+0.153652
## [41] train-rmse:0.511188+0.017941 test-rmse:1.221451+0.157500
## [42] train-rmse:0.501407+0.019836 test-rmse:1.220071+0.156303
## [43] train-rmse:0.493454+0.020099 test-rmse:1.217135+0.156892
## [44] train-rmse:0.483489+0.019172 test-rmse:1.214359+0.158560
## [45] train-rmse:0.474136+0.016880 test-rmse:1.211311+0.158835
## [46] train-rmse:0.464390+0.016348 test-rmse:1.211861+0.158429
## [47] train-rmse:0.455061+0.016626 test-rmse:1.211155+0.159185
## [48] train-rmse:0.448994+0.016212 test-rmse:1.211460+0.162386
## [49] train-rmse:0.442588+0.016263 test-rmse:1.207681+0.160379
## [50] train-rmse:0.436885+0.016191 test-rmse:1.208078+0.162173
## [51] train-rmse:0.429533+0.017825 test-rmse:1.205187+0.162039
## [52] train-rmse:0.421527+0.015034 test-rmse:1.204715+0.163453
## [53] train-rmse:0.415900+0.016442 test-rmse:1.204079+0.161818
## [54] train-rmse:0.410680+0.016252 test-rmse:1.202907+0.160987
## [55] train-rmse:0.404237+0.015997 test-rmse:1.201325+0.159816
## [56] train-rmse:0.398991+0.017547 test-rmse:1.201816+0.161125
## [57] train-rmse:0.393616+0.018201 test-rmse:1.201894+0.159642
## [58] train-rmse:0.387720+0.018418 test-rmse:1.203116+0.158470
## [59] train-rmse:0.383533+0.018136 test-rmse:1.202230+0.158378
## [60] train-rmse:0.379716+0.018603 test-rmse:1.202743+0.159397
## [61] train-rmse:0.372921+0.017584 test-rmse:1.201657+0.159949
## Stopping. Best iteration:
## [55] train-rmse:0.404237+0.015997 test-rmse:1.201325+0.159816
##
## 48
## [1] train-rmse:7.682904+0.057008 test-rmse:7.685049+0.254248
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:6.115531+0.045206 test-rmse:6.136740+0.225061
## [3] train-rmse:4.902681+0.036116 test-rmse:4.928370+0.183833
## [4] train-rmse:3.963773+0.028263 test-rmse:4.004956+0.168973
## [5] train-rmse:3.237120+0.025891 test-rmse:3.289500+0.142193
## [6] train-rmse:2.680009+0.021937 test-rmse:2.768703+0.127255
## [7] train-rmse:2.247840+0.018881 test-rmse:2.376366+0.117090
## [8] train-rmse:1.920674+0.018563 test-rmse:2.087498+0.107437
## [9] train-rmse:1.661398+0.021451 test-rmse:1.865397+0.080248
## [10] train-rmse:1.463352+0.017866 test-rmse:1.723682+0.086188
## [11] train-rmse:1.307895+0.021031 test-rmse:1.612448+0.081761
## [12] train-rmse:1.185086+0.022369 test-rmse:1.527452+0.092719
## [13] train-rmse:1.087735+0.020376 test-rmse:1.474137+0.092990
## [14] train-rmse:1.014872+0.017792 test-rmse:1.425301+0.102070
## [15] train-rmse:0.949945+0.015683 test-rmse:1.401331+0.116787
## [16] train-rmse:0.891209+0.020331 test-rmse:1.367549+0.113139
## [17] train-rmse:0.847273+0.019443 test-rmse:1.352776+0.116726
## [18] train-rmse:0.807352+0.019444 test-rmse:1.337888+0.119390
## [19] train-rmse:0.768977+0.021109 test-rmse:1.322545+0.127258
## [20] train-rmse:0.737204+0.013327 test-rmse:1.312870+0.134545
## [21] train-rmse:0.709944+0.011189 test-rmse:1.302529+0.140467
## [22] train-rmse:0.682847+0.013379 test-rmse:1.292522+0.143370
## [23] train-rmse:0.656574+0.012560 test-rmse:1.280291+0.142719
## [24] train-rmse:0.635273+0.013546 test-rmse:1.277245+0.142611
## [25] train-rmse:0.614527+0.010825 test-rmse:1.270238+0.144828
## [26] train-rmse:0.595864+0.012528 test-rmse:1.261235+0.147830
## [27] train-rmse:0.577831+0.011765 test-rmse:1.255715+0.148032
## [28] train-rmse:0.561913+0.011345 test-rmse:1.255813+0.150560
## [29] train-rmse:0.548221+0.010785 test-rmse:1.253154+0.154826
## [30] train-rmse:0.534191+0.010219 test-rmse:1.248368+0.155591
## [31] train-rmse:0.518697+0.012177 test-rmse:1.243872+0.155243
## [32] train-rmse:0.507358+0.012542 test-rmse:1.243061+0.157485
## [33] train-rmse:0.494164+0.011963 test-rmse:1.240090+0.158473
## [34] train-rmse:0.482769+0.011359 test-rmse:1.238271+0.162310
## [35] train-rmse:0.471337+0.012420 test-rmse:1.239318+0.157945
## [36] train-rmse:0.460892+0.012499 test-rmse:1.236444+0.157778
## [37] train-rmse:0.449268+0.014720 test-rmse:1.234451+0.157053
## [38] train-rmse:0.437389+0.012064 test-rmse:1.232718+0.161978
## [39] train-rmse:0.429228+0.012289 test-rmse:1.232364+0.158602
## [40] train-rmse:0.418409+0.010422 test-rmse:1.229594+0.157274
## [41] train-rmse:0.411375+0.011127 test-rmse:1.226507+0.158878
## [42] train-rmse:0.402905+0.011245 test-rmse:1.226287+0.158277
## [43] train-rmse:0.394709+0.012475 test-rmse:1.228179+0.157436
## [44] train-rmse:0.387921+0.012732 test-rmse:1.227829+0.157664
## [45] train-rmse:0.378941+0.013088 test-rmse:1.225003+0.157094
## [46] train-rmse:0.370386+0.013185 test-rmse:1.226954+0.155671
## [47] train-rmse:0.362505+0.010108 test-rmse:1.228187+0.154432
## [48] train-rmse:0.354815+0.010753 test-rmse:1.230927+0.153863
## [49] train-rmse:0.349321+0.011132 test-rmse:1.230955+0.153976
## [50] train-rmse:0.344040+0.012037 test-rmse:1.230766+0.152920
## [51] train-rmse:0.335990+0.014116 test-rmse:1.230798+0.153567
## Stopping. Best iteration:
## [45] train-rmse:0.378941+0.013088 test-rmse:1.225003+0.157094
##
## 49
## [1] train-rmse:7.554308+0.052782 test-rmse:7.550169+0.268254
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.961228+0.036958 test-rmse:5.955231+0.259913
## [3] train-rmse:4.794106+0.026666 test-rmse:4.788623+0.247893
## [4] train-rmse:3.956415+0.018986 test-rmse:3.962652+0.223826
## [5] train-rmse:3.370281+0.013139 test-rmse:3.387853+0.187395
## [6] train-rmse:2.970501+0.012412 test-rmse:2.993186+0.143852
## [7] train-rmse:2.702790+0.012850 test-rmse:2.734175+0.105170
## [8] train-rmse:2.525371+0.012988 test-rmse:2.560751+0.081494
## [9] train-rmse:2.409178+0.014233 test-rmse:2.450052+0.069609
## [10] train-rmse:2.332148+0.015368 test-rmse:2.372331+0.052773
## [11] train-rmse:2.278822+0.015928 test-rmse:2.314711+0.052288
## [12] train-rmse:2.240335+0.016978 test-rmse:2.280749+0.055145
## [13] train-rmse:2.211329+0.018260 test-rmse:2.255706+0.061833
## [14] train-rmse:2.187501+0.019548 test-rmse:2.230003+0.067027
## [15] train-rmse:2.167690+0.020611 test-rmse:2.219057+0.069803
## [16] train-rmse:2.150142+0.021158 test-rmse:2.209589+0.073818
## [17] train-rmse:2.134006+0.021611 test-rmse:2.190956+0.070251
## [18] train-rmse:2.119480+0.022062 test-rmse:2.180253+0.075520
## [19] train-rmse:2.105274+0.022776 test-rmse:2.167505+0.083114
## [20] train-rmse:2.091992+0.023033 test-rmse:2.163643+0.082860
## [21] train-rmse:2.078988+0.023644 test-rmse:2.150830+0.089280
## [22] train-rmse:2.066105+0.024345 test-rmse:2.139964+0.089088
## [23] train-rmse:2.053816+0.025055 test-rmse:2.120284+0.097856
## [24] train-rmse:2.041916+0.025707 test-rmse:2.103381+0.091615
## [25] train-rmse:2.030294+0.026524 test-rmse:2.089251+0.089009
## [26] train-rmse:2.019114+0.027231 test-rmse:2.084326+0.091291
## [27] train-rmse:2.008082+0.027670 test-rmse:2.077392+0.092641
## [28] train-rmse:1.997179+0.027725 test-rmse:2.068954+0.094914
## [29] train-rmse:1.987251+0.028052 test-rmse:2.061389+0.092398
## [30] train-rmse:1.977224+0.028552 test-rmse:2.054625+0.083321
## [31] train-rmse:1.967394+0.029238 test-rmse:2.045817+0.084945
## [32] train-rmse:1.958340+0.029555 test-rmse:2.032350+0.088339
## [33] train-rmse:1.949386+0.029872 test-rmse:2.024716+0.086946
## [34] train-rmse:1.940614+0.030456 test-rmse:2.015937+0.081377
## [35] train-rmse:1.931898+0.030784 test-rmse:2.008816+0.078704
## [36] train-rmse:1.923338+0.031201 test-rmse:1.996969+0.079608
## [37] train-rmse:1.914860+0.031484 test-rmse:1.991870+0.082690
## [38] train-rmse:1.906533+0.031907 test-rmse:1.984709+0.085571
## [39] train-rmse:1.898162+0.032322 test-rmse:1.975247+0.087362
## [40] train-rmse:1.890429+0.032835 test-rmse:1.974917+0.088976
## [41] train-rmse:1.882610+0.033335 test-rmse:1.961644+0.087990
## [42] train-rmse:1.874659+0.034002 test-rmse:1.954412+0.093140
## [43] train-rmse:1.866876+0.034772 test-rmse:1.949332+0.093202
## [44] train-rmse:1.859336+0.035496 test-rmse:1.947086+0.096669
## [45] train-rmse:1.851903+0.036155 test-rmse:1.939934+0.095389
## [46] train-rmse:1.844792+0.036637 test-rmse:1.936237+0.095143
## [47] train-rmse:1.837703+0.037164 test-rmse:1.928658+0.095832
## [48] train-rmse:1.830814+0.037574 test-rmse:1.922637+0.092603
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## [59] train-rmse:1.762247+0.041000 test-rmse:1.866588+0.095602
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## [61] train-rmse:1.751005+0.041570 test-rmse:1.860533+0.101685
## [62] train-rmse:1.745457+0.041867 test-rmse:1.855802+0.097884
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## [65] train-rmse:1.729365+0.042911 test-rmse:1.829551+0.099372
## [66] train-rmse:1.724182+0.043181 test-rmse:1.826574+0.100454
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## [68] train-rmse:1.713963+0.043663 test-rmse:1.817995+0.102053
## [69] train-rmse:1.708965+0.043970 test-rmse:1.816951+0.100431
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## [71] train-rmse:1.699283+0.044560 test-rmse:1.807151+0.100024
## [72] train-rmse:1.694691+0.044908 test-rmse:1.803826+0.103069
## [73] train-rmse:1.690261+0.045164 test-rmse:1.802906+0.105256
## [74] train-rmse:1.685815+0.045465 test-rmse:1.799640+0.104065
## [75] train-rmse:1.681399+0.045830 test-rmse:1.796705+0.104444
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## [77] train-rmse:1.672692+0.046581 test-rmse:1.792632+0.107758
## [78] train-rmse:1.668359+0.046797 test-rmse:1.789889+0.106358
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## [80] train-rmse:1.660070+0.047093 test-rmse:1.781123+0.104404
## [81] train-rmse:1.656070+0.047223 test-rmse:1.779295+0.105457
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## [83] train-rmse:1.648213+0.047394 test-rmse:1.766427+0.110047
## [84] train-rmse:1.644214+0.047600 test-rmse:1.763225+0.111451
## [85] train-rmse:1.640336+0.047782 test-rmse:1.757111+0.112509
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## [96] train-rmse:1.600085+0.049044 test-rmse:1.721133+0.123529
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## [100] train-rmse:1.587095+0.049477 test-rmse:1.712476+0.127816
## [101] train-rmse:1.583946+0.049604 test-rmse:1.713414+0.123572
## [102] train-rmse:1.580922+0.049752 test-rmse:1.708748+0.122122
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## [104] train-rmse:1.574851+0.050044 test-rmse:1.703347+0.125995
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## [108] train-rmse:1.563109+0.050534 test-rmse:1.690740+0.130249
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## [117] train-rmse:1.538636+0.050958 test-rmse:1.672092+0.135276
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## [119] train-rmse:1.533473+0.050978 test-rmse:1.666749+0.139563
## [120] train-rmse:1.530989+0.051083 test-rmse:1.664361+0.141699
## [121] train-rmse:1.528572+0.051179 test-rmse:1.663188+0.141176
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## [124] train-rmse:1.521350+0.051375 test-rmse:1.655334+0.140760
## [125] train-rmse:1.518985+0.051481 test-rmse:1.654809+0.141830
## [126] train-rmse:1.516738+0.051541 test-rmse:1.652931+0.141106
## [127] train-rmse:1.514487+0.051596 test-rmse:1.650040+0.141629
## [128] train-rmse:1.512267+0.051651 test-rmse:1.647848+0.143802
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## [130] train-rmse:1.507958+0.051737 test-rmse:1.644790+0.142476
## [131] train-rmse:1.505832+0.051762 test-rmse:1.644325+0.141005
## [132] train-rmse:1.503687+0.051795 test-rmse:1.641028+0.141669
## [133] train-rmse:1.501563+0.051815 test-rmse:1.640310+0.145048
## [134] train-rmse:1.499405+0.051909 test-rmse:1.636699+0.141846
## [135] train-rmse:1.497285+0.051987 test-rmse:1.634717+0.142500
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## [144] train-rmse:1.479668+0.052488 test-rmse:1.609951+0.149865
## [145] train-rmse:1.477803+0.052566 test-rmse:1.611342+0.150776
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## [158] train-rmse:1.455671+0.052954 test-rmse:1.599914+0.156069
## [159] train-rmse:1.454092+0.053000 test-rmse:1.597598+0.154405
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## [161] train-rmse:1.451014+0.053046 test-rmse:1.592000+0.154766
## [162] train-rmse:1.449484+0.053066 test-rmse:1.590780+0.155359
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## [166] train-rmse:1.443555+0.053085 test-rmse:1.579863+0.161713
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## [169] train-rmse:1.439307+0.053101 test-rmse:1.574648+0.159018
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## [182] train-rmse:1.422426+0.053309 test-rmse:1.559539+0.165232
## [183] train-rmse:1.421222+0.053317 test-rmse:1.559516+0.166334
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## [188] train-rmse:1.415405+0.053311 test-rmse:1.557872+0.168722
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## [190] train-rmse:1.413185+0.053294 test-rmse:1.555023+0.169685
## [191] train-rmse:1.412086+0.053290 test-rmse:1.551460+0.166799
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## [193] train-rmse:1.409953+0.053307 test-rmse:1.551348+0.166949
## [194] train-rmse:1.408884+0.053334 test-rmse:1.551826+0.166645
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## [196] train-rmse:1.406802+0.053360 test-rmse:1.548455+0.167822
## [197] train-rmse:1.405797+0.053373 test-rmse:1.549965+0.171569
## [198] train-rmse:1.404806+0.053387 test-rmse:1.549703+0.171012
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## [200] train-rmse:1.402829+0.053405 test-rmse:1.546086+0.169949
## [201] train-rmse:1.401878+0.053413 test-rmse:1.545923+0.170244
## [202] train-rmse:1.400923+0.053411 test-rmse:1.545744+0.171207
## [203] train-rmse:1.399934+0.053373 test-rmse:1.544724+0.171440
## [204] train-rmse:1.398962+0.053363 test-rmse:1.544048+0.171895
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## [206] train-rmse:1.397087+0.053366 test-rmse:1.540509+0.173157
## [207] train-rmse:1.396164+0.053377 test-rmse:1.539201+0.173949
## [208] train-rmse:1.395238+0.053385 test-rmse:1.536908+0.173839
## [209] train-rmse:1.394319+0.053394 test-rmse:1.538507+0.174937
## [210] train-rmse:1.393397+0.053415 test-rmse:1.537758+0.174614
## [211] train-rmse:1.392506+0.053413 test-rmse:1.537930+0.175268
## [212] train-rmse:1.391627+0.053400 test-rmse:1.537259+0.176085
## [213] train-rmse:1.390773+0.053388 test-rmse:1.536169+0.175805
## [214] train-rmse:1.389909+0.053395 test-rmse:1.533785+0.175170
## [215] train-rmse:1.389062+0.053412 test-rmse:1.531196+0.177499
## [216] train-rmse:1.388233+0.053418 test-rmse:1.530616+0.177042
## [217] train-rmse:1.387408+0.053425 test-rmse:1.529077+0.175317
## [218] train-rmse:1.386590+0.053435 test-rmse:1.528014+0.175846
## [219] train-rmse:1.385797+0.053429 test-rmse:1.528411+0.175962
## [220] train-rmse:1.385002+0.053432 test-rmse:1.528284+0.175385
## [221] train-rmse:1.384194+0.053412 test-rmse:1.527102+0.175171
## [222] train-rmse:1.383415+0.053394 test-rmse:1.527052+0.174992
## [223] train-rmse:1.382631+0.053375 test-rmse:1.526517+0.174930
## [224] train-rmse:1.381853+0.053360 test-rmse:1.524391+0.173945
## [225] train-rmse:1.381088+0.053342 test-rmse:1.523400+0.173821
## [226] train-rmse:1.380308+0.053352 test-rmse:1.523752+0.174000
## [227] train-rmse:1.379559+0.053355 test-rmse:1.523406+0.175972
## [228] train-rmse:1.378811+0.053361 test-rmse:1.523693+0.177136
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## [230] train-rmse:1.377357+0.053367 test-rmse:1.520471+0.177509
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## [232] train-rmse:1.375940+0.053341 test-rmse:1.520110+0.177324
## [233] train-rmse:1.375246+0.053331 test-rmse:1.520350+0.180004
## [234] train-rmse:1.374573+0.053331 test-rmse:1.520165+0.179511
## [235] train-rmse:1.373912+0.053331 test-rmse:1.519684+0.179249
## [236] train-rmse:1.373243+0.053327 test-rmse:1.518287+0.182629
## [237] train-rmse:1.372572+0.053325 test-rmse:1.517837+0.182441
## [238] train-rmse:1.371903+0.053332 test-rmse:1.516851+0.182246
## [239] train-rmse:1.371239+0.053345 test-rmse:1.518066+0.184104
## [240] train-rmse:1.370594+0.053349 test-rmse:1.517500+0.184506
## [241] train-rmse:1.369963+0.053349 test-rmse:1.517618+0.184622
## [242] train-rmse:1.369339+0.053353 test-rmse:1.517323+0.184475
## [243] train-rmse:1.368724+0.053362 test-rmse:1.516350+0.183954
## [244] train-rmse:1.368115+0.053361 test-rmse:1.515787+0.183420
## [245] train-rmse:1.367506+0.053360 test-rmse:1.515171+0.184279
## [246] train-rmse:1.366893+0.053351 test-rmse:1.515012+0.184202
## [247] train-rmse:1.366292+0.053349 test-rmse:1.514496+0.184133
## [248] train-rmse:1.365700+0.053346 test-rmse:1.515933+0.183192
## [249] train-rmse:1.365120+0.053335 test-rmse:1.515402+0.183474
## [250] train-rmse:1.364529+0.053339 test-rmse:1.513913+0.183756
## [251] train-rmse:1.363950+0.053335 test-rmse:1.513339+0.184833
## [252] train-rmse:1.363363+0.053326 test-rmse:1.512350+0.185669
## [253] train-rmse:1.362797+0.053319 test-rmse:1.511073+0.185087
## [254] train-rmse:1.362233+0.053303 test-rmse:1.510886+0.185108
## [255] train-rmse:1.361677+0.053286 test-rmse:1.510263+0.185429
## [256] train-rmse:1.361135+0.053271 test-rmse:1.510462+0.184832
## [257] train-rmse:1.360598+0.053272 test-rmse:1.509040+0.184257
## [258] train-rmse:1.360074+0.053265 test-rmse:1.508690+0.184320
## [259] train-rmse:1.359559+0.053264 test-rmse:1.508284+0.184997
## [260] train-rmse:1.359043+0.053265 test-rmse:1.507869+0.185446
## [261] train-rmse:1.358522+0.053267 test-rmse:1.506853+0.186315
## [262] train-rmse:1.358000+0.053265 test-rmse:1.505126+0.185597
## [263] train-rmse:1.357474+0.053267 test-rmse:1.505947+0.186392
## [264] train-rmse:1.356967+0.053263 test-rmse:1.505210+0.186719
## [265] train-rmse:1.356476+0.053265 test-rmse:1.504775+0.187268
## [266] train-rmse:1.355985+0.053273 test-rmse:1.503412+0.188994
## [267] train-rmse:1.355504+0.053269 test-rmse:1.503157+0.188596
## [268] train-rmse:1.355034+0.053260 test-rmse:1.504178+0.187235
## [269] train-rmse:1.354556+0.053254 test-rmse:1.503758+0.188033
## [270] train-rmse:1.354073+0.053249 test-rmse:1.502400+0.189830
## [271] train-rmse:1.353600+0.053241 test-rmse:1.503557+0.191450
## [272] train-rmse:1.353140+0.053233 test-rmse:1.503641+0.191912
## [273] train-rmse:1.352689+0.053226 test-rmse:1.502907+0.191865
## [274] train-rmse:1.352239+0.053222 test-rmse:1.502300+0.191801
## [275] train-rmse:1.351787+0.053215 test-rmse:1.500664+0.190928
## [276] train-rmse:1.351350+0.053207 test-rmse:1.500177+0.190478
## [277] train-rmse:1.350907+0.053192 test-rmse:1.499629+0.190617
## [278] train-rmse:1.350469+0.053184 test-rmse:1.499810+0.191337
## [279] train-rmse:1.350040+0.053167 test-rmse:1.498543+0.190503
## [280] train-rmse:1.349622+0.053164 test-rmse:1.499404+0.190183
## [281] train-rmse:1.349210+0.053166 test-rmse:1.498703+0.189903
## [282] train-rmse:1.348801+0.053164 test-rmse:1.498689+0.190287
## [283] train-rmse:1.348400+0.053163 test-rmse:1.497209+0.189960
## [284] train-rmse:1.347995+0.053157 test-rmse:1.497005+0.190007
## [285] train-rmse:1.347585+0.053150 test-rmse:1.496909+0.190830
## [286] train-rmse:1.347178+0.053137 test-rmse:1.496772+0.190580
## [287] train-rmse:1.346786+0.053129 test-rmse:1.497318+0.189486
## [288] train-rmse:1.346401+0.053121 test-rmse:1.497460+0.189575
## [289] train-rmse:1.346010+0.053121 test-rmse:1.496802+0.190544
## [290] train-rmse:1.345625+0.053121 test-rmse:1.496624+0.190587
## [291] train-rmse:1.345248+0.053121 test-rmse:1.496204+0.190678
## [292] train-rmse:1.344874+0.053108 test-rmse:1.495424+0.193029
## [293] train-rmse:1.344492+0.053098 test-rmse:1.495596+0.193652
## [294] train-rmse:1.344109+0.053088 test-rmse:1.496765+0.194829
## [295] train-rmse:1.343738+0.053072 test-rmse:1.494995+0.194353
## [296] train-rmse:1.343376+0.053053 test-rmse:1.495693+0.194532
## [297] train-rmse:1.343024+0.053041 test-rmse:1.496435+0.193518
## [298] train-rmse:1.342668+0.053026 test-rmse:1.495600+0.193304
## [299] train-rmse:1.342321+0.053026 test-rmse:1.496724+0.192696
## [300] train-rmse:1.341977+0.053022 test-rmse:1.495500+0.192620
## [301] train-rmse:1.341636+0.053017 test-rmse:1.495197+0.193265
## Stopping. Best iteration:
## [295] train-rmse:1.343738+0.053072 test-rmse:1.494995+0.194353
##
## 50
## [1] train-rmse:7.523080+0.053198 test-rmse:7.519408+0.263317
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.900232+0.041375 test-rmse:5.894119+0.245192
## [3] train-rmse:4.705416+0.032721 test-rmse:4.714387+0.221928
## [4] train-rmse:3.828286+0.026431 test-rmse:3.835366+0.189068
## [5] train-rmse:3.193784+0.026583 test-rmse:3.233101+0.150043
## [6] train-rmse:2.743786+0.011802 test-rmse:2.787922+0.130510
## [7] train-rmse:2.434268+0.008722 test-rmse:2.496815+0.102312
## [8] train-rmse:2.226536+0.005896 test-rmse:2.312603+0.078005
## [9] train-rmse:2.086618+0.005209 test-rmse:2.177169+0.066370
## [10] train-rmse:1.988646+0.007183 test-rmse:2.108809+0.067851
## [11] train-rmse:1.915149+0.007108 test-rmse:2.052083+0.077731
## [12] train-rmse:1.862759+0.008609 test-rmse:2.002692+0.089311
## [13] train-rmse:1.822287+0.008607 test-rmse:1.971016+0.087004
## [14] train-rmse:1.789654+0.010075 test-rmse:1.935106+0.090387
## [15] train-rmse:1.763215+0.011691 test-rmse:1.908545+0.092378
## [16] train-rmse:1.733788+0.014760 test-rmse:1.890754+0.098838
## [17] train-rmse:1.708471+0.018623 test-rmse:1.874541+0.111978
## [18] train-rmse:1.686265+0.019013 test-rmse:1.856780+0.105273
## [19] train-rmse:1.659718+0.012283 test-rmse:1.827531+0.104321
## [20] train-rmse:1.640713+0.013218 test-rmse:1.815863+0.103359
## [21] train-rmse:1.621219+0.014047 test-rmse:1.795140+0.102760
## [22] train-rmse:1.603049+0.015440 test-rmse:1.792090+0.103561
## [23] train-rmse:1.586645+0.016866 test-rmse:1.780601+0.100888
## [24] train-rmse:1.568313+0.017028 test-rmse:1.772652+0.110080
## [25] train-rmse:1.551854+0.016689 test-rmse:1.757738+0.109005
## [26] train-rmse:1.535082+0.016570 test-rmse:1.742742+0.117275
## [27] train-rmse:1.510836+0.013803 test-rmse:1.722508+0.113208
## [28] train-rmse:1.497483+0.013431 test-rmse:1.715775+0.110647
## [29] train-rmse:1.482764+0.015444 test-rmse:1.707364+0.114271
## [30] train-rmse:1.467144+0.016816 test-rmse:1.694897+0.112216
## [31] train-rmse:1.454696+0.016578 test-rmse:1.681395+0.110578
## [32] train-rmse:1.440950+0.017653 test-rmse:1.672053+0.110044
## [33] train-rmse:1.427605+0.017640 test-rmse:1.664795+0.109366
## [34] train-rmse:1.416030+0.018361 test-rmse:1.659621+0.108485
## [35] train-rmse:1.403301+0.018955 test-rmse:1.651662+0.112958
## [36] train-rmse:1.392052+0.019591 test-rmse:1.640163+0.122647
## [37] train-rmse:1.378860+0.019340 test-rmse:1.630334+0.127412
## [38] train-rmse:1.366875+0.018697 test-rmse:1.623828+0.126850
## [39] train-rmse:1.350888+0.019570 test-rmse:1.609976+0.131995
## [40] train-rmse:1.339094+0.020317 test-rmse:1.603984+0.135154
## [41] train-rmse:1.328560+0.021178 test-rmse:1.594749+0.137156
## [42] train-rmse:1.317540+0.022222 test-rmse:1.584553+0.139770
## [43] train-rmse:1.308457+0.022415 test-rmse:1.578181+0.140409
## [44] train-rmse:1.299712+0.022821 test-rmse:1.571802+0.141530
## [45] train-rmse:1.290658+0.022761 test-rmse:1.565541+0.139868
## [46] train-rmse:1.282371+0.023685 test-rmse:1.563124+0.139066
## [47] train-rmse:1.273803+0.023236 test-rmse:1.555632+0.139631
## [48] train-rmse:1.265029+0.021102 test-rmse:1.553981+0.139158
## [49] train-rmse:1.257087+0.021651 test-rmse:1.547751+0.140283
## [50] train-rmse:1.247797+0.022717 test-rmse:1.542279+0.142959
## [51] train-rmse:1.239373+0.023296 test-rmse:1.537733+0.144021
## [52] train-rmse:1.232066+0.024011 test-rmse:1.536652+0.148490
## [53] train-rmse:1.223757+0.024812 test-rmse:1.527087+0.148920
## [54] train-rmse:1.212602+0.029345 test-rmse:1.522207+0.152673
## [55] train-rmse:1.204852+0.029297 test-rmse:1.511699+0.160440
## [56] train-rmse:1.189965+0.023656 test-rmse:1.493171+0.139930
## [57] train-rmse:1.182528+0.024153 test-rmse:1.490105+0.137903
## [58] train-rmse:1.175416+0.024785 test-rmse:1.486398+0.139765
## [59] train-rmse:1.168882+0.025125 test-rmse:1.480979+0.133967
## [60] train-rmse:1.157848+0.029992 test-rmse:1.476444+0.133719
## [61] train-rmse:1.150255+0.030279 test-rmse:1.472476+0.135457
## [62] train-rmse:1.138778+0.032898 test-rmse:1.464846+0.138933
## [63] train-rmse:1.132535+0.033019 test-rmse:1.460006+0.139742
## [64] train-rmse:1.121874+0.026099 test-rmse:1.447473+0.141430
## [65] train-rmse:1.115630+0.025692 test-rmse:1.443629+0.143219
## [66] train-rmse:1.109373+0.025668 test-rmse:1.438451+0.142960
## [67] train-rmse:1.103753+0.026045 test-rmse:1.439346+0.144264
## [68] train-rmse:1.092750+0.023960 test-rmse:1.422523+0.133391
## [69] train-rmse:1.084237+0.025519 test-rmse:1.414439+0.138048
## [70] train-rmse:1.078284+0.026916 test-rmse:1.415111+0.138800
## [71] train-rmse:1.071967+0.027161 test-rmse:1.415327+0.139351
## [72] train-rmse:1.065834+0.026533 test-rmse:1.411607+0.141883
## [73] train-rmse:1.058586+0.026313 test-rmse:1.409765+0.142101
## [74] train-rmse:1.053644+0.026465 test-rmse:1.405499+0.143789
## [75] train-rmse:1.049436+0.026792 test-rmse:1.400625+0.147166
## [76] train-rmse:1.044451+0.026475 test-rmse:1.399802+0.146136
## [77] train-rmse:1.040017+0.026682 test-rmse:1.399062+0.143595
## [78] train-rmse:1.035105+0.026859 test-rmse:1.394411+0.143146
## [79] train-rmse:1.031392+0.027103 test-rmse:1.392796+0.143656
## [80] train-rmse:1.027381+0.027244 test-rmse:1.390356+0.144756
## [81] train-rmse:1.023308+0.027544 test-rmse:1.387151+0.144444
## [82] train-rmse:1.019464+0.027761 test-rmse:1.382318+0.146044
## [83] train-rmse:1.014484+0.028932 test-rmse:1.377713+0.146970
## [84] train-rmse:1.007176+0.030445 test-rmse:1.377766+0.149125
## [85] train-rmse:1.002471+0.031810 test-rmse:1.375816+0.150503
## [86] train-rmse:0.998516+0.031626 test-rmse:1.375390+0.152055
## [87] train-rmse:0.995196+0.031645 test-rmse:1.374266+0.152884
## [88] train-rmse:0.988511+0.029712 test-rmse:1.369484+0.153293
## [89] train-rmse:0.984604+0.029486 test-rmse:1.368057+0.156111
## [90] train-rmse:0.981524+0.029387 test-rmse:1.366802+0.156232
## [91] train-rmse:0.976213+0.030018 test-rmse:1.366143+0.154934
## [92] train-rmse:0.973067+0.030129 test-rmse:1.364117+0.154617
## [93] train-rmse:0.967253+0.025477 test-rmse:1.357773+0.154810
## [94] train-rmse:0.963802+0.025716 test-rmse:1.357729+0.154944
## [95] train-rmse:0.960696+0.025909 test-rmse:1.357855+0.155775
## [96] train-rmse:0.957916+0.026164 test-rmse:1.357537+0.157257
## [97] train-rmse:0.955223+0.026124 test-rmse:1.355059+0.157689
## [98] train-rmse:0.952646+0.026087 test-rmse:1.352061+0.159113
## [99] train-rmse:0.949326+0.026596 test-rmse:1.351504+0.161652
## [100] train-rmse:0.945582+0.027069 test-rmse:1.348308+0.162953
## [101] train-rmse:0.941407+0.027914 test-rmse:1.346666+0.165706
## [102] train-rmse:0.930568+0.031919 test-rmse:1.335692+0.160064
## [103] train-rmse:0.927541+0.032332 test-rmse:1.334150+0.159664
## [104] train-rmse:0.925068+0.032398 test-rmse:1.334397+0.158901
## [105] train-rmse:0.916584+0.024431 test-rmse:1.321035+0.160354
## [106] train-rmse:0.913564+0.024669 test-rmse:1.319940+0.160440
## [107] train-rmse:0.909780+0.025294 test-rmse:1.320063+0.160105
## [108] train-rmse:0.907649+0.025366 test-rmse:1.319712+0.159515
## [109] train-rmse:0.905299+0.025590 test-rmse:1.315501+0.162621
## [110] train-rmse:0.901009+0.025949 test-rmse:1.312262+0.161634
## [111] train-rmse:0.897003+0.025281 test-rmse:1.309637+0.163121
## [112] train-rmse:0.894715+0.025129 test-rmse:1.309156+0.165539
## [113] train-rmse:0.892490+0.025343 test-rmse:1.307987+0.165637
## [114] train-rmse:0.889880+0.024883 test-rmse:1.306838+0.165052
## [115] train-rmse:0.887659+0.025284 test-rmse:1.304771+0.165925
## [116] train-rmse:0.885276+0.025838 test-rmse:1.305670+0.167419
## [117] train-rmse:0.880378+0.024977 test-rmse:1.298018+0.162912
## [118] train-rmse:0.878001+0.024759 test-rmse:1.297869+0.163337
## [119] train-rmse:0.875402+0.024851 test-rmse:1.297374+0.162187
## [120] train-rmse:0.872596+0.024206 test-rmse:1.295686+0.162468
## [121] train-rmse:0.870686+0.024313 test-rmse:1.298761+0.163701
## [122] train-rmse:0.862461+0.018760 test-rmse:1.288252+0.165454
## [123] train-rmse:0.857688+0.023242 test-rmse:1.283050+0.160008
## [124] train-rmse:0.853661+0.023244 test-rmse:1.281083+0.160169
## [125] train-rmse:0.851500+0.023448 test-rmse:1.279110+0.159872
## [126] train-rmse:0.848777+0.023153 test-rmse:1.276987+0.161120
## [127] train-rmse:0.847032+0.023025 test-rmse:1.277733+0.162218
## [128] train-rmse:0.845292+0.023042 test-rmse:1.277392+0.162937
## [129] train-rmse:0.843134+0.022486 test-rmse:1.277584+0.162683
## [130] train-rmse:0.841555+0.022564 test-rmse:1.277337+0.162661
## [131] train-rmse:0.839673+0.022726 test-rmse:1.278971+0.165385
## [132] train-rmse:0.838245+0.022390 test-rmse:1.276333+0.165297
## [133] train-rmse:0.836592+0.022441 test-rmse:1.276220+0.165927
## [134] train-rmse:0.834875+0.022845 test-rmse:1.276097+0.168196
## [135] train-rmse:0.833144+0.023367 test-rmse:1.277692+0.167859
## [136] train-rmse:0.831387+0.023327 test-rmse:1.275509+0.168620
## [137] train-rmse:0.830157+0.023518 test-rmse:1.275531+0.168704
## [138] train-rmse:0.825540+0.020755 test-rmse:1.268293+0.170258
## [139] train-rmse:0.824153+0.020898 test-rmse:1.267757+0.169480
## [140] train-rmse:0.822490+0.020549 test-rmse:1.268457+0.171033
## [141] train-rmse:0.821057+0.020731 test-rmse:1.269180+0.171609
## [142] train-rmse:0.819850+0.020692 test-rmse:1.268062+0.171377
## [143] train-rmse:0.817948+0.020459 test-rmse:1.269500+0.173910
## [144] train-rmse:0.816138+0.020454 test-rmse:1.269352+0.173478
## [145] train-rmse:0.814622+0.021342 test-rmse:1.268680+0.172901
## Stopping. Best iteration:
## [139] train-rmse:0.824153+0.020898 test-rmse:1.267757+0.169480
##
## 51
## [1] train-rmse:7.510920+0.054535 test-rmse:7.507551+0.257130
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.869266+0.039440 test-rmse:5.865302+0.242149
## [3] train-rmse:4.652252+0.031774 test-rmse:4.661731+0.223446
## [4] train-rmse:3.750789+0.026281 test-rmse:3.755353+0.190111
## [5] train-rmse:3.093019+0.019034 test-rmse:3.141244+0.162072
## [6] train-rmse:2.622744+0.015775 test-rmse:2.691289+0.124128
## [7] train-rmse:2.290294+0.013397 test-rmse:2.370094+0.111111
## [8] train-rmse:2.053472+0.009285 test-rmse:2.159831+0.089083
## [9] train-rmse:1.889465+0.010243 test-rmse:2.031347+0.069616
## [10] train-rmse:1.771555+0.012047 test-rmse:1.933087+0.074807
## [11] train-rmse:1.685804+0.013922 test-rmse:1.863389+0.060699
## [12] train-rmse:1.622483+0.015501 test-rmse:1.815401+0.057909
## [13] train-rmse:1.567574+0.015893 test-rmse:1.773536+0.058313
## [14] train-rmse:1.523127+0.017495 test-rmse:1.755487+0.058350
## [15] train-rmse:1.482315+0.017864 test-rmse:1.721987+0.053984
## [16] train-rmse:1.450196+0.019259 test-rmse:1.695286+0.058087
## [17] train-rmse:1.418628+0.020756 test-rmse:1.677361+0.058635
## [18] train-rmse:1.391246+0.024714 test-rmse:1.665101+0.067211
## [19] train-rmse:1.361332+0.022086 test-rmse:1.637333+0.071709
## [20] train-rmse:1.332700+0.022742 test-rmse:1.612596+0.060321
## [21] train-rmse:1.308377+0.023837 test-rmse:1.592023+0.057057
## [22] train-rmse:1.283521+0.026034 test-rmse:1.579371+0.065173
## [23] train-rmse:1.262381+0.028084 test-rmse:1.566751+0.065894
## [24] train-rmse:1.241657+0.029544 test-rmse:1.552839+0.074272
## [25] train-rmse:1.220830+0.028757 test-rmse:1.539687+0.077896
## [26] train-rmse:1.203690+0.029485 test-rmse:1.521834+0.075023
## [27] train-rmse:1.186788+0.028744 test-rmse:1.510135+0.072558
## [28] train-rmse:1.170492+0.029970 test-rmse:1.496875+0.075901
## [29] train-rmse:1.153709+0.029008 test-rmse:1.487847+0.086940
## [30] train-rmse:1.137408+0.030670 test-rmse:1.478308+0.084595
## [31] train-rmse:1.122703+0.030569 test-rmse:1.474315+0.088789
## [32] train-rmse:1.106771+0.030082 test-rmse:1.466591+0.090942
## [33] train-rmse:1.089881+0.028680 test-rmse:1.449500+0.090845
## [34] train-rmse:1.076941+0.027590 test-rmse:1.438022+0.098288
## [35] train-rmse:1.062045+0.026247 test-rmse:1.426597+0.099150
## [36] train-rmse:1.048599+0.023692 test-rmse:1.417866+0.105439
## [37] train-rmse:1.033864+0.024687 test-rmse:1.402029+0.099936
## [38] train-rmse:1.020919+0.025786 test-rmse:1.396572+0.104349
## [39] train-rmse:1.010778+0.025919 test-rmse:1.395588+0.105946
## [40] train-rmse:0.998665+0.027118 test-rmse:1.388041+0.100889
## [41] train-rmse:0.988734+0.026967 test-rmse:1.381625+0.104442
## [42] train-rmse:0.978965+0.027228 test-rmse:1.375684+0.107204
## [43] train-rmse:0.969452+0.027348 test-rmse:1.371012+0.108043
## [44] train-rmse:0.958662+0.026520 test-rmse:1.365516+0.111374
## [45] train-rmse:0.948514+0.024850 test-rmse:1.365141+0.117615
## [46] train-rmse:0.937854+0.024097 test-rmse:1.358401+0.118269
## [47] train-rmse:0.928413+0.025054 test-rmse:1.353787+0.120218
## [48] train-rmse:0.919131+0.026352 test-rmse:1.350015+0.124544
## [49] train-rmse:0.908872+0.026703 test-rmse:1.347315+0.127087
## [50] train-rmse:0.899679+0.026391 test-rmse:1.341242+0.129665
## [51] train-rmse:0.891310+0.028289 test-rmse:1.336089+0.133010
## [52] train-rmse:0.884743+0.027833 test-rmse:1.330905+0.134947
## [53] train-rmse:0.877126+0.027616 test-rmse:1.327576+0.135647
## [54] train-rmse:0.869578+0.026105 test-rmse:1.328097+0.138295
## [55] train-rmse:0.860592+0.024605 test-rmse:1.327811+0.138029
## [56] train-rmse:0.853369+0.024349 test-rmse:1.325722+0.138341
## [57] train-rmse:0.844534+0.028106 test-rmse:1.315788+0.135356
## [58] train-rmse:0.835808+0.030100 test-rmse:1.312679+0.137572
## [59] train-rmse:0.826950+0.031701 test-rmse:1.310289+0.135950
## [60] train-rmse:0.819726+0.032237 test-rmse:1.305689+0.134891
## [61] train-rmse:0.812586+0.033241 test-rmse:1.300817+0.133464
## [62] train-rmse:0.805150+0.033015 test-rmse:1.296374+0.135129
## [63] train-rmse:0.797132+0.033194 test-rmse:1.296442+0.133988
## [64] train-rmse:0.792432+0.033311 test-rmse:1.294910+0.134112
## [65] train-rmse:0.786387+0.033981 test-rmse:1.292740+0.135702
## [66] train-rmse:0.779788+0.031033 test-rmse:1.293332+0.137661
## [67] train-rmse:0.774145+0.030360 test-rmse:1.292016+0.136708
## [68] train-rmse:0.769428+0.029931 test-rmse:1.289672+0.137549
## [69] train-rmse:0.763807+0.029153 test-rmse:1.287997+0.140100
## [70] train-rmse:0.758847+0.028523 test-rmse:1.285076+0.143531
## [71] train-rmse:0.753594+0.028828 test-rmse:1.281098+0.142550
## [72] train-rmse:0.749197+0.028309 test-rmse:1.281869+0.142515
## [73] train-rmse:0.745003+0.028341 test-rmse:1.280007+0.144897
## [74] train-rmse:0.740023+0.028848 test-rmse:1.283999+0.143015
## [75] train-rmse:0.736024+0.028058 test-rmse:1.284401+0.146434
## [76] train-rmse:0.728888+0.028741 test-rmse:1.282374+0.146774
## [77] train-rmse:0.725427+0.028753 test-rmse:1.281480+0.146935
## [78] train-rmse:0.720858+0.030459 test-rmse:1.278967+0.146603
## [79] train-rmse:0.715832+0.030736 test-rmse:1.279592+0.147661
## [80] train-rmse:0.710115+0.031505 test-rmse:1.273824+0.145696
## [81] train-rmse:0.705468+0.032568 test-rmse:1.273397+0.144662
## [82] train-rmse:0.702951+0.032369 test-rmse:1.272782+0.146441
## [83] train-rmse:0.698551+0.031371 test-rmse:1.273469+0.147140
## [84] train-rmse:0.692436+0.032927 test-rmse:1.274320+0.147173
## [85] train-rmse:0.686239+0.031522 test-rmse:1.265467+0.147942
## [86] train-rmse:0.682193+0.031640 test-rmse:1.263383+0.150173
## [87] train-rmse:0.679037+0.031106 test-rmse:1.262345+0.150445
## [88] train-rmse:0.674966+0.032246 test-rmse:1.259704+0.148601
## [89] train-rmse:0.671611+0.032302 test-rmse:1.260808+0.147818
## [90] train-rmse:0.667597+0.031974 test-rmse:1.260651+0.146769
## [91] train-rmse:0.664440+0.030481 test-rmse:1.258207+0.147664
## [92] train-rmse:0.660095+0.031204 test-rmse:1.253737+0.143698
## [93] train-rmse:0.657987+0.031390 test-rmse:1.252731+0.145981
## [94] train-rmse:0.654752+0.030822 test-rmse:1.251969+0.145601
## [95] train-rmse:0.651190+0.031178 test-rmse:1.253908+0.149137
## [96] train-rmse:0.647327+0.033610 test-rmse:1.253182+0.147773
## [97] train-rmse:0.644150+0.033345 test-rmse:1.252749+0.147705
## [98] train-rmse:0.641962+0.032890 test-rmse:1.252717+0.148729
## [99] train-rmse:0.636217+0.032961 test-rmse:1.250031+0.148121
## [100] train-rmse:0.634231+0.033511 test-rmse:1.249194+0.148473
## [101] train-rmse:0.631097+0.033385 test-rmse:1.249701+0.148305
## [102] train-rmse:0.628211+0.032322 test-rmse:1.248672+0.148623
## [103] train-rmse:0.626567+0.032116 test-rmse:1.249781+0.146140
## [104] train-rmse:0.624340+0.032110 test-rmse:1.249494+0.147470
## [105] train-rmse:0.621730+0.031743 test-rmse:1.249901+0.147571
## [106] train-rmse:0.618935+0.030915 test-rmse:1.250175+0.147379
## [107] train-rmse:0.615754+0.031274 test-rmse:1.250821+0.148466
## [108] train-rmse:0.613770+0.031490 test-rmse:1.249103+0.148142
## Stopping. Best iteration:
## [102] train-rmse:0.628211+0.032322 test-rmse:1.248672+0.148623
##
## 52
## [1] train-rmse:7.505902+0.055057 test-rmse:7.502623+0.258791
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.857099+0.041298 test-rmse:5.867470+0.242511
## [3] train-rmse:4.629260+0.030341 test-rmse:4.647738+0.215169
## [4] train-rmse:3.715686+0.024982 test-rmse:3.739438+0.183881
## [5] train-rmse:3.038351+0.014826 test-rmse:3.093326+0.165819
## [6] train-rmse:2.544839+0.011020 test-rmse:2.623694+0.132198
## [7] train-rmse:2.189829+0.007520 test-rmse:2.297764+0.111053
## [8] train-rmse:1.934335+0.013154 test-rmse:2.069305+0.076833
## [9] train-rmse:1.752171+0.014771 test-rmse:1.928689+0.078218
## [10] train-rmse:1.618407+0.016263 test-rmse:1.821230+0.074914
## [11] train-rmse:1.517497+0.018771 test-rmse:1.746538+0.072332
## [12] train-rmse:1.442690+0.015476 test-rmse:1.689534+0.059058
## [13] train-rmse:1.377362+0.018308 test-rmse:1.641460+0.069335
## [14] train-rmse:1.329540+0.018112 test-rmse:1.611120+0.074769
## [15] train-rmse:1.288791+0.019571 test-rmse:1.584227+0.077954
## [16] train-rmse:1.252045+0.018298 test-rmse:1.560127+0.077435
## [17] train-rmse:1.213634+0.022077 test-rmse:1.529489+0.089063
## [18] train-rmse:1.180151+0.025908 test-rmse:1.518442+0.088764
## [19] train-rmse:1.148256+0.025712 test-rmse:1.492181+0.098468
## [20] train-rmse:1.123535+0.025328 test-rmse:1.478787+0.102045
## [21] train-rmse:1.097848+0.027396 test-rmse:1.467399+0.104106
## [22] train-rmse:1.074001+0.028091 test-rmse:1.444736+0.097103
## [23] train-rmse:1.045235+0.020579 test-rmse:1.432973+0.108909
## [24] train-rmse:1.023304+0.022011 test-rmse:1.422378+0.116273
## [25] train-rmse:1.003690+0.021686 test-rmse:1.409551+0.118417
## [26] train-rmse:0.984726+0.021186 test-rmse:1.406035+0.118835
## [27] train-rmse:0.966265+0.022506 test-rmse:1.396851+0.116848
## [28] train-rmse:0.946214+0.023580 test-rmse:1.387661+0.122559
## [29] train-rmse:0.931716+0.023516 test-rmse:1.379168+0.124971
## [30] train-rmse:0.916382+0.020853 test-rmse:1.373160+0.130151
## [31] train-rmse:0.899852+0.017097 test-rmse:1.364473+0.138392
## [32] train-rmse:0.886679+0.017921 test-rmse:1.361492+0.133349
## [33] train-rmse:0.868732+0.017962 test-rmse:1.351739+0.132894
## [34] train-rmse:0.855124+0.016871 test-rmse:1.348599+0.138821
## [35] train-rmse:0.842563+0.018543 test-rmse:1.339372+0.144343
## [36] train-rmse:0.829254+0.017354 test-rmse:1.336599+0.143580
## [37] train-rmse:0.818323+0.017294 test-rmse:1.327918+0.147072
## [38] train-rmse:0.806218+0.016648 test-rmse:1.319952+0.145552
## [39] train-rmse:0.792268+0.014147 test-rmse:1.313468+0.149597
## [40] train-rmse:0.780405+0.013611 test-rmse:1.313284+0.150996
## [41] train-rmse:0.770607+0.013021 test-rmse:1.309582+0.153224
## [42] train-rmse:0.762148+0.013370 test-rmse:1.305975+0.156538
## [43] train-rmse:0.753840+0.013413 test-rmse:1.301472+0.155671
## [44] train-rmse:0.744894+0.012520 test-rmse:1.299806+0.156110
## [45] train-rmse:0.731165+0.014639 test-rmse:1.289084+0.153990
## [46] train-rmse:0.722567+0.015396 test-rmse:1.288974+0.156008
## [47] train-rmse:0.715257+0.016316 test-rmse:1.283228+0.155072
## [48] train-rmse:0.708031+0.016670 test-rmse:1.279938+0.153778
## [49] train-rmse:0.698026+0.014064 test-rmse:1.279213+0.156413
## [50] train-rmse:0.689322+0.013351 test-rmse:1.278403+0.158356
## [51] train-rmse:0.682009+0.012904 test-rmse:1.279206+0.161018
## [52] train-rmse:0.673018+0.010406 test-rmse:1.277769+0.162858
## [53] train-rmse:0.665664+0.012042 test-rmse:1.277224+0.164805
## [54] train-rmse:0.657142+0.011034 test-rmse:1.274288+0.163061
## [55] train-rmse:0.649933+0.008607 test-rmse:1.272078+0.161954
## [56] train-rmse:0.645209+0.008525 test-rmse:1.272323+0.163854
## [57] train-rmse:0.639284+0.010273 test-rmse:1.272166+0.163594
## [58] train-rmse:0.633723+0.009887 test-rmse:1.271869+0.164113
## [59] train-rmse:0.627569+0.011274 test-rmse:1.270629+0.161954
## [60] train-rmse:0.620760+0.010594 test-rmse:1.268022+0.163108
## [61] train-rmse:0.615569+0.010237 test-rmse:1.267796+0.162313
## [62] train-rmse:0.607634+0.009082 test-rmse:1.267311+0.164375
## [63] train-rmse:0.600258+0.009303 test-rmse:1.264169+0.162514
## [64] train-rmse:0.596286+0.009404 test-rmse:1.262830+0.160988
## [65] train-rmse:0.591092+0.009188 test-rmse:1.261316+0.160804
## [66] train-rmse:0.586736+0.010377 test-rmse:1.261506+0.161430
## [67] train-rmse:0.582935+0.009966 test-rmse:1.262258+0.159798
## [68] train-rmse:0.577173+0.011258 test-rmse:1.260478+0.160296
## [69] train-rmse:0.572257+0.011950 test-rmse:1.258691+0.160010
## [70] train-rmse:0.565893+0.010255 test-rmse:1.256566+0.161623
## [71] train-rmse:0.561890+0.009939 test-rmse:1.256224+0.159826
## [72] train-rmse:0.557309+0.008405 test-rmse:1.255903+0.159351
## [73] train-rmse:0.551670+0.008508 test-rmse:1.255789+0.159703
## [74] train-rmse:0.548090+0.007364 test-rmse:1.254286+0.159806
## [75] train-rmse:0.544468+0.007561 test-rmse:1.254059+0.160008
## [76] train-rmse:0.541724+0.008051 test-rmse:1.253776+0.159939
## [77] train-rmse:0.537591+0.008362 test-rmse:1.253606+0.158741
## [78] train-rmse:0.533308+0.007901 test-rmse:1.252444+0.159949
## [79] train-rmse:0.530249+0.007398 test-rmse:1.251921+0.159753
## [80] train-rmse:0.524663+0.007077 test-rmse:1.250520+0.159207
## [81] train-rmse:0.521744+0.007438 test-rmse:1.251317+0.158999
## [82] train-rmse:0.519356+0.007765 test-rmse:1.249299+0.158902
## [83] train-rmse:0.515998+0.007801 test-rmse:1.249576+0.158748
## [84] train-rmse:0.514056+0.007925 test-rmse:1.249518+0.159503
## [85] train-rmse:0.511596+0.008052 test-rmse:1.249158+0.158825
## [86] train-rmse:0.508276+0.008343 test-rmse:1.249313+0.159160
## [87] train-rmse:0.503567+0.008597 test-rmse:1.246311+0.160227
## [88] train-rmse:0.500518+0.008351 test-rmse:1.245830+0.158431
## [89] train-rmse:0.495027+0.005311 test-rmse:1.243940+0.158486
## [90] train-rmse:0.492395+0.004810 test-rmse:1.242705+0.159841
## [91] train-rmse:0.490543+0.004832 test-rmse:1.241999+0.160554
## [92] train-rmse:0.487119+0.004424 test-rmse:1.244207+0.161292
## [93] train-rmse:0.481418+0.010366 test-rmse:1.244967+0.161312
## [94] train-rmse:0.474925+0.014658 test-rmse:1.244533+0.161522
## [95] train-rmse:0.472443+0.014473 test-rmse:1.244671+0.161632
## [96] train-rmse:0.470614+0.014208 test-rmse:1.244814+0.161926
## [97] train-rmse:0.467977+0.013692 test-rmse:1.245111+0.161404
## Stopping. Best iteration:
## [91] train-rmse:0.490543+0.004832 test-rmse:1.241999+0.160554
##
## 53
## [1] train-rmse:7.501690+0.055479 test-rmse:7.502858+0.259411
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.845968+0.042192 test-rmse:5.863011+0.234254
## [3] train-rmse:4.606404+0.031748 test-rmse:4.630660+0.200274
## [4] train-rmse:3.681485+0.026386 test-rmse:3.735739+0.160666
## [5] train-rmse:2.992910+0.020637 test-rmse:3.068286+0.146786
## [6] train-rmse:2.478891+0.021060 test-rmse:2.580977+0.119053
## [7] train-rmse:2.108350+0.018690 test-rmse:2.257293+0.103322
## [8] train-rmse:1.834223+0.014635 test-rmse:2.011133+0.092405
## [9] train-rmse:1.637885+0.017267 test-rmse:1.851329+0.075079
## [10] train-rmse:1.491678+0.019676 test-rmse:1.745319+0.072075
## [11] train-rmse:1.384466+0.018946 test-rmse:1.660820+0.072017
## [12] train-rmse:1.301531+0.020318 test-rmse:1.622126+0.065284
## [13] train-rmse:1.232456+0.024338 test-rmse:1.569222+0.080519
## [14] train-rmse:1.167374+0.022435 test-rmse:1.533464+0.082652
## [15] train-rmse:1.121521+0.020344 test-rmse:1.500349+0.099085
## [16] train-rmse:1.078928+0.023252 test-rmse:1.484661+0.108686
## [17] train-rmse:1.045080+0.023097 test-rmse:1.465353+0.107091
## [18] train-rmse:1.007427+0.023696 test-rmse:1.449543+0.104583
## [19] train-rmse:0.978238+0.021809 test-rmse:1.435217+0.113967
## [20] train-rmse:0.947294+0.023292 test-rmse:1.422534+0.124131
## [21] train-rmse:0.922686+0.023197 test-rmse:1.408344+0.129622
## [22] train-rmse:0.893967+0.017418 test-rmse:1.400039+0.130408
## [23] train-rmse:0.870506+0.017590 test-rmse:1.380588+0.135617
## [24] train-rmse:0.849769+0.019063 test-rmse:1.368080+0.144867
## [25] train-rmse:0.826790+0.020381 test-rmse:1.358103+0.150251
## [26] train-rmse:0.804156+0.021182 test-rmse:1.350748+0.148300
## [27] train-rmse:0.785791+0.023502 test-rmse:1.343590+0.156256
## [28] train-rmse:0.768019+0.023429 test-rmse:1.336256+0.155330
## [29] train-rmse:0.754228+0.024352 test-rmse:1.331410+0.159292
## [30] train-rmse:0.735762+0.025276 test-rmse:1.325987+0.162336
## [31] train-rmse:0.720261+0.023960 test-rmse:1.326046+0.164747
## [32] train-rmse:0.707434+0.021564 test-rmse:1.317900+0.168198
## [33] train-rmse:0.693342+0.022705 test-rmse:1.308618+0.169598
## [34] train-rmse:0.680150+0.021230 test-rmse:1.301955+0.165696
## [35] train-rmse:0.668961+0.021607 test-rmse:1.294952+0.166849
## [36] train-rmse:0.657407+0.020832 test-rmse:1.289075+0.167004
## [37] train-rmse:0.642367+0.020541 test-rmse:1.283832+0.165562
## [38] train-rmse:0.631365+0.022219 test-rmse:1.281747+0.168530
## [39] train-rmse:0.619413+0.020606 test-rmse:1.275267+0.167942
## [40] train-rmse:0.608687+0.021248 test-rmse:1.273266+0.171314
## [41] train-rmse:0.598611+0.021105 test-rmse:1.273239+0.171243
## [42] train-rmse:0.590560+0.020673 test-rmse:1.269906+0.171148
## [43] train-rmse:0.581258+0.022127 test-rmse:1.268473+0.171113
## [44] train-rmse:0.573426+0.021389 test-rmse:1.269289+0.172919
## [45] train-rmse:0.564590+0.020885 test-rmse:1.269170+0.175757
## [46] train-rmse:0.552400+0.017582 test-rmse:1.267002+0.179341
## [47] train-rmse:0.542748+0.016156 test-rmse:1.266313+0.179788
## [48] train-rmse:0.536904+0.016486 test-rmse:1.267088+0.183535
## [49] train-rmse:0.529935+0.016870 test-rmse:1.265406+0.182951
## [50] train-rmse:0.523555+0.018720 test-rmse:1.262557+0.183014
## [51] train-rmse:0.514852+0.017695 test-rmse:1.263459+0.183774
## [52] train-rmse:0.507254+0.017778 test-rmse:1.260649+0.181588
## [53] train-rmse:0.502325+0.017826 test-rmse:1.259505+0.180837
## [54] train-rmse:0.496401+0.018291 test-rmse:1.260232+0.181803
## [55] train-rmse:0.488290+0.015257 test-rmse:1.260505+0.185450
## [56] train-rmse:0.483031+0.015271 test-rmse:1.255925+0.183550
## [57] train-rmse:0.477319+0.013901 test-rmse:1.254540+0.184385
## [58] train-rmse:0.470959+0.011922 test-rmse:1.255220+0.185627
## [59] train-rmse:0.464420+0.012071 test-rmse:1.254845+0.186854
## [60] train-rmse:0.461535+0.012096 test-rmse:1.254266+0.188466
## [61] train-rmse:0.454812+0.009623 test-rmse:1.249444+0.191793
## [62] train-rmse:0.448992+0.011441 test-rmse:1.249022+0.191019
## [63] train-rmse:0.442629+0.013624 test-rmse:1.250306+0.188997
## [64] train-rmse:0.438304+0.015425 test-rmse:1.249828+0.189172
## [65] train-rmse:0.433898+0.016487 test-rmse:1.250019+0.187442
## [66] train-rmse:0.429931+0.017926 test-rmse:1.249099+0.185708
## [67] train-rmse:0.424534+0.017476 test-rmse:1.247373+0.185207
## [68] train-rmse:0.421099+0.017849 test-rmse:1.247412+0.183515
## [69] train-rmse:0.417594+0.017928 test-rmse:1.246878+0.184625
## [70] train-rmse:0.411979+0.015999 test-rmse:1.246764+0.183610
## [71] train-rmse:0.407441+0.015749 test-rmse:1.247542+0.183569
## [72] train-rmse:0.403939+0.016891 test-rmse:1.247894+0.184118
## [73] train-rmse:0.398962+0.016545 test-rmse:1.245512+0.182866
## [74] train-rmse:0.394188+0.014326 test-rmse:1.244830+0.182652
## [75] train-rmse:0.388555+0.013377 test-rmse:1.243824+0.183266
## [76] train-rmse:0.384704+0.015858 test-rmse:1.244038+0.183589
## [77] train-rmse:0.380709+0.014230 test-rmse:1.245520+0.185270
## [78] train-rmse:0.378331+0.013809 test-rmse:1.245901+0.185360
## [79] train-rmse:0.374361+0.013528 test-rmse:1.246513+0.185446
## [80] train-rmse:0.370441+0.012969 test-rmse:1.248286+0.185610
## [81] train-rmse:0.364822+0.011331 test-rmse:1.247144+0.184425
## Stopping. Best iteration:
## [75] train-rmse:0.388555+0.013377 test-rmse:1.243824+0.183266
##
## 54
## [1] train-rmse:7.500734+0.055707 test-rmse:7.503625+0.251818
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.839574+0.042874 test-rmse:5.858078+0.230176
## [3] train-rmse:4.591629+0.033312 test-rmse:4.613396+0.185723
## [4] train-rmse:3.663419+0.029790 test-rmse:3.707306+0.160021
## [5] train-rmse:2.953073+0.023333 test-rmse:3.014316+0.133938
## [6] train-rmse:2.430078+0.022687 test-rmse:2.533704+0.119140
## [7] train-rmse:2.045863+0.023812 test-rmse:2.185575+0.097649
## [8] train-rmse:1.760803+0.025165 test-rmse:1.957899+0.092690
## [9] train-rmse:1.541892+0.021587 test-rmse:1.774024+0.093809
## [10] train-rmse:1.378775+0.029822 test-rmse:1.648127+0.083685
## [11] train-rmse:1.256893+0.029932 test-rmse:1.567290+0.078243
## [12] train-rmse:1.160873+0.036019 test-rmse:1.493814+0.071646
## [13] train-rmse:1.088662+0.033773 test-rmse:1.449082+0.087560
## [14] train-rmse:1.029321+0.034029 test-rmse:1.412040+0.087798
## [15] train-rmse:0.973365+0.035618 test-rmse:1.376697+0.094084
## [16] train-rmse:0.923482+0.032381 test-rmse:1.363205+0.098603
## [17] train-rmse:0.888884+0.033711 test-rmse:1.348570+0.107244
## [18] train-rmse:0.853488+0.031266 test-rmse:1.325403+0.118167
## [19] train-rmse:0.820398+0.030780 test-rmse:1.316443+0.126301
## [20] train-rmse:0.796352+0.029320 test-rmse:1.308214+0.126020
## [21] train-rmse:0.770784+0.028575 test-rmse:1.301108+0.131675
## [22] train-rmse:0.744659+0.027333 test-rmse:1.283295+0.139453
## [23] train-rmse:0.722623+0.029098 test-rmse:1.269990+0.141120
## [24] train-rmse:0.701840+0.031774 test-rmse:1.256972+0.133821
## [25] train-rmse:0.678345+0.030590 test-rmse:1.249758+0.139798
## [26] train-rmse:0.658859+0.026643 test-rmse:1.247255+0.146773
## [27] train-rmse:0.639253+0.024930 test-rmse:1.241910+0.145429
## [28] train-rmse:0.624292+0.023502 test-rmse:1.242980+0.150744
## [29] train-rmse:0.609555+0.024202 test-rmse:1.235894+0.152345
## [30] train-rmse:0.596815+0.025186 test-rmse:1.234151+0.152629
## [31] train-rmse:0.582142+0.024561 test-rmse:1.231745+0.153292
## [32] train-rmse:0.565798+0.027867 test-rmse:1.227746+0.157054
## [33] train-rmse:0.554895+0.026427 test-rmse:1.225881+0.155183
## [34] train-rmse:0.542318+0.025449 test-rmse:1.221962+0.155407
## [35] train-rmse:0.532164+0.026971 test-rmse:1.218503+0.159485
## [36] train-rmse:0.519305+0.025642 test-rmse:1.215445+0.157592
## [37] train-rmse:0.510347+0.026873 test-rmse:1.212579+0.160254
## [38] train-rmse:0.501261+0.026959 test-rmse:1.210489+0.159141
## [39] train-rmse:0.489976+0.028320 test-rmse:1.208100+0.162155
## [40] train-rmse:0.480335+0.027154 test-rmse:1.210566+0.164070
## [41] train-rmse:0.468728+0.027446 test-rmse:1.210720+0.163338
## [42] train-rmse:0.459383+0.024670 test-rmse:1.210011+0.165227
## [43] train-rmse:0.451579+0.026493 test-rmse:1.205842+0.166604
## [44] train-rmse:0.443157+0.025995 test-rmse:1.206466+0.164264
## [45] train-rmse:0.436349+0.026794 test-rmse:1.206926+0.166907
## [46] train-rmse:0.425784+0.022332 test-rmse:1.203557+0.169758
## [47] train-rmse:0.418717+0.019268 test-rmse:1.203124+0.167749
## [48] train-rmse:0.411972+0.019596 test-rmse:1.201779+0.168983
## [49] train-rmse:0.405081+0.020842 test-rmse:1.203291+0.171346
## [50] train-rmse:0.399336+0.020295 test-rmse:1.202156+0.171803
## [51] train-rmse:0.394314+0.020861 test-rmse:1.202404+0.172308
## [52] train-rmse:0.386014+0.020070 test-rmse:1.202762+0.171755
## [53] train-rmse:0.379935+0.021095 test-rmse:1.203047+0.172324
## [54] train-rmse:0.375164+0.022106 test-rmse:1.202080+0.174469
## Stopping. Best iteration:
## [48] train-rmse:0.411972+0.019596 test-rmse:1.201779+0.168983
##
## 55
## [1] train-rmse:7.500734+0.055707 test-rmse:7.503625+0.251818
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.836894+0.043236 test-rmse:5.861467+0.220654
## [3] train-rmse:4.584451+0.034142 test-rmse:4.618150+0.176143
## [4] train-rmse:3.641613+0.029645 test-rmse:3.685269+0.148300
## [5] train-rmse:2.926980+0.020913 test-rmse:3.000448+0.128588
## [6] train-rmse:2.394976+0.012898 test-rmse:2.507286+0.131455
## [7] train-rmse:1.998874+0.017113 test-rmse:2.155835+0.097879
## [8] train-rmse:1.701588+0.020172 test-rmse:1.901240+0.092804
## [9] train-rmse:1.479061+0.025498 test-rmse:1.734150+0.073458
## [10] train-rmse:1.310473+0.022058 test-rmse:1.597041+0.073648
## [11] train-rmse:1.172162+0.021860 test-rmse:1.506851+0.074694
## [12] train-rmse:1.067904+0.021591 test-rmse:1.451694+0.088673
## [13] train-rmse:0.991897+0.021382 test-rmse:1.411836+0.097463
## [14] train-rmse:0.920635+0.017973 test-rmse:1.365816+0.111712
## [15] train-rmse:0.868692+0.017336 test-rmse:1.339619+0.131632
## [16] train-rmse:0.819945+0.023162 test-rmse:1.322786+0.137239
## [17] train-rmse:0.781241+0.020654 test-rmse:1.303709+0.141859
## [18] train-rmse:0.743209+0.018004 test-rmse:1.282437+0.146787
## [19] train-rmse:0.712669+0.019997 test-rmse:1.274037+0.143561
## [20] train-rmse:0.689166+0.020248 test-rmse:1.268188+0.150615
## [21] train-rmse:0.662770+0.015050 test-rmse:1.260130+0.153307
## [22] train-rmse:0.639286+0.019273 test-rmse:1.248177+0.148837
## [23] train-rmse:0.617931+0.017331 test-rmse:1.246853+0.155639
## [24] train-rmse:0.597628+0.019216 test-rmse:1.240335+0.151135
## [25] train-rmse:0.577828+0.015502 test-rmse:1.236614+0.154218
## [26] train-rmse:0.559482+0.011738 test-rmse:1.232884+0.155741
## [27] train-rmse:0.545464+0.012727 test-rmse:1.228331+0.160213
## [28] train-rmse:0.528435+0.011965 test-rmse:1.224325+0.162436
## [29] train-rmse:0.515060+0.011433 test-rmse:1.226446+0.161817
## [30] train-rmse:0.502804+0.010027 test-rmse:1.226741+0.166296
## [31] train-rmse:0.488893+0.011213 test-rmse:1.225637+0.166644
## [32] train-rmse:0.472934+0.013648 test-rmse:1.221963+0.162548
## [33] train-rmse:0.464394+0.013627 test-rmse:1.220091+0.163739
## [34] train-rmse:0.451298+0.016660 test-rmse:1.218448+0.164101
## [35] train-rmse:0.442397+0.015521 test-rmse:1.219198+0.166072
## [36] train-rmse:0.429862+0.015526 test-rmse:1.217976+0.164890
## [37] train-rmse:0.416166+0.013065 test-rmse:1.213626+0.165857
## [38] train-rmse:0.408751+0.013227 test-rmse:1.214165+0.165391
## [39] train-rmse:0.398155+0.011136 test-rmse:1.214885+0.165792
## [40] train-rmse:0.386036+0.013006 test-rmse:1.212696+0.166001
## [41] train-rmse:0.380237+0.012685 test-rmse:1.213619+0.165905
## [42] train-rmse:0.372852+0.010343 test-rmse:1.213479+0.167133
## [43] train-rmse:0.362640+0.007950 test-rmse:1.214517+0.168503
## [44] train-rmse:0.353681+0.009077 test-rmse:1.210619+0.167537
## [45] train-rmse:0.344780+0.008982 test-rmse:1.210879+0.168438
## [46] train-rmse:0.338822+0.008657 test-rmse:1.213347+0.169404
## [47] train-rmse:0.330602+0.011009 test-rmse:1.212874+0.169629
## [48] train-rmse:0.323899+0.007087 test-rmse:1.213474+0.169101
## [49] train-rmse:0.318347+0.008347 test-rmse:1.213609+0.168045
## [50] train-rmse:0.312812+0.008511 test-rmse:1.213975+0.170195
## Stopping. Best iteration:
## [44] train-rmse:0.353681+0.009077 test-rmse:1.210619+0.167537
##
## 56
## [1] train-rmse:7.377807+0.051207 test-rmse:7.373679+0.266919
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.705260+0.034140 test-rmse:5.703851+0.264563
## [3] train-rmse:4.520452+0.024100 test-rmse:4.516231+0.241671
## [4] train-rmse:3.702929+0.017071 test-rmse:3.719042+0.203624
## [5] train-rmse:3.156887+0.011811 test-rmse:3.187219+0.170894
## [6] train-rmse:2.801925+0.012242 test-rmse:2.835341+0.121967
## [7] train-rmse:2.575166+0.013762 test-rmse:2.604944+0.082933
## [8] train-rmse:2.431911+0.014261 test-rmse:2.466770+0.064291
## [9] train-rmse:2.341250+0.015580 test-rmse:2.389252+0.057428
## [10] train-rmse:2.281361+0.016691 test-rmse:2.322012+0.055304
## [11] train-rmse:2.239101+0.016948 test-rmse:2.283129+0.056475
## [12] train-rmse:2.207995+0.017590 test-rmse:2.249940+0.066725
## [13] train-rmse:2.183585+0.019342 test-rmse:2.220335+0.067438
## [14] train-rmse:2.162816+0.020909 test-rmse:2.202501+0.072274
## [15] train-rmse:2.143753+0.022192 test-rmse:2.193531+0.075630
## [16] train-rmse:2.125825+0.022560 test-rmse:2.187521+0.076682
## [17] train-rmse:2.110205+0.022408 test-rmse:2.178459+0.080627
## [18] train-rmse:2.095841+0.023012 test-rmse:2.159811+0.077230
## [19] train-rmse:2.081802+0.023867 test-rmse:2.144923+0.080201
## [20] train-rmse:2.068400+0.024718 test-rmse:2.135975+0.082677
## [21] train-rmse:2.055448+0.025580 test-rmse:2.123329+0.083243
## [22] train-rmse:2.042336+0.026398 test-rmse:2.107297+0.089818
## [23] train-rmse:2.029409+0.027191 test-rmse:2.093135+0.092017
## [24] train-rmse:2.016778+0.027488 test-rmse:2.081582+0.097346
## [25] train-rmse:2.004582+0.028190 test-rmse:2.069704+0.088032
## [26] train-rmse:1.993075+0.028765 test-rmse:2.062717+0.092120
## [27] train-rmse:1.981780+0.029133 test-rmse:2.052375+0.095216
## [28] train-rmse:1.971010+0.029606 test-rmse:2.046468+0.097256
## [29] train-rmse:1.960823+0.029873 test-rmse:2.042256+0.094850
## [30] train-rmse:1.950694+0.030103 test-rmse:2.036838+0.092443
## [31] train-rmse:1.940840+0.030419 test-rmse:2.022736+0.088881
## [32] train-rmse:1.931242+0.030844 test-rmse:2.012814+0.086445
## [33] train-rmse:1.921861+0.031383 test-rmse:1.994128+0.084190
## [34] train-rmse:1.912814+0.032003 test-rmse:1.991090+0.078125
## [35] train-rmse:1.903751+0.032743 test-rmse:1.984841+0.078180
## [36] train-rmse:1.894841+0.033370 test-rmse:1.973726+0.082626
## [37] train-rmse:1.886219+0.034036 test-rmse:1.965829+0.081540
## [38] train-rmse:1.877848+0.034463 test-rmse:1.960581+0.081912
## [39] train-rmse:1.869632+0.034767 test-rmse:1.955092+0.088649
## [40] train-rmse:1.861655+0.035181 test-rmse:1.949095+0.088889
## [41] train-rmse:1.853719+0.035655 test-rmse:1.941684+0.090442
## [42] train-rmse:1.845804+0.036319 test-rmse:1.940864+0.095070
## [43] train-rmse:1.837940+0.036746 test-rmse:1.928140+0.094995
## [44] train-rmse:1.830331+0.037351 test-rmse:1.920867+0.095403
## [45] train-rmse:1.822731+0.038083 test-rmse:1.914723+0.096679
## [46] train-rmse:1.815364+0.038739 test-rmse:1.908481+0.096870
## [47] train-rmse:1.808205+0.039272 test-rmse:1.903497+0.100570
## [48] train-rmse:1.801100+0.039715 test-rmse:1.899322+0.100598
## [49] train-rmse:1.794380+0.040115 test-rmse:1.891608+0.104622
## [50] train-rmse:1.787629+0.040403 test-rmse:1.886721+0.102320
## [51] train-rmse:1.780955+0.040577 test-rmse:1.881913+0.100375
## [52] train-rmse:1.774319+0.040781 test-rmse:1.878183+0.099015
## [53] train-rmse:1.767750+0.040830 test-rmse:1.873013+0.095867
## [54] train-rmse:1.761290+0.041075 test-rmse:1.869670+0.098780
## [55] train-rmse:1.755013+0.041454 test-rmse:1.867272+0.094998
## [56] train-rmse:1.748958+0.041854 test-rmse:1.863337+0.094859
## [57] train-rmse:1.742958+0.042316 test-rmse:1.854112+0.092090
## [58] train-rmse:1.737191+0.042762 test-rmse:1.846279+0.094667
## [59] train-rmse:1.731636+0.043158 test-rmse:1.842794+0.092663
## [60] train-rmse:1.726162+0.043520 test-rmse:1.835202+0.092006
## [61] train-rmse:1.720583+0.043973 test-rmse:1.831159+0.095960
## [62] train-rmse:1.715145+0.044290 test-rmse:1.821870+0.093843
## [63] train-rmse:1.709714+0.044614 test-rmse:1.816125+0.100879
## [64] train-rmse:1.704356+0.044696 test-rmse:1.807289+0.102867
## [65] train-rmse:1.699105+0.044834 test-rmse:1.803444+0.102748
## [66] train-rmse:1.693979+0.045036 test-rmse:1.797654+0.108127
## [67] train-rmse:1.688840+0.045170 test-rmse:1.793303+0.107187
## [68] train-rmse:1.683794+0.045393 test-rmse:1.792772+0.107841
## [69] train-rmse:1.678951+0.045537 test-rmse:1.791373+0.108658
## [70] train-rmse:1.674106+0.045797 test-rmse:1.790913+0.103222
## [71] train-rmse:1.669300+0.046233 test-rmse:1.788552+0.106261
## [72] train-rmse:1.664640+0.046609 test-rmse:1.786894+0.107599
## [73] train-rmse:1.660076+0.046908 test-rmse:1.779108+0.106987
## [74] train-rmse:1.655723+0.047054 test-rmse:1.777811+0.106414
## [75] train-rmse:1.651316+0.047202 test-rmse:1.772867+0.105675
## [76] train-rmse:1.646957+0.047418 test-rmse:1.770379+0.104038
## [77] train-rmse:1.642566+0.047656 test-rmse:1.764154+0.109480
## [78] train-rmse:1.638203+0.047815 test-rmse:1.758817+0.114731
## [79] train-rmse:1.634023+0.048052 test-rmse:1.754945+0.114590
## [80] train-rmse:1.629914+0.048211 test-rmse:1.749192+0.110253
## [81] train-rmse:1.625968+0.048395 test-rmse:1.746719+0.111790
## [82] train-rmse:1.622053+0.048501 test-rmse:1.740570+0.112736
## [83] train-rmse:1.618148+0.048611 test-rmse:1.736555+0.112894
## [84] train-rmse:1.614318+0.048762 test-rmse:1.734338+0.115092
## [85] train-rmse:1.610546+0.048927 test-rmse:1.729804+0.116633
## [86] train-rmse:1.606781+0.049079 test-rmse:1.730611+0.119859
## [87] train-rmse:1.603148+0.049239 test-rmse:1.729259+0.120151
## [88] train-rmse:1.599494+0.049393 test-rmse:1.725419+0.118263
## [89] train-rmse:1.595869+0.049500 test-rmse:1.721476+0.116778
## [90] train-rmse:1.592265+0.049576 test-rmse:1.718390+0.117516
## [91] train-rmse:1.588692+0.049637 test-rmse:1.714420+0.118897
## [92] train-rmse:1.585189+0.049726 test-rmse:1.709802+0.123491
## [93] train-rmse:1.581733+0.049860 test-rmse:1.706216+0.123021
## [94] train-rmse:1.578324+0.050028 test-rmse:1.702747+0.126153
## [95] train-rmse:1.574990+0.050150 test-rmse:1.702624+0.129829
## [96] train-rmse:1.571609+0.050291 test-rmse:1.702195+0.128718
## [97] train-rmse:1.568335+0.050360 test-rmse:1.700595+0.126829
## [98] train-rmse:1.565116+0.050477 test-rmse:1.694138+0.127513
## [99] train-rmse:1.561904+0.050571 test-rmse:1.695048+0.127262
## [100] train-rmse:1.558803+0.050725 test-rmse:1.693320+0.130547
## [101] train-rmse:1.555700+0.050854 test-rmse:1.691106+0.131398
## [102] train-rmse:1.552686+0.050974 test-rmse:1.689818+0.132088
## [103] train-rmse:1.549681+0.051014 test-rmse:1.687584+0.133553
## [104] train-rmse:1.546671+0.050943 test-rmse:1.684261+0.134133
## [105] train-rmse:1.543815+0.051003 test-rmse:1.679293+0.132626
## [106] train-rmse:1.540996+0.051052 test-rmse:1.673062+0.136284
## [107] train-rmse:1.538241+0.051146 test-rmse:1.672565+0.136872
## [108] train-rmse:1.535423+0.051245 test-rmse:1.666857+0.134963
## [109] train-rmse:1.532702+0.051405 test-rmse:1.663670+0.134609
## [110] train-rmse:1.530027+0.051475 test-rmse:1.660336+0.132898
## [111] train-rmse:1.527436+0.051556 test-rmse:1.658526+0.137675
## [112] train-rmse:1.524806+0.051565 test-rmse:1.654998+0.137685
## [113] train-rmse:1.522196+0.051582 test-rmse:1.652469+0.138717
## [114] train-rmse:1.519617+0.051617 test-rmse:1.650605+0.140848
## [115] train-rmse:1.517079+0.051622 test-rmse:1.648427+0.140315
## [116] train-rmse:1.514570+0.051631 test-rmse:1.650044+0.143208
## [117] train-rmse:1.512129+0.051631 test-rmse:1.647370+0.143447
## [118] train-rmse:1.509714+0.051683 test-rmse:1.647538+0.145416
## [119] train-rmse:1.507275+0.051781 test-rmse:1.645338+0.142594
## [120] train-rmse:1.504847+0.051941 test-rmse:1.641539+0.142954
## [121] train-rmse:1.502486+0.052017 test-rmse:1.639826+0.143348
## [122] train-rmse:1.500186+0.052066 test-rmse:1.633124+0.143598
## [123] train-rmse:1.497906+0.052121 test-rmse:1.632852+0.143054
## [124] train-rmse:1.495672+0.052168 test-rmse:1.630854+0.144194
## [125] train-rmse:1.493463+0.052187 test-rmse:1.628240+0.146167
## [126] train-rmse:1.491299+0.052201 test-rmse:1.625412+0.147811
## [127] train-rmse:1.489129+0.052178 test-rmse:1.623653+0.148459
## [128] train-rmse:1.487003+0.052246 test-rmse:1.618926+0.151228
## [129] train-rmse:1.484924+0.052287 test-rmse:1.620463+0.151261
## [130] train-rmse:1.482818+0.052394 test-rmse:1.620452+0.152679
## [131] train-rmse:1.480750+0.052434 test-rmse:1.616899+0.151575
## [132] train-rmse:1.478693+0.052480 test-rmse:1.616726+0.150791
## [133] train-rmse:1.476697+0.052520 test-rmse:1.615269+0.151701
## [134] train-rmse:1.474772+0.052577 test-rmse:1.612277+0.153730
## [135] train-rmse:1.472840+0.052611 test-rmse:1.612134+0.153557
## [136] train-rmse:1.470921+0.052628 test-rmse:1.607325+0.152116
## [137] train-rmse:1.469081+0.052638 test-rmse:1.606058+0.153670
## [138] train-rmse:1.467233+0.052666 test-rmse:1.602904+0.152089
## [139] train-rmse:1.465343+0.052698 test-rmse:1.602602+0.151885
## [140] train-rmse:1.463483+0.052725 test-rmse:1.600734+0.151990
## [141] train-rmse:1.461628+0.052836 test-rmse:1.601405+0.153539
## [142] train-rmse:1.459822+0.052941 test-rmse:1.599566+0.154385
## [143] train-rmse:1.458083+0.053021 test-rmse:1.596182+0.153503
## [144] train-rmse:1.456357+0.053091 test-rmse:1.593872+0.152649
## [145] train-rmse:1.454638+0.053143 test-rmse:1.591574+0.154193
## [146] train-rmse:1.452913+0.053201 test-rmse:1.590331+0.153769
## [147] train-rmse:1.451182+0.053253 test-rmse:1.590937+0.156266
## [148] train-rmse:1.449478+0.053258 test-rmse:1.591783+0.156849
## [149] train-rmse:1.447803+0.053256 test-rmse:1.586635+0.154431
## [150] train-rmse:1.446200+0.053248 test-rmse:1.586134+0.154149
## [151] train-rmse:1.444626+0.053254 test-rmse:1.583772+0.154764
## [152] train-rmse:1.443047+0.053247 test-rmse:1.581209+0.157588
## [153] train-rmse:1.441520+0.053230 test-rmse:1.579413+0.158264
## [154] train-rmse:1.439997+0.053221 test-rmse:1.578416+0.157401
## [155] train-rmse:1.438516+0.053175 test-rmse:1.578007+0.158600
## [156] train-rmse:1.437046+0.053176 test-rmse:1.578988+0.159057
## [157] train-rmse:1.435577+0.053169 test-rmse:1.576815+0.160556
## [158] train-rmse:1.434074+0.053198 test-rmse:1.574429+0.160676
## [159] train-rmse:1.432567+0.053203 test-rmse:1.573703+0.160683
## [160] train-rmse:1.431101+0.053185 test-rmse:1.570477+0.160268
## [161] train-rmse:1.429668+0.053182 test-rmse:1.565110+0.162124
## [162] train-rmse:1.428278+0.053161 test-rmse:1.563932+0.163285
## [163] train-rmse:1.426901+0.053154 test-rmse:1.564084+0.164534
## [164] train-rmse:1.425541+0.053138 test-rmse:1.564028+0.164448
## [165] train-rmse:1.424220+0.053143 test-rmse:1.564553+0.165082
## [166] train-rmse:1.422863+0.053152 test-rmse:1.563722+0.165796
## [167] train-rmse:1.421523+0.053199 test-rmse:1.562311+0.165329
## [168] train-rmse:1.420182+0.053217 test-rmse:1.559599+0.165902
## [169] train-rmse:1.418856+0.053282 test-rmse:1.557757+0.166082
## [170] train-rmse:1.417541+0.053371 test-rmse:1.557031+0.167424
## [171] train-rmse:1.416275+0.053369 test-rmse:1.555907+0.168542
## [172] train-rmse:1.415044+0.053348 test-rmse:1.553866+0.167871
## [173] train-rmse:1.413851+0.053365 test-rmse:1.552353+0.168626
## [174] train-rmse:1.412673+0.053368 test-rmse:1.551157+0.168900
## [175] train-rmse:1.411493+0.053380 test-rmse:1.550171+0.168883
## [176] train-rmse:1.410318+0.053398 test-rmse:1.550224+0.170309
## [177] train-rmse:1.409142+0.053398 test-rmse:1.550289+0.172467
## [178] train-rmse:1.407991+0.053385 test-rmse:1.548628+0.172505
## [179] train-rmse:1.406842+0.053388 test-rmse:1.548218+0.171629
## [180] train-rmse:1.405710+0.053422 test-rmse:1.548745+0.170923
## [181] train-rmse:1.404601+0.053430 test-rmse:1.548776+0.170115
## [182] train-rmse:1.403492+0.053421 test-rmse:1.546731+0.170248
## [183] train-rmse:1.402380+0.053432 test-rmse:1.543666+0.171123
## [184] train-rmse:1.401306+0.053437 test-rmse:1.543267+0.172796
## [185] train-rmse:1.400239+0.053442 test-rmse:1.540115+0.172834
## [186] train-rmse:1.399186+0.053450 test-rmse:1.540657+0.173155
## [187] train-rmse:1.398158+0.053453 test-rmse:1.540370+0.173390
## [188] train-rmse:1.397142+0.053433 test-rmse:1.540324+0.173872
## [189] train-rmse:1.396127+0.053422 test-rmse:1.539913+0.173062
## [190] train-rmse:1.395122+0.053424 test-rmse:1.540210+0.174952
## [191] train-rmse:1.394123+0.053457 test-rmse:1.538872+0.174503
## [192] train-rmse:1.393136+0.053462 test-rmse:1.537361+0.173642
## [193] train-rmse:1.392178+0.053462 test-rmse:1.537229+0.174582
## [194] train-rmse:1.391221+0.053441 test-rmse:1.536240+0.175202
## [195] train-rmse:1.390248+0.053407 test-rmse:1.535074+0.175856
## [196] train-rmse:1.389321+0.053414 test-rmse:1.533342+0.175812
## [197] train-rmse:1.388410+0.053414 test-rmse:1.532863+0.174882
## [198] train-rmse:1.387520+0.053412 test-rmse:1.533150+0.175678
## [199] train-rmse:1.386625+0.053417 test-rmse:1.532996+0.176550
## [200] train-rmse:1.385746+0.053415 test-rmse:1.532025+0.176899
## [201] train-rmse:1.384884+0.053429 test-rmse:1.531135+0.176259
## [202] train-rmse:1.384025+0.053426 test-rmse:1.530192+0.176232
## [203] train-rmse:1.383175+0.053407 test-rmse:1.529520+0.175003
## [204] train-rmse:1.382331+0.053395 test-rmse:1.528079+0.175187
## [205] train-rmse:1.381487+0.053393 test-rmse:1.525944+0.175148
## [206] train-rmse:1.380671+0.053389 test-rmse:1.523853+0.175729
## [207] train-rmse:1.379851+0.053386 test-rmse:1.524422+0.178417
## [208] train-rmse:1.379044+0.053391 test-rmse:1.523345+0.179157
## [209] train-rmse:1.378257+0.053411 test-rmse:1.524640+0.180254
## [210] train-rmse:1.377462+0.053439 test-rmse:1.522282+0.181410
## [211] train-rmse:1.376687+0.053445 test-rmse:1.521273+0.181828
## [212] train-rmse:1.375916+0.053440 test-rmse:1.521723+0.181703
## [213] train-rmse:1.375133+0.053426 test-rmse:1.521500+0.182625
## [214] train-rmse:1.374367+0.053409 test-rmse:1.521369+0.183482
## [215] train-rmse:1.373617+0.053399 test-rmse:1.519464+0.182838
## [216] train-rmse:1.372864+0.053394 test-rmse:1.517834+0.182625
## [217] train-rmse:1.372133+0.053378 test-rmse:1.517640+0.182522
## [218] train-rmse:1.371432+0.053368 test-rmse:1.517480+0.182263
## [219] train-rmse:1.370741+0.053358 test-rmse:1.516068+0.181817
## [220] train-rmse:1.370051+0.053343 test-rmse:1.516557+0.180891
## [221] train-rmse:1.369365+0.053335 test-rmse:1.515254+0.181854
## [222] train-rmse:1.368697+0.053325 test-rmse:1.514798+0.181423
## [223] train-rmse:1.368027+0.053323 test-rmse:1.514369+0.182989
## [224] train-rmse:1.367353+0.053336 test-rmse:1.512639+0.182116
## [225] train-rmse:1.366673+0.053326 test-rmse:1.511433+0.183226
## [226] train-rmse:1.366011+0.053324 test-rmse:1.510894+0.184039
## [227] train-rmse:1.365357+0.053326 test-rmse:1.510335+0.183837
## [228] train-rmse:1.364709+0.053319 test-rmse:1.510783+0.184710
## [229] train-rmse:1.364074+0.053310 test-rmse:1.510908+0.184217
## [230] train-rmse:1.363434+0.053308 test-rmse:1.509696+0.183679
## [231] train-rmse:1.362806+0.053303 test-rmse:1.509234+0.186068
## [232] train-rmse:1.362190+0.053297 test-rmse:1.509975+0.185411
## [233] train-rmse:1.361598+0.053308 test-rmse:1.509527+0.185862
## [234] train-rmse:1.361007+0.053310 test-rmse:1.508602+0.185966
## [235] train-rmse:1.360412+0.053314 test-rmse:1.507707+0.185831
## [236] train-rmse:1.359833+0.053319 test-rmse:1.507025+0.184711
## [237] train-rmse:1.359247+0.053312 test-rmse:1.505435+0.186654
## [238] train-rmse:1.358662+0.053304 test-rmse:1.506004+0.187460
## [239] train-rmse:1.358099+0.053299 test-rmse:1.504810+0.187248
## [240] train-rmse:1.357548+0.053295 test-rmse:1.503241+0.186384
## [241] train-rmse:1.357005+0.053297 test-rmse:1.504676+0.188401
## [242] train-rmse:1.356464+0.053297 test-rmse:1.503957+0.188637
## [243] train-rmse:1.355928+0.053303 test-rmse:1.504075+0.190225
## [244] train-rmse:1.355396+0.053299 test-rmse:1.503079+0.190118
## [245] train-rmse:1.354866+0.053294 test-rmse:1.504146+0.189933
## [246] train-rmse:1.354337+0.053272 test-rmse:1.504107+0.189556
## [247] train-rmse:1.353813+0.053259 test-rmse:1.503716+0.189910
## [248] train-rmse:1.353295+0.053248 test-rmse:1.503034+0.188523
## [249] train-rmse:1.352784+0.053243 test-rmse:1.502477+0.187803
## [250] train-rmse:1.352299+0.053236 test-rmse:1.501809+0.187160
## [251] train-rmse:1.351817+0.053237 test-rmse:1.501245+0.188046
## [252] train-rmse:1.351339+0.053234 test-rmse:1.501407+0.189183
## [253] train-rmse:1.350872+0.053234 test-rmse:1.499409+0.189237
## [254] train-rmse:1.350414+0.053222 test-rmse:1.498702+0.189604
## [255] train-rmse:1.349961+0.053211 test-rmse:1.500109+0.189877
## [256] train-rmse:1.349496+0.053199 test-rmse:1.499762+0.190694
## [257] train-rmse:1.349024+0.053184 test-rmse:1.499091+0.191455
## [258] train-rmse:1.348566+0.053175 test-rmse:1.498126+0.191932
## [259] train-rmse:1.348115+0.053165 test-rmse:1.498184+0.191257
## [260] train-rmse:1.347675+0.053154 test-rmse:1.496896+0.192278
## [261] train-rmse:1.347247+0.053143 test-rmse:1.497236+0.192189
## [262] train-rmse:1.346821+0.053135 test-rmse:1.495791+0.193772
## [263] train-rmse:1.346395+0.053131 test-rmse:1.495894+0.193251
## [264] train-rmse:1.345979+0.053125 test-rmse:1.497121+0.192304
## [265] train-rmse:1.345571+0.053118 test-rmse:1.496854+0.192721
## [266] train-rmse:1.345156+0.053115 test-rmse:1.496213+0.192590
## [267] train-rmse:1.344738+0.053115 test-rmse:1.495754+0.192970
## [268] train-rmse:1.344333+0.053107 test-rmse:1.495278+0.192877
## [269] train-rmse:1.343929+0.053097 test-rmse:1.493876+0.192215
## [270] train-rmse:1.343526+0.053081 test-rmse:1.493849+0.192051
## [271] train-rmse:1.343133+0.053073 test-rmse:1.494069+0.191647
## [272] train-rmse:1.342740+0.053065 test-rmse:1.493469+0.191430
## [273] train-rmse:1.342357+0.053060 test-rmse:1.492815+0.191148
## [274] train-rmse:1.341978+0.053053 test-rmse:1.493346+0.190878
## [275] train-rmse:1.341593+0.053034 test-rmse:1.492517+0.192052
## [276] train-rmse:1.341212+0.053013 test-rmse:1.493678+0.193090
## [277] train-rmse:1.340837+0.052993 test-rmse:1.494631+0.192872
## [278] train-rmse:1.340473+0.052994 test-rmse:1.494449+0.193305
## [279] train-rmse:1.340119+0.052988 test-rmse:1.493554+0.192912
## [280] train-rmse:1.339764+0.052980 test-rmse:1.492959+0.193705
## [281] train-rmse:1.339419+0.052979 test-rmse:1.492693+0.193204
## Stopping. Best iteration:
## [275] train-rmse:1.341593+0.053034 test-rmse:1.492517+0.192052
##
## 57
## [1] train-rmse:7.343519+0.051656 test-rmse:7.339890+0.261680
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.637765+0.038928 test-rmse:5.631937+0.241943
## [3] train-rmse:4.421253+0.030096 test-rmse:4.433045+0.215797
## [4] train-rmse:3.561427+0.026165 test-rmse:3.580057+0.175215
## [5] train-rmse:2.968157+0.025025 test-rmse:3.014354+0.131861
## [6] train-rmse:2.564466+0.024433 test-rmse:2.631174+0.093846
## [7] train-rmse:2.293082+0.012272 test-rmse:2.378722+0.074283
## [8] train-rmse:2.118910+0.013850 test-rmse:2.220546+0.055001
## [9] train-rmse:2.005548+0.015912 test-rmse:2.117749+0.047033
## [10] train-rmse:1.926791+0.019007 test-rmse:2.056001+0.056100
## [11] train-rmse:1.868991+0.020609 test-rmse:2.016819+0.080439
## [12] train-rmse:1.824871+0.024272 test-rmse:1.983205+0.075845
## [13] train-rmse:1.783406+0.015062 test-rmse:1.938519+0.086879
## [14] train-rmse:1.748020+0.012380 test-rmse:1.904695+0.092398
## [15] train-rmse:1.722346+0.014451 test-rmse:1.883510+0.101285
## [16] train-rmse:1.697699+0.015973 test-rmse:1.861467+0.097308
## [17] train-rmse:1.674629+0.017773 test-rmse:1.840153+0.098376
## [18] train-rmse:1.653281+0.019480 test-rmse:1.826919+0.106660
## [19] train-rmse:1.630920+0.020320 test-rmse:1.817617+0.114139
## [20] train-rmse:1.611530+0.022076 test-rmse:1.805119+0.104546
## [21] train-rmse:1.591227+0.022828 test-rmse:1.788761+0.114089
## [22] train-rmse:1.565987+0.027614 test-rmse:1.766345+0.081203
## [23] train-rmse:1.544055+0.034688 test-rmse:1.742914+0.075317
## [24] train-rmse:1.527861+0.035276 test-rmse:1.735488+0.076351
## [25] train-rmse:1.512199+0.036852 test-rmse:1.721105+0.078665
## [26] train-rmse:1.492008+0.044820 test-rmse:1.707633+0.078903
## [27] train-rmse:1.477177+0.045931 test-rmse:1.692137+0.076462
## [28] train-rmse:1.462143+0.045556 test-rmse:1.678155+0.071783
## [29] train-rmse:1.446709+0.045369 test-rmse:1.671322+0.082174
## [30] train-rmse:1.431188+0.045828 test-rmse:1.664267+0.083014
## [31] train-rmse:1.418602+0.045809 test-rmse:1.646606+0.088294
## [32] train-rmse:1.405696+0.047079 test-rmse:1.638281+0.091024
## [33] train-rmse:1.387423+0.046542 test-rmse:1.625803+0.094767
## [34] train-rmse:1.375736+0.047310 test-rmse:1.615635+0.095298
## [35] train-rmse:1.363599+0.048706 test-rmse:1.608707+0.100362
## [36] train-rmse:1.346257+0.054596 test-rmse:1.591009+0.082099
## [37] train-rmse:1.334427+0.056406 test-rmse:1.582018+0.086326
## [38] train-rmse:1.324139+0.056297 test-rmse:1.573029+0.084969
## [39] train-rmse:1.309723+0.061430 test-rmse:1.560900+0.085423
## [40] train-rmse:1.298550+0.062077 test-rmse:1.555671+0.084838
## [41] train-rmse:1.288888+0.062523 test-rmse:1.546126+0.086503
## [42] train-rmse:1.276258+0.066427 test-rmse:1.531420+0.072305
## [43] train-rmse:1.266675+0.066407 test-rmse:1.520251+0.081594
## [44] train-rmse:1.256544+0.065714 test-rmse:1.511418+0.082543
## [45] train-rmse:1.247746+0.065691 test-rmse:1.506749+0.088341
## [46] train-rmse:1.239271+0.065636 test-rmse:1.498520+0.091727
## [47] train-rmse:1.231431+0.066182 test-rmse:1.495958+0.094101
## [48] train-rmse:1.222267+0.065839 test-rmse:1.489895+0.093980
## [49] train-rmse:1.214309+0.067062 test-rmse:1.482176+0.094063
## [50] train-rmse:1.204815+0.065520 test-rmse:1.478705+0.096149
## [51] train-rmse:1.193464+0.071533 test-rmse:1.470480+0.096134
## [52] train-rmse:1.185921+0.071706 test-rmse:1.465042+0.095858
## [53] train-rmse:1.179262+0.071684 test-rmse:1.467066+0.095613
## [54] train-rmse:1.167350+0.071507 test-rmse:1.455488+0.097481
## [55] train-rmse:1.157902+0.069589 test-rmse:1.453141+0.098116
## [56] train-rmse:1.150381+0.069844 test-rmse:1.451478+0.096853
## [57] train-rmse:1.140969+0.072181 test-rmse:1.436748+0.094139
## [58] train-rmse:1.132142+0.076654 test-rmse:1.427651+0.092832
## [59] train-rmse:1.125291+0.077141 test-rmse:1.421837+0.094566
## [60] train-rmse:1.119173+0.076621 test-rmse:1.416377+0.099053
## [61] train-rmse:1.106011+0.068870 test-rmse:1.396401+0.094534
## [62] train-rmse:1.100110+0.069643 test-rmse:1.395759+0.092609
## [63] train-rmse:1.091360+0.070329 test-rmse:1.389441+0.090519
## [64] train-rmse:1.085810+0.070312 test-rmse:1.387450+0.092232
## [65] train-rmse:1.080070+0.069801 test-rmse:1.382229+0.097291
## [66] train-rmse:1.073183+0.071632 test-rmse:1.378142+0.099398
## [67] train-rmse:1.067875+0.070954 test-rmse:1.373246+0.100762
## [68] train-rmse:1.059840+0.068163 test-rmse:1.368553+0.104756
## [69] train-rmse:1.043146+0.059781 test-rmse:1.360817+0.111713
## [70] train-rmse:1.038144+0.059347 test-rmse:1.354134+0.117774
## [71] train-rmse:1.033556+0.059853 test-rmse:1.349393+0.118858
## [72] train-rmse:1.028952+0.059979 test-rmse:1.349151+0.121831
## [73] train-rmse:1.022748+0.059300 test-rmse:1.344790+0.119550
## [74] train-rmse:1.018008+0.060211 test-rmse:1.343838+0.121108
## [75] train-rmse:1.013164+0.061947 test-rmse:1.342941+0.120079
## [76] train-rmse:1.008909+0.061808 test-rmse:1.338999+0.122633
## [77] train-rmse:1.004402+0.061343 test-rmse:1.337915+0.123716
## [78] train-rmse:0.993170+0.058228 test-rmse:1.324469+0.124491
## [79] train-rmse:0.988269+0.057702 test-rmse:1.324640+0.128764
## [80] train-rmse:0.981511+0.057549 test-rmse:1.317705+0.131122
## [81] train-rmse:0.976616+0.058655 test-rmse:1.317250+0.130322
## [82] train-rmse:0.972975+0.059059 test-rmse:1.314427+0.130345
## [83] train-rmse:0.969743+0.059055 test-rmse:1.313682+0.130150
## [84] train-rmse:0.966377+0.059272 test-rmse:1.310841+0.130654
## [85] train-rmse:0.960384+0.056228 test-rmse:1.308495+0.130513
## [86] train-rmse:0.956190+0.057436 test-rmse:1.305285+0.133037
## [87] train-rmse:0.952456+0.057439 test-rmse:1.304045+0.133874
## [88] train-rmse:0.945828+0.057349 test-rmse:1.299504+0.132911
## [89] train-rmse:0.939976+0.060279 test-rmse:1.297298+0.132758
## [90] train-rmse:0.931984+0.055340 test-rmse:1.294052+0.135957
## [91] train-rmse:0.927309+0.054163 test-rmse:1.292732+0.138948
## [92] train-rmse:0.924052+0.054242 test-rmse:1.292555+0.138578
## [93] train-rmse:0.921279+0.054313 test-rmse:1.290522+0.138466
## [94] train-rmse:0.918263+0.054377 test-rmse:1.287678+0.140553
## [95] train-rmse:0.915400+0.054008 test-rmse:1.285175+0.142528
## [96] train-rmse:0.912735+0.054450 test-rmse:1.285127+0.144930
## [97] train-rmse:0.909747+0.054580 test-rmse:1.287509+0.144414
## [98] train-rmse:0.899231+0.047871 test-rmse:1.268868+0.144169
## [99] train-rmse:0.894532+0.047203 test-rmse:1.267077+0.145276
## [100] train-rmse:0.889988+0.048976 test-rmse:1.264831+0.144786
## [101] train-rmse:0.887240+0.048761 test-rmse:1.264227+0.146123
## [102] train-rmse:0.885061+0.048785 test-rmse:1.262150+0.146603
## [103] train-rmse:0.882797+0.048951 test-rmse:1.258721+0.147652
## [104] train-rmse:0.880708+0.049132 test-rmse:1.256693+0.146716
## [105] train-rmse:0.876574+0.051508 test-rmse:1.257832+0.144644
## [106] train-rmse:0.873266+0.051634 test-rmse:1.255884+0.144343
## [107] train-rmse:0.870763+0.051781 test-rmse:1.255068+0.146062
## [108] train-rmse:0.868403+0.051405 test-rmse:1.255374+0.147747
## [109] train-rmse:0.862818+0.047987 test-rmse:1.244921+0.150038
## [110] train-rmse:0.861108+0.048053 test-rmse:1.245071+0.148867
## [111] train-rmse:0.858875+0.048138 test-rmse:1.242932+0.150136
## [112] train-rmse:0.856704+0.048222 test-rmse:1.242060+0.150542
## [113] train-rmse:0.854488+0.047802 test-rmse:1.241912+0.150644
## [114] train-rmse:0.851002+0.047198 test-rmse:1.241015+0.150470
## [115] train-rmse:0.849198+0.046983 test-rmse:1.239867+0.151338
## [116] train-rmse:0.846028+0.046037 test-rmse:1.240464+0.156321
## [117] train-rmse:0.843617+0.046567 test-rmse:1.241474+0.156529
## [118] train-rmse:0.842143+0.046665 test-rmse:1.240196+0.156309
## [119] train-rmse:0.840178+0.046544 test-rmse:1.238059+0.156369
## [120] train-rmse:0.835913+0.049234 test-rmse:1.235399+0.153107
## [121] train-rmse:0.833208+0.050362 test-rmse:1.235673+0.152833
## [122] train-rmse:0.831669+0.050350 test-rmse:1.235295+0.152797
## [123] train-rmse:0.829869+0.050288 test-rmse:1.234968+0.153378
## [124] train-rmse:0.828451+0.050078 test-rmse:1.234088+0.153455
## [125] train-rmse:0.826757+0.050099 test-rmse:1.234372+0.153113
## [126] train-rmse:0.823900+0.049106 test-rmse:1.235204+0.154747
## [127] train-rmse:0.822506+0.049178 test-rmse:1.235645+0.154907
## [128] train-rmse:0.820995+0.049160 test-rmse:1.234844+0.154376
## [129] train-rmse:0.818901+0.049937 test-rmse:1.234334+0.155446
## [130] train-rmse:0.817427+0.050066 test-rmse:1.233262+0.155653
## [131] train-rmse:0.815752+0.049753 test-rmse:1.232725+0.154238
## [132] train-rmse:0.814092+0.049426 test-rmse:1.232287+0.155459
## [133] train-rmse:0.812509+0.049596 test-rmse:1.233680+0.156042
## [134] train-rmse:0.807409+0.046155 test-rmse:1.234213+0.157125
## [135] train-rmse:0.805730+0.046767 test-rmse:1.232674+0.158144
## [136] train-rmse:0.804409+0.047223 test-rmse:1.231371+0.158664
## [137] train-rmse:0.802875+0.047245 test-rmse:1.231082+0.159590
## [138] train-rmse:0.800515+0.046638 test-rmse:1.230229+0.159148
## [139] train-rmse:0.798651+0.046242 test-rmse:1.229452+0.159365
## [140] train-rmse:0.792573+0.047062 test-rmse:1.225710+0.159108
## [141] train-rmse:0.791236+0.046930 test-rmse:1.225473+0.159681
## [142] train-rmse:0.789999+0.046753 test-rmse:1.224723+0.160473
## [143] train-rmse:0.789063+0.046715 test-rmse:1.224167+0.160344
## [144] train-rmse:0.786036+0.048732 test-rmse:1.220705+0.159137
## [145] train-rmse:0.784847+0.049330 test-rmse:1.220673+0.158612
## [146] train-rmse:0.783818+0.049204 test-rmse:1.220824+0.158262
## [147] train-rmse:0.782577+0.049155 test-rmse:1.220818+0.158089
## [148] train-rmse:0.781507+0.049020 test-rmse:1.220211+0.158098
## [149] train-rmse:0.780164+0.048983 test-rmse:1.219229+0.159298
## [150] train-rmse:0.778979+0.049145 test-rmse:1.219178+0.157659
## [151] train-rmse:0.777624+0.049905 test-rmse:1.218060+0.156719
## [152] train-rmse:0.775780+0.049537 test-rmse:1.217669+0.157099
## [153] train-rmse:0.773802+0.049420 test-rmse:1.217863+0.157012
## [154] train-rmse:0.771781+0.048562 test-rmse:1.219242+0.156500
## [155] train-rmse:0.770792+0.048659 test-rmse:1.219748+0.157505
## [156] train-rmse:0.769387+0.048542 test-rmse:1.221294+0.159756
## [157] train-rmse:0.767308+0.048149 test-rmse:1.224377+0.161312
## [158] train-rmse:0.765948+0.048436 test-rmse:1.223767+0.161400
## Stopping. Best iteration:
## [152] train-rmse:0.775780+0.049537 test-rmse:1.217669+0.157099
##
## 58
## [1] train-rmse:7.330158+0.053125 test-rmse:7.326849+0.255100
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.602812+0.037075 test-rmse:5.601525+0.245347
## [3] train-rmse:4.361028+0.026937 test-rmse:4.371819+0.221021
## [4] train-rmse:3.474439+0.022906 test-rmse:3.501569+0.169901
## [5] train-rmse:2.852313+0.021270 test-rmse:2.899095+0.140589
## [6] train-rmse:2.420799+0.015361 test-rmse:2.496526+0.126303
## [7] train-rmse:2.125725+0.014109 test-rmse:2.215932+0.081434
## [8] train-rmse:1.927079+0.015436 test-rmse:2.058916+0.073592
## [9] train-rmse:1.792655+0.019301 test-rmse:1.941982+0.070481
## [10] train-rmse:1.699984+0.023348 test-rmse:1.862827+0.057162
## [11] train-rmse:1.622053+0.022087 test-rmse:1.810834+0.064436
## [12] train-rmse:1.567079+0.024604 test-rmse:1.775986+0.077747
## [13] train-rmse:1.520490+0.022873 test-rmse:1.744990+0.070568
## [14] train-rmse:1.477998+0.024998 test-rmse:1.705897+0.067928
## [15] train-rmse:1.441539+0.022058 test-rmse:1.673070+0.069365
## [16] train-rmse:1.410372+0.023788 test-rmse:1.652193+0.067194
## [17] train-rmse:1.379688+0.025762 test-rmse:1.638276+0.080910
## [18] train-rmse:1.351978+0.024902 test-rmse:1.618216+0.087291
## [19] train-rmse:1.327389+0.023369 test-rmse:1.597089+0.091773
## [20] train-rmse:1.303511+0.024888 test-rmse:1.581334+0.088423
## [21] train-rmse:1.282370+0.027434 test-rmse:1.567811+0.085678
## [22] train-rmse:1.258066+0.028069 test-rmse:1.546336+0.091399
## [23] train-rmse:1.235707+0.026019 test-rmse:1.536748+0.092827
## [24] train-rmse:1.214330+0.026486 test-rmse:1.523800+0.102924
## [25] train-rmse:1.193797+0.022097 test-rmse:1.510836+0.113236
## [26] train-rmse:1.171501+0.023643 test-rmse:1.502925+0.110341
## [27] train-rmse:1.154868+0.024289 test-rmse:1.491119+0.119584
## [28] train-rmse:1.134331+0.025218 test-rmse:1.475459+0.118299
## [29] train-rmse:1.114776+0.028581 test-rmse:1.460994+0.121062
## [30] train-rmse:1.097584+0.032299 test-rmse:1.454022+0.120224
## [31] train-rmse:1.081878+0.032279 test-rmse:1.443895+0.120744
## [32] train-rmse:1.067811+0.034016 test-rmse:1.438405+0.120402
## [33] train-rmse:1.055793+0.033555 test-rmse:1.431516+0.119625
## [34] train-rmse:1.041720+0.031601 test-rmse:1.424775+0.126412
## [35] train-rmse:1.026835+0.033623 test-rmse:1.414404+0.125671
## [36] train-rmse:1.012709+0.034297 test-rmse:1.397491+0.128728
## [37] train-rmse:0.998067+0.031613 test-rmse:1.388699+0.134429
## [38] train-rmse:0.984931+0.029222 test-rmse:1.382306+0.138196
## [39] train-rmse:0.972749+0.030634 test-rmse:1.375452+0.136927
## [40] train-rmse:0.957787+0.028860 test-rmse:1.358786+0.126997
## [41] train-rmse:0.947626+0.029183 test-rmse:1.354474+0.126061
## [42] train-rmse:0.936325+0.030464 test-rmse:1.347869+0.127567
## [43] train-rmse:0.923803+0.031796 test-rmse:1.343915+0.121988
## [44] train-rmse:0.912045+0.032611 test-rmse:1.339600+0.125505
## [45] train-rmse:0.902768+0.032125 test-rmse:1.338957+0.135083
## [46] train-rmse:0.890359+0.027526 test-rmse:1.336760+0.137737
## [47] train-rmse:0.881199+0.028783 test-rmse:1.331536+0.140616
## [48] train-rmse:0.872715+0.027858 test-rmse:1.327744+0.137382
## [49] train-rmse:0.862968+0.028688 test-rmse:1.321688+0.143398
## [50] train-rmse:0.851281+0.025844 test-rmse:1.314811+0.148412
## [51] train-rmse:0.842571+0.028397 test-rmse:1.314123+0.144540
## [52] train-rmse:0.833717+0.026783 test-rmse:1.310351+0.145358
## [53] train-rmse:0.827064+0.026147 test-rmse:1.307995+0.147443
## [54] train-rmse:0.821267+0.026488 test-rmse:1.307204+0.146755
## [55] train-rmse:0.812858+0.029093 test-rmse:1.303400+0.146405
## [56] train-rmse:0.806378+0.029188 test-rmse:1.300946+0.147588
## [57] train-rmse:0.799630+0.028543 test-rmse:1.301599+0.149321
## [58] train-rmse:0.791728+0.028388 test-rmse:1.297004+0.148987
## [59] train-rmse:0.782610+0.026110 test-rmse:1.286094+0.145232
## [60] train-rmse:0.774079+0.027072 test-rmse:1.281949+0.144661
## [61] train-rmse:0.769414+0.026921 test-rmse:1.281939+0.143956
## [62] train-rmse:0.764330+0.026984 test-rmse:1.280156+0.144445
## [63] train-rmse:0.758379+0.027386 test-rmse:1.280780+0.146494
## [64] train-rmse:0.748992+0.027824 test-rmse:1.274800+0.147713
## [65] train-rmse:0.742563+0.028193 test-rmse:1.266949+0.145598
## [66] train-rmse:0.736591+0.027976 test-rmse:1.265417+0.147032
## [67] train-rmse:0.731583+0.028464 test-rmse:1.262355+0.153011
## [68] train-rmse:0.726589+0.027893 test-rmse:1.261077+0.153585
## [69] train-rmse:0.721300+0.027195 test-rmse:1.263937+0.151243
## [70] train-rmse:0.717682+0.026893 test-rmse:1.263157+0.151428
## [71] train-rmse:0.713002+0.027720 test-rmse:1.261480+0.151532
## [72] train-rmse:0.708573+0.027450 test-rmse:1.261093+0.151533
## [73] train-rmse:0.703660+0.029683 test-rmse:1.261418+0.151450
## [74] train-rmse:0.696922+0.028605 test-rmse:1.257587+0.157418
## [75] train-rmse:0.692382+0.029296 test-rmse:1.260931+0.156665
## [76] train-rmse:0.686734+0.029271 test-rmse:1.257534+0.157579
## [77] train-rmse:0.677899+0.029124 test-rmse:1.248959+0.152762
## [78] train-rmse:0.673866+0.029074 test-rmse:1.249521+0.153111
## [79] train-rmse:0.668706+0.029441 test-rmse:1.245365+0.152651
## [80] train-rmse:0.663688+0.027953 test-rmse:1.245211+0.151734
## [81] train-rmse:0.659651+0.026404 test-rmse:1.245335+0.152761
## [82] train-rmse:0.656672+0.026392 test-rmse:1.243194+0.151985
## [83] train-rmse:0.653246+0.026471 test-rmse:1.242849+0.153103
## [84] train-rmse:0.649184+0.027934 test-rmse:1.241716+0.152409
## [85] train-rmse:0.646417+0.027390 test-rmse:1.242098+0.152221
## [86] train-rmse:0.643894+0.027928 test-rmse:1.241533+0.151104
## [87] train-rmse:0.640803+0.027640 test-rmse:1.240807+0.150480
## [88] train-rmse:0.637772+0.026921 test-rmse:1.239990+0.149337
## [89] train-rmse:0.634886+0.027281 test-rmse:1.234931+0.148364
## [90] train-rmse:0.630531+0.028682 test-rmse:1.232830+0.147084
## [91] train-rmse:0.625982+0.029596 test-rmse:1.234330+0.146264
## [92] train-rmse:0.623190+0.029831 test-rmse:1.234937+0.145576
## [93] train-rmse:0.620724+0.029459 test-rmse:1.234225+0.145785
## [94] train-rmse:0.616743+0.028940 test-rmse:1.230491+0.145391
## [95] train-rmse:0.614324+0.029672 test-rmse:1.228567+0.146445
## [96] train-rmse:0.611910+0.030192 test-rmse:1.229887+0.144460
## [97] train-rmse:0.609222+0.031478 test-rmse:1.230757+0.145659
## [98] train-rmse:0.607444+0.032001 test-rmse:1.229831+0.145034
## [99] train-rmse:0.604594+0.031899 test-rmse:1.230215+0.146666
## [100] train-rmse:0.602385+0.032232 test-rmse:1.230765+0.146207
## [101] train-rmse:0.600598+0.032088 test-rmse:1.231186+0.145839
## Stopping. Best iteration:
## [95] train-rmse:0.614324+0.029672 test-rmse:1.228567+0.146445
##
## 59
## [1] train-rmse:7.324617+0.053692 test-rmse:7.321514+0.256872
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.589787+0.039233 test-rmse:5.599891+0.239852
## [3] train-rmse:4.338941+0.028741 test-rmse:4.363274+0.208873
## [4] train-rmse:3.431830+0.020827 test-rmse:3.466378+0.173174
## [5] train-rmse:2.781433+0.013788 test-rmse:2.844909+0.153111
## [6] train-rmse:2.329618+0.014403 test-rmse:2.414963+0.125221
## [7] train-rmse:2.012359+0.013220 test-rmse:2.142601+0.107005
## [8] train-rmse:1.797700+0.014446 test-rmse:1.955004+0.091625
## [9] train-rmse:1.642936+0.010251 test-rmse:1.824592+0.064634
## [10] train-rmse:1.529342+0.011344 test-rmse:1.748191+0.071077
## [11] train-rmse:1.447509+0.012965 test-rmse:1.687977+0.070954
## [12] train-rmse:1.380635+0.012639 test-rmse:1.634285+0.067986
## [13] train-rmse:1.330104+0.012447 test-rmse:1.601290+0.066787
## [14] train-rmse:1.286259+0.011165 test-rmse:1.560284+0.066491
## [15] train-rmse:1.244717+0.014584 test-rmse:1.530236+0.081320
## [16] train-rmse:1.209941+0.014896 test-rmse:1.516323+0.082352
## [17] train-rmse:1.176373+0.012707 test-rmse:1.495040+0.083913
## [18] train-rmse:1.145030+0.013011 test-rmse:1.467314+0.104667
## [19] train-rmse:1.116214+0.014485 test-rmse:1.447901+0.105348
## [20] train-rmse:1.089775+0.013669 test-rmse:1.433016+0.101605
## [21] train-rmse:1.063495+0.011749 test-rmse:1.423714+0.108180
## [22] train-rmse:1.040649+0.014702 test-rmse:1.410601+0.112549
## [23] train-rmse:1.016955+0.010322 test-rmse:1.393258+0.115750
## [24] train-rmse:0.992076+0.009615 test-rmse:1.385308+0.114803
## [25] train-rmse:0.972810+0.007996 test-rmse:1.372125+0.122661
## [26] train-rmse:0.950232+0.009504 test-rmse:1.362769+0.129797
## [27] train-rmse:0.932140+0.011188 test-rmse:1.356356+0.128914
## [28] train-rmse:0.914810+0.011309 test-rmse:1.351319+0.130657
## [29] train-rmse:0.899196+0.011515 test-rmse:1.337450+0.137771
## [30] train-rmse:0.882036+0.009136 test-rmse:1.331380+0.137503
## [31] train-rmse:0.864520+0.011463 test-rmse:1.326656+0.135961
## [32] train-rmse:0.849276+0.012551 test-rmse:1.319131+0.143079
## [33] train-rmse:0.835166+0.014390 test-rmse:1.312679+0.146279
## [34] train-rmse:0.821198+0.011525 test-rmse:1.306248+0.149458
## [35] train-rmse:0.804551+0.009828 test-rmse:1.302567+0.153401
## [36] train-rmse:0.791313+0.009672 test-rmse:1.299540+0.154719
## [37] train-rmse:0.779514+0.011046 test-rmse:1.292036+0.153649
## [38] train-rmse:0.767371+0.011872 test-rmse:1.285319+0.152086
## [39] train-rmse:0.755603+0.011397 test-rmse:1.285994+0.159672
## [40] train-rmse:0.745798+0.010503 test-rmse:1.282244+0.161873
## [41] train-rmse:0.734723+0.011476 test-rmse:1.278662+0.163370
## [42] train-rmse:0.724492+0.011781 test-rmse:1.276200+0.168869
## [43] train-rmse:0.715549+0.012231 test-rmse:1.273098+0.173433
## [44] train-rmse:0.708188+0.012916 test-rmse:1.273494+0.171187
## [45] train-rmse:0.699486+0.014854 test-rmse:1.274539+0.168444
## [46] train-rmse:0.689832+0.012309 test-rmse:1.271238+0.168453
## [47] train-rmse:0.680530+0.013379 test-rmse:1.268773+0.167751
## [48] train-rmse:0.670535+0.014906 test-rmse:1.263273+0.169295
## [49] train-rmse:0.663811+0.014974 test-rmse:1.263413+0.170584
## [50] train-rmse:0.655987+0.016028 test-rmse:1.259898+0.169012
## [51] train-rmse:0.645836+0.017075 test-rmse:1.260317+0.169650
## [52] train-rmse:0.639740+0.017107 test-rmse:1.257165+0.169572
## [53] train-rmse:0.630812+0.018749 test-rmse:1.255645+0.169213
## [54] train-rmse:0.622068+0.019820 test-rmse:1.255143+0.168435
## [55] train-rmse:0.614231+0.019665 test-rmse:1.246906+0.163752
## [56] train-rmse:0.608577+0.019229 test-rmse:1.250061+0.161482
## [57] train-rmse:0.602162+0.017176 test-rmse:1.248742+0.163925
## [58] train-rmse:0.596691+0.016919 test-rmse:1.246525+0.162157
## [59] train-rmse:0.591057+0.016924 test-rmse:1.245079+0.163103
## [60] train-rmse:0.586314+0.017135 test-rmse:1.244266+0.163063
## [61] train-rmse:0.580950+0.018829 test-rmse:1.243378+0.164530
## [62] train-rmse:0.575456+0.016865 test-rmse:1.244397+0.163183
## [63] train-rmse:0.571423+0.017136 test-rmse:1.242805+0.162092
## [64] train-rmse:0.565229+0.017342 test-rmse:1.242353+0.163089
## [65] train-rmse:0.560436+0.019191 test-rmse:1.241045+0.163081
## [66] train-rmse:0.555961+0.019976 test-rmse:1.240279+0.162280
## [67] train-rmse:0.551623+0.019392 test-rmse:1.236867+0.161645
## [68] train-rmse:0.543386+0.016875 test-rmse:1.235917+0.159840
## [69] train-rmse:0.538917+0.015666 test-rmse:1.236826+0.162283
## [70] train-rmse:0.533751+0.016141 test-rmse:1.236881+0.161278
## [71] train-rmse:0.529140+0.016070 test-rmse:1.236447+0.161540
## [72] train-rmse:0.523659+0.017175 test-rmse:1.234994+0.160617
## [73] train-rmse:0.520375+0.017119 test-rmse:1.234926+0.164697
## [74] train-rmse:0.516600+0.017925 test-rmse:1.234170+0.163201
## [75] train-rmse:0.512405+0.017732 test-rmse:1.236102+0.162486
## [76] train-rmse:0.505889+0.016595 test-rmse:1.236893+0.162256
## [77] train-rmse:0.500873+0.018333 test-rmse:1.235601+0.162440
## [78] train-rmse:0.497066+0.019456 test-rmse:1.235146+0.162529
## [79] train-rmse:0.493207+0.020930 test-rmse:1.235246+0.162312
## [80] train-rmse:0.489134+0.020965 test-rmse:1.234439+0.162115
## Stopping. Best iteration:
## [74] train-rmse:0.516600+0.017925 test-rmse:1.234170+0.163201
##
## 60
## [1] train-rmse:7.319946+0.054155 test-rmse:7.321748+0.257577
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.577043+0.039963 test-rmse:5.594723+0.231228
## [3] train-rmse:4.311103+0.030027 test-rmse:4.333684+0.202307
## [4] train-rmse:3.389877+0.023484 test-rmse:3.423992+0.165746
## [5] train-rmse:2.727118+0.017586 test-rmse:2.805640+0.139817
## [6] train-rmse:2.255862+0.021126 test-rmse:2.345459+0.108338
## [7] train-rmse:1.924645+0.021032 test-rmse:2.068236+0.106430
## [8] train-rmse:1.693622+0.026590 test-rmse:1.877859+0.086707
## [9] train-rmse:1.518497+0.025929 test-rmse:1.761107+0.079893
## [10] train-rmse:1.400870+0.026177 test-rmse:1.665831+0.080059
## [11] train-rmse:1.306046+0.024431 test-rmse:1.583353+0.080835
## [12] train-rmse:1.233886+0.025884 test-rmse:1.548941+0.083822
## [13] train-rmse:1.172457+0.024942 test-rmse:1.515242+0.085768
## [14] train-rmse:1.121191+0.018461 test-rmse:1.485193+0.086880
## [15] train-rmse:1.075455+0.020396 test-rmse:1.463857+0.103597
## [16] train-rmse:1.035630+0.022154 test-rmse:1.440015+0.104823
## [17] train-rmse:1.000252+0.023627 test-rmse:1.421476+0.110039
## [18] train-rmse:0.965833+0.021953 test-rmse:1.403689+0.118068
## [19] train-rmse:0.934603+0.018601 test-rmse:1.387845+0.122860
## [20] train-rmse:0.908385+0.020764 test-rmse:1.370916+0.125823
## [21] train-rmse:0.885543+0.020645 test-rmse:1.363561+0.123998
## [22] train-rmse:0.859739+0.023121 test-rmse:1.357274+0.133270
## [23] train-rmse:0.830361+0.022830 test-rmse:1.332952+0.131342
## [24] train-rmse:0.810413+0.024008 test-rmse:1.320607+0.138305
## [25] train-rmse:0.786275+0.025999 test-rmse:1.304189+0.135790
## [26] train-rmse:0.765164+0.020700 test-rmse:1.298448+0.141320
## [27] train-rmse:0.747970+0.019323 test-rmse:1.293059+0.139455
## [28] train-rmse:0.730488+0.019149 test-rmse:1.281792+0.141309
## [29] train-rmse:0.714608+0.018383 test-rmse:1.280215+0.148682
## [30] train-rmse:0.699007+0.018420 test-rmse:1.279239+0.150294
## [31] train-rmse:0.682838+0.019131 test-rmse:1.276920+0.150793
## [32] train-rmse:0.669810+0.016944 test-rmse:1.265066+0.154651
## [33] train-rmse:0.658182+0.018652 test-rmse:1.258512+0.154241
## [34] train-rmse:0.642854+0.018520 test-rmse:1.249450+0.153455
## [35] train-rmse:0.629972+0.021001 test-rmse:1.250390+0.152085
## [36] train-rmse:0.618867+0.021653 test-rmse:1.248979+0.154863
## [37] train-rmse:0.607577+0.020721 test-rmse:1.250187+0.154235
## [38] train-rmse:0.596254+0.018874 test-rmse:1.248873+0.156996
## [39] train-rmse:0.584360+0.022087 test-rmse:1.244024+0.156296
## [40] train-rmse:0.575895+0.022099 test-rmse:1.239763+0.155591
## [41] train-rmse:0.569450+0.022575 test-rmse:1.239021+0.155184
## [42] train-rmse:0.561999+0.023803 test-rmse:1.241323+0.157437
## [43] train-rmse:0.551239+0.022733 test-rmse:1.239721+0.154316
## [44] train-rmse:0.543961+0.021537 test-rmse:1.240262+0.153370
## [45] train-rmse:0.533995+0.021981 test-rmse:1.235565+0.156796
## [46] train-rmse:0.527973+0.022388 test-rmse:1.234875+0.158370
## [47] train-rmse:0.520147+0.022865 test-rmse:1.233373+0.158162
## [48] train-rmse:0.510821+0.020888 test-rmse:1.235697+0.156012
## [49] train-rmse:0.503575+0.019848 test-rmse:1.233510+0.155489
## [50] train-rmse:0.496927+0.021349 test-rmse:1.233820+0.155568
## [51] train-rmse:0.491398+0.021368 test-rmse:1.236062+0.156115
## [52] train-rmse:0.484292+0.022344 test-rmse:1.237427+0.158111
## [53] train-rmse:0.475905+0.020078 test-rmse:1.234863+0.158237
## Stopping. Best iteration:
## [47] train-rmse:0.520147+0.022865 test-rmse:1.233373+0.158162
##
## 61
## [1] train-rmse:7.318862+0.054412 test-rmse:7.322546+0.249401
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.569446+0.040835 test-rmse:5.588905+0.226898
## [3] train-rmse:4.292926+0.031028 test-rmse:4.324159+0.188218
## [4] train-rmse:3.364945+0.028692 test-rmse:3.423808+0.154206
## [5] train-rmse:2.681449+0.022174 test-rmse:2.774670+0.140472
## [6] train-rmse:2.193948+0.020711 test-rmse:2.325187+0.117880
## [7] train-rmse:1.846631+0.018365 test-rmse:2.037956+0.101734
## [8] train-rmse:1.599951+0.019203 test-rmse:1.842753+0.098967
## [9] train-rmse:1.412613+0.025751 test-rmse:1.692873+0.082229
## [10] train-rmse:1.273415+0.018060 test-rmse:1.588347+0.083689
## [11] train-rmse:1.164089+0.023641 test-rmse:1.514345+0.085173
## [12] train-rmse:1.087066+0.022857 test-rmse:1.460752+0.099505
## [13] train-rmse:1.026152+0.025103 test-rmse:1.424622+0.100559
## [14] train-rmse:0.964955+0.029213 test-rmse:1.388556+0.106463
## [15] train-rmse:0.924444+0.031991 test-rmse:1.373151+0.111541
## [16] train-rmse:0.883378+0.027985 test-rmse:1.345622+0.118319
## [17] train-rmse:0.844192+0.028142 test-rmse:1.319595+0.122514
## [18] train-rmse:0.809014+0.023748 test-rmse:1.312827+0.128869
## [19] train-rmse:0.778821+0.020001 test-rmse:1.287831+0.140315
## [20] train-rmse:0.749423+0.023127 test-rmse:1.280886+0.135856
## [21] train-rmse:0.726257+0.022722 test-rmse:1.268133+0.131898
## [22] train-rmse:0.704187+0.024522 test-rmse:1.262981+0.136363
## [23] train-rmse:0.679177+0.021553 test-rmse:1.258634+0.141037
## [24] train-rmse:0.658866+0.022989 test-rmse:1.250554+0.143641
## [25] train-rmse:0.642288+0.021895 test-rmse:1.249258+0.143468
## [26] train-rmse:0.622362+0.022681 test-rmse:1.242255+0.145006
## [27] train-rmse:0.606134+0.022209 test-rmse:1.237665+0.144221
## [28] train-rmse:0.591890+0.023065 test-rmse:1.233046+0.142665
## [29] train-rmse:0.577815+0.021832 test-rmse:1.230750+0.147219
## [30] train-rmse:0.561946+0.018908 test-rmse:1.228352+0.155986
## [31] train-rmse:0.548668+0.019793 test-rmse:1.223425+0.156265
## [32] train-rmse:0.534171+0.023457 test-rmse:1.220832+0.157173
## [33] train-rmse:0.522538+0.021331 test-rmse:1.217306+0.158479
## [34] train-rmse:0.511565+0.023238 test-rmse:1.215289+0.158673
## [35] train-rmse:0.502591+0.022874 test-rmse:1.213388+0.161780
## [36] train-rmse:0.492052+0.021672 test-rmse:1.211538+0.160616
## [37] train-rmse:0.480879+0.023730 test-rmse:1.207750+0.161248
## [38] train-rmse:0.471050+0.023184 test-rmse:1.206635+0.157917
## [39] train-rmse:0.461225+0.022938 test-rmse:1.206707+0.159559
## [40] train-rmse:0.453450+0.022679 test-rmse:1.205906+0.159947
## [41] train-rmse:0.444272+0.021042 test-rmse:1.206120+0.161059
## [42] train-rmse:0.436657+0.020637 test-rmse:1.204789+0.163171
## [43] train-rmse:0.429804+0.021306 test-rmse:1.203229+0.160560
## [44] train-rmse:0.421922+0.021412 test-rmse:1.201338+0.159404
## [45] train-rmse:0.414029+0.019369 test-rmse:1.202929+0.160793
## [46] train-rmse:0.405056+0.021869 test-rmse:1.201794+0.160505
## [47] train-rmse:0.399050+0.023045 test-rmse:1.200376+0.162448
## [48] train-rmse:0.394716+0.022910 test-rmse:1.200167+0.162021
## [49] train-rmse:0.389737+0.022713 test-rmse:1.199359+0.163366
## [50] train-rmse:0.381690+0.021228 test-rmse:1.199040+0.163202
## [51] train-rmse:0.378241+0.020598 test-rmse:1.197807+0.162782
## [52] train-rmse:0.369928+0.019965 test-rmse:1.198671+0.162593
## [53] train-rmse:0.363511+0.021303 test-rmse:1.198906+0.161970
## [54] train-rmse:0.355183+0.018844 test-rmse:1.197149+0.161738
## [55] train-rmse:0.349085+0.015932 test-rmse:1.197302+0.163026
## [56] train-rmse:0.342605+0.013598 test-rmse:1.196871+0.162000
## [57] train-rmse:0.335913+0.016078 test-rmse:1.196673+0.160923
## [58] train-rmse:0.331728+0.016440 test-rmse:1.197169+0.159622
## [59] train-rmse:0.327161+0.016970 test-rmse:1.196809+0.160159
## [60] train-rmse:0.322300+0.016030 test-rmse:1.196493+0.160167
## [61] train-rmse:0.317830+0.016074 test-rmse:1.197248+0.159790
## [62] train-rmse:0.314419+0.016845 test-rmse:1.198472+0.160515
## [63] train-rmse:0.311425+0.015351 test-rmse:1.199150+0.160908
## [64] train-rmse:0.307498+0.014130 test-rmse:1.198593+0.160209
## [65] train-rmse:0.303499+0.013397 test-rmse:1.199356+0.159727
## [66] train-rmse:0.298282+0.011729 test-rmse:1.197860+0.158578
## Stopping. Best iteration:
## [60] train-rmse:0.322300+0.016030 test-rmse:1.196493+0.160167
##
## 62
## [1] train-rmse:7.318862+0.054412 test-rmse:7.322546+0.249401
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.566230+0.041302 test-rmse:5.592322+0.216858
## [3] train-rmse:4.283992+0.031409 test-rmse:4.323845+0.169368
## [4] train-rmse:3.348786+0.030234 test-rmse:3.398454+0.140729
## [5] train-rmse:2.651709+0.018546 test-rmse:2.732812+0.142072
## [6] train-rmse:2.153559+0.017874 test-rmse:2.279077+0.122683
## [7] train-rmse:1.791248+0.022713 test-rmse:1.982844+0.088370
## [8] train-rmse:1.525179+0.027460 test-rmse:1.765394+0.076413
## [9] train-rmse:1.326920+0.027601 test-rmse:1.601397+0.085982
## [10] train-rmse:1.175504+0.030066 test-rmse:1.507634+0.080624
## [11] train-rmse:1.062130+0.024296 test-rmse:1.433796+0.089848
## [12] train-rmse:0.976327+0.026946 test-rmse:1.382941+0.096560
## [13] train-rmse:0.903941+0.020498 test-rmse:1.357208+0.112984
## [14] train-rmse:0.846169+0.020578 test-rmse:1.335198+0.112428
## [15] train-rmse:0.799342+0.023675 test-rmse:1.315480+0.117276
## [16] train-rmse:0.756426+0.023126 test-rmse:1.296012+0.123140
## [17] train-rmse:0.724712+0.020732 test-rmse:1.285940+0.126627
## [18] train-rmse:0.694216+0.020342 test-rmse:1.272822+0.133010
## [19] train-rmse:0.668210+0.016040 test-rmse:1.266953+0.140856
## [20] train-rmse:0.643181+0.016973 test-rmse:1.260935+0.141738
## [21] train-rmse:0.619843+0.017347 test-rmse:1.253225+0.145054
## [22] train-rmse:0.592485+0.022107 test-rmse:1.247144+0.146907
## [23] train-rmse:0.571195+0.020168 test-rmse:1.245091+0.152678
## [24] train-rmse:0.550633+0.019749 test-rmse:1.244471+0.158068
## [25] train-rmse:0.530729+0.021947 test-rmse:1.239389+0.156812
## [26] train-rmse:0.515211+0.021876 test-rmse:1.233060+0.155742
## [27] train-rmse:0.501452+0.019956 test-rmse:1.228388+0.156987
## [28] train-rmse:0.487126+0.022928 test-rmse:1.227285+0.156223
## [29] train-rmse:0.472732+0.019613 test-rmse:1.223171+0.161395
## [30] train-rmse:0.461316+0.020707 test-rmse:1.222125+0.163495
## [31] train-rmse:0.447965+0.024468 test-rmse:1.219590+0.163725
## [32] train-rmse:0.436484+0.024506 test-rmse:1.219955+0.164419
## [33] train-rmse:0.423527+0.022110 test-rmse:1.218577+0.164879
## [34] train-rmse:0.408090+0.018885 test-rmse:1.213735+0.164830
## [35] train-rmse:0.397291+0.016373 test-rmse:1.212832+0.163762
## [36] train-rmse:0.386731+0.018351 test-rmse:1.211406+0.163386
## [37] train-rmse:0.377501+0.017653 test-rmse:1.211143+0.163211
## [38] train-rmse:0.370297+0.016765 test-rmse:1.210180+0.165782
## [39] train-rmse:0.359426+0.016446 test-rmse:1.209542+0.164830
## [40] train-rmse:0.350387+0.012621 test-rmse:1.209976+0.168757
## [41] train-rmse:0.343288+0.013370 test-rmse:1.209686+0.169169
## [42] train-rmse:0.335007+0.015388 test-rmse:1.208583+0.167442
## [43] train-rmse:0.329026+0.016520 test-rmse:1.208130+0.168117
## [44] train-rmse:0.316835+0.012942 test-rmse:1.207959+0.168906
## [45] train-rmse:0.311552+0.014191 test-rmse:1.207669+0.170116
## [46] train-rmse:0.306210+0.014028 test-rmse:1.208517+0.169414
## [47] train-rmse:0.298889+0.012691 test-rmse:1.209086+0.169725
## [48] train-rmse:0.292980+0.012663 test-rmse:1.208938+0.170109
## [49] train-rmse:0.288155+0.014052 test-rmse:1.209251+0.170219
## [50] train-rmse:0.282783+0.013593 test-rmse:1.210768+0.169262
## [51] train-rmse:0.275904+0.011491 test-rmse:1.209616+0.168000
## Stopping. Best iteration:
## [45] train-rmse:0.311552+0.014191 test-rmse:1.207669+0.170116
##
## 63
## [1] train-rmse:7.201810+0.049632 test-rmse:7.197702+0.265536
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.457991+0.031908 test-rmse:5.457369+0.261221
## [3] train-rmse:4.266353+0.021652 test-rmse:4.265121+0.234746
## [4] train-rmse:3.477821+0.015423 test-rmse:3.497645+0.191068
## [5] train-rmse:2.975860+0.012073 test-rmse:3.003872+0.147272
## [6] train-rmse:2.667016+0.012964 test-rmse:2.705606+0.103338
## [7] train-rmse:2.477382+0.015022 test-rmse:2.511159+0.067988
## [8] train-rmse:2.362439+0.015532 test-rmse:2.400549+0.054486
## [9] train-rmse:2.291153+0.016826 test-rmse:2.340440+0.053023
## [10] train-rmse:2.243564+0.017806 test-rmse:2.292684+0.052599
## [11] train-rmse:2.209489+0.017975 test-rmse:2.254616+0.061749
## [12] train-rmse:2.182202+0.018687 test-rmse:2.219310+0.077914
## [13] train-rmse:2.159601+0.020162 test-rmse:2.202402+0.076736
## [14] train-rmse:2.139847+0.021975 test-rmse:2.182495+0.073743
## [15] train-rmse:2.121449+0.023577 test-rmse:2.173391+0.082822
## [16] train-rmse:2.104415+0.023710 test-rmse:2.166708+0.079823
## [17] train-rmse:2.088129+0.023471 test-rmse:2.158338+0.085502
## [18] train-rmse:2.073114+0.024055 test-rmse:2.145985+0.089749
## [19] train-rmse:2.059054+0.024567 test-rmse:2.134264+0.094278
## [20] train-rmse:2.045059+0.025377 test-rmse:2.113529+0.093512
## [21] train-rmse:2.031371+0.026485 test-rmse:2.094364+0.090883
## [22] train-rmse:2.018111+0.027693 test-rmse:2.074784+0.092328
## [23] train-rmse:2.005365+0.028734 test-rmse:2.064675+0.088766
## [24] train-rmse:1.993068+0.029102 test-rmse:2.056348+0.095766
## [25] train-rmse:1.980758+0.029188 test-rmse:2.051714+0.096874
## [26] train-rmse:1.968843+0.029434 test-rmse:2.041760+0.092855
## [27] train-rmse:1.957365+0.029828 test-rmse:2.037709+0.094115
## [28] train-rmse:1.946374+0.030346 test-rmse:2.030917+0.095285
## [29] train-rmse:1.935627+0.030647 test-rmse:2.020805+0.096192
## [30] train-rmse:1.925248+0.030950 test-rmse:2.011733+0.097129
## [31] train-rmse:1.915286+0.031601 test-rmse:2.005879+0.095319
## [32] train-rmse:1.905566+0.032342 test-rmse:1.996085+0.085204
## [33] train-rmse:1.895883+0.033093 test-rmse:1.987623+0.083398
## [34] train-rmse:1.886756+0.033858 test-rmse:1.978705+0.075986
## [35] train-rmse:1.877632+0.034384 test-rmse:1.968658+0.076457
## [36] train-rmse:1.868616+0.034738 test-rmse:1.952108+0.080339
## [37] train-rmse:1.859748+0.035450 test-rmse:1.949446+0.082076
## [38] train-rmse:1.851011+0.036183 test-rmse:1.936917+0.082708
## [39] train-rmse:1.842467+0.036780 test-rmse:1.932619+0.085237
## [40] train-rmse:1.834187+0.037365 test-rmse:1.922370+0.085767
## [41] train-rmse:1.826036+0.037984 test-rmse:1.916373+0.088648
## [42] train-rmse:1.818045+0.038560 test-rmse:1.908216+0.091770
## [43] train-rmse:1.810120+0.039159 test-rmse:1.901459+0.090044
## [44] train-rmse:1.802358+0.039759 test-rmse:1.895202+0.090250
## [45] train-rmse:1.794826+0.039948 test-rmse:1.892880+0.096644
## [46] train-rmse:1.787431+0.040251 test-rmse:1.885911+0.097224
## [47] train-rmse:1.780380+0.040561 test-rmse:1.883652+0.099962
## [48] train-rmse:1.773073+0.041064 test-rmse:1.877818+0.098449
## [49] train-rmse:1.766214+0.041300 test-rmse:1.874341+0.097366
## [50] train-rmse:1.759473+0.041580 test-rmse:1.868340+0.092157
## [51] train-rmse:1.752798+0.041836 test-rmse:1.863519+0.092875
## [52] train-rmse:1.746207+0.042168 test-rmse:1.860920+0.094216
## [53] train-rmse:1.739651+0.042560 test-rmse:1.854958+0.097028
## [54] train-rmse:1.733380+0.042889 test-rmse:1.847013+0.098461
## [55] train-rmse:1.726999+0.043271 test-rmse:1.842459+0.095282
## [56] train-rmse:1.720854+0.043592 test-rmse:1.836947+0.098467
## [57] train-rmse:1.714858+0.043908 test-rmse:1.827165+0.096196
## [58] train-rmse:1.708972+0.044317 test-rmse:1.820172+0.097751
## [59] train-rmse:1.703320+0.044690 test-rmse:1.812655+0.097429
## [60] train-rmse:1.697783+0.045022 test-rmse:1.809491+0.095617
## [61] train-rmse:1.692265+0.045300 test-rmse:1.807441+0.096802
## [62] train-rmse:1.686826+0.045571 test-rmse:1.802114+0.099288
## [63] train-rmse:1.681477+0.045853 test-rmse:1.798773+0.099169
## [64] train-rmse:1.676028+0.046130 test-rmse:1.793340+0.101601
## [65] train-rmse:1.670733+0.046505 test-rmse:1.787201+0.105687
## [66] train-rmse:1.665685+0.046677 test-rmse:1.781012+0.108420
## [67] train-rmse:1.660621+0.046791 test-rmse:1.778869+0.104827
## [68] train-rmse:1.655621+0.046983 test-rmse:1.775645+0.105880
## [69] train-rmse:1.650847+0.047152 test-rmse:1.771001+0.104524
## [70] train-rmse:1.646177+0.047323 test-rmse:1.765899+0.107337
## [71] train-rmse:1.641559+0.047578 test-rmse:1.765810+0.110475
## [72] train-rmse:1.636886+0.047925 test-rmse:1.759522+0.109166
## [73] train-rmse:1.632372+0.048161 test-rmse:1.757513+0.108619
## [74] train-rmse:1.627956+0.048238 test-rmse:1.753504+0.111658
## [75] train-rmse:1.623599+0.048407 test-rmse:1.750900+0.112313
## [76] train-rmse:1.619249+0.048504 test-rmse:1.744252+0.115663
## [77] train-rmse:1.614889+0.048532 test-rmse:1.734243+0.119497
## [78] train-rmse:1.610633+0.048684 test-rmse:1.731615+0.120227
## [79] train-rmse:1.606578+0.048884 test-rmse:1.728761+0.120324
## [80] train-rmse:1.602569+0.049106 test-rmse:1.724688+0.121279
## [81] train-rmse:1.598659+0.049296 test-rmse:1.720965+0.120517
## [82] train-rmse:1.594678+0.049560 test-rmse:1.721243+0.124553
## [83] train-rmse:1.590904+0.049617 test-rmse:1.717229+0.124209
## [84] train-rmse:1.587184+0.049712 test-rmse:1.712506+0.125184
## [85] train-rmse:1.583494+0.049846 test-rmse:1.712996+0.127890
## [86] train-rmse:1.579866+0.050018 test-rmse:1.707208+0.128748
## [87] train-rmse:1.576293+0.050138 test-rmse:1.705040+0.129953
## [88] train-rmse:1.572684+0.050198 test-rmse:1.702074+0.129616
## [89] train-rmse:1.569029+0.050296 test-rmse:1.699336+0.130230
## [90] train-rmse:1.565489+0.050348 test-rmse:1.700635+0.129032
## [91] train-rmse:1.561957+0.050428 test-rmse:1.704472+0.126988
## [92] train-rmse:1.558564+0.050556 test-rmse:1.698511+0.126970
## [93] train-rmse:1.555274+0.050677 test-rmse:1.695152+0.127497
## [94] train-rmse:1.551980+0.050779 test-rmse:1.687366+0.124975
## [95] train-rmse:1.548763+0.050881 test-rmse:1.687424+0.124433
## [96] train-rmse:1.545536+0.051009 test-rmse:1.685846+0.129437
## [97] train-rmse:1.542308+0.051091 test-rmse:1.684874+0.131491
## [98] train-rmse:1.539177+0.051152 test-rmse:1.679663+0.135671
## [99] train-rmse:1.536141+0.051239 test-rmse:1.672297+0.140541
## [100] train-rmse:1.533143+0.051320 test-rmse:1.668874+0.142777
## [101] train-rmse:1.530194+0.051386 test-rmse:1.664966+0.141162
## [102] train-rmse:1.527233+0.051476 test-rmse:1.660263+0.139989
## [103] train-rmse:1.524328+0.051527 test-rmse:1.655277+0.141670
## [104] train-rmse:1.521467+0.051516 test-rmse:1.652020+0.141763
## [105] train-rmse:1.518608+0.051516 test-rmse:1.649667+0.141832
## [106] train-rmse:1.515882+0.051559 test-rmse:1.647633+0.139676
## [107] train-rmse:1.513244+0.051601 test-rmse:1.645792+0.140635
## [108] train-rmse:1.510688+0.051645 test-rmse:1.641047+0.138223
## [109] train-rmse:1.508128+0.051672 test-rmse:1.638535+0.137611
## [110] train-rmse:1.505593+0.051746 test-rmse:1.637232+0.135790
## [111] train-rmse:1.503060+0.051823 test-rmse:1.635803+0.137654
## [112] train-rmse:1.500567+0.051882 test-rmse:1.634594+0.139601
## [113] train-rmse:1.498139+0.051978 test-rmse:1.633896+0.142260
## [114] train-rmse:1.495734+0.052041 test-rmse:1.631737+0.147912
## [115] train-rmse:1.493345+0.052160 test-rmse:1.628060+0.146666
## [116] train-rmse:1.490952+0.052309 test-rmse:1.623503+0.148480
## [117] train-rmse:1.488575+0.052344 test-rmse:1.620690+0.148124
## [118] train-rmse:1.486307+0.052392 test-rmse:1.619280+0.148304
## [119] train-rmse:1.484034+0.052499 test-rmse:1.617113+0.150041
## [120] train-rmse:1.481756+0.052579 test-rmse:1.616856+0.149532
## [121] train-rmse:1.479486+0.052659 test-rmse:1.616542+0.150225
## [122] train-rmse:1.477226+0.052701 test-rmse:1.615522+0.150034
## [123] train-rmse:1.474972+0.052706 test-rmse:1.616280+0.151129
## [124] train-rmse:1.472808+0.052705 test-rmse:1.613145+0.151987
## [125] train-rmse:1.470639+0.052721 test-rmse:1.615929+0.151917
## [126] train-rmse:1.468571+0.052705 test-rmse:1.611297+0.156471
## [127] train-rmse:1.466533+0.052697 test-rmse:1.610405+0.159656
## [128] train-rmse:1.464540+0.052725 test-rmse:1.606779+0.161348
## [129] train-rmse:1.462564+0.052730 test-rmse:1.604200+0.162355
## [130] train-rmse:1.460577+0.052777 test-rmse:1.601789+0.161269
## [131] train-rmse:1.458609+0.052819 test-rmse:1.598657+0.161252
## [132] train-rmse:1.456659+0.052849 test-rmse:1.597626+0.159764
## [133] train-rmse:1.454744+0.052889 test-rmse:1.592214+0.157871
## [134] train-rmse:1.452899+0.052936 test-rmse:1.589579+0.157543
## [135] train-rmse:1.451078+0.052948 test-rmse:1.589201+0.154111
## [136] train-rmse:1.449316+0.053001 test-rmse:1.587280+0.157030
## [137] train-rmse:1.447605+0.053044 test-rmse:1.585034+0.157978
## [138] train-rmse:1.445900+0.053087 test-rmse:1.585678+0.158148
## [139] train-rmse:1.444158+0.053131 test-rmse:1.582606+0.158547
## [140] train-rmse:1.442458+0.053164 test-rmse:1.579907+0.160191
## [141] train-rmse:1.440751+0.053217 test-rmse:1.578176+0.158450
## [142] train-rmse:1.439098+0.053237 test-rmse:1.578202+0.157234
## [143] train-rmse:1.437454+0.053258 test-rmse:1.576216+0.157003
## [144] train-rmse:1.435865+0.053277 test-rmse:1.572462+0.161856
## [145] train-rmse:1.434288+0.053302 test-rmse:1.571294+0.160053
## [146] train-rmse:1.432732+0.053330 test-rmse:1.572994+0.163428
## [147] train-rmse:1.431186+0.053321 test-rmse:1.570656+0.163788
## [148] train-rmse:1.429629+0.053315 test-rmse:1.570788+0.162231
## [149] train-rmse:1.428076+0.053324 test-rmse:1.569572+0.162514
## [150] train-rmse:1.426546+0.053335 test-rmse:1.569470+0.162241
## [151] train-rmse:1.425035+0.053365 test-rmse:1.568624+0.161542
## [152] train-rmse:1.423598+0.053390 test-rmse:1.568611+0.162366
## [153] train-rmse:1.422163+0.053392 test-rmse:1.566586+0.162665
## [154] train-rmse:1.420688+0.053337 test-rmse:1.565203+0.162805
## [155] train-rmse:1.419229+0.053273 test-rmse:1.563254+0.162725
## [156] train-rmse:1.417847+0.053269 test-rmse:1.559263+0.162785
## [157] train-rmse:1.416495+0.053275 test-rmse:1.556874+0.162559
## [158] train-rmse:1.415142+0.053280 test-rmse:1.555830+0.164058
## [159] train-rmse:1.413798+0.053349 test-rmse:1.554291+0.167135
## [160] train-rmse:1.412476+0.053390 test-rmse:1.554844+0.166782
## [161] train-rmse:1.411180+0.053417 test-rmse:1.553599+0.167913
## [162] train-rmse:1.409845+0.053394 test-rmse:1.551068+0.167444
## [163] train-rmse:1.408597+0.053384 test-rmse:1.549564+0.167620
## [164] train-rmse:1.407354+0.053376 test-rmse:1.548301+0.166849
## [165] train-rmse:1.406120+0.053350 test-rmse:1.547123+0.167968
## [166] train-rmse:1.404878+0.053316 test-rmse:1.545881+0.167782
## [167] train-rmse:1.403674+0.053309 test-rmse:1.544867+0.168242
## [168] train-rmse:1.402502+0.053313 test-rmse:1.545200+0.170168
## [169] train-rmse:1.401330+0.053334 test-rmse:1.541584+0.169889
## [170] train-rmse:1.400156+0.053337 test-rmse:1.540247+0.171505
## [171] train-rmse:1.399029+0.053376 test-rmse:1.539303+0.170583
## [172] train-rmse:1.397893+0.053402 test-rmse:1.537806+0.171494
## [173] train-rmse:1.396796+0.053426 test-rmse:1.536307+0.172061
## [174] train-rmse:1.395694+0.053441 test-rmse:1.535476+0.172562
## [175] train-rmse:1.394611+0.053472 test-rmse:1.533590+0.171350
## [176] train-rmse:1.393522+0.053504 test-rmse:1.534906+0.173353
## [177] train-rmse:1.392481+0.053519 test-rmse:1.533963+0.173769
## [178] train-rmse:1.391454+0.053529 test-rmse:1.533474+0.174472
## [179] train-rmse:1.390417+0.053519 test-rmse:1.535929+0.175121
## [180] train-rmse:1.389407+0.053512 test-rmse:1.533745+0.174221
## [181] train-rmse:1.388402+0.053488 test-rmse:1.531009+0.176968
## [182] train-rmse:1.387431+0.053488 test-rmse:1.530874+0.176241
## [183] train-rmse:1.386497+0.053491 test-rmse:1.529936+0.176234
## [184] train-rmse:1.385530+0.053497 test-rmse:1.528640+0.176049
## [185] train-rmse:1.384569+0.053477 test-rmse:1.528246+0.176656
## [186] train-rmse:1.383636+0.053460 test-rmse:1.527199+0.177913
## [187] train-rmse:1.382717+0.053456 test-rmse:1.526588+0.176717
## [188] train-rmse:1.381804+0.053464 test-rmse:1.526398+0.177211
## [189] train-rmse:1.380853+0.053466 test-rmse:1.526630+0.176816
## [190] train-rmse:1.379953+0.053443 test-rmse:1.524888+0.176803
## [191] train-rmse:1.379040+0.053418 test-rmse:1.524696+0.176732
## [192] train-rmse:1.378148+0.053390 test-rmse:1.522727+0.177044
## [193] train-rmse:1.377288+0.053364 test-rmse:1.522293+0.177150
## [194] train-rmse:1.376437+0.053331 test-rmse:1.521152+0.178226
## [195] train-rmse:1.375606+0.053306 test-rmse:1.522673+0.179230
## [196] train-rmse:1.374787+0.053302 test-rmse:1.522337+0.180945
## [197] train-rmse:1.373968+0.053299 test-rmse:1.521275+0.179070
## [198] train-rmse:1.373171+0.053304 test-rmse:1.520528+0.178819
## [199] train-rmse:1.372371+0.053309 test-rmse:1.519299+0.177834
## [200] train-rmse:1.371582+0.053318 test-rmse:1.518068+0.178452
## [201] train-rmse:1.370813+0.053329 test-rmse:1.516337+0.181118
## [202] train-rmse:1.370020+0.053344 test-rmse:1.515231+0.181134
## [203] train-rmse:1.369255+0.053343 test-rmse:1.512514+0.178780
## [204] train-rmse:1.368514+0.053349 test-rmse:1.512672+0.179788
## [205] train-rmse:1.367782+0.053343 test-rmse:1.514219+0.179698
## [206] train-rmse:1.367058+0.053330 test-rmse:1.514281+0.181978
## [207] train-rmse:1.366340+0.053322 test-rmse:1.513640+0.183068
## [208] train-rmse:1.365631+0.053310 test-rmse:1.513059+0.183087
## [209] train-rmse:1.364919+0.053312 test-rmse:1.514479+0.184906
## Stopping. Best iteration:
## [203] train-rmse:1.369255+0.053343 test-rmse:1.512514+0.178780
##
## 64
## [1] train-rmse:7.164372+0.050111 test-rmse:7.160793+0.260016
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.386446+0.036893 test-rmse:5.381956+0.237926
## [3] train-rmse:4.159824+0.028925 test-rmse:4.172955+0.211835
## [4] train-rmse:3.322261+0.027308 test-rmse:3.346224+0.156828
## [5] train-rmse:2.779536+0.024143 test-rmse:2.817318+0.117753
## [6] train-rmse:2.419282+0.033409 test-rmse:2.487176+0.082664
## [7] train-rmse:2.195386+0.036986 test-rmse:2.283900+0.047779
## [8] train-rmse:2.053011+0.033653 test-rmse:2.157263+0.050189
## [9] train-rmse:1.960040+0.033742 test-rmse:2.065704+0.059271
## [10] train-rmse:1.891741+0.036388 test-rmse:2.012463+0.072909
## [11] train-rmse:1.844461+0.038357 test-rmse:1.981567+0.077884
## [12] train-rmse:1.803238+0.037067 test-rmse:1.957442+0.098743
## [13] train-rmse:1.758493+0.033476 test-rmse:1.915132+0.121579
## [14] train-rmse:1.729669+0.034936 test-rmse:1.886396+0.116357
## [15] train-rmse:1.698739+0.031794 test-rmse:1.857200+0.102188
## [16] train-rmse:1.673942+0.034427 test-rmse:1.839659+0.099932
## [17] train-rmse:1.651366+0.035663 test-rmse:1.822050+0.099817
## [18] train-rmse:1.630153+0.035817 test-rmse:1.803453+0.098268
## [19] train-rmse:1.609061+0.035391 test-rmse:1.797679+0.107768
## [20] train-rmse:1.588133+0.035097 test-rmse:1.788782+0.108094
## [21] train-rmse:1.567923+0.037074 test-rmse:1.768622+0.105863
## [22] train-rmse:1.544535+0.040988 test-rmse:1.756634+0.112533
## [23] train-rmse:1.528251+0.043256 test-rmse:1.738480+0.111881
## [24] train-rmse:1.496993+0.035541 test-rmse:1.698352+0.085651
## [25] train-rmse:1.482115+0.036004 test-rmse:1.682538+0.087379
## [26] train-rmse:1.467603+0.036470 test-rmse:1.672214+0.089657
## [27] train-rmse:1.452556+0.036277 test-rmse:1.665771+0.084567
## [28] train-rmse:1.437842+0.037710 test-rmse:1.652552+0.080723
## [29] train-rmse:1.422280+0.039427 test-rmse:1.638339+0.082922
## [30] train-rmse:1.403375+0.049284 test-rmse:1.633521+0.078549
## [31] train-rmse:1.383945+0.048498 test-rmse:1.618411+0.088970
## [32] train-rmse:1.369341+0.047890 test-rmse:1.612278+0.086873
## [33] train-rmse:1.355750+0.046976 test-rmse:1.604398+0.091739
## [34] train-rmse:1.341915+0.049010 test-rmse:1.592432+0.101676
## [35] train-rmse:1.329517+0.049571 test-rmse:1.575389+0.108828
## [36] train-rmse:1.312434+0.049483 test-rmse:1.562693+0.111725
## [37] train-rmse:1.302111+0.049911 test-rmse:1.559358+0.109883
## [38] train-rmse:1.290997+0.050848 test-rmse:1.547409+0.111411
## [39] train-rmse:1.280847+0.051809 test-rmse:1.534487+0.111961
## [40] train-rmse:1.269376+0.051941 test-rmse:1.530177+0.113292
## [41] train-rmse:1.259474+0.051986 test-rmse:1.522240+0.115023
## [42] train-rmse:1.250930+0.052668 test-rmse:1.517874+0.113415
## [43] train-rmse:1.241636+0.054545 test-rmse:1.511972+0.114603
## [44] train-rmse:1.232328+0.054363 test-rmse:1.505669+0.111900
## [45] train-rmse:1.223589+0.054712 test-rmse:1.506441+0.116419
## [46] train-rmse:1.215306+0.054737 test-rmse:1.499723+0.116808
## [47] train-rmse:1.208041+0.055260 test-rmse:1.492999+0.117415
## [48] train-rmse:1.200200+0.055578 test-rmse:1.485653+0.121588
## [49] train-rmse:1.192505+0.055307 test-rmse:1.487313+0.120128
## [50] train-rmse:1.177631+0.055934 test-rmse:1.479262+0.123369
## [51] train-rmse:1.168818+0.057952 test-rmse:1.473757+0.123309
## [52] train-rmse:1.161963+0.058532 test-rmse:1.465104+0.119014
## [53] train-rmse:1.148366+0.058893 test-rmse:1.443894+0.104848
## [54] train-rmse:1.141877+0.059063 test-rmse:1.439459+0.107934
## [55] train-rmse:1.134700+0.059827 test-rmse:1.433145+0.104505
## [56] train-rmse:1.128953+0.059942 test-rmse:1.429492+0.101373
## [57] train-rmse:1.113586+0.060527 test-rmse:1.418387+0.106366
## [58] train-rmse:1.105703+0.062302 test-rmse:1.414167+0.116919
## [59] train-rmse:1.098514+0.060355 test-rmse:1.407569+0.122997
## [60] train-rmse:1.092366+0.061984 test-rmse:1.405380+0.122322
## [61] train-rmse:1.086538+0.061682 test-rmse:1.400322+0.120498
## [62] train-rmse:1.078496+0.056591 test-rmse:1.396318+0.121725
## [63] train-rmse:1.068838+0.050708 test-rmse:1.387763+0.122856
## [64] train-rmse:1.063250+0.051328 test-rmse:1.389405+0.127285
## [65] train-rmse:1.057674+0.050658 test-rmse:1.384955+0.127781
## [66] train-rmse:1.050430+0.051129 test-rmse:1.378269+0.126222
## [67] train-rmse:1.041165+0.050380 test-rmse:1.375416+0.124670
## [68] train-rmse:1.036586+0.050622 test-rmse:1.373934+0.122889
## [69] train-rmse:1.030136+0.050619 test-rmse:1.371015+0.121826
## [70] train-rmse:1.026012+0.050946 test-rmse:1.368907+0.118791
## [71] train-rmse:1.021524+0.051117 test-rmse:1.365633+0.121462
## [72] train-rmse:1.014017+0.051078 test-rmse:1.358960+0.121364
## [73] train-rmse:1.009442+0.050984 test-rmse:1.358357+0.124947
## [74] train-rmse:1.004829+0.051390 test-rmse:1.357709+0.124278
## [75] train-rmse:0.996704+0.051369 test-rmse:1.355826+0.127511
## [76] train-rmse:0.986785+0.050660 test-rmse:1.348482+0.127038
## [77] train-rmse:0.979018+0.049922 test-rmse:1.340418+0.121345
## [78] train-rmse:0.974098+0.050939 test-rmse:1.336497+0.126226
## [79] train-rmse:0.970347+0.051529 test-rmse:1.335701+0.126300
## [80] train-rmse:0.967094+0.051567 test-rmse:1.331526+0.125875
## [81] train-rmse:0.963127+0.051070 test-rmse:1.330436+0.127842
## [82] train-rmse:0.950674+0.037110 test-rmse:1.307307+0.126697
## [83] train-rmse:0.946925+0.036580 test-rmse:1.308355+0.130819
## [84] train-rmse:0.942934+0.036351 test-rmse:1.307404+0.129233
## [85] train-rmse:0.939278+0.037335 test-rmse:1.303192+0.127041
## [86] train-rmse:0.936080+0.037707 test-rmse:1.299947+0.126992
## [87] train-rmse:0.932969+0.038676 test-rmse:1.298390+0.125913
## [88] train-rmse:0.925806+0.045876 test-rmse:1.297180+0.127125
## [89] train-rmse:0.916576+0.039565 test-rmse:1.281505+0.132895
## [90] train-rmse:0.913768+0.039916 test-rmse:1.280786+0.133707
## [91] train-rmse:0.910531+0.039810 test-rmse:1.279249+0.133535
## [92] train-rmse:0.907515+0.040122 test-rmse:1.281095+0.133030
## [93] train-rmse:0.903848+0.040366 test-rmse:1.277496+0.130768
## [94] train-rmse:0.893996+0.037501 test-rmse:1.265926+0.127259
## [95] train-rmse:0.889159+0.036751 test-rmse:1.263561+0.127298
## [96] train-rmse:0.885490+0.036699 test-rmse:1.261522+0.127083
## [97] train-rmse:0.882876+0.036663 test-rmse:1.262239+0.126972
## [98] train-rmse:0.879580+0.036378 test-rmse:1.260867+0.125372
## [99] train-rmse:0.876430+0.035808 test-rmse:1.259130+0.125651
## [100] train-rmse:0.873892+0.035984 test-rmse:1.261071+0.128044
## [101] train-rmse:0.870668+0.036643 test-rmse:1.263367+0.130041
## [102] train-rmse:0.868158+0.036868 test-rmse:1.262263+0.130731
## [103] train-rmse:0.865733+0.037946 test-rmse:1.260377+0.129290
## [104] train-rmse:0.863396+0.038096 test-rmse:1.259215+0.128552
## [105] train-rmse:0.860010+0.039763 test-rmse:1.256792+0.124816
## [106] train-rmse:0.857486+0.039944 test-rmse:1.256271+0.125076
## [107] train-rmse:0.855510+0.040196 test-rmse:1.254526+0.126227
## [108] train-rmse:0.852925+0.039614 test-rmse:1.253949+0.126284
## [109] train-rmse:0.850398+0.039617 test-rmse:1.253535+0.127280
## [110] train-rmse:0.848613+0.039604 test-rmse:1.255296+0.127183
## [111] train-rmse:0.840913+0.035270 test-rmse:1.244623+0.131338
## [112] train-rmse:0.836609+0.034733 test-rmse:1.241931+0.134034
## [113] train-rmse:0.834442+0.034400 test-rmse:1.241375+0.133890
## [114] train-rmse:0.830320+0.034283 test-rmse:1.242232+0.136425
## [115] train-rmse:0.826134+0.032551 test-rmse:1.238729+0.140099
## [116] train-rmse:0.824694+0.032643 test-rmse:1.238704+0.143849
## [117] train-rmse:0.822937+0.032699 test-rmse:1.238468+0.143090
## [118] train-rmse:0.821104+0.033408 test-rmse:1.237986+0.144396
## [119] train-rmse:0.819068+0.033460 test-rmse:1.237335+0.143369
## [120] train-rmse:0.817377+0.033962 test-rmse:1.238318+0.144296
## [121] train-rmse:0.815098+0.034468 test-rmse:1.237272+0.143696
## [122] train-rmse:0.809368+0.038787 test-rmse:1.238663+0.142619
## [123] train-rmse:0.807686+0.038918 test-rmse:1.237497+0.143327
## [124] train-rmse:0.806177+0.039440 test-rmse:1.236103+0.143254
## [125] train-rmse:0.802472+0.038828 test-rmse:1.235143+0.142821
## [126] train-rmse:0.800701+0.038931 test-rmse:1.236440+0.142841
## [127] train-rmse:0.799265+0.038889 test-rmse:1.236427+0.141513
## [128] train-rmse:0.797034+0.039052 test-rmse:1.236510+0.142226
## [129] train-rmse:0.795871+0.039110 test-rmse:1.236340+0.142990
## [130] train-rmse:0.794689+0.039178 test-rmse:1.236847+0.140204
## [131] train-rmse:0.793154+0.039266 test-rmse:1.238164+0.142804
## Stopping. Best iteration:
## [125] train-rmse:0.802472+0.038828 test-rmse:1.235143+0.142821
##
## 65
## [1] train-rmse:7.149771+0.051716 test-rmse:7.146526+0.253068
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.345518+0.034890 test-rmse:5.345011+0.242600
## [3] train-rmse:4.089439+0.026351 test-rmse:4.109422+0.208327
## [4] train-rmse:3.222557+0.019219 test-rmse:3.269696+0.165815
## [5] train-rmse:2.638080+0.017891 test-rmse:2.695916+0.132097
## [6] train-rmse:2.249044+0.017383 test-rmse:2.357835+0.113241
## [7] train-rmse:1.993320+0.015394 test-rmse:2.132806+0.078512
## [8] train-rmse:1.827198+0.016391 test-rmse:1.984869+0.070874
## [9] train-rmse:1.716386+0.019601 test-rmse:1.901554+0.064665
## [10] train-rmse:1.633417+0.023027 test-rmse:1.826047+0.053184
## [11] train-rmse:1.574583+0.023078 test-rmse:1.768293+0.047064
## [12] train-rmse:1.520766+0.022010 test-rmse:1.734277+0.057463
## [13] train-rmse:1.477446+0.020901 test-rmse:1.712290+0.064753
## [14] train-rmse:1.439616+0.022865 test-rmse:1.682816+0.058483
## [15] train-rmse:1.403896+0.027848 test-rmse:1.660666+0.065720
## [16] train-rmse:1.374344+0.029491 test-rmse:1.630015+0.069077
## [17] train-rmse:1.342349+0.023600 test-rmse:1.607817+0.081245
## [18] train-rmse:1.317083+0.023726 test-rmse:1.590595+0.081453
## [19] train-rmse:1.284502+0.025361 test-rmse:1.558428+0.090750
## [20] train-rmse:1.259792+0.026085 test-rmse:1.543465+0.084943
## [21] train-rmse:1.236130+0.027143 test-rmse:1.541845+0.081531
## [22] train-rmse:1.215049+0.027965 test-rmse:1.525238+0.083350
## [23] train-rmse:1.189993+0.030199 test-rmse:1.509767+0.094142
## [24] train-rmse:1.170200+0.031091 test-rmse:1.496293+0.096086
## [25] train-rmse:1.148396+0.032359 test-rmse:1.469285+0.089908
## [26] train-rmse:1.130163+0.030493 test-rmse:1.461462+0.093241
## [27] train-rmse:1.112320+0.032547 test-rmse:1.455942+0.097223
## [28] train-rmse:1.097991+0.033221 test-rmse:1.444654+0.102119
## [29] train-rmse:1.077278+0.036101 test-rmse:1.428110+0.102987
## [30] train-rmse:1.062671+0.035471 test-rmse:1.418799+0.104992
## [31] train-rmse:1.046117+0.038495 test-rmse:1.407947+0.101413
## [32] train-rmse:1.032133+0.040242 test-rmse:1.396261+0.101616
## [33] train-rmse:1.010311+0.032460 test-rmse:1.387346+0.104983
## [34] train-rmse:0.996105+0.031543 test-rmse:1.377811+0.112495
## [35] train-rmse:0.983829+0.031437 test-rmse:1.374038+0.118467
## [36] train-rmse:0.968925+0.031486 test-rmse:1.366897+0.118760
## [37] train-rmse:0.956210+0.031915 test-rmse:1.362348+0.120101
## [38] train-rmse:0.943969+0.034263 test-rmse:1.353019+0.128061
## [39] train-rmse:0.930470+0.036396 test-rmse:1.350505+0.127628
## [40] train-rmse:0.917953+0.038199 test-rmse:1.345770+0.130549
## [41] train-rmse:0.907518+0.038319 test-rmse:1.339173+0.127267
## [42] train-rmse:0.897374+0.037501 test-rmse:1.331153+0.126947
## [43] train-rmse:0.887684+0.037371 test-rmse:1.321597+0.137686
## [44] train-rmse:0.878395+0.037004 test-rmse:1.318707+0.142703
## [45] train-rmse:0.867929+0.037483 test-rmse:1.314483+0.144666
## [46] train-rmse:0.858070+0.040074 test-rmse:1.305780+0.144528
## [47] train-rmse:0.849600+0.039729 test-rmse:1.305924+0.145775
## [48] train-rmse:0.837581+0.034318 test-rmse:1.302525+0.148526
## [49] train-rmse:0.827768+0.032507 test-rmse:1.296273+0.152061
## [50] train-rmse:0.820033+0.032288 test-rmse:1.289336+0.151797
## [51] train-rmse:0.811961+0.032166 test-rmse:1.287728+0.151728
## [52] train-rmse:0.803648+0.031982 test-rmse:1.287357+0.155895
## [53] train-rmse:0.797078+0.032942 test-rmse:1.283294+0.158066
## [54] train-rmse:0.787768+0.036429 test-rmse:1.279280+0.158480
## [55] train-rmse:0.778831+0.035434 test-rmse:1.279798+0.160722
## [56] train-rmse:0.769792+0.037434 test-rmse:1.269945+0.152962
## [57] train-rmse:0.764486+0.037018 test-rmse:1.271766+0.154926
## [58] train-rmse:0.757862+0.036319 test-rmse:1.271249+0.154330
## [59] train-rmse:0.751731+0.036381 test-rmse:1.269572+0.154641
## [60] train-rmse:0.744410+0.035639 test-rmse:1.265454+0.152816
## [61] train-rmse:0.737621+0.035501 test-rmse:1.261997+0.156450
## [62] train-rmse:0.731065+0.036811 test-rmse:1.257168+0.152422
## [63] train-rmse:0.725582+0.037167 test-rmse:1.256134+0.152684
## [64] train-rmse:0.716579+0.034224 test-rmse:1.257811+0.153246
## [65] train-rmse:0.712438+0.035116 test-rmse:1.257697+0.155218
## [66] train-rmse:0.706839+0.035024 test-rmse:1.256808+0.155406
## [67] train-rmse:0.701861+0.034722 test-rmse:1.256587+0.156432
## [68] train-rmse:0.695093+0.032289 test-rmse:1.252331+0.157723
## [69] train-rmse:0.691029+0.032151 test-rmse:1.253329+0.157022
## [70] train-rmse:0.686805+0.031879 test-rmse:1.255677+0.156092
## [71] train-rmse:0.683012+0.032404 test-rmse:1.254289+0.156425
## [72] train-rmse:0.678604+0.033733 test-rmse:1.253713+0.156971
## [73] train-rmse:0.674597+0.032820 test-rmse:1.249819+0.158158
## [74] train-rmse:0.670585+0.032756 test-rmse:1.248967+0.159260
## [75] train-rmse:0.665621+0.032240 test-rmse:1.248363+0.158274
## [76] train-rmse:0.662415+0.032440 test-rmse:1.250104+0.155932
## [77] train-rmse:0.657296+0.032934 test-rmse:1.246760+0.156016
## [78] train-rmse:0.653950+0.032427 test-rmse:1.246783+0.156957
## [79] train-rmse:0.650791+0.032725 test-rmse:1.248814+0.156403
## [80] train-rmse:0.646746+0.032102 test-rmse:1.246765+0.157634
## [81] train-rmse:0.641717+0.032118 test-rmse:1.245447+0.157847
## [82] train-rmse:0.636109+0.033086 test-rmse:1.243869+0.158114
## [83] train-rmse:0.633262+0.034210 test-rmse:1.244055+0.159186
## [84] train-rmse:0.630201+0.034719 test-rmse:1.242228+0.157112
## [85] train-rmse:0.627026+0.034563 test-rmse:1.241011+0.156562
## [86] train-rmse:0.623597+0.034005 test-rmse:1.241847+0.157486
## [87] train-rmse:0.618958+0.031232 test-rmse:1.237221+0.157499
## [88] train-rmse:0.614248+0.032030 test-rmse:1.237188+0.157012
## [89] train-rmse:0.611079+0.030563 test-rmse:1.238135+0.156712
## [90] train-rmse:0.609056+0.030592 test-rmse:1.238839+0.155942
## [91] train-rmse:0.606027+0.030404 test-rmse:1.237648+0.156893
## [92] train-rmse:0.603947+0.030562 test-rmse:1.237856+0.156468
## [93] train-rmse:0.600975+0.031346 test-rmse:1.237930+0.156426
## [94] train-rmse:0.597511+0.031602 test-rmse:1.234221+0.153628
## [95] train-rmse:0.594903+0.032306 test-rmse:1.232933+0.150024
## [96] train-rmse:0.591672+0.032223 test-rmse:1.232341+0.150093
## [97] train-rmse:0.589795+0.032076 test-rmse:1.232512+0.149783
## [98] train-rmse:0.587077+0.031470 test-rmse:1.233137+0.149364
## [99] train-rmse:0.583544+0.031449 test-rmse:1.232940+0.150029
## [100] train-rmse:0.581092+0.031240 test-rmse:1.234652+0.150198
## [101] train-rmse:0.579294+0.031188 test-rmse:1.234157+0.151156
## [102] train-rmse:0.578048+0.031414 test-rmse:1.235271+0.152164
## Stopping. Best iteration:
## [96] train-rmse:0.591672+0.032223 test-rmse:1.232341+0.150093
##
## 66
## [1] train-rmse:7.143685+0.052327 test-rmse:7.140787+0.254948
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.330526+0.036618 test-rmse:5.345684+0.233180
## [3] train-rmse:4.061875+0.029686 test-rmse:4.084839+0.192156
## [4] train-rmse:3.168735+0.018896 test-rmse:3.220727+0.161896
## [5] train-rmse:2.552306+0.016409 test-rmse:2.621257+0.113075
## [6] train-rmse:2.145518+0.016389 test-rmse:2.283615+0.100079
## [7] train-rmse:1.867513+0.022624 test-rmse:2.020173+0.081579
## [8] train-rmse:1.681915+0.019505 test-rmse:1.861418+0.077115
## [9] train-rmse:1.557320+0.017730 test-rmse:1.778775+0.063299
## [10] train-rmse:1.465385+0.020250 test-rmse:1.716141+0.072909
## [11] train-rmse:1.384938+0.026914 test-rmse:1.668747+0.057332
## [12] train-rmse:1.328351+0.026471 test-rmse:1.609827+0.075715
## [13] train-rmse:1.278123+0.034066 test-rmse:1.578184+0.076297
## [14] train-rmse:1.230981+0.038479 test-rmse:1.549629+0.083451
## [15] train-rmse:1.195216+0.032965 test-rmse:1.535013+0.091622
## [16] train-rmse:1.161401+0.034070 test-rmse:1.519427+0.095997
## [17] train-rmse:1.129006+0.031055 test-rmse:1.489072+0.096187
## [18] train-rmse:1.094768+0.028888 test-rmse:1.463336+0.114190
## [19] train-rmse:1.066882+0.026013 test-rmse:1.443531+0.117640
## [20] train-rmse:1.042144+0.030386 test-rmse:1.427025+0.119415
## [21] train-rmse:1.020474+0.031034 test-rmse:1.416671+0.126163
## [22] train-rmse:0.994856+0.027634 test-rmse:1.405015+0.130334
## [23] train-rmse:0.974929+0.024685 test-rmse:1.394258+0.133582
## [24] train-rmse:0.954019+0.025890 test-rmse:1.379633+0.133584
## [25] train-rmse:0.931873+0.029078 test-rmse:1.371757+0.141226
## [26] train-rmse:0.914284+0.028988 test-rmse:1.354625+0.147750
## [27] train-rmse:0.892647+0.021875 test-rmse:1.351766+0.154515
## [28] train-rmse:0.878710+0.020632 test-rmse:1.346074+0.155017
## [29] train-rmse:0.862126+0.018573 test-rmse:1.340600+0.155412
## [30] train-rmse:0.845850+0.023471 test-rmse:1.334216+0.157185
## [31] train-rmse:0.831955+0.022094 test-rmse:1.319622+0.152829
## [32] train-rmse:0.817895+0.021188 test-rmse:1.310041+0.150380
## [33] train-rmse:0.801131+0.019382 test-rmse:1.303322+0.153360
## [34] train-rmse:0.789401+0.018029 test-rmse:1.298707+0.158818
## [35] train-rmse:0.777684+0.018308 test-rmse:1.301923+0.161786
## [36] train-rmse:0.765814+0.015713 test-rmse:1.297463+0.167579
## [37] train-rmse:0.751197+0.018120 test-rmse:1.289264+0.164911
## [38] train-rmse:0.740954+0.018758 test-rmse:1.286881+0.167114
## [39] train-rmse:0.730063+0.019051 test-rmse:1.282123+0.167914
## [40] train-rmse:0.720187+0.016259 test-rmse:1.281275+0.172343
## [41] train-rmse:0.712543+0.015165 test-rmse:1.281892+0.171718
## [42] train-rmse:0.702710+0.014200 test-rmse:1.281486+0.173903
## [43] train-rmse:0.692065+0.015401 test-rmse:1.279789+0.174824
## [44] train-rmse:0.681232+0.021073 test-rmse:1.269618+0.169938
## [45] train-rmse:0.674356+0.019961 test-rmse:1.268523+0.170648
## [46] train-rmse:0.660505+0.019322 test-rmse:1.266451+0.168208
## [47] train-rmse:0.652558+0.017622 test-rmse:1.266737+0.171304
## [48] train-rmse:0.644674+0.017353 test-rmse:1.263749+0.169093
## [49] train-rmse:0.637916+0.017457 test-rmse:1.264772+0.169075
## [50] train-rmse:0.630631+0.017318 test-rmse:1.261624+0.170639
## [51] train-rmse:0.624249+0.015669 test-rmse:1.260667+0.171408
## [52] train-rmse:0.618097+0.014473 test-rmse:1.259907+0.175107
## [53] train-rmse:0.611643+0.014807 test-rmse:1.259059+0.173208
## [54] train-rmse:0.600466+0.014963 test-rmse:1.257477+0.172707
## [55] train-rmse:0.595731+0.015284 test-rmse:1.254192+0.171455
## [56] train-rmse:0.591608+0.017005 test-rmse:1.252077+0.168445
## [57] train-rmse:0.587499+0.016729 test-rmse:1.247614+0.169922
## [58] train-rmse:0.580973+0.015284 test-rmse:1.245698+0.171664
## [59] train-rmse:0.574236+0.017384 test-rmse:1.243869+0.171679
## [60] train-rmse:0.569511+0.017747 test-rmse:1.242092+0.171083
## [61] train-rmse:0.565207+0.016919 test-rmse:1.242535+0.169306
## [62] train-rmse:0.561078+0.017645 test-rmse:1.239465+0.171537
## [63] train-rmse:0.556848+0.017386 test-rmse:1.242039+0.170613
## [64] train-rmse:0.552629+0.017730 test-rmse:1.242256+0.167897
## [65] train-rmse:0.546992+0.016461 test-rmse:1.241717+0.165624
## [66] train-rmse:0.544034+0.016552 test-rmse:1.241635+0.165953
## [67] train-rmse:0.539231+0.017341 test-rmse:1.242548+0.166910
## [68] train-rmse:0.533271+0.016179 test-rmse:1.242155+0.167231
## Stopping. Best iteration:
## [62] train-rmse:0.561078+0.017645 test-rmse:1.239465+0.171537
##
## 67
## [1] train-rmse:7.138533+0.052833 test-rmse:7.141013+0.255745
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.316646+0.037501 test-rmse:5.337499+0.227562
## [3] train-rmse:4.027993+0.028738 test-rmse:4.053287+0.184844
## [4] train-rmse:3.124071+0.021394 test-rmse:3.170466+0.156616
## [5] train-rmse:2.495503+0.017975 test-rmse:2.587355+0.135151
## [6] train-rmse:2.060026+0.022415 test-rmse:2.181743+0.110100
## [7] train-rmse:1.769010+0.026897 test-rmse:1.946507+0.100514
## [8] train-rmse:1.566056+0.034388 test-rmse:1.792344+0.067117
## [9] train-rmse:1.414719+0.034457 test-rmse:1.687838+0.063598
## [10] train-rmse:1.305033+0.041847 test-rmse:1.614552+0.055478
## [11] train-rmse:1.222235+0.040351 test-rmse:1.557772+0.052912
## [12] train-rmse:1.162138+0.039989 test-rmse:1.514115+0.056076
## [13] train-rmse:1.108923+0.038034 test-rmse:1.484280+0.065025
## [14] train-rmse:1.063688+0.035914 test-rmse:1.461429+0.076442
## [15] train-rmse:1.024736+0.035723 test-rmse:1.442165+0.087252
## [16] train-rmse:0.982638+0.033041 test-rmse:1.416831+0.091421
## [17] train-rmse:0.945934+0.035999 test-rmse:1.394012+0.105286
## [18] train-rmse:0.913027+0.036741 test-rmse:1.376556+0.102994
## [19] train-rmse:0.886277+0.036279 test-rmse:1.367146+0.110345
## [20] train-rmse:0.856793+0.032585 test-rmse:1.354614+0.124820
## [21] train-rmse:0.830138+0.032433 test-rmse:1.344862+0.132643
## [22] train-rmse:0.810039+0.029928 test-rmse:1.340629+0.128699
## [23] train-rmse:0.783496+0.031656 test-rmse:1.330340+0.126025
## [24] train-rmse:0.758369+0.028498 test-rmse:1.318437+0.136433
## [25] train-rmse:0.737706+0.029351 test-rmse:1.307417+0.139404
## [26] train-rmse:0.719345+0.030132 test-rmse:1.309304+0.140400
## [27] train-rmse:0.703499+0.029088 test-rmse:1.303087+0.147034
## [28] train-rmse:0.687215+0.023749 test-rmse:1.290697+0.153911
## [29] train-rmse:0.672156+0.023977 test-rmse:1.283948+0.157995
## [30] train-rmse:0.657652+0.025527 test-rmse:1.278982+0.164121
## [31] train-rmse:0.642780+0.027555 test-rmse:1.272264+0.168840
## [32] train-rmse:0.630066+0.028611 test-rmse:1.265710+0.166273
## [33] train-rmse:0.617185+0.025763 test-rmse:1.260891+0.172484
## [34] train-rmse:0.605855+0.025749 test-rmse:1.258151+0.170389
## [35] train-rmse:0.595377+0.023796 test-rmse:1.259228+0.175345
## [36] train-rmse:0.582285+0.020328 test-rmse:1.258719+0.175874
## [37] train-rmse:0.570789+0.021463 test-rmse:1.251036+0.178049
## [38] train-rmse:0.560123+0.022804 test-rmse:1.247915+0.175172
## [39] train-rmse:0.547178+0.018684 test-rmse:1.246746+0.177825
## [40] train-rmse:0.537415+0.019664 test-rmse:1.248976+0.175499
## [41] train-rmse:0.529327+0.018650 test-rmse:1.243491+0.177169
## [42] train-rmse:0.518930+0.019537 test-rmse:1.243660+0.176886
## [43] train-rmse:0.509746+0.020053 test-rmse:1.238720+0.175006
## [44] train-rmse:0.499497+0.020700 test-rmse:1.237550+0.173663
## [45] train-rmse:0.492529+0.021947 test-rmse:1.234225+0.174420
## [46] train-rmse:0.487137+0.020800 test-rmse:1.234115+0.173510
## [47] train-rmse:0.479239+0.021964 test-rmse:1.234053+0.172455
## [48] train-rmse:0.471424+0.021100 test-rmse:1.231756+0.174157
## [49] train-rmse:0.465365+0.021262 test-rmse:1.228426+0.173027
## [50] train-rmse:0.459435+0.021935 test-rmse:1.230482+0.172452
## [51] train-rmse:0.453618+0.022423 test-rmse:1.230139+0.171772
## [52] train-rmse:0.444810+0.022334 test-rmse:1.228824+0.169104
## [53] train-rmse:0.439471+0.021506 test-rmse:1.226029+0.168670
## [54] train-rmse:0.434232+0.021112 test-rmse:1.225581+0.171160
## [55] train-rmse:0.429620+0.018471 test-rmse:1.226428+0.175004
## [56] train-rmse:0.424188+0.017018 test-rmse:1.229568+0.174051
## [57] train-rmse:0.416357+0.019470 test-rmse:1.227734+0.173913
## [58] train-rmse:0.412200+0.019722 test-rmse:1.227426+0.174260
## [59] train-rmse:0.407413+0.018580 test-rmse:1.227150+0.173441
## [60] train-rmse:0.403836+0.019309 test-rmse:1.227092+0.174136
## Stopping. Best iteration:
## [54] train-rmse:0.434232+0.021112 test-rmse:1.225581+0.171160
##
## 68
## [1] train-rmse:7.137312+0.053122 test-rmse:7.141834+0.246996
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.308328+0.038787 test-rmse:5.330348+0.224466
## [3] train-rmse:4.011516+0.032622 test-rmse:4.040434+0.175957
## [4] train-rmse:3.097256+0.028325 test-rmse:3.137383+0.147241
## [5] train-rmse:2.449108+0.020215 test-rmse:2.550125+0.146296
## [6] train-rmse:2.003611+0.013497 test-rmse:2.157959+0.122012
## [7] train-rmse:1.690623+0.024651 test-rmse:1.895520+0.094919
## [8] train-rmse:1.465232+0.021417 test-rmse:1.751660+0.088059
## [9] train-rmse:1.306637+0.024883 test-rmse:1.630815+0.096021
## [10] train-rmse:1.185560+0.025846 test-rmse:1.558888+0.096360
## [11] train-rmse:1.101661+0.019412 test-rmse:1.489744+0.118215
## [12] train-rmse:1.031021+0.024204 test-rmse:1.448250+0.124593
## [13] train-rmse:0.970676+0.027054 test-rmse:1.422760+0.120414
## [14] train-rmse:0.914574+0.023521 test-rmse:1.396580+0.126566
## [15] train-rmse:0.875295+0.017392 test-rmse:1.375892+0.133722
## [16] train-rmse:0.831369+0.018377 test-rmse:1.361363+0.135742
## [17] train-rmse:0.800947+0.016994 test-rmse:1.351340+0.142522
## [18] train-rmse:0.771555+0.018022 test-rmse:1.342029+0.141079
## [19] train-rmse:0.744061+0.018390 test-rmse:1.325745+0.146381
## [20] train-rmse:0.717473+0.018324 test-rmse:1.316752+0.149816
## [21] train-rmse:0.688859+0.018176 test-rmse:1.298610+0.158899
## [22] train-rmse:0.663987+0.019473 test-rmse:1.286179+0.162326
## [23] train-rmse:0.645026+0.016494 test-rmse:1.286357+0.161337
## [24] train-rmse:0.627272+0.017923 test-rmse:1.280982+0.165277
## [25] train-rmse:0.607044+0.015452 test-rmse:1.277296+0.164448
## [26] train-rmse:0.591509+0.016165 test-rmse:1.275125+0.163562
## [27] train-rmse:0.576213+0.019135 test-rmse:1.265619+0.168324
## [28] train-rmse:0.563699+0.018508 test-rmse:1.266056+0.165831
## [29] train-rmse:0.549497+0.019027 test-rmse:1.264655+0.169794
## [30] train-rmse:0.532082+0.017837 test-rmse:1.264261+0.171858
## [31] train-rmse:0.522017+0.019709 test-rmse:1.262024+0.172222
## [32] train-rmse:0.509500+0.020168 test-rmse:1.263943+0.173743
## [33] train-rmse:0.500626+0.018461 test-rmse:1.262317+0.175955
## [34] train-rmse:0.489864+0.017119 test-rmse:1.261731+0.177251
## [35] train-rmse:0.478424+0.014536 test-rmse:1.264915+0.176849
## [36] train-rmse:0.467006+0.014086 test-rmse:1.266795+0.177631
## [37] train-rmse:0.457567+0.012817 test-rmse:1.266074+0.178836
## [38] train-rmse:0.449718+0.010384 test-rmse:1.262582+0.178965
## [39] train-rmse:0.438137+0.008415 test-rmse:1.261646+0.180286
## [40] train-rmse:0.432036+0.009696 test-rmse:1.262667+0.182172
## [41] train-rmse:0.421645+0.010074 test-rmse:1.258725+0.181633
## [42] train-rmse:0.416816+0.009717 test-rmse:1.259161+0.181368
## [43] train-rmse:0.406834+0.013025 test-rmse:1.259589+0.177671
## [44] train-rmse:0.399935+0.016863 test-rmse:1.256560+0.179215
## [45] train-rmse:0.392252+0.017060 test-rmse:1.260093+0.178755
## [46] train-rmse:0.386443+0.017149 test-rmse:1.258495+0.182104
## [47] train-rmse:0.379409+0.017310 test-rmse:1.257224+0.181680
## [48] train-rmse:0.372344+0.017581 test-rmse:1.257752+0.181299
## [49] train-rmse:0.366549+0.018353 test-rmse:1.259659+0.180620
## [50] train-rmse:0.362419+0.017952 test-rmse:1.259993+0.181627
## Stopping. Best iteration:
## [44] train-rmse:0.399935+0.016863 test-rmse:1.256560+0.179215
##
## 69
## [1] train-rmse:7.137312+0.053122 test-rmse:7.141834+0.246996
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.303677+0.039464 test-rmse:5.332754+0.212247
## [3] train-rmse:3.998404+0.033382 test-rmse:4.043921+0.159172
## [4] train-rmse:3.073334+0.031412 test-rmse:3.140075+0.130601
## [5] train-rmse:2.411857+0.021555 test-rmse:2.537402+0.128057
## [6] train-rmse:1.954379+0.017033 test-rmse:2.126676+0.117820
## [7] train-rmse:1.625180+0.027272 test-rmse:1.854390+0.092246
## [8] train-rmse:1.388633+0.030583 test-rmse:1.667133+0.092485
## [9] train-rmse:1.213536+0.030827 test-rmse:1.554487+0.094346
## [10] train-rmse:1.082491+0.031365 test-rmse:1.470629+0.101003
## [11] train-rmse:0.991626+0.030834 test-rmse:1.423943+0.114081
## [12] train-rmse:0.920196+0.031954 test-rmse:1.384019+0.114300
## [13] train-rmse:0.857832+0.029576 test-rmse:1.361189+0.126412
## [14] train-rmse:0.800582+0.029761 test-rmse:1.344254+0.129892
## [15] train-rmse:0.762020+0.025682 test-rmse:1.331704+0.136571
## [16] train-rmse:0.720853+0.021430 test-rmse:1.316242+0.147710
## [17] train-rmse:0.685886+0.021870 test-rmse:1.305829+0.151131
## [18] train-rmse:0.652955+0.025610 test-rmse:1.294034+0.148858
## [19] train-rmse:0.621364+0.021659 test-rmse:1.285706+0.154395
## [20] train-rmse:0.597055+0.021662 test-rmse:1.278336+0.161477
## [21] train-rmse:0.578588+0.021439 test-rmse:1.273253+0.166374
## [22] train-rmse:0.557432+0.019067 test-rmse:1.270316+0.165458
## [23] train-rmse:0.533355+0.018655 test-rmse:1.265650+0.170829
## [24] train-rmse:0.512388+0.017160 test-rmse:1.265739+0.172372
## [25] train-rmse:0.496678+0.016950 test-rmse:1.267653+0.169577
## [26] train-rmse:0.477301+0.016981 test-rmse:1.261760+0.171738
## [27] train-rmse:0.461160+0.016155 test-rmse:1.255955+0.173119
## [28] train-rmse:0.446892+0.017879 test-rmse:1.256616+0.175789
## [29] train-rmse:0.431807+0.017948 test-rmse:1.256087+0.175184
## [30] train-rmse:0.421683+0.017843 test-rmse:1.254589+0.173970
## [31] train-rmse:0.411595+0.016860 test-rmse:1.254382+0.170951
## [32] train-rmse:0.400652+0.020893 test-rmse:1.254523+0.171764
## [33] train-rmse:0.388191+0.019769 test-rmse:1.251448+0.173096
## [34] train-rmse:0.379727+0.019738 test-rmse:1.251763+0.172283
## [35] train-rmse:0.371433+0.021260 test-rmse:1.253342+0.172625
## [36] train-rmse:0.362180+0.020741 test-rmse:1.255325+0.172804
## [37] train-rmse:0.350041+0.019498 test-rmse:1.250011+0.172014
## [38] train-rmse:0.342426+0.020927 test-rmse:1.251371+0.172791
## [39] train-rmse:0.334253+0.019393 test-rmse:1.250689+0.171411
## [40] train-rmse:0.326589+0.020858 test-rmse:1.249383+0.171922
## [41] train-rmse:0.318178+0.023198 test-rmse:1.250452+0.173059
## [42] train-rmse:0.310505+0.021832 test-rmse:1.252776+0.170649
## [43] train-rmse:0.305173+0.022384 test-rmse:1.250221+0.171312
## [44] train-rmse:0.296835+0.022818 test-rmse:1.250078+0.172377
## [45] train-rmse:0.291397+0.022193 test-rmse:1.249571+0.173076
## [46] train-rmse:0.278523+0.019859 test-rmse:1.251135+0.173258
## Stopping. Best iteration:
## [40] train-rmse:0.326589+0.020858 test-rmse:1.249383+0.171922
##
## 70
## [1] train-rmse:7.026356+0.048057 test-rmse:7.022277+0.264102
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.219930+0.029815 test-rmse:5.214355+0.258267
## [3] train-rmse:4.031154+0.019484 test-rmse:4.032050+0.227361
## [4] train-rmse:3.279577+0.014247 test-rmse:3.303640+0.177838
## [5] train-rmse:2.824524+0.012750 test-rmse:2.856531+0.130172
## [6] train-rmse:2.558604+0.014223 test-rmse:2.591059+0.083244
## [7] train-rmse:2.402601+0.016150 test-rmse:2.440235+0.058345
## [8] train-rmse:2.310487+0.016266 test-rmse:2.351722+0.050573
## [9] train-rmse:2.253226+0.017690 test-rmse:2.294818+0.059452
## [10] train-rmse:2.214395+0.018615 test-rmse:2.261316+0.059770
## [11] train-rmse:2.185155+0.019320 test-rmse:2.232598+0.065655
## [12] train-rmse:2.160250+0.019765 test-rmse:2.205579+0.078555
## [13] train-rmse:2.138285+0.021013 test-rmse:2.183969+0.079202
## [14] train-rmse:2.118764+0.022547 test-rmse:2.163909+0.075722
## [15] train-rmse:2.100142+0.023667 test-rmse:2.160997+0.080370
## [16] train-rmse:2.083636+0.024356 test-rmse:2.148629+0.089605
## [17] train-rmse:2.067355+0.024885 test-rmse:2.140592+0.093683
## [18] train-rmse:2.051812+0.025048 test-rmse:2.130326+0.094574
## [19] train-rmse:2.036692+0.025922 test-rmse:2.117145+0.094621
## [20] train-rmse:2.022023+0.026579 test-rmse:2.097508+0.095666
## [21] train-rmse:2.007787+0.027284 test-rmse:2.071855+0.091806
## [22] train-rmse:1.993974+0.028814 test-rmse:2.063329+0.088838
## [23] train-rmse:1.981039+0.029649 test-rmse:2.045030+0.091038
## [24] train-rmse:1.969262+0.030159 test-rmse:2.037900+0.095233
## [25] train-rmse:1.957460+0.030263 test-rmse:2.031789+0.099574
## [26] train-rmse:1.945760+0.030233 test-rmse:2.021362+0.096119
## [27] train-rmse:1.934184+0.030461 test-rmse:2.009817+0.084792
## [28] train-rmse:1.922935+0.030463 test-rmse:2.001161+0.077842
## [29] train-rmse:1.911923+0.030988 test-rmse:1.993243+0.082192
## [30] train-rmse:1.900934+0.031814 test-rmse:1.984810+0.084091
## [31] train-rmse:1.890469+0.032522 test-rmse:1.980676+0.090744
## [32] train-rmse:1.880588+0.033664 test-rmse:1.972287+0.087739
## [33] train-rmse:1.870890+0.034556 test-rmse:1.965387+0.085598
## [34] train-rmse:1.861481+0.035418 test-rmse:1.960122+0.083089
## [35] train-rmse:1.852225+0.036204 test-rmse:1.949404+0.084731
## [36] train-rmse:1.843025+0.036801 test-rmse:1.934348+0.089513
## [37] train-rmse:1.833966+0.037322 test-rmse:1.918266+0.085734
## [38] train-rmse:1.825090+0.038059 test-rmse:1.910699+0.089466
## [39] train-rmse:1.816199+0.038322 test-rmse:1.903667+0.090833
## [40] train-rmse:1.807627+0.038943 test-rmse:1.898791+0.093026
## [41] train-rmse:1.799361+0.039611 test-rmse:1.890679+0.094201
## [42] train-rmse:1.791078+0.040259 test-rmse:1.888503+0.091936
## [43] train-rmse:1.782947+0.041000 test-rmse:1.880700+0.096168
## [44] train-rmse:1.775087+0.041516 test-rmse:1.873494+0.093660
## [45] train-rmse:1.767766+0.041529 test-rmse:1.866050+0.096951
## [46] train-rmse:1.760607+0.041676 test-rmse:1.861913+0.101491
## [47] train-rmse:1.753351+0.041855 test-rmse:1.862206+0.099649
## [48] train-rmse:1.746311+0.042109 test-rmse:1.854623+0.098864
## [49] train-rmse:1.739169+0.042353 test-rmse:1.848658+0.096234
## [50] train-rmse:1.732297+0.042664 test-rmse:1.843338+0.097126
## [51] train-rmse:1.725641+0.043054 test-rmse:1.835294+0.093820
## [52] train-rmse:1.719073+0.043372 test-rmse:1.832284+0.100190
## [53] train-rmse:1.712558+0.043881 test-rmse:1.825372+0.100771
## [54] train-rmse:1.706239+0.043987 test-rmse:1.817409+0.101378
## [55] train-rmse:1.700090+0.044238 test-rmse:1.814973+0.104269
## [56] train-rmse:1.694151+0.044595 test-rmse:1.806876+0.106586
## [57] train-rmse:1.688263+0.045007 test-rmse:1.800304+0.101193
## [58] train-rmse:1.682453+0.045383 test-rmse:1.797541+0.101973
## [59] train-rmse:1.676909+0.045740 test-rmse:1.794720+0.103507
## [60] train-rmse:1.671328+0.046052 test-rmse:1.789259+0.102899
## [61] train-rmse:1.665677+0.046330 test-rmse:1.784853+0.108488
## [62] train-rmse:1.660239+0.046596 test-rmse:1.780753+0.107769
## [63] train-rmse:1.654902+0.046857 test-rmse:1.779703+0.103627
## [64] train-rmse:1.649535+0.047065 test-rmse:1.771266+0.103125
## [65] train-rmse:1.644337+0.047478 test-rmse:1.767829+0.111099
## [66] train-rmse:1.639184+0.047835 test-rmse:1.761587+0.114216
## [67] train-rmse:1.634217+0.048198 test-rmse:1.759282+0.114774
## [68] train-rmse:1.629366+0.048503 test-rmse:1.751907+0.116912
## [69] train-rmse:1.624676+0.048649 test-rmse:1.749036+0.116053
## [70] train-rmse:1.620076+0.048810 test-rmse:1.745524+0.111792
## [71] train-rmse:1.615490+0.048927 test-rmse:1.739969+0.109708
## [72] train-rmse:1.610877+0.049158 test-rmse:1.735444+0.111999
## [73] train-rmse:1.606408+0.049387 test-rmse:1.727944+0.122265
## [74] train-rmse:1.602039+0.049507 test-rmse:1.722085+0.119933
## [75] train-rmse:1.597663+0.049507 test-rmse:1.717722+0.122581
## [76] train-rmse:1.593405+0.049564 test-rmse:1.716070+0.122613
## [77] train-rmse:1.589251+0.049710 test-rmse:1.712261+0.125345
## [78] train-rmse:1.585160+0.049837 test-rmse:1.705511+0.125359
## [79] train-rmse:1.581193+0.049894 test-rmse:1.706317+0.131657
## [80] train-rmse:1.577291+0.049967 test-rmse:1.704908+0.129086
## [81] train-rmse:1.573426+0.050049 test-rmse:1.700195+0.132542
## [82] train-rmse:1.569564+0.050088 test-rmse:1.695695+0.131480
## [83] train-rmse:1.565779+0.050170 test-rmse:1.692632+0.133908
## [84] train-rmse:1.562029+0.050209 test-rmse:1.691601+0.134093
## [85] train-rmse:1.558299+0.050301 test-rmse:1.691431+0.131658
## [86] train-rmse:1.554670+0.050432 test-rmse:1.690399+0.130407
## [87] train-rmse:1.551157+0.050592 test-rmse:1.687457+0.131720
## [88] train-rmse:1.547677+0.050692 test-rmse:1.681598+0.135079
## [89] train-rmse:1.544313+0.050795 test-rmse:1.677784+0.130228
## [90] train-rmse:1.541038+0.050896 test-rmse:1.674310+0.131522
## [91] train-rmse:1.537710+0.051027 test-rmse:1.671994+0.132496
## [92] train-rmse:1.534439+0.051011 test-rmse:1.668853+0.132743
## [93] train-rmse:1.531193+0.051161 test-rmse:1.663071+0.138800
## [94] train-rmse:1.528009+0.051314 test-rmse:1.661371+0.140833
## [95] train-rmse:1.524870+0.051346 test-rmse:1.660075+0.141500
## [96] train-rmse:1.521691+0.051363 test-rmse:1.656762+0.141730
## [97] train-rmse:1.518662+0.051429 test-rmse:1.652773+0.139188
## [98] train-rmse:1.515663+0.051541 test-rmse:1.646736+0.138851
## [99] train-rmse:1.512692+0.051701 test-rmse:1.645194+0.140214
## [100] train-rmse:1.509798+0.051815 test-rmse:1.644008+0.141248
## [101] train-rmse:1.506883+0.051843 test-rmse:1.640497+0.143347
## [102] train-rmse:1.504043+0.051836 test-rmse:1.637736+0.146741
## [103] train-rmse:1.501362+0.051859 test-rmse:1.637868+0.147009
## [104] train-rmse:1.498655+0.051954 test-rmse:1.633225+0.147062
## [105] train-rmse:1.496015+0.052035 test-rmse:1.628443+0.146026
## [106] train-rmse:1.493432+0.052124 test-rmse:1.629602+0.146494
## [107] train-rmse:1.490876+0.052204 test-rmse:1.628436+0.150837
## [108] train-rmse:1.488370+0.052390 test-rmse:1.622981+0.147661
## [109] train-rmse:1.485896+0.052529 test-rmse:1.620509+0.148508
## [110] train-rmse:1.483426+0.052610 test-rmse:1.618911+0.148091
## [111] train-rmse:1.480995+0.052623 test-rmse:1.616947+0.148369
## [112] train-rmse:1.478562+0.052672 test-rmse:1.613953+0.149041
## [113] train-rmse:1.476253+0.052754 test-rmse:1.610785+0.147115
## [114] train-rmse:1.474011+0.052814 test-rmse:1.607462+0.149290
## [115] train-rmse:1.471797+0.052884 test-rmse:1.608445+0.150473
## [116] train-rmse:1.469628+0.052889 test-rmse:1.609163+0.151584
## [117] train-rmse:1.467492+0.052947 test-rmse:1.606255+0.155083
## [118] train-rmse:1.465372+0.053012 test-rmse:1.607423+0.157998
## [119] train-rmse:1.463275+0.053043 test-rmse:1.605922+0.156147
## [120] train-rmse:1.461181+0.053041 test-rmse:1.604611+0.154574
## [121] train-rmse:1.459085+0.053044 test-rmse:1.604658+0.158652
## [122] train-rmse:1.456961+0.053102 test-rmse:1.602559+0.159561
## [123] train-rmse:1.454849+0.053124 test-rmse:1.598684+0.160965
## [124] train-rmse:1.452805+0.053198 test-rmse:1.597841+0.162445
## [125] train-rmse:1.450745+0.053200 test-rmse:1.595711+0.162457
## [126] train-rmse:1.448691+0.053192 test-rmse:1.591413+0.163169
## [127] train-rmse:1.446680+0.053191 test-rmse:1.587202+0.160493
## [128] train-rmse:1.444765+0.053219 test-rmse:1.587062+0.159860
## [129] train-rmse:1.442869+0.053249 test-rmse:1.583703+0.160361
## [130] train-rmse:1.441031+0.053245 test-rmse:1.579478+0.160881
## [131] train-rmse:1.439215+0.053224 test-rmse:1.579518+0.161490
## [132] train-rmse:1.437428+0.053240 test-rmse:1.577519+0.163097
## [133] train-rmse:1.435619+0.053238 test-rmse:1.575552+0.164650
## [134] train-rmse:1.433846+0.053230 test-rmse:1.570582+0.163113
## [135] train-rmse:1.432139+0.053198 test-rmse:1.572061+0.161053
## [136] train-rmse:1.430477+0.053182 test-rmse:1.568198+0.161337
## [137] train-rmse:1.428837+0.053154 test-rmse:1.568406+0.159696
## [138] train-rmse:1.427260+0.053147 test-rmse:1.567118+0.160524
## [139] train-rmse:1.425651+0.053158 test-rmse:1.563022+0.161588
## [140] train-rmse:1.424076+0.053180 test-rmse:1.561863+0.160174
## [141] train-rmse:1.422527+0.053186 test-rmse:1.557366+0.163760
## [142] train-rmse:1.420952+0.053183 test-rmse:1.555720+0.163680
## [143] train-rmse:1.419415+0.053242 test-rmse:1.554957+0.164925
## [144] train-rmse:1.417886+0.053278 test-rmse:1.553998+0.165931
## [145] train-rmse:1.416389+0.053367 test-rmse:1.552635+0.166404
## [146] train-rmse:1.414872+0.053407 test-rmse:1.550822+0.166291
## [147] train-rmse:1.413378+0.053454 test-rmse:1.552664+0.168397
## [148] train-rmse:1.411940+0.053486 test-rmse:1.553114+0.168590
## [149] train-rmse:1.410543+0.053503 test-rmse:1.551247+0.168190
## [150] train-rmse:1.409156+0.053477 test-rmse:1.550434+0.167278
## [151] train-rmse:1.407796+0.053454 test-rmse:1.550342+0.166480
## [152] train-rmse:1.406469+0.053468 test-rmse:1.551044+0.168076
## [153] train-rmse:1.405161+0.053472 test-rmse:1.552477+0.168693
## [154] train-rmse:1.403886+0.053505 test-rmse:1.550527+0.167551
## [155] train-rmse:1.402609+0.053521 test-rmse:1.548532+0.167250
## [156] train-rmse:1.401325+0.053513 test-rmse:1.547212+0.168899
## [157] train-rmse:1.400065+0.053516 test-rmse:1.546503+0.168539
## [158] train-rmse:1.398803+0.053505 test-rmse:1.547123+0.169499
## [159] train-rmse:1.397567+0.053465 test-rmse:1.546127+0.168901
## [160] train-rmse:1.396348+0.053403 test-rmse:1.543881+0.169640
## [161] train-rmse:1.395186+0.053397 test-rmse:1.541891+0.171665
## [162] train-rmse:1.394056+0.053397 test-rmse:1.538685+0.172212
## [163] train-rmse:1.392901+0.053409 test-rmse:1.537234+0.173577
## [164] train-rmse:1.391769+0.053440 test-rmse:1.535103+0.174193
## [165] train-rmse:1.390619+0.053474 test-rmse:1.529992+0.172362
## [166] train-rmse:1.389562+0.053463 test-rmse:1.530358+0.173875
## [167] train-rmse:1.388442+0.053432 test-rmse:1.529663+0.174459
## [168] train-rmse:1.387357+0.053417 test-rmse:1.527421+0.174799
## [169] train-rmse:1.386296+0.053415 test-rmse:1.527566+0.175601
## [170] train-rmse:1.385266+0.053413 test-rmse:1.527553+0.176891
## [171] train-rmse:1.384229+0.053421 test-rmse:1.527260+0.176560
## [172] train-rmse:1.383211+0.053428 test-rmse:1.526614+0.177380
## [173] train-rmse:1.382209+0.053411 test-rmse:1.525639+0.177799
## [174] train-rmse:1.381212+0.053416 test-rmse:1.525079+0.178995
## [175] train-rmse:1.380242+0.053430 test-rmse:1.522623+0.181771
## [176] train-rmse:1.379296+0.053435 test-rmse:1.521218+0.181810
## [177] train-rmse:1.378361+0.053434 test-rmse:1.520991+0.181968
## [178] train-rmse:1.377461+0.053439 test-rmse:1.521681+0.182068
## [179] train-rmse:1.376561+0.053429 test-rmse:1.520355+0.182280
## [180] train-rmse:1.375663+0.053428 test-rmse:1.519933+0.184600
## [181] train-rmse:1.374765+0.053401 test-rmse:1.518445+0.184367
## [182] train-rmse:1.373886+0.053397 test-rmse:1.517940+0.182042
## [183] train-rmse:1.373042+0.053401 test-rmse:1.517654+0.182445
## [184] train-rmse:1.372199+0.053376 test-rmse:1.516763+0.182590
## [185] train-rmse:1.371363+0.053351 test-rmse:1.515667+0.182254
## [186] train-rmse:1.370547+0.053330 test-rmse:1.513557+0.181507
## [187] train-rmse:1.369732+0.053310 test-rmse:1.513943+0.181911
## [188] train-rmse:1.368923+0.053301 test-rmse:1.513888+0.181706
## [189] train-rmse:1.368120+0.053282 test-rmse:1.513915+0.182093
## [190] train-rmse:1.367312+0.053267 test-rmse:1.513350+0.182402
## [191] train-rmse:1.366516+0.053273 test-rmse:1.513024+0.183045
## [192] train-rmse:1.365730+0.053281 test-rmse:1.515116+0.183364
## [193] train-rmse:1.364956+0.053275 test-rmse:1.514381+0.183375
## [194] train-rmse:1.364189+0.053263 test-rmse:1.512913+0.183403
## [195] train-rmse:1.363440+0.053253 test-rmse:1.513438+0.184447
## [196] train-rmse:1.362692+0.053234 test-rmse:1.513354+0.183320
## [197] train-rmse:1.361954+0.053224 test-rmse:1.512451+0.183388
## [198] train-rmse:1.361221+0.053214 test-rmse:1.509295+0.184933
## [199] train-rmse:1.360506+0.053218 test-rmse:1.509063+0.185068
## [200] train-rmse:1.359813+0.053199 test-rmse:1.508833+0.186973
## [201] train-rmse:1.359124+0.053199 test-rmse:1.508543+0.186721
## [202] train-rmse:1.358465+0.053194 test-rmse:1.507498+0.187842
## [203] train-rmse:1.357826+0.053197 test-rmse:1.506561+0.187497
## [204] train-rmse:1.357186+0.053199 test-rmse:1.506313+0.187740
## [205] train-rmse:1.356556+0.053190 test-rmse:1.506757+0.187456
## [206] train-rmse:1.355923+0.053198 test-rmse:1.505952+0.187003
## [207] train-rmse:1.355321+0.053223 test-rmse:1.504604+0.186470
## [208] train-rmse:1.354725+0.053243 test-rmse:1.503050+0.188221
## [209] train-rmse:1.354127+0.053256 test-rmse:1.503471+0.188569
## [210] train-rmse:1.353543+0.053255 test-rmse:1.503027+0.188754
## [211] train-rmse:1.352962+0.053244 test-rmse:1.502997+0.190660
## [212] train-rmse:1.352386+0.053235 test-rmse:1.502982+0.192083
## [213] train-rmse:1.351807+0.053231 test-rmse:1.501879+0.192343
## [214] train-rmse:1.351242+0.053224 test-rmse:1.499701+0.191734
## [215] train-rmse:1.350675+0.053226 test-rmse:1.500987+0.191842
## [216] train-rmse:1.350122+0.053223 test-rmse:1.500275+0.192146
## [217] train-rmse:1.349580+0.053230 test-rmse:1.499674+0.192183
## [218] train-rmse:1.349049+0.053239 test-rmse:1.499724+0.193556
## [219] train-rmse:1.348530+0.053231 test-rmse:1.499283+0.193252
## [220] train-rmse:1.347994+0.053217 test-rmse:1.498220+0.194207
## [221] train-rmse:1.347459+0.053207 test-rmse:1.498262+0.194362
## [222] train-rmse:1.346943+0.053191 test-rmse:1.498947+0.193734
## [223] train-rmse:1.346436+0.053173 test-rmse:1.498144+0.193547
## [224] train-rmse:1.345944+0.053161 test-rmse:1.498765+0.192985
## [225] train-rmse:1.345457+0.053143 test-rmse:1.497203+0.194458
## [226] train-rmse:1.344966+0.053135 test-rmse:1.497554+0.193212
## [227] train-rmse:1.344485+0.053127 test-rmse:1.496750+0.193942
## [228] train-rmse:1.343999+0.053115 test-rmse:1.495669+0.193572
## [229] train-rmse:1.343511+0.053105 test-rmse:1.495241+0.193229
## [230] train-rmse:1.343023+0.053092 test-rmse:1.494171+0.192695
## [231] train-rmse:1.342565+0.053076 test-rmse:1.494156+0.192822
## [232] train-rmse:1.342110+0.053062 test-rmse:1.493114+0.191817
## [233] train-rmse:1.341656+0.053051 test-rmse:1.493818+0.191070
## [234] train-rmse:1.341217+0.053045 test-rmse:1.493826+0.191046
## [235] train-rmse:1.340789+0.053038 test-rmse:1.494370+0.193105
## [236] train-rmse:1.340358+0.053026 test-rmse:1.493751+0.193091
## [237] train-rmse:1.339940+0.053003 test-rmse:1.493219+0.193270
## [238] train-rmse:1.339523+0.052991 test-rmse:1.492377+0.195171
## [239] train-rmse:1.339115+0.052976 test-rmse:1.491681+0.195258
## [240] train-rmse:1.338717+0.052962 test-rmse:1.491620+0.195196
## [241] train-rmse:1.338325+0.052952 test-rmse:1.489838+0.195122
## [242] train-rmse:1.337928+0.052958 test-rmse:1.490527+0.196453
## [243] train-rmse:1.337533+0.052964 test-rmse:1.489394+0.195177
## [244] train-rmse:1.337148+0.052961 test-rmse:1.488729+0.194924
## [245] train-rmse:1.336760+0.052954 test-rmse:1.490524+0.196065
## [246] train-rmse:1.336384+0.052937 test-rmse:1.489947+0.195515
## [247] train-rmse:1.336013+0.052926 test-rmse:1.488949+0.195772
## [248] train-rmse:1.335646+0.052913 test-rmse:1.488004+0.197069
## [249] train-rmse:1.335287+0.052900 test-rmse:1.487354+0.196658
## [250] train-rmse:1.334944+0.052883 test-rmse:1.486706+0.196159
## [251] train-rmse:1.334603+0.052867 test-rmse:1.487078+0.196030
## [252] train-rmse:1.334262+0.052853 test-rmse:1.486359+0.196706
## [253] train-rmse:1.333914+0.052856 test-rmse:1.487838+0.197773
## [254] train-rmse:1.333569+0.052836 test-rmse:1.487266+0.197863
## [255] train-rmse:1.333217+0.052813 test-rmse:1.485380+0.198939
## [256] train-rmse:1.332880+0.052800 test-rmse:1.485067+0.197411
## [257] train-rmse:1.332566+0.052801 test-rmse:1.485036+0.198059
## [258] train-rmse:1.332253+0.052795 test-rmse:1.486363+0.197231
## [259] train-rmse:1.331952+0.052786 test-rmse:1.485795+0.196806
## [260] train-rmse:1.331649+0.052777 test-rmse:1.484946+0.197186
## [261] train-rmse:1.331348+0.052765 test-rmse:1.484921+0.197204
## [262] train-rmse:1.331052+0.052753 test-rmse:1.485103+0.197218
## [263] train-rmse:1.330754+0.052741 test-rmse:1.485047+0.196949
## [264] train-rmse:1.330460+0.052724 test-rmse:1.484653+0.196638
## [265] train-rmse:1.330166+0.052712 test-rmse:1.484146+0.196332
## [266] train-rmse:1.329872+0.052706 test-rmse:1.485922+0.195744
## [267] train-rmse:1.329583+0.052699 test-rmse:1.485442+0.195317
## [268] train-rmse:1.329294+0.052693 test-rmse:1.485167+0.195736
## [269] train-rmse:1.329013+0.052680 test-rmse:1.485810+0.196672
## [270] train-rmse:1.328741+0.052665 test-rmse:1.484339+0.197483
## [271] train-rmse:1.328471+0.052654 test-rmse:1.483815+0.197833
## [272] train-rmse:1.328201+0.052642 test-rmse:1.483136+0.198276
## [273] train-rmse:1.327935+0.052628 test-rmse:1.482454+0.198368
## [274] train-rmse:1.327678+0.052619 test-rmse:1.482178+0.198812
## [275] train-rmse:1.327422+0.052607 test-rmse:1.481034+0.199724
## [276] train-rmse:1.327169+0.052594 test-rmse:1.480906+0.199526
## [277] train-rmse:1.326925+0.052582 test-rmse:1.480979+0.199615
## [278] train-rmse:1.326679+0.052573 test-rmse:1.480329+0.199194
## [279] train-rmse:1.326433+0.052559 test-rmse:1.480758+0.199154
## [280] train-rmse:1.326193+0.052549 test-rmse:1.481715+0.199701
## [281] train-rmse:1.325947+0.052540 test-rmse:1.480850+0.199521
## [282] train-rmse:1.325712+0.052535 test-rmse:1.481272+0.201070
## [283] train-rmse:1.325473+0.052527 test-rmse:1.481504+0.201376
## [284] train-rmse:1.325242+0.052523 test-rmse:1.480999+0.201553
## Stopping. Best iteration:
## [278] train-rmse:1.326679+0.052573 test-rmse:1.480329+0.199194
##
## 71
## [1] train-rmse:6.985669+0.048566 test-rmse:6.982148+0.258323
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.141150+0.033978 test-rmse:5.142435+0.228104
## [3] train-rmse:3.912043+0.021054 test-rmse:3.926359+0.205699
## [4] train-rmse:3.103670+0.022382 test-rmse:3.141471+0.160501
## [5] train-rmse:2.590827+0.024975 test-rmse:2.647628+0.114209
## [6] train-rmse:2.280305+0.032239 test-rmse:2.363743+0.059172
## [7] train-rmse:2.091784+0.031062 test-rmse:2.188790+0.035186
## [8] train-rmse:1.976154+0.034144 test-rmse:2.092748+0.036346
## [9] train-rmse:1.893010+0.037165 test-rmse:2.023839+0.053784
## [10] train-rmse:1.834800+0.040755 test-rmse:1.996150+0.075765
## [11] train-rmse:1.789472+0.043682 test-rmse:1.949912+0.077076
## [12] train-rmse:1.753429+0.043228 test-rmse:1.918510+0.078299
## [13] train-rmse:1.723003+0.044375 test-rmse:1.881879+0.074928
## [14] train-rmse:1.685035+0.030230 test-rmse:1.840207+0.086008
## [15] train-rmse:1.659958+0.031952 test-rmse:1.824314+0.093539
## [16] train-rmse:1.631602+0.035649 test-rmse:1.808190+0.099073
## [17] train-rmse:1.603898+0.040605 test-rmse:1.788767+0.100194
## [18] train-rmse:1.578794+0.042646 test-rmse:1.763551+0.100141
## [19] train-rmse:1.556436+0.043429 test-rmse:1.749999+0.096155
## [20] train-rmse:1.536206+0.046479 test-rmse:1.743926+0.112738
## [21] train-rmse:1.511187+0.046738 test-rmse:1.710406+0.079465
## [22] train-rmse:1.487109+0.048823 test-rmse:1.689009+0.071847
## [23] train-rmse:1.468885+0.050391 test-rmse:1.672063+0.067332
## [24] train-rmse:1.449142+0.049311 test-rmse:1.655658+0.072342
## [25] train-rmse:1.433412+0.049729 test-rmse:1.639019+0.076778
## [26] train-rmse:1.418178+0.049440 test-rmse:1.621552+0.076677
## [27] train-rmse:1.402357+0.049271 test-rmse:1.610969+0.072154
## [28] train-rmse:1.386001+0.050472 test-rmse:1.600819+0.071262
## [29] train-rmse:1.371082+0.050427 test-rmse:1.592723+0.065790
## [30] train-rmse:1.356922+0.050492 test-rmse:1.587718+0.065390
## [31] train-rmse:1.343412+0.049862 test-rmse:1.576528+0.072091
## [32] train-rmse:1.330809+0.049122 test-rmse:1.563852+0.076104
## [33] train-rmse:1.309290+0.033642 test-rmse:1.554890+0.093958
## [34] train-rmse:1.296066+0.033365 test-rmse:1.547894+0.098006
## [35] train-rmse:1.284543+0.033875 test-rmse:1.539322+0.094865
## [36] train-rmse:1.268416+0.045255 test-rmse:1.527964+0.098847
## [37] train-rmse:1.256350+0.045744 test-rmse:1.517722+0.097176
## [38] train-rmse:1.245531+0.046651 test-rmse:1.506566+0.097015
## [39] train-rmse:1.235578+0.046383 test-rmse:1.499997+0.096078
## [40] train-rmse:1.225828+0.046459 test-rmse:1.494097+0.096051
## [41] train-rmse:1.216619+0.046890 test-rmse:1.486521+0.100844
## [42] train-rmse:1.207087+0.047007 test-rmse:1.481740+0.103063
## [43] train-rmse:1.198200+0.047914 test-rmse:1.475664+0.098939
## [44] train-rmse:1.189425+0.047683 test-rmse:1.470149+0.099337
## [45] train-rmse:1.181723+0.048413 test-rmse:1.462543+0.099547
## [46] train-rmse:1.174099+0.049073 test-rmse:1.456972+0.097729
## [47] train-rmse:1.166178+0.049053 test-rmse:1.456756+0.100101
## [48] train-rmse:1.150849+0.042689 test-rmse:1.451528+0.104720
## [49] train-rmse:1.141887+0.042636 test-rmse:1.444111+0.107256
## [50] train-rmse:1.134627+0.043393 test-rmse:1.434252+0.111034
## [51] train-rmse:1.123246+0.050442 test-rmse:1.427228+0.117872
## [52] train-rmse:1.115133+0.052345 test-rmse:1.423657+0.116190
## [53] train-rmse:1.107846+0.052427 test-rmse:1.419282+0.116428
## [54] train-rmse:1.101606+0.052628 test-rmse:1.415985+0.115974
## [55] train-rmse:1.090688+0.056787 test-rmse:1.410404+0.117966
## [56] train-rmse:1.083999+0.057617 test-rmse:1.404668+0.119248
## [57] train-rmse:1.077937+0.059054 test-rmse:1.400710+0.120958
## [58] train-rmse:1.070897+0.058330 test-rmse:1.397416+0.120299
## [59] train-rmse:1.061352+0.056287 test-rmse:1.394646+0.125109
## [60] train-rmse:1.054104+0.055569 test-rmse:1.390679+0.128113
## [61] train-rmse:1.048563+0.056013 test-rmse:1.382790+0.136304
## [62] train-rmse:1.041874+0.055933 test-rmse:1.380143+0.129588
## [63] train-rmse:1.034191+0.059974 test-rmse:1.380218+0.132882
## [64] train-rmse:1.029119+0.059980 test-rmse:1.381658+0.131466
## [65] train-rmse:1.015685+0.055863 test-rmse:1.374996+0.130867
## [66] train-rmse:1.009236+0.055700 test-rmse:1.367552+0.132395
## [67] train-rmse:1.004721+0.055858 test-rmse:1.361585+0.130972
## [68] train-rmse:0.998065+0.054655 test-rmse:1.358027+0.133501
## [69] train-rmse:0.987547+0.052562 test-rmse:1.340312+0.119628
## [70] train-rmse:0.982918+0.052344 test-rmse:1.336183+0.123200
## [71] train-rmse:0.977657+0.054171 test-rmse:1.334808+0.124042
## [72] train-rmse:0.973254+0.053949 test-rmse:1.332158+0.131255
## [73] train-rmse:0.968130+0.053540 test-rmse:1.331421+0.132562
## [74] train-rmse:0.963717+0.053314 test-rmse:1.327080+0.133357
## [75] train-rmse:0.958992+0.053732 test-rmse:1.325215+0.134385
## [76] train-rmse:0.949216+0.051752 test-rmse:1.321140+0.141189
## [77] train-rmse:0.943977+0.054981 test-rmse:1.318939+0.140342
## [78] train-rmse:0.940725+0.055054 test-rmse:1.319952+0.139299
## [79] train-rmse:0.936975+0.055268 test-rmse:1.319356+0.137266
## [80] train-rmse:0.933420+0.054988 test-rmse:1.317236+0.138594
## [81] train-rmse:0.930151+0.055157 test-rmse:1.311758+0.138810
## [82] train-rmse:0.922098+0.057105 test-rmse:1.298608+0.133477
## [83] train-rmse:0.918717+0.057192 test-rmse:1.298152+0.132673
## [84] train-rmse:0.911826+0.056583 test-rmse:1.297968+0.134297
## [85] train-rmse:0.907401+0.056822 test-rmse:1.301940+0.136865
## [86] train-rmse:0.904520+0.056967 test-rmse:1.298853+0.137454
## [87] train-rmse:0.901439+0.056756 test-rmse:1.298438+0.137618
## [88] train-rmse:0.898699+0.056590 test-rmse:1.295830+0.137294
## [89] train-rmse:0.895100+0.056870 test-rmse:1.294997+0.136516
## [90] train-rmse:0.891829+0.058054 test-rmse:1.296030+0.137357
## [91] train-rmse:0.888892+0.057764 test-rmse:1.293667+0.139376
## [92] train-rmse:0.883804+0.063330 test-rmse:1.292305+0.139700
## [93] train-rmse:0.878376+0.063606 test-rmse:1.284945+0.137557
## [94] train-rmse:0.875964+0.063695 test-rmse:1.285608+0.137273
## [95] train-rmse:0.873884+0.063886 test-rmse:1.282539+0.135164
## [96] train-rmse:0.870770+0.063858 test-rmse:1.281353+0.136068
## [97] train-rmse:0.864783+0.060717 test-rmse:1.276077+0.135886
## [98] train-rmse:0.861850+0.060211 test-rmse:1.275202+0.137479
## [99] train-rmse:0.859122+0.061080 test-rmse:1.276357+0.137415
## [100] train-rmse:0.856271+0.062548 test-rmse:1.274013+0.134990
## [101] train-rmse:0.853861+0.062596 test-rmse:1.274204+0.136764
## [102] train-rmse:0.851820+0.062747 test-rmse:1.274156+0.137485
## [103] train-rmse:0.849560+0.063155 test-rmse:1.275266+0.138824
## [104] train-rmse:0.847013+0.062827 test-rmse:1.273657+0.139418
## [105] train-rmse:0.844687+0.062470 test-rmse:1.272547+0.139620
## [106] train-rmse:0.839306+0.064008 test-rmse:1.265571+0.139449
## [107] train-rmse:0.836913+0.063717 test-rmse:1.263890+0.140054
## [108] train-rmse:0.835010+0.064316 test-rmse:1.265382+0.140279
## [109] train-rmse:0.831050+0.063661 test-rmse:1.264671+0.139251
## [110] train-rmse:0.829013+0.064322 test-rmse:1.264904+0.140650
## [111] train-rmse:0.826404+0.065185 test-rmse:1.264669+0.139329
## [112] train-rmse:0.824574+0.065075 test-rmse:1.266009+0.138748
## [113] train-rmse:0.821889+0.064424 test-rmse:1.264177+0.139904
## Stopping. Best iteration:
## [107] train-rmse:0.836913+0.063717 test-rmse:1.263890+0.140054
##
## 72
## [1] train-rmse:6.969788+0.050311 test-rmse:6.966611+0.251033
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.097812+0.038057 test-rmse:5.102324+0.225835
## [3] train-rmse:3.835733+0.033230 test-rmse:3.856261+0.185271
## [4] train-rmse:3.001683+0.026402 test-rmse:3.059129+0.145395
## [5] train-rmse:2.450853+0.018130 test-rmse:2.529153+0.126932
## [6] train-rmse:2.108497+0.014784 test-rmse:2.205720+0.080455
## [7] train-rmse:1.893731+0.016932 test-rmse:2.036905+0.074259
## [8] train-rmse:1.756886+0.016832 test-rmse:1.916802+0.069855
## [9] train-rmse:1.651539+0.022625 test-rmse:1.832023+0.062244
## [10] train-rmse:1.581282+0.024605 test-rmse:1.779617+0.067071
## [11] train-rmse:1.528926+0.026360 test-rmse:1.736316+0.063583
## [12] train-rmse:1.476577+0.018798 test-rmse:1.711848+0.071074
## [13] train-rmse:1.438885+0.019072 test-rmse:1.682006+0.072839
## [14] train-rmse:1.399636+0.026886 test-rmse:1.651128+0.073816
## [15] train-rmse:1.364280+0.030672 test-rmse:1.626966+0.080291
## [16] train-rmse:1.333770+0.033591 test-rmse:1.605641+0.076219
## [17] train-rmse:1.305269+0.034468 test-rmse:1.579499+0.072577
## [18] train-rmse:1.277058+0.034525 test-rmse:1.560084+0.083257
## [19] train-rmse:1.251983+0.035655 test-rmse:1.542170+0.089002
## [20] train-rmse:1.227204+0.036153 test-rmse:1.529331+0.092069
## [21] train-rmse:1.197656+0.029908 test-rmse:1.518955+0.106762
## [22] train-rmse:1.176184+0.029019 test-rmse:1.511272+0.107357
## [23] train-rmse:1.154545+0.031232 test-rmse:1.485479+0.115386
## [24] train-rmse:1.134308+0.031820 test-rmse:1.474369+0.121851
## [25] train-rmse:1.113824+0.029195 test-rmse:1.453506+0.110601
## [26] train-rmse:1.094942+0.029771 test-rmse:1.445938+0.108567
## [27] train-rmse:1.070910+0.022762 test-rmse:1.432365+0.112613
## [28] train-rmse:1.055156+0.024552 test-rmse:1.430800+0.115857
## [29] train-rmse:1.035949+0.024060 test-rmse:1.419880+0.121877
## [30] train-rmse:1.022435+0.023956 test-rmse:1.412990+0.126762
## [31] train-rmse:1.007179+0.023585 test-rmse:1.403951+0.127760
## [32] train-rmse:0.989666+0.022728 test-rmse:1.389022+0.134373
## [33] train-rmse:0.975187+0.021297 test-rmse:1.377536+0.137461
## [34] train-rmse:0.957718+0.022191 test-rmse:1.357975+0.133269
## [35] train-rmse:0.943734+0.023012 test-rmse:1.349511+0.135306
## [36] train-rmse:0.932085+0.023653 test-rmse:1.347402+0.143922
## [37] train-rmse:0.918917+0.023415 test-rmse:1.343263+0.144657
## [38] train-rmse:0.909332+0.023560 test-rmse:1.338943+0.144081
## [39] train-rmse:0.898695+0.023392 test-rmse:1.338835+0.146536
## [40] train-rmse:0.888187+0.023715 test-rmse:1.337634+0.146011
## [41] train-rmse:0.873116+0.027931 test-rmse:1.333065+0.145529
## [42] train-rmse:0.862497+0.025598 test-rmse:1.328723+0.146085
## [43] train-rmse:0.853188+0.026476 test-rmse:1.327965+0.143139
## [44] train-rmse:0.840221+0.028691 test-rmse:1.316415+0.149217
## [45] train-rmse:0.829991+0.026986 test-rmse:1.313054+0.151594
## [46] train-rmse:0.819903+0.027956 test-rmse:1.304890+0.153015
## [47] train-rmse:0.812230+0.027884 test-rmse:1.302612+0.155642
## [48] train-rmse:0.802891+0.026801 test-rmse:1.298544+0.154031
## [49] train-rmse:0.796830+0.026885 test-rmse:1.294939+0.153753
## [50] train-rmse:0.791132+0.027431 test-rmse:1.296069+0.157300
## [51] train-rmse:0.781070+0.031601 test-rmse:1.295549+0.158304
## [52] train-rmse:0.775210+0.031989 test-rmse:1.296419+0.160065
## [53] train-rmse:0.765245+0.030250 test-rmse:1.292093+0.161184
## [54] train-rmse:0.759133+0.031005 test-rmse:1.290991+0.161474
## [55] train-rmse:0.753823+0.031234 test-rmse:1.288547+0.163808
## [56] train-rmse:0.747124+0.031612 test-rmse:1.284263+0.161783
## [57] train-rmse:0.742556+0.032501 test-rmse:1.283938+0.159453
## [58] train-rmse:0.736158+0.031080 test-rmse:1.280061+0.160929
## [59] train-rmse:0.727609+0.029093 test-rmse:1.277787+0.160709
## [60] train-rmse:0.722012+0.028278 test-rmse:1.278887+0.165816
## [61] train-rmse:0.715748+0.028068 test-rmse:1.276835+0.165783
## [62] train-rmse:0.711104+0.028088 test-rmse:1.276823+0.165549
## [63] train-rmse:0.705682+0.027580 test-rmse:1.276001+0.165780
## [64] train-rmse:0.698527+0.028503 test-rmse:1.277598+0.165578
## [65] train-rmse:0.692730+0.028494 test-rmse:1.277529+0.166423
## [66] train-rmse:0.687134+0.028683 test-rmse:1.274276+0.164658
## [67] train-rmse:0.682612+0.028742 test-rmse:1.275664+0.164614
## [68] train-rmse:0.679006+0.029206 test-rmse:1.271026+0.165310
## [69] train-rmse:0.674228+0.030183 test-rmse:1.267431+0.165399
## [70] train-rmse:0.669377+0.030747 test-rmse:1.267622+0.164298
## [71] train-rmse:0.664221+0.030132 test-rmse:1.263256+0.163182
## [72] train-rmse:0.660075+0.031049 test-rmse:1.261595+0.164397
## [73] train-rmse:0.652961+0.029714 test-rmse:1.259763+0.164489
## [74] train-rmse:0.648933+0.029447 test-rmse:1.260128+0.165442
## [75] train-rmse:0.641989+0.026723 test-rmse:1.254509+0.162623
## [76] train-rmse:0.638432+0.027602 test-rmse:1.254705+0.164327
## [77] train-rmse:0.635972+0.027469 test-rmse:1.253556+0.166280
## [78] train-rmse:0.632996+0.027447 test-rmse:1.252216+0.165988
## [79] train-rmse:0.629973+0.028282 test-rmse:1.253022+0.167353
## [80] train-rmse:0.626283+0.026620 test-rmse:1.252745+0.167067
## [81] train-rmse:0.620457+0.028691 test-rmse:1.252481+0.166641
## [82] train-rmse:0.615475+0.028330 test-rmse:1.249076+0.165305
## [83] train-rmse:0.612638+0.027675 test-rmse:1.249070+0.165020
## [84] train-rmse:0.608389+0.028071 test-rmse:1.247507+0.163861
## [85] train-rmse:0.605720+0.028036 test-rmse:1.246612+0.164729
## [86] train-rmse:0.601828+0.028697 test-rmse:1.246438+0.165336
## [87] train-rmse:0.598006+0.031941 test-rmse:1.246791+0.165725
## [88] train-rmse:0.596169+0.032147 test-rmse:1.246751+0.166480
## [89] train-rmse:0.591163+0.030454 test-rmse:1.246308+0.166255
## [90] train-rmse:0.586908+0.027902 test-rmse:1.246436+0.166132
## [91] train-rmse:0.584983+0.027812 test-rmse:1.248069+0.169899
## [92] train-rmse:0.581338+0.028562 test-rmse:1.247508+0.170101
## [93] train-rmse:0.578261+0.028659 test-rmse:1.246580+0.169510
## [94] train-rmse:0.575799+0.028097 test-rmse:1.246689+0.169329
## [95] train-rmse:0.573037+0.027637 test-rmse:1.246276+0.170128
## [96] train-rmse:0.571012+0.027660 test-rmse:1.245316+0.168865
## [97] train-rmse:0.568286+0.027600 test-rmse:1.241970+0.168723
## [98] train-rmse:0.566081+0.027586 test-rmse:1.241319+0.169198
## [99] train-rmse:0.564336+0.027481 test-rmse:1.239970+0.167495
## [100] train-rmse:0.562524+0.027328 test-rmse:1.240562+0.168408
## [101] train-rmse:0.559796+0.026756 test-rmse:1.241494+0.170613
## [102] train-rmse:0.557593+0.026548 test-rmse:1.241815+0.170854
## [103] train-rmse:0.553992+0.027681 test-rmse:1.243522+0.171646
## [104] train-rmse:0.551479+0.029463 test-rmse:1.243314+0.172069
## [105] train-rmse:0.550215+0.029401 test-rmse:1.245135+0.174532
## Stopping. Best iteration:
## [99] train-rmse:0.564336+0.027481 test-rmse:1.239970+0.167495
##
## 73
## [1] train-rmse:6.963134+0.050966 test-rmse:6.960471+0.253016
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.081203+0.035964 test-rmse:5.096390+0.227794
## [3] train-rmse:3.801087+0.028714 test-rmse:3.816571+0.188826
## [4] train-rmse:2.935717+0.018145 test-rmse:2.983368+0.150369
## [5] train-rmse:2.359766+0.013530 test-rmse:2.446213+0.098834
## [6] train-rmse:1.995816+0.013512 test-rmse:2.130526+0.086110
## [7] train-rmse:1.750353+0.017749 test-rmse:1.915340+0.061707
## [8] train-rmse:1.593503+0.014643 test-rmse:1.799107+0.078185
## [9] train-rmse:1.479830+0.018252 test-rmse:1.720087+0.072665
## [10] train-rmse:1.395931+0.023109 test-rmse:1.665359+0.070904
## [11] train-rmse:1.330765+0.022663 test-rmse:1.620733+0.082367
## [12] train-rmse:1.280601+0.023381 test-rmse:1.576393+0.097905
## [13] train-rmse:1.233297+0.030122 test-rmse:1.536900+0.106087
## [14] train-rmse:1.187275+0.027349 test-rmse:1.509927+0.119479
## [15] train-rmse:1.150766+0.023464 test-rmse:1.489549+0.123890
## [16] train-rmse:1.117500+0.025177 test-rmse:1.467812+0.135894
## [17] train-rmse:1.086163+0.024309 test-rmse:1.449498+0.140498
## [18] train-rmse:1.054939+0.023378 test-rmse:1.434602+0.138364
## [19] train-rmse:1.025627+0.025276 test-rmse:1.424108+0.144000
## [20] train-rmse:1.001011+0.027693 test-rmse:1.415727+0.151618
## [21] train-rmse:0.973879+0.028230 test-rmse:1.386825+0.139926
## [22] train-rmse:0.952095+0.029280 test-rmse:1.381445+0.148554
## [23] train-rmse:0.928369+0.028813 test-rmse:1.366710+0.153353
## [24] train-rmse:0.904088+0.026004 test-rmse:1.358343+0.157914
## [25] train-rmse:0.884732+0.025332 test-rmse:1.353576+0.160390
## [26] train-rmse:0.865982+0.024844 test-rmse:1.348165+0.161131
## [27] train-rmse:0.847971+0.024123 test-rmse:1.332368+0.167056
## [28] train-rmse:0.831434+0.021000 test-rmse:1.325628+0.177565
## [29] train-rmse:0.814950+0.021506 test-rmse:1.319239+0.178597
## [30] train-rmse:0.799812+0.021270 test-rmse:1.316468+0.177797
## [31] train-rmse:0.784395+0.020877 test-rmse:1.311838+0.174436
## [32] train-rmse:0.772678+0.018749 test-rmse:1.307425+0.179099
## [33] train-rmse:0.756417+0.017350 test-rmse:1.300404+0.179030
## [34] train-rmse:0.740192+0.017189 test-rmse:1.294471+0.184905
## [35] train-rmse:0.724325+0.020365 test-rmse:1.290441+0.187923
## [36] train-rmse:0.713970+0.019874 test-rmse:1.289374+0.191514
## [37] train-rmse:0.702415+0.022447 test-rmse:1.283382+0.193252
## [38] train-rmse:0.692460+0.022297 test-rmse:1.282054+0.191021
## [39] train-rmse:0.683320+0.022479 test-rmse:1.282419+0.193784
## [40] train-rmse:0.673863+0.021669 test-rmse:1.282411+0.189800
## [41] train-rmse:0.666310+0.021771 test-rmse:1.279759+0.190831
## [42] train-rmse:0.656412+0.021923 test-rmse:1.273913+0.192641
## [43] train-rmse:0.645751+0.022920 test-rmse:1.277277+0.192136
## [44] train-rmse:0.636568+0.023144 test-rmse:1.274497+0.197069
## [45] train-rmse:0.630673+0.022734 test-rmse:1.273024+0.197577
## [46] train-rmse:0.623581+0.022451 test-rmse:1.271019+0.200306
## [47] train-rmse:0.617438+0.023367 test-rmse:1.268149+0.197268
## [48] train-rmse:0.609639+0.023216 test-rmse:1.269483+0.196781
## [49] train-rmse:0.602689+0.022902 test-rmse:1.263422+0.193882
## [50] train-rmse:0.593417+0.020877 test-rmse:1.261944+0.194602
## [51] train-rmse:0.584496+0.020974 test-rmse:1.261099+0.192740
## [52] train-rmse:0.577913+0.020563 test-rmse:1.259687+0.191965
## [53] train-rmse:0.569245+0.018780 test-rmse:1.259880+0.191558
## [54] train-rmse:0.561065+0.019254 test-rmse:1.260413+0.193728
## [55] train-rmse:0.556073+0.019616 test-rmse:1.257602+0.194027
## [56] train-rmse:0.549898+0.021442 test-rmse:1.256088+0.193448
## [57] train-rmse:0.544296+0.019621 test-rmse:1.257160+0.192454
## [58] train-rmse:0.538999+0.019461 test-rmse:1.251789+0.190438
## [59] train-rmse:0.533434+0.018097 test-rmse:1.251407+0.190904
## [60] train-rmse:0.527890+0.019101 test-rmse:1.248537+0.190208
## [61] train-rmse:0.523374+0.018603 test-rmse:1.247889+0.190177
## [62] train-rmse:0.518160+0.018642 test-rmse:1.244724+0.190593
## [63] train-rmse:0.508506+0.017189 test-rmse:1.241954+0.191461
## [64] train-rmse:0.504410+0.016138 test-rmse:1.242755+0.191083
## [65] train-rmse:0.500459+0.015289 test-rmse:1.242390+0.189635
## [66] train-rmse:0.496072+0.015487 test-rmse:1.244425+0.187807
## [67] train-rmse:0.491925+0.015572 test-rmse:1.243903+0.186820
## [68] train-rmse:0.488000+0.015293 test-rmse:1.244367+0.187416
## [69] train-rmse:0.484895+0.015681 test-rmse:1.245667+0.188938
## Stopping. Best iteration:
## [63] train-rmse:0.508506+0.017189 test-rmse:1.241954+0.191461
##
## 74
## [1] train-rmse:6.957476+0.051519 test-rmse:6.960685+0.253914
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.064951+0.036454 test-rmse:5.084575+0.225193
## [3] train-rmse:3.771214+0.031876 test-rmse:3.807530+0.173757
## [4] train-rmse:2.886489+0.020975 test-rmse:2.945040+0.152141
## [5] train-rmse:2.284456+0.022540 test-rmse:2.393352+0.105654
## [6] train-rmse:1.892346+0.022109 test-rmse:2.061231+0.099727
## [7] train-rmse:1.636293+0.021683 test-rmse:1.866190+0.085519
## [8] train-rmse:1.456368+0.025159 test-rmse:1.724098+0.072327
## [9] train-rmse:1.327642+0.018899 test-rmse:1.647202+0.077034
## [10] train-rmse:1.233761+0.026549 test-rmse:1.576306+0.085616
## [11] train-rmse:1.163670+0.028394 test-rmse:1.525641+0.090559
## [12] train-rmse:1.101621+0.029027 test-rmse:1.487537+0.094965
## [13] train-rmse:1.047729+0.025581 test-rmse:1.469800+0.106406
## [14] train-rmse:1.001086+0.027234 test-rmse:1.439632+0.115630
## [15] train-rmse:0.960435+0.024148 test-rmse:1.420220+0.125516
## [16] train-rmse:0.925016+0.024305 test-rmse:1.407455+0.133537
## [17] train-rmse:0.888852+0.027270 test-rmse:1.395033+0.127722
## [18] train-rmse:0.862594+0.025204 test-rmse:1.383917+0.134679
## [19] train-rmse:0.831319+0.022690 test-rmse:1.370676+0.142291
## [20] train-rmse:0.809403+0.023274 test-rmse:1.365354+0.150881
## [21] train-rmse:0.784474+0.022501 test-rmse:1.352769+0.155129
## [22] train-rmse:0.763492+0.022966 test-rmse:1.346985+0.153630
## [23] train-rmse:0.738762+0.018480 test-rmse:1.338614+0.159707
## [24] train-rmse:0.721167+0.017008 test-rmse:1.335508+0.160127
## [25] train-rmse:0.701914+0.015247 test-rmse:1.328210+0.163657
## [26] train-rmse:0.681357+0.015057 test-rmse:1.319847+0.166506
## [27] train-rmse:0.667183+0.015029 test-rmse:1.309189+0.170453
## [28] train-rmse:0.650326+0.017827 test-rmse:1.302056+0.177210
## [29] train-rmse:0.635911+0.017061 test-rmse:1.298969+0.175755
## [30] train-rmse:0.622535+0.016352 test-rmse:1.296188+0.172353
## [31] train-rmse:0.610045+0.014894 test-rmse:1.294015+0.173299
## [32] train-rmse:0.596850+0.015009 test-rmse:1.288548+0.173084
## [33] train-rmse:0.584882+0.016012 test-rmse:1.290381+0.171500
## [34] train-rmse:0.573242+0.014834 test-rmse:1.284100+0.175793
## [35] train-rmse:0.562783+0.014745 test-rmse:1.281533+0.174343
## [36] train-rmse:0.555005+0.014369 test-rmse:1.279095+0.175694
## [37] train-rmse:0.545194+0.014429 test-rmse:1.279123+0.176652
## [38] train-rmse:0.536970+0.016489 test-rmse:1.283067+0.175144
## [39] train-rmse:0.527474+0.019238 test-rmse:1.282654+0.175649
## [40] train-rmse:0.515769+0.017506 test-rmse:1.278333+0.176344
## [41] train-rmse:0.508096+0.017752 test-rmse:1.274457+0.179814
## [42] train-rmse:0.497988+0.015741 test-rmse:1.274206+0.182756
## [43] train-rmse:0.491212+0.015288 test-rmse:1.273846+0.184916
## [44] train-rmse:0.482427+0.013446 test-rmse:1.268564+0.185573
## [45] train-rmse:0.472005+0.013512 test-rmse:1.265561+0.184273
## [46] train-rmse:0.466910+0.013208 test-rmse:1.262272+0.184533
## [47] train-rmse:0.458780+0.011890 test-rmse:1.263700+0.184072
## [48] train-rmse:0.450859+0.009922 test-rmse:1.263782+0.183404
## [49] train-rmse:0.445730+0.009433 test-rmse:1.265273+0.186093
## [50] train-rmse:0.438847+0.009777 test-rmse:1.265457+0.187111
## [51] train-rmse:0.433287+0.010672 test-rmse:1.267688+0.187168
## [52] train-rmse:0.423765+0.012099 test-rmse:1.264956+0.183772
## Stopping. Best iteration:
## [46] train-rmse:0.466910+0.013208 test-rmse:1.262272+0.184533
##
## 75
## [1] train-rmse:6.956108+0.051839 test-rmse:6.961522+0.244604
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.054462+0.036840 test-rmse:5.076050+0.223101
## [3] train-rmse:3.746282+0.030914 test-rmse:3.785072+0.168559
## [4] train-rmse:2.846803+0.025426 test-rmse:2.920417+0.129121
## [5] train-rmse:2.246829+0.021828 test-rmse:2.379318+0.120412
## [6] train-rmse:1.839171+0.031675 test-rmse:2.036993+0.106466
## [7] train-rmse:1.549160+0.033794 test-rmse:1.799751+0.079407
## [8] train-rmse:1.352810+0.030775 test-rmse:1.663677+0.080935
## [9] train-rmse:1.207378+0.022201 test-rmse:1.572177+0.090232
## [10] train-rmse:1.115393+0.018543 test-rmse:1.522454+0.108742
## [11] train-rmse:1.038430+0.022054 test-rmse:1.497174+0.123508
## [12] train-rmse:0.971808+0.022622 test-rmse:1.470290+0.138164
## [13] train-rmse:0.922514+0.020185 test-rmse:1.440326+0.154858
## [14] train-rmse:0.871694+0.029068 test-rmse:1.415605+0.149860
## [15] train-rmse:0.828060+0.020925 test-rmse:1.380614+0.168754
## [16] train-rmse:0.797754+0.021377 test-rmse:1.369333+0.176585
## [17] train-rmse:0.764788+0.024144 test-rmse:1.360442+0.186575
## [18] train-rmse:0.730406+0.019112 test-rmse:1.346868+0.188292
## [19] train-rmse:0.701094+0.015505 test-rmse:1.322397+0.184389
## [20] train-rmse:0.678917+0.013517 test-rmse:1.313589+0.190722
## [21] train-rmse:0.655745+0.013904 test-rmse:1.304740+0.193333
## [22] train-rmse:0.634463+0.012746 test-rmse:1.303000+0.196482
## [23] train-rmse:0.614085+0.012873 test-rmse:1.300859+0.199358
## [24] train-rmse:0.594882+0.009830 test-rmse:1.293688+0.197003
## [25] train-rmse:0.577248+0.007549 test-rmse:1.289853+0.195822
## [26] train-rmse:0.560652+0.006196 test-rmse:1.281425+0.195767
## [27] train-rmse:0.545897+0.007735 test-rmse:1.282722+0.193984
## [28] train-rmse:0.530380+0.007321 test-rmse:1.282217+0.198121
## [29] train-rmse:0.517476+0.006680 test-rmse:1.281887+0.196099
## [30] train-rmse:0.505665+0.010934 test-rmse:1.277860+0.195780
## [31] train-rmse:0.492270+0.007793 test-rmse:1.271327+0.192709
## [32] train-rmse:0.477915+0.009453 test-rmse:1.268032+0.193349
## [33] train-rmse:0.467317+0.008071 test-rmse:1.268226+0.195533
## [34] train-rmse:0.453008+0.003376 test-rmse:1.262606+0.193554
## [35] train-rmse:0.445563+0.005214 test-rmse:1.266177+0.190294
## [36] train-rmse:0.434632+0.004790 test-rmse:1.263342+0.188565
## [37] train-rmse:0.425736+0.004583 test-rmse:1.259607+0.191213
## [38] train-rmse:0.418550+0.005004 test-rmse:1.261492+0.193482
## [39] train-rmse:0.408349+0.005773 test-rmse:1.257071+0.191778
## [40] train-rmse:0.400473+0.007733 test-rmse:1.254544+0.190685
## [41] train-rmse:0.390919+0.009934 test-rmse:1.254325+0.190926
## [42] train-rmse:0.384723+0.009355 test-rmse:1.252985+0.192196
## [43] train-rmse:0.378665+0.007782 test-rmse:1.254203+0.193370
## [44] train-rmse:0.370615+0.006512 test-rmse:1.254166+0.193184
## [45] train-rmse:0.365747+0.006330 test-rmse:1.255518+0.194895
## [46] train-rmse:0.358150+0.005703 test-rmse:1.255876+0.195414
## [47] train-rmse:0.351694+0.005735 test-rmse:1.261118+0.192708
## [48] train-rmse:0.344824+0.004771 test-rmse:1.261759+0.192454
## Stopping. Best iteration:
## [42] train-rmse:0.384723+0.009355 test-rmse:1.252985+0.192196
##
## 76
## [1] train-rmse:6.956108+0.051839 test-rmse:6.961522+0.244604
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:5.049765+0.037977 test-rmse:5.076842+0.210563
## [3] train-rmse:3.730874+0.032998 test-rmse:3.782795+0.156028
## [4] train-rmse:2.825443+0.030110 test-rmse:2.918628+0.126710
## [5] train-rmse:2.203614+0.028109 test-rmse:2.346965+0.103274
## [6] train-rmse:1.776315+0.028609 test-rmse:1.984866+0.084976
## [7] train-rmse:1.483040+0.034485 test-rmse:1.774504+0.080641
## [8] train-rmse:1.272266+0.033933 test-rmse:1.636192+0.087709
## [9] train-rmse:1.119770+0.027854 test-rmse:1.540157+0.095107
## [10] train-rmse:1.014519+0.028226 test-rmse:1.485351+0.092562
## [11] train-rmse:0.934641+0.024741 test-rmse:1.441669+0.101424
## [12] train-rmse:0.873987+0.023933 test-rmse:1.418842+0.125688
## [13] train-rmse:0.817091+0.026388 test-rmse:1.389932+0.131146
## [14] train-rmse:0.770884+0.027159 test-rmse:1.377017+0.144559
## [15] train-rmse:0.728004+0.019690 test-rmse:1.364180+0.159270
## [16] train-rmse:0.691620+0.020171 test-rmse:1.355867+0.162691
## [17] train-rmse:0.661624+0.021292 test-rmse:1.341917+0.164514
## [18] train-rmse:0.630876+0.022611 test-rmse:1.328997+0.171700
## [19] train-rmse:0.605335+0.018659 test-rmse:1.322453+0.175789
## [20] train-rmse:0.583856+0.021143 test-rmse:1.318685+0.182054
## [21] train-rmse:0.561790+0.017602 test-rmse:1.315538+0.183057
## [22] train-rmse:0.538397+0.013986 test-rmse:1.304086+0.182943
## [23] train-rmse:0.520856+0.010778 test-rmse:1.301368+0.182554
## [24] train-rmse:0.501697+0.010426 test-rmse:1.296311+0.176669
## [25] train-rmse:0.484856+0.011129 test-rmse:1.295086+0.180892
## [26] train-rmse:0.469408+0.010211 test-rmse:1.291170+0.180283
## [27] train-rmse:0.458194+0.011261 test-rmse:1.287949+0.183571
## [28] train-rmse:0.443567+0.010438 test-rmse:1.287372+0.180290
## [29] train-rmse:0.431295+0.009654 test-rmse:1.286595+0.182664
## [30] train-rmse:0.416082+0.012345 test-rmse:1.283335+0.184858
## [31] train-rmse:0.405520+0.014381 test-rmse:1.284428+0.185480
## [32] train-rmse:0.393214+0.016615 test-rmse:1.285912+0.187926
## [33] train-rmse:0.380334+0.015686 test-rmse:1.285983+0.182533
## [34] train-rmse:0.372779+0.014986 test-rmse:1.284630+0.183209
## [35] train-rmse:0.364200+0.015166 test-rmse:1.286130+0.183153
## [36] train-rmse:0.352325+0.014422 test-rmse:1.286588+0.184321
## Stopping. Best iteration:
## [30] train-rmse:0.416082+0.012345 test-rmse:1.283335+0.184858
##
## 77
## [1] train-rmse:6.851487+0.046483 test-rmse:6.847448+0.262610
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.990714+0.027764 test-rmse:4.985845+0.254116
## [3] train-rmse:3.814792+0.017594 test-rmse:3.817925+0.219340
## [4] train-rmse:3.106002+0.013457 test-rmse:3.126478+0.164517
## [5] train-rmse:2.699505+0.013790 test-rmse:2.735479+0.113631
## [6] train-rmse:2.472553+0.015458 test-rmse:2.502477+0.069684
## [7] train-rmse:2.344862+0.016634 test-rmse:2.390355+0.053050
## [8] train-rmse:2.271348+0.017608 test-rmse:2.322438+0.052451
## [9] train-rmse:2.224043+0.018702 test-rmse:2.262377+0.055691
## [10] train-rmse:2.191138+0.020063 test-rmse:2.234235+0.055336
## [11] train-rmse:2.164846+0.021077 test-rmse:2.209676+0.077110
## [12] train-rmse:2.141263+0.021289 test-rmse:2.189842+0.077671
## [13] train-rmse:2.119387+0.022269 test-rmse:2.170167+0.078974
## [14] train-rmse:2.099929+0.023253 test-rmse:2.150137+0.073041
## [15] train-rmse:2.080804+0.024030 test-rmse:2.138265+0.078466
## [16] train-rmse:2.063761+0.024821 test-rmse:2.125065+0.083219
## [17] train-rmse:2.047102+0.026196 test-rmse:2.117929+0.087410
## [18] train-rmse:2.031492+0.026735 test-rmse:2.107605+0.090061
## [19] train-rmse:2.016050+0.027460 test-rmse:2.090915+0.095846
## [20] train-rmse:2.000747+0.028072 test-rmse:2.077945+0.095233
## [21] train-rmse:1.985849+0.028639 test-rmse:2.064429+0.095416
## [22] train-rmse:1.971468+0.029897 test-rmse:2.043203+0.089551
## [23] train-rmse:1.958562+0.030789 test-rmse:2.024807+0.092392
## [24] train-rmse:1.946517+0.031012 test-rmse:2.019592+0.092930
## [25] train-rmse:1.934554+0.031278 test-rmse:2.010890+0.091981
## [26] train-rmse:1.922991+0.031816 test-rmse:1.997140+0.085441
## [27] train-rmse:1.911421+0.031880 test-rmse:1.990705+0.086621
## [28] train-rmse:1.900273+0.032186 test-rmse:1.988537+0.088319
## [29] train-rmse:1.888948+0.032542 test-rmse:1.977284+0.091570
## [30] train-rmse:1.878079+0.033287 test-rmse:1.966508+0.085757
## [31] train-rmse:1.867319+0.034064 test-rmse:1.953570+0.082621
## [32] train-rmse:1.857409+0.034967 test-rmse:1.947108+0.084551
## [33] train-rmse:1.847398+0.036207 test-rmse:1.939439+0.081369
## [34] train-rmse:1.837499+0.037121 test-rmse:1.928138+0.079471
## [35] train-rmse:1.827808+0.037713 test-rmse:1.917650+0.086741
## [36] train-rmse:1.818156+0.038454 test-rmse:1.908917+0.088323
## [37] train-rmse:1.808861+0.039087 test-rmse:1.904296+0.092145
## [38] train-rmse:1.799975+0.039720 test-rmse:1.893112+0.095728
## [39] train-rmse:1.791366+0.040130 test-rmse:1.884879+0.106465
## [40] train-rmse:1.782769+0.040424 test-rmse:1.880015+0.106288
## [41] train-rmse:1.774114+0.041252 test-rmse:1.874315+0.103168
## [42] train-rmse:1.765856+0.041574 test-rmse:1.866505+0.103543
## [43] train-rmse:1.757700+0.041971 test-rmse:1.864639+0.100747
## [44] train-rmse:1.749874+0.042478 test-rmse:1.855099+0.107549
## [45] train-rmse:1.742356+0.042703 test-rmse:1.855794+0.105471
## [46] train-rmse:1.735109+0.042990 test-rmse:1.848055+0.106203
## [47] train-rmse:1.728034+0.043243 test-rmse:1.839391+0.101617
## [48] train-rmse:1.720950+0.043564 test-rmse:1.830151+0.099190
## [49] train-rmse:1.714004+0.043996 test-rmse:1.825967+0.098649
## [50] train-rmse:1.707128+0.044359 test-rmse:1.815854+0.097441
## [51] train-rmse:1.700547+0.044588 test-rmse:1.806424+0.098048
## [52] train-rmse:1.694085+0.044740 test-rmse:1.803878+0.099749
## [53] train-rmse:1.687627+0.044948 test-rmse:1.798568+0.096282
## [54] train-rmse:1.681314+0.045154 test-rmse:1.791725+0.099192
## [55] train-rmse:1.675152+0.045459 test-rmse:1.786515+0.099190
## [56] train-rmse:1.669098+0.045748 test-rmse:1.783671+0.105721
## [57] train-rmse:1.663366+0.046077 test-rmse:1.780154+0.103999
## [58] train-rmse:1.657776+0.046324 test-rmse:1.778578+0.103779
## [59] train-rmse:1.652063+0.046599 test-rmse:1.777533+0.108902
## [60] train-rmse:1.646500+0.046891 test-rmse:1.776520+0.104119
## [61] train-rmse:1.640950+0.047319 test-rmse:1.772969+0.105735
## [62] train-rmse:1.635454+0.047449 test-rmse:1.766248+0.106563
## [63] train-rmse:1.630163+0.047682 test-rmse:1.762254+0.111616
## [64] train-rmse:1.624940+0.048066 test-rmse:1.754410+0.112536
## [65] train-rmse:1.619845+0.048270 test-rmse:1.753026+0.117386
## [66] train-rmse:1.614876+0.048733 test-rmse:1.749853+0.115666
## [67] train-rmse:1.609928+0.049074 test-rmse:1.736443+0.119589
## [68] train-rmse:1.604966+0.049409 test-rmse:1.733364+0.122146
## [69] train-rmse:1.600228+0.049595 test-rmse:1.728875+0.119680
## [70] train-rmse:1.595579+0.049851 test-rmse:1.725334+0.121297
## [71] train-rmse:1.591092+0.050139 test-rmse:1.721689+0.123867
## [72] train-rmse:1.586577+0.050336 test-rmse:1.716267+0.121802
## [73] train-rmse:1.582063+0.050395 test-rmse:1.712479+0.120194
## [74] train-rmse:1.577846+0.050458 test-rmse:1.707582+0.124157
## [75] train-rmse:1.573660+0.050457 test-rmse:1.702084+0.127474
## [76] train-rmse:1.569654+0.050448 test-rmse:1.694731+0.130047
## [77] train-rmse:1.565699+0.050491 test-rmse:1.690730+0.128458
## [78] train-rmse:1.561701+0.050516 test-rmse:1.684897+0.128476
## [79] train-rmse:1.557789+0.050739 test-rmse:1.682260+0.133483
## [80] train-rmse:1.553823+0.050808 test-rmse:1.678778+0.133900
## [81] train-rmse:1.549987+0.050854 test-rmse:1.678070+0.134622
## [82] train-rmse:1.546121+0.050960 test-rmse:1.677193+0.132881
## [83] train-rmse:1.542384+0.051043 test-rmse:1.672491+0.135070
## [84] train-rmse:1.538871+0.051063 test-rmse:1.670070+0.132668
## [85] train-rmse:1.535302+0.051153 test-rmse:1.666294+0.132230
## [86] train-rmse:1.531811+0.051167 test-rmse:1.664188+0.135442
## [87] train-rmse:1.528428+0.051133 test-rmse:1.658065+0.139594
## [88] train-rmse:1.525140+0.051141 test-rmse:1.657056+0.137945
## [89] train-rmse:1.521933+0.051196 test-rmse:1.656432+0.141947
## [90] train-rmse:1.518712+0.051193 test-rmse:1.649580+0.139982
## [91] train-rmse:1.515569+0.051183 test-rmse:1.644473+0.141251
## [92] train-rmse:1.512452+0.051191 test-rmse:1.643003+0.141049
## [93] train-rmse:1.509322+0.051261 test-rmse:1.639425+0.143218
## [94] train-rmse:1.506273+0.051379 test-rmse:1.637136+0.144037
## [95] train-rmse:1.503254+0.051438 test-rmse:1.639333+0.146411
## [96] train-rmse:1.500191+0.051542 test-rmse:1.634579+0.145098
## [97] train-rmse:1.497199+0.051677 test-rmse:1.635228+0.148694
## [98] train-rmse:1.494266+0.051836 test-rmse:1.633046+0.149669
## [99] train-rmse:1.491468+0.052039 test-rmse:1.630511+0.150362
## [100] train-rmse:1.488757+0.052139 test-rmse:1.627585+0.151190
## [101] train-rmse:1.486095+0.052265 test-rmse:1.621521+0.151045
## [102] train-rmse:1.483485+0.052359 test-rmse:1.618896+0.151687
## [103] train-rmse:1.480894+0.052460 test-rmse:1.616067+0.149386
## [104] train-rmse:1.478361+0.052560 test-rmse:1.609877+0.146813
## [105] train-rmse:1.475798+0.052681 test-rmse:1.606426+0.146569
## [106] train-rmse:1.473336+0.052783 test-rmse:1.606286+0.146347
## [107] train-rmse:1.470922+0.052928 test-rmse:1.604890+0.151136
## [108] train-rmse:1.468536+0.053032 test-rmse:1.603320+0.150434
## [109] train-rmse:1.466060+0.053166 test-rmse:1.601785+0.150170
## [110] train-rmse:1.463707+0.053226 test-rmse:1.600121+0.151423
## [111] train-rmse:1.461429+0.053260 test-rmse:1.598704+0.153041
## [112] train-rmse:1.459171+0.053289 test-rmse:1.595884+0.153311
## [113] train-rmse:1.456891+0.053269 test-rmse:1.593819+0.153226
## [114] train-rmse:1.454662+0.053296 test-rmse:1.589611+0.153261
## [115] train-rmse:1.452469+0.053281 test-rmse:1.587225+0.153067
## [116] train-rmse:1.450343+0.053316 test-rmse:1.585380+0.158386
## [117] train-rmse:1.448258+0.053370 test-rmse:1.584081+0.157411
## [118] train-rmse:1.446181+0.053387 test-rmse:1.586667+0.160176
## [119] train-rmse:1.444114+0.053358 test-rmse:1.587050+0.164463
## [120] train-rmse:1.442115+0.053351 test-rmse:1.585549+0.162110
## [121] train-rmse:1.440161+0.053318 test-rmse:1.585396+0.160319
## [122] train-rmse:1.438239+0.053281 test-rmse:1.581048+0.162808
## [123] train-rmse:1.436381+0.053250 test-rmse:1.577991+0.162825
## [124] train-rmse:1.434473+0.053200 test-rmse:1.575391+0.163594
## [125] train-rmse:1.432563+0.053126 test-rmse:1.573831+0.164455
## [126] train-rmse:1.430770+0.053126 test-rmse:1.571102+0.164383
## [127] train-rmse:1.428961+0.053145 test-rmse:1.566957+0.165635
## [128] train-rmse:1.427183+0.053162 test-rmse:1.565202+0.165652
## [129] train-rmse:1.425461+0.053196 test-rmse:1.559906+0.163374
## [130] train-rmse:1.423734+0.053204 test-rmse:1.561271+0.164282
## [131] train-rmse:1.422078+0.053249 test-rmse:1.559372+0.164822
## [132] train-rmse:1.420441+0.053297 test-rmse:1.558291+0.167857
## [133] train-rmse:1.418798+0.053305 test-rmse:1.556713+0.167573
## [134] train-rmse:1.417197+0.053324 test-rmse:1.556143+0.168320
## [135] train-rmse:1.415612+0.053340 test-rmse:1.553917+0.168224
## [136] train-rmse:1.414008+0.053314 test-rmse:1.552166+0.169612
## [137] train-rmse:1.412466+0.053277 test-rmse:1.548632+0.169508
## [138] train-rmse:1.410910+0.053221 test-rmse:1.544935+0.171153
## [139] train-rmse:1.409421+0.053201 test-rmse:1.544411+0.170971
## [140] train-rmse:1.407953+0.053227 test-rmse:1.542398+0.170213
## [141] train-rmse:1.406523+0.053268 test-rmse:1.542686+0.168268
## [142] train-rmse:1.405081+0.053307 test-rmse:1.542917+0.167106
## [143] train-rmse:1.403608+0.053410 test-rmse:1.545771+0.169286
## [144] train-rmse:1.402234+0.053426 test-rmse:1.544569+0.172931
## [145] train-rmse:1.400892+0.053443 test-rmse:1.544281+0.171450
## [146] train-rmse:1.399540+0.053460 test-rmse:1.545155+0.171479
## Stopping. Best iteration:
## [140] train-rmse:1.407953+0.053227 test-rmse:1.542398+0.170213
##
## 78
## [1] train-rmse:6.807448+0.047018 test-rmse:6.803991+0.256594
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.906122+0.028943 test-rmse:4.913806+0.234815
## [3] train-rmse:3.682603+0.017113 test-rmse:3.706251+0.208314
## [4] train-rmse:2.916032+0.012017 test-rmse:2.955440+0.153475
## [5] train-rmse:2.463461+0.017567 test-rmse:2.523779+0.119446
## [6] train-rmse:2.197402+0.023518 test-rmse:2.297415+0.072819
## [7] train-rmse:2.034017+0.024755 test-rmse:2.157662+0.049095
## [8] train-rmse:1.935739+0.027858 test-rmse:2.072712+0.063196
## [9] train-rmse:1.865576+0.038389 test-rmse:1.994016+0.048918
## [10] train-rmse:1.816771+0.041139 test-rmse:1.955717+0.059990
## [11] train-rmse:1.777732+0.043632 test-rmse:1.933448+0.058321
## [12] train-rmse:1.744651+0.044060 test-rmse:1.895937+0.054731
## [13] train-rmse:1.711520+0.050154 test-rmse:1.866305+0.054802
## [14] train-rmse:1.675956+0.054860 test-rmse:1.839825+0.059366
## [15] train-rmse:1.649960+0.057284 test-rmse:1.822026+0.059110
## [16] train-rmse:1.624569+0.059394 test-rmse:1.798114+0.055387
## [17] train-rmse:1.600471+0.062361 test-rmse:1.788229+0.055628
## [18] train-rmse:1.570958+0.059265 test-rmse:1.767870+0.047214
## [19] train-rmse:1.548693+0.060211 test-rmse:1.754007+0.042073
## [20] train-rmse:1.526571+0.064231 test-rmse:1.742391+0.044491
## [21] train-rmse:1.506048+0.065714 test-rmse:1.732253+0.055197
## [22] train-rmse:1.486750+0.065165 test-rmse:1.716822+0.054780
## [23] train-rmse:1.468828+0.067513 test-rmse:1.702010+0.054851
## [24] train-rmse:1.452186+0.067629 test-rmse:1.686951+0.057454
## [25] train-rmse:1.429601+0.068293 test-rmse:1.666930+0.068077
## [26] train-rmse:1.412867+0.068533 test-rmse:1.665337+0.070383
## [27] train-rmse:1.396871+0.069228 test-rmse:1.653549+0.068049
## [28] train-rmse:1.380821+0.071026 test-rmse:1.637591+0.067518
## [29] train-rmse:1.362458+0.074260 test-rmse:1.622466+0.077162
## [30] train-rmse:1.346926+0.073521 test-rmse:1.599316+0.086080
## [31] train-rmse:1.322451+0.075647 test-rmse:1.590106+0.085502
## [32] train-rmse:1.303835+0.079369 test-rmse:1.574571+0.078618
## [33] train-rmse:1.291205+0.081191 test-rmse:1.568441+0.080835
## [34] train-rmse:1.275936+0.083075 test-rmse:1.562156+0.081846
## [35] train-rmse:1.264888+0.083491 test-rmse:1.557024+0.083266
## [36] train-rmse:1.252506+0.081819 test-rmse:1.550033+0.085047
## [37] train-rmse:1.241250+0.082209 test-rmse:1.542890+0.083780
## [38] train-rmse:1.211957+0.059936 test-rmse:1.509860+0.095190
## [39] train-rmse:1.201447+0.059734 test-rmse:1.501221+0.099818
## [40] train-rmse:1.187630+0.062666 test-rmse:1.495133+0.105689
## [41] train-rmse:1.177973+0.062565 test-rmse:1.486329+0.107526
## [42] train-rmse:1.167156+0.059338 test-rmse:1.475604+0.109158
## [43] train-rmse:1.157996+0.060036 test-rmse:1.469753+0.110842
## [44] train-rmse:1.148796+0.061313 test-rmse:1.461953+0.113869
## [45] train-rmse:1.135640+0.063489 test-rmse:1.445154+0.110428
## [46] train-rmse:1.126962+0.064346 test-rmse:1.439946+0.112521
## [47] train-rmse:1.111957+0.052754 test-rmse:1.433946+0.119304
## [48] train-rmse:1.098835+0.057416 test-rmse:1.423554+0.120032
## [49] train-rmse:1.091230+0.058983 test-rmse:1.420994+0.119147
## [50] train-rmse:1.083797+0.058426 test-rmse:1.420789+0.124262
## [51] train-rmse:1.076889+0.058680 test-rmse:1.412557+0.123217
## [52] train-rmse:1.065786+0.061060 test-rmse:1.407510+0.128069
## [53] train-rmse:1.059380+0.061133 test-rmse:1.407637+0.123920
## [54] train-rmse:1.053059+0.061079 test-rmse:1.405041+0.129103
## [55] train-rmse:1.047318+0.060961 test-rmse:1.399832+0.129792
## [56] train-rmse:1.040686+0.062133 test-rmse:1.399656+0.131827
## [57] train-rmse:1.033880+0.062511 test-rmse:1.395942+0.131026
## [58] train-rmse:1.027251+0.064371 test-rmse:1.392866+0.131738
## [59] train-rmse:1.019520+0.064314 test-rmse:1.385799+0.130540
## [60] train-rmse:1.012628+0.066405 test-rmse:1.378537+0.133054
## [61] train-rmse:1.006240+0.065653 test-rmse:1.378214+0.133501
## [62] train-rmse:0.992683+0.053076 test-rmse:1.371415+0.143466
## [63] train-rmse:0.988203+0.053436 test-rmse:1.366741+0.145333
## [64] train-rmse:0.982994+0.054207 test-rmse:1.365115+0.147488
## [65] train-rmse:0.978393+0.054247 test-rmse:1.360720+0.151541
## [66] train-rmse:0.974154+0.054265 test-rmse:1.356759+0.153022
## [67] train-rmse:0.970290+0.054307 test-rmse:1.355483+0.153074
## [68] train-rmse:0.966300+0.054312 test-rmse:1.352824+0.153596
## [69] train-rmse:0.962148+0.054827 test-rmse:1.352161+0.154679
## [70] train-rmse:0.957270+0.053681 test-rmse:1.350814+0.154856
## [71] train-rmse:0.947322+0.046472 test-rmse:1.343933+0.159583
## [72] train-rmse:0.936101+0.037161 test-rmse:1.333901+0.165006
## [73] train-rmse:0.926894+0.032943 test-rmse:1.317938+0.169251
## [74] train-rmse:0.913898+0.042375 test-rmse:1.298919+0.155209
## [75] train-rmse:0.910110+0.042730 test-rmse:1.298869+0.156793
## [76] train-rmse:0.906253+0.042867 test-rmse:1.296456+0.157821
## [77] train-rmse:0.900179+0.041385 test-rmse:1.295440+0.161991
## [78] train-rmse:0.896406+0.041774 test-rmse:1.291783+0.160941
## [79] train-rmse:0.892286+0.041753 test-rmse:1.293419+0.161071
## [80] train-rmse:0.889055+0.041835 test-rmse:1.292631+0.161793
## [81] train-rmse:0.885668+0.041366 test-rmse:1.291202+0.160355
## [82] train-rmse:0.882973+0.041447 test-rmse:1.291797+0.158254
## [83] train-rmse:0.877127+0.039384 test-rmse:1.283141+0.162945
## [84] train-rmse:0.871544+0.039368 test-rmse:1.276959+0.167526
## [85] train-rmse:0.868873+0.040020 test-rmse:1.272556+0.167320
## [86] train-rmse:0.866418+0.040260 test-rmse:1.273478+0.172448
## [87] train-rmse:0.859887+0.039099 test-rmse:1.270690+0.173632
## [88] train-rmse:0.856425+0.039033 test-rmse:1.269426+0.174279
## [89] train-rmse:0.854199+0.039099 test-rmse:1.269997+0.173655
## [90] train-rmse:0.852210+0.039113 test-rmse:1.268865+0.173718
## [91] train-rmse:0.845893+0.039389 test-rmse:1.254291+0.175829
## [92] train-rmse:0.841669+0.041065 test-rmse:1.253049+0.175362
## [93] train-rmse:0.838882+0.041058 test-rmse:1.254144+0.175662
## [94] train-rmse:0.835982+0.040853 test-rmse:1.254114+0.175409
## [95] train-rmse:0.833996+0.040797 test-rmse:1.254997+0.175684
## [96] train-rmse:0.831560+0.040709 test-rmse:1.254285+0.176783
## [97] train-rmse:0.828961+0.040667 test-rmse:1.256822+0.174225
## [98] train-rmse:0.827084+0.040586 test-rmse:1.256752+0.175542
## Stopping. Best iteration:
## [92] train-rmse:0.841669+0.041065 test-rmse:1.253049+0.175362
##
## 79
## [1] train-rmse:6.790241+0.048909 test-rmse:6.787139+0.248991
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.857591+0.035862 test-rmse:4.863660+0.222247
## [3] train-rmse:3.598458+0.032193 test-rmse:3.618012+0.177607
## [4] train-rmse:2.793557+0.019725 test-rmse:2.847092+0.152938
## [5] train-rmse:2.293338+0.014301 test-rmse:2.381242+0.110237
## [6] train-rmse:1.988679+0.013873 test-rmse:2.111408+0.077349
## [7] train-rmse:1.803424+0.015920 test-rmse:1.975290+0.061205
## [8] train-rmse:1.685902+0.021971 test-rmse:1.872671+0.069426
## [9] train-rmse:1.594810+0.018065 test-rmse:1.788346+0.065186
## [10] train-rmse:1.532182+0.021622 test-rmse:1.746769+0.058977
## [11] train-rmse:1.483967+0.024032 test-rmse:1.706672+0.053525
## [12] train-rmse:1.437338+0.023704 test-rmse:1.678895+0.054054
## [13] train-rmse:1.397689+0.023609 test-rmse:1.651790+0.054399
## [14] train-rmse:1.352839+0.029899 test-rmse:1.621277+0.059135
## [15] train-rmse:1.317725+0.027162 test-rmse:1.593806+0.071322
## [16] train-rmse:1.284755+0.031471 test-rmse:1.575405+0.076650
## [17] train-rmse:1.253962+0.034134 test-rmse:1.560594+0.075643
## [18] train-rmse:1.226745+0.030585 test-rmse:1.533498+0.079545
## [19] train-rmse:1.196906+0.033098 test-rmse:1.497395+0.083023
## [20] train-rmse:1.171996+0.033993 test-rmse:1.487438+0.096305
## [21] train-rmse:1.146641+0.032258 test-rmse:1.472746+0.096208
## [22] train-rmse:1.121347+0.030100 test-rmse:1.458977+0.099749
## [23] train-rmse:1.100688+0.031286 test-rmse:1.446380+0.105027
## [24] train-rmse:1.081470+0.030276 test-rmse:1.429507+0.107903
## [25] train-rmse:1.056591+0.028685 test-rmse:1.417524+0.113310
## [26] train-rmse:1.039802+0.027755 test-rmse:1.405062+0.119172
## [27] train-rmse:1.023156+0.028711 test-rmse:1.396393+0.123083
## [28] train-rmse:1.006032+0.031798 test-rmse:1.384818+0.120433
## [29] train-rmse:0.989258+0.032584 test-rmse:1.376644+0.124947
## [30] train-rmse:0.974136+0.032451 test-rmse:1.372476+0.127147
## [31] train-rmse:0.958342+0.034165 test-rmse:1.366828+0.135175
## [32] train-rmse:0.942858+0.033270 test-rmse:1.356043+0.136088
## [33] train-rmse:0.926567+0.028675 test-rmse:1.348711+0.141588
## [34] train-rmse:0.915123+0.028323 test-rmse:1.345825+0.139805
## [35] train-rmse:0.900269+0.028461 test-rmse:1.335015+0.150846
## [36] train-rmse:0.881266+0.035289 test-rmse:1.316841+0.148800
## [37] train-rmse:0.869105+0.034154 test-rmse:1.310278+0.149006
## [38] train-rmse:0.857027+0.032710 test-rmse:1.302438+0.150324
## [39] train-rmse:0.846471+0.032067 test-rmse:1.301475+0.148938
## [40] train-rmse:0.836389+0.031481 test-rmse:1.296520+0.149291
## [41] train-rmse:0.826516+0.032423 test-rmse:1.290488+0.148893
## [42] train-rmse:0.815540+0.032536 test-rmse:1.283520+0.154892
## [43] train-rmse:0.804239+0.030307 test-rmse:1.284108+0.161876
## [44] train-rmse:0.794013+0.030333 test-rmse:1.280359+0.162131
## [45] train-rmse:0.785365+0.031601 test-rmse:1.276007+0.164752
## [46] train-rmse:0.773460+0.037338 test-rmse:1.270956+0.161976
## [47] train-rmse:0.765067+0.037510 test-rmse:1.263517+0.160366
## [48] train-rmse:0.758113+0.036454 test-rmse:1.257592+0.162397
## [49] train-rmse:0.751161+0.037610 test-rmse:1.256950+0.163546
## [50] train-rmse:0.744538+0.036849 test-rmse:1.256246+0.163844
## [51] train-rmse:0.736493+0.036731 test-rmse:1.252805+0.159438
## [52] train-rmse:0.729081+0.038589 test-rmse:1.248236+0.158646
## [53] train-rmse:0.722427+0.037544 test-rmse:1.250160+0.161688
## [54] train-rmse:0.716161+0.037582 test-rmse:1.247387+0.162392
## [55] train-rmse:0.710333+0.037563 test-rmse:1.245593+0.162769
## [56] train-rmse:0.705080+0.037211 test-rmse:1.245880+0.161921
## [57] train-rmse:0.699752+0.037355 test-rmse:1.245511+0.164423
## [58] train-rmse:0.693162+0.038308 test-rmse:1.244447+0.165569
## [59] train-rmse:0.686435+0.038149 test-rmse:1.242289+0.161517
## [60] train-rmse:0.681973+0.039012 test-rmse:1.242236+0.161350
## [61] train-rmse:0.674141+0.036001 test-rmse:1.245149+0.160336
## [62] train-rmse:0.670414+0.035682 test-rmse:1.246447+0.161887
## [63] train-rmse:0.666372+0.036560 test-rmse:1.247258+0.162004
## [64] train-rmse:0.660111+0.036652 test-rmse:1.247044+0.161914
## [65] train-rmse:0.654995+0.036135 test-rmse:1.243997+0.163643
## [66] train-rmse:0.650744+0.037649 test-rmse:1.244875+0.164519
## Stopping. Best iteration:
## [60] train-rmse:0.681973+0.039012 test-rmse:1.242236+0.161350
##
## 80
## [1] train-rmse:6.782995+0.049607 test-rmse:6.780599+0.251076
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.839909+0.033925 test-rmse:4.852426+0.224804
## [3] train-rmse:3.557779+0.027313 test-rmse:3.582748+0.177602
## [4] train-rmse:2.727720+0.017660 test-rmse:2.806758+0.141015
## [5] train-rmse:2.199410+0.014427 test-rmse:2.329875+0.095357
## [6] train-rmse:1.868915+0.024551 test-rmse:2.013555+0.066328
## [7] train-rmse:1.656158+0.023795 test-rmse:1.832571+0.059439
## [8] train-rmse:1.521580+0.024016 test-rmse:1.760561+0.055005
## [9] train-rmse:1.423789+0.026800 test-rmse:1.691013+0.054265
## [10] train-rmse:1.352813+0.030109 test-rmse:1.651437+0.074549
## [11] train-rmse:1.291672+0.025432 test-rmse:1.612536+0.085940
## [12] train-rmse:1.244998+0.027297 test-rmse:1.580052+0.093954
## [13] train-rmse:1.200326+0.023911 test-rmse:1.538991+0.100245
## [14] train-rmse:1.159405+0.023111 test-rmse:1.509645+0.114093
## [15] train-rmse:1.120760+0.020254 test-rmse:1.503335+0.111173
## [16] train-rmse:1.079739+0.019285 test-rmse:1.476526+0.118169
## [17] train-rmse:1.045247+0.017663 test-rmse:1.464253+0.119942
## [18] train-rmse:1.017142+0.016914 test-rmse:1.449461+0.120774
## [19] train-rmse:0.990378+0.017628 test-rmse:1.437170+0.129668
## [20] train-rmse:0.968624+0.018562 test-rmse:1.421733+0.134141
## [21] train-rmse:0.946045+0.016017 test-rmse:1.401974+0.135208
## [22] train-rmse:0.923588+0.014068 test-rmse:1.394518+0.138562
## [23] train-rmse:0.898347+0.011753 test-rmse:1.377396+0.129374
## [24] train-rmse:0.881969+0.011488 test-rmse:1.367433+0.131868
## [25] train-rmse:0.862612+0.012233 test-rmse:1.362111+0.135424
## [26] train-rmse:0.843801+0.014021 test-rmse:1.344737+0.142599
## [27] train-rmse:0.828294+0.014948 test-rmse:1.333896+0.142725
## [28] train-rmse:0.805134+0.016871 test-rmse:1.330028+0.142627
## [29] train-rmse:0.788934+0.014382 test-rmse:1.323164+0.142381
## [30] train-rmse:0.774441+0.012415 test-rmse:1.318851+0.144098
## [31] train-rmse:0.760092+0.010678 test-rmse:1.311595+0.146786
## [32] train-rmse:0.747194+0.011568 test-rmse:1.313824+0.149252
## [33] train-rmse:0.735547+0.012673 test-rmse:1.311148+0.149557
## [34] train-rmse:0.725058+0.013303 test-rmse:1.306959+0.150952
## [35] train-rmse:0.712402+0.008706 test-rmse:1.301996+0.153847
## [36] train-rmse:0.700904+0.011815 test-rmse:1.298595+0.160079
## [37] train-rmse:0.688672+0.012131 test-rmse:1.297632+0.159407
## [38] train-rmse:0.677902+0.011161 test-rmse:1.293086+0.156967
## [39] train-rmse:0.668677+0.010293 test-rmse:1.291952+0.157580
## [40] train-rmse:0.659231+0.009956 test-rmse:1.284246+0.155041
## [41] train-rmse:0.649859+0.009666 test-rmse:1.281814+0.157514
## [42] train-rmse:0.643261+0.009982 test-rmse:1.281451+0.162059
## [43] train-rmse:0.632856+0.011203 test-rmse:1.280551+0.162575
## [44] train-rmse:0.624615+0.011855 test-rmse:1.279602+0.161760
## [45] train-rmse:0.616996+0.011221 test-rmse:1.280183+0.162438
## [46] train-rmse:0.609228+0.011624 test-rmse:1.280145+0.163228
## [47] train-rmse:0.600170+0.012799 test-rmse:1.275902+0.160180
## [48] train-rmse:0.592982+0.014234 test-rmse:1.272675+0.157693
## [49] train-rmse:0.582369+0.005754 test-rmse:1.270773+0.156880
## [50] train-rmse:0.575842+0.006990 test-rmse:1.266770+0.153940
## [51] train-rmse:0.568842+0.007189 test-rmse:1.266409+0.156697
## [52] train-rmse:0.560553+0.006887 test-rmse:1.265227+0.155983
## [53] train-rmse:0.554047+0.004735 test-rmse:1.265280+0.153655
## [54] train-rmse:0.547961+0.005890 test-rmse:1.264663+0.155499
## [55] train-rmse:0.542542+0.005364 test-rmse:1.267366+0.154330
## [56] train-rmse:0.538062+0.007557 test-rmse:1.268300+0.151586
## [57] train-rmse:0.534508+0.007823 test-rmse:1.268721+0.151045
## [58] train-rmse:0.528511+0.007717 test-rmse:1.269833+0.149726
## [59] train-rmse:0.522425+0.007424 test-rmse:1.271333+0.152261
## [60] train-rmse:0.518822+0.007210 test-rmse:1.270765+0.152588
## Stopping. Best iteration:
## [54] train-rmse:0.547961+0.005890 test-rmse:1.264663+0.155499
##
## 81
## [1] train-rmse:6.776807+0.050207 test-rmse:6.780794+0.252084
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.820900+0.034425 test-rmse:4.843898+0.221789
## [3] train-rmse:3.523312+0.028768 test-rmse:3.559032+0.166501
## [4] train-rmse:2.671398+0.020612 test-rmse:2.762615+0.139911
## [5] train-rmse:2.109425+0.025354 test-rmse:2.241607+0.091031
## [6] train-rmse:1.754859+0.023239 test-rmse:1.926348+0.088178
## [7] train-rmse:1.530029+0.024028 test-rmse:1.757812+0.075075
## [8] train-rmse:1.370969+0.023343 test-rmse:1.641048+0.062761
## [9] train-rmse:1.266449+0.025025 test-rmse:1.587511+0.083056
## [10] train-rmse:1.186868+0.021656 test-rmse:1.538432+0.086553
## [11] train-rmse:1.119232+0.019748 test-rmse:1.499113+0.107280
## [12] train-rmse:1.059406+0.014949 test-rmse:1.470140+0.117372
## [13] train-rmse:1.006164+0.011904 test-rmse:1.443915+0.127241
## [14] train-rmse:0.968497+0.010944 test-rmse:1.424056+0.141765
## [15] train-rmse:0.934664+0.011186 test-rmse:1.400216+0.152358
## [16] train-rmse:0.895825+0.013866 test-rmse:1.374956+0.161557
## [17] train-rmse:0.866257+0.014689 test-rmse:1.364135+0.171799
## [18] train-rmse:0.834095+0.010405 test-rmse:1.351657+0.180334
## [19] train-rmse:0.808255+0.010032 test-rmse:1.338837+0.185829
## [20] train-rmse:0.784886+0.012405 test-rmse:1.334774+0.187151
## [21] train-rmse:0.764202+0.012102 test-rmse:1.324330+0.189681
## [22] train-rmse:0.742671+0.007672 test-rmse:1.321953+0.187057
## [23] train-rmse:0.721502+0.005539 test-rmse:1.304780+0.180999
## [24] train-rmse:0.697777+0.007013 test-rmse:1.294595+0.188973
## [25] train-rmse:0.677122+0.008972 test-rmse:1.290983+0.187841
## [26] train-rmse:0.661888+0.007045 test-rmse:1.286594+0.193333
## [27] train-rmse:0.649084+0.006624 test-rmse:1.282676+0.188468
## [28] train-rmse:0.634128+0.006538 test-rmse:1.285433+0.190266
## [29] train-rmse:0.620499+0.008674 test-rmse:1.283129+0.192478
## [30] train-rmse:0.604586+0.004117 test-rmse:1.282546+0.191751
## [31] train-rmse:0.591056+0.005386 test-rmse:1.277810+0.197861
## [32] train-rmse:0.579317+0.007071 test-rmse:1.276823+0.200483
## [33] train-rmse:0.567415+0.005870 test-rmse:1.276835+0.202263
## [34] train-rmse:0.556664+0.005468 test-rmse:1.275476+0.203388
## [35] train-rmse:0.545796+0.008827 test-rmse:1.275043+0.203584
## [36] train-rmse:0.535735+0.008226 test-rmse:1.268762+0.206500
## [37] train-rmse:0.524464+0.009193 test-rmse:1.265291+0.204666
## [38] train-rmse:0.515621+0.009896 test-rmse:1.265671+0.204531
## [39] train-rmse:0.506764+0.008761 test-rmse:1.262446+0.204602
## [40] train-rmse:0.498972+0.008310 test-rmse:1.266083+0.202611
## [41] train-rmse:0.491274+0.009489 test-rmse:1.267280+0.204413
## [42] train-rmse:0.479602+0.008666 test-rmse:1.269856+0.203523
## [43] train-rmse:0.473964+0.008920 test-rmse:1.268583+0.204189
## [44] train-rmse:0.465962+0.008348 test-rmse:1.263467+0.199414
## [45] train-rmse:0.457853+0.006070 test-rmse:1.262869+0.200576
## Stopping. Best iteration:
## [39] train-rmse:0.506764+0.008761 test-rmse:1.262446+0.204602
##
## 82
## [1] train-rmse:6.775278+0.050563 test-rmse:6.781639+0.242227
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.809715+0.036016 test-rmse:4.839656+0.203004
## [3] train-rmse:3.496515+0.030778 test-rmse:3.535058+0.147933
## [4] train-rmse:2.631149+0.026115 test-rmse:2.718371+0.135957
## [5] train-rmse:2.051157+0.030005 test-rmse:2.192708+0.108865
## [6] train-rmse:1.677909+0.028403 test-rmse:1.875903+0.095060
## [7] train-rmse:1.428669+0.028556 test-rmse:1.712835+0.088285
## [8] train-rmse:1.267955+0.038253 test-rmse:1.584837+0.086674
## [9] train-rmse:1.145649+0.037341 test-rmse:1.511669+0.089766
## [10] train-rmse:1.052206+0.034866 test-rmse:1.459356+0.093878
## [11] train-rmse:0.986879+0.038260 test-rmse:1.431210+0.114033
## [12] train-rmse:0.928719+0.033868 test-rmse:1.393911+0.141103
## [13] train-rmse:0.877496+0.032139 test-rmse:1.358765+0.143966
## [14] train-rmse:0.833135+0.029045 test-rmse:1.346319+0.150798
## [15] train-rmse:0.795682+0.024807 test-rmse:1.337881+0.158437
## [16] train-rmse:0.762151+0.027335 test-rmse:1.321717+0.163103
## [17] train-rmse:0.731720+0.020105 test-rmse:1.314440+0.166887
## [18] train-rmse:0.702864+0.018732 test-rmse:1.310076+0.171522
## [19] train-rmse:0.677585+0.013958 test-rmse:1.302089+0.181149
## [20] train-rmse:0.654277+0.013395 test-rmse:1.297148+0.183776
## [21] train-rmse:0.628592+0.010707 test-rmse:1.287641+0.187072
## [22] train-rmse:0.610218+0.009883 test-rmse:1.280881+0.187375
## [23] train-rmse:0.588623+0.015193 test-rmse:1.271714+0.189400
## [24] train-rmse:0.569216+0.012646 test-rmse:1.261990+0.186866
## [25] train-rmse:0.551473+0.012479 test-rmse:1.254100+0.181587
## [26] train-rmse:0.538714+0.015017 test-rmse:1.251260+0.183848
## [27] train-rmse:0.522716+0.015215 test-rmse:1.247895+0.185184
## [28] train-rmse:0.511312+0.013775 test-rmse:1.245325+0.186919
## [29] train-rmse:0.497812+0.010730 test-rmse:1.248767+0.187851
## [30] train-rmse:0.485846+0.012531 test-rmse:1.243895+0.188544
## [31] train-rmse:0.471484+0.013148 test-rmse:1.244950+0.183839
## [32] train-rmse:0.456180+0.014584 test-rmse:1.239783+0.183219
## [33] train-rmse:0.444449+0.013353 test-rmse:1.238537+0.188979
## [34] train-rmse:0.433334+0.008585 test-rmse:1.238903+0.186985
## [35] train-rmse:0.425645+0.007350 test-rmse:1.241803+0.186420
## [36] train-rmse:0.414783+0.007004 test-rmse:1.243067+0.189958
## [37] train-rmse:0.405891+0.007952 test-rmse:1.245455+0.187903
## [38] train-rmse:0.397570+0.008558 test-rmse:1.243903+0.189582
## [39] train-rmse:0.392680+0.007411 test-rmse:1.243238+0.189766
## Stopping. Best iteration:
## [33] train-rmse:0.444449+0.013353 test-rmse:1.238537+0.188979
##
## 83
## [1] train-rmse:6.775278+0.050563 test-rmse:6.781639+0.242227
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.805389+0.036085 test-rmse:4.835142+0.210778
## [3] train-rmse:3.481521+0.030734 test-rmse:3.540392+0.154831
## [4] train-rmse:2.599061+0.024211 test-rmse:2.703255+0.115296
## [5] train-rmse:2.007231+0.019210 test-rmse:2.171386+0.114522
## [6] train-rmse:1.613603+0.021921 test-rmse:1.854058+0.099340
## [7] train-rmse:1.341875+0.040087 test-rmse:1.646312+0.083080
## [8] train-rmse:1.162668+0.037582 test-rmse:1.554130+0.094866
## [9] train-rmse:1.032732+0.027280 test-rmse:1.455903+0.104081
## [10] train-rmse:0.931804+0.024132 test-rmse:1.402146+0.109364
## [11] train-rmse:0.861146+0.020512 test-rmse:1.366826+0.138434
## [12] train-rmse:0.798929+0.015960 test-rmse:1.340666+0.150789
## [13] train-rmse:0.753614+0.019971 test-rmse:1.317096+0.154575
## [14] train-rmse:0.713814+0.016958 test-rmse:1.300564+0.164562
## [15] train-rmse:0.672311+0.019868 test-rmse:1.293111+0.168582
## [16] train-rmse:0.647420+0.019220 test-rmse:1.286598+0.173913
## [17] train-rmse:0.617537+0.022255 test-rmse:1.281151+0.173605
## [18] train-rmse:0.585686+0.020519 test-rmse:1.267299+0.178530
## [19] train-rmse:0.563662+0.017842 test-rmse:1.264891+0.177375
## [20] train-rmse:0.539314+0.019832 test-rmse:1.255354+0.168911
## [21] train-rmse:0.518040+0.016954 test-rmse:1.251880+0.167582
## [22] train-rmse:0.496287+0.012773 test-rmse:1.251449+0.171530
## [23] train-rmse:0.476660+0.017727 test-rmse:1.246467+0.170728
## [24] train-rmse:0.458739+0.018923 test-rmse:1.247120+0.174228
## [25] train-rmse:0.442517+0.016796 test-rmse:1.241296+0.176168
## [26] train-rmse:0.429413+0.012990 test-rmse:1.240839+0.174134
## [27] train-rmse:0.418218+0.013199 test-rmse:1.239410+0.175048
## [28] train-rmse:0.410064+0.011711 test-rmse:1.242719+0.173920
## [29] train-rmse:0.398911+0.012826 test-rmse:1.239854+0.175008
## [30] train-rmse:0.388991+0.015494 test-rmse:1.240159+0.173726
## [31] train-rmse:0.375760+0.012945 test-rmse:1.237002+0.174651
## [32] train-rmse:0.363197+0.015772 test-rmse:1.238294+0.174847
## [33] train-rmse:0.356473+0.015834 test-rmse:1.236029+0.172785
## [34] train-rmse:0.343468+0.015117 test-rmse:1.235357+0.168912
## [35] train-rmse:0.335474+0.013043 test-rmse:1.234484+0.170417
## [36] train-rmse:0.325314+0.014580 test-rmse:1.235887+0.170650
## [37] train-rmse:0.314933+0.016520 test-rmse:1.236458+0.169174
## [38] train-rmse:0.306939+0.019825 test-rmse:1.239145+0.168992
## [39] train-rmse:0.296422+0.015268 test-rmse:1.238094+0.173420
## [40] train-rmse:0.289873+0.012320 test-rmse:1.237938+0.172027
## [41] train-rmse:0.280586+0.011801 test-rmse:1.238354+0.171253
## Stopping. Best iteration:
## [35] train-rmse:0.335474+0.013043 test-rmse:1.234484+0.170417
##
## 84
## [1] train-rmse:6.677250+0.044909 test-rmse:6.673260+0.261055
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.770800+0.025835 test-rmse:4.766795+0.249507
## [3] train-rmse:3.616810+0.016031 test-rmse:3.622476+0.210681
## [4] train-rmse:2.955699+0.013153 test-rmse:2.980157+0.150516
## [5] train-rmse:2.596877+0.014738 test-rmse:2.625328+0.099845
## [6] train-rmse:2.404950+0.016837 test-rmse:2.437980+0.059551
## [7] train-rmse:2.300967+0.017557 test-rmse:2.349963+0.052999
## [8] train-rmse:2.240431+0.018441 test-rmse:2.287313+0.058777
## [9] train-rmse:2.200269+0.019723 test-rmse:2.240625+0.059488
## [10] train-rmse:2.170867+0.020934 test-rmse:2.210629+0.065495
## [11] train-rmse:2.146165+0.022377 test-rmse:2.185374+0.074503
## [12] train-rmse:2.123947+0.022864 test-rmse:2.174265+0.078905
## [13] train-rmse:2.102034+0.023160 test-rmse:2.162026+0.084543
## [14] train-rmse:2.081616+0.023985 test-rmse:2.148059+0.085072
## [15] train-rmse:2.062469+0.024300 test-rmse:2.124667+0.082869
## [16] train-rmse:2.044677+0.025311 test-rmse:2.114956+0.089919
## [17] train-rmse:2.027384+0.026963 test-rmse:2.092930+0.090825
## [18] train-rmse:2.011001+0.027740 test-rmse:2.080033+0.083123
## [19] train-rmse:1.995258+0.028686 test-rmse:2.067788+0.087071
## [20] train-rmse:1.979454+0.029827 test-rmse:2.060081+0.096874
## [21] train-rmse:1.965028+0.029884 test-rmse:2.042910+0.100486
## [22] train-rmse:1.950435+0.030622 test-rmse:2.023497+0.093484
## [23] train-rmse:1.937099+0.031443 test-rmse:2.012730+0.093099
## [24] train-rmse:1.924621+0.031750 test-rmse:2.004170+0.090340
## [25] train-rmse:1.912438+0.032079 test-rmse:1.995081+0.096595
## [26] train-rmse:1.900433+0.032737 test-rmse:1.980198+0.088497
## [27] train-rmse:1.888781+0.033111 test-rmse:1.971439+0.085534
## [28] train-rmse:1.877329+0.033781 test-rmse:1.966918+0.087550
## [29] train-rmse:1.865956+0.034328 test-rmse:1.952097+0.082637
## [30] train-rmse:1.855533+0.035076 test-rmse:1.938930+0.088040
## [31] train-rmse:1.845196+0.035917 test-rmse:1.929769+0.086371
## [32] train-rmse:1.834871+0.036895 test-rmse:1.921364+0.084164
## [33] train-rmse:1.824750+0.037889 test-rmse:1.913289+0.086183
## [34] train-rmse:1.814623+0.038932 test-rmse:1.909101+0.088266
## [35] train-rmse:1.804680+0.039652 test-rmse:1.897364+0.086542
## [36] train-rmse:1.794863+0.040135 test-rmse:1.885590+0.092485
## [37] train-rmse:1.785364+0.040996 test-rmse:1.883159+0.098162
## [38] train-rmse:1.776128+0.041497 test-rmse:1.884480+0.100688
## [39] train-rmse:1.767212+0.041899 test-rmse:1.877019+0.097555
## [40] train-rmse:1.758669+0.042154 test-rmse:1.866504+0.093293
## [41] train-rmse:1.749912+0.042812 test-rmse:1.859873+0.095192
## [42] train-rmse:1.741588+0.043063 test-rmse:1.852007+0.096927
## [43] train-rmse:1.733170+0.043330 test-rmse:1.847407+0.100673
## [44] train-rmse:1.725386+0.043686 test-rmse:1.838601+0.096528
## [45] train-rmse:1.717713+0.044242 test-rmse:1.827903+0.103381
## [46] train-rmse:1.710492+0.044657 test-rmse:1.822008+0.101850
## [47] train-rmse:1.703452+0.045064 test-rmse:1.815811+0.101306
## [48] train-rmse:1.696409+0.045507 test-rmse:1.804413+0.101746
## [49] train-rmse:1.689498+0.045950 test-rmse:1.796447+0.098351
## [50] train-rmse:1.683074+0.046249 test-rmse:1.798494+0.100142
## [51] train-rmse:1.676702+0.046478 test-rmse:1.794033+0.103406
## [52] train-rmse:1.670043+0.046569 test-rmse:1.789500+0.104920
## [53] train-rmse:1.663834+0.046697 test-rmse:1.780088+0.101038
## [54] train-rmse:1.657725+0.046752 test-rmse:1.771630+0.106755
## [55] train-rmse:1.651858+0.046847 test-rmse:1.767218+0.106301
## [56] train-rmse:1.645919+0.046892 test-rmse:1.758921+0.106518
## [57] train-rmse:1.640124+0.047007 test-rmse:1.754722+0.108127
## [58] train-rmse:1.634524+0.047016 test-rmse:1.758110+0.101277
## [59] train-rmse:1.628828+0.047509 test-rmse:1.751576+0.097103
## [60] train-rmse:1.623045+0.048035 test-rmse:1.747616+0.099880
## [61] train-rmse:1.617649+0.048434 test-rmse:1.748019+0.102419
## [62] train-rmse:1.612253+0.048738 test-rmse:1.741398+0.103242
## [63] train-rmse:1.606848+0.049088 test-rmse:1.735591+0.112334
## [64] train-rmse:1.601635+0.049295 test-rmse:1.734962+0.112590
## [65] train-rmse:1.596559+0.049410 test-rmse:1.726669+0.124742
## [66] train-rmse:1.591808+0.049530 test-rmse:1.721737+0.126593
## [67] train-rmse:1.586929+0.049906 test-rmse:1.717082+0.125798
## [68] train-rmse:1.582242+0.050098 test-rmse:1.712806+0.128238
## [69] train-rmse:1.577486+0.050262 test-rmse:1.705142+0.130986
## [70] train-rmse:1.572807+0.050436 test-rmse:1.700732+0.128974
## [71] train-rmse:1.568304+0.050646 test-rmse:1.698824+0.128536
## [72] train-rmse:1.563929+0.050806 test-rmse:1.694851+0.130547
## [73] train-rmse:1.559579+0.050891 test-rmse:1.690301+0.130170
## [74] train-rmse:1.555271+0.051078 test-rmse:1.684082+0.129467
## [75] train-rmse:1.551299+0.051135 test-rmse:1.683010+0.127781
## [76] train-rmse:1.547389+0.051153 test-rmse:1.681076+0.127884
## [77] train-rmse:1.543428+0.051135 test-rmse:1.678043+0.133609
## [78] train-rmse:1.539670+0.051122 test-rmse:1.677661+0.135953
## [79] train-rmse:1.535957+0.051202 test-rmse:1.670946+0.133240
## [80] train-rmse:1.532132+0.051427 test-rmse:1.670757+0.135757
## [81] train-rmse:1.528502+0.051532 test-rmse:1.663317+0.138618
## [82] train-rmse:1.524908+0.051635 test-rmse:1.663596+0.136962
## [83] train-rmse:1.521362+0.051651 test-rmse:1.659465+0.133963
## [84] train-rmse:1.517924+0.051667 test-rmse:1.656919+0.136929
## [85] train-rmse:1.514498+0.051713 test-rmse:1.648797+0.138285
## [86] train-rmse:1.511086+0.051712 test-rmse:1.645162+0.140356
## [87] train-rmse:1.507733+0.051650 test-rmse:1.636991+0.145521
## [88] train-rmse:1.504528+0.051642 test-rmse:1.636812+0.145776
## [89] train-rmse:1.501272+0.051679 test-rmse:1.633309+0.146636
## [90] train-rmse:1.498123+0.051708 test-rmse:1.632337+0.146225
## [91] train-rmse:1.495099+0.051777 test-rmse:1.628900+0.147751
## [92] train-rmse:1.492155+0.051809 test-rmse:1.625387+0.144555
## [93] train-rmse:1.489209+0.051878 test-rmse:1.620857+0.141440
## [94] train-rmse:1.486302+0.051979 test-rmse:1.621159+0.146046
## [95] train-rmse:1.483457+0.051978 test-rmse:1.616210+0.145625
## [96] train-rmse:1.480654+0.052036 test-rmse:1.612667+0.147987
## [97] train-rmse:1.477882+0.052120 test-rmse:1.610778+0.148943
## [98] train-rmse:1.475113+0.052257 test-rmse:1.611536+0.150040
## [99] train-rmse:1.472308+0.052334 test-rmse:1.607108+0.148035
## [100] train-rmse:1.469728+0.052479 test-rmse:1.604524+0.147529
## [101] train-rmse:1.467145+0.052611 test-rmse:1.604371+0.145012
## [102] train-rmse:1.464541+0.052702 test-rmse:1.601960+0.147384
## [103] train-rmse:1.461992+0.052825 test-rmse:1.599557+0.147551
## [104] train-rmse:1.459657+0.052902 test-rmse:1.598159+0.148492
## [105] train-rmse:1.457295+0.052944 test-rmse:1.597576+0.150018
## [106] train-rmse:1.454921+0.053035 test-rmse:1.594847+0.154179
## [107] train-rmse:1.452564+0.053204 test-rmse:1.593851+0.156494
## [108] train-rmse:1.450265+0.053294 test-rmse:1.590910+0.155610
## [109] train-rmse:1.447953+0.053408 test-rmse:1.591862+0.155439
## [110] train-rmse:1.445649+0.053439 test-rmse:1.587288+0.160402
## [111] train-rmse:1.443419+0.053465 test-rmse:1.584730+0.162187
## [112] train-rmse:1.441274+0.053527 test-rmse:1.581740+0.164893
## [113] train-rmse:1.439182+0.053504 test-rmse:1.580802+0.163774
## [114] train-rmse:1.437104+0.053485 test-rmse:1.580060+0.162732
## [115] train-rmse:1.435108+0.053458 test-rmse:1.576089+0.161189
## [116] train-rmse:1.433166+0.053423 test-rmse:1.572526+0.160148
## [117] train-rmse:1.431208+0.053384 test-rmse:1.567301+0.155916
## [118] train-rmse:1.429323+0.053358 test-rmse:1.568279+0.160813
## [119] train-rmse:1.427436+0.053280 test-rmse:1.568360+0.158576
## [120] train-rmse:1.425527+0.053243 test-rmse:1.568175+0.158113
## [121] train-rmse:1.423619+0.053304 test-rmse:1.567240+0.161315
## [122] train-rmse:1.421759+0.053316 test-rmse:1.568270+0.163515
## [123] train-rmse:1.419932+0.053318 test-rmse:1.566746+0.165028
## [124] train-rmse:1.418097+0.053332 test-rmse:1.562040+0.165568
## [125] train-rmse:1.416326+0.053301 test-rmse:1.558402+0.167025
## [126] train-rmse:1.414610+0.053305 test-rmse:1.554758+0.166550
## [127] train-rmse:1.412944+0.053261 test-rmse:1.553147+0.165884
## [128] train-rmse:1.411314+0.053219 test-rmse:1.550766+0.166947
## [129] train-rmse:1.409736+0.053203 test-rmse:1.547412+0.168035
## [130] train-rmse:1.408166+0.053209 test-rmse:1.544548+0.170670
## [131] train-rmse:1.406658+0.053178 test-rmse:1.542619+0.171118
## [132] train-rmse:1.405156+0.053179 test-rmse:1.540908+0.171580
## [133] train-rmse:1.403668+0.053226 test-rmse:1.541385+0.174124
## [134] train-rmse:1.402243+0.053228 test-rmse:1.540859+0.173978
## [135] train-rmse:1.400821+0.053215 test-rmse:1.540316+0.173953
## [136] train-rmse:1.399385+0.053217 test-rmse:1.539295+0.174211
## [137] train-rmse:1.397994+0.053232 test-rmse:1.538156+0.175430
## [138] train-rmse:1.396622+0.053236 test-rmse:1.537453+0.175023
## [139] train-rmse:1.395248+0.053272 test-rmse:1.536795+0.175212
## [140] train-rmse:1.393888+0.053301 test-rmse:1.534715+0.174693
## [141] train-rmse:1.392538+0.053322 test-rmse:1.534133+0.172035
## [142] train-rmse:1.391220+0.053345 test-rmse:1.533750+0.170780
## [143] train-rmse:1.389944+0.053411 test-rmse:1.532325+0.171568
## [144] train-rmse:1.388639+0.053481 test-rmse:1.528755+0.175590
## [145] train-rmse:1.387399+0.053485 test-rmse:1.529660+0.173807
## [146] train-rmse:1.386168+0.053499 test-rmse:1.527053+0.173658
## [147] train-rmse:1.384998+0.053542 test-rmse:1.526433+0.173702
## [148] train-rmse:1.383841+0.053597 test-rmse:1.525596+0.172180
## [149] train-rmse:1.382710+0.053619 test-rmse:1.524834+0.173080
## [150] train-rmse:1.381582+0.053628 test-rmse:1.524105+0.177214
## [151] train-rmse:1.380463+0.053600 test-rmse:1.524977+0.176574
## [152] train-rmse:1.379325+0.053543 test-rmse:1.523964+0.175909
## [153] train-rmse:1.378217+0.053518 test-rmse:1.525150+0.176509
## [154] train-rmse:1.377134+0.053484 test-rmse:1.523088+0.175325
## [155] train-rmse:1.376054+0.053449 test-rmse:1.523278+0.176398
## [156] train-rmse:1.374988+0.053411 test-rmse:1.521584+0.176234
## [157] train-rmse:1.373922+0.053403 test-rmse:1.521264+0.176835
## [158] train-rmse:1.372890+0.053431 test-rmse:1.521853+0.178271
## [159] train-rmse:1.371900+0.053437 test-rmse:1.520391+0.179094
## [160] train-rmse:1.370941+0.053430 test-rmse:1.518173+0.178273
## [161] train-rmse:1.369975+0.053423 test-rmse:1.517719+0.178503
## [162] train-rmse:1.369007+0.053423 test-rmse:1.515977+0.178018
## [163] train-rmse:1.368013+0.053383 test-rmse:1.516494+0.178871
## [164] train-rmse:1.367094+0.053388 test-rmse:1.515984+0.180127
## [165] train-rmse:1.366188+0.053352 test-rmse:1.512102+0.182750
## [166] train-rmse:1.365298+0.053327 test-rmse:1.510834+0.182931
## [167] train-rmse:1.364421+0.053301 test-rmse:1.509343+0.184501
## [168] train-rmse:1.363548+0.053298 test-rmse:1.509295+0.184894
## [169] train-rmse:1.362717+0.053322 test-rmse:1.509558+0.187668
## [170] train-rmse:1.361881+0.053348 test-rmse:1.509892+0.187273
## [171] train-rmse:1.361077+0.053370 test-rmse:1.509304+0.187117
## [172] train-rmse:1.360287+0.053376 test-rmse:1.508709+0.186587
## [173] train-rmse:1.359516+0.053355 test-rmse:1.507783+0.187625
## [174] train-rmse:1.358757+0.053338 test-rmse:1.505126+0.187949
## [175] train-rmse:1.358005+0.053327 test-rmse:1.506428+0.187685
## [176] train-rmse:1.357265+0.053299 test-rmse:1.506395+0.188203
## [177] train-rmse:1.356528+0.053288 test-rmse:1.505115+0.187166
## [178] train-rmse:1.355791+0.053273 test-rmse:1.504901+0.188245
## [179] train-rmse:1.355058+0.053265 test-rmse:1.507003+0.188758
## [180] train-rmse:1.354344+0.053261 test-rmse:1.505820+0.188297
## [181] train-rmse:1.353622+0.053258 test-rmse:1.505031+0.188958
## [182] train-rmse:1.352897+0.053275 test-rmse:1.503467+0.190352
## [183] train-rmse:1.352207+0.053276 test-rmse:1.502301+0.189503
## [184] train-rmse:1.351522+0.053259 test-rmse:1.501760+0.189692
## [185] train-rmse:1.350863+0.053265 test-rmse:1.503671+0.188995
## [186] train-rmse:1.350229+0.053263 test-rmse:1.502644+0.188141
## [187] train-rmse:1.349590+0.053257 test-rmse:1.501905+0.188543
## [188] train-rmse:1.348943+0.053243 test-rmse:1.502899+0.188963
## [189] train-rmse:1.348331+0.053229 test-rmse:1.503157+0.189492
## [190] train-rmse:1.347727+0.053203 test-rmse:1.502934+0.191232
## Stopping. Best iteration:
## [184] train-rmse:1.351522+0.053259 test-rmse:1.501760+0.189692
##
## 85
## [1] train-rmse:6.629745+0.045469 test-rmse:6.626362+0.254828
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.678645+0.026442 test-rmse:4.689304+0.230338
## [3] train-rmse:3.469374+0.016383 test-rmse:3.492947+0.200084
## [4] train-rmse:2.742691+0.016938 test-rmse:2.794714+0.138899
## [5] train-rmse:2.336640+0.024310 test-rmse:2.418361+0.101445
## [6] train-rmse:2.105661+0.030253 test-rmse:2.219528+0.058404
## [7] train-rmse:1.963548+0.032853 test-rmse:2.086319+0.047547
## [8] train-rmse:1.882283+0.030734 test-rmse:2.008880+0.061338
## [9] train-rmse:1.824013+0.033452 test-rmse:1.963659+0.065242
## [10] train-rmse:1.778467+0.037468 test-rmse:1.926514+0.084023
## [11] train-rmse:1.742222+0.039959 test-rmse:1.896305+0.075137
## [12] train-rmse:1.703005+0.026577 test-rmse:1.865905+0.082721
## [13] train-rmse:1.670704+0.024859 test-rmse:1.843766+0.097752
## [14] train-rmse:1.642724+0.026530 test-rmse:1.820323+0.089186
## [15] train-rmse:1.617293+0.027695 test-rmse:1.806373+0.085962
## [16] train-rmse:1.582598+0.031637 test-rmse:1.785686+0.086263
## [17] train-rmse:1.557472+0.032763 test-rmse:1.769729+0.094680
## [18] train-rmse:1.534330+0.032103 test-rmse:1.747822+0.091072
## [19] train-rmse:1.510783+0.032448 test-rmse:1.736695+0.097399
## [20] train-rmse:1.486240+0.031892 test-rmse:1.725155+0.101904
## [21] train-rmse:1.466699+0.033609 test-rmse:1.711989+0.097663
## [22] train-rmse:1.446645+0.033558 test-rmse:1.692361+0.103043
## [23] train-rmse:1.429016+0.034042 test-rmse:1.679110+0.095724
## [24] train-rmse:1.404066+0.033753 test-rmse:1.644295+0.116606
## [25] train-rmse:1.388326+0.034120 test-rmse:1.631701+0.115169
## [26] train-rmse:1.372236+0.035587 test-rmse:1.622790+0.129427
## [27] train-rmse:1.356268+0.034445 test-rmse:1.614333+0.127764
## [28] train-rmse:1.341361+0.034289 test-rmse:1.597318+0.132717
## [29] train-rmse:1.324551+0.033040 test-rmse:1.582975+0.134339
## [30] train-rmse:1.302693+0.019787 test-rmse:1.575202+0.143432
## [31] train-rmse:1.275933+0.025890 test-rmse:1.536362+0.120536
## [32] train-rmse:1.255850+0.031092 test-rmse:1.519133+0.115301
## [33] train-rmse:1.241193+0.031351 test-rmse:1.507234+0.109259
## [34] train-rmse:1.230527+0.031018 test-rmse:1.498397+0.109044
## [35] train-rmse:1.220384+0.031174 test-rmse:1.492490+0.112496
## [36] train-rmse:1.203431+0.035817 test-rmse:1.468099+0.094759
## [37] train-rmse:1.193501+0.036130 test-rmse:1.458563+0.098919
## [38] train-rmse:1.183702+0.035886 test-rmse:1.452356+0.103594
## [39] train-rmse:1.174895+0.035975 test-rmse:1.440502+0.103319
## [40] train-rmse:1.165018+0.037736 test-rmse:1.435484+0.106956
## [41] train-rmse:1.148897+0.045537 test-rmse:1.429347+0.106841
## [42] train-rmse:1.136591+0.049457 test-rmse:1.419449+0.104835
## [43] train-rmse:1.125277+0.048259 test-rmse:1.407192+0.109292
## [44] train-rmse:1.116208+0.048096 test-rmse:1.404233+0.112268
## [45] train-rmse:1.107497+0.047735 test-rmse:1.401985+0.111142
## [46] train-rmse:1.098366+0.047367 test-rmse:1.397189+0.109219
## [47] train-rmse:1.083600+0.037886 test-rmse:1.390221+0.120051
## [48] train-rmse:1.074835+0.040037 test-rmse:1.377293+0.128436
## [49] train-rmse:1.067717+0.039226 test-rmse:1.377797+0.126571
## [50] train-rmse:1.060734+0.039024 test-rmse:1.375423+0.125906
## [51] train-rmse:1.054134+0.038748 test-rmse:1.375401+0.124980
## [52] train-rmse:1.045560+0.037377 test-rmse:1.368927+0.127745
## [53] train-rmse:1.039461+0.037768 test-rmse:1.365670+0.131223
## [54] train-rmse:1.030683+0.042857 test-rmse:1.358637+0.123102
## [55] train-rmse:1.014816+0.037157 test-rmse:1.349313+0.130559
## [56] train-rmse:1.007585+0.035882 test-rmse:1.344370+0.134416
## [57] train-rmse:0.999587+0.034127 test-rmse:1.336303+0.139189
## [58] train-rmse:0.994184+0.034009 test-rmse:1.335921+0.143442
## [59] train-rmse:0.988408+0.033621 test-rmse:1.331634+0.146335
## [60] train-rmse:0.983805+0.033417 test-rmse:1.330054+0.147083
## [61] train-rmse:0.978486+0.034560 test-rmse:1.325128+0.147481
## [62] train-rmse:0.972288+0.037763 test-rmse:1.327830+0.146734
## [63] train-rmse:0.968080+0.037740 test-rmse:1.326656+0.145703
## [64] train-rmse:0.963198+0.037300 test-rmse:1.326388+0.143399
## [65] train-rmse:0.952764+0.032001 test-rmse:1.321796+0.148426
## [66] train-rmse:0.947579+0.031040 test-rmse:1.319701+0.148168
## [67] train-rmse:0.943997+0.031320 test-rmse:1.315247+0.151259
## [68] train-rmse:0.939645+0.031722 test-rmse:1.312091+0.149220
## [69] train-rmse:0.934333+0.031834 test-rmse:1.312703+0.150074
## [70] train-rmse:0.925419+0.039856 test-rmse:1.310434+0.149661
## [71] train-rmse:0.920929+0.040052 test-rmse:1.310761+0.152038
## [72] train-rmse:0.916680+0.039584 test-rmse:1.308296+0.153857
## [73] train-rmse:0.912895+0.039149 test-rmse:1.308254+0.154940
## [74] train-rmse:0.909370+0.038948 test-rmse:1.307652+0.157315
## [75] train-rmse:0.903984+0.042925 test-rmse:1.303422+0.155887
## [76] train-rmse:0.899975+0.043130 test-rmse:1.302370+0.156979
## [77] train-rmse:0.896288+0.043036 test-rmse:1.298538+0.155774
## [78] train-rmse:0.893428+0.043736 test-rmse:1.298330+0.154890
## [79] train-rmse:0.890439+0.044053 test-rmse:1.299891+0.156605
## [80] train-rmse:0.887229+0.043681 test-rmse:1.295726+0.156202
## [81] train-rmse:0.881187+0.042593 test-rmse:1.293745+0.158311
## [82] train-rmse:0.877730+0.043030 test-rmse:1.291561+0.160085
## [83] train-rmse:0.864136+0.039260 test-rmse:1.265460+0.162044
## [84] train-rmse:0.861406+0.039743 test-rmse:1.265307+0.161285
## [85] train-rmse:0.857031+0.039083 test-rmse:1.265055+0.161676
## [86] train-rmse:0.849147+0.039485 test-rmse:1.262900+0.161798
## [87] train-rmse:0.845850+0.039139 test-rmse:1.261734+0.162570
## [88] train-rmse:0.839347+0.038428 test-rmse:1.254464+0.155216
## [89] train-rmse:0.835678+0.039862 test-rmse:1.253547+0.155708
## [90] train-rmse:0.832814+0.040212 test-rmse:1.255724+0.157697
## [91] train-rmse:0.830469+0.040881 test-rmse:1.253116+0.158505
## [92] train-rmse:0.824049+0.038948 test-rmse:1.239628+0.165086
## [93] train-rmse:0.820424+0.037899 test-rmse:1.241144+0.163587
## [94] train-rmse:0.817474+0.038860 test-rmse:1.237421+0.160632
## [95] train-rmse:0.815502+0.039173 test-rmse:1.237822+0.160893
## [96] train-rmse:0.813711+0.038956 test-rmse:1.237190+0.162499
## [97] train-rmse:0.810613+0.038695 test-rmse:1.236245+0.162101
## [98] train-rmse:0.808915+0.039107 test-rmse:1.238353+0.160344
## [99] train-rmse:0.806551+0.039432 test-rmse:1.235515+0.159210
## [100] train-rmse:0.803452+0.038953 test-rmse:1.233496+0.158801
## [101] train-rmse:0.801895+0.039078 test-rmse:1.232239+0.159142
## [102] train-rmse:0.799077+0.040632 test-rmse:1.232283+0.158546
## [103] train-rmse:0.797395+0.040748 test-rmse:1.232993+0.158789
## [104] train-rmse:0.793249+0.039546 test-rmse:1.230916+0.157775
## [105] train-rmse:0.791459+0.039389 test-rmse:1.230299+0.158716
## [106] train-rmse:0.789360+0.039434 test-rmse:1.230481+0.160974
## [107] train-rmse:0.787416+0.039488 test-rmse:1.229254+0.161635
## [108] train-rmse:0.785892+0.039456 test-rmse:1.228931+0.162191
## [109] train-rmse:0.783894+0.039045 test-rmse:1.229978+0.161921
## [110] train-rmse:0.778709+0.036741 test-rmse:1.227398+0.162283
## [111] train-rmse:0.776492+0.036674 test-rmse:1.229660+0.161620
## [112] train-rmse:0.774689+0.036281 test-rmse:1.229683+0.161421
## [113] train-rmse:0.773019+0.036907 test-rmse:1.228566+0.162113
## [114] train-rmse:0.771314+0.037449 test-rmse:1.229960+0.163114
## [115] train-rmse:0.770033+0.037419 test-rmse:1.229084+0.162704
## [116] train-rmse:0.767211+0.038199 test-rmse:1.229575+0.163489
## Stopping. Best iteration:
## [110] train-rmse:0.778709+0.036741 test-rmse:1.227398+0.162283
##
## 86
## [1] train-rmse:6.611167+0.047510 test-rmse:6.608144+0.246941
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.626128+0.033628 test-rmse:4.634334+0.218560
## [3] train-rmse:3.376781+0.030667 test-rmse:3.398873+0.169968
## [4] train-rmse:2.612706+0.021174 test-rmse:2.663267+0.141696
## [5] train-rmse:2.161250+0.020814 test-rmse:2.256490+0.092689
## [6] train-rmse:1.894782+0.014893 test-rmse:2.035846+0.067671
## [7] train-rmse:1.738002+0.016371 test-rmse:1.918480+0.070555
## [8] train-rmse:1.638425+0.020469 test-rmse:1.831278+0.059221
## [9] train-rmse:1.559213+0.018258 test-rmse:1.764218+0.064073
## [10] train-rmse:1.504068+0.020834 test-rmse:1.733438+0.059172
## [11] train-rmse:1.456110+0.020717 test-rmse:1.687750+0.054479
## [12] train-rmse:1.408783+0.020955 test-rmse:1.673300+0.060413
## [13] train-rmse:1.371496+0.024018 test-rmse:1.652489+0.054271
## [14] train-rmse:1.332981+0.024588 test-rmse:1.613432+0.067632
## [15] train-rmse:1.292570+0.020978 test-rmse:1.591009+0.077705
## [16] train-rmse:1.261010+0.021301 test-rmse:1.566561+0.088289
## [17] train-rmse:1.230153+0.026974 test-rmse:1.536762+0.077807
## [18] train-rmse:1.204534+0.026888 test-rmse:1.515449+0.083969
## [19] train-rmse:1.178351+0.025569 test-rmse:1.496202+0.089798
## [20] train-rmse:1.146167+0.021942 test-rmse:1.484115+0.085825
## [21] train-rmse:1.123032+0.020003 test-rmse:1.466756+0.101199
## [22] train-rmse:1.095852+0.022472 test-rmse:1.447106+0.102168
## [23] train-rmse:1.073970+0.022988 test-rmse:1.430778+0.105165
## [24] train-rmse:1.055254+0.023491 test-rmse:1.416206+0.107996
## [25] train-rmse:1.037381+0.022863 test-rmse:1.403814+0.111631
## [26] train-rmse:1.017557+0.028317 test-rmse:1.399682+0.113588
## [27] train-rmse:0.998710+0.024908 test-rmse:1.388632+0.118311
## [28] train-rmse:0.979339+0.022678 test-rmse:1.375763+0.119147
## [29] train-rmse:0.959917+0.025722 test-rmse:1.360437+0.110005
## [30] train-rmse:0.944416+0.025799 test-rmse:1.341669+0.116597
## [31] train-rmse:0.928706+0.025956 test-rmse:1.331662+0.117738
## [32] train-rmse:0.914539+0.025992 test-rmse:1.326452+0.121673
## [33] train-rmse:0.901074+0.025801 test-rmse:1.320762+0.125638
## [34] train-rmse:0.889088+0.023160 test-rmse:1.313237+0.128984
## [35] train-rmse:0.872398+0.027527 test-rmse:1.307782+0.131495
## [36] train-rmse:0.861957+0.027744 test-rmse:1.303145+0.134216
## [37] train-rmse:0.851617+0.027822 test-rmse:1.293938+0.140786
## [38] train-rmse:0.839119+0.026722 test-rmse:1.284774+0.138580
## [39] train-rmse:0.826974+0.027068 test-rmse:1.279931+0.140700
## [40] train-rmse:0.815715+0.024819 test-rmse:1.273530+0.142153
## [41] train-rmse:0.807067+0.024985 test-rmse:1.280441+0.142674
## [42] train-rmse:0.799053+0.025392 test-rmse:1.277931+0.140344
## [43] train-rmse:0.786570+0.024719 test-rmse:1.277842+0.141080
## [44] train-rmse:0.779007+0.025482 test-rmse:1.272250+0.146784
## [45] train-rmse:0.770030+0.026756 test-rmse:1.269863+0.154077
## [46] train-rmse:0.762986+0.027994 test-rmse:1.268580+0.155041
## [47] train-rmse:0.752335+0.024692 test-rmse:1.263764+0.158729
## [48] train-rmse:0.744573+0.023360 test-rmse:1.260918+0.161559
## [49] train-rmse:0.737901+0.024158 test-rmse:1.259846+0.160162
## [50] train-rmse:0.730376+0.023812 test-rmse:1.255567+0.160510
## [51] train-rmse:0.725257+0.023496 test-rmse:1.252255+0.162135
## [52] train-rmse:0.714674+0.029865 test-rmse:1.248870+0.155545
## [53] train-rmse:0.707880+0.030156 test-rmse:1.250119+0.156773
## [54] train-rmse:0.702007+0.030029 test-rmse:1.253769+0.157953
## [55] train-rmse:0.693555+0.029287 test-rmse:1.254191+0.158684
## [56] train-rmse:0.687437+0.030256 test-rmse:1.254829+0.158933
## [57] train-rmse:0.681862+0.029901 test-rmse:1.253645+0.160294
## [58] train-rmse:0.676921+0.030223 test-rmse:1.253829+0.160897
## Stopping. Best iteration:
## [52] train-rmse:0.714674+0.029865 test-rmse:1.248870+0.155545
##
## 87
## [1] train-rmse:6.603301+0.048251 test-rmse:6.601206+0.249125
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.607070+0.031658 test-rmse:4.623000+0.220141
## [3] train-rmse:3.337076+0.027732 test-rmse:3.368353+0.159975
## [4] train-rmse:2.538310+0.015192 test-rmse:2.615381+0.127898
## [5] train-rmse:2.051457+0.013217 test-rmse:2.179444+0.080302
## [6] train-rmse:1.761922+0.021139 test-rmse:1.935975+0.079587
## [7] train-rmse:1.590645+0.026706 test-rmse:1.808359+0.062158
## [8] train-rmse:1.470917+0.027704 test-rmse:1.723992+0.046452
## [9] train-rmse:1.384980+0.029942 test-rmse:1.660668+0.023883
## [10] train-rmse:1.313573+0.035836 test-rmse:1.601939+0.045778
## [11] train-rmse:1.248108+0.028152 test-rmse:1.563386+0.060443
## [12] train-rmse:1.199665+0.023901 test-rmse:1.531271+0.064426
## [13] train-rmse:1.158953+0.023484 test-rmse:1.504848+0.053805
## [14] train-rmse:1.118832+0.022803 test-rmse:1.476679+0.073319
## [15] train-rmse:1.080400+0.026046 test-rmse:1.454853+0.071125
## [16] train-rmse:1.043722+0.027034 test-rmse:1.444375+0.073780
## [17] train-rmse:1.015321+0.025772 test-rmse:1.417310+0.089134
## [18] train-rmse:0.985072+0.022114 test-rmse:1.395829+0.091533
## [19] train-rmse:0.959981+0.024363 test-rmse:1.388079+0.101596
## [20] train-rmse:0.932694+0.024135 test-rmse:1.372637+0.106926
## [21] train-rmse:0.912197+0.021337 test-rmse:1.362121+0.109933
## [22] train-rmse:0.888309+0.024205 test-rmse:1.349586+0.113669
## [23] train-rmse:0.872386+0.024414 test-rmse:1.338769+0.113094
## [24] train-rmse:0.852130+0.023660 test-rmse:1.329371+0.119023
## [25] train-rmse:0.831357+0.025235 test-rmse:1.325016+0.125582
## [26] train-rmse:0.815512+0.025471 test-rmse:1.319581+0.126801
## [27] train-rmse:0.800908+0.023680 test-rmse:1.313423+0.132862
## [28] train-rmse:0.780880+0.024607 test-rmse:1.301285+0.123514
## [29] train-rmse:0.764851+0.025463 test-rmse:1.300375+0.121402
## [30] train-rmse:0.752126+0.024928 test-rmse:1.293156+0.124023
## [31] train-rmse:0.734871+0.025436 test-rmse:1.284305+0.120783
## [32] train-rmse:0.718277+0.025418 test-rmse:1.286331+0.125268
## [33] train-rmse:0.702605+0.025096 test-rmse:1.282098+0.128689
## [34] train-rmse:0.689073+0.020708 test-rmse:1.280396+0.130236
## [35] train-rmse:0.679058+0.020211 test-rmse:1.275105+0.133683
## [36] train-rmse:0.666817+0.023882 test-rmse:1.272107+0.134283
## [37] train-rmse:0.656231+0.024883 test-rmse:1.261706+0.128359
## [38] train-rmse:0.644982+0.026211 test-rmse:1.259057+0.128203
## [39] train-rmse:0.634409+0.024267 test-rmse:1.262200+0.135000
## [40] train-rmse:0.627044+0.024650 test-rmse:1.261883+0.133159
## [41] train-rmse:0.618732+0.026577 test-rmse:1.257959+0.133248
## [42] train-rmse:0.607730+0.026739 test-rmse:1.253652+0.131200
## [43] train-rmse:0.600810+0.025985 test-rmse:1.253189+0.133420
## [44] train-rmse:0.592031+0.027650 test-rmse:1.245008+0.132947
## [45] train-rmse:0.580266+0.025864 test-rmse:1.243826+0.133424
## [46] train-rmse:0.572987+0.024381 test-rmse:1.243054+0.135766
## [47] train-rmse:0.565057+0.024199 test-rmse:1.242466+0.134321
## [48] train-rmse:0.556579+0.022894 test-rmse:1.242139+0.137586
## [49] train-rmse:0.551289+0.024132 test-rmse:1.244630+0.141112
## [50] train-rmse:0.545355+0.022667 test-rmse:1.245026+0.140083
## [51] train-rmse:0.539211+0.021878 test-rmse:1.242864+0.140786
## [52] train-rmse:0.534304+0.020842 test-rmse:1.247182+0.139808
## [53] train-rmse:0.528785+0.020329 test-rmse:1.244920+0.141338
## [54] train-rmse:0.523144+0.020426 test-rmse:1.247443+0.143298
## Stopping. Best iteration:
## [48] train-rmse:0.556579+0.022894 test-rmse:1.242139+0.137586
##
## 88
## [1] train-rmse:6.596553+0.048902 test-rmse:6.601378+0.250251
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.584417+0.031867 test-rmse:4.610929+0.218927
## [3] train-rmse:3.293325+0.026664 test-rmse:3.335495+0.163138
## [4] train-rmse:2.471998+0.017812 test-rmse:2.576673+0.140065
## [5] train-rmse:1.953065+0.020236 test-rmse:2.115321+0.084300
## [6] train-rmse:1.648405+0.027105 test-rmse:1.859156+0.076409
## [7] train-rmse:1.444231+0.039616 test-rmse:1.735355+0.055317
## [8] train-rmse:1.307042+0.035341 test-rmse:1.623787+0.070634
## [9] train-rmse:1.199547+0.030240 test-rmse:1.572329+0.086573
## [10] train-rmse:1.128356+0.026631 test-rmse:1.515438+0.112670
## [11] train-rmse:1.070907+0.027391 test-rmse:1.491729+0.119537
## [12] train-rmse:1.024862+0.028363 test-rmse:1.465227+0.125859
## [13] train-rmse:0.974624+0.018131 test-rmse:1.441081+0.130128
## [14] train-rmse:0.934220+0.018536 test-rmse:1.421670+0.142521
## [15] train-rmse:0.894129+0.017597 test-rmse:1.399833+0.142493
## [16] train-rmse:0.863960+0.019532 test-rmse:1.375584+0.149287
## [17] train-rmse:0.830149+0.027052 test-rmse:1.355349+0.147864
## [18] train-rmse:0.798783+0.024070 test-rmse:1.342654+0.157742
## [19] train-rmse:0.768790+0.020005 test-rmse:1.324647+0.160870
## [20] train-rmse:0.747135+0.018300 test-rmse:1.323054+0.163595
## [21] train-rmse:0.725805+0.018039 test-rmse:1.313809+0.166737
## [22] train-rmse:0.704750+0.015023 test-rmse:1.306708+0.169112
## [23] train-rmse:0.686441+0.017903 test-rmse:1.299314+0.169278
## [24] train-rmse:0.669453+0.015828 test-rmse:1.293354+0.167800
## [25] train-rmse:0.656222+0.015114 test-rmse:1.293728+0.173278
## [26] train-rmse:0.639952+0.011532 test-rmse:1.287174+0.176774
## [27] train-rmse:0.623240+0.012278 test-rmse:1.286002+0.178087
## [28] train-rmse:0.609611+0.010848 test-rmse:1.284803+0.184688
## [29] train-rmse:0.597488+0.009937 test-rmse:1.286644+0.188853
## [30] train-rmse:0.583404+0.011145 test-rmse:1.281792+0.185367
## [31] train-rmse:0.569976+0.012063 test-rmse:1.277730+0.184695
## [32] train-rmse:0.557892+0.011755 test-rmse:1.277165+0.184370
## [33] train-rmse:0.544966+0.009468 test-rmse:1.278169+0.186776
## [34] train-rmse:0.532928+0.011740 test-rmse:1.273448+0.185782
## [35] train-rmse:0.523940+0.011458 test-rmse:1.271635+0.183563
## [36] train-rmse:0.515311+0.011605 test-rmse:1.269233+0.183375
## [37] train-rmse:0.504424+0.014056 test-rmse:1.265990+0.183610
## [38] train-rmse:0.494465+0.010832 test-rmse:1.262042+0.182778
## [39] train-rmse:0.485518+0.010929 test-rmse:1.263210+0.182834
## [40] train-rmse:0.476886+0.013729 test-rmse:1.263897+0.184221
## [41] train-rmse:0.468854+0.012727 test-rmse:1.264782+0.188715
## [42] train-rmse:0.461310+0.011769 test-rmse:1.270399+0.187259
## [43] train-rmse:0.456086+0.011264 test-rmse:1.272106+0.186981
## [44] train-rmse:0.446470+0.013476 test-rmse:1.269032+0.186914
## Stopping. Best iteration:
## [38] train-rmse:0.494465+0.010832 test-rmse:1.262042+0.182778
##
## 89
## [1] train-rmse:6.594853+0.049294 test-rmse:6.602220+0.239862
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.572588+0.034929 test-rmse:4.606884+0.197797
## [3] train-rmse:3.272615+0.035818 test-rmse:3.321764+0.147887
## [4] train-rmse:2.427828+0.023373 test-rmse:2.541243+0.126028
## [5] train-rmse:1.900187+0.029684 test-rmse:2.076207+0.100898
## [6] train-rmse:1.559687+0.034936 test-rmse:1.803018+0.096806
## [7] train-rmse:1.341932+0.030325 test-rmse:1.648544+0.103900
## [8] train-rmse:1.183288+0.031765 test-rmse:1.540596+0.099103
## [9] train-rmse:1.073120+0.034074 test-rmse:1.472319+0.108054
## [10] train-rmse:0.994855+0.033528 test-rmse:1.426596+0.124299
## [11] train-rmse:0.930551+0.034564 test-rmse:1.407600+0.138214
## [12] train-rmse:0.869338+0.031086 test-rmse:1.366876+0.138008
## [13] train-rmse:0.822710+0.030098 test-rmse:1.349461+0.149954
## [14] train-rmse:0.785625+0.031288 test-rmse:1.335552+0.142497
## [15] train-rmse:0.748290+0.020558 test-rmse:1.320725+0.150202
## [16] train-rmse:0.720324+0.022224 test-rmse:1.321032+0.163578
## [17] train-rmse:0.689381+0.022660 test-rmse:1.309338+0.177086
## [18] train-rmse:0.661963+0.021127 test-rmse:1.302467+0.178357
## [19] train-rmse:0.642019+0.019853 test-rmse:1.299104+0.177785
## [20] train-rmse:0.620382+0.017089 test-rmse:1.294617+0.188696
## [21] train-rmse:0.595918+0.020267 test-rmse:1.281965+0.188100
## [22] train-rmse:0.579245+0.018920 test-rmse:1.281149+0.190554
## [23] train-rmse:0.561924+0.013865 test-rmse:1.279705+0.196014
## [24] train-rmse:0.545955+0.013129 test-rmse:1.276058+0.198635
## [25] train-rmse:0.524478+0.009601 test-rmse:1.268723+0.193943
## [26] train-rmse:0.507591+0.009406 test-rmse:1.263453+0.197307
## [27] train-rmse:0.496066+0.010320 test-rmse:1.258131+0.197020
## [28] train-rmse:0.480465+0.013385 test-rmse:1.256086+0.198065
## [29] train-rmse:0.471832+0.014131 test-rmse:1.254353+0.198997
## [30] train-rmse:0.460276+0.013748 test-rmse:1.250905+0.200059
## [31] train-rmse:0.453068+0.014253 test-rmse:1.251506+0.200669
## [32] train-rmse:0.442519+0.017680 test-rmse:1.248577+0.198208
## [33] train-rmse:0.431956+0.018512 test-rmse:1.249978+0.197146
## [34] train-rmse:0.415506+0.018262 test-rmse:1.250266+0.193048
## [35] train-rmse:0.405873+0.016446 test-rmse:1.251042+0.191215
## [36] train-rmse:0.396255+0.011509 test-rmse:1.250089+0.191216
## [37] train-rmse:0.389153+0.010631 test-rmse:1.253029+0.192024
## [38] train-rmse:0.380761+0.013674 test-rmse:1.253282+0.190995
## Stopping. Best iteration:
## [32] train-rmse:0.442519+0.017680 test-rmse:1.248577+0.198208
##
## 90
## [1] train-rmse:6.594853+0.049294 test-rmse:6.602220+0.239862
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.567412+0.034724 test-rmse:4.608260+0.203106
## [3] train-rmse:3.251728+0.032962 test-rmse:3.309955+0.150762
## [4] train-rmse:2.394771+0.020272 test-rmse:2.524303+0.129904
## [5] train-rmse:1.848043+0.023084 test-rmse:2.027660+0.127556
## [6] train-rmse:1.494208+0.020096 test-rmse:1.739469+0.120480
## [7] train-rmse:1.240727+0.022486 test-rmse:1.573099+0.104438
## [8] train-rmse:1.088855+0.022362 test-rmse:1.483047+0.131735
## [9] train-rmse:0.973134+0.018501 test-rmse:1.416157+0.146592
## [10] train-rmse:0.887320+0.019366 test-rmse:1.365482+0.156909
## [11] train-rmse:0.827349+0.016767 test-rmse:1.344418+0.163426
## [12] train-rmse:0.765973+0.022930 test-rmse:1.323038+0.171969
## [13] train-rmse:0.717854+0.019307 test-rmse:1.301539+0.182800
## [14] train-rmse:0.679131+0.017437 test-rmse:1.294375+0.191812
## [15] train-rmse:0.644853+0.013081 test-rmse:1.287657+0.192809
## [16] train-rmse:0.616007+0.011194 test-rmse:1.281356+0.194437
## [17] train-rmse:0.591867+0.011839 test-rmse:1.274764+0.195784
## [18] train-rmse:0.565243+0.006127 test-rmse:1.267383+0.203937
## [19] train-rmse:0.541092+0.004606 test-rmse:1.260239+0.202508
## [20] train-rmse:0.517299+0.006019 test-rmse:1.264314+0.201439
## [21] train-rmse:0.499057+0.005286 test-rmse:1.259871+0.198835
## [22] train-rmse:0.477324+0.011375 test-rmse:1.252321+0.199483
## [23] train-rmse:0.460147+0.011749 test-rmse:1.251276+0.196544
## [24] train-rmse:0.441959+0.010021 test-rmse:1.252200+0.196513
## [25] train-rmse:0.427905+0.011950 test-rmse:1.249582+0.194242
## [26] train-rmse:0.412050+0.011948 test-rmse:1.246122+0.195223
## [27] train-rmse:0.402598+0.010887 test-rmse:1.248743+0.195966
## [28] train-rmse:0.391870+0.011452 test-rmse:1.248398+0.195534
## [29] train-rmse:0.378466+0.009810 test-rmse:1.246871+0.198197
## [30] train-rmse:0.368335+0.012027 test-rmse:1.244039+0.199600
## [31] train-rmse:0.355313+0.012805 test-rmse:1.239751+0.197496
## [32] train-rmse:0.343839+0.008803 test-rmse:1.236178+0.197720
## [33] train-rmse:0.333463+0.009502 test-rmse:1.238582+0.199232
## [34] train-rmse:0.320140+0.006487 test-rmse:1.237498+0.197469
## [35] train-rmse:0.312806+0.008482 test-rmse:1.235365+0.195412
## [36] train-rmse:0.301096+0.009416 test-rmse:1.237141+0.194733
## [37] train-rmse:0.292620+0.010977 test-rmse:1.237962+0.195285
## [38] train-rmse:0.284360+0.008271 test-rmse:1.238587+0.193258
## [39] train-rmse:0.277407+0.008938 test-rmse:1.240923+0.193622
## [40] train-rmse:0.266952+0.006722 test-rmse:1.240304+0.190144
## [41] train-rmse:0.257998+0.007222 test-rmse:1.241072+0.189302
## Stopping. Best iteration:
## [35] train-rmse:0.312806+0.008482 test-rmse:1.235365+0.195412
##
## 91
## [1] train-rmse:6.503695+0.043337 test-rmse:6.499766+0.259430
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.560319+0.024040 test-rmse:4.557352+0.244413
## [3] train-rmse:3.436740+0.014849 test-rmse:3.445222+0.201416
## [4] train-rmse:2.826395+0.012734 test-rmse:2.865402+0.131174
## [5] train-rmse:2.513061+0.015155 test-rmse:2.544458+0.086163
## [6] train-rmse:2.351592+0.018065 test-rmse:2.386604+0.053326
## [7] train-rmse:2.267319+0.019104 test-rmse:2.313365+0.053527
## [8] train-rmse:2.215795+0.019990 test-rmse:2.264432+0.062857
## [9] train-rmse:2.180938+0.021220 test-rmse:2.219395+0.065263
## [10] train-rmse:2.153388+0.022224 test-rmse:2.197531+0.071689
## [11] train-rmse:2.129235+0.023745 test-rmse:2.171968+0.077858
## [12] train-rmse:2.107525+0.024872 test-rmse:2.157151+0.086041
## [13] train-rmse:2.085668+0.024845 test-rmse:2.144869+0.080133
## [14] train-rmse:2.064964+0.025408 test-rmse:2.134195+0.085702
## [15] train-rmse:2.045361+0.025779 test-rmse:2.117245+0.096228
## [16] train-rmse:2.026963+0.026281 test-rmse:2.102618+0.100127
## [17] train-rmse:2.009241+0.027515 test-rmse:2.080112+0.089869
## [18] train-rmse:1.991752+0.028710 test-rmse:2.063876+0.085659
## [19] train-rmse:1.975034+0.030362 test-rmse:2.045281+0.093446
## [20] train-rmse:1.959326+0.031591 test-rmse:2.040191+0.100607
## [21] train-rmse:1.944845+0.031723 test-rmse:2.019640+0.106106
## [22] train-rmse:1.930604+0.032022 test-rmse:2.004427+0.097179
## [23] train-rmse:1.917175+0.032634 test-rmse:1.998477+0.092451
## [24] train-rmse:1.903977+0.032982 test-rmse:1.982027+0.085130
## [25] train-rmse:1.891258+0.033412 test-rmse:1.972634+0.087439
## [26] train-rmse:1.878756+0.034268 test-rmse:1.957799+0.084650
## [27] train-rmse:1.866723+0.035003 test-rmse:1.953603+0.085880
## [28] train-rmse:1.855143+0.035658 test-rmse:1.944829+0.087180
## [29] train-rmse:1.844123+0.036727 test-rmse:1.941226+0.084278
## [30] train-rmse:1.833484+0.037708 test-rmse:1.926449+0.084734
## [31] train-rmse:1.822924+0.038684 test-rmse:1.920260+0.085715
## [32] train-rmse:1.812493+0.039354 test-rmse:1.907153+0.082903
## [33] train-rmse:1.802379+0.040044 test-rmse:1.899399+0.086550
## [34] train-rmse:1.792342+0.040600 test-rmse:1.896546+0.086789
## [35] train-rmse:1.782371+0.041077 test-rmse:1.887828+0.088447
## [36] train-rmse:1.772757+0.041551 test-rmse:1.872226+0.092109
## [37] train-rmse:1.763060+0.042133 test-rmse:1.864744+0.098200
## [38] train-rmse:1.753630+0.042649 test-rmse:1.863824+0.099832
## [39] train-rmse:1.744519+0.043036 test-rmse:1.856064+0.098153
## [40] train-rmse:1.735482+0.043432 test-rmse:1.854622+0.102020
## [41] train-rmse:1.726661+0.043962 test-rmse:1.840136+0.101362
## [42] train-rmse:1.718677+0.044437 test-rmse:1.831872+0.102053
## [43] train-rmse:1.710632+0.044713 test-rmse:1.822763+0.103491
## [44] train-rmse:1.703051+0.045114 test-rmse:1.813704+0.104713
## [45] train-rmse:1.695265+0.045547 test-rmse:1.811958+0.106860
## [46] train-rmse:1.687831+0.046069 test-rmse:1.804144+0.106876
## [47] train-rmse:1.680766+0.046478 test-rmse:1.793022+0.110548
## [48] train-rmse:1.673746+0.046877 test-rmse:1.789966+0.106054
## [49] train-rmse:1.666909+0.047292 test-rmse:1.785499+0.105516
## [50] train-rmse:1.660237+0.047580 test-rmse:1.779684+0.103308
## [51] train-rmse:1.653661+0.047845 test-rmse:1.772649+0.105694
## [52] train-rmse:1.647540+0.048107 test-rmse:1.769993+0.097609
## [53] train-rmse:1.641386+0.048156 test-rmse:1.764222+0.099045
## [54] train-rmse:1.635236+0.048062 test-rmse:1.757403+0.101737
## [55] train-rmse:1.629140+0.048225 test-rmse:1.753268+0.104772
## [56] train-rmse:1.623167+0.048362 test-rmse:1.746724+0.104883
## [57] train-rmse:1.617355+0.048464 test-rmse:1.738289+0.108888
## [58] train-rmse:1.611662+0.048756 test-rmse:1.722443+0.112896
## [59] train-rmse:1.606320+0.049028 test-rmse:1.722860+0.116326
## [60] train-rmse:1.600984+0.049364 test-rmse:1.719875+0.116264
## [61] train-rmse:1.595668+0.049686 test-rmse:1.720434+0.116205
## [62] train-rmse:1.590392+0.049933 test-rmse:1.719163+0.121808
## [63] train-rmse:1.585273+0.050251 test-rmse:1.716219+0.123839
## [64] train-rmse:1.580257+0.050511 test-rmse:1.710347+0.124407
## [65] train-rmse:1.575384+0.050696 test-rmse:1.706143+0.128296
## [66] train-rmse:1.570390+0.050730 test-rmse:1.700618+0.130143
## [67] train-rmse:1.565611+0.050798 test-rmse:1.696911+0.130907
## [68] train-rmse:1.561024+0.050943 test-rmse:1.689777+0.131177
## [69] train-rmse:1.556354+0.050956 test-rmse:1.689143+0.136205
## [70] train-rmse:1.551652+0.051038 test-rmse:1.682105+0.137519
## [71] train-rmse:1.547198+0.051077 test-rmse:1.677335+0.136560
## [72] train-rmse:1.542798+0.051237 test-rmse:1.673490+0.134870
## [73] train-rmse:1.538627+0.051297 test-rmse:1.671791+0.140080
## [74] train-rmse:1.534504+0.051442 test-rmse:1.671865+0.139480
## [75] train-rmse:1.530613+0.051569 test-rmse:1.666002+0.141092
## [76] train-rmse:1.526806+0.051622 test-rmse:1.661182+0.142274
## [77] train-rmse:1.523033+0.051632 test-rmse:1.659039+0.141848
## [78] train-rmse:1.519393+0.051750 test-rmse:1.649889+0.143686
## [79] train-rmse:1.515829+0.051797 test-rmse:1.648956+0.143391
## [80] train-rmse:1.512258+0.051865 test-rmse:1.649032+0.140382
## [81] train-rmse:1.508765+0.051957 test-rmse:1.644925+0.148847
## [82] train-rmse:1.505304+0.052049 test-rmse:1.642081+0.148893
## [83] train-rmse:1.501941+0.052084 test-rmse:1.637170+0.149860
## [84] train-rmse:1.498599+0.052095 test-rmse:1.632493+0.150323
## [85] train-rmse:1.495226+0.052061 test-rmse:1.627674+0.149182
## [86] train-rmse:1.491915+0.052090 test-rmse:1.624107+0.152511
## [87] train-rmse:1.488739+0.052162 test-rmse:1.615628+0.150161
## [88] train-rmse:1.485667+0.052319 test-rmse:1.611292+0.150007
## [89] train-rmse:1.482618+0.052446 test-rmse:1.608116+0.150172
## [90] train-rmse:1.479666+0.052498 test-rmse:1.605068+0.145979
## [91] train-rmse:1.476727+0.052690 test-rmse:1.601950+0.143131
## [92] train-rmse:1.473861+0.052831 test-rmse:1.600856+0.146545
## [93] train-rmse:1.471002+0.052886 test-rmse:1.599389+0.147190
## [94] train-rmse:1.468094+0.052948 test-rmse:1.600568+0.149082
## [95] train-rmse:1.465325+0.053070 test-rmse:1.603304+0.153951
## [96] train-rmse:1.462631+0.053127 test-rmse:1.598555+0.154721
## [97] train-rmse:1.459988+0.053083 test-rmse:1.595735+0.155517
## [98] train-rmse:1.457405+0.053107 test-rmse:1.593129+0.154729
## [99] train-rmse:1.454909+0.053077 test-rmse:1.590700+0.155443
## [100] train-rmse:1.452430+0.053019 test-rmse:1.588108+0.157290
## [101] train-rmse:1.450025+0.052933 test-rmse:1.588100+0.155667
## [102] train-rmse:1.447570+0.052942 test-rmse:1.588942+0.152338
## [103] train-rmse:1.445114+0.053048 test-rmse:1.586606+0.152928
## [104] train-rmse:1.442647+0.053242 test-rmse:1.587395+0.157741
## [105] train-rmse:1.440359+0.053221 test-rmse:1.583066+0.154601
## [106] train-rmse:1.438104+0.053205 test-rmse:1.582082+0.156664
## [107] train-rmse:1.435912+0.053159 test-rmse:1.582500+0.161179
## [108] train-rmse:1.433730+0.053124 test-rmse:1.579082+0.162899
## [109] train-rmse:1.431610+0.053165 test-rmse:1.576603+0.163400
## [110] train-rmse:1.429483+0.053264 test-rmse:1.574916+0.163275
## [111] train-rmse:1.427469+0.053267 test-rmse:1.572793+0.167453
## [112] train-rmse:1.425514+0.053321 test-rmse:1.572105+0.166541
## [113] train-rmse:1.423550+0.053391 test-rmse:1.566968+0.167102
## [114] train-rmse:1.421616+0.053439 test-rmse:1.563483+0.165838
## [115] train-rmse:1.419724+0.053518 test-rmse:1.560918+0.164161
## [116] train-rmse:1.417897+0.053520 test-rmse:1.556123+0.161397
## [117] train-rmse:1.416056+0.053527 test-rmse:1.556903+0.163942
## [118] train-rmse:1.414274+0.053502 test-rmse:1.553946+0.166390
## [119] train-rmse:1.412475+0.053469 test-rmse:1.554085+0.168801
## [120] train-rmse:1.410729+0.053430 test-rmse:1.555219+0.168156
## [121] train-rmse:1.409019+0.053394 test-rmse:1.555156+0.169698
## [122] train-rmse:1.407321+0.053376 test-rmse:1.555711+0.170399
## [123] train-rmse:1.405609+0.053408 test-rmse:1.551990+0.170992
## [124] train-rmse:1.403953+0.053429 test-rmse:1.549399+0.171325
## [125] train-rmse:1.402337+0.053460 test-rmse:1.547015+0.172079
## [126] train-rmse:1.400743+0.053441 test-rmse:1.544967+0.173971
## [127] train-rmse:1.399183+0.053423 test-rmse:1.541211+0.174293
## [128] train-rmse:1.397650+0.053399 test-rmse:1.539651+0.175403
## [129] train-rmse:1.396119+0.053398 test-rmse:1.536939+0.177087
## [130] train-rmse:1.394649+0.053400 test-rmse:1.535009+0.175782
## [131] train-rmse:1.393182+0.053414 test-rmse:1.532778+0.176112
## [132] train-rmse:1.391783+0.053409 test-rmse:1.532883+0.176524
## [133] train-rmse:1.390416+0.053444 test-rmse:1.531438+0.177296
## [134] train-rmse:1.389090+0.053437 test-rmse:1.530616+0.175383
## [135] train-rmse:1.387773+0.053400 test-rmse:1.530181+0.178196
## [136] train-rmse:1.386430+0.053426 test-rmse:1.529371+0.177063
## [137] train-rmse:1.385186+0.053451 test-rmse:1.528863+0.176126
## [138] train-rmse:1.383962+0.053450 test-rmse:1.526622+0.180859
## [139] train-rmse:1.382731+0.053449 test-rmse:1.524891+0.181225
## [140] train-rmse:1.381522+0.053454 test-rmse:1.524051+0.179594
## [141] train-rmse:1.380331+0.053464 test-rmse:1.523169+0.181880
## [142] train-rmse:1.379145+0.053486 test-rmse:1.522217+0.182955
## [143] train-rmse:1.377956+0.053465 test-rmse:1.521766+0.182355
## [144] train-rmse:1.376821+0.053467 test-rmse:1.522685+0.181401
## [145] train-rmse:1.375698+0.053469 test-rmse:1.519473+0.181453
## [146] train-rmse:1.374592+0.053441 test-rmse:1.518307+0.181004
## [147] train-rmse:1.373499+0.053441 test-rmse:1.517641+0.178195
## [148] train-rmse:1.372434+0.053416 test-rmse:1.517328+0.178077
## [149] train-rmse:1.371395+0.053417 test-rmse:1.518743+0.178594
## [150] train-rmse:1.370354+0.053417 test-rmse:1.516953+0.179680
## [151] train-rmse:1.369313+0.053408 test-rmse:1.516197+0.178652
## [152] train-rmse:1.368301+0.053409 test-rmse:1.512507+0.178336
## [153] train-rmse:1.367296+0.053399 test-rmse:1.511485+0.178062
## [154] train-rmse:1.366326+0.053401 test-rmse:1.510097+0.178926
## [155] train-rmse:1.365361+0.053373 test-rmse:1.510181+0.178556
## [156] train-rmse:1.364354+0.053305 test-rmse:1.509320+0.179154
## [157] train-rmse:1.363426+0.053316 test-rmse:1.511090+0.183173
## [158] train-rmse:1.362530+0.053313 test-rmse:1.512382+0.185883
## [159] train-rmse:1.361623+0.053307 test-rmse:1.512792+0.185718
## [160] train-rmse:1.360724+0.053314 test-rmse:1.511229+0.184920
## [161] train-rmse:1.359901+0.053322 test-rmse:1.509169+0.188612
## [162] train-rmse:1.359081+0.053354 test-rmse:1.509581+0.189828
## [163] train-rmse:1.358267+0.053360 test-rmse:1.508578+0.189754
## [164] train-rmse:1.357432+0.053369 test-rmse:1.507492+0.190966
## [165] train-rmse:1.356650+0.053371 test-rmse:1.507065+0.190927
## [166] train-rmse:1.355861+0.053360 test-rmse:1.505156+0.189528
## [167] train-rmse:1.355082+0.053334 test-rmse:1.502983+0.191489
## [168] train-rmse:1.354308+0.053322 test-rmse:1.501553+0.191421
## [169] train-rmse:1.353561+0.053297 test-rmse:1.503315+0.191647
## [170] train-rmse:1.352836+0.053285 test-rmse:1.500589+0.192452
## [171] train-rmse:1.352105+0.053281 test-rmse:1.499255+0.191482
## [172] train-rmse:1.351396+0.053255 test-rmse:1.499269+0.191842
## [173] train-rmse:1.350709+0.053254 test-rmse:1.498712+0.191085
## [174] train-rmse:1.350033+0.053248 test-rmse:1.498319+0.191474
## [175] train-rmse:1.349368+0.053244 test-rmse:1.496816+0.190771
## [176] train-rmse:1.348733+0.053224 test-rmse:1.496401+0.191099
## [177] train-rmse:1.348093+0.053211 test-rmse:1.496264+0.191183
## [178] train-rmse:1.347437+0.053204 test-rmse:1.495178+0.192815
## [179] train-rmse:1.346775+0.053192 test-rmse:1.495981+0.191763
## [180] train-rmse:1.346126+0.053181 test-rmse:1.496032+0.192205
## [181] train-rmse:1.345492+0.053159 test-rmse:1.495404+0.190808
## [182] train-rmse:1.344864+0.053127 test-rmse:1.493475+0.191393
## [183] train-rmse:1.344257+0.053128 test-rmse:1.492790+0.191685
## [184] train-rmse:1.343653+0.053116 test-rmse:1.494651+0.193803
## [185] train-rmse:1.343081+0.053100 test-rmse:1.495876+0.194838
## [186] train-rmse:1.342497+0.053044 test-rmse:1.498856+0.196402
## [187] train-rmse:1.341946+0.053047 test-rmse:1.497569+0.193597
## [188] train-rmse:1.341388+0.053068 test-rmse:1.498164+0.192946
## [189] train-rmse:1.340814+0.053080 test-rmse:1.496715+0.194660
## Stopping. Best iteration:
## [183] train-rmse:1.344257+0.053128 test-rmse:1.492790+0.191685
##
## 92
## [1] train-rmse:6.452603+0.043919 test-rmse:6.449304+0.253018
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.459874+0.023986 test-rmse:4.472986+0.225781
## [3] train-rmse:3.274163+0.017361 test-rmse:3.295099+0.176981
## [4] train-rmse:2.598537+0.017003 test-rmse:2.638764+0.105529
## [5] train-rmse:2.238507+0.020014 test-rmse:2.319695+0.057794
## [6] train-rmse:2.026607+0.019797 test-rmse:2.115649+0.045407
## [7] train-rmse:1.910583+0.018110 test-rmse:2.034953+0.065862
## [8] train-rmse:1.833281+0.018211 test-rmse:1.968381+0.060171
## [9] train-rmse:1.783126+0.020318 test-rmse:1.918245+0.083561
## [10] train-rmse:1.741660+0.022560 test-rmse:1.887922+0.099690
## [11] train-rmse:1.700376+0.028192 test-rmse:1.846569+0.083458
## [12] train-rmse:1.667681+0.031305 test-rmse:1.806838+0.087330
## [13] train-rmse:1.634174+0.026200 test-rmse:1.786137+0.091852
## [14] train-rmse:1.605739+0.027378 test-rmse:1.768720+0.098179
## [15] train-rmse:1.570795+0.032063 test-rmse:1.727546+0.055877
## [16] train-rmse:1.543445+0.036717 test-rmse:1.705589+0.062417
## [17] train-rmse:1.515795+0.039804 test-rmse:1.685719+0.069137
## [18] train-rmse:1.488581+0.035178 test-rmse:1.674207+0.066282
## [19] train-rmse:1.450861+0.038100 test-rmse:1.644059+0.042162
## [20] train-rmse:1.425800+0.042976 test-rmse:1.614999+0.057355
## [21] train-rmse:1.403937+0.045611 test-rmse:1.601185+0.063385
## [22] train-rmse:1.383131+0.045941 test-rmse:1.570816+0.073760
## [23] train-rmse:1.363805+0.046217 test-rmse:1.555714+0.071638
## [24] train-rmse:1.347194+0.047177 test-rmse:1.540066+0.069553
## [25] train-rmse:1.328357+0.050407 test-rmse:1.521038+0.068028
## [26] train-rmse:1.297999+0.027207 test-rmse:1.515610+0.079899
## [27] train-rmse:1.279920+0.027087 test-rmse:1.504833+0.083386
## [28] train-rmse:1.261992+0.028592 test-rmse:1.493683+0.084025
## [29] train-rmse:1.246595+0.029909 test-rmse:1.473592+0.087186
## [30] train-rmse:1.231651+0.030714 test-rmse:1.457627+0.089341
## [31] train-rmse:1.215909+0.030665 test-rmse:1.451231+0.092364
## [32] train-rmse:1.200829+0.033902 test-rmse:1.434856+0.080362
## [33] train-rmse:1.186732+0.033713 test-rmse:1.423789+0.079524
## [34] train-rmse:1.167225+0.024012 test-rmse:1.410494+0.088848
## [35] train-rmse:1.155510+0.023085 test-rmse:1.404104+0.090070
## [36] train-rmse:1.144871+0.023463 test-rmse:1.392574+0.086493
## [37] train-rmse:1.133354+0.023193 test-rmse:1.373693+0.094840
## [38] train-rmse:1.122253+0.022671 test-rmse:1.367362+0.092893
## [39] train-rmse:1.109650+0.024859 test-rmse:1.358628+0.087578
## [40] train-rmse:1.099567+0.024118 test-rmse:1.353137+0.090609
## [41] train-rmse:1.090553+0.024342 test-rmse:1.345513+0.096429
## [42] train-rmse:1.079450+0.026550 test-rmse:1.337938+0.104478
## [43] train-rmse:1.070971+0.026868 test-rmse:1.335803+0.105652
## [44] train-rmse:1.061346+0.027035 test-rmse:1.338031+0.113973
## [45] train-rmse:1.053145+0.027359 test-rmse:1.328345+0.118033
## [46] train-rmse:1.042005+0.029417 test-rmse:1.316844+0.110994
## [47] train-rmse:1.032500+0.026706 test-rmse:1.307170+0.113227
## [48] train-rmse:1.024300+0.028621 test-rmse:1.305447+0.117920
## [49] train-rmse:1.016999+0.028954 test-rmse:1.299368+0.114984
## [50] train-rmse:1.004503+0.025144 test-rmse:1.292791+0.119781
## [51] train-rmse:0.997857+0.025096 test-rmse:1.284529+0.124805
## [52] train-rmse:0.989123+0.024535 test-rmse:1.278622+0.122371
## [53] train-rmse:0.973665+0.032710 test-rmse:1.269833+0.124943
## [54] train-rmse:0.967608+0.033261 test-rmse:1.264746+0.125328
## [55] train-rmse:0.962472+0.033838 test-rmse:1.264216+0.124537
## [56] train-rmse:0.956570+0.034503 test-rmse:1.267387+0.128512
## [57] train-rmse:0.950107+0.035558 test-rmse:1.265645+0.129127
## [58] train-rmse:0.944698+0.036502 test-rmse:1.264393+0.131516
## [59] train-rmse:0.939339+0.037158 test-rmse:1.258980+0.134839
## [60] train-rmse:0.933401+0.037682 test-rmse:1.250301+0.132059
## [61] train-rmse:0.927690+0.037380 test-rmse:1.247297+0.136009
## [62] train-rmse:0.923649+0.037629 test-rmse:1.247550+0.140562
## [63] train-rmse:0.918228+0.035834 test-rmse:1.247182+0.141280
## [64] train-rmse:0.913045+0.035440 test-rmse:1.243650+0.141461
## [65] train-rmse:0.906257+0.039084 test-rmse:1.241311+0.141517
## [66] train-rmse:0.902279+0.039010 test-rmse:1.239395+0.143628
## [67] train-rmse:0.898367+0.039423 test-rmse:1.235121+0.146448
## [68] train-rmse:0.894716+0.039919 test-rmse:1.232902+0.147348
## [69] train-rmse:0.889826+0.039791 test-rmse:1.231763+0.149998
## [70] train-rmse:0.883108+0.038679 test-rmse:1.228581+0.150173
## [71] train-rmse:0.879446+0.038839 test-rmse:1.226875+0.149121
## [72] train-rmse:0.875580+0.038931 test-rmse:1.226128+0.152048
## [73] train-rmse:0.866944+0.046579 test-rmse:1.226414+0.153822
## [74] train-rmse:0.863374+0.046539 test-rmse:1.224308+0.152663
## [75] train-rmse:0.858570+0.046220 test-rmse:1.223144+0.153784
## [76] train-rmse:0.853755+0.046203 test-rmse:1.220847+0.152762
## [77] train-rmse:0.850826+0.046644 test-rmse:1.220051+0.154627
## [78] train-rmse:0.846746+0.046762 test-rmse:1.218725+0.156327
## [79] train-rmse:0.843687+0.046452 test-rmse:1.222657+0.153664
## [80] train-rmse:0.840888+0.046197 test-rmse:1.221595+0.154636
## [81] train-rmse:0.837782+0.046640 test-rmse:1.217349+0.151344
## [82] train-rmse:0.834609+0.046827 test-rmse:1.219128+0.149355
## [83] train-rmse:0.831509+0.046823 test-rmse:1.218363+0.149472
## [84] train-rmse:0.828768+0.046639 test-rmse:1.217324+0.150306
## [85] train-rmse:0.824120+0.043298 test-rmse:1.216890+0.150908
## [86] train-rmse:0.821824+0.043384 test-rmse:1.216571+0.149694
## [87] train-rmse:0.819990+0.043485 test-rmse:1.216033+0.149743
## [88] train-rmse:0.814621+0.046708 test-rmse:1.218940+0.147033
## [89] train-rmse:0.806165+0.038014 test-rmse:1.212175+0.148192
## [90] train-rmse:0.801979+0.035694 test-rmse:1.214388+0.153329
## [91] train-rmse:0.798079+0.034975 test-rmse:1.209261+0.149339
## [92] train-rmse:0.795491+0.035490 test-rmse:1.208559+0.149370
## [93] train-rmse:0.792953+0.036429 test-rmse:1.210878+0.149703
## [94] train-rmse:0.790290+0.036102 test-rmse:1.209988+0.149430
## [95] train-rmse:0.787378+0.035735 test-rmse:1.211135+0.146843
## [96] train-rmse:0.785891+0.035911 test-rmse:1.209415+0.146243
## [97] train-rmse:0.784005+0.036439 test-rmse:1.209278+0.146257
## [98] train-rmse:0.782318+0.036596 test-rmse:1.209331+0.145556
## Stopping. Best iteration:
## [92] train-rmse:0.795491+0.035490 test-rmse:1.208559+0.149370
##
## 93
## [1] train-rmse:6.432603+0.046117 test-rmse:6.429668+0.244878
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.403065+0.031741 test-rmse:4.413530+0.217669
## [3] train-rmse:3.171056+0.024783 test-rmse:3.212409+0.156750
## [4] train-rmse:2.450898+0.018031 test-rmse:2.520937+0.120846
## [5] train-rmse:2.044775+0.019631 test-rmse:2.154789+0.083705
## [6] train-rmse:1.811173+0.015980 test-rmse:1.957766+0.062533
## [7] train-rmse:1.676093+0.015663 test-rmse:1.852728+0.067194
## [8] train-rmse:1.588053+0.019348 test-rmse:1.793512+0.057219
## [9] train-rmse:1.521738+0.023089 test-rmse:1.731683+0.060669
## [10] train-rmse:1.464687+0.016757 test-rmse:1.694969+0.067597
## [11] train-rmse:1.419536+0.014516 test-rmse:1.673471+0.070536
## [12] train-rmse:1.370055+0.014758 test-rmse:1.625056+0.040828
## [13] train-rmse:1.323599+0.025459 test-rmse:1.604123+0.046691
## [14] train-rmse:1.288647+0.021815 test-rmse:1.578795+0.059284
## [15] train-rmse:1.255911+0.019178 test-rmse:1.551637+0.071509
## [16] train-rmse:1.222130+0.023777 test-rmse:1.523815+0.071287
## [17] train-rmse:1.195144+0.022917 test-rmse:1.489418+0.071405
## [18] train-rmse:1.167269+0.022852 test-rmse:1.477825+0.079407
## [19] train-rmse:1.135993+0.020235 test-rmse:1.460087+0.105048
## [20] train-rmse:1.109147+0.021718 test-rmse:1.443076+0.093911
## [21] train-rmse:1.086344+0.023978 test-rmse:1.424390+0.092348
## [22] train-rmse:1.062501+0.029148 test-rmse:1.405266+0.102558
## [23] train-rmse:1.041039+0.031088 test-rmse:1.389276+0.102074
## [24] train-rmse:1.020349+0.031124 test-rmse:1.379043+0.108257
## [25] train-rmse:0.999965+0.030501 test-rmse:1.369789+0.109070
## [26] train-rmse:0.985357+0.030131 test-rmse:1.359172+0.115348
## [27] train-rmse:0.965515+0.032461 test-rmse:1.348598+0.117423
## [28] train-rmse:0.946840+0.033433 test-rmse:1.330642+0.117225
## [29] train-rmse:0.930644+0.033195 test-rmse:1.309405+0.117070
## [30] train-rmse:0.915906+0.034645 test-rmse:1.298511+0.117431
## [31] train-rmse:0.903448+0.035929 test-rmse:1.294098+0.122206
## [32] train-rmse:0.890223+0.036181 test-rmse:1.290129+0.124468
## [33] train-rmse:0.867898+0.034837 test-rmse:1.275403+0.124920
## [34] train-rmse:0.854431+0.034684 test-rmse:1.265820+0.132281
## [35] train-rmse:0.842932+0.037143 test-rmse:1.260762+0.133688
## [36] train-rmse:0.832969+0.037222 test-rmse:1.260193+0.129552
## [37] train-rmse:0.817577+0.038333 test-rmse:1.245675+0.125694
## [38] train-rmse:0.806318+0.038475 test-rmse:1.244589+0.129269
## [39] train-rmse:0.798077+0.037564 test-rmse:1.242463+0.130715
## [40] train-rmse:0.786862+0.037050 test-rmse:1.236708+0.135078
## [41] train-rmse:0.777683+0.038571 test-rmse:1.229797+0.133884
## [42] train-rmse:0.769695+0.037603 test-rmse:1.230776+0.135035
## [43] train-rmse:0.758623+0.041774 test-rmse:1.225780+0.132131
## [44] train-rmse:0.750343+0.043120 test-rmse:1.219713+0.129388
## [45] train-rmse:0.741219+0.043279 test-rmse:1.212052+0.126513
## [46] train-rmse:0.733913+0.044588 test-rmse:1.210539+0.126124
## [47] train-rmse:0.727372+0.045616 test-rmse:1.207162+0.125597
## [48] train-rmse:0.720465+0.044343 test-rmse:1.202915+0.129522
## [49] train-rmse:0.712253+0.041541 test-rmse:1.199630+0.124618
## [50] train-rmse:0.703201+0.043878 test-rmse:1.199301+0.131955
## [51] train-rmse:0.694867+0.042964 test-rmse:1.198230+0.131983
## [52] train-rmse:0.689182+0.043073 test-rmse:1.196414+0.132830
## [53] train-rmse:0.681657+0.046088 test-rmse:1.195983+0.132959
## [54] train-rmse:0.677054+0.046120 test-rmse:1.192340+0.131769
## [55] train-rmse:0.671796+0.046046 test-rmse:1.192490+0.131696
## [56] train-rmse:0.666254+0.047039 test-rmse:1.188561+0.129948
## [57] train-rmse:0.661464+0.047054 test-rmse:1.192168+0.131252
## [58] train-rmse:0.656873+0.046021 test-rmse:1.193681+0.135797
## [59] train-rmse:0.647784+0.038712 test-rmse:1.192998+0.136611
## [60] train-rmse:0.642473+0.037855 test-rmse:1.197383+0.134036
## [61] train-rmse:0.639060+0.037588 test-rmse:1.196786+0.134554
## [62] train-rmse:0.631958+0.036411 test-rmse:1.197220+0.135487
## Stopping. Best iteration:
## [56] train-rmse:0.666254+0.047039 test-rmse:1.188561+0.129948
##
## 94
## [1] train-rmse:6.424089+0.046899 test-rmse:6.422334+0.247160
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.381831+0.029600 test-rmse:4.403653+0.219221
## [3] train-rmse:3.131718+0.021484 test-rmse:3.170830+0.154298
## [4] train-rmse:2.372594+0.012817 test-rmse:2.462866+0.125278
## [5] train-rmse:1.936754+0.019946 test-rmse:2.065504+0.073959
## [6] train-rmse:1.684651+0.022198 test-rmse:1.851090+0.080148
## [7] train-rmse:1.536010+0.025656 test-rmse:1.732116+0.073604
## [8] train-rmse:1.428082+0.025015 test-rmse:1.657171+0.062975
## [9] train-rmse:1.352167+0.024616 test-rmse:1.607786+0.084937
## [10] train-rmse:1.288610+0.027935 test-rmse:1.566379+0.089174
## [11] train-rmse:1.237713+0.033601 test-rmse:1.527688+0.082168
## [12] train-rmse:1.182418+0.024389 test-rmse:1.479684+0.092038
## [13] train-rmse:1.139115+0.023855 test-rmse:1.460928+0.103417
## [14] train-rmse:1.103890+0.026485 test-rmse:1.442676+0.115555
## [15] train-rmse:1.069376+0.021386 test-rmse:1.425865+0.123250
## [16] train-rmse:1.018470+0.020368 test-rmse:1.398530+0.118301
## [17] train-rmse:0.990263+0.023286 test-rmse:1.388881+0.114417
## [18] train-rmse:0.962882+0.023737 test-rmse:1.371663+0.118508
## [19] train-rmse:0.936373+0.024071 test-rmse:1.356575+0.132958
## [20] train-rmse:0.914578+0.027430 test-rmse:1.344857+0.132591
## [21] train-rmse:0.892082+0.026773 test-rmse:1.335124+0.137395
## [22] train-rmse:0.869055+0.028983 test-rmse:1.321288+0.130043
## [23] train-rmse:0.847407+0.028698 test-rmse:1.316984+0.140367
## [24] train-rmse:0.828133+0.027604 test-rmse:1.310644+0.153930
## [25] train-rmse:0.809182+0.025661 test-rmse:1.306433+0.154497
## [26] train-rmse:0.792577+0.028616 test-rmse:1.300162+0.151448
## [27] train-rmse:0.772908+0.026634 test-rmse:1.284401+0.145067
## [28] train-rmse:0.757319+0.027155 test-rmse:1.269234+0.141112
## [29] train-rmse:0.740218+0.025702 test-rmse:1.270554+0.140895
## [30] train-rmse:0.720394+0.028223 test-rmse:1.269978+0.138903
## [31] train-rmse:0.704498+0.022943 test-rmse:1.267446+0.138397
## [32] train-rmse:0.689469+0.022277 test-rmse:1.265059+0.141997
## [33] train-rmse:0.677168+0.023530 test-rmse:1.255489+0.141294
## [34] train-rmse:0.666797+0.022337 test-rmse:1.249747+0.140906
## [35] train-rmse:0.655807+0.024931 test-rmse:1.247855+0.145921
## [36] train-rmse:0.647365+0.023449 test-rmse:1.243280+0.148874
## [37] train-rmse:0.633999+0.017029 test-rmse:1.237652+0.145823
## [38] train-rmse:0.620643+0.015431 test-rmse:1.242379+0.146507
## [39] train-rmse:0.610444+0.017615 test-rmse:1.242158+0.147262
## [40] train-rmse:0.602587+0.016312 test-rmse:1.237630+0.147750
## [41] train-rmse:0.593402+0.017699 test-rmse:1.236490+0.148600
## [42] train-rmse:0.587272+0.017104 test-rmse:1.231577+0.149124
## [43] train-rmse:0.581708+0.019073 test-rmse:1.227436+0.146532
## [44] train-rmse:0.573424+0.019192 test-rmse:1.230135+0.143334
## [45] train-rmse:0.563845+0.018938 test-rmse:1.231399+0.144224
## [46] train-rmse:0.557575+0.020950 test-rmse:1.230321+0.139972
## [47] train-rmse:0.552332+0.022454 test-rmse:1.233036+0.142073
## [48] train-rmse:0.542271+0.023687 test-rmse:1.232400+0.146492
## [49] train-rmse:0.537445+0.023916 test-rmse:1.232966+0.146166
## Stopping. Best iteration:
## [43] train-rmse:0.581708+0.019073 test-rmse:1.227436+0.146532
##
## 95
## [1] train-rmse:6.416753+0.047601 test-rmse:6.422475+0.248417
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.356933+0.030383 test-rmse:4.393720+0.216575
## [3] train-rmse:3.082903+0.021967 test-rmse:3.166234+0.163385
## [4] train-rmse:2.293571+0.022165 test-rmse:2.412036+0.118219
## [5] train-rmse:1.832109+0.027485 test-rmse:2.040251+0.086813
## [6] train-rmse:1.552316+0.029424 test-rmse:1.811847+0.073453
## [7] train-rmse:1.378449+0.021259 test-rmse:1.697172+0.058305
## [8] train-rmse:1.244716+0.024975 test-rmse:1.595147+0.072231
## [9] train-rmse:1.155160+0.019890 test-rmse:1.533309+0.076934
## [10] train-rmse:1.091288+0.020013 test-rmse:1.505006+0.078592
## [11] train-rmse:1.028302+0.026662 test-rmse:1.466534+0.085274
## [12] train-rmse:0.980168+0.026076 test-rmse:1.443000+0.097739
## [13] train-rmse:0.932225+0.014453 test-rmse:1.397938+0.115395
## [14] train-rmse:0.895486+0.012023 test-rmse:1.386803+0.126557
## [15] train-rmse:0.867911+0.012149 test-rmse:1.368548+0.137932
## [16] train-rmse:0.835312+0.013194 test-rmse:1.355520+0.126164
## [17] train-rmse:0.803524+0.013846 test-rmse:1.343171+0.126294
## [18] train-rmse:0.772554+0.013131 test-rmse:1.337879+0.129521
## [19] train-rmse:0.741415+0.009367 test-rmse:1.322262+0.143489
## [20] train-rmse:0.719125+0.010359 test-rmse:1.312421+0.148695
## [21] train-rmse:0.698390+0.011438 test-rmse:1.299741+0.147386
## [22] train-rmse:0.677410+0.012625 test-rmse:1.292948+0.149926
## [23] train-rmse:0.657353+0.007241 test-rmse:1.287000+0.154689
## [24] train-rmse:0.640033+0.010675 test-rmse:1.281703+0.156088
## [25] train-rmse:0.626948+0.011989 test-rmse:1.283947+0.161295
## [26] train-rmse:0.605811+0.012610 test-rmse:1.277526+0.155009
## [27] train-rmse:0.590117+0.011519 test-rmse:1.274669+0.153442
## [28] train-rmse:0.573028+0.012854 test-rmse:1.267369+0.158254
## [29] train-rmse:0.558919+0.012869 test-rmse:1.269555+0.165116
## [30] train-rmse:0.546856+0.010987 test-rmse:1.267760+0.165794
## [31] train-rmse:0.537488+0.009296 test-rmse:1.269419+0.166131
## [32] train-rmse:0.525674+0.012954 test-rmse:1.262289+0.163933
## [33] train-rmse:0.515304+0.015239 test-rmse:1.262333+0.165965
## [34] train-rmse:0.504278+0.016352 test-rmse:1.264775+0.164630
## [35] train-rmse:0.493449+0.015699 test-rmse:1.263287+0.163094
## [36] train-rmse:0.483264+0.016275 test-rmse:1.265232+0.165294
## [37] train-rmse:0.475082+0.019939 test-rmse:1.264781+0.165720
## [38] train-rmse:0.468522+0.020844 test-rmse:1.262704+0.165133
## Stopping. Best iteration:
## [32] train-rmse:0.525674+0.012954 test-rmse:1.262289+0.163933
##
## 96
## [1] train-rmse:6.414868+0.048035 test-rmse:6.423305+0.237511
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.344137+0.031117 test-rmse:4.378870+0.180860
## [3] train-rmse:3.050740+0.027325 test-rmse:3.105413+0.137490
## [4] train-rmse:2.248678+0.021644 test-rmse:2.366016+0.120972
## [5] train-rmse:1.762645+0.023851 test-rmse:1.949884+0.114553
## [6] train-rmse:1.458133+0.036514 test-rmse:1.727042+0.094999
## [7] train-rmse:1.279336+0.033547 test-rmse:1.596814+0.106648
## [8] train-rmse:1.139996+0.037583 test-rmse:1.510264+0.105162
## [9] train-rmse:1.045145+0.033660 test-rmse:1.457050+0.098281
## [10] train-rmse:0.975263+0.036545 test-rmse:1.417109+0.117558
## [11] train-rmse:0.902948+0.037565 test-rmse:1.404794+0.111856
## [12] train-rmse:0.850820+0.036468 test-rmse:1.377751+0.130502
## [13] train-rmse:0.800647+0.028851 test-rmse:1.350714+0.138315
## [14] train-rmse:0.762494+0.021166 test-rmse:1.341424+0.142704
## [15] train-rmse:0.727051+0.016298 test-rmse:1.329044+0.145152
## [16] train-rmse:0.694061+0.018514 test-rmse:1.327655+0.155911
## [17] train-rmse:0.664413+0.016814 test-rmse:1.319150+0.158455
## [18] train-rmse:0.639406+0.017809 test-rmse:1.314528+0.159563
## [19] train-rmse:0.607836+0.017653 test-rmse:1.295273+0.145897
## [20] train-rmse:0.583973+0.014682 test-rmse:1.285633+0.158353
## [21] train-rmse:0.566113+0.015997 test-rmse:1.285646+0.165873
## [22] train-rmse:0.551582+0.015840 test-rmse:1.281950+0.163457
## [23] train-rmse:0.532452+0.018844 test-rmse:1.282031+0.166391
## [24] train-rmse:0.516139+0.015946 test-rmse:1.278274+0.172463
## [25] train-rmse:0.504129+0.015877 test-rmse:1.284931+0.173688
## [26] train-rmse:0.490981+0.017138 test-rmse:1.277442+0.173491
## [27] train-rmse:0.475265+0.017240 test-rmse:1.275360+0.171806
## [28] train-rmse:0.458932+0.013682 test-rmse:1.268635+0.167243
## [29] train-rmse:0.440476+0.015635 test-rmse:1.265193+0.168954
## [30] train-rmse:0.429983+0.014635 test-rmse:1.264423+0.169064
## [31] train-rmse:0.418837+0.011483 test-rmse:1.265768+0.173234
## [32] train-rmse:0.407810+0.011331 test-rmse:1.263711+0.172315
## [33] train-rmse:0.396112+0.012667 test-rmse:1.264396+0.173451
## [34] train-rmse:0.387678+0.012161 test-rmse:1.265671+0.172504
## [35] train-rmse:0.380543+0.012795 test-rmse:1.265050+0.171690
## [36] train-rmse:0.371390+0.015385 test-rmse:1.262710+0.171839
## [37] train-rmse:0.362030+0.016853 test-rmse:1.261501+0.168337
## [38] train-rmse:0.355331+0.016705 test-rmse:1.264214+0.168432
## [39] train-rmse:0.350227+0.017471 test-rmse:1.264585+0.171510
## [40] train-rmse:0.338456+0.015987 test-rmse:1.264666+0.171001
## [41] train-rmse:0.329024+0.014330 test-rmse:1.263264+0.171617
## [42] train-rmse:0.321583+0.015531 test-rmse:1.266153+0.170466
## [43] train-rmse:0.314382+0.017931 test-rmse:1.263703+0.171157
## Stopping. Best iteration:
## [37] train-rmse:0.362030+0.016853 test-rmse:1.261501+0.168337
##
## 97
## [1] train-rmse:6.414868+0.048035 test-rmse:6.423305+0.237511
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.338762+0.031247 test-rmse:4.379525+0.190165
## [3] train-rmse:3.029448+0.024649 test-rmse:3.087889+0.157817
## [4] train-rmse:2.207980+0.021990 test-rmse:2.345707+0.126658
## [5] train-rmse:1.709214+0.024857 test-rmse:1.906011+0.114451
## [6] train-rmse:1.386979+0.024268 test-rmse:1.676182+0.109694
## [7] train-rmse:1.168068+0.030566 test-rmse:1.504934+0.102949
## [8] train-rmse:1.037299+0.035503 test-rmse:1.423821+0.124140
## [9] train-rmse:0.925614+0.038156 test-rmse:1.367489+0.129133
## [10] train-rmse:0.844968+0.034398 test-rmse:1.323751+0.143605
## [11] train-rmse:0.781044+0.033273 test-rmse:1.318545+0.151023
## [12] train-rmse:0.730986+0.031284 test-rmse:1.305333+0.161446
## [13] train-rmse:0.688280+0.036522 test-rmse:1.298135+0.163611
## [14] train-rmse:0.646131+0.029268 test-rmse:1.272760+0.175685
## [15] train-rmse:0.612820+0.027794 test-rmse:1.257965+0.171232
## [16] train-rmse:0.582503+0.026760 test-rmse:1.251132+0.178147
## [17] train-rmse:0.555220+0.027109 test-rmse:1.246053+0.178945
## [18] train-rmse:0.526757+0.025058 test-rmse:1.245904+0.175114
## [19] train-rmse:0.504596+0.023443 test-rmse:1.243500+0.176710
## [20] train-rmse:0.485738+0.023037 test-rmse:1.247314+0.169924
## [21] train-rmse:0.466479+0.018644 test-rmse:1.242593+0.172990
## [22] train-rmse:0.447542+0.016682 test-rmse:1.235909+0.174529
## [23] train-rmse:0.433948+0.016404 test-rmse:1.231118+0.173653
## [24] train-rmse:0.414465+0.018600 test-rmse:1.233000+0.169711
## [25] train-rmse:0.400305+0.018672 test-rmse:1.231791+0.167503
## [26] train-rmse:0.388296+0.020406 test-rmse:1.231614+0.162860
## [27] train-rmse:0.378803+0.020303 test-rmse:1.230583+0.162428
## [28] train-rmse:0.365986+0.021394 test-rmse:1.231837+0.162535
## [29] train-rmse:0.346358+0.019532 test-rmse:1.229149+0.164907
## [30] train-rmse:0.335689+0.020020 test-rmse:1.234610+0.164779
## [31] train-rmse:0.325011+0.019809 test-rmse:1.234600+0.167640
## [32] train-rmse:0.314527+0.017749 test-rmse:1.233339+0.168308
## [33] train-rmse:0.301070+0.016603 test-rmse:1.233548+0.168847
## [34] train-rmse:0.290826+0.014908 test-rmse:1.232754+0.163670
## [35] train-rmse:0.284187+0.015335 test-rmse:1.231936+0.164240
## Stopping. Best iteration:
## [29] train-rmse:0.346358+0.019532 test-rmse:1.229149+0.164907
##
## 98
## [1] train-rmse:6.330877+0.041766 test-rmse:6.327024+0.257730
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.359408+0.022387 test-rmse:4.357663+0.238808
## [3] train-rmse:3.274038+0.014094 test-rmse:3.285592+0.191603
## [4] train-rmse:2.716604+0.012806 test-rmse:2.760620+0.116498
## [5] train-rmse:2.445408+0.015465 test-rmse:2.479918+0.074379
## [6] train-rmse:2.309065+0.018761 test-rmse:2.346120+0.051488
## [7] train-rmse:2.239462+0.019651 test-rmse:2.292285+0.049752
## [8] train-rmse:2.194949+0.020843 test-rmse:2.244640+0.067768
## [9] train-rmse:2.163363+0.021998 test-rmse:2.202155+0.069380
## [10] train-rmse:2.136868+0.023085 test-rmse:2.185617+0.076105
## [11] train-rmse:2.113088+0.024358 test-rmse:2.161036+0.075930
## [12] train-rmse:2.090673+0.025514 test-rmse:2.144703+0.085451
## [13] train-rmse:2.069518+0.026170 test-rmse:2.131825+0.086544
## [14] train-rmse:2.048664+0.026528 test-rmse:2.118028+0.086080
## [15] train-rmse:2.028399+0.026788 test-rmse:2.108109+0.094564
## [16] train-rmse:2.009183+0.027588 test-rmse:2.080041+0.097194
## [17] train-rmse:1.990738+0.028571 test-rmse:2.061726+0.095269
## [18] train-rmse:1.972631+0.029648 test-rmse:2.045993+0.087857
## [19] train-rmse:1.955399+0.031404 test-rmse:2.031839+0.094386
## [20] train-rmse:1.939524+0.032361 test-rmse:2.021687+0.103342
## [21] train-rmse:1.925337+0.032941 test-rmse:2.004457+0.106058
## [22] train-rmse:1.911481+0.032963 test-rmse:1.987843+0.106075
## [23] train-rmse:1.898021+0.033542 test-rmse:1.980248+0.097283
## [24] train-rmse:1.884270+0.033811 test-rmse:1.968984+0.096052
## [25] train-rmse:1.870876+0.034324 test-rmse:1.955166+0.095238
## [26] train-rmse:1.857891+0.035750 test-rmse:1.942843+0.084169
## [27] train-rmse:1.845735+0.036507 test-rmse:1.937111+0.083010
## [28] train-rmse:1.834038+0.037396 test-rmse:1.928498+0.087871
## [29] train-rmse:1.822496+0.038512 test-rmse:1.917949+0.093095
## [30] train-rmse:1.811511+0.039903 test-rmse:1.909018+0.088818
## [31] train-rmse:1.800983+0.040606 test-rmse:1.901191+0.091543
## [32] train-rmse:1.790519+0.041302 test-rmse:1.888686+0.093388
## [33] train-rmse:1.780534+0.041651 test-rmse:1.884368+0.093137
## [34] train-rmse:1.770454+0.041756 test-rmse:1.875545+0.090343
## [35] train-rmse:1.760323+0.042344 test-rmse:1.865466+0.090808
## [36] train-rmse:1.750583+0.042656 test-rmse:1.862770+0.090469
## [37] train-rmse:1.740759+0.043020 test-rmse:1.850715+0.091472
## [38] train-rmse:1.731194+0.043622 test-rmse:1.843192+0.099876
## [39] train-rmse:1.722217+0.043907 test-rmse:1.833403+0.105590
## [40] train-rmse:1.713196+0.044343 test-rmse:1.821577+0.103444
## [41] train-rmse:1.704674+0.044689 test-rmse:1.818422+0.095553
## [42] train-rmse:1.696745+0.045120 test-rmse:1.818266+0.106960
## [43] train-rmse:1.688820+0.045650 test-rmse:1.806219+0.107554
## [44] train-rmse:1.681176+0.046283 test-rmse:1.800789+0.105359
## [45] train-rmse:1.673807+0.046666 test-rmse:1.794409+0.109161
## [46] train-rmse:1.666510+0.047001 test-rmse:1.788160+0.112497
## [47] train-rmse:1.659374+0.047750 test-rmse:1.783341+0.114081
## [48] train-rmse:1.652669+0.048111 test-rmse:1.775339+0.115815
## [49] train-rmse:1.645961+0.048523 test-rmse:1.769521+0.115323
## [50] train-rmse:1.639227+0.048651 test-rmse:1.761829+0.110152
## [51] train-rmse:1.632541+0.048839 test-rmse:1.753511+0.114134
## [52] train-rmse:1.626200+0.048935 test-rmse:1.752735+0.103647
## [53] train-rmse:1.620074+0.049125 test-rmse:1.748229+0.100542
## [54] train-rmse:1.613921+0.049134 test-rmse:1.740601+0.103335
## [55] train-rmse:1.608084+0.049276 test-rmse:1.733902+0.106389
## [56] train-rmse:1.602191+0.049542 test-rmse:1.731468+0.107463
## [57] train-rmse:1.596318+0.049651 test-rmse:1.714228+0.116601
## [58] train-rmse:1.590671+0.049741 test-rmse:1.708600+0.120901
## [59] train-rmse:1.585228+0.050072 test-rmse:1.710407+0.123422
## [60] train-rmse:1.579915+0.050272 test-rmse:1.706491+0.123211
## [61] train-rmse:1.574616+0.050368 test-rmse:1.706049+0.126642
## [62] train-rmse:1.569580+0.050404 test-rmse:1.701800+0.131001
## [63] train-rmse:1.564583+0.050575 test-rmse:1.694604+0.130998
## [64] train-rmse:1.559657+0.050892 test-rmse:1.691214+0.132084
## [65] train-rmse:1.554966+0.051127 test-rmse:1.685125+0.131008
## [66] train-rmse:1.550351+0.051251 test-rmse:1.678827+0.129682
## [67] train-rmse:1.545802+0.051371 test-rmse:1.674847+0.130665
## [68] train-rmse:1.541028+0.051404 test-rmse:1.677937+0.131363
## [69] train-rmse:1.536335+0.051366 test-rmse:1.671203+0.135327
## [70] train-rmse:1.531838+0.051589 test-rmse:1.666603+0.137695
## [71] train-rmse:1.527540+0.051459 test-rmse:1.662754+0.135758
## [72] train-rmse:1.523377+0.051580 test-rmse:1.660200+0.137905
## [73] train-rmse:1.519376+0.051653 test-rmse:1.658983+0.142430
## [74] train-rmse:1.515542+0.051836 test-rmse:1.652408+0.141636
## [75] train-rmse:1.511707+0.052049 test-rmse:1.649596+0.141426
## [76] train-rmse:1.507897+0.052211 test-rmse:1.648144+0.140684
## [77] train-rmse:1.504197+0.052176 test-rmse:1.639264+0.141599
## [78] train-rmse:1.500616+0.052150 test-rmse:1.636449+0.142142
## [79] train-rmse:1.497085+0.052090 test-rmse:1.630390+0.149994
## [80] train-rmse:1.493713+0.052094 test-rmse:1.627203+0.144938
## [81] train-rmse:1.490360+0.052165 test-rmse:1.622447+0.145223
## [82] train-rmse:1.486980+0.052241 test-rmse:1.618020+0.148024
## [83] train-rmse:1.483729+0.052259 test-rmse:1.616628+0.150948
## [84] train-rmse:1.480514+0.052271 test-rmse:1.614049+0.151544
## [85] train-rmse:1.477398+0.052351 test-rmse:1.610751+0.153181
## [86] train-rmse:1.474278+0.052398 test-rmse:1.608306+0.152694
## [87] train-rmse:1.471230+0.052446 test-rmse:1.602804+0.151221
## [88] train-rmse:1.468432+0.052578 test-rmse:1.603036+0.151279
## [89] train-rmse:1.465658+0.052616 test-rmse:1.598148+0.150968
## [90] train-rmse:1.462885+0.052696 test-rmse:1.595031+0.153081
## [91] train-rmse:1.459949+0.053007 test-rmse:1.595125+0.158848
## [92] train-rmse:1.456983+0.053259 test-rmse:1.587680+0.159144
## [93] train-rmse:1.454153+0.053382 test-rmse:1.586858+0.162133
## [94] train-rmse:1.451495+0.053420 test-rmse:1.582146+0.163151
## [95] train-rmse:1.448882+0.053498 test-rmse:1.580855+0.162711
## [96] train-rmse:1.446334+0.053491 test-rmse:1.575244+0.163374
## [97] train-rmse:1.443838+0.053443 test-rmse:1.575073+0.160146
## [98] train-rmse:1.441407+0.053462 test-rmse:1.574360+0.161642
## [99] train-rmse:1.439061+0.053471 test-rmse:1.576353+0.161273
## [100] train-rmse:1.436730+0.053507 test-rmse:1.578945+0.159857
## [101] train-rmse:1.434454+0.053463 test-rmse:1.578632+0.159471
## [102] train-rmse:1.432212+0.053501 test-rmse:1.579969+0.162417
## [103] train-rmse:1.429873+0.053661 test-rmse:1.581030+0.162826
## [104] train-rmse:1.427618+0.053610 test-rmse:1.576862+0.160295
## Stopping. Best iteration:
## [98] train-rmse:1.441407+0.053462 test-rmse:1.574360+0.161642
##
## 99
## [1] train-rmse:6.276071+0.042367 test-rmse:6.272866+0.251159
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.250284+0.021582 test-rmse:4.266104+0.220915
## [3] train-rmse:3.095537+0.018704 test-rmse:3.117638+0.165033
## [4] train-rmse:2.472643+0.016620 test-rmse:2.521349+0.116253
## [5] train-rmse:2.140337+0.026063 test-rmse:2.231038+0.060121
## [6] train-rmse:1.965796+0.029484 test-rmse:2.081055+0.057990
## [7] train-rmse:1.865996+0.031854 test-rmse:2.010128+0.065442
## [8] train-rmse:1.800074+0.034569 test-rmse:1.964814+0.096221
## [9] train-rmse:1.744374+0.028696 test-rmse:1.916747+0.088044
## [10] train-rmse:1.700237+0.032617 test-rmse:1.893623+0.093586
## [11] train-rmse:1.661082+0.039244 test-rmse:1.850327+0.096514
## [12] train-rmse:1.629175+0.043818 test-rmse:1.815343+0.097670
## [13] train-rmse:1.597474+0.047060 test-rmse:1.788289+0.092071
## [14] train-rmse:1.565771+0.047755 test-rmse:1.760174+0.092682
## [15] train-rmse:1.529316+0.051069 test-rmse:1.725031+0.049384
## [16] train-rmse:1.497966+0.051645 test-rmse:1.704250+0.053100
## [17] train-rmse:1.460572+0.041532 test-rmse:1.670309+0.060213
## [18] train-rmse:1.433253+0.047234 test-rmse:1.660683+0.069720
## [19] train-rmse:1.404326+0.051488 test-rmse:1.629235+0.052818
## [20] train-rmse:1.382004+0.052898 test-rmse:1.617416+0.056227
## [21] train-rmse:1.362332+0.054482 test-rmse:1.597423+0.050159
## [22] train-rmse:1.339264+0.059866 test-rmse:1.586039+0.050081
## [23] train-rmse:1.315301+0.057476 test-rmse:1.563470+0.058836
## [24] train-rmse:1.296321+0.056430 test-rmse:1.542792+0.068761
## [25] train-rmse:1.279177+0.057851 test-rmse:1.533189+0.069604
## [26] train-rmse:1.256181+0.061721 test-rmse:1.521814+0.073359
## [27] train-rmse:1.240147+0.062503 test-rmse:1.508520+0.069586
## [28] train-rmse:1.222546+0.063707 test-rmse:1.491439+0.060091
## [29] train-rmse:1.207661+0.064259 test-rmse:1.474579+0.070119
## [30] train-rmse:1.192102+0.065099 test-rmse:1.461363+0.071284
## [31] train-rmse:1.178748+0.066252 test-rmse:1.453905+0.071553
## [32] train-rmse:1.165992+0.066666 test-rmse:1.447340+0.071193
## [33] train-rmse:1.150471+0.064749 test-rmse:1.447558+0.077833
## [34] train-rmse:1.138131+0.064112 test-rmse:1.433861+0.088758
## [35] train-rmse:1.126607+0.064706 test-rmse:1.433592+0.091555
## [36] train-rmse:1.116169+0.065368 test-rmse:1.427023+0.085648
## [37] train-rmse:1.104891+0.065318 test-rmse:1.423422+0.085861
## [38] train-rmse:1.094516+0.063320 test-rmse:1.415124+0.087750
## [39] train-rmse:1.085406+0.063875 test-rmse:1.403911+0.090039
## [40] train-rmse:1.077247+0.064211 test-rmse:1.391919+0.089787
## [41] train-rmse:1.067871+0.063958 test-rmse:1.388878+0.088587
## [42] train-rmse:1.059794+0.065552 test-rmse:1.381326+0.089031
## [43] train-rmse:1.047162+0.067870 test-rmse:1.365864+0.093251
## [44] train-rmse:1.039883+0.068264 test-rmse:1.364069+0.094289
## [45] train-rmse:1.030842+0.069126 test-rmse:1.353893+0.102864
## [46] train-rmse:1.022658+0.070163 test-rmse:1.350529+0.104140
## [47] train-rmse:1.015874+0.070307 test-rmse:1.346580+0.109506
## [48] train-rmse:1.006600+0.067537 test-rmse:1.338350+0.118164
## [49] train-rmse:0.998439+0.068182 test-rmse:1.337979+0.122726
## [50] train-rmse:0.987781+0.069194 test-rmse:1.328354+0.126135
## [51] train-rmse:0.969560+0.059696 test-rmse:1.306041+0.116397
## [52] train-rmse:0.956796+0.054236 test-rmse:1.301078+0.118714
## [53] train-rmse:0.947602+0.051730 test-rmse:1.294819+0.119841
## [54] train-rmse:0.941573+0.051232 test-rmse:1.290058+0.123238
## [55] train-rmse:0.936821+0.051589 test-rmse:1.283643+0.119559
## [56] train-rmse:0.930179+0.051151 test-rmse:1.280485+0.121334
## [57] train-rmse:0.923642+0.052199 test-rmse:1.279792+0.121302
## [58] train-rmse:0.917723+0.050185 test-rmse:1.277363+0.123751
## [59] train-rmse:0.912700+0.049019 test-rmse:1.275759+0.122039
## [60] train-rmse:0.906331+0.049327 test-rmse:1.271116+0.124628
## [61] train-rmse:0.901036+0.048717 test-rmse:1.272140+0.123581
## [62] train-rmse:0.894779+0.050654 test-rmse:1.268426+0.125805
## [63] train-rmse:0.890587+0.050487 test-rmse:1.267965+0.125824
## [64] train-rmse:0.885288+0.049580 test-rmse:1.264525+0.127099
## [65] train-rmse:0.881056+0.049230 test-rmse:1.262017+0.131320
## [66] train-rmse:0.876936+0.050039 test-rmse:1.259945+0.132150
## [67] train-rmse:0.871781+0.050586 test-rmse:1.256542+0.131742
## [68] train-rmse:0.868767+0.050409 test-rmse:1.255809+0.133530
## [69] train-rmse:0.865194+0.050334 test-rmse:1.252823+0.135130
## [70] train-rmse:0.860736+0.051681 test-rmse:1.252670+0.137861
## [71] train-rmse:0.857114+0.051945 test-rmse:1.251977+0.135909
## [72] train-rmse:0.854110+0.052001 test-rmse:1.252474+0.137000
## [73] train-rmse:0.849809+0.051729 test-rmse:1.251473+0.138295
## [74] train-rmse:0.841342+0.045510 test-rmse:1.235269+0.135732
## [75] train-rmse:0.837763+0.044943 test-rmse:1.237290+0.137075
## [76] train-rmse:0.835169+0.044924 test-rmse:1.233973+0.136460
## [77] train-rmse:0.831884+0.045867 test-rmse:1.233648+0.136647
## [78] train-rmse:0.827094+0.050160 test-rmse:1.231924+0.137891
## [79] train-rmse:0.824944+0.050203 test-rmse:1.231806+0.136587
## [80] train-rmse:0.821758+0.049796 test-rmse:1.229654+0.136157
## [81] train-rmse:0.816093+0.054040 test-rmse:1.229094+0.136456
## [82] train-rmse:0.812871+0.053407 test-rmse:1.231669+0.136250
## [83] train-rmse:0.809693+0.053422 test-rmse:1.232012+0.134817
## [84] train-rmse:0.807171+0.054296 test-rmse:1.236119+0.141852
## [85] train-rmse:0.805010+0.054231 test-rmse:1.235813+0.141407
## [86] train-rmse:0.800760+0.051971 test-rmse:1.235537+0.141989
## [87] train-rmse:0.798103+0.051788 test-rmse:1.235620+0.141972
## Stopping. Best iteration:
## [81] train-rmse:0.816093+0.054040 test-rmse:1.229094+0.136456
##
## 100
## [1] train-rmse:6.254594+0.044725 test-rmse:6.251754+0.242802
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.189278+0.029594 test-rmse:4.203469+0.213763
## [3] train-rmse:2.986030+0.022481 test-rmse:3.033444+0.151150
## [4] train-rmse:2.314234+0.015259 test-rmse:2.392299+0.106791
## [5] train-rmse:1.952184+0.016956 test-rmse:2.067876+0.068782
## [6] train-rmse:1.755157+0.012607 test-rmse:1.916046+0.052306
## [7] train-rmse:1.638985+0.014083 test-rmse:1.813965+0.068405
## [8] train-rmse:1.558923+0.015400 test-rmse:1.767849+0.055380
## [9] train-rmse:1.496276+0.015265 test-rmse:1.726654+0.042581
## [10] train-rmse:1.443779+0.019843 test-rmse:1.692825+0.041258
## [11] train-rmse:1.399052+0.026091 test-rmse:1.659075+0.044873
## [12] train-rmse:1.352620+0.027597 test-rmse:1.631348+0.060257
## [13] train-rmse:1.315243+0.029810 test-rmse:1.606638+0.056245
## [14] train-rmse:1.279252+0.031256 test-rmse:1.573980+0.061781
## [15] train-rmse:1.245455+0.032815 test-rmse:1.545983+0.072136
## [16] train-rmse:1.208392+0.035452 test-rmse:1.516615+0.075992
## [17] train-rmse:1.178677+0.038459 test-rmse:1.495373+0.080891
## [18] train-rmse:1.138561+0.034774 test-rmse:1.477516+0.083380
## [19] train-rmse:1.111518+0.031961 test-rmse:1.463402+0.098660
## [20] train-rmse:1.086315+0.033636 test-rmse:1.435110+0.089161
## [21] train-rmse:1.063115+0.031847 test-rmse:1.424243+0.095165
## [22] train-rmse:1.044470+0.032747 test-rmse:1.415902+0.094357
## [23] train-rmse:1.022719+0.031219 test-rmse:1.394279+0.095742
## [24] train-rmse:1.004615+0.029047 test-rmse:1.376036+0.092903
## [25] train-rmse:0.988288+0.029219 test-rmse:1.367184+0.106417
## [26] train-rmse:0.956701+0.023351 test-rmse:1.354674+0.111869
## [27] train-rmse:0.940895+0.022017 test-rmse:1.343939+0.115426
## [28] train-rmse:0.926194+0.020657 test-rmse:1.330908+0.121295
## [29] train-rmse:0.905755+0.023801 test-rmse:1.325793+0.124628
## [30] train-rmse:0.889913+0.028093 test-rmse:1.322052+0.133033
## [31] train-rmse:0.878199+0.027632 test-rmse:1.315688+0.128651
## [32] train-rmse:0.863573+0.028271 test-rmse:1.309803+0.136359
## [33] train-rmse:0.851598+0.028430 test-rmse:1.304092+0.134830
## [34] train-rmse:0.839881+0.025307 test-rmse:1.302833+0.139793
## [35] train-rmse:0.828561+0.023556 test-rmse:1.288108+0.142848
## [36] train-rmse:0.819277+0.023073 test-rmse:1.285515+0.144447
## [37] train-rmse:0.807910+0.021839 test-rmse:1.283456+0.145115
## [38] train-rmse:0.794061+0.025701 test-rmse:1.280745+0.143772
## [39] train-rmse:0.786645+0.026115 test-rmse:1.276308+0.145106
## [40] train-rmse:0.775780+0.021674 test-rmse:1.275925+0.145737
## [41] train-rmse:0.767654+0.020218 test-rmse:1.272970+0.147374
## [42] train-rmse:0.753581+0.022208 test-rmse:1.271604+0.148203
## [43] train-rmse:0.742909+0.024449 test-rmse:1.270355+0.150540
## [44] train-rmse:0.731776+0.026165 test-rmse:1.261406+0.141739
## [45] train-rmse:0.721456+0.024175 test-rmse:1.260897+0.143575
## [46] train-rmse:0.715637+0.023712 test-rmse:1.254118+0.147904
## [47] train-rmse:0.710043+0.024156 test-rmse:1.252045+0.147507
## [48] train-rmse:0.703688+0.023143 test-rmse:1.248488+0.148974
## [49] train-rmse:0.698084+0.024820 test-rmse:1.247112+0.146968
## [50] train-rmse:0.689605+0.022790 test-rmse:1.246283+0.147699
## [51] train-rmse:0.683911+0.022283 test-rmse:1.244875+0.150105
## [52] train-rmse:0.675498+0.023354 test-rmse:1.241282+0.149367
## [53] train-rmse:0.668627+0.027372 test-rmse:1.238421+0.147462
## [54] train-rmse:0.662418+0.027462 test-rmse:1.234303+0.147149
## [55] train-rmse:0.655940+0.027545 test-rmse:1.234653+0.144756
## [56] train-rmse:0.650256+0.027056 test-rmse:1.230477+0.141043
## [57] train-rmse:0.644913+0.026354 test-rmse:1.229435+0.142643
## [58] train-rmse:0.640972+0.025750 test-rmse:1.229166+0.142553
## [59] train-rmse:0.634113+0.025073 test-rmse:1.228184+0.145220
## [60] train-rmse:0.630351+0.024631 test-rmse:1.228007+0.145114
## [61] train-rmse:0.625254+0.025425 test-rmse:1.228053+0.153411
## [62] train-rmse:0.621083+0.024946 test-rmse:1.226540+0.151405
## [63] train-rmse:0.616598+0.025397 test-rmse:1.225821+0.151503
## [64] train-rmse:0.613089+0.024647 test-rmse:1.225644+0.153205
## [65] train-rmse:0.606824+0.025747 test-rmse:1.221804+0.154192
## [66] train-rmse:0.601944+0.024466 test-rmse:1.222545+0.155189
## [67] train-rmse:0.598802+0.024004 test-rmse:1.221928+0.156349
## [68] train-rmse:0.593756+0.023843 test-rmse:1.220428+0.155864
## [69] train-rmse:0.589675+0.023881 test-rmse:1.219732+0.155679
## [70] train-rmse:0.587308+0.023388 test-rmse:1.218093+0.154702
## [71] train-rmse:0.582408+0.024147 test-rmse:1.219247+0.153536
## [72] train-rmse:0.576566+0.024772 test-rmse:1.221894+0.154836
## [73] train-rmse:0.572243+0.025094 test-rmse:1.222362+0.155808
## [74] train-rmse:0.569333+0.025977 test-rmse:1.220684+0.154493
## [75] train-rmse:0.565437+0.027257 test-rmse:1.218220+0.152311
## [76] train-rmse:0.563376+0.026965 test-rmse:1.218042+0.150677
## [77] train-rmse:0.560653+0.026456 test-rmse:1.216864+0.150929
## [78] train-rmse:0.557707+0.025882 test-rmse:1.217688+0.149633
## [79] train-rmse:0.554807+0.025686 test-rmse:1.219346+0.151449
## [80] train-rmse:0.553052+0.025721 test-rmse:1.220333+0.150486
## [81] train-rmse:0.550416+0.025272 test-rmse:1.220906+0.149650
## [82] train-rmse:0.545985+0.025270 test-rmse:1.221487+0.148480
## [83] train-rmse:0.543713+0.025449 test-rmse:1.220949+0.150168
## Stopping. Best iteration:
## [77] train-rmse:0.560653+0.026456 test-rmse:1.216864+0.150929
##
## 101
## [1] train-rmse:6.245400+0.045549 test-rmse:6.244026+0.245181
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.165232+0.027180 test-rmse:4.202703+0.206435
## [3] train-rmse:2.940302+0.018664 test-rmse:2.982529+0.148950
## [4] train-rmse:2.230944+0.011088 test-rmse:2.327113+0.099550
## [5] train-rmse:1.839933+0.014509 test-rmse:1.997955+0.082703
## [6] train-rmse:1.612614+0.023991 test-rmse:1.806045+0.052894
## [7] train-rmse:1.477429+0.030919 test-rmse:1.711406+0.053652
## [8] train-rmse:1.379970+0.026247 test-rmse:1.647775+0.051668
## [9] train-rmse:1.297225+0.027293 test-rmse:1.586217+0.067269
## [10] train-rmse:1.244422+0.029365 test-rmse:1.554705+0.060527
## [11] train-rmse:1.194832+0.025430 test-rmse:1.509301+0.081236
## [12] train-rmse:1.140956+0.019359 test-rmse:1.490767+0.102242
## [13] train-rmse:1.092540+0.030520 test-rmse:1.455245+0.087253
## [14] train-rmse:1.041789+0.039718 test-rmse:1.427539+0.084463
## [15] train-rmse:1.007252+0.032234 test-rmse:1.406844+0.103710
## [16] train-rmse:0.975437+0.030490 test-rmse:1.391473+0.107836
## [17] train-rmse:0.946949+0.028679 test-rmse:1.376867+0.115054
## [18] train-rmse:0.918058+0.029939 test-rmse:1.363009+0.113715
## [19] train-rmse:0.889262+0.029204 test-rmse:1.351696+0.115057
## [20] train-rmse:0.866251+0.031833 test-rmse:1.336903+0.129833
## [21] train-rmse:0.843815+0.030336 test-rmse:1.329493+0.133362
## [22] train-rmse:0.820057+0.026848 test-rmse:1.323566+0.134621
## [23] train-rmse:0.798159+0.028075 test-rmse:1.311058+0.137005
## [24] train-rmse:0.777175+0.027596 test-rmse:1.307119+0.138768
## [25] train-rmse:0.758360+0.026279 test-rmse:1.297781+0.142772
## [26] train-rmse:0.743144+0.024995 test-rmse:1.290573+0.153216
## [27] train-rmse:0.724540+0.022282 test-rmse:1.278027+0.155468
## [28] train-rmse:0.706758+0.019508 test-rmse:1.261557+0.147943
## [29] train-rmse:0.692812+0.019566 test-rmse:1.265241+0.153937
## [30] train-rmse:0.679941+0.018879 test-rmse:1.261681+0.151742
## [31] train-rmse:0.663101+0.021795 test-rmse:1.253642+0.150655
## [32] train-rmse:0.649074+0.021043 test-rmse:1.252811+0.150587
## [33] train-rmse:0.638506+0.021289 test-rmse:1.252293+0.150723
## [34] train-rmse:0.625707+0.017038 test-rmse:1.244834+0.153455
## [35] train-rmse:0.615098+0.015920 test-rmse:1.240755+0.158167
## [36] train-rmse:0.604439+0.017543 test-rmse:1.241814+0.164496
## [37] train-rmse:0.594870+0.019014 test-rmse:1.240393+0.163558
## [38] train-rmse:0.586618+0.016820 test-rmse:1.233217+0.165820
## [39] train-rmse:0.576147+0.016704 test-rmse:1.231088+0.160816
## [40] train-rmse:0.565756+0.014872 test-rmse:1.235731+0.164780
## [41] train-rmse:0.559702+0.015488 test-rmse:1.236699+0.163744
## [42] train-rmse:0.550638+0.018233 test-rmse:1.236851+0.162783
## [43] train-rmse:0.541076+0.018460 test-rmse:1.230582+0.165044
## [44] train-rmse:0.532423+0.015759 test-rmse:1.229004+0.165346
## [45] train-rmse:0.523484+0.014924 test-rmse:1.231886+0.164404
## [46] train-rmse:0.516518+0.017040 test-rmse:1.232491+0.163032
## [47] train-rmse:0.510777+0.016072 test-rmse:1.231435+0.163893
## [48] train-rmse:0.505418+0.015659 test-rmse:1.232373+0.167259
## [49] train-rmse:0.499785+0.015959 test-rmse:1.234635+0.170322
## [50] train-rmse:0.495609+0.016633 test-rmse:1.234528+0.170942
## Stopping. Best iteration:
## [44] train-rmse:0.532423+0.015759 test-rmse:1.229004+0.165346
##
## 102
## [1] train-rmse:6.237445+0.046305 test-rmse:6.244130+0.246578
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.138109+0.028326 test-rmse:4.173827+0.205866
## [3] train-rmse:2.887988+0.021037 test-rmse:2.968399+0.159713
## [4] train-rmse:2.141865+0.024751 test-rmse:2.287038+0.097713
## [5] train-rmse:1.725532+0.033067 test-rmse:1.919095+0.081702
## [6] train-rmse:1.476462+0.031004 test-rmse:1.727829+0.069890
## [7] train-rmse:1.323456+0.031322 test-rmse:1.633261+0.053368
## [8] train-rmse:1.222208+0.031911 test-rmse:1.575885+0.041202
## [9] train-rmse:1.136187+0.021931 test-rmse:1.502840+0.070622
## [10] train-rmse:1.068118+0.021562 test-rmse:1.476307+0.077280
## [11] train-rmse:1.002624+0.025865 test-rmse:1.448244+0.098845
## [12] train-rmse:0.955785+0.016786 test-rmse:1.418851+0.114776
## [13] train-rmse:0.912943+0.012143 test-rmse:1.383110+0.121747
## [14] train-rmse:0.875319+0.012146 test-rmse:1.363066+0.129887
## [15] train-rmse:0.839418+0.007814 test-rmse:1.356327+0.130582
## [16] train-rmse:0.808989+0.007986 test-rmse:1.349380+0.128089
## [17] train-rmse:0.781587+0.007788 test-rmse:1.349416+0.133965
## [18] train-rmse:0.750057+0.006155 test-rmse:1.342121+0.134784
## [19] train-rmse:0.725458+0.006368 test-rmse:1.337414+0.139638
## [20] train-rmse:0.700198+0.006200 test-rmse:1.322958+0.150391
## [21] train-rmse:0.673534+0.009149 test-rmse:1.304626+0.148273
## [22] train-rmse:0.650815+0.009741 test-rmse:1.299451+0.148465
## [23] train-rmse:0.631041+0.006913 test-rmse:1.299440+0.153202
## [24] train-rmse:0.613966+0.008412 test-rmse:1.297276+0.156639
## [25] train-rmse:0.598670+0.009213 test-rmse:1.297628+0.158165
## [26] train-rmse:0.585233+0.009012 test-rmse:1.294932+0.158311
## [27] train-rmse:0.569523+0.010319 test-rmse:1.287220+0.153288
## [28] train-rmse:0.558243+0.010296 test-rmse:1.286458+0.155320
## [29] train-rmse:0.546664+0.010817 test-rmse:1.280773+0.153163
## [30] train-rmse:0.530824+0.011851 test-rmse:1.284938+0.155062
## [31] train-rmse:0.520131+0.012358 test-rmse:1.283581+0.157916
## [32] train-rmse:0.509725+0.012876 test-rmse:1.282920+0.160755
## [33] train-rmse:0.501213+0.014166 test-rmse:1.279348+0.159484
## [34] train-rmse:0.492414+0.014648 test-rmse:1.281742+0.158110
## [35] train-rmse:0.483189+0.013093 test-rmse:1.284302+0.159134
## [36] train-rmse:0.473718+0.016377 test-rmse:1.282727+0.161823
## [37] train-rmse:0.458327+0.016878 test-rmse:1.280679+0.165081
## [38] train-rmse:0.449659+0.013766 test-rmse:1.281906+0.170063
## [39] train-rmse:0.440889+0.010784 test-rmse:1.277126+0.169061
## [40] train-rmse:0.435009+0.011607 test-rmse:1.277292+0.170765
## [41] train-rmse:0.428150+0.012270 test-rmse:1.279522+0.170773
## [42] train-rmse:0.421661+0.014695 test-rmse:1.277832+0.174401
## [43] train-rmse:0.414319+0.011857 test-rmse:1.279936+0.174122
## [44] train-rmse:0.407806+0.013583 test-rmse:1.276480+0.173451
## [45] train-rmse:0.399846+0.017002 test-rmse:1.272067+0.176811
## [46] train-rmse:0.391961+0.018434 test-rmse:1.268868+0.174714
## [47] train-rmse:0.385918+0.015927 test-rmse:1.273056+0.171996
## [48] train-rmse:0.379154+0.016257 test-rmse:1.272931+0.172315
## [49] train-rmse:0.374155+0.015501 test-rmse:1.272344+0.174210
## [50] train-rmse:0.369140+0.014807 test-rmse:1.273003+0.175038
## [51] train-rmse:0.363827+0.014706 test-rmse:1.270636+0.173161
## [52] train-rmse:0.360387+0.016291 test-rmse:1.269948+0.173492
## Stopping. Best iteration:
## [46] train-rmse:0.391961+0.018434 test-rmse:1.268868+0.174714
##
## 103
## [1] train-rmse:6.235359+0.046784 test-rmse:6.244938+0.235175
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.123226+0.029101 test-rmse:4.166322+0.169124
## [3] train-rmse:2.851707+0.030892 test-rmse:2.904482+0.121992
## [4] train-rmse:2.088213+0.020728 test-rmse:2.222424+0.124241
## [5] train-rmse:1.645565+0.026323 test-rmse:1.870605+0.094857
## [6] train-rmse:1.368562+0.050405 test-rmse:1.661236+0.100839
## [7] train-rmse:1.201710+0.049809 test-rmse:1.542663+0.102074
## [8] train-rmse:1.081362+0.053257 test-rmse:1.481630+0.101109
## [9] train-rmse:1.001452+0.043850 test-rmse:1.423358+0.109831
## [10] train-rmse:0.934188+0.038958 test-rmse:1.409961+0.116253
## [11] train-rmse:0.873156+0.039013 test-rmse:1.377120+0.137568
## [12] train-rmse:0.831153+0.039586 test-rmse:1.370611+0.137687
## [13] train-rmse:0.787759+0.040897 test-rmse:1.353172+0.141868
## [14] train-rmse:0.743817+0.036862 test-rmse:1.335799+0.155205
## [15] train-rmse:0.708406+0.032420 test-rmse:1.327290+0.156667
## [16] train-rmse:0.672244+0.025986 test-rmse:1.317059+0.163400
## [17] train-rmse:0.648674+0.022532 test-rmse:1.306540+0.171596
## [18] train-rmse:0.623165+0.022402 test-rmse:1.297503+0.172473
## [19] train-rmse:0.596337+0.021500 test-rmse:1.282506+0.175281
## [20] train-rmse:0.574362+0.023910 test-rmse:1.276192+0.173561
## [21] train-rmse:0.559626+0.021920 test-rmse:1.274558+0.174857
## [22] train-rmse:0.539939+0.020009 test-rmse:1.274265+0.178372
## [23] train-rmse:0.525106+0.019942 test-rmse:1.272590+0.179424
## [24] train-rmse:0.501803+0.023855 test-rmse:1.270043+0.180038
## [25] train-rmse:0.488445+0.023520 test-rmse:1.266854+0.181419
## [26] train-rmse:0.473222+0.022080 test-rmse:1.260882+0.182998
## [27] train-rmse:0.456397+0.021983 test-rmse:1.259368+0.184346
## [28] train-rmse:0.445176+0.019656 test-rmse:1.261996+0.187207
## [29] train-rmse:0.429939+0.018313 test-rmse:1.259502+0.184242
## [30] train-rmse:0.414666+0.020590 test-rmse:1.257488+0.181533
## [31] train-rmse:0.402707+0.018937 test-rmse:1.255123+0.182927
## [32] train-rmse:0.389636+0.017999 test-rmse:1.255896+0.182743
## [33] train-rmse:0.381930+0.020135 test-rmse:1.252715+0.184816
## [34] train-rmse:0.368799+0.022347 test-rmse:1.253025+0.185177
## [35] train-rmse:0.359795+0.020134 test-rmse:1.253557+0.185717
## [36] train-rmse:0.351661+0.019163 test-rmse:1.253791+0.187206
## [37] train-rmse:0.342109+0.019802 test-rmse:1.251440+0.186029
## [38] train-rmse:0.332687+0.021586 test-rmse:1.251748+0.186596
## [39] train-rmse:0.319674+0.023358 test-rmse:1.252269+0.185178
## [40] train-rmse:0.312459+0.023648 test-rmse:1.250070+0.185737
## [41] train-rmse:0.305738+0.023308 test-rmse:1.251046+0.186446
## [42] train-rmse:0.299090+0.024048 test-rmse:1.254712+0.191739
## [43] train-rmse:0.291069+0.021313 test-rmse:1.254748+0.192352
## [44] train-rmse:0.285891+0.021859 test-rmse:1.254770+0.191364
## [45] train-rmse:0.278613+0.023647 test-rmse:1.254545+0.191431
## [46] train-rmse:0.273439+0.025340 test-rmse:1.255502+0.191242
## Stopping. Best iteration:
## [40] train-rmse:0.312459+0.023648 test-rmse:1.250070+0.185737
##
## 104
## [1] train-rmse:6.235359+0.046784 test-rmse:6.244938+0.235175
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.116959+0.029453 test-rmse:4.169600+0.178266
## [3] train-rmse:2.830137+0.029311 test-rmse:2.892826+0.134992
## [4] train-rmse:2.050791+0.016648 test-rmse:2.196506+0.113346
## [5] train-rmse:1.583995+0.022822 test-rmse:1.814232+0.095177
## [6] train-rmse:1.274856+0.040582 test-rmse:1.590838+0.090471
## [7] train-rmse:1.093339+0.030326 test-rmse:1.483766+0.109015
## [8] train-rmse:0.972954+0.038641 test-rmse:1.411844+0.117315
## [9] train-rmse:0.886778+0.040774 test-rmse:1.379578+0.134563
## [10] train-rmse:0.809347+0.029255 test-rmse:1.350338+0.151241
## [11] train-rmse:0.747659+0.028766 test-rmse:1.316736+0.154367
## [12] train-rmse:0.695636+0.029887 test-rmse:1.302183+0.165402
## [13] train-rmse:0.650889+0.022727 test-rmse:1.293255+0.179231
## [14] train-rmse:0.617566+0.022424 test-rmse:1.278844+0.179639
## [15] train-rmse:0.586778+0.025997 test-rmse:1.274716+0.183688
## [16] train-rmse:0.557824+0.027031 test-rmse:1.266768+0.195737
## [17] train-rmse:0.529107+0.024908 test-rmse:1.263903+0.197164
## [18] train-rmse:0.504506+0.021464 test-rmse:1.264412+0.195208
## [19] train-rmse:0.482894+0.017647 test-rmse:1.256060+0.195925
## [20] train-rmse:0.464240+0.018862 test-rmse:1.256491+0.192190
## [21] train-rmse:0.443966+0.015088 test-rmse:1.252654+0.190153
## [22] train-rmse:0.424711+0.015057 test-rmse:1.247282+0.189446
## [23] train-rmse:0.409549+0.017349 test-rmse:1.242799+0.188315
## [24] train-rmse:0.391039+0.015923 test-rmse:1.243864+0.186803
## [25] train-rmse:0.376385+0.018077 test-rmse:1.245498+0.186129
## [26] train-rmse:0.362947+0.017006 test-rmse:1.247787+0.187221
## [27] train-rmse:0.350891+0.017437 test-rmse:1.245135+0.186714
## [28] train-rmse:0.340528+0.018741 test-rmse:1.246491+0.186889
## [29] train-rmse:0.330790+0.017857 test-rmse:1.246985+0.189932
## Stopping. Best iteration:
## [23] train-rmse:0.409549+0.017349 test-rmse:1.242799+0.188315
##
## 105
## [1] train-rmse:6.158859+0.040200 test-rmse:6.155096+0.255945
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.168201+0.020889 test-rmse:4.167875+0.232667
## [3] train-rmse:3.128158+0.013738 test-rmse:3.142591+0.180757
## [4] train-rmse:2.624027+0.013133 test-rmse:2.673245+0.102762
## [5] train-rmse:2.390836+0.015718 test-rmse:2.428532+0.065179
## [6] train-rmse:2.274800+0.019556 test-rmse:2.313955+0.052568
## [7] train-rmse:2.216269+0.020510 test-rmse:2.271618+0.053983
## [8] train-rmse:2.176536+0.021804 test-rmse:2.217928+0.079562
## [9] train-rmse:2.146792+0.022834 test-rmse:2.192276+0.074866
## [10] train-rmse:2.120187+0.024076 test-rmse:2.173389+0.076443
## [11] train-rmse:2.096294+0.025360 test-rmse:2.155379+0.077077
## [12] train-rmse:2.073747+0.026697 test-rmse:2.135454+0.089983
## [13] train-rmse:2.052892+0.027201 test-rmse:2.120411+0.094986
## [14] train-rmse:2.032259+0.028312 test-rmse:2.109695+0.094022
## [15] train-rmse:2.012173+0.029179 test-rmse:2.083563+0.096844
## [16] train-rmse:1.992077+0.029934 test-rmse:2.068811+0.088203
## [17] train-rmse:1.972994+0.030070 test-rmse:2.042754+0.080370
## [18] train-rmse:1.953923+0.031099 test-rmse:2.029691+0.088826
## [19] train-rmse:1.936680+0.032576 test-rmse:2.014537+0.097154
## [20] train-rmse:1.920693+0.033211 test-rmse:2.005284+0.106707
## [21] train-rmse:1.906445+0.033639 test-rmse:1.996157+0.104312
## [22] train-rmse:1.892315+0.033970 test-rmse:1.980946+0.106001
## [23] train-rmse:1.878299+0.034674 test-rmse:1.961558+0.097834
## [24] train-rmse:1.864693+0.034955 test-rmse:1.951211+0.091505
## [25] train-rmse:1.851127+0.035657 test-rmse:1.939161+0.093727
## [26] train-rmse:1.838353+0.036722 test-rmse:1.926289+0.084792
## [27] train-rmse:1.825927+0.037787 test-rmse:1.920812+0.085027
## [28] train-rmse:1.813794+0.038821 test-rmse:1.912635+0.091099
## [29] train-rmse:1.801926+0.040023 test-rmse:1.902258+0.090049
## [30] train-rmse:1.790474+0.041331 test-rmse:1.890452+0.096492
## [31] train-rmse:1.779867+0.042225 test-rmse:1.879993+0.095934
## [32] train-rmse:1.769302+0.042913 test-rmse:1.870246+0.097502
## [33] train-rmse:1.759245+0.043309 test-rmse:1.858835+0.097232
## [34] train-rmse:1.749218+0.043636 test-rmse:1.847732+0.096247
## [35] train-rmse:1.739026+0.044051 test-rmse:1.853407+0.094753
## [36] train-rmse:1.729015+0.044189 test-rmse:1.841667+0.101882
## [37] train-rmse:1.719483+0.044402 test-rmse:1.831986+0.103060
## [38] train-rmse:1.709869+0.044670 test-rmse:1.823104+0.106223
## [39] train-rmse:1.700657+0.045020 test-rmse:1.817980+0.107083
## [40] train-rmse:1.692082+0.045434 test-rmse:1.809879+0.101400
## [41] train-rmse:1.683689+0.045891 test-rmse:1.804679+0.102945
## [42] train-rmse:1.675686+0.046376 test-rmse:1.800841+0.103344
## [43] train-rmse:1.667871+0.046913 test-rmse:1.786979+0.100869
## [44] train-rmse:1.660524+0.047493 test-rmse:1.780459+0.103867
## [45] train-rmse:1.653333+0.048023 test-rmse:1.776085+0.107751
## [46] train-rmse:1.646130+0.048268 test-rmse:1.774495+0.108123
## [47] train-rmse:1.639046+0.048650 test-rmse:1.769875+0.118765
## [48] train-rmse:1.632485+0.048907 test-rmse:1.765236+0.116707
## [49] train-rmse:1.625677+0.049335 test-rmse:1.762207+0.115742
## [50] train-rmse:1.619160+0.049523 test-rmse:1.752121+0.115197
## [51] train-rmse:1.612486+0.049516 test-rmse:1.741137+0.116328
## [52] train-rmse:1.606132+0.049675 test-rmse:1.732434+0.116303
## [53] train-rmse:1.599773+0.049800 test-rmse:1.711816+0.120162
## [54] train-rmse:1.593777+0.049965 test-rmse:1.711838+0.119577
## [55] train-rmse:1.587862+0.050038 test-rmse:1.712981+0.121825
## [56] train-rmse:1.582045+0.050092 test-rmse:1.705749+0.121375
## [57] train-rmse:1.576364+0.050247 test-rmse:1.704093+0.120938
## [58] train-rmse:1.570901+0.050389 test-rmse:1.696375+0.123855
## [59] train-rmse:1.565479+0.050654 test-rmse:1.690497+0.131577
## [60] train-rmse:1.560282+0.051017 test-rmse:1.690368+0.128669
## [61] train-rmse:1.555257+0.051247 test-rmse:1.691022+0.130648
## [62] train-rmse:1.550283+0.051365 test-rmse:1.685351+0.129362
## [63] train-rmse:1.545348+0.051512 test-rmse:1.680145+0.127923
## [64] train-rmse:1.540304+0.051628 test-rmse:1.674873+0.135075
## [65] train-rmse:1.535599+0.051738 test-rmse:1.672419+0.135499
## [66] train-rmse:1.530932+0.051801 test-rmse:1.669389+0.137057
## [67] train-rmse:1.526086+0.051818 test-rmse:1.663932+0.140840
## [68] train-rmse:1.521429+0.051959 test-rmse:1.657116+0.142762
## [69] train-rmse:1.517177+0.051880 test-rmse:1.653048+0.140548
## [70] train-rmse:1.512883+0.051821 test-rmse:1.650276+0.142923
## [71] train-rmse:1.508929+0.051768 test-rmse:1.643868+0.142674
## [72] train-rmse:1.505121+0.051770 test-rmse:1.639637+0.148079
## [73] train-rmse:1.501379+0.051974 test-rmse:1.638980+0.149552
## [74] train-rmse:1.497717+0.052110 test-rmse:1.632473+0.149465
## [75] train-rmse:1.494053+0.052303 test-rmse:1.634695+0.147643
## [76] train-rmse:1.490377+0.052421 test-rmse:1.630995+0.148828
## [77] train-rmse:1.486697+0.052348 test-rmse:1.627949+0.151309
## [78] train-rmse:1.483164+0.052411 test-rmse:1.613740+0.148841
## [79] train-rmse:1.479722+0.052482 test-rmse:1.610131+0.149181
## [80] train-rmse:1.476445+0.052540 test-rmse:1.602870+0.157457
## [81] train-rmse:1.473177+0.052569 test-rmse:1.601507+0.154598
## [82] train-rmse:1.469856+0.052692 test-rmse:1.602021+0.152943
## [83] train-rmse:1.466768+0.052718 test-rmse:1.601419+0.157319
## [84] train-rmse:1.463759+0.052681 test-rmse:1.599342+0.155990
## [85] train-rmse:1.460841+0.052717 test-rmse:1.598791+0.155259
## [86] train-rmse:1.457899+0.052759 test-rmse:1.595659+0.154900
## [87] train-rmse:1.455074+0.052894 test-rmse:1.592274+0.154414
## [88] train-rmse:1.452357+0.053039 test-rmse:1.590527+0.152688
## [89] train-rmse:1.449698+0.053138 test-rmse:1.585157+0.152423
## [90] train-rmse:1.446883+0.053237 test-rmse:1.584579+0.155061
## [91] train-rmse:1.443957+0.053508 test-rmse:1.580020+0.156822
## [92] train-rmse:1.441366+0.053645 test-rmse:1.577833+0.159943
## [93] train-rmse:1.438850+0.053694 test-rmse:1.574180+0.157830
## [94] train-rmse:1.436330+0.053783 test-rmse:1.571215+0.159034
## [95] train-rmse:1.433800+0.053891 test-rmse:1.572229+0.165382
## [96] train-rmse:1.431473+0.053903 test-rmse:1.572276+0.161544
## [97] train-rmse:1.429213+0.053909 test-rmse:1.572117+0.160643
## [98] train-rmse:1.426970+0.053916 test-rmse:1.568321+0.162575
## [99] train-rmse:1.424759+0.053822 test-rmse:1.565867+0.164548
## [100] train-rmse:1.422561+0.053769 test-rmse:1.569041+0.159698
## [101] train-rmse:1.420410+0.053677 test-rmse:1.565490+0.161507
## [102] train-rmse:1.418307+0.053671 test-rmse:1.563662+0.163582
## [103] train-rmse:1.416284+0.053624 test-rmse:1.561975+0.164013
## [104] train-rmse:1.414184+0.053534 test-rmse:1.563529+0.166815
## [105] train-rmse:1.412123+0.053432 test-rmse:1.559176+0.166172
## [106] train-rmse:1.410090+0.053329 test-rmse:1.558789+0.167750
## [107] train-rmse:1.408082+0.053327 test-rmse:1.554819+0.169043
## [108] train-rmse:1.406119+0.053304 test-rmse:1.550444+0.173842
## [109] train-rmse:1.404225+0.053322 test-rmse:1.544761+0.169577
## [110] train-rmse:1.402368+0.053355 test-rmse:1.543259+0.169300
## [111] train-rmse:1.400495+0.053376 test-rmse:1.540416+0.170089
## [112] train-rmse:1.398722+0.053364 test-rmse:1.538477+0.170051
## [113] train-rmse:1.397055+0.053361 test-rmse:1.537354+0.169440
## [114] train-rmse:1.395390+0.053344 test-rmse:1.536044+0.169150
## [115] train-rmse:1.393761+0.053405 test-rmse:1.536560+0.172338
## [116] train-rmse:1.392167+0.053464 test-rmse:1.534479+0.169932
## [117] train-rmse:1.390546+0.053531 test-rmse:1.534689+0.170075
## [118] train-rmse:1.388996+0.053551 test-rmse:1.533837+0.168873
## [119] train-rmse:1.387472+0.053576 test-rmse:1.532845+0.169697
## [120] train-rmse:1.385985+0.053557 test-rmse:1.530692+0.170244
## [121] train-rmse:1.384504+0.053562 test-rmse:1.530003+0.171278
## [122] train-rmse:1.383020+0.053564 test-rmse:1.531294+0.172660
## [123] train-rmse:1.381562+0.053631 test-rmse:1.529621+0.173919
## [124] train-rmse:1.380204+0.053674 test-rmse:1.528343+0.181660
## [125] train-rmse:1.378870+0.053652 test-rmse:1.528021+0.180578
## [126] train-rmse:1.377533+0.053631 test-rmse:1.524258+0.181898
## [127] train-rmse:1.376182+0.053579 test-rmse:1.523557+0.182781
## [128] train-rmse:1.374878+0.053561 test-rmse:1.520601+0.184045
## [129] train-rmse:1.373597+0.053568 test-rmse:1.519996+0.184188
## [130] train-rmse:1.372396+0.053542 test-rmse:1.518943+0.185216
## [131] train-rmse:1.371244+0.053547 test-rmse:1.517682+0.183942
## [132] train-rmse:1.370124+0.053533 test-rmse:1.516704+0.183986
## [133] train-rmse:1.369039+0.053530 test-rmse:1.515156+0.185967
## [134] train-rmse:1.367949+0.053501 test-rmse:1.514959+0.184202
## [135] train-rmse:1.366784+0.053460 test-rmse:1.513318+0.184575
## [136] train-rmse:1.365644+0.053400 test-rmse:1.511858+0.183536
## [137] train-rmse:1.364557+0.053320 test-rmse:1.510948+0.181727
## [138] train-rmse:1.363476+0.053253 test-rmse:1.511428+0.184785
## [139] train-rmse:1.362490+0.053213 test-rmse:1.512024+0.184846
## [140] train-rmse:1.361521+0.053162 test-rmse:1.513500+0.184453
## [141] train-rmse:1.360550+0.053152 test-rmse:1.512818+0.184387
## [142] train-rmse:1.359609+0.053154 test-rmse:1.509859+0.183707
## [143] train-rmse:1.358682+0.053141 test-rmse:1.507429+0.182290
## [144] train-rmse:1.357795+0.053126 test-rmse:1.506617+0.183157
## [145] train-rmse:1.356898+0.053113 test-rmse:1.508032+0.185802
## [146] train-rmse:1.355977+0.053098 test-rmse:1.505539+0.187855
## [147] train-rmse:1.355064+0.053106 test-rmse:1.503785+0.186264
## [148] train-rmse:1.354158+0.053118 test-rmse:1.503578+0.185717
## [149] train-rmse:1.353282+0.053122 test-rmse:1.501901+0.188005
## [150] train-rmse:1.352436+0.053131 test-rmse:1.500229+0.187054
## [151] train-rmse:1.351608+0.053151 test-rmse:1.500968+0.186246
## [152] train-rmse:1.350799+0.053152 test-rmse:1.500265+0.184535
## [153] train-rmse:1.349975+0.053140 test-rmse:1.500090+0.187731
## [154] train-rmse:1.349167+0.053134 test-rmse:1.501511+0.190796
## [155] train-rmse:1.348395+0.053115 test-rmse:1.500244+0.191354
## [156] train-rmse:1.347639+0.053107 test-rmse:1.498488+0.190945
## [157] train-rmse:1.346909+0.053076 test-rmse:1.498495+0.190892
## [158] train-rmse:1.346196+0.053045 test-rmse:1.498537+0.191992
## [159] train-rmse:1.345502+0.053028 test-rmse:1.496441+0.192775
## [160] train-rmse:1.344801+0.053024 test-rmse:1.495559+0.194188
## [161] train-rmse:1.344109+0.053027 test-rmse:1.496222+0.194000
## [162] train-rmse:1.343456+0.053058 test-rmse:1.495738+0.194885
## [163] train-rmse:1.342793+0.053057 test-rmse:1.496830+0.194340
## [164] train-rmse:1.342150+0.053050 test-rmse:1.494693+0.194941
## [165] train-rmse:1.341502+0.053042 test-rmse:1.494275+0.194934
## [166] train-rmse:1.340863+0.053085 test-rmse:1.494572+0.196613
## [167] train-rmse:1.340254+0.053085 test-rmse:1.494006+0.195615
## [168] train-rmse:1.339651+0.053074 test-rmse:1.492990+0.197106
## [169] train-rmse:1.339057+0.053066 test-rmse:1.490688+0.197649
## [170] train-rmse:1.338476+0.053054 test-rmse:1.490132+0.196360
## [171] train-rmse:1.337887+0.053041 test-rmse:1.491144+0.195421
## [172] train-rmse:1.337315+0.053038 test-rmse:1.489407+0.194488
## [173] train-rmse:1.336765+0.053029 test-rmse:1.489511+0.193051
## [174] train-rmse:1.336240+0.053013 test-rmse:1.488431+0.193763
## [175] train-rmse:1.335726+0.052990 test-rmse:1.487292+0.194419
## [176] train-rmse:1.335231+0.052968 test-rmse:1.488071+0.191970
## [177] train-rmse:1.334741+0.052953 test-rmse:1.489799+0.191772
## [178] train-rmse:1.334245+0.052942 test-rmse:1.488453+0.193532
## [179] train-rmse:1.333727+0.052940 test-rmse:1.487434+0.193969
## [180] train-rmse:1.333241+0.052917 test-rmse:1.486938+0.194532
## [181] train-rmse:1.332766+0.052896 test-rmse:1.485772+0.198769
## [182] train-rmse:1.332312+0.052882 test-rmse:1.486279+0.198748
## [183] train-rmse:1.331855+0.052859 test-rmse:1.485833+0.198119
## [184] train-rmse:1.331424+0.052845 test-rmse:1.485161+0.198886
## [185] train-rmse:1.330995+0.052829 test-rmse:1.484809+0.198181
## [186] train-rmse:1.330578+0.052801 test-rmse:1.482923+0.197232
## [187] train-rmse:1.330181+0.052779 test-rmse:1.483813+0.196931
## [188] train-rmse:1.329796+0.052762 test-rmse:1.483753+0.196973
## [189] train-rmse:1.329397+0.052746 test-rmse:1.484258+0.198899
## [190] train-rmse:1.329000+0.052730 test-rmse:1.485757+0.200394
## [191] train-rmse:1.328626+0.052724 test-rmse:1.485857+0.200486
## [192] train-rmse:1.328246+0.052702 test-rmse:1.485845+0.200752
## Stopping. Best iteration:
## [186] train-rmse:1.330578+0.052801 test-rmse:1.482923+0.197232
##
## 106
## [1] train-rmse:6.100201+0.040815 test-rmse:6.097103+0.249245
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:4.050014+0.019261 test-rmse:4.068808+0.215712
## [3] train-rmse:2.933563+0.020367 test-rmse:2.960622+0.152387
## [4] train-rmse:2.370209+0.010615 test-rmse:2.421023+0.095957
## [5] train-rmse:2.088718+0.022289 test-rmse:2.186990+0.059989
## [6] train-rmse:1.932136+0.032725 test-rmse:2.062075+0.034195
## [7] train-rmse:1.847593+0.037934 test-rmse:1.974398+0.057800
## [8] train-rmse:1.783557+0.043516 test-rmse:1.942800+0.087270
## [9] train-rmse:1.717192+0.029170 test-rmse:1.877702+0.091357
## [10] train-rmse:1.666259+0.019394 test-rmse:1.823454+0.115630
## [11] train-rmse:1.628583+0.019731 test-rmse:1.796703+0.112875
## [12] train-rmse:1.590297+0.029115 test-rmse:1.766967+0.112864
## [13] train-rmse:1.556003+0.030491 test-rmse:1.742265+0.115706
## [14] train-rmse:1.523739+0.031085 test-rmse:1.717954+0.110895
## [15] train-rmse:1.482540+0.030821 test-rmse:1.673907+0.061708
## [16] train-rmse:1.454374+0.032014 test-rmse:1.652582+0.061087
## [17] train-rmse:1.429343+0.033299 test-rmse:1.633425+0.072467
## [18] train-rmse:1.404177+0.035875 test-rmse:1.623712+0.077597
## [19] train-rmse:1.383568+0.036218 test-rmse:1.614725+0.081657
## [20] train-rmse:1.364070+0.036709 test-rmse:1.590292+0.087228
## [21] train-rmse:1.338130+0.038912 test-rmse:1.568039+0.064544
## [22] train-rmse:1.317379+0.039694 test-rmse:1.559745+0.070294
## [23] train-rmse:1.300562+0.040461 test-rmse:1.531243+0.077366
## [24] train-rmse:1.281267+0.042349 test-rmse:1.524564+0.080688
## [25] train-rmse:1.264912+0.043307 test-rmse:1.513222+0.080923
## [26] train-rmse:1.244144+0.045337 test-rmse:1.506540+0.084745
## [27] train-rmse:1.220040+0.040865 test-rmse:1.488699+0.083898
## [28] train-rmse:1.201978+0.040951 test-rmse:1.476036+0.096465
## [29] train-rmse:1.186155+0.041981 test-rmse:1.461968+0.095799
## [30] train-rmse:1.169598+0.043789 test-rmse:1.446892+0.093224
## [31] train-rmse:1.156393+0.044015 test-rmse:1.431918+0.099064
## [32] train-rmse:1.142648+0.048674 test-rmse:1.423414+0.101992
## [33] train-rmse:1.127689+0.047410 test-rmse:1.411537+0.102720
## [34] train-rmse:1.116204+0.048685 test-rmse:1.410249+0.100000
## [35] train-rmse:1.103582+0.048652 test-rmse:1.396383+0.097760
## [36] train-rmse:1.091135+0.048515 test-rmse:1.384073+0.095700
## [37] train-rmse:1.079972+0.048618 test-rmse:1.380293+0.094563
## [38] train-rmse:1.068798+0.047616 test-rmse:1.382310+0.105117
## [39] train-rmse:1.060598+0.048076 test-rmse:1.377197+0.106288
## [40] train-rmse:1.051714+0.048125 test-rmse:1.371329+0.109927
## [41] train-rmse:1.041580+0.048037 test-rmse:1.368083+0.111707
## [42] train-rmse:1.033951+0.048324 test-rmse:1.354874+0.117266
## [43] train-rmse:1.023958+0.047602 test-rmse:1.350412+0.119811
## [44] train-rmse:1.011528+0.047390 test-rmse:1.324584+0.136162
## [45] train-rmse:1.003330+0.047499 test-rmse:1.322539+0.136485
## [46] train-rmse:0.995220+0.046615 test-rmse:1.322799+0.133630
## [47] train-rmse:0.987305+0.047132 test-rmse:1.321187+0.133320
## [48] train-rmse:0.980738+0.047645 test-rmse:1.315067+0.133813
## [49] train-rmse:0.972248+0.046468 test-rmse:1.310994+0.135708
## [50] train-rmse:0.965768+0.046507 test-rmse:1.303931+0.137135
## [51] train-rmse:0.951871+0.045941 test-rmse:1.286684+0.155433
## [52] train-rmse:0.939074+0.036898 test-rmse:1.284120+0.161789
## [53] train-rmse:0.933185+0.036937 test-rmse:1.281907+0.158218
## [54] train-rmse:0.925650+0.033297 test-rmse:1.273484+0.162314
## [55] train-rmse:0.915893+0.030562 test-rmse:1.260739+0.157009
## [56] train-rmse:0.910505+0.030612 test-rmse:1.260864+0.156226
## [57] train-rmse:0.905663+0.031228 test-rmse:1.260510+0.156383
## [58] train-rmse:0.899964+0.030139 test-rmse:1.258562+0.157157
## [59] train-rmse:0.895046+0.030189 test-rmse:1.258905+0.156201
## [60] train-rmse:0.888324+0.032883 test-rmse:1.257214+0.151253
## [61] train-rmse:0.883633+0.033180 test-rmse:1.248251+0.154688
## [62] train-rmse:0.880105+0.033375 test-rmse:1.240563+0.156902
## [63] train-rmse:0.875994+0.033160 test-rmse:1.245033+0.152595
## [64] train-rmse:0.872173+0.033599 test-rmse:1.244220+0.155271
## [65] train-rmse:0.866950+0.033980 test-rmse:1.242310+0.155432
## [66] train-rmse:0.863196+0.034391 test-rmse:1.241039+0.153310
## [67] train-rmse:0.856852+0.038832 test-rmse:1.241291+0.151852
## [68] train-rmse:0.853563+0.038961 test-rmse:1.241261+0.152039
## Stopping. Best iteration:
## [62] train-rmse:0.880105+0.033375 test-rmse:1.240563+0.156902
##
## 107
## [1] train-rmse:6.077189+0.043342 test-rmse:6.074451+0.240708
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.984703+0.027427 test-rmse:4.001316+0.209838
## [3] train-rmse:2.815095+0.022978 test-rmse:2.857825+0.138027
## [4] train-rmse:2.198848+0.015406 test-rmse:2.297962+0.091267
## [5] train-rmse:1.877698+0.020415 test-rmse:2.009468+0.051985
## [6] train-rmse:1.699431+0.020148 test-rmse:1.863456+0.059877
## [7] train-rmse:1.588991+0.020510 test-rmse:1.780808+0.073832
## [8] train-rmse:1.511408+0.023792 test-rmse:1.719648+0.057464
## [9] train-rmse:1.450569+0.022087 test-rmse:1.683555+0.046097
## [10] train-rmse:1.403697+0.022502 test-rmse:1.653053+0.062839
## [11] train-rmse:1.363359+0.023819 test-rmse:1.629691+0.067541
## [12] train-rmse:1.325918+0.026725 test-rmse:1.597613+0.071301
## [13] train-rmse:1.282968+0.029958 test-rmse:1.561358+0.065473
## [14] train-rmse:1.244600+0.024320 test-rmse:1.531260+0.077430
## [15] train-rmse:1.204756+0.028006 test-rmse:1.503847+0.086203
## [16] train-rmse:1.168486+0.023274 test-rmse:1.479435+0.087324
## [17] train-rmse:1.139172+0.026550 test-rmse:1.463507+0.109959
## [18] train-rmse:1.108453+0.027304 test-rmse:1.448486+0.116075
## [19] train-rmse:1.082268+0.025983 test-rmse:1.431669+0.119199
## [20] train-rmse:1.056921+0.030080 test-rmse:1.422212+0.118610
## [21] train-rmse:1.031467+0.027971 test-rmse:1.409035+0.124700
## [22] train-rmse:1.012686+0.029765 test-rmse:1.397326+0.115667
## [23] train-rmse:0.994862+0.029822 test-rmse:1.380951+0.117489
## [24] train-rmse:0.974407+0.030268 test-rmse:1.370151+0.121944
## [25] train-rmse:0.953981+0.029094 test-rmse:1.359909+0.118399
## [26] train-rmse:0.934674+0.029254 test-rmse:1.345839+0.123406
## [27] train-rmse:0.918774+0.031645 test-rmse:1.334698+0.118709
## [28] train-rmse:0.900347+0.030759 test-rmse:1.315678+0.127687
## [29] train-rmse:0.888205+0.030972 test-rmse:1.311928+0.125698
## [30] train-rmse:0.876945+0.030653 test-rmse:1.308557+0.129795
## [31] train-rmse:0.862159+0.028005 test-rmse:1.293918+0.122526
## [32] train-rmse:0.848115+0.029522 test-rmse:1.286537+0.129247
## [33] train-rmse:0.833215+0.028961 test-rmse:1.275016+0.132787
## [34] train-rmse:0.821356+0.028643 test-rmse:1.278094+0.135597
## [35] train-rmse:0.810718+0.031099 test-rmse:1.269706+0.135842
## [36] train-rmse:0.797312+0.028089 test-rmse:1.265303+0.138822
## [37] train-rmse:0.776514+0.028286 test-rmse:1.254403+0.136724
## [38] train-rmse:0.767026+0.027060 test-rmse:1.249601+0.140103
## [39] train-rmse:0.757048+0.026408 test-rmse:1.241686+0.141447
## [40] train-rmse:0.747346+0.027349 test-rmse:1.237207+0.140080
## [41] train-rmse:0.738320+0.026972 test-rmse:1.234402+0.139957
## [42] train-rmse:0.728547+0.026248 test-rmse:1.229715+0.144596
## [43] train-rmse:0.718806+0.028617 test-rmse:1.225143+0.143332
## [44] train-rmse:0.710077+0.029247 test-rmse:1.222274+0.142150
## [45] train-rmse:0.704430+0.029508 test-rmse:1.220949+0.140536
## [46] train-rmse:0.695720+0.029983 test-rmse:1.221302+0.142163
## [47] train-rmse:0.689414+0.030824 test-rmse:1.221065+0.143990
## [48] train-rmse:0.683904+0.031673 test-rmse:1.220964+0.140837
## [49] train-rmse:0.673879+0.033244 test-rmse:1.220537+0.146293
## [50] train-rmse:0.664504+0.029964 test-rmse:1.218467+0.148445
## [51] train-rmse:0.658483+0.029537 test-rmse:1.215954+0.148748
## [52] train-rmse:0.649544+0.027721 test-rmse:1.211469+0.144015
## [53] train-rmse:0.642061+0.028104 test-rmse:1.209072+0.144977
## [54] train-rmse:0.638221+0.029046 test-rmse:1.207875+0.145871
## [55] train-rmse:0.633942+0.029130 test-rmse:1.209256+0.145760
## [56] train-rmse:0.629311+0.029006 test-rmse:1.214579+0.142647
## [57] train-rmse:0.624274+0.028388 test-rmse:1.214961+0.141036
## [58] train-rmse:0.616232+0.027478 test-rmse:1.207490+0.140499
## [59] train-rmse:0.611595+0.028150 test-rmse:1.205274+0.138554
## [60] train-rmse:0.606842+0.027592 test-rmse:1.206490+0.134507
## [61] train-rmse:0.603002+0.027479 test-rmse:1.207966+0.133731
## [62] train-rmse:0.598026+0.026373 test-rmse:1.208249+0.132766
## [63] train-rmse:0.593386+0.027595 test-rmse:1.208996+0.139303
## [64] train-rmse:0.590377+0.027157 test-rmse:1.207920+0.139535
## [65] train-rmse:0.586489+0.028162 test-rmse:1.207789+0.139971
## Stopping. Best iteration:
## [59] train-rmse:0.611595+0.028150 test-rmse:1.205274+0.138554
##
## 108
## [1] train-rmse:6.067282+0.044204 test-rmse:6.066333+0.243182
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.957109+0.024898 test-rmse:3.978158+0.197400
## [3] train-rmse:2.771499+0.017711 test-rmse:2.823178+0.148435
## [4] train-rmse:2.095014+0.013152 test-rmse:2.207670+0.105618
## [5] train-rmse:1.742595+0.015877 test-rmse:1.900681+0.096663
## [6] train-rmse:1.549591+0.021132 test-rmse:1.752421+0.084316
## [7] train-rmse:1.424827+0.023313 test-rmse:1.665453+0.074178
## [8] train-rmse:1.333706+0.029110 test-rmse:1.604992+0.063016
## [9] train-rmse:1.269990+0.027073 test-rmse:1.563187+0.067436
## [10] train-rmse:1.212325+0.024945 test-rmse:1.521276+0.085551
## [11] train-rmse:1.154469+0.031202 test-rmse:1.483213+0.081395
## [12] train-rmse:1.103241+0.029487 test-rmse:1.457808+0.098373
## [13] train-rmse:1.051793+0.029258 test-rmse:1.428490+0.102762
## [14] train-rmse:1.010095+0.029280 test-rmse:1.404899+0.120672
## [15] train-rmse:0.977906+0.026965 test-rmse:1.388631+0.121935
## [16] train-rmse:0.951674+0.026911 test-rmse:1.372050+0.121085
## [17] train-rmse:0.922634+0.020379 test-rmse:1.362085+0.129365
## [18] train-rmse:0.897309+0.015659 test-rmse:1.343160+0.140001
## [19] train-rmse:0.872201+0.016409 test-rmse:1.325152+0.144396
## [20] train-rmse:0.842980+0.014163 test-rmse:1.318527+0.139764
## [21] train-rmse:0.818116+0.017000 test-rmse:1.307177+0.144300
## [22] train-rmse:0.796530+0.017999 test-rmse:1.300209+0.141528
## [23] train-rmse:0.779814+0.019075 test-rmse:1.289591+0.148632
## [24] train-rmse:0.761962+0.018053 test-rmse:1.278343+0.150862
## [25] train-rmse:0.744308+0.022817 test-rmse:1.280940+0.150836
## [26] train-rmse:0.726569+0.020718 test-rmse:1.283304+0.156406
## [27] train-rmse:0.706499+0.020903 test-rmse:1.277524+0.154772
## [28] train-rmse:0.683755+0.018948 test-rmse:1.264469+0.159505
## [29] train-rmse:0.668137+0.016423 test-rmse:1.253333+0.149636
## [30] train-rmse:0.658530+0.015573 test-rmse:1.250358+0.148905
## [31] train-rmse:0.646042+0.013474 test-rmse:1.244114+0.152838
## [32] train-rmse:0.629742+0.011070 test-rmse:1.235711+0.156494
## [33] train-rmse:0.619105+0.009483 test-rmse:1.236171+0.156141
## [34] train-rmse:0.609311+0.010849 test-rmse:1.240367+0.159892
## [35] train-rmse:0.598375+0.009714 test-rmse:1.245721+0.160044
## [36] train-rmse:0.586008+0.011314 test-rmse:1.243050+0.157070
## [37] train-rmse:0.577185+0.011151 test-rmse:1.238881+0.156666
## [38] train-rmse:0.566993+0.011551 test-rmse:1.230241+0.153553
## [39] train-rmse:0.557626+0.011349 test-rmse:1.228453+0.150988
## [40] train-rmse:0.550687+0.011278 test-rmse:1.227861+0.148468
## [41] train-rmse:0.545251+0.011017 test-rmse:1.229100+0.149657
## [42] train-rmse:0.536903+0.013853 test-rmse:1.229865+0.152156
## [43] train-rmse:0.528948+0.009785 test-rmse:1.231493+0.152400
## [44] train-rmse:0.522435+0.010113 test-rmse:1.230240+0.152111
## [45] train-rmse:0.517086+0.009471 test-rmse:1.229181+0.150805
## [46] train-rmse:0.511860+0.008654 test-rmse:1.231118+0.149256
## Stopping. Best iteration:
## [40] train-rmse:0.550687+0.011278 test-rmse:1.227861+0.148468
##
## 109
## [1] train-rmse:6.058671+0.045017 test-rmse:6.066392+0.244734
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.927992+0.026351 test-rmse:3.963612+0.192928
## [3] train-rmse:2.702508+0.025274 test-rmse:2.756823+0.130478
## [4] train-rmse:2.007874+0.021115 test-rmse:2.142097+0.106607
## [5] train-rmse:1.631526+0.018025 test-rmse:1.836310+0.068460
## [6] train-rmse:1.415632+0.022468 test-rmse:1.686639+0.068171
## [7] train-rmse:1.274708+0.018213 test-rmse:1.608723+0.062239
## [8] train-rmse:1.178592+0.023983 test-rmse:1.527489+0.060190
## [9] train-rmse:1.087846+0.021299 test-rmse:1.463670+0.078855
## [10] train-rmse:1.027423+0.020988 test-rmse:1.438825+0.085928
## [11] train-rmse:0.961853+0.023630 test-rmse:1.415088+0.105673
## [12] train-rmse:0.917857+0.023111 test-rmse:1.375163+0.109753
## [13] train-rmse:0.878160+0.021905 test-rmse:1.358335+0.110493
## [14] train-rmse:0.844523+0.020946 test-rmse:1.344580+0.105953
## [15] train-rmse:0.807811+0.020076 test-rmse:1.340967+0.112249
## [16] train-rmse:0.779288+0.021643 test-rmse:1.320453+0.123102
## [17] train-rmse:0.751491+0.021515 test-rmse:1.303589+0.131704
## [18] train-rmse:0.725693+0.026047 test-rmse:1.297817+0.136212
## [19] train-rmse:0.702360+0.027104 test-rmse:1.295998+0.142118
## [20] train-rmse:0.677428+0.027850 test-rmse:1.287511+0.150247
## [21] train-rmse:0.655670+0.024706 test-rmse:1.285644+0.149781
## [22] train-rmse:0.636955+0.026880 test-rmse:1.280207+0.144382
## [23] train-rmse:0.619037+0.030337 test-rmse:1.268041+0.141189
## [24] train-rmse:0.604006+0.028603 test-rmse:1.261066+0.143463
## [25] train-rmse:0.588425+0.027022 test-rmse:1.253167+0.145135
## [26] train-rmse:0.574702+0.025442 test-rmse:1.255162+0.146150
## [27] train-rmse:0.554942+0.025808 test-rmse:1.249343+0.154401
## [28] train-rmse:0.542211+0.027941 test-rmse:1.247169+0.153884
## [29] train-rmse:0.533939+0.029521 test-rmse:1.247702+0.152025
## [30] train-rmse:0.520285+0.029426 test-rmse:1.246755+0.154837
## [31] train-rmse:0.508376+0.030127 test-rmse:1.241315+0.151912
## [32] train-rmse:0.497005+0.030514 test-rmse:1.240203+0.156065
## [33] train-rmse:0.488543+0.031805 test-rmse:1.243058+0.153922
## [34] train-rmse:0.479903+0.029277 test-rmse:1.243132+0.154513
## [35] train-rmse:0.463748+0.027670 test-rmse:1.237068+0.149082
## [36] train-rmse:0.456555+0.028133 test-rmse:1.236962+0.149436
## [37] train-rmse:0.442729+0.024672 test-rmse:1.236283+0.150176
## [38] train-rmse:0.434100+0.023210 test-rmse:1.233255+0.153593
## [39] train-rmse:0.424802+0.023219 test-rmse:1.234697+0.152856
## [40] train-rmse:0.418894+0.024269 test-rmse:1.232292+0.151964
## [41] train-rmse:0.410363+0.023329 test-rmse:1.230068+0.154575
## [42] train-rmse:0.403727+0.024174 test-rmse:1.227785+0.155091
## [43] train-rmse:0.393848+0.019679 test-rmse:1.227045+0.154656
## [44] train-rmse:0.386979+0.019926 test-rmse:1.225794+0.156056
## [45] train-rmse:0.375276+0.018172 test-rmse:1.225315+0.155252
## [46] train-rmse:0.369739+0.020344 test-rmse:1.224360+0.154651
## [47] train-rmse:0.361650+0.022036 test-rmse:1.224703+0.154045
## [48] train-rmse:0.356679+0.021580 test-rmse:1.226327+0.151757
## [49] train-rmse:0.351311+0.021855 test-rmse:1.227292+0.151378
## [50] train-rmse:0.344733+0.021612 test-rmse:1.223602+0.150110
## [51] train-rmse:0.338014+0.021188 test-rmse:1.224818+0.150249
## [52] train-rmse:0.332649+0.019674 test-rmse:1.226933+0.147014
## [53] train-rmse:0.329097+0.019458 test-rmse:1.227336+0.145832
## [54] train-rmse:0.320426+0.017282 test-rmse:1.229748+0.144930
## [55] train-rmse:0.314777+0.014624 test-rmse:1.231225+0.144248
## [56] train-rmse:0.309171+0.013770 test-rmse:1.231485+0.144256
## Stopping. Best iteration:
## [50] train-rmse:0.344733+0.021612 test-rmse:1.223602+0.150110
##
## 110
## [1] train-rmse:6.056370+0.045544 test-rmse:6.067166+0.232851
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.911367+0.027353 test-rmse:3.959382+0.164647
## [3] train-rmse:2.666770+0.032396 test-rmse:2.727670+0.113725
## [4] train-rmse:1.943007+0.023351 test-rmse:2.098148+0.109931
## [5] train-rmse:1.539552+0.029554 test-rmse:1.793190+0.087939
## [6] train-rmse:1.298579+0.043881 test-rmse:1.635477+0.073381
## [7] train-rmse:1.150037+0.040692 test-rmse:1.520684+0.077550
## [8] train-rmse:1.042296+0.034050 test-rmse:1.469553+0.093100
## [9] train-rmse:0.946578+0.026102 test-rmse:1.428067+0.097975
## [10] train-rmse:0.879737+0.029363 test-rmse:1.403466+0.100010
## [11] train-rmse:0.825533+0.019725 test-rmse:1.373994+0.114269
## [12] train-rmse:0.774336+0.023022 test-rmse:1.346296+0.106932
## [13] train-rmse:0.738635+0.024288 test-rmse:1.340467+0.108983
## [14] train-rmse:0.699587+0.027741 test-rmse:1.316967+0.115318
## [15] train-rmse:0.663705+0.024711 test-rmse:1.308266+0.119558
## [16] train-rmse:0.635496+0.017621 test-rmse:1.299345+0.135752
## [17] train-rmse:0.612763+0.018795 test-rmse:1.295944+0.135634
## [18] train-rmse:0.589023+0.019085 test-rmse:1.289166+0.137310
## [19] train-rmse:0.565110+0.012797 test-rmse:1.285349+0.137137
## [20] train-rmse:0.540727+0.011948 test-rmse:1.277273+0.137833
## [21] train-rmse:0.522898+0.009414 test-rmse:1.273954+0.140418
## [22] train-rmse:0.506204+0.010533 test-rmse:1.270256+0.144453
## [23] train-rmse:0.487010+0.012165 test-rmse:1.263157+0.144402
## [24] train-rmse:0.469945+0.016674 test-rmse:1.264641+0.143600
## [25] train-rmse:0.458934+0.017203 test-rmse:1.260555+0.138966
## [26] train-rmse:0.443115+0.018330 test-rmse:1.259249+0.140235
## [27] train-rmse:0.428432+0.018117 test-rmse:1.261449+0.139325
## [28] train-rmse:0.415829+0.019363 test-rmse:1.260552+0.140551
## [29] train-rmse:0.402849+0.018875 test-rmse:1.258015+0.141026
## [30] train-rmse:0.389619+0.020190 test-rmse:1.255654+0.138544
## [31] train-rmse:0.377161+0.017672 test-rmse:1.255029+0.135929
## [32] train-rmse:0.368772+0.016597 test-rmse:1.254553+0.133974
## [33] train-rmse:0.357726+0.014682 test-rmse:1.257355+0.137166
## [34] train-rmse:0.344734+0.016354 test-rmse:1.255470+0.136618
## [35] train-rmse:0.336175+0.014814 test-rmse:1.254333+0.136138
## [36] train-rmse:0.327158+0.014274 test-rmse:1.254180+0.133922
## [37] train-rmse:0.319865+0.014276 test-rmse:1.254822+0.135444
## [38] train-rmse:0.306215+0.015324 test-rmse:1.251721+0.132923
## [39] train-rmse:0.296488+0.015878 test-rmse:1.253647+0.131738
## [40] train-rmse:0.289606+0.015961 test-rmse:1.254594+0.130857
## [41] train-rmse:0.281642+0.014973 test-rmse:1.256012+0.130265
## [42] train-rmse:0.276497+0.015330 test-rmse:1.255571+0.130588
## [43] train-rmse:0.269510+0.012995 test-rmse:1.256111+0.131343
## [44] train-rmse:0.262615+0.013256 test-rmse:1.254318+0.128686
## Stopping. Best iteration:
## [38] train-rmse:0.306215+0.015324 test-rmse:1.251721+0.132923
##
## 111
## [1] train-rmse:6.056370+0.045544 test-rmse:6.067166+0.232851
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
##
## [2] train-rmse:3.904299+0.027968 test-rmse:3.964018+0.172356
## [3] train-rmse:2.641570+0.030332 test-rmse:2.707926+0.129332
## [4] train-rmse:1.903251+0.019348 test-rmse:2.056928+0.110278
## [5] train-rmse:1.475092+0.032196 test-rmse:1.718929+0.078203
## [6] train-rmse:1.203363+0.047375 test-rmse:1.537997+0.077387
## [7] train-rmse:1.039518+0.045240 test-rmse:1.434962+0.099673
## [8] train-rmse:0.926311+0.043043 test-rmse:1.382576+0.118284
## [9] train-rmse:0.851449+0.040060 test-rmse:1.352891+0.126814
## [10] train-rmse:0.782557+0.024745 test-rmse:1.333512+0.130596
## [11] train-rmse:0.721918+0.027265 test-rmse:1.292630+0.145303
## [12] train-rmse:0.678850+0.023024 test-rmse:1.289246+0.145461
## [13] train-rmse:0.636856+0.020943 test-rmse:1.284034+0.142572
## [14] train-rmse:0.605162+0.022863 test-rmse:1.279650+0.150068
## [15] train-rmse:0.568477+0.018351 test-rmse:1.268070+0.162779
## [16] train-rmse:0.536751+0.017649 test-rmse:1.258463+0.155899
## [17] train-rmse:0.510305+0.015432 test-rmse:1.255277+0.156696
## [18] train-rmse:0.487938+0.017106 test-rmse:1.254953+0.153808
## [19] train-rmse:0.467291+0.013493 test-rmse:1.259732+0.150319
## [20] train-rmse:0.447581+0.015560 test-rmse:1.253116+0.148658
## [21] train-rmse:0.430468+0.017511 test-rmse:1.253448+0.146047
## [22] train-rmse:0.408885+0.018553 test-rmse:1.248670+0.144612
## [23] train-rmse:0.391066+0.016915 test-rmse:1.253156+0.141918
## [24] train-rmse:0.377500+0.016659 test-rmse:1.250863+0.141002
## [25] train-rmse:0.363700+0.019734 test-rmse:1.248364+0.139914
## [26] train-rmse:0.352041+0.022001 test-rmse:1.250083+0.140442
## [27] train-rmse:0.333527+0.012596 test-rmse:1.248891+0.141249
## [28] train-rmse:0.320676+0.017530 test-rmse:1.248430+0.142748
## [29] train-rmse:0.311437+0.014623 test-rmse:1.252370+0.140800
## [30] train-rmse:0.302115+0.013271 test-rmse:1.251629+0.144310
## [31] train-rmse:0.285485+0.017311 test-rmse:1.253276+0.141961
## Stopping. Best iteration:
## [25] train-rmse:0.363700+0.019734 test-rmse:1.248364+0.139914
##
## 112## max_depth eta
## 94 3 0.36